1 journal of regional socio -economic issues, vol. 10

1 Journal of Regional Socio-Economic Issues, Vol. 10, Issue 3, September 2020

Journal of Regional Socio-Economic Issues, Vol. 10, Issue 3, September 2020 2

JOURNAL OF REGIONAL SOCIO-

ECONOMIC ISSUES (JRSEI)

Volume 10, Issue 3, September 2020

Journal of Regional & Socio-Economic Issues (Print) ISSN 2049-1395

Journal of Regional & Socio-Economic Issues (Online) ISSN 2049-1409

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JOURNAL OF REGIONAL

SOCIO-ECONOMIC ISSUES (JRSEI) ISSN No. 2049-1409

Aims of the Journal: Journal of Regional Socio-Economic Issues (JRSEI) is an international

multidisciplinary refereed journal the purpose of which is to present papers manuscripts linked

to all aspects of regional socio-economic and business and related issues. The views expressed

in this journal are the personal views of the authors and do not necessarily reflect the views of

JRSEI journal. The journal invites contributions from both academic and industry scholars.

Electronic submissions are highly encouraged (mail to: [email protected]).

Indexed by Copernicus Index, DOAJ (Director of Open Access Journal), EBSCO, Cabell’s Index

International Institute of Organized Research (I2OR) database

The journal is catalogued in the following catalogues: ROAD: Directory of Open Access Scholarly

Resources, OCLC WorldCat, EconBiz - ECONIS, CITEFACTOR, OpenAccess

Chief-Editor Prof. Dr. George M. Korres: University of the Aegean, Department of Geography,

[email protected]

Editorial Board (alphabetical order) Assoc. Prof. Dr. Zacharoula S. Andreopoulou, Aristotle University of

Thessaloniki, Faculty of Forestry and Natural Environment, School of

Agriculture, Forestry & Natural Environment, [email protected]

Dr. Stilianos Alexiadis, Ministry of Reconstruction of Production, Environment

& Energy Department of Strategic Planning, Rural Development, Evaluation &

& Statistics, [email protected]; [email protected]

Prof. Dr. Maria Athina Artavani, Department of Military Science,

Hellenic Military Academy, Greece, [email protected]

Prof. Dr. Elias G. Carayannis: School of Business, George Washington University,

USA, [email protected]; [email protected]

Emeritus Prof. Dr. Christos Frangos, University of West Attica, Athens,

[email protected]

Emeritus Prof. Dr. Andreas Demetriou, Department of Military Science,

Hellenic Military Academy, Greece, [email protected] Ass. Professor Dr Vicky Delitheou, Department of Economics and Regional

Development, Panteion University of Social and Political Sciences of Athens,

Email: [email protected]

Prof. Dr. Hanna Dudek: Warsaw University of Life Sciences, [email protected]

Prof. Dr. George Gkantzias: Hellenic Open University, [email protected]

Prof. Dr. George Halkos, Department of Economics, University of Thessaly,

[email protected]

Prof. Dr. Richard Harris: Durham University, [email protected]

Assoc. Prof. Dr. Olga-Ioanna Kalantzi, Department of Environment, University

of the Aegean, Email: [email protected]

Prof. Dr. Stephanos Karagiannis, Panteion University, [email protected]

http://jrseb.yolasite.com/

mailto:[email protected]



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Ass. Prof. Dr. Marina-Selini Katsaiti, Department of Economics & Finance, College of

Business & Economics, United Arab Emirates University, UAE,

[email protected]

Emeritus Prof. Dr. Christos Kitsos, University of West Attica, [email protected]

Assoc. Prof. Dr. Aikaterini Kokkinou, Department of Military Science, Hellenic

Military Academy, Greece, [email protected]

Prof. Dr. Elias A. Kourliouros, Department of Economics, University of Patras,

[email protected]; [email protected]

Emeritus Prof. Dr. Dimitrios Lagos, Department of Business Administration, University

of the Aegean, [email protected]

Assoc. Prof. Dr. Charalambos Louca: Head of Business Department, Director of

Research Department, [email protected]

Prof. Dr. Evangelos Manolas, Department of Forestry & Management of the

Environment & Natural Resources, School of Agricultural & Forestry Sciences,

Democritus University of Thrace, [email protected]

Prof. Dr. Emmanuel Marmaras†: Technical University of Crete

Prof. Dr. Ioannis Th. Mazis, National and Kapodistrian University of Athens,

Faculty of Turkish Studies and Modern Asian Studies,

School of Economics and Political Sciences, [email protected];

[email protected];

Prof. Dr. Maria Michailidis: Department of Management & MIS, University of Nicosia,

[email protected]

Prof. Dr. Photis Nanopoulos, Former Director of Eurostat, [email protected]

Prof. Dr. Nikitas Nikitakos, Department of Shipping Trade and Transport, University of

the Aegean, Email: [email protected]

Dr. Pablo Ruiz-Nápoles, Faculty of Economics, Universidad Nacional

Autonoma de Mexico, [email protected]

Assistant Professor Dr. Efstratios Papanis, Department of Sociology, University of the

Aegean, [email protected]

Assoc. Prof. Gerasimos Pavlogeorgatos (PhD), Department of Cultural Technology and

Communication, University of the Aegean, [email protected]

Prof. Dr. George Polychronopoulos, Professor, Member of the Board of Trustees,

University of West Attica, [email protected]

Prof. Dr. Kiran Prasad, Professor Sri Padmavati Mahila University

[email protected]; [email protected];

Dr. Efthymia Sarantakou, Architect Engineer, adjunct lecturer at the Hellenic Open

University & at Technological Educational Institute of Athens, [email protected]

Professor Yevhen Savelyev, Vice-Rector, Ternopil National Economic

University, Ukraine,[email protected];

Prof. Dr. Anastasia Stratigea, National Technical University of Athens, School of Rural

& Surveying Engineering, Deparment of Geography & Regional Planning,

[email protected]

Prof. Paris Tsartas, Harokopeio University, Athens, Greece, [email protected]

Prof. Dr. George O. Tsobanoglou, University of the Aegean, Department of Sociology,

[email protected]

Professor Dr. George Tsourvakas, School of Economic and Political Studies,

Department of Journalism and Mass Communications, Aristotle University of

Thessaloniki, [email protected]

Prof. Dr. George Zestos, Christopher Newport University, [email protected]


























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Table of Contents

Editorial Board 3

Table of Contents 5

Paper 1: Employment, education and vocational training in Greece: Micro-

level analysis and interaction effects, (by Stavros Rodokanakis) 6-21

6

Paper 2: Community Involvement in event planning: Cases from Greek

Festival Market (by Sofoklis Skoultsos and Alexios- Patapios Kontis) 22-35

22

Paper 3: Employee training in Athens luxury hotels and its relation to job

efficiency and company loyalty (by Papageorgiou Athina, Kikilia Ekaterini

and Varelas Sotirios) 36-45

Paper 4: Sharing economy in time of economic crisis: The owners'

perspective of Airbnb rentals in Greek cities (by Sofoklis Skoultsos, Anna

Kyriakaki, Alexios – Patapios Kontis, and Despina Sdrali) 46-62

Paper 5: The Challenge of Spatial Information Accessibility for Agricultural

Policies: Case of Pakistan (by Asmat Ali and Muhammad Imran) 63-101

36

46

62

Paper 6: Assessing the Reform Options of the Public Pension Scheme of the

Republic of Cyprus (by Charalambos N. Louca, George M. Korres and

George O. Tsobanoglou) 102-132

102

Paper 7: The Royal City Andrapolis of «The Acts of Thomas» (by Alex

Kordosis) 133-136

133

Call for Papers 137

Instructions to Authors

138


Employment, education and vocational training in Greece: Micro-level

analysis and interaction effects

Abstract:

This article examines the impact that level of education and vocational training had on the

Greek labour market, and especially on the chances of finding a job, during the period 1988-

2000, and tests human capital theory and matching theory following an extended interaction

effects analysis. It is not asserted categorically here that the mismatch between supply and

demand for labour in Greece was due to training mismatch alone, but it is contended that this

should be seriously considered as one of the reasons for the unemployment problem in

Greece, at least for the period prior to the current crisis.

Keywords: J08 Labor Economics Policies; J24 Human Capital; Skills; D04 Microeconomic

Policy: Formulation, Implementation, and Evaluation; C54 Quantitative Policy Modeling;

I280 Education: Government Policy.

Stavros Rodokanakis1

1 Corresponding-Address: Dr Stavros Rodokanakis (Visiting Fellow), Department of Social and Policy Sciences,

University of Bath, Claverton Down, Bath BA2 7AY, England. E-mail: [email protected]

https://papers.ssrn.com/sol3/JELJOUR_Results.cfm?code=J08

https://papers.ssrn.com/sol3/JELJOUR_Results.cfm?code=J24

https://papers.ssrn.com/sol3/JELJOUR_Results.cfm?code=D04

https://papers.ssrn.com/sol3/JELJOUR_Results.cfm?code=C54



1. Research Questions and Methodology

Since the early 1990s, there was a shift from ‘passive’ to active labour market policies

(ALMPs) in European countries. In the case of Greece, it is questionable whether vocational

training from the end of the 1980s onwards was accompanied by any real improvement in

matching supply with demand or increasing people’s chances of finding a job.

In particular, the human capital theory is tested, as well as the matching theory. What

has become clear is that the EU approach to vocational training has been very much

influenced by the human capital theory. The matching theory is also considered, because in

contrast to the human capital view under this perspective too much education leads to a lack

of training and consequently, an over-educated often unemployable workforce, which

appears to be the case in Greece and other Southern European countries (Liagouras et al.,

2003; Tsakloglou and Cholezas, 2005; Dolton and Marcenaro-Gutierrez, 2009; Thomaidou

et al., 2009; Karamessini, 2010). The key research question of the paper is what was the

impact of EU funded vocational training on the Greek labour market and individual job

seekers who undertook this training from 1988 to 2000?

At the micro-level, whether the vocational training courses and educational level

increased the chances of finding a job at the participant level is examined econometrically.

The research will determine whether or not the programmes did help the unemployed to get

any work and addresses the following question, namely ‘What was the impact of the

training programmes at the participant level?’. This question was operationalized by

empirically testing the following sub-questions:

• Did the social and demographic characteristics of an individual in Greece affect the

probability of finding employment during the period under investigation?

• Did the introduction of training courses funded by the EU have a statistically significant

effect on the probability of finding employment?

• Did university graduates in Greece face greater difficulties in finding a job compared to

those less educated (as relevant literature and aggregate statistics have suggested - see

Meghir et al., 1989; OECD, 1990; Eurostat: Education and Employment Prospects, 1995;

Iliades, 1995; IN.E./GSEE-ADEDY, 1999; Katsikas, 2005)?

2. Micro-Level of Analysis I: Theoetical Approaches

2.1 Training as human capital and the matching theory

The theory of human capital (Becker, 1964; Ben-Porath, 1967; Mincer 1974) has been

criticized for not being able to explain comprehensively the functions of vocational training,

for it merely considers it as an investment (Papakonstantinou, 1998). There has also been a

considerable amount of empirical research on the closely related topics of education and

skills, including Prais (1995) and Murray and Steedman (1998); closely related to the

current research is study on the increasing role of skilled labour in the economy (Berman et

al., 1994; Machin and van Reenen, 1998).

By contrast, the advocates of matching theory claim that under-education will result

in an increased necessity for more training. However, it is not yet clear whether training can

make up for inadequacies in formal education (substitution) or if it can just add to variations

in human capital (complementarity) that are already present. It could be the case that it is

only the features of the job (level and kind of job) in which the substitution aspects of

training are to be found, and that it is only in those aspects of formal education (level and

breadth) that the complementarity nature of training is obvious (van Smoorenburg and van

der Velden, 2000).


2.2 Literature review on the impact of training at the micro-level

The findings show that the more expensive programmes with a significant amount of

training appear to have been the most effective at increasing employment prospects (see

Brodaty et al., 2001; van Ours, 2001; Kluve and Schmidt, 2002; Raaum and Torp, 2002;

Kluve et al., 2005). However, national studies during the early to mid-2000s did not find

positive impacts of training on employment (Gerfin and Lechner, 2000; Regner, 2002).

Other studies that found mixed effects of participation in training programmes on

employment/unemployment are those of Lechner and Wunsch (2009), Fitzenberger et al.

(2010), Lechner et al. (2011), and McGuinness et al. (2014), depending on the section of

the population being targeted, but overall they reported a positive linkage.

Concerning the research on training in the 2000s (Larsson, 2002 - on earnings as

well; Stenberg, 2003 - on mobility between branches and on earnings; Weber and Hofer,

2003; Graversen, 2004; Hujer et al., 2004; Rosholm and Svarer, 2004; Centeno et al., 2005

- on earnings as well; Hogelund and Holm, 2005; Aakvik and Dahl, 2006; Meadows and

Metcalf, 2008; Rosholm and Skipper, 2009), no positive impact of training on employment

probability in European labour markets was found. According to Rosholm and Skipper

(2009) training raised the unemployment rate of participants but this effect disappeared over

time and this would indicate a locking-in effect, i.e. technical knowledge which is specific

to a particular production process and is not transferable to other processes. Other research

(Malmberg-Heimonen and Vuori, 2005; Steiger, 2005; Andren and Andren, 2006 -

unobservables slightly increased the effect for those treated; Lechner et al., 2007 - on

earnings as well; Cueto and Mato, 2009 - the locking-in effect found regarding trainees

suggested decreasing labour mobility; Lechner et al., 2011 - on earnings as well) found that

the employment effects of training were mixed, i.e. there were positive and negative results.

The findings suggest that training programmes seem to have had some positive

effects on employment and no effects on earnings. Moreover, the effects on the former

appear to diminish over time. The negative effects reported by several evaluations can be

explained, on the one hand, by a locking-in effect, and on the other by the fact that some

participants seemed to enrol in training merely in order to collect unemployment insurance

benefits (Cueto and Mato, 2009).

Micro-econometric analyses usually confirm that training had “mixed” results, but

nearly always a statistically insignificant impact on the participants’ prospects of

employment. Training might help an unemployed person to return to work faster and

because another unemployed worker therefore finds a job more slowly the training

programme is lacking effectiveness (Boone and van Ours, 2004). On the other hand, macro-

economic studies have reached the conclusion that training was the only category of active

employment policy that appears to have had a notable positive effect on the overall

performance of the labour market (CEC, 2006:145).

2.3 Mismatch and over-education

According to Chevalier and Lindley (2007), over-education can be defined as not being in a

graduate job when a person has a degree, thus resulting in skill mismatching. In general, the

vast majority of studies during the period 1990-2000 have indicated greater prevalence of

over-education rather than under-education (Green et al., 1999).

Despite a great deal of American and European empirical evidence being available

on the subject of over-education, it has been argued that “a solid relation [regarding the

over-education / under-education literature] with a formal theory of the labour market is

lacking” (Hartog, 1997).

According to human capital theory, over-education is not a permanent occurrence

and is the result of a poor match between employer and employee. This appears to go

against the empirical evidence, which suggests there is always a large percentage of the


labour force that is over-educated. It is possible that there is always over-education in the

labour market generally, but for each individual it is short lived. However, it could also be

the case that an individual chooses to be over-educated for a position, temporarily, so as to

remain in touch with the labour market in order to find a better job in the future. From this

point of view, over-education could be thought of as being a sort of human capital

investment (Green et al., 1999).

A different interpretation of over-education is offered by matching theory. Under

this lens, there might be a poor match between employer and employee causing over-

education, often resulting in the worker looking for a better match elsewhere. The fact that

both over- and under-education are to be found lends credence to the opinion that they are

both indications of the occurrence of poor matching in the labour market. In this instance,

over-education would be just short lived for the worker concerned (Green et al., 1999).

However, when persistent over-education occurs, according to Patrinos (1997), the focus

needs to be shifted away from the individual and his/her characteristics towards institutions

and policies regarding employment and vocational training across the focal society and its

political economy. The next section presents the micro-econometric work of the research.

3. Micro-Level of Analysis II: Econometric Analysis for Greece

3.1 The econometric analysis: The logit model for applying the micro-data of the

Greek LFS

In this research, the individual anonymised records (micro-data) of the 1992, 1994 and 2000

Labour Force Survey (LFS) for both employed and unemployed (1.5% of the total population

of each area) are examined, covering the spring and early summer, namely from the 14th to

26th week of the year. The reason these years are chosen is because 1992 was the first year in

the Greek LFS questionnaire with detailed questions on training, 1994 was the first year after

the end of the Community Support Framework (CSF)-1, whereas 2000 was one year after the

end of the CSF-2.

A logistic regression model is used for studying differences between those that did

participate in training programmes and those that did not. Moreover, regression models allow

for group comparisons by adjusting for demographic and socioeconomic variables. All three

years have merged together in order to take advantage of the time-series features of the data

(three time-sets of observations in 1992, 1994 and 2000) and used dummies for the years

instead. One logit model for all three areas under examination (Central Macedonia, Attica

and the rest of Greece) with all the main effects, all variables of interest, plus all the control

variables has been generated and has been run in a pooled format. Namely, all the available

data have pooled together into one database. Also, some of the categorical variables with few

observations (types of training) have been aggregated in order to increase the observations

within each cell, so as to avoid exceptionally large coefficients and confidence intervals.

The base (or reference) categories are those with which the rest of the corresponding

variables are compared. The reference categories are chosen so as to match the needs of the

research.2 In the next sub-section, the first part of the micro-level econometric analysis of the

paper is discussed.

3.2 Main effects

The Table 1 presents the results (main effects), namely, the estimated coefficients (B), the

standard errors (S.E.) and the p values for each explanatory variable in the logistic regression

2 Τhe working age population is between 14-65 years old. However, SPSS would not accept these age limits,

defaulting to 13 and 66 years old, so people from 15 to 64 years of age were included, which the programme was

able to compute.


for unemployment in Greece. Column “Sig.” (level of statistical significance or p value)

provides the coefficients for the variables and those above 0.05 are not statistically

significant. In Table 1, bk is the log of the odds, whereas Exp (bk) is the odds ratio.

The descriptive statistics of the logit model are summarised in Table 2 (see in

appendix). After taking into account missing records, restricting the sample by age (15-64

years old) and removing the non-active population, Table 3 (see in appendix) shows the

numbers of records eligible for analysis in the LFS samples.

Table 1: Results (main effects) for Greece, 1992, 1994 and 2000 (parameter

estimates kb, standard errors (s.e.), p-values, exponent of kb

)

Variables bk S.E. Sig. Exp (bk)

Gender 0 .915 0 .019 0.000 2.497

Marital status -0.398 0.039 0.000 0.671

Aged 15-24 ref. ref. ref. ref.

Aged 25-34 -1.023 0.024 0.000 0.359

Aged 35-44 -1.706 0.029 0.000 0.182

Aged 45-64 -1.977 0.031 0.000 0.139

University graduates ref. ref. ref. ref.

MSc or PhD holders 0.110 0.183 0.546 1.116

TEI graduates 0.371 0.046 0.000 1.449

12 years of schooling 0.601 0.038 0.000 1.824

9 years compulsory education 0.550 0.044 0.000 1.734

Primary school graduates and below 0.518 0.040 0.000 1.679

Rest of Greece ref. ref. ref. ref.

Attica 0.083 0.041 0.046 1.086

Central Macedonia -0.075 0.041 0.064 0.927

Rural areas ref. ref. ref. ref.

Athens area 0.738 0.050 0.000 2.091

Thessaloniki area 0.787 0.054 0.000 2.196

Rest of urban areas 0.899 0.030 0.000 2.457

Semi-urban areas 0.518 0.037 0.000 1.679

Non-participation in training course(s) ref. ref. ref. ref.

Training -0.013 0.052 0.808 0.987

Citizenship 0.077 0.058 0.179 1.080

Year 1992 ref. ref. ref. ref.

Year 1994 0.025 0.022 0.266 1.025

Year 2000 0.055 0.033 0.096 1.056

Constant -2.262 0.062 0.000 0.104

Females, non-married individuals, people in the age group 15-24 years old, people

who lived either in the Athens or Thessaloniki areas, the other urban areas or semi-urban

areas were more likely to be unemployed than males, married people, people aged between 25

to 64 and those in rural areas. University graduates had more chances of finding a job

compared to all other educational categories apart from MSc or PhD holders (these

differences were not found significant). These results are in contrast to some studies which

have asserted the opposite. The variable ‘immigrant status’ was found to be statistically non-

significant. Most importantly, the participation in vocational training programmes did not

seem to reduce the odds of unemployment, that is, training was found to be statistically non-

significant during the first and the second CSFs. This means that the results of training


variables are not compatible with the human capital theory. In other words, the more trained a

person was did not affect his chances of finding a job, in Greece, during the time period of

CSFs 1 and 2. The same results on training were found for other Greek regions and the entire

country as well (see Livanos, 2007 and 2009; Rodokanakis, 2010a and 2010b; Rodokanakis

and Vlachos, 2013 and 2019). The exceptions are the findings for the region of Eastern

Macedonia and Thrace in 2000 concerning the training variables ‘apprenticeship’ and

‘continuing vocational training (CVT)’ (less likely to be unemployed than the non-trainees -

see Rodokanakis and Vlachos, 2012).

Whether or not someone lived in Central Macedonia in 1992, 1994 or 2000 was

statistically non-significant. By contrast, people who lived in the region of Attica were more

likely to be unemployed than those living in the rest of Greece. Both of the years 1994 and

2000 were found to be statistically non-significant, i.e. the variable ‘time’ did not influence

the probability of being unemployed.

In the main, the econometric results of this paper for Greece confirm the human

capital theory concerning education, namely, university graduates had higher probabilities of

finding a job than people from lower educational categories. However, this was not the case

in the field of training, since this variable was found to be statistically non-significant. Thus, it

would appear that matching theory has better explanatory power than human capital theory in

the Greek context. This is because the former perspective holds that those with more

education need less training and in Greece there are many over-educated people.

3.3 Interaction effects among variables

For the 1992, 1994 and 2000 samples together, I fitted the interaction effects between

education and gender, age groups and education, age groups and areas, age groups and years,

gender and years, as well as education and residence location, years and education, and years

and areas. Also, I fitted the interaction effects between training and age groups, training and

level of education, training and geographical areas or residence location, and training and

years.

In all tables of the interaction effects analysis, as with the main effects, the variable

“MSc or PhD holders” was statistically non-significant. According to Table 4-1, females

when compared to males, who were both Technological Educational Institutions (TEI)

graduates, had lower probabilities of being employed in comparison to the case where both

males and females were University graduates. In addition, females who were TEI graduates

were 1.46 times less likely to be employed than males and this was similar for the remaining

three educational categories in terms of gender.

Also, concerning age group and educational category, someone who was between 15

and 24 years old in the four educational categories was less likely to be unemployed in

relation to those in this age group who were university graduates. The same applied to those

aged 25-34. Moreover, people in the age group 45-64 were less likely to be employed than

those in the same age group who were university graduates. In addition, in Attica, people aged

35-64 were more likely to be unemployed than those between 15 and 24 in the same region.

Furthermore, someone aged 15-44 in all residential locations (Athens area, Thessaloniki area,

semi-urban areas and rest of the urban areas) had less probability of being unemployed when

compared to those in the same age group in rural areas; however, the opposite was the case in

the age group 45-64. Moreover, females, when compared to males, had a lower probability of

being unemployed in 1994 than in 1992. Age groups 15-24 and 25-34 in 2000 were less likely

to be unemployed than the same age groups in 1992. In contrast, people in the age groups 35-

44 and 45-64 were less likely to be employed than those in the same age groups in 1992.


Table 4.1: Interactions with education and gender, age groups and education, age

groups and areas, age groups and years, gender and years (variables in the equation)


Gender and University graduates ref. ref. ref. ref.

Gender and MSc or PhD holders -0.286 0.385 0.457 0.751

Gender and TEI graduates 0.381 0.095 0.000 1.464

Gender and twelve years of schooling 0.559 0.078 0.000 1.748

Gender and nine years compulsory education 0.902 0.089 0.000 2.465

Gender and primary school graduates and below 0.681 0.079 0.000 1.976

Aged 15-24 and University graduates ref. ref. ref. ref.

Aged 15-24 and MSc or PhD holders -0.976 1.241 0.431 0.377

Aged 15-24 and TEI graduates -1.130 0.187 0.000 0.323

Aged 15-24 and twelve years of schooling -0.905 0.156 0.000 0.404

Aged 15-24 and nine years compulsory education -1.487 0.174 0.000 0.226

Aged 15-24 and primary school graduates and below -1.929 0.155 0.000 0.145


Aged 25-34 and MSc or PhD holders 0.510 0.638 0.424 1.666



Aged 25-34 and nine years compulsory education -0.931 0.154 0.000 0.394

Aged 25-34 and primary school graduates and below -1.063 0.128 0.000 0.345


Aged 35-44 and MSc or PhD holders 0.651 0.717 0.364 1.918



Aged 35-44 and nine years compulsory education 0.047 0.177 0.791 1.048

Aged 35-44 and primary school graduates and below 0.052 0.146 0.723 1.053


Aged 45-64 and MSc or PhD holders -0.329 0.624 0.599 0.720

Aged 45-64 and TEI graduates 0.652 0.157 0.000 1.919

Aged 45-64 and twelve years of schooling 0.431 0.126 0.001 1.539

Aged 45-64 and nine years compulsory education 0.717 0.146 0.000 2.049

Aged 45-64 and primary school graduates and below 0.850 0.120 0.000 2.339

Aged 15-24 and Attica ref. ref. ref. ref.

Aged 25-34 and Attica -0.082 0.104 0.433 0.922

Aged 35-44 and Attica 0.265 0.119 0.026 1.303

Aged 45-64 and Attica 0.481 0.121 0.000 1.618

Aged 15-24 and Central Macedonia ref. ref. ref. ref.

Aged 25-34 and Central Macedonia 0.031 0.101 0.759 1.031

Aged 35-44 and Central Macedonia 0.067 0.120 0.578 1.069

Aged 45-64 and Central Macedonia -0.012 0.126 0.927 0.989

Aged 15-24 and rural areas ref. ref. ref. ref.

Aged 15-24 and Athens area -1.329 0.149 0.000 0.265

Aged 15-24 and Thessaloniki area -1.650 0.166 0.000 0.192

Aged 15-24 and rest of urban areas -1.245 0.096 0.000 0.288

Aged 15-24 and semi-urban areas -0.903 0.115 0.000 0.405













Aged 45-64 and Athens area 1.506 0.086 0.000 4.509

Aged 45-64 and Thessaloniki area 1.324 0.112 0.000 3.758

Aged45-64 and the rest of urban areas 1.097 0.088 0.000 2.994

Aged 45-64 and semi-urban areas 0.813 0.103 0.000 2.255

Gender and year 1992 ref. ref. ref. ref.

Gender and year 1994 -0.099 0.045 0.028 0.906

Gender and year 2000 0.060 0.052 0.243 1.062

Aged 15-24 and year 1992 ref. ref. ref. ref.

Aged 15-24 and year 1994 -0.060 0.054 0.268 0.942

Aged 15-24 and year 2000 -0.961 0.074 0.000 0.382


Aged 25-34 and year 1994 -0.013 0.055 0.815 0.987

Aged 25-34 and year 2000 -0.331 0.065 0.000 0.718


Aged 35-44 and year 1994 -0.118 0.059 0.046 0.889

Aged 35-44 and year 2000 0.351 0.070 0.000 1.420


Aged 45-64 and year 1994 0.085 0.060 0.157 1.089

Aged 45-64 and year 2000 0.449 0.076 0.000 1.567

Constant -0.502 0.130 0.000 0.605

According to Table 4-2, people aged 25-64 who had participated in vocational training

courses were more likely to be unemployed than those 15-24 years old who had also done so.

In addition, those who undertook training and were residents of the Thessaloniki area or the

rest of the urban areas had more chances of finding a job in relation to people in agrarian

areas who also participated in such courses.

Table 4.2: Interactions with training (variables in the equation)


Gender and training 0.025 0.101 0.806 1.025

Aged 15-24 and training ref. ref. ref. ref.

Aged 25-34 and training 0.408 0.112 0.000 1.503



University graduates and training ref. ref. ref. ref.


MSc or PhD holders and training -18.519 8563,539 0.998 0.000

TEI graduates and training 0.179 0.324 0.580 1.196

Training and twelve years of schooling 0.146 0.325 0.652 1.158

Training and nine years compulsory

education 0.005 0.353 0.989 1.005

Training and primary school graduates

and below -0.249 0.411 0.545 0.780

Training and the rest of Greece ref. ref. ref. ref.

Training and Attica -0.261 0.197 0.186 0.771

Training and Central Macedonia 0.452 0.250 0.070 1.572

Training and rural areas ref. ref. ref. ref.

Training and Athens area -0.422 0.250 0.091 0.656

Training and Thessaloniki area -0.992 0.305 0.001 0.371

Training and the rest of urban areas -0.408 0.179 0.022 0.665

Training and semi-urban areas -0.014 0.216 0.947 0.986

Training and year 1992 ref. ref. ref. ref.

Training and year 1994 0.360 0.212 0.089 1.433

Training and year 2000 0.021 0.183 0.908 1.021

Constant -2.243 0.062 0.000 0.106

According to Table 4-3, those who had been educated up to lyceum graduate level (12

years of schooling) living in the rest of the urban areas and not in rural ones, had a higher

probability of being unemployed than university graduates residing in the same areas. This

was also the case for those in semi-urban areas and in the Athens area, but in the Thessaloniki

area this was so only for people with up to a high-school graduate level of education (nine

years compulsory education). University graduates in Attica were less likely to be

unemployed than in the rest of Greece. In both Attica and Central Macedonia, TEI, lyceum

and high-school graduates were more likely to be employed than their corresponding

educational categories in the rest of the country. Only those completing primary school

education or below this level in both the NUTS-2 regions under investigation were more

likely to be unemployed than the same educational category in the rest of Greece.

Table 4.3: Interactions with education and areas* (variables in the equation)


University graduates in Athens area ref. ref. ref. ref.

MSc or PhD holders in Athens area 18.970 14594,110 0.999 17330000

TEI graduates in Athens area 0.116 0.171 0.496 1.123

Twelve years of schooling in Athens area 0.492 0.140 0.000 1.635

Nine years compulsory education in

Athens area 1.187 0.153 0.000 3.277

Primary school graduates and below in

Athens area 2.049 0.142 0.000 7.762

University graduates in Thessaloniki area ref. ref. ref. ref.

MSc or PhD holders in Thessaloniki area 18.777 14594,110 0.999 14280000

TEI graduates in Thessaloniki area -0.204 0.202 0.314 0.816

Twelve years of schooling in Thessaloniki

area 0.157 0.163 0.335 1.170






University graduates in the rest of urban

areas ref. ref. ref. ref.

MSc or PhD holders in the rest of urban

areas 19.015 14594,110 0.999 18110000

TEI graduates in the rest of urban areas 0.206 0.176 0.244 1.228

Twelve years of schooling in the rest of

urban areas 0.607 0.144 0.000 1.834

Nine years compulsory education in the

rest of urban areas 0.986 0.157 0.000 2.680

Primary school graduates and below in the

rest of urban areas 1.905 0.144 0.000 6.721

University graduates in semi-urban areas ref. ref. ref. ref.

MSc or PhD holders in semi-urban areas 0.442 16746,825 1.000 1.555

TEI graduates in semi-urban areas 0.458 0221 0.038 1.581

Twelve years of schooling in semi-urban

areas 0.635 0.182 0.000 1.886

Nine years compulsory education in semi-

urban areas 0.814 0.198 0.000 2.258


semi-urban areas 1.493 0.181 0.000 4.450

University graduates in the rest of Greece ref. ref. ref. ref.

University graduates in Attica -0.640 0.085 0.000 0.527

University graduates in Central Macedonia 0.003 0.109 0.981 1.003

MSc or PhD holders in the rest of Greece ref. ref. ref. ref.

MSc or PhD holders in Attica -0.283 0.634 0.656 0.754

MSc or PhD holders in Central Macedonia 0.176 0.762 0.817 1.192

TEI graduates in the rest of Greece ref. ref. ref. ref.

TEI graduates in Attica -0.855 0.077 0.000 0.425

TEI graduates in Central Macedonia -0.389 0.105 0.000 0.678

Twelve years of schooling in the rest of

Greece ref. ref. ref. ref.

Twelve years of schooling in Attica -0.807 0.050 0.000 0.446

Twelve years of schooling in Central

Macedonia

-0.384 0.069 0.000 0.681

Nine years compulsory education in the

rest of Greece ref. ref. ref. ref.

Nine years compulsory education in Attica -0.395 0.069 0.000 0.674


Central Macedonia

-0.246 0.095 0.010 0.782

Primary school graduates and below in the

rest of Greece ref. ref. ref. ref.


Attica

0.395 0.069 0.000 1.485



Central Macedonia

0.246 0.095 0.010 1.279

Constant -0.502 0.130 0.000 0.605

*Rural areas are set as reference across all interactions with areas

According to Table 4-4, all educational categories (apart from MSc or PhD holders) in

1994 were more likely to be unemployed than university graduates and the same was true for

2000. Also, in 1994, those living in Athens or Thessaloniki were more likely to be

unemployed than people in rural areas, whilst the opposite was the case for those living in

semi-urban areas in 1994 and 2000, as well as in the rest of the urban areas in 2000. Finally,

in 1994 it was more likely that someone was unemployed in Attica than in the rest of Greece.

Table 4.4: Interactions with years and education, and with years and areas* (variables

in the equation)


Year 1994 and University graduates ref. ref. ref. ref.

Year 1994 and MSc or PhD holders -0.313 0.46 0.496 0.731

Year 1994 and TEI graduates 0.407 0.123 0.001 1.502

Year 1994 and twelve years of schooling 0.447 0.102 0 1.563

Year 1994 and nine years compulsory education 0.446 0.115 0 1.563

Year 1994 and primary school graduates and below 0.485 0.105 0 1.624

Year 2000 and University graduates ref. ref. ref. ref.

Year 2000 and MSc or PhD holders -0.649 0.436 0.136 0.522

Year 2000 and TEI graduates -0.059 0.129 0.65 0.943

Year 2000 and twelve years of schooling 0.16 0.108 0.14 1.173

Year 2000 and nine years compulsory education 0.163 0.124 0.188 1.177

Year 2000 and primary school graduates and below 0.53 0.113 0 1.698

Year 1994 and rural areas ref. ref. ref. ref.

Year 1994 and Athens area 0.17 0.071 0.016 1.186

Year 1994 and Thessaloniki area 0.188 0.096 0.051 1.207

Year 1994 and rest of urban areas 0.044 0.072 0.543 1.045

Year 1994 and semi-urban areas -0.294 0.087 0.001 0.745

Year 2000 and rural areas ref. ref. ref. ref.

Year 2000 and Athens area -0.084 0.08 0.295 0.919

Year 2000 and Thessaloniki area -0.1 0.105 0.338 0.904

Year 2000 and rest of urban areas -0.173 0.08 0.03 0.841

Year 2000 and semi-urban areas -0.275 0.095 0.004 0.759

Year 1994 and the rest of Greece ref. ref. ref. ref.

Year 1994 and Attica 0.213 0.052 0.000 1.238

Year 1994 and Central Macedonia 0.126 0.069 0.067 1.134

Year 2000 and the rest of Greece ref. ref. ref. ref.

Year 2000 and Attica -0.029 0.055 0.601 0.971

Year 2000 and Central Macedonia 0.140 0.073 0.057 1.150

Constant -1.914 0.103 0 0.147

* Year 1992 is set as reference across all interactions with years

4. Conclusion

The econometric results for Greece (main effects) support the human capital theory with


regards to education, i.e. university graduates had a higher probability of finding

employment than people with lower education levels. Hence, the findings of this paper

support the main policy lessons of human capital theory in the field of education. However,

this was not so in the case of training, as this variable emerged as being statistically non-

significant.

Regarding the interaction effects analysis, the findings in relation to education

support the human capital theory, with the exceptions to this being the relation between

educational level and age groups 15-34. Hence, most of the educational variable findings

concerning this aspect of the analysis did not support human capital theory. In particular, the

more educated a person was did not mean an improvement in his/her position in the Greek

labour market during the period 1988-2000. These findings on education are consistent with

those of some studies and aggregate statistics mentioned in section 1, which assert that

university graduates in Greece were not in a better position in the labour market than non-

university degree holders with regards to the probability of finding a job.

The results of the interaction effects analysis for training are not different from the

findings of the main effects, with the exceptions being the age groups 25 to 64, who were

more likely to be unemployed in relation to those 15-24 years old and concerning training,

people who lived in Thessaloniki or in the rest of the urban areas were more likely to be

employed than those living in rural ones. In other words, the chances of finding a job did not

change when training is counted as an additional qualification in relation to the other

characteristics of individuals in the LFS. These results were expected, first, because the

findings on all training variables in the logit model were non-significant and second, because

the number of training records used was apparently even smaller when the interaction effects

were examined, hence logically, the same outcomes would be expected.

The results support matching theory better than human capital theory, because the

former supports the perspective that more education leads to less training and Greece has

many over-educated people. This supports even more the stance that the human capital

theory could (and still can) not provide an explanation for the training configuration found in

Greece. One of the contributions of this paper is that, given the experience in Greece, it

appears that abstract micro-level theories of skills mismatch, like the human capital theory,

cannot be applied in political economies where labour markets cannot absorb high skills and

where demand for jobs requiring these is weak.

The mismatch between supply and demand for labour could be partially attributed to

the training mismatch, but this was only one cause of the unemployment problem. The

econometric findings on training are in line with those of Livanos (2007 and 2009) and to the

best of my knowledge, our studies are the only econometric studies for Greece on this topic

based on LFS micro-data.

The problems of today had already been identified in the late 1980s, but apparently

little has been done to rectify them. Of course the current crisis has not been magnified in

Greece because of the ineffectiveness and inefficiency of these training programmes, but has

to do with the structural problems already existing in the economy and the labour market

well before the crisis. The country entered the euro under these conditions and dynamics and

their non-resolution contributed, to an extent, to the country’s current economic and social

bottlenecks.

It is concluded that the cascading of EU money for training and the EU training

policies, were not effective in the case of Greece. Training could be ineffective even in

Northern European countries, but the reasons underlying this were not be the same as those

in Greece. One key particularity of that nation’s circumstance is the very high levels of

unemployment amongst the skilled labour force.

In other words, the human capital theory on training does not appear to be applicable


in the Greek context. Still, for the period under investigation, the high-skilled labour force

could not be absorbed in Greece. It appears that a strategic plan to create demand for training

was absent.

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APPENDIX

TABLE 2: DESCRIPTIVE STATISTICS OF THE LOGIT MODEL

The reference or base categories are in bold

Variables/Area/Year Frequencies Percent

Employed 138,405 90,4%

Unemployed 14,628 9,6%

Males 94,943 62,0%

Females 58,090 38,0%

Non-married 10,339 6,8%

Married or divorced or widows 142,694 93,2%

Aged 15-24 19,395 12,7%

Aged 25-34 39,975 26,1%

Aged 35-44 39,708 25,9%

Aged 45-64 53,955 35,3%

Training 4,208 2,7%

Non-participation in training

Greek Citizenship

148,825

149,881

97,3%

97,9%

Foreigner Ctizenship 3,152 2,1%

Athens area 45,994 30,1%

Thessaloniki area 12,662 8,3%

Rest of urban areas 36,433 23,8%

Semi-urban areas 18,989 12,4%

Rural areas 38,955 25,5%

MSc or PhD holders 554 0,4%

University graduates 15,048 9,8%

TEI graduates 11,358 7,4%

Twelve years of schooling 40,762 27,8%

Nine years compulsory education 14,532 9,9%

Primary school graduates and below 64,561 44,0%

Central Macedonia 24,398 15,9%

Attica 53,773 35,1%

Rest of Greece 74,862 48,9%

Year 1992 53,297 34,8%

Year 1994 65,858 43,0%

Year 2000 33,878 22,1%

Table 3: Numbers of records eligible for analysis in the LFS samples

Year Geographical level No. of records

1992 Greece 53,297

Central Macedonia 9,290

Attica 20,301

Rest of Greece 23,706

1994 Greece 65,858


Attica 22,399


2000 Greece 33,878


Attica 11,073



Community Involvement in event planning: Cases from Greek Festival

Market

Abstract:

It has been stated from various researchers that local communities play significant role to the

development and success of festivals and events. The latter may have a positive or negative

contribution to local society, economy, tourism and culture and their success is based mainly

on event planning orientation. Current paper approaches community involvement and

examines its levels to Greek festivals. A quantitative research took place to festival managers

and factor analysis was implemented in order to develop factors that include categories of

community involvement variables. Additionally, cluster analysis revealed that there are

different groups of festivals based on their mentality against community involvement. Those

groups presented certain characteristics and further statistical analysis showed that there is

strong correlation between several attributes and cluster categorization.

Keywords: community involvement, festivals, factor analysis, cluster analysis

Sofoklis Skoultsos1 and Alexios- Patapios Kontis2

1 Corresponding-Address: Dr. Sofoklis Skoultsos, Adjunct Lecturer, University of The Aegean. Email:

[email protected]. Sokratous 13, N. Irakleio Attikis, 14122. 2 Corresponding-Address: Dr. Alexios- Patapios Kontis, Adjunct Lecturer, University of The Aegean. Email:

[email protected]




1. Introduction

During last decades event industry has been developed rapidly at a global level. Various

destinations and local authorities approach all kind of events and festivals as means for further

development of local societies. Taking into account the strong relation between tourism and

events, the latter have been exploited in order to increase tourism flows at the host destination.

These tourism flows can be used as means of economic development (Ridolfi et al. 2017).

Additionally, events now represent a significant part of the tourism industry, especially when

they are related directly to culture. Additionally, destinations exploit culture as an alternative

offered experience and product to differentiate from the traditional mass tourism (Sarantakou,

Tsartas, & Bonarou, 2018; Sarantakou & Kontis, 2016) This fact, highlights the global trend

to special interest tourism products that have been argued from various researchers (Tsartas &

Sarantakou, 2016). As a result, various researchers have been focused on events as a separate

field of study and form of tourism. Impacts, motivation, event design, event experience,

events as tourism products etc. are some of the main issues discussed in the event literature.

Besides the above, another issue that is of great importance is event planning. Planning is

essential in order to achieve success in festival management and inevitably contains a wide

range of different aspects. Current paper focuses on level of community involvement as an

aspect of event planning. Managers for Greek festivals were selected as sample in order to

examine the level of local people engagement to festival management and implementation.

2. Literature Review

It is widely accepted that an event exploits, creates and rejuvenates the host area and can

affect a lot the local community (Picard & Robinson, 2006, p. 11). Although each event lasts

for a short period of time, their impacts (positive or negative) are obvious either for Mega,

Hallmark or Local events. Undoubtfully, there is a correlation between the level of these

impacts and the size of the event (Allen J., O’Toole W., McDonnell I. & Harris R., 2002).

Events can affect local economy, culture, society and tourism. The most difficult impacts to

be measured are the ones on the society and culture, although they are very important for the

host areas (Mason & Cheyne, 2000). Several researches tried to adapt measuring tools for

social impacts from the local people perspective (Delamere, 2001; Delamere et.al., 2001).

Events - especially the ones included in the category of Hallmark Events - can be

closely related to life and identity of local people. It has to be mentioned that various

researches examined the identity as key element of festival (Azara & Crouch, 2006; McCabe,

2006;). Arcodia & Whitford (2006) examined the concept of social capital and how it can be

developed through practices like building community resources and social cohesion. They

came up with the conclusion that positive social impacts should be the initial goal for event

managers.

Needless to say, in certain situations events may cause negative impacts to the local

society. Specifically, the large amount of people that visits a limited area for a short period of

time can contribute to rapid changes at the local environment and everyday life of local

people such as: price inflation, increase of crime, traffic conjunction etc. (Dwyer et.al, 2000;

Arcodia & Whitford, 2007; Bowdin et.al., 2011). All the mentioned impacts have a strong

negative result on the approach of local people to the festival, especially in cases were local

people are neglected from the organization of the festival. Various researchers argued that the

negative impacts of an event or festival can be relieved when local people are involved

(directly or indirectly) with the management and the whole organization of the festival or

event (Skoultsos, 2009). Moreover, long-term success of an event depends a lot on the

acceptance of the festival from locals. As a result, it can be stated that community

involvement is essential and should be considered as a major factor upon which a festival’s

success relies. As Getz (2007, p. 308) stated: “The ultimate in community acceptance is that


events become recognized as permanent ‘institutions’ in their community… ‘institutions’ can

only emerge through strong community support as measured by loyal attendance, committed

volunteers and political support…”.

The concept of community may be described with various definitions. Individuals can

self-identify themselves … “in terms of their home in terms of their home location or their

sense of belonging to a specific social group. Consequently, several distinct “communities”

may exist within a geographical area or neighborhood” (Rogers & Anastasiadou, 2011, p.

388). Another interesting definition is the one that highlights the importance of decision

making. Community is a “mutually supportive, geographically specific, social unit” (Mann,

2000, p. 18).

Apart from definition of community, various researchers have been focused on

community involvement and its importance for festivals, by examining the perspective of

local people (Fredline, 2000; Gursoy et.al., 2004). It is widely accepted that community

involvement leads to social coherence, increased sense of offering to the society, enhanced

local identity (especially at small and local festivals), community pride of local people etc.

(Hall, 1992; Mason & Beaumont-Kerridge, 2004; Fredline et.al., 2003; Arcodia & Witford,

2007; Skoultsos, 2009; Bowdin et.al., 2011). Additionally, an event may positively affect the

local community because it is an opportunity for leisure, personal development, recognition

(through volunteering) and social bonding (The Association of Festival Organizers, 2004).

The support and involvement of local community to a festival may be implemented in

various ways such as: volunteering, responsibilities of management, attendance, participation

decision making etc. All these forms of community involvement vary according to mindset

and managerial approach of festival managers. Moscardo (2007) argued that community

involvement should be one of the main elements of any festival and proposed a framework in

order to reach regional development through this particular mentality. Later on, Rogers &

Anastasiadou (2011) developed a framework to assess community involvement that consisted

of five indicators: involvement of schools; volunteering opportunities; participation in

decision making; accessibility; and business cooperation. The ultimate goal of the current

framework was to help authorities to improve their perspective on community involvement of

their festivals.

3. Research aims and objectives

Regarding the changing environment in the Greek Festival Market according to financial

crisis, event planning has been changing throughout the last decade. Moreover, taking into

account the importance of community involvement in the success of festival’s

implementation, current paper aims to explore the level of community involvement in Greek

festivals. Secondly, tries to highlight possible factors that may affect the approach that festival

managers have on the issue of community involvement. Based on the above analysis, the

following hypotheses were formulated in order to be tested through primary research:

H1. Variables of community involvement can be divided to different factors.

H2: There are different groups of festivals according to community involvement levels

at Greek festival market.

H3: Specific features of the festival affects levels of community involvement.

H3.1. There are specific characteristics of the surrounded area that affect the

approach of community involvement.

H3.2. There are specific characteristics of the perspective of festival managers

that affect the approach of community involvement

4. Methodology

The methodology that was finally implemented was quantitative research by questionnaire to

festival managers from the Greek festival market. The questionnaire contained a total number


of 20 questions. Seventeen of them were closed questions and three of them were open so as

the managers could express their opinion on several issues. The questionnaire was divided in

three sections: 1. Demographics of the festival (years of implementation, duration, host area

etc.), 2. Level of Community involvement (variables derived from event literature, opinions

etc.), 3. Contribution to local tourism product (efforts done for including the festival to the

local tourism product, success, opinions, perceptions on issues related to event tourism etc.).

Table 1: Variables (V) of community involvement to be tested

V1: There are local networks that contribute to the discovery of local artists (or

producers)

V2: There is a differentiated pricing policy for locals or/and for repeat visitors.

V3: There is a providence to include sponsorships from local enterprizes to the

festival.

V4: Promotional activities take place to the host area in order to increase the

sense of celebrating the festival’s identity and the inclusion of local citizens.

V5: Free (or affordable price) entrance.

V6: Local people are (or can be) volunteers at the festival.

V7: The majority of the festival’s employees are local citizens.

V8: Shows related to the festival’s program are performed for kids in public

places.

V9: Local citizens participate as volunteers at various positions in festival’s

management.

V10: Increase of participants’ social networking has been spotted because of the

festival.

V11: There is an assurance that the promotional activities of the festival aim to

attract local people.

V12: There are strong bonds between festival’s managers and local associations.

V13: Participation of local artists (or producers) in the festival program is a

strategy for the festival.

V14: Local people participate in decision making processes regarding festival’s

management issues.

V15: Taster shows are held around the festival’s area.

V16: There is an organizational committee that is responsible for communicating

with local associations.

V17: There is a providence for pricing facilitations (e.g. free transportation

coupons, discounts) to certain social groups of local citizens (e.g. students and

unemployed) during the festival.

As mentioned above, the scope of the primary research was to test H1, H2 and H3

hypotheses. According to this, factor and cluster analysis were implemented. Specifically,

factor analysis was used to decrease the number of variables (related to community

involvement) to a lower number of factors. Seventeen variables were selected and all of them

were based on event literature and previous research work of Rogers & Anastasiadou (2011)

(Table 1.). Selected variables were tested on a 5-point Likert Scale (5=Totally agree totally

1=Disagree). Furthermore, cluster analysis was used to examine if there are discernible

groups of festivals according to their specific characteristics. It was implemented in two steps:

Firstly, hierarchical cluster analysis was conducted to reveal the statistically reliable number

of clusters to be chosen. Further elaboration of data using k-means cluster was conducted for

three- and four- cluster solutions.


4.1 Data Collection and Sample

In order to collect primary data to test the hypotheses, an online survey was conducted from

September 2017 to December 2017. The goal was to approach Greek festival managers

without focusing on a specific type or kind of festivals so as to avoid certain characteristics of

the respondents (or the festivals involved). The questionnaire was sent to a list of 150 festival

managers’ contacts. Each respondent had the opportunity to complete only one on-line

questionnaire (through online platform) and was informed that all the data were filled

anonymously. Finally, the total number of filled questionnaires was 126 and 104 were

appropriate for further analysis. Statistical software of SPSS was used for the statistical

analysis of the results. Initially, descriptive statistics were used to analyze sample’s

sociodemographic characteristics.

5. Results

5.1 Demographics

Table 2 and Table 3 present the basic “demographics” of the festivals participated in the

survey. The vast majority of the festivals have “Music” in their theme and activities and are

held during the summer period. Moreover, most of them have been implemented for 5 to 10

years and they last mainly from 2 to 5 days. All the other characteristics (Surrounded area,

average number of attendees per day) presented more scattered answers.

Table 2: “Demographics” of the participated festivals – part 1

Years of

implementation

Main theme of the

Festival3

Season of the year

up to 2 years 11.54% Music 75% Summer 59.62%

2 to 5 years 23.08% Film 25% Autumn 23.08%

5 to 10 years 40.38% Sports 14% Winter 1.92%

10 to15 years 11.54% Gastronomy 20% Spring 15.38%

15 to 20 years 7.69% Dance 41%

more than 20

years

5.77% Other 50%

Table 3: “Demographics” of the participated festivals – part 2

Surrounded Area Average number of

attendees per day

Duration

less than 2000

residents

17.31% less than 100 1.92% 1 day 3.85%

2.000 to 10.000

residents

17.31% 100-500 25.00% 2 days 38.46

%

10.000 to 50.000

residents

25.00% 500-1.000 36.54% 3 to 5 days 38.46

%

50.000 to 100.000

residents

23.08% 1.000-5.000 25.00% more than 5

days

19.23

%

more than 100.000

residents

17.31% 5.000-10.000 9.62%

less than 2000

residents

17.31% more than

10.000

1.92%

3 The respondent had the opportunity to fill more than one answer.


Regarding the connection between the festival’s theme and activities with local

tradition, answers were diffused into two main categories: 44% answered that part of the

offered activities are directly linked to local tradition and 44% that there is no connection

between local tradition and the festival’s theme and activities. Only 12% answered that there

is a direct connection of the festival with the local tradition. Concerning the ratio of locals

and tourists, approximately 55% managers (54.35%) answered that due to their estimations

both categories are included at festival’s crowd. 34.78% declared that the festival attracts only

locals. Another critical question was the one for the festival manager’s perception upon the

importance of community involvement in the management of festivals. The answers were

divided with the following descending order: “Very Important” (50%), Absolutely essential

(26%), “Of Average Importance (22%), “Of little importance” (2%) and “Not important at all

(0%).

Table 4 presents the relative results in terms of festival’s relation with local tourism

product. Clearly, the majority (47,83%) of the participated managers clarified that they did

not make any effort to include the festival in the local area’s tourism product. Almost one

third of the sample declared that although they tried to make this connection their efforts were

unsuccessful. As a result, only 41% stated that their festival has been effectively (fairly and

adequately) utilized from the tourism industry. On the other hand, most of them believe that

the festival is successful. Only 4.4% stated that their festival is less successful. Taking

account, the answers about the impacts, it is obvious that festivals are not considered to have a

high positive impact on local tourism by managers. However, managers declared that they

have an extremely positive impact on society and on local culture.

Table 4: Festival’s relationship with local tourism product and level of success

Efforts have been made

to include festival in the

tourism product of the

local area.

The festival has been

effectively utilized by the

tourism industry.

How much successful

your festival can be

considered?

Yes,

successfully

21.74% Not at all 28.26% Not at all 0.0%

Yes,

unsuccessfully

30.43% Slightly 30.43% Less

successful

4.4%

No 47.83% Fairly 23.91% Adequately

successful

47.8%

Adequately 17.39% Very

successful

37.0%

Fully 0.00% Totally

successful

10.9%

Table 5: The festival has been positively affected:

Not at all Slightly Moderately Very Extremely

economy 8.7% 4.4% 32.6% 32.6% 21.7%

tourism 13.0% 15.2% 30.4% 19.6% 21.7%

culture 0.0% 4.4% 4.4% 28.3% 58.7%

society 2.2% 4.4% 10.9% 26.1% 52.2%

environment 6.8% 11.4% 36.4% 15.9% 27.3%


Table 6: Factors affecting the level of success for festivals:

Not

importan

t

Less

importan

t

Of

Average

importanc

e

Very

importan

t

Of Great

importanc

e

Stable Frequency 0.0% 2.2% 33.3% 44.4% 20.0%

Early Scheduling 0.0% 0.0% 2.2% 19.6% 78.3%

Appropriate placement of

employees (specialized in

event management) 0.0% 2.2% 10.9% 43.5% 43.5%

Level of event's production

quality (e.g. infrastructure) 0.0% 0.0% 19.6% 34.8% 45.7%

Holistic Experience 0.0% 0.0% 8.7% 43.5% 47.8%

Creation of bonds with local

society 0.0% 2.2% 15.2% 41.3% 41.3%

Effective promotion 0.0% 0.0% 2.2% 32.6% 65.2%

Regarding the factors that affect the level of success of a festival, the most important

one is “early scheduling” and secondarily “effective promotion”. The majority of answers

(82.6%) regarding “creation of bonds with local society” were equally split to “very

important” and “of great importance”.

5.2 Factor Analysis

First of all, regarding credibility of factor analysis, researchers state that in order to complete

factor analysis, number of respondents has to be five times greater than the number of the

selected items (Siomkos, 2005, p. 286). In the current study the number of participants was

104 and number of selected items 17 so the credibility is well stated. Table 7 shows the

variables that have been tested in the factor analysis. The majority of them gathered mean

over 3 with the highest at 3.92 and the lowest 1.87. Results from analysis (principal

components factor analysis with varimax rotation) revealed 5 factors with an eigenvalue of at

least 1.0 and explained 66.3% of Total variance. Scree plot (Figure 1) also shows that at least

5 is the appropriate number of derived factors. Table 8 presents the final results of factor

analysis: means for each variable item and its Standard Deviation4. The results clearly show

that there are variables that can be combined in five factors which can be described with the

following names: 1. Involvement of locals to management, 2. Strengthening the “bonds”, 3.

Networking, 4. Promotion to locals, 5. Communication of activities to the host area.

Table 7: Means and Standard Deviation of each community involvement variable

Community Involvement Variable Means Std. Deviation

V5: Free (or affordable price) entrance 3.92 1.18

V6: Local people are (or can be) volunteers at the festival. 3.92 1.094

V10: Increase of participants’ social networking has been spotted

because of the festival.

3.90 1.048

V11: There is an assurance that the promotional activities of the

festival aim to attract local people.

3.65 0.833

V13: Participation of local artists (or producers) in the festival

program is a strategy for the festival.

3.63 0.966

V12: There are strong bonds between festival’s managers and 3.60 1.102

4 Answers was based on 5-point Likert scale (1=not important, 5=extremely important).


local associations.

V4 Promotional activities take place to the host area in order to

increase the sense of celebrating the festival’s identity and the

inclusion of local citizens.

3.56

0.912

V9: Local citizens participate as volunteers at various positions in

festival’s management.

3.48 1.174

V7: The majority of the festival’s employees are local citizens. 3.33 1.194

V3: There is a providence to include sponsorships from local

enterprises to the festival. 3.31

1.107

V8: Shows related to the festival’s program are performed for

kids in public places.

3.27 1.217

V17: There is a providence for pricing facilitations (e.g. free

transportation coupons, discounts) to certain social groups of

local citizens (e.g. students and unemployed) during the festival.

3.13 1.293

V14: Local people participate in decision making processes

regarding festival’s management issues.

3.08 1.163

V16: There is an organizational committee that is responsible for

communicating with local associations. 3.04

1.114

V1: There are local networks that contribute to the discovery of

local artists (or producers)

2.44

1.05

V15: Taster shows are held around the festival’s area. 2.31 1.207

V2. There is a differentiated pricing policy for locals or/and for

repeat visitors. 1.87

1.098

Figure 1: Scree Plot

5.3 Cluster Analysis

Regarding cluster analysis’ results, dendrogram of Hierarchical analysis revealed that two-

clusters solution should be accepted. Nevertheless, k-means cluster analysis for three- and

four- cluster solutions was implemented in order to test other alternatives. Finally, the

solution of two clusters was chosen as the most effective differentiation of the sample. Table

9 presents the respective results of cluster analysis and the mean values of importance of each

created cluster to each of the tested variables. The first cluster represents more than the 2/3 of

the sample (69,2%) and presented a mean score of 3 to all variables included in the factor


analysis (2.93 including V17). Clearly, the 2nd cluster showed that all the tested variables of

community involvement took place in their management at high level.

Table 8: Community involvement variables divided in to 5 factors

Community development factors

and items

Factor

Loadin

g

Eigenvalu

e

Variance

explained

(%)

Cronbac

h Alpha

F1: Involvement of locals to

management

2.815 17.596 0.802

V6: Local people are (or can be)

volunteers at the festival.

.822

V9: Local citizens participate as

volunteers at various positions in

festival’s management.

.696

V14: Local people participate in

decision making processes

regarding festival’s management

issues.

.663

V7: The majority of the festival’s

employees are local citizens.

.612

V4: Promotional activities take

place to the host area in order to

increase the sense of celebrating the

festival’s identity and the inclusion

of local citizens.

.544

F2: Strengthening the “bonds” 2.110 13.189 0.612

V17: There is a providence for

pricing facilitations (e.g. free

transportation coupons, discounts)

to certain social groups of local

citizens (e.g. students and

unemployed) during the festival.

.706

V12: There are strong bonds

between festival’s managers and

local associations.

.684

V8: Shows related to the festival’s

program are performed for kids in

public places.

.630

V13: Participation of local artists

(or producers) in the festival

program is a strategy for the

festival.

.377

F3: Networking 2.005 12.533 0.617

V5: Free (or affordable price)

entrance.

.858

V1: There are local networks that

contribute to the discovery of local

artists (or producers)

.701

V10: Increase of participants’ social

networking has been spotted

because of the festival.

.464

F4: Promotion to locals 1.955 12.218 0.546


V3: There is a providence to

include sponsorships from local

enterprises to the festival.

.885

V11: There is an assurance that the

promotional activities of the festival

aim to attract local people.

.546

F5: Communication of activities

to the host area

1.774 11.088 0.668

V15: Taster shows are held around

the festival’s area.

.834

V16: There is an organizational

committee that is responsible for

communicating with local

associations.

.535

Total Variance Explained 66.624%

5.4 Characteristics of the revealed clusters

Furthermore, authors implemented additional statistical tests in order to check the correlation

between various answers of the questionnaire and cluster categorization. Specifically, chi-

square tests were implemented and the following variables (derived from the whole

questionnaire) appear to have a strong correlation with the final categorization of the clusters:

Characteristics of the surrounded area (x2=14.986, df=4, p=0,005). It seems that festivals

held at areas with low population tend to be categorized to the second cluster.

Theme of festival (Sports x2=8.206, df=1, p=0,004 and Dance x2=9.559, df=1, p=0,002):

It seems that sports and dance festivals tend to be included to the 2nd cluster.

Importance of festival’s acceptance from local citizens (x2=31.732, df=4, p=0,000): There

is a strong correlation between the opinion of the managers for the importance acceptance

among local people and cluster division. Managers of the 2nd cluster’s festivals appear to

believe that acceptance from local people is extremely important.

“Efforts have been made to include festival in the tourism product of the local area”

(x2=42.692, df=3, p=0,000) and “The festival has been effectively utilized by the tourism

industry” (x2=16.992, df=4, p=0,002): There is strong correlation between the answers in the

current question and cluster categorization. It seems that cluster 2 includes the majority of the

cases of festivals that made successful efforts to include the festival in the local area’s tourism

product. The same tendency appeared for the second question respectively.

The festival contributes positively to local economy (x2=18.092, df=5, p=0,003), tourism

(x2=19.649, df=5, p=0,001) and society (x2=24.899, df=6, p=0,000): Additionally, managers

of the festivals included in the 2nd cluster, tend to be more confident in terms of positive

contribution of the festival to economy, tourism and society.

Perspective on the definition of community (x2=16.162, df=4, p=0,003): Finally,

correlation found between managers’ perspective of community and cluster categorization. It

seems that the majority of the managers included in the second cluster, approach the concept

of community as “anything common that can support any group of people”.


Table 9: “Community involvement” factor means for each cluster

Overall mean

(n=104)

Cluster 1 –

(n=72)

Cluster 2

(n=32)

F1: Involvement of locals to

management

3.47 3.06 4.40

F2: Strengthening the “bonds” 3.41 3.06 4.19

F3: Networking 3.42 3.15 4.04

F4: Promotion to locals 3.48 3.13 4.28

F5: Communication of activities to

the host area

2.67 2.40 3.28

Total to variables included in the

factor analysis5

3.35 3 4.13

Note: Answers was based on 5-point Likert Scale (5=Totally agree totally 1=Disagree)

6. Analysis and Discussion

At first, the above results reflect that the majority of Greek festivals are implementing

practices of community involvement in their management at least to a moderate extent. Table

7 shows clearly that none of the tested variables exceeded number 4 in the relative Likert

Scale and it can be stated that although managers comprehend the importance of community

involvement, this belief is not reflected by a relative and clarified mindset as well. It is

important to mention that only 30.8% showed a willingness to engage local people to their

management practices.

Additionally, the managers’ answers on the positive impact of festivals enlighten the

fact that festivals considered to be catalysts for local culture and social coherence, according

to their opinion. It can be arbitrarily derived that festival managers aim on positive

contribution to local culture and society and as a result they actually believe that their festival

contributes to these sectors. Social cohesion and festival’s approval from the locals are

considered to be important factors in the mentality of managers, which complies with

previous researches (Moscardo, 2007; Arcodia & Witford, 2006; Skoultsos 2014). Moreover,

the responses reveal several difficulties that Greek festivals should overcome. Specifically,

lack of “Early scheduling” has been referred as one of the main problems for Greek festivals

in previous findings (Skoultsos, 2014) which has been increased due to various obstacles

based on economic crisis and difficulties in sponsorships and grants from public sector.

Festival managers do not associate utilization of tourism industry and inclusion in the

tourism product with the success of a festival. The latter is an important outcome which

reflects the mindset of the majority of the participants. These results comply with previous

research which revealed that the vast majority of Greek festivals do not aim -actually - to the

tourism industry and festivals’ themes and programs are based on the preferences and needs

of locals (Skoultsos, 2014). Initially, this should not be approached as an obstacle to for

community involvement. However, as the current paper shows, there is a correlation between

efforts to connect the festival with the local tourism product and success of respective efforts

with the level of community involvement. To an extent, it can be argued, that the mentality of

festival managers to involve local community to the festival management and activities lead

to better results regarding positive tourism impacts. In other words, festivals that aim directly

to be included in the local tourism product should inevitably implement activities of engaging

locals to their activities and management. These findings comply with previous researches

which stated that decision making and in general participation in event-related activities may

increase the willingness of local to act as hosts in the future (Pappas, 2014; Pappas 2017).

5 Removed variable: V2. There is a differentiated pricing policy for locals or/and for repeat visitors.


Regarding the hypotheses set at the beginning of the survey, H1 and H2 are verified

due to the results of factor and cluster analysis. Different factors were revealed together with

distinctive clusters of festivals. Finally, H3 is partly verified because several variables proved

to have strong correlation with the cluster categorization and as a result revealed clusters

include festivals with different attributes. Specifically, cluster 2 includes festivals with high

community involvement levels and specific characteristics (see section “characteristics of

revealed clusters”). The main outcome from the above results is that differences in mentality

of festival managers leads to different results regarding the success of the festival. Finally, it

should be mentioned that a wider idea of community is important in order to be more active to

increase community involvement.

6.1 Limitations

Predictably current research has several limitations. These limitations are related to the fact

that selected variables were derived from literature. It would be better to be derived through

further qualitative research to festival managers and local people. In this case, tested variables

would be adopted to specific attributes of Greek festivals.

6.2 Conclusions & future research

Results of current research are useful for festival managers in order to comprehend the

importance of community involvement. It has to be highlighted that community involvement

is a feature widely accepted as necessary for the festival’s success. Nevertheless, managers

participated in the survey declared that they don’t implement practices of community

involvement at a high level and consequently festivals are not successful in terms of

integration in the local tourism product. In general, festival managers should comprehend that

community involvement play a significant role to the tourism exploitation of a festival and

event planning should integrate local citizens to event-related activities and decision-making

processes.

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Employee training in Athens luxury hotels and its relation to job

efficiency and company loyalty

Abstract:

The aim of this study was to evaluate the level of satisfaction from the training provided by

Athens luxury hotels to their employees and its correlation with job satisfaction and company

commitment. For this we evaluated a total of 108 completed questionnaires using SPSS for

Windows. We found that the correlation between adequate training and job satisfaction was

quite high, with the employees demanding more complete, specialized and personalized

training programs. We also found that hotel employees are not satisfied with work

environment, financial benefits, reward policies and job recognition, this resulting in low

commitment to the company. Athens five star hotels should adjust human resources policy

accordingly in order to increase loyalty.

Key words: Job satisfaction, company loyalty, training programs, luxury hotels

Papageorgiou Athina1, Kikilia Ekaterini2 and Varelas Sotirios3

1 Papageorgiou Athina, Assistant Professor, University of West Attica, 2 Ag, Spyridonos Str., 12243 Egaleo,

Athens Greece. Email: [email protected] 2 Kikilia Ekaterini, Professor, University of West Attica, 2 Ag, Spyridonos Str., 12243 Egaleo, Athens Greece.

Email: [email protected] 3 Varelas Sotirios, Lecturer, Neapolis University, Paphos, 2 Danais Avenue, 8042 Paphos, Cyprus. Email:

[email protected]





1. Introduction

Current highly competitive tourism business environment has significantly changed the way

tourism enterprises operate, as they are obliged to adopt novel techniques in strategic

management and performance measurement in order to meet both national and international

competition (Kaleka & Morgan 2017, Krishnamoorthy & D'Lima 2014).

In order to become competitive, tourism enterprises need to quickly respond to

external environment changes and strategic decisions that other similar tourism businesses

adopt, and also try to be one-step-ahead of their competitors. For this, self-assessment and

strategy improvements are needed in order to gain competitive advantages (Aguiar-Quintana

et al 2016, Krishnamoorthy & D'Lima, 2014). Among other measures, continuous training

and specialization of staff are of paramount importance, as they reflect stability, commitment

to quality and the ability to change, while enhances employee job satisfaction and

commitment to the company (Amirtharaj et al 2011, Strietska-Ilina & Tessaring 2005).

2. Aim

The aim of this study was to identify the degree of satisfaction that the employees of luxury

hotels of Athens gain from the various training systems applied and its correlation with job

satisfaction and company commitment.

3. Material and method

To meet the aims of this study we used a structured questionnaire. We firstly recorded

responders’ demographics (part one, “demographics”) and then asked details about applied

staff training programs (part two, “measures related to staff training”), i.e. continuous training

and re-training, program adequacy and the extent to which these procedures met the

responders’ needs and expectations. With the next set of questions (part three, “evaluation of

11 statements”) we asked responder’s opinion on eleven specific statements regarding their

training while with the final set (part four, “characteristics of training programs applied”), we

recorded employees’ opinion on job satisfaction, personal income sufficiency and hotel

rewards policies.

The reliability of the questionnaire was assessed using Cronbach alpha index. When

the index value is greater than 0.7 there is strong consistency in the sample and it should

therefore be considered reliable (Tavacol & Dennick 2011). In this study internal consistency

reliability for the "measures related to staff training" was found to be 0.869, for the

"evaluation of 11 statements" it was 0.850 and for the "characteristics of training programs

applied" was 0.938, indicating satisfactory levels of our questionnaire reliability.

The survey was conducted between January and March 2019. We presented the

questionnaire to human resources management of 23 five-star Athens hotels through Google

Forms. Participation was voluntary and anonymous. A total of 108 completed questionnaires

were obtained and analyzed using the 24th edition of the SPSS for Windows statistical

package.

4. Results

Responders’ demographics are presented in Table 1.


Table 1: Responders’ demographics

n %

Sex Male 36 33,3

Female 72 66,7

Age (years) 18-25 24 22,2

26-35 37 34,3

36-45 27 25

46-55 19 17,6

56-65 1 0,9

over 65 0 0

Education High School 16 14,8

University 55 50,9

MSc 25 23,1

PhD 0 0

Other 12 11.1

Years of employment

at the Hotel

Less than 1 16 14,8

1-5 47 43,5

6-10 19 17,6

11-15 19 17,6

16-20 4 3,7

More than 20 3 2,8

Years of employment

Totally

Less than 1 6 1,5

1-5 34 31,5

6-10 19 17,6

11-15 31 28,7

16-20 8 7,4

More than 20 10 9,3

Contract Permanent 61 56,5

Seasonal 47 43,5

Job position General Manager 0 0

Human resources manager 1 0,9

Other department manager 14 13

Employee 88 81,5

Other 5 4,6

The vast majority of responders (95.4%) noted that their hotel offers some form of

staff training. When asked about their preferred method of training (a multi-answer question)

employers seem to prefer counseling by the instructor when a problem occurs (71.7%),

followed by advanced training on certain matters (67.3%), counseling with monitoring groups

(57.9%) and presentations by the department’s head (54.2%). Studied hotel managers

however seem to prefer on the job training (93.33%), followed by audiovisual training

(40.0%), lectures (26.66%) and training using the internet (20.0%), while computer assisted

training methods and apprenticeship are popular in only 13.33% and 6.66% of cases

respectively. Most employees (65.4%) stated that the department’s head is actively involved

in their training while in only 10% of cases the department’s head is absent. When asked

about their satisfaction from the training provided, responders’ views were divided: 54% of

them stated that they do not undergo sufficient training to be effective in their job, while

43.9% were satisfied. Overall, and in contrast to the previous answers, training provided


seems to meet the expectations of almost half of the participants (45.8%), while 25% of them

disagree.

Responders were then asked if they are familiar with the overall strategy of their

company and 72.9% gave a positive answer. When asked about their salary, 71% stated that

they are not satisfied, while only 40% agree that their company’s reward policy is

satisfactory. Also, only 30% of responders stated that their work is fully recognized by their

superiors.

When asked about their personal life, only 22.5% of responders stated that they are

satisfied, while 46% find it difficult to achieve a satisfactory combination of work and

personal life. Communication between the employees also seems to be unsatisfactory, as only

2% of responders stated that they communicate well with their colleagues and 86.9% do not.

Overall, only 47.6% of responders stated that they are satisfied from their work; however

88.8% of them would like to be a client of their hotel.

When asked to evaluate the educational programs implemented by their company (a

multi-answer question), responders stated that the majority of educational programs are job-

related (58.5%) and valuable (54.2%). In addition, 46.7% were satisfied with the possibility of

acquiring new knowledge, while the majority of staff training programs were moderately or

not at all satisfactory, as they did not contribute to retention (76.6%), efficient use of time

(76.6%) and enhancing personal growth (74.8%), with the latter being particularly

contradictory to previous answers.

Table 2: Characteristics of training programs

0% 20% 40% 60% 80% 100%

Practical value

Plenitude of training

Aquiring new knowledge

Personal enhancement

Relevance to the job

Effective time use

Interest maintenance

Clarity

Characteristics of training programs and level of satisfaction

None Limited Average High Very high n.a.

We then cross-tested:

The effect of certain demographic variables on the response of hotel employees regarding

training programs and company loyalty. To evaluate this we used gender, type of

employment (permanent or seasonal), total years of service, age and the educational level

of participants as independent variables.

The effect of years of service against the “eleven statements” group, sub-divided into two

groups, job satisfaction and education.


Demographics against employees' views on the usefulness of applied staff training

measures.

Any relationship between demographics and respondents' perceptions on certain

characteristics of the educational programs: practical value, completeness of training,

enhancement of personal development, relevance to employment, efficient use of time,

maintenance of interest and clarity.

Regarding “sex” and “overall job satisfaction”, t-test showed no difference (t = -1.505,

df = 105, p = .135) and the same was true for “education level” (t = -1.268, df = 105, p =

.208). On the contrary, women seem to experience a greater “lack of recognition from their

superiors” (t = -2.052, df = 105, p = .043) than men.

When analyzing participants' views on the usefulness of implemented specific

measures related to staff training, we found statistically significant differences in favor of

women in two statements, "Discussion with monitoring groups, to share experiences in

implementing ideas" (t = 2.252, df = 58.508, p = .028) and "Summary presentation of current

issues" (t = 2.057, df = 105, p = .042).

Applying t-test on job satisfaction among permanent or seasonal employees we found

a statistically significant difference between employees' satisfaction and their employment

status (t = -2.510 df = 105, p = .014) as seasonal employees were less satisfied.

Participants' views on the usefulness of the training they received were unaffected by

their employment status (t = -1.500, df = 105, p = .137), while a marginal statistically

significant difference was seen in the responses to "Completeness of training" (t = 2.011, df =

105, p = .045) with permanent employees showing a higher degree of satisfaction. The effect

of work experience on participants' responses was tested by one-way independent sample

analysis of variance (ANOVA, Ostertagova & Ostertag 2013). Participants were divided into

six year-of-service groups, but no statistically significant difference (p > 0.05) was found

between employees’ satisfaction and years of service [F (2.213) = 1.451, p = .213 > .05, n 2 =

.067]. The same was true for the relationship between the service groups and the employees'

views on the plenitude of the training course [F (1.257) = .389, p = .855 > .05, n 2 = .019].

Using the ANOVA test for the same groups in relation to the statement "I know the

overall strategy of the company ", employees with fewer years on the job know less about the

business strategy [F (2.915) = 4.488, p = .001 (p < .05)]. In relation to the proposal "My

income is satisfactory", those with at least 20 years of service agree more than employees

having 1-10 years of experience, who strongly disagree [F (3.339) = 4.458, p = .001 (p <

.05)]. Finally, in relation to the statement "In my work I receive the recognition that I

deserve", those with 1-15 years of service fully disagree [F (2.200) = 2.861, p = .019 (p <

.05)].

The age of the participants (same distribution as before) against job satisfaction was

another independent variable tested using the ANOVA test. It did not show any statistically

significant correlation between age and job satisfaction [F (1.926) = 1.580, p = .185 > .05, n 2

= .058] or education [F (1.948) = .770, p = .547> .05, η 2 = .029]. We did not perform any

post-hoc test, as there were not enough participants in these age groups, this being a limitation

of this study. In contrast, we identified statistically significant differences when examining the

relationship between age and the proposal "I know the overall strategy of the company", [F

(2.019) = 2.857, p = .027 (p < .05)], "My income is satisfactory" [F (2.523) = 3.128, p = .018

(p < .05)], "In my work I receive the recognition that I deserve" [F (2.018) = 2.554, p = .043

(p < .05)] and "There is a good balance between my professional and personal life" [F (2.423)

= 3.002, p = .022 (p < .05)].

We then checked whether the educational level of the employees involved in the

research influenced their views. The ANOVA test did not show statistically significant

differences for the dependent variables used, as for training the results were F (2.324) = .924,

p = .453 (p > .05), n 2 = .035 and for satisfaction F (2 .526) = .413, p = .799 (p > .05), η 2 =


.016. Therefore, employees' views on the adequacy of the training they receive, as well as

their job satisfaction, are not affected by their educational level.

To test if employee’s work commitment is influenced by the training they receive, we

used Pearson's coefficient r test (Sedgwick 2012). The analysis showed a moderately positive

correlation r = 0.497 that was statistically significant (p < .05) for the proposals "Overall, I am

satisfied with my work" and "I receive sufficient training to be effective in my work".

Similar results emerged from the correlation of the responds on the proposals

"Overall, I am satisfied with my work in the business" and "The training provided by

management is desirable and fulfills my expectations". The coefficient r = 0.475 indicates a

moderately positive correlation (p < 0.05) between the employees' work satisfaction and their

view on training provided (i.e. if it is desirable and fulfills their expectations), as seen in Table

3.

Table 3: The relation between the satisfaction from the job and the satisfaction from the

training programs

Completely

agree

Rather

agree

Not agree

or disagree

Rather

disagree

Completely

disagree

Satisfaction from the job 3.7 40.2 27.1 27.1 1.9

Satisfaction from training 3.7 43.9 27.1 24.3 0.9

0

5

10

15

20

25

30

35

40

45

50

Satisfaction from the job and the training programs

Satisfaction from the job Satisfaction from training

5. Discussion

Modern tourism enterprises, in order to make managerial decisions regarding staff training,

need to perform well-structured periodic staff evaluations in order to apply novel methods for

improving employee performance through training and specialization (Alomari et al 2017,

Haynes & Fryer 2000). This evaluation provides information useful to define clear and

objective goals and develop certain actions to achieve these goals, including adequate training

methods for the employees (Ashton 2017, Aguiar-Quintana et al 2016, Krishnamoorthy &

D'Lima 2014).

This study investigated whether appropriate training is associated with job satisfaction

and business loyalty. Results showed that the vast majority of five-star hotels in Athens

(95.4%) provide some kind of training that is addressed to 90.7% of employees. The

preferred method is on-the-job-training, probably because it is job-related and cost-effective,

as compared to other methods, i.e. audiovisual or computer related and apprenticeship.

Responders on the other hand seem to prefer counseling (71.7%), as it provides clear

answers, reduce work stress and fully support them. Upgraded training in specific topics also


appears popular (67.3%), reflecting their need to approach issues quickly and thoroughly,

while brief presentations on topics of interest is another strong option (54.2%).

We also found that hotel employees are not completely satisfied with both their work

environment and financial benefits a finding that is consistent with other studies (Dadeer et al

2017, Chi & Gursoy 2009). Tourism industry, although offering career opportunities even in

times of economic crisis, cannot provide high salaries, except for very few high-level jobs.

Employees state however that they are familiar with the company strategy, a finding that

shows that there is a good communication between them and hotel administration. This is

very important for job satisfaction, employee commitment and employee productivity (Anku

et al 2018, Bhatti & Qureshi 2007).

Responders state that the reward policies implemented by the company are not

satisfactory and do not meet their expectations, while they also think that they do not receive

the recognition that they deserve (60% in both cases). It is possible that hotels do not see

motivation as a priority in order to urge their employees to achieve higher goals than the set

ones (Putra et al 2017). It is also likely that this is related to the particularly high rate of staff

change (new recruitments) seen in hotels, as many prefer to hire new staff rather than leverage

existing ones in different positions (Solnet et al 2019, Anku et al 2018, Lertwannawit et al

2009).

The inability to achieve a balance between professional and personal live is a worrying

finding. Unstable working hours, shifts, days off when it suits the hotel and not the employee

and working on public holidays are probably contributing to this (Paek et al 2015, Bednarska

2013). As a result, hotel employees find it difficult to socialize and keep pace with their

family or friends.

In addition, several issues regarding employee intra-communication were identified, as

only 13% of respondents stated that such a communication was satisfactory. The positive

work climate, largely shaped by the communication between employees, is a key factor for

employee loyalty (Matzler & Renzl 2006). Our findings probably result from high

competition between colleagues, work under pressure and bad working conditions, including

work environment issues (Dardeer et al 2017). Also in many cases hotel staff does not see a

department issue as an overall business problem, perceiving themselves firstly as department

members. It is worth noting, however, that 48% of respondents stated that they were satisfied

with their job, while 9 out of 10 would like to be clients of the hotel they work, probably due

to the high quality of service five star hotels offer.

Regarding education, several important findings were revealed. Firstly, 65% of

responders stated that their supervisor is actively involved in improving their work and

training. It seems that communication between the supervisor and the employees is extremely

important in team work and also confirms the previous finding that the hotel management

prefers on-job training by the supervisor.

The answer to whether the training that employees receive in order to be effective in

their work is sufficient was inconclusive. 44% of responders stated that they find it effective,

27% "neither agree nor disagree", and 27% disagreed with the proposal. This result could

reflect the personality of the employees and the degree to which everyone trusts himself or

herself. It might also be affected by work experience, as un-experienced employees definitely

seek more training programs (UNWTOa 2020).

The evaluation of the educational programs gives us some important findings

regarding the quality of these programs. Employees stated that about half of the educational

programs organized by their hotels are job-related, has a practical value and offers the

potential to acquire new knowledge. Also, four out of ten employees seem satisfied with the

programs, regarding completeness and clarity. On the contrary, these programs seem to fail to

maintain interest, efficient time use and personal development enhancement, as almost eight

out of ten employees were not satisfied when asked about these parameters.


The evaluation of the educational programs applied is important for hotels, as their

quality and efficiency must always be tested. About half of the responders find these

programs to be job-related, of practical value and effective in providing new knowledge,

while four out of ten employees appear satisfied with the programs in terms of completeness

and clarity. However almost 80% of responders think that they do not maintain interest, are

not time consuming and do not lead to personal development. It seems that the organizers of

these educational programs emphasize on the content rather than their presentation and

personalization. The programs are relevant and useful, but not attractive to all. This may be

due to limited time and resources or the use of widely used programs that are not tailored to

meet the needs of each particular hotel or department. Low levels of employee satisfaction

mean that the content and specificity of these programs need to be redesigned. UNWTO aims

to solve this problem by providing the TedQual Certification System that is composed of

several evaluation criteria that are universally applicable to tourism education, training and

research programs aiming to measure the effectiveness of the pedagogical system and

evaluate to which extent the needs of the certain tourism sector are covered (UNWTOb 2020).

Regarding sex, it seems that it does not affect company loyalty and overall satisfaction

of applied training programs, but affects women’s impression on certain methods of training

and also job recognition. Indeed, in our study women seem to make greater efforts to be

recognized in their work than men, while consultation with their supervisors or monitoring

groups on various everyday issues is not considered to be practical and useful to women

employees.

Regarding work status (permanent or seasonal) it did not strongly affect responders’

view on the training programs offered. No differences were seen as for the usefulness of

training, while employees with permanent positions are more satisfied that seasonal working

responders. This could be explained by the fact that seasonal staff may not have the same

amount of training time as the permanent staff, thus considering training programs

incomplete. On the contrary, seasonal hotel workers are much less satisfied with their work

than permanent employees, probably due to professional uncertainty, high work load and

constant working pressure.

Regarding the years of service, it did not affect job satisfaction but several

discrepancies were seen on various other issues. Participants with fewer years of service do

not have a good understanding of the company strategy, as do employees with more work

experience in the hotel, who appear to be more familiar with the company's goals. The same

was true regarding income and job recognition, as seasonal personnel has lower salaries and

do not get jobs requiring high responsibility and expertise. Combining age and years of

service, older employees are more familiar with the overall strategy of the business than the

younger ones, with the latter being less satisfied with their income and job recognition,

probably because they do not hold positions of responsibility and do not get promoted as

quickly as they expect. Another interesting finding is that the age group reporting most

dissatisfied with the balance between work and personal life is the middle age group (36-55

years), probably because at this age family is a very important aspect of their life, resulting in

an imbalance between professional and personal time (Bednarska 2013).

Regarding the level of education, it did not affect responders’ views in general, since

neither the questions related to the evaluation of educational programs nor the questions on

job satisfaction showed any significant differences between the various levels of employees’

education.

Finally, job satisfaction is mildly associated with the satisfaction they receive from

training, as 43.9% of participants who stated that they are satisfied with their training are also

satisfied with their job. This probably means that adequate and specific training may lead to

higher levels of job satisfaction.


In summary, this study proved the necessity of quality staff training programs, as the

vast majority of participants see training as a necessary precondition for a highly satisfactory

job performance. The first key question of this research on the relationship between adequate

training and job satisfaction was positively answered, as the correlation between these two

factors was found to be quite high, despite the fact that employees showed skepticism

regarding the training programs offered, demanding more complete, more specialized and

more personalized ones. The second key question however was not positively answered, as

hotel employees are not satisfied with the work environment, financial benefits, reward

policies and job recognition by their superiors. This means that Athens five star hotels, in

order to increase loyalty, should adjust their human resources policy accordingly.

Future research could compare luxury hotels operating in Attica and other areas, both

in Greece and abroad and also compare 5 and 4/3 star hotels performance, in order to identify

areas for further improvement.

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https://www.unwto.org/UNWTO-academy


Sharing economy in time of economic crisis: The owners' perspective of

Airbnb rentals in Greek cities

Abstract:

This paper focuses on the impacts that sharing economy has on the life of Airbnb rental

owners based on a primary research to Greek cities and destinations. Firstly, the current paper

presents the theoretical background of sharing economy and P2P business models and

presents secondary data for the relative Greek market. The primary data of this paper were

gathered through an online questionnaire which was delivered to owners of Airbnb house

rentals and discuss their perspective of the socioeconomic changes that sharing economy has

to their own lives. The results showed that there are certain reasons due to which the owners

decided or were “actuated” to join the Airbnb platform, such as financial crisis and income

opportunities, rental taxation etc. Owners argued that there are various positive

socioeconomic impacts that highlight the importance of sharing economy to local societies:

increased income, economic opportunities, enhanced opportunities for travelling (demand

side), additional demand for local businesses and so on. The current paper argues that there

are specific characteristics for each society that should be considered to examine positive and

negative aspects of sharing economy impacts and decide which policy is the most appropriate

for each destination.

Keywords: Sharing economy, Airbnb rentals, Airbnb Owners’, Greece

Sofoklis Skoultsos1, Anna Kyriakaki2, Alexios – Patapios Kontis3, and Despina Sdrali4

1 Corresponding-Address: Sofoklis Skoultsos, Department of Tourism Economics and Management, University

of the Aegean, 8, Michalon Str., Chios, Greec. Email: [email protected] 2 Corresponding-Address: Anna Kyriakaki, Academic Faculty lab Instructor (PhD), Business School, University

of the Aegean. Email: [email protected] 3 Corresponding-Address: Alexios – Patapios Kontis, Department of Tourism Economics and Management,

University of the Aegean. Email: [email protected] 4 Corresponding-Address: Despina Sdrali, Assistant Professor, Department of Home Economics and Ecology,

Harokopio University. Email: [email protected]

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1. Introduction

In recent years, technology innovation and supply-side flexibility have created a growing

interest in sharing economy (Zervas et al., 2017). Sharing economy as an alternative and

highly flexible economic model allows people to obtain, give and share access to products and

services, coordinated through community-based online services (Hamari et al., 2016). Since

the later part of the last decade, travel and tourism are at the forefront of the sharing economy

and many tourism related activities, such as accommodation, transportation and

entertainment, are offered through sharing economy platforms (Skoultsos et al., 2017).

The strengthening of the tourism industry, the increase of arrivals and overnight stays,

and the economic crisis as well, have boosted the sharing economy. On the other hand,

tourists are more open to self-guided holidays (OECD, 2016). They use -even elderly ones-

technology advances and seek for information from social surrounding or anonymous tourists

who post on the internet. Furthermore, tourists have rethought their values, lifestyles and

patterns of consumption (Sdrali et al., 2015). They are seeking to push the boundaries of their

comfort zone, searching for authentic vacation and an involvement and interaction with local

people (Tsartas et al., 2017). Sharing economy platforms have been thus promoted as

opportunities for customization of the experience, for “meeting” the locals and for authentic

experience that are different from the traditional tourism product (Sarantakou et al., 2018;

Juul, 2017).

In addition, sharing economy has received a great deal of attention by the potential

entrepreneurs due to the economic crisis and its consequences (such as unemployment,

decreased income, inequality, poverty etc.), which opened up opportunities for new forms of

employment.

One of the most discussed examples of the sharing economy is Airbnb; a popular

online marketplace for short-term rentals which was founded in 2008 and since then it has

spread quickly. As a result, various researchers focused their interest to enlighten all the

different aspects of this new type of businesses and examined specific services and sharing

economy platforms (e.g. Airbnb). The most research has been conducted to investigate why

tourists choose to stay in Airbnb accommodation and explore the ways they use the service

(e.g. Farmaki and Stergiou, 2019; Stollery and Jun, 2017; Guttentag, 2015). Researches

showed that customers choose these types of services based on a wide range of reasons such

as: lower prices, increased alternative choices, enhanced experiences (e.g. living like a local),

etc. Other researchers focused on the impacts that P2P models have on the operation of

traditional businesses. For example, they try to reveal the impacts that short term rentals have

on the operation of hotels and destinations. However, limited research has been conducted

regarding the impacts on the life of suppliers of sharing economy services and their own

perspective on this new business models.

This paper aims to study and analyze the socio-economic profile and the basic reasons that the

individuals rent theirs homes via Airbnb platform but also their perceptions about social and

economic impacts that sharing economy has on based on a primary quantitative research to

several cities and destinations in Greece.


2.1 The rise of sharing economy

Rauch and Schleicher (2015) claim that the modern sharing economy has two features, based

on the idea that it may be monetized (e.g. earning income from sharing assets) or non-

monetized (e.g. swapping or gifting platforms). According to Szabó (2017), sharing has two

forms; the “pure sharing”, which works without any profit or business consideration, and the

mixed version of sharing, which uses the potential of community, being driven by business

interest from the intermediary’s side.


The sharing economy has opened up new opportunities for the providers and the

consumers as well. The sharing economy platforms have seen huge growth which is due to a

number of parameters, such as technological, economic and social.

Specifically, the speed and adoption of technological innovation has a considerable

impact on the aspects of our lives, such as communication, production and distribution

(Kontis et al., 2019; Oskam and Boswijk, 2016; Katsoni, 2016; Fountoulaki et al., 2015;

Kavoura and Katsoni, 2013). People have become familiar with the internet and especially

younger generation has steadily become more comfortable with purchasing goods and

services online. Mobile devices have equipped with applications that make people searching

for and accessing the available guest room, car, parking space or home-cooked meal even

from people they have never met. People share and trade their own goods, becoming

producers/suppliers and sellers without many times having the use of money (Sigala, 2017).

For Gansky (2010), the sharing economy benefits refer to the “triple bottom line”

included a greener commerce, greater profits and rich social experiences. However, economic

benefits may be considered as the strongest driver of the sharing economy. The sharing

economy is able to contribute to economic development, create new jobs, support jobs in

other industries, generate new income resources and give the individuals the capability of

micro-entrepreneurship.

Sharing aims at goods and services which would be otherwise unaffordable for the average

consumer (Szabó, 2017), becoming less dependent on ownership (Skoultsos et al., 2017;

Frenken and Schor, 2017). Participating in the sharing economy platforms can maximize the

users’ utility as they can choose lower-cost options (Zervas, et al., 2017; Belk, 2010;

Lamberton and Randall, 2012). Recent research has shown that people with low income are

the ones who mainly benefit from the sharing economy (Bradshaw, 2015; Fraiberger &

Sundararajan, 2015; Kodransky & Lewenstein, 2014). However, for Chiang (2015) people

who use sharing economy services seem to be “affluent”.

Moreover, there are many “green” sharing examples, such as carpooling, ride-hailing,

bike-sharing and exchanging used clothing, that produce fewer goods to deliver value to users

(Voytenko et al., 2016; Schor and Wengronowitz, 2017; Sigala, 2017). However, at the same

time, an opposite effect is generated (Szabó, 2017). According to Schor (2014) and Szabó

(2017), accommodation and transportation sharing services cannot always lead to a reduction

of carbon emissions, and the environmental impact may be increased. Why? The users are

travelling more often than before, and the traffic jams are increasing. Thus, the environmental

effects of the sharing economy platforms are seemed complex (Kontis et al., 2020; Sarantakou

et al., 2019; Frenken and Schor, 2017; Douglas & Lubbe, 2014; Kontis, 2014; Berne, et al.,

2012).

Another reason why individuals join sharing platforms is “social capital” (Botsman and

Rogers, 2010). The interested parties are allowed to meet new people, and may make friends,

share their culture and gain inter-cultural experiences (Farmaki and Stergiou, 2019; Böcker

and Meelen, 2016). However, the increasing number of people motivated by economic

reasons and the quality of ratings may contribute to the declining importance of social ties on

the sharing economy platforms (Frenken and Schor, 2017).

Sharing entails a high degree of risk due to the information asymmetries. Therefore,

trust seems to be the cornerstone of the sharing platforms and the base among its users

(Szabó, 2017). According to the PwC report (2015), the sharing economy is referred as “the

trust economy”, and for Slee (2013), the two main aspects of sharing economy are

coordination and trust. For this reason, the digital platforms, through rating and reputation

systems, are able to make sharing less risky and more appealing (Frenken and Schor, 2017).

However, according to Overgoor et al. (2012) and Zervas et al. (2017), ratings are considered

inflated and not very accurate. Ma et al. (2017), having examined how hosts describe

themselves on their Airbnb profile pages, found that they use a variety of disclosure strategies


which cause differences in perceived trustworthiness scores. The findings suggested that

longer self-descriptions and a mix of assessment signals in combination with conventional

ones that provide information about one’s hospitality or interests, are perceived to be more

trustworthy.

Concluding, despite the positive impacts of the sharing economy, it is argued that it

may display problems as well, regarding the safety and insurance issues, the establishing trust

and lack of information asymmetry, and environmental impacts (European Parliament’s

Committee on Transport and Tourism, 2015).

2.2 The sharing economy in the tourism sector. The example of Airbnb

The strengthening of the tourism industry, the increase of arrivals and overnight stays, and the

economic crisis as well, have boosted the sharing economy. According to OECD (2016), the

sharing economy is forecast to reach USD 335 billion by 2025. Much of this growth is in the

tourism sector and especially in the following four sub-sectors of it; i.e. accommodation,

transportation, dining and travel planning.

It is worth mentioning that short-term rental services within the sharing economy are

no longer regarded as alternative ones, but a widespread choice (Quinby, 2016). The

European Association of Hotels and Cafes estimates that in Europe the size of the sharing

economy accommodation is double than the size of the traditional hotel accommodation. A

typical example is France, where 40% of the available accommodation is available through

the sharing economy platforms (European Commission, 2016).

One of the most discussed examples of the sharing economy accommodation is

Airbnb.com. According to the website of Airbnb (2020), it has 7 million listings in 220

countries worldwide, while in 2008 -the year of establishment- it had 1.5 million listings in

190 countries worldwide. Airbnb also accounts for a similar number of room nights as some

major hotel brands (Guttentag, 2015). Furthermore, it was valued at 31 billion U.S. dollars in

2017 (25.5 in 2015), with total equity funding of around 3.3 billion U.S. dollars (2.3 in 2015).

Airbnb create opportunities for local people to rent their home for short stays and

generate additional income or/and support them to explore new business ventures (Kontis, et

al., 2018). For example, Airbnb hosts in Australia earned a median income of $4,920 in 2015-

16; a supplement to a household’s main sources of income, which may be handy for living

expenses, to pay down debt or increase savings.

In addition, despite the lack of official data, Airbnb can provide benefits to local

communities, as the Airbnb guests use to spend money in the area where they stay, rather than

in areas which traditionally benefit from tourism. With the majority of Airbnb

accommodation situated outside major hotel districts and nearby local neighborhoods,

travelers are likely to visit restaurants, bars, attractions and shops in the area where they stay.

Thus, a range of local businesses is enhanced.

Moreover, tourists are able to live like a local, staying at a host’s private space and

using services in a local neighborhood. Vacation, through the Airbnb platform, can be made

more “authentic”. The guests are allowed to meet a real neighborhood and local people and

enjoy a personal connection with their host and the surrounding community. According to

Sigala (2017), the experience of domesticity and sociality may represent a stronger driver for

using collaborative commerce, as the feelings produced are something that no one can get in

the traditional tourism industry. Furthermore, Airbnb gives the guests the opportunity to

decide what to visit and do under the direction of the local hosts, in order to “live like a local”

(Paulauskaite, et al., 2017).

On the other hand, Airbnb hosts enjoy a number of benefits arising from their

activities on the platform. These benefits are not limited to the income earned from the Airbnb

stays. The hosts also benefit from the interactions with their guests and the broader


community and can experience new cultures without leaving their hometown. Schor (2015)

found that half of the Airbnb hosts were motivated by social interaction and socialized with

their guests. In addition, Ladegaard (2018) showed that socially oriented hosts from Boston

were eager to interact with foreign guests who were different enough to be interesting and

similar enough to be comfortable as well.

Platforms like Airbnb connect people and this personal connection can range from the

mere exchange of services to a conversation or lasting friendship. The ability of Airbnb to

facilitate social interactions as well as accommodation is a key benefit of the platform.

However, Airbnb is considered “guilty” for increasing competition amongst the

traditional accommodation establishments and the sharing ones and creating property

conflicts amongst the local residents (Stergiou and Farmaki, 2019; Gutiérrez et al., 2017)

causing fears of rising rents (Avdimiotis and Poulaki, 2019) and property prices. At last but

not least, it is argued that Airbnb may display problems regarding peer-to-peer discrimination

(Ge et al., 2016), and less employment for lower-educated blue and pink collar workers

(economic inequality) and lower earnings for related markets, while neighbours may also

experience nuisance and feelings of danger from strangers (Frenken and Shor, 2017).

2.3 How to organize an Airbnb

Customer segments: Airbnb is a marketplace where travelers (guests) and locals who have a

shareable space (hosts) are connected around the world. Airbnb hosts offer their properties for

rent for days, weeks or months, and Airbnb guests can search for and book any of these

properties, subject to host approval (Zervas et al., 2017). People who want to become Airbnb

hosts can offer a whole house or a private room or a shared room.

The process: The hosts can list their space on Airbnb at no charge and upload

personal characteristics and information on their place, the number of people that it can

accommodate, the amenities it offers, its price, its availability, and photos and a map showing

the approximate location. Then, the search for the guests follows via the website’s tools. A

guest seeking to rent a property on Airbnb can view the desired destination and dates and can

browse by price range and size of the property. Geographical neighborhoods, amenities, host

language and room types can be some other criteria. Upon finding a space, a request is sent.

Airbnb presents the guest’s request to the host who accepts or declines it, and after the

booking is made, Airbnb charges both the hosts and the guests.

Key element: Trust is the key element of the sharing economy. According to Zervas

et al., (2017), trust is difficult enough to be built due to the information asymmetries the users

face. This happens because the guests and the hosts typically know little about each other.

Thus, Airbnb, in order to build trust among its participants, beyond self-supplied information,

encourages them to verify their identity. The users can do so by linking their Airbnb account

with other website accounts (e.g., Facebook, LinkedIn), and by providing a working email

address and phone number, and a copy of their passport or driver’s license. Moreover, once

the guest completes his/her trip, both the host and the guest are allowed for 14 days to review

and rate, on a scale from one to five stars, each other on the Airbnb website.

Airbnb in Greece: The new business model, in which digital platforms create an open

market for temporary use of goods or services, mainly provided by individuals, has recorded

during the last decade rapid growth also in Greece within the sharing economy. As in other

destinations, sharing economy model extends to a spectrum of business sectors, among which

the most developed in Greece is that of private short-term accommodations through digital.

The familiarization with social networks, the wide use of online markets and electronic

services such as the expansion of mobile devices and the increasing access of Greeks to the

Internet are some of the main factors for the development of the sharing economy and related

web-based electronic platforms and mobile applications. In Greece there is a number of short-


term rental digital platforms, such as Airbnb, TripAdvisor, Booking.com, HomeAway,

HouseTrip, Flipkey, Trivago, 9Flats, Roomorama, Stay in Athens and trip2Athens.

The worldwide rapid growth of sharing economy found the majority of public

authorities unprepared. The legal initiatives that were undertaken during the development of

the “sharing economy” phenomenon have resulted in wide variation in the legislation

governing the operation of short-term rentals between countries or specific destinations. The

expansion of short-term rentals and the reactions of established Greek tourism stakeholders

have showed up the need for an explicit institutional demarcation and operating framework,

ensuring equal competitions terms between new and traditional tourism providers. The Greek

parliament, in order to regulate key issues of this new market such as tax evasion and safe and

quality provision of services by unlicensed operators, has made a series of legislative

interventions (Laws 4179/2013, 4336/2015, 4446/2016, and 4472/2017), whose results will be

visible over time. The long-lasting lack of a relevant institutional framework and the gradual

implementation of recent laws allowed the establishment of an efficient and appropriate

framework for official registration of short-term rentals just in last period. As consequently

precise data on the evolution of listed properties and short-term rentals sizes listed properties

were not available from official sources (official authorities and bodies).

Based on data from private international database AirDNA (Table1), total active

rentals in Greece that distributed via share economy platforms ware 132 in 2010 and arise to

126.231 at the middle of 2018 (increasing 956 times or 95.630%).

Table 1: Total number of listed short-term rentals in Greece, 2010-2018

Year Total of active rentals Annual increase % Annual increase

2010 132

2011 747 615 466%

2012 2.298 1.551 208%

2013 5.390 3.092 135%

2014 12.063 6.673 124%

2015 26.455 14.392 119%

2016 57.307 30.852 117%

2017 96.217 38.910 68%

2018* 126.231 30.014 31%

Source: www.airdna.co, Athanasiou & Kotsi (2018)

* The data for 2018 refer to the end of the first half of the year. Every property in Greece

is covered from October 2016 onwards

Αs expected, tourism destinations across the country with the largest tourist flows

attract the majority of active accommodations via share economy platforms. In 2018 five

regions of the country (Attica, South Aegean, Crete, Central Macedonia and Ionia Islands)

possess over of 80% of the total number of listings properties distributed via share economy

platforms (Table 2)


Table 2: Listed short-term rentals in Greece by region, 2010-2018

Source: www.airdna.co, Athanasiou & Kotsi (2018)

* The data for 2018 refer to the end of the first half of the year. Every property in Greece is

covered from October 2016 onwards

The expected benefits and the enthusiasm of independent micro-entrepreneurs have led to

frenetic growth rates with insufficient time period and limited information for the

sustainability of these investments.

Table 3: Facts for Airbnb in Greece, 2017-2018

Metric 12/2017 12/2018 Growth Rate

Available Entire Place Listings 59,379 72,144 21%

Booked Entire Place Listings 17,138 22,079 29%

Listed Nights 473,163 585,766 24%

Booked Nights 249,577 270,021 8%

Occupancy 52.7% 46.1% -13%

Average Daily Rate €152.96 €126.44 -17%

RevPAR €80.68 €58.28 -28%

Source: www.airdna.com

According the report of Mark Saltana (AirDNA, 2019) diverging trends in some of the

key performance indicators of short-term leases through Airbnb were shaped. On the one

hand there has been an increase in both "booked nights" and "total listings properties", but on

the other hand there has been a decrease in all short-term rental metrics such as Occupancy,

Average Daily Rate (ADR), and Revenue Per Available Rental (RevPAR). Despite the

increase in demand (booked nights +8%), the increase of supply, which surpass the increase

in demand (Available Entire Place Listings +21% & Booked Entire Place Listings +29%)

results in increased competition and leads to both lower occupancy and lower prices (ADR

and RevPAR).

http://www.airdna.com/


3. Research Methodology

This paper aims at studying and analyzing the impacts that sharing economy has on the life of

Airbnb rental owners based on a primary quantitative research to Greek cities and

destinations. More specifically, the main survey aims are the following:

1. the determination of the demographic characteristics of the Airbnb houses owners (e.g.

age, income, education, etc.) and of the general characteristics of the Airbnb houses in

Greece (e.g. houses’ location, years of cooperation with the Airbnb, etc.),

2. the identification of certain reasons due to which the owners decided or were

“motivated” to join the Airbnb platform (e.g. financial crisis, income opportunities,

rental taxation etc.),

3. the investigation of the personal impacts (economic and social) that the sharing

economy (especially Airbnb) has both on the life of Airbnb rental owners and in

general ways,

4. the investigation of the key factors that influence the owners’ perceptions and attitudes

towards the legislative framework but also the impacts (social and economic) of

sharing economy and specifically of Airbnb.

4. Sample and data collection

For achieving the above goals, a primary quantitative research was undertaken by means of a

structured questionnaire. The research focused on a representative sample of 218 Airbnb

rental owners. Research was conducted via e-mail questionnaires sent to Greek owners

Airbnb houses, from May 2018 to December 2018. Taking into consideration the figures of

the population and the research objectives, the purposive sampling method was applied as this

method is widely used, mainly because of its simplicity in terms of statistical inference

(Iosifidis, 2017). According to the aims of the research, individuals/owners from different

Greek destination/region, belonging to both sexes and exercising diverse professions etc., are

included so as to guarantee sample representativeness (Levy & Lemeshow, 2013; Iosifidis,

2017). The distribution of rental houses of Airbnb in the sample almost follows the allocation

of hotel beds per region, with the regions of Attica and Central Macedonia being superior

because of the existence of the largest urban centers (Athens and Thessaloniki, respectively)

of Greece (see Table 4).

4.1 Survey instrument

To collect the research data was used an e-questionnaire with totally 28 close-ended questions

(five-point Likert scale, multiple choice true/false, and general multiple-choice ones). The

researchers draw from research aims and previous researches for the formulation of the

questions (Boswijk, 2016; Guttentag, 2015; Zervas et al., 2017). The questionnaire consisted

of five separate sections. The first section analyzed the “Demographic Profile or the

Respondents”, the second was about “General Characteristics of Airbnb houses”, the third and

fourth section covered the “Economic impacts of Airbnb” and the “Social impacts of Airbnb”,

respectively, and the fifth section consisted the “Attitudes of legislate framework about

Airbnb”. The questionnaire was available in Greek language. The statistics package SPSS 24

was used for the processing and the analysis of the data.


Table 4: Geographical distribution of the Sample (Greek owners Airbnb houses)

5. Results & Discussion

5.1 Demographics of the owners

Regarding age of the participants in the survey, most of them are between 36 to 55 years old

(~60% of the sample). Also, it has to be noted that there is a significant percentage from 26-

35 years old (23.9%) which is the age that has been affected from the financial crisis the most.

Also, 36.4% of the sample have an annual income between 10.000 – 20000 euros. It is

clarified that for the majority of the owners the income from the Airbnb rentals is

supplementary. Another interesting characteristic is that the majority of the sample is working

at the private sector either as an employee (29.1%) or as an owner (25.6%). The majority of

the sample declared that cooperates with Airbnb from 1-2 years. This result highlights the fact

that a large number of rentals have been added recently at Airbnb platform in Greece.

Table 5: “Demographics” of the owners


Table 6: Responds from the owners Prior use of the apartment (%) Source of information about

Airbnb alternative (%)

Total days annually of

renting the apartment at

Airbnb platform (%)

Country house 35 Friends / Relatives 50.4 0 – 30 days 8.4

Apartment for

permanent renting

36.4 Social Media 10.5 31 – 60 days 20.3

Rooms to let 4.2 Internet 31.5 61-90 days 23.1

Permanent residence 27.3 Media (TV,

Newspapers etv.)

4.9 91-120 days 16.1

Other 11.9 Other 2.8 121-150 days 9.1

More than 150 days 23.1

Gained income from Airbnb

rentals for you is (%)

Do you agree with the

following sentences5 (mean)

Do you agree with the

following sentences6 (mean)

Basic income 14.9 Airbnb rentals develop

imperfect competition

in the tourism market

2.15 Legal framework is

necessary for the

appropriate

operation of the

market

3.80

Additional income 85.1 Airbnb rentals decrease

the demand of hotels

and relative businesses

of the traditional

tourism market

2.36 Legal framework

may cause

finanacial costs for

the owners

3.2

Airbnb rentals increase

the taxes - leakages for

national economy

2.77 Legal framework

will decrease the

offer of Airbnb

rentals

3.48

5.2 Factor Analysis

Regarding credibility of factor analysis, it has been stated that in order to complete factor

analysis, number of respondents has to be five times larger than the number of the selected

items (Siomkos, 2005, p. 286). In the current study the number of participants was 218 (from

286 due to missing values) and number of selected items 17 so the credibility is well stated.

Table 7 shows the variables that have been tested in the factor analysis and refer to the

perspective of the owners in terms of the expected benefits either for them or for the host

destinations in general. Most of the examined variables presented a mean over 3 with the

highest at 4.44 and the lowest 1.997. It has to be highlighted that only three of them have a

mean below 3. This fact depicts high expectations from the perspective of the owners. It has

to be highlighted that the variables 1 to 5 that have presented the highest means are variables

that depict the fact that the choice of entering in the Airbnb platform actually was an expected

solution in real-life problems – such as decreased family and personal income. In other words,

the decision of renting the apartment was not less affected by factors such as trends or cultural

and social comprehension.

Table 7: Means and Standard Deviation of each variable

Variables of expected results from Airbnb rentals Mean Std. Deviation

1. Additional Personal income 4.44 .926

2. Additional family income 4.36 .987

5 Answers was based on 5-point Likert Scale (5=Totally agree totally 1=Disagree) 6 Answers was based on 5-point Likert Scale (5=Totally agree totally 1=Disagree) 7 Answers was based on 5-point Likert Scale (5=Totally agree totally 1=Disagree)


3. Exploitation of my own property (as initial motive) 4.07 1.067

4. Exploitation of personal property (in general) 4.06 .894

5. Solution during the financial crisis 4.01 1.160

6. Increasing travel capability (affordable holidays) 3.91 .932

7. Increasing travel capacity (increasing travelling) 3.60 1.078

8. Social and cultural improvement of the rentals' area. 3.57 1.179

9. Distinctive touristic image for some of the areas 3.52 1.053

10. Personal employment 3.51 1.067

11. Creation of new touristic areas inside the cities 3.49 .990

12. Cultural exchange and comprehension 3.35 1.125

13. Increasing repeat visitors 3.29 1.075

14. Social bonds between hosts and visitors 3.18 1.092

15. Employment solution 2.98 1.504

16. Current trend 2.02 1.179

17. Need for socializing 1.99 1.129

Results from analysis (principal components factor analysis with varimax rotation)

revealed 4 factors with an eigenvalue of at least 1.811 and explained 63.172% of Total

variance. Table 8 presents the results of factor analysis: factor loading for each variable item

and the relative eigenvalue. Also, the reliability of each factor is presented from the relative

number of Cronbach Alpha. The results clearly show that examined variables can be

combined to four factors which can be described with the following titles: 1. Benefits for

destinations, 2. Social and cultural benefits, 3. Personal benefits, 4. Additional alternative &

solution.

Table 8: Variables of expected benefits from Airbnb rentals (4 factors)

Variables of expected benefits

from Airbnb rentals

Factor

Loading

Eigenvalu

e

Variance

explained

(%)

Cronbac

h Alpha

F1: Benefits for destinations 4.743 27.900 .884

Increasing travel capacity

(increasing travelling)

.839

Distinctive touristic image for some

of the areas

.838

Creation of new touristic areas

inside the cities

.813

Increasing travel capability

(affordable holidays)

.763

Social and cultural improvement of

the rentals' area.

.734

Increasing repeat visitors .546

F2: Social and cultural benefits 2.242 13.191 .838

Need for socializing .763

Social bonds between hosts and

visitors

.634

Cultural exchange and

comprehension

.603


F3: Personal benefits 1.943 11.427 .674

Exploitation of my own property

(as initial motive)

.817

Additional Personal income .665

Exploitation of personal property

(in general)

.545

Personal employment .540

F4: Additional alternative &

solution

1.811 10.654 .534

Solution during the financial crisis .856

Employment solution .639

Additional family income .555

Total Variance Explained 63.172%

5.3 Cluster Analysis

Regarding cluster analysis’ results, dendrogram of Hierarchical analysis revealed that two-

clusters solution should be accepted. Nevertheless, k-means cluster analysis for three- and

four- cluster solutions was implemented in order to test other alternatives. Finally, the

solution of two clusters was chosen as the most effective differentiation of the sample. Table

9 presents the respective results of cluster analysis and the mean values of the variables for

each factor of each created cluster. First cluster represents the 39.4% of the sample and

presented a mean score of 2.93 to all the factors. Specifically, 1st cluster’s respondents

declared as important only the variables of 3 and 4 factor which means that focused on

personal benefits and alternative solution of employment and family income. On the other

hand, second cluster represents more than the half of the sample (60,6%) and presented a

mean score of 3.94 to all variables included in the factor analysis. Clearly, the majority of the

sample declared that the expected benefits of Airbnb rentals can affect positively both

individuals and destinations.

Table 9: “Expected benefits” factor means for each cluster

Overall

mean

(n=218)

Cluster 1

(n=86)

Cluster 2

(n=132)

F1: Benefits for destinations 3.55 2.82 4.03

F2: Social and cultural benefits 2.79 2.10 3.24

F3: Personal benefits 4.05 3.62 4.34

F4: Additional alternative & solution 3.78 3.19 4.16

Total to variables included in the factor

analysis8 3.54 2.93 3.94

Note: Answers was based on 5-point Likert Scale (5=Totally agree totally 1=Disagree)

5.4 Characteristics of the revealed clusters

Furthermore, authors implemented additional statistical tests in order to check the correlation

between various answers of the questionnaire and cluster categorization. Specifically, chi-

square tests were implemented and the following variables (derived from the whole

questionnaire) appear to have a strong correlation with the final categorization of the clusters:

8 Removed variable: “Current trend”


Days of rental (x2=18.964, df=5, p=0,002): It seems that owners that rent their houses for

0-30 days tend to be categorized to the first cluster and vice versa.

Legal framework will decrease the offer of Airbnb rentals (x2=29.465, df=4, p=0,000):

Owners from the 1st cluster expect that the application of the legal framework will decrease

the offer of Airbnb rentals (maybe this is because of the fact that they are more focused on

their personal benefits – so the resistance from this type of owners will be stronger)

Annual personal income (x2=19.995, df=4, p=0,001): Owners from the 1st cluster – in

terms of percentages – tend to declare higher personal annual income.

Airbnb rentals decrease the demand of hotels and relative businesses of the traditional

tourism market. (x2=20.757, df=4, p=0,000): Owners from the 2nd cluster seems to disagree in

higher percentages than the owners from the 1st cluster.

From the above analysis it is clarified that sharing economy has been a benefit for

most of the owners mainly. Airbnb rentals provided with alternative and additional personal

and family income

6. Conclusion

The main conclusions of the survey are the following:

The economic crisis in Greece and the income opportunities are consisted of main reasons

due to which the owners decided or were “actuated” to join the Airbnb platform. Other factors

which affected on the above decision are current trends, cultural and social impacts, etc.

The perceptions of the owners in terms of the expected benefits from the sharing economy

are concluded in four factors: Benefits for destinations, Social and cultural benefits, Personal

benefits and Additional alternative & solution.

The majority of the sample argued that there are various positive socioeconomic impacts

that highlight the importance of sharing economy to local societies, such as increased income,

economic opportunities, enhanced opportunities for travelling (demand side), additional

demand for local businesses etc.

Owners' recognition that the sharing economy, on the one hand, is conducive to

increased travels and expanding tourist demand and on the other hand has multiple benefits

for themselves and for host destinations, leads to an understanding of the rapid spread of the

phenomenon. However, several issues and aspects of the sharing economy remain to be

investigated.

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The Challenge of Spatial Information Accessibility for Agricultural

Policies: Case of Pakistan

Abstract Agriculture is directly linked to the socio-economic development of every region. Agriculture

impacts us all, whether we are seeking food security, better health or striving to conserve

natural resources. Goal 2 of Sustainable Development Goals (SDGs) underlines the

significance of agriculture as a means to achieve food security. United Nations in its recently

published report titled “World Economic Situation and Prospects 2020”(UN, 2020) has

declared agriculture as one of the global priorities for achieving high-quality health care and

formal employment opportunities. Agriculture is a spatial subject. Policy makers demand

unrestricted access to spatial data of various kinds in order to address agricultural issues and

for evidence-based policy-making. Therefore, what types of spatial datasets are required for

agricultural policy-making is a relevant question which is the objective of this paper. This

paper also explores agriculture in Pakistan, main challenges faced by the agriculture sector of

the country, and how many as well as what kind of spatial datasets are required to address

these policy challenges.

Keywords: Agriculture, Spatial information, Socio-economic, Challenges, Pakistan

Asmat Ali1 and Muhammad Imran2

1Corresponding-Address: Asmat Ali, PhD Student, Institute of Geoinformation & Earth Observation, PMAS-

ARID Agriculture University, Rawalpindi, Pakistan Email: [email protected] 2Corresponding-Address: Muhammad Imran, Institute of Geoinformation & Earth Observation, PMAS-ARID

Agriculture University, Rawalpindi, Pakistan. Email: [email protected]




1. Introduction

Agriculture refers to the set of process and activities consisting in cultivating soils, producing

crops and rearing animals; it includes harvesting, milking, breeding animals, and keeping

animals for farming purposes (INSPIRE, 2013). The agricultural system in the world is facing

tremendous pressure due to rising population, urbanization, climate change and environmental

stresses(Ghosh, 2019; Zhai et al., 2020). The Pakistan Vision 2025 (Government of Pakistan,

2014) strategy also notes, “The combination of increasing global population, changing

consumption patterns, stagnant agricultural technology, finite cropland, and growing water

stress in the most populous areas has raised the spectre of famines and persistent food

scarcity” (p.17). The Food and Agriculture Organization (FAO) of the United Nations has

estimated that food production will have to be increased by at least 60 per cent to meet the

needs of the world’s expected population of 9 billion by 2050 (FAO, 2014a). This is a great

challenge for global agriculture as one in eight people currently face food insecurity. This is

the reason, agriculture is a global priority because it has direct relationship to economics as

finds (Gardner, 1978; Y. Liu et al., 2020; Mihai, Florin & Latu, 2020). Kamilaris, Assumpcio,

Blasi, Torrellas, & Prenafeta-Boldú (2018) also argue for the same, “The central role of the

agricultural sector is to provide adequate and good-quality food to an increasing human

population and, because of its importance and relevance, it is on the focus of the global policy

agendas”. United Nations in its various reports and findings has underlined the need to invest

in agriculture sector not only for achieving food security and better health care as well as

economics but also to provide employment opportunities to the quickly growing population of

the world.

2. Agriculture in Pakistan

Pakistan is an agricultural country. It has an area of 796096 km2 and population of 204.65

million(Government of Pakistan, 2020). Agriculture in Pakistan consists of a vast spread of

crops, livestock, fisheries, rangelands, and forestry(I. Khan & Khan, 2018). The country has

four provinces namely Punjab, Sindh, Khyber Pakhtunkhwa (KPK) and Balochistan. Punjab

is the agricultural heartland of Pakistan, accounting for 73% of the total cropped area of the

country whereas Sindh province account for the second largest cropped area (14%) of the

country followed by KPK (7%) and Balochistan (5%)(Zulfiqar & Thapa, 2017). Pakistan is

the sixth most populated country in the world and agriculture is the backbone of its

economy(Ali & Erenstein, 2017; Government of Pakistan, 2016, 2018b, 2015; M, 2016; A.

Ullah et al., 2020). Pakistan has huge potential to enhance economic contribution of

agriculture sector through improved productivity(Government of Pakistan, 2019a). The

growth or decline of the agriculture sector directly affects other sector like service sector as

notes (Government of Pakistan, 2019a), “Services [sector] growth was adversely affected by

the slowdown in agriculture”(p.6). Agriculture in Pakistan is not only a source for providing

food, feed and fiber but also contributes to growth as a source of raw materials for industry,

and therefore also plays a vital role in Pakistan’s exports earnings.

2.1 Sustainable Development Goals

In January 2016, the United Nations General Assembly launched 17 Sustainable Development

Goals (SDGs) which are intended to be achieved by the year 2030. In collection of the 17

SDGs, Goal 1. End poverty in all its forms everywhere, Goal 2. End hunger, achieve food

security and improved nutrition and promote sustainable agriculture, Goal 3. Ensure healthy

lives and promote well-being for all at all ages, Goal 8. Promote sustained, inclusive and

sustainable economic growth, full and productive employment and decent work for all, Goal

10. Reduce inequality within and among countries, Goal 13. Take urgent action to combat

climate change and its impacts and Goal 15. Protect, restore and promote sustainable use of


terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and

reverse land degradation and halt biodiversity loss are directly or indirectly related to

agriculture.

On 16th February 2016, the Parliament of Pakistan unanimously approved the 17

Sustainable Development Goals (SDGs) as the national development agenda(Government of

Pakistan, 2019a). On one hand, there are many synergies among SDGs, and they are mutually

supportive, such as poverty eradication (Goal 1) and hunger eradication (Goal 2) as points out

the report (United Nations, 2019). “On the other hand, the 2030 Agenda also implies trade-

offs, e.g. between employment generation and rising productivity (both targets of Goal 8) and

between construction of physical infrastructure (Goal 9) and preserving people’s settlements

(Goal 11)” (Basnett & Bhattacharya, 2015). Countries therefore has to prioritize among

SDGs, due to the limited resources and national circumstances (Donoghue & Khan, 2019).

Government of Pakistan has prioritized for achieving SDGs, and has taken certain steps

including data portal development that is being tested before its launch (Government of

Pakistan, 2019a). Agriculture sector can facilitate in achieving objectives of SDGs 1, 2, 5, 6,

8, 13, 16, and 17(Government of Punjab, 2018). The important role of agriculture in

achieving the SDGs is described in Table 1.

Table 1: Role of agriculture in achieving SDGs

Sustainable Development Goal Importance of Agriculture

Goal 1. End poverty in all its forms

everywhere.

Agriculture is important for ensuring food security

and reducing poverty(Ali & Erenstein, 2017).

Agricultural progress is a potent force in reducing

poverty (OECD & Brooks, 2012). Agriculture

remains in general two to three times more effective

at reducing poverty than an equivalent amount of

growth generated in other sectors (Christiaensen et

al., 2011).

Goal 2. End hunger, achieve food

security and improved nutrition and

promote sustainable agriculture.

The central role of the agricultural sector is to

provide adequate and good-quality food to an

increasing human population and, because of its

importance and relevance, it is on the focus of the

global policy agendas(Kamilaris et al., 2018).

Goal 3. Ensure healthy lives and

promote well-being for all at all ages.

Rapid access to [agricultural] data and information

is crucial to the economic, environmental, and social

well-being of our global society(National Research

Council, 1993).

Without agriculture, it is impossible to live a healthy

live as agriculture is the means to provide food for

living a healthy life.

Goal 8. Promote sustained, inclusive

and sustainable economic growth, full

and productive employment and

decent work for all.

With more than two-thirds of the world’s poor living

in rural areas, higher rural incomes are a prerequisite

for sustained poverty reduction and reduced hunger

(OECD & Brooks, 2012).

Goal 10. Reduce inequality within and

among countries.

Agriculture can help to reduce economic inequality

among countries.

Goal 13. Take urgent action to combat

climate change and its impacts.

The effects of climate change can be observed

across regions. In Europe, for example, heat waves

have become more frequent and intense. This has

caused extensive damage in agriculture and forests

to the point that some forest areas are on the brink of


Sustainable Development Goal Importance of Agriculture

collapse (UN, 2020)

Climate affects agriculture. The role of agriculture is

not only crucial in mitigating, but also in adapting to

climate change (FAO, 2019).

Goal 15. Protect, restore and promote

sustainable use of terrestrial

ecosystems, sustainably manage

forests, combat desertification, and

halt and reverse land degradation and

halt biodiversity loss.

Intense heatwaves and dry spells are likely to cause

widespread wildfires and agricultural losses (UN,

2020).

As shown in Table 1, it can be concluded that for achieving all SGDs in general and

specifically the SDGs mentioned above agriculture sector can play a pivotal role as finds

Mihai, Florin & Latu (2020), “To achieve all range of SGDs across the globe, proper attention

must be paid to rural development perspectives such as quality of life improvement,

sustainable agriculture, rural resilience, and circular economy and reduced inequalities”.

3. Role of Agriculture in National Economy

Agriculture is the engine of economic growth, and agricultural growth is the cornerstone of

poverty reduction. Agriculture is one of the pillars of Pakistan’s economy(Government of

Pakistan, 2014). The agriculture sector of Pakistan contributes 18.9 percent to GDP and

absorbs 42.3 percent of labor force(Government of Pakistan, 2018b). Agriculture is also an

important source of foreign exchange earnings and stimulates growth in all sectors as finds

(Government of Pakistan, 2019a), “Therefore, policies aiming to improve agriculture growth,

improve governance in all sectors”(p.8). Agriculture is still a major source of employment and

income for a vast majority of the poor in Pakistan (M. H. Khan & Imam, 1985). Agriculture

problems in Pakistan disturb the economic growth, in 1947, the contribution of

agriculture towards GDP was 53% but it fell by 21% in the last year (Azam & Shafique,

2017). The contribution of agriculture sector in the GDP of Pakistan has decreased and the

sector is facing severe stagnation in productivity and declining growth(Ghazal et al., 2015;

Government of Pakistan, 2014, 2019a; I. Khan & Khan, 2018) as shown in Table 2 and Figure

1.

Table 2: Role of agriculture in national economy (%)

(Source: Pakistan Bureau of Statistics)

Year Share in

GDP

Employment Exports Imports

2014-15 20.9 43.5 16.8 18.7

2015-16 19.8 42.3 16.5 18.7

2016-17 19.5 42.3 15.5 17.7

2017-18 18.9 42.3 17.8 16.0

2018-19 18.5 38.5 17.1 17.5

From Table 2, it is evident that agriculture's share in economy and employment has

declined. Similarly, agriculture's share in exports and imports is not a healthy sign, too. Like

rest of the world countries, there are two basic principles of agriculture in Pakistan, too.

Firstly, to provide suitable quantity of food to the population, secondly provision of

employment to the people connected to agriculture. It is a fact that economic progress and

prosperity of a country like Pakistan depends on its agriculture. Moreover, raising living

standards in poorer states would also require increasing productivity in the agricultural


sector(OECD, 2017). Due to this reason, policy makers always try to make utmost progress in

these two fields. Although Pakistan is also trying to make progress in the field of agriculture

and industry, however, unfortunately a tangible speed and progress is not visible in the fields

as finds (Malik et al., 2016). Pakistan is considered as the best agricultural country due to its

four seasons, fertile land and canal irrigation system. Due to these features, it is one of the top

list agricultural country in crops, fruits and vegetables. In the past, foreign invasions took

place to fulfil their food necessities. The dilemma is, presently the region is unable to meet the

food demands of millions of its residents (Fatima et al., 2014). In spite of hard work, neither

there is an increase in the production nor a decrease in problems of farmers. Contrary to this,

most of the agricultural countries in the world are using spatial techniques and tools such as

Geographical Information Systems (GIS) and Remote Sensing (RS) to gain maximum

production by investing relatively less efforts and time. Unfortunately, in Pakistan neither

there is a strong agricultural policy nor Government of Pakistan (GOP) can tap the benefits by

using scientific inventions in agriculture like use of GIS and RS. Although, benefiting GIS

and RS for agriculture like many other countries is obviously not new in Pakistan. Most GIS

and RS applications cannot be developed with single dataset. Therefore, multiple spatio-

temporal datasets collected and locked by various organizations are required to be compiled

that are accurate, structured and up to date, as well. This implies that sharing of the spatial

datasets is crucial for developing reliable GIS applications to support policy making process

for efficient decision making in the areas such as exploration and monitoring of natural

resources, climate change as well as managing and mitigating natural disasters that are a

constant threat to agriculture.

Figure 1: Declining contribution of agriculture sector in GDP

(Source: World Bank)

The above scenario demand immediate attention to find out the root causes of

declining agriculture in Pakistan because agriculture’s declining share in GDP and

employment is inevitable as economies develop(Byerlee et al., 2009; Cervantes-Godoy et al.,

2008). Poor agricultural productivity can be related to the low use of improved seed, use of

inappropriate fertilizer, inadequate irrigation, and lack of incentives for farmers in the absence

of remunerative markets as finds Duncombe (2018). The annual plan 2019-20 (Government

of Pakistan, 2019a) also aim at, “There is dire need to enhance agricultural productivity and to

introduce high value crops including horticulture, livestock, poultry and fisheries to enhance

income of the farmer, reduce imports and expand export base of the country” (p.ix). Large


part of Pakistan is tropical and for such regions long-term growth policies should be

reoriented to favor small farmers instead of big agribusiness players to maintain food security

and social equity (Boron et al., 2016).

Since the emergence of Pakistan, the reigning governments have failed to adopt a viable

mechanism for formulation and implementation of public policies (Sirajul, 2015). The task for

governments is to make sure that the right policies and institutions are in place (OECD,

2010). But in case of Pakistan, politically elected government had always served their own

interests rather than public issues (Sirajul, 2015). Habib (2015) suggested that for the survival,

growth and stability of agricultural sector it is compulsory for every government to make an

effective policy.

4. key Issues to Agriculture in Pakistan

4.1 Review of Existing Policy Documents

In order to identify issues to agriculture in Pakistan, first of all, existing policy documents are

critically reviewed and analyzed as the policy documents are authoritative, and original

information source, as well as governing tools (Macheridis, 2015). The review of existing

policy documents also facilitates to understand the institutional settings and the overall policy

context in a certain domain and their inter-linkage in a region. This study critically reviewed

twelve relevant policy documents. The scope of these policy documents ranges from

agriculture, economic development, digitization of sectorial information to climate change.

The detail of policy documents reviewed is listed in Table 3.

Table 3: Policy documents reviewed for identification of issues to agriculture

Annual Plan 2019-20

National Food Security Policy (2018)

National Water Policy (2018)

Pakistan Statistical Year Book (2018)

Pakistan Economic Survey (2018-19)

Digital Pakistan Policy (2018)

Pakistan Vision 2025 (2014)

National Climate Change Policy (2012)

National Environment Policy (2005)

Agriculture Policy Khyber Pakhtunkhwa: A Ten-Year Perspective

(2015-2025)

Agriculture Policy of Sindh (2018)

Punjab Agriculture Policy (2018)

Annual Plan 2019-20 (Government of Pakistan, 2019a) identify climate change, shortage of

irrigation water, use of low quality inputs such as inferior seed and fertilizers, and reduction in

sown area as the main issues faced by the agriculture sector of Pakistan.

National food security policy 2018 (Government of Pakistan, 2018c) highlight key issues

faced by agriculture sector of Pakistan. The major issues mentioned in the policy document

include, problems with the quality, quantity, and timing of supply of agricultural inputs,

inefficient utilization of land and water resources, lack of infrastructure and technologies,

trade restrictions, climate change effects on agriculture and livestock. And un-capitalized

potential of mountain agro-ecological zones, degradation of natural resources, low priority to

mainstreaming women contribution and slow rate of diffusion of technological innovations.

National water policy 2018(Government of Pakistan, 2018d) mention major challenges

faced by the water sector of Pakistan such as climate change, trans-boundary water issues,


siltation of existing dams, replacement of water storage bodies and lack of equity in water

allocation at a regional levels. The issues including scarcely of fresh water resources and lack

of awareness about the impending threat of water scarcity, salt imbalances in irrigated lands

and the decreasing soil quality resulting in reduced yields are also stated in the policy

document.

Pakistan statistical year book (2018) was reviewed. From the statistical data given in the

book, it reveals that labour force working in the agriculture sector is gradually decreasing

whereas temperature of the region is increasing due to climate change. The share of

agriculture in GDP is also on the decline.

Pakistan economic survey 2018-19(Government of Pakistan, 2019b) attribute the under-

performance of agriculture sector due to reduction in the area of cultivation, lowe water

availability and drop in fertilizer off take as well as climate change. The issue of food security

is also underpinned in the document.

The digital Pakistan policy(Government of Pakistan, 2018a) highlight issues including lack

of agriculture information portal and integration of national as well as provincial databases in

addition to duplication of efforts in data collection. The policy underscores the need to

“revamp Geographical Information Systems (GIS) for Pakistan to monitor the environment

and plan sustainable agriculture” (p.14). The policy underpins the preparation of agriculture

related information in GIS format such as digital mapping, land use, soil types, meteorology,

ecology, oceanography, hydrology and agricultural records in order to integrate national and

provincial databases to avert duplication and ensure synergy (p.17).

Pakistan vision 2025(Government of Pakistan, 2014) identify, slow rate of technological

innovation, limited adoption of progressive farming techniques, problems with input supply,

limited investment in construction and maintenance of infrastructure. The policy also

mentions trade restrictions, pest and livestock disease problems, limited amounts of credit for

agricultural production and the lack of agriculture-specific financing as the major factors

underlying the poor performance of the agriculture sector.

National climate change policy(Government of Pakistan, 2012) mention issues including,

increased health risks, stress on water resources, migration of agriculture related population,

siltation of major dams and threat to coastal areas due to projected sea level rise.

National environment policy(Government of Pakistan, 2005) highlight issues which have

direct relevance for agriculture including pollution of fresh water bodies and coastal areas, air

pollution, soil degradation, lack of water management, deforestation, natural disasters and

climate change.

Agriculture policy of KPK(Government of Khyber Pakhtunkhwa, 2017), identify issues to

agriculture in the province including climate change and natural hazards, lack of

transportation infrastructure , food insecurity, increasing cost of agricultural inputs and quality

concerns, shrinking financial resources, low productivity and profitability, declining water

supply and land degradation, poorly managed natural resources and gender biases in

agriculture. The policy also underscores post-harvest losses, inadequate agriculture research

and development (R&D) system.

Punjab agriculture policy 2018(Government of Punjab, 2018), highlight challenges faced

the agriculture in the province including, lack of coherent policies, out dated agriculture

produce marketing system and outdated institutional structure, climate change and poor profit

for farmers. Lack of investment in human capital and diversification, depleted natural

resources and low growth as well as productivity are mentioned as issues to the agriculture,

too.

Agriculture policy of Sindh 2018 (Government of Sindh, 2018) mention major issues to the

agriculture in the province including problems with agricultural inputs (seed, site specific

fertilizer), poor infrastructure (Transport and storage), lack of institutional credit, poverty,

gender biases and climate change. The issues also include malnutrition, lessening agricultural


labor force, low yield, water availability, traditional cultivation methods, lack of food

processing technology and limited quality control facilities.

4.2 Review of Scientific Literature

Zulfiqar & Thapa (2017) in their study identified environmental, economic and social issues

that hamper agricultural sustainability at provincial level in Pakistan. They used secondary

data, covering the period of 2005/06–2012/13 for their study. Crop diversification, soil

salinity, and the use of organic and inorganic fertilizers and pesticides were the indicators

considered by them for environmental sustainability analysis. For economic sustainability

analysis, they used, change in overall crop production and stability of crop production as

indicators. Whereas employment of rural labor force and food security were used as indicators

for social sustainability analysis. They find, “there are regional differences in agricultural

sustainability in Pakistan”. For example, they noted that inorganic fertilizer, pesticides and

groundwater for irrigation are being over utilized in Sindh and Punjab. On the other hand,

limited use of fertilizer and pesticides in some areas and altogether no use in other areas of

KPK and Balochistan is impeding sustainable agricultural production. Moreover, they find

that in the coastal areas of Balochistan, groundwater for irrigation is further reinforcing

agricultural unsustainability. They recommended “to formulate effective regional agricultural

policies based on local level research and revise agricultural extension structure in order to

incorporate need-based services with better dissemination of information and farm level

trainings”.

I. A. Khan & Khan (2018) through SWOT (Strength, Weakness, Opportunity and

Threat) analysis, identified issues faced by the agriculture sector of Pakistan. The main issues

identified by the authors include, land and water productivity/sustainability, climate change,

stagnant yields, diversification, postharvest losses and markets, social disparity and gender,

malnutrition and food security (p.5).

Land and water productivity/sustainability

Peerzado, Magsi, & Sheikh, (2019) carried out study to explore the nexuses between

urbanization and agricultural land conversion in Hyderabad, Pakistan. They find that “70

percent of agricultural land has been sold and converted in urbanization in Hyderabad district”

and only 16 percent of the population is engaged in agricultural farming. According to them,

reasons for selling agricultural land are, “due to certain economic, social, financial and

agricultural related reasons”. They highlight that this scenario can cause shortage of food in

addition to issues like social, cultural, environmental and economic instability not only in the

study area but also in the country.

R. Ullah, Shivakoti, Kamran, & Zulfiqar (2019) conducted a reach study in province

of Khyber Pakhtunkhwa, Pakistan. They identified that land ownership status plays important

role in adaptation and mitigation of climate change threats. Farmers who own no land are the

least interested in adaptation and mitigation of climate change threats. On the other hand,

farmers who own land try to adopt the latest technological means to combat climatic threats.

Saltwater intrusion is also one of the problems being faced in coastal areas of Pakistan. The

Indus Delta, located near Karachi is an example of seawater intrusion and land degradation

due to climatic and environmental conditions(Rasul et al., 2012). Salinisation and

waterlogging in the provinces of Punjab and Sindh is due to over-irrigation (Chandio et al.,

2011).

I. A. Khan & Khan (2018) argue that “subsistence-oriented farming practices, uneven

distribution of ownership rights, and fragmentation” (p.6) hamper optimal utilization of land

and water resource. Moreover, they find that the ground water level is declining.

Accordingly, they suggest “to build new water reservoirs and the silting up of existing


storage” (p.7). Moreover, they find that subsoil water being used for agriculture is negatively

affecting soil health and organic matter content, and therefore sustainability of the entire

agricultural system of the country. Aslam and Prathapar (2006) also found that major river

basins such as Indus Basin in Pakistan is undergoing salinisation. In Pakistan, about 6.2 Mha

of agricultural land is affected by salinity(NIAB, 1997).

Figure 2: Important issues to agriculture policy in Pakistan

Source:(I. Khan & Khan, 2018)

Akhter & Erenstein (2017) conducted study for assessing farmer use of climate change

adaptation practices and impacts on food security and poverty in Pakistan. They find that, the

majority of farms in Pakistan are small-scale with 90% of the farms being two hectares or less

and “one-third of the farmers are pure tenant farmers having no land ownership”. They argue

that the farmer with no land ownership or having small size farms can not adapt to climate

changes. However, “Farmers with large landholdings are likely to have more capacity to try

out and invest in climate risk coping strategies”.

It is argued that despite the fact, Pakistan owns one the best canal system in Pakistan,

the sources of irrigation are scarce in the country as water is wasted in distributaries and fields

due to lack of integrated water management strategy as well as system. This can also be

attributed to illiteracy among farmers as most of them cannot learn new methods for irrigation

such as drip irrigation. The dilemma is, large quantity of rain, fresh and river water is lost in

oceans and there are no adequate measures to store this water. On the other hand, excessive

load-shedding affects tube wells, and this is another setback for watering the crops. There is

no specified land use system in the country and not a secure source of water, therefore

farming results in low productivity (A. Ullah et al., 2020).

There is a close relationship between water resources and climate change, “Water

resources are inextricably linked with climate; this is why the projected climate change has

such serious implications for Pakistan’s water resources”(Government of Pakistan, 2012).


Climate change

Agriculture in Pakistan has been negatively affected by climate change(Shukla et al., 2019,

p.452). Abbas et al. (2017) finds that from 1980 to 2014, there is an average shift of 4.6 days

per decade earlier in the case of spring maize growing periods whereas sowing of autumn

maize has been delayed 3.0 days per decade. Similarly, Tariq et al. (2018) found that due to

rise in temperature from 1980 to 2016, there are shifts in sowing, emergence, anthesis, and

maturity for fall and spring crops. Extreme weather condition and natural disasters negatively

impact agricultural yield, food production and food security(Dizon et al., 2019).

Climate change worsens the adverse effects of weather on agriculture. Climate change

has been noted as a new threat to agriculture and food security (Ahmad, Farooq, & Umar,

2016; Gahukar, 2011). In Pakistan, India, Nepal, and China, farmers have noticed an increase

in floods, landslides, drought, and disease, due to climate change(Dizon et al., 2019). These

events can be attributed to a decline in the production of main crops and an increase in food

insecurity (Hussain, Rasul, Mahapatra, & Tuladhar, 2016). In Pakistan most of farmers are

poor and less educated therefore they are not able to employ climate change adaptation

strategies for improving agricultural yield and ultimately food security(Ahmad, Mustafa, &

Iqbal, 2016; Akhter Ali & Erenstein, 2017). The rainfall has two-fold effects i.e. less than

average rainfall or more than average both has negative effects on agricultural productivity.

For example, in Pakistan, more than average rainfall is the source of floods that cause huge

losses in agriculture (de Vries & Asmat, 2016; Dorosh et al., 2010). Therefore, the food prices

were high causing more food insecurity in Pakistan (Shukla et al., 2019,p.516).

R. Ullah, Shivakoti, Kamran, & Zulfiqar (2019) find that agriculture in the region is

facing several threat due to climate change and the threats are beyond the control of the local

farmers. They highlighted the “significant role of land ownership status, along with

perceptions of risk sources and attitude towards risk, on farmers’ decisions of adopting off-

farm diversification and credit reserves” to cope with climate change issues.

Srivastava (2019), provided evidence of the adverse climate change effects on

agriculture, livestock production, PHM of fresh produce, food quality, and food security.

I. A. Khan & Khan (2018) find that Pakistan is highly vulnerable to climate change

affects. Accordingly, they call for disaster preparedness in order to stop potential loss of crops

and livestock. The authors assert that due to improved “access to software and data gathering

devices (GIS)”, government should focus on developing decision support systems as it would

be useful in sustainable agriculture policy formulation.

Stagnant yields

LeGouis, Oury, & Charmet (2020) conducted study to investigate the causes of stagnation in

productivity of wheat as is the case with Pakistan, too. They identified three groups of factors

are; a decrease in genetic progress, resistance to change agricultural practices and unfavorable

climatic conditions. They conclude that climate change is one of the main reasons causing

stagnation in agricultural productivity. Liu et al. (2016) and Wiesmeier, Hübner, & Kögel-

Knabner (2015) also find that stagnation in productivity is largely due to climate change

impacts.

I. A. Khan & Khan (2018) highlight that “agriculture sector of Pakistan is facing

severe stagnation in productivity and declining growth” (p.9). They attribute the declining

growth of agriculture sector of the country to lack of interest of the farmers in their native

profession, and yield gap in all the major crops such as wheat, rice, maize, cotton, and

sugarcane. They compare yield with other countries of the region and world and find huge

yield gaps. They find that “major reasons for this difference are unavailability of quality seed,

inappropriate sowing (methods and time), weeds, lack of balanced fertilizers, partial

mechanization, and excessive use of unfit irrigation water” (p.10).


Diversification

Prăvălie (2016) finds that desertification is affecting 38 of 48 countries in Asia and it is a

serious problem in Pakistan (Irshad et al., 2007; Lal, 2018). According to Ponce (2020),

farmers concerns over low or no support from governments for adaptation to climate changes

hamper diversification in agricultural systems which is not good for the sustainability of the

system as a whole. Therefore, climate change can be considered as one of the main reasons

that impede diversification in crops although “Crop diversification, as an agricultural practice,

could lead to climate adaptation and mitigation” (Piedra-Bonilla et al., 2020).

R. Ullah, Shivakoti, Kamran, & Zulfiqar (2019) in their study found that ownership

status of agricultural land help to adopt crop diversification as the farmers not having their

own agricultural land are forced to follow the instruction of their landlords.

Agriculture in Pakistan is dominated by five crops; wheat, cotton, rice, maize, and

sugarcane (I. Khan & Khan, 2018). They argue that the reasons for this include political

economy and the interest of the farmers on food security rather than profitability. They call

for giving incentives to the minor crop growers as it would help to achieve diversification.

Postharvest losses (PHL) and markets

According to Srivastava (2019), postharvest losses (PHL) can be attributed to climate change,

“Climate change has a strong impact on the food industry as it affects cultivation, postharvest

management (PHM), food loss, food quality, and food security”. The effects the changing

climate will have on post-harvest agriculture are well established and this calls for investment

in PHL mitigation(Chegere, 2019).

I. A. Khan & Khan (2018) find that due to lack of storage capacity and access to

agricultural markets that includes “poor transportation, inadequate grading, very heavy spread

in price between consumer and farmer, and tough competition with imported goods” (p.16)

contribute to postharvest losses.

It is argued that small land holder farmers usually do not have transportation means to

take their agricultural outputs to the markets and a middle man appears in most of the cases

who buy their products at low price and then sell it at higher price by transporting the

agricultural products to the big markets.

Social disparity and gender

The participation of women in agricultural activities can help to overcome gender barriers,

enhance gender equality and food security(Shukla et al., 2019, p.70).

However, I. A. Khan & Khan (2018) cite various statistics, to conclude that there is

wide social and gender disparity in Pakistan. According to them, this impedes sustainable

agriculture in the country. The existence of poor physical infrastructures including education,

health facilities, safe drinking water, and sanitation in rural areas cause social disparity in the

country. They argue that although rural women take part in agriculture, but “women are far

less likely to own income-generating assets such as land and livestock or to have a say in

household economic decisions” (p.17).

National food security policy(Government of Pakistan, 2018c) support above findings

and categorically states, “ Low priority to mainstreaming women contribution in value added

agriculture and family nutrition”. Therefore, it is evident that women are given a secondary

role in decision-making in the country.

Malnutrition and food security

Agriculture is two to three times more effective at reducing poverty than other sectors

(Christiaensen et al., 2011) because agriculture tends to employ more poor

people(Christiaensen & Martin, 2018; Ligon & Sadoulet, 2018). Enhancing agricultural

productivity can also improve food security(Dizon et al., 2019). However, the link between

agricultural growth and food security is less clear (Kirk et al., 2018).

Research in India (for example Headey, Chiu, & Kadiyala, 2012; Kolady, Srivastava,

& Singh, 2016; Vepa, Umashankar, Bhavani, & Parasar, 2014) shows that states with higher


growth in agricultural output have less food insecurity especially among children and women.

Similarly, in Bangladesh, rapid growth in rice yields improved food security situation

(Headey & Hoddinott, 2016; Yu, 2012). Higher growth in agriculture can be achieved by

making use of more inputs (fertilizer, seed, and the like), bringing more land under

cultivation, and enhancing the productivity of inputs and agricultural land(Dizon et al., 2019).

I. A. Khan & Khan (2018) argue that “micronutrient deficiency, known as hidden

hunger, is widespread in Pakistan and well characterized among rural areas” (p.18). The issue

of malnutrition and food security is relatively higher in rural areas as compared to urban

centers, they conclude.

There is a direct relationship of climate change and food security as finds (Ali &

Erenstein, 2017),”…. increasing weather variability and climate change have threatened the

agricultural sector and thereby, have become major barriers to achieving food security and

alleviating poverty in Pakistan”. The climate change “threats lead to major survival concerns

for Pakistan, particularly in relation to the country’s water security, food security and energy

security”(Government of Pakistan, 2018c).

Summary of the main issues to agriculture in Pakistan

From the review of existing policy documents and literature cited above, it reveals that main

issues faced by agriculture sector of Pakistan include, climate change, low availability of

water, low profitability and productivity, deprived physical infrastructure, food insecurity,

quality issues, degradation of natural resources (land, water), outdated cultivation methods,

issues with supply of agricultural inputs and lack of adoption of the latest technology. The

most of the issues faced by the agriculture sector of Pakistan are directly or indirectly related

to climate change.

5. The Role of RS/GIS in Agriculture

Due to large variations in climatic conditions, crops have to suffer from different types of

stresses leading to reduced crop productivity and year to year variability. The conventional

methods of acquiring weather and crop growth status information are reliable, but they are

labour intensive and time consuming. However, recently remote sensing (RS) and

geographical information system (GIS) technologies are gaining importance for acquiring

spatio-temporal meteorological and crop status information for complementing the traditional

methods. Under such conditions, rapidly emerging remote sensing and geospatial technology

can be of great help for crop growth monitoring, identification and management of different

types of stresses and regional yield estimations to sustain the natural resources and

agricultural productivity. Remote sensing data can greatly contribute to the monitoring by

providing timely, synoptic, cost-efficient and repetitive information about the earth’s surface

(Justice et al., 2002). Atzberger (2013) has illustrated five major applications of remote

sensing in agriculture including biomass and yield estimation, vegetation vigor and drought

stress monitoring, assessment of crop phonological development, crop acreage estimation and

cropland mapping, mapping of disturbances and land use land cover changes in addition to

precision agriculture and irrigation management. There was a need to develop fast track and

reliable procedures to make crop forecasts and estimations early in the season or end of

season. Pakistan Space and Upper Atmosphere Research Commission (SUPARCO), the

Space Agency of Pakistan started developing crop area estimation procedures and crop yield

models, based on the application of satellite remote sensing, GIS technology, agronomy, agro-

meteorology, statistics and other allied disciplines.

Korduan, Bill, and Bölling (2004) find that in agriculture, the importance of geo-

information is rising up. Agriculture is dealing with the integration of agricultural applications

to the natural heterogeneity outside in the fields. They argue that using geospatial


technologies would help in increasing yield and decreasing the means of production while the

environment remains conserved. To assemble necessary information regarding spatial

variability requires spatial data about the soil, the crop, the terrain, the land machinery, and

the applications. For analysis, GIS should be used and that would require collection, and

management of several datasets and therefore much of the time is spent in data management.

Due to availability of various data capturing devices, which support different data formats,

and improperly spatial reference systems one has to transform the data between proprietary

GIS and coordinate systems.

Imran (2013) carried out four studies to develop services for accessing, integrating,

and assessing data and models in the agriculture domain. He finds data integration

problematic due to three reasons i.e. technical, conceptual and institutional. The technical

reasons include various data formats and varying spatial scales. Conceptual barriers include

different interpretations of the data due to lack of common standards and semantics. Whereas

institutional issues such as ownership and copyrights impede data sharing. Accordingly, he

recommends the use of an SDI framework for integrating and upscaling of spatial datasets in

order to develop agricultural services.

Ghazal, Kazmi, & Zubair (2015) conducted study in Pakistan for monitoring and

mapping spatio-temporal dynamics of vegetation cover using Remote Sensing and GIS. They

concluded, that, geospatial technologies are “ideal for mapping the extent of agricultural and

other green bodies”. Bordogna et al. (2016) explored integration of multisource

heterogeneous geospatial data and time series in a case study for agriculture using OGC

standard interoperable SDI architecture and a geospatial data and metadata workflow. They

argue that geospatial information is becoming quite important not only for environmental

researchers, geographers, and social scientists but also among public authorities and citizens

for various purposes such as agriculture. They find that four factors play key role in favoring

SDI development; legislation and new strategies, standardization activities, increasing trend

of available free and open software and decreasing trend of market prices for hardware

resources.

Tóth & Kučas, (2016) find that geospatial information plays a key role in the

implementation of Common Agricultural Policy (CAP) in Europe. However, agricultural

decision makers have to work in order to optimize data integration for achieving transparency.

They underscore the need to establish a framework for information exchange between the

stakeholders. Relating information to location plays a fundamental role in agriculture. The

national spatial data infrastructures (SDI) may provide valuable input for the CAP as the

collected data can be shared within SDI. SDI will also contribute to the maintenance of the

digital topographic databases. They highlight that data sharing will save public resources.

They emphasize on standardization of geographic information in the agricultural domain.

Abdelrahman, Natarajan, and Hegde (2016) integrated RS and GIS for identification

of suitable land for agriculture in Chamrajnagar, India. They identify that remote sensing (RS)

data is useful for estimating biophysical parameters and indices besides cropping systems

analysis, and land-use and land-cover estimations during different seasons and however, “RS

data alone cannot suggest crop suitability for an area unless the data are integrated with the

site-specific soil and climate data”.

Sharma, Kamble, & Gunasekaran (2018) conducted a systematic literature review

(SLR) and selected 120 research articles published during 2000–2018 available in the ISI

Web of Science (WoS) database on applications of GIS in agriculture. They look at GIS as a

computer-based tool analytical tool which exploit spatial data. Accordingly, they mention

following as GIS components, “

Storage of spatial data in digital form.

Management and integration of spatial data collected from different

sources into the GIS system.


Retrieval and conversion of the spatial data in the required formats.

Performing data analytics to convert data into useful information.

Developing different models based on the information.

Display of information model and decision making”.

They find, “GIS is transforming the agriculture sector in incredible ways. The

hyperspectral and multispectral images obtained through the geospatial data is found to be

very useful for analyzing parameters such as crop health and soil moisture”. They concluded

with a call for establishing a framework for GIS data sharing, “There is a need for developing

novel frameworks incorporating different layers or platforms for efficient collection of data,

storage of data, data analysis and information sharing”. They further discussed the need for

data integration, “GIS data can be of high value to the practitioners if it is further integrated

with the data collected from the other sources for deriving meaningful insights”. They found

that GIS data can be helpful for improved decision and policy making in agriculture such as

land suitability analysis, site search selection, impact assessment, resource allocation, and

developing knowledge-based systems.

Honda et al. (2014) provide evidence that farming based on detailed geospatial

information increase yield and reduce the cost of fertilizers and other input resources. They

implemented an agricultural information service platform and tested it on geospatial data

infrastructure for crop modeling. They conclude that usage of web services from geospatial

infrastructure has advantage for the agricultural information service system.

(Kliment et al., 2015) implemented a simple SDI to automate the workflows for

publishing huge amount of metadata, geospatial data and remote sensing images on the Web

in an interoperable and distributed way. They carried out it in a real case study to support the

national agricultural sector of Italy. They found it quite handy for sharing geospatial

information resources with the stakeholders.

Edeme, Nkalu, Idenyi, & Arazu (2020) examined the positive affect of infrastructural

development on agricultural output utilizing panel autoregressive distributed lag (PARDL)

methodology. Physical as well as non-physical infrastructures such as ICT positively impact

positively agricultural productivity, they conclude.

Policies for agriculture consist of government decisions that influence the level and

stability of input and output prices, public investments affecting agricultural production, costs

and revenues and allocation of resources. These policies affect agriculture either directly or

indirectly. Improved agricultural production has been seen as one of the overall objectives for

poverty reduction in the country. The objectives of agricultural sector strategy have been

increasing agricultural growth, seen as important for increasing rural incomes and ensuring

equitable distribution. Due to limited availability of high potential land, it has been envisaged

that increasing agricultural production will have to come from intensification of production

through increased use of improved inputs, diversification especially from low to high value

crops, commercialization of smallholder agriculture, and increased value addition through

stronger linkages with other sectors.

The development of the agriculture sector is therefore important for the development

of the economy as a whole. In the following sections, we review some of the key policy issues

and concerns with respect to the sector’s development.

6. National Food Security Policy

The national food security policy(Government of Pakistan, 2018c) aim to:

Create a modern, efficient and diversified agricultural sector that can ensure a stable and

adequate supply of basic food for the country’s population, and provide high quality products

to its industries for export. It ensures attractive incomes and decent employment for those who

live and work in rural areas; use the resource base in an efficient and sustainable manner;


flexibly adapt to climate change and be resilient enough to quickly recover from shocks and

emergencies; and ensure that all sections of the population have stable access to adequate,

nutritious and safe foods necessary for a healthy life.

Within this context, the primary function of Government of Pakistan is to act as a

regulator and facilitator, creating an enabling environment that allows the dynamic subsectors,

such as production of high value livestock and horticulture products for both domestic and

export markets, to grow rapidly. A conducive investment environment also needs to be

created in rural areas for processing, storage and value-chain development to provide

employment opportunities, beyond those that can be offered by agriculture itself. At the same

time more proactive policies are needed to ensure inclusive growth that will draw in

vulnerable group such as small farmers, share croppers and non-agriculture workers; address

resource scarcity and degradation issues particularly related to land and water; and bring

about a rapid reduction in hunger and malnutrition. In line with the above, the key elements

of a new agriculture policy for Pakistan will be more innovation and technology based

agriculture that makes efficient and sustainable use of natural resources; redirect public sector

agriculture expenditure by focusing agriculture subsidies to socio-economic groups that need

it most such as small farmers, landless, women, and nomads and transhumant, and public

investments in creation of knowledge, technology and essential infrastructure that would

facilitate and encourage private investments by raising profitability of agriculture and rural-

based activities; and ensure that food is accessible to all sections of the population, in

particular vulnerable groups such as children and women, and is prepared, stored and

consumed in a way that ensures nutritional security.

To better understand the root causes of declining agriculture and failure of agricultural

policies in Pakistan, it is useful to explore that how agricultural policies are made in Pakistan.

7. Agricultural Policy Making Process in Pakistan

Agriculture is the most important sector of the country’s economy (Ahmad & Farooq, 2010;

Akram, Alam, & Iqbal, 2018; Awan & Yaseen, 2017; M. H. Khan & Imam, 1985; Malik et

al., 2016; Government of Pakistan, 2017). The agriculture sector of Pakistan is a provincial

subject. However, the sector is assisted by various federal and provincial government

departments. Federal institutions are mainly responsible for formulating and coordinating

national policies and strategies, such as the import and export of agricultural inputs and

commodities, setting prices of inputs and outputs, enforcing phytosanitary and quarantine

regulations, approval of varieties and seed certification, standardization, compliance of grades

and standards, providing support to national research institutions, and managing marine

fisheries resources (Greer & Jagirdar, 2006). There is a mess of institutions involved in

agriculture policymaking in the country as mentioned in a report published by Asian

Development Bank in 2006. According to the report, there is often excessive government

intervention and too many regulatory departments which are the major impediments in the

promotion of the sector. Policy decisions are mostly based on crisis management and the need

to provide a quick fix to a problem, the report concludes. Ahmad and Farooq (2010) while

discussing major flaws in the existing wheat policies find that considerable inefficiencies in

managing wheat surpluses as the quantities procured were beyond the storage capacities

available with the government departments—hiring private storage facilities at a huge cost to

the nation. It means that the government do not have data of storage capacities or even it has

the data but not making use of relevant data for managing wheat surpluses. Similarly,

Spielman et. al (2016) finds that there is decline in evidence-based policy making related to

agricultural sector issues in Pakistan. According to him, “as a result, growth in the rural

economy has lost momentum, leaving Pakistan's rural population to face continuing high

levels of poverty, and food insecurity, as well as limited access to the public services and

markets required for a modern economy”.


Commenting on the policy making process in Pakistan, Saeed (2013) finds that, “As

far as the development of policies is concerned, Pakistan has a unique process of developing

public policies”. Because, “Pakistan inherited a very strong bureaucratic structure which was

developed to cater the needs of the colonial powers” (Wilder, 2009). Therefore, “The policy

making was basically the domain of the colonial rulers and then bureaucracy was trained to

perform this task at local level” (Saeed, 2013). Whereas according to Sial (2011), “State has

lost its capacity to frame its policies according to its national priorities. Its parliament seems

to have imperfect control over decision-making process. Parliament doesn‘t seem to have self-

regulating capacity” (p. 127). Islam (2001) notes that “the legislatures have traditionally

played minimal role in the country's governance”. Saeed (2013) concludes in a policy making

case study in Pakistan that, “Pakistan ever since its independence has been under the influence

of super powers and donor agencies and most of the policies adopted and implemented in the

Pakistan has been suggested or enforced by them and the role of legislature in this regard has

not been significant at all”. Commenting on the policy formulation process in Pakistan,

Husain (2013) notes that, “Policymaking in Pakistan deviates from the ideal process”.

Meaning that the process is not in accordance with policy literature. According to him, there

are six deviations. First, “The stakeholder consultation is either superficial or the views of the

stakeholders, if found at variance with those of the formulators, do not find any place in the

revised documents”. Similarly, “Ministers feel personally offended if their policy documents

are criticized by other ministers”. “Some slick players with the gift of the gab can make

impressive PowerPoint presentations and mesmerise the audience. They create the impression

that things are going well while the facts are to the contrary”. Therefore, “underlying

problems remain unaddressed”.

Fifth, the capacity of the ministries and provincial departments in preparing policy

papers is limited. They do not have the necessary expertise or competence in the subject to

come up with evidence-based options. The use of systematic data is normally shunned. And

finally, the communication strategy of explaining the rationale and disseminating the policy

widely is almost non-existent in most cases. As the success of the policy depends upon people

outside the government their understanding and support are absolutely crucial.

Investigating public policy making in Pakistan, Hussain (2008) finds that policy

making has been captured by feudal elites and other powerful interest groups. According to

him, “…policy makers work under pressures of deadlines” Therefore, [data] are of no use to

them if they are made available after the deadlines. He is of the view that the policy

recommendations are not implement-able as the researcher; do not take cognizance of the

constraints faced by the policy makers. He asserts that public policy can be informed and

made effective if these are based on primary data collection, surveys. Structured interviews,

field visits and observations. Commenting on the need of multidisciplinary approach, he

argues that, “Most public policy Issues in Pakistan can be resolved only if we dissect them

under a multidisciplinary microscope rather than a single lens prism or a par1icular

discipline’. It means we must have data of various disciplines to inform policy making.

Having multiple datasets would help to stop ‘policy makers from indulging in irresponsible

policies, low return pet projects and rent seeking by their political cronies” (Husain, 2001).


Figure 3: Policy-making process in Pakistan

“The financial, technical and human resources play critical role in formulation and

implementation of public policy. In case of Pakistan, the country has always has been in short

of financial resources to implement the public welfare projects and there is no proper

utilization of resources in any sphere of development or policy making process. Due to the

corruption and inefficiency of government functionaries, the available resources are always

mismanaged and underutilized. The injustice taxation policy is the main reason of shortages

of financial resources in the country. Since the inception of Pakistan, government has failed to

implement a viable taxation policy where every citizen irrespective of their positions and

political influences should pay the required taxes and evaders liable to be punished according


to the law. Due to this reason, the budget formulation history of the country has depicted that

most of the presented budgets had been in deficit. It is prudent that there must be sufficient

resources for the successful formulation and implementation of public policies to obtain the

desired objectives” (Sirajul, 2015). Jabeen, Jadoon, & Salman (2016) analyzed public policy

making process and strategies in the specific context of Pakistan. Their findings are that,

“existing process and strategies of policy making are quite generic, linear and mainstream

which provide an overly simplistic and general understanding of the approach in which public

policies are formulated”. Political interests play an important role in policy making of the

country.

Khalid, Mushtaq, & Naveed (2016) argue that public policy means the actions

of the government in order to solve the problems being faced by the nation. They find that

there are three elements of a public policy i.e. what is the problem, its players and their

policies. They identify factors which cause failures of public policies in Pakistan. According

to them, the factors include, lack of relevant information, lack of coordination among

government institutions, irrelevant education of the politicians, lack of financial resources,

corruption and untrained civil servants in the specific discipline, political instability and lack

of research.

From above discussion, it can be concluded that policy making capabilities of the

government institutions in the country can be seen as poor. The reasons can include; lack of

coordination, lack of usage of scientific data and research, underperforming institutions,

exclusion of some relevant stakeholders and relying on inaccurate and incomplete data. There

is a need to change the current policy making process as governments in Pakistan secure their

own interests, and the vast majority of people including farmers are far from the basic

necessities and facilities(M. Abbas et al., 2016). Therefore, data should be used for evidence

based policy making instead of telling tales as “to assess the growth in global demand for

agricultural output, one needs estimates from many sources (Norton, 2016). The study suggest

“to enhance the development policymaking capacity of the institutions, by arranging capacity-

building and training programmes for the policymakers” (United Nations, 2019) in the areas

of agricultural development planning and financial analysis etc.

8. Specifics of Agricultural Information and Policies

Agriculture is spatial in nature. All agricultural activities take place at some geographic place.

Similarly, “all agricultural information is located in a certain geographic space, and possesses

spatial variation. Therefore, all agricultural information may be called agricultural geographic

information”(Y. Q. Huang et al., 2012). It is argued that almost all the problems faced by

agriculture are spatial. Agricultural policies are crafted for addressing these spatial problems

and obviously spatial decisions are made. The final decision is based on two main questions:

1) what should be done? (Action) and 2) where it should be implemented (Location)?

(Chakhar & Mousseau, 2008; Malczewski, 1999; Malczewski & Rinner, 2015). The first

question refers to the policy or decision-making process, and the second concerns the

‘receptor’ of the final decision, namely, the most suitable location for the development of a

specific agricultural activity. Most application that are built in agriculture exploit spatial

information. Farmers consider themselves as ‘information poor’, and news about new

agricultural technologies that improve productivity is not reaching them(Government of

Pakistan, 2014). Agricultural information consists of four interrelated components, namely

spatial position, spatial relationship, attribute, and temporal property(Fonseca et al., 2003).

Agricultural information “products (such as irrigation data, soil survey data, and cropland

data) are widely used in many research areas, ranging from agricultural sustainability, food

security, and biodiversity, to natural resources monitoring, disaster assessment, carbon and

accounting bioenergy”(Han et al., 2014). Thus, the information plays an important role in


agricultural production(Y. Q. Huang et al., 2012) and decision as well as policy making. The

information is produced by various organizations. Therefore it has different data formats and

specifications thus making it difficult to integrate and share (Y. Q. Huang et al., 2012). In this

situation, users can not make use of the information at different spatial scales for agricultural

assessment and analysis(Yang et al., 2011).

The critical review of scientific literature indicates that spatial data of various kinds is

required for sustainable agriculture, agricultural policies and practices. For understanding the

local agricultural context of a region and to predict precisely requires accurate “geospatial

data on land use, productivity, soils, climate, water availability, institutions, and so on”

(Norton, 2016). Information and education are critical for the policy makers, farmers and

consumers to make appropriate policy (Government of Pakistan, 2018c).

8.1 Importance of Data for Policy-Making

Information and education are critical for the policy makers, farmers and consumers to make

appropriate policy (Government of Pakistan, 2018c). Baumgartner and Jones (2015) explored

the role of information in public policies. They argue that, “To understand a complex issue,

governments must gather information about the issue”. They further suggest two types of

information: entropic information and expert information. Entropic information, which can be

described as incorporating ‘diverse viewpoints into a decision-making process’ (p. 78), is a

good fit for problem discovery, while expert information is useful for problem’s solution.

While highlighting the need of more information gathering, they find that, “the more

information one gathers, the more one understands the multidimensional character of the

issue, and the more one might be tempted to create a range of public policy programs

designed to address different elements of [the problem].

They define relationship between information and public policy as, “Information is critical

for problem solving, and hence how governments recognize, organize, and respond to

information is essential to understand public policy, the outputs of government activities”.

8.2 Role of Spatial Information within Policy-making

Monmonier (1982) argue that map is the principal medium for communicating geographic

information. According to him, map-making (cartography) is a policy science. He notes that

most of the maps are made by governments and enormous amounts are spent by governments

and private firms for the collection and presentation of spatial data. He argues the role of

cartographers as individuals in map making “but the important decisions are institutional—

federal, political or corporate, rather than individual”. He highlights that “It is the information,

not the map, that will be the principal product, and it is the agency or information network,

not the map-maker, that will be the primary provider”. He underpins, “in communicating

geographic information, public policy is a more significant factor than the eye-brain system”.

He asserts that “if the information is not collected, organized, and disseminated, then the best

design talent and the most astute theories of graphic communication are of no avail”. He

argues that major constraints to collection, organization and dissemination of GI are

institutional and political, not technical in nature.

While discussing cartography as a policy science, he stresses the need to understand

“institutional aspects of information management’. According to him, “…policy specialists in

economics study and debate government and private-sector programmes to increase

employment and productivity, so too are policy specialists in geographic cartography needed

to evaluate the effectiveness of public and private efforts to collect, organise and disseminate

geographic information”. He recognized the need of policy makers and scientists for better

information and to “evaluate the effectiveness of public and private efforts to collect, organise

and disseminate geographic information”. He points out that, “Cartographers and geographers

have a stake in policy science, for in the same sense that there is need for a policy on energy


and natural resources, there is need for a policy on mapping and geographic information.

Geographic information has the potential of becoming a major pillar of modern (Monmonier,

1982) public policy making process. A policy oriented geographic information concentration

has much (Monmonier, 1982) to do with policy making process.

Coppock (1974) grasped the reality of increasing awareness about spatial dimensions

of public policies. He argues, “it is highly appropriate to consider the role of geography [and

geographic information] in policy-making”. He further sheds light on the crucial fact that

policy makers themselves generally don’t have awareness regarding the spatial dimensions of

the problems or policies that may be due to the reason that geography get relatively low

importance in the academic institutions. According to him, “Understanding policies and

processes of decision-making is thus essential to an understanding of the contemporary

geography”. It is also important to have “the ability to analyze the spatial dimensions of the

problems and, more particularly, to handle, analyze and interpret spatially distributed data”.

In agriculture, resource management is significantly important and it has a spatial dimension

according to (Coppock, 1974). Geographic information can help to understand, identify and

mobilize the necessary resources according to local needs of the various regions. He finds

that spatial dimension is a major ingredient of all problems and resource management. To

offer solution for the large-scale projects which policy-makers face require handling of large

quantities of data, he concludes.

The implementation of GIS--coupled with a rigorous approach to information

management- provides the public sector with a significant opportunity to improve the

effectiveness of policy and the efficiency of programmes (Worrall & Bond, 1997).

Geographic information is critical to promote economic development, improve our

stewardship of natural resources and to protect the environment. Modern technology now

permits improved acquisition, distribution, and utilisation of geographic (or geospatial) data

and mapping. The National Performance Review has recommended that the Executive Branch

develop, in cooperation with State, local and tribal governments and the private sector, a

coordinated National Spatial Data Infrastructure to support public and private sector

applications of geospatial data in such areas as transportation, community development,

agriculture, emergency response, environmental management and information

technology(Coordinating Geographic Data Acquisition and Access, the National Spatial Data

Infrastructure, Executive Order 12906, Federal Register 59,17671-17674, 1994).

Harris & Hooper (2004) assessed through textual analysis, the concept and practice of ‘spatial

planning’ in the context of the formulation of public policies in Wales providing a spatial

context for major development decisions and for the allocation of resources. They reviewed

various sectorial policy documents published by the National Assembly of Wales for spatial

content. They concluded that “a spatial planning approach, if successfully developed and

enhanced, offers real value in the development of public policy” whereas “space and place are

in fact relevant and significant in the development of public policy”.

Barjolle, Sylvander, & Thévenod-Mottet (2011) find that GI has been instrumental in

EU common agricultural policy (CAP) and rural development. They argue, “Producers [of

agricultural products] may be at an advantage or a disadvantage by virtue of their

geographical location and their proximity to consumer centres”. They note, “for adjusting

supply and demand on generic markets for agricultural commodities, states have set up [GI]

systems for sector regulation in the context of their agricultural policies”.

Simukanga, Muhone, Mulenga, Phiri, & Nyirenda (2018) implemented an agricultural

geographical information system in South Africa. They argue that, “By understanding

geography and people’s relationship to location, we can make informed decisions”. They

highlight the importance of GIS and spatio-temporal data for policy makers, “Researchers and

policy makers may integrate spatial, temporal, and socio-economic data [using GIS] in order


to get better manageability, higher and quality productivity”. They note that reliable crop

information is essential for informed decision and policy making.

Huang, CHEN, YU, HUANG, & GU (2018) assessed available remote sensing data resources

for precision agriculture. They described and adapted a five-layer-fifteen level (FLFL)

satellite remote sensing data management structure for precision agriculture. They argue,

“The key of agricultural remote sensing is, with global positioning data and geographic

information, to produce spatially-varied data for subsequent precision agricultural

operations”.

8.3 Spatial Data for Agricultural Policies

Geospatial data and technologies are being widely used because of their capability to improve

development and planning of a nation(Majeed & Hanafiah, 2018). Masser (2007) underlines

the importance of location as a key factor in policy making. He argues that spatial databases

need to be assembled and made available at the national level for informing national policies.

The role of better data for better policies is also endorsed by (FAO, 2014b).

The spatial data required for agricultural policies can include the following:

Geospatial data

Precision agriculture demands collection, storage, sharing and analysis of large amounts of

geospatial data(Nash et al., 2009). Geospatial data coupled with web and mobile application

can help policymakers to understand, visualize and analyze the impact of agriculture on the

environment(Kamilaris et al., 2018). Geospatial data includes topographic data that is used as

base map to integrate various kinds of thematic information such as soil, rainfall and

population. Geospatial data can include satellite imagery, too.

Hydrological data

Rehman et al. (2019) identified three major factors impeding agricultural productivity in

Pakistan including water availability. Hydrological data helps to understand the potential

availability of water to crops, livestock and fisheries etc. Agriculture is usually the applicant

for water(Norton, 2016). Therefore, hydrological data relates to water resources such as lakes,

rivers, irrigation channels, wells and tube wells etc.

Digital Elevation Model (DEM)

Digital elevation model (DEM) plays an important role in agricultural planning such as

extraction of drainage networks(Wu et al., 2019), cultivation planning(Jasinski et al., 2005)

and appraisal of agricultural lands(Sakai & Chikatsu, 2008).

Cadaster (Land ownership) data

Information related to the location, size, boundaries and ownership of land parcels comes

under this category. This is important to know who owns a land parcel so that necessary

agricultural inputs can be delivered to the right person. Moreover, land ownership information

can be effectively used to allot land to agrarians who donot own agricultural land but possess

formal education in agricultural sciences(Elahi et al., 2020). For example, in 2009, the

Government of Punjab significantly increased the yield of major crops such as wheat, rice,

cotton, and sugarcane by 16.5, 14.2, 12.3, and 23.2%, respectively by allotting land to

agrarians (Elahi et al., 2020). Having land ownership information will be helpful to get back

state land encroached by people and therefore contribute towards timely and better

management of agricultural land(Elahi et al., 2015; Jehangir et al., 2007; Jiang et al., 2017; Li

et al., 2017). Also, governments from time to time announce subsidies on agricultural inputs

such as seeds, pesticides and fertilizers etc for farmers. Therefore, having land ownership

information would be helpful in implementing and informing agricultural policies.

Land use data

Land use data helps to identify the human activity or economic function on a specific piece of

land, e.g. urban use, industrial use, etc. In Pakistan, due to non-existence of dedicated land use

system, changes in land use are occurring almost every day and there are ambiguities in the


existing policies in defining different forms of formally and informally recognized land rights

(A. Ullah et al., 2020). Therefore, there is dire need to have data on land use in the country.

Data regarding actual use of land such as agricultural, residential, commercial etc. This type

of data helps to determine the magnitude of land being used for agriculture and therefore

helps to understand the pattern of increasing or decreasing agricultural land in a region. The

data can be helpful to analyze the required resources such as water, energy and physical

infrastructure for the development of agriculture in a specific area.

Land cover data

Land cover data is used to identify type of features present on the land. It refers to a physical

property or material, e.g. water, sand, etc.

Remote Sensing

The data captured by remote sensing satellites “assist with the assessment of crop condition

and crop condition anomalies, which can then be used to infer information on yield, area and

production”(Fritz et al., 2019). Similarly, Zhang et al. (2019) finds that “high spatial

resolution (HSR) remote sensing images are a reliable source for classifying urban land use.

Crops data

Data related to location and types of major crops grown is also useful as it helps to

understand, visualize and analyze the area under major crops such as wheat, rice, sugarcane

and maize etc as finds (Ali Chandio et al., 2016).

Research data

Data generated by researchers during their agricultural research is also an important outcome

as the data can be re-used by other stakeholders including but not limited to governments and

researchers. Neylon, (2017) asserts that research data is a quite useful outcome of a research

that can be benefited in some other research studies.

Soil data

Accurate information about soils is required for land resource management, monitoring, and

policy-making(Lucà et al., 2018). Therefore, soil is vital for informed decision and policy

making.

Agricultural census

It includes data such as employment statistics, land utilization, area under important crops,

livestock population, agricultural machinery statistics, crop yields and fertilizer usage as

fertilizers are the main inputs that are used to achieve high and fast rates of agricultural

returns(Rehman et al., 2019).

Weather / meteorological data

In order to predict and advise farmers, weather or meteorological data is of prime importance.

Zhai et al. (2020) argue for the same, “ … meteorological information, soil conditions,

marketing demands, and land uses, can be collected, analyzed, and processed for assisting

farmers in making appropriate decisions and obtaining higher profits”. Data of temperatures,

rainfall, sunshine duration, moisture, humidity, and wind speed can include this category of

data.

Pest and disease data

Pest attacks cause a major damage to agriculture. Chougule, Jha, & Mukhopadhyay, (2016)

find that, “[agriculture decision support systems] ADSSs are able to warn farmers about

possible occurrences of pests and diseases, helping them to take certain precautions to avoid

further losses”. But it requires data on pests and diseases to prevent the potential loss. This

information will also be of great use for companies engaged in preparation of pesticides.

Data on natural hazards

Natural hazards such as droughts, floods and landslides have direct and negative impact on

agricultural productivity. Simukanga et al. (2018) argues for the same, “ [food security] is

affected by factors such as poverty, health, food production, political stability, infrastructure,


access to markets, and natural hazards”. Therefore, having historical and predicted datasets on

natural hazards can help in mitigation and preparedness efforts while making sustainable

agricultural policies for a specific region.

From the literature cited above, it reveals that various kind of spatial datasets are

needed for evidence-based agriculture policy making. The summary of the key data sets

required is listed in Table 4 and Table 5. Table 4: Main spatial datasets to address agriculture issues

Spatial data References

Geospatial Data Fritz et al. (2019), Kamilaris et al. (2018); Nash et al.

(2009); Khan (2011)

Hydrological data Fritz et al. (2019); Rehman et al. (2019); Norton

(2016); Ali1 & Ahmad, (2012); Padilla-Pérez & Gaudin

(2014); Moro (2016); ADB (2009); Raja (2015); Bai et

al. (2016)

Cadastre (land ownership)

data

Elahi et al. (2015); Jehangir, Masih, & Ahmed (2007);

Jiang et al. (2017); Li et al. (2017)

Land use data A. Ullah et al. (2020), Zhai et al. (2020); Khan, (2011)

Crops data Fritz et al. (2019); Ali Chandio, Jiang, Ali Joyo, &

Rehman (2016);Government of Pakistan (2016); Raja

(2015); Reidsma, Ewert, Lansink, & Leemans (2010);

Bai et al. (2016); Khan (2011)

Research data Neylon (2017)

Soil data Fritz et al. (2019); Zhai et al. (2020); Lucà, Buttafuoco,

& Terranova (2018);OECD/FAO (2016); Reidsma et

al. (2010); Padilla-Pérez & Gaudin (2014)

Agricultural census Fritz et al. (2019); Rehman et al. (2019), Morton

(2007); Padilla-Pérez & Gaudin (2014); Raja (2015)

Weather / meteorological

data

Zhai et al. (2020); Fritz et al. (2019); OECD/FAO

(2016); Khan (2011)

Pest and disease data Chougule, Jha, & Mukhopadhyay (2016)

Data on natural hazards Simukanga et al. (2018)

Remote Sensing Zhang et al. (2019); Fritz et al. (2019); Reidsma et

al.(2010); Khan (2011)

Land cover data Khan (2011); Fritz et al. (2019)

Climate change

Khan (2011); Government of Pakistan (2016);

Government of Pakistan (2015); Fatima & Yousaf

(2014); Dinar, (1993); CCAFS (2016); Shirgure &

Shirgure (2013); Steenwerth et al. (2014); Moro

(2016); ADB (2009); OECD (2015);Zhai et al. (2020)

Impact of Climate Change on agriculture

The negative impact of climate change on agriculture is well acknowledged in theory and

practice. It can cause for example desertification, salinisation, land degradation and soil

erosion, saltwater intrusion due to rise in sea level and flooding. In the context of Pakistan,

following literature was sighted.

Prăvălie (2016) finds that desertification is affecting 38 of 48 countries in Asia and it

is a serious problem in Pakistan (Irshad et al., 2007; Lal, 2018). Major river basins such as

Indus Basin in Pakistan is undergoing salinisation (Aslam & Prathapar, 2006). In Pakistan,

about 6.2 Mha of agricultural land is affected by salinity(NIAB, 1997). Land degradation

affects people and ecosystems throughout the planet and it is a driver of climate

change(Shukla et al., 2019, p.53).


Saltwater intrusion is also one of the problems being faced in coastal areas of

Pakistan. The Indus Delta, located near Karachi is an example of seawater intrusion and land

degradation due to climatic and environmental conditions(Rasul et al., 2012). Salinisation and

waterlogging in the provinces of Punjab and Sindh is due to over-irrigation (Chandio et al.,

2011).

Agriculture in Pakistan has been negatively affected by climate change(Shukla et al.,

2019, p.452). Abbas et al. (2017) finds that from 1980 to 2014, there is an average shift of 4.6

days per decade earlier in the case of spring maize growing periods whereas sowing of

autumn maize has been delayed 3.0 days per decade. Similarly, Tariq et al. (2018) found that

due to rise in temperature from 1980 to 2016, there are shifts in sowing, emergence, anthesis,

and maturity for fall and spring crops.

In developing countries, floods negatively impact access to food and livelihoods. In 2010, the

unprecedented rainfall in Indus valley of Pakistan led to flooding, affecting the lives and

livelihoods of 20 million people. There is evidence that these effects were due to climate

change (Mann et al., 2017). Accordingly, the food prices were high causing more food

insecurity in the country(Shukla et al., 2019,p.516).

From above cited literature it reveals that climate change is the major threat to agriculture.

Therefore, it needs a special mention. Location information (spatial data) typically plays an

important role in climate change studies(Below et al., 2015; Hinkel, 2011; Tiwari et al., 2008;

Vincent, 2007). Paudyal, Dev Raj and McDougall, Kevin and Apan (2011) in their research

identified main spatial datasets to study climate change in a region.

Table 5: Main spatial datasets to address climate change

(Source: Paudyal, Dev Raj and McDougall, Kevin and Apan, 2011)

Spatial Data Local State National

Atmospheric ×

Climate × ×

Land Use/Land Cover

Digital Elevation Model × ×

Demography/Population

Distribution × ×

Soil and Geography ×

Aquifer and Ground Water × ×

Surface Water ×

Watershed/Catchment × ×

Landownership/Cadastre ×

Infrastructure × ×

Protected Areas × × ×

Source of Pollution × ×

Topographic base ×

Ariel Photograph/Orthophoto ×

Buildings ×

Transport × ×

Air Quality × ×

Ecosystems Zones × ×

Vegetation ×

Rainfall × ×


From Table 4 and 5, it reveals that 25 spatial datasets of various kinds are required to

address issues faced by agriculture. Practically, it is impossible for a single organization to

collect, maintain and provide these datasets due to mandate constraints. But it does not

include datasets required for agricultural monitoring and land management which is described

next.

For agricultural monitoring

Fritz et al. (2019) in their recent study compared eight main global and regional agricultural

monitoring systems based on the input data and models being used. They identified forty

thematic datasets required for agricultural monitoring. The authors found fundamental gaps in

the data and data collection methods. Accordingly, they suggest to harmonize data collection

methods.

Figure 4: The level of importance of datasets for agricultural monitoring

Source: Fritz et al. (2019)

For land management

Erb et al. (2017) from an inventory of the main data sets available for different land

management practices, found that ten various kinds of datasets are crucial for land

management in order to understand and address the “global sustainability challenges such as

climate change, biodiversity loss and food security”. The datasets include forestry harvest,

tree species, grazing and mowing harvest. crop harvest and residue management, crop species,

N-fertilization, tillage, irrigation and wetland drainage, and fire management.

9. Inventory of necessary spatial datasets for agriculture: The synthesis

To synthesis all the above mentioned spatial datasets, Table 6 lists all the necessary spatial

datasets required for agricultural practices and policy-making. The table also mentions user

as well as producer organizations of the datasets in Pakistan.

Table 6: Use–produce–origin description for types of geographic information necessary for

agricultural policies

(adapted from Chantillon et al., 2017)

Type of data Description Use—Produce—Origin

Hydrological Data (It is the data regarding

water resources)

• 9 user organisations

• 4 producing organisations



• The data mainly originate from Water and

Power Development Authority (WAPDA).

Cadastre (land ownership) Data

(It is the data which defines the geographic

extent of the past, current, and future rights

and interests in real property and the spatial

information needed to describe that

geographic extent)



• The data mainly originate from the

provincial governments.

Land Use Data

(It is the data which tells us about the human

use of land. Land use involves the

management and modification of natural

environment or wilderness into built

environment such as fields, pastures, and

settlements)





Crops Data (It tells us about the useful

information regarding crops maps, crop

intensity and crops management)





Research Data (It tells us about the

information that has been collected, observed,

generated or created to validate original

research findings)




academic institutions.

Soil Data (It tells us about any information

regarding the soil and their types)





Agricultural Census Data (The Census of

Agriculture is a census conducted that

provides the only source of uniform,

comprehensive agricultural data)




provincial governments and is consolidated at

the federal level.

Weather / Meteorological Data (The weather/

meteorological data provides us the useful

information regarding weather conditions and

also the changes in them)



• The data mainly originate from the Pakistan

Meteorological Department.

Pest and Disease Data (This data give

information regarding any animal or plant

detrimental to humans or human concerns,

including crops, livestock and forestry,

among others)




Natural Hazards Data (It is the data regarding

the natural disasters including floods,

droughts and land sliding)




provincial governments and National Disaster

Management Authority (NDMA).

Remote Sensing Data (Remote sensing is the • 5 user organisations



science of obtaining information about

objects or areas from a distance, typically

from aircraft or satellites)


• The data mainly originate from Space and

Upper Atmosphere Research Commission

(SUPARCO).

Land cover Data (Land cover data documents

how much of a region is covered by forests,

wetlands, impervious surfaces, agriculture)





(SUPARCO).

Digital Elevation Model Data

(It represents the relief of a surface between

points of known elevation)





(SUPARCO).

Demography/Population Data

(Data collected about the characteristics of

the population, e.g. age, gender and income)



• The data mainly originate from Pakistan

Bureau of Statistics (PBS).

Distribution Data

(The distribution of a statistical data set (or a

population) is a listing or function showing

all the possible values (or intervals) of the

data and how often they occur i.e. temporal

aspects)




Bureau of Statistics (PBS).

Aquifer and Ground Water Data

(Aquifer/Ground Water Data is all the

groundwater present in the area)





Surface Water Data

(It tells us about the water over the surface of

the earth)





Watershed/Catchment Data

(The Catchment data/watershed data tells us

about the area from which a city, service or

institution attracts a population that uses its

services)



• The data mainly originate from Climate,

Energy & Water Research Institute

(CEWRI).

Infrastructure Data

(Infrastructure data informs about

fundamental physical facilities and systems

serving a region such as telecom, transport

and health facilities)



• The data mainly originate from Planning

Commission of Pakistan.

Protected Areas Data

(Protected areas give information about the

locations which receive protection because of

their known natural, ecological or cultural

values)





Source of Pollution Data • 4 user organisations



(It gives information about the three sources

of pollution; mobile, stationary and area

sources. For example, cars, buses, planes,

trucks, and trains are moble sources of

pollution. Stationary sources can include

power plants, oil refineries, industrial

facilities, and factories. Area sources include

agricultural areas, cities, and wood burning

fireplaces)



Ministry of Climate Change.

Topographic Base Map Data

(Topographic data are information about

natural and man-made features that appear on

the earth)



• The data mainly originate from Survey of

Pakistan (SoP).

Ariel Photograph (It is airborne imagery) • 4 user organisations



Pakistan (SoP).

Buildings Data

(It refers to the characteristics of buildings)



• The data mainly originate from provincial

governments.

Transport Data

(It is the data related to the means of

transportation)




Pakistan (SoP).

Air Quality Data

(It indicates how clean or unhealthy air is in a

region)



• The data mainly originate from Ministry of

Climate Change.

Ecosystems Zones Data

(It deals with the precipitation intensity,

variability and annual amounts across a

region)


• Could not be specified.

• Could not be specified.

Vegetation Data

(It refers to type of plant)



• The data mainly originate from

SUPARCO.

Rainfall Data

(It is the data regarding rainfall)


•1 producing organisations


Meteorological Department.

Agricultural Production Data

(It refers to the production of agricultural

crops)







In Situ Measurements

(These are measurements that are made on

ground)


•5 producing organisations


Pakistan.

As shown in Table 6, thirty (30) types of geographic information is necessarily

required for agricultural policy-making. It is also evident from the table that several

organizations at various spatial scales ranging from national to local level are engaged in

producing the information. The number of user organizations of the information are up to 32

in certain cases. This scenario calls for implementation of national spatial data infrastructure

(NSDI) for efficient management and sharing of the data to inform agricultural policies and

practices.

10. Conclusions and recommendations

Agriculture in Pakistan, “despite its falling share in national income, continues to attract

considerable attention in policy debates because of its strategic importance to the livelihood of

farming communities, poverty reduction and national food security”(Birthal et al., 2020). The

literature cited and discussion made so far in this chapter reveals that agriculture in Pakistan is

facing several issues such as low availability of water, low productivity, deprived physical

infrastructure, food insecurity, degradation of natural resources (land, water), outdated

cultivation methods, issues with supply of agricultural inputs and lack of adoption of the latest

technology coupled with climate change threats. This situation calls for sound decision and

policy making at national and provincial levels. For evidence-based decision and policy

making, spatial data is an indispensable element. But finding the 30 spatial datasets (see Table

6) that are consistent and compatible is problematic(Ackrill, 2000, p.80). The timely

availability of spatial information can impact directly and positively agricultural policy-

making. From an agricultural policy perspective, governments have to prioritize resource

mobilization such as seeds, fertilizers and pesticides in the first place. In the second place,

establishment of physical infrastructures such as transportation, water and food storage

infrastructure should be setup in order to overcome postharvest losses. For resource

mobilization, developing physical infrastructures and studying the impact of agricultural

policy (Scozzafava, 2008) access to spatial data of various types is a pre-condition. In this

background, the study proposes the establishment of spatial data infrastructure (SDI) as the

means to assemble and share geographic information held by various organizations for

effective and evidence-based policy making including but not limited to agriculture.

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Assessing the Reform Options of the Public Pension Scheme of the Republic

of Cyprus1,2

Abstract:

Empirical evidence had revealed that the public pension scheme of the Republic of Cyprus (as

it was functioning and before its reform into its current situation), could generate undesirable

and widely distributed economic impacts. This study, focuses on examining alternative

reform scenarios aiming to identify the possible reforms of the public pension scheme as well

as the required extend of the reforms in order to enhance the financial sustainability of the

Public Pension Scheme. The suggested reform options include parametric changes to the

providences of the scheme such as increasing the financial contribution of the civil

employees, extending the retirement age, decreasing benefits and modifying the methodology

for determining the level of pensions as well as for readjusting pensions. The objective of this

research project, is to identify the reform options of the Public Pension Scheme of the

Republic of Cyprus, that can enhance the sustainability of the public pension scheme and of

course to keep both parties, the government and the civil servants, satisfied. The paper

concludes that effective institutional intervention with the suggested reforms can enhance the

sustainability of the public pension scheme and will benefit the budgetary balance of the

Republic of Cyprus. Finally, the paper concludes and gives some suggestions for further

research.

Keywords: Republic of Cyprus; public pension scheme; reform options; economic impacts.

JEL: H55 - Social Security and Public Pensions

Charalambos N. Louca3, George M. Korres4 and George O. Tsobanoglou5

1 Acknowledgments: Our sincere thanks to the Research Promotion Foundation for funding the research project,

“Sustainability and Modernization of the Public Pension Scheme of the Republic of Cyprus”. We thank the

Muhanna and Co. Actuarial Services, for their valuable contribution to the research project, as well as the

Administrations of the American College and of the University of the Aegean for their support. Thanks to the

Treasury of the Republic of Cyprus as well as to the Department of Public Administration and Personnel for

their support throughout the research project. 2 In memory of our beloved friend and scientific partner,

Mr. George M. Psaras, FCAA, Managing Actuary, Social Insurance, Pension and Provident Funds, Muhanna and

Co. Actuarial Services.

Note: This research project was completed in 2008 but the results pointing out the suggested reform options

were not published. This is the first time that they see the light of publicity. 3 Corresponding-Address: Associate Professor Dr. Charalambos N. Louca, Associate Professor, Head of

Business Department and Director of Research Department, American College, Eleftheria Square, P.O.Box

22425, Nicosia, Cyprus. Tel: +357 22 36 80 00, Fax: +357 22 36 80 01. Email: [email protected] 4 Corresponding-Address: Professor Dr. George M. Korres, Professor, University of the Aegean, School of

Social Sciences, Department of Geography. Email: [email protected] 5 Corresponding-Address: Professor Dr. George O. Tsobanoglou, President, International Sociological

Association, Research Committee on Socio-technics & Sociological Practice (ISA-RC26). Professor, University

of the Aegean, Department of Sociology. Email: [email protected]





1. Introduction

The public pension scheme of the Republic of Cyprus provided supplementary retirement and

widow pensions to government employees. It was almost entirely financed by the general

taxation system, since participation of employees in the financing of the public pension

scheme was limited to 0.75% contribution rate of their gross earnings, to pay for widows’ or

survivors’ pension. A brief description of the public pension scheme before the reforms of

2011 are applied is given below.

Early retirement was allowed from the age of 45 but the pension was frozen and

could only be paid at the age of 55 (at the age of 58 for government employees who joined

employment on or after the 1st of July, 2005) without any actuarial reduction of benefits. The

mandatory retirement age for the civil servants was the 63rd year of age and the 60th for the

teachers. Retirement for the police force, was the age between 60 and 61, for the employees in

the army the 52nd and the 60th year of age depending on the position and the rank of the

people in the army.

The pension was calculated on the basis of the final salary at an accrual rate that produces

a retirement benefit equivalent to 50% of that salary after 33 1/3 years of service or 400

months.

A lump sum gratuity was paid when an employee retired and is a multiple of the annual

pension. Today the maximum coefficient of the lump sum is 15/3 of the annual pension.

The Public Pension Scheme of the civil servants exercised substantial pressures upon

the public economics of the Republic of Cyprus. In 2006 the expenditures of the Scheme were

2.2% of the GDP and it was expected that by 2040 to be 2.8% of the GDP. According to

Eurostat (2015), the total expenditures on pensions including pensions of government

employees, was 10.8% of the GDP. In case that the expected rate of GDP is not attained, then

the cost of the Public Pension Scheme can fluctuate between 2.8% and 4.5% of GDP (table 1).

An expenditure of 2.8% of the GDP implies an average pay-as-you-go cost of 32.7% of the

wage bill. This by far exceeds the contributions of the public employees, which were only

0.75% of the wage bill, before the reforms of the scheme were applied in August 2011. After

August 2011, the employees’ contributions had risen to 5.1% of the wage bill.

Table 1: Cost Sensitivity of the Scheme Relative to the Increasing Rate of GDP

Projections of GDP, increasing by a lower percentage

rate than the rate before the economic recession of

2013

Long-term

Expenditures of

the Scheme as a %

of GDP

Projection of GDP – According to the Ministry of Finance

(before the economic recession of 2013)

2.8%

Projection of GDP by 0.5% lower increasing rate 3.3%

Projection of GDP by 1% lower increasing rate 3.9%

Projection of GDP by 1.5% lower increasing rate 4.5%

Source: Louca, Ch., Korres, G., Tsobanoglou, G. and Kokkinou, A. (2011)

The present value of the accumulated pension benefits of the current pensioners and

of the civil employees is estimated to be 8.34 bil Euros. If it were to develop an independent

fund for covering pensions, this should be endowed with an amount of 8.34 bil Euros, plus a

long-term financial contribution of 37% of the retirement remunerations (Louca, et al 2011).

What had leaded to high expenditures of the scheme can be identified in the following:

The civil servants did not contribute to the Scheme. Their contribution of 0.75% of the

wage bill, was supposed to cover the 50% of the cost of the widow pension.


The pensions were revised based on the inflation rate and on the general wage rises given

to the employed civil servants.

The retirement age did not rise to the 63rd year of age for all the civil servants. Currently,

for the majority of the government employees the retirement age, is the 65th year of age.

Other problems identified, can be the early retirement without any actuarial decrease in

pensions.

Changes in the above mentioned issues could decrease the annual expenditures of the

Public Pension Scheme of the Republic of Cyprus and secure its long-term sustainability.

Currently in the Republic of Cyprus, there is a continuous trend towards reforming the

public pension Scheme of the government employees. The reforms aim at reducing the cost of

the system to the government and at improving equity between the civil servants and the

employees of the private sector. Reforms can be both parametric and structural. The

parametric reforms to the Public Pension Scheme that this paper, studies are:

(a) the permanent contribution rate of public employees, to increase from 0.75% to 5.1%

of the wage bill; and

(b) the indexation of pensions to be based only on inflation.

This paper, aims to identify and suggest additional possible reforms of the Public Pension

Scheme of the Republic of Cyprus so that to guarantee its sustainability. The suggested

reforms are based on the actuarial projections presented in the paper, “assessing the public

pension scheme of the Republic of Cyprus” (Louca, et al 2011).

Thus, the objectives of the study are:

1. To identify the possible reform options of the Public Pension Scheme of the Republic of

Cyprus that can enhance the financial sustainability of the scheme and improve growth

prospects in Cyprus.

2. To assess the impacts of these reform options on the actuarial liability, on the long-term

total expenditures as a % of GDP in prices of 2007, on the normal contribution rate and on

the pay-as-you-go-cost for the period 2007 – 2070.

For achieving these objectives, the main actuarial projections under the existed

arrangement are firstly presented. Secondly, possible simulation scenarios of the demographic

and financial assumptions considered are studied. Finally, based on the simulation scenarios

studied, possible reform options of the Public Pension Scheme of the Republic of Cyprus are

proposed.

Following this section of the paper, a brief literature review is outlined, the

methodology applied follows as well as the demographic and financial assumptions that are

taken into consideration in the projections of the study are presented. Then the main projected

positions under the existed arrangement are presented, including projections of the fiscal

stance and the liabilities. The outcomes of the simulation scenarios of the assumptions

considered are then presented. In the next section, based on the outcomes of these simulation

scenarios, possible reform options to the scheme are proposed. The paper, finally concludes

and gives some suggestions for further research.


The European Commission, faces the challenge of delivering adequate and sustainable

pensions in Europe (European Commission, 2010 and European Commission, Economic and

Financial Affairs, 2017).

Countries in the EU such as France, the United Kingdom (Ex EU member), Finland,

Sweden, Germany, Italy, Austria, Greece, Portugal, Spain, the Netherlands, Belgium and

Dania have reformed their public pension schemes before other member countries of the

European Union.

Academics very early have shown interest in reforming social insurance systems.

Specifically, Prof. Barr, N. (2000), Diamond, P. (2002), Barr, N. and Diamond, P. (2009),


have determined the requirements for reforming pensions and have developed guidelines for

governments that would like to proceed with reforms to their pension systems. Cogan, F. and

Olivia S. Mitchel (2002), have proposed to the EU a system of dual arrangement. Palacios, R.

and Whitehouse, E. (2006) have studied the public pension schemes of civil servants in

different countries as well as the reforms of the public pension schemes in those countries.

Capretta, C. (2007), has studied the impacts of the population ageing on the sustainability of

the pension schemes of the civil servants in twelve economic developed countries.

3. Methodology

For achieving the objectives of the study, two software were developed; one for projecting the

income and expenditure of the Scheme and one for estimating the cost of the actuarial liability

of the Scheme.

The first software aims to project the cash flow of the Scheme for the next fifty

years. This software assesses the long-term financial situation of the Scheme and examines

the ratio of income to expenditure (Cichon, 1999). This method is mostly used for schemes

which are not fully funded.

The second software aims to estimate the cost of the Scheme and its actuarial

liability (American Academy of Actuaries, 2004) at the date of the estimation. This method is

mostly used for schemes which are fully funded.

3.1. Software for projecting income and expenditure

For estimating the income and expenditure of the Scheme we firstly proceeded with the

demographic and financial projections. The demographic projections include: (a) projection of

the employed civil employees covered by the Scheme; (b) projection of the retired civil

employees; and (c) projection of the widows and orphans.

The financial projections include: (a) projection of wages; (b) projection of employment of

the civil employees; and (c) Projection of benefits.

3.1.1. Demographic projections

(a) Projection of the employed civil employees covered by the Scheme

The projection of the number of insured employees is based on:

the expected number of the insured members of the Scheme in each year;

the number of the insured employees of the previous year who are expected to remain

insured;

the number of the new insured employees who enter the Scheme; and

the expected age distribution of new employees for every year.

(b) Projection of the retired civil employees

The projection of the number of the retired civil employees is based on:

the expected mortality which differs by age and sex; and

the expected probability of new members to retire (according to age as well as sex in

each year, due to the long-term assumption that the retirement age is increased to the

63rd year for certain categories of civil servants).

In appendix A, table 2, the data of the retired employees in 2006 is presented which is used

for projecting the number of the retired employees in the Scheme.

(c) Projection of widows and orphans

The projection for the beneficiaries of widow pension is based on:

The expected mortality which depends on age; and


The expected mortality of the married insured employees and pensioners. These changes

according to age and year, are used for the projection of the number of the widow

pensioners of the scheme.

3.1.2 Financial projections

The projection of wages depends upon the wage level of the employees at the date of their

retirement. Therefore, the second part of the software covers, (a) the projection of wages

based on the assumptions presented at the end of this section in table 3; (b) the projection of

the period of employment of the civil employees; and (c) the projection of benefits.

(a) Projection of wages

The projection of the average wage at each age level depends upon the estimated rate of wage

rise which is expected during the period of the projection. Specifically, the projection depends

upon:

the general wage rises;

the wage-rises due to inflation; and

the wage-rises due to service and promotion.

(b) Projection of the period of employment of the civil employees

The benefits received are directly related to the period of employment of the civil servants.

Based on data received from the Department of Public Administration and Personnel of the

Ministry of Finance, the future expected service until retirement is estimated.

(c) Projection of benefits

The demographic projections in combination with the projection of wages and the projection

of the period of service are used for the projection of benefits. Specifically, the projection of

the retirement pension includes the following two components:

The pension paid to the current retired pensioners. This is the pension paid during the

previous year and readjusted based on the rate by which pensions rise and according to

the relevant mortality.

The pension paid to the new retirees.

In the projections the benefit received as a supplementary pension from the Social Insurance

Fund is considered as well.

3.2 Software for estimating the actuarial liability

According to the provisional Law for establishing, registering, functioning and supervising

pension funds, the actuarial liability is estimated for fully funded funds. The Public Pension

Scheme of the Republic of Cyprus is not in this category but for the objectives of the study

both the actuarial liability and the cost of the Scheme are estimated.

The actuarial liability is the present value of the benefits which correspond to the

service of the members of the Scheme and have been earned by the date of the estimate.

Normal contribution rate is the percentage rate of contribution which is necessary to

cover the cost of the benefits for the service after the estimation date.

The difference between the actuarial liability and the assets of a fund is the actuarial

surplus or deficit.

According to the International Financial Reporting Standards (IFRS 19), the method

used for financing the fund must be the Projected Unit method (Matt Smith, 2005). It is

“Projected”, because it takes into consideration the projected assumptions until retirement in

estimating the value of the benefits. It is “Unit”, because it studies the cost of a one-year


future employment. The method presumes that the Fund will continue to accept new members

(Table 2).

Table 2: Entry of New Members into the Scheme

Age Distribution Percentage of New Employees in the

Pension Scheme in each Age Group

< 20

20 – 25

25 – 30

30 – 35

35 – 40

40 – 45

45 – 50

50 +

0.00%

20.00%

46.00%

17.50%

9.00%

5.75%

1.75%

0.00%

Total 100.00%

The method used, differentiates also the benefits that have been accumulated until the

date of the estimation (and the capital/provident that has been accumulated in order to cover

these benefits) from the benefits that will arise as a result of the future employment. The first

group of benefits emerges from the actuarial liability and the second group of benefits

emerges from the normal rate of contribution that must be paid in order to cover the cost of

benefits that will arise from future employment.

The main long-term financial objectives of the Fund are:

to secure that the available capital is sufficient to cover the value of the benefits for the

service that has been already offered, taking into consideration the projected final

wages; and

to secure that the normal rate of contribution is sufficient to cover the benefits that will

arise from the future service.

The Actuarial estimation is carried out in three stages as explained below:

(a) Stage one – Past service

The first stage is carried out in order to identify if the accumulated funds at the date of

estimation are sufficient to cover the accumulated benefits (i.e., the benefits that have been

accumulated for the service offered up to the date of the estimation).

The actuarial value of the benefits (actuarial liability) is then compared with the

actuarial value of the accumulated funds.

The objective of this first stage, is to estimate the monetary value of the benefits that

will arise in the future, as a result of the service offered up to the estimation date. This will

enable us to estimate the necessary funds required to cover the value of the benefits that have

been accumulated up to the estimation date. The estimation takes into consideration the wage

rises that will emerge in the future.

Therefore, stage one, determines the value of any difference between the actuarial

liability and the available funds for covering the responsibilities of the Fund.

(b) Stage two – Normal contribution rate

At this second (2nd) stage the normal contribution rate is estimated. This is the contribution

rate which is necessary to cover the cost of benefits for the service that will be offered after

the estimation date. This contribution rate, is estimated for each employee, by differentiating

the present value of the expected benefit that will be accumulated within the year after the

estimation date (taking into consideration any wage rises until the retirement date, the date of

death or the date of withdrawal from the service), from the present value of the annual wage.

This method which is used for financing the Fund, estimates an average contribution rate for

the whole Fund, as a percentage of the total overall wages of the members of the Fund. This


contribution should be sufficient to cover the cost of the benefits that will be accumulated in

the year after the estimation date.

(c) Stage three – Disposal of the actuarial surplus and financing the actuarial deficit

At this stage the normal contribution must be regulated so that to eliminate any difference

(positive or negative) between the assets of the Fund and the actuarial value of the benefits as

described in stage one. This kind of regulation can take the form of a lump sum paid (or

returned if it is appropriate), during a fixed time period or even the regulation of the normal

contribution rate during an appropriate time period. As was already mentioned, the Projected

Unit Method assumes that the Fund will continue to accept new members. The normal

contribution rate will not be strongly affected if the distribution of the members according to

sex, age and wage is stable. However, the normal contribution rate will be strongly affected if

the distribution of the members, changes substantially from one estimate to another. This may

happen for example either in case that a substantial number of new members enter the Fund or

old members of the Fund leave it (Louca, et al, 2011). For the objectives of the study, the

demographic and financial assumptions given in table 3 are considered.

Table 3: Description of Assumptions

Demographic Assumptions

Mortality

70% of PA90 (Appendix B, table 4)

with a gradual decrease to 50% in the

next 50 years.

Retirement Rate Retirement at normal age with a very

low percentage at early retirements.

Entry Rate of New Members in the

Scheme

Starting rate of 2.5% with a gradual

decrease to 1% in the next 10 years

and a final fixed rate of 0.5%

Entry of New Members According to table 2

Financial Assumptions

Percentage Rate of Investment 5.5%

Salary Increase 7%

Increase on the Basic Salary 4%

Increase in Pensions

Public Pension Scheme: 3.5%

Supplementary Pension from the

Social Insurance Fund: 2%

In the next paragraph the main projected positions under the present arrangement are

presented.

2. Projected Positions Under the Existed Main Arrangement

It was estimated that up to 2017, the employed civil employees will be increasing by 1% per

year and after 2017 by 0.5% per year. The ratio of the employed civil servants to the retired

ones (ratio of protection) is currently 2:1 (Table 4). In the future, it is estimated that the ratio

of protection will decrease because (a) the pension scheme has reached its maturity, (b) of the

decreasing rate by which the civil servants will enter the public service and (c) of the rising

rate of life expectancy.


Table 4: Demographic Projections 2007 – 2070

2007 2008 2009 2010 2015 2020 2030 2040 2050 2060 2070

Employed

civil servants 31,752 32,503 33,123 33,735 35,891 37,723 40,877 42,999 45,244 47,622 50,065

Civil servants

in pension 12,465 12,895 13,448 13,990 16,370 17,735 19,275 24,212 28,285 30,788 33,085

Beneficiaries of

widow/widower

and orphan

pension

2,443 2,571 2,698 2,826 3,507 4,228 5,536 6,347 6,891 7,730 8,677

Ratio of

protection 2.1 2.1 2.1 2.0 1.8 1.7 1.6 1.4 1.3 1.2 1.2

Source: Treasury of the Republic of Cyprus and authors’ projections (Louca, et al 2011)

3.1 Projection of expenditures and GDP

Based on data from the Treasury of the Republic of Cyprus, the long-term projections of

expenditures (pensions and lump sum benefits) given and to be given to beneficiaries, as a %

of GDP (up to 2060) in real prices of 2007 are given in table 5. The projections are estimated

based on the methodology adopted by the Commission of Economic Policy of the EU. The

long-term assumptions are determined by the convergence program of the EU (European

Commission, 2008).

Table 5: Projections of Total Expenditures and Contributions 2007 – 2070 (£ - Cyprus

Pounds, in Millions) 2007 2008 2009 2010 2015 2020 2030 2040 2050 2060 2070

Total

Expenditure

s

187.

4

202.

4

227.

5

243.

7

331.

6

425.

8

733.

6

1,429.

8

2,150.

4

3,220.

3

4,915.

7

Contributio

ns 4.9 5.4 5.8 6.3 8.1 10.6 17.5 21.9 30.2 45.4 67.5

Expenditure

as a % of

GDP in

Prices of

2007

2.1

%

2.1

%

2.2

%

2.2

%

2.1

%

2.1

%

2.2

% 2.8% 2.8% 2.9% 2.9%


Cyprus Pound (£) = € 0.585274

The contributions are for covering the pensions to widows/widowers and orphans.

Up to a wage ceiling and as determined by the Social Insurance Law, the contribution rate is

0.75% of the annual wages. Beyond this wage ceiling the contributions increase to 1.75% of

the annual employees’ wages.

Table 6: Projection of Total Wages (£ - Cyprus Pounds) and of the Pay as you go Cost 2007

– 2070 (£-million) 2007 2008 2009 2010 2015 2020 2030 2040 2050 2060 2070

Total

Annual

Wages

654.6 710.9 760.4 806.7 1,032.9 1,327.5 2,117.2 2,836.5 4,077.5 6,173.4 9,168.1

Pay as

you go

Cost

28.6% 28.5% 29.9% 30.2% 32.1% 32.1% 34.6% 50.4% 52.7% 52.2% 53.6%



Table 6, presents the projections of the annual expenditure for wages up to 2070. The

projected values are based on: (a) data from the Ministry of Finance, (b) the assumption for

inflationary wage rise, (c) the general wage rise; and (d) the annual wage rise. The projection

for the pay as you go cost estimated, is the percentage of the current employees’ wages that

would have been necessary to be paid as a contribution within a given year in order to cover

the total annual cost of pensions and benefits of the Public Pension Scheme.vAlthough the

Scheme is financed by the government’s budget, by studying the cost of expenditure as a

percentage of the civil employees’ wages, we get the required contribution necessary to cover

the annual expenditure of the Scheme in case that the pensions were financed by an

independent fund. The mean of the pay as you go cost for the period 2007-2070 is 43.2%.

3.2. Actuarial liability and normal percentage rate of contribution

For achieving the objective of this study, we have estimated the financial situation of the

Public Pension Scheme by applying the pre-financed method assuming that the Scheme is

financed by an independent fund. In such a case, a fund to be able, (a) to cover all the

biometric risks (death, disability and longevity), (b) to guarantee a given return on investment

and (c) to guarantee given defined benefits, must have adequate reserves for covering these

risks.

Based on data acquired from the Ministry of Finance and the Department of Treasury

of the Republic of Cyprus, we estimated the actuarial liability of the hypothetical fund and the

normal percentage rate of contribution that must be paid for covering the benefits of the

Scheme (table 7). These results will be used for comparison with the outcomes of the reform

options of the Scheme studied in this paper.

Table 7: Actuarial Liability as at 31/ 12 / 2006

Actuarial liability for past service 4,882,000,000

Actuarial deficit (Actuarial liability for past and future service

minus the present value of contributions) 7,578,000,000

Normal Percentage rate of Contribution 36.5%

Actuarial liability as a % of GDP 58.2%

Source: Authors’ projections (Louca, et al 2011)

3.2.1. Actuarial liability for past service

The actuarial liability for past service represents the present value of the benefits granted to

the current civil servants based on the years of service until the estimated retirement date plus

the pensions granted to the current pensioners.

The actuarial liability is presented in table 7 and includes the current civil servants and the

current pensioners.

3.2.2. Actuarial liability for future service

The actuarial liability for future service of the current civil servants and the retired ones is

estimated as well. Due to the fact that the future service of the civil servants is taken into

account, it is necessary to deduct the present value of the future contributions of the civil

servants (table 7).

3.2.3. Contribution rate – future service

The normal contribution rate is the percentage rate of contribution necessary to cover the cost

of the benefits given to the civil servants after the estimated date. During the time that the

research was carrying out, the normal contribution rate (according to the study) is 36.5% of

the wages excluding the 13th salary (table 7). According to the method of financing the

Scheme and the assumptions made for this study, the normal contribution rate must stay fixed


if the age distribution, the sex distribution and the wage distribution also remain stable. The

revised contribution rate which is the sum of the normal contribution rate (36.5%) and the

percentage rate required to finance the actuarial deficit (70.4%), according to the estimates of

this study, is 106.9 % of the total wages.

3.3. Public economics and the financial liabilities of the Scheme

Regarding the wage level of the civil servants, Cyprus was ranked 3rd in the European Union.

According to the report of the Ministry of Finance for the Stability Program (2008), the wages

of the civil employees were 17% of the GDP. However, according to the present study, the

wages are only 7.2% of the GDP. This is so, because only the full-time civil servants are

considered, without taking into account the part-time civil servants. The high wages and the

generous benefits offered by the Public Pension Scheme, substantially increase the cost of the

Scheme and the expected future expenditure.

3.3.1. Current expenditure as a percentage of GDP

In 2006 the GDP in Cyprus was ₤8.380 billions (Central Bank of Cyprus, 2006).

If the Scheme should have been fully-funded, then the actuarial liability should have

been substantial in relation to the GDP of Cyprus.

4. Simulation Scenarios

The outcome of the projections for the future is uncertain and the degree of uncertainty

increases as the projection period increases. For this reason, an analysis of the outcomes of the

possible changes in the economic and demographic assumptions originally considered is

made. These simulation scenarios aim to identify the impacts of any possible changes in the

economic and demographic assumptions considered, on the expenditures and the

contributions of the Public Pension Scheme. The simulation analysis takes into account

changes in inflation and interest rates as well as changes in the mortality rate and the number

of the retired civil servants.

4.1. Impact of Inflation changes

The Scheme’s benefits are estimated on the basis of the last salary of the employee before

retirement. The assumption for salary rises, substantially affects the projections of

expenditures and contributions as well as the actuarial responsibility of the Scheme and the

normal contribution rate. The main assumptions concerning salary rises, are: 2% inflation

rate, 1.5% general salary rises and 3.5% annual salary rises. This gives an assumption for a

total annual salary rise of 7%.

In these scenarios, we assume that the inflation rate can be either 1% higher or 1%

lower. That is, the total annual salary rise, is assumed to be either 6% or 8%.

Also, the inflation rate, affects other financial assumptions such as:

The income of the employees under the General Social Security Scheme;

The pensions of the civil servants under the public pension scheme; and

The supplementary pension from the General Social Security Scheme.

If we assume that the inflation rate is by 1% lower, this will result to a lower actuarial

responsibility by 20%. If the salaries rise by 1%, then the actuarial responsibility will be by

22.3% higher.

In table 8, the results of the simulation scenarios concerning the impacts of a change

in the inflation rate on the actuarial responsibility, on the normal contribution rate and on the

actuarial responsibility as a percentage of GDP are presented.


Table 8: Inflation Impacts

Inflation rate: 1% Inflation rate: 3%

Actuarial Responsibility 4,060,000,000 5,971,000,000

Normal Contribution Rate 27.6% 49.1%

Actuarial Responsibility as a % of

GDP 48.5% 71.2%

Source: Authors’ calculations.

From the financial projections we can conclude that both expenditures and

contributions decrease when the inflation decreases and; rise as the inflation rises. The

analytical results of the projections are presented in appendix C, tables 1-3.

4.2. Impact of changes in the percentage rate of investment

The percentage rate of investment is an important parameter which substantially affects the

responsibilities of the Public Pension Scheme.

In the basic financial assumptions, the annual long-term percentage rate of investment

is estimated to be 5.5%. Alternative rates of 4.5% and 6.5% are considered to test their impact

on the actuarial liability and the normal contribution rate as well as on the actuarial liability as

a % of GDP (table 9).

Table 9: Impact of the Percentage Rate of Investment of the Actuarial Liability and the

Normal Contribution Rate

Percentage rate of

investment:

4.5%

Percentage rate of

investment:

6.5%

Actuarial Liability 5,853,000,000 4,146,000,000


Actuarial Liability as a % of GDP 70% 49.5%


When the percentage rate of investment decreases by 1% the actuarial liability

increases by 20% and the normal contribution rate increases by 12.4%. However, when the

percentage rate of investment increases by 1%, then the actuarial liability decreases by 17.7%

and the normal contribution rate decreases by 8.6%. See also table 4 in Appendix C. The

projections of income and expenditures of the Scheme are not affected by the percentage rate

of investment.

4.3. Impact of changes in the mortality rate

The main assumption for the probability of mortality is based on the PA (90), decreased by

25% in the beginning. In the long-term within a time period of thirty years it is decreased by

50%. According to this assumption the life expectancy at the age of 60 is 20.5 years. If the

mortality rate is higher and the life expectancy at the age of 60 is 18 years, then the actuarial

liability and the normal contribution rate decrease. The results shown in table 10 are

comparatively lower that the ones of the current scheme shown in table 7.

Table 10: Impact of Mortality Rate on the Actuarial Liability and the Normal Contribution

Rate

Higher mortality rate

Actuarial Liability 4,463,000,000

Normal Contribution Rate 34.1%

Actuarial Liability as a % of GDP 53%



In Appendix C, the tables 5 to 7, show the impacts of the mortality rate on the

demographic and financial projections. We may underline that the mortality rate does not

affect the number of the employed civil servants, since the ones who pass away will be

replaced by new employees. However, in the case of the retired employees and of those who

receive widow and orphan pensions, the higher mortality rate will result to a decrease in their

numbers and in the total expenditures of the Scheme since the pensions will be paid for a

shorter period of time. Indicatively, the average pay-as-you-go cost for the period 2007-2070,

decreases to 36.3%, whereas in the current Scheme, the average pay-as-you-go cost is 43.2%.

4.4. Impact of a rise in the number of the employed civil servants

The main projected outcomes are based on the assumption that the number of the civil

servants will increase by 2.5% annually and that within the next ten years, this rate will

gradually decrease to 1%. Beyond this time period, the long-term assumption is that the

number of the civil servants will increase only by 0.5% per year. The scenario that the number

of the civil servants remains fixed after the year 2025 has also been studied.

This assumption affects only the demographic and the financial projections since the

actuarial liability, refers only to the current civil servants. The analytical results of these

projections are presented in Appendix C, in tables 8 and 9.

In this scenario, the numbers of both the employed and the retired employees are

lower. However, the decrease in the number of the employed civil servants is greater and this

has a negative impact on the ratio of protection.

Also the expenditures of the scheme are lower but the long-term pay as you go cost

increases since the number of the employed civil servants who will contribute to the Scheme

is lower.

5. Alternative Reform Options

In this paragraph, we study alternative reform options that include parametric changes in the

current providences of the Scheme. The objective, is to identify the reform options that have a

positive impact on the finances of the Scheme. Specifically, we study the following scenarios:

a. A rise in the contributions of the civil servants to the Scheme;

b. Estimating the pension rise based on the inflation rate;

c. Extending the retirement age;

d. Rising the contributor factor (coefficient) of the lump sum benefit;

e. Changing the methodology in estimating the pension benefit;

f. Decreasing the contributor factor of the widow pension;

g. Decreasing the benefit for the premature retirement; and

h. Combined reform scenarios.

5.1. A rise in contributions

The contributions of the civil employees to the Scheme, cover a very small percentage of the

cost of the widow pension. In this section, we examine the total contribution of the civil

servants to the Scheme as well as their contribution to the widow pension.

5.1.1. Total contribution

The cost of benefits of the Public Pension Scheme is equal to 36.5% of the total wages paid to

the civil employees by the government of Cyprus, and this creates a substantial financial

problem.

For this reason, we are discussing three alternative scenarios: a 5%, a 7.5% and a

10% contribution of the civil employees to the Scheme. In these scenarios we assume that,


this is the total contribution of the civil servants, including the contribution for the widow

pension.

A rise in the contributions of the civil employees for financing the benefits of the

Scheme will decrease its cost as a % of the GDP. The projection of the expenditures has been

examined in connection with the projection of the GDP. The results of these projections are

given in table 11 and in appendix D, table 10. It is clear, that even with a 10% contribution,

the total cost cannot be completely covered.

Table 11: Cost of the Scheme as a % to GDP, in real prices of 2007

2007 2008 2009 2010 2020 2030 2040 2050 2060

Without

Contribution 2.1% 2.1% 2.2% 2.2% 2.1% 2.2% 2.8% 2.8% 2.9%

5%

Contribution 1.7% 1.7% 1.8% 1.9% 1.8% 1.9% 2.5% 2.6% 2.7%

7.5%

Contribution 1.6% 1.6% 1.7% 1.7% 1.7% 1.8% 2.4% 2.5% 2.5%

10%

Contribution 1.4% 1.4% 1.5% 1.5% 1.5% 1.6% 2.3% 2.3% 2.4%

Source: Authors’ calculations

As a conclusion, the long term cost of expenditure in relationship to GDP decreases to

2.4% if the civil employees contribute the 10% of their salaries.

5.1.2. Contribution for covering the cost of the widow pension

In this section we are examining the contributions of the civil employees for covering the

widow pension.

Up to a ceiling fixed by the General Social Security System (GSSS), the civil

employees contribute the 0.75% of their annual remunerations. For remunerations above this

ceiling, the civil employees contribute the 1.75% of their annual remunerations. The objective

of this contribution is to finance part of the widow pension.

The mean of the pay as you go cost of the Scheme for the period 2007-2070 is

currently 43.2% (table 7). The cost for the widow pensions is 7%. Even with contributions of

7%, only up to 2037, the contributions will be adequate to cover the total cost of the widow

pensions (Appendix D, table 11). After 2037 the contributions must rise to 10%.

In 2007, the contributions covered only the 30% of the annual expenditure of the

Scheme. In the long-term the contributions will cover only the 10%. For covering the total

expenditure of the Scheme, the contribution should have been 7%. 3.5% to be paid by the

civil employees and 3.5% by the government.

In Appendix D, tables 10 and 11, the projections of the contributions of, 5%, 7%,

7.5% and 10% are presented as well as the projections for the total expenditures and the

expenditure for the widow pension.

5.2. Estimating the pension rise based only on the inflation rate.

Currently, pension rises, are estimated based on the inflation rate and the general salary rises

of the incumbent civil employees. Due to the fact that the general salary rises are granted to

the incumbent civil employees based on their productivity, it is not logical to grant it to the

retirees as well.

In this paragraph we examine the scenario that pensions are readjusted on the basis

of the inflation rate only. That is by 2% annually and not by 3.5% as it is currently the case.


Table: 12: Impact of 2% Pension Rise on the Actuarial Liability and the Normal Contribution

Rate

Readjustment of Pensions by 2%



Actuarial Liability as a % of GDP 47.8%


Comparing the actuarial liability and the normal contribution rate as at 31/12/2006

(see table 7), to the ones when pensions rise only by 2%, we observe a decrease of 21.7 % in

the actuarial liability and a decrease of 7.2% in the normal contribution rate. In Appendix D

(tables 12 and 13), the impact of the new pension readjustment of 2% on the financial

projections, is shown as well. The expenditures are lower and the average pay as you go cost

for the period 2007-2070, is only 37% in comparison to 43.2% in the current Scheme.

5.3.Extending the retirement age

In this paragraph we discuss the impact of extending the retirement age on the actuarial

liability and on the normal contribution rate. Specifically, we examine the case of extending

the retirement age of the public educators to the age of 63. In the case of the military surgeons

and the policemen, for those who currently retire at the age of 55, we examine the case of

extending their retirement at the age of 60. For those who currently retire at the age of 60, we

examine the case of extending their retirement at the age of 63.

Examining this scenario is necessary, due to the recent extension of the retirement

age of the civil employees to 63, as well as due to the demographic changes in Cyprus.

As a consequence of extending the retirement age, there will be an increase in the

number of contributing years (positive impact), an increase in the benefits paid (negative

impact) and fewer years for paying the benefit (positive impact).

If we compare the results of table 7 with those of table 13, we can see that the

actuarial liability decreases by 7.4% and the normal contribution rate by 4.2%.

Table 13: Impact of Extending the Retirement Age on the actuarial Liability and the Normal

Contribution Rate

Extending the Retirement Age to the

60th and the 63rd Year of Age





Comparing the financial projections presented in Appendix D, table 16 with the

results in table 5, we observe a decrease in the expenditures and this is due to the shorter time

period during which the benefit is paid. Although the benefit paid is greater due to the

extended years of service and the higher salaries received, the period during which the benefit

is paid is substantially lower. Specifically, in this scenario the average retirement age

increases by two years.

The results of the demographic projections are presented in Appendix D, table 15. If

we compare the results of the current scheme presented in table 4 with the results of this

scenario presented in table 15, Appendix D, we see that the number of the retirees in the

projected years is substantially lower and the ratio of protection is higher.


5.4. Impact of rising the contributor factor (coefficient) of the lump sum benefit

To an employee who has completed 400 months of service to the public sector (full service),

the pension currently paid by the Scheme is equal to 50% of the employee’s last monthly

salary. In the past the pension paid was 2/3 (66.66%) of the employee’s last monthly salary.

However, it was agreed to decrease it to 50% of the last monthly salary and the remainder of

the sum to be paid as a lump sum. Therefore, in order to convert this sum into a lump sum it

was necessary to take into consideration the life expectancy rate at the retirement age; being at

the age of 60. Based on the life expectancy rate, this lump sum was fixed at 12.5/3 of the

pension. In 1988 it was revised at 14/3 of the pension and since then, it remains fixed. Today,

for all employees whose retirement age has been extended to the 63rd year of age, the

coefficient of the lump sum increases by ½ a unit for every 12 months of service beyond the

400 months, with a maximum coefficient of 15/3.

Based on data from the statistical service of the Republic of Cyprus, the average life

expectancy for both men and women at the age of 60 is around 22.5 years. Although the age

of retirement for the majority of the civil employees is the age of 63, we shall examine the

level of the coefficient at the age of 60, so that to take also into consideration the members of

the scheme who retire earlier.

According to the current life expectancy, the assumption for the percentage rate of

investment and the assumption for the estimated rise in pensions, the lump sum coefficient is

estimated to be 17.5/3. If we consider that the retirement age, is the age of 63, then the

coefficient amounts to 16.3/3. In this section we examine two scenarios: in the first scenario,

the coefficient is 16.3/3 and in the second scenario is 17.5/3. Their impact on the actuarial

liability and on the normal contribution rate, is presented in table 14.

Table 14: Impact of Changing the Lump Sum Coefficient, on the Actuarial Liability and on

the Normal Contribution Rate

Lump Sum Coefficient =

16.3/3

Lump Sum Coefficient =

17.5/3

Actuarial Liability 4,983,000,000 5,043,000,000


Actuarial Liability as a %

of GDP 59.5% 60.2%


In comparison to the current Scheme (table 7), the actuarial liability increases by 2%

in the first scenario and by 3.3% in the second scenario. The normal contribution rate

increases by 1.4% and by 2.2% respectively. In Appendix D, tables 18 and 19, the impact of

the alternative lump-sum coefficients on the financial projections are presented. The average

pay-as-you-go cost for the period 2007-2070 increases to 44.7% and 45.5% respectively for

both scenarios in comparison to 43.2% (table 6) which is currently the average pay as you

cost of the Scheme.

5.5. Changing the methodology in estimating the pension benefit

According to the current regulations, the pension benefit is estimated based on the last salary

before retirement. In this scenario, we examine the case that the pension benefit is estimated

based on the average salary of the last two years of employee’s service, before retirement.


Table 15: Impact of Estimating the Pension Benefit, based on the average salary of the last

two years of Service, on the Actuarial Liability and the on the Normal Contribution Rate

Pension Benefit Based on the

Average Salary of the Last Two

Years of Employment





Comparing tables 7 and 15, we see that when the new methodology for estimating

the pension benefit is applied, the actuarial liability and the normal contribution rate decrease

by 1.5% and 0.7% respectively. In the Appendix D, the tables 20 and 21, show the impact of

the new methodology for estimating the pension benefit, on the financial projections. The

average pay-as-you-go cost for the period 2007-2070 is 42% in comparison to the current

Scheme which is 43.2%.

5.6. Decreasing the contributor factor (coefficient) of the widow pension

In 1988 the contributor factor for estimating the widow pension increased from 50% to 75%

of the pension benefit that should had been received by the passed away civil employee.

In this section, we examine the scenario of decreasing the contributor factor to 60%.

The 60% is chosen to be more acceptable in comparison to the 50%, because the 60% is the

contributor factor for the widow pension used by the General Social Insurance Fund.

Table 16: Impact of Decreasing the Contributor Factor of the Widow Pension, on the

Actuarial Liability and on the Normal Contribution Rate

Widow Pension: 60% of

the retirement pension





Comparing the data in table 16 with that of table 7, it can be seen that the actuarial

liability decreases by 2.3% and the normal contribution rate by 1.3%. The projections for the

expenditures are presented in the Appendix D, table 23. The average pay-you-go-cost for the

period 2007-2070 is 41.9% in comparison to 43.2% in the current Scheme. The pay as you go

cost for the widow pension only, decreases to 5.7% from 7% in the current Scheme.

Therefore, if the contribution of the civil employees is revised, in case of full financing by the

civil employees, their contribution will be 5.7%. In case that the cost of financing will be

equally shared between the civil employees and the government then their contribution will be

2.8%.

5.7. An actuarial decrease of the premature (early) retirement pension benefit

According to the revised law of 2005, eligible to premature retirement are only the civil

employees who have completed five years of service and are not younger than 48. To the

employees who apply for premature retirement, a lump sum benefit is paid but the pension

benefit is not paid until the retiree reaches the age of 58. As from the date of the premature


retirement until the date that the pension benefit will be paid to the retiree, the pension benefit

rises annually by the same percentage by which the public pensions rise.

Employees employed before 2005, can apply for a premature retirement at the age of

45 but the pension benefit will be paid at the age of 55.

In both cases, the premature pension benefit does not actuarially decrease and does

not take into account the longer period of time during which the retired employee will receive

the pension benefit. In this section, we estimate the actuarial decrease of the premature

pension benefit, so that, the present value of the total pension benefits that the retired

employee will receive, to be equal to the value of the pension benefits that would have been

received in case of normal retirement (table 17). On average, for each year before normal

retirement, the premature pension benefit decreases by 5%.

Table 17: Actuarial Decrease of the Pension Benefit for Premature Retirement

Retirement Age Actuarial Decrease

55 24%

56 23%

57 21%

58 19%

59 17%

60 15%

61 10%

62 5%


5.8. Combined reform scenarios

In this section, the impact of a combination of alternative scenarios discussed above is

examined. Specifically, we are examining a scenario which takes into account the following

changes:

Pensions rise based on the inflation rate.

The extension of the retirement age to 63 for those employed in the public

education and to 60 for those employed in the police and the army.

Increasing the lump sum benefit to 16.3/3 of the pension benefit.

Decreasing the widow pension to 60% of the pension benefit.

The pension benefit is estimated based on the average salary of the employee in the

last two years of service before retirement.

Table 18: Impact of a Combined Scenario on the Actuarial Liability and the Normal

Contribution Rate

Impact of a

Combined

Scenario





In this combined scenario, comparing the data of tables 7 and 18, the actuarial liability

decreases by 32.6% and the normal contribution rate by 10.4%.

Concerning the financial projections (Appendix D, table 25), it is worthy to note that, the

long-term expenditures as a percentage of GDP decrease substantially to 2.2%. Also, the


average pay-as-you-go cost decreases to 33.1% in comparison to 43.2% in the current

scheme. This gives us an approximate decrease of 10%.

6. Conclusions and Suggestions for Further Research

In this paper, a number of alternative reform options of the public pension scheme of the

Republic of Cyprus are examined and their impact on the financial projections has been

identified. The objective is to identify the trend and the extend of the impact that these reform

options have on the financial projections and mainly on the pay as you go cost, the actuarial

liability, the normal contribution rate and the total expenditures of the Scheme.

The reform options examined and the outcomes of their implementation are:

1. An increase in the contributions of the civil employees for financing the Scheme: based on the outcomes of the study even with a 10% contribution the total cost of

expenditure cannot be covered. However, this contribution decreases the expenditures as a

% of GDP to 2.4%.

2. A rise in the contribution of the widow pension: with a 7% contribution, the cost of

widow pension can be covered only up to 2037.

3. The pension rise is estimated based on a 2% inflation rate: In this case, the actuarial

liability decreases by 21.7% and the normal contribution rate decreases by 7.2%.

4. The retirement age is extended to 63 for the public academics and to 60 and 63 for

those employed in the army and the police respectively: the study reveals that the

actuarial liability decreases by 7.4% and the normal contribution rate by 4.2%.

5. Rising the contributor factor (coefficient) of the lump-sum benefit: two scenarios are

examined; 16.3/3 and 17.5/3 of the pension. In both scenarios the normal contribution rate

increases by 1.4% and 2.2% respectively and the actuarial liability increases by 2% and

3.3% respectively. The average pay-as-you-go cost increases to 44.7% and to 45.5%

respectively.

6. The pension benefit is estimated based on the average salary received in the last two

years of the employees’ service: the actuarial liability decreases by 1.5% and the normal

contribution rate by 0.7%. The average pay-as-you-go cost for the period 2007-2070,

decreases from 43.2% to 42%.

7. Decreasing the contributor factor (coefficient) of the widow pension from 75% to

60% of the pension: as a result, the actuarial liability decreases by 2.3% and the normal

contribution rate by 1.3%. The average pay-as-you-go cost for the period 2007-2070

decreases to 41.9% from 43.2% which is currently.

8. Actuarial decrease of the pension benefit for early retirement: on average, for each

year before normal retirement, the pension benefit decreases by 5%.

9. Combined reform scenario: based on this scenario, the actuarial liability decreases by

32.6% and the normal contribution rate decreases by 10.4%. In the long-term the

expenditures decrease to 2.2% of GDP. Also, the average pay-as-you-go cost decreases to

33.1% from 43.2% which is now (e.g., the period the research was carried out).

In table 19 the financial impacts of the alternative reform options studied are pointed out.

It can be seen that the greatest positive impacts have, (a) the extension of the retirement age;

(b) the pension rise to be estimated based on the inflation rate; (c) decreasing the widow

pension to 60% of the pension; and (d) estimating the pension benefit based on the average

salary of the employee in the last two years of service before retirement. Of course any rise in

the contributions of the employees such as a rise in total contributions to 10% will positively

affect the financial balances of the Scheme.

Changes on the above mentioned areas, will lead to a decrease in the annual

expenditures of the Scheme which will make it sustainable in the long-term. Moreover, they

will assist in decreasing the gap between the benefits offered by the Social Insurance System


to the employees in the private sector and the benefits that the Public Pension Scheme offers

to the retired civil servants.

Table 19: Impacts of Alternative Reform Options

Financial Impacts

Reform Options Actuarial

liability

Long-term Total

Expenditures as a

% of GDP in

Prices of 2007

Normal

contribution

rate

Average Pay as you

go cost for the

period

2007-2070

1. Pension rise of 2%,

based on the inflation

rate

21.7%

decrease

0.5% decrease 7.2%

decrease

6.2% decrease

2. Extending the

retirement age

7.4%

decrease

0.2% decrease 4.2%

decrease

3.3% decrease

3. Rising the

contributor factor of

the lump sum to

16.3/3 and 17.5/3 of

the pension

2% and 3.3%

increase

3% and 3.1%

increase

1.4% and

2.2%

increase

1.5% and 2.3%

increase

5. Decreasing the

contributor factor of

the widow pension to

60% of the retirement

pension

2.3%

decrease

0.1% decrease 1.3%

decrease

1.3% decrease

6. Combined reform

scenarios:

* Rising the

retirement age

(positive impact).

* Increasing the lump

sum benefit to 16.3/3

of the pension

(negative impact).

* Decreasing the

widow pension to

60% of the pension

(positive impact).

* The pension benefit

is estimated based on

the average salary of

the employee in the

last two years of

service before

retirement (positive

impact).

32.6%

decrease

0.5% decrease 10.4%

decrease

10.1% decrease

Based on- this study, a more complete suggestion for reforming the public pension

scheme of the Republic of Cyprus can include not only parametric changes but also structural

changes. A trend which is worthwhile to mention and it can be an issue for further research as

well, is the complete elimination of the public pension scheme of the civil servants and the


development of one pension fund for all the employees of the Republic of Cyprus, both in the

public and in the private sector. Robert Palacios and Edward Whitehouse (2006) have

compared schemes within the same country covering private sector workers as well. They

also reviewed key policy issues related to pension schemes covering civil servants and other

public sector workers. In particular, they have identified that, there is little justification for

maintaining parallel schemes in the long run.

This paper which is the outcome of the undertaken funded research, contributes to

the academic society as well as to the community of the Republic of Cyprus, in a way that the

presented outcomes of the research project, may enable the decision-makers responsible for

determining public policies as well as the social partners, to have a better picture of the

impacts of the alternative reform options on the finances of the Public Pension Scheme of the

Republic of Cyprus. This may lead to a productive dialogue between the Government and the

interested social partners, in an effort to design and implement an efficient and operationally

applicable programme for reforming the Public Pension Scheme of the Republic of Cyprus.

Definitely an economically sustainable Public Pension Scheme will benefit the society of

Cyprus.

The results of the research project have been utilized by the government of the

Republic of Cyprus at its negotiations with troika during the period of the economic recession

of 2013. Still the results can be of benefit to the social partners in their further discussions

with the government of The Republic of Cyprus, for further developing the pension system of

the civil employees.

Notes

1. The exchange rate of the Cyprus Pound (£) = € 0.585274. All the monetary values given

in the tables of this paper are in Cyprus Pounds (₤). This is because the currency of the

Republic of Cyprus during the period when the research project was carried out was the

Cyprus Pound (₤). Cyprus entered the Euro Currency Area in January 2008.

7. References

American Academy of Actuaries (2004). Fundamentals of current pension funding and

accounting for private sector pension plans, an analysis of the pension committee of

the American academy of actuaries.

Barr, N. (2000). Reforming Pensions: Myths, Truths, and Policy Choices. IMF Working

Paper, Fiscal Affairs Department, 2000.

Barr, N. and Diamond, P. (2009). Pension Reform. A Short Guide, Oxford University Press.

Capretta, J.C. (2007). Global ageing and the sustainability of public pension systems, a report

of the ageing vulnerability index project, Center for Strategic and International

Studies, Washington, 2007.

Central Bank of Cyprus (2006). Annual Economic Indicators.

Cichon Michael (1999). National Defined – Contribution Schemes: Old Wine in New Bottles.

International Social Security Review, 52(4), pp. 87 – 105.

Cogan, J. and Mitchell, O. (2002). The Role of Social Security in Household Decisions: VAR

Estimates of Saving and Fertility Behaviour in Germany. Econometric Society

Conference.

Diamond, P. (2002). Social Security Reform, Oxford University Press.

European Commission. (2008). The 2009 Ageing Report Underlying Assumptions and

Projected Methodologies for the EU 27 Member States 2007 – 2060, Economic and

Financial Affairs, Directorate – General, European Economy 7, 2008.


European Commission (2010). Joint Report on Pensions Progress and key challenges in the

delivery of adequate and sustainable pensions in Europe. Directorate-General for

Economic and Financial Affairs.

European Commission, Economic and Financial Affairs, (2017).

Eurostat (2015). Social Protection Statistics.

Louca, Ch., Korres, G., Tsobanoglou, G. and Kokkinou, A. (2011). Assessing the public

pension scheme of the Republic of Cyprus. Pensions, 16(3), pp. 151-167.

Matt Smith. Projected Unit Credit. Office of the State Actuary, presentation (2005).

Ministry of Finance. Demographic and Financial Assumptions, Nicosia (2006).

Ministry of Finance. Annual Report, Nicosia (2008).

Palacios, R. and Whitehouse, E.R. (2006). Civil-service pension schemes around the world.

The World Bank, Social Protection Discussion Paper 0602, Washington, 2006.

Republic of Cyprus, Pension Law of 1997 (N. 97(I)/97) (as it was revised by Law 3(I)/98),

Republic of Cyprus Gazette, 3207, Nicosia (1997).


APPENDIX A:

Table 1: Members of the Public Pension Scheme of the Civil Servants of the Republic of

Cyprus by Age Distribution as in 2006 Men Women

Age

Distribution

Total Number

of Members in

the Scheme

Average

Period of

Service

Average

Monthly

Salary

Total Number

of Members in

the Scheme

Average

Period of

Service

Average

Monthly

Salary

<20 4 0.6 785 2 0.1 635

20-25 540 2.0 794 179 1.7 680

25-30 1,867 4.7 870 2,095 2.8 945

30-35 2,408 8.0 1,060 2,688 6.5 1,150

35-40 1,897 10.8 1,319 2,200 9.5 1,261

40-45 1,543 13.4 1,543 2,016 10.8 1,433

45-50 2,281 18.3 1,737 2,240 13.6 1,671

50-55 2,348 22.2 1,915 2,387 17.1 1,908

55-60 2,278 26.0 2,211 1,558 19.5 2,096

60-65 292 30.7 2,455 118 19.8 2,262

65+ 2 34.7 5,279 0 0.0 0

Total 15,460 15.1 1,534 15,483 11 1,465

Table 2: Retired Employees y Age Distribution as in 2006

Men Women

Age

Number of

Retired

Employees

Average

Monthly Pension

Number of

Retired

Employees

Average Monthly

Pension

30-35 4 428 0 0

35-40 1 743 2 201

40-45 6 148 2 314

45-50 50 1,007 10 400

50-55 198 919 38 436

55-60 939 813 358 841

60-65 2,693 882 1,118 899

65-70 2,377 762 747 722

70-75 1,402 821 373 775

75-80 762 818 192 804

80-85 407 782 62 660

85-90 182 713 24 770

90-95 53 639 20 655

95-100 16 706 2 412

>100 6 385 6 479

Total 9,096 820 2,954 808


Table 3: Receivers of Widow/Orphan Pension by Age Distribution as in AS IN 2006

Age Number of

Widows/Orphans

Average Monthly

Pension

<35 15 264

35-40 14 319

40-45 20 195

45-50 40 197

50-55 89 308

55-60 174 433

60-65 256 621

65-70 297 612

70-75 417 499

75-80 347 461

80-85 325 377

85-90 188 348

90-95 109 300

95+ 30 303

Total 2,321 457


APPENDIX B:

Table 4: Mortality – PA90

Ηλικία Άνδρες Γυναίκες Ηλικία Άνδρες Γυναίκες

30 0.000584 0.000301 66 0.027296 0.013291

31 0.00057 0.000301 67 0.029778 0.014797

32 0.000613 0.000332 68 0.032479 0.016471

33 0.000666 0.000371 69 0.035416 0.018331

34 0.000725 0.000413 70 0.038608 0.020396

35 0.000791 0.000461 71 0.042075 0.022689

36 0.000864 0.000514 72 0.045838 0.025233

37 0.000947 0.000573 73 0.04992 0.028053

38 0.001041 0.000639 74 0.054346 0.031179

39 0.001149 0.000712 75 0.059139 0.034641

40 0.001274 0.000794 76 0.064326 0.038472

41 0.001419 0.000885 77 0.069935 0.042708

42 0.001589 0.000987 78 0.075992 0.047387

43 0.001788 0.0011 79 0.082528 0.052551

44 0.002022 0.001227 80 0.089572 0.058243

45 0.002298 0.001367 81 0.097153 0.06451

46 0.002623 0.001524 82 0.105301 0.0714

47 0.003006 0.001699 83 0.114047 0.078963

48 0.003456 0.001895 84 0.123418 0.087253

49 0.003984 0.002112 85 0.133444 0.096321

50 0.004601 0.002354 86 0.14415 0.106223

51 0.005318 0.002624 87 0.155561 0.117011

52 0.006147 0.002925 88 0.167699 0.128736

53 0.007099 0.003261 89 0.18058 0.141448

54 0.008184 0.003634 90 0.194221 0.155192

55 0.009412 0.004051 91 0.208629 0.170007

56 0.010789 0.004515 92 0.223809 0.185924

57 0.012321 0.005031 93 0.23976 0.202968

58 0.013517 0.005607 94 0.256471 0.22115

59 0.014765 0.006247 95 0.273928 0.240469

60 0.016127 0.006961 96 0.292106 0.26091

61 0.017612 0.007755 97 0.310973 0.282443

62 0.019232 0.00864 98 0.330491 0.305019

63 0.020997 0.009624 99 0.350609 0.328574

64 0.02292 0.010719 100 0.371274 0.353024

65 0.025015 0.011937 101 1 1


APPENDIX C: Analytical Results of the Simulation Scenarios

1. Impact of inflation

Table: 1: Actuarial liability and normal contribution rate

Actuarial Liability Inflation rate: 1% Inflation rate: 3%

Active civil employees 2,402,000,000 3,958,000,000

Retired Employees 1,509,000,000 1,834,000,000

Beneficiaries of widow pension 149,000,000 179,000,000

Total Actuarial Liability 4,060,000,000 5,971,000,000

Normal contribution rate 27.6% 49.1%

Table 2: Expenditures and contributions (₤ mil.) with 1% inflation rate

2007 2008 2009 2010 2015 2020 2030 2040 2050 2060 2070

Πληθωρισμός 1%

Total

expenditur

es

186.

1

198.

9

220.

8

233.

5

302.

2

369.

9

580.

6

1,027.

4

1,415.

6

1,944.

0

2,706.

2

Contributi

ons 4.9 5.3 5.6 5.8 7.2 8.9 13.3 15.3 19.3 26.5 35.7

Pay as you

go cots

28.4

%

28.5

%

30.1

%

30.7

%

32.6

%

32.6

%

35.3

%

51.4

%

54.3

%

54.3

%

56.0

%

Expenditur

es as a %

of GDP in

prices of

2007

2.0% 2.1% 2.2% 2.3% 2.2% 2.1% 2.2% 2.8% 2.9% 3% -

Table 3: Expenditures and contributions (₤ mil.) with 3% inflation rate

2007 2008 2009 2010 2015 2020 2030 2040 2050 2060 2070

Inflation rate: 3%

Total

expenditur

es

188.

7

204.

6

230.

6

248.

4

349.

8

464.

5

857.

8

1,821.

8

2,987.

7

4,912.

6

8,287.

5

Contributi

ons 4.9 5.3 5.7 6.1 8.3 11.4 20.7 28.7 43.7 72.4 118.5

Pay as you

go cots

28.8

%

29.1

%

30.7

%

31.1

%

32.6

%

32.1

%

33.7

%

48.6

%

50.4

%

49.7

%

51.3

%

Expenditur

es as a %

of GDP in

prices of

2007

2.1% 2.1% 2.2% 2.2% 2.2% 2% 2.1% 2.6% 2.6% 2.7% -

2. Impact of the percentage rate of investment

Table 4: Actuarial liability and normal contribution rate

Actuarial liability Percentage rage of

investment: 4.5%

Percentage rate of

investment: 6.5%


Retired employees 1,833,000,000 1,513,000,000





3. Impact of mortality


Actuarial liability Higher mortality rate

Active civil employees 2,857,000,000

Retired employees 1,464,000,000

Beneficiaries of widow pension 142,000,000

Total Actuarial Liability 4,463,000,000

Normal contribution rate 34.1%

Table 6: Demographic projections

2007 2008 2009 2010 2015 2020 2030 2040 2050 2060 2070

Active

civil

employees

31,72

0

32,50

4

33,12

2

33,73

5

35,89

0

37,72

2

40,87

8

43,00

0

45,24

4

47,62

3

50,06

5

Retired

employees

12,34

6

12,65

8

13,09

1

13,50

8

15,24

1

15,98

1

16,55

9

20,85

1

24,29

2

25,87

3

27,51

0

Beneficiari

es of

widow

pension

2,485 2,650 2,811 2,971 3,784 4,570 5,765 6,374 7,034 8,042 8,715

Ratio of

Protection 2.1 2.1 2.1 2.0 1.9 1.8 1.8 1.6 1.4 1.4 1.4

Table 7: Expenditures and contributions (₤ million)

2007 2008 2009 2010 2015 2020 2030 2040 2050 2060 2070

Total

expenditur

e

186.

8

200.

9

224.

8

239.

7

319.

5

401.

9

674.

2

1,319.

6

1,977.

9

2,906.

4

4,353.

7

Contributi

ons 4.9 5.4 5.8 6.2 8.1 10.6 17.4 21.8 30.1 45.3 67.3

Pay as you

go cost

28.6

%

28.3

%

29.6

%

29.8

%

31.0

%

30.4

%

32.0

%

46.7

%

48.6

%

47.2

%

47.6

%

Expenditur

es as a %

of GDP in

prices of

2007

2.1% 2.0% 2.1% 2.2% 2.2% 2% 2.1% 2.6% 2.6% 2.6% -

4. Projections of retired and active employees

Table 8: Demographic projections

2007 2008 2009 2010 2015 2020 2030 2040 2050 2060 2070

Active civil

employees 31,752 32,503 33,123 33,735 36,138 37,772 38,244 38,248 38,248 38,249 38,250

Retired

employees 12,465 12,895 13,448 13,990 16,370 17,736 19,280 24,126 27,753 28,621 29,111

Beneficiaries

of widow

pension

2,443 2,571 2,698 2,826 3,507 4,228 5,535 6,329 6,819 7,516 8,165

Ratio of

Protection 2.1 2.1 2.1 2.0 1.8 1.7 1.5 1.3 1.1 1.1 1.0


Table 9: Expenditures and contributions (₤ million)

2007 2008 2009 2010 2015 2020 2030 2040 2050 2060 2070

Total

expenditur

es

187.

4

202.

4

227.

5

243.

7

331.

6

425.

8

733.

8

1,426.

3

2,114.

2

2,968.

4

4,380.

0

Contributi

ons 4.9 5.4 5.8 6.3 8.2 10.6 16.9 20.1 25.8 37.1 52.7

Pay as you

go cost

28.6

%

28.5

%

29.9

%

30.2

%

32.0

%

32.0

%

36.1

%

55.3

%

61.0

%

59.0

%

61.3

%

Expenditur

es as a %

of GDP in

prices of

2007

2.1% 2.1% 2.2% 2.2% 2.2% 2.1% 2.2% 2.8% 2.8% 2.7% -


APPENDIX D: Analytical Results of alternative reform scenarios 1. Rise in contributions

Table 10: Total expenditures and employees’ contributions of 5%, 7.5% and 10% (₤ millions)

2007 2008 2009 2010 2015 2020 2030 2040 2050 2060

Total

expenditures

187.

4

202.

4

227.

5

243.

7 331.6 425.8 733.6

1,429.

8

2,150.

4

3,220.

3

Contribution

s 5%

31.1

5

34.1

4

36.7

8

39.1

8 50.41 64.75

103.9

0 139.78 199.91 302.41

Contribution

s 7.5%

43.6

1

47.7

9

51.5

0

54.8

5 70.58 90.65

145.4

6 195.69 279.88 423.37

Contribution

s 10%

62.2

9

68.2

8

73.5

6

78.3

6

100.8

2

129.5

1

207.8

0 279.56 399.82 604.82

Table 11: Expenditures and contributions for widow pensions (₤ millions)

2007 2008 2009 2010 2015 2020 2030 2040 2050 2060

Expenditures

for widow

pension

16.4 19.2 22.2 25.3 44.5 68.7 125.8 200.1 338.0 597.5

Current

contributions 4.9 5.4 5.8 6.3 8.1 10.6 17.5 21.9 30.2 45.4

Contributions

7% 44.1 47.8 51.5 54.8 70.6 90.7 145.5 195.7 279.9 423.4

2. Revising the method of readjusting of pensions


Actuarial liability Readjustment of Pensions by 2%






Table 13: Expenditures and contributions after readjusting pensions by 2% (₤ millions)

2007 2008 2009 2010 2015 2020 2030 2040 2050 2060 2070

Total

expenditur

es

185.

3

198.

1

220.

7

234.

3

305.

9

378.

8

633.

1

1,241.

7

1,802.

0

2,619.

9

3,998.

3

Contributi

ons 4.9 5.4 5.8 6.3 8.1 10.6 17.5 21.9 30.2 45.4 67.5

Pay as you

go cost

28.3

%

27.9

%

29.0

%

29.0

%

29.6

%

28.5

%

29.9

%

43.8

%

44.2

%

42.4

%

43.6

%

Expenditur

es as a %

of GDP in

prices of

2007

2.0

%

2.0

%

2.1

%

2.1

% 2.1% 1.9% 1.9% 2.4% 2.4% 2.4% -


3. Extension of the retirement age


Actuarial liability

Retirement at the Age of 63

for the academia and 60 for the

police and the army






Table 15: Retirement age – demographic projections

2007 2008 2009 2010 2015 2020 2030 2040 2050 2060 2070

Active

civil

employees

31,75

2

32,50

3

33,12

3

33,73

5

35,89

3

37,72

5

40,87

5

42,99

7

45,24

6

47,62

3

50,06

3

Retired

employees

12,46

5

12,89

5

13,44

8

13,99

0

15,32

0

16,12

2

17,02

3

21,18

7

24,83

9

26,65

3

28,16

2

Beneficiari

es of

widow

pension

2,443 2,571 2,698 2,826 3,506 4,225 5,523 6,300 6,767 7,479 8,261

Ratio of

Protection 2.1 2.1 2.1 2.0 1.9 1.9 1.8 1.6 1.4 1.4 1.4

Table 16: Extending the retirement age – expenditures and contributions (₤ millions)

2007 2008 2009 2010 2015 2020 2030 2040 2050 2060 2070

Total

expenditur

es

187.

4

202.

4

227.

5

243.

7

290.

9

391.

4

644.

0

1,346.

3

1,992.

9

2,964.

0

4,421.

1

Contributi

ons 4.9 5.4 5.8 6.3 8.4 11.2 18.7 23.5 31.2 47.0 70.2

Pay as you

go cost

28.6

%

28.5

%

29.9

%

30.2

%

27.4

%

28.4

%

29.0

%

45.4

%

47.5

%

46.6

%

46.4

%

Expenditur

es as a %

of GDP in

prices of

2007

2.1% 2.1% 2.2% 2.2% 2% 2% 2% 2.6% 2.6% 2.7% -

4. A change in the contribution factor of the lump sum benefit

Table 17: Actuarial liability and normal contribution benefit

Actuarial liability Lump-sum benefit =

16.3/3

Lump-sum benefit = 17.5/3


Retired employees 1,659,000,000 1,659,000,000





Table 18: Lump sum benefit (16.3/3) – expenditures and contributions (₤ millions)

2007 2008 2009 2010 2015 2020 2030 2040 2050 2060 2070

Total

expenditures 194.2 210.0 237.2 254.0 344.2 441.1 765.4 1,489.0 2,217.0 3,314.5 5,074.2

Contributions 4.9 5.4 5.8 6.3 8.1 10.6 17.5 21.9 30.2 45.4 67.5

Pay as you go

cost 29.7% 29.5% 31.2% 31.5% 33.3% 33.2% 36.2% 52.5% 54.4% 53.7% 55.3%

Expenditures

as a % of

GDP in prices

of 2007

2.1% 2.1% 2.3% 2.3% 2.3% 2.2% 2.3% 2.9% 2.9% 3% -

Table 19: Lump-sum benefit (17.5/3) – expenditures and contributions (₤ millions)

2007 2008 2009 2010 2015 2020 2030 2040 2050 2060 2070

Total

expenditures 197.7 213.9 242.3 259.4 350.7 449.1 782.0 1,519.9 2,251.7 3,363.6 5,156.9

Contributions 4.9 5.4 5.8 6.3 8.1 10.6 17.5 21.9 30.2 45.4 67.5

Pay as you go

cost 30.2% 30.1% 31.9% 32.2% 34.0% 33.8% 36.9% 53.6% 55.2% 54.5% 56.2%

Expenditures

as a % of

GDP in prices

of 2007

2.2% 2.2% 2.3% 2.3% 2.4% 2.2% 2.4% 3% 3% 3.1% -

5. A change in estimating the pension benefit


Actuarial liability

Estimated pension received based on the

average salary of the last two years of

service






Table 21: Retirement pension – expenditures and contributions (₤ millions)

2007 2008 2009 2010 2015 2020 2030 2040 2050 2060 2070

Total

expenditures 187.1 201.7 226.3 242.1 327.6 418.5 715.7 1,388.3 2,078.2 3,109.7 4,751.1

Contributions 4.9 5.4 5.8 6.3 8.1 10.6 17.5 21.9 30.2 45.4 67.5

Pay as you go

cost 28.6% 28.4% 29.8% 30.0% 31.7% 31.5% 33.8% 48.9% 51.0% 50.4% 51.8%

Expenditures

as a % of

GDP in prices

of 2007

2.1% 2.0% 2.2% 2.2% 2.2% 2.1% 2.2% 2.7% 2.7% 2.8% -


6. A change in the contribution factor of the widow pension


Actuarial liability Widow pension – 60%






Table 23: Widow-pension – expenditures and contributions (₤ millions)

2007 2008 2009 2010 2015 2020 2030 2040 2050 2060 2070

Total

expenditur

es

186.

9

201.

3

225.

8

241.

3

325.

3

414.

3

709.

8

1,390.

5

2,083.

0

3,100.

9

4,726.

1

Contributi

ons 4.9 5.4 5.8 6.3 8.1 10.6 17.5 21.9 30.2 45.4 67.5

Pay as you

go cost

28.6

%

28.3

%

29.7

%

29.9

%

31.5

%

31.2

%

33.5

%

49.0

%

51.1

%

50.2

%

51.5

%

Expenditur

es as a %

of GDP in

prices of

2007

2.1% 2.0% 2.2% 2.2% 2.2% 2.1% 2.2% 2.7% 2.8% 2.8% -

7. A combination of reform scenarios


Actuarial liability Combined reform scenario






Table 25: Combined reform scenarios – expenditures and contributions (₤ millions)

2007 2008 2009 2010 2015 2020 2030 2040 2050 2060 2070

Total

expenditur

es

191.

2

203.

8

227.

6

240.

8

265.

3

348.

0

554.

2

1,198.

7

1,670.

0

2,398.

0

3,586.

5

Contributi

ons 4.9 5.4 5.8 6.3 8.4 11.2 18.7 23.5 31.2 47.0 70.2

Pay as you

go cost

29.2

%

28.7

%

29.9

%

29.8

%

25.0

%

25.2

%

25.0

%

40.4

%

39.8

%

37.7

%

37.6

%

Expenditur

es as a %

of GDP in

prices of

2007

2.1% 2.1% 2.2% 2.2% 1.8% 1.7% 1.7% 2.4% 2.2% 2.2%


The Royal City Andrapolis of «The Acts of Thomas»

Abstract:

In the Acts of the apostle Thomas it is mentioned that Thomas sailed to the royal city of

Andrapolis. Andrapolis should not be identified with any of the cities of India (Βarygaza or

any other city of western India) nor with the island of Socotra, but with the harbor Μηδέκιον

ἄντρον in the strait of Deire, today Bab el Mandeb of the peninsula of Arabia. Thomas arrived

in India by ship, after leaving Andrapolis.

Key words: Thomas, Andrapolis, Homerites, India, Medekion Antron

Alex Kordosis1

1 Corresponding-Address: Alex Kordosis, Degree in Political Science and History, Panteion University. Msc

Political, Economic & International Relations in the Mediterranean, University of the Aegean.Phd candidate,

University of the Aegean. Email: [email protected]



1. The Royal City Andrapolis of «The Acts of Thomas»

In the Acts of the apostle Thomas (2nd or 3rd c. A.D.)2 we find the names of the apostles with

the information that the world was divided among them, in order to preach the Gospel. India

fell by lot and division to Judas Thomas, named also Twin3, but he was disinclined to accept

the mission.4 Thomas was not willing to go to India, because India was a strange and distant

country. Then, as it is said in the Acts, our Lord sold Thomas to Hâbbân (Αββάνην),5 an agent

of Gondapharos (an Indian king), who had come in Jerusalem to hire the services of a

qualified builder. So Hâbbān and Thomas boarded a ship and sailed, having favorite wind, to

the royal city Andrapolis.6 On their arrival the townspeople were found keeping the bridal of

the King’s daughter; the newcomers were invited to take part in the rejoicings, and a Hebrew

flute girl was brought on the scene. She attracted Thomas’s attention, and she discovered a

fellow patriot in him. An attendant at the feast strikes Thomas, and the apostle predicted the

man’s imminent punishment: the man, who was a cup-bearer, was to be killed by a wild

beast- a lion (?) prowling in the neighborhood- while on his way to fetch water; the

punishment was realized. Dogs tore the body to pieces, and one of them brought the right arm

which had struck the Apostle into the banquet place. All were amazed by the occurrence: the

king urged Thomas to come in to the bridal chamber and bless the new couple. The young

couple in the sequel vowed chastity; on hearing this, the king is indignant and orders Thomas

‘the sorcerer’ to be arrested». However, Thomas with Hâbbân escaped, sailing on a boat for

India.

The question we must answer concerns the identification of the royal Andrapolis.

Andrapolis is the city of Andhra. Some scholars suggest that Andrapolis was the city of the

Andhra kings, known as Satakarnis.7 Satakarnis is similar to the toponyms Sandaruk or

Sanadruk, which was the name of Andrapolis in the syrian edition of the Acts of Thomas.8

Τhis similarity led some scholars to identify Andrapolis with Barygaza (Bharuch) of India,9 a

city of the kings of Andhra. Barygaza is known from the text of “Periplus of Erythra” (1st c.

A.D.). It was a port as well as an industrial city. Barygaza imported such raw materials as

copper, tin and lead and exported silk cloth, introduced from China, cotton cloth etc.10 It was

a very significant harbor. The island of Socotra, in the Indian Ocean, east of the cape

2. Β. Ντόβας, Άγιος απόστολος Θωμάς. Η εικόνα του ως αποστόλου των Παρθών, των Ινδών και των Κινέζων,

μέσα από τη χριστιανική γραμματεία και τις τοπικές εκκλησιαστικές παραδόσεις. Διπλωματική εργασία, Ιωάννινα

2014, p. 13. 3. Acta Philippi et Acta Thomae, accedunt Acta Barnabae, ed M. Bonnet, Acta Apostolorum Apocrypha, partis

II, vol. II. Darmstadt, 1959, p. 100: «κατὰ κλῆρον οὔν ἔλαχεν ἡ Ἰνδία Ἰούδᾳ Θωμᾷ τῷ καὶ Διδύμῳ…». 4. Α. Ε. Medlycott, Apostle Thomas an Inqury with a critical analysis of the Acta Thomae, London 1905, p. 179:

«Νow the oldest record of the division of the world among the Apostles assigns Parthia to Thomas. This was

stated by Origen (a.d. 200-254) in his Commentary on Genesis, now lost, but the passage has been recovered for

us by Eusebius….in his Hist. Eccl., lib III. C 1». 5. Μedlycott, Apostle Thomas, 180: «…. It can by no means be accepted that our Lord was forced by Thomas’

conduct to sell him as a slave to Hâbbân». We must note that Gondapheros was a real king of North India

(Gudnaphar), see Nτόβας, Άγιος απόστολος Θωμάς, σ. 57-60. Some coins of this king are saved with his name

on them (ΓΟΝΔΟΦΑΡΟΣ) 6. Acta Philippi et Acta Thomae, p. 104. 7. Nτόβας, Άγιος απόστολος Θωμάς, σ. 52. 8. Μedlycott, Apostle Thomas, p.184: «The story then proceeds to what occurred on the landing at Sandaruk (or

Sanadruk), the Greek has Andrapolis instead; both Latin versions omit the name. Gutschmidt thought he found

here an allusion to the Andhra race. This race, according to Caldwell, formed the western branch of the Telegu

race, but between it and the sea lay the Konkani on the western shores of India (see the excellent map of

ancientnIndia by Reinaud, Mémoire sur l’ Inde). The change of Sandaruk into Andrapoli V, Andrapolis, comes

about by dropping the sibilant letter and adding the termination poli V». 9. J. Kurikilamkatt, First voyage of the apostle Thomas to India. Ancient Christianity in Baruch and Taxila,

Bangalore 2005, p. 40-68. Nτόβας. Άγιος απόστολος Θωμάς, σ. 52. 10. Τhe Periplus Maris Erythraei. Text with Introduction, translation, and commentary, by L. Casson, Princeton,

1914, p. 22, 76 κ.ε


Guardafui, is another place identified with Andrapolis.11 Ιn early byzantine time Socotra was

named island of Dioscuridus. Their residents were Christians (Nestorians), a mixture of Arabs

and Indians and even some Greeks. The island was «barren and damp, with rivers, crocodiles,

a great many vipers, and huge lizards».12

Αs mentioned in the Acts, after the incident with the new couple, Thomas, by taking a

ship, arrived at India.13 We, therefore, must place Andrapolis outside India, as A.E. Medlycott

notes: «Βut the town referred to in the text ought not to be in India, for in two succeeding

passages we are led to know that it was later the Apostle entered ‘in the realm of India’: the

passages are at the close of this and the beginning of the next Act. The poem of Jacob of

Sarug, which, as we said, incorporates the first two Acts of the story, also supports the

interpretation that the wedding feast which comes after the landing occurred before the

Apostle had entered India, based no doubt on the Acts».14

Which was the country where apostle Thomas with Hâbbân had arrived?

The existence of the Hebrew flute girl shows that Jews lived in Andrapolis. If we bear

in mind that the usual route to India was through the Red Sea and the harbors of Aethiopia

and Yemen (Adulis, Opone, Cane etc)15 and also that a great number of Jews lived in the land

of Homerites (Himyar),16 we must search for Andrapolis in the land of Homerites, in Yemen

(ancient Felix Arabia).

In another source, the Life of Saint Gregentius, we may read that the saint arrived to

Amlem (Αxom) from Alexandria and after boarding a ship (from Adulis), he travelled the Red

Sea to Μηδέκιον ἄντρον and to the capital city of the Himyars, Taphar.17 We think that antron

is the first part of Andrapolis (Antron+πόλις=city of Antron). Where was Andrapolis situated?

G. Fiaccadori wrote about this Medekion antron:«Τhe expression Medekion includes

another Arabic designation for Bāb al-Mandab, the ancient Maddabān (Mdbᶯ in South

Arabian epigraphy), i.e. the passage between the southern end of the Red Sea towards the

Indian Ocean: Μaḍīq literally “straight, canal”, which is also found in the Arabic version of

the Martyrion of Arethas, is likely to date from before the tenth century. It renders here,

στενός τόπος (“narrow place”) of the Greek original almost contemporary to the events

in Najrān; and becomes Maḍiq in the ensuing Ethiopic translation, not later than the thirteenth

century. The original form of the name in the Bios must have been Μηδέκιον, as transmitted

by the D.F.; ἄντρον, “cave”, but also “inner chamber closet”, seems to be a gloss, albeit a

peculiar one, of Maḍīq, conveying the same meaning “place of depth, narrowness or

straightness”, i.e. στενὸς τόπος as an outlet or passage to go through. The most important

harbour in this area is Mocha/al Mukhā, the ancient Muckhawān (Mkhwᶯ) in south Arabian

Inscriptions), which provides the closest access to Taphar/Zafar and then to Ṣan‘ā’ via

Dhamār, overland.”18 Mucka or Muza was the harbor of the capital Taphar-Zaphar and

11. M.M. Ninan, «Land and Sea routes of the Early Christian Missionaries to India»: Selected works of M.M.

Ninan, vol 3, San Jose, 2011, p. 385. Nτόβας, Άγιος απόστολος Θωμάς, p. 52. 12. Τhe Periplus Maris Erythraei, p. 68, 69: «….so huge that people eat the flesh and meet down the fat to use in

place of oil». 13. Acta Philippi et Acta Thomae, p. 123, 124. 14. A.E. Medlycott, Apostle Thomas, p. 184. 15. The Periplus Maris Erythraei, p. 8,9-10. Plinii, Natural History, ed. Loeb, V 104. Cosmas Indicopleustes 2.54

11.17. E. Warmington, The commerce between the Roman empire and India, London 1974, p. 48, 54, 55, 138.

J.I. Miller, The spice trade of the Roman empire, 29 BC to A.D. 641, Oxford 1969, σ. 145, 207. L. Boulnois, The

Silk road, transl. in English D. Chamberlin, London 1966, p. 54. 16. M. Detoraki, Le martyre de Saint Arethas et de ses compagnons, édition critique, étude et annotation.

Traduction par J. Beaucamp, Paris, 2007, passim. 17. A. Berger, Life and works of Saint Gregentios, Archbishop of Taphar, with a contribution by G. Fiaccadori,

Berlin-N. York 2006, p. 392. 18. G. Fiaccadori, «Gregentios in the land of the Hamerites». Ιn: Βerger, Life and works of Saint Gregentios, p.

50-51.


consequently the royal port. This is the reason that the “Acts of Thomas” name Andrapolis

«royal city». We don’t know Thomas itinerary, from Jerusalem. Most probably he arrived in

Andrapolis taking a ship in the port Aelana (today Aqaba) in the Red Sea.

2. References:

Acta Philippi et Acta Thomae acedunt Acta Barnabae, ed. M. Bonnet: Αcta

Apostolorum, Apokrypha, partis II, vol. II, Darmstadt 1959.

A. Berger. Life and works of Saint Gregentios, Archbishop of Taphar, with a contribution

by G. Fiaccadori, Berlin, N. York, 2006.

L. Boulnois, The Silk road, transl. in English D. Chamberlain, London 1966.

M. Detoraki, Le martyre de Saint Arethas et de ses compagnons, édition critique, étude

et annotation : Traduction par J. Beaucamp, Paris, 2007.

G. Fiaccadori, “Gregentios in the land of the Homerites”, In: Α. Βerger, Life and works of

saint Gregentios, N. York, 2006.

J. Kurikilamkatt, First voyage of the Apostle Thomas to India. Ancient Christianity in Baruch

and Taxila, Bangalore, 2005.

A.E. Medlycott, Apostle Thomas, an inquiry with a critical analysis of the Acta Thomae,

London 1905.

J. Miller, The spice trade of the Roman empire, 29 BC. to AD 641, Oxford 1969.

M.M. Ninan “Land and sea routes of the Early Christian Missionaries to India”: Selected

works of M.M Ninan, vol. 3, San Jose, 2011.

B. Nτόβας, Άγιος Απόστολος Θωμάς. Η εικόνα του ως αποστόλου των Πάρθων, των Ινδών και

των Κινέζων, μέσα από τη χριστιανική γραμματεία και τις τοπικές εκκλησιαστικές

παραδόσεις, διπλωματική εργασία, Ιωάννινα, 2014.

(The) Periplus Maris Erythraei. Text with introduction, translation, and commentary, by L.

Casson, Princeton 1914.

E. Warmington, The commerce between the Roman empire and India, London 1974.


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Each suitable article is blind-reviewed by two members of the editorial review board. A

recommendation is then made by the Editor-in-Chief. The final decision is made by the

Editor-in-Chief. If a revision is recommended, the revised paper is sent for a final approval to

the Chief-Editor.

Instructions to Authors: In order for a paper to be submitted to the Journal for publication, the following should be

taken into consideration:

1. All papers must be in English.

2. Papers for publication should be sent both in electronic format (MS Word and MS Excel

for charts) to the Chief Editor (mail to: [email protected]).

3. The Editor takes for granted that:

the submitted paper contains original, unpublished work that is not under consideration

for publication elsewhere;

authors have secured any kind of permission necessary for the publication from all

potential co-authors, along with having agreed the order of names for publication;

authors hold the copyright, have secured permission for the potential reproduction of

original or derived material and are ready to transfer copyright of the submitted paper to

the publisher, upon acceptance for publication.

4. The cover page should include the name of the author and coauthors, their affiliations, and

the JEL category under which the paper primarily belongs. The cover page is the only

page of the manuscript on which the names and affiliations of the authors and coauthors

should be listed.

5. Submission of manuscripts in electronic form: Authors must submit electronic

manuscripts. The submission should only contain the file(s) of the papers submitted for

publication, in MS Word and MS Excel for charts. If more than one file, a compressed file

(.zip) should be submitted instead.




6. Formatting requirements: Everything should be double-spaced (main text, footnotes,

bibliography, etc.)

7. Footnotes should be as few and as short as possible (preferably devoid of tables or

formulae), marked in the manuscript by superscripts in Arabic figures.

8. Formulae should be numbered by consecutive, Arabic figures (such as (1), (2), etc.),

placed on the right-hand side of the page.

9. Tables and Figures should be numbered consecutively in Arabic figures and have a

heading and a title.

10. References are citations of literature referred to in the text and should not appear as

footnotes. Abbreviations are only accepted in the authors' first names. Place all references,

alphabetized by author's last name (with last name first), on separate pages in a section

titled "References" at the end of the paper. Indent the second and subsequent lines of each

reference.

Journals Include all authors, article title, full title of journal, volume number, issue number, month,

year, and full page numbers. Example:

Michael Mahmood. "A Multilevel Government Model of Deficits and Inflation,"

Economic Journal, 24, 2, June 2010, pp. 18-30.

Books Include name of author, full title of book, edition, city and state (or country) of publisher,

name of publisher, and year of publication. Example:

Shapiro, John. Macroeconomics, 4th ed., New York, NY: Harcourt Brace Jovanovich,

2009.

Use the following style when an author's work appears in a publication edited by another:

George Summers, "Public Policy Implications of Declining Old-Age Mortality," in Gary

ed., Health and Income, Washington, DC: The Brookings Institution, 1987, pp. 19-58.

Public Documents Include the department or agency responsible for the document, title, any further

description such as number in a series, city and state (or country) of publication,

publisher, and date of publication. Example:

World Bank. Educational Attainment of Workers, Special Labor Force Report 186,

Washington, 2010.