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NUST JOURNAL OF SOCIAL SCIENCES AND HUMANITIES

Vol.1 (July-December 2015) pp. 1-25

Energy Demand Elasticity in Pakistan: An Inter-temporal Analysis from

Household Survey Data of PIHS 2001-02 and PSLM 2010-11

ASHFAQUE HASAN KHAN, UMER KHALID, and LUBNA SHAHNAZ

This study analyzes household consumption patterns of different forms of energy in Pakistan over two

survey rounds (2001-02 and 2010-11), while comparison with an earlier study based on 1984-85 data extends the

analysis over a 25 years horizon. Income elasticities of different types of fuels have been computed using the

Extended Linear Expenditure System. The analysis shows a differential pattern of energy use across the urban and

rural areas of the country as well as changes over time, with rural households spending proportionately more on

fuels throughout this period.

1. INTRODUCTION

Access to affordable and uninterrupted energy is a major pre-requisite for growth and

development of any country. Being one of the main inputs to the industrial sector, availability of

adequate energy directly affects the industrial output. At the household level, per capita energy

consumption is an important determinant of household welfare, with expenditure on energy

forming a significant share of total household consumption expenditure. Energy use has been

stipulated to impact household welfare through a number of channels. In the context of

households in a developing country using modern forms of energy such as Liquefied Petroleum

Gas (LPG) for cooking purposes, Kanagawa and Nakata (2007) have identified four such

mechanisms, which include education, health, income, and environment. Recent research by

Barnes, et al. (2011) and Khandker, et al. (2012) has also investigated the effects of energy

poverty on household welfare by examining the relationship between income-poverty and

energy-poverty in India and Bangladesh, respectively.

In view of the importance of ensuring availability of cheap energy to their people, many

developing countries, including Pakistan, have historically been subsidizing domestic fuel prices

to ensure access to affordable energy. However, the fiscal burden of these subsidies has been

becoming increasingly unsustainable for the governments, due to the global rise in fuel prices,

especially since 2008, as a result of which prices are being gradually increased to move towards

full cost recovery. Moreover, as the share of household spending on energy is likely to differ

substantially across income levels, general subsidies provided across the board to all households

may by regressive in nature and would serve to reduce access of low income population

segments to modern energy sources. In order to better target and direct subsides towards the most

disadvantaged social groups, it is important to understand “the structure of the correlations

Ashfaque Hasan Khan <[email protected]> is Principal/Dean School of Social Sciences and Humanities

(S3H), National University of Sciences and Technology (NUST), Sector H-12, Islamabad, Pakistan. Umer Khalid

<[email protected]> is Industrial Policy Advisor, Economic Reforms Unit, Ministry of Finance, Islamabad,

Pakistan. Lubna Shahnaz < [email protected]> is a Visiting Faculty, School of Social Sciences and

Humanities (S3H), National University of Sciences and Technology (NUST), Sector H-12, Islamabad, Pakistan.

mailto:[email protected]



2 Khan, Khalid, and Shahnaz

between energy and income levels” [Rodriguez-Oreggia and Yepez-Garcia (2014)]. In a recent

World Bank study [Rama, et al. (2015)], it was found that energy subsidies disproportionately

benefit the rich in South Asia, including Pakistan. In case of Pakistan, the poorest 40 percent (or

first two income quintiles) households received less than 30 percent of the total electricity

subsidies, while the richest 20 percent (or fifth income quintile) households got around 40

percent of the subsidies.

The fuel price rises are likely to have both income and substitution effects. In order to

quantify the impact of these effects across different sectors of the economy, sector-specific

demand elasticities of different fuels provide vital information for the policy makers. However,

no recent estimates of demand elasticities at the household level are available for Pakistan.

Burney and Akhtar (1990) estimated income and price elasticities of households’ expenditure on

different fuels using data from the 1984-85 Household Income and Expenditure Survey (HIES).

These estimates are somewhat dated now and hold little policy relevance in today’s environment,

as energy prices have been continuously undergoing deregulation in the country since the 1990s.

The purpose of this study is to examine the inter-temporal pattern of household

expenditure on energy consumption using data from Pakistan Integrated Household Survey

(PIHS) 2001-02 and Pakistan Social and Living Standards Measurement Survey (PSLM) 2010-

11. It computes the price and income elasticities using the Extended Linear Expenditure System

methodology the one also employed by Burney and Akhtar (1990) so as to have a set of

consistent estimates spanning over a period of 25 years. The use of micro data on household

energy expenditures enables examination of the variations in the patterns of consumption of

households with different socio-economic characteristics. The analysis of household patterns of

energy consumption is carried out separately for the urban and rural areas of the country, as well

as by expenditure quintiles. The income and price elasticities estimated using recent household

survey data and the changes observed over the last decade can have direct relevance for energy

pricing policy in Pakistan.

Rest of the paper is organized as follows. Section II discusses the trends in overall energy

consumption in Pakistan over the last two decades, with special focus on the household sector.

The theoretical framework and model used for estimation of income and price elasticities is

presented in Section III. Section IV gives details of the household survey datasets used for

empirical analysis, while Section V presents an examination of the patterns of household’s

energy expenditures. The analysis of income and price elasticities is presented in Section VI,

while the final Section contains some concluding remarks and policy recommendations.

2. STYLIZED FACTS ABOUT ENERGY CONSUMPTION IN PAKISTAN

Commercial energy consumption in Pakistan has been steadily increasing from 1990

onwards1. It stood at 17 Million tonnes Oil Equivalent (MTOE) in 1990-91 but more than

1 Commercial energy consumption, which refers to use of commercial fuels that are traded in markets, does not include fuel

sources, such as firewood and agricultural wastes commonly used in the rural areas of the country. According to some estimates,

fuels such as biomass account for 36 percent of total energy consumption in the rural sector (Asif, 2009). Amur and Bhattacharya

Energy Demand Elasticity in Pakistan: An Inter-temporal Analysis 3

doubled to 40 MTOE in the last two decades (Figure 1), showing an annual average growth of 4

percent during this period2. In terms of per capita energy consumption, it increased from 0.15

tonnes Oil Equivalent (TOE) in 1990 to 0.22 TOE in the last two decades (Figure 1), exhibiting

an average growth of 1.9 percent per annum. Despite witnessing reasonable growth over the last

23 years, Pakistan’s per capita energy consumption levels are much lower than those of the

developed nations, comparing unfavourably with the OECD average per capita energy

consumption of 3.1 TOE. Per capita energy consumption level of Pakistan is also lower

comparison to many of its developing country peers, like China (1.8 TOE), Thailand (1.6 TOE),

Iran (2.9 TOE) and India at 0.6 TOE (IEA, 2011).

Figure 1. Commercial Energy Consumption in Pakistan

Source: GoP (various issues).

The energy mix in the country has undergone significant changes over the last 23 years.

In 1990-91, oil had the highest share in final energy consumption, accounting for 46 percent of

the energy consumed, followed by natural gas at 31 percent (see, Table 1 and Figure 2). The

share of oil peaked at 48.3 percent in 1995-96, which was offset by a decline in the share of

electricity. In the post 1995-96 period, the share of oil witnessed a secular decline reaching as

low as 28 percent in 2009-10, while the share of natural gas continued to rise, as a result of

which, the shares of oil and natural gas in final energy consumption by 2012-13 have been

reversed compared to their 1990-91 position (see, Table 1 and Figure 2).

Rising oil prices on the one hand and a relatively cheaper gas prices on the other

contributed to the reversal of the shares of oil and gas during the post 1995-96 period.

Notwithstanding this reversal, oil and natural gas combined have remained the predominant fuels

in final energy consumption in Pakistan during the period under review, representing 76-79

(1999) estimated total biomass consumption of 22.6 MTOE representing 44 percent of the primary energy needs of Pakistan, with

the household sector accounting for 86 percent of total biomass consumption. 2Details of the data used for analysis in this section are provided in Appendix 1.

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Total Energy Consumption - MTOE (Left Axis)Per Capita Energy Consumption - TOE (Right Axis)


percent of total energy consumption. The share of electricity in total energy consumption has

remained stable during the entire period under review at 15-16 percent, while the share of coal is

observed to have initially declined up to 1999-2000 and has risen thereafter (see, Table 1 and

Figure 2).

Table 1. Share of Different Sources of Energy in Total Commercial Energy Consumption

(Percent)

Year Oil Gas Electricity Coal

1990-91 46.0 30.8 15.1 8.0

1991-92 46.6 29.6 15.1 8.8

1992-93 47.2 30.1 15.3 7.4

1993-94 47.4 30.1 14.9 7.7

1994-95 47.7 30.8 15.2 6.3

1995-96 48.3 30.8 14.7 6.3

1996-97 48.0 30.3 15.4 6.3

1997-98 46.9 32.3 15.5 5.4

1998-99 47.7 32.0 14.6 5.7

1999-00 47.3 33.0 14.7 5.0

2000-01 45.9 33.3 15.7 5.1

2001-02 43.3 34.8 16.1 5.8

2002-03 41.3 35.9 16.3 6.4

2003-04 38.5 36.0 16.2 9.3

2004-05 36.5 37.6 15.6 10.3

2005-06 32.0 41.1 16.2 10.6

2006-07 29.4 42.6 16.4 11.5

2007-08 29.3 41.9 15.2 13.7

2008-09 29.0 45.2 15.3 10.4

2009-10 27.9 45.4 15.6 11.0

2010-11 29.0 44.5 16.2 10.4

2011-12 29.0 45.2 15.6 10.1

2012-13 30.4 44.9 15.6 9.1


Figure 2. Share of Different Sources of Energy in Total Commercial Energy Consumption

(Percent)


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The share of different sectors in commercial energy consumption during the period under

review is illustrated in Figure 3. The figure shows that the industrial sector has been the largest

consumer of energy in Pakistan, followed by the transport and household sectors. It can be

observed that the share of industrial sector in total commercial energy consumption peaked in

2006-07 reaching 44 percent of total energy consumption, when the economy as well as industry

were experiencing high rates of growth. Correspondingly, the share of transport sector was at its

lowest point at 27 percent as far as use of commercial energy is concerned. Subsequently, the

share of industry started declining especially in the wake of the 2008 global oil price hike, while

the share of transport rose correspondingly. It can further be seen that the share of household

sector in commercial sector consumption has been witnessing a secular increase, especially in the

post 2008 period, reaching a peak of 25 percent in 2012-13. The fall in share of industry and the

corresponding rise in share of transport can be attributed to a number of factors. Firstly, the

slowing down of industrial growth due to declining growth momentum of the economy reduced

demand for energy. Secondly, the rising oil prices post 2008 led to substitution of petrol/ diesel

for CNG in the transportation sector, increasing energy demand in this sector. It is also pertinent

to point out that energy supply, particularly of electricity, to the industrial sector has been

rationed in recent years due to the power shortages being experienced by the country3.

Table 2. Households Commercial Energy Consumption

Year HH Energy Consumption

(MTOE)

HH Energy Consumption

(% of Total Energy Consumption)

1990-91 3.5 20.66

1991-92 3.3 18.22

1992-93 3.6 18.47

1993-94 3.8 18.58

1994-95 4.3 20.23

1995-96 4.7 20.51

1996-97 4.8 21.32

1997-98 5.4 22.94

1998-99 5.3 22.16

1999-00 5.7 22.58

2000-01 5.8 23.07

2001-02 5.9 23.03

2002-03 6.1 23.16

2003-04 6.3 21.67

2004-05 6.8 21.22

2005-06 7.1 20.78

2006-07 7.6 21.12

2007-08 8.0 20.42

2008-09 8.1 21.67

2009-10 8.4 21.56

2010-11 8.7 22.46

2011-12 9.4 23.39

2012-13 10.1 25.18


3 A detailed analysis of the factors responsible for the fall in share of industry and rise in share of transport is beyond the scope of

this paper.


Figure 3. Share of Different Sectors in Total Commercial Energy Consumption (%)


Trends in households’ use of commercial energy during the period 1990-2012, as

presented in Table 2 and Figure 3, show that the household sector consumed 3.5 MTOE (20.7

percent) of energy in 1990-91 from the total energy consumption of around 17 MTOE, which

increased to 10 MTOE (25.2 percent) in 2012-13. Households’ use of commercial energy has

grown at a faster pace than the per annum growth in total energy consumption (5.1 percent vs. 4

percent) during the period under review, resulting in rising share of household sector in

commercial energy consumption as indicated in the preceding analysis.

The analysis of household energy use by source shows large inter-fuel substitution during

the period, with the share of oil declining consistently from a significant 31 percent of total

household energy use in 1990-91 to a negligible 1 percent by 2012-13 (see, Figure 4). This

decline has been matched by a sharp increase in the share of natural gas from 44.6 percent to 70

percent an increase of 25.4 percentage points. The share of electricity on the other hand,

witnessed an increase of only 4.9 percentage points during the same period. Currently, natural

gas is the predominant source of commercial energy being consumed by the household sector,

accounting for around 70 percent of total household energy use in 2012-13. The share of coal in

household energy use has been almost nil during the period under review.

The preceding analysis of energy consumption in the country highlights that per capita

commercial energy consumption has increased steadily since the 1990s with the growth being

higher for the household sector, reflecting improving living standards. Moreover, the share of the

household sector in total commercial energy consumption has also risen during the period. This

period has also seen a rising trend in fuel prices faced by the household sector, as the government

has been compelled to gradually reduce subsidies provided to the domestic sector on commercial

fuels due to mounting fiscal pressures. These developments are an important motivation for the

present study, which seeks to carry out an in-depth assessment of patterns of household spending

on fuels and the changes observed over time.

0369

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Domestic Commercial Industrial


Figure 4: Share of Different Sources of Energy in Household Energy Consumption (%)


3. THEORETICAL FRAMEWORK

The study estimates an Extended Linear Expenditure System (ELES), which was first

formulated by Lluch (1973) and used by Burney and Akhtar (1990) to analyze households’

consumption patterns of different fuels in Pakistan using 1984-85 micro level data. In

comparison with its predecessor the Linear Expenditure System (LES), the ELES gives better

estimates of price elasticities4. Moreover, the ELES permits measurement of the impact of

relative prices on household savings through endogenizing the total household consumption

expenditure.

It is pertinent to point out that there are better and more flexible demand systems

available for analyzing household consumption decisions. In this regard, the Almost Ideal

Demand System (AIDS) given by Deaton and Muellbauer (1980a) is preferable to the ELES as

the utility function underlying this system assumes additive preferences, implying that the

marginal utility of one good is independent of the quantity of other goods consumed. This has

been shown to be not a very plausible assumption, in the case of food commodities [Alderrman

(1988)]. However, the application of AIDS requires data on prices of the commodities under

examination, which are not available for different fuels from the PIHS/ PSLM datasets5. In view

of this data constraint, the ELES has been used for analysis as it permits estimation of price

elasticities in the absence of price data.

4 See Lluch, et al. (1977) for details. 5 For details, see Appendix 3.

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The ELES is based on the standard constrained utility maximization problem of how

much to spend on various goods, given a fixed budget per unit of time6. The household

expenditure behaviour can be expressed by the following relationship, which assumes that

spending decisions are made on a per capita basis, and that with the exception of income and

prices all other socio-economic factors like age, education and gender do not influence

consumption;

ei = pixi= piri + βi (y – ∑ piri) … (1)

where, i = 1, 2, …, n goods, ei is households’ per capita expenditure on good i, pi is the price of

good i, xi is households’ per capita quantity consumed of good i, y is households’ per capita

income, while (ri,, βi) are the parameters to be estimated. The βi’s show the marginal propensity

to consume of good i with ∑ βi = µ is the overall marginal propensity to consume. The parameter

ri represents the basic needs or subsistence quantity of good i if it is positive, while ∑pjrj

indicates total subsistence expenditure. The expression (y - ∑pjrj) denotes supernumerary

income. The relationship shown by Equation (1) is referred to as the ELES.7When the

expenditure equations for all goods are added up, an aggregate consumption function of the

following form is obtained:

E = (1 - µ) ∑ piri + µy … (2)

where, E is the total household consumption expenditure. Equation (2) enables identification of

∑piri in the absence of price data which helps in obtaining price elacticities from the cross-

section data.

As ri appears in all the equations, the system of equations described by Equation (1)

needs to be estimated simultaneously. This imposes cross-equation restrictions which, in general,

require maximization of the likelihood function. In the case of cross-section data, however, since

each household faces identical commodity prices, the term piri is independent of the unit of

observations. Thus, it can be replaced by ri*. This stochastic specification of the ELES can then

be written as:

eih = αi +βiyh + ∈ih … (3)

where, h = 1, 2, …, H households, αi = ri* - βi∑ri*and ∈ihis the error term with usual classical

properties.

6 The ELES can be derived from the utility maximization behavior. The underlying utility function is Stone-Geary type where the

preferences are directly additive, i.e.,

U(x) = fi (xi) = βilog (xi – ri)

With xi> – ri, βi>0, and ∑ βi= µ. 7 A LES differs from an ELES in the sense that instead of y, total household expenditure (E) appears in the equation. Thus,

instead of supernumerary income, there is an expression (E-∑pjrj) referred to as supernumerary expenditure. The coefficient of

(E-∑pjrj) denoted say as βi* is interpreted as marginal budget shares, i.e., marginal propensity to consume out of total

expenditure, such that ∑βi* =1. The βi* can be obtained from βi* as βi*= βi/µ.


The system of equations as described by relation (3), is one of identical regressors in

which every left-hand side variable is regressed upon the same set of exogenous variables.

Estimation of each of its equations separately for different commodities, by the Ordinary Least

Squares (OLS) method, is equivalent to the system’s maximum likelihood estimation. The

maximum likelihood estimates of µ, ri* and ∑ri* can be estimated from the OLS estimates of αi

and βi using the following relationship:

µ = ∑βi

∑ri* = ∑ai/(1 - µ)

ri* = ai+ β∑ ri*

The relevant demand elasticities can then be computed as follows:

(i) Income Elasticity of Good i: ηiy = βi(y/ei)

(ii) Own-price Elasticity of Good i: ηii = (1 - β)i) (ri*/ei) – 1

(iii) Cross-price Elasticity of Good i: ηij = – βi(rj*/ei)

(iv) Income Elasticity of Total Expenditure: ηEy = µ(y/E)

The formula for the cross-price elasticity indicates that for a cross-price elasticity to be

positive either βi* must be negative, i.e., good i be inferior, or rj* must be negative, i.e., good j be

a luxury. This implies that in ELES the uncompensated cross-price elasticities, under normal

circumstances, can assume only negative values. Thus no conclusions can be derived from

negativity of these elasticities. This, it may be pointed out, is true for the LES as well.

4. DATA

This study is based on the micro level data of the Pakistan Integrated Household Survey

(PIHS) 2001-02 and Pakistan Social and Living Standards Measurement (PSLM) Survey 2010-

11, compiled by the Pakistan Bureau of Statistics. The data from PIHS 2001-02 are based on a

nationally representative sample of 14,676 households, out of which 5,514 (37.6 percent)

households were residing in the urban areas, while 9,162 (62.4 percent) were resident of the rural

areas of the country8. The PSLM 2010-11 data are also based on a nationally representative

sample of 16,313 households, with 6,572 households (40.3 percent) living in urban areas and

9,741 (59.7 percent) residing in rural areas9. The household income and expenditure module of

both survey rounds is compatible with each other, reporting expenditures on the same range of

commodities and can thus be used for inter-temporal comparison.

Total household expenditure, comprising of expenses on both durable and non-durable

goods as well as services, has been categorized into two broad groups – fuel and non-fuel

8 The definitions of urban and rural areas adopted in both survey rounds, i.e., PIHS 2001-02 and PSLM 2010-11 is given in

Appendix 2. 9 The sample from both survey rounds excludes households for which the reported total consumption expenditure was zero or

missing.


expenditures. The expenditure on fuels has been further disaggregated into expenditures on

different types of fuel – firewood, kerosene oil, natural gas, electricity, and other-fuels10. The

other fuels category includes household expenses on coal and other biomass fuels such as dung

cakes and crop residue, which are important sources of energy, especially for the rural

households11.

5. TRENDS IN HOUSEHOLD ENERGY CONSUMPTION

The overall picture of households’ expenditures on different fuels during 2001-02 and

2010-11 is presented in Table 312. The data show that a large proportion of urban households

reported having zero expenditure on kerosene oil, other fuels and firewood, with this proportion

rising between 2002-11. Electricity, followed by natural gas is seen to be the dominant fuel used

by urban households with access to both these fuel types rising between 2001-02 and 2010-11.

On the other hand, in the rural areas, majority of households are seen to have zero expenses on

natural gas in both the years under review, owing to lack of access to gas in rural areas, with this

share declining in 2010-11. The main fuel types utilized in the rural areas include firewood and

electricity, with the share of households reporting zero expenses on electricity declining sharply

during 2002-11, implying increasing access to electricity by rural households due to the village

electrification programme pursued by the government, on the one hand and inflow of remittances

increasing the demand for electrical appliances on the other13.

The analysis of the average monthly expenditure and expenditure shares of different

fuels, using constant prices of 2001-02 shows that the rural households spent proportionately

more on fuels compared to the urban households during both 2001-02 and 2010-11, but more so

in 2010-11 (Table 3)14. This finding is consistent with that of Burney and Akhtar (1990), who

attributed the higher average spending of rural households to the lower oil equivalent energy

provided by firewood and other-biomass fuels used mainly by the rural households in

comparison to electricity and natural gas. This implies that for a given amount of oil equivalent

of energy, rural households have to spend a higher amount on purchase of fuels in comparison to

their urban counterparts.

Real expenditures on fuel increased only marginally for the urban households during the

period 2001-02 to 2010-11, from Rs.678 per month to Rs.688 per month (Table 3), showing an

annual average growth of just 0.2 percent per annum. In comparison, non-fuel expenditures of

urban households grew at a higher rate of 1.4 percent per annum during the same period. On the

10 Both the surveys do not include price and quantity information on main fuel types – piped gas and electricity, which precludes

analysis in terms of actual household energy consumption. Thus, expenditure on energy is used as a proxy for energy use. 11 Details of the household energy expenditures included in both the surveys are presented in Appendix 3. 12 It is pertinent to point out here that the household use of energy obtained from the survey datasets includes both commercial

and non-commercial energy consumed during the reference period, i.e., one month. Therefore, estimates of commercial energy

consumption for the domestic sector as discussed in section II are not directly comparable with the estimates of household energy

use obtained from the survey datasets as they exclude non-commercial sources, which are likely to higher in rural areas of the

country. See, footnote 1 for further details. 13 The share of households using electricity in the rural areas increased at a faster rate between 1984-2001, i.e., more than

doubling from 30 percent in 1984-85 as indicated by Burney and Akhtar (1990) to 65.8 percent in 2001-02. 14 The analysis by nominal prices is presented in Appendix Table 1.


other hand, real expenditures of rural households increased at a faster pace on both fuels (2.8

percent) and non-fuels (2.6 percent).

The expenditure share of urban households on fuels declined slightly from 7.3 percent in

2001-02 to 6.6 percent in 2010-11, while in case of rural households it increased marginally

(Table 3). A comparison with the earlier estimates by Burney and Akhtar (1990) based on 1984-

85 data reveals that the average household expenditure share on fuels was much lower at 4.9

percent and 5.9 percent, in the urban and rural areas, respectively. The increase in the household

budget share on fuels over the period 1984-2001 can be attributed to both increases in fuel prices

and greater access as well as higher utilization of energy on account of more widespread use of

household appliances owing to the rise in income levels over the years. The higher use of

household appliances over time is supported by Khan and Khalid (2010) who found that

expenditure share on durable goods increased between 1984-85 and 2000-01 for both urban and

rural households, with the increase being higher for rural households (2.5 times)15.

Within the fuel category, urban households had highest expenditure shares on electricity

followed by natural gas, while their rural counterparts spent proportionately more on electricity

and firewood, during both the years under review. The expenditure share of natural gas in the

urban sector declined during 2002-11, while share of electricity increased marginally. This fall in

expenditure share of natural gas in urban areas may be driven by the fall in average size of urban

households16 resulting in lower use of gas for cooking purposes as well as better efficiency

overtime of appliances using natural gas. In the rural areas, the expenditure shares of both

electricity and firewood increased as well but more so on electricity during the same period

(Table 3). Comparison of these results with the earlier estimates by Burney and Akhtar (1990)

show that the increase in average expenditure shares on fuels during the period 1984-2001 was

driven by higher budgetary outlays on electricity, which went up by 2.8 times for urban

households and a substantial 5.6 times for rural households. This lends further support to the

premise that the increase in expenditure shares on fuels during 1984-2001 was driven mainly by

higher demand for electricity stemming from greater use of household appliances by both urban

and rural households across the country. The increase in fuel expenditure share of rural

household over the period 2002-11 is also driven mainly by higher expenditures on electricity

due to more widespread use of electrical appliances.

In per capita terms, the average real household expenditure on both fuels and non-fuels

increased at a higher annual rate in the rural areas as compared to the urban areas during the

period 2002-11 (2.5 percent and 1.8 percent vs. 3.6 percent and 3.5 percent, respectively)17.

Urban households’ recorded highest per capita expenditures on electricity and natural gas in both

2001-02 and 2010-11, with expenditure on electricity increasing by 1.9 percent, while those on

natural gas declining by 1.3 percent per year during this period. Per capita spending of rural

households on electricity and firewood was highest during both the years, with expenditures on

15 The durable goods category includes expenditures on household appliances, such as refrigerators, freezers, electric fans, air

coolers, air conditioners, etc. 16 The average household size fell from 6.9 in 2001-02 to 6.2 in 2010-11. 17 The analysis by nominal per capita expenditures is presented in Appendix Table 1.


electricity increasing by a higher annual rate between 2001-02 and 2010-11 compared to

firewood (5.0 percent vs. 3.4 percent (Table 3).

Table 3. Households’ Expenditure on Different Fuels

Year Households with zero

reported expenditures

(% of total HHs)

Average household

expenditure

(Rs. per month)

Average household

expenditure (% of total

household expenditure)

Average household

expenditure per

capita

(Rs. per month)

2001-02 Urban Rural Urban Rural Urban Rural Urban Rural

Firewood 69.71 21.87 63.52 160.98 0.68 2.67 9.38 24.9

Kerosene oil 82.14 45.56 13.7 26.65 0.15 0.44 2.43 4.62

Natural gas 31.01 87.4 171.89 23.87 1.84 0.4 29.84 3.92

Electricity 4.99 34.16 412.49 160.74 4.42 2.67 72.78 25.4

Other Fuels 87.03 47.82 16.39 70.59 0.18 1.17 2.47 11.48

Total Fuel 677.99 442.82 7.26 7.35 116.91 70.3

Total Non-Fuel 8,663.4 5,580.5 92.74 92.65 1,476.3 847.1

2010-11 In 2001-02 prices (General CPI)

Firewood 79.43 30.76 56.53 197.89 0.54 2.66 9.04 32.50

Kerosene oil 95.75 78.14 2.24 14.41 0.02 0.19 0.36 2.43

Natural gas 20.22 81.02 147.18 40.30 1.41 0.54 26.23 6.98

Electricity 1.08 14.68 470.53 218.00 4.51 2.93 84.92 36.93

Other Fuels 91.92 51.46 11.58 82.43 0.11 1.11 1.93 14.05

Total Fuel 688.1 553.03 6.59 7.42 122.48 92.89

Total Non-Fuel 9,751.9 6,896.7 93.41

92.58

1,714.11 1,111.19

Source: Authors’ calculations using PIHS 2001-02 and PSLM 2010-11.

Table 4 presents the average real monthly household expenditure (total and fuel) and

income in per capita terms for the sample of urban and rural households during 2001-02 and

2010-11 by expenditure quintiles18. The per capita household income is higher than the per

capita household expenditure across all expenditure quintiles in both 2001-02 and 2010-11

except for the first quintile in 2010-11 in the rural areas19. The highest growth in real per capita

household monthly income during the period 2002-11 for urban households is observed in the

fourth quintile, followed by the fifth and third quintiles, with growth being lowest for households

in first quintile. In case of rural households, the highest growth in real per capita income is seen

18 Average monthly household expenditures by quintile in nominal terms are shown in Appendix Table 2. 19 Income not spent is savings. The average per capita household savings for the periods 2001-02 and 2010-11 reveals interesting

facts. The average per capita household savings declined substantially in urban areas for the first two quintiles and increased

equally strongly for the third, fourth and fifth quintiles, more so for the fifth quintile during 2002-11. In the rural areas, the

average per capita household savings declined substantially for the first quintile but exhibited a continuous rise with second

quintile onwards, more so for the fifth quintile. It can be deduced from the findings that the economic well-being of the poorer

segments of households (first and second quintile) have deteriorated and that income disparity between the poorest households

and the richest one has widened during the two sample period. A word of caution is essential here as household income is

generally under-reported in household surveys, as people are reluctant to provide true income in fear of taxation. It is beyond the

scope of the present study to dwell more on this issue.


for households in the fifth quintile, followed by those in the second and fourth quintiles, with

growth being lowest for households in the first quintile. Such findings suggest worsening of

income inequality between the richest and poorest segments of society.

Per capita real expenditure on fuels is seen to rise with income across all expenditure

quintiles, with the exception of second and third quintile in urban areas and second quintile in

rural areas during both the sample years20. Average annual growth in real per capita expenditure

on fuels during the period 2002-11 is observed to be negative for urban households in the first

and second quintiles, while being highest for households in the fifth quintile. Growth in real per

capita expenditure on fuels is seen to be higher for rural households across all quintiles in

comparison to urban households, with rural households witnessing highest growth in the second

and third expenditure quintiles (Table 4). This growth in per capita expenditures on fuels in rural

areas is likely to be driven by the higher demand for household appliances due to rising income

levels of rural households. Recent years have seen a higher flow of resources to the rural areas,

as a result of rising farm support prices, as well as increasing flows of foreign and domestic

remittances [for more on this, see Khan and Khalid, 2010].

Table 4. Average per Capita Real Household Expenditure and Income by Expenditure Quintiles

Expenditure

quintiles

2001-02

Average per capita total

household monthly

expenditure (Rs.)

Average per capita household

monthly income (Rs.)

Average per capita monthly

expenditure on fuel (Rs.)

Urban Rural Urban Rural Urban Rural

First 894.30 746.32 1,013.57 828.63 91.86 67.66

Second 958.90 780.07 1,032.14 795.06 90.15 62.23

Third 1,067.83 875.67 1,175.71 897.99 89.83 65.58

Fourth 1,239.99 1,024.43 1,340.72 1,046.39 98.54 75.32

Fifth 2,649.61 1,449.76 3,090.78 1,508.00 165.18 92.20

2010-11

In 2001-02 Rs. – General CPI

In 2001-02 Rs. – fuel & lighting

CPI

First 1,044.18 959.11 1,063.39 955.68 90.11 85.34

Second 1,155.05 1,013.33 1,163.32 1,048.56 87.44 83.43

Third 1,297.87 1,138.08 1,456.29 1,165.92 90.71 87.76

Fourth 1,576.17 1,299.53 1,708.91 1,365.25 106.07 92.13

Fifth 3,119.97 1,930.42 3,865.11 2,302.67 184.52 121.66

Average annual growth rate, 2002-11 (%)

First 1.86 3.17 0.55 1.70 -0.21 2.90

Second 2.27 3.32 1.41 3.54 -0.33 3.79

Third 2.39 3.33 2.65 3.32 0.11 3.76

Fourth 3.01 2.98 3.05 3.39 0.85 2.48

Fifth 1.97 3.68 2.78 5.86 1.30 3.55


20 Appendix Table 3 shows the quintile-wise per capita expenditures on different fuels in nominal terms.


The disaggregation of real per capita fuel expenditure by type of fuels given in Table 5

shows that per capita expenditures on electricity and natural gas, by and large, increased across

each expenditure quintile for both urban and rural households. Real per capita expenditures on

kerosene oil and other fuels, on the other hand are observed to generally decline across the five

quintiles for urban and rural households during both the years under review, with few exceptions.

These include rising real per capita expenditures on kerosene oil across the fourth and fifth rural

expenditure quintiles in 2001-02 and the fifth expenditures quintile for other fuels in case of rural

households during both the years reviewed. In case of firewood, real per capita expenditures go

down across quintiles in the urban sector, while they increase for the rural households indicating

that firewood is an inferior energy form for urban households, while being a normal fuel type for

the rural households, owing to the lack of widespread availability of natural gas in rural areas.

Table 5. Average per Capita Real Household Expenditure by Fuel Type and Expenditure

Quintile

Expend

quintiles

2001-02

In 2001-02 Rupees

Firewood Kerosene oil Natural gas Electricity Other fuels

Urban Rural Urban Rural Urban Rural Urban Rural Urban Rural

First 16.60 23.05 4.29 4.63 19.37 1.47 44.51 22.15 7.08 16.36

Second 13.21 23.09 3.70 4.31 19.74 2.60 50.10 20.75 3.40 11.48

Third 11.14 25.23 2.75 4.29 24.14 3.70 49.13 23.23 2.66 9.15

Fourth 7.93 27.90 2.18 4.73 28.38 5.97 58.35 28.10 1.70 8.61

Fifth 5.42 27.29 1.25 5.62 41.95 8.90 115.55 40.81 1.01 9.58

2010-11

First 18.95 31.73 0.75 3.00 17.29 2.65 48.16 30.27 4.96 17.69

Second 12.43 31.90 0.39 2.45 18.66 4.45 53.23 30.86 2.73 13.78

Third 9.68 31.58 0.39 2.40 20.81 6.35 58.07 35.25 1.75 12.18

Fourth 6.32 32.95 0.28 2.08 25.33 8.11 72.77 37.38 1.37 11.62

Fifth 4.29 32.62 0.20 1.68 37.42 17.73 141.93 57.21 0.69 12.41

Average annual growth rate, 2002-11 (%)

First 1.57 4.18 -9.17 -3.91 -1.19 8.92 0.91 4.07 -3.33 0.90

Second -0.66 4.24 -9.94 -4.80 -0.61 7.91 0.69 5.41 -2.19 2.23

Third -1.46 2.80 -9.54 -4.90 -1.53 7.96 2.02 5.75 -3.80 3.68

Fourth -2.26 2.01 -9.68 -6.23 -1.19 3.98 2.75 3.67 -2.16 3.88

Fifth -2.32 2.17 -9.33 -7.79 -1.20 11.02 2.54 4.47 -3.52 3.28


In terms of annual growth in per capita expenditures on different fuel types between the

period 2001-02 and 2010-11, the analysis shows that negative growth is recorded in real per

capita expenditures on firewood for urban households across the second to fifth expenditure

quintile. Real per capita expenditures for kerosene oil are observed to decline for both urban and

rural households across all quintiles, with the fall being higher for urban households in each

quintile. Real per capita expenditures are also seen to fall for urban households in case of natural

gas and other fuels. On the other hand, real per capita expenditures of rural households on natural

gas witness highest growth in comparison to the other fuel types across all expenditure quintiles


owing to the government’s policy of providing affordable access to natural gas to the rural

population as much as possible, with households in the fifth quintile showing an average annual

growth of 11 percent during 2002-11 (Table 5).

The analysis in Table 6 shows that the share of fuel in average household real monthly

expenditure declines for both the urban and rural households across all expenditure quintiles,

during both 2001-02 and 2010-11 periods21. This finding may indicate that all fuel types are

necessities as the Engel’s Law stipulates that the share of expenditure on necessities declines

with rise in total income/ expenditure. In addition, it can be seen that in 2001-02, the urban

households allocated a larger proportion of their total expenditure to fuel compared to their rural

counterparts across all expenditure quintiles, except the top one. On the other hand, in 2010-11,

rural households are observed to have a higher share of expenditures on fuel in comparison to

urban households, across all expenditure levels owing to the government’s policy of rural

electrification as well as providing access to natural gas to the rural households, as much as

possible.

Table 6. Pattern of Household Real Expenditure and Income by Expenditure Quintile

Expenditure

quintiles

2001-02

Average household monthly

expenditure (Rs.)


income (Rs.)

Share of fuel in household

monthly expenditure (%)


First 2,949.22 2,906.34 3,259.55 3,136.27 10.39 9.09

Second 4,455.27 4,412.42 4,623.06 4,436.89 9.50 7.97

Third 5,830.82 5,785.74 6,225.01 5,934.80 8.73 7.42

Fourth 7,814.36 7,742.77 8,454.58 7,887.57 8.05 7.22

Fifth 16,643.90 13,458.73 19,080.88 14,030.61 6.25 6.26

2010-11 In 2001-02 Rs.

First 3,738.25 3,608.92 3,768.66 3,520.48 8.62 8.77

Second 5,428.90 5,380.84 5,338.72 5,404.61 7.57 8.09

Third 7,084.97 7,062.91 7,832.84 7,169.32 7.05 7.59

Fourth 9,443.31 9,361.95 10,009.32 9,667.83 6.62 7.04

Fifth 19,114.55 15,864.08 23,254.48 18,555.39 5.98 6.36


The breakup of the share of household monthly expenditure on fuels by different

fuel types shown in Table 7 reveals the differential patterns of spending by households across the

consumption expenditure distribution. The expenditure share of firewood, kerosene oil and other

fuels declines across each successively higher expenditure quintile for both urban and rural

households in both the years. In case of natural gas, during both 2001-02 and 2010-11, the

budget share of rural households rises across the expenditure quintiles owing to higher

availability of natural gas as a result of government policy. The expenditure share of urban

households on natural gas increases persistently upto the fourth quintile and then drops sharply

for the top quintile. It is a possibility that the richest quintile households in urban areas may be

substituting natural gas with electricity for heating and cooking purposes.

21 This analysis in nominal terms is given in Appendix Table 4.


Table 7. Share of Different Fuels in Total Real Household Expenditure (%)

Expenditure

quintiles

2001-02



First 2.03 3.08 0.44 0.58 1.80 0.16 5.03 2.87 1.09 2.39

Second 1.71 2.99 0.40 0.54 1.90 0.25 5.01 2.62 0.47 1.57

Third 1.28 2.89 0.27 0.46 2.20 0.37 4.67 2.61 0.32 1.09

Fourth 0.80 2.79 0.19 0.45 2.23 0.47 4.64 2.63 0.18 0.88

Fifth 0.32 2.05 0.06 0.31 1.63 0.54 4.18 2.68 0.06 0.68

2010-11

First 2.02 3.30 0.08 0.32 1.45 0.24 4.49 3.02 0.58 1.90

Second 1.27 3.14 0.04 0.25 1.50 0.35 4.50 2.92 0.26 1.43

Third 0.90 2.84 0.03 0.22 1.55 0.47 4.40 2.96 0.16 1.11

Fourth 0.50 2.62 0.02 0.17 1.57 0.53 4.41 2.81 0.11 0.91

Fifth 0.22

1.91

0.01

0.10

1.26

0.81

4.46

2.83

0.03

0.71


6. RESULTS AND ANALYSIS

The estimated income and price elasticities of household spending on different fuels are

analyzed and discussed in this section. The results of the regression model given in Equation (3)

are presented in Table 8 for both the urban and rural areas for the periods 2001-02 and 2010-11.

The coefficients are highly significant statistically during both the years under consideration; and

with the exception of kerosene oil and other fuels in the urban areas for 2001-02 and firewood,

kerosene oil and other fuels for urban households in 2010-11, have the anticipated signs.

Table 8. Results of OLS Regression

2001-02 2010-11

Urban Rural Urban Rural

α Β α β α β α β

Firewood 7.948 0.001 22.710 0.002 24.064 -0.001 70.224 0.002

(23.18)* (7.86)* (65.80)* (11.45)* (31.30) (-6.98)* (59.61)* (8.74)*

Kerosene oil 2.561 0.000 4.293 0.000 0.964 0.000 5.717 0.000

(14.00)* (-1.37) (12.99)* (1.81)* (12.33)* (-2.99)* (29.06)* (0.08)

Natural gas 19.784 0.006 2.318 0.002 47.479 0.003 3.875 0.004

(31.14)* (29.51)* (11.85)* (14.62)* (38.90)* (23.28)* (5.66)* (29.15)*

Electricity 27.073 0.025 19.715 0.006 123.616 0.015 54.507 0.011

(16.47)* (52.06)* (42.58)* (21.98)* (31.95)* (39.12)* (41.96)* (39.97)*

Other Fuels 2.896 0.000 10.951 0.001 5.181 0.000 31.359 0.001

(18.75)* (-5.12)* (44.06)* (3.86)* (17.24)* (-4.15)* (44.91)* (4.00)*

Total Fuel 60.262 0.031 59.988 0.011 201.305 0.018 165.682 0.018

(32.63)* (57.38)* (81.19)* (25.10)* (47.10)* (40.46)* (85.20)* (43.69)*

Total

Non-Fuel 584.338 0.494 703.693 0.150 2537.072 0.301 1702.721 0.302

(34.11)* (97.44)* (114.05)* (41.76)* (58.38)* (68.34)* (95.39)* (81.27)*

Note: Figures in parentheses are t-statistics.

* Denotes coefficient as statistically significant at the traditional level of significance, i.e., 5 percent.


The negative coefficients of income for kerosene oil and other fuels for the urban

households in 2001-02 indicate that urban households considered them as inferior goods. The

analysis further reveals that even after ten years (2010-11), firewood, kerosene oil and other fuels

remained an inferior fuel for urban households. The intercept term for all fuel types is positive

with a small numerical value for both 2001-02 and 2010-1122, showing that all fuels are a

necessity having low levels of consumption expenditures. This is also corroborated by the

positive ri* shown in Table 9.

Table 9. Marginal Expenditure Shares and Minimum Required Expenditure for Different Fuels

2001-02

Marginal Expenditure Share (%) Minimum Required Expenditure (Rs.)


Firewood -0.001 0.016 8.00 22.85

Kerosene oil 0.000 0.004 2.56 4.31

Natural gas 0.011 0.010 20.13 2.42

Electricity 0.047 0.031 28.65 20.07

Other Fuels 0.000 0.002 2.88 10.99

Fuel 0.056 0.063 62.21 60.64

Non-Fuel 0.944 0.937 1154.30 827.82

2010-11

Firewood -0.002 0.009 23.95 70.59

Kerosene oil 0.000 0.000 0.96 5.72

Natural gas 0.010 0.011 48.07 4.58

Electricity 0.044 0.029 126.76 56.33

Other Fuels 0.000 0.002 5.16 31.46

Fuel 0.052 0.052 204.90 168.67

Non-Fuel 0.948

0.948

3631.44

2440.52

The analysis of marginal expenditure shares given in Table 9 shows that the marginal

propensity to consume for different fuels is quite low for both urban and rural households during

both the sample periods. The rural households are observed to have a relatively higher marginal

consumption share of different fuels during both the years, except for electricity. The marginal

expenditure share on fuels is seen to be higher in 2001-02 for both urban and rural households.

Table 9 indicates that if household per capita expenditure increases by one rupee, the urban

households will spend additional 5.6 percent and 5.2 percent, respectively on fuels in 2001-02

and 2010-11. In comparison, their rural counterparts will spend an additional 6.3 percent and 5.2

percent in 2001-02 and 2010-11, respectively. The earlier estimates of marginal budget shares

obtained by Burney and Akhtar (1990) were lower at 2.4 percent and 2.9 percent, respectively

for the urban and rural areas. The increase in marginal expenditure shares observed since the

mid-1980s can be attributed to the widespread use of electrical appliances owing to the greater

22 The higher numerical values for 2010-11 reflect the effect of inflation or increase in fuel prices over the period 2002-11.


inflow of workers’ remittances, village electrification program of the government and

availability of gas in both urban and rural areas, more so in urban areas. Among the different fuel

types, both the urban and rural households have a higher allocation on electricity during the two

sample periods (4.7 percent vs. 3.1 percent and 4.4 percent vs. 2.9 percent, respectively).

The income elasticities for different fuels are reported in Table 10. The numerical value

of all income elasticities is seen to be below unity, indicating that all fuel types are a necessity

for both the urban and rural households in the country. The negative sign for kerosene oil and

other fuels for the urban areas in 2001-02 and for firewood, kerosene oil and other fuels for the

urban areas in 2010-11 imply that these are inferior fuel for them. The income elasticities are

observed to be higher for urban households in 2001-02, while they are higher for natural gas,

electricity and other fuels in rural households in 2010-11, reflecting the changing patterns of fuel

use for rural households due to greater availability of natural gas and electricity. The fall in

income elasticity for electricity in the urban areas during 2002-11 can be attributed to growing

energy shortages experienced by Pakistan in the second half of 2000, as demand exceeded

available supply resulting in higher hours of load shedding by consumers. It is also worth

mentioning that electricity was surplus in the early 2000s, due to which there were no supply

side constraints.

The uncompensated (Marshalian) own and cross-price elasticities of the different fuel

types estimated from the regression results are reported in Table 11, for both 2001-02 and 2010-

11. All the estimated price elasticities have the anticipated negative sign, with the exception of

kerosene oil and other fuels for urban households in 2001-02 and firewood, kerosene oil and

other fuels for urban households in 2010-11. The magnitude of all estimated price elasticities is

very small although non-zero, which shows that household consumption of different fuels is

price inelastic.

Table 10. Income Elasticites for Different Fuels

Fuel

1984-84 2001-02

2010-11


Firewood -0.088 0.301 0.154 0.088 -0.128 0.084

Kerosene oil 0.154 0.272 -0.055 0.072 -0.141 0.000

Natural gas 0.436 - 0.336 0.407 0.233 0.761

Electricity 0.351 0.712 0.629 0.223 0.383 0.375

Other Fuels 0.220 0.257 -0.171 0.047 -0.139 0.053

Source: Authors’ calculations using PIHS 2001-02 and PSLM 2010-11, Burney and Akhtar (1990).

Table 11. Uncompensated Price Elasticities for Different Fuels

Fuel

2001-02

Urban Rural

Firewood

Kerosene oil

Natural gas

Electricity

Other Fuels

Firewood

Kerosene oil

Natural gas

Electricity

Other Fuels

Firewood -0.1484 -0.0002 -0.0017 -0.0024 -0.0002

-0.0845 -0.0004 -0.0002 -0.0019 -0.0010

Kerosene oil 0.0002 0.0526 0.0006 0.0009 0.0001

-0.0017 -0.0670 -0.0002 -0.0015 -0.0008

Natural gas -0.0015 -0.0005 -0.3291 -0.0053 -0.0005

-0.0097 -0.0018 -0.3831 -0.0085 -0.0047

Electricity -0.0028 -0.0009 -0.0070 -0.6163 -0.0010

-0.0053 -0.0010 -0.0006 -0.2143 -0.0026

Other Fuels 0.0008 0.0002 0.0019 0.0027 0.1656

-0.0011 -0.0002 -0.0001 -0.0010 -0.0439

2010-11

Urban Rural

Firewood

Kerosene oil

Natural gas

Electricity

Other Fuels

Firewood

Kerosene oil

Natural gas

Electricity

Other Fuels

Firewood 0.1236 0.0000 0.0012 0.0032 0.0001

-0.0814 -0.0002 -0.0001 -0.0016 -0.0009

Kerosene oil 0.0007 0.1354 0.0014 0.0036 0.0001

0.0000 -0.0045 0.0000 0.0000 0.0000

Natural gas -0.0011 0.0000 -0.2255 -0.0059 -0.0002

-0.0178 -0.0014 -0.7232 -0.0142 -0.0079

Electricity -0.0018 -0.0001 -0.0037 -0.3769 -0.0004

-0.0088 -0.0007 -0.0006 -0.3604 -0.0039

Other Fuels 0.0007 0.0000 0.0013 0.0035 0.1305

-0.0012 -0.0001 -0.0001 -0.0010 -0.0512

Note: Figures along the diagonal are own price elasticities while figures off-diagonal are cross price elasticities.

En

ergy D

eman

d E

lasticity in

Pa

kistan

: An In

ter-temp

ora

l An

alysis

19

Khan, Khalid, and Shahnaz 20

7. CONCLUSION AND POLICY IMPLICATIONS

The purpose of this study has been to examine the inter-temporal patterns of household

consumption expenditures on energy by using two sets of micro-data for the years 2001-02 and

2010-11. The paper has computed income and price elasticities for different categories of fuels

using the Extended Linear Expenditure System, for the period 2001-02 and 2010-11 and has

compared these with elasticities estimated by Burney and Akhtar (1990) to see whether

expenditure patterns have undergone structural changes over the last 25 years in Pakistan. The

study has also examined the household expenditure patterns on different fuels for both urban and

rural households as well as for different expenditure quintiles. In addition, the study has also

analyzed the structural changes in overall energy consumption in Pakistan over the last two

decades.

Analysis of micro level data suggests that electricity followed by natural gas have been the

dominant fuel for urban households with access to both these fuel types rising during 2002-11. On

the other hand, firewood and electricity have been the main fuels for rural households during the

period with access to electricity rising for these households. It is also found that rural households

spent proportionately more on fuels compared with their urban counterparts – a finding consistent

with Burney and Akhtar (1990). Furthermore, per capita real expenditure on fuels was observed to

rise with the rise in income across all the five expenditure quintiles with few exceptions.

Quintile wise household per capita expenditure witnessed a rise in electricity and natural

gas for both urban and rural households during 2002-11, while expenditure on kerosene oil,

firewood and other fuels observed decline during the period, thus indicating them as inferior fuels.

The results of the study validate the Engel’s Law by showing quintile-wise decline in the share of

fuel in real household monthly expenditure in both urban and rural areas during the sample

period. The Engel Law states that as income/ expenditure of the household increases, the share of

expenditure on necessities declines. By similar account, firewood, kerosene oil and other fuels are

found to be inferior fuels as the share of expenditure on these fuels declines across quintiles in

both urban and rural areas over the study period. On the other hand, the expenditure share of rural

households on natural gas across expenditure quintiles rises during both the sample period. In

addition, the expenditure share of urban households on natural gas continued to exhibit a rising

trend until the fourth quintile but drops harply for the fifth quintile (richest households) apparently

indicating a substitution away from natural gas towards electricity for cooking and heating

purposes, as they could afford it.

The analysis of marginal expenditure shares suggests that the marginal budget shares for

different fuels are found to be on lower side, for both urban and rural households across the

sample years. However, the marginal budget shares are found to be relatively higher for rural

households compared with their urban counterparts for all fuel types, except electricity.

Notwithstanding low marginal budget shares for 2001-02 and 2010-11 for different fuels these are

found to be higher than those obtained by Burney and Akhtar (1990) for the period 1984-85.

The income elasticities for different fuels are found to be less than unity, indicating that all

fuel types are a necessity for both urban and rural households. Firewood, kerosene oil and other


fuels, as expected are found to be inferior fuels for urban households in both sample periods. All

estimated own price elasticities, though small in magnitude, are found to have the expected

negative signs with few exceptions (firewood, kerosene oil and other fuels). The low price

elasticities indicate that these fuels are price inelastic.

The policy implications that stem out of the study suggest that as income of the household

increases the demand for fuel would not rise proportionately as all fuels are found to be a

necessity. Secondly, the low price elasticites of different fuels suggest that as their prices fall,

their demand will not increase substantially because people will continue to buy these fuels

according to their needs. Thus, the fuel import bill will not necessarily decline sharply.

Furthermore, if the government reduces subsidy on different fuels, their prices will go up by

definition but their demand may not decline appreciably. This is because these are necessity and

the household will continue to demand according to their needs. The government may like to

reduce subsidy across the consumption quintiles excepting first and second (the poorest

households) to improve the country’s budgetary situation without adversely affecting the

relatively affluent classes.

APPENDIX

Appendix 1: Energy Consumption Data from Energy Yearbook

The analysis of macro trends in energy use presented in section II are based on data

obtained from various issues of the Pakistan Energy Yearbook. The Energy Yearbook gives

estimates of final commercial energy consumption in the country expressed in tones oil

equivalent. The starting point for computation of this data is the commercial energy supplies of

different types of primary energy sources (natural gas, petroleum products, liquefied petroleum

gas, coal, electricity and nuclear) available in the country in a particular year from both

indigenous and imported sources. From this primary energy supply, the transformations of

different sources of energy, which includes energy utilized by gas processing plants, petroleum

refineries and electric power stations are netted out. Next, the diversions of primary energy

supplies, in terms of transport and distribution losses, auxiliary consumption of energy sector, and

consumption for non-energy uses along with statistical difference are subtracted. The residual

obtained is the final energy use in the economy, which is used in the analysis in section II of the

paper. This final energy use is disaggregated into the following categories; domestic, commercial,

industrial, agriculture, transport and other government. The analysis of household energy

consumption presented in this section employs data available under the domestic category.

Appendix 2: Definition of Urban and Rural Areas in PIHS 2001-02 and PSLM 2010-11

The universe of both surveys – PIHS 2001-02 and PSLM 2010-11 consist of all urban and

rural areas of the four provinces of Pakistan, excluding military restricted areas. Details of the

urban and rural areas sampling frames developed by the Pakistan Bureau of Statistics (PBS) are

given below.


Urban area

The urban area sampling frame has been developed using Quick Count Record Survey

technique, under which all as cities/towns of the urban domain of the sampling frame have been

divided into small compact areas known as Enumeration Blocks (E.Bs). Each enumeration block

comprises about 200-250 households. Each Enumeration Block has been further divided into low,

middle and high-income group, keeping in view the status of the majority of households. In PIHS

2001-02, a total of 22,800 enumeration blocks in all urban areas of the country were used for

sampling. In PSLM 2010-11, the urban areas sampling frame consisted of 26,698 enumeration

blocks which had been updated through Economic Census conducted in 2003.

Rural areas

With regard to the rural areas, the lists of villages/mouzas/dehs according to Population

Census, 1998 have been used as sampling frame. In this frame, each village/mouza/deh is

identifiable by its name, Had Bast number and cadastral map, etc. The rural frame of both PIHS

2001-02 and PSLM 2010-11 comprised of 50,588 mouzas/villages/dehs.

Appendix 3: Energy Expenditure Data from Household Surveys (PIHS and PSLM)

The data pertaining to household expenditures on energy analyzed in Section V and VI of

the paper has been obtained from the PIHS 2001-02 and PSLM 2010-11 datasets. This data has

been extracted from the fuel and lighting category of monthly household expenditures reported in

the HIES modules of both the surveys (under item code 2700). The items covered under the fuel

and lighting category along with their definition is consistent across both these surveys so their

results are directly comparable23. The fuel types covered under the fuel and lighting category in

both PIHS and PSLM are presented in the table below:

Fuel type Item Code

Fire wood 2701

Kerosene oil 2702

Char coal 2703

Coal hard & soft peat 2704

Dung cake (dry) 2705

Gas (pipe), Gas (cylinder) 2706

Electricity 2707

Match box, Candles, Mantle, etc. 2708

Bagasse, Agricultural wastes for fuel purposes (cotton sticks, sawdust, shrubs,

weeds, tobacco sticks, etc.),

2709

Piped gas and cylinder gas appear as separate items in PIHS 2001-02.

For the purpose of analysis in the paper, the above 9 categories have been regrouped into

23 It needs to be clarified that the fuel and lighting category of household energy expenses excludes expenditures on petrol/ diesel

incurred for running of motor car/ motorcycle. These expenditures are captured under the ‘personal transport and travelling’

category in both the surveys under item code 4301.


the following 5 main categories, while one item - match box, candles, mantle, etc., has been

dropped from analysis due to having no direct link with household energy use:

Fuel type Items Included

Fire wood Fire wood

Kerosene oil Kerosene oil

Natural gas

Gas (pipe), Gas (cylinder)

Electricity

Electricity

Other fuels

Coal hard & soft peat, Dung cake (dry), Bagasse, Agricultural wastes for fuel

purposes

Appendix Table 1: Households’ Expenditure on Different Fuels

Year

2001-02

Average household expenditure

(Rs. per month)

Average household expenditure per capita

(Rs. per month)


Firewood 63.52 160.98 9.38 24.90

Kerosene oil 13.70 26.65 2.43 4.62

Natural gas 171.89 23.87 29.84 3.92

Electricity 412.49 160.74 72.78 25.40

Other Fuels 16.39 70.59 2.47 11.48

Total Fuel 677.99 442.82 116.91 70.32

Total Non-Fuel 8,663.41 5,580.50 1,476.25 847.11

2010-11

Firewood 133.37 466.85 21.33 76.68

Kerosene oil 5.29 33.99 0.85 5.74

Natural gas 347.21 95.07 61.88 16.46

Electricity 1,110.02 514.28 200.32 87.12

Other Fuels 27.31 194.45 4.56 33.13

Total Fuel 1,623.20 1,304.64 288.94 219.14

Total Non-Fuel 23,005.51 16,269.83 4,043.73 2,621.40


Appendix Table 2: Average Per Capita Household Expenditure and Income by Expenditure

Quintile (Rs.)

Expenditure

quintiles

2001-02

Average per capita total

household monthly

expenditure

Average per capita household

monthly income

Average per capita monthly

expenditure on fuel


First 894.30 746.32 1,013.57 828.63 91.86 67.66

Second 958.90 780.07 1,032.14 795.06 90.15 62.23

Third 1,067.83 875.67 1,175.71 897.99 89.83 65.58

Fourth 1,239.99 1,024.43 1,340.72 1,046.39 98.54 75.32

Fifth 2,649.61 1,449.76 3,090.78 1,508.00 165.18 92.20

2010-11

First 2,463.31 2,262.63 2,508.64 2,254.54 215.40 203.99

Second 2,724.86 2,390.52 2,744.37 2,473.63 209.02 199.43

Third 3,061.80 2,684.83 3,435.53 2,750.50 216.82 209.78

Fourth 3,718.32 3,065.72 4,031.48 3,220.74 253.53 220.22

Fifth 7,360.28 4,554.04 9,118.13 5,432.19 441.07 290.80



Appendix Table 3: Average Per Capita Household Expenditure by Type of Fuel and expenditure

Quintile (Rs.)

Expenditure

quintiles

2001-02



First 16.60 23.05 4.29 4.63 19.37 1.47 44.51 22.15 7.08 16.36

Second 13.21 23.09 3.70 4.31 19.74 2.60 50.10 20.75 3.40 11.48

Third 11.14 25.23 2.75 4.29 24.14 3.70 49.13 23.23 2.66 9.15

Fourth 7.93 27.90 2.18 4.73 28.38 5.97 58.35 28.10 1.70 8.61

Fifth 5.42 27.29 1.25 5.62 41.95 8.90 115.55 40.81 1.01 9.58

2010-11

First 45.31 75.85 1.78 7.16 41.33 6.32 115.12 72.36 11.86 42.29

Second 29.70 76.25 0.94 5.84 44.60 10.64 127.25 73.76 6.54 32.93

Third 23.13 75.49 0.94 5.73 49.75 15.18 138.81 84.27 4.19 29.11

Fourth 15.12 78.75 0.68 4.97 60.54 19.38 173.94 89.35 3.26 27.77

Fifth 10.25 77.98 0.47 4.01 89.44 42.38 339.25 136.75 1.65 29.67


Appendix Table 4: Pattern of Household Expenditure on Fuel by Expenditure Quintile

Expenditure

quintiles

2001-02


expenditure (Rs.)


income (Rs.)

Share of fuel in household monthly expenditure (%)


First 2,949.22 2,906.34 3,259.55 3,136.27 10.39 9.09

Second 4,455.27 4,412.42 4,623.06 4,436.89 9.50 7.97

Third 5,830.82 5,785.74 6,225.01 5,934.80 8.73 7.42

Fourth 7,814.36 7,742.77 8,454.58 7,887.57 8.05 7.22

Fifth 16,643.90 13,458.73 19,080.88 14,030.61 6.25 6.26

2010-11

First 8,818.86 8,513.75 8,890.61 8,305.12 8.74 8.89

Second 12,807.26 12,693.88 12,594.52 12,749.95 7.67 8.20

Third 16,714.07 16,662.03 18,478.36 16,913.06 7.14 7.69

Fourth 22,277.60 22,085.67 23,612.87 22,807.25 6.71 7.13

Fifth 45,092.91 37,424.76 54,859.37 43,773.80 6.06 6.44


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Washington, D.C.: The World Bank.


Vol. 1 (July-December 2015) pp. 26-55

Exploring New Pathways to Gender Equality in Education: Does Information

and Communication Technology Matter?

Ayesha Qaisrani and Ather Maqsood Ahmed

Through the use of the System Generalized Method of Moments Technique, this study aims to establish

links between Information and Communication Technologies (ICTs), gender equality in education and economic

growth, for segregated levels of education. The study focuses on the decade of 2000-2010 for the case of Lower

Middle Income countries. Through simultaneous solution of the models, it is concluded that ICTs do have some

potential to promote gender equality but the relationship is not strong enough, either due to lack of relevant

statistical data or due to inefficient integration of ICTs into the society. It is, however, deduced that the strongest

factor promoting gender equality is the average schooling of adult population. Furthermore, the study finds out

that for lower middle income countries, gender equality at lower levels of education plays an important

role in economic growth than gender equality in higher education.

Keywords: Education, Gender Equality, ICTs, Economic Growth.

1. INTRODUCTION

The 21st century has brought with itself a new revolution in the global realm – the

information society, which has changed the global macroeconomic landscape [Chetty (2012)].

The importance of technology cannot be denied as it has changed the way we live, the way we

work, the way we make decisions and the way we correspond with each other. Advancements in

Information Communication Technologies (ICTs) not only have the capability to improve the

technological arena, but they also have the potential to bring about social and economic

improvements (ibid.).

ICTs have the ability to transfer knowledge and information, introducing new methods of

learning, communication and working, thereby increasing the productivity of the people [Vu

(2014)]. ICTs involve all those technological mechanisms through which information is dispersed

and processed. In the past decade, the technological scenario has undergone rapid innovations,

from personal computers to laptops, from landline telephones to cellular phones, from internet to

broadband, and the more recent upgrade to 3G and 4G technologies, but it must be kept in mind

that older means like the radio, newspaper and television are also included in the definition of ICT

[Wamala (2012)].

The potential of ICTs to enhance people’s productivity can be used to address the

inequalities that the developing world faces in terms of economic opportunities [UNCTAD

(2014)]. Of all the social issues and inequalities, gender inequality has received significant

concern in recent years as a growth inhibiting factor [Kabeer and Natali (2013); Klasen (1999)].

Ayesha Qaisrani <[email protected].> works as a Research Associate at Sustainable Development Policy

Institute (SDPI), Islamabad, Pakistan. Ather Maqsood Ahmed <[email protected]> is Professor and

HOD Economics at the School of Social Sciences and Humanities (S3H), National University of Sciences and

Technology (NUST), Sector H-12, Islamabad, Pakistan.

mailto:[email protected].


27 Qaisrani and Ahmed

According to Sen (1999), growth is enhanced when people are free to make their decisions

regarding education, savings, health, ownership, labour participation etc. This freedom requires

the availability of equal opportunities to population. When a society restricts this freedom of

opportunities to roughly half of its population, i.e. women, the repercussions will be evident

in its macroeconomic indicators as well, like growth-stagnancy, , poverty, income

inequality, unemployment, illiteracy, etc.

Growth of an economy depends upon its resources – physical and human, among other

things. Investments leading to increased physical and human capital result in higher levels of

growth [Barro (2001)]. Human capital involves a quantitative aspect, i.e., the number of workers

and a qualitative aspect, i.e., the skills and capabilities of those workers. According to the Human

Capital Report [WEF (2013)], human capital is not just an aggregate function of education and

skills, it also conceptualizes health, physical, social and economic contexts of a society. When

countries ignore a major proportion of their population while making human capital investment

decisions, they are, in fact, missing out a great deal in their progress by not benefitting from the

potential skills and capabilities of females, which would otherwise promote growth. This leaves

half of the population socially and economically deprived of their basic human rights and

opportunities, increasing dependency, higher fertility rates, lower per capita incomes and overall

suppression of women in social and domestic contexts.

The prospects of ICT to address gender inequality have only recently come into the

limelight. The potential of ICT to empower women with opportunities to education and the

resulting significance of gender equality in education to economic growth has been a motivation

to carry out a detailed research in this field. Little existing literature has addressed this issue

quantitatively [Chen (2004); Kucuk (2013)]. Most studies have been survey based or based on

project analysis [Maier and Reichart (2007), Wheelar (2007) and Punie, et al. (2006)]. Some

literature also focuses on the existence of gender inequality in the access and use of ICTs

[Reinen and Plomp (1997); Huyer and Sikoska (2003)]. These studies focus on the barriers faced

by women in benefiting from ICTs, i.e., the gender divide within the ICT sector.

The current study differs from existing literature in the sense that it takes on the impact of

ICTs on gender equality in education at three separate levels of education, i.e., primary,

secondary and tertiary, and then instantaneously examines the effect of gender equality at these

levels on growth. It focuses on selected lower/ lower-middle income countries (as per the

definition of World Bank) and takes into account the decade of 2000-2010. Another major

contribution of this study is the use of latest technique, the System Generalized Method of

Moments, to tackle the issue of endogeneity and recursive nature of the system. Based on the

objectives of the study, this study tests whether or not ICTs promote gender equality at different

levels of education and whether this gender equality in education contributes to economic growth.

Rest of the paper is organized as follows. Section 2 throws some light on the existing

literature that focuses on the thematic area of ICTs and gender equality. Section 3 defines the

theoretical and methodological framework guiding the study. Section 4 presents a discussion of

results, while section 5 concludes the study and gives policy recommendations.

28 Exploring New Pathways to Gender Equality in Education

2. REVIEW OF LITERATURE

Numerous literature exits on the impact of gender inequality in education on growth, but

the literature involving the impact of ICTs on gender inequality at different stages of education

and simultaneously checking their impact on economic growth is relatively new. Studies

involving gender inequalities and their respective impact on growth find their nature in the

endogenous growth models which owe a great deal to the contributions of Romer (1986) and

Lucas (1988).

Contrary to Barro’s (1998) results that female education has an insignificant relation with

economic growth, a large number of studies affirm that gender equality in education positively

contributes to economic growth [Dollar and Gatti (1999); Schultz (2002); Klasen (2002);

Knowles (2002)]. Barro justified his finding by saying that benefits of female education are not

realized in many countries’ labour markets because of their culture and thus the impact on

economic growth is not well-observed, and in a later study the author confirmed that there in fact

exists an indirect relationship between female education and economic growth by means of

decline in fertility rate [Barro (2001)]. Nevertheless, evidence regarding the importance of gender

equality in education for economic growth is numerous and much stronger [Lewis and Lockheed

(2008); Yumusak, et al. (2013)].

Investing in female education is an investment in human capital, which then translates into

economic growth. Dollar and Gatti (1999) carried out a study with the purpose of finding

empirical relation between gender equality in terms of education and economic growth of the

economy. They used religious differences, regional characteristics and civil freedom as

determinants of gender inequality among other variables. Findings showed that investing in the

education of females positively impacts economic growth; religious factors influence gender

inequality negatively; and that there is a positive effect of higher income on gender equality.

Similarly, Schultz (2002) explains the inter-generational positive impacts of investing in female

education which can boost economic growth in the long run. The author found that increase in the

education of females improves the quality of children in terms of education, health and nutrition.

Conversely speaking, gender inequality is in fact attributed to have a negative impact on

economic growth. Pervaiz, et al. (2011) studied the impact of gender inequality for the case of

Pakistan over the years of 1972-2009. By taking the growth of real per capita GDP and an index

for gender inequality (for education, employment and health), the authors found that gender

inequality index had a significant negative relation with growth of per capita real GDP,

thereby, acting as a deterrent to growth.

Over the years, there has been growing interest in the role of ICTs as a means for

achieving development agendas [Sandys (2005); Gurumurthy (2004); Wheeler (2007); UNCTAD

(2014a)]. Hafkin and Taggart (2001) threw light on the importance of ICT for development and

the need for gender issues to be highlighted from the inception of ICT in the society before the

divide of access between men and women in a society increases. There are many barriers to

access of technology by women which include language, geographical location, time constraints,

costs, social and cultural norms, IT skills etc and in order to narrow the gender gap in technology,

these barriers must be worked upon. These findings were reconfirmed by Daly’s (2003) study

Qaisrani and Ahmed 29

which took into account cultural and social norms which have the capacity to affect the relation

between ICT and gender equality and entailed different scenarios in which ICT does or does not

impact gender equality. The author reflected the idea that culture and institutional policies that

discriminate against women before the ICT revolution will most likely not let ICT empower

women. For gender equality to be achieved, a revolution is needed at the cultural and institutional

level.

A noteworthy contribution in this field is by Chen (2004) as he empirically tested the

potential of ICTs to impact gender equality and economic growth. He used panel data for 78

countries over the time span of 1960 to 2002 and found affirmative role of ICTs for enhancing

gender equality in education and employment which ultimately contributes to economic growth.

Similarly, Kucuk (2013) empirically tested the determinants of gender equality and included ICT

as an important causal factor. Instead of using a panel data like Chen, Kucuk used cross-sectional

data for Middle East and North Africa (MENA) region to analyze the impact of religion and oil

in the extent of gender inequality. The relevant result for our thematic area is that ICTs were

observed to be beneficial for gender equality in the MENA region.

Other examples include a report by InfoDev (2010) which focuses on the role of ICTs for

enhancing gender equity in education in South Asian countries. The study presents theoretical

underpinnings following which ICTs can play a positive role for gender equity. These

technologies can bring down social, cultural and economic barriers faced by women and bring

access of education within their reach. The report found that the case of ICTs for advancement of

female education is stronger when one considers informal education. One can cite the example of

the animated TV advertisement of sexual harassment bill in Pakistan which proved to be highly

effective in spreading awareness of the bill among the masses.

A study by UNCTAD (2014b) throw light on how with the increasing access of ICTs in

the developing countries, women are overcoming barriers of culture, system and norms. Though

the study focuses on the role of ICTs in women entrepreneurship, the information drawn can also

be applicable for increasing female education. The results are similar to an earlier study by

Wheeler (2007) who finds that ICTs and particularly internet makes information more accessible,

allows women to maintain social connections and builds social awareness, ultimately leading to

empowerment.

There exists a research lacuna when it comes to determining the scope of ICTs for gender

equality for the case of Pakistan. Some examples that do exist mostly look at the issue through a

theoretical perspective. One reason could be the lack of gender segregated ICT data for Pakistan.

For instance, Saghir, et al. (2009) throw light on the barriers faced by women of Pakistan in

accessing ICTs. Computer illiteracy is highlighted as a major determinant for the lesser

involvement of girls in ICT related aspects. Furthermore, existing low levels of education, pricey

ICT infrastructure and cultural norms are other hindering factors in this regard.

3. FRAMEWORK AND METHODOLOGY

3.1. Theoretical Framework

In this section, the channel through which ICT leads to gender equality and how gender


equality ultimately adds up to economic growth is explained. As the access to and use of ICT

becomes prevalent in the society, it can increase female education levels by two pathways: by

easing access to education and by the improvement in the quality of education. Access to

education is made easier by the option of distant learning or e-learning. Media channels like the

television, internet, video conferencing and the radio are very convenient methods of imparting

education to distant places. Even if not used as a means of education, these technologies can

instill awareness in the population of the rights of girls to education, thereby, convincing the

orthodox minds against gender discrimination in education. Realizing the importance of

education, there will be lesser dropout rates, leading to increased educational attainment amongst

the population. An enlightened human resource will lead to an increase in the total factor

productivity, leading to economic growth.

A wide variety of educational resources are now in the reach of those who have access to

new technology like the internet. This has brought about improvements in the syllabi being

taught to children, keeping local courses in line with the changing needs of societies. New and

improved methods of teaching being used worldwide can be adopted to increase the quality of

education being imparted. Better educated citizens with high quality education would contribute

significantly to economic prosperity. Appendix 1 explains this process in the form of a flowchart.

Apart from the direct benefits of education to women, educated women have an important

role in the household scheme of work. Educated mothers are more likely to refrain from

discrimination between their male and female children in terms of food, health, education,

etc. They are an important source of bringing up their children in a better way, leading them to

become better citizens. Moreover, increased female education leads to reduction in their fertility

rates [Duflo (2004)]. The increased quality and reduced quantity of children help in building up a

better and stronger human resource which is extremely important for poverty reduction and

economic growth.

Increased educational attainment of the population leads to a virtuous cycle in the

economy. Education helps in building human capital which leads to efficient allocation of human

resource. Better educated and skilled labour results in increased production and economic

prosperity. With increased female education, they will be in a better position to benefit from

better working opportunities and reaping higher earnings. With higher earnings, savings would

increase resulting in more capital formation. Greater capital formation would lead to higher total

factor productivity, thereby, resulting in economic growth.

On the other hand, increased educational opportunities to women not only improve

economic prospects to them, but also lead to awareness of their political roles and rights in the

society. Education informs women about their rights and therefore, gives them power to

participate in the decision making process of their society. It also leads to their representation in

the government, key positions in bureaucracy and political parties etc. which gives them voice,

power of mobility and confidence [Tasli (2007)]. Women in strategic positions can help bring a

structural change in the system in favor of women, easing up the way for other women to

progress and contribute towards the overall economic prosperity. Appendix 2 shows the

flowchart of this process.


3.2. Methodological Framework

In economic literature, a variety of models have been presented to explain the growth

process of an economy. The economic growth models have undergone many cycles of evolution

starting from the classical model of Mercantilists to the neo classical models of Solow-Swan

(1956) and finally to the more recent endogenous growth models and those which

incorporate human capital as a determinant of economic growth.

In the neo-classical models, the emphasis was on the accumulation of capital which

determined the output per worker, both of which lead to economic growth. Capital in turn was

created by increased savings. Moreover, increases in population growth also rendered to

economic growth. In these models, technology was considered an exogenous variable.

The production function of the neo-classical models was:

Q = A f (L, K) ... (1)

where, Q= Output, A= Technology, L= Labour and K= capital.

Here, A can also be termed as the productivity, as increases in this variable lead to increases

in output with the given level of inputs.

However, the models that evolved through it, called the endogenous growth

models, took technology as an endogenous determinant of growth. According to those,

Q= f (L, K, A) … (2)

The more recent Augmented Solow Model incorporates human capital as an exogenous

determinant of the growth of an economy. Contributions by Mankiw, Romer and Weil (1992)

founded that human capital promotes economic growth through the channel of technology.

The study is based on the criteria that ICT advancements can lead to gender equality in

education and employment which can enhance growth. Gender equality in education once

established can have impacts which are far reaching and capable of getting round the poverty and

inequality traps. For this reason, we consider a systematic approach, based on a set of equations

to determine the ultimate effect of ICT on gender equality and economic growth.

An effort is made to determine the impact of Information and Communication

Technologies on gender equality on different levels of education, i.e., primary, secondary and

tertiary. For that, the ratio of female to male enrolments is taken at all these levels separately to

analyze whether or not ICTs aid in gender equality in education, and if so, at what level of

education is the impact the greatest.

Taking Chen’s (2004) research as reference, we include a set of independent and control

variables that determine gender equality. Female to male ratio of enrolment is adopted as a

general measure of gender equality in education (ibid.). This indicator is recognized by the

United Nations as a measure for Goal 3 of the Millennium Development Goals which relates to

promoting gender equality and empowering women.

Despite considerable advancement in the field of ICTs, data availability on the

availability, access and use of ICTs remains limited, especially for the case of developing


countries. Effort is being put in to determine gender segregated ICT statistics by some

organizations (International Telecommunications Union, LIRNEAsia, Women in Global Science

and Technology Research etc.), but they have only accumulated data for developed countries for

now. There is a huge gap in the data available for least developed and low income countries.

Prime reasons for this are the non-availability of human resource or funding to conduct ICT

surveys, the absence of national baseline data on ICT sector [Hafkin (2013)]. In these countries,

use of indicators like computers, internet connections, mobile and landline phones, fax modems,

etc., dominate the statistics [Gurumurthy (2004)]. The ICT proxies used by Chen in his study

included: number of computers per 1000 persons, number of internet users per 1000 persons,

number of phones per 1000 persons, ICT expenditure as a percentage of GDP; and ICT

expenditure per capita. We use a set of indicators representing ICTs for which data was readily

available for all the sample countries. These indicators include: number of telephone users,

number of mobile users and number of internet users per 100 people. These indicators represent

the integration of population through ICT devices which facilitate flow of information and

communication. It should be noted that although landline telephones are considered an old proxy

for measuring connectivity, a large number of households, especially in developing countries still

rely on landline phones for basic connections and services like access to the internet [Hamilton

(2010)].

Per capita GDP is included as a measure of economic development. According to

findings of Dollar and Gatti (1999), Goldin, et al. (2006) and Jayachandran (2014), female to

male enrolment in schools and colleges tends to decline with increase in economic development.

Public spending on education is also an important variable which may have a negative or

positive impact on gender equality in education depending on the benefit incidence [Esim

(2000); Sabir (2002)]. Average years of schooling represent the general level of education of the

economy and are expected to play a positive role in improving gender equality in education

[Afzal, et al. (2013)].

(𝐹𝑒𝑚𝑎𝑙𝑒 𝑡𝑜 𝑀𝑎𝑙𝑒 𝑅𝑎𝑡𝑖𝑜 𝑜𝑓 𝐸𝑛𝑟𝑜𝑙𝑚𝑒𝑛𝑡)𝑖𝑡𝑗 = 𝛼𝑜 + 𝛼1(𝑃𝑒𝑟 𝐶𝑎𝑝𝑖𝑡𝑎 𝑅𝑒𝑎𝑙 𝐺𝐷𝑃)𝑖𝑡 +

𝛼2(𝑃𝑢𝑏𝑙𝑖𝑐 𝑆𝑝𝑒𝑛𝑑𝑖𝑛𝑔 𝑜𝑛 𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛)𝑖𝑡 +

𝛼3(𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝑌𝑒𝑎𝑟𝑠 𝑜𝑓 𝑆𝑐ℎ𝑜𝑜𝑙𝑖𝑛𝑔 𝑜𝑓 𝐴𝑑𝑢𝑙𝑡 𝑃𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛)𝑖𝑡 + 𝛼4(𝐼𝐶𝑇)𝑖𝑡𝑘 + µ𝑖𝑡 … (3)

where, i represents each country cross-section under study, t represents time period (year), j

represents the level of schooling (primary, secondary and tertiary) and k represents the ICT proxy

from telephone, internet and mobile. A brief description of the variables is as follows with the

source of the definition mentioned.

The study is extended by analyzing the impact of gender inequality obtained from the

previously mentioned models at three educational levels, on the growth rates of economies. The

basic purpose is to see the economic importance of achieving gender equality. For this, taking

guidance from models developed by Klasen (2002) and Anderson (2010), measures of gender

equality in education in the growth equation, along with certain control variables are taken into

account.

The following model is estimated:


(𝐺𝑟𝑜𝑤𝑡ℎ 𝑖𝑛 𝑃𝑒𝑟 𝐶𝑎𝑝𝑖𝑡𝑎 𝐼𝑛𝑐𝑜𝑚𝑒)𝑖𝑡 = 𝛽0 + 𝛽1(𝐺𝑟𝑜𝑠𝑠 𝐶𝑎𝑝𝑖𝑎𝑙 𝐹𝑜𝑟𝑚𝑎𝑡𝑖𝑜𝑛)𝑖𝑡 +

𝛽2(𝑇𝑟𝑎𝑑𝑒 𝑂𝑝𝑒𝑛𝑛𝑒𝑠𝑠)𝑖𝑡 + 𝛽3 (𝑃𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛 𝐺𝑟𝑜𝑤𝑡ℎ)𝑖𝑡 +

𝛽4 (𝐸𝑠𝑡𝑖𝑚𝑎𝑡𝑒𝑑 𝐹𝑒𝑚𝑎𝑙𝑒 𝑡𝑜 𝑀𝑎𝑙𝑒 𝑅𝑎𝑡𝑖𝑜 𝑜𝑓 𝑆𝑐ℎ𝑜𝑜𝑙 𝐸𝑛𝑟𝑜𝑙𝑚𝑒𝑛𝑡)𝑖𝑡𝑗 + 𝛾𝑖𝑡𝑗 … (4)

A brief definition of the variables along with their sources are mentioned in Table 1.

Table 1. Data Sources and Definitions

Variable Name Source Definition

Female to Male

Ratio of Primary

Enrollment

World

Bank

It measures the proportion of females relative to males enrolled in public and private

primary schools of the country.

Female to Male

Ratio of Secondary

Enrollment

World

Bank

It defines the percentage of females enrolled in private and public secondary schools

relative to males.

Female to Male

Ratio of Tertiary

Enrollment

World

Bank

It takes the percentage of females relative to males enrolled at private and public tertiary

schooling institutions of the country.

Per Capita

Real GDP

World

Bank

Per capita income is taken in terms of constant US dollars for the base year 2005. It is

calculated by dividing the real GDP of a country in a given year with the average

population number in that given year. It indicates the economic prosperity of an economy.

Average Years of

Schooling

World

Bank

Adult population is taken as the population which is above the age of 25. Barro-Lee’s 5

year estimates were used to entail the general education level of the population, which

were interpolated to obtain annual data.

Public Spending on

Education

World

Bank

It is the total expenditure on education by the government expressed as a percentage of

GDP. It includes the current as well as capital expenditure carried out for educational

purposes. Any government spending on private or public educational institutes,

administration and also any subsidies provided come under this category.

No. of Telephone

Users Per 100

People

World

Bank

This variable informs us about the degree of connectivity through landline telephone

network.

No. of Mobile Users

Per 100

People

World

Bank

Mobile users approximate the degree of integration of population through the use of

cellular networks. Population covered by cellular network availability is an approximation

of penetration of the telecommunication network in the region which tells us the reach of

the network. No. of Internet

Users Per 100

people

World

Bank

This variable indicates the access of the internet and the World Wide. Web by the

population. The access to internet entails the access to communication and transfer of

information and education.

The time period under study is 2000-2010 and the dataset is a balanced panel dataset.

Despite the fact that system GMM can be applied to unbalanced panel data, it has been argued

that due to the lags of independent variables introduced as instruments, it is difficult to calculate

accurate results with models having endogeneity issues [Flannery and Hankins (2012); Baum

(2013)]. The study focuses on lower/ lower middle income countries. According to the

definition of World Bank (2012), those countries with per capita income between $1,036 and

$4,085 are defined as lower/ lower middle income countries. We have included lower/lower

middle income countries as the socioeconomic dynamics of these countries are extremely

different than those prevalent in the high income countries. Moreover, the policy implications


may also be very different for these countries. Out of 48 countries that fit the criteria, eleven

countries were shortlisted which had complete data availability for the variables in use.

These countries are: Pakistan, India, Nepal, Kenya, Morocco, Nicaragua, Paraguay, Syria,

Indonesia, Bolivia and Philippines.

One of the major limitations in this study is the use of proxies for ICT indicators. This

was due to the unavailability of appropriate variables and statistics for the integration and use of

ICT in the economies. While ITU has a huge database regarding ICTs, due to lack of

national statistics, data for Pakistan was not available, which is our main country of

concern. For that reason, the study relies on the number of telephone, mobile and internet users

per 100 people. Although, they do give an approximation of the usage of the ICTs, but the results

would have been better provided ideal data were available for ICT usage.

Another shortcoming in the data was availability of the average years of schooling

estimates on 5-yearly basis. Barro-Lee’s projections of mean number of schooling of adult age

population, i.e., above the age of 25 were used. To convert the 5-yearly data into annual data,

method of Moving Average was used. The mean schooling of population changes very slowly,

so, the growth rates were calculated between the 5 year intervals and interpolated to get annual

observations.

On the whole, the indicators used to proxy for certain variables provide sufficient

information about the required variable, but availability of better and more accurate statistical

measures could improve the study.

4. DESCRIPTIVE STATISTICS AND DISCUSSION OF RESULTS

4.1. Descriptive Statistics

This section focuses on the discussion of the trends and patterns of the dataset used, which

lays the foundation of the estimations. An overview of the selected variables is given for each

country under study. Appendix 11 lists down the summary statistics for the eleven countries

selected.

A glance at the summary statistics’ tables show that during the years 2000-2010,

almost all economies selected in this study have attained gender equality at primary level, with

the exception of Pakistan where the enrolment ratio is lowest, averaging at 76.87%. On the

other hand, Bolivia has the highest average female to male primary enrolment rate of 99.1%. A

major reason for the low average ratio is that Pakistan only started implementing free and

compulsory education for all goal of MDGs in 2004, whereas, other countries had started working

on it since 2000. There are fewer primary schools for girls in Pakistan than boys1.Another major

reason for the wide gap is the unavailability of female teachers to teach at primary level in most

rural areas of Pakistan.

1 See Mumtaz, (2014) reducing the Gender Gap/ Engendering PRSP2.


In case of gender equality at secondary level enrolments, it was observed that the three

South Asian nations lagged behind the other nations in the sample. Pakistan still had the

lowest average for female to male secondary enrolment ratio, but surprisingly the ratio was

higher than at primary level. On the other extreme, countries like Nicaragua, Paraguay and

Philippines had greater female enrolment which outnumbered male enrolment rates, with average

values of ratio 113.9%, 103.1% and 110.3%, respectively. These statistics show the changing

perception and value towards female education. Besides the social and cultural change, these

countries also have better educational institutes, ensuring that quality education is being

provided which encourages female enrolment rates. Moreover, efforts made by the states to

make secondary and higher education affordable have greatly driven higher female enrolment

rates in these countries. Nicaragua, in particular, gained from providing special scholarships for

secondary education to those girls who attend school regularly and perform well in class [Herz

(2011)].

The situation of female enrollments at the tertiary level has two extremes. At one extreme

lie countries like India, Nepal and Kenya where the female to male enrollment rates are as

low as 69.3%, 46.1% and 59.5%, respectively. These economies are a long way from achieving

gender parity at the tertiary level. One reason is that tertiary education is extremely costly, with

the private sector being more expensive than the public sector. The public sector, even though

affordable, imparts lower quality education and more than often, the opportunity cost for

enrolling in a higher level institute is greater for girls than not opting for it [Nagarajan (2014)].

At the other extreme, countries like Paraguay and Philippines had females outnumbering

males in tertiary enrollment rates, with the average ratio being 134.1% for Paraguay and 122.7%

for Philippines. At these levels, the disparity is against male education. This dramatic trend is a

result of greater acceptance of gender egalitarian practices, modernization and expansion of

higher educational facilities, and due to the better performance of girls in secondary education

which encourages them to enroll at higher educational institutions [Mather and Adams (2007)].

Apart from these factors, the major reason is the high private and social returns to female

education as compared to male education in these countries [DeSilva and Bakhtiar (2011)]. Such

gender gap in education in favor of women exists when there is a large gender wage gap in the

labor force in favor of men and in order to close the wage gap, females study more than males.

By observing other variables included in the study, it was noticed that those countries

which had high number of average years of schooling for adult population (generally higher than

4 years of schooling) had a less gender gap at all levels of enrolment. With average years of

education of 8.3 years each, both Philippines and Bolivia display higher levels of female

enrolment rates than other countries. Better educated society, therefore, can be a promoter of

female participation in education.

The trends of all ICT proxies have been constantly on the rise, with the rates of

growth being highest for mobile phones and internet. The use of telephone has dwindled in

developed nations because they have been substituted with wireless technology but the rate of

landline telephones is still growing in developing nations, particularly in low and lower/ lower

middle income countries as they remain the main source of communication and also because

internet in developing nations is still connected with landline telephone to a large extent.

Therefore, telephones connections have risen over the period of 2000-2010 but at a gradual rate.


4.2. Discussion of Results

To establish the link between ICTs and gender equality on one hand and the link between

gender equality and economic growth on the other, a recursive approach is to be used. A system

of recursive models exists when the dependent variable of one of the models is used as an

independent variable in another model, in a step-wise manner. However, our model also showed

evidence of endogeneity and heteroscedasticity among some of the variables, which was

confirmed by Hausman Test of Endogeneity and Park Test for Heteroscedasticity (see,

Appendices 3 and 4) To encounter these problems, an instrumental variable (IV) technique is

needed. System Generalized Method of Moments, being an IV technique is the most appropriate

technique for this study. The GMM technique was introduced by L Hansen in 1982.

The properties of GMM include consistency, asymptotic normality and efficiency.

Although a relatively new approach, the GMM has gained popularity because of its independence

from unnecessary assumptions [Hall (2009)], the method has an edge over other Instrumental

Variables techniques as it inherently addresses the issue of endogenity within the system and uses

a richer set of instruments that can be determined from within the system. A Generalized Method

of Moments approach is based on the specifications of certain moment conditions. A moment

condition is explained as a function of the parameters, variables and instruments, in a way that

their expectation is zero.

g(θ) = E [ f (wt, θ0 , zt) ] ... (5)

In this equation, θ defines a vector of parameters, wt includes the variables used and zt

includes the instruments. The expectation of a function of all three of these is zero for a moment

condition to be defined.

In case of system GMM, same set of instruments are used for all equations employed in

the system. The stronger the correlation of the instrument variable with the endogenous variable

more is the strength of the instrument for defining the endogenous variable. System GMM is

based on the principle that good instruments are only those generated from the system internally.

These internally generated instruments could be either differences or lags of independent

variables. Lags of independent variables, however, prove to be more strongly correlated with the

endogenous variable and uncorrelated with the error term [Roodman (2009)]. Moreover, in panel

data studies, it is very difficult to separate out external instruments which would prove to be

sufficient to explain the endogenous variables.

Validity and correct identification of the model depends upon the correct choice of

instruments. The correct choice is confirmed by the Sargan J-statistic which has the null

hypothesis of valid over-identification of restrictions as opposed to invalid on alternative

hypothesis. In other words, if the null hypothesis is accepted, it means that the instrument

specification is correct. The Sargan J-statistic follows the chi-square distribution, with degrees of

freedom being equal to the difference between number of instruments and number of parameters.

In this study, up to two lags of independent variables were introduced as instruments for each


regression.

On a broad note, results of the study are in accordance with the theoretical background.

ICT has been observed to be a likely contributor to gender equality in education and impact on

growth from that gender equality has been positive. Almost all of the control variables have also

shown coefficient signs as per expectations and are statistically significant.

We refer to Tables 2 and 3 for detailed discussion of coefficients. Among the ICT proxies

used for estimation (telephone lines, mobile usage and internet usage), only telephone lines per

100 people has given positive and statistically significant coefficients for female to male

enrolment ratios at primary, secondary and tertiary level of education. Judging from the

coefficients, there exists a marginal difference in the magnitude of the impact of ICTs on gender

equality at each level. In statistical terms, a 1% increase in the use of ICT in the society results in

a 0.21%, 0.25% and 0.28% increase in female to male ratio of primary, secondary and tertiary

enrolment respectively. These results conform to the earlier empirical evidence of Chen (2004)

and Kucuk (2013), the findings of which report that ICTs’ integration in the society promote

gender equality in education.

The coefficients of ICT remained positive when other proxies for ICT were used,

but the significance of the coefficients varied. Appendices 5-10 report the coefficients for system

GMM regressions when other proxies were used for female to male enrolment ratios at each

educational level. The significance of the coefficients, however, varied, for different proxies of

ICTs. A plausible conclusion drawn from this result is that ICTs do have the potential to promote

gender equality at different educational levels, but the strength of their impact depends upon the

integration of these technologies in the society.

Table 2. System GMM Results for Female to Male School Enrolments

Independent Variable

Female to Male

Primary

Enrollment Ratio

Female to Male

Secondary

Enrollment Ratio

Female to Male

Tertiary

Enrollment Ratio

Constant 78.4***

(1.8)

75.7***

(6.0)

23.2***

(1.3)

Per Capita Income 0.002

(0.001)

0.003**

(0.001)

0.023***

(0.0005)

Average Years of Schooling of

Adults

1.25***

(0.27)

4.32***

(0.36)

11.42***

(0.252)

Public Spending on Education 1.34***

(0.29)

-2.25***

(0.48)

-5.89***

(0.16)

ICT (Telephone) 0.210*

(0.12)

0.253***

(0.14)

0.28***

(0.04)

J-STAT 0.47 0.25 0.24

J-STAT Probability 0.92 0.97 0.62

Standard errors are reported in parenthesis.

(*) represents significance at 10%, (**) significance at 5% and (***) significance at 1%.


Table 3. System GMM Results for Growth in Per Capita Income

Independent Variable

Growth of Per Capita Income

Female to Male

Primary Enrollment Ratio

Female to Male

Secondary Enrollment

Ratio

Female to Male

Tertiary

Enrollment Ratio

Constant -18.7***

(3.9)

-9.5***

(3.6)

-2.1***

(0.47)

Trade Openness 0.41***

(0.09)

0.28***

(0.1)

0.00003***

(0.00)

Gross Capital Formation 0.06***

(0.02)

0.08***

(0.01)

0.11***

(0.01)

Growth of Population -1.13***

(0.08)

-0.93***

(0.14)

-0.12***

(0.03)

Female to Male Ratio of Primary

Enrollment

0.08***

(0.02)

--

--

Female to Male Ratio of

Secondary Enrollment

--

0.025**

(0.01)

--

Female to Male Ratio of

Tertiary Enrollment

--

--

0.001

(0.002)

J-STAT 0.47 0.25 0.34 J-STAT Probability 0.92 0.97 0.62

Standard errors are reported in parenthesis.

(*) represents significance at 10%, (**) significance at 5% and (***) significance at 1%.

Lower/ lower-middle income countries have a very limited integration of ICTs into their

system, even though the integration has observed an increasing trend over the years. Also, most of

the lower/ lower middle income countries have large proportion of population living in the rural

areas. The reach of ICTs in those areas is even more limited. Even though the use of ICTs has

been observed to enhance quality of education being imparted in such countries (with the use of

computers, internet, etc.), most schools, especially, primary schools in rural areas do not have

access to such technologies. Moreover, impact of ICTs on promotion of female education would

be better assessed if gender specific statistics are available [Hafkin (2001)]. With information on

the degree of access of ICTs to females, it would give more accurate results of how these

technologies are helping in the educational empowerment of women.

Another factor behind the varying significance of ICT indicators is that social issues,

reined by cultural norms, take time to change. The widespread use of the new ICTs has only

started recently. Even though the results indicate that ICTs, in fact, do contribute to the

achievement of gender equality at all levels, but the strength of the impact would be more evident

when these technologies have attained a deep-rooted integration in the whole society. With more

time, better data and improved quality of ICTs being used in the society, the positive impact

would be strengthened.

As far as other variables are concerned, it was found that the average years of schooling of

the adult population is the major contributor towards gender equality. The coefficient is positive

and highly significant for all three educational levels considered. The coefficient is largest for


tertiary level of education, indicating that 1 year increase in the average years of schooling of

adult population results in 11.4% increase in the female to male ratio of tertiary education. The

coefficients for primary and secondary education are 1.25% and 4.32%, respectively. The

conclusion drawn is that in a more enlightened society, there is less discrimination against female

education, which in turn encourages the females of the society to opt for higher education. In

other words, a well-educated society would promote gender equality in education. Moreover, in

the regression results, the coefficients of average years of schooling of adult population are largest

in magnitude, showing that in achieving gender equality, the most important factor is the

awareness of the importance of gender equality, which comes through greater education. This

finding confirms earlier results of El Sanabary (1989).

The estimated coefficient of public spending on education is positive and statistically

significant when primary education equality is considered, indicating that a rise in the educational

expenditure by the government raises the female to male ratio of primary enrollment. In statistical

terms, a 1% rise in the public spending on education leads to 1.34% rise in gender equality at the

primary level. One reason for this is the obligation of the governments, under the Millennium

Development Goals, to provide free and universal primary education to all. Following this, almost

all countries, have achieved this target of universal primary education. Thus public spending, at

the primary level, promotes gender equality.

For secondary and tertiary education, the coefficient’s sign is negative and is highly

significant. This lays emphasis on the importance of target incidence of public spending. If the

incidence of the public spending is poorly targeted, then it could actually worsen the gender

equality. Statistically, a 1% rise in the public spending on education hampers gender equality by

2.3% at secondary level and 5.9% at the tertiary level. The variable used for this study represents

the overall magnitude of government expenditure on education, regardless of how much part of

that expenditure is spent on boys or girls. Morgan (2007) has highlighted the issue of gender blind

budget allocations, which can hinder a country’s progress towards gender equality by ignoring the

dissimilar effects of public spending on males and females. The impacts of such policies, like

increased budget spending on education, are not gender neutral. Different societal groups on the

basis of age, gender, class etc., have different impacts from an overall government spending

[Balmori (2003)].

Without conclusive evidence from gender-segregated data on public spending on

education, it cannot be ascertained, contrary to the findings of the study, that government

expenditure on education in fact exacerbates gender inequality. Certain examples support this

finding e.g., when the public spending on education was segregated for gender in Ghana, it

showed that females benefitted considerably less from that spending than males, leading to

increased gender inequality [Budlender, et al. (2002)]. Moreover, a study by Elson (1999) found

that educational expenditures in some countries unintentionally favour boys over girls. The author

quoted the example of Pakistan and Kenya, where the incidence of public educational expenditure

shows that Pakistan spent 56 Rupees per male and only 26 Rupees per female (according to

World Bank, 1995) and Kenya spent 670 Shillings per male and 543 Shillings per male. Such

examples support the negative sign of the coefficient of public spending expenditure in this study.

Moreover, in developing countries, particularly lower/ lower middle income countries, the

concentration of educational public spending is more on developing infrastructure and capacity


building, rather than on quality of education or social aspects of education. In such a case,

government’s education expenditure might not involve certain needed action to address issues like

gender inequality, thereby, worsening the situation.

To capture the impact of level of economic prosperity, per capita income has been used in

terms of constant US dollar terms. Results show a positive but statistically insignificant

coefficient for the case of gender equality in primary education. This shows that income level of

population is not a significant contributor to attain gender equality at this level. This can be

supported by the free provision of primary education to all by the state, which redeems

population’s income insignificant, since the state itself has taken the responsibility to educate all

children at the primary level.

Per capita income has proved to be positive and statistically significant contributor to

achieve gender equality at higher levels of education. With 1 dollar rise in the per capita income

of the population, the female to male secondary enrolment ratio rises by 0.003%, and the female

to male tertiary enrolment ratio rises by 0.023%. This indicates that female enrolments at higher

levels of education are positively affected by higher per capita incomes. A rich nation is more

likely to attain gender equality at secondary and tertiary levels than a poorer nation. There are

costs to attain education, and with higher income, parents are better able to cover the educational

expenditures to send their daughters to schools, who were previously ignored in terms of

education. This finding is supported by Chen (2004) who claimed that higher levels of economic

development contribute to gender equality.

When the impact of gender equality at each educational level was observed in economic

growth, it was found to be positive and highly significant for primary and secondary female to

male levels of enrolment, but statistically insignificant for tertiary education. There have been

numerous studies in literature on the impact of gender equality in education on growth, but

literature that segregates the educational levels and then checking the impact on growth is very

limited. It is evident that gender equality in primary enrolment contributes most to economic

development. With reference to Table 3, a 1% rise in the female to male primary enrolment ratio

results in 0.08% increase in economic growth. In second place, gender equality in secondary

enrolment contributes 0.025% to growth. Furthermore, a 1% rise in gender equality in tertiary

enrolment results in 0.001% increase in growth, however, it is statistically insignificant.

It is to be noted that gender equality in education, captured by enrolment ratios does not

take into account dropout rates or actual level of educational attainment. Therefore, there’s a

possibility that impact of such gender equality on growth could be a bit misleading. Another

factor to be kept in mind is that the time period under study is just ten years, whereas, social

changes like achievement in gender equality could take a longer time period to show their impact

on growth.

As far as the insignificance of the coefficient of gender equality at tertiary level is

concerned, Wolff and Gittleman (1993) stated that for lower/ lower middle income

countries, initial levels of education contribute more to growth as compared to higher levels of

education. In developing countries, primary and secondary education levels matter most as they

lead to higher levels of productivity of final goods and help in adjusting to and adoption of

foreign technology. Tertiary education levels have only attained importance recently in these


countries; therefore, its impact on growth is not significant as yet. According to Vandenbussche,

et al. (2004) and Periera and Aubyn (2004), developing countries are just beginning to close their

technological gap, and for them “less skilled human capital formation is more important”. Higher

education or tertiary level skills gain importance when countries are at the brink of their

technological frontier. Moreover, we may also support this by using Barro’s (1998) conclusions

that the benefits of rising female education levels (especially at tertiary levels) are not realized due

to the cultural factors due to which better educated females are not able to participate in the labour

force to contribute to the economic gains.

To account for growth, certain control variables have been introduced. The coefficients of

trade openness, defined as the sum of imports and exports as a ratio of GDP, are positive and also

statistically significant, thereby, indicating that with trade liberalization, countries gain more in

terms of economic growth. According to the results, if trade is liberalized by 1%, growth in per

capita income rises by an average of 0.23%. This gain is achieved through better allocation of

resources as countries make efforts to specialize in certain industries, leading to productivity rise.

Moreover, as a country liberalizes its trade, it gains from technology transfers and rising factor

productivity. In case of developing countries, like the ones under study, trade openness is

associated with imports of intermediate and capital goods, which are then used in the domestic

industry, mostly export oriented [Parikh and Stirbu (2004)]. The positive and statistically

significant coefficients for growth-openness nexus is supported by the findings of Klasen (2002),

Andersson (2010), Ulasan (2012), etc.

The coefficient of population growth rate is significant and negative in all three growth

equations. A high rate of population growth rate indicates high dependency ratios on one hand

and lower capital per worker on the other. Both the situations are growth hampering. In lower/

lower middle income countries particularly, a higher population growth rate worsens the situation

as inflation, unemployment and poverty rates are already very high. With increasing

population growth, per capita income would decline, there will be more mouths to feed, more

jobs to be provided and more children to be educated. For every 1% rise in the population growth

rate, growth of per capita GDP declines by 0.72% on average according to the results. This

finding is supported by Cincotta and Engelman (1997), Maleka and Rehab (2004) and Afzal

(2009).

Investment is added in the equation as gross capital formation as a percentage of GDP.

The coefficients show a highly significant positive sign for all three growth equations. This is in

accordance with a priori expectation that higher levels of investment positively contribute to

economic growth. For every 1% rise in the gross capital formation, lower/ lower middle income

economies grow by an average of 0.08%. Gross capital formation is associated with infrastructure

development, increasing levels of productivity, stimulation of aggregate demand and creation

of jobs. The positive significant impact of capital formation conforms to earlier findings of

Klasen (2002), Pavelescu (2007), Andersson (2010) and Kumo (2012).

It was, however, noted that by changing the specification and instruments of the growth

model slightly, the signs and significant of the control variables did not show robustness. Bear in

mind that the factors that lead to growth generally have an effect in the longer run. A period of 10

years is too short to get robust results of impact of different factors on the economies’ growth, but

since the study focused on the impact of information revolution on gender equality and the


resulting impact on growth, the last decade was the most appropriate to be considered for study

as ICTs only began to be extensively used in developing countries in this time period.

This study, however, is an important contribution as it attempts to correct for endogeneity

and simultaneous bias in the estimations, which was previously not accounted for in gender

specific growth modeling [Birdsall, et al. (1997); Andersson (2010)].

The overall results are comparable to those concluded by Chen (2004) to some degree.

While the signs of coefficients of ICTs echo the findings of Chen, the significance of the

coefficients varies. It may be because of the difference in the sample size, as Chen included the

timeframe from 1960-2002 or it may also be because of the wide dataset used by Chen

comprising of data for 78 countries. Our study draws out the impact of ICTs on a limited number

of countries, based on their differentiated socioeconomic conditions.


It is drawn from the empirical findings that even though ICT positively influences gender

equality in education, significance of ICT proxies vary at each educational level. This indicates

the lack of thorough integration of ICTs in lower/ lower middle income countries. Either the

diffusion of the technologies is not deep rooted into the societal system, or their quality is not

good which restricts the positive impact of ICTs to be strong and robust with all proxies used.

Furthermore, for the impact to be vigorous, it must be evaluated over a longer time span as social

changes require some time to take place.

Another important conclusion drawn from the study is that gender equality is most

strongly affected by the average education of the adult population. It is observed that as the

average number of years of schooling of the adult population rises, the prospects for gender

equality rise at each level, with the strongest impact on higher education. More educated

societies show less incidence of gender discrimination in education. With increasing learning,

they realize the benefits of educating the females of their society and encourage them to gain

education.

This study shows that for growth in lower/ lower middle income countries, gender

equality is more important in primary and secondary education level than at the tertiary level.

Countries with less skilled labour force benefit from higher rate of returns to lower levels of

education. The proportion of people (both males and females) gaining higher education is very

less so far; therefore, the impact on growth of gender equality in higher education has not yet

been considerable. As countries advance, highly skilled labour force starts to play a more

important role and the impact of gender equality in higher education on growth matters more.

To promote gender equality and enhance growth, the governments of lower/lower middle

income countries should take the following measures:

1. To benefit, in the true essence from ICTs, governments should invest in ICT infrastructure

and ensure a deep diffusion of quality ICT facilities in the society, especially in educational

institutions.


2. Keenly devised distant learning programs should be initiated for that proportion of females

who face barriers to leave home for education and the content of the syllabus should address

the gender issues faced by women.

3. More important than the quantity, states need to adopt a holistic approach to improve the

quality of public spending on education for attaining gender equality. Governments in the

lower/ lower middle income countries need to make targeted efforts to reduce gender

inequality at all levels of education. Some considerations could be making new schools for

females, employing female instructors to be role models for young girls, making educational

institutes closer to the community’s vicinity, providing transport facility to girls to and from

schools, providing grants and scholarships to girls based on need and merit, training teachers

to be more gender friendly, etc.

4. Print and electronic media, which have the highest intensity of diffusion in lower/ lower

middle income countries, should initiate mass awareness programs regarding the benefits of

female educating and also highlight the importance of overall literacy levels. A more

educated society would be more liberal towards female education.

5. Efforts need to be done to collect gender segregated statistics for access, use and benefits of

ICTs. Indicators depicting the actual level of integration of ICTs in the society need to be

developed, along with those measuring the quality of such facilities. This would provide us

with comprehensive information about the impact analysis of ICTs.

The study is quite comprehensive in relating the ICTs to gender equality and analyzing the

impact of gender equality on growth, however, there is still room for improvement in knowledge

base. This study analyzes the cross-country relationships on a macro level. For in depth analysis

of impact of ICTs on gender equality, micro level studies should be carried out to see how

ownership and access to ICTs empower women.

Moreover, aspects other than gender equality in education should be considered, e.g.,

equality in labor force participation, equality in decision making and political positions, equality

in property ownerships etc. Even for the case of education, measures other than female to

male enrolment ratios should be used to define gender equality. Besides the impact of ICTs on

gender equality, the issue of gender divide in the use of ICTs should be analyzed. Women face

numerous barriers in accessing these technologies; in return, they reap fewer benefits than men

from productivity gains.

Furthermore, the empirics of this study could be reassessed by using different sample

countries and/or increasing the number of years under study. Other factors that could possibly

impact gender equality and economic growth could be included to re-specify the models. As till

date, Generalized Method of Moments is the best available technique to address the

endogeneity and simultaneous bias problems, which such models face. Future researches could

include instruments other than lagged values of independent variables and examine the resultant

impacts.


Appendix

Appendix 1: The Link between ICT Innovations and Gender Equality

ICT ADVANCEMENT

FEMALE EDUCATION

EASE OF ACCESS

MORE ENROLMENT

LESS DROPOUTSLOW COST OF EDUCATION

QUALITY IMPROVEMENT

HUMAN CAPITAL ACCUMULATION

TFP INCREASES

FEMALE EMPLOYMENT

ACCESS TO LABOUR MARKET

INCREASED WOMEN

PARTICIPATIO

NEW EMPLOYMENT

OPPORTUNITIES

MORE EDUCATED

POPULATION

GENDER EQUALITY


Appendix 2: The Mechanics between Gender Equality and Economic Growth

POVERTY REDUCTION AND ECONOMIC GROWTH

GENDER EQUALITY

IMPACT ON HH

BETTER EDUCATED MOTHERS

BETTER EDUCATION AND HEALTH CARE

FOR CHILDREN

HEALTHIER AND EDUCATED NEXT

GENERATION

QUALITY OF CHILDREN INCREASES

AND QUANTITY DECREASES

IMPACT ON ECONOMY

HUMAN CAPITAL ACCUMULATION

EFFICIENT HUMAN RESOURCE

ALLOCATION

GE IN EMPLOYMENT AND WAGES

HIGHER EARNINGS AND HIGHER

SAVINGS

CAPITAL FORMANTION

TFP INCREASES

WOMEN PARTICIPATION IN

HIGHER VALUE ADDED JOBS

IMPACT ON SOCIETY

AWARENESS OF RIGHTS AND

RESPONSIBILITES

PARTICIPATION OF WOMEN IN DESICION

MAKING

ACCESS TO PRODUCTIVE RESOURCES

REPRESENTATION IN GOVERNMENT


Appendix 3: Hausman Test of Endogeneity

Sr. No. Endogenous Variable Probability

1 PCY Residual 0.005

2 Adult Schooling Residual 0.0002

3 Investment 0.093

Appendix 4: Park Test of Heteroscedasticity

Sr. No Variable Causing Heteroscedasticity Probability

1 Per Capita Income 0.0002

2 Public Spending on Education 0.0001

3 Average Years of Adult Schooling 0.0000

Appendix 5: System GMM Estimations for Gender Equality at Primary Education with

ICT Proxies

Independent Variable Female to Male Primary Enrollment Rate

Constant 76.9***

(3.31)

82.5***

(3.07)

Per Capita Income 0.001

(0.001)

0.005***

(0.001)

Average Years of Adult Schooling 1.39***

(0.303)

0.66***

(0.33)

Public Spending on Education 1.67***

(0.53)

0.87***

(0.27)

Internet -- 0.20***

(0.07)

Mobile 0.07

(0.07)

--

J-Stat

0.36

0.49

J-Stat Probability 0.94 0.92

(***), (**) and (*) represent statistical significance at 1%, 5% and 10% level of confidence, respectively. Values in parentheses represent

standard errors.


Appendix 6: System GMM Estimations for Growth in Per Capita Income through GE in

Primary Education with ICT Proxies


Independent Variable MOBILE INTERNET

Constant -9.29*

(2.25)

-13.08*

(8.01)

Trade Openness 0.04

(0.108)

0.51**

(0.25)


(0.02)

0.04**

(0.02)


(0.12)

-1.19***

(0.08)

Female to Male Primary Enrollment

0.03*

(0.02)

0.07***

(0.02)

J-Stat 0.36 0.49


(***), (**) and (*) represent statistical significance at 1%, 5% and 10% level of confidence, respectively. Values in parentheses represent standard errors.

Appendix 7: System GMM Estimations for Gender Equality at Secondary Education with

ICT Proxies

Independent Variable Female to Male Secondary

Enrollment Ratio

Constant 76.01***

(4.99)

68.3***

(4.16)

Per Capita Income 0.005*

(0.003)

0.005***

(0.001)


(0.67)

3.87***

(0.50)

Public Spending on Education -1.5*

(0.86)

-0.38

(0.93)

Internet

--

0.023

(0.09)

Mobile 0.008

(0.03)

--

J-Stat 0.12 0.19





Secondary Education with ICT Proxies



Constant -12.7**

(3.7)

-10.4**

(3.6)

Trade Openness 0.37***

(0.12)

0.31***

(0.09)


(0.03)

0.09***

(0.02)


(0.14)

-1.08***

(0.13)

Female to Male Secondary Enrollment 0.02**

(0.01)

0.03*

(0.02)

J-Stat 0.12 0.19



Appendix 9: System GMM Estimations for Gender Equality at Tertiary Education with

ICT Proxies


Independent Variable Female to Male Tertiary Enrollment

Constant 18.59***

(3.92)

33.6***

(7.92)

Per Capita Income 0.02***

(0.001)

0.02***

(0.004)


(0.68)

9.86***

(0.96)

Public Spending on Education -5.9***

(0.39)

-5.7***

(0.62)

Internet -- 0.17

(0.35)

Mobile 0.08**

(0.03)

--

J-Stat 0.23 0.07




Tertiary Education with ICT Proxies



Constant -11.5**

(2.82)

-12.34**

(3.89)

Trade Openness 1.4E-6*

(8.3E-7)

4.9E-6***

(1.4E-5)


(0.03)

0.07*

(0.04)


(0.05)

-0.99***

(0.10)

Female to Male Tertiary Enrollment 0.006

(0.004)

0.003

(0.005)

J-Stat 0.23 0.07



Appendix 11: Summary Statistics

Variable Pakistan India Nepal Kenya Morocco

Mean S.D. Range Mean S.D. Range Mean S.D. Range Mean S.D. Range Mean S.D. Rang

e Female to Male Primary Enrolment

Ratio

76.8

7.1

17.33

95.20

6.50

17.6

92.9

9.9

29.7

96.8

1.5

4.2

90.5

2.4

8.9 Female to Male Secondary

Enrolment Ratio

77.43

1.00

3.35

81.93

7.57

21.8

79.9

10.1

30.8

93.5

3.9

15.01

84.3

2.8

8

Female to Male Tertiary Enrolment

Ratio

82.9

2.57

7.10

69.3

2.02

6.7

46.1

13.8

38.5

59.5

6.02

17.3

82.8

5.4

15.9

Public Spending on Education

2.3

0.42

1.12

3.5

0.40

1.2

3.6

0.6

1.8

6.5

0.7

2.2

5.6

0.12

0.4 Average Years of Adult

Schooling

4.3

0.56

1.60

4

0.28

0.9

2.8

0.3

0.89

5.9

0.24

0.7

3.9

0.32

1.01

Growth Rate of Per Capita Income

2.6

2.0

5.9

5.7

2.6

6.9

2.5

1.7

6.5

1.1

2.3

6.2

3.5

2.0

6.2

Gross Capital Formation

18.5

2.6

7.34

31.9

5.4

13.8

24.4

8.7

35.3

18.08

1.5

4.9

30.3

4.3

2.15

Growth Rate of Population 1.9 0.16 0.51 1.5 0.14 1.4 1.52 0.37 1.06 2.7 0.02 0.10 0.99 0.10 0.9

Per Capita Income

684.2

70.9

178.3

762.1

154.9

422

327.0

26.7

80.3

529.9

30.3

79.90

1983.1

246

740.2

Trade Openness

33.8

3.15

9.98

38.6

9.5

25.6

46.1

4.01

12.09

63.9

7.5

18.2

68.9

6.9

18.3

Telephone

2.87

0.50

1.51

3.5

0.47

1.6

1.94

0.68

1.8

1.07

0.32

0.4

6.4

3.08

8.78

Internet

5.2

2.7

7.9

2.9

0.8

7.0

1.5

2.2

7.7

5.4

4.5

13.7

18.4

17.4

51.4

Mobile

21.7

24.2

56.8

16.9

20.1

60.1

7.4

10.2

30.0

21.3

21.4

60.2

46.5

29.7

91.9

50

Exp

lorin

g N

ew P

ath

wa

ys to G

end

er Eq

ua

lity in E

du

catio

n

Variable

Nicaragua

Paraguay

Syria

Indonesia

Philippines

Bolivia

Mean

S.D

.

Range

Mean

S.D

.

Rang

e

Mean

S.D.

Range

Mean

S.D.

Rang

e

Mea

n

S.D.

Range

Mean

S.D.

Rang

e Female to Male Primary

Enrolment Ratio

98.5

1.2

3.6

96.6

0.4

2.8

95.8

1.8

4.9

98.6

1.9

6.8

98.8

0.65

2.7

99.1

0.4

1.2

Female to Male Secondary

Enrolment Ratio

113.9

2.3

8.1

103.1

1.3

4.7

95.9

3.9

10.6

99.4

1.1

3.1

110.3

1.2

3.8

96.9

1.2

3.8

Female to Male Tertiary

Enrolment Ratio

97.2

3.2

13.2

134.1

8.7

31.2

88.18

3.2

10.3

86.9

6.6

22.3

122.68

6.9

21.2

94.2

3.4

5.2

Public Spending on

Education

3.3

0.7

2.3

3.8

0.4

0.8

5.1

0.6

2.2

2.9

0.34

1.3

2.8

0.26

0.8

6.6

0.7

0.8

Average Years of Adult

Schooling

5.1

0.3

1.2

6.8

0.6

2.3

4.8

0.07

0.2

5.03

0.30

1.09

8.3

0.2

0.7

8.3

0.58

1.8

Growth Rate of Per Capita

Income

1.7

2.1

7.3

1.1

4.7

16.7

1.9

1.8

5.7

3.7

0.83

6.9

2.8

1.8

6.3

1.9

1.4

4.7

Gross Capital Formation

27.4

3.3

11.3

16.04

0.9

4.6

22.2

5.2

14.2

25.7

3.5

8.9

20.2

2.5

5.9

15.3

2.2

7.1

Growth Rate of Population

1.3

0.09

0.3

1.9

0.1

1.3

2.7

0.8

2.3

1.4

0.03

1.02

1.8

0.2

0.46

1.8

0.2

1.4

Per Capita Income

1159

79.6

207.3

1524

94.6

311.7

1561.2

106.9

315.4

1295.2

164.4

483.9

1209.4

119.9

342.7

1042.9

80.1

215.0

Trade Openness

73.5

8.8

23.1

95.3

10.1

28.2

74.1

7.4

21.3

60.1

6.6

20.8

96.1

4.2

25.3

62.5

6.04

16.2

Telephone

3.4

0.56

1.5

5.5

0.5

2.9

15.5

3.3

9.5

7.6

5.01

13.8

4.13

0.27

1.09

7.2

0.79

2.4

Internet

3.6

2.8

9.1

8.1

7.1

19.1

7.9

7.0

19.6

4.5

3.1

9.0

6.9

6.3

23.9

8.0

6.7

21.0

Mobile

28.5

24.7

66.1

51.2

30.5

77.6

21.8

20.0

56.7

30.7

29.7

86.3

46.5

28.1

81.2

31.4

22.9

65.3

Qa

isran

i and

Ah

med

51


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http://itidjournal.org/index.php/itid/article/view/257/127


Vol.1 (July-Decmber 2015) pp. 56-72

Psychological Distress Experienced by Women with Primary Infertility

in Pakistan: Role of Psycho-Social and Cultural Factors

SEHAR-UN-NISA HASSAN, ERUM KHURSHID, and SAEEDA BATOOL

This study aims to examine the predictive role of psycho-social factors in psychological distress

among women with primary infertility and to explore the nature of mental pressures faced by these women.

A sample of 200 women with primary infertility was recruited from various infertility clinics in Rawalpindi

and Islamabad. A demographic sheet, Urdu versions of General Health Questionnaire, Couple’s

Satisfaction Index-4 (CSI-4) a Self-Report Questionnaire (SCQ) were used to assess psychological

distress, marital satisfaction, personal and other family members’ desire for child, available social support,

and nature of mental pressures faced by women. About 82% of these women reported distress. The

standard multiple regression analysis showed that low marital satisfaction (β =-0.716; p<0.001); woman’s

non-work status (β =0.183; p<.001) and high personal desire to have child (β =0.136; p=0.006) were

significant predictors. Low social support from mother-in-law (β = 0.286; p<0.001) and high personal (β =

-0.188; p<.01) and husband’s desire to have child (β = -0.288; p<.001) influenced marital satisfaction.

Besides factors such as criticism, loneliness, inquiries made by other people, fear of husband’s second

marriage, quarrelsome in-laws were reported as stressors. Women with primary infertility are at increased

risk to experience psychological distress attributable to several social and cultural factors.

Keywords: Primary infertility, Psychological distress, Psycho-social factors

1. INTRODUCTION

Infertility is defined as “a disease of the reproductive system defined by the

failure to achieve a clinical pregnancy after 12months or more of regular unprotected

sexual intercourse” [Zegers-Hochschild, et al. (2009); pg 4]. However, infertility is not

only a major reproductive health problem but also a substantialsocial and psychological

issue. It is directly linked to maintenance of women’s social status and acceptance in

society as wives and mothers [Bell (2009)].

The rates of infertility among Pakistani women are on the rise reaching up to

almost 22%; (3.5% primary and 18.4% secondary) [Tahir, Shahab, Afzal, Subhan, Sultan,

Kazi, and Dil (2004)]. A recent cross-sectional survey of 7,628 out-patients from

Gynecology and Obstetrics Department at the Federal Government Services Hospital,

Islamabad found that frequency of infertility in this population was 7% [Shaheen,

Subhan, Sultan, Subhan and Tahir (2010)]. It has been commonly observed that in

psychological

Sehar-un-nisa Hassan <[email protected]> is Assistant Professor at Department of Behavioral

Sciences, School of Social Sciences and Humanities (S3H), National University of Sciences and

Technology (NUST), Sector H-12, Islamabad, Pakistan. ErumKhurshid, is a graduate of Fatima Jinnah

Women University, Rawalpindi, Pakistan. [email protected] is Assistant Professor at

Department of Economics, School of Social Sciences and Humanities (S3H) National University of

Sciences and Technology (NUST), Sector H-12, Islamabad, Pakistan.



Hassan, Khurshid,and Batool 57

Pakistani society, blame for not having a child is usually placed on the women. This

blame then invites more serious problems for women like husband’s second marriage,

divorce, physical and emotional harassment [Hussain (2010)].Sometimes wives who do

not have children are also deprived of their share in inheritance or asked to go back to

their parental home without being divorced. These consequences are reported in both

primary and secondary infertility cases [Sami and Ali (2006)]. Infertility problem has

been known to cause huge damage to Pakistani women as well [Bhatti, Fikree and Khan

(1999)]; however, not much attention has been given to identify and address the social,

psychological and cultural factors which are associated with psychological distress

among women suffering from primary infertility.

Investigating the role of these factors among infertile Pakistani women is worth

researching as dynamics of infertility experiences and help-seeking behaviours of couples

vary depending upon their ethnic and religious backgrounds [Culley, Hudson and Norton

(2013)]. Also differences exist in perceptions of people who are living in low-income,

middle-income or advanced Western countries [Greil, Slauson-Blevins and McQuillan

(2003)]. In many technologically advanced countries, infertility is also viewed as

volitional [Sundby (1999)]. Despite of rapid globalization, Eastern women’s role in home

and society is actually determined by motherhood. It becomes women’s responsibility to

complete the family by reproducing children after marriage. In cases of failure, the

women’s status and position at her home becomes questionable [Sami and Ali (2006)].

To deal with these social pressures, stigmas and fear of losing one’s identity at home and

society, these women expose themselves to extensive infertility treatments. The

availability of technologically advanced treatment methods for infertility has created

hope and at the same time is a source of great distress for women due to low success rates

and high costs [Jin, Wang, Liu, Zhang, Zeng, Qiu and Huang (2013)]. In Pakistan, there

is no well-established health insurance system and most agencies or employers also do

not cover for infertility treatments. When couples from middle and lower middle classes

opt for infertility treatments, it is often associated with increased financial burden,

physiological complications and emotional outcomes in case of failure of treatment

[Bhatti, Fikree and Khan(1999); Hussain (2010)]. Moreover, social correlates of

infertility such as complex network of social expectations, demands and relationships

appears to transform this personal health problem into a social agony [Daar and Merali

2002)].

Several factors such as illiteracy, unemployment, poor work conditions are found

to be associated with high rates of depression among people in low and middle income

countries [Nisar,Billo and Gadit (2004)]. Local studies [Mumford,et al.(2000); Luni,

Ansari and Jawad(2009)] have shown that rates of distress are generally high particularly

among women living in low socio-economic conditions, low levels of education and

unemployed. However, studies have also shown that infertility remains a significant risk

factor for psychiatric morbidity when controlling other factors. For instance, findings of

a comparative study showed women without children had high rating on depression scale

58 Psychological Distress Experienced by Women with Primary Infertility in Pakistan

than women with children [Guz, Ozkan, Sarisov and Yanik (2003)]. Finding from a case-

control study showed that infertile women were two times more likely to report

depression then women in control group [Domar, Clapp, Slawsby and Orav (2000)].

Studies have indicated that infertile women showed much higher levels of emotional

distress than their male partners and prevalence of depression ranges from 8% to 54%

among infertile women [Deka and Sarma (2010)].

Marital satisfaction has been found to be associated with mental well-being

among married couples [(Hashim, Khrushid and Hassan (2007)]. However, it becomes

more important in case of couples struggling with infertility. Western studies have also

documented that women with primary infertility often report social isolation, low levels

of marital satisfaction, high levels of stress and guilt [Edelmann and Laffont (1997)].

The existing literature recognizes the role of social support in promoting mental wellness

in diverse populations [Wang, Cai, Qian and Peng (2014)]. Despite increased awareness

about causes of infertility, it is also a common phenomenon in Pakistan that women are

often victimized and blamed for infertility by their dear and near ones. Women with

primary infertility report high levels of social alienation and isolation [Van Balen and

Bos (2009)] thus looking specifically at the role of social support and is very much

pertinent.

A systematic review of literature on psychiatric morbidity among infertile women

suggests [Hussain (2010)] that previous studies conducted in Pakistan have broadly

identified the problems faced by women due to infertility [Sami and Ali (2006); Bhatti,

Fikree and Khan (1999); Begum and Hassan (2014)].However, there is limited research

[Qadir, Khalid and Medhin (2015)], which have specifically examined the role of psycho-

social and cultural factors by combining quantitative and qualitative modes of inquiry.

Findings of study will broaden our understanding on how marital satisfaction, social

support, personal and social expectations are relevant factors to address infertile women’s

vulnerability for psychological distress.

Theoretical Background

By laying its foundation on Social Model of Health and Stress theories, this

research aims at identifying some of the significant determinants of psychological

distress among infertile women. Social Model of Health [Baum, Revenson and Singer

(2001)] recognize the role of social, economic, cultural and environmental factors on

people’s health. The existing literature on psychological distress among infertile women

calls for continued progress in the identification of role of social and cultural factors in

determining women’s vulnerability for psychological distress [Greil, Slauson-Blevins and

McQuillan (2010)]. The Social Model of Health emphasizes empowerment of individuals

and communities and promotion of health and well-being through targeting these specific

social, cultural and environmental determinants [Baum, Revenson and Singer (2001)].

Stress theories suggest that social stress is caused by anything which prevents a person


from achieving desired goals or maintain valued roles [Aneshensel (1992)]. Infertility

becomes a stressful experience because women who are not able to conceive within first

few years of marriage usually face lot of pressure from family and society in traditional

societies. Failures to achieve success in this matter create difficulties in maintaining their

valued roles as motherhood is considered as the primary role for a woman in these

cultures. Addressing these social, cultural and environmental determinants of infertility

related stress is important for women’s emotional and psychological health.

In the light of empirical evidences and common observations, following

hypotheses were developed:

1. Rates of psychological distress will be high among infertile women.

2. There will be low levels of marital satisfaction among infertile women.

3. Factors such as (woman age, education, occupational status, monthly income,

family members, years of married life, marital satisfaction, personal desire to have

children, husband’s desire to have children, expectations of other family members

and social support) will be significantly associated with psychological distress.

4. In multiple regressionmodel, low marital satisfaction will significantly predict

psychological distress independent of other factors.

5. Considering Pakistani society as a traditional society, women are likely to report

different kinds of mental pressures faced by them due to infertility.

2. METHOD

Study Design

A cross-sectional study design was employed. Both quantitative and qualitative

modes of inquiry were used. Quantitative data provides statistical evidence on nature and

strength of relationship between study variables whereas qualitative data increased its

richness by identifying any other cultural and social pressures faced by women due to

infertility.

Sample

Participants were recruited from three infertility clinics of Rawalpindi and

Islamabad, Pakistan. The eligibility criteria included, diagnosed with primary infertility

and has not adopted any child, age range >20 and <45 years, length of marriage at least 3

years. The literature [Menken, Trussell and Larsen (1986)] suggests fertility changes

with age as well there are variations in distress among women with infertility [Greil,

Shreffler, Schmidt and McQuillan (2011)]. Thus dynamics of distress due to infertility

are very different for women who are in their teens than those who are in late 40s [Liu

and Case (2011)]. The inclusion criterion for woman’s age (>20 and <45 years) was

selected to gain more conclusive evidence about dynamics of distress due to infertility

among married women in this age range. A total of 234 women were accessed to


participate in the study out of which 212 women completed self-report questionnaires.

Complete data was available on 200 survey forms.

The demographic characteristics of participants are as follow. The age range was

(20-45 year) with mean and median of 32 yrs. The range for monthly income was

(Rs.10,000-87,218 ) with median of (Rs.35,000 ) and mean of (Rs.64,930) thus median is

a better indicator here. The mean for years of education was 12.5 with S.D. 3.5. The

range for years of married life was from 3-26 years and median was 8.7. Majority of

women were living in joint family system (62%) and were housewives (58.5%).

Measures

Demographic sheet was used to obtain information about age, education,

occupation, length of marital life, approximate monthly income, family system, number

of total family members and numbers of earning family members.An Urdu version of

General Health Questionnaire (GHQ-12) [Minhas and Mubassshar (1996)] was used to

assess psychological distress. GHQ-12 is a well-known self-report psychiatric screening

instrument. The General Health Questionnaire (GHQ) was originally developed by

Goldberg in the 1970s which was 60-item questionnaire to assess current mental health.

This scale has been translated into many different languages and has been extensively

used in research and clinical settings in various countries across the world [Goldberg

(1988); Jacob, Bhugra and Mann(1997);Montazeri, Harirchi, Shariati, Garmaroudi, Ebadi

andFateh(2003)]. It includes items which assess levels of depression, unhappiness,

anxiety, psychological disturbance, social impairment and psychological well-being of

respondents. Each item is accompanied by four response options as “not at all”, “no more

than usual”, “rather more than usual”, and “much more thanusual”. The cutoff score for

GHQ-12 is 11. The alpha reliability reported by previousstudies range from .77-.93

[Goldberg and Williams (1988); Minhas and Mubassshar(1996)]. The alpha reliability of

this measure in this study was also found adequate (α=.93).

The short Urdu version of Couple’s Satisfaction Index-4 (CSI-4) [Qadir, DeSilva,

Prince and Khan (2005)] was used to assess martial satisfaction. It is comprised of four

items. Item# 1 is scored on 0-7 Likert scale, where 0 stands for “extremely unhappy” and

7 stands for “could notpossibly be any happy”. Range for items 2, 3 and 4 is from 0-6,

where 0 stands for “not at all true” and 6 stands for “ absolutely and completely true”.

The scale implies that higher the scoreson CSI-4, higher is the satisfaction from marriage.

The scale has adequate psychometric properties with alpha reliability of .94 [Funk and

Rogge (2007)]. The internal reliability of scale demonstrated in this study was (α=.96).

A self-report questionnaire (SRQ) was employed to assess social support in

context of infertility experience. The scale has been used in previous study from India

[D’Souza, Noronha, Judith and Nayak (2014)] and alpha reliability was .90. On this

scale, women were asked a question “How much following people support you in the

Fig.1. Conceptual Model to Illustrate Predictors of Psychological Distress and Low Martial Satisfaction

Among Infertile Women

Increased Psychological

Distress

Social

pressures

(Qualitative

data)

Low

Marital

satisfaction

Personal

high desire to

have child Husband

high desire

to have

child

Expectation

s of others

(parents, in-

laws)

Low Social

support

Demographic factors

(age, education,

employment, income)

Ha

ssan

, Kh

ursh

id, a

nd B

ato

ol 6

1


worry of being childlessness?” Participants were asked to rate the social support available

to them from (Father, Mother, Brothers, Sisters, Father-in-law, Mother-in-law, Sister-in-

law, Friends and Neighbors) on a five-point Likert scale (Very low to very high). The

same questionnaire also contains items which assess personal desire, husband’s desire

and other family members desire to have children on a five point rating scale (Very low

to Very high).

Qualitative Data

An open-ended questionnaire was used to obtain information about nature of

mental pressures faced by women due to infertility. The responses were transcribed and

coded by employing categorical strategy. This involves breaking down the narrative data

and rearranging it to produce categories [Teddlie and Tashkori (2009)]. The codes/catego

ries obtained through content analysis are then quantified by employing simple frequency

counts. This analytical strategy was well-suited to attain aims of analysis for this part of

study.

The study aims at assessing the role of psycho-social factors such as woman’s

age, education, occupational status, family system, marital satisfaction, social support and

cultural factors in determining psychological distress among women with primary

infertility. The use of above-mentioned tools and modes of inquiry was justified in

context of study objectives. A pilot administration of questionnaire was carried out on

five participants to assess and address any problems faced by participants in terms of

understanding and responding to these questionnaires. Participants of pilot survey did not

report any significant issue in this regard.

Ethical considerations

Prior approval was obtained from the ethical review committee of the institution.

Consent was obtained from the administration of healthcare institution to conduct the

study. Complete information about nature of study and information about available resour

ces to seek mental health services/support was shared with study participants through Inf-

ormed Consent. Confidentiality and anonymity of participant was maintained by

administration of questionnaires in private space and by coding of the data sets. The

debriefing session at end of interview were conducted to help women cope with any

stress caused by participation in this research.

3. RESULTS

General Psychological Distress and Marital Satisfaction

Analysis of responses showed that (N=164/200; 82%) scored above than cutoff


score as assessed by GHQ-12, thus providing evidence that rates of general psychological

distress experienced by infertile women is high. Women showed low to moderate level

of marital satisfaction as assessed by CSI-4 with mean (M) of 13.5 and standard deviation

(S.D) of 6.5. This pattern of findings support hypotheses 1 and 2 as majority of infertile

women had psychological distress and experienced low to moderate levels of marital

satisfaction.

Determinants of Psychological Distress in Infertile Women

A standard multiple regression analysis was performed to identify significant

determinants for psychological distress in infertile women. Standard multiple regression

was used to answer: a) what is the size of the overall relationship between psychological

distress (the predicted variable) and the independent (predictor) variables i.e. socio-

demographic variables (age, years of education, occupational status, family monthly

income, number of earning family members, years of marital relation, family system) and

psycho-social factors, i.e., (marital satisfaction, social support, personal desire to have

children, husband’s desire to have children, close relative’s desire to have children) and

b) how much does each independent (predictor) variable uniquely contributed to that

relationship? All predictor variables were entered into the regression equation at once as

per rules of standard multiple regression.

Inspection of correlations between independent and dependant variables showed

that woman’s years of education (r=-.13; p<.05), work status (ρ=-.28; p<.001), family

monthly income (r=-.16; p<.05) family system (ρ=.26; p<.001), number of family

members (r=.22; p<.001), number of earning family members (r=.17; p<.001), marital

satisfaction (r=-.78; p<.001), woman’s personal desire to have children (r=.34; p<.001),

husband’s desire to have children (r=.31; p<.001), social support from mother-in-law (r=-

.23; p<.001), father-in-law (r=-.17; p>.01) and sister-in-law (r=-.25; p<.05) were

significantly associated with psychological distress. Rest of the predictor variables (age,

years of married life, parent’s desire to have child, parent-in-law’s desire to have child,

social support from parents, siblings, neighbours, friends) showed insignificant

relationship with outcome variable. These variables were thus not entered in regression

model.

Inspection of inter-correlations among independent variables suggested some of

the independent variables were highly and significantly associated with each other such

as age with years of married life (r=.85; p<.001), family system with number of family

members (r=.73; p<.001), and number of family members with number of earning family

members(r=.83; p<.001). Both age and years of marital relation showed very low and

insignificant association with outcome variable thus excluded from multiple regression

analysis. Number of family members was used as proxy for family system and number

of earning family members in regression model due to high inter-correlation values

among them.


Above-mentioned demographic and psycho-social variables were entered in

regression model. The analysis of findings showed there was independence of residuals,

as assessed by a Durbin-Watson statistic of 1.44. The partial regression analysis showed

that linear relationship existed between predictors and outcome variables. The tolerance

values for all variables lie between (.48-.92) and VIF were greater than 1 but less than 3

thus indicating no multi-collinearly. The inspection of P-P Plots showed little deviations

thus demonstrating good model fit. A value of R=0.82, indicated an adequate level of

prediction. Adj. R2 value was 0.66 (66%) thus showing this much of variance in outcome

variable is explained by predictor variables. The regression model is a good fit of the

data as indicated by F (10, 189) = 40.339, p < .001. The standard multiple regression

analysis showed low levels of marital satisfaction was the most significant predictor for

psychological distress (β=-.716; p<.001) followed by woman’s occupational status

(β=.183; p<.001) and personal desire to have children (β =.136; p=.006) (Table 2). The

part correlations also suggest that 36% of variance in outcome variable is actually

explained by low levels of martial satisfaction.

Another interesting observation was related to significant association of marital

satisfaction with other predictor variables, i.e., work status (.15; p<.01); family system

(r=.19 p<.005 ); number of family members (r=.09; p<.01); personal desire to have

children (r=-.28; p<.001); husband’s desire to have children (r=-.28; p<.001); support

from mother-in-law (r=.37; p<.001); support from father-in-law (r=.27; p<.001) and

support from sister-in-law (r=.30; p<.001). However, the correlation values in all cases

were below (r<.39) thus these variables were entered in regression model to see their

independent contributions. This pattern of findings also suggests the need to explore the

role of demographic and psycho-social variables in marital satisfaction among infertile

women.

Table 1.Standard multiple regression analysis to identify determinants of

psychological distress in infertile women (N=200)

Variable Association with

psychological distress

b β

Years of education -.132* .114 .021

Occupation status -.287*** -3.126*** -.183***

Approx. monthly income -.162** -2.38E-006 -.025E-006

No. of family members .225** .143 .082

Marital Satisfaction -.787*** -.916*** -.716***

Personal desire to have children .349*** 1.87** .136**

Husband desire to have children .310*** .113 .009

Social Support from Mother-in-law -.239*** .429 .074

Social Support from Father-in-law -.178** .166 .026

Social Support from Sister-in-law -.259*** -.396 -.072

*p<.01; **p<.05; ***p<.001; Occupational Status 1=Housewife 2=Working

b=Unstandardized coefficients; β =Standardized coefficients


Predictors of Marital Satisfaction in Infertile Women

Standard multiple regression analysis was performed to identify which

(demographic and psycho-social variables) significantly influence marital satisfaction.

The analysis of findings showed there was independence of residuals, as assessed by a

Durbin-Watson statistic of 2.11. The partial regression analysis showed that linear

relationship existed between predictors and outcome variable. A value of R=0.51,

indicated an adequate level of prediction. Adj. R2 value was 0.239 thus showing (24%)

of the variance is explained by predictor variables. The regression model is a good fit of

the data as indicated by F (9, 191) = 8.79, p < .001. Variables i.e. low social support

from mother-in-law (β = .286; p<.001) and high personal desire (β = -0.188; p<.01) and

high husband’s desire (β = -0.288; p<.001) to have children significantly predicted

marital satisfaction.

Table 2: Standard multiple regression analysis to identify predictors of marital

satisfaction in infertile women (N=200) Variable Association with

marital satisfaction

b β

Years of education .064 .268 .064

Years of married life .089 .106 .089

Monthly Income .175 1.322E-005 .175

No. of family members -.160** -.219** -.160**

Personal desire to have children -.281*** -.264* -.188*

Husband desire to have children -.288*** -.293*** -.288***

Social Support from Mother-in-law .375*** .291*** .286***

*p<.01; **p<.05; ***p<.001; b=Unstandardized coefficients; β =Standardized coefficients.

Analysis of Responses on Open-Ended Question

It was hypothesized that women are likely to face variety of mental pressures

faced by them due to infertility thus an open-ended question was used to inquire about

the same. The responses to open-ended questions were transcribed and coded by

employing categorical strategy. This involves breaking down the narrative data and

rearranging it to produce categories [Teddlie and Tashkori (2009)]. The codes/categories

obtained through content analysis are then quantified by employing simple frequency

counts.

Nature of Mental Pressures Faced by Women Due to Infertility

Analysis of responses showed that ‘inquires made by other people regarding

women’s infertility’ and ‘tendency of people to give different kinds of advice’ were the


most commonly experienced mental pressures as reported by (19%) of women. Feelings

of loneliness were reported by 9% of women. Other commonly reported pressures were

quarrelsome and abusive husband and in-laws (9%) and fear of husband’s second

marriage (8.5%). Feelings of insecurity and criticism by relatives were reported by

(3.5%) of women in this sample and almost similar percentage of women (4%) reported

that they feel fed up trying different treatments for infertility.

4. DISCUSSION

The percentage of population affected by infertility is on rise; reaching up to 9%

to 30% in low income countries [Petraglia, Serour and Chapron(2013)]. Various health

and lifestyle factors are responsible for infertility in couples [Homan, Davies and Normal

(2007)]. The results of the present study showed that a large segment of women (82%) in

this study sample who were seeking treatments for primary infertility were experiencing

general psychological distress as assessed by General Health Questionnaire (GHQ)

consistent with existing evidence [Minucci(2013)]. The involuntary childlessness has

been found to be significantly associated with distress in women [McQuillan, Greil,

White and Jacob (2003)].

The study also examined role of social, psychological and cultural factors in

Pakistani society which are associated with psychological distress among women seeking

treatments for infertility. Women distressed by infertility status in Pakistan often seek

variety of traditional and non-traditional treatments which sometimes even complicate

their existing reproductive health conditions as well as act as a source of mental distress

for them [Sami and Ali(2006)]. Identification of specific social and cultural factors

associated with psychological distress in infertile women will help in educating

professionals as well as family members in order to address these issues; thus, enhancing

the quality of life for these women and improving treatment outcomes in many cases.

This is in line with the recommendations made by researchers from other parts of world

[Ombelet, Cooke, Dyer, Serour and Devroey (2008)].

Findings from present study revealed that low levels of marital satisfaction, non-

occupational status and woman’s own strong desire to have children were significant

predictors of psychological distress. The pattern of findings is not an unexpected pattern

of findings, keeping in view the social structure of our society and findings from other

studies. Edelmann and Laffont (1997) reported that infertility has a negative impact on

sexual and marital satisfaction of women. Some recent cross-sectional studies from

metropolitan cities of Pakistan [Sami and Ali (2006); Sultan (2010)] reported that marital

discord was more likely to be experienced by infertile women and act as a major source

of psychological distress in these women.

Infertile women who are primarily living as housewives are likely to experience

low levels of marital satisfactions and high personal desire for children due to role

expectations and stigmas associated with infertility. Previous studies [Minucci (2013);


McQuillan, Greil, White and Jacob (2003)] also reported some of the social and

psychological implications related to infertility which include loss of identity, low self-

esteem, feelings of isolation and inadequacy. These escalate the woman’s desire to have a

child and increases the levels of distress. The mental pressures reported by women in this

study also confirmed that infertility brings considerable sufferings to the lives of these

women. These women feel more stressed when they have to face questions and blamed

for infertility. They also face domestic abuse and threats of husband’s second marriage.

All these social factors add to their own subjective feelings of distress related to

infertility. A study from Sri-Lanka reported that psychological distress among Sri Lankan

infertile women was found to be associated with their desire and importance of having

children, the educational status of women, recent treatment experiences and lack of

marital support or communication [Lansakara, Wickramasinghe and Seneviratne (2011)].

Marital satisfaction which is an important determinant of psychological distress

among women in this study itself found to be predicted by other factors such as social

support and personal desire to have children.In the past few years, the role of social

support in dealing with life stressors has been increasingly emphasized [Martins,

Peterson, Almeida and Costa, (2011)]. Since the major stressor for infertile women in

traditional societies are actually the societal pressures and stigmas associated with

infertility, therefore, it was interesting to explore the nature of social support available to

an infertile woman which is also meaningful to her in terms of decreasing her risk for

psychological distress. About one fourth of participants reported that support is available

to them from their own parents, siblings, friends and neighbours; even though, it did not

decrease their vulnerability for psychological distress. However, support from mother-in-

law and sister-in-law turned out to be a significant protective factor. This is in line with a

longitudinal study which showed a relationship between unsupportive social interactions

and low levels of psychological adjustment among women with fertility problems

[Mindes, Ingram, Kliewer and James (2003)]. Findings emphasize the significance of

educational programs which not only address the physical but psychological, emotional

and social aspects of infertility experiences.

Findings showed that employment status of women was negatively associated

with psychological distress; thus emerged as strong protective factor. These results are

also consistent with the literature in the late 1990s from advanced countries. For instance,

[Sundby (1999)] reported that infertile women are motivated to fill the gap of

childlessness in their lives. Their occupation motivates them to do something rather than

just thinking about their infertility which decreases their vulnerability for psychological

distress. Findings from a recent study [Lykeridou, et al.(2011)] concluded that factors

such as low social class and maladaptive coping strategies might add risk to stress and

anxiety in infertile women. Alhassan, Ziblim and Muntaka (2014) reported high levels of

depression among infertile women in Ghana who were unemployed and had low or no

formal education. While exploring health-related quality of life in Iranian infertile

couples who were undergoing infertility treatments, researchers [Rashidi, Montazeri,


Ramezanzadeh, Shariat, Abedinia and Ashrafi (2008)] also found that low socio-

economic status is a significant risk factor for psychological distress in infertile couples.

Another study found that among Indian women the impact of infertility is exacerbated

due to associated stigma, socio-cultural meanings and external pressures from society.

The study also identified similar patterns such as duration of marriage or infertility

increases the distress. However, education and socio-economic status act as protective

factors [Widge (2002)]. These evidences about role of socio-economic and occupational

status also highlight the significance of considering these aspects while designing any

intervention plan for such females around the globe especially in south Asian

communities.

Overall the findings of study supported that specific social, psychological and

cultural factors in Pakistani society play a key role in increasing women’s vulnerability

for psychological distress, in addition to socio-demographic factors such as

disadvantageous occupational and socio-economic status acting as universal risk factors

for distress among females with primary infertility. The data for this study was collected

from fertility centers which are providing relatively advanced infertility treatments in

Pakistan. Such a high prevalence of psychological distress in this sample of women is

alarming and requires attention from health-care professionals and policy makers.

This also indicates the need to create awareness in the society about increasing

social support and social acceptance for women suffering from infertility. This further

enhances the need to develop structured programmes which includes education and

counselling of couples and immediate family members. Moreover, means of mass

communication can be used to educate people and address the intolerance and negative

attitudes shown by society at large for infertile women.

Implications for Practice and/or Policy

The positive role of psycho-social interventions in infertility treatments has been

demonstrated from Western countries [Read, et al. (2014)]. Keeping in view the complex

role of social, psychological and cultural factors, the study findings support the

recommendations made by [Minucci (2013)], a need for multidisciplinary teams in

infertility treatment centers comprising of a psychologist, a counsellor and a bioethicist

who would cater to the specific needs of infertile couples and facilitate them in coping

with infertility related stress. In Pakistan, it is even more important to understand and

address these issues where a wide gap exists between social classes. Families from

affording classes are ready to invest vast amounts of financial and emotional resources in

the quest to have a child, whereas women from low socio-economic classes do not even

have access to peri-natal and post-natal health care services. The problem of primary

infertility and associated psychological distress is a universal phenomenon and findings

provide insights about universal factors as well increased our understanding about role of

specific social and cultural factors. Understanding the implications will guide to enhance


cultural appropriateness of various interventions/treatment programs.

Limitations of Study

A comparative group would have strengthened the research design of study to

gain more conclusive evidence. Cross-sectional research with only one group of infertile

women provides only a glimpse of the situation. Data was collected only from infertility

clinics of two cities thus it does not tell us about infertility related experiences of women

unable to seek healthcare services or seeking non-medical treatments. Instead of open-

ended questions, in-depth interviews could have provided deeper insight about distress

related experiences of infertile women.

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Vol.1 (July-December 2015) pp. 73-85

Factors Shaping Exports of Cultural Goods from Pakistan

SABA SALIM and ZAFAR MAHMOOD

In the era of economic globalization, cultural goods trade has assumed a vital role in overall bilateral trade.

It has become an emerging and transformative force behind socio-cultural-economic development and an important

source of inclusive growth. Once virtually unlocked, trade in cultural goods is now fast growing with world-wide

market openings. Trade liberalization in cultural goods thus needs to be treated as a priority policy issue in

multilateral and regional trade negotiations. Despite large potential of exports in cultural goods, Pakistan has been

unsuccessful in realizing it. This is mainly because of lack of due attention given to it by the policymakers. In this

regard, this paper makes a beginning to investigate the determinants of cultural goods exports from Pakistan for the

period 2003-2012 with its 157 trading partner countries. The Gravity model is used to identify factors that determine

exports of cultural goods. Six major categories of cultural goods are used for the purpose of estimation. Results

indicate that size of Pakistan and its trading partner countries’ markets as well as distance among them are important

determinants of exports in cultural goods. Specifically, cultural goods exports are strongly and positively influenced

by the growth of the GDP in Pakistan, while the trading partner countries’ GDP growth negatively influence cultural

goods’ exports. Distance, representing transaction costs and trade barriers, negatively affect exports of cultural

goods; while colonial ties, common language, common border and land area of the trading partners positively

influence the export of cultural goods. Exports of cultural goods to landlocked countries are lower than other trading

partner countries.

Keywords: Gravity model, Cultural goods exports, Pakistan.

1. INTRODUCTION

Trade in cultural goods1 has become an emerging and transformative force behind socio-

cultural-economic development. It has turned out to be an important source of inclusive

economic growth. At present, about 7% of the world GDP constitutes of creative and cultural

goods. Nevertheless, only a handful of countries are the main players in global trade of cultural

goods. Production and trade potential of cultural goods, however, had remained largely

unexplored and unlocked. Now with market openings world-wide, cultural goods trade has been

rising at a faster pace.2 Therefore, liberalization of cultural goods trade needs to be considered as

Saba Salim <[email protected]> is a graduate of the School of Social Sciences and Humanities (S3H),

National University of Sciences and Technology (NUST), Sector H-12, Islamabad, Pakistan. Zafar Mahmood

<[email protected] > is Professor of Economics and HOD Research at the School of Social Sciences and

Humanities (S3H), National University of Sciences and Technology (NUST), Sector H-12, Islamabad, Pakistan.

1 Cultural goods represent disperse thoughts, signs and standard of living, while providing facts and amusement to form a group,

and recognize and influence cultural behaviour. Unlike the conventional goods, they carry information about production location,

people preferences, social attributes and cultural values (Cheptea, 2007). Cultural goods include antiques, musical instrument,

jewelry, crafts, paintings, newspaper, visual arts, etc. (UNESCO, 2000 and 2005). 2 For example, the world markets witnessed a surge in trade of cultural goods from US$47.8 billion in 1980 to US$213.7 billion

in 1998 and to US$424.4 billion in 2006 (UNESCO, 2013). A large proportion (almost 40%) of trade in cultural goods originates

from China, USA and UK (Drew, 2007). Other major countries include Hong Kong, France, India and Germany.

74 Salim and Mahmood

an important contemporary policy issue in the multilateral trade negotiations.

Countries with common cultural and historical attributes and ties are often seen engaged

in trade in cultural goods. If trade is built around comparative advantage, then cultural diversity

between countries enhances trade at faster pace [Cyrus (2011)].

Countries utilize modern media technology for building their image and get acceptance

and adaptation of their culture and cultural goods in the world. Factors like common language,

history, religious beliefs, and colonial affiliation played an important role in creating world-wide

demand for their cultural goods.

As trade in cultural goods is increasing, governments have started paying attention for its

development and promotion. This interest has motivated many academicians to work specifically

on this issue. For instance, Disdier, et al. (2010) examined the determinants of trade in cultural

goods. Their study suggested that trade in cultural goods reveals some specific characteristics.

That is, common language fosters exchange of cultural goods and past colonial relationships

influence consumers’ preferences for cultural heritage goods. The study also found that cultural

goods are traded over smaller distance as compared with conventional goods.

Marvasti and Canterbery (2005) investigated the determinants of US motion pictures exports to

33 countries. The study revealed a positive impact of language, education and religion on exports

of motion pictures. The study also found that trade barriers applied by importing countries are

raised with the growth of US exports of motion pictures.

Lili (2011) found that China’s trading partners’ economic size, GDP per capita, land area

and level of technological application have a positive impact on its exports of cultural goods. The

study further found that China’s FTAs have little impact on its exports of cultural goods.

Pakistan has a strong and rich cultural heritage, which has roots to ancient times. Its culture has

the influence of many foreign cultures dating back to the colonial eras; each of them brought

several cultural influences. Thus, Pakistan has a pleasant blend of diverse cultures representing

distinctive music, arts, antiques and sculptures. Despite the cultural richness, Pakistan has so far

been unsuccessful in realizing the export potential of cultural goods unlike other countries.

Nevertheless, lately trend appears to be changing as Pakistani electronic media and private

industry has started showcasing its soft image and culture world-wide. Consequently, Pakistan’s

export of cultural goods that were $277.75 million in 2003 has increased by more than six-folds

to $1,764.75 million in 2012 (For details see Appendix Table 1).

Nevertheless, trade has not received due attention in academic or policymaking circles in

Pakistan because of lack of recognition and understanding of the available potential of cultural

goods export. In this paper, therefore, we make a beginning by investigating the determinants of

Pakistan’s exports of cultural goods to its 157 trading partners. Our estimation is based on the

Gravity model of international trade. The data used for estimation are drawn from UN-COM-

Trade database using the six-digit level HS classification proposed by the UNESCO (2000).

Rest of the paper is divided into four sections. Section 2 presents the theoretical

framework used in the paper. Variable construction and data used along with data sources are

reported in section 3. Empirical results are discussed in section 4. Finally, section 5 concludes

the paper and draws policy implications from the empirical findings.

Factors Shaping Exports of Cultural Goods from Pakistan 75

2. THEORETICAL FRAMEWORK

Given the nature and pattern of trade in cultural goods the Gravity model of international

trade is most suitable for such a study. This model is motivated by the Newton’s law of gravity,

whereby the gravitational force between two bodies is determined by their distance and mass.

The Gravity framework in economics was introduced by Tinbergen (1962) and its theoretical

foundation was provided by Anderson (1979). The Gravity model embodies an appropriate

framework to test the marginal effect on bilateral trade flows of the determining variables [Lewer

and Berg (2008)].

The basic Gravity equation is as the following:

𝑇𝑖𝑗= 𝐺(𝑌𝑖𝑌𝑗

𝐷𝑖𝑗) … (1)

where, Tij is bilateral trade volume, Yi is country i's GDP, Yj is country j's GDP, Dij is the

distance between countries i and j, and G is a constant. Eq. (1) can be re-written in log natural

form as:

𝑙𝑛 𝑇𝑖𝑗 = 𝑙𝑛 𝐺 + 𝛼1 ln 𝐺𝐷𝑃𝑖 + 𝛼2 ln𝐺𝐷𝑃𝑗 + 𝛼3 ln 𝐷𝑖𝑗 + 𝜀𝑖𝑗 … (2)

Eq. (2) describes the value of bilateral trade as a function of the market size of the importer and

exporter countries as well as the distance between them. Both market sizes create push and pull

effects on the value of bilateral trade, and are characterized by the GDP. Distance, representing

trade barriers, is generally measured by geographic distance between two countries (absolute

distance). It is anticipated that large distance between trading partners leads to a decrease in

trade, as trade becomes more complicated to handle and as such enhances transaction costs.

Based on Eq. (2), we use the following empirical-specification (Eq. (3)) to link exports

(Exppj) from Pakistan to its jth trading partner with core and additional variables: Yp is the GDP

of Pakistan, AREAj is area of the jth trading partner, CONTIGpj is contiguity between Pakistan

and the jth trading partner, COMMLANGpj is common language between Pakistan and the jth

trading partner, LLj is whether the jth trading partner is landlocked or not, and COLpj is whether

Pakistan and the jth trading partner have colonial ties:33

ln 𝐸𝑥𝑝𝑝𝑗𝑡 = 𝑙𝑛𝛼0+ 𝛼1𝑙𝑛𝑌𝑝𝑡 + 𝛼2𝑙𝑛𝑌𝑗𝑡 + 𝛼3𝑙𝑛𝐷𝑝𝑗 + 𝛼4𝑙𝑛𝐴𝑅𝐸𝐴𝑗 + 𝛼5𝑙𝑛𝐶𝑂𝑁𝑇𝐼𝐺𝑝𝑗 +

𝛼6𝐶𝑂𝑀𝑀𝐿𝐴𝑁𝐺𝑝𝑗 + 𝛼7𝐿𝐿𝑗 + 𝛼8𝐶𝑂𝐿𝑝𝑗 + 𝑡 … (3)

3. VARIABLE CONSTRUCTION AND DATA

In the following, we describe the construction of the variables and their theoretical

relationship with the dependent variable as well as the data sources used:

3See, Lionetti and Patuelli (2010).


Country Economic Sizes (Y): Economic scale or size is measured by the national incomes of

trading countries. The greater the economic size of a country, the larger is its potential ability to

supply and demand. Thus, larger countries tend to trade more with each other and countries that

are of similar sizes also trade more [Feenstra (2004)]. GDP data for Pakistan and its trading

partners are obtained from World Development Indicators published by the World Bank.

Distance (Dpj): Distance proxies for transportation costs and trade barriers. Trade costs are likely

to increase with the distance between trading partners. Leamer and Levinsohn (1995) found a

robust negative relationship between distance and trade volume. Other studies including Teresa

(2011), Lionteii and Patuelli (2010), and Disdier, et al. (2010) also found a negative relationship

between distance and trade in cultural goods. Distance data between Pakistan and its trading

partner countries from capital to capital city are obtained from Centre d'Etudes Prospectives et

d'Informations Internationales, France.

Common Language (COMMLANG): This variable indicates whether the exporting country and

its trading partner share the same language or not. Common language makes it easy to interact,

communicate, collect material, build business relations and helps in the process of signing

contracts. Thus, reducing transaction costs, and eventually leading to a positive impact on

bilateral trade. Besides, when language is common then cultural goods are easily accepted by the

residents of the destination country. Following Zigano and Mayer (2006), we use a dummy

variable of common official language between Pakistan and its trading partner countries.

Information on Common language is obtained from Centre d'Etudes Prospectives et

d'Informations Internationales, France.

Common Border—Contiguity (Contg): Countries that share a common border are often well

aware of each other’s consumers’ choices and trading prospects. Moreover, common borders

imply relatively short distance. Because of these reasons, mutual trade is less costly. We have

used a dummy variable to reflect a common border by using information obtained from Centre

d'Etudes Prospectives et d'Informations Internationales France.

Common History (COL): Members of the same colonial empire upsurges the information about

trading partner’s organizations and business practices. Colonial relationship reduces cultural

differences between countries and thus reduces transaction costs in trade. Lionetti and Patuelli

(2010) and Cheptea (2007) found a positive relationship between bilateral trade and colonial

links. Following these studies, we use a dummy variable on the basis of information obtained

from Centre d'Etudes Prospectives et d'Informations Internationales, France.

Landlocked Countries (LL): When a country is landlocked and does not have a shipping port or

direct access then the trade-related costs are high. This is because they may have to rely on other

countries to transport their goods. We use a dummy variable on the basis of information obtained

from Centre d'Etudes Prospectives et d'Informations Internationales, France.

Land Area (AREAj): People of countries with large land area normally have greater

acceptability and tolerance for cultural diversity. Therefore, it is likely that the relationship

between land area and trade is positive. Information on land area is obtained from Centre

d'Etudes Prospectives et d'Informations Internationales, France.

Data for the dependent variable Exppj are obtained from UN COMTRADE database.


4. RESULTS AND DISCUSSION

Empirical findings reported in this section are based on Pakistan’s cultural goods exports

listed in Appendix-B with 157 trading partners listed in Appendix-C, for the period 2003 to

2012. The analysis is further extended to the six-digit HS codes level, for six sub-categories:

books, jewelry, crafts and paintings, newspapers and other printed matter, musical instruments

and visual arts (Appendix-A).

To describe the main features of the data used in this study, summary statistics are

reported in Table 1. The table elaborates Pakistan’s exports in cultural goods with reference to

mean, median, standard deviation and minimum and maximum values of variables. The mean

exhibits that the value of jewelry is the highest amongst all export categories, which shows the

highest level of exports. Crafts and paintings have the second highest mean value. Books have

the lowest average export value.

Table 1. Summary Statistics

Variable Mean Median Max. Min. Std. Dev.

Total Exports 4784628 63605 1520000 10.00 51944447

Exports: books 40350 5014 690140 1.00 102456.8

Exports: jewelry 14395455 15288 1.52E+09 12.00 1.12E+08

Exports: musical instruments 51999 8641 879250 3.00 125297.2

Exports: visual arts 105710 16845 5231345 5.00 351742.8

Exports: crafts and paintings 2027119 97052 1.18E+08 17.00 8558559

Exports: newspaper & printed matter 13275 1497 336963 1.00 37241.69

Dpj 6433 5308 16694.83 374.65 3931.216

AREAj 987687 238538 17075400 25.00 2393874

The mean value of distance shows that the average radius of the reach of Pakistan’s

exports of cultural goods is 6432 km. The maximum average distance of Pakistan’s exports is

recorded as 16,694.83 km whereas minimum average distance recorded to 374.65 km. The

average area of a country to whom Pakistan exported its cultural goods during 2003 and 2012 is

987,686.7 Sq km.

4.1. Unit Root Test

We begin with the evaluation of the time series data in terms of their being stationary or

non-stationary so that a valid and reliable estimation approach is identified. The null and

alternative hypotheses used to conduct unit root test are as follows:

𝐻0: All the variables exhibit unit root.


𝐻1 : All the variable exhibit unit root.

As N>>T (where N is number of (1165) observations and T (10 years) is time period),

therefore stationarity should not be a problem. Nonetheless, we use different tests to check for

the stationarity of variables. Results of this test are reported in Table 2, which show that all

variables are stationary. So we reject the null hypothesis that variables exhibit unit root. Distance

and area variables fail to show any result because they are time independent. Rests of the

variables included in the model are dummy variables.

Table 2. Unit Root Test

4.2. Generalized Least Square

We estimate the Gravity equation by using of the Common Constant method4. Panel

Estimated Generalized Least Squares (EGLS) method is applied to estimate the equation with

country weights and correction of standard errors for problems of autocorrelation and

heteroscedasticity. This method is suitable for unbalanced panel data set as it can handle a vast

range of data that are unequally spaced and have problem of hetroscadasticity [Baltagi and Wu

(1999)]. OLS and GLS have same model equation but only difference is that residuals do not

need to follow same assumptions as of OLS [Orlaith (2010)]. We also applied SUR (PCSE) to

get rid of problem of autocorrelation.

Table 3 shows regression results for overall exports5 in cultural goods of Pakistan with its

trading partners. Estimates are reported for relationships between dependent variable and

independent variables including home and host country’s GDP, distance, land area and a set of

4 Yu and Park (2011), Chang, et al. (2008) and Hwang (2012) used pooled least squares method to estimate the

Gravity equation. In our case, we tried both fixed and random effects models but results were not consistent as our

data set is an unbalanced panel. Fixed effects model generated dummies equal to the cross sections. We have 157

cross-sections and the Gravity model also consists of dummies so inclusion of more dummies created singularity

problem. This is why fixed effects model is not suitable for our study. It may be noted that the Common Constant

method works under the principal assumption that there are no differences among cross-sectional data sets. This

method, also known as “pooled OLS” method, assumes common constant α for all the cross sections in the model.

We cannot use Pesaran’s CD test because of lower and missing number of observations in selected countries. 5 Results of individual categories according to UNESCO definition are shown in Appendix-A.

Test name Trade value (TV) GDPi GDPj

coefficient Prob coefficient prob coefficient prob

Hadri Z-stat 20.7024 0.0000 24.5459 0.0000 25.0959 0.0000

Levin, Lin & Chu t* -98.2744 0.0000 -29.1971 0.0000 -47.2511 0.0000

Im, Pesaran and Shin W-stat -5.81882 0.0000 -2.00355 0.0226 -4.67089 0.0000

ADF - Fisher Chi-square 240.559 0.0129 268.888 0.0003 245.018 0.0000

PP - Fisher Chi-square 597.026 0.0000 604.218 0.0000 336.876 0.0000


dummies. Most of the Gravity model variables are found to be statistically significant at 1% level

of significance.

Table 3. Total Cultural Goods Exports

Dependent Variable: LOG(Grand Total Exports)

Methods: Panel EGLS (Cross-section weights)

Total panel (unbalanced) observations: 1164

Cross-section SUR (PCSE) standard errors & covariance (d.f. corrected)

Variable Coefficient Std. Error t-Statistic Prob

C 15.42654 0.619010 24.92133 0.0000***

LOG(GDP i) 0.103590 0.029035 3.567735 0.0004***

LOG(GDP j) -0.069319 0.017915 -3.869371 0.0001***

LOG(DPj) -0.921862 0.058249 -15.82618 0.0000***

LOG(AREA) 0.302063 0.022882 13.20081 0.0000***

COMMLANGpj 0.276463 0.099237 2.785881 0.0054***

CONTIGpj 0.075339 0.116200 0.648360 0.5169

LLj -2.127878 0.146204 -14.55420 0.0000***

COLpj 4.759326 0.096127 49.51085 0.0000***

R-squared 0.699445 Adjusted R-squared 0.697364

F-statistic 335.9869 Prob(F-statistic) 0.0000***

*** indicates that estimated coefficient is statistically significant at 1% level.

The result depicts that Pakistan’s GDP is statistically significant at 1% level and has a

positive sign. It shows a direct relation between GDP growth rate of Pakistan and its exports of

cultural goods, implying that when the domestic economy grows it generates large exportable

surpluses and thus export more. The coefficient for the home country GDP growth indicates that

a 1% growth in Pakistan’s GDP leads to a 0.10% growth in exports of cultural goods. This result

is consistent with the findings of other studies including Disdier, et al. (2010) and Yu and Park

(2011).

Estimated coefficient shows that a 1% increase in the growth of the trading partner

country’s GDP decreases Pakistan’s exports by 0.06%. This result is contrary to the theoretical

prediction about the relationship. The intuition behind this result is that richer countries

themselves have more space for producing various kinds and varieties of goods. So when they

produce more they decrease their imports of cultural goods from countries like Pakistan who do

not have much cultural influence abroad. In such a situation, the substitution effect appears

stronger than the income effect.

The estimated result reveals that the relationship between distance and cultural goods

exports is negative and statistically significant at 1% level. This implies that economic distance

is a hindrance in cultural goods exports. The estimated coefficient indicates that a 1% increase in

distance leads to 0.92% decrease in cultural goods exports. Our result is consistent with studies

of Frankel (1997) and Wall (1999).

The estimated coefficient of land area is positive and statistically significant at 1% level.

This implies that when land area of the trading partner country increases by 1% then exports of


cultural goods increases by 0.30%. This finding is consistent with the results found by Lili

(2011).

Common language shows a direct and statistically significant link with exports of cultural

goods. Its estimated coefficient is 0.28, which shows that for those countries with whom Pakistan

shares language, it exports 0.28 times more of cultural goods than with countries who do not

have a common language with it.

Pakistan shares common border with India, Iran, Afghanistan, and China. According to

the estimated coefficient the sign of the relationship is positive but the result is statistically

insignificant. Nevertheless, the result shows that export of cultural goods with common border

countries increases by 0.08 times as compared with the rest of the countries. These results are in

line with theoretical predictions and those found by Disdier, et al. (2010).

The estimated coefficient of landlocked countries exhibits a negative relationship with

exports of Pakistan’s cultural goods trade and is highly significant. Thus, if the trading partner

country is landlocked then exports of cultural goods from Pakistan decreases by 2.12 times as

compared to countries that are not landlocked. Our results are consistent with studies of Dollar

and Kraay (2003), and Francois and Manchin (2007).

Colonial link is statistically significant at 1% level and its coefficient is positive. Its

coefficient indicates that for countries with whom Pakistan had a colonial link its export of

cultural goods increases by 4.75 times as compared with rest of the trading partner countries. Our

results are consistent with the study of Lionetti and Patuelli (2010).


Empirical analysis based on the Gravity model led us to conclude that exports in cultural

goods are strongly influenced by the GDP growth of Pakistan but negatively affected by the

GDP growth in the trading partner countries. Distance which is a proxy for the cost of

transportation and trade barriers negatively affects exports of cultural goods. Land area of

importing countries boosts exports of cultural goods as it creates greater acceptability of diverse

foreign cultures and cultural goods. Exports of cultural goods sharply increase with those trading

partner countries that have colonial ties and share a common language with Pakistan. Under the

present circumstances, common border with importing countries is a weak factor to promote

exports of cultural goods. Countries that are landlocked are generally isolated from participating

in global trade import relatively less from Pakistan than its other trading partner countries.

It is evident from the preceding analysis that Pakistan has vast potential for export growth

in cultural goods provided corrective policy measures are adopted. On the basis of the empirical

findings, we draw the following policy implications for the promotion of exports of cultural

goods:

Increase domestic production of cultural goods by enhancing productivity and efficiency

of domestic industries producing them.

Ensure quality of cultural goods commensurating with the income levels of trading

partners.


Reduce trade barriers (e.g., the distance) by using modern electronic and social media

technology, advertisement and promotional activities world-wide.

Lower border restrictions and facilitate exports to increase exports of cultural goods to

neighboring countries.

Focus on countries with large land areas to tap their higher and wider acceptability for

diverse foreign cultures and products.

Target countries having common language with Pakistan for the promotion of cultural

goods exports. This initiative would enhance competitiveness by reducing the cost of

transaction.

Develop cost effective air links and cargo services to boost exports of cultural goods to

landlocked countries.

APPENDIX

Appendix Table 1: Exports of Pakistan’s Cultural Goods by Categories (Million US dollars)

Year Paintings News

Papers

Other

Printed

Matter

Crafts Antiques Jewelry Books

Musical

Instrument

s

Visual

Arts Total

2003 0.09 1.38 0.14 225.74 0.1 25.14 2.76 2.12 20.28 277.75

2004 0.18 0.71 0.32 258.23 0.23 29.04 2.76 2.71 12.68 306.86

2005 0.14 0.79 0.23 290.59 0.15 20.62 2.1 2.88 12.02 329.52

2006 0.28 0.21 0.1 247.61 0.29 24.06 2.73 4.42 8.13 287.83

2007 0.02 0.09 0.16 224.63 0.03 120.32 2.53 3.46 5.81 357.05

2008 0.14 0.07 0.26 189.08 31.25 239.83 2.3 4.19 7.3 474.42

2009 0.05 0.06 0.29 132.56 0.05 478.91 2.67 3.64 5.53 623.76

2010 0.12 0.03 0.11 132.5 0.12 590.24 2.47 3.58 5.2 734.37

2011 0.23 0.06 0.37 134.93 11.76 469.32 2 3.59 2.08 624.34

2012 0.09 0.17 0.3 121.24 0.09 1,634.07 2.75 3.18 2.86 1764.75

Source: UN COMTRADE, 2013.

Appendix Table 2: Cultural Goods Exports Share (%)

Country Share

UK 15.43

USA 34.97

China 20.4

India 18.01

Germany 10.51

Pakistan 0.68

Source: UNCOMTRADE, 2012


Appendix-A: Individual Category Results

Variable Musical

Instrument

Jewelry Visual Arts Books Newspapers

& Other Printed

Matter

Crafts &

Painting

C 10.81988***

(1.607423)

13.23392***

(2.829634)

12.45317***

(0.754473)

21.22402***

(1.321552)

10.41851***

(1.237455)

15.13304***

(0.559182)

GDPI 0.149002***

(0.032022)

0.096057**

(0.045737)

0.135496***

(0.047326)

0.02596

(0.06345)

0.241554***

(0.033265)

0.151528***

(0.037085)

GDPJ -0.112768***

(0.018578)

-0.098893***

(0.037761)

-0.037926***

(0.013981)

-0.003586

(0.020767)

0.008505

(0.018731)

-0.071687***

(0.019511)

LOG(DISTANCE) -0.468076**

(0.183749)

-1.568454***

(0.329757)

-0.677601***

(0.085415)

-1.809288***

(0.174928)

-0.755626***

(0.143148)

-0.803194***

(0.078981)

LOG(AREA) 0.149689***

(0.02305)

0.699936***

(0.053404)

0.206697***

(0.020848)

0.169348***

(0.033903)

0.157957***

(0.034505)

0.244917***

(0.023573)

CONTIG - -3.266117***

(0.688133)

0.621901***

(0.177068)

-1.627817***

(0.469107) -

0.219701

(0.17575)

SMCTRY -2.902227***

(0.403885)

-6.593509***

(0.840408) - -

0.034226

(0.322359)

-1.359257***

(0.289381)

LANDLOCKED -2.214824***

(0.294244)

-1.670197***

(0.29152)

-1.952095***

(0.144642)

-2.286794***

(0.288439)

-0.97099***

(0.185445)

-1.352253***

(0.138445)

COL 3.598144***

(0.140692)

2.90007***

(0.217915)

3.115939***

(0.194101)

3.955425***

(0.188582)

3.524505***

(0.262178)

4.122904***

(0.117747)

COMLANG_OFF 0.120811

(0.115213)

3.737919***

(0.325239) -

1.594269***

(0.198552)

0.392203**

(0.183838) -

R-squared 0.769949 0.704348 0.433499 0.232209 0.510356 0.577875

F-statistic 157.3028 60.61782 83.73731 23.13652 56.15384 163.5915

No of observations 385 239 774 621 440 965

Appendix-B: Commodities included in the study

Domain HS Code Description

Musical Instruments

830610 Bells, gongs and the like

920590 Wind musical instruments (excl. brass-wind instruments)

920890 Fairground organs, mechanical street organs, mechanical singing birds, musical saws and

other musical instrument; decoy calls of all kinds; whistles, call horns

920290 Guitars, harps and other string musical instruments (excl. with keyboard and those played

with a bow)

920510 Brass wind instruments (for example, clarinets, trumpets bagpipes)

920600 Percussion musical instruments (for example drums, xylophones, cymbals, castanets,

maracas)

920810 Musical boxes

920190 Harpsichords and other keyboard stringed instruments (excl. pianos)

920110 Upright pianos

920710 Keyboard instruments other than accordions

Paintings and Crafts

970190 Collages and similar decorative plaques

491191 Pictures, designs and photographs

970110 Paintings, drawings and pastels, executed entirely by hand, other than drawings of

heading

570110 Carpets of wool or fine animal hair, knotted

581099 Embroidery in the piece, in strips or in motifs


570190 Carpets of materials n.e.s. , knotted

570210 Handmade rugs

500720 Woven fabric >85% silk (except noil silk)

581100 Quilted textile products in the piece

580890 Other braids in the piece; ornamental trimmings in the piece, without embroidery; other

than knitted or crocheted

570232 Carpets of manmade yarn, woven pile, not made up, n.e.s.

580640 Fabrics consisting of warp without weft assembled by means of and adhesive

580631 Narrow woven fabrics: Other woven fabrics of cotton

581010 Embroidery in the piece, in strips or in motifs without visible ground

600293 Knit or crochet fabric of manmade fibres, n.e.s.

580810 Braids in the piece; ornamental trimmings in the piece, without embroidery; other than

knitted or crocheted

581091 Embroidery in the piece, in strips or in motifs: Other embroidery of cotton

581092 Embroidery in the piece, in strips or in motifs

580610 Narrow woven fabrics: Woven pile fabrics (including terry toweling and similar terry

fabrics) and chenille fabrics

580620 Narrow woven fabrics: Other woven fabrics, containing by weight 5% or more of

elastomeric yarn or rubber thread

580639 Narrow woven fabrics: Other woven fabrics of other textile materials

580632 Narrow woven fabrics: Other woven fabrics of man-made fibers

580900 Woven fabrics of metal thread and woven fabrics of metallized yarn of heading

580500 Hand-woven tapestries of the type Gobelins, Flanders, Aubusson, Beauvais, etc.

Jewelry

711320

Articles of jewelry and parts thereof of base metal clad with precious metal

711620 Articles of precious or semi-precious stones (natural, synthetic or reconstructed)

711319 Articles of jewelry and parts thereof of other precious metal, whether or not plated or clad

with precious metal

711411 Articles of goldsmiths' or silversmiths' wares and parts thereof of silver, whether or not

plated or clad with other precious metal

Visual Arts

442090 Wood marquetry and inlaid wood; caskets and cases for jewelry or cutlery, and similar

articles, of wood; wooden articles of furniture

701890 Glassware articles including statuettes

960110 Worked ivory and ivory articles

960190 Bone, tortoiseshell, horn, antlers, coral, mother-of-pearl and other animal carving

material, and articles of these materials (including articles obtained by molding)

442010 Statuettes and other ornaments, of wood

691310 Statuettes and other ornamental ceramic articles of porcelain or China

392640 Statuettes and other ornamental articles in plastic

830629 Statuettes and other ornaments, of base metal, not plated with precious metal

970300 Original sculptures and statuary, in any material

Books

490110 Printed reading books, brochures, leaflets and similar printed matter

490199 Printed books, brochures and similar printed matter

490191 Dictionaries and encyclopedias and serial installments thereof

Newspapers and Other

Printed Matter

490900 Postcards, printed or illustrated; printed greeting cards

490300 Children's picture, drawing or coloring books

491000 Calendars of any kind, printed, including calendar blocks


490210 Newspapers, journals and periodicals, whether or not illustrated or containing advertising

material appearing at least four times a week

490290 Other newspapers, journals and periodicals

Source: UNESCO (2000).

Appendix C: List of Partner Countries Included in the Study

Afghanistan, Albania, Algeria, Angola, Argentina, Armenia, Australia, Austria, Azerbaijan, Bahamas, Bahrain,

Bangladesh, Barbados, Belarus, Belgium, Belize, Benin, Bermuda, Bolivia, Bosnia, Botswana, Brazil, Brunei,

Bulgaria, Burkina Faso, Burundi, Cambodia, Cameroon, Canada, Chile, China, Colombia, Congo, Costa Rica, Cote

d’Ivoire, Croatia, Cyprus, Czech Republic, Denmark, Djibouti, Dominican, Ecuador, Egypt, El Salvador, Equatorial

Guinea, Estonia, Ethiopia, Fiji, Finland, France, Gambia, Georgia, Germany, Ghana, Greece, Greenland, Grenada,

Guatemala, Guinea, Guinea-Bissau, Guyana, Haiti, Honduras, Hong Kong SAR China, Hungary, Iceland, India,

Indonesia, Iran, Iraq, Ireland, Italy, Jamaica, Japan, Jordan, Kazakhstan, Kenya, Korea Rep., Kuwait, Kyrgyz Republic,

Latvia, Lebanon, Liberia, Libya, Lithuania, Luxembourg, Macao SAR China, Madagascar, Malawi, Malaysia,

Maldives, Mali, Malta, Mauritius, Mexico, Moldova, Mongolia, Morocco, Mozambique, Namibia, Nepal, Netherlands,

New Zealand, Niger, Nigeria, Norway, Oman, Panama, Papua New Guinea, Paraguay, Peru, Philippines, Poland,

Portugal, Qatar, Romania, Russian Federation, Rwanda, Saudi Arabia, Senegal, Serbia, Seychelles, Sierra Leone,

Singapore, Slovak Republic, Slovenia, South Africa, Spain, ,Sri Lanka, St. Lucia, St. Vincent and Grenadines, Sudan,

Suriname, Swaziland, Sweden, Switzerland, Syria, Tajikistan, Tanzania, Thailand, Togo, Tonga, Trinidad and Tobago,

Tunisia, Turkey, Turkmenistan, Uganda, Ukraine, UAE, United Kingdom, United States, Uruguay, Uzbekistan,

Venezuela Rep., Vietnam, Yemen Rep., Zambia, Zimbabwe.

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http://hdl.handle.net/1765/16826

https://imr.kangwon.ac.kr/board/dl.asp?fn...Eui%20Burm%20Park).pdf

https://imr.kangwon.ac.kr/board/dl.asp?fn...Eui%20Burm%20Park).pdf

http://mpra.ub.uni-muenchen.de/26469

BOOK REVIEW

David Neumark and William L. Wascher. Minimum Wage. Cambridge, Massachusetts:

The MIT Press. October 2008. 392 pages. (Paperback): $25.00/ £18.95.

The existing literature on minimum wage has outstanding contributions by David

Neumark and William Wascher, both in terms of volume and diversity. Particularly, David

Neumark is considered an authority and the most go-to labour market economist. He has

published more than 45 research papers on the topic and numerous briefs, reports, and

manuscripts. In this book, the authors have engaged in a number of topics that are of key

importance not only in the area of labour economics but also in development economics and

macroeconomics—these include effects of minimum wage laws on employment, poverty,

education, skills as well as role of minimum wages when entwined with other laws, mostly

related wages or subsidies. The book provides a wholesome analysis of nearly two decades of

literature on minimum wages and is a valuable resource for researchers interested in this area.

The book not only summarizes authors’ own research and perspective but also presents a

review of a large number of other research papers on the same questions. In terms of the number

of research papers that have been discussed on each topic, the book has been invariably

sufficient. However, the book subsides from the more recent research on the topic—of course;

we cannot blame authors for research published after 2008 (the year of publication). Moreover,

in discussing literature, the areas where literature seems convoluted, the authors have given more

weight to their own research and perspectives.

The book starts with an abridged but a valuable narrative of the history of minimum wage

in the United States. It tracks the history and illustrates how changes in the minimum wage laws

over time resulted in present minimum wage laws. It also discusses the difficulties that minimum

wage law had to face because of the legislation process and the opposition from various

groups. It traces back the links of modern debates to discussions among early economists on the

efficacy of minimum wage laws in achieving the desired goals, particularly focusing on the

controversies in the theory and empirical evidence of the time.

One of the most important questions painstakingly discussed is the impact of minimum

wage on employment. Authors divide the literate on minimum wage and its impact on

employment into four eras; the early time series research, which was unreliable because of little

variations in the minimum wage; the first wave of new minimum wage research, which used

panel data and case study methodologies; third generation of minimum wave research, which

highlighted issues in new wave of minimum wage research; and recent works on minimum

wage, that try to establish relationships and make sense out of disagreements in empirical

research outcomes. The authors’ reviewed literature and international evidence from developing

and developed countries and found the evidence of negative impact of minimum wage on

employment. Authors identified 33 studies based on the most reliable evidence, out of which

“more than 80 percent point to negative employment effects” (p. 39). They found “the

literature—when read broadly and critically—as largely solidifying the view that minimum

wages reduce employment of low-skilled workers" (p. 106).

Book Review 87

However, a closer look makes the evidence unconvincing as the authors have

circumscribed the literature that provides evidence against negative impacts of minimum wage.

Most of the studies, presenting negative impacts of minimum wage are state-panel approaches

actually pioneered by the authors during the new wave of minimum wage research. On the other

hand, in analyzing the literature, authors have not considered the anomalies in results arising

because of the heterogeneity based on local economic conditions—particularly in the case

of state-panel studies. The sample used by authors contained much of the variations in minimum

wages during 1986-95—the same period when unemployment was growing rapidly. In contrast,

there is sizeable research that reported no or positive impact of the minimum wages both in

developed and developing countries, for instance, Dube, et al. (2010) found no adverse impact

of minimum wage; and showed that most of the studies does not account for local economic

conditions and find negative impact of employment due to spatial heterogeneities. Similarly,

Stewart (2004) used quasi-experiment and different-in-difference estimator, appealing

methodology in this regard, and found no adverse impact of minimum wages in case of Europe.

Similar issues carry on when the author address second most important question the

impact of minimum wages on distribution and on the people living near or on the poverty line—

and do not find any strong negative impact on poverty. A more profound analysis, in terms of

variety of data sources and estimation techniques—particularly in the case of distributional

effects—can be found in Myth and Measurement: The New Economics of Minimum Wage. It has

not only criticized the earlier literature but also updated some of significant works. Contrary to

the authors (of Minimum Wage), they have found that minimum wage compresses the wage

distribution and reduces inequality.

Authors also discussed the consequences of minimum wages on schooling, skills, prices

and profits. They found little evidence to support the direction of impact on schooling and skills

acquisition in short run—while the literature on long-run impact is virtually absent. Most of the

literature, on schooling and skills acquisition, is mostly from the early 1980s that investigated

only the indirect and contemporaneous impact. Moreover, the evidence on the impact of

minimum wage on skills and schooling is inconclusive, firstly, because literature in this area has

not matured enough. Secondly, the studies that found the relationship, if any, have

methodological issues as noted by authors.

Lastly, authors endeavored to reason the forces at play in favour and against the

minimum wage in the form of backing from the general public, labour unions, politicians and

other interest groups. The review they provided in this regard is interesting as it discusses the

reasons of general misconceptions about the minimum wage and its consequences as well as the

motives behind the competing interest groups. It is also backed by evidence mostly comprising

survey studies on who is for or against the minimum wage.

In the end, surprisingly, authors do not clearly oppose the minimum wage or suggest any

version of it that might have a positive impact on employment or reduce inequality. In fact, they

seem confounded while answering the question: “do the conclusions lead to a relatively clear

sense of whether minimum wages constitute good social policy? We believe they do” (p. 286).

They suggested Earned Income Tax Credit, health insurance for children and other subsidies

88 Book Review

instead of the minimum wage; but then admitted that they “by no means experts with regard to

all of these policies” (p. 294).

While arriving at conclusions at numerous other instances it seems that the authors’

approach towards the critical review of research work is evasive often giving more weight to

their own research in terms of in-depth analysis of results or to the research works that are in line

with outcomes of their own. However, the Minimum Wage is a useful read for anyone who is

interested in research in the area of minimum wage. It covers important policy issues often

interlinked and related to minimum wage. For the coverage of vast and influential literature on

the topic alone, Minimum Wage stands out to be the most comprehensive book on the subject.

Imtiaz Ahmad

PhD Scholar,

School of Social Sciences and Humanities (S3H),

National University of Sciences and Technology (NUST),

Islamabad, Pakistan

REFERENCES

Card, D., and A. B. Krueger (1997) Myth and Measurement: The New Economics of the

Minimum Wage. Princeton, New Jersey: Princeton University Press.

Dolado, J., F. Kramarz, S. Machin, A. Manning, D. Margolis, and C. Teulings (1996) The

Economic Impact of Minimum Wages in Europe. Economic Policy, 11:23, 319-72

Dube, A., T. W. Lester, and M. Reich (2010) Minimum Wage Effects Across State Borders:

Estimates Using Contiguous Counties. The Review of Economics and Statistics, 92:4,

945-964.

Stewart, M. B. (2004) The Impact of the Introduction of the U.K. Minimum Wage on the

Employment Probabilities of Low-Wage Workers. Journal of the European Economic

Association, 2:1, 67-97.

Book Review 89

Rob Potter, Dennis Conway, Ruth Evans and Sally Lloyd-Evans. Key Concepts in

Development Geography. Sage Publications. 2012. 289 pages. (Paperback): $40.32/ €33.99.

Key Concepts in Development Geography is a valuable addition to rural development

literature and comprehensive analysis of meanings, ideas and practices in rural development

arena. The book provides broad overviews of the philosophy of human geography, economic and

cultural development and offers mainstream themes on comprehensible elaboration of time and

space, not only to attract novice readers, professional developmental actors, but more

importantly the development geographers. The book seeks to fill the gap on offering detailed

description and discussion of the concepts laying at the heart of theoretical and empirical

research in the contemporary development issues. Therefore, the book appears to tackle all three

distinct segments: Theory, Practice, and Strategies.

The book provides conceptual developments over time along with approximately twenty-

five entries on the core concepts that constitute the theoretical and abstract toolkit of

developmental partners and implementing organizations (e.g., NGOs) working within a specific

sub-discipline in rural development. Correspondingly, each subject provides a detailed portrayal

of the concept as well as outlining contested descriptions and approaches, the evolution of

concept and the way it has been used to understand any particular human and economic

phenomenon. In so doing, each section constitutes an invaluable companion guide to readers

grappling with understanding developmental industry that we inhabit.

The book starts with an excellent introduction, which among other things, highlights the

biggest known threat to development; inequality and grinding poverty –foremost global and not

affluence and wealth. Whereas, inequality is considered as a quite as pressing issue for thinking

about ‘development’ as poverty, not least since both are interconnected through the same

systemic courses but their severe effects on the local poor. In a nutshell, the introduction briefly

aims to locate ‘development studies’ within other interrelated and interdependent disciplines

(e.g., geography) to provide decadal history, although less inclusively as done by Adam Szirmai

(2005), regarding the development intellect. Thereafter, the book is divided into five main

sections and four sub-sections, each prefaced with its own short but comprehensive introduction

to get insight into the chapter.

In to the debate, the book, after explaining explicitly the very basic subject matters of

understanding development arena (meanings of development; measuring development; space and

development etc.), engages the reader to complex issues of communal understanding, perceiving

development and puzzle of modernity, post-modernism and post-structuralism, neoliberalism,

globalization and sustainability. At this point time, the book, perhaps, not surprisingly certain

any challenging remits to above historical trends to rural development, but conveys the rich

philosophies and histories of development in a very professional and compressed manner with

valuable criticism, suggesting detailed literature on individual aspects at each section’s end. The

90 Book Review

strain between pedagogic endowment of fundamental information and the critical understanding

is also evident in places.

The thrust of the third chapter is more analytical than descriptive. Here the authors dig

the cave towards economic opportunities though sustainable livelihood strategies and division of

world labour – decent work, threats in the informal sector; the digital economy and new

workspaces, likewise labour deployment in global aid and trade. The debate moves on to

connecting ‘people, popular culture and rural development’. Finally, wrapping-up with

conventional issues of culture, civil society, social capital and transnationalism in development.

On this subject matter, Ravallion (2010), however, gave more worthy insight into uneven

geographies of capital and wealth. He also explores the theme from poverty perspective and the

increasing share of world's poor in the ‘middle income economics’. Which this book fails to

achieve that gives quite substantial implications for thinking about the nature of poverty, and the

role of governments and external agencies based on geographical distribution.

By the same token, the section on contemporaneous topics did not seem strikingly the

contemporary issues. Although all of the arguments are certainly concerned about ongoing

importance of ‘culture in development’ (dominant enlightenment values and moralities), except

section on Tobin Tax that backs the ongoing discussion. Also, the pressing present-day issue in

the development debate, which includes the ever growing and influencing role of non-state

powerful actors (e.g., foundations) in the global policy formulation with cultural sensitive

thought is overlooked, which was highlighted by Tylor (1871) and could be explored to back the

debate of historical events in development and literature on societal cultural inclusion. Yet, the

difficult choices are to be made and the role of (so-called) ‘rising powers’ as development actors

should be acknowledged. As the emerging trend in international development and ought to have

been included, anthropologists like Schech and Haggis (2000), discussed the topic more in its

‘exotic authenticity’ and strove the meanings, values and socio-cultural practices of closed

societies.

The book provides a time-shifted focus on different ideological and theoretical

approaches to socio-cultural and politico-economic development, underpinned by the

international legal framework of culture and human rights. The politic-cultural idea is then

connected to rights discourse, the potential tensions and challenges associated with rights-based

and need-based approaches to development, hence, focusing more on the polarized nature of the

universalism –cultural relativism to meet human needs and flourishing i.e., people’s identity

formation as well as territorial affiliations. The developmental potential of global culture and

source destination of global and local societies is then examined to single out the migration–

development nexus.

Key Concepts in Development finds its niche as an excellent and supremely accessible

guide to nearly all key issues in the development industry to provide a clearly stated, well

informed and strongly structured pathway analyzed literature. Similarly, applied strategies ar

also well elucidated. Whatever deficiencies exist, they do not, to any marked degree, militate

against the valuable contribution of this book to global rural development, inequality and

Book Review 91

incorporating local poor (on understanding societal culture) in drawing together theory and

practice in particular.

Umer Khayyam, PhD

Assistant Professor,

Department of Development Studies,

School of Social Sciences & Humanities (S3H),

National University of Social Sciences & Technology (NUST),

Islamabad, Pakistan.

REFERENCE

Ravallion, M. (2010) The Developing World’s Bulging (but vulnerable) Middle Class. World

Development, 38:4, 445-454.

Schech, S., and Haggis, J. (2000) Culture and Development: A Critical Introduction.

Szirmai, A. (2005) The Dynamics of Socio-economic Development: An Introduction. Cambridge

University Press.

Tylor, E. B. (1871) Primitive Culture: Researches into the Development of Mythology,

Philosophy, Religion, Art, and Custom (Vol. 2). Murray.


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