a

EMPLOYMENT PROFILES FOR KENYA, 1994-96

by

John Thinguri Mukui

(Consultant)

Background Report Prepared for the Seventh National Development Plan, 1994-96, Office of the Vice President and Ministry of Planning and National

Development, Nairobi, Kenya

NOVEMBER 1993

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EMPLOYMENT PROFILES FOR KENYA, 1994-96

TABLE OF CONTENTS

1 INTRODUCTION .......................................................................................................................... 1

2 REGULAR EMPLOYMENT DATA FROM CBS .......................................................................... 2

CONCEPTS AND DEFINITIONS USED IN REGULAR ESTABLISHMENT-BASED SURVEYS ................. 2

ANNUAL ENUMERATION OF EMPLOYEES AND SELF-EMPLOYED PERSONS .................................... 4

SURVEY OF DOMESTIC SERVANTS ............................................................................................................. 7

SURVEY OF URBAN INFORMAL SECTOR ................................................................................................... 9

SURVEY OF INDUSTRIAL PRODUCTION.................................................................................................. 12

3 EMPLOYMENT DATA FROM AD HOC CBS SURVEYS ........................................................ 13

RURAL NONFARM ENTERPRISES SECTOR .............................................................................................. 14

SURVEY OF RURAL NON-AGRICULTURAL ENTERPRISES, 1985 ......................................................... 15

THE NATIONAL MANPOWER SURVEY, 1986-1988 ................................................................................. 15

LABOUR FORCE SURVEYS........................................................................................................................... 18

EMPLOYMENT IN SMALL FARM SECTOR ................................................................................................ 25

4 EMPLOYMENT DATA FROM OTHER AGENCIES ................................................................ 26

MINISTRY OF LABOUR AND MANPOWER DEVELOPMENT ................................................................ 26

REGISTRAR GENERALS OFFICE ................................................................................................................ 27

NATIONAL SOCIAL SECURITY FUND ....................................................................................................... 28

5 DELAYS IN RELEASING LABOUR STATISTICS ..................................................................... 29

6 SOME ILLUSTRATIONS OF USES OF LABOUR DATA ......................................................... 30

COMPUTING LABOUR COMPONENT OF GDP ........................................................................................ 30

DISTRIBUTION OF WAGES: AN ILLUSTRATION ..................................................................................... 31

7 EMPLOYMENT PROFILES, 1990-96 ......................................................................................... 35

8 SUMMARY AND CONCLUSION .............................................................................................. 42

TERMS OF REFERENCE .................................................................................................................... 44

STATISTICAL ANNEX ........................................................................................................................ 47

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EMPLOYMENT PROFILES FOR KENYA, 1994-961

1 INTRODUCTION

1. The purpose of the paper is twofold. First is to evaluate the scope and quality of labour

statistics generated by Government ministries and agencies. The analysis covers the modern sector,

small farms, rural nonfarm and the urban informal sector. While employment and earnings data for

the modern sector are collected through the annual census of establishments and Government

nominal rolls, data for the other sectors are derived using very restrictive assumptions; or residually

from the knowledge of composition and magnitude of the labour force. This creates special problems

in that errors of measurement and labour force summary statistics (e.g. unemployment rates) affect the

magnitude of estimates for the sectors whose employment is derived residually. Secondly, review of

the labour statistics offers a basis for constructing employment profiles and projections for the period

1994-96. The employment profiles will be used as an input to the seventh National Development Plan

1994-96. The Kenya government has traditionally created employment profiles for the entire

population as an input to development planning process beginning with the 1979-83 Development

Plan.

2. The Central Bureau of Statistics (CBS) is the Government department responsible for

collection, processing, analyzing, and disseminating statistics in Kenya. Among data collection

activities, CBS collects employment and earnings data on an annual basis through establishment-based

postal surveys, and on ad hoc basis through household-based personal interviews.

3. CBS collects data on employment and earnings through the following regular surveys:

i Annual Enumeration of Employees and Self-Employed Persons;

ii Survey of Urban Informal Sector;

iii Survey of Domestic Servants; and

iv Annual sectoral surveys, e.g. Survey of Industrial Production, Business Expectation Enquiry,

Transport Statistics, Hotel Statistics, etc.

4. There are ad hoc surveys that generate employment and earnings data, among other variables the surveys are designed to monitor. Such surveys are carried out solely by CBS or by CBS in

conjunction with other ministries. The surveys include population censuses, household budget

surveys, labour force surveys and manpower surveys. The most regular and important source of

employment and earnings data in the modern sector is the Annual Enumeration of Employees and Self-employed Persons.

5. There are other Government and nongovernmental institutions involved with collection or

culling of employment and earnings data. Among them are the line ministries such the ministries of

Labour and Education. The Registrar of Companies, a unit within Attorney Generals Chambers, keeps

files for all registered companies. Information contained in the files is used to update the frame CBS

uses for collecting employment data through establishment-based surveys. Other institutions

compiling data on employment and earnings include the Federation of Kenya Employers (FKE), the

1 Some of the material is drawn from my paper titled Kenya: The Coverage of Official Labour Statistics,

Background report prepared for the Presidential Committee on Employment, Nairobi, Kenya, 13 December

1990.

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Central Organization of Trade Unions (COTU), educational institutions, and researchers.

International institutions such as the International Labour Organization (ILO) and the World Bank

also cull employment and earnings data from primary sources in compiling country profiles.

6. The paper is divided into six parts. Sections 2 and 3 are a rundown on the regular and

occasional censuses and surveys conducted by CBS to gather labour data. Section 4 focuses on other

official institutions engaged in collection and analysis of labour data, while Section 5 briefly discusses

delays in official release of labour statistics. The definitions used by CBS in classifying official labour

data (based on persons by main economic activity) are differentiated from those of labour force

surveys (based on allocation of time per economic activity). Section 6 deals with the various uses of

labour data, narrowing down to issues of size distribution of wage earnings and the compilation of

labour costs component of Gross Domestic Product (GDP). Section 7 presents employment profiles for

the entire population using labour data and population/labour force projections.

2 REGULAR EMPLOYMENT DATA FROM CBS

CONCEPTS AND DEFINITIONS USED IN REGULAR ESTABLISHMENT-BASED SURVEYS

Modern Sector

7. The modern sector is defined to include all establishments in urban areas, large scale farms and

other modern enterprises located in rural areas plus that part of the public sector which is engaged in

activities of an enterprise nature. In this context, the agricultural establishments covered by the

Labour Enumeration (LE) survey are only those located in the former scheduled areas2, and exclude

the very large number of small holdings outside the former scheduled areas. Other very small

non-agricultural rural-based establishments are also excluded.

Economic Activity

8. The term economic activity as defined by the United Nations System of National Accounts

(SNA) covers those activities that produce goods and services that fall within the SNA production

boundary. The CBS classification closely follows this definition. However, modifications are made to

fit Kenyas economic structure, mainly at the group level (see Kenya Statistical Digest, Volume XVI, No. 1, March 1978). The economic activities used in analyzing employment and earnings data

collected by CBS follow the following hierarchy:

1. Major division

2. Division

3. Major Group

4. Group

There are ten Major Divisions:

1. Agriculture, Hunting, Forestry and Fishing

2 In the colonial period, Scheduled Areas was land alienated to exclusively European settlement, which is largely

coterminous with the white Highlands but since independence is referred to as Large Farm Areas.

Nonscheduled Areas was land reserved for African settlement, and is referred to as Small Farm Areas. About 7.4

million acres of land were reserved for the exclusive use of Europeans, with about 3.5 million acres in mixed

farming areas and 3.9 million acres used mostly for plantations producing coffee, tea or sisal, or for ranching.

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2. Mining and Quarrying

3. Manufacturing

4. Electricity and Water

5. Construction

6. Wholesale and Retail trade, Restaurants and Hotels

7. Transport and Communications

8. Finance, Insurance, Real estate and Business Services

9. Community and Social Services

10. Activities not adequately defined

Establishment

9. To determine the economic activity of an employed person, we must refer to the activity of

the unit in which the person works. The unit is the establishment, and in the framework of the United

Nations International Standard Industrial Classification of economic activities (ISIC), is defined as the

smallest statistical unit. An establishment is an entity which exclusively or principally carries out a

single type of economic activity at a single physical location. In the case of commercial banks, an

establishment would be a branch at a specific location, say, Kenya Commercial Bank, Tom Mboya

Street. Likewise in the hotel industry, a chain of hotels with different locations and names, but under

the same management, would be considered as separate establishments. This should also apply to

farms and estates. There are, however, some complications in this definition because some units are

hard to locate physically due to the nature of their activities. Thus, construction workers such as

masons may carry out daily activities at different worksites which are far away from each other, while

self-employed taxi drivers have boundless worksites.

Firm or Enterprise

10. Since industrial classification is based on homogeneity of activities carried out by the

economic unit, it is important to distinguish enterprises from establishments. The enterprise,

according to ISIC, is the smallest legal entity (or group of legal entities) which encompasses and

directly or indirectly manages all of the functions necessary to carry out the economic activities of the

establishments. The enterprise therefore does not represent unit of physical location nor necessarily of

kind of activity. For illustrative purposes, Kenya Breweries Ltd is a firm with different establishments:

one producing glass for bottles, and another distilling and bottling the drink itself, etc.

Occupation

11. Occupation refers to the type of work (duties) one was performing during the reference

period, regardless of the economic activity in which one may be employed or type of training

received. The main groupings are:

011 - Architects, engineers and surveyors

013 - Draughtsmen and engineering technicians

014 - Doctors, dentists, pharmacists and veterinarians

015 - Nurses and other paramedical staff

016 - Chemists, physicists, biologists, zoologists and agronomists

017 - Technicians in physical science and life sciences

018 - Statisticians, mathematicians, systems analysts and economists

021 - Statistical officers, mathematical technicians and related technicians

023 - Aircraft and ship officers

024 - Lawyers and jurists

025 - Teachers and lecturers with university degrees

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026 - Other teachers

027 - Librarians, sociologists, journalists, curators and related scientists

030 - Ministers of religion and other social and cultural workers

041 - General managers and salaried directors

042 - Middle level executives and departmental heads

043 - Qualified accountants and auditors

050 - Accounts assistants, cashiers, bank tellers, bookkeepers and bank clerks

061 - Other professionally-qualified personnel not elsewhere covered

062 - Production supervisors and general foremen

063 - Skilled workers: (a)

Miners, metal processors, paper makers, chemical processors, tanners, food and

beverage processors, tobacco preparers, tailors and shoemakers

064 - Skilled workers: (b)

Blacksmiths, mechanics, electrical fitters, plumbers and sound equipment operators

065 - Skilled workers: (c)

Printers, bookbinders and photo engravers

066 - Skilled workers: (d)

Bricklayers, carpenters, painters and other construction trades

067 - Drivers of transport, material handling and earthmoving equipment

071 - General clerks, typists and office machine operators

072 - Secretaries and stenographers

073 - Other skilled workers in retail and wholesale establishments

075 - Security personnel, caretakers, messengers, waiters, bartenders, cooks, firemen, launderers

and hairdressers

081 - Loggers, fishermen and hunters

082 - Semi-skilled workers

083 - Unskilled workers and other labourers

099 - Casual employees

ANNUAL ENUMERATION OF EMPLOYEES AND SELF-EMPLOYED PERSONS

Survey Description and Organization

12. The Annual Enumeration of Employees and Self-Employed Persons, commonly known as the

Labour Enumeration (LE) survey, is an annual mail survey that covers all registered establishments in

the modern sector. The survey chiefly collects earnings and employment data. These statistics are

collected under the Statistics Act (Cap 112 of the Laws of Kenya), specifically under The Statistics (Employment) Regulations gazetted each year. Statistics on number and size of establishments are reported in the annual Statistical Abstract showing (a) number of establishments by employment groups (0 employees, 1-4, 5-9, 10-19, 20-49 and 50+), (b) total number of employees by employment

groups, and (c) number of establishments/businesses opening and closing.

Coverage

13. The survey covers about 26,000 establishments in the modern sector of the economy,

excluding central Government and educational institutions under the Teachers Service Commission

(TSC). These establishments are grouped into two classes by size of employment. The first category,

referred to as Large Scale, comprises about 4,500 establishments which employ 20 or more persons.

The second, known as Small Scale, covers about 21,500 establishments employing less than 20 persons.

For the first category, a detailed questionnaire (FORM LE/9-/L) is administered to the respondents,

while the latter uses a brief schedule labelled FORM LE/9-/S. In addition, Government departments

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are mailed Form LE/9-/C to capture information on casual employees as they are not included in the

payroll.

Survey Frame

14. The basis of data collection for any establishment-based surveys in CBS is the Master File

(MF). Therefore, the LE survey uses the MF to mail questionnaires to establishments. The MF is a

register of firms in the modern sector of the economy. The MF takes the establishment rather than the

firm as the unit of the register.

15. The main sources of MF units are administrative records. The names of newly registered

establishments are obtained from the Registrar Generals office in the Attorney Generals Chambers.

Other important sources used in updating the MF include:

i The register maintained by National Social Security Fund (NSSF);

ii The register maintained by the Ministry of Industry;

iii Commissioner of Insurance, Central Bank of Kenya, and professional/business organizations

such as the Kenya Association of Manufacturers;

iv Physical listing of new establishments by district-based CBS officers, when such

establishments are not contained in the district-specific checklists supplied to the districts;

v Physical listing of new establishments by headquarters staff during listing exercise (rarely

carried out due to financial constraints); and

vi The media, i.e. the Business Directory, the Telephone Directory, newspapers, magazines,

periodicals, etc.

16. The lists are counterchecked with the active MF to avoid any duplication or omission of a

record. Feedbacks from filled returns, field follow-ups by district and headquarters personnel, and

other CBS establishment-based surveys are used to update the MF by monitoring the closure of an

establishment, or changes in name, ownership, activity, status or geographical location.

17. With respect to the establishments of the Central Government and Teachers Service

Commission, data are obtained from their respective nominal rolls pertaining to 30th June of every

year. These are therefore the most reliable datasets on public employment and earnings. In addition,

the Government hires works-paid casual employees for specific short-term projects undertaken

mostly by the following Ministries: Water Development, Public Works, Transport and

Communications, Environment and Natural Resources, and Agriculture. Data pertaining to such

employees are gathered through the Survey of Government Casuals as they are not included in the

payroll.

Data Processing

18. The LE survey information is analyzed in the mainframe computer at the Government

Computer Services (GCS). The receipt of the survey forms is monitored through a specifically designed

checklist, where each responding establishment is ticked off against its name, and the date the

establishments questionnaire is received entered into the appropriate column. The returns are edited

and taken to the keying room as they are received from the responding establishments. After closing

the survey in the month of March of each year, hard copies from the tapes containing the data are

availed to the Labour Statistics Section to proofread or validate. After validation (cleaning tapes),

required tabulations are generated by taking the following steps:

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i Data collected by the three survey forms (i.e. FORM LE/9-/L, FORM LE/9-/S and FORM

LE/9-/C) are merged to form the Primary Survey File;

ii The primary survey file is enriched with the Estimation file (a file that contains computer

generated employment and earnings estimates for establishments that would not have

responded by the close of the survey) to form the Enriched Survey File;

iii The enriched survey file is merged with two magnetic tapes containing information on civil

servants and teachers employed by the TSC to form the Final Employment File;

iv Thirteen Standard Tables are then produced by the mainframe computer and given to the

Labour Statistics Section. The Labour Statistics Section uses these tables to update employment

and earnings series appearing in various publications of the CBS. The same tables are also used

in meeting data requests from different users.

Classification and Analysis

19. Employment and earnings data compiled from the LE survey are presented in the following

CBS publications: (a) the Economic Survey - 12 Tables, (b) the Statistical Abstract - 25 Tables, and (c) Employment and Earnings in the Modern Sector - 29 Tables. Geographical, activity, and occupational codes are assigned to the Master File to facilitate analysis of employment data by area, industry and

occupation. For instance, urban versus rural analysis can be carried out with ease; for data are available

by sub-location, which can be aggregated to location, division and district levels. Furthermore, all

urban towns, defined as urban centres with a population of 2,000 or more persons, are given codes in

the Master File. Therefore, to get urban employment, one needs to sum up employment in all urban

centres in the country. Likewise, employment data are presented on a classification based on the 1968

edition of the United Nations International Standard Industrial Classification of economic activities

(ISIC) at four-digit level.

Limitations

20. One of the weaknesses of this survey has been the low response rate. Response rate for

small-scale establishments averages about 28 percent, while that for large-scale establishments is

around 58 percent on average. The rates for the latter are higher because of follow-up work, which is

targeted at establishments employing 20 or more persons. The low response rates lead to

understatement of employment figures. In an endeavour to improve response rate, thereby reducing

estimation, follow-up of non-responding firms is done two months from the date of posting the

questionnaire to the establishment. However, due to budgetary constraints, follow-up concentrates

only on the large firms.

21. Data manipulation includes estimation for non-responding establishments. Although an

estimation method for non-responding establishments is activated by the computer at the analysis

stage, the method collapses where an establishment fails to respond for three or more consecutive

years. If an establishment fails to respond in the first year, estimates are made on the assumptions that

its employment has not changed and that earnings followed the general trend of the responding firms.

For those establishments which did not respond in the two previous years, estimates are derived on

the assumption that employment followed a similar pattern as that of establishments of the same size

in the same economic activity which had responded for three consecutive years 3. The automatic

3 For an establishment falling under activity j that responds in year n but defaults in years n+1 and n+2, the computer carries forward the figures (x) for year n and uses the growth rate for the establishments in activity j which responded for year n+1 to get employment figure for year n+1 as (x+e). For year n+2, the figure (x+e) for the defaulting establishment is subjected to the growth rate of total employment in the responding

establishments falling under activity j.

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adjustments done by the computer are then manually scrutinized on a firm-by-firm basis to determine

whether the estimates are reasonable. If an establishment does not respond for three years in a row, it

is assumed to have gone out of business and is therefore removed from the Master File.

22. To improve on the response rates, the following measures are recommended:

i Use of CBS field infrastructure, i.e. District Statistical Officers (DSO) and district-based

enumerators in the establishment-based surveys, and especially during the follow-up phase of

the surveys should be encouraged;

ii The LE survey could be carried out on a sample basis. Before such a decision is taken, a

complete listing of establishments should be carried out, estimation procedures formulated,

and appropriate computer programs compiled.

23. The other main constraint to the LE survey has been defects in the Master File. The Master

File is known to contain establishments that have closed down, and some that are dormant. It is also

known to omit establishments that are known to exist. It has proved difficult for the headquarters staff

to identify these establishments and to carry out the necessary corrections. The LE Section does not

have adequate personnel and funds to investigate these cases adequately.

24. It is against this background that there is a feeling that the Master File should have at least

70,000 establishments as opposed to the 45,000 establishments it currently lists. This figure can be

derived by:

i Projecting the number of establishments from way back in 1980, by use of annual registration

of companies - as given in Table 70 of the Statistical Abstract; and growth rates of the economy by sectors since then;

ii Using the records held with the NSSF, which give the number of employers registered with

the Fund at 39,000. Although employers are legally required to register with the Fund if they

employ 5 or more persons, many employers evade registration with the Fund for varied

reasons; and employers are registered at firm/enterprise level, implying that some of the

39,000 firms may be made up of more than one establishment.

25. Understating the number of establishments in the Master File has serious implication on the

reliability of data emanating from any establishment-based surveys. In particular, employment data

are seriously understated mainly due to defects in the Master File. Labour force and unemployment

projections carried out by the Long Range Planning Unit of the Ministry of Planning and National

Development give current employment estimates which are far above the total employment figures

given in the 1993 edition of the Economic Survey.

26. A Census of Establishments to update the current Master File is therefore highly

recommended. In the meantime, other sources should be fully utilized in complementing the LE

survey so as to come up with realistic employment figures. In this regard, NSSF data files should be

scanned for employment figures (current contributors). Employment data from household-based

surveys and censuses should be utilized as benchmark data, which should be updated by LE and other

establishment-based regular surveys.

SURVEY OF DOMESTIC SERVANTS

27. Domestic servants are defined as people who perform household chores and include ayahs,

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cooks and maids. Data on employment of domestic servants used to be collected by the LE section

through a mail annual survey called Enumeration of Domestic Servants. A list of respondents was

compiled using the prevailing edition of the Kenya Post Office Telephone Directory. The survey

covered about 43,686 persons with private postal addresses. These persons were mailed Form

LE/19../D, and requested to give the number of domestic employees and monthly salaries/earnings for

the employees. Results of the survey were analyzed to give estimates of domestic servants and the

average earnings. The information was used to update CBS publications, e.g. Table 4.6 in the 1984

Economic Survey and Table 213 in the 1983 Statistical Abstract.

28. However, since its inception in the early 1970s, the survey persistently suffered from a serious

response problem to the extent that the effective returns for 1981, 1982 and 1983 were 4.8 percent,

17.7 percent and 13.5 percent of the total questionnaires dispatched, respectively. The most serious

cause for the low response rate was the survey frame. First, the use of Post Office Directory frame was

based on the wrong assumption that only those people operating a Post Office box employ domestic

servants; and that every person whose name appears in the Post Office Directory employs domestic

servants. Numerous people who employ domestic servants were therefore excluded from the frame.

Also included in the frame were a lot of people who were not employing domestic servants. Secondly,

the frame offered no possibilities for physical follow-ups, i.e. there were no quick ways of visiting the

respondents, nor were there means of identifying employers of domestic servants, if such employers

are not listed in the Post Office Directory.

29. Consequently, and mainly due to low response rate, possibilities of using other frames started

being explored. In 1982, a question on domestic servants was incorporated in the 1982 Rent Survey.

The analysis on this question showed that, although the figures on employment of domestic servants

were low, response rates were higher compared to response in the Survey of Domestic Servants.

However, it was not until 1987 when the old frame of Postal Telephone Directory was abandoned, and

the National Sample Survey and Evaluation Programme (NASSEP) frame was adopted as the most

appropriate frame for collecting data on employment of domestic servants. In this regard, the Rent

Survey questionnaire was modified to incorporate both Rent Survey and Enumeration of Domestic

Servants. The Rent Survey became the Survey of Rent and Domestic Servants.

30. Data from the new survey started being used in 1988. However, the results have been giving

low figures due to the following reasons:

i The observation unit of the frame for Rent Survey is the residential structure, while there may

be no discernible relationship between a structure and domestic servant. For instance, a

structure may be occupied by people who do not require hired domestic help, such as single or

unmarried persons.

ii The rent survey frame is biased towards low income residential areas, for like any other

NASSEP frame, it is determined by the population size. The frame does not therefore

adequately represent high income groups who are the main consumers of domestic services.

31. An estimation method has been adopted to come up with reasonable figures. However, the

estimation procedure is hard to perfect given the abovementioned bias in the frame. Reliable data on

domestic servants can be obtained if a specific sample of the frame for the Survey of Rent and

Domestic servants is prepared by stratification so as to give more weight to middle and upper income

groups.

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SURVEY OF URBAN INFORMAL SECTOR

32. The informal sector was brought about by lack of full employment in the modern sector. In

many African countries, for example, there was a shortage of manpower at the time of independence

and therefore the public and private modern sectors managed to absorb all the job seekers. However,

over time, the modern sector has not managed to absorb all the labour force. Employment stagnation

in the agricultural sector which used to be the main employer in the rural areas, coupled with peoples

preference for white collar jobs, has led to an exodus of people from rural to urban areas with the

expectation of obtaining better-paying jobs. Consequently, both population and labour force in urban

areas has been increasing rapidly in the last two decades. On the realisation that no jobs are

forthcoming in urban areas, the urban unemployed have tended to engage in informal sector income

generating activities.

33. The unemployment problem in the developing countries has been worsened by economic

stagnation and high population growth. The government, which is the major employer, has been

forced to cut down the size of its labour force or has enacted policies to keep out new entrants or

hastened the exit of existing labour (e.g. through reduction of retirement age) to curtail budget

deficits. The resultant effect is that there has been massive unemployment and these people have had

to seek recourse in the informal sector. Also engaged in the informal sector are low-income earners in

the formal sector to supplement their low salaries in the formal sector.

34. The informal sector acts as a sponge and absorbs the excess labour especially in urban areas.

According to the International Labour Office, the major job creator is the informal sector, which is

responsible for creating 59 percent of the jobs in the African urban areas as compared to the modern

sector which employs only an estimated 25 percent. This implies that jobs generated directly by

investment in the formal sector, where much of the development efforts have in the past been

concentrated, accounts for only a small fraction of the total employed. Later studies in Africa

confirmed the ILO findings that small scale labour-intensive industries are indeed efficient and

profitable but are discriminated against in accessing borrowed capital from the formal money market4.

The informal sector also acts as a training ground for those with no skills. Many apprentices learn on

the job.

Definition of the Informal Sector

35. There is no precise definition of the informal sector. Different people perceive it differently.

According to Sethuraman (1976) 5, an enterprise can be considered as belonging to the informal sector

if it has at least one of the following characteristics:

i Employ not more than 10 persons;

4 Derek Byerlee, Carl K. Eicher, Carl Liedholm, and Dunstan S.C. Spencer, Rural Employment in Tropical

Africa: Summary of Findings, Working Paper No. 20, African Rural Economy Program, Department of

Agricultural Economics, Michigan State University, East Lansing, February 1977. 5 S.V. Sethuraman, The Urban Informal Sector: Concept, Measurement and Policy, International Labour Review, 114(1), July-August 1976. See also, International Labour Office, Resolution concerning statistics of employment in the informal sector, adopted by the Fifteenth International Conference of Labour Statisticians,

Geneva, 19 to 28 January 1993. The latter observed that regular data collection on the informal sector should

preferably be based on a household survey approach, with households as reporting units and individual

household members as observation units, while occasional in-depth studies should preferably be based on an

establishment survey approach or a mixed household and enterprise survey approach, or a combination of both,

with the informal sector units themselves and their owners as observation and reporting units.

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ii Act not in compliance with labour and administrative provisions;

iii Employ workers who are members of the entrepreneurs family;

iv Lack working timetable or fixed working days;

v Lack capital from official financial institutions;

vi Employ mostly workers with less than 6 years of schooling;

vii Not use mechanical or electric power;

viii Have no fixed premises or occupy rudimentary or temporary sites.

36. According to a Study on the Role of the Informal Sector in African Economies6, some four major approaches appear to have been accepted as a basis for understanding the sector:

i Labour Force: Activities are categorized according to the characteristics of the labour. The

implication is that the informal sector uses levels of education as the discriminating factor in

allocation of labour. The major employer of unskilled or semiskilled labour is considered to be

the informal sector.

ii Labour Force and Volume of Capital: The informal sector is defined as the sum total of

activities at the lowest level of the small and medium-scale enterprises.

iii Sources of Factors of Production: Informal sector relies heavily on family own factors of

production e.g. own labour and savings.

iv Lack of registration: Activities are generally not registered, neither pay tax nor respect salary

and labour legislation.

37. As per Central Bureau of Statistics definition, informal sector consists of semi-organized and

unregulated activities largely undertaken by self-employed persons in the open markets, in market

stalls, in undeveloped plots or on street pavements within urban centres. They may or may not have

licenses from local authorities for carrying out such activities as tailoring, carpentry, black-smithing,

grocery, kiosks, meat and maize roasting, sale of apparel and shoes, open air restaurants, repair of

footwear, car repair, shoe shining, hair cutting, etc (Central Bureau of Statistics, Employment and Earnings in the Modern Sector 1981, 1984)7.

38. It is evident that the informal sector is the aggregate of activities that results from the need for

earning ones living because other sectors of the economy are unable to provide adequate employment

opportunities for the rapidly growing labour force. These activities are heterogeneous in nature,

operating in unregulated and competitive markets, often having very little capital and equipment, and

use simple labour-intensive technologies. In most cases, the business owners operate outside of the

legal requirements of the local government authorities.

Data Collection Methodology

39. Information on informal sector is collected through an annual sample survey in all urban areas

which, according to the 1979 population census, had more than 2,000 persons. The purpose of the

survey is to provide information to assist in the planning of the sector. The survey has been a useful

source in determining the contribution of the informal sector in eradicating unemployment. The

6 United Nations, Economic Commission for Africa, Study on the role of the informal sector in African

economies, Seventh Session of the Joint Conference of African Planners, Statisticians and Demographers, Addis

Ababa, Ethiopia, 2-7 March 1992. 7 Also cited in: S.O. Noormohamed, The usefulness and limitations of CBS data: An illustration, in: Kenya Symposium on Statistical Surveys, Central Bureau of Statistics, Ministry of Planning and National Development, 1988.

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specific objectives of the survey are to provide data which will assist in determining:

i The contribution of the sector towards creating employment opportunities in the country;

ii The relative importance of the sector and its contribution in the Kenya economy;

iii The size and geographical distribution of informal sector establishments; and

iv The occupational characteristics of the workers engaged in the sector.

40. The informal sector activities have high entry and exit rates. It is therefore not possible to

maintain an up-to-date frame. Sample units are systematically selected and interviewed at the same

time. Data on people engaged and their earnings are collected, and for classification purposes, type of

business and activity. From 1973 to 1975, the sample survey was undertaken in Nairobi, Mombasa,

Kisumu and Nakuru. Since 1976, the sample survey has covered all urban centres having populations

of 2,000 or more, excluding those in North Eastern Province. Analysis of data from the 1984 survey of

the urban informal sector is reported in Economic Survey 1985.

41. The enumerators use the tally method to count the number of establishments and the number

of persons engaged. This information is recorded for the five major economic activities: manufacturing

(ISIC group 3), construction (ISIC 5), wholesale and retail trade, hotels and restaurants (ISIC 6),

transport and communication (ISIC 7), and services (ISIC 9). Economic activities not elsewhere

classified are denoted by zero. At the end of the tally exercise, it is then possible to determine the total

number of activities per each major ISIC group. Thereafter, a 10 percent systematic sample is derived

for each ISIC group. This will give a fairly representative sample of all the informal sector activities

existing within a given urban area. Maps are usually provided to assist the enumerators to identify the

boundaries of their areas of operation. The urban areas are normally divided into small enumeration

areas to ensure complete coverage of the informal sector activities without omission and to avoid

double counting.

Data Limitations

42. As already mentioned, the maintenance of an efficient sampling frame has not been

practicable. The following problems have contributed to this scenario:

a) Lack of a sample frame due to high incidence of entry and exit.

b) In general, respondents have low levels of education and therefore do not keep proper books

of accounts. This limits the scope of the quantitative information that can be collected.

c) Inadequacy of funds to survey a large number of informal sector units.

d) Respondents conceal information on the belief that the survey data will be used for taxation

purposes.

e) The data collected relates to the urban informal sector establishments only. It is not clear as to

what is the structure and proportion of the informal sector in rural areas. Even for the urban

areas, activities located in households are omitted. However, to overcome this problem, CBS

proposes to introduce a two-stage sampling: households, then establishments. Towards this

end, a pilot survey was undertaken in June 1993 in five major towns: Nairobi, Mombasa,

Kisumu, Nakuru and Eldoret. The results are yet to be analyzed.

f) Gross earnings are reported without deducting costs of inputs e.g. raw materials, wages and

rent, thereby making it difficult to derive net earnings. This is due to the difficulty of soliciting

reliable response on sources and costs of inputs and wares.

g) Total employment figures relate to total persons engaged without classifying them by

employer/employee status.

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h) The lack of a predetermined frame gives enumerators the discretion to decide on the

establishments to interview, without necessarily following the laid-down procedures. This

does not guarantee completeness of coverage and reliability of data collected. The problem

makes it difficult to give the general trend in total employment in the sector as the coverage in

each town might change from year to year and the number of towns included changes over

time.

i) The survey does not normally cover certain economic activities e.g. the matatu subsector.

43. To arrive at the total wage employment in the modern sector, data from all the above sources

are aggregated and published under the title, Employment and Earnings in the Modern Sector. However, it should be noted that the data have shortcomings:

i Data on wage employment covers both rural and urban sectors but those of small scale

enterprises pertain to urban areas only. A lot of informal sector activities in rural areas are

therefore left out.

ii Due to lack of a suitable sample frame and definitional problems, data on domestic servants are

not reliable and could be underestimated.

A big proportion of labour data that go into the calculation of labour costs components of GDP are

provided by this source. However, it seems that the statistics are underestimated. Caution therefore

needs to be exercised in their interpretation.

SURVEY OF INDUSTRIAL PRODUCTION

44. CBS conducts other regular establishment-based surveys to monitor performance of various

sectors of the economy. For example, Business Expectation Enquiry and Survey of Industrial

Production (SIP) generate employment data focused on the industrial sector. The surveys cover a

sample, usually of firms employing 50 or more persons, and vary in coverage. In addition, while the LE

survey takes the establishment as the statistical unit of enquiry (respondent), most of the other surveys

take firm as the respondent, mainly because employment data is not the domain of their study.

45. Since the first industrial production survey was conducted for reference year 1954, others

have been conducted for reference years 1956, 1957, 1961 and annually since 1963. Enquiries for

reference years 1961, 1963, 1967 and 1972 took the form of a census. Up to and including 1963, all

establishments engaging 5 or more persons were covered, and from 1964 to 1969 the coverage was

restricted to firms engaging 50 or more persons. Surveys for 1970 and 1971 covered firms engaging

fifty or more persons plus a 25 percent sample of those engaging between 20 and 49 persons. In 1972,

the coverage was extended to all establishments (see 1979 Statistical Abstract).

46. The survey covers three sectors: mining and quarrying, manufacturing, and building and

construction. Firms covered are stratified by employment. In the surveys of 1973-1976, firms engaging

fifty or more persons were covered in full and a sample (25 percent) was selected from the group

employing between twenty and forty nine persons. Firms employing less than twenty persons were

not covered because they presented particular response problems. For convenience, the latter group of

firms is divided into three groups: those employing 5 to 19 persons; those employing 1 to 4 persons;

and rural non-agricultural enterprises employing 1 to 4 persons.

47. Information for the category of firms employing less than 20 persons was estimated. Data on

persons engaged, gross earnings, and inputs (e.g. wages, water, transport) was collected through a mail

13

questionnaire supplemented by secondary statistics from the ex-community Government

Corporations. To obtain aggregate statistics for the annual SIP, data for firms employing 20-49 persons

are grossed up (blown-up) to obtain totals for this category of firms. However, every five years, a

Census of Industrial Production (CIP) is undertaken. The CIP differs from the SIP because it covers all

firms employing 20 or more employees, and only takes a 50 percent sample of firms employing 5 to 19

persons and 25 percent of those employing 1 to 4 persons (see 1980 Statistical Abstract). In addition, CBS undertakes a quarterly Business Expectation Enquiry for all industrial sectors (excluding

electricity and water), which includes employment and earnings statistics.

3 EMPLOYMENT DATA FROM AD HOC CBS SURVEYS

48. There are ad hoc surveys such as population censuses, household budget surveys, labour force surveys and manpower surveys, which give reliable estimates of the stock of employees in the country.

Such surveys provide benchmark data that should be used in both projecting and revising employment

data. Some ad hoc surveys conducted to meet specific data needs include:

a) Rural Non-farm Activity Survey, 1977

b) Survey of Rural Non-Agricultural Enterprises, 1985

c) National Manpower Survey, 1986-88

d) Labour Force Survey, 1977/78

e) Urban Labour Force Survey, 1986

f) Rural Labour Force Survey, 1988/89

g) Household Budget Survey, 1974/75, and

h) Household Budget Surveys: Rural (1981/82) and Urban (1982/83)

With exception of Survey of Rural Non-Agricultural Enterprises 1985 and National Manpower Survey

1986-88, all others used the household as the unit of investigation. This has special significance in that

households are the suppliers of labour.

49. Although CBS is the principal collector of labour data in the country, other Government

departments have assisted in the collection and analysis of labour data. For instance, the Manpower

Planning Department of the Ministry of Manpower Development and Employment conducted the

National Manpower Survey in 1986-89 to update the Manpower Survey of 1972 as the results of the

1981/82 Manpower Survey were not published. To update the Labour Force Survey of 1977/78, the

Long Range Planning Unit of the Ministry of Planning and National Development conducted the

Urban Labour Force Survey in 1986 and later in 1988-89 undertook the Rural Labour Force Survey.

50. Participation of CBS in surveys undertaken by other Government departments has always

been very significant due to the following reasons:

a) CBS maintains the Register of Establishments which serves as the frame for

establishment-based surveys. Without the register, the National Manpower Survey would

have been difficult, if not impossible.

b) CBS has a well developed infrastructure for household-based surveys including a master

sample frame and trained permanent field enumerators.

c) All national surveys are undertaken under the Statistics Act and any institution intending to

conduct such a survey must obtain prior clearance from the Director of Statistics in accordance

with the Act.

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51. The input of the CBS in these surveys has been mainly technical covering questionnaire

design, sampling and data collection. Data processing and analysis is carried out by the department

sponsoring the survey. Analysis of the 1986-89 National Manpower Survey was done by the Ministry

of Manpower Development and Employment while analysis of the 1986 Urban Labour Force Survey

and 1988/89 Rural Labour Force Survey was done by the Long Range Planning Unit. The arrangement

of pooling resources and skills has worked well and the results are produced and published timely.

This applies not only to labour-related surveys but to all the surveys undertaken jointly by the Central

Bureau of Statistics and other Government departments.

RURAL NONFARM ENTERPRISES SECTOR

52. According to Rural Household Budget Survey of 1981/82, nonfarm enterprises accounts for

about 17 percent of household incomes and is the main source of income for an estimated 9.9 percent

of rural households (see Table 1 below). In spite of its importance to rural incomes, data pertaining to

employment in rural nonfarm enterprises is scanty. CBS conducts sample surveys on rural nonfarm

enterprises on ad hoc basis, the latest of which was undertaken in 1985/86. The surveys cover establishments in rural trading centres with a population of less than 2,000 persons. Unfortunately, the

surveys do not provide proper estimates of total size of the rural nonfarm sector, though useful for

assessing its sub-sectoral composition.

Table 1: Average Household Net Monthly Income by Main Source and Other Sources (KShs) Main source of net income

Farm Non-farm Wages/Salaries Other Sources Total

Farm enterprises 548 255 168 165 399

Non-farm enterprises 76 742 39 130 140

Salaries and wages 57 59 630 29 177

Other sources 92 216 20 337 114

Average total 773 1,272 857 661 830

% of income

Farm enterprises 70.9 20.0 19.6 25.0 48.1

Non-farm 9.8 58.3 4.6 19.7 16.9

Salaries and wages 7.4 4.6 73.5 4.4 21.3

Other sources 11.9 17.0 2.3 51.0 13.7

Average total 100.0 100.0 100.0 100.0 100.0

Percent of households 58.7 9.9 21.4 10.0 100.0

Source: F. A. Opondo, Rural Household Budget Survey 1981/82: Highlights, in: Kenya Symposium on Statistical Surveys, Central Bureau of Statistics, September 1988

53. Most of rural nonfarm activities are household-based, usually undertaken on part-time basis

using unpaid family labour. According to a survey of rural Kenyan households, undertaken in 1977

within the Integrated Rural Surveys to determine the nature and extent of nonfarm activities, at least

50 percent of the households were engaged in at least one of such activities and nearly one out of four

engaged in two or more (see Rural Non-Farm Activity Survey 1977, Social Perspectives, Volume 2, No. 2, June 1977; and The Integrated Rural Surveys 1976-79, Basic Report). The survey covered activities which are rural in nature, and therefore excluded formal sector activities such as teaching and

employment in government. Estimation of employment in nonfarm activities is intrinsically difficult.

Due to the inadequacy of data, estimates of employment in rural nonfarm enterprises are often derived

15

as a residual by subtracting modern wage employment, small farm and pastoral employment, from

estimates of the rural labour force.

SURVEY OF RURAL NON-AGRICULTURAL ENTERPRISES, 1985

54. The Survey of Rural Non-Agricultural Enterprises was undertaken from December 1985 to

April 1986 and had 1985 as the reference period. The survey had three main objectives:

a) To obtain information that would help estimate the contribution of rural-based activities to

wage employment and earnings;

b) To secure information on rural sectoral contribution to Gross Domestic Product; and

c) To shed light on the sectoral dispersion of rural non-agricultural enterprises.

55. Three sectors were covered: manufacturing, distribution, and services. The industries

excluded from the survey are building and construction, mining and quarrying, transport, and

handicrafts. The survey was establishment-based, and therefore excluded household-based enterprises

e.g. tailoring and beer brewing. All establishments included in the Master File were also excluded

since they are covered in the Annual Enumeration of Employees and Self-Employed Persons. The survey covered a sample of 67 rural and market centres selected from 525 centres listed in the

1979-1983 Development Plan. Information was collected from all unregistered establishments in each

selected centre with identifiable activities and engaging not more than 10 persons (working for pay or

profit). Care was taken not to include any centre with a population of more than 2,000 persons as this

would fall outside the definition of a rural centre. Information was obtained through personal

interviews by permanent field enumerators who were trained prior to commencement of the survey.

Information regarding persons engaged, and gross earnings and inputs (e.g. wages) were collected.

This made it feasible to compute gross product for this sector8. The data was grossed up to obtain

district, province and national totals taking into account the rural and market centres not included in

the sample survey.

56. Information on small rural non-agricultural enterprises for the years 1973, 1974, 1975 and

1976 is reported in the Report on Surveys of Industrial Production. However, only three sectors were covered: mining and quarrying, manufacturing, and building and construction. As it appears, this

information was collected as a subset of the Survey of Industrial Production. This means that

distribution and services were not included. In effect, therefore, comparison with the results of the

1985 survey is rather difficult.

THE NATIONAL MANPOWER SURVEY, 1986-1988

57. Manpower surveys facilitate the study of some characteristics of employed persons and assist

in determining their current and future stocks, and supply and demand patterns. Until recently,

manpower surveys were designed to collect data on high-level manpower as its supply was regarded as

a critical factor in development. This approach changed gradually as it became evident that the lower

levels of manpower were also important. The first manpower survey was conducted in 1964 and aimed

at collecting data on high-level manpower. Two more surveys were undertaken in 1967 and 1972 and

extended their coverage to include high and middle-level manpower but still excluded low-level

8 L.A. Ojiambo, Small Scale Business Enterprises Sector (informal sector) in rural Kenya: Concepts and

Preliminary GDP estimates for 1985, in: Kenya Symposium on Statistical Surveys, Central Bureau of Statistics, Ministry of Planning and National Development, 1988.

16

manpower. An attempt was made to carry out a comprehensive survey in 1982/83 but due to some

constraints the survey did not succeed. Arising from the non-completion of the 1982/83 survey, the

need for manpower data necessitated the execution of the 1986/88 National Manpower Survey. The

survey covered 2,201 establishments in the modern sector out of 2,483 that were sampled. The total

number of establishments in the modern sector was 42,378. The reference period of the survey was 1st

February to 30th June 1987 and achieved a response rate of above 80 percent.

58. The objectives of the 1986-88 National Manpower Survey were to:

a) Determine the stock and distribution of various categories of manpower in the modern sector;

b) Collect and determine characteristics of employed persons in the modern sector of the

economy;

c) Collect information which would assist in determining the number of vacant posts within

different sectors of the economy and assess the possible areas of emphasis in trying to alleviate

the unemployment situation;

d) Assess the relationship between demand and supply of manpower in the economy in order to

determine both current and future manpower balances;

e) Determine ways of improving the existing system of occupational classification in Kenya;

f) Assist in the formulation of long-term manpower development and utilization policies and

determine the profile of wages and earnings by occupation and economic activity;

g) Assist government in planning and programming educational and vocational training systems

more appropriate to the economys manpower requirements; and

h) Contribute towards the development of a sustained manpower databank which would

improve Kenyas capability for overall manpower planning.

59. Survey work was done in two phases: Phase 1 covered employees and information was

collected on a form designated as Form A, and Phase 2 covered employers and used a form designated

as Form B. The response rates achieved were 88.6 percent and 96.6 percent for phase I and phase II,

respectively. Non-response is attributed to the inadequacy of the frame, in particular the Register of

Establishments. It was in some cases found to include some establishments which had ceased to exist

and some agricultural establishments that had been subdivided into smaller units. Important

characteristics of the employees covered in the survey included age, sex, citizenship, and highest level

of education and training attained.

Methodology Used in the 1986-1988 Manpower Survey

60. The survey adopted a revised and updated 1982 Kenya National Occupational Classification

System (KNOCS) which was harmonized with national needs and the 1986 Draft Report of the ILO

International Standard Classification of Occupations (ISCO). In addition, the register of

establishments in the modern sector maintained by the CBS was updated. The survey was based on a

sample of establishments randomly selected from three sampling frames, i.e. (a) Register of

Establishments maintained by CBS for private sector, state corporations and municipalities, (b)

Nominal Civil Service (CS) roll, and (c) a list of educational institutions maintained by the Teachers

Service Commission (TSC). For the purpose of sample selection, establishments were the Primary

Sampling Units (PSU) and all manpower within the selected establishments were interviewed. In all

cases, a one-stage selection design was maintained resulting in simple estimation methodology. The

establishments were identified as falling in four categories: private establishments, municipalities,

TSC, and CS.

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61. The instrument used for data collection was the questionnaire which had two forms, A and B.

Form A collected the name of establishment, the name of the employee, age, sex, highest level of

education reached, training background, mode of training, current and previous occupation, number

of previous employers, remuneration in those occupations, and whether housed or not. Form B was

used to countercheck the accuracy of information reported in Form A and collected data on

promotions, entries, exits and the number of vacant posts in various occupations.

62. A pilot survey was conducted to test the questionnaire design, field logistics, survey

methodology, and sample frame. The findings of the pilot survey indicated shortcomings and

inadequacies in sample frame, logistical arrangements, questionnaires and KNOCS. Consequently, the

sample frames were updated, logistical arrangements were streamlined, the questionnaire variables

were rearranged, and KNOCS was improved and expanded in readiness for the main survey. Data

collection was through the enumerator-canvasser method. The country was divided into nine regions,

conforming with provincial boundaries except Rift Valley province which was subdivided into two

regions.

Limitations and Constraints in the 1986-1988 National Manpower Survey

63. As noted in An Overview Report of National Manpower Survey 1986-1988 (1988), while most of the objectives were addressed, four main areas were not fully addressed due to various inadequacies

in the data:

a) Data on new occupations which were not in the Kenya National Occupational Classification

System (KNOCS) were not analyzed.

b) Information on manpower supply was incomplete because only output from local education

and training institutions was considered. This was because data on Kenyans training abroad is

either sketchy or unavailable.

c) The questionnaire used could not determine whether or not employees not physically housed

by the employer were given house allowance.

d) Complete analysis of employment dynamics could not be ascertained because the data from

Phase II of the survey had not been weighted.

64. Survey results indicated that Kenya National Occupational Classification System required

further refinement as new occupations were encountered that were nonexistent in the KNOCS. The

revised 1986 KNOCS was an improvement on the 1982 version. In spite of this revision, a number of

occupations were still lumped together which made the classification imprecise in a number of cases.

For example, because certain occupations could not get their exact equivalents in KNOCS, they were

lumped under category other. These shortcomings were particularly more evident in medical and

technical occupations, especially at the middle level. It was therefore recommended that the KNOCS

be further refined to make it more precise and hence more relevant to the Kenyan labour market. In

addition, information on whether those not physically housed by their employers, especially in the

private sector, were given house allowance was not easy to determine due to the questionnaire design.

65. The register of establishments maintained by the CBS was found to be inadequate. Some

establishments had either ceased to exist or had been subdivided into a number of smaller ones which

had since changed their names. Such cases were common in agriculture, and transport and

communications. The coverage in these two industries was relatively low due to this problem. There

was also non-response of employees in some establishments due to their non-availability at the time of

the survey.

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66. With regard to the development of manpower databank for use in manpower planning, it was

observed that manpower data was not gathered and analyzed in a well coordinated manner. The

Central Bureau of Statistics, for instance, collects employment data on an annual basis through its

postal labour enumeration surveys which has problems of reliability due to low response rates.

Another problem is with regard to the occupational classification which is over-aggregated thereby

reducing its utility for manpower planning. This was the basis for the recommendation that the

Ministry of Manpower Development and Employment should establish a national manpower

information system to act as the countrys manpower databank. The system should be updated

periodically through undertaking of sectoral manpower surveys and other specialized studies on the

countrys human resources development and utilization. The ministry should also coordinate the

various aspects of human resource planning and development.

LABOUR FORCE SURVEYS

67. Kenya has undertaken three major labour force surveys which, taken together, provide a

wealth of information on the structure and composition of the labour force. The surveys are:

i The 1977/78 Labour Force Survey (LFS)

ii The 1986 Urban Labour Force Survey (ULFS)

iii The 1988/89 Rural Labour Force Survey (RLFS)

The last two surveys were undertaken in recognition of the fact that important changes had occurred

since 1977/78 and of the need for furthering the understanding of the dynamics of labour force

activity in Kenya. The ULFS and RLFS were designed to complement each other so as to provide an

overview of the Kenyan labour force. A summary of the findings from the 1977/78 LFS are given in

Economic Survey 1981 and the 1986 ULFS in Economic Survey 1990, while analysis of women and the labour force based on the three labour force surveys is summarized in Economic Survey 1991.

68. The outline begins by giving a brief theory that applies to the unemployment and

underemployment situation in Kenya. It then gives an outline on the application of labour force

concepts in the Kenyas surveys. And finally, it gives a brief account of each labour force survey and a

comparison among them.

Labour Force concepts

69. The term unemployment is taken to mean open unemployment, as opposed to any other form

of labour underutilization and includes both voluntary and involuntary unemployment. The notion of

voluntary unemployment does not imply that workers want to remain unemployed indefinitely; it

means that at least for a period of time, they prefer to wait and search rather than to accept

employment at the going wage, and is related to the gap between modern and informal sector wages,

as well as alternative income opportunities like family support and/or charity.

70. The causes of labour underutilization in the Kenyan economy may be described as a

consequence of the dualistic nature of the economy which is defined by the following stylized facts:

a) Coexistence of modern and more traditional technology and related conditions within the

same time and space;

b) Superiority of, and consumer preferences for, modern sector products;

c) Relative capital intensity of modern sector products which were typically designed for

high-income demand;

19

d) Scarcity of capital and skilled entrepreneurship;

e) Segmentation of factor markets, especially of the labour market, into a higher-wage modern

segment and the lower-wage informal and agricultural segments; and

f) Various policy biases in favour of the modern capital-intensive sector.

71. A theory of dualistic economy explains how these conditions interact and generate labour

underutilization, income inequality and poverty. It relates the existence of underemployment to the

characteristics of the dual economy and treats unemployment as an extreme form of

underemployment. In a theory of labour markets with well-behaved demand and supply functions,

involuntary unemployment result from downward wage rigidity; and a general weakness of labour

demand relative to its supply due to rapid population growth and migration on the supply side, and

mismatch between labour quality requirements and availabilities as well as scarcity of other

complementary factors on the demand side. The latter is caused by the relative capital intensity and

dominance of modern sector goods and services combined with the scarcity of capital and skilled

entrepreneurship.

72. Stylized facts of urban unemployment in Kenya, abstracted from the 1986 ULFS are:

Urban unemployment, as measured in the survey, is not high by the standards of developed

countries;

The survey did not find a lot of disguised unemployment at least by some definitions; and

The youth unemployment is very high compared with overall levels and female

unemployment is more than male unemployment.

73. Labour force participation rate is the proportion of persons in a given age group who either

worked a minimum of one hour in the last week (or yesterday in the case of 1977/78 LFS), or were the

head of a farm or business in the last week (yesterday). All labour force measures are made with

respect to a specific time period and generally refer to either the usually active population, or the

currently active population.

74. The currently active population refers to a short reference period, either one day or one week.

The ULFS and RLFS measurements of the currently economically active population used both a

one-week and a one-day reference period. The one-week reference period permits comparison

between the two surveys, while the one-day reference period permits comparison with the 1977/78

LFS.

75. The working age population: In rural Kenya, the working age population includes all persons

aged over seven years for both LFS and RLFS. The non-working age population was defined as all

persons seven years of age or less. The non-working age population was excluded from all labour force

analysis. The participation rate measures the proportion of the working age population who belonged

to the labour force. In both the LFS and RLFS, rural participation rates were very close to 100 percent,

and hence rates of open unemployment were almost zero.

76. Economic activity of labour was defined to include all persons who either (a) worked one hour

in an economic activity during last week/yesterday, (b) were the head of a family farm, or (c) looked

for work in the last year. All persons who did not do any of these activities were defined as

economically inactive. The economically inactive persons belonged to the following groups: (a) the

voluntarily inactive (approximately one in every 8 Kenyans in urban areas chooses to be voluntarily

inactive, majority of whom are females, e.g. housewives and/or engaged in childrearing activities); (b)

20

discouraged workers who represent a small proportion of the urban population between 15 and 64

years of age (tend to be dominantly female and 90 percent are less than 40 years of age); and (c) still at

school fulltime.

77. Employment was derived from the data on hours spent in specific activities in the last

week/yesterday. A person was considered to have been employed only if had worked a minimum of

one hour in an economic activity; or if no hours of work were recorded in the last week, a person must

be the head of the family farm (in the case of LFS and RLFS). A person was considered to be fully

employed if he worked a normal work week (or day), or if he worked less than the normal number of

hours, but did not look for an opportunity to work more hours.

78. Unemployment and Underemployment: The normal time worked was defined to be 40 hours

per week. A person was underemployed if he worked less hours than the norm, and had shown that he

was not satisfied with his present employment by looking for a different job or an extra job. The rate of

underemployment was the number of underemployed persons as a proportion of all employed

persons. In the case of both the LFS and the RLFS, it was difficult to capture visible underemployment

without conducting a monthly survey, since many agricultural jobs were highly seasonal with

alternating slack and intensive periods.

79. In addition, the urban labour force sampled only from those who were actually in urban areas,

while people in rural areas could be unemployed and searching for urban jobs by, say, counting on the

help of friends and writing letters while residing in the rural areas. These people would not be counted

as urban unemployed. This implicit unemployment model assumes that those who do not have

desirable urban jobs are trying to get one and must balance between being in the rural area, among the

urban unemployed, or among those employed in the urban informal sector. Consequently, the ULFS

attempts to discover the proportion of those employed that is dissatisfied and searching for other

work.

80. Four definitions for unemployment rates have been derived depending on the type of job

search considered to be valid, and whether underemployment is included or not. The four definitions

are best distinguished using the following table:

Employment Status Valid Job Search Criteria

Active Search Only Active and Passive Search

Conventional Employment Status A C

Underemployment included B D

81. Two definitions of job search activity are included in the ULFS: (a) the norm one, which

included a direct approach to an employer, union hall or labour exchange, answering a newspaper

advertisement, etc; and (b) a passive one which included asking friends and relatives as an

acceptable job search activity for being included in the labour force. There are, as a result, two

different measures of underemployment, one counting only those who qualify under only the

traditional or normal job search methods and one including those who qualify on the basis of having

used the passive methods. Including the amount of underemployment as part of overall

unemployment (A to B) increases urban unemployment by a meagre 0.6 percent. By the broadest

definition of search (including passive search), overall urban unemployment goes up by 2.9 percent,

from 14.8 percent to 17.7 percent, i.e. in moving from definition A to C.

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82. Definition A is the most restrictive and D the least restrictive. Thus, unemployment and

participation rates increase from A to D. Urban unemployment rates increase from the most restrictive

definitions of labour force and employment status to the least restrictive. Using the conventional

definition of unemployment, the overall participation rate is 81.5 percent for men and 54.4 percent for

women, and 69.3 percent overall. Irrespective of the underlying definitions, all aggregate participation

rates are higher for males than females by about one-third, except in the 15-19 age cohort. In terms of

age-groups, overall participation rates are lowest for 15-19 age group, evidently because of schooling,

and highest for the 40-44 age-group. Participation rates decline gradually for age cohorts beyond

40-44. The definition of unemployment is of importance not only for overall participation rates but

also for certain population disaggregation.

83. A person was considered underemployed under the following conditions:

a) He/she reported in the survey that he/she was unhappy with his/her current job because it was

part-time;

b) He/she had looked for a job in the last week; and

c) The hours worked was below the hours norm specified for his/her particular education level

and sex.

84. An appropriate hours norm was established by first examining the distribution of hours

worked for all fulltime workers of a given sex and education level. If the individual has more than one

job, total hours worked includes the sum of the hours he has worked in all jobs. Since the distributions

are so obviously non-normal, the hours norm was selected empirically using the bottom decile of the

distribution. This defines individuals in the bottom 10 percent of hours worked as underemployed.

The inclusion of underemployment in this manner has implications for participation rates. Individuals

who do not satisfy these three criteria for underemployment are treated as partially in the labour

force.

85. A similar procedure was used for low wage individuals. In order to be considered

underemployed, they had to be:

a. Actively seeking work in the last week, and

b. Earning less than the wage norm for their particular sex and education level.

86. Each underemployed individual was considered to be partially employed and unemployed

based on these criteria. The wage norm was set at the bottom decile of the wage distribution, and each

individuals employment rate calculated with respect to these wage norms. The treatment of

underemployment using these criteria has no effect on participation rates.

87. Based on these definitions, the following are some of the urban labour force surveys results:

a) Inclusion of urban underemployment increases the conventional (definition A)

unemployment rate by 1.6 percent.

b) Extending valid job search to network through friends and relatives increases the rate by

about 1 percent from 15.4 percent to 16.4 percent.

c) The combined effect of (a) and (b) is to increase the aggregate unemployment rate by a little

over 3 percent to 18.2 percent.

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d) These effects are also noticeable when males and females are analyzed separately. Though

female rates are double male rates, they are equally sensitive to the definitional extension of

valid job search.

e) The observations on unemployment rate-age patterns persist for all unemployment

definitions. However, with the inclusion of underemployment, the unemployment rate

increases proportionately more in the younger age groups than in the older cohorts, suggesting

a greater degree of underemployment for younger persons. Similarly, the inclusion of passive

job search raises the unemployment rate more for the youngest and oldest age groups, than for

those between 30 and 54 years.

The Methodology Used in the Labour Force Surveys

88. In 1977/78, a labour force survey was undertaken as a module within the Integrated Rural

Surveys. The objective of this survey was to examine labour supply and its utilization. As detailed in

the Integrated Rural Surveys 1976-79, all the rural households covered by the survey and 3,000 urban households were interviewed each month for a period of 12 months. All members of households,

including those attending school fulltime, were interviewed. Information collected included hours

worked on and off the holding, occupation and industry of employment, training received, and

whether or not respondents were looking for work. Results of the survey were analyzed and limited

copies of the report published in 1986. A summary of results was included in the 1981 edition of the

Economic Survey.

89. In order to update the 1977/78 survey, an urban labour force survey was undertaken in 1986

and its rural component undertaken in 1988/89, using the NASSEP II frame. The two surveys were a

joint project of CBS and the Long Range Planning Unit of the Ministry of Planning and National

Development. The Rural Labour Force Survey was undertaken in two phases (July to November 1988

and February to June 1989) in order to capture seasonality of rural employment, while the Urban

Labour Force Survey was a single round. The two surveys were based on slightly different definitions

from those used in 1977/78 survey which limited the comparisons which could be made. The basic

difference is in the length of the reference period. In the 1977-78 survey, a single day reference period

or yesterday approach was used while the 1986 Urban Labour Force Survey and 1988-89 Rural

Labour Force Survey used yesterday approach and one week approach. All these surveys gathered

basically the same information but the 1988-89 Rural Labour Force Survey collected additional

information on urban to rural migration for purposes of job search.

90. The coverage of the 1977/78 LFS was the entire country, both rural and urban. The aim of the

survey was to measure employment and unemployment. Urban unemployment was 6-7% of the

labour force, which was probably underestimated. It concluded that unemployment in rural areas was

virtually nonexistent and the concept of unemployment cannot therefore be usefully applied to rural

areas. But there did exist significant labour underutilization, as measured by the number of hours

worked per day.

91. The 1986 ULFS was designed with the key objective of updating information on:

a) Estimation of participation rates and unemployment rates, as well as examination of the

importance of several determinants of these rates;

b) Analysis of the structure and composition of the labour force; and

c) Examination of the magnitude of open unemployment and of the significance of

underemployment.

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92. The survey results using one day reference period showed that the urban unemployment rate

remained the same as in 1977/78, while the participation and unemployment rates of females

increased. The overall rate of urban unemployment of 16.2 percent was lower than in most other

African countries.

93. The 1988/89 RLFS was designed to provide an overview of the Kenyan rural labour force. The

objectives of the RLFS were:

a) To provide information on labour activities of rural residents;

b) To provide measures of participation, unemployment and underemployment; and

c) To complement the 1986 ULFS and therefore provide a complete scenario of the labour force

in Kenya in recent years.

94. The 1988/89 RLFS covered about 95 per cent of the rural population in 30 out of 39 rural

districts (i.e. excluding Nairobi and Mombasa). The districts not covered included Lamu, Tana River,

Garissa, Mandera, Wajir, Isiolo, Marsabit, Samburu and Turkana. A 10 per cent sample was drawn

from the NASSEP II based on the 1979 census. A pilot survey conducted in March 1988 revealed that

the concept of employment was particularly difficult as most farmers and family farm workers did not

consider themselves to be employed. The questionnaire was subsequently redesigned to capture the

hours of work spent in various activities. The overall response rate for Phase I was 78.1 percent (8,102

households) and 77.5 percent for Phase II (7,869 households).

95. Due to cost and time constraints, it was not possible to conduct a monthly survey to fully

capture the seasonality of rural employment. The timing of the survey, therefore, was designed to

provide two periods within which varying amounts of activity were recorded in rural areas. The

periods were chosen based on normal agricultural seasons in Kenya. The information collected in

RLFS included:

a) The structure of the economically active population in rural areas;

b) Patterns of employment, in particular, the nature and extent of nonfarm activities;

c) Hours of work in various activities;

d) Open unemployment in rural areas;

e) The magnitude of underemployment in rural areas; and

f) The educational attainment of respondents by age and sex.

96. One major problem in the interpretation of the data arises from the definition of employment.

Only few rural respondents report as unemployed in the technical sense; but this may be obscuring a

high level of underemployment and hidden unemployment. This means that the unemployment rate

of 0.3 percent calculated on the basis of the Rural Labour Force Survey of 1988-89 could be hiding a

substantial level of unemployment and underemployment. However, the 16.2 percent urban

unemployment rate may be more representative of the picture in urban areas due to the nature of

urban economies in comparison with rural areas.

Comparisons among the Labour Force Surveys

97. In comparing the 1977/78 LFS with the 1986 ULFS, the primary concern is whether

statistically significant changes in unemployment or participation rates have occurred over the 8-year

period. Despite comparable sampling designs and sample composition, the results must be made

compatible by adequately accounting for the differences in conceptualization and questionnaire

structure. The most significant difference is the length of the reference period used.

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98. As mentioned above, the three surveys were based on slightly different definitions of labour

force activity and unemployment. The most significant of these differences is the length of the

reference period used in the survey. The results suggest that the ULFS respondents are generally

representative of the urban population of Kenya with respect to both the age-sex breakdown and the

level of formal education.

99. While the 1977/78 survey covered both urban and rural areas of the country, the 1986 ULFS is

limited to urban areas. Comparisons between the two surveys are thus limited to characteristics of the

urban labour force as follows:

a) The urban areas used in the 1977/78 survey are based on urban areas defined for the 1969

census, while the urban areas sampled for the 1986 survey