Identification
Survey ID Number
KEN-KNBS-LFS-1999-v02.
Title
The Integrated Labour Force Survey, 1998/99, Second round
Subtitle
Second round
Country
| Name | Country code |
|---|---|
| Kenya | KEN |
Study type
Labor Force Survey [hh/lfs]
Abstract
The 1998/99 Integrated Labour Force Survey (ILFS) was the first of its kind to integrate three related surveys (labour force, informal sector and child labour modular surveys) into a single cost-effective survey. It was conducted over the whole country on the household-based NASSEP III sample frame, and covered 11,049 households giving a response rate of 86.2 per cent. As such, the survey collected a wide range of representative information that can be used in the design, implementation, monitoring and evaluation of various policies and programmes. In particular, it provides indicators such as school enrolments rates, housing conditions, access to amenities and facilities, income and expenditures, unemployment rates, and income and expenditure levels which should provide invaluable inputs into the monitoring and evaluation of the economic reforms and poverty reduction programmes that are being implemented by the Government.
The key objectives of the survey were to update data on the labour force, determine the size and output of the informal sector, and estimate the extent of child labour. A rich data bank has been created as a by-product of data processing exercise, which can be used to carry out further analysis of the information collected by the survey.
In designing and implementing the survey, CBS worked closely with other stakeholders through the Inter-Ministerial Steering Committee (IMSC) that was formed to provide overall guidance on the implementation of the survey. The committee was composed of representatives from Ministry of Labour and Human Resource Development, Ministry of Education Science and Technology, and the Macro Planning and Human Resources and Social Services departments in the Ministry of Finance and Planning. A Technical Working Group (TWG) was formed as the survey's secretariat that undertook day-to-day activities on the implementation of the survey.
The Surveyed Population
Age-sex Structure
The age-sex pyramid of the surveyed population depicts a youthful population, with those aged below 15 years absorbing 42.3 per cent of the population, leading to a dependency ration of 85.3 per cent. The sex ratio was 0.997 for the whole population and 1.06 at birth (age 0-4). The average household size was 4.2 persons (3.3 persons in urban areas and 4.7 persons in rural areas).
Marital status and migration patterns
An estimated 42.7 per cent of the population aged over 12 years had never married. Of those ever married, 51.3 per cent were in current marriage, 3.5 per cent widowed and 3.6 per cent separated or divorced. There was evidence of early marriages where 5.0 percent of the population aged 13-17 reported they were currently married.
Education and Literacy
There were 3.6 million children in primary and 0.9 million children in secondary schools, giving gross enrolment ratios of 89.1 percent and 30.7 percent respectively. Student sex ratio, or ratio of males for females, in primary schools was 1.08, while that for secondary schools was 1.20. About 16.4 percent of the Kenyan population aged over 5 years and over reported to have had no formal education at all. Those with primary education constituted 59.0 per cent of the referenced population while 19.7 percent had attained secondary education. Only 1.1 per cent had attained university education.
Housing and amenities
About 31.0 per cent of the households had a permanent dwelling unit. Majority of the rural households reported that they owned both the dwelling units they lived in and the land on which it was built, while almost all the urban residents lived in rented dwelling units. About 12.5 per cent of households, mainly in the rural areas, reported they had no toilet facilities. The commonest type of waste disposal was pit latrine, but flush toilet was prevalent in urban areas. Most of the rural households travelled long distances to fetch water, while 80.4 percent of the urban households had water within 50 meters.
Firewood was the commonest type of cooking fuel in rural areas, while paraffin (53.3 per cent) and charcoal (22.6 per cent) were the main types of cooking fuels in urban areas. About 77.2 per cent of responding households were using paraffin to light their houses, with 90.5 per cent in rural areas. Urban areas mainly relied on paraffin (50.7 per cent) and electricity (41.8 per cent) as the chief sources of lighting.
Migration Patterns
The overall out-migration rate was 13.2 percent, with rural areas losing a large portion of its population to urban areas. Among the eight provinces, Nairobi, Western and Central experienced significant out-migration of over 15.0 percent. Overall, urban areas were net gainers in population flows within the country.
Household expenditure
Overall mean monthly expenditure per household amounted to Kshs 6,343. Monthly mean expenditures for rural households were estimated at Kshs 4,101, while the urban equivalent was Kshs 10,826. There were expenditure differentials between male- and female-headed households, where mean monthly expenditures for female-headed households in rural areas was Kshs 2,986, quite below he monthly expenditure of Kshs 4,620 for male-headed households. Similarly, mean expenditure for male-headed households in urban areas was almost twice that of female-headed households.
The Labour Force Participation
Economic activity
The results show that there were 15.9 million persons aged 15-64 (the working population) of which 77.4 per cent reported to be economically active. Most of the active population was youth between 24-34 years of age. About 14.6 percent of the economically active were unemployed. Some 3.6 million persons reported to be economically inactive, representing 22.6 per cent of the population aged 15-64 years. Majority of the inactive population was full time students (47.3 per cent). Only 2.0 per cent of the inactive population reported they were out of the labour force because they were retired.
Participation Rates
The overall labour force participation rate for the population aged 15 - 64 years stood at 73.6 per cent. Urban areas had higher labour force participation rate of 86.4 per cent compared to rural areas with a rate of 73.8 per cent. Males had a slightly higher participation rate of 74.7 per cent compared to that of females at 72.6 per cent. The results show that participation rates increase along the age spectrum to about 95.2 for the age group 40 - 44 before levelling to 80. 1 per cent for the age cohort 60 - 64. Also, participation rates tend to rise with the level of formal education, rising from 83.7 per cent for those with no education to over 98.8 per cent for those who have completed post-graduate education.
Employment
The number of employed persons aged 15-64 years stood at 10.5 million persons, giving employment rate of 85.4 per cent. The overall employment sex ratio was 1.08, but females dominated rural based small-scale farming and pastoralist activities, with a sex ratio of 0.67. Rural area absorbed 70.1 per cent of the employed persons. The working population was largely made up of unpaid family workers (39.6 per cent), mostly working in the rural areas and paid employees, largely concentrated in urban areas (33.4 per cent). Self-employed persons constituted 23.8 per cent of the employed. Of the three sectors of the economy, small-scale farming and pastoralist activities engaged 42.1 per cent of workers. Informal sector and formal or modern sector absorbed 31.6 per cent and 26.3 per cent of the total workforce.
Occupations and industry
Most of the employed persons reported to be skilled agricultural and fishery workers (37.3 per cent), largely self-employed based in rural areas. Professionals were mainly in paid employment, and accounted for only 1.2 per cent of the employed persons. The agricultural activities absorbed 63.1 per cent of the employed persons. The other major employers were the service industries with community, social and personal services accounting for 6.1 per cent of the employed. The least popular industries were private households with employed persons, and electricity and water supply. The number of females employed in activities traditionally dominated by males such as construction, mining and quarrying was notably low. However, females were concentrated in agricultural activities, trades, and educational services.
Hours of work
Most workers reported 40 working hours per week with a significant proportion of the urban population working above the average hours. Urban workers generally reported to have worked for longer hours than workers in rural areas. Gender analysis shows that females worked for fewer hours than males, particularly in the rural areas. However, females who worked in urban areas (in private households as housemaids) were working quite above 40 hours in a week.
Wage levels
Average earnings amounted to KShs 7,766 per month, with the main source of employee's remuneration being basic salary, which formed 81.3 per cent of the overall earnings per person. Earnings in urban were almost double the average earnings in rural areas. There were significant disparities in earnings by gender as females were earnings wages quite below their male counter parts in both rural and urban areas.
Unemployment
There were 1.8 million unemployed persons aged 15-64 years, giving an overall unemployment rate of 14.6 per cent. The urban unemployment rate had risen from -- per cent in 1989 to 25.1 per cent by 1999. Like wise, unemployment in the rural areas was high at 9.4 per cent, but less acute then in urban areas. Most of the unemployed were youth and females. Most of the unemployed persons (94.2 per cent) were looking for paid employment during the one-week reference period. It is also worth noting the shift from subsistence farming, as more jobs searchers were ready to start self-employment (mainly found in mostly in the expanding informal sector) than farming activities in the small-scale and pastoralist sector. The main mode of job search in both urban and rural areas was to ask friends or relatives (41.3 per cent), followed by a direct approach to the employer (32.8 per cent).
Under-employment
About 4.8 per cent of the 10.5 million working persons were under-employed. Majority of the underemployed worked for between 18 to 25 hours during the survey's reference week. Males constituted 65.1 per cent of the under-employed persons.
Working Children
Size and School Status
Despite concerted global and domestic efforts aimed at combating the child labour practices, 1.9 million children (17.4 percent of the children aged 5-17) were reported to have worked either in the last week or at any other time within the 12 months preceding the survey. These were composed of 984,168 boys and 909,596 girls. The proportion of working children to the total population of children aged 5-17 years was significantly higher in the rural (19.7 per cent) than the urban (9.0 per cent) areas. About 8 per cent of the 7.4 million schooling children worked during the year, while 37.4 per cent of the 3.5 million out of school children reported to have worked. Majority of the working children (78.7 per cent) worked as unpaid family workers in family farms or businesses. About 18.5 per cent of these children reported to have worked for pay, while only 1.6 per cent operated their own businesses.
Occupation and industry
Most of the children were engaged in elementary occupations, with 34.0 per cent engaged as commercial agriculture and fishery workers, 23.6 per cent as subsistence agricultural and fishery workers, and. 17.9 per cent engaged as domestic and related helpers, cleaners and launders. There were more girls than boys working as domestic related helpers (mainly as maids), and as personal care and related service workers such as hairdressing. Boys (especially older boys aged 15-17 years) were a majority in the activities that are traditionally male dominated, such as fishing and fishing services, quarrying of stones and clay and building activities.
Working Conditions
The results show that children work for long hours, with 38.5 per cent of children working for more than 41 hours in a week; mainly in the private households as domestic servants, fishing and fishing services, mining of stones and clay and road transport. Consequently, many children were reported to have either fallen sick or injured in their places of work. Majority of the children in wage employment (63.6 per cent) earned less than KShs 900 per month, slightly below the statutory minimum wage of KShs 998 per month set for unskilled employees aged below 18 years who work in the agricultural sector (Regulations of Wages Order, 1998).
Reasons for Working
Poverty was a cause of child labour as 21.3 per cent of the working children were from very poor households with a monthly income of less than Kshs 2,001, while 57.9 per cent came from households with monthly income below Kshs 6,001. The situation is supported by reasons given for working, where 27.5 per cent of parents reported that they released their children for work so as augmenting the household income. Similarly, 22.9 per cent of the working children reported to have worked so as to augment household income, while 22.2 per cent reported that they worked to support themselves.
Child labour
Not all work undertaken by the 1.9 million working children was child labour - which was defined as work undertaken by children aged 5-17 years, which prevents them from attending school, and is exploitative, hazardous or inappropriate for their age. Going by schooling indicator, child labour in Kenya can therefore be estimated at 1.3 million children. Further, the survey revealed some worst forms of child labour where 15 thousand children were engaged in activities that are unsafe and risky for young persons, e.g. fishing, mining and quarrying, building and construction, and road transport. Even though, there were no reported cases of worst forms of child labour child with respect to slavery or recruitment of children for use in armed conflict.
The Informal Sector
The enterprises and operators
The number of informal sector enterprises was estimated at 2.7 million. About 70.0 per cent of the enterprises were based in rural areas. Many of the enterprises were engaged in wholesale and retail trades (64.5 per cent) and the manufacturing activities (24.0 per cent). Males owned 53.0 per cent of the enterprises in rural areas, while females owned 55.3 per cent of the urban-based enterprises.
Informal sector activities were carried out in a variety of worksites, with commercial premises hosting 39.5 per cent, residential houses 18.0 per cent, and open markets 12.9 per cent. However, most of the activities in the urban areas were carried out on the roadside and pavements.
Majority of the informal sector activities were carried out without licences, while 15.2 per cent were authorised by local authorities. Among the 8 provinces Nyanza hosted most of the enterprises (22.5 per cent) followed by Rift Valley, (20.9 per cent), Central (18.2 per cent) and Eastern (13.7 per cent) provinces.
Employment creation
Employment in the informal sector was estimated at 3.6 million. Urban areas absorbed 34.1 per cent of the informal sector employment. Most of the employees were self- employed (75.4 per cent) followed by wage employees (19.2 per cent).
Nationally there were more males than females working in the informal sector, giving a sex ration of 1.2. Wholesale and retail trades absorbed 56.6 per cent, while manufacturing activities absorbed 23.0 per cent of the informal sector employment.
Goods and services
The informal sector produces and offers a wide spectrum of commodities and services, where the latter represented about 66.9 per cent of the total output. The most prominent commodities were textile fibres and their wastes (60.5 per cent of the enterprises). The informal sector activities also included small-scale extractive industries such as stone quarrying and sand harvesting. Services offered by the informal sector businesses were mainly retail trades (71.1. per cent of reporting enterprises), catering and selling of drinks (11.2. per cent), among other services.
Gross Domestic Product (GDP)
Value added of the informal sector activities amounted to a monthly figure of KShs 6.0 billion, with an operating surplus of KShs 4.6 billion.
Recommendations
The rapid changes in the structure of the labour force and the increasing role played by the expanding informal sector in our economy suggest that surveys of this kind be conducted more regularly. Information collected by such surveys will facilitate better understanding of the dynamics of the labour market in general and the informal sector in particular. However, future sample surveys must be conducted on an updated sample frame so as to minimise sampling errors and facilitate generation of realistic district level estimates.
Also, the wealth of data collected by the 1998/99 ILFS should be used for in-depth analysis, especially on the informal sector module data.
The key objectives of the survey were to update data on the labour force, determine the size and output of the informal sector, and estimate the extent of child labour. A rich data bank has been created as a by-product of data processing exercise, which can be used to carry out further analysis of the information collected by the survey.
In designing and implementing the survey, CBS worked closely with other stakeholders through the Inter-Ministerial Steering Committee (IMSC) that was formed to provide overall guidance on the implementation of the survey. The committee was composed of representatives from Ministry of Labour and Human Resource Development, Ministry of Education Science and Technology, and the Macro Planning and Human Resources and Social Services departments in the Ministry of Finance and Planning. A Technical Working Group (TWG) was formed as the survey's secretariat that undertook day-to-day activities on the implementation of the survey.
The Surveyed Population
Age-sex Structure
The age-sex pyramid of the surveyed population depicts a youthful population, with those aged below 15 years absorbing 42.3 per cent of the population, leading to a dependency ration of 85.3 per cent. The sex ratio was 0.997 for the whole population and 1.06 at birth (age 0-4). The average household size was 4.2 persons (3.3 persons in urban areas and 4.7 persons in rural areas).
Marital status and migration patterns
An estimated 42.7 per cent of the population aged over 12 years had never married. Of those ever married, 51.3 per cent were in current marriage, 3.5 per cent widowed and 3.6 per cent separated or divorced. There was evidence of early marriages where 5.0 percent of the population aged 13-17 reported they were currently married.
Education and Literacy
There were 3.6 million children in primary and 0.9 million children in secondary schools, giving gross enrolment ratios of 89.1 percent and 30.7 percent respectively. Student sex ratio, or ratio of males for females, in primary schools was 1.08, while that for secondary schools was 1.20. About 16.4 percent of the Kenyan population aged over 5 years and over reported to have had no formal education at all. Those with primary education constituted 59.0 per cent of the referenced population while 19.7 percent had attained secondary education. Only 1.1 per cent had attained university education.
Housing and amenities
About 31.0 per cent of the households had a permanent dwelling unit. Majority of the rural households reported that they owned both the dwelling units they lived in and the land on which it was built, while almost all the urban residents lived in rented dwelling units. About 12.5 per cent of households, mainly in the rural areas, reported they had no toilet facilities. The commonest type of waste disposal was pit latrine, but flush toilet was prevalent in urban areas. Most of the rural households travelled long distances to fetch water, while 80.4 percent of the urban households had water within 50 meters.
Firewood was the commonest type of cooking fuel in rural areas, while paraffin (53.3 per cent) and charcoal (22.6 per cent) were the main types of cooking fuels in urban areas. About 77.2 per cent of responding households were using paraffin to light their houses, with 90.5 per cent in rural areas. Urban areas mainly relied on paraffin (50.7 per cent) and electricity (41.8 per cent) as the chief sources of lighting.
Migration Patterns
The overall out-migration rate was 13.2 percent, with rural areas losing a large portion of its population to urban areas. Among the eight provinces, Nairobi, Western and Central experienced significant out-migration of over 15.0 percent. Overall, urban areas were net gainers in population flows within the country.
Household expenditure
Overall mean monthly expenditure per household amounted to Kshs 6,343. Monthly mean expenditures for rural households were estimated at Kshs 4,101, while the urban equivalent was Kshs 10,826. There were expenditure differentials between male- and female-headed households, where mean monthly expenditures for female-headed households in rural areas was Kshs 2,986, quite below he monthly expenditure of Kshs 4,620 for male-headed households. Similarly, mean expenditure for male-headed households in urban areas was almost twice that of female-headed households.
The Labour Force Participation
Economic activity
The results show that there were 15.9 million persons aged 15-64 (the working population) of which 77.4 per cent reported to be economically active. Most of the active population was youth between 24-34 years of age. About 14.6 percent of the economically active were unemployed. Some 3.6 million persons reported to be economically inactive, representing 22.6 per cent of the population aged 15-64 years. Majority of the inactive population was full time students (47.3 per cent). Only 2.0 per cent of the inactive population reported they were out of the labour force because they were retired.
Participation Rates
The overall labour force participation rate for the population aged 15 - 64 years stood at 73.6 per cent. Urban areas had higher labour force participation rate of 86.4 per cent compared to rural areas with a rate of 73.8 per cent. Males had a slightly higher participation rate of 74.7 per cent compared to that of females at 72.6 per cent. The results show that participation rates increase along the age spectrum to about 95.2 for the age group 40 - 44 before levelling to 80. 1 per cent for the age cohort 60 - 64. Also, participation rates tend to rise with the level of formal education, rising from 83.7 per cent for those with no education to over 98.8 per cent for those who have completed post-graduate education.
Employment
The number of employed persons aged 15-64 years stood at 10.5 million persons, giving employment rate of 85.4 per cent. The overall employment sex ratio was 1.08, but females dominated rural based small-scale farming and pastoralist activities, with a sex ratio of 0.67. Rural area absorbed 70.1 per cent of the employed persons. The working population was largely made up of unpaid family workers (39.6 per cent), mostly working in the rural areas and paid employees, largely concentrated in urban areas (33.4 per cent). Self-employed persons constituted 23.8 per cent of the employed. Of the three sectors of the economy, small-scale farming and pastoralist activities engaged 42.1 per cent of workers. Informal sector and formal or modern sector absorbed 31.6 per cent and 26.3 per cent of the total workforce.
Occupations and industry
Most of the employed persons reported to be skilled agricultural and fishery workers (37.3 per cent), largely self-employed based in rural areas. Professionals were mainly in paid employment, and accounted for only 1.2 per cent of the employed persons. The agricultural activities absorbed 63.1 per cent of the employed persons. The other major employers were the service industries with community, social and personal services accounting for 6.1 per cent of the employed. The least popular industries were private households with employed persons, and electricity and water supply. The number of females employed in activities traditionally dominated by males such as construction, mining and quarrying was notably low. However, females were concentrated in agricultural activities, trades, and educational services.
Hours of work
Most workers reported 40 working hours per week with a significant proportion of the urban population working above the average hours. Urban workers generally reported to have worked for longer hours than workers in rural areas. Gender analysis shows that females worked for fewer hours than males, particularly in the rural areas. However, females who worked in urban areas (in private households as housemaids) were working quite above 40 hours in a week.
Wage levels
Average earnings amounted to KShs 7,766 per month, with the main source of employee's remuneration being basic salary, which formed 81.3 per cent of the overall earnings per person. Earnings in urban were almost double the average earnings in rural areas. There were significant disparities in earnings by gender as females were earnings wages quite below their male counter parts in both rural and urban areas.
Unemployment
There were 1.8 million unemployed persons aged 15-64 years, giving an overall unemployment rate of 14.6 per cent. The urban unemployment rate had risen from -- per cent in 1989 to 25.1 per cent by 1999. Like wise, unemployment in the rural areas was high at 9.4 per cent, but less acute then in urban areas. Most of the unemployed were youth and females. Most of the unemployed persons (94.2 per cent) were looking for paid employment during the one-week reference period. It is also worth noting the shift from subsistence farming, as more jobs searchers were ready to start self-employment (mainly found in mostly in the expanding informal sector) than farming activities in the small-scale and pastoralist sector. The main mode of job search in both urban and rural areas was to ask friends or relatives (41.3 per cent), followed by a direct approach to the employer (32.8 per cent).
Under-employment
About 4.8 per cent of the 10.5 million working persons were under-employed. Majority of the underemployed worked for between 18 to 25 hours during the survey's reference week. Males constituted 65.1 per cent of the under-employed persons.
Working Children
Size and School Status
Despite concerted global and domestic efforts aimed at combating the child labour practices, 1.9 million children (17.4 percent of the children aged 5-17) were reported to have worked either in the last week or at any other time within the 12 months preceding the survey. These were composed of 984,168 boys and 909,596 girls. The proportion of working children to the total population of children aged 5-17 years was significantly higher in the rural (19.7 per cent) than the urban (9.0 per cent) areas. About 8 per cent of the 7.4 million schooling children worked during the year, while 37.4 per cent of the 3.5 million out of school children reported to have worked. Majority of the working children (78.7 per cent) worked as unpaid family workers in family farms or businesses. About 18.5 per cent of these children reported to have worked for pay, while only 1.6 per cent operated their own businesses.
Occupation and industry
Most of the children were engaged in elementary occupations, with 34.0 per cent engaged as commercial agriculture and fishery workers, 23.6 per cent as subsistence agricultural and fishery workers, and. 17.9 per cent engaged as domestic and related helpers, cleaners and launders. There were more girls than boys working as domestic related helpers (mainly as maids), and as personal care and related service workers such as hairdressing. Boys (especially older boys aged 15-17 years) were a majority in the activities that are traditionally male dominated, such as fishing and fishing services, quarrying of stones and clay and building activities.
Working Conditions
The results show that children work for long hours, with 38.5 per cent of children working for more than 41 hours in a week; mainly in the private households as domestic servants, fishing and fishing services, mining of stones and clay and road transport. Consequently, many children were reported to have either fallen sick or injured in their places of work. Majority of the children in wage employment (63.6 per cent) earned less than KShs 900 per month, slightly below the statutory minimum wage of KShs 998 per month set for unskilled employees aged below 18 years who work in the agricultural sector (Regulations of Wages Order, 1998).
Reasons for Working
Poverty was a cause of child labour as 21.3 per cent of the working children were from very poor households with a monthly income of less than Kshs 2,001, while 57.9 per cent came from households with monthly income below Kshs 6,001. The situation is supported by reasons given for working, where 27.5 per cent of parents reported that they released their children for work so as augmenting the household income. Similarly, 22.9 per cent of the working children reported to have worked so as to augment household income, while 22.2 per cent reported that they worked to support themselves.
Child labour
Not all work undertaken by the 1.9 million working children was child labour - which was defined as work undertaken by children aged 5-17 years, which prevents them from attending school, and is exploitative, hazardous or inappropriate for their age. Going by schooling indicator, child labour in Kenya can therefore be estimated at 1.3 million children. Further, the survey revealed some worst forms of child labour where 15 thousand children were engaged in activities that are unsafe and risky for young persons, e.g. fishing, mining and quarrying, building and construction, and road transport. Even though, there were no reported cases of worst forms of child labour child with respect to slavery or recruitment of children for use in armed conflict.
The Informal Sector
The enterprises and operators
The number of informal sector enterprises was estimated at 2.7 million. About 70.0 per cent of the enterprises were based in rural areas. Many of the enterprises were engaged in wholesale and retail trades (64.5 per cent) and the manufacturing activities (24.0 per cent). Males owned 53.0 per cent of the enterprises in rural areas, while females owned 55.3 per cent of the urban-based enterprises.
Informal sector activities were carried out in a variety of worksites, with commercial premises hosting 39.5 per cent, residential houses 18.0 per cent, and open markets 12.9 per cent. However, most of the activities in the urban areas were carried out on the roadside and pavements.
Majority of the informal sector activities were carried out without licences, while 15.2 per cent were authorised by local authorities. Among the 8 provinces Nyanza hosted most of the enterprises (22.5 per cent) followed by Rift Valley, (20.9 per cent), Central (18.2 per cent) and Eastern (13.7 per cent) provinces.
Employment creation
Employment in the informal sector was estimated at 3.6 million. Urban areas absorbed 34.1 per cent of the informal sector employment. Most of the employees were self- employed (75.4 per cent) followed by wage employees (19.2 per cent).
Nationally there were more males than females working in the informal sector, giving a sex ration of 1.2. Wholesale and retail trades absorbed 56.6 per cent, while manufacturing activities absorbed 23.0 per cent of the informal sector employment.
Goods and services
The informal sector produces and offers a wide spectrum of commodities and services, where the latter represented about 66.9 per cent of the total output. The most prominent commodities were textile fibres and their wastes (60.5 per cent of the enterprises). The informal sector activities also included small-scale extractive industries such as stone quarrying and sand harvesting. Services offered by the informal sector businesses were mainly retail trades (71.1. per cent of reporting enterprises), catering and selling of drinks (11.2. per cent), among other services.
Gross Domestic Product (GDP)
Value added of the informal sector activities amounted to a monthly figure of KShs 6.0 billion, with an operating surplus of KShs 4.6 billion.
Recommendations
The rapid changes in the structure of the labour force and the increasing role played by the expanding informal sector in our economy suggest that surveys of this kind be conducted more regularly. Information collected by such surveys will facilitate better understanding of the dynamics of the labour market in general and the informal sector in particular. However, future sample surveys must be conducted on an updated sample frame so as to minimise sampling errors and facilitate generation of realistic district level estimates.
Also, the wealth of data collected by the 1998/99 ILFS should be used for in-depth analysis, especially on the informal sector module data.
Kind of Data
Sample survey data [ssd]
Unit of Analysis
A labour force survey had the following units of analysis: individuals, households, and establishments.
Version
Version Description
v1.3
Version Date
2008-11-16
Coverage
Geographic Coverage
National coverage
Universe
The survey covered all de jure household members, all persons above 5 years
Producers and sponsors
Primary investigators
| Name | Affiliation |
|---|---|
| Kenya National Bureau of Statistics | Ministry of State for Planning National Development and Vision 2030 |
Funding Agency/Sponsor
| Name | Abbreviation | Role |
|---|---|---|
| International Labour Organisation | ILO | Financial support |
Other Identifications/Acknowledgments
| Name | Role |
|---|---|
| International Programme for Elimination of Child Labour | Technical and financial assistance during the design stage |
| United Nations Development Fund | Funded the data collection phase |
| The World Bank | Funded the data processing and report writing phases |
Sampling
Sampling Procedure
The sample for the 1998/99 Integrated Labour Force Survey (ILFS) was drawn from the NASSEP III master sample frame, which was developed from the population count of the 1989 Population and Housing Census. The frame covered all the districts (excluding Turkana, Marsabit, and Samburu) that were in existence during its inception in 1989. The master sample frame, which is a two stage stratified cluster design, is multi-purpose for household-based surveys.
In the design of NASSEP III, the Enumeration Areas (EAs) of the 1989 population census were the Primary Sampling Units (PSUs). The PSUs were selected using the Probability Proportional to Size (PPS) method, and were then segmented into smaller units of about 100 households, constituting one Measure of Size (MOS). One segment from each PSU was selected randomly for the creation of a “cluster”. The frame was further categorised into urban and rural sub-strata. The urban component comprised 329 clusters (of which 209 are operational) spread over 63 urban centres, with population 10,000 and over, including all district headquarters with the exception of Marsabit, Mararal and Lodwar towns. The rural component of the frame had a total of 952 clusters (of which 930 are operational) spread over 34 districts as constituted in 1989, but excluded Turkana, Marsabit, Samburu and the North Eastern districts of Wajir, Garissa and Mandera. In creating the rural component of the frame, each of the 34 districts covered was treated as a stratum. The allocation of the PSUs to the rural districts was done proportionately to the population size. The allocation of the clusters to the districts varied between 12 and 36 clusters, with sparsely populated districts assigned fewer clusters than densely populated districts.
Sample Size Determination
The child labour phenomenon was used in determining the appropriate sample size, so as to increase the chances of capturing working children in sampled households since the child labour incidence is a rare event. First, it was estimated that children aged 5-17 years constituted 37.0 percent of the listings of 1996, and also in the December 1998 population projections. Also, the proportion of working children falling in this age interval was estimated to lie between 15 percent and 19 percent (using the results of the 1989 Population and Housing Census). Using a margin of error of 5 percent and a confidence level of 95 percent with an adjustment for the design effect of 2.0, a sample size of 54,000 persons was estimated for the survey. Working with average household size of 4.2 persons, the sample size translated into 12,814 households, which were selected by a systematic selection of every tenth household in each cluster. Where the calculated number fell below 10 households, a minimum of 10 households was taken in all such cases. The resultant sample size was observed to be sufficient to provide national and provincial estimates.
In the design of NASSEP III, the Enumeration Areas (EAs) of the 1989 population census were the Primary Sampling Units (PSUs). The PSUs were selected using the Probability Proportional to Size (PPS) method, and were then segmented into smaller units of about 100 households, constituting one Measure of Size (MOS). One segment from each PSU was selected randomly for the creation of a “cluster”. The frame was further categorised into urban and rural sub-strata. The urban component comprised 329 clusters (of which 209 are operational) spread over 63 urban centres, with population 10,000 and over, including all district headquarters with the exception of Marsabit, Mararal and Lodwar towns. The rural component of the frame had a total of 952 clusters (of which 930 are operational) spread over 34 districts as constituted in 1989, but excluded Turkana, Marsabit, Samburu and the North Eastern districts of Wajir, Garissa and Mandera. In creating the rural component of the frame, each of the 34 districts covered was treated as a stratum. The allocation of the PSUs to the rural districts was done proportionately to the population size. The allocation of the clusters to the districts varied between 12 and 36 clusters, with sparsely populated districts assigned fewer clusters than densely populated districts.
Sample Size Determination
The child labour phenomenon was used in determining the appropriate sample size, so as to increase the chances of capturing working children in sampled households since the child labour incidence is a rare event. First, it was estimated that children aged 5-17 years constituted 37.0 percent of the listings of 1996, and also in the December 1998 population projections. Also, the proportion of working children falling in this age interval was estimated to lie between 15 percent and 19 percent (using the results of the 1989 Population and Housing Census). Using a margin of error of 5 percent and a confidence level of 95 percent with an adjustment for the design effect of 2.0, a sample size of 54,000 persons was estimated for the survey. Working with average household size of 4.2 persons, the sample size translated into 12,814 households, which were selected by a systematic selection of every tenth household in each cluster. Where the calculated number fell below 10 households, a minimum of 10 households was taken in all such cases. The resultant sample size was observed to be sufficient to provide national and provincial estimates.
Response Rate
The survey, as stated earlier, covered 1,109 clusters out of the 1,139 selected clusters, giving 97.4 percent response rate. The remaining 30 clusters constituting 2.6 per cent were not covered, mainly due to inaccessibility caused by flooding and insecurity prevailing in these clusters.
At household level, 11,049 out of 12,814 selected households participated in the survey, giving 86.2 percent response rate. The household response rates varied between districts and urban/rural sub-strata as shown in Annex 1. The lowest response rate was recorded in Garissa district while the highest response rates were observed in Mandera and Embu districts. The rural component had a higher response rate of 87.5 percent, that is 9,111 respondents out of a total of 10,413 selected households. In the urban areas there was a response rate of 80.7 percent based on 1,938 households that responded from 2,401 selected households. Among the provinces, the lowest response rate was experienced in North Eastern where 74.6 percent while Eastern Province had the highest response rate of 92.7 percent.
At household level, 11,049 out of 12,814 selected households participated in the survey, giving 86.2 percent response rate. The household response rates varied between districts and urban/rural sub-strata as shown in Annex 1. The lowest response rate was recorded in Garissa district while the highest response rates were observed in Mandera and Embu districts. The rural component had a higher response rate of 87.5 percent, that is 9,111 respondents out of a total of 10,413 selected households. In the urban areas there was a response rate of 80.7 percent based on 1,938 households that responded from 2,401 selected households. Among the provinces, the lowest response rate was experienced in North Eastern where 74.6 percent while Eastern Province had the highest response rate of 92.7 percent.
Weighting
As to the weighting procedures, weighting of the sample data was done because the selection process of the sample was not self-weighting; and in the accompanying computation process, adjustment was done for cluster and household non-response. In addition, the adjustments took into consideration both the listed populations in the clusters and population growth.
Data Collection
Dates of Data Collection
| Start | End |
|---|---|
| 1998-12-21 | 1999-01-30 |
Data Collection Mode
Face-to-face [f2f]
Supervision
Training for field staff was undertaken in two tiers: 6 days training of trainers, which was conducted at a central point; and a week's training of enumerators in 8 training venues spread over the country.
The fieldwork was undertaken in 21 consecutive days during the months of December 1998 and January 1999. About 250 enumerators who are permanent employees of CBS based in each of the surveyed districts collected the data. Fifty District Statistical Officers (DSOs) supervised data collection at district level. In addition, 32 district coordinators were constituted to coordinate the survey in each of the districts; while the 250 CBS field enumerators, who are permanent employees of CBS based in each of surveyed districts, manned the NASSEP clusters and collected the data.
The enumerators had a range of responsibilities, which included:
Locating the sampled households within the assigned clusters by use of cluster maps;
Establishing rapport with respondents to gain their consent to be interviewed;
Conducting personal interviews and recording answers using the questionnaire by following instructions given during their training and elaborated in the enumerators' reference manual;
Checking the completed questionnaires to ensure that all questions are asked and the responses are neatly and legibly recorded;
Returning to the respondents where necessary to clarify suspect entries and for appointments to finish uncompleted interviews;
Preparing debriefing notes for the supervisor on the problems encountered; and
Forwarding all completed questionnaires to the supervisor or the DSO.
The fieldwork was undertaken in 21 consecutive days during the months of December 1998 and January 1999. About 250 enumerators who are permanent employees of CBS based in each of the surveyed districts collected the data. Fifty District Statistical Officers (DSOs) supervised data collection at district level. In addition, 32 district coordinators were constituted to coordinate the survey in each of the districts; while the 250 CBS field enumerators, who are permanent employees of CBS based in each of surveyed districts, manned the NASSEP clusters and collected the data.
The enumerators had a range of responsibilities, which included:
Locating the sampled households within the assigned clusters by use of cluster maps;
Establishing rapport with respondents to gain their consent to be interviewed;
Conducting personal interviews and recording answers using the questionnaire by following instructions given during their training and elaborated in the enumerators' reference manual;
Checking the completed questionnaires to ensure that all questions are asked and the responses are neatly and legibly recorded;
Returning to the respondents where necessary to clarify suspect entries and for appointments to finish uncompleted interviews;
Preparing debriefing notes for the supervisor on the problems encountered; and
Forwarding all completed questionnaires to the supervisor or the DSO.
Data Collectors
| Name | Abbreviation | Affiliation |
|---|---|---|
| Kenya National Bureau of Statistics | KNBS | Ministry of Planning |
Questionnaires
Questionnaires
The ILFS questionnaire presented as Appendix II consisted of three modules with a total of thirteen forms set up as follows:
The labour force module consisting of four forms LFS/1/98 through LFS/IV/98, which solicited demographic and labour force particulars;
The informal sector module consisting of three forms LFS/V/98 through LFS/VII/98, which solicited information on informal sector businesses; and
The child labour module consisting of six forms LFS/VIII/98 through LFS/XIII/98, which collected information on working children aged 5-17 years in an attempt to determine the size and structure of child labour in Kenya.
The labour force module consisting of four forms LFS/1/98 through LFS/IV/98, which solicited demographic and labour force particulars;
The informal sector module consisting of three forms LFS/V/98 through LFS/VII/98, which solicited information on informal sector businesses; and
The child labour module consisting of six forms LFS/VIII/98 through LFS/XIII/98, which collected information on working children aged 5-17 years in an attempt to determine the size and structure of child labour in Kenya.
Data Processing
Data Editing
The field staff in the districts where the survey was conducted undertook the initial editing of the questionnaires. The edited questionnaires were then forwarded to the CBS head office for further editing and processing using FoxPro 2.0 software. Thereafter the data were verified and validated up to the analysis stage to identify and correct invalid codes, duplicates, missing variables and other internal inconsistencies.
Data Appraisal
Estimates of Sampling Error
It is observed that estimates based on sample survey data are potentially subject to two types of errors, namely, sampling and non-sampling errors. The latter are not easy to control since they arise from factors external to the sample design, which include coding and data entry errors among others. Sampling errors are however controlled through the design of the sample and are measured by use of variances. In the ILFS, the stratified cluster design was used. This is a complex design and its variance estimates were based on the ultimate cluster method for variance estimation. The Cenvar program in the IMPS was used in the estimation of the variances of some selected variables. The standard error estimates in Appendix I were therefore obtained and reflect the situation of the data.
The variances of the parameters were either for the totals or ratios. (For the fomulae kindly check the report)
It is observed from the estimates of the variances that most of the estimates have consistently small standard errors, with the exception of categories with very few observations. Consequently, it was found that the coefficient of variation (CV) for most of the variable were in the neighbourhood of 10 percent or below with exception of North Eastern Province which had a very small sample in the survey, or categories of the population with rare occurrence such as people with university level education or ages exceeding 50 years in some provinces, which had very few observations.
The design effect in most cases was not high with the exception of the categories having small observations in the North Eastern Province. It is, therefore, observed that estimates based on the ILFS are fairly reliable and provide a good reflection of the employment situation and associated characteristics in the country.
The variances of the parameters were either for the totals or ratios. (For the fomulae kindly check the report)
It is observed from the estimates of the variances that most of the estimates have consistently small standard errors, with the exception of categories with very few observations. Consequently, it was found that the coefficient of variation (CV) for most of the variable were in the neighbourhood of 10 percent or below with exception of North Eastern Province which had a very small sample in the survey, or categories of the population with rare occurrence such as people with university level education or ages exceeding 50 years in some provinces, which had very few observations.
The design effect in most cases was not high with the exception of the categories having small observations in the North Eastern Province. It is, therefore, observed that estimates based on the ILFS are fairly reliable and provide a good reflection of the employment situation and associated characteristics in the country.
Access policy
Access authority
| Name | URL | |
|---|---|---|
| Director | data@knbs.co.ke | www.knbs.co.ke |
Contacts
| Name | Affiliation | URL | |
|---|---|---|---|
| Director | Kenya National Bureau of Statistics | director@knbs.co.ke | www.knbs.co.ke |
Confidentiality
Confidentiality of respondents is guaranteed by Articles ..... to ....... of the National Statistics Act of [date].
Before being granted access to the dataset, all users have to formally agree:
1. To make no copies of any files or portions of files to which s/he is granted access except those authorized by the data depositor.
2. Not to use any technique in an attempt to learn the identity of any person, establishment, or sampling unit not identified on public use data files.
3. To hold in strictest confidence the identification of any establishment or individual that may be inadvertently revealed in any documents or discussion, or analysis. Such inadvertent identification revealed in her/his analysis will be immediately brought to the attention of the data depositor.
Access conditions
The dataset has been anonymized and is available as a Public Use Dataset. It is accessible to all for statistical and research purposes only, under the following terms and conditions:
1. The data and other materials will not be redistributed or sold to other individuals, institutions, or organizations without the written agreement of the Kenya National Bureau of Statistics.
2. The data will be used for statistical and scientific research purposes only. They will be used solely for reporting of aggregated information, and not for investigation of specific individuals or organizations.
3. No attempt will be made to re-identify respondents, and no use will be made of the identity of any person or establishment discovered inadvertently. Any such discovery would immediately be reported to the Kenya National Bureau of Statistics.
4. No attempt will be made to produce links among datasets provided by the Kenya National Bureau of Statistics, or among data from the Kenya National Bureau of Statisticsand other datasets that could identify individuals or organizations.
5. Any books, articles, conference papers, theses, dissertations, reports, or other publications that employ data obtained from the Kenya National Bureau of Statistics will cite the source of data in accordance with the Citation Requirement provided with each dataset.
6. An electronic copy of all reports and publications based on the requested data will be sent to the Kenya National Bureau of Statistics.
The original collector of the data, the Kenya National Bureau of Statistics, and the relevant funding agencies bear no responsibility for use of the data or for interpretations or inferences based upon such uses.
1. The data and other materials will not be redistributed or sold to other individuals, institutions, or organizations without the written agreement of the Kenya National Bureau of Statistics.
2. The data will be used for statistical and scientific research purposes only. They will be used solely for reporting of aggregated information, and not for investigation of specific individuals or organizations.
3. No attempt will be made to re-identify respondents, and no use will be made of the identity of any person or establishment discovered inadvertently. Any such discovery would immediately be reported to the Kenya National Bureau of Statistics.
4. No attempt will be made to produce links among datasets provided by the Kenya National Bureau of Statistics, or among data from the Kenya National Bureau of Statisticsand other datasets that could identify individuals or organizations.
5. Any books, articles, conference papers, theses, dissertations, reports, or other publications that employ data obtained from the Kenya National Bureau of Statistics will cite the source of data in accordance with the Citation Requirement provided with each dataset.
6. An electronic copy of all reports and publications based on the requested data will be sent to the Kenya National Bureau of Statistics.
The original collector of the data, the Kenya National Bureau of Statistics, and the relevant funding agencies bear no responsibility for use of the data or for interpretations or inferences based upon such uses.
Citation requirements
Kenya National Bureau of Statistics, Labour Force Survey-1999, Version 1.2 of the public use dataset (Nov2008), provided by the National Data Archive. www.nda_popstan.or
Disclaimer and copyrights
Disclaimer
The user of the data acknowledges that the original collector of the data, the authorized distributor of the data, and the relevant funding agency bear no responsibility for use of the data or for interpretations or inferences based upon such uses.
Copyright
(c) 2008, Kenya National Bureau of Statistics
Metadata production
DDI Document ID
DDI-KEN-KNBS-LFS-1999-v02
Producers
| Name | Abbreviation | Role |
|---|---|---|
| Kenya National Bureau of Statistics | KNBS | Documentation of the study |
Date of Metadata Production
2008-11-07
DDI Document version
Version 1.1 (Nov 2008).