Worldwide, nearly 60 percent of workers are working in the informal economy. The share is higher the lower the level of development, with nearly 90 percent of employees in low-income countries working in the informal sector.

In 2023, the average net wage of informal employees in the service sector in Indonesia was 2.17 million Indonesian rupiah a month. In comparison, informal employees in Indonesia's agriculture sector had an average monthly salary of 1.48 million Indonesian rupiah. The salaries in Indonesia in that year varied greatly, with those formally employed earning significantly more than those in informal and casual employment.

Poverty entails more than a scarcity of material resources—it also involves a shortage of time. To examine the causal benefits of reducing time poverty, we conducted a longitudinal field experiment over six consecutive weeks in an urban slum in Kenya with a sample of working mothers, a population who is especially likely to experience severe time poverty. Participants received vouchers for services designed to reduce their burden of unpaid labor. We compared the effect of these vouchers against equivalently valued unconditional cash transfers (UCTs) and a neutral control condition. Participants completed a detailed survey. As part of this survey, they provided contact information and then they completed a series of subjective well-being measures from prior research. Specifically, participants answered questions about their overall subjective well-being and their positive and negative emotions. Participants then completed demographic items including their gender, age, marital status, whether they were currently living with their partner, or were the head of the household. They also reported on the highest level of formal education they had completed, the number of children they had living at home, their current childcare status, and they answered a series of income questions including how many people they financially supported, how many people relied on their income. Third, they answered a series of employment questions including whether they currently worked for pay, how many jobs they worked, what kinds of jobs they worked in, how much money they earned per month, how their earnings and employment status had changed during the COVID-19 pandemic, and whether they were currently looking for new employment opportunities and why. Fourth, respondents answered questions about the earnings of their household members and the amount of savings and debt that they had, and how these estimates had changed during the COVID-19 pandemic. Participants also reported how many hours they spent on unpaid labor in the past 7 days, and whether they had experienced any of the negative impacts of COVID-19 for their own health and their concerns with COVID-19 exposure. Participants also reported how valuable they felt their time was on a series of different measures. Lastly, participants reported how much money they expected to earn in the next six months as well as their predictions for their expenses over the next 6 months.

In 2023, service workers earned the highest among informal workers in Bali, with an average net wage of approximately 2.8 million Indonesian rupiah monthly. The lowest average net income of about 1.5 million Indonesian rupiah was earned by Balinese agricultural workers. Wages in Bali The monthly minimum wage in Bali stood at around three million Indonesian rupiah, ranking on the lower end compared to other provinces in Indonesia. On average, formal workers in Bali earn over 55 percent more than those in informal employment. Despite being one of the largest contributors to the Balinese economy, the agricultural sector has an average income below the minimum wage for both informal and formal workers, the lowest among all economic sectors. Cultural expenditures among Balinese People in Bali spend over 58 percent of their income for non-food purposes, a significantly higher rate when compared to the national average. This spending includes religious and cultural celebrations, some of which are widely known such as Galungan, Nyepi, and Ngaben ceremonies. Renowned for its rich culture and traditions, alongside its picturesque nature landscapes, Bali attracts millions of domestic and international tourists each year, making it a prime global travel destination.

The data and programs replicate tables and figures from "Family Migration and Structural Transformation", by Cao, Chen, Xi and Zuo. Please see the ReadMe file for additional details.

Abstract

The Urban Employment and Unemployment Survey program was designed to provide statistical data on the size and characteristics of the economically active and the inactive population of the country on continuous basis. The variables collected in the survey: socio-demographic characteristics of household members; economic activity during the last seven days and six months; including characteristics of employed persons such as hours of work, occupation, industry, employment status, and earnings from paid employment; unemployment and characteristics of unemployed persons.

The general objective of the 2014 Urban Employment and Unemployment Survey is to provide statistical data on the characteristics and size of the economic activity status i.e. employed, unemployed population of the country at urban levels on annual basis. The specific objectives of the survey are to: • collect statistical data on the potential manpower and those who are available to take part in various socio-economic activities; • update the data and determine the size and distribution of the labour force participation and the status of economic activity for different sub-groups of the population at different levels of the country; and also to study the socioeconomic and demographic characteristics of these groups; • identify the size, distribution and characteristics of employed population i.e. working in the formal or informal employment sector of the economy and earnings from paid employees and its distribution by occupation and Industry...etc; • provide data on the size, characteristics and distribution of unemployed population and rate of unemployment; • provide data that can be used to assess the situation of women’s employment or the participation of women in the labour force; and • generated time series data to trace changes over time;

Geographic coverage

The survey covered all urban parts of the country except three zones of Afar and six zones of Somali, where the residents are pastoralists.

Analysis unit

• Household
• Individual

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2007 Population and Housing Census was used as frame to select 30 households from the sample enumeration areas.

The country was divided into two broad categories. 1) Major urban centers: All regional capitals and five other major urban centers were included in this category. This category had a total of 16 reporting levels. A stratified two-stage cluster sample design was implemented to select the samples. The primary sampling units were EAs, from each EA 30 households were selected as a second stage unit.

2) Other urban centers: In this category, all other urban centers were included. This category had a total of 8 reporting levels. A stratified three stage cluster sample design was adopted to select samples from this category. The primary sampling units were urban centers and the second stage sampling units were EAs. From each EA 30 households were selected at the third stage.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire that was used to collect the data had six sections:

Section - 1: Area identification of the selected household: this section dealt with area identification of the respondents such as region, zone, wereda, etc.

Section - 2: Socio- demographic characteristics of households: it consisted of the general socio-demographic characteristics of the population such as age, sex, education, status and type of migration, disability, literacy status, educational Attainment, types of training and marital status.

Section – 3: Economic activities during the last seven days: this section dealt with a range of questions which helps to see the status and characteristics of employed persons in a current status approach such as hours of work in productive activities, occupation, industry, status in employment, earnings from employment, job mobility, service year for paid employees employment in the formal and informal sector and time related under employment.

Section – 4: Unemployment and characteristics of unemployed persons: this section focused on the size, rate and characteristics of the unemployed population.

Section – 5: Economic activities during the last twelve months: this section consists of the usual economic activity status refereeing to the long reference period i.e. engaged in productive activities during most of the last twelve months, reason for not being active, status in employment, main occupation and industry with two digit codes.

Section – 6: Economic activities of children age 5-17 years: this section comprises information on the participation of children age 5-17 years in the economic activities, whether attending education, reason for not attending education, whether they were working during the last seven days, reason for working, for whom they are working, types of injury at work place, whether using protective wear while working and frequency of working periods, and orphan hood status.

Cleaning operations

The filled-in questionnaires that were retrieved from the field were first subjected to manual editing and coding. During the fieldwork, field supervisors and statisticians of the head and branch statistical offices have checked the filled-in questionnaires and carried out some editing. However, the major editing and coding operation was carried out at the head office. All the edited questionnaires were again fully verified and checked for consistency before they were submitted to the data entry by the subject matter experts.

Using the computer edit specifications prepared earlier for this purpose, the entered data were checked for consistencies and then computer editing or data cleaning was made by referring back to the filled-in questionnaire. This is an important part of data processing operation to maintain the quality of the data. Consistency checks and rechecks were also made based on frequency and tabulation results. This was done by senior programmers using CSPro software in collaboration with the senior subject matter experts from Manpower Statistics Team of the CSA.

Response rate

Response rate of the survey was 99.8%

Sampling error estimates

Estimation procedures, estimates, and CV's for selected tables are provided in the Annex II and III of the survey final report.

Abstract

The Informal Sector Survey was designed by the Central Statistics Office (CSO) to answer a number of key policy questions on the existence of informal sector in Botswana. This was under perception that in recent years, there is rapid multiplication of informal sector activities all over Botswana and that informal sector has grown over time.

At the time of planning the survey, the only current relevant data sources within CSO were the 1991 Population Census, the 1994 Household Income and Expenditure Survey and the 1995/96 Labour Force Survey. Although this was the case these were not specific to informal sector but rather proxies that could be used.

It is acknowledged that in many developing countries, contribution of informal sector to household income is so significant to an extent that in some of these countries it exceeds that of formal sector. Employment creation in the informal sector is another factor that makes a significant contribution to the economies of developing countries. Indeed, for many people informal sector is a major domain in which they make a humble living.

The conditions of Botswana's informal sector activities in detail are not known empirically. The ISS was, therefore intended to collect data that would provide information required for shedding light on, among others, the following:

i. Contribution of informal sector to the economy's total output; ii. Types of major activities in the informal sector; iii. Proportion of workforce employed in the informal sector; iv. Proportion of household income generated by the informal sector; v. Size of capital investment in informal sector; vi. Extent of informal sector's contribution to the competitive market in the economy as a whole.

In broad terms this will also provide data to complement existing data collected through 'Formal sector' surveys. All these are expected to broaden and improve data coverage to enable monitoring and strengthen on the National/Regional technical expertise.

The working definition of "informal sector business" was tailored to enterprise not registered as a company; 5 or less paid employees; informal accounts or none; expenditure not easily distinguishable from household expenditure; enterprise often temporary or mobile or in owner's home.

Geographic coverage

National

Analysis unit

• Households
• Individuals

Universe

Only private dwellings were within the scope of the survey. Institutional dwellings (prisons, hospitals, army barracks, hotels, etc) were not within the scope of the survey. Only towns (cities) and villages were in the scope of the survey. Cattle posts and lands areas were outside of the scope of the survey because informal sector activities either minimal or non-existence in these areas.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for the Informal Sector Survey was designed to provide estimates of informal sector indicators at the national level, separate estimate each for 5 cities/towns, 19 urban villages and one global estimate for rural area. A stratified two-stage probability sample design was utilized for the selection of the sample.

Of the 84,833 listed households 13400 (15.8 percent) households were identified to be business households. Out of these 13,400 identified business households, 9916 (74.0 percent) business households were selected. Only 8,420 business households (84.7 percent) responded and their data were usable. The business household response rates in urban villages were more as compared to cities/town and rural areas. In the interviewed business households, 5,580 individuals were found with informal sector activities. Of these 4,656 were successfully interviewed yielding a response rate of 83.4 percent.

The first stage was the selection of block as Primary Sampling Units (PSUs) selected with probability proportional to measures of size (PPS), where measures of size (MOS) were the number of dwellings/households in the block. A total of 447 out of 1,738 blocks (EAs) were selected with PPS.

At the second stage of sampling, the households were systematically selected from a fresh list of occupied business households prepared at the beginning of the survey's fieldwork (i.e. listing of households for the selected blocks). Overall 9,114 households were drawn systematically.

Mode of data collection

Face-to-face [f2f]

Research instrument

Household Questionnaire: A Household Questionnaire was administered in each selected household. The Household questionnaire was divided into four major sets of questions, namely i. Socio-Demographic Characteristics ii. Education and Social Characteristics iii. Employment Status and Other Economic Characteristics iv. Eligibility Criteria for being an Informal Sector Respondent

Individual Questionnaire: All the eligible individuals from the household questionnaire were asked the questions on the individual questionnaire. The individual questionnaire was divided into 6 parts, each dealt with a specific topic.

In 2023, around 20 million people were employed in Kenya, this was an increase of some 900,000 individuals from the previous year. The employees belonged mostly to the informal sector. Roughly 16.7 million worked in informal conditions, whereas close to 3.3 million were employed in the formal sector. The informal sector constitutes an important part of the Kenyan economy, being related to employment creation, production, and income generation. Trends in the informal labor market and economic sectors The largest employment activities for people in the informal sector were in wholesale and retail trade, as well as hotels and restaurants, with 9.32 million people employed in these areas in 2022. Moreover, the hospitality sector in the country was the fastest-growing economic sector with a quarterly growth rate of 21.5 percent of the GDP. However, the largest economic sector as an added value to the GDP was the agricultural sector. Navigating unemployment challenges in Kenya Kenya’s unemployment rate is following a decreasing trend, which dropped below five percent at the end of 2022. However, unemployment among the youth in the same period was fairly high at 13.4 percent. The cohort with the highest level of unemployment was among the age group between 20 to 24 years old, with an unemployment rate of over 15 percent.

Goal 8Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for allTarget 8.1: Sustain per capita economic growth in accordance with national circumstances and, in particular, at least 7 per cent gross domestic product growth per annum in the least developed countriesIndicator 8.1.1: Annual growth rate of real GDP per capitaNY_GDP_PCAP: Annual growth rate of real GDP per capita (%)Target 8.2: Achieve higher levels of economic productivity through diversification, technological upgrading and innovation, including through a focus on high-value added and labour-intensive sectorsIndicator 8.2.1: Annual growth rate of real GDP per employed personSL_EMP_PCAP: Annual growth rate of real GDP per employed person (%)Target 8.3: Promote development-oriented policies that support productive activities, decent job creation, entrepreneurship, creativity and innovation, and encourage the formalization and growth of micro-, small- and medium-sized enterprises, including through access to financial servicesIndicator 8.3.1: Proportion of informal employment in total employment, by sector and sexSL_ISV_IFEM: Proportion of informal employment, by sector and sex (ILO harmonized estimates) (%)Target 8.4: Improve progressively, through 2030, global resource efficiency in consumption and production and endeavour to decouple economic growth from environmental degradation, in accordance with the 10-Year Framework of Programmes on Sustainable Consumption and Production, with developed countries taking the leadIndicator 8.4.1: Material footprint, material footprint per capita, and material footprint per GDPEN_MAT_FTPRPG: Material footprint per unit of GDP, by type of raw material (kilograms per constant 2010 United States dollar)EN_MAT_FTPRPC: Material footprint per capita, by type of raw material (tonnes)EN_MAT_FTPRTN: Material footprint, by type of raw material (tonnes)Indicator 8.4.2: Domestic material consumption, domestic material consumption per capita, and domestic material consumption per GDPEN_MAT_DOMCMPT: Domestic material consumption, by type of raw material (tonnes)EN_MAT_DOMCMPG: Domestic material consumption per unit of GDP, by type of raw material (kilograms per constant 2010 United States dollars)EN_MAT_DOMCMPC: Domestic material consumption per capita, by type of raw material (tonnes)Target 8.5: By 2030, achieve full and productive employment and decent work for all women and men, including for young people and persons with disabilities, and equal pay for work of equal valueIndicator 8.5.1: Average hourly earnings of employees, by sex, age, occupation and persons with disabilitiesSL_EMP_EARN: Average hourly earnings of employees by sex and occupation (local currency)Indicator 8.5.2: Unemployment rate, by sex, age and persons with disabilitiesSL_TLF_UEM: Unemployment rate, by sex and age (%)SL_TLF_UEMDIS: Unemployment rate, by sex and disability (%)Target 8.6: By 2020, substantially reduce the proportion of youth not in employment, education or trainingIndicator 8.6.1: Proportion of youth (aged 15–24 years) not in education, employment or trainingSL_TLF_NEET: Proportion of youth not in education, employment or training, by sex and age (%)Target 8.7: Take immediate and effective measures to eradicate forced labour, end modern slavery and human trafficking and secure the prohibition and elimination of the worst forms of child labour, including recruitment and use of child soldiers, and by 2025 end child labour in all its formsIndicator 8.7.1: Proportion and number of children aged 5–17 years engaged in child labour, by sex and ageSL_TLF_CHLDEC: Proportion of children engaged in economic activity and household chores, by sex and age (%)SL_TLF_CHLDEA: Proportion of children engaged in economic activity, by sex and age (%)Target 8.8: Protect labour rights and promote safe and secure working environments for all workers, including migrant workers, in particular women migrants, and those in precarious employmentIndicator 8.8.1: Fatal and non-fatal occupational injuries per 100,000 workers, by sex and migrant statusSL_EMP_FTLINJUR: Fatal occupational injuries among employees, by sex and migrant status (per 100,000 employees)SL_EMP_INJUR: Non-fatal occupational injuries among employees, by sex and migrant status (per 100,000 employees)Indicator 8.8.2: Level of national compliance with labour rights (freedom of association and collective bargaining) based on International Labour Organization (ILO) textual sources and national legislation, by sex and migrant statusSL_LBR_NTLCPL: Level of national compliance with labour rights (freedom of association and collective bargaining) based on International Labour Organization (ILO) textual sources and national legislationTarget 8.9: By 2030, devise and implement policies to promote sustainable tourism that creates jobs and promotes local culture and productsIndicator 8.9.1: Tourism direct GDP as a proportion of total GDP and in growth rateST_GDP_ZS: Tourism direct GDP as a proportion of total GDP (%)Target 8.10: Strengthen the capacity of domestic financial institutions to encourage and expand access to banking, insurance and financial services for allIndicator 8.10.1: (a) Number of commercial bank branches per 100,000 adults and (b) number of automated teller machines (ATMs) per 100,000 adultsFB_ATM_TOTL: Number of automated teller machines (ATMs) per 100,000 adultsFB_CBK_BRCH: Number of commercial bank branches per 100,000 adultsIndicator 8.10.2: Proportion of adults (15 years and older) with an account at a bank or other financial institution or with a mobile-money-service providerFB_BNK_ACCSS: Proportion of adults (15 years and older) with an account at a financial institution or mobile-money-service provider, by sex (% of adults aged 15 years and older)Target 8.a: Increase Aid for Trade support for developing countries, in particular least developed countries, including through the Enhanced Integrated Framework for Trade-related Technical Assistance to Least Developed CountriesIndicator 8.a.1: Aid for Trade commitments and disbursementsDC_TOF_TRDCMDL: Total official flows (commitments) for Aid for Trade, by donor countries (millions of constant 2018 United States dollars)DC_TOF_TRDDBMDL: Total official flows (disbursement) for Aid for Trade, by donor countries (millions of constant 2018 United States dollars)DC_TOF_TRDDBML: Total official flows (disbursement) for Aid for Trade, by recipient countries (millions of constant 2018 United States dollars)DC_TOF_TRDCML: Total official flows (commitments) for Aid for Trade, by recipient countries (millions of constant 2018 United States dollars)Target 8.b: By 2020, develop and operationalize a global strategy for youth employment and implement the Global Jobs Pact of the International Labour OrganizationIndicator 8.b.1: Existence of a developed and operationalized national strategy for youth employment, as a distinct strategy or as part of a national employment strategySL_CPA_YEMP: Existence of a developed and operationalized national strategy for youth employment, as a distinct strategy or as part of a national employment strategy

We investigate the factors that influence the selection and productivity of informal service-oriented family enterprises in Nigeria. Using nationally representative micro-data from the Nigerian General Household Survey (2010–2015), we employed random-effect probit and selectivity-adjusted regression models to estimate and analyze the results. The findings reveal that the location of informal Non-Farm Household Enterprises (NFHEs)–whether home-based or non-home-based—significantly impacts the wholesale, retail, personal, and consultancy service sectors operated by informal NFHEs. This impact remains significant even after accounting for variations in individuals, households, or locational characteristics. Furthermore, when considering selectivity in the earnings equation, we found that home-based informal enterprises exhibit lower productivity compared to non-home-based enterprises, a difference that varies across sectors. Overall, factors such as the gender of business owners, educational levels, geopolitical zones, infrastructure, and business characteristics play a crucial role in determining the locational and productivity disparities among service-oriented enterprises in Nigeria. Key recommendations stemming from this study include addressing gender-based segregation and economic disparities, prioritising financial inclusion for small business development, bridging infrastructure gaps, and implementing policies that acknowledge and bolster the informal sector.

Abstract

The Quarterly Labour Force Survey (QLFS) is a household-based sample survey conducted by Statistics South Africa (Stats SA). It collects data on the labour market activities of individuals aged 15 years or older who live in South Africa. Starting in 2005, Stats SA undertook a major revision of the Labour Force Survey (LFS) which was conducted twice per year since 2000. This revision resulted in changes to the survey methodology, the survey questionnaire, the frequency of data collection and data releases, and the survey data capture and processing systems. The redesigned labour market survey is the QLFS which is now the principal vehicle for disseminating labour market information on a quarterly basis.

The objective of the QLFS is to collect quarterly information about persons in the labour market , i.e., those who are employed by sector(formal,informal,agriculture and Private households); those who are unemployed and those who are not economically active.

Geographic coverage

the QLFS has national coverage

Analysis unit

individuals

Universe

Households in the nine provinces of South Africa

Kind of data

Données échantillonées [ssd]

Sampling procedure

The Labour Force Survey frame has been developed as a general-purpose household survey frame that can be used by all other household surveys irrespective of the sample size requirement of the survey. The sample size for the QLFS is roughly 30 000 dwellings.

The sample is based on information collected during the 2001 Population Census conducted by Stats SA. In preparation for Census 2001, the country was divided into 80 787 enumeration areas (EAs). Some of these EAs are small in terms of the number of households that were enumerated in them at the time of Census 2001. Stats SA's household-based surveys use a Master Sample of Primary Sampling units (PSUs) which comprises EAs that are drawn from across the country. For the purposes of the Master Sample, the EAs that contained fewer than 25 households were excluded from the sampling frame, and those that contained between 25 and 99 households were combined with other EAs of the same geographic type to form Primary Sampling Units (PSUs). The number of EAs per PSU ranges between one and four. On the other hand, very large EAs represent two or more PSUs.

The sample is designed to be representative at provincial level and within provinces at the metro/non-metro level. Within the metros, the sample is further distributed by geography type. The four geography types are: urban formal, urban informal, farms, and tribal. This implies that, for example, within a metropolitan area the sample is designed to be representative at the different geography types that may exist within that metro.

The current sample size is 3 080 PSUs. It is equally divided into four subgroups or panels called rotation groups. The rotation groups are designed in such a way that each of these groups has the same distribution pattern as that which is observed in the whole sample. They are numbered from one to four and these numbers also correspond to the quarters of the year in which the sample will be rotated for the particular group. The sample for the redesigned Labour Force Survey is based on a stratified two-stage design with probability proportional to size (PPS) sampling of primary sampling units (PSUs) in the first stage, and sampling of dwelling units (DUs) with systematic sampling in the second stage.

Sample rotation Each quarter, a ¼ of the sampled dwellings rotate out of the sample and are replaced by new dwellings from the same PSU or the next PSU on the list. Thus, sampled dwellings will remain in the sample for four consecutive quarters. It should be noted that the sampling unit is the dwelling, and the unit of observation is the household. Therefore, if a household moves out of a dwelling after being in the sample for, say two quarters and a new household moves in then the new household will be enumerated for the next two quarters. If no household moves into the sampled dwelling, the dwelling will be classified as vacant (unoccupied).

Mode of data collection

Interview face à face [f2f]

Research instrument

the questionnaire is composed by 5 sections: - Section1, Biographical information (marital status, language, migration, education, training, literacy, etc.)
- Section2, Economic activities in the last week : The questions in this section determine those individuals, aged 15-64 years, who are employed and those who are not employed.
- Section 3, Unemployment and economic inactivity : This section determines which respondents are unemployed and which respondents are not economically active. - Section 4, Main work activities in the last week : This section contains questions about the work situation of respondents who are employed. It includes questions about the number of jobs at which the respondent works, the hours of work, the industry and occupation of the respondent as well as whether or not the person is employed in the formal or informal sector etc., - Section 5 covers earnings in the main job for employees and own-account workers aged 15 years and above.

Cleaning operations

Automated editing and imputation QLFS uses the editing and imputation module to ensure that output data is both clean and complete. There are three basic components, called functions, in the Edit and Imputation Module:

Function A: Record acceptance Function B: Edit and imputation Function C: Clean up, derived variables and preparation for weighting

Function A: Record acceptance This function is divided into three phases:

First phase: Pre-function A The first phase ensures that the records contain valid information in selected Cover Page questions required during edit and imputation and during the subsequent weighting and variance estimation. Any blanks or other errors that need to be corrected are done here before processing of the record can proceed.

Second phase: Function A record acceptance The second phase ensures that there is enough demographic and labour market activity information to ensure that editing and imputation can be successfully completed.

Third phase: Post Function A clean up This phase ensures that certain data are present where there is evidence that they should be. This for example, involves: · Ensuring that if there is written material in the job description questions then there are corresponding industry and occupation codes for them. · Ensuring that partial blanks or non-numeric characters that appear in questions where the Survey Officer is required to enter numbers are validated.
· Ensuring that where there is written material in the space provided for “Other - specify” that the corresponding option is marked.

Function B: Edit and imputation Having determined in Function A that the content of the record would support extensive editing and imputation, this function carries out those activities. Editing is the detection of errors in the captured questionnaire. Imputation is the correction of the detected errors.

Function C: Clean up, derived variables and preparation for weighting Function C includes all of the “post E&I clean up” functions such as “Off-path cleaning”, “Result Code validation”, verification of the presence of industry and occupation codes, and the generation of all derived variables.

Response rate

Response rates: first Quarter: 93.0% second quarter: 90.9% third quarter: 92.3% forth quarter: 91.4%

Sampling error estimates

Because estimates are based on sample data, they differ from figures that would have been obtained from complete enumeration of the population using the same instrument. Results are subject to both sampling and non-sampling errors. Non-sampling errors include biases from inaccurate reporting, processing, and tabulation, etc., as well as errors from non-response and incomplete reporting. These types of errors cannot be measured readily. However, to the extent possible, non-sampling errors can be minimised through the procedures used for data collection, editing, quality control, and non-response adjustment. The variances of the survey estimates are used to measure sampling errors. The variance estimation methodology is discussed below. (i) Variance estimation The most commonly used methods for estimating variances of survey estimates from complex surveys such as the QLFS, are the Taylor-series Linearization, Jackknife Replication, Balanced Repeated Replication (BRR), and Bootstrap methods (Wolter, 2007). The Fay’s BRR method has been used for variance estimation in the QLFS because of its simplicity.

(ii) Coefficient of variation It is more useful in many situations to assess the size of the standard error relative to the magnitude of the characteristic being measured (the standard error is defined as the square root of the variance). The coefficient of variation(cv) provides such a measure. It is the ratio of the standard error of the survey estimate to the value of the estimate itself expressed as a percentage. It is very useful in comparing the precision of several different survey estimates, where their sizes or scale differ from one another.

(iii) P-value of an estimate of change The p-value corresponding to an estimate of change is the probability of observing a value larger than the particular observed value under the hypothesis that there is no real change. If p-value <0,01 then the difference is highly significant; if p-value is between 0,01 and 0,05 then the difference is significant; and if p-value >0,05 then the difference is not significant.

Distribution and association of socio-economic characteristics and income downfall.

Abstract

Statistical information on all aspects of socio-economic activities is essential for the designing, monitoring, evaluation of development plans and policies. Labour force surveys are one of the important sources of data for assessing the role of the population of the country in the economic and social development process. These surveys provide data on the main characteristics of the work force engaged or available to be engaged in productive activities during a given period and its distribution in the various sectors of the economy. It is also useful to indicate the extent of available and unutilized human resources that must be absorbed by the national economy to ensure full employment and economic well being of the population. Furthermore, the information obtained from such surveys is useful for the purpose of macro-economic monitoring and evaluation of human resource development planning. The other broad objective of statistics on the labour force is for the measurement of relationship between employment, income and other social and economic characteristics of the economically active population for the purpose of formulating, monitoring and evaluation of employment policy and programs. Seasonal and other variations and changes over time in the size and characteristics of the employment and unemployment can be monitored using up-to-date information from labour force surveys.

CSA has been providing labour force and related data at different levels and with varying content details. These include the 1976 Addis Ababa Manpower and Housing Sample Survey, the 1978 Survey on Population and Housing Characteristics of Seventeen Major Towns, the 1980/81 and 1987/88 Rural Labour Force Surveys, and the 1984 & 1994 Population and Housing Census. A comprehensive national labour force result representing both urban and rural areas was also provided based on the 1999 Labour Force Survey. The 1996 and 2002 Surveys of Informal Sector and most of the household surveys also provide limited data on the area. Moreover, some information can be derived from small, large and medium scale establishment surveys.

As the sector is dynamic and sensitive to economic and social changes, it is important to have up to date data that will show current levels and that will be used for trend and comparative analysis. Earlier data in this regard were not regular and up to date. Thus, to fill-in the data gap in this area, a series of current and continuous labour force surveys need to be undertaken. Recognizing this fact and in response to request from different data users, the CSA had launched a Bi-annual Employment and Unemployment Survey program starting October, 2003 G.C.

This survey is the second in the series. Like the first round, it covered only urban areas of all regions with the exception of Gambella.

Objectives of the survey The Bi-annual Employment and Unemployment Survey program was designed to provide statistical data on the size and characteristics of the economically active and the non-active population of the country on continuous basis. The data will be useful for policy makers, planners, researchers, and other institutions and individuals engaged in the design, implementation and monitoring of human resource development projects and the performance of the economy.

The specific objectives of this survey were to: - Up date data on the size of work force that is available to participate in production process; - Determine the status and rate of economic participation of different sub-groups of the population; - Identify those who are actually contributing to the economic development (employed) and those out of the sphere; - Determine the size and rate of unemployed population; - Provide data on the structure of the working population; - Obtain information about earnings from paid employment; - Identify the distribution of employed population in the formal/informal sector of the economy; - Generate data to trace changes over time.

Geographic coverage

The 2004 Urban Bi-annual Employment and Unemployment Survey (UBEUS) covered only urban parts of the country. Except three zones of Afar, six zones of Somali regions, where the residents are pastoralists, and every part of Gambella region, all urban centers of the country were considered in this survey.

Analysis unit

• Household
• Individual aged 10 years and above

Universe

All households in the selected samples, except residents of collective quarters, homeless persons and foreigners.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample Design and Sample Size: Information from the listing of the 1994 Population and Housing Census was utilized to develop the sampling frame for the 2004 Urban Bi-annual Employment and Unemployment Survey. It was by taking in to account of cost and precision of major variables that determination of sample size was achieved. Moreover, in order to judge precisions of major variables, the 1999 Labor Force Survey result was the main source of information that was taken into consideration.

Except Harari, Addis Ababa and Dire Dawa, where all urban centers of the domain were incorporated in the survey, in other domains a three stage stratified cluster sample design was adopted to select the samples from each domain. The primary sampling units (PSU's) were urban centers selected systematically using probability proportional to size; size being number of households obtained from the 1994 Population and Housing Census. From each selected urban centers enumeration areas (EA's) were selected as a second-stage sampling unit (SSU). The selection of the SSU's was also done using probability proportional to size; size being number of households obtained from the 1994 Population and Housing Census. For each sampled EA a fresh list of households was prepared at the beginning of the survey. Thirty households from each sample EA were selected at the third stage. The survey questionnaire was finally administered to those thirty households selected at the last stage.

The selection scheme for Harari, Addis Ababa and Dire Dawa was similar to the case explained above. However, in these three domains instead of a three-stage design a two-stage stratified cluster sample design with enumeration areas as PSU and households (from the fresh list) as secondary sampling unit was used.

Note: Distribution of sampling units (planned and covered) by domain (reporting level) is given in Summary Table 2.1 of the 2004 Urban Bi-annual Employment Unemployment Survey Round 2 report.

Mode of data collection

Face-to-face [f2f]

Research instrument

Same questionnaire used for the first round survey was administered in this round (round 2).

The questionnaire was organized in to five sections; Section - 1: Area identification of the selected household: this section dealt with area identification of respondents such as region, zone, wereda, etc.,

Section - 2: Demographic characteristics of household: it consisted of the general socio-demographic characteristics of the population such as age, sex, education, states & types of training and marital status.

Section - 3: Economic activity during the last six months: this section covered the usual economic activity status, number of weeks of Employment /Unemployment and reasons for not usually working.

Section - 4: Productive activities during the last seven days: this section dealt with the status and characteristics of employed persons such as hours of work occupation, industry, employment status, and Earnings from employment.

Section - 5: Unemployment and characteristics of unemployed persons: the section focused on the size and characteristics of the unemployed population.

Note: The questionnaire is provided as external resource.

Cleaning operations

Data Editing, Coding and Verification: The filled-in questionnaires that were retrieved from the field were first subjected to manual editing and coding. During the fieldwork the field supervisors, Statisticians and the heads of branch statistical offices have checked the filled-in questionnaires and carried out some editing. However, the major editing and coding operation was carried out at the head office. All the edited questionnaires were again fully verified and checked for consistency before they were submitted to the data entry. After the data was entered, it was again verified using the computer.

Data Entry, Cleaning and Tabulation: Using the computer edit specification prepared earlier for this purpose, the entered data were checked for consistencies and then computer editing or data cleaning was made by referring back to the filled-in questionnaire. This is an important part of data processing operation in attaining the required level of data quality. Consistency checks and re-checks were also made based on tabulation results. Computer programs used in data entry, machine editing and tabulation were prepared using the Integrated Microcomputer Processing System (IMPS).

Response rate

As regards the response rate of the survey, a total of 99 urban centers were selected and incorporated in to the survey. To be covered by the survey, 527 enumeration areas was initially selected, and the survey could successfully be carried out in 507 (96.20%) out of all the 527 of the EA's. The total number of expected households that were to be interviewed was 15810; however, due to different reasons 740 sample households were not interviewed, including households from 20 EAs of Gambella Region. As a result only 15070 households were actually covered by the survey, which made the ultimate response rate of the survey 95.32 %.

Sampling error

Abstract

Statistical information on all aspects of the population is vital for the design, implementation and evaluation of economic and social development plan and policy issues. Labour force survey is one of the most important sources of data for assessing the role of the population of the country in the economic and social development process. It is useful to indicate the extent of available and unutilized human resources that must be absorbed by the national economy to ensure full employment and economic well being of the population. It is also an input for assessing the meeting of the Millennium Development Goals (MDGs) and the country's poverty reduction strategy framework for PASDEP (Plan for Accelerated and Sustained Development to End Poverty). Statistics on the labour force further deals with the measurement and the relationship between employment, income and other social and economic characteristics of the economically active and non active population. Seasonal and other variations as well as changes over time in the size and characteristics of the employment and unemployment can be monitored using up-to-date information from labour force surveys.

Thus, data on economic activity together with other labour force data would be of a springboard for a clear formulation, monitoring and evaluation of employment policies, programs and strategies on human resource development and various socio-economic plans at different levels in the country. This survey results provide data on the main characteristics of the work force engaged or available to be engaged in the production of economic goods and services and its distribution in the various sectors of the economy during a given reference period. Statistical information on all aspects of the population is vital for the design, implementation and evaluation of economic and social development plan and policy issues. Labour force survey is one of the most important sources of data for assessing the role of the population of the country in the economic and social development process. It is useful to indicate the extent of available and unutilized human resources that must be absorbed by the national economy to ensure full employment and economic well being of the population. It is also an input for assessing the meeting of the Millennium Development Goals (MDGs) and the country's poverty reduction strategy framework for PASDEP (Plan for Accelerated and Sustained Development to End Poverty). Statistics on the labour force further deals with the measurement and the relationship between employment, income and other social and economic characteristics of the economically active and non active population. Seasonal and other variations as well as changes over time in the size and characteristics of the employment and unemployment can be monitored using up-to-date information from labour force surveys.

Thus, data on economic activity together with other labour force data would be of a springboard for a clear formulation, monitoring and evaluation of employment policies, programs and strategies on human resource development and various socio-economic plans at different levels in the country. This survey results provide data on the main characteristics of the work force engaged or available to be engaged in the production of economic goods and services and its distribution in the various sectors of the economy during a given reference period.

Objectives of the Survey: The 2009 Urban Employment and Unemployment Survey program was designed to provide statistical data on the characteristics and size of the economic activity status i.e. employed, unemployed and the non-active population of the country at urban levels on annual basis. The data obtained from this survey will be useful for policy makers, planners, researchers, and other institutions and individuals engaged in the design, implementation and monitoring of human resource development projects and to assess and understand the performance of the economy.

The specific objectives of the 2009 Urban Employment and Unemployment Survey are: - collect statistical data on the potential manpower and those who are available to take part in various socio-economic activities; - up date the data and determine the size and distribution of the labour force participation and the status of economic activity for different sub-groups of the population; and also to study the socio-economic and demographic characteristics of these groups; - identify those who are actually contributing to the economic development (working population) and those out of the sphere the economy; - identify the size, distribution and characteristics of employed population i.e. working in the formal or informal employment sector of the economy and earnings for paid employees, type of occupation and Industry...etc; - provide data that can be used to assess the situation of women's employment or the participation of women in the labour force; - provide data on the size, characteristics and distribution of unemployed population and rate of unemployment; - identify the size and characteristics of children aged 5-17 years that were engaged in economic activities; - provide the generated time series data to trace changes over time

Geographic coverage

The 2009 Urban Employment and Unemployment Survey (UEUS) covered only urban parts of the country. Except three zones of Afar, six zones of Somali, where the residents are pastoralists all urban centers of the country were considered in this survey.

Analysis unit

• Household
• Individual aged 10 years and above

Universe

This survey follows household approach and covers households residing in conventional households and thus, population residing in the collective quarters such as universities/colleges, hotel/hostel, monasteries and homeless population etc., are not covered by this survey.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling Frame: The list of households obtained from the 2007 Population and Housing Census is used to select EAs. A fresh list of households from each EA was prepared at the beginning of the survey period. The list was then used as a frame in order to select households from sample EAs.

Sample Design: For the purpose of the survey the country was divided into two broad categories. That is major urban center and other urban center categories. Category I:- Major urban centers:- In this category all regional capitals and four other major urban centers that have a high population size as compared to others were included. Each urban center in this category was considered as a reporting level. The category has a total of 15 reporting levels. In this category, in order to select the sample, a stratified two-stage cluster sample design was implemented. The primary sampling units were EAs of each reporting level. Then from each sample EA 30 households were selected as a Second Stage Unit (SSU).

Category II: - Other urban centers: Urban centers in the country other than those under category I were grouped into this category. A domain of other urban centers is formed for each region. Consequently 8 reporting levels were formed in this category. Harari, Addis Ababa and Dire Dawa do not have urban centers other the one previously that grouped in category I. Hence, no domain was formed for these regions under this category.

A stratified three stage cluster sample design was adopted to select samples from this category II. The primary sampling units were other urban centers and the second stage sampling units were EAs. From each EA 30 households were finally selected at the third stage and the survey questionnaires administered to all of them.

Sample Size and Selection Scheme Category I:- In this category 371 EAs and 11,130 households were selected. Sample EAs from each reporting level in this category were selected using probability proportional to size systematic sampling; size being number of households obtained from the 2007 population and housing census. From the fresh list of households prepared at the beginning of the survey, 30 households per EA were systematically selected and covered by the study.

Category II:- 82 urban centers, 270 EAs and 8,100 households were selected in this category. Urban centers from each domain and EAs from each urban center were selected using probability proportional to size systematic method; size being number of households obtained from the 2007 Population and housing census. From the listing of each EA then 30 households were systematically selected and the study performed on the 30 households ultimately selected.

The distribution of planned and covered EAs and households and the Estimation procedures are given in the appendix in the final report.

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey questionnaire is organized into six sections; Section - 1: Area identification of the selected household: this section deals with area identification of respondents such as region, zone, wereda, etc. Section - 2: Particulars of household members: it consists of the general socio-demographic characteristics of the population such as age, sex, educational status, types of training and marital status. Section - 3: Economic activity during the last seven days: this section deal with whether persons were engaged in productive activities or not during the last seven days prior to date of interview, the status and characteristics of employed persons such as occupation, industry, employment status, hours of work, employment sector /formal and informal employment/ and earnings from paid employment. Section - 4: Unemployment rate and characteristics

Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all : Overall, economic trends in the Pacific region have been positive, yet inconsistent. The restricted economic bases of Pacific Island countries are highly sensitive to external economic shocks (including commodity price fluctuations, supply chain disruptions and financial stress), natural events (such as cyclones, floods and droughts) and costs of adaptation to climate change; nternal employment factors in the Pacific compound these external factors. The size of the informal economy; gender gaps and imbalances; and high youth unemployment/underemployment are issues that can be monitored under Goal 8; Tourism is an important sector of growth and development in the Pacific, providing foreign exchange earnings, employment and income earning opportunities for many Pacifc islanders. Tourism is one of the region’s few economically viable sectors, and its share in national GDPs is monitored in this goal.

Find more Pacific data on PDH.stat.

Abstract

Labour force surveys are one of the most important sources of data for assessing the role of the population of the country in the economic and social development process. These surveys provide data on the main characteristics of the work force engaged or available to be engaged in productive activities during a given period and its distribution in the various sectors of the economy. It is also useful to indicate the extent of available and unutilized human resources that must be absorbed by the national economy to ensure full employment and economic well being of the population. Moreover, it further provides an input for assessing the meeting of the Millennium Development Goals (MDGs) and the country's poverty reduction strategy framework (PASDEP-Plan for Accelerated and Sustained Development to End Poverty). The other broad objective of statistics on the labour force is for the measurement of relationship between employment, income and other social and economic characteristics of the economically active population for the purpose of formulating, monitoring and evaluation of employment policy and programs. Seasonal and other variations and changes over time in the size and characteristics of the employment and unemployment can be monitored using up-to-date information from labour force surveys.

The Central Statistical Agency (CSA) has been providing labour force and related data at different levels and with varying content details. These include the 1976 Addis Ababa Manpower and Housing Sample Survey, the 1978 Survey on Population and Housing Characteristics of Seventeen Major Towns, the 1980/81 and 1987/88 Rural Labour Force Surveys, and the 1984 & 1994 Population and Housing Census. A comprehensive national labour force data representing both urban and rural areas was also provided based on the 1999 and 2005 Labour Force Surveys. The 1996 and 2002 Surveys of Informal Sector and most of the household surveys also provide limited data on the area. Moreover, some information can be derived from small, large and medium scale establishment surveys.

Considering the dynamic and sensitive nature of the sector and also in response to the demands of different data users, the CSA had launched a Bi-annual Employment Unemployment Survey program starting from October 2003 GC. In this way, the Agency had conducted two rounds in October 2003 and April 2004 and the results were published in Statistical Bulletin 301 and 319. The 2005 Labour Force Survey (LFS) had been conducted to update the 1999 National Labour force survey. Here after, based on data need assessment it was decided to undertake the continuous survey annually instead of bi-annually.

Objectives of the survey The Employment and Unemployment Survey program was designed to provide statistical data on the size and characteristics of the economically active and the non-active population of the country on continuous basis. The data will be useful for policy makers, planners, researchers, and other institutions and individuals engaged in the design, implementation and monitoring of human resource development projects and the performance of the economy.

The specific objectives of this survey were to: - Up date data on the size of work force that is available to participate in production process; - Determine the status and rate of economic participation of different sub-groups of the population; - Identify those who are actually contributing to the economic development (employed) and those out of the sphere; - Determine the size and rate of unemployed population; - Provide data on the structure of the working population; - Obtain information about earnings from paid employment; - Identify the distribution of employed population in the formal/informal sector of the economy; and - Generate time series data to trace changes over time.

Geographic coverage

The 2006 Urban Annual Employment and Unemployment Survey covered only urban parts of the country. Except three zones of Afar and six zones of Somali regions, where the residents are pastoralists, all urban centers of the country were considered in this survey.

Analysis unit

• Household
• Individual aged 10 years and above

Universe

All households in the selected samples, except residents of collective quarters, homeless persons and foreigners.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample Design and Sample Size: Information from the listing of the 2004 Urban Economic Establishment Census was utilized to develop the sampling frame for the 2006 Urban Annual Employment and Unemployment Survey. It was by taking into account of cost and precision of major variables that determination of sample size was achieved. Moreover, in order to judge precisions of major variables, the 1999 Labor Force Survey result was the main source of information that was taken into consideration.

Except Harari, Addis Ababa and Dire Dawa, where all urban centers of the domain were incorporated in the survey, in other domains a three stage stratified cluster sample design was adopted to select the samples from each domain. The primary sampling units (PSU's) were urban centers selected systematically using probability proportional to size; size being number of households obtained from the 2004 Urban Economic Establishment Census. From each selected urban centers enumeration areas (EA's) were selected as a second-stage sampling unit (SSU). The selection of the SSU's was also done using probability proportional to size; size being number of households obtained from the 2004 Urban Economic Establishment Census. For each sampled EA a fresh list of households was prepared at the beginning of the survey. Thirty households from each sample EA were selected at the third stage. The survey questionnaire was finally administered to those thirty households selected at the last stage.

The selection scheme for Harari, Addis Ababa and Dire Dawa was similar to the case explained above. However, in these three domains instead of a three-stage design a two-stage stratified cluster sample design with enumeration areas as PSU and households (from the fresh list) as secondary sampling unit was used.

Note: Distribution of sampling units (planned and covered) by domain (reporting level) is given in Summary Table 2.1 of the 2006 Urban Employment Unemployment Survey report.

Mode of data collection

Face-to-face [f2f]

Research instrument

Almost similar questionnaire that were used for the first and second rounds is administered in this survey.

The questionnaire was organized into five sections: Section - 1: Area identification of the selected household: this section dealt with area identification of respondents such as region, zone, wereda, etc.,

Section - 2: Demographic characteristics of household: it consisted of the general socio-demographic characteristics of the population such as age, sex, education, status & types of training and marital status.

Section - 3: Productive activities during the last seven days: this section dealt with the status and characteristics of employed persons such as hours of work, occupation, industry, employment status, and earnings from paid employment.

Section - 4: Unemployment and characteristics of unemployed persons: the section focused on the size and characteristics of the unemployed population.

Section - 5: Economic activity during the last six months: this section covered the usual economic activity status, number of weeks of employment /unemployment and reasons for not usually working.

The questionnaire used in the field for data collection purpose was prepared in Amharic language. Both Amharic and English versions of the questionnaires are provided as external resource.

Cleaning operations

Data Editing, Coding and Verification: The filled-in questionnaires that were retrieved from the field were first subjected to manual editing and coding. During the fieldwork the field supervisors, Statisticians and the heads of branch statistical offices have checked the filled-in questionnaires and carried out some editing. However, the major editing and coding operation was carried out at the head office. All the edited questionnaires were again fully verified and checked for consistency before they were submitted to the data entry. After the data was entered, it was again verified using the computer.

Data Entry, Cleaning and Tabulation: Using the computer edit specification prepared earlier for this purpose, the entered data were checked for consistencies and then computer editing or data cleaning was made by referring back to the filled-in questionnaire. This is an important part of data processing operation in attaining the required level of data quality. Consistency checks and re-checks were also made based on tabulation results. Computer programs used in data entry, machine editing and tabulation were prepared using the Integrated Microcomputer Processing System (IMPS).

Response rate

As regards the response rate of the survey, a total of 99 urban centers were selected and incorporated into the survey. To be covered by the survey, 527 enumeration areas was initially selected, and the survey could successfully be carried out in 525 (99.62%) out of all the 527 of the EA’s. The total number of expected households that were to be interviewed was 15,810; however, due to different reasons 235 sample households were not interviewed. As a result only 15,575 households were actually covered by the survey, which made the ultimate response rate of the survey 98.51 %.

Sampling error estimates

Estimation procedures of total, ratio and

Abstract

The Labour Force Survey (LFS) is a household survey designed to be carried out every two years by the Central Statistical Office in collaboration with the Ministry of Labour and Social Security. Since its inception in 1986, the major objective of the LFS has been to measure the size of the labour force and its characteristics (age, sex, industry, sector of employment, education, e.t.c). The first Zambia Labour Force Survey was conducted in 1986 to satisfy a need for reliable and timely data on the labour market. Successive labour force surveys were conducted in 2005, 2008 and 2012. The LFS provides Key Indicators of Labour Market (KILM) namely: labour force participation rate, employment-population ratio, status in employment, employment by sector, employment by occupation, part time work, hours of work, unemployment, youth unemployment, time-related underemployment, informal sector employment, income, inactivity. The main purpose of the 2014 LFS was to measure the size of the labour force and its characteristics with the view to providing guidance in the formulation and implementation of labour market policies and programmes. The specific objectives of the LFS included:

• Measuring the size of the employed population both in the Formal and Informal sectors.
• Assessing levels of unemployment so that job creation efforts could be intensified.
• Measuring income levels among Paid employees, Self employed and Employers.
• Assessing the incidence and prevalence of child labour.

Geographic coverage

The whole country.

Analysis unit

• Individuals
• Households

Universe

The 2014 LFS was a nation-wide survey covering household population in all the ten provinces and in both rural and urban areas. The survey excluded populations in institutions such as prisons, refuge camps, hospitals, or barracks.

Kind of data

Sample survey data [ssd]

Sampling procedure

The total population of Zambia was estimated at 14,983,315 in 2014, of which 49.1 percent were male and 50.9 percent were female. This population was spread across 2,934,096 households. The population shows an increase of 4.2 percent from the population of 14,375,601 recorded in 2012. A larger proportion of the total population accounting for 58.4 percent was in rural areas while 41.6 percent was in urban areas. A representative sample of 11, 520 households was selected at two stages. In the first stage, 576 Enumeration Areas (EAs) were selected from the 2010 Census sampling frame. In the second stage, households in each of the selected EAs were first listed followed by the selection of 20 households for enumeration.

Sample Allocation and Selection: The total sample of 11,520 households was first allocated between rural, urban and the provincial domains in proportion to the population of each domain according to the 2010 Census frame. The proportional allocation does not however allow for reliable estimates at lower domains like district, constituency or ward. Adjustments to the proportional allocation of the sample were made to allow for reasonable comparison to be achieved between strata or domains. Therefore, disproportionate allocation was adopted, for the purpose of maximizing the precision of survey estimates. The disproportionate allocation is based on the optimal square root allocation method designed by Leslie Kish. The sample was then selected using a stratified two-stage cluster design.

Sampling deviation

The sample was designed to provide estimates at national (rural and urban) and provincial level. Zambia is administratively divided into ten provinces. Each province is in turn subdivided into districts. For statistical purposes, each district is subdivided into Census Supervisory Areas (CSAs) and these are demarcated into Enumeration Areas (EAs). The Census mapping exercise of 2006-2010 in preparation for the 2010 Census of Population and Housing, demarcated the CSAs within wards, wards within constituencies and constituencies within districts. The 2010 Census produced at least 25,000 EAs stratified as rural and urban. This constituted the sampling frame. Information borne on the list of EAs from the sampling frame includes number of households and the population size. The total number of households in the frame was used to determine the selection of Primary Sampling Units (PSUs).

Mode of data collection

Face-to-face [f2f]

Research instrument

The main questionnaire (Form B) had nine sections, namely:

a) Demographic Characteristics b) Education, Literacy and Skills Training c) Economic Activity d) Employment e) Hours of Work and Underemployment f) Income - Part A and Part B g) Unemployment/Potential Labour Force h) Previous Work Experience i) Household Chores

Cleaning operations

Following completion of data collection, completed forms A and B were edited and sent to Head Office for data editing, capture and processing. Data editing took place at a number of stages throughout the processing. These included:

1. Field editing
2. Office editing and coding
3. During data entry
4. Structure checking and completeness
5. Secondary editing
6. Strucural checking of SAS data files

Response rate

In 2014, the labour force participation rate was 77.7 percent. The labour force participation rate in rural areas was higher at 80.2 percent relative to 74.7 percent recorded in urban areas. Males had a lower participation rate of 77.1 percent compared to 78.2 percent for females.

Data appraisal

A series of data quality tables and graphs are available to review the quality of the data and in addition to this, external resources such as the 2014 Labour Force Survey report has been attached.

Abstract

The Central Statistical Agency (CSA) has been providing labour force and related data at different levels and with varying details in their content. These include the 1976 Addis Ababa Manpower and Housing Sample Survey, the 1978 Survey on Population and Housing Characteristics of Seventeen Major Towns, the 1980/81 and 1987/88 Rural Labour Force Surveys, the 1984 and 1994 Population and Housing Census, and 2003 and 2004 Urban Bi-annual Employment Unemployment Survey. The 1996 and 2002 Surveys of Informal Sector and most of the household surveys undertaken by the Agency also provide limited information on the area. Still pieces of information in relation to that of employment can also be derived from small, large and medium scale establishment surveys.

Till the 1999 Labour Force Survey (LFS) there hasn't been a comprehensive national labour force survey representing both urban and rural areas. This 2005 LFS is the second in the series. Like the National Labour Force Survey of 1999, it covered both the urban and rural areas of all regions.

The specific objectives of this survey are to: - generate data on the size of work force that is available to participate in production process; - determine the status and rate of economic participation of different sub-groups of the population; - identify those who are actually contributing to the economic development (i.e., employed) and those out of the sphere; - determine the size and rate of unemployed population; - provide data on the structure of the working population; - obtain information about earnings from paid employment; - identify the distribution of employed population working in the formal/informal enterprises; and - provide time series data and trace changes over time.

Geographic coverage

The survey covered all rural and urban parts of the country except all zones of Gambella region excluding Gambella town, and the non-sedentary population of three zones of Afar & six zones of Somali regions.

Analysis unit

Household Individual

Universe

The survey covered all households in selected sample areas except residents of collective quarters, homeless persons and foreigners.

Kind of data

Sample survey data [ssd]

Sampling procedure

SAMPLING FRAME: The list of households obtained from the 2001/2 Ethiopian Agricultural Sample Enumeration (EASE) is used to select EAs from the rural part of the country. For urban sample EAs on the other hand the list consisting of households by EA, which was obtained from the 2004 Ethiopian Urban Economic Establishment Census, (EUEEC) was used as a frame. A fresh list of households from each urban and rural EA was prepared at the beginning of the survey period. The list was then used as a frame for selecting sample households of each EAs.

SAMPLE DESIGN: For the purpose of the survey the country was divided into three broad categories. That is; rural, major urban center and other urban center categories.

Category I: Rural: - This category consists of the rural areas of 8 regions and two city administrations found in the country. Regarding the survey domains, each region or city administration was considered to be a domain (Reporting Level) for which major findings of the survey are reported. This category totally comprises 10 reporting levels. A stratified two-stage cluster sample design was used to select samples in which the primary sampling units (PSUs) were EAs. Households per sample EA were selected as a second Stage Sampling Unit (SSU) and the survey questionnaire finally administered to all members of sample households.

Category II:- Major urban centers:- In this category all regional capitals and 15 other major urban centers that had a population size of 40,000 or more in 2004 were included. Each urban center in this category was considered as a reporting level. The category has totally 26 reporting levels. In this category too, in order to select the samples, a stratified two-stage cluster sample design was implemented. The primary sampling units were EAs. Households from each sample EA were then selected as a Second Stage Unit.

Category III: - Other urban centers: Urban centers in the country other than those under category II were grouped into this category. Excluding Gambella a domain of other urban centers is formed for each region. Consequently seven reporting levels were formed in this category. Harari, Addis Ababa and Dire Dawa do not have urban centers other than that grouped in category II. Hence, no domain was formed for these regions under this category. Unlike the above two categories a stratified three stage cluster sample design was adopted to select samples from this category. The primary sampling units were urban centers and the second stage sampling units were EAs. Households from each EA were finely selected at the third stage and the survey questionnaires administered for all of them.

SAMPLE SIZE AND SELECTION SCHEME: Category I: - Totally 830 EAs and 24,900 households were selected from this category. Sample EAs of each reporting level were selected using Probability Proportional to Size (PPS) systematic sampling technique; size being number of household obtained from the 2001/2 Ethiopian Agricultural Sample Enumeration. From the fresh list of households prepared at the beginning of the survey 30 households per EA were systematically selected and surveyed.

Category II: - In this category 720 EAs and 21,600 households were selected. Sample EAs from each reporting level in this category were also selected using probability proportional to size systematic sampling; size being number of households obtained from the 2004 EUEEC. From the fresh list of households prepared at the beginning of the survey 30 households per EA were systematically selected and covered by the study.

Category III:-127 urban centers, 275 EAs and 8,250 households were selected in this category. Urban centers from each domain and EAs from each urban center were selected using probability proportional to size systematic selection method; size being number of households obtained from the 2004 EUEEC. From the fresh listing of each EA 30 households were systematically selected and the study carried out on the 30 households ultimately selected.

Note: Distribution of number of samples planned and covered from each domain are given in the Summary Table 2.1, Table 2.2 and Table 2.3 of the 2005 National Labour Force Survey report which is provided as external resource.

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey has used a structured questionnaire to produce the required data. Before taking its final shape, the draft questionnaire was tested by undertaking a pre-test. The pre-test was conducted in Addis Ababa, Sendoffs, Teji and their vicinity. Based on the findings of the pre-test, the content, layout and presentation of the questionnaire was amended comments and inputs on the draft contents of the survey questionnaire obtained from user-producer forum were also incorporated in the final questionnaire.

The contents of the questionnaire and methods used in this survey were further improved based on comment of international consultant. The consultancy was obtained as part of a joint World Bank/IMF project to improve statistics of countries in Anglo-phone Africa participating in the General Data Dissemination System (GDDS).

The questionnaire was organized in to five sections; Section 1 - Area identification of the selected household: this section dealt with area identification of respondents such as region, zone, wereda, etc.,

Section 2 - Socio- demographic characteristics of households: it consisted of the general sociodemographic characteristics of the population such as age, sex, education, status and type of disability, status and types of training, marital status and fertility questions.

Section 3 - Productive activities during the last seven days: this section dealt with a range of questions which helps to see the status and characteristics of employed persons in a current status approach such as hours of work in productive activities, occupation, industry, employment status, and earnings from employment. Also questions included are hours spent on fetching water, collection of firewood, and domestic chores and place of work.

Section 4 - Unemployment and characteristics of unemployed persons: this section focused on the size and characteristics of the unemployed population.

Section 5 - Economic activities during the last twelve months: this section covered the usual economic activity status (refereeing to the long reference period), number of weeks of employment /unemployment/inactive, reasons for inactivity, employment status, whether working in the agricultural sector or not and the proportion of income gained from non-agricultural sector. The questionnaire used in the field for data collection was prepared in Amharic language. Most questions have pre-coded answers. A copy of the questionnaire translated to English is provided as external resource.

Cleaning operations

Data Editing, Coding and Verification: The filled-in questionnaires that were retrieved from the field were first subjected to manual editing and coding. During the fieldwork the enumerator, the field supervisors, Statisticians and the heads of branch statistical offices have done some editing. However, the major editing operation was carried out at the head office. All the edited questionnaires were again fully verified and checked for consistency before they were submitted to the data entry.

Response rate

Ultimately 100.00 % EAs and 99.84% household were covered

Abstract

The Poverty, Income, Consumption, and Expenditure Survey 2017 is the main data source for the compilation of the informal sector, living conditions, poverty levels, and weights for the Consumer Price Index (CPI). The survey is based on a sample of 32,256 households, representative at Province and District Levels.

The objectives of the survey are to: - Estimate private consumption expenditure and disposable income of the household sector - Compile the production account of the agricultural sector - Study income/expenditure disparities among socio-economic groups - Estimate the contribution of the informal sector to GDP in Zimbabwe - Estimate the size of household transfer incomes within and outside the country - Calculate weights for the Consumer Price Index (CPI) - Calculate the poverty line, measure the poverty rate and inequality - Provide data useful to formulate national policies for social welfare programmes - Obtain data for poverty mapping - Obtain data useful in measuring the demographic dividend for Zimbabwe

Geographic coverage

• National Coverage: 62 administrative districts of Zimbabwe
• Rural and Urban areas
• Land-use sectors: Communal Lands (CL), Small Scale Commercial Farms (SSCF), Large Scale Commercial Farms (LSCF), Resettlement Areas (includes Old Resettlement Areas (ORA), A1 Farms and A2 Farms), Urban Council Areas (UCA), Administrative Centres (AC), and Growth Point (GP) and Other Urban Areas (OUA), e.g. Services Center and Mines.

Analysis unit

• Individuals
• Households

Universe

The sample is representative of the whole population of Zimbabwe living in private households. The population living in collective households or in institutions such as military barracks, prisons and hospitals are excluded from the sampling frame.

Kind of data

Sample survey data [ssd]

Sampling procedure

At the first sampling stage, the sample EAs for the PICES 2017 are selected within each stratum (administrative district) using random systematic sampling with Probability Proportional to Size (PPS) from the ordered list of EAs in the sampling frame. The measure of size for each EA are based on the total number of households identified in the 2012 Population Census sampling frame. The EAs within each district are ordered first by rural and urban codes, land-use sector, ward and EA number. This provides implicit land-use and geographic stratification of the sampling frame within each district, and ensures a proportional allocation of the sample to the urban and rural areas of each district.The Complex Samples module of the SAS software is used for selecting the sample EAs systematically with PPS within each stratum at the first stage. The module uses the “SURVEY SELECT” sampling procedure.

At the second sampling stage, a random systematic sample of 14 households are selected with equal probability from the listing of each sample EA. Reserve households are selected for replacements. The reason why the replacement of non interview households are considered was to maintain the effective sample size and enumerator workload in each sample EA. Four households are selected for possible replacement, and thus a total of 18 households are selected from each sample EA. A systematic subsample of 4 households are then selected from the 18 households, and the remaining 14 sample households are considered the original sample for the survey. A spreadsheet is developed for selecting the 14 sample households and 4 reserve households for possible replacement in each sample EA. This spreadsheet includes items for the identification of the sample EA, and formulas for the systematic selection of households once the total number of households listed has been entered.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

The PICES 2017 data entry is conducted by the ZIMSTAT Data Entry Unit using the CSPro software to enter the data. Data entry was done from January 2018 to June 2018. Data is captured twice by different people for purposes of verification. Data from the daily record books (the household food consumption diaries) have been entered from July to November 2018. SAS and STATA software is used for data processing. Data cleaning is done at all stages i.e. during data entry and data processing to check for the consistency of the data. Tables are then generated for use in report writing.

Response rate

Out of a total of 32,256 sampled households, a total of 31,195 households successfully completed interviews. This gives a response rate of 96.7 percent of the sampled households.

Sampling error estimates

The standard error, or square root of the variance, is used to measure the sampling error, although it may also include a small variable part of the non-sampling error. The variance estimator should take into account the different aspects of the sample design, such as the stratification and clustering. Programs available for calculating the variances for survey data from stratified multi stage sample designs such as the PICES 2017 include STATA and the Complex Samples module of SPSS as well as SAS and Wesvar. All these software packages use an ultimate cluster (linearized Taylor series) variance estimator. The Complex Samples module of STATA is used with the PICES 2017 data to produce the sampling errors.

Abstract

The survey was undertaken bi-annually from 1965-1985 by the South African Department of Manpower (now the South African Department of Labour) and the standard form used the department's own classification scheme. In 1996 the name changed to the Occupational Survey. In 1998 it was replaced by the Survey of Occupations by Race and Gender, but this was discontinued after the pilot study in 1998. The two series of Manpower Surveys are not always comparable. Data users need to read the data quality notes provided for further details on data comparability.

The Manpower Survey is a survey of enterprises in South Africa that collected industry and occupation data by gender and race for each enterprise. It covered both the private and public sector, but excluded workers in the informal sector and agricultural sector, and domestic workers in private households. Enterprise details for the survey sample were obtained from government sources, and the survey instrument was a form mailed to enterprise managers.

The dataset available from DataFirst is not at firm level, but is rather the industry and occupation data by gender and race available from the survey reports. This country-level data is from the Manpower Surveys conducted in 1965-1994, unearthed in a project to find and share historical South African microdata. The data was obtained with the assistance of Lucia Lotter, Anneke Jordaan and Marie-Lousie van Wyk from the Human Sciences Research Council's Research Use and Impact Assessment Department. The project was made possible by an exploratory grant obtained by Andrew Kerr and Martin Wittenberg of DataFirst from the Private Enterprise Development in Low-Income Countries (PEDL) research initiative. PEDL is a joint research initiative of the Centre for Economic Policy Research (CEPR) and the Uk Department For International Development (DFID). It aims to develop a research programme focusing on private-sector development in low-income countries.

Geographic coverage

The survey had national coverage, but excluded the "independent" " homelands" of Bophuthatswana and Transkei (excluded from 1979) Venda (1981) and the Ciskei (1983).

Analysis unit

Individuals and institutions

Universe

The universe of the survey were enterprises in the formal non-agricultureal sector in South Africa

Kind of data

Sample survey data

Sampling procedure

The survey sample is based on lists of companies obtained from the databases of the Compensation Fund and Unemployment Insurance Fund of the South African Department of Labour) and the South African Tourism Board. During the time the surveys were conducted by the Department of labour (1965-1985), the sample of companies was 250,000. The survey was taken over by the Central Statistical Service (now Statistics South Africa) in 1987 who rationalised the sample to 12,800 companies in 1989, and later to 8500.

The sample excludes domestic workers in private household, and workers in the agricultural and informal sectors. The firms were classified into industries, based on the Standard Industrial Classification of all Economic Activities. Where these firms consisted of more than one establishment in more than one sector the firm was classified according to the sector in which it is predominantly engaged. Thus, although workers in the agricultural sector are not covered these may be included in firm data for those firms which include more than one establishment, and where one of the establishments is involved in agricultural production.

Entities in the "independent" " homelands" were excluded from the survey. These included Bophuthatswana and Transkei (excluded from 1979) Venda (1981) and the Ciskei (1983).

Mode of data collection

Mail Questionnaire [mail]

Research instrument

The 1965-1985 questionnaire from the Department of Labour has 5 Sections: Section A: To be completed for all employees except artisans, apprentices and “Bantu” building workers Section B: To be completed for male artisans and apprentices only Section C: To be completed for women artisans and apprentices only Section D: To be completed for “Bantu” building workers only (“skilled Bantu building workers and learners registered in terms of the Bantu Building Workers' Act”) Section E: To be completed for all employees (total number of employees)

The 1987-1994 questionnaire from the Central Statistical Service has 4 Sections: Section 1: To be completed for all employees except artisans, apprentices Section 2: To be completed for artisans only (men and women) Section 3: To be completed for apprentices only (men and women) Section 4: To be completed for all employees (total number of employees)

The variable

Response rate

Since the questionnaire was completed by company managers, the response rate of the sample is very high (around 90 per cent)

Data appraisal

DATA RELIABILITY The Manpower survey enables investigations of long-term changes in the occupational and racial division of labour during the period 1965-1994. It is the only data source for this period that distinguishes artisans and apprentices from other manual workers, which allows analysis of these occupations over time. However, the data is not reliable at disaggregated levels because of the following:

(1) Both agriculture and the informal sector are excluded from the survey universe. These sectors are major employers in the South African economy. (2) Domestic workers in private households are also excluded from the sample. (3) The survey does not cover the unemployed and is therefore not representative of the economically active population. (4) Although this is an occupational survey, the information on occupations is derived from samples based on total employment within industries. (5) A new sample was drawn by the Central Statistical Service when they took over the Manpower Survey from the Department of Manpower in 1987, causing a break in the series.

QUESTIONNAIRES Finally, the variable

CODING OF INDUSTRIES The Standard Industrial Classification codelist used to code industries in the survey seems to have been the ISIC revision 2.The codelist is provided here with the data, and is also available from the United Nations Statistics Division's website, at http://unstats.un.org/unsd/cr/registry/regdnld.asp?Lg=1