The Labour Force Survey provides estimates of employment and unemployment which are among the timeliest and important measures of performance of the Canadian economy. With the release of the survey results only 10 days after the completion of data collection, the LFS estimates are the first of the major monthly economic data series to be released. The Canadian Labour Force Survey was developed following the Second World War to satisfy a need for reliable and timely data on the labour market. Information was urgently required on the massive labour market changes involved in the transition from a war to a peace-time economy. The main objective of the LFS is to divide the working-age population into three mutually exclusive classifications - employed, unemployed, and not in the labour force - and to provide descriptive and explanatory data on each of these. LFS data are used to produce the well-known unemployment rate as well as other standard labour market indicators such as the employment rate and the participation rate. The LFS also provides employment estimates by industry, occupation, public and private sector, hours worked and much more, all cross-classifiable by a variety of demographic characteristics. Estimates are produced for Canada, the provinces, the territories and a large number of sub-provincial regions. For employees, wage rates, union status, job permanency and workplace size are also produced. These data are used by different levels of government for evaluation and planning of employment programs in Canada. Regional unemployment rates are used by Employment and Social Development Canada to determine eligibility, level and duration of insurance benefits for persons living within a particular employment insurance region. The data are also used by labour market analysts, economists, consultants, planners, forecasters and academics in both the private and public sector.This public use microdata file contains non-aggregated data for a wide variety of variables collected from the Labour Force Survey (LFS). It contains both personal characteristics for all individuals in the household and detailed labour force characteristics for household members 15 years of age and over. The personal characteristics include age, sex, marital status, educational attainment, and family characteristics. Detailed labour force characteristics include employment information such as class of worker, usual and actual hours of work, employee hourly and weekly wages, industry and occupation of current or most recent job, public and private sector, union status, paid or unpaid overtime hours, job permanency, hours of work lost, job tenure, and unemployment information such as duration of unemployment, methods of job search and type of job sought. Labour force characteristics are also available for students during the school year and during the summer months as well as school attendance whether full or part-time and the type of institution.LFS revisions: Labour force surveys are revised on a periodic basis, either to adopt the most recent geography, industry and occupation classifications; to use new observations to fine-tune seasonal adjustment factors; or to introduce methodological enhancement. Prior LFS revisions were conducted in 2011, 2015 and 2021. The most recent revisions to the LFS were conducted in 2023. The first major change was a transition to the National Occupational Classification (NOC) 2021 V1.0, with all LFS series from 1987 onwards having been revised to the new classification. The second major change were methodological enhancements to LFS data processing, applied to all LFS series beginning Jan 2006. The third major change was a revision of seasonal adjustment factors, applied to LFS series Jan 2002 onward. A list of prior versions of this LFS dataset can be found under the ‘Versions’ tab.

Abstract

This survey intends to: -

· Measure the labour force or economically active population size in relation to the general population in the country. · Identify and analyse the factors leading to the emergence and growth of Labour Force in the country. · Monitor the labour force participation. · Identify and measure the informal sector from within the labour force. · Monitor other Key Indicators of the Labour Market such as employment rates,unemployment rates, hours of work, average income and/or wages etc.

Furthermore, the survey seeks to examine the relationships of socio-economic factors such as education, health, social security, employment within the labour force, and more importantly to measure the causes and effects of children’s involvements in economic activities with special focus on the conditions and environment under which affected children operate.

The main objective of the 2012 LFS was to collect data on the social and economic activities of the population, including detailed information on employment, unemployment, underemployment, wages, informal sector, general characteristics of the labour force and economically inactive population. The survey was designed to specifically measure and monitor Key Indicators of the Labour Market (KILM) such as employment levels, unemployment, income and child labour in Zambia. However, indicators on child labour are not part of this 2012 LFS report. There will be a separate report on child labour later. The measurement of the KILM was with a view to informing users and policy-makers for decision-making. The methodology used in carrying out the survey and the design of questionnaire conform to internationally acceptable standards.

Geographic coverage

The 2012 Labour Force Survey (LFS) was a nation-wide survey covering household population in all the ten provinces and, in both rural and urban areas. The survey covered a representative sample of 11, 520 households, which were selected at two stages. In the first stage, 576 Standard Enumeration Areas (SEAs) were selected from a sampling frame developed from the 2010 Census of Population and Housing. In the second stage, households in each of the selected SEA were first listed/updated and then 20 households for enumeration were selected. 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 results.

Analysis unit

The unit of analysis was Households and Individuals ( Men and Women of 5 years and older). Additionally, the analysis focused on national level at both rural/urban and provincial level. The micro-data has provisions to generate major indicators at district and constituency levels. As much as possible the micro-data have also been analyzed by sex and age.

Universe

The survey covered all de jure household members (usual residents) in non-institutionalised housing units, all women and men aged 5 years and older

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample was designed to allow separate estimates at national level for rural and urban areas. Further, it also allowed for provincial estimates. A cluster, which is equivalent to a Standard Enumeration Area (SEA), was the primary sampling unit in the ?rst stage. In the second stage, a household was a sampling unit for enumeration purposes. 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 in turn demarcated into Standard Enumeration Areas (SEAs). 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. As at the time of the survey, Zambia had 74 districts, 150 constituencies, 1,430 wards and about 25,000 SEAs. Information borne on the list of SEAs from the sampling frame also includes number of households and the population size as at the last update of the SEA. The number of households determined the selection of primary sampling units (PSU). The SEAs are stratifed as urban and rural. 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 results. The proportional allocation does not however allow for reliable estimates for lower domains like district, ward or constituency. 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 stratifed two-stage cluster design.

Sampling deviation

There was no deviation from sample design.

Mode of data collection

Face-to-face [f2f]

Research instrument

Two types of questionnaires (Form A and Forma B) were used to collect data from the household members. Form A was used in the first stage for listing purposes while Form B was used in the second stage for collecting detailed data from the selected households. It was a requirement for each household member to provide responses during the face-to-face interview to the questions that were asked.

The main questionnaire has ten sections namely:

a. Demographic Characteristics b. Education, Literacy and Skills Training c. Economic Activity d. Employment e. Hours of Work and Underemployment f. Income g. Unemployment/Job Search h. Previous Work Experience i. Household Chores j. Working Conditions (i.e. Forced labour)

Cleaning operations

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

At the end of the field work and editing in the provinces, a total of at least 11,000 of completed questionnaires, representing a 99.8 percent response rate were sent to Head Office for data processing.

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 2012 Labour Force Survey report has been attached.

The Egypt Labor Market Panel Survey, carried out by the Economic Research Forum (ERF) in cooperation with Egypt’s Central Agency for Public Mobilization and Statistics (CAPMAS). Over its twenty-year history, the ELMPS has become the mainstay of labor market and human development research in Egypt, being the first and most comprehensive source of publicly available micro data on the subject. The 2018 wave of the Egypt Labor Market Panel Survey (ELMPS) is the fourth wave of a longitudinal survey carried out by the Economic Research Forum (ERF) in cooperation with the Egyptian Central Agency for Public Mobilization and Statistics (CAPMAS). The 2018 wave follows previous waves in 1998, 2006 and 2012. Over its twenty-year history, the ELMPS has become the mainstay of labor market and human development research in Egypt, being the first and most comprehensive source of publicly available micro data on the subject. The ELMPS is a wide-ranging, nationally representative panel survey that covers topics such as parental background, education, housing, access to services, residential mobility, migration and remittances, time use, marriage patterns and costs, fertility, women’s decision making and empowerment, job dynamics, savings and borrowing behavior, the operation of household enterprises and farms, besides the usual focus on employment, unemployment and earnings in typical labor force surveys. ELMPS 2018 also provided more detailed information on health, gender role attitudes, food security, hazardous work, community infrastructure and the cost of housing. It incorporated specific questions on vulnerability, coping strategies and access to social safety net programs. (Krafft, C, Assaad, R., and Rahman, K .,2019) In addition to the survey’s panel design, which permits the study of various phenomena over time, the survey also contains a large number of retrospective questions about the timing of major life events such as education, residential mobility, jobs, marriage and fertility. The survey provides detailed information about place of birth and subsequent residence, as well information about schools and colleges attended at various stages of an individual’s trajectory, which permit the individual records to be linked to information from other data sources about the geographic context in which the individual lived and the educational institutions s/he attended. The data may be accessed through the ERF Data Portal: http://www.erfdataportal.com/index.php/catalog/157

Abstract

The Migration Cost Surveys (MCS) project is a joint initiative of the Global Knowledge Partnership on Migration and Development (KNOMAD) and the International Labor Organization (ILO). The project was initiated to support methodological work on developing a new Sustainable Development Goal (SDG) indicator (10.7.1) on worker-paid recruitment costs. The surveys of migrant workers conducted in multiple bilateral corridors between 2015 and 2017 provide new systematic evidence of financial and some non-financial costs incurred by workers to obtain jobs abroad. The compiled dataset is divided into two waves (2015 and 2016) based on the questionnaire version used in the surveys.

Geographic coverage

Multinational coverage: - Ethiopia - India - Nepal - Pakistan - Philippines - Vietnam - Guatemala - Honduras - El Salvador

Analysis unit

KNOMAD-ILO Migration Costs Surveys (KNOMAD-ILO MCS) have the following unit of analysis: individuals

Universe

Surveys of migrants from the following corridors are included:

• Ethiopia to Saudi Arabia • India to Qatar • Nepal to Qatar • Pakistan to Saudi Arabia and United Arab Emirates • Philippines to Qatar • Vietnam to Malaysia • Guatemala, Honduras and El-Salvador to Mexico

Kind of data

Sample survey data [ssd]

Sampling procedure

All surveys conducted for this project used either convenience or snowball sampling. Sample enrollment was restricted to migrants primarily employed in low-skilled positions, who departed to the destination country, typically no more than 5 years prior to the interview year. All but two surveys using the 2015 questionnaire were conducted in the country of origin by interviewing returning migrants.The exceptions were the surveys of Vietnamese migrants in Malaysia and migrants from Guatemala, Honduras and El-Salvador in Mexico, which were administered in the destination countries (Malaysia and Mexico, respectively). Their customized questionnaires are worded in present tense when it comes to various aspect of stay in the destination country. The content of the variables remains analogous to the surveys of returnees. Please refer to Annex Table 1 of the 2015 KNOMAD-ILO MCS User Guide for a summary description of the included samples in the 2015 KNOMAD-ILO MCS dataset.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The 2015 KNOMAD-ILO Migration Costs Surveys consists of 6 survey modules:

A. Respondent Information B. Information on costs for current job C. Borrowing money for the foreign job D. Job search efforts and opportunity costs E. Work in foreign country F. Job environment

Sampling error estimates

n/a

Data appraisal

n/a

Abstract

The Ghana Living Standards Survey Round Six (GLSS6) like previous rounds focuses on the household as the key socio-economic unit and provides valuable information on the living conditions and well-being of households in Ghana. This report summarizes the main findings of the sixth round of the GLSS which was conducted by the Ghana Statistical Service (GSS) from 18th October 2012 to 17th October 2013.

A nationally representative sample of 18,000 households in 1,200 enumeration areas was covered in the survey. Of this number, 16,772 were successfully enumerated leading to a response rate of 93.2 percent. Detailed information collected on Demographic characteristics of households, Education, Health, Employment, Migration and Tourism, Housing conditions, Household Agriculture, Household Expenditure, Income and their components and Access to Financial Services, Credit and Assets, Governance Peace and Security.

The main objectives of the sixth round of the Ghana living Standards Survey Round Six are to: . Provide information on the patterns of household consumption and expenditure at a lower level of disaggregation. . Serve as the basis for the construction of a new basket for the next re-basing of the Consumer Price Index. . Provide information for up-dating the country's National Accounts. . Provide information on household access to and use of financial services. . Estimate the number of persons in the labour force (Employed, Under-employed and Unemployed) and their distribution by sex, major age-groups, educational level, disability status, geographical and rural/ urban spread, as well as the ecological manifestations of these. . Estimate the number of child workers (or children in employment) aged 5-17 years, and its distribution by sex, major age-groups, educational status, geographical, ecological and rural/urban spread, etc.

Publications of the GLSS 6 survey include * GLSS 6 Main Report * Poverty Profile in Ghana, 2005 - 2013 * GLSS 6 Labour Force Report * GLSS 6 Child Labour Report * GLSS 6 Governance Peace and Security Report * GLSS 6 Water Quality Testing Report. * GLSS 6 Community Report

Geographic coverage

Nationally Region

Analysis unit

Individuals, Households, Communities

Universe

The survey covered all household members

Kind of data

Sample survey data [ssd]

Sampling procedure

The sixth round of the Ghana Living Standards Survey (GLSS6), like the previous rounds, was designed to provide nationally and regionally representative indicators. It applied the same sampling methodology, the same questionnaires and covered the same broad range of topics such as education, health, employment, housing conditions, migration and tourism among others.

To cater for the needs of the Savannah Accelerated Development Authority (SADA) areas and also provide nationally representative quarterly labour force statistics, the number of primary sampling units (PSUs) and households were increased from 580 and 8,700 to 1,200 and 18,000 respectively - an increase of about 107% over the GLSS5 figures. (See Appendix 1 Tables A1 and A2).

A two-stage stratified sampling design was adopted. At the first stage, 1,200 enumeration areas (EAs) were selected to form the PSUs. The PSUs were allocated into the 10 regions using probability proportional to population size (PPS). The EAs were further divided into urban and rural localities of residence. A complete listing of households in the selected PSUs was undertaken to form the secondary sampling units (SSUs). At the second stage, 15 households from each PSU were selected systematically. Hence the total sample size came to 18,000 households nationwide. (Refer to Appendix 1 in main GLSSS 6 report.)

Sampling deviation

No deviation from the sample

Mode of data collection

Face-to-face [f2f]

Research instrument

Six different questionnaires were used for the GLSS 6 survey: PART A, PART B, SECTION 10, COMMUNITY , PRICE and GOVERNANCE PEACE AND SECURITY questionnaires:

PART A Questionnaire comprise: Section 1: Household roster collecting information on age, sex, marital status, nationality, religion etc. Section 2: Education- General education, Educational carreer, Literacy and Apprenticeship. Section 3: Health - Health conditions, Preventive health, Immunisation, Post natal care, Fertility, Contraceptive use and HIV awareness and Health insurance. Section 4: Employment and time use, activity status and characteristics of main and secondary jobs, underemployment, unemployment,
employment search and housekeeping activities for last 7days and 12 months. Section 5: Migration, Domestic and Outbound tourism. Section 6: Identification of household members for agriculture and Non farm enterprises. Section 7: Housing characteristics (type of dwelling, utilities and housing expenses), Information technology.

PART B Questionnaire sought information on : Section 8: Agricultural assets, Land, Livestock and Equipment, Farm details, Harvest and disposal of crops, Seasonality of sales and purchases of key staples, Other agricultural income in cash and kind, Processing of agricultural produce and Consumption of own produce. Section 9: Household expenditure on food and non food, frequently purchased and less frequently purchased items, Availability of selected consumer items. Section 11: Income transfer and receipts by households, Income and miscellaneous income and expenditure, Migration and Remitances of returned
and current migrants , Improvement to dwelling Section 12: Credit, assets, consumer goods and Savings.

SECTION 10 Questionnaire sought information on Basic characteristics of non farm enterprises, Wage earnings, Employment, Revenue of enterprises, (closing stock, sales and exports), Wholesale and retail activities, Preparation of meals, Other revenue, Expenditure of enterprises and assets of enterprises.

COMMUNITY Questionnaire: Section 1: Demographic information of the community ( total population, ethnic groupings etc) Section 2: Economy and infrastructure Section 3: Education Section 4: Health Section 5: Agriculture

PRICE Questionnaire consist of Food and Non food quantity and prices of selected items.

GOVERNANCE PEACE AND SECURITY Questionnaire sought information on ; Part A: Theft, Robery and Burglary, Part B: Sexual offences Part C: Violence and Security Part D: Safety Part E: Peace and Social Cohesion Part F: Political Engagement Part G: Governance- Effectiveness of Government agencies

Cleaning operations

Data editing took place at a number of stages throughout the processing, including: a) Office editing and coding by field editors b) Using range checks during data capture c) Structure , range and completeness checks d) Secondary editing using batch editing rules developed in CSPro 4.1 e) Consistency check in all sections using STATA

Response rate

A nationally representative sample of 18,000 households in 1,200 enumeration areas was covered in the survey. Of this number, 16,772 were successfully enumerated leading to a response rate of 93.2 percent

Sampling error estimates

Sampling errors were calculated for some key variables. Refer to the GLSS 6 Main Report Appendix 1 attached to external resources for sampling error estimates.

analyze the consumer expenditure survey (ce) with r the consumer expenditure survey (ce) is the primo data source to understand how americans spend money. participating households keep a running diary about every little purchase over the year. those diaries are then summed up into precise expenditure categories. how else are you gonna know that the average american household spent $34 (±2) on bacon, $826 (±17) on cellular phones, and $13 (±2) on digital e-readers in 2011? an integral component of the market basket calculation in the consumer price index, this survey recently became available as public-use microdata and they're slowly releasing historical files back to 1996. hooray! for a t aste of what's possible with ce data, look at the quick tables listed on their main page - these tables contain approximately a bazillion different expenditure categories broken down by demographic groups. guess what? i just learned that americans living in households with $5,000 to $9,999 of annual income spent an average of $283 (±90) on pets, toys, hobbies, and playground equipment (pdf page 3). you can often get close to your statistic of interest from these web tables. but say you wanted to look at domestic pet expenditure among only households with children between 12 and 17 years old. another one of the thirteen web tables - the consumer unit composition table - shows a few different breakouts of households with kids, but none matching that exact population of interest. the bureau of labor statistics (bls) (the survey's designers) and the census bureau (the survey's administrators) have provided plenty of the major statistics and breakouts for you, but they're not psychic. if you want to comb through this data for specific expenditure categories broken out by a you-defined segment of the united states' population, then let a little r into your life. fun starts now. fair warning: only analyze t he consumer expenditure survey if you are nerd to the core. the microdata ship with two different survey types (interview and diary), each containing five or six quarterly table formats that need to be stacked, merged, and manipulated prior to a methodologically-correct analysis. the scripts in this repository contain examples to prepare 'em all, just be advised that magnificent data like this will never be no-assembly-required. the folks at bls have posted an excellent summary of what's av ailable - read it before anything else. after that, read the getting started guide. don't skim. a few of the descriptions below refer to sas programs provided by the bureau of labor statistics. you'll find these in the C:\My Directory\CES\2011\docs directory after you run the download program. this new github repository contains three scripts: 2010-2011 - download all microdata.R lo op through every year and download every file hosted on the bls's ce ftp site import each of the comma-separated value files into r with read.csv depending on user-settings, save each table as an r data file (.rda) or stat a-readable file (.dta) 2011 fmly intrvw - analysis examples.R load the r data files (.rda) necessary to create the 'fmly' table shown in the ce macros program documentation.doc file construct that 'fmly' table, using five quarters of interviews (q1 2011 thru q1 2012) initiate a replicate-weighted survey design object perform some lovely li'l analysis examples replicate the %mean_variance() macro found in "ce macros.sas" and provide some examples of calculating descriptive statistics using unimputed variables replicate the %compare_groups() macro found in "ce macros.sas" and provide some examples of performing t -tests using unimputed variables create an rsqlite database (to minimize ram usage) containing the five imputed variable files, after identifying which variables were imputed based on pdf page 3 of the user's guide to income imputation initiate a replicate-weighted, database-backed, multiply-imputed survey design object perform a few additional analyses that highlight the modified syntax required for multiply-imputed survey designs replicate the %mean_variance() macro found in "ce macros.sas" and provide some examples of calculating descriptive statistics using imputed variables repl icate the %compare_groups() macro found in "ce macros.sas" and provide some examples of performing t-tests using imputed variables replicate the %proc_reg() and %proc_logistic() macros found in "ce macros.sas" and provide some examples of regressions and logistic regressions using both unimputed and imputed variables replicate integrated mean and se.R match each step in the bls-provided sas program "integr ated mean and se.sas" but with r instead of sas create an rsqlite database when the expenditure table gets too large for older computers to handle in ram export a table "2011 integrated mean and se.csv" that exactly matches the contents of the sas-produced "2011 integrated mean and se.lst" text file click here to view these three scripts for...

Abstract

The 2021 Tonga HIES is the new update of this kind, after the 2015/2016, 2009 & 2001 versions. This survey aims to provide indicators on Household Living Standard using monetary aspect (amount of income and expenditure), non-monetary aspect (calory consumed, assets own, imputed rents…) and more social approach (education, health, food security status…). Survey outputs have multiple uses in various domains such as public health (food nutrition analysis), economic development (poverty), system of National Account (consumption aggregates), and they represent a key source of information to populate many National SDGs.

Geographic coverage

National coverage.

Analysis unit

Household and Individual.

Universe

Household Income and Expenditure Survey (HIES) covered all persons who were considered to be usual residents of private dwellings.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Tonga 2021 HIES sample design is based on a two stages sample design, where each stage corresponds to a random selection: - Stage 1: random selection of census blocks (using the probability of selection proportional to size) - Stage 2: random selection of households (from the selected blocks). Within each selected blocks, 12 households were randomly selected (uniform probability) The survey aims to disseminate results at strata level where stratas are defined on geographical combination of provinces and urban rural areas. The sample sizes were calculated at the strata level, with the aim of minimizing the sampling error (and relative sampling error) within each strata.

The sampling frame used was the 2016 population census.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The 2021 Tonga Household Income and Expenditure Survey (HIES) questionnaire was developed in English and Tongan language and it follows the Pacific Standard HIES questionnaire structure. It is administered on CAPI using Survey Solution, and the diary is no longer part of the form. All transactions (food, non food, home production and gifts) are collected through different recall sections during the same visit. The traditional 14 days diary is no longer recommended in the region. This new method of implementing the HIES present some interesting and valuable advantages such as: cost saving, data quality, time reduction for data processing and reporting. Only a sub sample of the selected HIES sample was asked to fill a 2-week diary (in addition to the core recall HIES questionnaire). But only the recall sections are used to compute the HIES outputs and aggregates, this sub sample HIES diary was made for research purposes.

Below is a list of all modules in this questionnaire: -Household ID -Demographic characteristics -Education -Health -Functionality -Communication -Alcohol -Other individual expenses -Labour force -Fisheries & hunting -Handicraft -Dwelling -Assets -Home maintenance -Vehicle -International trips -Domestic trips -Household services -Financial support -Other household expenditure -Ceremonies -Remittances -Food insecurity -Livestock & aquaculture -Agriculture parcel -Agriculture vegetables -Agriculture rootcrops -Agriculture other plants -Agriculture fruits -Legal services.

The survey questionnaire can be found in this documentation.

Cleaning operations

Once the data was collected using Survey Solutions, it was then edited on Stata (version 15).

Response rate

The final response rate including replacement from List B can be found below:

-Tongatapu urban: 95% -Tongatapu rural: 93.3% -Vava'u: 92.5% -Ha'apai: 96.1% -Eua: 99.2% -Ongo Niua: 96.6% -Total Tonga: 94.8%

Abstract

This research is an Indicator Survey conducted in Tonga from April 27 to Sept. 30, 2009, as part of the Enterprise Survey initiative. An Indicator Survey, which is similar to an Enterprise Survey, is implemented for smaller economies where the sampling strategies inherent in an Enterprise Survey are often not applicable due to the limited universe of firms.

The objective of the survey is to obtain feedback from enterprises on the state of the private sector as well as to help in building a panel of enterprise data that will make it possible to track changes in the business environment over time, thus allowing, for example, impact assessments of reforms. Through interviews with firms in the manufacturing and services sectors, the survey assesses the constraints to private sector growth and creates statistically significant business environment indicators that are comparable across countries.

Questionnaire topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs/labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, land and permits, taxation, business-government relations, and performance measures.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study is the establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

The whole population, or the universe, covered in the Enterprise Surveys is the non-agricultural economy. It comprises: all manufacturing sectors according to the ISIC Revision 3.1 group classification (group D), construction sector (group F), services sector (groups G and H), and transport, storage, and communications sector (group I). Note that this population definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, except sub-sector 72, IT, which was added to the population under study), and all public or utilities-sectors.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for Tonga was selected using stratified random sampling. Two levels of stratification were used in this country: industry and establishment size.

Industry stratification was designed in the way that follows: the universe was stratified into manufacturing industries, and one services sector.

Size stratification was defined following the standardized definition for the rollout: small (5 to 19 employees), medium (20 to 99 employees), and large (more than 99 employees). For stratification purposes, the number of employees was defined on the basis of reported permanent full-time workers. This seems to be an appropriate definition of the labor force since seasonal/casual/part-time employment is not a common practice, except in the sectors of construction and agriculture.

Regional stratification did not take place as only the main island of Tongatapu where the capital of Nuku'alofa is located. Tongatapu is also the largest island of Tonga's three island groups and is the home of the majority of the formal business community for the country.

Due to limited data sources available in Tonga on registered businesses, the final sample frame was obtained from a combined dataset obtained from the Tonga National Statistics Office. The list provided by the NSO was limited to including information on the sector and location of enterprises, with no details on the number of employees. Therefore, original sample counts were not able to be stratified by enterprise size. The modified sample frame was used to select the sample of establishments for the full survey. This database contained the following information: -Name of the firm -Contact details -Location -ISIC code.

Given the impact that non-eligible units included in the sample universe may have on the results, adjustments may be needed when computing the appropriate weights for individual observations. The percentage of confirmed non-eligible units as a proportion of the total number of sampled establishments contacted for the survey was 42% (139 out of 333 establishments). Breaking down by industry, the following numbers of establishments were surveyed: Manufacturing - 78, Services - 72.

Mode of data collection

Face-to-face [f2f]

Research instrument

The current survey instruments are available: - Services Questionnaire - Manufacturing Questionnaire - Screener Questionnaire.

The Services Questionnaire is administered to the establishments in the services sector. The Manufacturing Questionnaire is built upon the Services Questionnaire and adds specific questions relevant to manufacturing.

The standard Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs/labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures. Over 90% of the questions objectively ascertain characteristics of a country’s business environment. The remaining questions assess the survey respondents’ opinions on what are the obstacles to firm growth and performance.

Cleaning operations

Data entry and quality controls are implemented by the contractor and data is delivered to the World Bank in batches (typically 10%, 50% and 100%). These data deliveries are checked for logical consistency, out of range values, skip patterns, and duplicate entries. Problems are flagged by the World Bank and corrected by the implementing contractor through data checks, callbacks, and revisiting establishments.

Response rate

Complete information regarding the sampling methodology, sample frame, weights, response rates, and implementation can be found in "Description of Tonga Implementation 2009" in "Technical Documents" folder.

Abstract

The primary purpose of this survey was to gather more accurate and detailed information on income and expenditure levels and flows in the Marshall Islands (MHL) and to update and revise the MHL Consumer Price Index (a separate series of publications document the CPI revision efforts).

Geographic coverage

National coverage; urban and rural.

Analysis unit

Household and Individual.

Universe

All usual household residents in private dwellings.

Kind of data

Sample survey data [ssd]

Sampling procedure

SAMPLE SIZE: In determining an appropriate sample size for a survey of this nature, numerous factors come into the equation. These include:

a) The degree of accuracy required for key estimates; b) The population size of the country; c) The manner in which the sample is selected; d) Cost or staffing constraints which may exist; e) Whether or not estimates are required for sub-populations; f) The level of variability in the data being collected.

Each of these factors have different magnitudes of importance, but the major priority should always be on selecting a sample big enough to produce results of suitable accuracy. Many of these issues are generally known as well - for instance:

· A user group may pre-specify what level of accuracy they may wish to achieve for the survey · The population of a country can normally be estimated to a reasonable level of accuracy · The sample selection technique adopted is known · Cost and staff constraints are generally known, and · A user group can once again provide information on whether estimates for sub-populations are required.

The Marshall Islands 2020 Household Income and Expenditure Survey (HIES) aims to release outputs at the Urban Rural level and National level.

The sampling strategy has been developed around a stratification of urban and rural domains of the Marshall Islands. This stratification aims to improve the robustness of the indicators at urban and rural level. The urban sector has been stratified in 2 different atolls (Majuro and Kwajalein) and the rural region has been stratified in group of atolls that show similarities as follow: - Rural 1: peri urban atolls; located close to Majuro and Kwajalein (Arno, Mili) - Rural 2: most important rural atolls with facilities (boarding schools; health center…) (Jaluit; Wotje) - Rural 3: atolls where population benefit from US Government for nuclear testing (Enewatak, Kili, Utirik) - Rural 4: other atolls

The targeted sample size has been determined around 880 households based on the results of the previous 2011 population census that provided the mean and standard deviation of the total cash household income at the strata level:

The cluster size has been determined at 12 households.

-Urban 1 & 2: respectively 400 and 150 households spread across 33 and 13 EAs lead to 7.9% RSE in Urban domain
-Rural: respectively 2, 3, 10 and 13 EAs in rural1, 2, 3 and 4 (24, 36, 120 and 150 households) lead to 7.3% RSE in Rural domain -At the National level this total sample size of 880 households spead across those 6 stratas as mentioned lead to a RSE of 7.1%

SAMPLE SELECTION: The 2020 Marshall Islands HIES is based on a stratified cluster sampling strategy. The households are selected in 2 steps: - Step1: the random selection of EA based on the sampling strategy parameters (Primary sampling unit) - Step2: the random selection of 12 households (+6 replacements) within each selected EA

The final probability of selection combines both probabilities of EA getting selected within the strata and households getting selected with the EA.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaire was produced in English and Marshallese languages. The English questionnaire can be found in the External Resources.

Below is the list of all questionnaire modules: -1. Household ID -2. Household member roster -3. Person details: Profile; Education; Health; Physical; Communication; Alcohol & tobacco; Other individual expenses; Labour force; Fisheries hunting; Handicraft & home processed food) -4. Food away from home: Breakfast; Lunch; Dinner; Snacks; Hot drinks; Bottked water; Non-alcoholic drinks -5. Own production -6. Deprivation (persons) -7. Food recall -7.1. Partaker -8. Non-food recall -9. Household details: Dwelling characteristics; Household assets; Other household items & services; Ceremonies; Remittances; Food insecurity; Copra production; Livestock & aquaculture; Agriculture; Legal services -10. Deprivation and financial inclusion (household) -11. Migrant worker -12. Geographic information + photo.

Cleaning operations

Data editing was done using the software Stata.

Response rate

Below are the response rates by urban-rural region for Set A (households selected from the sample): -Urban: 85.3% -Rural: 89% -NATIONAL: 86.7%

Below are the response rates by urban-rural region for Set B (households selected from the sample + replacements): -Urban: 99.8% -Rural: 95.8% -NATIONAL: 98.3%

Sampling error estimates

-RELATIVE SAMPLING ERRORS (RSEs): Below are the RSEs for total expenditure throughout all COICOP divisions, by urban and rural areas: .Urban: 3.6%; Mean expenditure: 5,119; Lower 95% confidence interval: 4,747; Upper 95% confidence interval: 5,490. .Rural: 6.3%; Mean expenditure: 3,280; Lower 95% confidence interval: 2,870; Upper 95% confidence interval: 3,691. .NATIONAL: 3.3%; Mean expenditure: 4,659; Lower 95% confidence interval: 4,348; Upper 95% confidence interval: 4,969.

Below are the RSEs for total income throughout all PACCOI divisions, by urban and rural areas: .Urban: 7.6%; Mean income: 3,612; Lower 95% confidence interval: 3,061; Upper 95% confidence interval: 4,162. .Rural: 9.2%; Mean income: 3,585; Lower 95% confidence interval: 2,928; Upper 95% confidence interval: 2,928. .NATIONAL: 6.2%; Mean income: 3,605; Lower 95% confidence interval: 3,161; Upper 95% confidence interval: 3,161.

The detailed relative sampling errors (RSEs) for the 2019 Marshall Islands Household Income and Expenditure Survey (HIES) will be included in the Appendix section of the final analytical report (when released).

Abstract

The Migration Cost Surveys (MCS) project is a joint initiative of the Global Knowledge Partnership on Migration and Development (KNOMAD) and the International Labor Organization (ILO). The project was initiated to support methodological work on developing a new Sustainable Development Goal (SDG) indicator (10.7.1) on worker-paid recruitment costs. The surveys of migrant workers conducted in multiple bilateral corridors between 2015 and 2017 provide new systematic evidence of financial and some non-financial costs incurred by workers to obtain jobs abroad. The compiled dataset is divided into two waves (2015 and 2016) based on the questionnaire version used in the surveys. This document describes surveys conducted using the 2016 version of the MCS questionnaire.

Geographic coverage

Multinational coverage - India - Philippines - Nepal - Uzbekistan - Kyrgyz Republic - Tajikistan - Countries in Western Africa

Analysis unit

KNOMAD-ILO Migration Costs Surveys (KNOMAD-ILO MCS) have the following unit of analysis: individuals

Universe

Surveys of migrants from the following corridors are included: • India-Saudi Arabia • Philippines to Saudi Arabia • Nepal to Malaysia, Qatar and Saudi Arabia • Kyrgyzstan, Tajikistan, Uzbekistan to Russia • West African countries to Italy

Kind of data

Sample survey data [ssd]

Sampling procedure

All surveys conducted for this project used either convenience or snowball sampling. Sample enrollment was restricted to migrants primarily employed in low-skilled positions. There is variation in terms of when migrants were interviewed in their migration life-cycle. Two surveys of recruited workers - that is workers who are recruited in their home countries for jobs abroad - namely Filipinos and Indians to Saudi Arabia, were conducted with migrants returning to their origin countries (for visits or permanently). The surveys of non-recruited migrants - Central Asian migrants to Russia and West African migrants to Italy - were administered in the destination countries, which permitted multiple bilateral migration channels to be documented (at cost of smaller sample sizes in some corridors, particularly with Italy as destination). The survey instruments for non-recruited migrants were worded in present tense for various aspect of stay in the destination country. The content of the variables remains analogous to the surveys of returnees. Finally, the survey of Nepalese migrants was conducted with migrants who were departing to their destination countries within a two-week period. Please refer to Annex Table 1 of the 2016 KNOMAD_ILO MCS Guide for a summary description of the samples included in the 2016 KNOMAD-ILO MCS dataset.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The 2016 KNOMAD-ILO Migration Costs Surveys consists of 7 survey modules: A. Respondent information B. Information on costs for current job C. Borrowing money for the foreign job D. Job search efforts and opportunity costs E. Work in foreign country F. Job environment G. Current status and contact information

Sampling error estimates

n/a

Data appraisal

n/a

Abstract

The Consumer Expenditure Survey (CE) program provides a continuous and comprehensive flow of data on the buying habits of American consumers. These data are used widely in economic research and analysis, and in support of revisions of the Consumer Price Index. To meet the needs of users, the Bureau of Labor Statistics (BLS) produces population estimates for consumer units (CUs) of average expenditures in news releases, reports, issues, and articles in the Monthly Labor Review. Tabulated CE data are also available on the Internet and by facsimile transmission (See Section XV. APPENDIX 4). The microdata are available online at http://www/bls.gov/cex/pumdhome.htm.

These microdata files present detailed expenditure and income data for the Diary component of the CE for 2003. They include weekly expenditure (EXPD) and annual income (DTBD) files. The data in EXPD and DTBD files are categorized by a Universal Classification Code (UCC). The advantage of the EXPD and DTBD files is that with the data classified in a standardized format, the user may perform comparative expenditure (or income) analysis with relative ease. The FMLD and MEMD files present data on the characteristics and demographics of CUs and CU members. The summary level expenditure and income information on the FMLD files permits the data user to link consumer spending, by general expenditure category, and household characteristics and demographics on one set of files.

Estimates of average expenditures in 2003 from the Diary survey, integrated with data from the Interview survey, are published in Consumer Expenditures in 2003. A list of recent publications containing data from the CE appears at the end of this documentation.

The microdata files are in the public domain and with appropriate credit, may be reproduced without permission. A suggested citation is: "U.S. Department of Labor, Bureau of Labor Statistics, Consumer Expenditure Survey, Diary Survey, 2003".

STATE IDENTIFIER

Since the CE is not designed to produce state-level estimates, summing the consumer unit weights by state will not yield state population totals. A CU's basic weight reflects its probability of selection among a group of primary sampling units of similar characteristics. For example, sample units in an urban nonmetropolitan area in California may represent similar areas in Wyoming and Nevada. Among other adjustments, CUs are post-stratified nationally by sex-age-race. For example, the weights of consumer units containing a black male, age 16-24 in Alabama, Colorado, or New York, are all adjusted equivalently. Therefore, weighted population state totals will not match population totals calculated from other surveys that are designed to represent state data.

To summarize, the CE sample was not designed to produce precise estimates for individual states. Although state-level estimates that are unbiased in a repeated sampling sense can be calculated for various statistical measures, such as means and aggregates, their estimates will generally be subject to large variances. Additionally, a particular state-population estimate from the CE sample may be far from the true state-population estimate.

INTERPRETING THE DATA

Several factors should be considered when interpreting the expenditure data. The average expenditure for an item may be considerably lower than the expenditure by those CUs that purchased the item. The less frequently an item is purchased, the greater the difference between the average for all consumer units and the average of those purchasing. (See Section V.B. for ESTIMATION OF TOTAL AND MEAN EXPENDITURES). Also, an individual CU may spend more or less than the average, depending on its particular characteristics. Factors such as income, age of family members, geographic location, taste and personal preference also influence expenditures. Furthermore, even within groups with similar characteristics, the distribution of expenditures varies substantially.

Expenditures reported are the direct out-of-pocket expenditures. Indirect expenditures, which may be significant, may be reflected elsewhere. For example, rental contracts often include utilities. Renters with such contracts would record no direct expense for utilities, and therefore, appear to have no utility expenses. Employers or insurance companies frequently pay other costs. CUs with members whose employers pay for all or part of their health insurance or life insurance would have lower direct expenses for these items than those who pay the entire amount themselves. These points should be considered when relating reported averages to individual circumstances.

The Diary survey PUMD are organized into five major data files for each quarter: 1. FMLD - a file with characteristics, income, and summary level expenditures for the household 2. MEMD - a file with characteristics and income for each member in the household
3. EXPD - a detailed weekly expenditure file categorized by UCC 4. DTBD - a detailed annual income file categorized by UCC
5. DTID - a household imputed income file categorized by UCC

Analysis unit

Consumer Unit

Kind of data

Sample survey data [ssd]

Sampling procedure

A. SURVEY SAMPLE DESIGN

Samples for the CE are national probability samples of households designed to be representative of the total U. S. civilian population. Eligible population includes all civilian noninstitutional persons.

The first step in sampling is the selection of primary sampling units (PSUs), which consist of counties (or parts thereof) or groups of counties. The set of sample PSUs used for the 2003 sample is composed of 105 areas. The design classifies the PSUs into four categories:

• 31 "A" certainty PSUs are Metropolitan Statistical Areas (MSA's) with a population greater than 1.5 million. • 46 "B" PSUs, are medium-sized MSA's. • 10 "C" PSUs are nonmetropolitan areas that are included in the CPI. • 18 "D" PSUs are nonmetropolitan areas where only the urban population data will be included in the CPI.

The sampling frame (that is, the list from which housing units were chosen) for the 2003 survey is generated from the 1990 Population Census 100-percent-detail file. The sampling frame is augmented by new construction permits and by techniques used to eliminate recognized deficiencies in census coverage. All Enumeration Districts (ED's) from the Census that fail to meet the criterion for good addresses for new construction, and all ED's in nonpermit-issuing areas are grouped into the area segment frame.

To the extent possible, an unclustered sample of units is selected within each PSU. This lack of clustering is desirable because the sample size of the Diary Survey is small relative to other surveys, while the intraclass correlations for expenditure characteristics are relatively large. This suggests that any clustering of the sample units could result in an unacceptable increase in the within-PSU variance and, as a result, the total variance.

Each selected sample unit is requested to keep two 1-week diaries of expenditures over consecutive weeks. The earliest possible day for placing a diary with a household is predesignated with each day of the week having an equal chance to be the first of the reference week. The diaries are evenly spaced throughout the year. During the last 6 weeks of the year, however, the Diary Survey sample is supplemented to twice its normal size to increase the reporting of types of expenditures unique to the holidays.

B. COOPERATION LEVELS

The annual target sample size at the United States level for the Diary Survey is 7,800 participating sample units. To achieve this target the total estimated work load is 11,275 sample units. This allows for refusals, vacancies, or nonexistent sample unit addresses.

Each participating sample unit selected is asked to keep two 1-week diaries. Each diary is treated independently, so response rates are based on twice the number of housing units sampled.

Mode of data collection

Computer Assisted Personal Interview [capi]

Response rate

The response rate for the 2003 Diary Survey is 73.4%. This response rate refers to all diaries in the year.

The Labour Force Survey provides estimates of employment and unemployment which are among the timeliest and important measures of performance of the Canadian economy. With the release of the survey results only 10 days after the completion of data collection, the LFS estimates are the first of the major monthly economic data series to be released. The Canadian Labour Force Survey was developed following the Second World War to satisfy a need for reliable and timely data on the labour market. Information was urgently required on the massive labour market changes involved in the transition from a war to a peace-time economy. The main objective of the LFS is to divide the working-age population into three mutually exclusive classifications - employed, unemployed, and not in the labour force - and to provide descriptive and explanatory data on each of these. LFS data are used to produce the well-known unemployment rate as well as other standard labour market indicators such as the employment rate and the participation rate. The LFS also provides employment estimates by industry, occupation, public and private sector, hours worked and much more, all cross-classifiable by a variety of demographic characteristics. Estimates are produced for Canada, the provinces, the territories and a large number of sub-provincial regions. For employees, wage rates, union status, job permanency and workplace size are also produced. These data are used by different levels of government for evaluation and planning of employment programs in Canada. Regional unemployment rates are used by Employment and Social Development Canada to determine eligibility, level and duration of insurance benefits for persons living within a particular employment insurance region. The data are also used by labour market analysts, economists, consultants, planners, forecasters and academics in both the private and public sector.This public use microdata file contains non-aggregated data for a wide variety of variables collected from the Labour Force Survey (LFS). It contains both personal characteristics for all individuals in the household and detailed labour force characteristics for household members 15 years of age and over. The personal characteristics include age, sex, marital status, educational attainment, and family characteristics. Detailed labour force characteristics include employment information such as class of worker, usual and actual hours of work, employee hourly and weekly wages, industry and occupation of current or most recent job, public and private sector, union status, paid or unpaid overtime hours, job permanency, hours of work lost, job tenure, and unemployment information such as duration of unemployment, methods of job search and type of job sought. Labour force characteristics are also available for students during the school year and during the summer months as well as school attendance whether full or part-time and the type of institution.LFS revisions: Labour force surveys are revised on a periodic basis, either to adopt the most recent geography, industry and occupation classifications; to use new observations to fine-tune seasonal adjustment factors; or to introduce methodological enhancement. Prior LFS revisions were conducted in 2011, 2015 and 2021. The most recent revisions to the LFS were conducted in 2023. The first major change was a transition to the National Occupational Classification (NOC) 2021 V1.0, with all LFS series from 1987 onwards having been revised to the new classification. The second major change were methodological enhancements to LFS data processing, applied to all LFS series beginning Jan 2006. The third major change was a revision of seasonal adjustment factors, applied to LFS series Jan 2002 onward. A list of prior versions of this LFS dataset can be found under the ‘Versions’ tab.

Abstract

The Annual Survey of Industries (ASI) is the principal source of industrial statistics in India. It provides statistical information to assess and evaluate, objectively and realistically, the changes in the growth, composition and structure of organized manufacturing sector comprising activities related to manufacturing processes, repair services, gas and water supply and cold storage. The survey has so far been conducted annually under the statutory provisions of the Collection of Statistics (COS) Act, 1953 and the rules framed there-under in 1959 except in the State of Jammu & Kashmir where it is conducted under the J&K Collection of Statistics Act, 1961 and rules framed there under in 1964. From ASI 2010-11 onwards, the survey is to be conducted annually under the statutory provisions of the Collection of Statistics (COS) Act, 2008 and the rules framed there-under in 2011 except in the State of Jammu & Kashmir where it is to be conducted under the J&K Collection of Statistics Act, 1961 and rules framed there under in 1964.

ASI schedule is the basic tool to collect required data for the factories registered under Sections 2(m)(i) and 2(m)(ii) of the Factories Act, 1948. The schedule for ASI, at present, has two parts. Part-I of ASI schedule, processed at the CSO (IS Wing), Kolkata, aims to collect data on assets and liabilities, employment and labour cost, receipts, expenses, input items: indigenous and imported, products and by-Products, distributive expenses, etc. Part-II of ASI schedule is processed by the Labour Bureau. It aims to collect data on different aspects of labour statistics, namely, working days, man-days worked, absenteeism, labour turnover, man-hours worked etc.

Geographic coverage

The ASI extends its coverage to the entire country upto state level.

Analysis unit

The primary unit of enumeration in the survey is a factory in the case of manufacturing industries, a workshop in the case of repair services, an undertaking or a licensee in the case of electricity, gas & water supply undertakings and an establishment in the case of bidi & cigar industries. The owner of two or more establishments located in the same State and pertaining to the same industry group and belonging to same scheme (census or sample) is, however, permitted to furnish a single consolidated return. Such consolidated returns are common feature in the case of bidi and cigar establishments, electricity and certain public sector undertakings.

Universe

The survey cover factories registered under the Factory Act 1948.

Kind of data

Sample survey data [ssd]

Sampling procedure

Pls refer Annual Survey of Industries 2017-18 Volume 1

Mode of data collection

Face-to-face [f2f]

Research instrument

Annual Survey of Industries Questionnaire is divided into different blocks:

BLOCK A.IDENTIFICATION BLOCK - This block has been designed to collect the descriptive identification of the sample enterprise. The items are mostly self-explanatory.

BLOCK B. TO BE FILLED BY OWNER OF THE FACTORY - This block has been designed to collect the particulars of the sample enterprise. This point onwards, all the facts and figures in this return are to be filled in by owner of the factory.

BLOCK C: FIXED ASSETS - Fixed assets are of a permanent nature having a productive life of more than one year, which is meant for earning revenue directly or indirectly and not for the purpose of sale in ordinary course of business. They include assets used for production, transportation, living or recreational facilities, hospital, school, etc. Intangible fixed assets like goodwill, preliminary expenses including drawing and design etc are excluded for the purpose of ASI. The fixed assets have, at the start of their functions, a definite value, which decreases with wear and tear. The original cost less depreciation indicates that part of value of fixed assets, which has not yet been transferred to the output. This value is called the residual value. The value of a fixed asset, which has completed its theoretical working life should always be recorded as Re.1/-. The revalued value is considered now. But depreciation will be taken on original cost and not on revalued cost.

BLOCK D: WORKING CAPITAL & LOANS - Working capital represents the excess of total current assets over total current liabilities.

BLOCK E : EMPLOYMENT AND LABOUR COST - Particulars in this block should relate to all persons who work in and for the establishment including working proprietors and active business partners and unpaid family workers. However, Directors of incorporated enterprises who are paid solely for their attendance at meeting of the Board of Directors are to be excluded.

BLOCK F : OTHER EXPENSES - This block includes the cost of other inputs as both the industrial and nonindustrial service rendered by others, which are paid by the factory and most of which are reflected in the ex-factory value of its production during the accounting year.

BLOCK G : OTHER INCOMES - In this block, information on other output/receipts is to be reported.

BLOCK H: INPUT ITEMS (indigenous items consumed) - This block covers all those goods (raw materials, components, chemicals, packing material, etc.), which entered into the production process of the factory during the accounting year. Any material used in the production of fixed assets (including construction work) for the factory's own use should also be included. All intermediate products consumed during the year are to be excluded. Intermediate products are those, which are produced by the factory but are, subjected to further manufacture. For example, in a cotton textile mill, yarn is produced from raw cotton and the same yarn is again used for manufacture of cloth. An intermediate product may also be a final product in the same factory. For example, if the yarn produced by the factory is sold as yarn, it becomes a final product and not an intermediate product. If however, a part of the yarn produced by a factory is consumed by it for manufacture of cloth, that part of the yarn so used will be an intermediate product.

BLOCK I: INPUT ITEMS - directly imported items only (consumed) - Information in this block is to be reported for all imported items consumed. The items are to be imported by the factory directly or otherwise. The instructions for filling up of this block are same as those for Block H. All imported goods irrespective of whether they are imported directly by the unit or not, should be recorded in Block I. Moreover, any imported item, irrespective of whether it is a basic item for manufacturing or not, should be recorded in Block I. Hence 'consumable stores' or 'packing items', if imported, should be recorded in Block I and not in Block H.

BLOCK J: PRODUCTS AND BY-PRODUCTS (manufactured by the unit) - In this block information like quantity manufactured, quantity sold, gross sale value, excise duty, sales tax paid and other distributive expenses, per unit net sale value and ex-factory value of output will be furnished by the factory item by item. If the distributive expenses are not available product-wise, the details may be given on the basis of reasonable estimation.

Cleaning operations

Data submitted by the factories undergo manual scrutiny at different stages.

1) They are verified by field staff of NSSO from factory records.

2) Verified returns are manually scrutinized by senior level staff before sending to data processing centre.

3) At the data processing centre these are scrutinized before data entry.

4) The entered data are subjected to computer editing and corrections.

5) Tabulated data are checked for anomalies and consistency with previous results.

Sampling error estimates

Relative Standard Error (RSE) is calculated in terms of worker, wages to worker and GVA using the formula (Pl ease refer to Estimation Procedure document in external resources).

Data appraisal

To check for consistency and reliability of data the same are compared with the NIC-2digit level growth rate at all India Index of Production (IIP) and the growth rates obtained from the National Accounts Statistics at current and constant prices for the registered manufacturing sector.

Abstract

The survey was conducted in Nicaragua between October 2016 and June 2017 as part of Enterprise Surveys project, an initiative of the World Bank. The objective of the survey is to obtain feedback from enterprises on the state of the private sector as well as to help in building a panel of enterprise data that will make it possible to track changes in the business environment over time, thus allowing, for example, impact assessments of reforms. Through interviews with firms in the manufacturing and services sectors, the survey assesses the constraints to private sector growth and creates statistically significant business environment indicators that are comparable across countries. Only registered businesses are surveyed in the Enterprise Survey.

Data from 333 establishments was analyzed. Stratified random sampling was used to select the surveyed businesses. The data was collected using face-to-face interviews.

The standard Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs and labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures. Over 90 percent of the questions objectively ascertain characteristics of a country's business environment. The remaining questions assess the survey respondents' opinions on what are the obstacles to firm growth and performance.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study is the establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

The whole population, or the universe, covered in the Enterprise Surveys is the non-agricultural economy. It comprises: all manufacturing sectors according to the ISIC Revision 3.1 group classification (group D), construction sector (group F), services sector (groups G and H), and transport, storage, and communications sector (group I). Note that this population definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, except sub-sector 72, IT, which was added to the population under study), and all public or utilities sectors.

Kind of data

Sample survey data [ssd]

Sampling procedure

Three levels of stratification were used in this country: industry, establishment size, and region.

Industry stratification was designed in the way that follows: the universe was stratified into Manufacturing industries (ISIC Rev. 3.1 codes 15- 37), Retail industries (ISIC code 52) and Other Services (ISIC codes 45, 50, 51, 55, 60-64, and 72).

For the Nicaragua ES, size stratification was defined as follows: small (4 to 20 employees), medium (21 to 50 employees), and large (51 or more employees). These categories differ from the global ES size definitions - small (5 to 19 employees), medium (20 to 99 employees), and large (100 or more employees).

Regional stratification was done across four regions: Managua (department), East (departments of Masaya, Granada, and Carazo), West (departments of Chinandega and Leon), and North (departments of Esteli, Jinotega, and Matagalpa). Due to several cells without any realized interviews, the stratification regions East, West, and North were combined in one.

The sample frame consisted of listings of firms from two sources: For panel firms the list of 336 firms from the Nicaragua 2010 ES was used, and for fresh firms (i.e., firms not covered in 2010) the sample frame was comprised of a list randomly drawn from the Economic Census, provided by the Banco Central de Nicaragua. Standardized size categories provided by the Census were used.

The quality of the frame was assessed at the onset of the project through visits to a random subset of firms and local contractor knowledge. The sample frame was not immune from the typical problems found in establishment surveys: positive rates of non-eligibility, repetition, non-existent units, etc. In addition, the sample frame contains no telephone/fax numbers so the local contractor had to screen the contacts by visiting them. Due to response rate and ineligibility issues, additional sample had to be extracted by the World Bank in order to obtain enough eligible contacts and meet the sample targets.

Given the impact that non-eligible units included in the sample universe may have on the results, adjustments may be needed when computing the appropriate weights for individual observations. The percentage of confirmed non-eligible units as a proportion of the total number of sampled establishments contacted for the survey was 22.8% (326 out of 1,430 establishments).

Mode of data collection

Face-to-face [f2f]

Research instrument

The structure of the data base reflects the fact that two different versions of the survey instrument were used for all registered establishments. Questionnaires have common questions (core module) and respectfully additional manufacturing- and services-specific questions.

The eligible manufacturing industries have been surveyed using the Manufacturing questionnaire (includes the core module, plus manufacturing specific questions).

Retail firms have been interviewed using the Services questionnaire (includes the core module plus retail specific questions) and the residual eligible services have been covered using the Services questionnaire (includes the core module).

Each variation of the questionnaire is identified by the index variable, a0.

The last complete fiscal year is January to December 2015. For questions pertaining to monetary amounts, the unit is the Nicaraguan Córdoba (NIO).

Cleaning operations

Data entry and quality controls are implemented by the contractor and data is delivered to the World Bank in batches (typically 10%, 50% and 100%). These data deliveries are checked for logical consistency, out of range values, skip patterns, and duplicate entries. Problems are flagged by the World Bank and corrected by the implementing contractor through data checks, callbacks, and revisiting establishments.

Response rate

Survey non-response must be differentiated from item non-response. The former refers to refusals to participate in the survey altogether whereas the latter refers to the refusals to answer some specific questions. Enterprise Surveys suffer from both problems and different strategies were used to address these issues.

Item non-response was addressed by two strategies: a- For sensitive questions that may generate negative reactions from the respondent, such as corruption or tax evasion, enumerators were instructed to collect "Refusal to respond" (-8) as a different option from "Don't know" (-9). b- Establishments with incomplete information were re-contacted in order to complete this information, whenever necessary.

Survey non-response was addressed by maximizing efforts to contact establishments that were initially selected for interview. Attempts were made to contact the establishment for interview at different times/days of the week before a replacement establishment (with similar strata characteristics) was suggested for interview. Survey non-response did occur but substitutions were made in order to potentially achieve strata-specific goals.

The number of interviews per contacted establishments was 0.233. This number is the result of two factors: explicit refusals to participate in the survey, as reflected by the rate of rejection (which includes rejections of the screener and the main survey) and the quality of the sample frame, as represented by the presence of ineligible units. The number of rejections per contact was 0.393.

The Labour Force Survey provides estimates of employment and unemployment which are among the timeliest and important measures of performance of the Canadian economy. With the release of the survey results only 10 days after the completion of data collection, the LFS estimates are the first of the major monthly economic data series to be released. The Canadian Labour Force Survey was developed following the Second World War to satisfy a need for reliable and timely data on the labour market. Information was urgently required on the massive labour market changes involved in the transition from a war to a peace-time economy. The main objective of the LFS is to divide the working-age population into three mutually exclusive classifications - employed, unemployed, and not in the labour force - and to provide descriptive and explanatory data on each of these. LFS data are used to produce the well-known unemployment rate as well as other standard labour market indicators such as the employment rate and the participation rate. The LFS also provides employment estimates by industry, occupation, public and private sector, hours worked and much more, all cross-classifiable by a variety of demographic characteristics. Estimates are produced for Canada, the provinces, the territories and a large number of sub-provincial regions. For employees, wage rates, union status, job permanency and workplace size are also produced. These data are used by different levels of government for evaluation and planning of employment programs in Canada. Regional unemployment rates are used by Employment and Social Development Canada to determine eligibility, level and duration of insurance benefits for persons living within a particular employment insurance region. The data are also used by labour market analysts, economists, consultants, planners, forecasters and academics in both the private and public sector.This public use microdata file contains non-aggregated data for a wide variety of variables collected from the Labour Force Survey (LFS). It contains both personal characteristics for all individuals in the household and detailed labour force characteristics for household members 15 years of age and over. The personal characteristics include age, sex, marital status, educational attainment, and family characteristics. Detailed labour force characteristics include employment information such as class of worker, usual and actual hours of work, employee hourly and weekly wages, industry and occupation of current or most recent job, public and private sector, union status, paid or unpaid overtime hours, job permanency, hours of work lost, job tenure, and unemployment information such as duration of unemployment, methods of job search and type of job sought. Labour force characteristics are also available for students during the school year and during the summer months as well as school attendance whether full or part-time and the type of institution.LFS revisions: Labour force surveys are revised on a periodic basis, either to adopt the most recent geography, industry and occupation classifications; to use new observations to fine-tune seasonal adjustment factors; or to introduce methodological enhancement. Prior LFS revisions were conducted in 2011, 2015 and 2021. The most recent revisions to the LFS were conducted in 2023. The first major change was a transition to the National Occupational Classification (NOC) 2021 V1.0, with all LFS series from 1987 onwards having been revised to the new classification. The second major change were methodological enhancements to LFS data processing, applied to all LFS series beginning Jan 2006. The third major change was a revision of seasonal adjustment factors, applied to LFS series Jan 2002 onward. A list of prior versions of this LFS dataset can be found under the ‘Versions’ tab.

Abstract

This research was conducted in Indonesia between August 2009 and January 2010 as part of the Enterprise Survey initiative.

The objective of the survey is to obtain feedback from enterprises in client countries on the state of the private sector as well as to help in building a panel of enterprise data that will make it possible to track changes in the business environment over time, thus allowing, for example, impact assessments of reforms. Through interviews with firms in the manufacturing and services sectors, the survey assesses the constraints to private sector growth and creates statistically significant business environment indicators that are comparable across countries.

The standard Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs/labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures. Over 90% of the questions objectively ascertain characteristics of a country’s business environment. The remaining questions assess the survey respondents’ opinions on what are the obstacles to firm growth and performance. The mode of data collection is face-to-face interviews.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study is the establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

The whole population, or the universe, covered in the Enterprise Surveys is the non-agricultural economy. It comprises: all manufacturing sectors according to the ISIC Revision 3.1 group classification (group D), construction sector (group F), services sector (groups G and H), and transport, storage, and communications sector (group I). Note that this population definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, except sub-sector 72, IT, which was added to the population under study), and all public or utilities-sectors.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for Indonesia was selected using stratified random sampling. Three levels of stratification were used in this country: industry, establishment size, and region.

Industry stratification was designed in the way that follows: the universe was stratified into 6 manufacturing industries, 1 services industry -retail -, and two residual sectors. Each manufacturing industry had a target of 160 interviews. The services industry and the two residual sectors had a target of 120 interviews. For the manufacturing industries sample sizes were inflated by about 33% to account for potential non-response cases when requesting sensitive financial data and also because of likely attrition in future surveys that would affect the construction of a panel. An additional 85 interviews were added to the survey half way through the fieldwork. Targets were adjusted such that the manufacturing sectors' targets were increased to 160-180 interviews.

Size stratification was defined following the standardized definition for the rollout: small (5 to 19 employees), medium (20 to 99 employees), and large (more than 99 employees). For stratification purposes, the number of employees was defined on the basis of reported permanent full-time workers. This seems to be an appropriate definition of the labor force since seasonal/casual/part-time employment is not a common practice, except in the sectors of construction and agriculture.

Regional stratification was defined in four regions: Bali, Banten, DKI Jakarta, Jawa Barat, Jawa Tengah, Jawa Timur, Lampung, Sulawesi Selatan, and Sumatera Utara. These are the largest population and economic centers of the Indonesia constituting over 70% of firms and 68% of employment in Indonesia.

The sample frame used in the Indonesia was obtained from Central Bureau of Statistic (Badan Pusat Statistik BPS). Sampling was conducted by the World Bank team in Washington D.C. This database contained the following information: -Name of the firm -Location -Contact details -ISIC code -Number of employees (except for services establishments).

Given the impact that non-eligible units included in the sample universe may have on the results, adjustments may be needed when computing the appropriate weights for individual observations. The percentage of confirmed non-eligible units as a proportion of the total number of sampled establishments contacted for the survey was 19% (489 out of 2532 establishments). Breaking down by industry, the following numbers of establishments were surveyed: 15 (Food) - 392, 17 (textiles) - 135, 18 (Garments) - 141, 24 (Chemicals) - 108, 25 (Plastic & Rubber) - 111, 26 (Non-metallic mineral products) - 151, Other manufacturing - 141,
Retail - 133, Other services - 132.

Mode of data collection

Face-to-face [f2f]

Research instrument

The current survey instruments are available: - Core Questionnaire + Manufacturing Module [ISIC Rev.3.1: 15-37] - Core Questionnaire + Retail Module [ISIC Rev.3.1: 52] - Core Questionnaire [ISIC Rev.3.1: 45, 50, 51, 55, 60-64, 72] - Screener Questionnaire.

The “Core Questionnaire” is the heart of the Enterprise Survey and contains the survey questions asked of all firms across the world. There are also two other survey instruments- the “Core Questionnaire + Manufacturing Module” and the “Core Questionnaire + Retail Module.” The survey is fielded via three instruments in order to not ask questions that are irrelevant to specific types of firms, e.g. a question that relates to production and nonproduction workers should not be asked of a retail firm. In addition to questions that are asked across countries, all surveys are customized and contain country-specific questions. An example of customization would be including tourism-related questions that are asked in certain countries when tourism is an existing or potential sector of economic growth.

The standard Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs/labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures. Over 90% of the questions objectively ascertain characteristics of a country’s business environment. The remaining questions assess the survey respondents’ opinions on what are the obstacles to firm growth and performance.

Cleaning operations

Data entry and quality controls are implemented by the contractor and data is delivered to the World Bank in batches (typically 10%, 50% and 100%). These data deliveries are checked for logical consistency, out of range values, skip patterns, and duplicate entries. Problems are flagged by the World Bank and corrected by the implementing contractor through data checks, callbacks, and revisiting establishments.

Response rate

Complete information regarding the sampling methodology, sample frame, weights, response rates, and implementation can be found in "Description of Indonesia Implementation 2009" in "Technical Documents" folder.

The Labour Force Survey provides estimates of employment and unemployment which are among the timeliest and important measures of performance of the Canadian economy. With the release of the survey results only 10 days after the completion of data collection, the LFS estimates are the first of the major monthly economic data series to be released. The Canadian Labour Force Survey was developed following the Second World War to satisfy a need for reliable and timely data on the labour market. Information was urgently required on the massive labour market changes involved in the transition from a war to a peace-time economy. The main objective of the LFS is to divide the working-age population into three mutually exclusive classifications - employed, unemployed, and not in the labour force - and to provide descriptive and explanatory data on each of these. LFS data are used to produce the well-known unemployment rate as well as other standard labour market indicators such as the employment rate and the participation rate. The LFS also provides employment estimates by industry, occupation, public and private sector, hours worked and much more, all cross-classifiable by a variety of demographic characteristics. Estimates are produced for Canada, the provinces, the territories and a large number of sub-provincial regions. For employees, wage rates, union status, job permanency and workplace size are also produced. These data are used by different levels of government for evaluation and planning of employment programs in Canada. Regional unemployment rates are used by Employment and Social Development Canada to determine eligibility, level and duration of insurance benefits for persons living within a particular employment insurance region. The data are also used by labour market analysts, economists, consultants, planners, forecasters and academics in both the private and public sector.This public use microdata file contains non-aggregated data for a wide variety of variables collected from the Labour Force Survey (LFS). It contains both personal characteristics for all individuals in the household and detailed labour force characteristics for household members 15 years of age and over. The personal characteristics include age, sex, marital status, educational attainment, and family characteristics. Detailed labour force characteristics include employment information such as class of worker, usual and actual hours of work, employee hourly and weekly wages, industry and occupation of current or most recent job, public and private sector, union status, paid or unpaid overtime hours, job permanency, hours of work lost, job tenure, and unemployment information such as duration of unemployment, methods of job search and type of job sought. Labour force characteristics are also available for students during the school year and during the summer months as well as school attendance whether full or part-time and the type of institution.LFS revisions: Labour force surveys are revised on a periodic basis, either to adopt the most recent geography, industry and occupation classifications; to use new observations to fine-tune seasonal adjustment factors; or to introduce methodological enhancement. Prior LFS revisions were conducted in 2011, 2015 and 2021. The most recent revisions to the LFS were conducted in 2023. The first major change was a transition to the National Occupational Classification (NOC) 2021 V1.0, with all LFS series from 1987 onwards having been revised to the new classification. The second major change were methodological enhancements to LFS data processing, applied to all LFS series beginning Jan 2006. The third major change was a revision of seasonal adjustment factors, applied to LFS series Jan 2002 onward. A list of prior versions of this LFS dataset can be found under the ‘Versions’ tab.

Abstract

Throughout the Middle East, unemployment rates of educated youth have been persistently high and female labor force participation, low. Researchers from the World Bank studied the impact of a randomized experiment in Jordan designed to assist female community college graduates find employment. One group of graduates was given wage subsidy vouchers that could be redeemed by their employers for up to six months for a value equivalent to the prevailing minimum wage; a second group was invited to attend 45 hours of soft skills training; a third group was offered both interventions; and the fourth group formed the control group.

To conduct the study, researchers chose eight public community colleges with the largest female enrolment numbers. Four colleges were in Central Jordan (Amman University College, Princess Alia University College, Al-Salt College, Zarqa University College) and four in Northern and Southern Jordan (Al-Huson University College for Engineering, Irbid University College, Ajloun University College, and Al-Karak University College).

Four individual level survey questionnaires were administered during the impact evaluation study. The baseline survey was conducted in July 2010, the midline - in April 2011, the first endline was carried out in December 2011, and the second endline - in January 2013.

Geographic coverage

Amman, Salt, Zarqa, Irbid, Ajloun, and Karak.

Analysis unit

Female community colleges graduates from the class of 2010.

Kind of data

Sample survey data [ssd]

Sampling procedure

Researchers chose the eight public community colleges with the largest female enrollment numbers, which comprise over 85% of total female public community college enrollment. In July 2010 just before final graduation exams, data collectors conducted baseline surveys for most of the 404 male and all of the 1,776 female second-year students from these eight colleges. In August 2010, the researchers merged the baseline data with administrative data on examination results, which revealed that 324 men and 1,418 women passed their examinations. Of the 1,418 women who passed their examinations, the researchers randomly assigned by computer 1,349 of these graduates to be in the experimental sample. However, two of these graduates were male but incorrectly recorded as female. They were subsequently dropped from the sample.

The experimental sample of 1,347 was stratified into 16 strata and randomly assigned by computer into three treatment groups and a control group. The strata were created based on the following four characteristics: whether or not (1) the community college was in Amman (Amman, Salt, and Zarqa) or outside Amman, (2) an individual's Tawjihi examination score at the end of high school was above the sample median, (3) an individual indicated at baseline that she planned to work full-time and thought it at least somewhat likely that she would have a job within 6 months of graduating, and (4) she is usually permitted to travel to the market alone. Within each of the 16 strata, 22.2% of the students were allocated to receive the wage voucher only, 22.2% allocated to receive the soft skills training only, 22.2% allocated to receive both, and 33.3% allocated to the control group. This resulted in 299 or 300 in each treatment group, and 449 in the control group.

Sampling deviation

The only deviation from the sample design involved dropping two graduates from the sample because they were incorrectly recorded as female.

Mode of data collection

Other [oth]

Research instrument

All questionnaires were initially developed in English and subsequently translated into Modern Standard Arabic.

The questionnaire design process was based on standard labor force survey questions, academic literature on well being, mental health, and female empowerment, and inputs from Al Balqa Community Colleges, the Chamber of Commerce, the Ministry of Planning and International Cooperation, the Social Security Corportation, the Department of Statistics, Dajani Consulting, Business Development Center, and local firms.

The questionnaires were piloted and adjusted accordingly in each survey round.

Response rate

In the midline, first endline, and second endline surveys, researchers successfully followed up with 92%, 96%, and 92% of graduates in the sample, respectively. In the first and second endline surveys, the team collected a portion of the survey data (3% and 9%, respectively) by proxy through their relatives. This survey experienced very few problems with outright rejections to answer the survey questions although the proxy responses reflect graduates or their families refusing to allow the graduate respond for herself. The vast majority of attrition comes from disconnected cell phones and the inability to completely track individuals down.

The attrition rates are low and slightly vary by treatment status. The wage voucher group has the lowest attrition (3% midline, 1% 1st endline, 4% 2nd endline), which is likely due to the additional information gathered through monitoring the voucher usage. On the other hand, the control group experienced the highest attrition (11% midline, 7% 1st endline, 11% 2nd endline), which is likely because there was no additional contact with the control group outside of the surveys.

Abstract

This survey was conducted in Timor-Leste between September 2015 and June 2016, as part of the Enterprise Survey project, an initiative of the World Bank. The objective of the survey is to obtain feedback from enterprises on the state of the private sector as well as to help in building a panel of enterprise data that will make it possible to track changes in the business environment over time, thus allowing, for example, impact assessments of reforms. Through interviews with firms in the manufacturing and services sectors, the survey assesses the constraints to private sector growth and creates statistically significant business environment indicators that are comparable across countries. Only registered businesses are surveyed in the Enterprise Survey.

Data from 126 establishments was analyzed. Stratified random sampling was used to select the surveyed businesses. The data was collected using face-to-face interviews.

The standard Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs/labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures. Over 90% of the questions objectively ascertain characteristics of a country's business environment. The remaining questions assess the survey respondents' opinions on what are the obstacles to firm growth and performance.

Geographic coverage

Dili

Analysis unit

The primary sampling unit of the study is the establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

The whole population, or universe of the study, is the non-agricultural economy. It comprises: all manufacturing sectors according to the group classification of ISIC Revision 3.1: (group D), construction sector (group F), services sector (groups G and H), and transport, storage, and communications sector (group I). Note that this definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, except sub-sector 72, IT, which was added to the population under study), and all public or utilities-sectors.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample was selected using stratified random sampling. Two levels of stratification were used in this country: industry and establishment size.

Industry stratification was designed in the way that follows: the universe was stratified into manufacturing and services industry - Manufacturing (ISIC 3.1 codes 15 - 37), and Services (ISIC codes 45, 50, 51, 52, 55, 60-64, and 72).

For the Timor-Leste ES, size stratification was defined as follows: small (5 to 19 employees), medium (20 to 99 employees), and large (100 or more employees).

Regional stratification did not take place as all interviews took place in and around Dili.

The sample frame consisted of listings of firms from two sources: First, for panel firms the list of 150 firms from the Timor-Leste 2009 ES was used. Second, for fresh firms (i.e., firms not covered in 2009), data from National Statistics Directorate (by way of PDT) was used.

The quality of the frame was enhanced by the verification process conducted by Mekong Economics. However, the sample frame was not immune from the typical problems found in establishment surveys: positive rates of non-eligibility, repetition, non-existent units, etc.

Given the impact that non-eligible units included in the sample universe may have on the results, adjustments may be needed when computing the appropriate weights for individual observations. The percentage of confirmed non-eligible units as a proportion of the total number of sampled establishments contacted for the survey was 6.8% (15 out of 220 establishments).

Mode of data collection

Face-to-face [f2f]

Research instrument

The structure of the data base reflects the fact that two different versions of the survey instrument were used for all registered establishments. Questionnaires have common questions (core module) and respectfully additional manufacturing- and services-specific questions. The eligible manufacturing industries have been surveyed using the Manufacturing questionnaire (includes the core module, plus manufacturing specific questions). Retail firms have been interviewed using the Services questionnaire (includes the core module plus retail specific questions) and the residual eligible services have been covered using the Services questionnaire (includes the core module). Each variation of the questionnaire is identified by the index variable, a0.

Cleaning operations

Data entry and quality controls are implemented by the contractor and data is delivered to the World Bank in batches (typically 10%, 50% and 100%). These data deliveries are checked for logical consistency, out of range values, skip patterns, and duplicate entries. Problems are flagged by the World Bank and corrected by the implementing contractor through data checks, callbacks, and revisiting establishments.

Response rate

Survey non-response must be differentiated from item non-response. The former refers to refusals to participate in the survey altogether whereas the latter refers to the refusals to answer some specific questions. Enterprise Surveys suffer from both problems and different strategies were used to address these issues.

Item non-response was addressed by two strategies: a- For sensitive questions that may generate negative reactions from the respondent, such as corruption or tax evasion, enumerators were instructed to collect "Refusal to respond" (-8) as a different option from "Don't know" (-9). b- Establishments with incomplete information were re-contacted in order to complete this information, whenever necessary.

Survey non-response was addressed by maximizing efforts to contact establishments that were initially selected for interview. Attempts were made to contact the establishment for interview at different times/days of the week before a replacement establishment (with similar strata characteristics) was suggested for interview. Survey non-response did occur but substitutions were made in order to potentially achieve strata-specific goals.

The number of interviews per contacted establishments was 0.57. This number is the result of two factors: explicit refusals to participate in the survey, as reflected by the rate of rejection (which includes rejections of the screener and the main survey) and the quality of the sample frame, as represented by the presence of ineligible units. The number of rejections per contact was 0.15.

Abstract

This research was conducted in Vietnam between June 2009 and January 2010 as part of the Enterprise Survey initiative.

The objective of the survey is to obtain feedback from enterprises in client countries on the state of the private sector as well as to help in building a panel of enterprise data that will make it possible to track changes in the business environment over time, thus allowing, for example, impact assessments of reforms. Through interviews with firms in the manufacturing and services sectors, the survey assesses the constraints to private sector growth and creates statistically significant business environment indicators that are comparable across countries.

The standard Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs/labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures. Over 90% of the questions objectively ascertain characteristics of a country’s business environment. The remaining questions assess the survey respondents’ opinions on what are the obstacles to firm growth and performance. The mode of data collection is face-to-face interviews.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study is the establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

The whole population, or the universe, covered in the Enterprise Surveys is the non-agricultural economy. It comprises: all manufacturing sectors according to the ISIC Revision 3.1 group classification (group D), construction sector (group F), services sector (groups G and H), and transport, storage, and communications sector (group I). Note that this population definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, except sub-sector 72, IT, which was added to the population under study), and all public or utilities sectors.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for Vietnam was selected using stratified random sampling. Three levels of stratification were used in this country: industry, establishment size, and region.

Industry stratification was designed in the way that follows: the universe was stratified into 6 manufacturing industries, 1 services industry -retail -, and two residual sectors. Each manufacturing industry had a target of 160 interviews. The services industry and the two residual sectors had a target of 120 interviews. For the manufacturing industries sample sizes were inflated by about 33% to account for potential non-response cases when requesting sensitive financial data and also because of likely attrition in future surveys that would affect the construction of a panel. An additional 85 interviews were added to the survey half way through the fieldwork. Targets were adjusted such that the manufacturing sectors' targets were increased to 160-180 interviews.

Size stratification was defined following the standardized definition for the rollout: small (5 to 19 employees), medium (20 to 99 employees), and large (more than 99 employees). For stratification purposes, the number of employees was defined on the basis of reported permanent full-time workers. This seems to be an appropriate definition of the labor force since seasonal/casual/part-time employment is not a common practice, except in the sectors of construction and agriculture.

Regional stratification was defined in five regions containing 14 provinces: Red River Delta (Hanoi, Ha Tay, Hai Duong, and Hai Phong), the North Centre Coast (Thanh Hoa, Nghe An), Mekong River Delta (Can Tho, Long An, Tien Giang), South Centre Coast (Khanh Hoa, Da Nang) and South East (Ho Chi Minh City, Binh Duong, Dong Nai).

Two frames were used for Vietnam. The sample frame containing fresh contacts used in the Vietnam was obtained from the 2008 Vietnam General Statistics Office. A frame containing firms that had participated in the 2005 survey constituted a second frame of panel contacts. Each database contained the following information: -Name of the firm -Location -Contact details -ISIC code -Number of employees.

Given the impact that non-eligible units included in the sample universe may have on the results, adjustments may be needed when computing the appropriate weights for individual observations. The percentage of confirmed non-eligible units as a proportion of the total number of sampled establishments contacted for the survey was 23% (734 out of 3131 establishments). Breaking down by industry, the following numbers of establishments were surveyed: 15 (Food) - 127, 17 (Textiles) -120, 18 (Garments) - 120, 26 (Non-metallic mineral products) - 123, 28 (Metal & Fabrication) - 122, Other manufacturing - 196, Retail & IT - 128, Other services - 117.

Mode of data collection

Face-to-face [f2f]

Research instrument

The current survey instruments are available: - Core Questionnaire + Manufacturing Module [ISIC Rev.3.1: 15-37] - Core Questionnaire + Retail Module [ISIC Rev.3.1: 52] - Core Questionnaire [ISIC Rev.3.1: 45, 50, 51, 55, 60-64, 72] - Screener Questionnaire.

The “Core Questionnaire” is the heart of the Enterprise Survey and contains the survey questions asked of all firms across the world. There are also two other survey instruments- the “Core Questionnaire + Manufacturing Module” and the “Core Questionnaire + Retail Module.” The survey is fielded via three instruments in order to not ask questions that are irrelevant to specific types of firms, e.g. a question that relates to production and nonproduction workers should not be asked of a retail firm. In addition to questions that are asked across countries, all surveys are customized and contain country-specific questions. An example of customization would be including tourism-related questions that are asked in certain countries when tourism is an existing or potential sector of economic growth.

The standard Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs/labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures. Over 90% of the questions objectively ascertain characteristics of a country’s business environment. The remaining questions assess the survey respondents’ opinions on what are the obstacles to firm growth and performance.

Cleaning operations

Data entry and quality controls are implemented by the contractor and data is delivered to the World Bank in batches (typically 10%, 50% and 100%). These data deliveries are checked for logical consistency, out of range values, skip patterns, and duplicate entries. Problems are flagged by the World Bank and corrected by the implementing contractor through data checks, callbacks, and revisiting establishments.

Response rate

Complete information regarding the sampling methodology, sample frame, weights, response rates, and implementation can be found in "Description of Vietnam Implementation 2009" in "Technical Documents" folder.