Abstract

The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.

Geographic coverage

National coverage

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 universe of inference includes all formal (i.e., registered) private sector businesses (with at least 1% private ownership) and with at least five employees. In terms of sectoral criteria, all manufacturing businesses (ISIC Rev 4. codes 10-33) are eligible; for services businesses, those corresponding to the ISIC Rev 4 codes 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95 are included in the Enterprise Surveys. Cooperatives and collectives are excluded from the Enterprise Surveys. All eligible establishments must be registered with the registration agency. In the case of India, the definition of registration of the 6th Economic Census (EC) was used, where registration can be from any of the following: Shops and Commercial Establishments Act; Companies Act, 1956; Factories Act, 1948; Central Excise/Sales Tax Act; Societies Registration Act; Co-operative Societies Act; Directorate of Industries; KVIC/KVIB/DC: Handloom/Handicrafts; Registered with other relevant agencies.

Kind of data

Sample survey data [ssd]

Sampling procedure

The WBES use stratified random sampling, where the population of establishments is first separated into non-overlapping groups, called strata, and then respondents are selected through simple random sampling from each stratum. The detailed methodology is provided in the Sampling Note (https://www.enterprisesurveys.org/content/dam/enterprisesurveys/documents/methodology/Sampling_Note-Consolidated-2-16-22.pdf). Stratified random sampling has several advantages over simple random sampling. In particular, it:

• produces unbiased estimates of the whole population or universe of inference, as well as at the levels of stratification
• ensures representativeness by including observations in all of those categories
• produces more precise estimates for a given sample size or budget allocation, and
• may reduce implementation costs by splitting the population into convenient subdivisions.

The WBES typically use three levels of stratification: industry classification, establishment size, and subnational region (used in combination). Starting in 2022, the WBES bases the industry classification on ISIC Rev. 4 (with earlier surveys using ISIC Rev. 3.1). For regional coverage within a country, the WBES has national coverage.

Note: Refer to Sampling Structure section in "The India 2022 World Bank Enterprise Survey Implementation Report" for detailed methodology on sampling.

Mode of data collection

Face-to-face [f2f]

Research instrument

The standard WBES questionnaire covers several topics regarding the business environment and business performance. These topics include general firm characteristics, infrastructure, sales and supplies, management practices, competition, innovation, capacity, land and permits, finance, business-government relations, exposure to bribery, labor, and performance. Information about the general structure of the questionnaire is available in the Enterprise Surveys Manual and Guide (https://www.enterprisesurveys.org/content/dam/enterprisesurveys/documents/methodology/Enterprise-Surveys-Manual-and-Guide.pdf).

The questionnaire implemented in the India 2022 WBES included additional questions covering contractual disputes, COVID-19, green economy, delayed payments, invoice discounting (TReDS or similar services), government support, attitudes towards taxes, training costs, and childcare support. These questions were selected in collaboration with the members of the WB local country team.

Response rate

Overall survey response rate was 61.8%.

Abstract

This survey was conducted in India between June 2013 and December 2014 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.

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.

Data from 1,247 establishments was analyzed. Stratified random sampling was used to select the surveyed businesses. Data was collected using 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 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. Three levels of stratification were used: industry, establishment size, and region.

For stratification by industry, the universe was stratified into seven manufacturing industries (food, textiles, garments, chemicals, non-metalic minerals, motor vehicles, other manufacturing) and two service sectors (retail and other services).

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).

Regional stratification was defined in 5 regions: Punjab, Sindh, KPK, Balochistan, and Islamabad.

The sample frame for Manufacturing establishments was from the Pakistan Bureau of Statistics (PBS). For Retail and Other Services establishments, Nielsen Pakistan provided the sample frame through desk research. For confidentiality purposes, PBS randomly drew the sample of fresh manufacturing establishment to be interviewed based on the sample design provided by the World Bank.

The combination of the PBS-provided sample along with the services lists from Nielsen were then used as the sample frame for the Pakistan Enterprise Survey with the aim of obtaining interviews at 1,320 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 4.1% (116 out of 2,841 establishments).

Mode of data collection

Face-to-face [f2f]

Research instrument

The structure of the database reflects the fact that two different versions of the questionnaire were used for 3 categories of businesses (manufacturing, retail, and other services/non-retail). The Manufacturing Questionnaire includes all common questions asked to all establishments and some specific questions relevant to manufacturing firms. The Services Questionnaire, administered to retail and other services/non-retail establishments, includes all common questions asked to all establishments and some specific questions relevant retail and other services firms. Each variation of the questionnaire is identified by the index variable, a0.

All variables are named using, first, the letter of each section and, second, the number of the variable within the section, i.e. a1 denotes section A, question 1. Variable names proceeded by a prefix "SAR" or "IND" indicate questions specific to the South Asia region or India only, therefore, they may not be found in the implementation of the rollout in other countries. All other suffixed variables are global and are present in all country surveys over the world. All variables are numeric with the exception of those variables with an "x" at the end of their names. The suffix "x" denotes that the variable is alpha-numeric.

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 the refusal to respond as a different option from don’t know. 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 0.16. 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 0.12.

According to a survey conducted between 2020 and 2021, over 57 percent of the beneficiaries had very good experience in hospitals empanelled under Pradhan Mantri Jan Arogya Yojana (PM-JAY) scheme. About 3.3 percent of respondents reported their experience to be satisfactory. PM-JAY is the largest health assurance scheme in the world fully funded by the government.

Abstract copyright UK Data Service and data collection copyright owner.

The Work Attitudes and Spending surveys (WAS) are intended to give insight into household spending. In particular, they focus on whether the husband/head of household has complete control over household decisions, or whether the wife has significant control. They also attempt to explain the causes of women's control, or lack of it, including factors such as earnings, education, attitudes, religion, ethnic group and birthplace.

For the third edition (April 2008), the existing study materials were replaced, and further data from India (gathered in 2007), Nigeria (2003 and 2005), Kenya (2004) and Egypt (2005-2006), and accompanying documentation, were added to the dataset. The data are available as combined files, which include data from all surveys, and also as raw data files for individual countries. See READ file for full details, and for a complete edition history.


Main Topics:

The following types of information are covered by each WAS survey:

• household composition (for example, number of adults);
• household spending;
• household durable goods ownership;
• employment and earnings;
• attitudes, mainly the measurement of 'feminist' or 'machismo' views;
• demographic information, such as age;
• household financial management (i.e. who organises money).

Standard Measures
Likert Scales were used, many of which are based on the British Household Panel Study (BHPS) questionnaire (held at the UK Data Archive under SN 5151).

According to a survey on online purchases from e-commerce platforms across India in June 2020, a majority of respondents preferred to have the actual image of the product packaging with principal display panel and minimum retail price or MRP tag. In the same survey, a majority of the respondents also looked for the country of origin while purchasing products on e-commerce platforms.

Abstract

This survey is the first detailed study on the phenomena of teacher absenteeism in Indonesia obtained from two unannounced visits to 147 sample schools in October 2002 and March 2003. The study was conducted by the SMERU Research Institute and the World Bank, affiliated with the Global Development Network (GDN). Similar surveys were carried out at the same time in seven other developing countries: Bangladesh, Ecuador, India, Papua New Guinea, Peru, Uganda, and Zambia.

This research focuses on primary school teacher absence rates and their relations to individual teacher characteristics, conditions of the community and its institutions, and the education policy at various levels of authority. A teacher was considered as absent if at the time of the visit the researcher could not find the sample teacher in the school.

This survey was conducted in randomly selected 10 districts/cities in four Indonesian regions: Java-Bali, Sumatera, Kalimantan-Sulawesi, and Nusa Tenggara.

Geographic coverage

Java-Bali, Sumatera, Kalimantan-Sulawesi and Nusa Tenggara regions

Analysis unit

• Teachers
• Schools

Kind of data

Sample survey data [ssd]

Sampling procedure

Information from Indonesian Statistics Agency (BPS) and the Ministry of Education was used as a basis to build a sample frame. The data gathered included the amount of total population, a list of villages and primary school facilities in each district/city. Due to limited time and resources, this research only focused on primary schools. In Indonesia, there are two types of primary education facilities: primary schools and primary madrasah. Primary schools are regulated by the Ministry of National Education, using the general curriculum, while primary madrasah are regulated by the Ministry of Religious Affairs, using a mixed (general and Islamic) curriculum.

A sample of districts/cities and schools (consisting of primary schools and primary madrasah) were selected using the following steps. First, Indonesia was divided into several regions based on the number of total population: Java-Bali, Sumatera, Kalimantan-Sulawesi, and Nusa Tenggara. Indonesian provinces that were suffering from various conflicts (such as Aceh, Central Sulawesi, Maluku, North Maluku, and Papua) were removed from the sample selection process. Then, from each region, a total of five districts and cities were randomly selected, taking into account the population of each district/city.

Second, 12 schools were selected in each district/city. Before choosing sampled schools, researchers randomly selected 10 villages in each district/city to be sampled, taking into account the location of these villages (in urban or rural areas). One of the 10 villages was a backup village to anticipate the possibility of a village that was too difficult to reach. In each village sampled, researchers asked residents about the location of primary schools/madrasah (both public and private) in these villages. They started visiting schools, giving priority to public primary schools/madrasahs. To meet the number of samples in each district/city, additional samples were selected from private schools.

Third, in each school sampled, the researcher would request a list of teachers. If a school visited was considered to be large, such as schools with more than 15 teachers, then the researcher would only interview 15 teachers chosen randomly to ensure that survey quality could be maintained despite the limited time and resources. Each school was visited twice, both on an unannounced date. From the 147 primary schools/madrasah in the sample, 1,441 teachers were selected in each visit (because this is a panel study, the teacher absence data that were used were taken only from teachers that could be interviewed or whose data were obtained from both visits). If there were teachers whose information was only obtained from one of the visits, then their data was not included in the dataset panel.

Mode of data collection

Face-to-face [f2f]

Research instrument

The following survey instruments are available:

• Teacher Questionnaire, First Visit
• Teacher Questionnaire, Second Visit.

Cleaning operations

Detailed information about data editing procedures is available in "Data Cleaning Guide for PETS/QSDS Surveys" in external resources.

The STATA cleaning do-file and the data quality report on the dataset can also be found in external resources.

As per a survey conducted in 2023, 53 percent of population in India are of the opinion that same-sex marriage should be legal, while 43 percent oppose it. India's Supreme Court recently declined to legalize same-sex marriage while accepting the current government's offer to set up a panel to evaluate and consider granting legal rights and benefits for same-sex couples.

Abstract

There is a widespread policy view that a lack of job opportunities at home is a key reason for migration, accompanied by suggestions of the need to spend more on creating these opportunities so as to reduce migration. Self-employment is widespread in poor countries, and faced with a lack of existing jobs, providing more opportunities for people to start businesses is a key policy option. But empirical evidence to support this idea is slight, and economic theory offers several reasons why the self-employed may in fact be more likely to migrate.

The "Self-employment and Migration", World Development study conducted two sets of analysis: 1) It put together panel surveys from 8 countries to look descriptively at the relationship between self-employment and migration; 2) It re-analyzed 7 randomized experiments that increased self-employment to look at their impacts on migration.

Kind of data

Sample survey data [ssd]

Mode of data collection

Face-to-face [f2f]

Research instrument

Derived data, see paper and readme document found under 'Documentation' for sources

The access control market is expected to strengthen its boundaries at a steady CAGR of 12.4% during the forecast period. The market is expected to hold a share of US$ 19.05 billion in 2023 while it is anticipated to cross a value of US$ 61.31 billion by 2033.

Attributes	Details
Access Control Market CAGR (2023 to 2033)	12.4%
Access Control Market Size (2023)	US$ 19.05 billion
Access Control Market Size (2033)	US$ 61.31 billion

Country-wise Insights

Region	CAGR (2023 to 2033)
The United States	7.5%
China	4.7%
India	6.6%
Germany	2.7%
The United Kingdom	4.0%

The United States Market CAGR (2023 to 2033)	7.5%
The United States Market Sales Volume (2023)	4,328.4

China Market CAGR (2023 to 2033)	12%
China Market Sales Volume (2023)	1,605.8

India Market CAGR (2023 to 2033)	9.8%
India Market Sales Volume (2023) (Units)	890.4

Category-wise Insights

Segment	Top Component
Top Sub-segment	Hardware
CAGR (2023 to 2033)	13.3%
CAGR (2017 to 2022)	8.8%

Segment	Top System
Top Sub-segment	Iris Recognition Access Control Systems
CAGR (2023 to 2033)	16%
CAGR (2017 to 2022)	10.8%

Abstract

This Impact Evaluation (IE), a randomized controlled trial, tested the effectiveness of using the women’s self-help group platform of Bihar’s JEEViKA program to address the immediate and underlying determinants of undernutrition among women and children andimprove nutrition outcomes. JEEViKA is a rural livelihoods project, supported by the World Bank in Bihar that supports self-helpgroups (SHGs) – savings and credit-based groups of about 15-20 women, mostly targeted towards those from poor households –with the aim of improving their livelihoods and enhancing household incomes. The JEEViKA Multisectoral Convergence (JEEViKA-MC)pilot, developed by the Bihar Rural Livelihoods Promotion Society with technical support from the World Bank, was designed to leverage these SHGs to provide two complementary sets of interventions—health and nutrition behavior change communication (BCC) to improve women’s knowledge and household practices, and efforts to improve service access through convergence —alongside the existing core package of JEEViKA. The core JEEViKA interventions include: the organization of rural women into SHGs,training and strengthening the SHGs, federation of the SHGs into Village Organizations (VOs) and Cluster-Level Federations (CLFs),bank linkages for the SHGs and their federations, and improvement of livelihoods and women’s empowerment through extensionservices and related interventions. Within this target population, households with young children, mothers of young children, andpregnant women were the primary focus of the JEEViKA-MC pilot.

The IE assessed changes household knowledge and behaviours, as well as in nutrition outcomes of women and children in the pilot areas as compared to areas that did not receive the two additional interventions. Two rounds of panel data - at baseline conducted in April–May 2016, and at endline conducted in October - November2018, of women with children 6 - 23 months of age at baseline, were used to assess the following outcomes of the JEEViKA MC pilotas compared to non-intervention areas, i.e., areas with only the core JEEViKA interventions. - The primary outcomes assessed werewomen’s body mass index (BMI) and reported dietary diversity for children aged 6 -23 months. - Secondary outcomes for womenincluded reported dietary diversity, and health, hygiene, and nutrition knowledge and practices. For children, secondary outcomesincluded anthropometric outcomes, infant and young child feeding practices, and morbidity among children. For households,outcomes included household food security, use of government programs as well as JEEViKA food security-related services, and adoption of hygiene and sanitation practices (including handwashing and use of latrines).

The International Food Policy ResearchInstitute (IFPRI) was contracted to conduct the IE.

Geographic coverage

While the JEEViKA program covers the majority of districts in Bihar, the JEEViKA-MC pilot interventions were introduced in 12 village administrative units, called Gram Panchayats (GPs) of Saur Bazaar, Sonbarsa Raj, and Pattarghat blocks of Saharsa district of Bihar.

Sampling procedure

The impact evaluation used a cluster-randomized controlled trial design. It was conducted across three pilot implementation blocks that had mature self-help groups (i.e. groups formed in 2011). Of the 24 available comparable village administrative clusters, called gram panchayats (GPs), allocated 12 to receive the JEEViKA-MC pilot treatment interventions and another 12 as a comparison group. Cluster randomization was done through simple random sampling. The total number of 120 villages were selected, 60 in each arm. Complete listing of all households in each of these 120 villages was obtained. From this household listing, 25 households were selected as per village that had a woman who: • belonged to a household where at least one woman was a member of a JEEViKA SHG. • had at least one child age 6–23 months. The sampling of 25 households allowed for oversampling of 5 households per village, to ensure that 20 households per village responded to the survey. Thus, the total sample was 20 (HHs per village) * 5 (villages per Gram Panchayat) * 24 (Gram Panchayats) = 2,400 respondents in total: 1,200 in the control and 1,200 in the treatment arm of the study.

For the baseline survey, 5 villages were chosen at random from each of the 24 Gram Panchayats. In cases where there were fewer than 5 villages per Gram Panchayat all villages in the Gram Panchayat were included in the survey and the number of households per Gram Panchayat was increased.

The same households were surveyed during the endline as well.

Mode of data collection

Face-to-face [f2f]

Research instrument

The full set of questionnaires are available for download under the downloads tab.

Response rate

The baseline survey was carried out in 131 villages. 2,246 households were interviewed with respondent women who met the sampling criteria—1,164 in the treatment areas and 1,082 in the comparison areas. At endline, 2,246 baseline households were revisited and 2119 could be re-interviewed (those with baseline respondent women available), for an attrition rate of only 5.65 percent. The most common reasons for attrition among the respondents were migration for work, permanent relocation, temporary absence from the village, and death.

Anthropometric data was collected for 2,116 respondent women from the baseline, re-interviewed the mothers of 2,084 index children (35 were not alive), and anthropometric data for 2,006 index children from the baseline was collected. In addition to the index child, if the mother had given birth to one or more children since the baseline, at endline information on the youngest of those children between the ages of 6 and 23 months was collected. There were 805 such youngest children, and anthropometric data were available for all of them, with no dates of birth missing.