Enterprise Survey 2009-2019, Panel Data
North Macedonia, 2008 - 2019
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MKD_2009-2019_ES-P_v01_M
Enterprise Survey 2009-2019, Panel Data
| Name | Country code |
|---|---|
| North Macedonia | MKD |
Enterprise Survey [en/oth]
As part of its strategic goal of building a climate for investment, job creation, and sustainable growth, the World Bank has promoted improving business environments as a key strategy for development, which has led to a systematic effort in collecting enterprise data across countries. The Enterprise Surveys are an ongoing World Bank project in collecting both objective data based on firms' experiences and enterprises' perception of the environment in which they operate.
The ES currently cover over 190,000 firms in 152 countries, of which 143 have been surveyed following the standard methodology. This allows for better comparisons across countries and across time. Data are used to create statistically significant business environment indicators that are comparable across countries. The ES are also used to build a panel of enterprise data that will make it possible to track changes in the business environment over time and allow, for example, impact assessments of reforms.
The document dataset covers the Enterprise Survey (ES) panel data collected in North Macedonia in 2009, 2013 and 2019.
Macedonia ES 2009 was conducted in 2008 and 2009, while Macedonia ES 2013 was conducted between November 2012 and May 2013, and North Macedonia ES 2019 was conducted between December 2018 and October 2019. The objective of the Enterprise Survey is to gain an understanding of what firms experience in the private sector.
As part of its strategic goal of building a climate for investment, job creation, and sustainable growth, the World Bank has promoted improving the business environment as a key strategy for development, which has led to a systematic effort in collecting enterprise data across countries. The Enterprise Surveys (ES) are an ongoing World Bank project in collecting both objective data based on firms’ experiences and enterprises’ perception of the environment in which they operate.
Sample survey data [ssd]
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.
Version
Version 01. Edited, anonymous dataset for public distribution.
Scope
The scope of the study includes:
- characteristics of establishment;
- infrastructure and services;
- sales and supplies;
- degree of competition;
- capacity;
- land and permits;
- innovation;
- crime;
- finance;
- business-government relations;
- labor;
- business environment;
- Performance
- Green Economy Module:
- Environment-related aspects
- Exposure to environmental impacts
- Management and the environment
- Environmental policy and regulation
- Environmental impact of the establishment
Coverage
National
Regions covered are selected based on the number of establishments, contribution to employment, and value added. In most cases these regions are metropolitan areas and reflect the largest centers of economic activity in a country.
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.
Producers and sponsors
| Name |
|---|
| The World Bank Group |
| The European Bank for Reconstruction and Development |
| The European Investment Bank |
| Name |
|---|
| The World Bank Group |
| The European Bank for Reconstruction and Development |
| The European Investment Bank |
Sampling
The sample for Macedonia 2009 ES, Macedonia 2013 ES and of 2019 North Macedonia ES were selected using stratified random sampling, following the methodology explained in the Sampling Manual for Macedonia 2009 ES and for Macedonia 2013 ES, and in the Sampling Note for 2019 North Macedonia ES. Stratified random sampling was preferred over simple random sampling for several reasons:
a. To obtain unbiased estimates for different subdivisions of the population with some known level of precision.
b. To obtain unbiased estimates for the whole population. 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.
c. To make sure that the final total sample includes establishments from all different sectors and that it is not concentrated in one or two of industries/sizes/regions.
d. To exploit the benefits of stratified sampling where population estimates, in most cases, will be more precise than using a simple random sampling method (i.e., lower standard errors, other things being equal.)
e. Stratification may produce a smaller bound on the error of estimation than would be produced by a simple random sample of the same size. This result is particularly true if measurements within strata are homogeneous.
f. The cost per observation in the survey may be reduced by stratification of the population elements into convenient groupings.
Three levels of stratification were used in this country: industry, establishment size, and region. The original sample design with specific information of the industries and regions chosen is described in Appendix C of the North Macedonia 2019 ES Implementation Report and in Appendix E of the Macedonia 2013 Implementation Report.
Industry stratification was done as follows: Manufacturing – combining all the relevant activities (ISIC Rev. 3.1 codes 15-37), Retail (ISIC 52), and Other Services (ISIC 45, 50, 51, 55, 60-64, 72).
As it is standard for the ES, the North Macedonia ES was based on the following size stratification: small (5 to 19 employees), medium (20 to 99 employees), and large (100 or more employees).
Regional stratification for North Macedonia ES 2019 was done across three regions: Skopje; Eastern Macedonia comprising Northeastern, Eastern, Southeastern, and Vardar regions; and Western Macedonia comprising Polog, Southwestern and Pelagonia regions. For Macedonia 2013 ES, regional stratification was defined in 4 regions (city and the surrounding business area) throughout Macedonia. And for Macedonia ES 2009, regional stratification was defined in 4 regions which are Eastern, North- West & West, Skopje, and South.
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 (-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. However, there were clear cases of low response. The following graph shows non-response rates for the sales variable, d2, by sector. Please, note that for this specific question, refusals were not separately identified from “Don’t know” responses.
Under stratified random sampling, weights should be used when making inferences about the population. Any estimate or indicator that aims at describing some feature of the population should take into account that individual observations may not represent equal shares of the population.
However, there is some discussion as to the use of weights in regressions (see Deaton, 1997, pp.67; Lohr, 1999, chapter 11, Cochran, 1953, pp.150). There is not strong large-sample econometric argument in favor of using weighted estimation for a common population coefficient if the underlying model varies per stratum (stratum-specific coefficient): both simple OLS and weighted OLS are inconsistent under regular conditions. However, weighted OLS have the advantage of providing an estimate that is independent of the sample design. This latter point may be quite relevant for the ES as in most cases the objective is not only to obtain model-unbiased estimates but also design-unbiased estimates (see also Cochran, 1977, pp 200 who favors the used of weighted OLS for a common population coefficient.)
From a more general approach, if the regressions are descriptive of the population then weights should be used. The estimated model can be thought of as the relationship that would be expected if the whole population were observed. If the models are developed as structural relationships or behavioral models that may vary for different parts of the population, then, there is no reason to use weights.
Survey instrument
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.
Data collection
| Start | End |
|---|---|
| 2008 | 2009 |
| 2012-11 | 2013-05 |
| 2018-12 | 2019-10 |
| Name |
|---|
| Ipsos |
| Ipsos- North Macedonia |
The surveys were implemented following a 2-stage procedure. Typically, first a screener questionnaire is applied over the phone to determine eligibility and to make appointments. Then a face-to-face interview takes place with the Manager/Owner/Director of each establishment. However, sometimes the phone numbers were unavailable in the sample frame, and thus the enumerators applied the screeners in person. Interviews were conducted using Computer-assisted personal interviewing (CAPI) in North Macedonia. The variables a4b and a6c contain the industry and size of the establishment from the screener questionnaire.
Depositor information
| Name | Affiliation |
|---|---|
| Joshua Seth Wimpey | The World Bank Group |
Data Access
| Is signing of a confidentiality declaration required? |
|---|
| Yes |
Where necessary please site the source as "Enterprise Analysis Unit - World Bank Group https://www.enterprisesurveys.org"
Disclaimer and copyrights
The user of the data acknowledges that the original collector of the data, the authorized distributor of the data, and the relevant funding agency bear no responsibility for use of the data or for interpretations or inferences based upon such uses.
Contacts
| Name | Affiliation | |
|---|---|---|
| Enterprise Analysis Unit | The World Bank Group | enterprisesurveys@worldbank.org |
Metadata production
DDI_MKD_2009-2019_ES-P_v01_M_WB
| Name | Affiliation | Role |
|---|---|---|
| Development Economics Data Group | The World Bank Group | Documentation of the DDI |
2020-07-15
Metadata version
Version 1 (July 2020)
2020-07-15