Mapping the Landscape of Transactions: The Governance of Business Relations in Latin America 2017-2018
Argentina, Bolivia, Ecuador, Peru, Paraguay, Uruguay, 2017 - 2018
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Reference ID
LAC_2017_ES_v01_M_v01_A_LT
Producer(s)
David C. Francis, Nona Karalashvili, Peter Murrell
Collection(s)
Metadata
Created on
Sep 10, 2018
Last modified
Apr 27, 2021
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10062
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Identification
Survey ID Number
LAC_2017_ES_v01_M_v01_A_LT
Title
Mapping the Landscape of Transactions: The Governance of Business Relations in Latin America 2017-2018
Country/Economy
| Name | Country code |
|---|---|
| Argentina | ARG |
| Bolivia | BOL |
| Ecuador | ECU |
| Peru | PER |
| Paraguay | PRY |
| Uruguay | URY |
Study type
Enterprise Survey [en/oth]
Abstract
The survey data accompanies the working paper, "Mapping the Landscape of Transactions the Governance of Business Relations in Latin America”. This paper provides a picture of the landscape of transactions and one of the central motivations for this analysis is to ascertain whether there are meaningful patterns that emerge from datasets on how firms make agreements with their suppliers and customers.
A new set of survey questions is used to map governance structures that firms employ to support the successful implementation of transactions. Without imposing any a priori model, latent class analysis (LCA) discovers meaningful patterns of governance structures that readily match constructs in the literature. All governance structures use bilateralism. Bilateralism and formal institutions are sometimes complemented but never substitutes. For each firm, LCA provides estimates of the posterior probability that the firm uses each of the discovered governance structures.
These estimates can be used by researchers to go further, testing their own hypotheses relevant to Williamson’s discriminating alignment agenda using additional data from the Enterprise Surveys or elsewhere. Variations in the effectiveness of different governance structures across countries and across different types of firms and transactions are explored. Regional variation within countries is greater than cross-country variation. Foreign-owned firms, exporters, larger firms, and better-managed ones are more likely to use governance structures that complement bilateralism with use of the legal system or with the help of paid third-parties.
The responses were used to a unique set of questions posed in 2017 and 2018 as part of the ES implemented in six Latin American countries: Argentina, Bolivia, Ecuador, Paraguay, Peru, and Uruguay. The surveys are based on interviews with business owners and top managers in a sample of officially registered firms with at least five employees in the manufacturing and services sectors. The surveys are designed to be nationally representative, using a stratified survey design.
A new set of survey questions is used to map governance structures that firms employ to support the successful implementation of transactions. Without imposing any a priori model, latent class analysis (LCA) discovers meaningful patterns of governance structures that readily match constructs in the literature. All governance structures use bilateralism. Bilateralism and formal institutions are sometimes complemented but never substitutes. For each firm, LCA provides estimates of the posterior probability that the firm uses each of the discovered governance structures.
These estimates can be used by researchers to go further, testing their own hypotheses relevant to Williamson’s discriminating alignment agenda using additional data from the Enterprise Surveys or elsewhere. Variations in the effectiveness of different governance structures across countries and across different types of firms and transactions are explored. Regional variation within countries is greater than cross-country variation. Foreign-owned firms, exporters, larger firms, and better-managed ones are more likely to use governance structures that complement bilateralism with use of the legal system or with the help of paid third-parties.
The responses were used to a unique set of questions posed in 2017 and 2018 as part of the ES implemented in six Latin American countries: Argentina, Bolivia, Ecuador, Paraguay, Peru, and Uruguay. The surveys are based on interviews with business owners and top managers in a sample of officially registered firms with at least five employees in the manufacturing and services sectors. The surveys are designed to be nationally representative, using a stratified survey design.
Kind of Data
Sample survey data [ssd]
Unit of Analysis
The primary sampling unit of the study is the establishment.
Version
Version Description
- v0.1: Edited anonymous dataset for public distribution
Scope
Notes
The scope of this survey includes:
- Resolving or preventing problems with suppliers
- Resolving or preventing problems with customers
- Resolving or preventing problems with suppliers
- Resolving or preventing problems with customers
Coverage
Geographic Coverage
The surveys are designed to be nationally representative (implemented in Argentina, Bolivia, Ecuador, Paraguay, Peru, and Uruguay).
Universe
The surveys are based on interviews with business owners and top managers in a sample of officially registered firms with at least five employees in the manufacturing and services sectors.
Producers and sponsors
Primary investigators
| Name | Affiliation |
|---|---|
| David C. Francis | The World Bank |
| Nona Karalashvili | The World Bank |
| Peter Murrell | University of Maryland |
Funding Agency/Sponsor
| Name | Abbreviation |
|---|---|
| World Bank Group’s Knowledge for Change Program | KCP |
Sampling
Sampling Procedure
The sampling methodology of the World Bank’s Enterprise Survey generates sample sizes appropriate to achieve two main objectives: first, to benchmark the investment climate of individual economies across the world and, second, to conduct firm performance analyses focusing mainly on how investment climate constraints affect productivity and job creation in selected sectors.
To achieve both objectives the sampling methodology:
- Generates a sample representative of the whole non-agricultural private economy that substantiates assertions about this part of the economy, not only about the manufacturing sector. The overall sample should include, in addition to selected manufacturing industries, services industries and other relevant sectors of the economy; and
- Generates large enough sample sizes for selected industries to conduct statistically robust analyses with levels of precision at a minimum 7.5% precision for 90% confidence intervals about:
i. Estimates of population proportions (percentages), at the industry level; and
ii. Estimates of the mean of log of sales at the industry level.
STRATIFICATION
The population of industries to be included in the Enterprise Surveys and Indicator Surveys, the Universe of the study, includes the following list (according to ISIC, revision 3.1): all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Also, to limit the surveys to the formal economy the sample frame for each country should include only establishments with five (5) or more employees. Fully government owned firms are excluded as the Universe is defined as the non-agricultural private sector.
SAMPLE SIZE
Overall sample sizes for both Enterprise Surveys and Indicator Surveys are determined by the degree of stratification of the sample. The overall sample size depends on the decision of the sample size for each level of stratification. In all ES and IS the objectives of stratification are to allow an acceptable level of precision for estimates, at, first, different first, within size levels (small, medium, and large), second, at the different levels of regional stratification, and third, for the different sectors of stratification (which, as explained before, are chosen depending on the size of the economy).
Given that both the Enterprise Survey and the Indicator Survey include more than 100 indicators the computation of the minimum sample size required is complicated since it depends on the variance of each indicator. However, many of the indicators computed from the survey are proportions, such as percentage of firms that engage in X activity or chose Y action. In this case the computation of the sample size is simplified by the fact that the variance of a proportion is bounded. Assuming the maximum variance (0.5) the minimum level of precision is guaranteed.
To achieve both objectives the sampling methodology:
- Generates a sample representative of the whole non-agricultural private economy that substantiates assertions about this part of the economy, not only about the manufacturing sector. The overall sample should include, in addition to selected manufacturing industries, services industries and other relevant sectors of the economy; and
- Generates large enough sample sizes for selected industries to conduct statistically robust analyses with levels of precision at a minimum 7.5% precision for 90% confidence intervals about:
i. Estimates of population proportions (percentages), at the industry level; and
ii. Estimates of the mean of log of sales at the industry level.
STRATIFICATION
The population of industries to be included in the Enterprise Surveys and Indicator Surveys, the Universe of the study, includes the following list (according to ISIC, revision 3.1): all manufacturing sectors (group D), construction (group F), services (groups G and H), transport, storage, and communications (group I), and subsector 72 (from Group K). Also, to limit the surveys to the formal economy the sample frame for each country should include only establishments with five (5) or more employees. Fully government owned firms are excluded as the Universe is defined as the non-agricultural private sector.
SAMPLE SIZE
Overall sample sizes for both Enterprise Surveys and Indicator Surveys are determined by the degree of stratification of the sample. The overall sample size depends on the decision of the sample size for each level of stratification. In all ES and IS the objectives of stratification are to allow an acceptable level of precision for estimates, at, first, different first, within size levels (small, medium, and large), second, at the different levels of regional stratification, and third, for the different sectors of stratification (which, as explained before, are chosen depending on the size of the economy).
Given that both the Enterprise Survey and the Indicator Survey include more than 100 indicators the computation of the minimum sample size required is complicated since it depends on the variance of each indicator. However, many of the indicators computed from the survey are proportions, such as percentage of firms that engage in X activity or chose Y action. In this case the computation of the sample size is simplified by the fact that the variance of a proportion is bounded. Assuming the maximum variance (0.5) the minimum level of precision is guaranteed.
Response Rate
Non-response rates due to respondents spontaneously answering “Don’t Know” (which was not displayed as a possible option in the ‘show card’ listing possible responses). Fewer than 3% of the respondents chose at least one “Don’t Know” across the six questions about the methods of governing relations with suppliers and customers. The question with the most frequent occurrence of “Don’t Know” on relations with suppliers is on paid private dispute resolution (1.4% of the sample); for relations with customers, the question about personal trust had the highest item non-response (1.2% of the sample). Given the low item non-response rates, in our application of LCA we drop observations that have at least one “Don’t Know” in the relevant series of questions. This leaves 3,350 observations on relations with suppliers (97.7% of the sample), and 3,339 observations on relations with customers (97.3% of the sample).
Weighting
The data includes sampling weights. All results are obtained with proper use of these weights and thus refer to the entire population of establishments in the six countries.
Data Collection
Dates of Data Collection
| Start | End | Cycle |
|---|---|---|
| 2017-03 | 2018-03 | Argentina |
| 2017-01 | 2017-06 | Bolivia |
| 2017-03 | 2018-10 | Ecuador |
| 2017-02 | 2017-08 | Paraguay |
| 2017-03 | 2018-03 | Peru |
| 2017-03 | 2017-12 | Uruguay |
Data Collection Mode
Computer Assisted Personal Interview [capi]
Data Collection Notes
Interviews were conducted face-to-face using tablet devices (CAPI) and covered a wide range of topics. All interviews were conducted by local contractors in Spanish.
Questionnaires
Questionnaires
As part of the implementation of the surveys, twelve newly designed questions were administered, six concerning interactions with the firms’ suppliers and six on customer interactions. These questions were on the effectiveness of various methods of preventing or resolving problems when implementing agreements. When designing questions to be administered in a long survey and addressed to firms of all types, in different institutional settings, both conceptual and practical issues immediately arise.
Data Processing
Data Editing
The model parameters that authors use to estimate posterior probabilities are obtained from the software Latent GOLD (Vermunt and Magidson 2016), which does not provide exact parameters and applies some rounding (See the Excel file with estimated model parameters, attached as Related Material). As a result, the estimates of posterior probabilities calculated from the estimated model parameters differ somewhat from the estimates that are obtained directly from the Latent GOLD output.
Access policy
Contacts
| Name | Affiliation | |
|---|---|---|
| Nona Karalashvili | The World Bank Group | nkaralashvili@worldbank.org |
| Peter Murrell | University of Maryland | murrell@econ.umd.edu |
Citation requirements
Francis, David C.; Karalashvili, Nona; Murrell, Peter. 2018. Mapping the landscape of transactions: the governance of business relations in Latin America (English). Policy Research working paper; no. WPS 8564; Paper is funded by the Knowledge for Change Program (KCP). Washington, D.C.: World Bank Group.
http://documents.worldbank.org/curated/en/524361534957836994/Mapping-the-landscape-of-transactions-the-governance-of-business-relations-in-Latin-America
http://documents.worldbank.org/curated/en/524361534957836994/Mapping-the-landscape-of-transactions-the-governance-of-business-relations-in-Latin-America
Access authority
| Name | Affiliation | |
|---|---|---|
| Nona Karalashvili | The World Bank | nkaralashvili@worldbank.org |
| Peter Murrell | University of Maryland | murrell@econ.umd.edu |
Disclaimer and copyrights
Disclaimer
The user of the data acknowledges that the original collector of the data, the authorized distributor of the data, and the relevant funding agency bear no responsibility for use of the data or for interpretations or inferences based upon such uses.
Metadata production
DDI Document ID
DDI_LAC_2017_ES_v02_M_v01_A_LT_WB
Producers
| Name | Abbreviation | Affiliation | Role |
|---|---|---|---|
| Development Data Group | DECDG | World Bank | Study documentation |
Date of Metadata Production
2018-09-06
DDI Document version
DDI Document - Version 02 - (04/27/21)
This version is identical to DDI_LAC_2017_ES_v01_M_v01_A_LT_WB but country field has been updated to capture all the countries covered by survey.
v01 (September 2018)
This version is identical to DDI_LAC_2017_ES_v01_M_v01_A_LT_WB but country field has been updated to capture all the countries covered by survey.
v01 (September 2018)