rafmacalaba/data-use-mentions-extended huggingface dataset:
past week. The response rate averaged 79% for firms interested in Agruppa (81% in treatment blocks, 77% in control blocks), and 69% for not-interested firms (70% in treatment blocks, 68% in control blocks). Tables A4.1 and A4.2 break this down by survey round, showing a tendency for response rates to fall over time, reflecting some survey fatigue.<sup>13</sup> We then collected two longer surveys at six-months and 12 months after the launch of Agruppa in a block. In addition to the information collected in the high-frequency surveys, these questionnaires also asked about business opening hours, sales of some other products, pricing strategies, crime, record-keeping, and work-life balance. The response rates for interested firms were 78% at six months (80% in treatment blocks, 75% in control blocks), and 76% at 12 months (77% in treatment blocks, and 74% in control blocks), and were again lower for uninterested firms (Table A4.1). We were able to collect at least sales prices in most stores interviewed (Table A4.2), but item non-response rates were very high for sales and profits (Table A4.3), so that profits and sales are only available for 35 percent of interested firms on average (similar by treatment status). In our regressions we control for block pair fixed effects (which controls for issues like overall insecurity that affect response rates) and for the baseline values of the outcome of interest (adjusting for an imbalance on initial outcomes caused by attrition).

# **4. Impacts on Firms**

We analyze the impacts of this intervention by assessing the extent to which it was able to reduce travel times and travel costs to Corabastos and improve life quality for firm owners, whether it changed their input costs and sale prices, whether this resulted in changes in sales and profits, and how competing firms were then affected.

#
Policy Research Working Paper 8882

# **Abstract**

Individual-level employment data have a wide range of applications. They are used to monitor labor markets and the Sustainable Development Goals, contribute to understanding and explaining socioeconomic conditions, and may help to design and inform labor market policies. This paper is relevant for academics and policy makers who want to understand the main survey design issues behind the collection of individual-level employment data in nationally representative household surveys and the implications for data quality, particularly for women and young people. The paper reviews four survey programs (Living Standards Measurement Study, Living Standards Measurement Study– Integrated Surveys on Agriculture, Labor Force Surveys, and Demographic and Health Surveys) in 14 developing countries. First, the paper reviews the Sustainable Development Goals to identify a core set of labor market indicators and briefly discusses the International Labour Organization’s definitions of key concepts that shape these indicators. Second, it assesses whether the Sustainable Development Goals labor market indicators are captured in the reviewed surveys. Third, it takes stock of current approaches to collect employment data and discusses critical survey design features, such as the structure of the labor module and the wording of the questions. Fourth, the paper examines whether these survey design features are gender and age neutral. Data from the Living Standards Measurement Study–Integrated Surveys on Agriculture are used to illustrate these issues. The paper concludes by proposing short- and medium-term objectives to improve the data quality in the Living Standards Measurement Study–Integrated Surveys on Agriculture.

This paper is a product of the Development Data Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at sam.desiere@kuleuven.be and vcosta@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in
together with the regulatory costs means that informal rms are much more prevalent in lower levels of productivity and are much smaller. Ulyssea (2018) develops and estimates an equilibrium model of informality using data from the Survey of the Urban Informal Sector (Pesquisa de Economia Informal Urbana, ECINF), a survey of informal rms conducted by IBGE in 2003. He considers two margins of informality. First, the decision of rms to not register their business (extensive margin). Second, the choice to hire workers o the books (intensive margin). The model integrates the three leading views of informal rms La Porta and Shleifer (2014) in a unied setting. The rst view argues that the informal sector is a reservoir of potentially productive entrepreneurs who are kept out of formality by high regulatory costs. The second sees informal rms as parasite rms that are productive enough to survive in the formal sector but choose to remain informal due to the cost advantages of not complying with taxes and regulations. The third argues that informality is a survival strategy for low-skill entrepreneurs who are too unproductive to ever become formal. The results show that the largest proportion of informal rms in Brazil (48.8 percent) correspond to the view that informal rms correspond to low-skill entrepreneurs who are too unproductive to ever become formal. The view that informal rms are to a large extent parasite rms also corresponds to a signicant fraction of informal rms (41.9 percent). On the other hand, the view that informal rms are potentially productive rms hindered by high regulatory costs corresponds to only 9.3 percent of informal rms in Brazil. This evidence on heterogeneity of rm productivity in the Brazilian informal sector suggests that the potential eects of formalization policies focused on the reduction of formal sector entry costs are probably limited. On the other hand, human capital policies that increase the skill of informal entrepreneurs may have signicant eects. We will return to these issues when we discuss the
Policy Research Working Paper 9326

# **Abstract**

The Syrian crisis has led to rapid and large-scale population displacement. This paper uses several sources of data, including the United Nations Higher Commissioner for Refugees’ registration database and multi-country, multitopic surveys collected in 2015‒16, to characterize service delivery in the context of a rapid influx of displaced populations. The study encompasses infrastructure services, such as electricity and garbage disposal, and social services, such as health and education, and considers both measures of access to services and their perceived quality.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at nkrishnan@worldbank.org, flaviorussoriva@gmail.com, dsharma5@worldbank.org, and tvishwanath@worldbank.org..

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
international trade costs, changes in the geographic concentration of industries can go either way, depending on the relative magnitude of changes in these costs. Since domestic transport and international trade costs are partly determined by the same underlying forces – international trade still requires domestic transportation at some point – any analysis of the changes in transport costs on the geographic concentration of industries needs to control for changes in international trade costs broadly defined. We will control for these costs using measures of trade exposure – both for imports and exports and for different groups of countries – though these are only proxies for international trade costs since they are an amalgam of transport costs, foreign productivity, and other forces.

Second, changes in the trading environment of one sector, intermediates in this case, have a direct impact on the geographic structure of the other sector, final in this case. As the mass of imported intermediates increases, holding trade and transport costs fixed, the final sector disperses more although it is not directly but only indirectly affected (through the input-output links). This suggests that we need to control for general equilibrium effects that are channeled through the impacts of changes in the geographic structure of some sectors on the other sectors. We will control for these effects novel measures equilibrium by constructing microgeographic that proxy access to potential local inputs and outputs.

Last, a large set of parameters that are fairly stable over time influence the degree of geographic concentration across industries in any cross section. To control for these factors, we will use panel data and focus on changes and not on levels. Doing so obviously does not solve the problem of industry-specific time-varying unobservables, but it is a step forward compared to purely cross-sectional analyses of the determinants of geographic concentration that most of the literature (e.g., Rosenthal and Strange, 2001; Ellison _et al._ , 2010) has used until now (yet, see Storeygard, 2016, for an approach using time-varying proxies for transport costs).

# **3 Measurement and descriptive evidence*
# **3. Public and Private Wages in India (knowledge to date)**

The paucity of evidence on the magnitude of the wage differentials for the Indian labor market as a whole is usually blamed on the scarcity of suitable data. Even though many agencies collect some information on employment and wages, the comparability of public and private sector wages is complicated because wage data are rarely collected in conjunction with information on the qualifications of workers. Thus, the notion of alternative wages is rarely used. Yet, some data from formal sources exist and several authors have analyzed wages and wage differentials using individual-level data. Overall, the information on the differences in wages is limited to the _factory sector_ , to the segment of the labor market, which employs highly skilled workers, and to some geographic areas.

Data on wages from the 1997-98 Annual Survey of Industries (Central Statistical Organization, Government of India), which covers the _factory sector_ , suggest that the public sector offers higher remuneration for workers compared to the private sector. There are substantial variations in pay within the public sector. Workers employed in units owned by the Central government receive wages that are nearly twice as high as the sector average; followed by workers in units owned jointly in the public sector. Workers in “wholly” private enterprises appear to receive the lowest wages among all considered categories (Table 4).<sup>2</sup> Several authors have attempted to account for differences in the human capital of workers in the public and private sectors while investigating wage differentials. Most of these studies used the survey of Degree Holders and Technical Personnel (DHTP) conducted, along with the 1981 Census of India, on behalf of the Division of Scientific and Technical Personnel of the Council for Scientific and Industrial Research. Data were collected on the basis of a 20% Census sample in 12 states and using the complete enumeration samples in other states and union territories.

> 2 The 1999-2000 Annual Survey of Industries data on wages has not been released as
vectors of sample welfare _yc,s_<sup>_∗_,</sup><sup>_c_=1</sup><sup>_, . . . , C_,obtaintheEBestimators</sup><sup>_τ_ˆ</sup><sup>_EB_</sup> _c_<sup>_∗_</sup> , using MC simulation if needed. The estimation procedure is exactly the same EB procedure based on the original sample, but using the bootstrap sample data _yc,s_<sup>_∗_,</sup><sup>_c_= 1</sup><sup>_, . . . , C_and the corresponding</sup> bootstrap model parameter estimates _θ_<sup>ˆ</sup><sup>_∗_</sup> from step 4.

> 11Note that here welfares are generated for all households in the census, sampled and non-sampled.

9
Partial Effect of Fractionalizaton (ELF)<br>−.01 Partial Effect of Executive Constraints (INS<br>0 0 −.6<br><!-- End of picture text --> _Note(s)_ : Panels (a) and (b) illustrate the results for a nominal size of 10 percent. The break underlying panels (c) and (d) are estimated with a nominal size of 20 percent. The average partial effects shown in panels (a) to (d) are based on column (5) of Table 2 and are computed over the entire range of the variable on the horizontal axis while all other variables take on their respective realizations. The dashed lines are upper and lower 95 percent confidence limits. _Source_ : Authors’ analysis based on data from the Penn World Tables version 9.0 (series _rgdpna/pop_ ). Additional data come from the Polity IV project, Desmet et al. (2012), and the Federal Reserve Economic Data (FRED) database.

The estimated effects are both economically and statistically significant across a wide range of situations. Figure 3 plots the average partial effect with respect to one variable of the interaction term over representative values of the other, including a 95% confidence interval. The vertical axis on the right measures the average predicted semi-elasticity of the expected duration with respect to fractionalization or executive constraints. The effect 22
autonomous republic, and 1 independent city (Tashkent), crossed with their urban areas (except for the City of Tashkent, which is entirely urban). The survey was conducted entirely on tablet devices (CAPI), enabling validation using crossreferencing and other techniques to ensure accuracy. The survey was conducted over the course of a 1.5-month period in May/June 2018. Although the L2CU study collected data continuously, the baseline is the only source that provides a comprehensive consumption and expenditure module.

# **II.IV – Kyrgyz Republic HBS 2016**

Poverty rates and other welfare estimates for Kyrgyzstan rely on the 2016 round of the Kyrgyzstan HBS, conducted by the National Statistical Agency. For household consumption, the survey is usually considered nationally representative, representative at the oblast (region) level, and separately representative for rural and urban areas. The survey uses a stratified sample design with strata corresponding to seven regions + 1 large city (Bishkek) crossed by their urban and rural areas. A complete consumption module (using a diary approach) is gathered in the HBS, covering both food and non-food items. Information on household composition, income, employment, and related topics is also collected. All data are collected in-person, and in most recent years using a CAPI system.

19
# **Appendix 4. Student Perception: Captivate Index**

Throughout the student questionnaire we asked students a series of questions about specific teachers and their practices in order to construct an index that aims to measure if teaching practices inspire curiosity and interest and whether teachers are able to hold the student’s attention in class and provide the basis for continuing interest. The questions were based on the Student Tripod Survey, a pre-specified well-known questionnaire validated by the Measures of Effective Teaching (MET) project sponsored by the Bill and Melinda Gates Foundation.<sup>26</sup> Column (1) of Table A4.1 lists the questions used to build this index, which we named Captivate Index. These questions were randomly mixed in the student survey instrument. Students gave categorical answers of the type “strongly agree”, “agree”, “disagree” and “strongly disagree”. We aggregated these answers into an index using a maximum likelihood principal components estimator. According to our estimation, only one factor is retained because it has an Eigenvalue over one. Specifically, the Eigenvalue is 2.327 and the Cronbach’s alpha reliability coefficient for our sample is 0.759. Column (2) shows the loading associated with each variable.

After the prediction was computed to produce the index, we standardized it using the mean and standard deviation of the Control Group. The index ranges between -6 to 2, where -6 indicates that the student strongly disagrees in all the questions included in the index. This means that, for that student, teacher practices do not inspire curiosity and interest or fails to keep his attention in class. In contrast 2 indicates that the student strongly agrees in all the questions that make up the index suggesting that, for that student, teacher practices do inspire curiosity and interest or are successful in keeping his full attention in class.

> 26 Tripod survey is the US’s leading provider of classroom-level survey assessments for K-12 education. Tripod surveys are in their 18th
# Globally Engaged Firms in the COVID-19 Crisis<sup>∗</sup>

Cristina Constantinescu (World Bank) Ana Margarida Fernandes (World Bank) Arti Grover (World Bank)<sup>†</sup> Stavros Poupakis (University College London) Santiago Reyes (World Bank)

## **JEL classification** : D22, F14, L20, L25, O10.

**Keywords** : COVID-19, Crisis, Firms, Recovery, Trade, Exporters, Global Value Chains.

∗The authors are immensely grateful to the many World Bank Group colleagues and counterpart institutions that have supported the Business Pulse Survey (BPS) data collection, without whom this work would not have been possible. Likewise, we thank the Enterprise Analysis Unit of the Development Economics Global Indicators Department of the World Bank Group for making the Enterprise Survey data available. We are thankful to the members of the EITFE team for their efforts in coordinating BPS data collection. We thank Gene Grossman, Mona Haddad, Denis Medvedev, Antonio Nucifora, Marcelo Olarreaga and Daria Taglioni for their guidance and strategic support of the BPS initiative, and the Umbrella Facility for Trade trust fund (financed by the governments of the Netherlands, Norway, Sweden, Switzerland and the United Kingdom), as well as the USAID, the IFC, and the World Bank for their financial support. We thank Bishakha Barman, Yewon Choi, and Shwetha Eapen for their research assistance. The authors are also grateful to the following colleagues for their feedback at the concept stage of this project: Asya Akhlaque, Caroline Freund, Mary Hallward-Driemeier, Leonardo Iacovone, Michele Ruta, Daria Taglioni, Gonzalo Varela and Erik von Uexkull. We thank Beata Javorcik for kindly sharing the data on letter of credit intensity and Arthur Johannes Giesberts for his generosity in sharing his tool for converting text variables into 4-digit industry codes. The views expressed in this paper are solely those of the authors and do not necessarily reflect those of the World Bank, its Executive Directors, or the countries
mapped buildings are randomly selected using enumeration section maps, and households living in those buildings are interviewed; if there are no households inside, then the closest household is selected. For camps, survey investigators start from the centre of the camp and walk towards the outside in a different randomly selected paths. They number each household encountered in the way, and randomly select two households to interview. For all three types of sampling, when multiple households are found living in the same building or tent, a single household is randomly selected.

In connection with these data, it is necessary to bear in mind that the results of the survey remain relatively imprecise (CNSA, 2010a). Indeed, the sampling was operated according to a census that was de facto made obsolete in the wake of the earthquake, given the population displacements entailed by the disaster. Moreover, some camps had at that point been set up but had not yet been registered by the IOM, especially in rural areas (Jacmel, Léogâne). On several sites, there were no lists available, so that local authorities were called upon to identify camps and then determine the number of households there. Finally, there was no gaining access to few areas which were out of bounds (remoteness or landslips caused by the earthquake) that may induce a small bias in the analysis.

The questionnaire examined food insecurity and the dynamics of poverty as well as the impact of displacements in non-directly affected areas. Food and non food emergency assistance allocation efficiency is also assessed in the wake of the earthquake.

# _(b) Recall bias_

Data analysis does not incorporate sampling weights nor adjusts for clustering within households. Indeed, as the situation evolves rapidly, our analysis focuses on targeting and behaviors and, although households had been chosen randomly, does not present results fully representative of the population. One important issue is, however, to assess the recall bias due to distant answers from the earthquake. In the June (EFSA II) survey, households were interviewed on their
- 101 -

Primary Insurance Rates for Residential Properties Basis Points in Relation to Sums Insured - 1999

|Antigua & Barbuda|120 (high risk)|
|---|---|
|Barbados|70 (medium risk)|
|Dominica|70 (medium risk)|
|Grenada|38 (low risk)|
|St. Kitts & Nevis|110 (high risk)|
|St. Lucia|60 (medium risk)|
|St. Vincent & the Grenadines|40 (low risk)|
|Trinidad & Tobago|35 (low risk)|

Given the differentiated risk zones, sub-regional risk pooling would enable more optimal diversification of risk under a sub-regional insured portfolio which can then be more efficiently transferred to the reinsurance markets and/or the capital markets. Pooling of risks before reinsuring them lowers the aggregate portfolio's single event exposure thus reducing the capital carrying cost of funding catastrophe risk, or conversely, increasing the available capital for the same level of risk aggregated (see graph below).

Shift in Capital Supply Requirement With **Diversified** Risk Portfolio **A B** Risk Capital **/ Capital** Cost ($) For the three risk sub-zones under a non-pooled scenario, curve B reflects the capital requirement with regard to the three risk level exposures individually. However, under a pooled and further diversified portfolio, the lesser risk capital required to cover catastrophe "hits" at any point in time over the entire portfolio, shifts the required capital supply curve back (curve A). Thus, at the same risk levels shown by the connecting horizontal lines, the risk based capital needed to fund the fair actuarial catastrophe risks becomes less reflecting a more efficient funding arrangement.

# **Risk Pooling**

The following chart based on empirical data shows the reduction in the "portfolio" expected loss potential, as achieved through pooling of catastrophe risks. The chart is based on data collected on market values of private and public properties and