rafmacalaba/data-use-mentions-extended huggingface dataset:
in the implementation of the República Bolivariana de Venezuela 2006 Enterprise Survey, there were slight deviations in the restrictive criteria (such as the exact length of the survey instrument) typical for World Bank Enterprise Surveys (WBES). This deviation from the WBES afforded the flexibility to run a survey methodology experiment.<sup>2</sup> The paper is focused on methodological, not policy issues. The details of the experiment are as follows. Firms were randomly split into two groups. One group received the standard ES questionnaire with perceptions of the business environment obstacles at the end of every section. The other group received the standard ES questionnaire with one modification - the perceptions of the business environment obstacles lumped together at the very beginning of the questionnaire. We then compare the perceptions of the business environment across the two groups.

Our results indicate that of the 15 business environment obstacle ratings, only 4 show a statistically significant difference across the groups – political instability, corruption, electricity, and business licensing and permits. After using a regression analysis framework to account for several factors, only two business environment elements have a statistically significant difference across the two groups – business licensing and permits, and corruption. The latter finding is consistent with Friesenbichler et al (2017) who uncover answer biases for the corruption perception in firm surveys. We then explore if changes in the order of the perceptions-based questions have any effect on the objective measures in the survey for business licensing and permits, and corruption. We find no significant effects. Thus, objective measures may be unaffected by the placement of the perceptions-based questions. This follows a long literature that has studied the effects of question order in surveys across a wide range of fields (Bradburn and Mason, 1964; Bishop and Smith, 2001; Rohwedder et al., 2006; Holbrook et al., 2007; Lumsdaine and Exterkate, 2013; Bard and Weinstein, 2017).

> 2 The increasing popularity of the use of the WBES has raised questions about the validity of various
to the tax administrative and survey data. The general patterns across the different data sources are compared to see whether they suggest a similar trajectory in terms of the impact of COVID-19 on formal firms in Zambia. The tax administrative data is adjusted for inflation, drawing on monthly inflation rates released by the central bank of Zambia, so that comparisons over time can be made in real terms. The survey data is weighted to ensure it is representative of formal firms in Zambia that share the key characteristics that the original ES sampling frame is based on (e.g., having more than five employees, operating in specific sectors, located in urban areas). The survey weights also factor in the small amount of attrition between the baseline and follow-up surveys; however, this has negligible impact because there was a very high response rate across all three rounds. In addition, both data sources are winsorized at 2.5 and 97.5 percent to ensure that the average trends are not entirely driven by outliers.

The next part of the descriptive analysis is to solely use the tax administrative data for the subset of firms that share similar characteristics to the firms that participate in the survey. The first step is to only focus on firms that file each month (i.e. they are ‘perfectly compliant’). This is necessary as the overall trends in the tax administrative data are partly influenced by which firms choose to file in a given month and there is seasonality in when firms choose to file. A drawback from restricting the focus to these firms is that they are likely to be quite distinct from other formal firms. For example, they may be quite profitable and/or large so each month they always exceed the threshold for filing a tax return, whereas other firms may have sales that fluctuate around the threshold for filing. Another difference between the ‘perfectly compliant’ firms and other formal firms is that they may have a lower risk tolerance and/or greater exposure to the tax authority, which means
into 100 and 0 in the indicators database for easier analysis. For questions with multiple options, separate indicators are created to enable direct analysis. Most of these transformations and indicator definitions are self-explanatory.

The two databases – the raw data with the country-level questions mapped into a common set and the indicators data – is publicly available on the WBES data portal.<sup>6</sup> The databases will be constantly maintained and updated with additional data as the World Bank's Enterprise Analysis unit completes additional surveys of informal businesses around the world.

> 6 As noted above, data can be downloaded from the Enterprise Surveys data portal: <u>https://login.enterprisesurveys.org/ and the city-level Informal Sector Enterprise Surveys indicators can be viewed or</u> downloaded from the Informal Sector Enterprise Surveys page of the Enterprise Surveys website: <u>https://www.enterprisesurveys.org/en/informal-businesses.</u>

10
# **The Labor Content of Exports Database**<sup>**1**</sup>

Massimiliano Calì (World Bank) Joseph Francois (University of Bern and CEPR) Claire H. Hollweg (World Bank) Miriam Manchin (University College London) Doris Anita Oberdabernig (University of Bern) Hugo Rojas-Romagosa (CPB) Stela Rubinova (Graduate Institute) Patrick Tomberger ( University of Bern )

**Keywords:** International trade and labor markets, labor compensation, jobs **JEL classification codes:** J2, J3, F16, F66

> 1 The corresponding author can be contacted at mcali@worldbank.org. The authors thank Paul Brenton, Jakob Engel, Mona Haddad, Tom Farole, Russell Hillberry, and Marinos Tsigas for useful inputs, and Alen Mulabdic for outstanding research assistance.
Figure 3.1 Incidence of Conflict in Côte d‟Ivoire: 2001 to 2006 <!-- Start of picture text --> Conflict events<br>250<br>194<br>200<br>145<br>150 120<br>100<br>54<br>32<br>50<br>11<br>0<br>2001 2002 2003 2004 2005 2006<br><!-- End of picture text --> Source: Authors‟ calculation based on the ACLED database In this study we use two main data sources. The data on local incidences of conflict are taken from the Armed Conflict Location and Event Database (ACLED). The latter<sup>1</sup> as described by Raleigh, Hegre, and Carlson (2009) compiles exact locations, dates, and additional characteristics of individual battle events in states affected by civil war. The conflict data for Côte d‟Ivoire are available for the period from 1997 to 2010. The ACLED database on Côte d‟Ivoire reports a total number of 965 conflict events between 1998 and 2008. It tracks rebel activity and distinguishes between territorial transfers of military control from governments to rebel groups and vice versa. The conflict events are disaggregated into six categories: (i) Battle - government regains territory, (ii) Battle - no change of territory, (iii) Battles - rebels overtake territory, (iv) Non-violent activity by a conflict actor, (v) riots/protests, and (vi) Violence against civilians. In Figure 3.1, we show the total number of reported conflicts per year for the period starting from 2001 to 2006. The conflict intensity reached its peak between 2002 and 2004 with a total of 459 conflict events.

For empirical purposes, we disaggregate the conflict events into 50 departments, which are nested into 19 regions in Côte d‟Ivoire. To decipher the causes and consequences of conflict at the local level, many
from the goal of a product that allows borrowers, fearful of default, to take on higher-risk higher-return investments with the comfort of knowing they have some flexibility to repay. We see these results as motivating, for both lenders and researchers, to continue to learn more about how products can better “match cash flows” both with respect to timing and risk.

# **References**

Ahlin, Christian, Selim Gulesci, Andreas Madestam, and Miri Stryjan. 2020. “Loan Contract Structure and Adverse Selection: Survey Evidence from Uganda.” _Journal of Economic Behavior & Organization_ 172 (C): 180–95.

- Armendariz de Aghion, Beatriz, and Jonathan Morduch. 2010. _The Economics of Microfinance_ . 2nd ed. Cambridge, MA: MIT Press.

- Athey, Susan, Julie Tibshirani, and Stefan Wager. 2019. “Generalized Random Forests.” _The Annals of Statistics_ 47 (2): 1148–78.

Banerjee, Abhijit, Dean Karlan, and Jonathan Zinman. 2015. “Six Randomized Evaluations of Microcredit: Introduction and Further Steps.” _American Economic Journal: Applied Economics_ 7 (1): 1–21. https://doi.org/10.1257/app.20140287.

Barboni, Giorgia, and Parul Agarwal. 2023. “How Do Flexible Microfinance Contracts Improve Repayment Rates and Business Outcomes? Experimental Evidence from India.” _Working Paper_ , February. https://papers.ssrn.com/abstract=4358795.
br>HH member smokesj 3 ee —<br>Functioning refrigerator in household} 3 oe Ost<br>Female oa ope<br>Single birth} © —2#—— 2<br>Urban ——<br>Total HH size (N)j a +<br>Total children under five in HH (N) ° 43<br>Total HH income per month (1 = 1000 PHP) (log) s ——<br>In poorest quintile of total HH income per capita] 6 a<br>Mother has high school or tertiary education} % ——<br>Mother is married —+—<br>Mom wasa teenager at time of birth —_—+—<br>Mother hasa job or business Te<br>0.5 1.0 1.5 2.0 0.5 1.0 1.5 2.0<br>Relative Risk (95% Cl)<br><!-- End of picture text -->

Source: Author's calculations based on 2015 National Nutrition Survey (NNS). Notes: Only the relative risk estimates for significant risk factors are labeled. Age, age squared and age cubed (in months) is also used as a control but is not shown in the chart. The unadjusted regression model does not control for socio-economic factors.
(NPC) measured as the ratio of the value of agricultural output in domestic prices to its value in border prices. Its log represents a rate of difference between the output valued in domestic and border prices. Using panel data on 14 industrial countries between 1955-1987 Honma finds that (i) NPC declines with comparative advantage in agriculture (measured as the ratio of labor productivity in agriculture to labor productivity in industry), (ii) there is inverted-U shaped relationship between the NPC and the share of agriculture in output (or employment), with a threshold value of that share equal to 4.5%; that is, NPC rises as the share in agriculture increases to 4.5% and falls beyond that, (iii) NPC increases as the terms of trade (measured as 6
capita gross domestic product decreased by 15% between 2015 and 2017, from an average of US$963 in 2015 to US$823 in 2017 (in 2010 purchasing power parity (PPP)). In terms of overall development, Chad is</mark> ranked 187 of 189 countries on the Human Development Index (World Bank 2019). Due to these challenges, Chad struggled to meet many of the Millennium Development Goals in 2015, and barring unforeseen economic growth or great increases in official development assistance, the country is not likely to meet many of the Sustainable Development Goals objectives set for 2030.

4
of violence and thus provide a basis for inference over the effects of NSP on the reduction of violence in already insecure regions. The other eight districts represent ‘marginal’ areas which may be at risk of increased violence. Collectively, the sample thus provides for the estimation of effects for areas which have already succumbed to the insurgency and for areas which are vulnerable.

> 8 Our evaluation assesses the effects of this bundled development treatment - both the creation of the elected gender balanced local institution and the allocation of funds - on an array of outcomes ranging from security to women’s rights (Beath, Christia, Enikolopov 2012), economic wellbeing and access to services, governance, and state building. Preliminary results for this analysis in the form of a report are available in Beath et. al. (2010) with additional academic papers presently in progress. Results on security were not considered separately in the report since at that time the data on security incidents was not available.

> 9 The differences are likely to be driven by the fact that the villages that are located closer to big cities and provincial centers received NSP between 2003 and 2007, i.e. before the start of the impact evaluation and, are thus, excluded from the analysis.

9
**Figure 9. Work category by** **<u>gender and by age cohort</u>**

<!-- Start of picture text --> Unpaid Worker 36% 64% Unpaid Worker 41% 59%<br>Own Account Worker 76% 24% Own Account Worker 13% 87%<br>Employer 10% 90%<br>Employer 82% 18%<br>Paid Employee 18% 82%<br>Paid Employee 79% 21%<br>0% 20% 40% 60% 80% 100%<br>0% 20% 40% 60% 80% 100%<br>Youth Adults<br>Male Female<br><!-- End of picture text --> _Source:_ World Bank staff calculations based on NHBPS 2014/15.

Before the separation from South Sudan in July 2011, Sudan was the largest country in Africa in terms of land mass. Sudan is now the third largest country in Africa behind Algeria and the Democratic Republic of Congo. Since the late 1990s, Sudan’s economy has been driven by oil. That said, the mainstay for the greater share of the population has always been agriculture. According to a UN World Food Programme (WFP) study that was conducted in 2006, Sudan was estimated to have 84 million ha of arable land. However, only 17 million ha, or 20 percent, of that land was being cultivated (Karrar, Abbadi, and Adam 2006). Some of Sudan’s agricultural land is irrigated. However, the vast majority of it—upward of 90 percent according to a 2003 governmental study—is dependent solely on rainfall (Ibnouf 2011).<sup>7</sup> Employment in agriculture is more common among women, among the poor, and in rural areas. Nearly 70 percent of Sudanese occupations are focused on elementary occupations and skilled agriculture. Both elementary occupations<sup>8
Besides the substantial heterogeneity across sectors, we also find substantial, but also intuitive, heterogeneity across countries. For the CEFTA countries, the initial MFN tariff rate and the initial openness explain quite well the resulting trade effects. For the EU countries and the ROW, the effects are small in all sectors. The obvious reason here is that for the EU countries trade with the CEFTA countries is only a small fraction of their overall exports.

Let us next turn to our border scenario. Note that for the border scenario we also report results for services, which we could not include in our tariff scenario, as tariff data for services are not available. We find substantial increases of total exports for all 6 CEFTA countries in manufacturing, agriculture, and mining, and these increases are larger than in the tariff scenario. This highlights that only relying on tariff data to investigate the effects of the integration between the CEFTA countries and the EU may miss a substantial part of potential effects from trade liberalization. The comprehensive measure of the change in bilateral trade costs based on differences of the border estimates can be useful to quantify these additional gains.

Comparing across sectors, we find the largest increases again in agriculture, followed by manufacturing. Besides heterogeneity across sectors, we again find substantial heterogeneity among countries. Albania, Bosnia and Herzegovina, and North Macedonia are predicted to be the countries with the largest gains. Again, gains for the EU countries and ROW are small.

For services, we also find for some CEFTA countries, namely Albania, North Macedonia, and Serbia, substantial trade effects. The reason that we do not find substantial total export increases for the other CEFTA countries is the very low level of services exports of these countries to the EU.

# **5 Conclusion**

We introduced a simple econometric approach to quantify ex-ante the “deep” impact of trade liberalization and the “hard” effects of protection with the empirical structural gravity model, and we demonstrated the effectiveness of our methods by quantifying the partial and
proportion of women who were from villages included in both the 2008 and 2010–11 surveys and who had heard about the HealthyBaby voucher also increased from 22 percent in 2008 to 75 percent in 2010–11. In addition, 78 percent of those living in villages with a voucher user in 2010–11 had heard about the HealthyBaby voucher, compared with 69 percent of those from villages with no voucher user. Results from the household surveys further show that women who had heard about the HealthyBaby voucher were more likely to be aware of possible dangers during pregnancy, childbirth, or immediately after 21
in these countries are 8%, 16%, and 38%, respectively.<sup>5</sup> Do these numbers provide a good yardstick with which to judge the relevance of race to an understanding of inequality in these countries? Should South African and Brazilian policy-makers worry much more about racial differences in incomes than do their American counterparts? Does the small percentage of income inequality attributable to race in the U.S. mean that racial inequality is not a pertinent economic and social issue?

Conventionally, between-group inequality is calculated as a function of two arguments: differences among groups in mean incomes and the relative size and number of the groups. The figures above are based on four population groups for Brazil, three for South Africa, and five for the U.S., but the population shares of the white groups versus non-white groups differ tremendously.<sup>6</sup> In each country, the mean income of the nonwhite groups is much below that of the white group, but the non-white groups form the majority in South Africa (80%), half of the population in Brazil (50%), and a minority in the U.S. (28%). The difference in between-group inequality observed between these three countries could in fact be due largely to the difference in population shares of the racial groups instead of the differences in relative mean incomes of these groups.<sup>7</sup> The conventional between-group share is calculated by taking the ratio of observed between-group inequality to total inequality. Total inequality, however, can be viewed as the between-group inequality that would be observed if every household in the

> 5 These figures have been calculated by the authors using data from PNAD (2001) for Brazil, IES(2000) for South Africa, and LIS(2000) for the U.S.

> 6 The racial groups used in our analysis are “White”, “Black”, “Pardo”, and “other”
10 indicators that did not feature in the MDGs, but do feature in, or are consistent with, the SDGs. To make way for the newer indicators, and for the extensive financial protection data, some of the MDG-era indicators included in previous versions of the HEFPI database have been retired. The retained MDG-era indicators feature prominently in the official and supplemental MDG monitoring indicators (Wagstaff and Claeson 2004), as well as in indicator lists for current global goalmonitoring exercises, such as the ‘Countdown to 2030 for Maternal, Newborn, and Child Survival’ (cf. Victora et al. 2015) and the SDGs (e.g. SDG target 2 on ending hunger and improving nutrition and SDG 3.8 on achieving UHC).<sup>4</sup> The SDG-era indicators also feature in current global goalmonitoring exercises, including broad exercises like the SDGs, as well as in more specific exercises like the UN General Assembly’s 2011 Political Declaration on NCDs.<sup>5</sup>

> 4 The SDG indicators are listed at <u>https://unstats.un.org/sdgs/indicators/indicators-list/.</u> 5 The indicators being used to monitor progress on the NCDs are listed at <u>http://www.who.int/nmh/global_monitoring_framework/en/.</u>
across agglomerations after the 2009 recession. Together, our results suggest that similar recessions and recoveries at a macroeconomic scale may be driven by very different changes at more disaggregate spatial scales. This has a bearing on the attendant changes in spatial disparities within a country and the policies designed to remedy them.

This analysis also suggests that remote-sensed data on nighttime luminosity may improve our understanding of changes in sub-national economic activity across the business cycle. While these changes in luminosity are correlated with changes in manufacturing and construction activity, they are less useful for estimating changes in the service and agriculture sectors. However, our analysis demonstrates the usefulness of remote-sensed nighttime lights data when estimating the sub-national impacts of business cycles in rapidly urbanizing economies.

The results highlight one major challenge with conducting any empirical analysis involving sub-national changes in economic activity. We have shown that there is considerable heterogeneity across locations and time. Further, this heterogeneity is not random, but clustered across both dimensions. Two promising types of empirical analysis of sub-national economic changes are especially challenging when dealing with heterogenous clusters within a country. First, as Sun and Shapiro (2022) note, traditional approaches to estimating causal relationships between aggregate shocks and sub-national economic outcomes incorrectly assume that the baseline exposure to an event (such as baseline oil production intensity or suitability of land for potato cultivation) is exogenous. They conjecture that it is likely that the magnitudes of the impacts themselves are heterogenous and correlated with the exposure variables that are typically used, and will, therefore, result in biased estimates.<sup>24</sup> Second, although remote-sensed data are large, machine learning approaches to estimating meso-level changes also need to allow for clustering in the changes in economic activity over time and location. Khachiyan et al. (2022) develop an approach to estimating local economic activity using machine learning that is both novel and has considerable potential to guide policies. However, their approach does not incorporate the fact that modern economies consist of heterogenous spatial clusters of economic activity. Further, the R-squared
**Table A.3.3: Results of Simulation 3 by region, income group, and lending type. Effect on proficiency (share)**

||||Post-COVID-19<br>||
|---|---|---|---|---|
|Aggregate|Baseline|Optimistic|Intermediate|Pessimistic|
|EAP|36%|41%|43%|46%|
|ECA|31%|39%|42%|46%|
|LAC|53%|60%|64%|68%|
|MNA|55%|61%|65%|68%|
|NAC|17%|22%|25%|28%|
|SAR|||||
|SSF|||||
|Grand Total|40%|47%|50%|53%|
|HIC|25%|32%|35%|39%|
|UMC|51%|58%|61%|65%|
|LMC|69%|74%|75%|77%|
|LIC|-|-|-|-|
|Grand Total|40%|47%|50%|53%|
|Part I|23%|30%|33%|37%|
|IBRD|49%|57%|60%|63%|
|IDA/Blend|80%|82%|84%|86%|
|Grand Total|40%|47%|50%|53%|

Source: Authors’ calculation. Share Students Below Minimum Proficiency (BMP). Simulation 3 results based on the latest available PISA and PISA-D of 92 countries. Unweighted average. Student coverage as share of lower secondary enrollment: 100% NAC; 95% LAC; 94% EAP; 91% ECA; 76% SAR; 39% MNA; 3% SSA.
by size, sector and location. Any accounts collected in local currency units are converted to constant 2005 US dollars at purchasing-power-parity. Rates of growth are scaled to an annual basis. Table 1a reports sample counts by country and table 1b reports the means and standard deviations of our main variables of interest for two separate country samples based on OECD membership. Table 1c contains a detailed listing of the Enterprise Survey questions underlying the establishment-level business environment indicators used in this study.

Country-level data on the competitiveness of the business environment are taken from the IFC/World Bank Doing Business (DB) reports. Strongly positive correlations among the major DB variables and among their categorized aggregated indicators suggest that national regulation policies come in ̳packages.‘ In line with recent work by Loayza, Oviedo and Serven (2010) and Djankov, McLiesh and Ramalho (2006), we examine the effects of business regulations on 13