rafmacalaba/data-use-mentions-extended huggingface dataset:
in the Dominican Republic. The first source of firm-level information is the tax forms from the Direccion General de Impuestos Internos (DGII), which compiles tax declarations for the universe of formal firms required to file taxes in the Dominican Republic between 2007 and 2021. The number of firms each year averages around 90,000, ranging from 55,000 in 2007 to close to 129,000 in 2021. Through these tax declarations, we have the necessary information to track and compute several firm performance variables, including sales, production, value added, and productivity. Also key to our analysis is firms’ expenses and liabilities, as this will be the conduit through which a CO2 tax affects firms. The database also includes relevant firm characteristic variables such as the economic sector of activity and geographical location. We complement this information with data from the National Energy Information System (SIEN). SIEN reports energy consumption and energy generation by type of source and economic activity, as well as CO2 emissions by aggregate sectors. SIEN also details how energy is used in the main areas of consumption. This is complemented by data on supply flows, including supply, transformation centers, and final energy consumption. With this information, it is possible to estimate total emissions 9
in one state in India, our estimates using a nationally-representative panel dataset of rural public schools provides complementary evidence on the likely role of improved monitoring on reducing teacher absence.

In interpreting the result on school inspections, it is useful to consider why there might be variation in the frequency of inspections across villages and what this would imply for a causal interpretation. One obvious explanation is that inspectors are more likely to visit more accessible villages, but the data do not support this hypothesis since there is no correlation between changes in the remoteness index and changes in inspection rates (Table 7).

District-level interviews on school governance in India suggest two important reasons for the variation in inspection frequency (Centre for Policy Research, 2012).<sup>24</sup> The first is staffing. Districts are broken down further into administrative blocks, and schools within blocks are organized into clusters. School supervision is typically conducted by “block education officers” and “cluster resource coordinators”. We find that a significant fraction of these posts are often unfilled. For instance, in 19 percent of the cases (where we have data) even the position of the “District Education Officer (DEO)”, the senior-most education official in a district, was vacant. Further, there is high turnover in education administration (the average DEO had a tenure in office of just one year) creating periods when the positions are vacant during transitions. The lack of supervisory staff at the block-level is even more acute, as 32% of these positions were estimated to be vacant in 2010 (the year of our survey) even by an official government report (13th JRM Monitoring Report, 2011). Our interviews suggest that these staffing gaps at the block and cluster level are the most important source of variation in inspection frequency within districts, since blocks and clusters without supervisory staff are much less likely to get inspected.

The second source of variation in inspections is the diligence of the concerned supervisory officer. Even if all the positions of supervisory
Policy Research Working Paper 10147

# **Abstract**

This paper proposes a method to analyze interval-censored data, using multiple imputation based on a heteroskedastic interval regression approach. The proposed model aims to obtain a synthetic data set that can be used for standard analysis, including standard linear regression, quantile regression, or poverty and inequality estimation. The paper presents two applications to show the performance of the method. First, it runs a Monte Carlo simulation to show the method’s performance under the assumption of multiplicative heteroskedasticity, with and without conditional normality. Second, it uses the proposed methodology to analyze labor income data in Grenada for 2013–20, where the salary data are interval-censored according to the salary intervals prespecified in the survey questionnaire. The results obtained are consistent across both exercises.

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 gcanavire@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
We therefore control for regional GDP per capita and the college share of a region’s population to capture these general equilibrium forces, and argue that residual variation in trust across regions, conditional on their level of development, should more precisely target differences in the differential cost to hiring professional managers relative to family ones.

# III. DATA

Our data come from several sources.

## **_III.A. Bureau van Dijk Amadeus_**

Firm-level data comes from Bureau van Dijk’s (BvD) Amadeus dataset for 2011. This database collects standardized data from over 50 countries across Western Europe, Eastern Europe and Central Asia. The source for the data is generally the official company registrar office in each country, which requires all incorporated firms to submit annual company filings. The database includes standardized financial accounts data, although variable availability differs across countries. This database was also used by Bloom and Van Reenen (2007) as the sampling frame for the WMS and we will use this dataset to cross-check our classification of “family-owned family-managed” firms.<sup>14</sup> The region of each firm is also reported, although the granularity of this data varies by country. We query the Google Maps API to find a full set of regional identifiers that enable us to assign firms to regions reported in other datasets.<sup>15</sup> Following Klapper, Laeven and Rajan (2006), we restrict our sample according to several criteria in order to ensure cross-country comparability. First, we include only private limited liability companies, since these firms are almost always required to file statements (coverage of other types of firms is uneven).<sup>16</sup> Second, we restrict our sample to manufacturing, retail and services industries. Third, we drop firms with less than 10 employees. This removes some of the cross-country variance regarding reporting requirements for small firms, and removes suspicious listings which may be holding companies rather than active firms. Finally, we drop firms
2. **Public Sector Motivation index** (8 questions): Questions on commitment to serve the public, capacity to resolve conflict among people, faith in government’s role in improving society.

We developed a new module to understand the attitudes of health workers towards their work, and group these questions into an index of professional identity and efficacy:

## • : **Public providers’ surveys**

**Professional identity and efficacy** (5 items): whether respondent would have preferred another profession; whether feels a sense of inner pride/fulfillment when doing their work; whether gets social status from being in the profession; whether feel they can improve outcomes through their effort; whether they have to take permission for every little thing.

Finally, we asked frontline health workers and their supervisors within the bureaucracy about management culture. During the field work to develop the survey instruments, we learnt that frontline health workers attend regular meetings with their supervisors at the block level. We thus targeted the questions to understand management culture by specifically referencing the types of issues that come-up in these regular meetings with supervisors. We start by reporting these results.

# **4 Management and professional culture**

Health workers in Bihar attend regular meetings with their supervisors, which provides an opportunity to measure management and professional culture through survey questions targeted at the content of these meetings. Among the lowest tier health workers–the ASHAs–94 percent report meeting with the ANM of the village health center, their immediate supervisor, and 85 percent report meetings organized at the next level, by the Medical Officer In-Charge (MOIC) of the Block Primary Health Center, at least monthly. Similarly, 95 percent of ANMs report regular meetings (at least monthly) convened by their supervisor (block-level MOIC). In the survey of supervisors (Medical Officers in Charge, Civil Surgeons, various programme 18
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to 8.3 percent, indicating that poor households were becoming poorer. Simulations also indicated that income inequality  the Gini coefficient  would rise from 39.3 to 41.3.

When compared with recently released post-crisis data, our ex-ante simulation model performed reasonably well in predicting the direction and magnitude of changes in poverty. Although, postcrisis EU-SILC data are not available, the household budget surveys (HBS) indicate that poverty rates increased from 10.12 percent in 2008 to 18.05 percent in 2009: an increase of about 8.0 percentage points.<sup>13,14</sup> and our simulation model predicted a poverty increase of about 6.0 percentage points. Similarly, between 2008 and 2009, the poverty gap increased by 3.6 percentage points compared to our predicted increase of 2.4 percentage points. Some differences may have arisen because our income data sources differ from those of the HBS. However, poverty numbers from post-crisis HBS data show the usefulness of our model in predicting exante effects of a crisis, despite many data limitations.

Much of the social impact of the crisis can be traced through crisis effects on the labor market. In 2009, GDP contracted 18 percent and the sectors most severely affected were trade hotels and  restaurants, construction, and manufacturing a loss of more than 126,000 formal jobs, comprising about 11.2 percent of the workforce. The large contraction in overall employment reflects employment contractions of 19 percent in trade, hotels, and restaurants, 16 percent in construction, and 14 percent in manufacturing. Finally, substantial differences exist in the impact of the financial crisis across regions and specific population groups in Latvia.

The Government of Latvia implemented several programs to mitigate crisis impacts on households, including increasing the threshold rate of the Guaranteed Minimum Income program, the main national poverty-targeted program; and a self-targeted public works program for unemployed people. Our simulations indicate that these measures may have helped cushion crisis impacts for some of the hardest hit households
future time periods. For the future time periods, separate model runs were conducted under lowand high-emission scenarios (SSPs 1-1.7 and 3-3.7 respectively). Urbclim temperature output was then used to calculate Wet Bulb Globe Temperature (WBGT), a heat stress index that takes not just temperature into account but also humidity and radiation from the sun and nearby surfaces. Model outputs included a suite of climate variables at 30-meter horizontal resolution including expected daily air temperature values (daily minimum, mean and maximums); number of days per year exceeding temperature thresholds; and WBGT heat stress values.

The simulations highlight the presence of significant Urban Heat Island (UHI) effects for the three cities as well as significant expected increases in heat exposure in future. Lucknow, with its inland location, experiences a stronger UHI effect than do the coastal cities of Chennai and Surat (Figure 1). The night-time UHI effect for Lucknow reaches as high as 5°C. UHI intensity is seen to increase with proximity to the dense urban core; the satellite city of Barabanki, with its lower density of buildings and sealed surfaces, also experiences a UHI intensity some 2°C lower than central Lucknow does. The spatially explicit estimates of future heat stress variables are a key input for the modeling conducted under this paper, while the significant increase in heat exposure – the magnitude of which differs depending upon global emissions scenarios – are themselves informative results for adaptation planning (see Section 3).

5
38 <u>Table 6: Survey ‘collections’ used in financial protection side of HEFPI database</u>

|Collection|<br>Title in full|<br>Type of collection|<br>Institution|<br>Geographic<br>focus|<br>Type of survey(s)|<br>Further information on data and access|
|---|---|---|---|---|---|---|
|CWIQ|Core Welfare<br>Indicators<br>Questionnaire|Multi-country<br>survey initiative<br>– somewhat<br>standardized|World Bank<br>originally, but<br>have been used by<br>other institutions<br>too|<br>Developing<br>world|Multipurpose survey|Some CWIQ surveys publicly accessible via<br>the World Bank Microdata Catalog.<sup>35</sup>More<br>CWIQ surveys can be accessed by World Bank<br>staff via the World Bank Microdata Library.<sup>36</sup><br>See also the Institute for Health Metric’s<br>Global Health Data Exchange (GHDx) entries<br>for CWIQ surveys.<sup>37</sup>|
|E123|Enquêtes 1-2-3|Multi-country<br>survey initiative<br>– somewhat<br>standardized|DIAL Research<br>Unit, France|Developing<br>world|Multipurpose survey|Details available (in French only) on the<br>Enquêtes 1-2-3 page of the DIAL website.<sup>38</sup>|
|EAPPOV|World Bank East<br>Asia & Pacific<br>harmonized<br>household survey<br>collection|Ex post<br>harmonized|World Bank|World Bank’s<br>East Asia &<br>Pacific region|
WPS3819 LOBBYING AND AGRICULTURAL TRADE POLICY IN THE UNITED STATES

**KISHORE GAWANDE*** BUSH SCHOOL OF GOVERNMENT, TEXAS A&M UNIVERSITY

# **BERNARD HOEKMAN**

WORLD BANK, GROUPE D’ECONOMIE MONDIALE, SCIENCES POLITIQUES, AND CEPR

**Abstract:** This paper studies whether political campaign contributions influence agricultural protection in the United States in the manner suggested by the political economy model of Grossman and Helpman (1994).  This is the first attempt to test this model using agricultural data. We test the model using a detailed cross-sectional data set of agricultural protection, subsidies, and PAC contributions in the late 1990s. The model is qualitatively affirmed by the data. We make a novel attempt to solve a puzzle about the model’s quantitative implications, also found in recent studies.  This solution makes the simple model consistent with the complicated decision-making process in real world government.  The results imply the underpinnings of a political economy equilibrium that will be hard to dislodge.

# World Bank Policy Research Working Paper 3819, January 2006

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

**_________________________________** *Helen and Roy Ryu Professor of International Affairs, Bush School of Government. Texas A&M University, College Station, TX 77843-4220. Email: <u>kgawande@tamu.edu.  Phone: (979) 458-8034.</u>

We thank Marcelo Olarreaga for access to valuable data on agricultural protection
women and hence their “purity” more needing of protection and control. Communities also tend to concentrate in specific neighborhoods and socially segregate themselves even in urban areas, which can further strengthen the influence of social norms. Using the regression approach described in the methods section and by focusing on more affluent, better educated, and urban women within each cohort, this section addresses the questions of whether the trends are different and the gender gap is closing for these groups of women in South Asia.

This heterogeneity analysis for labor market participation shows that women in the top 20 percent of the wealth distribution have enjoyed stronger progress when compared with their poorer counterparts. However, more educated women, or women living in urban areas do not seem to have followed a different path than less educated or rural women. So, this confirms that the stagnant trends and decadal

> 24 Although IPV prevalence is not static and likely to change over the life cycle, we do not show the age by cohort graphs because the variable has too few data points in the DHS data set to claim any meaningful result.

20
it would be incorrect to apply inequality or distributional measures, which are derived from surveys which measure one thing, to means which are derived from national accounts, which measure another (Deaton., 2003, p.35).

14 Income from property is notoriously underestimated in household surveys (even leaving aside the fact that most surveys do not include capital gains at all). Concialdi (1997, p. 261) claims that the best available French household surveys underestimate capital incomes by about 40 percent. Wagner and Grabka (1999) estimate German property income to be underestimated by almost one-half compared to National Accounts data. In Japan, according to Ishizaki (1985) only 12 percent of property income is “captured” by household surveys (quoted in Bauer and Mason, 1992, p. 407).

15 We speak of upscaling rather than downscaling because GDI per capita is normally greater than household per capita disposable income.

9
poverty (Beck, Demirgüç-Kunt, and Levine 2007); increased consumption (Karlan and Zinman 2012; Burgess and Pande 2005); increased productivity (Allen et al. 2016; Bruhn and Love 2009); greater investment in preventive health (Dupas and Robinson 2013; and Cramer 2023); and overall higher levels of savings (Karlan, Ratan, and Zinman 2014).

Partially through its facilitation of increased savings, formal financial inclusion holds significant implications for financial health during economic downturns. Owning an account allows adults

> 1 Unless otherwise indicated in the text or through a footnote, all data in this paper is from the Global Findex 2021. The Global Findex 2021 Database, the fourth edition since 2011, includes comparable indicators of financial inclusion for 139 economies, including 15 economies in 2022. The data is collected through nationally representative surveys of about 1,000 adults per country. To download the complete database and for more information on the survey methodology, see: https://globalfindex.worldbank.org.

3
others, United Nations data (Venieris and Gupta 1983). Some authors have also used the data of the project of international comparison of products on the basis of purchasing power parities (Landau 1983, based on Kravis 1979, and Ram 1986, based on Summers and Heston 1984). Finally, some studies do not specify the data source (cf. table 1, column 2).

The choice of source is not without importance for the measurement of growth, as is evident in various studies. This is shown by Ahmad and Kwon (1981) for the difference between IMF and World bank data, and Plane (1986) for the difference between World Bank and UNCTAD data. We have tried to assess the magnitude of the difference between the two rates calculated using World Bank and IMF statistics for GDP at market prices over the period 1970-81 (see table 2). The differences between these two rates are found to be greater and more frequent than the differences previously observed between GDP and GNP growth rates: over the period 1970-81 the absolute gap exceeds 0.5 in 14 cases out of 55 and 1Z in 9 cases; over the period 1974-81 it exceeds 0.52 in 18 cases out of 37 and 12 in 11 cases; in some countries (Honduras, Malaysia), it is substantial.
32 households are probably satisfied with it; besides, they may not be able to afford new housing Second, pensioner households constitute a large share of the owners, and they have no need to move. On the other hand, a high proportion of municipal housing is still inhabited by households for whom housing remains a pressing concern and who are ready to join the housing market.

As for the type of settlement as a factor of this process, it manifests itself most strongly in Moscow and to a lesser extent in the provincial towns.

Similar trends can be observed in the other former Soviet republics, where after privatization part of the housing remained in state or municipal ownership. Surveys conducted in 1995 in Estonia (Arpaillange, 1995a), for example, demonstrated that only a very strong desire to obtain a more suitable home motivates the dwellers who envision moving in the next five years. This hope is shared by about 15% of those surveyed; tenants display a stronger desire (about a third of the total inumber), while owners have a much more stable attitude (13%), especially if they own a house (only 9% of those living in individual houses would like to move).

These findings are also consistent with those of surveys conducted in Lithuania (Arpaillange, 1995b), where nearly all housing is now privately owned. Lithuanian researchers have found that residential mobility has been increasing as a consequence of the privatization program and the tremendous changes that occurred in the economy since independence: 25% of households have moved in the past five years. Over the past few years, mobility in Lithuania was 5% a year on average as compared to 3.5% in Moscow, 4% in Vladimir and 4.5% in Gorodets.

In short privatization has made a large contribution to the establishment of the housing market, and this could not help but increase residential mobility. Housing privatization has also made better headway in small towns, which may be one of the reasons for its success in