rafmacalaba/data-use-mentions-extended huggingface dataset:
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1.69 years later than Hutu women (all three figures are significantly different in means across Hutu and Tutsi). Other socio-economic characteristics differ significantly as well across Hutu and Tutsi, including education and wealth. However, it seems that these differences in fertility behavior and socio-economic status are driven by the place of residence: 14 percent of Tutsi women live in urban areas, compared to 5 percent of Hutu women. A regression analysis confirms this: When regressing the total number of children on multiple characteristics (including ethnicity, age, education, place of residence, and partnership characteristics), ethnicity is no longer statistically significant.

Fifth, we run all estimates based on the full sample of women in order to enhance the comparability and precision of the estimates. In both short-term and long-term analyses, the impact of conflict exposure is only slightly smaller in terms of the magnitude of effects and the level of significance compared to the original results derived from restricted samples.

# **7 Conclusions**

The paper analyzes the effects of mass violent conflict on fertility for conflict-survivors in the case of the genocide in Rwanda in 1994 using individual-level data. To enable an identification of the genocide at the micro-level, two types of proxies for conflict exposure are constructed using three waves of RDHS data from before and after the genocide. With this estimation strategy, we identify the ‘pure’ effects of genocide on fertility over and above conflict-related effects like urbanization, destroyed infrastructure, and weaker health infrastructure – which in turn also affect fertility. We study the short- and long-term effects of genocide and differentiate the analysis by cohort, kinship relation and gender of the deceased persons.

Our approach is unique in that we estimate the effects of genocide on fertility using multiple measures of conflict exposure and disaggregating in more dimensions than done previously. In Rwanda, this is important as by law collecting a key conflict proxy, ethnicity, is not possible. In other contexts using conflict proxies may be important if data-sets, which do not contain any direct identification
Borrowers were then sorted for each model specification according to two different criteria: (A) to determine the number of borrowers who would default at given acceptance rates; and (B) to estimate the number of firms that would receive credit at a given default rate.

Table 4A presents information on the percent of borrowers who would be expected to default at given approval rates for Argentina and Brazil. The exercise shows that for any target approval rate, a loan portfolio selected using the complete information model has a lower default rate than the one selected using only negative information. For example, in the case of Argentina, if one wanted to extend credit to 60% of the sample population, then having access to positive information would reduce the default rate from 3.81% to 2.98%, a reduction of approximately 20% on portfolio losses.

In Table 4B the gains from using positive information in terms of increased access to credit is demonstrated, controlling for the riskiness of borrowers. In this ex-post exercise, borrowers again are sorted in order of increasing predicted default probability and the percentage of approved loan applications is computed once the portfolio default rate reaches the set target. In both cases, more borrowers gain access to credit for a given default rate when using the complete information model than in the case of only negative information. Taking the case of Brazil, if a target default rate of 3% is desired, then the full model permits more than 82% of the sample population to obtain credit whereas this figure is only 55.8% for the negative only model – a difference of more than 30% in terms of providing access to credit.

# **_Individual bank versus  shared information_**

Having established the importance of including both positive and negative information, we now turn to the more general issue: the value of sharing information. Using data from the Argentine PCR we estimate the following equation for individual financial institutions:

# Pr. ( Deafult ) _i_ =  αINDIVIDUAL _i_ + βSHARED _i_ +
- Ethiopia’s Rural Capacity Building Project (RCBP) was implemented in 10 regions of the country beginning in 2007.  RCBP strengthens agricultural extension services through raising awareness of improved and environmentally sustainable techniques, as well as better targeting farmers’ needs, including a focus on women farmers — who constitute (depending on the data source) between 29 percent and 45.5 percent of agricultural labor (FAO, 2011; Palacios-Lopez et al, 2017).

- Malawi’s national Farm Input Subsidy Program for smallholder farmers, part of the country’s <u>Agriculture Sector-Wide Approach (ASWAp), includes female-headed households as targets for access to inputs,</u> extension and on-farm technologies, as well as supporting access to input and output markets (Deijl et al, 2017).

<u>Access to finance for self-employment</u>

- Mexico’s Expanding Rural Finance project, launched in 2015, is targeted towards small farmers and micro, small and medium enterprises (MSMEs) in rural areas. In 2017, women agricultural producers and MSMEs accounted for more than 70 percent of beneficiaries.

- Afghanistan’s <u>Women Economic Empowerment - Rural Development Program</u> (WEE-RDP), launched in 2018, will cover 34 provinces. A main focus will be on improving access to finance for women-owned MSMEs.

# Understanding how rural employment responds to market factors

Better data on factors enabling women’s economic mobility, and opportunity costs for employment, can also shed light on how rural women respond to changes in broader economic shifts. Using recent crosscountry data from developing-country labor force surveys, Figure 1 shows that LFP among younger rural women follows a U-shaped pattern (discussed in Goldin, 1995; Mammen and Paxson, 2000) — where women’s LFP is higher in poorer countries, declines initially with growth as they pursue more education and leisure, and then rises again with increases in income/expected earnings opportunities. However, employment for older age groups is less responsive to
managed then net of its effect on local wages, rents and income, the size of the population should have no effect on forests. The population variable appears significant in the OLS specification for forest area per household measure, but as we control for village fixed effects, it becomes indistinguishable from 0. On the other hand, the only variable that remains statistically significant after controlling for the possible endogeneities is the household income variable, which suggests that the only channel through which remittances have an effect on forests is through the household income. Though significant only in the OLS specification, the coefficient on land price is consistently negative confirming the prediction we had from theory. Finally, given the results from our reduced form regressions in Table 6 we would expect the depopulation of the youngest age group due to migration and the consequent inflow of remittances both to raise wages. However, from the equilibrium regressions it appears that net of their contributions to raising incomes, wages have no independent effect on either measure of forest area.

# **5.3 Robustness**

Both rounds of the living standards survey contained a community questionnaire that collected information from village representatives on the subjective perception of whether overall forest area 25
**Figure 9. Scatterplots of the change in ln Employment and change in Exports a. 2016-2020 b. 2013-2020**

**b. 2013-2020**

<!-- Start of picture text --> Change in ln Employment and ln Export Sales, Egypt 2016-2020 Change in ln Employment and ln Export Sales, Egypt 2013-2020<br>-4 -2 0 2 4 6 -2 0 2 4 6<br>Change in ln Export Sales Change in ln Export Sales<br>c. 2013-2016<br>Change in ln Employment and ln Export Sales, Egypt 2013-2016<br>-4 -2 0 2 4<br>Change in ln Export Sales<br>4 5<br>2<br>0 0<br>Change in ln Employment -2 Change in ln Employment<br>-4 -5<br>6<br>4<br>2<br>0<br>Change in ln Employment<br>-2<br>-4<br><!-- End of picture text --> Source: Authors’ elaboration using data from the Enterprise Survey for Egypt, 2013, 2016, and 2020.

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Figure 10: Covariate Shocks: Price Shocks and Crime <!-- Start of picture text --> (a) Rainfall<br>1600.00<br>1400.00<br>1200.00<br>1000.00<br>800.00<br>600.00<br>400.00<br>200.00<br>0.00<br>Rainfall (2014) Avg. Rainfall (2003-2015)<br>(b) Conflict<br>450<br>400<br>350<br>300<br>250<br>200<br>150<br>100<br>50<br>0<br>Number of conflict Events (2014) Avg. Number of Conflict events (2012-2015)<br><!-- End of picture text --> _Source_ : Authors’ calculation using ACLED and CHRS databases.

26
this study are the responsibility of the authors and do not necessarily reflect the views of their employers, funding agencies, or governments. This study was registered in the AEA RCT Registry with unique identifying number AEARCTR-0001602

> _†_ The World Bank. Development Research Group. Email: ldinartediaz@worldbank.org

> _‡_ Asia School of Business, Kuala Lumpur, and Massachusetts Institute of Technology, Cambridge. Email: pegana@mit.edu.

1
, involving revenue sharing disputes between mining companies, local authorities, and local populations (Arellano-Yanguas, 2011). Poor management of the _Canon_ also appears to add to the discontent (Hinojosa, 2011).

In this paper we use variation in mining across Peruvian districts to investigate the impact of mining activity and government transfers on local socioeconomic outcomes. The analysis uses a unique, district level dataset that merges administrative data on local mining production, transfers from central to local government, and census and survey-based measures of households’ average consumption, poverty, and inequality.

Our empirical approach improves upon the existing literature in two related aspects. First, since we are able to identify the location where the mineral is extracted down to the lowest administrative

> 2 In Peru, sub-national administrative units are called departments, provinces, and districts, in decreasing order of size.

2
to measurement error. The quality of the welfare measure is likely to be particularly poor when the data are sparse. (These issues are discussed in detail in the following section.) Since FGT measures are more sensitive than the headcount to errors, the use of these indicators should be restricted to situations where all of the consumption data being compared are reasonably comprehensive. " '° What may appear to be a straightforward solution to the problem of inconsistent consumption data is to discard some of the available information and base comparisons only on those components of consumption which are common to all countries or time periods. While simple, there are, however, a number of drawbacks to this approach. First, discarding data is likely to worsen the measure of household welfare. Second, if one is interested in a cardinal magnitude for the poverty rate, rather than ordinal comparisons, then the appropriate basis is total expenditure. As shown in Section 11, FGT (a2 1) measures of poverty, for example, would have a lower values when food expenditure alone is used in their calculation. Finally, this approach may be impossible to implement, either because detail about sub-categories of expenditure are unavailable or because there are no common elements in the sub-categories used in the different datasets.

16
The Evolution of Built-up Areas in Ghana since 1975<sup>1</sup> Marcel Fafchamps2 Forhad Shilpi3 JEL Classification : R12, O18, R10, R14

**Keywords:** Human settlement pattern; transition matrix; roads; agglomeration; urbanization forecasts

> 1 We would like to acknowledge Varnitha Kurli for excellent research assistance in extracting all relevant GIS data used in the study.  Our sincere thanks to Brian Blankespoor for his help with the GIS data. We are grateful to the World Bank for funding this research. All views expressed in the paper are those of authors and should not be attributed to the Bank or its affiliates.

> 2 Stanford University, NBER, and IZA. Email: fafchamp@stanford.edu

> 3 The World Bank, 1818 H Street, Washington DC, USA.
# **6 Figures**

**Figure 1:** Trends in Labour Force Participation and Farm Work by Gender

<!-- Start of picture text --> Labour Force Participation Farm Work<br>55 61 66 68 55 61 66 68 55 61 66 68 55 61 66 68<br>Male Female Male Female<br>.75<br>.75<br>.5<br>.5<br>LFP<br>Farm Workers<br>.25 .25<br>0 0<br><!-- End of picture text --> _Notes_ : The above figures shows trends in labour force participation and engagement in farm work (conditional on participating in labour force) over time, and disaggregated by gender. NSS survey rounds 55, 61, 66 and 68 correspond to the 1999-00, 2004-05, 2009-10 and 2011-12 years.

36
the survey following the disaster, the empirical analysis focuses on the change in IPV prevalence in the Southern Highlands relative to the rest of the country.

In this analysis, we first focus on the men’s survey, which includes a range of questions about the acceptability of intimate partner violence (IPV). The question states ”in your opinion, is a husband justified in hitting or beating his wife in the following situations,” and then presents a range of situations. The situations are ”if she goes out without telling him,” ”if she neglects the children,” ”if she argues with him,” ”if she refuses to have sex with him,” and ”if she burns the food.” And respondents are able to respond ”yes,” ”no,” or ”don’t know.” The question only asks about a hypothetical scenario about a husband and wife and does not ask if the respondent would hit or beat his wife in each situation.

In addition to the men’s survey, we also focus on a subset of the women’s survey that administered a domestic violence module. In a random subset of the households that were selected to be administered the men’s questionnaire, one woman who was eligible for the women’s survey was randomly selected to respond to the added module. The module was only fielded in households in which privacy from other household members could be secured, resulting in a sample of 4,873 women (DHS 2019). The questions about abuse on which we focus are the questions about the incidence of IPV, which are asked only of the 3,945 women in the sample that had either been married or had lived with a man at one point in their lives.

The enumerator began fielding the module with a statement that guaranteed the anonymity of the respondent and that nobody else in the household would be informed of the responses. The extensive module asked whether the respondent’s husband or partner engaged in
. The curriculum of the entrepreneurship training was composed of a financial and business skills section (entry cost, business plan, management, costs registry) as well as a socio-emotional skills section (self-esteem, listening and empathy, stress management, and problem solving). According to the program monitoring data, given grants ranged from 25,000 to 1,500,000 CFA francs, with an average amount per girl of 55,900 CFA francs (228 USD purchasing power parity 2019).<sup>3</sup> The high variation in the size of the grant is due to the varying number of business associates (i.e., beneficiaries who associate to work on a business plan) and to the predicted volume of sales of the IGA. The majority of the created IGA were small-scale activities in the food processing sector or in the food and nonfood retail sectors. The IGA intervention, also implemented by the IRC, was delayed due to institutional constraints and the COVID-19 pandemic, which reduced the duration from 12 months initially planned to only 6 months.

# **2.2.3 Husbands’ and Future Husbands’ Clubs**

In addition to safe spaces, husbands’ and future husbands’ clubs, sometimes referred to as husbands’ clubs for fluidity, were implemented to involve men and boys as partners in family health and family planning. The clubs aimed to engage men in discussions on gender

> 3Conversion factor = 245.51. Source: https://data.worldbank.org/indicator/PA.NUS.PPP?locations=CI.

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Policy Research Working Paper 10312

# **Abstract**

This paper provides novel evidence on the relationship between firm size and effective corporate tax rates, using full-population administrative tax data from 13 countries. In all countries, small firms face lower effective corporate tax rates than mid-sized firms due to reduced statutory tax rates and a higher propensity to register losses. In most countries, effective corporate tax rates fall for the largest firms due to the take-up of tax incentives. As a result, a third of the top 1 percent of firms face effective corporate tax rates below the global minimum tax of 15 percent. The minimum tax could raise corporate tax revenue by 27 percent in the median sample country.

This paper is a product of the Development Research Group, Development Economics and the Macroeconomics, Trade and Investment 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 pbachas@worldbank.org, abrockmeyer@worldbank.org, and csemelet@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
<br>4<br>2<br>0<br>AFR EAP ECA LAC MNA SAR<br><!-- End of picture text -->

- D. Probability of default of financial institutions (%) 2017 regions

<!-- Start of picture text --> 3.5 25 pctl<br>median<br>3<br>75 pctl<br>2.5<br>2<br>1.5<br>1<br>0.5<br>0<br>AFR EAP ECA LAC MENA SAR<br><!-- End of picture text -->

- F. Banks’ claims on total government and public non‐financial entities (% of GDP) 2017

<!-- Start of picture text --> 40<br>25 pctl<br>35 median<br>75 pctl<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>AFR EAP ECA LAC MNA SAR<br><!-- End of picture text --> Note: pctl means percentile. Sources: NPLs / Total Loans, FSI IMF; Probability of default, National University of Singapore – Credit Research Initiative; Bank sovereign exposure / Bank assets Fitch Connect; Claims data IFS IMF.

46
educational data and the resulting choice of measure for inequality in achievement. It also utilizes information on student background more comprehensively than all previous studies we are aware of, and is additively decomposable both across circumstances and population subgroups. The measure is also isomorphic to (inverse) measures of educational mobility.

We report our measures of inequality in educational achievement and opportunity for the 57 countries that took part in the PISA 2006 exercise. Each measure was computed separately for each of the three tests applied by PISA: mathematics, reading and science. But there was a good measure of agreement between their rankings, and we often refer only to the math results in the text.<sup>3</sup> We find considerable variation in the standard deviation of test scores, from lows of around 80 (for Indonesia, Estonia and Finland) to highs near 110 (in Belgium and Israel).<sup>4</sup> Similarly stark variation exists in our measure of inequality of opportunity, from 0.10 – 0.15 for Macau (China), Australia, and Hong Kong SAR, China, up to 0.33 – 0.35 in Bulgaria, France and Germany. Inequality of opportunity is uncorrelated with mean achievement and only weakly (negatively) correlated with GDP per capita. Broadly speaking, it is higher in continental Europe (except for Italy) and Latin America than in Asia and Scandinavia, with the US and the UK in intermediate positions. It is negatively correlated with the share of public educational spending that accrues to primary schools, and positively correlated with the proportion of technical and vocational enrollment at the secondary level (a measure of “educational tracking”).

The paper is organized as follows. Section 2 describes the data sets we use. Section 3 considers the implications of test score standardization and of the PISA sampling frame for the measurement of inequality in educational achievement, and reports the standard deviation in test scores for our sample of countries. Section 4 proposes our measure of
antenatal consultations to create an index of non‐clinical quality of care, which primarily focuses on the interpersonal behavior of the providers. The index gives equal weight to the following six indicators: filling an antenatal care card, taking sufficient time for the consultation (at least 10 minutes), explaining procedures, encouraging questions, suggesting a third person attend the consultations, and using visual aids. On average, the providers score 0.57 on this index. We do not find a relationship between socioeconomic status and this measure of non‐clinical quality, regardless of the regression specification (columns 7 to 9 in table 2).

Table C.3, in appendix C, presents the regression results when each component included in the direct observation quality indexes is regressed individually on wealth. The regressions include the same controls as those used in column 6 in table 2. For eight of the 21 items representing clinical quality, there is a positive relationship with the wealth index, with p‐values below 0.10. The relationship is particularly strong for diagnostic testing, which often requires additional fees and out‐of‐pocket 10
input assumptions are based on expert opinion and data from similar contexts (such as other Pacific Islands), but not the Solomon Islands itself. In addition, the selection of the rainfall event (in the lack of real rainfall data) as well impact of disruption on particular gradients is based on expert opinion. As such, the current analysis serves as an input to decision making that should be considered alongside expert, experience-based guidance. Another limitation of the study is that it is based on elevation data – the results could be further improved with knowledge on drainage and locations of bridges, which are more vulnerable to flooding events.

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