rafmacalaba/data-use-mentions-extended huggingface dataset:
requires a close identification with the issues involved in the original litigation, even if it does not depend on being able to litigate. The crucial question, therefore, is to what extent does SE litigation generate non-excludable goods that are broadly shared across social classes? The concern, which Gauri and Brinks call _policy area inequality_ , is that litigation will turn the state‘s attention away from policy areas that are the primary concern of the poor, and toward policy areas that are not shared spaces with the poor. As further explained below, our focus in this paper is precisely on policy area inequality.

This is, in the end, the challenge we take on most directly. Where they seem to dominate, we will tally the socio-economic status of the litigants themselves, but our focus is on the broader, indirect effects, which seem more important to the overall distributive effect of litigation. In order to examine the consequences of this impact, we identify the areas in which the results of judicial decisions generalized to broader populations, and we determine as best we can the socio-economic composition and size of the beneficiary class. We then calculate a weighted average of the expected beneficiaries in each class, to evaluate the extent of policy area inequality. In the next section we summarize the results of our investigation for the five countries included in the Gauri and Brinks study, and then compare these results to our theoretical expectations.

# **Empirical Analysis**

The unit of analysis is litigation in a particular policy area – for example, medications cases in Brazil, or HIV/AIDS cases in South Africa. We look at these courses of litigation and classify them according to their expected effects: we look at the broad categories developed by Gauri and Brinks – Provision, Regulation and Obligation – and at national level characteristics of the legal system. For each subset of cases, we examine whether the impact is the product of 16
not available across time; we instead use a product-specific Consumer Price Index from the BLS. For an additional 46 industries (mainly in the service sector), no consistent PPI or CPI is available for our entire time period; we instead use the economy-wide PPI from the BLS. For eleven of the industries, coverage begins in 1985; we freeze prior years to the 1985 level. The series are simple averages across monthly values, and are not seasonally adjusted.

We also create a series of import and export prices from BLS data. These data are downloaded at the 1, 2, and 3-digit SITC level. We then use the concordance between SITC and SIC codes received from Robert Feenstra and used in Feenstra et al. (2002) to create price series data at the SIC level. These data are then matched to our CPS industry coding scheme using a concordance created by the authors mapping each 3-digit SIC code into a corresponding CPS industry code. We then construct export and import price indices at the 1, 2 and 3-digit CPS industry codes.

A supplemental import price data series was also provided at the NAICS level generously by Robert Lawrence. We use a concordance between NAICS and SIC codes to create a series of import prices at the SIC level, which are then mapped to our CPS industry classification scheme. For many-to-one matches between the SIC code and CPS industry codes, we use the SIC code with non-missing import prices.

These data are used in our supplementary online materials and summary statistics for these variables are available upon request.

# J. Summary Statistics

In Appendix Table 1, we present summary statistics for our sample of individual CPS workers, and their assigned values for offshoring, trade, and technology measures. We present summary statistics for the entire sample (1983-2002), the 1983 sample, and the 2002 sample of workers. In Panel A, we report over 3.4 million workers in the data, with a noticeable decline in the fraction of workers who
of the overall returns, showing considerable decline. Returns in the informal sector exhibit less variability, ranging from only 5.7 to 7.3 percent during the whole ten year period. Returns in the formal sector are higher than returns in the informal sector and the spread is increasing after 1996.

Table 2: Shares of Sectors of Employment: 1992-2002

|Formal|Informal|Employees|Self-employed|
|---|---|---|---|
|1992<br>65.3|34.7|71.4|19.9|
|1995<br>54.5|45.5|67.8|26.9|
|1996<br>54.9|45.1|67.3|27.6|
|1997<br>53.1|46.9|68.1|26.6|
|1998<br>51.1|48.9|64.9|29.5|
|1999<br>49.2|50.8|62.3|31.2|
|2000<br>48.0|52.0|60.5|33.1|
|2001<br>47.6|52.4|60.8|29.9|
|2002<br>46.0|54.0|62.4|29.3|
|Source: Encuesta de Hogarespor Mue|stro|||

Figure 2: Shares of employment for Informal Sector, Employees and Self-employed: 1992-2002 <!-- Start of picture text --> 80<br>70<br>60<br>50 Informal<br>40 Employees<br>30 Self-employed<br>20<br>10<br>0<br>1992 1995 1996 1997 1998 1999 2000 2001 2002<br><!-- End of picture text --> 7
Policy Research Working Paper 10726

# **Abstract**

How did the economic crisis caused by the Covid-19 pandemic impact poor households in Sub-Saharan Africa? This paper tackles this question by combining 73 High-Frequency Phone Surveys collected by national governments in 14 countries with older nationally representative surveys containing information on household consumption. In particular, it examines how outcomes differed according to predicted per capita consumption quintiles in the first wave of the survey, and in subsequent waves by households’ predicted per capita consumption. The initial shock affected households throughout the predicted welfare distribution. Households in the bottom 40 percent responded by sharply increasing farming activities between May and July of 2020 and gradually increasing ownership of nonfarm enterprises starting in August. This coincided with an improvement in welfare, as measured by a decline in food insecurity and distressed asset sales among these households during the second half of 2020. With respect to education, children in the bottom quintile were 15 percentage points less likely to engage in learning activities than those in the top quintile in the immediate aftermath of the crisis, and the engagement gap between the bottom 40 and top 60 widened in the summer before narrowing in the fall due to large declines in engagement among the top 60. Poorer households were slightly more likely to report receiving public assistance immediately following the shock, and this difference changed little over the course of 2020. The results highlight the widespread impacts of the crisis both on welfare and children’s educational engagement, the importance of agriculture and household non-farm enterprises as safety nets for the poor, and the substantial recovery made by the poorest households in the year following the crisis.

This paper is a product of the the Poverty and Equity Global Practice and 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
in the sample. When this is not possible, however, XGBoost appears to offer a notable advantage when predicting into non-sampled areas. Further research could explore whether and how re-weighting the sample can improve the accuracy of out-of-sample predictions. Related to this is the difficulty of predicting accurately at the tails of the welfare distribution, suggesting potential benefits from oversampling tails. Further research can also explore how the relative accuracy of different methods depends on the size and structure of the sample, as this analysis only considers one type of sample.

A key contribution of the paper is to evaluate the accuracy of the uncertainty estimates generated by a two-stage residual block bootstrap. These estimates perform reasonably well on average, as coverage rates average approximately 89 percent for BRF, 87 percent for XGBoost, and 84 percent for Cubist, all of which exceed the 78 percent average for EBP. Overall, the results indicate that the combination of Extreme Gradient Boosting or Cubist Regression, the random effect block residual bootstrap, and publicly available geospatial data offer a practical way to improve on EBP estimates when geolocated survey data are available.

While our results offer useful input for poverty estimates at a more disaggregated level that can result in more cost-effective budget planning, particularly in contexts where recent census data are unavailable, it should be noted that these results may not extend to other welfare outcomes. In addition, while we include seven different countries, it is not possible to claim generalizability in all contexts. The relative performance of different machine learning methods may well depend on the specific outcomes and model features under consideration. For example, one method may work well for poverty but may work less well for food insecurity or children’s malnutrition status. The performance of different methods will also depend on the nature of the sample data. We encourage further research on comparing the performance of different methods, contexts, and outcomes. Further research could also shed light on whether models are sufficiently stable to use for intertemporal prediction. It is also useful to evaluate the performance of these methods in
sources.

The final sample used in the analysis includes 313 external sovereign debt restructurings in 91 countries between 1815 and 2015.<sup>24</sup> This number represents a lower bound, since we summarize multiple restructurings into a single event if they resulted from the same default event (for example, when the negotiation process of a given default results in separate restructurings for different creditor groups or bond currencies, such as restructurings of USD vs. GBP bonds; see Meyer [2021]). More specifically, we combine a total of 358 individual restructurings into the final event sample of 313 cases, so that each default receives just one haircut estimate. We use restructuring amounts in USD as weights when combining cases. Appendix C provides more details and a breakdown by country. Figure 1 shows the yearly distribution over 200 years for the entire 358 case sample, while in the remainder of the analysis we use the final sample of 313 restructuring cases (with combined restructurings).

> 24 For 68 of the 91 defaulters, we could also construct monthly bond price series. For the remaining 23 countries, we estimated haircuts but have no price data. In addition, we collected price data for another 23 countries that never defaulted on their external debt obligations (see Section 4), bringing the total pricing sample to 91 countries.

10
Chart 7 Characteristics of Rate Cycles over Different Sub-periods <!-- Start of picture text --> Duration Number of Rate Changes (in-sync)<br>150 25<br>120 20<br>90 15<br>60 10<br>30 5<br>0 0<br>Tightening Easing Tightening Easing<br>Average Pace of Rate Changes Initial Velocity<br>2 4<br>1 2<br>0 0<br>-1 -2<br>-2 -4<br>Tightening Easing Tightening Easing<br>Amplitude (total rate change)<br>12<br>6<br>1970-1984 1985-1998<br>0 1999-2007 2008-2019<br>2020-2024 (May)<br>-6<br>-12<br>Tightening Easing<br>Number<br>Rate change over first 6 months (in pp)<br>Months<br>Rate change (in pp)<br>Total rate change (in pp)<br><!-- End of picture text --> Sources: Authors’ calculations using the turning points for rate cycles listed in Appendix Table 1. Data from January 1970 – May 2024. Notes: Chart shows means across all tightening or easing phases within the given time period, with data ending on May 31, 2024. The number and pace of rate adjustments only include "in-sync" rate adjustments, i.e., the rate increases for tightening phases and decreases for easing phases. Velocity and amplitude are the total change in rates (in any direction) over the first six months of the phase or the entire phase, respectively. Means are calculated for each economy (weighted by the length of each phase within the relevant period) and then over the sample as a whole. Members of the euro area are included as individual countries through 1998, and then the euro area is included from 1999.

25
**_Tackling the Global Profitarchy:_ Gender and the Choice of Business Sector**

**Authors** : Markus Goldstein, Paula Gonzalez Martinez and Sreelakshmi Papineni<sup>1</sup>

JEL: J16 L25 L26 Keywords: Gender, Entrepreneurship and Firms.

1 This paper is a product of the World Bank Africa Gender Innovation Lab (AFRGIL). Goldstein (email: <u>mgoldstein@worldbank.org);</u> Gonzalez Martinez (pgonzalezmartine@worldbank.org) and Papineni (email: <u>spapineni@worldbank.org). This paper uses data from the</u> _Future of Business_ survey which is a collaborative effort between Facebook, the World Bank and the Organization of Economic Co-operation and Development (OECD). We thank Laura McGorman and JW Schneider at Facebook, Mariarosa Lunati at OECD, Kathleen Beegle, Caren Grown and Josh Wimpey at the World Bank for comments; and Chandni Raja for superb research assistance. We are grateful to the World Bank Group's Umbrella Facility for Gender Equality (UFGE) for financial support. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.
# **I. Introduction**

Studying the effects of exchange rates is a hardy perennial of international macroeconomics. But nearly all the empirical research is focused on the impact of exchange rate changes on the country experiencing or undertaking them.<sup>1</sup> There is less evidence quantifying the effect of exchange rate movements on the exports of competitor countries, a classic case of spillover that in its adverse manifestation is dubbed the ―beggar-thy-neighbor‖ effect.

This paper examines the spillover effect of movements in China’s exchange rate on exports of other developing countries in third country markets. The Chinese currency provides a suitable opportunity to study the spillover dimension for three reasons. First, China, by virtue of being the world’s largest exporter of goods, is likely to have quantitatively more significant competitive consequences for other countries than nearly any other exporter. Second, China is also a highly diversified exporter so that it potentially competes with a broad range of countries and across the product spectrum. Finally, reflecting China’s dominant size and encompassing scope, its exchange rate policy has been one of the most controversial aspects of international macroeconomics during the 2000s. More recently, it has been in the spotlight because of the consequences of a possibly undervalued renminbi on demand and output in industrial countries experiencing high unemployment and excess capacity.

The spillover effect is estimated with the help of highly disaggregated trade data which facilitates the use of a novel methodology to exploit the variation across exporters, importers, products, and time. We use disaggregated trade data at the 6-digit level spanning 124 developing country exporters and 57 large importers over the period 2000-2008. Our empirical approach is motivated by an analytical framework that we develop based on Feenstra, Obstfeld, and Russ

> 1 This is generally true of the older, voluminous literature on the trade consequences of exchange rates (Goldstein and Khan (1985) provides a survey and other contributions include Hooper, Johnson and Marquez (2000); Thursby and Thursby (1987)). It is
advantages to using household-level data.<sup>2</sup> Most importantly, we can control for a host of household-level variables–notably flexible functions of initial household income–that may be confounded with local inequality. This helps rule out some potential explanations for the inequality-growth relationship, in particular those that rely on the aggregation of non-linear effects of own-income. With household-level data we are also able to explore “within-village” heterogeneity of the impact of inequality on growth: Are the poor hurt more than the rich? Are households with higher educated members immune to the impact? We are also able to broaden the set of outcomes from “income growth” to other economic variables, like the composition of income, e.g., concentration in agriculture, or participation in off-farm employment, that inform the question of how inequality may affect growth.

Repeated observations at the village-level allow us to evaluate the stability and consistency of the “treatment” of higher inequality: We examine whether all variation in inequality is the same. First, we explore the impact of cross-sectional differences of initial inequality across villages. We then determine whether rising inequality within villages, or changing inequality across villages, affects household growth in the same way. These multiple sources of “treatment” provide identifying information on the nature of any causal relationship between inequality and growth. The village-dimension of the panel also allows us to address whether unobserved heterogeneity confounds the impact of initial inequality.

While we are able to provide robust estimates of the correlation between village inequality at the outset of reforms and subsequent growth, there remain disheartening limits as to what we can learn from even these data. Paramount among these limits, the endogeniety problem is intractable: Solving it requires finding instruments that predict initial income inequality, but are otherwise excludable from a growth equation, and in particular are uncorrelated with _any_ institutions that may be related to subsequent growth. The next best thing is to trace the correlation of income inequality through various _observable_ institutional channels that affect growth. While we previously
**Figure 2. 2  Number of cashed checks per person in Colombia**

<!-- Start of picture text --> 4<br>3.0<br>3<br>3 2.4<br>2.0<br>2 1.8<br>1.6<br>2<br>1<br>1<br>0<br>1999 2000 2001 2002 2003<br>No. of cashed checks<br>Source: Superintendencia Bancaria<br>No. of cashed checks per capita<br><!-- End of picture text --> 7. **Proposed tax refunds will only be available to people with bank accounts.** In an economy with a low savings rate such as Colombia, tax returns contribute to the savings capacity of low income people. This is supported by evidence from income tax returns in the United States. Recently introduced legislation in Colombia intends to return 2% of the 10% sales tax charged on purchases with debit and credit cards as a tax rebate<sup>28</sup> . The purpose of this law is to foster payments with cards, which makes evading VAT more difficult. The amount will be refunded through financial institutions by crediting the bank account of the purchaser. The tax refund is therefore only available to buyers who have a bank account. While the measure encourages the use of lower cost non-cash transactions among the banked, it precludes people without a bank account from access to an additional source of savings.

8. **Under the current regulation, no obligation exists for banks to open accounts for low income clients.** In view of their higher risk and cost of serving such clients, banks are often reluctant to provide such services. Recognizing the importance of access to basic financial services, other countries have introduced measures to make basic account services available to all clients with limited costs. These vary from government guidelines on basic
51)|(3.35)|(3.41)|
|Time trend|-0.0097|0.0231|0.0377*|
|(=0 in 2000)|(-0.50)|(1.27)|(1.75)|
|n|1,342|1,358|1,344|
|R-squared|0.423|0.846|0.874|
|RMSE|3.88|3.76|3.21|

_Note:_ Figures in parentheses are robust t-statistics. *, **, ***: significant at 10, 5, and 1 percent respectively. Annual data from 1996–2013 for low- and middle-income countries. RMSE = root mean square error, or sample standard deviation.

26
adaptive cluster sampling”, with stratification (SACS), which was developed in bio-statistics (Thompson 1990, 1991) to efficiently measure elusive and clustered populations: the former characteristic being mostly self-evident and the latter—that informality tends to be highly clustered— being well-established in the literature (see, e.g., World Bank 2018, Harris, 2014, Gulyani and Talukdar, 2010).

SACS is implemented through the selection of primary sampling units (PSUs) of square-block areas. In the case of the Harare survey, the administrative area of the city was divided into 200-by200-meter squares, stratified by the classification of informal business activity (low, medium, high), with a simple random sample of PSUs in a fourth stratum made up of known markets. The idea of SACS is that the discovery of some target units (i.e., informal businesses) is useful and can make fieldwork more efficient. Implementers set a threshold for expansion, where enumerators will move (16)
# **Figure VI-2: Spectral Density Function of Filtered Series**

## **A- Oil exporters**

<!-- Start of picture text --> Bahrain: Spactral Density Function of Filtered Series<br>0.00 0.10 0.20 0.30 0.40 0.50<br>Frequency<br>Evaluated at the natural frequencies<br>Kuwait: Spactral Density Function of Filtered Series<br>0.00 0.10 0.20 0.30 0.40 0.50<br>Frequency<br>Evaluated at the natural frequencies<br>UAE: Spactral Density Function of Filtered Series<br>0.00 0.10 0.20 0.30 0.40 0.50<br>Frequency<br>Evaluated at the natural frequencies<br>6<br>6.00<br>4<br>4.00<br>2<br>2.00<br>0<br>0.00<br>log Periodogram-2<br>-2.00<br>-4<br>-4.00<br>-6<br>-6.00<br>6<br>6.00<br>4<br>4.00<br>2<br>2.00<br>0<br>0.00<br>log Periodogram-2<br>-2.00<br>-4<br>-4.00<br>-6<br>-6.00<br>6<br>6.00<br>4<br>4.00<br>2<br>2.00<br>0<br>0.00<br>log Periodogram -2<br>-2.00<br>-4<br>-4.00<br
.48 to 0.92 in East Asia (King and Winthrop, 2015). Gender patterns in competencies tend to translate into the fields of study that young men and women choose in post-secondary education and also their choice of occupation as adults. In the U.S., for instance, more young women than young men are proceeding to college, but they tend to concentrate in certain areas of study, with consequences for their future jobs (Jacob, 2002).

> 6 This gender disparity among students continues into adulthood: Data on 22 OECD countries show that men do significantly better than women on numeracy; on average, 52 percent of men score in the top two performance brackets compared to 42 percent of women, and a one-standard deviation increase in numeracy skills is associated with an average 18 percent wage increase among prime-age workers (Hanushek et al., 2015).

4
|Time assigned to practical training (%)|-0.000699<br>(0.000557)||||
|---|---|---|---|---|
|Internships outside institution are mandatory|0.0169<br>(0.0205)||||
|Mandatory internships at the end of the program|-0.0448<br>(0.0294)||||
|_Link with productive sector_|||||
|Collect employment data for graduates more than once per year|-0.0186<br>|-0.0260*<br>|-2.686**<br>|-0.198**<br>|
||(0.0187)|(0.0132)|(1.350)|(0.0910)|
|Communicate with local firms about their needs more than once per|-0.0371*<br>||||
|year|(0.0204)<br>||***||
|Industry helps with student evaluation or curriculum design|-0.0137<br>||-4.414<br>||
||(0.0169)<br>||(1.214)<br>||
|Industr has internshi areements with HEI|-0.0440**||-1.799||
|y p g|(0.0218)<br>||(1.492)||
|Industr has areements to train facult|0.0363*||||
|y g y|(0.0198)||||
|Industry lends or provides equipment to program for student|-0.0174||||
|training|(0.0188)<br>||||
|Sbd (