rafmacalaba/datause-encoder-data huggingface dataset:
counter-party for cross-border bank loans across all regions, with the non-bank sector not a major originator of cross-border liabilities.

**Table 3.4g. Multilateral Loans, Percent of GDP**

||Country|2002|2007|2009|2010|
|---|---|---|---|---|---|
|New Member States|Bulgaria<br>|12.6<br>|3.8<br>|3.1<br>|2.9<br>|
||Czech Republic|0.2|0.0|0.0|0.0|
||Estonia|0.5|0.1|0.1|0.1|
||Hungary|0.8|0.1|9.3|9.2|
||Latvia|3.0|0.3|5.6|8.2|
||Lithuania|2.8|0.1|0.1|0.1|
||Poland|1.2|0.4|1.0|1.3|
||Romania|5.7|1.5|7.8|11.5|
||Slovakia|0.8|0.4|0.3|0.2|
||Slovenia|0.3|0.1|0.0|0.0|
|Candidates and Potential Candidates|Albania|12.5|8.4|7.7|8.1|
||Bosnia and Herzegovina|18.9|10.0|10.6|12.4|
||Croatia|2.2|1.9|2.0|2.6|
||Macedonia, FYR|||||
||Serbia|||||
||Turkey|11.9|2.3|2.9|2.4|
|Eastern Partnership Countries|Armenia|30.8|12.2|22.6|23.8|
||Georgia|23.5|11.1|20.6|22.6|
||Moldova|29.1|13.5|11.0|13.8|
||Ukraine|9.7|1.9|12.2|11.8|

_Source_ : World Bank, Joint External Debt Hub. Note: This table reports multilateral loans, total.

In terms of the issuance of international debt securities, Tables 3.4j and 3.4k show that Emerging Europe has a similar profile to Emerging Asia and Emerging Latin America, with cross-border bond debt much smaller than cross-border loans.

**Table 3.4h. Loans from Banks, Percent of GDP**

||2002|2007|2009|2010|
|---|---|---|---|---|
|Euro Periphery|70|116|131|128|
|Emerging Latin America|14|8|9|8|
|Emerging Asia|13|13|11|11|
|New Member States|11|37|41|37|
|Candidates and Potential Candidates|11|23|27|24|
|Eastern Partnership Countries|3|8|8|7|

_Source_ : BIS. Note: This table reports cross-border loans from BIS banks.

**Table 3.4i. Loans from Banks to Nonbanks, Percent of GDP**

||2002|2007|2009|2010|
|---|---|---|---|---|
|Euro Periphery|16|29|33|35|
|Emerging Latin America|10|5|6|4|
|Emerging Asia|5|4|4|4|
|New Member States|5|12|14|13|
|Candidates and Potential Candidates|7|12|15|14|
|Eastern PartnershipCountries|2|2|3|3|

_Source_ : BIS. Note: This table reports cross-border loans from BIS banks to nonbanks.

Finally, Tables 3.4l and 3.4m show some striking differences in the structure of crossborder banking across the region. In the Euro Periphery, the consolidated exposures of international banks is much smaller than the locational exposures, reflecting the role of the Euro Periphery in re-intermediating wholesale bank flows (primarily in Ireland). In Emerging Latin America and Emerging Asia, there is little difference between consolidated and locational 16
# Women’s Police Stations and Domestic Violence: Evidence from Brazil

Elizaveta Perova* and Sarah Reynolds<sup>+</sup> JEL Classification: I130, J120, J160 Keywords: domestic violence, women’s police stations

*The World Bank

+School of Public Health, University of California Berkeley. Corresponding Author. sar48@berkelely.edu

## **Acknowledgements:**

The authors would also like to thank Pablo Fajnzylber, Louise Cord and Luis Felipe Lopez Calva for guidance, and, for helpful comments and suggestions, Aude Sophie Rodella, Pedro Olinto, and the participants of _34 Encontro Brasileiro de Econometria, Universidad de Los Andes Seminario CEDE_ , University of San Francisco International & Development Economics Seminar, and the University of California, Berkeley, Demography Brown Bag Seminar. Qualitative work included in this study was led by Miriam Muller. The views presented in the papers are of the authors alone, and we are solely responsible for any remaining errors. Our views should not be attributed to the World Bank, its Executive Directors, or the countries they represent.
areas, it does show that the recorded distribution of the prevalence of COVID-19 in Mali is dramatically skewed toward Bamako. Second, insights from Google’s Community Mobility Reports, shown in Figure 2, suggest that individuals living in Bamako have experienced relatively large changes in the time spent at various types of places relative to a baseline period in early 2020. Consistent with our claim, these changes in Bamako are larger than reported changes for individuals in all of Mali. Third, as documented in Figure 3 and Figure 4, according to our COVID-19 phone panel survey data individuals in urban areas are more likely to report taking health-related precautions and negative economic impacts due to the coronavirus pandemic than individuals in rural areas. As predicted by Reardon _et al._ (2020), this evidence highlights the reality that although the coronavirus pandemic increased challenges related to food security everywhere, the pandemic is much more disruptive in urban areas than in rural areas in Mali.

The difference-in-differences identification strategy rests on the assumption of parallel counterfactual trends in latent food security between households in urban and rural areas (or, in some specifications, between households in Bamako and all else) in the absence of the coronavirus pandemic. While this identifying assumption cannot be tested directly, we argue that if bias exists in our empirical strategy, then equation (1) generates estimates of the lower bound effect of the pandemic on food security. We perform a number of robustness and sensitivity checks on our results. First, in each table reporting regression results we show two variations of our core specification: one that does not include household fixed effects and one that does include household fixed effects. The household fixed effects allow us to account for time-invariant omitted heterogeneity between households which may bias our results. Second, in the Supplemental Appendix, we show results that exclude all households in Bamako from the regression specification and therefore estimate differences between urban and rural areas outside Bamako. Although these estimates suffer from limited statistical power, we find results that are qualitatively similar to our core results.

# **5 Results**

In this section we report three sets of results. First, we report descriptive results which come directly from the COVID-19 phone survey, targeted at understanding the responses to and socio-economic effects of the coronavirus pandemic. While these results do not specifically quantify the effect of the coronavirus pandemic on food security, they do provide insight of how individuals in Mali perceive the effect of the pandemic in their own lives. Second, we report estimation results based on variations of the regression specification in equation (1). These results provide some of the first estimates of the effect of the coronavirus pandemic in low- and middle-income countries. As we have previously discussed, we see these results as estimates of the lower bound of the effect of the pandemic on food security. Third, we discuss and implement several robustness tests.

11
**Microcredit is concentrated in a relatively small number of leading institutions.** The top 5 MFIs in MENA represent 43 percent of total outreach, and 3 of these are from Morocco (Annex A2.4 has a country breakdown by borrowers and by loan portfolio). 70 percent of microfinance institution (MFI) branches are in Morocco.  Microcredit is not well developed in the Gulf Cooperation Council (GCC) countries, where only Bahrain and Saudi Arabia have microfinance institutions that report to the regional microfinance network (Sanabel).

**After years of overall slow growth, the rate of growth of the microcredit sector in MENA finally started to catch up with global growth rates between 2006 and 2008.** Worldwide growth rates recorded by the Micro Banking Bulletin<sup>7</sup> were 21 percent for borrowers and 35 percent for portfolio, whereas for the MENA region they were 19 percent and 30 percent respectively.  Outreach contracted in 2009 by 3 percent (78,785 borrowers), although total loan portfolio increased marginally, driven primarily by a decline in the size of the Moroccan microfinance sector. This was a consequence of reconsolidation and losses following the microfinance over-indebtedness crisis in Morocco, which is explored in more detail in Annex 5. The total number of microcredit borrowers in Morocco more than doubled from 2005 to 2007, but then fell by almost a third from 2007 to 2009.  Growth rates for individual Arab countries varied, with the highest rates in 2009 recorded in Iraq (45 percent) and Lebanon (37 percent). (see Annex tables A2.1, A2.2 for growth rates by country).

**Microcredit in MENA reaches relatively poor clients compared to other regions** (except Asia), and a reasonably high proportion of women, at 63 percent of total microcredit borrowers (although only 31% employees of Sanabel member MFIs are female). The proportion of women microcredit clients ranges from as low as 18 percent in Iraq, where female workforce participation has declined since the most recent conflict, to as high as 81 percent in Yemen and 83 percent in Jordan where many MFIs target women as part of their mission. Table 4 compares 24 larger and better performing MFIs from across MENA with their counterparts globally, on outreach, gender, efficiency and portfolio quality.

**Table 4. Comparative Outreach, Cost and Portfolio Quality Statistics by Region**

||**Outreach**|**Percent of**|**Operating**<br>**Expense/Loan**|**Portfolio at Risk**|
|---|---|---|---|---|
||**(Income)**|**Women clients**|**Portfolio**|**(>30 days)**|
|**MENA **|**15.8**|**65.4 **|**21.4 **|**2.4 **|
|Africa|67.8|57.2|32.7|4.7|
|Asia|17.8|93.8|17.2|1.5|
|ECA|68.3|43.1|15|2.0|
|LAC|25.5|59.6|22.3|4.2|

Source: 2008 MFI Benchmarks MIX Micro Banking Bulletin. Median values. Outreach is average loan balance per borrower/GNI per capita.

> 7 The Bulletin has a database of 600 microfinance institutions from 84 countries, including most of the leading institutions by size and performance as recorded by the Microfinance Information Exchange.

10
<!-- Start of picture text --> 100 $4.0<br>—— Number of countries with trade operations (left axis)<br>80 —i— Number of trade-related operations (left axis)<br>—— Value of commitments on trade (right axis) $3.0 5<br>60 3<br>$2.0 5“i<br>S<br>40 c<br>:2<br>>$1.0 8<br>20<br>ie] $0.0<br>2001 2002 2003 2004 2005 2006 2007 2008 2009<br><!-- End of picture text -->
include additional factors that determine per capita consumption such as whether the household receive public assistance and more detailed asset variables.

# **_4.2 Role of remittances in maintaining consumption after 1998 flood in Bangladesh_**

A devastating flood in Bangladesh in July-September 1998 covered more than two-thirds of the country and caused 2 million metric tons of rice crop losses and threatened the livelihoods of millions through food shortages (del Ninno et al. 2001). Three waves of representative household surveys were conducted after a flood in 1998 in rural Bangladesh in 7 flood-affected regions (thanas) within four to sixteen months after the flood by the International Food Policy Research Institute (IFPRI) to understand how households cope with the flood (see del Ninno et al. 2001). The first round was conducted in November- December 1998, the second round in April- May 1999 and the third round was in November- December 1999. These surveys provide information on the pre-flood asset holding and the migration and remittance histories of households (see annex table 1). The first round of the survey contains information on various measures of the severity of flood at the village level, such as the depth of water in the house, number of days water remained in the house, number of days evacuated, cost of repair and a flood index developed by IFPRI using the above flood measures.

Of the 734 households which are available in all the three surveys, 493 were affected by the 1998 flood. Using propensity score matching technique using the household characteristics discussed in Section 4.2, we identified 469 households which are comparable in terms of household characteristics and other determinants of remittance-receiving status. Among these 469 households, around 118 or 25 percent of households receive remittances. The latter group includes households that receive remittances either from within Bangladesh or from other countries, since information on specific sources is not available from the surveys.

In table 3, we examine the impact of remittances on per capita monthly household consumption sixteen months after the flood for households in the flood affected areas. The analysis is performed on all households comparable to remittance-receiving households in terms of observable characteristics. We find that remittances have a positive and significant effect on per capita monthly household consumption. Since the average household size is 6.4, a thousand taka increase in remittances to the remittancerecipient households in the six months prior to the survey leads to about a 156 taka (=6.4 x 24.37) increase in monthly household consumption expenditure of the average household (including those do not receive remittances).<sup>11</sup>

> 11 That would imply a marginal propensity of consumption of 62% out of additional remittances (since the estimated increase in consumption above is the average increase for the matched sample which includes

11
**Figure 14: Export Growth in BRICS Countries, 2006–08 vs 2011–12) a: 2006** – **08**

<!-- Start of picture text --> 150 134 135 139<br>128 130<br>100 BRA RUS IND CHN ZAF<br>50<br>22 24<br>16<br>0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 7 5<br>0<br>‐17 ‐14 ‐8 ‐7 ‐10 ‐15<br>‐30<br>‐50 ‐39 ‐38<br>‐48<br>Intensive Margin Extensive Margin<br>‐100<br>Increase of Decrease in Extinction of Introduction of Introduction of Introduction of Product<br>existing products existing products exports of new products in new products in existing products diversification in<br>in established in established products in new markets established in new markets established<br>markets markets established markets markets<br>markets<br><!-- End of picture text -->

# **b: 2011** – **12**

<!-- Start of picture text --> 200<br>145<br>150 134 134 BRA RUS IND CHN ZAF<br>119<br>108<br>100<br>50<br>32<br>0 0 0 0 0 2 0 0 0 0 0 0 0 0 10 6 12 3 15<br>0<br>‐5 ‐2<br>‐11 ‐10 ‐9<br>‐20<br>‐50 ‐35 ‐35<br>‐48 ‐46<br>Intensive Margin Extensive Margin<br>‐100<br>Increase of Decrease in Extinction of Introduction of Introduction of Introduction of Product<br>existing products existing products exports of new products in new products in existing products diversification in<br>in established in established products in new markets established in new markets established<br>markets markets established markets markets<br>markets<br><!-- End of picture text --> _Source:_ WITS _Note_ : The analysis excludes 2009, the year of the trade crisis, in order to focus on periods of trade growth.

1
framework has been used to study child development before, but it has not been widely used as a basis to test for catch-up growth. We show how the framework allows both forms of catch-up to be tested in a unified setting. In addition, we apply a novel estimator that relies on minimal statistical assumptions regarding the data generating process. The merit of this procedure is demonstrated with longitudinal data from the four Young Lives countries – Ethiopia, India, Peru and Vietnam. On a technical level, we find the proposed fixed-effects estimator with individual slopes outperforms conventional correlated random effects (mixed effects) models. On the substantive level, the procedure reveals limited evidence for catch-up growth in either stature or body mass across the four countries.

Our third contribution is to verify the extent to which estimates of catch-up are sensitive to the ways in which anthropometric outcomes are transformed, often via standardization procedures. This point has been acknowledged before (e.g., Leroy et al. 2015), but few studies include a systematic comparison of the degree to which statistical tests of different forms of catch-up depend on the specific metric or transformation chosen. We find that externally-standardized _z_ -scores and associated binary transformations (e.g., stunted / not stunted) tend to produce more positive assessments of the presence of catch-up growth compared to tests based on absolute differences. As this may be driven by changes in the variance of the reference distribution, we propose that the ratio between the outcome for a given child and the expected outcome for a child of the same age and gender in a reference population is used as a complementary measure.

Our final contribution is to extend the latent growth framework to show how the determinants of variation in mean child size and growth velocity can be investigated via a second stage of analysis. This provides a consistent and straightforward approach to identifying factors that account for differences in growth trajectories across children. As applied to the Young Lives data we find significant heterogeneity in the magnitude (and direction) of different drivers across the four countries. We also find that community characteristics explain a non-trivial share of the variance in child size and growth velocity.

The remainder of the paper is structured as follows: Section 2 reviews conventional definitions of catch-up and briefly summarizes recent literature. Addressing the shortcomings of extant empirical approaches, Section 3 sets out our proposed alternative procedure based on a latent growth framework; and we discuss alternative empirical estimation strategies. Section 4 applies our recommended approach to the Young Lives data and discusses the results. Section 5 concludes.

2
<!-- Start of picture text --> Figure la Trends in Total Fertility<br>PRE-TRANSIT IONAL CO.INTA IES<br>7 7 7=<br>2<br>1955 1990 1995 19I70 1975 1990 1995<br>Figure It. Trends in Total Fertility<br>TRANSIT ONAL rOtaNfQIS<br>7<br>__ 3 , .3 ,<br>4 2 -,<br>S<br>19S 1960 1965 1970 1975 19B0 1995<br>YEAR<br><!-- End of picture text --> <!-- Start of picture text --> Figure Ic. Trer,c;-. in Total Fertility<br>POST-TRANSITIONAL COrNITQIS<br>2.4 39<br>2.5<br>Sa4d<br>1 4<br>195 1 960 . .1S5 . 1..70. 1S75 19e0 19e<br>,e^o<br><!-- End of picture text --> 7
Michael Bell, Hoe Be Khor, Kalpana Kochhar, Jun Ma, Simon N'guiarba, and Rajiv Lill, 1993, hina id the Threshold of a Market Economy, , No.107, Intrational Monetuay Fund, Washington DC.

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Persson, Matte, Torsten Persson, and Lars E.O. Svensson, 1987, "Time Consistency of Fiscal and Monetay Policy," Econor, Vol.55, pp.1419-1432, (November).

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Rogers, Carol, 1987, "Expenditure Taxes, Income Taxes, and Time-Inconsistency," Jrnalof Public lommics. Vol. 32, 215-230.

The State COUcil, 1989, 'Decisions of Industrial Policy,' People's Daily, March 18.

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, 1991, 'Perspectives on the Design of Intergovernmental Fiscal Relations," Policy 74
While it is true that there is no general trend toward higher inequality within countries when looking at all the countries of the world, the picture is not so favorable if one looks only at rich countries and only at the last decade. The Luxembourg Income Study (LIS) has produced comparable, high-quality income distribution data for most of the rich countries. This work finds no obvious trends in inequality up through the mid-to-late 1980s. Over the past decade, on the other hand, there have been increases in inequality in most of the rich countries. Because lowskilled workers in these countries are now competing more with workers in the developing world, it is certainly plausible that global economic integration creates pressures for higher inequality in rich countries, while having effects in poor countries that often go the other way. The good news from the LIS studies is that “Domestic policies and institutions still have large effects on the level and trend of inequality within rich and middle-income nations, even in a globalizing world.... [G]lobalization does not force any single outcome on any country.” (Smeeding, 2002) In other words, among rich countries some have managed to maintain stable income distributions in this era of globalization through their social and economic policies (on taxes, education, welfare).

_Trend #5. Wage inequality is rising worldwide._ Much of the concern about globalization in rich countries relates to workers and what is happening to wages and other labor issues. The most comprehensive examination of globalization and wages used International Labour Organization data on very detailed occupational wages going back two decades (Freeman, Oostendrop, and Rama, 2001). These data look across countries at what is happening to wages for very specific occupations (bricklayer, primary school teacher, nurse, auto worker). What the study found is that wages have generally been rising faster in globalizing developing countries than in rich ones, and faster in rich ones than in non-globalizing developing countries (Figure 24