rafmacalaba/data-use-mentions-extended huggingface dataset:
data and provides a full reproducibility package, so that interested users can explore alternative organizations of the data that suit their specific purposes.

The WGI data sources cover different sets of economies, and some data sources provide information across many of the six aggregate WGI indicators while others contribute only to one. To get a better sense of the relative importance of the four types of indicators, Table 2 summarizes the number of country-year-source data points in the WGI corresponding to the four types of indicators. The six aggregate indicators over the past 25 years are based on a total of just over 230,000 country-year-source data points. Roughly one-third of these data points, or almost 77,000, come from the four commercial business information providers, which tend to cover the largest sets of countries and more dimensions of governance. Just over one-quarter of the country-year-source data points, or about 62,000, come from data sources produced by NGOs. Finally, surveys and public sector providers account for about 20 percent each of the total number of country-year-source data points or about 45,000 data points each.

# 3. Constructing Aggregate Governance Indicators

In this section we summarize the methodology used to combine the individual indicators of governance, as organized into six dimensions described in the previous section, into six composite measures of governance. The discussion here closely follows the presentation of the WGI methodology in Kaufmann, Kraay and Zoido-Lobaton (1999), Kaufmann, Kraay and Mastruzzi (2004, 2011), as our basic methodology has not changed since these earlier presentations.

We rely on a statistical method known as an “Unobserved Components Model” (UCM) to construct the aggregate indicators.<sup>14</sup> The basic premise underlying this approach is straightforward. Each individual indicator of governance provides an imperfect signal of the broader concept of governance to which it is assigned. Since this broader concept of governance cannot be measured directly, we have a signal extraction problem: how do we best extract the most informative signal of the underlying concept of
the “Poverty Assessment Tool” (PAT) developed by the IRIS Center for USAID, see Schreiner (2014a) for a comparison.<sup>8</sup> These approaches predict a household’s poverty status on the basis of a small number of questions, mostly relying on nonnested double sampling, and then aggregate these predictions to obtain estimates of poverty at the national level (and possibly other administrative levels). SPS and PAT have been applied to

> 5In its version of the non-nested double sampling approach infrequent conventional surveys, which collect both _y_ and _x_ for all households, are alternated with more frequent low cost surveys that only collect _x_ . The infrequent full surveys are used to estimate (and update) the prediction model which is then used to predict poverty into the subsequent low cost surveys that are conducted for the years in between the full surveys.

> 6Furthermore, they do not provide a full break-down of the costs involved; only selected cost components are reported which excludes transport costs for example.

> 7Security concerns denote an important motivation for reducing the time of data collection by means of faceto-face interviews.

> 8The poverty scorecard takes a more pragmatic approach to building its prediction model as it emphasizes simplicity and ease of implementation. See Schreiner (2014b) for further details on the Simple Poverty Scorecard.

4
10|1.41|0.34|
|<br>_Of which: - Purchased_|_0.51_|_0.30_|_0.14_|_0.26_|_0.47_|_-_|_-_|_-_|_0.04_|
|_- Own account_|_0.54_|_0.58_|_0.36_|_0.49_|_1.88_|_-_|_-_|_1.41_|_0.30_|
|**Total Expenditures**|**7.21**|**8.15**|**5.37 **|**5.60**|**10.74**|**11.25**|**10.84 **|**7.62 **|**4.72**|

Notes: (1) US estimate is an average for 1998-2000. (2) Japan R&D excludes social science, and social science R&D is in the 2.0 percent of the residual term of innovative property. (3) Brazil firm-specific human capital excludes services.

Sources: The original data source of the US is CHS (2005), of Japan is Fukao et al. (2009), and of the UK, Germany, France, Italy and Spain is van Ark et al. (2009). The source of China estimates is Hulten and Hao (2011). This table is based on their Table 2, which contains downward revisions of the non-China data to be consistent with the results of another EU-funded project. GDP in this table is the sum of conventional GDP plus new intangibles. Estimates of investment have been converted to estimates of expenditures using CHS formulae.

17
0.1750)|0.6736|-0.0018|-0.0073|
|Thaba-Tseka|0.0088<br>(0.0933)|0.0066<br>(0.0808)|0.3069|0.0022|0.0178|
|_Faculty_||||||
|Agriculture|0.0374|0.0632|0.0000|-0.0258|-0.0836|
||(0.1899)|(0.2434)||||
|Education|0.1778<br>|0.2696<br>|0.0000|-0.0917|-0.1566|
||(0.3824)|(0.4438)||||
|Health Sciences|0.0541<br>(0.2262)|0.0692<br>(0.2538)|0.0107|-0.0151|-0.0444|
|Humanities|0.0758<br>(0.2646)|0.1265<br>(0.3324)|0.0000|-0.0507|-0.1194|
|Law|0.0430|0.0356|0.1275|0.0073|0.0266|
||(0.2029)|(0.1856)||||
|Science & Technology|0.1272<br>(0.3333)|0.1234<br>(0.3289)|0.6346|0.0039|0.0083|
|Social Sciences|0.2401|0.3125|0.0000|-0.0723|-0.1147|
|Observations|(0.4272<br>3419|(0.4636)<br>3194||||

_Source_ : Own calculations using NUL students’ records data for the academic
Policy Research Working Paper 10142

# **Abstract**

Despite decades of economic growth, gender inequality in South Asia remains remarkably high. Although not the only one, social norms are a crucial driver of various gender outcomes, including differential economic participation. Using repeated cross-sectional data from nationally representative surveys, this paper explores the long term trends of gender outcomes and social norms (proxied by attitudes towards gender roles) in South Asia. The results corroborate the evidence that there has been almost no progress in gender equality in South Asia over the past half-century. There has been little progress on female labor force participation, marriage age, agency, intimate partner violence, and preference for sons, with education being the only exception. The lack of progress is apparent among all socioeconomic groups, including women who live in urban areas, are educated, and have higher incomes. Gender attitudes also remain unchanged, and in some cases, have become more conservative and have a negative relationship with gender outcomes. Better measurements of social norms and better understanding of how their constraining role can be loosened may be critical for achieving gender equality in the region.

This paper is a product of the Office of the Chief Economist, South Asia Region and 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 mbussolo@worldbank.org, jezebuihe@worldbank.org, amunozboudet@worldbank.org, s.poupakis@gmail.com, trahman4@worldbank.org, and nsarma@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
# **<<B>>** Household Surveys (Primary Data)

The target populations in both the baseline and follow-up household surveys were women aged between 15 and 49 who had experienced a pregnancy or birth during the 12 months prior to the survey as well as men of similar age group whose partner was pregnant or had given birth over the same period. Two visits were made to each village. The purpose of the first visit was to seek the cooperation of the local council chair in generating a list of households in which a pregnancy or birth had occurred in the previous 12 months and to take global positioning system (GPS) coordinates to ensure that all the villages were within the prescribed geographical location. During the second visit, a survey was administered to all women living in those households meeting the inclusion criteria. A total of 2,266 women and 177 men participated in the baseline survey, and another 2,313 women and 582 men participated in the follow-up survey.

In both surveys, respondents provided information on household assets and amenities, health-related household arrangements, food security, household expenditures on goods and services, individual background characteristics (age, education level, religious affiliation, and marital and employment status), general health status and health care utilization, childbearing experiences and intentions, family planning knowledge and use, trust and social cohesion in the community, and awareness, use, and perceptions regarding vouchers. Also sought were men’s perceptions regarding the importance and timing of ANC, delivery care, and PNC for their partners, and their willingness to pay (WTP) for such services. However, findings from the men’s WTP questions are not presented here. In 2008 women were asked detailed questions about their two most recent births, including the use of ANC, delivery care, and PNC, and experiences and management of any complications. In the follow-up survey, women provided detailed information on all births in the five years prior to the survey. Table A1 in the appendix presents the
since the early 2000s. Since the creation of the Federal Secretariat for Women’s Policies in 2003, anti-VAW policies and legislation have been strengthened. Efforts include the enactment of the Maria da Penha Law (MdP) in 2006, which has been internationally lauded as one of the most innovative and comprehensive legal instruments in the world to addresses the issue of domestic violence (UNIFEM 2008).<sup>4</sup> The Maria da Penha Law establishes special courts and stricter sentences for offenders and mandates other instruments for prevention, including specialized police stations and shelters for women. The law also seeks to build civil society capacity to monitor and evaluate policies designed to combat VAW in Brazil.<sup>5</sup> However, despite a robust legal framework to prevent and protect women from domestic violence, several barriers challenge the effective implementation of laws and policies. Some critics defend that, although the established policy and legal frameworks are progressive in many aspects, they also embed regressive components such as the prioritization of punitive measures to the detriment of protective and preventive efforts (Machado et al. 2017).

Although there is a lack of official and systematic data that adequately measure the extent of VAW in Brazil in its physical and non-physical forms,<sup>6</sup> existing data reveal the pervasiveness of the issue. A specialized World Health Organization survey from 2000 showed that approximately 41

> 4 Law cited alongside Spanish Laws (2004) and the Mongolian Law on violence against women. Progress of the World’s Women 2008/2009, UNIFEM 2008, p. 76.

> 5 An overview of related laws, degrees as well as amendments to MdP can be found under:

> <u>http://www.compromissoeatitude.org.br/legislacao-sobre-violencia-contra-as-mulheres-no-brasil/</u>

> 6 There are two main sources of VAW data: data from specialized surveys and administrative data. The highest prevalence rates are obtained by specialized surveys that follow WHO ethical and safety standards. However
Policy Research Working Paper 9284

# **Abstract**

School closures due to COVID-19 have left more than a billion students out of school. This paper presents the results of simulations considering three, five and seven months of school closure and different levels of mitigation effectiveness resulting in optimistic, intermediate and pessimistic global scenarios. Using data on 157 countries, the analysis finds that the global level of schooling and learning will fall. COVID-19 could result in a loss of between 0.3 and 0.9 years of schooling adjusted for quality, bringing down the effective years of basic schooling that students achieve during their lifetime from 7.9 years to between 7.0 and 7.6 years. Close to 7 million students from primary up to secondary education could drop out due to the income shock of the pandemic alone. Students from the current cohort could, on average, face a reduction of $355, $872, or $1,408 in yearly earnings. In present value terms, this amounts to between $6,472 and $25,680 dollars in lost earnings over a typical student’s lifetime. Exclusion and inequality will likely be exacerbated if already marginalized and vulnerable groups, like girls, ethnic minorities, and persons with disabilities, are more adversely affected by the school closures. Globally, a school shutdown of 5 months could generate learning losses that have a present value of $10 trillion. By this measure, the world could stand to lose as much as 16 percent of the investments that governments make in the basic education of this cohort of students. The world could thus face a substantial setback in achieving the goal of halving the percentage of learning poor and be unable to meet the goal by 2030 unless drastic remedial action is taken.

This paper is a product of the Education 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
Using HBS data presents some challenges, the main one being the absence of data or responses that match exactly with the hypotheses we made in this paper. For instance, the HBS does not collect information related to transaction and search costs. Moreover, the data set that is available to us is a household data set, and does not include any information about local assets and conditions. This will require us to use proxies in some instances without sacrificing the robustness of the analysis and the precision of the results.

# **5.0 The analysis**

The 2007 HBS indicates that distances to the markets have been increasingly affecting rural prices. Figures 3 and 4 below show that in 2000/ 01 maize prices remained constant as household distance to markets increased. But by 2007, the price of maize had become more responsive to distances decreasing the further the households were from the market. This implies that maize growing households that live further away from markets have become more vulnerable. It may also partly explain why the HBS recorded a decline in rural consumption by the bottom ten percent of the rural population.

_Figures 3 and 4: Farm gate price of maize and distance to market_

# **Maize flour**

# **Maize grain**

<!-- Start of picture text --> 500<br>400 2007<br>300<br>200<br>2000/01<br>100<br>0<br>0 2 4 6 8 10 12 14 16 18 20<br>Distance to market (km)<br>Price per kg of maize flour<br><!-- End of picture text --> <!-- Start of picture text --> 250<br>2007<br>200<br>150<br>100<br>2000/01<br>50<br>0<br>0 2 4 6 8 10 12 14 16 18<br>Distance to market
|---|---|---|---|---|
|**Panel(A): national rates**|||||
|AROPE +|45.0|46.2|38.6|53.6|
|**Panel(B): raw headcount of each d**|**imension**||||
|AROP|22.9|18.1|13.7|21.2|
|low work intensity|6.3|4.4|2.9|3.2|
|material and social deprivation +|19.0|4.2|3.0|24.5|
|Education|29.2|34.0|22.7|38.4|
|Health|4.8|10.3|11.7|14.7|

Note: Percentage of individuals (using survey weights). AROPE + is an extension of AROPE. Material and social deprivation + extends the material and social deprivation included in the AROPE to incorporate if the person lives in an overcrowded house (that is, if the average number of people per room available to the household is greater than two). A person is educationally deprived if at least one adult in the household has only attained primary education (or less). A person is deprived in health if at least one adult in the household has unmet needs for medical examination or treatment during the last twelve months.

Source: Own estimates based on 2022 EU-SILC. We report the survey year and not the income year. AROPE definition based on EU2030 target.

**Nearly half of the individuals who are AROPE + in Bulgaria and Romania are deprived in more than one dimension.** In contrast, in Croatia and Poland, 65.6 percent and 73.8 percent of the individuals who are AROPE + are deprived in only one dimension (
empirical analysis using data for 21 emerging market economies and a panel vector autoregression approach.

The aforementioned studies have focused on different aspects of the impact of exchange rate regimes on macroeconomic dynamics in recipient economies, none of which directly addresses the economic growth consequences, although their results do carry some implications for that. Thus, the focus of this study is to examine directly, how exchange rate flexibility influences the way remittances affect economic growth. Notably, it departs from studies by Guliano and Ruiz-Arranz (2009) and Lartey (2013) which focus on the impact of remittances on economic growth without consideration for the role of exchange rate regimes.

The next section explores the data and considers an empirical examination of the extent to which exchange rate flexibility affects the relationship between remittances and economic growth. Section 4 discusses the results and the policy implications thereof, and section 5 presents some concluding remarks.

# 3 Data and Methodology

## 3.1 Characteristics of the data

The data set used constitutes annual observations that span the period, 1970-2007, for 135 developing and transition countries.<sup>2</sup> The data for exchange rate regimes is based on the Reinhart and Rogoff de-facto exchange rate regime (COARSE) classification, whereas the data for remittances and all other variables are from the World Bank’s World Development Indicators (WDI) database. Table 2 presents a description of the exchange rate regime classification scheme. Table 3 provides some basic descriptive statistics for the data on the main variables used in the analysis. The exchange rate regime varies between categories 1 and 5, with an increase indicating a movement towards a more flexible exchange rate regime. GDP per capita growth rate is highly variable across countries over the sample period, ranging between about -50 percent to about 91.7 percent, with a mean of about 7 percent. Remittances are characterized by a low of US$9,980 and a high of US$37.2 billion, with an average of US$687 million. When expressed as
wider sample.

An additional concern is whether we incorrectly measure the location of multi-plant establishments. For example, a multi-plant establishment in the US might produce across many locations but register in Delaware. The level of trust in Delaware would therefore be an inaccurate measure of the level of trust between the firm and its managers where it operates. Unfortunately, our firm-level dataset does not permit us to observe the location of firms’ plants and address this issue directly. Instead, we take an indirect route and restrict the sample to a group of smaller firms who are much more likely to be single-plant establishments. For these firms, the location of registration should be the same as where production occurs. Columns (4) and (10) consider firms with between 10 and 250 employees. While this approach to deal with multi-plant firms is imperfect, the stability of our estimates across this restricted sample suggests this unlikely to have too much of an effect on our results.

Lastly, to match the sample used by the World Management Survey, columns (5) and (11) use a minimum size cutoff of 50 employees. The results are qualitatively unchanged.

_Alternative Measure of Firm Size._ In the model, firms differ in a single attribute (productivity). Since there is only one input (labor), this is perfectly correlated with productivity under our assumption of common input prices and motivates our use of firm employment to proxy heterogeneity across firms. There are, of course, other ways to measure firm size, the most common of which is capital or assets. Our framework can easily be extended to a Cobb-Douglas technology over labor and capital. Intuitively since there

> 27We also drop firms with more than 5000 employees to trim outliers. These constitute 0.01% of observations in our sample.

20
are CRI. By examining the spatial distribution of the vulnerable households, we can identify their location within the countries and gain a better understanding of their exposure to natural hazards to design more efficient policies targeting for the most vulnerable populations.

The rest of the paper is organized into four sections. Section 2 presents the methodology, where we discuss our approach to modeling per capita household income within the vulnerability to poverty framework with climate disaster risk. We also describe the decomposition by PI, CRI, and IRI. Section 3 provides our data sources, including household surveys, natural disasters, climate, and extreme weather data. Section 4 presents the main results, counting the incidence of vulnerability to poverty in fifteen Latin American countries and the spatial distribution of vulnerable households. Finally, Section 5 concludes the paper with a summary of our findings and their potential implications for policy makers.

# **2 Methodology**

Following Chaudhuri (2003), the main difference between poverty and vulnerability to poverty is risk. The potential sources of risk are diverse. Households can experience risks from adverse aggregate shocks and adverse idiosyncratic shocks specific to individual families (i.e., ranging from general ones like macroeconomic shocks or changes in international prices due to harvests that suffer from droughts or floods to more specific ones like the principal income household earner losing her job). The nature and magnitude of household risks lead to wide variability in household's income or consumption. A household's vulnerability to poverty depends on income volatility and the ability to smooth consumption. While consumption is a smoother measure, income is typically more readily available in household surveys conducted in Latin America and the Caribbean (LAC). Our objective is to incorporate as many countries as possible into our analysis. To achieve this, we proposed a model considering the per capita total family income, acknowledging that the main predicted variable of the model to measure vulnerability is more volatile than other well-being proxies, such as consumption.

4
of water and sanitation under each scenario for each country is then given by: where _CM DHS_ is under-5 child mortality in the most recent DHS survey, _CM cf_ is the predicted (counterfactual) child mortality rate, and _CM MDG_ is the country-specific child mortality rate target set in the MDGs. From the estimated absolute reductions in child mortality rates we can also derive the total number of deaths prevented (DP) per year and

> 13 Due to a lack of computational power, we were unfortunately not able to control for regional (sub-national) fixed effects in our estimation. But as we show in a previous paper (Günther & Fink, 2010) the inclusion of regional fixed effects has little impact on the point estimates obtained.

13