rafmacalaba/data-use-mentions-extended huggingface dataset:
migrant’s current contacts. These contacts are determined prior to the decision to become acquainted with a political figure in the city, and are also likely to have path dependencies. Hence they represent the lower bound of current contacts available to an individual choosing to gain access to their political representatives.

To address the potential endogeneity of wealth, we adapt a strategy used by Munshi (2003), exploiting our knowledge of households’ year of migration and district of origin to incorporate the extensive Indian climate data set, gathered by the World Bank. This dataset extends from 1955 to 1987, providing detailed information on prices and crop yields for virtually all districts of India. These crops (wheat and maize) have been in widespread production around India over the last 40 years. Wheat was the central crop in India’s Green Revolution, while maize is traditionally considered an inferior good. The shifts in production levels between these crops provide useful indicators of the economic situation in the migrant’s home district.

We take five year averages of crop revenue (the product of local agricultural prices and the district crop yield) preceding the individual’s departure from their home district. To construct our instrument set, we find the deviations in revenue from this average in the year of the individual’s departure. Unlike rainfall, the use of crop revenue deviations provides an instrument that is unaffected by the widespread adoption of water storage and irrigation technologies. Yet, since such deviations are largely the result of exogenous weather shocks, a fall in revenue is likely to increase the “push” factors for out-migration. Such push factors are also far more likely to matter for the poor, since wealthier households are better protected from agricultural risk. Thus, the exclusion restriction we propose is that district-wide deviations from revenue are likely to be correlated with household wealth but not to their access to power networks in the city.<sup>xvi</sup> We test for this explicitly with Hansen’s over-identification test.

20
A simple example of a poverty measure using attainment-aggregation is the usual headcount index of poverty:<sup>10</sup> where _Fy_ is the distribution function for aggregate consumption _y_ and _z_ is the poverty line in that space. To keep things simple for expository purposes (including graphing), suppose that there are two attainments in amounts _x_ 1 and _x_ 2, with prices _p_ 1 and _p_ 2, so _y=p_ 1 _x_ 1+ _p_ 2 _x_ 2.

The second approach measures poverty in each of the dimensions separately and then aggregates the dimension-specific “deprivations” into a composite index. Formal treatments of this approach can be found in Tsui (2002), Bourguignon and Chakravarty (2003), Duclos et al. (2006) and Alkire and Foster (2007). The Alkire-Santos MIP is an example. I shall call this “deprivation aggregation.” To see more clearly how this second approach works, consider again the two continuous attainments, _x_ 1 and _x_ 2, with distribution functions _F_ 1 and _F_ 2 respectively. Poverty lines, denoted _z_ 1 and _z_ 2, are defined in each space and the weights on deprivations are _w_ 1 and _w_ 2 ( _w_ 1+ _w_ 2=1). Then a simple example of a poverty measure using deprivation-aggregation is the weighted incidence of poverty across the two dimensions:<sup>11</sup> This is only one possible way of aggregating deprivations. Alternatively one can focus on the joint distribution, and ask what proportion of the population is poor in at least one of the two dimensions (Bourguignon and Chakravarty, 2003). Letting _F_ 12 denote the joint distribution function, the poverty measure is then . Alternatively, one might ask what proportion is poor in both dimensions, i.e., . The measure proposed by Alkire and Foster (2007) goes further in introducing an extra parameter, such that a household is

> 10 The headcount index has a number of well-known limitations, and there is a large
countries (Eichler and Levine, 2009; Meessen et al., 2011).

The purpose of this paper is to evaluate the impact of Rwanda’s national P4P scheme on individual and couple HTC. Building on the lessons from pilot experiences in a few provinces, Rwanda initiated in 2006 a national P4P scheme at the health center level to improve health services delivery, including HIV/AIDS services. We use data from a prospective impact evaluation we nested into the national scale-up of P4P in Rwanda, producing evidence from an

> 1 An important exception is from Thornton (2008) who demonstrated that cash value vouchers doubled the percentage of individuals who obtained their HIV test results, given that they had been tested.

2
contingent on school participation in Mexico and Brazil, but they do not have educational performance data to estimate effects on educational quality.<sup>4</sup> Research on the determination of educational quality in Latin America would be particularly welcome since Latin America is the region featuring the highest rates of return to education among the studies surveyed in Psacharopoulos and Patrinos (2004). Also, the returns to education typically increased in Latin America (including Argentina and Colombia) during the 1990s (cf. Pritchett 2004), which has been attributed to the economy-wide reforms that affected technological progress and created higher returns to education in the adaptation to the generated disequilibria (Behrman et al. 2000). While economy-wide reforms such as capital-market and trade liberalization, privatization and tax and labor-market reforms influence the profitability of educational investments, particularly during transitions to new equilibria, the same can be expected of the education system itself, which will lower the longrun returns to education if it does not succeed in producing basic skills to a satisfactory degree.

The remainder of the paper is structured as follows. Section 2 describes the database of the PIRLS 2001 international student achievement study and presents descriptive statistics on educational achievement and student and school characteristics in the selected Latin American and comparison countries. Section 3 derives the empirical model and its econometric implementation. Section 4 presents results on the relationship between student background and educational performance in the different countries. Section 5 estimates the relationship between school characteristics, both material and institutional, and educational performance. Section 6 summarizes the main findings and concludes.

# **2. The Database**

## **_2.1 The PIRLS 2001 International Student Achievement Test_**

The dataset used in the analysis is the PIRLS 2001 International Database. The PIRLS assessment was conducted in 2001 by the International Association for the Evaluation of

> 4 Cf. Hanushek (1995), Glewwe (2002), Pritchett (2004) and Glewwe and Kremer (2005) for reviews of research on the determinants of educational quality in
# **Table 9:  Total Issuance Costs as Percent of Issue Size (for US$100 mn issues)**

||**Brazil**<br>**C**|**hile**<br>**M**|**exico**|
|---|---|---|---|
|Domestic debt|2.39%|2.74%|1.18%|
|Domestic equity|4.39%|1.62%|3.93%|
|International debt|2.22%|2.22%|2.22%|

The next table takes another view of the transactions costs data. For each category of fees, Table 10 shows its percentage contribution to the total fee costs. The first point of note is that investment banking fees clearly dominate the total costs. The exception is Chile, where even though banks charge Chilean firms the usual ballpark range of 1% fees, the tax represents a higher amount (1.6% of face).

**Table 10:  Comparative Costs: Category Fees as a Percent of Total Costs**

||Brazil|Chile<br>**Domestic De**|Mexico<br>**bt**|Brazil<br>**D**|Chile<br>**omestic Equ**|Mexico<br>**ity**|**Intl. Debt**|
|---|---|---|---|---|---|---|---|
|Investment Banking|86.93%|36.56%|67.75%|91.22%|92.39%|89.06%|56.21%|
|Legal|1.11%|1.83%|3
many aspects of social norms. As expected, the measure of gender norms from WVS and FLFP are highly correlated (see Appendix Figure A.2). It is therefore not surprising that repeating our estimations yields results that are broadly consistent with using the WVS to classify countries as more or less traditional (Panel A of Tables A.16 and A.17 in the Appendix).<sup>30</sup>

In addition, we use information from Women, Business and the Law (WBL) (World Bank, 2024). WBL measures many aspects of laws and policies critical for women’s economic empowerment, including laws and regulations related to women’s agency and freedom of movement, protection against workplace discrimination based on gender, mandates of equal remuneration for women and men for work of equal value, constraints to a woman’s ability to start and run a business, among others. Panels B and C of the Appendix Tables A.16 and A.17 present results when using the WBL index and the entrepreneurship component of the WBL index to classify countries in more or less traditional.<sup>31</sup> When using the WBL index and the component relating to entrepreneurship, the

> 28Ideally, the minimum sample size should possibly be 100, however, due to constraints on sample size for firms reporting loan amount, we use a lower threshold.

> 29Using this threshold, the number of more traditional countries is 20 while the remaining 41 countries are classified as less traditional.

> 30When using FLFP, the number of more traditional countries is 28 while the remaining 33 countries are classified as less traditional.

> 31When using the WBL index, 32 countries are classified as more traditional and 29 as less traditional, although the figures are 52 and 9 when using the entrepreneurship component. This unbalanced division in the latter is explained by

18
whether governance affects investment policies. First, we use data on governance arrangements for central banks' reserve management operations as an independent variable collected through the RAMP surveys. Second, we utilize RAMP survey data on the composition and risk of reserve portfolios as dependent variables describing a central bank's investment policy. We then deploy three types of control variables to isolate empirically the impact of the governance structure and investment policies.

# **4.1 Governance and Macroeconomic Variables**

We compiled governance variables from multiple sources, collected at the national level, to assess the broader governance environment as a control variable to isolate the effect of the governance arrangements at the central bank level. We also use macroeconomic variables and data on reserve adequacy to further isolate the effects (see Table 1 for a summary).

7
of the adult population holds a job, almost nine out of ten women are not working, and over half of active youth are unemployed. The quality of available work is low with high levels of informal employment. Kosovo still has high poverty rates, with about 18 percent of Kosovars living in poverty according to the most recent 2017 Household Budget Survey (HBS) data. As labor is the main source of income for the majority of the population in Kosovo (above 60 percent for all quintiles), low employment rates and low wages contribute to material deprivation for workers and their families. Importantly, poverty is related to labor market attachment, and growth in labor income has been the main driver of poverty reduction in recent years — either because of higher employment rates or because of increased labor earnings.

**In this section we present trends during the period 2012-2018 in labor force participation, unemployment, and informality, factors important to the design of an optimal minimum wage policy.**

Data sources and definitions

**The analysis presented in this paper relies heavily on the 2012-2018 Kosovo Labor Force Survey (LFS), a continuous household survey, with data collected each week of the year by the Kosovo Agency of Statistics (KAS).** The survey collects detailed data on labor market indicators as well as other standard sociodemographics including age, gender, employment status, economic activity, occupation and other variables related to the labor market.  The data are representative at the urban and rural level. The sampling frame was based on the data and cartography from the 2011 Kosovo Census, and a stratified two-stage sample design was used for the 2012-2018 Kosovo LFS. In our analysis, we focus on the working age population (age 15-64) and use survey weights computed by KAS to adjust the estimates for the survey design.

**Measuring individual earnings is challenging, in part because of the quality of wage data in the LFS.** Wages are collected in intervals or brackets and so estimating reliable point estimates is difficult
# **INTRODUCTION**

It is widely recognized that the Brazilian economy is facing considerable competitiveness challenges.<sup>2</sup> After several years of strong expansion, the recent slowdown seems related to supply-side difficulties stemming from a wide range of inefficiencies and rising costs, instead of insufficient aggregate demand. Such inefficiencies and costs have increasingly burdened economic activity and have shown signs of aggravation in recent years.

This note examines in detail Brazilian export performance over the last 15 years, focusing not only on growth and composition, but also on different performance dimensions, including diversification, sophistication, and firm dynamics. The analysis uses international comparisons to better situate the Brazilian performance, and explores different databases, including firm-level data recently published by the World Bank. This note uses a recent diagnostic toolkit developed by the World Bank (Reis and Farole 2012). The objective is to generate hypotheses to identify factors that have been inhibiting exports and industrial production expansion.

# **EXPORT PERFORMANCE: 1998–2011**

# **Growth, composition, and destination**

The aggregate performance of Brazilian foreign trade can be considered favorable in the last 10 years. In the wake of strong global economic growth, the expansion of international trade, as well as favorable commodity prices, Brazilian exports of goods and services grew 262 percent between 2000 and 2010, almost twice the global average, 135 percent. As a result, exports have also grown as a proportion of gross domestic product (GDP), from 10 percent of GDP in 2000 to a peak of 16.4 percent in 2004, falling back to 11.2 percent in 2010.

Brazil’s export performance is less stellar when compared with other emerging countries. In fact, the Brazilian export expansion was significantly below the 439 percent growth in the other BRICS countries (a group that includes Brazil, the Russian Federation, India, China, and South Africa). Among BRICS, the most successful case was India, which increased the ratio of exports to
Africa. However, the main conceptual focus remains on individual and household predictors of intra-household agreement. Ambler et al. (2017), using the same 2011-12 Bangladesh Integrated Household Survey (BIHS) as Seymour and Peterman (2018), use participation in decision-making in household activities, owned assets and the purchase of new assets to analyze patterns of disagreement, setting up the analysis to distinguish between cases where the woman recognizes _any_ degree of decision-making involvement on her part versus cases where the husband recognizes her decision-making involvement. The authors then examine the degree of association with five measures of women’s well-being outcomes and find that cases of disagreement where women recognize their involvement, but men do not, are also positively associated with improved outcomes for women, but often to a lesser extent than when men agree that women are involved.

We build on this theoretical and empirical literature in what follows. Using the DHS, we look at intra-household assessments of women’s decision-making power in 23 Sub-Saharan African countries. We unpack the multidimensionality of power; not only looking just at agreement, as in the majority of the past literature, or whether the woman acknowledges any degree of decisionmaking involvement. Instead, we operationalize Adams’ “given power” and “taking power” by utilizing survey cross-reporting to build relative measures of power assignation along the spectrum of decision-making responses. To our knowledge, ours is the first paper to link spouses’ varying perceptions of decision-making roles to power theory and assess the relationship of this specific facet of power to a range of well-being outcomes for the household, including children’s outcomes.

8
Again, by specifying the logarithm of the variance ht as a function of ηt, Nelson ensured that the conditional variance stays positive even if some of the coefficients in (8) are

> negative. Introducing  _t_  1 /<sup>_h_</sup> _t_  1 permits us to analyze whether positive or negative values

of ηt will increase or decrease the conditional variance.

# **4. Estimation results**

# _5.1 The intervention equations_

We estimate the parameters of the intervention criteria, equations (4.1) and (4.2), with observations ItB and ItS as a standard probit model. These estimates are presented in Table 2.

# **Table 2. Probit estimates of the criteria functions for intervening. (Standard errors are in parentheses.)**

|**_Dependent Variab_**|**_le M_**<br>**_t_**<br>**_B_**|**_Dependent Va_**|**_riable M_**<br>**_t_**<br>**_S_**|
|---|---|---|---|
|_Coefficients_|_Estimates_|_Coefficients_|<br>_Estimates_|
|δ11|0.3810|δ21|-1.1235|
||(0.0633)||(0.0821)|
|δ12|-0.3855|δ22|1.0463|
||(0.0769)||(0.0817)|
|δ13|-8.0346|δ23|-4.8294|
||(0.5882)||(0.3789)|
|Observations|1469||1469|
|Log Likelihood|-879.26||-647.79|

The results indicate that the central bank seems to be consequent in its intervention decisions. That is, the probability of intervening buying (selling)
and cash-flow projections and estimated probabilities of repayment to obtain a more forward-looking measure.<sup>19</sup> Consistency rules between the ratings of different lenders to the same borrower are often established e.g.; two lenders cannot input ratings that differ by say more than one point on the scale otherwise the system rejects both. Depending on the sophistication of the system, including ratings will require a higher degree of supervisory oversight to ensure that ‘ratings’ are consistent and to resolve disputes. Nearly 60% (33/57) of the PCRs surveyed, collect rating information including all Latin American countries

> 17 These questions were not asked in the subsequent 2002 PCR survey.

> 18 Other answers to the question on the most important supervisory use of PCR data included: to track conglomerate borrowing (3); for use in calculating provisions (6); to identify discrepancies between institutions in risk ratings of borrowers (5); for information on the status of credits (7); and other (2).

17
# **Panel C. Log GDP per capita**

# **Panel D. COVID-19 Policy Stringency Index**

<!-- Start of picture text --> 0 20 40 60 80<br>7 8 9 10 11 Average over the period by country of Oxford Stringency Index<br>Natural logarithm GDP per capita High Inc EAP ECA LAC<br> EAP ECA LAC MENA SA SSA MENA SA SSA<br>100 100<br>80 80<br>60<br>60<br>40<br>40<br>20<br>20 Financial Policy Response Activity Index<br>Financial Policy Response Activity Index<br>0<br>0<br><!-- End of picture text --> Source: World Bank COVID-19 Financial Sector Policy Response Database, Hale et al. (2020); World Health Organization (WHO) and World Bank World Development Indicators; authors’ calculations. Note: For the Policy Stringency Index (Panel D), the chart shows the average between January 30<sup>th</sup> and September 1<sup>st</sup> , 2020.

# **3 Determinants of the Financial Sector Policy Response to COVID-19: Methodological Framework**

# _3.1 Modeling the time until the first policy measure is taken in each category_

The variable of interest is the time elapsed (in days) between the date when a country undertook its first policy measure in a specific category (e.g., _Banking Sector_ ) and January 30, 2020, the date when WHO declared COVID-19 to be a Public Health Emergency of International Concern (PHEIC).<sup>10</sup> An “event” is recorded the first time a country takes a policy measure in one of the four categories described in section 2.1. Thus, we are analyzing four separate events by following each country during the period between January 30, 2020, and
# **4. Estimation Results and Discussion**

# **4.1. TA-Level Aid Distribution**

Table 2 presents results for a random effects models using weather variables mentioned in aid distribution documents in the first column and the flood and weather shock dummies from the crop production analysis in the second column.<sup>19</sup> In the first specification, both low and high rainfall deviations increased aid distribution, while higher estimated maize yield differences and higher December-January SPI values decrease aid distribution. The explanatory power is reasonably high at .46 R<sup>2</sup> -overall and .58 R<sup>2</sup> -between. However, looking at column 2, we see that neither drought nor flood shocks have a significant impact on aid distribution. On the other hand, as shown in Online Appendix 1, both drought and flood shocks have significant negative impacts on maize yields. Additionally, the high rainfall difference has a significant positive impact on maize yields _and_ aid distribution, while December-January SPI has a negative impact on both maize yields _and_ aid distribution. The results thus suggest that the reliance by MVAC on certain rainfall variables provides a source of exogenous variation in aid distribution through geographic targeting discrepancies that are captured in the descriptive statistics.

> 19 Full results are found in Online Appendix 3.

21
we plot the estimated difference-in-differences coefficient for each year, which measures the ii differential change for domestic listed firms relative to foreign listed firms. ββ̂ The regression results confirm that equity issuances only started to diverge across the two groups in the years following the implementation of the Stock Connect program. The difference in equity raised over assets between domestic and foreign listed firms was not statistically significant during the QFII implementation in 2002, the RQFII implementation in 2011, or the QFII/RQFII expansions in 2013. It became statistically significant in 2015, when domestic listed firms increased the equity to assets ratio by 4 percentage points (p.p.) relative to foreign listed firms. This difference increased to more than 10 p.p. in 2016 (Figure 3, Panel A). By 2020, the cumulative amount of equity raised over assets for domestic listed firms reached 50 percent, 21 p.p. higher than that for foreign listed ones (Figure 3, Panel B).<sup>25</sup> Since comparisons between foreign and domestic listed firms may be affected by confounding domestic events unrelated to internationalization, we now focus on the group of domestic listed firms. We estimate difference-in-differences regressions to compare the issuance activity of connected and unconnected domestic listed firms (Table 4 and Figure 4). We present the results from the full sample (left-hand panels) and the PSM sample (right-hand panels). Both groups of firms show similar equity issuance patterns before the Stock Connect program was implemented. Since then, connected firms raised substantially more equity than unconnected firms.

Results with the full sample show that the differences between connected and unconnected firms became significant in 2015. The amount of equity raised over assets was about 4 p.p. higher for connected firms than for unconnected firms in 2015 and 6 p.p. higher in 2016 (Figure 4, Panel A). Differences between connected and unconnected firms were still significant (but smaller) during the MSCI incorporation process (2018-20). By 2020