rafmacalaba/datause-encoder-data huggingface dataset:
# Table 6:  Weather Risk Model: Fixed-Effects Estimates

# Dependent Variables [Log(Variable/Population)]

||(1)<br>Floods<br>Killed|(2)<br>Floods<br>Killed|(3)<br>Floods<br>Affected|(4)<br>Floods<br>Affected|(5)<br>Droughts<br>Affected|(6)<br>Droughts<br>Affected|
|---|---|---|---|---|---|---|
|Female Primary|-0.018||-0.017||-0.020||
|Enrollment Rate|(4.67)**||(2.70)**||(2.05)*||
|Female Secondary||-0.017||-0.015||-0.011|
|Enrollment Rate||(5.36)**||(2.87)**||(1.16)|
|GDP Per Capita|-0.137|-0.122|-0.120|-0.107|||
|($’000)|(6.22)**|(5.46)**|(2.91)**|(2.53)*|||
|Precipitation|0.010|0.010|0.016|0.016|-0.013|-0.014|
|(mm.)|(3.28)**|(3.23)**|(3.61)**|(3.60)**|(1.75)|(1.79)|
|Constant|-12.833|-13.438|-6.630|-7.297|-0.883|-1.752|
||(28.38)**|(36.28)**|(9.29)**|(12.92)**|<br>(1.08)|(2.67)**|
|Observations|933|929|1051|1047|323|322|
|Countries|120|120|134|134|83|82|
|R-squared|0.46|0.46|0.41|0.42|0.54|0.53|

Absolute value of t statistics in parentheses * significant at 5%; ** significant at 1% 31
# 1. Introduction

Nigeria has been steadily growing and was considered one of the fastest growing economies in the world in the decade surrounding the global financial crisis. At the same time home to the largest number of people living in extreme poverty. In 2018, it surpassed India that had been leading this rank before. “End poverty in all its forms everywhere” by 2030 defined as the Sustainable Development Goal (SDG) 1 is thus an ambitious goal for Nigeria. Given the established consensus that children may suffer more and in different ways from poverty than adults (Abuda and Delamonica, 2018), it is important to study poverty distinguishing between these two groups. Along these lines, Newhouse et al. (2016) showed based on data from 2010 that Nigeria is facing one of the highest child poverty rates among a sample of 89 developing countries and that children are disproportionally more affected by poverty than adults. Considering that 44 percent of the Nigerian population is estimated to be below the age of 15<sup>1</sup> , reducing child poverty is therefore particularly important to achieve SDG 1. While some may argue that economic growth will bring along poverty reduction, there is a concern that Nigeria’s strong economic growth has not been inclusive, as its effects on poverty remain unclear (Ajakaiye et al., 2016a,b). This view is supported by Dang and Dabalen (2019) and Ichoku et al. (2012) findings, that Nigeria has experienced non pro-poor growth. Moreover, World Bank (2016) showed that despite estimating a poverty growth elasticity of -0.6 and a reduction in the share of the population considered poor, Nigeria nevertheless experiences an increase in the number of poor. This means that Nigeria’s population is growing at a higher rate, than the growth elasticity of poverty needed to result also in the reduction in the absolute number of poor people.

However, the United Nations definition of poverty extends beyond monetary poverty that may be directly affected through growth, also reflected in the SDGs 2, 3, 4 and 6. During the 1995 world summit in Copenhagen absolute poverty was defined as “Absolute poverty is a condition characterized by severe deprivation of basic human needs, including food, safe drinking water, sanitation facilities, health, shelter, education and information. It depends not only on income but also on access to social services.” (UN,1995, p.38).

While Nigeria is working on improving its human capital through different programs and policy interventions, there is a need for evidence on where to find the neediest children along these dimensions, to allow for more targeted and evidence-based policy making and interventions, given limited resources.

> 1 See World Bank Indicators Population ages 0-14 (% of total population) <u>https://data.worldbank.org/indicator/SP.POP.0014.TO.ZS?locations=NG</u>

2
# **Table 3:  Large Changes in Governance, 2002-2005**

||**Gove**<br>**2005**|**rnance**<br>**2002**|**Score**<br>**Change**|**Agree**|**No**<br>**change**|**Dis-**<br>**agree**|**Agree/**<br>**(agree+**<br>**Disagree)**|**Sources**<br>**Added**|**Sources**<br>**Subtracted**|
|---|---|---|---|---|---|---|---|---|---|
|**Voice & Accountability**||||||||||
|UNITED ARAB EMIRATES|-1.08|-0.49|-0.59|4|1|1|0.80|2|0|
|RUSSIA|-0.85|-0.45|-0.40|8|0|3|0.73|2|1|
|SINGAPORE|-0.29|0.50|-0.79|4|1|2|0.67|3|0|
|IRAQ|-1.47|-2.15|0.69|6|0|0|1.00|1|0|
|KENYA|-0.12|-0.69|0.57|5|1|1|0.83|3|1|
|LIBERIA|-0.92|-1.57|0.65|5|0|0|1.00|1|1|
|UKRAINE|-0.26|-0.68|0.43|6|2|1|0.86|2|1|
|SERBIA AND MONTENEGRO|0.12|-0.30|0.42|7|1|0|1.00|3|0|
|**Political Stability**||||||||||
|BANGLADESH|-1.65|-0.73|-0.92|4|0|3|0.57|1|1|
|BOLIVIA|-1.15|-0.21|-0.95|5|0|2|0.71|0|0|
|IRAQ|-2.82|-1.80|-1.01|4|1|1|0.80|1|0|
|TOGO|-1.22|0.03|-1.24|3|0|0|1.00|2|0|
|THAILAND|-0.55|0.31|-0.86|7|0|2|0.78|1|1|
|ANGOLA|-0.82|-1.66|0.84|6|0|0|1.00|1|1|
|GEORGIA|-0.80|-1.83|1.03|2|1|1|0.67|2|1|
|**Government Effectiveness**||||||||||
|ZIMBABWE|-1.42|-0.81|-0.61|6|0|3|0.67|2|0|
|* ST. KITTS AND NEVIS|1.00|-0.27|1.28|0|0|2|0.00|1|0|
|* ST. VINCENT AND THE GRENADINES|1.07|-0.20|1.27|1|0|1|0.50|1|0|
|SLOVAK REPUBLIC|0.95|0.46|0.49|9|1|0|1.00|2|0|
|**Regulatory Quality**||||||||||
|VENEZUELA|-1.15|-0.57|-0.58|6|1|2|0.75|1|0|
|* VANUATU|0.05|-1.23|1.29|2|0|0|1.00|1|0|
|IRAQ|-1.61|-2.30|0.69|4|0|1|0.80|0|0|
|**Control of Corruption**||||||||||
|GEORGIA|-0.57|-1.07|0.50|5|0|2|0.71|2|1|
|TURKEY|0.08|-0.45|0.52|8|2|1|0.89|2|1|
|* SOLOMON ISLANDS|0.02|-1.35|1.37|2|0|0|1.00|1|0|
|* VANUATU<br>* 2005 estimates based on fewer than thre|0.26<br>e data s|-0.81<br>ources.|1.07|1|1|0|1.00|1|0|

38
Lewis and Lockheed India. Enrollment, however, does not ensure completion, and the large gender gaps in primary school completion rates signal serious issues.

**_Ethnic and linguistic gaps._** In general, “marginalized” ethnic-linguistic groups are disadvantaged with respect to schooling, often on account of geographical isolation. Thus, in India, children from the lowest caste groups and from tribal groups trail majority children in school enrollment by about 10 percentage points (Census of India 2001). In Ecuador indigenous children are 30 percent more likely to drop out of schools in rural areas than non-indigenous children (Hall and Patrinos 2006). In Bolivia, the primary completion rate of indigenous children, 55 percent, is 25 percentage points lower than that of mainstream children (81 percent). In Laos, PDR, rural young adults have completed about 3 years of schooling, whereas urban young adults have completed about 8 years (King and van de Walle, 2007). In Vietnam restrictions on mobility and inequities in school provision lead to significantly fewer years of education among rural minorities (van de Walle and Gunewardena 2001). In Guatemala rural residence is correlated with delayed primary school entry, lower grade for age, lower rate of primary completion, and lower secondary enrollment (Hallman and Peracca 2007).

**_Double disadvantage._** In each of the above cases, the girls in the marginalized groups trail not only the mainstream children but also the boys in the marginalized groups. Thus, these girls are “doubly disadvantaged.” They are disadvantaged as girls and, in addition they are disadvantaged because they come from impoverished families, tribal, ethnic, or linguistic “minority” communities, geographically remote settings, or lower castes. They are less likely to participate in education and more likely to stay in school only briefly if they enroll at all (Lewis and Lockheed 2006). The exact number of “doubly disadvantaged” girls who are out of school is elusive, since education data are rarely disaggregated by both gender and group identity. Examples from a few countries (see Annex A for details) suggest that gender gaps are significant within ethnic/linguistic groups with respect to:

- <u>having ever enrolled in school</u> in Guatemala, Bolivia, and China (Hallman and Peracca 2007; Jimenez 2004; Hannum 2002);

- <u>current participation in schooling</u> in Guatemala, Ecuador, India and Nigeria (Hallman and Peracca 2007; Garcia Aracil and Winkler 2004; Census of India 2001; Unesco Institute of Statistics 2006);

8
A consistent picture also emerges if we consider the building blocks of the U-shape hypothesis, i.e. trends in the economic transformation away from agriculture, female education and fertility rates. Figure 2 shows the change of these three parameters between 1990 and 2010 for all groups of countries considered.<sup>5</sup> It is immediately evident how well the MENA region has performed. The region is the best performer in terms of fertility rates, the second best performer in terms of female secondary education and performs above average also for the reduction in the share of agricultural values added. The only area where the MENA region has been below the regions’ average is GDP per capita but the MENA region is still above all other regions with the exception of South Asia and Sub-Saharan Africa. The statistics refer to relative percentage changes and the MENA region still lags behind other regions in terms of these indicators. However, the relative performance of the MENA region has been very positive as compared to other regions in the world. According to this figure, the MENA region has done most things right to turn the corner of FPRs. This largely supports results found in World Bank (2013). This last study also shows that the ratio of female to male unemployment rates is higher in the MENA region than elsewhere suggesting that labor demand for women is one of the culprits of low FPR in the region.

**Figure 2 – Relative percentage changes in Fertility, Female Education and Share of Agricultural Value Added between 1990 and 2010 (mean values across countries in region).**

<!-- Start of picture text --> Average<br>SAS<br>GDP per capita<br>SSF<br>MEA Fertility Rates<br>EAS<br>Female Secondary<br>Education<br>LCN<br>Share of Agricultural<br>ECS Value Added<br>NAC<br>-100 -50 0 50 100 150 200<br><!-- End of picture text --> Source: World Bank Open Data. EAS: East Asia and the Pacific; ECS: Europe and Central Asia; LCN: Latin America and the Caribbean; MEA: Middle East and North Africa; NAC: North America; SAS: South Asia; SSF: Sub-Saharan Africa.

In spite of the positive developments observed for the main drivers of the U-shape hypothesis, we saw in Figure 1 that FPRs in the MENA region are significantly lower than the world taken as a whole. Figure 3 compares FPRs by age cohort between the world, the MENA region and Morocco for three points in time, 1990, 2000 and 2010. The figure reports the average FPR for women between the age of 20 and 59 divided into five years’ cohorts. For example, point 20 represents the average FPR for age cohort 20-24 and point 25 the average FPR for age cohort 25-29. We can see that, worldwide, women have improved

> 5 Note that for the share of agricultural value added we considered the average values for the two periods 1990-1999 and 2000-

> 2010 given that agricultural output is subject to significant annual variations.

9
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20
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46
# I Introduction

One of the major tenets of the ...eld of law and economics is that the legal environment a¤ects economic behavior and outcomes. In studying the legal environment, it is crucial to distinguish between the letter of legal rules and their application or enforcement. Much of the evidence we have on how private law is enforced comes from legal disputes between private parties. The empirical analysis of legal disputes has focused disproportionately on cases litigated at trial, since almost all available data is on trial outcomes. However, ...led cases are not a random sample of the universe of underlying legal disputes, and cases litigated at trial are not a random sample of those ...led.

The literatures on law and economics have examined many areas of dispute resolution, including such topics as litigation, arbitration, settlement, and the selection of cases for trial. A key limitation of the empirical tests of theories in this literature has been the lack of data on settled legal disputes. We exploit the fact that Mexican labor law obliges parties in employment disputes to seek rati...cation of settlements, and also mandates that courts approve and record the details of settlements of ...led lawsuits. We use a data set from labor tribunals in Mexico that provides extensive information about settled cases as well as tried cases in order to address a few of the major testable implications of arbitration and litigation theory.

Our data analysis has yielded several interesting ...ndings. First, lawsuits that go to trial receive signi...cantly lower ...nal payments. Second, ...nal-payment amounts are lower when several workers are grouped in a case against a single ...rm, and these cases settle with lower probabilities. Finally, we ...nd evidence that workers who exaggerate their claims settle less often, and may be punished in terms of ...nal-payment amounts. These results have important implications for many of the theoretical models proposed in the literature.

The paper is organized as follows. Section 2 reviews previous theoretical and empirical work in the areas of arbitration, litigation, and settlement. Section 3 explains the relevant details of the legal environment, namely Mexican labor law relating to the type of lawsuits we examine, as well as rules governing the labor courts in Mexico. Section 4 describes the information available in the data set we use. Section 5 presents our statistical analyses. Section 6 concludes and relates our results to the broader literature.

# II Previous Work

This paper is related generally to the literature on bargaining and dispute resolution, and more speci...cally to the work on arbitration and litigation. The usual framework in this area is a game theoretic model in which parties decide whether to settle a dispute privately or bring it before an arbitrator or a court. When adjudication is costly, parties have complete information, and they are assumed to behave according to standard rational decision theory, all disputes terminate in private settlement. Hence, incomplete information is the predom2
# **3. Empirical strategy**

To estimate whether mangroves can reduce hurricane damage, we divide the Caribbean region into 1 square kilometer grid cells and construct a cell-year panel for the 2000–12 period. The panel combines measures of economic activity, potential hurricane damage, and mangrove protection.

First, to assess the impact of hurricanes on economic activity, we estimate the impact of a damage function on our nightlight data set, which is our proxy for economic activity. Specifically, we follow Del Valle et al. (2020) and consider the following two-way fixed effect equation: N i i i ii ii = αα+ ββff + ππ + μμ + εε Where N i is nightlight intensity (in logarithms) of cell in year , i is the damage index,<sup>17</sup> are year fixed effects, are cell fixed effects, and i is the error term. The inclusion of cell fixed effects aims to ii capture the fact that certain areas in the Caribbean may have been exposed to more frequent and greater ii tt ff ππ ii incidence of hurricanes, leading to the reallocation of segments of the population—and thus economic μμ εε activity—or to the implementation of disaster prevention actions. Issues related to time-varying common shocks and nightlight time comparability are addressed by including year fixed effects. The estimation method is based on Correia (2016), which accounts for linear models with many levels of fixed effects (as in our case).

Second, to measure the level of protection, we use two data sets with different calculations of mangrove width and test for a potential nonlinear relationship. Our interest is to study the effectiveness of this potential mitigation property as the level of mangrove protection—that is, mangrove width—increases.

# **3.1.Modeling the windstorm model**

We use the wind speed data to obtain a damage index using the transformation and parameters proposed by Emanuel (2011) in the following expression: Where i represents the wind speed in cell at year , is the threshold below which damage is unlikely to occur, which is set at 50 knots or roughly equal to 92.6 kph, and is the wind speed at which half of TT all structures are expected to be destroyed set as 150 knots or roughly equivalent to 277.8 kph. VV ii tt VV HH VV **Storm surge-prone area** : As we mentioned at the beginning of this section, the i represents the damage index that provides an informative measure of wind damages from excess rainfall and storm surge. But, as storm surge is often considered one of the most harmful aspects of hurricanes, we further investigate ff whether coastal lowlands are disproportionately affected by hurricanes. Specifically, we create a coastal lowland indicator variable identifying continuous areas along the coast that are less than 10 meters above

> 17 For this exercise, we used the _f_ damage index with . However, the impact was also estimated with , with similar results. HH VV = 203 kkpph 13 HH VV = 278 kkpph

13
**Figure 9. Who Captures Government Transfers?**

<!-- Start of picture text --> Share of Total Benefit by Income Quintile Share of Total Benefit by Probability Quintile<br>1 2 3 4 5 1 2 3 4 5<br>Income Index Probability Index<br>Fraction of Total Benefit Fraction in the Population Fraction of Total Benefit Fraction in the Population<br>Source: Authors’ estimates<br>Note: The share of total transfer is approximated using the share of the estimated benefit used in figure 8 given to each income quintile of the population.<br>30<br>25<br>20<br>%15<br>10<br>5<br>0<br>40<br>35<br>30<br>25<br>%20<br>15<br>10<br>5<br>0<br><!-- End of picture text -->

# **6. Final Remarks**

Access to opportunities in the form of basic goods and services is limited in Côte d’Ivoire. These opportunities, including access to education and good habitation, can improve the likelihood that a child will maximize his or her human potential and pursue a productive life as a citizen. The guiding principle is one of equality of opportunity: that a child’s circumstances at birth (for example, gender, location, and parental, social, and economic status) should not determine a child’s access to opportunities.

Two empirical exercises were conducted: (i) construction and analysis of an inequality-sensitive coverage rate of opportunities and (ii) a benefit incidence analysis of access to education using an equality of opportunity framework. The first exercise identified that opportunities not only have low coverage rates, but they are unevenly distributed among different groups in society. Variables such as region and location and household head characteristics differ along the probability distribution of access to opportunities. The 29
the expense of group factors. After the crisis, the trend goes in reverse and ’deglobalization’ takes place: the role of common factors and, in particular, global factors, experiences a decline.

Lastly, we ...nd that countries’exposure to global cycles, as captured by the estimated loadings on the global factors, is signi...cantly related to their structural and policy framework. Financial openness and ...nancial depth amplify the impact of global cycles on both the in‡ow and the out‡ow sides of capital ‡ows. Additionally, a less-‡exible exchange rate regime has the same e¤ect on the gross in‡ow side. The latter result implies that exchange rate pegs reduce, to an economically signi...cant extent, countries’ ability to insulate their ...nancial conditions from changes in global ...nancial centers –suggesting that, in spite of the global ...nancial cycle, the Mundellian Trilemma continues to characterize the trade-o¤s in the choice of exchange rate regime, capital account openness, and monetary policy autonomy.

The rest of the paper is organized as follows. Section 2 describes the two-level latent factor model employed in the estimation. Section 3 turns to the data. Empirical results are presented in Section 4. We ...rst report the factor model estimates, and then discuss how the factors relate to global ’push’variables. Next, we consider how commonality changes over time, and investigate the determinants of countries’exposure to global conditions. Lastly, we extend the empirical setting to encompass developing countries. Finally, Section 5 concludes.

# 2 Analytical framework

Our primary objective is to analyze the comovement of gross capital ‡ows across countries, and assess the respective roles of global, group and idiosyncratic factors. For this purpose, we study the behavior of capital in‡ows and out‡ows in a large panel dataset. To capture the cross-sectional dependence of capital ‡ows, we use a latent factor model. Speci...cally, we consider a simple two-level factor model: Here ym;it denotes the chosen measure of gross capital in‡ows or out‡ows for the i-th country of group (or region) m over period t; Gt is a set of rG unobserved common (world) factors, and Fm;t is a set of rm unobserved factors speci...c to group m. In turn, �m;i and �m;i are the respective factor loadings, and um;it is an idiosyncratic component that may be heteroskedastic and serially and/or cross-sectionally (weakly) correlated. Stacking all the observations for group m at time t, we can write 5