rafmacalaba/datause-encoder-data huggingface dataset:
# **1 Introduction**

The number of countries that have developed the necessary tools to measure absolute monetary poverty and that are now able to track their own performance in terms of poverty reduction has grown considerably in LAC and worldwide in recent years. However, less progress has been achieved to accurately compare poverty estimates across countries. In practice, poverty measurement differs from country to country in many respects, including: (i) the methodology to estimate the poverty line (e.g., relative vs. absolute lines, the minimum amount of calories, the choice of the reference group, or the procedure to go from extreme to moderate poverty lines); (ii) the choice of the individual welfare measure (i.e., income vs. consumption); (iii) the construction of the welfare aggregate (e.g., items included in the income or consumption aggregate or the treatment of implicit rent of own housing); (iv) the design of surveys (e.g., differences in questionnaire design); and (v) many other adjustments and considerations (e.g., adult equivalent scales vs. per capita values, economies of scale, and regional prices).<sup>1</sup> Consequently, the comparison of official poverty estimates across countries is generally misleading. Thus, any attempt to generate meaningful poverty comparisons and to aggregate national poverty indicators into regional and global ones requires a standardized measure of household welfare and a common poverty line.

An emerging body of research has proposed some methods to obtain standardized measures of monetary poverty that are comparable across countries and independent of the official methodologies applied in each country. The seminal work by Ravallion, Datt, and van de Walle (1991) proposed one of the first global extreme poverty lines at $1.01 (rounded to $1) per person per day at 1990 PPPs based on 1985 prices surveys performed by the ICP.<sup>2</sup> This global extreme poverty line corresponded to the average of the poverty lines of the eight most deprived economies in the world and became the foundation for the United Nations’ first Millennium Development Goals of halving the proportion of people with incomes lower than $1 a day between 1990 and 2015. This line was then revised by Chen and Ravallion (2001) to $1.08 per person per day at 1993

> 1 In LAC, National Statistical Offices (NSOs) are generally in charge of monitoring poverty over time. Most of the countries in the region measure poverty based on per capita income, thought there is a growing tendency to consider consumption as the welfare measure.

> 2 Ahluwalia, Carter, and Chenery (1979)used India’s poverty line to estimate poverty at a global level based on the 1975 PPPs. This was the first attempt to measure global poverty, though it was based on income and consumption data from only 25 countries in the world (Ferreira et al. 2015).

2
**Table 5.4: Years of Completed Education for Age Groups, by Locality and Poverty** **<u>Condition, Selected Years 1992-2002</u>**

|||**19**|**92**|**19**|**96**|**20**|**02**|
|---|---|---|---|---|---|---|---|
|||**Poor**|**Non-**<br>**poor**|**Poor**|**Non-**<br>**poor**|**Poor**|**Non-**<br>**poor**|
|**Region **<br>||**Age **<br>|**group 1**<br>|**2-24**<br>||||
|**Rural**<sup>**1**</sup>|No education|12.71|5.72|7.85|5.48|5.27|4.25|
||Primary incomplete|40.27|27.48|34.46|16.47|21.57|14.19|
||Primary complete|38.77|41.87|43.13|44.06|51.12|42.51|
||Secondary complete|7.87|21.8|13.69|29.6|20.53|30.45|
||Higher education|0.37|3.13|0.88|4.39|1.52|8.59|
|**Urban**|No education|6.63|2.85|3.64|2.94|5.66|1.75|
||Primary incomplete|24.9|9.48|19.55|5.73|22.41|5.28|
||Primary complete|46.55|33.73|43.77|30.77|41.56|30.56|
||Secondary complete|20.7|39.14|28.28|41.39|25.29|39.22|
||Higher education|1.22|14.8|4.76|19.16|5.08|23.19|
|||**Age **<br>|**group 2**<br>|**5-45**<br>||||
|**Rural**|No education|37.67|20.9|21.67|13.09|23.15|12.84|
||Primary incomplete|43.66|40.75|44.57|30.41|39.14|27.49|
||Primary complete|16.66|27.34|25.62|29.85|29.26|31.37|
||Secondary complete|1.96|7.74|7.46|18.77|7.32|19.55|
||Highereducation|0.04|3.26|0.67|7.89|1.13|8.76|
|**Urban**|No education|19.27|6.37|12.13|4.25|11.79|3.18|
||Primary incomplete|34.93|16.37|29.11|10.46|26.13|9.28|
||Primary complete|35.85|29.28|36.7|22.6|31.96|21.04|
||Secondary complete|8.68|24.06|17.3|30.45|25.07|34.05|
||Highereducation|1.27|23.9|4.77|32.23|5.05|32.44|
|Source: E<br>than 2,500|NIGH 1992, 1996 and 20<br>inhabitants.|02.<sup>1</sup>Rura|l area de|fined as|localitie|s with l|ess|

The probability of falling below the poverty line fell for secondary school graduates during 1992-1996 and increased again from 1996-2002 reaching a level that is not statistically significantly different from 1992. As unemployment increased after the crises in 1996, the labor market returns adjusted a little, as can be noticed in Table 5.3. The income per capita regression analyses presented confirm that returns to secondary education decreased before the crises and decreased only slightly again during 1996-2002. Moreover, the head of household’s returns to secondary education in rural areas were statistically and significantly lower in 2002 (67 percent) than in 1992 (81 percent), controlling for other individual and household characteristics.

Human capital is composed of many components and an important one, apart from formal education, is skills. In the labor market literature, skills and experience are often proxied by the age of a worker. We include the age and age squared in the regression, the latter to capture possible nonlinearities in data. In all models, age is positively associated with the probability of escaping poverty during 1992-2002. Moreover, during this period skills become more poverty reducing; one more year decreases the likelihood of experiencing 30
79 Figure A12: Distribution of Landholding Sizes <!-- Start of picture text --> −6 −4 −2 0 2 4 −5 0 5<br>Log of landholding size (in ha) Log of landholding size (in ha), residualized<br>(a) Unadjusted (b) Adjusted for Region<br>.4 .4<br>.3 .3<br>.2 .2<br>Density Density<br>.1 .1<br>0 0<br><!-- End of picture text --> _Notes_ : The figures show kernel density estimates for (the log of) the size of households’ landholdings (measured in ha). A unit of observation is a household. The data comes from the 2016 edition of the HIES. Panel A12a shows unadjusted estimates. Panel A12b shows estimates for a residualized version of the log landholding size variable which is obtained by extracting the residual from a regression of that variable on a set of district dummy variables, with the regression estimated by Weighted Least Squares (WLS) using the statistical weights provided by the HIES. Densities are estimated using Epanechnikov kernels with optimal bandwidth (0.2021 in Panel A12a and 0.1647 in Panel A12b) and statistical weights.

sample. We then estimate the dispersion parameter _σLn_<sup>2for district</sup><sup>_n_as:</sup> where _|_ S _n| ≡_<sup>�</sup> _h_<sup>_′_</sup> _∈n_<sup>1 is the number of surveyed households in district</sup><sup>_n_that own land.</sup>

Given estimates _σ_ ˆ _Ln_<sup>2ofthedispersionparameters,wecanthenestimatethescaleparameters</sup> _μn_ so as to match the average landholding size in each district. Specifically, the properties of the log-normal distribution imply that the average landholding size in district _n_ is exp( _μn_ + _σLn_<sup>2</sup><sup>_/_2), so</sup> we set the empirical analogue of this expression equal to the average landholding size observed in the data and rearrange terms to obtain our estimator of _μn_ : where _Ln_ is total arable land (in ha) and _Nn_<sup>_F_is the number of farmers in district</sup><sup>_n_.</sup>

# **C.D Evolution of Rice and Tea Agriculture over Time**

We combine cutting-edge remote sensing data (described in Appendix A.C.2) with detailed weather data (described in Appendix A.J) to show the evolution of yields of rice and tea (and, for rice, of cultivated area) over time.
most minor case (an exception is Belize). As a result, the appeals process is often used by the losing party to delay enforcement even further after the already protracted court of first instance proceedings. 35 The LAC index for formalism is higher than that of both the African and E. Asian index.

Some examples help illustrate the severity of the problem in the LAC region: In Colombia, it takes on average 527 days to resolve a case of small debt collection in the courts, while in Peru it takes 441, and even in Argentina it takes nearly a year. The eviction of a delinquent tenant through court order can be even more cumbersome – 440. Looking at data in the other regions shows similar results.

The project discussed here does not set absolute standards of formalism for any country. For example, the degree of procedural complexity in the UK, while low by the standards of developed countries may not be low enough for Mozambique. And reform towards greater simplicity in the courts of Mozambique for example, will continue to have little relevance if there are few courts in the country and distances traveled are large. Another thing that is missing is information on who actually uses the courts for the disputes considered. Two other caveats need to be mentioned in considered these results: while the project focuses on small disputes relative to GDP, in many countries the amounts chosen may still be large relative to the income of the vast majority of the population, especially where inequality is high.

Any reform of the judicial system needs to assess how it will affect behaviour.

> 35 Common Law LAC countries exhibit substantially less regulation of dispute resolution, all of its major components and substantially faster resolution than their Civil Law counterparts.  Because of their small size, Caribbean countries are often omitted in international surveys and unfortunately, data on corruption containment from the Common Law LAC countries is virtually missing (exceptions are Jamaica and Trinidad and Tobago).

34
Table 4. Labor Market Outcomes

||M<br>|Gender of the<br>ales<br>|household hea<br>Fe<br>|d<br>males<br>|
|---|---|---|---|---|
||Mean|SD|Mean|SD|
|**A. Head's labor market activities**|||||
|Jobless|0.032|0.177|0.297|0.457|
|Day labor|||||
|Agriculture|0.322|0.467|0.143|0.350|
|Non-agriculture|0.157|0.364|0.133|0.340|
|Paid employment|0.028|0.165|0.118|0.322|
|Self employment|0.461|0.498|0.309|0.462|
|**B. Household labor outcomes**|||||
|Monthly household expenditure (BDT)|9,198|4,780|5,370|3,802|
|Number of employed adults|2.44|1.05|1.44|0.87|
|Employment ratio|0.869|0.295|0.864|0.472|
|Migration (yes=1)|0.541|0.499|0.350|0.477|
|Remittances if positive|2,121|2,444|2,579|5,212|
|**Num. obs**<br>Notes: This table presents employment status|6, <br>of the head of|022<br>the household,|7<br>and labor mark|90<br>et outcomes of|

Notes: This table presents employment status of the head of the household, and labor market outcomes of all adults in the household. The jobless category includes those out of the labor force and the unemployed.

Although female-headed households have substantially lower monthly expenditures, per capita expenditures vary little with the gender of household heads. The overall employment ratio in these households, that is, the number of employed adults over the total number of adults, is about 87 percent and does not vary by gender of the head of household. This suggests that almost the full capacity of adults in poor rural households, regardless of gender of the household’s head, are engaged in economic activities, and loss of employment and labor earnings could have significantly negative consequences.

Moreover, migration to seek employment opportunities is quite common. More than one-half of the sampled households have migrant workers who did not reside in the households but sent remittances in the past year. The male-headed households are significantly more likely to have migrant workers. The presence of another male household member may influence one’s decision to migrate given that the majority of migrant workers are males. Regarding remittances, however, more resources are transferred to female-headed households, which is likely an effort to compensate for their lack of incomes.

15
# **<mark>Table 8 - Impact of FD Law on States’ Outcomes Given Their Public Debt and Fiscal Spending Composition</mark>**

<mark>This table reports OLS estimates of the impact of the FD Law on outcomes at the state-quarter, state-year and firm-bank-month levels for states</mark> of varying ex ante public indebtedness and spending composition. _GDPs,q_ is the GDP growth rate of state _s_ in quarter _q_ . _Employment Totals,q_ is the growth rate of employment in state _s_ in quarter _q_ . _Povertys,y_ is the share of the population in state _s_ and year _y_ that is considered poor (i.e., individuals that cannot fulfill one of six basic needs: education, access to health, access to social security, basic housing services, access to food, and basic income). _Moderate (Extreme) Povertys,y_ is the share of the population in state _s_ and year _y_ that cannot fulfill at most (more than) two basic needs. The variable _Ginis,y_ is the Gini coefficient of state _s_ in year _y. Volumef,b,m_ is the loan volume (in logs) issued to firm _f_ , by bank _b_ in month _m. DebtStates,16Q1_ is a measure of public indebtedness of state _s_ in the quarter prior to the FD Law and is calculated as the ratio of public debt of a state over its net income. _NonInfrastructures,2015_ is the ratio of public spending in all categories except for infrastructure over the total public spending of state _s_ in 2015. _Postt_ is an indicator variable that equals one from in periods following the FD Law. Standard errors are reported in parentheses and are doubled clustered at the state and quarter levels (Columns 1 and 2), state and year levels (Columns 3 to 6), and state and month levels (Column 7). *, **, *** denote significance at the 10, 5 and 1 percent levels. Detailed variable definitions are provided <u>in Table A1 in the Appendix.</u>

||GDPs,q|Employment<br>Totals,q|Povertys,y|Moderate<br>Povertys,y|Extreme<br>Povertys,y|Ginis,y|Volumef,b,m|
|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Postt*DebtStates,16Q1*Non-<br>Infrastructures,2015|0.062*|0.039*|-0.176|-0.396***|0.219***|0.386|0.637***|
||(0.036)|(0.021)|(0.150)|(0.122)|(0.080)|(0.246)|(0.198)|
|Postt*DebtStates,16Q1|0.004*|0.001***|-0.009*|-0.017***|0.007**|0.002|0.019***|
||(0.003)|(0.000)|(0.005)|(0.004)|(0.003)|(0.008)|(0.006)|
|Postt*Non-Infrastructures,2015|-0.061|0.084***|0.434***|0.465***|-0.025|0.275*|-0.503***|
||(0.062)|(0.001)|(0.151)|(0.130)|(0.075)|(0.162)|(0.178)|
|Observations|480|420|150|150|150|150|1,252,105|
|R-squared|0.473|0.184|0.964|0.920|0.969|0.608|0.803|
|State FE|Yes|Yes|Yes|Yes|Yes|Yes|-|
|Quarter FE|Yes|Yes|No|No|No|No|-|
|Year FE|-|-|Yes|Yes|Yes|Yes|-|
|Firm-Bank FE|No|No|No|No|No|No|Yes|
|Bank-Month FE|No|No|No|No|No|No|Yes|
|Sector-Month FE|No|No|No|No|No|No|Yes|

55
have been persuasive when pointing to particular cases, the sheer number of cases, as well as the variation in tendencies over time and among court benches, have made reaching a general conclusion difficult. This paper has argued that complaints related to separation of powers concerns are better understood as criticisms of the impact of judicial interventions on sectoral governance, and that structured case studies of sectoral governance are necessary to assess those criticisms. On the issue of inequality, this paper contributes to an overall assessment by systematically examining the relative magnitude, case composition, and geographical origins of, as well as legal representation and the claimant’s social class in, PIL and Fundamental Rights cases that reached the Indian Supreme Court.

The analysis of PIL “cases” shows that they do not appear to consume a significant share of the resources of the Supreme Court; they constitute less than 1% of the overall case load. The subject matter of PIL cases and orders remains difficult to discern because over 70% of them are classified as “other,” which is problematic from the point of view of judicial transparency. Concerns regarding inequality appear to be validated by some of the quantitative data on Fundamental Rights cases. On the one hand, although the number of Fundamental Rights cases related to women and children’s rights and to the concerns of scheduled caste/scheduled tribe/other backward classes, as well as cases explicitly called a “PIL” in the Court’s written opinions, appear to have increased; and the rate of increase has been similar for claimants belong to both marginalized and advantaged population segments. The share of cases from BIMARU states also appears to be climbing. On the other hand, a very low share of the cases are brought by cooperative entities, such as NGOs, which is a useful predictor of the likelihood that benefits will generalize to the larger population. Most striking, win rates for Fundamental Rights claims are now significantly lower when the claimant is from an advantaged social group than when he or she is from a marginalized group. That constitutes a social reversal both from the original objective of public interest litigation and from the relative win rates in the 1980s.

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Policy Research Working Paper 7692

# **Abstract**

In the past decades, natural disasters have caused substantial human and economic losses in Central America, with strong adverse impacts on gross domestic product per capita, income, and poverty reduction. This study provides a regional perspective on the impact of hurricane windstorms on socioeconomic measures in the short term. Apart from modeling the socioeconomic impact at the macro and micro levels, the study incorporates and juxtaposes data from a hurricane windstorm model categorizing three hurricane damage indexes, which lends a higher level of detail, nuance, and therefore accuracy and comprehensiveness to the study. One standard deviation in the intensity of a hurricane windstorm leads to a decrease in growth of total per capita gross domestic product of between 0.9 and 1.6 percent, and a decrease in total income and labor income by 3 percent, which in turn increases moderate and extreme poverty by 1.5 percentage points. These results demonstrate the causal relationship between hurricane windstorm impacts and poverty in Central America, producing regional evidence that could improve targeting of disaster risk management policies toward those most impacted and thus whose needs are greatest.

This paper is a product of the Social, Urban, Rural and Resilience Global Practice Group. 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://econ.worldbank.org. The authors may be contacted at oishizawa@worldbank.org, jjmiranda@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 papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
We selected two representative housing types: a high-value two-story home constructed with brick walls, concrete floor and clay roof (referred to as “Masonry”) and a middle-income two- to-three story home constructed with mixed wall, concrete floor and asbestos roof (referred to as “Mixed Wall”). The replacement value of the representative high-value building is assumed to be 19,200 USD and 7,000 USD for the middle-income one (based on estimates from Silver, 2007).

# **_Exceedance Probability Curve and Selected Disaster Risk Reduction Measures_**

Given flood hazard data available for Jakarta, our analysis is based on approximate flood extent maps and limited depth estimates for two past floods in January/February 2002 and February 2007 (Dartmouth Flood Observatory, 2008). Our hazard analysis also uses a 30-year monthly rainfall time series, observed at the Jakarta Observatory (NOAA global database) within the catchment of the Ciliwung and an elevation map based on data from the NASA Shuttle Radar Topography Mission. Based on these inputs, two probabilistic flood depth curves are generated, representing a higher (‘max hazard’) and lower (‘min hazard’) hazard location. Using two curves also allows us to test the sensitivity of findings to the hazard approximation.

<!-- Start of picture text --> 0.2<br>0.2<br>Mixed Wall in Max Hazard<br>0.2<br>0.1 Mixed Wall in Min Hazard<br>0.1<br>0.1 Masonry in Max Hazard<br>0.1<br>Masonry in Min Hazard<br>0.1<br>0.0<br>0.0<br>0.0<br>0.0 5.0 10.0 15.0 20.0 25.0<br>Loss Amount (Thousands USD)<br> Exceedance Probability<br>Annual<br><!-- End of picture text -->

**FIGURE 2: FLOOD RISK IN JAKARTA - EP CURVES FOR TWO BASE LINE STRUCTURES IN TWO DIFFERENT HAZARD LOCATIONS WITH NO DISASTER RISK REDUCTION**

11
17 In the second stage, three sets of estimated coefficients from the first stage are pooled over time and regressed on a set of trade-related industry characteristics. The dependent variables in this stage are: (i) the industry premia coefficients from (1), _wpjt_ ; (ii) the industry-specific skill premia coefficients from (1), _spjt_ ; and (iii) the constant terms _λ0jt_ for each occupation from (2). Each of these variables _vjt_ ={ _wpjt_ ; _spjt_ ; _λ0jt_ ) is regressed (in first differences) on a set of trade-related variables ( _Tjt_ ), including industry- specific effective rates of protection, import- and export-weighted exchange rates; import penetration and export shares: In addition to estimating a two-stage employment model, which we have not seen in the trade literature, there are other differences between these estimations and those reported in Pavcnik et al. (2004). Our data comes from a nationally representative household survey (the PNAD), and include workers in agriculture and non-tradable industries (for which industry-specific exchange rates can be constructed,<sup>24</sup> and affect relative prices). Rather than relying on the manufacturing sector in the six largest metropolitan areas of Brazil, our sample is therefore much more broadly representative of the country. Possibly as a result, some of our estimation results are different than those in Pavcnik et al. (2004), and we discuss them briefly in the next section.

But the main purpose of estimating equations (1)-(3) is to use them in the decomposition of all changes in the full wage distribution between 1988 and 1995. Following Juhn, Murphy and Pierce (1993) – henceforth cited as JMP – one can decompose the difference between the wage distribution prior to the liberalization (say, in 1988) and the wage distribution afterwards (say, in 1995) into three components: one due to changes in observed worker characteristics ( _X_ ); one due to changes in the returns to those characteristics (measured by regression coefficients _β_ ) and a final one due to changes in 24 We use economy-wide import- and export-weighted exchange rates for the non-tradables sector.