rafmacalaba/datause-encoder-data huggingface dataset:
HOI (C1,C2)≤HOI(C1). Similarly, D(C1,C2)≥D(C1). The impact of adding a circumstance A can be given by: Where _N_ is the set of all circumstances, which includes _n_ circumstances in total; _S_ is a subset of _N_ (containing _s_ circumstances) that does not contain the particular circumstance C1. _D(S)_ is the dissimilarity index estimated with the set of circumstances _S_ . D(S∪{C1}) is the dissimilarity index calculated with set of circumstances _S and_ the circumstance C1. We can define the contribution of circumstance C to the dissimilarity index as: In other words, the sum of the contributions of all circumstances to the dissimilarity index adds up to 100 percent – a critical property satisfied by the Shapley value decomposition.

To measure the contribution of each circumstance to inequality in access to a basic service, we apply the above procedure on the dissimilarity index (equation (2)). We apply it for all seven circumstances and the outcomes of interest. Consider any outcome (e.g. whether a blood sample taken from the mother during pregnancy ), defined as a discrete (0-1) variable, with “1” denoting “yes” and “0” denoting “no”. Our objective is to obtain the conditional probabilities of access to this opportunity for each child based on his/her circumstances. In order to do so, a simple logistic model, linear in the parameters β, where the event I corresponds to “whether a blood sample taken from the mother during pregnancy” and C is the set of circumstances. The following logistic regression is fitted using DHS data: Where ck denotes the row vector of variables representing n circumstances and βk a corresponding column vector of parameters. From the estimation of the above regression one obtains estimates of 14
2 A second goal is micro-level stocktaking. Analysis of trends in economic policies and performance over the duration of Indonesia's recent outwardoriented industrial surge has been abundant at the aggregate level. Yet surprisingly little is known as to what lies behind these aggregate statistics: who are the new export participants? how is industrial organization changing in the emerging sectors? what features of the microenvironment have been most supportive -- and most disruptive -- of export efforts? This section will provide detailed micro-level information (collected via firm surveys) on those aspects of industrial organization and the microenvironment for exporters that are.of special relevance for SMEs. A third goal is to evaluate micro-level policy for SMEs, especially for SME exporters. Indonesia's "first generation" of policy reforms focused appropriately on macro and trade policies. However, with the maturing of these micro-reforms the question arises as to whether micro-level policies and institutions are adequately supportive of SMEs. Particular attention will begiven to the operation of marketing, technological and financial supDort systems for SMEs. Large enterprises typically have resources and functional skills within the firm to explore and exploit opportunities available in the market. But, lacking such resources and skills, SMEs may benefit significantly from support systems -- both private and public networks external to the firm -- to complement their internal efforts. The questions thus arise as to what have been the marketing, technological and financial support systems that have so far facilitated dynamism by Tndonesia's SMEs, and whether the existing support systems are adequate to sustain SME dynamism in the future. This section will analyze the micro-environment for SME exporters, and suggest some reforms that could accelerate participation in Indonesia's manufactures export
was no school at the premises. This could partly be attributed to the large share of “ghost schools” in Sierra Leone.<sup>3</sup> The total number of schools for which data are available at baseline and endline, by district, is summarized in Table 1.

Comparing baseline characteristics for treatment and control schools, we find that the sample is balanced across a wide range of characteristics including infrastructure, student-teacher ratio, school fees, test scores, and many others (Table 2). Of 18 such characteristics, a significant difference (at the 90% level or higher) between treatment and control schools is found only for the student-classroom ratio. Thus, the two groups were well balanced at baseline.

# **3.3 Data**

Baseline data collection took place in May 2008 and final or endline data collection took place in December 2009. The data collected included a head-teacher survey, a classroom teacher survey, a student survey, and student exams.

The data on the school as a whole were obtained through direct observation (e.g., the condition of the buildings, the number of classrooms and other facilities) and a comprehensive interview with the head teacher addressing school finances, record keeping, community participation, management practices, etc. Data were also collected from teachers for grades 4 and 5 through teacher interviews.

One hundred students were selected randomly at each school and were given a written numeracy and literacy test. At baseline (May 2008) the student tests were administered to students in Grades 3 and 4 and at endline the same tests were administered again to the same cohort, which was now in Grades 5 and 6 (Dec 2009). Although we have a large number of tested students, only a small fraction were able to be definitively matched from baseline to endline. Therefore, to measure impacts on student performance, our preferred estimates rely on endline scores only, controlling for schoolcohort-subject mean at baseline and – beyond that – relying on the assumption of balance on average across schools at baseline. We also report results for the change in score of those students who were matched for baseline and endline, but these results may be affected by selective attrition in test participation.

> 3 A 2008 news item highlighted the challenge of “ghost schools,” which the Government was seeking to identify and shut down (Fofana 2008). In 2011, the Government of Sierra Leone announced that they had discovered over 5,000 “ghost teachers,” teachers that do not exist but are used to collect payroll on behalf of corrupt principals and officials (Coker 2011).

7
# Saving for Dowry:

# Evidence from Rural India<sup>_∗_</sup>

S Anukriti<sup>_†_</sup> Sungoh Kwon<sup>_‡_</sup> Nishith Prakash<sup>§</sup> _Note: The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ _JEL Codes_ : J1, D14, O15 Keywords: Dowry, Marriage Payments, India, Savings, Labor Supply

> _∗_ We thank Achyuta Adhvaryu, Prashant Bharadwaj, Sonia Bhalotra, Lucas Coffman, Mausumi Das, Anusar Farooqui, Erica Field, Andrew Foster, Delia Furtado, Parikshit Ghosh, Jessica Goldberg, Shoshana Grossbard, Rachel Heath, Seema Jayachandran, Rob Jensen, Adriana Kugler, Hyun Lee, Arthur Lewbel, Annemie Maertens, Subha Mani, J.V. Meenakshi, Mushfiq Mobarak, Rohini Pande, Krishna Pendakur, Laura Schechter, John Strauss, Chris Udry, Shing-Yi Wang, David Weil, and seminar and conference participants at the AEA-Econometric Society Joint Meetings, Boston College, Delhi School of Economics, Fordham University, Georgetown University, IFPRI, Indian Statistical Institute, NEUDC, New Frontiers in Development Economics Conference, SEA Meetings, University of British Columbia, University of Connecticut, and University of Southern California for their helpful comments and suggestions.

> _†_ Development Research Group, The World Bank and IZA. `sanukriti@worldbank.org` .

> _‡_ Korea Institute of Public Finance. `sokwon@kipf.re.kr` .

> §Department of Economics and Human Rights Institute, University of Connecticut, IZA, HiCN, and CReAM. `nishith.prakash@uconn.edu` .
WPS4249

# **What is the Impact of International Remittances on Poverty and Inequality in Latin America?**<sup>**1**</sup>

**Pablo Acosta (Corporación Andina de Fomento) Cesar Calderón, Pablo Fajnzylber and Humberto Lopez (The World Bank)**

## **Abstract**

Workers’ remittances have become a major source of income for developing countries. However, little is still known about their impact on poverty and inequality. Using a large cross-country panel dataset, we find that remittances in Latin American and Caribbean (LAC) countries have increased growth and reduced inequality and poverty. These results are robust to the use of different instruments that attempt to correct for the potential endogeneity of remittances. Household survey-based estimates for ten LAC countries confirm that remittances have negative albeit relatively small inequality and poverty reducing effects, even after imputations for the potential home earnings of migrants.

Keywords: Remittances, growth, inequality, poverty, Latin America.

## World Bank Policy Research Working Paper 4249, June 2007

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> 1 Paper forthcoming in _Word Development_ . We thank four anonymous referees for valuable comments, Maria Soledad Martinez Peria for providing us with the data on remittances, and Guillermo Beylis and Nam Suk Kim for valuable research assistantship.

1
37

**Table 7. Tobit Analysis of Determinants of Rural Income in Egypt**

|||Annual Per Ca|pita Household I|ncome From:||
|---|---|---|---|---|---|
|Variable|Non-Farm|Agricultural|Transfer|Livestock|Rental|
|Land||||||
|Amount of land owned|46.104|171.802|3.572|18.021|82.622|
|by household|(-2.778)**|(5.761)**|(0.323)|(2.644)**|(2.354)**|
|Amount of land rented|-148.908|-367.564|-62.105|66.099|61.333|
|in by household|(-3.124)**|(-4.176)**|(-1.934)|(4.328)**|(2.354)*|
|Labor||||||
|Household size|-32.311<br>(-2.819)**|24.372<br>(1.078)|0.698<br>(0.079)|2.743<br>(0.519)|16.669<br>(1.730)|
|Mean age of all household|42.572|-18.454|22.539|0.577|2.584|
|members|(4.145)**|(-0.980)|(3.400)**|(0.130)|(0.319)|
|Mean age of all members<br>squared|-0.691<br>(-4.968)**|0.451<br>(1.966)*|-0.083<br>(-1.042)|-0.006<br>(-0.118)|0.144<br>(1.502)|
|Number of household males<br>over 15 years|65.319<br>(2.188)*|91.615<br>(1.518)|-69.169<br>(-2.933)**|8.910<br>(0.626)|13.718<br>(0.534)|
|Capital||||||
|Mean education of household<br>males over 15 years|21.019<br>(1.237)|11.225<br>(0.322)|-28.622<br>(-2.210)*|-5.474<br>(-0.670)|-15.169<br>(-1.005)|
|Mean education of household|2.122|-2.850|1.819|0.409|1.165|
|males squared|(1.894)|(-1.192)|(2.094)*|(0.743)|(1.151)|
|Value of farm equipment<br>owned|0.008<br>(1.235)|-0.003<br>(-0.234)|-0.003<br>(-0.548)|0.008**<br>(2.768)|0.004<br>(0.881)|
|Value of enterprises owned|0.020<br>(5.966)**|-0.005<br>(-0.746)|-0.006<br>(-1.374)|0.001<br>(0.052)|0.004<br>(1.411)|
|Sigma|832.5|1615.3|574.9|381.9|610.7|
|Constant|-276.325<br>(-1.314).|-1093.366<br>(-2.465)*|-777.171<br>(-4.580)**|-347.700<br>(-3.177)**|-1042.868<br>(-4.991)*|
|Log likelihood|-6841.5|-6573.9|4812.7|<br>-5611.1|-3575.3|

Notes: N = 1301 households. Numbers in parentheses are T-statistics (two-tailed). Governorate-level dummy variables are not reported. The dependent variable is annual per capita household income from the particular income source.

* Significant at the .05 level.

** Significant at the .01 level.
tween segregation and test score gaps than with test score mean. A one standard deviation change in the level of segregation raises the locality mean test scores by .78 points while it will increase the gap between the wealthiest and the poorest groups by 2.6 points, i.e. the change in the gap is three times as large as the change in average test scores. Similar effects are found using provincial data. The model of peer effects estimated in this paper is able to account for the observed pattern.

Figure 2: Segregation and school achievement <!-- Start of picture text --> by province<br>CFE<br>BUE TFG BUE CHU SFEGBA MENTUC<br>LPA CRBSGO RNG<br>LRI SJN ERI<br>SCR TFGLPA SJN CRBERICHUSLS SFEGBACFE MEN RNG SAL SCR CAT CHASLSJUJMISCOR SAL<br>MIS TUC<br>FOR JUJ<br>LRI CAT CHASGOCOR FOR<br>.15 .2 .25 .3 .35 .15 .2 .25 .3 .35<br>Segregation index Segregation index<br>slope= 24.4 (p-value = .16) slope= 30.15 (p-value = .19)<br>by localities<br>0 .2 .4 .6 .8 0 .2 .4 .6<br>Segregation index Segregation index<br>slope= 6.91 (p-value = .013) slope= 22.8 (p-value = .001)<br>75<br>15<br>70<br>65 10<br>Mean test score 60<br>Gap in test scores<br>55 5<br>100 40<br>80 20<br>60 0<br>Mean test score 40 -20<br>Gap in test scores<br>20 -40<br><!-- End of picture text --> Note: OLS regressions, with no controls. Standard errors are clustered at the provincial level. Gap in (Spanish) test scores is the difference between the average achievement among the upper group of socioeconomic status and the lower group. Segregation index is the proportion of between school variance in number of books over total variance. Source: ONE 2000, Argentina.

10
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Policy Research Working Paper 5998

# **Abstract**

The Thailand Village Fund is the second-largest microcredit scheme in the world. Nearly 80,000 elected local Village Fund committees administer loans that reach 30 percent of all households. The value of Village Fund loans has remained steady since 2006, even without new infusions of government funds, and loans go disproportionately to the poor. Based mainly on a custom-built survey of more than 3,000 Village Funds conducted in 2010, this paper evaluates the performance of Village Funds, which it argues are best modeled as altruistic, and do not appear to be subject to elite capture. As expected, profit rates are difficult to model, but the regression analysis shows that loan recovery rates, total lending, credit ratings, and the proportion of loans going to the poor are all higher when a Village Fund borrows additional funds from a formal bank and on-lends to households, as was done by one in five Village Funds. An economic analysis suggests that Village Fund benefits exceed the costs. Most Village Funds are social rather than financial intermediaries; they have little incentive to take risks or to innovate, which explains why Village Fund lending has not kept pace with the growth of the Thai economy.

This paper is a product of the Agriculture and Rural Development Team, Development Research 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 author may be contacted atskhandker@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
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44
Policy Research Working Paper 5561

# **Abstract**

This paper quantifies hurricane damage caused by climate change across the US. A damage function is estimated from historic hurricane data to measure the impacts at each location given the storm’s strength. The minimum barometric pressure of each storm turns out to be a better indicator of damages than the traditional measure of maximum wind speed. A hurricane generator in the Atlantic Ocean is then used to create 5000 storms with and without climate change. Combining the location and intensity of each storm with the income and population projected for each location, it is possible to estimate a detailed picture of how hurricanes will impact each state with and without climate change. Income and population growth alone increase expected baseline damage from $9 to $27 billion per year by 2100. Climate change is expected to increase damage by another $40 billion. Over 85 percent of these impacts are in Florida and the Gulf states. The 10 percent most damaging storms cause 93 percent of expected damage.

This paper is a product of the Global Facility for Disaster Reduction and Recovery, Finance Economics and Urban Department. 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 author may be contacted at Robert.mendelsohn@yale.edu.

_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