rafmacalaba/datause-encoder-data huggingface dataset:
Table B9: Which villages were reallocated across treatments? - Individual Variables

||(1)|(2)|(3)|
|---|---|---|---|
|Subsample:|All villages|Treatment group|Control group|
|Variables(Y)||||
|ALL HOUSEHOLD MEMBERS||||
|1. Female|0.0157|0.0201|0.0096|
||(0.0391)|(0.0579)|(0.0334)|
|2. Usually lives here|0.0149***|0.0076|0.0254***|
||(0.0046)|(0.0057)|(0.0039)|
|3. Stayed here last night|0.0079|0.0173***|-0.0139|
||(0.0110)|(0.0042)|(0.0106)|
|4. Age|4.1418***|3.3682***|5.3807***|
||(0.4620)|(0.3959)|(0.3977)|
|RESPONDENTS ONLY||||
|5. Age|0.1662|2.5454|-3.4066|
||(2.6551)|(1.8592)|(4.9482)|
|6. Ever attended school|-0.1374***<br>(0.0293)|-0.1263***<br>(0.0411)|-0.1556***<br>(0.0352)|
|7. Only primary school|0.2397***<br>(0.0263)|0.2603***<br>(0.0356)|0.2192***<br>(0.0400)|
|8. Literate|-0.1209***<br>(0.0434)|-0.1390***<br>(0.0450)|-0.0918<br>(0.0799)|
|9. Muslim religion|0.1997***<br>(0.0353)|0.1697***<br>(0.0472)|0.2294***<br>(0.0527)|
|10. Tigre tribe|0.0386<br>(0.1958)|0.3009**<br>(0.1298)|-0.3789***<br>(0.0676)|
|11. Married|-0.0826***|-0.0525**|-0.1232***|
||(0.0205)|(0.0210)|(0.0268)|

Note: Variables 5-11: sample restricted to respondents only. For each variable Y, we report the coefficient _β_ 1 estimated from LS regression _Yi_ = _β_ 0 + _β_ 1∆ _i_ + _✏i_ , where ∆ _i_ is a dummy =1 if person _i_ lives in a village whose treatment status was changed, and =0 otherwise. Sample restricted to treatment group in column (2) and to control group in column (3). Robust standard errors in parentheses. Observations clustered at village level. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

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This paper explores the religious divide in sanitation practices in India, considering in particular the role of location-specific factors in driving observed differences between Hindus and Muslims. To do so, the analysis uses data from three nationally-representative surveys: the Indian Human Development Survey (IHDS), the District Level Household and Facility Survey (DLHS-3), and the National Family Health Survey (NFHS-3). Each data set provides detailed information on latrine ownership or latrine use at the household level.

The study examines the role of household religion in predicting differences in household sanitation practices. If differences in household sanitation practices are being driven by religion itself, then household sanitation practices should continue to differ substantially by religion when comparing Hindus and Muslims within the same geographic areas.<sup>4</sup> The estimates show unconditional differences between Hindus and Muslims in latrine ownership and latrine use, similar in magnitude to Geruso and Spears (2018), but these differences are reduced by approximately two-thirds after controlling for measures of urbanization and other location characteristics. When replacing these location characteristics with location fixed effects, the analysis estimates a similarly large reduction in the HinduMuslim gap. Latrine ownership remains 5.3 percentage points lower in Hindu households than in Muslim households, using IHDS data in the preferred specification (95% confidence – interval: 1.5pp lower 9.1pp lower). This estimate is similar across specifications, once controlling for local characteristics or location fixed effects, and are similar in DLHS-3 data and NFHS-3 data. While there remains some difference in latrine ownership and latrine use, the unconditional differences by household religion largely reflect differences in household location.

> The analysis then extends to consider other sanitation practices and does not find differences between Hindus and Muslims in reported handwashing or observed fecal material near

> 4Note that religion may influence average sanitation practices in locations, which would be absorbed into location fixed effects, whereas the main analysis focuses on how religion differentially affects households of that religion. Using a machine learning technique (LASSO), this paper also estimates that average sanitation practices in locations are more predicted by locations’ rural status and female literacy than by locations’ religion shares.

2
POVERTY AND INFRASTRUCTURE ACCESS IN SLUMS

# 5   Conclusions

The preceding analysis shows that much can be learned from a comparative study of African cities. Here we underscore what we consider to be three salient conclusions. A first key finding of our work is that, within the admittedly small sample of cities that we examined, neither human capital nor income appears to fully explain the observed variation in infrastructure access. This finding, if borne out by additional work, presents a substantial puzzle—and challenges the idea that reduction in income poverty is sufficient to improve the lives of slum residents.

A second key insight from this work is that the challenges facing slum residents vary considerably from neighborhood to neighborhood, and still more from city to city. This heterogeneity raises an important set of social science questions: How do variations in poverty and human capital relate to variations in urban infrastructure? What institutions mediate this relationship? What lessons do betterserved places offer to places still struggling to meet basic needs? The remarkable variation in the data also suggests an important lesson for policymakers: Interventions must be adapted to local conditions.

The third lesson is primarily methodological. The current paper shows how a multisectoral approach reveals insights that sector-specific studies would almost certainly miss. Efforts to understand urban poverty must recognize multiple dimensions. We show how visual presentations of carefully chosen indicators—grouped into what we term the development diamond, the living conditions diamond, and the infrastructure polygon—can provide a simple yet powerful tool for comparative analysis.

Demographic data from Africa (and from around the world) show that future generations will live mostly in cities. But under what living conditions? This remains unclear. If we are to chart a course to a prosperous, sustainable urban future, then we need to understand clearly where we are today, and the play of forces that got us here. The research described here points to a rich set of issues for additional research. Priorities include:

- _Better data_ . The power of comparative analysis proceeds from good data drawn from many different cities. Furthermore, scholars require longitudinal (panel) data in order to draw conclusions regarding urban outcomes and their underlying causes. Data that sheds light specifically on informal sources of income will be particularly useful.

- _Better theory._ One line of investigation that seems ripe is the institutional context of improvements in slum conditions. How do the key institutional players and the rules of the game vary from city to city? Are there important success stories that can help ground a theory of the institutional antecedents for systematic improvements in slum conditions?

- _Better analytical frameworks._ These will help us arrive at better interpretations of multisectoral and multidimensional data on living conditions and poverty, to translate such data into well-tailored urban interventions, and to assist in communicating the justification for selected interventions.

23
- Cameron, A. Colin and Douglas L. Miller. 2013. “A Practitioner’s Guide to Cluster-Robust Inference.” Working paper, UC Davis.

- Canay, Ivan A. 2011. “A Simple Approach to Quantile Regression for Panel Data.” _Econometrics Journal_ 14 (3):368–386.

- Card, David. 2001. “Estimating the Return to Schooling: Progress on Some Persistent Econometric Problems.” _Econometrica_ 69 (5):1127–1160.

- Case, Anne and Christina Paxson. 2008. “Stature and Status: Height, Ability, and Labor Market Outcomes.” _Journal of Political Economy_ 116 (3):499–532.

- Checkley, William, Gillian Buckley, Robert H. Gilman, Ana M.O. Assis, Richard L. Guerrant, Saul S. Morris, K ̊are Mølbak, Palle Valentiner-Branth, Claudio F. Lanata, and Robert E. Black. 2008. “Multi-Country Analysis of the Effects of Diarrhoea on Childhood Stunting.” _International Journal of Epidemiology_ 37 (4):816–830.

- Chetty, Raj. 2009. “Sufficient Statistics for Welfare Analysis: A Bridge Between Structural and Reduced-Form Methods.” _Annual Review of Economics_ 1:451–488.

- Coffey, Diane, Aashish Gupta, Payal Hathi, Nidhi Khurana, Dean Spears, Nikhil Srivastav, and Sangita Vyas. 2014. “Revealed Preference for Open Defecation: Evidence from a New Survey in Rural North India.” Working paper, r.i.c.e.

- Currie, Janet. 2009. “Healthy, Wealthy, and Wise: Socioeconomic Status, Poor Health in Childhood, and Human Capital Development.” _Journal of Economic Literature_ 47 (1):87–122.

- Currie, Janet and Tom Vogl. 2013. “Early-Life Health and Adult Circumstance in Developing Countries.” _Annual Review of Economics_ 5:1–36.

- Cutler, David, Winnie Fung, Michael Kremer, Monica Singhal, and Tom Vogl. 2010. “Early-life Malaria Exposure and Adult Outcomes: Evidence from Malaria Eradication in India.” _American Economic Journal: Applied Economics_ 2 (2):72–94.

- Cutler, David and Grant Miller. 2005. “The Role of Public Health Improvements in Health Advances: The Twentieth-Century United States.” _Demography_ 42 (1):1–22.

- Desai, Sonalde, Amaresh Dubey, B.L. Joshi, Mitali Sen, Abusaleh Shariff, and Reeve Vanneman. 2007. “India Human Development Survey (IHDS).” Computer file, icpsr22626-v2, University of Maryland, National Council of Applied Economic Research, New Delhi and Inter-University Consortium for Political and Social Research, Ann Arbor.

- Esrey, Steven A., Jean-Pierre Habicht, and George Casella. 1992. “The Complementary Effect of Latrines and Increased Water Usage on the Growth of Infants in Rural Lesotho.” _American Journal of Epidemiology_ 135 (6):659–666.

- Galiani, Sebastian, Paul Gertler, and Ernesto Schargrodsky. 2005. “Water for Life: The Impact of the Privatization of Water Services on Child Mortality.” _Journal of Political Economy_ 113 (1):83– 120.

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Appendix Table 6: RDD estimates of gaps in teacher characteristics associated with teachers hired pre- and post-FPE (bandwidths of 5, <u>7, and 10 years)</u>

||Bandwidth||Urban|Age|Male|Born in<br>district|“Perman<br>ent”<br>contract<br>status|Secondar<br>y or<br>more|Diploma<br>or more|Bachelor<br>s or more|Teaching<br>certificati<br>on|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Pooled|5|Coef|-0.024|1.697|0.025|0.030|-0.053|0.016|-0.009|-0.018|-0.050|
|||SE|(0.043)|(0.571)|(0.050)|(0.048)|(0.036)|(0.028)|(0.039)|(0.025)|(0.030)|
|||Pval|0.571|0.003|0.615|0.539|0.135|0.573|0.809|0.472|0.092|
||7|Coef|-0.019|2.028|0.054|0.082|-0.025|0.017|0.016|-0.011|-0.020|
|||SE|(0.036)|(0.480)|(0.041)|(0.039)|(0.030)|(0.022)|(0.033)|(0.021)|(0.025)|
|||Pval|0.606|0.000|0.196|0.037|0.408|0.445|0.636|0.599|0.421|
||10|Coef|0.050|2.326|0.053|0.086|-0.014|0.001|0.075|0.002|0.016|
|||SE|(0.031)|(0.421)|(0.036)|(0.034)|(0.026)|(0.018)|(0.028)|(0.017)|(0.022)|
|||Pval|0.108|0.000|0.141|0.011|0.581|0.947|0.007|0.922|0.475|
|Kenya|5|Coef|0.041|2.841|-0.129|-0.082|-0.071|0.032|0.055|0.034|-0.020|
|||SE|(0.092)|(1.297)|(0.100)|(0.097)|(0.080)|(0.025)|(0.091)|(0.048)|(0.068)|
|||Pval|0.661|0.030|0.200|0.400|0.377|0.200|0.546|0.478|0.770|
||7|Coef|-0.064|2.997|-0.082|-0.003|0.012|0.029|0.112|0.033|0.082|
|||SE|(0.079)|(1.122)|(0.082)|(0.082)|(0.070)|(0.019)|(0.080)|(0.041)|(0.060)|
|||Pval|0.417|0.008|0.319|0.971|0.865|0.131|0.161|0.418|0.173|
||10|Coef|0.063|4.483|-0.049|-0.054|0.130|0.038|0.295|0.028|0.303|
|||SE|(0.070)|(1.027)|(0.071)|(0.072)|(0.062)|(0.021)|(0.072)|(0.036)|(0.056)|
|||Pval|0.374|0.000|0.490|0.453|0.038|0.073|0.000|0.444|0.000|
|Madagascar|5|Coef|-0.170|3.288|0.089|0.112|0.033|-0.019|-0.006|0.007|-0.056|
|||SE|(0.121)|(1.848)|(0.132)|(0.124)|(0.102)|(0.116)|(0.121)|(0.088)|(0.093)|
|||Pval|0.161|0.076|0.500|0.368|0.745|0.870|0.959|0.941|0.546|
||7|Coef|-0.186|3.084|0.132|0.151|-0.008|-0.073|-0.061|-0.065|-0.040|
|||SE|(0.107)|(1.595)|(0.117)|(0.108)|(0.089)|(0.102)|(0.105)|(0.077)|(0.086)|
|||Pval|0.083|0.054|0.260|0.163|0.924|0.479|0.564|0.398|0.647|
||10|Coef|-0.144|2.129|0.080|0.167|-0.044|-0.184|-0.144|-0.066|-0.037|
|||SE|(0.092)|(1.359)|(0.099)|(0.090)|(0.078)|(0.088)|(0.089)|(0.064)|(0.070)|
|||Pval|0.121|0.118|0.420|0.064|0.569|0.039|0.106|0.298|0.593|

Notes: Table reports coefficient estimates (Coef), Standard Errors (SE), and p-values derived from estimating Equation (1). Each estimate and associated standard error and p-value are the result of a separate regression model. Pooled model includes country fixed effects.

A-11
year. However, the empirical identification of the length of the growing season is in itself challenging because rainfall in Nigeria has considerable spatial variability; from a tropical climate in the south to a semi-arid climate of the Sahel.

This fact is explicit in Figures 5, which shows the seasonal distribution of rainfall for three weather stations located at different latitudes: Maiduguri (north), Abuja (central), and Port-Harcourt (south). Annual precipitation in Nigeria is highest in the coastal areas and decrease inland towards the north. The graph in panel (a) also reflects the typical double maxima and the little dry season (popularly known as August break) of the coastal part of the country, as well as the single maximum rainfall regime of northern Nigeria. It also confirms that the length of the growing season varies enormously at different parts of the country. Therefore, to account for this variability we divide the country in three distinct zones using the estimates of the length of the growing season presented in Odekunle (2004).<sup>19</sup> The resulting mean lengths are approximately 6 months in the south (from early May to the end of October), 4 months in the central part of the country (from June to September), and only 2 months in the north (from July to August). The spatial distribution of DHS clusters into these regions is depicted in Figure 6 along with the interpolated normal level of rainfall during the growing season for each DHS cluster.

As opposed to rainfall patterns, normal temperatures are relatively uniform and the seasonal variation is small, thereby allowing a wide range of suitable crops in relation to temperature conditions. This happens especially in the central-south part of the country where the mean monthly long-term temperature fluctuates between 25 to 30 degrees Celsius. The period between February and April is the hottest during the year (this period corresponds to the dry season). Northern Nigeria presents more variability in terms of temperature that ranges between 22 and 33 degrees.

# Figure 6

> 19 Specifically, Odekunle (2004) used 40 years of daily rainfall data from five weather stations (Ikeja, Kaduna, Illorin, Ondo, and Kano) representing different climates, to predict the date of onset and cessation of rains. The point of onset corresponds to the time when cumulative rainfall reaches 7-8% of the annual total rainfall, whereas that of rainfall cessation corresponds to 90%.

17
6). When looking at total taxes over GDP, the SSA region lags behind ECA, EAP and LAC, but it is better positioned compared to MENA and SA.

**Figure 6. Trends in taxation (total taxes as % GDP) by region**

<!-- Start of picture text --> EAP ECA LAC MENA SA SSA<br>26%<br>24%<br>22%<br>20%<br>18%<br>16%<br>14%<br>12%<br>10%<br>8%<br>2 0 0 0 2 0 0 2 2 0 0 4 2 0 0 6 2008 2010 2012 2014 2016 2018 2020<br><!-- End of picture text --> _Source:_ Government Revenue Database (GRD) – United Nations University UNU - WIDER.

<mark>Regional-level analysis hides considerable country heterogeneity. Public sector performance indeed varies substantially across countries (Table 1). CPIA scores show that a small subset of countries stands out, such as Rwanda and Cabo Verde which have been among the better-performing countries in terms of public sector performance for more than a decade. Similar trends emerge when zooming into budget and PFM performance: Public Expenditure and Financial Accountability (PEFA) assessments of budget execution, for example, show that some countries, such as Rwanda, Ghana and Zambia, outperform their regional neighbors with regards to the level of predictability and control over budget execution (Figure 7).</mark>

**_Table 1. How have African countries performed from a governance perspective in the past decade?_**

||**CPIA Public Sector Management and Institutions**|
|---|---|
|Top5performers(2021)|Cabo Verde,Rwanda,Kenya,Senegal and Ghana|
|Bottom 5 performers (2021)|South Sudan, Somalia, Sudan, Guinea-Bissau, Central African Republic|
|Most positive trend since 2005<br>(>=0.5pt)|Zimbabwe, Togo, Côte d’Ivoire, Rwanda|
|Most negative trend since 2005 (<=-<br>0.5 pt)|Sudan, Tanzania, Mali, Madagascar, Guinea-Bissau, Eritrea|
|No change since 2005|Niger, Nigeria, Senegal|

Data source: CPIA Africa Report 2022.

11
43 threshold might lead to the loss of all benefits, implying a withdrawal rate in excess of 100 percent, that is, a reduction in net income.”

# MIG vs. ITS: A Comparison

Highpercentageof targeting errors. Let us look first at the targeting issue if the minimal income guarantee is adopted. The key issue is how to identify the poor, that is, ascertain the exact amount of income. This is difficult because a large part of income of the poor and the "near poor” is derived from informal activities which are almost impossible to monitor. In addition, even formal sector income from the small legalized and legitimate private businesses often goes unreported to tax authorities. What in addition makes targeting difficult is that at some realistic minimal guaranteed income, lots of people are both slightly below that income or slightly above. That means that approximately, 10 to 12 percent of the population might find itself within 10 percentage point of the MIG. (These values are derived from the elasticities ranging between 0.5 and 0.6, discussed in Section 2.) Many of these people may try to claim the benefit. As the data from a Polish study show, the "excess" error (benefits given to those whose income is above the poverty line) is concentrated in income groups slightly above the poverty line (see Figure 6). Forty percent of the "excess" error (both in terms of people and money spent) concerns those with income 30 percent or less above the poverty line.

<!-- Start of picture text --> Figure 6<br>Distribution of the non-poor who receive social assistance<br>(Poland, 1993)<br>16<br>44<br>12<br>10<br>8<br>6<br>4<br>2<br>8<br>10 20 3 40 50 60 70 BI 90 100 150 200 250 300 350 400 41 500>500<br>% above the poverty line<br>Notes: All the non-poor who have received social assistance= 100.<br>Poverty line= minimum pension.<br><!-- End of picture text --> "Very often one finds that the same income threshold is used to determine eligibility for social assistance, subsidized utilities and child care, lower transportation fare etc. By going over that threshold, family might lose more in lost benefits than it gets in higher money (pre-social assistance) income.
richest quintile. The poorest households are also most reliant on the remittances that they receive from abroad: remittances account for 11 percent of household income in the poorest quintile households, and about 8 percent in the richest quintile. The two most important uses of remittance income are for food and to buy or improve housing (combined, these account for about 82 percent of the primary use of remittance income). Expenditure on food is substantially more common among poorer households. It is important to note however that as remittance income tends to relax household budgets in general (the income is “fungible”) this is a rather subjective measure of the welfare impact of remittance income.

**Figure 2: Migration and Use of Remittances**

<!-- Start of picture text --> 30%<br>Primary use for remittances<br>25% EducationOther<br>Health<br>20% Pay debt<br>Wedding<br>15%<br>Impr./buy<br>10% housing<br>5%<br>0%<br>Poorest 2 3 4 Richest<br>Food<br>Share with Migrant Household Member<br>Average Remittance Share of Income<br><!-- End of picture text --> _Notes: Listening to the Citizens of Uzbekistan Baseline Survey, Author’s calculations_ These relationships can be comprehensively summarized using a simple ordinary least squares (OLS) regression framework, though such cross-sectional comparisons are only descriptive. Table (4) reports such a set of descriptive regressions for which the dependent variable is equal to one when a household member is a current migrant, and zero otherwise. The coefficient in column (1) is negative and significant, indicating that households with current migrants abroad have lower per capita consumption on average than those that do not. This comparison controls for which province the household lives in, whether the household resides in an urban location, and the size of the household. Column (2) highlights however that this relationship is even stronger if one reduces consumption by the value of remittances sent over the same period. After accounting for the increased consumption that is assumed to be purchased using remittances provided by migrant members, households with migrants have substantially lower (log transformed) per capita consumption than other households.

Columns (3-4) further clarify the importance and contribution of remittances in household welfare. The near-zero coefficient for the log of total household income in column (3) can be interpreted as signifying that households that currently have a migrant member do not have different income than those without migrant members (in fact, average income is slightly higher when ignoring zero values). However, after removing remittance income, column (4) reveals that households with a migrant 9
# **Overdraft Facility Policy and Firm Performance: An Empirical Analysis in Eastern European Union Industrial firms**<sup>**1**</sup>

Leopoldo Laborda Castillo<sup>*</sup> World Bank (USA) and University of Alcalá (Spain) Jose Luis Guasch** World Bank (USA) and University of California, San Diego (USA)

# **Keywords**

Overdraft Facility Policy, Propensity Score Matching, Technical Efficiency, Data Envelopment Analysis (DEA), Bootstrapping.

# **JEL Classifications**

C40, H43, H8, L25, L26, L53

* STC at The World Bank and Associate Professor at the University of Alcalá: c/ Trinidad no 1, Colegio de Trinitarios, Alcalá de Henares, 28801, Madrid (Spain).  Phone: +34 91 8820399. e-mail: llabordacastillo@gmail.com

** Former Senior Adviser at the World Bank and Professor of Economics at University of California, San Diego (USA) e-mail: jguasch@worldbank.org

> 1 This paper is forthcoming in the _Journal of Business Economics and Management._ The authors are thankful to Esperanza Lasagabaster and two anonymous referees for very useful comments.
**_Government effectiveness_** (EFFECTIVE) and **_rule of law_** (RULE) are from the World Bank Institute’s Worldwide Governance Indicators Database. Government effectiveness is “measuring the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government’s commitment to such policies” (Thomas, 2010). Rule of law captures “perceptions of the extent to which agents have confidence in and abide by the rules of society, and in particular the quality of contract enforcement, the police and the courts, as well as the likelihood of crime and violence” (Thomas, 2010). The measure of both variables for each country is a point in the range of -2.5 (lowest effectiveness or rule of law) to 2.5 (highest effectiveness or rule of law). As a result, the expected sign of both variables is negative.

**_Burden of government regulation_** (BURDEN) is from Global Competitiveness Database and it measures “how burdensome is it for businesses in your country to comply with governmental administrative requirements (e.g., permits, regulations, reporting)? [1 = extremely burdensome; 7 = not burdensome at all]” (World Economic Forum, 2013). Similar to other control variables the expected sign is negative.

23