rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 10: Distribution of Unitary Public Expenditures on** **_Health Center Care_ Net of Private Household Contributions, Paraguay, 2009 (Ages 0–17)**

**a.                                                               b.**

<!-- Start of picture text --> Unitary public expenditure on health center care Unitary public expenditure on health center care<br>by quintiles of incomes (2009) by quintiles of probability (2009)<br>213<br>184 178 173<br>177 157<br>148<br>138 79 140 113 105 133 108 99 116 86<br>40 37 30<br>-46 -39 -73 -64 -69 -46 -50 -69 -56 -62<br>Q1 (Poorest) Q2 Q3 Q4 Q5 (Richest) Q1 (Least) Q2 Q3 Q4 Q5 (Most)<br>Public expenditures Household expenditures Net benefit Public expenditures Household expenditures Net benefit<br>Source : Author’s estimates from INEC’s EPH 2009; MSPyBS (2010); IPS (2011); Cuesta and Suarez-Becerra (2013).<br>Figure 11: Distribution of Unitary Public Spending on Hospital Care<br>Contributions, Paraguay, 2009 (Ages 0–17)<br> a. b.<br>Unitary public expenditure on hospital care Unitary public expenditure on hospital care<br>by quintiles of incomes (2009) by quintiles of probability (2009)<br>662<br>607<br>554<br>513<br>458 467 463 479 495 477<br>613<br>470 525 500 379 419 451 389<br>388 405<br>-84 -60 -49 -44 -88<br>-70 -43 -82 -62 -54<br>Q1 (Poorest) Q2 Q3 Q4 Q5 (Richest) Q1 (Least) Q2 Q3 Q4 Q5 (Most)<br>Public expenditures Household expenditures Net benefit Public expenditures Household expenditures Net benefit<br>200<br>200<br>US$ of 2009 100 0 US$ of 2009 100 0<br>-100 -100<br>600<br>600<br>400 400<br>200<br>US$ of 2009 200 US$ of 2009<br>0<br>0<br>-200<br><!-- End of picture text -->

**Figure 11: Distribution of Unitary Public Spending on** **_Hospital Care_ Net of Private Household Contributions, Paraguay, 2009 (Ages 0–17)**

_Source_ : Author’s estimates from INEC’s EPH 2009; MSPyBS (2010); IPS (2011); Cuesta and Suarez-Becerra (2013).

# **4. Two Policy Applications of OIA**

The BIAs and OIAs presented so far have a diagnostic nature, that is, they describe the extent to which the distribution of public spending is progressive. In appendix 2, the BIA and OIA describe instead the share of public benefits captured by each quintile of beneficiaries. The obvious question is how this information can provide value added in terms of effectively guiding policy decisions. The following sections provide two concrete illustrations of applications of OIA to guide policy making in the intersection of social spending, distribution, and equality of opportunities. The first relates to the use of the distribution of opportunities as an additional targeting criterion to complement conventional meanstested instruments. The second application continues the tradition of ex ante microsimulations to inform how certain policy interventions could improve equal access to opportunities more cost-effectively.

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climate performance of the countries in our survey.

**Table 5: Criteria for selecting and investment location according to surveyed EPZ firms, rankings by country (top 5 highlighted)**

|**Investment criteria**|**African zones**|**Non‐African**<br>**zones**|
|---|---|---|
|**Cost andquality of utilities**|1|3|
|**Access to transport infrastructure**|2|2|
|**Business regulatory environment**|3|5|
|**Tariffs, duties, and rules of origin**|4|8|
|**Level of corporate taxes**|5|6|
|**Access to highly skilled labor**|6|4|
|**Access to suppliers**|7|7|
|**Access to low cost labor**|8|1|
|**Availability / cost of land and buildings**|9|10|
|**Access to local and regional markets**|10|9|
|**Access to technology**|11|11|

Source: SEZ Investor Surveys Table 5 summarizes investors’ ranking of the relative importance of eleven criteria for selecting an investment location<sup>18</sup> . Among the African countries surveyed, there was remarkable similarity in the average respondent rankings, particularly among the top 3 factors. What is also striking is that the top 3 factors ranked by investors are issues over which the EPZ program and individual zone management should have at least a decent measure of control – the cost and quality of utilities, access to (efficient) transport, and the business regulatory environment. The other two factors rated as highly important by investors are tariffs and duties and the level of corporate taxes, the latter of which at least can also be controlled by zones through incentives. Factors like labor, technology, and markets, which are more linked with wider issues of national competitiveness, were ranked lower in importance by investors in African zones. A positive interpretation of these results is that these results suggest that zone programs have significant power over the issues which matter most to investors. A more pessimistic view is that the results are evidence of a selection bias – i.e. investors in the African zones have already discounted deeper sources of competitiveness and their responses suggest they are likely to be footloose.

In comparison, respondents from the non‐African zones surveyed also identified utilities and transport as among their most important investment criteria. Their point of differentiation with the investors in the African zones is that they place much greater emphasis on access to labor, both of the low wage (ranked as the most important criteria by investors in the non‐African zones and only 8<sup>th</sup> by investors in the African zones) and high‐skill variety, and are much less concerned with tariffs and market preferences. Across countries, there were no significant, consistent criteria ranked differently by foreign and domestic investors. However, it is worth noting that in several countries, local investors ranked

> 18 The specific question was as follows: Please rate each of the factors below in terms of their importance to your company in considering a location for investment (rate each on a scale of 1‐5, with 1 being "least important" and 5 being "most important"). Average ratings for each factor in a country were then converted into a rank.

23
_Brazilian spatial labor dynamics Page 6_ neighboring dynamic areas. Agroclimate is postulated as an exogenous determinant of employment growth. This allows for the possibility that agricultural labor is shifting between agroclimatic zones – and perhaps, especially, out of less favorable zones – in response to changing agricultural market conditions.

We model capital investment as

# ∆ ln _K_ = ∆ ln _K_ ( _MARKETACCESSt_ − 1, _GOVERNANCE_ , _AGROCLIMATE_ , ∆ ln _MP_ ) ()3

Better market access (measured by lower transport costs to São Paulo and to the nearest state capital) is associated with lower price levels and hence higher returns to capital. Better local government is expected to increase the município’s attractiveness for investment. Increases in the market potential indicate location near a dynamic region; this potentially endogenous instrument must however be instrumented.

Because we lack reliable data on capital, we substitute (3) into (1). While we have data on mean years of education of the work force, we lack information on employees’ educational quality. Moreover, a dynamic município may experience changes in mean educational level due to compositional effects, depending on the relative educational level of in-migrants, out-migrants, and stayers. Hence we use initial educational level of workforce, and initial educational level of public school teachers (a proxy for local educational quality) in place of educational change in (1). We interpret the result as describing the demand curve while allowing for the endogenous response of capital investment to initial conditions.

# **Data**

We examined patterns of employment and wage change over the period 19912000. The basic spatial unit of analysis was the município. A complication, however, is that some municípios experience splits over the study period, the total number growing from 4491 to 5507. In complicated cases, two municípios are reorganized into three. To address this problem, we merged municípios into 4267 Minimum Comparable Areas (MCAs) – consisting of sets of one to three municípios whose borders were constant over the study period.

Much of the data stem from the Brazilian Population Censuses of 1991 and 2000.<sup>4</sup> The key variables of interest were employment: total number of people reporting any employment and wage: reported labor income divided by the number of workers. Self-employed workers and farmers are counted as workers, and their earnings are counted as labor income. Table 6 gives the sources and definitions of variables employed in this paper. Table 7 provides summary statistics for these variables. Some of them are mapped in the appendix.

A unique feature of this analysis is the use of market potential and related variables. Market potential for a target MCA i is defined as the weighted sum of personal incomes of all other MCAs, where the weight is a decreasing exponential of distance to the target município:

> 4 To be precise, they stem from the 12,5% Census sample of 1991 and 5% Census sample of 2000. Both samples represent município level data.
<!-- Start of picture text --> DIAGiA.iN,/ C3<br>-MARGINAL REVENUE PRODUCT OF CAPITAL<br>SA.NMIPLE VALUES: MALE ENTERPRISES<br>5 J ' ~~~~~~~~~~~~~~~~~~~~~~~~~~~0.0196<br>{ * ' ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~0.020S_<br>0.016<br>l<br>I F~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~0<br>0196<br>r 0.016<br>N~~ ~ ~ .. F°0i':^<br>0.0076<br>r 0.0076<br>/0.0106<br>--. | , / L t 0.0106<br>26<br>P0.00<br>0.0106<br>A /t 08^6S0<br>d \0.0076 A~~~~~~~~~~~~~~~~~~~~~ 0 .0016<br>/\ - ~~~~~~~~~~~~~~~~~~~~~~~~~0.0006<br>/ < '0~~~~~~~~~~~~~~~~~~~~~~~0.0004<br>/ \ '*~~~~~~~~~~~~~~~~~~~~~~~~0.0014<br>/ * -~~~~~~~~~~~~~~~~~~~~~~~~~0.0024<br>/ , '-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~0. 0034<br>0 100 200 300<br>ENTERPRISES OROERED BY NRP OF CAPITAL<br>84<br><!-- End of picture text -->
|**Table (B.5)**<br>Exploring Secondary Outcomes: Priming Effects on Information Misperceptions<br>Dep. Variable<br>(1)<br>(2)<br>**Perception about Size**<br>**Perception about Education**<br>Treatment (Priming=1)<br>6.225***<br>-0.391***<br>(0.885)<br>(0.142)<br>R-squared<br>0.015<br>0.003<br>Observations<br>3,273<br>2,901<br>Av. dep. var before treatment<br>29.18<br>6.84<br>_Notes:_ The dependent variable _Yi_ represents the outcome for individual _i_ as measured in the survey of their personal beliefs about the Venezuelans’ share of<br>Colombian population and the Venezuelans’ average years of education. The variable_Treatment (Priming=1)_is the assigned treatment status to COVID priming<br>of the individual. _∗∗∗_signifcant at the 1%, _∗∗_signifcant at the 5%, _∗_signifcant at the 10%. Standard errors are reported in parentheses.|**Table (B.6)**<br>Heterogeneous Effects: Losing job during the pandemic<br>Dep. Variable<br>(1)<br>(2)<br>(3)<br>(4)<br>(5)<br>**Altruism**<br>**Policy Altruism**<br>**Opinion on Effort**<br>**Opinion on Economy**<br>**Opinion on Taxes**<br>Priming = 1_×_Lose Job = 1<br>0.137<br>-0.002<br>0.003<br>0.053<br>-0.031<br>(0.090)<br>(0.087)<br>(0.087)<br>(0.086)<br>(0.084)<br>Treatment (Priming = 1)<br>-0.146*<br>-0.018<br>-0.074<br>-0.109<br>-0.162**<br>(0.080)<br>(0.078)<br>(0.077)<br>(0.077)<br>(0.075)<br>Because of the pandemic you lost your job<br>-0.048<br>-0.111*<br>-0.092<br>-0.198***<br>-0.062<br>(0.063)<br>(0.061)<br>(0.060)<br>(0.060)<br>(0.059)<br>Marginal Effects [Lose Job = 1]<br>-0.010<br>-0.020<br>-0.071_∗_<br>-0.056<br>-0.193_∗∗∗_<br>( 0.040)<br>( 0.039)<br>( 0.039)<br>( 0.038)<br>( 0.038)<br>R-squared<br>0.001<br>0.002<br>0.003<br>0.006<br>0.010<br>Observations<br>3,130<br>3,383<br>3,345<br>3,359<br>3,412<br>_Notes:_ The dependent variable_Yi_ represents the outcome for individual_i_as measured in the survey and standardized using the mean and standard deviation of the<br>control group. The variable_Treatment (Priming=1)_is the assigned treatment status to COVID priming of the individual. _∗∗∗_signifcant at the 1%, _∗∗_signifcant at<br>the 5%, _∗_signifcant at the 10%. Standard errors are reported in parentheses.|
|---|---|

28
Table 1: Benchmark regressions for transmission channels associated with foreign claims by international banks in developing countries, 2004Q1–2011Q1<sup>_†_</sup>

||_B1_|_B2_|_B3_|_B4_|
|---|---|---|---|---|
|Lagged foreign claims|0.772<br>(0.03)<sup>_∗∗∗_</sup>|0.772<br>(0.03)<sup>_∗∗∗_</sup>|0.781<br>(0.03)<sup>_∗∗∗_</sup>|0.771<br>(0.03)<sup>_∗∗∗_</sup>|
|Libor-OIS|0.030|-0.025|-0.019|-0.030|
||(0.01)<sup>_∗∗∗_</sup>|(0.01)<sup>_∗_</sup>|(0.02)|(0.02)<sup>_∗_</sup>|
|1st lag|||0.010<br>(0.01)||
|2nd lag|||0.017<br>(0.01)||
|3rd lag|||-0.022<br>(0.01)<sup>_∗∗_</sup>||
|Libor-OIS _×_ crisis||||-0.000<br>(0.04)|
|Global bank CDS|-0.014|-0.001|-0.004|0.002|
||(0.01)<sup>_∗∗_</sup>|(0.01)|(0.01)|(0.01)|
|Global bank CDS_×_ crisis||||-0.012<br>(0.01)|
|Asset volatility|-0.062|-0.060|-0.067|-0.068|
||(0.02)<sup>_∗∗_</sup>|(0.02)<sup>_∗∗_</sup>|(0.02)<sup>_∗∗∗_</sup>|(0.04)<sup>_∗_</sup>|
|Asset volatility _×_ crisis||||0.018<br>(0.06)|
|Loan demand|0.009|-0.002|-0.006|-0.017|
||(0.03)|(0.03)|(0.04)|(0.04)|
|Infation|-0.019|-0.022|-0.022|-0.022|
||(0.01)<sup>_∗∗∗_</sup>|(0.01)<sup>_∗∗∗_</sup>|(0.01)<sup>_∗∗∗_</sup>|(0.17)|
|Growth|-0.131|-0.168|-0.159|-0.173|
||(0.15)|(0.15)|(0.15)|(0.19)|
|GDP|0.308|0.289|0.289|0.282|
||(0.04)<sup>_∗∗∗_</sup>|(0.04)<sup>_∗∗∗_</sup>|(0.04)<sup>_∗∗∗_</sup>|(0.04)<sup>_∗∗∗_</sup>|
|Crisis||0.108<br>(0.02)<sup>_∗∗∗_</sup>|0.078<br>(0.03)<sup>_∗∗∗_</sup>|0.153<br>(0.12)|
|Adj. _R_<sup>2</sup>|0.770|0.771|0.776|0.762|
|_R_<sup>2 </sup>(within)|0.771|0.772|0.777|0.772|
|Estimator|FE|FE|FE|FE|
|N|3,435|3,435|3,429|3,435|

> _†_ All variables are in log form. Heteroskedasticity and autocorrelationrobust standard errors are reported in parentheses, with the exception of specification ( _B4_ ), where errors are bootstrapped. A constant term was included in the regressions, but not reported.<sup>_∗_</sup> indicates significance at 10 percent level,<sup>_∗∗_</sup> indicates significance at 5 percent level, and<sup>_∗∗∗_</sup> indicates significance at 1 percent level.

12
<u>Table 1. Summary statistics for the younger cohort at baseline (2009)</u>

|||<br>**Treatment**|h|**Ci**|**D**<br>l|**ifferences**|
|---|---|---|---|---|---|---|
||Early (Batch 1)|Late (Batch 2)|Bot<br>(Batch 1 & 2)|**omparson**|Eary –<br>Late|Both – Comparison|
||(1)|(2)|(3)|(4)|(5)|(6)|
|A ()|1.520|1.499|1.509|1.508|-0.021|0.001|
|ge years|(0.287)|(0.286)|(0.286)|(0.286)|(0.012)|(0.013)|
|Household size|4.678|4.729|4.704|4.964|0.052|-0.259*|
||(1.529)|(1.568)|(1.549)|(1.705)|(0.095)|(0.102)|
|Wealth z-score (SD)|-0.0125|-0.0301|-0.0217|0.0520|-0.018|-0.074|
|..|(1.036)|(0.962)|(0.998)|(1.003)|(0.082)|(0.073)|
|Pti SD|-0.0120|-0.0316|-0.0222|0.0532|-0.020|-0.075|
|arenng z-score (..)|(1.006)|(0.968)|(0.986)|(1.031)|(0.070)|(0.073)|
|Mth’ d i i l 1 = Y|0.512|0.514|0.513|0.504|0.002|0.008|
|oers eu s prmary or ess ( es)|(0.500)|(0.500)|(0.500)|(0.500)|(0.029)|(0.030)|
|Ml 1 = Y|0.491|0.516|0.504|0.525|0.025|-0.021|
|ae ( es)|(0.500)|(0.500)|(0.500)|(0.500)|(0.023)|(0.019)|
|Bod Mass Index (BMI)|14.62|14.53|14.57|14.66|-0.092|-0.091|
|y|(2.078)|(2.117)|(2.098)|(2.097)|(0.107)|(0.102)|
|Citi kill SD|-0.0979|0.00907|-0.0421|0.101|0.107|-0.143*|
|ognve ss (..)|(1.055)|(0.982)|(1.019)|(0.947)|(0.076)|(0.061)|
|Fi kill SD|-0.0323|-0.00911|-0.0202|0.0483|0.023|-0.069|
|ne motor ss (..)|(0.988)|(1.045)|(1.018)|(0.954)|(0.059)|(0.055)|
|G kill SD|-0.0798|-0.0134|-0.0452|0.108|0.066|-0.153**|
|ross motor ss (..)|(1.013)|(1.017)|(1.015)|(0.954)|(0.057)|(0.047)|
|L kill SD|-0.0216|-0.00990|-0.0155|0.0371|0.012|-0.053|
|anguage ss (..)|(0.984)|(1.031)|(1.008)|(0.979)|(0.058)|(0.052)|
|Observations|1042|1137|2179|910|||

p<0.001***; p<0.01**; p<0.05* Note: Early (Batch 1) villages received the project first in 2009 and late (batch 2) villages received the project later in 2010. Comparison villages never received the project. Standard deviation in parentheses in columns (1) to (4). Standard errors clustered at village level in parentheses in columns (5) to (6).

9
# Abstract

In this paper, we have assessed the incidence and determinants of pesticide poisoning among rice farmers in Vietnam’s Mekong Delta. Blood cholinesterase tests suggest that the incidence of poisoning from exposure to organophosphates and carbamates is quite high in Vietnam. Using the medical test results as benchmarks, we find that farmers’ self-reported symptoms have very weak associations with actual poisoning.

Regression analysis of blood tests reveals a lower incidence of poisoning for farmers who avoid the most toxic pesticides and use protective items. We also find very large provincial differences in poisoning incidence after we control for individual factors. Our results highlight the potential importance of negative externalities, and suggest that future research on pesticide-related damage should include information on local water, air and soil contamination.

2
Policy Research Working Paper 8444

# **Abstract**

This paper presents an overview and meta-analysis of the effects of water, sanitation, and hygiene interventions around the world. It is based on 136 impact evaluations (randomized and quasi-experimental studies) that explore the effects of water, sanitation, and hygiene interventions on health and non-health outcomes, ranging from behavior change—such as the adoption of water treatment—to school attendance rates, to a reduction in diarrhea. The selected impact evaluations were divided into five groups, and meta-regressions with fixed effects (at the regional level) and random effects were performed, controlling for each study’s characteristics (implementing organization, sample sizes, type of publication, number of publication views, and so forth). All results are reported as changes in odds ratios, with respect to the standard deviation of reported effects. Water, sanitation, and hygiene interventions were found to increase the likelihood of behavior changes and the adoption of new hygiene practices by 17 percent. The smallest effects were observed from water, sanitation, and hygiene interventions aimed at reducing the rates of child mortality and non-diarrheal disease. Water, sanitation, and hygiene interventions implemented in schools showed statistically significant results in reducing school absenteeism and dropouts. Similarly, the results showed a statistically significant aggregate likelihood of increased access to safe water and improved water quality, as well as increased water treatment options—a difference of one-fifth with respect to the standard deviation of the average effect size reported. Finally, the results showed that water, sanitation, and hygiene interventions reduced the likelihood of the incidence of diarrheal and enteric disease by 13 percent, which is consistent with findings in other meta-analyses of the same subject.

This paper is a product of the Water Global Practice. 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://www.worldbank.org/research. The authors may be contacted at landres@ worldbank.org or cborjavega@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 do not include the two other large minorities in this analysis. They are Adivasis (or Scheduled Tribes) and “Other Backward Castes” (OBC). Adivasis are in many ways a self-contained category. They own at least subsistence land and so, when they cannot get benefits from job quotas, either due to lack of education or due to lack of access to information about vacancies, or due to the fact that these vacancies remain unfilled, they have subsistence agriculture to fall back on. As a last resort, they end up as casual laborers (Das, 2006). They are outside the purview of the caste system and so are not “ritually dominated” in the same way as Dalits are. That said, the situation of Adivasis is so much worse than that of any other category in terms of poverty and lack of overall access to human and other capital, that understanding their labor market outcomes needs independent theoretical and empirical work. OBCs on the other hand, could well be dominant castes in many areas and in any case are so heterogeneous in both their socioeconomic status and their ritual positions that the classification of “OBC” only works as an administrative construct - certainly not as a generic social or economic one.

**III. Key Hypothesis, Data and Methods** : Our main interest in this paper is to find out what educated Muslims and educated Dalits do.  We start from the assumption that for uneducated individuals, the casual labor market is the default option.  But with some human capital – particularly secondary education and above – the chances of getting salaried jobs and better quality self-employment increase. For Dalits reserved jobs in government should take care of some of the supply of educated labor from amongst them.  The remainder of the educated persons ought to be in self-employment, since they should have the requisite skills.  In the case of Muslims, since they have no quotas in government jobs, they would be more likely to be in self-employment than Dalits are.  While we are unable to test if such engagement is better or worse for them than regular salaried work, disproportionate engagement in selfemployment nevertheless could be an indication of their lack of options in the “primary” or the more coveted market.  Thus, as pointed out earlier, due to data limitations, rather than look at earnings in selfemployment as the marker of minority enclaves, we look at entry into self-employment as that marker of enclaves.

Data for this analysis come from the Employment and Unemployment Schedule (Schedule 10) of the National Sample Survey 61<sup>st</sup> Round, conducted in 2004-05. All analysis is weighted and the multivariate analysis is conducted separately for urban and rural men in the age-group 15-59 years, **excluding current students,** and based on usual principal status activity only. The reason for excluding students is that in the age-group 15-25 many are still students and this affects the labor force participation rates. If we take only those individuals who are available for employment, we can come to a more precise understanding of who is employed and who stays out of the labor market.

7