rafmacalaba/datause-encoder-data huggingface dataset:
interpret.<sup>10</sup> We observe the highest positive effect of water and sanitation infrastructure on children’s health in urban areas and Sub-Saharan Africa (SSA) for flush toilets. According to our estimates children in households with access to a flush toilet in those sub-groups show about 17% lower odds for diarrhea when compared to children using open defecation. Our control variables show the expected direction.

In line with the reviewed meta-studies, we find that improved sanitation has a somewhat higher positive effect on diarrhea than water infrastructure. In contrast to previous studies, the effect we estimate for sanitation is, however, significant smaller: about half of the previously reported estimates on improved sanitation, which showed reductions in diarrhea between 20% and 30%. While one may interpret our lower coefficients as evidence of measurement (attenuation) bias, an at least equally likely explanation is that the short-term effects typically observed in localized controlled trials should be expected to fade out over time as the quality of water and sanitation infrastructure deteriorates in the absence of external maintenance and support.

Last, all sub-samples show a considerable and statistically significant difference between basic and advanced sanitation technologies, whereas we cannot find a remarkable difference for basic and more advanced water infrastructure. This last finding was already previously stated by Esrey (1996). Cairncross and Kolsky (1997), however, challenge the former study both on data quality and methodological grounds, arguing that pushing towards the highest possible sanitation standards _“could lead to the unwarranted rejection of affordable low-cost sanitation for the poor in developing countries.”_ We did our best to be very careful in our statistical estimation, including as many as possible control variables<sup>11</sup> and 172 DHS surveys. The difference we find between simple and more advanced sanitation technologies is smaller than in the study of Esrey (1996), but still seems to be statistically significant. This, however, does not automatically mean – as presumed by Cairncross and Kolsky (1997) – that flush toilets are necessarily the first best

> 10 For example, a 13% percent reduction in the odds implies a reduction of diarrhea from 19% (e.g. for open defecation, see Figure 3) to 16.8 %, which is equal to an 11.5% reduction in diarrhea risk. The odds for 0.19 are 0.19/0.81=0.235. A reduction of 13% in the odds of 0.235 leads to odds of 0.202, which transfers into a diarrhea risk of 0.168 (0.202=0.168/0.832).

> 11 Most importantly, note that piped water was controlled for in all specifications, which means that flush toilets do not pick up the effect of piped water into the household.

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18
were, on average, 11 years old in their last interview from the baseline rounds. By 2004, they were almost 23 years old (Table 1).

# _III.2 Descriptive statistics_

Our definition of child labor is the total hours spent working in economic activities and chores in the previous week (including fetching water and firewood, preparing meals, and cleaning the house). Economic activities for children consist predominately of farming, including tending crops in the field, processing crops, and tending livestock. We include chores as well as economic activities because the concept of child labor typically (e.g., in the ILO standard) encompasses both,<sup>4</sup> although we will distinguish between the two to explain differences in our results for boys and girls. Children in the sample work on average 16.8 hours per week, of which 10 are spent on chores (Table 1). Girls spend on average 2.5 hours more than boys working on household chores; this difference is more pronounced among older girls. More than 90 percent of children have worked at least one hour in one of the baseline waves.

We use two instrumental variables: household crop shocks and rainfall shocks. Household crop shocks are measured as an indicator variable of crop lost to pests and fire during the baseline interview period. Rainfall was measured at 21 weather stations from 1980 onward. For each household, we use rainfall data from the nearest weather station. We construct the rainfall shock as the deviation of the total rainfall in the short and long rainy seasons preceding the interview from its 25-year average, scaled by its standard deviation. Table 1 shows that the average proportion of crop loss is 0.34. The mean of the rainfall shock is 0.21 (i.e., one-fifth of a

> 4It should also be mentioned that the concept of child labor does not necessarily refer to simply any work done by a child, but, rather, work that stunts or limits the child’s development or puts the child at risk. However, in survey data it is difficult to isolate the portion of time spent working on the farm that qualifies under this nuanced definition.

7
**Table 5. The macroeconomic effects of a 5 percent climate-induced shock to capital stock with reconstruction financed by reduction in public expenditure**

||Year 0|Year 1|Year 2|Year 3|Year 4|Year 5|Year 10|Year 20|Year 30|
|---|---|---|---|---|---|---|---|---|---|
|**Economic Activity**||||||||||
|<br>GDP|0.0|-3.6|-3.5|-3.3|-2.9|-2.5|0.2|0.2|-0.2|
|Private consumption|0.0|-2.5|-2.7|-2.5|-2.2|-1.9|-0.5|0.1|-0.1|
|<br>Government consumption|0.0|-44.2|-44.6|-44.9|-45.1|-45.0|0.4|0.5|-0.1|
|Total investment|0.0|3.4|3.9|4.5|5.0|5.7|0.2|-0.5|-0.5|
|Exports|0.0|0.1|0.4|0.6|0.9|1.1|1.2|0.2|-0.2|
|Imports|0.0|-4.1|-4.2|-4.1|-3.9|-3.7|-1.5|-0.8|-0.1|
|Current account balance<br>(pp of GDP)|0.0|0.8|0.8|0.7|0.7|0.6|0.2|0.2|0.1|
|**Prices**||||||||||
|Producer prices|0.0|0.4|0.3|0.1|-0.0|-0.1|-0.4|0.1|0.1|
|Consumer prices|0.0|-0.1|-0.2|-0.3|-0.3|-0.3|0.0|0.1|0.0|
|Real exchange rate|0.0|-0.2|-0.5|-0.8|-1.1|-1.4|-1.5|-0.3|0.2|
|Import prices|0.0|-0.0|0.1|0.2|0.4|0.6|1.3|0.4|-0.1|
|Export prices|0.0|-0.1|-0.3|-0.4|-0.5|-0.6|-0.3|0.1|0.1|
|<br>**Fiscal**||||||||||
|Revenues|0.0|-3.1|-3.3|-3.1|-2.9|-2.6|-0.2|0.3|-0.0|
|Expenditures|0.0|-1.3|-1.9|-2.2|-2.3|-2.3|-0.5|0.3|0.1|
|<br>Deficit (pp of GDP)|0.0|-0.3|-0.2|-0.1|-0.1|-0.0|0.0|-0.0|-0.0|
|<br>Debt (pp of GDP)|0.0|1.2|1.3|1.3|1.2|1.1|-0.1|-0.2|0.1|
|**Supply Side**||||||||||
|<br>Potential GDP|0.0|-3.0|-2.6|-2.1|-1.7|-1.1|-0.2|-0.2|-0.3|
|Capital stock|-5.0|-4.2|-3.5|-2.8|-2.0|-1.3|-0.4|-0.4|-0.4|

_Sources:_ MFMod, World Bank; Authors’ calculations. _Note:_ Tables show deviations as percent from a baseline scenario _._ 26
However, these hypotheses fail to get any support in the data, as Table 8 shows. Both treatments fail to significantly affect beliefs about politicians, and both appear to increase respondents’ beliefs about how widespread abuse of public money is. Many explanations for this are of course possible: for example, increasing the salience of public comptrollers might also simultaneously increase the salience of corruption. Alternatively, the mechanism survey instruments and/or their position at the very end of the survey might make them prone to measurement error, which might explain the null result on attitudes towards politicians. In any event, these results do not appear to account for why the treatments are effective at improving tax morale.

Table 8: Mechanism models (OLS)

||**Politicians care**|**Abuse money**|
|---|---|---|
|**(Intercept)**|0.343***<br>(0.003)|0.693*** (0.003)|
|**Top down/Control**|0.004<br>(0.004)|0.019*** (0.004)|
|**Bottom up/Control**|0.007|0.029*** (0.004)|
||(0.005)||
|**N**|65435|65435|
|**p**|0.3|0.0|
|**Log-likelihood**|-44238.4|-41293.9|
|**AIC**|88484.8|82595.9|

Notes: Each column shows an OLS model predicting different variables capturing the possible experimental mechanisms of beliefs about politicians caring about ordinary people and politicians abusing money.

# **Treatment effect heterogeneity**

Finally, we investigate how treatment effects vary based on covariates. In line with the theory of retributive justice, we had hypothesized in the pre-analysis plan that the top-down accountability treatment should have larger effects for “outsider” groups such as women, and lower for groups of taxpayers with larger direct exposure to the tax system such as business owners. However, there is no support for this in the data: as Table 9 shows, there is very little evidence that business owners react differently from other respondents. In fact, we find no evidence that these treatments vary across taxpayer groups at all. This suggests that the treatment effects are not driven by a simple calculation about expected costs or benefits (which would vary across different types of taxpayers).

Table 9: Conditional Average Treatment Effects (OLS) by taxpayer type

||**Tax index**<br>**(OLS)**|**Tax fine**<br>**(OLS)**|**Tax morale**<br>**(OLS)**|
|---|---|---|---|
|(Intercept)|5.029***|2.443***|2.586***|
||-0.013|-0.008|-0.007|
|Top down/Control|0.239***|0.116***|0.123***|
||-0.019|-0.011|-0.011|

16
the share of immigrant students in the school on natives’ grades, but report a small negative effect on levels of eligibility for upper secondary school. Although it is not the focus of the paper, it is to the best of our knowledge the only study prior to this paper that estimates distinct effects of immigrant concentration by age at migration.

This paper also relates to the thin literature in economics looking at the impact of age at migration on the educational attainment of foreign-born students. In the Dutch context, Ohinata and van Ours (2012) investigate the effect of age of migration on test scores of immigrant children at age 9 or 10, using data from the 2007 Trends in International Mathematics and Science Study (TIMSS). They find that immigrant children who entered at age 5 or older have a much lower science test score than children who entered as babies, suggesting assimilation effects. Other studies, such as Cortes (2006), Bohlmark (2008) or van Ours and Veenman (2006) focus on the impact of age at migration on educational attainment of first-generation immigrants at older ages.

# **3 Background and Institutional Setting**

## **3.1 Immigrants in the Netherlands**

In 2011, the Netherlands was populated by a population of 1.77 million immigrants, representing around 11 percent of the country’s population.<sup>9</sup> As in most European countries, the majority of immigrants residing in the Netherlands come from lower-income countries. First, large groups of immigrants came from former Dutch colonies, mainly Indonesia, Suriname and the Dutch Antilles, starting from the 1940s. The independence of Indonesia in 1949 and of Suriname in 1975 led to large influxes of migrants from these two countries. Another large inflow occurred around 1980 9This figure includes both first and second generation migrants.

8
# Research Coqponents of World<sup>Bank Education Projects,</sup> since 1982.

|Area|Country and Loan/Credit<br>Nuber|Approved|<br>||<br>Research Components<br>|Current Status of Studies,<br>including<br>Canpleted Docunents/Data|
|---|---|---|---|---|
|EENEA|Yemen AR CR 1340-YAR|1983|Fifth<br>Education.<br>(i)<br>Evaluation<br>of District<br>Training<br>Centers (para. 2.18 p.15).<br>(ii)<br>Evaluation<br>and pre-investmnent studies<br>for<br>follow<br>up projects<br>(2.21 16)|(i)<br>In progress<br>- uidertaken<br>byEROC<br>(ii)<br>canpleted by EDUCONSULT<br>(no docum.ent found)<br>Also: Abdu Ali<br>lawned<br>Qasim (1983).<br>"Initial<br>study concerning the establishment<br>of the General<br>Teacher's Institute."<br>(doc.#253.929 (A))|
|ENENA|Yemen AR CR<sup>1203-YAR</sup>|1982|Fourth Education.<br>Educational<br>Research and<br>Development Center to uidertake<br>four studies:<br>(i)<br>Establish<br>system to measure productivity<br>and efficiency<br>of primary schools;<br>(ii)<br>study supply and retention<br>of Yemeni<br>teachers;<br>(iii)<br>review curricula<br>for teacher training;<br>(iv)<br>educational<br>facilities<br>inventory<br>(para.<br>2.06<br>p.12).|(i)<br>and (ii)<br>coapleted;<br>(iii)<br>in progress-<br>scope of study enlarged.<br>(iv)<br>in progress (95X complete)<br>humhed<br>At-Haj Khali (1981).<br>"Preliminary<br>comprehensive study on the<sup>current</sup><br>situation<br>and<br>development of curricula,<br>and proposed program<sup>for</sup><br>Second Five Year Plan."<br>(doc. # 253.071 (A))|
|ENEMA|Yewen AR CR<sup>1470-YAR</sup>|1984|Sixth Education.<br>(i)Review: role of National<br>Institute<br>for Public Adninistration;<br>NIPA<br>educational<br>and training<br>programs; personnel<br>and physical<br>resources of NIPA.<br>(ii)<br>Evaluate "Special<br>study of Education<br>Administration<br>and Finance."<br>(iii)<br>Implement feasibility<br>studies<br>(para 2.21<br>p.17).|(i)<br>Completed by NIPA;<br>(ii)<br>completed and currently<br>urder Bank/IDA review:<br>(iii)<br>completed by EDUCONSULT<br>06/86.|
|EhENA|YemenAR CR 1645-YAR|1986<br>||Technical Training.<br>Noresearch component.|Confirmed.<br>|
|EMENA|YemenPORCR 1222-YDR|1982<br>||Third Education.<br>Twostudies:|(i)<br>To be coMleted<br>by the IL0;<br> <br> <br>|
||||(i)<br>manpower planning;<br>I|(ii)<br>completeo and recently<br>expanded to include<br> the remaining govenorates|

57
**Index** Sets Symbol Description Set Members c Food/Fred Crops /maize, sorghun'., soybeans/ I Factor Inputs /capital, labor, fertilizer/ a Animal Stocks /cattle, pigs, brollers, layers/ I Livestock Comm. /beef, pork, poultry, eggs, milk/ Variables Name Description PR(c) Production of crop c FD(c) Animal feed demand, crop c HD(c) Human food demand, crop c GSADJ(c) Government is.upiy adjustment, crop c RPG(c) Real price guarantee, crop c RBP(c) Real border price, crop c PCC(c) Per capita human consumption, crop c P(i) Real price of factor I PCON Per capita human consumption, all commoditles POF Index of the relative price of food POP Populatlon of Mexico INV(a) Stocks In Mexico of animal type a INVUS(a) Stocks in US of animal type a QP(D) Production of livestock commodity I PCL(I) Per capita consumption, I!vestock commodity<sup>I</sup> NEXP(I) Net exports, livestock commodity I PP(I) Real producer price, Mexico, livestock<sup>comm. I</sup> PPUS(I) Real producer price in US, livestock<sup>comm. I</sup> RP(I) Real consumer price, Mexico, livestock<sup>comm. I</sup> PTORT Real consumer price, Mexico, maize tortillas

The variables P(l), PCON and POF are<sup>llnkage variables from the Mexican</sup> macroeconomic model; and the variables<sup>RBP(c), INVUS(a) and PPUS(I) are linkage varlables</sup> from the US (and rest of the world) agricultural<sup>markets model. The varla! les RPG(c) are</sup> agricultural policy variables, while the variables P(l)<sup>and NEXP(I) may also be policy</sup> variables. The variable POP Is exogenous. All other variables are endogenous.
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42
to changes in the volatility of the underlying commodity. Usually, this is the at-the-money option, but occasionally it is the near-the-money option<sup>4</sup> . This contract is used to extract .' ISD fc. three reasons. First, because this contract price is sensitive to volatility, it should therefore return the most accurate measure of volatility. Second, for at-the-money options on futures contracts, the value added by the American feature is the smallest (Ramaswamy and Sundaresan, 1985). While we correct for the American feature, the correction is not perfect and we prefer to use the ISDs that are least influenced. Third, at-the-money options have the smallest bias when volatility is not constant. The Black model is (approximately) linear hi volatility for at-the-money optioL.s, which implies that the at-the-money implit I volatility estimates will result in only a small bias when volatility is stochastic (Hull and White, 1987).

So our first set of forecasts is the ISD extracted from the contract with the highest derivacive of the call price with respect to volatility, in other words the a which solves the equation (5)

> where _C'(t,T,X,)_ is the observed call price at time t for a contract with time to expiration 7 and exercise price _Xi, C(-)_ is th, Barone-Adesi and W'haley (1987) formula given in (4) above, and i is chosen to maximize T. . Throughout this paper, we refer to this T-period forecast of volatility at time t as ISDATteT.

Using ISDAT, however, ignores information about volatility that is available in other contracts. We therefore propose two other market-based forecasts of volatility which do not ignore the potentially useful inforrnation in the prices of options which are not at-the-money. First, we use a weighted average of all the ISD's that can be computed on a given date _t_ for a given time to expiration T, with the weights being the derivatives of the option price with rebpect to volatility. More specifically, the forecast we use is

# (6)

where _°TXj_ is the ISD from a call option with T days to expiration and with exercise price Y,, and _7TX,_ is the derivative of the price cf this call option with respect to volatility. We chose this weighting mechanism because options which are far away from the money have prices which are

> **4 "At-the-money" difference in the refers to the option whose exercise price is closest to the spot price of the underlying asset, ISD's from the at-the-money option and the near-the-money option are likely to be very smail** rhi **To illustrate, in previous work by the authors using soybean data. the correlation between at-the- noney ISD's an,j** just-in-the-money ISD's from contracts with greater than 260 days to maturity is 0.9891.

5
We end this paper with some words of advice for future evaluations of the ongoing SBM program in the Philippines. Barrera-Osorio et al.’s (2009) review of SBM studies concludes that retrospective evaluations of such program are extremely difficult to implement and recommends using prospective methods. The SBM program in the Philippines is an ongoing exercise, and the analysis in our paper suggests that it was successful with respect to its objectives of improving student achievement, although the effects were small. However, as the Department of Education in the Philippines moves forward with SBM and goes to scale across the country, it is important to collect data simultaneously in a systematic manner to enable scientific evaluations. The current data has several limitations which make it difficult to espouse conclusive statements about the success of the program and how it might generalize beyond the 23 districts to the country as a whole. A new evaluation approach could enable the Department of Education to study both the implementation processes (what really makes SBM work) and assess impact in a more rigorous fashion.

17