rafmacalaba/datause-encoder-data huggingface dataset:
17 Figure 6 presents a similar exercise for Vavuniya, based on actual rainfall and irrigation tank level data.<sup>26</sup> For the Maha season, the years 1996, 2008 and 2014 were particularly bad, when crop losses were incurred due to dry spells. The simulated pay-outs (blue bars) show that the pure rainfall index insurance product would have triggered pay-outs in all these years. However, it would have also triggered pay-outs in years such as 2003, 2012 and 2015, when pay-outs would not have been necessary for farmers with access to major irrigation. Once again, a pure rainfall index product would have triggered more pay-outs than necessary, leading to a more expensive product and not addressing the lower risk profile of irrigated farming. The rainfall and irrigation index (red bars) would have triggered pay-outs only in the specific bad years, which would be a more relevant product for major irrigated farms as well as lead to a lower premium.

**Figure 6. Product Simulated Pay-outs for Vavuniya (in LKR)**

<!-- Start of picture text --> 18,000<br>16,000<br>14,000<br>12,000<br>10,000<br>8,000<br>6,000<br>4,000<br>2,000<br>0<br>Rainfall index Rainfall and Irrigation index<br><!-- End of picture text --> Source: Author constructed based on Meteorological data (Meteorological Department of Sri Lanka) and MCM data (Irrigation Department of Sri Lanka).

Based on information collected on damages for the Maha season in Anuradhapura through focus group discussions with farmers, it has been established that severe crop losses occurred in the district in the Maha seasons of 2003, 2004, 2008 and 2011. This was also validated by an analysis of damage ratios.

> 26 Based on Pavatkulam tank, from Irrigation Department, Ministry of Irrigation and Water Resources Management, Sri Lanka.
imports covered by trade barriers,<sup>an extremely effective barrier</sup> that excludes almost all imports<sup>would receive little weight.</sup> The coverage ratio only suggests<sup>that barrie-s to trade exist,</sup> but cannot measure their effect<sup>(for a discussion of the</sup> relationship between these<sup>administrative measures and other</sup> indicators of openness, see Pritchett<sup>(1990)).</sup> Related efforts have focused<sup>on using measures derived from</sup> a careful examination of tariffs<sup>and non-tariff barriers.</sup> Thomas, Halevi, and Stanton (1991)<sup>use a measure of trade</sup> liberalization based on a country-by-country<sup>assessment. They</sup> find that countries which liberalized<sup>in the 1980s increased</sup> their rate of GDP growth significantly,<sup>even when other effects</sup> were taken into account, including<sup>external financing, changes in</sup> the terms of trade, movements<sup>in the real exchange rate, and</sup> faster growth in OECD countries.

Research on trade and growth<sup>using both price-based or</sup> administrative measures has<sup>increased in the last several years.</sup> Studies based on these types<sup>of measures (See Section II in Table</sup> 1) have generally found a positive<sup>relationship between trade and</sup> growth. Although more recent<sup>efforts to identify the impact of</sup> openness on economic growth have<sup>relied on the use of cross-</sup> sectional averages of time-series<sup>data, one exception to this</sup> trend is Bhalla and Lau (1991),<sup>which examines the association</sup> between openness and growth using<sup>annual data. Bhalla and Lau</sup> (extending Lau, Jamison, and<sup>Louat (1991)) also construct annual</sup> measures of capital stock and educational<sup>stocks, which allows</sup> 6
Most importantly, however, the results and the caveats point to a clear need for further research in the field about the actual operations of this program. As a first-order priority, we note that actual take-up numbers and disbursements are unknown. We have very limited information about dissemination among the general population, as the formally commissioned evaluation study consists of a selected sample of program applicants (MODE 2000). Some operational details of the program also remain unclear, such as the need to hold a BPL card or quotas for certain ethnic groups. With some idea of actual program operations, we may for instance, be able to exploit additional sources of exogenous variation such as differences in launch date across different areas of Haryana. To reiterate, the novel design, scale and potential social impact of this program all emphasize the importance of further understanding its effects, and the need to monitor related developments in the future.

19
What of the impact of interventions? Cuba, with its extensive interventions for pregnant women and young children, has achieved significantly better performance on literacy assessments, scoring two standard deviations higher, than any other Latin American country (Carnoy and Marshall 2005, as cited in Mustard 2006: 39).

Fogel (2002) and Alderman, Behrman, and Hoddinott (2003) show the importance of specific nutrition interventions in bolstering cognitive development, physical stature and strength, earlier school enrollment and more regular school attendance, greater schooling and learning, increased adult productivity, and healthier offspring.

A recent 35-year longitudinal study of the long-term impacts of nutrition intervention during early childhood provides striking results (Behrman in Spence and Lewis, forthcoming; Melgar and others 2008). Two nutrition supplements were randomly assigned to low-income children in rural Guatemala; children who consumed the protein-rich supplement achieved dramatically better educational performance and labor force earnings. Women who received the protein-rich supplement during their first three years of life attained 1.17 more years of schooling, their infants’ birth weight was 179 grams heavier, and their children were a third taller than those of women who consumed the calorie-based supplement as children. Men who consumed the highprotein supplement in the first two years of their childhood earned an average wage 46 percent above that of men who consumed the calorie-based supplement.

Thus the evidence on the value of interventions in the preschool years is striking. Indeed, recent evidence (see Figures 6 and 7) suggests that the economic rate of return to preschool attendance dwarfs the returns to university or job training (Carneiro and Heckman 2003) and that the lack of attention to early childhood development has high long-run costs (Heckman 2007).

**Figure 6. Returns to Different Levels of Education Based on Family Background**

<!-- Start of picture text --> rate of<br>return to Early childhood<br>investment<br>Preschool programs<br>in human<br>capital School programs<br>Job training/continuing<br>education programs<br>preschool post- age<br>school<br><!-- End of picture text --> _Source:_ Adapted from Cunha and others 2006.

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In addition to the accumulation of human capital and subsequently wage growth, Table 2 points to other reasons for why policymakers may want to nudge workers from the informal to the formal sector. Most importantly, formal workers are much more likely to receive job benefits. When asked about twelve potential job benefits, formal workers with permanent (temporary) contracts report receiving 11.12 (9.32) compared to 1.96 among those working in the informal sector. In addition, workers in formal employment, especially those with permanent contracts, are more satisfied on the job and plan to stay in the job for longer.<sup>15</sup>

# **3 Experimental design**

## **3.1 Sample and randomization**

Mexico’s upper secondary education comprising grades 10 to 12 is divided into two tracks: schools offering a general academic curriculum preparing students for university and vocational (technical and technological) schools that prepare students predominantly to work after graduating. Since the government was concerned about unintended consequences of the wage incentive for those planning to continue their education, we focused our recruitment on students planning to work (either full or part-time) after graduation. Of students in our sample, 80% thus attend vocational schools and 20% attend general schools. All schools are located within 80km of the industrial zone of San Luis Potosi, an area with a sizeable manufacturing industry located in north-central Mexico.

School officials informed parents and legal guardians in the 13 participating schools that were part of our evaluation about the study and asked them to authorize their children’s participation. To avoid conflicts with exam dates and other school events, the research team scheduled activities in coordination with each school director. Activities took place in the last quarter before graduation. We asked students enrolled in 12th grade for consent to participate in the study and administered an in-person baseline survey that included modules about socio-demographic characteristics, employment trajectories, and career expectations.<sup>16</sup> Approximately 90% of invited students agreed to participate. Table 3 shows baseline char-

> 15In Mexico, law-mandated job benefits are the same for formal jobs regardless of the type of contract (temporary or permanent). Some firms offer additional benefits to workers, which, in our sample, seem to be more prominent for workers under a permanent contract.

> 16Within a school, we randomly selected whether the morning or afternoon shift was included in the study. Within a shift, all students were invited to participate.

14
lived (Galárraga and others 2017). But on the other hand, in both the Tanzania (cash transfers conditioned on remaining STI negative) and the Lesotho (lottery incentives for being STI free) studies, the effects were shown to be sustained in one-year post-intervention follow-up studies, even though the sustained effects were only found among men in Tanzania (Björkman Nyqvist and others 2018; de Walque, Dow and Nathan 2014).

For smoking cessation incentives, Cahill, Hartman-Boyce and Perera (2015) conclude that they seem to increase quitting rates while they are in place, but that fewer programs managed to yield sustained effects. Nevertheless, Volpp and others (2009), Etter and Schmid (2016), Giné, Karlan and Ziman (2010) and Halpern and others (2015) all report sustained effects 6 months or 12 months after the incentives were withdrawn. Loewenstein, Price and Volpp (2016) also found that incentives for a healthy diet among elementary school children could have sustained impacts in the short term (5 weeks) after their withdrawal.

Overall the evidence on the sustainability of the incentives is mixed and the jury is still out. Most of the evaluations that found impacts after incentives have been discontinued did not go beyond a 12 months post-intervention follow-up. However, it is worth noting that none of the studies reported negative effects in the long term that could be explained by the destruction of intrinsic motivation.

# **_Taxes or rewards?_**

The design of the incentives could also affect their sustainability. Obviously taxing harmful behaviors will bring revenue to the government and is likely to be more cost-effective than financial rewards given to discourage them. Taxes on tobacco and alcohol (and less frequently on unhealthy food) have been called “sin taxes” and presented as “win-win” measures 21
predator; differential threats to important regional subspecies that must be preserved (Bengal, Indochinese, Malayan and Sumatran tigers); divided national jurisdictions; differences in countries’ institutional capabilities, conservation management costs, and willingness to pay for conservation; and, not least, widely-differing and rapidly-changing opportunities for commercial exploitation of remaining habitat areas.

Cost-effective resource allocation under these dynamic conditions involves frequent reassessment of threats and opportunities in many habitat areas scattered across the tiger range countries. Timely analysis requires near-real-time forest monitoring information, which is now available from FORMA (Forest Monitoring for Action), a new database developed by a research consortium that includes the Center for Global Development (http://www.cgdev.org/forma), the World Resources Institute, the University of Maryland, and Resources for the Future, in consultation with World Bank staff, Conservation International, the Nature Conservancy and WWF. Drawing on data from NASA’s MODIS system, FORMA provides monthly updates on forest clearing at high spatial resolution for tropical Asian countries with tiger habitat.

From a formal analytical perspective, saving the tiger involves optimal spatial resource allocation with a limited budget, a short time horizon (to prevent extinction), a complex, constantly-changing spatial distribution of potential conservation benefits and costs, and the prospect of rapid, irreversible losses in areas where conservation is weak. Combining the FORMA information with a spatially-disaggregated database of economic, demographic and geographic information, previous papers in this series have focused on identifying area-specific drivers of habitat destruction (Dasgupta, et al. 2012a), and testing the effects of interventions intended to protect habitat (Dasgupta, et al., 2012b).

3
# Figure II: Share of Family Firms Across Countries

<!-- Start of picture text --> (a) GDP (b) Rule of Law<br>.6 BIHBGR<br>BIH BGR .5<br>RUS<br>.5<br>RUS<br>.4<br>ITA<br>.4 SRB SVN EST<br>SRB EST SVN ITA .3 CZE ESPPRT<br>.3 CZE ESP .2 CHE<br>PRT POLLVALTU DEU NLD<br>.2 POLLVALTU DEUNLD CHE .1 UKR FRA<br>FRA<br>.1 UKR 0<br>8 9 10 11 12 −1 0 1 2<br>Log 2011 GDP pc Rule of Law Index<br>Data shows share of FOFM firms in each country against 2011 GDP. Data shows share of FOFM firms in each country against RoL index.<br>(c) Trust (WVS) (d) Enforcing Contracts Rank (WB DBS)<br>.6 .6<br>BIH BGR BGR BIH<br>.5 .5<br>RUS RUS<br>.4 .4<br>ITA ITA<br>SVN SVN<br>EST SRB EST SRB<br>.3 ESP CZE .3 ESP CZE<br>PRT<br>.2 CHE .2 CHE<br>LTU POL LVA DEU NLD DEU LTULVA NLD POL<br>FRA FRA<br>.1 UKR .1 UKR<br>0.2 0.3 0.4 0.5 0.6 0 50 100 150<br>Trust Enforcing Contracts Rank<br>Data shows share of FOFM firms in each country against WVS trust score. Data shows share of FOFM firms in each country against enf contracts rank from WB DBS.<br>Figure III: Trust as a Proxy for Rule of Law and Enforcing Contracts<br>(a) Rule of Law (b) Enforcing Contracts<br>2 FIN NOR 150 ITA<br>GBR DEU CHE NLD 100 MKD BIH SRB<br>FRA BGRSVK CZE<br>1 SVN EST ESP CZE 50 TUR SVN POL ESPUKRGBR<br>LTUPOLLVA EST NLD<br>HUN 25<br>SVK LVA CHE<br>ITA LTU HUN<br>0 TUR BGR RUS<br>MKD BIH FIN<br>SRB FRA<br>5 DEU<br>UKRRUS NOR<br>−1<br>0.2 0.4 0.6 0.8 0.2 0.4 0.6 0.8<br>Trust Trust<br>Plots WGI rule of law score against WVS trust country average. Correlation is 0.60. Plots DB Contract Enforcement Rank against WVS trust country average. Correlation is −0.41.<br>Share of Family Firms Share of Family Firms<br>Share of Family Firms Share of Family Firms<br>Rule of Law Score<br>Enforcing Contracts Rank<br><!-- End of picture text --> 43
<!-- Start of picture text --> petceri<br>. f - > - - O ' X 4 - 0 ',<br>S -- ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ - - - - - - -<br>.. . ---------. , - - ----<br>-- - ~~~~~~~~~~~~~~~1- ----------- ~<br>t X w- --. k .............. Om u0-n C~~~~~~~~: -------- -- --- 0 n -<br>3" 9 D ., S X . A j . - - ..... , _ m c t X . - C-<br>-------------, ----------------- -------- -- ------------ ----------------- m<br>CDt 3---; * v m<br>_; m<br><!-- End of picture text -->
that the household head's principal occupation is agriculture. And, in most countries, except El Salvador and Honduras, households benefit from social protection that can be adapted as a safety net to respond to natural disasters. Moreover, the results show, on average, a significant proportion of fragile infrastructure and buildings susceptible to natural disasters and limited access to toilet facilities. In El Salvador and Mexico, the percentage of households living in fragile dwellings is to around one-quarter, and the percentage of households with access to toilet facilities linked to the sewer ranges, on average, from 23 percent to 68 percent, depending on the country.

**Table 2.** Average of households with coping and adaptation mechanisms.

|**Category**|**Variable**|**Brazil**|**Domini-**<br>**can**<br>**Rep.**|**Ecuador**|**El**<br>**Salvador**|**Hondu-**<br>**ras**|**Mexico**|**Peru**|
|---|---|---|---|---|---|---|---|---|
||HH head is salaried in<br>the main occupation|38.9%|38.4%|43.3%|40.6%|35.8%|56.8%|33.9%|
||HH head is informal in<br>the main occupation|27.0%|36.6%|50.6%|39.6%|42.8%|32.5%|51.5%|
|Income<br>|The non-labor income<br>per capita PPP 2017|194.98|116.47|115.15|88.19|39.55|89.32|100.81|
|diversification<br>and<br>|<br>Ln. non-labor income<br>per capita PPP 2017|2.75|2.92|2.94|2.14|1.67|2.23|3.55|
|occupation<br>protection|Labor income as a<br>share of total income|67.1%|74.7%|73.3%|75.6%|78.6%|77.9%|71.8%|
||Dependencyrate<br>HH head's principal<br>income is from<br>agriculture|1.8<br>6.4%|1.7<br>8.4%|1.7<br>23.8%|2.1<br>14.3%|2.2<br>23.9%|1.8<br>12.8%|1.9<br>26.6%|
|Social<br>protection|HH receives benefits<br>from social assistance|19.3%|26.3%|18.6%|1.9%|12.2%|28.9%|47.2%|
||Dwelling's materials of<br>low quality|3.2%|5.9%|19.7%|25.6%|11.4%|25.8%|12.6%|
|Housing|Toilet facilities linked<br>to sewer|67.7%|23.2%|64.0%|35.6%|38.3%|62.1%|73.0%|
||Access to electricity|99.8%|99.1%|99.0%|97.6%|94.1%|99.5%|95.6%|
||Overcrowding|3.5%|3.9%|28.9%|8.7%|17.0%|10.6%|8.9%|
||Car|49.4%|20.9%|23.3%|20.2%|23.6%|42.5%|11.8%|
|Assets|Fridge|98.1%|87.9%|80.8%|75.7%|72.4%|86.3%|54.5%|
|ownership|Computer|41.7%|16.7%|41.7%|16.7%|17.2%|27.4%|32.7%|
||Washingmachine|65.9%|78.7%|41.0%|21.1%|.|67.9%|30.5%|
|TICs|Access to Internet|37.7%|25.9%|36.4%|23.3%|27.4%|40.6%|35.9%|
|Education|Years of education of<br>HH head|8.5|8.7|8.6|6.8|6.2|8.8|8.9|
||Number of hh members|3.0|3.1|6.0|3.5|4.1|3.6|3.6|
|Demographics|Age household head|48|49|51|50|50|50|53|
||Male household head|52.8%|63.1%|71.3%|62.9%|65.8%|71.3%|68.8%|

Source: authors' calculations based on the SEDLAC database.

For hazard variables ( ), we exploit two types of data, the number of occurrences of natural disasters in the region in the past and the potential damage expected from them. For the first one, we use the number of natural disasters that occurred in 2000-2020 at the state level from HH the EM-DAT of the Université Catholique de Louvain, Belgium. This database offers a 8
First, we test for endogeneity of the intervention variable and its interaction with RELPOL separately in the cases of MENA and non-MENA countries. As the Durbin-Wu-Hausman endogeneity test statistic shows, we cannot reject the exogeneity null of these two variables in the case of non-MENA countries even at the 10% significance level. However, we reject the null in the case of MENA countries at the 1% significance level. Therefore, we use a linear probability model (LPM) to estimate equation (8.1),<sup>36</sup> while in the case of MENA we employ a two-stage linear probability model (TSLPM). The choice of TSLPM rather than a two-stage logit model is based on the conclusion of Angrist and Kruger (2001) that a linear regression in the first stage generates consistent second-stage estimates in case of an endogenous dummy variable. Moreover, using nonlinear models such as probit or logit to generate fitted values in the first stage for use in the second-stage does not generate consistent estimates unless the nonlinear model happens to be exactly right.

In the first stage, we estimate simultaneously the system of equations (9.1) and (9.2): This system represents a reduced-form specification for the intervention and interaction variables and includes a vector of instruments, X3it. We consider as potential instruments for intervention the number of bordering countries to country _i_ (BORD) and the total length of the border of country _i_ in kilometers (TOTBORD). This choice of variables is motivated by Gleditsch (2007) who finds

> 36 We obtain similar results with the logit.

34
# **6. Educational Spending and Student Outcomes**

The previous discussion has stressed the importance of cognitive skills for economic outcomes – individual earnings, income distribution, and economic growth. By virtually any standard, the evidence indicates a strong causal impact of skills on these outcomes. Yet simply knowing that skill differences are important does not provide a guide to policies that might promote more skills. Indeed, a wide variety of policies have been implemented within various countries without much evidence of success in either achievement or economic terms. This history has led some to believe that education is not a useful policy lever to achieve economic development goals.

We believe that the disappointing results of the past generally reflect pursuing policies for which there is little empirical support. But the past outcomes should not be generalized to statements about all policies.

Substantial research has gone into understanding why achievement differs across students and across countries. While controversies have existed about the interpretation of various evidence, some considerable agreement now exists about what kinds of approaches are unreliable avenues for change. There is perhaps less agreement on the precise approaches that might be followed, but even here there is a growing consensus about the general sets of policies that have shown promise based on more credible research approaches.

The work on achievement determinants is generally labeled “education production functions.” The extensive work has taken a variety of perspectives and approaches. The general objective is to sort out the causal impacts of school factors (things that can potentially be manipulated through policy) from other influences on achievement including family background, peers, neighborhood influences, and the like.

The most important generally available evidence relates to the effects of resources. Many policies undertaken involve substantial flows of resources – direct spending, changes in teacher salaries, reductions in class size, and the like – made within the context of current school organization. The empirical evidence clearly documents the difficulties with such policies. Simply providing more resources gives, according to the available evidence, little assurance that student performance will improve significantly.

This section reviews the international evidence on resources, spending, and student outcomes. The next section discusses larger questions of institutional design.

59