rafmacalaba/datause-encoder-data huggingface dataset:
in the inequality aversion of girls, while one cannot reject the hypothesis that it had no effect on the inequality aversion of boys.

List et al. (2021) argue that one reason for studying children’s decision-making is that “children are active participants in their human capital production process”; their preferences and decisionmaking abilities may influence investments in their human capital with long term-consequences. We provide some support for this hypothesis. Our results show that aversion to disadvantageous inequality predicts scores in _Prova Rio_ , standardized Math and Portuguese exams administered to 3<sup>rd</sup> grade students studying in public schools in Rio de Janeiro. These results are consistent with a model that predicts that children who dislike falling behind will put more effort and study harder to avoid scoring lower than her peers. The association between aversion to disadvantageous inequality and grades is robust to controlling for IQ and to controlling for all other measures of economic preferences and decision-making abilities.

This paper contributes to the literature on the causal effects of education on economic preferences and on decision-making abilities (Bettinger & Slonim 2006; Jakiela et al. 2015; PerezArce 2017; Andreoni et al. 2019; Banks et al. 2019; Cappelen et al. 2020; Chuan et al. 2022) and to a growing literature on children’s decision-making (see e.g. Harbaugh et al. 2001; Castillo et al. 2011; Castillo et al. 2018; Sutter et al. 2019; Castillo et al. 2019; Brocas & Carrillo 2020; Castillo et al. 2020<sup>5</sup> ; List et al. 2021). We are aware of only two other randomized experiments that have studied the causal effects of early education on children’s decision-making (Bettinger & Slonim 2006; Cappellen et al. 2020).<sup>6</sup> One distinction of this study is to study the effect not only on economic preferences but also on decision-making abilities.

The rest of the paper is structured as follows. Section 2 describes the lottery system that generated the exogenous variation in daycare attendance, shows that control and treatment groups were balanced in terms of their characteristics, and that the lottery is a strong predictor of daycare attendance. Section 3 discusses the four incentivized experiments that were administered to measure children’s preferences. Section 4 estimates the effects of daycare attendance. Section 5 investigates whether the effects of daycare attendance on aversion to disadvantageous inequality

> 5 See also the special issue of Journal of Economic Behavior & Organization, “Understanding Cognition and Decision Making by Children.” Journal of Economic Behavior 179 (2020): 623-806.

> 6 Bettinger & Slonim (2006) leverage a lottery that allocated scholarships among families with children in kindergarten all the way to 8<sup>th</sup> grade. Alan & Ertac (2018) and Alan et al. (2019) study educational interventions specifically designed to increase patience and to increase grit.

5
Estimating the Economic and Distributional Impacts of the RCEP Figure 10. Trade. Productivity kick scenario relative to baseline (in billion USD dollars, 2035) <!-- Start of picture text --> Electrical equipment and machinery<br>Chemical, rubber, plastics<br>Metals<br>Wearing apparel and leather<br>Food and beverages<br>Motor vehicles and parts<br>Textiles<br>Other manufactures<br>Wood and paper products<br>Fossil fuels<br>Petroleum, coal products<br>Trade<br>Business services<br>Other transport equipment<br>Meat products<br>Non metallic minerals<br>Crops<br>Tourist services<br>Public administration and services<br>Air transport<br>Road transport<br>Other extraction<br>Communications<br>Basic pharmaceuticals<br>Financial services and insurance<br>-60 -10 40 90 140 190 240<br>Imports from ROW Exports to ROW Intra RCEP trade<br><!-- End of picture text --> Source: World Bank staff calculations using the ENVISAGE model.

Under all RCEP scenarios, sectors that expand the most are the manufacturing products such as wearing apparel; textiles; electrical equipment; and transport equipment (Figure 11). Those sectors show the highest increase in output in percentage change and might benefit substantially from an increase in productivity related to a lower protection level on industrial inputs. Tourist services and petroleum and coal are the non-manufacturing sectors that also expand significantly.

The productivity kick assumption affects the impact on output by sectors. Under a full RCEP scenario without productivity increase, the main sectors expanding remain the same, but the increase in output is much smaller. Wearing apparel and leather, textiles, electrical equipment and machinery and petroleum and coal products are the sectors that expand the most. Without productivity increases, some sectors contract, mainly basic pharmaceuticals, business services, and crops, but the fall in output is very slight.
phenomenon and members tend to come from economically deprived urban areas. Gangs tend to flourish in environments where the established social order has broken down and social capital is low.

Youth violence is closely bound up with violence against children. There is a significant relationship between exposure to violence or victimization as a child and a later propensity for violent conduct.<sup>35</sup> It is estimated that 6 million minors in the region are the object of severe maltreatment and that 80,000 die each year as a result of injuries caused by their parents, relatives or others.<sup>36</sup> A recent study found that over 2 million children and youth, and 23 % of families experience abuse in urban Colombia, and that in Mexico City 1 million children and 13 % of households do so.<sup>37</sup> This study also finds that abuse has a significant negative effect on human capital: it affects on children’s educational attainment and adult labor wages. The public health literature makes a strong case for the prevention of violence against children and domestic violence as a form of primary violence prevention.

With the rapid rise of crime and violence levels, citizen security has been identified as a key concern of the Latin American electorate. Latinobarómetro polls quote delinquency among the top concerns of the population along with unemployment, inflation, poverty and corruption.<sup>38</sup> The same poll reports low levels of inter-personal trust. Polls suggest that violence tolerance levels differ across societies and the perceptions of violence levels are not always matched by reality. Interestingly, Latin Americans seem to feel less secure than they actually are, according to research by Smulovitz (2004) on Argentina and Basombrío (2004) on Peru.<sup>39</sup>

> 35 Dahlberg (1998) quoted in Buvinic, Morrison and Orlando (2002). 36 De Roux (1994) quoted in Ayres (1998).

> 37

> 38

> Knaul and Ramírez (2005).

> Latinobarómetro (2002) quoted in Buvinic, Morrison and Orlando (2002).

> 39 Smulovitz (2003), Basombrío (2003) in Frühling and Tulchin with Golding (2003).

6
# **9.3 Direct transfers**

**The PECS only provides aggregate data of households’ income from social protection (e.g., government assistance).** This includes one variable for “total cash” and another variable for “total in kind”, the latter labeled as near-cash transfers in our model. Hence, it was not possible to disaggregate transfers households received for each of the four main specific social protection (cash transfer) programs in the country. Currently, the model infers that the cash-transfers reported in the PECS are from the CTP program based on cross-validation with other administrative data. However, if the team had access to the PMT formula of the CTP and threshold or the allocation methods for other programs, it would have been possible to do a more detailed simulation of social protection programs in the West Bank and Gaza.

## **9.3.1 Education**

**Data limitations in the PECS 2016/17 required that we made assumptions about beneficiaries of public education benefits.** The PECS 2016/17 survey does not have information on “current grade of education”; instead, this study inferred beneficiaries combining self-reported information on “highest grade of education achieved (plus one year)” and “currently enrolled in public education”. The analysis also imputes an average education benefit calculated at the national level; implicitly, this assumes that students in the West Bank and Gaza receive the same benefits across regions at each education level. Information on public expenditure by education level, disaggregated by region, would improve the calculation of education benefits. Lastly, due to PECS data limitations, it was not possible to analyze distributional effects of public expenditure from other levels of education (pre-primary and tertiary).

## **9.3.2 Health**

**The PECS does not have information on access to public health services.** By using the health-insurance approach (focusing on individuals reporting coverage of government health insurance in the PECS), the model implicitly assumes that eligible individuals do not have significant barriers to access public health services. However, in practice, usage of health services by the eligible population could be lower, particularly in Gaza where most residents use UNRWA-provided health services. Hence, using the healthinsurance approach could overestimate the in-kind health benefits among Gaza residents. We also calculated public health benefits at the national level, assuming that benefits for each health level are the same for residents in the West Bank and Gaza. However, having public health expenditure data disaggregated by health service and region would improve calculation of health benefits.

59
**_PoLh FTcal_** _Crbk 27_

# Table A. 1 (continued)

||Quantity|Price|Value|
|---|---|---|---|
|Accounting Option 4: Inventory Revaluation||||
|Stock of materials on 1.1 following revaluation|1<br>1|30<br>30|30<br>3Q|
|Total:|2||60|
|Extraordinary profits from inventory revaluation|||60-30<br>= 30|
|Materials consumed in production|1|30|30|
|HCA profit from sales = sales - cost of<br>materials used|||40-30<br>= 10|
|Total HCA profit = profit from sales +<br>extraordinary profits|||10+30<br>-40|
|Stock of materials on 31.12|1|30|30|
||1|30|la|
|Total:|2||60|
|Inventory accumulation, 1.1-31.12|||60-30<br>=30|
|Accounting identity: cash flow = HCA profit-<br>inventory accumulation|||40-30<br>-<br>10|
along with the creation of the new capital city, Bras ́ılia (Ayres et al., 2019). This impetus was continued under subsequent governments with a series of national development plans in 1967, 1973, and 1976 emphasizing the construction of hydroelectric dams and roads improvement and highway construction (Leturcq, 2018).<sup>2</sup> The plans required a large public investment effort toward infrastructure. In the initial Targets Plan, 71 percent of public investments targeted energy, transportation, and communications (Obhara,<sup> ̄</sup> 1974). These investments were sustained in the national development plans that followed. They were undertaken at a time when electricity demand increased due to population growth and urbanization. New investments then slowed down – starting with transport followed by electricity – during the “lost decade” of the 1980s and 1990s as Brazil’s governments focused on addressing the macroeconomic crisis (Raiser et al., 2017). This led to a progressive deterioration of the transport network as investments did not cover maintenance needs (Correa and Ramos, 2010).

All in all, infrastructure investments resulted in a significant expansion of the electricity and highway networks. Lipscomb, Mobarak, and Barham (2013) report that the transmission network in Brazil grew at an average rate of 8.9 percent per year between 1950 and 2000, resulting in a 72-fold increase from 2,359 kilometers to 167,443 kilometers over the period. As a consequence, household access to electricity rose from 48% in 1970 to 93% in 2000. The total length of highways increased from 41,189 km in 1970 (of which 3,488 km of radial highways originating from Bras ́ılia) to 90,788 km in 2000 (of which 7,440 km were radial highways originating from Bras ́ılia–calculation by authors based on DNIT data).

# **2.2 Data**

We start with creating a longitudinal dataset with four points in time (1970, 1980, 1996, and 2000) across 3,102 Brazilian municipalities for which economic data are available.<sup>3</sup> It covers more than 93 percent of the Brazilian population throughout the study period.<sup>4</sup> Our data comprise the municipal Gross Valued Added (GVA),

> 2The 1973 plan aimed to create 8 radial roads, 17 longitudinal highways, 24 transverse roads, 27 diagonal roads, and 62 connecting highways (Pereira and Lessa, 2011).

> 3Note that the data is unequally spaced in time, which will not affect the scope and magnitude of our results as shown in Section 4.4.

> 4We work with administrative boundaries at the municipality level from the Brazilian Institute of Geography and Statistics ( _Instituto Brasileiro de Geografia e Estat ́ıstica, IBGE_ ) using the 1970 data as a baseline reference. The 568 missing municipalities for which economic data are not available only represent 6 percent of the country’s population in 1970 and 7 percent in 2000.

4
# IPF Results: Water Catchment

After calculating the SEI and FEI scores for each catchment project according to the weighted additive models above, the projects are ranked by each score, by region. The calculations for SEI and FEI scores are detailed in Annexes 3 and 4, while the graphical results of rankings by each index are summarized in Figures 5, 6, 7, and 8. Figures 5 (SEI) and 7 (FEI) display results region by region, whereas Figures 6 (SEI) and 8 (FEI) display the full set of projects pooled into a single ranked group according to each index.

In each ranking chart, the projects that were actually approved for funding by the government are marked with a checkmark above the project’s score. Generally speaking, irrigation projects with higher SEI and FEI scores tended to be those approved for funding, though some projects with outlying low SEI scores were among the approved set.

## Water Catchment SEI Results

Project rankings by SEI for each region are detailed in Figure 5. In each ranking chart, the projects actually selected for funding are marked with a checkmark.

Figure 5. Water Catchment Project SEI Scores and Ranking, by Region <!-- Start of picture text --> Biobio SEI Maule SEI<br>100 100<br>90 90 ✔ ✔<br>80 80<br>70 70 ✔ ✔<br>60 60 ✔<br>50 50<br>✔ ✔<br>40 40<br>30 ✔ ✔ 30<br>20 20<br>10 10<br>0 0<br>Project ID Project ID<br>O'Higgins SEI ✔✔ Valparaiso SEI<br>100 100<br>90 90<br>8070 ✔✔ ✔ 8070<br>60 60<br>50 50 ✔ ✔✔<br>40 40<br>30 30 ✔<br>20<br>20<br>10<br>10 ✔<br>0<br>0<br>Project ID<br>Project ID<br>5 3 4 2 10 9 7 11 13 12 8 1 6 14 20 22 15 24 21 17 16 26 23 25 27 19 18<br>36 46 35 29 30 31 37 41 42 43 32 33 34 28 47 48 39 40 38 44 45 49 56 58 57 51 50 55 54 60 61 52 53 59<br><!-- End of picture text --> ✔ - Project approved for funding by GoC Source: Authors’ calculations 29
Policy Research Working Paper 9165

# **Abstract**

Many economists believe that the returns to migration are high. However, credible experimental estimates of the benefits of migration are rare, particularly for low-skilled international migrants and their families. This paper studies a natural experiment in Bangladesh, where low-skilled male migrant workers to Malaysia were selected via a large-scale lottery program. This study tracked the households of lottery applicants and surveyed 3,512 lottery winners and losers. Five years after the lottery, 76 percent of the winners had migrated internationally compared with only 19 percent of the lottery losers. Using the lottery outcome as an instrument, the paper finds that the government intermediated migration increased the incomes of migrants by over 200 percent and their household per capita consumption by 22 percent. Furthermore, low-skilled international migration leads to large improvements in a wide array of household socioeconomic outcomes, including female involvement in key household decisions. Such large gains arise, at least in part, due to lower costs of government intermediation.

This paper is a product of the Social Protection and Jobs 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/prwp. The authors may be contacted at mshrestha1@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
# **6. Bibliography**

Adams, Richard, Alfredo Cuecuecha, and John Page. "The Impact of Remittances on Poverty and Inequality in Ghana." _World Bank Policy Research Working Paper 4732_ , 2008.

- Asante, Y. "Migration-Development Interrelationships." In _Migration Research Study in Ghana_ , by K. A. Twum-Baah, J. S. Nabila and A. F. Aryee. Accra: Ghana Statistical Service, 1995.

- Batse, Z. M. K. "Measuring Internal Migration." In _Migration Research Study in Ghana_ , by K. A. Twum-Baah, J. S. Nabila and A. F. Aryee. Accra: Ghana Statistical Survey, 1995.

- Beals, R. E,, M. B. Levy, and L. N. Moses. "Rationality and Migration in Ghana." _Review of Economics and Statistics_ , 1967.

- Caldwell, J. C. "Determinants of Rural-Urban Migration in Ghana." _Population Studies: A Journal of Demography_ , 1968.

- Codjoe, Gosway, Clemens Breisinger, and Xinshen Diao. "Local Impacts of a Global Crisis: Food Price Transmission and Poverty Impacts in Ghana." _IFPRI Working Paper_ , 2008.

- Fafchamps, M., and R. Hill. "Price Transmission and Trader Entry in Domestic Commodity Markets." _Economics of Development and Cultural Change_ , 2008: 729766.

- Gbortsu, E. P. S. "Socio-Demographic Profile of Internal Migrants." In _Migration Research Study in Ghana_ , by K. A. Twum-Baah, J. S. Nabila and A. F. Aryee. Accra: Ghana Statistical Service, 1995.

- Ghana Statistical Service. "Ghana Living Standards Survey--Report of the Fourth Round (GLSS4)." Accra, Ghana, 2000.

Ghana Water Directorate. "Status of Ghana's Water and Sanitation." 2008.

Hine, J. L. "Transport and Marketing Priorities to Improve Food Security in Ghana and the Rest of Africa." _Regional Food Security and Rural Infrastructure: an International Symposium._ Glessen, 1993.

- Hine, J., J Riverson, and E Kwakye. _Accessibility, Transport Costs and Food Marketing in the Ashanti Region of Ghana._ Crowthorne: Transport and Road Research Laboratory, 1983.

- Jensen, Henning Tarp, Willem van den Andel, and Magnus Ebo Duncan. "A Social Accounting Matrix (SAM) for Ghana for the year 2004." _Institute of Food and Resource Economics, University of Copenhagen Working Paper Series 2009(2)_ , 2009.

- Litchfield, J., and H. Waddington. "Migration and Poverty in Ghana: Evidence from the Ghana Living Standards Survey." _Sussex Migration Working Paper_ , 2003.

14
28

# SUMWIY **OF** MtI^TITATIWE **ANALySIS OF PIOJECT** COOTEIT

The percentages of projects with given elements in their designs, by year and by region, are shown In this table. In the following table, the presence or absence of an element in a given project is indicated by a I or a 0.

|ELEMENTS<br>IN<br>PROJECT<br>DESIGN<br>pr|All<br>oject|Year a<br>Pre-<br>s<br>1982|gorove<br> <br>1982|d<br>1983|1984|1985|1986|R<br>L<br>1967 &|egion<br>at.Am<br>Carib|. South<br>. Asia|East<br>Asia|M.E.&<br>N.Afr.|East<br>Africa<br>A|West<br> <br>frica|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|PROJECT<br>CHARACTERISTICS|||||||||||||||
|Type|||||||||||||||
|A. basic health services|88|83|75|86|100|<br>92|75|100|88|<br>80|71|<br>83|100|92|
|B. marpower|90|83|100|100|<br>100|<br>92|75|86|75|<br>100|<br>100|<br>100|<br>100|75|
|C. institutionat<br>development|100|100|<br>100|100|<br>100|<br>100|100|<br>100|100|<br>100|<br>100|<br>100|<br>100|100|
|D. urban health|6|0|0|0|33|0|13|14|0|0|0|<br>0|22|8|
|E. disease<br>control|13|17|0|14|0|15|13|14|13|<br>0|29|0|11|17|
|F. poputation|58|17|25|57|33|62|as|86|25|80|43|17|**IOU**|83|
|G. nutrition|27|67|0|0|0|15|25|71|25|<br>20|29|0|11|50|
|Targets,<br>nunericaL|58|100|<br>75|57|33|62|50|29|88|<br>80|57|<br>83|67|8|
|Management capacity|||||||||||||||
|A. weak|96|100|100|100|100|92|88|100|100|100|71|100|100|100|
|B.good|6|17|0|0|0|8|13|0|13|0|29|0|0|0|
|INFORMATION<br>FOR PROJECT<br>DESIGN|||||||||||||||
|Client fertility,<br>family planning|||||||||||||||
|A. basic information|88|33|75|100|100|92|100|100|75|80|86|100|100|83|
|B. KAP & fertility<br>practices|58|33|50|86|67|54|as|29|75|80|43|67|78|25|
|C. determinants of behavior|15|17|25|14|0|8|25|14|25|20|0|0|33|8|
|Client health status, behavior|||||||||||||||
|A. basic information|83|33|100|<br>71|100|85|100|100|88|20|86|83|100|92|
|B.KAP|19|17|0|14|33|8|25|43|0|0|14|33|44|8|
|C. determinants of behavior|6|0|0|14|0|0|0|29|0|0|0|17|22|0|
|Client nutrition<br>status, behavio|r||||||||||||||
|A. basic information|81|83|100|29|67|92|88|100|88|40|71|83|100|92|
|B. KAP/consumption|38|67|75|14|33|31|25|43|50|20|43|67|33|25|
|C. determinants of behavior|10|50|0|0|33|0|0|14|25|20|14|0|11|0|
|Provider practices|||||||||||||||
|A. basic description|42|67|25|43|0|46|50|29|38|80|29|50|22|33|
|S.dbterminants|6|0|0|14|0|8|0|14|0|20|0|17|11|0|
|TRAINING<br>B SUPERVISION|||||||||||||||
|Managemen**t**raining|||||||||||||||
|A. senior<br>& middle managers|69|33|100|57|100|85|50|71|50|80|86|67|100|58|
|<br>B. supervisors<br>of field<br>staff|52|50|25|43|33|54|50|86|**38**|80|43|<br>33|56|67|
|C. salaried<br>field staff|29|17|25|14|33|38|38|29|25|40|43|33|22|25|
|IEC training<br>for field staff|58|50|75|71|33|46|63|71|50|60|14|67|89|50|
|Estab/inwove<br>field supervisio|n||||||||||||||
|A. general|83|83|75|86|67|77|88|100|75|80|100|50|89|92|
|S. provide<br>transport,<br>allowanc|e 63|33|50|86|0|46|88|100|50|<br>S0|29|<br>33|89|67|