rafmacalaba/datause-encoder-data huggingface dataset:
experimental variation in migration, this assumption is unlikely to hold. Indeed, the seminal migration selectivity model of Borjas (1987) has migrants deciding whether or not to migrate in part on the basis of the εi they would expect to have at home versus abroad. It is easy to think of a whole range of typically unmeasured variables, such as entrepreneurial prowess, ambition, language proficiency, and health status which would both affect whether or not someone migrates, and also directly affect their income or other outcome of interest. Likewise liquidity constraints will likely determine both the pattern of self-selection of migrants (McKenzie and Rapoport, 2010), as well as the range of job opportunities and consumption-smoothing opportunities that individuals will have at home. As a result, equation (2) will almost always be violated in practice, so that linear regression on equation (1) will result in biased estimates of the impact of migration.

Equation (2) therefore says that we can only estimate the causal impact of migration if the only reason one person migrates and another does not is random (conditional on observed characteristics). That is, ideally we would randomly choose some people to migrate and others not to, and then by comparing these two groups, get the impact of migration. This is precisely what the experimental approach attempts to do.

# **3. Examples of the Experimental Approach**

# **3.1 Policy Experiments**

Several countries use visa lotteries to choose among numerous applicants desiring to immigrate through a particular migration category that has a fixed quota. The most famous of these is the United States Diversity Visa Lottery (commonly known as the Green Card Lottery), which each year makes available 50,000 visas, to be drawn randomly among eligible applications from countries with low rates of immigration to the United States. For the 2010 lottery, over 13.6 million qualified entries were received, with 102,800 applicants drawn as winners, under the assumption that half of these would migrate.<sup>3</sup> Whilst this is the most well-known example of a migration lottery, it has not been used for research yet.

> 3 Source: http://travel.state.gov/visa/immigrants/types/types_4574.html [accessed February 26, 2010].

3
0 _._ 92SD (or 2 months of schooling) but with strong implications for distribution, with greater impacts on 9<sup>_th_</sup> grade students from low quantiles and significantly reduced impacts for 5<sup>_th_</sup> grade students.

We use detailed data on teachers, principals and public spending to analyze several mechanisms for these findings. First, there are significant reductions in principals “ _Designated_ " to schools and replaced by those going through formal selection processes. Second, there is a strong indication that both principals and teachers in _Ceará_ are more likely to be involved in training and report it as useful for their daily work compared to similar principals and teachers in the immediate municipalities outside _Ceará_ ’s border. Fourth, after the RBF reform, teachers in _Ceará_ are more likely to be hired on 40-hour contracts (both grades), check school homework more often (5<sup>_th_</sup> grade), and cover at least 80% of the curriculum (both grades). Finally, schools in _Ceará_ report significantly less frequent lack of pedagogical materials at the same time that significantly more teachers consider these books to be at least “good”.

Exploring different formats of the RBF model in _Ceará_ in time provides a clear contribution to public policy design. Particularly, introducing the RBF in combination with technical assistance boosts learning impacts in at least two _−_ fold relative to RBF alone. Elaborating textbooks and providing training to teachers, based on student test scores, is the main source of these impacts. Another important contribution to policy design is that redistributing fiscal resources exclusively based on education performance tends to widen learning gaps. Such inequalities are significantly reduced, when penalizing municipalities where there is a high percentage of students below certain thresholds of performance.

All these gains in student learning in _Ceará_ were obtained without increasing public spending on education. This means that the large gains in student learning and education quality followed the efficient allocation of public spending to municipalities in _Ceará_ .

42
percent, including 472 individuals who had not been able to be interviewed at baseline. In total 5,057 individuals were interviewed at both baseline and follow-up.

The attrition rate of 6 percent at follow-up compares favorably with the attrition rates in other evaluations of vocational training in developing countries (18.5 percent in Attanasio et al, 2011; 23 percent in Maitra and Mani, 2012; 38 percent in Card et al, 2011, and 46 percent in Cho et al, 2013). Table 1 examines whether attrition from either the baseline or follow-up surveys is related to treatment status, both for the full sample, and then by the four gender by age group stratum. To do this, we estimate the following regression: Attritioni = α+ βAssignedtoTraining+ ∑457s=1 θsδi,s + εi (1) Where _i_ denotes individuals, _s_ denotes a course*gender*age group lottery, and δi,s is a dummy variable indicating whether individual _i_ applied for lottery _s_ .<sup>12</sup> This controls for randomization strata (Bruhn and McKenzie, 2009) as well as controlling for individuals who applied for more than one course (Abdulkadiroglu et al, 2011). We are then interested in β, the impact on attrition of being assigned to receive vocational training.

Table 1 reports the results. Attrition at baseline is 2.7 percentage points lower for the treatment group than the control, while attrition at follow-up is 1.4 percentage points lower. This differential attrition comes from relatively higher attrition from males in the control group than for females or for treated males, while there is no significant differential attrition for females. Whilst statistically significant, this difference in attrition rates by treatment status is small in absolute terms. We examine the sensitivity of our results to differential attrition by employing Lee’s (2009) bounding approach. In addition, attrition is near zero in our administrative data (only 6 individuals could not be matched). This gives us several outcome measures which are not subject to concerns about attrition. Furthermore, using this administrative data, we are unable to reject that attrition rates in the follow-up survey are unrelated to the likelihood of having found a formal job by the time of this survey (p=0.807).

# **4.2 Administrative Data on Employment from Social Security Records**

Our sample of applicants was linked by ISKUR to official data on worker payments filed in the social security system. Due to updates and delays in this system, it took us two years to receive this data from when it was requested, and we were limited in the information that could

> 12 There are 457 strata, reflecting the non-empty cells from 130 courses*2 genders*2 age groups.

10
successful the stipend program can be is inexorably linked to the state of infrastructure available for girls’ education.

The evidence presented in Figures 14 – 16 suggests that this may indeed be the case. Figure 14 shows that in almost all parts of the province, at the primary level girls’ school are generally half of all schools available. At the middle school level (Figure 15) however, there is much more variability. In a number of districts girls’ schools account for as little as one-third of all schools. At the level of high school (Figure 16) the picture is bleaker with no district able to document gender parity in the number of schools. This state of affairs feeds back into the problem of having teachers to teach in these schools. If there aren’t enough girls’ schools at higher levels of education where are the female teachers of tomorrow going to get their education? This suggests one reason why rural schools might lag behind their urban counterparts – a dearth of well-qualified teachers in rural areas.<sup>14</sup> As the program continues and expands it will need to confront these issues to maintain the success it has already documented.

> 14 In results not reported in this paper (for the sake of brevity) it appears that a simple index of facilities does not differentiate schools in their ability to increase enrollments under the CCT program. I am currently engaged in gaining access to information from the census forms on teacher qualifications. This might shed more light on how urban schools are able to consistently out-perform their rural counterparts.

21
land. In contrast to the results for larger farmers, wage growth is insignificant in both columns. Land transactions are very small and are not responding to increased labor costs.<sup>8</sup> Table 3 to be inserted Table 3 shows results on machine investments.<sup>9</sup> The sample was again split into two groups based on initial farm size in 2007. In Column 1, we find that an increase in the real agricultural wage induces machine investments among relatively large farmers. However, this is not observed among relatively small farmers (Column 2). Therefore, we can conclude that larger farmers tend to not only expand their operation but also to invest in machines to substitute for labor when real agricultural wage increases.

Table 4 to be inserted Table 4 summarizes the results on estimate of a profit function.<sup>10</sup> The dependent is the growth of crop income (after deducting all costs except family labor) taking the difference in log crop incomes between 2007 and 2010. Thus, negative income observations were dropped from the sample. The first three columns use all farmers with positive crop incomes in both years. Three results emerge. First, selfcultivated land, both owned and rent-in, has significant positive effects on crop profit (Column 1). Second, machine investment also has a marginally significant effect on crop profit (Column 2). Third, if we include the interaction term between cultivated land and machine investment (Column 3), the effects of land and machine investment remain robust, and the interaction has a positive effect on crop profit, but this is marginally insignificant.

> 8 In Indonesia, rice production rather stagnated in 2009 and 2010, which resulted in an increase of rice imports, and the negative production shock could be a reason for smaller rent-in among small holders but since the survey captured the crop year of 2009/10, the decision on land rentals should have been made at the end of the crop year 2008/09 and moreover the observations of negative crop incomes were excluded in the above analysis, such a possibility is quite small.

> 9 The survey did not capture details of machine rentals.

> 10 In the preliminary analysis, per-ha profit did not show good results, which implies that productivity was not

affected by machines and lanldholding size.

11
- [4] P. L. Joskow. Restructuring to promote competition in electricity: In general and regarding the poolco vs. bilateral contract debate. Presented at the American Economic Association Meetings, January 1996.

- (53 M. Klein. Competition in network industries. Policy Research Working Paper 1591, The World Bank, April 1996.

- [61 J.-J. Laffont, P. Rey, and J. Tirole. Network competition: I. overview and nondiscriminatory pricing. Mimeo, IDEI, April 1996.

- [71 J.-J. Laffont, P. Rey, and J. Tirole. Network competition: Ii. price discrimination. Mimeo, IDEI, April 1996.

- [8] J,-J. Laffont and J. Tirole. Creating competition through interconnection: Theory and practice. _Journal of Regulatory Economics,_ 10(3):227-56, November 1996.

- [91 S. Littlechild. The transmission price control review of the national grid company: Third consultation. Office of Electricity Regulation, United Kingdom, May 1996.

- [10] J. MacKie-Mason and H. R. Varian. Pricing the internet. Mimeo, Department of Economics, University of Michigan, February 1994.

- [11] T.-O. Nasser. _Imperfect Markets for Power: Competition and Residual Regulation in the Electricity Industry._ PhD thesis, Department of Economics, Massachusetts Institute of Technology, June 1997.

- [12] S. Oren, P. Spiller, P. Varaiya, and F. Wu. Nodal prices and transmission rights: A critical appraisal. _The Electricity Journal,_ 8(3):24-35, March 1995.

- [13] .J. Perez-Arriaga, F.J. Rubio, J.F. Puerta, J. Arceluz, and J. Marin. Marginal pricing of transmission services: An analysis of cost recovery. _IEEE Thansactions on Power Systerms,_ 10(l):546-553, 1995.

- [14] F. Schweppe, M. Caramanis, R. Tabors, and R. Bohn. _Spot Pricing of Electricity._ Norwell, MA: Kluwer, 1988.

- [15] F. Wu, P. Varaiya, P. Spiller, and S. Oren. Folk theorems on transmission access: Proofs and counterexamples. _Journal of Regulatory Economics,_ 1996. Forthcoming.

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# 1. Introduction

It is estimated that non-communicable diseases (NCDs) cause over 70% of deaths worldwide (WHO, 2022). Among these, an estimated 7.9 million deaths and 187.7 million disability-adjusted life-years (DALYs) were attributable to dietary risk factors in 2019 (Qiao et al., 2021). Unhealthy diets - linked to leading NCDs like diabetes, heart disease, stroke, and cancer - are characterized by low consumption of whole grains, fruit, vegetables, nuts and seeds, fiber, polyunsaturated fatty acids, omega-3 oils, calcium, or milk, and/or high consumption of sodium, trans-fats, processed and red meat, and sugary drinks (as classified by Global Burden of Disease studies) (PAHO, 2023).

Increased dietary risk has contributed to an unprecedented rise in overweight and obesity. The first report from the Lancet Commission on Obesity described this rise, together with undernutrition and climate change, as a “Global Syndemic”, a synergy of epidemics sharing common societal drivers including food and agriculture, transportation, urban design, and land use systems (Swinburn et al., 2019). Due to factors such as limited political leadership, coordination, and public demand, and strong commercial lobbies, no country has managed to reverse the rise in obesity prevalence over the last four decades, despite three decades of World Health Assembly commitments and the UN designation of 2016-2025 as the “Decade of Nutrition” (Swinburn et al., 2019). The Americas is the World Health Organization (WHO) region with the highest prevalence of overweight and obesity, estimated at 62.5% and 28% respectively in 2016 (PAHO, 2023).

In addition to contributing to obesity and overweight (which in turn increases NCD risk), ultra-processed foods have been independently linked to a range of negative health outcomes, informing policy efforts to reduce their consumption to promote healthy diets and reduce the burden of diet related NCDs, as recommended by the Framework for Action from the Second International Conference on Nutrition (2014). Fiscal policies – both taxes and subsidies – have gained increasing traction as tools to influence healthier consumption. While more substantial research has been conducted on taxation of alcohol, tobacco, and sugar-sweetened beverage (so-called “health taxes” or “sin taxes”), studies on fiscal policies to influence healthy food consumption have been more limited. In particular, evidence on impacts of such

> 1 World Bank Health, Nutrition, and Population Global Practice

> 2 World Bank Health, Nutrition, and Population Global Practice

> 3 World Bank Health, Nutrition, and Population Global Practice

2
and γj allows for region-specific intercepts.

The variables contained in the vector Zi come from administrative and survey data. The 2014 Census provides information on Districts’ population and urbanization rates, while poverty rates are estimates for 2013 (World Bank, 2016b)<sup>21</sup> . We complement these socioeconomic data with average students’ score at the UCE in 2011, a nationally administered exam students take at the end of lower secondary school, as the best available proxy for education in the population.<sup>22</sup> Our survey also gathered data on the population of radio stations operating in Uganda at the time of the survey, from which we derive a variable of the number of radio stations headquartered in a district. Access to radio, and to media in a more general sense, has been shown to impact the public scrutiny of local politicians (Ferraz and Finan, 2008) and affect citizens’ investment in health and education (Khemani and Keefer, 2015; Khemani and Keefer, 2014). We also consider that the existence of local radio stations might proxy for other unobservables such as the dynamism of local markets.

In addition, we compile data on local vote shares from the 2011 elections, the last one before our survey. The main variables are the vote share of the presidential candidate of the NRM, the national ruling party (labeled "Voter Attachment to National Party"), at the district level, and voting Herfindhal Indices<sup>23</sup> for district politicians<sup>24</sup> .

The lower panel in Table 4 presents descriptive statistics of administrative data for the 75 Districts included in the survey. The median District in our sample has approximately 250,000 people and is overwhelmingly rural<sup>25</sup> . Poverty rates, at 23 percent in the median District, are only slightly higher than the national rate of 20 percent in 2013. The numbers also show the subdued level of political competition in presidential elections: the candidate

> 21These are small areas estimates produced by the World Bank, using data from the 2013 UNHS and 2014 Census data. For details on methodology see for example Ebers, Lanjow and Lanjow (2002).

> 22Note that higher values of the UCE score are indicate of lower education performance, as described in the data Annex. We include 2011 UCE performance since those cannot be influenced by current politicians, who were elected in that year.

> 23The Herfindahl Index is most commonly used as measure of firm competition in a given market, but has been widely used to measure political competition . The Herfindahl Index is obtained, at the electoral area level, by the sum of the squares of candidates’ votes. Higher values are associated with a less competitive environment (the index ranges from 1/N to 1, where N is the number of candidates).

> 24We compute all the results at the electoral area level and then obtain the district-level average across all areas.

> 25According to the 2014 Census only 16 percent of the population in Uganda lived in urban areas.

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# Table **1** Commodity price instability, 1950-1986

|||Coefficient of va<br>|riation, percentages<br>|
|---|---|---|---|
|Commodity|1950-69<br>5-yr<br>price<br>MA<br>change|197<br>5-yr<br>MA|0-86<br>price<br>change|
|Cocoa|21<br>25|22|28|
|Coffee|12<br>16|24|35|
|Tea|7<br>11|19|23|
|Sugar|35<br>39|38|47|
|<br>Cotton|6<br>13|13|19|
|Jute|20<br>22|12|18|
|Rubber|16<br>24|18|23|

Source: Newbery (1990, Table 5.1) Notes: Price change is the standard deviation of 2 Pt- p-) / (p, + p, ,. 5-year MA is the CV of deviations frm the S-year moving average.

46
**Figure 10. TFP growth difference (percentage points)**

<!-- Start of picture text --> 0.35<br>0.3<br>0.25<br>0.2<br>0.15<br>0.1<br>0.05<br>0<br>Innovation Infrastructure Education Institutions Efficiency<br><!-- End of picture text --> Note. The comparison is between a scenario of convergence to the 75<sup>th</sup> percentile index among HIC in each component and a baseline with constant levels

**Figure 11. TFP growth path**

<!-- Start of picture text -->
1.6%<br>1.3%<br>1.0%<br>0.7%<br>0.4%<br>2024 2027 2030 2033 2036 2039 2042 2045 2048<br>Baseline Moderate Ambitious<br><!-- End of picture text -->

Source: LTGM simulations, World Bank Staff’s calculations The moderate and ambitious reform scenarios assume the TFP determinant index converges to HI country levels, but with higher targets within that group for the ambitious scenario. In the moderate reform scenario, each subcomponent index for innovation, education, market efficiency, infrastructure, and institutions, increases linearly to the 75<sup>th</sup> percentile among HI countries by 2050, which results in TFP growth accelerating from 0.6% to 1.1% by 2035, and then maintaining that pace for the next decade (Figure 11).<sup>13</sup> The ambitious scenario is simulated similarly, but we assume that each subcomponent reaches the highest overall index among HI countries by 2050. While this target is extremely optimistic, it is mostly the rate of improvement that drives TFP growth, and many countries have had periods of rapid reform. The ambitious reform scenario results in TFP growth accelerating to 1.6% by 2035, though with some deceleration in the following decade. However, even with 1.6% TFP growth in the ambitious scenario, Peru is unable to reach HI status, with growth only reaching 3%. Instead, complementary reforms are needed to physical and human capital accumulation, which we discuss next.

> 13 Although the resulting path for TFP growth is moderate, the pace of reform would have to be fast.

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# _Promoting strategic research agendas_

Despite an increase in funding for research on intervention effectiveness in recent years, resources fall short of what is required to close important evidence gaps. Moreover, studies conducted in isolation or without sufficient attention to existing research agendas can lead to a fragmented evidence base lacking in relevance (Ioannidis, 2006), presenting a barrier to our ability to reach generalizable conclusions beyond individual study contexts.

Gap maps can help address these concerns. They can be a useful tool for developing a strategic approach to building the evidence base in a particular sector and ensure available resources are spent efficiently. They help identify areas of high policy relevance where evidence is lacking and can feed into setting research priorities. Furthermore, by providing easy access to and overview of existing studies, evidence gap maps can be used to identify promising interventions that warrant replication in different contexts, thereby enhancing the reliability of conclusions about intervention effectiveness (Valentine et al., 2011), highlighting sources of contextual heterogeneity and addressing concerns about external validity.

Overlaying a map of impact evaluations with a map of systematic reviews provides a visual representation of different evidence gaps. Firstly, it highlights ‘absolute gaps’ where there are few or no studies and primary studies should be targeted. Identifying these gaps might be of particular relevance for funders of impact evaluations who want to target their funding towards important evidence gaps. It can also be useful for researchers who want to identify areas where their work can add particular value.

Secondly, it identifies areas with substantial un-synthesized primary evidence. Many systematic reviews fail to provide policy relevant findings due to a lack of primary evidence. While highlighting the lack of evidence is an important finding in itself, it can be discouraging if substantial time and resources have been invested in a review. Thus, evidence gap maps can help mitigate this issue by identifying areas with sufficient primary evidence for systematic reviews to be worthwhile, and where primary studies should be prioritized over evidence synthesis. This use of gap maps might be particularly relevant for commissioners and producers of systematic reviews, such as the Campbell and Cochrane Collaborations.

Thirdly, mapping the availability and quality of existing evidence syntheses highlights areas with systematic reviews of either low quality or with sufficient new evidence to warrant an 19