rafmacalaba/datause-encoder-data huggingface dataset:
chance of demonstrating and having 0.95 connections to a demonstration farmer, for a given promoted crop.

In farmer groups assigned to the decentralized treatment arm, the level of decentralization of demonstration activities is successfully higher than in the other treatment arms, with 4.62 demonstration farmers each setting up a demonstration plot for each promoted crop. Consequently, for each promoted crop, farmers in decentralized groups have a 22.3% chance of demonstrating and have on average 2.68 connections to a demonstrator.

# **4 Experimental results**

We leverage the experimental variation produced by our RCT to produce three key results. First, we show that assigning demonstration resources to farmer groups increases group members’ adoption of the promoted improved seed varieties (Figure 4a; Section 4.1). The resulting adoption fully offsets the negative impact of flood-saline events over the course of our study, as the improved seed increases crop resilience to saline flooding (Figure 4b; Section 4.2). Second, the social and geographical decentralization of demonstration to many farmers additionally increases technology adoption in the group (Figure 4c; Section 4.4). Third, these pooled estimates mask important dynamics (Section 4.3): in the long run, the impacts of demonstration on adoption persist but fall (Figure 4a), and the additional impacts of decentralizing demonstration on adoption vanish (Figure 4c). We discuss potential explanations of these dynamics in Section 4.5, and argue they are most consistent with increased learning (which we formalize in Section 5). We present analysis of robustness in Section 4.6.

## **4.1 Demonstration plots increase adoption**

**Empirical strategy** We start by estimating farmer-crop level impacts of assigning demonstration resources to a farmer group, comparing outcomes in control villages and demonstration villages in the two dry seasons that follow the demonstration season.

As discussed in Section 3.3, we focus our analysis throughout the paper on promoted crops; we do so for power, as we do not expect effects of demonstration on 21
importance of girls’ education could mitigate potential negative effects of targeting girls for enrollment, rather than focusing on both genders.

To evaluate this program, we use a cluster-randomized experiment in 230 primary schools and individual-level panel data on enrollment, retention, attendance, and test scores in English, Hindi, and Math. We examine whether the program met its objectives: improving enrollment – especially of girls, increasing learning – across all abilities, and supporting school management committees. To measure whether the program was successful at targeting – to girls and students of lower-ability – we examine how the program differentially affected girls (vs. boys), and initially low-performing students (vs. higher performing). We also discuss threats that may arise from differential retention and enrollment.

We find large and statistically significant positive impacts on student enrollment – especially among girls – in the two years of the program. Further, these effects are large, representing increases of 8.1% and 11.7% in the first and second year of the program, respectively. While estimated effects are larger for girls, the differences between girls and boys are not statistically significant. Further, we find no statistically significant effects on the types of students (i.e., high vs. low ability) who enroll in program schools.

In terms of learning, we find large positive gains in all subjects, among both boys and girls. In the first year of the program, the impacts on post-program test scores are highest: 0.323 standard deviation across all subjects (0.317 in Hindi, 0.256 in English, and 0.369 in Math). Students in treatment schools are also 22.7 percentage points more likely to improve their scores in the first year when comparing pre-program with post-program tests. There are no significant differences in the effect of the program on learning gains by student gender. Second year post-program impacts are similar, although somewhat smaller, at 0.156 SD (0.127 in Hindi, 0.159 in English, and 0.136 3
_39_ Table 6—School Infrastructure (Year 1)

||Spending|Number|purchased|Facility|present (Y|/N)|Other|
|---|---|---|---|---|---|---|---|
||(1)<br>Amount|(2)<br>Desks|(3)<br>Chairs|(4)<br>Computers|(5)<br>Library|(6)<br>Sports|(7)<br># Rooms Upgraded|
|High|25,460.31***|5.97***|3.76***|0.20***|0.11***|0.10**|0.70***|
||(8,787.82)|(1.63)|(1.40)|(0.05)|(0.04)|(0.04)|(0.26)|
|Low Treated|19,251.19**|8.71***|6.13**|0.17***|-0.03|-0.03|0.47|
||(8,702.52)|(2.45)|(2.76)|(0.06)|(0.05)|(0.04)|(0.40)|
|Low Untreated|-1,702.36|1.31|0.87|0.04|-0.03|0.02|0.16|
||(8,376.89)|(1.40)|(1.19)|(0.04)|(0.04)|(0.03)|(0.26)|
|Baseline|0.09***<br>(0.03)|0.10*<br>(0.05)|0.12*<br>(0.07)|0.26***<br>(0.04)|0.32***<br>(0.04)|0.23***<br>(0.05)|0.71***<br>(0.06)|
|R-squared|0.06|0.09|0.08|0.20|0.20|0.11|0.57|
|Observations|798|810|811|822|822|822|822|
|# Schools (Rounds)|798 (1)|810 (1)|811 (1)|822 (1)|822 (1)|822 (1)|822 (1)|
|Mean Depvar|57,258.48|14.59|10.92|0.39|0.35|0.19|6.36|
|Test pval (H=0)|0.00|0.00|0.01|0.00|0.01|0.02|0.01|
|Test pval (_L_<sup>_t_</sup>=0)|0.03|0.00|0.03|0.01|0.58|0.49|0.24|
|Test pval (_L_<sup>_t_</sup>=H)|0.50|0.31|0.45|0.60|0.01|0.01|0.59|

Notes: * p _<_ 0.10, ** p _<_ 0.05, *** p _<_ 0.01 a) This table examines outcomes relating to school infrastructure using data from round 1. Column 1 is the annual (fixed) expenditure on infrastructure– e.g. furniture, fixtures, or facilities. Columns 2-3 refer to the number of desks and chairs purchased; columns 4-6 are dummy variables for the presence of particular school facilities; and column 7 measures the number of rooms upgraded from temporary to permanent or semi-permanent classrooms. Closed schools take on a value of 0 in all columns. b) Regressions are weighted to adjust for sampling and include strata fixed e↵ects, with standard errors clustered at the village level. The number of observations and unique schools are the same since we use one round of data. Observations may vary across year 1 and 2 due to attrition and missing values. The mean of the dependent variable is its baseline value. c) The bottom panel shows p-values from tests that either ask whether we can reject a zero average impact for high (H=0) and low treated ( _L_<sup>_t_</sup> =0) schools, or whether we can reject equality of coefficients between high and low treated ( _L_<sup>_t_</sup> =H) schools.
# **2 Data and Methodology**

By way of background, zones and other cluster-based programs have steadily increased in South Asia. Starting from the first export processing zone (EPZ) in Gujarat in 1965, India has developed over 3,350 zones, according to the Ministry of Commerce and Industry. Bangladesh, Nepal, and Bhutan have all announced plans to establish and expand Special Economic Zones (SEZs), industrial districts and parks. The passage of SEZ Acts and Policies signal more to come (Galal, 2021).

However, despite the growing prevalence of these programs, a lack of data has obstructed their systematic assessment. This paper is able to do so thanks to a unique World Bank survey designed to provide detailed information on firms within and outside zones. The survey covered 1,201 firms inside different types of zones and 1,166 firms outside zones in Bangladesh, Bhutan, India, and Nepal. For data collection, the sample frame for firms outside zones was based on administrative data from government sources.<sup>1</sup> The sample frame for firms inside zones relied on a database collected from zone operators. The same process was followed in all four countries. Fieldwork was conducted between September 2020 and January 2021 and successfully surveyed 2,367 of the 2,400 target enterprises. Maps of the number and types of zones are presented in Figure 1. For the sake of simplification, the term zone is used in the rest of the paper as a generic umbrella to cover all zone types, but when necessary, the specific zone type will be mentioned explicitly.

Table 1 presents sample descriptives for firms inside and outside zones. The survey was designed to ensure that firms outside the zones were from the same districts as the zones and representative of their district. Within each district, on average 52% of firms were located inside zones. Most firms in the survey are located in India, followed by Bangladesh, Nepal, and Bhutan. The largest sectors represented among the firms are in the food sector, followed by plastics, and metals. Firms inside zones tend to be slightly younger, have more employees, and less capital than those outside zones.

Table 2 presents additional descriptives for firms within zones by the type of zone.

> 1The administrative data were drawn from the following sources: India - The Annual Survey of Industries, 2015; Bangladesh - The Bangladesh Economic Census, 2013; Bhutan - An establishment census obtained from the Ministry of Economic Affairs; and for Nepal - an establishment census obtained from the Ministry of Industry, Commerce, and Supplies.

4
- 25 -

Table 9: INCIDENCE AND DURATION<sup>OF APPRENTICESHIPS</sup>

||Abidjan|Other Cities|Rural|
|---|---|---|---|
|Percent of Population<br>with Apprenticeship|13.4|11.5|5.7|
|(number of cases in sample)|<br>(261)|(271)|(325)|
|Male|69.7|73.8|82.5|
|Female|30.3|26.2|17.5<br>|
||100.0|100.0|100.0|
|Ivorian|78.5|84.1|84.6|
|Non-Ivorian|21.5|15.9|15.4|
||100.0|100.0|100.0|
|Duration (years)||||
|1|2.5|4.5|8.4|
|1|20.9|24.5|21.4|
|2|22.1|20.4|24.5|
|3|_zi.3_|19.7|18.0|
|4-5|20.1|19.3|13.7|
|6-9|10.2|9.7|10.6|
|10+|2.9|1.9|3.4|
|Total|100.0|100.0|100.0|
|% with also VTE|2.3|5.2|1.5|
Figure 3: Flowchart of study selection process for burden of malnutrition associated with diets

|**Identification of studies via database**|**Identification via other methods**|
|---|---|
|Records identified from:<br>Web of Science Core<br>Collection Database<br>(n = 2786)<br>Cochrane Database<br>(n = 305)<br>Records removed_before_<br>_screening_:<br>Duplicate records removed<br>(n = 451)<br>**Identification**|Records identified from:<br>Citation searching (n = 11)|
|Records screened<br>(n = 2335)<br>Records excluded<br>(n = 2124)||
|Reports sought for retrieval<br>(n = 211)<br>Reports not retrieved<br>(n = 0)<br>**Screening**|Reports sought for retrieval<br>(n = 11)|
|Reports assessed for eligibility<br>(n = 211)<br>Reports excluded:<br>Diet intervention, only<br>RCTs (n = 46)<br>|Reports assessed for eligibility<br>(n = 11)|
|No LMIC studies (n = 33)<br>No primary meta-analysis<br>(n = 26)<br>Diet exposure not relevant<br>(n = 17)<br>Outcome not relevant (n =<br>14)<br>Other (n = 3)<br>Total studies included in review<br>(n = 82)<br>**Included**<br>Studies included in review<br>(n = 71)||

<!-- Start of picture text --> Identification<br><!-- End of picture text --> <!-- Start of picture text --> Screening<br><!-- End of picture text --> <!-- Start of picture text --> Included<br><!-- End of picture text --> 18
- 31 -

# CONCLUSIONS

Given the caveats regarding the data on which this paper is based, a number of conclusions from World Bank experience relating to the issue of increased productivity of job training, and the propositions regarding the rolo of public secondary vocational schools and a new vision for education, can be drawn.

First, the shift away from investments in secondary vocational schools towards non-formal training (and university level training) is clear, as is the increasing emphasis in middle income countries on non-formal training systems. Thus it is reasonable to conclude that, over the past decade, the World Bank and its Borrowers have begun to move away from the secondary mode, at least in terms of new investment.

At the same time, there is some evidence, at least, that in strong economies with continuing demand for skilled workers in the -- modern sector, a variety of training modes can be effective including secondary institutions. In such circumstances, however, these institutions appear to take on a number of the characteristics associated with non-formal modes, becoming less like the traditional secondary school. Those developing countries with a considerable existing investment in secondary vocational schools may thus find, in the experience of other countries, directions for reform and rehabilitation. Such efforts, of course, must be fundamentally grounded in the labor market realities they confront. In many
years. Initially developed by Nguyen (2012) and further refined by Masaki et al. (2020), these models are based on a simple modification of the unit-level approach: rather than modeling householdlevel income as a function of household-level characteristics, unit-context models instead use only area-level covariates from the census. Specifically, the unit-context model replaces the survey-based covariates _xiv_ in Eq. (15) with census aggregates at the municipality and the PSU level, _xi_ and _xic_ , and thus relaxes the assumption that the survey-based covariates match the moments of those from the census. Like the area-level model, the unit-context model is a direct competitor to the machinelearning approach because it is feasible to implement with the same data. For this reason, we use the unit-context model as reference model as we evaluate the performance of machine-learning methods. However, it is worth noting that the unit-context model has been shown to produce biased and noisy poverty estimates, largely due to its inability to explain between-household variation in income (Corral et al., 2022).

# **4 Model Validation**

The coefficient of determination or _R_<sup>2</sup> is the most commonly used metric for validating poverty maps generated using machine-learning methods (see Jean et al. (2016), Pokhriyal and Jacques (2017), Steele et al. (2017), Lee and Braithwaite (2020), Yeh et al. (2020), or Chi et al. (2022)). In this section, we first critically assess this validation approach by discussing three ways in which the _R_<sup>2</sup> can be misleading in terms of model assessment. We then argue that a better alternative is to validate poverty estimates using the mean squared error, which is not subject to many of the limitations of the _R_<sup>2</sup> . Finally, we outline another validation procedure that focuses more directly on what is of ultimate concern with poverty mapping, namely informing the targeting of resources for the purposes of poverty alleviation.

Let _yi_<sup>_a_denotetheactualortruepovertyindicatorforthe</sup><sup>_ith_geographicalentity.Further,let</sup><sup>_y_</sup> _i_<sup>_d_</sup> and _yi_<sup>_p_denote the direct estimate and model-based prediction of the poverty indicator, respectively.</sup> The standard method for assessing the predictive performance of model-based poverty estimates calculates the _R_<sup>2</sup> from a regression like the following: where _α_ and _δ_ are the regression coefficients and _ζ_ represents the error term.<sup>21</sup> The _R_<sup>2</sup> for the above regression can be written as follows: where _σd_<sup>2isthevarianceofthedirectestimateand</sup><sup>_σ_</sup> _ζ_<sup>2isthevarianceof</sup><sup>_ζ_.</sup> _Rd_<sup>2thusconveys</sup>

> 21Note that any regression of this form can only be estimated for the sampled regions with direct estimates.

14
# BENEFIT INCIDENCE ANALYSIS IN DEVELOPING COUNTRIES

# L Introduction

Measuring benefits of govermnent expenditures across income, race and other characteristics of individuals is an elusive empirical issue. As interesting and as difficult as it is to allocate tax burdens to individuals, the profession knows even less about allocating benefits.1 In this paper, we review the recent literature on benefit incidence and offer suggestions concemning further research, focusing particular attention on benefit incidence in developing countries.

Benefit incidence studies have a long history, but the interest in benefit incidence surged as a result of Robert McNamara's optimism about the degree to which government spending can alter the income distribution and living standards of the poor in developing countries. According to McNamara, "Shifts in the pattems of public expenditure represent one of the most effective techniques a govenmment possesses to improve the conditions of the poor" (McNamnara, 1972, p. 17). Knowledge of benefit Incidence by income and other variables can be useful in reallocating public resources toward programs that benefit the poor.<sup>2</sup> The literature on benefit incidence has three distinct periods. In the eady literature or pre1975, benefits are allocated to households either on a per capita basis or in proportion to the income of the household. Both allocation mechanisms yield obvious conclusions about benefit incidence. There is also a preoccupation in the early literature with allocating the entire budget including the benefits of so-called pure public goods, such as defense.

Aaron and McGuire (1970) attempt to reduce the inherent arbitrariness of the allocation of pure public good benefits to households by deriving benefit measures based on a specific utility function. The parameters of their utility function suggest a strong pro-rich distribution of benefits, at least in developed countries.

DeWulf (1975) in his review of the many early studies quite rightly criticized them for not accounting for different income elasticities for different public services and a host of other problems. After the DeWulf critique, a second wave of benefit studies in both developed and
<mark>Table 5: Transition Matrices - Synthetic vs. Actual Panel Data</mark> <u><mark>Using Alternative Specifications,</mark></u> <mark>Peru 2008 and 2009</mark>

|<br> <br> <br> <br>Status in 2008, 2009<br>Lower Bound<br>Estimates<br>Truth<br>Upper Bound<br>Estimates<br>[1]<br>[2]<br>[3]|
|---|
|_Panel A: Using Actual Instead of Retrospective Data_|
|Poor,Poor<br>29.18<br>(21.89,25.31)<br>18.59|
|Poor,Non-poor<br>4.22<br>(8.54,10.93)<br>16.64|
|Non-poor,Poor<br>4.92<br>(9.25,11.72)<br>15.51|
|Non-poor,Non-poor<br>61.68<br>(54.16,58.16)<br>49.27|
|_Panel B: Using Actual Department of Residence Instead of Department of Birth_<br><br> <br>|
|Poor,Poor<br>29.46<br>(21.89,25.31)<br>17.55|
|Poor,Non-poor<br>5.30<br>(8.54,10.93)<br>16.55|
|Non-poor,Poor<br>4.63<br>(9.25,11.72)<br>16.55|
|Non-poor,Non-poor<br>60.60<br>(54.16,58.16)<br>49.36|
|_Panel C: Using Sub-national Controlsfrom Census at the Departmental-level_|
|Poor,Poor<br>28.49<br>(21.89,25.31)<br>17.63|
|Poor,Non-poor<br>4.77<br>(8.54,10.93)<br>16.43|
|Non-poor,Poor<br>5.60<br>(9.25,11.72)<br>16.47|
|Non-poor,Non-poor<br>61.13<br>(54.16,58.16)<br>49.47|
|Observations<br>2,368<br>2,368<br>2,368|
|Data source: INEI - Peruvian National Household Survey (ENAHO),2008-2009.<br>Note: R-squared is calculated for opposite halves of the total 2008 sample.<br>Results are constrained to the panel sample of households whose heads are<br>between 25 and 65 years old. Results are weighted using household-level survey-<br>sampling weights. Results in columns [1] and [3] are based on a model with<br>household time invariant characteristics, sub-national controls from census, and<br>interactions between household time invariant characteristics and sub-national<br>controls. Panel A adds asset ownership. Results in panel C are based on sub-<br>national controls from census at the departmental-level (instead of at the village-<br>level). Results in column [2] show actual panel mobility. 95 percent confidence<br>interval between parentheses. Upper bound estimations are based on 50<br>repetitions.|

28
# **1) Introduction**

In developed countries, premature mortality is negatively associated with income and education (Deaton and Paxson 2003; Lleras-Muney 2005). The same gradient is expected in developing countries, although, given the lack of vital registration systems it has been less easy to demonstrate. What has generally been shown is that the poor are more likely to experience poor health and less likely to have access to health services (Filmer 2005, Gwatkin, Wagstaff and Yazbeck, 2005). Additional information is available over a wide range of socioeconomic differences in health, nutrition and population including indicators on child mortality, malnutrition, fertility, immunization coverage, treatment of diarrhea and acute respiratory infections, antenatal care visits and delivery attendance, use of modern contraception as well as knowledge of HIV/AIDS prevention (Gwatkin, Rutstein, Johnson, Suliman, Wagstaff, and Amouzou. 2007). However adult mortality is not included among the indicators reported. Since adult mortality is the most objective measure of adult health (as opposed to self-reported health status or diseases), analyzing the socioeconomic gradient in premature adult mortality adds to the knowledge about the disparities in health in developing countries.

Bicego (1997), Timaeus and Jasseh (2004), Gakidou and King (2006) and Obermeyer, Rajaratnam, Park, Gakidou, Hogan, Lopez and Murray (2010) have analyzed adult mortality using various data sources (including the Demographic and Health Surveys—DHS—that we use in this study) and have gradually improved the methods for doing so, but they have limited their analysis on overall trends and on differences by gender and by age.

Part of our analysis looks at the extent of adult mortality in countries affected by conflicts and as such generalizes earlier analyses of the distribution and consequences of excess adult mortality in Cambodia (de Walque 2005, de Walque 2006) and Rwanda (de Walque and Verwimp, 2010). The HIV/AIDS epidemic is another important factor contributing to adult mortality, especially in Sub-Saharan Africa. Contrary to conflicts and famines, its impact is spread over a long period. Bicego (1997) and Timaeus and Jasseh (2004) have attempted to see if by looking at mortality by birth cohort and by period the impact of the HIV/AIDS epidemic could be detected in mortality trends. Using more recent data sets, we document large increases in adult mortality in countries heavily affected by HIV/AIDS, especially in southern Africa.

2
The striking results here are the high numbers for Latin America (60 to 94 percent), EasternmSouthern Africa (excluding Ghana, 75 to 92 percent) and East Asia (85 and 90 percent),. In these countries, the main explanation for why children do not complete primary school _is not that_ they don't start school, it is the fact that they drop-out. In the Western/Central African and South Asian countries, dropout explains less than half the shortfall from universal primary education for the poor, with values ranging from 10 percent in India to 49 percent in Comoros (which although it lies off the Eastern coast has the attainment patterns of Western/Central Africa).

,Of course this approach can only address the question of the physical _availability_ of schools, not true _access_ to education. In particular it is possible that children did attend first grade, but in classes of 100 or more, with no materials, indifferent (or worse) teaching, and deteriorating buildings. Not surprisingly there will be high drop-out, due not to physical availability but to access to an education, which when properly defined, includes quality.

A second approach to the impact of school availability would be to look directly at the relationship between the presence of schools and enrollment. For example, using the DHS data from India, Filmer and Pritchett (1998b) found in state by state regressions that there was only a weak relationship between the availability of schools and enrollment rates. In this paper we don't replicate the analysis country by country but, as a heuristic indication, we report the proportion of "Rich Rural Males" (RRM) who have completed at least grade 1. Since poverty is not completely regionally concentrated this group is likely to suffer from a similar lack of physical access to schools as less socially favored groups (the poor and females). If RRM have high enrollment this provides some evidence on the degree to which other groups are falling short due to their status, not school availability.

32