rafmacalaba/datause-encoder-data huggingface dataset:
the average effect on test score being statistically significant. Nevertheless, when we compare whether the program had a differential effect on the average learning score, we cannot reject that the effect for the low ability is the same as for high ability ones (p-value=0.18) and for missing ability ones (p-value=0.31). In summary, although we find larger coefficients for high-ability students than low-ability ones, we do not have enough statical power to rule out that the effect does not vary with initial level of ability.

We repeat a similar exercise using household income. We define as “high HH income” those students who report a monthly household income in the bracket between $3,501 MX and $7,000 MX or above, while those who report an income in a lower bracket are classified as “low HH income”.<sup>23</sup> Results are reported in Panel C in Table 6. Household income does not affect the program’s effect on the probability of taking the 12th grade ENLACE exam. The treatment effects on learning outcomes among low income students are not statistically different from zero, although the size of the effect is nontrivial for the math score (0.20 _σ_ ). Among high income students the effect is positive and marginally significant on Spanish (0.18 _σ_ ) and large for math (0.32 _σ_ ). When we consider the effect on the average learning score, we find a 0.12 _σ_ increase among low income students, as opposed to a 0.25 _σ_ (statistically significant at 5%) among high income students, and a 0.18 _σ_ increase among missing income students. We can reject the null hypothesis of no differential effect between low and high income students (p-value=0.029), while we can not reject when we compare low income students with those who do not report income information.

In summary, we found larger effects on the learning outcomes of girls than boys, mostly driven by a particularly large impact on the math score. We found no significant evidence of treatment heterogeneity in students’ ability, but significant differences associated with household income. The fact that we found an effect only among high income students makes unlikely that the average impacts that we find are driven by the infomation about the PRONABES higher education scholarship. As discussed in section 2.1, PRONABES only targets students with monthly household income below three minimum wages.

# **4.3 Results on Self-reported Effort**

In the simple theoretical framework outlined in section 3.1, information improves student’s performance through an increase in the level of effort. While objective measures of effort are not available, we use the self-reported measure of effort elicited in the 12th grade _ENLACE de contexto_ (described in section 3.3) to provide some preliminary evidence on whether the intervention induced students to work harder. 26% of the boys, as opposed to 18% of the

> 23In 2012, the median price adjusted household income was $4,880 MX.

16
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Policy Research Working Paper 9160

# **Abstract**

This paper proposes a methodology for identifying urban areas that combines subjective assessments with machine learning, and applies it to India, a country where several studies see the official urbanization rate as an under-estimate. For a representative sample of cities, towns and villages, as administratively defined, human judgment of Google images is used to determine whether they are urban or rural in practice. Judgments are collected across four groups of assessors, differing in their familiarity with India and with urban issues, following two different protocols. The judgment-based classification is then combined with data from the population census and from satellite imagery to predict the urban status of the sample. The Logit model, and LASSO and random forests methods, are applied. These approaches are then used to decide whether each of the out-of-sample administrative units in India is urban or rural in practice. The analysis does not find that India is substantially more urban than officially claimed. However, there are important differences at more disaggregated levels, with “other towns” and “census towns” being more rural, and some southern states more urban, than is officially claimed. The consistency of human judgment across assessors and protocols, the easy availability of crowd-sourcing, and the stability of predictions across approaches, suggest that the proposed methodology is a promising avenue for studying urban issues.

This paper is a product of the Office of the Chief Economist, South Asia Region, and the Office of the Chief Economist, Latin America and the Caribbean Region. 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 yli7@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
- 2) _Measuring impacts over longer-time frames:_ the returns to these programs will differ substantially if they merely speed up the process of gaining employment versus having lasting impacts. Yet most studies measure impacts over at most 1 to 2 years, leaving them to speculate about cost-benefit on the basis of assumptions about how impacts vary over time. Tracking impacts over longer-time periods is therefore needed. Studies which link participants to administrative records (such as Attanasio et al, 2015) offer one promising way to do this.

- 3) _Limiting attrition:_ When the likely impact on employment is only 2 to 3 percentage points, and attrition rates are 10, 20, or even more than 30 percent, any treatment effects are dwarfed by attrition, and bounds that incorporate this attrition will be completely uninformative. Limiting attrition is particularly difficult given that so many ALMPs focus on youth, who tend to be more mobile and difficult to track over time. Serious investment in limiting attrition, combined with the use of administrative data is needed.

- 4) _Continued and improved careful measurement of costs:_ I was pleasantly surprised by the number of studies which did report the costs of the intervention, although a number still lack this key information. More work is needed to make clear average versus marginal costs in understanding the cost structure as pilot programs expand.

- 5) _Pre-specifying outcomes and heterogeneity:_ a number of studies fail to find a significant impact on either employment or earnings, but then emphasize impacts on a particular subgroup (such as one gender, or one skill level) or for one outcome (such as formal employment). Pre-specification of the primary outcomes and key heterogeneity of interest lessens concerns about multiple hypothesis testing.

- 6) _Testing placebo effects:_ Several studies find impacts despite almost no direct hires through the program they study (e.g. Beam (2016), Galasso et al. (2004), Levinsohn et al. (2014)). These studies raise the possibility that simply doing _anything_ to support job-seekers may encourage them to keep exerting effort and search, so that what matters is their sense that someone wants them to succeed, not the particular policy pursued.<sup>5</sup> Testing more formally this sort of placebo effect would be interesting in further work.

- 7) _Understanding general equilibrium better:_ a key concern with many of these policies directed at particular job-seekers is that they merely change who gets the jobs firms are advertising, without increasing the total number of jobs available. The ideal would be

19
the national population from the World Development Indicators (WDI)). Whenever we are interested in the performance of our estimation or database, e.g. the split between consumption and income surveys or the size of the Pareto constants, observations are unweighted, as pointed out in the tables.

We use consumption PPPs to account for price differences across countries. Incomes obtained from PovcalNet are already converted to 2005 PPP dollars. For the additional surveys we replicate the approach in PovcalNet: as explained before, and after accounting for currency conversions<sup>39</sup> , we convert the average incomes into local currency units in 2005 prices using domestic consumer price indices (CPIs).<sup>40</sup> Then, we apply the 2005 PPP consumption exchange rates to convert into international dollars.<sup>41</sup> It is important to note that PPP exchange rates only exist at the country-level, so we ignore any price differences which exist within countries. As a result we probably over-state within-country inequality. As mentioned before, we treat the rural and urban areas of the three most populous developing countries China, India and Indonesia as separate “countries”. Due to a lack of disaggregated data, we assume a common CPI for rural and urban areas, but allow for different PPP exchange rates.<sup>42</sup> The vast majority of surveys cover the entire country except for several, mostly Latin American, countries which survey only urban areas.<sup>43</sup> We treat these surveys as representative of the entire country.<sup>44</sup> 39 Currency conversions include changes in the currency being used, such as the formation of the Eurozone, and currency redenominations as often observed in high-inflation environments.

40 As a first step, we use domestic CPI figures from the WDI. Where these are unavailable, we resort to the IMF’s World Economic Outlook or the country’s statistical offices directly.

41 The WDI does not provide PPPs for Kosovo, so we used the implied PPP GDP conversion factors reported in the IMF’s World Economic Outlook.

42 Data for China and Indonesia are provided by PovcalNet so they already incorporate adjustments for differential costs of living in rural and urban areas. For India, where we added one survey from the WYD, we use urban and rural PPP exchange rates of Rs 17.24 and Rs 11.40 respectively which are given in Ravallion (2008).

> 43 These countries include Argentina, Colombia, Ecuador, Honduras, Micronesia and Uruguay, and are all taken from PovcalNet.

20
2011 dataset, a plot of the average years of education of migrants to the Gulf against the migration prevalence rate at the _taluk_ (sub-district) level reveals that localities with higher rates of Gulf migration tend to have migrants with lower levels of education (see appendix figure A.3). Consequently, we would anticipate that spouses residing in areas with larger migrant networks may expect lower overseas wages relative to localities with lower incidences of Gulf migration.

Consistent with our prediction, when the migration prevalence rate is used instead of the median migrant income as a regressor, we find it to be negatively correlated with the reported earnings ratio and statistically significant at conventional levels.<sup>17</sup> Since migrant networks indirectly capture expectations, we preferred the use of the median income term in all subsequent analysis as a more direct measure of the potential information available to wives of their husbands’ foreign earnings.

# V. REMITTANCE BEHAVIOR

In this section, we provide empirical analysis of the remittance behavior postulated in predictions 3 and 4.

# _Remittances and Underreporting_

Our model predicts a positive association between the reported earnings ratio and remittances. Appendix figure A.4 is a scatterplot of annual remittances reported by the migrant as a share of his annual income against the constructed earnings ratio. Consistent with prediction 3, which applies both to levels and shares of income, the fitted linear line shows that the lower the reported earnings ratio, the lower the income share of remittances.

Interesting patterns emerge, when we plot together income shares of remittances

> 17. Adding the district-level migration prevalence rate to the existing specification with the median income of migrants (log) resulted in a multicollinearity issue. Both variables are negatively correlated with a pairwise correlation of  0.67 ( _p_ = 0.00 ). The regression with the migration prevalence rate is available upon request.

24
# **Appendix n:** Data

# A. Country coverage

The following is the list of countries covered in our study. Since the panel data set is unbalanced, we also indicate the time periods for which observations are available in each of the 70 countries.

|Countrv|1961-65|1966-70|1971-75|1976-80|1981-85|1986-90|1991-93|
|---|---|---|---|---|---|---|---|
|Algeria|||||x|x|x|
|Argentina|x|x|x|x|x|x|x|
|Australia|x|x|x|x|x|x|x|
|Austria|x|x|x|x|x|x|x|
|Bangladesh||||x|x|x|x|
|Bolivia|x|x|x|x|x|x|x|
|Brazil|x|x|x|x|x|x|x|
|Cameroon||x|x|x|x|x||
|Canada|x|x|x|x|x|x|x|
|Central African Rep.||x|x|x|x|x|x|
|Chile|x|x|x|x|x|x|x|
|Colombia|x|x|x|x|x|x|x|
|Costa Rica|x|x|x|x|x|x|x|
|Cyprus<br>|||x|x|x|x|x|
|Demnark|x|x|x|x|x|x|x|
|Ecuador|x|x|x|x|x|x|x|
|El Salvador|x|x|x|x|x|x|x|
|Finland|x|x|x|x|x|x|x|
|France|x|x|x|x|x|x|x|
|Gambia||||x|x|x|x|
|Germany|x|x|x|x|x|x|x|
|Ghana||x|x|x|x|x|x|
|Greece|x|x|x|x|x|x|x|
|Guatemala|x|x|x|x|x|x|x|
|Haiti||x|x|x|x|||
|Honduras|x|x|x|x|x|x|x|
|India|x|x|x|x|x|x|x|
|Indonesia||x|x|x|x|x|x|
|Ireland|x|x|x|x|x|x||
|Israel|x|x|x|x|x|x|x|
|Italy|x|x|x|x|x|x|x|
|Jamaica|x|x|x|x|x|x|x|
|Japan|x|x|x|x|x|x|x|
|Jordan|||||x*|x*|x*|
|Kenya||x|x|x|x|x|x|
|Korea||x|x|x|x|x|x|
|Malawi|||||x|x|x|
|Malaysia|x|x|x|x|x|x|x|
|Mauritius||||x|x|x|x|
|Mexico|x|x|x|x|x|x|x|

29
<mark>met regularly found beneficial impacts on several important domains for beneficiary women and their children.</mark><sup>13</sup>

- <mark>Finally, more attention should be paid to the targeting of large cash transfer programs.</mark> Failing to consider the local market price effects of CCT (or UCT) programs can overstate the net benefits of targeted cash transfers. In areas where individual targeting of social programs covers most of the households, offering the program on a universal basis would avoid such negative impacts at moderate, if any, additional cost.<sup>14</sup>

> 13Ahmed, Akhter, John Hoddinott, and Shalini Roy. 2019. Food Transfers, Cash Transfers, Behavior Change Communication and Child Nutrition: Evidence from Bangladesh. Working Paper.;Roy, Shalini, Melissa Hidrobo, John Hoddinott, and Akhter Ahmed. 2019. Transfers, Behavior Change Communication, and Intimate Partner Violence: Post‐Program Evidence from Rural Bangladesh. Review of Economics and Statistics, _forthcoming_ .

> 14 Filmer et al. (2018), _cited above_ .

8
wage subsidy literature in several ways. First, we test the effect of a wage incentive for youths who are just graduating from secondary school.<sup>7</sup> At this career point, hourly wages of informal jobs are relatively attractive (Shehu and Nilsson, 2014) and youths may heavily discount and underestimate the rapid (relative) wage growth of formal jobs. Short-term wage incentives offered during the school-to-work transition are thus particularly important, not least because the first job has long-lasting impacts on career trajectories and lifetime earnings (Neumark, 2002; Kahn, 2010; Oreopoulos et al., 2012). Second, the incentive is paid monthly and is thus more attractive than bonuses paid in the more distant future, especially if beneficiaries heavily discount future payouts.<sup>8</sup> Finally, we link our study sample to monthly social security data, which addresses concerns of selective attrition and surveyor demand effects. It also allows us to precisely trace out employment effects over time, which is important given the high rates of worker churn, and thus estimate cumulative treatment<sup>9</sup> We also contribute to a nascent literature that explores the role of worker retention and on-the-job human capital accumulation in developing countries. In line with Donovan et al. (2020), our findings suggest that high turnover rates, especially in the informal sector, do not represent a reallocation of workers to more productive jobs. Donovan et al. (2020) and Bick et al. (2018) also conclude that the steeper wage-tenure profiles in developing countries are less likely to be due to human capital accumulation and more likely to result from firm selection in the presence of uncertain match quality. By contrast, our results suggest that at the very early career stage, formal employment offers substantial training and learning opportunities that are associated with wage increases. Importantly, when workers switch between formal jobs they do not experience a loss in wages, suggesting that the rapid wage increase in formal employment is at least in part due to a gain in general rather than firmspecific skills. The wage incentive thus indirectly serves as a subsidy for workers to acquire human capital.

> 7While other wage subsidy programs also focus on youths, they do not administer the program before participants graduate. This is important as most graduates find employment shortly after graduating. At the same time it raises concerns about unintended consequences, e.g. with regard to educational decisions. 8 Indeed, one explanation for the muted effects of existing programs (e.g. Van der Klaauw and Van Ours (2013)) is that beneficiaries receive the bonus only after they hold a job for a certain period of time.

> 9Gains in statistical power from panel data are particularly large for outcomes with low autocorrelation, e.g. due to high job turnover rates (McKenzie, 2012). Our study is thus able to estimate employment effects more precisely than existing wage subsidy studies.

6
# **1.  Introduction**

Approximately 84% of Bangladesh’s people are directly or indirectly dependent on agriculture for their livelihood, and agriculture contributes about 24% of gross domestic product (Bangladesh Bureau of Statistics, 2001). Rice is the major staple crop, accounting for 76% of the cultivated area, 78% of the irrigated area, 52% of agricultural GDP, and 71% of caloric intake (Bangladesh Bureau of Statistics, 2001). Plant, animal and insect pests pose a constant threat to rice production, inflicting losses conservatively estimated at 10-15% annually (FAO, 2001). Farmers have used toxic chemicals extensively for pest control, because of their reputation for speed and effectiveness. However, rising use of chemical pesticides has also posed serious health risks, as well as threatening widespread ecological damage. These problems will undoubtedly increase if Bangladeshi farmers respond to rapidly-rising food demand by intensifying their use of chemicals for pest control.

In response to rising concern about the sustainability of conventional agriculture, the government has collaborated with international assistance agencies to promote Integrated Pest Management (IPM). IPM has no standard definition, but comprises approaches that range from carefully-targeted used of chemical pesticides to biological techniques that use natural parasites and predators to control pests (Sorby et al., 2003). Since chemical pesticides are expensive for poor farmers, IPM also offers the prospect of lower production costs and higher profitability. Of course, adoption of IPM may reduce overall profitability if it also lowers productivity, or induces more intensive use of other production factors. However, application of IPM techniques may also raise overall productivity, by encouraging more effective use of other inputs. Data scarcity has hindered a full accounting of IPM’s impact on profitability, health and local ecosystems.

Using new survey data, this paper attempts such an accounting for farmers in Bangladesh. We compare outcomes for farming with IPM and conventional techniques, using simple input-use accounting and estimation of conventional and frontier production functions, along with farmers’ assessments of their own health status and local ecological conditions.

The remainder of the paper is organized as follows. Section 2 describes recent trends in pesticide use, the associated problems, and the current status of IPM programs 2
**Table 11. Cross section, Energy, regional dummies interactions.**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||OLS|OLS|2SLS|2SLS|
||pcGDPgrowth|pcGDPgrowth|pcGDPgrowth|pcGDPgrowth|
|Constant|-2.058|-1.085|-0.784|-0.646|
||(1.159)*|(1.150)|(1.752)|(1.542)|
|GDPpc71|-0.000|-0.000|0.000|-0.000|
||(0.000)|(0.000)**|(0.000)|(0.001)|
|prim_expen_70|-0.048|-0.079|-0.053|-0.093|
||(0.038)|(0.037)**|(0.064)|(0.048)*|
|second_expen_70|0.000|0.002|0.001|0.002|
||(0.002)|(0.001)|(0.003)|(0.003)|
|Invt_GDP7195|0.197|0.188|0.106|0.182|
||(0.049)***|(0.047)***|(0.128)|(0.060)***|
|egc7195|-0.899|4.450|-3.038|-0.057|
||(2.244)|(1.454)***|(6.514)|(6.997)|
|EA*egc|4.625||32.843||
||(1.361)***||(30.305)||
|LI*egc||-18.451||-19.050|
|||(3.343)***||(16.486)|
|MI*egc||-3.719||0.000|
|||(1.186)***||(0.000)|
|Observations|48|48|44|44|
|R-squared|0.60|0.70|||
|Wu-Hausman F test,<br>p-value|||0.25|0.87|

Robust standard errors in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1%. Regional dummies: EA (East Asia), LI (low income countries), MI (middle income countries). Instruments: see Table 3 and 4.

31