rafmacalaba/data-use-mentions-extended huggingface dataset:
.

> 6This paper also adds to the scant literature of long-term effects of positive, policy-based treatments, especially in developing countries. In this way, this paper differs from studies that analyze the impacts of interventions early in life in developing countries such as Ito and Tanaka (2018) and Field et al. (2009) in that we focus on a host of adult outcomes rather than the child or adolescent outcomes.

> The study by Cas (2012) comes closest to the present study, which examines the impact of the same program as ours on education and cognition outcomes for children of age 11-17 years, using the data from the first four waves of IFLS. The analysis of this paper expands on this by focusing on adult cognition (as opposed to early and middle adolescents) by considering the latest fifth wave of IFLS and considering a wider variety of outcomes such as health and earnings of these adults. Please refer to the subsection S1.1 “Additional Literature Review” in the Appendix available at _The World Bank Economic Review website_ for more details.

7The authors are not aware of any large-scale intervention study other than Venkataramani (2012) and Cas (2012) to study later life cognition in developing countries for an early-life health intervention.

4
. Journal of Labor Economics 10: 412-437.

- Lazear EP (1979) Why is there mandatory retirement? The Journal of Political Economy 87: 1261-1284.

- Lee C (2009) Labor force participation of older males in Korea: 1955-2005, NBER Working Paper Series 14800, Cambridge, MA.

- Lee C and Lee J (2011) Employment status, quality of matching and retirement in Korea: Evidence from the Korean Longitudinal Study of Aging, Rand Working Paper Series WR-834.

- Lee R and Mason A (2011) _Population Aging and the Generational Economy: A Global Perspective_ , Abingdon, Oxon, UK: Edward Elgar Publishing.

27
bias. On the other hand, the limited information likelihood estimator (LIML) is a better-quality estimator and is more robust to the weak instrument problem. We report both the 2SLS and LIML results.

In addition to model (4) we also estimate two models for separate samples of imported goods and exported goods. Protection is modeled in the imported goods sample as and subsidies are modeled in the exported goods sample as Whereas in model (4) it is assumed that government’s policy preferences are constant regardless of whether their decision is about protection or about subsidies, (5) and (6) allow flexibility in this regard. Before turning to the task of testing the model, we discuss the data.

# **4. Data**

# _Protection_

The data on protection and subsidies we use are not only comprehensive in the range of instruments they cover, but are measured in a manner that fit the theoretical requirement of the model.<sup>23</sup> They have been constructed as part of a World Bank project on trade restrictiveness. We use three measures of protection and two measures of subsidies produced by those studies. The first measure of protection is the ad valorem equivalent (AVE) of core nontariff measures

> 23 We are grateful to Marcelo Olarreaga for access to this and other data that were constructed as part of the World Bank’s project on trade restrictiveness.

12
**Figure 2.6. Yemen: sectoral demand composition in 2020 (%)**

<!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>intermediate use* private cons government cons investment exports<br>percent<br><!-- End of picture text -->

*Includes trade and transport margins.

Source: Authors’ calculations based on the 2020 Yemen SAM.

# 2b. Literature overview

During the last decades, Yemen’s economy has been analyzed in a large number of applications of CGE models, an indication that it is relatively straightforward to apply such models even in a data-scarce environment like Yemen’s. This section provides a brief survey of the 15 Yemen studies that have been identified, with a focus on method and the issues addressed. The studies were published between 2007 and 2022 in academic journals, as book chapters, or as working papers. Most were undertaken by IFPRI as part of a research effort led by Clemens Breisinger; some work was also done at the World Bank. Readers interested in additional details are referred to the individual references for the studies.

The methodological framework of these models is rooted in the pioneering work at the World Bank in the late 1970s and early 1980s that is documented in Dervis, de Melo, and Robinson (1982). The approach that was developed may be described as neoclassical structuralist (Robinson 1989): its starting point is Walrasian general equilibrium theory, but it deviates from this theory whenever this is deemed necessary in order to incorporate essential features of the modeled economy.

In terms of the specifics of the Yemen applications, the IFPRI standard model (Lofgren et al. 2002), extended to recursive dynamics by Thurlow (2004),
_∗∗∗_</sup>|0.1145<br>(0.0055)<sup>_∗∗∗_</sup>|0.1097<br>(0.0059)<sup>_∗∗∗_</sup>|
|Age indicators|Yes|Yes|Yes|Yes|
|Year indicators|Yes|Yes|Yes|Yes|
|2-digit sector indicators|Yes|Yes|Yes|Yes|
|Observations|190,817|190,817|190,817|190,817|
|_R_<sup>2</sup>|0.2183|0.2313|0.2128|0.2336|

_Notes_ : All specifications include a constant. Coefficients are reported with robust standard errors in parenthesis, which are clustered by urban area of work.<sup>_∗∗∗_</sup> ,<sup>_∗∗_</sup> , and<sup>_∗_</sup> indicate significance at the 1, 5, and 10 percent levels. t&t stands for technical and technological.

of 0.054 (Duranton, 2016).<sup>7</sup> In columns (2) and (3), we instead examine how wages vary based on the cities where individuals went to college or high school, respectively. In column (2) we obtain a higher elasticity of 0.08 for college city size, while in column (3) we obtain a lower elasticity of 0.04 for high school city size.

In column (4) we control for work city, college city, and high school city sizes. We find that the elasticity with respect to work city size shrinks from 0.052 to 0.021 and becomes only marginally significant, while the elasticity with respect to college city size remains relatively high at 0.071 and the elasticity with respect to high
Policy Research Working Paper 10916

# **Abstract**

This paper investigates the extent to which real-time indicators derived from internet search, cell phones, and satellites predict changes in household socioeconomic indicators across approximately 300 administrative level-1 regions in 20 countries during the COVID-19 crisis. Measures of changes in socioeconomic status in each region are taken from high-frequency phone surveys. When using the first wave of data, fielded between April and August 2020, models selected using the least absolute shrinkage and selection operator explain 37 percent of the cross-regional variation in the share of households reporting declines in total income and 34 percent of the share of respondents reporting work stoppages since the onset of the crisis. Realtime indicators explain a lower amount of the within-region variation in income losses and current employment over time, with an R2 of 15 percent for current employment and 22 to 26 percent for the prevalence of income declines. When limiting the sample to urban regions, real-time indicators are far more effective at explaining within-region variation in income losses and current employment, with R2 values of approximately 0.54 and 0.38, respectively. Income gains, self-reported food insecurity, social distancing behavior, and child school engagement are more difficult to predict, with R2 values ranging from 0.06 to 0.17. Google search terms related to food, money, jobs, and religion were the most powerful predictors of work stoppage and income declines in the first survey wave, while those related to food, exercise, and religion better tracked changes in income declines and employment over time. Google mobility measures are also strong predictors of changes in employment and the prevalence of specific types of income declines. In general, satellite data on vegetation, pollution, and nighttime lights are far less predictive. Google mobility and search data, and to a lesser extent vegetation and pollution data, can provide a meaningful signal of regional economic distress and recovery, particularly during the early phases of a major crisis such as COVID-19.

This paper is a product of the Development Economics, the Poverty and
## **2.2 Roll-out of the MGNREGA Program**

MGNREGA was implemented in three phases. Backwardness status of the districts was used to determine roll-out priority. However, each state was provided representation in Phase I. The Planning Commission of India calculated and ranked the backward status of Indian districts (Planning Commission, 2003). The official ranking of backwardness of the districts in each state was based on the Scheduled Castes and Scheduled Tribes population in 1991, agricultural wages in 1996-97, and output per agricultural worker in 1990-93. In Phase I, 200 backward districts implemented the program in February 2006. The program was then introduced in additional 130 districts in Phase II in April 2007,<sup>9</sup> and all the remaining 270 districts received the program in the last phase in April 2008.<sup>10</sup> This variation in the timing of the program roll-out enables us to identify the causal effect of this scheme on schooling outcomes.<sup>11</sup>

# **3 Data**

The principal source of data is the annual panel of Indian schools called the District Information System for Education (DISE).<sup>12</sup> The data covers grades 1 through 8 in 1.13 million schools. School characteristics include: staff characteristics such as gender and qualification of teachers, infrastructure measures including availability of common toilets, gender specific toilets, drinking water facilities, and electrification, and enrollment by gender and grade. The data also include appearance and pass rates for school ex-

> 9The program commenced in May 2007 for 17 Phase II districts in Uttar Pradesh due to state legislative assembly elections

> 10Due to splitting of districts for which data for the parent and split district was not available in all years, the number of districts in our sample are 193, 123, and 254, respectively.

> 11Prior to February 2006, the government experimented with a pilot program (the Food for Work Program) in November 2004 in
>29</sup> Furthermore, household-level access to birthing assistance might also be potentially subject to clientelist targeting, with assistance provided conditional upon political support. Nichter (2011) suggests that in Brazil access to female sterilization, and generally to receiving attention from public health providers, can be subject to political manipulation rather than being broadly delivered. Could household-level measures of access to health services show a different association with vote buying than the measures obtained from government-records, and emphasized as the main results of the paper? Additionally, does access to birthing assistance exhibit a different or more robust correlation with vote buying than access to nutrition supplements, which might be relatively more amenable to clientelist targeting, and to influence from higher tier politicians?

To address these questions, we estimate the following specification with household data: HouseholdAccessijkl = δ0 + δ1VoteBuyingjkl + XijklBδh � + Wjklδb + Zklδm + θl + ωijkl (4) The dependent variables for access to health services reported by household _i_ , living in barangay _j_ , of municipality _k_ , in congressional district _l_ , consists in turn of the percentage of pregnancies for which the respondent reports receiving assistance from a trained municipal

> 29 On the other hand, political strategies that provide clientelist benefits in between elections might be traded-off against vote buying at the time of elections.

20
, and Wodon—alone or with coauthors.<sup>22</sup> The main contribution of this literature has been developing techniques that allow the production of poverty maps and of rigorous analysis of the evolution of poverty and its characteristics over time, making it possibly to identify the effects of various policy instruments used to alleviate poverty. Much of what is known about what works and what does not at the household level is due to these researchers.

Of particular interest to infrastructure policymakers is the effectiveness of targeted subsidies, one of the most common redistribution instruments used to reduce “infrastructure poverty.” Despite their popularity in policy circles, particularly for infrastructure, targeted subsidies (as well as safety nets) have long been held in low regard by academics for their alleged ineffectiveness (in terms of economic efficiency and incentive costs). But the verdict delivered by new databases is that in many cases these costs are modest. In other words, direct subsidies and cross-subsidies are not always as bad as they are made out to be. Moreover, these results seem to hold for both temporary and chronic poverty. (For a useful recent review of the debate and survey of the empirical evidence, see Ravallion 2003.)

> 21 See Estache and others (2004).

> 22 These publications include Lanjouw and Lanjouw (2001), Lanjouw and Ravallion (1999), Ebers and Lanjouw (2001), Ebers, Lanjouw, and Lanjouw (2003), Lanjouw, Quizon, and Sparrow (2001), Ravallion (1999a, 1999b, 2000, 2003), Ravallion and Lipton (1995), Galasso and Ravallion (2004), Van de Walle (1998a, 1998b, 2000, 2003), Van de Walle and Nead (1995), Wodon (1997), Wodon and Yitzhaki (2002), Duclos, Makdissi  and Wodon (2004),  Makdissi and Wodon (2004a, 2004b), Wodon, Ajwad and Siaens (2003) and Ajwad  and Wodon (2002, and forthcoming).
# Table A.12: Attitudes toward Violence

|Full sample<br>(1)|Rural<br>(2)|Urban<br>(3)|
|---|---|---|
|**Panel A: Women’s attitudes index**|||
|Fraction of female leaders<br>0.281<br>(0.257)|0.169<br>(0.282)|0.552<br>(0.350)|
|Observations<br>62,060|43,936|18,124|
|Outcome mean<br>-0.006|-0.049|0.098|
|**Panel B: Men’s attitudes index**|||
|Fraction of female leaders<br>-0.290|-0.353|-0.100|
|(0.288)|(0.331)|(0.347)|
|Observations<br>67,065|47,088|19,977|
|Outcome mean<br>0.036|0.003|0.115|
|State fxed efects<br>x|x|x|
|District characteristics<br>x|x|x|
|Individual characteristics<br>x|x|x|

_Notes:_ Data are from the 2015-16 National Family Health Survey (NFHS) of India. The table presents 2SLS estimates using the fraction of female leaders in a district in close elections as an instrument. The dependent variables are an index of women’s attitudes towards IPV in Panel A and an index of men’s attitudes towards IPV in Panel B. Column (1) presents estimates for the full sample, while columns (2) and (3) restrict the samples to those living in rural and urban areas, respectively. We define close elections as those
# **II – CONTEXT: SCHOOL ATTAINMENT, EARNINGS AND ADULT ENROLMENT**

To understand and assess the analysis of this paper, it is helpful to be familiar with the context. This section briefly introduces school attainment, earnings and adult enrolment.

Many adult Colombians have still not attained primary and secondary education. Further, tertiary education remains relatively elitist. Figure 1 presents attained schooling by agecohorts of a representative sample of Colombians living in urban areas. It shows both the progress in education over the last two decades and the large share of the population with little schooling. For example, only 20 percent of Colombians above 60 years have completed secondary education. For the age group 35 – 39, only half of the population has completed secondary education. Among the age cohort of 20 to 24 years, almost 40 percent have primary education or less. Extending this analysis to include the rural population would paint a bleaker picture of the gaps in education. Consequently, the majority of the Colombian labor force has still to attain secondary education.<sup>2</sup>

**Figure 1** – **Attained** **<u>Schooling by Age Group</u>**

<!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>20-24 25-29 30-34 34-39 40-44 45-49 50-54 55-59% 60-64<br>Less Primary Primary Completed Secondary Completed Tertiary Completed<br><!-- End of picture text --> Source: Encuesta de Hogares, 2000

> 2 Despite the above shortcomings, it should be noted that the Colombian formal education system expanded greatly during the last two decades. According to the World Development Indicators 2002, preschool gross enrollment grew from 11 percent in 1985 to 35
- <mark>Social Norms Atlas: Understanding Global Social Norms and Related Concepts. April 2021. The Social Norms Learning Collaborative. Washington, DC: Institute for Reproductive Health, Georgetown University.</mark>

- <mark>Talukder, A., Hasan, M. M., Razu, S. R., & Hossain, Z. (2020). Early marriage in Bangladesh: a cross-sectional study exploring the associated factors. Journal of International Women's Studies., 21(1), 68-78.</mark>

- <mark>United Nations Children’s Fund, Ending Child Marriage: A profile of progress in Bangladesh, UNICEF, New York, 2020.</mark>

- <mark>Verbeek, M. & Nijman, T. (1992), Can cohort data be treated as genuine panel data? Empirical Economics, 17, 9–23.</mark>

<mark>Verick, S. (2014). Female labor force participation in developing countries. IZA World of Labor.</mark>

- <mark>Weitzman, A. (2014). Women's and men's relative status and intimate partner violence in India. Population and Development Review, 40(1), 55-75.</mark>

- <mark>Weber, A. M., Cislaghi, B., Meausoone, V., Abdalla, S., Mejía-Guevara, I., Loftus, P., ... & Gupta, G. R. (2019). Gender norms and health: insights from global survey data. The Lancet, 393(10189), 24552468.</mark>

- <mark>World Bank. 2012. World Development Report 2012: Gender Equality and Development. World Bank. © World Bank. https://openknowledge.worldbank.org/handle/10986/4391.</mark>

- <mark>World Bank. 2017. “Jobless Growth
BLT payments (that is, cash transfers from the government to poor households) than panel households (24 percent). We also observe a greater use of concrete walls and lower use of bamboo for new households. A higher proportion of new endline households had soap available at the handwashing station and had access to improved sanitation, but a lower proportion of them appropriately dispose of children’s feces. Thus, Tables 3 and 4 suggest that the attrition was mostly random with respect to treatment assignment so doesn’t lead our households in treatment communities to differ systematically from those in the control communities. Further, the panel sample does not appear to differ markedly from the original random sample so should be reasonably representative of the population.

# **7. PROGRAM EXPOSURE**

During the study period, trained facilitators went into the eight project districts and supported the local governments in conducting triggering and follow-up activities in the communities. Of the 80 treatment villages, the endline survey data established that 53 villages (66 percent) were triggered (see Table 5).<sup>7</sup> The table also reports data from an earlier, additional short survey of community heads conducted in May 2010, which generated very similar figures to the endline data. Monitoring data collected as part of the TSSM program reports a higher percentage of treatment villages (83 percent) received the triggering.

Some control villages in the sample did receive the treatment. This was largely as a result of district governments changing their target communities after the randomization plan had been agreed upon. The endline survey data suggests that 13.8 percent of the control villages were exposed to the program (TSSM monitoring data gives a lower figure, at 4 percent).

The extent to which districts adhered to the randomization varied considerably.<sup>8</sup> Jombang, Bondowoso, Blitar, and (to a lesser extent) Madiun performed well in this respect. Banyuwangi and Probolinggo performed less well, with triggering detected in only 50 percent
auditor during the visit to the actual product in the market product dataset using the commercial name of the product when available. Although auditors were instructed to gather the commercial name during their visits, the staff failed to provide it in more than half of the visits. For visits without recorded product commercial name matching was done based on other product attributes like the type of savings account (transaction vs. investment), the interest rate quoted, and the presence of several fees. Even when a product offered was found in the market products dataset, the match could not sometimes be used because the market products dataset contained missing terms for the product. The resulting sample of matched products consists of 215 savings and 261 credit products.<sup>27</sup> From the different product terms collected (see Footnote 26) not all are equally relevant or important, because they may not apply or may not be charged. For this reason we develop a “transparency index” that reflects the disclosed cost as a percentage of the total (annual) cost of the product. The index ranges between 0 and 100 percent, where 0 percent indicates that no information about the cost of the product is disclosed and 100 percent means that the full cost of the product is disclosed. The index is thus a ratio where the numerator is the sum of product-relevant costs disclosed by the staff and the denominator is the total cost of the product. For example, if a savings product has a monthly management fee, then the annualized cost will be included in the denominator and will appear in the numerator if the staff mentioned it.<sup>28</sup> Because the total cost may include usage fees, we compute the transparency index with the usage profile suggested by the regulator and already used in Table 1. The transparency index is reported for all costs disclosed orally as well as the costs disclosed voluntarily by the staff, that is, those costs that were not prompted by the auditor. The transparency index is also computed