rafmacalaba/data-use-mentions-extended huggingface dataset:
**Figure 21. Services Exports, Value Added to Other Exports, and Level of Development, 2011**

<!-- Start of picture text --> 100 100 100<br>80 80 80<br>60 60 60<br>IND<br>ZAFTUR FRADEUUSA<br>40 40 IND 40<br>RUS<br>BRA CAN<br>POL<br>IND TUR FRAUSA CHN<br>20 TUR POLFRA USA 20 POL DEU CAN 20 IDN<br>DEUCAN CHNZAFBRA RUS<br>IDNCHNZAFBRA RUS IDN<br>0 0 0<br>6 8 10 12 6 8 10 12 6 8 10 12<br>GDP per capita PPP GDP per capita PPP GDP per capita PPP<br>G ross services exports<br>D irect value added services exports T otal value added services exports<br><!-- End of picture text --> Source: World Bank Trade Value Added Database and Francois et al., 2013. Notes: RUS = Russia, BRA = Brazil, IND = India, CHN = China, ZAF = South Africa, IDN = Indonesia, FRA = France, USA = United States of America, TUR = Turkey, CAN = Canada, POL = Poland, DEU = Germany.

45
# 1. Introduction

This paper focuses on methodological challenges related to collecting high‐quality individual‐ level employment data through household surveys in rural as well as urban areas of developing countries, paying close attention to issues that disproportionally affect women and young people. Employment data are key to studying a host of issues (Grosh & Glewwe, 2000; IFAD, 2016; World Bank, 2013, 2019). At the macro‐level, the structure of employment defines societies. Relatively slow but far‐reaching evolutions such as the structural transformation, the youth bulge, the demographic dividend and international competitiveness can only be monitored with aggregated labor market statistics that are consistent across countries and over time (Bick et al., 2018). At the micro‐level, employment determines one’s living conditions, as an extensive literature about livelihoods illustrates (Ellis, 1998; Haggblade et al, 2007).

In developing countries, individual‐level employment data are relatively scarce, especially in Sub‐Saharan Africa (SSA) (Headey et al., 2010; Szirmai et al., 2013; World Bank, 2013, p. 34). The ILO, for instance, reports that 37% of all year‐country observations of labor force participation in the period 1990‐2017 are based on actual data such as Labor Force Surveys (LFS), censuses or – in some cases – official government estimates (ILO, 2017). In SSA, the share of real observations to the total number of potential observations drops to 8.4%. Similarly, an ILO review showed that 160 of the 236 countries and territories worldwide (and 36 of 60 African countries) implemented an LFS or a household survey with a labor module from 2000 to 2010 (ILO, 2013b). At the same time, multi‐topic household surveys, such as the LSMS(‐ISA)<sup>4</sup> surveys supported by the World Bank, focus primarily on ‘final’ outcomes such as
# **1. Introduction**

Human capital levels in Pakistan are low, and the risks to human capital development begin in early childhood. Pakistan, the fifth most populous country in the world with 221 million people, has a low Human Capital Index (0.41), indicating that by age 18, a Pakistani child born today can only expect to be 41 percent as productive as she could be if she had access to full education and health. Many of the risks to development occur in the first 5 years of life, when brain development is exceptionally rapid and sensitive. Missed opportunities for nurturing care and stimulation cannot easily be made up later in life. Children in their early years require nurturing that emerges not only from adequate nutrition and health care, but also from safe environments with engaged caregivers who interact with their young children responsively and provide psychosocial stimulation through everyday activities such as talking and playing (Devercelli et al., 2022; World Health Organization, United Nations Children’s Fund, World Bank Group, 2018). Access to nurturing and stimulating environments in the early years impacts human development into adulthood.

Across Pakistan, child development before school entry and learning outcomes in school are undermined by widespread geographical, gender, and wealth inequalities. Over 11 million households or 21.9 percent of the population lives below the poverty line, and poverty disproportionately affects young children: 27.0 percent of children aged 0-2 years and 29.6 percent of children aged 3-5 years live in poverty, according to calculations using the 2018 Pakistan Social and Living Standards Measurement (PSLM) . Among 3- to 4-year-old children living in the lowest income quintile, just 26.0 percent are developing on track (Tomlinson et al., forthcoming). Two-thirds (62.8 percent) of the population is rural (World Bank, 2021a), and Pakistan also has one of the lowest gender parity indices in primary school (girls’ enrollment compared to boys’ enrollment) in the world: 0
off-trend fluctuations in NO2, which we now show are indicative of business cycles.

# **3.2 Time Series Characteristics**

Figure 1 presents the example of China, to provide some basic intuition regarding NO2’s superior ability to capture cyclical movements. In the left-hand side panels are NO2, while the right are lights. In the top row are raw data, while the bottom is the cyclical component. All panels cover the same period, from January 1, 2015 to January 1, 2021, which includes two significant downturns: August 24, 2015’s “Black Monday”, when the Shanghai main share index lost 8.49% of its value, and January 1, 2020, a rough estimate for the beginning of the COVID-19 pandemic. Both downturns are clearly evident in the raw data and the cyclical component of NO2, which fell significantly in their aftermath. However, the same may not be said for lights; whereas Black Monday is evident in the cyclical component, the pandemic is not. Thus, regardless of the fact that NO2 is overall downward-trending, the cyclical component seems to contain more information regarding changes in economic activity in this case.

Figure 2 changes the perspective to average world NO2, and back to the 2009 Financial Crisis. From this perspective as well, the potential for NO2 to indicate the timing of global business cycles is evident; for instance, the ramifications of the 2015 episode in China are also visible in world data, as are both the 2009 and 2020 crises.<sup>9</sup> Thus, a hypothesis we wish to test is whether NO2 reflects cyclical economic activity better than lights globally.

# **3.3 Cross-Sectional Characteristics**

Thus far we have emphasized time series fluctuations. But NO2 also contains important crosssectional information. While NO2 has broader applications than just the COVID-19 pandemic, this is a nonetheless an example of a particularly stark reduction in economic activity where we may see geographic heterogeneity clearly.

**China.*
05|0.04|0.04|0.04|

_Source_ : Authors’ calculations based on the 2018 survey “Getting to Know 8th Grade Students.” _Note_ : Conditional correlations are Ordinary Least Squares regressions. Yellow and blue highlights show significant correlations at least at the 95 percent level for the variables of interest and covariates, respectively. Grades are the average of four subjects—math, science, English, Bahasa Indonesia—of the last semester before the survey and come from the schools’ administrative data. They are standardized at the school level, with a mean of zero and a standard deviation of one. Measures of socioemotional skills are standardized for the entire sample (see annex 2 for definitions). Standard errors are clustered at the school level. The base level of the dummy ‘have lower expectations than aspirations’ includes 5 percent of students with higher expectations than aspirations.

_*p < 0.1 **p < 0.05 ***p < 0.01_ 18
points to the presence of rural workers who shift between the farm and non-farm sectors in response to employment opportunities generated by exogenous increases in precipitation, which positively affects agricultural production and increases the demand for farm labor. In the absence of such labor demand, these workers opt to remain self-employed in manufacturing and service activities.

# **3.2 Heterogeneity of Individual Predictors of Rural Non-Farm Employment across District Characteristics**

## **3.2.1 Heterogeneity by Secondary Education**

The empirical findings in Section 3.1 documented education and social identity to be key predictors of non-farm employment for rural workers. In particular, while secondary educa-

> 8 We use district-level rainfall data from the ICRISAT between 1975 and 2014 to assess the long-term district-specific distribution of annual rainfall.

17
>10</sup> Alesina, Favaro and Giavazzi (2019b) use cross-country panel data to analyze 9 To rule out that our results are driven by the most indebted states, we adopt a difference-in-difference approach where the treatment variable is discrete and equal to one for states above the median indebtedness in the quarter prior to the FD Law and zero otherwise. We find that our results remain very similar under this specification. Another threat to our identification is that the FD Law also established procurement rules intended to guarantee that subnational debt was obtained at the lowest financial cost. Such rules could have induced local governments with ex-ante lower debt to increase their indebtedness, in which case our estimates would be positively biased. To rule this out, we examine changes in bank lending by local governments and confirm that ex-ante less-indebted local governments did not expand their bank liabilities after the Law. Furthermore, we also find that our results hold when restricting the sample to states with ex-ante similar debt-to-net-income ratios but subject to different debt ceilings. Additionally, given that Mexico is an oil producer country, we also rule out that our results are explained by the global collapse in energy prices of mid-2014 (all our results hold if we drop from the sample the oil producing states). Finally, we rule out that our results are driven by variations in external conditions. Given that northern states are more exposed to external shocks due to stronger economic relations with the United States, we split the sample into northern and non-northern states and confirm that our results are not geographically concentrated.

10 Chodorow-Reich (2019) studies the effect of an increase in spending in one region of a monetary union, concluding that an average point estimate for a cross-sectional multiplier is 1.8. Using a panel of OECD countries, Guajardo, Leigh and Pescatori (2014) find that fiscal consolidation has contractionary effects on private demand an on GDP.

6
worldbank.org/curated/en/909741624654308046/pdf/Advancing-ArabicLanguage-Teaching-and-Learning-A-Path-to-Reducing-Learning-Poverty-in-the-Middle-East-andNorth-Africa.pdf.

- Harris, Douglas N., and Tim R. Sass. 2011. “Teacher Training, Teacher Quality and Student Achievement.” _Journal of Public Economics_ 95 (7–8): 798–812. https://doi.org/10.1016/j.jpubeco.2010.11.009.

- Hattie, John A. C. 2009. _Visible Learning: A Synthesis of over 800 Meta-Analyses Relating to Achievement_ . London, New York: Routledge.

- Jha, Jyotsna, and Sarah Pouezevara. 2016. _Measurement and Research Support to Education — Strategy Goal I — Boys’ Underachievement in Education: A Review of the Literature with a Focus on Reading in the Early Years_ . Washington, DC: United States Agency for International Development (USAID).

- Knoster, Kevin C. 2016. _Strategies for Addressing Student and Teacher Absenteeism: A Literature Review_ . Washington, DC: U.S. Department of Education, North Central Comprehensive Center.

- LaRoche, Sylvie, Marc Joncas, and Pierre Foy. 2020. “Sample Design in TIMSS 2019.” In _Methods and Procedures: TIMSS 2019 Technical Report_ , edited by Michael O. Martin, Matthias von Davier, and Ina V. S. Mullis, 3.1-3.33. TIMSS & PIRLS International Study Center, Lynch School of Education and Human Development, Boston College, and International Association for the Evaluation of Educational Achievement (IEA). https://timssandpirls.bc.edu/timss2019/methods/chapter-3.html.

- Legewie, Joscha, and Thomas A. DiPrete. 2012. “School Context and the Gender Gap in Educational Achievement.” _American Sociological Review_ 77 (3): 463–85. https://doi.org/10.1177/0003122412440802.

- Mullis, Ina V. S., Michael O. Martin, and Pierre Foy. 2013. “The Impact of
confirm that local food price changes are uncorrelated with the initial livelihood strategies and child human capital, implying that at least in this restricted dimension, price changes are exogenous to the household. This finding is important since we can exclude the possibility of potential endogeneity of local food price changes. As shown later, a full specification that includes all explanatory variables (village‐average) also confirms the result.

# **5. Empirical Results**

This section summarizes the empirical results. As discussed, we use the Laspayres index of main food commodities as the measure of price changes, based on quantities and calories. Results using both constructions are presented in the following tables.

Table 4 shows the benchmark results on changes in the height‐for‐age z scores. Columns 1 and 2 presents the key results using quantity‐based and calorie‐based price indices, respectively. The specifications include the non‐producer indicator that has the value of one if the proportion of main food consumption own produced is smaller than 0.035 and zero otherwise, the Laspeyres price index, their interaction term, age in months, age squared, male dummy, log of birthweight (kg), and province dummies. The results show a significant positive effect of the non‐producer indicator and a negative effect of its interaction with the price index. The price index itself is not significant. Both results in quantity and calories are qualitatively similar. The non‐producer households experienced higher child growth (without food price spikes) but they are more vulnerable to food price spikes than the producers, resulting in a significant reduction in child growth. Age has a significantly positive but convex effect. Children in Lumpung, East Java, NTB and North Sulawesi grew faster relative to those in Central Java.

Columns 3 and 4 include, as controls, household and village characteristics: the number of household members, household head’s age (years), household income (labor earnings in 2007, not including transfer incomes and non‐labor
Croke et al. (2016) school feeding. As noted, the estimated weight gain in kilograms per dollar spent from school feeding is less than 0.01 (from RCT studies; Galloway et al. (2009)). Given the $0.60 per year treatment cost of deworming in environments where two annual doses are required, an effect of just 0.006 kg would make deworming cost effective relative to school feeding (setting aside, for the time being, the issue of other outcomes).

Examining the random effects meta-analysis estimator that is the focus in TMSDG and taking as given the variance of the effect across studies that TMSDG estimate, and the standard errors TMSDG report for the underlying studies, the implied Minimum Detectable Effect (MDE) size at 95% confidence and 80% power is 0.28 kg.<sup>11</sup> As illustrated in Figure 3, a wide range of possible effect sizes lies above the magnitude at which deworming would be cost effective relative to other common interventions for similar populations (~0.006 kg), yet below the magnitude which TMSDG would be adequately powered to detect (~0.28 kg). For example, this range includes TMSDG's random effects estimate of 0.08 kg, for which there is only 13% statistical power to detect a significant effect at 95% confidence.

TMSDG also appears to be underpowered to detect effects that might be reasonable to expect from MDA given the estimated effects TMSDG report in studies of those known to be infected. This is notable because the suggestion that there are positive effects from treating individuals known to be infected, yet no impact from treating populations

> 11 These estimates were obtained using the method of Hedges and Pigott (2001). In particular, for a given E <u>E</u> E E E E <u>E</u> E E E effect size, we estimate power as: P P Φ �1.96
fees charged by schools in the locality. Finally, from the community module in the 1994 and 2000 DHS, we include information on the average time to travel from a central point in the cluster to a primary school.

Table 4 shows results from the analysis of school determinants taking into account school supply variables. The availability of schools in the urban/rural commune (both in terms of the number of schools and classes) is positively associated with the probability of attending school. A greater number of teachers is associated with a higher probability of school attendance,

> 14 The standard deviation of the wealth index is 1.96.

> 15 In order to reduce endogeneity problems with this variable, we use the left out mean for every household. 16 Table A.5 and Table A.6 in the appendix show the results from Table 3, run separately for urban and for rural areas.

> 17 The population of school age is defined as the number between 5 and 19 years old who lived in the urban/rural area of the commune at the time of the 2003 population census.

11
. Time-varying sensitivity is estimated by regressing the change in five-year-ahead inflation expectations on inflation shocks, as described in Appendix 3. Solid lines denote the median of estimates and the dotted lines indicate the median of 68 percent confidence intervals.

- B. Based on a sample of 24 advanced economies and 23 EMDEs during 2000H1-2018H1.

- C. Based on a sample of 24 advanced economies during 1995H1-2018H1.

- D. Based on a sample of 23 EMDEs during 2000H1-2018H1.

21
<br>squared|(-7.29)|(-5.92)|(-9.83)|(-6.03)|
|95% Confidence<br>Interval|-1.02 to -.5929|-1.2353 to -.6208|-.836 to -.5581|-1.2074 to -.6154|
|Maximum Point|$4295|$4595|$5358|$5564|
|CountriesinSample|50|50|50|50|

Note: parentheses indicate z-statistics. Source: Author’s calculations The coefficient estimates of the data grouped by industry fall within the 95% confidence intervals for the pooled data at both the 4- and 6-digit levels. Furthermore, the maximum points occur at very similar levels. That is, the data suggest that the observed relationship between discovery activity and income per capita is not significantly different across industries. This is illustrated in Figure 3, which shows the estimated relationship between expected discoveries and growth (the discovery curves) estimated individually for each of the 16 HS industry groups with discoveries (using the 4-digit data).

27
Policy Research Working Paper 10088

# **Abstract**

Oportunidades was an innovative anti-poverty program that put additional resources in the hands of women and their families and encouraged parents to invest in the human capital of their children. This program was the first in its kind, and early evaluations demonstrating its success informed its large expansion within Mexico and the implementation of similar conditional cash transfer programs across the world. However, the existing evidence, which arguably captures causal positive impacts, relies on a sample of very poor rural children. This paper conducts the first evaluation of the program using representative data. It focuses on child height as a marker of long-term nutritional status. The causal impact of the program on child height is isolated by exploiting insights from the biology of child growth in combination with the timing of the rollout of Oportunidades and the panel dimension of the survey. Height for age among children exposed during the first four years of life is contrasted with similar children who were not exposed. Consistent with previous evidence, this analysis finds positive and sizable effects on children who lived in rural poor communities incorporated at the beginning of the intervention. In contrast, the impacts of the program in rural localities incorporated later and in suburban and urban communities are, at best, very modest.

This paper is a product of the Poverty and Equity Global Practice and Duke University. 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 gfarfan@worldbank,org and mgenoni@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
# **2.3 Evaluation of performance**

In order to tune the algorithms listed above, compare the different approaches for nowcasting poverty presented in Table 1, and report the final out-of-sample errors, we rely on nested 5-fold cross validation (Stone, 1974; Iizuka _et al._ , 2003; Varma and Simon, 2006). Intuitively, nested cross-validation works by iteratively the into three different a on splitting sample subsamples; training subsample which variants of a particular machine learning method are estimated, a testing subsample on which these variants are compared against each other and the best performer is selected, and a validation subsample which for each machine learning method is used to calculate the out-of-sample errors. The data points going into these three subsamples are reassigned multiple times to maximize the power of the data. Nested cross-validation is an extension of regular k-fold crossvalidation, which reduces a bias with the latter towards selecting a model with many tuning options and a downward bias in the final out-of-sample errors obtained. Appendix A contains a more thorough and technical discussion of nested cross-validation.

Once we are done with nested 5-fold cross validation, we will have one out-ofsample estimate of the poverty rate for each household survey (except for the earliest one for each country, as the methods that rely on changes since the last data point will not work in those cases), for each machine learning method, and for 1. each approach to estimating poverty from Table To evaluate the performance of the various methods, our primary loss function will be the mean absolute deviation between the predicted and true poverty rate. To assure that we do not select models that only work well for a few countries that happen to have many poverty estimates, we weigh each country by the inverse of number of surveys, such that the total weight for each country equals one.

We use the mean absolute deviation rather than the mean squared error since we are interested in minimizing the deviations between the true and predicted poverty rates while giving equal weight to all deviations. Using the