rafmacalaba/data-use-mentions-extended huggingface dataset:
education, less likely to use a community coop and to have shared water source (Table 3). Our results do not qualitatively change if we include these variables in baseline controls.

# 7.2. Attrition

Of the originally selected 886 households, 450 were replaced either because the household barangay officials were not able to identify the household among current barangay residents, or because the field team could not confirm that a child of eligible age and gender was a member of approached household. We do not find difference in the likelihood to be lost to follow-up by treatment status (estimated coefficient is -0.016, with standard error of 0.044).

We also check whether attrition is affected by baseline characteristics in the control group, as well as for evidence of differential attrition by observable characteristics between treatment status. Specifically, we first estimate the probability of receiving the 4P transfer based on the wide range of 2008 characteristics and construct a propensity score. We then check for differential attrition in the following regression: 0 1T T 2 3T T (Eq.2) ii,bb bb ii bb ii ii,bb where is a binary variable equal to 1 if individual AA = ξξ + ξξ TT + ξξ PP _i_ was not tracked, + ξξ TT PP + ςςT T is a barangay level indicator of treatment and ii,bb is the value of propensity score from a regression of treatment status on 2008 bb AA TT household characteristics – we include all variables reported in Table 3. Coefficient ii 2 captures whether PP attrition differs depending on the baseline characteristics which capture the likelihood of participation in ξξ the 4P program among the control group. Coefficient 3 captures whether attrition differs by observable characteristics between our treatment and control group. Table 4 shows that none of the coefficients is ξξ significantly different from 0.

Even though we do not find evidence of differential attrition, given that the fraction of households that we were unable to track is high, we report Lee bounds (Lee, 2009) and Kling-Liebman bounds (
# **REFERENCES**

Adhvaryu, A., N. Kala and A. Nyshadnam, 2016. “Soft Skills to Pay the Bills: Evidence from Female Garment Workers”, mimeo, University of Michigan

- Almund, M. A. Duckworth, J. Heckman, and T. Kautz, 2011.  “Personality Psychology and Economics,” (with). In E. Hanushek, S. Machin, and L. Woessman, eds., _Handbook of the Economics of Education_ , Amsterdam: Elsevier. pp. 1-181.

- Athey, S., and G.W. Imbens, 2016. “The Econometrics of Randomized Experiments”, Banerjee, A. and E. Duflo, (eds.), _Handbook of Economic Field Experiments_ . Volume 1. Elsevier.

- Attanasio, O., S. Cattan, E Fitzsimons, C. Meghir, and M. Rubio-Codina, 2015. “Estimating the Production Function for Human Capital: Results from a Randomized Control Trial in Colombia”, _NBER Working Paper_ No. 20965

- Benet-Martinez, V. and O.P. John, 1998. “Los Cinco Grandes across cultures and ethnic groups: Multitrait multimethod analysis of the Big Five in Spanish and English”, _Journal of Personality and Social Psychology_ (75): 729-250.

Bernard, T. and A. Taffese, 2014. “Aspirations: An Approach to Measurement with Validation Using Ethiopian Data” _Journal of African Economies_ (2014) 23 (2): 189-224 Bernard, T., S. Dercon, K. Orkin, and A. Taffese, 2014. “The Future in Mind: Aspirations and Forward-Looking Behaviour in Rural Ethiopia”, _CEPR Discussion Papers_ 10244.

- Bertrand, M. and S. Mullainathan, 2001. “Do People Mean What They Say? Implications for Subjective Survey Data”, _American Economic Review_ , 91 (2): 67–72.

- <mark>Blattman, C., J. Jamison and M. Sheridan,
in 2000 (of which 7,440 km were radial highways originating from Bras ́ılia–calculation by authors based on DNIT data).

# **2.2 Data**

We start with creating a longitudinal dataset with four points in time (1970, 1980, 1996, and 2000) across 3,102 Brazilian municipalities for which economic data are available.<sup>3</sup> It covers more than 93 percent of the Brazilian population throughout the study period.<sup>4</sup> Our data comprise the municipal Gross Valued Added (GVA),

> 2The 1973 plan aimed to create 8 radial roads, 17 longitudinal highways, 24 transverse roads, 27 diagonal roads, and 62 connecting highways (Pereira and Lessa, 2011).

> 3Note that the data is unequally spaced in time, which will not affect the scope and magnitude of our results as shown in Section 4.4.

> 4We work with administrative boundaries at the municipality level from the Brazilian Institute of Geography and Statistics ( _Instituto Brasileiro de Geografia e Estat ́ıstica, IBGE_ ) using the 1970 data as a baseline reference. The 568 missing municipalities for which economic data are not available only represent 6 percent of the country’s population in 1970 and 7 percent in 2000.

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# 8. The Regional Dimension

The previous section presented an economic assessment of VTE at the national level. This level is relevant because many budget, investment, and other policy decisions are made for the country as a whole. Nevertheless, the CMte d'Ivoire is a far from homogeneous country. As some of the tables earlier in this paper indicated, there are wide divergences in education and earning levels between Abidjan, the other cities, and the rural areas. In this section we therefore Introduce a regional dimersion into the economic assessment of VTE.

The limitation of the CILSS data set, especially the small sample size, do. however, impose two restrictions on the regional analysis. First, only two levels of formal ITE can be distinguished: secondary level VTE and other VTE (combining post-secondary VTE and teacher training). Second, the number of cases of wage-earners in rural areas is too small and the estimated earnings function coefficients too imprecise to serve as a basis for an internal rate of return calculation. The latter is thus only presented for Abidjan and other cities. At any rate, given the prevalence of own account work in rural areas, the estimation of the returns to education should mainly be based on the earnings of the self-employed, and, as we said in section 7.5, the study of the role of education in own account work should follow a production function approach. This is outside the scope of this paper.
and homicide. We find that while unemployment increases mortality by Alzheimer’s and poisoning, it decreases homicide.

Unemployment seems to have no impact on either males or females, and for most age groups. The only exception is the 25-34 age group. We find that mortality of the 25-34 displays a clear negative relationship with unemployment. In other words, during the Great Recession, counties that have higher job losses actually have smaller numbers of deaths in the 25-34 age group. We will discuss the potential reasons for this phenomenon in section 4.

This paper is organized as follows: Section 2 provides a literature review; section 3 details our methodology and data sources; section 4 presents our regression analyses; section 5 discusses robustness checks and the possibility of reverse causation. Finally, section 6 concludes.

# **2. LITERATURE REVIEW**

The cyclicality of health persists as a topic of much debate. Brenner (1973, 1975, and 1979) in his seminal analyses on the issue, concluded that health deteriorates in economic downturns. The author hypothesized that physical and mental health worsen due to increasing stress and risk-taking (leading to medication with alcohol or legal and illegal drugs). However, this finding has been reputed and criticized on statistical grounds in subsequent studies (see Wagstaff, 1985 for a survey). Subsequent studies, such as Forbes and McGregor (1984), Joyce and Mocan (1993), after correcting for the problems, no longer find that health is pro-cyclical.

More recently, Christopher Ruhm has a series of works on the cyclicality of health (Ruhm 2000, 2005, 2015a, 2015b). Ruhm (2000) used a fixed effects model with state-level data from the U.S.’s 50 states and District of Columbia to control for states’ unobservable heterogeneity. The author found that surprisingly, health –measured by mortality- improves in recessionary periods, with one 4
funding were provided to some village funds in 2003 and 2004, and well-run funds were allowed to borrow up to a million baht more from the BAAC (or GSB or KTB).

_VF Management and Operation_ In this and subsequent sections we rely heavily on the results of a purpose-built questionnaire that was administered by the National Statistics Office in May 2010 to 3,091 village funds. Every village or urban area that was covered by the 2009 Socio-Economic Survey was included in the sample frame, and an attempt was made to gather information on the Village Fund in each of these localities by interviewing in person one of the Village Fund officers. In rural areas the villages and Village Funds apply to the same area, so the results of the survey can be matched village-to-village with those of the 2009 SES. However, in urban areas the areas covered by the Village Funds did not necessarily coincide with the enumeration areas used in the SES, and so the correspondence is imperfect: for each urban district covered by the SES, a Village Fund was interviewed, but the geographic coverage of the latter overlapped, but was not identical to, the urban enumeration area used by the SES.

The 2010 VF Survey shows that of the village fund committees that were in operation in 2009, 54% were established in 2002, and a further 36% in 2003 or 2004. By the end of 2005, 91% of villages had an operating VF committee, and almost all of the rest have set up their committees subsequently.

Villagers are not automatically “members” of the VF, but anyone aged 20 or older may join, typically by purchasing a share or making an initial deposit. The median number of VF members is 113 per village, of which slightly under half were men; this compares with a median number of households per village of 210, and population of 850 (see Table 3).

The village funds are run by local committees, with on average of eleven members, evenly split between men and women. There is
# **The Effects of Differential Exposure to COVID-19 on Educational Outcomes in Guatemala**<sup>**1**</sup>

Andres Ham<sup>2</sup> Emmanuel Vazquez<sup>3</sup> Monica Yanez-Pagans<sup>4</sup> _Keywords_ : education, dropout, promotion, school switching, government policy, COVID-19. _JEL Classification_ : H12, I21, I24, I25, I28.

> 1 This research was carried out as a background paper for the 2022 Central America Human Capital Review by the World Bank (P177403). The World Bank provided access to anonymized data to conduct the analyses in this paper.

> 2 Associate Professor, School of Government, Universidad de los Andes. Email: a.ham@uniandes.edu.co.

> 3 Senior Researcher, Center for Distributive, Labor and Social Studies (CEDLAS), Instituto de Investigaciones Económicas, Facultad de Ciencias Económicas, Universidad Nacional de La Plata. Email: <u>evazquez@cedlas.org.</u>

> 4 Senior Economist. Education Global Practice. The World Bank. Email: myanezpagans@worldbank.org.
normal distribution, i.e., ranging from approximately -2.5 to 2.5. The government effectiveness indicator is available from 1996 to 2019. There is a strong relationship between the SPI overall score and the government effectiveness indicator. The correlation between the two in 2019 is 0.77. Figure 10 offers the visual illustration of the strong and positive relationship between the SPI overall score and the government effectiveness indicator.

Finally, Figure 11 provides a graphical description of the relationship between the SPI and poverty and inequality. Unsurprisingly, the SPI has a negative relationship with the poverty rate (which is suggested by the positive relationship between a country’s SPI score and its income level discussed earlier). Yet, it is interesting to note that the SPI also has a negative, albeit somewhat weaker relationship with inequality, indicating that countries with more unequal allocation of resources have lower SPI scores. While further investigation is clearly needed, this result is encouraging. It suggests that improvements to a country’s SPI score reflect a more efficient NSS that can potentially help improve inequality, perhaps through higher-quality data and more transparency on its economic activities.

# **4. Issues for Further Consideration**

We propose in this paper to improve the World Bank’s SCI—which has been widely used to measure country statistical capacity—in different aspects, both conceptually and empirically. While our proposed new SPI offers a number of advantages over the SCI such as more indicators, more country coverage, and better conceptual motivations, it leaves room for further enhancement.<sup>16</sup> In this section, we briefly discuss several promising directions to expand the SPI as well as its practical relevance for policy recommendations.

> 16 We fully acknowledge that the increased numbers of indicators and countries covered for the SPI require more data compared to the SCI. The data for the SPI come from different sources, but mostly from established databases with

16
Belgium and Israel.

The second measurement issue that may compromise international inequality comparisons based on PISA test scores is the possibility of sample selection. The surveys are designed to be representative of the population of 15 year-olds enrolled in school, and attending grades 7 or above. While this stipulation covers most of the population of that age group in OECD countries, it purposively excludes substantial numbers in poorer countries. Selection into the sample is clearly correlated with determinants of test scores, leading to a classic problem of sample selection bias. Using information on characteristics of fifteen year-olds included in other, ancillary household surveys, we use sample re-weighting methods to assess the implications of the selection bias for our measures of educational inequality in achievement and opportunity. Results for Brazil, Indonesia, Mexico and Turkey suggest that the inequality measures are relatively robust to selection on the basis of three observed variables (gender, mother’s diverting some individuals from progress to tertiary education, it seems to reduce the impact of family background on adult literacy and promote further on-the-job training by offering more effective curricula to less well performing students.

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local firm numbers can be weighted based on what industry _k_ purchases or sells nationally.

I use the Herfindal measure to examine the degree of economic diversity in each district. I refer to this measure as urbanization economies ( _U jt_ ) in each district in a given year _t_ . Urbanization economies are a reference to large urban areas, which are industrially diverse, which enjoy access to large labour pools with multiple degrees of specialization, access to financial and professional services, better physical and social infrastructures etc. The Herfindahl Index, although it captures only the level of industrial diversity within a region, is a proxy for these larger urbanization economies. The Herfindal Index of a district _j_ ( _U jt_ ) is the sum of squares of firm shares of all industries in district _j_ : Unlike measures of specialization, which focus on one industry, the diversity index considers the industry mix of the entire regional economy. The largest value for _U j_ is one when the entire regional economy is dominated by a single industry. Thus a higher value signifies a lower level of economic diversity.

# **_First-Nature Geography Variables_**

In principle, improved access to consumer markets (including inter-industry buyers and suppliers) will increase the demand for a firm’s products, thereby providing the incentive to increase scale and invest in cost-reducing technologies. The proposed model will use the formulation proposed initially by Hanson (1959), which states that the accessibility at point A to a particular type of activity at area B (say, employment) is directly proportional to the size of the activity at area B (say, number of jobs) and inversely proportional to some function of the distance separating point A from area B. Accessibility is thus defined as the potential for opportunities for interactions with neighboring districts and is defined as: Where, _MAjt_ is the accessibility indicator estimated for location _j_ in year _t, Sm_ is a size indicator at destination _m_ (in this case, district population) in a given year, _d jm_ is a measure of distance
their time (or more time) to the activities with the highest earnings? One reason may be limited access to inputs and assets (e.g., high-return livestock) and lower human capital endowments (i.e., salaried/formal employment) among UP women. For instance, more nonUP than UP households own cows (28% vs. 9%) and fewer own chickens (26% vs. 40%). NonUP households also hold a larger number of animals per type (not shown). The TUP program aims to relax these constraints.

**Figure 3. Earnings per Hour for Main Income-generating Activities, by Gender (USD PPP)**

<!-- Start of picture text --> Men Women<br>2.03<br>Paid Labor: Other<br>0.78<br>2.89<br>Formal/Salaried Employment<br>2.39<br>1.85<br>Paid Labor: Agriculture<br>0.98<br>Own Livestock Rearing (HH) 1.14<br> - 0.50 1.00 1.50 2.00 2.50 3.00 3.50<br>USD PPP per hour<br><!-- End of picture text --> _Notes._ This figure is derived using non-UP and UP control group survey data at follow-up in 2018. It presents earnings per hour in different activities by gender. Other paid labor includes maid services, non-agricultural activities outside of the household, and other paid work. All monetary amounts are PPP-adjusted USD terms, set at 2018 prices using the Afghanistan CPI and PPP conversion factor from the IMF, unless otherwise stated.

# 3. THE TUP PROGRAM

The TUP program combines the transfer of a productive asset with structured training, mentoring, a basic cash stipend, and other complementary services for a defined period. The original TUP program was designed and implemented by BRAC in
Unfortunately, because of a quirk in the program, the total calculated length is a true geodesic distance, while distances by paving status are projected distances. However, this never causes a discrepancy of more than a few percent, and because the same projection is used for the whole continent, these errors are highly correlated within countries.

Of 485 populated lights, 464 (96 percent) received plausible routes. Of the remaining 21, 6 were in exclaves or islands, and 2 had centroids more than 5 km from the nearest domestic road. Three received no routes because they were on road segments disconnected from the primate by a gap of at least 100 meters. The remaining ten received implausible routes (because of suspicious gaps of longer than 100 meters in the road network) and were removed. To the extent that these cities are in fact less connected than others, or that government officials have not mapped their roads correctly or at all, they are more likely to be excluded from traditional data sources like censuses and surveys as well.

# **A.5 Pixel-level data-generating process**

The pixel-level data-generating process can be modeled as follows: where _j_ indexes pixels, which nest in cities, _s_ indexes satellite-years within a year, _yjist_ is measured pixel-level light, and _yjist_<sup>_∗_is true(latent) pixel-level light.Twononlinearities</sup> appear here, in addition to rounding to the nearest integer. Processing by NOAA converts to zero nearly all (1) individual pixel values of 1 or 2 and (2) clusters of less than 4 nonzero pixels. In both cases, NOAA’s algorithm interprets these patterns as random noise.

The relationship of interest is at the city level, as are all of the regressors, but the lights data are generated nonlinearly at the pixel level. Rather than estimate Equation (14) via maximum likelihood with approximately 5.6 million pixel-satelliteyears, I instead simply sum lights across pixels and satellites within a city: where _St_ is the number of
There are signs that productivity growth has stagnated in many areas, falling behind the pace needed to meet future demand, and leaving fewer opportunities for future global gains (Ray et al. 2012; Ray et al. 2013). Poor management of water, fertilizer and pesticide use globally, and specifically during Asia’s Green Revolution, have degraded land and water resources, and have likely played a role in declining yields (Pinstrup-Andersen and Hazell 1985; Pingali and Rosegrant 1994; Godfray et al. 2010a; Stevenson et al. 2013). Especially in Africa, inadequate fertilizer use has depleted the nutrient content of soils (Drechsel et al., 2001; Cobo, Dercon and Cadisch, 2010). Further ahead are the uncertain consequences of climate change on food production and prices (IPCC, 2014). What’s more, the anticipated transition to mechanization in fast-growing countries in Asia, where wages are increasing, may have the unexpected effect of undercutting future land productivity gains (Otsuka et al., forthcoming).

Using projections of food and biofuel demand by Bruinsma (2009) and Fischer (2009), Hall and Richards (2013) calculate that grain yields on current agricultural lands would need to grow by 1.2 to 1.3 percent per year pace (depending on uncertain policy-driven biofuel demand) to keep pace with growing demand through 2050. In their study, the researchers consider known but unexploited opportunities to improve yield potentials for rice, maize and wheat, and speculate about promising new technologies, but conclude that current and expected future rates of improvement in genetic potential will be insufficient to meet future demand.

Against this background, we discuss two alternative paths to global food security especially relevant for Sub-Saharan Africa. The first is to expand production by bringing more land and water resources into agriculture. In general, the potential for bringing more natural resources into agriculture in a sustainable way is limited, especially once competing resource demands and agricultural lands lost to urbanization are accounted for. Nevertheless, many researchers
# **Robustness to Changes in Parameters**

The quantitative predictions of our model depend on the calibrated parameter values. In this section we compute some robustness exercises by changing some parameters that are difficult to measure or subject to controversy. In particular, we consider changes in � , the capital share in output; in � , the depreciation rate of capital; and in � , the targeted level of foreign liabilities as percentage of GDP. Tables 1 and 2 present the results of these experiments, along with those corresponding to the benchmark calibration. Table 1 displays the national saving rate that is necessary to finance a GDP per‐capita growth rate of 4 percent under the three scenarios regarding TFP growth. The rows labeled “Baseline calibration” report results of the baseline model. The remaining rows report results of the different calibrations. Likewise, Table 2 reports projected per‐capita growth paths when the national saving rate remains fixed at 20 percentage points of GDP.

Consider first the capital share in output. Reliable estimates of � require reliable estimates of national account data on employee compensation. Gollin (2002) and Bernanke and Gürkaynak (2002) argue that, in many developing countries, series on employee compensation substantially understate the labor share in output because of the large number of self‐ employed workers or employees working outside the corporate sector. Adjusting these series with complementary data, these authors find that, in those countries for which these adjustments can be made, the capital share in output is about one third, consistent with the values obtained in developed countries.

In our benchmark calibration, the capital share in output � was set equal to 0.5, which is an average of available Egypt‐specific estimates. We now check the robustness of our results using ��0.35 , a value more aligned with the international evidence cited above. Under the new calibration, the main message of the paper is, in fact, reinforced. Take, for example, the economy with moderate TFP growth in Table 1