rafmacalaba/datause-encoder-data huggingface dataset:
aged 3-5 years are developmentally on track. The index is based on a 10-item module on milestones a child is expected to reach by ages 3 and 4. Table 1 describes each item and how it fits into the four domains under study – literacy-numeracy, learning, physical, and social development. In this analysis, ECDI and each of the four domains are represented by binary variables, which take a value of 1 if the criteria in Table 1 are met. If a child is on track in 3 out of 4 domains, he or she is considered to be overall developmentally on track on the ECDI scale. In order to reflect this, a composite Early Childhood Development Index (ECDI) is constructed for every child such that the ECDI will take a value of 1 if a child meets at least three out of the four domains above.

# **3.2      Empirical strategy**

Based on existing evidence, a negative correlation is expected between arsenic and early childhood development as measured by the ECDI and its four domains. Moreover, the association is expected to be stronger as the level of arsenic in drinking water increases. Logit regression analysis is used to estimate the association between arsenic contamination in drinking water and the combined ECDI and the four component domains, controlling for socio-economic and demographic factors that influence a child’s cognitive development. The following equation is estimated.

ECD���= ∝ +��h������ + �����h����� + ������ + ��������� + ���������� + ���� (1) Where ������ are the outcome variables of interest, which are: ECDI, literacy, physical, social and learning domains; and �h���, �����h���, and ��������� explanatory variables denote child, household and community characteristics. Arsenic exposure (��������� ) is specified in three ways: (1) above 10 ppb; (2) above 50 ppb; and (3) x≤ 10 ppb, 10< x ≤ 50 ppb, and x >50 ppb. To increase the sample size and improve robustness, the arsenic result from the tested household is applied to all households within the survey cluster.<sup>1</sup> This is based on the assumption that if a household water source is contaminated, then all households that use the same type of water source in the vicinity are likely to have similar levels of arsenic. Other explanatory variables in the three specifications include child’s age, child’s sex, whether he/she has ever been breastfed, maternal education, household location, household size, wealth quintile, type of sanitation facility, type of drinking water source, and water treatment. Breastfeeding (if a child has ever been breastfed) is included in the analysis to account for its impact on cognitive development and is expected to have a positive relationship with ECDI

> 1 Cluster is the primary sampling unit (PSU), which is composed of about 250 households from which 20 households are randomly sampled for the survey which (MICS, 2015).

8
# **1. Introduction**

The strong correlation between education category and health outcomes, even after controlling for income, has been recognized as a robust empirical observation in the social sciences and economic literature (Deaton and Paxson 2003; Fuchs 1982; Grossman 2004; Lleras-Muney 2004). The decision to smoke or not to smoke is a conscious choice that directly affects the health status and ultimately the mortality of individuals. It therefore provides an interesting opportunity to investigate how education, by influencing behaviors, affects health outcomes.

Smoking habits were not initially perceived as dangerous. The information revealing the health damaging consequences of cigarette smoking emerged gradually between 1950 and 1970. Variations in smoking prevalence across education groups during this period might inform us about the way individuals reacted to that information and how education is instrumental in accessing and processing this information.

It turns out smoking is the leading cause of premature adult mortality. Each year in the United States, tobacco use causes more than 440,000 deaths and results in more than 5.6 million years of potential life lost.<sup>1</sup> The impact on mortality of smoking, and the potential role of education in prevention suggest that traditional estimates of the returns to education—focusing exclusively on labor market outcomes—might be too low. This paper examines whether the effect of education on improving health outcomes can be considered as causal.

This issue is central in the economics literature on the interaction between health and education. Theoretical explanations for this correlation can be classified into three broad categories. One explanation stresses that education is an investment. Education will deliver a higher income, a higher consumption level in the future, and raises the value of staying alive (Becker 1993). More educated individuals are healthier because their investment in the future gives them the right incentives to protect their health. Another explanation, based on education entering as a factor in the health production function (Grossman 1972), emphasizes that education improves the access to health-related information and the processing of that information to make health-related decisions. A third view (Farrell and Fuchs 1986; Fuchs 1982) claims that the observed correlation between health and education is mainly due to unobservables, like the discount factor or the ability that causes the same individuals both to study longer and to take greater care of their health.

This paper analyzes smoking prevalence across education groups in the United States from 1940 to 2000. Despite the lack of surveys linking education and smoking

> 1 <u>http://www.cdc.gov/tobacco/issue.htm</u> (accessed on May 11, 2004).
However, ensuring that trade is adequately integrated into broader growth and development strategies, remains a challenge in the region. For the poorest countries in the Caribbean (such as Guyana and Haiti), for which the international community makes external assistance conditional on the elaboration of an explicit Poverty Reduction Strategy (PRS), it is essential to enhance their ability to bring trade needs into the PRS process. Doing so would allow these countries to generate additional resources, to address infrastructure constraints, the lack of human capital, and so on. In that sense, AFT would involve helping Caribbean countries to design and implement a trade agenda as part of a donor-supported, broader national development strategy. Countries could then decide whether to use resources allocated to trade reform either for specific projects identified within the prioritized list of tradecapacity building needs, or for direct budget support (in case of loss of fiscal revenue).<sup>17</sup> A review of existing PRSs in the region, however, indicates that trade reforms have received limited consideration (See World Bank (2008)).

For the richer countries in the Caribbean, donors should also ensure that AFT assistance is linked with broader development programs and complements or strengthens a country’s own plans, budgets, and structures. However, monitoring is more difficult in the case of these countries because they are not, in a sense, “required” to develop an explicit development strategy.

Regarding the form that aid should take, it is clear that for the poorest and least creditworthy countries in the Caribbean, direct grants to governments (in the form of additional contributions from ODA budgets) will continue to be the primary means of addressing development needs—particularly for lumpy investments in infrastructure, education, and health. Richer countries of the region, however, have limited access to ODA grant resources and concessional lending. Non-concessional lending (through multilateral institutions, in particular) and equity investment will therefore be key in addressing the

> 17 In practice, however, a key problem is to distinguish between infrastructure projects that are conducive to development in general, and those that have a direct effect on trade; in the words of Stiglitz and Charlton (2006, p. 6), “When you are building a road, how close does it have to be to the port to become and aid for trade

22
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adjustments for local factors.<sup>17</sup> We considered three possible adjustments to transfer the Mexico City damage estimate to the GCMA.

First, we double the damage estimate because Cairo has a population density about double that of Mexico City. The GCMA has a population of 17 million living in an area of about 1,660 square miles (Nippon Koei 2010). The greater Mexico City metropolitan region has a population of 19 million in an area of 3,691 square miles.

Second, we made some adjustment for differences in people‘s valuation of pollution-health risks. These risks are quantified using the value of a statistical life (VSL), which measures people‘s willingness to pay for reduced mortality risk, expressed per life saved. Parry and Timilsina (2010) implicitly assume a VSL for Mexico City of about $1.5 million—about one-fourth of that assumed for the United States by its Environmental Protection Agency and Department of Transportation. The VSL is commonly transferred among countries using their relative real per capita income, raised to the power of the income elasticity of the VSL (e.g., Cifuentes et al. 2005, 40-41). Typical estimates for this elasticity vary between about 0.5 and 1.0 (e.g., Viscusi and Aldy 2003, Miller 2000). Real per capita income in Egypt is approximately 40 percent of that in Mexico, where income is measured in terms of purchasing power parity equivalent (IMF 2009, World Bank 2008). Therefore, this range of elasticities suggests the VSL for Egypt should be about 40-60 percent of that in Mexico. We lean on the conservative side by assuming the VSL for Egypt is $600,000 (or about one-tenth of that for the United States). Thus, we scale back the local damage estimate by 60 percent.

Third, we did not make any adjustment for differences in automobile emissions rates. Our estimates of average miles per gallon for autos and microbuses in the GCMA are almost the same as those for Mexico City in Parry and Timilsina (2010). It is possible that vehicle emissions control equipment, for a given level of fuel economy, differs between the two cities, but we lack the data to reliably estimate the direction, let alone the magnitude, of any difference.

The net impact of the above adjustments is therefore a local pollution damage of 72 cents per gallon of gasoline. Following Parry and Small (2009), for public buses we assume local pollution costs per vehicle mile are three times those for auto.

_Marginal global pollution damages_ . Most estimates of the discounted (worldwide) damages from future global warming are in the order of about $10-$30 per ton of CO2, though studies using below market

> 17 Their estimate is also broadly in line with local pollution damage estimates for Mexico City in World Bank (2002) and also for Santiago discussed in Parry and Strand (2010).

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# **The Nutrition Sensitivity of Food and Agriculture in South Asia**

Felipe Dizon Anna Josephson Dhushyanth Raju _JEL codes_ : Q18, I1, I3 _Keywords_ : food, agriculture, nutrition, nutrition-sensitive agriculture

> Felipe Dizon, World Bank, <u>fdizon@worldbank.org; Anna Josephson, Department of Agriculture and Resource</u> Economics, University of Arizona, <u>aljosephson@email.arizona.edu;</u> Dhushyanth Raju, World Bank, <u>draju2@worldbank.org. We thank Derek Headey for comments, and Francesco Cuomo, Gabriel Martens, and</u> Zetianyu Wang for excellent research assistance. This study is a background paper for the South Asia regional report on undernutrition. It is funded by contributions from the United Kingdom Department for International Development and the European Commission, through the South Asia Food and Nutrition Security Initiative (grant number TF0A5366) administered by the World Bank.
value of depreciation allowances allowed by tax laws on ezsting accounting capital stock.

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> In a simple case without taxes, _Qt_ in the investment function becomes -

<!-- Start of picture text --> Qt= (9)<br>PKt<br>=pV*g<br><!-- End of picture text --> The first term on the right is simply's Tobin's q, the ratio between the value of the firm and the replacement cost of capital. Thus, if Tobin's q or the real shadow price of capital is greater than 1, investment will increase, and vice-versa if it less than 1.

# 2.3 The Intra-period CGE

The intertemporal investment and consumption are embedded in a computable general equilibrium model which incorporates many stylized features of a developing economy. Generally, these features foiow the family of CGE models constructed for developing countries by Dervis, de Melo, and Robinson (1982).1" Production is a nested function of inputs. Output in each sector is a CES combination of value added and material input (A29-31). Material input is a fixed-coefficient aggregation of inputs (A35-6). Value added, on the other hand, is another CES composite of labor and installed capital (A32-4). Labor market is fragmented - labor in each sector is a Cobb-Douglas combination of urban and rural types (A37-9); but sectoral wages are fixed proportions of wages for each labor type, reflecting differences in productivity (A40).

"A survey of such CGE models is found in Robinson (1988).

10
textbooks until July 2019 due to the severe resource constraints they experienced. Even though the sample districts originally selected for the baseline study were changed, the baseline information, including availability and distribution status of Grade 2 textbooks, was collected during the end-line survey. This data collection was made possible by MoGE’s phased procurement and distribution approach of textbooks for each grade. For example, no additional procurement or distribution of Grade 2 textbooks took place after 2016 until October/November 2019, when the end-line survey of this study was executed. This enabled the study to collect baseline information (availability of Grade 2 textbooks procured in 2016 and its distribution timeline) during the end-line survey.

Lastly, the 27 selected districts were randomly assigned to three groups—two treatment groups and one control group. Nine schools were randomly drawn from each district in those districts for the survey, amounting to a total of 243 schools. During the end-line survey, two-period outcome information (pre and post treatment) was collected at once: the outcome measure for the pre-treatment was Grade 2 textbook distribution and that for the post-treatment was Grade 4 textbook distribution. The primary survey respondents were teachers who were in charge of textbooks. Their answers were cross-checked with the documents and information from the MoGE head offices, such as DEBS’s surveys and textbook records/storage kept in schools.<sup>3</sup>

# _Measurement_

Following the textbook procurement schedule for early grades, this study focuses on the availability of Grade 2 (pre-treatment) and Grade 4 (post-treatment) textbooks procured in 2016 and 2018, respectively. This sample included both English and local language textbooks. Two different constructs were measured—one for the availability of textbooks and one for the textbook delivery mechanism. Outcome variables for textbook availability were measured by constructing (i) a dummy variable of whether the school received the textbooks from DEBS, (ii) the textbook-pupil ratio in the school, and (iii) a dummy variable of whether the school received the textbooks within one month after they were made available in DEBS. The textbook delivery mechanism construct captures how the textbooks reach schools—whether schools collect the books from DEBS offices or from zone-center schools or whether DEBS transport the books to schools.

> 3The surveyor was able to check records for the Grade 2 textbook for 56 percent of the schools and storage records for 70 percent of schools that kept the books on the premises.

8
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39
_really useful in analyzing a trade shock_ , it must allow us to analyze the welfare effects along – the whole transition path including (i) announcement effects, (ii) impact effects, (iii) the path to the new steady state, and (iv) changes to the steady state. Our approach achieves this.

We thus combine a desirable feature of the neoclassical models (duality) with a desirable property of the search models (gross flows).

This is a simplified and more tractable version of a more general model presented in Cameron, Chaudhuri and McLaren (2007). The general version is stochastic, can accommodate any number of industries and geographic regions within the country under study, and has the virtue that its main parameters can be econometrically estimated, as described in that paper. Here, we focus on the implications of the model for trade theory in a simple two-sector special case with one type of labor, which is simple and intuitive to use, allowing us to derive _analytic results_ for effects of trade shocks on _lifetime welfare_ taking into account the full transition path to the steady state. A companion paper (Artu ̧c, Chaudhuri and McLaren (2008)) study the model through numerical simulations. Finally, Artu ̧c, Chaudhuri and McLaren (2010) estimate the structural parameters of the model on US data and simulate the adjustment process, while Artu ̧c and McLaren (2010, 2012) do the same for Turkish data and for sectoral as well as occupational mobility, respectively.

The main results from this paper are: (i) Gross flows of labor always exceed net flows, so there are always workers moving across sectors, even in the steady state.

(ii) Equilibrium is unique, and there is no hysteresis, despite the presence of an unavoidable fixed cost to switching sectors.

(iii) Wage differentials persist across locations or sectors even in the long run, despite the fact that there are always some workers changing sectors. In particular, cancellation of a sector’s tariff protection leads not only to a short-run drop in that sector’s wage relative to the other sector, but also to a (smaller but still positive) drop in its relative wage in the long run. Thus, _a frictionless model is not a good predictor for the steady state behavior of the model_ .

4
# 3.2 Household Demographics

The four refugee groups and host communities differ in terms of gender of household head, age, and gender composition. Overall, it is notable that there are more adult women than men from each of the refugee groups – except Sudan. At the same time the majority of refugees are children, meaning very high dependency ratios as well as childcare responsibilities. In general, host communities have a higher proportion of adults than the four refugee groups. All refugee groups except Eritreans have higher percentages of children than host communities.

The South Sudanese refugee household are distinct because more than 90% refugee households are headed by women, whereas the majority of Eritrean, Somali, and Sudanese refugee households are headed by men (Figure 2). This arises because only 6% of South Sudanese refugees are adult men (Figure 3). According to Pape et al. (2018), the missing men among South Sudanese could be partly explained by the fact that adult men may be more likely to join the conflict or be recruited by armed groups; indeed 30% of survey respondents identified recruitment by armed groups as the main factor for family separation. However, similar patterns were also observed among the Sudanese (World Bank Group, 2019).

Another important difference is that Eritrean refugees have more working age men (who could be fleeing conscription), and there is a higher adult share among Eritreans.

<!-- Start of picture text --> 60<br>50<br>40<br>30<br>20<br>10<br>0<br>Male Female Male Female Male Female Male Female Male Female Male Female<br>Refugee Host Community South Sudanese Somalis Eritreans Sudanese<br>Children (0-14) Youth (15-24) Adults (25-64) Elderly (>65)<br><!-- End of picture text --> Sources: Author Calculation using Skills Profile Survey (2017), Ethiopia and _Pape et al. (2018)_ Figure 3: Demographics of refugee population in Ethiopia 10