rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 5965

# **Abstract**

Using data collected in rural Burkina Faso, this paper examines how children’s cognitive abilities influence households’ decisions to invest in their education. To address the endogeneity of child ability measures, the analysis uses rainfall shocks experienced in utero or early childhood to instrument for ability. Negative shocks in utero lead to 0.24 standard deviations lower ability z-scores, corresponding with a 38 percent enrollment drop and a 49 percent increase in child labor hours compared with their siblings. Negative education impacts are largest for in utero shocks, diminished for shocks before age two, and have no impact for shocks after age two. The paper links the fetal origins hypothesis and sibling rivalry literatures by showing that shocks experienced in utero not only have direct negative impacts on the child’s cognitive ability (fetal origins hypothesis), but also negatively impact the child through the effects on sibling rivalry resulting from the cognitive differences.

This paper is a product of the Human Development and Public Services Team, Development Research Group. 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://econ.worldbank.org. The authors may be contacted at akresh@illinois.edu, ebagby@mathematica-mpr.com, ddewalque@worldbank.org and harounan.kazianga@okstate.edu.

_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
_μ_<sup>0</sup> _si_<sup>=0</sup> 10. Following Hsieh and Klenow (2009), we set the capital shares for each industry equal to those of the United States as we suppose that the US economy is less distorted than Chile’s economy.

11. Most data on firm labor payments do not include labor benefits such as social security contributions. In the same manner as Hsieh and Klenow (2009), we scale each industry’s labor share by 3/2.

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If panel data is available, the joint and conditional probabilities defined above can be computed directly. If not, one can obtain estimates of these quantities by constructing synthetic cohorts based on repeated cross-sectional data. Using the data from BLSS 2017 and 2022, we estimate 2017 and 2022, as well as the mean squared errors and , using OLS on the following linear regression ββ̂ ββ̂ model: εεii,2017 εεii,2022 σσ� σσ� where _yij_ denotes the real per capita consumption in survey round _j_ for household _i_ , with _i_ = _1_ , ..., _n_ , and _j_ = _2017,2022_ . _xij_ is a vector of time-invariant household characteristics observed in both survey rounds, which include the age, gender and education of the household head, the district of residence of the household, and whether the household lives in a rural or urban area. Household heads’ ages are restricted to between 25 and 55 for the first period, and the age group is then adjusted for the second period according to the time elapsed between survey rounds ( _i.e._ household heads aged 30 to 60), so as to focus on a consistent group of households. The estimation of joint and conditional probabilities rests on the assumption that the population underlying the 2017 and 2022 BLSS samples is the same. If verified, this assumption allows to exploit the time-invariant household characteristics used to define the cohorts as the connectors of household consumption between the two periods. This is a strong and crucial assumption in the context at hand, due to the unprecedent shock generated by the COVID epidemic. In particular, we are concerned with any migration from rural to urban areas (between 2017 and 2022, the share of population living in rural areas decreased from 59.8 to 56.3 percent; Amendola et al. 2023). Even though the Monte Carlo simulations presented by Dang and Lanjouw (2023) show that results remain reasonably robust when this assumption is violated, we extend their framework by using a simulation based
reflecting mainly the steady increase in the country's relative wealth (especially since the early 1980s). In contrast, persistent current account deficits have resulted in an increasingly large debtor position for the **U.S** after the mid 1980s.

# **_5._ Conclusions**

This paper proposes to consider external sustainability from the perspective of equilibrium in net foreign asset positions. According to this approach, an external situation is sustainable if two conditions are met. First, in the long run, international and domestic investors achieve their desired portfolio allocation of assets across countries. And second, in the short run, the current and capital-account flows are consistent with the reallocation of international capital to achieve the investors' desired stock positions.

Based on a standard Markowitz-Tobin model of portfolio diversification, the paper develops a reduced-form model of net foreign asset positions. The model yields a long-run equilibrium condition in which the ratio of **NFA** to the total wealth of domestic residents is a negative function of investment returns in the country relative to the rest of the world, a positive function of investment risk in the country relative to the rest of the world, and an inverse function of the ratio of foreign-owned to domestic-owned wealth. This long-run equilibrium condition guides the empirical analysis of the paper.

In order to estimate the equilibrium condition, the paper uses a newly constructed data set of foreign asset and liability stocks for a large group of industrial and developing countries spanning the period from the 1960s to the present (see Kraay, Loayza, Serv6n, and Ventura **1999.)** With these data and estimates for the total capital stock in each country, the paper obtains a measure of the wealth of domestic residents in a country. In addition, the paper develops summary _measures_ of country returns and risks. These are based on a comprehensive set of macroeconomic, policy, and institutional variables.

The econometric methodology of the paper is an application of the Pooled Mean Group estimator recently proposed **
percent price discount compared to other areas, and that this discount can be five times higher in areas of higher flood risk and less resilience. ́Alvarez and Resosudarmo (2019) measure the correlation between the water level of floods in Jakarta in 2007 and housing rental prices and find that a 10 percent increase in the flood depth is associated with 1.24 percent lower rents. Using the same data sources as this study, Erman et al. (2020) and (WB, 2017) assess the relationship between rents and reported flood exposure in Accra, Ghana and Antananarivo, Madagascar respectively. Erman et al. (2020) identify a 27 percent price discount on rent among flood-affected households in Accra and (WB, 2017) find that flood-affected households pay 15 percent lower rents in Antananarivo, Madagascar.

Most of the literature uses hazard maps to identify flood plains or high-risk locations. For instance, Bin and Polasky (2004) look at both location within a floodplain and location outside a floodplain in Pitt County, North Carolina, US which was severely impacted by the major floods that followed hurricane Floyd in 1999. They find that properties located in floodplains were cheaper than equivalent properties located in other areas, and that the price discount in floodplains was significantly larger after the hurricane.

This study uses self-reported data from household surveys to assess the willingness to pay to avoid floods. The analysis relies on four household surveys conducted between 2016 and 2018 in four SSA cities that are the most economically and politically relevant in their countries: Accra, Antananarivo, Dar es Salaam, and Addis Ababa. Flood risk is captured in two ways: by having reported being affected by a flood in recent years and respondents’ perceived risk of future flooding. The use of household survey data provides an opportunity to expand the use of the hedonic pricing approach to evaluate the benefits of urban resilience and infrastructure investment in countries that rely heavily on household survey data and where hazard risk and property value data may not be readily available. The household survey data
In early 2021, the predominant reason for refusing vaccines was that the respondent did not trust vaccine producers. Although this remained a significant concern in all three countries, much more striking is the rising prevalence of the response that “counterindication” was the reason a respondent did not plan to be vaccinated. By August 2021 this accounted for the plurality of vaccine hesitant people in all three countries. The rise of counterindication is not solely an effect of composition, it also continuously rose in absolute terms in Uzbekistan and Kazakhstan. See annex 2 for more details on counterindication patterns in Central Asia.

Among those saying they will not vaccinate in Kazakhstan and Tajikistan, those who cited counterindication were the “stickiest” in their responses, in the sense that they were consistently the least likely to change their response. In Uzbekistan only the “other” category is stickier, and in Uzbekistan, many people providing “other” responses describe ineligibility due to various health criteria that are not formally counterindication restrictions on vaccination. For instance, a common response in the other category was the (mistaken) impression that vaccines were unsafe for people over age 60 or 65.

The prevalence of counterindication as a reason for vaccine hesitancy in Central Asia is likely linked to official guidance (please see annex 2 for more details). In Kazakhstan, Uzbekistan, and to a limited extent Tajikistan, vaccination programs explicitly excluded some at-risk groups, with public communications campaigns often listing these conditions explicitly. Eligibility restrictions were primarily based on perceived risk arising due to pre-existing health conditions. However, the guidance in some cases strongly contrasted with conventional practice elsewhere in the world where members of these same at-risk groups are often targeted for early vaccination with the same vaccines.<sup>8</sup>

# Methods

Our survey experiment was embedded in the Listening to Central Asia series of surveys in Tajikistan, Uzbekistan, and Kazakhstan, surveys that are designed to continuously monitor wellbeing and views on topical policy issues.<sup>9</sup> The surveys are nationally
include the UNICEF WCARO tool,<sup>4</sup> CARICOM costing model,<sup>5</sup> Estimator Cost Analysis Tool,<sup>6</sup> Brookings Childhood Cost Calculator,<sup>7</sup> ILO Care Policy Investment Simulator,<sup>8</sup> and UNESCO’s SDG 4.2 costing model.<sup>9</sup> For the case of Pakistan, UNICEF’s ECE Accelerator Costing and Simulation Model was preferred over other available models because it is open source, easy to adapt, and allows the planners and decision-makers to use the model iteratively.

The ECE accelerator simulation model was developed in 2021. Governments have been using the tool for ECE sub-sector planning. For example, Lesotho used the results of the simulation exercise to undertake focused advocacy with the government and public representatives to increase funding for ECE. The resulting analysis suggested that universal expansion would not be feasible given available resources. The government instead opted to support the expansion of one year of ECE. The costing exercise also resulted in leveraging additional funding from development partners for ECE.

# **Data and Methods**

## The approach

This paper analyzes data from the Pakistan Bureau of Statistics (2017 Census)<sup>10</sup> and the provincial Education Management Information Systems<sup>11</sup> on the population size, enrollment rates, and achievement rates (dropout and retention rates) in ECE. Data on existing infrastructure, human resources, ECE and education financing, and unit rates for key inputs such as teachers and basic ECE supplies were also collected from each provincial government. These inputs were categorized under 3 heads: salary, non-salary (such as payment of electricity bills), and development (such as construction of classrooms). Policy options and targets for ECE were then defined based on best practices and in line with national and provincial policies and implied service standards. Enrollment estimates from the PSLM
before January 2015. To address these concerns, we check for such instances through the _Global Trade Alert_ data, which records both trade restrictive and trade liberalizing measures for all countries since 2009.<sup>10</sup> To link the NTM dataset to our panel of manufacturing plants, we follow the literature on trade reforms.<sup>11</sup> We build two types of variables. The first is an NTM output measure,

> 8Given that our focus is on the effect on imports of NTMs imposed by the Indonesian government, we exclude from our analysis bilateral contingent measures and measures imposed by the exporting country.

> 9In those cases we attribute the date of entry into force of the measure as that of the first regulation. 10 The dataset can be accessed at its own web page.

> 11Our methodology closely follows Amiti and Konings (2007) and De Loecker et al. (2016).

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# **4. Education and Employment**

The potential contribution of education to improving labor productivity, lowering income inequality and promoting growth and development is well recognized, and virtually all governments set aside a substantial portion of their public expenditure budget for education. Since independence, the nations of sub-Saharan Africa have invested heavily in the education sector. The achievements in the sector have been impressive both in absolute terms and in relation to other sectors. However, primary enrollments have stagnated or even declined recently, and the quality of education has apparently declined. These reversals have occurred in an era of mounting fiscal austerity, population growth and strained political climate, all of which have hurt the education sector, and the deterioration in educational services have exacerbated the regions' economic and social problems. Between 1970 and 1980, while the world's population grew at an average annual rate of 1.9 percent, Africa's population grew at 2.9 percent (World Bank, 1993, P. 18), and is projected to grow even faster at a rate of 3.2 percent a year, while growth rates are declining in other parts of the world. Africa's rapid population growth creates serious problems for education. To keep pace with the growth of school-age children, more schools, teachers, books etc. are required.

Extensive experience from Africa and elsewhere provide strong evidence that increased investment in education can yield broad economic benefits-including higher incomes and lower fertility. It can accelerate the growth process, and act as a complement to other growth inducing factors. However, public expenditure on education, both absolutely and as a percentage of GNP has fallen in many countries. The economic evidcnce based on micro and macro-level data indicate significant returns to education. The micro-level analysis examine the relation between an individuals education level and his/her productivity in the labor market as a wage worker or self-employed worker; or by linking the education of individuals to the outcomes of household behavior, such as fertility, child mortality etc.
._044<br>(0_._002)|0_._004|

_Notes_ : Indicators extracted from EMIS 2017. The value displayed for t-tests are the differences in the means across the groups. Robust standard errors are reported in the second row (in parentheses). ***, **, and * indicate significance at the 1, 5, and 10 percent critical level.

Table 2: Balance: Impact Evaluation Sample: Treatment vs Control

|Variable|N|(1)<br>Control<br>Mean/SE|N|(2)<br>Treatment<br>Mean/SE|T-test<br>Diference<br>(1)-(2)|
|---|---|---|---|---|---|
|Number of Toilets|184|14_._973<br>(0_._734)|202|13_._267<br>(0_._530)|1_._705_∗_|
|Pupil Classroom Ratio|182|107_._964<br>(4_._526)|201|105_._360<br>(4_._364)|2_._604|
|Pupil Teacher Ratio|184|75_._928<br>(1_._984)|201|77_._714<br>(2_._343)|_−_1_._786|
|Enrollment Grades 1-4|184|618_._212<br>(31_._354)|202|577_._119<br>(27_._025)|41_._093|
|Enrollment Grades 5-8|184|316_._685<br>(21_._046)|202|285_._287<br>(17_._675)|31_._398|
|Repetition Rate|184|0_._258<br>(0_._008)|200|0_._248<br>(0_._008)|
the Rosstat’s official estimates are slightly higher), the trends are quite similar as shown in the next section.

Using the LiTS, informal workers are identified as those without a written contract in their main job. The 2016 survey contains a question that asks whether the individual had a written contract for the primary job, with possible responses including permanent with/without contract, temporary with/without contract, seasonal with/without contract, daily laborer and other. In previous survey rounds, the relevant question asked only whether an individual had a written contract in her main job or not. We define a worker as formal if she has a contract in her primary job, whether permanent, temporary or seasonal, and informal otherwise.

Using the ESS, informal workers are identified as those without a contract in their main job. Among employment-related questions, the survey asks whether the individual held a contract in the main job, and whether it was of unlimited or limited duration. We define a worker as formal if she held a contract in her main job, regardless of duration of contract, and informal otherwise.

# **Recent evolution of informal employment in Russia**

In this section, we proceed to estimate levels of informal employment and examine trends across surveys. We are able to extend the trend using recent estimates of informal employment as fortunately all data sources are updated through 2016. This allows a close comparison of trends over time across different surveys. For each survey, we focus on informal employment in the main job as described in the previous section. We observe a long-term increase in informality from the early 2000s (and even before that based on RLMS data, though not shown here) through 2016, with a short period of stagnation or even decrease in the second half of the 2000s. Encouragingly, this trend appears to be consistent across different data sources, including the official measure of informal employment monitored by Rosstat, which is presented alongside our estimates for comparison purposes. This result serves as a robust
groups of countries (for example, Amu et al. 2022; Barasa et al. 2021; Wang, Temsah, and Mallick 2014; National Population Commission NPC/Nigeria and ICF International 2014).

For LMICs where data was available from either the Demographic and Health Surveys or the OECD, close to half of the total population now reports that they have health insurance (Table 1). Coverage is nearuniversal for several countries in East and Central Asia and Latin America and middling in India and some Sub-Saharan African countries (Namibia, Sudan, Rwanda, Ghana). Other countries in Sub-Saharan Africa and South Asia are still below 5 percent coverage. Interestingly, most of the growth in coverage, when it occurred, is very recent. For instance, health insurance coverage in Rwanda increased from 41 percent in 2005 to 83 percent by 2019, from 56 percent in 1998 to 88 percent in 2013 in <mark>Türkiye</mark> and from 40 percent in 2012 to 61 percent in 2017 in Indonesia. In a historical context, this expansion is quite rapid as European 4
Table 3: Characterization of transient and chronic poverty in households from China over six years, 1985-1990.

||Chro<br>(mean ex<br>pov|nically poor<br>penditures below<br>erty line)|Transiently poor<br>only (mean<br>expenditures above|Never poor|
|---|---|---|---|---|
||Always<br>poor|Not persistently<br>poor|poverty line)||
|Full Sample|6.21|14.38|33.38|46.03|
|Guangdong|0.40|1.04|18.31|80.25|
|Guangxi|7.12|16.07|37.38|39.43|
|Guizhou|11.90|21.20|40.17|26.73|
|Yunnan|4.88|18.04|35.55|41.53|

Notes: Adapted from Jalan and Ravallion (1999), table 2.

Other panel evidence shows similar transitions in and out of poverty. A summary of research using panel data that matches poverty data in households with at least two observations in Baulch and Hoddinott (1999), reproduced here in table 4, shows that the fraction of households which have experienced an episode of poverty is at times much larger than either those who never experienced such an episode or those who were persistently poor. This is consistent with our findings that levels of vulnerability to poverty are much higher than poverty rates themselves.

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# **3.2 Exchange rate Management in India since the early 1990s—a steady move towards more flexibility**

India’s exchange rate regime has become progressively more flexible over time. After the breakdown of the Bretton Woods System in 1971, the rupee was initially pegged to the pound sterling, and then from 1975 with a basket of currencies. Following the balance of payments crisis in 1991 the exchange rate was depreciated twice in quick succession, by 9 per cent and 11 per cent on July 1, 1991 and July 3, 1991 and was floated subsequently. Even though officially the exchange rate was floated in 1993, in practice it continued to be managed. As per the Reinhart and Rogoff’s defacto classification of exchange rates, as well as the IMF’s classification, India’s exchange rate is broadly characterized as a pegged or managed float from the early 1990s until late 2000s, and as floating since 2010, Appendix Table 1. Increased flexibility of India’s exchange rate regime over time is confirmed in monthly percent changes in Figure 9 below as well as in higher frequency data (which we have not presented here for brevity).

**Figure 9: Monthly percent change in Nominal Exchange Rate confirm defacto Managed Float until late 2000s, and increased flexibility since 2008**

<!-- Start of picture text --> De facto peg and De facto crawling band<br>25 two devaluations De facto crawling peg/ that is narrower than or<br>managed floating equal to +/-2% Floating<br>20 (managed Floating)<br>15<br>10<br>5<br>0<br>‐5<br>‐10<br>Source: RBI Handbook of Statistics on the Indian Economy<br>% Chg. m-o-m<br>Jan‐91 Oct‐91 Jul‐92 Apr‐93 Jan‐94 Oct‐94 Jul‐95 Apr‐96 Jan‐97 Oct‐