rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 7561

# **Abstract**

School dropout is a growing concern in Central America, and in Latin America as a whole, because of its consequences for economic productivity, the inclusiveness of growth, social cohesion, and increasing youth risks. This paper utilizes more than two decades of household survey data to construct a systematic overview of school dropout at the primary and secondary levels in Central America, including recent trends, causes and consequences, and evidence on what works to reduce dropout. Within each country, poverty, rurality, and indigenous group membership are the strongest correlates of dropout, reflecting several underlying factors that affect the marginal benefits and costs of staying in school. Global and regional evidence increasingly points to common policy priorities for reducing dropout across Central America, including addressing remaining gaps in access at the pre-primary and secondary levels, improving the quality of education at all levels, and strengthening the coverage, targeting, and coherence of existing programs aimed at improving education outcomes. However, additional rigorous evaluations, including cost data, are needed to identify the most effective specific approaches in each country.

This paper is a product of the Education Global Practice 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 madelman@ 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 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
period flood under current socioeconomic conditions, climate change may expose 38‐46% of the population, depending on the severity of sea level rise. Climate change impacts may make frequent events as important as rare ones in terms of exposure: for instance, a 25‐year flood under future climate conditions exposes more people than a 200‐year flood under current conditions. While poor districts are not found to be more exposed to floods at the national level, the city‐level analysis of HCMC provides evidence that 68‐85 percent of slum areas are exposed to floods, a higher percentage than the rest of the city. In addition to showing the benefits of investing today in flood protection, this paper provides policy implications for the design of flood risk management strategies in Vietnam.

# **2. Literature review**

In the last 30 years, floods worldwide have killed more than 500,000 people and resulted in economic losses of more than US$500 billion (Kocornik‐Mina et al. 2015). It is therefore no surprise that a number of studies have examined the population and economic assets exposed to flood risk. At the global level, it is well documented that an increasing share of the population and economic assets lie in areas exposed to riverine and coastal flood risk today, and these trends show no sign of slowing down (UN‐ISDR 2015; Ceola, Laio, and Montanari 2014; Jongman et al. 2014). To compound these socioeconomic changes, climate change is expected to intensify many hazards and further increase exposure: the number of people exposed to river floods could increase by 4‐15% in 2030 and 12‐29% in 2080 (Winsemius et al. 2015).

But only a handful of global studies have examined how this distribution of flood exposure differs between rich and poor. Kim (2012) assesses these dynamics at the country‐level, and finds that poor countries tend to be more exposed to natural disasters, including floods, compared to rich countries
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GRC<br>CHN BRA MEX SVK JPN<br>15 IND<br>10<br>0 10 20 30 40 50 60<br>GDP per capita (thousand USD)(2)<br><!-- End of picture text -->

- (1) 2004 for Argentina and Serbia and Montenegro.

- (2) Calculated using current purchasing power parities.

_Source:_ OECD (2008), National Accounts of OECD Countries - online database, February, IMF (2008), Government Finance Statistics, International Monetary Fund, December; World Bank (2009), World Development Indicators - online database, February; World Bank (2008), and FYR Macedonia - Public Expenditure Review, Report No. 42155-MK, February; Indian Ministry of Finance (2008), Indian Public Finance Statistics 2007-2008; CEIC database for China.

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); v) reliability of the selfreported information on social media; difficulties in applying statistical definitions of migration.

<mark>Using the case study of a data science challenge involving West African mobile phone data, Taylor (2016) argues that big data carries with it the dual risk of rendering certain groups invisible, and of misinterpreting what is visible a</mark> nd, in addition, raises concerns about the lack of information concerning the context of behaviors and activities ‘observed’ using big data analysis. Others have argued that the distance between the researcher and the research looms large for remotely generated data, posing challenges to validity and ground truth, as well as for the reliability and interpretation of research results (Kraly and Hovy, 2020). Operationally, while the potential for using such data to better understand migration dynamics has yet to be fully explored, integrating multi-topic household surveys with varied sources of big data requires not only addressing the significant technical and ethical challenges just highlighted but also the development of new methodologies that consider complex interactions over differing geographic and temporal scales (Franklinos et al., 2020). Furthermore, to leverage big data as a meaningful source of information for migration analysis, national statistical offices would need to work 34
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the last five years prior to the year the ES was conducted. The data source for the variable is WDI, World Bank.

We account for the education level of women relative to men. There are many ways in which greater education for women can increase their participation in the labor market. For example, improved access to education for women adds to their skills and creates awareness among them about available opportunities in the labor markets; it raises the cost to women of staying home; more educated women in society helps to change social attitudes towards women’s work, making women’s labor market activity socially more acceptable (World Bank 2011, Contrareras and Plaza 2010). This could spuriously affect our main results if maternity leave and women’s access to education happen to be correlated. We guard against this possibility by controlling for the level of education among females relative to males in the country ( _Education_ ). The variable is constructed in two steps using data from WDI, World Bank. In the first step, average values over the last five years prior to the year the ES was conducted are obtained separately for gross female to male enrollment rate in primary education and in secondary education. In the second step, a simple average is taken over the primary and secondary enrollment rates obtained in the first step.

The overall structure of the economy – share of manufacturing, agriculture and services sectors – could have direct or indirect effects on female employment. For example, a larger agricultural or services sector (relative to manufacturing) may affect female relative to male employment as women tend to be concentrated (more than men) in the services and agriculture sectors. If the share of the manufacturing sector also varies systematically with the level of maternity leave, our main results could be spuriously affected. We follow the literature<sup>5</sup> in

> 5 See for example, Wamboye and Seguino 2014.

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influential currencies such as the Australian dollar and the South African rand are used as target currencies.

**_Exchange rate volatility and domestic price inflation._** Cross-country data reveal that developing economies that allow large exchange rate volatilities are those with relatively high inflation rates. This is depicted in Figure 1 where exchange rate volatility (measured by the standard error of regression reported in Table 2) is plotted against the inflation rate (average of the log differences of monthly CPI series during January 1990 - December 1996). Since both exchange rate volatility and CPI data are needed to draw this figure, the number of developing countries is limited to 124. These economies are grouped into 6 regions: Africa, East Asia, South Asia, Europe, the Middle East, and Latin America.

Figure 1 clearly demonstrates that developing countries with high inflation rates tend to have high volatility of exchange rates. Many developing countries in Africa, Europe, and Latin America exhibit high inflation rates as well as large exchange rate volatility, although there are several exceptions. Developing countries in the Middle East tend to have both low inflation rates and small exchange rate volatility. Inflation rates of the East Asian emerging market economies are generally low, thus enabling them to achieve relatively stable exchange rates.

# **3. Explaining the Estimated Currency Weights**

**_Trade with the anchor country or the anchor currency area._** What determines the estimated G-5 currency weights in each developing country's currency basket? The hypothesis tested here is that the estimated currency weights are explained either by (a) the country's share of trade with the respective anchor country (direct-trade based share), or (b) the country's share of trade with the currency area formned by the respective anchor country (currency-area based

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It could be argued that the national weather observations that NMHSs share globally through the WMO coordinated Global Telecommunication System (GTS), which are then assimilated by global NWPs, also provide a public good by improving NWP performance. However, sharing these data primarily results in improved national outputs from the GPCs, thereby most benefitting the country making and sharing the observations. Some observations like upper air soundings may also generate regional benefits. However in this assessment these instances are not considered.

As the focus of this research lies on the supply side and the majority of the costs of GPCs occur in Stage 1, we focus primarily on these stages, however also including GPC costs for training RSMCs and NMHSs to utilize their products (Stage 2). Further, as we are only interested in the additional costs incurred when using forecasting products from GPCs, we must assume the existence of basic capacities under Stages 2-4. While as reviewed in Section 1.4 this should be viewed as a major caveat of this assessment, under the constraints of this research, it would in any case be difficult to extract global NWP-specific costs from Stages 2-4. It is recognized that this assumption is particularly bold in LICs, many of whom lack basic capacities in Stages 2-4.

The costs of GPCs to provide forecasting products to LICs were analyzed using the data gathered from GPCs as described in Section 2.4. Average data ranges of the marginal set-up and running costs of providing the full suite of global forecasting products to an additional country or region were calculated. To estimate the costs of providing global forecasting products to all LICs, these figures are multiplied by the total number of LICs (31 countries). This is a very conservative estimation (resulting in higher than expected costs), as it ignores for example economies of scale when products are provided to regions. The results are shown in Table 3.1.

**_Table 3.1_** _: Summary of costs under conservative assumptions, in 2014 US$._

|**
showed that land use regulations, such as rights of way, set backs, and the amount of land provided for community facilities, could cumulatively increase costs markedly, making housing affordable only for higher-income groups. Ultimately, this work suggested that projects that did not consider the broader land policy dimensions could become enclaves that would not be replicable.

For the Bank, these emerging lessons resulted in a new emphasis on policy and institutions as fundamental aspects of project support. While these emphases had always been an important aspect of Bank shelter projects, with the coming of the 1990s this aspect of support received particular attention in places such as Mexico and Russia, and resulted in the fourth type of housing assistance mentioned above -- policy-based loans.

# **Subsequent Research Findings on the Bank’s Strategy.** Since the 1986 review, the

findings of earlier research were subjected to additional scrutiny both by further research and by events.

# **Evidence on the Effects of Policy on Housing Supply**

At the time of the last review, in contrast to the path-breaking work of Reid (1962) on housing demand, little was known about housing supply. In part to support the Bank’s 1993 _Housing Policy Paper: Enabling Markets to Work_ more empirically based cross-country evidence on sector performance was accumulated. This data collection effort was initiated under a World Bank-UN Habitat exercise called _The Housing Indicators Program,_ which was initiated in 1989. This program addressed the dearth of comparable cross-country (or city) data on housing and urban development outcomes. In order to fill this gap, data were collected on basic urban and housing indicators for a sample of 51 cities in both developed and developing countries. This program provided the first empirical basis to analyze the 11
difficult to measure with accuracy and can be quite variable. However, in India there are many surveys on income and consumption of slum dwellers that have been conducted by universities and NGOs, so it should be possible for every major city to have a fair idea of income distribution within the slum community and how these incomes overlap with the rest of the population.

The distribution of income among urban households is indispensable to assessing housing policy. The distribution of income evolves constantly and in a different way in each city. Earlier, the establishment of the traditional income categories such as EWS, LIG, MIG etc. were an attempt to understand housing needs based on income. Unfortunately these categories have been used as “norms” to establish prospective benefits or even housing “rights” rather than to quantify demand and 33
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- [17] Hadri, K. 2000. “Testing for stationarity in heterogeneous panel data.” Econometrics Journal 3: 148-161

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and natural resource exports as a share of total exports. For this information, we used the World Integrated Trade Solution (WITS) database on the SITC, Revision 1.0. Natural resource exports comprise the commodities in the SITC sections 0 – food and live animals, 1 – Beverages and tobacco, 2- Crude materials, inedible except fuels (excluding 22 – oil seeds, oil nuts and oil kernels), 3 – mineral fuels, lubricants and related materials, 4 – animal and vegetable oils and fats, and 68 no ferrous metals (Lederman and Maloney 2007). The value of these exports (in US dollars at current prices) is then normalized by either GDP or total merchandise exports.

## **3.3.1 Natural resource exports as a share of GDP**

Natural resource exports as a percentage of GDP in the Sub-Saharan Africa region declined from 17 percent in 1990-94 to 13.2 percent in 2010-14. The decline in this ratio for oil rich countries is comparable to that of the region, although the share of natural resources in GDP is the largest across all country groups (about 43 percent in 2010-14). The share of natural resource exports in GDP for countries rich in minerals and metals had the largest decline (from 27 percent in 1990-94 to 16 percent in 2010-14). Finally, natural resource exports of non-resource rich countries remained stable over time at about 9 percent of GDP.

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with price changes less than 3 percent and overestimate the quantity changes for firms with growth in prices over 3 percent. This would imply that productivity change is underestimated for firms that experience a change in price of less than 3 percent and overestimated for firms that experience a change in price greater than 3 percent.

Table 2: Price Changes in Transport Equipment Industry

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||Price Dec|line|Price Incr|ease|Firm Data|WPI|
|Year|% of Firms|Mean|% of Firms|Mean|Std. Dev.|Industry Level|
|1990|6.06|-0.21|93.94|0.13|0.11|0.11|
|1991|13.64|-0.07|86.36|0.11|0.10|0.09|
|1992|6.06|-0.22|93.94|0.18|0.15|0.11|
|1993|7.77|-0.07|92.23|0.12|0.12|0.07|
|1994|32.52|-0.07|67.48|0.09|0.12|0.03|
|1995|27.52|-0.12|72.48|0.13|0.17|0.07|
|1996|24.54|-0.11|75.46|0.13|0.15|0.08|
|1997|25.00|-0.11|75.00|0.11|0.14|0.
. <u>Conversely, valuable information can be inferred from private (internal to the firm) sources.</u> As acknowledged by Hsieh and Klenow (2009), their approach relies on restrictive assumptions such as (i) CES aggregation of differentiated products within a narrowly defined sector (allowing to derive TFPQ in the absence of information on input and output quantities), and (ii) constant mark-ups within the same sectors. Under these conditions, any variation in TFPR is attributable to resource misallocation. However, heterogeneous mark-ups reflecting differences in demand and/or quality within the sector or measurement errors could also explain variations in TFPR.

Bils et al. (2017) propose a methodology that exploits the panel structure of the data to correct for potential measurement errors (see Appendix B). The authors build on the Hsieh and Klenow (2009) approach and allow for additive<sup>5</sup> measurement error in revenues and intermediate inputs. We apply Bils et al.’s (2017) framework to firm-level data available from four ASEAN countries: Indonesia, Malaysia, the Philippines and Vietnam. Table 1 provides an overview of the data sources used. For robustness, we exclude from the sample 4-digit sectors with fewer than 5 firms.

> 5 Assuming additive rather than multiplicative measurement error yields more conservative estimates.

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