rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 7887

# **Abstract**

Natural disasters have an impact on poverty through many different channels—economic growth, health, schooling, behaviors—that are difficult to quantify. It is nonetheless possible to assess the short-term impacts of income losses. A counterfactual scenario is built of what people’s income would be in developing countries in the absence of natural disasters. This scenario uses surveys of 1.4 million households in 89 countries. Depending on where they live and work, what they consume, and the nature of their vulnerability, the additional income that each household in the survey could earn every year on average in the absence of natural disasters is calculated. The analysis concludes that if all disasters could be prevented next year, 26 million fewer people would be in extreme poverty—that is, living on less than $1.90 a day. A systematic analysis of the uncertainty suggests that this impact could lie between 7 million if all the most optimistic assumptions are combined, and 77 million if we retain only the most pessimistic assumptions.

This paper is a product of the Global Facility for Disaster Reduction and Recovery (GFDRR) and the Office of the Chief Economist of the Sustainable Development Global Practice Group of the World Ban, and a background paper to the GFDRR and World Bank report “Unbreakable: Building the Resilience of the Poor in the Face of Natural Disaster.” 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 shallegatte@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
...................................<br>_65_|

# Index of Tables

|Table 1 Illustrative prior assertions tested against survey evidence ............. ............|....<br>26|
|---|---|
|Table 2 Reform Interventions and anticipated performance pay offs in the Bolivian<br>public sector .............................................................|31|
|Table 3 Reform Interventions and anticipated performance pay offs in Bolivia Centr<br>Administrations ...................................................|al<br>64|
|Statistical Table I Rule credibility indicator and components (East Caribbean) ..........|. 46|
|Statistical Table 2 Policy credibility indicator and components (East Caribbean) .......|. 46|
|Statistical Table 3 Indicator of resource adequacy and predictability and components<br>(East Caribbean) ............................................|46|
|Statistical Table 4 Indicator of overall institutional environment and components (
fully reduced form analysis at the household level (very much in the spirit of Dercon, 2004). In particular, it examines the effect of natural disasters on household consumption, using disaggregated (objective) disaster measures derived from geo‐ referenced meteorological data and controlling for some of the coping strategies. The overall objective of the study is thus twofold: 1) to develop and illustrate a methodology to construct more objective and disaggregated natural hazard and disaster maps from primary meteorological data; and 2) to illustrate a methodology to estimate the expected welfare loss associated with natural disasters based on such natural hazard and disaster maps.

By deriving natural disaster measures from historical weather data several of the methodological and practical challenges associated with subjective measures can be mitigated. Furthermore, though not pursued in this study, the hazard and disaster maps thus obtained can also be used for planning and targeting purposes. In estimating the welfare loss, an estimator which simultaneously accounts for heteroskedasticity, survey design (cluster effects), and spatial correlation is also developed. Though oft ignored, as the effects of natural disasters permeate through the economy, spatial correlation in consumption following a disasters is likely. Ignoring this may cause bias and reduce the efficiency of the estimation procedures. To be clear, an in depth examination of the channels through which the different disasters affect welfare is not aspired. Nonetheless, a limited exploration of the effects of disasters on intermediary outcomes is presented to facilitate an intuitive interpretation of the reduced form findings. Potential persistence of the effects and heterogeneity across key socio‐economic groups is also explored.

The empirical application is to Vietnam, which UNISDR (2009) ranked fourth in the world in terms of the absolute number of people exposed to floods; tenth in terms of the absolute number of people exposed to high winds from tropical cyclones, and sixteenth in terms of the absolute number
.|
|Kimberly Clark|5/21/92 *|Government action: fine for water pollution.|
|Grupo Bimbo|3/19/92 *<br>2/14/93|Black list of air polluter.<br>Government action: initiate court action.|
|Telefonos de<br>Mexico|5/21/19<br>6/9/94|Government action: warning about tree cutting.<br>Government action: fine.|

Given the nature of these results, we have pooled together government and citizens' complaints and tested whether or not they had a statistically significant differential impact on market values when compared to all other negative events. Results in Table 8 indicate that they strongly do.

23
(Figure 5.A). For example, Burundi imposes controls on the price of coffee while Benin imposes controls on cashew nuts. Contrary to some EMDEs, advanced economies do not impose price controls on exportable commodities.

# **Figure 5. Price controls on exported goods and financial products**

<!-- Start of picture text --> A. Share of countries with price controls on B. Number and percentage of Chart on interest<br>export goods rate ceilings<br>Percent of economies Percent of economies<br>26<br>SAR 3 (38%)<br>MENA 6 (29%)<br>24<br>EAP 7 (44%)<br>ECA 7 (23%)<br>22<br>LAC 14 (48%)<br>SSA 24 (50%)<br>20<br>LICs Other EMDEs 0 5 10 15 20 25 30<br><!-- End of picture text --> Source: World Bank; World Integrated Trade Solution (WITS); World Trade Organization A. 2017 data. Listed price control policies are retrieved from the latest (2003-19) country Trade Policy Review publication. Countries that rely heavily on a single export defined as a country in which exports of one or more 4-digit HS category represents 10 percent or more of its total exports in 2017. Chart shows the share of all LICs and other EMDEs that relying heavily on a single export whose price is subject to price controls. Sample includes 12 low-income countries and 61 other EMDEs.

B. Replication of Figure 1 of Maimbo and Gallegos 2014.

While not covered in the price control data set, the financial sector is also often a target of price controls. Around 60 EMDEs have imposed ceilings on interest rates (Figure 5.B). These measures are often motivated by a desire to provide targeted support to strategic industries or to shield consumers from financial exploitation. For example,
**Destination country characteristics.** Potential destination-specific drivers of variation in FDI concentration across country groups are examined in a similar framework, adding interactions of variables with a developing country group indicator to distinguish characteristics particularly relevant for this group. Specifically, in order to investigate the role played by country characteristic _xit_ , we estimate the following equation:

As in the previous equation, _Yit_ denotes our measure of FDI concentration. Our main variable _xit_ of interest is _Uncertaintyit_ as a country-specific measure of the overall risk experienced in economy _i_ and year _t_ , including geopolitical and economic events (see Ahir et al. (2022)).<sup>2</sup> In addition, _Zit_ denotes time-variant country-level control variables, including the logarithm of population, logarithm of GDP and the average exchange rate over the three years preceding the investment. The combination of country-year controls and the set of fixed effects is introduced to account for local economic conditions other than uncertainty (such as developments in GDP, population, exchange rates; as well as regional and global trends) affecting investment.<sup>3</sup>

# **4 Stylized Facts**

This section outlines two key stylized facts about the increasing concentration of FDI in developing economies.

**Fact 1.** _FDI concentration is on the rise, especially in developing countries_ . Based on observations of capital expenditure for greenfield FDI projects, Figure 1 reveals that FDI concentration has been increasing in developing economies, particularly since the global financial crisis.<sup>4</sup> This upward trend has even accelerated in recent years. FDI concentration in high-income economies has followed a U-shaped pattern, declining until 2013 and rebounding since then at an accelerating pace. The more pronounced trajectory of the concentration of investment in developing countries becomes apparent after taking into account the time-invariant characteristics of the countries (Table 1).

> 2The measure is intended to capture both the risk and ambiguity components of uncertainty. While risk reflects the (known) probability of an outcome
Policy Research Working Paper 10626

# **Abstract**

Hidden hunger, or micronutrient deficiencies, is a serious public health issue affecting approximately 2 billion people worldwide. Identifying areas with high prevalence of hidden hunger is crucial for targeted interventions and effective resource allocation. However, conventional methods such as nutritional assessments and dietary surveys are expensive and time-consuming, rendering them unsustainable for developing countries. This study proposes an alternative approach to estimating the prevalence of hidden hunger at the commune level in Madagascar by combining data from the household budget survey and the Demographic and Health Survey. The study employs small area estimation techniques to borrow strength from the recent census and produce precise and accurate estimates at the lowest administrative level. The findings reveal that 17.9 percent of stunted children reside in non-poor households, highlighting the ineffectiveness of using poverty levels as a targeting tool for identifying stunted children. The findings also show that 21.3 percent of non-stunted children live in impoverished households, reinforcing Sen’s argument that malnutrition is not solely a product of destitution. These findings emphasize the need for tailored food security interventions designed for specific geographical areas with clustered needs rather than employing uniform nutrition policies. The study concludes by outlining policies that are appropriate for addressing various categories of hidden hunger.

This paper is a product of the Poverty and Equity Global Practice. 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 fmulangu@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
# **Attracting Private Solutions and Participation in the Power Sector in**

# **Sub-Saharan Africa — Findings from a Survey of Investors and Financiers**<sup>**1**</sup>

Benedict Probst<sup>1</sup> , Richard Holcroft<sup>2</sup> , Joern Huenteler<sup>1,3*</sup> , Ani Balabanyan<sup>3</sup> , Andrew Tipping<sup>2</sup> , Peter Robinson<sup>2</sup> 1University of Cambridge 2Economic Consulting Associates 3The World Bank, Energy & Extractives

*Corresponding author: jhuenteler@worldbank.org; +1 202 458 8646.

**Keywords:** Private investment; investment risks; electricity; financial viability; developing countries; emerging markets

**JEL codes:** F21, L94, L98

1 Any views presented here are the authors alone and should not be attributed to the World Bank or any other person or institution. The authors are very grateful for financial support from the Energy Sector Management Assistance Program (ESMAP). The team is grateful for peer review comments from [Vivien Foster, Arnaud Braud, and Sudeshna Banerjee]. Any shortcomings are the sole responsibility of the authors.
to conduct rigorous empirical analyses despite the obvious shortcomings of official statistical systems in conflict-affected countries. It has been shown that nighttime light intensity is highly correlated with GDP in developing countries (Donaldson and Storeygard 2016, Henderson et al. 2012). In South Asia, this high correlation holds even at the subnational level (Beyer et al. 2018). It has also been documented that data from mobile phone records can be exploited to understand a large array of social phenomena from land use to wealth and asset distribution to traditional measures of firm activity (Pei et al. 2014, Blumenstock et al. 2015, Blumenstock et al. 2018), among others. And the land surface devoted to poppy cultivation has already been used as a direct measure of the size of illicit activities (Lind et al. 2014).

While there is no readily-available data source to assess economic fluctuations in the informal sector, these can be inferred from the other high-frequency series considered. Aggregate output is the sum of output by the formal, informal and illicit sectors, and there is a sense of the relative weight of these sectors 3
in a co-learning model.

# **Method**

# **Search Strategy**

We searched the existing literature on in-service teacher training in developing countries to identify a sample of training programs which had been evaluated in terms of the impact they have on student learning. The resulting sample would serve first to inform a review of what we know to date about the effectiveness of different kinds of in-service teacher training in developing countries. Secondly, we would use this sample of studies to inform the design of our survey instrument – by including questions about relevant characteristics either reported or noticeably omitted by studies – and to pilot the instrument in interviews with the program implementers.

Our inclusion criteria for the search are impact evaluations of primary education interventions in low- and middle-income countries that either (a) focused on in-service teacher training, or included this as one component of a broader program, and (b) which reported impacts of the program on student test scores in math, language, or science. We include both published and unpublished papers and do not explicitly restrict by year of authorship.

In order to identify papers fulfilling the above criteria, we searched 10 meta-databases through EBSCOhost: the Education Resources Information Center (ERIC), Academic Search Complete, Business Source Complete, Econlit with Full Text, Education Full Text (H.W.Wilson), Education Index Retrospective:1929-1983, Education Source, Educational Administration Abstracts, Social Science Full Text (H.W.Wilson), Teacher Reference Center and EconLit. We looked for articles containing the terms _("teacher training" OR "teacher education" OR "professional development") AND (learning OR scores OR attainment) AND (“impact evaluation” OR effects) AND (“developing country 1” OR “developing country 2” OR ... “developing country N”),_ where “developing country” was replaced by country names _._ 9
<br>_(0.05)_|.<br>_(0.05)_|.<br>_(0.03)_|.<br>_(0.04)_|.<br>_(0.13)_|.<br>_(0.73)_|

_Note:_ This table presents group averages and differences in means for households matched across the two waves of the panel data. Columns (1)-(4) present average and s.d. for each variable described for ULGSP LGAs and comparator, for each of the survey waves. Columns (5)-(6) present differences in means for the specific groups and the p-value of a t-test for equality of means (clustering at LGA-level) 46
poverty reduction in countries that still have some extreme poverty to begin with and (ii) small absolute changes in poverty where baseline poverty rates are low tend to be large in relative terms, mechanically pushing up the absolute values of the estimated elasticities.<sup>9</sup> The curated sample consists of 575 comparable spells between 1981 and 2021, based on 715 nationally representative household surveys for 89 countries representing over 92 percent of the population in the “developing world”. The regional distribution of spells in the curated sample is shown in Panel B of Table 1. Compared to the full sample of 1,604 spells, the largest changes are observed in the ECA and SSA regions. As a result of their low poverty rates, the weighted share of ECA in the sample drops from 35 to 20 percent, while the share of SSA increases from 24 to 36 percent. There are no substantial changes in the weighted distribution of spells by time period (Table 2). About 20 percent of spells started before 2000, 40 percent started in the first decade of the 2000s and another 40 percent started after 2010. The distribution of spells by type of welfare aggregate is more skewed towards consumption in the curated sample, with 70 percent of spells using consumption as a measure of welfare and 30 percent using income. In this sample, the average descriptive GEP with respect to survey means and GDP per capita amount to - 2.1 and -0.9, respectively, while the passthrough between growth in GDP per capita and growth in survey means is approximately unity (indicating that a 1 percent change in per capita GDP has on average been associated with a 1 percent change in household consumption as measured from surveys).

> 9 For instance, a reduction in poverty from 1.7 percent to 1.1 percent, though small in absolute terms, comes down to a relative decrease of over 35 percent, which will result in a large elasticity (absolute value). Richer countries therefore tend to
Investment concentration could lead to an overdependence on a small number of global enterprises that can erode some of the developmental impact of foreign capital in developing countries. The trend can undermine competition, either through reduced market entry, or through excessive market power that stifles domestic producers. Furthermore, the concentration of foreign capital into fewer MNEs can also reduce the diffusion of critical technologies in developing economies, either through more effective and diverse appropriability strategies that these firms regularly employ (Mezzanotti & Simcoe 2023), or the decrease in labor market dynamism (De Loecker et al. 2020) or weaker incentives for innovation in the rest of the economy (Philippon & Gutierrez 2018).

The remainder of this paper is organized as follows. Section 2 describes the data on FDI projects we use to establish patterns of concentration. Section 3 discusses the measurement and empirical framework. Section 4 presents selected stylized facts about the evolution of investment concentration over time, its main features and associations. Section 5 provides conclusions and policy implications.

# **2 Data**

**Greenfield FDI.** Cross-border “greenfield investment” refers to the construction of new production facilities or expansion of existing ones. It differs from mergers and acquisitions - the other major component of FDI flows - in that it involves the generation of new productive capacity and jobs. These two components account for the bulk of cross-border direct investment globally. Information on greenfield FDI projects is compiled by the _Financial Times_ in real-time into the commercially available _fDi Markets_ database. Observations are recorded at the project level, and include the sector and subsector of investment activity, estimates of capital expenditure invested, as well as expected job creation from this investment. The dataset, which includes an identifier of the parent company, the country of origin and destination, has global coverage: 175 source countries, 200 destination countries, and observations matched with 53 ISIC Rev. 4 industries at the 2-digit level of aggregation. Historical extracts of the database span the years 2003 to 2022.

**Mergers and Acquisitions
***|-0.758***|
||(0.227)|(0.226)|
|Fragile state|-0.522**|-0.567***|
||(0.211)|(0.207)|
|HIPC|0.180|0.425|
||(0.294)|(0.305)|
|N. of Obs.|74|74|
|R-squared|0.63|0.65|

Notes: Syria and Yemen are excluded from the analysis since they are currently facing armed conflicts. Standard errors in parentheses, * p<0.10, ** p<0.05, *** p<0.01.

Column 2 in Table 1 adds an index that captures the presence of laws that require the publication of debt statistics and key DM documents. While most effects remain robust, the estimated coefficient for Eurobond drops considerably and loses statistical significance. This suggests that both factors are aligned in terms of data disclosure requirements. Notice that the index on legal requirements only reflects the obligation to publish, but does not capture the depth of the mandate or level of enforceability.
|ECA||0.218<sup>**</sup>|0.077|0.473<sup>*</sup>|-0.003|
|---|---|---|---|---|---|
|||(0.035)|(0.532)|(0.054)|(0.983)|
|LAC||0.129|-0.154|0.083|0.026|
|||(0.188)|(0.306)|(0.489)|(0.817)|
|MENA||-0.069|-0.306<sup>***</sup>|-0.060|-0.145<sup>*</sup>|
|||(0.306)|(0.002)|(0.671)|(0.052)|
|SA||-0.289<sup>***</sup>|-0.299<sup>**</sup>|-0.473<sup>***</sup>|-0.195|
|||(0.004)|(0.016)|(0.010)|(0.127)|
|Observations|237|237|127|111|78|
|Countries|85|85|49|59|42|
|R-squared|0.061|0.135|0.153|0.216|0.180|

_Notes:_ * p < 0.10, ** p < 0.05, *** p < 0.01. p-values in parentheses. Estimated via OLS. Standard errors are clustered at the country level. The omitted categories for
. Blignaut, M. Horridge, S. Hess, R. Mabugu and M. Mabugu (2006). Searching for Triple Dividends in South Africa: Fighting CO2 Pollution and Poverty while Promoting Growth. The Energy Journal, Vol. 27 (2), pp. 113-141.

Williams, Roberton C., III, Hal Gordon, Dallas Burtraw, Jared C. Carbone, and Richard D. Morgenstern (2015). The Initial Incidence of a Carbon Tax across Income Groups. National Tax Journal 68 (1):195-213.

World Bank (2022a). World Development Indicator Database. <u>https://databank.worldbank.org/home Retrieved on August 25, 2022.</u> World Bank (2023). States and Trends of Carbon Pricing 2023. World Bank, Washington, DC.