rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 10699

# **Abstract**

This paper examines the socioeconomic factors correlated with vulnerability to natural hazards, using unique data from the High-Frequency Phone Surveys conducted in Haiti in 2021, 2022, and 2023. The results indicate a high overall exposure to hazards, with a significant number of individuals living in households facing the threat of multiple hazards. The analysis finds that disaster preparedness is generally low, with the poorest households experiencing the most significant challenges. Households in the bottom two wealth quintiles are less likely to have the necessary supplies to prepare adequately for and respond to disasters compared to those in the upper quintiles. Moreover, the level of education of the household head and access to the internet are found to be correlated with the likelihood of having better disaster preparedness. This suggests that higher levels of education and internet access play a significant role in improving preparedness levels among households. Overall, these findings highlight the importance of addressing socioeconomic factors when developing strategies to enhance resilience to natural hazards. By focusing on improving disaster preparedness among the most vulnerable households and promoting education and internet access, policy makers can mitigate the negative impacts of natural disasters on affected communities.

This paper is a product of the Poverty and Equity Global Practice and the Urban, Disaster Risk Management, Resilience and Land 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 gcanavire@worldbank.org; ncarrasco@worldbank.org; mcardonabotero@worldbank.org; and onsababera@worldbank.org. A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<br>G<
Policy Research Working Paper 11076

# **Abstract**

Biodiversity is essential for ecological stability, human well-being, and economic progress, providing critical ecosystem services such as clean water, food, and climate regulation. However, it faces unprecedented threats, with extinction rates accelerating to 1,000 times the natural baseline due to habitat destruction, overexploitation, pollution, invasive species, illegal trade, and climate change. Effective conservation requires urgent, coordinated global action, as ecosystems and species habitats often transcend national borders. Collaboration among governments, industries, and communities is essential to restore habitats, protect endangered species, strengthen policies, and enforce conservation measures. The challenges of biodiversity conservation are particularly acute in geopolitically sensitive and overlapping regions, including non-determined legal status territories, fragile and conflict-affected situations, and transboundary ecosystems. In these areas, effective conservation is hindered by weak policies, inconsistent enforcement, and institutional fragility. This paper addresses these challenges by providing baseline data to guide conservation strategies. Using newly developed World Bank species occurrence maps based on open-access, date-stamped records from the Global Biodiversity Information Facility, the study evaluates species richness, endemism, and extinction risks across 35 non-determined legal status territories, 19 conflict-affected countries, 20 fragile states, 18 marine joint regimes, and 311 international river basins. The data sets reveal that these regions host numerous, often vulnerable, species. Biodiversity conservation emerges as a pathway for trust-building and collaboration, aligning stakeholders around shared goals such as climate resilience and sustainable livelihoods. Reliable and comparable data sets are critical for evidence-based planning, fostering dialogue and cooperation among divided groups. The estimates presented in this paper aim to support robust, data-driven strategies to safeguard biodiversity in geopolitically sensitive and overlapping regions, with far-reaching implications for global conservation and international cooperation.

This paper is a product of the Development Research Group and the Development Data Group, Development Economics and the Environment Global Department. 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
0.159|0.891|12460|
|Percentage below-2 SD|0.283|0.029|0.016|0.989|12803|
|Percentage below-3 SD|0.137|0.028|-0.012|0.377|12803|
|**Panel B: Adults aged 40 and above**<sup>**b**</sup>||||||
|Elevated blood pressure<sup>c</sup>|0.363|0.020|0.058|0.454|10535|
|Self-reported hypertension|0.264|0.028|0.063*|0.071|15450|
|Prescribed medication for hypertension|0.893|-0.019|0.044|0.128|3741|
|Took medication in past 24 hours<br>|0.676|-0.034|0.039|0.292|3714|
|Use local RHC or HH<sup>d</sup>|||0.076**|0.041|2002|
|**Panel C: Recently pregnant women**<sup>**c**</sup><br>||||||
|Use local RHC of HH<sup>d</sup>|||0.064|0.133|2879|
|ANC at the primary level<sup>e</sup>|0.769|0.029|-0.132|0.995|5,590|

Note: data from household surveys. Unless otherwise noted, regressions are of the difference-in-differences specification with facility fixed effects. Standard errors are clustered at the district and survey round level with wild bootstrapping. All regressions include controls for catchment areas’ exposure to the collaborative quality improvement and citizen scorecards interventions, region, household wealth quintile, and
the country. Section 3 examines the recent evolution of commonly-used measures of bank concentration such as the share of assets held by the top five banks and the HerfindahlHirschman (HH) index and compares these measures for Russia to those for Brazil, China, and India. Section 4 reviews the regulatory and supervisory framework governing bank entry and operations as well as bank insolvency and exit in Russia. The objective of this section is to determine whether the regulatory and supervisory framework promotes contestability. Section 5 describes the methodologies and data used to obtain the Panzar and Rosse H-statistic and the Lerner index of market power. Section 6 reports the estimates for the Lerner Index and the H- statistic. Here we compare Russia to Brazil, China, and India and also investigate differences across groups of banks within Russia. Finally, Section 7 concludes and discusses some policy recommendations.

# **2. The structure of the Russian banking sector**

The Russian banking sector consists of 1,007 banks.<sup>**5**</sup> Despite the large number of institutions operating in the country, the Russian banking sector is fairly concentrated and dominated by state-owned banks. The largest 20 banks control approximately 70 percent of total bank assets and six government-controlled banks account for 52 percent of total bank assets and 60 percent of deposits. The largest government-owned bank, Sberbank, is also the largest credit institution in the country and accounts for 25 percent of total assets of the Russian banking system.

> **5** This number and most of the statistics in this section come from the following publications produced by the Central Bank of Russia: “Review of the Banking Sector of the Russian Federation,” February 2010, and the “Bulletin of Banking Statistics”, January, 2010.

7
- **Uganda:** Oil was discovered in 2006. Although production has yet to start, the government has borrowed in anticipation of future oil revenues. The public debt ratio has nearly doubled since 2007, reflecting loans from Chinese state banks and other lenders to finance large hydropower and other infrastructure projects. With production dates being postponed, infrastructure projects affected by cost overruns, and the current account deficit reaching over 10 percent of GDP in 2015, fiscal risks and external financing risks have increased (World Bank 2015f).

- **Mozambique:** The discovery of massive gas deposits in 2012 has lifted medium to longterm growth prospects. However, the sharp fall in oil and gas prices since 2014, delays in mining infrastructure projects and highly expansionary fiscal policies have generated major short-term challenges. Public debt ratios have risen sharply from 2007, to finance government infrastructure spending. But with finances under pressure, the country has turned to the IMF for financial support (IMF 2016).

# **IV. Data description and stylized facts**

The data set comprises proprietary data from MINEX Consulting on 960 global mining discoveries in copper and gold between 1950 and 2014. The copper discoveries data set contains data on 46 developing and advanced countries covering 273 discoveries. The gold discoveries data set contains observations for 73 developing and advanced economies, and a total of 687 discoveries.

In addition, data are available on the size and quality of the deposits (Tier 1, 2 or 3 deposits with Tier 1 deposits referring to the highest quality deposits based on criteria that include concentration of ore and size of find). These data are augmented by other data including country specific institutional and governance variables (democracy, fragmentation/conflict); macroeconomic variables (government debt levels, inflation, public sector and external balances) and other control variables (levels of GDP per capita) and World Bank Income Classification categories to identify countries that are low-, middle- or high income. It is important to note that although the data
6.2% appears reasonable, consistent with the average of private capital depreciation rates for low‐ and middle‐income countries used in the IMF FAD Investment and Capital Stock Database 2017.<sup>14</sup> In panel B of Table 1, we calibrate the initial capital‐to‐output ratio, K�/Y� at 2.560 assuming steady‐state properties<sup>15</sup> and based on 30‐year averages of the investment‐to‐GDP share (22%), GDP growth (4.1%),<sup>16</sup> and aggregate depreciation rate (4.5% in PWT 8.1). Taking this approach at least provides us with some basis of setting an initial K�/Y� that is in between the PWT values for 2010 (2.384 based on PWT 9 and 2.632 based on PWT 8.1) especially since the PWT 9 value puts Syria on the border of the 75<sup>th</sup> percentile of lowest K/Y ratios, and is below the respective averages of LMI countries, low‐income countries as well as MENA countries.<sup>17</sup> Using the IMF FAD Investment and Capital Stock Database 2017 to calculate the shares of public and private capital shows that the share of public capital �K<sup>G</sup> /K� is relatively high in Syria, accounting for 0.610 of total capital. This is higher than the average for LMI countries of 0.399, but closer to the average for oil‐based economies in the Middle‐East and North Africa (MENA) region, 0.506. Still, Syria appears to have the highest ratio among oil‐based economies that have fuel exports accounting for less than 60% of exports (for which, the average is 0.397).<sup>18</sup> Taking
# **4.2  Sampling, Randomization, and Program Description**

The school block grant (BG) program was evaluated as part of a larger education research initiative (across 500 schools) known as the Andhra Pradesh Randomized Evaluation Studies (AP RESt), with 100 schools being randomly assigned to each of four treatment and one control groups.<sup>25</sup> We sampled 5 districts across each of the 3 socio-cultural regions of AP in proportion to population. In each of the 5 districts, we randomly selected one administrative division and then randomly sampled 10 mandals (the lowest administrative tier) in the selected division. In each of the 50 mandals, we randomly sampled 10 schools using probability proportional to enrollment. Thus, the universe of 500 schools in the study was representative of the schooling conditions of the typical child attending a government-run primary school in rural AP. Experimental results in this sample can therefore be credibly extrapolated to the full state of Andhra Pradesh.

The school year in AP starts in mid June, and baseline tests were conducted in the 500 sampled schools during late June and early July, 2005.<sup>26</sup> After the baseline tests were evaluated, 2 out of the 10 project schools in each mandal were randomly allocated to one of 5 cells (four treatments and one control). Since 50 mandals were chosen across 5 districts, there were a total of 100 schools (spread out across the state) in each cell. The geographic stratification allows us to estimate the treatment impact with mandal-level fixed effects and thereby net out any common factors at the lowest administrative level of government, and also improve the efficiency of the estimates of program impact.

Since no school received more than one treatment, we can analyze the impact of each program independently with respect to the control schools without worrying about any confounding interactions. The analysis in this paper is based on the 200 schools that comprise the 100 schools randomly chosen for the school block grant program and the 100
_Pt_ is the aggregate price level at time t.

Demand for the products of each sector can be derived: Consumption of good i strictly decreases with its relative price, and strictly increases with real income. And the model assumes income elasticity for the four sectors satisfy the following: This assumption draws upon various estimates of sectoral income elasticities from existing literature (Comin, Lashkari, and Mestieri 2021; Young 2013; Aguiar and Bils 2015). As incomes rise, consumer expenditure patterns tend to shift from goods to services, reflecting a growing preference for experiences over material possessions. Food exhibits the lowest income elasticity, as even the wealthiest individuals have finite nutritional needs. Similarly, manufactured goods have a limited income elasticity, as the utility gained from owning multiple TVs, smartphones, or cars diminishes beyond a certain point.

The challenge lies in estimating the income elasticity for high skill services. First, a major portion of financial services, ICT services, professional, scientific, and technical services primarily cater to businesses rather than individual consumers, making it difficult to derive income elasticity from household expenditure surveys. As an economy’s real income level increases, businesses become more sophisticated, driving up demand for these services to enhance productivity. Second, households often consume such services indirectly through other products or services, making it challenging to isolate and accurately measure the expenditure on these services. Third, some of these services like legal and accounting services are purchased infrequently, such expenses often fall out of the regular expenditures that surveys typically focus on. Fourth, some digital services could be accessed for free or through non-monetary means (e.g., data sharing), which is not captured in traditional expenditure surveys.

By far the highest income elasticity is observed in non-tradable, low-skill services including housing, education, healthcare, tourism, and personal services. Aguiar and M. Bils (2015) estimated that income elasticity in US households is 0.4-0.5 for food, 1-1.5 for manufactured goods, 1.2-1
# **References**

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and the less educated (Ceritoglu, Tunculer, Torun, and Tumen, 2015; Del Carpio and Wagner, 2015). The economic effects of SUTPs not only vary across different segments of the labor market, there are also strong regional differences in their economic effects. Using synthetic modelling methods, Ozturkler and Goksel (2015) estimate the impact of Syrian refugees on local prices, wages, inflation, and services in 10 cities with large refugee populations. Some of the salient negative effects have been increases in rental prices, increases in inflation at border cities, illegal hiring by small business, and decreases in wages. However, in some cities (Gaziantep, Adana, Kahramanmaras, and Mardin), the presence of refugees has improved the trade balance, and economic activity in these areas are projected to increase as economic integration with MENA deepens. Orhan and Gundogar (2015) also note both positive and negative aspects of SUTPs.

A primary contribution of this paper is the estimation of poverty at the sub-national level and among population groups of interest. Since migration, geographic, and welfare variables do not exist in a single data set, imputation techniques are required to overcome these limitations and to compute household level poverty. The imputation of income poverty is done using the Turkish Labor Force Survey (LFS), and with information and modeling parameters determined from the Survey of Income and Living Conditions (SILC). More details and validation of this methodology is discussed throughout this paper.

While explicit identification of Syrians in available surveys is not feasible, there is evidence of an increase in the amount of foreign-born individuals that is being captured in the LFS. The arrival year of foreign-born migrants is available in the data which allows for identification of “Settled Migrants” and “Recent Migrants”. The latter is used as a proxy for Syrian refugees for the purposes of this paper. National official surveys that are conducted under-report the refugee population. Yet, since about 10 percent of Syrian refugees are in camps and the remaining are residing throughout the
||Peru|Chile|Malaysia|Romania|Ecuador|Colombia|South Africa|LAC Median|UMIC Median|HIC Median|
|Overall determinant index*|43.8|56.4|60.8|55.2|38.8|44.6|50.4|40.4|44.9|78.9|
|Innovation|3.6|11.7|23.7|13.0|7.9|6.5|11.2|4.0|6.6|40.2|
|Education|53.2|62.5|55.5|66.4|52.1|48.8|51.2|41.9|51.1|71.5|
|Market Efficiency|67.0|71.3|82.3|71.7|48.1|68.3|79.3|55.5|63.2|86.9|
|Infrastructure|42.4|52.8|60.4|53.7|43.4|46.6|46.7|45.4|48.3|68.9|
|Institutions|56.8|76.1|67.0|63.7|50.0|55.2|60.0|53.4|53.6|82.0|
|*The composite index of the subc<br>Scale from 1 to 100 (best) as of 2|omponen<br>019 excep|t indexes f<br>t for group|or the five ca<br>medians|tegories of|the determi|nants|||||

Convergence to HI country values in the Innovation and Infrastructure sub-dimensions produces the largest boosts to growth, followed by Education and Institutions, and lastly market efficiency. To assess the sensitivity of TFP
4.44|-25.91|14.01|31406|
|GDP per capita (constant 2015 USD)|3976.76|3208.01|302.07|14086.02|31406|
|_Panel C: Outages related variables_||||||
|Outages frequency (occurrences per month)|7.31|8.69|0.00|31.00|16821|
|Outages intensity (hours per occurrence)|4.45|6.98|0.00|96.00|16416|
|_Panel D: Performance related variables_||||||
|Log revenue-based TFP (TFPR)|2.46|1.50|-3.23|8.83|31406|
|Cost of inputs per unit of sales (2009 USD)|0.40|0.22|0.00|0.99|31406|
|Sales per labor cost (2009 USD)|11.71|21.32|0.11|646.50|31406|
|Capacity Utilization (%)|73.48|21.12|0.00|100.00|30714|
|Losses due to Outages (% of annual Sales)|8.12|11.85|0.00|100.00|12813|

Asiedu et al. (2021) find that the probability of facing power constraints is lower for state-owned firms with majority ownership. However, a high level of private participation in a firm could reveal significant levels of firm attractiveness or performance.

Further details on the firm-level variables can be found in the Appendix (Table 16).

# **4.2.2 Country-level data**

At the country level, we control the quality of regulation, financial development, bank concentration, foreign aid, inflation, economic growth, and the level of wealth. Indeed, Agostino et al. (2020) establish strong evidence that better
Njuguna Ndung’u, A. Morales, and Lydia Ndirangu, “Cashing In on the Digital Revolution” IMF Finance & Development, June 2016, Vol. 53, No. 2 Ouedraogo, Idrissa M. and Pascal T. Kouaman (2014) "Governance and private investment in Sub-Saharan Africa" _International Journal of African Development_ 2(1):5-25 Rajan, Raghuram G., and Luigi Zingales (1998) "Financial dependent and growth" _The American Economic Review_ 88(3):559-586 Samuel, Jonathan, Niraj Shah and Wenona Hadingham. 2005. “Mobile Communications in South Africa, Tanzania and Egypt: Results from Community and Business Surveys in Africa: The Economic Impact of Mobile Phones.” Vodafone Policy Paper Series, Number 3." Stiglitz, Joseph E., and Andrew Weiss, “Credit Rationing in Markets with Imperfect Information,” _American Economic Review_ 71(3):393-410 Syverson, Chad (2011) “What Determines Productivity?” _Journal of Economic Literature_ 49(2): 326-365 Wright, Richard, Erdal Tekin, Volkan Topalli, Chandler McClellan, Timothy Dickonson, and Richard Rosenfeld (2014) “Less Cash, Less Crime: Evidence from the Electronic Benefit Transfer Program”, _NBER Working paper_ no. 19996 18
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