rafmacalaba/data-use-mentions-extended huggingface dataset:
Finally, our paper sheds new light on the implications of multiple, overlapping disasters and the challenges they pose to disaster risk management. The COVID-19 pandemic had major implications for the financial wellbeing of households, particularly those whose income was earned in close-contact professions. We document that deteriorating financial circumstances during the COVID-19 pandemic continued to result in reduced disaster risk preparedness even after the peak of the pandemic. The findings underline that addressing disasters will become increasingly complex as climate change will lead to a more volatile and riskier environment.

# **Acknowledgments**

We thank the World Bank’s LAC High Frequency Phone Survey team for leading the design of the survey, coordination and helpful advice. We thank Sergio Rivera for research assistance. We thank _Sistemas Integrales_ and their national partners for implementing the survey and all respondents for the time they took to answer our questions. We thank our development partners for their inputs on the disaster risk data collection module developed for this project. We gratefully acknowledge financial support from the European Union in the framework of the Caribbean Regional Resilience Building Facility, managed by the Global Facility for Disaster Reduction and Recovery (GFDRR) and from the Canada-Caribbean Resilience Facility (CRF). The sole responsibility of this publication lies with the authors. The European Union, Canada, and the World Bank are not responsible for any use that may be made of the information contained therein.

# **Declaration of interest**

The authors are not aware of any financial or personal relationships with other people or organizations that could inappropriately influence or bias this work.

# **Data availability statement**

The microdata used in this paper is publicly available through the World Bank Microdata <u>Catalogue</u> for four of the five countries: Belize (https://doi.org/10.48529/6zwy-a603), Dominica <u>(https://doi.org/10.48529/3sf6-h006), Haiti (https://doi.org/10.48529/dj1x-q178
uses Enterprise Survey observations for 14 countries and hydro‐power variation as an instrument to estimate that reducing the outages to a level comparable to that of South Africa would increase sales of firms in Sub‐Saharan Africa by 85%, a number that rises to 117% if firms do not own generators (Cole _et al._ , 2018). For 23 countries, a recent study by the World Bank (2019) estimates that a one percentage point increase in power outage frequency reduces firm‐level output by 3.3%. Furthermore, the same increase reduces firms’ share of sales from exports by 0.12%. Finally, low power reliability makes it impossible for countries to host certain high‐growth industries such as large data centers, thus further foregoing economic potential.

As the reliability of electricity supply significantly influences the production possibilities of firms, it also dynamically influences their longer‐term choices. Aterido and Hallward‐Driemeier (2010) show that for African firms, increased occurrences of power outages have a negative effect on employment growth – especially in the case of large firms. Mensah (2018) finds a negative effect of electricity shortages on employment in 21 countries in Africa that is substantial in size: overall, electricity outages decrease the probability of employment for an individual by 35% to 41%. Impact channels for this link include the discouragement of entrepreneurship by shortages, lower productivity and output by businesses, and reduced international competitiveness of firms. Indeed, a one percentage point increase in outages is estimated to reduce employment by 1.1% in the case of low‐skilled workers, and 0.35% for high‐skilled workers in a study considering a sample of 23 African countries (World Bank, 2019).

At the country level, Allcott et al. (2016) similarly use hydroelectric power supply as an instrument and estimate that electricity deficits reduce Indian manufacturing firms’ revenues by 5% to 10% on average. They do not, however, find that shortages
the regions whose schedules we have access to. The total tax liabilities for Amhara, for example, are much larger than for SNNP. The taxes also vary within each region depending on the availability of irrigation (Oromia), whether the land was used for specific highvalue crops (SNNP), and whether it is a Productive Safety Net Program (PSNP) ward (Amhara) (Table 1). In the Afar Regional State, landholders are required to pay land tax according to whether they use the land for grazing or crop cultivation (ANRS 2009). In Benishangul Gumuz region, taxes are assessed on the area of land, fertility of land, weather, and suitable infrastructure (BGRS 2010). There is no tax exemption for smallholders.

**Table 1: Features of the Rural Land Use Fee and Agricultural Income Tax**

||**Amhara**|**Oromia**|**SNNP**|
|---|---|---|---|
|_Taxes are assessed based on:_|Total land area. Taxes are|Total land area. Taxes are|Total land area. Taxes are|
||lower for Productive Safety|higher for farmers who use|higher for banana, coffee,|
||Net Program (PSNP)<br>_kebeles_ (wards)|irrigation|chat, apple, and pepper<br>plantations|

_Source_ : Amhara Regional State, Proclamation (No.161/2001), Proclamation to Amend Rural Land Use Payment and Agricultural Income Tax of Oromia Regional State’s Proclamation (No.99/2005), SNNP Regional State, A Revised Proclamation to provide for rural land use fee and agricultural activities income tax (No. 122/2008).

The average per hectare tax rates for Amhara, Oromia, and SNNP regions (calculated from the tax schedules) are regressive; they generally decline the larger the total landholding (Figure 1). Farmers with less
to identify the starting point then the track to confirm the path taken. Grid and segmentation can both use waypoints to confirm points are within selected areas, and it may also be possible to use tracks to confirm the listing process if strict protocols are used (ie. Start in the NE corner and continue clockwise) though the intersection of the interviewers paths may make results less clear.

# _4.4 Replacements_

Due to high transportation costs, most surveys in the developing world use replacements for non‐ response due to refusals or out of sample selections. This is done either through selecting additional households from the PSU listing exercise, as is recommended in the World Bank’s Living Standards Measurement Study (Grosh and Munoz, 1996), or selecting a neighboring structure based on field protocols, such as selecting the dwelling immediately to the right (Lowther et al, 2009). While replacements for out of sample selections with new random points does not introduce bias, it is inefficient and increases costs. For non‐response due to refusal, it is likely to be non‐random, and therefore replacements will create at least some degree of bias in the data. The reason and method for the replacement may influence the degree. If refusals tend to come from households in the highest and lowest wealth quintiles, as the opportunity cost of their time is high, and replacements come from the main part of the distribution, the use of replacements will attenuate the variation in the sample. This may cause the results to underestimate measures such as inequality that depend on accurately capturing the extremes of the distribution. When using a replacement method that uses near neighbors, if structures
Source: Ferri and Liu (2009).

103. The World Bank Enterprise Survey conducted in 2004 provides further evidence for the concentration of lending. While among large firms, 65 percent had a loan from a bank or financial institution, only 32 percent of medium-sized firms and 20 percent of small firms had a loan. Reflecting this lack of bank finance, many of the non-state sectors face a high degree of credit constraints and have to resort to the informal lending market. Also, survey data from the IFC for China (Investment Climate Surveys Draft Country Profile-2003) show that SMEs are less reliant on banks for financing and more reliant on informal sources.

104. The highly concentrated financial structure also artificially lowers the cost of credit and capital, acting as a subsidy to those enterprises with access to financial services (Table 1). The subsidy is mainly financed by people who hold deposits in financial institutions but cannot 66
country-specific time trend in reading achievement, using data from all PIRLS rounds available for this country. Column 5 provides similar results, but additionally adjusting for student age and grade. However, these adjustments are made using country-specific data only and not using information on age or grade effects from other countries’ data.

Table A2 provides model-based estimates of the learning loss for all ECA+ countries. The first column compares 2021 results to those from the previous rounds. The second column shows the estimate of linear trend departure, with the linear trend fixed at the same value for all ECA+ countries. These estimates are based on strong assumptions that there is no trend in the data or that the trend is the same for all countries. Column 3 reports similar estimates to those reported in Table A1 in column 4 but testing whether the change is statistically different from zero. Finally, column 5 provides results controlling for the country-specific time trend but also for age and grade effects estimated for all ECA+ countries.

Table A3 provides calculations for EU countries that did not participate in PIRLS 2021. Learning loss is estimated for these countries considering the UNESCO data about the number of weeks with full closure and taking the average EU estimates from Table 1 for the overall decline and the decline associated with one week of closures.

_Within-countries differences in the learning decline_ Some research has shown that boys suffered larger learning losses during the pandemic. However, interactions of the COVID-19-related effects for boys show no difference in the decline of achievement by gender. In general, we found no evidence of gender differences in the impact of school closures on reading achievement of 4<sup>th</sup> grade students in EU or ECA+ countries.

However, as expected, quantile regression results show that lower-achieving students experienced much larger losses. Considering principal-provided data, presented for the EU countries on Figure 1A, students at the 1<sup>st</sup> achievement decile (lowest achieving students) in schools affected for more than eight weeks suffer a loss
# **1. Introduction**

Subjective measures of well-being can capture dimensions of welfare that go beyond a narrow focus on consumption or income (Layard, 2005). There is increasing interest among academics and policymakers in developing measures of development and societal progress that capture the multifaceted nature of well-being<sup>2</sup> , in order “to shift emphasis from measuring economic production to measuring people’s well-being” (Stiglitz, Sen and Fitoussi, 2009).<sup>3</sup> A growing literature has established strong correlations of measures of happiness or satisfaction with life with income, health, marriage and employment.

Yet, issues of salience<sup>4</sup> , framing and scaling of subjective well-being measures, as well as the challenges in interpreting various types of well-being measures remain and need to be given careful consideration when employing such data to measure well-being over time, across space or population groups, or for policy evaluation. Ferrer-i-Carbonell (2005) notes that unobserved factors like personality traits can have a significant effect on subjective well-being. Lucas and Diener (2008) find that “correlations between subjective well-being and personality characteristics such as extraversion and neuroticism are stronger than correlations with any demographic predictor or major life circumstance that has been studied so far.” Also, Kahneman and Krueger (2006) note that subjective well-being measures have been found to be affected by the weather or trivial events such as finding a dime, and that in studies that collected repeated measures of subjective well-being over short periods of time these exhibited lower correlations across test-retest rounds than other variables like levels of education or earnings (Schwarz and Clore 1983, Lucas, Diener and Suh 1996). Last, Deaton (2011) cautions us that the ‘best possible life’ evaluations in the daily Gallup Poll appear to be affected more by Valentine’s day than by the doubling of unemployment in the United States.<sup>5</sup> On the other hand, subjective well-being measures have
|Variables|Povertyrate|
|---|---|
|Share of population <1 hour to a town|0.059*<br>(0.103)|
|Share of population 1-2 hours to a large city|0.080**<br>(0.026)|
|Share of population 1-2 hours to an intermediate city|0.090**<br>(0.012)|
|Share of population 1-2 hours to a small city|0.168***<br>(0.000)|
|Share of population 1-2 hours to a town|0.070**<br>(0.050)|
|Share of population 2-3 hours to a large city|-0.025<br>(0.494)|
|Share of population 2-3 hours to an intermediate city|0.009<br>(0.792)|
|Share of population 2-3 hours to a small city|0.083**<br>(0.022)|
|Share of population 2-3 hours to a town|0.045<br>(0.214)|
|Legend: * p<.05; ** p<.01; *** p<.001||

## Source: authors’ elaboration

These results shed a much finer lens on poverty along the rural–urban continuum (going well beyond the traditional rural–urban characterization) and help draw attention to the role of local governments in smaller towns and cities in fighting poverty, in their urban areas as well as their hinterlands. By exploiting recent advances in artificial intelligence and access to satellite imagery, the prediction accuracy of the disaggregated poverty measures could further be enhanced and the geographies in which the disaggregated poverty measures are predicted better aligned with the geography of the urban–rural catchment configurations (Engstrom, Newhouse, & Soundararajan, 2019; Jean et al., 2016)
# **Which Firms Drive the Gains from Connectivity and Competition? The Impact of India’s Golden Quadrilateral across the Firm Life Cycle**

Arti Grover,<sup>*</sup> William Maloney,<sup>†</sup> and Stephen D. O’Connell<sup>‡</sup>

**Keywords:** firm dynamics, connectivity, competition, productivity, infrastructure, highways, India, manufacturing.

**JEL classification:** D22, D24, O12, O14, O18, R12

**Acknowledgements:** We thank the World Bank’s Research Support Budget for funding this paper. We are grateful to Leslie A. Martin, Shanthi Nataraj and Ann Harrison for sharing their code on matching ASI cross-sectional data with the ASI establishment panel data. We are indebted to Denis Medvedev and William Kerr for their support and insightful comments on the research proposal. We also thank two anonymous reviewers for their suggestions that helped improve the planned analysis at the concept stage.

> * Corresponding author. International Finance Corporation, World Bank. Email: agrover1@worldbank.org

> † International Bank for Reconstruction and Development, World Bank. Email: wmaloney@worldbank.org

> ‡ Emory University. soconnell.work@gmail.com
# 5. An approximation of economic impacts

The analyses in this work so far describe exposure in terms of the percentage of a certain asset affected by a natural hazard. This section aims to transform these estimates into figures that might be considered more tangible and easily interpretable. Specifically, it approximates economic impacts in monetary terms and in jobs affected.

## 5.1. General approach and assumptions

To transform exposure estimates derived in section 4 to economic figures, province-level economic data, such as contribution to GDP, export and employment data, obtained in section 3.8, are combined with asset exposure estimates from section 4. This translates flood exposure estimates into economic exposure estimates providing an approximation of the economic impact of the natural shock. To illustrate this, assume a 100-year coastal flood exposes 40 percent of the rice paddy fields in coastal provinces. Total yearly export value of rice derived from coastal provinces is $1.2 billion, providing jobs to 2.2 million people. Following the employed methodology, given flood would put at risk $480 million worth of exports and some 880,000 jobs. Following this methodology, impact estimates are produced under two simplifying assumptions.

First, sectoral output must be evenly distributed across all assets of that sector within each province. For hotels, as an example, this assumes that all hotels contribute equal revenue to GDP. For agriculture this means that there are no differences in yield and productivity across provinces, implying that X percent of crop fields in a certain province contribute exactly X percent of national crop production, and therefore also contribute X percent to agriculture GDP, agricultural export and job creation. In reality there are many factors affecting agricultural yield, such as different soil resources, water resources, availability of irrigation systems, input, and so forth. For rice, due to data availability, calculations take differential provincial yields into account. Due to data limitations, for other crops and orchards, this is not possible.

Secondly, the analysis is simplified considerably by assuming equal
errors are clustered by product groups. All coefficients have the expected signs. Regarding the relative impact of standards, internationally harmonized mandatory standards seem to have the largest impact on Chinese exports. As consumption and tariff data have missing observations for some sectors in some countries, we exclude these variables from the baseline specification. As shown in column 3, estimates of standards coefficients do not change significantly as a result of the omission of tariffs and consumption variables. Importer dummies control for fixed importer-specific characteristics such as distance and historical linkages with China. Yet, some importer-characteristics evolve over time. In order to provide some temporal variation to importer-specific effects, we define 4-year time dummies and interact them with importer dummies, as well as with 10
THE DEMOCRATIC REPUBLIC OF CONGO’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# **Challenges**

Only a tiny fraction of the DRC’s hydropower potential is developed, and much of that has fallen into disrepair. Only 2,540 MW of the 100,000 MW potential has actually been developed as installed capacity, and only 1,100 MW of this meager capacity is actually in functioning order. There are two interconnected power systems in the country. This first connects the Inga site in Bas Congo to Kinshasa and Brazzaville, as well as to Katanga and on to Zambia. The second interconnects the two Kivu Provinces with Rwanda and Burundi. Both systems are dilapidated and need extensive rehabilitation.

As a result, power supply is heavily constrained and subject to blackouts, placing major limitations on private sector activity. About 40 percent of firms in the DRC own and operate their own backstop generator to shield themselves from frequent power interruptions, which cause significant production losses. This is one of the higher percentages of generator ownership in Africa (although the ratio exceeds 80 percent in Nigeria). The Platt’s power generation database indicates that almost half of the installed generation capacity in the DRC is owned and operated by private companies for the purpose of selfsupply—one of the highest ratios anywhere in Africa. Timber mills in the Kinshasa area spend up to 63 cents per kWh to run diesel-powered backup generators when needed. This represents a major constraint in the conversion of logs to higher-value timber products for export. In the Katanga region, mining companies depend primarily on power from the Inga hydro plant, but due to dilapidated infrastructure, supplies are highly unreliable, with 19 interruptions reported on average per month. Overall, the Katanga region is estimated to have a power supply deficit of 900 MW. Due to these deficiencies, mining companies have developed their own local hydroelectric schemes at a cost of around of 10 cents per kWh, compared with an estimated long-run marginal cost for grid electricity of less than 4 cents per kWh.

To turn
have tended to find the strongest gains to alleviating malnutrition in reduced levels of stunting (e.g., Fernald, Gertler, and Neufeld (2009); Rivera, Sotres-Alvarez, Habicht, Shamah, and Villalpando (2004); Maluccio and Flores (2005)).

Tables 8 and 9 show the same outcomes as tables 6 and 7, but only include new children born to women in households where the woman was interviewed at baseline. These are presumably the children most likely to be influenced by the changes in behavior shown in table 5, which only focused on the mothers with new children. There are no indications of any improvements in cognitive or anthropometric measures for these youngest children.

If complying with suggested best practices, such as exclusively breastfeeding a child for six months, one particular outcome that should improve among the youngest children is a reduction in rates of diarrhea. This would occur both because of increased nutrients from drinking breastmilk and from reduced likelihood of contamination from dirty water. For new children born since the baseline in both of the treatment groups, there is a substantial, albeit insiginficant reduction, in the likelihood of having diarrhea, as shown in column 8 of table 9. Though the reduction is only between 1 and 2 percentage points, this is a 15-25% lowering of the likelihood of having diarrhea over the 6.9 percent of children in the control group who had diarrhea in the past thirty days. This suggests that the treatments may have improved child outcomes, and perhaps some of the broader insignificant results are due to the short time window in which we observe the children. Since the intervention only took place over nine months, it may not have been enough time to sufficiently capture the gains, but by following up in the future we may be able to more accurately measure improvements in child outcomes if they have indeed improved.

This analysis has focused only on participants in the endline survey. The midline survey results are left to an appendix because of issues with attrition discussed in section 3.
equivalents for the imposed TTBs may improve upon the estimation considerably.

Second, we also take a first step toward examining the role of import market power on the interplay between the relative changes in trade policy affecting PTA partners and PTA non-partners. Given our desire in this paper to hew relatively closely to the Estevadeordal et al. (2008) approach so as to make our first set of results as comparable as possible, our market power variables may contain too much measurement error to have proven useful for this particular environment. The basic approach, however, could surely be improved upon in other empirical settings that are able to rely on more disaggregated trade policy data (typically made available after 1995) and further refinements to the elasticity estimates that seem to be improving over time.

Third, and more generally, examination of the patterns of the raw trade policy data and our results call into question whether it is accurate to _characterize_ that Argentina and Brazil have either a customs 29