rafmacalaba/datause-encoder-data huggingface dataset:
# **1.  Introduction**

Without international assistance, developing countries will adapt to climate change as best they can. Part of the cost will be absorbed by households and part by the public sector. Adaptation costs will themselves be affected by socioeconomic development, which will also be affected by climate change. Without a better understanding of these interactions, it will be difficult for climate negotiators and donor institutions to determine the appropriate levels and modes of adaptation assistance. This paper attempts to contribute by assessing the economics of adaptation to extreme weather events. We address several questions that are relevant for the international discussion: How will climate change alter the incidence of these events, and how will their impact be distributed geographically? How will future socioeconomic development affect the vulnerability of affected communities? And, of primary interest to negotiators and donors, how much would it cost to neutralize the threat of additional losses in this context?

From a narrow technical perspective, it might be desirable to address the latter question with a detailed engineering cost analysis of specific disaster prevention measures. However, as we show in the paper, existing cross-country information about relevant emergency preparedness programs is far too sparse to support systematic analysis and projection. And in any case, we believe that the effectiveness of such measures is contingent on the characteristics of the communities that employ them. We therefore adopt an alternative approach in this paper, focusing on the role of socioeconomic development in increasing climate resilience.

In principle, a full analysis of climate vulnerability should consider a broad range of losses, including deaths, injuries and economic damage. Our analysis relies on the most comprehensive data on weather-related losses, which are maintained by the Centre for Research on the Epidemiology of Disasters (CRED) at the School of Public Health of the Université Catholique de Louvain, Brussels. These data provide a detailed accounting of deaths and persons affected by weather-related disasters, but their coverage of economic 2
generally narrow in countries that reported the presence of IRCs in these markets, with only three countries showing a gap of zero. Actual deposit rates are negative in a few economies with deposit rate ceilings. Finally, actual deposit rates paid by commercial banks unsurprisingly show a general convergence towards floors, with a few economies showing negative real floors and actual rates.

- The degree of bindingness of lending IRCs is associated with other financial repression policies. Pairwise correlations suggest that the relative bindingness of real lending ceilings is associated with a higher share of state-owned commercial banks, which governments can use to direct credit, and more stringent _de jure_ capital controls, which can be used to influence the size and composition of capital flows. Correlations become stronger, including for other proxies of financial repression, when lending ceilings that are determined relative to a market benchmark are excluded from the sample.

Further research is needed to advance knowledge on IRCs and investigate their effects on welfare. The hope is that our database on IRCs can contribute to generate renewed interest and additional research on the topic, which can help provide concrete policy prescriptions. It would be important to complement our effort with country level analysis at the bank-product level over time to ascertain whether legal limits to interest rates in a country are in fact binding and what are the welfare implications. In this context, it would also be important to include _de facto_ IRCs in the analysis, especially those introduced by subsidized government programs. It would also be interesting to collect data on other forms of financial repression, especially lending quotas on which there is very limited knowledge, and investigate the interactions among various forms of financial repression. It is likely that IRCs in a country are part of a wider toolkit of financial repression policies, as we have preliminarily shown in this paper, and interaction and complementarity of policies may have different welfare outcomes. Finally, given the prospect that IRCs might become an increasingly important feature of domestic financial systems in the post Covid-19 world, it would be important to closely monitor these developments.

19
|P117363|2011|2011|2/27/2011|1 Closed|Yemen,Republic of|
|---|---|---|---|---|---|
|P117363|2011|2012|12/27/2011|2 Closed|Yemen,Republic of|
|P117363|2011|2013|7/4/2012|3 Closed|Yemen,Republic of|
|P117363|2011|2013|4/2/2013|4 Closed|Yemen,Republic of|
|P117363|2011|2014|1/22/2014|5 Closed|Yemen,Republic of|
|P117363|2011|2015|12/1/2014|6 Closed|Yemen,Republic of|
|P117363|2011|2015|6/30/2015|7 Closed|Yemen,Republic of|
|P117363|2011|2020|2/27/2020|8 Closed|Yemen,Republic of|
|P117365|2011|2011||0 Active|Niger|
|P117365|2011|2012|7/9/2011|1 Active|Niger|
|P117365|2011|2012|2/14/2012|2 Active|Niger|
|P117365|2011|2013|9/7/2012|3 Active|Niger|
|P117365|2011|2013|1/9/2013|4 Active|Niger|
|P117365|2011|2014|7/5/2013|5 Active|Niger|
|P117365|2011|2014|1/4/2014|6 Active|Niger|
|P117365|2011|2014|4/1/2014|7 Active|Niger|
|P117365|2011|2015|12/4/2014|8 Active|Niger|
|P117365|2011|2015|6/14/2015|9 Active|Niger|
|P117365|2011|2016|12/23/2015|10 Active|Niger|
|P117365|2011|2016|6/9/2016|11 Active|Niger|
|P117365|2011|2017|12/5/2016|12 Active|Niger|
|P117365|2011|2017|6/1/2017|13 Active|Niger|
|P117365|2011|2018|12/1/2017|14 Active|Niger|
|P117365|2011|2018|6/1/2018|15 Active|Niger|
|P117365|2011|2019|11/7/2018|16 Active|Niger|
|P117365|2011|2019|4/24/2019|17 Active|Niger|
|P117365|2011|2020|10/17/2019|18 Active|Niger|
|P117365|2011|2020|4/22/2020|19 Active|Niger|
|P117370|2011|2011||0 Closed|Zambia|
|P117370|2011|2019|5/9/2019|1 Closed|Zambia|
|P117371|2009|2010||0 Closed|OECS Countries|
|P117371|2009|2011||0 Closed|OECS Countries|
|P117371|2009|2012||0 Closed|OECS Countries|
|P117371|2009|2013||0 Closed|OECS Countries|
|P117377|2010|2010|6/28/2010|1 Closed|Argentina|
|P117377|2010|2011|2/23/2011|2 Closed|Argentina|
|P117377|2010|2012|9/7/2011|3 Closed|Argentina|
|P117377|2010|2012|3/21/2012|4 Closed|Argentina|
|P117377|2010|2013|11/17/2012|5 Closed|Argentina|
|P117377|2010|2014|7/9/2013|6 Closed|Argentina|
|P117377|2010|2014|12/30/2013|7 Closed|Argentina|
|P117382|2011|2011||0 Closed|Congo,Democratic Re|
|P117382|2011|2012|2/18/2012|1 Closed|Congo,Democratic Re|
|P117382|2011|2013|9/22/2012|2 Closed|Congo,Democratic Re|
|P117382|2011|2013|5/15/2013|3 Closed|Congo,Democratic Re|
|P117382|2011|2014|1/6/2014|4 Closed|Congo,Democratic Re|
|P117382|2011|2015|7/5/2014|5 Closed|Congo,Democratic Re|
Policy Research Working Paper 8888

# **Abstract**

Much micro-econometric evidence suggests that precipitation has wide ranging impacts on vital economic indicators such as agricultural yields, human capital, and even conflict. And yet paradoxically most macro-econometric evidence (especially in the climate economy literature) finds that precipitation has no robust and significant impact on various measures of aggregate economic output. This paper argues that spatial aggregation of weather at the country level explains this result. The paper uses annual subnational gross domestic product data to show a concave relationship between precipitation and local gross domestic product growth between 1990 and 2014. It then demonstrates that when the data are aggregated at larger spatial scales, the impact decreases and eventually vanishes. The impact of precipitation on aggregate economic activity is predominantly felt in developing countries; it is insignificant in developed countries. Agriculture is found to be the dominant pathway. The results have significant consequences for measuring the economic impacts of climate change.

This paper is a product of the Water 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 rdamania@worldbank.org, sdesbureaux@worldbank.org and ezaveri@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 affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
poorest households between urban and rural areas, several natural hazards threaten in higher proportion according to the respondents. For instance, hurricanes threaten 74 percent of rural and poor households, while only a 59 percent for urban and poor households, almost 15 percent higher. Likewise, vulnerability at the household level is also higher among households with a less educated head of household. Given the correlation between socio-economic status and education, the data by educational level of the household head was disaggregated to examine the vulnerability of households to natural hazards. Results show that certain natural hazards affect in higher proportion those with a lower level of education of the household's head. For instance, 61 percent of respondents that reported an educational level of the head of household equal to primary school or less reported living in a household exposed to cyclones, compared to 55 percent for those with heads of household with more than primary education (Table 6).

For earthquakes, data from the most recent earthquake on August 14, 2021, suggests that the educational level of the household head and the presence of children in the household correlate with the likelihood of being affected. In 2021, Haiti experienced an earthquake that resulted in 2,246 deaths, 12,673 injured and the destruction of infrastructure, with an estimated $1.6 billion in damage and losses.<sup>9</sup> We examine which socioeconomic factors are correlated with being affected by the earthquake in a multivariate regression framework. The results show that the households in the southern region are more likely to report being affected in all survey years, as expected, since the earthquake hit the southern peninsula of the country. The results in Figure 3a show that households where the household head has an educational level of secondary or tertiary or more decreases the chances by 9 and 10 percentage points of being affected by the earthquake, respectively; while living in rural area increases the likelihood of suffering from this hazard. The multivariate analysis for 2022 shows that households with 3 or more children have a higher probability of being vulnerable to earthquakes (by 22 percentage points). Additionally, there are no statistically significant differences by sex of head in reporting impacts from the earthquake. However, the absence of differences by sex of household head does not imply the absence of gender differences in impact given that this variable does not capture within-household gender differences.

> 9 <u>https://www.worldbank.org/en/country/haiti/overview</u>

6
<!-- Start of picture text --> WATER SUPPLY SPSP's!<br>PERCENTAGE OF HOUSEHOLDS RELYING ON SPSP's - DOCUMENTED<br>iSPSP C 0 -10%HH: Gs sPsP's Documented - HH Coverage NA ——— \jpepantionat Baundanes<br>GY 11 - 30% [ Anecdotal Information on SPSP's<br><!-- End of picture text --> <!-- Start of picture text --> HN 3) - 50% [ | No Data Available. excueenen ae<br>Ee 51 - 100% =i Developed Countries (OECD and Non-OECD); “Forwre: looutions documentedpresence: databasefoounty; Region, <i}<br><!-- End of picture text -->

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<!-- Start of picture text --> Overall losses* US$ 3,700bn<br><!-- End of picture text --> Insured losses* USS 970bn 5% 1% 1 Upper middle income economies (GNI 4,036 -— 12,475 USS) Low income economies (GNI 31,025 USS) ‘2 3%Hea i Income Groups 2012 (defined (defined by 7 High income economies (GNI 212,476 US$) Lower middle income economies (GNI 1,026 — 4,035 USS) Income Groups 2012 (defined (defined by World Bank, July 2012): “in 2012 values
It can be seen from Tables 3-7 that our qualitative results are generally robust to the choice of poverty measure. Similarly to Ravallion and Datt (1996), the growth elasticities tend to be highest (in absolute value) for SPG and higher for PG than H. As we show in the 1996 paper, the higher growth elasticity of PG than H implies that the depth of poverty (as measured by the mean poverty gap relative to the poverty line) is also reduced by growth. Similarly, the even higher elasticity of SPG implies that inequality amongst the poor (as measured by the coefficient of variation) is reduced by growth. Thus the impacts of growth within and between sectors are not confined to households in a neighborhood of the poverty line.

There are two notable exceptions to this pattern. The first is found in the pre-1991 data for urban areas, where we find a slightly lower elasticity for SPG than PG in the effects of urban growth on urban poverty (Table 7). This is suggestive of underlying adverse distributional effects amongst the poor in the urban economic growth process of the pre-reform period. The second exception is in the impacts of rural economic growth on rural poverty in the post-1991 period, for which we find a lower elasticity for SPG than PG in the post-1991 period (Table 7). It appears that an adverse distributional effect amongst the rural poor has emerged in the rural growth process of the pre-reform period.

Compared to our earlier findings, the most striking new result is the evidence that the urban economic growth process since 1991 has been appreciably more effective in reducing rural (and national) poverty. Since the regressions for rural poverty include rural mean consumption, the urban growth effect can be interpreted as a distributional effect. Evidence in support of such a distributional effect is provided by the following regression of changes in the rural log Gini index ( _G_ ) of inequality on the (share-weighted) urban and rural growth rates:<sup>33</sup> It can be seen that, unlike the pre-1991 period, higher growth rates of mean urban consumption since 1991 have reduced inequality within rural areas (significant at the 10% level). Rural consumption growth on the other hand has had the opposite effect.

> 33 We included the population shift effects (as in equation 6.2), but these were insignificant and are not reported.  The share-weighted urban and rural growth terms are instrumented as in Table 5.

23
Scheme 1 <!-- Start of picture text --> Farmer's name:<br>Plot ID: 1010201<br>Irrigation type: Canal<br>Crop: Piri-piri<br>30<br>24.82<br>25<br>= 20<br>g 1771<br>a<br>a<br>§ 15<br>3<br>E 10.43<br>—€ 10<br>6.19<br>|0 L<br>1 2 3 4<br>Growth stage<br>Numbcamperfd ores 28 22 20 5<br>in each stage:<br><!-- End of picture text -->
<!-- Start of picture text --> Addis Ababa Dakar<br>100% 100%<br>80% 80%<br>60% 60%<br>40% 40%<br>20% 20%<br>South of CBD North of CBD East of CBD<br>0% 0%<br>‐25 ‐22 ‐19 ‐16 ‐13 ‐10 ‐7 ‐4 ‐1 3 6 9 12 15 18 21 24 ‐4 ‐2 1 3 5 7 9 11 13 15 17 19 21 23 25<br>Buit‐up Paved Roads Open Space Built‐up Paved roads Open Space<br>Source: Author’s calculations using land classification from high resolution satellite imagery (see<br>annex A for more details) and Felkner, Lall, & Lee (Forthcoming). European cities were analyzed using<br>Urban Atlas data layers published by the European Environment Agency (EEA)<br>(http://www.eea.europa.eu/data‐and‐maps/data/urban‐atlas)<br><!-- End of picture text -->

# LAND COVER CLASSIFICATION

Poorly managed, dense urban areas can become congested, polluted, and poorly equipped to provide basic services for their inhabitants. In Africa, evidence of this challenge is apparent in the growth of informal settlements; tightly packed small houses that have been constructed from nondurable materials. Households in these dense neighborhoods often lack sufficient living space, sanitation, electricity, and safe water. Historically, many governments have been reluctant to grapple with such settlements, weary of the social and political risks associated with displacing vulnerable populations.<sup>21</sup> Furthermore, a lack of up‐to‐date and accurate spatial data on these areas adds to the challenge. In order for policy makers and planners to make well informed decisions, high quality maps are essential.

This study uses high resolution satellite imagery to detect and identify land that appears to have slum‐ like characteristics, or more accurately put, ‘irregular’ neighborhoods with tightly packed roofs, surrounded by minimal vegetation, and often irregular rooftop angles. This identification was achieved using a semi‐automated classification approach that examines the texture and structural composition of various neighborhoods, and then groups land with similar patterns to the same class. This method was originally developed to accurately detect shanties in cities throughout the world, and has been proven effective in a diverse set of cities.<sup>22</sup> It has since been adopted by the US Census Bureau and US Department of Energy’s Oakridge Laboratory.<sup>23</sup> In this work, we also explored its ability to detect commercial/industrial land, barren land, and vegetation. Commercial and industrial areas were 21 UN Habitatat. Urban Patterns for a Green Economy: Leveraging Density.

22 Jordan Graesser et al. 2012 Image based Characterization of formal and informal neighborhoods in an urban landscape. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 5 (4) August:1164‐ 1176.

23 High‐Resolution Urban Image Classification Using Extended Features: Data Mining Workshops (ICDMW), 2011 IEEE 11th International Conference Dec. 2011. Author: Vatsavai, R.R. Published by IEEE.

10
# **5. Potential Mechanisms and Heterogeneity in Impacts**

The observed changes in aggregate demand for various services due to the pandemic reflect a combination of demand and supply shocks. As we highlighted earlier, there are three major channels through which the coronavirus pandemic can affect demand for services. First, as the pandemic began to take hold in much of the world, governments have introduced multiple measures, including travel restrictions, social distancing, and lockdowns, to reduce the spread of the virus. These measures directly affect the supply of some services, while inducing demand for substitutes such as remote working. These business closures limit the availability of some services, such as international flights, which effectively put a total hold on air travel and tourism.

Second, the pandemic could affect consumer demand through income and wealth channels as workers get laid off or experience cuts in wages and salaries due to business closures and economic slowdowns. Third, as the pandemic takes hold, consumers will reduce economic activities and alter their demand for certain services (such as hotels, restaurants, local malls, and shopping centers, and travel and leisure) for fear of contracting the virus. Besides such fearinduced withdrawal of demand, people may also reduce the consumption of some services deemed non-essential due to economic anxiety and elevated uncertainties about the future under the shadow of the pandemic. Such anxieties and negative economic sentiments are known to reduce consumer confidence and aggregate spending. In this regard, early studies from Europe and the United States show a significant decline in consumer spending across various sectors. Using daily transaction-level data from Denmark, for instance, Andersen et al. (2020) find a 25 percent decline in overall household spending following lockdown.

To identify whether supply-side or demand-side shocks drive our results, we interact with the number of weekly confirmed cases with the implementation of social distancing and lockdown measures. More specifically, we interact with social distancing and lockdown implementation indicators with an indicator variable for whether a country has registered the first 100 cases. However, we cannot disentangle the change in demand due to income losses from that driven by anxieties. Similarly, the breadth and implementation of these policies vary across countries. But the variations in the spread of the pandemic and government responses give us some latitude to isolate the relative roles of demand- and supply-side factors in driving the average effect.

Panel A of Table 3 provides results on the impacts of the spread of the pandemic in the absence or presence of social distancing measures. In contrast, Panel B provides similar results by 22
Policy ReseaRch WoRking PaPeR 4525

# **Abstract**

This paper provides evidence of the presence and relevance of a credit-chain amplification mechanism by looking at its implications for the correlation of industries. In particular, it tests the hypothesis that an increase in the use of trade-credit along the inputoutput chain linking two industries results in an increase in their correlation. The analysis uses detailed data on the correlations and input-output relations of 378 manufacturing industry-pairs across 44 countries with different degrees of use of trade credit. The results provide strong support for this hypothesis and indicate that the mechanism is quantitatively relevant.

This paper—a product of the Growth and the Macroeconomics Team, Development Research Group—is part of a larger effort in the department to understand the determinants of macroeconomic volatility. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at craddatz@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 affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team