rafmacalaba/data-use-mentions-extended huggingface dataset:
>5.0<br>Only monetary poor<br>0.0<br>Energy and monetary poor<br>Energy poverty index<br><!-- End of picture text --> _Figure 16: Overlap of at-risk of poverty and multidimensional energy poverty rate (2019/20)_ _Source: Own estimates based on EU-SILC (2020)_

# 3.4. Big Data

## **We lastly experiment with big data to explore the potential of this data type to measure energy**

**poverty.** We note that this approach is experimental and should be cautiously taken. Nighttime lights have been used to measure urbanization dynamics (Zhou, 2012), population, local economic activity, and energy use (Goldblatt, 2016; Shi et al., 2014; N. Zhao,2017; Amaral et al., 2005; Townsend et al. 2010, Coscieme et al. 2014; Shi et al., 2014; Fehrer and Krarti (2018)). We follow the approach of Fehrer and Krarti (2018) and argue that nighttime light satellite data could be used as a proxy for building energy use intensity. The methodology presented in this study offers a replicable approach globally, providing opportunities for geospatial energy analysis without the challenges associated with disaggregated building energy use data collection. 42 We use the data made available by Li and Zhou (2017). Applying zonal statistics, we calculate the mean nighttime light data per municipality and plot it in Figure 17.

42 The study addresses the challenge of limited data for geospatial analysis of energy use in buildings by proposing the use of satellite-derived information, specifically nighttime light imagery from the VIIRS instrument aboard the SUOMI NPP satellite. Researchers evaluated this imagery as a predictor of building energy use intensity across states, counties, and cities in the United States. The findings indicate that nighttime lights can explain a significant portion of the variability in energy consumption, reaching up to 90%, depending on conditions and
# Bibliography

Estache, Antonio, Lisa Bagnoli, and Salvador Bertomeu, 2018. "Infrastructure Affordability in Developed and Developing Economies: Rules of Thumbs and Evidence," Working Papers ECARES 2018-02, ULB -- Universite Libre de Bruxelles.

Gandelman, Néstor, Tomás Serebrisky, and Suárez-Alemán, Ancor, 2018. “Household Spending on Transport in Latin America and the Caribbean: Understanding Transport Expenditure Patterns,” Documento de Investigación, Nro.115. Universidad ORT Uruguay. Facultad de Administración y Cien cias Sociales. ISSN 1688 - 6275.

Gandelman, Néstor, Tomás Serebrisky, and Suárez-Alemán, Ancor, 2019. "Household spending on transport in Latin America and the Caribbean: A dimension of transport affordability in the region," Journal of Transport Geography, Elsevier, vol. 79(C), pages 1-1.

Lebrand, Mathilde and Ewane Theophile, 2022. “Rising incomes, transport demand and Sector decarbonization”, forthcoming Mitra, S., Neki, K., Mbugua, L.W., Gutierrez, H., Bakdash, L., Winer, M., Balasubramaniyan, R., Roberts, J., Vos, T., Hamilton, E., Naghavi, M., Harrison, J., Job, R.F.S., Bhalla, K., 2021. “Availability of population-level data sources for tracking the incidence of deaths and injuries from road traffic crashes in low-income and middle-income countries,” BMJ Global Health 28
the overall design so that one does not re-invent the wheel and that the right answers are sought by asking the right people the right questions. In most countries, useful information on residential energy consumption may have already been gathered for other purposes. Such data can usually provide a starting point for identifying critical issues as well as selecting population groups and areas for in-depth research. Previous studies and surveys will help narrow the objectives and amrnunt of information sought in the household energy survey, thus saving time and money. Interviews with those previously involved in similar tasks can also help improve survey design and implementation. For instance. preliminary research may highlight the possible seasonality of fuel consumption, migration patterns or accessibility of survey sites, and thus aid in survey timing and logistics. The services of a sociologist or anthropologist can be used to assist in understanding household consumption patterns to better design and implement a relevant survey.

After reviewing the available information and identifying gaps in the data, care must be taken in determining which information will be most useful in understanding energy problems and their solutions. While it would be difficult and, indeed, unwise to assume an advance knowledge of the outcome of the survey, one can develop a general ret of expectations about how the database will be used. For example, in Morocco, a series of working papers were outlined which would then be written based on information generated by the household energy survey. Their structure was by no means fixed but they did provide useful guideposts in the important areas of sample and questionnaire design. In Indonesia, an interministerial committee on household energy was established to review the survey design process so as to guarantee that the survey would provide each ministry with information that would be useful. These are just two techniques which can be used as a check on ultimate worth of the survey.
Policy Research Working Paper 10995

# **Abstract**

This paper estimates the differentiated economic impact of natural hazard-related disasters (the specific disasters and climate shocks studied here being floods) when they occur in conflict versus non-conflict affected areas. Existing literature shows that disasters and climate shocks can cause significant distress to countries and people on an institutional and household level. However, assumptions are made that their impact tends to be larger in conflict-affected areas, with little evidence available on the differentiated extent of these damages. This paper investigates whether, and to what extent, the presence of conflicts has amplified the impacts of floods on economic activity and people, and hampered recovery. The paper applies a “top-down” approach to estimating the differential impacts of disasters and climate shocks between conflict and non-conflict affected areas using satellite-derived imagery of nightlight radiance as a proxy for economic activity, along with geospatial footprints of floods. The analysis considers two case studies: the 2019 tropical cyclones Idai and Kenneth and subsequent floods in Mozambique, and the July 2022 floods in Nigeria. Using difference-in-difference estimations, the analysis finds that there are significant differences in disaster and climate shock impacts and recovery between conflict and non-conflict affected areas. Particularly, there is a greater decline in economic activity and a longer recovery time in conflict affected areas, as proxied by the greater change in the intensity of nightlight radiance.

This paper is a product of the Urban, Disaster Risk Management, Resilience and Land 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 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 kbenbih@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
|(5.710)|(5.74)|(5.494)|(5.524)|
|Herfindahl-Hirschman Index|-5.197|-5.022|-7.021**|-6.775*|
||(3.662)|(3.709)|(3.446)|(3.482)|
|Age of Firm (logs)|-0.833**|-0.831**|-0.834**|-0.834**|
||(0.358)|(0.359)|(0.355)|(0.358)|
|Foreign Ownership (proportion)|0.103<br>(0.961)|0.130<br>(0.962)|0.183<br>(0.961)|0.218<br>(0.961)|
|Female Ownership Y:1 N:0|5.975***|5.987***|5.905***|5.890***|
||(0.652)|(0.654)|(0.652)|(0.653)|
|Skills Obstacle (0-4)|0.636**|0.636**|0.625**|0.622**|
||(0.269)|(0.269)|(0.270)|(0.269)|
|How Much Of An Obstacle: Transport?|<br>-0011|<br>-0016|<br>-001|<br>-0012|
||.<br>(0.209)|.<br>(0.209)|.<br>(0.209)|.<
were illiterate, 52% were literate or had up to primary school education, and 37% had a high school degree or higher (WFP and TRC, 2019b). Although Turkey introduced legislation to allow refugees to apply to work permits and work formally if they find an employer, very few formal sector employers have shown a willingness to hire refugees and as a result only 31,000 Syrian refugees in Turkey had official work permits by March 2019, or nearly 1.5% of the 2.1 million working-age population.<sup>11</sup> Until 2016, there were no concerted social assistance mechanisms for refugees living among host communities. With the signing of the EU-Turkey accord in 2016, and the creation of the EU Facility for Refugees in Turkey, the ESSN program was set up to fill this gap. The program was launched in November 2016, with the aim of meeting the basic needs of the most vulnerable refugees living in Turkey, and provide cash transfers for a total amount of one billion euros for its first two phases covering the period up to March 2020.<sup>12</sup> To be able to apply to the ESSN program, households need to have a valid registration ID and a registered address of residence in Turkey. To register and obtain a valid ID refugees need to approach the Provincial

> 9 The Turkish government has invested US$37 billion to provide social services to refugees (Regional Refugee and Resilience Plan / 3RP, 2019-2020).

> 10 TURKSTAT Labor Market Statistics June 2020, Employment by status in social security registration, June 2019, June 2020 data.

> 11 CNN Turk, 2019. News Article on Minister of Trade of Turkey’s statements on the number of Syrians with work permits in Turkey, June 14, 2019. Bakan Pekcan: 15 bin 159 Suriyeli şirket var https://www.cnnturk.com/ekonomi/bakan-pekcan15-bin-159-suriyeli-sirket-var.

> 12 The program has been recently extended until December 2021.

6
post-ultrasound declines in EFM across SES groups. The magnitudes of the absolute declines are larger in low-SES households where pre-ultrasound rates were higher, but these differences are not statistically significant, and the proportional declines are similar. This is not surprising because even if high-SES households were more affected by the introduction of prenatal sex detection because they showed a greater inclination to substitute sex-selective abortion for fertility-continuation, low-SES households are more “treatable” in any attempt to reduce mortality because it is easier to bring mortality down from initially higher levels.<sup>50</sup> A smaller change in investment can have larger survival impacts in low-SES households as they are more vulnerable to other causes of child mortality, such as infection.

**SES samples by caste.** We also split each SES-subsample by caste.<sup>51</sup> Unlike other dimensions of SES, caste is exogenous in that an individual is born into a caste and remains in it. The caste hierarchy has been preserved by the low prevalence of inter-caste marriages.<sup>52</sup> The upper-castes have historically laid greater emphasis on ritual purity and adherence to religious texts, which often compromises the position of women (Das Gupta et al. (2003), Das Gupta (2010)). In accordance with this, pre-ultrasound excesses in mortality and family size in girl-led families and post-ultrasound declines were, on average, larger in higher caste households. Interacting caste with other indicators of SES shows that at the low-end of the SES distribution, low castes are more gender-equal but at the high-end, low caste behavior is similar to that of high-castes. This is consistent with the process of _Sanskritization_ , wherein lower castes emulate the upper castes in seeking upward mobility (Srinivas (1962)).

**Heterogeneity by religion.** We examine heterogeneity by religion across the two largest religious groups in India, Hindus and Muslims, because they are known to differ in attitudes to abortion. Previous work shows that Muslims, who are more
<!-- Start of picture text --> 30<br>25<br>8<br>20<br>7<br>6<br>15<br>5<br>10 4<br>3<br>5 2<br>1<br>0<br>0<br>c. At-risk-of-poverty (AROP) rate anchored at a fixed moment in<br>time (2019) and household median income growth, Percent,<br>2007-2022<br>80 25<br>20<br>60<br>15<br>40<br>10<br>20<br>5<br>0 0<br>Percent<br>Poverty Headcount (Percent)<br>CY SI FI CZ BE DK IE MT LU FR NL EE PL AT LT PT HR IT SE ES HU SK LV EL BG RO<br>Poverty Headcount Daily Income<br><!-- End of picture text --> Poverty Rate, National anchored (adult equivalents) 2019 Daily median disposable income (2011 USD PPP per capita) _Source:_ For Poverty Headcount Rates (UMI): World Bank estimates using 2007-2022 EU-SILC UDB-C. Estimates are reported using income year, not survey year. No more recent estimates are available. For Anchored AROP: For 2006-2021, World Bank estimates using 2007-2022 EU-SILC UDB-C. This corresponds to the headcount poverty at 60 percent of the national median adult equivalent disposable income anchored to 2019 for time comparability. Estimates are reported using income year, not survey year. There is a break in the series in 2015, due to methodological changes. No more recent estimates are available.

**Income inequality rose between 2015 and 2018 but has declined since then. Despite the recent positive trends, income inequality persists at a notably high level, ranking the country as the most unequal country in the EU.** Despite solid income growth among poorer households, growth
early marriage, female genital mutilation, gendered social stigmas and gender‐based violence) are likely to prompt migration (Ferrant et al., 2014).

Gender is a crucial part of understanding the processes of migration among Indonesians, and the recent history of migration is likely to have both a discouraging effect (fear of ill-treatment) or an encouraging effect (better opportunities than informal labor) for women. Indonesia is one of the largest origin countries of migrants in the world. Opportunities to work abroad attract Indonesians because of a lack of domestic labor demand and higher potential earnings in destination countries (World Bank, 2017). Concerning liquidity constraints, using data from Indonesia, Bazzi (2017) shows that positive rainfall and price of rice stocks are associated with larger international migration flows. He shows that this phenomenon is linked to the role of liquidity constraints in the decision-making process of migration. Emigration was largely female before the introduction of migration restrictions towards multiple countries in the Middle-East, North- and East-Africa and Pakistan (Makovec et al., 2018). Indeed, female migrants were facing risks throughout their migration journey, leading the Indonesian government to implement these moratoriums as an attempt to address the cases of illtreatment (ranging from physical and sexual abuse, forced labor and unpaid wages) facing Indonesian women working abroad (World Bank, 2017). Makovec et al. (2018) show that migration restrictions towards Saudi Arabia increased the share of informal employment in Indonesia, formal labor markets being unable to absorb the excess labor supply.

# 3. Data and methods

## 3.1. Data

This study draws from household data collected for the impact evaluation of the Government of Indonesia’s _DESMIGRATIF_ program. The _DESMIGRATIF_ program aims to provide a holistic approach to promote safe international migration from Indonesia, based on the four pillars of: (i) better migration information and services, (ii) productive enterprises, (iii) community parenting, and (iv) financial cooperatives for potential migrants, return migrants, and
# **Appendix: Data Sources and Methodology**

This appendix describes in detail how we constructed the data set for each type of investment. We explain the data sources and assumptions made, including the treatment of missing and zero-valued observations. We aimed for consistent treatment across sources. We also discuss the challenges each data set presents, the rationale behind our decisions, and alternative approaches we tried.

# **A.1. Bank Loans and Deposits**

Cross-border loans and deposits are constructed from the Bank for International Settlements (BIS) locational banking statistics (LBS), the most comprehensive source of information on international banking, available since 1977. The LBS compile assets and liabilities of internationally active banks on a residency basis, in line with the balance of payments statistics (IMF 2009). In 2018, the LBS covered close to 8,000 banks (branches and subsidiaries) in 47 reporting countries comprising North and South countries as well major offshore financial centers, each reporting loans and deposits with more than 200 counterparty countries. The concentrated nature of global banking implies that our data coverage is high in terms of the value of positions, even if only 47 countries and jurisdictions report this information.

Banks report their gross assets and liabilities, along with breakdowns by currency, instrument, and the counterparty countries they lend to and borrow from. The instrument breakdown allows us to isolate the relevant investment type for our analysis: loans and deposits. That is, we strip out bank holdings of debt and equity to avoid double counting them with the portfolio investment already contained in the Coordinated Portfolio Investment Survey (CPIS) (which includes banks’ holdings of securities). This ensures that our data sets on loans and deposits and portfolio investment are mutually exclusive, in principle.

31
|3.6%|7.1%|2.7%|
|old|73.9%|51.0%|13.2%|5.8%|5.4%|4.0%|3.7%|2.5%|4.8%|2.1%|
|young|69.9%|56.1%|18.9%|9.4%|4.9%|4.9%|3.8%|3.0%|8.1%|2.2%|

Note: Data cover 2174 enterprises that responded to owner gender and age (youth=30 years and younger) questions across Ghana, Kenya, Mozambique, Nigeria, Senegal, South Africa, and Tanzania.

Figure 1 presents a complementary breakdown of divides in uses of DTs by owner gender-age subgroups. Young men-owned enterprises are consistently the largest users of internet-enabled DTs. While 13 percent of younger men-owned firms use a smartphone, only 3 percent of older womenowned firms use one. The digital divide is even larger with computer use, with less than 2 percent of young women-owned firms using one and more than 4 times more younger men-owned firms using one (8 percent).

_Figure 1. Divides in uses of DTs by owner gender-age subgroups_ <!-- Start of picture text --> 80%<br>76%<br>71%71%<br>69%<br>70%<br>62%<br>60% 58%<br>49%<br>50%<br>42%<br>40%<br>30%<br>21%<br>20%<br>17%<br>15%<br>13%<br>11% 11%<br>10% 8% 8%7% 7%<br>6% 5%
_ESTIMATING FOOD PRICE INFLATION_ _15_ mula for MAE, _MAEd|μ_ is the MAE calculated using observed data and the unconditional mean. Since the true data range is not observed, and averaging is known to improve ensemble performance, the quantity from equation 6 is likely a conservative estimate.<sup>24</sup>

� The focus next is on the quantity 1 _− NMAE_ , which is the share of the total absolute variation in the demeaned data explained by the imputation model. The _D_ values are averaged as follows.

where _Z_ is the Fischer Z-transformation and _Z_<sup>_−_1</sup> its inverse, and _w_ are the relative weights of the price component in the final price index. The final score from equation 7 roughly has the interpretation of the average _R_<sup>2</sup> of the food price index, using a robust calculation of out-of-sample errors.<sup>25</sup> The unit of measurements are harmonized so that each food item has equal weight after in the index once scaled to a comparable unit of measurement. Specifically, the food item specific predictions are aggregated into food price indexes by summing the prices of all foods in the basket, after bringing the prices to comparable units of measurement (1 kg for foods, 1 liter for fluids and vegetable oils, 1 dozen for single packaged eggs, 1 unit for foods that come in other units of measurement – such as some fruits that come in bundles). As a simple example, if the index consists of 1 kg of sorghum and 100 Kg of maize, the latter price is multiplied by 0.01 and an equally weighted index is constructed with the result.<sup>26</sup> Note such,24Notethe estimatedthat _MAENMAE|_ � _μ_ only _d_ usesis likely larger than the true value since the denominator is underestimatedthe observed data range while the true data range is likely larger. As in equation 6. There are further related challenges to the validation due to
# Figure A2.1. Global activity A. Output growth

## B. Output per capita growth

<!-- Start of picture text --> Percent Percent<br>8 6<br>6 4<br>4 2<br>2 0<br>0 -2<br>-2 -4<br>1950 1960 1970 1980 1990 2000 2010 2020 1950 1960 1970 1980 1990 2000 2010 2020<br>C. Industrial production growth D. Trade growth<br>Percent Percent<br>15 18<br>10 12<br>5 6<br>0 0<br>-5 -6<br>-10 -12<br>1950 1960 1970 1980 1990 2000 2010 2020 1950 1960 1970 1980 1990 2000 2010 2020<br>E. Unemployment rate F. Oil consumption growth<br>Percent Percent<br>8 10<br>6 5<br>4 0<br>2 -5<br>1950 1960 1970 1980 1990 2000 2010 2020 1950 1960 1970 1980 1990 2000 2010 2020<br>Sources: British Petroleum; Feenstra, Inklaar, and Timmer (2015); Haver Analytics; International Monetary Fund;<br>Kose and Terrones (2015); Organisation for Economic Co-operation and Development; World Bank.<br>Note: Aggregates for output, output per capita, and industrial production are market-weighted. Shaded areas indicate<br>global recessions in 1975, 1982, 1991, and 2009, and global downturns in 1958, 1998, 2001, and 2012. World aggregates<br>are not computed if data are available in less than 10 countries.<br><!-- End of picture text --> 66
are not available for Mexico, for small, family-owned US farms with sales less than $150,000 per year, 76% have profit margins of less than 10%, and only 16% report margins greater than 20% (USDA 2013). A 10% profit margin on 731 pesos per hectare of revenue suggests profits of less than 100 pesos per hectare – implying both that our WTA estimates display a correct order of magnitude to

> 13Analysis of household impacts from the 2011-2014 cohorts (Alix-Garcia, Sims, Orozco-Olvera & et al. 2018) suggests no significant gains in household wealth, but does find significant investment in communal assets and substantial increases in local land-cover management activities. Surplus payments are likely beneficial to the community and may result in external benefits through land conservation. We do not take a stand on the objective function of CONAFOR, which may wish to keep average payments for participating communities higher than estimated WTA.

> 14The estimates are derived from municipal level production revenues from principle crops and livestock in 2003 (the first year of the PSA program). We normalize the revenues by non-forested area of the municipality to calculate an average value of production per non-forested hectare. We regress these revenues on municipal averages of geographic covariates and predict the locality-level production values using the locality values of those covariates. These measures therefore provide a village-level approximation of agricultural revenues, but no measure of cost.

28