rafmacalaba/data-use-mentions-extended huggingface dataset:
Figure B3: Poverty rates across urban versus rural areas <!-- Start of picture text --> (A) $2.15 poverty line<br>Official definition DOU DB<br>80 80 80<br>60 60 60<br>40 40 40<br>20 20 20<br>Urban Rural Urban center Rural Core Town<br>Urban cluster Suburb Other rural<br>(B) $3.65 poverty line<br>Official definition DOU DB<br>100 100 100<br>80 80 80<br>60 60 60<br>40 40 40<br>20 20 20<br>Urban Rural Urban center Rural Core Town<br>Urban cluster Suburb Other rural<br>Poverty rate (%)<br>Poverty rate (%)<br><!-- End of picture text --> Source: International Urban Poverty Database. Note: Each boxplot shows the distributions of poverty rates over different geographic areas in 16 SSA countries. GHSPOP 1km is used for the DOU and DB methods. The dashed lines represent the average national poverty rate in the sample.

Figure B4: Comparison of urban poverty rates between official and DOU/DB urban definitions <!-- Start of picture text --> (A) DOU vs. official definition (B) DB vs. official definition (C) DB vs. DOU<br>60 MWI 60 60<br>50 50 50<br>40 40 MWI 40 MWI<br>TZA NER NER<br>UGA TZA TZA<br>NER<br>30 30 30<br>ETH ETH ETH<br>20 20 20<br>AGO AGO<br>10 BFATCD LSOAGO 10 BFATCD LSOUGA 10 TCDLSOBFA UGA<br>CIVGNB MRTGNBGHA GHAMRTGNB<br>G SENMRTIN GHA GINSENCIV SENGINCIV<br>GAB GAB GAB<br>10 20 30 40 50 60 10 20 30 40 50 60 10
likely to live in Cairo and Upper Egypt, both urban and rural, whereas undocumented migrants are more likely to live in rural Lower Egypt. As for their job in 2012 upon return, undocumented migrants have greater job tenure by approximately 2 years and are significantly less likely to have a work contract compared to documented migrants. Along the spectrum of sectors of employment and economic activities, both documented and undocumented migrants seem to be equally employed in the different economic sectors and activities.

In Table A.2 in the Online Appendix, we examine additional differences between documented and undocumented migrants with respect to their migration spell abroad. Documented migrants are found to have significantly higher monthly earnings and savings while abroad. They are also more likely to remit and to send higher remittances compared to undocumented migrants. In addition, documented migrants are found to have longer migration spells relative to undocumented migrants, by one year on average. However, there is no significant difference in terms of number of migration episodes between the two groups—as they both seem on average to have one migration spell. In the 1980s, a significantly higher proportion of undocumented migrants migrated compared to documented migrants. By contrast, in the 1990s and in the 2000s, a higher proportion of documented migrants migrated for the first time compared to undocumented migrants. In terms of destination choice, undocumented migrants were significantly more likely to choose Libya and Iraq. However, documented migrants were found to be significantly more likely to migrate to Saudi Arabia, United Arab Emirates, Kuwait, and other destination countries.<sup>12</sup> Looking at the average hourly wages in 2012 for non-migrants, returnees, legal, and illegal migrants in Table 3, we find that the average hourly wage for returnees is higher than non-migrants.<sup>13</sup> We also find that documented migrants have higher hourly wages compared to undocumented migrants upon return. In the second column, we report the difference in hourly wages between each group and non-migrants. Indeed, we find
preponderance of evidence here implies that different extraction methods do not introduce substantial mismeasurement into the analysis. We fail to reject Hypothesis 1. However, there is the possibility of heterogeneity across or within countries. As such, we next consider this same metric, disaggregated by country. Figures 10 and 11 present different extraction methods across all rainfall and temperature metrics, for each of the six countries. Now that we have divided the results by extraction method, rainfall/temperature, and country, each bar represents the share of significant coefficients from 1,008 regressions for rainfall and 288 for temperature. We simplify the graph by only presenting the share of coefficients with _p >_ 0 _._ 95.

We see more variation within a country based on extraction method than in our previous graphs. In Ethiopia, the two extraction methods based on Administrative area centerpoints are strongly different from Household bilinear. While there are no other pairwise comparisons in Ethiopia, or any of the other countries, that are strongly different, a number of pairwise comparisons are weakly different. However, there seems to be no pattern to which extraction methods are different.<sup>13</sup> The evidence for differences in extraction method using temperature data is equally noisy and without any apparent pattern of one extraction method over/under performing the others.

> 13In Malawi, Household simple and the two zonal means are weakly different from EA bilinear and Administrative area bilinear. In Niger, Administrative area simple is weakly different from modified EA bilinear. In Nigeria, Administrative zonal mean is weakly different from bilinear Household and modified EA. In Tanzania, EA simple and EA zonal mean are weakly different from bilinear Household and EA, as well as Administrative simple and zonal mean. In Uganda, no pairwise comparison is weakly different.

23
education provision (and there is limited mobility) this in turn should aect the likelihood of a private school existing in a village.

# 2.2 Data

We employ three data sources: (a) a complete census of private schools carried out by the Federal Bureau of Statistics in 2000; (b) administrative data on the location and date of construction of public schools in the Punjab province available from the province's Educational Management and Information Systems (EMIS 2001) augmented with the National Educational Census (NEC 2005); and (c) data on village-level demographics and educational proles from the 1998 and the 1981 population censuses of Punjab, which provide both baseline and contemporaneous information on village-level characteristics.

We restrict our analysis to rural areas in the province of Punjab, the largest province in the country which hosts 60 percent of the population, two-thirds of whom live in rural areas.<sup>7</sup> Since the EMIS and the other datasets do not employ a common village coding scheme, we had to match villages in the dierent databases on the basis of their names. Using a combination of a phonetic algorithm and manual post-match, we were able to match over 90 percent of the villages across databases (23,064 of the 25,266 unique Punjabi villages in the 1981 census).<sup>8</sup> In our nal estimation sample, we restrict attention to villages that did not receive a girls' or boys' secondary school prior to 1981 and did not have such secondary schools in their neighboring villages. This reduces our sample to 9,333 villages, but aords two

> 7Not all data sets (e.g., EMIS, 1981 Census) were readily accessible for other provinces, and urban areas could not be matched at the granular level necessary to exploit the cross-sectional variation in private school location and GSS presence that we utilize in the paper.

> 8We also augment the public schooling data from the EMIS with more
GSMA (2018). The Mobile Gender Gap Report 2018.

- Hasbi, M. and A. Dubus, 2019. "Determinants of Mobile Broadband Use in Developing Economies: Evidence from Sub-Saharan Africa," Working Papers hal-02264651, HAL.

- Hjort, J. and J. Poulsen. 2019. "The Arrival of Fast Internet and Employment in Africa _." American Economic Review_ , 109 (3): 1032-79.

- Kaila, H. and F. Tarp. 2019. “Can the Internet Improve Agricultural Production? Evidence from Viet Nam”. _Agricultural Economics,_ 675-91.

- Klonner, S. and P. Nolen. 2010. “Does ICT Benefit the Poor? Evidence from South Africa.”, Proceedings of the German Development Economics Conference, Hannover.

- Marandino, J. and P. Wunnava. 2014. “The Effect of Access to Information and Communication Technology on Household Labor Income: Evidence from One Laptop per Child in Uruguay.” IZA Discussion Paper 8415, Institute for the Study of Labor, Bonn.

- Menon, N. 2011. "Got Technology? The Impact of Computers and Cell Phones on Productivity in a Difficult Business Climate: Evidence from Firms with Female Owners in Kenya," IZA Discussion Papers 5419, Institute of Labor Economics (IZA).

- Nsabimana, A. and P. Funjika, 2019. “Mobile phone use, productivity and labour market in Tanzania”, WIDER Working Paper, No. 2019/71, ISBN978-92-9256-705-7, The United Nations University World Institute for Development Economics Research (UNU-WIDER), Helsinki,

- Paunov, C. and V. Rollo, 2015. Overcoming Obstacles: The Internet's Contribution to Firm Development, _The World Bank Economic Review_ , Volume 29, Issue suppl_1, Pages S192–S204.

- Ritter, P. and M. Guerrero. 2014. “The Effect of the Internet and Cell Phones on Employment and Agricultural Production in Rural Villages in Peru.” Working paper, University of Piura,
Branches are, instead, not independently incorporated and fully owned by their foreign parent bank.

Foreign-owned banks represent about 50 percent of the total number of banks in Latin America and Eastern and Central Europe. Those included in our sample capture more than 80 percent of the assets controlled by all foreign banks, on average, across countries. Hungary, Peru and Bolivia are the exception, where the foreign banks in our sample account for less than 60 percent of the assets of the foreign banks in the system, mainly because the biggest foreign banks in these countries are owned by banks from neighboring countries that are not among the world’s top 100 banks.<sup>11</sup> Overall, we capture 247 out of 387 foreign controlled institutions or about 65 percent of the foreign operations in the countries in our sample.

Among the banks in our sample with operations in Latin America and/or in Eastern Europe, there seems to be a preference for subsidiaries (Table 2), which account for 65% of the number of foreign bank operations in Latin America and for 82% of those in Eastern Europe.

The operations of the top 100 banks by country of origin (or home-country) are shown in Table 3. It is clear that U.S., German, and Japanese banks are dominant both in terms of assets and number of institutions. There are 20 U.S. banks, 16 German banks, and 11 Japanese banks in the top 100 list. Regarding our sample of host countries, we observe that U.S. and German banks have operations in 15 out of the 20 host countries we consider. Italian and Dutch banks operate in 14 and 12 countries, respectively. The actual number of branches and subsidiaries in the hostcountries that make up our sample can also be observed in Table 2. U.S. banks have 26 subsidiaries and 24 branches, followed by German banks with 42 subsidiaries and 6 branches.

> 11 Peru's low share data is more subtle
farms differed from each other in the size of their reaction to the onset of the conflict in 2022 ( _β1 - β2_ ) or subsequent adaptation ( _γ1 - γ2_ ) and differences in the level of adaptation by small ( _γ1 - β1_ ) and large ( _γ2 - β2_ ) farms. The results suggest that although output market participation did not differ between the two groups, small farmers’ value of output per hectare decreased marginally less than that of large farms in both the first year (by 7% versus 10%) and the second year (by 12.5% versus 16%) of the war. Although part of this was compensated by an increase in input cost that was about 35% larger for small as compared to large farms, this translated into a slightly less pronounced drop in profits for 2022 (by US$110) and 2023 relative to 2022 (by US$63). The modest size of these differences as well as the descriptive statistics reported earlier suggest that Ukrainian farmers failed to capitalize on the diversification opportunities that were inherent in the relative price changes triggered by the war. Identifying barriers to doing so and ways to reduce them would be relevant.

# **5. Conclusion and policy implications**

The effects of conflict may be felt in areas not directly affected by military activity, and their nature and magnitude may change over time. To account for these factors in analyzing the economic impact of the war in Ukraine, we combined remotely sensed and farm survey evidence. We showed that freely available satellite imagery allows constructing a measure of conflict at high levels of spatial and temporal granularity that is more suitable for quantifying the intensity of the effects of the conflict on agriculture than existing alternatives. We employed regression analysis that linked this variable to a 2019–23 VC-level panel of area cultivated with winter and summer crops for the entire territory of Ukraine (including areas beyond Ukrainian control). This allowed us to analyze separately the direct (due to damage to a specific field) and
Somali regions.<sup>10</sup> Several terrorist attacks occurred during fieldwork of the SHFS.<sup>11</sup> The analysis exploits detailed household data with dates of interviews and GPS positions to evaluate the immediate impact of attacks against civilians in Somalia. The types of incidents considered correspond to attacks from Al-Shabaab against civilians.<sup>12</sup> We concentrate on measuring the effect within a week due to data availability given that i) the questionnaires used a recall period of 7 days for food items, which is the main component of the consumption aggregate; and ii) only one household was interviewed 8 days or more after an incident.

# 3.1 Data sources

The main sources of data correspond to detailed household information on sociodemographic characteristics, perceptions and poverty conditions from Waves 1 and 2 of the Somali High Frequency Survey, as well as location and time of attacks from The Armed Conflict Location & Event Data Project (ACLED). In this way, household data from the SHFS is combined with geo-tagged information on attacks to identify households exposed to terrorist incidents.

Wave 1 includes 4,117 households interviewed between February and March 2016, ‐ which are representative of 9 of the 18 Somali pre war regions, as the remaining areas were inaccessible for security reasons at the time of fieldwork. Wave 2 expanded the ‐ coverage to 17 pre war regions, interviewing 6,092 households between December 2017 and January 2018.<sup>13</sup> ACLED data records locations and intensity of armed conflict coded by researchers who collect information from secondary sources, NGO reports, local and international news reports and research publications (Raleigh and Dowd 2015). The database is unique due to its geographical level of precision when reporting the latitude and longitude of the attacks, indicating the location of an incident (Figure 2). All the violent incidents reported in ACLED that occurred a week before the start and end of data collection of Wave 2 of the SHFS were considered as
_Figure 1: Officials prioritize improvements to school completion over improvements to learning for economic growth._ <!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>MNG LAO PHL IDN VNM Non-EAP<br>MOE MOF All<br>increase<br> mprove test scores<br>instead of<br>Share of officials choosing<br>number of students finishing secondary<br><!-- End of picture text --> Source: CGD–World Bank survey, 2022. The figure aggregates the answers obtained for the question “For the economy to grow faster, the most important thing your country could do in education would be to: (1) Increase the number of children finishing secondary school, (2) improve test scores, (3) Do not know, (4) other: specify. The “other” replies were then identified as either linked to quality (test scores) or quantity (more secondary completion) by looking at individual responses.

# 3.2 Policy makers underestimate learning poverty by large margins in EAP

Policy makers in EAP underestimate learning poverty by a larger margin than officials from other countries. Learning poverty is defined as the percentage of children aged 10 who are unable to read and understand age-appropriate text. Senior education and finance officials in EAP underestimate learning poverty by an average of 31 percentage points. The gap between officials’ estimates and World Bank estimated learning poverty levels is larger in EAP countries than in non-EAP countries sampled. This is true for both the 2020 and 2022 survey waves. Non-EAP officials underestimate the learning poverty level by an average of 23 percentage points, while EAP officials underestimate it by 31 percentage points.

This underestimation of learning poverty is an overestimation of
7a). While consumption poverty declined faster during 2005‐2012 from the previous decade, no such acceleration is seen for non‐monetary indicators (Figure 7b).

Education attainment among adults has risen appreciably since the mid‐2000s, mainly due to increase in the share of adults who have completed at least secondary school. The decline in illiteracy among adults – from 38 percent in 2005 to 30 percent in 2012 – is matched by a 9 percent increase in the share of adults who have completed secondary education or more during this period (Figure 8). This indicates a positive trend of skilling of the Indian labor force as secondary school enrollments and completion have increased over the years. This trend could also have an impact in terms of reducing vulnerability, given that secondary education among household heads is associated with higher chances of upward mobility and lower chances of downward mobility for the household, as mentioned earlier.

**Figure 8: Improving literacy and education, mainly due to rising secondary school completion**

<!-- Start of picture text --> (a) Proportion of adults (%), 2005 (b) Proportion of adults (%), 2012<br>Secondary<br>and above, Secondary<br>23% and above Illiterate<br>Illiterate,<br>32% 30%<br>38%<br>Middle<br>completed, Literate or<br>16% Literate or primary Middle primary school<br>completed, school completed17% completed21%<br>23%<br><!-- End of picture text --> _Source:_ Employment, unemployment surveys conducted by the National Sample Survey Office (NSSO), 2005 & 2012; ‘adult’ defined as ages 15 and older.

# 4. Spatial pattern of poverty reduction<sup>16</sup>

Poverty rates declined between 1994 and 2012 across all major states in India. Furthermore, as Figure 9a shows, the acceleration in
**Figures 4(a)-(d):  Global Capital Flows, Over Time and From Different Perspectives**

<!-- Start of picture text --> (a) Countries' International Financial Position, (b) World Trade and Gross Capital Inflows ,<br>1980-2009, Ratio of World GDP Share of World GDP<br>4<br>35%<br>3.5<br>30% World<br>3 Trade<br>25% (% of<br>2.5 GDP)<br>20%<br>2<br>15%<br>1.5<br>Global<br>10%<br>1 cross-<br>border<br>0.5 5% inflows<br>0 0%<br>Source: OECD Sources: OECD, IMF, WDI<br>25% (c) Gross Inflows , Share of World GDP, (d) Advanced Economies, Gross Capital Flows,<br>By Country Aggregates % of World GDP<br>20% 20<br>Advanced Economies<br>15 Inflows (liabilities)<br>Emerging and developing<br>15% 10<br>economies<br>5<br>10% 0<br>-5<br>5% -10<br>-15<br>Outflows (liabilities)<br>0%<br>-20<br>-5% Sources: IMF andWDI<br>Sources: IMF, WDI, IMF(2011d)<br>1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009<br>1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009<br>1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
sample reports information on income, and 43 percent on expenditures.

All income and expenditures data are in 2005 PPP US dollars. For each survey, we first correct current units for inflation using the national CPIs, and then convert them into 2005 US dollars PPP using the International Comparison Program (ICP) PPP conversion. Since the ECAPOV, PovCal and SEDLAC surveys are used to compute World Bank poverty figures, we used for these surveys the same conversion, weights and methodology that has been used to compute internationally comparable poverty data.

For the analysis in this paper, we have collapsed yearly observations into five-year averages. We have also dropped from the analysis countries with population of less than two million, and have 4
- Santos Silva, J.M.C. and Silvana Tenreyro (2006), “The Log of Gravity.” _The Review of Economics and Statistics_ 884, 641–658. url: https://doi.org/10.1162/rest.88.4.641.

- Teti, Feodora A. (2020), “30 Years of Trade Policy: Evidence from 5.7 Billion Tariffs.” _ifo Working Papers_ 334.

- Timmer, Marcel P., Erik Dietzenbacher, Bart Los, Robert Stehrer, and Gaaitzen J. _de_ Vries (2015), “An Illustrated User Guide to the World Input-Output Database: the Case of Global Automotive Production.” _Review of International Economics_ 233, 575–605. url: http://doi.wiley.com/10.1111/roie.12178.

- Yotov, Yoto V. (2021), “The Variation of Gravity within Countries (or 15 Reasons Why Gravity Should Be Estimated with Domestic Trade Flows).” _Drexel Economics Working Paper Series WP 2021-12_ .

- (2022), “Gravity at Sixty: The Bijou of Trade.” _Drexel Economics Working Paper Series WP 2022-01_ . url: https://ideas.repec.org/p/ris/drxlwp/2022%7B%5C_%7D001.html.

- Yu, Zhihao (2000), “A Model of Substitution of Non-Tariff Barriers for Tariffs.” _The Canadian Journal of Economics / Revue canadienne d’Economique_ 334, 1069–1090. url: http://www. jstor.org.emedien.ub.uni-muenchen.de/stable/2667392.

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Also, female headed households rely more on the fuelwood than the male headed households. For instance, 81 percent of all female headed households used firewood compared to 68.1 percent for males,‖ according to the Population and Housing Census the Popular Report Aug 2002 as reported in Muchiri (2008). As a result, targeting an intervention to fuelwood users would also effectively target that intervention to female-headed households. Another implication of higher poverty rates among female-headed households is that when uptake of new technologies is income-elastic, female headed households will be less likely to adopt if (and because) they generally have lower incomes (e.g., kerosene and LPG in Andra Pradesh, ESMAP 2004).

In the Lao PDR Rural Electrification Project, it was found that female headed households made up 43% of poor households, who would not be able to afford fees for connecting to the electricity grid. According to World Bank (2010c), ―this led the project team to design a pilot component, known as _Power to the Poor_ , which targeted the poorest, especially the women-headed households. With initial support from the Global Environment Facility (GEF), Aus-AID, and Gender Action Plan funds, the national electricity company, EDL, set up a revolving loan fund to provide these households interestfree loans to cover 80 percent of their connection and wiring costs. All women-headed households were eligible for support. Within a few years, electrification rates in pilot areas increased by about half (from 63 to 90 percent) and more than a quarter for women-headed households (from 75 to 96 percent) (Boatman, J. et al. 2009).‖ Perhaps more relevant to program design is that the legal, cultural, or other institutional constraints faced by women may also affect the ability of female-headed households to switch fuels, adopt technology, or establish an electricity connection, even conditional on a given household income level (cf. Meinzen-Dick et al. 2010 conclusion that there is widespread empirical evidence of ―gender-based disparities in adoption