rafmacalaba/datause-encoder-data huggingface dataset:
**Appendix Table 6: Children in monetary** **<u>poor households in 2022 at the $3.65 line in 2017 PPP by region</u>**

|**Region**|**Children in**<br>**poverty**<br>**(millions)**|**Headcount**<br>**poverty rate**<br>**(%)**|**Share of**<br>**poor (%)**|**Share of**<br>**population**<br>**(%)**|
|---|---|---|---|---|
|**East Asia and Pacific**|55.1|13.3|6.7|19.4|
|**East Asia and Pacific without China**|49.7|24.5|9.7|14.9|
|**South Asia**|300.2|46.7|36.6|30.1|
|**South Asia without India**|74.3|39.4|14.5|13.8|
|**Sub-Saharan Africa**|409.2|69.4|49.9|27.6|
|**Sub-Saharan Africa without Nigeria**|332.2|68.6|64.9|35.5|
|**Latin America and Caribbean**|24.7|15.1|3.0|7.7|
|**Europe and Central Asia**|4.2|2.1|.5|9.4|
|**Middle East and North Africa**|26.7|21.6|3.3|5.8|
|**Total**|820.0|38.4|100.0|100.0|

**Appendix Table 7: Children in monetary poor households in 2022 at $3.65 PPP (LMIC) in in 2017 PPP in 2013, 2017 and 2022 by region (%)**

|**Region**|2013|2017|2020|2022|
|---|---|---|---|---|
|**East Asia and Pacific**|28.2|17.5|14.6|13.3|
|**East Asia and Pacific without**<br>**China**|38.6|27.8|25.7|24.5|
|**South Asia**|65.8|58.6|53.2|46.7|
|**South Asia without India**|54.7|45.2|43.5|39.4|
|**Sub-Saharan Africa**|73.8|71.0|70.3|69.4|
|**Sub-Saharan Africa without**<br>**Nigeria**|74.2|70.9|69.6|68.6|
|**Latin America and Caribbean**|19.9|18.5|16.9|15.1|
|**Europe and Central Asia**|2.9|2.4|2.2|2.2|
|**Middle East and North Africa**|15.9|22.0|22.9|21.6|
|**Total**|47.8|43.3|41.3|38.9|

**Appendix Table 8: Children in monetary** **<u>poor households in 2022 at the $6.85 line in 2017 PPP by region</u>**

|**Region**|**Children in poverty**<br>**(millions)**|**Headcount poverty**<br>**rate(%)**|**Share of poor (%)**|**Share of population**<br>**(%)**|
|---|---|---|---|---|
|**East Asia and Pacific**|174.0|42.0|12.3|19.7|
|**East Asia and Pacific without**<br>**China**|117.0|57.7|13.6|15.2|
|**South Asia**|557.0|86.7|39.2|30.5|
|**South Asia without India**|157.1|83.3|18.2|14.1|
|**Sub-Saharan Africa**|537.8|91.1|37.9|28.0|
|**Sub-Saharan Africa without**<br>**Nigeria**|436.9|90.3|50.7|36.3|
|**Latin America and Caribbean**|64.5|39.5|4.5|7.8|
|**Europe and Central Asia**|18.5|10.8|1.3|8.2|
|**Middle East and North Africa**|68.2|55.2|4.8|5.9|
|**Total**|1420.0|67.4|100.0|100.0|

13
from any of the interventions on any awareness measure. The evidence suggests that the intervention did not raise community awareness of the existence, functioning, or purpose of SMCs. This finding is in line with evidence from India suggesting that the provision of information alone frequently fails to raise community awareness of the functioning of existing community engagement institutions (Banerjee et al., 2010).

# **4.4 Threats to internal validity**

Using geospatial analysis (GIS), we find that five percent of intervention villages are located 1-2 kilometers from the nearest control village.<sup>48</sup> This proximity may endanger the validity of the impact estimates if contamination of treatment occurred in control schools and villages.<sup>49</sup> In order to test that contamination did not occur, we isolated all treatment schools in villages located at least 1 kilometer from control schools, and we conducted the analysis on this limited sample to identify any significant changes in the size of coefficients and standard errors. The results on additional teachers, teacher absenteeism, and primary school enrollment for boys all continue to be statistically significant.<sup>50</sup> In addition, the results remain consistent in magnitude compared to the main results tables, with negligible changes in effect sizes, suggesting that contamination has not biased the impact estimates reported within the full sample and fully functional sample of schools.

> 48 The mean distance between a treatment school and the nearest control school is 7.41km; the median 5.83km, and the standard deviation 12.67km.

> 49 The Stable Unit Treatment Value Assumption (SUTVA) requires that the treatment of one unit should be unaffected by the assignment of treatment to other units (Wooldridge, 2012). The relatively close distances between control and treatment schools heighten the probability of contamination, a possible violation of the SUTVA assumption. 50 See Table A6, in Appendix A.

44
- Jacobs, CIM & Antea Paviotti. 2017. “Social integration of internally displaced people in urban settings.”.

- Jenson, Jane. 1998. _Mapping social cohesion: The state of Canadian research_ . Canadian policy research networks Ottawa.

- JIPS. 2012. “Profile at a glance: Uganda.” https://www.jips.org/uploads/2019/03/ JIPS-ProfileataGlance-Uganda-2011.pdf.

- Landau, Loren B. 2014. “Urban refugees and IDPs.” _The Oxford Handbook of refugee and forced migration studies_ 1.

- Marbach, Moritz & Guido Ropers. 2018. “Not in My Backyard: Do Increases in Immigration Cause Political Violence?”.

- Mels, Cindy, Ilse Derluyn, Eric Broekaert & Yves Rosseel. 2010. “The psychological impact of forced displacement and related risk factors on Eastern Congolese adolescents affected by war.” _Journal of Child Psychology and Psychiatry_ 51(10):1096–1104.

- Miller, Joy. 2007. Uganda’s IDP policy. Technical report Brookings-Bern Project on Internal Displacement.

- Milner, James & Gil Loescher. 2011. “Responding to protracted refugee situations: Lessons from a decade of discussion.”.

- Pham, Phuong N, Laila Fozouni, Abdulrazzaq Al-Saiedi, Kevin Coughlin & Patrick Vinck. 2021. “Association between distress and displacement settings: a cross-sectional survey among displaced Yazidis in northern Iraq.” _BMC Public Health_ 21(1):1–11.

- Pham, Phuong N, Patrick Vinck & Eric Stover. 2008. “The Lord’s Resistance Army and forced conscription in northern Uganda.” _Human Rights Quarterly_ 30:404.

- Pham, Phuong & Patrick Vinck. 2010. “Transitioning to peace: A population-based survey on attitudes about social reconstruction and justice in Northern Uganda.” _UC Berkeley Initiative for Vulnerable Populations_ .

- Pham, Phuong, Thomas O’Mealia, Carol Wei, Kennedy Kihangi Bindu, Anupah Makoond & Patrick Vinck. 2021. “Hosting new neighbors: Perspectives of host communities on social cohesion in eastern DRC.”. _Unpublished Working paper_ . Commissioned as part of the “Preventing Social Conflict and Promoting Social Cohesion in Forced Displacement Contexts Series.” Washington, DC: World Bank Group.

- Revkin, Mara Redlich. 2021. “Competitive governance and displacement decisions under rebel rule: evidence from the Islamic State in Iraq.” _Journal of conflict resolution_ 65(1):46–80.

- Roberts, Bayard, Eliaba Yona Damundu, Olivia Lomoro & Egbert Sondorp. 2009. “Post-conflict mental health needs: a cross-sectional survey of trauma, depression and associated factors in Juba, Southern Sudan.” _BMC Psychiatry_ 9(1):1–10.

29
Figure 3 presents the maps of the spatial accessibility of job opportunities in Amman, Beirut, and Cairo. In our sample, in Amman, households have on average access to 27% of total jobs within 60 minutes using public transportation and walking. In Beirut, the same figure is at 21% and in Cairo (a larger city), it is at 16%. These accessibility levels are lower than many cities in developing countries where such analysis has been done. Peralta-Quiros et al. (2019) perform a benchmarking of 11 cities in Africa, according to this benchmarking, all three cities can be classified as worst performers in connecting people with employment opportunities.<sup>22</sup>

# _b. Measuring availability_

Availability focuses on the availability of public transport close to the residential locations. In practice, we look at the proximity to transit stops factored by the frequency of service. This indicator is used to assess the density of service within immediate reach of the household, but without considering the destinations of the transit.

The same street grid data and transit network data that was used for measuring accessibility is also used to measure availability. For each household, we compute the number of public transport ‘runs’, that is the number of vehicle departures at all stops available within a 10 minute walking time over the course of an hour. The number of runs is normalized to create an index between 0 and 100%.<sup>23</sup> A value of 100% means that public transport is highly available within 10-minute walking distance from an hour while a value of 0 means that no public transport is available within a 10-minute walking distance of a household.

Figure 4 presents the maps of the availability of public transport in Amman, Beirut, and Cairo. In Amman, for our sample of households, the average value of the index is 41%. In Beirut, the same figure is at 29%, and Cairo it is at 52%.

> 22 Benchmarking data is only available for Africa.

> 23 To normalize, we divide the number of runs by 60 and truncate the values equal to or over 1 to be equal to 100%. This means that we have an index of an average number of bus reachable each minute between 8 am and 9 am, at a maximum distance of 10-minute walk, with the maximum being 1 bus per minute (in this case the index equals 100%).

15
childbearing and she does not enter the next spell. This is done for all four spells, so fertility is capped at four. Because the samples might differ in composition between periods, the results for the two periods are presented for both women who married in the 1985–1994 period and women who were married in the 1995–2006 period. The presented results are averages of 80 repetitions.

Table 6: Predicted Lifetime Fertility, Sex Ratio, and Abortions for Women with 8 or More Years of Education

|Sample period|1985–|1994<br>1995|–2006|
|---|---|---|---|
|Estimation Results from|Period 2|Period 3<br>Period 2|Period 3|
|||Urban||
|Fertility|2_._3|2_._1<br>2_._3|2_._0|
|Percentage boys|52_._7|53_._0<br>52_._6|53_._2|
|Abortions per 100 births|3_._0|3_._6<br>2_._9|3_._8|
|Abortions per 100 women|7_._1|7_._6<br>6_._7|7_._8|
|||Rural||
|Fertility|2_._8|2_._6<br>2_._7|2_._5|
|Percentage boys|52_._5|52_._7<br>52_._4|52_._7|
|Abortions per 100 births|2_._5|2_._8<br>2_._4|2_._9|
|Abortions per 100 women|6_._9|7_._2<br>6_._6|7_._3|

**Note.** Prediction of fertility and abortions are based on the results above. The samples consist of all women who married in a given period. All numbers presented are for women predicted to have one or more births. Results are average values of 80 repetitions of the predictions.

Fertility has fallen over time; predicted completed fertility for urban women went from 2.3 during the 1985–1994 period to just barely over 2 during the 1995–2006 period. Hence, fertility for urban women is likely to be below replacement. Rural women also showed a decline in fertility over time going from 2.8 to 2.5.

For all groups, the number of abortions expected before childbearing is over increased substantially between the two periods. Ignoring the compositional effects and comparing the first and last columns for urban women, the number of abortions per 100 women have gone up by almost 10 percent (7.1 to 7.8) and the number of abortions per 100 births have gone up by over 25 percent (3.0 to 3.8). For rural women, abortions per 100 women increased 5 percent (6.9 to 7.3), while abortions per 100 births increased over 15 percent (2.5 to 2.9).

Of particular note is the high level of abortions for the 1985–1994 period, especially for ru47
In the first three years of the 1990s earnings were reduced 36 percent. Eurthermore, benefits such as, for examnple, housing and schooling allowances enjoyed by manufacturing sector workers were cut. Earnings in the manufacturing sector were eroded, being 68 percent above average real earnings in 1979 and only 36 percent above in 1990 (Knight; 1996). A decline in real earnings of this magnitude may indicate that the Zimbabwean labor market is rather flexible.

The first government after Independence launched income policies in an attempt to obtain a more equal wage distribution. It actively intervened in the wage determination and launched a minimum wage law. The minimum wage increased rapidly in real terms in the following two years. One consequence of the minimum wage law and the requirement for permission to lay-off and dismiss workers is that employers are unable to offer lower wages, despite being obliged to give greater job security.<sup>4</sup> In recent years collective bargaining plays a more important role in setting wages in Zimbabwe. The combination of income policies and a rise in the minimum wage following Independence placed a legal boundary on potential, downward real wage adjustments (see Fallon and Lucas; 1993).

**Table 2**

|||**Average real**|**earnings by sector (**|**1979=100)**||
|---|---|---|---|---|---|
|Year|All**sectors**|**Agriculture**|**Domestic Service**|**Mining**|**Manufacturing_**|
|1989|114.7|133.3|101.8|130|93.6|
|1993|85|86.3|47|113.1|71.1|

_Source:_ CSO quarterly.

Also real manufacturing wages fell in the 1980s and 1990s. As earlier mentioned, the number of employees in manufacturing rose during the 1980s, whereas it was roughly

> 4 The 1980 Employment Act requires explicit permission from the Ministry of Labor's Retrenchment Committee before any wage or salary employee can be laid-off. Furthermore, contract workers are protected during the contract period. However, employees employed up to six weeks, the so-called casual workers, are totally excluded from the permission clause.

10
Amhara and Afar in late June 2021.<sup>26</sup> Thus, Tigray is the region affected in the first phase of the war. The other regions in Ethiopia that were not affected by the first phase of the war serve as controls. We then relax this assumption using the granular data from ACLED and apply alternative definitions of control group to probe the robustness of our results. We also restrict this control sample to the three highland regions of Ethiopia (i.e., Amhara, Oromia, and SNNP) given their similarities to Tigray in both agroecological and livelihood contexts. As shown in Section 3, these regions share comparable trends in food security and participation in livelihood activities before the war. If the identifying parallel trends assumption holds in our context, the parameter associated with the interaction term, 2, identifies the impact of the war on food security and labor market outcomes. _h_ r is an error term and captures other unobservable factors that may affect food αα security and labor market outcomes. εε Multiple pre- and post-war-onset round data allows us to estimate time-varying effects of the conflict and relax the assumption of homogenous and time-invariant treatment effects needed to identify the difference-in-difference parameters in equation (1) (de Chaisemartin and D’Haultfœuille, 2020; Goodman-Bacon, 2021). Moreover, multiple pre-war round data allow us to test the parallel trend assumption in the pre-war period. For these purposes, we also estimate an alternative DID specification with month-specific controls and outcomes: _h_ h + ∑ (2) MM1 MM2 rrmm _h_ mm mm=1 mm rr mm=MM1+1 mm rr YY = αα + αα δδ TTWWTTW TT × 11(DD= WW) + ∑ γγ TTWWTTW TT × 11(DD= rrmm WW) + εε where _h_ is a measure of food insecurity and labor market outcomes associated with household _h_ living in region rrmm _r_ observed in month _m_ , h represents household-specific fixed effects, and YY is a vector of month or round dummies. Pre-war survey rounds range from 1 to _M1_ while post-mm αα αα war-onset rounds range from _M+1_ to _M2_ . The third (interaction) term in equation (2) captures Tigray specific pre-war trends and hence the parameters associated with this term serve us to indirectly test the parallel trend assumption. The fourth (interaction) term in equation (2) captures Tigray specific trends immediately after the outbreak of the war in November 2020, and the parameters associated with this term identify the impacts of the war on labor market and food

> 26 Because the conflict spilled over to other regions after June 2021 in ways that are difficult to relate to the administrative area codes observed in our data, we limit our study to the period in which the conflict was within Tigray.

22
# People in Harm’s Way: Flood Exposure and Poverty in 189 Countries

## **Jun Rentschler**<sup>**1**</sup> **, Melda Salhab**<sup>**1,2**</sup>

1 Climate Change Group, The World Bank, Washington DC, USA 2 Center for Advanced Spatial Analysis, University College London, London, UK

**JEL codes:** Q54, O18, P36 **Keywords:** Flood, exposure, disaster, poverty

**Acknowledgments:** This working paper contributes to the World Bank’s 2020 flagship report Poverty and Shared Prosperity. It has benefited from helpful comments, feedback, and inputs by Anahid Basiri, Alexandru Cojocaru, Minh Cong Nguyen, Samuel Freije-Rodriguez, Stephane Hallegatte, Mike Toman, Benjamin Stewart, Sophie de Vries Robbe, and Michael Woolcock.
than their Turkish counterparts. In terms of NIM, they have the best record as well.

Figure 1 compares operating expenses between foreign and local banks. As can be seen, operating expenses of foreign banks start at a higher level than local private banks and then go hand-in-hand for a while. Foreign banks later reduce their costs below Turkish banks, but there is a big increase in 1988 and 1989. In the following years, Turkish banks have higher ratios and there does not seem to be a persistent and clear trend for both groups.

The evolution of foreign banks' market share in terrns of assets, loans, and deposits shows that their shares have fluctuated but did not increase over time. In order to have a longer-term perspective we extended the analysis back to 1970. In terms of assets, we see that their share increased from 3.6 percent of total assets to 5 percent in 1997. With respect to loans, there is a decrease compared to the situation in 1970. The same is also true for deposits, accounting for only 3.4 percent of all deposits in the banking system. These are interesting results considering the number of entries. There is no visible increase in their traditional bank outputs and hence shares.

This implies two things. First, the shares of individual foreign banks relative to the banking system must be smaller now. There are more foreign banks but their shares did not increase proportionally. Second, if this is so, these banks are highly specialized and service niche areas and their profitability reflects substantial revenues from fee-based services.

Foreign banks are, in general, smaller than local banks, the largest being the Ottoman Bank, established in Turkey in 1863, which is not really perceived as a foreign bank. Total bank assets were about US$95 billion in 1997, of which US$4.5 billion 9
Taiwan in 1990 based on their HW study. Wang and Mullahy (2006)’s stated preference study in China implies a ratio of about 70:1. Vassanadumrongdee and Matsuoka (2005) in a stated preference study of WTP in the context of traffic accidents report a much higher ratio (158:1); however, Bhattacharya, Alberini and Cropper (2007) report a VSL for commuters in Delhi, India of $150,000 USD, implying a VSL/Y ratio of only 15. These results suggest that the VSL/Y ratio may be lower in middle than in high income countries.

_How Should Benefits Transfers Be Conducted?_ In practice, benefits transfers are likely to be based on income, with the elasticity of the VSL (ε in equation (9)) capturing differences in other factors—attitudes toward risk, discount rates, survival probabilities—that vary across countries and may be correlated with income. The preceding paragraph suggests that, interpreted in this way, ε > 1. There is, however, no reason to believe that the income elasticity of the VSL is constant, as equation (9) assumes. It could vary with income, increasing as income decreases.

Given these considerations, how should benefits transfers be conducted? Transferring VSL estimates using equation (10) is likely to overstate the VSL in low and middle income countries. One possibility is to use a ratio of VSL/Y in middle income countries of 80:1, based on Robinson (2008). Another possibility would be to transfer VSL estimates from the US to other countries using an elasticity of 1.0 and an elasticity of 1.5 to provide a range of estimates.<sup>31</sup> Table 8 illustrates the implications of this transfer for different groups of countries. The range of VSL values for a middle income country with per capita GDP = $5,700 is $257,000 to $710,000 (2000 USD), while it is between $730,000 and $1.4 million (2000 USD) for an upper-middle income country with per capita GDP of $11,400.<sup>32</sup> For a country with PPP per capita GDP of $1,150, the mean 2007 per capita income of “least developed countries” according to the UN definition, the VSL ranges from $23,000 to $143,000 (2000 USD).

> 31 Some support for the 1.5 elasticity is provided by Costa and Kahn (2005) and Hammitt, Liu and Liu

> (2000) who use time series variation in the coefficient of risk in an hedonic wage equation ( _γ0_ in equation (8)) to infer the elasticity of the VSL with respect to per capita GDP.  The authors estimate a series of hedonic wage equations at different points in time—Costa and Kahn using micro data for the US (19401980) and Hammitt et al. (2000) using micro data for Taiwan (1982-1997).  The elasticity of the VSL with respect to per capita GDP is approximately 1.5 in Costa and Kahn and 2.0 in Hammitt et al. (2000). 32 The transfers in Table 8 are based on 2007 PPP per capita GDP figures and a US VSL of $5.4 million (2000 USD) from Kochi et al. (2006).

20
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