rafmacalaba/datause-encoder-data huggingface dataset:
- 31 -

# **REFENCES**

Krueger, Anne O., Maurice Schiff & Alberto Valdes, 1988, "Agricultural Incentives In Deveioping Countries: Measuring the Effect of Sectoral & Economywido Policies", World Bank Econormic Review, 2(3):255-271.

Newbery, David & Nicholas Stern, editors, 1978, The Theory of Taxation for **Deveobph** Countries, Now York: Oxford University Press (for The World Bank), July.

Newbery, David, 1987, "Agricultural Taxation: The Main Issues", Chapter 13, in Newbery & Stern, eds.

O'Mara, Gerald T., editor, 1988, Efficency hI rrigatbn, Washington DC: rhe World Bank, March.

O'Mara, Gerald T., 1988, "The Efficient Use of Surface Water & Groundwater In irrigatlon: An Overview of the Issues", Chapter 1 In O'Mara, ed.

Radosevich, George, 1988, "Legal Consideratlons for Coping with Externalities In Irrigated Agriculture", Chapter 3 In O'Mara ed.

Sah, RaaJ Kumar & Joseph Stiglitz, 1987, "The Taxation & Pricing of Agricultural & Industrial Goods In Developing Economies", Chapter 16 In Newbery & Stern, eds. Stelnberg, David I., 1983, rriatlon & AID's Experience: A Consideration Based on Evaluatlons, Program Evaluation Report No.8, USAgency for International Development, August.

Wade, Robert, 1979, "The Social Response to irrigation: An Indian Case Study", Journal of Development Studies 16(1):3-26.

Wade, Robert, 1982, "The System of Administrative and Political Corruption: Canal Irrigation In South India", Journal of Development Studles 18(3):287-328.
# 6. Conclusions

After more than two decades of civil war and conflict, Somalia remains a fragile state subject to conflict and violence. The Federal Government of Somalia aims to provide the political and security conditions for improving the development trajectory of the country and increasing the welfare conditions of its population. The challenge of improving security conditions will be larger in the coming years as countries participating in The African Union Mission in Somalia (AMISOM) are considering whether to withdraw from Somalia.<sup>32</sup> Terrorist groups and their attacks are one of the threats to the government and its stability, representing a risk for the well-being of the population and limiting the capacity of the government and international partners to design and implement effective development policies.

This paper documents the immediate (within a week) impact of terrorist attacks from Al-Shabaab against civilians in Somalia. We combine micro-data from two waves of the Somali High Frequency Survey and employ a difference-in-difference approach comparing outcomes of households exposed to terrorist attacks against households who were not exposed to the incidents, before and after the events. Our estimates are robust to the use of clustered and HAC standard errors, different samples and control groups considered, besides that a similar composition of repeated-cross sections and a falsification test – measuring the impact before the events occurred– support the validity of our empirical strategy. We further confirm the results through an instrumental variables approach, for which we obtain a valid shift-share type of instrument that exploits the spatial variation of incidents and changes in the number of US air/drone attacks against Al-Shabaab.

Our estimates indicate a sizable immediate effect on consumption for households exposed to attacks with a decrease of 33%, mainly driven by a decline in food consumption. For some households, the reduced consumption brings their expenditure level below the poverty line, increasing the share of poor population. Among the poor, the negative effect results in consumption levels further away from the poverty line. The impact on consumption seems to be explained by a smaller share of household members (aged 15 to 50) working and earning income after an attack. In addition, we document that the negative impact on consumption is clustered within a 4 kilometer radius from the incident and has a heterogeneous effect, not affecting households in the top 20% of the consumption distribution. Besides, OLS estimates –comparing Wave 2 households in Mogadishu before

> 32 The African Union Mission in Somalia is an active, regional peacekeeping mission operated by the African Union with the approval of the United Nations Security Council.

23
not adjust _p_ values for these five comparisons. The most conservative approach, a Bonferroni correction, would substitute .02 and .01 for the usual 0.1 and 0.05 critical values. In general, we will see the main findings pass even this conservative test.

This theory and the main outcomes were pre-specified but not formally registered. Our study predated the social science registry, but we outlined predictions for the main measures in a 2012 National Science Foundation proposal 1225697.<sup>31</sup> Naturally these five are not the only conceivable outcomes of interest. There are alternative mechanisms (e.g. therapy could affect behavior by changing peers, post-traumatic stress, or substance abuse). Other outcomes enhance our interpretation (such as the effects of cash on economic inputs and outputs that generate income). We collected survey data for many such outcomes, and every one of these is reported in this paper’s tables. But note that the treatment effects on these other outcomes are exploratory and suggestive, primarily meant for for interpretation and hypothesis generation.

**Self-reported data** One threat to identification comes from systematic measurement error in self-reported data, especially measurement error correlated with treatment status. In the absence of administrative data such as arrest records, we developed a technique to validate select survey variables through intensive observation. Blattman et al. (2015) reports the approach in detail, and we summarize in Section 7 and Appendix F.

**Spillovers** Another threat to identification comes from spillovers. Our recruiting strategy— working in large neighborhoods, recruiting less than 1% of adult men in those areas, and less than 15% of high-risk men we could identify on the street—was designed to reduce equilibrium effects such as a change in the returns to illicit work. We do not have the data or research design, however, to confirm that these equilibrium effects were minimized.

Another potential spillover involves interactions within and between treatment arms, especially therapy. For example, because of peer effects and the emphasis on social norms, there could be positive spillovers from treating groups of friends. If so, the coefficients on therapy would overestimate the effect of therapy in isolation. Alternatively, to the extent that control subjects interact with and learn from treatment subjects, they may acquire some of the lessons, leading us to underestimate therapy’s impact.

> component variables would give more weight to outcomes that are typically measured with many different questions (such as aggressive behavior) versus one that can be precisely measured with a small number of variables (such as drug selling), which we find inappropriate. Nonetheless, Appendix E.1 shows robustness to an index that averages all survey questions rather than composite measures, or uses covariance weighting rather than mean

> 31http://chrisblattman.com/documents/research/2012.01.13_STYL_NSF_proposal.pdf. The fifth outcome, values, arose out of the review process and other comments and does not appear in this document.

16
# **Table 1: Top five of countries with high nominal lending rates and lending-deposit spreads in 2017 by region**

|**Regions**|**Countries with Highest Nominal Lending**<br>**Rate (%)**|**Countries with Highest Lending Deposit**<br>**Spreads (%)**|
|---|---|---|
|East Asia & Pacific|Mongolia (20.0)<br>Micronesia, Fed. Sts. (16.1)<br>Timor-Leste (13.2)<br>Myanmar (13)|Micronesia, Fed. Sts. (15.6)<br>Timor-Leste (12.5)<br>Solomon Islands (10.3)<br>Papua New Guinea (7.87)|
||Indonesia (11.0)|Mongolia (7.05)|
|Europe & Central Asia|Tajikistan (29.6)<br>Kyrgyz Republic (19.8)<br>Azerbaijan (16.5)<br>Ukraine (16.3)|Tajikistan (26.0)<br>Kyrgyz Republic (17.0)<br>Azerbaijan (8.11)<br>Ukraine (7.25)|
||Armenia (14.4)|Albania (5.82)|
||Brazil (46.9)<br>Argentina (31.2)|Brazil (38.4)<br>Paraguay (14.1)|
|Latin America & Caribbean|Venezuela, RB (21.0)<br>Honduras (19.2)|Guyana (11.9)<br>Jamaica (11.1)|
||Paraguay (18.0)|Honduras (11.0)|
||Egypt, Arab Rep. (18.1)|Algeria (6.25)|
|Middle East & North Africa|Lebanon (8.09)<br>Algeria (8)<br>Qatar (4.7)|Egypt, Arab Rep. (6.08)<br>Qatar (1.79)<br>Lebanon (1.18)<br>-|
||Bhutan (14.2)<br>Sri Lanka (11.5)|Bhutan (11.4)<br>Maldives (6.57)|
|South Asia|Maldives (10.1)<br>Bangladesh (9.54)|Bangladesh (3.93)<br>Pakistan (3.73)|
||India (9.50)|Sri Lanka (2.56)|
||Madagascar (60)<br>Malawi (38.5)|Madagascar (45)<br>Congo, Dem. Rep. (16.4)|
|Sub-Saharan Africa|Gambia, The (29)<br>Mozambique (27.8)|São Tomé and Príncipe (15.4)<br>Sierra Leone (13.8)|
||Uganda (21.2)|Gambia, The (13.2)|

Source: World Bank Finstats 2019.

40
Table 1. Thailand

||(1)<br>NETCOGGD|(2)<br>LEPR|(3)|(4)<br>RDEPR<br>(2-3)|
|---|---|---|---|---|
|1972|6.86|5.0|4.85|0.15|
|1973|5.54|5.0|15.50|-10.50|
|1974|4.60|6.0|24.32|-18.32|
|1975|4.83|6.0|5.33|0.67|
|1976|4.83|6.0|4.15|1.85|
|1977|4.45|8.0|7.60|0.40|
|1978|4.01|8.0|7.92|0.08|
|1979|3.34|8.3|9.89|-1.65|
|1980|3.12|12.0|19.70|-7.70|
|1981|3.42|12.5|12.66|-0.16|
|1982|4.97|13.0|5.26|7.74|
|1983|4.04|13.0|3.73|9.27|
|1984|6.62|13.0|0.86|12.14|
|1985|5.72|13.0|2.43|10.57|
|1986|7.63|9.8|1.84|7.91|
|1987|7.59|9.5|2.47|7.03|

Note: NETCOGGD COG - GD - DEPR - i - RDEPR (COG-GD)/GDP where Commercial banks' claims on government and Government deposits at commercial banks Deposit Rate Inflation Rate Using the CPI Real Deposit Rate (DEPR-ir) Source: IFS.
past less of a priority for national governments and it is only recently and with the numbers of forcibly displaced people increasing that this is becoming an issue. Experiments are now being conducted with innovative technologies such as remote sampling with aerial or satellite imagery and also with non‐ sampling techniques, but there is still no blueprint for sampling refugees and IDPs and more generally very mobile populations.

**Unit of analysis** . As for other studies based on micro data, the primary unit of analysis for refugees and IDPs is the individual and/or household. The added difficulty in the context of refugees and IDPs is how to identify the household and allocate individuals to single households. Refugee households are complex by definition. Some household members may stay behind or join later, others may leave or shuttle between places. Some families travel as clans with multiple families living together, others travel as single families. Sometimes individuals or families try to register in multiple places to claim more benefits. Deciding what constitutes a household or a family is not simple. The UNHCR has its own definitions of family and household but it is not always straightforward for field workers to apply such definitions. Again, while for national household budget surveys these questions are largely resolved, for mobile populations such as refugees and IDPs they are not and this is of key importance for economists working with unit data.

**Welfare** . How to measure the welfare of refugees? Welfare is perhaps the main outcome to measure for economists. Welfare economists would typically use income, expenditure or wealth to measure welfare but refugees and IDPs have a very peculiar situation when it comes to these measures. They have probably left behind most of their wealth and assets with the exception of what they could carry with them. Income from work, if any, is informal and very low whereas other incomes are mostly represented by cash grants provided by humanitarian organizations. Expenditure is probably restricted in terms of items purchased and largely driven by what can be purchased with food vouchers. Expenditure is also done in bulk, when money becomes available and in places that offer low prices or predetermined stores that accept food vouchers. Should food vouchers be considered income or expenditure? As we will see further in the paper, the study on the welfare of Syrian refugees addressed many of these questions but was limited to refugees in areas that benefitted from an unusually rich set of data. There is still no blueprint on how to measure welfare of forcibly displaced populations.

**Multidimensional deprivations** . Refugees and IDPS also have special needs that exist but are rare in regular populations. Refugees and IDPs are typically traumatized, injured or ill, in need of psychological and health assistance. They have difficulties in accessing basic services such as health and education because they often have no legal rights in the host country or the host country may simply not have the

19
|Agro-biodiversityand Adaptation|GarryCharlier|
|---|---|
|Agro-biodiversityand Adaptation|GarryCharlier|
|Agro-biodiversityand Adaptation|GarryCharlier|
|Agro-biodiversityand Adaptation|GarryCharlier|
|Agro-biodiversityand Adaptation|GarryCharlier|
|Agro-biodiversityand Adaptation|GarryCharlier|
|Agro-biodiversityand Adaptation|GarryCharlier|
|Agro-biodiversityand Adaptation|GarryCharlier|
|Agro-biodiversityand Adaptation|GarryCharlier|
|Support to Internal Audit Agencyto improve Internal Auditors S|kills Robert Wallace DeGraft-Hanson|
|Support to Internal Audit Agencyto improve Internal Auditors S|kills Robert Wallace DeGraft-Hanson|
|Second PovertyReduction Support Credit|Olasupo Olusi|
|Second PovertyReduction Support Credit|Olasupo Olusi|
|Monitoring& Evaluation of the PRSP and Reform Programs|Ingrid B. Ivins|
|Monitoring& Evaluation of the PRSP and Reform Programs|Ingrid B. Ivins|
|Monitoring& Evaluation of the PRSP and Reform Programs|Ingrid B. Ivins|
|Governance and Institutional Development Project II|Anne-Lucie Lefebvre|
|Governance and Institutional Development Project II|Anne-Lucie Lefebvre|
|Governance and Institutional Development Project II|Anne-Lucie Lefebvre|
|Governance and Institutional Development Project II|Anne-Lucie Lefebvre|
|Governance and Institutional Development Project II|Anne-Lucie Lefebvre|
|Governance and Institutional Development Project II<br>|Anne-Lucie Lefebvre<br>|
|Governance and Institutional Development Project II|Anne-Lucie Lefebvre|
|Governance and Institutional Development Project II|Anne-Lucie Lefebvre|
|Governance and Institutional Development Project II|Anne-Lucie Lefebvre|
|Governance and Institutional Development Project II|Anne-Lucie Lefebvre|
|Governance and Institutional Development Project II|Anne-Lucie Lefebvre|
|Governance and Institutional Development Project II|Anne-Lucie Lefebvre|
|Governance and Institutional Development Project II|Anne-Lucie Lefebvre|
|Community-based Facilitator Training (British Council)|John Victor Bottini|
|Conditional Cash Transfers Project|Bojana Naceva|
|Conditional Cash Transfers Project|Bojana Naceva|
|Conditional Cash Transfers Project|Bojana Naceva|
|Conditional Cash Transfers Project|Bojana Naceva|
|Conditional Cash Transfers Project|Bojana Naceva|
|Conditional Cash Transfers Project|Bojana Naceva|
|Conditional Cash Transfers Project|Bojana Naceva|
|Conditional Cash Transfers Project|Bojana Naceva|
|Conditional Cash Transfers Project|Bojana Naceva|
|Conditional Cash Transfers Project|Bojana Naceva|
|Conditional Cash Transfers Project|Bojana Naceva|
|Conditional Cash Transfers Project|Bojana Naceva|
|Conditional Cash Transfers Project|Bojana Naceva|
|Conditional Cash Transfers Project|Bojana Naceva|
|Conditional Cash Transfers Project|Bojana Naceva|
|Conditional Cash Transfers Project|Bojana Naceva|
|Conditional Cash Transfers Project|Bojana Naceva|
22 the use of software and telecommunication systems that are not specifically tailored to the needs of Islamic banks.<sup>49</sup> Equally important for the operation of Islamic as well as conventional banks is the presence of a conducive institutional environment and an efficient regulatory framework. Such poor supporting institutional infrastructure exposes Islamic banks to systemic risks related to institutional, legal and regulatory issues. At the forefront of these is institutional risk resulting from the lack of consensus among _Fiqh_ scholars on contractual rules governing financial transactions. For instance, while some _Fiqh_ scholars consider the terms of a _murabaha_ or _istisna_ contract to be binding to the buyer, others argue that the buyer has the option to rescind from the contract even after making an order and paying the commitment fee.<sup>50</sup> This raises Islamic banks’ exposure to counter-party risks arising from the unsettled nature of contracts, and may lead to potential litigation problems. A related issue is the general confusion created by the heterogeneous interpretations of the fundamental _Shariah_ rules resulting in differences in financial reporting, auditing and accounting treatments by Islamic banks.<sup>51</sup> Moreover, the lack of standardized contracts for Islamic financial instruments as well as the absence of effective litigation and dispute resolution systems create a business environment risk. Poor enforceability of contractual agreements ultimately increases Islamic banks’ exposure to counter-party risks of default and delinquency.<sup>52</sup> While the imposition of penalty in the case of late payment is not accepted according to _Shariah_ law, some banks enforce the penalty as a deterrent mechanism and use the collected sums for charitable causes.<sup>53</sup>

> 49 Khan & Ahmed (2001).

> 50Khan & Ahmed  (2001).

> 51As previously discussed, this results in lack of transparency and comparability in their financial statements; see Archer & Ahmed (2003). 52 Khan & Ahmed (2001).

> 53 Archer & Ahmed (2003).
# **Big Sisters**

Pamela Jakiela, Owen Ozier, Lia Fernald, and Heather Knauer<sup>_∗_</sup>

**JEL codes:** O12, J13, J16, D13

**Keywords:** sisters, girls, girl effect, girl power, Family Care Indicators, early childhood, human capital, household structure, parental investments, natural experiment

> _∗_ Jakiela: Williams College, BREAD, CGD, IZA, and J-PAL, email: pamela.jakiela@williams.edu; Ozier: Williams College and the World Bank Development Research Group, BREAD, IZA, and J-PAL, email: owen.ozier@williams.edu; Fernald: University of California at Berkeley, email: fernald@berkeley.edu; Knauer: University of Michigan, email: heather.knauer@gmail.com. Orazio Attanasio, Maya Eden, Dave Evans, Deon Filmer, Joe Henrich, Amanda Glassman, Matthew Jukes, Vikram Maheshri, John Ototo, Ania Ozier, Justin Sandefur, Dana Schmidt, and Tara Watson provided helpful comments. We are grateful to William Blackmon, Julian Duggan, Laura Kincaide, and Elyse Thulin for excellent research assistance. IRB approval for the larger RCT was provided by the University of California at Berkeley, by the Maseno University Ethics Review Committee, and by Innovations for Poverty Action. This research was funded by Echidna Giving and by the Strategic Impact Evaluation Fund and the Early Learning Partnership at the World Bank. All errors are our own.

The findings, interpretations and conclusions expressed in this paper are entirely those of the authors, and do not necessarily represent the views of the World Bank, its Executive Directors, or the governments of the countries they represent.
data on the occurrence and impacts of over 22,000 mass disasters worldwide since 1900 (Yamamura, 2015; Cappelli _et al._ , 2021). Another source of disaster data is DesInventar, which provides records of disaster at the country level from 1980 (Rodriguez-Oreggia _et al._ , 2012; Keerthiratne and Tol, 2018). The advantage of such databases is their wide coverage and the standardization of disaster data, enabling cross-country comparisons. However, they often lack detailed local-level data, which can be crucial for understanding variations in disaster impacts within countries. Moreover, these databases predominantly focus on large-scale disasters, potentially excluding smaller, localized disasters that can have a substantial cumulative impact on poverty and inequality.<sup>8</sup> The reliance on official reports or media coverage for disaster validation may also underrepresent disasters in areas with less robust reporting systems. These limitations highlight the importance of incorporating additional data sources to provide a more comprehensive understanding of disaster impacts.

Another nuanced approach in the study of disaster impacts combines traditional survey techniques with geospatial and localized risk information (i.e., footprint data). Geo-referencing is employed to further validate self-reported exposure data, thus combining ground truth with computational models. Such methodologies aim to generate a more intricate understanding of localized risks and household experiences, and therefore bring a multidimensional perspective that blends traditional and high-tech methods. Specifically, recent studies use community flood maps and satellite imagery to identify flood risks or use self-reported survey data that were validated with a newly developed flood map, offering a more complex understanding of households’ experiences during flood events (Erman _et al._ , 2019; Erman _et al._ , 2020). A particularly significant development is the use of reanalysis data, which combines satellite observations, ground measurements, and statistical models to offer near-global coverage of

> 8 To be included in EM-DAT, a disaster must meet at least one of the following criteria: 10 or more people reported killed, 100 or more people reported affected, declaration of a state of emergency, or a call for international assistance.

15
# **Section II Public Pension Schemes**

# **A. Expenditures and Revenues**

Definitions. In this study, pension spending is defined as _old age/retirement, survivors/death and invalidity/disability payments based on past contribution records and non-contributory, universal flat or means-tested programs specifically targeted toward the old._ The data reported are thought to include all major pension spending programs including the main scheme and special schemes covering public sector employees. Due to the dispersion of programs in some countries, however, some of the smaller schemes may not be included. In cases where these omitted schemes were thought to represent a significant percentage of the spending, the country was excluded. In some cases, data on separate programs from different sources are added together to get the total pension spending. Administrative costs as opposed to benefit payments are excluded and treated separately (see "Administrative Costs" below"). In-kind services such as medical care or housing for the old are excluded as is poverty assistance not based on age. Public pension spending here refers only to direct spending on benefits and ignores tax expenditures. Data are for years between 1985 and 1993 and include single-year estimates based on data on different pension schemes from different years.

_Pension revenues here refer to the combined employer/employee payroll taxes, income from the investment of pension reserves and direct and indirect government subsidies to the pension scheme._ The revenues of various social insurance programs are, in many cases, not allocated between pensions and other programs such as unemployment. For this reason, the sample of countries with pension spending data is larger than the sample with data for pension revenues. In addition, more recent and more comprehensive data are sometimes available for pension spending than for pension revenues and the sources may differ. In contrast to pension spending data, revenues are sometimes shown for the main scheme only and therefore may exclude smaller, secondary schemes. In most cases, however, the revenues of all major schemes are available and are included.

3
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