rafmacalaba/data-use-mentions-extended huggingface dataset:
, using a dynamic model of the reinsurance sector based on a solvency constraint for reinsurance rms. We thus enrich Winter (1994) rst dynamic model of the competitive insurance market. This solvency constraint is represented by a bounded probability of default, and the model is related to the classical capacity constraint hypothesis. Furthermore, we take into account the strategic eects of rms individual behavior on the market equilibrium, completing the traditional competitive view of the insurance industry (Hardelin and Lemoyne de Forges, 2009).

Model results show a market dynamics that reproduces many real-world observations, suggesting that the capacity-constraint hypothesis is able to explain the observed variability in reinsurance prices and quantities, provided that strategic behavior is taken into account. We nd path-dependency, i.e. the fact that the consequence of a one shock depends on previous years losses and reinsurers response. We characterize and quantify the resilience of the sector through the time necessary to restore reinsurance capacity after large disasters. We provide a sensitivity analysis of the key parameters including higher loss levels and series of losses. We show that improving the ability of the reinsurance market to cope with large losses and reducing reinsurer default probability that is consolidating reinsurance rms with regard to risk has a cost, due to its negative impact on market prices and capacity. Thus we exhibit a regulation trade-o between market resilience and eciency.

The paper is organized as follows: Section 2 briey describes the main characteristics of the reinsurance market for natural catastrophe risks. Section 3 presents the underlying economic

> 1See Munich Re's long-term statistics: http://www.munichre.com/en/ts/geo\_risks/ natcatservice/long-term\_statistics\_since\_1950/default.aspx

> 2Capacity refers to the term used by Cummins et al. (2002) and dened as follows: For any losses for which the reinsurance companies are liable, the capacity of the reinsurance market is the proportion of the liabilities that is deliverable to their customers, given the
<mark>remaining age groups decreased in the refugee population compared to pre‐crisis Syria. The pattern is similar for male refugees. Men under 20 represent 59 percent of the male refugee population, compared to 47.6 percent in 2010. Young adult males seem to be missing from the refugee population as the proportion of young adult male refugees is lower than their share in the population of Syria in 2010: 8.2 percent of Syrian male refugees are between 20 and 24 years old compared to 10.4 percent in Syria in 2010. The reduction in the share of males ages 20–24, 2.2 percentage points, compares to a 1.3 percentage point decrease in the proportion of female refugees in the same age group.</mark>

# Refugee Education

<mark>The Government of Jordan opened its schools and health services to refugees living in the communities. Nevertheless, many Syrian children remain outside school and numbers were highest in the years just after their arrival. The proportion of Syrian school‐age children enrolled in formal education was 53 percent in 2014, up from 44 percent in 2013 (UNHCR 2014). Household poverty is one of the reasons behind these low enrollment figures. However, boys and girls face different barriers to staying in school. UNICEF (2017) reports that for boys, child labor, school violence, and the high costs of schooling (for transportation and stationery supplies) are the main barriers to their enrollment. For girls, barriers include the distance to the nearest school, the high cost of transportation, the need to help with household chores, health problems, and families refusing to educate their daughters. According to the JD‐HV database, the most common reasons parents gave for their children not attending school were financial constraints (35 percent), lack of capacity in schools (29 percent), or that children were required to work to support their family (14 percent). By 2016, the enrolment rate of Syrian refugee school‐
**Figure 13. Financial resilience is much higher in high‐income countries than in developing economies**

<!-- Start of picture text --> High‐income economies<br>Developing economies<br>0% 20% 40% 60% 80%<br>Percentage of adults who could raise emergency funds<br>Savings<br>Money from working<br>Family or friends<br>Borrowing from a bank, an employer, or a private lender<br>Other<br><!-- End of picture text --> _Source_ : Global Findex database.

_Note_ : “Other” includes all respondents who chose “selling assets,” “other sources,” “don’t know,” or “refuse” as their response.

About two‐thirds of adults in ECA reported that they would be able to come up with emergency funds. In the euro area, the share rose from 70 percent in 2014 to 76 percent in 2017. In the developing economies of the region, the share declined from 64 percent to 61 percent, though the decline is not significant. The gender gap in the share is about 6 percentage points, with men more likely to report that they could come up with emergency funds than women. This gap remained constant between 2014 and 2017.

When asked about their source of emergency funds, 52 percent of respondents in the euro area who reported that they were able to come up with the funds stated that the main source was their savings. In the developing economies of the region, the share was 18 percent. More than half of the adults in developing ECA who said they could come up with the funds reported that the main source of funding would be family or friends (figure 14).

24
result that an increase in rates widens intermnediation spreads at first. Spreads then narrow, as lending rates gradually adjust, but the catch-up in estimated to be incomplete, so that a positive long-tern equilibrium relationship is estimated to exist between intermediation spreads and the level of interest rates.

Here again our main approach was to use a simple error-correction formulation in a pooled cross-section and time series. As indicated in the appendix there are more countries for which sufficient bank rate data exist than is the case for wholesale rates. An error-correction model explaining movements in lending rates by those in deposit rates in 32 countries estimated on quarterly data 1980-97 suggests a rapid pass-through: over 81 per cent of any change in deposit rates being picked-up in lending rates in the same quarter, and over 93 per cent by the second quarter. However, such a relationship can hardly be considered causal, as both bank rates are likely to be influenced by the same exogenous factors.

Instead, therefore we modeled the determination of both deposit and lending rates as being jointly influenced by wholesale rates, using the model: where superscripts 1, m, and p denote deposit, wholesale and lending rates respectively. (Country identifier has been suppressed.) 24
’s DGSTRI are, however, recorded under a broader category of measures that also include regulations related to telecommunications and digital infrastructure, as well as restrictions on the use of communication services. The policies explicitly dealing with data flows are therefore harder to discern. Moreover, the index does not cover the domestic use of data as done by ECIPE’s DTRI.

Other measures relevant for the TFiS conceptualization of measures relate to the ones specifically mentioned regarding Mode 2 and Mode 3 (see above). However, available databases that contain relevant measures on these points such as visa procedures and insurance coverage are scarce and often non-existing yet. Nonetheless, there are two databases that express some cost dimension of visas, which are the Henley Passport Index and the Passport Index by Arton Capital. Given that these two indexes are broad-brushed and do not necessarily take stock of the level of visa fees (although they do cover when visa fees are exempted for their ranking), or the length and complexity of visa procedures, it is hard to exactly differentiate which components of these indexes qualify for a TFiS measure.

# **_Sector-specific measures_**

The main databases that document sector-specific measures in services that would fall under TFiS are both the World Bank’s and the OECD’s **Services Trade Restrictiveness Index (STRI)** . Both sources record restrictions in specific services sectors of which the OECD’s covers for a wider range of services and categories of measures.<sup>11</sup> More importantly, the OECD’s STRI also incorporates various supplementary 11 The World Bank STRIs have a greater focus on discriminatory policies than the OECD and cover a wider set of developing countries. However, the OECD includes more categories of measures that are non-discriminatory in nature and related to the business operations and / or domestic regulations, such as competition and transparency.

12
If the article includes several estimated effects, the intent-to-treat parameter is the preferred estimate (Croke et al. 2016). The intent-to-treat parameter is the estimated effect of participating in the subsidy program (i.e. receiving vouchers/cash) on yield or income, and not the effect on people who actually buy subsidized inputs with the vouchers. Ideally, the parameters in the meta-analysis would come from randomized trials, which is the gold standard for program evaluation, but there are few such studies, so we also include quasi-experimental studies in this meta-analysis. We examine the relationship between experimental estimates and effect size for robustness in Section 3.3.

# **3. Results**

# **3.1 Sample characteristics**

A description of the 39 articles at the full text screening stage is included in Table 1. The top three reasons for exclusion at the abstract screening stage were “Not an input subsidy or price intervention” (199 articles), “Not a low- or middle-income country” (82 articles), and “Not related to agriculture” (39 articles). At the full-text screening stage, the top three reasons for exclusion were “Non-causal method” (11 articles), “Not impact on output, yield, or income” (8 articles), and “Non-original research or review article” (5 articles). Of the 39 articles, 28 examine countries in Sub-Saharan Africa, and the final metaanalysis sample includes 11 articles with data from Sub-Saharan countries and 1 from a non-African country (China). Our primary outcome of interest, productivity, is examined in 21 of the 39 articles, and 11 of them are included in the meta-analysis. Our secondary outcome of interest, income or expenditure, is studied in 8 articles, and 7 are included in the meta-analysis.

The methods used to analyze program impact varies in the articles we have identified. Studies using non-causal and non-quantitative methods are excluded. Some studies employ multiple methods to show robustness and estimate several treatment
# Geospatial Analysis of Displacement in Afghanistan<sup>∗</sup>

_Anais Dahmani-Scuitti_<sup>+</sup> _, Erwin Knippenberg, Walker Kosmidou-Bradley, Johanna Lee Belanger_<sup>∗∗</sup> JEL Classification: C15, C21, O12, O15, L94, R23 Keywords: migration, displacement, geospatial analysis, econometric regressions, nighttime light

## Policy Statement

Robust monitoring systems are crucial in humanitarian interventions, particularly in the most vulnerable areas. These areas, prone to natural disasters and riven by conflict, are often hard to reach, making on-the-ground data collection logistically difficult, time-consuming, and expensive. This paper explores how remote sensing data, particularly geospatial data, can be used to monitor displacement flows in a conflict zone affected by drought. Using the 2018 drought in Northeastern Afghanistan as a case study, it shows how nighttime lights collected by satellite can be used to quantify the displacement of populations. These results can be used to predict whether a settlement was a net receiver of displacement flows in 2018-2020, providing insights that can inform humanitarian assistance.

> ∗ We declare that we have no relevant or material financial interests that relate to the research described in this paper. The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of the World Bank or any affiliated organizations, its Board of Executive Directors, or the governments they represent. The World Bank does not guarantee the accuracy of the data included in this work. This research was supported through the World Bank Budget (BB) for the Afghanistan Poverty and Equity Program FY 21-22 as part of its core analytical activities.

> + Corresponding author, adahmaniscuitti@worldbank.org

> ∗∗ _Dahmani-Scuitti:_ World Bank, Poverty & Equity Global Practice, Washington DC, USA; _Knippenberg:_ World Bank, Poverty & Equity Global Practice, Washington DC, USA; _Kosmidou-Bradley:_ World Bank, Poverty & Equity Global Practice, Dakar, Senegal; _Lee Belanger_ : World Bank, Poverty
|Currency overvaluation (t-1)|||0.133***|0.089***|
||||[0.031]|[0.022]|
|Currency mismatch (t-1)|||-0.002|-0.000|
||||[0.001]|[0.001]|
|FDI (t-1)|||-0.019|0.009|
||||[0.047]|[0.031]|
|No. of observations|123|1,229|490|555|
|No. of countries|8|49|30|37|
|Estimated on annual data 1985-2018 e<br>*** p<0.01, ** p<0.05, * p<0.1|xcluding 2009. St|andard errors in b|rackets.||

49
Aside from these commonly available price data, there are other types of price data, such as the price data collected for the PPP calculation under the ICP, scanner data collected by private companies, and price inventories of specific goods and services (agricultural products, housing, and so on). For example, recent research that takes advantage of scanner data demonstrates the impacts of quality (or item) variations on the costs of living across cities. Handbury and Weinstein (2015) use detailed barcode data to address heterogeneity bias (stemming from the comparison of different goods in different locations) and variety bias (stemming from not correcting for the fact that some goods are unavailable in some locations), finding that prices are lower (as opposed to higher) in larger cities in the United States. Feenstra, Xu, and Antoniades (2017) analyze China in a similar way and compare it to the United States. Other research with scanner data includes Handbury (2013), which constructs a spatial price index for different income levels (that is, non-homothetic) across the U.S. metropolitan areas. It finds that price levels for high-income households tend to be overestimated when a standard homothetic index is used.

6
raion-level dummy variables. The dependent variable is the natural logarithm of an individual’s monetary and in-kind wages from his or her main occupation (the occupation where the individual reported the highest number

> 24 I also tested the effect of having relatively fewer males as compared to women in the working age group on labor market participation. For this test, I used two raion-level sex ratios of men to women for 1989. The first ratio is for the age group 20-49 and the second one includes ages 15 to 64. The age groups cut-off points are based on the cut-offs for the population numbers published by the State Statistical Committee of Tajikistan and based on the 1989 Census. The linear probability regressions are estimated with robust standard errors that control for heterogeneity at the raion level to control for effects that are common for individuals living in the same community. Unfortunately, I can not use fixed effects at the raion level in this model as the sexratio variables are available only at a raion level and is constant for all observations in a particular raion. The estimated coefficient on the sex ratio for the 20-49 year olds has a positive impact on the employment by men and women in 2007, but is significant only in the regressions for the sample of men (results not reported), suggesting that an increase in the number of men relative to women prior to the war had a positive and significant effect on the labor force participation of men.

26
distributed this value for the 20 regions using a re-proportioning method through domestic demand. For instance, _Piemonte_ ’s share is equal to the domestic demand for the Piedmont electricity sector, divided by the domestic demand for the Italian electricity sector. The shares of each region were then multiplied by the amount of €1.6 billion to obtain the regional figure for importing the electricity sector;

- Import of services: data refer to 2016 and are taken by the ISTAT-ICE report from the Bank of Italy. Data refers to “ _debiti per importazioni di servizi per tipologia e ripartizione territoriale_ ” (i.e., debts on imports of services). These data are reported for macro-regions (northwest, northeast, middle, south, and islands), so we need to regionalize these macro-regions into our 20 Nuts-II levels. For the regionalization of the import of services, the domestic demand is used in the same way it is presented for the import of goods. The initial data are not broken down by macro-areas for the transport category but fall under the item "non-distributable data" (approximately €20 million). For this reason, data on the number of companies in the transport sector whose reference markets are foreign countries were used to distribute the €20 million (data on permanent ISTAT censuses). Once the regional shares were calculated, they were applied to the €20 million to obtain the final regional figure;

- Export of goods: as for goods imports, the first step is to split the electricity/water category through 2 digit data. However, the 2 digit data shows €350 million not distributed among the regions. While we solved the import problem using shares calculated on domestic demand, shares on the number of employees by region and sector have now been used for services exports. It is plausible that the value of exports of goods can be approximated using the shares of the number of employees in the same sector within a specific region. In addition to the shares on employees, shares relating to the
# **Footnotes**

1 Blimpo (2014) evaluated the effect of financial incentives on secondary school children in Benin and found large gains on test scores. This policy, however, did not target access directly and provided no additional resources upfront.

2 Schools serve as test centers. In almost all cases, students take the exams at the school they attend.

3 The major sub-national units in The Gambia are 6 regions. Region 1 includes the capital Banjul, with regions 2–6 at increasing remove heading east along the Gambia River bisecting the country. Below these subnational units, there are 43 districts as of 2013.

> 4 For purposes of this paper, public schools refer to both government and grant-aided schools, the latter of which are publicly funded but administered privately. Both types of public schools are eligible for the scholarship program, while private schools are not.

> 5 We follow the Gambian convention in referring to the 2000–2001 academic year as 2001, to 2001–2002 as 2002, and so on.

> 6 The average value changed over time because of changes in the exchange rate (the average value of a US dollar per Gambian Dalasi was approximately 13, 15, 20, 27 between 2000 and 2003) and also changes in the composition of students covered (middle and high school students) over time as the program got scaled.

7 Although it is possible that girls were previously paying less than 100% of the nominal fee, leading to an increase in school resources due to the policy, denial of school services for non-payment was a common practice.

8 We omit exam data from 2004 because student gender is missing for that year.

9 The map in figure S1 of the supplemental appendix shows secondary schools in 2011, the most recent year for which location data are available. Region 1 schools and Upper Basic (middle) schools are excluded from the estimation sample but shown on the map to illustrate the locations of secondary schools throughout the system. Not all schools in the sample
**Figure 9. Characteristics of Urban Informal Firms Owners, %**

<!-- Start of picture text --> Female Age groups Education<br>8.3 9.4 3.9 2.9 4.9 0.0 7.0<br>15.8<br>10.7<br>35.3<br>52.3<br>48.4<br>44.0<br>81.0 77.7 90.6<br>60.7 81.2<br>50.5 51.8 34.0 64.7<br>44.6 31.5<br>28.6<br>12.2 13.7 9.7<br>10.7 12.8 0.0<br>3.0 5.5 Total LE-SE HE-SE Emplo<br>Total LE-SE HE-SE Emplo yer<br>yer No educati on Primary Secondary<br>Total LE-SE HE-SE Emplo 15-24 25-54 55+ Vocational University<br>yer<br>60 100 100<br>80 80<br>40 60<br>60<br>40<br>40<br>20<br>20<br>20<br>0<br>0<br>0<br><!-- End of picture text --> Source: Own elaboration based on IES. Notes: Weighted average including Mozambique, Zambia and Zimbabwe.

# **5.2 Distribution of Job-Related Characteristics by Employment Type**

Urban informal and formal workers are employed in similar sectors, with some variation across informal employment types. The sector of employment can be roughly categorized into four groups: agriculture, industry, services, and other (not classified).<sup>53</sup> Figure 9 shows that 44.3 percent of urban informal jobs are in the services sector, only six
# Introduction

<mark>Is privacy a luxury for the rich world?</mark><sup>1</sup> <mark>This paper seeks to understand how much data privacy laws cost to implement and enforce. Relying on industry surveys, government studies, and government agency budgets, this paper compares the costs of private sector implementation and public sector enforcement for the United States, European Union, and, to a limited extent, China. We conclude that data privacy is not outside the reach of the poorer parts of the world, though the rules should be written with differing resources for compliance and enforcement.</mark> <mark>The focus of this project is to help provide the informational base needed to support the practical realization of data privacy protections. Like some other legal domains, data privacy laws are subject to an “enforcement gap”—“that is, a wide disparity between the stated protections on the books and the reality of how companies respond to them on the ground.”</mark><sup>2</sup> <mark>A decade ago, Kenneth Bamberger and Deidre Mulligan observed that “no one has conducted a sustained inquiry into how corporations actually manage privacy and what motivates them.”</mark><sup>3</sup> <mark>Their study helped understand how companies were responding to regulations and enforcement. But even a decade later, we know too little about the costs of compliance or the costs of enforcement. Despite the rapid embrace of laws designed to regulate the use of personally identifiable information, there is a remarkable scarcity of studies of their costs.</mark><sup>4</sup> <mark>The absence of data makes it difficult to assess possible regulatory measures in the area. Some in developing nations may be worried about the costs for small and medium-sized companies of compliance with new regulations. Governments too may also be concerned about the additional costs of enforcement of new
secondary activity.<sup>15</sup> Table 3 shows the magnitude of the bias induced by this methodology, and it is large. One indication of the size of the bias is found by comparing primary and secondary activity. Many of those who reported a “non-economic” activity as their primary employment status also reported an economic activity as their secondary employment status. These respondents should be counted as active.

# **Table 3: Effect of including inactivity response questions in primary employment status question on measurement of economic participation**

||**1992**<br>Unadjusted<br>(from primary<br>activityonly)|**/93**(all questio<br> <br>Including<br>secondary<br>activity|ns)<br>**Difference**|**2005/**<br>Unadjusted<br>(from primary<br>activityonly)|**06**(12-month re<br>Including<br>activity|call)<br>**Difference**|
|---|---|---|---|---|---|---|
|LFPR|78.3|86.6|**8.3**|78.6|89.4|**10.8**|
|Labor Force|6,470,667|7,172,816|**702,149**|9,935,893|11,304,948|**1,369,055**|

_Source_ **_:_** _Author calculation based on the IHS 1992/93, UNHS 2005/06 Note: 12 month recall questions used._

> 15 To the extent that this happened, it also indicates that even the interviewers were not aware of how to measure economic activity either.  If the literate