rafmacalaba/data-use-mentions-extended huggingface dataset:
Table 4: Subsample Analysis

||Wo<br>C/R|rld Bank a<br>PS1|id 1995–2<br>PS2|014<br>PS3|
|---|---|---|---|---|
|Mission|0.11<sup>_∗∗_</sup>|0.12<sup>_∗∗∗_</sup>|0.07<sup>_∗∗_</sup>|0.10<sup>_∗∗∗_</sup>|
||(0.05)|(0.03)|(0.03)|(0.03)|
|Ethnic dummies|Yes|Yes|Yes|Yes|
|Mean dependent variable|0.43|0.36|0.29|0.35|
|_R_-squared|0.52|0.66|0.66|0.68|
|Observations|844|799|604|794|

_Source_ : Authors’ analysis based on mission locations from Beach (1903); aid projects from AidData, World Bank geocoded research release, version 1.3; and control variables from multiple sources, detailed in appendix A.

_Note_ : “C/R” stands for coast or river subsample. “PS” refers to different subsamples obtained by propensity score matching. Estimation is by ordinary least squares. Conley (1999) standard errors are given in parentheses, with the cutoff at 2 degrees (220 km). The dependent variable is a dummy for at least one project commitment in the sample period. Control variables include: log distances to coast, explorer route, colonial railway, and Arab trade route; a third-order polynomial in the 18th-century population; presence of a city as of 1800; country dummies; precolonial ethnic dummies; average altitude; Terrain Ruggedness Index; percentage area within 10 km
gains, perhaps because of<br>low level of payments|
|13|Opportunidades,<br>Mexico, 1997|Oportunidades beneficiaries received 12.2% more prenatal<br>procedures compared with non-beneficiaries (adjusted mean<br>78.9,95% Confidence Interval (Cl): 77.5-80.3; P<0.001). Higher<br>perceived quality of care among users - which may be result of <br>greater sense ofempowerment achieved through the overall<br>program.Some small-scale studies suggesthoweverthat<br>publicfacilities were not always able to cope with the increase <br>in demand (e.g. running out of drugs)|Oportunidades beneficiary statuswas<br>|associated with 127.3 g higher birthweight<br>|among participatingwomen anda 4.6<br> |percentage point reduction in low<br>|birthweight. Also lower incidence of illness<br>amongst beneficiary children, reduced<br> |stunting and increased growth. Maternal and<br>child mortality delined in programme areas,<br>but this is based on administrative data alone<br>anduncontrolled|
response rates. Participants who did not complete a single survey were less likely to be female, less likely to have access to an alternative phone, and more likely to have been offered a pre-paid credit of 10 SDG.

As additional socioeconomic characteristics of households were available from the NBHS for about half of the SSEPS households, an additional set of regressions was run to explore the association between survey participation and the level of educational attainment of the household head, poverty status, and television ownership. Results from this set of regressions (Annex A) show that respondents belonging to households in which the head member completed post-secondary education were more likely to complete at least one survey. The poverty status of the household and ownership of a television were not associated with the rate of survey completion.

# _Call Center Outcomes_

Horizon Contact Center kept records of the attempts to reach respondents (Figure 5). In December, among 3617 dials, 36 percent resulted in a contact with a respondent and 19 percent resulted in a completed survey. The efficiency of the calling process improved significantly in January—for every 100 dialed numbers, 34 resulted in a completed survey—but subsequently fell off in February and March where out of every 100 calls, only 17 and 15, respectively, resulted in a completed survey.

Major challenges related to reaching the correct respondent involved an unreachable number (37 percent of calls) and no network coverage (18 percent).<sup>8</sup> Problems with the network increased over the course of the four month period, peaking at 32 percent of all dialed numbers in March. Calls to Bentiu and Torit in December and January and Aweil and Bor in March were cited as being particularly and consistently problematic due to poor network connectivity. The fraction of unreachable numbers was particularly high in February, when 47 percent of all dialed numbers were unreachable.

> 8 Unreachable numbers exist when a number is out of service or a connection cannot be made to the number being called. No network refers to the
_Access 2009_ introduced statistics on the use of financial services in 139 countries and mapped a broad range of policies and initiatives supporting financial inclusion, focusing on data collection and measurement challenges. Building on this, _Financial Access 2010_ updated statistics on the use of financial services and analyzed changes that took place in 2009 – a turbulent year for the financial sector in most countries around the world. As more countries provided more and higher quality data after the 2009 report, the focus shifted to the analysis of financial inclusion policies around the world (see World Bank, 2008; CGAP and the World Bank, 2009 and 2010).

This paper uses the _Financial Access_ database to count the number of unbanked individuals in the world, and analyze the change of access to formal financial services around the world. Next, it reviews the role of the main financial regulator in relation to the implementation of financial inclusion reforms and mandates to give a broad global picture of the state of financial inclusion policies.

Our findings indicate that 56 percent of adults worldwide are unbanked, although the numbers differ across high-income and developing countries (17 percent and 64 percent, respectively). Overall, we predict a slight improvement in access to financial services in 2009: approximately 50 million more adults have accounts, although the adult population increased by approximately 79 million. The results of the econometric analysis confirm the earlier findings in the literature that higher deposit and/or loan correlation is associated with higher economic and financial development as measured by GDP per capita, the amount of electricity use, the availability of explicit deposit insurance and better credit environment, etc. Although access to deposit services has improved in 2009, the global financial crisis took its toll: volume of deposits and loans shrank. The world as a whole added 65 deposit accounts per 1,000 adults but the number of outstanding loans remained more or less the same.<sup>2</sup> At the same time, global retail networks, consisting of financial institution branches, ATMs, and POS terminals
should at least be 1:6,000 and ideally better than 1:3,000, as is the case in most industrialized economies (Boon 2005).

The remainder of this section examines the state of the formal and informal infrastructure for remittances in Sri Lanka. As is the case in other regions, the financial remittance market includes three principal actors—commercial banks, money transfer businesses, and informal money transfer agents. Slowly, other actors—such as the post office and microfinance institutions—have begun exploring opportunities in the remittance industry but their participation is still in its infancy.

# **Commercial Banks**

_State commercial banks, especially Bank of Ceylon, have long dominated the official remittance business, in part because of their large branch networks._ In 2004, their network comprised 618 branches compared to 1734 for the entire banking system and together they channeled close to 70 percent of private remittances.<sup>10</sup> Table 3 summarizes the contribution of the main commercial banks to private remittances during the last 3 years.

_The retail product range of the commercial banks is fairly broad, ranging from checking accounts, deposits, credit, insurance, and securities._ For migrants and their relatives, banks offer check products as well as electronic and internet based products. In a recent survey of financial institutions, Hettiarachi (2005) found the following were the most widely used instruments for transferring money: (i) Telegraphic Transfers based on SWIFT messages; (ii) drafts/checks drawn on banks;<sup>11</sup> and (iii) international money orders issued by banks and post offices.

5
. .|51|
|18|FDI Infows in Post-Confict . . . . . . . . . . . . . . . . . . . . . . . .|52|
|19|Trust and Confict in the Cross Section . . . . . . . . . . . . . . . . . .|70|
|A1|Night light in 2013 and Recovery Length . . . . . . . . . . . . . . . . .|86|

# **List of Tables**

|1|Confict Incidence and Economic Performance: Country-level data . . .|11|
|---|---|---|
|2|Confict Incidence and Country Economic Performance: Ethnic-level data|15|
|3|Recovery Growth After Confict: Country Level . . . . . . . . . . . . .|19|
|4|Recovery Growth After Confict: Ethnic Group Level . . . . . . . . . .|20|
|5|Growth and Confict in the Cross-section . . . . . . . . . . . . . . . . .|22|
|6|Impact of Civil Confict on the Stock of Refugees: Country-level Analysis|31|
|7|Output and Confict Related Aggregate Loss in Human Capital<br>. . . .|40|
|8|Peace and Foreign Investment . . . . . . . . . . . . . . . . . . . . . . .|52|
|9|Fragility and Investment Flows<br>. . . . . . . . . . . . . . . . . . . . . .|55|
|10|Risk Factors and Investment Flows . . . . . . . . . . . . . . . . . . . .|57|
|11|Robustness Check: Risk Factors and Investment Flows . .
Interruption to road services due to landslides can cause direct economic losses due to infrastructure damage and indirect losses that result from the impact on socio-economic activities that rely on roads and highways.

To address the landslide issue in Chittagong, Islam et al. (2017) and Ahmed et al. (2018) developed landslide susceptibility mappings. Islam et al. (2017) demonstrated that quality mapping can be made through open-source spatial data, multi-criteria evaluation, and experts’ knowledge. Ahmed et al. (2018) compared four methodologies to develop landslide susceptibility maps, including (a) machine learning methods – the random forest, artificial neural network, (b) support vector machine, (c) multiple linear regression, and (d) principal component analysis and multiple linear regression, and concluded (a) as the best model for developing the landslide early warnings, validating by receiver operating characteristics. This study will use machine learning methods to quantify the weights that are used for landslide susceptibility mapping that informs decision making of investments for protection.

# **3. Methodology**

Fig. 2 shows a flowchart of the hybrid machine learning and sensitivity analysis approach introduced in this study for determining weight uncertainty in landslide susceptibility mapping. The approach consists of seven interlinked, iterative steps. These steps are numbered from 1 to 7 in Fig. 2.

## **3.1. Trigger and factor data**

Spatially distributed data for the factors and triggers contributing to landslide occurrence are collected in the first step of the proposed approach. The factors considered in this study 7
labor regulation index receives the highest loading, while the other six indices get only low positive or negative weights. Analogously, the third principal component loads heavily only on the tax regulation index. The close connection between the principal components and our regulation categories is clearly demonstrated by the pattern of correlation coefficients shown in Table 2.<sup>14</sup> Figure 1 provides a regional comparison of the overall, product market, fiscal, and labor regulation indices. In addition, figure 2 depicts scatter plots of these indices against the (the log of) GDP per capita of all countries in the sample. Looking at the first three panels, it becomes clear that employment regulation, fiscal burden, and product market regulation stand apart from each other. Indeed, while there seems to be little relation between a country’s average income and the strength of labor regulation, the relationship with income is positive and significant in the case of fiscal burden and clearly negative in the case of product market regulation. The overall regulation index is negatively related to per capita GDP, which is not surprising given the large weight of product market regulation in the overall index.

# **Regulation and governance**

The effect of regulation on firm dynamics and, ultimately, macroeconomic performance is likely to depend on the institutional context in which regulation is enacted. For example, countries with better institutions create regulatory environments that actually try to improve business conditions rather than privilege a few interest groups; they also limit the interaction between regulation and abuses of power in the public sector that place many firms in disadvantage, sometimes forcing them to operate in the informal sector (see for instance De Soto, 1986).

We use an index of “governance quality” in order to assess the quality of regulation itself and the general context that determines how regulation functions. We construct this index using measures from the International Country Risk Guide, which evaluates a country’s risk for international investment by looking at its social and political situation. Specifically,
4 The second approach to evaluating the relation between ownership and performance focuses on comparing the performance of state and private firms operating under reasonably identical conditions: at the same time, in the same markets, within the same environment. This "synchronic" approach confronts its own difficulties. In order to capture the effect of ownership cleanly, a study must assure the absence of a selection bias. Such a bias might occur, for example, if state ownership was introduced in the first place to shore up firms that could not compete in the market (so that state firms were handicapped from the beginning). Or, if one wants to avoid this problem by comparing the performance of privatized: (rather than originally private) firms to that of state companies, selection bias may occur if better firms were chosen for privatization.8 A reliable study of the effects of ownership on performance must combine the two approaches, comparing state and private firms operating at the same time, but using historical data, _in addition_ to the synchronic comparisons, to eliminate the possibility of selection bias. This is the route we follow in this paper. We focus on comparisons of performance between state and privatized firms, leaving out the issue of the differences between either of these types of 1989 literature, predominantly based on small samples and lacking statistical significance, is listed and classified in Boardman and Vining (1989). An earlier review, together with an argument that private ownership is more efficient, may be found in De Alessi (1980). Among more recent studies, Caves (1990) surveys the impact of British privatization policies of the 1980; in a case-type study, Galal _et aL_ (1994) examine the pre- and post-privatization performance of 12 firms in developing countries.

Most of the literature on the postcommunuist transition and enterprise restructuring also follows the historical approach and is by now very large. Frydman and Rapaczynski (1994) provide references to the initial discussions which focused on the merits and demerits of different privatization programs. The first performance-focused
# 1.10. Case 2: Construction of Bypass Road in a Tsunami-Prone Area

The second case project is another bypass road intended to reduce travel time and provide an alternative route to avoid a tsunami-prone area (see Figure 9 ). The existing National Highway No. 42 runs through the nearby coastal area along the Pacific Ocean, which is characterized by steep slopes and serves as the only primary route available to connect cities along the coastal area. If the existing highway is disrupted, there is no alternative route available for the coastal cities it serves. This road section is exposed to the high possibility of a tsunami caused by the Nankai Trough Earthquake which is expected to occur in the next few decades. The proposed bypass runs outside the tsunami-prone area, and, therefore, works as the alternative route during disruption of the existing national highway. Most residents live along the existing national highway and would lose access to the major hospital if the road is obstructed. Therefore, even with the bypass, the population suffering due to the tsunami would remain the same. However, the critical impact of the bypass would be on the recovery time since it allows for additional points to start road clearance work.

## Indicator Data

Table 2 lists the data in Case 2. The economic loss calculations required knowing the traffic volume of the ordinary and disrupted states between the representative O-D pair (KamitondaKushimoto). The existing national highway is the only primary route connecting towns in the coastal area. If the existing highway is disrupted, the only available detour is a secondary route including narrow roads in the mountainous area. While it usually takes 60 minutes between Kamitonda and Kushimoto under the ordinary state, the disruption of the highway would result in a 119-minute travel time. The bypass consists mostly of tunnels and runs through areas away from the coast and hence, is free from the risk of a tsunami. The travel time between 26
19 areas are found in the largest cities, while 29% are in towns with less than 15,000 inhabitants. Similarly, 27% of underweight urban children reside in the largest cities, but 30% reside in the smallest towns.

In a final robustness check, we ask whether our observed gradient between poverty and city-size is somehow an artifact of the focus, in this paper, on small area estimates of poverty for each city rather than direct measures of poverty for such localities.<sup>21</sup> We have already noted that direct measures of poverty for individual towns and cities are not generally available in developing country settings. The household surveys that underpin poverty analysis in these countries do not generally cover sufficiently large samples to permit poverty measurement at this detailed level. As was described in Section 2, the small area estimation procedure applied in the present paper combines household survey with unit-record population census data in an effort to circumvent this small sample problem. The approach takes advantage of the full population coverage of the population census and then applies statistical techniques to insert into the census an indicator of per capita expenditure or income for each household. This is necessary because in most developing (and developed) countries, the population census fails to collect detailed income or expenditure data.

India offers an opportunity to probe the contention that our findings are merely an artifact of the methods we have employed. The Indian National Sample Survey Organization (NSSO) fields a very large sample survey every five years with a sample size that is sufficiently large to permit a breakdown of urban areas into city-size categories.<sup>22</sup> Table 9 draws on a World Bank study (World Bank 2010) to illustrate that at the national level for the years 1983, 1993 and 2004/5, National Sample Survey data show a clear gradient in poverty by city size. This gradient holds both at the national

> 21 Tarozzi and Deaton (2007) have recently expressed a concern that the small area estimation procedure employed
the national<br>social insurance system| Employee<br> Requires some information on the<br>nature of contract, and benefits<br>provided on the job|Not related to the characteristics<br>of the firm, only characteristics of<br>the job; informal workers may<br>work in informal firms|
|Micro, small, and<br>medium enterprises<br>(MSMEs)|<br>Enterprises which may be<br>incorporated and registered or<br>not.|Firm (in firm surveys)|Some include HEs with employee<br>in microenterprises. Size<br>categories varyacross countries.|
|Informal firm|Definition varies by country.<br>Usually a firm which is not<br>incorporated, licensed, or<br>registered. May or may not<br>have informal employees.|Can include HEs if:<br> data is collected in household<br>survey, and<br> survey instrument includes an<br>enterprise model<br>Some firm surveys (e.g., ICA) include<br>small (micro) firms, which may be<br>labeled as informal (criteria varies).<br>These are usually not HEs, as they<br>have nonfamilyregular employees.|Informality of firms has to be<br>defined with respect to national<br>regulations. An informal firm is<br>not in compliance with<br>regulations, regardless of size. But<br>there tends to be a correlation<br>with size of employment<br>(although not with revenues).|

2
# **A1.1 Business Environment Risk Intelligence (BERI)**

BERI S.A. is a private source of analysis and forecasts of the business environment in a number of countries. The firm was founded in 1966 and is headquartered in Geneva, Switzerland. As of May 1999, BERI can be reached at http:I/www.beri.com.

BERI has two services that include variables of interest for the purpose of this paper: The Business Risk Service, and The FORELEND or Lender Risk Rating. We use data from the February 1998 version of Business Risk Service. Both services are supervised by Dr. F.T. Haner, founder and senior editor. Eighteen analysts analyze various data sources and produce initial draft reports, relying on an international network of sources for intelligence on countries. BERI convenes two permanent panetls of about 105 experts from all over the world. These panels provide country ratings and qualitative observations on the basis of these initial reports. One panel assesses political conditions, and the other offers perspectives on the business operating environment. These ratings are constructed using the Delphi method, in which panelists are also supplied with the ratings they produced in previous assessments as well as the panel average score for each measure.

BRS monitors 50 countries three times per year, assessing 57 criteria separated into three indices. The Political Risk Index (PFI) focuses on sociopolitical conditions in a country. Diplomats and political scientists rate the present condition of eight causes and two symptoms of political risk, using a scale from 7 (no problem) to 0 (prohibitive problem). The Operation Risk Index (ORI) identifies major bottlenecks for business development, rating 15 criteria on a scale of 0 (unacceptable conditions) to 4 (superior conditions). The R factor assesses a country's willingness to allow foreign companiles to convert and repatriate profits and to import components, equipment and raw materials. It is composed of 4 sub-indices, one of which assesses the quality of legal framework in terms of