rafmacalaba/data-use-mentions-extended huggingface dataset:
are disrupted: the higher these three impacts, the higher the criticality of the link.

# 2.2. Exposure of critical links

Once we have identified the critical links, we overlay the network with hazard layers to identify which ones are also exposed to natural disasters.

<mark>We used a database of flood scenarios produced by Global Flood Risk using the IMAGE Scenarios model—GLOFRIS (Ward et al., 2013; Winsemius et al., 2013). This model simulates daily discharge and runoff at a horizontal resolution of 0.5° x 0.5°. Deltares simulated eight return periods – RP5, RP10, RP25, RP50, RP100, RP250, RP500, and RP100. The model was forced using daily meteorological fields of precipitation, temperature, and radiation for four time periods: 1960‐99, which represents the baseline climate; 2010‐49 representing 2030; 2030‐69 representing 2050; and 2060‐99 representing 2080. The meteorological data for the baseline climate are WATCH Forcing data (Weedon et al., 2011). The future meteorological data are provided by the ISI‐MIP project, and consist of bias‐corrected data (Hempel et</mark> 6
most refined measure feasible for India at this time. Incumbent establishments, which are used to model existing activity in the district-industry and Marshallian spillovers, are defined as firms that are three years old or more. We principally define entry measures through employment in these new establishments. We also test robustness using an entry share measure based upon establishment counts.

Most of the Indian surveys described next unfortunately do not record whether new establishments belong to larger, multi-plant firms or whether they are independent enterprises. While it would be advantageous to study single-unit starts separately from multi-unit expansions, we are unable to do so consistently. Encouragingly, we are able to separate establishment types for organized manufacturing, and we find very similar results to those presented below when modeling the single-unit start-up entry rate separate from multi-unit expansions, but we are unable to ascertain this stability across all of the sectors we study. The Indian context is one in which a major limitation for development is the growth and replication of successful initial businesses. Thus, from this perspective, many policy makers are as concerned about encouraging entry of new expansion establishments as they are initial start-ups.

7
.639_||_0.139_|_0.292_|
|China Growth*Late||-0.04|0.0351||-0.011|0.035|
|||_0.355_|_0.453_||_0.242_|_0.178_|
|CRB Commodity Index Growth|||-0.0429***|||-0.028|
||||_0.000_|||_0.179_|
|Constant|0.003**|0.020***|0.00247|0.001*|0.014|0.007*|
||_0.012_|_0.000_|_0.701_|_0.093_|_0.249_|_0.092_|
|Observations|942|942|942|71|70|70|
|R-squared|0.440|0.446|0.457|0.518|0.603|0.620|
|**G7 Growth+G7 Growth*Late**|1.030***|1.075***|1.250***|1.068***|1.225***|1.417***|
||_0.000_|_0.000_|_0.000_|_0.000_|_0.000_|_0.000_|
|**China Growth+China Growth*Late**||-0.185***|0.012||-0.083|0.058|
|||_0.004_|_0.872_||_0.234_|_0.161_|
25 same results as when relying on production ((3)-(4)). Lastly, for both cement production and consumption, if we use 10-year panel regressions with several lags of the variables of interest, we do not find significant differences between the types of countries.<sup>33</sup> Therefore, although NRXGDP countries have more tall buildings, in particular concrete towers, they do not use more cement, which might imply eviction effects within the construction sector. Of course, with economic development and rising land values, tall buildings replace short structures (Jedwab et al., 2020b). However, we here compare NRXGDP and MFGSERV countries with _similar_ levels of urban economic development, thus focusing on “excesses” that might lead to “gaps” in the rest of the urban sector.

To study urban construction more generally, we turn to information on the value added of the construction sector (in PPP; constant 2005 international dollars) from 1970 to date.<sup>34</sup> We use the long-difference model and consider as the dependent variable the log sum of construction GDP over the period 1970-2020 while simultaneously controlling for log urban population in both 1970 and 2020, thus capturing _construction GDP per urban capita_ . Again, we observe no significant difference ((5)-(6)). If we use 10-year panel regressions with several lags of the variables of interest, we also do not find significant differences between the types of countries (not shown). However, one caveat with these analyses is that both cement use and construction include infrastructure.

Finally, if residential construction is indeed crowded out in NRXGDP countries, one might see higher slum shares there. However, to project prestige NRXGDP may more willingly adopt slum clearance policies. Data on slum shares are only available for the most recent years (United Nations, 2020b). If we use the cross-sectional version of the long-difference model (thus not controlling for slums c. 1960), we find smaller
# IV – When Do Migrants Consider Going Abroad for Work?

The average monthly remittance payment recorded in the L2CU study is, alone, more than twice as large as the average salary in Uzbekistan. Surely, a large share of labor migrants travels abroad simply because of the stark differences in wages, and there is little else needed to explain their behavior. However, potentially there are also economic challenges and hardships that increase the need that particular migrants feel to go abroad, and that also increase the salience of the average wage differential. This section aims to quantify the contribution of these factors in the migration decision.

In contrast to the preceding section that focuses largely on the static data from the baseline survey and administrative data collected in the L2CU study, the following two sections take advantage of the panel design of the phone-based survey conducted with a subset of more than 1,500 households that participated in the baseline. This type of information allows additional analyses that account for common confounding factors that complicate the interpretation of simple cross-sectional comparisons. However, again beginning with these simple comparisons helps to establish the overall association between the decision to migrate and the measures of interest. For this reason, table (6) reports descriptive OLS regressions like those reported in section (3). The difference between the estimates reported in table (4) and table (6) comes from the difference in data sources and the definitions of the variables. Table (6) focuses on responses for households that either consider sending a migrant during the period in question, or when a migrant departs (while table (3) focused instead on migrants that were already abroad at the time of the baseline survey).

The descriptive statistics are grouped into three broad categories: (i) shocks, (ii) labor market issues, and (iii) personal views. For ease of exposition the results are presented in separate tables (6-8), though the distinction between these in some cases is arbitrary. As was the case with the results reported
there is no direct question on mobile internet adoption in the surveys. However, such usage can be captured through combining information gained through multiple survey questions regarding whether respondents: (1) have used the internet or not, (2) own a mobile phone, and (3) whether the type of mobile phone they own allows internet access. An individual is counted as using mobile internet if he or she has used the internet and also owns an internet-enabled phone.<sup>9</sup> Results show that Latin American countries have the highest rate of mobile internet adoption: 65.6 percent of the total population. The rural and urban divide is significant across all regions (figure 1), especially in Africa, where the adoption rate among urban residents is more than twice that among rural residents. Asian countries in the sample have the widest gender gap: only 8.4 percent of females used mobile internet in 2017. Those who are at the bottom 40 percent of the national income distribution have a lower adoption rate across regions, while the gap is minimal in Latin America.

**Figure 1. Mobile internet adoption, by subgroup, across regions (% of population) a. Rural vs. urban and male vs. female b. Poor vs. not and educated vs. not**

> 8 The majority of the Asian countries covered in the sample are in South Asia.

> 9 Internet-enabled phones include smartphones and feature phones. Admittedly, this constructed variable cannot completely rule out cases of individuals owning an internet-enabled phone who in fact accessed the internet through Wi-Fi–enabled fixed broadband. However, data from Telegeography show that household fixed-broadband penetration is low (at 13.5 percent) across countries in the sample; thus, such cases should be minimal. The estimated rate is as low as 3.6 percent among African countries, and 7.7 percent among the Asian countries covered.

5
ically interdependent, we study the impact of regional integration and cross-border linkages when assessing the impact of future infrastructure investments. Currently, connectivity across the borders is poor due to a lack of road infrastructure, high border delays, and volatile security situations that make trade or transportation of goods too costly. The paper complements the analysis of key infrastructure investments with additional trade facilitation measures at the borders.

The paper is divided in two parts. The first part uses reduced-form analysis to quantify the impacts of past investments in electricity, internet, and road infrastructure on the structure of employment in Cameroon, Nigeria, and Chad over the past two decades. We show that major past infrastructure investments have had a large impact on the structure of the local economies. The second part uses a spatial general-equilibrium model, based on Moneke (2020), to assess the aggregate and spatial impacts of future infrastructure investments in the region. Reducedform results capture the localized effects in the areas that have been affected, but do not capture the general-equilibrium effects and spillovers due to the network nature of transport infrastructure. The general-equilibrium model captures the spillover effects that a localized investment has on the rest of the country and all the countries in the Lake Chad region and generates welfare estimates for the entire region and all its subregions.

We first provide evidence on how past investments and their combinations have impacted structural transformation in Cameroon, Nigeria, and Chad. Geo-identified data on the infrastructure expansion of roads and electricity for Cameroon, Nigeria, and Chad are collected over time and across space, and linked with information on local economic activity based on household-survey data. Two instrumental variables are developed to overcome the endogeneity concerns for road and electricity investments. Following Moneke (2020) and Gaggl et al. (2019), straight transmission lines connecting the main cities with newly opening hydropower dams in Cameroon and power plants in Nigeria are used for electrification, the construction of an hypothetical least-cost network expansion using a minimum spanning tree algorithm is used for road expansion. The paper finds different effects
# **Table A6: Characteristics of ELA Participants**

**Means, clustered standard errors from OLS regressions in parentheses P-value of t-test of equality of means with relevant control group in braces**

||<br>**Participants**||
|---|---|---|
||**Low Disruption**<br>**High Disruption**<br>**(1)**<br>**(2)**|**Difference**<br>**(3)**|
|**_A. Basic Characteristics_**|||
|**Age**|17.3<br>17.2|{.594}|
||[3.73]<br>[3.79]||
|**In any relationship**|.565<br>.631|{.982}|
|**Married**|.268<br>.272|{.324}|
|**Age at marriage**|16.3<br>16.6|{.415}|
||[2.82]<br>[2.64]||
|**Age of husband at marriage**|31.82<br>31.29|{.553}|
||[7.62]<br>[5.86]||
|**Has children**|0.478<br>0.434|{.275}|
|**If in relationship: Experienced any**<br>**form of intimate partner violence**|0.492<br>0.535|{.128}|
|**_B. Empowerment and Aspirations_**|||
|**Gender Empowerment Index [0-100]**|.175
the public. Third, financial crises can occur regardless of financial liberalization, but financial liberalization combined with poor transparency increases the probability of a crisis. This does not imply that countries should not liberalize their financial system or that financial liberalization always results in a crisis. It implies only that countries that liberalize their financial sector should make every effort to increase transparency. Fourth, if financial institutions become vulnerable, it is very likely that they will rollover bad loans and hence their condition is likely to deteriorate farther rather than improve. The reason is that by rolling bad loans over banks do not have to reduce the book value of their capital and consequently other loans. Thus, delaying adjustments (e.g., bail out, closing down banks) may be costly in the sense that it is likely to increase the magnitude of the crises.

In order to test whether indeed financial liberalization combined with poor transparency increases the probability of a banking crisis - we construct a data set of 56 countries from 1977 to 1997. The empirical results suggest that financial liberalization increases the probability of a crisis during the five years following the liberalization. We further find that the probability of a crisis following liberalization is higher in countries with poor transparency than in countries that are transparent.

This paper is constructed as following. In the next section we present a dynamic model of credit and investment and discuses the role of transparency. Section 3 discusses how financial liberalization with poor transparency may lead to financial crisis and discusses some policy implications. Section 4 presents empirical evidence in support of the model. Section _5_ concludes.

3
Figure 11. Children in wealthier households have been more likely to be engaged in more interactive educational activities than poorer ones _Interactive distance learning by country and quintile_ <!-- Start of picture text --> Q1 Q2 Q3 Q4 Q5<br>100<br>80<br>60<br>40<br>20<br>0<br>Pooled Indonesia Lao PDR Mongolia Myanmar Philippines Vietnam<br>before the pandemic<br>children attending school<br>Percent of households with<br><!-- End of picture text --> Note: Modes of interactive distance learning include mobile apps, online or in-person meetings/sessions with a teacher or tutor, or other online learning platforms. 95% confidence intervals for difference from Q1 are shown (rather than from zero). The pooled regression includes country and period fixed effects. Education data are only available for Round 4 in Myanmar and Round 2 and 4 in Indonesia. Source: HFPS

# 5. Prospects for inclusive recovery

This paper presents evidence from the High Frequency Phone Surveys (HFPS) indicative of the risk of rising inequality both in the short-and long-terms across a selected set of EAP countries.

Unlike in developed countries, work stoppages and labor income have been relatively widespread at times of economic closure and downturn, although there is some evidence that the top 20 have been able shield themselves more than workers at the bottom of the distribution when economic activity resumed. The data on potentially harmful coping mechanisms, food insecurity, and distance learning suggest that the impacts could be long lasting, suggesting that the recovery may be uneven.

These results from high-frequency survey data, should be taken as indicative of trends, in the absence of official household and labor force surveys. The HFPS, while timely and informative for looking at how households have weathered during the pandemic on a number of dimensions, is not without limitations. As mentioned in the paper, limited numbers of survey rounds and a respondent sample can only provide a
order to ensure that projects such as this attract the most qualified talent, it is important not only that salaries be provided but that they do not distort local market conditions. As noted earlier, some teachers under the project received payments that far exceeded local norms. Others struggled to receive any compensation at all. Neither situation is desirable and can be avoided if project design stipulates mechanisms (if not levels) of compensation.

Third, the quality of project playgroups – as measured by classroom observations – is a key predictor of sustainability. We find that direct observations of the playgroups are able to capture elements of the centers that are predictive of sustainability, beyond what can be predicted from, student’s school readiness, teachers’ education and teaching experiences alone. Given that these are community-based services and that parents are often able to directly observe quality – both the state of physical infrastructure as well as the quality of teacher and child interactions – it is imperative that projects build in mechanisms for tracking and improving quality throughout the project.

We also find that market conditions, as measured by the number of kindergartens per 100 children, and community support, as measured by the degree of parental involvement in project playgroups are both strong predictors of sustainability. Both variables indicate community-wide support for investing in early childhood education. As such, market conditions need to be studied and efforts made to adapt to these at the design stage. This will bear not only on what types of services are established but how much these services charge and how much they remunerate their teachers.

Finally, when we jointly examine the categories of predictors (finance, quality, market conditions, supplementary services, and community support), local market condition is the strongest predictor category of a center remaining open three years after the project ended. For 23
The survey is nationally representative of the formal private sector. It is built based on a stratified random sampling frame designed by the WBES team. Three variables are used to construct the strata: firm size, sector, and geographic area within a country. Under the WBES sampling framework, firms are divided into three categories according to their size: small, medium-sized, and large. Small firms are those with 5-19 full-time employees; mediumsized firms have 20-99 full-time employees; and the large ones have more than 99 full-time employees. The industries are classified according to the ISIC Revision 3.1 classification at 2-digits. The regions within a country are defined by the WBES team. The database also includes sampling weights that can be used to mimic nationally representative samples in the empirics.

The WBES collects data on a broad range of variables related to firm production, performance, and the business environment in which firms operate. It has information on variables related to production such as sales, capital, labor, materials, investment, exports, and manager’s education, among others. Due to the lack of information on prices at the firm-level, we use the consumer price index from the World Bank’s World Development Indicators to deflate sales, capital, materials, and investment, thus transforming nominal values into 2010dollar values. Firms’ labor is equal to the number of permanent employees that work for the firm. The survey collects data on the percentage of firms’ sales that are exported. Last, a firm’s managerial capability is measured by the number of years of experience of the manager. The novelty of the WBES is that it also collects information on technology adoption at the firm-level. Thus, at every wave, firms are asked whether they use a business email to communicate with clients and suppliers and whether they have a business website in order to carry out their operations. We exploit this variation in time, across countries, sectors, and firms to estimate the effects of digital business solutions on productivity and factor demand.
remoteness, and relatedly, dispersion.

> 11 Part of the motivation for this work was a desire, for the purpose of empirical analysis, to apply a consistent approach to identifying ‘remote’, outer islands in different PICs. It became clear that it is not immediately obvious how to empirically measure remoteness of a particular community, within the broader geographical context of a country as a whole. This was part of a broader task of analyzing socio-economic characteristics of Pacific Island communities from a spatial geography perspective, with a particular interest in describing living conditions for people living on more remote islands (see World Bank, 2019). World Bank, 2019. ‘Socio-economic analysis: A spatial analysis of population and housing census data’, a background paper prepared for _A Review of Spatial Development Issues in the Pacific Islands Countries_ (P166346).

> 12 At the other extreme—where a country has no disaggregated data at all (that is, data analogous to just one ‘local area’)—our indicators would be completely insensitive to its internal settlement patterns.

27
adaptive social protection 26 the Government of Sri Lanka in 1995, with the main goal of reducing poverty in the country.

# 4.1 Proxy means test (PMT)

A proxy means test (PMT) is an index of observable and verifiable household characteristics, such as the quality of the household’s dwelling, ownership of durable goods, demographic structure, and the education and employment status of adult household members [9]. PMT scores provide a proxy for household incomes, and are commonly used to determine or optimize eligibility for social safety net programs in situations Since the PMT essentially ranks households by welfare level, the same formula can be used for targeting a range of different welfare programs, adopting different eligibility cutoff scores for each program and possibly adding other criteria as well. a common for various based on a PMT Having targeting approach programs, single score, ensures consistency of targeting across programs, minimizes overlaps, and provides a basis for future harmonization of programs.

Samurdhi program benefits are currently distributed on the basis of self-reported income. However, the Sri Lankan government is in the process of updating this system, and one component of this reform is to identify beneficiaries using a standard vector of household attributes in place of self-reported incomes. This shift is expected to significantly improve the targeting of Samurdhi benefits to the poor in Sri Lanka.

In this analysis, we construct four illustrative disaster-responsive adaptive social protection (ASP) systems. Like Samurdhi, these ASP systems distribute cash transfers to designated households on a one-time or monthly basis. Enrollment and disbursements for each ASP alternative are based disaster-affected status, existing Samurdhi enrollment, and PMT scores, as described in the following sections. In some cases, we use a PMT score of 887 as a cutoff for benefits. This value corresponds to the 25th percentile of the “true" per capita consumption distribution in Sri Lanka, as measured by the detailed HIES survey [10].