rafmacalaba/data-use-mentions-extended huggingface dataset:
black male workers, who also benefit from a smaller wage premium when employed in the mining industry. These patterns are robust to the inclusion of controls for individual attributes and occupational categories.

While suggestive of the importance of coercion and rent-sharing in the resource industry, the differential income path of former homeland communities in democratic South Africa could at least in part reflect alternative explanations that operate at the communitylevel. First, they might be explained by government interventions aimed at addressing the historically uneven access to infrastructure.<sup>3</sup> In the South African context, Dinkelman (2011) shows that mass roll-out of the electricity grid to rural households in KwaZuluNatal generated sizable employment gains among females and led to pay rises among males. If improvements in access to infrastructure are correlated with initial employment in the mining industry, our estimates would be biased. Second, since the homelands were spread across the country, the effects of their progressive integration in the South African economy might be expected to depend on relative changes in the degree of proximity to markets and connectivity (Harris, 1954; Krugman, 1991; Hanson, 1996; Fujita et al., 2001; Redding and Sturn, 2008). Third, the end of apartheid might lead to migration flows from the former homelands. Heterogeneity in the demographic composition of these flows across locations might challenge our interpretation of the results.

To further strengthen the credibility of our empirical findings, and to assess the role of these alternative explanations, we use the community-level data to provide several additional pieces of evidence. First, we show that our results remain unaltered when accounting for changes in access to several infrastructure items, notably electricity, piped water, refuse and phone. Second, we show that the main findings hold when accounting for the role of market access and connectivity, while also providing evidence that greater proximity to markets was associated with stronger income growth. Third, we find little evidence that population growth differed systematically across local communities from former homelands with varying degrees of exposure to resource
do after return, and whether to migrate again. They have to overcome legal barriers and pay upfront fixed costs, often financed through borrowing. They face numerous sources of uncertainty due to macroeconomic conditions that affect labor demand at the destination or individual conditions that impact their employers.

These decisions are also interconnected: the outcome of each decision depends on and influences the other decisions. In other words, the temporary migration process has dynamic repercussions on the migrants’ human capital, savings, labor supply, occupational choices, and, ultimately, their consumption paths and lifetime welfare (Dustmann and Kirchkamp

> 1Identifying the impacts of temporary migration on origin countries requires following the employment trajectories of workers across borders over time and collecting detailed household, consumption, savings and investment data, which are hard to achieve with standard cross-sectional (household or labor force) surveys or administrative registration data.

> 2While several papers look at the aggregate association between remittances and economic growth in origin countries, results are mixed or inconclusive, and suffer from a number of methodological limitations (Clemens and McKenzie 2018). Also see Cazachevici, Havranek, and Horvath (2020) for a recent meta-nalysis and review of the literature on the topic.

2
data mining techniques, Chowdavarapu and Manikanandan (2016) showed that the extraction and water point type significantly predicts functionality. In addition, there could be an endogenous relationship regarding the observed high levels of non- functionality of water points. Certain types of water pumps are more expensive to sustain than others, and this could be challenging for communities which lacked the financial resources and hence are unable to replace or repair water pumps, leaving them dysfunctional. Meanwhile, a survey of 512 water points in Kenya for example found that 44 percent of submersible pumps were not functioning while only 2 percent of handpumps were not (Goodall et al., 2016). Similar results were found for Timor Leste (Willets, 2012).

Finally, a number of studies have also found the importance of hydrological factors in explaining water point functionality. For Tanzania, Fisher (2015) found a statistically significant relationship between functionality and groundwater storage. Water points in areas of low storage is associated with higher functionality. Similarly, using data from the 2015 Nigeria National Water and Sanitation Survey, Andres et al. (2018) found that hydrogeological factors are significant in explaining water point

> 14 Additionally, Koehler et al. (2015) also suggested that the sustainability of rural water supply can be further improved by taking advantage of advances in monitoring and payment technologies.

8
NIGER’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 13. Expansion of standposts and improved latrines in Niger**

Population gaining access per year between 1998–2006 <!-- Start of picture text --> a. Water b. Sanitation<br>5 5<br>4 4<br>3.4 3.7 3.4 3.4 3.7 3.4<br>3 3<br>2 2<br>1 1<br>0 0<br>National Urban Rural National Urban Rural<br>-1 -1<br>-2 -2<br>Piped Water Standposts Flush Toilets Improved Latrines<br>Wells/Boreholes Surface Water Traditional Latrines Open Defecation<br>Annualized Growth Annualized Growth<br>Population gaining access per year (%) Population gaining access per year (%)<br><!-- End of picture text --> _Source:_ WHO Joint Monitoring Program 2010, from the Demographic and Health Surveys for 1998 and 2006.

# Power

## **Achievements**

The performance of NIGELEC,<sup>10</sup> Niger’s power utility, improved significantly after the introduction of an internal restructuring program in 2006. System losses accounted for 27 percent of the power produced in 2005 and dropped to 17 percent in 2009, below what is observed in power utilities on average in SubSaharan African low- and middle-income countries. But system losses are still above the international best practice of 10 percent. At 96 percent of billings, NIGELEC’s collection ratio exceeds that of other African MICs and LICs, and collections have increased as the government is paying its bills.

Recovery of costs accompanies NIGELEC’s performance improvements. At $0.17 per kilowatt-hour (kWh), Niger’s consumers pay approximately $0.07 more for power than the average African country that derives its power from thermal-based resources (figure 14). With this level
to the number of subsectors that are identified as climate policy relevant.<sup>6</sup> On top of the CPRS sectors we add the companies that provide water supply, sewerage, and waste management services (NACE E) since we find that the aggregate GHG emissions in this sector are relatively high but it falls outside the CPRS classification into six high-level sectors of the economy (i.e., fossil fuels, utilities and electricity, energy-intensive, buildings, transportation, and agriculture). The detailed mapping can be found in Annex 2. Since the CPRS is a binary classification (i.e., either relevant or not) this does not allow us to rank sectors in the amount of transition risk present. We therefore also obtain Eurostat data on CO2 and GHG emissions. We first use this data to estimate the amount of exposure of CGSs to the sectors with the highest GHG emissions. We then proceed by identifying those sectors that could potentially cause the highest absolute losses to CGSs by defining a risk metric that combines exposure with GHG emissions. Using equation (1) we identify sectors _i_ that are highly relevant for CGSs from a

> 6 To map our 2-digit level exposure data to the 4-digit CPRS we weigh 2-digit sectors using a factor 1.0 if all the subsectors are in CPRS, a factor 0.5 if two or more subsectors are in CPRS, and 0 if one or no subsectors are in CPRS.

8
population of non-agricultural firms within each country.<sup>4</sup> The BEEPSs contain both qualitative and quantitative information about the firms and the business environment where they operate. We exploit the sections of the surveys that focus on the business perceptions about the most relevant obstacles to investment, the relative importance of different constraints to boosting employment and productivity, and the effects of the country-specific environment on firms’ international competitiveness.

To ensure comparability across countries and over time, we convert in US dollar at constant prices the values of the variables of interest from the BEEPS expressed in local currencies. The WDI and GDF provide exchange rate time series and estimates of the GDP deflator used to convert the nominal time series in real terms. The use of the GDP deflator implies that the inflation rate has been constant across sectors in a given country and during a specific year. The use of sectorspecific price indexes for each ISIC sector would be preferable but these series are not available for the countries we study. We account for country-specific factors such as general economic conditions with GDP (constant 2000 US$), likelihood to obtain credit with the number of commercial bank branches (per 100,000 adults), domestic credit provided by banking sector (% of GDP), and the extra-cost of borrowing (lending rate minus deposit rate, %).

The United Nations Statistics Division (UNSD) manages the National Accounts Estimates of Main Aggregates that report the gross value added at constant prices (in USD) by country and sector for all the years and countries in the BEEPS. The TPIS data set reports bilateral export volumes and we can then estimate the demand shocks based on the strategy proposed by Coulibaly, Sapriza, and Zlate (2012). The authors construct an index of demand as a function of the firms’ exports-to-sales ratio, the sector-specific exposure to various foreign destinations, and real GDP growth across destinations as a proxy for the change in demand. We provide details regarding the construction of these indices in Section 3.1
in the spirit of microsimulations, these rules allow predicting new imputed housing prices for the damaged dwellings. Fourth, the predicted average value of all dwellings in each geographic area is computed. And fifth, plugging these new neighborhood effects in the hedonic price function allows predicting new imputed housing prices for all dwellings. Steps four and five are then repeated until imputed housing prices converge to stable values.

The proposed approach is illustrated in the case of Nepal, a country with a high vulnerability to natural disasters (World Bank 2022). The focus is on the cost of the 2015 Gorkha earthquakes. At the aggregate level, this exercise yields a disaster cost similar in magnitude to the one jointly reported by the Post-Disaster Needs Assessment (PDNA) conducted in the months that followed (National Planning Commission 2015). At a more disaggregated level, however, the estimated disaster cost is significantly higher in the most severely affected districts, and lower elsewhere. The main reason for the discrepancy with the official assessment stems from the strength of neighborhood effects in the worst-hit localities.

# **2. Relationship to the literature**

The focus on the value of dwellings marks a departure from previous approaches to assess the cost of disasters, which have tended to rely on other _indicators_ . Simplifying, those other indicators include national accounts, field surveys of physical assets, and satellite images.

Before-and-after comparisons of national accounts data on aggregate output and its components provide a straightforward way to assess the impact of disasters in developing countries. Given the large number of events occurring worldwide in every single year, the number of observations available to conduct comparisons of this sort is substantial. Large samples, in turn, allow to qualify impacts depending on the type of disaster, its magnitude and the country’s development level (Loayza et al. 2012). Some studies have even focused on narrower sets of disasters, such as storms (Ishizawa and Miranda 2019), hurricanes (Strobl 2012), and earthquakes (Webb et al. 2002).

Studies
7 Where governments conduct frequent retail price surveys and post them on the Web, the study uses monthly average prices for the country or, if country-wide average data are not available, average prices in the capital city. For countries with price control, _official_ prices averaged over the month (if prices were adjusted once or more in January) are used, even if black market prices were markedly higher, because the focus of this study for countries with price control is not on the actual prices paid by consumers but government pricing policies. In a handful of cases where one or more grades of a given fuel are subsidized but not others, the subsidized prices are used to highlight policy consequences. Details about fuel characteristics and sources of price information can be found in table A2.1 in appendix 2 and reference FOB prices in table A2.3. The inter-regional differences in the reference FOB prices varied by 7 percent or less for gasoline, diesel, and kerosene. LPG prices in contrast varied by as much as 60 percent by region—propane prices were much lower in the United States than elsewhere in January 2012, and the lowest and highest butane prices in January 2009 differed by almost 50 percent.

The pass-through coefficients as defined in this study need to be interpreted with caution. Retail prices depend not only on the reference world prices, taxes, and subsidies, but also on a number of other factors:

- _Timing of fuel purchase_ . Particularly in small markets, there may be a considerable time lag between fuel purchase and fuel sale, and world fuel prices may change markedly during that period. The smaller the market, the longer is the time interval between purchase on the world market and final retail sale. This is arguably the largest source of uncertainty in this analysis. The monthly average benchmark price of regular gasoline in Singapore was about the same in November 2008 and January 2009, but 20 percent lower in December 2008, resulting in an under-estimation of pass-through for gasoline purchased in December 2008. In Europe, the
**Table 5.8. Robustness check: Impact of PqTR reduction on repetition rate (PqTR 100)**

||**Overall**|**Lower Primary**|
|---|---|---|
|Control in 2018|0.268***|0.277***|
||(0.013)|(0.015)|
|Time (year=2019)|-0.017|-0.016|
||(0.018)|(0.020)|
|Lagged Treatment|-0.002|0.005|
||(0.015)|(0.017)|
|Lagged Treatment (DiD)|-0.026<br>(0.020)|-0.033<br>(0.023)|
|Control (N)|73|73|
|Treatment (N)|253|253|
|r2|0.03|0.04|

_Data Source: EMIS 2018 and 2019 Note: Robust standard errors in parentheses. * p<0.10, ** p<0.05, *** p<0.01 Compares rates at end 2017/18 and end 2018/19. Treated schools are those where PqTR brought below 100 in 2018._ 28
having experienced conflict in the past year; researchers followed conflicts that had taken place in 2002 in all six villages chosen from that district. Conflicts here included battles between rival _silat_ (martial arts) groups, clashes over village head elections, and a dispute between a community and a state-run mining enterprise.

There appeared to be a similar trend of underreporting in the NTT districts. In Manggarai, the westernmost district on the island of Flores, only one of six villages reported conflict; yet, according to the research teams on the ground, all six had experienced significant unrest in the previous calendar year. Whereas PODES did pick up conflict over land between groups in Tanah Rata village, it missed a range of other land conflicts across the district. In Sikka, the figures from PODES and the field research were closer: PODES reported two villages as having experienced conflict; the qualitative assessment determined that three villages had been the location for conflict in the previous year.

The _impacts_ of conflict were also underreported in the PODES. Across the 25 villages, PODES reported three injuries and no deaths as resulting from conflict. Analysis of the qualitative primary data from the research sites, as well as secondary sources including figures from the police, health care providers and newspaper sources, would indicate that the human costs of violent conflict in the villages studied were in fact much higher. Likewise, a large percentage of the cases followed in the qualitative research involved property (private, company or state-owned) being burnt down; these financial costs were, by and large, not picked up by PODES.

# **_3.4. Insights from Comparison of PODES and Qualitative Data Sources_**

The comparison of the broad PODES data with that gathered in the more in-depth qualitative work hints at some of the weaknesses in the PODES dataset. There appears to be a 18
s (1974) contingent claim pricing framework to measure bank default risk and its contribution to systemic risk. Using a sample of 1,872 publicly traded banks in 63 countries over the period 1997 to 2009, we investigate the impact of bank concentration and competition, as measured by the Lerner index of bank market power. Our results suggest a positive relationship between competition and systemic stability, consistent with the view that greater competition encourages banks to take on more diversified risks, making the banking system less fragile to shocks. We also examine the impact of the larger institutional and regulatory environment on this relationship. Correlated risk 2
the Survey of Consumer Finance and finds evidence consistent with both informational problems.<sup>4</sup> The paper is also related to a framed experiment conducted by Giné et al. (2010) in Peru that shows that dynamic incentives can be important. In addition, there is a theoretical and empirical literature on the impact of credit bureaus that are also related to this paper. The exchange of information about borrowers should theoretically reduce adverse selection (Pagano and Jappelli, 1993) and moral hazard (Padilla and Pagano, 2000). Empirically, de Janvry, McIntosh and Sadoulet (forthcoming) study the introduction of a credit bureau in Guatemala and find that it did contribute to efficiency in the credit market. Finally, the paper is related to the literature motivated by the rise in personal bankruptcies in the US in the last decades (Livshits et al. 2010).

The remainder of this paper is organized as follows. Section 2 describes the experimental design and survey data and Section 3 presents the intuition of a simple model of loan repayment. Section 4 describes the regression specifications, and Section 5 presents the empirical results. Section 6 provides additional discussion and robustness checks. Section 7 presents the benefitcost analysis of introducing biometric technology, and Section 8 concludes.

# **2. Experimental Design and Survey Data**

The experiment was carried out as part of the Biometric and Financial Innovations in Rural Malawi (BFIRM) project, a cooperative effort among Cheetah Paprika Limited (CP), the Malawi Rural Finance Corporation (MRFC), the University of Michigan, and the World Bank. CP is a privately owned agri-business company established in 1995 that offers extension services and high-quality inputs to smallholder farmers via an out-grower paprika scheme. MRFC is a government-owned microfinance institution and provided financing for the in-kind loan package for 1/2 to 1 acre of paprika. Loaned funds were not disbursed in cash, but rather took the form of

> 4 Ligon, Thomas and Worrall (1999) write down competing models of risk-sharing
<!-- Start of picture text --> 35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Public Sector Banks Private Banks<br>2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br><!-- End of picture text --> Note: Data is from Reserve Bank of India. Years refer to respective fiscal years.

There has been a long downward trend in the high NPA level since the late 1990s. The PSBs had a higher non-performing asset ratio at that time. As PSBs gradually reduced their NPAs, the NPAs continued to grow at private sector banks till early 2000s. In recent years the ratio of nonperforming assets has increased in PSBs. Besides, there is also a stark contrast between the profitability of PSBs and private banks. Public sector banks have continued to record negative profitability ratios since March 2016. Return on assets of public sector banks was -0.1 percent in September 2017, and its return on equity was -2.0, compared to 1.4 percent and 11.9 percent respectively for private banks.<sup>43</sup>

**Figure 24: Gross NPAs to Gross Advances of the Banking System**

> 43 The allocative efficiency of the public-sector-dominated Indian banking sector is considered to be low, holding back potential investments and economic growth. Banerjee et al (2004) characterize the Indian public banks as “lazy”, since the lending decisions of their managers are not based on the current or expected profitability of firms; they under-lend to the private sector and overinvest in government securities. They explain that the employees of the public-sector banks are treated as public servants by law, and hence if they take decisions which result in direct financial gain to a third party, they may be held guilty of corruption. The bankers thus choose to lend less to the private sector;
defined as a dummy variable indicator equal to one if the individual reported in 2012 that job search was a purpose of Internet use. Relying on the 2018 data, I track individuals who were interviewed in 2012 and identify whether they found a job following their unemployment spell in 2012. The ELMPS 2018 provides retrospective information on employment status and job mobility, which allows identifying if any individual changed his employment status/job and the year of new status or new job. This makes it possible to determine whether the unemployed individuals in 2012 found a job, and if so also the year in which they started the new job. In Table 4, I examine the impact of Internet job search in 2012 on the probability of being employed within a 5 years timeframe, conditional on being unemployed in 2012. Unemployed individuals are defined according to the labor market definition of labor force participation, so individuals who engage in subsistence economic activities are considered as unemployed according to this definition. Table 4 therefore reports results estimating equation (1). I include the control variables gradually to check the sensitivity of the results with respect to the covariates included in the regression specification. For instance, in column (1), I control for home Internet access, gender, and marital status. In column (2), I additionally control for age and educational attainment dummies, while in column (3) I also control for living in rural versus urban areas. Finally, column (4) includes the full set of control variables in addition to controlling for geographical regions’ dummies. Controlling for Internet access at home, I find that unemployed individuals in 2012 who use the Internet for job search are significantly and consistently more likely to find a job within 5 years. Using the Internet for job search increased the likelihood that a previously unemployed found a job by 10 percentage points—a particularly large increase, given that the mean of the dependent variable is 0.038.

The results presented in Table 4 may be concealing heterogenous effects. In Table