rafmacalaba/data-use-mentions-extended huggingface dataset:
i) mainline faults per 100 telephone calls for telecommunications, (ii) electricity generation losses as a percent of total electricity output, and (iii) the percentage of paved roads in good condition and (iv) diesel locomotive utilization as a percentage of the total rolling stock. Each of these indicators was normalized so that performance in a particular category is measured in terms of standard deviations from the cross-country average level of performance. Then a simple average across performance indicators was taken to obtain an aggregate index for each country."<sup>5</sup> As discussed at the beginning, the impact of public capital on economic growth is likely to vary according to how it is financed. There are two basic options considered in the growth equation estimated below. One is debt finance, which implies a higher level of taxes (and its distorting consequences). The other option is the reorientation of public expenditures from government consumption to investment spending, which would maintain the level of debt and taxation constant. Following Barro

> '<sup>4</sup> The 46 developing countries used in these regressions coincide with the same sub-set of countries used in Hulten's (1966) analysis.

> **_15It_** is interesting to note that Mexico ranked second highest among the 46 develbping countries in terms of the public capital efficiency index. (Mauritius ranked the highest and Nigeria ranlked last.) The parameters underlying Mexico's efficiency index are (a) 13% of electric power generation output is lost, (b) 85% of paved roads are in good condition, and (c) 64% of diesel inventory is in use.
used in the analysis.

This study investigates to what extent the conventional wisdom on SME lending holds in practice – the conventional view is that large banks are not attracted to SME lending and that the SME business is dominated by small banks and based on relationship lending. Their results show that the conventional view is not prevalent in practice. Like the study by Beck, Demirguc-Kunt and Martinez Peria (2008), they find that the SME segment is perceived to be profitable and that most banks are interested in serving SMEs, including large and foreign banks. Almost all the banks in the sample have separate SME units and offer a wide range of products and services. In addition to relationship lending, banks apply different transactional technologies such as credit scoring, risk-rating tools, and special products such as leasing and factoring. There are significant differences across banks and countries regarding the use of particular techniques, but these technologies allow banks to compensate for weaknesses in the enabling environment. Interestingly, the use of government supported programs is reported to be low.

# **3.  The MENA Survey**

Our survey (henceforth the MENA survey) was developed and conducted jointly with the Union of Arab Banks (UAB), which is the regional association of all banking associations in the region and has a membership of about 330 banks. The MENA survey has 50 questions distributed into four broad sections. Like the two previous surveys, we included qualitative questions in three broad areas: the strategic approach to SME lending, the main products offered to SMEs, and the risk management techniques employed. Moreover, like the survey by Beck, Demirguc-Kunt and Peria (2008), we also included a fourth and quantitative section designed to measure the extent of SME lending, the share of investment loans in total SME loans, and other variables.
Policy Research Working Paper 6219

# **Abstract**

This study provides estimates of social and financial costs of environmental damage in India from three pollution damage categories: (i) urban air pollution; (ii) inadequate water supply, poor sanitation, and hygiene; and (iii) indoor air pollution. It also provides estimates based on three natural resource damage categories: (i) agricultural damage from soil salinity, water logging, and soil erosion; (ii) rangeland degradation; and (iii) deforestation. The estimates are based on a combination of Indian data from secondary sources and on the transfer of unit costs of pollution from a range of national and international studies. The study estimates the total cost of environmental degradation in India at about 3.75 trillion rupees (US$80 billion) annually, equivalent to 5.7 percent of gross domestic product in 2009, which is the reference year for most of the damage estimates. Of this total, outdoor air pollution accounts for 1.1 trillion rupees, followed by the cost of indoor air pollution at 0.9 trillion rupees, croplands degradation cost at 0.7 trillion rupees, inadequate water supply and sanitation cost at around at 0.5 trillion rupees, pasture degradation cost at 0.4 trillion rupees, and forest degradation cost at 0.1 trillion rupees.

This paper is a product of the Disaster Risk Management and Climate Change, South Asia Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at mmani@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished.
Missing from this discussion has been information on how COVID-19 has affected the ability and intent of African men to migrate. Such data are rare, since another consequence of the pandemic was to shut down data collection efforts in most countries. We use a large-scale panel survey of Gambian households fielded in 2019 (pre-pandemic) and again in September and October 2020 (during the COVID-19 global pandemic) to examine how intentions to migrate of males aged 18 to 33 have changed due to COVID-19, directly asking about the effect of COVID-19. We take a comparative perspective, examining changes in intentions to migrate to Europe, and changes in intentions to migrate to neighboring Senegal. We provide a simple model to illustrate the main channels through which migration intentions will be affected, noting that the reduction in home income is likely to increase the desire to migrate, while lower income earning opportunities and worsening health conditions abroad will act to reduce this desire, and higher costs and lower wealth may make liquidity constraints more binding. Our first empirical result is that the net effect has been to reduce the intention to migrate to Europe for 31 percent of youth, and to Senegal for 34 percent of youth; with fewer than 4 percent increasing this likelihood.

We then use our survey questions and model to examine the channels through which this reduction in migration intentions occurs, and to examine which youth have had their migration intentions most affected by the pandemic. We find the largest reduction in the intention to migrate for individuals who were uncertain about their migration intentions pre-COVID-19. For these youth, the expected utility of different destinations was not that different from one another, so that the reductions in earnings opportunities abroad and increase in migration costs would be enough to change their migration decision. In contrast, those who had little intention to migrate to begin with are less affected, while a small number of those who definitely wanted to migrate appear to have doubled-down on their decision and become even more likely to move. Our results also highlight the importance of liquidity constraints, as we
**Figure 2:** Distribution of perceived oxen skill level for main household members

<!-- Start of picture text --> None 1 2 3 or higher<br>Reported oxen skill<br>Producer Boys<br>Spouse Girls<br>Note: Oxen skill reported by the household head for all household members on a 0-10 scale.<br>100<br>80<br>60<br>40<br>Percent of sample<br>20<br>0<br><!-- End of picture text --> The presence of social norms regarding handling oxen is reinforced by the existence of district-level patterns in the likelihood that women work with oxen. In our survey data, almost a third of households reside in districts where other households’ spouses performed fewer than one in three agricultural oxen tasks (sowing/weed, plow, or transport) over the prior year, while another third reside in districts where women performed at least 1.33 tasks. The intra-household labor impacts from the distribution of oxen are likely to critically depend on norms around the appropriateness of women working with oxen.

Women’s likelihood of working with oxen is highly correlated with broader district-level descriptive norms regarding women’s decision-making over their own health care, large household purchases, and visits to family and relatives (correlation coefficient of 0.75). Districts in which women are less likely to work with oxen are also those where women have less overall decision-making power. Regional patterns in these norms are shown in Figure 3.<sup>11</sup> Underlying these patterns in women’s decision-making is variation in kin-

> 11 We use the three standard DHS women’s decision-making questions (decision-making around women’s health care, large household purchases, and visits to family and relatives) to define a household-level decision-making index. We then take the median within each third-level administrative region, or districts (d ́epartements), and characterize
in 1996 to a surplus of +28% in 2016. In South Asia, the net trade surplus doubled from +4% to +8% over the same 20-year period.

**Figure 4:  Evolution of exports and the trade balance for the broad textile industry in 3 regions**

<!-- Start of picture text --> 2% 40%<br>1% 20%<br>0% 0%<br>1995 2000 2005 2010 2015<br>-1% -20%<br>-2% -40%<br>Total exports (SSA) Trade balance (SSA) Apparel bal. (SSA)<br>Total exports (China) Trade balance (China) Apparel bal. (China)<br>Total exports (India+) Trade balance (India+) Apparel bal. (India+)<br><!-- End of picture text --> Note: All fractions and growth rates are calculated from nominal values in current USD. Total exports sum over five product categories in the textile industry and is expressed as a fraction of global trade. The trade balance subtracts total imports from exports. The apparel balance shows net exports limited to apparel products (the

> 4 The five segments of the textile industry: agri-inputs, textile equipment, textile, apparel, leather & footwear.

8
<!-- Start of picture text --> Poverty by income concept (PGT scenario)<br>40 39.0<br>33.4<br>=a)DQ 30.6 29.3<br>£ 30<br>°<br>a<br>oa<br>D<br>8<br>=<br>o<br>2 320<br>o<br>2<br>><br>o4 11.63<br>& 98 10.8<br>10 79<br>0<br>Market Net market Disposable Consumable<br>G9 Headcount HS Gap<br>Slslotes: Authors’ estimates based on PECS 2016/2017; LFS 2017.<br>1] The official national poverty headcount matches poverty at Disposable Income.<br>2] We do not calculate poverty at Final income because we value the health and education transfers at the average cost of provision.<br><!-- End of picture text -->
- (b) Disaggregation and geo-mapping of data by location (current location and location of habitual residence; urban, peri-urban or rural location), accommodation (organized camp versus noncamp), and demographics (age and sex);

- (c) Expanded coverage of data collection exercises to include all areas of affected countries (security permitting);

- (d) Improved coverage and detailed data on displaced populations living outside of organized camps;

- (e) Improved coverage of ‘flows’, i.e. new displacement, durable solutions (returns, integration, resettlement), births, deaths, and in the case of IDPs, the numbers that flee across international borders becoming refugees;

- (f) Systematic data collection beginning from the earliest moment following displacement, following up as populations disperse, and continuing until sustainable/durable solutions have been achieved; and

- (g) Better aggregation, analysis and presentation of forced displacement data currently compiled separately by UNHCR, IOM, IDMC and UNRWA.

Additional efforts are required to address the gaps in the data required for development policy and planning These data are critical for informing the design of development policies and assistance programs. Specifically, additional data is required in the following areas:

- (a) Data on the socio-economic welfare, specific vulnerabilities and needs of displaced populations (broadly consistent with data collected and analyzed for poverty work, i.e. demographics, income and expenditure data, prices, living standards, access to infrastructure, services and local governance etc.);

- (b) Estimates of the size of affected host populations and data on the impact of displacement on host communities;

- (c) Estimates of the numbers of returnees yet to overcome the socio-economic vulnerabilities associated with their displacement, as well as data on their socio-economic welfare (broadly consistent with data collected and analyzed for poverty work, i.e. demographics, income and expenditure data, prices, living standards, access to infrastructure, services and local governance etc.);
residues. Hence, trying to achieve consistently high quality in a cluster might turn out to be too expensive for many potential participants.

The management of the clusters themselves is another potential problem area. The leadership might be tempted to exploit their institutional position and informational advantage to extract undue rents from the group trade activities. This risk seems particularly pertinent in a context of many small-scale traders being illiterate and lacking the market knowledge and accounting skills to effectively supervise the cluster management. Rent seeking might also occur from intermediaries, such as truck-transport or storage facility providers, who find themselves in a dominant position and can extract excessive fees for their services from group trade participants.

# **3. Data and empirical strategy**

Given the advantages and challenges of group trade discussed above, the empirical analysis addresses two questions related, respectively, to the determinants of group trade participation and the long-term viability of group trade. The analysis draws on field surveys on SSCBT, which were augmented by information on the physical and policy characteristics of traded products.

Empirical information on SSCBT at the eastern borders of DRC was collected through cross-sectional field surveys in December 2021. The surveys were carried out in eight border crossing locations: six sites at the border between DRC and Burundi (Gatumba, Kavimvira, Kiliba, Rumonge, Nyamoma, Uvira) and two near the DRC border with Rwanda (Goma, 10
# **3 Data**

## **3.1 Texas Department of Public Safety (TDPS) Data**

We use confidential data on arrests reported to the Texas Department of Public Safety (TDPS) by arresting agencies every 7 to 30 days, as required by the Texas Code of Criminal Procedures, Chapter 66.252. Our data span 2010 through 2017 and cover every Texas county. Texas state law requires that counties maintain at least a 90% data completeness rate over a rolling five year period in order to be eligible for certain state funds. Completeness means that the data reflect the most up-to-date status or disposition of each case. We received our data in 2019, so at least 90% of the cases through 2017 have been deemed to accurately reflect their most up-to-date status in our data (Department of Public Safety, 2019).

The TDPS arrest disposition data come in several parts. We combine files providing data on the individual arrested, the circumstances of the arrest, details of any prosecution, details of any court trial, and details of the subsequent sentencing or appeal. The individual data provide a unique ID for each individual arrested in our data, as well as the sex, race, ethnicity, and date of birth. The arrest data include the date of arrest, the date of offense, the arresting agency, the level of the arrested offence (for instance, misdemeanor A), the arrest charge (for instance, manslaughter), and the address of the defendant at the time of the arrest.<sup>7</sup> Each arrest charge is given a unique entry in the data. For example, if an individual is arrested on October 1, 2010 and charged with assault and resisting arrest, we will have two records for that individual, one for each charge. If they are then arrested again in 2011 for another charge, we will have a third entry for them. Each of these incidents can be linked to the same individual with their
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at 5-km resolution to establish a template for tracking methane emissions reduction at priority sites. We demonstrate its potential effectiveness with case studies for large sources of methane emissions from energy, agriculture and waste. Then we extend the template to create and analyze emissions trend reports for 775 high-priority global sites.

The remainder of the paper is organized as follows. Section 2 analyzes the global distribution of methane emissions sources using “bottom-up” estimates from the Emissions Database for Global Atmospheric Research (EDGAR).<sup>2,3</sup> In Section 3, we use the results to identify high-priority sites where rapid reduction of methane emissions would accelerate the global transition. Section 4 develops a strategy for sampling satellite-measured CH4 concentrations in sites that account for 50% of global methane emissions. Section 5 introduces our monitoring approach with illustrative case studies for landfills (Spain), irrigated rice production (India) and oil production (Iraq). In Section 6, we extend our approach to 775 high-priority areas and use the results to analyze the distribution of emissions trends globally, by source sector, and by World Bank income group. Section 7 presents the priority-area reports in detailed tables, maps and an accompanying Excel file, while Section 8 summarizes and concludes the paper.

> 1 https://www.globalmethanepledge.org/#about

> 2 https://edgar.jrc.ec.europa.eu/

> 3 EDGAR’s “bottom-up” approach estimates emissions by combining sectoral activity data with broadly-calibrated emissions factors (Crippa et al. 2020; Solazzo et al. 2021).

3
(DNP). 135 of the 200 firms exported at least once during these 11 years. These data provide export values at the 6-digit product and destination country level for each firm. For example, perfumes and cosmetics exported by a firm to Ecuador in 2018, or leather products exported by a firm to Chile in 2020. Using these data, we first measure the extensive margin of whether a firm is exporting at all, and then construct measures of export variety (number of countries, number of products, number of countryproduct combinations). We define export innovation as exporting a new country-product combination, and sum up the total value of exports. We merge these export data with data on formal employment provided by the Ministry of Health (the PILA), and use this 14
The algorithm above traces every link of state participation once the starting node is correctly determined as a public authority. However, we identified cases where ORBIS incorrectly classified a governmentrelated entity as a corporation, and it was therefore not included in the algorithm (see decision tree in Figure 7). To correct this, we analyzed the national legal definition for each country to identify additional starting nodes that needed to be included in the exercise.<sup>38</sup> These additional starting nodes were included in the algorithm to map additional subsidiaries and firms that were not identified in the first run. Another tool that we employed to minimize the measurement error consisted of using the information from the GUO variable described in section 2 to identify potential additional entities as starting nodes. Inclusion of GUO variable also addresses cases where a firm is linked to a single public authority through multiple ownership “paths” and each individual path does not exceed the threshold, but in aggregate the public authority holds shares above the threshold.

The iterative use of the algorithm to identify SOEs in ORBIS, combined with additional information from the supplementary database yields an unprecedented coverage of SOEs. The application of the algorithm to ORBIS may fail in cases where the entity at the starting node is wrongly classified or not even included at all in ORBIS, or where some ownership link is not recorded. We used the supplementary database to complement the entities identified through the algorithm and then applied the algorithm to the new set of entities identified. This iterative process helps retrieve a universe of SOEs larger than previously identified in many countries.<sup>39</sup> In sum, the SOE registry for a specific country is built through five complementary steps to minimize the measurement error. The full list of SOEs in a country (SOE registry) is built through a combination of (i) ownership trees run through the public authorities identified in the ORBIS ‘entities file’, (ii) ownership trees built on additional entities identified as government-owned firms through the review of
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