rafmacalaba/data-use-mentions-extended huggingface dataset:
Our measure of policy uncertainty is the Economic Policy Uncertainty (EPU) index compiled by Baker, Bloom and Davis (2016) and available with monthly frequency from <u>www.PolicyUncertainty.com. Baker, Bloom and Davis (2016) have developed the EPU index for</u> 22 countries using frequency counts of newspaper articles that contain terms pertaining to the trio of economy, policy, and uncertainty. The 22 countries are: Australia, Brazil, Canada, Chile, China, Colombia, France, Germany, Greece, India, Ireland, Italy, Japan, Mexico, Netherlands, Russian Federation, Republic of Korea, Singapore, Spain, Sweden, United Kingdom, and United States. The EPU index is strongly related to other measures of economic uncertainty, such as stock market volatility, and economic variables such as industrial production and unemployment rates.

**Figure 2.** **_Import volume growth and policy uncertainty, by country and year: for goods and services imports and for goods imports and using level or change of policy uncertainty_**

<!-- Start of picture text --> 0.4 0.4<br>0.3 y = ‐0.0691x + 0.3787 0.3 y = ‐0.0155x + 0.0597<br>R2 = 0.0839 R2 = 0.0026<br>0.2 0.2<br>0.1 0.1<br>0 0<br>‐0.1 ‐0.1<br>‐0.2 ‐0.2<br>‐0.3 ‐0.3<br>‐0.4 ‐0.4<br>‐1.5 ‐1 ‐0.5 0 0.5 1 1.5<br>3 3.5 4 4.5 5 5.5 6<br>Log‐difference of Economic Policy Uncertainty (EPU)<br>
financial markets also tend to be related to a smaller distance, likely due to stronger macrofinancial linkages.

The overall results remain stable when the three countries with the largest bond issuance are excluded from the sample. For a subset of firms for which we matched bond-level data to firms’ balance sheet data, the main results are robust to the inclusion of balance sheet variables, with firms having larger asset size and more profitable firms rated closer to the sovereign rating. Our main results are robust to possible sample selection bias in bond issuance.

Given the increasing access of emerging market sub-sovereign entities to global debt markets, it is important to note that their cost of foreign currency debt financing is influenced by sovereign credit ratings, especially for public enterprises and banks. In a similar spirit as the predictive model of “shadow” rating used to estimate the credit risk of unrated _countries_ (Basu et al., 2013; Ratha, De and Mohapatra, 2011), the findings of this study could provide useful insights to evaluate the creditworthiness of unrated firms. Finally, this study calls for further careful assessment of the sub-sovereign rating decisions based on bond-level and corporate financial factors after incorporating the externality which stems from country and global risk factors.

21
<sup>3</sup> These facts alone do not provide a full picture of the COVID-19 impact on irregular migration flows, but they do suggest irregular migration attempts from Northern Africa to Europe through the Mediterranean Sea continue to be an issue, despite the border closures and travel bans.

> 1 See <u>https://www.iom.int/news/covid-19-control-measures-gap-sar-capacity-increases-concern-about-invisibleshipwrecks</u> [accessed 29 January, 2021].

> 2 Mixed Migration Centre “Quarterly Mixed Migration Update Q4 2020: West Africa”, January 2021. <u>http://www.mixedmigration.org/resource/quarterly-mixed-migration-update-wa-q4-2020/</u> [accessed 29 January, 2021].

> 3 See UNHCR data available at <u>https://data2.unhcr.org/en/documents/download/84312</u> and FRONTEX data available at <u>https://frontex.europa.eu/along-eu-borders/migratory-map/</u> [accessed 29 January, 2021].

2
to closure by their second follow-up. Our paper builds on this work and not only shows no significant spillovers, but significant growth at the market level.

The remainder of the paper is structured as follows: Section 2 discusses the selection of our sample and randomization procedure; Section 3 the training and mentoring interventions; Section 4 our data collection and estimation approach; Section 5 impacts on primary and secondary outcomes; Section 6 mechanisms for these results; Section 7 cost-effectiveness; and Section 8 concludes.

# **2. Selection of the Sample, Randomization Procedure, and Baseline Characteristics**

# **2.1 Selecting a Sample**

A participatory process involving the ILO, government ministries, and organizations serving female entrepreneurs<sup>1</sup> was used to select four counties for the study: Kakamega and Kisii in the Western region, and Embu and Kitui in the Eastern region. These regions are largely rural, with an average population for each county of approximately 1 million, and the majority of the population below the poverty line. In each of the four counties, field staff from Innovations for Poverty Action, Kenya, mapped out all market centers deemed as “medium” or “large” outside of the main cities and conducted a market census of all female-owned businesses between June and November 2013 (see timeline in Appendix 1). After listing, an eligibility filter was applied (Appendix 2) and a baseline survey administered to 3,537 individuals located in 157 separate markets.

These markets are typically small and remote, largely consisting of women<sup>2</sup> operating a limited variety of businesses such as selling fruits, vegetables, grains, and dried fish products from tables; and offering services like hairdressing, dressmaking and small food kiosks. The market is a designated place in the village, and authorities typically build several stalls for the sellers to display

> 1 Stakeholders consulted included the Department of Micro and Small Enterprise Development (DMSED) of the Ministry of Labour,
of communication, which allowed observations, both critical and complementary, to constantly inform innovations in design and implementation.

Unfortunately, KDP is among a very small group of World Bank funded participatory projects that have made an effort to build effective monitoring systems. We reviewed the monitoring and evaluation systems in place in all participatory projects in the World Bank from 1999 to 2007. In our review, we examined a range of project documents, including the Project Appraisal Document (PAD), which is among the main documents assessed by the World Bank’s Executive Board before approving a loan. The PAD should ideally include a detailed account of the monitoring system and the manner in which the project will be evaluated. We also looked at Implementation Status Reports (ISRs) and Implementation Completion Reports (ICRs) for a random sample of 20 percent of these projects to assess the effectiveness of proposed monitoring and evaluation (M&E) systems in the PAD. The former are typically prepared by the project manager after every supervision mission. The latter are self-evaluations of projects screened by the Independent Evaluation Group. Finally, we assessed information from project supervision documents that synthesized the results of regular project visits by Bank operational task teams. The analysis also encompassed a survey of project managers (task team leaders, in World Bank parlance), to gather information that was not available in project documents.

The PADs were striking in their similarity to each other. Often their language was cut and paste from one project to the next, suggesting a singular lack of attention to context. The documents and the survey also revealed a pervasive inattention to monitoring systems. Only 40 percent of PADs included a monitoring system as an essential part of the project design, and a third did not even bother to mention basic monitoring requirements such as a management information system (MIS). When monitoring was mentioned, it usually involved collecting extremely imprecise indicators, and even this was done irregularly. The most important reasons given for the paucity of effective monitoring were poor human and technical
_Figure 5: Distribution of monthly per capita expenditure, nominal and spatially adjusted_ <!-- Start of picture text --> Spatially Adjusted<br>Nominal<br>0 200000 400000 600000<br>Per capita Montly HH Expenditure<br>Density<br><!-- End of picture text --> _Notes:_ The vertical line indicates the monthly value of the $2.15 poverty line (2017 PPP) _Source_ : World Bank staff calculations, based on HNAP summer 2022 Demographic and WASH survey As shown in Figure 5, the spatially adjusted and unadjusted (nominal) welfare metrics display a very similar distribution, although the spatially adjusted one is relatively more concentrated. Table 7 below reports national level poverty estimates at LIC $2.15 (2017 PPPP) as well as LMIC $3.65 (2017 PPP) international poverty lines based on the two welfare metrics.

_Table 7: National poverty estimates based on nominal and spatially adjusted welfare_

||Nominal|SpatiallyAdjusted|
|---|---|---|
|$2.15 2017 PPP|26.92|25.62|
||(0.008)|(0.008)|
|$3.65 2017 PPP|69.34|70.41|
||(0.011)|(0.001)|

_Note:_ Standard errors in parenthesis _Source_ : World Bank staff calculations, based on HNAP summer 2022 Demographic and WASH survey As shown in Figure 6, the distribution by governorate and the welfare quintiles constructed on each of the two welfare metrics are substantially different, with clear implications in terms of poverty profile (Figure 7).

_Figure 6: Distribution by_ _<u>governorate and quintiles of spatially adjusted and nominal monthly per capita expenditure</u>_ <!-- Start of picture text --> spatially adj<br>nominal<br>spatially adj<br>nominal<br>spatially
# **Table 2. Business Climate for FDI: ranking of 29 African countries, average 1995-1997**

Country FDI/business climate **a** ICRG political riskb Institutional Investor

|Namibia|1|1|NA|
|---|---|---|---|
|Mali|2|12|13|
|Mozambique|3|11|18|
|Zambia|4|4|14|
|Chad|5|NA|NA|
|Senegal|6|13|6|
|Angola|7|18|20|
|Benin|8|NA|12|
|Mauritius|9|NA|I|
|Cote d'Ivoire|10|8|8|
|Tanzania|11|5|10|
|Uganda|12|15|11|
|Central African Republic|13|NA|NA|
|Ghana|14|7|4|
|Madagascar|15|9|NA|
|Burundi|16|NA|NA|
|Rwanda|17|NA|NA|
|Zimbabwe|18|4|3|
|Congo, Rep.|19|14|19|
|Nigeria|20|17|15|
|Niger|21|20|NA|
|Guinea|22|19|17|
|Malawi|23|6|7|
|Cameroon|24|16|9|
|Kenya|25|5|5|
|South Africa|26|2|2|
|Ethiopia|27|10|15|
|Sierra Leone|28|21|22|
|Congo, Dem. Rep.|29|22|21|

Sources: Author's own calculations; Pigato (1999).

**a** The business climate index is defined as net FDI inflows normalized by GDP and the total value of natural resources in each host country.

b Political risk rating based on the opinion of banks, TNCs
# **Figure 3 Non-residential depreciation rates in OECD countries**

<!-- Start of picture text --> % per year<br>Switzerland<br>Belgium<br>New Zealand<br>Sweden<br>United States<br>Germany<br>France<br>Denmark<br>Netherlands<br>Finland<br>Hungary<br>Slovenia<br>Iceland<br>Mexico<br>Norway<br>Chile<br>Czech Republic<br>Israel<br>Slovak Republic<br>Estonia<br>Greece<br>Luxembourg<br>Poland<br>China<br>Ireland<br>Italy<br>Austria<br>Australia<br>United Kingdom<br>Canada<br>Japan<br>Spain<br>Portugal<br>Korea<br>0 1 2 3 4 5 6 7<br><!-- End of picture text --> _Source:_ OECD Economic Outlook Database, June 2017

# **_Estimates of non-residential gross fixed asset investment by sector_**

The official Chinese statistics provide estimates for the total nominal value of gross fixed capital formation from 1952 to 2016. There is not any breakdown of the total by sector or by the type of asset. It should be noted, though, that the definition of gross fixed capital formation differs from that found in the standard implementation of the System of National Accounts. In particular, expenditure on a project is counted as capital formation from the start of the project rather than as work-inprogress and being assigned to fixed capital formation when the project is completed and handed over to the final user.

# _Different measures of capital formation_

There is another statistical concept that, superficially, appears close to the concept of fixed capital formation: namely total fixed asset investment (FAI). It has the advantage of being broken down by asset and by sector since 2004 and by asset since 1981. The coverage
.

This paper assesses the potential for low-cost augmentation of ground-based air quality monitoring in Dhaka, Bangladesh, with more comprehensive coverage from global information sources. We test the potential contributions of two satellite information platforms: Sentinel-5P, operated by the European Space Agency (ESA), and NASA’s MODIS. Our assessment includes four dimensions: (1) Goodness-of-fit to ground-monitoring data; (2) spatial resolution; (3) update frequency; and (4) cost. We also investigate the potential contribution of Google Traffic in a spatial panel estimation exercise that relates traffic congestion to outdoor air pollution in the Dhaka region.<sup>1</sup> The remainder of the paper is organized as follows. Section 2 introduces prior research on the health impacts of PM2.5 and NO2. In Section 3, we introduce the data sources for our analysis. Section 4 uses econometric modeling to test the relationships between locally-monitored air pollution and measures provided by Sentinel-5P, MODIS and Google Traffic. Section 5 assesses the predictive accuracy of the estimated models. Section 6 discusses the relative benefits and costs of the new data sources, while Section 7 summarizes and concludes the paper.

> 1 In a global study, Dora et al. (2011) find that urban road transport often accounts for more than 50% of dangerous outdoor air pollutants (e.g., fine particulates, nitrogen oxides, carbon monoxide, ozone, sulphur dioxide).

2
# **4. Data**

We analyze poverty spells in 18 countries throughout the world, roughly corresponding to changes in the decade from 2000 to 2010. Most Latin American countries in the sample use income-based measures of poverty and data is taken from a harmonized database of household surveys from Latin American countries compiled in the Socio-Economic Database for Latin America and the Caribbean (SEDLAC)<sup>9</sup> . For countries that use consumption-based measures of poverty, data are taken from household surveys in the case of Bangladesh, Peru, and Thailand. For Moldova and Romania, household surveys were standardized by the World Bank. For Ghana and Nepal, we use the Rural Income Generating Activities (RIGA) datasets, a harmonized database of household surveys compiled in a joint effort by the United Nations Food and Agriculture Organization FAO and the World Bank. Table A1 in the Annex provides more detail of the countries, exact years, and surveys included in this study.

# **5. Results**

Demographics, labor income, public transfers, remittances: Which factor contributes the most to observed reductions in poverty? The key result that emerges is that the most important contributor to the reduction in poverty has been the growth in labor income per adult. In 10 of the 16 countries with substantial declines in moderate poverty, changes in labor income and employment explain more than half of the change in moderate poverty, and in another 4 countries, they account for more than 40 percent of the reduction in poverty (figure 10 and table 7).<sup>10</sup> This is true regardless of the decomposition path that is taken, as these results come out of the Shapley-Shorrocks estimates. Interestingly, in most cases, it was the growth in labor income throughout the decade that contributed the most, rather than an increase in the share of occupied adults.

Although changes in labor income are the main contributors to changes in poverty in most countries, demographics also matter. In particular, a higher
borrowed money in the past 12 months. Globally, 36 percent of men and 32 percent of women borrowed in the past year – from sources including formal financial institutions (bank, credit union or microfinance institution), store credit, family or friends, an employer, or an informal lender. The gender gap of 4 percentage points in borrowing remains consistent across high income and developing countries. In developing countries, a slightly larger percentage of adults report having borrowed money in the past year (37 percent vs. 32 percent in developing countries). In high-income countries, 33 percent of men reported borrowing as compared to 29 percent of women. The relatively low share of adults with loans in high income countries might be explained by the widespread ownership of credit cards (40 percent vs. only 7 percent in developing countries) that give adults access to short-term loans as needed.

Not surprisingly, the highest fraction of adults (14 percent) with formal loans can be found in high income countries (Figure 6 Panel A). In East Asia Pacific, Europe and Central Asia, Latin America and Caribbean, and South Asia, between 8 and 9 percent of the population reports having loans from formal financial institutions. The gender gap in the use of formal credit is most pronounced in high income economies at 4 percentage points. In the developing world, the gender gap with respect to formal credit is only around 1 percentage point or insignificant. However, in developing countries, the overwhelming number of loans originates from informal sources and the gender gap for those loans ranges between 3 and 4 percentage points (Figure 6 Panel B).

# **3. Explanatory Variables**

To shed light on the factors associated with gender differences in the access to and use of financial services, our empirical analysis combines individual-level data on the use of financial 13
Policy Research Working Paper 7534

# **Abstract**

This paper exploits data from a rotating panel that follows individuals for four quarters to shed light on the factors driving the time use decisions and restrictions faced by Mexican youth. The results of the analysis imply that: (i) once youth aged 15 to 18 years old leave school, it is very unlikely that they will return; (ii) being “neither in work nor in school” (Nini) is a highly persistent condition; and (iii) marriage (perhaps motivated by teen pregnancy) increases the probability of girls leaving school and raising children by themselves, which may in turn increase their future likelihood of being Ninis, as well as the probability of their children growing up to become Ninis, potentially creating an intergenerational transmission of Nininess. Similar results are found for other countries in the region (Brazil and Argentina).

This paper is a product of the Education Global Practice Group. 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 authors may be contacted at jbaron@ 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. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Figure 5. Physical climate change risks due to natural disasters**

- A. Annual number of disasters, 1981‐2017

<!-- Start of picture text --> 450<br>400<br>350<br>300<br>250<br>200<br>150<br>100<br>C. Number of affected countries,<br>1981‐2017<br>140<br>130<br>120<br>110<br>100<br>90<br>80<br>70<br>60<br>50<br>40<br>E. Disaster damage by region, USD billion<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>EAP ECA LAC MENA SA SSA<br>Note: Regional median per period in blue. 25 th and 75 th percentiles are<br>bottom and top of box, respectively<br>No. weather related disasters<br>1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017<br>No. Affected countries<br>1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017<br>USD billion<br><!-- End of picture text -->

- B. Share of disasters by type, 1981‐2017

<!-- Start of picture text --> 50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Drought Extreme Flood Landslide Storm Wildfire<br>temperat<br>D. Number of disasters by region,<br>1981‐2017<br>100<br>90<br>80<br>70<br>60<
districts in which this study takes place,<sup>6</sup> the schools that fell in the 90th percentile in terms of unutilized SMC funds had PKR 77,500 and PKR 62,000, respectively, each equivalent to at least three years of accumulated funds.<sup>7</sup> Private schools are virtually non-existent in Sindh. Ethnic and caste fragmentation means that, despite the large number of public schools, competition between schools is also not a viable source of pressure for improvement. Hence, there is no market-based _exit_ option available to parents (Hirschman, 1970). In the absence of accountability through school choice, and in an environment marked by low democratic pressure on service providers, the only option for dissatisfied parents is to directly _voice_ concerns with the educators (ibid). This makes Sindh a suitable context for exploring alternative approaches to community engagement.

# **2.2 Sample**

Sampling for the study was done in accordance to the districts: (1) being representative of the overall education profile of rural Sindh; (2) having a sufficient number of schools to meet the statistical requirements of our analysis; and (3) not posing excessive security risk for field teams. Using the PSLM survey, we ranked the 28 districts of Sindh by: i) proportion of adults who ever attended school, and ii) net enrollment rates of primary-age children (5-12 years). Also, using the

> 6 The districts in question were Matiari and Mirpurkhas. See section 2.2 for details of selected study districts. 7 We report cost estimates from School Improvement Plans prepared by SMC members during capacity building under ELECTIONS-CAPACITY; participants were instructed to prepare SIPs to spend all accumulated reserves (Figure A3, in Appendix A). In 2013, as the result of initial findings from the baseline survey and pilot activites, the Government of Sindh revamped the scope of the use of SIP Grants, specifically requiring all SMCs in the province to submit bank statements and members
: Women should earn less than men to Agree: Women should spend less time<br>sustain peace in the family working than men to dedicate time at home<br>Urban Urban<br>Rural Rural<br>National 34% National 77%<br>Urban Urban<br>Rural Rural<br>National 42% National 73%<br>Urban Urban<br>Rural Rural<br>National 20% National 74%<br>0% 10% 20% 30% 40% 50% 50% 60% 70% 80% 90%<br>UZB UZB<br>KGZ KGZ<br>KAZ KAZ<br><!-- End of picture text --> _Source: Listening to Central Asia surveys, 2022_ 5