rafmacalaba/data-use-mentions-extended huggingface dataset:
unprotected child labor market countrywide. Data from ILO’s 2015 Labor Force Survey (LFS) indicates that 9.3 percent of Myanmar’s total child population (12.14 million children aged 5-17 years) were engaged in child labor at the time of the survey. Working for pay or profit, child workers fall into one of four main categories: (1) children aged 5-17 engaged in hazardous work<sup>3</sup> for at least one hour per week; (2) children

> 1 In this study, a child is defined as anybody between the ages of 5 and 17.

> 2 What is child labor? https://www.ilo.org/ipec/facts/lang--en/index.htm.

> 3 The definition of hazardous work includes the following: (1) work which exposes children to dust and fumes; fire, gas and flames, loud noise or vibration; extreme cold or heat; (2) work underground, under water, at dangerous heights or in confined spaces; (3) work with dangerous machinery, equipment and tools, or which involves the manual handling or transport of heavy loads; (4) work in an unhealthy environment which may, for example, expose children to hazardous substances, agents or processes, or to temperatures, noise levels, or vibrations damaging to their health; and (5) work under particularly difficult conditions such as working long hours or during the night or work where the child is unreasonably confined to the employer’s premises (Department of Labour 2016).

2
, for several reasons. Chinese official loans and related credit events are not on the radar screen of credit rating agencies such as Moody’s or Standard & Poor’s. Data on Chinese defaults and restructuring is also not collected systematically by international organizations such as the OECD, the IMF or the Paris Club. Chinese lenders themselves do not share information on their overseas loans in arrears or default and on whether debt restructurings took place and at what terms. In effect, as Gelpern et al. (2021) document, China’s loan contracts often explicitly rule out being part of a transparent, multilateral, coordinated debt relief initiative with comparable treatment. Most Chinese debt restructuring deals are arranged bilaterally and outside the public domain.

As a result, defaults or restructurings of Chinese overseas debts remain largely “hidden”. As we discuss, “hidden defaults” are neither new nor unique to Chinese creditors. OECD 2
the Law dataset, in 101 of 190 economies, women face at least one legal constraint that prevents them from operating a business in the same way as men (World Bank 2023). Significant improvement over the last 50 years has led to most economies allowing women to sign contracts, register a business, and open a bank account in the same manner as men. However, only 47 percent of economies prohibit gender discrimination in access to credit, and nearly 40 percent of economies limit women’s property rights, including right to inherit assets.<sup>34</sup> Furthermore, socio-cultural factors linked to restricted gender norms are still pervasive in some regions and hinder the implementation of laws; for example, Braunmiller and Dry (2022) observed this in the Democratic Republic of Congo.

One crucial gender norm concerns the division of time for household activities. If women are overburdened by care responsibilities and domestic work, their ability to work longer hours in their business is limited. This can partly explain the gender gap in profits (World Bank 2021). The prevalence of gender-based violence (GBV) may also affect women’s choice of business sector, location, and networking activities. Promising interventions include legal reforms, information campaigns and discussions around

> 31 For example, a survey with informal retailers on a digital platform in Egypt shows that, compared to male retailers, women retailers are less likely to know how to use the mobile application and are more likely to end up with stockouts due to not tracking inventory (Mignano and Kipnis 2022).

> 32 IFC (2022) describes the experience of digital business-to-business distribution platforms in Nigeria, Egypt, and the Philippines.

> 33 See Burga et al. (2021) for a toolkit providing practical guidance to help teams working on women's entrepreneurship projects apply digital solutions to project design and policy advice.

> 34 Based on the author’s own calculation using the WBL 2023 dataset (World Bank 2023).

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and evaluation of<br>RRSF identify for each<br>African country<br>weaknesses and<br>strengths in order to<br>support interventions;<br>• Provide a general idea<br>of the performance of<br>African countries’<br>statistical systems.|• Aid NSOs in identifying<br>areas of improvement;<br>• Assist NSOs and donors to<br>justify the need for funding<br>for training;<br>• Provide a measure of the<br>impact of capacity building<br>activities by being<br>administered at two points<br>in time, before and after.|• Provide an assessment of country<br>statistical capacity over time in a cost-<br>effective, sustainable way;<br>• Provide guidance to the WB teams in<br>assessing the progress and<br>sustainability of the Bank supported<br>projects;<br>• Inform Systematic Country<br>Diagnostics;<br>• Provide a monitoring tool for<br>countries’ SDGs data production<br>capacity.|

**Sources** : Adapted with modifications from Cameron et al. ( _2021_ ).

19 Partnership in Statistics for Development in the 21st Century (PARIS21): Statistical Capacity Building Indicators; United Nations Economic Commission for Europe: Global Assessment; Food and Agriculture Organization of the United Nations: Country Capacity Indicators; United Nations Economic Commission for Africa: African Statistical Development Index; US Census Bureau: Tool for Assessing Statistical Capacity, World Bank: Statistical Capacity Indicator.

20 The pilot study was conducted in Asia Pacific countries in 2012. In-depth country assessment will be carried out, following the established guidelines.

21 Referred to the most recent GA report on National Statistical System of Mongolia. No specific indicators were referred to, but the report focused on assessing different specific areas of the Mongolian statistical system.

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values should be treated as missing, since it was argued, it was a far bigger assumption to assume these missing values are all zero. For the sake of robustness, several samples, which have varying degrees of missing values, are estimated to ensure that their presence do not lead to spurious results.

# _5. Data_

The paper draws upon data from three main sources. Migration data are obtained from Özden et al (2011), which details five origin-destination matrices that comprise every nation state, major territory and dependency from across the globe (226*226). The dataset is based upon the foreign-born concept of migration and each matrix corresponds to one of the last five completed census rounds, 1960-2000.<sup>15</sup> The data correspond to economic migrants and every effort has been made to remove refugees. Although the proportion of illegal migration captured in the dataset is unknown, it is still likely that a fairly large proportion of illegal migrants are captured in national censuses. This dataset is superior to those previously estimated in terms of its broad global coverage and the number of decades to which it refers. It

> 15 In this paper, the version of the data chosen from Ozden et al (2011) is that which equates the migration data to a specific year, for example, 1970 or 1980 as opposed to the version which pertains to census rounds or decades, for example 1994-2005.

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<!-- Start of picture text --> 40<br>30<br>20<br>10<br>0<br>1 year 2 years 3 years<br>Post treatment years<br>Learning related supplies<br>Health related supplies<br>Repairs<br>Upgrades<br>Rent & utilities<br>Transportation<br>Construction<br>Percent of double grant spent<br><!-- End of picture text -->

**Figure 4:** Allocation of double grant

Note: The figure shows the means and 95% confidence intervals of allocation of the double grant in treatment schools. "Learning related supplies" are spending on books, writing utensils (e.g., pencils, pens, chalk, etc.), and writing surfaces (e.g., paper, chalkboard, notebooks). "Health related supplies" are spending on first aid kits, basic personal hygiene products, and cleaning supplies. "Repairs" refer to funds spent on fixing broken equipement, furniture, and space in schools. "Upgrades" refer to funds spent on purchasing new equipment and furniture. "Rent & utilities" are spending related to real estate, electricity, water, etc. "Transportation" is spending related to transportation for teachers and students. "Construction" is spending on the infrastructure of classrooms and teacher dormitories. Data source: Parent association survey.
52,433 vertebrates (amphibians, birds, fish, mammals, reptiles); 213,268 arthropods; 32,355 molluscs; Table 1 provides a more detailed accounting. We limit the exercise to GBIF species in the kingdoms two methods to remove spurious observations from locales such as zoos and botanical gardens. The first is exclusion of isolated outlier occurrences before map estimation, which happens automatically in alphahull. The second occurs after map estimation, when we exclude alphahull-bounded point sets with fewer than three observations. Many species maps have multiple bounded areas; in those maps, our approach imposes a conservative interpretation of the evidence. We drop species maps with single bounded areas when they contain fewer than three observations.

3. Global Species Distributions 3.1 Spatial Selection Bias GBIF occurrence reports are often produced by voluntary exercises that do not employ scientific sampling methods. In consequence, spatial point densities in species occurrence reports are positively related to physical accessibility, population density and income (Garcia-Rosello et al. 2023; Borgelt et al. 2022; Isaac and Silman 2011; Reddy and Davalos 2003). Other things equal, more species sightings occur near transport arteries; in areas where there are more inhabitants to identify species; and in areas where more inhabitants have disposable income sufficient to support species search and the cost of reporting. These factors complicate attempts to map species population densities from occurrence reports (e.g., Kass et al. 2022). Alphahull estimation of boundaries is different, because it focuses on exterior points in spatial sets. However, accurate representation still requires a critical minimum number of sightings in areas that are not advantaged by transport access, population density or disposable income. As Feeley and Smith (2011) note, the accuracy of boundary estimation improves as more sightings occur in non-advantaged million new reports per day. The following section tests alphahull boundary mapping from the current provided by existing expert maps.

GBIF species and the smaller inventory of expert range maps, with two primary objectives. The first is incorporating more
A: The 535-household subsample that is matched to CDR. Panel B: The 535-household matched sample, plus the 472 households that do not have a phone; this is meant to approximate targeting performance if CDR from all mobile networks were available. Sample weights are applied as described in Section 2.5. Panel C: All 2,814 observations from the TUP survey, including households matched to CDR, households that own phones not matched to CDR, and households without phones, with sample weights applied. For Panels B and C, we simulate two types of CDR-based targeting: targeting households without phones first and targeting households without phones last.

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# **Tables:**

|Table 1: Causes of Corruption: Full Specification.............................................19|
|---|
|Table 2: Corruption in Developing Countries...................................................22|
|Table 3: Decentralization and Corruption: Unitary vs. Federal States.......................23|

# **Appendices:**

|Appendix A: Sources for the 1998 Corruption Perceptions Index...........................29|
|---|
|Appendix B: Sample Space........................................................................30|
|Appendix C: Data Sources and Descriptions....................................................31|
|Appendix D: Composite Measures................
order was implemented on March 23 (eventually lifted on May 4). The government was also very active in introducing a series of measures to help employers and workers weather the economic consequences of the lockdown. These included various forms of tax and rent relief for businesses, unemployment benefit extensions, financial support and social insurance coverage for employees whose contracts have been suspended, financial support for the self-employed, and prohibitions on dismissals for businesses shut down by state order.

Nonetheless, as elsewhere, the pandemic and lockdown are having major economic impacts in Greece. The Hellenic Statistical Authority (ELSTAT) reports that on a seasonally adjusted basis, GDP declined by 1.6% in the first quarter compared to the fourth quarter of 2019.<sup>2</sup> In its Summer Forecast, the European Commission predicts that economic activity will decrease by 9% in 2020.<sup>3</sup> In this situation, a significant deterioration in labor market conditions would be expected.

The main empirical contribution we make in this paper is to describe in some detail how the Greek labor market has evolved in the first few months of the pandemic. Relying on a range of sources including administrative data, survey data, and data from online job posting sites, we document the drop in employment following the imposition of the lockdown and the subsequent flat employment trend through the first months that followed. Two things are particularly noteworthy about the patterns we have observed. First, while Greece did not experience the major declines in employment that some other OECD countries did in March, April, and May, these are normally months when employment growth is substantial in the heavily seasonal Greek economy. Second, employment in these months in 2020 differed from the story in previous years not because the lockdown fueled large numbers of separations but rather it choked off new hiring in what should have been expansionary months. In fact, compared to recent years, separations were comparable or even lower. We attribute this largely to the regulations and wage subsidies
in Malaysia. Fisman (2001) uses a ‘Suharto Dependency Index’ in Indonesia. Rijkers et al. (2017) leverage a data set connecting firms to the holdings of the Ben Ali family in Tunisia. Likewise such connections can be extended to school networks, as well as close advisors and friends (see, for example, Bertrand et al. (2018)’s analysis in France). Kubinec et al. (2021) employ both self-reported political connections on a 1 to 10 scale and a measure of aggregated relationships between firms and the state; they find that the self-reported scale tends to be more theoretically valid than the aggregated observed measure.

The specificity of connections across several disparate economies, in turn, presents a challenge. The nature of connections in one economy may very well not translate to another. Take the role of the military, which may evoke a wide range of the meanings of political connections depending on the context of a specific place. Our need for a measure with plausible, directional consistency toward political influence, gives us a reason to favor a direct measure of connections that is widely applicable. For this, we define political connections using the measure from the survey: _Has the owner, CEO, top manager, or any of the board members of this firm ever been elected or appointed to a political position in this country?_ We note that this measure is not necessarily ideal in the sense that it is not fully comprehensive. Previous

> 18Firm size is determined by the total number of workers and excludes firms with fewer than 5 workers. The size categories are small (5–19 workers), medium (20–99), and large (100+).

> 19The surveys’ population of inference excludes certain ownership structures, such as non-profits or 100 percent government-owned establishments. For more details on the survey methodology, refer to the Data Appendix or see `https://www.enterprisesurveys.org/en/methodology` .

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emissions originating from fuel use and production activities. A detailed description of the model, assumptions and corresponding equations is given in Annex 1.

4. This is the first time that a CGE model for India has looked at the trade-offs between economic growth and ―local‖ pollution mitigation.<sup>5</sup> The open economy model incorporates links among 57 sectors — various sectors within agriculture, manufacturing and services — of the Indian economy as well as links between the economic output of these sectors and air pollution emissions, principally PM10 and emissions of SO2 and NOx which give rise to health effects. Other CGE models for India have so far included only 11 to 36 sectors and have not tracked emissions such as PM10.

5. The model‘s database developed by the GTAP network<sup>6</sup> (GTAP database version 8 for 2007) includes data from the India‘s National Accounts. This was complemented with statistics on urban pollutants (from national statistical sources) and macroeconomic variables (i.e. growth rate projections and total factor productivity (TFP) from the literature). Specifically, the model was extended by several external inputs, such as demographics, labor productivity and labor supply, and corrected for environmental health impacts, sectoral coefficients for PM10 emissions.

# **Methodology**

6. In terms of the methodology, first, an economic growth scenario was developed, reflecting the most likely path that the Indian economy could follow from 2010 through 2030. This path represents the "economic baseline". The GTAP model was calibrated to reproduce actual GDP growth rates in the country during 2007-2010 and growth projections in line with World Economic Outlook projections.<sup>7</sup> While the recent IMF survey of the Indian economy suggests a robust 7-8 percent growth in the next few years in spite of a global economic slowdown, it will be necessary, according to the IMF, to focus on reinvigorating the structural agenda, rather than relying on monetary and fiscal
reports indicate that women’s response to the initiative was positive. For example, from July 2021 to March 2022, the percentage of women traveling by bus in Tamil Nadu increased from 40% to 61% (Sundaram, 2022). According to Goswami (2021), women made the of riders on Delhi buses March 2021 after the up majority Transport Corporation by program’s launch.

To identify the causal impact of the program on women’s labor outcomes, we collated data from several sources. Our main empirical analysis is based on the Consumer Pyramids Household Survey (CPHS) data maintained by the Centre for Monitoring the Indian Economy (CMIE). The rich CPHS data is a panel of about 160,000 households across all major Indian states after 2014. It includes comprehensive information not only on household expenditures, members’ demographic characteristics, and employment status but also on their time use patterns and allocation of time on various activities. We bolster the findings with a large primary survey of women from Delhi that was collected after the program’s launch in Delhi.

Our identification approach compares women in treated states (i.e., Punjab and Tamil Nadu) to their geographical neighbors, serving as our control group. The implementation of the in 2021 variation. To address con- policy provides temporal endogeneity cerns, we implement a synthetic differences-in-differences strategy (SDID) proposed by Arkhangelsky et al. (2021) at the state level. This approach combines the synthetic control method (Abadie and Gardeazabal, 2003) with the difference-in-differences strategy.<sup>25</sup> We compare the pre-trends in our treated and the synthetic control group using event study models and do not discern differential trends.

We examine the effect of the treatment on on In first expenditure commuting. doing so, we determine if women’s travel demand is responsive to the cost of transportation. We find evidence consistent with elastic demand for transportation for women using the CPHS data. Overall expenditure on travel, specifically on a category
which considers scale (size of a city, size of its economy including the size of private sector activity) or services (access to infrastructure and social services) as something produced rather than given, requiring cities to constantly perform to remain influential in a global world. New literature which focuses on connectivity of cities within a network includes that of Sassen, (2000), Castells, (2000), and Doel and Hubbard (2002). Uncovering the variables that drive the dynamics of city growth and performance interests us, and we particularly look at the role of governance, using a set of empirical tests.

From the data we have been able to gather from various sources, we can look at the effect of both the scale issue as well as the quality of services and investigate the Friedman (1986) and Townsend (2001) hypotheses.<sup>6</sup> 5 Kyu Sik Lee and Alex Anas in “The Benefits of Alternative Power Tariffs for Nigeria and Indonesia” (1996), demonstrate how companies locate in a city and if they do not find the services they need either invest in their own generation, thereby exiting the system, or put pressure to get better services, with potential improvements in local governance, or relocate to cities with better services.

6 The Taylor database gives us data on financial/banking companies located in a city, as well as data on the availability of key companies in law, accounting, and advertising, all measures of indicators (a), (d), and (f) in the Friedman (1986) hypothesis. From the UN and KLM databases we get measures of key infrastructure endowments (indicator (b)). City size (indicator (c)) is obtained from the website: http://www.citypopulation.de. We use the Kearney composite measure of globalization for measure (e) and have collected web statistics that show whether a city has a website and if it posts budget information or business information
by Danon et al. (2023). It can be played on a tablet and

> Guatemala, we asked teachers to do it for this study. As for 2023, data were collected for school reports in all three countries.

> 34We are able to measure school dropout only for students who (i) were not enrolled in the same school (or in any other school in our sample) or (ii) reported they were going to abandon the educational system (due to migration to another country, for example).

> 35Teachers in Honduras and Guatemala score performance between 0 and 100, and teachers in El Salvador report math grades with a score between 0 and 10.

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