rafmacalaba/data-use-mentions-extended huggingface dataset:
assembly. Because of variation in the extent to which headmasters effectively disseminated information about the scholarship, there was a sense that awareness was inadequate in some areas, and as a result the NGO held additional community meetings in September and October to reinforce knowledge about program rules in advance of November 2001 district exams. After these meetings, enumerators began collecting school attendance data during unannounced visits.

The 2002 student survey indicates that girls had excellent knowledge of the program: 88% of cohort 1 and 2 girls in Busia district claimed to have heard of the program, and knowledge levels were only slightly worse in Teso district (85%). Girls had somewhat better knowledge about program rules governing eligibility and winning than boys: Busia girls were 8 percentage points more likely than boys to know that “only girls were eligible for the scholarship” (86% to 78%) and 7 percentage points more likely to know exactly how many scholarships would be awarded (32% to 25%), and patterns are again similar in Teso district (not shown). Girls in Busia were only slightly more likely than boys to report that their parents had mentioned the program to them at home (72% to 70%).

In June 2001, a tragic incident occurred in which lightning struck a primary school in Teso district (Korisai Primary School), severely damaging the school, killing seven students and injuring 27 others. Because ICS had been involved with an assistance program in that school, and due to certain strange coincidences (the names of certain lightning victims were the same as the names of ICS staff members who had recently visited the school) the deaths were associated with ICS in the eyes of some community members, and the incident led several schools to pull out of the Girls Scholarship Program. Of the original 58 sample schools in Teso district, five pulled out of the program at that time, and one Busia district school located near the Teso border also pulled out. Figure 1 presents the location of the lightning strike and
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X)

|Table B1O. Coverage of Health and Social<br>**Insurance**<br>Insurance<br>from Informal Sector Surveys conducted by the author|_<er_<br>~~~~~<br>Taxicabs|245 hour<br>~~~~~~~~~<br>per week<br>Taxicabs|Entire<br>~week<br>Samnple<br>Taxicabs|Entire<br>Samnple<br>Kiosks<br>|Entire<br>Sample<br>Stre<br>Markets<br>|Entire<br>Samnple<br>et Services &e<br>v<br>Vendors<br>|
|---|---|---|---|---|---|---|
|Number of Observations (actual number may vary depending on<sup>the</sup><br>question)|99|101|308|245|137|80|
|Social or Health Insurance contributions from informal activity<br>income|||||||
|Yes|24.2|52.6|37.7|41.6|31.9|42.5|
|Any coverage under Health Insurance|||||||
|Yes|69.4|56.6|62.9|54.7|43.5|45.6|
|Any coverage under Social Insurance|||||||
|Yes|66.4|44.4|55.1|38.2|13.2|16.3|
|Spouse covered by Health or Social Insurance<br>Yes|56.8|53.
list of economies included in the _common_ sample.

# _3.2. Description of variables_

# _Dependent variable: Labor productivity_

Labor productivity is measured as the natural logarithm of sales per worker, computed as the revenue deflated and represented in 2009 USD during the last fiscal year divided by the number of employees (see also Amin & Okou, 2020). We use labor productivity instead of the TFP measure (Francis et al., 2020) because information for the latter is only available for less than 40% of the observations and is not available for firms in the non-tradable sectors.

# _Independent variable: Urban density_

We use urban density instead of urban population size to measure agglomeration since recent research argues that in the developing world agglomeration externalities predominantly arise from density rather than absolute market size (Henderson et al., 2019). Urban density is obtained from 9
**Table 3.4. Matching of household surveys to livelihood zones**

||Ethiopia|Lesotho|Malawi|Mauritania|Mozambique|Niger|Nigeria|Zambia|Zimbabwe|Total|
|---|---|---|---|---|---|---|---|---|---|---|
|Maize mixed|2,913|377|26,172|0|2,610|0|0|5,580|12,614|50,266|
|Highland|10,651|2,444|0|0|227|0|29|0|1,695|15,046|
|Roots and<br>lowland|0|0|0|0|0|0|5,705|0|0|5,705|
|Agropastoral|0|0|0|0|0|2,315|2,278|0|0|4,593|
|Pastoral|0|0|0|11,463|0|2,178|0|0|0|13,641|

_Source: World Bank. Note:_ Livelihood zones are generated from Dixon et al. (2019). Agropastoral and pastoral regions in Ethiopia are not included in the analysis. Pastoral includes pastoral and arid pastoral oasis farming systems. Highland includes highland perennial and highland mixed farming systems. Roots and lowland combines root and tuber crop, cereal root crop, and humid lowland tree crop farming systems.

**Table 3.5. Descriptive statistics for variables by** **<u>year and country</u>**

|**Year**|**Country**|**Average per**<br>**capita**<br>**consumption**<sup>**a
tax treaty policy in Sub-Saharan Africa. Building on a panel data set of 41 African economies from 1985 to 2015, we examine the effects of concluding DTTs with investment hubs empirically. In Africa, Mauritius has positioned itself as a conduit country for investment into the continent. As part of its strategy, Mauritius taxes corporate income at 15 percent, which can be reduced to an effective rate of 3 percent,<sup>4</sup> and negotiated favorable tax treaties with many African economies. We take advantage of the fact that 11 1 The authors would like to thank Geerten Michielse for his guidance and contributions throughout the preparation of this draft. The authors would also like to thank Punam Chuhan-Pole, Matt Collin, Ruud De Mooij, Alexander Klemm, Jeffrey Owens, Victoria Perry, Alex Sienaert, Marijn Verhoeven, and participants of the Vienna University of Economics (WU) research seminar on the future of international taxation for providing helpful comments. The views expressed in this paper are those of the authors and do not necessarily represent the views of the IMF, the World Bank, their Executive Boards, or management.

2 Definitions of “investment hubs” or “tax havens” differ across institutions and over time and there is no single globally accepted definition. The OECD has developed a list of havens as part of its work on harmful tax practices in 1998 and has since revised the list and approaches to define havens. Other efforts at defining the concept have been made by researchers, legislators, CSOs and regional and international organizations. A close link also exists to the discussion and definition of offshore financial centers (OFC), providing financial services to non-residents.

3 Several CSOs, including the Tax Justice Network (2015), Oxfam, ActionAid (2016) are publishing lists of tax havens, tax treaty policy decisions and examples of cost. In the G20/OECD led Base Erosion and Profit Shifting (BEPS) process (2015) several measures that will help reduce the risks of tax treaty abuse have been agreed
. For example, Gutierrez, Krings, and Blondel (2013) created a predictive model to extract indicators of wealth, inequality, and segregation from mobile phone data. They suggest that network data sets represent a starting point to understand socioeconomic variables in countries with no resources to conduct large surveys. At the same time, these researchers are also cautious, warning that traditional surveys are still needed to build more accurate models or to assess the quality of mobile phone data.

Data quality can vary according to the specific scope of a study. For example, in the previous Blumenstock et al. (2018) paper using private firms’ mobile phone data in Afghanistan, the authors disclaim that registered corporate lines are unlikely to represent all firms, particularly small and informal firms operating outside urban areas. In developing countries, the large size of the informal economy can skew results interpretation. Similarly, mobile phone information is limited to people able to afford a cell phone. Even though mobile phone use is increasingly widespread, it is important to note the limitations of these data sets A last drawback is related to data availability and individuals’ privacy. Since private firms usually operate cell phone networks, it can be problematic to obtain user data due to commercial, ethical, and regulatory concerns. It is hard to estimate the cost of these data sets, but the main obstacle for obtaining them may be regulatory and administrative hurdles. Researchers must prove that they will use the information only for research purposes.

# **Geospatial data**

# <u>Method</u>

24
mechanisms that cause the disparity in overall life satisfaction between refugees in and outof-camps. I analyzed them directly from the ATT of propensity score specification. The result on household earnings (income per capita) shows that camp refugees are significantly more likely to have lower income per capita than out-of-camp refugees. Camp refugees typically earn an average of JD33.65 per capita per month, whereas out-of-camp refugees earn about JD48.53. After controlling for pre-crisis characteristics, refugee households living in camps earn on average JD14.77 per household member less than refugees living out-of-camps. Ginn (2020) showed that camp residents' low income is not associated with assistance dependency as male camp residents are more likely to show readiness to work than out-of-camp counterparts. But the challenge is that camp residents often lack access to jobs compared to out-of-camp residents. Although camp officials provide some assistance for camp refugees to secure resident permits and help them in job search, Ginn showed that males living in camps are less likely to have worked at least one hour in the last 30 days. There is also concern that out-of-camp refugees' additional income may be canceled by the high rent they pay; but as I will show, this additional income helped many out-of-camp refugees meet basic needs.

Material deprivation is a function of whether households meet their basic or psychological needs such as food, water, clothing, and a comfortable place to sleep. The abject poverty measure allows estimation of the share of households at the risk of deprivation with respect to these basic needs. About 62 percent of households living in camps are at risk of living in abject poverty compared to 28 percent for those living outside of camps. The difference after controlling for pre-crisis characteristics is 36—a very significant finding. Indeed, refugees are entitled to some form of blockchain cash transfer, but even after controlling for amount of aid received, the result still shows that camp refugees are more at risk of deprivations in basic needs. The camp refugees are also less likely to say they are able to lend up
be allowed to enter and leave camps freely; Syrian refugees should be given housing assistance through shelter programs that subsidize their rent; the international community should spend more money to support refugees. All outcomes are standardized with respect to the control group, averaged, then standardized with respect to the control group again.

Altruism to Syrians ranges from 0-5 JD and reports how much of a 5JD transfer was allocated to a charity serving Syrian refugees vs a charity serving vulnerable Jordanians vs kept for self. The altruism exercise was incentivized for one third of the sample, who were actually given 5 JD to allocate between the 3 choices. The rest of the sample was asked to make a hypothetical choice.

- The regressions also have enumerator fixed effects, community-level controls (Irbid/Mafraq governorate and population quartile), refugee household-level controls (shelter program, baseline number of children, baseline number of children plus adults), the Marlowe–Crowne scale, and respondent gender, education, and age. In the parentheses are robust standard errors clustered at the locality level. Statistical significance represented by * (10%), ** (5%), and *** (1%). Q-values are calculated per Anderson (2008) and correspond to Family 1 in the pre-analysis plan.

39
_to consumable income. Results are calculated at the $1.25 daily poverty line, 2005-PPP. A positive poverty reduction effect means that poverty is declining, while a negative means that poverty is rising._ _Box 2. Measuring_ _<u>poverty in Uganda</u>_ The poverty line was set in 1998 using 1993 data by estimating the amount of expenditure needed to satisfy the minimum daily calorie requirements and basic non-food needs. Appleton et al. (1999) identified the 28 commonly consumed food items and the corresponding amount consumed to meet 3,000 calories per adult equivalent. Calorie requirement varies by age and gender, and hence the 3,000 calories is per adult equivalence. Based on the population structure then, the average per capita calorie need was 2,283 calories.

The minimum expenditure on basic non-food needs was estimated using the classic approach of Ravallion and Bidani (1994) by identifying the non-food expenditure of households that are just on the food poverty line. The justification for using these households’ non-food expenditure as a reference is that the poor have sacrificed some of their need for calories to buy the non-food items. Therefore, these non-food expenditures should also be regarded as meeting essential needs. The non-food expenditure was allowed to vary by region and rural/urban areas in order to account for spatial differences prices (Appleton et al., 1999). It includes, among other items, rent, utility fees, as well as education and health expenditures. The poverty line is the sum of expenditure on basic food and non-food items. Since 1993, the Consumer Price Index has been used to update this poverty line.

_Source: Appleton et al (1999)._

# 4.3. Fiscal impoverishment

An individual receiving benefits and paying taxes experiences increases and decreases (respectively) in income or purchasing power. When an individual who is measured as poor at _post-fiscal_ income experienced a greater burden from revenue collection instruments (i.e. she paid more in taxes) than she
South Asian countries to shed light on the high gender employment gap in the region. After standardizing definitions of labor-related variables across different survey instruments, we compare employment by gender across countries and time by looking at different population age groups, economic sectors, location (urban or rural), type of employment contract, and institutional structure to explore factors that could explain the gap.

The aggregate figures show that since 2011 employment rates have been relatively stable in all countries except for Bangladesh, where the gap between men and women’s employment has widened. In fact, in Sri Lanka and Pakistan the different recall periods used in different surveys do not significantly affect employment rates between men and women. In Bhutan, on the other hand, using a yearly recall period rather than a weekly one does generate a difference of up to 20 percentage points, depending on the specific year. In Nepal, Afghanistan, and India, our results do not lead to definite conclusions.

> 9While the surveys for Sri Lanka, Bhutan, and Nepal had more precise data on education attainment (completion of each individual grade from 1 to 12), the data for Pakistan was not as detailed and contained fewer categories (primary, secondary, etc.) instead having completion of individual grades). Therefore, the education indicators in other countries were adapted to be comparable to those in the Pakistan survey.

19
# **1 Introduction**

The overdraft facility<sup>2</sup> policy from private financial institutions provides financial support to increase firms‘ accessibility to private financing sources. However, according the IMF (2005) the overdraft facility policy has often been criticized for its negative effects by impairing the development of an innovative private financial sector and for making firm highly dependent on government support policy measures. Even though there have been some qualitative remarks on the effectiveness of the policy, an evaluation of the overdraft facility policy has not been conducted systematically in terms of methodology and data, especially in Eastern European Union Industrial firms. This study aims to fill the gap in the existing literatures.

In this work, we evaluate the effect of the overdraft facility policy in terms of human capital (production workers trained), technological capital (expenditures of R&D), competitiveness (sales export level) and the firm‘s technical efficiency in the Eastern European Union Industrial firms, by comparing overdraft facilitated firms and non- overdraft facilitated firms. The amount of funds allocated to the overdraft facility policy is huge and the number of targeted firms is large. Therefore, we need to investigate its effectiveness and to provide background information for further evolution of the policy.

This work utilizes an original dataset that covers manufacturing firms in 11 Eastern European countries (Bulgaria, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Moldova, Poland, Romania, Slovak Republic, and Slovenia). Moreover, we analyze the effectiveness of the overdraft facility in four industrial sectors (Beverages, Food, Garments, and Metals and machinery).

Methodologically, reliable policy evaluation should solve the ̳selectivity‘ problem. According to Muthen and Jöreskog (1983), selectivity problems can occur whenever one tries to estimate population parameters from a nonrandom sample. The sample may be nonrandom because only firms with certain characteristics are selected into the sample (sample selection), or because firms participate voluntarily in the sample (self-selection). Selective samples can also
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019)|(0.014)|
|Uganda|0.226***|-0.009|0.171***|0.177***|0.135***|0.059***|
||(0.026)|(0.011)|(0.023)|(0.018)|(0.021)|(0.014)|
|Observations|5,595|5,595|5,595|5,595|5,595|8,719|
|R-Squared|0.015|0.035|0.223|0.012|0.033|0.011|

_Note_ : Each column reports results from a single regression of a binary variable equal to 1 if the respondent stated that the household relied on that strategy to cope with the loss of income and 0 if they did not. The binary variable is regressed on indicators for each country, with Malawi as the base case, along with an indicator equal to 1 if the household was located in an urban area, as opposed to a rural area. Data come from the most recent round of phone survey data available. This is round 3 in Ethiopia and Nigeria, round 2 in Malawi, and round 1 in Uganda. Robust standard errors are reported in parentheses (*** p<0.01, ** p<0.05, * p<0.10).

51
Glick, Peter. 2008. 'What policies will reduce gender schooling gaps in developing countries: Evidence and interpretation', _World Development_ , 36: 1623‐46.

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- Kim, Jooseop, Harold Alderman,
# **Figure 9: Latent bond factors and observed global factors**

Each panel compares the largest two extracted latent factors _퐹푡_ from (5) with observed global factors. All variables shown are standardized to facilitate interpretation.

# **(a) First latent factors**

<!-- Start of picture text --> Federal funds rate (r=49.2%) TED spread (r=-7.5%) VIX index (r=-60.1%)<br>Latent factor Latent factor Latent factor<br>MSCI World Index (r=89.2%)<br>Latent factor<br>EMBI (r=87.1%)<br>GBI-EMU (r=98.2%) EMBI-div (r=88.2%)<br>GBI-EM (r=22.1%) EMBI+ (r=86.6%) Debt/GDP (r=82.1%) Reserves/GDP (r=65.2%)<br>Latent factor Latent factor Latent factor Latent factor<br>(b) Second latent factor<br>TED spread (r=-18.0%) VIX index (r=-32.0%)<br>Latent factor Latent factor<br>Federal funds rate (r=-13.2%) MSCI World Index (r=2.1%)<br>Latent factor Latent factor<br>EMBI (r=31.5%)<br>GBI-EMU (r=-0.1%) EMBI-div (r=27.3%)<br>GBI-EM (r=74.6%) EMBI+ (r=33.6%) Debt/GDP (r=43.2%) Reserves/GDP (r=46.4%)<br>Latent factor Latent factor Latent factor Latent factor<br>20092010201120122013201420152016201720182019 20092010201120122013201420152016201720182019 20092010201120122013201420152016201720182019 20092010201120122013201420152016201720182019