rafmacalaba/data-use-mentions-extended huggingface dataset:
contributions (excluding pensions) results in a 5-point reduction in the Gini coefficient when comparing market income plus pensions with disposable income** . Most of the impact comes from direct transfers, especially child benefits (noncontributory and means-tested), unemployment insurance, disability benefits, and other noncontributory and means-tested transfers. The direct tax effect is non-significant in terms of inequality reduction. Also, subsidies and indirect taxes reduce the Gini coefficient from disposable to consumable income by 1.9 Gini points, with an individual positive effect of VAT. The most considerable effect comes from in-kind transfers (health and education) that help reduce inequality by 6.3 Gini points (See Figure 4 panel a). Specifically, the most significant effects come from health and upper-secondary education. Similar results were obtained in the 2018 CEQ.

**However, the redistributive impact of direct taxes and transfers continues to be among the lowest in Europe.** Overall, Bulgaria's redistributive impact through direct taxes and transfers ranks among the lowest in Europe. With results second only to Hungary, Bulgaria falls within the group of countries achieving minimal redistribution through direct taxes and transfers, excluding contributory pensions. The

21
). AZTool iteratively combines and recombines sets of geographic building blocks to generate larger zones optimized to meet a set of pre-defined user-specified constraints. Such specified constraints include population threshold (Min, Max and target) and compactness of the shape. Criteria and constraints used in the current study are reported in table 2. Compactness is a minor constraint used so that shapes ‘tend’ to be compact and avoid difficult shapes such as snake-like shapes. Donut is one output area surrounding another. The Area constraint was not originally included as a constraint in AZTool, however, we modified the .xml file to include the area constraint for the purpose of this study.

Table 2. Illustrates the criteria were set in AZTool to generate sensible EAs in Somalia rural area.

|**Criteria**|**Hard Constraint**|**Soft Constraint**|**On or off**|
|---|---|---|---|
|Shape compactness||Yes|on|
|Population|Min=0 and Max=750|Target=650|on|
|Area|Min=0.1 km<sup>2</sup>and Max=9 km<sup>2</sup>|Target=8.8 km<sup>2</sup>|on|
|Donuts|Yes||on|

# **2.6 Computation of EA probability of selection**

The split and merge algorithm results in a partition of the country into regions that satisfy the definition of an EA. We calculate the population of each EA using the population density map and compute the probability of selection. The probability of sampling for each EA was defined according to the expected population within each EA divided by the total population in the regional stratum, recalling that the country was divided into 18 pre-war regions and further subdivided into urban/rural strata.

10
# 1 Introduction

To measure the trade restrictiveness of a country’s trade policy regime, one needs to overcome two important aggregation hurdles: aggregation of different forms of trade policies and aggregation across goods with very different economic importance.

Regarding the fi rst aggregation problem, trade policy can take many different forms: tariffs, quotas, non-automatic licensing, antidumping duties, technical regulations, monopolistic measures, subsidies, etc. How can one summarize in a single measure the trade restrictiveness of a 10 percent tariff, a 1000 tons quota, a complex non-automatic licensing procedure and a $1 million subsidy? Often the literature relies on outcome measures, e.g., import shares. The rationale is that import shares summarize the impact of all these trade policy instruments. The problem is that they also measure differences in tastes, macroeconomic shocks, and other factors, which should not be attributed to trade policy. Another approach that is also often followed is to simply rely on tariff data and hope that all other instruments are (perfectly) correlated with tariffs. These are obviously unsatisfactory solutions. A more adequate approach to solve this fi rst problem is to bring all types of trade policy instruments into a common metric.<sup>1</sup> Regarding the second aggregation problem, trade policy is set at the tariff line level and there are often more than 5000 tariff lines in a typical tariff schedule. How can one summarize all this information in one aggregate and economically meaningful measure? Commonly used aggregation procedures include simple average, import-weighted averages and frequency or coverage ratios; none of which has a sound theoretical basis. For example, imports subject to high protection rates are likely to be small and therefore will be attributed small weights in an import-weighted aggregation, which would underestimate the restrictiveness of those tariffs. In the extreme case, goods subject to prohibitively high tariffs have the same weight as goods subject to zero tariffs: a zero weight. Similarly, when computing simple average tariffs, very low tariffs on economically meaningless goods

>
**Figure 6: Percentage of Job Advertisements Using Keyword Clusters by Occupational Level, 2014**

Source: Authors’ calculations using Babajob data.

The use of text analysis can provide far richer information on the employee characteristics demanded by employers than can traditional surveys. By associating key cognitive, non-cognitive and technical skills with different occupations, the data can inform job seekers of the skills they should strive to acquire in order to become more competitive in the job market. The text analysis can be extended if shortlisted job seekers possess the demanded skill sets, by analyzing their CVs. These analyses can also provide important indicators to policy makers and educational institutions regarding the types of skills training that should be prioritized.

# **5.3. Observing Job-Search Behavior and Improving Skills Matching**

Behavioral economics has emerged as an important tool of international development research, as it enables a more comprehensive analysis of the psychological, social and cultural factors that affect decision making and influence social and economic behavior (World Bank, 2015). While development economists are attempting to collect more detailed information on the personality traits and psychological characteristics of individuals, the methodological limitations of surveys, questionnaires and other traditional techniques, which often rely on self-reporting of behavioral or personality information, can make it difficult to capture behavioral data. As a result, these data are often collected through experimental research methods.

The extensive transactional information saved on online job portals offers a unique source of observed behavioral information on job-seeking and recruiting trends. Job seekers face a variety of constraints when applying for a job, such as their urgency to find work, the availability of jobs in their chosen field, as well as salary and location preferences. These diverse constraints result in substantial differences in job-seeking behavior, which standard sample surveys cannot capture. While the literature on the use of online job-portal data in behavioral analysis is currently very limited, the application of behavioral analysis to non16
, we could observe that poorer households are less likely to take a loan to cope (columns (4)-(5)).

# **4.3 Robustness Checks**

We conduct two sensitivity analyses and check the robustness of the results in Table 1. To begin with, the rapid response phone survey sample consists of Kenyan nationals as well as refugees. Refugees might have different coping strategies from nationals and thus we exclude the refugee sample and cross-check the gender differences in coping among the Kenyan nationals.

14
returns are the difference between realized returns and the expected returns implied by a market model. Specifically, A A , (1) bb,tt bb,tt bb bb tt = AA −αα� −ββ̂ AARR where A A are the abnormal returns for bank _b_ at time _t_ , is the realized stock return of the bb,tt bb,tt bank, and is the market return. For each bank, and AA are the intercept and slope tt bb bb coefficients, respectively, of an OLS regression of bank AARR αα� _b_ ’s stock returns on a constant and the ββ̂ domestic market return estimated using monthly data between May 2018 and December 2019.<sup>12</sup> We compute monthly cumulative abnormal returns, which is the variation in stock prices from the last day of a month to the last day of the following month.

The empirical analysis, outlined in equation 2, consists of cross-sectional regressions that relate the monthly cumulative abnormal returns of banks to a series of bank characteristics. The dependent variable A A is the cumulative abnormal return of bank _b_ from country _c_ from month bb,tt _t-1_ to month _t_ . We refer to L L L as the bank illiquidity or liquidity risk interchangeably. bb To calculate this variable, we multiply the 2019 average cash holdings of bank LLAALL rrLLr _b_ by -1. is the bb measure of exposure for each bank to the oil sector as explained above. corresponds to the OOLLOO bb ex-ante total assets of a bank, measured in IHS. is an indicator variable that equals SSLLSSAA bb PPLLPPOOLLPP PPb rr

> 12As noted by Karolyi & Stulz, (2003), there are two alternatives in estimating the market model for a cross-country sample of stocks. The first estimates alpha and beta in the market model using a domestic market index, while the second uses a world market index. We present results using the “domestic CAPM” model and confirm most of our findings when we use the world CAPM model. We also
capitalization of 0.28). In other words, in terms of growth, moving from Pakistan to (more financially developed) Spain benefits highly financially dependent industries more.

We next analyze the effects of a different degree of competition in the financial system. We again compare an industry at the 75th percentile of financial dependence, shipbuilding and repairing, with one at the 25th percentile, manufacture of other non metallic mineral products. Using the OLS regression results presented in Table 4 (column 5), we find that the difference in growth between these industries is 1.2 percentage points higher in Sweden, a country at the 75th percentile of degree of competition (with an _H_ -statistic of 0.82) than in India, a country at the 25th percentile of degree of competition (with an _H_ -statistic of 0.61). In other words, in terms of growth, moving from India to (more competitive) Sweden again benefits highly financially dependent industries more. Since the average growth in real value added is 3.5% these effects are substantial. It is important to observe that, the effects of financial development and competition on growth are of similar magnitude.

# **5. ROBUSTNESS TESTS**

We next conduct a number of robustness tests. We start by using a different sample period. In the basic test, we use the same time period for growth in value added as in RZ, 1980—1990. However, the data from which we estimated the banking system competition measures covered the 1987—1996 period and thus do not exactly coincide with the period of our dependent variable (1980—1990). We also have growth data over the period 1980—1997,<sup>10</sup> which provides for a larger overlap with the period for which we estimate the degree of banking

> 10 We extend the growth rates into the 1990s using data from the UNIDO database, the same source used in RZ.

26
fines reduces tax evasion. The last two predictions are exactly the same as in Allingham and Sandmo (1972) model.

# **3 Empirical Approach**

This section estimates the impact of trust on tax morale using equation (7). From the theoretical model to the empirical approach, I assimilate tax evasion to tax morale due to data constraints. Unlike tax evasion data that are scant at the individual level, information on tax morale are relatively available, extensively used as a proxy of attitude toward taxes<sup>6</sup> and cover a couple of African countries. Moreover, experimental evidence and results from crosscountry surveys suggest that tax morale is an important predictor of tax evasion (Cummings et al., 2009). The authors highlight, for instance, that individuals that perceive tax avoidance to be moral are more likely actually to evade their taxes. The next sections present the empirical specification, the data, and the econometric issues before discussing the findings.

## **3.1 Empirical specification**

The econometric specification is based on the linearized form of the latent tax morale from equation (7): where, _i_ , _l_ and _c_ are respectively individual, location (city) and country indexes. _θilc_ denotes tax morale, _V Tilc_ trust in public institutions, _HTilc_ trust in the neighbourhood. _Kic_ refers to individual taxpayer characteristics and _ηc_ the country fixed effects which allow controlling for important omitted variables at the country such as the deterrence probability, the culture, norms about tax morale, and formal institutions. In the WVS,<sup>7</sup> respondents are asked to

> 6See for instance Ali et al. (2014), Frey and Torgler (2007), Torgler (2006) and Torgler (2005).

> 7The Afrobarometer data contain similar questions on tax morale and trust in public institutions. However, to the best of my knowledge the survey does not collect information on trust in the neighborhood and in civil service.

10
- or worker-level data might yield different elasticity estimates relative to micro firm- or worker-level data because with spatial aggregation the information on industry, firm and worker attributes is lost. Even within micro data, productivity measures derived from firm level data versus those collected through household or labor force surveys vary. This may be due to the type of firms in the data set. For instance, some countries have a cut-off on employee size in their firm census data, while comparable wage elasticity using household surveys may have a different threshold or criterion. In particular, elasticity estimates using firm level data might be lower than worker level data. For instance, Chun et al. (2019) find the wage elasticity for China to be negative 0.113 using firm level data compared to other studies that find huge estimates of ranging from 0.07 to 0.29 when using worker-level data (Combes et al. 2019).

# _Spatial scale_

Studies evaluate the spatial extent of local spillovers ranging from the broadest administrative level (regions) to the finest administrative level (e.g. villages and neighborhood). Most studies end up somewhere in the middle – administrative level 2 or 3 (e.g. municipalities or districts). The spatial scope of agglomeration effects depends on the nature of activity. For example, while knowledge and technology intensive activities will need to be co-located, other interactions such as input–output linkages can take place at a larger scale. A common approach is to consider an individual or location defined at a fine scale and to draw rings with increasing radius around it. Agglomeration benefits dissipate with distance and are rarely significant beyond a threshold distance and hence broader spatial scales yield lower benefits (See Rosenthal and Strange, 2003; Desmet and Fafchamps, 2005 for evidence on the US and Rice et al., 2006 for the UK ; Di Addario and Patacchini, 2008 for Italy).

5
affected, a state of emergency was declared, or international assistance was provided. We used EMDAT data from 2000 to 2023 from each region in the world for comparison (Table 3).<sup>5</sup> We find that, in our sample, the share of studies on subSaharan Africa is similar to the reported incidence of climate shocks in EMDAT. The share of studies on South Asia is higher than the reported incidence of shocks. Other regions are underrepresented. The results show that the coverage for floods and heat are lower in our sample, while it is higher for droughts in our sample of studies relative to the reported incidence in EMDAT.

> 5 Last accessed November 28, 2023.

7
collection in Pakistan. _ICTD Working Paper 33_ . Brighton: International Centre for Tax and Development.; Khan, A., Khwaja, A., & Olken, B. (2016). Tax farming redux: Experimental evidence on performance pay for tax collectors. _Quarterly Journal of Economics_ , 131 (1):219-271.

> 118 This is a possible explanation, for example, for evidence that large firms pay lower effective tax rates than mid-sized firms in Ethiopia. Mascagni, G., & Mengistu, A. (2016). The corporate tax burden in Ethiopia: Evidence from anonymized tax returns. _ICT Working Paper 48_ . Brighton: International Centre for Tax and Development.

> 119 Zucman, G. (2014). Taxing across borders: Tracking personal wealth and corporate profits. _Journal of Economic Perspectives_ , 28 (4):121-148.

> 120 Klemm, A. (2009). Causes, benefits and risks of business tax incentives. _IMF Working Paper WP/09/21_ . Washington: International Monetary Fund.

> 121 Prichard, W., & Jibao, S. (2010). Building a fair, transparent and inclusive tax system in Sierra Leone. Freetown: Christian Aid Sierra Leone.

32
More recently, Baker and Milligan (2008) find that the introduction of the right to leave in Canadian provinces delays mothers’ return to work shortly after birth but increases the likelihood that they will return to their pre-birth employer. Similarly, Lalive and Zweimüller (2009) use the period before and after this time as a natural experiment to analyze the effects of these changes in entitlement on fertility and mothers’ labor market outcomes in Germany. Based on variation across births on either side of policy introduction, the authors show that extended leave delays return to work of mothers, even after the benefits are exhausted, resulting in significant reductions in female employment and earnings during the first three years after birth, but only minor effects beyond three years. Hanratty and Trzcinski (2009) examine the impact of the expansion of Canadian paid family leave and find no significant change in return-to-work levels by the 16th month after birth. Recent cross-country evidence from Europe (Del Boca, Pasqua and Pronzato 2009) explores the relationship between conditions in the labor market, a range of social policies (including parental leave) and women’s employment outcomes. The authors find that social policies have a large and significant impact on the probability of a woman being in employment, but that the relationship is much stronger for less-educated women for whom the relative costs of participation in the labor market are higher. They also find that while a legal entitlement to maternity leave has positive implications for women’s labor market outcomes, as it helps women to keep a formal connection to their employers, longer leave durations harm women’s career opportunities in the long term. Akgunduz and Plantenga (2013) present evidence of an inverse U-shaped relationship between maternity leave and women’s labor market outcomes. Using data from 16 European countries between 1970 to 2010, Akgunduz and Plantenga (ibid) find that parental leave is associated with increases in women’s labor market participation, particularly when participation is measured in terms of hours worked; however, longer durations of parental leave are
each representative process would be studied without addressing issues of service quality as this would require a much more complex approach to data collection. For example, for access to education, _Living Life_ observes how burdensome it can be to enroll a child in school, the required documentation, and the fees charged, but it does not include any measure of quality of the education provided.

Even within this limited scope, the initial plan for data collection was very ambitious as it entailed two respondent data groups. For each area of study and in each country, data would be collected through interviews with several experts who would answer questions about processes and regulations applying to a specific prototypical case (contributor data). Second, data was to be collected through a representative survey of citizens who would answer questions about their own experiences (user data). The objectives were to collect information on both the legal framework (or _de jure_ situation) that the common citizen may not know as well as to assess the actual experience of the users of public services ( _de facto_ situation). However, while this represented the optimal approach, the limited budget was insufficient to pilot the user data component. As a result, only the contributor data collection methodology was developed and tested in the pilot.

7 | P a g e
|_Additional Growth Drivers_|||||||
|Secondary enrollment rate|0.004<sup>***</sup>|-0.002|0.002<sup>**</sup>|0.002<sup>*</sup>|-0.003<sup>*</sup>|0.002<sup>**</sup>|
|<br>_(initial level, logs)_|_(0.000)_|_(-0.102)_|_(-0.003)_|_(-0.035)_|_(-0.044)_|_(-0.004)_|
|ICRG Political risk index|0.006<sup>***</sup>|0.0002|0.005<sup>***</sup>|0.006<sup>***</sup>|0.001<sup>*</sup>|0.005<sup>***</sup>|
|_(principal components)_|_(0.000)_|_(-0.470)_|_(0.000)_|_(0.000)_|_(-0.015)_|_(0.000)_|
|Credit to private sector|-0.011<sup>***</sup>|-0.007<sup>***</sup>|-0.009<sup>***</sup>|-0.011<sup>***</sup>|-0.006<sup>***</sup>|-0.009<sup>***</sup>|
declining trend, in particular among the EU candidate and EU neighborhood countries, and in both of these groups a decline in national savings ensued.

The case of emerging Europe also appears to be consistent with what economic theory would predict: capital flows to high-growth countries (Figure 3; left chart). Hence, the allocation puzzle—that, paradoxically, capital flows to low-growth countries instead of high-growth countries—does not appear true in Europe either. These figures also suggest that the underlying strength of the link between foreign savings and growth might vary across groups. Still, an increase in current account deficits (i.e., capital inflows that happen to be absorbed) and an increase in growth are common among two of these country groups in emerging Europe (Figure 3; right chart). The opposite is the case, however, among emerging markets outside Europe—that is, high growth in incomes occurs jointly with an increase in current account surpluses (i.e., the allocation puzzle holds).

**Figure 3. Current Account Balances (% GDP) and Growth in Emerging Economies**

<!-- Start of picture text --> 20<br>15<br>10<br>5<br>0<br>-5<br>-40 -20 0 20 40 -40 -20 0 20 40<br>Current Account Balance, % of GDP<br>Non-European EME Emerging Europe EU10 EU Candidates EU Neighborhood<br><!-- End of picture text --> Source: Authors’ calculations based on World Bank and IMF data (2011). Note: Period 1997-2008.

In terms of the composition, capital flows take the form of FDI, portfolio, and banking (or other) flows. Clearly, FDI in emerging Europe played a key role; it is particularly large across all three groups of emerging European countries and is positively correlated with growth (Figure 4). This is even more evident in
*|-0.039|-0.114|-1.434***|0.147|
||(0.306)|(0.001)|(0.920)|(0.734)|(0.000)|(0.740)|
|OECD DSTRI|-2.259**|2.465*|-0.106|3.562***|3.475*|1.783|
||(0.036)|(0.070)|(0.962)|(0.009)|(0.062)|(0.356)|
|WEF NRI|1.045***|1.607***|1.819***|1.203***|1.591***|1.763***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Control GRAV|Yes|Yes|Yes|Yes|Yes|Yes|
|Control C|Yes|Yes|Yes|Yes|Yes|Yes|
|FE exp + imp|Yes|Yes|Yes|Yes|Yes|Yes|
|Obs|2898|2898|2898|2898|2898|2898|
|P-R2|0.988|0.969|0.963|0.967|0.937|0.976|

Note: * p<0.10; ** p<0.05; *** p<0.01. The dependent variable is bilateral digital services exports (DSX) using the underlying gross trade data from the TiVA database. Sector groupings in each column can be found in Annex Table A2. Robust standard errors are clustered at the country-pair level.