rafmacalaba/data-use-mentions-extended huggingface dataset:
impact on markup dispersion is not significant across age and productivity groups. Older productive firms have both higher productivity and higher markups when regulations are made more complex; younger productive firms benefit from regulations that simplify processes. In several cases, low-productivity firms confronted by competitive pressures have even lower productivity and markups. This suggests that competition occurs leads to churning at the lower end of the productivity distribution, while market leaders, defined as the top 25 percent in that distribution, are able to escape competition and maintain or increase their market shares.

The subsector results reveal that the types of regulation and enforcement data collected relate to the regulator’s implicit objectives. The objections for infrastructure and telecommunications, as defined by the laws and enforcement rulings, are not necessarily 23
An important advantage of the BIS _Consolidated Banking Statistics_ is that they net out intrabank claims so, for example, loans between a bank’s headquarter and its branches and or subsidiaries overseas are not included. On the other hand, the BIS data does not adequately discriminate between cross-border and local foreign claims. While cross-border claims are mainly denominated in foreign currency, local claims can be denominated either in the local currency of the country where the claimholder resides or in a foreign currency. The latter is particularly true in the case of highly dollarized countries (such as many in Latin America). In general, BIS data on local claims only captures those denominated in local currency. Foreign currency denominated local claims are combined with cross-border claims and reported under what the BIS calls “international claims”. Thus, BIS data is largely ill-suited for an analysis of the determinants or the implications of the mix of cross-border versus local international bank claims.

Due to the limitations of the existing BIS information, the data used in our analysis had to be specially requested, through the BIS, from the Italian and Spanish central banks. The exception was the U.S., which is the only country that since 1997 reports separate information on cross-border and local claims to the BIS. For all three countries, the data available to us aggregates claims held by all banks vis-à-vis each host country. Thus, like in the case of the BIS data, the information we have is not bank, but rather country level data. For the U.S., we have information for the period 1997-2002. In the case of Spain and Italy the data covers the period 1998-2002. None of these countries collected separate information on local and cross-border 8
the intergenerational occupational mobility of the youth beneficiaries of the program in rural areas. The analysis on this subject is considered an approximation that will contribute to our understanding of extent to which PROSPERA has achieved its ultimate objective, which is an improvement of the occupation of youths, in comparison with their characteristics of origin, encouraging the process of breaking the intergenerational transmission of poverty.

The data from the Evaluation of Rural Households Survey (ENCEL) collected in 2017 enabled us to study the intergenerational occupational mobility and the occupational attainment process experienced by the beneficiary youths of PROSPERA two decades after the intervention began and, at a more advanced moment in their lives, in which a greater proportion of them has entered the labor market and achieved a more stable labor status. Based on a quantitative analysis, which uses the ENCEL 2017 as the main source of data, the objective of this study is to provide answers to two sets of research questions:

1. What are the characteristics of the intergenerational occupational mobility in the rural youth beneficiaries? Do differences by sex, ethnic background or migratory status exist? What is the

> 3 Throughout its history, the program has been named Program of Education, Health and Nutrition (Progresa), Oportunidades Human Development Program (Oportunidades) and currently Prospera, Social Inclusion Program (PROSPERA). The name PROSPERA will be used in this paper, except for those occasions in which it is more appropriate to use the name corresponding to a specific historic period.

> 4 Currently, there are 64 similar social programs in different regions of the world (World Bank, 2018c: 37).

2
(2016) is a descriptive conceptual framework for the policy-making process and is appropriate for social science research questions in multiple fields. The limited relevant empirical work in energy includes Schmidt & Sewerin, (2019), who analyze policy mixes in nine developed countries, although that study does not consider policy interactions.

# 6 CONCLUSIONS AND POLICY IMPLICATIONS

Achieving decarbonization worldwide requires a robust understanding of how effective different renewable energy and climate policies are in a broad range of countries and their dynamics over time. Research on the topic is especially important in developing countries, where the literature is relatively scarce.

This study sheds light on these important issues by conducting a first systematic assessment of how seven renewable energy policy packages affect the energy mix in developing countries over time.

We rely on the background data behind the RISE indicators published by the ESMAP at the World Bank. We address several well-known econometric issues in the existing literature that uses similar datasets, including omitted variables and simultaneity and reverse causality between energy policy packages and outcomes. We estimate thousands of indicatorinstrument-outcome-level country and time fixed-effects regressions over regional panels covering more than 100 developing countries and four decades of energy sector policies.

We credibly evaluate the robustness of indicators’ measurement, quality of instrumental variables used, and significance and direction of estimated energy policy package coefficients. Notably, we find no major measurement differences when we try different policy instrument aggregation outcomes, allowing us to conclude that the aggregation method used by the World Bank is robust to potential under- and overweighting problems. We also find no major differences in the second-stage estimates obtained by 34
fact that interviewees provide a recorded description of product and process innovations allows the user to verify the identified innovations and reclassify wrongly attributed cases to their respective category or invalidate cases that do not constitute an innovation at all. This exercise has been conducted by Cirera and Sabetti. 2019, who provided “clean innovation data” (see Appendix for methodological description). For the overall sample of South Asia firms, the cleaning exercise conducted by the authors has decreased both product innovation - from 53% to 51% - and process innovation rates from 64% to 58%. Although the cleaning exercise reduced innovation rates for most countries, Nepal is an exception where product innovation increased from 10% to 12% as the result of reclassification from process innovations.

7
# **3.2.1 Observable Characteristics**

To measure how much of the association between household income and informal consumption shares can be explained by observable characteristics, we estimate the following regression separately for each country: where _i_ indexes a household, _Xi_ are household characteristics and each observation is weighted by the relevant household survey weight. Table 2 shows the average of the slope coefficients _β_ across countries, the average upper and lower bounds of the 95% confidence intervals, and the number of countries for which the coefficient is statistically significant at the 5% level. Column 1 displays results from the specification without controls. Column 2 controls for household demographics: household size and age, education and gender of the household head.<sup>16</sup> We find that these characteristics do not explain the correlation between informal expenditure and income and, if anything, the IEC slopes become slightly steeper.

Columns 3 and 4 add controls for households’ location, either with an indicator for whether the household lives in a rural area (column 3) or with survey block fixed effects (column 4).<sup>17</sup> These controls allow us to test whether the association between informal budget shares and household expenditure is due to poorer households living in areas with worse access to formal retailers. Despite large differences in average informal budget shares between rural and urban households, controlling for rural locations only explains 13% of the slopes.<sup>18</sup> Controlling for survey blocks explains just over 20% of the differences in informal budget shares between poor and rich households.

In columns 5 to 8 we test whether non-homothetic across preferences goods play a role – when richer households spend more on goods predominantly sold in formal stores. We run a product-level version of specification (2) with goods fixed effects and compute an average goods-level estimate of _β_ for each country.<sup>19</sup> We gradually consider variations within narrower goods categories: we first consider food
Fourth, regarding international factors (see e.g. Burnside & Dollar, 2000; Demir, 2016), contrary to the lack of significant effect of the interactive terms with the variables FDI inflows and terms of trade (see Table A4), the presence of the official development assistance (ODA) reduces the tax revenues gap between Non-Cooperative and Cooperative countries (column [9]). Indeed, since ODA often comes with measures related to improved allocation and greater transparency, such measures can equally translate into various government activities including in terms of tax collection, which may ultimately improve tax revenue mobilization.

Finally, Table A4 in the Online Appendix equally reports estimations of interactive terms with other variables (see e.g. Tanzi, 1992; Sachs & Warner, 1995; Gupta, 2007; Corsetti et al., 2012; Debrun & Kinda, 2016), namely the time length since a country was included on the FATF list, the difference between the estimated propensity score and its sample average, the phase of the business cycle, education, and mineral and natural resources. These variables are not found to affect the tax revenues gap between Non-Cooperative and Cooperative countries, despite being consistent with what one may expect—for example, the coefficient of the interactive term with education is positive and with an associated p-value of 0.25. Altogether, the results presented in this section complement our previous findings, as they emphasize different environments that may affect the impact of being on the FATF list on tax revenues.

# **IX. Concluding remarks**

Recent work draws attention to the fragility of domestic tax revenues—a vital resource for the developing world—to illicit financial flows (IFFs). To cope with two major challenges in the relationship between IFFs and tax revenues—related with the mere measurement of IFFs and reverse causality—we exploited the Financial Action Task Force (FATF) data in an impact assessment analysis. Propensity score matching estimations revealed a significant tax revenue loss in countries associated with important
technology. At the same time, lower dismissal costs decrease firms’ costs of retaining workers by diminishing their bargaining power.

<u>Anesi and De Donder (2013)</u> study the political economy of combining employment protection and unemployment insurance policies. They show that the election process and the formation of political parties could influence the relative weights of the policies. In the direct citizen-candidate model, low-skilled workers are decisive and push for maximum employment protection and unemployment benefits. Allowing for the party formation results in policy equilibria in which high-skilled workers form a coalition with the unemployed and support a combination of policies with high unemployment benefits and lower job protection levels.

Several recent papers analyze the impact of the COVID-19 pandemic on the labor market and its relationship to social policies. <u>Eymeoud et al. (2021)</u> contrast US and European labor market trends during the pandemic. They demonstrate that the EU countries avoided a sharp increase in unemployment in the early stages of the pandemic by funding short-time work programs. However, these programs may slow the reallocation of workers from shrinking to growing sectors of the economy during the recovery.

<u>Aidukaite et al. (2021) and Beland et al. (2021)</u> study the labor market response to the COVID-19 crisis in Hungary, Lithuania, Poland, the Slovak Republic, and the OECD countries. Both papers find that labor market policies in these countries depended on pre-pandemic social policy choices and focused mostly on job protection measures. Finamor and Scott (2021) document a negative association between the generosity of unemployment insurance and employment in the US during the pandemic. Notably, the employment gap remained constant even after the expansion of benefits expired, indicating that fear of infection and childcare options could be important factors explaining the persistent rates of labor inactivity in the US.

# **3. Data**

The analysis in this paper relies on a new country-level data set of expenditure on social protection measures implemented in response to the
br>70<br>65<br>60<br>55<br>50<br>Greece Ireland Portugal Spain<br>2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br>2000 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br><!-- End of picture text --> <!-- Start of picture text --> 0<br>10<br>20<br>30<br>40<br>50<br>60<br>70<br>80<br>Greece Ireland Portugal Spain<br>2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br><!-- End of picture text --> Higher rank means less cumbersome regulation Source: The World Bank, Ease of Doing Business Database.

**Figure 14.c: Control of corruption score               Figure 14d: Product market regulation**

<!-- Start of picture text --> 1998 2003 2008 2013<br>0<br>5<br>10<br>15<br>20<br>25<br>30<br>35<br>Greece Ireland Portugal Spain<br><!-- End of picture text --> Percentile rankings (0 lowest rank, 100 best performance) Source: The World Bank, Worldwide Governance Indicators.

<!-- Start of picture text --> Among 34 OECD members, higher rank (lower number)<br>means less regulation.<br>Source: OECD<br><!-- End of picture text --> _<u>Business climate and governance: when policy, institutions and perceptions follow each other.</u>_ In comparative terms, Ireland, by far the richest country in the group, has always had the best business

> 75 In Portugal the number was 16 pp higher and in
producers, purchased by government and supplied to households)” (OECD, 2013a, p.130). We cannot measure gross fixed capital formation by the public sector because the TiVA database, like other ICIO data, does not provide a split between public and private gross fixed capital formation. We measure social transfers in kind with the entries under the “General Government Expenditure” component of final demand. To measure intermediate consumption, we take the column vectors from the input-output matrix that correspond to government entities. The European Commission (2017) proposes three ways to define these entities in an input-output matrix: (i) a “narrow” classification that includes only the “Public Administration” column; (ii) a “typical” definition that adds the “Health” and “Education” columns to the “Public Administration” one; and (iii) a

> 21See this note: `http://www.oecd.org/industry/ind/tiva-2018-differences-tiva-2016.pdf` from the OECD on the subject.

16
enrollment and attendance in the developing world. The idea underlying such claim is that educated parents by and large understand the importance of achieving basic education and therefore feel responsible to send their kids to school. The reverse is true for non-educated parents who started to work at an early age. This is evident in Wahba [28] that shows that Egyptian parents, who were child laborers themselves, would most likely send their children to work. In other words, for those parents education may not necessarily be considered as an investment.

Some studies also consider cultural aspects such as gender issues that may influence some disparities in enrollment and attendance. Tansel [25] points to gender as one characteristic that should not be neglected when analyzing the determinants of education. He noted that the effect of income on the schooling of girls was larger than that of boys. Al-Samarrai and Peasgood [2] find that household characteristics such as parental education may have a totally different impact on the education of females and males in Tanzania. Using some descriptive statistics, Bonifaz and Ochoa [8] find that Bolivia does not present a significant gender gap in terms of total school attendance, even though minor differences emerge when considering the socioeconomic status and the living area.

> 9 See Basu [5] for an analytical survey.

6
exchange rate between the origin and destination currencies in year _t_ , and let _e_<sup>_US_</sup> _odt_<sup>betheaverage</sup> real exchange rate between the destination and the USD in year _t_ . Our bilateral exchange rate variables in logs are defined as follows: Finally, GDP data are from the World Bank’s World Development Indicators (WDI).

# **3.3 Trade policy variables**

We use data on both tariffs and non-tariff measures. For tariffs, we use data on Most Favored Nation (MFN) and preferential tariffs at the HS6 level from TRAINS. For each origindestination-product-year ( _odpt_ ) quadruplet, we compute the bilateral applied tariff. As we are mostly interested in the role played by differences in trade policy across markets, rather than in the effect of variations in trade policy in a given market over time, we also compute the average bilateral applied tariff over the period. This allows us to smooth out missing values without much loss of information. In the empirical analysis we will show that our results are robust to using time-varying bilateral tariffs.

For non-tariff measures we use ad-valorem equivalents (AVEs) at the destination countryproduct level estimated in Cadot and Gourdon (2014, 2016). The Ad-valorem equivalents were computed for 45 countries and three types of measures, type-A measures (SPS), type-B measures (TBT), and “other” measures (essentially quantitative restrictions).<sup>18</sup> The bulk of the variations in NTM AVEs (more than 80 percent) is attributable to the application of SPS and TBT regulations. Section B.1 of the online appendix provides more information on the NTM source data and how they were converted into estimated AVEs.

In Section B.2 we provide additional descriptive statistics on our trade policy measures. Table A.1 shows the (unweighted) average levels of bilateral applied tariffs and NTMs AVEs for the 45 countries covered by both tariff and NTM data. The lowest levels of tariffs
notch less the reduction in the bunching mass at the old notch (as a result of the reform). However, when looking at the raw data, there are clear time trends in the number of households locating at the old and new notch in the pre-reform period. Thus, our identification challenge is to understand how the number of households reporting incomes at the old and new notch would have evolved in the post-reform period had the reform not occurred.

At a high level, our identification strategy is based on the following insight: the incentives to report an income at and above the original notch are affected by the reform, but the incentives to report an income below the original notch are unaffected by the reform. Hence, we can use portions of the reported income distribution below the original notch as control groups for underlying time trends in the reported income distribution at and above the original notch. To do so, we use a generalized difference-in-difference strategy as employed in, for example, Wolfers (2006) or Mora and Reggio (2013). A standard difference-in-difference estimator requires that the control groups and treatment groups have “parallel trends” pre-reform that would have persisted in the post-reform period had the reform not occurred. Similarly, the generalized difference-indifference estimator requires that there exist stable relationships, which are well approximated by relatively low order polynomials, between treatment and control groups which existed pre-reform that would have persisted in the post-reform period had the reform not occurred. In our setting, we discretize the reported income distribution into bins, using bins below the original notch as control bins while using bins that include the original and new notches as treatment bins. We then use the estimated relationships between treatment and control bins in the pre-reform period to create counterfactuals for how the treatment bins would have evolved absent the reform. Fundamentally, there are complex structural relationships between the number of people reporting in each income bin; these relationships are determined by how the true income distribution is evolving over time, how the true income distribution is mapped to the
informed by applications to the United States by Ioannides and Zabel (2003) and to China by Song and Zenou (2012).

The estimated change in imputed housing prices in the aftermath of a disaster can in turn be decomposed into two elements. First is the fall in the value of a dwelling resulting from the physical destruction it suffered; this first element reflects the change in the present value of living conditions and can be linked with the damage concept. Second is the fall stemming from the destruction experienced by the surrounding area; this second element indicates the change in the present value of earnings opportunities and access to services associated with the dwelling, similar in spirit to the loss concept.

The only two requirements to implement the proposed approach are the availability of disaggregated information on imputed housing prices and dwelling characteristics before the disaster, and of geographically disaggregated information on the condition of physical assets in its aftermath. In practice, data on housing prices and characteristics can often be obtained from a representative household survey, and data on the extent of destruction of assets from either a field survey or satellite images.

The first step toward implementing the proposed approach is to use household survey data to estimate a hedonic price function for the value of dwellings before the disaster, with the specification including a neighborhood effect. Second, plausible rules are used to allocate the local destruction rates of physical assets of different types to individual households in the survey. Third, 3