rafmacalaba/data-use-mentions-extended huggingface dataset:
46 threshold. A household is defined as impoverished if it is not poor based on consumption or income <u>gross of out-of-pocket health expenditures, but is poor based on consumption or income net of out-of-</u> pocket health expenditures. To take into account differences in household size and weights, we use an ‘aweight’ equal to the product of household size and household weight when computing the population-level incidence of (i.e. the percentage of households incurring) catastrophic and impoverishing expenditures.

# <u>Data processing process</u>

The data-processing process begins by generating in the microdata standardized or harmonized variables that correspond to our indicators – see Figure 11. (Surveys that do not allow us to estimate out-of-pocket expenditures and income or (non-health) consumption are, of course, excluded.) Population rates of catastrophic expenditures (CATA) (and, where income is used, rates for specific quintiles) are then computed from each standardized microdata set. The computation of rates of impoverishment (IMPOV) require supplementary data at the national level on PPPs and the CPI; these data are merged into the microdata and the impoverishment rate is then computed. The data are then consolidated into a ‘meso data set’, which has one row per country-year-surveyadaptation-indicator combination, e.g. Estonia/2010/HBS/ECAPOV adaptation/catastrophic expenditures/10% threshold. For quality checking we compare the country-year datapoints to data from other sources (see below for details) and the necessary data are merged into the meso data and then the quality checks are performed. Rejected datapoints are dropped, and the resultant data set is the financial protection HEFPI meso data set.
income (see the red lines in Figure 7)<sup>14</sup> . To account for possible nonlinearities we include a quadratic term. Figure 7 shows that for mobile operators, while market concentration is smaller in MENA when income is low, it quickly increases with a steep positive slope, while the slope for EAP is negative. For banking, asset concentration for both EAP and MENA is rising faster than the rest of the world, but MENA is above EAP in levels of asset concentration. This evidence is consistent with a popular notion that MENA does not fare well in market competition. For example, according to the World Bank’s Doing Business data, MENA countries are generally ranked very low in starting a business (e.g. Saudi Arabia is ranked 141, Egypt 109, Algeria 150, Iraq 155).<sup>15</sup> Results are robust to using regional dummies for all regions of the world (see Appendix Table A10).

> 14 For Mobile Operators, market concentration is calculated as annual average of quarterly HHI, based on market share of mobile operators provided by GSMA. For Banking, market concentration is calculated as assets of three largest banks as a share of assets of all commercial banks, data source is World Bank Database on Financial Development and Structure which was first constructed by Beck et al (2000).

> 15 As Arezki et al. (2018) argue, “MENA governments seeking to protect incumbents, especially in sectors like banking and telecommunications, impose excessive and outdated regulations that deter new actors from entering the market. This short-circuits competition, undermines the diffusion of general-purpose technology, and blocks the type of adaptation and evolution that underpins a vibrant private sector”.

17
- **Ruggedness:** This study implements the terrain ruggedness index of Riley, DeGloria and Elliot (1999) using the tri() function in R.<sup>40</sup> The algorithm uses five neighboring pixels to calculate each pixel’s index from the elevation data. Our study uses the average of the ruggedness index at the census tract level.

- **Rivers and lakes:** Information on surface water bodies comes from the Google Earth Engine Data Catalog and is available at https://developers.google.com/earth-engine/ datasets/catalog/MERIT_Hydro_v1_0_1. The data comes from the MERIT Hydro dataset with a 3 arc-seconds spatial resolution (90 _mts_<sup>2</sup> ). Our variables take the value of one if a river or lake passes by a census tract.

- **Precipitation:** Precipitation was obtained from the Global Climate Database created by Hijmans et al. (2005) that is available at http://www.worldclim.org/. This data provides a historic time series of rainfall in millimeters from 1960 to 2018 at the 2.5 minutes spatial resolution (21 _km_<sup>2</sup> ) with a monthly periodicity. This study standardizes the series from 1960 to 1979 and calculates the standardized average of rainfall for each census tract from 1975 to 1979.

- **Temperature:** Maximum temperature was obtained at the Global Climate Database created by Hijmans et al. (2005) and is available at http://www.worldclim.org/. This data provides a historic time series of temperature in Celsius from 1960 to 2018 with a monthly periodicity at the 2.5 minutes spatial resolution (21 _km_<sup>2</sup> ) with a monthly periodicity. This study standardizes the series from 1960 to 1979 and calculates the standardized average of temperature for each census tract from 1975 to 1979.

- **Historical crop yield:**
. Contracts are therefore typically complex and a farmer is unlikely to even have a good idea about what the claim payments from the product would have been in the recent past. Poor understanding is particularly detrimental to farmers who are eligible but not compelled to purchase WBCIS insurance.

One potential regulatory response would be to mandate that all weather indexed insurance contracts include a ten year history of what the claim payments would have been, using data from the contractual weather station. If such weather data is not available then data from the nearest government-approved weather station should be used, with an explicit caveat. Requiring insurers to disclose such information would allow farmers to assess whether there were claim payments in bad historical years, and may be considered as a similar type of regulation to requiring that banks state yields for loans in standardized forms, for example as an APR. Hill and Robles (2011) reports on a pilot in Ethiopia in which such information disclosure led to greater understanding of the products by farmers.

Such regulation would, of course, offer incentives to insurers to design products for which the average claim payment over the last ten years was high but the expected claim payment in future years was low. While this is regrettable, the advantages to consumers from such regulation seem likely to outweigh any such disadvantages.

Another cause for concern are reports that insurance companies are designing and pricing products based on historical weather data but then adjusting the length of the sales window based on up-todate weather forecasts for the season, with shorter sales windows where payouts are expected and longer sales windows where payouts are not expected. Such practices might be expected to increase insurer profits but are bad for farmers and premium subsidy-paying central and state governments, and should be addressed by regulators and government.

11
and some extensions, and explores directly the empirical consequences of using FAI rather than FDI data. Section 6 applies the results to assess the implications for real inward investment of the prospective global corporate minimum tax rate. Section 7 concludes.

# **2 Channels of Tax Spillover**

To fix ideas, consider a multinational potentially investing through controlled affiliates in _N_ countries. Suppose too, for now, that profit shifting through artificial transactions between them is not possible: relaxing this will be one of the later extensions. Denoting by _Kj ≥_ 0 the tangible capital that the multinational invests in its affiliate in jurisdiction _j_ , and by _Fj_ ( _Kj_ ) some output associated with that capital, we suppose taxable receipts of the affiliate in the typical host jurisdiction _h_ to be of the form _Rh_ [ _F_ 1( _K_ 1) _, .., Fh_ ( _Kh_ ) _, .., FN_ ( _KN_ )].<sup>11</sup> The key feature this allows for is the possibility that receipts of an affiliate located in any country _h_ may depend on the production of related affiliates in other countries. This serves to capture a range of possible patterns of multinational activity.

The simplest such possibility is that each affiliate serves its own local and competitive market, selling its product at price _Ph_ , so that _Rh_ = _Ph.Fh_ ( _Kh_ ); this is in effect the familiar closed economy case, with no cross effects across the distinct affiliates. But there are many other possibilities captured by this structure. One, a case of particular interest in analyzing the consequences of closer economic integration, is that the affiliates sell a homogeneous product into a single integrated market within which the multinational has some degree of market power, so that _Rh_ = _P_ (<sup>�</sup><sup>_N_</sup> _i_ =1<sup>_Fi_)</sup><sup>_Fh_,where</sup><sup>_P_(</
<mark>Gruber, Jonathan. "The Incidence of Payroll Taxation: Evidence from Chile."</mark> _<mark>Journal of Labor Economics</mark>_ <mark>15.S3 (1997): 72-101.</mark> <mark>Huttunen, Kristiina, Jukka Pirttilä, and Roope Uusitalo. "The employment effects of low-wage subsidies." Journal of Public Economics 97 (2013): 49-60.</mark> <mark>Korkeamäki, Ossi, and Roope Uusitalo. "Employment and wage effects of a payroll-tax cut— evidence from a regional experiment." International Tax and Public Finance 16.6 (2009): 753772.</mark> <mark>Korkeamäki, Ossi. "The Finnish payroll tax cut experiment revisited." Government Institute for Economic Research Working Papers 22 (2011).</mark> <mark>Kramarz, Francis, and Thomas Philippon. "The impact of differential payroll tax subsidies on minimum wage employment." Journal of Public Economics 82.1 (2001): 115-146.</mark> Murphy, Kevin J. "The impact of unemployment insurance taxes on wages." Labour Economics 14.3 (2007): 457-484.

Månsson, Jonas, and AMM Shahiduzzaman Quoreshi. "Evaluating regional cuts in the payroll tax from a firm perspective." The Annals of Regional Science (2012): 1-25.

Scarpetta,S., A. Sonnet and T. Manfredi (2010), "Rising Youth Unemployment During The Crisis: How to Prevent Negative Long-term Consequences on a Generation?", OECD Social, Employment and Migration Working Papers, No. 106, OECD Publishing, Paris.

Skedinger, Per. “Effects of payroll tax cuts for young workers”. Nordic Economic Policy (2013): 125.

Statistics Sweden. “Labour Force Surveys (LFS)” (2015) Swedish Government, “Government budget statement for 2015” (2014), p. 238 Yellen, J
# Table 5: **The Effect of Blackouts on Worker Labor and Wages**

All specifications include time fixed effects, firm fixed effects, and a vector of dummies indicating the number of days (of 5) for which a response about blackout status was reported by the firm owner. Standard errors are clustered at the neighborhood level. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

||(1)<br>Any<br>Worker<br>Present|(2)<br>Total<br>Worker<br>Days|(3)<br>Total<br>Worker<br>Hours|(4)<br>Wages/<br>Worker Hours<br>(GhC)|
|---|---|---|---|---|
|**Firms With Workers, Only**|||||
|# Blackout days reported (out of 5 days)|0.03***|0.32***|3.13***|-0.02|
||(0.01)|(0.10)|(0.96)|(0.02)|
|Outcome Variable Average|0.84|8.32|77.51|0.10|
|Observations|1,097|1,097|1,097|917|

Source: Author calculations from The Hohoe Garment Makers Project Data 20
# **3. Creditor losses in historical perspective**

This section shows that creditor losses due to default and restructuring events occur fairly often in external sovereign debt markets. However, the losses are almost always partial. Debt repudiations (unilateral debt cancelations) are rare, and defaults typically end in a negotiated settlement with haircuts well below 100%.

As explained in the introduction, we follow the international macro literature and use the term “haircut” to describe the size of creditor losses suffered in a sovereign default and debt restructuring. Finance scholars and practitioners also refer to “loss given default” (LGD), or one minus the “recovery rate” of a bond, which is exactly what our haircut estimates aim to capture for sovereign defaults. There is no consensus on the exact measurement of corporate bond recovery rates, however (see the survey by Altman 2011). Most but not all contributions use bond prices to approximate creditor losses in a corporate default, but the dating to choose the relevant bond price differs. Some contributions choose an (arbitrary) date at the start of default, for example, the bond price 90 days post-default (Moody’s 2011a; Jankowitsch, Nagler, and Subrahmanyam 2014), while others focus on bond prices at the exit from default (e.g., Moody’s 2007). The latter approach is sometimes called “ultimate recovery rates” and is conceptually close to ours since we compute haircuts at the restructuring date (default exit). Moreover, in Appendix C2.3 we show that our haircut estimates have a high correlation with bond prices at the start of default (for 94 cases for which prices were available).<sup>23</sup> In sum, our haircut estimates can be regarded as a valid measure of sovereign LGDs in the vein of existing corporate LGD databases.

# 3.1. Sovereign debt restructurings, 1815–2016

To estimate haircuts requires moving well beyond identifying the incidence of debt crises, as in
_56_ (1), 63–90.

- , “Collective labor supply and welfare,” _Journal of Political Economy_ , 1992, _100_ (3), 437–467.

- **Connell, J., B. Dasgupta, R. Laishley, and M. Lipton** , _Migration from Rural Areas: The Evidence from Village Studies_ , London: Oxford University Press, 1976.

- **Cook, T.D. and V.C. Wong** , “Empirical tests of the validity of the regression discontinuity design,” _Annales d’Economie et de Statistique_<sup>_ ́_</sup> , 2008, _91–92_ , 127–150.

- **Corak, M.** , “Death and Divorce: The Long-Term Consequences of Parental Loss on Adolescents,” _Journal of Labor Economics_ , 2001, _19_ (3), 682–715.

- **Cort ́es, P.** , “The Feminization of International Migration and its effects on the Children Left behind: Evidence from the Philippines,” Working Paper, Boston University School of Management, Boston, MA 2010.

- **Cull, R. and K. Scott** , “Measuring Household Usage of Financial Services: Does it Matter How or Whom You Ask?,” _World Bank Economic Review_ , 2010, _24_ (2), 199–233.

- **Dahl, G.B. and L. Lochner** , “The impact of family income on child achievement: Evidence from the earned income tax credit,” _American Economic Review_ , 2012, _102_ (5), 1927–1956.

- **Duflo, E.** , “Child health and household resources in South Africa: Evidence from the Old Age Pension program,” _American Economic Review Papers & Proceedings_ , 2000, _90_ (2), 393–398.

- **, R. Glennerster, and M. Kremer**
enced historically during a crisis period. Secondly, we take out countries for which the selected minimum values were positive, that is, when the change in GDP growth during the crisis years was greater than zero. This is necessary to avoid the inclusion of a country experience that contradicts the definition of a systemic financial crisis.<sup>16</sup> Thirdly, we employ an outlier robust method to compute the average response of the countries that have a crisis trimmed means. The trimmed means experienced financial by computing are calculated in such a way that only the data points that fall within the 2.5<sup>_th_</sup> and the 97.5<sup>_th_</sup> percentile are included in the computation. This ensures that only the center of the empirical data, where 95% of the probability mass lies, is included in the averaging. The of extreme values on the is thus eliminated. The influence average response effectively censoring rules applied to CPI inflation, the lending rate and changes in the exchange rate are analogous to the ones applied to real GDP growth.

One last requirement that is imposed is that for each country a full set of macroeconomic Stress scenarios needs to be present. That is, we require data for each of the four macroeconomic variables (real GDP growth, CPI inflation, the lending rate and the change in the exchange rate) to be available for the trimmed mean response to be computed. If, for instance, data on lending rates are not available for a particular country, while the remaining three macroeconomic variables are present, then this scenario, and potentially the country itself, is excluded from the calculation of the average crisis response. The resulting model free Stress scenario based on the historical cross-country crisis experience

> 16 To provide an example of such a scenario, consider Lebanon which is identified by the Laeven and Valencia (2008) database to have gone through a Systematic Banking and Currency Crisis in 1990. However, (annual) real
36|2.3|105|2.8|
|Sub-Saharan Africa|8|1.9|10|1.2|15|1.0|38|1.0|
|**Memo:**|||||||||
|BRICS 5 countries|9|2.4|26|3.1|70|4.5|382|10.1|
|OPEC 12 members|10|2.5|8|0.9|15|1.0|30|0.8|

Source: Based on World Bank World Development Indicator database.
internalize the possibility of facing tighter credit constraints in bad states of nature, they may also consume less and save more in good times.

The second part described the econometric methodology and the empirical specification of the model, which controls for various standard determinants of private savings. The third part presented and discussed the regression results, based on panel data for non-oil commodity exporters of sub-Saharan Africa covering the period 1980-96. Overall, they suggest that transitory movements in the terms of trade have a positive effect (albeit less 16
in the sample have sales credit). We also include a dummy variable indicating whether a firm has a positive share of investment financed with external funds (this applies to 24 percent of firms in the sample).

The World Bank Enterprise Surveys also provide important information on firm characteristics, including size, age, sector,<sup>5</sup> export activity, and ownership as well as whether a firm is an independent, single establishment. The sample used in this paper is stratified by size, age, sector, region, and other firm characteristics. (Variable descriptions and distributions are reported in tables 1, 2 and 3.) Firms are divided into four categories by size: _micro_ (1–10 permanent employees), _small_ (11–50), _medium_ (51–200), and _large_ (more than 200). The sample includes mostly micro firms (39 percent of the total) and small firms (37 percent); only 16 percent are medium-size and 7 percent large. Firms are divided into three categories by age: _young_ (1–5 years),<sup>6</sup> _mature_ (6–15), and _older_ (more than 15). Most are mature (47 percent) or older (41 percent); only 11 percent are young firms. Ownership is defined as being _foreign_ or _government_ if “10 percent or more” of the firm is foreign or government owned; 12 percent of the firms in the sample are foreign owned and only 2 percent are government owned. _Exporter_ is a dummy variable indicating that direct exports account for 10 percent or more of a firm’s sales; 13 percent of the sample firms are exporters.

Whether a firm has a single establishment or multiple ones matters for firm growth, especially in the manufacturing sector (see Dunne, Roberts, and Samuelson 1989). We therefore include a dummy variable indicating whether a firm is an independent, single establishment. Most of the firms in
positive effect of school size and no effect of class size. The current paper extends that analysis by drawing on a much bigger sample of thousands of Ukrainian schools. The size of the current dataset not only provides for more precise estimates but also allows for the analysis of more precise questions, like an analysis by gender or by type of settlement. In addition, the current dataset allows us to analyze the Ukrainian plan to close schools with less than 100 students.

# **IV. Data and Analysis**

In this paper, we use several datasets which were matched and combined into one comprehensive dataset. From the Ministry of Education, we obtained data, for all Ukrainian schools, on the total number of students, the number of students by grade, the number of classes by grade and the total number of teachers and staff.

Data about performance on the EIT were obtained from the yearly public data files, posted on the EIT site, which provide, for each school, the distribution of students‟ scores over 10 intervals. In addition, from the Ukrainian Centre for Education Quality Monitoring we obtained mean and median scores data and information about the gender and the language choice of students<sup>22</sup> .

A more detailed description of these sources and the matching process can be found in the appendix A1.

## _a. Descriptive Statistics for 2010_<sup>_23_</sup>

For 11683 Ukrainian schools we have information on both 2010 EIT test scores (from the EIT database) and information on input variables (from the Ministry of Education).

Given that our main variables of interest, school size and class size, but also other input variables are very different depending on whether or not the school is located in an urban or rural area, we provide the descriptive statistics for rural and urban schools separately.

> 22 The correspondence between those two sources of data was high but not perfect. For a few schools, we had no mean scores and for somewhat more schools
households at the top 60 percent. An increase in unemployment and a reduction of social government programs may hit poorer households harder, given their more intense dependency on transfers and higher risk of becoming unemployed (or lower employability).

_Figure 4: Unequal Distribution of Employment and Assets, such as human capital, and of consumption of food across the Russian Population, 2011_ <!-- Start of picture text --> 0.6 0.6<br>Total Occupation<br>0.5 0.5<br>Share of food in total<br>0.4 consumption (right axis) 0.4<br>Unskilled in<br>0.3 Non Agri 0.3<br>Skilled in No Agri<br>0.2 0.2<br>0.1 0.1<br>Skilled in Agri<br>Unskilled in Agri<br>0 0<br>0 10 20 30 40 50 60 70 80 90<br>Population percentiles<br>Share of food<br>Percent share of total population<br><!-- End of picture text --> Source: HBS data (2011) To get some insights on the implications of these assumption we carried out two additional simulations. In the first one we assume that all those who lose their jobs in the contracting sectors – up to 3.1 percent of total employment – become unemployed. In the second one we analyze the implication of a cut in government transfers.

Abstracting from multiplier effects,<sup>19</sup> what would be the distributional impact of the increase in unemployment? The first step of the simulation was to identify those who are more likely to become unemployed.<sup>20</sup> Not surprisingly, the results of the econometric specification show that less qualified,

> 19 Clearly, if employment goes down (or equivalently if unemployment increases), GDP will also go down and that
in an aggregate indicator that they wish to influence.

Of course, we do not claim that perceptions data are the _only_ relevant type of data for measuring governance. Objective data on the specific laws and regulations “on the books” that shape the regulatory environment also are useful, even though they may differ substantially from their implementation in practice. Similarly, indirect measures based on data patterns such as gaps between spending on infrastructure and infrastructure actually built, or the performance of politically connected firms relative to non-connected firms, or patterns in tax return and procurement data can provide

> 10 Previous vintages of the WGI aggregate indicators are available on the WGI website at www.govindicators.org. In addition, we occasionally need to revise the historical source data if we discover an error we have made in processing the data and assigning it to governance clusters.  This also contributes to revisions of the historical aggregate WGI measures.  All revisions to the historical dataset, as well as the addition or deletion of data sources, is separately described at www.govindicators.org for each annual update of the WGI.

9