rafmacalaba/data-use-mentions-extended huggingface dataset:
In this paper, we propose to use these threshold regression models in order to take into account this original dimension in the estimation of the rates of return on public capital stocks, i.e. the potential presence of threshold e¤ects. Applying the so-called production function approach introduced by Aschauer (1989), we consider various speci...cations of public capital stock augmented production functions. At this stage, a remark has to be made. So far, we indi¤erently used the terms of public capital and infrastructure. However, the notion of infrastructure does not fully correspond to the concept of public sector investment expenditure. This distinction is particularly important when it comes to interpret the threshold e¤ects. Indeed, it would be naïve to directly interpret the threshold e¤ects in the productivity of public capital as network e¤ects: the public investments are so heterogeneous, that the level of public investments or the public capital stocks can not be used to reveal information on the completion of the main infrastructure networks. For this reason, in this paper, we only consider the threshold e¤ects of the productivity of the infrastructure investments.

More precisely, we propose to re-evaluate the marginal productivity of infrastructure stocks using threshold regression models, in which the threshold variable may be de...ned as the level of the infrastructure stock actually available. The main concern with these models is that they require an important number of observations to be estimated since they depend on an important number of parameters. This concern is particularly relevant in our context, since the time dimension of the series of infrastructure stocks is generally not su¢ cient to estimate such models, even if we consider only two regimes<sup>1</sup> . One solution is to use panel data as it is generally done in the literature devoted to linear representations of the productivity of infrastructure. In a seminal paper, Hansen (1999) proposed the ...rst procedure to estimate and to test the threshold
%|15.7%|29.4%|58.5%|-|
||Large|24.1%|20.0%|35.0%|66.8%|-|
||All|25.8%|30.6%|44.6%|42.7%|-|
|Afi|Small|23.8%|28.4%|42.2%|40.2%|-|
|rca|Medium|33.0%|37.1%|51.5%|39.3%|-|
||Large|31.1%|46.5%|58.4%|51.4%|-|
||All|20.5%|16.8%|28.2%|67.9%|-|
|Russian|Small|13.2%|11.7%|18.6%|55.6%|-|
|Federation|Medium|24.9%|22.8%|35.2%|67.7%|-|
||Large|38.7%|22.4%|48.8%|83.8%|-|
||All|5.4%|6.3%|8.5%|63.1%|-|
|Tk|Small|4.3%|6.8%|8.0%|58.8%|-|
|urey|Medium|5.1%|4.5%|7.0%|55.4%|-|
||Large|14.2%|9.1%|17.6%|84.0%|-|

Source: Author’s elaboration from Enterprise Survey (2014) Further, the composition of the innovation portfolio highlights
Figure C.1. Socioeconomic Index by Date of Arrival <!-- Start of picture text --> Socioeconomic Index<br>PEP<br>Date of Arrival<br>Socioeconomic Index Fitted values<br>01jan2015 01jul2016 01jan2018 01jul2019 01jan2021<br>.8<br>.7<br>.6<br>Before Migration<br>Socioeconomic Index<br>.5<br><!-- End of picture text --> 56
, column (3) includes both the number of active armed conflicts and the unemployment rate in the destination. We find that both variables are positively associated with return migration. While an additional armed conflict in the destination country increases the probability of return migration by 2.8 percentage points, we find that a one percent increase in unemployment rate at destination is associated with 0.7% increase in the return probability. The results from our structural equation are reported in column (4) and are consistently robust to controlling for unemployment rates at destination in the return migration equation.

A related issue is that conflicts might have consequences on the Egyptian economy through, for instance, changes in trade flows, foreign direct investments, or remittances. In Table A.13 in the Online Appendix, we check the robustness of our results to controlling for the following variables in Egypt in the return migration equation: FDI (% of GDP), remittances (% of GDP), and the exports and imports of goods and services (% of GDP).<sup>28</sup> Our results from the structural equation in column (4) are also consistently robust to controlling for these variables.

> 28 Data comes from the World Bank Development Indicators. These variables are matched with the year of migration for return migrants and at the time of the survey in 2012 for current migrants. The results were also robust to controlling for these variables in the structural equation in column (4).

26
**Figure 1:** Mapping of Ebola Cases and Sample

<!-- Start of picture text --> 10 ° N 10 ° N<br>9.5 ° N 9.5 ° N<br>9 ° N 9 ° N<br>8.5 ° N 8.5 ° N<br>8 ° N 8 ° N<br>7.5 ° N<br>7.5 ° N<br>7 ° N<br>7 ° N<br>13 ° W 12.5 ° W 12 ° W 11.5 ° W 11 ° W 10.5 ° W<br>13 ° W 12.5 ° W 12 ° W 11.5 ° W 11 ° W 10.5 ° W<br>Log(VHF Entries + 1)<br>2 4 6 8 Sections in Ebola Sample (160) Sections only in RCT Sample<br>(a) Heat Map of VHF Cases by Section (b) Sections Used in Ebola Analysis<br><!-- End of picture text --> Figure 1(a): the number of entries (logged) by section in the Viral Hemorrhagic Fever (VHF) database maintained by the CDC during the Ebola crisis. Figure 1(b): map of all sections that contain clinics that were part of the original randomized experiment. Sections in light gray are excluded, because they contain more than one clinic from the original RCT.

# **3.3 Randomization**

## **3.3.1 Matching and Blocking**

We grouped the 254 clinics in our sample into matched triplets. Clinics in a triplet fall within the same district and exhibit similar levels of utilization and performance at baseline.<sup>18</sup> Within each triplet, we then randomize clinics into control, community monitoring (CM), or non-financial awards (NFA). This results in
<!-- Start of picture text --> −2 0 2 4 6<br>Trade Proximity in Sticky Products<br>−1 0 1 2 3<br>Trade Proximity in Non−Sticky Products<br>3<br>2<br>1<br>0<br>−1<br>Change in GDP synchronization<br>−2<br>3<br>2<br>1<br>0<br>−1<br>Change in GDP synchronization<br>2<br>−<br><!-- End of picture text -->

**Figure 6.** Change in sticky trade (top panel) and non-sticky trade (bottom panel) and change in GDP synchrony. Each dot represents a region-income pair and the difference between the 2005-2014 and the 1995-2004 time windows. Source: WDR 2020 team, based on World Bank’s World Development Indicators (database) and a classification of sticky vs. non sticky trade from Martin et al. (2018).

27
variety of channels.<sup>17</sup> Separating the domestic content from the foreign content contained in a country’s exports—as achieved in the LACEX data—is a necessary step when disentangling the impact of production across multiple borders. However, when defining GVC-trade as value addition across different geographical locations governed by

> 16 Exceptions include Kucera et al. (2012).

> 17 For example, the requirement of GVCs to meet strict quality standards often leads to increased mechanization, and the scale economies possible from accessing larger global markets may make capital investments more viable. Less labor-intensive methods of production may result, lowering the number of jobs for a given volume of output/exports. GVCs may also have distributional consequences in terms of the nature of skills demand and their implications for wages. On the other hand, if greater foreign value added in exports leads to an increase in total exports, by complementing the domestically available intermediate products and skills, it may lead to higher demand for labor in an industry. However, if it substitutes domestic production, it can lead to a lower share of domestic value added in exports and thereby lower labor demand. As another alternative, it could substitute the direct labor within an industry, but complement the indirect labor (Farole 2015).

27
and crime_

This section reports the estimated impacts of the intervention on a variety of outcomes of interest related to social behavior, violence and crime. Each of these outcomes of interest is observed for treatment and control groups both at baseline, prior to the intervention, and in the follow‐up survey (FUS) after the intervention. For both treatment and control groups, we evaluate the changes over time, test the statistical significance of these changes, and calculate the difference‐in‐differences (DD) estimates.

# 5.2.1 Impacts on social behavior/environment

Here we report the results from looking at the social behavior and environment indicators. The program may have improved outcomes for participants by altering the people they interact with. One indicator of this is whether youth report hanging out with friends late at night, which is a strong correlate of anti‐social behavior and crime. We find that for the treatment group, the reported incidence declined from 36.8% at baseline to 29.6% post‐intervention), or 18.7 percentage points (significant at a 5% level). For the control group, the incidence rose slightly from 42.2% to 44.2%, which is not statistically significant. Hence, the Difference in Difference estimate is 8.8 percentage points, significant at the 5% level, which is a decline of 24.2 percentage points relative to the baseline value for the treatment group. These results are reported in Table 5. They are also presented graphically in Figure 4 (Panel A). At baseline, those in treatment group 20
12|--|--|--|--|
|Both|1,867|0.30|0.30|0.01|0.69|--|--|--|--|
|Discussed visit with neighbors(1=Yes)|1,867|0.41|0.41|0.35|0.50|--|--|--|--|
|Panel B: Information about political discussions||||||||||
|Discuss political issues with women in neighborhood (1=Yes)|2,637|0.44|0.55|0.50|0.62|0.40|0.30|0.51|0.18|
|Issues raised during conversations||||||||||
|Party/Candidate Positions|847|0.61|0.62|0.61|0.63|0.59|0.58|0.61|0.59|
|Importance of voting|847|0.86|0.85|0.85|0.86|0.86|0.89|0.83|0.88|
|Importance of voting in accordance to own preferences<br>Notes: Data come from follow-up survey. Column (1) refers to th<br>data for treated women. Columns (6)-(8) report data for control<br>women in control clusters.|847<br>e numbe<br>women i|0.52<br>r of ob<br>n treat|0.53<br>servati<br>ed clus|0.53<br>ons for<br>ters. Co|0.53<br>column<br>lumn (9|0.47<br
problems across countries in terms of the survey instruments used. The Estonia and Poland data sets document in great detail the employment dynamics experienced by these countries between 1989 and 1995, using both official government data and information collected through a labor force survey.

# **Responses to Crises and Institutional Changes**

Following the transition from a planned to a market economy, Eastern European and fonner Soviet countries experienced a larger-than-expected decline in aggregate activities. Recent important studies have looked for causes of this output collapse. _Ex ante,_ researchers agreed that output may initially decline before resuming its growth as the market reallocated resources from less to more productive uses. Borensztein and Ostry (1995) hypothesize that the sharp fall was mainly the result of a supply side shock: as prices were liberalized, state owned enterprises were unable to pay for inputs. Commander and Coricelli (1992), and Borensztein, et al. (1993) emphasize a demand shock: the increase in prices reduced the domestic demand for final goods. Berg (1994), Atkeson and Kehoe (1996), and Shimer (1995) argue that structural adjustments related to the transition itself were the main cause of the initial decline. Finally, Murphy, et al (1992) and Shleifer and Vishny (1993) focuse on the political economy of the transition process and emphasized the perverse effects of a partial price liberalization reform and government corruption.)<sup>3</sup> The World Bank coverage of output behavior in transition economies is somewhat limited and unsystematic across countries, hampering cross-country analysis. I]n addition, the country-specific analyses are based on firm surveys designed to test specific hypotheses, preventing researchers from evaluating competing hypotheses even within the same country. Nonetheless, existing surveys have provided us with great insights into the effects of transition on output.

> 13 Estrin, Schaffer and Singh (1993) offer new insights on the structural restructuring process that naturally accompanies a change in regime for some Eastern European countries by integrating their industry-level analysis with firm-level data
(0.000864)|0.000439<br>(0.000443)|0.000686*<br>(0.000413)|0.000899*<br>(0.000480)|0.00117**<br>(0.000463)|0.00111*<br>(0.000641)|0.00132**<br>(0.000644)|0.00102<br>(0.000874)|0.00121<br>(0.00189)|
|Trade Openness|0.000747|0.00170|0.00341|0.00588|0.00706*|0.00567|0.00645|0.00914|0.0149|
|_(% of GDP, logs)_|(0.00695)|(0.00356)|(0.00332)|(0.00386)|(0.00372)|(0.00516)|(0.00518)|(0.00703)|(0.0152)|
|Financial Openness|0.00105|0.00129|0.00134|0.00133|0.00128|0.00132|0.00151|0.00263|0.00299|
|_(Chinn‐Ito Index)_|(0.00181)|(0.000926)|(0.000863)|(0.00100)|(0.000968)|(0.00134)|(0.00135)|(0.00183)|(0.00395)|
|_External Factors_||||||||||
|Foreign Growth|0.000199|-0.000163|-0.000155|-0.000205|-0.000221|-0.000349|-0.000396|-0.000531|-0.000300|
||(0
## 2.4 Pros and cons of effective progressivity metrics

Effective progressivity metrics have significant advantages. Most obviously, effective progressivity considers the interaction of both the tax schedule and the underlying income distribution. Effective progressivity measures can be used to examine not just the PIT and social contributions, but also the VAT and excise taxes. The key drawback of the effective progressivity approach is that it is data intensive. It requires information on both the tax system and on the underlying distribution of income. The difficulty in obtaining data for IDA countries was illustrated by the fact that even the wide ranging CEQ analyses cover only six IDA countries, and only two based on income survey data. Furthermore, any microdatabased analysis is resource intensive.

In addition to the difficulty obtaining data, there are also a wide range of data limitations that impact effective progressivity analysis. In particular, top incomes are typically underreported in income surveys. Evasion, particularly by high-income taxpayers, will also not be captured in income survey data. If consumption data is used for PIT modelling, it will provide only an imperfect proxy for income data due to savings patterns. Additional problems arise when modelling indirect taxes (beyond the conceptual difficulties noted above). For example, expenditure is typically underreported in household budget surveys, due to a range of factors including imperfect recall and intentional underreporting of some expenditure categories. VAT exemptions are difficult to model, and ideally input-output data is needed to estimate the impact of the denial of input tax credits. The impact of informality can also pose modelling challenges in developing countries, particularly if a place of purchase variable is not available. There are also challenges associated with modelling _ad quantum_ excise taxes with expenditure data.

# 3 Measuring structural tax progressivity

Structural progressivity focuses purely on the income tax rate schedule itself, ignoring the underlying distribution of income, and produces local measures of progressivity. This section first presents a range of local measures of structural progressivity before presenting examples of their use, and finally summarizing their strengths and weaknesses.

## 3
# Figure I: Withholding Systems and GDP per capita

<!-- Start of picture text -->
** **<br>*<br>**<br>N=31 N=35 N=53 N=60 N=19 N=10 N=36 N=16<br>Mean GDP pc Median GDP pc 95% CI<br>VAT WHNo VAT WH IT WH No IT WH VAT WH BroadVAT WH Targeted IT WH BroadIT WH Targeted<br>40<br>30<br>20<br>GDP pc<br>10<br>0<br><!-- End of picture text -->

Notes: This gure displays the mean and median GDP per capita, and the 95% condence interval of the mean, for dierent subsamples of countries. GDP per capita is measured in thousands of current USD from the World Development Indicators for 2013. The number below each bar displays the sample size. The stars reect the signicance levels of the mean dierence between two adjacent bars. The rst and second bar refer to countries that use and do not use withholding on the VAT/sales tax respectively. The third and fourth bar refer to countries that use and do not use withholding on business income taxes (i.e. income taxes on corporations and the self-employed/unincorporated businesses) respectively. The fth and sixth (seventh and eight) bar further divide the subsample of countries with VAT (income tax) withholding into countries that use a broad withholding regime (that applies across sectors), and those that use a targeted withholding regime, applicable only to certain sectors (e.g. construction, shing). The analysis is based on a sample of 118 countries for which data was available from the PKF International Worldwide Tax Guide 2015, recent EY International Tax Alerts, PWC Tax Summaries, or the secondary sources referenced in the introduction. The results are robust to using
events and measurement error on the other hand. If the wardrobe had been upgraded, Nve would expect future ratings on average to be as nice as today's. However, if today's nice rating was caused by an idiosyncrasy or measurement error, we would expect future ratings to revert to the old, lower levels. Hence, both transitory events and measurement error would show up as many movements up and down the fashion hierarchy but all these movements would tend to be undone in the next period. This intuition underlies the procedure that distinguishes persistent and transitory shocks. This procedure is presented formally in the next section.

Second, one might obtain three independent measures of the quality of a person's outfit; for example the outfit's price, age and the interviewer's rating. If there were no measurement error, all three measures would always move in the same direction. We can then find the measurement error in each measure by seeing how often that measure moves in a different direction than the other two. Once we know the amount of measurement error in the interviewer's ratings, we can calculate the likelihood that an increase in the ratings was due to the person wearing a nicer outfit. One can interpret two of the measures of fashion quality as instruments for the third measure. This instrumental variables procedure is developed formally in the next section.

# 3. **Data and Methodology**

## **_Data_**

The data for Russia come from the Russian Longitudinal Monitoring Survey (RLMS), which is a nationally-representative socioeconomic survey of the Russian Federation. We use the panel component of waves 5 through 8 which were fielded in the falls of 1994, 1995, 1996 and 1998. This yields a sample of 7,382 individuals in 2,256 households with complete demographic, income and expenditure information. More details about this dataset can be found on the website of the University of North Carolina at Chapel Hill (www.cpc.unc/projects/rlms/
Q2 2011 Q3 2011 Q4 2011 Q1 2012 Q4 2009 Q1 2010 Q2 2010 Q3 2010 Q4 2010 Q1 2011 Q2 2011 Q3 2011 Q4 2011 Q1 2012<br><!-- End of picture text --> Source: International Institute of Finance, Emerging Market Lending Survey.

**33. Host country banking systems may also be vulnerable to parent bank attempts to cut back their host operations and transfer resources from subsidiaries.** In addition to affecting local credit conditions, such attempts may trigger mutually harmful policy responses from host authorities, hurting the banking group in turn—a source of tension that requires close coordination and cooperation between home–host regulatory and supervisory authorities:

- As noted, some European banks have been withdrawing, to varying degrees, from CEE, Latin America, Asia, and the Middle East: for example, by cutting back on capitalintensive activities; reducing jobs; and withdrawing from jurisdictions with less profitable operations and higher dollar funding costs to focus on ―core‖ markets, including the home market as well as strategically important host markets (see appendix table A.3). While ―home bias‖ has emerged, in part, as a result of the need to shrink balance sheets
Policy Research Working Paper 9669

# **Abstract**

In April 2015, the Government of Ukraine abruptly raised the tariffs of natural gas to residential customers, which were previously well below the cost of acquiring gas and delivering it to households. The tariff increase—700 percent—caused considerable distress to the population and led the government to scale up its existing energy assistance program, the housing and utilities subsidy program. This paper examines the welfare effect of the program and potential redesigns of the program. Using several waves of Ukraine’s Household Budget Survey, the analysis finds that electricity, gas, and fuels account for a considerable share of household income. After the tariff hike, the average household that did not receive the housing and utilities subsidy spends 11 percent of its income on electricity, gas, and fuels, implying that it meets the definition of “fuel poor.” The average share for households that do receive the subsidy is 6–8 percent. The housing and utilities subsidy cuts the rate of fuel poverty in half. It also brings considerable consumer surplus gains of 6–7 percent of income. This comes at a high price tag for the government, as the budget for the housing and utilities subsidy is 1–2.5 percent of gross domestic product. Considerable savings would be achieved with only a small loss of consumer surplus if the housing and utilities subsidy was cut in half. Linking the subsidy solely to income would also attain considerable savings, but at a high loss of welfare. The housing and utilities subsidy could also be paired with social tariffs, or an energy efficiency subsidy, with major savings for the government.

This paper is a product of the Social Protection and Jobs Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors