rafmacalaba/datause-encoder-data huggingface dataset:
|7|Banerjee,<br>Oetzel and<br>Ranganatha<br>n (2006)|40 developing economies<br>across Latin America,<br>Europe, Africa and Asia|1990-2000|Power, natural gas,<br>telecommunications,<br>transport, and water<br>supply and sanitation|**Econometrics**<br>_(Pooled OLS)_|<br>Property rights<br><br>Bureaucratic quality|
|---|---|---|---|---|---|---|
|8|Lüthi and<br>Wüstenhage<br>n (2012)|63 European photovoltaic<br>developers investing in<br>Europe and abroad|2008|Power|**_Econometrics_**<br>_(Choice_<br>_experiment)_|<br>Duration of administrative process,<br><br>Feed-in tariff level and price cap|
|9|Mengistu (<br>2013)|PPI deals from 133 low-<br>and middle-income<br>countries|1995-2008|Power,<br>telecommunications,<br>water and sanitation|**Econometrics**<br>(Cross-country<br>panel)|<br>Size of the service sector is most important predictor for<br>PPI<br><br>SSA countries with common law origins substantially<br>more likely to get PPI (likely, due to more developed<br>financial markets and property rights)|
|||48 international equity|||||
||Lamech and|investors from mainly||||<br>Overall regulatory quality (rule of law, respect for rights|
|10|Saeed<br>(2003)|industrialized countries<br>investing in developing<br>countries|2000-2003|Power|**Survey**|of investors and judicial processes free of political<br>interference)|
|11|Noel and<br>Brzeski (<br>2005)|8 equity investors<br>investing in Europe and<br>Central Asia|2003-2004|Power, water and<br>other local<br>infrastructure (_not_<br>_specified_)|**Survey**<br>(_informal_<br>_survey)_|<br>Adequate cash flow<br><br>Adequate contractual framework<br><br>Policy risk<br><br>Exit possibility for first-round equity investors|
|12|Akampurira,<br>Root and<br>Shakantu(<br>2009)|28 responses (19<br>government entities, 9<br>private sector) in Uganda|2007|Power|**Survey**|<br>Regulatory and legal framework<br><br>Access to finance<br><br>Swift approval process,<br><br>Social acceptance|
|13|Bürer and<br>Wüstenhage<br>n (2009)|60 venture capital and<br>private equity funds<br>mainly from North<br>America and Europe|2007|Power|**Survey**|<br>Security of cash flow|

6
## **c. Operating Expenses**

<!-- Start of picture text --> 6.2 6.2<br>6.1<br>6<br>5.1<br>4.7<br>4 3.8<br>3.5 3.5<br>3.1<br>3.1<br>2.6 2.6 2.5<br>2.2 2.2<br>2 1.8<br>1.4<br>1.1<br>0.7 0.6<br>0<br>BhutanBulgariaSolomon IslandsSouth AfricaCroatiaEthiopiaGhanaUkraineKosovoIndonesiaUruguayAlbania KenyaRomania BrazilGeorgiaBurundiArgentina Peru<br>Percent of GDP<br><!-- End of picture text --> _Source_ : World Bank Infrastructure SOEs Database. _Note_ : Data are for 2009–18.

# **_4.2 Performance_**

SOE underperformance can turn into an important source of fiscal risk and macroeconomic instability, depending on the volatility and size of SOE losses. This section looks at three indicators of profitability to gain a deeper understanding of how infrastructure SOEs perform.

Figure 2 shows the return on average assets (ROAA) for all the SOEs in the sample, with and without adjusting for the operating subsidies they receive. This figure, and the following ones, reveals the high level of subsidies in infrastructure SOEs. The power sector is the least subsidized and the best-performing sector,<sup>6</sup> with and without subsidies, followed by airlines and airports (without subsidies). Railways is by far both the most heavily subsidized sector and the worstperforming one, with negative ROAA even after accounting for subsidies. This performance raises concerns about the sustainability and fiscal risk of railway SOEs. The figures also show that when the net income of the SOEs in our sample is adjusted for operating subsidies, ROAA becomes very negative.

> 6 In Sub-Saharan Africa, for instance, only Umeme, Uganda’s private utility company, has remained profitable. Trimble and others (2016) find that only 2 of 39 African countries they study—the electric utilities of Uganda and the Seychelles—were fully recovering their costs of supply (before taking system expansion into account).

8
source of variation has implications for thinking about the likely effectiveness of hiring new supervisory staff (some of whom may be less diligent). We discuss these in section 5.3.

# **4.3 Correlates of Changes in Student Outcomes**

While the focus of our analysis has been on teacher absence, we also briefly consider the correlations between improvements in school quality (seen in Table 1) and learning outcomes. Table 8 presents panel regressions of the form in equation (1), where changes in normalized mean math test scores at the village level are regressed on changes at the village-level, including teacher absence, school facilities, and monitoring. Changes in household-level inputs such as growth in mean parental education and increases in the fraction of students taking private tuition are positively correlated with test score gains (significant in most specifications). School-level variables are not consistently significant, though higher STRs and higher teacher absence are both correlated with lower test scores (and significant in some specifications). However, there is no positive correlation between the improvements in most of the standard measures of school quality and student learning outcomes - including school infrastructure, mid-day meals, and teacher qualifications and training.

We only treat these results as suggestive because the data (with a seven-year gap in mean village-level test scores) is not ideal for testing the impact of school characteristics on test scores. The ideal specifications would use annual panel data on student test scores matched to these characteristics and estimate value-added models of student learning. Nevertheless, the main findings here are consistent with those in studies set in low-income countries using data sets that are better suited to study student learning outcomes, which find that teacher absences significantly hurt student learning, and that school infrastructure, teacher qualifications, and teacher training are typically not correlated with improved student learning.<sup>25</sup>

> 25Duflo, Hanna and Ryan (2012) show experimentally that lower teacher absence raises test scores, while Muralidharan (2012) shows this in value-added estimates with five years of annual panel data on test scores in the state of Andhra Pradesh matched with the absence rate of the teacher of each student that year. (Das et al., 2007) show that high teacher absences in Zambia (mainly due to teachers falling sick) lead to significantly lower student test score gains. Muralidharan (2012) also shows that school infrastructure and teacher qualifications are not correlated with improvements in learning outcomes in the control schools (which represent the ‘business as usual’ scenario). See (Muralidharan, 2013) for a review of this evidence with a focus on India.

17
US antidumping data (Blonigen, 2000), thus eliminating a substantial entry barrier to researchers interested in examination of the US process.<sup>7</sup> The global antidumping database project described in this paper seeks to emulate the basics of Blonigen’s data collection efforts and complement the data generated for the United States by establishing a relatively comprehensive, publicly available database on other countries’ use of antidumping. The purpose of this paper is to briefly describe the collected data, so as to provide researchers a starting point for examining other (non-US) users of the controversial trade policy. Our theory is that providing such data will eliminate some entry barriers to research in this area. We hope that additional entry by researchers will result in improved quality of empirical research on the global use of antidumping, like the Blonigen data collection effort has done for the United States’ use of the policy. A last goal would be that results of scientific research would lead to additional transparency and understanding as to what is actually taking place under the guise of antidumping laws and policy around the world.

With respect to coverage, table 1.1 illustrates the countries that are included in the detailed portion of our database, as a subset of antidumping users (WTO members) over the 1995-2004 period. While close to 50 countries have now adopted antidumping laws and initiated antidumping investigations (Zanardi, 2004), it is still the case that most of the use of antidumping – as measured, for example, by total number of investigations or measures imposed – is highly concentrated into around a dozen countries. Thus while the current database contains detailed antidumping use data for (only) 15 of these policy-imposing countries, these countries collectively make up a substantial fraction of the policy’s use – e.g., these countries were responsible for 85% of the antidumping investigations and 87% of antidumping measures imposed by all WTO members over the 1995-2004 period.

> 7 Thanks go to Bruce Blonigen for his collecting and original conception of publicly posting the antidumping data for the United States at his website at <u>http://darkwing.uoregon.edu/~bruceb/adpage.html. His generation of this</u> public good has arguably spawned additional research into antidumping, improving transparency and allowing for more in depth research to improve scientific understanding over the implications of the policy instrument. The current project is largely the result of researchers recognizing the Blonigen contribution and wondering why no one had done something comparable for other countries’ use of AD. Additional personal thanks are also due to Tom Prusa for generously sharing his personal collections of antidumping data as well. Furthermore, Robert Staiger and Frank Wolak made data on the use of US antidumping for the 1980-1986 period (Staiger and Wolak, 1994) available on the NBER data CDs earlier in the 1990s (Feenstra, 1997).

3
caseload data on all 5,297 civil and commercial cases that entered court between 2012 and 2015. Second, we exploit the granularity of our court data by tracing each case through the legal procedure and constructing case-level markers of speed (duration) and procedural formalism (number of steps in the procedure at both pre-trial and decision phases, and number of overturned steps). Court-level studies tend to be limited to richer economies (Chang & Schoar, 2007).<sup>1</sup> Even then, court data typically only record duration (Alencar & Ponticelli, 2016; Chemin, 2009b,a; Coviello et al., 2015; Lichand & Soares, 2014; Visaria, 2009). Detailed hearing-level data allow us to measure steps taken by judges to avoid dilatory actions by the parties. We also shed light on potential efficiency-quality trade-offs, using data on final judgments and intention to appeal to capture the quality of decisions. Understanding the trade-offs associated with changing the rules of the game in bureaucracies is particularly important in courts, as judges evolve in complex, multi-tasking environments with substantial authority and independence (Holmstrom & Milgrom, 1987; Finan et al., 2017).

We find the reform significantly increased the speed of justice through a reduction in procedural formalism with no adverse effect on the quality of legal decisions. We document a large reduction in pre-trial duration of 46.1 days (0.32 SD) off of a 157 day baseline, as judges are 49% more likely to meet the four-month deadline (an increase of 23.9 percentage points from a pre-reform level of 48.7%). We show this effect is attributable to a reduction in formalism. The number of case-level pre-trial hearings is reduced (0.31 SD), and the decisiveness of hearings increases: the number of desk-rejected and fast-tracked cases increase (by 16.9 and 9.2 percentage points, respectively), while judges are 48% more likely to issue a strict deadline for an adjournment. Both smaller and larger disputes are similarly affected by the reform, and the decree is equally applied by originally faster and slower judges. We later discuss robustness of our event study design to selection on level, selection on trend, selection on shock, and selection on trend shock.

> 1Chemin (2009b) uses yearly court-level data to identify the impact of a legal reform in Pakistan, exploiting district-level variation in coverage. Alencar & Ponticelli (2016) exploit yearly variation in case duration across courts to isolate the role of court efficiency on the impacts of a bankruptcy reform in Brazil.

2
1. _Firm Longitudinal Data_ : Our main source of information comes from ISSS, the Social Security agency in El Salvador. The data cover the universe of firms operating in the formal sector independent of their economic sector and size.<sup>13</sup> We use monthly data between 2010 and 2018 on annual wages, number of employees per firm separated by gender, and number of branches per firm. The data also include information on firms’ general characteristics— such as location (municipality and department), economic sector and activity, the year it started operating, and whether the firm stopped operating.<sup>14</sup> Once a firm becomes formal, it is followed over time until it goes out of business.

As described Table 1, our sample consist of the universe of firms operating in the formal sector, which employ an average of 25 workers. On average, the firms in our sample pay a monthly salary of US$310 per worker, with an important difference between men and women—20.1 percent more to male workers. In our data set, we observe the location of the branch of each firm. Since a firm can have branches in many municipalities, we impute the location of each firm according to the location of the biggest branch. On average, we find that there are 80 firms operating at the municipality level. The average firm is in a municipality where returnees represent about 0.05 percentage points of the local population.

2. _Repatriations:_ data on returned migrants comes from records of the General Directorate of Migration ( _Direcci ́on General de Migraci ́on y Extranjer ́ıa_ , DGME) and are available at the individual level. In total we have information on 403,851 repatriations of Salvadoran citizens from 1995 to 2018. Starting in 2011, we have rich administrative information on specific characteristics on all returnees. Upon their arrival in El Salvador, each returned migrant has to visit the repatriation center in the country. In this center, a migration officer greets each returnee and conducts an interview collecting information on the date of repatriation, date of birth, marital status, level of education, gender, time spent abroad, country in

> 13We exclude firms in public administration, education, health, social work, and utilities (e.g., electricity, gas and water sectors) since they are managed or heavily regulated by the government and do not capture the full dynamics of the private sector.

> 14The economic sector classifications follow those of the United Nations’ International Standard Industrial Classification (ISIC) of All Economic Activities, Revision 3.

10
Orissa Figure 7a: Annual Percentage Change in Stamp Duty Revenues and Rates Figure 7b: Stamp Duty Revenues and Rates <!-- Start of picture text --> Orissa<br>50<br>40<br>30<br>20<br>10<br>0<br>-10<br>-20<br>Year<br>Tax Revenue Stamp Rate<br>Figure 8a: Annual Percentage Change<br>in Stamp Duty Revenues and Rates<br>Punjab<br>60<br>40<br>20<br>0<br>-20<br>-40<br>-60<br>Year<br>Tax Revenue Stamp Rate<br>1980-81 1985-86 1990-91 1995-96 2000-01<br>1980-81 1985-86 1990-91 1995-96 2000-01<br>Annual % Change<br>Annual % Change<br><!-- End of picture text --> <!-- Start of picture text --> Orissa<br>10000 16.0%<br>9000 14.0%<br>8000<br>12.0%<br>7000<br>6000 10.0%<br>5000 8.0%<br>4000 6.0%<br>3000<br>4.0%<br>2000<br>1000 2.0%<br>0 0.0%<br>Year<br>Tax Revenue Stamp Rate<br>1980-81 1985-86 1990-91 1995-96 2000-01<br>Rs. Lakhs Rate (%)<br><!-- End of picture text --> Punjab Figure 8b: Stamp Duty Revenues and Rates

# **Punjab**

<!-- Start of picture text --> 40000 14.00%<br>35000 12.00%<br>30000 10.00%<br>25000<br>8.00%<br>20000<br>6.00%<br>15000<br>10000 4.00%<br>5000 2.00%<br>0 0.00%<br>Year<br>Tax Revenue Stamp Rate<br>1980-81 1985-86 1990-91 1995-96 2000-01<br>Rate (%)<br>Rs. Lakhs<br><!-- End of picture text --> 75
Elvery, J. (2020). “Getting to Accuracy: Measuring Covid-19 by Mortality Rates and Percentage Changes,” Federal Reserve Bank of Cleveland. April 8.

EuroMOMO (2020). “Pooled Number of Deaths by Age Group,” EuroMOMO. https://www.euromomo.eu/graphs-and-maps/ Fenz, K. and H. Kharas. (2020). “A Mortality Perspective on COVID-19: Time, Location, and Age,” Brookings. March 23. https://www.brookings.edu/blog/future-development/2020/03/23/amortality-perspective-on-covid-19-time-location-and-age/ Giesecke, Johan (2020). “The Invisible Pandemic,” The Lancet. May 5. https://doi.org/10.1016/S0140-6736(20)31035-7 Global Health Data (2018). “Baby ‘Boom’ and ‘Bust’”: Nations’ Rate of Childbirth Vary Significantly,” Global Health Data. November 2018.

Huang C. and others (2020). “Clinical Features of Patients Infected with 2019 Novel Coronavirus in Wuhan, China,” The Lancet, January 24.

International Monetary Fund (2020). “World Economic Outlook,” International Monetary Fund. April. https://www.imf.org/en/Publications/WEO/Issues/2020/04/14/weo-april-2020 Istituto Superiore di Sanità (2020). “Age-Specific Mortality Rates COVID-19", National Report. https://www.epicentro.iss.it/coronavirus/bollettino/Bollettino-sorveglianza-integrata-COVID19_30-marzo-2020.pdf Johns Hopkins University and Medicine (2020). “COVID-19 Dashboard,” Coronavirus Resource Center. Center for Systems Science and Engineering. https://coronavirus.jhu.edu/map.html Korean Ministry of Health and Welfare (2020). “COVID-19 Update,” May 6.

La Porta, R. and A. Shleifer (2014). “Informality and Development,” Journal of Economic Perspectives—Volume 28, Number 3—Summer 2014—Pages 109–126.

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Onder, G., Rezza, G. and S. Brusaferro (2020). “Case-Fatality Rate and Characteristics of Patients Dying in Relation to COVID-19 in Italy,” March 23. JAMA. doi:10.1001/jama.2020.4683 Pilling, D. (2020). “Covid-19 Death Toll Raises Hopes Africa May Be Spared Worst,” April 28. https://on.ft.com/3aI2qvu Richardson, S., Hirsch, J. and M. Narasimhan. (2020). “Presenting Characteristics, Comorbidities, and Outcomes Among 5700 Patients Hospitalized With COVID-19 In The New York City Area,” JAMA Network. April 22.

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https://www.washingtonpost.com/graphics/2020/local/retropolis/coronavirus-deadliestpandemics/ 26
percent) or other taxes (2.4 percent) (World Bank. 2017).

Furthermore, the additional tariff band of 35 percent implemented under ECOWAS (e.g., by Guinea) exceeds the WTO bindings; yet, estimates indicate that eliminating it would have a negligible impact on revenues. Since 2015, the members of ECOWAS adopted the CET—for 90 percent of tariff lines, the ECOWAS CET was based on the WAEMU CET, which pre-dated it since it was implemented in 2004, but the remaining 130 tariff lines are subject to a higher 35 percent tariff rate, which has had only a negligible impact on tax revenues (IMF, 2015). More important, the ECOWAS CET exceeds bindings at the WTO for all member States except for Guinea-Bissau and Togo. Moreover, numerous other duties and levies imposed by member States are in fact bound at zero, posing a contradiction with bindings. An observation of the latest available applied MFN tariff for 2017 at the 6-digit level, shows Niger and Mali applied tariff lines with deviations from the CET, amounting to 100+ discrete tariff rates spread across the entire tariff schedule (if not bands), some with minor deviations from the 5 band (0, 5, 10, 20, 35) structure, while other deviations are significant.

Table 2: Average Tariff Rates in Niger

|Tariff Line|Average<br>Tariff<br>Rate|
|---|---|
|1. Simple average applied MFN rate|18.1|
|Agriculturalproducts(WTO definition)|22.4|
|Non-agriculturalproducts(WTO definition)|17.4|
|Agriculture,hunting,forestryand fishing (ISIC 1)|23.6|
|Extractive(ISIC 2)|11.2|
|Manufacturing (ISIC 3)|17.8|
|2. Effective Applied Tariffs*|18.6|
|3. Tariff lines dutyfree(% of all tariff lines)|0.6|
|4. Simple average rate(lines dutiable)|18.2|
|5. Non-ad valorem tariffs(% of all tariff lines)|0.0|
|6. Tariffquotas(% of all tariff lines)|0.0|
|7. National tariffpeaks(% of all tariff lines)a|0.0|
|8. International tariffpeaks(% of all tariff lines)b|48.1|
|9. Overall standard deviation of applied rates|9.6|
|10. Applied rates "nuisance" (% of all tariff lines) c|0.0|

_Source_ : Pitigala et al. (2017, 2018 a, b).

Some simulations on the adjusted CET rates in WAEMU find significant trade and welfare costs. The revision of the CET with the additional 35 percent band has potentially important consequences, as it may increase the cost of living for households from 7 to 10 percent and therefore decrease their welfare from 2 to 5 percent (Gourdon and Maur, 2014). In the case of Guinea, due to differing consumption patterns of households and the nature of the tariff structure, tariffs are regressive across the income distribution both for the 4-band UEMOA tariff and the ECOWAS CET. For the former, average tariffs range from roughly 12 percent for the poorest to 9 percent for the richest, and for the CET from 13 to 10 percent. The impact on households is also regressive in the case of the ECOWAS CET: poor households are disproportionately affected—the consumption-weighted average welfare cost amounts to 5 percent for the lowest 5th percentile compared to 3 percent for the 95th percentile.

10