rafmacalaba/data-use-mentions-extended huggingface dataset:
US; however, the relative amount to the market capitalization and GDP is comparable. As expected, since the compliance costs are a fixed cost, smaller companies tend to be affected more negatively as the regulatory burdens increase.<sup>45</sup>

> 41 One way to overcome the disincentive to list caused by the increased disclosure and governance requirements is to impose those requirements on all companies – not just listed companies – above a certain size, and a number of countries, including, for example, Sri Lanka and Tunisia, have done so.  However, it is important to ensure that such requirements are then enforced.

42 The different approaches taken by developed markets to requiring disclosure of executive compensation amply demonstrates these divergent cultural sensitivities.

43 A reluctance to disclose is not unique to emerging markets. In the United States, for example, when the SEC proposed to increase disclosure for companies traded on Nasdaq’s over-the-counter Bulletin Board, a significant number of companies delisted, rather than take on the new disclosure burden. Bushee and Leuz, 2004 44 The Korea Securities Research Institute, “Policy Recommendations for Reducing Compliance Costs of Public Companies,” June 2005. The data for listed companies were collected from 201 companies (113 in KSE and 88 in KOSDAQ) out of 1,566. In addition, focused interviews with senior mangers from 20 companies in each market were carried out to complement the survey and to estimate the absolute amount of compliance costs.

45 To a certain extent at least, this is expected: those companies that do not list are most likely to complain about the burdens, and some of those companies might in fact not be able to list should they desire to do so.

15
properly control for this potential endogeneity issue, a dynamic panel GMM estimation technique is implemented (Arellano and Bond, 1991; Arellano and Bover, 1995). The system GMM technique controls for endogeneity by using lags of the endogenous variables as instruments in the econometric equation. It also controls for eventual autocorrelation issues that could appear after introducing lags of the dependent variable as regressors. ESpendit−1 , Fisconit−1 and GDPgwhit are treated as endogenous variables in the system GMM regression.<sup>3</sup>

# **3. Data**

Table 1-Panel A shows the data used to estimate equation 1, and Panel B describes the data used to estimate equation 4. Data are collected from the World Economic Outlook and the World Development Indicators databases. The data cover the period 1990-2019. To address the data availability issues, all variables are averaged by a three-year interval.

Panel A data show that about 9.5% of the observations experienced a relative decline of total government expenditure over the last decades and the average debt service-to-reserve ratio varies between 0.5% and 397%. The average debt-to-GDP ratio is over the IMF's recommended benchmark of 40% in about 60% of the countries, showing that several countries may be affected by debt sustainability issues. The data are available for a total of 50 low- and middle-income countries from 1991 to 2019 to estimate equation 1.

Education spending per school-age individual (in primary, secondary, and tertiary) is the dependent variable in equation 4 (Table 1-Panel B). Because countries included in the analysis have different demographic structures, the size of the school-age population may vary across countries. Government spending on education is usually designed to cover the school-age population, and education expenditure by school-age child appears to accurately capture government's effort toward education financing.

Panel B shows that the average spending per school-age child is about $300. The spending per child hugely varies across income groups, from $39.5 in low-income countries to $636
elasticities are small limits the use of taxes as an effective tool to substantially reduce consumption. Taxes are also considered a blunt instrument as they disregard where and at what time of the day the driving occurs. In contrast, congestion tolls, which have been shown to be effective in dealing with congestion and pollution (see Börjesson et al. 2012; Coria et al. 2015) and more efficient than gasoline taxes (Parry 2002), can be spatially and temporally differentiated, charging driving on the most congested roads and during peak hours. Clearly congestion charges would be an alternative mechanism to achieve the goals that did not occur with PYP.

# _The Effect of PYP on Vehicle Registrations and Vehicle Sales_

The lack of evidence of a decrease in CO levels in both PYP phases might be explained by an expansion in the vehicle stock or in the number of trips made. One way to analyze this type of behavioral response is to evaluate possible increases in the number of registered vehicles and sales of new cars due to the program. Data on monthly vehicle registrations (January 1997–November 2010) from the Transport Agency of Bogotá, and on new vehicle sales (July 2000–September 2011) from Econometria S.A., are used to evaluate this effect. The model for vehicle registrations can be represented as: where _V_ t is vehicle registrations in logs at period _t_ , ���� is a set of lags of the dependent variable, �� is the error term, and other variables are as described above<sup>27</sup> . �� and �� measure the effect of PYP on vehicle registrations. Classical ADF tests indicate nonstationarity of the registered regular cars and private vehicles. Hence, the dependent variable is the first difference of monthly vehicle registrations. Using BIC, the identified

> 27. Due to lack of data, the model does not control for vehicle registration fees.

19
three minutes or longer<sup>6</sup> – and (2) resulting in consumers being out of power.<sup>7</sup> Netherlands reports all disruptions of at least 5 seconds, and Cyprus and Malta do not have a classification. Countries included in the dataset represent the EU-28 (without Hungary, Denmark, and Bulgaria) plus Serbia.

The 463 significant outages are grouped in 128 unique categories detailing the causes of the disruption. We label each of these categories as natural shocks, non-natural causes, load-shedding, or other. Nonnatural causes include technical problems on both supply and consumer sides as well as human error. Unlike in the US, vandalism on the electricity grid and fuel supply shortages play subordinate roles and thus are not reported as individual categories.

# 2.1.3. Bangladesh

The Bangladesh Rural Electrification Board (BREB) has recorded daily reports of division-level load shedding volumes since at least November 2009. Processing these daily reports is challenging as they are published as individual pdf scans of a non-harmonized form sheet with a layout that changes over the years. These scans have been converted into readable datafiles, from which daily division-level load shedding volumes were extracted through an automated text processing algorithm. The robustness of the resulting time series has been tested through manual comparison with the original form sheets. The load shedding volumes for divisions Chittagong, Rajshahi, Rangpur, Sylhet and Barisal have been found to be robust. However, given the lack of power outage data in Barisal, we exclude this division from the analysis. The remaining data is summarized in Figure 3.6 in annual aggregates.

Moreover, data on storm-induced outages are retrieved from daily records published at the district level by the Bangladesh Rural Electrification Board. The data reports outages that can be attributed to climatic shocks – in particular storms – during the period 2010-2017. The BREB publishes reports for 64 of its 79 power supply or Palli Biduyt Samity (PBS) zones<sup>8</sup> . However,
# **S5 Wage distribution among Formal Workers in Household Surveys and Social Security Records**

This appendix assesses the cross-sectional differences in wage distributions between the administrative databases and the household surveys. Figure S5.1 follows Kumler, Verhoogen, and Frias (2015) and presents kernel estimates of the wage density for all formal salaried workers in the various countries from the administrative data (solid black line) and monthly take-home wages from the household survey data (dashed black line). The figure presents results for 2011, a year for which data was available for all the countries. The pattern is clear: the wage distribution is very similar for formal workers between administrative data and household surveys. There is more measurement error in the household survey data. The wage distribution based on administrative data is slightly to the left of the household survey distribution, but the differences are very small and the means are similar (in line with supplementary online appendix table S1.3). In the three countries, the spikes in density are high around the minimum wage and there is a large drop-off to the left of the spike, showing reasonable compliance and suggesting that many of these workers are now in the spike, although, as everywhere in Latin America, some workers are to the left of the spike (indicating noncompliance).<sup>38</sup>

> 38These results contrast with Mexico, where the minimum wage is too low to be binding and the distribution of wages using administrative data lies largely on the left of the household survey distribution.

69
"relaxation" in macro stringency as a result of the real devaluation, which has led to an improvement in enterprise liquidity.<sup>34</sup> Further, the government has not been able to borrow commercially, and has reaffirmed its desire to stay the course (see Slay (1999) for an account of the government's behavior post-meltdown - hyperinflation was avoided but although there has been no reversal, further implementation of reform has been on hold). No new net government borrowing and greater pressure on the energy monopolies to pay taxes means a reduction in the ability to finance implicit subsidies. This push factor, combined with the pull of a large and sustained real devaluation, has lowered NCS use, consistent with the analytical framework presented in this paper.<sup>35</sup> However, the nonpayments system is not about to disband itself spontaneously. Thus, while surveys by the Russian Economic Barometer indicate that NCS as a share of sales in medium-sized and small enterprises has come down from 50 percent, it has stabilized at 35 percent for the last three months of 1999.36 Similarly, arrears as a share of GDP have fallen at end-1999, but this is at least partly, if not substantially, due to inflation erosion. Likewise, while Gazprom and RAO UES have increased the share of cash in their sales, the level achieved as of the end of 1999 is still low by central European standards -- 33 percent for Gazprom, 39 percent for RAO UES. At the same time, there has been no perceptible change in other factors that perpetuate nonpayments. One important example is the politics of tax sharing between the subnational and federal governments, which is such that the former may well encourage noncash tax payments (part of NCS) to retain more taxes at the local level.<sup>37</sup> Another critical factor (discussed next) is the personal enrichment enabled by a new form of industrial organization spawned by nonpayments. Thus, the core of
open to question. Choi et al’s empirical study of the high court judges of the 50 US states provides little evidence that raising salaries would improve judicial performance.

More generally, there is mixed evidence to suggest that court budgets affect judicial effectiveness. Voigt and El Bialy (2013) analyze the determinants of judicial efficiency based on data of the 47 member states of the Council of Europe. They found three variables that had a negative correlation with judicial efficiency: (i) the increase in the budget of the judiciary, (ii) the introduction of court specialization, and (iii) the establishment of judicial councils, a body that 23
|Spatial GMM<br>SE<br>(Cutoff = 1|Spatial GMM<br>Est.<br>(Cutoff = 2)|Spatial GMM<br>SE<br>(Cutoff = 2)|
|---|---|---|---|---|
|Intercept|-.04462965|.19892652|.00716942|.22615147|
|ln Teacher Qualification in 1991|.00093057|.00426831|.00001553|.00453575|
|Years of Schooling in 1991|.06330124|.00894142|.06656591|.01025243|
|Total Rainfall|.00010275|.00002402|.00010328|.00002395|
|Government Accountability|-.01646986|.02421216|-.02936971|.02324613|
|Delta ln Employment|-.68066132|.16307582|-.76383001|.18872485|
|ln Transport Cost SP in 1995|-.04688983|.01363969|-.042707|.01531044|
|ln Transport Cost Capital in 1995|-.04913757|.01864702|-.05431722|.02134752|
|Delta ln Transferences|.17948617|.0803842|.15744109|.09175749|
|Delta ln Market Potential|.58104956|.19482417|.52911681|.22650729|
|crit. fn. test of overid. restrictions|33.939201|33.939201|21.138334|21.138334|
household i, (i.e., ), in survey round with sample size , jj yy and i be a vector of household head characteristics observed for household i in survey round j. These ii= 1, ... , NN jj NN household head characteristics, observed in both survey years, can include time-invariant variables (e.g., XX household head’s gender, ethnicity, place of birth, etc.), deterministic variables (e.g., such as age, education, literacy, etc.), and retrospective questions collected in round 2 about round 1 such as assets (Cruces et al, 2015; Dang and Lanjouw, 2018). Changes in household composition can help identify matching errors, which could lead to spurious measures of change. To avoid this, the estimation samples are constrained to household heads’ age: 25 to 65 in the first cross-section and adjust this age range accordingly in the second cross-section. The linear model where -th household’s log per capita income<sup>7</sup> i is explained solely by household head characteristics i for each survey round is given by: ii yy XX i i i (1) TT where i is an error term and yy = ααii + ββi is a vector of ii XX + εε _K_ regressors, and jj = 1, 2 is the intercept. In this context, + i would represent the portion of log income explained exclusively by deterministic and time-ii ii εε XX αα αα invariant household head characteristics. TT ii ββ XX

> 5 Lucchetti et al (2020) use 36 panels from Argentina, Chile, Peru, and Nicaragua to validate their estimates of mobility using harmonized variables frequently used in many regional and global studies. After the validation exercises, the authors implemented the LASSO-PMM method in 43 countries worldwide.

> 6 The donor survey is the one that contains the actual income data from the first-round survey. The receiver is the second-round survey where the income is imputed
respectively, from the first approach is fairly stable across parametric (OLS) and nonparametric (matching) models. Similarly, and given that the consequences of model misspecification are more serious in the RDD approach, the empirical models in this design were run for a number of low-order polynomial functions as well as other more flexible functional forms including third- and fourth-order polynomials and nonparametric models. The direction – and even in some cases the magnitude – of the RDD estimates to test for school completion are robust to the inclusion of different polynomial terms in the control function.

Finally, another concern is that the higher rates of matching survey data and test records observed for the treatment group may be driven by differences in the merging procedures and quality of information rather than by the effect of the program. In particular, considering that part of the information used to construct the treatment and control groups come from different datasets, it could be that the individual-level variables used to merge them (name, date of birth and national identification number) may be more accurate for people in the treatment group. For instance, surveyors that collected the data for the short-term impact evaluation could have been more careful to correctly keep the information to identify individuals in the treatment group for the subsequent rounds of data collection. Similarly, as part of the regular updates of the data entered in the information systems of FA (SIFA), program administrators may be more likely to correct mistakes in names, birth dates, and national identification numbers of program beneficiaries. As a result, individuals in the treatment group may be more likely to be matched with records in the _Icfes_ database, not because they are more likely to take the test, but because of better information quality. In practice, however, measures computed from the matching algorithms such as the _Levenshtein_ distance do not indicate systematic differences in the accuracy of data between the treatment and control groups. Therefore, it seems unlikely that this could be a reason to find children covered by the FA program to be more likely
did this person live 1 year ago?’ We should note these two questions seem to create a certain level of confusion which possibly arises from when the survey is conducted within that year. Suppose the survey is conducted in September 2006 and the migrant arrived in the United States in March 2005. The answer to the first question will be that the migrant moved to the United States during 2005. In other words, this question is likely to capture more than 12 months of arrivals. That is why, the total number of recent arrivals (current or previous year) obtained by summing up each individual survey year is actually more than the number of people who arrived during 2001-11 based on the 2012 ACS data. For the second question, some people will interpret ‘1 year ago’ as November 2005 and state where they were living in the United States. Other respondents will interpret one year as the previous calendar year and report the foreign country they were in before coming to the United States. As a result, around 40% of the people who stated they migrated during that year or the previous year also reported a location in the United States to this second question. We drop these observations from our sample. This adjustment brings our total number of observations to 9.03 million, which is now 10% less than the number of people who arrived during 2001-11 according to the 2012 ACS data.

15
Learning from Developing Country Power Market Experiences: The Case of Colombia <!-- Start of picture text --> COP/kWh<br>1,200<br>1,000<br>800<br>600<br>400<br>200<br>0<br>1 5 9 1 5 9 1 5 9 1 5 9 1 5 9 1 5 9 1 5 9 1 5 9 1 5 9 1 5 9 1 5 9 1 5 9 1 5 9<br>2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br>Average contracts price Pool price<br>Figure 6-4 Comparison of the evolution of contracts and pool price.<br><!-- End of picture text --> Figure 6-4 Comparison of the evolution of contracts and pool price.

Source: Own elaboration based on data collected by the World Bank.

# 6.1.2 Short-term wholesale prices

Short-term wholesale energy prices in Colombia increased by 158% in real terms from 2005 to 2016. Prices rose from 32 USD/MWh in 2005 to 100 USD/MWh in 2016, but then fell to 37 USD/MWh in 2017 (in nominal USD). The evolution of prices (see Table 6-3) highlights an upward trend of pool prices since 2011 due to low hydro inflows (XM, 2013, 2014, 2015); and price spikes that occurred during 2009-10 and 201516 due to El Niño phenomena. The monthly pool price is depicted in Figure 6-5. It is worth noting that prices fell in 2017 due to normal hydrological inflows (compared to the very low inflows seen during 2016), and also slow demand growth (XM, 2017b).

Pool price spikes occurred during 2009-10, and even more severe price spikes during 2015-2016, primarily due to reduced hydro availability because of El Niño climatic phenomena (see Table 6-3 and Figure 6-5, in nominal COP/kWh). It has been argued that pool price spikes
based work and therefore at higher risk are largely concentrated among lower wage deciles. This includes personal care, food services, and production jobs (figure 3). Occupations that are intensive in face-to-face interactions that relate most closely to physical proximity are similarly concentrated among lower wage deciles. However, the correlation is stronger for HBW than for F2F because several well-paid occupations, such as professors and managers, ae amenable to remote work, but ideally require intense F2F interactions.

Lastly, a larger share of women’s employment is also accounted for by occupations that are intensive in these face-to-face interactions (figure 4). The gender exposure measure is constructed by taking the F2F occupational scores and multiplying them by the share of employment by gender in each occupation as a share of total employment by gender. For example, a value of 100 for female workers would imply all women are employed in the occupation with the highest F2F score, while a score of 0 would mean all women are employed in the occupation with the lowest F2F score. The results clearly indicate much stronger effects for women than for men. It is also worth noting that women are more exposed according to the F2F index than the HBW measure. The reason is that women in the United States are less likely to work in manufacturing, mining, and construction occupations, which cannot be performed at home, but are less intensive in F2F interactions. Hence, as lockdowns are lifted, the longer lasting effects may be on female employment, as discussed above.

Overall, our results indicate the COVID-19 outbreak is likely to disproportionately affect vulnerable groups and could exacerbate inequality.

**Figure 4: Women are more exposed to face-to-face interactions than men**

<!-- Start of picture text --> 60<br>52<br>50<br>39<br>40<br>30<br>20<br>10<br>0<br>Male Female<br><!-- End of picture text -
Policy Research Working Paper 10205

# **Abstract**

During the last quarter century, job tenure in Europe has shortened. Using data from Eurostat Labor Force Surveys of 29 countries from 1995 to 2020 and applying an age-period-cohort decomposition to analyze changes in tenure for specific birth cohorts, this paper shows that tenure has shrunk for cohorts born in more recent years. To account for compositional changes within cohorts, the analysis estimates the probability of holding jobs of different durations, conditional on individual and employment-related characteristics. The estimations demonstrate that, over time, the likelihood of having a medium- or long-term job decreased and holding a short-term job increased. The paper also finds that stricter job protection legislation appears to decrease the probability of holding a short-term job, and higher trade openness and ICT-related technological change are correlated with an increase of that probability.

This paper is a product of the Office of the Chief Economist, Europe and Central Asia Region. 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 may be contacted at mbussolo@worldbank.org, dcapelle@imf.org, mlokshin@worldbank.org, itorre@worldbank.org, and hernanwinkler@gmail.com.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.