rafmacalaba/datause-encoder-data huggingface dataset:
The remainder of this paper is structured as follows. Section 2 provides a brief review of the existing studies on the welfare impacts of the COVID-19 pandemic and describes the contributions of this paper to the existing literature. Section 3 describes the data used in the analysis, defines the main variables that the study relies upon to examine the different dimensions of welfare impacts, and discusses some of the data-related caveats. Section 4 outlines the main findings related to the patterns of employment losses and subsequent dynamics across different countries and population groups. Section 5 looks at how the broader welfare impacts and the response to the shock could lead to scarring and reinforce inequality in the longer-term if the losses are left unmitigated. Section 6 concludes.

# **2 Welfare impacts of COVID-19: Existing evidence and knowledge gaps**

A number of studies have documented the multifaceted welfare impacts of the COVID-19 pandemic both across and within countries, focusing primarily on the labor market impacts such as job and income losses. These resulted from the restrictions to mobility and economic activity that were imposed by governments in order to contain the spread of the coronavirus as well as the ensuing impact of global demand and supply shocks.<sup>3</sup> In developing countries, analyses mostly rely on HFPS data collected by the World Bank, as many countries had to put on hold the collection of household data through regular surveys such as Household Budget Surveys or Labor Force Surveys during 2020-2021. HFPS data revealed high levels of work stoppages early in the pandemic, particularly in the industry and services sectors, lack of payment for work performed, as well as income losses at the household level that correlate with the degree of stringency of the measures that were imposed to restrict mobility and economic activity (Bundervoet et al. 2022; Khamis et al. 2021; Kugler, Viollaz, et al. 2021). For instance, Bundervoet et al. (2022) find, based on a sample of 31 countries, that more than a third of respondents reported having stopped working since the onset of the pandemic, and almost two-thirds of households reported a decrease in total income. These job and income losses were associated with significantly higher levels of food insecurity at the household level.

The early analyses conclude that the welfare impacts precipitated by the economic crisis were not evenly distributed either across or within countries. Across countries, employment and incomes dropped sharply almost everywhere, and nearly all LICs and LMICs saw poverty increase in 2020. Globally, the pandemic led to a broad shock across the global income

> 3There is a substantive literature documenting the important benefits of public health policies to flatten the curve of COVID-19 contagion through masking, social distancing, contact tracing and vaccination in the context of developing countries that is beyond the scope and focus of this paper.

3
international firms to assess whether the evolution of _q_ differs across distinct methods of internationalization.

The data for identifying and dating each firm’s international activities come from the following sources. First, besides the Bank of New York’s standard database (the Complete Depositary Receipt Directory) that contains information on current depositary receipt activities, we received access to their historical databases and reports on (i) depositary receipt program initiation dates, (ii) termination dates (if any), (iii) capital raisings, and (iv) trading activity. These data form a comprehensive database on American and Global depositary receipt programs. The historical data start in January 1956, but most programs begin after 1980. Second, Euromoney provides the dates when firms raise equity capital in international markets, including cross-listings and issuance of Global Depositary Receipts (GDRs). Thus, the Euromoney data substantively enhance the identification of international firms. The Euromoney database we use covers 8,795 cross-border equity issuances and cross-listing operations from 5,665 firms in 86 countries over the period January 1983 - April 2001. Finally, information on dating the initiation of international equity market activities was augmented with data from the London Stock Exchange (LSE), NASDAQ, and New York Stock Exchange (NYSE) on listing dates by foreign corporations.<sup>7</sup> To measure firm valuation we use Tobin’s _q_ based on data from Worldscope (Thomson Financial Company), Standard & Poor’s Emerging Markets Data Base (EMDB), and Bloomberg. Given data availability, we calculate _q_ as the market value of equity plus the book value of debt

> 7 We also have data from the Frankfurt Stock Exchange’s Regulated Unofficial Market (Open Market), where shares from more than 60 countries are traded.  The decision to have shares traded in this market is not made by the issuing firm; rather, the decision is made by trading participants, who only need to notify the Deutsche Bourse of the type of securities to be traded and inform the issuer.  There are no legal obligations for the issuing firm.  Thus, we do not consider these firms as international firms.  In the regressions presented below, we exclude these Open Market firms unless we have other information that indicates that they have chosen to cross-list, issue depositary receipts, or raise capital abroad.  In robustness tests, we categorized these firms as domestic firms and confirmed all the paper’s findings.

8
<!-- Start of picture text -->
1.0<br>: 7 | | I | | | |<br>0.0 | J | | : | il a | l l<br>“0.5 | |<br>-1.0<br>SeSe)yy &ofxo& seS aeoOmSS>fF aNx~@ o‘Ioea>osFkiSiJ PSXey PFeSsSF > FSxS Sev9 FSYe~x&22 ~oNSSe@<br>OsSeSs» + SeseS)is2<br>RSSs ws ’Ss<br>2000-2011 m 2012-2022<br><!-- End of picture text -->

<!-- Start of picture text --> ;1.0 | | | | | | | | | | | | |<br>Sereenl Wi dbhe<br>“0.5 |<br>-1.0<br>woeFFSSoe “FSFS s SP&HFK®SB© FEFFaSKshEF §ELFFESPeSFSFE CPSFEFLYKSO ghCESESX SSMS<br>eJSS SRS >y& ocS<br>we & <<br>RS es<br>2000-2011 m 2012-2022<br><!-- End of picture text -->

<!-- Start of picture text -->
1.0<br>; | | | |<br>0.0 | | | | I 4 1 = : L l = I I |<br>-0.5 | | [ |<br>-1.0<br>PSPFPJ S we2 SMPeS& SS WP L LS—S &e SsKo££ &we _&gkys oSF oO swe£ S WEeo< S e<br>we FF KF Ss ww ‘ ee SF<br>ei& oo éCaSS <&S<br>S)Ss w KSES<br>2000-2011 2012-2022<br><!-- End of picture text -->

<!-- Start of picture text -->
1.0<br>; | | | | | | i | |<br>00 = 1 Ir ry a , a | | = ___<br>-0.5 | |<br>-1.0<br>yess FFLoo xe> s s &S FPF>SFC&SX FFOS~~SK~ &PEe csx _PwPoeeS OooOSFLPF> SoO.S &&ys&&9 SF3D> *Ss3eo<br>s¥Ssx SJS» 3veegx»<br>aS eg‘S sSS<br>2000-2011 TM 2012-2022<br><!-- End of picture text -->

<!-- Start of picture text --> ;1.0 | | | | | | | | | |<br>aren |i ho l<br>0.5 |<br>-1.0<br>.SFFFPSF SKFFFSaOSFF®WwHF EKsKOM weSSCFEF KFESFRSFMKF vOKKSF MHFY ESESF SFSSS$“$sESSFSC SFSF<br>seFPvs okSe s >ys&<br>we & &<br>w »<br>2000-2011 mTM 2012-2022<br><!-- End of picture text -->

<!-- Start of picture text -->
1.0<br>; 1<br>0.0 ry = | "a | i<br>-0.5<br>-1.0<br>RSSs° eRy,o >8x ¢ © &x2 sSRN<br>we RS ¢ 2 s* es<br>s8RS oSs<br>x&<br>2000-2011 TM 2012-2022<br><!-- End of picture text -->
Table A1: Top 10 Destinations of China’s Processing Exports

||2000||2007||
|---|---|---|---|---|
|Rank||USD (Bil)||USD (Bil)|
|1|United States|35.17|United States|152.51|
|2|Hong Kong SAR, China|31.02|Hong Kong SAR, China|150.00|
|3|Japan|23.17|Japan|60.25|
|4|Germany|5.62|Netherlands|29.08|
|5|Korea, Republic of|5.34|Germany|29.00|
|6|Netherlands|3.90|Korea, Republic of|26.70|
|7|United Kingdom|3.90|Singapore|19.04|
|8|Singapore|3.62|United Kingdom|17.41|
|9|Taiwan, China|2.92|Taiwan, China|13.22|
|10|France|2.10|France|11.81|

Source: China’s Customs Trade Data.

17
**Figure 4. Short-Term Income Growth Rate for Immobile Individuals, Russia 1994-2015**

<!-- Start of picture text --> 175<br>150<br>125<br>100<br>75<br>50<br>25<br>0<br>First tercile<br>-25<br>Second tercile<br>Third tercile<br>-50<br>1994-1998 1998-2004 2004-2009 2009-2015<br>Growth rate(%)<br><!-- End of picture text -->

**Note:** Estimation results are obtained based on total household income per capita. The terciles are defined using the cross-sectional sample for each year. All numbers are deflated with December to December regional CPIs and weighted with population weights, where the first survey round in each period is used as the base year.

45
would expect from adding men to the sample whose stress is not well captured by the average induction risk). Overall, we continue to find evidence that stress, in the form of induction risk, worsens later-life health.

# **_Variations in Sample Inclusion Criteria_**

We also examine the sensitivity of results to variation in the sample inclusion criteria. These results are presented in Appendix Table A3, with Panel A reprinting our main results on induction risk from Tables 4 and 5 for easy comparison.<sup>17</sup> In Panel B, we present results when those who ever served in the military, not just during the Vietnam War era, are dropped from the sample. In Panel C, we include only non-Vietnam-veteran men who responded without a proxy to the NHIS survey. The purpose for doing this is to eliminate any additional reporting error due to health being proxy-reported instead of self-reported. In Panel D, we include in the sample all birth cohorts of male non-Vietnam-veterans back to 1930, who would have been of military age starting in 1948 and thus subject to the same post World War II draft system as those in our main sample. In Panel E, we limit the sample to those born between 1930 and 1936 who did not serve in the military, but were age-eligible to serve during the Korean War. These results are generally similar to those of the main specification; exposure to a higher induction risk tends to be associated with a higher probability of obesity and worse self-assessed health.

# **_Model Estimates for Veterans of Various Wars_**

We estimate additional falsification tests, using data for male veterans of wars other than Vietnam in Appendix Table A4. The results generally confirm that veterans, no matter which war they served in, do not exhibit the negative correlation between induction risk (that was found

> 17 We do not present estimates on induction risk for specification 1 because these estimates are very similar to those from specification 2.

22
the SPRs only cover PCSB schools before 07. In cases in which we cannot find proficiency data, we make imputations (see below).

- Math proficiency: constructed similarly as reading proficiency.

- Year of Opening: source: SPRs, FOCUS, web searches. The variable stores the Fall of the first academic year that the school is open.

- Year of Closing: first year that the school is no longer open. The sources are the Center for Education Reform , current SPRs and PCSB listings of charter schools, current NCLB reports, and web searches.

- Reason for Closing: classified as academic, financial or mismanagement. Source: Center for Education Reform, SPRs, web searches.

• Multi-campus: an indicator variable that equals 1 if the school belongs to an organization that has multiple campuses by the end of the sample period. Percent of low-income students is missing for 9 out of 228 observations. These schools have low enrollments. To most of these cases we impute the school’s average percent of low-income students, calculated over the years for which the school does have data. In the case of missing proficiency rates (36 out of 228 observations), we make imputations similar to those described for public schools, the only difference being that we used school-fixed effects (as opposed to campus-fixed effects) in the predicting regressions.

# **A.1.3 Private Schools**

The starting point is the list of private schools from the Private School Survey (PSS). Since PSS is biennial, we use the 2003, 2005 and 2007 waves. PSS classifies schools as regular, vocational, special ed, and other/alternative. For the years of interest, 92 percent of the schools in Washington, D.C. are classified as regular, and the remaining schools are classified as other/alternative. Although an alternative public school usually serves students with behavioral problems, an alternative private school is usually a regular school with a specialized curriculum. Hence, we keep most alternative schools. We eliminate vocational schools because they enroll ungraded students exclusively. We also eliminate special ed schools, early childhood centers (as long as they never have enrollment in regular grades during the sample period), and other schools that only teach ungraded students.

Since PSS does not have a 2004 wave, we assign 2004 values to the variables through linear interpolation of 2003 and 2005, and similarly for 2006. For instance, we calculate percent white for 2005 as the average between percent white in 2003 and in 2005. In cases in which a school does not report to the survey in a particular year, we make imputations based on the school’s reported data for the other years. If a school does not appear again in PSS after a particular wave, we assume that the last year of operation is the year of the last wave in our data.

For the list of variables below, PSS is the main source of data:

- Address: if needed, we supplement PSS with web and Internet archive searches. We geocode all addresses.

44
Technology Strategies for Low-Carbon Economic Growth: A General Equilibrium Assessment<sup>_∗_</sup> Ian Sue Wing<sup>1</sup> and Govinda Timilsina<sup>2</sup>

> 1Dept. of Earth & Environment, Boston University

> 2Development Research Group, World Bank

JEL classification: C68, Q48, Q54, Q55, Q65 Keywords: Static Computable General Equilibrium, Green Growth and Sustainability, Low Carbon Development, Climate Change Mitigation, Energy Efficiency

> _∗_ Comments by Jan Gaska and Mun Ho are gratefully acknowledged. The authors also acknowledge the financial support from Knowledge for Change (KCP) Trust Fund of the World Bank.
relevant supranational authorities (central banks in regional monetary unions) are also included in the database given their role as benchmarks to ensure a globally coordinated policy response.<sup>4</sup>

# _2.1 Classification of measures_

# _Measures that focus on liquidity and funding conditions_

In the first phase of the pandemic, in many countries market liquidity evaporated and funding markets were severely strained. Large capital outflows from many emerging markets amplified market moves and eclipsed the outflows seen during the 2007-2009 Global Financial Crisis. Many central banks reacted by easing monetary conditions and injecting domestic currency liquidity through banks. In some cases, exceptional measures were taken and liquidity was also provided through standing credit facilities to nonbank financial institutions and corporates in affected sectors, offering lending on favorable terms and longer tenors. The primary objectives of these measures were safeguarding liquidity conditions and ensuring the smooth flow of credit from banks to the real economy (Cavallino and De Fiore, 2020 and Lane, 2020). Central banks in advanced, and for the first time in several emerging markets, have relied on local government bond purchases to ease financial conditions and restore liquidity in local capital markets (Benigno et al., 2020). Moreover, confronted with outflows that caused currency depreciation and volatility and US dollar shortages, several monetary authorities in EMDEs intervened in foreign exchange markets and established temporary swap lines with other central banks, notably the US Federal Reserve.

# _Measures that focus on the banking sector_

Many authorities around the world implemented temporary relief measures in support of borrowers and to ensure the flow of credit to the real economy while safeguarding banks’ resiliency (Drehmann et al., 2020). These measures seek to avoid a rise of insolvencies of cash-strapped, but otherwise viable businesses,<sup>5</sup> by providing direct support to borrowers in the form of, inter alia, public guarantees for bank loans, state

> 4 In addition to EMDEs, the following jurisdictions are tracked in the database: United States, United Kingdom, Japan, European Union/European Monetary Union (European Central Bank, Single Supervisory Mechanism and other European Union agencies), Standard Setting Bodies (SSBs), Australia, Canada, Germany, France, Italy, and Spain.

> 5 Around 50 percent of firms do not have sufficient cash buffers to cover their debt-servicing and operating costs as a result of the COVID-19 shock (Banerjee et al., 2020). Moreover, a simple balance sheet stress test based on pre-COVID-19 data suggests that non-financial companies in EMDEs may be vulnerable to liquidity and earnings shocks (Feyen et al., 2020).

6
# **Figure 4**

Patterns in Offshoring to Low Income Countries and Import Penetration by Industry <!-- Start of picture text --> Offshoring to Low Income Countries and Task Content Import Penetration and Task Content<br>.4 .5 .6 .7 .4 .5 .6 .7<br>Industry Share of Routine Jobs in 1983 Industry Share of Routine Jobs in 1983<br>.6<br>.4<br>.4<br>.3<br>.2 .2<br>0 .1<br>Percentage Point Increase in Employment in LIC '02-'83 -.2 Percentage Point Increase in Import Penetration '02-' 83 0<br><!-- End of picture text --> _Notes_ : Change in low wage employment is defined as the change between 1983 and 2002 in the share of employees hired by US based multinationals in low income countries using World Bank classification. Import penetration is taken from Bernard et al. (2006). Industry share of routine jobs is computed from information about individual job characterics using Department of Labor definitions. See data appendix for details.
**_-6-_**

# (2) The Earnings Function Method

This method is derived from Mincer (1974) and involves the fitting of a function specified as: where S is the number of years of schooling of individual i, and EX and EX<sup>2</sup> are years of experience and its square, respectively. Often weeks-worked or hours-worked are added as independent variables to this function as compensatory factors. We call the above a "basic earnings function." In this semi-log specification the coefficient of S **(O)** can be interpreted as the average private rate of return to one additional year of schooling, regardless of the educationa **l** evel this year of schooling refers to.

The earnings function method can be used to estimate returns to education at different levels by converting the continuous years of schooling variable (S) into a series of dummy variables, say PRIM, SEC, UNIV, to denote the fact that a person has completed the corresponding level of education, and that of course there are also people in the sample with no education in order to avoid matrix singularity.

Then, after fitting the following "extended earnings function": (4)
FIGURE 8 <!-- Start of picture text --> REAL VALUE OF M2 (MONTHLY 1960-89)<br>250.<br>159 . .. ,,... . ... ,.<br>I 0 . . . . . . . . . . . . . . . . .<br>1969 1965 197? 1975 19989 1995 1990<br>= M2R<br><!-- End of picture text --> The debt policy' of the government has much to do with the performance of the financial portfolio of the private sector, the reason being that the government has gradually become the "borrower of first resort" of the economy and as a consequence most of the financial assets of the private sector are either directly or indirectly the result of loans to the public sector (except, of course, the holdings of foreign exchange).

Tables 6 and 7 provide estimates of the domestic interest earning debt of the Treasury and the Central Bank. Practically all of the Central Bank debt is directly held by commercial banks under the form of compulsory reserve requirements ("depositos indisponiDles") or, at times, under voluntary holdings of Central Bank's CD's. The commercial banks, in turn, obtain their funds by raising interest earning deposits from the public. What we therefore observe in practice is a system in which most of the public's deposits at commercial banks are lent to the Central Bank and used to finance the fiscal deficit. Part of the deposits of the public may be lent to the private sector, but that amount has been gradually displaced in favor of lending to ttle Central Bank as it can be appreciated from data in Table 9 from where it follows that most of the lending capability generated by the public's demand for M2 is absorbed in the form of domestic liabilities of the Central Bank.

26
<!-- Start of picture text --> we Central African Rebel Events from 1990,2000 [te Ge<br>pe | 5,<br>pal i ‘<br>@® BattlesHeld territory ; ¢ oe= . éooooe foehme 4 £<br>@ Lost Territory 9a Pa é « Fe<br>@ Bases ‘Le e © 5¢ sac , §<br>® Rebel Presence = eo” Ay. ,<br>° Misc. ’ ¢ a ie ?. * i.<br>nd * ¢ + rs é te<br>§ ~ * ©<br>“ * A bet , i *.<br>ote ° :<br>e tec, 5<br>cE pn}<br>a *<br><!-- End of picture text -->

Figure 2: Events in ACLED (Armed Conflict Location and Event Data), 1990s, Central Africa
**<mark>Table 3: Repeat Migration in the RSE</mark>**

|**Financial**|Percent of RSE<br>workers<br>who returned in|Percent of return<br>workers<br>going back to|Percent of workers in<br>this season who have<br>worked in any previous RSE|
|---|---|---|---|
|**year**|the nextyear|the same employer|season|
|2007/08|52.2|86.1|0|
|2008/09|44.6|88.2|34.8|
|2009/10|52.3|86.6|59.5|
|2010/11|n.a.|n.a.|62.1|

<mark>Source: Merwood (2012)</mark> Table 4 provides a more recent update, looking at the RSE workers working in the 2012/13 season, which is the sixth year of the RSE. Overall, 31 percent of the migrants working in this season were in their first year of the program, 22 percent their second year, 16 percent their third, and 31 percent their fourth or higher season. The three main Pacific nations participating in the program all have worker breakdowns similar to these overall averages.

**<mark>Table 4: Seasonal Composition of RSE Workers in 2012/13 season</mark>**

|**Nationality**|**Number of**<br>|**Perc**|**entage of work**<br>|**ers by number o**<br>|**f seasons worked**<br>|
|---|---|---|---|---|---|
||**RSE workers 2012/13**|**1st Season**|**2nd Season**|**3rd Season**|**4th(or more) season**|
|Vanuatu|2829|34%|25%|13%|27%|
|Tonga|1573|31%|20%|17%|32%|
|Samoa|1137|28%|22%|19%|32%|
|Thailand|565|12%|21%|24%|44%|
|Solomon Islands|423|42%|23%|13%|22%|
|Indonesia|303|19%|12%|15%|54%|
|Malaysia|273|49%|14%|11%|26%|
|Kiribati|138|27%|29%|26%|19%|
|Others*|215|32%|16%|11%|41%|
|**Total**|**7456**|**31%**|**22%**|**16%**|**31%**|

<mark>Source: Ministry of Business, Innovation and Employment (2013). Analysis by Paul Merwood</mark> 14