rafmacalaba/data-use-mentions-extended huggingface dataset:
development has been concentrated in the capital cities (which are often the only cities of any appreciable size).

The next most significant factors for increasing household welfare in the FSU were the household's entrepreneurial activity, either through owning an enterprise or farming a private plot of land, and the household's link to the labor market, as proxied by the share of wages in total household income. However, the ranking of these factors was countryspecific. In Estonia, land ownership and owning an enterprise was significant but most important was the share of wages in household income, which can increase welfare by one-third. In Russia, land ownership was not significant,'<sup>6</sup> but the share of wages in household income and ownership of household businesses were important, raising household welfare 30 and 24 percent respectively. In Kyrgyz Republic, after location, ownership of a private plot had the largest effect on welfare, increasing it by 25 percent, while-household welfare increased 20 percent from increasing participation in the official economy (as captured by an increase in the share of wages in total household income).

In all three countries, the presence of an unemployed household head (or household member) was found to significantly decrease household welfare. In Estonia and Russia, specifications based on the number of unemployed demonstrated that adding an unemployed household member reduced household welfare per equivalent adult between

> 16 Ths is possibly due to recordinWmeasurement errors for this variable which is carried in the data set as hectares of land held by the household. When the raw data were examined, there were several improbable outliers. Unfortunately, removing these outliers did not significantly improve perfornance, nor did the subsitutation of a dummy variable for land ownership. The dummy variable did perform better in Russia than the number of hectares, so it was retained. Sonewhat similar measurement problems also plagued the Kyrgyz Republic data (the two surveys were conducted by the same consulting group), but the dununy variable was significant for Kyrgyz Republic. In Estomia,
If ETCR payments (W 91.9 trillion<sup>51</sup> in 2014) are included, ETP as a ratio of GDP is around 49 percent for 2014.

Figure 4 Credit, Debit, and Prepaid Card Transactions as Percentage of GDP <!-- Start of picture text --> 45<br>Prepaid Cards<br>40<br>Debit Cards<br>35 Credit Cards<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013<br><!-- End of picture text --> _Source:_ Authors’ aggregation of data from Bank of Korea (http://ecos.bok.or.kr).

_Notes:_ a. Debit card payments include check cards. b. Electronically traceable cash receipt payments are not included. c. Payments by all consumers, including not only wage and salary income earners but also self‐employed businesses.

Table 7 shows the ETP increase as compared to private final consumption expenditure. The proportion of ETP over private final consumption expenditure increased from 45.2 percent in 2005 to 88.9 percent in 2015. The absolute majority of private consumption, other than indispensable cash transactions, are now paid with ETPs in Korea, which brings the country near to having a cashless society.

> 51. ETCR payments were W 18.6 trillion in 2005, W 76.0 trillion in 2010, and W 91.0 trillion in 2014. Data retrieved from _Statistical Yearbook of National Tax_ (NTS 2006–2015).

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taken from PWT10.0.

**Infrastructure** is captured by an index, as often is the case in the literature (surveyed in Välilä, 2020). We therefore combine information on phone lines, mobile phones, internet coverage, availability of secure internet connections, and access to electricity from WDI. Since they have different sample coverage, we use the most available series (phone lines) to predict missing values of the second-most available (mobile phones), use those to predict missing values in the next series, etc. Each of those five updated infrastructure series is then Pearson standardized (i.e., transformed to mean 0 and standard deviation 1). Their weights in the final infrastructure index are the number of available original observations at each year, divided by the total number of original observations from all five series in the same year.

> 15 This shift transformation is consistent with the earlier approach in Araujo et al. (2016) and most common in the literature. We are aware of potential problems and that there are possible alternatives which researchers are free to explore using our data set. See also Karakaplan et al. (2020).

-8-
find that on average, as per capita expenditure increases by 10%, transport expenditures increase by 17% for all transport, by 10% for transport services, and by 20% for private transport. These findings imply that private transport is a luxury good whereas public transport is a normal good. We also document disparities between and within countries, for which more work needs to be done to understand how geographic, social, and cultural differences affect transport choices. Given that most of the increase in carbon emission in low- and middle income countries is expected from first-time acquisition of motor vehicles, we estimate an ordered logistic regression using vehicle ownership data at the household level. In line with the transport literature, we identify a non-linear relationship between the probability of having a car and income. We finally explore the relationship between income and car use by using a panel of country-level data on traveled distance per car. Standard household surveys do not include questions on traveled distance at the household level.

Turning to the last part of the paper, we simulate the implications of rising incomes on transport carbon footprints and carbon inequalities. We focus on vehicle ownership and use as 3
coverage that allows obtaining results in 4 of the 5 regions, in which the national territory is divided: Atlantic, Eastern, Central, Pacific and finally the capital cities of departments of the Amazon and Orinoco. Additionally, the survey is representative of the 32 capital cities of the country's departments and the following 6 prioritized municipalities: Rionegro, Soledad, San Andres de Tumaco, Barrancabermeja, Buenaventura, and Yumbo.

## **_Administrative Data_**

Additional information was required to complement the household survey information. Some of the sources used in the development of the model are the following<sup>28</sup> : (i) DIAN administrative data: Amount in returns for each of the income types and exempt income; (ii) Utilities Information System; (iii) Energy and gas rate bulletins; (iv) Investment Project monitoring system; (v) School Meals Program Report for January-December 2017; (vi) The information available in the Pension Fund; (vii) Local Fiscal Account; (viii) Information from the General System of Transfers; (ix) Resolution 6411 of 2016; (x) The tax statute in force in 2017 which is Law 1819 of December 29, 2016; (xi) The 2010 input-output matrix on 2005 basis<sup>29</sup> ; (xii) The matching between the products of the ENPH, the CPC 2.0 and the national accounts; and (xiii) The reference prices of alcoholic beverages and tobacco products for the year 2017.

# ii. Methodology

For the incidence analysis of each one of the fiscal interventions, and the impact on poverty and inequality of each one of the taxes and the social spending of the government, the Commitment to Equity methodology proposed by Lustig (2018) was followed. This method calculates per-capita income before and after each of the tax interventions, as shown in Figure III-1. More precisely, the following income definitions are used for each household:

- _Market income:_ This income
Column 3 shows that the plot size for cultivation is also smaller for commercial producers (Panel A). In contrast, we find no differences for subsistence crops (Panel B); this offers further evidence that the land fragmentation of the Civil War period still exists today. This result is confirmed in Column 4, which shows that the share of land owned by commercial farmers is much smaller within former FMLN areas. In Table D.16 in the Appendix, we also look at the Simpson index to measure land fragmentation. Consistent with these results, we find more land fragmentation in former FMLN areas relative to nearby locations.

# _Agricultural productivity_

Next, we analyze whether changes in economic production during the period of guerrilla control led to changes in agricultural productivity today. Table 7 presents the results of the spatial RD analysis for the total extension of land cropped (Panel A), the share of the land harvested (Panel B), and the actual crop yield in 2005 (Panel C). Consistent with qualitative evidence, we find that the production of export crops such as sugarcane dropped significantly. Moreover, the measures of productivity are much lower in later years in former guerrilla areas. The estimates confirm that actual crop yield was lower for all crops within controlled areas.

In the Appendix, we explore differences in the occupations of employed individuals currently living in former guerrilla areas relative to other areas. Unsurprisingly (and in line with previous results), we find that individuals in these areas work disproportionately in agriculture (specifically subsistence agriculture) but less in other occupations known to create more value added. These include, for example, agro-based industry and services (See Table D.17 and Figure D.15). These occupational differences between treated and control areas are maintained even as we increase the bandwidth around the discontinuity from two to 18 km, suggesting these differences are not affected by the location or creation of urban centers close to the discontinuity (See Figure D.16).

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On average, (self-reported) satisfaction with life in the transition region has remained constant compared to 2006 levels (recorded in LiTS I), but this reflects different dynamics across countries. Satisfaction with life dropped in 15 countries and increased in 13. These changes are correlated with the severity of the 2009-10 economic contraction. The countries where average life satisfaction levels declined the most - Latvia, Lithuania and Romania - experienced severe recessions. Conversely, average satisfaction with life increased in countries that had higher growth - for example, Azerbaijan, Mongolia and Poland. This is consistent with the view that income is an important determinant of satisfaction.<sup>16</sup> But recent growth (or lack of it) is insufficient to explain differences in the evolution of satisfaction with life across countries, as satisfaction _increased_ in many south-eastern European countries compared to 2006 levels. In terms of individual comparisons - within countries, and after taking into account such factors as income, status, employment, health status, education and so on - those who were subjectively affected by the crisis report significantly lower satisfaction with life than those who were not, and the greater the severity of the impact the lower their satisfaction with life. We show in table 6 results of OLS, ordered probit and probit regressions (with country fixed effects) of life satisfaction on crisis impact and individual characteristics. Consistent with the literature on the determinants of life satisfaction, we find that relative income (measured as the respondent's assessment of his position in a ten-step income ladder) is strongly correlated with life satisfaction. In fact, when relative income (with respect to others and in relation to the past) is included in the regression, the positive association between absolute income and life satisfaction becomes non-significant. Good health, higher education and asset ownership are also positively correlated with higher life satisfaction.

# **4.3. Perceptions of government performance**

The severity of the downturn was associated with more negative perceptions of the evolution of government performance (
and structural deep-rooted social problems, respectively.<sup>10</sup> This section estimates STMs using unbalanced quarterly data for the period 1960:Q1 through 2019:Q4 for six Latin American countries (Argentina, Brazil, Chile, Colombia, Mexico, and Peru) and 17 developed countries (Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, Netherlands, Norway, Portugal, Spain, Sweden, United Kingdom, and United States).<sup>11</sup>

## 2.1 Methodology and identi...cation strategy

We use the well-known Blanchard and Perotti (2002) identi...cation strategy that imposes timing restrictions by assuming that while government spending changes are allowed to contemporaneously a¤ect economic activity (i.e., within the quarter), it takes the government at least one quarter to

> 8 This paragraph signi...cantly relies on Galeano et al. (2021). See their paper for a detailed discussion of social transfers basic trends, composition, rigidity, and business cyclical aspects of social transfers.

> 9 In fact, the contemporaneous correlation between quarterly changes of real GDP and real unemployment insurance spending is -0.23 (statistically signi...cant). Pennings (2020) also provide some similar evidence for the United States.

> 10 In fact, the contemporaneous correlation between quarterly changes of real GDP and real pensions and family programs spending is 0.09 (statistically not signi...cant).

> 11 See Appendix 1 for description of data de...nitions, sources, and time coverage for each country.

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there are more crisis years experience a decline in inequality. The size of the effect indicated by the point estimate is not huge: a five-year long crisis is estimated to lower the log of Gini by 0.02, equivalent at the sample mean value to moving the Gini from 0.376 to 0.368.

Lopez’s finding can be seen as broadly consistent with the Indonesian evidence, and the other cases mentioned where Gini has declined around the time of crises.

By taking the number of crisis years in a given five-year period as the crisis variable, he is implicitly focusing on short and medium-term impacts of the crisis denouement. However, this variable entails heavy reliance on the accuracy of the crisis end-dates, whereas it is known that these dates are especially uncertain, essentially because the process of resolving failed financial institutions is a long-drawn-out one. Thus, in respect of several crises, no end-point is shown despite the fact that the crisis has been fully resolved. (Lesotho is a good example: taking the lack of an end-date as an indication that the crisis shown as beginning in 1988 was still running in 2002 would be misleading.) Furthermore, the alignment of the Gini and crisis measures is somewhat problematic, given that some of the Ginis are measured early within the five-year period and possibly before even a crisis that scores 0.6 or 0.8 on the index got under way.

These timing issues are likely to bedevil any work in this area; noting them does not detract from the useful exercise carried out by Lopez, but points to the desirability of looking further at the dynamics of the Gini around the known crisis dates.

It is worth mentioning here that another major difficulty in mechanically applying the crisis database is that many of the countries without reported crises are known to have rather dysfunctional controlled banking systems, but ones which have managed to remain solvent or nearly so.

_Gini coefficients before and after crises_ (a) Long-term effects Looking first at long-term effects, it is of interest to compare
# **6.1 Mapping Global CO2 Emissions**

Our econometric exercise measures marginal effects on atmospheric concentration anomalies from CO2 emissions by industry, fires, and non-industrial population-related sources. Concentration anomalies are measured in parts per billion; we convert to emissions volumes using a global adjustment. With 2019 as the benchmark year, Ritchie and Roser (2020) estimate global CO2 emissions of 35.4 gigatons (Gt) from fossil fuels and industrial sources. Bailis et al. (2015) estimate global CO2 emissions of 1.1 Gt from traditional woodfuels that are still used for heating in many locations. Adding the two yields a total estimate (GCO2) of 36.5 Gt.

Using the estimated model coefficients in Table 1, we predict industry- and population-related concentration anomalies for each of the 266,884 grid cells in our global database. The industry component (ICO2) for each cell is the sum of predicted effects for industry emissions in the cell and wind-displaced industry emissions from neighboring cells. The population component (PCO2) is the sum of predicted effects for heating degree days, cooling degree days, and income per capita, adjusted by the subway impact multiplier. We sum across cells to produce the global aggregate concentration anomaly:
section. Our preferred results are in column 7, where the STRI, DTRI, and WGI variables all have statistically significant coefficients. While their signs indicate that they all have the expected impact on bilateral trade, it is important to be able to compare them in terms of quantitative importance. To do that, we convert them to AVEs for each country pair (since the STRI varies bilaterally due to EU preferential data), then take the simple average at the country level.

Results are in Table 5. The first three columns report AVEs. The fourth shows the total effect of measures related to TFiS (the sum of columns two and three) as a percentage of the AVE related to policy restrictiveness (column 1). Focusing on the last column, we can see that for all countries in the sample, the trade cost impact of measures related to TFiS is much higher than that due to straightforward policy restrictiveness. The difference is at least 1.5 times and is frequently much higher. The evidence therefore strongly suggests that policies that do not affect traditional market access, but which nonetheless increase transparency and improve regulatory governance in a non-discriminatory fashion, would have great potential to reduce trade costs in services and thereby boost bilateral trade. Figure 3 shows that in particular many middle-income countries would benefit from improving institutional governance as proxied by the WGI given that their AVE in this area is comparatively big.

The final part of the empirical exercise consists of re-estimating the model from Table 4 column 7 using sectoral data. This exercise is necessarily approximate, as the sectoral definitions used to construct the STRI are different from the definitions used to record and harmonize the trade and production data. We can therefore only identify four sectors where there is a close enough correspondence to make the analysis meaningful: finance (using the commercial banking STRI), computer services (using the computer services STRI), transport (using the maritime transport STRI), and telecom (using the telecom STRI).

Results are in Table 6. There is
the financial fragility of many household businesses,<sup>24</sup> the observed rapid recovery indicates the resilience of this sector. More broadly, the result also speaks to the success of the Vietnamese government’s containment strategy in introducing early and effective domestic and international restrictions to suppress infections. By being able to contain the disease, the country was able to transition quickly to the reopening phase.

# **4.3 Heterogeneities by sector and business revenue size**

We next analyze the impact of the pandemic on household business activities and earnings across sectors and firm sizes. Table 3 shows that household businesses operating in the services industries were the most affected from the pandemic. These results are consistent with sector-specific quarterly output growth in Vietnam in 2020 A.5. The most detrimental impacts are observed in Transportation and Accommodation, Restaurants, and Entertainment (ARE) (panel A). There was an average 164-percent increase in the number of ARE firms filling for suspension, and 108 percent increase in business closure (percentage-change statistics shown in square brackets). The average work

> 21The government imposed a national lockdown on April 1, 2020. The lockdown was lifted after 22 days, on April 22, 2020. There were scattered targeted lockdowns imposed in different parts of the country since April 2020 in response to local outbreaks.

> 22Monthly income (column 2) is reported with a one month lag. For income, the LFS asks respondents for income earned during the last month. Thus, the “May X Post Covid” estimate reflects the income effect of the pandemic in April 2020.

> 23Data on the daily government lockdown stringency index (Figure A.3) was obtained from the Oxford COVID-19 Government Response Tracker (OxCGRT). Data on mobility (Figure A.4) is obtained from the Google’s COVID-19 Community Mobility Reports.

> 24This evidence is consistent with the finding in Bartik et al. (2020) for small businesses in the United States.

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rather muted. Calling for more research, she tentatively concluded that this was because of other distortions in factor markets.

More recent exercises have had more liberalizations to consider and better data, and although they show mixed results the general tendency is still towards small effects. For example, Rama (1994), applying a model of monopolistic competition to a panel of 39 sectors in Uruguay over 1979-86 found a significant positive relationship between protection and employment in manufacturing, but no significant effects on real wages. Reducing the protection rate within a sector by 1 percent led to an employment reduction of between 0.4 and 0.5 percent within the same year. Harrison and Hanson (1999) suggest that an implication is that during the years concerned the labor market in Uruguay was fairly competitive, with significant employment reallocation between sectors after the reforms.

Revenga (1997), using plant-level data for Mexico, found a no reduction in overall firmlevel employment following reductions in tariff levels, but that reductions in quotas had a significant but relatively small impact: a reduction in quota coverage from 90 percent to 10 percent of output was associated with a 4-6 percent reduction in output and, via that, a 2-3 percent decline in employment. Tariff reductions appeared to affect wages, however, because Revenga concludes, tariff liberalization eroded rents and thus had no effect on employment and output decisions. Similarly small employment effects elsewhere in Latin America are reported by, for example, Marquez and Pages-Serra (1998) for Latin America and the Caribbean in general, Levinsohn (1999) for Chile and Moreira and Najberg (2000) for Brazil.

Milner and Wright (1998) explore industry level data on Mauritius and find a slightly more encouraging response to liberalization. After an initially adverse wage effect they find fairly strong long-run growth in wages and employment in the exportables sector (mainly of female labor producing clothes). But they also find, surprisingly, growth in the import-competing sector, which they attribute to Mauritius’ overall strong economic performance. In fact, Mauritius