rafmacalaba/data-use-mentions-extended huggingface dataset:
# **6. Summary and Conclusions**

In this paper, we have used new data from Mexico and Brazil to analyze relationships linking economic development, the size distribution of manufacturing plants, and exposure to industrial pollution. Prior work in this field has generally been limited to water pollution and to medium size plants, owing to lack of data. In contrast the present study examnines air pollution, and emissions of airborne suspended particulates in particular, and encompasses small plants (with 1-20 employees) as well as medium, and large plants (with more than 100 employees).

The data for Mexico indicate that emissions per employee decline with plant size in the aggregate and also within most 3-digit sectors. With the exception of a few sectors (such as basic foods, petroleum products and industrial chemicals) the small plants are the more pollution intensive per employee (and presumably per unit of output).

Some 69 percent of Brazil's municipios report having some manufacturing within their boundaries. Among these industrial municipios, the share of the standard dirty six industries falls continuously with increases in the mean wage of household heads (which is taken as a proxy for level of economic development). These same data also show that the share of the smaller plants (no matter whether measured by the fraction of employment or proportion of plants) is greater among the low wage municipios. Conversely, the large plants are relatively more important in the high wage municipios.

At first glance, these findings seem to imply that the pollution has a 'regressive' bias and that most of the damage is inflicted by small factories. However, the introduction of scaling variables reverses this conclusion: Almost all the pollution damage is in the highest-income areas, and most of it is generated by large plants. The reversal occurs because production volume, plant scale and population density are highly correlated with economic development in Brazil and these effects are not offset by Large power plants are a major source of particulate pollution, and including them would obviously increase estimates of deaths
# **4. Robustness Tests, Anticipation Effects and Heterogeneity**

# **4.1 Robustness Tests**

This section presents an additional set of results to investigate whether our main results (presented in Table 1) are robust to additional controls or ways of identifying outliers, and also compares to the literature. The result tables are in Online Appendix 4.

**Alternative treatment of outliers and influential observations.** This subsection tests the robustness of our findings to alternative assumptions about winsorizing growth data and excluding influential observations. Online Appendix Table A4.1 Column 1-6 starts this analysis by re-estimating Table 1 including both influential observations (EE2011 and HR2016 in country-level regressions) and not winsorizing any growth data. Even in this case, we find little evidence of large multipliers (>1), with the exception of IV country-level results for CEE countries, which are now very large (2.36) and significant at 5%. This large and significant coefficient is due to the inclusion of one influential observation (specifically Croatia 2016); in Column 8 we remove both influential observations, and the CEE multiplier becomes insignificant (because the first stage is now weak). Including influential observations and not winsorizing also makes multipliers for OLS CEE (subnational) and All Europe (country level) insignificant at the 5% level (they were significant in Table 1). The insignificance of the former is due to the larger standard errors/smaller coefficient with winsorized data, and the insignificance of the latter is due mostly due to the inclusion of influential observation Estonia 2011 (excluding influential observations makes the latter significant in column 7).

An alternative approach is to winsorize at a different level. In our default results, we winsorized at the 90% level for the more-volatile NUTS2 data, and at the 98% level for the country-level data. Reversing this in Online Appendix Table A4.2 so NUTS2 data are winsorized at 98%, and country-level data are winsorized at 90%, generates broadly similar results
|(0.240)|(0.249)|(0.251)|(0.251)|(0.250)|
|log maintenance|-0.08|-0.14|-0.13|-0.14|-0.14|
||(0.183)|(0.186)|(0.185)|(0.185)|(0.184)|
|_N_|20544|20544|20544|20544|20544|
|Firms|5226|5226|5226|5226|5226|
|frm and year FE|Yes|Yes|Yes|Yes|Yes|
|log employment|No|Yes|Yes|Yes|Yes|
|industry-specifc trends|No|No|Yes|Yes|Yes|
|frm-specifc trends|No|No|No|Yes|Yes|
|imported inputs|No|No|No|No|Yes|

NOTES: IV-FE regressions of (log) employment on an exporter dummy. Column (1): firm fixed-effects and year fixed-effects; column (2): adds log total employment (firm size); column (3): adds controls for industry-specific trends (i.e., interactions between year dummies and industry dummies); column (4): adds initial conditions to control for firm-specific trends; column (5): adds the share of imported inputs. Data are from the Encuesta Nacional Industrial Anual (National Annual Industrial Survey), Chile 2001-2005.

21
upon eBird’s focus on avian life by including observations of all flora and fauna. Notably, unlike eBird, iNaturalist does not contain survey effort data, which renders it unsuitable as a source of wildlife data for our study. Nonetheless, the geolocated and timestamped observations in the iNaturalist database enable us to use it as a proxy for visits to protected areas.

Given that iNaturalist does not represent a comprehensive record of tourist visits, we configure the dependent variable as an indicator that equals 1 if any iNaturalist observations occur within a protected area in a specific year, and 0 otherwise. Consequently, the dependent variable in this analysis offers an extensive margin measure of whether any iNaturalist user visits took place.

There are no missing values in the data underlying the regression visualized in Figure S8. If a protected area receives no iNaturalist visits in a given year, the dependent variable simply registers as 0. The data we construct span the years 1998 to 2022. There are 3,625 observations because we have 145 protected areas (22 treatment group and 123 control group areas). The dependent variable’s mean value among control areas is 0.462, which means that in 46% of area-years, at least one iNaturalist observation was recorded within the boundaries of the protected area during that year.

While the majority of iNaturalist data is likely recorded by tourists, it is important to note that protected area staff can also upload wildlife observations to iNaturalist. If AP staff are more likely to upload observations than their counterparts at other protected areas, this would upwardly bias our estimate of the effect of AP management on tourist visits. To test the robustness of our results to the potential inclusion of protected area staff in iNaturalist data, we implement the following approach. We exclude all data uploaded by any iNaturalist user who records an observation inside the same protected area between 30 and 365 days from their last visit, as such users could plausibly be protected area staff. Reconstructing the panel data as per our primary specification results in the same
Does State Ownership Have Limits in Romania?

An Assessment of Firm Performance and Market Outcomes before and durin the COVID-19 Crisis<sup>1</sup> <u>g</u> Seidu Dauda,<sup>2</sup> Georgiana Pop,<sup>3</sup> and Mariana Iootty<sup>4</sup> Keywords: State-owned Enterprises , Competition, Firm Performance, Market Structure, Subisidies, COVID-19 JEL codes: L32, D41, L25, L22, H25

> 1 Comments to earlier versions of this paper were provided by Ekaterina Vostroknutova, Lead Economist, and Mary C. Hallward-Driemeier, former Senior Adviser, World Bank. The team is grateful to the Ministry of Finance of Romania for providing access to the firm-level data covering enterprises of all sizes from 2011 to 2020. This paper has been prepared for the World Bank Business of the State (2023): <u>https://www.worldbank.org/en/publication/business-of-the-state</u>

2 The World Bank, sdauda@worldbank.org.

> 3 The World Bank, <u>gpop@worldbank.org</u> (corresponding author).

4 The World Bank, <u>miootty@worldbank.org.</u>
_0.66_|
|Generalized entropy measure (e=3)|75.0|69.7|7.5|72.1|76.4|71.0|
||_48.4_|_44.4_||_46.5_|_48.7_|_45.8_|

_Note:_ Figures in _italics_ are the standard errors corrected for sample design effect. The figures in square brackets give the relative contribution of the three shocks to the total impact (not calculated for the inequality measures for which these contributions are not additive. _Source:_ Calculated from 1998 APIS data.

31
|39|40|40|158|

# **3.4 Key Measures of Corporate Vulnerability**

This section describes two key measures that will be used below in corporate stress testing and bank stress testing.

The proportion of firms in the industry that find it difficult to meet their interest payments with current earnings, i.e. for which interest coverage ratio falls below a certain threshold (such as below 1, or below 0.75) is one metric of sectoral level vulnerability. The higher proportion of firms in the industry that find it difficult to meet their interest payment, the more vulnerable that industry is, and the higher risk the banks with higher exposure to that industry will bear.

Table 7 reports proportion of firms with interest coverage below 0.75 by sector in 2006 (the latest year with corporate data) along with the number of firms in each sector. (Table 7 simply replicates several rows from Table 3 above). The transportation sector has the largest proportion of firms with industry coverage below 0.75: 30% of all firms, followed by industry, 27%, services and construction at 22%.

16
What is needed are instruments correlated with contractual choice, but uncorrelated with unobserved attributes of the plot, and which also vary across plots within the same household. Such variables are typically hard to come by in farm household surveys. In our data set, though, tenant cultivators report the characteristics of their landlord, including among other things his total landholdings. So, we have data on the plot owner’s landholdings regardless of whether the plot is leased in or owner-cultivated. Since farmers endowed with relatively more land tend to lease out a greater proportion of it, a given plot is more likely to be leased in if it belongs to a larger landowner. Our principal instrument for ∆Lc = Lci in equation 5 is therefore ∆Ao = Aoi − Aoj = Aoi − Acj, where Aoi denotes the total landholdings of the owner of plot i. For owner-cultivated plots (Lci = 0) this will just be the landholdings of the cultivator himself (i.e. Aoj = Acj).<sup>16</sup> Identi fi cation also requires that E [∆Ao∆εc] = 0. In other words, the difference in the total landholdings of the owner-cum-tenant and the landlord of his leased plot should not be correlated with differences in the unobserved attributes of the owner-cultivated and leased plots. We show why this should be the case in the next subsection.

Using the same argument, additional instruments can be constructed from other assets of the landowner (tubewells and tractors) as well as by allowing the impact of relative land endowments on the leasing decision to vary by the landholdings of the tenant-cultivator (interacting ∆Ao with the land endowment of the cultivator Ac). Note that the direct effect of the cultivator’s landowership on his input use has already been removed from equation 5 along with the fi xed effect νc.

# 3.2 Adverse selection and landholdings

To reiterate, our identi fi cation strategy requires zero correlation between the unobserved quality of a plot and the landholdings of its owner
statistics in parentheses:<br>* p<0.05 ** p<0.01 *** p<|0.001|||

Figure 7 displays the global distribution of the value-added component. Large positive outliers (colored light and dark red) denote areas where S5P CH4 anomalies are much higher than would be predicted with knowledge of field extents and EDGAR emissions alone. The converse is true for large negative outliers.

> 12  It may also be useful to interpret this as a three-step process.  The first step performs separate regressions of S5P CH4 anomalies and EDGAR emissions on IFPRI field extents.  The second step computes residuals for both regressions.  The third step regresses the S5P residuals on the EDGAR residuals and computes the residuals from that exercise.  These residuals incorporate the S5P CH4 anomalies that are not explained by EDGAR residuals, after both variables have been purged of field scale effects.

16
Small Open Economies with Scale Economies and Imperfect Comp,” _The American Economic Review_ , Vol. 74 (5), December, 1016-1032

- Harris, Richard and Catherine Robinson (2004), “Productivity Impacts and Spillovers from Foreign Ownership in the United Kingdom,” _National Institute Economic Review_ , Vol. 187: 58-75

- Harrison, Glenn H., Thomas F. Rutherford and David G. Tarr (1993), "Trade Reform in the Partially Liberalized Economy of Turkey," _The World Bank Economic Review_ , May.

- Harrison, Glenn H., Thomas F. Rutherford and David G. Tarr. (1996), `A` Increased Competition and Completion of the Market in the European Community: Static and Steady-State Effects, `@` _Journal of Economic Integration_ , 11(3), September 1996, 332-365.

- Harrison, Glenn H., Thomas F. Rutherford and David G. Tarr (1997), “Quantifying the Uruguay Round,” _Economic Journal_ , Vol. 107 (444), September, 1405-1430.

- Harrison, Glenn H., Thomas F. Rutherford and David G. Tarr (1997a), `A` Economic Implications for Turkey of a Customs Union with the European Union, `@` _European Economic Review_ , 41(3-5), 861-870.

- Harrison, Glenn H., Thomas F. Rutherford and David G. Tarr (2002), “Trade Policy Options for Chile: The Importance of Market Access,” _World Bank Economic Review_ , Vol. 16, Number 1.

- Harrison, Glenn H., Thomas F. Rutherford, David G. Tarr and Angelo Gurgel (2004) “Trade Policy and Poverty Reduction in Brazil,” _The World Bank Economic Review_ , Vol. 18, 289-317, 2004.

- Heston, Alan, Robert Summers and Bettina Aten (2006), Penn World Table Version 6.2, Center for International Comparisons of Production, Income and Prices at the University of Pennsylvania, September.

52
<mark>Third, a variety of cultural and historical factors may affect women’s response to childcare services. Klasen (2019) notes that despite global expansion of women’s education and rapid fertility declines, the trends in FLFP rates have been heterogeneous across developing countries. Klasen (2019) attributes this heterogeneity at least in part to d</mark> eep drivers of cultural differences. It is important to note, though, that cultural norms can change with institutional support, as illustrated, for example, in a study of Arab communities in Israel (Schlosser 2011). Similar to the populations of neighboring Arab nations, Israeli Arab women had a very low LFP rate. Once these communities had childcare centers, though, maternal LFP increased by 8 percentage points, an increase of almost 50 percent from the baseline rate of 17 percent.

<mark>Thus, we may expect the maternal labor market response to the availability of childcare in lowand middle- income countries to differ from the well-established positive relationship found in high-income countries.</mark> While country-level studies of impacts of childcare on maternal labor market outcomes abound, there have been few attempts to systematize this evidence from lower- and middle-income countries. Initial review of the evidence by Todd (2013) confirms the general finding that increasing availability and lowering the price of childcare increases maternal labor force participation (MLFP) in developing and transitioning economies. However, her review is part of a larger work on family-friendly policies and it is limited to the then scarce evidence on the topic of childcare, with only 3 studies having a causal inference research design. Burgeoning studies on childcare and maternal employment with more-plausibly-causal designs have been implemented since her 2013 research.

We have more overlap with the meta-analysis done by Harper, Austin, and Nandi (2017) —9 of the 13 studies they include—who provide an estimate of aggregated effect sizes of childcare and MLFP in lower- and middle-income countries. Limiting their meta-analysis to the 9 quasiexperimental or randomized studies, they
between establishments in the group that proxies for the response sample and the group of establishments that proxies for the non-response sample.

**Response bias.** To assess the relevance of response bias, we conducted a parallel pilot in Kenya where we re-interviewed 100 randomly selected establishments with a short version of the questionnaire. For those establishments, we randomly selected three business functions and asked about the presence of the relevant technologies. We estimated a probit model to assess the likelihood of consistent answers between the original and the back-check interviews, controlling for establishment-level fixed-effects. Reporting the use of a technology in the back-check interview is associated with 80.6% of the likelihood of reporting the use of the same technology in the original interview. Conversely, reporting that a technology is not used in the back-check interview, is associated with a 70.7% likelihood of not being reported in the original survey. These estimates do not differ between establishments of different sizes.<sup>13</sup>

**Validation using external sources.** We evaluate the quality and reliability of the data collected by comparing it to external sources in Korea (KED) and Brazil (RAIS). We focus on variables related to establishment size, productivity and technology. Table A.24 shows that the weighted sample averages of the labor variables in the FAT data (number of workers, average wages, share of college workers, share of low- and high-skill workers) are not statistically different from the averages in the universe of firms from the RAIS dataset. In the Brazil matched establishments, we find a strong correlation between FAT measures of log valueadded per worker and the log of average wages from RAIS (See Table A.23). In the Korean matched establishments, we find very high cross-establishment correlations (above 0.93) in the log levels and growth rates of sales and employment, as well as in log labor productivity (0.73).<sup>14</sup> Additionally, the average
Guido Lorenzoni, Ludwig Straub, and Ivan Werning, “Macroeconomic Implications of COVID-19: Can Negative Supply Shocks Cause Demand Shortages?,” Working Paper 26918, National Bureau of Economic Research April 2020.

Hall, Robert E. and Charles I. Jones, “Why do Some Countries Produce So Much More Output Per Worker than Others?,” _The Quarterly Journal of Economics_ , 1999, _114_ (1), 83–116.

and Marianna Kudlyak, “The Inexorable Recoveries of US Unemployment,” CEPR Discussion Papers 15646, C.E.P.R. Discussion Papers January 2021.

Hanushek, Eric A. and Ludger Woessmann, “The economic impacts of learning losses,” 2020, (225).

, Jens Ruhose, and Ludger Woessmann, “Knowledge Capital and Aggregate Income Differences: Development Accounting for US States,” _American Economic Journal: Macroeconomics_ , October 2017, _9_ (4), 184–224.

Hanushek, Eric and Ludger Woessmann, “Do better schools lead to more growth? Cognitive skills, economic outcomes, and causation,” _Journal of Economic Growth_ , December 2012, _17_ (4), 267–321.

- Harsanyi, John C., “Cardinal Welfare, Individualistic Ethics, and Interpersonal Comparisons of Utility,” _Journal of Political Economy_ , 1955, _63_ , 309–309.

- Heckman, James and Richard Robb, “Using Longitudinal Data to Estimate Age, Period and Cohort Effects in Earnings Equations,” in W.M. Mason and S.E. Fienberg S.E., eds., _Cohort Analysis in Social Research_ , Springer, New York, NY, 1985, pp. 137–150.

- Hendricks, Lutz, “How Important Is Human Capital for Development? Evidence from Immigrant Earnings,” _American Economic Review_ , 2002, _92_ (1), 198–219.

- and Todd Schoellman, “Human Capital and Development Accounting: New Evidence from Wage Gains at

- Migration,” _The Quarterly Journal of Economics_
the communist military force. In a placebo test, we also investigate casualty rates for the non-communist Kuomintang (Nationalist Party, or KMT), ruling party of China after 1927. There we do not find a relationship between warlord-era famines and subsequent KMT casualties.

Exactly why did famines push peasants into the arms of the Communists? Using land ownership surveys between 1918 and 1921, we find that famines raised landless tenancy: famine occurrence raised the fraction of tenant households by 1.7 percentage points in the following year. We also show that as tenant households rose, there was an increase with the 4
|0.36|0.13|0.15|0.44|

_Source_ : Authors’ calculations based SEAD data.

_Note_ : The regressors for the outcome are dummy variables taking the value of one if the skills count of a skills category of an occupation is above the mean. The 2017 Malaysian labor force survey does not report any workers in one of the 127 occupations from our occupational skills profile (paramedical practitioners, ISCO code 224).

_*p < 0.1 **p < 0.05 ***p < 0.01_

# **6. Complementarities among Digital, Cognitive, and Socioemotional Skills: The Case of Malaysia**

Cognitive and socioemotional skills correlate more strongly with basic digital skills than with intermediate and advanced digital skills in Malaysia. Basic digital skills correlate highly with all three subsets of cognitive skills and with one socioemotional skill in Malaysia. The correlation coefficient between basic digital skills and thinking, communication, organization (cognitive), and emotions (socioemotional) is around 0.5 (Table 7). Basic digital skills are not correlated with relationships or personal growth in Malaysia. In contrast, intermediate digital skills correlate with thinking and 26
The BVC proprietary data consist of all the transactions in Colombian stocks during the 2006-2017 period. The data contain detailed execution information, including a time stamp (to the second), price, direction, quantity, and the broker routing the trade.<sup>9</sup> Each transaction record has a unique investor identification number. In addition, the BVC classifies investors into three broad groups – domestic individuals, domestic institutions, and foreign investors.<sup>10</sup> The stock exchange further classifies domestic institutions into mutual funds, pension funds, non-financial corporations, brokers trading on their own accounts, and other institutions.

To obtain a more detailed classification of foreign investors we proceed as follows. For the exclusive purpose of this project, the BVC discloses the name of foreign institutions involved in the transactions. We search manually on the web and cross-reference the names with Factset Ownership – a database with extensive global coverage on institutional investors – and classify each foreign investor into one of the following groups: Active mutual funds, passive mutual funds, pension funds, exchange-traded funds (ETFs), hedge funds, government institutions (e.g., sovereign wealth funds and central banks), and investment firms. Other investors include foreign individuals, trust funds, and private endowments.

The BVC database has about 17 million records during our 12-year period. Domestic institutions represent over half of the traded value in domestic stocks. Consistent with international trends in cross-border portfolio investments (IMF, 2017), the presence of foreign investors in Colombia increases over time. The number of foreign investors rose from 324 in 2006 to almost 4000 at the end of 2017 and the share of total traded value by this group increased from 4.5% to 31.8%. In addition to the generalized growth of foreign flows to emerging markets after the 2008 global financial crisis, there are two key related events in Colombia in the period that contribute to explain the surge of foreign investors: (i) Colombian sovereign debt