rafmacalaba/data-use-mentions-extended huggingface dataset:
limitations. Endogeneity concerns are difficult to alleviate given the cross-sectional nature of the data. Furthermore, the survey data do not contain information on whether the family owners are the founders, or descendants of the founders, which has been found to be important in the literature (Villalonga and Amit, 2006). Regardless, this study makes an important contribution by highlighting the role that the external operating environment can have in the relationship between family management and management practices. The key implication is that the business environment matters, and for some cases it may be easier to address some of these concerns than to change the management or ownership structure of family firms.

12
<br>60.0<br>50.0<br>40.0<br>30.0<br>20.0<br>10.0<br>0.0<br>2017 2018 2019 2020<br>Old Model New Rescaling New No Rescaling<br>Old Model New Rescaling New No Rescaling Old Model New Rescaling New No Rescaling Old Model New Rescaling New No Rescaling Old Model New Rescaling New No Rescaling<br><!-- End of picture text --> Source: Own estimations based on SEDLAC.

# **Distributional effects**

This section presents the performance of the considered models' variations along the whole income distribution. The Growth Incidence Curves (GICs) for each country/year are calculated using the SEDLAC actual income data and the simulated income vector for each variation. The squared errors correspond to the difference in the annualized growth rate at each percentile of the income distribution. The MSEs correspond to the average squared error (MSE) across countries over time. Figure 7 presents the results. It is worth noting that these results do not include information on the 1-5 and 96-100 percentiles due to the high dispersion in the tails of the actual distribution.

Source: Own estimations based on SEDLAC.

Results show that differences between the proposed variations of the model are more significant in this case (a higher magnitude in the units of the vertical axis), and that is because more cut-offs are 22
002|-0.004|
||(0.004)|(0.005)|(0.006)|(0.003)|(0.005)|(0.006)|
|Constant|5.988***|6.389***|-0.401|4.722***|5.406***|-0.6836|
||(0.285)|(0.682)|(0.676)|(0.272)|(0.683)|(0 .671)|
|Includes Ability|No|No|No|Yes|Yes|Yes|
|Observations|8292|4111|12403|8292|4111|12403|

Notes: Standard errors are clustered at the community level (*** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1). A role model is someone who was born in the respondent’s district but is within _±_ 3 of respondent’s father age and belongs to top 50 percent education level within that age group of that district. Rural and Urban are defined based on location of birth. If the respondent was born in a big city or town, it was considered as Urban, and if the respondent was born in a village, it was considered Rural. Cognitive ability index is expressed in percentile; it is constructed taking the first principal component of Raven Test and two memory memory tests net of age and age square. Ability controls include cognitive ability index and its square.

39
therapy for patients with respiratory failure. Therefore, it is believed that when hospital care cannot be accessed, the case fatality rate (CFR) for a COVID-19 infection is higher. We assume that the COVID-related mortality is elevated by a factor of 3 for those patients who are in need of hospitalization but cannot receive it.<sup>20</sup> We denote by _πb_ ( _m_ ) the probability an individual of group _m_ requires hospitalization, conditional on being infected. The share of individuals in need of hospital beds at time _t_ , _Bt_ , is given by We assume that hospital bed allocation is random among those in need. Denoting by _h_ the number of hospital beds, the probability that an infected individual dies at period _t_ is given by As in Atkeson (2020) and Eichenbaum et al. (2021), we assume that it takes an average of 18 days from infection to either recover or die. To obtain weekly mortality probabilities, we multiply the rates obtained from Walker et al. (2020) by 7/18.<sup>21</sup> The number of hospital beds in each country is obtained from the World Bank’s World Development Indicators. It should be noted that the indicator is not measured frequently,

> 20For example, Yang et al. (2020) find that the CFR by the middle of February 2020 within the city of Wuhan, China, the presumed outbreak location of origin for COVID-19, was substantially higher (5.25 percent) than in regions outside Wuhan but within the same province (1.41 percent) and in regions outside the province (0.15 percent). While the CFRs within and outside Wuhan should vary for several reasons, including availability of testing, a key factor was the initial demand for hospital beds exceeded the supply.

> 21It has been noted that the Eichenbaum et al. (2021) SIR-macro model misses on the timing of COVID19 deaths for the US. Unfortunately, there is no practical way to
revolutions - 0.0309 * Number of assassinations - 0.0148 * Deviation from the PPP in 1960. For simplicity, the last three regressors are considered to be zero. We collect data for the other variables using the Barro-Lee and Summers-Heston databases. We use GDP per capita and the enrollment rates for primary and secondary school for three years before the start of the crisis. For government consumption scaled by GDP, we take the average ratio for the period between eight years and three years before the start of the crisis. The quadratic form in the model of GDP per capita implies a positive relation between GDP per capita level and growth for values of GDP per capita above $10,800.<sup>11</sup> We therefore restrict the realizations of the GDP per capita level variable to be no larger than $10,800. For each country, we insert the realizations of the variables at three years before the start of the crisis into the Barro equation and adjust for population growth rates to obtain the predicted GDP growth rates. Finally, we compare this predicted rate with the actual growth rates during the crisis and, following the IMF methodology, summarize the differential growth rates into an output loss variable.<sup>12</sup> For a country’s institutional and legal environment, we use three indicators. QUALITY OF INSTITUTIONS is a broad measure of institutional quality developed by

> 11 The quadratic form can be viewed as an approximation to a functional form that asymptotically approaches a zero relation between growth and level of per capita GDP, with the relation coming close to zero when real GDP is above $ 10,800 (Barro 1991).  For simplicity, we cut off the GDP per capita data at $10,800, thus forcing a flat relationship for higher actual GDP per capita.

12 There are also a number of other measures that can be used to estimate the output loss. These differ in terms of the assumptions made about trend growth and the timing of the
companies serving the region’s larger cities. Melendez (2008) analyzes subsidies for electricity, water, gas, and phone services in Colombia.

The majority of these studies use household survey data on expenditures to back-calculate household water and electricity use (Foster 2004; Foster and Araujo 2004; Melendez 2008).<sup>4</sup> This estimation strategy is also common in subsidy incidence analyses that examine electricity and water subsidies only. Appendix A summarizes studies that examine the incidence of subsidies in the water supply sector (for a review of subsidy incidence analyses in the electricity sector, see Cardenas and Whittington 2019). However, there are two main problems with this back-calculation approach. First, when self-reporting their electricity and/or water bill, the respondent may simply guess the past month’s bill because its amount is either not remembered or unknown. Second, researchers in this vein typically use the average price paid based on the existing tariff structure. Yet if the price structure used by a utility company is based on an IBT, then the result is an error in the estimation of the levels of water and electricity actually used.

Another factor that merits consideration is that, in these studies, the estimates of costs of production and delivery are based on national or regional cost estimates—not on data from the utility companies themselves (Foster 2004; Foster and Araujo 2004; Melendez 2008). Foster and Yepes (2006) focus on cost recovery levels of water and electricity utility companies in Latin America, but they do not have access to information on the full average cost of service provision. Hence they rely on data from Global Water Intelligence for estimates of operation costs and of maintenance and capital costs for water and electricity utility companies. There are very few studies in the literature that obtain or estimate the average unit cost based on data from the utility companies actually serving

> 4 Foster (2004) uses data from a household survey that asked respondents to present their electricity/water bills, when available
missing. By contrast, all bilateral positions of a reporting country (source or destination) are, in principle, observed. Since BIS reporting countries generally itemize every counterparty country on which its banks hold or owe an active (positive) position, any missing observation is most likely a true zero and we code it as such. This procedure could underestimate the number and value of active links for those reporting countries that do not require banks to report the complete breakdown of all counterparty countries when positions are small enough.

Another point concerns the handling of a change in reporting in 2012. Countries historically reported positions expressed in U.S. dollars rounded to the closest million. Thus, positions below US$0.5 million (a low threshold in the context of country-to-country investment) were reported as zero. When decimal reporting was introduced in the second quarter of 2012, hundreds of links below US$0.5 million started to be reported as positive, leading to an artificial jump in the number of active links in 2012. Although this jump does not affect our analysis of the intensive margin (because the value of these links is so small), it could bias the analysis of the extensive margin. We offset this break in the series by setting all links below US$0.5 million to zero. This ensures that all reported loans and deposits are subject to a constant reporting threshold throughout the sample period.

# **A.2. Portfolio Investment**

For portfolio investment, we rely on the CPIS database from the International Monetary Fund (IMF). The CPIS is a voluntary data collection exercise that assembles data on countries’ international holdings of equities and long-term and short-term debt securities. Countries report data on: (i) their holdings of portfolio investment assets issued by residents in other countries, and (ii) their portfolio investment liabilities issued by domestic residents and held by residents in other countries. No aggregation is needed because the data are already reported at the country level (all
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in the raw data on _SAH_ , we find that the deterministic component of _SAH_ has a marked gradient with respect to a broader concept of economic welfare in which the role of “non-income” factors is identified using self-assessments of economic welfare. We also show that this (unconditional) gradient is largely attributable to other covariates of health, notably age and education; our preferred estimation method reveals only a small conditional gradient in health.

# **2. Biases and methods**

We want to see how health status ( _H_ ) varies with economic welfare ( _W_ ) but we do not observe _H_ and _W_ directly. We must rely on proxies for each variable, entailing measurement errors. In the literature (referred to in the introduction), the proxy for _H_ is self-assessed health ( _SAH_ ) while for _W_ it is “income” ( _Y_ ). The measurement errors may well be uncorrelated with _H_ and _W_ , although we need not assume that this is so; in particular, we can readily allow the measurement error in _Y_ to be positively correlated with _W_ , as would happen if there is intertemporal variability of incomes that can be at least partly smoothed through savings or borrowing to make economic welfare less variable. We also allow the possibility that the measurement error in _SAH_ is correlated with _Y_ .

There will be two sources of bias in estimating the economic gradient of health by regressing _SAH_ on _Y_ (with or without controls). The first is the well-known attenuation bias that arises when a regressor is measured with error. The existence of a positive correlation between the measurement error and actual economic welfare, due to inter-temporal smoothing behavior, will strengthen the standard attenuation bias due to classical measurement errors in a regressor.

The second source of bias is intrinsic to the use of subjective data. It stems from the fact that the errors in using self-assessed health to indicate actual health status are unlikely to be statistically ignorable (“white noise”), but rather will be correlated with incomes and other 6
(by about 5-10%), very few studies found that these improvements in skills translated into positive effects on sales and profitability. This combination of small changes in business practices and low statistical power means that few studies find effects of training on sales or profitability, although a few studies find some positive short-term effects. By comparison, one-on-one consulting provided to larger firms can improve the performance of firms. Some of the reasons for limited impact of training programs include, short-time horizon of evaluations, and that the monitoring and evaluation surveys have high attrition rate such that the impact, especially on control group is not well measured. There may also be spillover effects on untreated control group of firms. There is also difficulty of measuring firm outcomes, such as revenues and profits, since firms in developing countries often do not keep records and are reluctant to share this information, which in turn might subject to changes in reporting following the business training.

# **_3.4.1 Content of training: What to train on?_**

There is a wide variety of technical skills training programs. Marketing and financial practices training programs are most common of these. In South Africa, experimental evidence on 852 (primarily micro) firms suggest that both training programs can increase firm profits. Twelve months after the program, treated firms with marketing training tended to have larger size (sales and employment) because they implemented better market research tactics, advertising, and were inclined to adjust to customer needs. By comparison, treated firms with finance training experienced larger changes in firm efficiency. These firms were more likely to adopt financial practices aimed at separating business and personal finances, keeping and analyzing business records, managing budgets more efficiently, and assessing working capital needs, implying a larger increase in output-input ratio relative to the other groups (Anderson et al., 2018). These results suggest that marketing training effects were larger for firms with less initial exposure to different market contexts, while the finance training effects were greater for firms with
8 could result from constraints in raising appropriate amounts of debt. In addition, the nature of these constraints may differ for firms of different sizes. For instance, Figure 7 suggests that only mega firms can issue bonds, and that micro firms get only tiny bank loans compared to their balance sheet size. To a detailed analysis of these issues the paper now turns.

**Figure 7:  Financing Structure of Chilean Firms by Size, 2003**

<!-- Start of picture text --> Financing structure of firms Corporate leverages, 2003<br>100% Average debt to equity ratio<br>80% 2.5 2.2<br>2.0<br>60%<br>2.0<br>40%<br>1.5 1.3<br>20%<br>1.0<br>0%<br>0.5<br>% bank loans % bonds -<br>% equity % supplier credit Chilean US firms Colombian<br>% intra company loans % other fin. resources firms firms<br>Source: own calculations based on FECUs, Bizstats, Superintendencia de Empresas de Colombia<br>Mega Large Medium Small Micro<br><!-- End of picture text -->

# **2.  Firm Access to Capital Market and Bank Financing**

# **_Foreign Capital Markets_**

Access to foreign capital markets is restricted to mega large firms. Firms with access to foreign capital markets (bonds or equity) are concentrated in three sectors - utilities (including the privatized utilities and the road concessions which issued infrastructure bonds), manufacturing and industry. They include exclusively mega large firms and nearly exclusively firms based in Santiago.

# **_Domestic bonds_**

The high cost of issuing bonds and minimum rating requirements by institutional investors create a minimum size for bond issues higher than in neighboring countries. As Figure 8 shows, the cost of issuing bonds
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and type of dwelling indicators<sup>41</sup> report relatively low overlaps. At the same time, nearly half of the monetary poor are also energy poor in the case of the affordability indicators. A high share of households are only energy poor on the building indicator (40.2 percent). These measures can be disaggregated at the NUTS3 level, as the SILC survey is representative at this level.

and welfare is created. To achieve this, the authors construct a harmonized dataset by combining EU-SILC and household budget survey data. Subsequently, they employ various imputation models and identify the most effective one to impute energy spending shares into the EU-SILC data.

> 40 Arrears on utility bills are captured in the SILC survey question: “During the last 12 months, did your household face some problems with payment on time of the expenditure on the dwelling you are living in? Ability to keep the home warm is captured in the SILC survey question: “Can you afford adequate heating of your dwelling?“ 41 Privately-owned inefficient multi-family apartment blocks with insufficient maintenance may have high energy poverty rates.

26
CONSUMPTION SUB-AGGREGATES 20 It remains possible that Engel curves in fact possess the desired linearity, or that changes in Engel curves due to changes in relative prices are negligible in real-world data. Accordingly, we use data from Rwanda, Uganda, and Tanzania and search across different goods to identify those that feature (nearly) linear Engel curves in a base period. We then use these goods to construct a sub-aggregate in a subsequent period. Consistent with theory, the performance of this method turns out to be extremely bad at measuring head-count poverty rates. One should _not_ construct sub-aggregates for this purpose by relying on the fact that Engel curves may be linear at some time and place, because they are unlikely to be linear in a different time or place since prices are likely to be different.

Perhaps there are better ways to construct a sub-aggregate? Our conditions are both necessary and sufficient, so theory tells us that the answer is “no”. Another idea often used in practice involves constructing a sub-aggregate by choosing goods with large expenditure shares. If one can do this in such a way that a very large percentage of _all_ households expenditures were in the sub-aggregate this would hold promise. But if Engel curves aren’t linear then there will be variation in expenditure shares across households, and identifying goods that have large shares on average will tend to select goods that have a low income elasticity. This is more or less the opposite of what one would wish, since expenditures on these goods will convey little information about underlying household resources.

Finally, as a practical matter, any measured consumption aggregate is likely to be an incomplete or a reduced aggregate approximation to the full aggregate. For instance, the number of consumption items (or categories) for which data are collected from households in LSMS surveys ranges from 37 to 305, with the mean being 137 and the median 130 (Beegle et al. 2010). Arguably the value of public goods and time and leisure should also be included in a comprehensive or “full” consumption
# **The Impact of COVID-19 on Electricity Generation: An Empirical Investigation**

# **1. Introduction**

Since early 2020, the COVID-19 pandemic has impacted human life in every corner of the world. During the first wave of COVID-19 (March–June 2020), the world went through unprecedented lockdowns that reduced the level of economic activity. However, the lockdowns also caused some positive environmental externalities, such as reduced local air pollution and greenhouse gas (GHG) emissions due to reduced energy consumption, particularly for transportation (Andreoni, 2021; Bauwens et al., 2020; Bao and Zhang, 2020; Liu et al., 2020; Le Quéré et al., 2020). The reductions in local air pollution and GHG were mainly seen in countries with the predominant use of fossil fuels for their energy supply.

Some studies (Sharma et al., 2020; Nakada and Urban, 2020; Menut et al., 2020; Chauhan and Singh, 2020) have sought to investigate the effect of COVID-19 and the accompanying public health measures on reducing local air pollutants in India, Brazil, China, Europe, and the United States. Most of these studies have reported a significant reduction in common pollutants due to the COVID-19 lockdowns. For example, using spaceborne NO2 column observations from January to April 2020, Bauwens et al. (2020) find that NO2 decreased by 40% in China and 20% - 38% in western Europe and the United States compared to the same period in 2019. In Hangzhou, a megacity in eastern China, aerosol particles data from January to March 2020 show that the concentrations of PM2.5-10, PM2.5, NOx, SO2, and CO fell by 58%, 47%, 83%, 11%, and 30%, respectively (Yuan et al., 2021). In contrast, Bekbulat et al. (2021) find that stay-at-home orders in the US did not significantly or permanently reduce pollution. Ozone, NO2