rafmacalaba/data-use-mentions-extended huggingface dataset:
<br>60,000<br>Phase I: early 1990s-early 2000s:<br>Steady liberalization<br>40,000 of capital flows, modest<br>pick up in inflows<br>20,000<br>0<br>Mln US $<br>1990-91 1991-92 1992-93 1993-94 1994-95 1995-96 1996-97 1997-98 1998-99 1999-00 2000-01 2001-02 2002-03 2003-04 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12 2012-13 2013-14<br><!-- End of picture text --> Source: RBI Handbook of Statistics on the Indian Economy. Capital flows refer to net capital flows.

> 12 Using methodology standard in the literature, using quarterly data we identify 2008q3-2009q2 as the period of a sudden stop. The criteria used is similar to that in Forbes and Warnock (2012b) wherein a sudden stop is identified if inflows fall one standard deviation below the average of previous 20 quarters for at least consecutive two quarters; and if in at least one quarter, flows fall two standard deviations below the prior years’ average.

> 13 However, since this event lasted only for a quarter, it does not qualify as a sudden stop as per the standard criteria used in the literature, which require the reversal of capital flows to last more than a quarter.

11
at 4 am and finishing at 4 am of the following day, using 15-minute intervals. Building on previous literature, using the time-use diary of the individuals, “market work” includes time spent on working or on the production of goods and services. In our dataset, this includes working or being employed; owning a business; staple grain farming; horticultural or high-value crop farming; raising large livestock (cattle, buffaloes), small livestock (sheep, goats, pigs), and poultry and other small animals (chickens, ducks, turkeys); and fishpond culture. “Unpaid work” activities include time spent on caregiving (including caring for children as a primary activity and caring for adults, the sick, or the elderly); shopping or getting services (including health services); cooking; other domestic work (including fetching water and fuel); and weaving, sewing, or textile work.<sup>13</sup> Lastly, we classify as “other activities” time spent on personal care, sleeping and resting, eating and drinking, attending school and doing homework, exercising, social activities and hobbies, religious activities, commuting to and from work or school, and other activities that cannot be classified as market work or unpaid work. For the econometric analysis, we use a Seemingly Unrelated Regression (SUR) framework to account for the interdependence of market work, unpaid work, and other activities that all take place within a 24-hour window. In this model, the three sets of activities are determined simultaneously by the same set of explanatory variables and are estimated jointly as a system: i (2) ii0 ii1θθ θθii iiXX ii ii where i refers to the number of hours per day spent by individual TT = ββ + ββ CC + ββ XX + εε on activity =1,2,3 corresponding to market work, unpaid work, and other activities, respectively, and , =0,1,2, and are as defined TT ii aa above. The
top of that, school closures had a direct effect on children’s learning ( _e_ . _g_ ., see the reviews of the evidence by Hammerstein _et al_ ., 2021, and Werner and Woessmann, 2021). Particularly in Latin America, the impact of the pandemic on these three dimensions has been among the strongest worldwide. Indeed, considering all these dimensions in their simulations—health crisis, economic downturn, and educational losses—Neidhöfer _et al_ . (2021) predict a large drop in the likelihood of completing secondary education for current cohorts aged 15 to 19 in Latin America. Subsequent studies based on real-time information from surveys or administrative data on learning losses, disconnection from school, and drop-out rates in 2020 confirm that the pandemic had a significant negative short-term effect on education in basically all countries in the region (see the review of the real-time evidence included in Lustig _et al_ ., 2021).

While these estimates refer to the year 2020, the situation did not improve substantially in 2021. Although Latin American economies recovered slightly with respect to the previous year, a high number of infections still limited school openings and in person learning in most countries.<sup>2</sup> Table 1 shows the number of weeks that schools were fully or partially closed during the period 2020-2021 in each country, the regular weeks of school that children would have had without the pandemic in these two years, as well as the number of cumulative COVID-19 cases and deaths at the end of 2021. In most countries, the share of weeks with closed schools exceeds 90% of the instructional time in the two academic years. The average across all countries is 85%. Interestingly, although surely the epidemiological situation was the main driver of the decision to close schools at the national level, at the cross-country level there seems to be no clear pattern of association between the relative number of cases and deaths and the number of weeks with closed schools in a country.

The closure of schools was accompanied by the efforts
Despite recent efforts to promote biofuels, the current share of biofuels in the global energy mix for transportation is only around 2 percent on energy equivalence basis.<sup>3</sup> One reason for this low market penetration<sup>4</sup> is that the price of fossil fuels does not include its negative external costs (e.g., environmental damage) to society. Particularly in the context of climate change mitigation, this could be addressed by imposing a carbon tax on fossil fuels. However, how much a carbon tax would change the relative economics of biofuels and its penetration in the energy supply mix is an empirical question.

During the last two decades, a plethora of research both on carbon taxation and on biofuels has been produced. This literature spans a wide range of issues, methodologies and disciplines, and a detailed review is beyond the scope of this paper. A number of studies use either general equilibrium or partial equilibrium models that incorporate biofuels, to simulate climate change mitigation policies. For instance, Janssen and de Vries (2000) simulate different GHG mitigation policies, such as a carbon tax in the energy system model TIME that includes biofuels, to estimate the costs of stabilizing the CO2 concentration in the atmosphere at 550 ppmv. However, the partial equilibrium approched employed in the TIME model does not account for economy-wide feedback effects of a carbon tax. Plevin and Mueller (2008) develop a bottom-up model to calculate the effects of a CO2 tax and other emission policies on the costs of producing ethanol from corn, but also their model is partial equilibrium, and the study can therefore not account for the spill-over effects of carbon policies on other sectors of the economy. McCormick and Kåberger (2005) identify the Swedish carbon tax as a facilitating factor for the expansion of bioenergy in the town of Enköping, but such a case study does not

> 3 Authors‘ estimation for year 2008 based on data for global gasoline and diesel consumption in the transport sector from the International Energy
economic growth, Vietnam experienced also a rapid social and demographic change. The population grew from 23.5 million in 1945 to 96.5 million in 2019 (World Bank, 2020). In 2018, about 70% of the population was under 35 years old. Between 1950 and 2019, life expectancy increased from 52 to 75 years, which is qurrently the highest in Southeast Asia (Khoa, 2021). However, the welfare generated by the economic growth is unevenly distributed with no substantial improvements. The national GINI index measuring income inequalities has been steadily around 42%-43% since the early 2000s. Inequalities are higher in rural areas, especially in Northern midland and mountain areas where in 2018 the GINI index increased to 44%, while urban areas in the South-East had a GINI index of about 37% (GSO data).

Health indicators improved along with the economic growth. Between 1990 and 2015, maternal mortality rates declined by 61%, while child mortality rates droped from 8.6% in 1964 to 2.1% in 2017. These achievements have been followed by a significant increase in public spending on the health care system, which however remains insufficient as households still spend a substantial amount of out-ofpocket money on private health care (Dang et al., 2021). Since 2000, about 15% of the households in Vietnam spent over 10% of their income on health care, and 5% of the population fell into poverty because of health care costs. From 1995 to 2006, the government share of the health care expenditure was about 30% while out-of-pocket payments were about 50%. After 2006, the proportion shifted to 40%-40% and in 2014 government spending accounted for over 50% (Khoa, 2021).

Currently, Vietnam has a mixed public-private health care system with two types of health insurance: the governmant’s social health insurance and private health insurance offered by private insurance companies. The coverage rate of the social health insurance increased from 12
events unless, of course, there was a big shock; in fact, according to the available data (table 2), with the notable exception of 2011, there is no sign of such an event.

Results from the second-stage of the 2SLS estimation reported in Table 5 suggest that income diversification is positively influencing Nigerian farmers’ welfare. Nevertheless, as discussed in section 2, it is possible that agents benefit differently from diversifying and thus base their decisions on their anticipated returns from it (Radchenko, 2014). In the specific context of Nigeria, we believe that the events that took place in-between the two survey rounds, namely the 2011 droughts and to some extent -but this is very difficult to model and restricted to a

> 12The selection of 2001 as a starting point is dictated by the availability of the geo-referenced high resolution precipitation data from NOAA climate prediction center.

> 13Referring to a common threshold employed in literature (Staiger and Stock, 1977), we consider a value of the F-statistic above 10 from the test of joint significance of the instruments in the first-stage regression as necessary to conclude that our instrument is sufficiently strong.

10
# **1. Introduction**

During 2020 and 2021, the COVID-19 pandemic affected the lives of everyone. To contain the spread of the disease, all governments imposed national lockdowns, travel restrictions, and social-distancing measures, including school closures. Latin America was not an exception. On average, in 2020 and 2021 schools were closed for 269 days. Given that a typical school year in the region takes 189 days, school closures represented a disruption of 1.42 years. National education systems provided remote learning options and other tools to hamper the learning interruptions, but undoubtedly the process of human capital formation of children and youths was affected. Due to this disruption, the pandemic will likely imply permanent lower levels of human capital for many individuals, and therefore lower earnings (see the surveys in Blanden _et al_ ., 2022 and Moscoviz and Evans, 2022).<sup>1</sup> In this paper we provide new evidence on the impact of the COVID-19 pandemic and the associated school closures on school dropouts and educational losses in Latin America. We take advantage of recently released microdata from national household surveys for 2020 that allows us to explore changes in school enrollment. To estimate the change in school enrollment associated with the pandemic, we run regressions of the outcome of interest—an indicator of whether the individual is enrolled in education or in private education—on a linear timetrend from 2009 to 2019, a binary indicator for the year 2020, and a set of controls. We show that in all countries enrollment rates in 2020 negatively deviate from the previous trend. On average (across the 12 Latin American countries in our sample, which add up to 87% of the regional school-age population), enrollment of children and young people aged 6 to 24 fell by around two percentage points. The decrease in enrollment in private education is also in this order of magnitude in absolute terms, but larger in relative terms because of the lower average baseline level. These results are in line with simulations

>
3. _H_ 0<sup>3-differentmeasurementtechnologiesforprecipitationandtemperaturehavethesame</sup> impact on estimates of agricultural productivity.

To test the first hypothesis, we extract remote sensing weather data for LSMS-ISA survey locations using ten different combinations of spatial feature and extraction method. The spatial features represent different obfuscation techniques (aggregation and displacement) and extraction methods (simple, bilinear, and zonal statistics) represent different approaches to dealing with both coarse spatial resolution and uncertainty in location. These data are then combined with household-level data on agricultural production. Following the model specification of Deschˆene and Greenstone (2007), we then estimate agricultural yield functions. This allows us to test if there are differences in the predicted effect of a weather metric on yield across the different obfuscation procedures. Similarly, to test the second hypothesis, we calculate 22 different weather metrics that are commonly used in the economics literature. For rainfall, these include, but are not limited to, mean daily rainfall, total seasonal rainfall, deviations from the long-run average of seasonal rainfall, and the longest intra-season dry spell. For temperature, these include, but are not limited to, measurements such as mean daily temperature, GDD, and long-run deviations in GDD. By testing each of these weather metrics individually and in combination with each other, we can determine which measure of rainfall or temperature is a consistent predictor of yields. And, by extension, which have poor predictive power or are predictive for only a certain crop or in a certain country. Finally, to test the third hypothesis, we conduct all of the above analysis on data from six different remote sensing precipitation data sets and three different remote sensing temperature data sets. This allows us to see if all data sources provide essentially the same results, or if results vary by data source. All told, we run more than 129,600 regressions to identify which weather metrics have strong predictive power over a large set of crops, countries, obfuscation methods, and data sources.

While our approach allows us to compare various combinations
also find that firms expand many years after they internationalize suggesting that market timing is not the only force underlying internalization.

However, Doidge, Karolyi, and Stulz (2009) argue that the findings in Gozzi et al. (2008) are the result of the inclusion of other cross-listings besides US exchange listings. They argue that a valuation premium is not predicted by theory for those non-exchange cross listings. Doidge, Karolyi, and Stulz (2009) show that a US cross-listing premium exists every year from 1990 to 2005 for exchange listed firms for which a premium is predicted by the bonding view.

Several other papers use firm-level data to examine the motivations for going abroad and the effect of internationalization at the firm level. Sarkissan and Schill (2004) find evidence of a proximity effect, that is, geographical proximity and other affinity factors such as trade links and common language determine cross-listing. Ayyagari and Doidge (2010) find that controlling shareholders use a U.S. cross-listing to sell their stake and facilitate changes in ownership and control. Others have examined the effect of equity market liberalizations (e.g. Henry, 2003; Patro and Wald, 2005) in reducing the cost of capital.

While most of the theories and empirical evidence are based on equity capital issuances around the world, the theories also hold implications for debt issuances. Recent studies suggest that international debt issuances are equally, if not more, important. Gozzi, Levine, and Scmukler (2010) study the extent of internationalization at the firm level by analyzing 168,513 equity and debt issues of firms from 116 countries over the period 1991-2005. They find that debt markets dwarf equity markets both in their internationalization (35% of debt capital was raised abroad compared to 10% of equity issues) and how corporations raise capital (firms raise four times more money through bond sales relative to equity issues).

# _5.4.3 Private Equity and Venture Capital_

Outside of bank financing and public debt and equity markets, private equity and venture
three times as much as that of roads ($0.15 per tonne-km), primarily because the state of the road system in the southern Democratic Republic of Congo is so poor that intermodal competition is rare. Assuming that there are no additional delays due to disrepair or other service disruptions, railways have a competitive edge in terms of average tariffs.

A comparison of the time required to move a given volume of imports from port of entry to destination revealed that road freight moving over the interregional corridors was faster than rail freight. The trains in southern Africa have rather low implicit velocities. Additionally, delays occur when cargo must be transferred from one national railway network to

**Figure 2.2 Comparison of road and rail freight tariffs**

<!-- Start of picture text --> 0.15<br>0.12<br>0.09<br>0.06<br>0.03<br>0.00<br>Road Spoornet TAZARA CCFB RSZ NRZ SNCC<br>US$ per ton-kilometer<br><!-- End of picture text --> _Source:_ AICD railway database **;** Teravaninthorn and Raballand 2009.

the other. (The average dwell time for imports at the frontier between two national rail networks is about three days.) On average, it takes between 400 hours (16 days) and 690 hours (28 days) to move freight along the southern African road network. By rail it takes 490 hours (20 days) to 1,200 hours (50 days) (figure 2.3). The slowest times recorded are for moving rail freight from Durban to Lubumbashi (in the Democratic Republic of Congo), with much of the delay occurring on the border between Zambia and the 14
most comprehensive assessment of the short-term impact of COVID-19 on businesses in terms of Our paper contributes to the quickly expanding literature assessing the economic impact of COVID-19. Much of the early research attempts to draw parallels with previous crises or uses macro simulations in combination with some ex-ante measures of vulnerability to the pandemic. Ludvigson et al. (2020), for example, extrapolate from past natural disasters to model COVID-19 as a series of large, multiple-period exogenous shocks.<sup>5</sup> Bonadio et al. (2020) use a quantitative framework to simulate a global lockdown as a contraction in labor supply for 64 countries, where labor supply is assumed to change with the fraction of work in the sector that can be done from home, interacted with the stringency with which countries imposed lockdown measures. They find a major contraction in GDP, with a quarter of the impact attributed to disruptions in global supply chains.

Another strand of the literature uses high-frequency, unconventional data to monitor the pan-

> 1At the same time, the shock could create pressures on firms to expand their use of digital technologies or increase competition which could also lead to higher productivity growth and better resource allocation.

> 2Data were collected jointly by World Bank, national authorities and partner organizations relying on an homogeneous questionnaire.

> 3Among richer countries, our dataset covers Cyprus, Greece, Italy, Poland, Romania and Slovenia. Data collected cover all regions where the World Bank is present: East Asia and Pacific (EAP), Europe and Central Asia (ECA), Latin America and the Caribbean (LAC), Middle East and North-Africa (MNA), South Asia (SAR), and Sub-Saharan Africa (SSA).

> 4Twenty-eight countries included businesses in agriculture in the sample, and these observations account for around 5% of the sample. Most are in the Philippines, Tanzania, Ghana, Kenya, and Tunisia.

> 5Using a costly disaster index, the authors estimate that under fairly conservative assumptions, the pandemic could lead to a
SNA training instructions specify that greater effort should be directed at larger transactions. Deaton cites OECD (2002, p. 179) where the SNA training instructions with respect to valuing home-production state that the time expended to collect this information should only be expended if the amount produced is sufficiently large with respect to the total supply in the nation of that particular item. In contrast, household budget surveys, and living standards surveys tend to focus on home production and more generally, include smaller transactions with greater probability than larger ones. The primary objectives of these surveys are typically to rank households, identify the poor, and measure consumption patterns. Conversely, estimates of 12
I then use individual level employment data from the Mexican Labor Market Survey (ENOE) to gauge whether the measured effect reflects movements in and out of social security registration instead of reflecting actual increases in employment and do not find this to be the case. ENOE data also show no evidence of displacement effects on employment in ineligible durable goods manufacturing industries.

The measured impact of wage subsidies on firms’ employment levels are larger than other estimates in the literature. Kangasharju (2007) show that wage subsidies for hiring unemployed workers in Finland raised employment in eligible firms by 9% and Betcherman, Daysal, and Pagés (2010) find that employment subsidies to firms that created new jobs in Turkey led to an increase in registered jobs in eligible provinces by up to 15%. In fact, the finding that the effect of wage subsidies was greater after the subsidy period than before is unique in the literature. Most papers measure the effect of wage subsidies during the period when the subsidies are being paid. Two exceptions are Card and Hyslop (2005) for Canada and Groh et al. (2015) for Jordan. Both of these papers find positive short‐term impacts on employment, but that these effects dissipate after the subsidy ended.

The findings thus suggest that the use of wage subsidies may be particularly effective during an economic crisis since they can be paid for a relatively short amount of time and still have lasting effects on employment. At the same time, the results raise a question about the channel through which the wage subsidies worked. The intention of the program was to allow firms to keep workers with job‐ specific skills, but the estimated effect on employment, while positive, is not statistically significant during the program’s duration. Larger, statistically significant effects, only emerge after the program ended. One possible explanation is that the program affected which types of workers subsidized firms kept vs. let go. That is, subsidized firms may have kept the workers with the most specialized and relevant skills in greater proportion