rafmacalaba/data-use-mentions-extended huggingface dataset:
therefore impossible to distinguish between immigrants who were educated at the time of their arrival and those who acquired education after they settled in the receiving country; for example, Mexican-born individuals who arrived in the US at age 5 or 10 and graduated from US high-education institutions are counted as highly-skilled immigrants. Hence, our de...nition of the brain drain is clearly determined by data availability. Existing data do not allow us to systematically eliminate foreign-born individuals arrived with completed schooling or after a given age threshold.

In the US, the proportion of foreign born who arrived before age 15 (i.e. those who spent part of their schooling time in the US) represents 7.5 percent of the immigration stock. Since we have no systematic data for the other countries, we cannot control for children immigration.

Finally, the concept of immigration is not fully homogenous between countries. When the information exists, we de...ne migration on the basis of the country of birth rather than citizenship. This concept is recommended by the United Nations<sup>8</sup> and better captures the decision to emigrate<sup>9</sup> . Another reason is that the concept of country of birth is time invariant, contrary to citizenship which changes with naturalization. The OECD statistics report that 14.4 millions of foreign born were naturalized between 1991 and 2000. Countries where the number of acquisitions of citizenship is particularly high are the US (5.6 million), Germany (2.2 million), Canada (1.6 million), Australia and France (1.1 million). Data by country of birth are available in a large majority of OECD countries. However, for some countries, immigrants are only classi...ed by citizenship (Germany, Greece, Italy, Japan and Korea).

The ...rst six columns in table 1 describe the availability of immigration data in OECD countries as well as the statistical sources. Some of these
poraneous differences between the treatment and control groups in the vicinity of the discontinuity. Formally, this requires that both lim _x↓x_<sup>_E_</sup> � _yi|xi_ = _<u>x</u>_ <u>�</u> and lim _x↑x_<sup>_E_</sup> � _yi|xi_ = _<u>x</u>_ <u>�</u> are continuous in the forcing variable _xi_ . If this assumption holds around the cutoff, then any discontinuity in outcomes observed at the cutoff can be attributed to the discontinuity induced by the treatment, in this case debt relief. As a test of this identifying assumption, Figure 4 plots the unconditional means of pre- program observables with accompanying local linear regressions to each side of the eligibility threshold. These graphical tests demonstrate that households in the vicinity of the program eligibility threshold are indeed similar along observable pre-program characteristics, and that there are no discontinuities in any of these variables in the neighborhood of the program threshold. Table D.3 in the Supplemental Appendix provides additional parametric tests for continuity and similarly demonstrates that there are no discontinuities in pre-program observables at the cutoff, indicating that the covariate continuity assumption is met.

The second identifying assumption is that the forcing variable, and therefore treatment status, is not subject to manipulation. Ex-ante manipulation of land status was highly unlikely for several reasons. First, the program was the first of its kind in India that made eligibility conditional on land pledged as collateral at the time the loan was disbursed, rather than the vintage or amount of outstanding debt. Second, the approved amount of agricultural loans is typically proportionate to the value of land pledged as collateral. This means that, in general, households have an incentive to over- rather than underreport their land to the bank, whereas households in the vicinity of the program cutoff would have had to manipulate their documented land downward to benefit from the program.
presents the decision tree that we use to identify and classify digital skills.

> 3 Cognitive and socioemotional skills are themselves complementary to each other. In the U.S., job requirements for cognitive and social skills are positively correlated with pay and firm performance, and more so when both skills are required (Deming and Kahn 2018). Longitudinal data of adults, also for the U.S., show that wage returns to cognitive and socioemotional skills are increasing (Weinberger 2014, Deming 2017). Online job postings collected in Ukraine show that the shares of postings requiring cognitive and socioemotional skills within an occupation are very similar (Muller and Safir 2019).

5
/mark><sup>34</sup> <mark>Step 2: Select all individuals who have the same characteristics as the individual selected in step 1 from HBS.</mark><sup>35</sup> <mark>Step 3: Construct a “synthetic” individual whose synthetic earnings are equal to the average of all individuals selected in step 2 (from HBS) and match him or her to the original individual selected in the LFS.</mark>

> 32 Because of data availability, we match the 2017 LFS with the 2017 HBS survey. In the main analysis, we use the 2018 LFS.

> 33 <mark>In probability theory, the support of a probability distribution can be loosely thought of as the closure of the set of possible values of a random variable having that distribution.</mark>

> 34 We match only employees with non-missing wages.

> 35 Specifically, we match individuals by the following characteristics: gender, age, urban status, education level, marital status, relationship to the head of the household, and occupation.

29
formula already appears suitable for estimation. However, some problems arise since it is well known that most macroeconomic time series contain unit roots and that the regression of one non-stationary series on another is likely to yield spurious results. As reported in the Appendix, the data for Guatemala is no exception. The estimation bias can be removed by transforming the time series to stationarity. This can be done by first differencing. In any case, this will create its own problems, notably because of the risk of losing valuable information on the long-run relationships of the variables.

One approach to dealing with this dilemma is to employ an error-correction model which combines long-run information with a short-run adjustment mechanism. This methodology has been used successfully in alternative growth studies. Examples of this are Nehru and Dareshwar (1994), Morales (1998), and Bassanini and Scarpetta (2001). The error-correction model can be estimated in different ways. Banerjee et al. (1993) show that the generalized one-step error-correction model is a transformation of an autoregressive distributed lag model. As such, it can be used to estimate relationships among non-stationary processes. Based on Hendry’s (1995) concept of general-to-specific modeling, the error-correction model of the human capital augmented production function for Guatemala can be specified as follows: For Guatemala, in line with much empirical cross-country research, the short-run effects of schooling on growth have been found insignificant and are as such excluded from the regressions. This suggests that only the _level_ of human capital has a long-run effect on economic _growth_ . As it stands, the equation can be estimated by ordinary least squares (OLS) or instrumental variables (IV) techniques, but the coefficients cannot be formed without knowledge of _α_ and _β_ . However, one can estimate the re-parameterized form: Estimates of the parameter γ 3 can now be used to calculate the required elasticities _α_ and _β_ . The loading coefficient _γ 3_ contains additional information because it can be interpreted as a measure of the speed of adjustment in which the system moves towards its
income. To outweigh this opposition from the top, poorer individuals (the potential winners from redistributive policies) would need to form a coalition. Countries where political influence is more concentrated would need to mobilize larger coalitions of individuals to counter-balance the political influence in the hands of the few.

We apply this methodology, using the most recent data from the World Bank PovcalNet database for over 120 developing countries. Our results show that many countries do not have the resources to finance their own development out of poverty. Moreover, the countries that have the arithmetic capacity to do so would likely face great resistance to implement redistributive policies as political power is concentrated among a few rich individuals whose interests diverge from those of the poor. These results suggest that domestic resource mobilization, beyond an issue of political will, implies a challenge related to the concentration of resources among powerful groups and their ability to influence policy decisions.

22
# **6 Conclusion and Discussion**

Throughout 2020 and 2021 there have been frequent media reports that a greater proportion of women are exiting the workforce during the COVID-19 pandemic than men because of competing demands on their time due to increased domestic and care work as a result of the closure of schools and childcare facilities. The COVID-19 pandemic worked to illuminate the unequal gender norms around domestic and care responsibilities that still exist in today’s society and its contribution to the gender gap in earnings. In this paper we utilize data on COVID-19 related school closure policies to explore the impact of an exogenous shock to the domestic and care-related activities within a country on the gender gaps in entrepreneurship.

Using high-frequency monthly data collected from over 150,000 firms across 50 countries surveyed on Facebook, we present evidence that female entrepreneurs were indeed more likely to have closed their business during the COVID-19 pandemic than male entrepreneurs. The closing of schools, a key part of the care infrastructure, leads to higher business closures, and women were more likely to close their business than men. Part of what is driving a significant amount of this disproportionate female closure is care work. Women entrepreneurs spend more time on care when schools closed and they do not get help from their spouses. Male entrepreneurs, on the other hand, do not increase the hours they spend on care, and get help from their spouses. In order to show that the excess female closure is due to childcare, we separately examine patterns for women with children and those who do not. The pattern of higher firm closure rates associated with school closure and increased care is significant and economically meaningful for women with children, it is not significant for women without children.

Finally, we show that female entrepreneurs in the most gender conservative societies, as defined by male individual beliefs, are significantly more likely to increase their time spent on care and chores, and close their business in response to a school closure policy, relative to women in more gender liberal societies.

30
.077**|0.079**|0.076**|0.077**|0.070**|
||(0.035)|(0.035)|(0.035)|(0.035)|(0.035)|(0.035)|
|No. of obs|35,439|35,439|35,439|35,439|35,439|35,439|
|R-squared|0.312|0.312|0.312|0.312|0.312|0.312|

_Note_ **_:_** Results are from 2019-2022 panel regressions where the unit of observation is the village council and the dependent variable is the NDVI for fields planted with winter wheat in each of the relevant years. Crimea and areas that have been occupied since 2014 are excluded. GDD is cumulative growing degree days above 0°C between October 1and May 31 approximated hourly by sinusoidal distribution of minimum and maximum temperature (d’Agostino and Schlenker, 2016); and fall, winter and spring months are Oct-Nov, Dec-Feb, and Mar-May, respectively. ‘Any’ (Col. 5) is the maximum over the 8 two-week periods considered whereas ‘conflict intensity’ (col 6) accounts for the temporal dimension by taking a value of 1 for any of the 8 two-week periods when any conflict activity was observed. See appendix table 1 for full specification including weather variables. Standard errors in parentheses. * p<0.10, ** p<0.05, *** p<0.010.

34
, and their measures use a clear methodology and are straightforward. Except for the Labor Market Indicators Database, all sources are public. Our sample covers 76 countries.<sup>8</sup> In most cases, data are based on surveys conducted in a single year (in the late 1990’s) in a large group of countries; for components with observations for more than one

> 8 We do not consider here the heterogeneity of regulation within countries.  This can be important in some cases, such as the added burden of local taxation in federal systems.  At present, however, very few data sources provide information on regulation at the regional level.  One of these sources is the Investment Climate Survey of The World Bank, which records data on investment climate and activities of over 14,000 firms in about 30 countries.

7
Structure,” World Bank Economic Review 14, 597-605.

- Beck, T., A. Demirg ̈u ̧c–Kunt, and R. Levine, 2010, “Financial Institutions and Markets across Countries and over Time: The Updated Financial Development and Structure Database,” World Bank Economic Review 24, 77-92.

- Beck, T., R. D ̈ottling, T. Lambert, and M. van Dijk, 2021, “Liquidity Creation, Investment, and Growth,” Working Paper.

- Beck, T., and R. Levine, 2002, “Industry Growth and Capital Allocation: Does Having a Market– or Bank–Based System Matter?” Journal of Financial Economics 64, 147–180.

- Beck, T., and R. Levine, 2004, “Stock Markets, Banks, and Growth: Panel Evidence,” Journal of Banking and Finance 28, 423–442.

- Becker, B., J. Chen, and D. Greenberg, 2013, “Financial Development, Fixed Costs, and International Trade,” Review of Corporate Finance Studies 2(1), 1–28.

- Bekaert, G., C.R. Harvey, C. Lundblad, and S. Siegel, 2007, “Global Growth Opportunities and Market Integration,” Journal of Finance 62(3), 1081-1137.

- B ́ek ́es, G., and B. Murak ̈ozy, 2012, “Temporary Trade and Heterogeneous Firms,” Journal of International Economics 87, 232–246.

- Ben-Nasr, H., 2016, “State and Foreign Ownership and the Value of Working Capital Management,” Journal of Corporate Finance 41, 217–240.

- Berman, N., and J. H ́ericourt, 2010, “Financial Factors and the Margins of Trade: Evidence from Cross-Country Firm-Level Data,”Journal of Development Economics 93(2), 206-217.

- Besedeˇs, T., 2013, “
>1|2.9|1|24.7|7|
|**Dom. Rep. -2009**|3,031|2,812|147|6.2||2.2 <br>|||||
|**Portugal -2005**|21,127|1,143|202|4.6|2|2.8 <br>1|||||

_Source:_ Lao PDR - authors‟ calculation based on customs data; Mali, Malawi, Senegal and Tanzania – Cadot et al (2010); Dominican Republic – Molina, Bussolo and Iacovone (2010); Portugal – Amador and Opromolla (2010).

This first look at Lao exports‟ composition is complemented by a look at the top products (disaggregated at the HS 6-digit and 2-digit level). Table 2 shows a list of products ranked by their total dollar value in the period for which data is available. The domination of copper and related products is immediately apparent (also its steady rise if calculated by year), as well as the important presence of wood, agricultural products and garments. At the 2-digit level, one also _Exporting from a small landlocked economy_ _7_
120-500, and > 500 ha groups, respectively.<sup>12</sup> To quantify the extent of informality in Ukraine’s agriculture sector, we use crop maps for 2020 elaborated using the methodology described in Kussul _et al._ (2017); Shelestov _et al._ (2017), and Shelestov _et al._ (2020) to identify the total area cultivated with crops in 2020 and then compare this area to what is covered by Form 29 as well as SAR.<sup>13</sup> Table 1 panels C and D highlight that, based on these maps, Ukraine’s total cultivated area amounts to 45.73 million ha, 33.87 mn ha in ACUs and 11.86 mn ha in AACs. With 27.8 mn. ha (21 mn ha in ACUs and 6.8 mn ha in AACs), SSSU’s Form 29 thus covers 61% of the country’s cultivated area in 2020, leaving an estimated 39% of cultivated area in informality.

Using enterprise IDs to match farms between SAR and Form 29 suggests that 7.5 mn ha nationally (6.4 mn. ha in ACUs and 1.1 mn ha in AACs) is cultivated by farms included both in SAR and Form 29 while 3.36 mn. ha, almost all in ACUs, are registered in SAR only. This suggests that in the 3-month period from Aug. 12 to Oct. 15, 2022, registration in SAR reduced the amount of land cultivated informally by 3.36 mn. ha or about 7% of the total. Still, 14.5 mn. ha, more than the total agricultural area of Poland or Germany, are cultivated by farmers that are neither part of SSSU’s sample frame nor registered in SAR, implying that this land is not reflected in any official statistics.

> 12 As a plausibility check, we compare mean farm sizes between Form 29 and SAR. For the above
>_∗_</sup>|1.126|1.043|0.941<sup>_∗∗∗_</sup>|
||(0.240)|(0.420)|(0.592)|(0.567)|(0.736)|(0.852)|(0.313)|
|_×_ Oil price|-0.119|0.091|-0.610|0.144|1.584<sup>_∗_</sup>|1.409|0.464|
||(0.301)|(0.838)|(0.973)|(0.857)|(0.784)|(0.929)|(0.560)|
|N|3392|3523|3523|3523|3523|3523|3523|
|R-sq|0.72|0.66|0.57|0.61|0.72|0.64|0.66|

The dependent variable is in inverse hyperbolic sine. The regressions include country and year fixed effects. Robust standard errors in parenthesis, and * stands for statistical significance at the 10% level, ** at the 5% level and *** at the 1% percent level.

the whole study period, with a long list of other countries accounting for the remaining 25%. I thus check if discoveries trigger arms imports from these large arms exporters in particular. The first surprising result is that arms imports from the United States, the largest arms exporter in the world, do not react at all to giant discoveries. This is true across specifications and datasets. For other countries the results are different whether we look at COMTRADE or SIPRI data. Using COMTRADE data I find arms imports from China to be particularly responsive to giant discoveries, while arms from France and Germany react strongly
We implement our preferred methods for all countries to estimate global poverty, benchmarking the results against poverty estimates published by the World Bank. We find that the models track poverty rates relatively well in general but with notable exceptions. We show that these errors are in part due to poverty estimates not being comparable across countries and within countries over time but surely also due to modeling errors.

On average, our preferred models predict income or consumption off by around 30%. While this is a large error, it is important to assess it against an appropriate benchmark. A random forest on all 1,000+ possible predictors does not lead to any gains in the out-of-sample error, suggesting that much of the remaining error is likely irreducible. Furthermore, at a global scale, a 30% error is small in comparison with the large observed income differences: The median welfare of the richest country in our sample is more than 100 times that of the poorest country, and the 75<sup>th</sup> percentile of medians is 5 times greater than the 25<sup>th</sup> percentile.

A long-standing literature has tried to overcome data gaps and predict distributions when limited information was available. Survey-to-survey imputations can be used when survey data on welfare is unavailable at a desired point in time, but data on correlates of households’ wellbeing is available together with welfare data from a prior household survey (Stifel and Christiansen 2007, Roy and Van Der Weide 2024). Alternatively, national accounts data can be used to extrapolate older welfare vectors forward in time (Mahler et al. 2022; Angrist et al. 2021). Others have estimated full distributions when grouped data or summary statistics are available (Chen 2018; Chotikapanich et al. 2012, Eckernkemper and Gribisch 2021, Jorda and Niño-Zarazúa 2019; Hajargasht et al. 2012). Wealth indices have been used to predict full distributions for individual countries that lack consumption or income data but have a Demographic and Health Survey (Filmer and Prichett 2001,
Table 3: Weighing Different Kinds of Evidence

||Pooled<br>(1)|Policymaker<br>(2)|Policy<br>Practitioner<br>(3)|
|---|---|---|---|
|Impact|1.07***|1.05***|1.10***|
||(0.01)|(0.02)|(0.02)|
|Quasi-Experimental|1.11|0.97|1.31|
||(0.13)|(0.15)|(0.22)|
|Experimental|1.40***|1.30*|1.55**|
||(0.16)|(0.19)|(0.29)|
|Diferent country, same region|1.15|1.05|1.31*|
||(0.12)|(0.15)|(0.20)|
|Same country|1.59***|1.33**|2.02***|
||(0.15)|(0.17)|(0.26)|
|Recommended by local expert|1.24***|1.28**|1.21*|
||(0.10)|(0.15)|(0.12)|
|Small C.I.|1.29***|1.06|1.61***|
||(0.11)|(0.12)|(0.20)|
|Observations|818|407|411|

This table reports the results of conditional logit regressions on which program was selected. Odds ratios are reported. “Impact” refers to the estimate
the bank is domiciled, and the rate of return of oil prices estimated using weekly data between May 2018 and December 2019.

Our final sample comprises 3,043 firms, of which 896 are commercial banks and 2,147 correspond to non-bank financials. Panels A and B of Table A1 in the Appendix present the distribution of banks and non-bank financial companies across the countries in our sample. Table 1 displays the summary statistics of the variables of interest for the banks in the sample.

As Table 1 shows, the daily return of bank stocks from January to early May 2020 was on average -0.4 percent, with the median bank obtaining returns of -0.3 percent. 12 percent of the banks in the sample are classified as public owned.<sup>9</sup> However, as Table A2 in the Appendix shows, there is large heterogeneity in the share of public banks across regions. Whereas state-owned banks in India represent 53 percent of all publicly traded banks in the country, in high-income countries they constitute just 2 percent.

As a measure of ex-ante liquidity of banks, we calculate the cash-to-total assets ratios of banks and average them over the 2019Q1-2019Q4 period. The liquidity ratio of the median bank in the sample is 7 percent, with banks in the 25<sup>th</sup> percentile holding as little as 2 percent of cash and equivalents relative to their total assets.

Bank size is calculated as the 2019Q1-2019Q4 average of the total assets for each bank. We then take the Inverse Hyperbolic Sine (IHS) to smooth outliers in the distribution. The average size of banks in the sample is 24 IHS assets, which corresponds to 13.4 billion dollars. Regulatory

> 9 We define public banks as those with a non-zero equity participation from the domestic government. Our findings are robust to alternative definitions of public banks where the minimum threshold of state ownership is set to 10% and 30%.

9