rafmacalaba/datause-encoder-data huggingface dataset:
programs. The benefit is the fraction of high school graduates who finish college, and the cost is the net public spending incurred to produce them. We maintain all our previous assumptions.

Lacking data on the annual cost of educating a college student, _C_ , we assume it is equal to the tuition paid by students from the highest income bracket at public universities (equal to 2.2 million COP on average; see Appendix Table A3). This is a lower bound of the true cost, as public colleges subsidize tuition even for the highest income students, and private universities— whose tuition might be closer to actual costs—charge more than 6 million COP on average. To the policymaker, the average net cost per graduate under policy _p_ as follows: where _Nt_<sup>_p_isthenumberofstudentsenrolledunderpolicy</sup><sup>_p_inyear</sup><sup>_t_,</sup><sup>_C_istheannualcostof</sup> educating a college student, and _T_<sup>_p_</sup> ( _·_ ) is the tuition paid by student _i_ under policy _p_ . By adding in the numerator over all individuals enrolled, equation (20) shows that the per-graduate net cost accounts for the cost of dropouts as well.

For each policy, Figure 15 shows the average net cost per graduate in terms of per capita GDP (panel a). For convenience, it also shows the fraction of high school graduates who finish college (panel b), which provides a measure of _G_<sup>_p_</sup> . Net cost per graduate is already high in the baseline—about the same as per capita GDP. Even if enrollment did not rise, free college would raise net cost per graduate just because fewer students would pay tuition. The combination of lower tuition revenues and higher enrollment raises net cost per graduate under all free college regimes. The cost rises the least under ability-and-need based free college and the most under universal free college, mostly reflecting each policy’s enrollment expansion.

**Figure 15: Free College Cost**

<!-- Start of picture text --> 3 30<br>2.5 25<br>2 20<br>1.5 15<br>1 10<br>.5 5<br>0 0<br>Baseline Univ. Need Abil.Abil.+NeedPerf. Perf.+Need Baseline Univ. Need Abil.Abil.+NeedPerf. Perf.+Need<br>a. Average net cost per college graduate b. Percent of high school graduates<br>(relative to GDP per capita) that complete college<br>Percent<br>Avg. net cost per grad / GDP pc<br><!-- End of picture text --> _Source_ : Model simulations for enrollment and graduates. See the text for (gross) cost assumptions. GDP per capita is from Colombia’s National Administrative Department of Statistics (DANE). _Notes_ : Average net cost per college graduate is computed as described in the text.

Consider, now, a policymaker seeking to maximize the number of graduates subject to a budget constraint. Universal and performance-based free college maximize the number of 43
Of the roughly 80 countries involved in the BRI, each one has its own set of environmental policies that must be understood to grasp the full regulatory landscape. There is a wide range of national, provincial, and local regulatory policies that could have a bearing on environmental risks from transportation infrastructure projects including: environmental assessment laws; environmental protection laws for public lands; environmental protection laws from private lands including easement laws; primary protected areas legislation; criminal codes addressing violation of environmental protection laws and related obligation; wildlife laws; construction permitting; wetlands protection and mitigation; vehicle emissions standards; damages law for hazardous materials spills and leaks; indigenous peoples rights laws/territory rights; zoning laws; imminent domain laws; and national procurement rules. In addition to national legislation, countries may have policies, guidelines, and strategies for addressing many of these issues. It is also important to track various complementary laws, regulations, and zoning ordinances from provincial or local governments, since these can sometimes have as much if not more influence over BRI projects than a central government (H. Liu and Lim 2018). There is a growing body of resources that document, archive, and analyze environmental laws. The Green Growth Knowledge Platforms (GGKP) provides an excellent meta‐analysis of 24 databases of environmental regulations, financial incentives, overarching policies and frameworks, and voluntary approaches (Booth 2017). Overall these databases provide comprehensive coverage of the electricity, agriculture, forestry, and fishing sectors in OECD countries. Coverage of other sectors and regions of the world – including most BRI countries – is less exhaustive, unfortunately. Legal Atlas is beginning to develop an interactive knowledge platform for all environmental regulations related to infrastructure projects in every BRI country, initially focusing on laws for linear infrastructure. Appendix 10 describes several databases that are particularly valuable for assessing environmental policies that may have a bearing on BRI transportation infrastructure. It should be noted that, while host country laws may constrain all the BRI investments, significant elements of the environmental risks that are associated with BRI transport corridors are transboundary risks, including impacts upon carbon emissions, forest cover, and biodiversity. Limiting these transboundary impacts can fairly be considered to be global or regional public goods provision, so we should not expect host countries to fully take these impacts into account in their own laws (Kaul, Grunberg, and Stern 1999) .

32
Lustig and Tommasi 2020). These studies find that Latin America was one of the regions hardest hit by the COVID-19 pandemic and the containment measures, with staggering costs in terms of lives, jobs, incomes and welfare.

Finally, there is a literature that studies the impact of the shock exploiting the World Bank’s HFPS surveys both around the globe, and specifically in Latin America (Ballon et al. 2021; Cucagna and Romero 2021; Khamis et al. 2021; Kugler et al. 2021; MejiaMantilla et al. 2021; Olivieri and Rivadeneira, 2021). The richness of these surveys allows exploring the impact of the shock in the labor market as well as in education, food security and other outcomes.

Our paper starts by making a contribution to this latter literature. By codifying the occupation variables and constructing a variable for the individual potential for WFH we are allowed to study some previously unexplored issues with HFPS data on the asymmetric impact of the shock. Our results also make a contribution to other strands of the literature. In particular, we shed some light on the role of teleworking in alleviating the costs of the crisis, and explore the mechanisms behind the gender asymmetries. Finally, our paper is also a contribution to a better understanding of the labor market impact of the pandemic and the social-distancing measures in the specific context of Latin America.

# **3 Data**

Our main source of data is the High-Frequency Phone Surveys (HFPS) conducted by the World Bank in 2020 to assess the impact of the COVID-19 pandemic. In this section we present this data set, explain how we codify the occupation variables, present basic descriptive statistics of job losses, and discuss an index of the individual’s potential for work from home.

## **3.1 The High-Frequency Phone Surveys**

One of the reactions to understand the impact of the pandemic was to collect new data, given that regular national household surveys were not well-suited to deal with this novel situation. One of the more ambitious initiatives was led by the World Bank, which implemented or supported several waves of high-frequency phone household surveys in over 100 countries, and harmonized the results. Thirteen Latin American countries participated in the survey: Argentina, Bolivia, Chile, Colombia, Costa Rica, Dominican Republic, Ecuador, El Salvador, Guatemala, Honduras, Mexico, Paraguay, and Peru. These countries represent around 60 percent of the region’s population.

The HFPS have a panel structure over three rounds conducted between May and August 2020.<sup>2</sup> The surveys collect information on multiple dimensions, such as changes

> 2The first round was conducted between May 8 and June 14, 2020, the second round from June 5 until July 16, and the third round from July 5 until August 25, 2020. In Ecuador there was a fourth round, which was collected between August 15 and 25, 2020.

5
decades, Bhutan has also achieved broad-based and inclusive poverty reduction. As noted by NSB and World Bank (2014), between 2007 and 2012 the proportion of the population with consumption below the official poverty line dropped by half and Bhutan was able to practically eradicate extreme poverty. Multidimensional poverty indices and indicators related to education and health outcomes have also seen significant improvements.

With regard to Bhutan’s labor market, overall conditions are more favorable in the country than in most other South Asian economies. For instance, data from MoLHR (2015) show that in 2014 Bhutan’s overall unemployment rate stood at 2.6 percent. Besides, the gaps between male and female labor force participation and employment rates have long been much narrower than in many other South Asian economies. While the overall education level of Bhutan’s labor force continues to be comparatively low, it has been improving rapidly due to a trend toward almost universal enrollment in primary education, increasing enrollment rates in secondary education, and an ever greater emphasis on tertiary education.

At the same time, Bhutan faces a number of significant labor market challenges and bottlenecks. These include high levels of informality and underemployment (in particular in rural areas), a limited formal social protection system and relatively elevated rates of unemployment for certain parts of the labor force, especially for young, well-educated city dwellers. Specific labor market challenges relate to the interplay between the public and the private sector. A vibrant private sector has yet to emerge in Bhutan. As documented in MoLHR (2015), the country’s public sector comprises almost 50 percent of employment outside agriculture. It also employs two-thirds of workers with tertiary education. In addition to that, the public sector is widely perceived as the preferred employer, including among educated youth who aspire to white-collar jobs. In a recent survey among unemployed youth in Bhutan, 50.3 percent of respondents said they would prefer to work for the government and another 32.4 percent aspired for a job in a state-owned enterprise (MoLHR and UNDP, 2014). Similar data from the 2014 edition of the Bhutan Labour Force Survey confirm that the majority of unemployed persons would like to work in the public sector. Highly educated persons are particularly keen to find a job in the public sector and the perceived superior salary, good benefit packages and high job security of public sector jobs are cited among the main reasons for why work in the public sector is so attractive.

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surveyed. Importantly, earth observation data suggest that 2016, the year the MAPS data was collected, was a drought year (FAO, no date). The data may thus reflect a similar situation as in 2015 in Ethiopia where self-reported losses where conspicuously high compared to average yields and suggest that in drought years we observe an “overreaction” in farmers’ reports.

# <u>What do we conclude and still need to know?</u>

While farmer reports and expert assessments show some overlap, they do not match very well and display some striking discrepancies that are not explainable by small differences that would be expected in the appraisal process. This may serve as further suggestive evidence that while eliciting losses on a percent scale gives a decent ordinal proxy in most cases, forming an exact point estimate is cognitively challenging, may lead to satisficing behavior, and is thus prone to error. This seems to be exacerbated in drought years where farmers’ reports are particularly high. At the same time, we cannot ascertain the accuracy of enumerator reports with confidence either as enumerators may err just as well when it comes to losses at the extensive and intensive margins. This makes it hard to proclaim enumerator assessments a credible ‘gold standard’. In order to better understand the accuracy in farmers’ self-reported losses, finding a dependable, “objective” measure of losses is thus still an open question for future methodological research.

# <u>Question 3: Can data from past harvests or unaffected plots serve as a suitable benchmark for attainable harvest?</u>

The FAO methodology heavily relies on the ability to obtain an accurate estimate of the attainable yield. For this, it suggests drawing on past harvest data to calculate the multi-year trend (or average) of realized yields as an indication of in-season attainable yields (Conforti, Markova and Tochkov, 2020). Alternatively, using same-season realized yields from plots unaffected by any disaster, for example average yields from an agriculturally comparable district, would be a possibility (FAO, 2020).

Simplifying Equation (1) from the framework in Section 3 and using that we can write where the counterfactual yield y<sup>c</sup> is a function of past yield realizations or 22
agile. On the other hand, the smaller scale (in terms employee size) of informal firms does not leave much scope for adjustments on either the extensive or the intensive margins. The overall effect depends on the net of the two effects and is an empirical question.

Our data suggest that the scale effect dominates such that informal firms have about an 8 and 14 percentage-point lower probability of adjusting on the extensive and intensive margins, respectively (Table 4, panel A column 1 and 5). The elasticity of adjustment is lowest among informal firms driven by necessity and highest among the parasite type, suggesting that the available scale of adjustment could be driving these results.<sup>14</sup> Shapley’s decomposition of the probability of employment adjustment at extensive and intensive margins suggests that informality explains 60% and 72%, respectively, of these adjustments (Figure 5, panels a and b). The size of the shock (change in sales) is much more important at the extensive margin, indicating that larger negative effects of the crisis on sales induce more adjustments on the extensive margin. Firm-level constraints (mainly uncertainty) explain 11% of the adjustments on the extensive margin, while a much smaller share on the intensive margin. The overall characteristics of informality explain a larger share of adjustments on the intensive margin, and so do the external attributes such as age, size, and sector.

Macro evidence suggests that the unemployment rate rises sharply during recessions, while the recovery is only slow and gradual during expansions. This mismatch of sharp spikes in unemployment during recessions and slow job gains during expansions is known as “cyclical asymmetry” (Andolfatto and Spewak, 2018; Andolfatto, 1997; Ferraro, 2018, 2023). Micro evidence in Cirera et al. (2021) confirms this pattern using BPS data. Our work suggests that the elasticity of change in employment with the drop in sales was comparable for both formal and informal firms in the first phase of the crisis. In the recovery phase, however, informal firms show a nearly inelastic response to employment growth with the recovery in sales. This is in contrast to formal firms where the change in employment is slower but still positive (Figure 6, panel a). Using a sharper definition of fully formal firms, we illustrate that not only do all types of informal firms show signs of relatively slow job recovery, and particularly the parasite ones, but that the fully formal firms are remarkably different (Figure 6, panel b). This implies that the degree of formality matters. Given the presence of a large informal sector in South Asian countries, this does not bode well for job recovery in the post-crisis period.

> 14Mean size in our sample is ten employees for necessity firms, 13 for De Soto, 12 for parasite firms, and 48 for fully formal. We test the robustness of our result to adding uncertainty and change in sales as controls in Table B4, and the results do not change.

18
deposits, mining has grown substantially in significance compared to manufacturing, while agriculture remains marginal in terms of value added to GDP (see Figure 2).

**_Figure 2: Contributions to GDP Growth, supply side_**

<!-- Start of picture text --> 8%<br>6%<br>4%<br>2%<br>0%<br>2008 2009 2010 2011 2012 2013 2014 2015<br>-2%<br>-4%<br>Agriculture Industry (excl. mining)<br> Mining Services<br>Taxes (net of subsidies) GDP at market prices<br><!-- End of picture text --> Source: World Bank (2017) The country has faced unsustainably high budget deficits (9.3% in 2016/17). Public expenditure has in the past years acted as a leading driver of growth. This has been particularly driven by the government wage bill, which comes to over 18% of GDP in recent years, among the highest in the world (World Bank 2016). This dwarfs the amount spent on grants and social benefits, as well as capital expenditure though particularly the latter has been increasing significantly as a share of total expenditure in recent years. This is exacerbated by political instability, high rates of HIV, tuberculosis and maternal and infant mortality,<sup>4</sup> as well as significant exposure to climate-related stresses, including a prolonged drought in 2015/16. Social protection transfers targeted towards the poor have not been effective.

Reliance on revenue-sharing within SACU in order to fund public expenditure means that fiscal policy is also in part outside of Lesotho’s control. It also makes trade liberalization a very difficult sell. These factors combine to greatly complicate any efforts for Lesotho’s policy makers to make substantial liberalization reforms for key imported inputs, even if the investors Lesotho is hoping to attract require lower-cost inputs in order for Lesotho to be a competitive location. However, the substantial depreciation of the maloti in recent years creates an opportunity for exporters to exploit its increased currency competitiveness. Furthermore, while Lesotho has only limited leverage within SACU, it could pursue targeted negotiations with South Africa and other SACU members on removing tariffs on inputs to access specific regional value chains that may also benefit the region.

> 4 Driven primarily by the high mortality and morbidity rates caused by the HIV-AIDS crisis, life expectancy in 2015 (50.0.years) was almost 10 years lower than it had been 1992 (59.6 years).

5
Table 4: US Antidumping and Safeguard Tariff Imposition: Import Growth and Industry Effects

||**Antidumping**<br>**and**<br>**safeguard**<br>**tariff policies**|**Add**<br>**political-**<br>**economy**<br>**covariates**|**Add steel**<br>**and**<br>**chemical**<br>**indicators**|**Predicted pr**<br>**safeguard**<br>**deviation in**<br>**variable,**|**obability of ant**<br>**tariff for one**<br>**crease in each**<br>**expressed in**|**idumping or**<br>**standard**<br>**explanatory**<br>**percent†**|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(1)|(2)|(3)|
|Growth of imports_ikt-1|6.11***|3.44***|3.34**|0.39|0.38|0.39|
||(1.71)|(1.14)|(1.37)||||
|�<br>�<br>�<br>�_k<br>�<br>�<br>1/<br>ln<br>f<br>m<br>f<br>x <sup>�</sup>|1.19***|0.71***|0.24***|0.66|0.63|0.39|
||(0.19)|(0.12)|(0.06)||||
|Standard deviation of<br>import growth_ik|-0.25***<br>(0.03)|-0.14***<br>(0.02)|-0.16***<br>(0.02)|0.08|0.10|0.09|
|Percent change in|4.91***|2.70***|2.75***|0.42|0.40|0.40|
|real exchange rate_it-1|(0.65)|(0.48)|(0.43)||||
|**_Domestic industry variables_**|||||||
|ln(Four firm conc. ratio)_k|--|0.25***<br>(0.10)|0.13<br>(0.11)|--|0.38|0.35|
|ln(Employment)_kt-1|--|1.04***<br>(0.14)|0.70***<br>(0.10)|--|0.62|0.47|
|Value-added/Shipments_kt-1|--|-3.58***<br>(0.64)|-1.08**<br>(0.54)|--|0.20|0.28|
|Inventories/Shipments_kt-1|--|6.82***<br>(0.98)|4.98***<br>(0.75)|--|0.47|0.41|
|Indicator for industry k is<br>steel|--|--|0.04***<br>(0.01)|--|--|0.33|
|Indicator for industry k is<br>chemicals|--|--|0.01***<br>(0.00)|--|--|0.39|
|**Predicted probability of antidum**<br> **expressed in percent,† at mea**|**ping or safeguar**<br>**ns**|**d tariff,**||0.32|0.32|0.32|
|**Observations**|81,943|81,943|81,943||||
|**Log-likelihood**|-1631.52|-1512.05|-1346.50||||

Notes: Dependent variable is a binary indicator that a US antidumping tariff or safeguard was imposed on exporting country i in industry k after an investigation initiated in year t. Probit model used to estimate all specifications. Huber-White robust standard errors in parentheses. ***, **,* indicate statistical significance of marginal effects at the 1%, 5% and 10% levels, respectively. †Predicted probabilities expressed in percent terms; e.g., 0.32 is a predicted probability of thirty-two hundredths of one percent, or 0.0032. 24
# **Appendix 4: Variable definitions**

# **1. Quality shock variable:**

# **<u>Aggregate shock (</u>** **_<u>AGGSHOCK</u>_** **<u>)</u>**

- _Fraction of bad aid boats in 07_ :  Proportion of faulty boats from donors in the village by 2007, which is the number of faulty aid boats divided by the total number of aid boats in the village. (from the _Panglima Laot_ survey in 2007)

# **<u>Idiosyncratic shock (</u>** **_<u>IDIOSHOCK</u>_** **<u>)</u>**

- _D_have a bad aid boat in 07/09_ :  Dummy variable indicating whether a household received an aid boat which was discovered to be poor quality by 2007/09 or not. (from the household survey in 2007/2009)

- _D_bad house in 07/09_ :  Dummy variable indicating whether a household received a temporary or permanent house from donors which had at least one type of quality issues such as a leaky roof, cracked wall, poor foundation, or faulty plumbing by 2007/09 or not. The summary statistics in Table 1 also reports the fraction of households facing each type of housing quality problem for the supplementary sample. (from the household survey in 2007/2009)

# **2. Household characteristics:**

- _Fish in 07/09_ :  Dummy variable indicating fishing as a primary job in 2007/09 or not.(from the household survey in 2007/2009)

- _Report catch in 07:_ Dummy variable indicating any family members reported some amounts of fish outputs in 2007 or not. (from the household survey in 2007/2009)

- _Boat aid in 07/09:_ Dummy variable indicating the receipt of a boat from donors by 2007/09 or not. (from the household survey in 2007/2009)

- _Buy in 07/09:_ Dummy variable indicating the receipt of a boat by investing own money by 2007/09 or not. (from the household survey in 2007/2009)

- _Boat owner in 07/09:_ Dummy variable indicating households owned a boat in 2007/09 or not. (from the household survey in 2007/2009)

- _Boat owner and captain pre-tsunami:_ Dummy variable indicating pre-tsunami boat owner and captain or not.  (from retrospective data in the 2005 household survey)

- _D_large boat pre-tsunami:_ Dummy variable indicating the use of boat with above average boat length (9 meter) pre-tsunami or not.  (from retrospective data in the 2005and 2009  household surveys)

- _High education pre-tsunami:_ Dummy variable indicating household heads' education level was above senior high school pre-tsunami or not.  (from retrospective data in the 2005 household survey)

- _D_debt to Toke Bangku:_ Dummy variable indicating household owed traditional debt to middlemen ( _Toke Bangku_ ) and was still obliged to repay after the tsunami or not. (from retrospective data in the 2005 household survey)

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