rafmacalaba/datause-encoder-data huggingface dataset:
# **Appendix B: Global Monitoring of School Closures Related to the COVID-19 Pandemic**

To determine the status of the school closure policy in each country we use the information gathered by UNESCO on school closures due to COVID around the world (UNESCO, 2020). UNESCO has monitored the status of the schooling system on a daily basis and categorized each country in one of the following 4 classifications:<sup>4</sup>

1. Closed due to COVID-19: Government-mandated closures of educational institutions affecting most or all of the student population.

2. Academic break: Schools across the country are on scheduled academic breaks for most or all the student population. All instructional activities are suspended during this period.

3. Fully open: Schools are open and deliver classes exclusively face-to-face for most or all of the student population.

4. Partially open: Schools are: (a) open in certain regions and closed in others; and/or (b) open for some grades, levels, or age groups and closed for others; and/or (c) open with reduced in-person class time, combined with distance learning (hybrid approach).

Table B1 below presents the status of the school closure policies for each of the countries included in our sample at the time of each survey wave. In the analysis for this paper we classify countries in our sample into three categories: (1) Schools closed partially or fully due to COVID-19, (2) Schools fully open, or (3) On academic break.

Graphs B1 to B3 plot the percentage of countries in each region that had a school closure policy in place at the time of each survey wave and shows there was significant variation in school closure policies over time. For example, at the time of the first survey wave in May 2020, approximately 82% of countries in Europe had a school closure policy in place due to COVID-19 (fully closed or partially closed); 18% of countries had schools fully open and no countries were on Academic Break. In July 2020, most of the countries surveyed in Europe were on a scheduled Academic Break. By the time of the final survey wave in October 2020, the school closure policies within Europe had changed whereby approximately 19% of European countries had a school closure policy related to COVID-19 in place and 81% of European countries had schools fully open. However, the pattern is not the same in all regions. For example, in the South Asia region the policies show a more constant trend whereby schools were closed in the majority of countries throughout the six month study period. Notably, in Latin America, Sub-Saharan Africa and South Asia there was a higher average rate of school closures due to COVID-19 (approximately 80% of countries had schools fully or partially closed due to COVID-19 averaged across the survey period) compared with Europe, Middle East and Asia and Oceania (approximately 40% of countries had schools fully or partially closed due to COVID-19 averaged across the survey period).

> 4 Please refer to the methodological note “Global Monitoring of School Closures caused by the COVID-19 Pandemic”

48
The decades-long military operations of the CCP resulted in heavy casualties. We collect data on all recorded communist war casualties (recorded as “martyrs”) from China’s Ministry of Civil Affairs, which uses this data set to pay pension and death benefits to family members of CCP war deaths.<sup>10</sup> The data set covers 1.24 million people who died in in the communist military insurrections between 1927 and 1949. It records their names, place of birth, and place and date of death. Our data set records 188 prefectures which are home to CCP war casualties. The mean number of CCP war casualties in a prefecture is 6,597; the median, 1,183.

# **4. Border Ruggedness and Famines**

In this section we test the warlord fragmentation hypothesis and the terrain ruggedness hypothesis. We first conduct the ordinary least square estimation, and then offer extensive sensitivity checks, as well as the instrumental variable estimation.

# **4.1. Baseline Results**

We first examine how the incidence of famines changed from unified central rule to the warlord era by the following specification: era by the following specification: F F F P (1) The dependent variable F Fii,ttF= ββ P is a dummy variable indicating the existence of tt + VVii + εεii,tt recorded famines in prefecture _i_ in yearii,tt _t_ . P P is a dummy variable indicating the warlordyears between 1917 and 1926. We control for prefecture fixed effectstt to hold all timeinvariant factors constant. We cluster standard errors at the prefecture level to allow the VVii unobservables correlated over time within the prefecture. Our sample here covers the prefectures from 1912 to 1926. The results are in Column 1 of Table 2.

Consistent with the warlord fragmentation hypothesis, the incidence of famines clearly rose drastically after the warlords took over. The coefficient indicates that after the arrival of the warlord period, the incidence of famines increased by 5.3 percentage points, or roughly 1⁄4 of one standard deviation (SD) of the famine indicator in our sample period.

> 10 The original wording is martyrs, and they were officially-designated CCP war casualties. For simplicity we refer to these as war casualties.

14
# 3 Data

The data for the empirical analysis of this paper come from the Nepal Living Standard Survey (NLSS) of 1995/96. The NLSS is a nationally representative survey which collected information from 3373 households spread among 274 primary sampling units (locally known as ‘wards’) covering 73 of Nepal’s 75 districts. In addition to the comprehensive information on household and its members’ characteristics, and household’s expenditure levels, the survey collected detailed information on agricultural activities including cropping pattern, crop production and sales. Of all households for which we have complete information (about 3344), 75% are engaged in crop production. Nearly all of the farmers engaged in crop production are located in rural areas (93%) and the rest located in and around rural towns. The farm households (about 2531 households) are distributed in 257 wards/villages. We drop wards/villages with less than three farm households from our sample. The empirical analysis of this paper is thus based on the sample of 237 villages/wards where at least three households are engaged in agricultural production. Both dependent and explanatory variables for the empirical analysis are de fi ned at the village level using the information on farmers residing in respective ward/village.

Panel A of Table 1 provides the summary statistics for di ff erent measures of agricultural specialization and commercialization. The Her fi ndahl index (HI), de fi ned in equation (7), is constructed from the cropping pattern observed at the village level. As already noted, the index takes a value of unity if all land in a village is devoted to a single crop and declines in value with an increase in the number of crops grown in a village. The median of the Her fi ndahl index is about 0.27 (mean=0.31). There are, however, considerable variations in the level of specialization across villages covering the entire spectrum from complete specialization (HI=1) to highly diversi fi ed cropping patterns. Our second measure of specialization (nonsubsistence specialization) is the share of land devoted to production of relatively high value non-cereal crops (fruits, vegetables, oilseeds, spices, cash and other crops). According to Table 1, cereal crops dominate the cropping pattern. On average, less than a third of total land during dry season is used to produce non-cereal crops, the median is smaller about 0.25.

10
countries for trade with each other. This is supported by the fact that nine of the eleven possible estimates in panels B and C are negative and more than half of them are statistically significant. The two positive estimates that we obtain are not statistically significant and are for Chemicals and Machines. According to our estimates the border gap is the widest in Food (about 22% tariff equivalent), Minerals (about 15% tariff equivalent), and Wood (about 10% tariff equivalent). Based on the analysis in this section, we conclude that CEFTA members have the potential to face significantly lower barriers to trade upon accession to the European Union, which may lead to significant gains in terms of welfare. We use our structural gravity estimates to quantify the potential for such gains in the next section.

# **4.2 On the GE of CEFTA-EU Harmonization**

We employ the standard structural gravity general equilibrium framework following Dekle, Eaton and Kortum (2007, 2008) to perform two counterfactual experiments that quantify the total GE impact on the exports of the countries in our sample.<sup>21</sup> The first experiment simulates harmonization of MFN tariffs by the CEFTA countries to the EU tariff rates. To perform these analyses, we use data on actual tariffs, as described in the Data Section 3.2, in order to change the vector of trade costs for the CEFTA members. As the tariff data were available at the HS17 8-digit classification, we aggregated them up using trade shares as weights to match the level of aggregation for the GE analysis. For the counterfactual analysis we set MFN tariffs to zero for trade between EU member countries and CEFTA member countries and set the MFN import tariffs of the CEFTA members for all their other trading partners besides the EU member countries to the MFN import tariff level of the EU.

The second experiment simulates accession of the CEFTA countries to the EU in terms of the trade costs that these countries face for their trade with the EU. To perform this analysis, we rely on the estimates from Section 4.1. Specifically, we change the vector of trade costs that are faced by the CEFTA members so that the trade costs that they face with the EU are the same as the trade costs among the existing EU members. The second approach has two advantages. First, as discussed in the previous section, the differences in the border effects that we obtain should capture

> 21A description of the framework can be found in Appendix B of the Supplementary Appendix.

21
**Table 10: Relationship between the employment rate of older women (55-64) and the natural log of hours worked by younger women by age group**

||(1)<br>15-24|(2)<br>25-34|(3)<br>35-44|(4)<br>45-54|
|---|---|---|---|---|
|Employment rate of old women (55-64)|-0.004***|-0.001|0.002***|0.001**|
||(0.001)|(0.001)|(0.000)|(0.000)|
|Poverty rate|-0.003***|0.000|-0.001*|0.001|
||(0.001)|(0.001)|(0.001)|(0.001)|
|Share of employment in the|-0.001|-0.004***|-0.005***|-0.005***|
|agriculture sector|(0.001)|(0.001)|(0.001)|(0.001)|
|Share of employment in the|0.004***|-0.000|-0.001|-0.002*|
|industrial sector|(0.001)|(0.001)|(0.001)|(0.001)|
|Share of younger women|-0.016***|-0.007***|0.004|0.008**|
|(15-24/25-34/35-44/45-54)|(0.003)|(0.002)|(0.003)|(0.004)|
|Share of population with a primary|-0.001|0.001*|0.002***|0.001|
|education or less|(0.001)|(0.001)|(0.001)|(0.001)|
|Natural log of GRP per capita|0.071***|0.052***|0.050***|0.046***|
|(constant 2010 IDR)|(0.011)|(0.009)|(0.007)|(0.008)|
|Growth in GRP per capita|-0.000|0.001|0.000|-0.000|
||(0.001)|(0.001)|(0.001)|(0.001)|
|Number of observations|272|272|272|272|
|R-squared|0.740|0.658|0.719|0.726|
|Year dummies|Yes|Yes|Yes|Yes|

Source: Author’s calculations using Sakernas data Notes: Robust standard errors in parentheses. ***p<0.01, **p<0.05, *p<0.1.

26
# **1 Introduction**

The spread of COVID-19 has caused huge negative shocks on the economy (Altig et al., 2020), households (Hanspal, Weber and Wohlfart, 2020; Kansiime et al., 2021), as well as businesses (Bartik et al., 2020; Meyer, Prescott and Sheng, 2021) across the globe. Apart from the sharp increase in poverty and inequality (Han, Meyer and Sullivan, 2020; Deaton, 2021), other negative impacts have been documented, including rising armed conflict (Bloem and Salemi, 2021; Ide, 2021), inter-personal violence (Evans, Lindauer and Farrell, 2020; Ag ̈uero, 2021), and elevated mental stress (Pfefferbaum and North, 2020). Several studies have pointed out that the impact of COVID-19 varies across different For households and countries are socio-demographic groups. example, poor more vulnerable than richer ones (Schellekens and Sourrouille, 2020; Mena et al., 2021), as these groups do not have as much resources to cope with such a profound negative shock. Moreover, the current COVID-19 crisis disproportionately affected women compared to men (Alon et al., 2020). Women are more likely to lose employment or income (Etheridge and Spantig, 2020; Kugler et al., 2021; Dang and Nguyen, 2021; Xue and McMunn, 2021), which is due to the fact that they are more likely to work in the service sector, and they have a higher burden of childcare compared to men.

Gender inequality is a challenge across the globe, especially in low-income economies (Sachs, 2012). However, the gender difference is likely to be elevated after the COVID19 pandemic (Alon et al., 2020). A large strand of literature shows that men and women have different preferences (Andersen et al., 2013), also during the COVID-19 time (Fan, Orhun and Turjeman, 2020). For example, women have been found to be more risk averse (Dohmen et al., 2011; Eckel and Grossman, 2008), more pro-social (Eckel and Grossman, 1998), more patient (Dittrich and Leipold, 2014), and more conducive to family welfare (Ashraf, 2009; Rubalcava, Teruel and Thomas, 2009). During the COVID19 crisis, women are more risk-averse, worry more about their and others’ health, and take more preventive actions (Fan, Orhun and Turjeman, 2020). In Kenya, women predominate in agriculture but their contributions are mostly unpaid. Moreover, they are less predominant in formal employment and women’s property rights are often through men (Ellis et al., 2007). It is important to understand the gender differences in the COVID-19 impact for the future policy design attempting at alleviating gender inequality as well as for post-COVID relief interventions.

This study pools the first five waves from a nationally representative survey conducted 1
These results might, possibly, give some guidance for future policy; in particular, they may provide arguments that governments in lower-income countries, when receiving additional financial flows, ought to relieve the marginal tax burdens on capital and labor whenever this is technically and politically feasible.

**Table 4.2: Estimates of MCPF for major countries in Sub-Saharan Africa, by tax type and total. Various years 1990-2000.**

|Country|||**T**<br>|**ax type**<br>|||
|---|---|---|---|---|---|---|
||Domestic|Exports|Imports|Capital|Labor|Average|
|Cameroon|1.10|1.08|1.07|1.53|1.27|1.14|
|DRC|1.01|1.02|10.1|1.43|1.38|1.10|
|Ethiopia|1.13|3.14|1.23|1.75|1.60|1.31|
|Ghana|1.03|1.17|1.10|1.50|1.26|1.17|
|Kenya|1.02|1.20|1.06|1.30|1.11|1.08|
|Madagascar|1.13|1.16|1.11|1.58|1.32|1.17|
|Mozambique|1.04|1.17|1.07|1.60|1.22|1.11|
|Nigeria|0.99|1.02|1.02|1.30|1.19|1.08|
|South Africa|1.09|1.02|1.00|1.29|1.11|1.12|
|Sudan|1.09|1.92|1.23|1.87|1.57|1.26|
|Tanzania|1.17|1.59|1.20|1.76|1.73|1.27|
|Uganda|1.10|0.82|0.97|1.40|1.30|1.11|
|Average, all<br>of Africa|1.09|1.32*|1.08|1.52|1.42|1.17|

* = average value for countries with positive export taxes (20 countries in total). Source: Warlters and Auriol (2005).

# **5. Rent Seeking, Corruption, and the Hidden Economy**

# **5.1 Introduction**

A ubiquitous issue in developing countries is the imperfect rule of law, one aspect of which is individuals’ and firms’ ability to escape taxes, and the widespread presence of rent seeking and corruption. By rent seeking we here mean that private effort and resources are devoted to seeking private gains which are not accompanied by social gains. By corruption we mean the use of public office for private gain.<sup>17</sup> Systematic tax evasion leads to the existence of an unregulated and untaxed “shadow economy”, which often absorbs a large fraction of economic activity.

Rent seeking and corruption can take a variety of forms. One is talented labor directed at socially lowproductive activities; another is intra-governmental haggling and disputes over budgets and responsibilities; a third private-sector bribe payments to public sector officials in returns for favors. Our objective here is not to go through all these cases and their implications, rather to point out some implications of the overall phenomenon, and its extent, for the effectiveness of climate-related finance.

# **5.2 Evidence on extent of the shadow economy and corruption**

Table 5.1 below shows the average shares of the shadow economy in overall economic activity, for some wide groups of countries, and the developments of these shares over the period 1999-2003. We see that the shadow economies on average comprise much smaller fractions of countries’ GDP in

> 17 See Gray and Kaufmann (1998).

19
used as identification when opening a US bank account. Study participants were made aware of a collaborating US bank that had an ongoing savings promotion among Hispanic immigrants, but the _matriculas consulares_ in principle could have been used at any number of US banks. Impacts of the treatment were assessed in an in-person follow-up survey. Assignment to the treatment is found to lead to increased opening of US bank accounts, higher savings in the US, and reduced remittances to Mexico. Among migrants who report they have “no control” over how remittances are used in Mexico, the abovementioned effects are larger, and there is also a large, positive and statistically significant treatment effect on migrant earnings.

Taken together with AAMY, the Chin, Karkoviata and Wilcox (2010) study reinforces the conclusion that migrants have a variety of types of demand for savings facilities. There is demand for savings in the US, as well as demand for savings in the country of origin, and providing access to appropriate savings devices can have large impacts on savings. What’s more, both studies underline the importance of migrant control over savings accounts in facilitating savings accumulation. We view such studies as just the tip of the proverbial iceberg. There is likely to be great potential for analogous future studies that partner with institutions to offer a variety of financial services to immigrants. Products that have yet to be investigated include credit, insurance, and direct payment facilities targeted towards the needs of migrants and their origin households.

# _Identifying barriers to migration_

While international and internal migration flows are large in magnitude, even greater numbers of individuals do _not_ migrate, even in the face of substantial wage differentials between less- and more-developed areas. There are likely to be large number of potential migrants who are deterred from migrating by a variety of barriers, such as imperfect information on migrant wages and job conditions, imperfect information on one’s own affinity for or returns from migrant work, lack of information on job-seeking procedures, and credit constraints (when migration or job search involves non-negligible fixed costs).

At the moment we know little as researchers about the relative importance of these various potential barriers to migration. Credible evidence on the importance of migration barriers has important policy implications as well. A number of developing countries – most 18
|Table 1. Summary o|f results of|previous stud<br>**Data used**|ies in low-|and middle-|income coun|tries<br>**Main resul**|**ts**|||
|---|---|---|---|---|---|---|---|---|---|
|**Author(s)**|**Country**<br>**(ies)**|**and**<br>**sampling**|**Estimation**<br>**method**|**Dependent**<br>**variable**|**Schooling**|**Cognitive**<br>**skills**|**Noncognitive skills**|**Gender **|**Control**<br>**variables**|
|Acosta, Muller &<br>Sarsoza (2015)|Colombia|STEP (Skills<br>Measurement|Structural<br>model with|Log hourly<br>earnings of|Not included|0.161*** for<br>reading|-0.026 (factors:<br>extroversion,|-0.198***<br>for female|Age, age-<br>squared;|
|||Survey) for<br>Colombia,<br>2012; 1,363<br>men and<br>women|cognitive<br>and<br>noncognitive<br>skills as<br>latent<br>variables|men and<br>women||proficiency in<br>OLS model;<br>0.483** in IV<br>model;<br>0.134*** in<br>structural<br>|openness; emotional<br>stability;<br>conscientiousness<br>and grit)|dummy<br>variable|mother's<br>education<br>(splines);<br>parental<br>involvement;<br>city<br>|
|||||||model|||dummies|
|Aslam, Bari & Kingdon|Pakistan|Research|OLS|Log|0.054***|<br>−0.048 when|0.061** for positive|0.581*** for|<br>Constant,|
|(2012)||Consortium<br>on<br>Educational<br>Outcomes<br>and Poverty<br>household<br>survey,<br>2006–2007,<br>in Punjab<br>and NWFP,<br>700 men and<br>women aged<br>15-60||earnings|when<br>including also<br>cognitive &<br>noncognitive<br>skills;<br>otherwise,<br>0.048***<br>when not<br>including<br>literacy score|conditioning<br>for schooling<br>and<br>noncognitive<br>skills;<br>otherwise,<br>0.436***<br>without<br>schooling but<br>with<br>noncognitive<br>skills|personality, .006 for<br>negative personality,<br>when no control for<br>schooling or<br>cognitive skills;<br>0.002 and -0.007,<br>respectively, with<br>controls for<br>schooling and<br>cognitive skills|male<br>dummy<br>variable<br>when<br>conditioning<br>for<br>schooling<br>and<br>cognitive<br>and<br>noncognitive<br>skills|experience<br>and<br>experience-<br>squared;<br>whether<br>sympathetic<br>to religious<br>parties or<br>causes|
|Cunningham, Parra|Peru|National|OLS|Log hourly|0.062*** -|(6.80)***|‐0.086** - ‐|‐0.168*** -|Age, age-|
|Torrado & Sarzosa||Skills and||earnings of|0.067*** for|(0.50)|0.093*** for|-.132*** for|squared;|
|(2016)||Labor<br>Market<br>Survey<br>(Encuesta||<br>men and<br>women;<br>gender as a<br>dummy|years of<br>schooling||kindness, ‐0.056* - -<br>0.058* for<br>cooperation, 0.067**<br>- 0.074** for|female<br>dummy|<br>language;<br>regional<br>dummies;<br>whether|
|||Nacional de<br>Habilidades,||variable|||emotional stability||firstborn;<br>economic|
|||<br>ENHAB),<br>2010, 770<br>men and<br>|||||||sector|
|Cunningham, Parra|Peru|women<br>National|Structural|Log hourly||Constant|0.3986* for stability,|‐0.1908***|Age, age-|
|<br>Torrado & Sarzosa||Skills and|model with|<br>earnings of||7.183|<br>‐0.0752 for|for female|<br>squared;|
|(2016)||Labor|cognitive|men and|||plasticity, and|dummy.|language;|
|||Market|and|women;|||0.0532 for grit||regional|

9
-6-

Methodology.

The systematic relationship between performance and environment can be represented in reduced form for country i in period t (t - 1,2,...,T) as: ai* is a measure of systematic country-specific contribution to economic performance. The three data matrices -- Xit, Sit and Pit -- include time series of variables measuring incentives (and disincentives) to economic performance. The external incentives in Xt can be either price-based (e.g., terms of trade, real interest rate) or macroeconomic (e.g., world demand, debt burden) in nature. The columns of Sit ar.d Pit measure secular economic trends and policy choice respectively. c it is the random component and is assumed independently distributed across time periods. b2 ci and gi are conformable vectors measuring country-specific responses to these incentives.

There are three important elements of country-specific behavior that can be decomposed in this analysis. First, a large ai in comparison with other countries indicates country-specific suc,...s given a stable international environment. Second, the country's economic structure as captured in tbi,ci,gi) can be relatively more or less successful in responding to changes in the environment. Third, government policy (Pit) can be more or less flexible in responding to changes in the environment. I illustrate the first two elements by transforming equation (1) to introduce a "normal" structural response to changes in the environment denoted by the vectors b, c and g for T periods. 4