rafmacalaba/datause-encoder-data huggingface dataset:
concerned with establishing early on property rights. The role of banking was also highly controversial, as shown in the early survey by Pleskovic 1994. Was a wellfunctioning credit market indispensable at the outset, or should the state continue, for a while, to oversee capital accumulation while human capital was being accumulated?

While privatization and the setting up of a proper banking system were always seen by economists as necessary steps, there has been some debate on what kind of market economy should be aimed at. The US model, where the bulk of corporate financing originates in stock markets, industrial policy is frowned upon and welfare programs are limited. The European model accepts social goals and relies to a greater extent on large banks and the state.

Thus, from the outset, there existed different transition paths and different ultimate objectives. Along with political conditions and the usual dose of history randomness, it is not surprising that the outcomes are sharply contrasted. This variety offers us much to learn from. Rather than offering fresh evidence and analysis, this paper surveys the fact and distillates the now voluminous literature on transition to draw a reasonable set of conclusions. It reviews a territory which has been much visited recently, e.g. by, Aslund, Boone, and Johnson 1996, Begg 1996, Blanchard 1997, Caprio 1995, de Melo, Denizer, Gelb 1996, Pleskovic 1994, and World Bank 1996.

# **The Broad Facts**

# _Growth_

Growth has followed the U-shape shown in Figure 1 which depicts the average of 15 countries, the Central and Eastern economies as well as Russia and Ukraine.' A number of economists - chiefly in international organizations - had been optimistic and expected a J-shape, expecting that fast growth would set in promptly as soon as market institutions would be in place. Others - chiefly former east-Europeanists 2
# 5.3 Cross-sectional regressions

In Table 11 we complement the panel regressions by conducting cross-sectional regressions to take initial steps to explore whether financial development, remittances, economic freedom, and ICT adoption can help explain the cross-sectional variation in crypto-assets volumes. We focus on 2020, the year for which we have complete data. Panel A documents the results for volumes for all transaction sizes whereas Panel B focuses on volumes associated with small transaction sizes only (<US$10k). We divide the crypto volumes by nominal GDP to account for scale differences between countries to obtain a country’s relative measure of crypto activity.

The results are broadly similar in both panels. In all regressions we control for the level of economic development as captured by log GDP per capita. Model 1 shows that economic development does not appear to play a significant role, but subsequent regressions (Model 4 and 5) which control for additional factors, suggest that less economically developed countries exhibit higher crypto volumes. Model 1 also shows that financial development in terms of financial sector depth or inclusion does not appear to play a substantive role, implying that crypto-assets are not perceived as an important substitute for traditional financial services. Model 2 suggests that countries with higher remittances received exhibit more crypto activity. This is consistent with the idea that crypto-assets may address some of the challenges associated with cross-border payments which can be slow and costly, but further research is necessary to substantiate this. A 1-percentage point increase in remittances to GDP is associated with a 3 to 4-percent increase in crypto-assets activity. Model 3 implies that countries with fewer monetary freedoms experience higher cryptoassets volumes (significance at the 5 percent level in both panels). A one-point decrease on the monetary freedoms scale (0-100), is associated with a 2.9 percent increase in crypto-assets volumes. This finding supports the idea that crypto-assets are more popular in countries with weaker price stability and more repressive price controls. We find however support for the opposite association for countries with more financial freedoms (i.e., more efficient banking systems and limited government control in the financial sector), perhaps because the financial sector under such conditions is more willing to facilitate crypto activity. Model 4 suggests that crypto activity is higher in countries with higher mobile penetration and fixed broadband subscriptions per capita. The coefficients are significant at the 5 percent level and the 1 percent level, respectively, and further suggest that the association with broadband subscriptions is stronger than mobile phones. One additional subscription out of 100 people is associated with around 5 percent increase in crypto activity. These findings are intuitive as crypto-assets usage requires basic ICT infrastructure 33
ings. However, the population segments that were targeted by the reform accumulate very little savings and we do not find any evidence that the pension reform crowded out private savings.

An analysis of the costs associated with the 2008 reform shows that the new pension system design is about twice as expensive as the old system. The cost of the new pension system could be reduced by about 8 percent by increasing the retirement age of women to be the same as that of men (age 65 instead of age 60) and/or by mandating that men make extra pension contributions to their spouse’s pension account if they have a nonworking spouse.

Overall, our analysis shows that the 2008 pension reform largely achieved the government’s stated goals of reducing old-age poverty, inequality and gender pension gaps. However, these benefits came at some cost, as the post-2008 pension reform is on average double the cost of the old system. The counterfactual simulations showed that increasing the mandatory retirement age of women to be the same as that of men would be a design change that would further reduce the gender pension gap and also substantially reduce monetary costs.

36
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26
zero).

# **4 New measure of reserve requirement shock**

This section presents a new narrative-based measure of reserve requirement changes `a la Romer and Romer (2010). Subsection 4.1 presents the criteria and sources to identify the motivation for each RR change. Armed with this classification scheme, Subsection 4.2 presents the new measure of RR shocks resulting from applying the criteria.

## **4.1 Sources and identifying motivation**

The sources of the narrative analysis are primary documents issued by policymakers both at international and country institutions at the time. Our key sources are IMF reports including Staff Reports and Recent Economic Developments, as well as central banks’ documents including working papers and monetary and financial stability reports, among others. Following Romer and Romer’s (2010) identification strategy, changes in RR are differentiated between those that were mainly motivated by current or projected fluctuations in output, which we will call endogenous, from those that were triggered by other reasons, which we will call exogenous. In the rest of this subsection, we present our categorization strategy and also provide, as examples, shorter versions of some of the narratives which are fully developed in the Online Appendix.

_Endogenous changes in reserve requirements_ are typically implemented to offset developments that would cause output growth to differ from trend. This includes macroprudential cases where policymakers were intentionally responding to current or projected economic activity, including those events related to fluctuations in capital flows. What follows is a brief description of two episodes of changes in RR that took place in Argentina. They involved an increase in RR in 1993 (during the Convertibility Plan euphoria) and a series of decreases in RR in 1995 (right after the Tequila crisis) and are categorized as endogenous (good times) and endogenous (bad times), respectively.

Endogenous (good times): In the third quarter of 1993, RR increased on all deposits by 3 percentage points. The strong economic upswing that followed the adoption of the Convertibility Plan in March 1991 continued at a more moderate pace in 1993, as real GDP 20
**When Face-to-Face Interactions Become an Occupational Hazard: Jobs in the Time of COVID-19**

# **Besart Avdiu and Gaurav Nayyar**<sup>**1**</sup>

The World Bank JEL Classification: J29, O29, Z00 Keywords: COVID-19, Jobs, Face-to-face interactions, Home-based work

> 1 The authors would like to thank Xavier Cirera, Denis Medvedev, and Hernan Winkler for valuable comments and suggestions.  The opinions expressed in the paper are those of the authors and do not necessarily reflect the views of the World Bank Group or its Board of Executive Directors.
# **Annex 4. Robustness: The Role of Human Capital**

## **Table A4.1. The Interaction between Human Capital and Level of Development**

|||Employme|nt ratio (ln)||Capital st|ock ratio||
|---|---|---|---|---|---|---|---|
||(1)<br>RE|(2)<br>RE|(3)<br>RE|(4)<br>FE|(1)<br>RE|(2)<br>RE|(3)<br>FE|
||**1996-2013**|**1996-2013**|**1996-2013**|**1996-2013**|**1996-2013**|**1996-2013**|**1996-2013**|
|**Pillar 1**|-0.002|-0.010|0.000|-0.007|-0.002|-0.000|0.002|
||(-0.02)|(-0.72)|(0.03)|(-0.57)|(0.09)|(-0.02)|(0.64)|
|**Pillar 2 and 3**<sup>a</sup>|0.000|-0.001|-0.000|-0.002|0.027***|0.026***|0.009|
||(0.07)|(-0.24)|(-0.07)|(-0.29)|(2.20)|(2.19)|(0.74)|
|**Pillar 4**||||||||
|Output gap|0.006***<br>(13.35)|0.006***<br>(13.87)|0.006***<br>(13.85)|0.006***<br>(13.82)|0.004***<br>(4.2)|0.004***<br>(4.23)|0.006***<br>(5.57)|
|Change in terms of trade|0.009|0.008|0.007|0.009|-0.055*|-0.055*|-0.021|
||(0.53)|(0.52)|(0.47)|(0.57)|(-1.49)|(-1.55)|(-0.57)|
|**Pillar 5 and 6**||||||||
|Human Capital|0.001***|0.001*|0.001***|0.000|0.001|0.001|-0.001|
||(3.58)|(1.57)|(2.70)|(0.07)|(0.71)|(1.09)|(-1.01)|
|Interaction income pc|<br>-0.000*<br>(-1.64)|<br>-0.000<br>(-1.27)|||<br>0.001***<br>(6.42)|<br>0.000***<br>(6.45)||
|Interaction EM dummy|||0.000<br>(0.56)|0.001**<br>(1.69)|||0.003****<br>(3.82)|
|**Pillar 7**||||||||
|Tax rate|-0.099***|-0.115***|-0.094***|-0.101***|-0.033|-0.034|0.056|
||(-3.38)|(-4.10)|(-3.26)|(-3.46)|(-0.53)|(-0.56)|(0.82)|
|Regulatory quality|<br>0.072***|<br>0.073***|<br>0.068***|<br>0.074***|<br>0.026|<br>0.033|<br>0.041*|
||(5.53)|(5.88)|(5.43)|(5.93)|(0.90)|(1.23)|(1.42)|
|Regulatory trade barriers|-0.005**|-0.006**|-0.006**|-0.007***|0.010*|0.010*|0.007|
||(-1.74)|(-1.89)|(-1.91)|(-2.27)|(1.40)|(1.44)|(0.99)|
|**Pillar 8**||||||||
|Women|-0.035***||-0.042***||-0.011|||
||(-2.42)||(-2.91)||(-0.02)|||
|CBR|0.034|0.040|0.027|0.032|0.236*|0.310**|0.114|
||(0.46)|(0.53)|(0.36)|(0.43)|(1.41)|(1.90)|(0.64)|
|**Pillar 9**|0.003|0.005|0.004|0.004|0.088***|0.084***|0.073***|
||(0.32)|(0.50)|(0.43)|(0.46)|(4.15)|(4.09)|(3.27)|
|**Pillar 12**|-0.005|-0.007*|-0.004|-0.007|0.0243***|0.023***|0.021**|
||(-1.06)|(-1.34)|(-0.85)|(-1.46)|(2.01)|(2.11)|(1.79)|
|Emerging market dummy|||||-0.488**<br>(-1.46)|||
|Time effect|Yes|Yes|Yes|Yes|<br>Yes|Yes|Yes|
|Constant|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Obs|496|496|496|496|496|496|505|
|Wald Test|390.8||416.0||1373.6||66.08|
|WaldTest p-value|0|0|0|0|0|0|0|

_Note: t_ statistics in parentheses. * p<0.2, ** p<0.1, *** p<0.05. a Infra index, WEF.

38
- 33 -

the excise becomes payable. No transfers can be<sup>made without observation by the officer. In</sup> some instances metering devices are now used to<sup>check output. The checking of strength is</sup> mainly designed to prevent adulteration.

3. Record Control. The direct physical control is supplemented<sup>by records control. The</sup> firms are required to record materials acquisitions,<sup>output, stock on hand, as well as details of</sup> the production process.

Shipments from the bonded warehouse must be accompanied<sup>by a consignment note</sup> giving details of the purchaser, etc., for each shipment,<sup>or for each day.</sup> 4. Payment and Returns. In some instances payment<sup>must be made whenever the</sup> commodity becomes subject to tax, that is, upon shipment<sup>from the bonded warehouse, and thus</sup> no arrears are possible (if the procedures are strictly followed).<sup>In other situations, payment of</sup> any unpaid amounts is made on a monthly basis. Returns<sup>giving detailed information on</sup> acquisitions of materials, output, transfers from bonded warehouses,<sup>etc. are required. These are</sup> checked against the records kept on a day-to-day basis,<sup>but there is typically no true audit.</sup> The picture is complicated by the fact that some shipments<sup>from the bonded warehouses</sup> are exempt from tax: for export, for industrial nontaxable<sup>use, etc. Adequate documentation</sup> must be provided for these--but inevitably they provide<sup>some avenues of potential leakage. In</sup> industrialized countries there is some tendency to move away<sup>from primary reliance on physical</sup> controls, with emphasis on accounts types of controls,1<sup>5but most developing countries are not</sup> yet ready for this change; the potential loss of revenue is too<sup>great.</sup> Motor Fuel Taxation. Usually there is only one oil refinery<sup>in a developing country, or</sup> at most a few. Strict physical control is neither possible<sup>nor necessary. Returns are required,</sup> with information on acquisition of petroleum and sale<sup>of various petroleum products, and</sup> indication of sales and tax due, which must be paid<sup>with the filing of the return.</sup> "<sup>5</sup> For a discussion of the issues,<sup>note the study, Alcohol and</sup> Tobacco Excise Taxes: Laws and Audits<sup>Need Modernizina, Report by</sup> Comptroller General of the United<sup>States, Washington, DC, 1977.</sup>
confidence;<sup>44</sup> finally, we standardize the remaining observations relative to the comparison group.

## **Fluid Intelligence and Fluid Intelligence after Stimuli**

Fluid intelligence refers to the capacity to think logically and solve problems in novel situations, independent of acquired knowledge. To collect a proxy for fluid intelligence, we use a tool similar to Raven’s Progressive Matrices (Raven, 1936).<sup>45</sup> This nonverbal assessment tool presents a series of visual puzzles missing a piece, and the individual is required to identify the correct piece from a set of options. Each puzzle is designed to be increasingly complex, requiring more abstract reasoning, pattern recognition, and problem-solving skills, which are key components of fluid intelligence. Before the participant solves each matrix, we randomly show negative and positive stimuli using emotionally laden videos from GAPED (Dan-Glauser, 2011). The outcome is measured by calculating the percentage of correct answers after the positive and negative stimuli; and we also calculate the total percentage of correct answers, irrespective of which stimuli were shown before the matrix. Then, these percentages are standardized with respect to the _Clubs_ group. Thus, an increase in the score after the negative stimuli can be thought of as an increase in emotion regulation, but also in Mullainathan and Shafir (2013)’s concept of “mental bandwidth”, which translates directly into better reasoning.

# **3.3 Baseline Summary Statistics**

Table 1 reports the average characteristics of the participant schools, and Table 2 shows the average characteristics of participant students. In both tables, column (1) exhibits statistics for the total sample, column (2) for the students in _Clubs_ , column (3) for any psychology-based intervention, and columns (4) and (5) for the _Virtue_ and _Mindful_ groups,

> 44To reduce the number of variables and ease the analysis, we only present in our tables the results with trimming at the 30th percentile, but the analyses conducted with these variables trimmed at the 10th, 20th, and 40th percentiles follow those with the variables trimmed at the 30th percentile.

> 45The Progressive Matrices are widely recognized for their ability to assess cognitive ability without being influenced by language and cultural background, making them a valuable tool in the study of fluid intelligence.

23
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26
take advantage of these datasets for targeting strategies, such as proxy means testing or geographic targeting. Another important limitation is that HBS often only collects limited information on disposable income, which makes it difficult to deduce reliable poverty indicators from these types of surveys.

**The current data environment in Bulgaria imposes serious restrictions on studying energy poverty.** Detailed information on income, living conditions, and access to social protection programs in Bulgaria is mainly gathered as part of the European Union Statistics on Income and Living Conditions (EU-SILC). Expenditure data, on the other hand, is primarily collected by HBS. Only relying on HBS to study energy poverty would result in very limited analyses due to the following reasons. First, the information on household income is less reliable and rigorous in the case of the HBS, as the survey is mostly designed to capture expenditure patterns, not income patterns. Figures A.1 and A.2 in the Appendix show that the distribution of equivalized household income, as reported in the HBS and EU-SILC, differs in the case of Bulgaria. Second, the HBS does not contain information on social protection programs. Consequently, it is not possible to study if energy poor households are sufficiently covered by social protection schemes. Third, the HBS only has very limited information to study potential drivers behind energy poverty. Detailed information on housing conditions, for example, is only available in the EUSILC. Ideally, to achieve efficient and effective policy interventions, one would want to observe all this information together. In addition, the current data environment also imposes limitations on identifying individuals who might be vulnerable to energy poverty.<sup>3</sup>

**Given that the sample of households between different types of surveys does not overlap, one cannot combine both datasets based on common household identifiers.** Due to the data environment described previously, one can only observe energy spending shares and energy poverty indicators in the HBS, while we can only observe reliable monetary poverty indicators as well as access to social

> 3 The concept of vulnerability to poverty more broadly speaking has gained more traction in recent years due to populations being increasingly exposed to both natural and human hazards (examples are by Klasen and Waibel (2015); Gao et al. (2020); Rude and Robayo (2023)).

3