rafmacalaba/datause-encoder-data huggingface dataset:
slightly their situation between 2006 and 2008, male-headed households were more likely to be poor than female-headed household both in 2006 and 2008.

Female-headed households tend to have a lower ratio of adults working to the total number of members than similar male-headed households. They are more likely to have no members in any economic activity, and to have wage workers only. They are less likely than male-headed households to have members in both wage employment and selfemployment (especially among households that are non-poor in 2008).

# _7.2 Participation in active and passive labor market programs_

As mentioned above, official statistics suggest that only about 12 percent of the labor force is insured against the risk of unemployment (MoLISA, 2010). It is still early days to assess the impact of the introduction of such programs in Vietnam, and in any case, the LFS and VHLSS datasets provide little information that can be used to understand such impact. The income module from the VHLSS 2008 reveals than less than 1 percent of households report having received one-time job loss allowance.<sup>23</sup> The above analysis suggests that those who are unemployed are individuals who can afford to stay unemployed. It is interesting to find out the structure of unemployment has changed over the period 2007-2009 - a period with the introduction of unemployment insurance and the advent of several economic crises – and whether women and men are affected differently and behaving differently in their job search.

# _Few use employment services_

Employment services (publicly or privately provided) can address potential inefficiencies in the process of matching workers with jobs. Such inefficiencies arise because information does not flow instantly, and some workers and firms are much better equipped to acquire information than others.

Few unemployed individuals use employment services in Vietnam. The two main ways in which unemployed individuals look for jobs are (i) through their family and friends (about 49 percent of the unemployed in 2009) and (ii) by applying directly with

> 23 We would need to know the percentage of unemployed who received unemployment benefits, but it is not possible to calculate this.

40
29 -

-

Table 9. Private School Effects after Holding Background and Peer Group Characteristics Constant, Dominican Republic, 1982-83

|Characteristics<br>of the Randomly<br>|<br>Pr<br>|edicted sc<br>|ore<br>|Dfeeta<br>||
|---|---|---|---|---|---|
|Chosen Student Set at Nean of:|Public|Private|Private|0-Pub.|F-Pub.|
|||0-Type|F-Type|||
|Public school subsample|10.04|10.97|8.12|0.93|-1.92|
|Private 0-type subsample|11.56|11.95|9.95|0.39|-1.60|
|Private F-type subsample|14.05|14.23|13.12|0.18|-0.93|

# RELATIVE COST OF PRIVATE AhD PUBLIC SCHOOLS

Two questions are now explored: what is the relative costeffectiveness of the three types of schools, and what is the value added by private schools?

Our cost data are admittedly incomplete. '"hen the information on school background was originally gathered in 1982-83, financial material was not included. Although we returned to the sample schools in 1987-88, we were able to obtain only very limited information on costs. Nevertheless, the data on salaries for teaching and non-teaching staff, which often accounts for as much as 90-95% of recurrent costs in education, do give useful indications.l Two types of evidence are presented to address the issue of relative cost-effectiveness. One relies on actual but partial data on unit costs, i.e., salary expenditures per student among the schools (Table 10). The other S' UNESCO data for the Dominican Republic show that teacher emoluments accounted for 95% of public general secondary education expenditures in 1983.
**_Electricity Sector Reform in Developing Countries, Jamasb, Mota, Newbery, Pollitt_**

|**Study**|**Hypothesis**|**Dependent**<br>**Variable(s)**|**Independent**<br>**Variable(s)**|**Control Variables**|**Notes: # of countries, times**<br>**period, data source**|
|---|---|---|---|---|---|
||_influences the extent of_<br>_reform._||_not always significant, correlated_<br>_with extent of reform._<br>- economic ideology (composite<br>indicators/indices)<br>_Result: economic orthodoxy_<br>_positively, and mostly significant,_<br>_correlated with reform._<br>-distributional conflict<br>(composite indicators/indices)<br>_Results: mostly negatively,_<br>_though not always significant,_<br>_correlated with reform score._|||
|**Bergara,**<br>**Henisz and**<br>**Spiller (1997)**|_H0: Well-defined and_<br>_credible political institutions_<br>_are positively correlated_<br>_with investment in the_<br>_electricity sector._|-log of public & private<br>generation capacity per<br>1000 population<br>(PI)|-political index (CL) based on<br>the following elements:<br>_-_**_judicial independence with_**<br>index of law & order,<br>_-formal constraints on executive_<br>_discretion_<br>(federalist state dummy,<br>dual executive/legislature<br>dummy, bicameral legislatures<br>dummy, index of legislators’<br>effectiveness,<br>index of military role in<br>government, index of gov.<br>particip. by org. religion,<br>degree of developm. of pol.<br>parties),<br>_-informal_<br>_constraints_<br>_on_<br>_executive discretion_<br>(index of quality of bureaucracy,<br>index of corruption in gov., index<br>of racial/nationalist tensions).<br>-political index (2) - as above but<br>index of law & order replaced<br>with the ‘tenure of high/supreme|-GDP per capita (log)<br>(CL)<br>-industry share in GDP<br>(log)<br>(CL)<br>-urbanisation % (log)<br>(CL)<br>-energy share of hydro<br>(log)<br>(SE)|The analysis uses data from a<br>total of 91 developed and<br>developing countries for<br>1987. The size of the actual<br>samples used in different<br>models are 38 and 87 or 86<br>countries depending on data<br>availability for variables.|

53
# References to Appendix C

<mark>Banfield, Jeffrey D. and Adrian E. Raftery. 1993. Model-Based Gaussian and Non-Gaussian Clustering.</mark> _<mark>Biometrics</mark>_ <mark>. Vol. 49, No. 3 (Sep.,), pp. 803-821</mark> Biernacki, C.; G. Celeux; G. Govaert. 2000. Assessing a mixture model for clustering with the integrated completed likelihood. _IEEE Transactions on Pattern Analysis and Machine Intelligence_ . Volume: 22, Issue: 7. Pages: 719 - 725

- Collins, L. M., & Lanza, S. T. 2010. _Latent class and latent transition analysis: With applications in the social, behavioral, and health sciences_ . New York: Wiley.

Masyn, Katherine. 2013. Latent class analysis and finite mixture modeling. In T. D. Little (Ed.) _The Oxford handbook of quantitative methods in psychology. Volume 2_ . pp. 551-611. New York, NY: Oxford University Press.

Vermunt, Jeroen K. and Jay Magidson. _Technical Guide for Latent GOLD 5.1: Basic, Advanced, and Syntax_ . January 9, 2016. Statistical Innovations Inc.

<u>http://www.statisticalinnovations.com</u> 24
an increase in non-performing loans (NPLs), which may weaken both financial stability as well as the sector’s capacity to contribute to the post-pandemic economic recovery. Asset quality could deteriorate significantly in the wake of the pandemic outbreak, especially in countries where firms and households exhibit high levels of debt (Reinhart, 2021b).

Financial sector authorities undertook a wide-ranging set of measures to preserve the stability and resilience of the banking system and bolster its capacity to support economic activity (Demirgüç-Kunt et al., 2020; Feyen et al., 2020 **;** Casanova et al., 2021). Some of these measures may have reduced the transparency of bank balance sheets.

To explore the value added of CDBP in environments where financial data are limited or opaque such as it is the case for many economies during the COVID-19 pandemic, we collect balance-sheet information for Q3 2020 for approximately 1,500 commercial banks headquartered in 59 EMDEs<sup>11</sup> using Fitch data. If balance-sheet information is not available for the third quarter of the year 2020, we use the latest balance sheet information available, although not older than 2019.<sup>12</sup> The key variables needed to compute the Consolidated Distance to Breakpoint (CDBP) are:

- NPL ratio (as % gross loans);

- Total Regulatory Capital (in USD millions);

- Risk-weighted Assets (in USD millions);

- Minimum Capital Requirement (as % of RWA);

- Gross Loans (in USD millions);

- Total Assets (in USD millions).

Figure 2 summarizes the country distribution of the CDBP across World Bank regions. In line with recent assessments (see for example Yuuki et al., 2021), the data suggest that EMDE banks are resilient in the short-term to credit shocks, although there is wide variation across regions and countries. The country median CDBP is 9.8 percentage points (pp). This percentage-point increase in the consolidated NPL ratio of “Banks at Risk” would render these banks undercapitalized in the median country. In 10 countries, a 5 percentage point increase in current NPL ratios is sufficient to deplete capital buffers of “Banks at Risk”. Two countries have “Banks at Risk” that are already undercapitalized. There is wide variation across regions. The interquartile range is larger for Sub-Saharan Africa (SSA), which exhibits the highest median CDBP. South Asia (SAR) has the lowest median CDBP at 3.8 percentage points, while Latin America and the Caribbean (LAC) has the most dispersed distribution (Figure 2, Panel A). East Asia and Pacific (EAP) countries show a tighter interquartile range and the tightest distribution.

> 11 In this paper, EMDEs are defined as non-high income countries, according to the 2021 World Bank classification available at: <u>https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups.</u> 12 For approximately a third of our sample, we us information from year 2019.

8
end of 2010 the company reported a debt of over 200 m. JD. However, due to disruptions of gas supply from Egypt in 2011, the cost of producing electricity in Jordan increased by several fold as producers had to switch to the use of expensive diesel and heavy fuel oil, of which share in fuel for power mix reached 80 percent in 2012 (from 29 percent in 2010). As the increased costs were not passed onto the final consumers, NEPCO assumed all the costs of increases in fuel prices and began running monthly deficits of an estimated 100 m. JD, which amounted to JD 1.2 billion annually (5.5 percent of GDP in 2011). This in turn has placed an enormous fiscal burden on the government, and this is one of the main reasons the government has stated its intention to follow fiscal consolidation plans in the context of an IMF SBA (Stand-byArrangement) program.

# **2. Distribution of Subsidies**

This section describes the distribution of expenditures on subsidized products and the distribution of subsidies across households in Jordan based on the 2010 Household Expenditures and Income Survey (HEIS), the most recent flagship consumption survey conducted by the Department of Statistics (DOS) of Jordan. The survey being outdated, all expenditures were inflated to be in 2013 year prices using nominal GDP per capita growth rates.<sup>2</sup>

## **2.1 Petroleum products**

Households in Jordan spent an estimated JD 856 million on subsidized petroleum products such as kerosene, LPG, gasoline<sup>3</sup> and diesel in 2013 (table 2). Expenditures on gasoline account for about twothirds of this amount, followed by LPG (24 percent), kerosene (6 percent) and diesel (5 percent). Wealthier households spend much larger amounts on subsidized petroleum products.

**<u>Table 2. Household expenditures on subsidized petroleum products, mln. JD</u>**

||kerosene|LPG|gasoline|Diesel|Total|
|---|---|---|---|---|---|
|Quintile 1|7|27|21|0|55|
|Quintile 2|9|33|55|0|98|
|Quintile 3|12|38|91|1|141|
|Quintile 4|12|45|139|2|199|
|Quintile 5|14|63|251|35|363|
|Total|55|206|557|38|856|

Source: Authors’ calculations based on extrapolated HEIS 2010 data. <u>Notes: Population quintiles based on spatially adjusted consumption per capita before the reform.</u> Expenditures on gasoline and diesel are relatively more important for wealthier households, while LPG and kerosene are relatively more important for less affluent households (table 3). For example, households in the wealthiest quintile spend an estimated 4.4 percent of their total expenditures on gasoline, while the bottom quintile spends only 1.9 percent. Conversely, the bottom quintile households spend 2.4 percent of their expenditures on LPG, while the wealthiest quintile spends 1.1 percent. Budget shares of each product can be clearly seen in figure 2 plotted over population percentiles ranked by consumption per capita. The positive slope means higher shares of the product in the total budget of the wealthier population. Petroleum

> 2 Wbopendata Stata ado (Azevedo, 2013) was used to retrieve information on GDP per capita from the WDI database as of 3 September 2014.

> 3 Gasoline here refers to Octane-90 only.

5
expression in the context of foreign exchange markets) – the faster the acceleration of the financial sector the greater its predictive power of a subsequent bust.

Section 2 overviews the data, section 3 outline the methodology, section 4 discusses the results. Section 5 concludes.

# **2. Data**

We obtain annual data on real value added and employment in 10 broad economic sectors covering a panel of 28 countries constructed by Timmer and de Vries (2009) through Groningen Growth and Development Centre (GGDC), 10-Industry Database (http://www.ggdc.net). The data cover the years 1947 through 2005; however, up to 1949 data on only 4 countries are available with the coverage jumping sharply to 26 in 1950 and to 28 in 1960. The 10 sectors are agriculture, mining, manufacturing, public utilities (electricity, gas, and water), construction, wholesale and retail (including hotels, restaurants); transport, storage, and communication; community, social, and personal services; government services, and finance, insurance, and real estate. Previous studies using the GGDC data include McMillan and Rodrik (2011). Following these authors, we increase the level of aggregation to 9 sectors by combing the data on community, social, and personal services with government services, because a number of countries, especially in Latin America, do not distinguish between the two when reporting employment or value added. We refer to the consolidated sector collectively as government.<sup>1</sup> The additional controls, including real GDP per capita, inflation rates, real interest rates, and the agricultural and industrial shares of the economy, were obtained from World Bank’s World Development Indicators (WDI) database. Political stability, rule of law, and regulatory quality indicators were obtained from the World Bank Governance Indicators database <u>(http://www.govindicators.org) from indexes constructed by Kaufmann et al (2009).</u> Data on government consumption as a share of GDP and annual value of imports and exports as a share of GDP were obtained from Penn World Tables (Heston, Summers and Aten (2009)). We construct de-facto financial openness measures using the updated and extended version of the

> 1 As Timmer and de Vries (2007) point out, some activities in government services are nevertheless traded through markets. For example many personal services, but also private education and health services should be part of ―market services‖. Government services may also include value added from public investment projects. However, the data is not detailed enough to distinguish market from non-market in these sectors.

5
Based on those indicators, among Asian developing countries, there are democracies with strong poverty rights/rule of law (Malaysia, Thailand, and India) and democracies where property rights/rule of law are measured to be less good (Bangladesh, Indonesia, Philippines). Similarly, there are less politically open countries measured by ICRG to have strong property rights/rule of law (China, Vietnam) and those measured to be less strong (Myanmar, Pakistan).

This pattern is similar throughout the developing world. If one leaves developed countries out of the analysis, there is no correlation between democracy as measured by Freedom House and ICRG’s property rights/rule of law index (Figure 2).<sup>5</sup> In the very long run there is likely to be a relationship: all the fully industrialized countries have both good economic governance and liberal democracy. But the lack of correlation between democracy and economic governance among developing countries suggests that the relationship is a long-run one, and probably a complex one with causality running both ways. Good economic governance leads to sustained growth, higher incomes, and a broader civil society, which are promising foundations for political reform. Wellfunctioning democracy in turn provides a mechanism of accountability and checks and balances on decision-making that tends to prevent the worst economic mistakes. (While democracies and authoritarian states tend to grow at about the same average rate, the growth rates of authoritarian countries have greater dispersion suggesting that authoritarian states tend to really get it right or really get it wrong, without much corrective mechanism.)

# **_4. China’s challenges_**

14
select into certain choices based on their initial internal expectations of likelihood of achievement of target.

In particular, we expect firms that are less motivated to undertake serious sustainability efforts (and hence more likely to fail to achieve pre-set targets) to be more likely to resort to measures such as setting later target dates or including penalty-minimizing call options. Such firms face a higher probability of penalty payouts, and hence have greater incentives to seek insurance against these payouts via structuring loopholes to minimize penalties. The existence of SLB premiums tends to attract more of these low-motivation firms to issue SLBs, which makes such decisions more relevant.

While it is not possible to gauge internal motivation of issuers to undertake sustainability efforts, we do have information on their past performance track on this front. Given that the majority 50.62 percent (Figure 3) of sustainability targets are related to reduction in CO2 emissions intensity, the paper exploits past CO2 emissions by the issuer as a proxy of their sustainability performance. Specifically, it examines if issuers with higher past CO2 emissions are more likely to exploit structuring loopholes to minimize penalty payouts relative to issuers with lower past CO2 emissions. We test formally for this relationship by conducting a bondlevel regression for the following specifications: TargetPeriodi = α+ β. CO2Emissionsi,t−1 + Controlsi + εi [5] Callablei = α+ β. CO2Emissionsi,t−1 + Controlsi + εi [6] Where TargetPeriodi refers to the time to target date from issue divided by the time to maturity from issue for bond _i_ ; CO2Emissionsi,t−1 refer to CO2 emissions by the issuer in the year preceding the issue; Callablei is a dummy variable that equals 1 if the SLB has a penaltyminimizing call option and 0 otherwise; Controlsi as the same as in equation [1]. Specification [5] is an OLS regression run only for SLBs with step-up penalties following specification [2]. Specification [6] is a logistic regression run for all SLBs for which CO2 emission data is available.

**<u>Table 7: Bond-Level Regression Analysis for Target Period with CO2 Emissions</u>**

||(1)<br>Model 1|(2)<br>Model 2|(3)<br>Model 3|(4)<br>Model 4|
|---|---|---|---|---|
|CO2Emissions (tens of<br>millions tonnes)|0.005***|0.007***|0.008***|0.013***|
||(0.001)|(0.002)|(0.002)|(0.003)|
|Investment grade dummy||0.120**||0.163***|
|||(0.050)||(0.052)|
|Amount issued (US$ billions)||0.094||0.117|
|||(0.086)||(0.088)|
|Tenor (years)||-0.005||-0.001|
|||(0.008)||(0.007)|
|Total assets (US$ billions)|||-0.001|-0.002***|
||||(0.001)|(0.001)|
|Leverage (%)|||-0.221|-0.064|
||||(0.156)|(0.165)|
|Public organization dummy|||-0.206**|-0.095|

20
trolled behavior with a perceived uncertainty about its benefits or detriment to the wellbeing of oneself or others (Trimpop, 1994). To measure risk-taking behavior, we used the Balloon Analogue Risk Task (BART)<sup>41</sup> . Participants were asked to maximize the number of points they could earn from pumping a balloon. They earned points for every pump but could lose all of their points if the balloon popped. Risk-taking was measured as the number of for the balloons that did not We the values of average pumps pop. adjusted this variable to reflect a different orientation of risk attitudes, so that risk-taking, reversed, defines the risk-aversion outcome.<sup>42</sup> If originally high scores indicated high risk-taking, reversing them means now lower scores indicate higher risk-taking. Thus, the greater the risk-taking reversed score, the more risk-averse the individual is. The data are also standardized relative to the comparison group for consistency.

# **Emotion Regulation**

This trait is defined as all of the conscious and nonconscious strategies we use to increase, maintain, or decrease one or more components of an emotional response (Gross, 2001). To measure this skill, we use the Reactiva application (Amangeldiyev and Egana-delSol, 2018). This platform synthesizes the pioneering technology developed by MIT’s Affective Computing Group (Picard, 1995; McDuff et al., 2015) with a bespoke platform tailored for conducting field research in behavioral economics. Specifically, we used the _affdex_ software development kit created by Affectiva, a spin-off of Affective Computing Group. Affectiva’s technology analyzes videos captured from the front camera of smartphones or tablets to proxy for emotions. It measures emotional reaction to different emotionally laden stimuli (negative and positive videos) based on face detection, facial-feature extraction, and expression classification. Via the front cameras of tablets, the computer vision algorithm identifies key landmarks on the face. The machine learning algorithm then an-

> 41For papers related to the validation of this instrument, refer to Lejuez et al. (2002).

> 42In Stata, _revrs_ is a module to reverse variable value order. Reversing with the _revrs_ command in Stata specifically means taking a variable’s values and changing their order so the highest becomes the lowest and vice versa, effectively flipping the scale. For example, if a scale originally runs 1 to 5, reversing it would make 1 correspond to 5, 2 to 4, and so on, altering the dataset to reflect the inverse value for each entry. We used it to reverse the order of the risk-taking variable values so that it reflects risk-averse behavior, our outcome of interest.

21
# **_Annex III: Graphical Representation and Diagnostics for Closed Economy Formulation_**

**Graph (1): Plot of Log of Levels and First Differences of Logs of Variables for Closed Economy VAR:**

<!-- Start of picture text --> 2020 2020 2020 2020 2020<br>2000 2000 2000 2000 2000<br>1980 1980 1980 1980 1980<br>1960 1960 1960 1960 1960<br>Drm2 Drgdp DINFL DInterestrt DDISC<br>0.2 0.0 0.2 0.0 10 -10 2 0 5 0<br>2020 2020 2020 2020 2020<br>2000 2000 2000 2000 2000<br>1980 1980 1980 1980 1980<br>1960 1960 1960 1960 1960<br>rm2 rgdp INFL Interestrt DISC<br>6 4 7 5 20 0 10 5 15 5<br><!-- End of picture text --> 54