rafmacalaba/datause-encoder-data huggingface dataset:
# **7 Conclusion**

The neoclassical model of trade predicts that international specialization will be jointly determined by cross-country differences in relative factor endowments and exogenous technologies. Our proposed model relaxes the Hicks-neutral technological differences assumption by allowing countries to adopt different technologies. The marriage of literatures on biased technical change and trade yielded a tractable theory, whereby differences in factor endowments and intellectual property rights bias technical change towards particular factors, and thus unit factor input requirements can vary across economies.

We tested this theoretical model with data on net exports of a single industry, computers, intellectual property rights, factor endowments, and other controls for 73 countries over the period 1980-2000. The descriptive and econometric results provide robust evidence suggesting that once technological choices are considered, countries exhibit different Rybczynski coefficients. This is partly due to differences in factor endowments, as in Schott’s multiplecone model of international specialization with identical technologies across countries. But the evidence also indicates that differences in intellectual property rights and the characteristics of technology trading partners, which also determine technology-adoption choices in our model but not in Shott’s theory, are associated with differences in factor intensities across countries.

# **References**

- [1] Acemoglu, D., 2002. Directed Technical Change. Review of Economic Studies, 7 (4), 781-809.

- [2] Barro, R. and L., Jong-Wha, 1993. International Comparisons of Educational Attainment. Journal of Monetary Economics, 32 (3) 363-394.

- [3] Bernanke, B.S. and R.S., Gurkaynak, 2002. Is Growth Exogenous? Taking Mankiw, Romer, and Weil Seriously, B.S. Bernanke and K. Rogoff (eds.) NBER Macroeconomics Annual 2001 (Cambridge, MA: MIT Press), 11-57.

26
shock produces a D 1 of 87.9 percent, a 38-percent increase. Analogously, the D 2 is driven by the ICR and reaches 28.5 percent at a (30, 30) shock, a 230-percent increase. DD DD

# **Exhibit 7. DaR1 and DaR2 under different shock scenarios to earnings and receivables** **_Panel 1: DaR1, % of total debt_**

||||**Recei**|**vables**|||
|---|---|---|---|---|---|---|
||**No Shock**|**10%**|**20%**|**30%**|**40%**|**50%**|
|**No Shock**|63.9|74.8|84.2|87.1|91.4|92.9|
|**gs**<br>**10%**|64.6|75.5|84.4|87.3|91.5|93.0|
|**nin**<br>**20%**|64.9|75.8|84.8|87.3|91.5|93.1|
|**Ear**<br>**30%**|71.6|82.4|85.4|87.9|91.9|93.3|
|<br>**40%**|72.7|83.3|86.1|88.5|92.4|93.8|
|**50%**|81.5|84.1|86.8|89.2|92.8|94.1|
|**_l 2: DaR2, % of tot_**|**_al debt_**||**Recei**|**vables**|||
||**No Shock**|**10%**|**20%**|**30%**|**40%**|**50%**|
|**No Shock**|12.4|12.9|13.1|13.7|14.3|14.9|
|**gs**<br>**10%**|15.2|15.7|16.4|17.0|17.7|18.3|
|**nin**<br>**20%**|17.2|17.7|18.4|19.2|19.9|20.5|
|**ar**<br>**30%**|20.3|20.9|27.6|28.5|29.4|30.2|
|**E**<br>**40%**|24.6|25.4|32.3|33.2|34.3|35.0|
|**50%**|30.0|38.8|45.7|46.7|48.0|48.9|

**_Panel 2: DaR2, % of total debt_**

Note: Sample only includes firms with non-missing ICR and QR.

**These results mask significant regional heterogeneity in pre-existing corporate balance-sheet vulnerabilities and their susceptibility to shocks.** Exhibit 8 presents D 1 and D 2 for each region without any shocks and a 30-30 percent shock to both earnings and receivables. All regions will see a significant increase in D 1 in a shock scenario. Firms in EAP and MNA in particular DD DD already entered the pandemic with the highest vulnerabilities. Absent any shock, 91.6 percent of debt in MNA was already vulnerable, and 47.2 percent was very vulnerable. A 30-30 shock would DD see highly vulnerable corporate debt in MNA rise to almost 64 percent. Firms in SA appear the most susceptible to a shock. A 30-30 percent shock would see highly vulnerable debt surge from 10.6 percent to almost 40 percent.

**The pandemic disproportionately affects certain sectors such as travel, hospitability, entertainment, tourism, energy and transportation. However, the debt of these firms does not appear to be significantly more vulnerable than in other sectors.** We consider the following sectors particularly vulnerable to the pandemic: Aerospace & Defense, Oil & Gas Producers, Oil Equipment, Services & Distribution, and Travel & Leisure. Our sample contains 582 firms identified as belonging to these sectors. Combined, these firms represent US$ 2.3 trillion in total assets and US$ 646 billion in debt. Prior to the pandemic, 9.1 percent of the debt was highly

13
Table 2. Households characteristics correlated with vulnerability (t-statistics from single variable regressions)

|Characteristic|Vulnerability (% change in<br>consumpion,2008 to 2009)|
|---|---|
|Female headed household|-1.06|
|Number of children in the household|-0.33|
|Size of the household|-1.48|
|Number of elderly in the household|2.44|
|HH head has a low level of education|-3.33|

A positive (negative) t-statistics indicates a less (more) vulnerable household.

# 6. <u>Results</u>

Our analysis has three parts: (i) we analyze the impact of the income shock on the entire sample; (ii) we restricted the analysis to the lowest quintile of the population (in order to analyze the impact of the income shock on the poor);<sup>10</sup> (iii) we restrict our analysis to other vulnerable groups.

Before elaborating our main results, Table 3 presents an overview of the main descriptive statistics used in the analysis.

||Households aff<br>Table 3. Descripti|<br>ected byincome<br>ve statistics|<br>shock|N|on affecte|<br>d|
|---|---|---|---|---|---|---|
|Variable|Obs<br>Me|an<br>|Std. Dev.|Obs|Mean|Std. Dev.|
|Household size|1762|2.970488|1.579336|6531|2.56117|1.437643|
|Male head of household|1808|0.8451327|0.361878|6956|0.744681|0.436072|
|Pct of employed in household|1762|0.512231|0.352159|6531|0.446603|0.369057|
|Asset index|1148|0.1510217|0.973648|4745|-0.0594|0.997216|
|Education level of the household head|1427|4.674142|1.85662|5184|4.49537|2.03727|
|Age of the household head|1501|52.71086|13.52604|5934|57.51196|14.73281|
|Log of real health expenditure|1183|5.70942|1.267061|4838|5.669439|1.206375|
|Log of real food expenditure|1786|8.210205|0.861647|6688|8.067133|0.78741|
|Number of doctor visits|1627|10.25937|6.214223|6167|8.865737|5.517154|

Tables 4, 5 and 6 present our difference-in-difference propensity score matching results.<sup>11</sup> Table 4 reports the results conducted on the entire sample. There are a few empirical regularities that emerge from Table 4. First, we see that there is no statistically significant difference between the crisis-affected and the non-affected group in the baseline scenario. This regularity holds for all three variables of interest: food expenditure, health expenditure, and doctors’ visits. Second, the follow up scenario, namely, the year of the crisis and after, follows a similar empirical regularity.

> 10 As a robustness check we also conduct the analysis on the upper quintile (as well as a combination of the two upper quintiles). The results of these robustness checks are available on request.

> 11 We use one-to-one matching as a basis for the difference-in-difference matching procedure. The diagnostic tests for the propensity score matching (PSM) exercise that is a basis for the difference-in-difference PSM are presented in Appendix 1.

13
**_Executive Summary_**

**_Paraguay's aguateros_** _-_ **_water supply_** _- There are about 300 to 400 private individuals and firms in Paraguay supplying good quality piped water to areas not served by the public water company. These range from 'mom-and-pop' operations supplying their own neighbourhood, up to larger companies serving as many as 800 connections._

_Unlike the public company, the aguateros allow payment of connection fees on instalment, making it easier for low income consumers to connect. A UNDP- World Bank Water and Sanitation Program review concluded "Aguaterias have been operating successfully for a good ten years and quality of service is increasing as experience, competition and customer activism have increased over time,. 2_ _Yemen's small scale_ **_electricity providers_** _- In the Yemen, the public utility meets urban electricity demand, but does not supply rural towns and villages. This gap is filled by a large number of small-scale providers. These range from individual households who generate for their own use and supply a few neighbours, to larger operators supplying up to 200 households and operating sophisticated billing systems. The result is that rural household electricity use in the Yemen is remarkably high compared to other middle income countries. While there is little prospect of significantly improving the proportion of rural grid-connected households in the next two decades, small scale private sector operators have demonstrated a willingness and innovative approaches to meeting the demand.3_ These examples show that where a monopoly supplier does not meet people's needs, other suppliers can enter the market. These suppliers are generally financial viable, sustainable, and customer responsive. They adopt innovative technology, and expand service to poor and marginal areas. Given a chance, such small-scale enterprises can evolve into substantial providers, and generate significant welfare gains in the communities they serve. This suggests that restricting entry into crucial sectors such as water and electricity may impose real costs, in particular on the poorest communities.

**_These entrants seldom duplicate investments_**

Entrants tend to concentrate on unserved areas, so that in practice wasteful duplication of investment seldom occurs. Our analysis suggests that it is only when there is no existing infrastructure, and the market is likely to be highly profitable, that a race for market share can result in duplication. These conditions exist in some markets, for example in the wireless

- 2 Solo, Tova Maria and Snell, Suzanne Water and Sanitation Services for the Urban Poor - Small Scale Providers: Profiles and Typology UNDP-World Bank Water and Sanitation Program, May 1998.

- 3 Republic of Yemen: Household Energy Strategy Study, Phase 1. A Preliminary Study of Northern Governorates. Report No. 126/91 Energy Sector Management Assistance Program (ESMAP) March 1991.

7
**Figure 6 OECD index of regulatory restrictions in engineering services**

<!-- Start of picture text --> Italy<br>Canada<br>Luxembourg<br>Germany<br>Greece<br>Japan<br>Slovak Republic<br>Turkey<br>Czech republic<br>Austria<br>Hungary<br>Korea<br>New Zealand<br>Spain<br>Mexico<br>Netherlands<br>Tunisia<br>Norway<br>Finland<br>0 1 2 3 4 5<br>Restrictiveness index<br><!-- End of picture text --> Data source: Based on OECD (2005a).

28
their real spending per capita in 2009, but the reductions measured for the other years were not statistically significant.

**Table 1.  Estimates for regression where the dependent variable is real per capita expenditure, Thailand, 2007-2010**

|Variable|coefficient|p-value|Variable(cont.)|coefficient|p-value|
|---|---|---|---|---|---|
|Dependent variable:||||||
|Real per capita expenditure||||||
|Region: Bangkok (omitted)|||No. disabled|-412.78|_0.00_|
|Center|-2080.29|_0.00_|No. government wkrs.|372.56|_0.00_|
|North|-3373.03|_0.00_|No. other employees|-193.05|_0.00_|
|Northeast|-2882.17|_0.00_|No. self-employed|-382.42|_0.00_|
|South|-2252.21|_0.00_|No. in sales|182.01|_0.00_|
|Urban|642.28|_0.00_|No. working on stalls|269.13|_0.00_|
|Size of household|-1074.13|_0.00_|No. of managers|1189.85|_0.00_|
|Household runs a business|75.31|_0.06_|No. of farmers|141.24|_0.00_|
|Most income from wages|90.33|_0.03_|No. of subsistence frmrs.|58.70|_0.04_|
|Most income fromprofits|321.10|_0.00_|No. in elementary svces.|-65.74|_0.03_|
|No. of males|100.62|_0.00_|No. ofprofessionals|766.50|_0.00_|
|No. aged under 5|-109.25|_0.00_|2007 q2|-37.40|_0.54_|
|No. aged 5-9|74.45|_0.03_|2007 q3|-25.47|_0.68_|
|No. aged 10-14|134.20|_0.00_|2007q4|-227.95|_0.00_|
|No. aged 15-19|209.45|_0.00_|2008 q1|-187.93|_0.02_|
|No. aged 20-29|-98.03|_0.00_|2008 q2|-321.07|_0.00_|
|No. aged 30-59|-2.66|_0.89_|2008 q3|-351.89|_0.00_|
|No. aged 60-64|-257.33|_0.00_|2008q4|-197.34|_0.01_|
|No. aged 65-74|-463.19|_0.00_|2009 q1|170.13|_0.01_|
|No. aged 75 and over|-484.05|_0.00_|2009 q2|99.54|_0.10_|
|Head is a widow|-250.84|_0.00_|2009 q3|50.55|_0.41_|
|Head is separated|62.17|_0.07_|2009q4|72.54|_0.23_|
|No. with just primary edn.|457.51|_0.00_|2010 q1|-39.42|_0.62_|
|No. with just second. Edn.|774.53|_0.00_|2010 q2|-220.46|_0.01_|
|No. with upr. Vocatl. Edn.|1244.85|_0.00_|2010 q3|-45.04|_0.57_|
|No. with university edn.|2230.56|_0.00_|2010q4|-133.99|_0.09_|
|No. withpost-graduate edn.|4085.72|_0.00_|Constant|9421.57|_0.00_|

Source: Data from Thailand Socio-Economic Surveys of 2007-2010. Data are pooled. Sample size: 176,141 households. Adjusted R<sup>2</sup> = 0.29. Quarterly fixed effects are relative to 2007:Q1.

**Table 2.  Measuring the Impact of the 2008-09 Recession on Real Expenditure, Thailand**

|Change in real expenditure per capita||
|---|---|
|compared to 2007:Q1|Number of|
|2007<br>2008<br>2009<br>2010|households|
|_baht/month_||

Losers and Winners in Recession: Thailand 2008-09 16
# **1. Introduction**

The return to investing in education, based on past empirical studies, is known to differ between individuals. For example, evidence from the United States (see for example, Ingram and Neumann 2005) shows that over the past decades, individuals with college education but without specific skills experienced the lowest benefits from investing in education. Therefore, labor markets may favor individuals with a wide range of (cognitive or non-cognitive) skills; for such individuals, skills are expected to interact positively with education, resulting in higher benefits from education investments.

Figure 1: Observable versus Unobservable Skills <!-- Start of picture text --> Schooling<br><!-- End of picture text --> <!-- Start of picture text --> Non-Schooling<br> (Life)<br><!-- End of picture text --> <!-- Start of picture text --> Observed Skills<br> (Such as numeracy,<br> reading, writing)<br><!-- End of picture text --> <!-- Start of picture text --> Unobserved skills<br>(such as diligence,<br>organization, teamwork)<br>etc<br><!-- End of picture text --> <!-- Start of picture text --> Observable<br><!-- End of picture text --> <!-- Start of picture text --> Unobservable<br><!-- End of picture text --> As highlighted in Figure 1, (observable) skills as well as (unobservable) skills, are generated through schooling as well as elsewhere in life (such as within the family). Other unobservables (innate ability) may be signaled via schooling. Ability is multidimensional. IQ has to be distinguished from what is measured by achievement tests, although it partly determines success on achievement 2
cohorts of children were exposed to the fighting (see Suri and Boozer (2007) who use a similar identification strategy to explore child labor and schooling issues).<sup>6</sup> We find children who were exposed to the conflict have significantly lower height for age z-scores, an effect that is robust to the inclusion of a variety of control variables, time trends, and alternative sample specifications.

Recent research suggests this finding could have serious implications for the future schooling and productivity of these children when they reach adulthood. Alderman, Hoddinott, and Kinsey (2006) use Zimbabwe data and find that improvements in childhood chronic malnutrition (measured as height for age) are associated with a higher number of schooling grades completed. Alderman et al. (2001) estimate the impact of child health on school enrollment in rural Pakistan and conclude that improvements in preschool height for age z-scores are likely to have substantial long-run productivity effects through inducing greater schooling and subsequent productivity gains. Maccini and Yang (2006) and Meng and Qian (2006) find that negative early-life environmental conditions (negative rainfall shocks in Indonesia and China’s Great Famine, respectively) lead to worse adult health and socioeconomic conditions.

There is also evidence demonstrating the adverse effects of prenatal health shocks. In research on the Dutch famine during World War II, Stein et al. (2004) examine the impact of prenatal exposure to the famine on body proportions at birth and find that exposure during late pregnancy negatively affected the child’s birth weight and body proportions. Stein et al. (2007) have also studied the long-term consequences of the same famine and find that exposure during early pregnancy is associated with a higher risk of adult obesity for women. Similarly, Almond (2006) finds that children who were in utero during the 1918 influenza pandemic experienced lower educational attainment, increased rates of disability, and lower socioeconomic status once they reached adulthood. Based on this logic, it is likely the civil war in Burundi will have longrun welfare impacts through its adverse effect on child health which in turn can negatively affect schooling, future adult health, and income levels. Following the end of conflict, improved child health should be a tangible peace dividend, although there will still be a generation of children who were exposed to the conflict and have to suffer the negative consequences of the war.

> 6 For the baseline regressions, we focus only on the provinces that experienced civil war at some point during this time period, and we exclude the two provinces in Burundi (Cankuzo and Rutana) that did not experience any conflict as these regions might be systematically different than regions where fighting occurred.  In the robustness specifications that include these two provinces, results are quantitatively similar.

2
- McKenzie, D., Stillman, S., Gibson, J., 2010. How important is selection? Experimental vs. nonexperimental measures of the income gains from migration. _Journal of the European Economic Association_ , _8_ (4), 913-945.

- Mitchell, M., Favara, M., Porter, C., Sánchez, A., 2020. Human capital development: New evidence on the production of socioemotional skills (Discussion Paper No. 13804), _Discussion Paper Series. Institute of Labor Economics (IZA), Bonn, Germany._

- Montenegro, C.E., Patrinos, H.A., 2021. A data set of comparable estimates of the private rate of return to schooling in the world, 1970–2014. _International Journal of Manpower ahead-of-print_ .

- Muralidharan, K., 2019. Reforming the Indian School Education System, in: _What the Economy Needs Now, edited by Abhijit Banerjee, Gita Gopinath, Raghuram Rajan, and Mihir Sharma, Juggernaut_ .

- Nunnally, J.C., 1978. Psychometric theory, 2nd edition. ed, McGraw-Hill series in _Psychology. McGraw-Hill, New York._

- Outes-Leon, I., Dercon, S., 2009. Survey attrition and attrition bias in Young Lives (Technical Note No.

- 5), _Young Lives Technical Notes. University of Oxford, Oxford, United Kingdom._

- Pierre, G., Sanchez Puerta, M.L., Valerio, A., Rajadel, T., 2014. STEP skills measurement surveys:

- Innovative tools for assessing skills (Discussion Paper No. 1421), _Social Protection and Labor Discussion Papers. World Bank, Washington, DC._

- Pritchett, L., 2013. The rebirth of education: Schooling ain’t learning. _CGD Books._

- Schultz, T. W., 1975. The value of the ability to deal with disequilibria. _Journal of Economic Literature_ , _13_ (3), 827-846.

- Shah, D., Khatoon, S., Bhatti, M.A., Tatla, A., 2018. Public financing in education sector (Report No. 282), _AEPAM Publications. Academy of Educational Planning and Management, Islamabad, Islamabad, Pakistan._

- Singh, R., Kesarwani, R., Mukherjee, P., 2018. ‘I will achieve everything on my own’: the association between early psychosocial skills and educational progression through adolescence in India. _Young Lives Working Paper 175._

- Subramanian, N., 2020. Workplace attributes and women’s labor supply decisions: Evidence from a randomized experiment in Pakistan. _Presented at the 2020 IZA/University of Luxembourg Workshop: Matching Workers and Jobs Online - New Developments and Opportunities for Social Science and Practice, IZA - Institute of Labor Economics, Online._

- Valerio, A., Sanchez Puerta, M.L., Tognatta, N., Monroy-Taborda, S., 2016. Are there skills payoffs in low- and middle-income countries? Empirical evidence using STEP data (Working Paper No. 7879), _Policy Research Working Paper. World Bank, Washington, DC_ .

- World Bank, 2018. World Development Report 2018: Learning to realize education’s promise. _World Bank, Washington, DC._

34
The theoretical framework motivates our estimation strategy. Our empirical model estimates the relationship between responses to climate change questions C i and age of the respondent controlling for respondent’s characteristics: C i i i i , (4) ii where, i is a set of individual characteristics such as the respondent’s age, gender, highest level = ββXX + γγGG + θθ + εε of education, marital status, religious affiliation, indicators of mental health, risk aversion, trust in XX government, main sources of information, household composition, and a proxy for household income. i is a vector of regional characteristics such as type of location (urban or rural). is time-invariant country _c_ fixed effect, and i is an innovation term. ii GG θθ εε

# **4. The data**

We use the latest 2023 round of the Life in Transition Survey (LiTS), implemented by the European Bank of Reconstruction and Development (EBRD) and the World Bank (EBRD 2023). It covers the transition economies of Europe and Central Asia and several comparator countries in Western Europe, the Middle East, and North Africa, including a total of 38 countries. The survey included a nationally representative sample of around 1,000 households per country.

The analysis focuses on climate beliefs and climate actions. Beliefs about climate change are captured by the following questions: “ _How convinced are you personally that climate change is real?_ ” and “ _How convinced are you personally that climate change is manmade?_ ” Responses are recorded on a 5-step Likert scale ranging from “entirely unconvinced” to “entirely convinced.” We distinguish between impacts on one’s self and impacts affecting the next generation. The former is based on responses to the yes/no question “ _Do you think climate change seriously affects or will seriously affect you during your lifetime?_ ”, while the latter is based on responses to the question “ _Do you think climate change seriously affects or will seriously affect the children of today during their lifetime?_ ”. We assess past impacts of climate change using the question, “ _Have you personally experienced disruption or damage due to flooding, drought, or other natural disasters? (Yes/No)._ ” We capture climate action through questions concerning support for government and personal action in the climate space. Support for government action is based on responses to the question 10