rafmacalaba/datause-encoder-data huggingface dataset:
# **1 Introduction**

In theory, abundant endowments of labor are associated with specialization in labor-intensive manufacturing, as long as trade costs are not prohibitive. In fact, the success of East Asia’s most dynamic economies is attributed precisely to their ability to integrate into the world economy through the efficient use of the one factor of production that they had in good supply: labor (World Bank, 1993). China’s progress and ability to reduce poverty in large scale and the East Asia region’s quick recovery from the late-1990s economic crisis, for example, have been based to a large extent on a model that relies on importing capital and know-how and exporting goods and services that require a great deal of labor (Gill et al., 2013). On the other hand, some countries such as the Dominican Republic are labor abundant and the economy has grown fast, but poverty and unemployment remained high. This is a very different story from that of East Asia and one that is suggestive that some countries might not be using efficiently their most abundant factor of production.

This paper explores the relationship between factor endowments and comparative advantage. In particular, we test the null hypothesis that the products exported by labor abundant countries are not labor intensive, but rather capital-intensive. In that context, we estimate Rybczynski coefficients for the world and for subsets of countries and test whether Rybczynski coefficients are different across different types of countries. We then use the estimated coefficients to assess whether the countries with a low or high ratio of skilled over unskilled labor have a comparative advantage in products that are capital-intensive or labor-intensive.

This paper studies the factor intensity of tradable industries across countries and over time. It is consistent with two neo-classical theories of international trade, namely Ricardian and factor proportions models. Ricardian theories rely on the assumption that countries differ in production technologies, whereas factor proportions models presume that countries utilize similar technologies in production and thus the patterns of trade of an economy are driven purely by international differences in relative factor abundance. There are two strands of empirical trade literature related to these models. The first one examines the implications 2
**Table 4: Correlates with the duration of aggregate measures**

||(1)|(2)|(3)|
|---|---|---|---|
|**Variables**|**lib_m**|**res_m**|**res_x**|
|Ln(Total_stepsj)|2.98|385.15***|-1.33|
||(2.80)|(4.08)|(1.84)|
|Ln(Total_timej)|0.50|-49.43***|-0.47|
||(1.34)|(2.29)|(0.69)|
|Eprocj|-0.33|-1.77***|-0.29|
||(0.35)|(0.13)|(0.20)|
|Ln(Num_DPAj)|0.11||-0.11|
||(0.33)||(0.18)|
|GPAj|1.27||1.37***|
||(0.85)||(0.51)|
|Ln(POPj)|0.14|3.77***|-0.20|
||(0.22)|(0.20)|(0.13)|
|Ln(MPj)|0.07|15.76***|-0.09|
||(0.23)|(0.27)|(0.10)|
|GEj|1.11**|10.06***|-0.29|
||(0.50)|(0.89)|(0.36)|
|Mshj|-11.95|-238.30***|-18.44**|
||(14.68)|(10.82)|(9.18)|
|RCAj|-2.43**|0.32|0.04|
||(1.01)|(0.47)|(0.55)|
|Ln(1+Tarj)|1.04**|38.76***|-0.13|
||(0.49)|(0.69)|(0.33)|
|Ln(Covid_casesj)|-0.11||0.42|
||(0.40)||(0.27)|
|Ln(Covid_deathsj)|0.14|-2.06***|-0.22|
||(0.31)|(0.16)|(0.25)|
|Constant|-10.91|-955.74***|10.36**|
||(7.52)|(9.43)|(4.28)|
|Observations|50|33|50|
|R-squared|0.20|1.00|0.31|

**_Note:_** _Robust standard errors, clustered by country, in parentheses. Levels of significance: *10%, **5%, ***1%._

# **7. Conclusion**

Controlling for country size, government effectiveness and economic factors that may influence trade policy, we find evidence that the use trade measures targeting medical products in the first seven months of the global COVID-19 pandemic is positively correlated with precrisis attributes of national public procurement regimes. Jurisdictions with PP systems that are characterized by more steps or stages and processes that take longer on average to complete made greater use of trade policy to increase domestic availability of protective equipment and medical supplies. At the same time, we find that GPA membership and participation in DPAs is associated with maintaining more open markets for medical products. Jurisdictions that have signed agreements that require nondiscrimination between foreign and domestic firms do more to reduce import barriers and are slower to reimpose import restrictions.

16
**Table 17. Child development production function for 1 year old cohort (later beneficiaries)**

||Projectplaygroup|Total months of En<br>Non Projectplaygroup|rolment between<br>Kindergarten|2009 and 2013 in<br>Religiousplaygroups|Primaryschool|
|---|---|---|---|---|---|
|**EDI**||||||
|Physical Health and Well-being|0.012***|0.013***|0.014***|0.025***||
||(0.002)|(0.004)|(0.004)|(0.005)||
|Social Competence|0.019***|0.020***|0.027***|0.011|le|
||(0.003)|(0.004)|(0.004)|(0.007)|ab|
|Emotional Maturity|0.003|0.007**|0.012***|0.016**|plic|
||(0.003)|(0.003)|(0.005)|(0.007)|ap|
|Language and Cognitive Development|0.033***|0.033***|0.057***|0.041***|Not|
||(0.003)|(0.004)|(0.004)|(0.007)||
|Communication and General Knowledge|0.024***|0.030***|0.031***|0.014**||
||(0.003)|(0.004)|(0.005)|(0.007)||
|**SDQ**||||||
|Total Difficulties|-0.002|-0.001|-0.014***|-0.017**||
||(0.003)|(0.003)|(0.004)|(0.007)||
|Emotional Problems|-0.001|-0.002|-0.006|-0.008||
||(0.003)|(0.003)|(0.004)|(0.007)|le|
|Social Conduct|0.003|0.007**|-0.006*|-0.013**|ab|
||(0.003)|(0.003)|(0.004)|(0.006)|plic|
|Hyper activity|-0.005*|-0.006*|-0.012***|-0.010|ap|
||(0.003)|(0.003)|(0.004)|(0.007)|Not|
|Peer problems|-0.004*|-0.002|-0.015***|-0.015**||
||(0.002)|(0.003)|(0.004)|(0.007)||
|Pro social behavior|-0.013***|-0.007**|-0.008**|0.003||
||(0.003)|(0.004)|(0.004)|(0.007)||

Notes: *** p<0.01, ** p<0.05, * p<0.1; Robust standard errors are in the parentheses. Each row is the result of a separate regression. The dependent variables are child development outcomes as measured by the EDI and the SDQ. Key variables of interest are months of enrollment in each type of service. Each regression controls for child and household characteristics as specified in the text.

53
Table 2. Distribution by Income Classification: COVID-19 Deaths, All-Cause Deaths and Total Population (percent)

|||Balance<br>(160 co|d group<br>untries)|Unbala<br>(all c|nced group<br>ountries)|
|---|---|---|---|---|---|
||Share in<br>COVID-19<br>deaths|Share in<br>expected<br>deaths from<br>all causes|Share in<br>population<br>|Share in<br>expected<br>deaths from<br>all causes|Share in<br>population|
|HICs|78.9|18.1|15.7<br>|18.6|16.2|
|UMICs|17.8|34.5|36.0<br>|32.7|34.1|
|LMICs|3.1|38.9|40.1<br>|38.3|39.7|
|LICs|0.2|8.5|8.1<br>|10.6|10.0|

Source: Johns Hopkins University; World Health Organization; World Population Prospects. Note: COVID-19 data for 160 countries (balanced group) and world (unbalanced group).

The starkly differential impact creates the impression of rich and poor countries being subjected to ‘two pandemics’. HICs currently claim 79 percent of recorded deaths globally due to COVID19 (Table 2). This leaves 21 percent for the developing world, which comprises 85 percent of the global population. Some 18 percent of the global death toll is claimed by UMICs, whereas just 3.1 and 0.2 percent, respectively, by LMICs and LICs. In other words, the two poorest groups of countries collectively represent almost half of the global population but account for only 3.3 percent of global deaths that have COVID-19 as the underlying cause. These large differences almost come across as if the world is affected by ‘two pandemics’.

# **Demography Contradicts the Excess Inequality between Rich and Poor Countries**

One natural way to interpret the seeming dichotomy between rich and poor countries would be to attribute it to differences in demographic structure (Figure 8). The strong age gradient in COVID19 fatalities has been well established by now.<sup>7</sup> Hence, the demographic diversity we witness around the world will matter greatly (World Bank, 2016). The world could be split up into roughly two groups: pre- and early-dividend countries, which continue to have high fertility rates and a still-rapidly increasing working-age share in their total populations, and late- and post-dividend countries, which are aging at rapid speed or have aged already very considerably.<sup>8</sup> This typology of demographic diversity carries over in large part to the income classification given the tight twoway connections between demography and development. As Figure 8.A illustrates, younger age cohorts are much more significant in the age distributions of LICs and LMICs than in those of UMICs and HICs.

> 7 See Baud and others, 2020; Begley, 2020, Belligoni, 2020; Courage, 2020; Dylan, 2020; Huang and others, 2020; Mahase, 2020; Onder and others, 2020; Richardson and others, 2020, Soneji and others, 2020; Wu and McGoogan, 2020.

> 8 See World Bank (2016) for a detailed discussion of the demographic typology.

15
Table 1. Common Dimensions and Indicators of Job Quality

|_Dimension_|_Areas_|_Indicators_|
|---|---|---|
|Earnings|Wage income|• Wage income above a threshold*|
|Job benefits|Social protection|• Job registered with social security administration*<br>• Job provides health insurance*<br>• Job provides pension*<br>• Jobprovides eligibilityfor unemployment insurance*|
||Employment benefits|• Paid annual leave*<br>• Paid sick leave*<br>• Paidparental leave*|
|Job stability|Formal employment|• Employment is bound by written contract*<br>• Employment is permanent, not temporary*<br>• Employer is formal entity*|
||Tenure|• Employed in current job more than a certain number<br>ofyears*|
||Work-life balance|• Excessive working hours*<br>• Work intensity<br>• Unsocial hours / shift work<br>• Involuntary part time<br>• Would like to work more*<br>• Has a secondpaidjob*|
|Working<br>conditions|Physical health and safety|• Dangerous work environments (noise, vibrations,<br>drastic temperatures, and so on)<br>• Manual labor(moving heavy loads, painful or tiring<br>positions)<br>• Accidents a work|
||Job satisfaction|• Skills Match<br>• Work autonomy<br>• On-the-job learning and training<br>• Good relationships with colleagues|

* These indicators are available in most household and labor force surveys across regions of the world and are used in the proposed job quality measure.

The earnings dimension measures whether a job pays sufficiently to lift a family out of poverty and into the lower-middle class. Labor earnings are the main source of material well-being among most individuals, especially in developing countries. This dimension not only captures whether a job generates income, but also whether labor income can support a minimum standard of living among workers and their families (World Bank 2012). As a measure of minimum living standards, this paper adopts the World Bank international poverty line of US$1.90 a day per capita (2011 purchasing power parity [PPP] US dollars). Focusing only on the well-being of individual workers, however, is not sufficient for understanding the welfare implications of employment among the population at large. A poverty headcount around the world is measured based on household and not individual income, and it is thus imperative to consider the needs of workers as well as their dependents, especially in developing countries where labor earnings are the main source of household income. For the harmonization exercise, the dependency ratio is 7
Policy Research Working Paper 8610

# **Abstract**

Bureaucratic complexity in citizen-government interactions can be an obstacle to accessing basic services and exercising civil rights. This paper presents a methodology to collect contributor-based data on the regulatory framework and compliance burden of six key citizen-government interactions: (i) civil registration and identification, (ii) voting, (iii) paying taxes, (iv) access to health care, (v) access to education, and (vi) access to utilities. The methodology was tested in Chile, Ghana, India, and Vietnam, and it provided an overview of the legal and regulatory frameworks. In addition, the study offers some interesting observations: (i) two of the four economies moved toward including digital components to civil registration and identification systems, making it easier to register births/deaths and obtain an identification document; (ii) three of the four economies require a specific voter ID to cast a ballot, which can discourage citizens from engaging in elections; (iii) patients accessing certain services at primary health care facilities may experience longer waits for treatment than those seeking access to the same services in hospitals; and (iv) a student’s ability to access quality education can be limited by school placement based on residential address in two of the four economies. The lessons learned suggest that future studies should use survey data collection to investigate the practical experience of citizens beyond procedures and legal requirements. In addition, focusing on fewer areas and including a larger set of countries could generate greater variability and more robust conclusions.

This paper is a product of the Global Indicators Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The authors may be contacted at vperotti@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Policy ReseaRch WoRking PaPeR 4444

# **Abstract**

This paper investigates the extent to which the health systems of the Western Balkans (Albania, Bosnia and Herzegovina, Montenegro, Serbia, and Kosovo) have succeeded in providing financial protection against adverse health events. The authors examine disparities in health status, healthcare utilization, and out-of-pocket payments for healthcare (including informal payments), and explore the impact of healthcare expenditures on household economic status and poverty. Methodologies include (i) generating a descriptive assessment of health and healthcare disparities across socioeconomic groups, (ii) measuring the incidence and intensity of catastrophic healthcare payments, (iii) examining the effect of out-ofpocket payments on poverty headcount and poverty gap measures, and (iv) running sets of country-specific probit regressions to model the relationship between health status, healthcare utilization, and poverty. On balance, the findings show that the impact of health expenditures on household economic wellbeing and poverty is most severe in Albania and Kosovo, while Montenegro is striking for the financial protection that the health system seems to provide. Data are drawn from Living Standards and Measurement Surveys.

This paper—a product of the Health, Nutrition and Population Division, Human Development Department of the Europe and Central Asia Region—is part of a larger effort in the department to understand issues of efficiency, sustainability and equity of health financing in the countries of the Western Balkans. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at cbredenkamp@worldbank.org and mgragnolati@ worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Table E2: IAT results

||Depend|ent variab|le: Depen|dent varia|ble: IAT|D-Score|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Advances instrument|0.065|0.110|0.110|0.059|0.105|0.105|
||(0.035)|(0.038)|(0.039)|(0.035)|(0.038)|(0.039)|
|Advances instrument _×_ Male respondent||-0.090|-0.090||-0.093|-0.093|
|||(0.070)|(0.070)||(0.070)|(0.071)|
|Male respondent||-0.046|-0.036||-0.022|-0.020|
|||(0.055)|(0.051)||(0.052)|(0.049)|
|Employed|||0.082|||0.048|
||||(0.075)|||(0.059)|
|Young (18-25 years-old)|||0.059|||0.058|
||||(0.044)|||(0.043)|
|Low education (middle school or less)|||-0.103|||-0.076|
||||(0.044)|||(0.046)|
|D-Score on Gender-Career IAT||||0.225|0.208|0.179|
|||||(0.059)|(0.064)|(0.062)|
|Constant|0.166|0.189|-0.348|0.106|0.122|-0.424|
||(0.027)|(0.040)|(0.076)|(0.030)|(0.041)|(0.074)|
|Observations|588|588|588|582|582|582|
|Respondents|294|294|294|291|291|291|
|Platform Fixed Efect|No|No|Yes|No|No|Yes|
|_Post-estimate test for difer_<br>ˆ<br><sup>ˆ</sup>|_ence betw_|_een instru_|_ments am_|_ong men_|||
|_β_<br>Advances_×_Male respondent + _β_<br>~~A~~dvances||0.020|0.020||0.012|0.012|
|P-value||0.733|0.735||0.836|0.837|

_Note:_ The dependent variable is the IAT D-score as calculated by Greenwald et al. (2003). Omitted category is safety instrument in columns (1) and (4); safety instrument, woman respondent in columns (2) and (5); and safety instrument, woman respondent, more than 25 years-old, unemployed, with high school or college degree in columns (3) and (6). Unit of observation is a respondent-instrument pair, so that there are two observations per respondent, one for the provokes advances instrument and one for the safety instrument. Columns (1)-(3) include all respondents who took both the Advances and the Safety tests. Columns (4)-(6) Include only respondents who finished all the tests. All specifications include sampling weights. Standard errors in parentheses, clustered at participant level.
# **Content**

|**1**<br>**INTRODUCTION ............................................................................................................................... 3**|
|---|
|**2**<br>**CONCEPTUAL MODEL ...................................................................................................................... 5**|
|2.1<br>KEY TERMS............................................................................................................................................ 5|
|2.2<br>FRAMEWORK........................................................................................................................................ 7|
|2.3<br>HYPOTHESES......................................................................................................................................... 8|
|2.4<br>EMPIRICAL SPECIFICATION..................................................................................................................... 9|
|**3**<br>**THE DATA ...................................................................................................................................... 12**|
|3.1<br>POVERTY ANDENVIRONMENTNETWORK(PEN) ....................................................................................... 12|
|3.2<br>CLIMATE DATA AND CONTEXTUAL VARIABLES............................................................................................ 12|
|3.3<br>HOW REPRESENTATIVE AND APPLICABLE ARE THEPENDATA? ..................................................................... 13|
|**4**<br>**ESTIMATING ENVIRONMENTAL INCOME OF THE RURAL POOR: A SNAPSHOT ................................ 15**|
|4.1<br>OVERALL INCOME................................................................................................................................ 15|
|4.2<br>ENVIRONMENTAL INCOME AMONG THE POOREST...................................................................................... 16|
|**5**<br>**ESTIMATING THE EFFECTS OF CLIMATE VARIABILITY ...................................................................... 22**|
|5.1<br>REPORTED SHOCKS AND COPING STRATEGIES............................................................................................ 22|
|5.2<br>OBSERVED CLIMATE SHOCKS AND INCOME................................................................................................ 25|
|**6**<br>**CONCLUSIONS ............................................................................................................................... 35**|
|6.1<br>MAIN FINDINGS................................................................................................................................... 35|
|6.2<br>DISCUSSION AND POLICY RELEVANCE....................................................................................................... 37|
|**REFERENCES ......................................................................................................................................... 39**|
|**APPENDIX: DESCRIPTIVE STATISTICS .................................................................................................... 43**|

2
that firms suffer from a decline in market values -upon announcetnent of adverse environmental news.<sup>4</sup> In this paper, we assess whether or not capital markets in Mexico, Chile, Argentina, and the Philippines react to the announcement of firm-specific environmental news. To our knowledge, the current analysis is the first of this<sup>nature</sup> performed in developing countries. Even in those countries where it is generally argued that the environmental regulations suffer from poor implementation, we show that capital markets react negatively (decrease in firms' value) to citizens' complaints targeted<sup>at</sup> specific firms. We also show that markets react positively (increase in firms' market value) to the announcement of rewards and explicit recognition of superior environmental performance. An immediate policy implication from the current analysis is that environmental regulators in developing countries may explicitly harness those market forces by introducing structured programs of information release on firms' environmental peformance, and empower communities and stakeholders through environmental education programs.<sup>5</sup> These results may also shed some new light on the pollution haven hypothesis. A large number of studies have examined the potential impact of environmental regulations on intemational competitiveness.6 Many of these have concluded that pollution intensive In the United States, these studies include, among others, analysis of the reaction of markets to releases of **the** _Toxics Release Inventory_ (Hnamilton (1995) and Konar and Cohen (1997)). Lanoie and Laplante (1994) analyze the reaction of capital markets to environmental news in Canada. For a survey of these studies, see Lanoie, Laplante and Roy (1997).

We know of at least two such programs currently in place in developing countries: in Indonesia (PROPER Prokasih) and the Philippines (Ecowatch). Similar programs are currently being developed in Mexico and Colombia. For further details. see Afsah et al. (1996). See for example, Jaffe et al. (1995), Kolstad and Xing (1994), Levinson (1992), Low and Yeats (1992), Stewart (1993), Tobey (1990), Walter (1992), and Wheeler and Moddy (1992).

6