rafmacalaba/datause-encoder-data huggingface dataset:
poverty line of R$81 in 2001. The impact of having completed grades 1-4 of primary education on the likelihood of being poor is the lowest. For high-school graduates, the estimated impact is much larger (4 times) than that of grade 1-4 of primary education. Furthermore, completed tertiary education reduces poverty more than completed secondary education. For university graduates, the likelihood of falling below the poverty line is 8 times lower than that of completed grade 1-4 of primary education. Moreover, it is interesting to note that the likelihood of falling below the poverty line is much lower for university graduates in Bahia than in Brazil as a whole, indicating that highly educated individuals is a scarce resource in Bahia and they therefore receive a wage premium. This is not the case for household heads that only partly or fully completed primary school. On the contrary, this group of individuals has a higher probability of being poor than in the rest of Brazil, indicating that the quality of primary education may not be as high in Bahia as elsewhere.

**Figure 7.1: Marginal Effects of Attained Education** **<u>on Poverty, Bahia and Brazil (2001)</u>**

<!-- Start of picture text --> Primary Education<br>Lower Secondary Education<br>Upper Secondary Education<br>Tertiary Education<br>-18% -16% -14% -12% -10% -8% -6% -4% -2% 0%<br>Bahia Brazil<br>Source: Authors estimations based on PNAD 2001.<br><!-- End of picture text --> _Labor market connection is important for the probability of falling into poverty_ . Households where the household head works in the formal sector were less likely to be poor in 2001. In Bahia, however, the effect on poverty of formal sector employment is less strong than in Brazil as a whole. This finding is in line with the fact that formal sector employment is lower in Bahia than in the nation. Turning to the sector of employment—agriculture, services, industry, social and other— is a significant correlate of poverty and Bahia is similar to the rest of the country. A household, whose head is employed in the public sector, is richer than one whose head is employed in the private sector. Additionally, household heads working in agriculture are 10 times more likely to be poor than household heads working in services and industry.

_Age of head of household and its members._ The older the head of the household, the lower the probability the household will be poor, albeit at a decreasing rate (Table 7.1). Households with young children are the most poverty prone; households with old people are the least. In Bahia and Brazil, families with children younger than 5 45
18 uncertain at the time how the price of an average share with a book value of CSK1.000 would develop.

**Table 10: Rank of funds by discounts of fund shares (as of June 30, 1994)**

||PPF|HCC|IB|CP|AB|YSE|CS|KB|ZB|CA|
|---|---|---|---|---|---|---|---|---|---|---|
|Discount 1/|78|72|72|62|58|56|53|51|37|28|
|Market value 2/|7.3|10.4|9.6|8.0|9.0|21.0|11.0|13.3|18.0|21.0|
|buy back promise 3/|11.0|10.3|11.0|15.0|n/a|-|12.0|<br>-|-|-|

1/ Discount of fund share in relation to net asset value per fund share 2/ Market value of investment [in 1,000 CZK] (= fund share price x number of fund shares per voucher book) 3/ Promise to buy back fund shares [in 1,000 CZK] (per voucher book). "-" means that fund gave no buy back promise.

Remark: for fund names and fund classification see Table 3. Source: Fund interviews.

How did Czech share prices develop after privatization? The total market capitalization in the Czech Republic in June 30, 1994 was estimated at $12.3 billion (Creditanstalt, 1994). It should be noted that around one-third of the companies involved in the first wave have not been traded even once and another third is rarely traded. Therefore it is difficult to say what 'market capitalization' really means. If one assumes that the above-stated figure mainly reflects the value of the two-thirds of regularly and occasionally traded shares, which is then divided over the number of outstanding shares (which includes rarely and non-traded shares), the average value of a Czech share is still far above the book value of CSK1,000 23 Thus, the average market value of a voucher book is far greater than the buy back guarantees of some funds. This might explain why the promise of voucher funds in that price range may have created an upper ceiling for the price of a fund share. There are two interpretations for this upper ceiling. First, market participants might consider the 'price' that was indicated by a fund itself as the 'real market price', i. e. the perception of what the price should be. A second interpretation might be that funds buying back their fund shares and selling them again on the secondary market create some kind of excess supply of their own shares. Since many fund shares are quoted at high discounts, buying back fund shares 23 The average value of a share at this point of time can be estimated at around CZK1,700.
concerns, especially since the poorest countries have made minimal contributions to the source of the problem.

# **6. Empirical Results**

Our IO analysis yields a rich array of results regarding the composition of trade in virtual carbon. We first use the MRIO approach to summarize total flows of carbon, in total and normalized to GDP, by country. We follow this with an examination of the composition of trade in virtual carbon using the BTIO approach. As noted above, the key advantage of the BTIO approach is that it allows the matching of bilateral trade flows to their carbon content.

Turning to the results for the MRIO approach to virtual carbon, Figures 1 and 2a and 2b decompose flows of carbon by country. The virtual carbon – from _domestic_ sources – required to produce domestic final demand is represented by the dark bars. Gray bars represent the virtual carbon – from _foreign_ sources – embodied in the country’s final demand. The outline bar measures how much carbon was actually emitted in all production processes inside the country. Note that these are emissions in the production sectors, meaning that household and government emissions are excluded. This is not to argue that these emissions are unimportant in the bigger picture of understanding the contribution to climate change of different economic sectors. However, since these emissions are not ‘virtually traded’ they fall outside the domain of analysis in this paper.

In Figure 1 we see – as expected – that the big emitters of carbon from production processes are the US, the EU, China and the aggregate of other middle income countries (excluding China, India, Brazil, Mexico, Russia and South Africa). Note that if the outline bar (emissions from total production) is higher than the sum of the dark and grey bars (domestic and foreign virtual carbon in domestic final demand), then the country is a net exporter of virtual carbon. The main net exporters of virtual carbon are China, Russia and other middle income countries, while the main net importers are the EU, USA and Japan.

Figures 2a and 2b look at carbon intensity of consumption (solid bar) and production (outline bar) by normalizing the numbers from Figure 1 to the GNI measured at market exchange rates and PPP. From a consumption perspective, the countries consuming the most (domestic and foreign virtual carbon) emissions per dollar of income are Russia, China, India and South Africa. Adjusting by PPP exchange rates increases the relative importance of the USA (now fourth) and diminishes that of India. To understand these numbers, consider India: one dollar of (PPP) income results in 0.4 tons of consumption driven emissions. This is much lower than China (almost 0.7) but higher than EU15 (0.26). So income growth in India will result in a higher 'demand' for emissions than income growth in the EU (assuming constant technology and PPP exchange rates). This reflects both India's production patterns but also the characteristics of India's demand for imported goods.

13
**Table 10a: Lower Income Exporters**

||(1)|(2)|(3)|
|---|---|---|---|
|VARIABLES|DeepProvisions|AnyLabor Clause|Main Categories|
|Trade Agreement|0.067|0.106|0.134|
||(0.129)|(0.131)|(0.130)|
|Tariff Preference Margin|0.020|0.027|0.028|
||(0.027)|(0.025)|(0.025)|
|Any Labor Clause||-0.225***||
|||(0.080)||
|1. Aspirational|||-0.158|
|goals/objectives|||(0.199)|
|2. Substance-related|||-0.612**|
|clauses|||(0.283)|
|3. Substance-related labor|||-0.002|
|clauses: investment|||(0.096)|
|4. Cooperation over|||0.148|
|labor clauses|||(0.122)|
|5. Institutions overseeing|||-0.016|
|labor commitments|||(0.255)|
|Anti-Dumping|-0.771***|-0.739***|-0.733***|
||(0.245)|(0.258)|(0.257)|
|Competition Policy|-0.021|-0.034|-0.016|
||(0.138)|(0.136)|(0.136)|
|Subsidies|-0.502***|-0.381***|-0.281**|
||(0.131)|(0.130)|(0.127)|
|Technical Barriers A|1.342***|1.194***|1.087***|
||(0.327)|(0.337)|(0.334)|
|Technical Barriers B|0.345|0.384|0.437|
||(0.330)|(0.331)|(0.336)|
|Technical Barriers C|0.313***|0.342***|0.174|
||(0.109)|(0.107)|(0.135)|
|Technical Barriers D|-0.196|-0.241|-0.075|
||(0.197)|(0.205)|(0.218)|
|Constant|16.355***|16.340***|16.325***|
||(0.042)|(0.044)|(0.049)|
|Observations|646,327|646,327|646,327|

**Notes:** Robust standard errors in parentheses (clustered on country pairs).. *** p<0.01, ** p<0.05, * p<0.1.  The data cover 1995-2018. Estimation in all columns include country-pair fixed effects and importer and exporter time period interaction terms estimated with Stata’s _ppmlhdfe_ command (Correia et al. 2019a, 2019b).

pg. 40
# **1 Introduction**

It was not until recently that the downsides of intensified industrialization have started gaining academic attention. One of the burgeoning topics is how increasingly frequent and severe industrial disasters have taken place around the world. Since the 1990s, the number of documented large-scale industrial disasters has increased by nearly fivefold (EM-DAT, 2017). According to the International Disaster Database from the Centre for Research on the Epidemiology of Disasters (CRED), the types of industrial disasters that nations experience include gas leaks, oil spills, nuclear explosions, and chemical contamination. These incidents often lead to disastrous environmental consequences with impacts felt for years. Developing countries, with laxer environmental standards and a strong desire to promote industries and attract foreign investment, are most likely to bear the brunt of these industrial disasters. Ironically, these countries usually lack the capacity to fully evaluate the causes and effects of disasters, hold perpetrators accountable, and provide timely assistance to the affected population. Existing studies on the effects of man-made environmental disasters in developing countries, due to capacity and budget constraints, and sometimes political sensitivities, are rare.

In this paper, we examine the labor-market impacts of _Formosa_ ,<sup>1</sup> an industrial marine pollution crisis breaking out in Vietnam that devastated the ecosystem and disrupted fishery activities in the country’s central coast in 2016. Our empirical analysis leverages a novel source of high-resolution satellite data on night-time boat detection in Vietnam’s marine exclusive economic zone (EEZ), and relates it to employment data from Vietnamese labor force surveys. Exploiting both the industry-specific and location-specific natures of the _Formosa_ shock, our identification strategy compares fishery workers that lived in the affected region to both non-fishery workers in the same area and other fishery workers outside the affected zone.

We estimate the impact of _Formosa_ by employing a series of difference-in-differences estimations (DiD) using individual-worker data, and show that the disaster sharply reduced average fishery income by as much as 42 percent in the rest of 2016. Utilizing high-resolution satellite’s boat-detection data at the monthly interval, we additionally estimate a direct impact of _Formosa_ on fishing activities, showing an average decline in fishing intensity of as much as 36 percent in the immediate period following the pollution breakout. Interestingly, we find that the negative impact of the disaster did not distribute evenly across locations; the

> 1As later discussed, we refer to the disaster as _Formosa_ following the name of the polluter that caused this marine pollution.

1
# **Table 4: OLS Estimates of Wage Determinants using Occupational versus Industry Offshoring Exposure, 1997-2002**

Dependent Variable: Log Wage

||Industry-Sp <br>|Offshoring<br>ecific Expo<br>|Measured by<br>sure,Manufactu<br>|ringOnly|Occupat<br>|Offshoring<br>ion-Specific|<br>Measured by<br>Exposure,All|Sectors|
|---|---|---|---|---|---|---|---|---|
|Variable|All<br>Occupations|Most<br>Routine|Intermediate<br>Routine|Least<br>Routine|All<br>Occupations|Most<br>Routine|Intermediate<br>Routine|Least<br>Routine|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Lagged log of low income|-0.008|-0.007|-0.019*|0.007|-0.111***|-0.200***|0.141***|0.323*|
|affiliate employment|(0.01)|(0.01)|(0.01)|(0.02)|(0.03)|(0.03)|(0.05)|(0.19)|
|Lagged log of high income|-0.009|-0.016|-0.01|0.004|0.098***|0.171***|-0.134***|-0.293*|
|affiliate employment|(0.01)|(0.01)|(0.01)|(0.02)|(0.03)|(0.03)|(0.05)|(0.16)|
|Lagged import penetration|0.152|0.222*|-0.03|0.027|-0.312**|-0.441***|-0.100|1.620|
|using 1979 weights|(0.1)|(0.12)|(0.15)|(0.21)|(0.14)|(0.12)|(0.75)|(1.83)|
|Lagged export share using|0.016|-0.102*|0.09|0.074|0.485**|1.006***|0.326|-0.791|
|1979 weights|(0.06)|(0.06)|(0.07)|(0.1)|(0.21)|(0.20)|(0.33)|(0.96)|
|Number of observations|132,104|71,985|36,982|23,137|886,984|291,894|337,059|258,031|
|R-squared|0.44|0.35|0.40|0.34|0.48|0.39|0.51|0.37|

# _Source_ : See Table 1.

_Note_ : Robust standard errors reported in parentheses below coefficient estimates. The classification of occupations into routine categories is determined by the proportion of tasks which are routine in each occupation, with low being occupations with more than 2/3, intermediate being between 1/3 and 2/3, and high being occupations with less than 1/3rd of tasks designated routine. Controls are included for lagged log price of investment, lagged total factor productivity, and lagged log real price of shipments among manufacturing workers. Wage specifications control for a worker's gender, age, race, experience, whether in a union, and include industry, year, education and state fixed effects. The occupation-specific exposure regressions also include 2-digit occupation fixed effects. Controls for lagged computer use rates are imputed by the worker's industry (columns 1-4) and by occupation (columns 5-8).
Table 9 demonstrates the strong bias towards intra-regional migration. Every diagonal element exceeds unity (except where it is zero because there is only one country in the region), very much so in some cases. Australia and New Zealand have a huge propensity to migrate between themselves, perhaps reflecting the integrated labor market created under the Closer Economic Relations Agreement, or maybe just their isolation. In second place is intra-African migration, more probably a reflection of Africans’ relative unwelcomeness elsewhere, than of anything positive. Among the offdiagonal elements of note are the strength of the EAP-Japan link; the close links between the USA and Canada; the bias towards the USA for LAC emigrants, which we have noted before, and the bias of US emigrants towards LAC, which we have not; the European tendency to choose other Northern destinations; and the strong relative movement from high-income MENA (the Gulf) to developing MENA. Again, these patterns are not all surprising, but it is useful to have them explicit and their quantification will, we hope, allow a good deal of subsequent research to explain them.

The bilateral data allow us to explore a number of possible factors behind migration, descriptively here, but possibly econometrically in future. One concerns the influence of a common language. For 163 of our countries we have been able to identify a predominant international language: Arabic, Chinese, English, French, Portuguese and Spanish.

**Figure 4:  Migration and Common Language**

<!-- Start of picture text --> Arabic<br>Chinese<br>English<br>French<br>Portuguese<br>Spanish<br>different<br><!-- End of picture text --> Figure 4 shows that over half of international migration (that on the right hand side of the pie) takes place between countries with a common language, the lion’s share of this being between English-speaking countries. Dividing each language group into North and South shows that much of the common language flow is within the North or within the South, but that the North-South dimension is also strong. Every Northern language group draws a large share of its immigrants from regions in the South which share its language and, where this is not the largest share, it is dominated by another welltrodden South-North path. The Arabic-North has a higher percentage from the English24
First, there is relatively lower competition in the financial sector of Macedonia compared to that of Slovakia. Though Macedonia has a higher number of banks per capita, the Macedonian banking sector is dominated by few large banks preventing competitive pressures to force increased lending and deposits, and to reduce interest rate margins. As a result, the lending rate and the interest rate margins in Macedonia have been higher compared to those in Slovakia (see Figure 1). Slovak banks, facing higher competition borrowed extensively funds from abroad. On the other hand, Macedonian banks, facing limited competition still remain net international creditors despite higher domestic interest rates and profit margins relative to Slovakia (see Table 3).

**<u>Table 3: Net External Position and Competitiveness of the Banking Sector</u>**

||1998|1999|2000|2001|2002|2003|2004|2005|2006|2007|
|---|---|---|---|---|---|---|---|---|---|---|
|**Macedonia**<br>|||||||||||
|Net external position<sup>1</sup><br>|2.4|3.9|5.5|13.0|9.3|10.2|10.8|7.7|7.7|4.7|
|Banking sector reforms<sup>2</sup><br>|2.7|2.7|2.7|2.7|2.7|2.7|2.7|2.7|2.7|2.7|
|Banking Efficiency<sup>3</sup>|...|...|3.3|3.6|3.5|3.5|3.6|4.8|...|...|
|**Slovakia**<br>|||||||||||
|Net external position<sup>1</sup><br>|5.2|4.7|8.3|7.3|2.3|-2.5|-6.9|-14.6|-5.7|-9.9|
|Banking sector reforms<sup>2</sup>|2.3|2.3|2.3|2.3|2.3|2.7|2.7|2.7|3.0|3.0|
|BankingEfficiency<sup>3</sup>|...|...|4.8|4.4|4.9|4.8|4.8|4.9|...|...|

_Note:_ 1/ Net of banking sectors’ external claims and liabilities; 2/ EBRD Banking reforms and interest rate liberalization index; 3/ World Bank Financial development indicator; _Source:_ IMF International Financial Statistics, EBRD, World Bank.

Second, the mix of macroeconomic policies, namely the de-facto fixed exchange rate and frequent fiscal surprises, have allowed Macedonian banks to enjoy higher riskfree returns (through FX arbitrage) relative to Slovak banks, thus further crowding out lending to the private sector and limiting banks’ competition (see Table 3 and Figure 2). Since their introduction in 2004, the average interest rate on the 3-month T-bills issued by the Macedonian Ministry of Finance was 7.5%, compared to 4.1% in the Slovak Republic during the same period. The real (CPI-adjusted) risk-free return was thus, on average, significantly positive (6.2) for Macedonian banks since 2004, while it was negative (-0.3) for Slovakian banks. This could also reduce the relative incentives of Macedonian banks to lend to the private sector.

Third, the extent of the involvement of the private sector in financial operations, especially borrowing, is important. Credit to the private sector barely reached 40% of 16
women (as shown by Arceo-Gómez and Campos-Vázquez, 2014). This is in line with qualitative work carried out in Mexico and Honduras (World Bank, 2014).

Figure 10. Proportional Change in the Probability of Being Nini to Changes in Demographic Characteristics, Youth Aged 15-18 y.o., by Gender <!-- Start of picture text --> (a) Male (b) Female<br>0.30 0.30<br>0.25 0.25<br>0.20 0.20<br>0.15 0.15<br>0.10 0.10<br>0.05 0.05<br>0.00 0.00<br>-0.05 -0.05<br>-0.10 -0.10<br>HH head Married Urban Age HH head Married Urban Age<br>woman woman<br><!-- End of picture text --> _Notes_ : The value of the bars represent the marginal effects on the probability of being Nini for the corresponding variables from the underlying estimation of multinomial logit models (one for each gender). The dependent variable in the multinomial models takes three states (Nini, Work Only, and Study Only). Only marginal effect for the outcome of Nini is presented in this graph. The models include controls for history of past decision indices, youth’s own education level, parental education level, household income quintiles, a set of demographic variables, and state economic condition variables. Complete results are presented in Appendix Table A1. _Source_ : Authors’ calculations based on model estimation using data from the _Encuesta Nacional de Ocupación y Empleo_ (ENOE), 2005:Q1 - 2013:Q4.

Other demographic characteristics, such as whether the household head is a woman (possibly related to high teenage pregnancy rates), or urban/rural status, show some significance for some of the outcome decisions, but the associations are in the majority of cases relatively smaller than those of the other variables discussed.

# _(vi) Macroeconomic conditions_

To capture the effects of macroeconomic conditions on the probability of being a _Nini_ we use quarterly inflation and the industrial production index (Appendix Table A1). For young men aged 15 to 18 years old, we find a negative association between good macroeconomic conditions (higher production) and the probability of being a _Nini_ , a positive association with _studying only_ , and a negative association with _working only_ (albeit only statistically significant at the 10 percent level). For young women, we find the same associations but with opposite signs in each case (statistical significance is also weaker). Inflation is not significant in any model specification for this age group.

28
To bring out the inconsistencies implied by this approach I present here an alternative interpretation of the standard model, which is based on a logically coherent, though special, treatment of the “leisure” variables. I then discuss the standard model further in the light of this interpretation.

# **3.2 An alternative interpretation of the standard household model**

The model can be interpreted as in fact containing a system of domestic production, but of a special kind. The empirical implementation of the model on household survey data is widely misunderstood to exclude such a system due to the interpretation of the “leisure” variables literally as pure leisures. To show that this is a mistake, the basic structure of the model is set out formally below for an n-person household.

With n individuals and one aggregate consumption good, x, the household utility function takes the form where li, i = 1, .., n, are the non-market time allocations of the n household members. Given the time constraints with hi denoting market labor supply and T the total time available, the household's full income budget constraint can be written as with as household full income, where wi is the wage rate and mi the non-wage income of individual i. The price of the consumption good is unity and the wage rates differ across individuals. Solution of the model yields the demand functions x = x(w1, ..., wn, Y) (5a) variables in his model include domestic work for other family members. They are not assigned 10