rafmacalaba/datause-encoder-data huggingface dataset:
fishery communities located downstream in contaminated zone were affected more heavily, compared to those located upstream and thus closer to safe waters. This, in consequence, seems to have induced different coping responses. Satellite data suggests a strong northward movement in fishing pattern of the affected upstream fishers. Being able to travel to safer fishing locations allowed these workers to maintain the number of work hours; however, their average monthly income still declined by as much as 45 percent in 2016. In contrast, fishers located downstream were more likely “trapped” inside the contaminated zone; their average income was cut by more than half. This adverse circumstance led to a significant reduction in fishery work hours and an increase in the likelihood of working secondary jobs.

Next, we study aspects of disaster recovery, examining the impacts of _Formosa_ on both fishing intensity and fishery income over time. We find a gradual reduction in disaster damage to both outcomes. We also show that, by the last quarter of 2016, while fishing activity in the affected region had largely recovered, the average monthly earnings of affected fishers were still only two-third of the base level in the first quarter (i.e. the pre-disaster period). Finally, we study potential spillover effects of _Formosa_ to other related industries in the affected provinces. Among the industries deemed eligible for _Formosa_ compensation by the government, we show that fishers in freshwater fisheries, especially those located downstream, actually benefited from the incident in terms of earnings.

We focus on examining the immediate impacts of _Formosa_ on fishery workers, and how the affected fishers adapted to this extreme environmental shock, for at least two important reasons. First, fishery is a major industry in Vietnam, accounting for 19.97 percent of the country’s total agricultural GDP in 2016, according to the Statistical Yearbook (General Statistics Office, 2016b). Fishery-related activities also make up a considerable share of the economics in the central coastal of Vietnam. At the four-digit Vietnam Standard Industrial Classification (VSIC) level, the single sub-industry of saltwater fishery accounts for 3.8 and 7.3 percents of total employment and income in coastal districts<sup>2</sup> of Ha Tinh, Quang Binh, Quang Tri, and Hue in 2015 (General Statistics Office, 2016a). Second, it is worth noting that most of the Vietnamese fishery sector is small and medium scale, where fishers predominantly operate nonpower or outboard-engine (i.e. low-powered) boats. This puts a considerable restriction on the length and types of the fishing voyage, and often makes fishery earnings highly vulnerable to the uncertainty in fishing conditions, such as unfavorable climates or extreme, albeit rare, incidents like _Formosa_ . Because we are interested in examining the immediate impact of the crisis, we confine the scope of the analysis to the period before any

> 2 District is a second-tier administrative unit, subordinated to a province.

2
**<u>Table 5: Effects of political reservation on labor supply at extensive and intensive margin</u>**

|||<br>**Participation**|||**No. of days wor**|**ked**|
|---|---|---|---|---|---|---|
||**Total**|**No NREGS**|**NREGS only**|**Total**|**No NREGS**|**NREGS only**|
|**Panel A**|||||||
|Res. now (α1)|0.000|-0.016|0.034|-0.038|-0.092**|0.128*|
||(0.008)|(0.010)|(0.022)|(0.038)|(0.045)|(0.067)|
|Res. before (α2)|0.027***|0.022**|0.063***|0.166***|0.124***|0.257***|
||(0.010)|(0.010)|(0.019)|(0.049)|(0.045)|(0.064)|
|Observations|66,362|66,362|66,362|66,362|66,362|66,362|
|R-squared|0.28|0.317|0.242|0.377|0.405|0.230|
|**Test:**|||||||
|F test(α1+α2=0; pval)|0.004|0.590|3.57e-05|0.013|0.546|5.54e-06|
|**Panel B**|||||||
|Res. now (α1)|-0.022|-0.033|0.027|-0.139|-0.167*|0.039|
||(0.027)|(0.022)|(0.041)|(0.117)|(0.094)|(0.145)|
|Res. before (α2)|-0.035|-0.011|-0.018|-0.131|-0.039|-0.158|
||(0.024)|(0.021)|(0.041)|(0.121)|(0.097)|(0.158)|
|Res now × fem (β1)|0.035|0.033|0.015|0.167|0.169|0.072|
||(0.050)|(0.045)|(0.062)|(0.229)|(0.191)|(0.224)|
|Res. before × fem (β2)|0.117**|0.076*|0.168**|0.571**|0.372*|0.645**|
||(0.046)|(0.042)|(0.072)|(0.230)|(0.191)|(0.275)|
|Observations|66,362|66,362|66,362|66,362|66,362|66,362|
|R-squared|0.285|0.318|0.249|0.381|0.406|0.236|
|Dep. Var Mean|0.76|0.693|0.252|3.65|3.324|1.466|
|... males|0.87|0.84|0.23|4.51|4.34|0.71|
|... females|0.63|0.51|0.27|2.69|2.11|0.92|
|**Test:**|||||||
|F test (α1+α2=0; p val)|0.062|0.090|0.707|0.088|0.083|0.548|
|F test (β1+β2=0; p val)|0.007|0.030|0.0434|0.013|0.016|0.0424|
|F test (α1+ β1=0; p val)|0.609|0.998|0.393|0.822|0.988|0.310|
|F test (α2+ β2=0; p val)|0.002|0.008|0.00198|0.001|0.003|0.00250|
|F test (α1+β1+α2+β2=0; p val)|0.002|0.021|0.00171|0.003|0.007|0.00238|

_Note:_ ‘Reserved now’ and ‘reserved before’ are indicator variables of whether village panchayats are reserved in the current or the previous panchayat periods and the sample is limited to those who worked under NREGS. Control variables included throughout but coefficients on which are not reported include household size, composition, land ownership, and the head’s marital status, gender, age, and education; village-level access to road, distance to town and district HQ, population, share of SCs, STs, and key religions; years since the last village election; _pradhan_ characteristics (education, caste, religion, previous tenure and candidacy for office) and for individual-level regressions individuals’ gender, marital status, age, education and their squared terms. Standard errors are clustered at panchayat level. Robust standard errors reported in parentheses and multiple hypotheses tests are adjusted using the Bonferroni method. *** p<0.01, ** p<0.05, * p<0.10.

20
**Table 5.1**

**Changes in employment by skill level and between and within sector decompositions. Annual Percentage Change, 1990s and 2000s. Summary of results** _Overall change_

|||1990s|||2000s||
|---|---|---|---|---|---|---|
||skilled|high school|dropouts|skilled|high school|dropouts|
|Argentina|0.81|0.05|-0.86|0.51|0.62|-1.13|
|Brazil|0.34|0.54|-0.88|1.04|0.68|-1.71|
|Chile|0.80|0.62|-1.42|0.17|0.50|-0.66|
|Costa Rica|0.65|-0.17|-0.48|0.84|0.08|-0.92|
|El Salvador|1.01|0.67|-1.68|0.36|0.45|-0.82|
|Mexico|0.51|0.46|-0.97|0.62|-0.15|-0.47|
|Nicaragua|0.68|-0.05|-0.63|1.49|0.16|-1.65|
|Panama|0.27|-0.08|-0.19|0.40|0.53|-0.93|
|Venezuela|0.71|0.27|-0.98|0.80|0.66|-1.46|
|Mean|0.64|0.26|-0.90|0.69|0.39|-1.08|
|_Between effect_|||||||
|||1990s|||2000s||
||skilled|high school|dropouts|skilled|high school|dropouts|
|Argentina|0.21|0.02|-0.23|0.04|-0.02|-0.02|
|Brazil|0.09|0.03|-0.12|0.33|0.06|-0.39|
|Chile|0.21|0.05|-0.26|0.12|-0.03|-0.09|
|Costa Rica|-0.14|-0.01|0.14|0.27|0.02|-0.30|
|El Salvador|0.46|0.21|-0.67|0.14|0.05|-0.19|
|Mexico|0.29|0.13|-0.42|0.13|0.04|-0.17|
|Nicaragua|0.16|0.07|-0.24|0.03|-0.01|-0.02|
|Panama|-0.12|-0.05|0.17|0.04|0.01|-0.06|
|Venezuela|-0.06|0.05|0.01|0.08|-0.02|-0.06|
|Mean|0.12|0.06|-0.18|0.13|0.01|-0.14|
|_Within effect_|||||||
|||1990s|||2000s||
||skilled|high school|dropouts|skilled|high school|dropouts|
|Argentina|0.60|0.03|-0.63|0.47|0.64|-1.11|
|Brazil|0.24|0.51|-0.75|0.70|0.62|-1.32|
|Chile|0.59|0.58|-1.17|0.04|0.53|-0.57|
|Costa Rica|0.78|-0.16|-0.62|0.57|0.05|-0.62|
|El Salvador|0.55|0.46|-1.01|0.22|0.41|-0.63|
|Mexico|0.22|0.33|-0.55|0.49|-0.19|-0.30|
|Nicaragua|0.51|-0.12|-0.39|1.46|0.17|-1.63|
|Panama|0.39|-0.02|-0.37|0.36|0.52|-0.87|
|Venezuela|0.77|0.22|-0.98|0.72|0.68|-1.41|
|Mean|0.52|0.20|-0.72|0.56|0.38|-0.94|

Source: own computations based on SEDLAC (CEDLAS and World Bank).

34
Policy Research Working Paper 9906

# **Abstract**

This paper studies external sovereign bonds as an asset class. It compiles a new database of 266,000 monthly prices of foreign-currency government bonds traded in London and New York between 1815 (the Battle of Waterloo) and 2016, covering up to 91 countries. The main insight is that, as in equity markets, the returns on external sovereign bonds have been sufficiently high to compensate for risk. Real ex-post returns average more than 6 percent annually across two centuries, including default episodes, major wars, and global crises. This represents an excess return of 3–4 percent above US or UK government bonds, which is comparable to stocks and outperforms corporate bonds. Central to this finding are the high average coupons offered on external sovereign bonds. The observed returns are hard to reconcile with canonical theoretical models and the degree of credit risk in this market, as measured by historical default and recovery rates. Based on an archive of more than 300 sovereign debt restructurings since 1815, the authors show that full repudiation is rare; the median creditor loss (haircut) is below 50 percent.

This paper is a product of the Office of the Senior Vice President and Chief Economist, Development Economics Vice Presidency. 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/prwp. The authors may be contacted at jmeyer@diw.de; carmen_reinhart@harvard.edu; and christoph.trebesch@ifw-kiel.de.

_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
trade theory of a positive demand shock for a low-middle income country. Our results show that labor market responses to export shocks differs across regional labor markets in the short-run — that is, between 2005 and 2010—but seem to dissipate in the longer run, 2005 to 2016, as neoclassical trade theory would predict. This suggests that trade shock effects on labor market outcomes differ regionally only temporarily in a country with labor markets where workers are mobile and with relatively low barriers to migration. In contrast, these effects seem to become more economy-wide driven by returns to factors and not industry or region specific as predicted by Stolper Samuelson theorem in the long run.

# References

- Acemoglu, Daron; David H. Autor; David Lyle. 2004. “Women, War, and Wages: The Effect of Female Labor Supply on the Wage Structure at Midcentury” Journal of Political Economy 112(3): 497-551.

- Artuç, Erhan, Shubham Chaudhuri, and John McLaren. 2010. “Trade Shocks and Labor Adjustment: A Structural Empirical Approach.” American Economic Review, 100 (3): 100845.

Artuç, Erhan, Gladys Lopez-Acevedo, Raymond Robertson, and Daniel Samaan. 2019. “Exports to Jobs: Boosting the Gains from Trade in South Asia (English).” South Asia Development Forum. Washington, D.C.: World Bank Group.

Autor, David H., David Dorn, and Gordon H. Hanson. 2013. "The China Syndrome: Local Labor Market Effects of Import Competition in the United States." American Economic Review, 103 (6): 2121-68.

Baldwin, Richard. 2009. “The Great Trade Collapse: Causes, Consequences, and Prospects.” CEPR (ISBN 1907142061) Bernard, Andrew B. & Stephen J. Redding & Peter K. Schott, 2007. "Comparative Advantage and Heterogeneous Firms," Review of Economic Studies, Blackwell Publishing, vol. 74(1), pages 31-66, Bangladesh Bureau of Statistics, 2015. “Population distribution and internal migration in Bangladesh.” 2015. Population monograph, Volume 6. Bangladesh Bureau of Statistics.

Bartik, Timothy. 1991. Who Benefits from State and Local Economic Development Policies? W.E. Upjohn Institute.

Blanchard, Olivier Jean and Lawrence F. Katz. 1992. “Regional Evolutions.” Brookings Papers on Economic Activity 1992 (1):1–75.

32
# **References**

- Ahmed, Amer and Laurent Bossavie (2021). _Towards Safe and Productive Migration in South Asia_ . World Bank Group: Washington DC.

- Ambrosini, William, Karin Mayr, Giovanni Peri, and Dragos Radu (2015). “The Selection of Migrants and Returnees in Romania: Evidence and Long-run Implications”. In: _Economics of Transition_ 23.4, pp. 753–793.

- Amir, Saman, Aphichoke Kotikula, Rohini P Pande, Laurent Loic Yves Bossavie, and Upasana Khadka (2018). “Female Labor Force Participation in Pakistan”. In:

- Arif, Ghulam Mohammad (1998). “Reintegration of Pakistani return migrants from the Middle East in the domestic labour market”. In: _The Pakistan Development Review_ , pp. 99–124.

- Barrett, Alan and Jean Goggin (2010). “Returning to the question of a wage premium for returning migrants”. In: _National Institute Economic Review_ 213, R43–R51.

- Borjas, George and Bernt Bratsberg (1996). “Who Leaves? The Outmigration of the ForeignBorn”. In: _The Review of Economics and Statistics_ 78.1, pp. 165–76.

- Borodak, Daniela and Matloob Piracha (2011). “Occupational choice of return migrants in Moldova”. In: _Eastern European Economics_ 49.4, pp. 24–46.

- Bossavie, Laurent and Anastasiya Denisova (2018). “Youth labor migration in Nepal”. In:

- Bossavie, Laurent, Joseph-Simon Gorlach, Caglar Ozden, and He Wang (2021). “Temporary Migration for Long-term Investment”. In:

- (2022). “Institutional Voids, Capital Markets and Temporary Migration: Evidence from Bangladesh”. In: _World Bank Policy Research Working Paper_ 9930.

- Bossavie, Laurent and Caglar Ozden (2022). “Impacts of Temporary Migration on Development in Origin Countries”.

- Bulmer, Elizabeth Ruppert, Ami Shrestha, Michelle Marshalia, et al. (2020). _Nepal Jobs Diagnostic_ . Tech. rep. The World Bank.

- Carletto, Calogero and Talip Kilic (2011). “Moving up the ladder? The impact of migration experience on occupational mobility in Albania”. In: _Journal of Development Studies_ 47.6, pp. 846–869.

20
**<mark>Work experience</mark>** <mark>: The majority of occupations do not require substantial related work experience: 77 percent require up to 1 year of experience while almost all of the remainder require between 1 and 5 years. In general, semi- and low-skilled workers require less than 6 months of related work experience, while most higher-skilled workers require between 1 and 3 years (</mark> **<mark>Figure 5</mark>** <mark>). University and higher education teachers, software developers, and civil engineers are the occupations requiring the most experience (3 or more years).</mark>

**Figure 5: Work experience requirements of the SDS’s 30 occupations by skill level**

_Share of workers reporting the level of work experience is required (%)_ <!-- Start of picture text --> Elementary occupations<br>Plant and machine operators and assemblers<br>Craft and related trades workers<br>Service and sales workers<br>Technicians and associate professionals<br>Professionals<br>0% 10% 20% 30% 40% 50% 60% 70% 80% 90%100%<br>Less than 6 months 6 months to 1 year 1 year to 3 years<br>3 years to 5 years 5 years to 10 years 10 years or more<br>Source: SDS.<br>Low<br>Semi<br>High<br><!-- End of picture text -->

**<mark>Digital tools.</mark>** <mark>Most occupations require the use of smartphones (77 percent), while a smaller share (40 percent) require the use of computers (</mark> **<mark>Figure 6</mark>** <mark>). Smartphones are widely used at work across all skill levels except for occupations involved with a production line (for example, machine operators and assemblers) (</mark> **<mark>Figure 7</mark>** <mark>). Computers are used in high-skilled occupations (for example, professionals and technicians and associate professionals). A third of these occupations also require the use of specific software such as AutoCAD for Civil Engineers and MISA accounting software for accountants.</mark>

13
<!-- Start of picture text --> Figure 10: Age distribution of owners of urban formal and informal enterprises<br>Formal Informal<br>.037725 .046261<br>.000212 .000203<br>15.3118 84.6882 9.59296 80.407<br>Age of Owner of Formal Enterpris Age of Ow ner of Informal Enterpr<br>Kernel Density Estimate Kernel Density Estimate<br>e n sity<br>Density D<br><!-- End of picture text --> <!-- Start of picture text --> .037725 .046261<br>.000212 .000203<br>15.3118 84.6882 9.59296 80.407<br>Age of Owner of Formal Enterpris Age of Ow ner of Informal Enterpr<br>Kernel Density Estimate Kernel Density Estimate<br>Source: UES, 2004.<br>e n sity<br>Density D<br><!-- End of picture text --> 30. In keeping with the sectoral composition of formal enterprises, discussed later, over onetenth operate out of construction sites. About 13 percent of the informal establishments operate through street and market stalls, or as mobile vendors. This lack of a fixed place of work and predominantly home-based work of informal enterprises, conforms to the picture of informality that exists in most developing countries in Asia.

<!-- Start of picture text --> Figure 11: Location of Urban Enterprises<br>70<br>60.79<br>60<br>50<br>42.92<br>37.58<br>40<br>30 24.82<br>20<br>10.68<br>10 3.96 8.27 3.29<br>0.41 0.36 0.9 2.67 2.46 0.9<br>0<br>Home based Factory, office, Farm Construction site Market/bazaar Street stall Mobile/other<br>workshop, shop, stall<br>kiosk<br>Formal Informal<br>Source: UES, 2004.<br><!-- End of picture text -->

# **_(ii) Behavior of Urban Enterprises_**

31. This section lays out the insights obtained from questions on the behavior of urban enterprises, including their growth trajectory, constraints to growth, startup capital, hiring practices and non-wage benefits provided.

32. (a) <u>Growth Trajectory and Volatility:</u> The growth of urban enterprises shows that Timorese urban firms are very new, and still exhibit large fluctuations. This suggest that the economy continues to stabilize, but also reflects the volatility of new entry small firms. It is 16
|**Table 4: Impacts of**|**20% ch**<br>**Soy, So**|**anges in**<br>**ybean**|**intern**<br>|**ational**<br>|**prices**<br>**Soy, So**<br>|<br>**ybean**<br>|
|---|---|---|---|---|---|---|
||<br>|<br>|**Mai**|**e &**|**Oil &**|**Other**|
||**Oil**|**&**|||||
|**Indicators**|<br>**Biod**<br>**(20**<br>|<br>**iesel**<br>**%)**<br>|**Bioet**<br>**(20**<br>|**hanol**<br>**%)**<br>|**agricu**<br>**prod**<br>**(-20**<br>|**ltural**<br>**ucts**<br>**%)**<br>|
||**y1 **|**y5 **|**y1 **|**y5 **|**y1 **|**y5 **|
|**_Macroeconomic Indicators_**|||||||
|GDP|1.9|2.2|0.3|0.4|0.5|-0.8|
|Trade Balance|2.4|2.8|0.3|0.4|0.8|-0.9|
|Fiscal Result|1.6|1.6|0.1|0.1|0.0|-0.9|
|Rate of Unemployment|-0.1|0.0|0.0|0.0|-1.2|0.0|
|**_Welfare Indicators_**|||||||
|Poorest Household|2.1|2.3|0.3|0.4|0.9|-0.4|
|Richest Household|1.5|1.8|0.4|0.5|-0.1|-1.2|
|**_Aggregate sectors Activity Level_**|||||||
|Agriculture|5.8|6.8|1.1|1.4|-12.2|-14.7|
|Manufactures|-2.4|-2.7|-0.4|-0.5|3.4|2.4|
|Services|0.1|0.0|0.1|0.1|1.4|0.1|
|**_Specific sectors Activity Level_**|||||||
|Soy|53.3|61.4|-5.2|-5.9|-17.5|-19.5|
|Maize|-22.8|-26.0|93.6|107.5|46.1|51.4|
|Sugar Cane|-10.9|-12.7|-1.8|-1.5|15.1|15.7|
|Rest of agriculture|-25.3|-29.0|-5.5|-6.2|-16.3|-20.1|
|Soybean oil|83.8|97.1|-0.5|-0.6|-87.7|-96.3|
|Diesel|-1.4|-1.4|-0.6|-0.5|0.1|-0.8|
|Gasoline|-1.6|-1.6|-0.6|-0.5|0.1|-0.8|
|Biodiesel|33.7|1222.2|-0.2|2.7|44.5|1722.2|
|Bioethanol|-2.7|-34.3|48.8|4323.3|2.6|-0.3|
|**_Gas emission index_**|2.9|3.3|0.6|0.7|-5.3|-7.0|
|N.B.: y1: 2007, y5: 2012. Figures<br>corresponding year.|are dev|iations fr|om perc|entage ch|ange in|the baseline of the|

The last column of Table 4 shows instead a reduction of prices of soy, soybean oil and other agricultural products (not maize, sugar cane); it can be seen that the opportunity cost of biodiesel production is reduced, and therefore the production of biodiesel is increased. The result for bioethanol is the consequence of the reduction of the activity level of the economy (as it is reduced the demand for gasoline) rather than the effect of relative prices<sup>10</sup> .

> 10 We have performed a sensitivity analysis to different degrees of capital mobility. The model was calibrated to a 10% of mobile capital, consistent with the observed economic variables in the baseline growth scenario. Considering an economy with 40% mobile capital, we have observed that macroeconomic aggregates do not present important changes but at sectoral level, more flexibility in the capital has a more significant effect in terms of the sectoral activity level. For instance, in the simulations of changes in international prices the capital moves to the more profitable sectors. Hence, biofuels would have a lower growth rate when the capital is more mobile because they are relatively more intensive in use of capital.

16
**Table 4. Controlling for Inflation Targeting**

|Dependent variable:|Fiscal balance/GDP<br>(%)|Total revenues/GDP<br>(%)|Total<br>expenditures/GDP (%)|
|---|---|---|---|
|Constant|-7.31***<br>(-7.95)|-7.22***<br>(-4.86)|-0.309<br>(-0.20)|
|Dummy fuel > 25%<br>exports|4.74***<br>(10.57)|9.41***<br>(14.87)|5.00***<br>(8.99)|
|Dummy ores and|0.383|0.412|0.371|
|metals > 10% exports|(1.29)|(0.84)|(0.73)|
|Hard peg dummy|1.44***|-2.77***|-4.26***|
||(2.96)|(-4.63)|(-8.33)|
|Political stability|0.0615***|0.472***|0.417***|
|(0 to 100)|(4.55)|(20.57)|(17.95)|
|Inflation targeting|0.091|-0.565|-0.722|
|dummy|(0.33)|(-0.85)|(-0.99)|
|Time trend|0.0172|0.323***|0.288***|
|(=0 in 2000)|(0.68)|(7.19)|(6.11)|
|n|1,378|1,392|1,378|
|R-squared|0.134|0.300|0.232|
|RMSE|4.75|8.03|7.97|

_Note:_ Figures in parentheses are robust t-statistics. *, **, ***: significant at 10, 5, and 1 percent respectively. Annual data from 1996–2013 for low- and middle-income countries. RMSE = root mean square error, or sample standard deviation.

11