rafmacalaba/datause-encoder-data huggingface dataset:
> <sup>20</sup>

variables are therefore more likely to turn into polarization and to drive insurgency in more credible political settings. To see if this is true, an interaction term between fragmentation and political variables are added to the regressions in Tables 1, 2 and 3. As in the natural resource case, the interaction effect should be insignificant above an institutional threshold that cannot be identified _ex ante_ . For the same reason, the interaction term is expected to be least significant in the Table 1 sample that includes the richest countries, where the most citizens are likely to be protected by institutions of credible commitment.

The first three columns of Table 4 report the estimated interaction coefficients of ethnic fragmentation and political variables from the corresponding regressions in Tables 1, 2 and 3. The earlier discussion concluded that the political variables are not necessary conditions for religious fragmentation to become religious polarization since religions, for the most part, are already organized. Consistent with this, religious interaction terms are nowhere significant and are not reported. As in the natural resource case, the ethnic interaction term is least significant in the sample including the richest countries. Nevertheless, the interaction with programmatic parties significantly increases the risk of civil war. The interaction term is significant in five of the remaining seven cases. Taken together, the results in Table 4 provide strong evidence that, although the political variable reduces the odds of conflict, this effect is weaker the more a country is ethnically fragmented or endowed with natural resources.

# **Institutions of credible commitment versus constitutional institutions**

Reynal-Querol (2002) concludes that more inclusive democracies (those with proportional representation as opposed to majoritarian or presidential systems) limit the odds of ethnic civil war. With only fifteen democracies experiencing civil war in the data used here, these propositions are difficult to investigate. However, the Database of Political Institutions contains variables with which to examine at least the robustness of the credibility variables of concern here and to revisit the question of how institutions affect conflict. The three key variables are controls for whether countries are parliamentary, or presidential or semi-presidential; whether they are majoritarian, as reflected in their district magnitudes (low indicates more majoritarian) and whether they use plurality or majoritarian electoral rules.

The addition of these formal institutional variables has no effect on the results reported in Tables 1 and 2: the significance of the credibilty variables is little changed. The
normalize the skewed distribution of outcomes (i.e. many observations of low income levels and a few observations of very high income levels).<sup>11</sup>

# **3. Rural incomes and changing living conditions**

This section shows the composition of rural income portfolios across different socioeconomic groups and regions, as well as changes in living conditions and the impediments to increasing prosperity – as perceived by households.

## **3.1 Income portfolios**

As calculated based on the VHLSS data, the following income categories are of importance in rural Vietnam:

- 1) _crop_ income: the output value minus production costs including rice, other staple crops, industrial crops<sup>12</sup> , fruit trees and crop by‐products;<sup>13</sup>

- 2) _livestock_ income: the value of animals and animal products produced (for selling and self‐ consumption) minus production costs;

- 3) _forestry_ income: the value of harvested trees and other forest products (such as firewood), hunted animals, as well as incomes from , forest protection and management minus the production costs;

- 4) _fishing_ income: the value of production and catch of fish and shrimp minus production costs;

- 5) _wage_ income: any cash and in‐kind wages and salaries including from agricultural wage employment and non‐agricultural wage‐employment in unskilled and skilled occupations;

- 6) _business_ income: revenues from businesses outside agriculture, forestry, aquaculture minus cost;

- 7) _transfer_ income include remittances, emergency assistance, insurance, donations, social benefits, support in health care and education;

- 8) _other_ income: including returns from investments, and earnings from weddings and funerals.

The data demonstrates that agriculture and other ecosystem‐based incomes remain an important income source for rural households. Crop, livestock, forestry and fishing incomes and agricultural wages make ca 45 percent of incomes of all rural households (Figure 1). Whereas a larger share of rural households is engaged in income activities related to crop cultivation, livestock, forestry or fishing, the incomes earned from wage‐employment in skilled occupations and from businesses are much higher (Table A.1). Interestingly, more than 90 percent of households across all groups receive some transfer income. Overall, there is little difference in the income composition for different socioeconomic groups between 2010, 2012 and 2014.

> 11 Taking the natural logarithm also improves the explanatory power of the various models and brings ease in interpreting the coefficients as percentage change of the outcome variable.

> 12 In order of importance, these include peanut, coconut, coffee, soya beans, tea, sugarcane, cashew, pepper.

> 13 While revenues are indicated for each crop, costs are only given for all crop production. To calculate incomes for different crops, it is assumed that each crop’s cost share is equivalent to it share in total crop revenues.

8
Table 2 shows descriptive statistics for the variables used in our main regressions. All bank-level variables are averaged by bank over the period 1999-2005, and country-level variables are averaged by country over the period under study. The dependent variables include the ratio of nonperforming loans to total loans, the bank Z-index, and equity to total assets. The key exogenous variable is the measure of bank market structure proxied by the Lerner index, but we also include traditional measures of concentration such as the HHI deposit and loan indices to ensure robustness.

We control for bank size and for foreign bank ownership. We also collect country-level data on business regulations and their enforcement from the World Bank Doing Business database to proxy for the business environment in a particular country (World Bank 2006.) Our control variable is an index of legal rights, which measures the degree to which collateral and bankruptcy laws facilitate lending. We include the log value of GDP per capita in all regressions to control for variations in economic development.

# **V. Empirical Results**

# _A. Main Regression Results_

Tables 3, 4, and 5 present our main regression results. We estimate GMM regressions with robust standard errors clustered at the country level to correct for within-country correlation. We test for the presence of heteroskedasticity in our data set and report the results of the BreushPagan/Godfrey/Cook-Weisberg statistic. We also run diagnostic tests for both the relevance (using the First Stage F-test) and the validity (using the Hansen’s J test) of the instruments of the degree of market power. The results support the presence of heteroskedasticity –and hence the use of the GMM estimator— and show that our instruments are both relevant and valid.

We use three dependent variables to proxy for financial stability: In Table 3 we measure loan portfolio risk with the ratio of nonperforming loans, and in Tables 4 and 5 we measure overall bank risk and leverage risk with the Z-index and bank capitalization ratio, respectively.

All regressions include either the Lerner index, HHI-deposit index, or HHI-loan index as a measure of industry competition, and which are instrumented with indicators of activity restrictions, banking freedom and the percent of foreign and government owned banks. In all cases, higher values of the Lerner index, HHI-deposit index, and HHI-loan index imply higher 11
# Table 2. Shares of World Markets for Big-5 Commodity Exports (%).

||MCoffee|Cotton|Sugar|Tea|Tobacco|
|---|---|---|---|---|---|
|MalawviI||||||
|1961-73|0.01|0.25|0.03|_J5.1_|<br>4.5|
|1974-87|0.03|0.05|0.26|2.6|3.3|
|Tanzania||||||
|1961-73|3.9|4.0|0.02|1.8|1.3|
|1974-87|1.6|1.1|0.05|_1.5_|0.6|
|Zimbabwe||||||
|1961-73|0.04|0.9|0.4|0.43|18.4|
|1974-87|0.02|1.3|_0.52_|0.6|5.6|
|Total (Malawi, Ta|nzania, & Zim|babwe||||
|1961-73|3.9|5.2|1.3|7.4|24.2|
|1974-87|1.8|2.4|0.8|4.6|9.5|

_5_
Dhiman, R.C., S. Pahwa, G.P.S. Dhillon, and A.P. Dash, 2010. Climate Change and Threat of Vector-borne Diseases in India: Are We Prepared? Parasitology Research 106:763-773.

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Dinar, A., 1998. Measuring the Impact of Climate Change on Indian Agriculture. World Bank Publications. pp. 266.

Ding, Y., S. Liu, J. Li, and D. Shangguan, 2006. The Retreat of Glaciers in Response to Recent Climate Warming in Western China. Annals of Glaciology 43: 97-105.

Diodato, N., G. Bellocchi, and G. Tartari, 2011. How do Himalayan areas respond to global warming? International Journal of Climatology. doi: 10.1002/joc.2340.

Dobhal, D.P., J.T. Gergan, and R.J. Thayyen, 2008. Mass Balance Studies of the Dokriani Glacier from 1992 to 2000, Garhwal Himalaya, Indian Bulletin of Glaciological Research 25:9–17.

Dobhal, D.P., J.T. Gergan, and R.J. Thayyen, 2004. Recession and Morphogeometrical Changes of Dokriani Glacier (1962–1995) Garhwal Himalaya, India. Current Science 86: 692-696.

Dobler, A., and B. Ahrens, 2011. Four climate change scenarios for the Indian summer monsoon by the regional climate model COSMO-CLM. Journal of Geophysical Research 116: D24, doi:10.1029/2011JD016329.

Du, M., S. Kawashima, S. Yonemura, X. Zhang, and S. Chen, 2004. Mutual influence between human activities and climate change in the Tibetan Plateau during recent years. Global and Planetary Change 41(3): 241-249.

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32
Policy Research Working Paper 10070

# **Abstract**

As the economies of Southeast Asia continue adopting digital technologies, policy makers increasingly ask how to prepare the workforce for emerging labor demands. However, little is known about the skills that workers need to adapt to these changes. Skills profiles in low- and middle-income countries are typically derived from data collected in the United States, which is known to inaccurately reflect their occupational skills. This paper uses online job postings data from Malaysia to identify the digital, cognitive, and socioemotional skills required for digital and non-digital occupations. The skills profiles for each occupation are then merged with labor force survey data from Cambodia, Malaysia, Thailand, and Vietnam to sketch skills profiles of the workforces in these countries. Using descriptive statistics and linear probability model regressions, the paper finds evidence that highly digital occupations require not only digital skills, but also cognitive and socioemotional skills. Similarly, virtually all occupations, regardless of the digital intensity of the job, require some basic or intermediate digital skills. Pairwise correlations and a factor analysis confirm the complementarity between digital skills and different subsets of cognitive and socioemotional skills. The data also confirm that, even with the excitement about the digital revolution, the bulk of employment in Southeast Asia is in low- (around two-thirds) or medium-digital (around onethird) occupations. Only between 1 and 5 percent of jobs are highly digital in the four countries studied. These findings suggest that as education and training systems adapt to teach basic digital skills, they will need to continue to foster cognitive and socioemotional skills.

This paper is a product of the Social Protection and Jobs Global Practice. 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 wcunningham@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
The data shown in the following table for imports of cabuya sacks per country of origin draws attention to the fact that in 1995 Nicaragua increased its exports to Costa Rica more than twofold compared to the previous year. This situation, together with the large increase of imports from other countries (including Bangladesh), meant an aggregate increase of 290% in the total imports of cabuya sacks to Costa Rica in that same year.

**Table 4.7   Costa Rica: Origin of cabuya sacks imports 1992-1995**

|||(In –units)<br>||
|---|---|---|---|
|**Year**|**Total imported**<br>**sacks**|**Sacks imported**<br>**from Nicaragua**|**Sacks imported from**<br>**Bangladesh and other**<br>**countries**|
|1992|371,490|332,409|39,081|
|1993|286,582|253,596|32,986|
|1994|209,218|209,218|0|
|1995|816,683|489,609|330,074|

Note: The periods correspond to January-August of each year. Source: MEIC’s resolution published in the Official Gazette No. 91, May 13, 1998.

These figures confirm the substantial increase in imports that would result in the eventual injury to the domestic production of jute sacks. Unfortunately, the case’s file and the respective MEIC’s resolution fail to provide any information or analysis of the impact (if any) of said increased imports on the domestic production of jute sacks.

Likewise, there are no data available on the sector’s evolution or its share in production and employment. However, given that one of the main uses of jute sacks is to package coffee for export, the behavior of coffee exports could well shed some light on the potential impact of the increased imports of cabuya sacks on domestic production.

According to figures from the Central Bank of Costa Rica, the volume of coffee exports (in 46-kilogram sacks) decreased by 3% in 1995, which is consistent with most of the years of that decade, with the exception of 1996, when the volume of coffee exports recorded an increase of 23%. Taking into account that jute sacks are not a perishable product and that coffee is usually traded in futures markets, it makes sense to consider that the increase in the volume of coffee exports in 1996 would explain part of the increase in jute sacks imports in 1995.

That means that around one tenth of the increased cabuya sacks imports recorded in 1995 had no direct effect on domestic production, since it was simply meeting an increase in the demand for the product to be used to package coffee for export.<sup>24</sup> However, even though there is no information on the behavior of the other activities that use jute sacks as an input, it seems reasonable to assume that the remaining 267 percentage points of increase in jute sacks imports did cause an important crowding out of domestic production in the local market.

> 24 Indeed, the positive expectations created by the increased sales of coffee abroad could have induced coffee businesses to increase their stock of packaging material.

35
||**Ababa**|%|7.9|31.3||54.7|41.9|2.7|0.7|28.7|98.2|66.6|99.5|Mean|4.3|3.2|1.7||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**Addis**|Obs|810|810|795|435|333|21|5|810|810|810|810|Obs|810|810|810||
||**Salaam**|%|22.3|20.7||56.7|36.2|4.8|0.4|31.3|42.7|83.9|90.4|Mean|4.1|3.7|2.0||
|ample|**Dar es**|Obs|1,335|1,335|1,335|783|483|64|5|1,335|1,335|1,335|1,335|Obs|1,335|1,053|1,335||
|y, total s|**anarivo**|%|8.1|47.2||43.5|34.1|16.8|5.6|5.2|18.7|59.2|99.3|Mean|4.2|2.1|1.9||
|s by cit|**Antan**|Obs|2,272|2,272|2,272|988|775|382|128|2,272|2,272|2,272|1,863|Obs|2,272|2,272|2,261||
|statistic|**ccra**|%|44.6|40.3||20.3|44.0|29.4|6.3|15.6|45.3|74.0|96.7|Mean|3.4|1.5|1.5||
|Descriptive|**A**|Obs|1,006|high<br>1,006|1,006|204|442|296|63|1,006|1,006|1,006|1,006|Obs|1,006|1,006|820||
|TABLED.1:|||**Exposure to foods**<br>Households directly affected by food|Households that perceive food risk as|**Dwelling tenure**|Owner|Rented|Free|Other|**Housing conditions**<br>Access to improved toilet facility|Access to improved drinking water|Access to collected solid waste|Lighting with electricity||Household size|Number of rooms|**Job density**|_Notes:_ Figures are weighted.|

36
# Policy Research Working Paper Series

|**Title**|Author|Date|Contact<br>for paper|
|---|---|---|---|
|WPS977 Income Security for Old Age:<br>Conceptual Background and Major|Estelle James|September 1992|D. Evans<br>37489|
|WPS978 How Restricting Carbon Dioxide<br>and Methane Emissions Would Affec<br>the Indian Economy|Charles R. Blitzer<br>t R. S. Eckaus<br>Supriya Lahiri<br>Alexander Meeraus|September 1992|WDR Office<br>31393|
|WPS979 Economic Growth and the<br>Environment|Dennis Anderson|September 1992|WDR Office<br>31393|
|WPS980 The Environment: A Now Challenge<br>forGATT|Piritta Sorsa|September 1992|WDR Office<br>31393|
|WPS981 After Socialism and Dirigisme:<br>Which Way?|Andres Solimano|September 1992|S. Fallon<br>37947|
|WPS982 Microeconomics of Transformation<br>in Poland: A Survey of State<br>Enterprise Responses|Brian Pinto<br>Marek Belka<br>Stefan Krajewski|September 1992|S. Husain<br>37139|
|WPS983 Legal Reform for Hungary's Private<br>Sector|Cheryl W. Gray<br>Rebecca J. Hanson<br>Michael Heller|October 1992|R. Martin<br>39065|
|WPS984 Barriers to Portfolio Investments<br>in Emerging Stock Markets|Asli Demirg0i;-Kunt<br>Harry Huizinga<br>b|October 1992|W. Patrawimolpon<br>37664|
|WPS985 Regional Integration, Old and<br>New|Jaime de Melo<br>Claudio Montenegro<br>Arvind Panagariya|October 1992|D. Ballantyne<br>37947|
|WPS986 The Administration<br>of Road User<br>Taxes In Developing Countries|Roy Bahl|October 1992|J. Francis-O'Connor<br>35205|
|WPS987 How the Epidemiological Transition<br>Affects Heafth Policy Issues in<br>Three Latin American Countries|Jose Luis Bobadilla<br>Cristina de A. Possas|October 1992|0. Nadora<br>31091|
|WPS988 Ewonomic Valuation and the<br>Natural World|David Pearce|October 1992|WDR Office<br>31393|
|WPS989 The Indian Trade Regime|M. Ataman Aksoy|October 1992|R. Matenda<br>35055|
|WPS990 Protection and Industrial Structure<br>in India|M. Ataman Aksoy<br>Francois M. Ettori|October 1992|R. Matenda<br>35055|
|WPS991 Environmental Costs of Natural<br>Resource Commodities: Magnitude<br>and Incidence|Margaret E. Slade|October 1992|WDR Office<br>31393|
KIRIDATI 2. Projections - NRRil by 2020

|T1r<br>p|_iLtIa|**tatio**|**Peiod**|rate||C|TFR|lbn|**Fmir**|In|s^i|
|---|---|---|---|---|---|---|---|---|---|---|---|
|1990|70|75.5|199094|1.67|32.4|10.7|4.04|53.6|60.4|53|so|
|199|77|76.4|1995-99|1.72|29.0|9.6|3.67|55.4|63.7|45|65|
|2000|64|73.6|2000-06|1.56|26.0|8.8|3.31|57.3|66.7|37|51|
|2005|91|65.9|2005-09|1.40|23.4|8.3|2.91|56.9|_A.3_|35|48|
|2010|96|57.8|2010-14|1.31|21.5|7.6|2.56|60.5|70.0|33|44|
|2015|104|53.9|2014-19|1.21|19.5|7.4|2.25|62.3|71.7|32|41|
|2020|1II|49.8|2020-24|1.14|18.5|7.1|2.14|".61|3.s|30|**3**|
|2025|117|W.5|2025-49|0.66|16.0|7.s|2.10|68.6|77.7|20|25|
|2050|1"|48.9|205074|0.41|13.6|9.5|2.07|74.0|82.4|7|9|
|2075|161|62.4|2075-99|0.16|12.7|10.9|2.07|77.6|65.3|4|6|
|2100|169|6.1|2100-24|0.12|12.1|10.9|2.06|60.4|67.6|3|4|
|2125<br>2150|174<br>_177_|70.6<br>72.5|2125-49|0.06|11.9|11.3|2.06|M1.4|6.5|2|3|

# 3. **Age** structure (percent): 1990 and 2025

<!-- Start of picture text --> 10 a 6 4 2 0 2 4 6 8 10<br><!-- End of picture text --> <!-- Start of picture text --> 75 +<br>70-74<br>65-89<br>60-64<br>55.59<br>50-54<br>45-49 40-44 ES93 _<br>35-39<br>30-34<br>25-29<br>20-24<br>15-19 R5<br>10-14<br>5-9<br>0-4<br>10 8 6 4 2 0 2 4 6 8 10<br><!-- End of picture text -->

# 4. Comparison of Country with Region and Income Group, 1985-9o

<!-- Start of picture text --> TFR *0 Growth rate<br>*~ 9.S<br>. . . .. .~~~~~~~~6<br>Comatry Regfon Income group Cointry RegIon Income groupC Co~mtry Rtegfon Income group<br><!-- End of picture text -->