rafmacalaba/datause-encoder-data huggingface dataset:
Table 1: Classification of skills

|**Skill classifcation**|**Occupations**<br>**I**|**SCO-08 (2 digits)**|
|---|---|---|
|**High-Skill**|Managers|11-14|
||Professionals|21-26|
||Technicians|31-35|
|**Medium-Skill**|Clerical support<br>Sales|41-44<br>52|
||Skilled agricultural workers|61-62|
|**Low-Skill**|Agricultural workers|63 & 92|
||Protective services|54|
||Food prep., cleaning, transp.|91 & 93-96|
||Personal care/services|51 & 53|

other maintenance basic occupations are defined as low-skill occupations in the NQF classification. This distinction is essential when interpreting the results since some of the changes in the premium for the same skill category between both classifications just capture differences in how some occupations enter the low skill category.

# **3.3 of innovative**

A critical step for the empirical analysis is the definition of innovative firms. One key challenge when working with employer-employee dataset is the lack of firm-level information, which in the case of RAIS is restricted to employment, wages, occupations, and geographical location. Innovation information is often found in other surveys. In the case of Brazil, the national innovation survey PINTEC (Industrial Research on Technological Innovation) is a representative survey for more than 17,000 firms every three years that includes information on firms’ expenditure on R&D. The survey, however, only includes manufacturing, services and electricity and does not cover all states of the country. Also, given the probabilistic sampling design and the three-year gap in data collection, the longitudinal dimension is not well in the data set.

Given the existing limitations in the data, a proxy of innovative activity can be developed using the share of occupations dedicated to research and research-related activities. There is an extensive theoretical and empirical tradition looking at innovation and technology from the angle of human capital. Endogenous growth models, such as Romer (1990) and Aghion and Howitt (1992) or more recently Bloom et al. (2020), assume that total factor productivity growth depends on the amount of human capital devoted to research. A number of empirical applications of this tradition has used some measures of human capital as a mea8
# **Achieving Urban Climate Adaptation in Europe and Central Asia**<sup>**1**</sup>

JoAnn Carmin Massachusetts Institute of Technology Yan Zhang World Bank .

1 This paper is one of a series of sector-based papers which serve as background to the report “Managing Uncertainty: Adapting to Climate Change in ECA Countries.” It benefited from review and comments from Marianne Fay, Ron Hoffer, Wael Zakout, Manuel Marino, Anthony Bigio, Kari Juha Haemekoski, and Nilufar Ahmad.
programs may expand access to external finance. We return to these possibilities in Section VII.

# **IV. Baseline Results**

This section presents basic descriptive statistics of median and mean survival times by sector and by country. In calculating these statistics, we cut the sample at the 5<sup>th</sup> and 95<sup>th</sup> percentile of survival times, a simple way to eliminate outliers that reduces the sample of firms from 6,897 to 6,345.

Retailers have the shortest survival time, whereby the median business runs out of savings in about 8 weeks of no revenues (Figure 2). Firms in the manufacturing sector have higher survival times on average – between 13 (metals and fabricated metal products) and 19 weeks (other manufacturing) – as profit margins (and hence retained earnings) tend to be higher. The median firm in the construction sector has liquidity to last 9 weeks, while firms in the manufacturing of chemical, plastics and mineral products can last 16 weeks.

<!-- Start of picture text --> Figure 2: Median Survival Time based on Fixed Costs by Sector<br>Retail<br>Other Services<br>Construction<br>Manufacturing of Food and Beverages<br>Manufacturing of Machinery, Computing, and Electrical ...<br>Manufacturing of Metals and Fabricated Metal Products<br>Manufacturing of Textiles, Apparel, and Leather Products<br>Other Manufacturing<br>Manufacturing of Chemical, Plastic, and Mineral Products<br><!-- End of picture text --> <!-- Start of picture text --> 12<br><!-- End of picture text --> <!-- Start of picture text --> 16<br><!-- End of picture text --> <!-- Start of picture text --> 20<br><!-- End of picture text --> <!-- Start of picture text --> Note : Data is extracted from the World Bank Enterprise Surveys. Number of Observations: 6,345<br><!-- End of picture text --> Figure 3 shows the median survival time by country, which ranges between 7 (Ukraine) and 16 weeks (Peru). Kazakh and Kenyan firms are as cash constrained as Ukrainian firms (also at 7 weeks) and have a survival time that is less than half that of the median Colombian firm (15 weeks). The median business in Italy, Jordan, and the Russian Federation can last 10 weeks, one week longer than the median business in Portugal and Turkey (9 weeks).

9
_Rural-urban gradient in Latin America and the Caribbean_ _page_ 16

# **Table 7.  Proportion of total population, by land category and low population density**

||**Land ca**|**tegory**|||||||
|---|---|---|---|---|---|---|---|---|
||**Suitab**<br>**rainfed**<br>**not for**|**le for**<br>**crops,**<br>**ested**|**Unsuita**<br>**rainfed**<br>**not for**|**ble for**<br>**crops,**<br>**ested**|**Good for**<br>**crops, fo**|**rainfed**<br>**rested**|**Unsuita**<br>**rainfed**<br>**fore**|**ble for**<br>**crops,**<br>**sted**|
||**All**|**< 150 /**<br>**sq. km.**|**All**|**< 150 /**<br>**sq. km.**|**All**|**< 150 /**<br>**sq. km.**|**All**|**< 150 /**<br>**sq. km.**|
|Argentina|76%|28%|21%|16%|2%|2%|1%|1%|
|Bolivia|28%|10%|50%|31%|16%|11%|5%|3%|
|Brazil|66%|33%|19%|10%|10%|8%|5%|3%|
|Chile|21%|7%|60%|24%|7%|4%|12%|9%|
|Colombia|20%|10%|55%|18%|7%|6%|18%|9%|
|Costa Rica|5%|5%|12%|8%|7%|7%|76%|26%|
|Cuba|82%|41%|3%|2%|8%|7%|8%|5%|
|Ecuador|60%|22%|35%|18%|2%|2%|3%|2%|
|Guatemala|7%|5%|50%|10%|7%|5%|36%|15%|
|Guyana|25%|12%|9%|3%|52%|41%|14%|8%|
|Honduras|19%|8%|28%|16%|11%|6%|42%|23%|
|Mexico|42%|13%|35%|19%|10%|4%|12%|7%|
|Nicaragua|42%|19%|29%|19%|9%|6%|20%|12%|
|Paraguay|68%|22%|3%|2%|25%|19%|4%|3%|
|Peru|8%|6%|80%|33%|7%|5%|5%|4%|
|Panama|31%|16%|19%|10%|13%|6%|38%|23%|
|Uruguay|98%|58%|2%|2%|0%|0%|0%|0%|
|Venezuela|48%|19%|17%|6%|22%|10%|13%|4%|
|_LCR Totals_|_48%_|_20%_|_32%_|_14%_|_10%_|_6%_|_11%_|_5%_|

Sources: 1) Population and density are from CIESIN Gridded Population of the World 3.

2) Rainfed crop suitability data are from FAO/IIASA Global Agro-Ecological Zones. Notes: 1) LCR totals include all Latin American and Caribbean countries, including those that do not appear in the table.

2) There were a small number of coastal gridcells that were missing country codes, and were therefore omitted from the above table.

3) The denominator for the percentage was total population as given by the tabulated cells, and excluding those few cells with omitted country codes.

4) “Unsuitable” include GAEZ classifications marginal, very marginal and not suitable.
# **Political Reservation and Female Labor Force Participation in Rural India**

**Klaus Deininger**<sup>**1**</sup> **, Songqing Jin**<sup>**2**</sup> **, Hari K. Nagarajan**<sup>**3**</sup> **, Sudhir K. Singh**<sup>**1, 2**</sup>

1 World Bank, Washington DC

> **2** Michigan State University, East Lansing MI

> **3** Indian Institute of Management Ahmadabad, India

**Key Words:** Political reservation, Female labor force participation, India, Public Work, NREGA. **JEL Codes:** H3; I38; O1

> 1 **Acknowledgement:** 1818 H St. NW, Washington DC, 20433; Tel 202 4730430, fax 202 522 1151. The authors can be contacted at <u>kdeininger@worldbank.org; Jins@msu.edu, hknagarajan@gmail.com, and ssingh12@worldbank.org,</u> respectively. <mark>We gratefully acknowledge funding support from World Bank, DFID, B</mark> rown University, ICSSR, and NIRD. The views presented in this paper are those of the authors and do not necessarily reflect those of the World Bank Group, its Executive Directors, or the countries they represent. <mark>We would like to thank the IRMA team led by J. P Singh, Anupam Chaterjee, Vipul Jain, and the Survey team led by T.K. Krishan for excellent support in data collection.</mark>
**Table 2: Sectoral Export and Import Shares in World Trade** (Percent)

|I|ndonesia|Thailand|Philippines|Singapore &<br>Malaysia|China|Korea &<br>Taiwan|Japan|USA|EU|Rest of<br>World|Total|
|---|---|---|---|---|---|---|---|---|---|---|---|
|_Shares in World Exports:_||||||||||||
|Grains|0.1|6.4|0.0|0.0|7.4|0.2|0.0|54.0|10.6|21.1|100.0|
|Other Agriculture|3.5|3.5|1.0|1.9|6.1|3.7|0.5|21.6|9.7|48.4|100.0|
|<br>Forestry & Fishery|3.6|5.9|1.2|7.9|4.7|7.5|1.8|17.1|5.4|45.0|100.0|
|<br>Energy & Minerals|5.0|0.5|0.3|1.9|2.4|0.2|0.4|3.9|6.1|79.3|100.0|
|<br>Food Processing|1.4|3.6|1.1|4.7|4.1|2.8|1.3|19.8|29.5|31.6|100.0|
|<br>Textiles|3.6|1.7|0.2|1.5|10.2|20.6|10.3|9.3|22.4|20.2|100.0|
|Apparel|3.5|3.8|2.1|3.1|28.5|10.1|0.8|4.4|13.4|30.4|100.0|
|<br>Other Light Manufact.|1.7|2.8|0.6|3.3|17.7|16.2|16.4|10.1|17.7|13.5|100.0|
|<br>Wood & Paper|4.5|0.7|0.5|3.5|2.4|3.9|3.0|18.9|17.4|45.2|100.0|
|<br>Basic Intermediates|1.0|_0.5_|0.3|3.8|2.6|5.4|10.3|17.3|27.9|31.0|100.0|
|Machinery & Equipment|0.2|0.8|0.3|4.1|2.6|7.0|23.8|22.7|23.4|15.1|100.0|
|<br>Services|0.4|0.8|0.7|2.4|2.8|4.4|8.7|17.9|27.5|34.5|100.0|
|Total|1.2|1.2|0.5|3.3|4.4|6.0|12.8|17.9|22.7|30.1|100.0|
|_Shares in World Imports:_||||||||||||
|Grains|2.9|0.7|1.3|3.3|9.9|9.3|20.0|2.5|5.9|44.1|100.0|
|Other Agriculture|_1.5_|1.4|0.3|2.5|4.8|7.7|14.0|14.0|37.5|16.3|100.0|
|Forestry & Fishery|0.1|3.7|0.3|1.7|4.0|5.2|41.6|14.8|21.9|6.8|100.0|
|<br>Energy & Minerals|0.6|1.4|0.9|2.8|2.2|6.3|21.3|22.0|36.0|6.4|100.0|
|Food Processing|0.7|0.9|0.9|3.0|5.2|4.0|15.8|15.2|22.5|31.9|100.0|
|Textiles|1.8|1.7|1.3|4.9|16.1|_5.1_|5.8|10.6|19.4|33.4|100.0|
|Apparel|0.0|0.0|0.0|0.9|1.3|0.7|10.2|32.4|33.4|20.9|100.0|
|Other Light Manufact.|0.5|0.8|0.2|3.0|5.8|2.6|8.1|31.6|25.4|21.9|100.0|
|Wood & Paper|0.6|1.1|0.3|1.7|4.2|3.8|9.6|20.6|33.2|25.0|100.0|
|Basic Intermediates|1.7|2.6|0.8|4.1|6.9|7.3|8.9|17.0|20.6|30.0|100.0|
|Machinery & Equipment|1.3|1.8|0.6|4.4|_5.5_|_5.5_|4.3|23.6|19.8|33.3|100.0|
|Services|0.6|0.5|0.3|2.0|3.0|4.1|12.8|12.5|24.8|39.5|100.0|
|Total|1.0|1.4|_0.5_|3.2|4.9|5.1|10.0|19.2|23.9|30.9|100.0|

Note: Calculated from APEC model data base derived from GTAP data (Hertel, 1996).

6
**Table 1:  Summary Statistics on Lending by Official Creditors**

||**1970-79**|**1980-89**|**1990-99**|**2000-2010**|
|---|---|---|---|---|
|**_Disbursements by Multilateral Creditors(Billions Constant_**|**_2005$US)_**||||
|African Development Bank|0.9|4.9|12.6|8.9|
|African Development Fund|0.3|3.5|6.4|7.4|
|Asian Development Bank|2.4|8.7|26.0|37.0|
|Asian Development Fund|1.2|5.4|11.9|13.5|
|European Investment Bank|1.5|4.2|11.6|21.7|
|International Bank for Reconstruction and Development|37.9|112.0|135.0|131.0|
|International Development Association|19.2|41.2|59.3|68.0|
|Interamerican Development Bank|6.8|20.6|41.9|62.7|
|Interamerican Development Fund|2.9|4.7|3.9|3.8|
|Other Multilaterals|5.5|25.9|49.6|65.1|
|**_Total Multilateral Disbursements_**|**_78.4_**|**_231.1_**|**_358.2_**|**_419.1_**|
|**_Disbursements by Bilateral Creditors(Billions Constant 200_**|**_5$US)_**||||
|Canada|10.2|11.0|6.2|1.9|
|China|4.8|2.8|3.3|26.6|
|France|7.7|14.5|15.5|8.2|
|Germany|18.5|23.0|32.5|9.6|
|Italy|1.7|4.8|5.6|1.6|
|Japan|20.5|53.1|105.0|72.7|
|Kuwait|5.8|6.8|4.1|4.5|
|Russia|0.0|0.2|2.5|5.6|
|Saudi Arabia|7.2|9.8|1.6|1.8|
|United Kingdom|6.2|2.7|1.3|1.3|
|United States|45.9|48.6|23.9|4.0|
|USSR|8.0|18.8|1.1|0.0|
|Other DAC Bilaterals|8.9|17.0|16.8|10.0|
|Other Non-DAC Bilaterals|13.8|16.8|6.5|9.6|
|**_Total Bilateral Disbursements_**|**_159.2_**|**_230.0_**|**_225.9_**|**_157.3_**|
|**_Total Disbursements All Creditors_**|**_237.6_**|**_461.0_**|**_584.2_**|**_576.4_**|
|**_Loan-Weighted Average Terms of New Commitments_**|||||
|Interest Spread Over 20 Yr US TreasuryBill Rate(%)|-2.6|-4.7|-2.0|-2.1|
|Grace Period(Years)|6.9|5.9|6.0|6.4|
|Maturity (Years)|25.2|22.9|21.4|22.6|

Note: This table provides summary statistics on disbursements on loans from official creditors to developing countries captured in the Debtor Reporting Statistics (DRS) database of the World Bank. Disbursements are measured in billions constant 2005 $US. "Other Multilaterals" includes all other multilateral creditors with loans recorded in DRS. "Other DAC Bilaterals" and "Other Non-DAC Bilaterals" include all other bilateral creditors with loans recorded in DRS. DAC refers to membership in the OECD's Development Assistance Committee as of 2012. Data on terms in the last three lines are calculated on a loan-weighted basis, weighting by initial loan commitment. The interest spread refers to the nominal interest rate on the loan less the 20-Year US T-Bill rate, except for the <u>period 1987-1992 when no 20-Year T-Bills were issued: during this period the 30-Year rate is used instead.</u> 35
# **Figure 54: East Asian Exports for Simple Manufactured* Products to US**

<!-- Start of picture text --> 100%<br>90%<br>Taiwan<br>80%<br>70%<br>60% Singapore<br>50%<br>Korea<br>40%<br>30%<br>20% Japan<br>10%<br>Hong Kong<br>0%<br>China<br>1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br><!-- End of picture text --> Source: U.S. Department of Commerce and U.S. International Trade Commission. *Simple manufactured products correspond to SITC Revision 4 codes 6 and 81–85, covering paper, textiles, rubber, metal, and clothing products.

<!-- Start of picture text --> Retail Price:<br>$500.00<br><!-- End of picture text -->

**Figure 55: The Production Value Chain of an iPhone**

<!-- Start of picture text --> Components:<br>$10.75 Japan<br>$60.60<br>$22.96 Korea<br>Assembly:<br>USA $6.50 China<br>$30.15<br>Germany<br>$48.04<br>Rest of<br>iPhone: $179.00<br>World<br><!-- End of picture text --> <!-- Start of picture text --> Retail Price: Assembly:<br>$500.00 USA $6.50 China<br>(Profit Margin: 64%)<br><!-- End of picture text --> Source: Rassweiler (2009).

71
<!-- Start of picture text --> ¥B<br><7<br>38<br>Se<br>fo}<br>&<br>nS<br>S<br>N<br>e<br>S8<br>ro<br>£<br>2<br>£ oO<br>at<br>a<br>a<br>an<br>oO<br>oO<br>o<br>a<br>a °<br>o 2020 2040 2060 2080<br>———= Bottom Billion -———= OECD<br><!-- End of picture text -->
Learning from Developing Country Power Market Experiences: The Case of Colombia <!-- Start of picture text --> Exports NCRE<br>1% 1%<br>Oil<br>Other free<br>customers 10%<br>11% Coal<br>Mining<br>7% 8%<br>Gas<br>Manufacturin 11%<br>g industries<br>14%<br>Regulated Hydro<br>67%<br>70%<br>Figure 2-2 Structure of Colombia’s electricity demand during Figure 2-3 Share of installed capacity (in MW) per<br>2015. technology in Colombia, 2015.<br><!-- End of picture text --> Figure 2-2 Structure of Colombia’s electricity demand during 2015.

Source: Own elaboration based on data collected by the World Bank.

The power generation fleet is largely hydro-based generation (70% of national installed capacity in 2015 with 11.5 GW), followed by gas, oil and coal-fired power plants with 11%, 10% and 8% of total installed capacity, respectively (see Figure 2-3). However, most of the oil-fired installed capacity adding to the 10% is dual gas power plants which have been increasingly declaring oil as their primary fuel due to the lack of gas supply contracts, as well as the regulatory measures that incentivized installation of liquids as primary fuels to guarantee security of supply (e.g. the Firm Energy Obligations, further discussed in section 4). Those “oil-fired” power plants are therefore not necessarily producing power fueled by diesel (UPME, 2016c). A very small amount of renewable generation is installed in Colombia.

The Colombian power market experiences some degree of market decoupling (or separation) due to transmission constraints, mostly driven by hydrological conditions. However, major problems concentrate in the transmission and distribution facilities of Distco Electricaribe (at or below 110 kV), which serves about 25% of Colombian electricity demand. Transmission congestion also exists between the center and north regions of the country.

Interconnectors exist with Ecuador and the República Bolivariana de Venezuela, totaling 600 MW in imports and 871 MW in exports. Two interconnection transmission lines with Ecuador sum up to 395 MW of import capacity, and 535 MW of export capacity. Three interconnectors with the República Bolivariana de Venezuela sum up to 205 MW of import capacity and 336 MW of export capacity.

# 2.2 Hydro Availability and Fuel Supply

The Colombian power system is highly dependent on hydro availability due to the composition of the generation fleet and the limited water storage capacity (only 6% of total capacity in reservoirs can store water for more than six months (Keay et al., 2018)). Hydro availability is volatile and largely unpredictable a year in the future, translating into highly volatile spot price outcomes. Moreover, climatic phenomena such as El Niño or La Niña can heavily impact the availability of hydro resources. As further discussed later in this case study, serious El Niño events caused major power shortages in 1992 and have recently 11
# **5. Estimation results**

# **5.1. Program impact**

We start first with examining the local impact of the 30A Program by plotting individuallevel outcomes based on the 2006 poverty rate of the districts where people reside. In Figures 5 and 6, we present RDD graphs both before and after the program's initiation in 2009. The graphs demonstrate that the disparity in self-employed farm work between the treatment and control groups was reduced after the program. Moreover, the treatment group experienced an increasing trend in self-employed farm work as well as wage jobs, compared to the control group. In Figure 6, we find that the treatment group received higher education subsidies than the control group, particularly after the program's implementation.

Using equation (3) we further estimate the effects of the 30A Program on various outcomes for households and individuals. The direct outcomes of the 30A Program are employment. We start with the program impact on employment of individuals aged 15 and older in Table 2.<sup>6</sup> Table 2 shows that there are no significant program effects on the likelihood of working (column 1). However, the program has significant effects on individuals’ probabilities of being self-employed in farm and nonfarm. Specifically, the program decreases the probability of self-employment in farm work by 0.11, while increasing the probability of self-employment in nonfarm work by 0.054 (columns 2 and 3). This suggests that the program encourages individuals to transition from self-employed farm work to selfemployed nonfarm work.

The program impact on the likelihood of having a wage job is positive, although it is not statistically significant (column 4). Similarly, the program effects on monthly wages of

> 6 According to Viet Nam’s Labor Code, the minimum age for working is 15. We also try to use the sample of individuals aged 15-64 (Table A.6 in the Appendix), and the program impact estimates are quite similar to those derived from the sample of individuals aged 15 and older.

16
Policy ReseaRch WoRking PaPeR 4602

# **Abstract**

Using economic data from more than 8,500 household surveys across 10 African countries, this paper examines whether the choice of farm type depends on the climate and agro-ecological zone of each farm. The paper also studies how farm type choice varies across farmers in each zone, using a multinomial logit choice model. Farmers are observed to choose from one of the following five types of farms: rainfed crop-only, irrigated crop-only, mixed rainfed (crop and livestock), mixed irrigated, and livestock-only farming. The authors compare current decisions against future decisions as if the only change were climate change. They focus on two climate scenarios from existing climate models: the Canadian Climate Centre scenario, which is hot and dry, and the Parallel Climate Model scenario, which is mild and wet. The results indicate that the change in farm types varies dramatically by climate scenario but also by agro-ecological zone. Policy makers must be careful to encourage the appropriate suite of measures to promote the most adapted farm type to each location.

This paper—a product of the Sustainable Rural and Urban Development Team, Development Research Group—is part of a larger effort in the department to mainstream research on climate change. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at Niggol.seo@yale.edu, Robert.mendelsohn@ yale.edu, Adinar@worldbank.org, Rashid.hassan@up.ac.za, and Pradeep.kurukulasuriya@undp.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