rafmacalaba/datause-encoder-data huggingface dataset:
# **B Summary statistics**

|Table 4: Summary|statistics|||
|---|---|---|---|
|Variable<br>|Mean|Std. Dev.|N|
|Outward FDI fows (US$ million)|286.06|1040.53|5504|
|Number of projects|2.74|7.29|5504|
|Average project size|95.59|218.43|2839|
|Credit to GDP ratio<br>|0.73|0.49|5504|
|Quality of fnancial institutions|10.26|3.28|5336|

24
<!-- Start of picture text --> First two children boys<br><!-- End of picture text --> <!-- Start of picture text --> (g) 1972-1984 (N=339) (h) 1985–1994 (N=899) (i) 1995–2006 (N=763)<br>Prob. boy (%) Prob. boy (%) Prob. boy (%)<br>0 5 10 15 20 0 5 10 15 20 25 0 5 10 15 20<br>Quarter Quarter Quarter<br>Prob. no birth yet Prob. no birth yet Prob. no birth yet<br>0 5 10 15 20 0 5 10 15 20 25 0 5 10 15 20<br>Quarter Quarter Quarter<br>85 85 85<br>80 80 80<br>75 75 75<br>70 70 70<br>65 65 65<br>60 60 60<br>55 55 55<br>50 50 50<br>45 45 45<br>40 40 40<br>35 35 35<br>30 30 30<br>1 1 1<br>.8 .8 .8<br>.6 .6 .6<br>.4 .4 .4<br>.2 .2 .2<br>0 0 0<br><!-- End of picture text --> Figure 10: (Continued) Predicted probability of having a boy and probability of no birth yet from nine months after second birth for rural women with 8 or more years of education by quarter. Predictions based on age 24 at second birth. Left column shows results prior to sex selection available, middle column before sex selection illegal and right column after sex selection illegal. N indicates the number of women in the relevant group in the underlying samples.

interest of space the graphs are not presented here.<sup>30</sup> As for the other spells there is no evidence of sex selection for the first period. Again, families without boys are the main users of sex-selective abortions. For urban women with three girls the predicted percentage boys is between 60 and 75 for both the 1985–1994 and 1995–2006 time periods. The numbers for rural women with three girls are in line with those for urban women. It is worth noting that there are only 211 and 155 observed births for urban women for the two time periods, and only 167 and 178 births for the rural women. Urban women with with two girls and one boy show a predicted percentage boys above 65 percent for all quarters, but this result is based on only 292 births. Finally, it is interesting that the probability of having a fourth child is higher for women with three boys than it is for women with two boys and one girl. This might be evidence of a slight gender balance preference, although there is absolutely no evidence that women are aborting boys and the number of observed births to both groups is very small.

> 30 Graphs and underlying estimation results are available on request.

37
<!-- Start of picture text --> °<br>an 100 0 O°<br>& °<br>><br>ZB 80 °°<br>uo} °<br>2a<br>8s o °<br>so=tTM 60 552 a@,<br>eal $o<br>2a °<br>ss 40<br>-_ °F<br>5 °<br>re>&<br>=iS 20 f<br>= 2,<br>of<br>0 20 40 60 80 100<br>Monthly cropland fires detected by MODIS, 2012-2017 (000s)<br><!-- End of picture text --> © Months 45 line
# **ABBREVIATIONS AND ACRONYMS**

|ACB|Akiba Commercial Bank|
|---|---|
|BARA|Business Activities Registration Act|
|BDS|Business Development Service|
|BEST|Business Environment Strengthening for Tanzania|
|BOT|Bank of Tanzania|
|BRAC|Bangladesh Rural Advancement Committee, Tanzania|
|BRELA|Business Registration Licensing Authority|
|CEM|Country Economic Memorandum|
|CRDB|Cooperative Rural Development Bank|
|CSSC|Christian Social Services Commission|
|DDSDP|Demand-driven Skill Development Program|
|FBO|Faith-based Organizations|
|FDC|<br>Focal Development Colleges|
|FGD|Focus Group Discussion|
|FINCA|Foundation for International Community Assistance|
|FSDT|<br>Financial Sector Deepening Trust|
|GEMA|Gender Education Management Association|
|GTZ|_Deutsche Gesellschaft für Technische Zusammenarbeit_|
|HBS|Household Budget Survey|
|HE|Household Enterprise|
|ISIC|International Standard Industrial Classification|
|IST|Informal Sector Training|
|ILFS|Integrated Labor Force Survey|
|ITEP|Individual Training Evaluation Program|
|LGA|Local Government Authority|
|MDA|Ministries, Departments, and Agencies|
|MFI|Microfinance Institutions|
|MITM|Ministry of Industry and Trade, Marketing|
|MKUKUTA|_Mkakati wa Kukuza Uchumi na Kupunguza_Umaskini Tanzania|
||(National Strategy for Growth and Poverty Reduction)|
|MKURABITA|_Mpango wa Kurasimisha Rasilimali na Biashara za Wanyonge_|
||Tanzania (National Business and Property Formalization Program)|
|MSME|Micro- and Small and Medium Enterprises|
|MNRT|Ministry of Natural Resources and Tourism|
|M&E|<br>Monitoring and Evaluation|
|NACTE|National Council for Technical Education|
|NSGRP|National Strategy for Growth and Reduction of Poverty|
|NBS|<br>National Bureau of Statistics|
|NGO|Nongovernmental Organization|
|NMP|<br>National Microfinance Policy|
|PIN|<br>Personal Identification Number|
|PMO|Prime Minister‟s Office|
|PTF|Presidential Trust Fund|
|PPA|Participatory Poverty Assessment|

v
using, among other inputs, labor. Farmers are endowed with land and labor and allocate time (net of leisure) to the “farm” labor market, the home (land) plot to produce primary inputs (grapes) for sale, or to work in nearby farms. In this framework, Porto derives additional welfare impacts given by the direct impact on wages. Also, a given farm can either sell or purchase labor in the “farm-labor” market, and, consequently the estimation of the first-order welfare effects needs to include the additional impacts that arise due to changes in wage earnings or paid wages. Porto (2005) finds that increases in the prices of agro-manufactured exports such as wines (a major export sector in Moldova) have sizeable poverty-reducing impacts. Wages respond positively to export prices and this causes first order gains that dominate both the consumption losses due to higher consumer prices and the profit losses due to higher wages paid to hired labor.

Porto (2006) studies a case where households consume products, sell labor in the market, but do not produce farm products. His model applies well to urban, middle-income economies, where farm activities are not relevant (and are not captured by the available household surveys either). Porto explores the distributional consequences of MERCOSUR—a regional trade agreement between Argentina, Brazil, Paraguay and Uruguay—in Argentina and finds welfare gains for the average poor and middle-income households (and negligible effects for the wealthiest households). This is because, on top of gains from price reductions due to tariff cuts, there are changes in wages that favor unskilled workers over skilled workers, and unskilled workers are concentrated at the bottom of the income distribution.

Three recent studies are also worth mentioning. Ferreira, Fruttero, Leite and Lucchetti (2013) examine the consequences of food price inflation in Brazil, a large food producer with a predominantly wage-earning agricultural labor force. They find large and negative consumption effects, which are also markedly regressive, as expected. However, they also uncover a positive and progressive income effect, particularly in rural areas. Overall, thus, the Brazilian middle-income household suffered larger proportional losses than the very poor or the richer households. Jacoby (2013) reaches a similar conclusion in his study of food prices in India. Specifically, once the wage gains are accounted for, he finds that rural households 13
# **B. Appendix: Empirical calculations of wage premia**

## **_B1.The SEDLAC dataset and sample_**

The evidence is drawn from the analysis of microdata from a large set of household surveys that are part of the _Socioeconomic Database for Latin America and the Caribbean_ (SEDLAC), jointly developed by CEDLAS at the Universidad Nacional de La Plata (Argentina) and the World Bank’s LAC poverty group (LCSPP). This database contains information on more than 200 official household surveys in the region (see _sedlac.econo.unlp.edu.ar_ ). All variables in SEDLAC are constructed using consistent criteria across countries and years, subject to the constraint of the survey questionnaires, and identical programming routines.

The document covers a set of 16 Latin American economies: Argentina, Brazil, Bolivia, Chile, Colombia, Costa Rica, Ecuador, El Salvador, Honduras, Mexico, Nicaragua, Panama, Paraguay, Peru, Uruguay and Venezuela. Only Guatemala is missing among the continental Latin American countries. The sample represents 97.5 percent of the total Latin American population.

In this study, the sample for each country includes all individuals aged 26-56 that have fully coherent answers about income and education in the corresponding household surveys. For the wage premia, and following Manacorda _et.al_ (2010), the analysis attempts to control for the secular increase in female labor force participation by computing premiums from a sample of male workers only.

## **_B2.Construction of wage premia_**

Skilled workers are defined as those with some college education (complete or incomplete), and the unskilled as those up to complete secondary education – but without college. Unskilled labor is further divided into high school graduates and those without a high school diploma – also referred to as “dropouts” in the analysis.

These wage premiums are estimated by means of a Mincer wage regression based on individual worker’s microdata. The Mincer equation consists of a regression of the logarithm of the hourly wages on dummies for educational levels, a potential experience variable (constructed as age minus years of education minus 6) and its powers up to the order of 4, and a series of regional and urbanrural controls. The remuneration for each input (ln _wkt_ ) is computed from this regression as a weighted average of the returns to education of each of the educational levels belonging to the corresponding input. The wage premiums are then obtained as the difference of these remunerations. These regressions have the following form: where _w_ is the wage for worker _i_ at time _t_ , the _D_ variables are indicators for the level of educational attainment (college complete and incomplete, secondary complete and incomplete, and primary complete – primary incomplete is the omitted category) with their corresponding _β_ coefficients, and the _X_ variables represent a set of individual characteristics, which include years of experience, region of residence and urban/rural status (when available).

37
Policy Research Working Paper 8516

# **Abstract**

The international comovement of equity returns has been viewed as reflecting either pervasive common shocks or local linkages between countries. This paper brings these perspectives together by assessing the comovement of equity returns in a dynamic model that allows for both common factors and spatial dependence, using quarterly data for 40 advanced and emerging countries over the past two decades, and including GDP growth, the real interest rate, and credit as fundamental variables. Estimation results employing a bias-corrected quasi-maximum likelihood approach provide strong indication that the cross-country dependence of equity returns results from both spatial effects and common shocks captured by a latent common factor—weak and strong dependence, respectively. The factor exhibits a robust negative correlation with market measures of aggregate risk. Countries’ exposure to the common factor rises with their extent of trade openness and the degree of rigidity of their exchange rate regime. Despite its simplicity, the empirical model fits the data well. All these results are robust to the use of alternative spatial weight matrices. The paper also shows that ignoring cross-country dependence leads to distorted parameter estimates and a marked deterioration of the explanatory power of the empirical model.

This paper is a product of the Development Research 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 gabate@worldbank.org and lserven@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
# PRE Wrarking PAper Sarias

|||||Contact|
|---|---|---|---|---|
|WPt662|Trends in Social Indicators<br>and<br>Social Sector Financing|Jacques van der Gaag<br>Elene Makonnen<br>Pierre Englebert|May 1991|B. Rosa<br>33751|
|WPS66|3 Bank Holding Companies:<br>A Better<br>Structure for Conducting<br>Universal<br>Banking?|Samuel H. Talley|May 1991|Z. Seguls<br>37665|
|WPS66|4 Should Employee<br>Participation<br>Be<br>Part of Privatization?|Barbara W. Lee|May 1991|G. Orraca-Te<br>37646|
|WPS66|5 Microeconomic<br>Distortions: Static<br>losses and their Effect on the<br>Efficiency of Investment|Ram6n L6pez|**May**1991|WDR Office<br>31393|
|WPS66|6 Agricufture<br>and the Transition<br>to the<br>Market-|Karen M. Brooks<br>Jose Luis Guasch<br>Avishay Braverman<br>Csaba Csaki|May 1991|C. Spooner<br>30464|
|WPS66|7 VERs Under Imperfect<br>Competition<br>and Foreign Direct Investment:<br>A Case Study of the U.S.-Japan<br>Auto VEFR|Jaime de**Meb**<br>David Tarr|May 1991|D. Ballantyna<br>337947|
|WPS66|8 Inflation Tax and Deficit Financing<br>in Egypt.|Hinh T. Dinh<br>Marcelo Glugale|May 1991|L. Santano<br>80553|
|WPS66|9 Are High Real Interest Rates Bad for<br>World Economic<br>Growth|Nemat Shafik<br>Jalaleddin<br>Jalali|May 1991|M. DMno<br>33739|
|WPS67|0 Inflation Adjustments<br>of Financial<br>Statements: Application<br>of<br>*****<br>Intemational<br>Accounting<br>Standard 29|Yaaqov Goldschmidt<br>-<br>Jacob Yaron<br> <br>=|May 1991|C.Spooner<br>30464|
|WPS67|1 Lessons from the Heterodox<br>Stabilization<br>Programs|Miguel A. Kiguel<br>Nissan Liviatan|May 1991|E. Khine<br>39361|
|WPS67|2 The Macroeconomics<br>of Public<br>Sector Deficits: The Case of Ghana|Roummn Islam<br>Deborah<br>L Wetzel|May 1991|R. Luz<br>34303|
|WPS67|3 The Macroeconomics<br>of Publc<br>Sector Defdeis: The Case of<br>Pakistan|Nadem U. Haque<br>Peter Montiol|May 1991|R. Luz<br>34303|
|WPS67|4 Distributional<br>Effects of Adjustment<br>Poicies: Simulations<br>for Two<br>Archetype Economies|Francois<br>Bourguignon<br>Jaime de Mdlo<br>AkJko Suwa|<br>May 1991|WDROffice<br>31393|
**Table 7:  Russian employers in innovative firms assessment of the most lacking skills**

||**Skills most lacking**<br>**(in decreasing order of skills**<br>**gap)**|**Skills where the perceived gap among innovative firm**<br>**employers most exceeds the perceived gap among**<br>**traditional firm owners**|
|---|---|---|
|Managers|Decision-making<br>Leadership<br>Foreign language<br>Teamwork<br>Openness to ideas<br>Problem-solving|Decision-making<br>Leadership<br>Foreign language<br>Openness to new ideas<br>Problem-solving|
|Specialists|Problem solving<br>Professional skills<br>Independent work<br>Cooperation<br>Decision-making<br>Planning work|Problem solving<br>Professional skills<br>Independent work<br>teamwork|
|Blue-collar|Conscientiousness<br>Professional skills<br>Problem-solving<br>Independent work<br>Conflict aversion|Problem solving|

_Source: Adapted from Vasiliev et al. (2013), pages 38-41._

**Table 8: Indian employers’ most important skills, by factor with mean factor loadings**

|**Core employability**|**Mean**|**Professional Skills**|**Mean**|**Communication Skills**|**Mean**|
|---|---|---|---|---|---|
|Integrity|4.48|Use of modern tools|4.08|English|4.26|
|Reliability|4.42|Apply math/science/|4.07|Communication||
|Teamwork|4.41|engineering knowledge||Written|4.07|
|Willingness to learn|4.4|Creativity|4.07|Communication||
|Entrepreneurship|4.35|Problem Solving|3.93|Reading|4.04|
|Self-discipline|4.26|System design|3.84|Technical skills|4.02|
|Self-motivation|4.22|Contemporary issues|3.83|Experiments/data|4.01|
|Flexibility|4.15|Customer service|3.51|analysis||
|Understand/take|4.14|||Verbal|4|
|directions||||communication||
|Empathy|3.92|||Basic computer<br>Advanced computer|3.95<br>3.71|
|**Average**|**4.27**|**Average**|**3.91**|**Average**|**4.1**|

Question asked: Employers were requested to rate on a scale from 1 (not at all) to 5 (extremely) how important each skill is for an engineering graduate to be an effective employee. The scores were used as input to a factor analysis, which returns the three factors in the Table. _Source: Blom and Saeki (2011)._ 40
personal income taxes paid by the labor force employed in productive sectors except fossil fuel and thermal electricity sectors. To incorporate this scenario, we first calculate a rate that will be used to adjust the wage rate and user cost of capital. The rate is calculated as follows: Labor price, PL and, capital price, PK are then adjusted by multiplying by (1 −pitcr) throughout the model. In the final set of scenarios, the carbon tax revenue is used to cut corporate income taxes of the firm (“Corporate Income Tax Cut” case). We have three cases under this scenario based on how the carbon tax revenues are reallocated to firms to cut their corporate income taxes. Under Scenario 4a (“CO2 Intensity” case), the carbon tax revenue is recycled to non-fossil fuel sectors inversely proportional to their CO2 intensities. This means a sector with a higher CO2 emission intensity gets a lower carbon tax revenue as a rebate. The rebate rate used to adjust corporate income tax etri adjusted for sectoral carbon intensity weights as YGAP etri = carbii. (48) (sek.QKiPKi) Where carbii is a weight computed from the SAM and used to adjust the rebate for all non-energy sectors according to their carbon intensity such that sectors with higher carbon intensity have a lower rebate then etri is used to adjust corporate tax expenditure in Equation 31 as follows: EExp= sek. ∑(te−etri i). PK⋅QKi + TEG+ ∑TEHh h + TEW (49) In Scenario 4b (“Capital account share” case), carbon tax revenue is recycled to non-fossil fuel sectors in proportion to their capital accounts. A sector with a higher capital account gets a higher rebate in this scenario, the tax rebate for corporates is computed as: QKiPKi YGAP etri = . (50) ∑QKi iPKi (sek.QKiPKi) In the last case (“Export Intensity” case), the carbon tax revenue is recycled to non-fossil fuel sectors in proportion to exports of their outputs. In this case, more export-oriented sectors get higher rebates from carbon tax revenues. Altogether, we have 9 cases. In all cases, we follow government budget neutrality, meaning that only the incremental government revenue due to the carbon tax is recycled.

Then, etr is used to modify equation 31 in the same way as 4a and 4b.
4

# Figure 4 Mo4el of sonttepqve U§e nd. Fertility in the Last ove tears In iunisia

<!-- Start of picture text --> Bacground Healfh<br>Characteristics Access Proportion ol<br>6 Yrs. Ago Time Modern<br>Temporary<br>Methods Used<br>nomuniFt y i Additional Airths<br>Mortality Children Proportion o in<br>Still Wanted Time Traditional | Last Five<br>_ducational | 5 Years Ago Methods Used | | Years<br>Opportunitie<br>\ ~~Probabilil<br>Number ot-FF-ai!fei Sterilized in |<br>5 years ago Last live year<br><!-- End of picture text -->
each climate model. Altogether 8 sets of 17,000 tropical cyclones are generated. For each track, we follow where the tropical cyclone makes landfall. Tropical cyclones that land on continents are assumed to terminate there. Tropical cyclones that pass over islands are assumed to continue and can strike multiple locations. The minimum barometric pressure and the maximum wind speed at landfall of each storm are recorded. The models are also used to predict the expected frequency of tropical cyclones in each ocean basin.

A damage function is then used to predict the damage that each storm will cause. The coefficient for storm intensity was estimated using aggregate damages per storm and storm characteristics at landfall from US storms since 1960 (NOAA 2009). Several storm characteristics were tested including maximum wind speed and minimum barometric pressure. Vulnerability measures were used to control for population density and income at each impacted coastal area. These data were inferred for each year from decennial Census data by county (US Census of Population 1960, 1970, 1980, 1990, 2000).

A separate damage analysis was then conducted of tropical cyclones around the globe (EMDAT 2009). The international data set was used to estimate the coefficients for vulnerability (income and population density). The observations in this data set are damages and fatalities per country and event. These were matched with national income and population. All dollar values were adjusted for inflation to USD 2010.

Some storms were predicted to cause so much damage that damages exceeded the capital stock in the path of the storm. Storm damages were truncated at a maximum where all the capital ashore is destroyed. A value for US storms was calculated that assumed a highly destructive storm would destroy five coastal counties completely (Mendelsohn et al. 2010). The average damage for the US for these highly destructive storms was $172 billion per storm in 2100. This average national value was then transferred to other countries in proportion to their GDP in 2100. Countries with higher future GDP have higher maximum values.

The model calculated damages for each storm in the data set given its intensity and which country it landed in. The expected damages were calculated by summing the product of the probability of each storm times the damage it causes. Separate estimates 8
As we pointed out earlier, children can devote their time to five activities and most children in C6te d'Ivoire combine several of these, especially work and school. For purposes of this analysis, and considering the limitations imposed by the CILSS sample size, we have classified children in 4 categories:

- (1) children attending school, and not reporting any work ("school only")

- (2) children attending school and reporting work ("school and work")

- (3) children not attending school and reporting work ("work only")

- (4) children not attending school and reporting no work or home care activities only ("home care")

The fourth category deserves some clarification. In the CILSS sample, 12% of children report no school attendance, no work inside or outside the home, and participation in home care activities. Another 10% report no school, no work, and no home care activities either. In the context of C6te d'Ivoire, it would be most unusual for children in the age group 7-17 to not attend school and to make no contribution at all to the household. We must consider the possibility of reporting errors for those cases. It is most likely that those children forgot to report home care activities, and we have therefore grouped them 21