rafmacalaba/datause-encoder-data huggingface dataset:
Osborne (2013) estimate of an occupation’s probability of automation using the ISCO classification mapping described above. This provided us with data on 41 countries and 73 data sets, of which 57 of these data sets contained the ISCO-88 classification of occupations, and 16 contained the ISCO-68 classifications. For each data set, we estimate the proportion of workers in automation-prone occupations, non-automation-prone occupations, and the informal agricultural sector. For our analysis, we only compare proportions derived from the same ISCO coding system. Finally, estimates use sample weights when data are not self-weighted.

The fourth data set used is the OECD’s Programme for International Student Assessment (PISA). The 2015 data for 70 economies are used in this analysis; economies without GDP per capita data are not shown in the plots with GDP per capita. These data are representative of 15-yearold students in grades 7 or higher and include internationally comparable measures of student achievement on literacy, program of study (including vocational), grade, gender, an index of economic, social and cultural status (ESCS), and an index of a student’s value of cooperating with others. We use this latter measure as a measure of non-cognitive skills. We use the PISA data to compare education quality of countries and to compare the cognitive and non-cognitive skills across countries and to examine how being in a non-academic track (which we refer to as vocational) versus an academic track program associates with cognitive and non-cognitive skills.

# _Empirical models and estimation methods_

To test the model’s assumption of the association of being in a non-automation-prone occupation with educational attainment and skills in the STEP and PIAAC data, two logistic regression models are estimated. Let �� be a binary variable denoting whether individual _i_ is in an automation prone occupation, �� be a binary vector denoting his or her highest level of schooling excluding general post-secondary, �� denote his or her literacy achievement, �� be a binary variable denoting whether individual _i_ is female, and �� denote his or her age. The first model is For STEP countries, we add the “Big 5” non-cognitive skills, denoted by vector ��, 22
_- Jointly with someone else outside the household - Someone outside the household/other - Decision not made_

**Vignettes:** for each situation below, respondent is asked: _(a) Are you like this person (Y/N)?  (b) If yes, are you completely the same or somewhat the same?  (c) If no, or are you completely different or somewhat different?_

_-Person’s actions in [X] are determined by situation; no other options. - Person is doing [X} because that's what spouse or other member of group/community_ (6) _tells her/him -Person is doing [X] because that's what he/she feels her family or community expect - wants them to approve of him/her as a good farmer/businessperson -Person makes his/her own choices about [X] based on what he/she thinks is best for family and business. If person changed his/her mind, he/she could act differently._

- Whether or not to use family planning to space or limit births

- Agricultural production - Getting inputs for agr. production - Types of crops to grow for agr. production - Taking/not taking crops to market - Livestock raising - Nonfarm business activity - Respondent’s own wage/salary employment - Major household purchases - Minor household expenditures - If respondent has a serious health problem - How to protect herself from violence - Whether and how to express religious faith - Kinds of tasks to do on a particular day - Whether or not to use family planning to space or limit births

- Separate module in the WEAI

56
|**government worker/employee**|government worker/employee|
|---|---|
|**private worker/employee**|private worker/employee|
|**unpaid family worker**|unpaid family worker|
|**casual non‐agriculture**|casual non‐agriculture (only in wave 4, in waves 1,<br>2 and 3 this category was combined with “private<br>worker/employee”)|
|**casual agriculture**|casual agriculture (only in wave 4, in waves 1, 2<br>and 3 this category was combined with “private<br>worker/employee”)|
|**Years mostly self‐employed**|Number of years between 1988 and the year of<br>the wave (1993, 1997, 2000 or 2007) in which the<br>individual was mostly self‐employed._Note: most_<br>_of the work history data are purely retrospective,_<br>_except the work data at the time of the surveys._<br>_E.g. in IFLS2, the respondents recall the work_<br>_history from 1987‐1996. This also applies to the_<br>_next two variables._|
|**Years mostly in government**<br>**employment**|Number of years between 1988 and the year of<br>the wave (1993, 1997, 2000 or 2007) in which the<br>individual was mostly a government employee.<br>_See note above._|
|**Years mostly in private sector**<br>**employment**|Number of years between 1988 and the year of<br>the wave (1993, 1997, 2000 or 2007) in which the<br>individual was mostly a private sector employee.<br>_See note above._|

In addition to employment category variables, we are interested in a range of individual characteristics that may relate to labor market outcomes. Below, in table B, we summarize statistics for two mutually exclusive samples of working individuals across waves 1, 2, 3 and 4 of IFLS. These samples are later used in estimations of earnings and only include those working 35 hours or more per week, with a non‐zero income. As we are particularly interested in youth, the first sample (Youth) consists of observations when individuals are between 15 and 38. While 24 is the common upper boundary age for youth, any individuals in wave 1 of the study that were 24 would be 38 by wave 4. Setting the maximum age to lower than 38 would mean losing observations for these youth in fixed effects estimation.

14
as the good is consumed in similar quantities by all types of households. Rural consumption is much higher than urban consumption, while the reverse was observed for rice. In terms of geographic patterns, consumption is lower in the areas of Kidal and Tombouctou than elsewhere.

Tables 3 and 4 provide consumption data for corn (maize) and wheat products. Consumption levels are substantially lower, at about 30,000 FCFA per household per year for corn, and 24,000 for wheat and related products such as bread. Slightly less than half of the population consumes corn, but the proportion reaches three fourths for wheat products. Corn is consumed most heavily in the Sikasso area, while consumption of wheat products is highest in the Kidal area. While corn consumption is not affected too much by the quintile of well-being of households, wheat products are as expected much more consumed by better off households, and this differences also reproduces itself when comparing urban and rural areas.

Together, the consumption of rice, millet and sorghum, corn and wheat products represents an outlay of about 320,000 FCFA per household per year. Using the same comparison as above, this represents about one fourth of what is needed for a typical poor household not to be poor. An increase of 25 percent of cereals price would then represent about five percent of what a poor family needs in order not to be poor. Yet this would take into account only the impact of higher prices on the cost of food, and not the extra revenues that some households would probably get as producers of food.

Beyond statistical tables, it is useful to visualize the data so as to better understand differences in consumption patterns between various households groups as defined by their level of consumption, since this is ultimately what affects the impact on poverty of price changes. We focus here on rice, millet and sorghum, and corn because these are the cereals for which average consumption is highest (especially in the case of rice and millet and sorghum), and these are also the cereals for which we observe both consumption and production in the country (as discussed below in the case of production). For the graphical analysis we use simple on-parametric techniques to present kernel estimates for various variables (this follows previous similar work by a number of authors, as noted in the introduction). All figures presented in this section as well as in following ones share a common variable for the horizontal axis, namely the level of well-being of households according to the logarithm of their consumption per equivalent adult.

Figure 1 first provides the distribution density for the logarithm of consumption in urban and rural areas as well as at the national level. As urban households are richer, the urban density is to the right of the rural density. In rural areas, the mode of the density is about a value of 12,
# **Stabilization and Association Process in the Balkans: Integration Options and their Assessment '**

Bartlomiej KaminskiX and Manuel de la Rocha'

> The authors would like to thank UK Department for International Development for funding research that led to this paper. The authors are grateful to Daniela Gressani, Constantine Michalopoulos, Marcelo Olarreaga, Maurice Schiff and Milica Uvalic for their insightful and detailed comments on successive versions of this paper. We are also indebted for excellent comments from staff of the European Commission and the International Monetary Fund. The usual caveats apply.

> X University of Maryland, College Park, MD 20742, email: bkaminski@gvpt.umd.edu.

' World Bank, 1818 H St, NW, Washington, D.C. 20433, email: mrochaeworldbank.org.
# **Figure 18. Growth, sovereign ratings and policy space in EMDEs**

# **A. Growth by group**

# **B. Growth Differential between EMDEs and**

# **advanced economies**

<!-- Start of picture text --> Percent 1990-2008 Average<br>10 2003-2008 Average<br>8<br>6<br>4<br>2<br>0<br>EMDEs EMDE EMDE Advanced<br>commodity commodity economies<br>importers exporters<br>C. Sovereign rating and commodity prices<br>Average sovereign rating Index<br>Commodity exporters<br>11 1000<br>Commodity price index (RHS)<br>BB+<br>11.5<br>BB12 500<br>12.5<br>BB-<br>13 0<br>E. 2015 fiscal balances<br>Oct. 2014 forecast<br>Percent of GDP<br>April 2016 estimate<br>0<br>-1<br>-2<br>-3<br>-4<br>-5<br>-6<br>Commodity exporters Commodity importers<br>2011 2012 2013 2014 2015 2011 2012 2013 2014 2015 2011 2012 2013 2014 2015 2011 2012 2013 2014 2015<br>2001 2003 2005 2007 2009 2011 2013 2015<br><!-- End of picture text --> <!-- Start of picture text --> Percentage points<br>6<br>4<br>2<br>0<br>-2<br>-4<br>1990 1995 2000 2005 2010 2015<br><!-- End of picture text -->

# **D. Stock of EMDE bonds maturing**

<!-- Start of picture text --> Percent of gross bond issuance in 2015<br>80 Sovereign<br>70 Corporate<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>2015 2016 2017 2018<br><!-- End of picture text -->

# **F. Policy interest rates**

<!-- Start of picture text --> Percent<br>Commodity exporters<br>10<br>Commodity importers<br>9<br>8<br>7<br>6<br>5<br>4<br>2010 2011 2012 2013 2014 2015 2016<br><!-- End of picture text --> Sources: World Bank, Standard and Poor’s, Haver Analytics, EPFR, International Monetary Fund. C. Sample includes 44 EMDE commodity exporters. Simple average across country group. Last observation is April 2016. D. Last observation is May 2016.

E. 2015 budget balances as predicted in October 2014 and estimated in April 2016. Simple average across country group. F. Sample includes 29 commodity exporters and 21 commodity importers. Simple average across country group. Last observation is April 2016.

40
**Table 1** Treatment-Control Balance at Baseline

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|**Outcome**|**ELA Only**<br>**Control Mean**|**Boys-ELA**|**No Goal**<br>**Control Mean**|**Goal -**<br>**No Goal**|
|**A. Intimate Partner Violence**|||||
|Psychological abuse often|0.017|0.006<br>(0.008)|0.018|0.006<br>(0.006)|
|Physical abuse often|0.008|0.010<br>(0.007)|0.011|0.000<br>(0.004)|
|Forced sex often|0.012|0.004<br>(0.006)|0.013|-0.001<br>(0.004)|
|Psychological abuse in last year|0.054|0.017<br>(0.017)|0.062|0.006<br>(0.010)|
|Physical abuse in last year|0.045|0.011<br>(0.016)|0.053|-0.007<br>(0.009)|
|Forced sex in last year|0.035|0.006<br>(0.013)|0.040|-0.007<br>(0.008)|
|**B. Sexual Activity**|||||
|Ever had sex|0.250|-0.001<br>(0.035)|0.261|0.006<br>(0.018)|
|Currently has partner|0.212|0.011<br>(0.034)|0.230|-0.006<br>(0.017)|
|Had partner past 2 years|0.266|0.010<br>(0.037)|0.279|0.003<br>(0.019)|
|Total sex partners ever|0.312|0.018<br>(0.054)|0.334|0.001<br>(0.026)|
|Hours with boyfriend in the<br>past day|0.030|0.014<br>(0.013)|0.041|-0.002<br>(0.011)|
|Observations<br>|1,074|3,178|2,313|3,178|
|_χ_<sup>2 </sup>p-value||.535||.867|

_Notes._ Column 1 shows control means for females in ELA only communities and column 3 shows control means for females not assigned to the _Goal_ treatment. Columns 2 and 4 show the difference between the means in the corresponding community- or individual-level treatment arms and the corresponding control group means, controlling for the randomization strata. In Panel A, Psychological abuse often (in last year), Physical abuse often (in last year), and Forced sex often (in last year) are binary indicators for the female reporting experiencing psychological abuse, physical abuse, or forced sex often (in last year) within the 12 months prior to survey, respectively. In Panel B, the measures are binary indicators for the female reporting ever having sex, having a current partner, and having a partner in the past 2 years; and continuous indicators for total number of sex partners ever and reported hours spent with her boyfriend in the past day. Standard errors, clustered at the club level, are presented in parentheses below coefficient estimates in columns 2 and 4. The _χ_<sup>2</sup> p-value in the last row is the p-value from a test of the joint significance of all outcomes in Panel A and B in predicting treatment assignment to the _Boys_ arm in column 2 and to the _Goal_ arm in column 4 using a logit model, controlling for randomization strata and clustering standard errors at the club level. .01 ***, .05 **, .10 *. _Source._ Female baseline data.

48
that the results obtained from the product Herfindahl and PC product indicators, and between 5 and 10 product indicators, demonstrate significant overlap. In the tables discussed in this section (Tables 5, 6, and 7), odd-numbered columns refer to regressions using the Herfindahl Products indicator, while even-numbered columns denote those using the 5 product indicator.

Table 5 systematically adds additional economic and structural control variables to – the main specification. These are initial GDP ( _E1 E2_ ), the GDP per capita growth rate – – ( _E3 E4_ ), a measure of human capital ( _E5 E6_ ), a measure of the volatility of government expenditure ( _E7_ – _E8_ ) and, finally, an indicator for the occurrence of natural disasters – ( _E9 E10_ ). Table 6 repeats the exercise, this time for a range of political and institutional – – controls, including: measures of government ( _P1 P2_ ) and institutional quality ( _P3 P4_ ), – – and indicators of political volatility ( _P5 P6_ ), civil conflict ( _P7 P8_ ), and assassinations – of public officials ( _P9 P10_ ).

Throughout these robustness checks, we find that the coefficients on the interaction term and the openness variable continue to carry the correct signs and are statistically and economically significant, both individually (the interaction term) and jointly (the interaction term and the openness indicator), across all specifications. The estimated threshold values (not reported) are not markedly different from those found in our preferred benchmark in Table 3.

Moreover, while the coefficients of the newly-introduced variable are statistically insignificant, they tend to carry the expected signs. For instance, a fast-growing country – (specifications _E3 E4_ ) is more likely to experience a reduction in its growth volatility; this is reasonable, since high-growth nations are more likely to enter into the league of high-income countries, which, as discussed before, have available to them more mechanisms for smoothing growth fluctuations. Greater volatility in government spending, in – contrast, is detrimental for growth stability ( _E7 E8_ ). Superior government and institu– tional quality ( _P1 P4_ ) exert a moderating effect on volatility, while the presence of civil – conflict ( _P7 P8_ ) has the opposite effect.

Our subsample analysis proceeds along two dimensions. We choose to restrict the sample from either end by deleting the final (2001–2005) (Table 7, columns _S1_ (product Herfindahl) and _S2_ (5 product) measures) and first (1976–1980) ( _S3_ and _S4_ , respectively) periods from the sample. The first restriction, which deletes the most recent period, examines the importance of recent history in influencing the outcomes of the analysis. The second, which pares the earliest period of the sample, tests the robustness to the exclusion of the period of increased global trade integration in the late 1970s and early 1980s, which followed the end of the Tokyo Round and led up to the important Uruguay 23
# **Table 8:  Pollution Factors and PM10 Concentrations**

# **(a) All Fuels**

|Fuel|Cooking<br>Location|Building<br>Material|Space|Abbrev.|Mean<br>PM10|Median<br>PM10|Houses|Difference in<br>Mean<br>(From BIOL)|t-<br>statistic|
|---|---|---|---|---|---|---|---|---|---|
|Biomass|Inside|Mud|Kitchen|BIMK|515|528|23|292|11.15|
|Biomass|Inside|Mud|Living|BIML|467|453|20|244|8.86|
|Biomass|Outside|Mud|Kitchen|BOMK|351|258|22|128|4.81|
|Biomass|Inside|Other|Kitchen|BIOK|250|220|74|27|1.46|
|Biomass|Outside|Mud|Living|BOML|244|218|29|21|0.87|
|**Biomass **|**Inside**|**Other**|**Living**|**BIOL**|**223**|**213**|**62**|||
|Biomass|Outside|Other|Kitchen|BOOK|203|191|101|-20|-1.16|
|Biomass|Outside|Other|Living|BOOL|166|162|116|-57|-3.41|
|Clean||||CLN|133|117|46|-90|-4.34|
|Overall|||||231|187|493|||

# **(b) Firewood Only**

|Cooking<br>Location|Building<br>Material|Space|Abbrev.|Mean<br>PM10|Median<br>PM10|Houses|Difference in<br>Mean<br>(From BIOL)|t-<br>statistic|
|---|---|---|---|---|---|---|---|---|
|Inside|Mud|Kitchen|BIMK|498|520|10|248|5.24|
|Inside|Mud|Living|BIML|475|443|9|226|4.59|
|Outside|Mud|Kitchen|BOMK|638|567|5|389|6.33|
|Inside|Other|Kitchen|BIOK|267|220|31|17|0.49|
|Outside|Mud|Living|BOML|293|301|4|44|0.65|
|**Inside**|**Other**|**Living**|**BIOL**|**250**|**210**|**21**|||
|Outside|Other|Kitchen|BOOK|210|193|35|-39|-1.15|
|Outside|Other|Living|BOOL|165|163|44|-84|-2.58|
|Overall||||263|201|159|||

The tables reveal a nearly-identical pattern of results for all fuels and firewood only, so we focus on the results for all fuels. For statistical comparison of means, our benchmark is the mean living-space PM10 concentration for households with biomass fuels, inside cooking and non-mudwall construction (acronym BIOL in the tables). The mean concentrations for these households are 223 ug/m<sup>3</sup> for all fuels, and 250 ug/m<sup>3</sup> for firewood. By comparison, cooking and living spaces have far greater pollution for inside 24
Our results also suggest that corporate governance affects the relationship between bank risk taking and the business cycle. Specifically, lending at banks with more shareholder-friendly corporate governance is more procyclical as such banks may care less about the riskiness of expanding credit at the height of the business cycle. In addition, we find that banks with ‘good’ corporate governance report less nonperforming loans at times of high economic growth. This suggests that banks with ‘good’ corporate governance bias their reporting of nonperforming loans downward during periods of rapid economic expansion so as to preserve capital to be able to continue to increase credit. Similarly, we see that banks with ‘good’ corporate governance have relatively low tangible capital ratios when the economy grows rapidly.

The higher bank risk associated with shareholder-friendly corporate governance should benefit banks to the extent that this increases the valuation of the implicit insurance provided by the financial safety net. As an extension, we examine the relationship between corporate governance and the estimated value of the implicit insurance provided by the financial safety net. We find that shareholder-friendly corporate governance is associated with a higher value of the implicit insurance, especially for large banks. This is consistent with the notion that large banks have an incentive to increase the value of the implicit insurance by increasing their risk, since their too-large-to-fail status increases their chances of collecting on this insurance.

Our study fits in an emerging literature that has addressed the impact of corporate governance on bank risk taking.<sup>2</sup> Pathan (2009) finds that small boards and boards that are not controlled by the CEO lead to additional bank risk as reflected in market measures of risk and the Z-score for a sample of US bank holding companies over the

> 2 Recent surveys are offered by Becht, Bolton, and Roell (2011) and Mehran, Morrison and Shapiro (2012).

4
**Table 7 List of Student Assistance Institutions**

|**Country**|**Program and Source**|**Type of assistance**|**Amount**<br>(US$ Mil.<br>PPP)|
|---|---|---|---|
|Brazil|Fundo de Financiamento ao Estudante do Ensino Superior<br>(FIES)|Loans|212.0|
|Colombia|Instituto Colombiano de Crédito Educativo y Estudios Técnicos|Scholarships|414.9|
||en el Exterior(ICETEX)|Loans|1199.9|
||Programa Nacional de Becas y Financiacion para la Educación<br>Superior(PRONABES)|Scholarships|146.7|
||Instituto de Financiamiento e Información para la Educación|Scholarships|9.3|
||(EDUCAFIN)|Loans|10.2|
|Mexico|Instituto de Crédito Educativo del Estado de Tamaulipas|Scholarships|6.4|
||(ICEET)|Loans|5.3|
||La Sociedad deFomento alaEducaciónSuperior(SOFES)|Loans|60.4|
||Instituto de CréditoEducativo del Estado de Sonora (ICEES)|Loans|30.7|
|Peru||Scholarships|16.7|
||Instituto Nacional de BecasyCréditos Educativos(INABEC)|Loans|31.7|

Note: Amount in PPP US$ 2003 prices.

# **2.2.2. Accessibility**

To estimate the four accessibility indicators, we use data from the education and demographic sections of the national household surveys. The education section provides data on school participation, the highest level of education completed, father’s educational level, and current level of education. The demographic section of the surveys describes age, gender, relationship of interviewee to household head, and parent-children relationships.

# **3. FINDINGS**

This section presents the findings of affordability and accessibility among the four LAC countries and the high-income countries. We present each indicator of affordability and accessibility as a percentage of GDP per capita at PPP. The detailed results are in the annex (Table 8 – 21).

# **3.1. Affordability**

For our analysis, it is helpful to define five groups of countries:

- _Latin America_ : Brazil, Colombia, Mexico and Peru

- _Japan_

- _Anglo-Saxon countries_ : Australia, Canada, New Zealand, United Kingdom, and United States

- _Continental Europe_ : Austria, Belgium, France, German, Ireland, and Italy

- _Northern Europe_ : Finland, Netherlands, and Sweden

12