rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> % of children under 2 years of age<br>All individuals Income quintile Mothers UrbanicuralHH<br>Adequate pre-natal conditions (maternal nutrition,Low birth weightbirth outcomes) (<2,500g)-- v v 1405 7 a 18 3 “ °o 13,15 of B<br>Mother has short stature (height < 145cm)- iy é § 8 g 19,12<br>Mother has low body mass index (BMI < 18.5)- 8 1hd6 19.44 13,13<br>Attended at least 4 antenatal visits- ¥ @ B 86 99 78 §5<br>Received assistance during delivery by skilled practitioner (Doctor, nurse- 8 gy $ & 8 a y<br>HH member smokes or drinks alcohol- gy 63,87 83 g8 6389<br>Adequate home care & feeding practices- 3 e 4B 2 8 29,31<br>Mother reads & plays with child- g 8 2 2 4% 3a<br>Mother always handwashes bf foodprep & feeding, bf/af eating & af toilet u- 8 % §s 78 §8 TM$1<br>Age appropriate feeding (custom)- y 8 3! 84 3! 87,89<br>Child is currently breastfed- y bal A 8 A 2 %<br>Child is currently fed solid or semi-solid foods- 8 75/6 498 Te 78<br>Age appropriate infant & young child feeding- g z y $ & 9 8<br>Adequate HH food security & diversity- 8 8 8 $ 8 i nv<br>Household is food secure- 8 g 2 8 g 24,46<br>HH consumed protein rich foods in past 7 days (meat & eggs & milk product- $ ¥ B 8 86 8 99<br>Dietary diversity score 10 or above (75th percentile or higher) - g 3 8 8 8 % 8<br>HH consumed protein intense diet in past 7 days (meat & eggs & milk produ- e ¥ B 2 8 2 30<br>Adequate post-natal maternal & child health care- 8 38 8. 6 3 40,3Z<br>Received postnatal check-up right after birth by skilled practitioner (Doc- § 8 98 98 98 78 §2<br>Underwent neonatal screening - e Bd §§ 8 8 & A<br>Age appropriate vaccine status- gy 66,7 66.8 6%g°<br>Had acute diarrhea in past 2 weeks- i 14) 6 1% d 7 1 5,1 :<br>Adequate HH water, sanitation infrastructure & environment- Cy a % g §5 e fo<br>Improved drinking water (main source is piped, borehole, protected dug wel- S &§ 9 §§ 8 % 8<br>Improved sanitation (toilet is water sealed)- 8 88 y 8 ¥ % 8<br>At least 75% of housholds in municipality have improved sanitation- & §§ 8 8 88 8 3<br>Household has electricity- § g ¥ Ed Ey 83 3<br>Functioning refrigerator in household- S 8 8 8 a 8 8<br>HH member smokes- g 4G,47 Ba oy 4851<br>Uses improved cooking fuel (Elec/LPG/Nat gas) & does not burn garbage- g 8 § g 8 2 9<br>0 25 50 75 100 0 25 50 75 100 0 25 50 75 100 0 25 50 75 100<br>© Poorest 20% 9 Some or complete college 9° Urban<br>© Richest 20% © Some or complete elementary; or no schooling © Rural<br><!-- End of picture text --> Source: Author's calculations using 2015 NNS and FIES data. Notes: Indicators in italics are not used as components of the aggregate indicators of overall adequacy in each of the five nutrition driver categories
|MAS Fabric (India|30%|582|36.4|255|6.8|
|---|---|---|---|---|---|
|Metro Hi-Tech Cooperative|90%|99|1.2|107|7.6|
|Mundra SEZ Textile & Apparel|100%|116|10.6|104|5.1|
|Next Gen Textile|60%|100|1.9|101|7.3|
|Palladam Hi-Tech Weaving|100%|65|0.7|55|4.7|
|Pochampally Handloom|100%|22|3.7|34|4.7|
|Pride india cooperative Textile|90%|27|0.3|58|7.4|
|Purna Global Textiles|60%|55|1.3|92|6.1|
|Rhythm Textile & Apparel|60%|19|1.3|125|6.1|
|Sayana Textile|90%|57|1.1|117|5.3|
|SIMA Textile Processing Centre|30%|248|24.8|112|9.1|
|Surat Super Yarn|90%|43|1.6|105|7.0|
|The Great Indian Linen & Textile<br>Infrastructure Company|30%|121|6.1|149|8.9|
|Vraj Integrated Textile|90%|70|3.3|115|7.0|
|**Average**|**77%**|**119**|**7.6**|**105**|**6.4**|

# _Correlations with estimated completion times_

<!-- Start of picture text --> No correlation between park size and estimated<br>completion time<br> 10.0<br> 9.0<br> 8.0<br> 7.0<br> 6.0<br>R2 = 0.036<br> 5.0<br> 4.0<br> 3.0<br>0 200 400 600 800 1,000<br>Park land area (acres)<br>Estimated Completion Time (y)<br><!-- End of picture text --> Figure Ia 41
**<u>Table 1: Mean characteristics for villages in the sample</u>**

||**Total**||**By r**|**egion**||
|---|---|---|---|---|---|
|||**N & NE**|**Coastal **|**Central **|**SW**|
|**General characteristics**||||||
|Number of households in village|427.04|507.55|327.02|423.17|439.90|
|Village population|1575.24|1813.61|1064.30|1762.00|1701.25|
|Share of agricultural households in village (%)|78.61|80.20|51.02|86.91|90.14|
|Per capita income (Yuan)|2989.54|2940.13|4596.84|2525.17|1983.21|
|Per capita cultivable land (Mu)|1.24|1.89|0.97|1.17|0.94|
|**Structure of village income**||||||
|Total village income in 2003 (10,000 Y)|22.41|15.66|58.25|9.55|4.87|
|.. of which from collective enterprises (%)|21.02|24.82|20.42|21.40|16.15|
|.. of which from private enterprises (%)|5.77|0.97|7.71|3.65|2.54|
|.. of which from taxes and fees (%)|7.98|14.17|2.86|37.13|15.77|
|.. of which from land compensation (%)|36.53|38.97|37.16|10.47|45.59|
|.. of which other (%)|28.69|21.07|31.85|27.34|19.95|
|Mean village expenditure (10000Y)|17.62|13.41|40.97|11.27|5.17|
|Mean value of village assets (10,000 Y)|55.62|48.57|114.82|25.77|26.81|
|**Governance and knowledge about the law (%)**||||||
|Villages with leaders elected (%)|70.18|70.11|75.72|64.89|68.16|
|Households holding land certificates|82.81|66.50|86.86|84.36|93.04|
|Villages w leaders knowing conditions for big readjustment(%)|53.41|67.93|38.73|39.36|58.74|
|Villages w. leaders knowing conditions for small readjustment (%)|17.80|13.04|17.34|25.53|18.83|
|Households knowing conditions for big readjustment (%)|53.31|65.52|40.26|33.51|60.78|
|Households knowingconditions for small readjustment(%)|19.32|19.62|23.66|26.49|12.90|

_Source:_ Own computation based on 722 villages from the 2005 NBS/WB survey. **a** The data in the last four rows (Leaders and household’s knowledge on the content of the law with regard to conditions under which the big and small adjustments are allowed) are based on the test results of leaders and farmers’ understanding of RCLC.

23
relationship that applies both to countries that underwent structural reforms and those that did not, such as Brazil.

**Figure 2:** Coverage of pensions as shares of the working age population vs. GDP per capita for

selected countries <!-- Start of picture text --> 4.8<br> Sweden<br>4.6<br>Italy<br>4.4<br>UK<br>4.2 Argentina US<br>4.0 Mexico Poland<br>Colombia Uruguay<br>3.8 Peru Chile Costa Rica<br>3.6<br> Dominican Rep<br>3.4 Bolivia El Salvador y = 0.4388Ln(x) + 2.561<br>3.2 2<br>R = 0.8706<br>3.0<br>0 10 20 30 40 50 60 70 80 90<br>Coverage, share of working age population (%)<br>2005<br>Log (GDP per capita PPP) in<br><!-- End of picture text --> Note: The year for coverage data varies between 2000 and 2005.

Sources: World Bank (forthcoming) and WDI.

**Figure 3:** Coverage rates: contributors as shares of economically active populations for selected LAC countries, 1990s and 2000s (percent)

<!-- Start of picture text --> 80<br>67<br>70 62 62 1990s 2000s<br>59<br>55<br>60 51<br>50 41<br>35<br>4030 2523 2630 2827<br>20 1411 1115<br>10<br>0<br>ArgentinaBolivia ChileColombiaCosta RicaEl SalvadorMexico PeruUruguay<br><!-- End of picture text --> Note: Data available are not identical across countries, representing the closest years to 1995 and 2004: Argentina (1995, 2004), Bolivia (1999, 2002), Chile (1996, 2003), Colombia (1996, 1999), Costa Rica (1995, 2004), El Salvador (1995, 2003), Mexico (1998, 2002), Peru (1999, 2003), and Uruguay (1995, 2004). Source: Rofman and Lucchetti (2006) Socio-demographic characteristics are also determinants of contribution patterns. Rofman and Luccetti (2006) confirm that there is significantly lower participation among younger, female and low-income workers, with the poorest quintile being practically excluded from the social security system in most countries. In some countries, there was an observed drop in the coverage of the lowest income quintile (e.g., Chile, Costa Rica, Uruguay, and Argentina).

Several studies suggest that there is a substantial movement of individual workers in and out of the formal and informal sectors, rather than a pure dichotomy. This flow between formality and informality ultimately results in a low density of contribution histories for many workers (World Bank 2007). Based on data from the Mexican Social Security Institute ( _Instituto Mexicano de Seguridad Social_ , IMSS), Levy (2006) notes that only 11.6 percent of low-wage workers affiliated to the IMSS spent the entire previous nine-year period in the IMSS system. Data from the Social Security Bank ( _Banco de Previsión Social_ , BPS) in Uruguay confirms that there is considerable labor movement in and out of the formal sector (Bucheli et al. 2006). The gaps in contribution histories, even among some of the countries with the highest pension coverage, such as Argentina, Chile, and Uruguay, raised concerns that membership density may not be sufficient to provide significant replacement rates for the covered population. The 2008 reform to the Chilean pension system places great emphasis on expanding coverage.<sup>19</sup>

> 19 See Appendix 2.

- 10 -
**Figure 3 Unit Values of Sri Lanka’s Apparel Exports to the EU-15 and the United States**

<!-- Start of picture text --> 70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008<br>EU-15 Unit Values (Euro/Kg) US Unit Values ($US/Dozen)*<br>unit values<br><!-- End of picture text --> _Source:_ EU-15: Eurostat; United States: U.S. International Trade Commission (USITC). _Note:_ For products exported to the United States, volume data were “not available” for four products,and for five products the volumes were reported in “numbers.” Both sets are measured in numbers that were converted into dozens.

On the other hand, the unit prices of Cambodia’s main export products are lower than the world average. For EU-15 exports, unit prices in 2005 were lower than in India, Sri Lanka, and Vietnam, but higher than in Bangladesh, China, and Pakistan (Tewari 2008). One of the reasons for the low prices of Cambodian apparel exports is that Cambodian apparel manufacturers concentrate on the production of basic products, while other countries export higher-value products. Unit prices went down after the end of the MFA, with U.S. import prices experiencing sharper declines than those of the EU. Between 2004 and 2008 the average price of apparel exports to the United States fell by 25 percent, from $52 to $39 per dozen, while the average price of apparel exports to the EU-15 declined by 7 percent, from €13.4 to €12.5 per kilogrambetween 2004 and 2008,according to the U.S. International Trade Commission (USITC) and Eurostat data (Staritz and Frederick 2011a) (see figure 4).

11
For scenario E, the steeper growth rate of liquid fossil fuels costs is taken from the U.S. Energy Information Administration High Oil Price case scenario for 2035 projections EIA (2010a, p. 86, Table 10). We calibrate the value of the logistic oil price growth rate, _κf_ , so that most of the additional increase in prices take place by mid-century (see Figure C.2).

For scenario scenario, T we set the land-use emissions' target in 2100 to 60% less compared to 2004. The global warming intensity, _θzt_ ( _zτ , t_ ) dening the land-use GHG emissions' target is a logistic function: where _zτ_ are GHG emissions in 2025, _zT_ are the GHG emissions cap in 2104, and _κμ_ is the targeted rate of decline in land-use GHG emissions. We calibrate the targeted rate of decline so that the target's stringency increases rapidly after it is introduced, with larger GHG emissions' reductions taking place by 2050.

Figure C.2: Projections of Exogenous Variables, 2005-2104 <!-- Start of picture text --> 10 300<br>9<br>250<br>8<br>7<br>200<br>6<br>5 150<br>4<br>100<br>3<br>2<br>50<br>1<br>0 0<br>2005 2030 2055 2080 2105 2005 2030 2055 2080 2105<br>Baseline Scenario A Baseline Scenario E<br>120% 4.00<br>3.50<br>100%<br>3.00<br>80% 2.50<br>60% 2.00<br>1.50<br>40%<br>1.00<br>20% 0.50<br>0.00<br>0%<br>2025 2050 2075 2100<br>2025 2050 2075 2100<br>Scenario T Scenario T<br>Crop Yield, t/Ha Oil price, $/bbl<br>GHG Emissions as % of Baseline GHG Shadow Price Index, 2025 = 1<br><!-- End of picture text --> 25
deviation increase in wealth is associated with a price increase of CDF 123 over the price paid by poorer women in the same facility.

Why do facilities charge different prices for the same quality of consultation? One reason might be that health providers are pro‐social and charge wealthier users higher fees to subsidize the provision of care to poorer users. Alternatively, this behavior might represent price discrimination for profit maximization. Given the autonomy in setting prices and the ability to distinguish women by their socioeconomic status, health providers can charge according to the willingness to pay by the distinct groups. One way to distinguish between the two options would be to compare the marginal cost of providing a consultation with the price charged to the poor. Under cross‐subsidization, the price for the poor will be less than the marginal cost. Unfortunately, the data do not allow for reliably estimating the marginal cost. However, the household survey data provide the composition of wealth in the different health zones. If the goal of the providers is fully or partially covering the costs of serving the poor by charging higher prices to wealthier women, the additional amount needed to be charged from each wealthier woman will decrease as the ratio of wealthy to poor increases. As a result, the higher is the number of wealthier women per poor woman in the health zone, we would expect a flatter wealth‐price gradient once controlling for the quality of consultations.

Table 7 presents the results of testing for heterogeneity in the price‐wealth relationship by the ratio of wealthy (top quintile) to poor (bottom quintile) households. When restricting the sample to health zones in which the number of women in the bottom quintile is greater than the number of women in the top quintile, we do not find a statistically significant price‐wealth relationship when we incorporate health zone dummies (column 1) or facility dummies (column 2). However, when the analysis is restricted to the sample of health zones where there are more women in the highest wealth quintile than in the bottom quintile, there is a larger and statistically significant relationship between price and wealth. When controlling for facility dummies, a one standard deviation increase in wealth is associated with a CDF 138 increase in price. The results are similar when we consider the number of women in the top two quintiles relative to the bottom two. The significance of the within‐facility price‐wealth relationship only when the ratio of wealthy to poor is above one suggests that the rationale for differential pricing is not purely pro‐social.

To summarize, we find a strong correlation between the price of consultations and their quality. We also find that wealthier women pay significantly more for their consultations conditional on quality; thus, the average price paid per antenatal care visit is substantially higher for wealthy than for poor women.

15
# **5. Individual outcomes and household wealth**

**_Wealth effects on nutritional status:_** Figure 1 plots the incidence of the three anthropometric indices of undernutrition against percentiles of the household wealth-index distribution. For women, we plot incidence for all women 15 to 49 years of age, and for women 20 to 49 years of age, given that younger women typically have a lower BMI. The wealth effect—whereby nutritional status improves with a higher DHS wealth index—is generally evident. However, aside from child stunting, the wealth effect is clearly weak in most countries. The incidence of being underweight is slightly higher for younger women, although the relationship with household wealth is very similar.  Child wasting in some countries shows little or no sign of the wealth effect (notably Gabon, Gambia, Senegal, Sierra Leone and Swaziland). Figure 2 gives the corresponding graphs using household consumption per capita. Similar comments apply.

The overall strength of the household wealth effect for each country can be assessed by regressing the standardized values for nutritional status (that is, the z-score for women’s BMI and height-for-age and weight-for-height z-scores for children) on the wealth index, which (as noted) is also a z-score. The regression coefficient gives the number of standard deviations of the nutritional indicator attributed to a one standard deviation increase in wealth. Table 7 gives results using the DHS, and also the analogous results using standardized consumption z-scores from the LSMS. Although the estimated wealth effects are statistically significant in almost all cases (the exceptions are for child wasting in a few countries), the coefficients appear to be generally quite low; for women’s BMI the mean regression coefficient is 0.26, while it is 0.29 for the height-for-age z-score and only 0.09 for weight-for-height. Even for the countries where the wealth effect on child stunting is highest (Burundi, Cameroon and Nigeria), a one standard deviation increase in wealth is only associated with a 0.5 standard deviation increase in the incidence of child stunting. And for about half the countries, the wealth effect on stunting is less than 0.3 standard deviation.

However, these results cannot tell us much about the efficacy of household wealth in predicting the incidence of undernourished individuals. Low wealth effects such as evident in Table 7 need not imply that the incidence of undernutrition is unresponsive to income or wealth differences (as demonstrated in Ravallion, 1990). Also, as shown in Section 3, even if household wealth and individual nutritional status are correlated, it does not follow that a large proportion of undernourished individuals will be found in the lower ends of the wealth distribution.

16
Thus, while pre-crisis debt accumulation is associated with stronger household vulnerability, this finding is not limited to risky foreign currency loans in Eastern Europe. This finding is in line with the findings of Brown and Lane (2011) and Brown et al. (2013) and suggests that foreign currency debt is not a major driver of the decline in household consumption in the region. This surprising result is likely driven by the fact that in most countries foreign currency loans were more likely to be extended to more creditworthy clients, i.e. clients which are less leveraged, have lower debt-service to income ratios or are “hedged” with foreign currency deposits or foreign currency (remittance) income (Fidrmuc et. al. 2011).

# **5 Conclusions**

This paper examines the impact of the recent banking crises in Europe and Central Asia on households’ incomes and consumption patterns. The analysis is based on cross-sectional householdlevel data taken from the 2010 _Life in Transition Survey_ . The survey covers eleven of the 22 countries in Europe and Central Asia which experienced a banking crisis between 2008 and 2011. We assess the impact of the crises on households using available indicators on changes in household income, consumption, education and health during 2008/2009. We further investigate potential macro-level, meso-level and micro-level mitigators of the impact of the crises on households.

Our analysis reveals that the transmission of recent banking crises in the ECA region to households largely mirrors the transmission mechanism of financial crises in the 1990’s. Households were predominantly affected through the labor market channel, whereby the reduction of wages was more severe than actual job-losses. The exposure of individual households to income shocks depends strongly on the economic development of the country as well as the income level and location of the household. In Eastern Europe and Central Asia more than twice as many households were hit by an income shock than in Western Europe. In Eastern Europe the urban and rich were more likely to experience income shocks. By contrast in Western Europe the rural population more likely experienced income shocks. We find that changes in household consumption patterns also mirror those in previous crises: Households reallocate spending from non-essential goods to staple foods in order to cushion income shocks. Households also cut back on health care while education is largely insulated from income shocks. Reductions in staple-food consumption are strongest among lowincome households.

28
positive effects (see Ottaviano et al., 2018 and Kerr et al., 2015 for examples). However, most of these studies focus on the impacts of highly educated migrants in developed countries with small informal sectors. The effects of migration on firm outcomes are also sizable in these studies. Ottaviano et al. (2018), for instance, find that a one percentage point increase in the share of immigrant workers in the local labor force produced a 2.8 to 3.4% increase in firm exports and a 3% effect on labor productivity. The sign difference between their results and ours may be explained by the type of migrants we are analyzing. Particularly, highly educated individuals are typically absorbed by the formal sector whereas IDP are predominantly absorbed by the informal sector.

Overall, our estimates suggest that larger IDP inflows are detrimental for the intensive and the extensive margins of production of formal firms in Colombia. We explore the potential mechanisms driving these effects in the next section.

# **V.2 Effects on prices**

We next explore the effects of IDP inflows on firm prices. Considering that IDP are a population shock, it is plausible as was pointed earlier that they may be impacting output or input prices directly through an increase in consumption; that is, through an aggregate demand shock in output and input markets. We consider this possibility by estimating equations (1) and (2) using the average sale or purchase value of all outputs and inputs used or produced by all formal firms in the manufacturing census between 1995 and 2010. All estimates include firm and year fixed effects as well as product fixed effects, which correspond to the four-digit classification of the International Standard Industry Classification (113 four-digit codes). The results of this exercise are presented in Table III. Although some of the results are statistically significant for nominal output prices, these effects are not consistent and disappear once a more robust set of controls is included in column (3). Consequently, our results suggest that IDP inflows have not had a significant impact on input or output prices.

18
# **TABLE OF CONTENTS**

# Page

|Int|roduction<br>.................................<br>1|
|---|---|
|1.|Trends In Vocational Schooling ..................................<br>1|
|2.|Vocational Schooling In Brazil ................<br>..................<br>4|
|3.|Labor Market Outcomes of Secondary Schooling.<br>6<br>Data.<br>6<br>|
||Methodology.<br>8|
|4.|Vocational Schooling, Occupation and Eamings<br>.9<br>Education-occupation<br>matchings<br>......................................<br>9<br>Eaamings<br>Functions Results for Vocational School Leavers .10<br>|
||Eamings Functions for Academic and Vocational Leavers .11|
|5.|Summary and Conclusions<br>.13|
|Re|ferences<br>..<br>15|
|Ta|bles<br>|
||Table 1 .......<br>4<br>|
||Table 2 .......<br>5<br>|
||Table3........<br>7<br>|
||Table 4.......<br>10<br>|
||Table 5 .......<br>11<br>|
||Table 6.......<br>12|
|Ch|arts<br>|
||Chart I .......<br>2<br>|
||Chart 2 .......<br>3<br>Chart 3 .......<br>3|

Ana-Maria Arriagada Is an Economist in the Human Resources Operation Divislon, Latin American and the Caribbean Country Department Il; Adrian Ziderman, formerly Senior Economist in the Education and Employment Division, Is consultant to the Division and Professor of Economics at Bar Ilan University, Israel.