rafmacalaba/datause-encoder-data huggingface dataset:
# C. Creriatics of Enterpss

Data on the eharacterigtics of the enterprises included in the first wave of coupon privatization are presented in Tables 2 and 3.1 Given the large number of firms included in the coupon scheme, it is not surprising that the sectoral composition mirrored that of the entire Czechoslovak economy. The largest number of firts were in the engineering and building and civil engineering sectors - which aceounted for **_458_** enterpries or about one-third of all enterprises in the coupon scheme. The food industry, with 148 enuerprises was the next most common sector. In general, firms included in the first wave were in productive sectors like manufacturing and services and not public utilities, whose privatizition raised complex isWas of regulation which were postponed tc the second wave. lhe largest enterprises in terms of book value tended to be !r b tinking, electric power, iron and steel, metallurgy, chemicals, pulp and paper, clothing and insurance.

Based on the data on past performance provided to the public, the most profitable sectors were the foreign trade companies, banks and insurance companies. The average profit/equity ratio in 1991 for the entire sample was 17.6, while these sectors had ratios four to seven times that level. Thus it is nct surprising that foreign trade, banking and insurance would emerge as some of the most popular shares in the bidding rounds. Factor shares, as reflected in capital/output and capital/labcr ratios, tended to vary as might be expected across economic activities. Sectors with high ratios of capital tended to be water supply; glass, china and stoneware; foreign trade and banking. Labor-intensive sectors tended to be design, research and development, clothing, security and services.

One of the most striking characteristics of the enterprises is their relatively low indebtedness, as evidenced by the low average debt/equity ratios. The most indebted firms tended to be in the financial sectors (banking and foreign trade), which is normal given the nature of these activities. Thereafter, the firms providing infrastructure and services tended to have some of the
**Figure 1: Durban Dwell Time (2006-2010) at DCT Pier 2**

<!-- Start of picture text --> 18.0 Import<br>16.0<br>Export<br>14.0<br>12.0 Tranships<br>10.0<br>8.0<br>6.0<br>4.0<br>2.0<br>0.0<br>2006-01 2006-03 2006-05 2006-07 2006-09 2006-11 2007-01 2007-03 2007-05 2007-07 2007-09 2007-11 2008-01 2008-03 2008-05 2008-07 2008-09 2008-11 2009-01 2009-03 2009-05 2009-07 2009-09 2009-11 2010-01 2010-03 2010-05 2010-07 2010-09 2010-11<br><!-- End of picture text --> _Source: Transnet Port Terminal_ Dwell time for transshipments is around 5 to 10 days with a few irregular peaks at around the 15 days mark, notably between July and September<sup>7</sup> . This is also related to the fact that “free time”<sup>8</sup> for transshipment is set at 7 days (with low charges below 15 days)<sup>9</sup> .

It should be noted that cargo is generally moved from the terminal to bonded warehouses before the expiry of the free storage period of 3 days offered by TPT. Therefore, the information provided does not capture all dwell time figures for the port. Additional data were also collected from those involved in cargo handling for the period from January to December 2010. The data are based on accounting information from arrival at port to the time cargo is passed on to the owner. The data shows that on average most cargo is held for less than 4 days (referred to as overstays).

According to the main shipping lines/warehouse owners, less than 10% of containers go beyond the 3 day period and less than 1% go beyond 28 days. Based on these multiple interviews, it was possible to reconstruct dwell time frequency (see Figure 2).

> 7 Transshipments account for approximately 20% of total cargo.

> 8 Free time is defined as the time from when the vessel completes discharge and the container is stored in the port area until collection for a specified period without incurring any port storage charges. For import containers this is set at 3 days and 7 days for transshipments.

> 9 Figure 1 suggests that dwell time for exports and imports is rather similar and could tend to demonstrate that the lower limit is rather close.

7
FIGURE 9 Pass-through associated with exchange rate shocks <!-- Start of picture text --> A. Exchange rate shocks B. EMDEs: Exchange rate shocks<br>Ratio Ratio Interquartile range Median<br>0.1 Interquartile range Median 0.1<br>0.0<br>-0.1<br>-0.2<br>0.0 -0.3<br>-0.1<br>All Advanced EMDEs Trade Exch. Inflation Energy Current<br>economies openness regime target account<br>Low High Float Peg. Non-IT IT Importer Exporter Deficits Surpluses<br><!-- End of picture text --> Note: Pass-throughs are defined as the ratio between the one-year cumulative impulse response of consumer price inflation and the one-year cumulative impulse response of the exchange rate change estimated from factor-augmented vector autoregression models for 29 advanced economies and 26 EMDEs over 1998-2017. A positive pass-through means that a currency depreciation is associated with higher inflation. Bars show the interquartile range and markers represent the median across countries. EMDEs = emerging market and developing economies; IT = inflation targeting. B. Countries with “high” trade openness are defined as those with above median trade-to-GDP ratios; all others are considered to have “low” trade openness. Exchange rate and IT regimes are based on IMF classifications. Energy exporters are defined as in World Bank (2018); all other countries are considered energy importers. Countries with current account deficits are those with a negative average current account balance over 1998-2017.

FIGURE 10 Average pass-through <!-- Start of picture text --> A. Exchange rate shocks B. EMDEs: Exchange rate shocks<br><!-- End of picture text --> <!-- Start of picture text --> Ratio Ratio<br>0.4 0.2<br>Interquartile range Median<br>0.3<br>0.2<br>0.1<br>0.1<br>0.0<br>0.0<br>-0.1<br>Full Post-1998 Full Post-1998<br>sample sample<br>Advanced economies EMDEs<br>India Russia China South Africa Brazil Turkey Mexico Poland<br><!-- End of picture text --> Note: Pass-throughs are defined as the ratio between the one-year cumulative impulse response of consumer price inflation and the one-year cumulative impulse response of the exchange rate change estimated from factor-augmented vector autoregression models for 29 advanced economies and 26 EMDEs over 1998-2017. A positive pass-through means that a currency depreciation is associated with higher inflation. Bars show the interquartile range and markers represent the median across countries. Shock-specific pass-throughs are aggregated using shares of currency movements accounted for by each type of shock as weights. EMDEs = emerging market and developing economies. A. Full sample estimations are over 1971 to 2017 but can vary at the country level depending on data availability.

32
and Herzegovina, Bulgaria, Croatia, Czech Republic, Estonia, Georgia, Hungary, Kyrgyz Republic, Latvia, FYR Macedonia, Moldova, Poland, Romania, Russian Federation, Serbia, Slovak Republic, Slovenia, Turkey and Ukraine. The resulting compilation, the IVS, assesses individual life satisfaction with the following question: _All things considered, how satisfied are you with your life as a whole these days?_ Responses are measured on a scale from 1 (dissatisfied) to 10 (satisfied).

The LiTS, a joint project of the European Bank for Reconstruction and Development (EBRD) and the World Bank, examines the impact of economic and political transition on individuals’ lives (and well-being), as well as their future hopes for Central Eastern Europe, the Baltic States, and South Eastern Europe. Its two waves, conducted in 2006 and 2010, covered approximately 58,000 individuals in 29 ECA countries (all except Kosovo). In the LiTS, individual life satisfaction is measured as follows: _To what extent do you agree with the following statements? All things considered, I am satisfied with my life now_ . Responses on this question are assessed on a scale from 1 (strongly disagree) to 5 (strongly agree).

The RLMS, a nationally representative survey of households in the Russian Federation, is designed to evaluate the impact of Russian reforms on welfare and health. Although the survey has collected data on about 10,000 individuals aged 14 and above 21 times since 1992, our analysis is based on 16 waves during the 1995–2013 period. The RLMS evaluates individual life satisfaction as follows: _To what extent are you satisfied with your life in general at the present time?_ Responses are assessed on a 5-point scale from 1 (not at all satisfied) to 5 (fully satisfied). Summary statistics for subjective well-being and respondent socio-demographics from all the above data sources are given in Appendix 1.

# _2.2 Estimation approaches_

# _2.2.1 Ordinary least squares (OLS): IVS_

For the IVS data, we adopt the standard regression approach applied in the majority of studies on subjective well-being (e.g. Blanchflower and Oswald, 2008). Because well-being is measured on an ordered 5-point or 10-point scale, a latent variable analysis might seem the most appropriate method. However, because the bias introduced by a standard ordinary least squares estimation (OLS) is fairly small (Ferrer-i-Carbonell and Frijters, 2004), we rely mostly on OLS. In general, the model is as follows: where ��� denotes the life satisfaction of individual _i_ . ���� is included either as a linear or multinomial function of age (i.e. quadratic and cubic terms) or as a set of 5-year age group 5
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Choi, N., M. Fedenia, H. Skiba, and T. Sokolyk, 2017, “Portfolio concentration and performance of institutional investors worldwide,” Journal of Financial Economics, 123, 189-208 Christakis, N.A. and P. D. Allison, 2006, “Mortality after the Hospitalization of a Spouse,” New England Journal of Medicine, 354, 719-730 Conroy, Robert M., Robert S. Harris, and Bruce A. Benet, 1990, “The effects of stock splits on bid-ask spreads,” Journal of Finance 45, 1285-95 Constantinides, G. M., 1986, “Capital market equilibrium with transaction costs,” Journal of Political Economy, 94, 842–862 Coval, J. D., D. A. Hirshleifer, and T. Shumway, 2005, “Can individual investors beat the market?” Working Paper, University of Michigan 37
**Table 2: Summary of study characteristics that describe costs associated with unhealthy diets** (listed in groups by type of dietary component assessed)

|Author (Year)|Country and<br>dataset|Life stage group|Diet Assessed|Nutrition/health Outcomes Assessed|Cost Assessed|
|---|---|---|---|---|---|
|GBD 2019 Risk<br>Factors<br>Collaborators<br>(2020)|Global (204<br>countries), GBD<br>1990-2019|General<br>population|13 dietary risk factors* (combined<br>and individually)|_[not reported in the article but deaths_<br>_and DALYs based on diet-related risk-_<br>_disease pairings for outcomes like_<br>_CVD, T2DM]_|Mortality, DALYs|
|Afshin et al.<br>(2019)|Global (195<br>countries), GBD<br>1990-2017|General<br>population|15 dietary risk factors<sup>‡</sup>|_[not reported in the article but deaths_<br>_and DALYs based on diet-related risk-_<br>_disease pairings for outcomes like_<br>_CVD, T2DM]_|Mortality, DALYs|
|Studies estimatingc|ost associated with|all dietaryrisk factors||||
|Zhang et al. (2023)|Global (204<br>countries), GBD<br>1990-2019|General<br>population|13 dietary risk factors*|CVD|Mortality, DALYs|
|Dong et al. (2022)|Global (204<br>countries), GBD<br>1990-2019|General<br>population|13 dietary risk factors relevant to<br>CVD outcomes*|all forms ofCVD|Mortality, DALYs|
|Machado et al.<br>(2022)|Brazil, GBD 1990-<br>2019|General<br>population|15 dietary risks‡|NCDs -CVD,diabetesand neoplasms|Mortality, DALYs|
|Qiao et al. (2022)|Global (204<br>countries), GBD<br>1990-2019|General<br>population|13 dietary risk factors*|23NCDsattributable to dietary risks in<br>GBD|Mortality, DALYs|
|Davila-Cervantes<br>(2020)|Mexico,<br>GBD 1990-2017|General<br>population|dietary riskswere CVD risk factor|CVDas outcome<br>• nutrition-related risk factor:high<br>BMI<br>• health-related CVD risk factors:<br>high systolicblood pressure, high<br>LDL cholesterol, high fasting<br>plasmaglucose|Mortality, DALYs|
|Duncan et al.<br>(2017)<br>Studies estimatingc|Brazil,<br>GBD 1990-2015<br>ost associated with|General<br>population<br>salt/sodium consump|Dietary risk factors(low in whole<br>grains, nuts and seeds, and fruits;<br>high in red and processed meat,<br>sweetened beverages)<br>tion|Diabetes, hyperglycemia(high fasting<br>plasma glucose)|Mortality, DALYs|
|Vega-Solano et al.<br>(2023)|Costa Rica|Adults|Saltconsumption|High blood pressure|Annual cost of<br>hospitalization,<br>consultations and<br>medications|

20
Table A8: Heterogeneous impacts of _Mais Educação_ by city size

|Dependent variable|Dropout rates|Test score<br>mathematics|Test score<br>Portuguese|
|---|---|---|---|
||(1)|(2)|(3)|
|Panel A: Impacts|on fifth-grade vari|ables||
|Dummy_Mais Educação_* year 2011|1.151|–3.476**|–2.259*|
||(1.321)|(1.498)|(1.361)|
|Dummy_Mais Educação_* year 2009|0.922|–4.074***|–2.689**|
||(1.112)|(1.462)|(1.348)|
|_Mais Educação *_log(city population)|5.197|4.678|7.956|
||(3.378)|(10.93)|(10.57)|
|_R_ <sup>2</sup>|0.667|0.786|0.789|
|Observations|2,988|2,988|2,988|
|Dummy year 2009|Yes|Yes|Yes|
|Dummy year 2011|Yes|Yes|Yes|
|School fixed effects|Yes|Yes|Yes|

<mark>Panel B: Impacts on ninth-grade variables</mark>

|Dummy_Mais Educação_* year 2011|–0.297|–3.852**|–0.766|
|---|---|---|---|
||(1.097)|(1.746)|(1.649)|
|Dummy_Mais Educação_* year 2009|–0.148|–5.373***|–2.572|
||(0.963)|(1.661)|(1.650)|
|_Mais Educação * l_og(city population)|0.382|2.410|7.768|
||(5.569)|(10.34)|(11.34)|
|_R_ <sup>2</sup>|0.736|0.755|0.741|
|Observations|2,595|2,595|2,595|
|Dummy year 2009|Yes|Yes|Yes|
|Dummy year 2011|Yes|Yes|Yes|
|School fixed effects|Yes|Yes|Yes|

_Sources:_ Authors’ calculations using MEC/FNDE administrative data, _Prova Brasil,_ School Census, and INEP. _Note:_ Treatment group are schools that joined _Mais Educação_ in 2008 and remained in the program until 2011. Municipal population variable is in log. This table reports coefficients from the ordinary least squares estimation of equation (6) in the paper. The dependent variable is in column (1) is Portuguese grades in _Prova Brasil,_ in column (2) mathematics grades in _Prova Brasil,_ and in column (3) dropout rates. All variables are at school level. ***significance at the 1% level; **significance at the 5% level; *significance at the 10% level. Robust standard errors, clustered at school level, are reported in parentheses. All regressions include school fixed effects and time dummies. All regressions are weighted by propensity score matching weights. Panel A reports these estimates for the schools from the fifthgrade sample. Panel B reports these estimates for schools from the ninth-grade sample. City size is captured by city population.
**Table 3. Percentage of agreement between rounds of assessments**

||**World**|**GWU s**|**tudents**|**MTurk-**|**India**|**MTurk**|**- USA**|
|---|---|---|---|---|---|---|---|
||**Bank**<br>**analysts**|Impromptu<br>judgment|Structured<br>protocol|Impromptu<br>judgment|Structured<br>protocol|Impromptu<br>judgment|Structured<br>protocol|
|**World Bank analysts**||||||||
|Structured protocol|100|82.3|83.7|85.6|88.2|87.6|89.1|
|**GWU students**||||||||
|Impromptu judgment||100|96.7|91.9|86.0|89.3|87.0|
|Structured protocol|||100|92.0|86.0|89.3|86.6|
|**MTurk- India**||||||||
|Impromptu judgment||||100|89.5|93.5|91.3|
|Structured protocol|||||100|92.6|93.0|
|**MTurk- USA**||||||||
|Impromptu judgment||||||100|93.6|
|Structured protocol|||||||100|

Given this high level of consistency across assessors and protocols, we pooled all seven rounds of assessments which resulted in 50 or more human judgments for each place in the sample. We then classified every place as urban or rural based on a majority rule over these 50 or more judgments and got only one tie among the 1,277 places in the sample.

Second, we assessed whether the size of administrative units affected human judgment. To do this, we classified the 1,277 places in the sample into 20 size quantiles and computed the share of places in each quantile that was considered urban based on the pooled classification. For the top three size quantiles the share of urban places is high and increases with their size. But for the other 17 quantiles there is no clear correlation between size and urban share (appendix 3).

We also investigated the relationship between the size of a place and the share of its surface that is builtup, which provides the first node in the decision tree followed by the assessors under the structured protocol. We found that the median built-up share varies across quantiles, but it displays no consistent relationship with sizes. This finding gave us further assurance that human judgment is not affected by the size of administrative units.

# _The classification of the sample_

Given the consistency of outcomes across assessors, protocols and sizes, we treated the classification of places resulting from the pooled assessments as the outcome of our human judgment exercise. This pooled classification turns out to be generally aligned with the official status of the places in the sample, but there are also some significant discrepancies.

For example, Jigani of Karnataka is administratively rural but meets most of the criteria to be considered urban in practice (figure 4a). The built-up area covers a substantial share of the place’s surface, the buildings are compact, there is a network of roads both inside the place and linking it to external markets, 14
12 <!-- Start of picture text --> Rate Real /Sf 1 Sn Sn + Sf1<br>of ISn + Sfo<br>Intereat ' If<br>0~~~~~~~~~~~~~~~.10<br>S/ Sn,sn h1 b Sang. Invostrnent<br><!-- End of picture text --> Figure 2: National Saving, Domestic Investment and the Current Account Deficit.

downwards indicating that there is more investment at lower interest rates. The national saving function Sn is nearly vertical indicating that national saving does not vary greatly with changes in the domestic real interest rate.

The effective cost at which foreign saving hegins to be supplied in any particular year depends on the country's debt position inherited from past borrowing. The effective cost of foreign borrowing is also the effective domestic real interest rate. At an effective interest rate of ro, domestic investment _I0_ exceeds nat onal saving Sn%. Hence, the inflow of foreign saving is positive and the country runs a current account deficit on its balance of payments equal to _IO-Sno._ The accumulation of debt resulting from the current account deficit in year 0 raises the foreign saving curve to _Sfi._ T'his change produces an effective cost of foreign borrowing of _r1_ in year 1. In this case, foreign debt accumulation _reduces_ domestic investment and raises national saving through a higher domestic real interest rate. As this process continues in subsequent years, the current account deficit declines until it reaches a steady-state equilibrium in which the debt/C NP ratio is constant. This is the stabilizing financial effect of foreign debt accumulation.8

> 8 The destabilizing effect of foreign debt accumulation in the form of government and government-
Figure 2.3 Employment polarization is increasing in China <!-- Start of picture text --> 0 20 40 60 80 100 0 20 40 60 80 100<br>Occupation's Percentile in 2005 Wage Distribution Occupation's Percentile in 2000 Education Distribution<br>2000 - 2005 2005 - 2010 2000 to 2015 2000 to 2005<br>2010 - 2015 2005 to 2010 2010 to 2015<br>.2 .15<br>.15 .1<br>.1<br>.05<br>.05<br>0<br>0<br>-.05<br>-.05<br>-.1 -.1<br>100 x Change in Employment Share 100 x Change in Employment Share<br>-.15 -.15<br><!-- End of picture text --> _Source_ : Du and Park 2017.

_Note_ : The figure shows the employment share for five-year periods between 2000 and 2015 by two measures of initial skills. The left panel shows it using initial levels of wages as proxies for the skill distribution, and the right panel using initial levels of education.

The new or transformed jobs require a fundamentally different set of skills. As most of these jobs involve nonroutine tasks, they may not be readily filled by the displaced workers. Regardless of whether labor markets are showing signs of polarization in developing countries, the tasks and skills that individuals use at work are changing. Work is becoming increasingly intensive in nonroutine cognitive and interpersonal skills. Tasks that are difficult to automate—intensive in the latter skills—are becoming a larger share of tasks performed by the workforce in the developed and developing world (Figure 2.4). The steady increase in demand for nonroutine cognitive and interpersonal tasks suggests that these skills are highly valued in the face of technological changes, globalization, and structural changes.

Figure 2.4 Nonroutine cognitive and interpersonal skills are highly valued everywhere <!-- Start of picture text --> High-income countries Low-income countries<br>2000 2002 2004 2006 2008 2010 2012 2000 2002 2004 2006 2008 2010 2012<br>Year Year<br>Non-routine congnitive or inter-personal<br>Routine cognitive or manual<br>Non-routine manual<br>100 100<br>80 80<br>60 60<br>Percent Percent<br>40 40<br>20 20<br>0 0<br><!-- End of picture text --> _Source_ : World Bank 2016.

Nonroutine interpersonal and cognitive skills are increasingly valued in China’s labor market, with demand steadily increasing between 2000 and 2015 (Figure 2.5). This suggests that the increase 13