rafmacalaba/datause-encoder-data huggingface dataset:
(e.g., graduate studies). Therefore, I also introduce a lag of the dependent variable as a regressor to control for serial correlation when analyzing the level of educational attainment.<sup>13</sup> Figure 3: Educational attainment vs. labor income Gini <!-- Start of picture text --> (a) (b)<br>R2=0.30 R2=0.34<br>2 3 4 5 6 7 0 5 10 15<br>Standard deviation of years of education Average years of education<br>70 70<br>60 60<br>50 50<br>40 40<br>Labor income Gini (0−100 scale) Labor income Gini (0−100 scale)<br>30 30<br><!-- End of picture text --> Source: Author’s calculations based on data from Latin American household surveys, 1995-2009, biannual and Polity IV Project.

Similar to Table 1, in Table 2 each duplet of columns corresponds to computing Equation (2) using 0.01, 0.03, 0.06 and 0.10 as values for _r_ respectively. Our results are consistent with our prior: democracy is related to the level of educational attainment. (Note that _β_ is biased downwards.) Furthermore, in columns (2), (4), (6) and (8), democracy and educational attainment show a slight non-monotonic relationship.

Now, let us consider the results of columns (2), (4), (6) and (8). A cohort with no existing stock of democratic capital experiences the following democratic educational effect: for each full decade of high-quality democracy (Polity2 = 10), democracy stock increases by approximately 100 points. Considering panels A and B, I find that the predicted impact on educational attainment of a decade of high-quality democracy is approximately between 0.09 and 0.19 years of education. Considering that the average educational attainment is 5.6 years, having 20 years of high quality democratic institutions would increase educational attainment (on average) by 5%.

> 13Verbeek and Vella (2005) show that if grouping variables are exogenous and relevant and cohorts sizes are large enough, it is possible to obtain and appropriate measure of _Y c,g,p,e−_ 1, equivalent to the lagged value of _Y c,g,p,e_ .

15
||_[0.13]_|
|---|---|
|Education: reference group, primary or lower||
|<br>secondary||
|Higher secondary|-0.218<sup>***</sup>|
||(0.031)<br><sup>***</sup>|
||_[0.01]_|
|University|-0.377<sup>***</sup>|
||(0.065)<br>_[0.01]_<sup>***</sup>|
|Missing education|-0.046|
||(0.056)|
||_[0.34]_|
|Employment status: reference group, out of||
|labor force||
|Employed|0.003|
||(0.017)<br>|
||_[0.84]_|
|Unemployed|0.098<sup>***</sup>|
||(0.033)<br>|
||_[0.01]_<sup>***</sup>|
|Missing employment status|-0.112|
||(0.107)|
||_[0.37]_|
|Income group: reference group, lowest||
|<br>income group.||
|2nd income group|-0.001|
||(0.025)<br>|
||_[1.00]_|
|3rd income group|-0.098<sup>**</sup>|
||(0.039)<br>|
||_[0.02]_<sup>*</sup>|
|4th income group|-0.196<sup>***</sup>|
||(0.046)<br>|
||_[0.01]_<sup>***</sup>|
|Highest income group|-0.453<sup>***</sup>|
||(0.061)<br>|
||_[0.01]_<sup>***</sup>|
|Missing|-0.230<sup>***</sup>|
||(0.031)|
||_[0.01]_<sup>***</sup>|
|Observations|45583|
|_R_<sup>2</sup>|0.177|

Notes: OLS regressions. Country and year dummies included in all regressions but not reported. Dependent variable in column 1 (Demand for redistribution) takes values 1 (strongly disagree that government should reduce income differences), 2 (disagree), 3 (agree) and 4 (strongly agree). Clustered standard errors at the country level in parentheses. Wild clustered bootstrap p-value estimations in brackets. Significance: * p<0.10, ** p<0.05, *** p<0.01.

The coefficients associated to the four country level variables are small and very imprecisely estimated. While most of these variables were shown to be important determinants of inequality perceptions (Table 2a-2c), they appear to have no impact on demand for redistribution. Some individual characteristics however do correlate with demand for redistribution. The population-weighted intergenerational elasticity in education is also not statistically significant, as in Table 2a, but it has the expected sign: larger elasticity (i.e. less mobility and thus less fairness) correlates with more demand for redistribution. Nevertheless, the coefficient is imprecisely estimated.

35
# **2.1  Assignment of reliability weights to the eight GCMs, based on their historical “goodness of fit” to the CRU data**

We use R<sup>2</sup> ’s rather than the simple correlation coefficients, because we want to discriminate strongly in favor of the best-fitting GCMs. Our approach is bivariate, because temperature and rainfall are jointly produced by the climate process. To assign reliability weights, we combine fine-gridded historical results for each GCM with monthly temperature and rainfall data for the period 1961-2000 (CRU), provided for Ethiopia by the Climatic Research Unit of the University of East Anglia, Norwich, UK. We compute R<sup>2</sup> ’s between CRU and each of the eight GCMs for temperature and rainfall separately. Each estimate is based on 178,560 observations (monthly during 1961-2000 for 372 Ethiopian grid squares). We add rainfall and temperature R<sup>2</sup> ’s to get a summary measure of explained variation for each GCM, specify the smallest value as the numeraire, and divide it into the others to get relative scores.

# **2.2   Establishment of forecasting benchmarks for each grid square**

We require separate benchmarks for the CRU and each of the GCMs. We establish forecasting benchmarks for each grid square using data for the period 1980-2000 (the most recent year in our CRU dataset). We begin by computing average annual rainfall and temperature for the nine datasets (CRU; 8 GCMs). Then we use a least-squares fit criterion to select the most representative joint monthly distribution of temperature and rainfall. For each variable, we compute squared monthly deviations from median values in each grid square. We calculate yearly sums of squared deviations for each grid square, and then rank the 21 results (1980-2000) separately for temperature and rainfall. We use ranks to ensure robust results, since temperature and rainfall metrics are quire different.

4
- <mark>Gieseman, Raymond (1987). "The Consumer Expenditure Survey: quality control by comparative analysis."</mark> _<mark>Monthly Lab. Rev.</mark>_ <mark>110: 8.</mark>

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<mark>Hurd, Michael D., and Susann Rohwedder (2013). "Expectations and Household Spending."</mark>

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36
# TABLE 4

# Responses to the Question 'If Boys and Girls are equal in the eyes of Parents, why do most of the Parents educate their Sons and not the Daughters?'

|1 ::There.-are<br>-no:<br>financi.al gains: toteprns<br>_16_<br>4<br>2 .it<br>::snot<br>-.customary to eductegrl.2561<br>2x'..,-<br>W<br>W<br>S'v<br>,<br>'-<br>.3...There<br>:is:,no<br>proper arrangement:<br>.educatLoM.-.--..-:.~<br>0<br>32~<br>.''4<br>Since boy' and grlg<br>are-not equ<br>:,.no<br>qestion-ofeducating<br>both.<br>133<br>84<br>"'' Poverty: prevents parents from educati3. '.<br>duhers.74.<br>6.Girlsbecometooindependent aftergetting:<br>educated<br>3<br>58<br>3..<br>7Ignorant parents donot value their children<br>.<br>:<br>education<br>, ,::<br>3.1<br>8. Purdabh is the reason for not educating girls.<br>44<br>2 8<br>9. Girls have to do household work. '8<br>'.l|
|---|
|10. Girls are not intellectually capable of getting<br>an education.<br>16<br>1.0|
|11. Education does not help girls in their future<br>life.<br>.<br>.<br>:<br>'.<br>.<br>12<br>0.8<br>':::-<br>:1,585<br>000,|

Source: iNasra M. Shah Pakistani Women: A Socioeconomic & Demographic Prorile. Pakistan Institute of Development Economics. Islamabad, 1986.
<!-- Start of picture text --> Education and Training<br>eH<br>Years of education<br>eH<br>Received any training in the past<br>4<br>Received training in EICT in the past<br>Kn<br>-4 -.2 0 2 4<br>Regression coefficient<br>e Male e Female = 90% Cl -— 90% Cl<br><!-- End of picture text -->
These findings were confirmed by Wagstaff and Nguyet Nga (2002) who conclude that the impressive gains in child survival in Vietnam have been concentrated heavily among the better off, leading to increased socio-economic inequalities. They identify poorer coverage of health services and in lower educational levels of women as among the causes of this differential decline in child mortality between the poor and non-poor.

## Infant and child mortality: Targets andfuture challenges

# Box Sa. MDG Goal Four: Reduce Child Mortality by Ovo thirds by 2015

## Vietnam Development Goal: reduce child mortality, child malnutrition and reduce the birth rate

Target 1: Reduce the infant mortality rate to 30 per 1000 live births by 2005 and 25 by 2010 and at a more rapid rate in disadvantaged regions (see below) Target 2: Reduce the under-5 mortality rate to 36 per 1000 live births by 2005 and 32 by 2010 Source: Socialist Republic of Vietnam (2002) National targets for reductions in mortality rates are less ambitious than the MDGs. These are set down in the ten-year health strategy and included in the CPRGS as a core outcome targets. These targets specify that the IMR will fall to 25 per 1000 live births and the USMR to 32 per 1000 live births, representing a decline of 44 percent in both rates over the 1990-2010 period. They are also more realistic, however, than the MDGs, and achievable if there is a stronger focus than hitherto on regions where mortality figures are highest (Wagstaff and Nga Nguyet Nguyen, 2002). Projections demonstrate that both rates will have to reduce more rapidly over the coming decade than in the past if national goals are to be achieved (Figures 10 and 11).

## Figure 10. Infant Mortality — progress made during 1990-1998 and projections for 2010 and 2015

<!-- Start of picture text --> lee<br>45 teat<br>oe<br>0<br>1990 1998 2010 2015<br><!-- End of picture text --> Source: ADB (2002a) 24
Table A6: Heterogeneous effects of payment delays on non durable spending

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|Panel A:|Male<br>HH head|Any previous<br>bank use|Close to<br>branch|Assets|Above median:<br>Patience|
|1-day delay|869.462**<br>(357.434)|1228.607**<br>(520.654)|952.246**<br>(438.515)|813.509*<br>(427.750)|430.304<br>(429.888)|
|8-day delay|46.452<br>(321.023)|764.800<br>(499.231)|42.299<br>(380.555)|-167.709<br>(370.209)|217.823<br>(411.239)|
|Observations|310|152|233|226|190|
|Mean of dependent variable in Immediate group|2362.97|1759.18|2456.62|2434.22|2076.89|
|R-squared|0.282|0.387|0.289|0.337|0.229|
|P-value: 1-day=8-day|0.036|0.423|0.043|0.024|0.671|
|Panel B:|Female<br>HH head|No previous<br>bank use|Far from<br>branch|Assets|Below median:<br>Patience|
|1-day delay|540.827<br>(443.654)|381.768<br>(344.910)|461.733<br>(365.426)|770.039**<br>(389.136)|940.626**<br>(372.254)|
|8-day delay|266.054<br>(444.633)|-368.425<br>(294.287)|31.080<br>(335.316)|231.328<br>(349.389)|-124.735<br>(317.100)|
|Observations|162|320|239|246|282|
|Mean of dependent variable in Immediate group|1665.30|2313.46|1830.13|1851.96|2195.73|
|R-squared|0.218|0.181|0.179|0.166|0.326|
|P-value: 1-day=8-day|0.553|0.027|0.267|0.181|0.004|
|P-value: equal efect of 1-day vs 0-day|0.545|0.152|0.374|0.938|0.348|
|P-value: equal efect of 8-day vs 0-day|0.674|0.039|0.982|0.414|0.490|
|P-value:equalefectof1-dayvs8-day|0341|0651|0401|0434|0149|
|<br>The top panel includes respondents from female-h<br>transactions at NBS in the 90 days preceding the <br>those with above-median values of a principle-ind<br>measured at baseline by amount required to prefe<br>three rows are from tests of equality of the 1-day <br>8-day delay coefcients, respectively, in Panels A <br>and the value of the outcome measured at survey <br>* p_<_0.10, ** p_<_0.05, *** p_<_0.01|.<br>eaded house<br> transfer, tho<br>ex of asset o<br>r a transfer i<br>delay coefc<br>and B. All s<br> 1. Exchange|.<br>holds and those <br>se who live abo<br>wnership, and th<br>n one month ins<br>ients, the 8-day <br>pecifcations incl<br> rate: MK 420 p|.<br> with above-<br>ve the media<br>ose with ab<br>tead of imm<br>delays, and t<br>ude village a<br>er USD. Ro|.<br>median valu<br>n distance f<br>ove-median <br>ediately. P-<br>he diferenc<br>nd week-of-<br>bust standa|.<br>es for number of<br>rom the bank branch,<br> patience, as<br>values in the bottom<br>es between 1-day and<br>frst-survey fxed efects,<br>rd errors in parentheses.|

48
<!-- Start of picture text --> Venezuelan Migration Shock, Mg effect<br>Promotion Rate<br>T<br>2°<br>N<br>So<br>g<br>Loc it<br>oe — ill _tk<br>aTINIEz IR4Yetye<br>Y4 WN Sel<br>2g |<br>2012 2013 2014 2015 2016 2017 2018<br>Year<br>'— Foreign Males +——~ Domestic Males<br>'—— Foreign Females *—~ Domestic Females<br><!-- End of picture text -->
lifting entry distortions, on the other hand, the most relevant protracting force in the transition is given by the dierence in the distribution of entrepreneurial talent between entering rms and incumbents. The distribution of entrepreneurial ability at entry is calibrated to match the life-cycle growth of rms in the U.S. which requires a substantial gap between the average productivity of entrants and incumbents. When entry distortions are lifted, a burst of new entrepreneurs enters the market, increasing the density on the left tail of the productivity distribution. As these entrepreneurs innovate and their abilities follow their stochastic course, the distribution converges sluggishly to the stationary one, protracting the dynamics of productivity in the aggregate.

We then evaluate the quantitative implications of the model in the context of China's growth acceleration since 1998, for which we can tightly discipline the initial level and subsequent reversal of distortions. In terms of the idiosyncratic distortions, we follow Hsieh and Klenow (2009) in measuring these as wedges from the rms' optimal conditions. In particular, we compute the regression coecient between the logarithm of distortions, log ( _TFPR_ ) , and the logarithm of rm-level productivity, log ( _TFPQ_ ) , from the Annual Survey of Industries between 1998 and 2005. We consider the value for 1998 as part of China's initial stationary allocation and consider the values thereafter as dictating the speed of reforms. In terms of entry distortions, we model these as a combination of overhead production costs and taxes to entrepreneurial prots. To calibrate their values, we appeal to moments in the data on which these distortions exert a rst-order eect. Concretely, we set the prot tax and the xed cost in the distorted stationary allocation to match the average rm size and the earnings share accounted for by the richest 1% of households in China in 1998. Then, we discipline the path of reversal of prot taxes to match the average rm size dynamics during the acceleration and let the dynamics of earnings inequality be used as validation of the model's quantitative t. Starting from the distorted stationary allocation and feeding the path of reversal of both types of distortions, we nd that the model can account for one-third of the productivity growth evidence by China between 1998 and 2011, and matches the dynamics of inequality remarkably closely.

The rest of the paper is organized as follows. Section 2 relates our work to the literature, in section 3 we provide the macro and micro facts that motivate our analysis, and section 4 presents the model with and without distortions. The calibration and quantitative explorations of the benchmark reforms and the case 4
electronics and IT, life science/pharmaceutics, environmental protection technique and equipment, new materials, photo-electronics and laser-mechanic-electronics, and technology for aerospace and earth space. A large portion of these companies are manufacturing firms, while many software companies, R&D and technical service companies are also covered in this category.

Table 8 shows the indicators of high- and new-tech enterprises in the industrial parks in Beijing and Shanghai in 2001. Beijing outpaces Shanghai in the number of high-tech firms and employees, their revenues, as well as their exports; however, Shanghai leads Beijing in the average size of firms, and with regard to the revenue and exports either per firm or per employee.

**Table 8: Indicators of high-tech and new tech enterprises in industrial parks of Beijing and Shanghai, 2001**

||Beijing|Shanghai|National in total|
|---|---|---|---|
|Number of firms|7911|405|24293|
|Number of employees|282720|73652|2761433|
|Revenue (billion Yuan)|125.6|83.8|1011.7|
|Export(million US$)|2928.9|2512.6|22664.4|

_Source_ : China Statistic Yearbook 2002.

# **Knowledge intensive business services and creative content providers**

As to knowledge intensive business services and creative content providers, there is even more vagueness in the classification. We select several categories as well as subcategories relevant to our firm survey from the published statistical materials (see Table 9). We find that Beijing surpasses Shanghai in the number of firms and employees in almost all the categories and subcategories.

**Table 9: Knowledge intensive business services and creative content providers in Beijing and Shanghai, 2001**

||Beij<br>|ing<br>|Shan<br>|ghai<br>|Nationa <br>|l in total<br>|
|---|---|---|---|---|---|---|
||Firms|Employees<br>(thousand)|Firms|Employees<br>(thousand)|Firms|Employees<br>(thousand)|
|Total|222766|6466.9|235347|8267.8|3025862|165022.2|
|Computer services|6981|153.5|3384|65.2|29256|522.5|
|Software development|4679|100.9|2353|47.5|20027|371.1|
|Computer system services|1230|24.8|574|7.7|4771|69.1|
|Information Consultancy|26682|304.8|7576|109.5|77787|934.2|
|Advertisement/marketing|5215|61.4|1983|28.5|27953|294.9|
|Business Consultant|8298|93.1|459|11.2|16350|234.1|
|Finance/security|1618|137.3|958|114.7|63271|3835.0|
|Culture and Arts|1991|40.2|693|15.8|5544|150.5|
|Printing Publishing|364|21.2|174|6.2|1629|90.8|
|Media|348|10.4|267|7.3|5159|148.0|

_Source_ : The Second National Survey on Basic Units, 2002.

14
cost reduces the weight-for-height z score. However, the number of days evacuated from home is positively associated with the weight-for-height z score. It is possible that children, who had been evacuated from the affected areas, grew better than those who could not.

Our results summarized in Table 2 demonstrate impacts of the 1998 flood on changes in the child’s anthropometric measures, consistent with other studies (e.g., Alderman et al., 2006). In the preliminary analysis, we could not find significant impacts on changes in the height-for-age z score in the one-year period.

# **_5.2. Human Capital vs. Disasters_**

In this subsection, we summarize the dynamic impacts of disasters on the transition from early childhood to schooling stages using flood and drought examples in Bangladesh, Ethiopia and Malawi. As discussed in Section 3, we use the height for age z score to capture pre-school biological human capital, which reflects nutrient intake and health inputs during early childhood.

# **5.2.1. Bangladesh**

In Bangladesh, this measure is obtained from round 1 in 1998, which was collected immediately after the flood. While one could argue that floods could immediately affect nutritional status, since height was measured right after the flood, the endogeneity issue is negligible. This is also one advantage of using the height for age z score, which is different from the weight for age z score as weight can fluctuate substantially in a relatively short period. In Ethiopia and Malawi, since the height for age z scores were taken from the initial round before the drought occurred, the above concern is not so important (see also discussions in Section 3).

# Table 3 to be inserted

In Table 3, we use water depth, the number of days covered by water, repair cost and the number of days evacuated from home as measures of flood exposure. The dependent variable is years of grades completed in 2004. Column 1 shows a benchmark result on the height effect on grades completed. The height for age z score has a significant and positive effect on grades completed. The specification includes union fixed effects and age dummies.

Columns 2 to 5 show the effects of flood and child height on schooling. First, we confirm that even with flood variables, the height for age z score has significantly positive effects on schooling. Second, interestingly, flood measures have positive effects on schooling (after six years). Water depth and the number of days submerged show significant positive effects on schooling. Third, we do not observe significant interaction terms of flood and the height z score.

Note that these findings seem contradictory to those on the short-term changes in child’s weight. Here we are looking at the outcome nearly 6 years after the flood. Therefore, if the recovery (catch-up) process started sometime before 2004, it is possible to observe that those who were affected could grow faster under certain conditions.

12