rafmacalaba/datause-encoder-data huggingface dataset:
financing pilots. Although the survey was not designed to be representative at the national or district level, it covered about 26 percent of the health zones in the country and is representative at that level.<sup>7</sup> In each health zone covered by the survey, five health centers were randomly chosen to be included in the sample, without distinguishing between health centers offering the minimal package of services and referral health centers offering a more complete package. In the catchment area of each of the chosen health centers, a village or urban quarter was randomly selected to be included as a sampling unit for the household survey. In addition to the health centers, health facility surveys were also conducted in general referral hospitals in most health zones. Appendix A provides further details on the sampling frame and overall study design.

Within the selected villages, 4,834 households with women who had live births in the 24 months prior to the survey were randomly selected for the household survey. The focus of the survey was the care received during the most recent pregnancy. Women who reported receiving at least one antenatal consultation were asked a series of recall questions about different components of the consultations, ranging from physical examinations and diagnostic tests to preventive treatments and counseling.

The facility assessments covered a wide range of aspects, ranging from staffing and management to infrastructure and availability of equipment and supplies. Observations of antenatal consultations were conducted in 316 facilities that were randomly selected for an in‐depth assessment (see appendix A). Most of the health centers offer antenatal consultations only one day per week; on average, antenatal care services were offered 1.3 days per week. Exit interviews were conducted with the women who received antenatal care in these facilities. For 22 percent of the women for whom an exit interview was completed, an observation of the consultation was not conducted.<sup>8</sup> A key advantage of using data collected through direct observations is the ability to overcome potentially large recall biases in maternal self‐reports. Direct observation data are also more precise, as they are collected by trained enumerators with a medical background, and thus contain more detail on the procedures implemented. In addition, the information collected is more complete, as the direct observation tools measure not only which procedures were conducted during the consultations, but also

> 7 The Performance‐Based Financing programs are the Projet de Renforcement du Système de Santé pour Améliorer les Résultats de Santé Maternelle et Infantile and Projet d’Urgence relatif à la Violence Sexuelle Basée sur le Genre et la Santé des femmes dans la Région des Grands Lacs.

> 8 Unfortunately, the data do not allow us to separate cases in which consent was not given for the observation (by the provider or user) from cases in which a member of the survey team was not available. A survey team spent a day in each facility and had to complete a set of interviews and observations.

6
# **4.1.2 Trading of Emissions Allowances**

The volume of emissions that an emitter (here sector) trades depends on the level of its initial allowance and the amounts of emissions it would like a release to keep its desired level of outputs. Since the level of emission allowances a sector receives depends on the allocation rule, the volume of trade is highly sensitive to the rule for allowance allocation. Since the amount of emission trading is also sensitive to the gap between the baseline emissions and emissions under the ETS (i.e., desired level of emission reduction of an emitter), baseline setting is a critical determinant of the volume of traded emissions and market clearing emission permit prices. Therefore, the volume of emissions traded and prices of emission permits are sensitive whether or not the CGE model is linked with the information from the bottom-up model, TIMES. Note that the TIMES model significantly dampens the baseline (it reduces baseline emissions by 15.5% in 2030).

Table 3(a) and (b) presents the volume of emission trading by sector and by provinces. As illustrated in these tables, there are huge differences in the volume of emission traded and prices of emission permits between the allowance allocation rules. If the CGE and TIMES models are not linked, the total volume of permits traded under the output-based allocation rule is seven times as high as that under the emission-based allocation rule. This is because the output-based rule offers much more allowances to the sectors which do not need (they produce more output not necessarily the emissions), so they have a large number of allowances to sell. On the other hand, sectors that produce more emissions receive a lower number of allowances, so they need to buy a large number of allowances. Thus, the size of the emission market is much larger under the output-based allocation rule as compared to the emission-based allocation rule. Larger the market would mean more flexibility for trade, thereby lowering the prices of permits. The market clearing permit price under the output-based allocation rule would be (¥406/tCO2). On the other hand, the total supply of emission allowances under the emission-based allocation rule are much smaller as compared to that under the output-based allocation rule, the market clearing prices of emission allowances would be very high, ¥706/tCO2.

When we linked the CGE and TIMES model the total (national level), baseline emissions would be smaller, and so does the amounts of emissions to be reduced to meet the NDC targets.

41
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**Table 3. Descriptive statistics.**

|Country group|Number<br>of<br>countries|GDP (log points),<br>change|Industry VA (% of<br>GDP), change|Services VA (% of<br>GDP), change|Skilled (% of<br>working age<br>population), change|Females (% of<br>employment),<br>change|
|---|---|---|---|---|---|---|
|Middle and low income|72|3.2|0.0|0.2|0.5|0.0|
|High income|32|1.8|‐0.2|0.4|0.3|0.0|
|Country group||Working age<br>population (% of<br>population), change|Older than 65 years<br>(% of population),<br>change|<br>Internet users (% of<br>population), change|Imports (% of GDP),<br>change|Exports (% of GDP),<br>change|
|Middle and low income|72|0.3|0.1|1.8|0.4|0.4|
|High income|32|0.0|0.2|3.5|0.9|1.2|

Note: each figure is the average change between each country’s first and last year in the sample, divided by the length of the period (in years).

We estimate the following equation: Where j stands for each of the five tasks considered, namely non-routine analytical, non-routine interpersonal, routine cognitive, routine manual and non-routine manual. Equation (1) estimates annual changes in the skill content of jobs as a function of annual changes in labor demand (∆X�,�,���� ) and labor supply (∆X�,�,���� ) factors. The term μ�,� is a control variable that captures unobserved heterogeneity across observations, by using an interaction of four dummy variables indicating the level of income of the country with year dummy variables.<sup>4</sup> This set of dummy variables controls for different non-parametric trends in the skill content of jobs across broad levels of income. We also control for the lagged level of GDP per capita to account for different linear trends in the skill content of jobs by level of economic development. The sample is an unbalanced panel of countries with annual frequency (see Appendix 1).

# **c. Results**

We estimate equation (1) applying, alternatively, the skill scores based on O*NET and the average STEP indexes from the pooled sample to all the countries. In addition to including GDP in first

> 4 We use the World Bank income level classification.

20
|**Country &**<br>**Region**|**World Bank**<br>**Income**<br>**Group (2020)**|**Author(s)**|**Study**<br>**Period**|**Methodology,**<br>**including**<br>**identification**<br>**strategy**|**Multiplier range**|**Type**|
|---|---|---|---|---|---|---|
|Japan|High Income|Miyamoto<br>_et al._<br>(2017)|1980 – 1995<br>1996 – 2014<br>(Zero lower<br>bound)|Local projection<br>method (estimates<br>impulse response<br>functions on shocks<br>as well as lags of<br>variables usually<br>entering a vector<br>autoregression)|0.6<br>1.5|Government Spending|
|Korea, Rep|High Income|Eskesen<br>and Lueth<br>(2009)||DSGE (simulated<br>impacts on the<br>output)|0.8|Government<br>Investment and<br>Consumption|
|Netherlands|High Income|Veld<br>(2016)||European<br>Commission’s macro<br>Quest model<br>simulations|0.5|Public Investment|
|Gulf<br>Cooperation<br>Council<br>Countries|High Income|Espinoza<br>and<br>Senhadji<br>(2011)|1975-2009|VAR linking real<br>GDP, real<br>government<br>expenditure and non-<br>oil real GDP. The<br>VAR estimated in<br>growth rates using<br>three lags|0.3 - 0.7<br>0.6 - 1.1|Current Expenditure<br>Capital Spending|
|Spain|High Income|Pereira and<br>de Frutos<br>(1999)|1970-1989|VAR (this approach<br>does not impose a<br>priori restrictions on<br>the dynamic<br>relations among the<br>different variables)|0.65 (after two years)|Public capital<br>accumulation|
|United States|High Income|Coenen_et_<br>_al._(2012)||DSGE (simulated<br>impacts on the<br>output)|1|Government Spending|
|United States|High Income|Erickson_et_<br>_al._(2015)|2001-2012|OLS with IV to<br>address endogeneity|1.5|Federal Government<br>Spending|
|United States|High Income|Ramey &<br>Zubairy<br>(2018)|1889-2015|Local projection<br>method (based on<br>sequential<br>regressions of the<br>endogenous variable<br>shifted several steps<br>ahead)|0.66 (after two years)<br>0.71 (after four years)|Government Spending|
|United States|High Income|Zandi<br>(2008)||Moody’s<br>Economy.com macro<br>model|1.59 (after one year)|Government Spending|
# **Table 1 Gross capital flows: descriptive statistics**

||Mean|CIF<br>Std.<br>Deviation|Coef. of<br>Variation|Mean|COD<br>Std.<br>Deviation|Coef. of<br>Variation|Correlation<br>CIF - COD|
|---|---|---|---|---|---|---|---|
|All countries|7.71|10.03|1.20|6.69|10.09|1.92|0.47|
|Advanced|11.99|12.15|0.92|12.03|12.24|0.96|0.90|
|Emerging|6.16|7.25|1.16|6.07|8.97|1.40|0.48|
|Developing|6.39|11.19|1.42|3.87|9.73|2.99|0.19|

Notes: this table reports group averages of the individual-country statistics (shown in Table A3). CIF = gross inflows; COD = gross outflows.

# **Table 2 Gross capital flows: cross-country comovement** **<u>(a) Average cross-country correlation</u>**

|Flow / Region|Advanced|CIF<br>Emerging|Developing|Advanced|COD<br>Emerging|Developing|
|---|---|---|---|---|---|---|
|Advanced|0.435||||||
|CIF<br>Emerging|0.143|0.209|||||
|Developing|-0.074|0.085|0.109||||
|Advanced|0.434|0.142|-0.087|0.488|||
|COD<br>Emerging|0.290|0.155|-0.009|0.315|0.251||
|Developing|0.133|0.089|-0.006|0.151|0.142|0.070|

## **<u>(b) Percentage of significant correlations</u>**

|Flow / Region|Advanced|CIF<br>Emerging|Developing|Advanced|COD<br>Emerging|Developing|
|---|---|---|---|---|---|---|
|Advanced|0.87||||||
|CIF<br>Emerging|0.55|0.58|||||
|Developing|0.58|0.53|0.54||||
|Advanced|0.82|0.53|0.60|0.90|||
|COD<br>Emerging|0.77|0.51|0.51|0.81|0.69||
|Developing|0.54|0.47|0.40|0.59|0.56|0.42|

Notes: In panel (a), entry ( _i,j_ ) is the average of the pairwise correlations between the flows of countries in group _i_ and those of countries in group _j._ In panel (b), entry ( _i,j_ ) indicates the percentage of all the pairwise correlations underlying the average shown in panel (a) that are statistically significant at the 95 percent level, with the standard error of a correlation _r_ approximated by the square root of (1 - r<sup>2</sup> )/( _T_ -1). Diagonal entries in each block of the table correspond to within-group correlations (excluding the own-country correlation), off-diagonal elements correspond to between-group correlations. CIF = gross inflows; COD = gross outflows.

31
and earnings improve with time in Ecuador.<sup>14</sup> Table 5 classifies Venezuelan migrants on the basis of their arrival in Ecuador. Let us first describe the size of each arrival cohort. We estimate that 11,140 Venezuelans arrived in 2016 (and remained in the country until the time of the survey). The flows increased 6-fold in 2017 to 66,305 arrivals in that year. Once again the annual inflow tripled in 2018 to an estimated 184,075 individuals. The inflows probably plateaued in 2019 and we estimate that about 63,664 Venezuelans settled in Ecuador in the first half of 2019.

The top panel of the table also shows that the average age of migrants has been falling over time. The average migrant arriving in 2016 was 32.3 years old whereas the average age of the most recent arrivals (2019) has fallen to 24.6 years. The data also show a 10 percentage-point increase in the share of females between 2016 and 2019 and a marked change in the skill composition of the Venezuelan arrivals in Ecuador. Among those who arrived in 2016, 70% had a college degree. In contrast, that proportion is only 29% among those arrived in 2019. In sum, these observations suggest that the most educated migrated first. But, as economic conditions deteriorate in Venezuela, individuals with a lower propensity to migrate, such as women and lower-educated workers, are also choosing to leave their home country.

The remaining panels in the table focus on labor market indicators for working-age individuals. It is helpful to focus on the two most recent arrival cohorts, which are fairly similar in terms of average age and educational attainment. The data show that the 2018 arrival cohort has a higher employment rate, higher earnings and a higher hourly wages than the 2019 arrival cohort. Thus, Venezuelan migrants do seem to experience a substantial improvement in their work conditions through the first year in Ecuador. However, the improvement appears to stall when comparing to the 2017 arrival cohort.

The bottom panels of the Table also stratify by education level. Focusing again on the two most recent arrival cohorts, we observe no sign of improvement in working conditions for migrants with at most primary schooling. In contrast, earnings and hourly wages appear to increase substantially for workers with medium and high education levels, one year after arrival. The data are less conclusive regarding whether the earnings for these workers keep improving with time in the country. We will return to this question in the next section.

> 14Our dataset is a single cross-section. Thus we will not be able to disentangle cohort effects from economic assimilation. Furthermore, the picture is complicated further by selective out-migration. As a result, our focus here is mainly descriptive.

10
<!-- Start of picture text --> c. HDI vs. inequality in reading d. IHDI loss vs. inequality in reading<br><!-- End of picture text --> <!-- Start of picture text --> R2=0.34<br>R2=0.38<br>.6 .7 .8 .9 1 0 10 20 30 40<br>Human development index (2012) IHDI loss (2012)<br>e. HDI vs. inequality in science f. IHDI loss vs. inequality in science<br>R2=0.39<br>R2=0.29<br>.6 .7 .8 .9 1 0 10 20 30 40<br>Human development index (2012) IHDI loss (2012)<br>30 30<br>20 20<br>10 10<br>Inequality index for reading (2012) Inequality index for reading (2012)<br>0 0<br>30 30<br>20 20<br>10 10<br>Inequality index for science (2012) Inequality index for science (2012)<br>0 0<br><!-- End of picture text --> Note: 95% confidence intervals added (shaded area). Source: Authors’ calculations based on data from the OECD, PISA 2012, and UNDP, HDI 2013.

# _3.1 Decomposition of inequality of opportunity_

From our decomposition exercise we find that socioeconomic status is the single most important circumstance contributing to inequality of opportunity in educational achievement (Figure 3). This is consistent with previous findings by the OECD (OECD, 2010b) and points out to a “silver spoon” type phenomenon; having a prosperous background vastly improves your chances of succeeding in life. We also find that, on average, education of the parents/guardians contribute to inequality of opportunity in education to a considerable extent. This is also consistent with previous findings – parental education can affect children’s academic outcomes through parents’ educational expectations of their children and the kind of educational stimulation they provide at home (Eccles, 2005; Davis-Kean, 2005). City size, on the other hand, reflects the often observed urban-rural divide, which favors urban areas over rural ones.

The decomposition results for reading proficiency reveal a large share of inequality of opportunity being explained by gender for most countries, which favors girls.<sup>12</sup> Although this phenomenon has 12 The share of women in or above level 2 of proficiency is 76% in reading; the corresponding share for men is 64%. Looking at individual countries, in every country, women outperform men on average.

9
Bollen and Grandjean, 1981, properly controlling for the non-monotonic relationship between economic development (by including both the logarithm of energy consumption per capita and its square as a measures) and income inequality (measured by the Gini coefficient), did not find any significant evidence to support the view that political democracy contributes to a more egalitarian distribution of income in a sample of fifty countries, once economic development is controlled for.

Bollen and Jackman, 1985, reexamine the argument presented in a previous study Rubinson and Quinlan, 1977, on the potential reciprocal relation between political democracy and income inequality, by constructing a simultaneous-equations model for income inequality and political democracy. Following Kuznets' insights, they specify inequality as a curvilinear function of the level of economic development and anticipate an inverted U-shaped curve, with a positively signed coefficient for economic development and a negatively signed coefficient for its square. The most important variables included in their investigation are: political democracy, measured by Bollen's index of political democracy; income inequality (the data are from World Bank sources); socioeconomic development, measured by the gross national product per capita; and population age structure, measured by the percentage of the population aged 0-14 years. The major result of this study is that, again, no significant effects between democracy and inequality in either direction is found.

Some other studies have tested the effect of the length of democratic experience on inequality. Hewitt, 1977, for example, in a sample of 25 industrialized countries failed to find a significant relationship, after properly controlling for other variables.

In an important study, Muller, 1988, extended the sample to 50 countries also using the number of years of democratic experience and controlling for the extent of economic development. The indicator of a country's level of democracy is Bollen's 18
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