rafmacalaba/datause-encoder-data huggingface dataset:
the countries in Europe and Central Asia have data protection laws explicitly governing the use, collection, and processing of personal data. Estonia, Moldova, and the United Kingdom have also imposed a full range of cybersecurity requirements on data processors and controllers. But when it comes to the regulatory environment for enablers, the region is still lagging. For instance, the Kyrgyz Republic is still in the process of drafting an e-commerce law.

**Figure 3. Scores on enablers and safeguards across regions**

<!-- Start of picture text --> 100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>North Europe & Latin Sub-Saharan East Asia & Middle East South Asia<br>America Central Asia America & Africa Pacific & North<br>Caribbean Africa<br>Enabler score Safeguard score<br><!-- End of picture text --> _Source:_ Author’s calculation based on results from the diagnostic.

# _4.3 Comparison across dimensions under the enabler and safeguard pillars_

In addition to the uneven development of regulatory environments for enablers and safeguards, country performance on the underlying dimensions also varies significantly (table 4). On average, countries in the higher-income group tend to perform better across all dimensions. However, no country income group demonstrates an advanced regulatory framework across all dimensions, indicating room for significant improvement. All income groups achieve their highest scores on the dimension of e-commerce, with more than 70 percent of the regulatory good practices adopted on average. Public intent data and cybersecurity and cybercrime are two other dimensions where most countries are performing relatively well, except for those in the low-income category. All income groups show a basic or evolving level of development for the regulatory framework related to cross-border data flows and private intent data. On average, only 17 percent of regulatory best practices are adopted under the private intent data dimension, ranging from 3 percent in low-income countries to 30 percent in high-income countries.

The average correlation coefficient among country scores across all seven dimensions is 0.27; the coefficients between two dimensions range from -0.22 (between regulations for sharing public intent data and protecting nonpersonal data) to 0.82 (between regulations for protecting personal data and allowing cross-border data flows) (table 5). The extent to which such bilateral correlation coefficients are statistically significant, and indeed rather large in some cases, suggest that governments may tend to simultaneously address certain clusters of issues in the regulatory framework. For instance, countries with regulatory frameworks that allow cross-border data flows are highly likely to allow access to private intent data, provide protection for personal data, and have strong cybersecurity regulations in place. Or again, countries 12
# Figure VI: Enrollment in Vocational Training across Countries

<!-- Start of picture text --> 80<br>CZE AUT<br>SVK SVN<br>60 CHE<br>POL ITA BEL NLD LUX<br>TUR<br>FIN<br>HUN NOR<br>40 MEX LVA PRT ISR FRAGBR<br>CRI DEUSWE<br>COL CHL EST<br>GRCRUS LTU JPNESP<br>20 KOR DNKAUS<br>NZL ISL IRL<br>BRA<br>0 ZAF CAN SAU<br>9.5 10 10.5 11 11.5<br>Log GDP per worker in PPP<br>Enrollment Share in Vocational Programs<br><!-- End of picture text --> _Notes_ : The figure plots the share of upper secondary students aged 15-19 enrollment in vocational programs in 2019, against the country’s log GDP per worker (in PPP terms) in 2019. Enrollment by program orientation is taken from OECD (2022), while GDP per worker is taken from version 10.0 of the Penn World Tables (Feenstra et al., 2015).

more general theme that the appropriateness of general and vocational education is contextspecific, with faster technological process and higher labor market uncertainty favoring the accumulation of general skills, and increases in the division of labor favoring specialization (Alon, 2018; Alon and Fershtman, 2023).

# **2.4.3 On the Job Accumulation**

**The accumulation of human capital on the job is an important determinant of wage growth over the life cycle** . Such accumulation is facilitated by both formal training programs provided by firms (Acemoglu and Pischke, 1998), as well as more informal learning opportunities associated with work experience (Bagger et al., 2014). Within countries, firms are heterogeneous in the quality of the learning environment they offer; for example, firms with more knowledgeable co-workers, (Jarosch et al., 2021) involved in exporting activities (Ma et al., 2020), of larger size (Arellano-Bover, 2020) and located in big cities (De La Roca and Puga, 2017) allow their workers to achieve faster human capital growth.

**The rate of wage growth over the life cycle is significantly higher in rich countries.** Lagakos et al. (2017) document this fact for 18 countries; while wages roughly double over the life cycle in countries like Germany and the United States, they only increase by about 50% in poorer countries like Jamaica and Mexico. Jedwab et al. (2021) extend this result to a broader and more diverse sample of 145 countries. While higher returns to experience in rich countries might also reflect lower labor market frictions and a more experience-biased organization

12
eligible to participate in the project. No municipalities refused to participate.

Comparison municipalities were selected based on the set of indicators used for the poverty ranking with the addition of two variables: the municipality population and land area. Specifically, the eight control municipalities were selected using cluster analysis.<sup>8</sup> To ensure that control municipalities share similar characteristics, a cluster analysis was computed for each of the four provinces in which the survey was to be fielded. The method starts with as many clusters as the number of municipalities in the province, which are then systematically clustered with those closest in Pythagorean distance along the various dimensions specified. There is significant within-municipality variation in our variables of interest.

From the outset, the operation included a rigorous impact evaluation collecting data from treatment and comparison municipalities before, during and after the intervention. The analysis takes advantage of a detailed household-level panel dataset collected to evaluate project impacts. The original sample covered 2,400 households in 135 villages in 16 municipalities, in 4 provinces of the Philippines. It includes municipalities in which the project is being implemented as well as comparison municipalities.

The first round of data collection took place in November 2003. In the 66 treatment villages included in the sample, project implementation started in October 2004. The second round of data collection took place in November 2006. The survey was fielded in the same 135 villages.<sup>9</sup>

> 8 The targeting mechanism used to select municipalities made more commonly used techniques such as propensity score matching impossible to implement.

> 9The survey used two stage stratified probability-proportional-to-size sampling to draw respondent households. First stage units are the villages. The villages in each selected municipality were stratified into three groups according to proximity to the poblaci ́on (municipal center). The first 1/3 in the ranking comprise stratum 1 (the villages nearest the poblacion), the next 1/3 stratum 2 and the last 1/3 comprise stratum 3. One quarter of the total number of villages were randomly selected from each stratum using probabilities

11
labor-intensive products as well as petroleum, and the Southern Cone countries that have vast natural resources, especially arable land.<sup>16</sup> The results are reported in Table 4. For the sake of brevity, we only report the coefficients of our variables. The first panel of the table shows the estimates using the bilateral data from OECD and UNCTAD. We find that the results for the three sub regions are similar to those of LAC as a whole. That is, we do not find a negative effect of either China or India in any of the sub regions, and there is still evidence of complementarities. It is noteworthy that the impact of China is larger on the Central American and Andean countries, whereas India has a larger effect on the Southern Cone.

The second panel of the table reports estimates of the impact of China and India on total U.S. stocks in the three sub regions. We continue to find no evidence of substitution effects. The positive effect for LAC as a whole, however, is only significant for the Southern Cone. The overall effect of India on U.S. stocks continues to be zero for all sub regions.

Finally, the last panel of table 4 reports the impact of China and India in FCS in the manufacturing sector. We find that there is heterogeneity across sub regions in this sector. The overall coefficient of China in the manufacturing sector is negative for Central America in the OLS specification, suggesting that China may be diverting manufacturing FCS away from this region. However, this finding is not robust to the use of the Poisson and Negative Binomial estimators. There is no evidence that growing FCS in China have had a negative impact on the FCS of the other two regions. The results for India are in general not significant and do not differ much across sub regions. In contrast,

> 16 Central American Countries: Costa Rica, Dominican Republic, El Salvador, Guatemala, Haiti, Honduras, Jamaica, Mexico, Nicaragua, Panama and Trinidad and Tobago. Andean Countries: Bolivia, Colombia, Ecuador, Peru and Venezuela. Southern Cone Countries: Argentina, Brazil, Chile, Paraguay and Uruguay.

18
average incomes during that period.

In the second part of the paper, we present predictions of the effects that changes in income inequality have on ASEAN countries' GDP per capita growth. These predicted effects are computed by combining estimates of the non-linear effect that a change in income inequality has on GDP per capita growth and data on ASEAN countries' initial GDP per capita. Brueckner and Lederman (2015) provide instrumental variables estimates of the effect that an exogenous change in income inequality has on GDP per capita growth. This paper draws on the estimates presented in that paper.

One of the main findings in this paper is that for the majority of ASEAN countries a rise of the middle class would have had a negative effect on economic growth if it would have occurred at an early stage of economic development, as measured by levels of GDP per capita in 1970. ASEAN economies' average income was too low in 1970 for a rise of the middle class to have generated a positive effect on economic growth. In contrast, for current values of average income a rise of the middle class has a positive effect on economic growth in all the ASEAN countries.

Consistent with the Galor and Zeira (1993) model, the paper documents that human capital accumulation is an important mechanism through which the rise of the middle class affects economic growth in ASEAN. At the early stage of development, as measured by 1970 levels of GDP per capita, an increase of the share of income held by the middle class would have had a negative effect on the share of the population with secondary and tertiary education in ASEAN countries. But for recent levels of ASEAN countries' GDP per capita the opposite is the case: An increase in the share of income held by the middle class would increase the share of the population with secondary and tertiary education. Borrowing constraints in investment in human capital<sup>1</sup> may explain the existence of this non-linear relationship between income inequality, economic growth, and education; see the model of Galor and Zeira (1993).

> 1 For a discussion of borrowing constraints in investment in human capital, see Chapman (2006); or Asian Development Bank (2011) for a discussion that has a specific focus on Asian countries.

3
**Table A1 – Balance table (with inverse probability weights applied)**

||**(1)**|**(2)**|**(3)**|**(4)**|**t-test**|**t-test**|**t-test**|
|---|---|---|---|---|---|---|---|
||**Control**|**Expert**<br>**advice**<br>**treatment**|**Social**<br>**norms**<br>**treatment**|**Relative**<br>**safety**<br>**treatment**||||
|**Variable**|**Mean/SE**|**Mean/SE**|**Mean/SE**|**Mean/SE**|**(1)-(2)**|**(1)-(3)**|**(1)-(4)**|
|**Male**|0.519|0.499|0.518|0.521|0.021|0.002|-0.002|
||[0.018]|[0.025]|[0.026]|[0.026]||||
|**40 years or older**|0.277|0.277|0.307|0.343|-0.000|-0.030|-0.066**|
||[0.016]|[0.022]|[0.024]|[0.024]||||
|**Highlands region**|0.388|0.367|0.383|0.326|0.021|0.005|0.063**|
||[0.018]|[0.024]|[0.026]|[0.024]||||
|**Islands region**|0.154|0.178|0.152|0.200|-0.024|0.001|-0.046*|
||[0.013]|[0.019]|[0.019]|[0.021]||||
|**Momase region**|0.276|0.263|0.253|0.313|0.012|0.022|-0.038|
||[0.016]|[0.022]|[0.023]|[0.024]||||
|**Southern region**|0.182|0.192|0.211|0.161|-0.009|-0.029|0.021|
||[0.014]|[0.019]|[0.022]|[0.019]||||
|**Observations**|740|415|358|377||||

Notes: ***, **, and * indicate significance at the 1, 5, and 10 percent critical level. The values displayed for t-tests are the differences in the means across the groups.

**Table A2 – Inverse probability weights for phone survey**

||**Male**||**Female**||
|---|---|---|---|---|
||**<40yrs**|**>=40yrs**|**<40yrs**|**>=40yrs**|
|**Highlands Region**|0.810|0.842|2.136|2.221|
|**Islands Region**|0.489|0.508|1.289|1.340|
|**Momase Region**|0.592|0.615|1.560|1.622|
|**Southern Region**|0.868|0.903|2.290|2.381|

**Table A3 – Inverse probability weights for online experiment**

||**Male**<br>||**Female**<br>||
|---|---|---|---|---|
||**<40yrs**|**>=40yrs**|**<40yrs**|**>=40yrs**|
|**Highlands Region**|0.911|1.530|2.390|4.015|
|**Islands Region**|0.414|0.696|1.087|1.826|
|**Momase Region**|0.365|0.613|0.958|1.610|
|**Southern Region**|1.058|1.777|2.776|4.664|

41
occupation of the head’s father.

The instrumenting equation is shown in Table 8. Our instruments are jointly signi fi cant and the F -statistic is only marginally below 10, which is considered su ffi cient to avoid a weak instrument problem. We also conduct an overidenti fi cation test, temporarily ignoring the selection issue. In all cases, we cannot reject the null hypothesis that instruments can be excluded from the adequacy regressions: p-values are 34% and above.

The model presented in Table 7 is reestimated with all regressors and controls, adding the residuals from the instrumenting equation in the adequacy regression. This approach is similar to that developed by Smith & Blundell (1986) for tobit and by Rivers & Vuong (1988) for logit. By analogy, it should also work here since the Heckman selection model is also based on the normal distribution. This approach yields a test of endogeneity as a by-product. Results are summarized in Table 9. Residuals from the instrumenting equation are signi fi cant in all regressions except health care, suggesting that endogeneity is indeed a problem. The main change compared to Table 7 is the massive increase in the consumption coe ffi cients. This is a typical outcome when correcting for measurement error. Distance coe ffi cients remain by and large unchanged.

Coe ffi cient estimates from Table 9 are used to compute (5.1) and (5.2). Two sets of calculations are reported in Table 10. The fi rst set evaluates the equivalent variation of reducing travel time to markets from the mean of 2 hours and 10 minutes to the minimum recorded travel time, which is 1 minute. For food consumption, clothing, housing, schooling, and total income we use formula (5.1). For health care we bracket ckm using λ r ≈ α<sup>r</sup> 2<sup>/αu</sup> 1<sup>asexplainedabove.We</sup> also compute the combined welfare cost of isolation using (5.2).<sup>28</sup> The second set evaluates the

> 28 In the surveyed population, average expenditure shares are as follow: food 66.3%; clothing 8.1%; housing 12.2%; schooling 2.8%; health 3.4%; other 7.2%. Adequacy questions thus cover items representing 92.8% of total consumption. Since we do not have an adequacy question for other goods, we ignore them in the calculation and renormalize shares to sum to 1. This is equivalent to assuming average subjective adequacy for other goods.

29
# **6.1 Health and education spending**

Collecting higher tax revenues allows governments to spend more on health and education, which is related to better growth outcomes and higher productivity (Baldacci et al. 2008). EMDEs spent about 7 percent of GDP on average on health and education per year well below the 11.1 perent spent in advanced economies. Figure 6A plots a simple binned scatterplot (where observations along the vertical and horizontal axis are partitioned and then binned into one single point). The scatterplot shows that there is a generally positive relationship between government’s future health and education spending. In addition, there seems to be some evidince of a discontinutity in average future government spending on education and health around the 15 percent of GDP threshold, which underscores the likelihood that low tax revenue undermines spending.

We use the same threshold methodology as above but here looking at whether there is a threshold at which tax revenue affects spending on health and education. We find a tax to GDP threshold over 30 percent of GDP. While the threshold dynamics are less important at over 30 percent of GDP, the regression indicates that below the threshold higher tax revenue is associated with higher future spending on health and education (figure 6B). A country with tax to GDP of 7 percent is predicted to spend 7.8 percent of GDP every year for the next ten years. This rises to 8.4 percent at 15 percent of GDP. Over a ten-year period, this is an additional 6.0 percentage points of GDP in cumulative spending on health and education.

**Figure 6 Impact of taxes on future health and education spending**

A. Scatterplot of tax revenue on future health and education spending B. Predicted average ten-year future spending on health and education <!-- Start of picture text --> 16<br>Percentage points<br>14<br>9.5<br>12<br>9.0<br>10<br>8.5<br>8<br>8.0<br>6<br>7.5<br>4 7.0<br>2 6.5<br>0 6.0<br>0 10 20 30 40 50<br>7% tax to 15% tax to 20% tax to 25% tax to<br>Tax to GDP GDP GDP GDP GDP<br>Average future health and<br>education spending (% of GDP)<br><!-- End of picture text --> _Sources:_ International Monetary Fund; Authors’ calculations. _Note:_ A. Average 10-year ahead health and education spending relative to GDP. Binscatter with 50 bins where observations along the vertical and horizontal axis are partitioned and then binned into one single point. Vertical line reflects 15 percent tax to GDP.

# **6.2 Progressivity and equity**

Taxation is a potent tool for promoting a more equitable income distribution. Piketty (2014) underscores that progressive taxation is a critical element of the social state. He asserts that it was central to the development of the social state and the transformation of inequality structures in the 11
not fi nd any correlation between teacher shocks and movements. Of course, it could still be that the correlation is in unobservables that do not co-vary positively with these observed characteristics; verifying (or not) the importance of selective matching versus precautionary investment remains an important agenda for our future research.

# 8 Conclusion and Caveats

This paper uses a household optimization framework to identify the impact of teacher-level shocks on students’ learning gains. Our data from Zambia show that shocks to teacher inputs have a substantial effect on student learning. In our preferred speci fi cation, shocks associated with a 5 percent increase in the teacher’s absence rate resulted in a decline in learning of 3.75 percent (English) and 4 percent (Mathematics) of the average gains across the two years. The identi fi cation strategy is based on a restriction of the sample, whereby we focus only on those students who remained with the same teacher in the two concurrent years that they were tested. This allows us to rule out changes in unobserved teacher characteristics as a confounding factor in the estimation procedure. We argue that the estimates thus arrived at are robust to omitted variable and misspeci fi cation bias.

What are the implications of this fi nding? To improve educational outcomes, governments should concentrate on providing resources at the school-level that cannot easily be substituted for by households. In a previous paper (Das and others 2004), we documented that educational funding is an important determinant of learning achievement. However, because schools spend the money received from the government on resources such as textbooks, there is a high degree of substitutability between public and private funding. Increases in the former crowd-out the latter. Contrariwise, this paper argues that households are unable to insure themselves against teacher-level shocks. Moreover, the estimated impact of teacher-level and student-level shocks are of roughly the same magnitude. This con fi rms fi ndings from other studies that teachers matter and further, raises the possibility that insurance at the school-level may be more bene fi cial than at the household-level. This is a policy priority worth investigating further.

Throughout we have assumed that the learning decline is the impact of negative shocks that result in higher teacher absence rather than the impact of absenteeism per se. Our interpretation is that the learning declines re fl ect the joint effect of from time away from class, decreased teaching quality when in class and less lesson preparation when at home. In attributing all teacher absence to negative shocks, we have probably been overly generous–it is likely that at least some portion of teacher absence is due to shirking rather than illness. If so, incentive schemes should work. In the 20