rafmacalaba/datause-encoder-data huggingface dataset:
education observed in the labor survey data can affect individual preferences only to the extent that COMIPEMS applicants can rely on this information. While we do not have information about students’ expectations at the moment of the application, we can observe students’ expected returns during the last year of high school, as measured by the _Enlace de contexto_ . Students’ subjective expectations about earnings five years after high school completion across the different high school tracks in the metropolitan area of Mexico City are broadly consistent with the ENOE data (see Figure 5).

Vocational options in Mexico are often considered of lower academic quality, but on average they display a better provision of school inputs that are more likely to favor labor market entry upon finishing high school, compared to the more academic tracks. As shown in Panel b in Table 5, vocational options display the lowest ratios of computers per student and the highest number of workshops per student. Although the number of students per teacher (18.55) is higher than the academic track (18.16), it is much lower than the technical track (20.46).

The vocational options also display tighter links with the characteristic of the local labor market. Figure 6 depicts the different areas of specialization of the technical programs offered within the COMIPEMS system according to whether the students in our sample opt for a technical or a vocational program in their solicited first option. Among those who choose a technical program, almost 40 percent choose a program that covers mixed, and presumably more general, specializations, as opposed to the vocational track where the programs chosen seem to be more geared toward career-specific training. Roughly one-third of the COMIPEMS applicants in our sample who choose a school from the vocational track as their first option opt for the auto repair specialization. According to the Mexican Economic Census, the auto-repair sub-sector in the municipality of Ecatepec stands above the 95th percentile in the distribution of all the service sub-sectors in terms of employees’ wages, number of firms, number of employees and (gross) value added. This evidence provides further support to the hypothesis that COMIPEMS applicants are aware of the labor market returns of the vocational options.

Overall, these different pieces of evidence seem consistent with the notion that for those students who expect to enter the labor market after high school completion, the vocational track seems a profitable and yet more costly alternative when compared to the other two more academicoriented tracks. A pilot survey conducted in 2009 among the _Oportunidades_ recipients in the city 18
# Table 1: Estimated Wage Differentials for One-digit Industries, Guatemala 1989

||I|ndustry Coefficie|nt|
|---|---|---|---|
||All Workers|White Collar|Blue Collar|
|Mining|0.185<br>(1.4)|0.292<br>(1.0)|0.152<br>(1.0)|
|Manufacturing Industry|-0.141|-0.111|-0.142|
||(3.9)|_(1.5)_|(3.5)|
|Electricity, Gas, Water|0.196|0.235|0.186|
||(2.7)|(1.6)|(2.2)|
|Construction|0.028|-0.089|-0.015|
||(0.7)|(0.8)|(0.4)|
|Commerce|-0.127|0.121|-0.100|
||(3.3)|(1.6)|(2.1)|
|Transportation|0.014<br>(0.3)|-0.014<br>(0.2)|0.048<br>(0-9)|
|Finance|0.104|0.130|0.099|
||(2.0)|(1.6)|(1.2)|
|Services|0.028<br>(0.8)|0.088<br>(1.3)|-0.043<br>(1.2)|
|Standard Deviation of<br>Differentials|0.126|0.150|0.118|
|R2|0.59|0.50|0.42|
|Sample Size|6,637|1,795|4,842|
|Source:<br>ENS 1989.||||
|Note:<br>t-statistics in parentheses.||||

The dispersion of interindustry wage differentials for different types of workers based on separate earnings functions for each type of worker are presented in Table 2. Dispersion is measured by the simple standard deviation of the resulting coefficients (differentials). Alternative samples for unionized/non-unionized workers as well as samples based on length of 3
94 necessarily raise the efficiency of the family **as** a production unit or even influence how it allocates its resources among ommbers. The effect o~` public transfers on intrahousehold consumption patterns is thus likely to be greatly reduced.

More promising are social interventions that work to change the constraints impinging on the family and thereby to motivate it to achieve its goals by investing more human capital resources in its female members. The private and social rates of return to wooen's education at the primary and secondary level are at least equal to those of oen. Thus, a more equal distribution of education between sen and women would not slow economic growth. In fact, because female education has greater effects on health, nutrition and fertility, it might actually accelerate development. This possibility warrants more study, particularly in South and West Asia and Africa where female education levels are especially low.

There is evidence to suggest that the economic bias of accounting only for market income has led to a misallazation of public development resources in favor of market vis-a-vis nonmarket sativities, and consequently men's rather than women's work. A reevaluation of women's nonmarket work has begun to show the pervasiveness of this bias. But this has not yet affected development planning. The rhetoric on "women and development" has been substantial in the last decade, but progress toward establishing an analytical framework free of the traditional market bias has been slow. This framework must be used to guide the collection of data from households and communities **tltat** will show where investments enhance women's productivity, broadly defined to include nonmarket or home production.

The traditional economic approach to modeling family decision-making as
The results of the paper suggest that more liberal bilateral air service agreements are associated with more passenger traffic, and with more city-pairs served by direct flights. There appears to be no statistically significant difference between the effects of bilateral and plurilateral agreements once we take into account the respective policy content. We use our empirical results in a counterfactual analysis in two steps.<sup>5</sup> First, we quantify the impact of deepening the ACAC agreement by assessing how much more traffic there would be among ACAC members if the policy commitments were extended to match those of the most liberal international agreements (e.g. UK-Singapore). Using country-level data we find that traffic flows would grow by approximately 30%. The city-pair estimates suggest that traffic along given routes would grow modestly (by 5 to 8%), but that there would be a significant increase in the number of city pairs served by direct international flights within the ACAC.

In the second step, we estimate the implications of Turkey’s accession to the ACAC agreement, assuming the more liberal commitments. This implies very large changes in the openness of the policy commitments, because Turkey’s existing agreements with countries in the region are quite restrictive, when such agreements exist at all. The country level analysis of Turkey’s accession to the ACAC at increased levels of policy commitments suggests that passenger traffic between Turkey and ACAC countries would more than double.<sup>6</sup> City level analysis suggests that the increase in traffic would occur both through the growth of traffic on given routes and substantial increases in the number of city pairs served.<sup>7</sup> In the full liberalization policy scenario we consider, passenger growth along existing routes is modest, but the likelihood that a direct flight would occur between a given pairing of a Turkish and an

> 5 The counterfactual analysis we conduct relies solely on parameters we estimate in the reduced form econometric models, and not on a fully specified model of the behavior of market participants.  As such we only calculate first order effects of liberalization, assuming that the coefficients remain constant and that other right hand side variables do not change in response to our policy shock.  We are potentially missing secondary effects that might operate, for example, through binding capacity constraints, or through spillovers to other markets.  But these effects are difficult to estimate without substantially more data and a richer model structure, so we employ transparent calculations that rely on the reduced form estimates.

> 6 A policy scenario in which Turkey enters at the existing level of ACAC commitments generates a rough doubling of passenger traffic between Turkey and the ACAC member states.

> 7 An increase in the number of cities served by international travel reflects an improvement in the overall quality of air transport services because more people have the ability to take a direct flight.

4
three stages. At each stage, incentives, in the form of prices or regulations, affect the development and adoption of new technologies: _Invention:_ an idea must be born.

_Innovation:_ new ideas are then developed into commercially viable products. Often, these two stages of technological change are lumped together under the rubric of research and development (R&D).

_Diffusion:_ to have an effect on the economy, individuals must choose to make use of the innovation.

I use this review to identify gaps in the literature at each of these three stages that merit further study to advance the promotion of green growth. I begin with a discussion of data available for measuring the various steps of technological change. I continue with a discussion of where environmental innovation comes from. Given that most innovation is concentrated in a few rich countries, this leads to a discussion of the remaining role for lower-income countries. I note a growing role for emerging economies, and identify three key questions pertaining to green R&D in developing countries. These questions all focus on the links between innovation and demand: (1) promoting the development of technologies with limited markets in high-income countries, (2) the role of adaptive R&D, and (3) the potential for emerging economies to meet the green technology needs of high-income countries.

Since much R&D occurs in high-income countries, I continue with a discussion of technology transfer. Beginning with diffusion across countries, differences among countries raise important questions, such as (1) understanding how the technological distance between countries affect the transfer of green technologies, (2) whether lessons learned from the recent successes of India and China are generalizable to smaller countries. Similarly, within countries, 4
**Figure 1: Some key macro indicators in recent years, by quarter**

<!-- Start of picture text --> Growth<br>percent change year on year<br>4- - Per capita private<br>consumption<br>3 ~~~ =~~~~_ F _ _-*Per capita GDP<br>1-<br>3-2<br>2-3<br>0.<br>,-3><br>-4 -i<br>-5-<br>97 01 97 Q2 97 03 97 Q4 98 01 98 Q2 98 03 98 Q4 99 Q1<br>Inflation<br>pertage change over previous year<br>- Food<br>14 -<br>12 - -G eneral CPI<br>10 .<br>44<br>0;<br>2<br>Um Et rate<br>p pn dchage, yw<-yew<br>4-<br>3-<br>2- °att[LtSLLLD [t<br>-2-<br>Jan- Pjx- JU- 0- Jar- x- Juj- 0- Jai- 8 p- JLi- 0- Jan, Ppx- Jul-<br>96 96 96 96 97 97 97 97 98 98 98 98 99 99 99<br><!-- End of picture text --> _Source:_ Compiled from data published by the National Statistics Office and the National Statistical Coordination Board.

33
To assess the quality of the data, we compare the total values obtained from aggregating the customs data at the country level with the total values obtained at the country level from Comtrade. We dropped the years in which the total values obtained from the customs data represented less than 70 percent or more than 130 percent of the total values obtained from Comtrade aggregates. Table 1 has the full list of countries and periods included in the final sample used in this paper and the complete list of countries and years for which data are excluded can be found in Table A1 in the Appendix.

Table 1 also shows the summary statistics with annual averages for the years available from 2004 to 2008. There is significant variation in the number and size of exporters across countries and also within countries. For example, Cambodia (KHM) has a relatively small number of exporters, yet they are relatively large on average. In contrast, Bulgaria (BGR) has a relatively large number of exporters that are relatively smaller. The correlation between number and size is 0.36. Within countries, we also observe a large difference between the median and the mean values per exporter—the mean values are, on average, 51 times larger than the median values per exporter. This reflects the incredibly skewed distribution of firm size.

Figure 1 shows the distribution of the 200 largest firms by size on average across countries in the sample. Figure 1.i shows firm rank against cross-country average exporter size on a log-log scale for year 2008.<sup>7</sup> We find that the relationship is nearly linear–the Pareto exponent (the slope of the regression of log(rank) on log(export size)) is 1.05, which is consistent with Zipf’s law.<sup>8</sup> Figure 1.ii highlights the skewed distribution in simpler terms. It records the average share per exporter by rank in total exports. The top firm across countries accounts for

> 7 We tried estimations for other years (2005, 2007) and results are very similar.

> 8 Following Gabaix and Ibragimov (2012), we also calculated the slope using their modification (firm rank - 1⁄2) against average export size.  The results are very similar with a slope of 1.09.

8
<!-- Start of picture text --> Figure 6: Relative Size of New Products’ Exports for Incumbent Exporters<br>Panel A. Averages in 2005-2007 Panel B. Averages in 2008-2009<br>.11<br>.17 .09<br>.06 .06 .06 .06 .06 .05<br>.05<br>.08 .04<br>.07 .04<br>.06 .05 .03 .06 .05 .05 .03 .04 .03 .02 .03<br>.02 .01<br>A.Natural Resource B.Simple Processing C.Manuf.D.Other A.Natural Resource B.Simple Processing C.Manuf. D.Other<br>LAC countries Other countries LAC countries Other countries<br>.2<br>.1<br>.15 .08<br>.06<br>.1<br>.04<br>.05<br>.02<br>Size of New Products Rel. to Incumbent Products 0 Size of New Products Rel. to Incumbent Products 0<br>Ecuador South Africa Costa Rica Peru Chile Bangladesh Cambodia Guatemala Nicaragua El Salvador Dominican Rep Mexico LAC countries Colombia Ecuador South Africa Costa Rica Peru Chile Bangladesh El Salvador Nicaragua Guatemala Cambodia Dominican Rep Mexico LAC countries<br><!-- End of picture text --> Notes: in Panel A the average for Ecuador covers 2006-2007. Colombia does not enter in Panel A due to lack of data. Brazil does not enter in the figure due to lack of access to the exporter-level customs data.

Finally, we examine the contribution of new products to short-run export growth of incumbent exporters in LAC and comparator countries. Figure 7 shows the average annual growth rate in total exports of incumbent exporters for each country during 2005-2007 in Panel A and 2008-2009 in Panel B. The average contribution of the three terms in the incumbent exporters’ export growth decomposition in Eq. (2) -- for new, incumbent, and exiting products -- is presented for each country in Panel C for 2005-2007 and in Panel D for 2008-2009. During the steady growth period, incumbent products contributed the largest share to the growth of incumbent exporters in every LAC country. New products contributed a meaningful share to export growth of incumbent exporters only in the Dominican Republic, El Salvador, Guatemala, and Nicaragua. A potential rationale for the importance of new products in these countries relates to the entry into force of the Central America Free Trade Agreement (CAFTA) agreement with the United States whereby incumbent exporters gained access to a very large market to which they likely started exporting new products. However, note that for El Salvador, the reduction in exports due to products dropped by incumbent exporters more than compensated for the increase in exports due to their new products. Interestingly, the importance of new products in explaining export growth of incumbent exporters was smaller in LAC than in its comparators in groups of natural resource countries and processing countries.

25
<!-- Start of picture text --> PanelA. RealExchange Rate Gap: Pane! B. Country Rating Gaps<br>06 a6 1<br>o4 i o4 i<br>a2 |i02 Hi<br>i0<br>o|<br>i02 i<br>i a i<br><!-- End of picture text -->

<!-- Start of picture text --> a4 i -06 i<br>-0.6 - 08 f<br>o 1 2 3 4 5 6 7 & 9 0 611 o a 2 3 4 5 6 7 5 9 1 #11<br>Panel C GDPper capita Grouth Gaps Panel D. National Saving Gaps<br>6 F O6<br>Cass Stabiizaton i Recovery Cass Stabilization Recovery<br>4 H a4 i<br>H H<br>2 i 02 i<br>H H<br><!-- End of picture text --> <!-- Start of picture text -->
-2 | 02<br>4 04 |<br>63 | : 0608 i:<br>° 1 2 3 4 5 6 7 3 9 rl ie © i 4 oO a 2 3 4 5 6 7 8 9 10 #11 12<br>Panel E. Investment Gaps Panel F. Current Acount Gaps<br>O6 { } Os f i<br>Cais Stabilization | Recovery Cass i i Recovery<br><!-- End of picture text -->

<!-- Start of picture text -->
| |<br>i<br>o 2 } o 2 | i<br>:<br>0.2 t 0.2 i<br>il 04 j<br>06 | 1 26 |<br>08 | i 08 ; }<br>0 3] 2 3 4 5 6 t 5 9 0 li 12 o 1 2 3 4 5 6 7 5 9 0 #11 #12<br>— LAC1 Low Savers —— LAC! High Savers<br><!-- End of picture text -->

Sources: Authors’ calculations based on UN, WDI and Institutional Investor data.

Notes: This figure shows the within group average policy-adjusted gaps for different variables for the high and low saving LAC1 countries for 1980-2012: real exchange rate (Panel A), country rating (Panel B), GDP per capita growth (Panel C), national saving (Panel D), investment (Panel E) and current account (Panel F). Each period is a three-year average. See Table B1 for the groups’ composition and Section 3 for the details of how the benchmarks are calculated.
14. Colmer, J., Hardman, I., Shimshack, J. & Voorheis, J. Disparities in PM2. 5 air pollution in the United States. _Science_ **369,** 575–578 (2020).

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18
of sponsorship was 9.3 years, a monthly income increase of $13–17 over an average lifetime of work implies a modest financial rate of return to child sponsorship of 3.7–5.0%.

Our estimations of impacts on wealth in adulthood find significant impacts on adult dwelling quality from child sponsorship on proxies for adult wealth, where we find that sponsored children—especially males—are more likely as adults to live in better housing (homes with electricity and with roof and floor made of superior construction materials). Formerly sponsored men are more likely to live in homes with indoor plumbing. Impacts on adult consumer good ownership, however, are more modest and appear to be limited to substantially greater ownership of mobile phones among both formerly sponsored men and women. We also present (modest) evidence suggesting that sponsored children have fewer children in adulthood, along with much stronger evidence that both formerly sponsored men and women are less likely to live with their parents in adulthood.

What about child sponsorship, in particular Compassion’s approach to child sponsorship, could be responsible for these significant effects on income and wealth in adulthood? In related research using a separate sample of _currently_ sponsored children we explore the hypothesis that child sponsorship may improve adult incomes not merely through relieving _external_ constraints that improve schooling access, nutrition, and health, but through addressing _internal_ constraints related to imparting a greater level of hopefulness about the future and instilling greater aspirations for schooling and adult vocation. Using data on currently sponsored children, we find in Glewwe, Ross, and Wydick (2014) a causal link between child sponsorship and elevated educational and vocational aspirations among children in Kenya, and higher levels of happiness, self-efficacy, and hopefulness based on a quantitative analysis of children’s self-portrait drawings in Indonesia. Although it is yet impossible to definitively identify these increased aspirations as a causal channel to the positive impact from sponsorship on income and wealth we find in this study, what is clear from our three pieces of research on child sponsorship is that child sponsorship increases aspirations and that child sponsorship also improves adult economic outcomes. We present a diagram in figure 9 of what appears to us to be the causal channel for the effects we observe from child sponsorship on income and wealth in adulthood.

Most conditional and unconditional cash transfer programs, and many—if not most— educational interventions, do not seek to directly address internal constraints of children, which are also related to the fostering of noncognitive (socio-emotional) skills. Our findings on the impacts of child sponsorship suggest that this may constitute a missed opportunity. Taken together, our results suggest that development programs that relieve tangible external 16