rafmacalaba/datause-encoder-data huggingface dataset:
# **Figure 14: Women in the Northern and Eastern provinces have poorer self‐reported writing skills**

Percent of individuals that never use writing skills <!-- Start of picture text --> 70%<br>65%<br>60%<br>55%<br>52%<br>49%<br>41% 42%<br>Men Women<br>Northern Eastern Western Other provinces<br>Source: STEP Survey 2012<br><!-- End of picture text --> In addition to poorer reading and writing skills, women in the Northern and Eastern provinces also report poorer numeracy skills. In the Northern and Eastern provinces, 13% and 17% of women report to have never used numeracy skills in the previous 12 months, compared with 14% in the rest of the country. Instead of a significant geographic gap, we see a large gender gap in numeracy skills. Much more women than men in the Northern and Eastern provinces report never having used numeracy skills in the previous 12 months. Taken together, a lack of reading, writing, and numeracy skills may be hurting the employment possibilities of women in the Northern and Eastern provinces.

**Figure 15: Women in the Northern and Eastern provinces have poorer self‐reported numeracy skills**

<!-- Start of picture text --> Percent of individuals that never use numeracy skills<br>17%<br>14% 14%<br>13%<br>12%<br>10%<br>5%<br>1%<br>Men Women<br>Northern Eastern Western Other provinces<br><!-- End of picture text --> Source: STEP Survey 2012 Another valuable perspective on skills shortages comes from employer surveys. Many employers in the Northern and Eastern provinces see skills shortage as a major constraint, as many report difficulty finding applicants to fill higher‐skilled vacancies. Figure 14 reports results from the Enterprise Survey 2012 for Sri Lanka, which surveyed employers about various aspects of their business. When asked if finding an adequately educated workforce was a constraint they faced, employers in the Northern and Eastern provinces (25% and 59%) were the most likely of all provinces to respond affirmatively. Moreover, employers in the Northern and Eastern provinces report difficulty finding applicants to fill higher‐skilled vacancies. All firms in the Northern and Eastern provinces report encountering difficulty finding 19
# **Introduction**

A country’s education level is critical for its economics success. For many years, the economics literature focused on the positive effects of education quantity on growth (Barro, 1991; Mankiw, 1992). However, a growing body of evidence suggests it is not only the _quantity_ of schooling, measured by average years of schooling or enrollment rates, but also the _quality_ of schooling, proxied by student achievement tests, that contributes to growth. It is not about being in school but what is learned in school that matters. Over 15 years of literature now supports this conclusion (Hanushek and Kimko, 2000; Pritchett, 2001; Hanushek and Woessmann, 2008; Hanushek and Woessmann, 2012). The evidence shows that in cross-country regressions when student achievement conditional on years of schooling – rather than years of schooling alone - is correlated with growth, the association and explanatory power of growth models is significantly higher. The most recent World Development Report (World Bank, 2017) highlights this finding. Moreover, Hanushek and Woessmann (2012) use differences-indifferences and instrumental variables methods and find a plausibly causal link between cognitive skills and growth.

This insight comes at a time when the availability and coverage of International Student Achievement Tests (ISATs) – which are carefully constructed, psychometrically-tested, standardized assessments – is growing. ISATs first started in the 1960s and are carried out by institutions such as the OECD and the International Association for the Evaluation of Educational Achievement (IEA). One of the largest ISATs, PISA, covered 71 countries in 2015, and another large ISAT, TIMSS, covered 65 countries in 2015. The growth of these assessments enables credible global comparison of education quality levels and changes over time.

While critically useful, these international achievement tests have a series of limitations. First, while PISA and TIMSS tests are highly correlated (Rindermann and Stephen, 2009), they have meaningful differences in both their rigor and scaling. Thus, when comparing them, it is important to adjust for these differences. Second, since these assessments only started being implemented consistently and in a standardized fashion in the 1990s and 2000s, they are limited in their ability to conduct longitudinal and panel analysis. Third, these assessments often include mostly OECD countries, omitting developing countries which have the most to gain from a quality education. For example, the first PISA in 2000 included 28 OECD countries and four non-OECD countries. While PISA has grown substantially, and in 2015 included 71 countries, none of these countries where from Sub-Saharan Africa. Thus, implications of studies 2
20 Table 2: <u>Volume of cotton</u><sup>a</sup> <u>production, yield, trade and utilization, 2005-06</u>

||Output<br>(Kt)|Change i<br>stocks<br> (Kt)|n<br>Exports<br>(Kt)|<br>Imports<br>(Kt)|<br>Utilization<br>(Kt)|Share of<br>supply<sup>b</sup><br>exported<br>(%)|<br> <br>National<br>share of<br>global<br>output(%|<br> <br>)<br>National<br>share of<br>global<br>exports<br>(%)|<br>Yield per<br>ha, % of<br>global<br>average|
|---|---|---|---|---|---|---|---|---|---|
|China|5819|9|10|2800|8600|0|23.5|0.1|163|
|United States|4735|408|3039|7|1296|70|19.1|37.5|122|
|India|4250|550|225|125|3600|6|17.1|2.8|63|
|Pakistan|2308|42|100|250|2415|4|9.3|1.2|103|
|Brazil|1191|-85|425|50|900|33|4.8|5.2|161|
|Uzbekistan|1100|14|837|1|250|77|4.4|10.3|110|
|Turkey|805|0|25|770|1550|3|3.2|0.3|181|
|<br>Australia|496|-97|582||11|98|2.0|7.2|258|
|Greece|358|6|258|5|100|73|1.4|3.2|144|
|Syria|298|-9|150||158|49|1.2|1.9|192|
|Egypt|263|-8|125|75|220|46|1.1|1.5|137|
|Burkina Faso|254|-14|264||4|99|1.0|3.3|64|
|Mali|250|-1|247||4|98|1.0|3.0|68|
|Turkmenistan|219|6|114||100|54|0.9|1.4|52|
|Tajikistan|162|6|132||25|85|0.7|1.6|80|
|Argentina|155|-5|50|20|130|31|0.6|0.6|63|
|Mexico|152|-33|45|287|428|24|0.6|0.6|169|
|Kazakhstan|147|5|134|5|12|94|0.6|1.7|99|
|Benin|140|-49|186||3|98|0.6|2.3|67|
|Côte d'Ivoire|124|11|103||10|91|0.5|1.3|62|
|Iran|120|0|10|10|120|8|0.5|0.1|114|
|Cameroon|112|-78|57|1|132|30|0.5|0.7|69|
|Spain|110|0|63|15|62|57|0.4|0.8|178|
|Sudan|96|0|92||4|96|0.4|1.1|67|
|Tanzania|96|-24|104||16|87|0.4|1.3|31|
|Paraguay|90|42|43||5|90|0.4|0.5|49|
|Nigeria|87|2|30|15|70|35|0.4|0.4|33|
|Zambia|76|0|55||20|72|0.3|0.7|39|
|Chad|72|-5|77||1|100|0.3|0.9|33|
|Zimbabwe|72|-13|58||26|68|0.3|0.7|36|
|Peru|70|1|2|23|90|3|0.3|0.0|118|
|Togo|70|-9|79||0|100|0.3|1.0|54|
|Myanmar|59|0|11||47|19|0.2|0.1|29|
|<br>Colombia|55|21||78|111|0|0.2|0.0|109|
|Azerbaijan|55|5|41||8|82|0.2|0.5|71|
|<br>Kyrgyzstan|38|0|39|3|3|103|0.2|0.5|121|
|<br>Uganda|37|-5|38||4|90|0.1|0.5|52|
|<br>Mozambique|25|-3|26||2|93|0.1|0.3|16|
|<br>Ethiopia|22|0|2||20|9|0.1|0.0|38|
|South Africa|21|0||39|60|0|0.1|0.0|73|

Source: ICAC (2005).

a Cotton, refers to ginned lint or raw cotton. It does not include seed cotton, linters, cotton mill waste, or cotton fibers subjected to any processing other than separation of lint from seed by the gin. Annual data are for the cotton year beginning 1 August.<sup>b</sup> Supply is output plus change in stocks.
private account if all the other participants did likewise, 10 in Cardenas et al. and 13 in Attanasio et al. Therefore, by design, our experiment was likely to generate a higher rate of contribution to the group account.<sup>26</sup> Henrich et al. (2005) show that, in a standard public goods games conducted in 5 small-scale societies, the percentage of “free-riders”, i.e. participants who contributed nothing into a public account, ranged from 0 to 5%; however the public good games employed had a much higher MPCR (0.40 or 0.50) and smaller groups (4 or 5 people).<sup>27</sup> Table 3 shows the percentages of parents investing in the group account in the 12 counties, ranging from 61% in Fier to 88% in Gjirokaster. A Chi-squared test shows that the between-county differences are statistically significant at the 1 percent level. At the district level, the variation in the percentage of cooperating parents is even more striking, ranging from 47% in the district of Lushnje to 95% in the district of Delvine (see Table A2 in Appendix). The differences in cooperating behavior across districts are statistically significant at the 1 percent level.<sup>28</sup> A linear probability model employing only the district dummies as explanatory variables shows that the 36 districts account for 6 percent of the variation in individual cooperativeness in the game, and for 25 percent of the variation in cooperativeness across schools.

**Table 3**

**Parents’ investment in the group account by county**

||**% of parents investing in the group**<br>**account**|
|---|---|
|**Full sample**|**0.75**|
|**Counties**||
|Gjirokaster|0.88|
|Korce|0.87|
|Kukes|0.84|
|Vlore|0.79|
|Diber|0.76|
|Elbasan|0.76|
|Tirane|0.74|
|Shkoder|0.71|
|Berat|0.69|
|Durres|0.69|
|Lezhe|0.68|
|Fier|0.61|
|Data source:|School Stakeholder Survey|

> 26 In choosing the parameters for our game, we were guided by the particular characteristics and historical background of Albania. In the fear of finding a too low level of contributions to the group account, we designed the game so that coordination among participants would not be as difficult as in previously employed versions of the dichotomous public goods experiment.

> 27 Cardenas and Carpenter (2008) review studies employing public goods experiments in the field, and report that average contribution range from 33% of endowment in Chile  (Heinrich and Smith , 2004) to 81% in Peru (Karlan, 2005).

> 28 The public good game was played also by 5 teachers. The behavior of teachers is remarkably similar to that of parents, with 76% of teachers investing in the group account, and variations among counties and districts being highly statistically significant. Given the focus of the paper on participatory accountability, here we focus only on the behavior of parents.

15
# **5.3 Sensitivity analysis**

As a final exercise to better identify causality in the innovation wage premium, we use an exogenous shock to workers to re-estimate the premium. Specifically, we reduce our data to those workers in establishments that were not innovative in 2014 and experienced closure (following Dahl and Sorenson (2010); Schmieder and von Wachter (2010); Rege et al. (2019)), and follow these workers over time; since some workers are reallocated to other firms that are innovative while most of the remaining workers change companies but continue in non-innovative firms. To minimize temporary workers reallocation we keep workers that remain employed during the whole period of analysis, so once they change firms they remain employed. We match workers using initial firms’ and workers’ characteristics and use initial firm size, initial wages, average firm wage growth in the past, and average firm growth in 2013-2014.

Table 11 shows the results equivalent to Table 10 but using only workers that have experienced layoffs and found work in another firm. The results support the findings in the previous section of a positive, between 7 and 8 log points, and persistent innovation wage premium. Workers that move to firms that are innovative earn between 5 and 8 log points higher wages than other workers that reallocate to non-innovative firms. The effects are significant and persistent in magnitude in t+2 and t+3.

Table 11: Do reallocated workers get higher wages after moving to innovative firms?

|||y: log of|wage||
|---|---|---|---|---|
||(1)<br>Mean|(2)<br>Mean|(3)<br>Dynamic|(4)<br>Dynamic|
|Mean efect|0.0846***<br>(0.00641)|0.0669***<br>(0.00632)|||
|Outcome t+1|||0.0790***<br>(0.0102)|0.0565***<br>(0.0101)|
|Outcome t+2|||0.0774***|0.0539***|
||||(0.0102)|(0.0100)|
|Outcome t+3|||0.0786***|0.0550***|
||||(0.0103)|(0.0101)|
|Individual-Year|Yes|Yes|Yes|Yes|
|Region-Sector||Yes||Yes|
|Observations|79144|79142|79144|79142|
|R-squared|0.838|0.840|0.837|0.840|

_Note:_ The dependent variable is log of hourly wage. Innovative firms is measured by a dummy = 1 if there is more than 1 PoTec workers for firm _f_ in year _t_ but none in _t −_ 1. Heteroskedasticity robust standard errors clustered at the firm level are reported in parenthesis. ***, ** and * respectively indicate 0.01, 0.05 and 0.1 levels of significance.

Figure 5 shows the wage trajectory of workers wage differentials, treated versus non27
_26_ _Mexico: The 1997 Pension Reform_

**Financial hypotheses.** The _real interest rate_ was assumed to be 3.5% throughout the projection period. Assumptions on returns are quite conservative given that the average real interest rate between 1983-1995 in Mexico was almost 9%. For sensitivity purposes, an even lower real interest rate (2%) was used for a number of scenarios. Lower returns mean lower savings in an individual's account and a higher probability of choosing benefits under the old PAYG scheme or requiring the MPG from the State.

<!-- Start of picture text --> Assumptions: Real Interest Rate<br>35%<br>25%<br>20%<br>15% - -- - - -~/ -- - \ - -<br>10%, ,--------, ,/, --- ----<br>0% ---------- \--<br>-5%<br>co It U) co r- co 0 0 c s so LO co CD 0 a 0<br>co cO CO CO D CO co 0a) a) a) a) a) 0) 0) 0 . co<br>a) 0) CD v) 0) a) a) 0) 0) a) 0 0) 0) 0) 0 D 0<br>|+ Observed . _Average Assurnption]<br>Assumptions: Increase in Real GDP<br>10<br>0 ------------------- -- - _------- ------------- __ - _ ---------- ____ _ --__ -<br>6 ------------ --------) -- ----N-- -- --- ----- -<br>-8<br>- 42.-E -- ------ -- - -- -- - -- - - - - - - -- - - - - - --- - -- - -- - -- -- -- --- -- ---- -<br>~~ -2<br>o X co 0a N 6 )co co a N<br>rs F PI co co co 0) :n O o oo<br>a 0) - CD t _ 0 CM 0) N ~~~~~~~~~~~~~~~~~N ) 0) 0 0 CD o CM o CD CN co<br>F-* Observed Assurmpto<br><!-- End of picture text -->

_GDP growth_ assumnptions suggest a modest recovery for the next six years (less than projections made by the SHCP) to start with a conservative scenario. Growth after 2000 stabilizes below the rates observed during the 1 970s.
When margin (the measure of enterprise profitability) is included in the base regression, most of the coefficients on the dummy variables indicating ownership are statistically insignificant. This suggests that enterprises owned by foreigners, 'insiders' (i.e., managers and workers) and privatized enterprises owned by outsiders pay similar levels of bribes to state-owned enterprises (the default category). In contrast, the coefficient on the dummy variables indicating that the enterprise is a _de novo_ enterprise (i.e., newly established private enterprises) is statistically significant and positive, suggesting that _de novo_ enterprises generally pay higher bribes than privatized or state-owned enterprises.28 The effect appears to be large in quantitative terms - _de novo_ private enterprises reported paying between about 0.5 and 0.9 percent more of revenues in bribes than similar state-owned enterprises in the different model specifications.<sup>2 9</sup> As discussed previously, there are several plausible reasons why _de novo_ enterprises might pay higher bribes than other enterprises. First, many studies have found that private enterprises in general and _de novo_ enterprises in particular perform better than state-owned enterprises in the transition economies (see Djankov and Murrell, 2000). As noted previously, both the 'speed money' and 'endogenous harassment' hypotheses suggest that more profitable enterprises should pay higher bribes than less profitable enterprises. Consequently, to the extent that the measure of profitability (i.e., 'margin') does not fully account for perfonnance differences between state and privately owned enterprises, the ownership dummies might be partially proxying for performance differences.<sup>3 0</sup> Consistent with the idea that performance differences between private and state-owned enterprises might partially explain the result, the coefficients on the dummy variables indicating _de novo_ private enterprises and privatized enterprises increase in magnitude and the coefficient on privatized enterprises becomes statistically significant when margin is excluded (see column 4 of Table 3). Second, as noted

> 28 This pattern is consistent with the pattern observed for total bribes (i.e., bribes to all government officials not just utilities) before controlling for other factors that might affect corruption (see, European Bank for Reconstruction and Development, 1999, p 125-26).

> 29 Note that the actual average reduction would be smaller than this since most enterprises did not report paying bribes and, therefore, bribes could not be reduced for these enterprises.

> 30 Although foreign-owned enterprises also appear to consistently out-perform state-owned enterprises in the transition economies (see Djankov and Murrell, 2000), the small number of these enterprises in the sample (see Table 2) might make it difficult for the coefficient on this dummy variable to achieve statistical significance.

16
# **V: Extensions and applications**

We have used comprehensive off-chain transactions data from what has been the world’s largest peer-to-peer crypto exchange platform over the past five years. The analysis provides evidence that strongly suggests that Bitcoin is used actively as a vehicle currency in international transactions; in most countries it is also used extensively as vehicle for domestic currency transactions. Thus, it runs counter the oft-expressed view of crypto currencies as a purely speculative asset class. As we have emphasized, the nature of the data and, in particular, our ability to trace transactions price and currency is completely different than for research analyzing on-chain transactions.

Nevertheless, as computationally intensive as our exercise may be, the LocalBitcoins.com data set represents only a small share of the universe of off-chain crypto transactions when one includes centralized exchanges (where the exchange acts as financial intermediary).

In principle, our methodology can be applied for any more targeted investigation in any particular country/region, as well as to data from any exchange that identifies trades in terms of the fiat currency used to purchase crypto, as long as a number of minimum conditions are fulfilled (see Appendix A.6 for the precise description). Of course, when applying our methodology to data from other exchanges, one must account of their individual structure and features, including the average speed of clearing fiat money payments, and how to factor in pro-rata transactions fees. Also, in some centralized exchanges, the fee structure makes it more feasible to engage in very high-frequency speculative trading, although these almost invariably involve buying and selling in the same currency and one can thus exclude the impact of such trades on aggregates by exclusively analyzing international crypto vehicle currency trades.<sup>35</sup> Although data from many exchanges is private, regulators can typically access data from centralized exchanges in their own local jurisdiction, or potentially beyond that given sufficient

> 35 Similarly Wash trades, a fairly common and unregulated phenomenon in crypto markets (Cong et al, 2020; Le Pennec et al., 2021), can also be controlled, by assessing international capital flows exclusively.

33
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# **IV. Methodological design**

This section describes the sources of information and the working sample, the characteristics of the study’s population sample, the design of the impact evaluation of the program, as well as the methods and variables used in the analysis of intergenerational occupational mobility and the determinants of occupational attainment.

# A. _Sources of information and working samples_

The central source of information is the database from the Evaluation of Rural Households Survey (ENCEL in Spanish) 1997-2017 of PROSPERA.<sup>16</sup> In addition to the information from ENCEL, the National Income and Expenditure Household Survey (ENIGH in Spanish) 2016, representing the makeup of the population at both the national and federal state levels, was used as a source of information to validate the socioeconomic strata, as well as compare the characteristics of our study group with the same age group from the population of the country.

For the analysis on intergenerational mobility and determinants of occupational attainment we used the baseline from the ENCEL, provided by the Socioeconomic Characteristics of the Households Survey (ENCASEH in Spanish) collected in 1997, as well as the 2017 round of ENCEL. In the construction of the comparison groups we used information of the whole panel of ENCEL, as well as historic administrative data of PROSPERA that, among other variables, details the period and amount of cash transfers received between 1997 and 2017 for each household of the selected youths. Finally, for the estimation of the weights of the propensity score method we used the ENCASEH 1997.<sup>17</sup> Between 1997 and 2017, ten rounds of the ENCEL have been collected: including seven rounds between 1997 and 2000 and also in 2003, 2007 and 2017. The rounds between 1997 and 2000, captured information from the entirety of the households residing in an experimental sample of 506 rural localities (320 treatment and 186 control) of high or very high marginality, located in seven states of the country: Guerrero, Hidalgo, Michoacán, Puebla, Queretaro, San Luis Potosí and Veracruz. However, the experimental design was lost starting in 2000 when the control localities were incorporated to the program. This has presented a methodological challenge for the impact evaluation, which has had to use quasi-experimental methodology.<sup>18</sup> The last round, collected in 2017, prioritized information that would enable the long-term evaluation of PROSPERA, essentially to answer questions about its effects on children enrolled in the program between 1997 and 2000. For this purpose, 15,457 youths that in 2017 were

> 16 The panel database comprises of variables from all rounds of ENCEL. See World Bank (2018b).

> 17 In some cases, where necessary information of ENCASEH 1997 was not available, the information from the closest round was used.

> 18 See INSP (2007) and Yaschine, Hernández and Urquieta (2008), for a discussion on the loss of the experimental design between 1997 and 2007.

13