rafmacalaba/datause-encoder-data huggingface dataset:
allows for further insight on differences across educational qualifications. Budria and Pereira (2005) used quantile regression analysis to estimate the returns to different education qualifications in nine European countries. They find that returns to education generally increase with quantiles and that education (especially tertiary) has a positive impact on within group dispersion.

Results on the male returns by education level and quantile are presented in Table 6. In all East Asian countries except Cambodia and China, the highest returns are observed for university qualifications. Likewise, in all Latin American countries except Colombia (males) and Bolivia, returns to university qualifications exceed all other levels. Returns to primary education (obtained using 2 years of forgone earnings for all countries), are generally lower compared to tertiary qualifications; exceptions are found only in the cases of Cambodia, (especially at the bottom of the earnings distribution), Mongolia (upper end of the distribution), China (middle section of the distribution), and Venezuela (but not by much).

These findings are of interest given similar recent findings, even from Africa. Existing literature based on empirical evidence from a large number of studies in both developed and developing countries implies an empirical tendency for private internal rates of return to education to decrease at higher levels of education. This was first observed by Becker (1964) for the United States (for other countries, see Psacharopoulos and Woodhall 1985; Psacharopoulos and Patrinos 2004). However, along with the frequently discussed evidence of an increase in the returns to college educated workers in the United States (see, for example, Katz and Autor 1999; Acemoglu 2002) and evidence of the same for a host of countries, such as Brazil, Chile, Colombia, Mexico, Korea, Taiwan (China), Indonesia, Thailand and China (Topel 1999; Schultz 2004). Schultz (2003) finds that evidence from recent representative surveys in Africa confirms the same regularity. It was found that in six African countries (Ghana, Cộte d’Ivoire, Kenya, South Africa, Nigeria and Burkina Faso) the wage gains associated with each year of higher education are relatively attractive by world standards at between 10 and 15 percent. Schultz (2003) finds it ironic that world leaders of higher education are reluctant to consider wage structures for policy purposes because they think that wage returns to higher education are low and that large returns to education are probably evidence of distorted labor markets.

19
principals may be investing more in earlier grades driven by a belief that learning delays emerge early in the life of the child. Indeed such a belief is actively promoted by PASEC.

Using data from the teachers' questionnaires at follow-up we investigate whether there were dierential impacts of school grants on observable investments in 3<sup>_rd_</sup> and 5<sup>_th_</sup> grade students in Panel A of Appendix Table 9.<sup>14</sup> Some of the variables we can study are classroom materials (e.g., textbooks/manuals, desks, tables, etc), and teacher training. We nd no dierential impact of the program in any of these. When we examine other classroom characteristics or teacher behaviors, the only interesting dierence to report concerns student (mis-)behavior in the classroom. While in third grade there was a positive impact of the program on student behavior as measured by the number of times a day a teacher needs to demand silence, in fth grade there was a negative impact of the program on student behavior measured by this variable, and by the number of times a teacher has to punish a child for impolite behavior.

Observable parental investments are not dierent between grades three and ve (see Panel B of Appendix Table 9), which is prima facie evidence that the dierences are attributable to the eectiveness or administration of grants between the grades.

# 5.2 Distributional Impacts

Whether the program has dierent eects across the distribution is an important question relating to targeting. In Figure 3 we show parameter estimates together with their 95% condence intervals from a quantile regression of the relevant test scores for grade three in the rst follow-up (rst column) and second follow-up (second column), on the treatment indicator and including the usual controls, clustered by school. The eects of the grant are generally spread over most of the distribution as shown by the index in the fourth row, although the results are less precise due to smaller sample sizes, particularly in the second follow-up. In the second follow-up, for mathematics the eects are larger at the lower end of the distribution, while for French, oral, and PPVT scores the eects are somewhat larger in the mid- to the upper end of the distribution.

In the remaining part of the paper we look in greater detail at these results and consider

> to a lack of power. If the point estimates for grade 5 were as large as those for grade 3 it is likely that we would be able to reject that they were statistically equal to zero. When designing our study we anticipated that with our sample we would be able to detect program impacts of between 0.2 and 0.3 standard deviations, which is in line with what we nd.

> 14Ideally we would want to do this using 2 _nd_ and, say, 4 _th_ grade students, but we do not have the follow-up data for these teachers, although we have baseline data for them.

15
Table 7: Unobservable abilities

|||Math|(z-score)|||Language|(z-score)||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Social networks|-0.328<sup>_∗∗∗_</sup>|0.057|0.042|0.049|-0.416<sup>_∗∗∗_</sup>|0.107|0.120|0.066|
||(0.085)|(0.077)|(0.063|)<br>(0.073)|(0.088)|(0.087)|(0.076)|(0.089)|
|Schooing in years||0.107<sup>_∗∗∗_</sup>|0.101<sup>_∗∗_</sup>|<sup>_∗_</sup><br>0.109<sup>_∗∗∗_</sup>||0.129<sup>_∗∗∗_</sup>|0.117<sup>_∗∗∗_</sup>|0.148<sup>_∗∗∗_</sup>|
|||(0.010)|(0.012|)<br>(0.020)||(0.011)|(0.013)|(0.016)|
|Age at hire||-0.006|-0.002|-0.003||-0.006|-0.003|0.000|
|||(0.007)|(0.007|)<br>(0.009)||(0.006)|(0.006)|(0.006)|
|Father’s pri.school||0.071|0.056|0.069||0.269<sup>_∗∗∗_</sup>|0.164<sup>_∗∗∗_</sup>|0.114<sup>_∗_</sup>|
|||(0.083)|(0.056|)<br>(0.058)||(0.071)|(0.058)|(0.061)|
|Mother’s pri.school||0.061|0.042|0.015||0.149|0.120|0.168|
|||(0.090)|(0.115|)<br>(0.123)||(0.093)|(0.100)|(0.121)|
|Firm fxed efect|||X||||X||
|Firm-occ fxed efect||||X||||X|
|R-squared|0.141|0.299|0.214|0.149|0.123|0.428|0.306|0.283|
|N|3396|3396|3396|3396|3396|3396|3396|3396|
|||Big 5 (z-|score)||||||
||(9)|(10)|(11)|(12)|||||
|Social networks|0.069<sup>_∗_</sup>|0.132<sup>_∗∗_</sup>|0.034|0.002|||||
||(0.039)|(0.052)|(0.038)<br>|(0.040)|||||
|Schooing in years||0.012|0.008|0.004|||||
|||(0.009)|(0.006)<br>|(0.006)|||||
|Age at hire||0.004|-0.002|-0.003|||||
|||(0.005)|(0.005)<br>|(0.005)|||||
|Father’s pri.school||0.075|-0.060<sup>_∗_</sup>|-0.051|||||
|||(0.058)|(0.032)<br>|(0.035)|||||
|Mother’s pri.school||0.008|0.015|0.002|||||
|||(0.066)|(0.038)<br>|(0.035)|||||
|Firm fxed efect|||X||||||
|Firm-occ fxed efect||||X|||||
|R-squared|0.109|0.124|0.053|0.058|||||
|N|3390|3390|3390|3390|||||

_Note_ . All the dependent variables are normalized to have the zero means and the one standard deviations. The other controls included are entry year fixed effects and geographical division fixed effects. Standard errors clustered within an establishment are in parentheses. Significance levels: _∗∗∗_ = 1%, _∗∗_ = 5%, _∗_ = 10%.

29
<!-- Start of picture text --> wv<br>o<br>3<br>i= nN - _~ _-——"<br>E eo<br>s |~—<br>e<br>Oo<br>0 20 40 60 80 100<br>Quantile<br>——-— QTT —— Quantiles of F,<br>—— ATE at Quantile of Baseline Y<br>Note. Based on 10,000 observations drawn from Simulation 1. See appendix 1 for details,<br><!-- End of picture text -->

lows us to compare treatment effects on quantiles that correspond to the same value of nd strategies to compare quantile effects across groups.

Distribution of Treatment Impacts Introduction Part 3 discusses methods to estim te the impact of a policy or program on (functions of) the marginal distribution of outcome __n this part, we are interested in the distribution of indiThe distribution of treatment effects is required to answer questions such as: What proportion of the population was hurt by the program? As described in Part 2, these questions cannot be answered by the methods in Part 3, because, rather than effects on distributions, they concern effe ts on individuals. The central difficulty of tudying the distribution of individual treatment effects i that they require a . In order to estimate the impact of treatment o person p antly, the ind vidual estimates are only of interest n thedistributio of treatment effects in or er to answer qu_ tions such as those ve. The estimated
norms” and we formally define it as: _λoj_ = 1 _− Aojf /Aojm_ .<sup>12</sup> The second gender distortion arises if men and women earn a different wage rate _per human capital unit_ in the same occupation-sector pair. We refer to this wage differential as female “wage discrimination”, which we formally define as the percentage difference between gender-specific wage rates _wojg_ in each occupation-sector pair: _τoj_ = 1 _− wojf /wojm_ .<sup>13</sup>

**Consumer Preferences.** Individuals have non-homothetic CES preferences across agriculture, manufacturing, and service goods, allowing sectoral expenditure shares to change with income levels (Comin et al. (2021)).<sup>14</sup> Preferences for a consumption bundle _C_ over sectors _k_ = _{A, M, S}_ are defined by the following constraint:

where _σ >_ 0 is the standard CES elasticity of substitution across sectors, _εk_ is the non-homothetic elasticity of substitution, and _θk_ are sectoral preference shifters. Setting _εk_ = 1 _∀k_ corresponds to the standard homothetic CES preferences. Individuals maximize their utility subject to the budget constraint � _k_<sup>_p_</sup> _k_<sup>_C_</sup> _k_<sup>=</sup><sup>_I_</sup> _ojg_<sup>(</sup><sup>_z_).Theincomeearned</sup> by an individual of gender _g_ and ability _z_ who works in occupation-sector pair _oj_ is equal to _Iojg_ ( _z_ ) = _wojg_ exp( _zκojg_ ), where _wojg_ is the wage rate per human capital unit in _oj_ , which can vary across genders due to gender wage discrimination, and _κojg_ are occupation-sector-specific returns to ability.

Services are a CES composite of home and market services _S_ = _{hs, ms}_ , following Ngai and Petrongolo (2017) and Ngai et al. (2024) so that:

> 12Our model and employment data cannot distinguish whether gender differences in utility costs, _λoj_ , stem from intrinsic gender-specific preferences or from restrictive and coercive gender norms. For example, the underrepresentation of women relative to men in engineering jobs could reflect either a genuine preference differential (in the absence of constraints) or societal factors such as stigma, discouragement, or discrimination of women. Given this limitation, we restrict our analysis to a growth accounting exercise and we refrain from any normative welfare analysis, in line with the literature (Hsieh et al., 2019; Chiplunkar and Goldberg, 2024; Ranasinghe, 2024a; Cuberes and Teignier, 2014; Lee, 2024).

> 13In the baseline model, we assume that men and women with the same ability draw _z_ have the same productivity in each occupation-sector pair. Hence, we do not allow for any gender-specific comparative advantage across occupation-sector pairs. We relax this assumption in Section 7.4.

> 14Such income effects have been emphasized in the literature as an important driver of structural transformation, e.g., Herrendorf et al. (2013); Alder et al. (2022); Comin et al. (2021); Conte (2022); Fan et al. (2023).

17
Table 2: Summary statistics panel data models

|**Variable**|**Mean**|**Std. Dev.**|**Min.**|**Max.**|
|---|---|---|---|---|
|CPIA cluster A|3.713|0.869|1|6|
|CPIA cluster B|3.513|0.706|1|5.7|
|CPIA cluster C|3.422|0.700|1|6|
|CPIA cluster D|3.226|0.717|1|5.5|
|Heritage|37.959|16.318|10|91.5|
|quality of public administration|3.148|0.683|1|6|
|quality of budget management|3.345|0.79|1|6|
|efcacy of revenue mobilization|3.477|0.711|1|6|
|transparancy and corruption|3.043|0.796|1|6|
|cumul. PSG cond.|27.562|30.897|0|210|
|cumul. civil service cond.|4.576|6.68|0|39|
|cumul. fnancial mgmt. cond.|8.664|11.008|0|66|
|cumul. tax policy cond.|5.661|8.577|0|73|
|cumul. anti-corruption cond.|0.448|1.302|0|10|
|cumul. econ. mgmt. cond.|11.92|14.365|0|116|
|cumul. struc. pol. cond.|40.341|42.469|0|220|
|cumul. social. pol. cond.|8.418|12.971|0|83|
|log GDP per capita (PPP)|8.012|1.017|5.076|10.352|
|aid over GDP|0.04|0.063|-0.019|0.806|
|Political Rights|3.755|1.987|1|7|
|log gross IDA|2.102|2.224|0|8.311|
|debt crisis t-1|0.007|0.085|0|1|
|growth t-2|4.474|6.679|-50.248|106.28|
|trade openness|0.37|0.212|0.06|1.162|
|programmatic party|0.553|0.497|0|1|
|transition to democracy|0.013|0.114|0|1|
|age of democracy|27.318|25.316|1|139|

34
the husband's satisfaction with the marriage. For the sake of comparison we also include the number of female children, even though their effect on the husband's satisfaction is less clear.

Outside options in the marriage market, ml and m2 are measured by ihe marriage squeeze ratio<sup>19</sup> -the ratio of the number of women to the number men at marriageable ages (defined as women aged 10- 19/ men aged 20-29). ml is measured at the year of the marriage while m2 is measured ten years after the marriage. m2 is measured with a ten year lag because we do not know the exact time when the violence occurred but our qualitative interviews suggest that most incidences of violence occur within the first ten years of marriage. Additionally, since the Indian census is conducted every ten years, a ten year lag permits us to obtain the value of the marriage squeeze ratio from the next period available in the census. For bothl m2 and m2 we use the census year closest to the year of marriage. We obtain the data from the census for Mysore district in Karnataka State - the geographic region within which these households choose their spouses20.

# **5. Econometric Results, Policy Implications and Conclusion**

Table I provides a few summary statistics about the community. The families in our sample are poor. The mean schooling of both women and men is very low, at 1.14 years for women and 1.46 years for men. The average annual income of the wife's parents is 5,506 rupees ($625 in PPP adjusted US dollars) while that of the husband's family is 6440 rupees (PPP $731), but note that the standard deviation of the wife's family's income is very large at 6572 rupees compared to 784 rupees for the husband's family income. One possible reason for this large variation is measurement error because we did not interview the parents of the wife directly, but asked the wife to give us an estimate of her parent's income. Fertility levels are rather low, the average family has 1.18 male children and 1.2 female children.

The average dowry paid is 11,840 rupees with a standard deviation of about six times that. The large variation is mainly because dowries are both negative and positive since this community has moved from paying brideprices (negative dowries) to dowries. The average of '9 See Rao(1993) for a more detailed explanation of this measure, and Bhat and lHalli (1999) for a extended discussion on past and future trends in the availability of grooms in India.

20 Ooru and Halli villages are located in Mysore district. Beedu village is in Coorg district but arranges marriages with families exclusively within Mysore district. We should note that the marriage squeeze variable is an approximation of the true availability of spouses for the potter families because it measures the ratio for all endogamous groups in the district. However, because it is largely driven by reductions in 19
# **5. Robustness** Tests18

This section assesses the robustness of the results to alternative measures of financial structure, financial development, and external dependence. First, recognizing that there is not a universally accepted definition of bank-based versus market-based, we decided to isolate those countries with extremely bank-based or market-based systems. Perhaps, very "unbalanced" financial systems are robustly linked with industrial performance, even though continuous measures used thus far are not. We construc **t** hree additional variables. _Unbalanced-Bank_ equals one if Bank Credit is greater than the sample median and Value Traded is less than the sample median, and zero otherwise.19 Unbalanced-Marketequals one if Value Traded is greater than the sample median and Bank Credit less than the sample median, and zero otherwise.20 Finally, _Unbalanced equals_ one if either _Unbalanced Bankor U nbalanced Market_ equals one, and zero otherwise. The results indicate that classifying countries as having unbalanced financial systems does not help explain industrial growth patterns across countries.

To assess further the robustness of our results, we also constructed measures of financial development and structure that only include the assets privately-owned banks and therefore exclude the assets of state-owned banks. Our findings are robust to the use of credit to the private sector by _privately-owned_ financial institutions. Using recently compiled data by La Porta, Lopez-de-Silanes and Shleifer (2000) on the public share in the commercial banking sector, we construc **t** wo new measures of (1) credit to the private sector by privately owned deposit money banks and (2) credit to the private sector by privately owned financial

> 18 These results are available on request in Appendix B.

> 19Austria, Chile, Denmark, Finland, and Portugal are classified as having unbalanced bank-based systems.

> 20 Australia, Brazil, India, New Zealand and Sweden are classified as having unbalanced market-based systems.

23
most severe global challenge − from 29 percent in 2021 to 23 percent in 2022 − is related to the beginning of the war in Ukraine.

There is no direct means of testing the competing priority hypothesis, so we draw on indirect evidence. We use responses to the following questions about general attitudes about respondents’ living situation: _To what extent do you agree with the following statements: “The economic situation in our country is better today than around 4 years ago”; “I have done better in life than my parents”; “My household lives better nowadays than around 4 years ago”; “All things considered, I am satisfied with my life now”; “Children who are born now will have a better life than my generation”; “All things considered, I am satisfied with my job as a whole.”_ Responses to these questions take five values ranging from “strongly disagree” to “strongly agree.” They capture a general sense of worry about respondents’ livelihoods. We assume that this general sense of worry would crowd out concerns about climate change, as they are less immediately linked to the respondent’s livelihood.

<u>Table 3</u> displays the aggregate responses. Respondents’ assessment of their life situation improved between 2016 and 2023 across all six dimensions, with the differences in averages being positive and statistically significant. Figure 3 shows qualitatively similar results at the country level. The response averages are higher in 2023 than in 2016 (the markers are above the 45-degree line), indicating that, on average, respondents’ life situation across these six dimensions improved in most countries, and the general sense of worry may have declined. In theory, this result should be associated with an increase in concerns about climate change – but this is not what we observe.

We substantiate these results with Google trend data. The competing priority hypothesis would require the relative number of searchers about climate change to decline between 2016 and 2023. But the opposite is observed: Between 2016 and 2023, the Google trend index of search topics increased by 31 percent for environmental and climate change topics and 47 percent for global warming; it rose by 36 percent for health, 31 percent for education, 16 percent for immigration policies and border issues, and by 2 percent for public safety. We find analogous changes in the index at the country level.<sup>6</sup>

> 6 These results are available from the authors on request.

10
# **4.5 Summary and Multiple Testing**

Across Tables 3 through 7 we have estimated the impact of In-Tech funding on five different summary indices, and 52 different individual outcomes.<sup>12</sup> Table 8 re-examines our estimates using the sharpened q-values of Anderson (2008) and Benjamini et al. (2006) to control for this multiple testing. When we look at the overall summary index level, we see strong impacts of the In-Tech funding on additionality, and on collaboration within the consortia. There is no significant impact on collaboration outside the consortia. The impact on research and innovation is significant when we use our full bandwidth with controls, or the local mean specification, and has a sharpened q- value of 0.109 using the full bandwidth without controls. The impact on commercialization remains significant after multiple-testing adjustment when either specification with the full eightpoint range is used.

When we switch to examining individual outcomes, we see all 13 individual outcomes with p- values below 0.05 using the full bandwidth have sharpened q-values below 0.10 after accounting for testing 52 different outcomes. The four outcomes with p-values between 0.05 and 0.10 are no longer significant at conventional levels after the multiple testing correction. Eleven of these 13 significant outcomes are specific to the particular project proposed for In-Tech – they show the project proposed is more likely to have been funded, started, and completed if it was awarded funding (additionality); that it generated more collaboration within the consortia on R&D; it led to publications and patents being more likely to be produced relating to this project; and that it led to a product or process out of this project being commercialized. The other two outcomes show some broader collaborative benefits – researchers receiving funding now are more likely to know firms to talk to, and lack of finance is less likely to be a barrier to collaboration.

# **5. Conclusions and Discussion**

Our regression-discontinuity analysis shows that the In-Tech project did have measurable impacts on the core outcomes of innovation it was designed to foster. It resulted in more collaboration, more patents and publications, and some commercialization of the resulting product that came out of the project. There is thus clear additionality, as the grant led research and innovation activities to be undertaken that would not otherwise have occurred. This additional innovation does not

> 12 We exclude here the breakdown in Table 3 by call for proposals, and their respective summary indices.

23