rafmacalaba/datause-encoder-data huggingface dataset:
**Table S6.1.** ITT and knowledge effects on number of adopted ISFM technologies and integrated adoption of the full ISFM package, ADE of treatments and ACME of overall, principles and how-to knowledge as mediating variables.

|||Numbe<br>|r of ISFM te<br>|chnologies a<br>|dopted<br>|||Integrate<br>|d adoption<br>|of full ISFM<br>|package<br>||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|**Panel A: Coefficient E**|**stimates**||||||||||||
|T1 (extension)|0.546***|0.406***|0.643***|0.437***|0.480***|0.380***|0.084**|0.074***|0.098**|0.081***|0.070*|0.066***|
||(0.163)|(0.075)|(0.175)|(0.080)|(0.162)|(0.075)|(0.042)|(0.025)|(0.042)|(0.026)|(0.041)|(0.024)|
|T2|0.609***|0.490***|0.744***|0.542***|0.568***|0.479***|0.108**|0.097***|0.125***|0.106***|0.098**|0.089***|
|(extension + video)|(0.161)|(0.074)|(0.171)|(0.077)|(0.157)|(0.074)|(0.043)|(0.024)|(0.043)|(0.024)|(0.042)|(0.023)|
|Overall knowledge<br>score|2.909***<br>(0.180)|1.443***<br>(0.147)|||||0.398***<br>(0.050)|0.254***<br>(0.057)|||||
|Principles knowledge<br>score|||1.663***|0.760***|||||0.227***|0.114***|||
||||(0.142)|(0.109)|||||(0.039)|(0.039)|||
|How-to knowledge<br>score|||||3.102***|1.422***|||||0.483***|<br>0.344***|
||||||(0.193)|(0.157)|||||(0.056)|(0.057)|
|(Pseudo)R-squared|0.214|0.559|0.144|0.545|0.223|0.557|0.052|0.282|0.038|0.274|0.065|0.293|
**-4**

**Figure:** E. **10.3**

# EL SALVADOR LABOR INTENSIVE EXPORTS

## LABOR INTENSIVE TOTAL EXPORTS

<!-- Start of picture text --> LABOR INTENSIVE EXPORTS BY PARTNER<br>1970<br><!-- End of picture text --> <!-- Start of picture text --> 80000<br>e -<br>o 60000<br>a,<br>'o 40000<br>: 20000<br>o _-<br>U<br>1970 1980 1990<br><!-- End of picture text --> <!-- Start of picture text --> LABOR INTENSIVE EXPORTS BY PARTNER<br>1980<br><!-- End of picture text --> <!-- Start of picture text --> NA (0.46%)<br>ROW (0. 16%) MEX (0.16%)<br>CA (99.22%)<br><!-- End of picture text --> <!-- Start of picture text --> ROW (0.77°% MEX (0 07%)<br>CA (97.23%)<br>INTENSIVE EXPORTS BY PARTNER1<br>1990<br>ROW ( E<br>NA (40.12%)<br>CA (57.60%)<br>MEX (071%)<br><!-- End of picture text -->

**LABOR** INTENSIVE EXPORTS BY PARTNER1 1990
The mother’s education is a major determinant of the opportunity cost of time and higher education is expected to lead to lower fertility, thereby increasing the use of sex-selective abortions. Father’s education had little effect on the hazards and the use of sex-selective abortions and is not included. Education is divided into three groups: No education, one to seven years of education, and eight and more years of education. The models are estimated separately for each education level.

The sex composition of previous children affects both the timing of fertility and the use of sex-selective abortions. The exact effects depend on whether the “funeral pyre” hypothesis holds and whether parents continue to try to conceive sooner after a girl than a boy after prenatal sex determination becomes available. Sex composition is captured by dummy variables for the possible combinations for the specific spell, ignoring the ordering of births. As an example, for the third spell three groups are used: Two boys, one girl and one boy, and two girls.

The area of residence is a dummy variable for the household living in an urban area.<sup>21</sup> Area of residence captures both access to prenatal sex determination techniques and the cost of children. Moving from rural to more urban area should increase both the cost of children and access to prenatal sex determination. Both are expected to lead to greater use of sex-selective abortions.

The sex composition of children, area of residence and their interactions are interacted with the piece-wise linear baseline hazard dummies. In other words, the baseline hazards are assumed to be different depending on where a woman lives and the composition of her previous children. As an example, for the second spell a separate regression is run for each education level and in each regression four different baseline hazards are estimated (first child a boy in rural area, first child a boy in urban area, first child a girl in rural area, first child a girl in urban area). Although this substantially increases the number of regressions and estimated parameters it reduces the potential problem of including other variables as proportional effects. In addition, it forces attention to how much data are really available to estimate the determinants of sex-selective abortions.

The remaining variables are expected to affect hazards proportionally. Although fecundity

> 21 NFHS uses four categories for area of residence: Large city, small city, town and countryside. To reach a sufficient sample size urban areas are merged into one group.

19
rural Argentina earn much higher wages than their less-educated counterparts. In 2003, the association with the wage level of primary, secondary, secondary, and tertiary education relative to no or incomplete primary education was positive at all quantiles, controlling for other individual characteristics. Compared to the wages of non-educated workers and those with incomplete primary, median wages of workers with complete tertiary education were 136 percent higher; the comparable premium for secondary schooling was 72 percent. Workers with complete primary education received a 27 percent higher return compared to peers with no complete education.<sup>13</sup> Returns across the wage distribution vary a lot for workers with complete upper secondary and tertiary education; i.e. workers with completed primary education (secondary and tertiary education) in the low end of the income distribution are being paid comparatively less (more) than their peers in the high end. This would seem to indicate that: (1) there is wide heterogeneity in the quality of education in rural areas across the wage distribution, and (2) the capacity of workers to convert their educational capital into higher earnings through labor market networks is not very similar for poorer and richer workers. Hence, poor people with secondary education seem to benefit more from good labor market connections or social networks than richer people.<sup>14</sup> Workers with complete secondary and tertiary education face decreasing returns across the wage distribution, however: those at the low end are paid proportionally more than those at the high end, indicating that workers with the same level of education are not compensated equally. The poorest (25th quantile) receive a wage premium when completing secondary education of 150 percent, while the richest (90th quantile) receive only 89 percent. One possible explanation is that social networks that facilitate labor market connections operate better among the poorer than the richer segments of the rural labor force.

> 13 Recent research shows that returns to education and skills in urban areas of Argentina have increased for all three levels of education in urban areas in the last decade, and it has been more pronounced for tertiary education (World Bank 2004).

> 14 Hence, workers with the same level of education are not compensated equally. This finding has also been found in the case of blacks in South Africa (Mwabu and Shultz 1996). However, here findings may be due to the fact that there are very few workers in the sample with completed tertiary education and therefore findings cannot be seen as robust.

21
25

|University|0.004|0.067|0.004|0.061|0.767|
|---|---|---|---|---|---|
|**Lived in Dar es Salaam all their life**|0.750|0.433|0.669|0.471|0.000|
|**Marital status**||||||
|Never married|0.721|0.449|0.796|0.403|0.000|
|Divorced/separated|0.243|0.429|0.169|0.375|0.000|
|Currently married|0.013|0.115|0.016|0.124|0.668|
|Widowed|0.013|0.111|0.015|0.120|0.662|
|Cohabiting|0.010|0.099|0.005|0.068|0.146|
|**Has at least one child**|0.744|0.437|0.668|0.471|0.000|
|**Neighborhood poverty ranking (1-**||||||
|**5)**|2.682|0.684|2.687|0.684|0.872|
|**Self-ranked social status (1-7)**|3.720|1.526|3.715|1.530|0.931|
|**Income**||||||
|Total income (30 days) Tsh - mean|256200|303441|252414|346987|0.788|
|Income from sex work (30 days)<br>Tsh - mean|238419|280312|235261|228049|0.774|
|**Place of work**||||||
|Pub/bar|0.405|0.491|0.419|0.494|0.505|
|Guesthouse|0.259|0.439|0.237|0.425|0.225|
|Street|0.078|0.269|0.106|0.309|0.023|
|Night club/disco|0.087|0.283|0.071|0.257|0.164|
|Brothel|0.069|0.254|0.067|0.250|0.802|
|**Clients and condom use**||||||
|Number of clients /week|8.372|6.861|8.463|6.753|0.755|
|Amount earned with a condom|13942|15091|12700|16267|0.071|
|Amount earned without a condom|19731|41189|16697|28809|0.075|
|**Ever had an STI**|0.065|0.247|0.076|0.266|0.323|
|**Last time tested for STIs**||||||
|Within the last month|0.023|0.151|0.016|0.124|0.193|
|1-2 months ago|0.089|0.284|0.097|0.297|0.482|
|3-6 months ago|0.164|0.370|0.167|0.373|0.835|
|6 months - 1 year ago|0.124|0.330|0.109|0.312|0.268|
|Over a year ago|0.194|0.396|0.171|0.377|0.154|
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19
||**Authors**|**Year**|**Type**|**Quality**|**Effect**|**Country context **|**Type of Job**|
|---|---|---|---|---|---|---|---|
|139|Springer et al.|2010|field experiment|5|failed|OECD|Craft|
|140|Stajkovic and Luthans|2001|field experiment|5|positive|OECD|Production|
|141|Stazyk|2010|observational|4|positive|OECD|Unclassified|
|142|Steel et al|2007|observational|3|positive|OECD|Craft|
|143|Straberg|2010|observational|3|neutral|OECD|Craft|
|144|Streib and Nigro|1993|observational|3|neutral|OECD|Unclassified|
|145|Vaghela et al.|2009|observational|3|positive|OECD|Craft|
|146|van Dijk, Sonnemans and va|2001|lab experiment|5|positive|OECD|Production|
|147|Vegas|2005|observational|3|positive|Developing|Craft|
|148|Vegas and Umansky|2005|observational|3|positive|Developing|Craft|
|149|Willems, Janvier and Hende|2006|observational|2|neutral|OECD|Unclassified|
|150|Witter et al|2011|observational|3|neutral|Developing|Craft|
|151|Woessmann|2010|observational|4|positive|OECD|Craft|
|152|World Bank|2001|observational|3|positive|Developing|Craft|
|153|Younget al|2007|observational|4|positive|OECD|Craft|

36
Trimble, C., M. Kojima, I. Perez Arroyo, and F. Mohammadzadeh. (2016). “Financial Viability of Electricity Sectors in Sub‐Saharan Africa: Quasi‐Fiscal Deficits and Hidden Costs”. _World Bank Policy Research Working Paper_ No. 7788.

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30
income level, whereas the opposite occurs with ICT, for which the richer the country, the greater its marginal impact on export performance.

Illustrative estimates show that improvements in infrastructure and border and transport efficiency halfway to the level of the regional top performer can be substantial. However, the high cost of investment in physical infrastructure is a factor to be considered. Of course, investment in physical infrastructure can also have large spillovers that should be taken into account in the cost-benefit analysis, but they are difficult to measure.

The net balance of costs and benefits cannot yet be stated with certainty for a given country. Such an assessment can only be made within the framework of specific infrastructure project appraisals, and it can only be addressed on a case-by-case basis. However, improvement in other areas, such as border and transport efficiency, where costs are considerably lower in comparison with investment in physical infrastructure, shows promising results for developing countries. Although general estimates on the yield of these projects is difficult to obtain, Helble, Mann, and Wilson (2009) use detailed data on aid flows to estimate the responsiveness of trade flows to specific types of foreign aid directed to enhancing trade competitiveness in developing countries. They find that relatively small amounts of aid targeted at policy and regulatory reform, in contrast to aid for broad sector-specific projects or trade-related infrastructure has relatively greater elasticity with respect to trade flows. Indeed, they estimate that a one percent increase in aid directed toward trade policy and regulatory reform yields an increase of 0.009 percent in trade. This implies that average marginal effect in the authors’ sample of that a one dollar increase in aid would result in an increase of about US$697 in trade.

Hallaert and Munro (2009) propose to adapt the growth diagnostics procedure developed by Haussman et al. (2005) to trade expansion, in order to pinpoint the most binding constraints affecting trade performance in a given country. An approach along these lines based on a more rigorous microeconomic setting can be a promising road to identify constraints to trade expansion in a given country.

In summary, this paper intends to provide policymakers information about the effectiveness of possible interventions in four areas of trade facilitation. We stress that the results presented here are targeted at stimulating discussion and helping policymakers and stakeholders arrive at a tentative prioritization of their efforts in this area. In the future, more detailed analysis is required in relation to particular reform programs, covering costs and benefits for a developing 28
# **6) OTHER TRADE IMPEDIMENTS**

It is by now accepted that institutional, infrastructure and regulatory burdens present obstacles to the movement of goods across borders. Obstacles include poor infrastructure (Limão and Venables, 2001), market regulations that restrict competition in transport (Teravanithorn and Raballand, 2008) and weak micro-level institutions, including port efficiency, customs environment, regulatory environment and policies affecting cost of entry (Johnson and others, 2007; Wilson and others, 2005; Njinkeu and others 2008). These micro-level institutional effects are often greater impediments to African trade than tariff barriers (Portugal-Perez and Wilson 2009).

For example, it costs more than twice as much to clear a standard 20-foot container for exports or imports in SSA and SADC countries as in the East Asia & Pacific (Table 7). Costs are particularly high in Zimbabwe, Botswana, Zambia and Congo (DR). The time taken to export and import is also high in SSA and SADC countries compared to other regions: more than three times that of the OECD and twice that of Latin America & Caribbean (Table 7).

**Table 7: Time** **<u>delays and trade costs</u>**

|Region or Economy|Documents<br>to export<br>(number)|Time to<br>export<br>(days)|Cost to<br>export<br>(US$ per<br>container)|Documents<br>to import<br>(number)|Time to<br>import<br>(days)|Cost to<br>import (US$ per<br>container)|
|---|---|---|---|---|---|---|
|East Asia & Pacific|6.7|23.1|909.3|7.1|24.3|952.8|
|Eastern Europe & Central Asia|6.5|26.8|1581.8|7.8|28.4|1773.5|
|Latin America & Caribbean|6.8|18.6|1243.6|7.3|20.9|1481|
|Middle East & North Africa|6.4|22.5|1034.8|7.4|25.9|1221.7|
|OECD|4.3|10.5|1089.7|4.9|11|1145.9|
|South Asia|8.5|32.4|1364.1|9|32.2|1509.1|
|Sub-Saharan Africa|7.8|33.6|1941.8|8.8|39.4|2365.4|
|SADC|7.4|35.1|1903.7|8.8|42.4|2348.3|
|Angola|11|65|2250|8|59|3240|
|Botswana|6|30|2810|9|41|3264|
|Congo, Dem. Rep.|8|44|2607|9|63|2483|
|Lesotho|6|44|1549|8|49|1715|
|Madagascar|4|21|1279|9|26|1660|
|Malawi|11|41|1713|10|51|2570|
|Mauritius|5|14|737|6|14|689|
|Mozambique|7|23|1100|10|30|1475|
|Namibia|11|29|1686|9|24|1813|
|South Africa|8|30|1531|9|35|1807|
|Swaziland|9|21|2184|11|33|2249|
|Tanzania|5|24|1262|7|31|1475|
|Zambia|6|53|2664|9|64|3335|
|Zimbabwe|7|53|3280|9|73|5101|

Source: World Bank Doing Business Survey. http://www.doingbusiness.org/ExploreTopics/TradingAcrossBorders/ [Accessed 10 June 2010] Yet simple unconditional cross-country comparisons such as these do not take into account the interdependent relationship between geography and trade costs. African countries are often far from developed markets and many countries are landlocked, all of which raise internal and external transport costs (Behar and Venables, 17
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trend assumption. This implies that in the absence of the quota intervention when accounting for the fixed effects, the outcome trends for the treated and untreated groups would follow a similar pattern as the trends between the two groups in the pre-treatment period. I found evidence from an event study analysis that the parallel trends assumption holds.

I use the universe of matriculation results of Kankor test-takers from the academic years 2013–2018. This dataset contains detailed applicant characteristics for 1.51 million participants, such as gender, test score, high school name, and the unit of study to which they were admitted. For this study, I focus _exclusively_ on a subset of 336,221 applicants admitted to public universities over six-years. I employ district-level monthly average luminosity data from 2014–2015 to indicate economic development<sup>3</sup> and identify applicants from areas of low socioeconomic status (SES). This categorization enables me to assess the impact of the quota intervention on the participation and score-related outcomes of female applicants with high and low<sup>4</sup> statuses.

The quota led to a nine-percentage point increase in the matriculation rate of female applicants in the treated concentrations, a 32% increase over the control group mean. Specifically, the quota raised the matriculation rate of female applicants from low-SES districts by three percentage points, a 30% increase compared to the control group mean. This expansion in women’s admissions to public IHEs has lessened the gender gap. However, it has also resulted in a reduced score threshold for female students’ admissions to treated units. This is revealed by a significant decrease of 0.04 units in the average score ratio of female-to-male applicants in the treated concentrations, a 4% reduction compared to the control group mean. This indicates that the preferential treatment enrolled female students with lower Kankor scores. This favored treatment might raise concerns about applicants’ academic performance post-admission,

> 3I acknowledge the coarse nature of this classification. However, given the lack of granular metrics at the applicant and/or district-level in Afghanistan, the use of nightlight data provides a plausible indicator of economic development at the district-level. I am unable to differentiate between the socioeconomic statuses of individual applicants level and instead rely on district classifications.

> 4I use the average of luminosity across district as the source of operationalization. Low-socioeconomic status indicates districts with an average luminosity value less than the mean.

3