rafmacalaba/datause-encoder-data huggingface dataset:
# **6. Conclusion**

So, does import competition affect product quality? We investigate this question using a rich dataset of Chilean plants and products and a regression framework where increases in unit values proxy for product quality improvements and transport costs are the exogenous measure of import competition. Our results show that import competition does have a positive, significant, and robust impact on product quality for Chilean plants. To the extent that these findings can be generalized to other middle-income countries, they suggest that increased exposure to import competition, including that from China and India, can be beneficial by encouraging their producers to follow the “high road” to competitiveness (Pietrobelli and Rabellotti, 2006). Moreover, in light of the evidence provided by Iacovone and Javorcik (2008) that Mexican plants invest in product quality upgrading before they export, our findings suggest that over time plants - including those with no export experience - may be able to progressively target more sophisticated export markets.

However, our evidence also suggests that import competition may be insufficient to enable quality upgrading where the “technology gap” between foreign competitors and local producers is high. In so far as quality upgrading for non-frontier products presents a less demanding task than more radical innovation, our findings suggest that while import competition encourages upgrading, other policy tools will be necessary for more radical innovations.

Our findings also suggest that the recent models of heterogeneous multi-product firms such as those of Bernard et al. (2006c) and Eckel and Neary (2006) that examine changes in firms’ product mix as a response to trade costs’ reductions have yet to exploit other interesting margins of adjustment such as the possibility of quality upgrading.

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equality of impacts by age and gender, nor do we find robust heterogeneity with respect to other individual characteristics. Consistent with these modest overall impacts, we do not find treated individuals to be in better mental health, to be any more likely to expect to be working in 2 years’ time, or to expect a higher future subjective well-being than individuals who are not trained. An expectations elicitation exercise reveals these impacts to be substantially smaller than anticipated by either program applicants or Employment Agency staff. Using administrative data to examine longer-term impacts, we confirm a small, but positive significant impact on formal employment over a one year horizon, which dissipates over time so that there is no significant impact on formal employment three years after training.

We then examine heterogeneity of impacts along a pre-specified causal chain of training impact in an attempt to understand why the impacts were lower than expected, and under what circumstances they are higher. We test which course characteristics associated with training quality matter, and we that find attendance rates for courses are relatively high, and there is little evidence of heterogeneity by course length, teacher quality measures, participant assessments of what the course teaches, or by the unemployment rates in the labor markets in which courses are taught. Instead, we find training to have stronger impacts when offered by private providers, with this heterogeneity being robust to adjustments for multiple hypothesis testing. Controlling for observable differences in other course characteristics, local labor market conditions and the applicants for different courses does not change this conclusion. This finding that impacts are higher with private provision is consistent with some non-experimental work (e.g. Jespersen et al, 2008) but to our knowledge this is the first time it has been found in an experimental setting. However, longer-term administrative data show that the private provider effect is no longer significant in the medium term. As a result, cost-benefit analysis suggests that even privately-run courses struggle to provide positive returns.

The remainder of the paper is structured as follows: Section 2 discusses the context and Turkey’s vocational training courses, Section 3 the experimental design, and Section 4 our data collection and estimation methodology. The main results are presented in Section 5, while Section 6 traces out a causal chain from training to employment. Section 7 examines in more detail the differences between private and public courses, and Section 8 discusses costeffectiveness. Section 9 concludes.

4
assignment and school mean exam score (Figure 2), all adjusted analyses presented account for these two stratification factors. Data for classes 1 and 5 combined were used for the health outcome analyses. However, as different assessments were administered for classes 1 and 5 for the evaluation of attention (e.g. pencil tap for class 1 and code transmission for class 5), literacy and numeracy outcomes, analyses were conducted for each class separately. Separate GEE analyses were conducted for the first and second follow-ups. No formal adjustment was made for multiple testing therefore p-values should be interpreted with due caution. However as specified in the statistical analysis plan formal testing was restricted to two primary and three secondary pre-specified outcomes.

For comparison purposes, we also obtained estimates from an unadjusted model that did not adjust for baseline outcome measures, child characteristics or study design (literacy group and mean school-exam score) and hence retained all study children assessed at follow-up regardless of whether they had baseline measures. Secondary analyses were conducted additionally adjusting for stunting, schoolfeeding program and SES on top of the pre-specified variables. These additional adjustments had no notable impact on the effect estimates and are not presented.

In order to gain power and account for missing data, random effects models, using a likelihood-based approach were fitted to the one-year and two-year follow-up data simultaneously (Tables S1, S2, S3, S4, S5, S6 and S7; Text S1). Additional sensitivity analyses were conducted to examine intervention effects when children who had transferred from their original school were excluded from the analyses (Table S8).

## **_Role of the funding source_**

The funders had no role in the study design, data collection, data analysis, data interpretation or writing of the report. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication.

# **Results**

## **_<mark>Trial profile and baseline data</mark>_**

<mark>One hundred and one schools were randomized to one of the two study groups (Figure 3). I</mark> n total, 7,337 children aged between 5 and 20 years (median: 10 years and IQR: 8-13 years) were randomly selected in January 2010 of which <mark>5,772 (78.7%) parents consented,</mark> with no real differences found between groups in terms of percentage of parents refusing and not attending the meetings. Overall, 5,233 children were initially enrolled, of which <mark>5,176 (98.9%) children were eligible for follow-up after the baseline assessments. Characteristics of the children included in each of the study groups are shown in Table 1. T</mark> he numbers of children per school ranged from 18 to 58 but overall were well balanced between groups (control: median, 52 inter-quartile range [IQR], 50-54 and intervention: median, 53 IQR, 50-55). A difference in percentage of children unavailable for the baseline health surveys was observed between the groups with 5.1% and 10.1% unavailable in the control and intervention groups respectively (Figure 3).

9
than produce them domestically, the financial system will shrink after trade opening, ceteris paribus.

We test the predictions of our model using a dataset compiled by Beck, Demirguc-Kunt, and Levine (2000). It consists of measures of financial development plus a variety of other country-level variables, including trade openness, for 22 OECD countries and 55 developing countries. The dataset is also available as a panel, reporting data at 5-year intervals from 1965 to 1995. The list of countries is presented in Table 1.

The key question is how do we proxy for financial comparative advantage: which countries should we expect to export financially dependent goods? The model gives the answer in terms of per capita income: a richer country will specialize in the financially intensive good. Perhaps at least as relevant empirically is the quality of institutions dimension: countries with better enforcement of contracts and property rights will be expected to export the financially dependent good under trade. In practice, of course, institutional quality and per capita incomes are extremely highly correlated (ρ ≈ 0.85), and thus the two sources of financial comparative advantage will tend to reinforce each other. Thus, in the present empirical work we will take per capita incomes as a sufficient statistic for the degree of financial comparative advantage.

Thus, we attempt to show that trade led to faster financial development in richer countries, and slower financial development in poorer countries. As a first pass, we split the sample into OECD and non-OECD countries,<sup>4</sup> and run the following basic specification in the two subsamples: where FinDevj is the change in country j’s financial development over the period 196595, Trade65j is a country’s trade openness in 1965 and Xj is a set of controls. Financial development is measured by the ratio of private credit to GDP, while trade openness is measured by (Exports+Imports)/GDP. The list of controls includes initial level of private credit to GDP, initial per capita GDP, a measure of human capital (average years of secondary schooling in the population), as well as legal origin dummies. Variable definitions and summary statistics are presented in Table 2.

The results are presented in Table 3. Column 1 estimates equation (16) for the OECD countries. In developed countries, trade openness has if anything a positive effect on sub-

> 4 As expected, the differences in per capita income across these two groups are pronounced. In the OECD sample, per capita GDP is $7354, while in the other sample, $1010.

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Figure 5: Labor Income by household type, % Figure 6: Main source of income reported by difference from baseline households (% of households) <!-- Start of picture text --> 50<br>Quintile 5 45<br>40<br>Quintile 4 35<br>30<br>Quintile 3 25<br>20<br>Quintile 2 15<br>10<br>Poorest 5<br>0<br>Quintile 5<br>Quintile 4<br>Quintile 3<br>Quintile 2<br>Poorest<br>-64 -63.5 -63 -62.5 -62 -61.5 -61<br>Total Male headed Female headed<br>Rural<br>%<br>Other sources<br>Urban<br>Non-agricultural business Agricultural activity Paid employment Help from others<br><!-- End of picture text --> <u>Source: Author’s calculations from HFPS 2020</u>

# 3.2. Impact on female-headed households

Around 23 percent of Chad’s households are female-headed, divided equally among rural and urban areas. Male-headed households tend to be two-parent households, whereas 70 percent of the female heads in Chad are either widowed, divorced, or separated (Figure A. 2 in the Appendix). Female-headed households face particular financial constraints related to often being single-income households and having a larger dependency ratio.<sup>11</sup> Based on ECOSIT 4, female working members of female-headed households are more likely to work as non-salaried non-farm workers or workers in non-family farms than female workers in male-headed households likely to work as family farm workers or be owners of family farms. There is an over-representation of female-headed households among the poorest urban quintile compared to male-headed households, and an under-representation in the highest urban quintile despite this group being the recipient of most of the household transfers of remittances from abroad<sup>12</sup> (Figure A. 4 in the Appendix). Almost 20 percent of the urban female-headed households fall into the poorest urban quintile versus 14 percent of male-headed households.

Results from the COVID-19 HFPS show that three months into the pandemic, 63 percent of the interviewed households reported having experienced a decrease in income from their main source of income. Non-agricultural business income is the main source of income reported by 39 percent of the households interviewed in the COVID-19 HFPS; followed by agricultural income, paid employment, and domestic remittances (Figure 6). There are no statistically significant differences between the proportion of total male and female-headed households reporting decreased income (Table 6, Column 1). However,

> 11 These characteristics are not unique to female headed households in Chad. See Milazzo and van de Walle (2015) for more on female headed households in Africa.

> 12 The share of remittance in income is very low in Chad. For comparison, Chad and Senegal have similar proportion of migrants relative to their total population, but the share of remittances in income in Senegal is 27 percent while the corresponding share in Chad is only 5 percent.

12
Table 9: Regression results from baseline specification for manufacturing only (continued)

||(13)|(14)|(15)|(16)|(17)|(18)|(19)|(20)|(21)|(21)|(22)|(24)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||<br>‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|<br>‐‐ TFPQ Hsieh|<br>and Klenow|<br>using countr|<br>y specific IO‐‐‐|<br>‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|<br>‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|<br>‐‐‐‐‐‐‐‐‐‐‐‐‐‐ TF|<br>PQ Hsieh and|<br>Klenow using|<br>EU‐wide IO‐‐‐|<br>‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|
|Overallt‐2|7.123*||||||‐6.482||||||
||(0.088)||||||(0.265)||||||
|Entryt‐2||‐9.129***|||‐10.406***|‐10.337***||‐4.288|||‐4.195|‐4.346|
|||(0.006)|||(0.009)|(0.010)||(0.247)|||(0.472)|(0.455)|
|Conductt‐2|||7.208**||7.538**|7.597***|||‐6.396||‐6.250|‐5.770|
||||(0.013)||(0.010)|(0.010)|||(0.140)||(0.143)|(0.178)|
|FDI Overallt‐2||||3.847||3.080||||2.574||8.144|
|||||(0.290)||(0.513)||||(0.674)||(0.190)|
|FE firm|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|YES‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|
|FE c‐y|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|YES‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|
|FE s‐y|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|YES‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|
|Observations|1392899|1495481|1414829|1587437|1392899|1392899|1382913|1485494|1404843|1570712|1382913|1382913|
|R2A|0.840|0.838|0.841|0.847|0.840|0.840|0.840|0.838|0.841|0.847|0.840|0.840|
|R2W|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|
|RMSE|0.509|0.515|0.507|0.517|0.509|0.509|0.508|0.514|0.507|0.515|0.508|0.508|

Note: * p<0.10; ** p<0.05; *** p<0.01. The dependent variable is in log. Robust standard errors clustered at the country‐industry‐year level.

39
9 feminism.<sup>22</sup> / Girls trained in a single-sex environment were<sup>also found to</sup> demonstrate lower levels of fear of success and were more likely<sup>to exercise</sup> leadership roles.-l No significant single-sex<sup>effects for boys were reported</sup> in any of these studies.

DeveloRing countries. Although single-sex education is<sup>somewhat</sup> more common in developing than developed countries,<sup>research on gender issues</sup> in education in developing countries is considerably more likely to focus on the lower school attendance rates of females than on the differential effectiveness of sex-segregated and coeducational schools. An exception is a recent study comparing the relative advantages of the two types of<sup>schools for</sup> male and female eighth graders in Thailand, using longitudinal data on mathematics achievement before and after eighth grade.2 2.Single-sex schools were found to be more effective for female students, where coeducational schools were more effective for male students. While<sup>the longitudinal</sup> nature of the data, as well as the econometric two-stage modeling of choice into school and effects of school on achievement, make these findings particularly strong, the fact that most of the single-sex schools were private<sup>whereas the</sup> coeducational schools were mostly run by the state, however, results in<sup>an</sup> unavoidable but unfortunate confounding of school grouping and school governance in that study.

In many developing countries, girls are less likely to attend<sup>school</sup> than boys. This causes a special problem for investigations of the relative effectiveness of single-sex and coeducational schools for the two genders, since those girls who do attend school come from more advantaged<sup>homes than</sup> boys, with girls more likely to have more educated parents<sup>who are members of</sup> the emergent professional classeF. _23i_ This was found to be particularly true
vices Colombia offers all forced migrants (regardless of status) or unwilling to exercise these rights for fear of being identified and deported. Table A.1 describes the services provided to all Venezuelan forced migrants and the additional services and benefits PEP offers.

The PEP program aimed to foster the integration of forced migrants into Colombia’s society and economy by providing access to formal labor markets and entrepreneurship, and by removing barriers to education, healthcare, and other public and private services.

# **II. B Rollout and eligibility**

The Colombian government first introduced two waves of the PEP program that targeted more affluent Venezuelans who migrated through official immigration checkpoints and had lawful migratory status. Nearly 182,500 permits were issued during these two waves but this excluded a large share of Venezuelans in Colombia because the majority had migrated through illegal border crossings, overextended stays, or with a temporary document ( _Tarjeta de Movilidad Fronteriza_ ) that only allowed short stays in border areas.<sup>7</sup> To address the large share of forced migrants still without regularized status, the Colombian government introduced a third PEP wave in August 2018 for all Venezuelans who had registered in the Administrative Registry of Venezuelan migrants (again, RAMV for its Spanish acronym). RAMV had been implemented between April 6 and June 8, 2018—two months prior to PEP’s enactment. Recall that RAMV was not designed to grant work permits and was not advertised in that way; it was only instituted to count the Venezuelan migrants who had not yet regularized their migratory status. However, in August 2018, just a few weeks before leaving office, Colombian President Juan Manuel Santos unexpectedly declared that forced migrants who had registered in RAMV were now eligible for a new wave of the regularization program: PEP-RAMV, the focus of this work. For brevity, we refer to PEP-RAMV simply as PEP. Figure 1 illustrates the rollout of RAMV and PEP.

Unlike previous regularization programs around the world, PEP did not impose eligibility

> 7The _Tarjeta de Movilidad Fronteriza_ facilitated the movement of Venezuelans who lived near the VenezuelanColombian border and crossed on a regular basis to shop, visit family members, and attend school, among other reasons. It only permitted free movement inside the border areas and no longer exists.

10
_Figure 7.Unfairess perceptions and Absolute Gini coefficient_

- _a) Over countries (Pooling all the countries and years)_

<!-- Start of picture text --> 100<br>80<br>60<br>Unfairness = -0.24 Abs Gini + 102.9<br>R2 = 0.04<br>40<br>80 90 100 110 120<br>Absolute Gini (index)<br>% Unfair or Very Unfair<br><!-- End of picture text -->

- _b) Over time (Cross country average, 1997-2015)_

<!-- Start of picture text --> 100 120<br>Absolute Gini coefficient (RHS)<br>90<br>100<br>80<br>% Unfair or very unfair (LHS) 80<br>70<br>60 60<br>1997 2000 2005 2010 2015<br>%<br><!-- End of picture text --> Note: Panel (a) of this figure presents the cross-county correlation between unfairness perceptions and the absolute Gini coefficient for all LA countries for which data is available over the 1997-2015 period. The absolute Gini was normalized so the average over the period is equal to 100 in every country. Figure does not include data points that were calculated through linear interpolations. Panel (b) shows evolution of the unweighted average absolute Gini and unfairness perceptions. To ensure the same set of countries is analyzed over time, a linear extrapolation of inequality indicators was made in the years in which income microdata was not available.

15
Therefore in the analysis we will allow the parameter β to vary across different types of firms and inputs. Also we will allow for a nonlinear effect of a demand shock on input smoothing by including a quadratic term (ΔYit)<sup>2</sup> in the above equation, as a firm which faces fixed adjustment costs will not adjust inputs initially for small negative shocks, unlike a firm facing convex adjustment costs.

# _Smoothing of labor inputs_

Different versions of the empirical model will be estimated for different indicators of input use. We start with the use of labor inputs. As already presented in Table 1, the CRBS 2009 survey asked the firm by which percentage the number of permanent workers had increased relative to the same period one year before.<sup>16</sup> On average, firms reduced the number of workers. However, most notable is that the firms reduced the input use of unskilled production and non-production workers much more than that of managers, professionals and skilled production workers. This may reflect differences in adjustment costs, as the hiring and firing of skilled workers is typically more costly than that of unskilled workers (Hamermesh and Pfann 1996).

In order to understand to which extent the changes in labor input use are the result of demand shocks, we first estimate model (5) for reported changes of each type of labor input controlling for the demand shock as experienced by the firm. The demand shock is measured as the percentage change in the firm’s total sales (domestic and foreign) in the period since one year ago. In terms of the vector Zit, we include dummies for the type of labor, sector, firm size and location. These dummies control for factors that affect the (relative) adjustments of each type of labor, such as differences in labor-output elasticity across type of labor, changes in relative prices of types of labor, and changes in raw materials and output prices. In the subsequent analysis we will also present estimates where we allow the regression coefficients (and specifically β) to vary by skill type of worker (see Table 6).

Column (1) of Table 5 presents the results of the regression. The standard errors are corrected for clustering at the firm level and t-values are reported in parentheses.<sup>17</sup> There is a positive correlation between the percentage change in permanent workers and the percentage change in sales. The coefficient

> 16 Of course firms may also employ temporary workers next to permanent workers. However, in Cambodia, the percentage of temporary workers is only 3.6% of total employment. Consequently, few firms report changes in the number of temporary workers and therefore the analysis focuses on permanent workers only.

> 17 A firm can have multiple observations if it employs different types of labor.

19