rafmacalaba/datause-encoder-data huggingface dataset:
is weak. For all of the estimations, the F statistic (at a 5% significance level) ensured that the maximal bias of the IV estimator relative to OLS was no larger than 5%. This fits the definition of a strong instrument according to Stock et al. (2002).

# _The Instrument’s Validity_

Changes in the amount of unanticipated regional mining revenues do not respond to local circumstances. They only depend on the price of minerals in the international market and on the volume of minerals produced prior to the change in the royalties’ legislation. Variations in the amount of unanticipated mining revenues do not lead directly to an increase in economic activity in the areas where the resource is located. Indeed, the correlation between the changes in per capita GDP and the changes in unanticipated mining revenues is quite small (0.203)<sup>10</sup> , indicating that changes in unanticipated mining revenues cannot affect per capita GDP (or, for that matter health) other than through changes in public expenditures. Furthermore, changes in these revenues have no impact on changes in consumer price indexes for commodities that could affect child well-being, such as food, medicines, and health care, as shown in Table S4.2 in the supplemental appendix<sup>11</sup> .

Unanticipated mining revenues are thus arguably unrelated to children’s nutritional outcomes provided that an increase in these revenues is uncorrelated with changes in net income per capita, expenditure categories, and migration patterns and that mining does not directly affect child health. First, I verify that increases in mining revenues do not directly increase income per

> 2002). One measure of whether a set of instruments is strong is whether the concentration parameter is sufficiently large.

> 10 I calculated this correlation using region-year data between 2002 and 2006. In addition, I conducted a regression of changes in unanticipated mining revenues on changes on GDP per capita and found no statistically significant effect of mining revenues on GDP per capita.

9
At the 36-month follow-up, a typical GL in an untrained group spends 55 hours per month working on the group enterprise and has net earnings of USD 6.91, compared with 33 hours and USD 4.31 for the average untrained RF member. These differences are statistically significant at the 95% level of confidence. However, if the groups have received training, these differences shrink substantially (even disappear) and become statistically indistinguishable: trained GL and RF members work 32 vs. 30 hours and earn USD 5.82 vs. USD 5.55 per month, respectively. The training intervention seems to have led to a more equitable distribution of the group profits between its members, i.e., reducing within-group inequality, without reducing the modest total net earnings of the enterprises. While there is some modest suggestion of the RF improving their earnings, and profits from the groups, the majority of the convergence comes from leaders reducing effort in the face of the training.

# **6. ENDLINE ANALYSIS**

# **6.1.  Longer Term Household Impacts of TASAF Using a Second Control Group**

The pipeline structure of the main TASAF study means that by endline we no longer have experimental identification of the impact of TASAF because both Early and Late groups are treated. Because we also drew eligible households who are not TASAF beneficiaries into the survey, we can point a different lens at our pipeline design and recover the ability to estimate absolute treatment effects in the endline, albeit non-experimentally. To gain maximum power on long-term TASAF effects we pool all eligibles and ignore other treatments (so we incorporate the trained groups as part of the TASAF treatment and will miss complementarities with the cross-cut UCT treatment, discussed in more detail in the next section). To be able to combine impacts across 25
# <u>5.3 Dropout and Childbearing Rates</u>

Table 6 presents the results of OLS estimates of the effects of each program component on the dropout and childbearing rates among baseline students (that is, students enrolled in upper primary school at the onset of the program). The condom debates and essay competitions took place too recently for us to be able to detect a reduction in childbearing from this intervention (the most recent childbearing data were collected 8 months after the debates were organized). However, we can look at the impact of the teacher training and the reduction in the cost of education over a 36-month period. While the teacher training had no impact on retention rates, the reduction in the cost of education helped students stay in school longer. Girls in schools were free uniforms were provided are 2.5 percentage points less likely to have dropped out (column 1). This corresponds to a 15% decrease. For boys, the decrease is also around 15% (column 2). Reducing the cost of education thus helped students stay in school longer, and, consequently, decreased the likelihood that they get married and have children. Girls in schools where free uniforms were provided are 1.5 percentage points less likely to have started childbearing (column 3). This corresponds to almost a 10% decrease in teen childbearing. They are also 1.4 percentage points (or 12%) less likely to be married. Among boys, those in schools with reduction in the cost of education are 0.8 percentage points, or 40%, less likely to be married (column 5). Overall, these results suggest that reducing the cost of education generates effective incentives for teenagers to avoid teen pregnancy or marriage. In rural Western Kenya, social pressures are likely to make these incompatible with school attendance for most pupils.

20
**<u>Table 2: Descriptive statistics of estates by lease status</u>**

||**Total**||**Non-agric**|**. estates**|||**Agric.**|**estates**||
|---|---|---|---|---|---|---|---|---|---|
|||**All**|<br>**North **|<br>**Center**|**South **|**All**|<br>**North **|<br>**Center**|**South **|
|**General characteristics**||||||||||
|Total area (1,000 ha)|1,487.44|138.68|45.52|20.30|72.86|1,348.76|230.63|871.61|246.52|
|Mean area (ha)|27.10|6.60|15.98|2.54|7.17|39.80|39.49|35.12|76.23|
|Signed before 1988 (%)|18.29|27.71|26.48|37.46|20.03|13.82|11.30|13.74|18.91|
|Signed 1988 to 1995 (%)|56.25|7.59|8.79|7.14|7.66|79.31|81.39|81.99|52.67|
|<br>Signed after 1995 (%)|25.47|64.70|64.74|55.40|72.32|6.88|7.31|4.27|28.41|
|<br>Length of lease (years)|40.71|76.77|81.09|64.19|86.24|24.35|24.52|23.41|32.46|
|Lease length <=21 years (%)|47.88|9.55|5.85|15.35|5.80|73.62|65.85|77.92|55.40|
|Lease length >21 years (%)|19.79|43.04|35.27|38.47|49.73|4.19|3.67|3.24|12.27|
|Size less than 10 ha (%)|42.60|97.83|97.05|98.44|97.57|8.37|7.98|5.86|28.29|
|Size 10 - 30 ha (%)|45.62|1.09|1.72|0.90|1.05|73.22|71.92|78.21|37.20|
|<br>Size 30 - 50 ha (%)|5.90|0.25|0.28|0.16|0.31|9.40|9.81|8.98|11.90|
|<br>Size 50 - 100 ha (%)|3.21|0.26|0.32|0.18|0.32|5.04|5.91|4.11|10.58|
|Size 100 - 500 ha (%)|2.12|0.33|0.14|0.24|0.45|3.24|3.65|2.22|10.30|
|Size above 500 ha (%)|0.55|0.25|0.49|0.09|0.31|0.74|0.74|0.61|1.73|
|**Formal documentation**||||||||||
|Has deed (%)|35.80|26.51|18.72|25.66|29.97|42.03|43.33|40.07|54.49|
|<br>Has offer (%)|65.49|49.51|39.06|49.33|53.34|76.22|68.76|79.53|64.93|
|Has offer but no deed (%)|34.47|30.56|26.61|33.09|29.76|37.09|27.70|41.91|17.86|
|Lease indeterminate (%)|32.41|47.54|58.91|46.38|44.57|22.25|30.50|18.90|32.45|
|Sketch plan (%)|56.76|65.81|58.18|67.65|65.81|51.65|52.07|52.85|41.94|
|SD plan (%)|35.53|16.14|23.08|16.73|14.42|46.47|46.61|45.94|50.23|
|<br>Deed plan (%)|7.71|18.05|18.74|15.63|19.77|1.87|1.32|1.21|7.83|
|<br>Annual rent (US$/ha)|10.69|26.66|23.18|23.22|30.35|0.79|0.37|0.53|3.59|
|_Lease term in 2016_||||||||||
|Lease expired (%)|45.35|9.16|5.15|15.03|5.46|69.65|63.13|74.64|43.98|
|Lease term <= 10 a (%)|2.42|2.09|2.66|2.46|1.57|2.64|1.55|2.43|6.27|
|Lease term > 10 a (%)|19.82|41.21|33.29|36.13|48.41|5.46|4.82|4.04|17.30|
|No. of obs.|58,733|23,593|3,728|9,236|10,629|35,140|6,181|25,560|3,399|

_Source:_ Own computation from the National Geographical Estates Database.

17
overtime become major markets for Vietnam’s exports of manufactured goods such as textiles, garments, and footwear (see Figure 2). The degree of concentration of trading partners, as measured by the Herfindahl Index, declined from 0.18 in 1992 to 0.11 in 2002 (IMF, 2003).<sup>3</sup>

**Figure 1.  Vietnam’s Export Structure by Sector**

<!-- Start of picture text --> 1992<br>2002<br>Rice Rice<br>12% 4% Crude oil<br>Crude oil 20%<br>Others 31% Others<br>23%<br>14%<br>Manufactures<br>6% Manufactures<br>Other primary Other primary 32%<br>commodities commodities<br>37% 21%<br><!-- End of picture text --> <!-- Start of picture text --> Source: ITC<br><!-- End of picture text -->

**Figure 2.  Vietnam’s Export Structure by Country/Region**

<!-- Start of picture text --> 1992 2002<br>Singapore China EU U.S. China<br>12% 3% 7% 14% 7%<br>Singapore EU<br>6%<br>24%<br>Japan<br>25%<br>Others Others Japan<br>53% 35% 14%<br><!-- End of picture text --> Source: IMF Direction of Trade Statistics Because of the linkages between trade and the environment (through scale, composition and technique effects), this rather dramatic shift in Vietnam’s trade patterns could have important implications for the environment and use of natural resources. Though the precise environmental impacts of the various bilateral, regional, or multilateral trade 3 The Herfindahl Index is the sum of the squared market shares of all trading partners; a decrease indicates reduced concentration or increased diversification.

3
ally exclusive groups (see Figure (1) below).<sup>10</sup> This framework allows us to use recently developed matching and propensity score weighted estimators (Huber et al. (2013)) with appropriate subsamples as “treatment” and “comparison” groups to explore potentially heterogeneous effects of family background on children’s schooling.<sup>11</sup> The central challenge in causal interpretation of the estimated intergenerational links is whether the observed intergenerational persistence can be driven primarily by genetic transmissions of ability and preference across generations. In the absence of any credible exclusion restrictions, we explore the role of genetic transmissions in two steps.<sup>12</sup> First, we implement sensitivity analysis using Rosenbaum bounds under the assumption that the estimates effects of economic status are biased upward because of a positive correlation in genetic ability and preference.<sup>13</sup> Second, we take advantage of the recently developed econometric approaches that corrects for selection on unobservables without imposing any exclusion restrictions (Millimet and Tchernis (2013), Lewbel (2012)). We use data from the 2002 round survey of the Chinese Household Income Project (CHIP) for our empirical analysis.

The substantive conclusions of this paper can be summarized as follows. First, we find evidence of substantial heterogeneity. Focusing on the children’s generation, the standard specification with parental education as the sole indicator of economic status shows that a child of a parent with more than primary schooling gains about a year more of schooling.

> according to, for example, the first principal component lacks any clear economic content.

> 10We focus on primary schooling as the education threshold to keep estimates comparable across three generations. The proportion of grandparents with of more than middle school is too small (4 percent) for any meaningful analysis. However, in an online appendix, we report estimates for parents-children sample using middle school as the relevant cut-off.

> 11Most of the existing analysis of intergenerational educational mobility in developing countries including China relies exclusively on the OLS estimator.

> 12Please see the discussion in section 4.2 below on the challenges in finding appropriate natural experiments to estimate heterogeneous effects of parent’s economic status in our context.

> 13To the best of our knowledge this is the first paper in the literature on intergenerational mobility to exploit Rosenbaum bounds to understand whether the estimated persistence can be driven by selection on genetic endowments.

4
**Table 5: Revealed Capability Intensity (RCI) of Final versus Intermediate Products**

|**Category**|**Capability**|**Final GVC**<br>**Products**|**Intermediate**<br>**GVC Products**|
|---|---|---|---|
|Fixed|Proximityto markets|0.049|0.372***|
||Natural capital|0.206|0.244|
|Long‐term|Human capital|0.087|0.189***|
|Policy<br>|Physical capital|‐0.066|0.227***|
|Variables|Institutional capital|‐0.02|0.24***|
|Short‐term|Logistics/connectivity|0.141|0.484***|
|Policy<br>|Wage competitiveness|0.076***|‐0.13|
|Variables|Market access|‐0.08|0.099***|
||Access to inputs|0.238***|0.194|

Source: Authors’ calculations

*** Indicates statistically significant difference at p <0.01.

**    Indicates statistically significant difference at p<0.05.

*      Indicates statistically significant difference at p<0.1.

# B. Which capabilities matter most for GVC participation, within sectors?

Circumstances that give rise to GVC participation vary by sector. We present RCI estimates for each of the 14 GVC sectors. Aggregated across products, the sector‐level RCI is the average of product‐level RCIs, weighted according to trade value. Table 6 shows that sectors like transport and machinery/electronics are associated with higher levels of capabilities like human, physical and institutional capital than sectors like clothing and textiles. Wages and access to inputs are shown to be most important for clothing. Proximity to markets and natural capital are most important for textiles. Figure 1 then graphically depicts requirements in four of the largest GVC sectors – electronics, transport, clothing and textiles – to help visualize which capabilities are most associated with which sector, but also to show that total capability requirements (all capabilities added up) are higher for more technology‐intensive sectors like machinery/electronics and transport compared to clothing and textiles.

There are a few anomalies in Table 6 for reasons described earlier. For example, market access is not shown to be very important for the export of garments. This does not apply in every country case: the growth of the garment industry in such countries as Haiti, Jordan and Lesotho was indeed tied to preferential access awarded by large markets. As mentioned above, the direct impact of these bilateral trade agreements is not fully incorporated into this model. Another example is that natural capital is one of the top requirements in footwear assembly, likely because of the dominance in this sector of China, which has an abundance of natural capital used not in footwear but in other sectors.

11
Table 3: Simulated v. actual number of children, women, Brazil

|||Simulated n|umber of ch|ildren in actu|al terms||||
|---|---|---|---|---|---|---|---|---|
|f||0|1|2|3|4|>4|Total|
|er o|0|2974068|62837|45063|8299|3915|549472|3643654|
|b|1|166252|2841511|20134|529|213|496346|3524985|
|um<br>|2|156287|64255|2710228|0|1212|442859|3374841|
|al n<br>ren|3|105332|96573|80433|1504710|6945|275641|2069634|
|tu<br>ild|4|56890|55098|69251|15938|662625|136922|996724|
|Ac<br>ch|>4|53675|71626|105656|32697|13047|660480|937181|
|Total||3512504|3191900|3030765|1562173|687957|2561720|14547019|
|||Simulated n|umber of ch|ildren in per|centage term|s|||
|of||0|1|2|3|4|>4||
|er|0|0.8162|0.0172|0.0124|0.0023|0.0011|0.1508||
|b|1|0.0472|0.8061|0.0057|0.0002|0.0001|0.1408||
|um<br>|2|0.0463|0.0190|0.8031|0.0000|0.0004|0.1312||
|al n<br>ren|3|0.0509|0.0467|0.0389|0.7270|0.0034|0.1332||
|tu<br>ild|4|0.0571|0.0553|0.0695|0.0160|0.6648|0.1374||
|Ac<br>ch|>4|0.0573|0.0764|0.1127|0.0349|0.0139|0.7048||

27
_Political Economy_ , **93** (5), 901–918.

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19
for micro firms. However, the Enterprise Survey’s Power Outage measure is associated with higher productivity for relatively larger firms—a surprising result. Given that two of the three sets of indicators of the quality of the electricity system suggest that good provision of electricity tends to be enabling for relatively small firms, and the Power Outage measure likely does not capture electricity provision as fully as the other two indicators, we tentatively conclude that electricity provision is enabling for relatively small firms.

_Human Capital_ . A better human capital environment also appears to level the playing field for smaller firms. Reducing the Enterprise Survey’s Skills Obstacle measure is associated with higher productivity only for micro firms. The World Bank’s HLO is associated with higher productivity only for micro and small firms; an increase of one standard deviation implies higher productivity of 30 percentage points for micro firms and 22 percentage points for small firms. Similarly, the Global Competitiveness Quality of Education System measure is most strongly and positively (though insignificantly) related to productivity for micro firms.

_Access to Finance_ . Access to finance has most positive associations with firm growth for small and medium firms, and this is true based on both the Enterprise Survey Overdraft indicator and the Global Competitiveness Financial Development indicator. In contrast, the positive association between access to finance (as measured by the Enterprise Survey Overdraft measure) and productivity is relatively stronger for larger firms. The results suggest that access to finance does different things for firms of various sizes: allowing smaller firms to grow faster, while allowing larger firms to enjoy higher productivity.

_Barriers to Entry_ . Finally, land access is likely particularly important for relatively small firms. The Enterprise Survey Land Access Obstacle measure is negatively and significantly associated with productivity for micro firms and small firms.

To summarize, particularly important BE elements for relatively small firms are corruption control, both basic and modern infrastructure, the human capital environment, and land access. Access to finance is also important for relatively smaller firms in terms of firm growth. For relatively large firms, physical safety appears to be relatively important, and access to finance is important for improving productivity.

# **7. Conclusions**

This paper expands earlier literature on the relationship between firm performance and the business environment using multiple popular data sources and measures, including both the de jure _Doing Business_ indicators at the country level, and the de facto _Enterprise Survey_ 23
# **Table 6.  The impact of takeover laws on capital markets and takeover activity**

||**Listed fi**|**rms to milli**|**on p**|**opulation**||**Marke**|**t cap of stock ex**|**change to GDP**||
|---|---|---|---|---|---|---|---|---|---|
|Numberofobs.|50|41||41||49|41|41||
|Rsquared|0.1369|0.4282||0.4220||0.3311|0.3770|0.3400||
||49.88<sup>~~b~~</sup>|63.74|~~a~~|41.68|~~c~~|159.43|~~b~~<br>163.02|<sup>~~b~~</sup><br>158.63|~~a~~|
|Takeover Law|(24.58)|(24.83)||(22.88)||(75.18)|(77.35)|(61.31)||
|||4.17|~~c~~||||12.09|<sup>~~c~~</sup>||
|Eff.judiciary||(2.44)|||||(6.23)|||
|||||5.79|~~d~~|||4.22||
|Inv.protection||||(3.59)||||(11.88)||
||2.48|-0.06||6.53|~~b~~|10.54|~~c~~<br>-2.74|9.51|~~d~~|
|Ln GNI/cap|(2.99)|(3.35)||(3.09)||(6.10)|(8.75) <br>|(6.51)||
||-16.74|-36.97|~~c~~|-70.09|~~a~~|-88.92|~~b~~<br>-56.59|-84.00||
|Constant|(24.41)|(18.41)||(28.53)||(39.99)|(52.40)|(73.15)||

||**Traded**|**value of stock e**|**xchange to GD**|**P**<br>**T**<br>**Volume**|**akeover Activit**<br>**Hostile dea**|**y( **<br>**ls**|**R-V 04)**<br>**Cross-bor**|**der**|
|---|---|---|---|---|---|---|---|---|
|Numberofobs.|50|41|41|41|41||41||
|Rsquared|0.4544|0.4699|0.5077|0.5414|0.3518||0.4969||
||96.95|<sup>~~a~~</sup><br>105.69|~~a~~<br>47.57|11.20|~~c~~<br>1.86|<sup>~~b~~</sup>|-41.84|~~a~~|
|Takeover Law|(35.10)|(37.67)|(36.05)|(6.54)|(0.88)||(9.93)||
|||3.11|||||||
|Eff.judiciary||(3.51)|||||||
||||13.13|~~b~~|||||
|Inv.protection|||(6.58)||||||
||13.22|<sup>~~a~~</sup><br>9.44|~~b~~<br>19.13|~~a~~<br>7.69|~~a~~<br>0.34|<sup>~~a~~</sup>|-1.07||
|Ln GNI/cap|(5.18)|(6.74)|(5.25)|(1.40)|(0.12)||(1.75)||
||-111.20|<sup>~~a~~</sup><br>-100.98|~~a~~<br>-173.71|~~a~~<br>-50.90|~~b~~<br>-3.11||72.55|~~a~~|
|Constant|(36.45)|(40.94)|(44.81)|(9.90)|(0.92)||(18.81)||

_Note: Data from World Bank World Development Indicators and La Porta et al (1998). GNI is measured by the Atlas method. The measures of takeover activity are based on Rossi and Volpin (2004). Volume refers to the percentage of domestic traded companies targeted in completed deals ion the 1990s. Hostile deals is the share of attempted hostile takeovers as a percentage of domestic traded companies. Cross-border ratio is the number of cross-boarder deals as target as a percentage of all completed deals. Significance levels are as follows: 1% (a), 5% (b), 10% (c), 15% (d). Regressions with robust standards errors._ 36