rafmacalaba/datause-encoder-data huggingface dataset:
across countries, which could explain the lack of uniformity in the results obtained for different economies.

On a related note, Alfaro and Chen (2015), exploring a cross‐country firm‐level data set, show that a 10 percentage point increase in the probability of a new multinational entry raises the aggregate weighted domestic productivity by 1.6 percent across countries. They find that both market reallocation and knowledge spillover are significant sources of productivity gain. However, a substantial share of productivity gains are channeled through competition effects.

Other examples of intra‐industry spillovers are also encouraging. Kee (2015) finds evidence for enhanced productivity among local firms that share a supplier with foreign firms. Görg and Strobl (2005) find productivity improving through labor movement spillovers, suggesting that firms which are run by owners who worked for multinationals in the same industry immediately prior to opening up their own firm have higher productivity levels than other firms. Keller and Yeaple (2009) show that technology spillovers arising from FDI could account for 14 percent of productivity growth in American manufacturing firms between 1987 and 1996.

# **_B. FDI and exports_**

FDI can provide the impetus for export sophistication, which is difficult for many developing countries seeking to leverage exports for growth. Swenson (2008) shows that the positive association between the presence of foreign affiliates and new export connections among private Chinese exporters may be driven by information spillovers. Using detailed Chinese trade statistics from 1997 to 2009, Chen and Swenson (2014) find that the presence of foreign affiliates in the same sector is associated with more and higher unit value trade transactions by Chinese firms.

More importantly, there is a vast literature documenting how many developing countries which used to depend on exports of commodities have managed to substantially increase the number of products exported, as well as the number of export destinations. Such countries have radically modified the composition of their exports by attracting, enabling, and retaining efficiency‐seeking FDI. Further, case studies have shown that countries that have managed to upgrade and diversify their export base achieve higher growth rates and greater welfare gains than those that simply keep trying to expand the goods and services that have traditionally dominated their exports. The experiences of different countries such as China, the Czech Republic, Costa Rica, Malaysia, Mexico, the Philippines and Vietnam, among others, illustrate how efficiency‐seeking FDI has been a key vehicle in achieving such objectives (Moran 2014). At the firm level, Freund and Pierola (2012) use data from 32 countries and demonstrate that individual firms matter for countries’ trade volumes and sectoral trade patterns. These so‐called “export superstars” have often entered the export market through foreign investment.

9
discuss the conditional effects of UN missions in our supplement and do not find evidence that the model fits differently in different ranges of the data.<sup>28</sup> Even with a more interactive model, UN missions have a statistically significant effect.

But we have already mentioned that the UN is more likely to send troops to difficult cases. This is verified by sorting cases with and without UN missions and comparing means of some of the covariates in the two groups. This type of selection can be dealt with propensity score matching methods. The propensity score is an estimate of the probability that a UN mission will be assigned to a peace process. We get such an estimate using all other variables in the model and then divide the data in two groups –the treatment group (with UN missions) and the control group (no UN)— and compare outcomes in cases with similar propensity scores. Since all other covariates are used to estimate the propensity score, differences in outcomes are attributed to the “treatment” (the UN operation). Matching estimation assumes that all the right variables have been identified and are exogenous and there can be several ways to balance the two groups, and different balancing rules can affect the results. The results we report here are based on widely used programs for matching (see supplement for a more detailed discussion).<sup>29</sup> We report average treatment effects on the treated (ATT) for _transformational and_ any _UN intervention_ using four matching methods.

Out of 119 observations with non-missing observations, there are 56 cases in the range of common support for the model including _transformational UN missions_ . The average treatment effect is substantial and stable, ranging from .46 to .75 and it is statistically significant using all four matching methods. However, nearest neighbor matching yields only a few matches (there are 8 treated and 5 control observations on common support), so parameter estimates will necessarily be more uncertain. The effects of _any UN intervention_ are also strong (the ATT ranges from .26 to .42). The range of common support is now larger and includes 89 observations. We find similarly strong results for UN missions using other matching estimators.<sup>30</sup> In our supplement, we

> 28 Supplement, pages 36-40; Additional Results, section 9.

29 We used Becker and Ichino’s (2002) program in stata for matching estimation. The seed number used for sampling observations in the bootstrap was 123456789. 30 Additional Results, sections 11 and 12. For a fuller discussion and several sources on matching. For a more extensive discussion, see our comment on Gary King and Langche 15
- 59 -

# United Nations, Yearbook of National Accounts Statistics, various issues.

# - indirect taxes (INDTAXN)

United Nations, Monthly Bulletin of Statistics, various issues.

- export price index for machinery and equipment from the major industrialized countries (XUVKIMP)

# Derivations

The derivations used for the variables included in the econometrics are as follows in alphabetical order:

|AVGWAG|=|TOTWAGN/TOTLAB|
|---|---|---|
|GDP|=|log(GDPNO)|
|GDPNO<br>|=|(GDPN-GDPPETRN)/GDPDEF<br>|
|GOVI<br>|=|log[GOVIN/IDEF]<br>|
|GVEX|=|log[GVEXN/GDPDEF]<br>|
|DBFGD|=|log[DBFGD/DBFGD]|
|GVINFN|=|GVIAGN+GVIELECN+GVICONN+GVITRNN+GVIUTILN|
|GVIINF|=|log[GVINFN/IDEF]<br>|
|ICOSTS|=|log[IDEF/GDPDEFI|
|ICTOTN|=|ICMACHN+ICBLDGN+ICTRANSN|
|IDEF|=|(0.194*WMACH*WP[DMACH)+(0.0806*WMACH*IMACfII)|
|||+(WBLDG*WPIBLDG)+(WTRANS*WPITRANS)<br>+(WOTHER*WPI)|
|IMACHI|=|XUVKIMP*EBM|
|INDTAX|-|INDTAXN/GDPDEF<br>|
|KP|=|(1-0.043)KP(-I)+PRIVI|
|MARKUP|=|log[WPUWAGEINDX]|
|NIGVEX|=|GOVEX-GOVI|
|PRIVI|=|log[PRIVIN/IDEF]|
|PROFIT|=|log(GDPNO-TOTWAG-INDTAX)|
|PVCRD|=|log[PRVCREDN/GDPDEF]|
|PVCRDY|=|log[PRVCREDN/GDPDEF/GDPNO]|
|R|=|log( I+DISCRATE)/(1 +(WPI-WPI(- 1 ))/WPI(- 1))|
|R(**W**||log(1+R)/(1+AVGWAG)|
|TOTWAG|=|TOTWAGN/WPI|
|WBLDG||ICBLDGN/ICTOTN|
|WMACH|=|ICMACHN/ICTOTN|
|WOTHER|=|ICOTHERN/ICTOTN|
|WTRANS|=|ICTRANSN/ICTOTN|
**Table 1: Biofuel Production, Top 15 Countries**

|||Fuel<br>|Ethanol<br>|||||Bi<br>|odiesel<br>||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Country|Major||Productio|n (Billio|n Liters|)|Major||Producti|on (Billio|n Liters)||
||Feedstock|2004|2005|2006|2007|2008|Feedstock|2004|2005|2006|2007|2008|
|US|Corn|13|15|18.3|24.6|34|Soybean|0.11|0.36|0.99|1.93|2.69|
|Brazil|Sugar cane|15|15|17.5|19|27|Soybean|||0.07||1.2|
|Germany|Wheat|0.02|0.2|0.5||0.5|Rapeseed|1.18|1.9|3.02|3.28|3.2|
|France|Sugar beet, wheat|0.1|0.15|0.25||1.2|Rapeseed|0.4|0.56|0.84|0.99|2.06|
|China|Corn, sugar cane|2|1|1|1.8|1.9|Soybean, rapeseed|||0.07||0.1|
|Argentina|Sugar cane|--|--|--|0.02|--|Soybean|||||1.2|
|Italy|Cereals|--|--|0.13||0.13|Oil seeds|0.36|0.45|0.51|0.41|0.68|
|Spain|Barley, Wheat|0.2|0.3|0.4||0.4|Oil seeds|0.01|0.08|0.11|0.19|0.24|
|India|Sugar cane, wheat|--|0.3|0.3|0.2|0.3|Soybean, rapeseed|||0.03||0.02|
|Canada|Wheat|0.2|0.2|0.2|0.8|0.9|Oil seeds||0.1|0.05||0.1|
|Poland|Rye|--|0.05|0.12||0.12|Rapeseed||0.11|0.13|0.09|0.31|
|Czech Republic|Sugar beet|--|0.15|0.02||--|Rapeseed|0.07|0.15|0.12|0.07|0.12|
|Colombia|Sugar cane|--|0.2|0.2|0.3|0.3|Soybean|--|--|--|--|--|
|Sweden|Wheat|--|0.2|0.14||0.14|Rapeseed|0.002|0.001|0.01|0.07|0.11|
|Malaysia|--|--|--|--||--|Oil palm|||0.14|||
|UK|--|--|--|--||--|Rapeseed|0.01|0.06|0.22|0.17|0.22|
|Denmark|Wheat|--|0.1|--||--|Oil seeds|0.08|0.08|0.09|0.1|0.15|
|Austria|Wheat|--|0.1|--||--|Oil seeds|0.06|0.1|0.14|0.3|0.24|
|Slovakia|Corn|--|0.1|--||--|Oil seeds|0.02|0.09|0.09|0.05|0.17|
|Thailand|Sugar cane, cassava|0.2|--|--|0.3|0.3|Oil Palm|||||0.40|
|Australia|Sugar cane|0.07|--|--|0.1|--|--|--|--|--|--|--|
|EU|Various|--|--|--|2.16|--|--|--|--|--|--|--|
|World Total||31|33|39|49.6|67||2.3|4.1|6.9|8.4|14.7|

Note: Ethanol figures do not include ETBE, a mixture of ethanol and isobutylene (petrochemical) used in low-concentration gasoline blends up to about 8-10% in fuels in parts of Europe, particularly France and Spain.

Source: REN21 (2005, 2006, 2008, 2009); Renewable Fuels Association (2008); EBB (2009); EIA (2009)
based on skin tone rather than as fixed traits determined by inheritance. Consequently, there exists more ambiguity in classifying race. Official statistics, and our main data source, divide individuals into five main racial categories: _branco_ (white), _preto_ (black), _pardo_ (brown), _amarelo_ (yellow), and _indigena_ (indigenous). The most salient disparities in labor market outcomes are observed between _branco_ individuals and the combined group of _preto_ and _pardo_ individuals, which together comprise 99% of the population. _Preto_ and _pardo_ workers are shown to exhibit relatively similar levels of education, employment, and wages (Garcia de Oliveira _et al._ , 1981; Gerard _et al._ , 2021; Miller and Schmutte, 2023; Silva, 2000). Thus, we pool _preto_ and _pardo_ into one “non-white” group and compare outcomes across white and non-white individuals, following a large strand of research on racial disparities in Brazil (e.g., Cornwell _et al._ , 2017; Derenoncourt _et al._ , 2021; Gerard _et al._ , 2021; Hirata and Soares, 2020; Miller and Schmutte, 2023; Telles, 2014).

**Separation regulations.** The Brazilian labor market exhibits a very high degree of labor turnover. Around 45% and 80% of terminated formal job spells have lasted less than one year and three years, respectively (Britto _et al._ , 2022). In our analysis, we distinguish between employer-initiated separations (“layoffs”) and employee-initiated separations (“quits”). Layoffs account for roughly three-quarters of all separations, while quits cover most of the remaining part. If workers are laid off without a just cause, they are entitled to various benefits, such as severance payments and unemployment insurance (UI). Severance payments are paid by the employer, who contributes every month 8% of workers’ earnings to a mandatory savings account (FGTS). Upon layoff, the worker can access the account’s balance, as well as an additional penalty for the firm equal to 40% of the balance. UI benefits amount to 1 to 1.76 minimum salaries and are paid for 3 to 5 months.<sup>6</sup> In contrast, if workers quit the job themselves, they are not entitled to any benefits. Quits are thus more likely to be driven by the workplace preferences of employees.

**Informality.** Brazil has a relatively large informal sector that accounts for roughly 45% of all workers in 2015 (Derenoncourt _et al._ , 2021). Our analysis will focus on separations from formal jobs and measure the racial composition among all formally employed coworkers which we can observe in administrative employment data. Survey evidence shows that formality rates are quite similar across racial groups (Gerard _et al._ , 2021), which suggests that conditioning on formality does not pose major limitations for understanding racial disparities in labor market

> 6Van Doornik _et al._ (2023) provide evidence that formal layoffs can be used to extract rents from the UI system by showing that firms are more likely to lay off workers when they become eligible for UI benefits, but continue to employ them informally while on benefits and recall them when the benefit period is over.

6
<!-- Start of picture text --> Educacion primariay secundaria<br>18.0<br>16.0<br>0<br>a9 ET—_<br>NN<br>2.0 SS<br>0.0<br>1 2 3 4 5 6 7 3B 9 10<br>—— Guatemala —— Honduras ———El Salvador<br>oe Nicaragua eee (Costa Rica eens Panama<br><!-- End of picture text --> <!-- Start of picture text --> Educacion terciaria<br>50.0<br>45.0<br>40.0<br>35.0<br>30.0<br>25.0<br>20.0<br>15.0<br>10.0<br>5.0<br>0.0<br>1 2 3 4 5 6 7 8 9 10<br>——Guatemala ——Honduras —— El Salvador<br>——Nicaragua —=CostaRica —===§Panama<br>CC<br><!-- End of picture text -->
parent, but the fact that the rate of substance abuse, of those with one parent in the household, are placed between those of the two extremes, lends support to the hypothesis of the importance of parental support in keeping youth away from substance abuse.

Figure 3.8: Connection between Parents and Substance Abuse, Argentina 2005 <!-- Start of picture text --> 80.0<br>70.0 Live with no parents<br>Live with one parent<br>60.0 Live with both parents<br>50.0<br>40.0<br>30.0<br>20.0<br>10.0<br>0.0<br>Females Males Females Males Females Males<br>Drugs Drink Smoke<br>Percent<br><!-- End of picture text --> _Source_ : Own calculations on ESCVJA (four urban areas) Drug abuse and school dropout are serious negative outcomes, and they are closely linked. Youth, who have tried drugs, are more likely to have dropped out of school or vice versa (Figure 3.9).<sup>7</sup> This is the case irrespective of gender. Keeping in mind that factors and outcomes are not clearly distinct, in the sense that a consequence or outcome caused by some factors, may be a factor for a new outcome, it is possible to consider the hypothesis of either drug abuse or school dropout causing the other. The testing of this hypothesis is left for future research.

Figure 3.9: School Dropout and Drug Abuse for Argentine Youth (percent), 2005 <!-- Start of picture text --> 100.0<br>90.0 Tried drugs<br>80.0 Never tried<br>70.0<br>60.0<br>50.0<br>40.0<br>30.0<br>20.0<br>10.0<br>0.0<br>Females Males Females Males<br>Dropouts Rest of sample<br>Percent<br><!-- End of picture text --> _Source_ : Own calculations on ESCVJA (four urban areas) 7 This relation is not simply explained by age differences, because dropout is distinct from graduating from school in the questionnaire.

14
21 Table 4: NRAs, CTEs, WRIs and TRIs, all covered farm products, by region, 1972 to 1976

||**1972**|**1973**|**1974**|**1975**|**1976**|
|---|---|---|---|---|---|
|**NRA (%)**||||||
|Asia (excl. Japan)|18|-10|-31|-13|4|
|Africa|-17|-25|-30|-20|-21|
|Latin America|-27|-31|-42|-35|-15|
|All developing countries|1|-17|-33|-18|-4|
|High-income countries|32|19|14|25|36|
|**CTE (%)**||||||
|Asia (excl. Japan)|-5|-32|-28|-8|1|
|Africa|-9|-18|-25|-13|-6|
|Latin America|-19|-26|-35|-28|-9|
|All developing countries|-9|-28|-29|-12|-2|
|High-income countries|32|18|14|26|36|
|**WRI (%)**||||||
|Asia (excl. Japan)|47|48|46|50|41|
|Africa|55|52|53|48|49|
|Latin America|38|41|52|49|27|
|All developing countries|47|47|48|49|40|
|High-income countries|59|43|41|51|72|
|**TRI (%)**||||||
|Asia (excl. Japan)|29|25|24|36|25|
|Africa|19|18|23|25|31|
|Latin America|21|25|33|33|16|
|All developing countries|25|24|25|34|24|
|High-income countries|27|18|13|24|36|

Source: Authors’ compilation based on data in Anderson and Valenzuela (2008) and Anderson and Croser (2009).
~~<u>|</u>~~

<!-- Start of picture text --> Brazil Chile Indonesia Mexico<br>8 120100 rae<br>no \A\<br>60<br>Mozambique Peru Phillipines South Africa<br>120<br>100 Ne as<br>60<br>80 yee \*\.<br>1995 2000 2005 2010 2015 2020 1995 2000 2005 2010 2015 2020 1995 2000 2005 2010 2015 2020 1995 2000 2005 2010 2015 aear<br><!-- End of picture text -->
# **C.4 Combined Instrument and Reduced Form Estimates, by Age**

The previous subsection, Appendix Section C.3, estimates the effect of the FPE and MTI reforms on schooling using five instruments. However, reduced form analysis across different ages would become cumbersome with the large number of variables; combining the impact of the two reforms would allow for easier graphical representation of the impact of the reforms across different ages. Cutting the data by age is only possible with the inclusion of both reforms, this allows for the use of the national sample and additional variation from the timing of the MTI implementation.

Creating a single instrument that measures exposure to both the FPE and MTI reforms, and predicts changes in schooling, requires a simplifying assumption from the five-instrument model used in Appendix Section C.3. The necessary baseline assumption is that the magnitude of the effect of an additional year of FPE on schooling is the same as each additional year of MTI, in either a positive (no script change) or negative (with script change) direction. For simplicity, it is also assumes that there is no interaction between the two reforms, only one of the six interaction terms in the first stage of Appendix Tables C.7 to C.9 has a p-value below 0.15. These assumptions yield a combined intensity measure that can be expressed using the following equation: _Izy_<sup>_MT I−T_</sup> is zero for all non-Tigray regions, and is shown to be positive in Appendix Tables C.7 to C.9. Similarly, _Izy_<sup>_MT I_</sup> , is only non-zero for MTI regions other than Tigray (as described in Appendix Section C.2), and shown to be negatively associated with schooling in Appendix Tables C.7 to C.9.<sup>24</sup> Additionally, the differences in the estimated effect of each intensity measure can also be statistically tested. Using the estimates from the DHS in Appendix Table C.9, the survey that contains all age specific outcomes of interest, the null hypothesis that each combination of the three intensity measures has an equal effect on schooling is tested in Appendix Table C.16. Across the four tests, the null hypothesis of equality cannot be rejected, and no p-value is larger than 0.5.

In addition to testing the equality of each first stage coefficient, the combined intensity measure (∆ _Izy_ ) is also used to re-estimate the effect of years of schooling on number of children born, and all outcomes from Appendix Tables C.10, C.11, and C.13. The estimates using the combined intensity measure can be found in Appendix Tables C.17 to C.20; in each case, the combined intensity measure yields estimates similar to the model using five instruments. Most importantly, the estimated effect of an additional year of schooling

> 24The additive portion of ∆ _Izy_ does not double count years in which both FPE and MTI are available in the region. Both reforms making one additional year of schooling available can never increase schooling by more than a single year.

A.32
an initial food poverty rate of 63 percent or more, at or above one standard deviation above the mean, a 1 percent increase in growth during 1992–2014 would lead to an annual decline in the poverty rate of almost 3.4 percent, whereas, in municipalities with initial food poverty at 20.0 percent or less, at or below one standard deviation below the mean, the elasticity is roughly _−_ 2 (table 8, panels b and c, column 1).

Table 8: Poverty-adjusted growth elasticities, 1992–2014

|||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|
|---|---|---|---|---|---|---|---|
|||**1992-2014**|**1992-2000**|**2000-2014**|**2000-2005**|**2005-2010**|**2010-2014**|
|||||**a. All mun**|**icipalities**|||
|||_−_1_._983***|_−_1_._885***|_−_2_._288***|_−_2_._280***|_−_1_._874***|_−_1_._990***|
|(1_−_|_Pit−τ_)_gi_(_yit_)|(0.151)|(0.135)|(0.163)|(0.195)|(0.118)|(0.116)|
|Obs|.|2,361|2,361|2,361|2,361|2,361|2,361|
|R<sup>2</sup>||0.499|0.421|0.453|0.489|0.432|0.451|
|||**b. Mun**|**icipalities w**|**ith relatively**|**low initial **|**food povert**|**y rates**|
|1|_P _|_−_1_._984***|_−_1_._756***|_−_1_._870***|_−_2_._247***|_−_1_._337***|_−_2_._182***|
|(_−_|_it−τ_)_gi_(_yit_)|(0.233)|(0.233)|(0.167)|(0.249)|(0.170)|(0.155)|
|Obs|.|426|426|436|436|383|440|
|R<sup>2</sup>||0.486|0.293|0.365|0.423|0.277|0.601|
|||**c. Muni**|**cipalities w**|**ith relatively **|**high initial **|**food pover**|**ty rates**|
|1|_P _|_−_3_._387***|_−_2_._863***|_−_2_._872***|_−_3_._911***|_−_2_._444***|_−_3_._082***|
|(_−_|_it−τ_)_gi_(_yit_)|(0.124)|(0.242)|(0.142)|(0.264)|(0.106)|(0.095)|
|Obs|.|433|433|458|458|425|457|
|R<sup>2</sup>||0.882|0.785|0.596|0.621|0.828|0.881|

_Source_ : World Bank calculations. _Note_ : The table presents estimates of the parameter _η_ in equation (8), weighted by municipal population at the initial year of each period under study. The dependent variable is the annualized growth in the food poverty headcount ratios of municipalities over the period. (1 _− Pit−τ_ ) _gi_ ( _yit_ ) are the annualized changes in mean per capita income at the municipal level over the period at August 2014 prices, adjusted by municipalities’ initial food poverty headcount ratio. Municipalities with low (high) initial food poverty rates are those with headcount ratios one standard deviation below (above) the mean headcount ratios for the whole sample. The intercepts are shown in table 45 in the ancillary file. Robust standard errors are in parentheses. *** _p < ._ 01, ** _p < ._ 05, * _p < ._ 1 Hence, contrary to the linear relationship by which the ordinary growth elasticity of poverty reduction falls in absolute value as poverty rates rise, it can be readily verified that the poverty-adjusted growth elasticity follows a concave relationship with poverty (figure 6). In other words, those municipalities with very high levels of food poverty in 1992 experienced sufficiently higher subsequent growth in mean per capita income to achieve substantial rates of poverty reduction by 2014, which unambiguously occurred (see figure 5, panel b), at least as substantial as in contexts of low poverty and relatively high income growth. A salient result is observed during the 29
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