rafmacalaba/datause-encoder-data huggingface dataset:
of punitive violence. Their findings, however, must be viewed with caution since results are based on correlations which cannot be causally interpreted.

Most of the subjects covered on these studies on the Vietnam draft were exposed to real combat. Instead, in our case we analyze the impact of conscription on crime by using a dataset on subjects that were drafted for military service in peace times (with the exception of two cohorts during the Malvinas war).

# **III. Data**

From 1901 through 1995 military service in Argentina was mandatory. Initially, young males were called to serve at the age of 21, and, later, at age 18. The last cohort serving at the age of 21 was the cohort born in 1955, whereas the first cohort serving at the age of 18 was the cohort born in 1958. Cohorts born in 1956 and 1957 were not called to serve in military service.

Each year a lottery system assigned a number between 1 and 1000 to each of the last three numbers of “candidate’s” national IDs. A cutoff number was announced and those “candidates” whose ID number corresponded to a lottery number above the cutoff were eligible to serve on the military service. Final selection of individuals for military conscription from the draft-eligible was based on the pre-induction physical examination and the examination for mental aptitude.<sup>3</sup> Among those lottery numbers eligible for conscription, the lowest numbers were assigned to the Army, the intermediate numbers to the Air Force, and the highest numbers to the Navy. Conscription in the Navy lasted for two years, whereas the duration was one year in the Army and the Air Force.

Exploiting this random assignment, we will try to answer whether conscription incentives or disincentives involvement in criminal activities. To answer this question we use individual-level datasets on men that went through the criminal justice process. We have two datasets provided by the Justice Ministry. One dataset has information of all men that went through criminal justice process since 1934 (about one million observations). This database includes information on the last three ID number and the

> 3 As pointed out by Angrist (1990), the fact that the selection process for entry into the military service was ultimately not random does not imply that the priority for induction was not randomly assigned. In our case, although some upper class youth could have used influences to avoid, shorten, or ameliorate their conscription time, this was basically not an option for low class youth, the ones more prone to involvement in criminal activities.

5
# **Figure 6: Ingrained income bias and endogeneity**

In a cross-country analysis, the hypothesized effect of natural capital on bond yields may in fact be driven by the unobserved level of income or development. Neglecting _푑푒푣푒푙표푝푖_ will bias the results and overestimate the effect of interest (dashed arrow).

Level of development or income _푑푒푣푒푙표푝푖_ ↗↙ ↘↖ _푛푎푡푐푎푝푖_ −−−−−−−−→ _푦푖푒푙푑푠 푖_ Average natural capital Average bond yields ˆ [The _훽_<sup>ˆ</sup> coefficient in _푦푖푒푙푑푠 푖_ = _훼_ + _훽_<sup>ˆ</sup> _푛푎푡푐푎푝푖_ can be interpreted as the percentage point change in the average yield associated with a 1% increase in average natural capital. The standard errors are reported below the coefficients. The stars indicate the level of significance with *** p<0.01, ** p<0.05, * p<0.1.] The statistical results suggest no substantial relationship for A-rated issuers, but provide strong evidence that resource-rich issuers with B-ratings tend to pay higher yields.

# **3.2 The ingrained income bias**

From a long-term growth perspective, this finding makes sense. Natural wealth is an essential input for long-term, sustainable growth. This raises the (natural) rate of interest which drives up inflation expectations and ultimately bond yields. Thus, countries with higher resource endowments are reasonably associated with higher growth potential. This is consistent with the fact that the wealth data was constructed with the explicit goal of capturing long-term growth potential in mind.

Although this finding is intuitively appealing, its simplicity does not rule out any alternative explanation. Higher yields can also indicate higher default risk, for example due to the resource curse. Natural wealth alone does not necessitate a positive economic growth path. In a system with little protection of private property rights and weak rule of law, resource rents can be misappropriated. This can lead to corruption and in extreme cases constitute a source for armed conflict. Neither of these foster the creditworthiness of a country. Political uncertainty and questionable ability for debt repayment will thereby raise the bond returns necessary for investors.

Hence, while it is true that countries richer in natural resources have higher yields, it is entirely unclear why. Upon further consideration, a much more detrimental problem emerges (Figure 6). The between-country view compares countries at their respective stages of development. However, as countries become more prosperous, the relevance of natural capital changes. The resource curse would predominantly affect resource-dependent economies. Countries further down their growth paths will have transitioned from resource-reliant growth into economies where industry and technology constitute the major share of GDP. One could even argue that more prosperous countries will in fact invest back into natural capital and protect ecosystem services. Comparing countries in this manner therefore becomes a comparison of income. Therefore, any conclusions about the effect of natural wealth on borrowing costs will be dominated by 12
stands for the stock of patents, for both the country and the rest of the world. _ui_ is country fixed effect, which we include in some of the estimations (see 6 below). In turn, we explore the possible knowledge spillovers emanating from the region or income group: In this case the the specification includes the stock of patents accumulated by the country, other countries in the same income or regional group, and the stock from the rest of the world.

In a second step we estimate the R&D specification that is analogous to RBR’s laboratory equipment: And the nested models specification (with two country groups, one by income and another by region, denoted by subscript j2) is given by: If knowledge spillovers are positive, then _↵_ 0 _, ↵_ 1 _, ↵_ 2 _>_ 0. However, the coefficients on foreign patent stocks could be zero or negative if instead of complementarity in knowledge, as is assumed in endogenous growth models, there is some degree of substitutability between the research carried out in a given country and the research carried out in the rest of the region or the rest of the world. This would happen, for example, if there is competition for patents across countries and if this effect dominates the public-good diffusion effect.

Finally, the choice of econometric estimators is not trivial. As mentioned above, various contributions have utilized count-data models, mainly Poisson or Negative Binomial estimators. Since country-specific effects (and time effects) play an important role in our application, we provide estimates of equations 2 and 4 above with a Negative Binomial estimator as proposed by Allison and Waterman (2002). The main difference with respect to Hall et al. (1984) concerns how one controls for country fixed effects. Allison and Waterman (2002) show simulation evidence indicating that the Negative Binomial estimator with dummy variables used to control for unit-specific fixed effects is superior to the estimator proposed by Hall et al. (1984). This is due to the fact that, in the presence of overdisperion in the data generation process, fixed effects affect the model prediction through two channels. One is the correlation between the unit fixed effects and the explanatory variables of interest, and the second is through the over-dispersion parameter. The approach proposed by Hall et al. (1984), which relies on unit-specific over-time average patent counts to control for does not control for biases due to the of on the unit-specific fixed effects influence fixed effects over-dispersion parameter. Hence, our estimations come from the Negative Binomial estimator with 9
# FIGURE 10: SHARE OF GOVERNMENT. HEALTH. EDUCATION. MILITARY. AND SCIENTIFIC SERVICES IN VALUE ADDED: USSR AND OECD

<!-- Start of picture text --> 25%7<br>DNESWE<br>20% D4K<br>{<br>NED NED<br>[RL~~~~~~~USCi USA<br>15%- _ GCR GMA ISL<br>~~~A IN BEL<br>USSR PNT GNR<br>/961%N ~~USSR<br>10%-<br>GRC<br>5%- I l l I I<br>8 8.5 9 9.5 10<br>LOG OF PER CAPITA INCOME<br><!-- End of picture text -->
# Research Co.qponents of Wortd<sup>Hank Education Projects,</sup> since 1902.

|Area|Country and Loan/Credit<br>Number|Approved||Research Components|Current Status of Studi-.,<br>including<br>Completed Documents/Data|
|---|---|---|---|---|
|||I|Istablished<br>under the Fourth Project<br>to<br>nalyze<br>job placement and performance<sup>of graduates of</sup><br>Irbid<br>NPITIto be extended to<sup>cover all</sup><br>MD0H<br>paramedical inst,tutions.|asample of graduates from<sup>19'S onwards. Refinement</sup><br>of the system is still<br>in progress.<br>Vocational<br>training<br>Corporation<br>(1986).<br>O"anpower<br>'urvey<br>in Jordan"(doc. UH09709)|
|EMENA|Jordan LN 2890-JO|1988|Seventh Education Project.<br>Strengtheuu technical<br>capacities<br>of Directorate<br>of Exminations<br>and<br>evaluate<br>imnct<br>of project<br>inputs;<br>(teacher<br>upgrading,<br>instructional<br>materials<br>school facilities)<br>on student achievement<br>(para.<br>3.15 p.18).<br>(ii)<br>evaluate Teacher Resource<sup>Center</sup><br>(iii)<br>Conduct studies<br>on relevance of higher<br>education<br>to labor market;<br>(iv)<br>financing<br>of students<br>abroad,<br>(v) effectiveness<br>of coummnity college<br>progr_s<br>(para 3.17 p. <sup>19 </sup>).|(i)<br>Work delayed due to basic<sup>and secondary</sup><br>educational<br>reform measures. Proposed<sup>assessment</sup><br>to start<br>01/90.<br>(ii)<br>8aseline assessrent scheduled<sup>for early</sup><br>1990<br>(iii)<br>and (iv):<br>the Gove,nwumentis preparing<br>areform of higher education<sup>underwhichthesewill</sup><br>be covered;<br>(v) r-A)leted<br>in 1969 - no doctment<sup>found.</sup>|
|ENEMA|Jordan LN 3'06-JO|1989|Developnent of lational<br>Center for Educational<br>Research and Develogment<sup>(UCERD) to monitor and</sup><br>evaluate<br>project,<br>provide<br>feed>ick<br>for design<br>modifications,<br>nd to develop, intoacenter<sup>for</sup><br>excellence<br>in educational<br>research.<br>To be fully<br>staffed<br>by March 1990.<br>Conpleted studies<br>to be<br>submitted to_Bank_for review<sup>and comment (pars</sup><br>3.46-9 p.28-9,<br>and Annex1.2).|Project<br>not yet initiated.|
|EMUA|<br>NoroccoLN 2149-41R|1982|Fifth<br>Education. Unspecified<br>future project<br>studies<br>and architectural<br>fees (pars. 2.14<br>p.12).|Studies carried<br>over to FY86 Education Sector<br>Reform Progrm.|
|ENEMA|NoroccoLU2479-MaR|1985|Vocational<br>Training.<br>Evaluate experience of<sup>two</sup><br>moiIte<br>training<br>units<br>(para. 2.12 p.1.*);<br>(ii)<br>study on vocational training<sup>coordination</sup>|<sup>(i)</sup><br>Noinforution<br>available;<br>(ii)<br>"Etude surladefinition<br>d'ue strategie de<br>developpment Se**ta**for_tion<br>dcsapprentis.0|

51
emerging market category or not).<sup>20</sup> Thus, it does not use most of the upgrades and downgrades occurring within our sample, which we exploit later.

The regressions for the extensive margin show that the inclusion of a country in an index is positively related to a country’s size, measured by market capitalization and GDP (Table 2, Panels A and B). Moreover, country risk and the quality of institutions are significant for the probability of including a country in an index. The regressions for the intensive margin (Table 2, Panel C) show that the country weight is positively correlated with the country’s market capitalization (with and without a free-float adjustment), GDP, GDP per capita, and the quality of institutions.

Although at the security level the benchmark weights are directly related to market capitalization, at the more aggregate country level this does not need to be the case. For this to occur, it is necessary that the market capitalization of a country’s selected securities, corrected by their individual inclusion factors, be proportional to the overall market capitalization of the country. Our results confirm that the aggregation process preserves some of the characteristics of the individual security-level composition of the index, so that the selection of securities across countries is related to the size of different markets after controlling for other factors.

Next, we explore the panel structure of our benchmark weight data to study the determinants of their time-series variation. We focus only on the intensive margin because we do not have many episodes of country upgrades and downgrades. Based on our cross-sectional results, we concentrate on the role of changes in a country’s market capitalization relative to other countries in the same benchmark. We estimate the following regression: ( ) ( ) (3) where ( ) is the change in the log-weight of country _c_ in benchmark _B_ between _t-1_ and _t_ , and ( ) is the difference between the net return of country _c_ and that of benchmark _B_ at time _t_ .<sup>21</sup> The coefficient of interest is , which captures the relation between relative returns and percentage changes in benchmark weights. The parameters , , and correspond to benchmark-country, benchmark-time, and country-time

> 20 In these estimations, we use only the MSCI indexes to analyze a consistent sample. For instance, FTSE classifies Korea as a developed country, while MSCI puts it under the emerging market category. We choose MSCI because it represents the majority of the benchmarks in our sample and it allows us to estimate well the extensive margin.

> 21 This derivation uses that the change in the market capitalization of a country relative to other countries in the same benchmark equals its relative gross return (assuming a constant number of securities in an index). It also uses that the log gross relative return,    ( ), is approximately equal to the difference in net returns ( ).

11
that are the exogenous mechanism linking wheat production to food aid shipments changed, starting with the 1985 Farm Bill and culminating in the 1996 Farm Bill which formally uncoupled government purchases from price or production targets (Willis and O’Brien, 2015).<sup>6,7</sup> This policy change implies that NQ’s hypothesized first stage relationship between US wheat production and food aid deliveries should be strongest prior to 1985 and should disappear or at least attenuate after 1996.

Thus, the latter portion of the NQ data offer a natural placebo test as the mechanism tapered off after that point. When we re-estimate for sub-samples before 1985, from 1985-1996, and after the wheat price stabilization policy ended in 1996, however, rather than finding the hypothesized positive first and second stage relationship in the pre-1985 period and no correlation in the latter period, we find a positive association both between aid deliveries and wheat production (first stage) and between conflict and aid as instrumented by wheat production (IV result) in both the early (pre-1985) and latter (post-1996) periods, as reflected in Appendix 1.<sup>8</sup> The results from the pre-1985 period where the posited policy mechanism was indeed active are statistically indistinguishable from those from the post-1996 period when it no longer existed. And the estimated relationship during the policy change period of 1985-1996 is negative, not positive, and statistically insignificant, 6 The details of the policy and how it affects the estimation strategy are described in more detail in Appendix 1, which also explains why the mechanism NQ posit is largely irrelevant today, given changes in both US farm and food aid policies.

7 Following the 1996 Farm Bill, the USDA was still enabled, though not obligated, to purchase wheat through the Commodities Credit Corporation (CCC). It is possible that wheat yields would remain associated with price or supply changes that could differentially create incentives to purchase wheat for aid under the Bill Emerson Humanitarian Trust or 416(b) programs in years with elevated wheat production, rather than draw on government-held stocks. However, such episodes have been relatively infrequent and when they have occurred, the bulk of the wheat procured has been used primarily for non-emergency Title II shipments that are monetized by the recipient NGO, i.e., not in emergency situations where food aid might prolong a conflict, as NQ hypothesize.

8 Except where additional data sources are added and explicitly noted, we use the dataset posted by Nunn and Qian on the replication files page at: <u>https://www.aeaweb.org/articles?id=10.1257/aer.104.6.1630. These data are described in further</u> detail in the original NQ paper. Where relevant, we refer to these data as the NQ replication file.

10
significant correlation between years of schooling and the derived schooling cognitive skills variable. Pasche (2007), using Swiss data, estimated equation 10 while encountering only moderate multicollinearity problems. In our case, however, the problem is severe. Variance inflator factors (VIF) for the two collinear variables are in excess of 50, rendering the estimated coefficients of years of schooling and _SCS_ uninterpretable.

We will now attempt to overcome the multicollinearity problem by modifying the methodology presented in equations 7-12, in particular instead of deriving an estimate of schooling cognitive skills using the significant schooling coefficients, we will derive an estimate of non-schooling cognitive skills instead. For this, we explore a suitable set of variables in the dataset to be used as non-schooling additional controls in equation 9. The set of non-schooling controls identified includes: fathers’ and mothers’ education, reading books at home frequently, urban residence and age, of which the first four enter with a statistically significant coefficient. Using the derived schooling and non-schooling cognitive skills in the earnings function confirms that there is no evidence of significant multicollinearity (apart from what arises of including experience and its square in the regression – which is unavoidable). The Variance Inflator Factors (VIF) are now between 2 and 3 (see Table A4 in the appendix, column 4) and the mean VIF is well below 10.

From Table 3, the average marginal return to schooling from the standard specification is 9 percent. There is a significant decrease of about 25 percent in the years of schooling coefficient once we control for full cognitive skills. After controlling for both types of cognitive skill (column 4), both schooling and non-schooling cognitive skills are responsible for the overall 23
2 Figure 1: Trade growth

# (A) U.S. export growth

(B) U.S. import growth <!-- Start of picture text --> Percent 2002-08 2010-14 Percent 2002-08 2010-14<br>14 14<br>12 12<br>10 10<br>8 8<br>6 6<br>4 4<br>2 2<br>0 0<br>Arm's-length Intra-firm Arm's-length Intra-firm<br><!-- End of picture text -->

- (C) Global GDP and foreign affiliates value added

(D) Top 5 multinationals, 2015 <!-- Start of picture text --> Percent 2002-08 2010-14 US$, billions Foreign sales Total sales<br>300<br>12<br>250<br>200<br>10<br>150<br>100<br>8<br>50<br>0<br>6<br>4<br>2<br>0<br>GDP Value added of<br>foreign affiliates AEs EMDEs<br>China Ocean América Móvil<br>GE (U.S.) BP (U.K.) China Ntl Offshore Petronas (MYS) (MEX)<br>Shell (U.K.) Toyota (JAP)<br>Total SA (FRA) Oil (CHN) Vale SA (BRA) Shipping (CHN)<br><!-- End of picture text --> Sources: U.S. Census Bureau, UNCTAD.

A.B. U.S. exports and imports of goods based on data from the U.S. Census Bureau. Global data is not available.

C. Nominal terms. Value added of foreign affiliates is based on estimates from various editions of UNCTAD’s World Investment Report.

D. Ranked by foreign assets in 2015. Excludes multinational companies in the financial sector. AEs stands for advanced economies. BRA=Brazil, CHN=China, FRA=France, JAP=Japan, MEX=Mexico, MYS=Malaysia.
# **A Appendix**

**Table A1:** The Role of Omitted Variables: Alternative Definition of Depth

||(1)<br>No Tariffs|(2)<br>WITS|(3)<br>New GTD|(4)<br>New GTD|(5)<br>MFN|(6)<br>Glob|(7)<br>Glob-MFN|
|---|---|---|---|---|---|---|---|
|**(A) Baseline (20**|**00-2015)**, F|E: i-s-t, j-s-|t, i-j-s|||||
|Deep TAs|0.18<sup>_∗∗_</sup>|0.14<sup>_∗_</sup>|0.07|0.08|0.05|0.06|0.05|
||(0.08)|(0.07)|(0.07)|(0.07)|(0.06)|(0.06)|(0.06)|
|Shallow TAs|-0.05|-0.09|-0.15<sup>_∗∗_</sup>|-0.14<sup>_∗∗_</sup>|-0.15<sup>_∗∗∗_</sup>|-0.08<sup>_∗_</sup>|-0.09<sup>_∗_</sup>|
||(0.06)|(0.06)|(0.06)|(0.06)|(0.05)|(0.05)|(0.05)|
|Enabl. Clause|0.05|0.04|0.01|0.00|0.03|0.04|0.04|
||(0.06)|(0.06)|(0.06)|(0.07)|(0.06)|(0.06)|(0.06)|
|_ln_(1+_τ_)||-1.17<sup>_∗∗_</sup>|-3.09<sup>_∗∗∗_</sup>|-3.06<sup>_∗∗∗_</sup>|-1.65<sup>_∗∗∗_</sup>|-1.27<sup>_∗∗_</sup>|-0.98<sup>_∗∗_</sup>|
|||(0.59)|(0.78)|(0.82)|(0.55)|(0.59)|(0.46)|
|MFN Openness|||||(0.01)<br><br>0.04<sup>_∗∗∗_</sup>||(0.01)<br><br>0.01|
|N|474,312|360,522|360,522|474,312|474,312|474,312|474,312|
|**(B) Long Sampl**|**e (Manuf.**|**and Agri., **|**1989-2015)**,|FE: i-s-t,j-s|-t, i-j-s|||
|Deep TAs|0.23<sup>_∗∗∗_</sup>|0.13<sup>_∗_</sup>|0.04|0.09|0.02|0.03|0.00|
||(0.08)|(0.08)|(0.08)|(0.08)|(0.06)|(0.07)|(0.06)|
|Shallow TAs|0.00|-0.06|-0.14<sup>_∗_</sup>|-0.10|-0.10|-0.09|-0.10|
||(0.08)|(0.09)|(0.08)|(0.08)|(0.07)|(0.07)|(0.06)|
|Enabl. Clause|0.01|-0.01|-0.05|-0.05|-0.02|-0.01|-0.00|
||(0.06)|(0.05)|(0.05)|(0.06)|(0.05)|(0.06)|(0.05)|
|_ln_(1 +_τ_)||-1.70<sup>_∗∗∗_</sup>|-3.58<sup>_∗∗∗_</sup>|-3.36<sup>_∗∗∗_</sup>|-1.98<sup>_∗∗∗_</sup>|-1.43<sup>_∗∗∗_</sup>|-1.05<sup>_∗∗_</sup>|
|||(0.54)|(0.62)|(0.60)|(0.52)|(0.47)|(0.49)|
|MFN Openness|||||0.05<sup>_∗∗∗_</sup><br>(0.01)||0.02<sup>_∗∗_</sup><br>(0.01)|
|N|683,290|392,839|392,839|683,290|683,290|683,290|683,290|

**Note:** The information on the depth is provided by DESTA (Dür et al. 2014), definitions of the different types of agreements (enabling clause, shallow TA, and deep TA) can be found in the main text. All columns include importer-sector-time, exporter-sector-time, and importer-exporter-sector fixed effects, columns (5) and (7) include time-varying border controls to account for general trends in globalization. In Panel (A) the data includes three broad sectors (agriculture, manufacturing, and services) for the years 2000 to 2015, Panel (B) has no services-trade but a longer time frame (1989-2015). Standard errors are clustered threeway for importer, exporter, and year.<sup>_∗_</sup> for _p <_ 0 _._ 1, _∗∗_ for _p <_ 0 _._ 05 and _∗∗∗_ for _p <_ 0 _._ 01.

I