rafmacalaba/datause-encoder-data huggingface dataset:
# **Crossovers: Female Entrepreneurs Who Enter Male Sectors Evidence from Ethiopia**

_By_ Salman Alibhai, Niklas Buehren, Sreelakshmi Papineni, and Rachael Pierotti<sup>_∗_</sup>

**Keywords:** Gender, Entrepreneurship, Firms, Sector Choice and Ethiopia. **JEL codes:** J16 L25 L26 O12

> _∗_ This paper is a product of the World Bank Africa Gender Innovation Lab (AFRGIL). Alibhai (email: aalibhai@worldbank.org); Buehren (email: nbuehren@worldbank.org); Papineni (email: spapineni@worldbank.org); Pierotti (email: rpierotti@worldbank.org). This study builds on the research framework of “Breaking the Metal Ceiling: Female entrepreneurs who succeed in male-dominated sectors in Uganda” (2011), and we thank the authors Francisco Campos, Markus Goldstein, Laura McGorman, Ana Maria Munoz Boudet, and Obert Pimhidza for their support during the conceptualization stage and for the survey instrument that was used to collect much of the data used in this paper. This study was integrated into the Women Entrepreneurship Development Project (WEDP), an ongoing lending operation of the Finance & Markets Global Practice, and we thank the Team Leader Francesco Strobbe for his support. We thank Seblewangel Ayalew Woreta for superb field assistance. We are grateful to the World Bank Group’s Umbrella Facility for Gender Equality and the Government of Canada for financial support.

_1_
**(contd) Table 1b: GDP per capita growth volatility and private credit as a share of GDP**

|**Dep. Var:**|GMM|OLS|OLS|OLS|GMM|GMM|
|---|---|---|---|---|---|---|
|**std_gdppc**|(7)|(8)|(9)|(10)|(11)|(12)|
|privatecredit|-0.0322***<br>(0.003)|-0.0103**<br>(0.045)|-0.0181***<br>(0.002)|-0.0266***<br>(0.000)|-0.0181**<br>(0.028)|-0.0347***<br>(0.002)|
|privatecredit<sup>2</sup>|0.0001**|0.0000|0.0000**|0.0001***|0.0001*|0.0001**|
||(0.016)|(0.149)|(0.050)|(0.001)|(0.078)|(0.018)|
|financ.open.|0.0000|0.0001|0.0004|-0.0000|0.0000|0.0000|
||(0.813)|(0.663)|(0.198)|(0.894)|(0.286)|(0.512)|
|tradeopen.|0.0045*|0.0061***|0.0026|0.0051***|0.0039*|0.0044**|
||(0.054)|(0.001)|(0.135)|(0.002)|(0.060)|(0.046)|
|initialgdppc|-0.1950|-0.1767**|0.0350|-0.0775|-0.0713|-0.0650|
||(0.268)|(0.022)|(0.679)|(0.179)|(0.585)|(0.577)|
|financialcrisis|1.1157***|||||1.1453***|
||(0.001)|||||(0.001)|
|ECA|0.7328<br>(0.173)||||||
|std_exchangerate||0.0307*|||||
|||(0.085)|||||
|tot_change|||0.0009||0.0033||
||||(0.854)||(0.529)||
|commodityexporter||||0.3070**<br>(0.043)|||
|Constant|4.5870***|3.5880***|2.6603***|3.6714***|3.2877***|3.8188***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|# of Obs.|900|474|542|978|545|900|
|R-squared||0.112|0.032|0.078|||
|Prob > F|0|0.000|0.000|0.000|0.003|0.000|
|# of countries|133||||146|133|

Robust p-values are presented in parentheses and significance at 1% (***), 5% (**), and 10%(*) levels are indicated.

11
|**Table A11: Base Regression R**|**esults for L**|**arge Firm**|**s: Log odds**|**ratios from l**|**ogit estimat**|**ion**|
|---|---|---|---|---|---|---|
|Dependent variable: R&D Activity|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|
|Informal Competition (Country-level|-0.166|-0.197|-0.265|-0.248|-0.274|-0.424|
|cell average)|(0.542)|(0.550)|(0.554)|(0.545)|(0.555)|(0.583)|
|GDP per capita (lagged, logs)||-0.021|0.013|-0.014|-0.013|-0.118|
|||(0.122)|(0.116)|(0.115)|(0.114)|(0.193)|
|Firm Size|||0.353***|0.298***|0.297***|0.298***|
||||(0.105)|(0.108)|(0.109)|(0.109)|
|HHI|||0.315|0.358|0.282|-0.100|
||||(0.601)|(0.647)|(0.612)|(0.595)|
|Age of Firm (logs)||||0.199*|0.187|0.177|
|||||(0.118)|(0.118)|(0.117)|
|Manager Experience (logs)||||0.104|0.098|0.138|
|||||(0.121)|(0.125)|(0.124)|
|Exports (proportion of sales)||||0.083|0.088|-0.016|
|||||(0.219)|(0.217)|(0.215)|
|Female Top Manger Y:1 N:0||||-0.083|-0.092|-0.092|
|||||(0.292)|(0.297)|(0.273)|
|Multi-firm Y:1 N:0||||0.552***|0.543***|0.574***|
|||||(0.170)|(0.172)|(0.170)|
|Power Outages (day hours)|||||0.021|0.018|
||||||(0.020)|(0.021)|
|Finance Obstacle (0-4 scale)|||||-0.037|-0.039|
||||||(0.060)|(0.060)|
|Overdraft Y:1 N:0|||||0.289*|0.282*|
||||||(0.161)|(0.161)|
|Courts Obstacle (Country-level cell||||||0.416**|
|average)||||||(0.167)|
|Population (lagged, logs)||||||-0.133**|
|||||||(0.065)|
|Inflation (%, annual)||||||0.007|
|||||||(0.007)|
|Gross Tertiary Enrollment Rate||||||0.686|
|||||||(0.856)|
|Gross Primary Enrollment Rate||||||0.519<br>(0.594)|
|Industry fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Constant|0.226|0.439|-1.646|-2.297|-2.361*|-0.679|
||(0.620)|(1.428)|(1.404)|(1.423)|(1.407)|(2.038)|
|Number of observations|5,721|5,721|5,721|5,721|5,721|5,721|
|Pseudo R-squared|0.065|0.065|0.076|0.090|0.094|0.104|

Standard errors in brackets. All standard errors are Huber-White robust and clustered on the country. Significance is denoted by *** (1%), ** (5%), * (10%).

79
Figure D.7. Smooth Condition Test Under Different Bandwidths (cont’d) <!-- Start of picture text --> (a) In-migration Share (b) Out-migration Share (c) Inside Highly Populated Area (1980)<br>.1 .2 .3 .4 .5 .6 .7 .8 .9 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 4 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 4 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 4<br>Bandwidth (Km) Bandwidth (Km) Bandwidth (Km)<br>Mean of Outcome: .14 Mean of Outcome: .01 Mean of Outcome: .71<br>(d) Aggregate Yield Index (1961–79) (e) Bean Agro-climatic Yield (f) Coffee Agro-climatic Yield<br>.1 .2 .3 .4 .5 .6 .7 .8 .9 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 4 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 4 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 4<br>Bandwidth (Km) Bandwidth (Km) Bandwidth (Km)<br>Mean of Outcome: 0 Mean of Outcome: 4.06 Mean of Outcome: 1.67<br>(g) Cotton Agro-climatic Yield (h) Maize Agro-climatic Yield (i) Wet Rice Agro-climatic Yield<br>.1 .2 .3 .4 .5 .6 .7 .8 .9 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 4 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 4 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 4<br>Bandwidth (Km) Bandwidth (Km) Bandwidth (Km)<br>Mean of Outcome: .71 Mean of Outcome: 9.94 Mean of Outcome: 8.630000000000001<br>.1 .005 .1<br>.05<br>.05 0<br>0<br>Coefficient magnitude 0 Coefficient magnitude -.005 Coefficient magnitude<br>-.05<br>-.05 -.01 -.1<br>.2 .06 .04<br>.04 .02<br>.1<br>.02 0<br>0<br>0 -.02<br>Coefficient magnitude Coefficient magnitude Coefficient magnitude<br>-.1 -.02 -.04<br>-.2 -.04 -.06<br>.01 .15 .3<br>.1<br>.2<br>.005<br>.05<br>.1<br>0<br>0<br>Coefficient magnitude Coefficient magnitude Coefficient magnitude 0<br>-.05<br>-.005 -.1 -.1<br><!-- End of picture text --> _Notes:_ The results follow the specification of equation (1). The estimates shown include 400 break fixed effects.

66
**Table 1. Technology Adoption Readiness and Growth**

||(1)<br>Rela|(2)<br>tive decadal gr|(3)<br>owth|
|---|---|---|---|
|Relative income|-0.0130***<br>(0.00186)|-0.0135***<br>(0.00168)|0.0302***<br>(0.00328)|
|Average technology readiness (-)|0.000243***<br>(0.0000146)|0.000243***<br>(0.0000133)||
|Relative income # Average<br>technology readiness (-)|0.000335***|0.000259***|0.00169***|
||(0.0000459)|(0.0000415)|(0.0000846)|
|Observations|6319|6319|6319|
|Country fixed effect|no|no|yes|
|Year fixed effect|no|yes|yes|
|R-square|0.115|0.286|0.451|

- Notes: Coefficient estimates from ordinary least squares regressions at the country year level. Standard errors are given in parentheses. * p<0.1, ** p<0.05, *** p<0.01. The dependent variable is the relative annualized overlapping decadal growth of real GDP per capita, compared to the growth in the US. Relative income is the relative real GDP per capita from the initial year of the decade (US’s real GDP per capita at the same year equals 1). Average technology readiness in the regression represents the average ranking for technology readiness. A higher number means a better ranking and higher technology readiness. The main variable of interest in all columns are the technology readiness, interacted with relative income from the initial year. The coefficient estimates associated with the constant are not reported to save space. Column (1) has no fixed effects, while column (2) is added with year fixed effects. In column (3), we added country fixed effect to replace the linear term of average technology readiness, in order to capture country-specific characteristics in addition to technology readiness. See Appendix Table A2 for the list of countries.

To address concerns about endogeneity associated with technology adopted, we instrumented Technology Readiness with variable capturing variables capturing the attitude toward innovation and risks presented in Hofstede et al (2010).<sup>13</sup> Attitudes toward innovation vary considerably across countries. These attitudes play a critical role in driving decision of governments, firms, individuals toward adoption of technology and innovation. Figure 5 provides illustrative evidence of the powerful relationship between attitude traits and technology readiness. The correlations validate that the most relevant psychological traits are power distance (the way in which power is distributed), avoidance of uncertainty, and individualism (see Figure 5). Other dimensions that might affect are tough versus tender, (short-term) normative versus (long-term) pragmatic, and indulgence versus restraint. We use all six dimensions to instrument technology readiness in the first table, and the results are presented in Table 2.

> 13 Data are from Hofstede Insights: https://hi.hofstede-insights.com/national-culture

10
- 21 -

Rather than present a large, and potentially confusing, array of DCC curves for the C6te d'Ivoire, Table 3 summarises our efforts to establish a welfare ordering of alternative commodity taxes using the criterion of marginal welfare dominance. Of the five promising excise tax candidates consisting of telephones, electricity, gasoline, alcoholic beverages and public transportation, we concluded that the taxation of telephone services dominates all of the other items in this group. Taxing electricity consumption, on the other hand, dominates the taxation of alcoho'lic beverages and public transportation but does not quite manage to dominate the taxation of gasoline. Gasoline taxation, by the same token, dominates a tax on either alcoholic beverages or public transportation. Finally, a tax on alcoholic beverages fails to dominate a tax on public transportation. It would be possible to extend this welfare ordering to include other items shown in Table 2 but tbs ranking shown in Table 3 are perhaps the most interesting ones for policy purposes and it is clear from Table 2 that taxes on food and cigarettes would be dominated by taxes on any of the five candidates already examined.

An interesting extension of the analysis is to consider the welfare implications of adjusting the mix of direct and indirect taxes in the C6te d'Ivoire. This welfare comparison can be achieved by viewing a tax on wages or rents and dividends as a tax on all of the expenditure made by workers or capitalists respectively. From Table 2 it appears that the weighted income elasticities for capital and labor incomes are relatively large and extremely close to one another in size. Moreover, while telephone consumption dominates either income component as a tax base, it is also apparent that neither electricity nor gasoline consumption does the

**MHas11*/Welfsre** Dominance & **the** Design of Excise Taxation in **the** Coto D'Ivoir/WThirsk:ab/5/18/88
**TABLE 7.1: Public Agency Level - Univariate OLS Regressions** Source of Data: Public Officials' Survey Dependent variable: Accessibility of Public Services

**Coefficient Robust t-stat N R-squared**

|**Corruption Indices**|||||
|---|---|---|---|---|
|Overall corruption|-0.493|[4.18]***|157|0.22|
|Perceived corruption|-0.642|[4.28]***|160|0.10|
|Corruption in personnel|-0.298|[2.84]***|159|0.10|
|Corruption in budget|-0.289|[4.35]***|159|0.14|
|Corruption in public contracts|-0.312|[3.79]***|160|0.16|
|Administrative Corruption|-0.349|[3.89]***|160|0.16|
|Corruption in laws/regulations|-0.379|[4.18]***|160|0.18|
|**Governance Indices**|||||
|Effectiveness of rules|0.505|[3.38]***|160|0.17|
|Effectiveness of rules in personnel|0.509|[3.39]***|161|0.17|
|Effectiveness of rules in budget|0.444|[3.40]***|160|0.14|
|Quality of Rules|0.515|[3.00]***|160|0.13|
|Quality of Personnel Rules|0.488|[2.56]**|161|0.10|
|Quality of Budget Rules|0.475|[3.43]***|160|0.12|
|Audit|0.481|[4.16]***|156|0.18|
|Audit in personnel|0.392|[3.74]***|159|0.12|
|Audit in budget|0.426|[4.38]***|160|0.16|
|Meritocracy|0.354|[3.72]***|159|0.12|
|Wage satisfaction|0.182|[2.38]**|161|0.04|
|Citizen voice|0.563|[4.07]***|161|0.17|
|Mission|1.071|[5.37]***|160|0.22|
|Resources|0.302|[3.06]***|161|0.07|
|Justice|-19.033|[3.68]***|161|0.10|
|Municipal|2.866|[0.84]|161|0.00|

- significant at 10%; ** significant at 5%; *** significant at 1%

35
# (6) **_7R =_** _2 tLy_ **_('p) T- (p)0p =_** _u 2 L pX-( LT_ **_(p'PLp_** _CrT_ **_CX-D'_**

so that **nR** is positive-and hence payments are progressive on ATP-if the concentration curve for ATP, y, lies above the concentration curve for payments, T. In eqn (5), Sis the average deduction rate; i.e., deductions, D, expressed as a proportion of pre-payment income, x. ,z in eqn (5) measures the progressivity of deductions on prepayment income, and is defined as which is positive if the Lorenz curve for pre-payment income lies above the concentration curve for deductions.

From eqn (5), it is evident that the progressivity of payments on pre-payment income reflects not just the progressivity of payments on ATP, but also the progressivity of deductions on pre-payment income. Thus if deductions are a higher proportion of prepayment income for the better-off than the poor (i.e., if D is progressive or incomeelastic), z, will be positive and deductions will exert a dampening effect on the progressivity of payments on pre-payment income. By contrast, if deductions are a smaller proportion of pre-payment income for the better-off than the poor (i.e., _D_ is regressive or _income-inelastic), rD_ will be negative and deductions will exert an enhancing effect on the progressivity of payments on pre-payment income. Payments will be more progressive on pre-payment income the higher is S(deductions as a proportion of pre-payment income) and the more income-inelastic deductions are.

One of the implications of this is that if one's interest is in seeing whether payments are appropriately linked to ATP, a progressivity analysis of payments on prepayment income will not help. WHO (World Health Organization 2000) argues that payments for health care _should be proportional to ATP._ In other words ,;< ought to be zero, or equivalently there should the same degree of inequality in payments as there is in ATP. In this sense, then, levying payments for health care in proportion to ATP is egalitarian. From eqn (5), it is clear that estimates of the progressivity of payments on pre-payment income cannot help us discern whether this condition is satisfied.

# 3.1.2. **Redistributive effect and ability to pay**

Similar problems arise in the context of redistributive effect. Following Pfdhler **_RS_** (1990), the RS index of health care payments, _ITT_ can also be decomposed into two _9_
Figure 22. Average monthly labor income of men 15+ by state of origin <!-- Start of picture text --> Men who migrated Men who did not migrate Percent difference<br>20,000 140<br>120<br>100<br>15,000<br>80<br>60<br>10,000<br>40<br>20<br>5,000 0<br>-20<br>0 -40<br>State of origin<br>Mexican pesos Percent difference<br>chs tla yuc nay coa gro pue agc oax gto ver tab col qro mor mex cam mich hgo zac jal slp tam dgo qroo nl sin cdmx bcs son chh bc<br><!-- End of picture text --> Source: Own estimates based on Population and Housing Census 2020. Abbreviation codes in Annex 1.

**Homeownership significantly differs among women who migrated and those who did not migrate. The same hold for men.** Regarding dwelling tenure, only 40.8 percent of women who migrated internally five years ago own the house they inhabit against 73.6 percent of those who did not migrate (Figure 23). There are no significant differences compared to men considering their migration status.

**Women and men who migrated tend to live in localities most populated.** Almost 60 percent of women who migrated reside in urban localities with 100,000 or more people versus 48.8 of their counterparts who did not migrate. On the contrary, 11.7 percent of women who migrated live in rural localities with less than 2,500 people versus 20.4 of those who did not migrate (Figure 24). A similar pattern is observed among men.

Figure 23. Percentage of women and men who own the house they inhabit by internal migration status <!-- Start of picture text --> 80 73.5 73.6<br>70<br>60<br>50 39.0 40.8<br>40<br>30<br>20<br>10<br>0<br>Men who migrated Men who did not Women who migrated Women who did not<br>migrate migrate<br><!-- End of picture text --> Source: Own estimates based on Population and Housing Census 2020.

18
Gasparini, L., and G. Cruces (2010). “A Distribution in Motion: The Case of Argentina,” in L. LópezCalva and N. Lustig, eds., _Declining Inequality in Latin America: A Decade of Progress?_ (Washington, DC: Brookings Institution Press and UNDP), pp. 100-133.

<mark>Gasparini, L., M. Cicowiez., and W. Sosa Escudero (2013).</mark> _<mark>Pobreza y Desigualdad en América Latina: Conceptos, Herramientas y Aplicaciones</mark>_ <mark>(Buenos Aires: Editorial Temas).</mark> <mark>Gasparini, L., G. Cruces, and L. Tornarolli (2016). “Chronicle of a Deceleration Foretold: Income Inequality in Latin America in the 2010s,” CEDLAS Working Paper 198 (Universidad Nacional de La Plata).</mark> <mark>Inchauste, G., J.P. Azevedo, B. Essama-Nssah, S. Olivieri, T. Van Nguyen, J. Saavedra-Chanduvi, and H. Winkler (2014).</mark> _<mark>Understanding Changes in Poverty</mark>_ <mark>(Directions in Development - Poverty, World Bank)</mark> .

López-Calva, L., and N. Lustig (2010). _Declining Inequality in Latin America: A Decade of Progress?_ (Washington, D.C.: Brookings Institution Press).

Riera-Crichton, D., C. Vegh, and G. Vuletin (2016). “Tax Multipliers: Pitfalls in Measurement and Identification,” _Journal of Monetary Economics_ , Vol. 79, pp. 30-48.

<mark>Sala-i-Martin, X. (2006). “The World Distribution of Income: Falling Poverty and ... Convergence, Period,”</mark> _<mark>Quarterly Journal of Economics,</mark>_ <mark>Vol. 121, No. 2, pp. 351-397.</mark> <mark>Stampini, M., and L. Tornarolli (2012). “The Growth of Conditional Cash Transfers in Latin America and the Caribbean: Did They Go Too Far?” IZA Policy Paper 49 (Institute of Labor Economics).</mark> <mark>Vegh, C., and G. Vuletin (2013). “O</mark> vercoming the Fear of Free Falling: Monetary Policy Graduation in Emerging Markets <mark>,” in D. Evanoff, C. Holthausen, G. Kaufman, and M. Kremer, eds.,</mark> _<mark>The Role of Central Banks in Financial Stability: How Has It Changed?</mark>_ <mark>, (Federal Reserve Bank of Chicago and European Central Bank), pp. 105-131.</mark> <mark>Vegh, C., and G. Vuletin (2015). “H</mark> ow is Tax Policy Conducted over the Business Cycle?” _American Economic Journal: Economic Policy_ , Vol 7, pp. 327-370.

Vegh, C., G. Vuletin, D. Riera-Crichton, J. P. Medina, D. Friedheim, L. Morano, and L. Venturi (2018). _From Known Unknowns to Black Swans: How to Manage Risk in Latin America and the Caribbean_ (LAC Semiannual Report, World Bank).

16
# Schooling and Youth Mortality: Learning from a Mass Military Exemption<sup>∗</sup>

Piero Cipollone<sup>†</sup> & Alfonso Rosolia<sup>‡</sup> Abstract We examine the relationship between education and mortality in a young population of Italian males. In 1981 several cohorts of young men from specific southern towns were unexpectedly exempted from compulsory military service after a major quake hit the region. Comparisons of exempt cohorts from least damaged towns on the border of the quake region with similar ones from neighbouring non-exempt towns just outside the region show that, by 1991, the cohorts exempted while still in high school display significantly higher graduation rates. The probability of dying over the decade 1991-2001 was also significantly lower. Several robustness checks confirm that the findings do not reflect omitted quake-related confounding factors, such as the ensuing compensatory interventions. Moreover, cohorts exempted soon after high school age do not display higher schooling or lower mortality rates, thus excluding that the main findings reflect direct effects of military service on subsequent mortality rather than a causal effect of schooling. We conclude that increasing the proportion of high school graduates by 1 percentage point leads to 0.1-0.2 percentage points lower mortality rates between the ages of 25 and 35.

Keywords: education, mortality, health, human capital. JEL Codes: I20, I12.

> ∗The views expressed in this paper do not necessarily reflect those of the Bank of Italy. We wish to thank Josh Angrist, Iwan Barankay, David Card, Sabrina D’Addario and Franco Peracchi for their comments as well as seminar participants at ESPE 2005, EEA 2006, SIE 2006, NBER Summer Institute 2007, EALE-SOLE 2010, the Bank of Italy, Fifth Brucchi Luchino Workshop, the University of California at Berkeley, the University of Warwick, ISER, the University of Rome “Tor Vergata”, the University of Bologna and the Banco de Espa ̃na. We also thank Maurizio Lucarelli and the staff of Istat’s safecenter in Rome (ADELE Lab), for their assistance in accessing the data. We are especially grateful to Debora Radicchia who worked with us at the early stages of this project. An unwritten version of this research has been routinely presented under the title “The Effects of Education on Youth Mortality”. Correspondence: alfonso.rosolia@bancaditalia.it

> †World Bank and Bank of Italy.

> ‡Bank of Italy and CEPR.