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Dishonesty and Public Employment. (2023). Rossi, Martín ; Cruces, Guillermo ; Schargrodsky, Ernesto.
In: Working Papers.
RePEc:sad:wpaper:168.

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  5. Appearing in this database implies that the individual was a formal employee in the public or private sector at some point during the 2010-2016 period. The reasons for not appearing in this wage-earner database include inactivity, unemployment, informal employment, self-employment, business ownership, international migration, or death. As described in the text, we deal with attrition from the latter two reasons by matching the data with the 2013 national electoral roll.
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  6. Appendix 3. Additional tables and figures for online publication Table A1. Population size and cutoff numbers by cohort Cohort born in Population Lottery cutoff number 1958 217,598 174 1959 214,979 319 1960 217,072 340 1961 212,113 349 1962 226,352 319 Total/Average 1,088,114 300 Notes: Lottery numbers (provided by the Argentine Army, Oficina de Reclutamiento y Movilización, Estado Mayor del Ejército Argentino) ranged from 1 to 1000. The average cutoff is weighted by each cohorts’ population. The unweighted average is also 300. The previous year’s cutoff for the 1958 cohort was 24.
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  16. Cruces, Guillermo, Martín A. Rossi, and Ernesto Schargrodsky. 2023. “Replication data for: Dishonesty and Public Employment.” American Economic Association [publisher], Interuniversity Consortium for Political and Social Research [distributor]. https://doi.org/10.3886/E185801V1.
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  19. Failed Medical Examination is a dummy variable that equals 1 if the individual failed the conscription medical examinations, and 0 otherwise. Public Sector Employee is a dummy variable that takes the value of 1 if the individual was a wage earner in the public sector at some point in the period 2010 to 2016, and 0 otherwise. Dummy variables are normalized to 0/100 so that results represent percentage points. In the 2SLS model in column (3), Failed Medical Examination is instrumented with Distance to Cutoff – the first stage of this regression is presented in column (1). The Montiel-Pflueger effective F statistic for this first-stage regression is 268.93 – critical value of 37.4 for a 5% worst-case bias (Pflueger and Wang 2015; Montiel Olea and Pflueger 2013). **Significant at the 5% level. ***Significant at the 1% level.
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  20. Figure 2. Reduced form: Public and private employment as a function of distance to the cutoff A. Public Employment B. Private Employment Note: The 295,611 observations correspond to the draft-exempt men in the 1958–1962 cohorts present in the 2013 electoral roll. The figure depicts a binned scatterplot of public and private employment levels for 12 quantiles of the normalized difference between the draft lottery number and the year’s eligibility cutoff. The average cutoff for the 5 cohorts was 300, so the distance below the cutoff (i.e., the exempt) was normalized for each cohort as (-300;0]. The plot was made using the binsreg package by Cattaneo et al. (2019).
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  21. Figure A1. Failure rate in medical examination as a function of draft lottery number by cohort with actual and previous year cutoffs Note: The total number of observations is 1,088,114 (see Table A.3 for number of observations for each cohort). Actual and previous year cutoffs in solid and dash lines, respectively (see Table A.1 for each specific value). Each figure depicts a binned scatterplot of the failure rate in medical examination by 40 quantiles of the draft lottery number for each cohort. All plots include a control for Air Force draftees which were subject to a more stringent medical examination at the time of incorporation. The plots were made using the binsreg package by Cattaneo et al. (2019).
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  33. Our dataset is a subset of these data that contains summary measures of employment histories for the 2010-2016 period for the universe of individuals in the five cohorts we consider (those born between 1958 and 1962). While we did not have access to full employment histories, this database is not a sample of individuals – it contains the information on the full population of formal employees for the cohorts under study.
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  38. Table A3. Failure rate in medical examination (in percentages) by cohort and draft eligibility group Cohort Draft Exempt Draft Eligible Difference 1958-1962 11.30 14.60-3.30*** (0.06) (0.04) (0.07) Observations 325,298 762,816 1,088,114 1958 12.70 14.75-2.06*** (0.17) (0.08) (0.20) Observations 37,405 180,193 217,598 1959 12.86 16.54-3.68*** (0.13) (0.10) (0.17) Observations 68,599 146,380 214,979 1960 11.16 13.62-2.46*** (0.12) (0.09) (0.15) Observations 72,875 144,197 217,072 1961 10.28 13.16-2.88*** (0.11) (0.09) (0.15) Observations 74,230 137,883 212,113 1962 10.28 14.77-4.49*** (0.11) (0.09) (0.15) Observations 72,189 154,163 226,352 Notes: Lottery numbers (provided by the Argentine Army, Oficina de Reclutamiento y Movilización, Estado Mayor del Ejército Argentino) ranged from 1 to 1000. Standard errors in parentheses. ***Difference significant at the 1% level.
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  39. Table A7. Medical examination, employment, and distance to cutoff (with control variables) (1) (2) (3) Failed Medical Examination Public Sector Employee Public Sector Employee Distance to Cutoff-0.0098*** -0.0017** (0.0006) (0.0008) Failed Medical Examination 16.87** (7.82) Observations 295,611 295,611 295,611 Estimation method OLS (First Stage) OLS (Reduced Form) 2SLS Dependent variable mean 10.99 16.58 16.58 Notes: Standard errors clustered at the last-3-ID-digits/cohort level are shown in parentheses. The observations correspond to draft-exempt men in the 1958-1962 cohorts present in the 2013 electoral roll. All models include cohort fixed effects and controls for six regions of residence at age 16, whether the individual is a naturalized citizen, and whether he is indigenous. Distance to Cutoff is the absolute value of the difference between each individual’s lottery number and the conscription cutoff for his cohort.
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    RePEc:nbr:nberte:0178.

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  59. Jackknife Instrumental Variables Estimation. (1995). Krueger, Alan ; Imbens, Guido ; Angrist, Joshua.
    In: NBER Technical Working Papers.
    RePEc:nbr:nberte:0172.

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  60. Average Causal Response with Variable Treatment Intensity. (1995). Imbens, Guido ; Angrist, Joshua.
    In: NBER Technical Working Papers.
    RePEc:nbr:nberte:0127.

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  61. Identification and Estimation of Local Average Treatment Effects. (1995). Imbens, Guido ; Angrist, Joshua.
    In: NBER Technical Working Papers.
    RePEc:nbr:nberte:0118.

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  62. Natural and Quasi- Experiments in Economics. (1994). Meyer, Bruce.
    In: NBER Technical Working Papers.
    RePEc:nbr:nberte:0170.

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  63. The misnorming of the U.S. military entrance examination and its effect on minority enlistments. (1993). Angrist, Joshua.
    In: Institute for Research on Poverty Discussion Papers.
    RePEc:wop:wispod:1017-93.

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  64. Identification of Causal Effects Using Instrumental Variables. (1993). Imbens, Guido ; Angrist, Joshua ; Rubin, D. B..
    In: NBER Technical Working Papers.
    RePEc:nbr:nberte:0136.

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  65. Estimating the Payoff to Schooling Using the Vietnam-Era Draft Lottery. (1992). Krueger, Alan ; Angrist, Joshua.
    In: NBER Working Papers.
    RePEc:nbr:nberwo:4067.

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  66. Instrumental Variables Estimation of Average Treatment Effects in Econometrics and Epidemiology. (1991). Angrist, Joshua.
    In: NBER Technical Working Papers.
    RePEc:nbr:nberte:0115.

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  67. Impact of Interupted Education on Earnings: The Educational Cost of the Chinese Cultural revolution. (). Meng, Xin ; Gregory, Robert.
    In: Canadian International Labour Network Working Papers.
    RePEc:mcm:cilnwp:40.

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