The database we apply in this study is under the constraint of confidentiality rule, which doesn’t allow us to directly know the identity of servicemen. Therefore, we cannot perfectly obtain all servicemen in the military and unfortunately lose some ob-servation in order to increase the possibility of having people who were more likely to serve around the policy.
6.3 Discussion
This paper suggests that 1.74 months of deduction in service leads to 1.56 months increase in working experience. However, the shortened service doesn’t have effect on time of finding jobs. In addition, the increased experience doesn’t affect their future income and the fraction of having a job in short or long term.
Nevertheless, the way we estimate serviceman’s income is worth of discussing.
The income of those who didn’t work were considered as 0 and this would affect the results when there’s a different fraction of people working around the cutoff. The reason that we put 0 on people’s income for those who didn’t have a job is because it includes the effect of whether they found a job. If we consider those without a job as a missing unit, we are estimating the income effect on people who have a job, which would be more reasonable if we merely want to know the change in scale of income. However, this would also lead to a problem. For servicemen on the left to the cutoff, the fraction of them having a job is lower due to seasonal factors and because their income is deemed as missing; therefore, we would underestimate the effect of policy.
In conclusion, when estimating effect of RD in this study, it might be better to consider jobless worker’s income as missing. For RD in 2004, we’ve known that there’s more people who retired after January found a job. And if we choose to estimate the income effect with jobless men, then we won’t be able to know the income effect on people who have a job. The effect we found, here, is the income effect plus the effect on the fraction of having a job. However, if we choose to estimate income without including jobless men, then we will have two decomposed effect on income. For those retired after January 2004 and had a job, their first job’s income is NTD 1,015 higher than those without deduction (P <.05). 3 years after starting service, servicemen with jobs earned NTD 1,651 more (P <.05) and for those who didn’t have a job, there’s 3 more percentage points of them found a job due to seasonal effect. 10 years after starting service, servicemen with jobs earned NTD 2,249 more (P <.1) and there’s no significant difference in the fraction of having a job between servicemen retired before and after January 2004.
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Nevertheless, when estimating using RD-DID, we should better include jobless workers because there’s no effect on the fraction of having a job and we are estimating the effect of policy through their job search process. Therefore, each way to deal with jobless people’s income might be used under certain purpose. However, in this study, we mainly focus on the results representing the effect of deduction policy using RD-DID design instead of trying to know what’s the seasonal influence on workers’ income from short term to long term using RD design. Hence, we include jobless workers in both RD and RD-DID designs to create a unified income estimation.
Except for the continuous deduction policies from 1999 to 2008, we suggest fur-ther studies can be conducted to discuss the service change in ROC military. After 2013, the compulsory service was changed into military training and the length of duty dropped from 1 year to 4 months, which is a larger decrease in service than this study.
Therefore, if there’s suitable data that can be acquired, people can further study the effect of compulsory service on future income and employers’ attitude to the value of having military service on labor’s skills. Other possible topics are studies estimating the effect of veterans’ health condition. Since regular military training may improve veterans’ exercise habit and cultivate self-discipline, there might be positive impact on lowering the possibility of having chronic diseases in the future.
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References
Angrist, J., & Krueger, A. B. (1994). Why do World War II veterans earn more than nonveterans? Journal of labor economics, 12(1), 74–97.
Angrist, J. D. (1990). Lifetime earnings and the Vietnam era draft lottery: evidence from social security administrative records. The American Economic Review, 313–
336.
Asali, M. (2015). Compulsory military service and future earnings: evidence from a quasi-experiment.
Bauer, T. K., Bender, S., Paloyo, A. R., & Schmidt, C. M. (2012). Evaluating the labor-market effects of compulsory military service. European Economic Review, 56(4), 814–829.
Card, D., & Cardoso, A. R. (2012). Can compulsory military service raise civilian wages? Evidence from the peacetime draft in Portugal. American Economic Journal:
Applied Economics, 4(4), 57–93.
Grenet, J., Hart, R. A., & Roberts, J. E. (2011). Above and beyond the call. long-term real earnings effects of British male military conscription in the post-war years.
Labour Economics, 18(2), 194–204.
Huang, H. (2017). The Revolution of Taiwan’s Military Defense:1982-2016. In (p. 229). ReadingTimes.
Hubers, F., & Webbink, D. (2015). The long-term effects of military conscription on educational attainment and wages. IZA Journal of Labor Economics, 4(1), 10.
Imbens, G., & van der Klaauw, W. (1995). Evaluating the Cost of Conscription in the Netherlands. Journal of Business & Economic Statistics, 207–215.
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Table 1: Descriptive Statistics of RD Sample
Before After
January 2004 January 2004
Service Term(Month) 21.76 20.05
(2.06) (1.68)
Age of Starting Service(Year) 21.06 21.29
(1.62) (1.66)
Age of Retiring From Service(Year) 22.87 22.96
(1.58) (1.63)
Lived in Taipei
While Starting Service 0.08 0.09
(0.28) (0.29)
Time of Finding Jobs 5.19 4.70
(5.22) (5.19)
Age of Finding First Job(Year) 23.48 23.45
(1.76) (1.73)
First Job’s Income 16,133.52 17,014.36
(11,004.90) (11,181.46) Income
(3 Years After Starting Service) 15,986.36 18,157.78 (13,117.72) (13,747.32) Working Experience
(3 Years After Starting Service) 8.75 10.62
(5.89) (6.40)
Fraction of Having a Job
(3 Years After Starting Service) 0.67 0.72
(0.47) (0.45)
Income
(10 Years After Starting Service) 28,151.14 29,372.19 (19,704.06) (19,962.62) Fraction of Having a Job
(10 Years After Starting Service) 0.85 0.86
(0.36) (0.35)
Number of observations 110,397 109,015
Notes:This table shows the means of variables from sample and stan-dard deviations are in parentheses. Column (1) indicates means on the left to the cutoff and Column (2) indicates means on the right to the cutoff. Data in RD design starts from January 2003 to January 2005.
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Table 2: Descriptive Statistics of RD-DID Sample
Treatment Control
2003 2004 2004 2005
July-Dec. Feb.-June July-Dec. Feb.-June
Service Term(Month) 21.84 19.73 20.33 19.96
(2.29) (1.66) (1.62) (1.42)
Age of Starting Service(Year) 20.93 22.17 20.48 22.39
(1.54) (1.50) (1.34) (1.41)
Age of Retiring From Service(Year) 22.75 23.81 22.17 24.06
(1.53) (1.49) (1.31) (1.40)
Lived in Taipei
While Starting Service 0.09 0.11 0.07 0.11
(0.28) (0.31) (0.26) (0.31)
Time of Finding Jobs 5.45 4.06 5.31 3.77
(5.27) (4.89) (5.40) (4.80)
Age of Finding First Job(Year) 23.37 24.25 22.66 24.39
(1.70) (1.58) (1.48) (1.46)
First Job’s Income 15,288.06 19,437.83 14,571.31 19,913.60
(10,448.64) (11,453.20) (9,926.39) (11,486.24) Income
(3 Years After Starting Service) 15,163.08 20,856.77 15,337.64 22,302.27 (12,449.64) (14,414.67) (11,953.44) (14,404.96) Working Experience
(3 Years After Starting Service) 8.39 11.63 9.67 11.78
(5.96) (6.10) (6.54) (5.91)
Fraction of Having a Job
(3 Years After Starting Service) 0.66 0.76 0.69 0.79
(0.47) (0.43) (0.46) (0.41)
Income
(10 Years After Starting Service) 26,625.55 34,685.15 24,106.70 35,686.19 (17,595.16) (21,268.58) (15,872.43) (20,935.36) Fraction of Having a Job
(10 Years After Starting Service) 0.84 0.89 0.82 0.90
(0.36) (0.31) (0.38) (0.30)
Number of observations 48,768 49,985 52,759 44,826
Notes:This table shows the means of variables from sample and standard deviations are in parentheses. Column (1) and (3) indicate means on the left to the cutoff and Column (2) and (4) indicate means on the right to the cutoff. Data in RD-DID design starts from July 2003 to June 2005.
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Table 3: The Effect of Deduction Policy on Service Term and Job Search
(1) (2) (3) (4) (5)
Panel A: Term
Deduction2004 -1.843***
(0.113)
T× Deduction2004 -1.744*** -1.778*** -1.712*** -1.858***
(0.151) (0.275) (0.141) (0.233)
Baseline Mean 21.84
Sample size 219,412 196,338 150,952 181,722 196,338
Panel B: Time of Finding Jobs
Deduction2004 -1.348***
(0.278)
T× Deduction2004 -0.342 -0.140 -0.258 0.0710
(0.449) (0.469) (0.466) (0.413)
Baseline Mean 5.45
Sample size 219,412 196,338 150,952 181,722 196,338
Panel C: Working Experience (3 Years After Starting Service)
Deduction2004 2.956***
(0.319)
T× Deduction2004 1.558** 1.306** 1.203* 1.094**
(0.567) (0.599) (0.587) (0.516)
Baseline Mean 8.39
Sample size 219,412 196,338 150,952 173,110 196,338
RDD Yes – – – –
RDD+DID – Yes Yes Yes Yes
Non-union – – – Yes –
Covariates Yes – Yes Yes Yes
Poly. model Linear Linear Linear Linear Quadratic
Bandwidth (months) 12 6 4 6 6
Standard errors in parentheses
* p < 0.1, ** p < 0.05, *** p < 0.01
Notes: Column (1) shows the estimated coefficients on Deduction in equation (1). Column (2) to column (5) respectively shows the coefficients in equation (2), controlling their age of starting service and cluster their retiring month. The outcome variables are the length of service (Panel A), time spent on finding first jobs (Panel B), and the working experience accumulated 3 years after starting service (Panel C). Column (1) displays RD estimations using linear function controlling the city they lived before starting service. Column (2) to (4) applies RD-DID design with different length of bandwidth and exclude servicemen who were insured under occupational union. Column (5) indicates outcome using quadratic function.
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Table 4: The Effect of Deduction Policy on Future Earnings
(1) (2) (3) (4) (5)
Panel A: First Job’s Income
Deduction2004 2224.0***
(675.0)
T× Deduction2004 540.0 260.3 418.5 -40.92
(842.8) (821.5) (862.2) (674.2)
Baseline Mean 15,288
Sample size 219,412 196,338 150,952 181,722 196,338
Panel B: Income, 3 Years After Starting Service
Deduction2004 2026.5***
(683.7)
T× Deduction2004 383.0 416.7 288.8 399.3
(877.6) (785.3) (916.3) (679.2)
Baseline Mean 15,163
Sample size 209,554 184,641 141,602 173,110 184,641
Panel C: Fraction of Having a Job (3 Years After Service)
Deduction2004 0.0321**
(0.0146)
T× Deduction2004 0.0170 0.00450 0.0105 -0.00845
(0.0186) (0.0197) (0.0190) (0.0205)
Baseline Mean 0.66
Sample size 209,554 184,641 141,602 173,110 184,641
Panel D: Income, 10 Years After Starting Service
Deduction2004 2608.6*
(1373.9)
T× Deduction2004 742.5 921.0 700.7 439.5
(792.3) (587.0) (867.8) (658.7)
Baseline Mean 26,625
Sample size 209,309 186,739 143,456 162,026 186,739
Panel E: Fraction of Having a Job (10 Years After Service)
Deduction2004 0.0133
(0.0143)
T× Deduction2004 -0.0101 -0.00885 -0.0127 -0.00824
(0.00897) (0.00922) (0.0114) (0.0143)
Baseline Mean 0.84
Sample size 209,309 186,739 143,456 162,026 186,739
RDD Yes – – – –
RDD+DID – Yes Yes Yes Yes
Non-union – – – Yes –
Covariates Yes – Yes Yes Yes
Poly. model Linear Linear Linear Linear Quadratic
Bandwidth (months) 12 6 4 6 6
Standard errors in parentheses
* p < 0.1, ** p < 0.05, *** p < 0.01
Notes:Column (1) shows the estimated coefficients on Deduction in equation (1). Column (2) to column (5) re-spectively shows the coefficients in equation (2), controlling their age of starting service and cluster their retiring month. The outcome variables are first monthly salary (Panel A), income received 3 years after starting service (Panel B), income received 10 years after starting service (Panel D), and the fraction of having a job (Panel C and E). Column (1) displays RD estimations using linear function controlling the city they lived before starting service. Column (2) to (4) applies RD-DID design with different length of bandwidth and exclude servicemen who were insured under occupational union. Column (5) indicates outcome using quadratic function.
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Figure
Figure 1: Age Distribution
Notes: This figure shows the percentage of age when servicemen started their service. The eligible age of having compulsory service is from 19 to 36. And we include servicemen who started service at their primary age between 19 to 25 so as to increase the possibility of finding out servicemen.
Figure 2: Age Distribution in Each Birth Cohort
Notes: This figure shows the number of servicemen born in different years and their age of starting service. For servicemen born in each cohort, they mostly served at the age of 20 and the number starts to decrease.
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Figure 3: Number of Servicemen
Notes:This figure shows the number of actual personnel, which contains regular and substitute servicemen reported in July each year, against the number of observation in NHIRD.
Figure 4: Fraction of Each Service Term
Notes: This figure presents the percentage of each service term. In this study, we set a wider range to capture servicemen in NHIRD and find that very few of them had unreasonable length of service.
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Figure 5: Age of Starting Service
Notes: This figure shows the average age of servicemen retired around January 2004. The retirement age on the right to the cutoff is 1.5 year greater than servicemen on the left.
Figure 6: Age of Finishing Service
Notes: The average age of servicemen retired after January 2004 is greater than those who finished service before. Therefore, we try to control their age of starting service and plot the residual on their age of finishing service. For the servicemen on the right to the cutoff, their retirement age should be 2 months younger than those on the left as they served two fewer months. In this figure, the average age on the right to the cutoff is 1 to 2 months lower.
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Figure 7: Number of Servicemen Retired in 2 Age Groups
Notes: This figure shows the number of servicemen finished their duty in each month. After January, the number of older servicemen surpassed younger servicemen, which explains the increasing age we found on the right to the cutoff.
Figure 8: Number of Servicemen Retired
Notes: For servicemen who were supposed to retire on January and February in 2004, they were all released on January 1. And for those who were supposed to retire in March, they also retired in January as well. Therefore, the number of servicemen retired on January is three times as many as it is in other months.
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Figure 9: Average Length of Service Term
Notes: This figure shows the average length of service for servicemen retired from January 2003 to January 2005. The average service increased when there’s fewer servicemen aged 22 to 25 retired.
Figure 10: Service Term Controlling Age of Starting Service
Notes: This residual plot shows the influence of different number of older servicemen disap-pear after controlling their starting age. The average term became smooth and decreased by 1.5 to 2 months for servicemen retired after January 2004.
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Figure 11: Service Term in 2004’s RD
Notes:This residual plot shows the change in service term after controlling starting age. The length of term dropped by 1.5 to 2 months for servicemen retired after January 2004.
Figure 12: Service Term in 2005’s RD
Notes:This residual plot shows the change in service term after controlling starting age. The length of service increased by less than 0.1 months for servicemen retired after January 2005.
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Figure 13: Time of Finding a Job in 2004’s RD
Notes: This residual plot shows the change in time of finding a job after controlling starting age. For servicemen retired after January 2004, servicemen with term deduction found a job 1 to 1.5 months earlier.
Figure 14: Time of Finding a Job in 2005’s RD
Notes: This residual plot shows the change in time of finding a job after controlling starting age. For servicemen retired after January 2005, servicemen found a job 1 month earlier even there was no deduction policy.
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Figure 15: Working Experience in 2004’s RD
Notes: This residual plot shows the change in working experience accumulated 3 years af-ter starting service, controlling starting age. For servicemen retired afaf-ter January 2004, they accumulated 2.5 more months of working experience,
Figure 16: Working Experience in 2005’s RD
Notes: This residual plot shows the change in working experience accumulated 3 years af-ter starting service, controlling starting age. For servicemen retired afaf-ter January 2005, they accumulated 1 more month of working experience,
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Figure 17: First Monthly Income in 2004’s RD
Notes:This residual plot shows the change in first job’s income, controlling starting age. For servicemen retired after January 2004, servicemen with term deduction received around NTD 2,500 more on first monthly income.
Figure 18: First Monthly Income in 2005’s RD
Notes:This residual plot shows the change in first job’s income, controlling starting age. For servicemen retired after January 2005, servicemen received around NTD 2,500 more on first monthly income.
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Figure 19: Income 3 Years After Starting Service in 2004’s RD
Notes: This residual plot shows the change in income 3 years after starting service, control-ling starting age. For servicemen retired after January 2004, servicemen with term deduction received around NTD 2,000 more on monthly income.
Figure 20: Income 3 Years After Starting Service in 2005’s RD
Notes: This residual plot shows the change in income 3 years after starting service, control-ling starting age. For servicemen retired after January 2005, servicemen with term deduction received around NTD 1,900 more on monthly income.
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Figure 21: Fraction of Having a Job 3 Years After Starting Service in 2004’s RD
Notes:This residual plot shows the change in the fraction of having a job 3 years after starting service, controlling starting age. For servicemen retired after January 2004, the fraction of having a job increased by 2 percentage points.
Figure 22: Fraction of Having a Job 3 Years After Starting Service in 2005’s RD
Notes:This residual plot shows the change in the fraction of having a job 3 years after starting service, controlling starting age. For servicemen retired after January 2005, the fraction of having a job increased by 1 percentage point.
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Figure 23: Income 10 Years After Starting Service in 2004’s RD
Notes: This residual plot shows the change in income 10 years after starting service, control-ling starting age. For servicemen retired after January 2004, servicemen with term deduction received around NTD 5,000 more on monthly income.
Figure 24: Income 10 Years After Starting Service in 2005’s RD
Notes: This residual plot shows the change in income 10 years after starting service, control-ling starting age. For servicemen retired after January 2005, servicemen with term deduction received around NTD 5,000 more on monthly income.
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Figure 25: Fraction of Having a Job 10 Years After Starting Service in 2004’s RD
Notes:This residual plot shows the change in the fraction of having a job 10 years after starting service, controlling starting age. For servicemen retired after January 2004, the fraction of having a job increased by 3 percentage points.
Figure 26: Fraction of Having a Job 10 Years After Starting Service in 2005’s RD
Notes:This residual plot shows the change in the fraction of having a job 10 years after starting service, controlling starting age. For servicemen retired after January 2005, the fraction of having a job increased by 4 percentage points.
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Appendix
Figure 27: Unemployment Duration of First-Time Job Seekers
Notes: This figure shows the unemployment duration of first-time job seekers in each year.
Around 2005, it took 6 months for job seekers to find their first job. Source: Directorate General of Budget, Accounting and Statistics (DGBAS), R.O.C.
Figure 28: Number of Job Seekers and Vacancies
Notes:This figure shows the number of job seekers and vacancies in each year. After January, the number of job vacancies increased tremendously, which explains the seasonal alteration in