This paper analyzes the effect of vocational rehabilitation (VR) programs on labor market earnings. The earnings effect is separated into the effect on employment probability, the effect on employment duration and the effect on monthly earnings. The value of output during program participation is also considered. These effects are evaluated empirically by use of a mixed proportional hazard rate model on a set of Norwegian register data covering more than nine years. The VR programs are divided into five groups: work training in ordinary firms (WTO), work training in a protected environment (WTP), wage subsidies (WS), education provided by the employment service (AMO) and ordinary education (EDU). Furthermore, the effect of each program is allowed to differ depending on the characteristics of the program and the participants.
The findings indicate that all programs apart from AMO lead to improved job matches in terms of employment duration. Especially re-education into a new profession (EDU) where the health problem may be less of a burden, provides longer employment spells. All employment stability effects are strengthened by time spent in each program.
In fact, increasing the program duration by one standard deviation results in a positive effect from AMO as well. In addition, participants older than 44 years experience
stronger effects on employment duration from all programs relative to the younger ones.
Work training (WTO, WTP and WS) increases the monthly earnings of the participants by around 5 percent. This effect can largely be ascribed to male participants and participants with short-term illnesses. In contrast, classroom training (AMO and EDU) has almost no impact on monthly earnings. A single exception is that male participants experience a 2 percent increase from participating in EDU.
All programs increase the labor market income of their participants over a six-year period. However, the effect of WTP is not significantly different from zero. The program effect on the employment probability has larger impact on total earnings than the program effect on job quality. Furthermore, in line with the existing literature, the more similar to a real job, the more effective the programs are. Classroom training provided through the public educational system is also more effective than classroom training provided by the labor market office. These differences between programs may reflect signaling effects as well as more relevant training. Not surprisingly, the additional labor market income from program experience is sensitive to the number of years
covered by the analysis. This applies especially to EDU, which has the largest effect on VR duration, thereby prolonging the time until employment actually occurs. Expanding the evaluation window from six to nine years doubles the additional income caused by EDU participation.
For all programs, except WTP, the income generated exceeds the operating costs.
WS generates the largest surplus, followed by EDU and WTO. The relative strength between the latter two depends on the number of evaluation years and how the production value during participation is valued. Over a nine-year period and including the production value generated by participation, the net surplus of a participant in either of these
programs amounts to around 200,000 NOK. Using the same criteria the surplus from WS is nearly 450,000 NOK per participant. This surplus, however, may be upwardly biased if the WS participants would have been employed by the program provider even without the subsidies (dead-weight loss). Anyway, this paper argues that the surplus of WS exceeds the one of WTO even if this possible bias is removed. One final thing worth noting is the possible bias in the program production value. If this production value compensates the cost faced by the program provider, or if participants displace other workers, this value may be upwardly biased. Reducing the impact of the program
production value would clearly benefit EDU relative to WTO and WTP. Based on these arguments, this paper concludes that WS, WTO and EDU all produce large surpluses.
The relative strength of these three programs however rests on assumptions regarding program production value and dead-weight loss, which is not completely examined within the model. The results for AMO and WTP, on the other hand, are less uplifting, although AMO produces a surplus of more than 30,000 NOK over a six-year period (although not statistically significant). A surplus from WTP on the other hand, rests entirely on the program production value, and even then it is close to zero over a nine-year period.
References
Aakvik, A. (2001), 'Bounding a Matching Estimator: The Case of a Norwegian Training Program', Oxford Bulletin of Economics and Statistics Vol: 63, No. 1: 115-143.
Aakvik, A. (2003), 'Estimating the Employment Effects of Education for Disabled Workers in Norway', Empirical Economics Vol: 28 No. 1: 515-533.
Aakvik, A., J. J. Heckman and E. J. Vytlacil (2005), 'Estimating Treatment Effects for Discrete Outcomes When Responses to Treatment Vary: An Application to Norwegian Vocational Rehabilitation Programs', Journal of Econometrics Vol:
125, No. 1-2: 15-51.
Abbring, J. H. and G. J. van den Berg (2003), 'The Nonparametric Identification of Treatment Effects in Duration Models', Econometrica Vol: 71, No. 5: 1491-1517.
Brinch, C. N. (2007), 'Nonparametric Identification of the Mixed Hazards Model with Time-Varying Covariates', Econometric Theory Vol: 23, No. 2: 349-354.
Carling, K. and K. Richardson (2004), 'The Relative Efficiency of Labor Market
Programs: Swedish Experience from the 1990's', Labour Economics Vol: 11, No.
3: 335-354.
Dahlberg, M. and A. Forslund (2005), 'Direct Displacement Effect of Labour Market Programmes', Scandinavian Journal of Economics Vol: 107, No. 3: 475-494.
Eberwein, C., J. C. Ham and R. J. Lalonde (1997), 'The Impact of Being Offered and Receiving Classroom Training on the Employment Histories of Disadvantaged
Women: Evidence from Experimental Data', Review of Economic Studies Vol:
64, No. 4: 655-682.
Ekhaugen, T. (2007), 'Long-Term Outcomes of Vocational Rehabilitation Programs:
Labor Market Transitions and Job Durations for Immigrants', Memorandum, University of Oslo, Department of Economics, 10/2007.
Frölich, M., A. Heshmati and M. Lechner (2004), 'A Microeconometric Evaluation of Rehabilitation of Long-Term Sickness in Sweden', Journal of Applied
Econometrics Vol: 19, No. 3: 375-396.
Gaure, S. and K. Røed (2007), 'How Tight Is the Labour Market? Sources of Changes in the Aggregate Exit Rate from Unemployment across the Business Cycle', Journal of Business Cycle Measurement and Analysis. Vol. 3, forthcoming.
Gaure, S., K. Røed and L. Westlie (2008), 'The Impacts of Labor Market Policies on Job Search Behavior and Post-Unemployment Job Quality', Essay 2 in this
dissertation.
Gaure, S., K. Røed and T. Zhang (2007), 'Time and Causality: A Monte Carlo
Assessment of the Timing-of-Events Approach', Journal of Econometrics Vol:
141, No. 2: 1159-1195.
Gerfin, M. and M. Lechner (2002), 'A Microeconometric Evaluation of the Active Labour Market Policy in Switzerland', The Economic Journal Vol: 112, No. 482:
854-893.
Gerfin, M., M. Lechner and H. Steiger (2005), 'Does Subsidised Temporary Employment Get the Unemployed Back to Work? An Econometric Analysis of Two Different Schemes', Labour Economics Vol: 12, No. 6: 807-835.
Greenberg, D. (1997), 'The Leisure Bias in Cost-Benefit Analyses of Employment and Training Programs', Journal of Human Resources Vol: 32, No. 2: 413-439.
Heckman, J. J., R. J. Lalonde and J. A. Smith (1999), 'The Economics and Econometrics of Active Labor Market Programs', in Handbook of Labor Economics, Chapter 31: Elsevier, 1865-2097.
Jespersen, S. T., J. R. Munch and L. Skipper (2007), 'Costs and Benefits of Danish Active Labour Market Programmes', Labour Economics Vol: In Press, Corrected Proof.
KD (2008), 'Orientering Om Forslag Til Statsbudsjettet 2008 for Universiteter Og Høyskoler', Kunnskapsdepartementet,
http://www.regjeringen.no/pages/2011220/F-4221_UH_08.pdf.
Kluve, J. (2006), 'The Effectiveness of European Active Labor Market Policy', IZA Discussion paper No. 2018.
Lindsay, B. G. (1983), 'The Geometry of Mixture Likelihoods - a General-Theory', Annals of Statistics Vol: 11, No. 1: 86-94.
Martin, J. P. (1998), 'What Works among Active Labour Market Policies: Evidence from OECD Countries' Experiences', OECD Labour Market and Social Policy
Occasional Papers, No. 35.
McCall, B. P. (1994), 'Identifying State Dependence in Duration Models', American Statistical Association 1994 Vol: Proceedings of the Business and Economics Section, No.: 14-17.
Meyer, B. D. (1990), 'Unemployment-Insurance and Unemployment Spells', Econometrica Vol: 58, No. 4: 757-782.
Prentice, R. L. and L. A. Gloeckler (1978), 'Regression-Analysis of Grouped Survival Data with Application to Breast-Cancer Data', Biometrics Vol: 34, No. 1: 57-67.
Røed, K. and L. Westlie (2007), 'Unemployment Insurance in Welfare States: Soft Constraints and Mild Sanctions', Essay 1 in this dissertation.
Raabe, M. (2005), 'Hovedtall for Utdanning', in Utdanning 2005 – Deltakelse Og Kompetanse (Norwegian): SSB.
Raaum, O., H. Torp and T. Zhang (2002), 'Do Individual Programme Effects Exceed the Costs? Norwegian Evidence on Long Run Effects of Labour Market Training', Memorandum No. 15, Department of Economics, University of Oslo.
Westlie, L. (2008), 'Norwegian Vocational Rehabilitation Programs: Improving Employability and Preventing Disability?' Essay 3 in this dissertation.