Assessing the effectiveness of health care cost containment measures: evidence from the market for rehabilitation care


This study empirically evaluates the effectiveness of different health care cost containment measures. The measures investigated were introduced in Germany in 1997 to reduce moral hazard and public health expenditures in the market for rehabilitation care. Of the analyzed measures, doubling the daily copayments was clearly the most effective cost containment measure, resulting in a reduction in utilization of about \(20\,\%\) . Indirect measures such as allowing employers to cut federally mandated sick pay or paid vacation during inpatient post-acute care stays did not significantly reduce utilization. There is evidence neither for adverse health effects nor for substitution effects in terms of more doctor visits.

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Fig. 1


  1. 1.

    This paper does not differentiate between different “types” of rehabilitation therapies. Distinctions are mostly made by German Social Law and are rather technical. Here the outcome variable in the empirical models captures all types of rehabilitation treatments. The reforms analyzed apply to “inpatient” medical rehabilitation, which was quantitatively, by far, the most dominant type of rehabilitation treatments. Vocational rehabilitation that intends to integrate disabled people into the labor market is disregarded here (cf. Jönsson and Skogman Thoursie 2012). In 2007, only 68,000 cases were counted in Germany (Rauch et al. 2008). “Outpatient” medical rehabilitation is carried out at the place of residence of the patient and has become increasingly popular in recent years; it is also outside the scope of this paper. In 1997, it accounted for only 1 % of all rehabilitation therapies (Rauch et al. 2008).

  2. 2.

    In the German system, there exist about 150 different MHI sickness funds (=health plans). Individuals are free to choose among those plans. The health plan coverage is heavily regulated under Social Law. Rehabilitation treatments are federally mandated benefits; coverage, including cost-sharing, is identical for all 150 health plans.

  3. 3.

    Using aggregated administrative data and dividing total spending (€7.6 billion) by the total number of days consumed (58.8 million) one obtains a daily cost estimate of about €130 in 1995 (German Federal Statistical Office 2013). Thus, the post-reform copay would equal 10 % of total costs.

  4. 4.

    If the respondent was interviewed in two subsequent waves, e.g., in 1994 and 1995, time-variant data from questions posed in the first year dealing with the first year are matched with retrospective data obtained from questions posed in the second year dealing with the first year. For example, in 1994 and 1995, respondents were asked about their current health status and about their insurance status during the previous year. Hence, the 1994 data on health status is used together with the 1995 data on insurance status if the respondent was interviewed in both years.

  5. 5.

    More than 80 % of all respondents were interviewed between January and April. Because of seasonal effects, doctor visits are always higher in the winter months, which is why we would substantially overestimate the annual number of doctor visits if we multiplied these figures by the factor four.

  6. 6.

    In contrast to the control variables, when the health measures are used as dependent variables, they are generated differently: Since one would like to test for health effects post rehabilitation, we leave the information as it was surveyed. This means that the models use information about rehabilitation care in the calendar year prior to the interview together with information about the respondents’ health status at the time of the interview, i.e., the health status is definitely measured after a potential rehabilitation therapy.

  7. 7.

    Puhani (2012) shows that the advice of Ai and Norton (2004) to compute the discrete double difference \(\frac{\Delta ^2 \Phi (.)}{\Delta post97 \Delta T}\) is not relevant in nonlinear models when the interest lies in the estimation of a treatment effect in a DID model. Using treatment indicators, the average treatment effect on the treated is given by \(\frac{\Delta \Phi (.)}{\Delta (post97 \times T)} = \Phi (\alpha + \beta \text {post97} + \gamma \text {T} + \delta \text {DID}+ x^{\prime }\psi + \rho + \phi )- \Phi (\alpha + \beta \text {post97} + \gamma \text {T} + x^{\prime }\psi + \rho + \phi \)), which is exactly what is calculated and presented throughout the paper.

  8. 8.

    The detailed descriptive statistics by treatment groups as well as the regression results showing the determinants of rehabilitation care are available upon request.

  9. 9.

    The usual threshold is 2 % of disposable household income; for people with chronic diseases it is 1 %.

  10. 10.

    The results for Model 1 are similar and available upon request.

  11. 11.

    The estimation results are available upon request.

  12. 12.

    This specification excludes non-working respondents, which is why the sample size drops to 17,878 obs. Note that this specification estimates the effect of the copayment doubling, and hence the decrease in rehabilitation care utilization, on workplace absences. Treatment Group 2 was not affected by the cuts in statutory sick pay.

  13. 13.

    This simple exercise multiplies the number of working respondents in Treatment Group 2 with the pre-reform treatment length from administrative data (see above) and subtract the total number of post-reform rehabilitation care absence days. Dividing the resulting decrease in the number of rehabilitation care-related absence days (4,725) by the number of working respondents in Treatment Group 2 (14,678), one obtains a reform-related decrease in absence days of 0.32 per employee.

  14. 14.

    Under the assumption that the necessary prescription for rehabilitation care “mechanically” triggers an additional 1–2 office visits per year, the estimated decrease in doctor visits might entirely be triggered by the decrease in rehabilitation care utilization. An alternative explanation could refer to a supply-side reform that was implemented in July 1997 and introduced quarterly budgets for outpatient physician reimbursement. However, from a theoretical point of view, it is unclear whether these budgets actually decreased annual office visits or just postponed re-appointments to the next quarter. Moreover, it is not clear how reimbursement incentives actually affect physician treatment behavior (Glied and Zivin 2002). As discussed in “The German market for rehabilitation care services” section, in Germany, the competition among primary care physicians for patients is intense and there exists free provider choice. Part of the decrease in office visits might also be triggered by an increase in copayments for prescription drugs and MHI-insured that went into effect in July 1997 (Winkelmann 2004). However, according to a survey conducted at the time of the reform, 80 % of all respondents claimed that it did not affect their number of office visits (Lauterbach et al. 2000). In addition, various hardship clauses and exemptions—especially for needy people—applied. If substitution effects in terms of more office visits were triggered by this reform, it would still not be a threat to the estimates and conclusions from Panel A. In that case we would underestimate the negative effects on office visits.

  15. 15.

    The results are available upon request.

  16. 16.

    The year 1996 is omitted in order to take potential anticipation effects into account.


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Correspondence to Nicolas R. Ziebarth.

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I would like to thank Christian Boehler, Brian Boulier, Roger Feldman, Amy Finkelstein, Martin Karlsson, Jochen Kluve, Michael Kvasnicka, Michael Marlow, Therese Nilsson, Nigel Rice, Hendrik Schmitz, Sita Slavov, Matt Sutton, Tom Siedler, Gert G. Wagner, and participants in seminars at the 2008 Latin American Meeting of the Econometric Society (LAMES), the 2009 Health Economists’ Study Group (HESG) Meeting in Manchester, the 2009 Annual Conference of the Royal Economic Society (RES), the 2009 Meeting of the European Economic Association (EEA), the 2009 conference of the European Association of Labour Economists (EALE), the 2011 meeting of the American Economic Association (AEA), the 2011 Insurance.Inequality.Health-Conference in Darmstadt, the 72nd International Atlantic Economic Conference (IAEC) in Washinton DC, the SOEP Brown Bag, and the Berlin Network of Labour Market Researchers (BeNA) for very helpful comments and discussions. I take responsibility for all remaining errors in and shortcomings of the article. The research reported in this paper is not the result of a for-pay consulting relationship. My employer does not have a financial interest in the topic of the paper which might constitute a conflict of interest.

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Ziebarth, N.R. Assessing the effectiveness of health care cost containment measures: evidence from the market for rehabilitation care. Int J Health Care Finance Econ 14, 41–67 (2014).

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  • Health care demand
  • Cost-sharing
  • Health effects
  • Preventive care
  • Substitution effects
  • SOEP

JEL Classification

  • G22
  • H51
  • I11
  • I12
  • I18
  • J14
  • J22