Email: T.J.Klein at uvt.nl
Tobias Klein studied economics at the University of Mannheim, the University of California at Berkeley, and University College London. He joined Tilburg University's faculty in 2007 after obtaining a Ph.D. from the University of Mannheim.
He is deputy managing editor of the Econometrics Journal, associate editor of Empirical Economics and the Review of Economics, and was editor of a special issue of Information Economics and Policy on current regulatory issues in media and entertainment markets.
His research is in health economics, empirical industrial organization, and econometrics. Among other things he currently works on the design of health insurance, the effects of patient cost sharing, consumer behavior, the effects of advertising, competition between online platforms, two-sided markets, and rating systems in online markets.
Tobias Klein's research interests are more broadly related to the idea that recent developments in information and communication technologies together with the availability of big data can help us to address research questions in a novel way if we combine data with tractable models of individual behavior. Insights gained in this way then gives rise to the opportunity to implement welfare-improving policies that are at the same time in the interest of the firms offering a service. This can even lead to the creation of new markets. Examples are the online rating mechanisms used by eBay, Airbnb, Tripadvisor, Yelp, Uber and others that discipline market participants via online ratings and lead to more transparency. Another example is advertising that is well-targeted may be valued by consumers if it reminds them of making a purchase that they intended to do.
A recurrent theme in his research is that seemingly small details may have economically important effects. In a recent paper he and his co-authors quantify the effect of framing patient cost sharing incentives in a different way. They find that this can affect yearly health care spending by as much as 7.2 percent.
He is co-organizer of the structural econometrics group and teaches econometrics and structural empirical organization at Tilburg University.
Bernal, Noelia, M.A. Carpio, and T.J. Klein (2017): "The effects of access to health insurance for informally employed individuals in Peru," Journal of Public Economics, 154, pp. 122-136.
French, E. with many others including T.J. Klein (2017): "End-Of-Life Medical Spending In Last Twelve Months Of Life Is Lower Than Previously Reported," Health Affairs, 36(7), pp. 1211-1217.
Klein, T.J., C. Lambertz and K.O. Stahl (2016): “Market Transparency, Adverse Selection, and Moral Hazard,” Journal of Political Economy, 124(6), pp. 1677-1713.
Karlsson, M., T.J. Klein, and N. Ziebarth (2016): “Skewed, Persistent and High before Death: Medical Spending in Germany,” Fiscal Studies, 37(3-4), pp. 527-559. This article is part of a special issue of Fiscal Studies with the title "Medical Spending around the Developed World". See French and Kelly (2016) for an overview.
Klein, T.J. (2013): “College Education and Wages in the U.K.: Estimating Conditional Average Structural Functions in Nonadditive Models with Binary Endogenous Variables,” Empirical Economics, 44(1), 135-161.
van der Heijden, E., T.J. Klein, W. Müller, and Jan Potters (2012): “Framing Effects and Impatience: Evidence from a Large Scale Experiment,” Journal of Economic Behavior and Organization, 84(2), pp. 701-711.
Bonsang, E. and T.J. Klein (2012): “Retirement and Subjective Well-Being,” Journal of Economic Behavior and Organization, 83(3), pp. 311-329.
Filistrucchi, L., T.J. Klein, and T.O. Michielsen (2012): Assessing Unilateral Merger Effects in a Two-Sided Market: An Application to the Dutch Daily Newspaper Market, Journal of Competition Law and Economics, 8(1), pp. 1-33. A shorter and less technical version with a slightly different focus appeared as: Filistrucchi, L., T.J. Klein, and T.O. Michielsen (2012): Assessing Unilateral Merger Effects in the Daily Newspaper Market, in: J. Harrington and Y. Katsoulakos (eds.): Advances in the Analysis of Competition Policy and Regulation, Edward Elgar Publishing.
Amann, R. and T.J. Klein (2012): Returns to Type or Tenure?, Journal of the Royal Statistical Society, Series A, 175(1), pp. 153-166.
Hullegie, P. and . Klein (2010): The Effect of Private Health Insurance on Medical Care Utilization and Self-Assessed Health in Germany, Health Economics, 19(9), pp. 1048-1062. A shorter version with additional results appeared as: Hullegie, P. and T.J. Klein (2011): “The effect of private health insurance on doctor visits, hospital nights and self-assessed health: Evidence from the German Socio-Economic Panel,” Schmollers Jahrbuch, 131(2), pp. 395-407.
Klein, T.J. (2010): Heterogeneous Treatment Effects: Instrumental Variables without Monotonicity?, Journal of Econometrics, 155(2), pp. 99-116.
Klein, T.J., C. Lambertz, G. Spagnolo, and K.O. Stahl (2009): The Actual Structure of eBays Feedback Mechanism and Early Evidence on the Effect of Recent Changes, International Journal of Electronic Business, 7(3), pp. 301-320.
van Dalen, R. and Klein, T. J. (2014): “Mededingingsbeleid voor internetmarkten met netwerkeffecten," Economisch Statistische Berichten, p. 44-49.
Filistrucchi, L., D. Geradin, E. van Damme, S. Keunen, T.J. Klein, T. Michielsen, and J. Wileur (2010): “Mergers in Two-Sided Markets—A Report to the NMa,” Nederlandse Mededingingsautoriteit, The Hague, Netherlands.
SELECTED WORKING PAPERS
Advertising as a reminder: Evidence from the Dutch State Lottery
Our minute-level data allow us to nonparametrically estimate the effect of an advertisement on online sales. Here we see that the effect of advertisements reaching many people lasts for about 30 minutes.
We use high frequency data on TV and radio advertising together with data on online sales for lottery tickets to measure the short run effects of advertising. We find them to be strong and to last for up to about 4 hours. They are the bigger the less time there is until the draw. We develop the argument that this finding is consistent with the idea that advertisements remind consumers to buy a ticket and that consumers value this. Then, we point out that in terms of timing the interests of the firm and the consumers are aligned: consumers wish to be reminded in a way that makes them most likely to consider buying a lottery ticket. We present direct evidence that this does not only affect the timing of purchases, but leads to market expansion. Then, we develop a tractable dynamic structural model of consumer behavior, estimate the parameters of this model and simulate the effects of a number of counterfactual dynamic advertising strategies. We find that relative to the actual schedule it would be valued by the consumers and profitable for the firm to spread advertising less over time and move it to the last days before the draw.
Does the framing of patient cost-sharing incentives matter? The effects of deductibles vs. no-claim refundsjoint with Arthur P. Hayen and Martin Salm
Individuals have to pay for medical care until they hit the cost-sharing limit. This figure shows that until then, expenditures are lower. The main point of our paper is to show that not only this financial incentive matters, but also how it is framed. One can see in the figure that expenditures are lower when financial incentives are framed in terms of a deductible.
In light of increasing health care expenditures, patient cost-sharing schemes have emerged as one of the main policy tools to reduce medical spending. We show that the effect of patient cost-sharing schemes on health care expenditures is not only determined by the economic incentives they provide, but also by the way these economic incentives are framed. Our analysis is based on claim-level data from a Dutch health insurer. We estimate the effects of within-year variation in economic incentives using an instrumental variables approach. We find that patients react to economic incentives much more strongly under a deductible policy than under a no-claims refund policy, resulting in substantially lower health care expenditures under a deductible policy. The overall effect is a reduction in yearly spending of 7.2 percent. Our preferred explanation is that individuals are loss-averse and respond differently to both schemes because they perceive deductible payments as a loss and no-claim refunds as a gain.
Price Competition in Two-Sided Markets with Heterogeneous Consumers and Network Effects
NET Institute Working Paper #13-20
We model a two-sided market with heterogeneous customers and two heterogeneous network effects. In our model, customers on each market side care differently about both the number and the type of customers on the other side. Examples of two-sided markets are online platforms or daily newspapers. In the latter case, for instance, readership demand depends on the amount and the type of advertisements. Also, advertising demand depends on the number of readers and the distribution of readers across demographic groups. There are feedback loops because advertising demand depends on the numbers of readers, which again depends on the amount of advertising, and so on. Due to the difficulty in dealing with such feedback loops when publishers set prices on both sides of the market, most of the literature has avoided models with Bertrand competition on both sides or has resorted to simplifying assumptions such as linear demands or the presence of only one network effect. We address this issue by first presenting intuitive sufficient conditions for demand on each side to be unique given prices on both sides. We then derive sufficient conditions for the existence and uniqueness of an equilibrium in prices. For merger analysis, or any other policy simulation in the context of competition policy, it is important that equilibria exist and are unique. Otherwise, one cannot predict prices or welfare effects after a merger or a policy change. The conditions are related to the own- and cross-price effects, as well as the strength of the own and cross network effects. We show that most functional forms used in empirical work, such as logit type demand functions, tend to satisfy these conditions for realistic values of the respective parameters. Finally, using data on the Dutch daily newspaper industry, we estimate a flexible model of demand which satisfies the above conditions and evaluate the effects of a hypothetical merger and study the effects of a shrinking market for offline newspapers.
Skewed, Persistent and High before Death: Medical Spending in Germany
The horizontal axis in this picture is the time until death. We see that medical spending sharply increases in the two years before death.
We use claims panel data from a big German private health insurer to provide detailed individual-level evidence on medical spending between 2005 and 2011. This includes evi- dence on the distribution of medical spending, the dependence of medical spending on age and other demographic characteristics, its persistence, and how medical spending evolves in the years before death. Our main findings are that health care spending more than dou- bles between ages 50 and 80 and that spending is very concentrated: the top 10% of all spenders are responsible for 53% of all medical spending in a given year. There is a fifty percent probability that individual expenditures lie in the same quintile of the distribution after five years, both for very high and very low cost individuals. Medical spending in the year before death is six times higher for the deceased, as compared to spending of every- body else, and accounts for 5.6% of lifetime spending. Females use more outpatient care and have higher spending in younger ages, whereas males have higher spending in older ages, particularly for inpatient care, and die younger. The presentation of these empirical facts is framed by an institutional discussion of the German health care system, a compari- son between publicly and privately insured, and a discussion of medical spending trends in aggregate-level data.
The effects of access to health insurance for informally employed individuals in Peru
This figure shows a measure of health care utilization against an eligibility index. Individuals are eligible for free health insurance if the index is negative. This suggests that the effect of free health insurance on utilization is positive.
In many countries large parts of the population do not have access to health insurance. Peru has made an effort to change this in the early 2000's. The institutional setup gives rise to the rare opportunity to study the effects of health insurance coverage exploiting a sharp regression discontinuity design. We find large effects on utilization that are most pronounced for the provision of curative care. Individuals seeing a doctor leads to increased awareness about health problems and generates a potentially desirable form of supplier-induced demand: they decide to pay themselves for services that are in short supply.
Dynamic Discrete Choice Models: Methods, Matlab Code, and Exercises
This document supports the first Matlab computing sessions in our PhD elective course Empirical Industrial Organization II in CentER Tilburg's Research Master in Economics program (230323) and in the Finnish Doctoral Programme in Economics (SEIO11). It contains some notes on the theory of dynamic discrete choice models and on methods for their computation and estimation. It is centered around some basic Matlab code for solving, simulating, and empirically analyzing a simple dynamic discrete choice model. Student exercises ask students to extend this code to apply different and more advanced computational and econometric methods to a wider range of models.
We present a simple example of a two-sided market in Matlab. There are logit models on either side of the market. We generate data from that model and then proceed as if one could in a structural empirical study. In particular, we recover marginal cost, conduct a SSNIP test, calculate UPP, and finally conduct a merger simulation. The zip file contains a document with a detailed explanation of the setup, slides that have been used for teaching, as well as Matlab code.