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The European Journal of Health Economics

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Open Access 01-11-2018 | Original Paper

Market structure and competition in the healthcare industry

Results from a transition economy

Authors: Martin Lábaj, Peter Silanič, Christoph Weiss, Biliana Yontcheva

Published in: The European Journal of Health Economics | Issue 8/2018

Abstract

The present paper provides first empirical evidence on the relationship between market size and the number of firms in the healthcare industry for a transition economy. We estimate market-size thresholds required to support different numbers of suppliers (firms) for three occupations in the healthcare industry in a large number of distinct geographic markets in Slovakia, taking into account the spatial interaction between local markets. The empirical analysis is carried out for three time periods (1995, 2001 and 2010) which characterise different stages of the transition process. Our results suggest that the relationship between market size and the number of firms differs both across industries and across periods. In particular, we find that pharmacies, as the only completely liberalised market in our dataset, experience the largest change in competitive behaviour during the transition process. Furthermore, we find evidence for correlation in entry decisions across administrative borders, suggesting that future market analysis should aim to capture these regional effects.

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For example, if the size of the market needs to triple to add an additional entrant, this suggests that the addition of that firm dramatically reduces firm profits. A discussion of the importance and the effects of competition in the healthcare industries can be found in Barros et al. [3].

Bresnahan and Reiss [2] identify towns or small cities in the continental United States that are at least 20 miles from the nearest town of 1000 people or more to estimate their econometric models.

A comprehensive survey of this literature is available in Gaynor and Town [6].

This section is based on the Health Systems in Transition report for Slovakia by Szalay et al. [15], which provides a detailed description of the healthcare system and of reforms in the Slovak Republic. It was prepared by national experts in collaboration with the European Observatory on Health Systems and Policies.

The fluctuation in the number of observations is due to changes in the definition of administrative units. These alterations are usually the product of towns growing larger and hence splitting into separate administrative units. MISR [20] and SOSR [21] provide additional information regarding the rationale behind these changes. As we expect these administrative decisions to reflect economic phenomena, we accept that split municipalities are likely to represent split markets.

We define young citizens as those who have not yet completed their fifteenth year and the elderly as anyone aged over 60.

In the description of the model below, the parameter of $\ln S$ is constrained to 1. In the estimation, this parameter is allowed to vary. It is denoted as $\alpha$ in the reported results (Table 4) and can be interpreted as $1/\sigma$ (see [22, p. 204]). All parameters are subsequently rescaled in order to arrive at the values relevant for the theoretical model.

The authors study towns which are located at least 100 miles away from a large metropolitan area (with more than 100,000 inhabitants) and are not closer than 20 miles to a town with a population of over 1000 people. In a more complex approach, Seim [23] allows for a more flexible definition of the market by using concentric circles around each seller. However, this increased flexibility is combined with the restriction that the competitive effect of additional entry is the same, regardless of the current number of incumbent firms. Additionally, information on the exact location of each seller is necessary, which is outside the scope of our dataset.

The Moran’s I statistic is calculated as:

$$\begin{aligned} I=\frac{n}{S_0}\frac{\sum _{i=1}^n\sum _{j=1}^nw_{ij}(x_i-\bar{x})(x_j-\bar{x})}{\sum _{i=1}^n(x_i-\bar{x})^2} \end{aligned}$$

where $w_{ij}$ is equal to the inverse distance between town i and town j squared if those are within 30 km of each other and 0 otherwise. $\bar{x}$ represents the average value of the variable, whereas $x_i$($x_j$) measures the variable at a given location i (j). n represents the total number of observations, whereas $S_0$ counts the number of positive connections between the observations ($S_0=\sum _{i=1}^n\sum _{j=1}^nw_{ij}$). The statistic measures the spatial correlation between the observations and compares this to a random distribution.

We set $w_{ij}=0$ if the towns are more than 30 km apart. This cut-off value corresponds to Bresnahan and Reiss [2], who use a 20-mile threshold. Furthermore, the use of exponential weights means that towns which are further than 30 km away are unlikely to get a high weight. Estimation experiments with alternative cut-off values provide very similar empirical results.

In particular we assume: $\beta \sim N(0,T)$, where $T=I_K 10^{12}$ and K is the number of covariates; $\theta _N \sim U(\theta _{N-1},\theta _{N+1})$ in the interval $[\theta _{N-1},\theta _{N+1})$; $\rho \sim \beta (1,1)$ in the interval $(-1,1)$. A detailed outline of the assumptions and implementation strategy is available in LeSage and Pace [25], pp. 279–299. For further information on the estimation steps, consult the documentation of the R package spatialprobit.

In the empirical analysis, we follow previous research and set $N^m = 7$ in order to have sufficient observations to identify each threshold. As additional competitive effects are likely to wane as more firms enter, we believe that the loss of information due to this censoring is likely to be minimal.

To ensure comparability with previous research, we also estimated the model using a non-spatial specification and focusing on rural areas. These results can be found in the Appendix.

A duopolistic market still required 1.36 times the per-firm population of a monopolist to break even (this is not shown in Fig. 3, which takes a competitive market with six firms as a benchmark).

The introduction of entry restrictions by the Slovak Chamber of Pharmacists may have led to censoring in the number of firms. As such, the coefficients estimated in 2001 may include both competitive and regulatory effects. To take this into account, we additionally report the estimates from a censored ordered probit model in Section “Counterfactual analysis”.

It is important to note that in the present analysis, the restrictions imposed through the regulation are not explicitly incorporated into the econometric model. In practice, we observe entry beyond the maximum number of firms which is allowed by the regulation. This is due to the fact that no licenses were revoked by the new legislation. As such, the observed market structure is the result of liberalised entry prior to self-regulation and new firm establishments which were subject to the restrictions. Section “Counterfactual analysis” deals with this issue in detail.

Unlike Schaumans and Verboven [12], we assume that the restrictions are not binding on markets with zero pharmacies. This assumption is based on the text of the legislation, which states that “[t]he committee of the regional chamber shall not issue a certificate to an applicant if he or she is to pursue the occupation of a pharmacist in a pharmacy less than 500 metres from another pharmacy or if the number of inhabitants per public pharmacy in the region goes below 5,000” [16]. While the precise meaning of the text is subject to interpretation, as it could imply that the number of inhabitants for the current incumbents should not be decreased or that the applicant is also taken into account when calculating this number. Since in rural areas the 5000 inhabitants rule is likely to be applied using a wider definition of the market than the village itself, we assume that the regulation is likely to be lenient with the goal of ensuring geographic coverage.

See Lábaj et al. [24] for an overview of entry threshold levels for other industries.

OECD: Focus on health spending, p. 2015. Technical report, OECD Health Statistics (2015)

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Barros, P.P., Brouwer, W.B., Thomson, S., Varkevisser, M.: Competition among health care providers: helpful or harmful? Eur. J. Health Econ. 17(3), 229–233 (2016)CrossRef

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Title: Market structure and competition in the healthcare industry
Results from a transition economy
Authors: Martin Lábaj
Peter Silanič
Christoph Weiss
Biliana Yontcheva
Publication date: 01-11-2018
Publisher: Springer Berlin Heidelberg
Published in: The European Journal of Health Economics / Issue 8/2018
Print ISSN: 1618-7598
Electronic ISSN: 1618-7601
DOI: https://doi.org/10.1007/s10198-018-0959-1

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