We used the DiD method in our effort to isolate the effect of the regulation, and, for purposes of this task, we have defined the regulated and unregulated firms and their quality before the year and after the reform the year We were able to gather data from firms that had a contract with Kela during both periods.
In order to control unobservable factors that could have an influence on the outcome, we have added other firm and market structure municipality level variables to the analyses. Our control variables are competition, potential patient capacity describing firm size , average rental rate a cost shifter and the amount of disabled individuals receiving physiotherapy a demand shifter. All of our control variables are justifiable and we face no bad control problem [ 21 ], because we are dealing with individual-level panel data and, e.
By examining reimbursements before and after the regulation, Kela reimbursed physiotherapy in the two pilot districts for firms in and for firms in To avoid a possible bias regarding competition as a control variable, we also estimate the model without competition.
Effects of Regulation on Drug Launch and Pricing in Interdependent Markets
In order to control possible changes in management styles etc. However, quality was scored by using the same scaling and hence quality is comparable despite different procurement and procuring mechanisms in and With service vouchers, on the other hand, only minimum quality requirements were verified by Kela, but excess quality of the service was not analysed or scored during the registration and, therefore, information on quality had to be gathered by conducting questionnaires on the firms.
From a total of five questionnaires and six reminders that were sent to the firms in January, February, March, April, November — three of them were electronic and two of them were traditional post questionnaires. To gather more data, a total of 33 service providers were interviewed by phone in April [ 19 ]. Data regarding capacity and price were obtained from Kela, as was the data on the number of disabled individuals receiving physiotherapy in municipalities.
The amount of population and the average level of rent in the municipalities were provided by Statistics Finland [ 22 , 23 ], and information on the number of physiotherapists in the local market and firm level risk rates were obtained from Suomen Asiakastieto Oy. The quality scoring was based on the same scoring as was conducted during competitive bidding organised by Kela in for the contract period for physiotherapists not providing services with service vouchers. The quality scoring of price-regulated firms was carried by the researcher between January and April This ensured that the quality analysed was the same for both regulated and non-regulated firms.
Firms that did not receive a contract with Kela were excluded.
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Our quality measure was previously used by Pekola et al. The maximum quality score was points. The qualities of the two contract periods were made comparable by multiplying the premises and their quality score points by 0. We argue that the quality parameters analysed in this study are valid because the scoring is uniform to all firms. Also according to the quality assurance standards by the Charted Society of Physiotherapy, quality physiotherapy includes a multitude of different quality factors [ 24 ] and Grimmer et al.
We have added several firm and market structure level-independent variables into our analyses in order to control factors that could have an effect on the outcome. The market-level variables are: the number of competitors firms providing physiotherapy operating in the municipality, the average rental rate in a municipality [ 23 ], the number of disabled individuals receiving physiotherapy in a municipality, and the amount of population in a municipality [ 22 ]. For the final model, we also added a company-type dummy variable to the model in order to control for firm level time-invariant fixed effects.
Firms producing physiotherapy are divided into six different company types. Other company types are limited partnership, partnership, limited company, foundation and association. When analysing the effects of regulation, one approach is to compare regulated and unregulated firms or markets [ 26 ]. We used the DiD method in our effort to isolate the effect of price regulation, and for this task we have defined the regulated and non-regulated firms and their quality before and after the reform.
3.1.6 Natural Monopoly: Regulation though Marginal Cost Pricing
The coefficient of interest the interaction term forms after the average gain over time in the control group is subtracted from the average gain over time in the treatment group. The method basically removes biases that could either be caused by permanent differences between the two groups or biases resulting from time trends unrelated to the regulation [ 28 ]. The control variables used in the estimation were included in the vector W it. Despite DiD regression is a fairly precise mechanism for estimating the effect of a treatment or a reform with non-experimental data, there are certain well-known caveats with the DiD analyses.
Parallel trend assumption is one of the most common problems with DiD estimation and, therefore; it should be tested that the two groups did not differ before the reform was implemented. Unfortunately we did not have access to additional data regarding multiple periods prior and post reform to have a better understanding of the parallel trend assumption in the quality of physiotherapy. However, in spite of this we use individual-level panel data, which enables us to control factors that vary across firms and factors that are unobservable.
We also aim to control factors that could have an effect on quality for other reasons than price regulation and, therefore, for the model 2 we have added previously mentioned pre-reform and time-varying control variables to increase the precision of our estimates. Another robustness check is executed with a slightly different quality measure.
As mentioned earlier, our original quality measure is the sum of different quality factors that were scored either during the procurement process competitive bidding or after firms replied to questionnaires that were sent during the research. Finally, we also added firm type dummy variables to the model in order to control for firm type time invariant factors in our analyses.
As there are previously mentioned deficits in DiD estimation and our data, in the final stage, we tested the robustness of our DiD estimates as well as the unobservable group-specific pre-regulation heterogeneity between the study group and the control group with Kernel Matching KM and balancing properties respectively. The basic idea with propensity score matching is to find matches for treated units from the control group [ 29 ]. Kernel matching uses all treated units and all controls in its estimation and thus this matching algorithm is used in this study because the number of treated firms is fairly small.
To increase the precision of the matching we also bootstrapped standard errors. Based on Rosenbaum and Rubin, matching is a method of selecting units from the control group that are similar to units in the study group with respect to the distribution of observed covariates [ 30 , 31 ]. The balancing test on the other hand, performs a balancing t -test of difference in means of the specified covariates between the control and treated groups during the pre-regulation period [ 32 ]. Kela piloted regulated price service vouchers in two insurance districts during the contract period Firms located in these districts had fixed prices and the prices needed to cover all costs of the service, as firms were not allowed to charge any extra fees from patients.
On the other hand, patient choice was also initiated in for the same service. Free choice was granted to all patients despite the procurement mechanism. Based on the previously described system, the service voucher reform could have induced firms to change their behaviour regarding quality. Based on theory regarding price regulation and quality competition, the effect on quality due to the reform is unambiguous. We have tested by using empirical data from physiotherapy, which theoretical prediction dominates the market.
We used DiD estimation techniques as well as Kernell matching in our effort to isolate the effect. We used firm level pre- and post-regulation data in our estimations. The data included both regulated and unregulated firms. We also used several control variables in the estimations. By removing the competition variable from the models, we aimed to remove the possibility of bad controls. However, the removal did not alter the results.
However, the effect is much more modest in this model. This could mean two things: either the difficulty of the scoring indeed overestimated the results a negative effect or firms decreased their quality most in this respect due to price regulation.
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The firms also have slightly higher quality before the reform, have somewhat lower financial risk and have more staff per firm yet weakly. The results regarding, e. As is suggested by previous literature [ 33 ], outcome values for quality after the reform are not included in the matching process. The balancing properties were also satisfied by using a set of baseline covariates. We were able to find matches from the control group for 92 regulated firms.
Overlap between treatment and comparison groups is presented in Fig. As can be seen, the density distribution of the propensity score is satisfying after the matching. These additional identical results confirm that our results are unbiased and the negative effect on quality was caused by the service voucher reforms.
Balancing properties as well as common support of regulated and non-regulated firms are presented in Figs. A regulatory policy that sets a price as a markup above marginal costs yields to the socially optimal price and quality if the regulator had full information about the market and its behavior [ 34 ]. If the regulated price is set under marginal costs, firms have an incentive to diminish quality.
The regulated price was negligibly different statistically from the pre-regulation period prices when the earnings index was controlled. Therefore it could be stated that the quality decrease was not caused by an inappropriate level of regulated price. We argue that competition did not incentivise firms to compete for patients on quality.
It is likely that unresponsiveness of firms to quality competition is caused by imperfect information. Despite free choice having been initiated in , comparable quality information is not provided for patients that would support their decision making. Therefore, the results from the empirical estimations are sensible and support theoretical findings that price regulation tends to decrease quality in health care.
With this study, we are able to analyse price regulation combined with the free choice of patients i. Our findings show that quality was decreased due to the reform. The aim was to test which theoretical prediction dominated in the market — price regulation and the possibility to cut costs through quality or quality competition, which by the general theory of competition with fixed prices should enhance quality unless factors such as imperfect information influence the incentives of firms.
Based on our findings, quality reduction was statistically significant in all models. All of our regression models, as well as KM, show that quality was reduced due to the service voucher reform which had fixed prices but also introduced free choice of patients. Most likely the result is caused by price regulation. Fixed prices alter the financial incentives of firms and Ellis has shown that patient selection and quality discrimination of hospitals is sometimes even boosted under competitive environment [ 4 ]. Also Meltzer et al. Gravelle and Masiero on the other hand point out the incentive effects of providers are lower in any capitation fee when information is imperfect [ 17 ].
The more a health care provider, e. Thus, the success of the pricing must be evaluated through the interests of patients and providers [ 35 ]. Due to the imperfect information, the interest of patients is difficult to stand out despite free choice and thus the financial incentives of firms regarding price regulation is solved by reductions in quality.
The result is sensible as the evaluated quality marks quality investments of firms rather that the outcome of care. Even though our results seem solid, ideally the assessment of the regulation to the behavior and performance of firms requires a fairly lengthy time series to avoid basing conclusions on possible transitional responses [ 26 ]. Unfortunately, we did not have access to several pre- or post-treatment periods and we were unable to test, e. This is definitely a shortcoming of our study.
Conversely, there are several issues that support the fact that firms were alike in both study and control groups. Firstly, all firms had to participate in competitive bidding before the implementation of the fixed-price service vouchers. Secondly, all firms had similar contracts with Kela and all firms were treating disabled individuals criteria of the disabled were defined by Kela.
Finally, all firms had to follow the minimum quality criteria defined by Kela. These issues support our understanding that the firms in both groups were similar before the reform. Another weakness of the DiD regression lies in the unobserved temporary effect e. In our study, this means that the quality of the firms needed to decline before the price regulation, which conversely, would have overestimated the impact of price reform and biased our estimate.
However, pre-regulation quality decline is not a possible option in our study because prior to the price regulation, all firms had to take part in competitive bidding and had contracts with Kela, which strongly forbids quality decline during the contract period and controls it with different contractual penalty instruments. For this reason, the negative change in quality had to happen after the price regulation was implemented and was not an anticipation effect.
An additionally compositional effect over time should not cause problems in this study as both regulated and unregulated firms are not going to get mixed. In our study, there is no such case in which firms with regulated insurance districts would have a chance to influence the prices or participate in competitive bidding instead and therefore, before and after, comparability is not compromised. Unfortunately due to missing data we were unable to perform proper response bias analyses on firms which participated in competitive bidding and had non-regulated prices during both periods but were not included in the study.
However, firms which participated in a service voucher pilot and had regulated prices and were not included in the data of this study due to missing quality data from both periods have been previously analysed by Pekola et al. The results indicate that firms that had regulated prices but were not included in this study were smaller firms based on potential patient capacity and perhaps had lower than average quality. Our study shows that quality was decreased due to the reform which regulated prices but also initiated free choice of patients.
We aimed to analyse which of the two mechanisms dominated in the market - cost containment due to price regulation or quality competition due to free choice of patients. As all of our regression models as well as our sensitivity analyses using kernel matching present similar results, we conclude that our results are robust. Regulators have invented different mechanisms, such as revenue-share penalties used in different industries such as telecommunications and electric power , which are designed to eliminate this undesirable behavior, but paradoxically, they may in fact encourage firms to do just the opposite - reduce investments in quality [ 36 ].
On the other hand, even Arrow mentions in his famous paper regarding the physician market that risk and uncertainty are significant elements of health and, therefore, information ends up having a market on its own [ 37 ]. As quality information of the service provided by the physicians is not apparent upon inspection by patients, quality deteriorates to the lowest level in the market, causing serious market failure [ 38 ]. However, by increasing information regarding the service quality of firms and initiating benchmarking has been shown to increase investments in quality [ 36 ].
Also benchmarking could have a positive effect on quality as well. Ultimately, it is undisputed that when patient choice is more and more widely introduced, different mechanisms that enhance information must be developed in order to enhance the ability of patients to choose providers, but also to incentivise providers toward quality investments. This also presumably has an impact on the financial incentives of firms in their effort to cut costs through quality reductions when prices are fixed.
We would like to thank the two anonymous referees for their remarks and recommendations on how to improve the paper. PP carried out the data gathering and data analyses as well as drafted the manuscript. IL substantially contributed to the data analyses, interpretation of the data, and provided comments on all drafts. HM supported the data gathering and provided comments on drafts.
All authors read and approved the final manuscript. Externality, Economics, Microeconomics, Market Economics. Love how the videos were brief, but informative. Very helpful class and exams weren't overly complicated. In the end, I felt as though I retained much of the knowledge. Another fine course from Professor Stein. I particularly appreciated the final set of lectures, but all were well presented, understandable, and relevant.
Monopolies come in various types: one price monopoly, natural monopoly, price discrimination and monopolistic competition. Price Regulation refers to the policy of price setting by a government agency, legal statute or a regulatory authority. After establishing the appropriate costs, a reasonable and fair amount of mark-up is added to determine the maximum or minimum prices.
This is commonly known as the Cost-plus Mark Up pricing methodology. This is the highest prices at which goods and services can be sold. In this case, selling above the maximum prices will be deemed illegal. This is the lowest prices at which goods and services can be sold. In this case, selling below the minimum prices will be deemed illegal.
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- Price Regulation.
Price Regulation may be imposed at different levels of the production and distribution channel. This includes at the retail marketplace, wholesale level, factory level and even at the farm gate level. Price control is a widely accepted instrument of competition policy in cases of natural monopoly, such as the case with utilities.
The Commission can make a recommendation to the Minister under Section 39 and Section 44 of the Commerce Commission Decree to bring certain goods and services under price control. The Commission makes a recommendation to the Minister only after it is satisfied after an in-depth study and research based on scientific methodologies that:. Once the Cabinet approves for the prices of goods and services to be controlled, the Commission obtains the Price Control Order and then determines and authorises price of goods and services in various qualities, quantities, grades and classes.
In these cases the prices are not set at what the competitive market prices should be, but higher prices are charged putting the consumers at a disadvantage. Price regulation then ensures a fair and reasonable pricing. Regulatory price control mechanism encourages prices that reflect what one would observe in a competitive environment.