Hospital strikes in the Portuguese National Health Service (NHS) are becoming increasingly frequent. This paper analyses the effect of different health professionals' strikes (physicians, nurses and diagnostic and therapeutic technicians - DTT) on patients’ outcomes and hospital activity. Patient-level data, comprising all NHS hospital admissions in mainland Portugal from 2012 to 2018, is used together with a comprehensive strike dataset with almost 130 protests. Pooled OLS is employed to study the impact of strikes on health outcomes. A Hazard model is also used to analyze changes in patients' length of stay. Data suggests that hospital operations are partially disrupted during strikes, with sharp reductions in surgical admissions (up to 54%) and a decline on both inpatient and outpatient care admissions. Controlling for hospital characteristics, time and regional patterns, and differences in patients’ composition, results suggest a 6% increase in hospital mortality for patients exposed to physicians’ strikes. Urgent readmissions increase for patients exposed to nurses or DTTs' strikes. Results suggest that legal minimum staffing levels defined during strikes, particularly during physicians' strikes, fail to prevent declines in the quality of care provided.
{"title":"License to Kill? The Impact of Hospital Strikes","authors":"Eduardo Costa","doi":"10.2139/ssrn.3414532","DOIUrl":"https://doi.org/10.2139/ssrn.3414532","url":null,"abstract":"Hospital strikes in the Portuguese National Health Service (NHS) are becoming increasingly frequent. This paper analyses the effect of different health professionals' strikes (physicians, nurses and diagnostic and therapeutic technicians - DTT) on patients’ outcomes and hospital activity. Patient-level data, comprising all NHS hospital admissions in mainland Portugal from 2012 to 2018, is used together with a comprehensive strike dataset with almost 130 protests. Pooled OLS is employed to study the impact of strikes on health outcomes. A Hazard model is also used to analyze changes in patients' length of stay. Data suggests that hospital operations are partially disrupted during strikes, with sharp reductions in surgical admissions (up to 54%) and a decline on both inpatient and outpatient care admissions. Controlling for hospital characteristics, time and regional patterns, and differences in patients’ composition, results suggest a 6% increase in hospital mortality for patients exposed to physicians’ strikes. Urgent readmissions increase for patients exposed to nurses or DTTs' strikes. Results suggest that legal minimum staffing levels defined during strikes, particularly during physicians' strikes, fail to prevent declines in the quality of care provided.","PeriodicalId":11036,"journal":{"name":"Demand & Supply in Health Economics eJournal","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84160218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper introduces a factor-augmented forecasting regression model in the presence of threshold effects. We consider least squares estimation of the regression parameters, and establish asymptotic theories for estimators of both slope coefficients and the threshold parameter. Prediction intervals are also constructed for factor-augmented forecasts. Moreover, we develop a likelihood ratio statistic for tests on the threshold parameter and a sup-Wald test statistic for tests on the presence of threshold effects, respectively. Simulation results show that the proposed estimation method and testing procedures work very well in finite samples. Finally, we demonstrate the usefulness of the proposed model through applications to forecasting stock market returns and the annual growth rate of industrial production, respectively.
{"title":"Inference for Factor-Augmented Forecasting Regressions with Threshold effects","authors":"Yayi Yan, Tingting Cheng","doi":"10.2139/ssrn.3389793","DOIUrl":"https://doi.org/10.2139/ssrn.3389793","url":null,"abstract":"This paper introduces a factor-augmented forecasting regression model in the presence of threshold effects. We consider least squares estimation of the regression parameters, and establish asymptotic theories for estimators of both slope coefficients and the threshold parameter. Prediction intervals are also constructed for factor-augmented forecasts. Moreover, we develop a likelihood ratio statistic for tests on the threshold parameter and a sup-Wald test statistic for tests on the presence of threshold effects, respectively. Simulation results show that the proposed estimation method and testing procedures work very well in finite samples. Finally, we demonstrate the usefulness of the proposed model through applications to forecasting stock market returns and the annual growth rate of industrial production, respectively.","PeriodicalId":11036,"journal":{"name":"Demand & Supply in Health Economics eJournal","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75552368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-03-31DOI: 10.1146/ANNUREV-RESOURCE-100518-094053
Amelia B. Finaret, W. Masters
The economics of human nutrition has changed greatly in recent years as researchers have moved beyond supply and demand of specific foods and total calories to functional aspects of diet quality, such as nutrient composition, sustainability, and a variety of credence attributes. New kinds of data and methods allow researchers to focus on beneficial or harmful attributes of dietary patterns and the cost-effectiveness of interventions aimed at improving health through diet. This review describes some of the recent literature in nutrition economics and its implications for food policy around the world. The new economics of nutrition is benefiting from a strong foundation in the behavioral and social sciences, building on evidence from the natural and health sciences to address fundamental aspects of human well-being and sustainable development.
{"title":"Beyond Calories: The New Economics of Nutrition","authors":"Amelia B. Finaret, W. Masters","doi":"10.1146/ANNUREV-RESOURCE-100518-094053","DOIUrl":"https://doi.org/10.1146/ANNUREV-RESOURCE-100518-094053","url":null,"abstract":"The economics of human nutrition has changed greatly in recent years as researchers have moved beyond supply and demand of specific foods and total calories to functional aspects of diet quality, such as nutrient composition, sustainability, and a variety of credence attributes. New kinds of data and methods allow researchers to focus on beneficial or harmful attributes of dietary patterns and the cost-effectiveness of interventions aimed at improving health through diet. This review describes some of the recent literature in nutrition economics and its implications for food policy around the world. The new economics of nutrition is benefiting from a strong foundation in the behavioral and social sciences, building on evidence from the natural and health sciences to address fundamental aspects of human well-being and sustainable development.","PeriodicalId":11036,"journal":{"name":"Demand & Supply in Health Economics eJournal","volume":"31 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84135647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In 2017-19, about 18 of my research articles were retracted. These retractions offer some useful lessons to scholars, and they also offer some useful next steps to those who want to publish in the social sciences. Two of these steps include 1) Choose a publishable topic, and 2) have a rough mental roadmap of what the finished paper might look. That is, what’s the positioning, the study, and the possible contribution.
The topics I’ve described here offer one set of roadmaps that could be useful. First, they were of interest to journals in medicine, behavioral economics, marketing, nutrition, psychology, health, and consumer behavior. Second, they each show what a finished paper might look like. They show the positioning, relevant background research, methodological tips, and key implications.
I find all of these topics super interesting and of practical importance. This document provides a two-page template for each one that shows 1) An overview why it was done, 2) the abstract (or a summary if there was no abstract), 3) the reason it was retracted, 4) how it could be done differently, and 5) promising new research opportunities on the topic.
Table 1 and Appendix D lay out an estimate of how much effort it might take to do studies on these topics, and Appendix B lays out other issues related to how these specific papers were investigated. I’ve also estimated what I think the practical impact each research project might have. These are my own subjective estimates, but you might find them a useful starting point if you’re looking for a tie-breaker between two different topics.
I would strongly encourage anyone who’s interested in publishing in these areas to closely follow the principles of open science. You can start by preregistering hypotheses and planned analyses, and following the other steps along the road to publication. Making specific hypotheses and testing them followed by open science principles will be the best next way forward on these topics.
Academia can be a tremendously rewarding career both you and for the people who benefit from you research. Best wishes in moving topics like these forward, and best wishes on a great career.
{"title":"Retracted Journal Articles and New Research Opportunities to Change Eating Behavior","authors":"B. Wansink","doi":"10.2139/ssrn.3716474","DOIUrl":"https://doi.org/10.2139/ssrn.3716474","url":null,"abstract":"In 2017-19, about 18 of my research articles were retracted. These retractions offer some useful lessons to scholars, and they also offer some useful next steps to those who want to publish in the social sciences. Two of these steps include 1) Choose a publishable topic, and 2) have a rough mental roadmap of what the finished paper might look. That is, what’s the positioning, the study, and the possible contribution.<br><br>The topics I’ve described here offer one set of roadmaps that could be useful. First, they were of interest to journals in medicine, behavioral economics, marketing, nutrition, psychology, health, and consumer behavior. Second, they each show what a finished paper might look like. They show the positioning, relevant background research, methodological tips, and key implications.<br><br>I find all of these topics super interesting and of practical importance. This document provides a two-page template for each one that shows 1) An overview why it was done, 2) the abstract (or a summary if there was no abstract), 3) the reason it was retracted, 4) how it could be done differently, and 5) promising new research opportunities on the topic.<br><br>Table 1 and Appendix D lay out an estimate of how much effort it might take to do studies on these topics, and Appendix B lays out other issues related to how these specific papers were investigated. I’ve also estimated what I think the practical impact each research project might have. These are my own subjective estimates, but you might find them a useful starting point if you’re looking for a tie-breaker between two different topics.<br><br>I would strongly encourage anyone who’s interested in publishing in these areas to closely follow the principles of open science. You can start by preregistering hypotheses and planned analyses, and following the other steps along the road to publication. Making specific hypotheses and testing them followed by open science principles will be the best next way forward on these topics.<br><br>Academia can be a tremendously rewarding career both you and for the people who benefit from you research. Best wishes in moving topics like these forward, and best wishes on a great career.","PeriodicalId":11036,"journal":{"name":"Demand & Supply in Health Economics eJournal","volume":"28 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81753274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
While economic studies often assume that labor markets are in equilibrium, there may be specialized labor markets that are likely in disequilibrium. We develop a new methodology to improve the estimation of a reduced form disequilibrium model from the existing models by incorporating survey-based shortage indicators into the model and estimation. Our shortage-indicator informed disequilibrium model includes as a special case the foundational model of Maddala and Nelson (1974). We demonstrate the gains in information provided by our methodology. We show how the model can be implemented by applying it to the market for anesthesiologists, a profession susceptible to disequilibrium. In this application, we find that our new disequilibrium model informed by a shortage indicator fits the data better than the Maddala-Nelson model, and has better out-of-sample predictive power.
{"title":"Improving Estimation of Labor Market Disequilibrium Using Shortage Indicators, with an Application to the Market for Anesthesiologists","authors":"M. Baird, Lindsay Daugherty, Krishna B. Kumar","doi":"10.2139/ssrn.3390116","DOIUrl":"https://doi.org/10.2139/ssrn.3390116","url":null,"abstract":"While economic studies often assume that labor markets are in equilibrium, there may be specialized labor markets that are likely in disequilibrium. We develop a new methodology to improve the estimation of a reduced form disequilibrium model from the existing models by incorporating survey-based shortage indicators into the model and estimation. Our shortage-indicator informed disequilibrium model includes as a special case the foundational model of Maddala and Nelson (1974). We demonstrate the gains in information provided by our methodology. We show how the model can be implemented by applying it to the market for anesthesiologists, a profession susceptible to disequilibrium. In this application, we find that our new disequilibrium model informed by a shortage indicator fits the data better than the Maddala-Nelson model, and has better out-of-sample predictive power.","PeriodicalId":11036,"journal":{"name":"Demand & Supply in Health Economics eJournal","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72809082","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The objective of this work was to assess the impact of the monetary policy of the central bank of the Russian Federation on the level of inflation using the structural autoregression model (SVAR). The results show significant inflation responses in the first months to different approximations of monetary policy. Thus, with an increase in the money supply (M2) by 1%, inflation in the first month increases by 0.23%, and with an increase in the interbank lending rate by the same amount, inflation increases by 0.125%. Inflation attenuation at the 90% level of significance occurs at 5 and 6 months, respectively. At the 95% level of significance, when the money supply changes, the inflation response dies out in the second month. When changing the interbank loan rate, the attenuation occurs in the fifth month.
{"title":"SVAR Modeling of Inflation Response to Monetary Policy in Russia","authors":"S. Smirnov, Vladimir Tlostanov","doi":"10.2139/ssrn.3373944","DOIUrl":"https://doi.org/10.2139/ssrn.3373944","url":null,"abstract":"The objective of this work was to assess the impact of the monetary policy of the central bank of the Russian Federation on the level of inflation using the structural autoregression model (SVAR). The results show significant inflation responses in the first months to different approximations of monetary policy. Thus, with an increase in the money supply (M2) by 1%, inflation in the first month increases by 0.23%, and with an increase in the interbank lending rate by the same amount, inflation increases by 0.125%. Inflation attenuation at the 90% level of significance occurs at 5 and 6 months, respectively. At the 95% level of significance, when the money supply changes, the inflation response dies out in the second month. When changing the interbank loan rate, the attenuation occurs in the fifth month.","PeriodicalId":11036,"journal":{"name":"Demand & Supply in Health Economics eJournal","volume":"79 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90786293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Hospital Readmission Reduction Program (HRRP) is a prominent Pay−for− Performance (P4P) program of the Centers for Medicare and Medicaid (CMS) intended to reduce hospital readmissions. In this article, I use a regression kink design to examine whether hospitals that were penalized under the HRRP changed the process of care for patients targeted and untrageted by the policy, as measured by the amount and composition of resource use (e.g. length of stay, and spending on radiology, pharmacy, and laboratory). Estimates indicate that hospitals penalized for excess heart attack (AMI) readmissions decreased AMI readmissions by 30% and increased spending on AMI patients by 20%. This additional care had no impact on mortality. Interestingly, I find that these hospitals also increased the quantity of care for patients with diagnoses not targeted by the HRRP. Hospitals penalized for excess readmissions for relatively more frequent conditions (pneumonia and heart failure) did not respond to the HRRP incentives. I show using a conceptual model of hospital behavior that as the number of patients in the targeted condition rises, the hospital’s marginal cost of reducing the penalty increases by relatively more than the marginal benefit. This intuitive result is novel and fundamental to the discussion on the relative incentive to reduce readmissions across medical diagnoses and how P4P programs can be optimized to reflect this differential cost.
{"title":"The Intended and Unintended Consequences of the Hospital Readmission Reduction Program","authors":"Engy Ziedan","doi":"10.2139/ssrn.3350492","DOIUrl":"https://doi.org/10.2139/ssrn.3350492","url":null,"abstract":"The Hospital Readmission Reduction Program (HRRP) is a prominent Pay−for− Performance (P4P) program of the Centers for Medicare and Medicaid (CMS) intended to reduce hospital readmissions. In this article, I use a regression kink design to examine whether hospitals that were penalized under the HRRP changed the process of care for patients targeted and untrageted by the policy, as measured by the amount and composition of resource use (e.g. length of stay, and spending on radiology, pharmacy, and laboratory). Estimates indicate that hospitals penalized for excess heart attack (AMI) readmissions decreased AMI readmissions by 30% and increased spending on AMI patients by 20%. This additional care had no impact on mortality. Interestingly, I find that these hospitals also increased the quantity of care for patients with diagnoses not targeted by the HRRP. Hospitals penalized for excess readmissions for relatively more frequent conditions (pneumonia and heart failure) did not respond to the HRRP incentives. I show using a conceptual model of hospital behavior that as the number of patients in the targeted condition rises, the hospital’s marginal cost of reducing the penalty increases by relatively more than the marginal benefit. This intuitive result is novel and fundamental to the discussion on the relative incentive to reduce readmissions across medical diagnoses and how P4P programs can be optimized to reflect this differential cost.","PeriodicalId":11036,"journal":{"name":"Demand & Supply in Health Economics eJournal","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88656884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We use the Nielsen Consumer Panel to investigate the impact of tobacco control policies on purchases of electronic cigarettes (e-cigarettes), cigarettes, and smoking cessation products. We measure product quantity, product type, nicotine content, and liquid volume of e-cigarettes, and product quantity and nicotine content of cigarettes. Higher cigarette excise taxes decrease both cigarette and e-cigarette purchases, suggesting that cigarettes and e-cigarettes are complements, and higher cigarette excise taxes reduce the aggregate amount of nicotine purchased from cigarettes and e-cigarettes. Cigarette smoke-free air laws decrease cigarette purchases, while e-cigarette smoke-free air laws do not affect cigarette or e-cigarette purchases.
{"title":"The Relationship Between Cigarettes and Electronic Cigarettes: Evidence From Household Panel Data","authors":"Chad Cotti, Erik T. Nesson, Nathan Tefft","doi":"10.2139/ssrn.3222998","DOIUrl":"https://doi.org/10.2139/ssrn.3222998","url":null,"abstract":"We use the Nielsen Consumer Panel to investigate the impact of tobacco control policies on purchases of electronic cigarettes (e-cigarettes), cigarettes, and smoking cessation products. We measure product quantity, product type, nicotine content, and liquid volume of e-cigarettes, and product quantity and nicotine content of cigarettes. Higher cigarette excise taxes decrease both cigarette and e-cigarette purchases, suggesting that cigarettes and e-cigarettes are complements, and higher cigarette excise taxes reduce the aggregate amount of nicotine purchased from cigarettes and e-cigarettes. Cigarette smoke-free air laws decrease cigarette purchases, while e-cigarette smoke-free air laws do not affect cigarette or e-cigarette purchases.","PeriodicalId":11036,"journal":{"name":"Demand & Supply in Health Economics eJournal","volume":"208 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77060954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The indeterministic relations between unobservable events and observed outcomes in partially identified models can be characterized by a bipartite graph. Given a probability measure on observed outcomes, the set of feasible probability measures on unobservable events can be defined by a set of linear inequality constraints, according to Artstein's Theorem. This set of inequalities is called the “core-determining class”. However, the number of inequalities defined by Artstein's Theorem is exponentially increasing with the number of unobservable events, and many inequalities may in fact be redundant. In this paper, we show that the exact core-determining class, i.e., the smallest possible core-determining class, can be characterized by a set of combinatorial rules of the bipartite graph. We prove that if the bipartite graph and the measure on observed outcomes are non-degenerate, the exact core-determining class is unique and it only depends on the structure of the bipartite graph. We then propose an algorithm that explores the structure of the bipartite graph to construct the exact core-determining class. We design and implement the model and algorithm in a set of examples to show that our methodology could efficiently discard the redundant inequalities that are not useful to identify the parameter of interest. We also demonstrate that, by using the inequalities corresponding to the exact core-determining class to perform set inference, the power of test statistics against local alternatives can be improved.
{"title":"Identifying and Computing the Exact Core-determining Class","authors":"Ye Luo, Hai Wang","doi":"10.2139/ssrn.3154285","DOIUrl":"https://doi.org/10.2139/ssrn.3154285","url":null,"abstract":"The indeterministic relations between unobservable events and observed outcomes in partially identified models can be characterized by a bipartite graph. Given a probability measure on observed outcomes, the set of feasible probability measures on unobservable events can be defined by a set of linear inequality constraints, according to Artstein's Theorem. This set of inequalities is called the “core-determining class”. However, the number of inequalities defined by Artstein's Theorem is exponentially increasing with the number of unobservable events, and many inequalities may in fact be redundant. In this paper, we show that the exact core-determining class, i.e., the smallest possible core-determining class, can be characterized by a set of combinatorial rules of the bipartite graph. We prove that if the bipartite graph and the measure on observed outcomes are non-degenerate, the exact core-determining class is unique and it only depends on the structure of the bipartite graph. We then propose an algorithm that explores the structure of the bipartite graph to construct the exact core-determining class. We design and implement the model and algorithm in a set of examples to show that our methodology could efficiently discard the redundant inequalities that are not useful to identify the parameter of interest. We also demonstrate that, by using the inequalities corresponding to the exact core-determining class to perform set inference, the power of test statistics against local alternatives can be improved.","PeriodicalId":11036,"journal":{"name":"Demand & Supply in Health Economics eJournal","volume":"96 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91487183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
• The U.S. health care system is based on markets. The system will work only as well as the markets that underpin it. • These markets do not function as well as they could, or should. Prices are high and rising, there are incomprehensible and egregious pricing practices, quality is sub-optimal, and the sector is sluggish and unresponsive, in contrast to the innovation and dynamism which characterize much of the rest of our economy. • Lack of competition has a lot to do with these problems. • There has been a great deal of consolidation in health care. There have been 1,519 hospital mergers in the past twenty years, with 680 since 2010. The result is that many local areas are now dominated by one large, powerful health system, e.g., Boston (Partners), Pittsburgh (UPMC), and San Francisco (Sutter). • Insurance markets are also highly consolidated. The two largest insurers have 70 percent of the market or more in one-half of all local insurance markets. • Physician services markets have also become increasingly more concentrated. Two-thirds of specialist physician markets are highly concentrated, and 29 percent for primary care physicians. There have been a very large number of acquisitions of physician practices by hospitals, so much so that 33 percent of all physicians, and 44 percent of primary physicians are now employed by hospitals. • Extensive research evidence shows that consolidation between close competitors leads to substantial price increases for hospitals, insurers, and physicians, without offsetting gains in improved quality or enhanced efficiency. Further, recent evidence shows that mergers between hospitals not in the same geographic area can also lead to increases in price. Just as seriously, if not more, evidence shows that patient quality of care suffers from lack of competition. • This is causing serious harm to patients and to the health care system as a whole. • Policies are needed to support and promote competition in health care markets. This includes policies to strengthen choice and competition, and ending distortions that unintentionally incentivize consolidation. • These include: – Focus and strengthen antitrust enforcement. – End policies that unintentionally incentivize consolidation. – End policies that hamper new competitors and impede competition. – Promote transparency, so employers, policymakers, and consumers have access to information about health care costs and quality.
{"title":"'Examining the Impact of Health Care Consolidation' Statement before the Committee on Energy and Commerce, Oversight and Investigations Subcommittee, U.S. House of Representatives","authors":"M. Gaynor","doi":"10.2139/ssrn.3287848","DOIUrl":"https://doi.org/10.2139/ssrn.3287848","url":null,"abstract":"• The U.S. health care system is based on markets. The system will work only as well as the markets that underpin it. \u0000 \u0000• These markets do not function as well as they could, or should. Prices are high and rising, there are incomprehensible and egregious pricing practices, quality is sub-optimal, and the sector is sluggish and unresponsive, in contrast to the innovation and dynamism which characterize much of the rest of our economy. \u0000 \u0000• Lack of competition has a lot to do with these problems. \u0000 \u0000• There has been a great deal of consolidation in health care. There have been 1,519 hospital mergers in the past twenty years, with 680 since 2010. The result is that many local areas are now dominated by one large, powerful health system, e.g., Boston (Partners), Pittsburgh (UPMC), and San Francisco (Sutter). \u0000 \u0000• Insurance markets are also highly consolidated. The two largest insurers have 70 percent of the market or more in one-half of all local insurance markets. \u0000 \u0000• Physician services markets have also become increasingly more concentrated. Two-thirds of specialist physician markets are highly concentrated, and 29 percent for primary care physicians. There have been a very large number of acquisitions of physician practices by hospitals, so much so that 33 percent of all physicians, and 44 percent of primary physicians are now employed by hospitals. \u0000 \u0000• Extensive research evidence shows that consolidation between close competitors leads to substantial price increases for hospitals, insurers, and physicians, without offsetting gains in improved quality or enhanced efficiency. Further, recent evidence shows that mergers between hospitals not in the same geographic area can also lead to increases in price. Just as seriously, if not more, evidence shows that patient quality of care suffers from lack of competition. \u0000 \u0000• This is causing serious harm to patients and to the health care system as a whole. \u0000 \u0000• Policies are needed to support and promote competition in health care markets. This includes policies to strengthen choice and competition, and ending distortions that unintentionally incentivize consolidation. \u0000 \u0000• These include: \u0000 \u0000– Focus and strengthen antitrust enforcement. \u0000 \u0000– End policies that unintentionally incentivize consolidation. \u0000 \u0000– End policies that hamper new competitors and impede competition. \u0000 \u0000– Promote transparency, so employers, policymakers, and consumers have access to information about health care costs and quality.","PeriodicalId":11036,"journal":{"name":"Demand & Supply in Health Economics eJournal","volume":"73 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72582020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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