Non-random sorting can bias observational measures of institutional quality and distort quality-based polices. I develop alternative quasi-experimental approaches to quality estimation that accommodate nonlinear causal effects, institutional specialization, and unobserved selection-on-gains. I use this framework to compute empirical Bayes posteriors of the quality of 4,821 U.S. hospitals, combining estimates from ambulance referral quasi-experiments with predictions from observational risk-adjustment models. Higher-spending, higher-volume, and privately-owned hospitals are of higher quality, and most healthcare markets exhibit positive Roy selection-on-gains. I then simulate Medicare reimbursement and consumer guidance programs based on different hospital quality measures. Higher-spending providers tend to see moderately larger performance-linked subsidies when quality posteriors replace conventional rankings, while teaching hospitals are reimbursed relatively less. Admissions policy simulations highlight limitations of consumer guidance programs in settings with unobserved Roy selection: redirecting patients to top-ranked hospitals may worsen expected survival when based on observational rankings, while quasi-experimental rankings appear to generate modest gains.
{"title":"Estimating Hospital Quality with Quasi-Experimental Data","authors":"Peter Hull","doi":"10.2139/ssrn.3118358","DOIUrl":"https://doi.org/10.2139/ssrn.3118358","url":null,"abstract":"Non-random sorting can bias observational measures of institutional quality and distort quality-based polices. I develop alternative quasi-experimental approaches to quality estimation that accommodate nonlinear causal effects, institutional specialization, and unobserved selection-on-gains. I use this framework to compute empirical Bayes posteriors of the quality of 4,821 U.S. hospitals, combining estimates from ambulance referral quasi-experiments with predictions from observational risk-adjustment models. Higher-spending, higher-volume, and privately-owned hospitals are of higher quality, and most healthcare markets exhibit positive Roy selection-on-gains. I then simulate Medicare reimbursement and consumer guidance programs based on different hospital quality measures. Higher-spending providers tend to see moderately larger performance-linked subsidies when quality posteriors replace conventional rankings, while teaching hospitals are reimbursed relatively less. Admissions policy simulations highlight limitations of consumer guidance programs in settings with unobserved Roy selection: redirecting patients to top-ranked hospitals may worsen expected survival when based on observational rankings, while quasi-experimental rankings appear to generate modest gains.","PeriodicalId":11036,"journal":{"name":"Demand & Supply in Health Economics eJournal","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78718905","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 prevalence of obesity has significantly increased over the last few decades. It was once a condition exclusive to mature adults, but has now become commonplace among children. The World Health Organization (WHO) declared childhood obesity to be one of the most serious public health challenges of the 21st century. According to the Centers for Disease Control 17% of children and adolescents between the age of 2 and 19 are obese in the U.S. Additionally, obesity status can cause children serious psychological harm as a result of social stigmatization, depression, and poor body image. In this paper, I investigate how technology coupled with the social experience affects children's behavior and whether it can facilitate the adoption of more active lifestyles. Using a fitbit like device - named SQORD - and designed specifically for children, I conduct a clustered randomized control trial in the Anchorage School District where we assign schools to either full device access or restricted access. I evaluate the effect of feedback type on the physical activity of elementary school children. One type of feedback allowed students access to a website to check their points and compare their activity levels to that of friends while the other type limited the information to leader-board print outs that they received once every two weeks. I provide evidence that the type of feedback affects physical activity in elementary school children in Alaska.
{"title":"The Effect of Feedback on Children's Physical Activity: A Randomized Control Trial in Alaska","authors":"Mouhcine Guettabi","doi":"10.2139/ssrn.3179820","DOIUrl":"https://doi.org/10.2139/ssrn.3179820","url":null,"abstract":"The prevalence of obesity has significantly increased over the last few decades. It was once a condition exclusive to mature adults, but has now become commonplace among children. The World Health Organization (WHO) declared childhood obesity to be one of the most serious public health challenges of the 21st century. According to the Centers for Disease Control 17% of children and adolescents between the age of 2 and 19 are obese in the U.S. Additionally, obesity status can cause children serious psychological harm as a result of social stigmatization, depression, and poor body image. In this paper, I investigate how technology coupled with the social experience affects children's behavior and whether it can facilitate the adoption of more active lifestyles. Using a fitbit like device - named SQORD - and designed specifically for children, I conduct a clustered randomized control trial in the Anchorage School District where we assign schools to either full device access or restricted access. I evaluate the effect of feedback type on the physical activity of elementary school children. One type of feedback allowed students access to a website to check their points and compare their activity levels to that of friends while the other type limited the information to leader-board print outs that they received once every two weeks. I provide evidence that the type of feedback affects physical activity in elementary school children in Alaska.","PeriodicalId":11036,"journal":{"name":"Demand & Supply in Health Economics eJournal","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88741560","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}
Inequality between private and public patients in Australia has been an ongoing concern due to its two tiered insurance system. This paper investigates the variations in hospital length of stay for hip replacements using Victorian Admitted Episodes Dataset from 2003/2004 to 2014/2015, employing a Bayesian hierarchical random coefficient model with trend allowing for structural break. We find systematic differences in the length of stay between public and private hospitals, after observable patient complexity is controlled. This suggests shorter stay in public hospitals due to pressure from Activity-based funding scheme, and longer stay in private system due to potential moral hazard. Our counterfactual analysis shows that public patients stay 1.4 days shorter than private in 2014, which leads to the 'quicker but sicker' concern that is commonly voiced by the public. We also identify widespread variations among individual hospitals. Sources for such variation warrant closer investigation by policy makers.
{"title":"A Panel Data Analysis of Hospital Variations in Length of Stay for Hip Replacements: Private Versus Public","authors":"Yan Meng, Xueyan Zhao, Xibin Zhang, Jiti Gao","doi":"10.2139/ssrn.3077763","DOIUrl":"https://doi.org/10.2139/ssrn.3077763","url":null,"abstract":"Inequality between private and public patients in Australia has been an ongoing concern due to its two tiered insurance system. This paper investigates the variations in hospital length of stay for hip replacements using Victorian Admitted Episodes Dataset from 2003/2004 to 2014/2015, employing a Bayesian hierarchical random coefficient model with trend allowing for structural break. We find systematic differences in the length of stay between public and private hospitals, after observable patient complexity is controlled. This suggests shorter stay in public hospitals due to pressure from Activity-based funding scheme, and longer stay in private system due to potential moral hazard. Our counterfactual analysis shows that public patients stay 1.4 days shorter than private in 2014, which leads to the 'quicker but sicker' concern that is commonly voiced by the public. We also identify widespread variations among individual hospitals. Sources for such variation warrant closer investigation by policy makers.","PeriodicalId":11036,"journal":{"name":"Demand & Supply in Health Economics eJournal","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89666717","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 presents some preliminary results of a study investigating the effect of telecare on the length of stay in hospital using linked administrative health and social care data in Scotland. We make various assumptions about the probability distribution of the outcome measure and formulate three Negative Binomial Models to that effect i.e. a basic Negative Binomial Model, a zero-inflated Negative Binomial Model and a zero-truncated Negative Binomial Model. We then bring the models to data and estimate them using a strategy that controls for the effects of confounding variables and unobservable factors. These models provide an alternative to the Propensity Score Matching technique used by the previous studies. The empirical results show that telecare users are expected to spend a shorter time in hospital than non-users, holding other factors constant. The results also show that older individuals, males, rural residents and individuals with comorbidities have a longer length of stay in hospital, on average, than their counterparts, all things equal. Future research will involve conducting a sub-group analysis, investigating the effectiveness of various telecare devices and determining the impact of telecare on admission to hospital.
{"title":"An Econometric Analysis of the Impact of Telecare on the Length of Stay in Hospital","authors":"Kevin Momanyi","doi":"10.2139/ssrn.3017182","DOIUrl":"https://doi.org/10.2139/ssrn.3017182","url":null,"abstract":"This paper presents some preliminary results of a study investigating the effect of telecare on the length of stay in hospital using linked administrative health and social care data in Scotland. We make various assumptions about the probability distribution of the outcome measure and formulate three Negative Binomial Models to that effect i.e. a basic Negative Binomial Model, a zero-inflated Negative Binomial Model and a zero-truncated Negative Binomial Model. We then bring the models to data and estimate them using a strategy that controls for the effects of confounding variables and unobservable factors. These models provide an alternative to the Propensity Score Matching technique used by the previous studies. The empirical results show that telecare users are expected to spend a shorter time in hospital than non-users, holding other factors constant. The results also show that older individuals, males, rural residents and individuals with comorbidities have a longer length of stay in hospital, on average, than their counterparts, all things equal. Future research will involve conducting a sub-group analysis, investigating the effectiveness of various telecare devices and determining the impact of telecare on admission to hospital.","PeriodicalId":11036,"journal":{"name":"Demand & Supply in Health Economics eJournal","volume":"68 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91473275","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 analyze detailed data on plan designs from the Kaiser Family Foundation Employer Health Benefits Survey for 331 firms that offered employees both a qualifying high-deductible health plan and a lower-deductible option. For an employee at these firms selecting the lower-deductible option will decrease the deductible by $1,300 on average. However, the cost of that additional coverage for the employee, from increased employee premiums and forgone firm contributions to health savings accounts, is nearly as large, averaging $1,100. In 65% of firms the high-deductible option would result in lower maximum possible spending for the employee for the year. Further, we estimate based on simplified plan representations that the high-deductible plan financially dominates the lower-deductible option for employees at roughly half of the firms. Employees facing a range of possible medical-spending distributions would save on average over $500 per year with the high-deductible option, often with no additional annual financial risk. While we cannot pin down the mechanism behind these patterns conclusively, the evidence is consistent with firms passing through lower average costs for high-deductible plans generated by adverse selection patterns to the employees choosing those plans. These results raise questions about the net effect of offering employees choices over plans with different coverage levels. Rather than creating a classic trade-off between risk and expected spending, at many firms plan options generate disparities in overall benefit value for employees who opt into different levels of coverage.
{"title":"How Common are Dominated Health Plan Options? Evidence from Employer Health Benefits with High-Deductible Plans","authors":"Chenyuan Liu, Justin R. Sydnor","doi":"10.2139/ssrn.3060675","DOIUrl":"https://doi.org/10.2139/ssrn.3060675","url":null,"abstract":"We analyze detailed data on plan designs from the Kaiser Family Foundation Employer Health Benefits Survey for 331 firms that offered employees both a qualifying high-deductible health plan and a lower-deductible option. For an employee at these firms selecting the lower-deductible option will decrease the deductible by $1,300 on average. However, the cost of that additional coverage for the employee, from increased employee premiums and forgone firm contributions to health savings accounts, is nearly as large, averaging $1,100. In 65% of firms the high-deductible option would result in lower maximum possible spending for the employee for the year. Further, we estimate based on simplified plan representations that the high-deductible plan financially dominates the lower-deductible option for employees at roughly half of the firms. Employees facing a range of possible medical-spending distributions would save on average over $500 per year with the high-deductible option, often with no additional annual financial risk. While we cannot pin down the mechanism behind these patterns conclusively, the evidence is consistent with firms passing through lower average costs for high-deductible plans generated by adverse selection patterns to the employees choosing those plans. These results raise questions about the net effect of offering employees choices over plans with different coverage levels. Rather than creating a classic trade-off between risk and expected spending, at many firms plan options generate disparities in overall benefit value for employees who opt into different levels of coverage.","PeriodicalId":11036,"journal":{"name":"Demand & Supply in Health Economics eJournal","volume":"55 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80201243","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 this paper, we use factor-augmented HAR-type models to predict the daily integrated volatility of asset returns. Our approach is based on a proposed two-step dimension reduction procedure designed to extract latent common volatility factors from a large dimensional and high-frequency returns dataset with 267 constituents of the S&P 500 index. In the first step, we apply either LASSO or elastic net shrinkage on estimates of integrated volatility of all constituents in the dataset, in order to select a subset of asset return series for further processing. In the second step, we utilize (sparse) principal component analysis to estimate latent common asset return factors, from which latent integrated volatility factors are extracted. Although we find limited in-sample fit improvement, relative to a benchmark HAR model, all of our proposed factor-augmented models result in substantial out-of-sample predictive accuracy improvement. In particular, forecasting gains are observed at market, sector, and individual-stock levels, with the exception of the financial sector. Further investigation of the factor structures for non-financial assets shows that industrial and technology stocks are characterized by minimal exposure to financial assets, inasmuch as forecasting gains associated with factor-augmented models for these types of assets are largely attributable to the inclusion of non-financial stock price return volatility in our latent factors.
{"title":"Latent Common Return Volatility Factors: Capturing Elusive Predictive Accuracy Gains When Forecasting Volatility","authors":"Ming-Yen Cheng, Norman R. Swanson, Xiye Yang","doi":"10.2139/ssrn.2998304","DOIUrl":"https://doi.org/10.2139/ssrn.2998304","url":null,"abstract":"In this paper, we use factor-augmented HAR-type models to predict the daily integrated volatility of asset returns. Our approach is based on a proposed two-step dimension reduction procedure designed to extract latent common volatility factors from a large dimensional and high-frequency returns dataset with 267 constituents of the S&P 500 index. In the first step, we apply either LASSO or elastic net shrinkage on estimates of integrated volatility of all constituents in the dataset, in order to select a subset of asset return series for further processing. In the second step, we utilize (sparse) principal component analysis to estimate latent common asset return factors, from which latent integrated volatility factors are extracted. Although we find limited in-sample fit improvement, relative to a benchmark HAR model, all of our proposed factor-augmented models result in substantial out-of-sample predictive accuracy improvement. In particular, forecasting gains are observed at market, sector, and individual-stock levels, with the exception of the financial sector. Further investigation of the factor structures for non-financial assets shows that industrial and technology stocks are characterized by minimal exposure to financial assets, inasmuch as forecasting gains associated with factor-augmented models for these types of assets are largely attributable to the inclusion of non-financial stock price return volatility in our latent factors.","PeriodicalId":11036,"journal":{"name":"Demand & Supply in Health Economics eJournal","volume":"28 4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87914351","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}
How do the Affordable Care Act health insurance coverage expansions affect payment for medical care provided through liability insurance, such as auto insurance? Theoretically, expanding coverage might lead to a substitution of health insurance disbursements for automobile insurance disbursements. Alternatively, expanding health insurance coverage might increase utilization of medical care, increasing auto liability claims payments. The net effect of these two mechanisms can only be determined empirically. We evaluate the health insurance-auto insurance interaction by examining the 2010 ACA dependent coverage expansion. Prior to 2010, individuals 19 and older were excluded from health insurance coverage under their parental health insurance plan. In September 2010, as part of the ACA, individuals were allowed to continue health insurance coverage until age 26. We use this policy change and claims data from insurers representing approximately 60% of the automobile passenger market to evaluate the effects of expanding health insurance coverage on auto liability claim payments. Using a difference-in-difference research design, we find an approximate 10% reduction in the total BI claim count in the policy-affected 19-25 ages when compared to the control group of individuals 26-34. Conditional on filing a claim, we also find an approximate 9% reduction in the mean total auto insurance paid amount in the 19-25 ages compared to the 26-34 ages. We do not identify any effects of the policy on the PIP auto insurance line.
{"title":"The Effect of Health Insurance Coverage Expansions on Auto Liability Claims and Costs","authors":"S. Kadiyala, Paul S. Heaton","doi":"10.2139/ssrn.3087503","DOIUrl":"https://doi.org/10.2139/ssrn.3087503","url":null,"abstract":"How do the Affordable Care Act health insurance coverage expansions affect payment for medical care provided through liability insurance, such as auto insurance? Theoretically, expanding coverage might lead to a substitution of health insurance disbursements for automobile insurance disbursements. Alternatively, expanding health insurance coverage might increase utilization of medical care, increasing auto liability claims payments. The net effect of these two mechanisms can only be determined empirically. We evaluate the health insurance-auto insurance interaction by examining the 2010 ACA dependent coverage expansion. Prior to 2010, individuals 19 and older were excluded from health insurance coverage under their parental health insurance plan. In September 2010, as part of the ACA, individuals were allowed to continue health insurance coverage until age 26. We use this policy change and claims data from insurers representing approximately 60% of the automobile passenger market to evaluate the effects of expanding health insurance coverage on auto liability claim payments. Using a difference-in-difference research design, we find an approximate 10% reduction in the total BI claim count in the policy-affected 19-25 ages when compared to the control group of individuals 26-34. Conditional on filing a claim, we also find an approximate 9% reduction in the mean total auto insurance paid amount in the 19-25 ages compared to the 26-34 ages. We do not identify any effects of the policy on the PIP auto insurance line.","PeriodicalId":11036,"journal":{"name":"Demand & Supply in Health Economics eJournal","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90536345","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}
James M. Carson, C. Ellis, R. Hoyt, Krzysztof Ostaszewski
There are large, upfront, fixed costs to writing a life insurance policy. Both agent commission and direct underwriting costs (e.g., fees for physicals and blood tests) are fully paid a few years into contracts that can last 10-30 years. Because of these upfront costs, insurers can actually lose money on policies when the consumer lapses early into the contract, even if no death benefit is ever paid out. Thus, to properly price contracts, insurers must estimate lapse risks. However, consumers will often have private knowledge of their lapse likelihood, leading to adverse selection. We develop a model of insurance pricing under heterogeneous lapse rates with asymmetric information about lapse likelihood within the context of an optional two-part tariff as a screening device for future policyholder behavior. We then test for consumer self-selection using detailed, policy-level data on life insurance backdating (a common practice that resembles a two-part tariff). We are able to identify, through a control function approach, the information about lapse risk a consumer reveals when they choose to backdate. Our contribution to the literature is twofold: we are the first to consider life insurance lapsing as a form of adverse selection; we also explore, both theoretically and empirically, the role of optional two-part tariffs as a screening mechanism using life insurance backdating as our primary example. We find that consumers who are less likely to lapse self-select into the two-part tariff pricing structure and also document consumer behavior consistent with sunk cost fallacy.
{"title":"Sunk Costs and Screening: Two-Part Tariffs in Life Insurance","authors":"James M. Carson, C. Ellis, R. Hoyt, Krzysztof Ostaszewski","doi":"10.2139/ssrn.2863171","DOIUrl":"https://doi.org/10.2139/ssrn.2863171","url":null,"abstract":"There are large, upfront, fixed costs to writing a life insurance policy. Both agent commission and direct underwriting costs (e.g., fees for physicals and blood tests) are fully paid a few years into contracts that can last 10-30 years. Because of these upfront costs, insurers can actually lose money on policies when the consumer lapses early into the contract, even if no death benefit is ever paid out. Thus, to properly price contracts, insurers must estimate lapse risks. However, consumers will often have private knowledge of their lapse likelihood, leading to adverse selection. We develop a model of insurance pricing under heterogeneous lapse rates with asymmetric information about lapse likelihood within the context of an optional two-part tariff as a screening device for future policyholder behavior. We then test for consumer self-selection using detailed, policy-level data on life insurance backdating (a common practice that resembles a two-part tariff). We are able to identify, through a control function approach, the information about lapse risk a consumer reveals when they choose to backdate. Our contribution to the literature is twofold: we are the first to consider life insurance lapsing as a form of adverse selection; we also explore, both theoretically and empirically, the role of optional two-part tariffs as a screening mechanism using life insurance backdating as our primary example. We find that consumers who are less likely to lapse self-select into the two-part tariff pricing structure and also document consumer behavior consistent with sunk cost fallacy.","PeriodicalId":11036,"journal":{"name":"Demand & Supply in Health Economics eJournal","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88898142","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}
Healthcare investments are faced by the need to match growing expenses, due to ageing population trends, with public budget constraints. Infrastructural PF packages are by now popular and effective, although they are rigid and long-termed. Big data-driven value chains add unprecedented information to project financing (PF) and public private partnerships (PPPs), especially in healthcare investments. Big data and Internet of Health sensors, currently adopted in telemedicine, can be applied even to PF strategies, providing useful information to data-driven business plans. Public and Private Partners interact through networking big data and interoperable databases, boosting value co-creation, improving Value for Money, and reducing risk. Policy makers can conveniently use networked big data to enrich their feasibility plans, whereas private managers may extract precious information from public healthcare databases. Big data can also help shortening supply chain passages, boosting economic marginality and easing the sustainable planning of smart healthcare investments.
{"title":"Big Data-Driven Healthcare Project Financing","authors":"Roberto Moro Visconti","doi":"10.2139/ssrn.2925790","DOIUrl":"https://doi.org/10.2139/ssrn.2925790","url":null,"abstract":"Healthcare investments are faced by the need to match growing expenses, due to ageing population trends, with public budget constraints. Infrastructural PF packages are by now popular and effective, although they are rigid and long-termed. Big data-driven value chains add unprecedented information to project financing (PF) and public private partnerships (PPPs), especially in healthcare investments. Big data and Internet of Health sensors, currently adopted in telemedicine, can be applied even to PF strategies, providing useful information to data-driven business plans. Public and Private Partners interact through networking big data and interoperable databases, boosting value co-creation, improving Value for Money, and reducing risk. Policy makers can conveniently use networked big data to enrich their feasibility plans, whereas private managers may extract precious information from public healthcare databases. Big data can also help shortening supply chain passages, boosting economic marginality and easing the sustainable planning of smart healthcare investments.","PeriodicalId":11036,"journal":{"name":"Demand & Supply in Health Economics eJournal","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75827146","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 analyse how children’s disability affects intra-household investment decisions. By means of a general preference model, we show that variation in family size and health conditions can be used to infer whether parents are averse to inequality in the distribution of quality among their children or if, instead, they care more about efficiency. In particular, we exploit the fact that parents of only children cannot possibly exhibit inequality aversion. We apply our identification strategy to Mexican cross-sectional data and find evidence that parents are inequality averse. Specifically, our results show that inequality aversion induces an average increase of 0.7-0.8 years of schooling for disabled individuals when non-disabled siblings are present. We also show that the effect differs by the gender of the child. Particularly, parental inequality aversion is relevant for males but not for females. While parental inequality aversion does not close the schooling gap between disabled and non-disabled males, its estimated effect is economically relevant, as it represents about 13-15 percent of the disability gap in education, which amounts to 5.3 years of schooling in Mexico.
{"title":"Testing for Parental Inequality Aversion. Evidence from Mexico","authors":"Anastasia Terskaya","doi":"10.2139/ssrn.3084645","DOIUrl":"https://doi.org/10.2139/ssrn.3084645","url":null,"abstract":"We analyse how children’s disability affects intra-household investment decisions. By means of a general preference model, we show that variation in family size and health conditions can be used to infer whether parents are averse to inequality in the distribution of quality among their children or if, instead, they care more about efficiency. In particular, we exploit the fact that parents of only children cannot possibly exhibit inequality aversion. We apply our identification strategy to Mexican cross-sectional data and find evidence that parents are inequality averse. Specifically, our results show that inequality aversion induces an average increase of 0.7-0.8 years of schooling for disabled individuals when non-disabled siblings are present. We also show that the effect differs by the gender of the child. Particularly, parental inequality aversion is relevant for males but not for females. While parental inequality aversion does not close the schooling gap between disabled and non-disabled males, its estimated effect is economically relevant, as it represents about 13-15 percent of the disability gap in education, which amounts to 5.3 years of schooling in Mexico.","PeriodicalId":11036,"journal":{"name":"Demand & Supply in Health Economics eJournal","volume":"78 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83319094","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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