We develop an integrated, holistic and systematic model of behavioural change in fixed psychologies by making use of a model of the psychology of behaviour. We identify and explain three simple and comprehensive means by which behavioural change is affected by the interaction of the individual's psychology with their environment. This has value for the pure theory of human behaviour, for applied theory (making hypotheses to appraise and to be tested by datasets), and for the practice of policy by demonstrating a fairly simple causal structure underlying the seemingly highly individuated and complex psychological processes which support behavioural change.
{"title":"On Changing Behaviour in Fixed Psychologies: An Integrated, Holistic, Systematic Approach","authors":"B. Markey-Towler","doi":"10.2139/ssrn.2919165","DOIUrl":"https://doi.org/10.2139/ssrn.2919165","url":null,"abstract":"We develop an integrated, holistic and systematic model of behavioural change in fixed psychologies by making use of a model of the psychology of behaviour. We identify and explain three simple and comprehensive means by which behavioural change is affected by the interaction of the individual's psychology with their environment. This has value for the pure theory of human behaviour, for applied theory (making hypotheses to appraise and to be tested by datasets), and for the practice of policy by demonstrating a fairly simple causal structure underlying the seemingly highly individuated and complex psychological processes which support behavioural change.","PeriodicalId":11036,"journal":{"name":"Demand & Supply in Health Economics eJournal","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83164106","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}
Shortage of health workers in rural areas is one of the biggest problems faced in India’s health sector. This paper examines the current status of health workforce and human resource challenges in the public health sector in rural India. The findings show that the public health sector in rural areas across the country has suffered from poor availability of health workers even after more than a decade of implementation of the National Rural Health Mission. The density of health workers is abysmally low compared to the global norms. The paper highlights various human resource challenges in the rural health sector, such as, health workers shortage, lack of female practitioners, large scale vacancy, unbalanced skill mix, uneven distribution of health workers among states, absenteeism, and the quality of medical education. The paper emphasises on a compact package of interventions comprising regulatory measures, monetary and non-monetary incentives, workforce management, public-private partnerships, and task shifting, etc. to ameliorate the shortage of health workers in rural areas.
{"title":"Human Resource Challenges in the Public Health Sector in Rural India","authors":"Dilip Saikia","doi":"10.2139/ssrn.2985393","DOIUrl":"https://doi.org/10.2139/ssrn.2985393","url":null,"abstract":"Shortage of health workers in rural areas is one of the biggest problems faced in India’s health sector. This paper examines the current status of health workforce and human resource challenges in the public health sector in rural India. The findings show that the public health sector in rural areas across the country has suffered from poor availability of health workers even after more than a decade of implementation of the National Rural Health Mission. The density of health workers is abysmally low compared to the global norms. The paper highlights various human resource challenges in the rural health sector, such as, health workers shortage, lack of female practitioners, large scale vacancy, unbalanced skill mix, uneven distribution of health workers among states, absenteeism, and the quality of medical education. The paper emphasises on a compact package of interventions comprising regulatory measures, monetary and non-monetary incentives, workforce management, public-private partnerships, and task shifting, etc. to ameliorate the shortage of health workers in rural areas.","PeriodicalId":11036,"journal":{"name":"Demand & Supply in Health Economics eJournal","volume":"84 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75172684","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}
Historically, Medicare has operated under the assumption that health care providers respond to reductions in reimbursement through increased provision of services to offset declines in practice revenue; however, recent empirical work has found either small offsetting effects or evidence supporting a traditional supply response. Using multiple identification techniques and datasets, including distance matching a sample of physicians in close proximity but subject to distinct reimbursement rates and approximating physician practice costs, this study finds strong evidence in support of the offsetting assumption.
{"title":"Do Physicians Engage in Offsetting Behavior? Empirical Evidence from Medicare Part B","authors":"Christopher S. Brunt, Joshua R. Hendrickson","doi":"10.2139/ssrn.2877310","DOIUrl":"https://doi.org/10.2139/ssrn.2877310","url":null,"abstract":"Historically, Medicare has operated under the assumption that health care providers respond to reductions in reimbursement through increased provision of services to offset declines in practice revenue; however, recent empirical work has found either small offsetting effects or evidence supporting a traditional supply response. Using multiple identification techniques and datasets, including distance matching a sample of physicians in close proximity but subject to distinct reimbursement rates and approximating physician practice costs, this study finds strong evidence in support of the offsetting assumption.","PeriodicalId":11036,"journal":{"name":"Demand & Supply in Health Economics eJournal","volume":"67 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83257973","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 develop a new class of random-graph models for the statistical estimation of network formation that allow for substantial correlation in links. Various subgraphs (e.g., links, triangles, cliques, stars) are generated and their union results in a network. The challenge in estimating the frequencies with which subgraphs 'truly' form is that subgraphs can overlap and may also incidentally generate new subgraphs, and so the true rate of formation of the subgraphs cannot generally be inferred just by counting their presence in the resulting network. We provide estimation techniques for recovering the rates at which the underlying subgraphs were formed from the observation of a single (large) network. We provide results on identification of the true underlying rates of subgraph formation from various statistics, as well as a new Central Limit Theorem for correlated random variables that establishes asymptotic normality for our estimators. We also show that if the network is sparse enough then direct counts of subgraphs are consistent and asymptotically normal estimators. We illustrate the models with applications.
{"title":"A Network Formation Model Based on Subgraphs","authors":"Arun G. Chandrasekhar, M. Jackson","doi":"10.2139/SSRN.2660381","DOIUrl":"https://doi.org/10.2139/SSRN.2660381","url":null,"abstract":"We develop a new class of random-graph models for the statistical estimation of network formation that allow for substantial correlation in links. Various subgraphs (e.g., links, triangles, cliques, stars) are generated and their union results in a network. The challenge in estimating the frequencies with which subgraphs 'truly' form is that subgraphs can overlap and may also incidentally generate new subgraphs, and so the true rate of formation of the subgraphs cannot generally be inferred just by counting their presence in the resulting network. We provide estimation techniques for recovering the rates at which the underlying subgraphs were formed from the observation of a single (large) network. We provide results on identification of the true underlying rates of subgraph formation from various statistics, as well as a new Central Limit Theorem for correlated random variables that establishes asymptotic normality for our estimators. We also show that if the network is sparse enough then direct counts of subgraphs are consistent and asymptotically normal estimators. We illustrate the models with applications.","PeriodicalId":11036,"journal":{"name":"Demand & Supply in Health Economics eJournal","volume":"659 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85372684","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}
By linking provincial pesticide usage reports from several Chinese statistical yearbooks (1998-2011) with the Chinese Longitudinal Healthy Longevity Survey (1998-2011), this study provides new evidence that pesticides adversely affect health outcomes via drinking water exposure. We follow a difference-in-difference-in-differences framework to compare health outcomes between people who drink surface water and ground water in regions with different intensities of rice pesticide use before and after 2004, when China shifted from taxing agriculture to subsidizing agricultural programs. The results indicate that a 10% increase in rice pesticide use unfavorably alters a key medical disability index (Activities of Daily Living or ADL) by 1% for rural residents 65 and older. This is equivalent to 2.13 and 0.64 million dollars in medical and family care costs, respectively. Further, we provide suggestive evidence of an intergenerational transfer of caring burden by showing pesticide use reduces out-migration of the offspring in affected households. The results are robust to a variety of robustness checks and falsification tests.
{"title":"Pesticide Use and Health Outcomes: Evidence from Agricultural Water Pollution in China","authors":"Wangyang Lai","doi":"10.2139/ssrn.2751458","DOIUrl":"https://doi.org/10.2139/ssrn.2751458","url":null,"abstract":"By linking provincial pesticide usage reports from several Chinese statistical yearbooks (1998-2011) with the Chinese Longitudinal Healthy Longevity Survey (1998-2011), this study provides new evidence that pesticides adversely affect health outcomes via drinking water exposure. We follow a difference-in-difference-in-differences framework to compare health outcomes between people who drink surface water and ground water in regions with different intensities of rice pesticide use before and after 2004, when China shifted from taxing agriculture to subsidizing agricultural programs. The results indicate that a 10% increase in rice pesticide use unfavorably alters a key medical disability index (Activities of Daily Living or ADL) by 1% for rural residents 65 and older. This is equivalent to 2.13 and 0.64 million dollars in medical and family care costs, respectively. Further, we provide suggestive evidence of an intergenerational transfer of caring burden by showing pesticide use reduces out-migration of the offspring in affected households. The results are robust to a variety of robustness checks and falsification tests.","PeriodicalId":11036,"journal":{"name":"Demand & Supply in Health Economics eJournal","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87735608","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 Coordination Reform was introduced in Norway in 2012 including a fee for bed-blocking in hospitals. To study this, we introduce a Stackelberg game where the hospital is the leader and the care institution is the follower. The reform does not necessarily lead to less bed-blocking as this depends on the relative strength of the players’ concern for income and patients’ health, and the optimal discharge date before the reform. Testing the results with data, we find a large negative effect on bed-blocking and discharge date. Thus, financial incentives may count more than health incentives, or health effects of bed-blocking are insignificant.
{"title":"Using Fees to Reduce Bed-Blocking: A Game between Hospitals and Care Providers","authors":"S. Kverndokk, H. Melberg","doi":"10.2139/ssrn.2877111","DOIUrl":"https://doi.org/10.2139/ssrn.2877111","url":null,"abstract":"The Coordination Reform was introduced in Norway in 2012 including a fee for bed-blocking in hospitals. To study this, we introduce a Stackelberg game where the hospital is the leader and the care institution is the follower. The reform does not necessarily lead to less bed-blocking as this depends on the relative strength of the players’ concern for income and patients’ health, and the optimal discharge date before the reform. Testing the results with data, we find a large negative effect on bed-blocking and discharge date. Thus, financial incentives may count more than health incentives, or health effects of bed-blocking are insignificant.","PeriodicalId":11036,"journal":{"name":"Demand & Supply in Health Economics eJournal","volume":"59 36 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78358230","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 2014, the United States Food and Drug Administration announced that chain restaurants with 20 or more locations would be required to put calorie labels on the menu. The merits of the policy depend in large part on three empirical issues: 1) if calorie labels help correct calorie under- or overestimation biases; 2) if the labels lead to changes in consumer behavior, which may improve physical health; and 3) if they have an impact on psychological health. This paper presents data from an online experiment (N = 1,323) in which participants were randomly presented with pictures of food and drink items from major fast-food companies either with or without calorie labels. The following findings are reported. First, there was calorie overestimation bias among participants, and the respondents thought, on average, that products contained more calories than was actually the case. Second, calorie labels both made participants perceive the products as healthier, and made them more likely to intend to purchase said items. Third, calorie labels did not have any discernible effects either on the expected utility from consuming the products, or on the participants? experienced well-being. Thus, while calorie labels did not appear to have any negative effects on psychological health, they did seem to correct a calorie overestimation bias, which may inadvertently improve the perceived healthiness of foods and beverages high in calories, and could also potentially lead consumers to buy more, rather than fewer, such products.
{"title":"Calorie Overestimation Bias and Fast Food Products: The Effects of Calorie Labels on Perceived Healthiness and Intent to Purchase","authors":"Simon Hedlin","doi":"10.2139/ssrn.2847480","DOIUrl":"https://doi.org/10.2139/ssrn.2847480","url":null,"abstract":"In 2014, the United States Food and Drug Administration announced that chain restaurants with 20 or more locations would be required to put calorie labels on the menu. The merits of the policy depend in large part on three empirical issues: 1) if calorie labels help correct calorie under- or overestimation biases; 2) if the labels lead to changes in consumer behavior, which may improve physical health; and 3) if they have an impact on psychological health. This paper presents data from an online experiment (N = 1,323) in which participants were randomly presented with pictures of food and drink items from major fast-food companies either with or without calorie labels. The following findings are reported. First, there was calorie overestimation bias among participants, and the respondents thought, on average, that products contained more calories than was actually the case. Second, calorie labels both made participants perceive the products as healthier, and made them more likely to intend to purchase said items. Third, calorie labels did not have any discernible effects either on the expected utility from consuming the products, or on the participants? experienced well-being. Thus, while calorie labels did not appear to have any negative effects on psychological health, they did seem to correct a calorie overestimation bias, which may inadvertently improve the perceived healthiness of foods and beverages high in calories, and could also potentially lead consumers to buy more, rather than fewer, such products.","PeriodicalId":11036,"journal":{"name":"Demand & Supply in Health Economics eJournal","volume":"67 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82546886","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 many health care systems payers reward physicians for reaching predetermined performance targets. These targets may be based on measures for which own performance is difficult to predict. This paper uses a principal-agent model to analyse physicians’ response to a target-based performance payment and the role uncertainty about own performance plays. It is shown that physicians’ response depends on their type (determined by abilities and preferences), the size of the performance payment, and their uncertainty about own performance. Only in the presence of uncertainty do all physician types respond to the target payment, and they respond by increasing effort. Meanwhile, increased uncertainty leads some physician types to reduce the magnitude of their response and other types to increase their response. Therefore, when designing target-based payment schemes it is important to perform baseline measurements to assess the distribution of physician types and to predict physicians’ ability to assess own performance.
{"title":"Physician Response to Target-Based Performance Payment","authors":"A. Oxholm","doi":"10.2139/ssrn.2913543","DOIUrl":"https://doi.org/10.2139/ssrn.2913543","url":null,"abstract":"In many health care systems payers reward physicians for reaching predetermined performance targets. These targets may be based on measures for which own performance is difficult to predict. This paper uses a principal-agent model to analyse physicians’ response to a target-based performance payment and the role uncertainty about own performance plays. It is shown that physicians’ response depends on their type (determined by abilities and preferences), the size of the performance payment, and their uncertainty about own performance. Only in the presence of uncertainty do all physician types respond to the target payment, and they respond by increasing effort. Meanwhile, increased uncertainty leads some physician types to reduce the magnitude of their response and other types to increase their response. Therefore, when designing target-based payment schemes it is important to perform baseline measurements to assess the distribution of physician types and to predict physicians’ ability to assess own performance.","PeriodicalId":11036,"journal":{"name":"Demand & Supply in Health Economics eJournal","volume":"77 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88563237","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}
Itai Gurvich, K. O’Leary, Lu Wang, Jan A. Van Mieghem
Collaboration is important in services, but may lead to interruptions. Professionals exercise discretion on whether to preempt individual tasks to switch to collaborative tasks. Task switching can introduce setup times, often mental and unobservable, when resuming the preempted task and thus can increase workload.We analyze and quantify how collaboration, through interruptions and setup times, affects workload. We introduce an episodal workflow model that captures the interruption dynamics — each switch and the episode of work it preempts — present in settings where collaboration and multitasking is paramount. We then deploy the model in a field study of hospital medicine physicians — “hospitalists.” A hospitalist’s patient-care routine includes visiting patients and consulting with other caregivers to guide patient diagnosis and treatment.A rigorous empirical analysis is presented using a dataset assembled from direct observation of physician activity and pager-log data. We estimate that a hospitalist incurs a total setup time of 5min per patient per day, which represents a significant 20% of the workload: caring for 14 patients per day, a hospitalist spends more than one hour each day on setups. Switches causally lead to longer documentation time in general but the magnitude of the effect depends on the trigger: when the switch is triggered by the hospitalist the setup time is smaller.
{"title":"Collaboration, Interruptions and Changeover Times: Workflow Model and Empirical Study of Hospitalist Charting","authors":"Itai Gurvich, K. O’Leary, Lu Wang, Jan A. Van Mieghem","doi":"10.2139/ssrn.2616926","DOIUrl":"https://doi.org/10.2139/ssrn.2616926","url":null,"abstract":"Collaboration is important in services, but may lead to interruptions. Professionals exercise discretion on whether to preempt individual tasks to switch to collaborative tasks. Task switching can introduce setup times, often mental and unobservable, when resuming the preempted task and thus can increase workload.We analyze and quantify how collaboration, through interruptions and setup times, affects workload. We introduce an episodal workflow model that captures the interruption dynamics — each switch and the episode of work it preempts — present in settings where collaboration and multitasking is paramount. We then deploy the model in a field study of hospital medicine physicians — “hospitalists.” A hospitalist’s patient-care routine includes visiting patients and consulting with other caregivers to guide patient diagnosis and treatment.A rigorous empirical analysis is presented using a dataset assembled from direct observation of physician activity and pager-log data. We estimate that a hospitalist incurs a total setup time of 5min per patient per day, which represents a significant 20% of the workload: caring for 14 patients per day, a hospitalist spends more than one hour each day on setups. Switches causally lead to longer documentation time in general but the magnitude of the effect depends on the trigger: when the switch is triggered by the hospitalist the setup time is smaller.","PeriodicalId":11036,"journal":{"name":"Demand & Supply in Health Economics eJournal","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84363288","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}
Choosing a college is not as easy as choosing a product because the decision must consider the value of the future. However, there are some similarities in selecting universities, especially private universities. The purposes of this study to observe the factors that create student loyalty models and the loyalty of students at private colleges, so that the marketing managers of private universities are able to improve and maintain the students' effectiveness. The research is conducted on 5 private roommate colleges are selected randomly from the 27 private universities in Bandung, Indonesia. There are 250 respondents. The sampling technique is conducted by quota sampling on each of the private universities. The Data are analyzed by using structural equation modeling (SEM) and a data processing using AMOS program. Descriptively, the confidence of students in PHE relatively high, while loyalty, satisfaction and image of the PHE are very low. Results of student's loyalty building model from the three models offered, second model is the best model that can be used to explain the loyalty of students at PHE.
{"title":"Creating Students Loyalty Model in Private Higher Education","authors":"M. Gunarto, L. Wibowo, R. Hurriyati","doi":"10.2139/ssrn.3185585","DOIUrl":"https://doi.org/10.2139/ssrn.3185585","url":null,"abstract":"Choosing a college is not as easy as choosing a product because the decision must consider the value of the future. However, there are some similarities in selecting universities, especially private universities. The purposes of this study to observe the factors that create student loyalty models and the loyalty of students at private colleges, so that the marketing managers of private universities are able to improve and maintain the students' effectiveness. The research is conducted on 5 private roommate colleges are selected randomly from the 27 private universities in Bandung, Indonesia. There are 250 respondents. The sampling technique is conducted by quota sampling on each of the private universities. The Data are analyzed by using structural equation modeling (SEM) and a data processing using AMOS program. Descriptively, the confidence of students in PHE relatively high, while loyalty, satisfaction and image of the PHE are very low. Results of student's loyalty building model from the three models offered, second model is the best model that can be used to explain the loyalty of students at PHE.","PeriodicalId":11036,"journal":{"name":"Demand & Supply in Health Economics eJournal","volume":"30 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84607472","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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