Pub Date : 2021-02-16DOI: 10.1101/2021.02.12.21251642
I. Skarda, M. Asaria, R. Cookson
We present a novel dynamic microsimulation model that undertakes stochastic transition modelling of a rich set of developmental, economic, social and health outcomes from birth to death for each child in the Millennium Birth Cohort (MCS) in England. The model is implemented in R and draws initial conditions from the MCS by re-sampling a population of 100,000 children born in the year 2000, and simulates long-term outcomes using life-stage specific stochastic equations. Our equations are parameterised using effect estimates from existing studies combined with target outcome levels from up-to-date administrative and survey data. We present our baseline projections and a simple validation check against external data from the British Cohort Study 1970 and Understanding Society survey.
{"title":"LifeSim: A Lifecourse Dynamic Microsimulation Model of the Millennium Birth Cohort in England","authors":"I. Skarda, M. Asaria, R. Cookson","doi":"10.1101/2021.02.12.21251642","DOIUrl":"https://doi.org/10.1101/2021.02.12.21251642","url":null,"abstract":"We present a novel dynamic microsimulation model that undertakes stochastic transition modelling of a rich set of developmental, economic, social and health outcomes from birth to death for each child in the Millennium Birth Cohort (MCS) in England. The model is implemented in R and draws initial conditions from the MCS by re-sampling a population of 100,000 children born in the year 2000, and simulates long-term outcomes using life-stage specific stochastic equations. Our equations are parameterised using effect estimates from existing studies combined with target outcome levels from up-to-date administrative and survey data. We present our baseline projections and a simple validation check against external data from the British Cohort Study 1970 and Understanding Society survey.","PeriodicalId":37916,"journal":{"name":"International Journal of Microsimulation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48330391","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}
{"title":"Comparative analysis of different techniques to impute expenditures into an income data set","authors":"","doi":"10.34196/ijm.00226","DOIUrl":"https://doi.org/10.34196/ijm.00226","url":null,"abstract":"","PeriodicalId":37916,"journal":{"name":"International Journal of Microsimulation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48588416","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}
S. Kuypers, J. Boone, Johannes Derboven, F. Figari, G. Verbist
While microsimulation techniques have been widely used for the analysis of the distribution of income, this has not been the case for the distribution of wealth. A major reason for this has been the lack of appropriate input data. In Europe this has recently changed among others by the launch of the Eurosystem Household Finance and Consumption Survey (HFCS). In this paper we explain how microsimulation analysis of wealthrelated taxes and policies is enhanced by using the HFCS as input data for EUROMOD, the EUwide taxbenefit microsimulation model. Pilot databases for Belgium and Italy were explored in Kuypers et al. (2016). This paper builds further on that work by extending the coverage to 17 countries and introducing the simulation of new wealthrelated policies in EUROMOD. We explain the processes used to build the input data and to code the wealthrelated policies in EUROMOD and highlight some important advantages and drawbacks. Finally, we put forward some research questions which may be addressed by using this enhanced model. JEL classification: C18, C88, D31, H24 DOI: https:// doi. org/ 10. 34196/ ijm. 00223
{"title":"Enhancing microsimulation analysis of wealth-related policies in EUROMOD","authors":"S. Kuypers, J. Boone, Johannes Derboven, F. Figari, G. Verbist","doi":"10.34196/ijm.00223","DOIUrl":"https://doi.org/10.34196/ijm.00223","url":null,"abstract":"While microsimulation techniques have been widely used for the analysis of the distribution of income, this has not been the case for the distribution of wealth. A major reason for this has been the lack of appropriate input data. In Europe this has recently changed among others by the launch of the Eurosystem Household Finance and Consumption Survey (HFCS). In this paper we explain how microsimulation analysis of wealthrelated taxes and policies is enhanced by using the HFCS as input data for EUROMOD, the EUwide taxbenefit microsimulation model. Pilot databases for Belgium and Italy were explored in Kuypers et al. (2016). This paper builds further on that work by extending the coverage to 17 countries and introducing the simulation of new wealthrelated policies in EUROMOD. We explain the processes used to build the input data and to code the wealthrelated policies in EUROMOD and highlight some important advantages and drawbacks. Finally, we put forward some research questions which may be addressed by using this enhanced model. JEL classification: C18, C88, D31, H24 DOI: https:// doi. org/ 10. 34196/ ijm. 00223","PeriodicalId":37916,"journal":{"name":"International Journal of Microsimulation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49059695","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}
{"title":"Editorial","authors":"","doi":"10.34196/ijm.00221","DOIUrl":"https://doi.org/10.34196/ijm.00221","url":null,"abstract":"","PeriodicalId":37916,"journal":{"name":"International Journal of Microsimulation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46982681","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}
IrpetDin is a dynamic microsimulation model, developed by IRPET (Regional Institute for Economic Planning of Tuscany) to study the future sociodemographic structure of the population and to evaluate the effects of social security programmes in Italy and in Tuscany over the medium to longterm. The model, based on the Eurostat Survey on Income and Living Conditions, makes projections from 2009 to 2050 and it is organised in modules: demography, education, labour and income and social security. IrpetDin produces realistic projections even for the Region of Tuscany and models education and labour with details. Probabilities and rates are estimated differently for Italy and Tuscany, trough regional administrative data. Education careers are completely simulated, from the choice of secondary school to dropout, from university enrolment to graduation. Labour supply is endogenously determined while labour demand is driven from IRPET’s macro model. The matching of labour supply and demand is modelled by sector of activity and education, in order to estimate the quantitative and the qualitative mismatch. JEL classification: C1, C2 DOI: https:// doi. org/ 10. 34196/ ijm. 00224
{"title":"IrpetDin. A Dynamic Microsimulation Model for Italy and the Region of Tuscany","authors":"M. Maitino, L. Ravagli, N. Sciclone","doi":"10.34196/ijm.00224","DOIUrl":"https://doi.org/10.34196/ijm.00224","url":null,"abstract":"IrpetDin is a dynamic microsimulation model, developed by IRPET (Regional Institute for Economic Planning of Tuscany) to study the future sociodemographic structure of the population and to evaluate the effects of social security programmes in Italy and in Tuscany over the medium to longterm. The model, based on the Eurostat Survey on Income and Living Conditions, makes projections from 2009 to 2050 and it is organised in modules: demography, education, labour and income and social security. IrpetDin produces realistic projections even for the Region of Tuscany and models education and labour with details. Probabilities and rates are estimated differently for Italy and Tuscany, trough regional administrative data. Education careers are completely simulated, from the choice of secondary school to dropout, from university enrolment to graduation. Labour supply is endogenously determined while labour demand is driven from IRPET’s macro model. The matching of labour supply and demand is modelled by sector of activity and education, in order to estimate the quantitative and the qualitative mismatch. JEL classification: C1, C2 DOI: https:// doi. org/ 10. 34196/ ijm. 00224","PeriodicalId":37916,"journal":{"name":"International Journal of Microsimulation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42578854","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}
Over the past several decades, the United States has experienced a dramatic rise in obesity rates, due to both a rightward shift of the body mass index (BMI) distribution and a pushing out of the right tail. This shift has led to increases in obesityrelated chronic diseases, particularly diabetes, as well as impacts on longevity, medical expenditures, and quality of life. Microsimulation modeling is a potentially useful tool for assessing the impacts of policies targeting this epidemic, but reliably assessing policies requires a model that performs well in projecting health risk factors and disease outcomes. This research assesses the outofsample and external validity of a microsimulation model of the U.S. adult population.There are two research questions addressed in this analysis: 1. How well does the Future Adult Model (FAM) perform in projecting BMI and diabetes over a tenyear horizon compared to the host data? 2. How well do the microsimulation model’s predictions compare to external surveillance data of BMI and diabetes?FAM is an economicdemographic microsimulation model of the United States population over the age of 25. For this validation exercise, all Markov transition models are estimated using the 1999-2007 waves of the PSID. The simulation is then run from 2007-2017. For internal consistency, simulated outcomes in 2017 are compared to actual PSID outcomes. Population means and selected quantiles are compared between the simulation and the host data. Receiver operating characteristic (ROC) curves are used to assess model performance for binary outcomes using the area under the curve (AUC) statistic. For external validation, simulated outcomes for 2007-2017 are compared to the Behavioral Risk Factors Surveillance System (BRFSS), a large, nationallyrepresentative survey of the United States population.After ten years of simulation, FAM BMI projections for men and women compare well to both PSID and BRFSS data throughout much of the distribution. The 99th percentile differs significantly, with FAM underestimating the right tail of the BMI distribution. Individual assignment of obesity and severe obesity performs well using AUC as a criteria. Initial differences in the diabetes prevalence between PSID and BRFSS data are preserved in FAM projections. FAM is initially 1.9 percentage points below BRFSS for women 25 and older and is 1.6 percentage points below BRFSS for women 35 and older after ten years of simulation. Men 25 and older are 1.2 percentage points lower initially and are 0.8 percentage points lower after ten years of simulation. Individual assignment of diabetes incidence does not perform as well as clinical models with richer predictors. Researchers using FAM should be cognizant of these strengths and limitations of the microsimulation model. JEL classification: C6, I1, J1 DOI: https:// doi. org/ 10. 34196/ ijm. 00225
{"title":"Validating risk factor and chronic disease projections in the Future Adult Model","authors":"B. Tysinger","doi":"10.34196/ijm.00225","DOIUrl":"https://doi.org/10.34196/ijm.00225","url":null,"abstract":"Over the past several decades, the United States has experienced a dramatic rise in obesity rates, due to both a rightward shift of the body mass index (BMI) distribution and a pushing out of the right tail. This shift has led to increases in obesityrelated chronic diseases, particularly diabetes, as well as impacts on longevity, medical expenditures, and quality of life. Microsimulation modeling is a potentially useful tool for assessing the impacts of policies targeting this epidemic, but reliably assessing policies requires a model that performs well in projecting health risk factors and disease outcomes. This research assesses the outofsample and external validity of a microsimulation model of the U.S. adult population.There are two research questions addressed in this analysis: 1. How well does the Future Adult Model (FAM) perform in projecting BMI and diabetes over a tenyear horizon compared to the host data? 2. How well do the microsimulation model’s predictions compare to external surveillance data of BMI and diabetes?FAM is an economicdemographic microsimulation model of the United States population over the age of 25. For this validation exercise, all Markov transition models are estimated using the 1999-2007 waves of the PSID. The simulation is then run from 2007-2017. For internal consistency, simulated outcomes in 2017 are compared to actual PSID outcomes. Population means and selected quantiles are compared between the simulation and the host data. Receiver operating characteristic (ROC) curves are used to assess model performance for binary outcomes using the area under the curve (AUC) statistic. For external validation, simulated outcomes for 2007-2017 are compared to the Behavioral Risk Factors Surveillance System (BRFSS), a large, nationallyrepresentative survey of the United States population.After ten years of simulation, FAM BMI projections for men and women compare well to both PSID and BRFSS data throughout much of the distribution. The 99th percentile differs significantly, with FAM underestimating the right tail of the BMI distribution. Individual assignment of obesity and severe obesity performs well using AUC as a criteria. Initial differences in the diabetes prevalence between PSID and BRFSS data are preserved in FAM projections. FAM is initially 1.9 percentage points below BRFSS for women 25 and older and is 1.6 percentage points below BRFSS for women 35 and older after ten years of simulation. Men 25 and older are 1.2 percentage points lower initially and are 0.8 percentage points lower after ten years of simulation. Individual assignment of diabetes incidence does not perform as well as clinical models with richer predictors. Researchers using FAM should be cognizant of these strengths and limitations of the microsimulation model. JEL classification: C6, I1, J1 DOI: https:// doi. org/ 10. 34196/ ijm. 00225","PeriodicalId":37916,"journal":{"name":"International Journal of Microsimulation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45784157","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}
D. Schofield, Owen Tan, R. Shrestha, R. Rajkumar, Sarah West, Morgan Rice, N. Kasparian, J. Boyle, Louise Christie, M. Leffler, L. Murray, R. Tanton, Jinjing Li, T. Roscioli, M. Field
Intellectual disability (ID) is associated with far reaching economic and psychosocial outcomes. People with ID require a wide range of supports from both governments, and families. No study has thoroughly assessed the economic and social costs of care for individuals with familial ID. Understanding the comprehensive costs of ID is important for policy makers to decide on resources required to support the families affected by the condition. In the Australian Economic and Psychosocial Impacts of Caring for Families Affected by Intellectual Disability (EPIC- ID Study), we developed a microsimulation model, IDMOD, to provide a holistic perspective of the economic costs of ID for use in cost- effectiveness studies related to genomic testing and precision medicine for familial ID. This paper describes the construction of IDMOD. The model base population are individuals who were referred to the Genetics of Learning Disability (GoLD) clinics. Through a detailed questionnaire, we collected information including health expenditure, income, education, welfare payments, savings, housing and residential care, and support received for purchase of aids and equipment, employment for both people with ID and their carers. Both government and patient costs were included. Data on quality of life, psychological wellbeing, relationship strength and social inclusion are also collected before and after genomic diagnostic testing. Patients’ use of medical services, prescription medicines, and hospital services were captured via linked datasets. Lost income, assets, and tax were imputed via synthetic matching to records from the Static Incomes Model (STINMOD). Each unit record in the model was weighted using input from the Survey of Disability, Ageing, and Carers (SDAC) to reflect the total familial ID population in Australia. The model will provide data on the economic costs of familial ID in Australia, and the associated effects of implementing genomic testing and precision medicine for this population group.
{"title":"IDMOD: An Australian microsimulation model of lifetime economic and social factors in familial intellectual disability","authors":"D. Schofield, Owen Tan, R. Shrestha, R. Rajkumar, Sarah West, Morgan Rice, N. Kasparian, J. Boyle, Louise Christie, M. Leffler, L. Murray, R. Tanton, Jinjing Li, T. Roscioli, M. Field","doi":"10.34196/ijm.00212","DOIUrl":"https://doi.org/10.34196/ijm.00212","url":null,"abstract":"Intellectual disability (ID) is associated with far reaching economic and psychosocial outcomes. People with ID require a wide range of supports from both governments, and families. No study has thoroughly assessed the economic and social costs of care for individuals with familial ID. Understanding the comprehensive costs of ID is important for policy makers to decide on resources required to support the families affected by the condition. In the Australian Economic and Psychosocial Impacts of Caring for Families Affected by Intellectual Disability (EPIC- ID Study), we developed a microsimulation model, IDMOD, to provide a holistic perspective of the economic costs of ID for use in cost- effectiveness studies related to genomic testing and precision medicine for familial ID. This paper describes the construction of IDMOD. The model base population are individuals who were referred to the Genetics of Learning Disability (GoLD) clinics. Through a detailed questionnaire, we collected information including health expenditure, income, education, welfare payments, savings, housing and residential care, and support received for purchase of aids and equipment, employment for both people with ID and their carers. Both government and patient costs were included. Data on quality of life, psychological wellbeing, relationship strength and social inclusion are also collected before and after genomic diagnostic testing. Patients’ use of medical services, prescription medicines, and hospital services were captured via linked datasets. Lost income, assets, and tax were imputed via synthetic matching to records from the Static Incomes Model (STINMOD). Each unit record in the model was weighted using input from the Survey of Disability, Ageing, and Carers (SDAC) to reflect the total familial ID population in Australia. The model will provide data on the economic costs of familial ID in Australia, and the associated effects of implementing genomic testing and precision medicine for this population group.","PeriodicalId":37916,"journal":{"name":"International Journal of Microsimulation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46372534","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 the 1980s and 1990s the US employment rate increased steadily, and by 2000 it was one of the highest among the rich democratic nations. Since then it has declined both in absolute terms and relative to other countries. We use an in-depth comparison between the United States and the United Kingdom to probe the causes of America's poor recent performance. Contrary to a common narrative, a comparative perspective suggests that the decline in US labour force participation is not confined to the (white) male population; the divergence in the female participation rate is even more pronounced. We do not find evidence that the poor US performance is linked to cyclical patterns, such as the 2008-09 Great Recession; instead, it is a more pervasive, longer-run phenomenon. The relative decline of US participation rates compared to the UK is attributable to shifts in socio-demographic characteristics, such as education, and to shifts in the impact of those characteristics, which have become more adverse to participation.
{"title":"What happened to the 'Great American Jobs Machine'?","authors":"Matteo G. Richiardi, B. Nolan, L. Kenworthy","doi":"10.34196/IJM.00211","DOIUrl":"https://doi.org/10.34196/IJM.00211","url":null,"abstract":"In the 1980s and 1990s the US employment rate increased steadily, and by 2000 it was one of the highest among the rich democratic nations. Since then it has declined both in absolute terms and relative to other countries. We use an in-depth comparison between the United States and the United Kingdom to probe the causes of America's poor recent performance. Contrary to a common narrative, a comparative perspective suggests that the decline in US labour force participation is not confined to the (white) male population; the divergence in the female participation rate is even more pronounced. We do not find evidence that the poor US performance is linked to cyclical patterns, such as the 2008-09 Great Recession; instead, it is a more pervasive, longer-run phenomenon. The relative decline of US participation rates compared to the UK is attributable to shifts in socio-demographic characteristics, such as education, and to shifts in the impact of those characteristics, which have become more adverse to participation.","PeriodicalId":37916,"journal":{"name":"International Journal of Microsimulation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69827020","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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