An Unhealthy America: The Economic Burden of ... - Milken Institute

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An Unhealthy AmericaMilken Instituteaffect a state’s productivity. By looking at both young dependents and life expectancy at 65, we can capture the effectsto two sides of a spectrum and derive their individual impacts on productivity. The negative and significant regressioncoefficient on young dependents indicates that states with high fertility rates will experience decreased growth in thefuture due to the large number of dependents per capita—until those dependents enter the labor force.Production Function Results - Dependent Variable: Log (Real GDP by State)VariablesExpenditures PRC* Expenditures/PRCCoefficientDisease(Billions) (Millions) (Thousands)0.258*Cancer 48.1 10.6 4.5Log(Life Expectancy at 65)(2.05)Breast Cancer 5.5 1.1 4.8Colon Cancer 3.9 0.3 11.5 0.506**Log(Percentage Lung Cancer of Population With 6.3 a Bachelor’s 0.4 Degree) 17.1 (19.31)Prostate Cancer 4.3 1.0 4.1 0.750**Log(Unadjusted Other Cancers Labor Force) 28.0 7.7 3.6 (26.17)Pulmonary Conditions 45.2 49.2 0.90.196**Diabetes 27.1 13.7 2.0Log(Capital Stock)(14.84)Hypertension 32.5 36.8 0.9Heart Disease 64.7 19.2 3.4 -0.311**Stroke Log (Young Dependent per Capita) 13.6 2.4 5.6 (-7.09)Mental *Significant Disorders at the 5% Level 45.8 30.3 1.5Total **Significant at the 1% Level 277.0 162.2 1.7PRC: Source: Population Milken Reporting Institute ConditionReaction Functions 1Expenditures Variables PRC* Expenditures/PRCDisease Dependent (Billions) (Millions) Explanatory (Thousands)Log(Percentage Cancer of Population With a 48.1 10.6 4.5Bachelor’s Breast Degree) Cancer 5.5Log(Life Expectancy 1.1 at 65) 4.8Colon Cancer 3.9 0.3 11.5Log(Labor Lung Force Cancer Participation Rate 6.3Log(Life Expectancy 0.4 at 65) 17.1Prostate Cancer 4.3Log(Percentage 1.0 of Population With 4.1 aLog(Capital Other Stock Cancers per Employee) 28.0Bachelor’s Degree) 7.7 3.6**Significant Pulmonary at the Conditions 1% Level45.2 49.2 0.91.Controlling Diabetesfor Other Effects 27.1 13.7 2.0Source: Hypertension Milken Institute32.5 36.8 0.9Heart Disease 64.7 19.2 3.4Stroke 13.6 2.4 5.6Mental Disorders 45.8 30.3 1.5Total 277.0 162.2 1.7PRC: Population Reporting ConditionCoefficient1.80**(3.95)0.55**(2.87)0.56**(4.76)[ 175 ]
An Unhealthy AmericaMilken InstituteComparison TablesThe baseline intergenerational model is built on the projection of independent variables, given the same growth trend(1970–2003) and baseline PRC projections. The projections themselves represent the embedded investment from futureimprovements in health.Inserting the variable life expectancy at 65 into the optimistic projections, we use the most recently available six years(1997–2003) of NCHS life table data. We insert the optimistic projections for the two leading causes of chronic diseasedeath—cancer and heart disease—to obtain expected mortality rates for the over-65 population. By computing thecoefficients between life expectancy and mortality rates, along with forty-year historical trends, we find that in 2023,the optimistic life expectancy will be roughly 0.7 year longer than that of the baseline projection. By 2050, optimistic lifeexpectancy at 65 will increase 1.7 years over the 2050 baseline.The impact of life expectancy on the percentage of population with a bachelor’s degree varies over time. Generally,the greater impact should occur within the first twenty years, from 2003 to 2023, and increase at a slower rate until 2050.We control for median earnings by educational attainment, since higher relative incomes will make the acquisition ofhigher degrees more appealing. We plug this newly created optimistic projection of the percentage of populationwith a bachelor’s degree variable into a reaction function to calculate the optimistic capital stock output. Decisions toinvest in capital stock (software, equipment, and structures) are determined by the percentage of the population withhigher education degrees.The percentage of population with a bachelor’s degree and life expectancy at 65 both have impacts on the laborforce size, whose magnitude for each will vary according to an “S” curve. This reaction function shows that decisions toinvest in better health will have a positive and significant impact on a person’s life, as well as work force longevity.This model design departs from existing literature by not just projecting domestic regional markets. It also relays thespillover effects of health that have not been captured in any previous models. Better health enables a worker to remainin the labor pool longer. Feedback into the production function will demonstrate by how much this will increase eachstate’s productivity.Now that we have optimistic data from 2004 through 2050 for each variable, we can use the coefficients from theproduction function to generate optimistic output (state GDP) from 2004 to 2050. The gap between optimistic andbaseline presents a difference of 17.59 percent by 2050. This gap totals $1.201 trillion by 2023 and widens to $5.668trillion by 2050. We can also compare this gap with that in the baseline/optimistic scenarios for indirect impacts anddirect costs from previous chapters.When other models fail to account for the interaction of health with other variables, they risk a pervasive understatingof GDP—by double-digit percentages. Such errors underscore the importance and potential contribution of thisresearch in the field of health economics.[ 176 ]
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