Cause & Effect: Ma Health Insurance Mandate

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Abstract

In 2006, the state of Massachusetts passed a health care reform policy intended to provide a low cost and high quality insurance coverage to all of the residents.  This study examines this policy change and the effects it had on several different factors including insurance coverage, health and employment status using empirical data.  It was an aspiring plan that inevitably leads to the formation of the United States current health care reform.

1.        Introduction

        An emergence of economic studies has begun to examine empirical causes and effects in the health care industry.  We understand the importance of health care, specifically, health care insurance.   As with all insurance coverage, you pay a little up front to hedge against the risk of having to pay a larger bill in the future.  Still, many Americans decide not to buy health care insurance.  

        This paper focuses on the Massachusetts health care policy reform that mandated all state resident’s to buy insurance.  Evaluating this change in policy can bring light to see what effect this reform had on several dependent variables, such as health status and part time employment.  The main highlight of this study, though, will be the impact on health insurance coverage.  Did the policy increase the number of insured people?  The motivation here lies in theory; health care costs should decrease with a larger pool of an insured community.  By mandating that everyone purchase health insurance, the overall expenditures of health care are spread out more evenly among individuals.

2.        Background

        There continues to be controversy over the ability to identify solutions to the United States health care problem.  In 2009, President Obama and the House of Representatives revealed their plan to repair this broken health-care system called the Affordable Health Care for America Act.  The reform was a similar structure to Governor Mitt Romney’s plan for Massachusetts in 2006; a mandate that every resident of Massachusetts obtain health insurance coverage, or they must pay a tax penalty.

        The Massachusetts reform’s official title is An Act Providing Access to Affordable, Quality, Accountable Health Care.  The policy change passed in 2006, and was implemented in 2007.  The Commonwealth Health Insurance Connector Authority governs the reform.  It’s this Board’s concern that all Massachusetts’ residents over the age of 18 are insured.  It’s also their duty to ensure that the health care coverage is affordable and of the highest quality.  A few key points[1] of the enacted law is as follows:

• Individuals have 63 days after becoming a resident, or 63 days after dropped coverage to obtain and maintain new coverage.
• Strict guidelines as to what qualifies as “Creditable coverage” and is determined by the board of the connector.
• All firms with 10 or more employees must provide an employer-sponsored health insurance program.

3.        Empirical Framework

        There are several research designs for estimating causal effects.  The five most valuable econometric methods are random assignment, regression, instrumental variables, regression discontinuity designs, and differences in differences[2].  While each methodology has it’s own unique design and tradeoffs, I will use the differences in differences technique to estimate the effects of this policy.  

        A simplified equation for differences in differences has four main components: a dependent variable, an interaction term, a dummy variable for treatment, and a dummy variable for time.  A dummy variable takes on values 0 and 1 to report an absence or occurrence of some categorical outcome.  Dummy variables plan an intricate part for difference in difference modeling, are used profusely with most of the variables in this paper.  Throughout the rest of this study, treatment groups will take on the value of 1, while the control groups will have a 0 value.

        This simplified model would be sufficient if there were only two groups and two time periods for our measurements of causal effect.  It is an extremely useful tool because random assignments are rare, therefore making treatment and control groups different in many unobservable ways.  Controlling for all these differences would be a very daunting task, and almost nearly impossible.  For this study we are using multiple states and time periods so it’s beneficial to use fixed effects.  Thus, our OLS model will be as follows:

Yjt = β0 + β1INTERACTIONjt + β2STATEFEj + β3YEARFEt + εjt         (Equation 1)

        Here, Y is my dependent variable.  I will use 3 different dependent variables throughout this paper to see what kind of effects the policy had on them.  The three variables include health insurance coverage, health status, and employment status.  Subscript j denotes a group and subscript t denotes a time period.   The β coefficients are as follows:

• β0 – The intercept for the linear regression line.
• β1 – On average and holding everything else constant, this is the causal effect of the reform policy on our dependent variable.  The interaction term is the treatment dummy variable (in this case Massachusetts) multiplied by the dummy variable for after the policy was implanted (in this case the years after treatment are 2008, 2009, and 2010).
• β2 – On average and holding everything else constant, this coefficient controls for fixed differences between the other states that are being compared.
• β3 – On average and holding everything else constant, this coefficient controls for fixed differences between the years that are being compared.

        Equation 1 ends with our error term, denoted ε.  The error term is the difference between the observed Y and the fitted values from the regression model.  When I regress the estimated equation, the goal is to make the predicted value of Y as close as possible to the true population Y value accomplished by choosing values that minimize the sum of squared residuals.

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