Back to Appendix E: Medicare Dependence and Health StatusFrom Planning for the Future
p. 97-100, published 2002
The estimated likelihoods shown on page 36 are based on the relationship between limitations in physical functioning and gender, age, race, ethnicity, marital status, education, income and location of residence in an urban or rural area. We use probit models to estimate the relationship between limitations in six categories of physical functioning and these socioeconomic and demographic characteristics. Six dependent variables equal 1 if a person has been limited for greater than three months or less than three months and 0 if the person is not limited at all in each of the six categories of physical functions described. To see the actual question used to generate these variables please refer to Appendix A, question 34. The six different categories of physical functioning range from the most vigorous activities such as running, lifting heavy objects, or participating in strenuous sports to the most basic activities of eating, dressing, bathing, or using the toilet. These are the only two categories of physical functioning shown in the figure on page 36. The independent variables in the model are listed below, along with parameter estimates in Table F.1:
GENDER –– This dichotomous variable is equal to 1 for males and 0 for females.
LESSHS, HSGRAD, SOMEPSE, BAORMORE — The survey respondents were asked to please circle the last grade in school you completed. LESSHS equals 1 if they circled “grade school” or “some high school.” HSGRAD is the reference group and refers to those individuals that circled “graduated high school” or “GED.” SOMEPSE equal 1 if they circled “1 or 2 years college, no degree,” “graduated junior or community college,” or “vocational/technical degree.” BAORMORE equals 1 if they circled “Bachelor’s degree,” “some graduate school work,” or “graduate degree (ex: MA, MS, PhD, JD).
Q1INC, Q2INC, Q3INC, Q4INC, MISINC –– These dichotomous variables reflect if the individual’s total household income from all sources before taxes is in the first, second, third, or fourth quartile, or missing, respectively. The first quartile is left out of the model and is therefore the comparison group. The variable equals 1 if the individual’s income falls in the quartile (or is missing in the case of MISINC) and 0 if it does not. The first quartile are those persons with total household incomes below $15,000, the second quartile incomes fall between $15,000 and $29,999, the third quartile incomes fall between $30,000 and $49,999, and the fourth quartile incomes are those $50,000 or higher.
AGED –– This is a continuous variable that represents the person’s age.
WHITE –– This dichotomous variable equals 1 if a person is white and non-Hispanic and it equals 0 otherwise.
MARRIED –– This dichotomous variable equals 1 if a person is married and it equals 0 otherwise.
METROCO –– This dichotomous variable is equal to 1 if the individual lives in an urban county and 0 otherwise. We used Beale Codes to categorize the respondent’s county. If the county is designated as “0” through “3” then METROCO equals 1. Otherwise, if the Beale Code is equal to 4 through 9 then METROCO equals 0.
The next stage of this analysis involved comparing two groups from our sample––the retired respondents and the nonretired respondents. To do this we estimated the means for each of these two groups for all the independent variables used in the models, except age. Table F.1 lists the means for the two different groups. The nonretired group has higher incomes, as expected, but also higher educational attainment levels, on average. These two characteristics have been shown repeatedly by researchers to affect health outcomes significantly. These means were used to estimate the likelihood the “typical” retiree would be limited in each of the physical functioning categories at each age compared to the likelihood the “typical” nonretiree would be limited at each age. Although not shown here, for each of the categories analyzed, the likelihood that a retiree would be limited was greater at each age than the likelihood of a nonretired Kentuckian regardless of the physical functioning category. As can be seen in Table F.1, many of the same independent variables, namely education and income, are statistically significant and have the same signs in the other four categories of physical functioning as in the two shown in the text. These results suggest that we may see better health statuses in the area of physical functioning among coming retirees in their later years than those that are typical of current Kentucky retirees.
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