Medicaid Utilization Data
Cross-section data originating from the 1986 Medicaid Consumer Survey. The data comprise two groups of Medicaid eligibles at two sites in California (Santa Barbara and Ventura counties): a group enrolled in a managed care demonstration program and a fee-for-service comparison group of non-enrollees.
data("Medicaid1986")A data frame containing 996 observations on 14 variables.
Number of doctor visits.
Length of observation period for ambulatory care (days).
Total number of children in the household.
Age of the respondent.
Annual household income (average of income range in million USD).
The first principal component (divided by 1000) of three health-status variables: functional limitations, acute conditions, and chronic conditions.
The second principal component (divided by 1000) of three health-status variables: functional limitations, acute conditions, and chronic conditions.
Availability of health services (0 = low access, 1 = high access).
Factor. Is the individual married?
Factor indicating gender.
Factor indicating ethnicity ("cauc" or "other").
Number of years completed in school.
Factor. Is the individual enrolled in a demonstration program?
Factor indicating the managed care demonstration program:
Aid to Families with Dependent Children ("afdc") or
non-institutionalized Supplementary Security Income ("ssi").
Journal of Applied Econometrics Data Archive.
Gurmu, S. (1997). Semi-Parametric Estimation of Hurdle Regression Models with an Application to Medicaid Utilization. Journal of Applied Econometrics, 12, 225–242.
## data and packages
data("Medicaid1986")
library("MASS")
library("pscl")
## scale regressors
Medicaid1986$age2 <- Medicaid1986$age^2 / 100
Medicaid1986$school <- Medicaid1986$school / 10
Medicaid1986$income <- Medicaid1986$income / 10
## subsets
afdc <- subset(Medicaid1986, program == "afdc")[, c(1, 3:4, 15, 5:9, 11:13)]
ssi <- subset(Medicaid1986, program == "ssi")[, c(1, 3:4, 15, 5:13)]
## Gurmu (1997):
## Table VI., Poisson and negbin models
afdc_pois <- glm(visits ~ ., data = afdc, family = poisson)
summary(afdc_pois)
coeftest(afdc_pois, vcov = sandwich)
afdc_nb <- glm.nb(visits ~ ., data = afdc)
ssi_pois <- glm(visits ~ ., data = ssi, family = poisson)
ssi_nb <- glm.nb(visits ~ ., data = ssi)
## Table VII., Hurdle models (without semi-parametric effects)
afdc_hurdle <- hurdle(visits ~ . | . - access, data = afdc, dist = "negbin")
ssi_hurdle <- hurdle(visits ~ . | . - access, data = ssi, dist = "negbin")
## Table VIII., Observed and expected frequencies
round(cbind(
Observed = table(afdc$visits)[1:8],
Poisson = sapply(0:7, function(x) sum(dpois(x, fitted(afdc_pois)))),
Negbin = sapply(0:7, function(x) sum(dnbinom(x, mu = fitted(afdc_nb), size = afdc_nb$theta))),
Hurdle = colSums(predict(afdc_hurdle, type = "prob")[,1:8])
)/nrow(afdc), digits = 3) * 100
round(cbind(
Observed = table(ssi$visits)[1:8],
Poisson = sapply(0:7, function(x) sum(dpois(x, fitted(ssi_pois)))),
Negbin = sapply(0:7, function(x) sum(dnbinom(x, mu = fitted(ssi_nb), size = ssi_nb$theta))),
Hurdle = colSums(predict(ssi_hurdle, type = "prob")[,1:8])
)/nrow(ssi), digits = 3) * 100Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.