Dental Hygiene Regulation and Access to Oral Health Care: Assessing the Variation across the U. S. States Tanya Wanchek University of Virginia

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Empirical Model
The difficulty in estimating the model is that wages and employment rates are endogenous to each other and both simultaneously affect access to care. This two-way causality means that OLS estimates of the structural model of supply and demand will be biased and inconsistent. Therefore, to simultaneously estimate the supply and demand equations, we employ a three stage least squares (3SLS) estimation technique (Zellner and Theil 1962). The 3SLS estimator accounts for contemporaneous correlation in the error terms across equations, allowing for a consistent covariance matrix across the error terms of the model (Greene 2000). Consequently, 3SLS is the most efficient estimator of a system of equations in larger samples. One disadvantage of the 3SLS method, however, is if the system is not correctly specified, then the errors are compounded through all the estimates. Heteroscedasticity is also a concern. We test for heteroscedasticity using Pagan-Hall and scatter plot tests; both fail to reject homoscedasticity.
The first stage of the 3SLS estimation method solves for all endogenous variables in the system by rewriting the endogenous variables as a function of the exogenous variables. The fitted values of each endogenous variable from the OLS estimation of the reduced form equations are used as instruments in the structural equations. The second stage involves estimating the structural equations separately using the first stage fitted values. The final stage involves using the covariance matrix as a weighting matrix as well as the instruments derived in the first stage to jointly estimate the equations in the structural model. Using instruments to estimate endogenous variables ensures consistency, while joint estimation ensures asymptotic efficiency (Wacziarg 2001). We estimate the system of equations using a 3SLS procedure with a STATA software package.
Data
A summary of the mean, standard deviation, minimum and maximum is presented in Table 1. In the supply equation, the dependent variable is the DH employment per 100,000 population. The explanatory variables include the inflation-adjusted mean DH wage, DH graduates per 100,000 population, practice years required, state or regional exams accepted for licensure by credentials, and dental and dental assistant employment per 100,000 population. We also control for inflation-adjusted income using the national consumer price index (CPI), density of population, a state-specific cost of living index, and the year, where 2002 is the base year. We create a practice years index by taking the inverse of the number of years that a state requires a dental hygienist to practice out-of-state for licensure by credentials. Through this conversion, a high practice year index implies liberal entry restrictions, while a low practice year index represents stringent entry restrictions with zero being the limit of not allowing entry by credentials. We also transform employment, wage, income, and density by taking the logarithm to account for nonlinearities. To increase the sample size, and therefore the efficiency of the estimator, we pool data from 2002, 2004, and 2006, which are the years that data on dental office visits is available.
Insert Table 1.
The demand equation consists of mean DH wage as the dependent variable. Explanatory variables include DH employment per 100,000 population, the DHPPI index of practice restrictions, and mean, inflation-adjusted wage of dentists and dental hygienists to capture to price of substitutes or complements. We also control for state-level cost of living, CPI-adjusted income, density, and year. We considered the potential for nonlinearity in the index, but found that the square of the DHPPI was insignificant.
In the access to care equations, the dependent variable is the percent of the population who visited a dental office or dental clinic within the past year. The dependent variable is broken down into subgroups in each of the regressions in order to test whether more stringent occupational regulation has a differential effect on certain segments of the market. The first system of equations includes males and females, the second system is by household income level, and the third system is by age. Among the explanatory variables are dentists, DHs, and dental assistants per capita to capture availability of providers and the wage of each of these professionals as proxy variables to capture the price of services. Income, density, cost of living, and year are also included as control variables.

Results
Male/ Female Regression
The 3SLS estimation addresses three hypotheses of the model. The first hypothesis—liberal licensure requirements increase the number of DHs employed—is consistent with the results of the pooled regression (Table 2, Eq. 1). The three proxy variables for entry restrictions (DH graduates, practice years, and exams accepted for credentials) indicate that liberal entry restrictions are correlated with higher DH employment. The number of DH graduates is significantly correlated with DH employment at the 1 percent confidence level. States that facilitate entry by offering more educational opportunities increase the number of DHs practicing in the state. The practice year index is positively correlated at the 5 percent confidence level, indicating that fewer practice years required for licensure by credentials increases DH employment. Finally, the number of exams a state will accept in licensure by credentials is positively correlated with DH employment at the 10 percent confidence level.2 Thus, facilitating entry through newly trained individuals or through in-migration increases DH employment rates. In addition, DH wage and employment are positively correlated, as expected in a supply relationship.
The second hypothesis—liberal practice restrictions increase DH wages—is also supported by the results (Table 2, Eq. 2). Liberal practice restrictions, represented by a high DHPPI index, are positively correlated with higher wages. This result is consistent with Wing et al. (2004). The coefficient of the DHPPI index is 0.002, which translates into a one-point increase on the DHPPI index (out of 100) being correlated with a 0.2 percent increase in wages. Thus, for the average wage of \$60,000, the pooled regression predicts an increase of \$120 wage for every point increase in the index.
In addition, DH employment and wage are negatively correlated, as expected in a demand equation. Higher wages of dentists and dental assistants are positively correlated with DH wages, suggesting that DHs are substitutes for other oral health care providers. Higher cost of living and lower density increases wage. The negative correlation between wage and density is surprising because we expect urban areas to have higher wages.
The third hypothesis—wage and employment jointly influence access to care—is observed in the outcome equations (Table 2, Eq. 3-4). DH wages are negatively correlated with male visits to the dental office, but are insignificant for female visits. To the extent that DH wage is a proxy for cost of services, then the results suggest that males are more price sensitive to the cost of dental visits. The number of providers, including DH, dentists, and dental assistants, are positively correlated with number of visits for both males and females. This correlation suggests that availability matters and may be the primary determinant for females. Interestingly, income per capita is not significant. The density of the state, however, is significant, suggesting that living in a rural area and having to travel further to a dental office may hinder dental visits.
Insert Table 2

Income Regression
Income and socio-economic status are thought be primary determinants of access to health care in the U.S. In order to better understand how regulations influence different market segments, we repeat the pooled regression using the five income groups—less than \$15,000, \$15-25,000, \$25-\$35,000, \$35-50,000, and \$50,000 or higher—reported by the CDC (2002, 2004, 2006) (Table 3). The results of the supply and demand equations are similar to the male/female regression. The outcomes equations (Table 3, Eq. 3-7) reveal that DH wages are only a significant predictor of visits to a dental office for middle-income groups (\$15-25,000, \$25-35,000, and \$35-50,000). To the extent that wage is a proxy for price, the lowest and highest income groups are likely less sensitive to the price of dental care. These two groups are least likely to pay the full cost of care. The lowest income group likely relies on public services, while many in the higher income group likely have insurance that at least partially covers dental office visits. Furthermore, among those in the higher income group, oral health services are a smaller share of income per unit of service. Therefore, the significance reflects the relatively higher price sensitivity of the middle-income groups. The wage of dentists and dental assistants is not significant for any group.
DH employment rates significantly influences the likelihood of visiting the dentist’s office for all but the middle-income group (\$25-35K), at the one percent significance level. Visits for all income groups are positively correlated with dentists and dental assistants per capita. It is unclear why the middle-income group is not correlated with DH, but is correlated with the availability of other oral health care providers. Because the regression is in log-linear form, the results can be interpreted as supply elasticities. The largest effect of DHs on access to care is among the lowest income groups, where the coefficient of 0.5 indicates that a 1 percent increase in per capita DH employment would result in a 0.5 percent increase in dental visits. Alternatively, a 1 percent increase in DH employment per capita among the highest income group results in only a .19 percent increase in dental office visits.
The density of a state is a significant predictor of visits for the higher income groups, as well as for the \$15-\$25,000 income group. The importance of the density variable suggests these individuals may be sensitive to travel time from rural areas to oral health care providers and travel time may be a primary determinant for higher income individuals. The dummy variable for the year 2006 is negative for all income groups, with a higher coefficient among lower income groups. This indicates that dental visits were significantly lower in 2006 than in 2002, the base year. Given that 2006 was not a recession year, why this decline occurred merits further inquiry.
Insert Table 3
Age Regression
Separating out age groups provides useful information on whether occupational regulation affects different ages in different ways. The supply and demand results are similar to the previous two regressions (Table 4, Eq. 1-2). In the outcome equations, age groups varied in several respects. First, relatively younger individuals (age 18 through 44) were less likely to visit a dental office when DH wages were high, whereas individuals 45 and over were not effected by DH wage. One possible explanation is that as individuals get older visiting a dentist becomes more of a necessity. Younger individuals tend to go more for preventative care and may have a lower perceived need for care. Alternatively, older individuals with more advanced careers may be more likely to have dental insurance.
Results on availability of providers are mixed. While DH employment is significant for all age groups, the number of dentists per capita is not significant among younger individuals (18 to 34 years old). One reason may be that younger individuals rely more on dental hygienists for teeth cleaning, as opposed to older individuals who utilize dentists for restorative work. Interestingly, the insignificance of dental employment among younger individuals is consistent with Kleiner and Kudrle (2000). If dental employment has little effect on access to care among the young, then we would not observe dental licensure requirements influencing the outcomes of young recruits.
The year dummy variable for 2006 is significant for 18 to 44 year-olds, but not for individuals age 45 and up, indicating that between 2002 and 2006 there was a decline in younger individuals visiting a dental office. Similar to the change in visits found in the prior regressions, this finding merits further investigation.
Insert Table 4
The results of the 3SLS estimation in all three outcome groups—male/female, income, and age—are consistent with the model’s predictions. In the supply equation, stringent licensure requirements are negatively correlated with DHs employed per capita. In the demand equation, stringent practice restrictions are negatively correlated with DH wages. Furthermore, for most groups of individuals, DH employment rates are positively correlated with access to care, while high DH wage rates discourage dental visits, particularly among younger individuals, medium income individuals, and males.
Summary
Using a supply and demand framework and a 3SLS estimation method, we find that entry requirements are negatively correlated with DH employment, practice restrictions are negatively correlated with DH wages, and both wage and employment affect access to care, as observed in the prevalence of dental office visits. These results are consistent with a state’s entry and practice regulations jointly affecting access to oral health care. Not only does the regulatory environment in a state influence overall oral health care, but it appears to have a differential effect on certain groups. When comparing males and females, males appear to be more sensitive to the cost of DH services, where DH wage is a proxy for cost. Among different income and age groups, middle-income individuals and younger individuals are the most sensitive to price. The availability of providers appears to matter for all market segments.
This analysis has important policy implications both for overall oral health, as well as for reducing disparities. High caries and low dental visitation rates have prompted public health officials across the U.S. to proclaim a need to take action to improve oral health. This study suggests that by increasing the number of DHs working in a state through expanding educational opportunities, facilitating in-migration of DHs from other states, and implementing less stringent scope of practice regulations, states can significantly improve access to oral health care and reduce many of the disparities that exist. Furthermore, these results can be applied to other licensed occupations. Policymakers often do not appreciate the full implications of occupational regulations on health outcomes. This study highlights the need to consider occupational regulations as an important component of health care reform in the current health care debate.
Acknowledgements
The author would like to thank William Shobe for suggesting the topic, Leora Friedberg helpful comments on an early draft, and participants at the International Symposium on the Government Regulation of Occupations and two anonymous referees for their insightful comments.

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Appendix

Graph 1. Graph 2.

Stringent entry and practice restrictions Liberal entry and stringent practice restrictions

Graph 3. Practice and Entry Restrictions by State

Source: DHPPI – Wing et al. (2005); DH Graduates – U.S. Department of Education, Integrated Postsecondary Education Data System (IPEDS). Mean DHPPI is 39; mean DH graduates per 100,000 is 2.8.

Graph 4. Adults Visiting Dental Office, BRFSS 2006

Source: CDC (2006). The average is 70 percent.

Table 1. Means, Standard Deviations, Minimum and Maximum, 2006
 Mean Standard Deviation Minimum value Maximum value DH Labor Market DH Wage (\$) 55,408 7,129 44,770 74,470 DH Employment (per 100,000) 57.7 15.9 32.6 95.0 Entry Restrictions Exams 1.9 1.6 0.0 5.0 Practice yrs 1.7 1.6 0.0 5.0 DH Grads (per 100,000) 2.8 1.3 0.6 7.0 Practice Restrictions DHPPI (median = 39) 44 20.4 10.0 97.0 Outcomes (% population) visit male 67.8 5.4 56.6 79.5 visit female 71.8 5.4 59.1 82.6 visit less15K 46.0 7.2 30.6 60.7 visit 15-25K 54.1 6.0 42.3 67.0 visit 25-35K 63.2 5.0 53.4 74.7 visit 35-50K 70.7 4.6 59.5 80.3 visit 50K plus 81.9 3.5 75.3 87.8 visit 18-24 69.6 5.5 55.5 78.6 visit 25-34 66.9 5.6 55.6 77.5 visit 35-44 72.0 5.3 59.8 83.3 visit 45-54 72.9 6.2 58.8 86.8 visit 55-64 71.7 6.7 56.9 83.9 visit 65 plus 66.0 7.1 46.4 78.2 Other providers D wage (\$) 128,590 21,724 77,490 164,220 DA wage (\$) 15,154 1,779 12,088 18,695 D employment (per 100,000) 29.6 8.4 32.5 52.9 DA employment (per 100,000) 92.2 18.1 62.5 162.8 State control variables Income (per capita) (\$) 33,670 5,521 24,664 52,811 Cost living 94.8 15.4 66.4 131 Density 362 1311 1.1 9378

Data Sources: DH Employment, D Employment, DA Employment - Bureau of Labor Static (BLS), Department of Labor’s Occupational Employment Statistics Survey and U.S. Census Bureau population estimates. DH, D, DA Wage - BLS, Department of Labor’s Occupational Employment Statistics Survey and U.S. Census Bureau population estimates. Adjusted for inflation using CPI estimates. Practice years and Exams - American Dental Hygiene Association, http://www.adha.org/careerinfo/licensure.htm (accessed 10/10/07). DH Grads - U.S. Department of Education, Integrated Postsecondary Education Data System (IPEDS). DHHPI - Wing et al. (2004). Visits –Behavioral Risk Factor Surveillance System (BRFSS) survey, CDC (2002, 2004, 2006). Income - Bureau of Economic Analysis. Density - U.S. Census Bureau. Cost living - Aten (2005).

Data is missing for: Dental employment - Alaska, North Dakota, Oregon, Utah, Vermont in 2002; Alaska, Colorado, Hawaii, Minnesota, Oregon, Rhode Island, Tennessee, Utah in 2004; DH employment –Washington D.C., Utah in 2002; Dental assistant employment – Rhode Island in 2004; DH wage – South Carolina in 2004; DHPPI – Delaware in all years. Dental Visits – Hawaii is missing dental office visit data for all years. New Jersey and Arkansas are missing dental office visit data by income group for 2002 and Alaska for 2006. Nevada and New Jersey are missing dental office data by age for 2002, Arizona for 2004 and 2006, and Alabama for 2006.

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