Tài liệu Oganixation of dental care industry

Organization of Dental Care Industry BY THANH-AN NGUYEN-LE M.D., Pham Ngoc Thach University of Medicine, 2007 M.P.H., University of Illinois at Chicago, 2010 THESIS Submitted as partial fulfillment of the requirements for the degree of Doctor of Philosophy in Public Health Sciences in the Graduate College of the University of Illinois at Chicago, 2015 Chicago, Illinois Defense Committee: Anthony T. Lo Sasso, Chair and Advisor, Division of Health Policy and Administration Robert Kaestner, Department of Economics Lindsey Leininger, Mathematica Policy Research Marko Vujicic, American Dental Association, Health Policy Institute Coady Wing, Indiana University, School of Public and Environmental Affairs This dissertation is dedicated to my parents Nguyen Van Hung and Le Thi Thanh My, to my husband Doan Trung Kien and my daughter Doan Thao Minh. ii ACKNOWLEDGMENTS I would like to express my deepest appreciation to my advisor, Professor Anthony T. LoSasso, for all of his support and guidance over the past years. I always feel impressed and grateful for the time Professor LoSasso devotes for his students including me, given his busy schedule. His immense knowledge, insightful guidance and encouragement have always inspired me after each of our regular meetings. This dissertation would not be possible without him. I am also grateful to other members of my dissertation committee, Professor Robert Kaestner, Professor Lindsey Leininger, Dr. Marko Vujicic, and Professor Coady Wing, for their valuable feedback that improved my research in many ways. My dearest gratitude is for my family. To my mom, for nurturing me and my learning dream, being the mother, the teacher, and the best friend, all at the same time. To my husband, for sacrificing his own opportunities to always be on my side, support me in my life, share with me the challenges, and not let me give up. Last but not least, I would like to thank the Vietnam Education Foundation and the Division of Health Policy and Administration at UIC School of Public Health for giving me the opportunity to fulfil my dream of studying. TANL iii TABLE OF CONTENTS CHAPTER PAGE 1. COMPETITION AND MARKET STRUCTURE IN THE DENTAL INDUSTRY .. 1 1.1 Introduction ............................................................................................................. 1 1.2 The theory ............................................................................................................... 3 1.3 The model specification .......................................................................................... 5 1.3.1 The cost function and its derivatives. ..................................................................... 6 1.3.2 The demand function. ........................................................................................... 10 1.4 Data description and variable construction ........................................................... 10 1.5 Missing Data ......................................................................................................... 26 1.6 Results ................................................................................................................... 46 1.7 Estimation stability ............................................................................................... 67 1.8 Conclusion ............................................................................................................ 85 2. THE GENDER GAP IN DENTIST EARNINGS ........................................................ 86 2.1 Introduction ........................................................................................................... 86 2.2 Methods................................................................................................................. 88 2.2.1 The Oaxaca-Blinder decomposition ..................................................................... 88 2.2.2 The DiNardo-Fortin-Lemieux decomposition ...................................................... 88 2.3 Data sources .......................................................................................................... 91 2.4 Results ................................................................................................................... 92 2.4.1 An Overview of Gender Differences in Dentists’ Earnings and Related Characteristics ....................................................................................................... 92 2.4.2 The Oaxaca-Blinder decomposition ................................................................... 103 2.4.3 The DiNardo-Fortin-Lemieux decomposition .................................................... 119 2.5 Conclusion .......................................................................................................... 124 CITED LITERATURE ............................................................................................................ 126 VITA ......................................................................................................................................... 132 iv LIST OF TABLES TABLE PAGE I. NET INCOME BY EMPLOYMENT STATUS......................................................... 12 II. DESCRIPTIVE STATISTICS OF ENTIRE PRACTICE VARIABLES ................... 13 III. CHANGE IN ENTIRE PRACTICE CHARACTERISTICS OVER TIME ............... 14 IV. OBSERVED AND PREDICTED INPUT FACTOR PRICES................................... 21 V. CONSTRUCTION OF DENTIST AND HYGIENIST VISIT PRICE ...................... 22 VI. DESCRIPTIVE STATISTICS OF DENTIST AND HYGIENIST VISIT PRICES .. 23 VII. DIFFERENCES IN CHARACTERISTICS BETWEEN MISSING AND NONMISSING CASES (C, YD, YH) ................................................................................. 32 VIII. DIFFERENCES IN CHARACTERISTICS BETWEEN MISSING AND NONMISSING CASES (WD, WH, WO) ........................................................................... 34 IX. DIFFERENCES IN CHARACTERISTICS BETWEEN MISSING AND NONMISSING CASES (XD, XH) ..................................................................................... 36 X. DESCRIPTIVE STATISTICS OF VARIABLES INCLUDED IN THE ESTIMATION ............................................................................................................ 38 XI. TREND OF VARIABLES IN THE ESTIMATION FOR INCLUDED OBSERVATIONS (C, YD, YH) ................................................................................ 39 XII. TREND OF VARIABLES IN THE ESTIMATION FOR INCLUDED OBSERVATIONS (WDP, WHP, WOP) .................................................................... 42 XIII. DIFFERENCES BETWEEN INCLUDED AND EXCLUDED OBSERVATIONS IN THE ESTIMATION .............................................................................................. 45 XIV. COST FUNCTION RESULTS ................................................................................... 47 XV. DEMAND FUNCTION RESULTS ........................................................................... 51 XVI. ESTIMATION RESULTS WITH BOOTSTRAPPED STANDARD ERRORS ....... 58 XVII. CHARACTERISTICS OF EXTREME VALUES OF RETURNS TO SCALE BY TRANSLOG ESTIMATION (RT) ...................................................................... 65 v LIST OF TABLES (continued) XVIII. COMPARISON OF COEFFICIENTS IN THE DEMAND FUNCTIONS FOR DENTIST VISIT AND HYGIENIST VISIT ............................................................. 70 XIX. THE TRANSLOG COST FUNCTION ESTIMATIONS FROM DIFFERENT DATA SETS ............................................................................................................... 73 XX. THE DIEWERT COST FUNCTION ESTIMATIONS FROM DIFFERENT DATA SETS .............................................................................................................. 78 XXI. ESTIMATIONS FROM DIFFERENT DATA SETS................................................. 84 XXII. CHARACTERISTICS OF FEMALE AND MALE DENTISTS OVER TIME IN SDP DATA ............................................................................................................ 94 XXIII. UNCONDITIONAL DIFFERENCE IN MEANS BETWEEN FEMALE AND MALE DENTISTS IN SDP DATA............................................................................ 97 XXIV. CHARACTERISTICS OF DENTISTS IN THE CENSUS AND ACS DATA ....... 100 XXV. THE OB DECOMPOSITION OF EARNINGS DIFFERENTIALS FROM SDP DATA 1982 TO 2011 ............................................................................................... 107 XXVI. THE OB DECOMPOSITION OF THE CENSUS AND ACS DATA ..................... 115 vi LIST OF FIGURES FIGURE PAGE 1. Kernel density estimate for the main variables ................................................................. 18 2. Kernel density estimate for observed and predicted input prices ..................................... 19 3. Changes in percentage of time spent for procedures in dental practice ............................ 24 4. Price of dentist visit and hygienist visit over time ............................................................ 25 5. Percentages of missing values in the main variables (not imputed). ................................ 27 6. Percentages of missing values of raw and imputed variables. .......................................... 30 7. Demand curves for dentist visit and hygienist visit .......................................................... 55 8. Marginal cost of dentist (A) and hygienist (B) visit by Diewert and translog estimation 57 9. Price elasticity of demand for dentist visit (A) and hygienist visit (B) ............................ 60 10. Market power index for dentists (A) and hygienists (B) by Diewert estimation .............. 62 11. Market power index for dentists (A) and hygienists (B) by translog estimation .............. 63 12. Returns to scale by Diewert (A) and translog (B) estimation ........................................... 64 13. Earnings densities and the unexplained difference, SDP data, 1982-1996..................... 120 14. Earnings densities and the unexplained difference, SDP data, 1997-2011..................... 121 15. Earnings densities and the unexplained difference, IPUMS data, 1990, 2000, and 2007-2011.... 122 vii LIST OF ABBREVIATIONS ACS American Community Survey ADA American of Dental Association DFL DiNardo, Fortin, and Lemieux GP General Practitioner HHI Herfindahl-Hirschman Index HIE Health Insurance Experiment IPUMS Integrated Public Use Microdata Series OB Oaxaca Blinder SDP Survey of Dental Practice viii SUMMARY This research examines the organization of the dental care industry in two different aspects: the competition among dental care providers at practice level and the earnings of dentists at individual level. The dental care market in the United States is mainly made up of numerous private solo practice providers. However, it could not be characterized as the perfectly competitive market due to the complexity in the relationship among consumers, sellers, and payers, as well as the professional regulations. Chapter one uses repeat cross-section data from 1981 to 2011 of the American Dental Association Survey of Dental Practice (SDP) to estimate the degree of market power in the private dental practice, how it has changed over time, and the extent of any economies of scale in the provision of dental services. I identify market power by estimating an index measuring the ability of a firm to markup the price above the marginal cost. This parameter is estimated from the price index, the demand function and the system of equations including the cost function and labor input factor demand functions. Both specifications of flexible cost functions - the generalized Leontief and the translog cost functions - show that the dentist services market is monopolistic competitive while the hygienist services market is close to perfectly competitive. I also find that private dental practice shows significant economies of scale and demand for dental services are inelastic. The results provide important information about practice organization and pricing behaviors for public welfare and reimbursement policy. Chapter two explores the gender difference in dentist earnings. Previous research found an unexplained gap when accounted for age, experience, working hours, parental status, region and race. Using national data of the Survey of Dental Practice from 1982 to 2012, I add specialty, entrepreneurship, productivity, and practice size to the explanatory factors for more complete specification. I also use the census data and the American Community Survey data from the Integrated Public Use Microdata Series to examine the role of race, marital status, and parental status, which are not available in the SDP. The Oaxaca-Blinder decomposition finds the earnings gap in the SDP data reducing from 74% in 1982-1986 to 31% in 2007-2011, with the decrease in the explaining role of observable characteristics from 40% to ix SUMMARY (continued) 4% and the relatively consistent unexplained part contributing to 23%-29% of the differential. The census data shows a larger earnings gap, which could be explained by the lower percentage of female dentists as practice owners as compared to the SDP data. Race, marital status, and parental status are unobservable in the SDP analysis, but the census analysis shows that these factors only explain for 4-6% of the earnings difference. Thus, if all factors are combined in an analysis, I still expect an unexplained earnings difference of about 20% which remains relatively unchanged over time. The semiparametric DiNardo-Fortin-Lemieux approach finds that the observable characteristics have stronger effect on the gap for dentists with lower earnings; while for those at higher earnings distribution, the unexplained gap is large and consistent. x 1 1. 1.1 COMPETITION AND MARKET STRUCTURE IN THE DENTAL INDUSTRY Introduction In the United States dental care is mainly provided by dentists in private solo practice. This should create a competitive market with plethora of suppliers. However, given the complexity in the relationship among consumers, sellers, and payers, as well as the professional regulations, the extent to which dental health care markets could be described as perfectly competitive is unclear. It has long been known that dentistry in the U.S. is an internally well-organized industry in which dentists are operating in a strongly regulated environment but professional regulations are mostly made by market participants (Lipscomb and Douglass 1982). Local and state dental societies are mainly made up of dentists. These entities decide on local and state regulations, and ultimately impact national policy under which dentists practice their profession. Lipscomb and Douglass (1982) concluded that “the professional structure has reinforced dentistry's leverage over governmental legislation and regulations affecting the profession thereby influence the market of dental care”. This is a reason for the common perception that dental practice can exploit some market power to control price. However, recent judgement against the North Carolina State Board of Dental Examiners (the Board) in the case against the Federal Trade Commission is a sign that such dynamics in dental care market could be changing. The Board which is made up of six elected dentists, one hygienist and one consumer member was accused of violating antitrust laws for excluding non-dentists from the teeth-whitening services market. The main question of the case is whether a state agency involving market participants is exempt from antitrust laws. The Supreme Court decides that a state regulatory board operated by market participants is subject to federal antitrust laws unless having active state supervision (Supreme Court of the United States 2015). The Court decision will affect state regulatory boards and could particularly change the legal environment of dental practice and dental care market. Thus, studying the competitive landscape and market structure of the dental care market is policy relevant. Furthermore, understanding the degree of competition, which in turn affects pricing behaviors in dental care market, is important for public welfare and reimbursement policy. 1 2 In medicine, there is a rich literature with the objective of characterizing the market structure of hospitals and physicians. In some studies, researchers directly measure the degree of competition using the Hirschman-Herfindahl Index (HHI) (Farley, 1985; Zwanziger & Melnick, 1988; Gruber, 1994; Kessler & McClellan, 2000) or the function of some structural variables such as provider density, population density, market size, or entry barriers (Joskow, 1980; Robinson & Luft, 1985; Wilson & Jadlow, 1982; Gruber, 1994). In other studies, researchers infer the market structure by giving evidence of competitive or anticompetitive conduct such as barriers to entry (Kessel 1970; Rayack 1967), advertising bans (Feldman and Begun 1978; Hass-Wilson 1986), or pricing behavior (Kessel 1958; McCarthy 1985; Gunning and Sickles 2013) . Measurement of competition is useful to study its effect on prices, quality of care, and health outcomes, as well as to examining the determinants of market characteristics such as antitrust work (L. C. Baker 2001). Empirical studies dealing with the structure of the dental care market are surprisingly sparse and dated. Most economic papers on the dental care market focus on estimating price elasticity of demand or supply, rather than identifying market structure or measuring the degree of competition. Since the 1970s, several estimates of the demand for dental care have been published by Feldstein (1973), Holtmann and Olsen (1976), Phelps and Newhouse (1973), and Maurizi (1975). However, they were criticized for methodology issues including lack of controlling for price variation, environmental and personal characteristics, or under-identifying of demand curves (Manning and Phelps 1979). Later estimates of price and co-insurance elasticity of demand by Hu (1981) and Mueller and Monheit (1988) are also questioned due to small sample size and specification errors (Grembowski et al. 1988). The number of studies on the supply side is even smaller and they provided mixed results: Kushman and his team made several efforts to identify whether the dental care market was monopolistic or competitive. In a study using the supply and demand equation approach they concluded a monopolistic model of dentists’ behavior (Kushman and Scheffler 1978), while in another paper using the marginal product and wage approach they found that dentists’ behavior was consistent with competitive profit-maximizing (Kushman 3 et al. 1978). As a result, Kushman (1981) suggested that there should be a model including some characteristics of both market structure extremes. The answer to the question of how competitive the dental care market is remains unclear based on the prior evidence. Additionally, most studies used crosssectional data from several decades ago. There is a need for a study specifying the structure of dental care market using recent time-series data. As data on market shares of dental practices are unavailable, I will not directly measure competition using HHI or practice density. Instead, I will characterize the market structure by estimating the market power of dental practices in pricing in relative to their marginal costs. The method is from Bresnahan (1989) and will be described in the next section. The research objective is to estimate the amount of market power in the private dental practice market, as well as the extent of any economies of scale in the provision of dental services. The use of the repeat cross-section data allows for describing the trend of these estimations over time. 1.2 The theory Economic theory predicts that profit-maximization in a competitive market with costless entry and exit implies cost-minimization. The equilibrium output level y is chosen such that the marginal revenue equals the marginal cost. In the perfectly competitive market, firms are price-takers which produce at the output level y such that the marginal cost equals the equilibrium price MR  MC  P . The monopolist, on the other hand, has to reduce price in order to sell an additional output (dP/dy <0); thus, the marginal revenue is always less than price, as given by: MR   d[ P * y] dP y dP   P y  P 1   dy dy  P dy  As the price elasticity of demand is given by eD  P dy , I have y dP [1] 4   1  1  MR  P 1    P 1   eD   eD   [2] Combining the formula with the condition for profit maximization, MR  MC , Lerner (1934) proposed a measurement of monopoly power: Lerner index  P  MC 1  P eD [3] The Lerner index implies that the extent to which a monopolist sets the price above the marginal cost depends on the inverse of the price elasticity of demand. Firms in markets between the two extremes, perfect competition and monopoly, also have some market power to set the price above the marginal cost. If the market is highly inelastic, a small increase in price would not significantly change the quantity demanded, which allows firms to markup more. The extent of the markup implies the degree of market competition and the elasticity of demand. Based on prior evidence, it is reasonable to presume that the private dental practice market structure is neither perfect competitive nor monopolistic, and use the theoretical framework as described in Bresnahan (1989) to measure market power, the ability of providers to markup price above marginal cost, given the elasticity of demand for dental care services. Considering the general cost function of a private dental practice i with quantity of output yi, the marginal cost (MC) function is given by: Ci  C ( yi , wi , Zi , ,  ci )  MCi  Ci  C1 ( yi , wi , Zi , ,  i ) yi [4] where wi is a vector of factor prices, Zi are exogenous variables that shift cost,  are parameters to be estimated, and i is the error term. The demand function is given by: 5 yi  D( Pi , X i , ,  di )  yi  D1 Pi [5] where Xi are exogenous variables that shift demand,  are parameters to be estimated, and di is the error term. The marginal revenue is: MRi  Pi  yi Pi y   Pi  i  yi D1 [6] Combining [4] and [6] I have the condition for profit-maximizing or cost-minimizing equilibrium: C1  Pi  yi  D1 (C  P )    1 i D1 yi [7] Equation [7] illustrates the relationship between the marginal cost incurred by a firm and the price it sets. In perfect competition, price equals marginal cost and thus   0 . The marginal revenue for a monopolist is MR  P  y / D1 which means   1 . The parameter  is therefore an index of the market competition, measuring the ability of a firm to markup the price above the marginal cost, occurring in market structures departing from perfect competition. Credibly estimating the parameter  is the main purpose of this research. 1.3 The model specification The market power index  will be estimated from the demand function and the system of three equations including the cost function and two labor input factor demand functions. The method is adapted from Escarce and Pauly (1998) and Gunning and Sickles (2013), who estimate physician practice cost functions as well as the competition and market power of physicians in private practice. Due to the large number of parameters to be estimated in the cost function, Gunning and Sickles (2013) add the factor 6 demand functions into the system to increase efficiency (as the factor demand functions are the derivative of the cost function with respect to the factor prices, the parameters of the factor demand functions are restricted to be identical to the corresponding parameters in the cost function). The specifications of the equations are detailed below. 1.3.1 The cost function and its derivatives. I use the multi-product flexible cost function approach, which is common in recent empirical work to estimate the costs of hospital and physician practice production with multiple outputs. The two outputs in my context are dentist visits and hygienist visits. Prior to the widespread use of the flexible cost functions, ad hoc models which estimated average cost as a function of various determinants were used but often criticized for the lack of theoretical foundation in the assumptions of a production function. The flexible cost function, on the other hand, is derived from a theoretically appropriate production function without imposing a priori restrictions on factor substitution elasticities. The most appealing feature of the flexible cost function that serves my objective is its measurement of scale and scope economies. The original flexible function only includes output quantities and input prices. I employ the “hybrid” flexible form which adds other explanatory variables that could affect costs and thus combines the distinct features of the ad hoc function and the original flexible function. I estimate two different widely used cost functions in order to compare the robustness of my results across the different underlying models. The translog cost function. The most popular flexible function in empirical work is the translog cost function developed by Christensen, Jorgenson, and Lau (1973). The advantage of this form is the interpretability of coefficients and the manageable number of parameters to be estimated, compared to the quadratic and the Diewert cost functions. When first proposed, it was applied to single product firms and did not permit zero output level. In 1980, Caves, Christensen, and Tretheway developed the generalized translog multiproduct cost function, which replaced the natural log for the outputs in the original translog function by the Box-Cox transformation. This way, the cost function could allow for zero output level and for imposition of linear homogeneity in input prices restrictions. Linear homogeneity is the required 7 property of the cost function for the existence of a duality relationship between the cost and the production function. I apply the generalized translog cost function for two outputs: dentist visits (yd) and hygienist visits (yh). The Box-Cox transformation is given by: yiB   yi  1 /  [8] where λ is the Box-Cox parameter; yiB represents ydB and yhB, which are the Box-Cox transformation for dentist visits and hygienist visits. I estimated a regression of total cost on untransformed outputs, input factor prices, and control variables to obtain the maximum likelihood estimate of the Box-Cox parameter. Based on this regression I obtained   0.9530704 . The hybrid generalized translog cost function has the form: 3 1 1 2 2 ln C   0   d ydB   h yhB   d ydB   h yhB   dh ydB yhB    di ydB ln wi 2 2 i 1 3 3 4 1 3    hi yhB ln wi    i ln wi   ij ln wi ln w j   m Z m   T  1 2 i , j 1 i 1 i 1 m 1 [9] where wi are input factor prices including wage of dentists (w1), wage of hygienists (w2), and office price (w3); T is a vector of year indicators; Zm are the non-input cost shifters including practice location (urban or rural area), ownership (incorporated or unincorporated), practice size, and an indicator of public program participation; and  is the error term. From the cost function given by equation [9], I can derive the marginal cost functions: 8 C MCd  yd  C *  ln C yd  1  2     d ydB    2  d ydB     dh ydB yhB  3    di ydB ln wi    C      y  y  y yd i 1 d d d     2  1   2     d  yd  1 /      3      d  yd  1 /      y y  1 /  ln w y  1 /          dh hB d di i d 2    C    yd yd yd yd i 1     3    d yd 1  yd  1  1  C   d yd    dh yhB yd 1   yd 1 di ln wi     i 1     3   [10]  C   d   d ydB   dh yhB    di ln wi  y , i 1  1 d for dentist visits. Similarly, MCh  C yh  C *  ln C yh   3    C   h   h yhB   dh ydB    hi ln wi  yh 1 , i 1 for hygienist visits Differentiating the cost function with respect to input prices yields the input factor demand equations. I estimate two labor input factors including dentists and hygienists.  ln C   d 1 ydB   h1 yhB  1  11 ln w1  12 ln w2  13 ln w3  ln w1 [11]  ln C   d 2 ydB   h 2 yhB   2   22 ln w2  12 ln w1   23 ln w3  ln w2 [12] ln X d  ln X h  I estimate the translog cost function parameters by the seemingly unrelated regression of three equations [9], [11] and [12]. I impose the following restrictions for the cost function to be homogeneous of degree one and symmetric in factor prices: 9 3  i 1 i 1 11  12  13  0 12   22   23  0 13   23  33  0 d1  d 2  d 3  0  h1   h 2   h 3  0 ij   ji [13] The Diewert cost function. Another form of the cost function that I use, for comparison and robustness checking with the translog form, is the generalized Leontief cost function developed by Diewert (1971). An advantage of the Diewert cost function is that it can handle zero values of outputs and inputs, which appear in the data as a practice can have either dentists or hygienists or both. In addition, as the Diewert cost function is linear homogeneous in factor prices by construction, I do not need to impose restrictions on parameters as in the case of the translog form thus allow more flexibility. The hybrid generalized Leontief cost function has the form: C   0  yd y 3 2 d  i 1 di 3  1i  j 3 wi  y  dij wi1/ 2 w1/j 2  yd   di wi  yh i 1 3 2 h  i 1 hi  1i  j 3 3  hij wi1/ 2 w1/j 2  yh   hi wi i 1 3 3 4 i 1 i 1 m 1 wi  yd yh  i wi    i wi   m Z m   T  1 [14] The marginal cost functions derived from the generalized Leontief cost function are specified as: MCd  3 3 3 C    dij wi1/ 2 w1/j 2    di wi  2 yd   di wi  yh  i wi yd 1i  j 3 i 1 i 1 i 1 3 3 3 C MCh     hij wi1/ 2 w1/j 2    hi wi  2 yh   hi wi  yd   i wi yh 1i  j 3 i 1 i 1 i 1 [15] The two labor input factor demand equations to be estimated together with the cost function in the seemingly unrelated regression are: 10 Xd  Xh  C w1 C w2   1 2 1 2 3 yd   d 1 j j 2 yd   d 2 j j 1,3 w1/j 2 1/ 2 1 w w1/j 2 w1/2 2  yd  d 1   yd  d 2  1 2 1 2 3 yh   h1 j j 2 yh   h 2 j j 1,3 w1/j 2 1/ 2 1 w w1/j 2 w1/2 2  yh h1  yd2  d 1  yh2  h1  yd yh 1  1 [16]  yh h 2  yd2  d 2  yh2  h 2  yd yh  2   2 [17] 1.3.2 The demand function. I specify the demand function linearly in price, its second-order term, practice characteristics (vector of variables Z m as in the cost equation) and time variable. 6 4 y  0  1P  2 P 2   i Z m  10T   3 [18] i 3 m 1 1.4 Data description and variable construction I use 31 years of data from the American Dental Association (ADA) Survey of Dental Practice (SDP) from 1982 to 2012. The SDP is an annual survey conducted by the ADA sent to a random sample of general practitioners and specialists in private practice across the U.S., regardless of ADA membership. The sample is drawn with simple random probability method, from the ADA Sampling Frame which includes all active dentists who graduated from an accredited dental school in the U.S. and work in private practice. The response rates vary from 30 to 50 percent (American Dental Association, 2011; Vujicic, Lazar, Wall, & Munson, 2012). The questionnaire includes a core set of questions that remain roughly unchanged over the years and other questions that vary depending on whether a short form or long form version of the survey was administered in a given year. In 1982, a part of the sample received the short form questionnaire while the rest received the long form. In all other years, the entire sample received the same questionnaire. The long form was sent out every third year. The survey asked about the experiences