Does the way physicians are paid influence the way they practice? The case of Canadian family physicians’ work activity

Health Policy 98 (2010) 203–217

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Does the way physicians are paid influence the way they practice? The case of Canadian family physicians’ work activity Sisira Sarma a,∗ , Rose Anne Devlin b , Bachir Belhadji c , Amardeep Thind d a b c d

Department of Epidemiology & Biostatistics, Schulich School of Medicine & Dentistry, The University of Western Ontario, London, Ontario N6A 5C1, Canada Department of Economics, University of Ottawa, Ottawa, Ontario K1N 6N5, Canada Chronic and Continuing Care Division, Health Policy Branch, Health Canada, Ottawa, Ontario K1A 0K9, Canada Department of Epidemiology and Biostatistics & Centre for Studies in Family Medicine, University of Western Ontario, London, Ontario N6A 5C1, Canada

a r t i c l e

i n f o

Keywords: Physician remuneration Work activity Canadian family physician IV Method Joint model

a b s t r a c t Objectives: To investigate the impact of the mode of remuneration on the work activities of Canadian family physicians on: (a) direct patient care in office/clinic, (b) direct patient care in other settings and (c) indirect patient care. Methods: Because the mode of remuneration is potentially endogenous to the work activities undertaken by family physicians, an instrumental variable estimation procedure is considered. We also account for the fact that the determination of the allocation of time to different activities by physicians may be undertaken simultaneously. To this end, we estimate a system of work activity equations and allow for correlated errors. Results: Our results show that the mode of remuneration has little effect on the total hours worked after accounting for the endogeneity of remuneration schemes; however it does affect the allocation of time to different activities. We find that physicians working in non-fee-for-service remuneration schemes spend fewer hours on direct patient care in the office/clinic, but devote more hours to direct patient care in other settings, and more hours on indirect patient care. Conclusions: Canadian family physicians working in non-fee-for-service settings spend fewer hours on direct patient care in the office/clinic, but devote more hours to direct patient care in other settings and devote more hours to indirect patient care. The allocation of time in non-fee-for-service practices may have some implications for quality improvement. © 2010 Elsevier Ireland Ltd. All rights reserved.

1. Introduction Primary health care is the basic foundation upon which an efficient and effective health care system rests. Access to high quality primary health care means better chronic disease management, the coordination and integration of health care services, and continuity of care. Simply put, nations with strong primary health care systems are asso-

∗ Corresponding author. Tel.: +1 519 661 2111x87583; fax: +1 519 661 3766. E-mail addresses: [email protected] (S. Sarma), [email protected] (R.A. Devlin), [email protected] (B. Belhadji), [email protected] (A. Thind). 0168-8510/$ – see front matter © 2010 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.healthpol.2010.06.019

ciated with better health outcomes in comparison to those with weaker systems regardless of the way in which primary health care is measured, ceteris paribus [1,2]. In fact, some evidence suggests that the lack of access to family physicians not only affects the health and well-being of the population adversely but also entails substantial financial burden on the health care system in terms of increased demand for specialized health care services which are in fact avoidable [3]. From an efficiency perspective, ensuring universal access to family physician services is the crucial element of an efficient and effective publicly funded health care system. Despite the recognition of the importance of primary health care, lack of access to a family physician or gen-

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eral practitioner is becoming increasingly an issue in many developed countries. Like elsewhere, concern is growing in Canada regarding the lack of access to a family physician as many older family physicians are reducing their work hours or retiring altogether, and existing physicians are unwilling or unable to take on new patients. In 2005, the Canadian Community Health Survey reported that about 3.9 million Canadians do not have a regular family physician;1 Decima polls commissioned by the College of Family Physicians of Canada during the 2003–2005 period reported that about 5 million Canadians are without a family physician.2 These developments pose major challenges as provincial governments seek to reconcile increased demand for health care services with budgetary constraints. Consequently, a great deal of emphasis is now being placed on trying to improving the performance of existing health care resources. Physicians are a major player in this regard not only with respect to the amount of time they devote to providing health care services, but also as mobilizers of other health and human resources. In order to utilize the scarce physician workforce efficiently and meet the emerging challenges of physician shortage, there is a need to understand the link between the mode of remuneration and the work activities of physicians. Traditional methods of remunerating physicians consist of fee-for-service, salary and capitation. These payment schemes seem to have evolved as a result of institutional and historical contexts rather than efficiency considerations per se. Recent years have witnessed a growing international debate in search of alternative methods of remuneration to enhance efficiency, improve quality and reduce costs of health care services. Traditional remuneration schemes are replaced by more innovative schemes–like the blending of several elements of these schemes combined with incentives for targeted services and pay-for-performance provisions. Although a number of recent papers examine the typology and evolution of physician remuneration schemes in the international context (see, for example, Jegers et al. [4], Devlin et al. [5], Fujisawa and Lafortune [6], Wendt [7], Wranik and DurierCopp [8]),3 research on the way and the extent to which a change in the mode of remuneration influence physician’s allocation of time to various work activities is sparse. Because physicians are paid by provincial health ministries in Canada, the form that this remuneration takes can be a formidable tool for influencing physician behaviour. Recognizing this has led policy makers to move away from the traditional fee-for-service structure and seek innovative ways of remunerating family physicians. The introduction of new modes of remuneration in Canada’s health care system leads us to wonder how they affect the work activities of practicing physicians. Although some recent studies established a link between the mode of remuneration and direct patient care contacts or volume

1

Canadian Community Health Survey, 2005. Statistics Canada, Ottawa. 2 The College of Family Physicians of Canada, News Release, Nov 2, 2006. 3 Although physicians in many European countries do not choose how they are remunerated, physicians in the US and Canada do have some control over the way in which they are paid.

of services performed by family physicians (e.g., Basu and Mandelzys [9], Devlin and Sarma [10], Kantarevic et al. [11], Sarma et al. [12]), little is known about the impact of remuneration on variety of work activities undertaken by the family physician. The existing literature on the underlying factors influencing the work activities of Canadian family physicians is primarily descriptive in nature (see the discussion in the next section). We extend this sparse literature and examine the impact of the mode of remuneration on work activity of family physicians. More specifically, we seek to answer the following interrelated research questions: Does the way in which physicians are paid influence weekly hours of work on: (a) direct patient care in office/clinic?, (b) direct patient care in other settings?, and (c) indirect patient care? How do the results change if we take into account the possibility that physicians self-select into particular remuneration schemes (i.e., the endogeneity of remuneration schemes) by undertaking a simultaneous modeling of the choice of remuneration scheme and the allocation of time to work activities? 2. Materials and methods 2.1. Background literature The existing empirical literature on the work activities of Canadian physicians focuses largely on how they vary with physician demographic characteristics. These descriptive studies suggest that physician work hours on direct patient care has been declining. For example, Buske [13] found that physicians under the age of 45 were spending 20% less time providing direct patient care in 2003 compared to 1982. Using a cohort analysis, Crossley et al. [14] found a 16 percent decline in the hours spent seeing patients (7.2 h) by family physicians during 1982–2003; between 3.3 and 4.6 h of the decline is due to changes in male physician hours worked, between 2 and 2.5 h of the total fall is due to change in the gender composition of physicians, and between 0.6 and 1.4 h of the fall is attributable to changes in the female physician hours worked. Survey data show that weekly hours worked by family physicians fell from 52 in 1998 to 49 in 2004.4 As can be seen from Table 1, over the 1998 and 2004 period the largest decline in the hours worked came from direct patient care activities. These reductions in physician work hours may be due both to changes in the demographics of existing practicing family physicians, and to changes in their work behaviour. For instance, female physicians, who are becoming an increasing proportion of practicing family physicians, typically work fewer hours on average than male physicians. Similarly, family physicians are aging, and elderly physicians are known to work fewer hours than younger physicians.

4 It is difficult to explain the small increase in the total hours worked in 2001 and 2002 compared to 2000 levels. One plausible explanation could be that initial adjustment on the part of physicians due to the introduction of primary care reform models in Canada is responsible for this increase. Given the small sample size, it may also be plausible that the differences are due to sampling error in surveys preceding 2004.

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Table 1 Average hours worked per week by family physicians, 1998–2004a . Activities

1998 (1681)

1999 (1647)

2000 (1434)

2001 (1539)

2002 (1427)

2003 (1196)

2004 (10,629)

Direct patient care Health facility committees Managing practice Other indirect patient care Research Administration Teaching CME/CPD Other

36.8 1.1 2.0 5.2 0.7 1.6 0.8 3.0 1.1

36.3 1.1 2.3 5.4 0.7 1.8 0.8 3.0 1.0

35.1 0.9 2.3 5.7 0.7 1.6 0.8 3.0 0.9

35.3 0.8 2.1 5.3 0.8 1.7 0.8 3.0 1.3

35.7 0.9 2.4 5.4 0.8 1.6 0.9 3.1 0.9

35.4 0.9 2.0 5.7 0.7 1.4 0.8 2.9 1.1

34.7 0.6 1.5 5.7 0.6 1.7 0.7 2.9 .94

Total

52.1

52.5

51.0

51.1

51.6

50.9

49.2

a

Exclude on-call activities. Figures in parentheses are the corresponding samples. Data Sources: Physician Resource Questionnaire Survey, 1998–2003. Results available at: http://www.cmaj.ca/misc/prqindex.shtml (accessed May 2009). National Physician Survey, Canada, 2004. Results available at: http://www.nationalphysiciansurvey.ca/nps/ (accessed May 2009). Table 2 Average hours worked on different activities by type of remuneration. Weekly activity

FFS

Alternative

Mixed

NFFS

Total

(a) Patient care in office/clinic (b) Homecare (c) Patient care in emergency room (d) Hospital based activities (e) Institutional based activitiesa (f) Other direct patient care Direct Patient Care (a + b + c + d + e + f) (g) Teaching/education (h) Indirect patient careb (i) Health facility committees (j) Managing practice (k) Research (l) Administrationc (m) CME/CPDd (n) Othere Total work hours

29.75 0.69 2.13 3.11 0.81 0.08 36.56 0.31 6.19 0.36 1.76 0.21 0.27 2.99 0.53 49.64

18.90 0.84 5.47 5.49 1.41 0.21 32.32 1.07 5.10 0.81 1.10 1.05 3.48 2.74 1.44 48.35

20.84 0.74 5.06 5.34 1.46 0.23 33.67 1.05 5.43 0.82 1.29 0.96 2.45 2.74 1.15 50.44

14.78 1.05 6.35 5.80 1.30 0.17 29.44 1.10 4.47 0.79 0.73 1.22 5.47 2.75 2.00 44.31

25.04 0.75 3.58 4.14 1.07 0.14 34.72 0.65 5.70 0.56 1.46 0.59 1.71 2.88 0.94 49.06

a

Nurse home, rehabilitative facility, etc. Charting, reports, phone call, meeting patient’s family, etc. c Management of University program, chief of staff, department head, Ministry of Health, etc. d Continuing medical education or professional development, such as courses, reading, videos, tapes, seminars, etc. e Participation in professional or specialty organizations, medico-legal activities. Data Source: Authors compiled from the 2004 National Physician Survey master file. b

The percentage of female physicians practicing in Canada has increased from 7% in 1961 to 40% in 2008 [15,16]—a fact that is particularly notable when one considers that female physicians work fewer hours on average than their male counterparts, and they tend to practice less intensively [17]. For instance, in 2002 female physicians worked an average of 49 h per week compared to 56 h for male physicians [18] and provided about one-third fewer services than male physicians [17]. The proportion of physicians aged 65 and over has also been rising: from 7% in 1981 to 11% in 2001 [17], and older physicians work fewer hours than younger ones [17–19]. These Canadian findings are largely consistent with the international evidence [14]. But, demographics may not explain everything. Significant changes also have occurred in the way in which physicians are being paid. Since the inception of Canadian Medicare in 1966, physicians have been remunerated primarily on the basis of fee-for-service (FFS), but this approach has been on the decline since the mid-1990s [20]. The 2004 National Physician Survey (NPS) reports, for

instance, that only 52% of family physicians receive 90% or more of their professional income from FFS. Indeed, the share of payments to physicians under alternative remuneration schemes increased from 13% of total clinical payments (CD$1.31 billion) in 2000–2001 to 21% of total clinical payments (CD$2.98 billion) in 2005–2006 [20]. Remuneration schemes create financial incentives which may alter the labour supply of family physicians. We can see from Table 2 that family physicians, on average, work about 49 h per week, excluding “on-call” time: it also reveals that physicians remunerated under FFS and mixed remuneration schemes work longer hours on average than those paid under a non-FFS scheme.5 Perhaps even more significantly, this table shows that the amount of time devoted to direct patient care in an office or clinic varies widely under different remuneration schemes: 30 h under

5 These results are based on the raw data from the 2004 NPS. See Table 3 for the definition on remuneration schemes.

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FFS, 21 h under mixed, and 15 h under non-FFS. A part of the gap between the total hours of work and the h devoted to direct patient care in an office or clinic can be explained through differences in the allocation of time. For instance, Table 2 reports that family physicians under a NFFS remuneration scheme spend a relatively higher number of hours on research and administration than those who are remunerated under FFS. A study conducted more than a decade ago by Ferrall et al. [21] using survey data from 1990 CMA Physician Resource Questionnaire and controlling for a number of physician and practice characteristics find that physicians paid by fee-for-service work 5.5 fewer hours per week on direct patient care than those paid by salary. But fee-forservice physicians see patients for 5.9 more hours per week than do salaried physicians.6 Personal preferences and life styles of the new generation of family physicians appear to be markedly different than their older predecessors [22], suggestive of a more balanced approach to managing their professional and personal lives. Alternative remuneration schemes tend to support this change in life style in part because income is more predictable and is often combined with generous benefit packages. In addition, physicians today are more likely to have a professional spouse [13,23] and hence considerable non-practice income at their disposal. Economic theory tells us that access to outside income tends to reduce an individual’s (in this case, the physician’s) number of hours worked. Clearly, understanding the relative importance of the demographic and behavioural factors in explaining the work activity of family physicians is a crucial step for the design of effective health policy. Rather than the hours worked, a few authors have looked at the labour supply of physicians [24]. Saether [24] argues that the elasticity of hours with respect to wage is relatively small in the labour supply of physician literature and discusses the importance of economic incentives and non-pecuniary job characteristics in affecting the labour supply of physicians. Some earlier studies find evidence of a backward-bending physician supply curve [25–28], while others find a positive relationship between fees and physician labour supply [29,30]. 2.2. Methods Family physicians face a variety of options when it comes to putting together a practice. In this paper, we focus on two important decisions: the type of remuneration, and hours devoted to different professional activities, and assume that they are chosen in such a way as to maximize the physician’s utility. On the remuneration front, we begin by modeling the choice as a binary one (fee-forservice scheme versus an alternative one as defined below), but subsequently extend the analysis to a multinomial choice setting. The choice of remuneration is hypothesized to depend on preferences over work and leisure, the

6 Note that their definition of “salary remuneration” is imprecise at best as they define salary if over 50% of professional income is obtained from salary.

availability of practice opportunities, and opportunities for non-practice income, among other factors. The physician must also decide upon how many hours to devote to direct and indirect patient care activities–which we expect to be influenced by the remuneration scheme chosen. Note that we are unable to estimate a pure labour supply function, because we do not have information on wage rates, hence our models can best be characterized as pseudo-reduced form expressions for labour supply rather than pure labour supply functions as such.7 It is the remuneration scheme that determines the relative price of leisure (forgone income) per period of time. If physicians have some control over the way in which they are compensated, it can affect the link between work activities and remuneration. In this case, we say that the remuneration scheme is endogenous to the problem of determining how many hours to devote to direct and indirect patient care activities per week. Our basic reducedform model is specified as: ln Hi = ˛ + ıRi∗ + ˇ Xi + εi ,

(1)

where ln Hi is the natural logarithm of hours worked per week on direct or indirect patient care, Xi is a vector of exogenous characteristics (including physician and practice characteristics), ˛ is the intercept term, and εi is the error term. Ri∗ is a latent variable that determines the regime in which individual physician is remunerated and is specified as: Ri∗ =  +   Zi + ui ,

(2)

where Zi is a vector of characteristics that influences the decision regarding the remuneration choice,  is the intercept term, and ui is the error term. The parameters to be estimated are ˇ,  and ı: we are interested in the estimates of ı. The observed realization of the latent variable Ri∗ takes the following form: Ri = 1 if Ri∗ > 0 Ri = 0, otherwise. If ı = 0, then Eqs. (1) and (2) are independent and can be estimated separately. In this case, the remuneration scheme in place does not affect work intensity. If the null hypothesis ı = 0 is rejected, then the remuneration scheme is endogenous to the work activity equation. Estimation of the model characterized by Eqs. (1)–(2) requires exogenous instruments that are correlated with the endogenous variables but uncorrelated with the error term in the structural equation. Finding instruments that have no direct effect on physician work activities but strongly correlated with the choice of remuneration

7 In the Canadian context, wage rates will tend to be fixed across the remuneration schemes as fees are negotiated between the provincial government and the respective medical associations. This is typical of most Canadian studies analyzing the labour supply of physicians; it is argued that several elements of the Canadian health care system reduce the impact of not including wage rate in the labour supply equation [5,11]. Nevertheless, systematic data on earnings (both practice and non-practice income) at the individual physician level in future surveys or linkage from other sources to the physician survey data would enable to estimate the structural models of the labour supply consistent with economic theory.

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scheme is a challenge, further complicated by the fact that the assumptions are not directly testable. Thus, researchers generally rely on theoretical intuition and statistical tests for the validity of instruments. The theoretical basis for finding instruments is based on the idea that physicians’ self-selection mechanism is governed by unobserved personal preferences, abilities, or some sort of sorting [10,12,31–33]. Two instruments are considered to capture these dimensions for the problem at hand. First is the preference for FFS which essentially captures the labour-leisure choice of physicians. In particular, an indicated preference for FFS is suggestive of a relatively low value of leisure and/or a high value of income. Second are preferences for teaching, research or non-work related activities that led a family physician to pursue a career in family medicine at the beginning of his/her career. However, since this constructed variable may be correlated with the availability of teaching, research or non-work related activities, we use the average value of this variable at the health region level as an instrument.8 Recent developments in weak instruments methodology enabled us to test econometrically the relevance of instruments, over-identifying restrictions, and weak-instrument hypotheses [34–37]. It is well known in the context of the IV method of estimation, that the standard errors are inconsistent in the presence of known or arbitrary heteroskedasticity. A variety of test results suggest that the null hypothesis that the disturbance term is homoskedastic is rejected against the alternative hypothesis of unknown heteroscedasticity. In the presence of an unknown form of heteroscedasticity, the two-step generalized method of moments (GMM) estimator yields consistent and efficient estimates [34–37]. Since the GMM estimator is the preferred specification, we present the results from this model. Heteroscedasticity corrected standard errors are determined using the ‘robust’ option of STATA in all our regression results. All of the signs of the estimated coefficients on our instruments conform to expectations. Our findings show that physicians who prefer to be paid by a FFS remuneration scheme are less likely to choose alternative remuneration schemes over FFS. Similarly, physicians who choose a family medicine career due to teaching, research and other non-work related interests at the beginning of their career are more likely to choose alternative remuneration schemes. Various tests reported at the bottom of the tables in the Appendix confirm that the instruments satisfy the exogeneity assumption. The Kleibergen–Paap rank test statistic shows that our instruments are not weak instruments in the presence of arbitrary heteroscedasticity and non-identically and independently distributed errors [34–36]. The total hours worked per week on all activities is restricted to physicians who work at least twenty and no more than 80 hours per week in order to eliminate extreme outliers in the sample. Based on this restriction, we constructed our dependent variables (all in natural logarithms for greater than 1 hour): hours worked per week

8 Using the average score of this variable at the health region level renders it strictly exogenous at the individual physician level.

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on direct patient care in office/clinic, direct patient care in settings other than office/clinic, and indirect patient care including teaching and research. Since these three decision variables are related, the error terms are likely to be correlated across equations. Indeed, the Breusch-Pagan Chi-squared test statistic based on OLS estimates rejects the null hypothesis that the error terms are uncorrelated implying that these equations are not independent. Thus, it is preferable to estimate the equations as a system and allow for correlated errors across equations. We estimate the system of equations using the maximum likelihood “cmp” subroutine in STATA (written by David Roodman [38] to estimate several types of mixed-process models). Although the coefficients based on the IV estimator are not greatly affected, the estimated correlations across the error terms are statistically significant supporting the joint estimation (i.e., simultaneous estimation of remuneration scheme and work activities) of these equations.

2.3. Data and variables The data for this study come from the 2004 National Physician Survey, a census survey of all licensed physicians practicing in Canada carried out from February to June 2004. This survey contains information on several key variables like physicians’ allocation of time, methods of remuneration and personal demographic characteristics. The NPS was sent to all licensed physicians in Canada, but we are only interested in the responses of family physicians. Of the 30,903 eligible family physicians, 11,041 answered the survey yielding a response rate of about 36%.9 After deleting those observations with non-responses and the cases for which the self-reported source of income from all remuneration types did not add up to 100 percent and excluding those practicing in the Territories, we are left with 10,457 observations with which to conduct our analysis. The type of remuneration scheme is a key variable of interest in our model and is treated as endogenous in the hours worked equations. We examine three situations: (a) Alternative vs. FFS (Alternative = 1, FFS = 0), (b) Mixed vs. FFS (Mixed = 1, FFS = 0), and (c) Non-FFS vs. FFS (NFFS = 1, FFS = 0). Alternative remuneration refers to a situation where family physicians are paid other than by FFS; so, FFS and alternative are mutually exclusive. Mixed remuneration refers to the situation where 90% or more of professional income comes from a combination of payment schemes, including FFS. The mixed remuneration classification is akin to some form of blended remuneration scheme which combines the elements prospective and retrospective payment systems. Non-fee-for-service (NFFS) is when 90% or more of professional income comes from sources other than FFS. Note that Mixed and NFFS schemes are the subsets of alternative remuneration category.

9 Although the response rate is about 36%, the analysis of the respondents and non-respondents show that those who replied to the survey are representative of the physician population with respect to age, gender and medical specialty [39].

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Table 3 Variable definitions. Variable Dependent variables ln H ln H1 ln H2 ln H3 Remuneration schemes FFS Alternative Mixed NFFS Physician characteristics Female Married IMG Group

Child ≤ 6 Dual-physician Age 35 Age 35 44 Age 45 54 Age 55 64 Age 65 Urban British Columbia Alberta Saskatchewan Manitoba Ontario Quebec New Brunswick Nova Scotia Price Edward Island Newfoundland Instruments Prefer FFS Prefer Other

Definition Natural log of hours worked on all activities (≥20 h and ≤80 h) Natural log of hours worked per week on direct patient care (i.e., item (a) in Table 2) in office/clinic: >1 h conditional on 20 ≤ H ≤ 80 Natural log of hours worked per week on direct patient care in other settings (i.e., items (b)–(f) in Table 2): >1 h conditional on 20 ≤ H ≤ 80 Natural log of total hours worked per week on indirect patient care (i.e., items (g)–(n) in Table 2): >1 h conditional on 20 ≤ H ≤ 80 =1 if 90%+ of professional income is obtained from FFS, =0 otherwise =1 if 90%+ of professional income is obtained from other than FFS, =0 otherwise =1 if 90%+ of professional income is obtained from a combination of alternative payment schemes, =0 if 90%+ of professional income is obtained from FFS =1 if 90%+ of professional income is obtained from non-FFS remuneration schemes, =0 if 90%+ of professional income is obtained from FFS Female = 1, male = 0 Married/living with partner = 1, Single or separated or divorced = 0 International medical graduate = 1, Canadian medical graduate = 0 =1 if group practice (defined as physicians who share at least three of the following six items with other physicians: office space, equipment, expenses, patient records, on-call and staff), =0 if solo practice =1 if the respondent has children less than or equal to 6 years old, =0 otherwise =1 if the respondent’s spouse is a physician (i.e., dual physician-family status), 0 = otherwise =1 if the respondent is less than 35 years, =0 otherwise =1 if the respondent is 35–44 years, =0 otherwise =1 if the respondent is 45–54 years, =0 otherwise =1 if the respondent is 55–64 years, =0 otherwise =1 if f the respondent is 65 years and above, =0 otherwise. =1 if population primarily being served by the physician’s practice is inner city or urban/suburban, =0 if population being served is small town, rural, geographically isolated/remote British Columbia = 1, otherwise = 0 Alberta = 1, otherwise = 0 Saskatchewan = 1, otherwise = 0 Manitoba = 1, otherwise = 0 Ontario = 1, otherwise = 0 Quebec = 1, otherwise = 0 New Brunswick = 1, otherwise = 0 Nova Scotia = 1, otherwise = 0 Price Edward Island = 1, otherwise = 0 Newfoundland and Labrador = 1, otherwise = 0 =1 if a family physician prefers to be paid by FFS, =0 otherwise =1 if teaching opportunities and/or research opportunities and/or ability to pursue non-work related interests led a family physician to choose Family Medicine career, =0 otherwise

Guided by the literature and the availability of information in the NPS, we include a host of exogenous variables in the equations looking at the number of hours worked by physicians in direct and indirect patient care activities. These variables are defined in Table 3 and discussed in greater detail in the following section. Table 4a presents the corresponding descriptive statistics. A comparison of the characteristics of the sample actually used in our analysis and those dropped out shows that the sample characteristics are very similar except a higher proportion of the elderly respondents are dropped out. These descriptive results are reported in Table 4b. 3. Results The regression framework described in section 3 is used to disentangle the effect of the mode of remuneration on hours worked. First, we estimate the total hours

worked on all activities and the IV results are reported in Table 5. Once we take into account the endogeneity of remuneration scheme, we find that compared to FFS, alternative remuneration, mixed remuneration and NFFS have no statistically significant effect on total hours worked. The coefficient on mixed remuneration is positively significant at the 10% level, although it was statistically insignificant in the OLS. The OLS coefficients on Alternative and NFFS are negatively significant, although the magnitudes are small. The non-significance of the results based on IV regression suggest that after accounting for the physician and practice characteristics adequately, there is no clear relationship between the mode of remuneration and the total hours worked. We now extend the analysis to examine how the mode of remuneration affects hours worked on direct and indirect patient care. We estimate three competing models for three different types of activity: weekly work hours on direct patient care

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Table 4a Descriptive statistics. Variable

Obs

Mean

Std. Dev.

Min

Max

H ln H H1 ln H1 H2 ln H2 H3 ln H3 FFS Alternative Mixed NFFS Group Child ≤ 6 Dual-physician Female Married IMG Age 35 Age 35 44 Age 45 54 Age 55 64 Age 65 Urban British Columbia Alberta Saskatchewan Manitoba Ontario Quebec New Brunswick Nova Scotia Newfoundland Prince Edward Island Prefer FFS Prefer Other Mean(Prefer Other)

8593 8593 7033 7033 5340 5340 8352 8352 10457 10457 8858 7364 10457 10457 10457 10285 10294 9998 10243 10243 10243 10243 10243 10457 10457 10457 10457 10457 10457 10457 10457 10457 10457 10457 10232 10457 10457

47.536 3.820 28.746 3.256 14.524 2.286 15.351 2.514 0.551 0.448 0.349 0.217 0.717 0.216 0.160 0.398 0.861 0.196 0.123 0.281 0.335 0.194 0.068 0.609 0.145 0.107 0.032 0.037 0.380 0.208 0.028 0.037 0.020 0.005 0.263 0.035 0.035

12.970 0.296 10.827 0.518 12.451 0.925 10.585 0.677 0.497 0.497 0.477 0.412 0.451 0.412 0.367 0.490 0.346 0.396 0.328 0.449 0.472 0.396 0.251 0.488 0.352 0.310 0.176 0.188 0.486 0.406 0.166 0.190 0.140 0.072 0.440 0.183 0.012

20 2.996 1.25 0.223 1.073 0.071 1.5 0.405 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

80 4.382 62 4.127 80 4.382 80 4.382 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0.139

in an office or clinic, weekly work hours on direct patient care in other settings, and weekly work hours on indirect patient care. The IV results are presented in Tables A.1–A.3 in the appendix and the corresponding joint models are presented in Tables 6–8. Tables 6–8 present the estimated coefficients from the joint model for alternative, mixed and NFFS remuneration scheme, respectively. Each table presents one column for each of the dependent variables (all in natural logarithms): hours worked per week on direct patient care in office/clinic (H1 ), in settings other than office/clinic (H2 ), and on indirect patient care including teaching and research (H3 ). Once we take into account the endogeneity of remuneration choice and joint estimation, we find that compared to FFS, alternative remuneration, mixed remuneration and NFFS reduce hours worked on direct patient care in an office/clinic in the range of 37–44%.10 We can use the OLS estimates to determine the magnitude of this effect if this endogeneity were not taken into account–and we find OLS estimates underestimate a reduction of hours worked of

10 Note that we use Halvorsen-Palmquist adjustment procedure ((exp(ˇ) − 1) × 100) for the percentage interpretation of a dummy variable in a semi-logarithmic model [40].

around 10%. In contrast, family physicians in alternative, mixed and NFFS remuneration schemes work 61%, 54%, and 66% more hours on direct patient care in settings other than office/clinic, respectively, when compared to FFS physicians. The OLS estimates marginally underestimate these effects with the exception of NFFS. Similarly, other forms of remuneration prompt physicians to work significantly more hours in indirect patient care relative to those in the FFS scheme. We find that family physicians in alternative remuneration devote 66% more hours on indirect patient care, mixed remuneration leads to 63% more hours on indirect patient care and NFFS physicians work 96% more hours compared to FFS physicians. The OLS procedure substantially underestimates the effect of remuneration on indirect patient care. Another important characteristic of a family practice is whether or not the physician works with a group. The literature typically defines a group practice as a situation in which more than one physician shares the workload, revenues and expenses according to a predetermined formula. In the Canadian context, physicians may work in a group but only share a minimal amount. We therefore define a group practice as a situation in which physicians share at least three of the following six items with other family physicians: office space, equipment, expenses, patient records, on-call and staff. Our results show that

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Table 4b Descriptive statistics of sample used and excluded. Variable

FFS Alternative Mixed NFFS Group Child ≤ 6 Dual-physician Female Married IMG Age 35 Age 35 44 Age 45 54 Age 55 64 Age 65 Urban British Columbia Alberta Saskatchewan Manitoba Ontario Quebec New Brunswick Nova Scotia Newfoundland Prince Edward Island Prefer FFS Prefer Other Mean(Prefer Other)

H = 8593

Excluded observations

Obs

Mean

Obs

Mean

8593 8593 7228 6068 8593 8593 8593 8463 8468 8239 8429 8429 8429 8429 8429 8593 8593 8593 8593 8593 8593 8593 8593 8593 8593 8593 8414 8593 8593

0.547 0.453 0.349 0.225 0.720 0.215 0.161 0.403 0.861 0.187 0.127 0.282 0.343 0.191 0.057 0.628 0.143 0.105 0.031 0.035 0.387 0.208 0.028 0.038 0.019 0.005 0.256 0.035 0.035

1864 1864 1630 1296 1864 1864 1864 1822 1826 1759 1814 1814 1814 1814 1814 1864 1864 1864 1864 1864 1864 1864 1864 1864 1864 1864 1818 1864 1864

0.570 0.430 0.348 0.180 0.697 0.221 0.155 0.373 0.861 0.235 0.104 0.275 0.298 0.205 0.119 0.521 0.155 0.117 0.037 0.044 0.344 0.2091 0.028 0.036 0.026 0.004 0.294 0.031 0.034

a group practice leads to no change in hours worked on direct patient care in an office/clinic, fewer work hours on direct patient care in settings other than office/clinic in the range of 21–25%, and decreases indirect patient care by about 7% for alternative and NFFS in relation to FFS physicians. We find that being married to another physician has a negative impact on hours worked on direct patient care in an office/clinic. Family physicians with dual-physician family status work 11–12% fewer hours on direct patient care per week in office/clinic while it has no effect on other direct and indirect patient care. This result is consistent with previous evidence on the impact of dual-physician family status on labour supply of physicians [23,41,42]. Sobecks et al. [42] find that dual-physician families enjoy much higher levels of household income, even compared to those who have non-physician professional spouses. Thus, our finding corroborates the idea that non-practice income opportunity significantly reduces the labour supply of physicians. As mentioned earlier, age, gender and the presence of dependent children in the household are known to influence the labour supply behaviour of physicians. In addition to the direct effects of age, gender and children, the interaction between age and gender and gender and small children may also influence this behaviour.11 We therefore examine

11 We thank an astute reviewer of this journal for suggesting that we to consider these interaction effects.

both the direct and interaction effects of age, gender and dependent children. Physicians are classified into one of five age groups: up to 35, 35–44, 45–54, 55–64, and over 65 years of age. The middle group is the reference category. Gender is represented by a dummy variable which takes on the value 1 if the family physician is female, 0 otherwise. The literature finds that female physicians are likely to work less intensively on average compared to their male counterparts because of child bearing and rearing, and the fact that females typically spend relatively more time on household chores or other non-market activities [14,17,18,21,29,43–47]. The presence of dependents in the family is captured by the dummy variable child ≤ 6 which takes on the value 1 if there is a child aged six or less in the household, 0 otherwise. We can see that the estimated coefficients on the age dummies are negative, although statistically insignificant in some instances, for direct patient care hours in office/clinic and indirect patient care. Compared to those aged 45–54 years old, physicians aged 35–44 years old as well as 55–64 years old spend about 5–8% fewer hours on direct patient care in an office/clinic; but physicians aged 65 and above spend 18–19% less time on direct patient care in office/clinic. The negative sign on the estimated coefficient on gender conforms to our expectation: female physicians tend to spend about 11–14% less time on direct patient care in office/clinic compared to their male counterparts. They also devote about 24–26% fewer hours per week to direct patient care in settings other than office/clinic; the direct effect of gender on indirect patient care is not statistically significant. Our results show that the effect of having a child aged six or less in the household leads to a reduction in direct patient care hours in office/clinic by about 7–9%. These findings are largely consistent with the literature in the United States that reports that physicians with young children work fewer hours on direct patient care than those without young children [29,42,43]. The effect of having a child aged six or less in the household, however, leads to an increase in direct patient care hours in settings other than office/clinic in the range of 12–19%, but no statistically significant effect on indirect patient care hours. Now turning to the interaction effects, we find that female physicians having a child aged six or less leads to a reduction in the direct patient care hours by about 20–23% in settings other than an office/clinic. The effect of having a child aged six or less is also to reduce the indirect patient care of female physicians by about 10%, except in the NFFS sample where the effect is statistically insignificant. Having a young child has no statistically significant effect on direct patient care. The interaction effects between age dummies and gender are jointly significant, but many of the individual interaction effects are statistically insignificant. The interaction between age and gender suggests that as female physicians become older, they tend to devote somewhat more hours to direct patient care, although the interaction coefficients are mostly insignificant. The impact of a being married (defined as also living with a partner) on hours worked by physicians is, a priori,

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Table 5 Family physician’s weekly hours worked on all activities: IV GMM estimation. (1) Alternative Alternative Mixed

(2) Mixed

(3) NFFS

−0.009 (0.019) 0.048* (0.026)

Observations

−0.011 (0.007) −0.005 (0.012) −0.069*** (0.009) −0.142*** (0.007) −0.034** (0.015) −0.039*** (0.011) −0.063*** (0.011) −0.223*** (0.019) −0.126*** (0.018) 0.055*** (0.021) 0.024 (0.018) 0.063*** (0.021) 0.099* (0.058) 0.015 (0.010) 0.036*** (0.009) −0.011 (0.007) 3.958*** (0.017) 7931

−0.023*** (0.008) −0.010 (0.013) −0.069*** (0.010) −0.137*** (0.008) −0.035** (0.017) −0.037*** (0.012) −0.059*** (0.012) −0.213*** (0.020) −0.132*** (0.019) 0.047** (0.023) 0.017 (0.020) 0.066*** (0.023) 0.087 (0.061) 0.004 (0.011) 0.042*** (0.010) 0.005 (0.008) 3.957*** (0.018) 6689

−0.032 (0.030) −0.012 (0.009) −0.002 (0.014) −0.073*** (0.011) −0.140*** (0.008) −0.035* (0.019) −0.035*** (0.014) −0.066*** (0.013) −0.220*** (0.021) −0.122*** (0.022) 0.054** (0.025) 0.009 (0.021) 0.065*** (0.024) 0.089 (0.062) 0.017 (0.012) 0.038*** (0.010) 0.016* (0.009) 3.960*** (0.019) 5568

Selected econometric test results Under identification test (Kleibergen-Paap rk LM statistic) Weak identification test+ (Kleibergen-Paap rk Wald F-statistic) Overidentification test (Hansen J statistic) Endogeneity test (Durbin-Wu-Hausman)

984.89 727.16 1.61 [0.20] 2.04 [0.15]

639.48 406.53 1.82 [0.18] 4.69 [0.03]

776.54 536.92 1.652 [0.20] 4.34 [0.04]

NFFS Group Child ≤ 6 Dual-physician Female Age 35 Age 35 44 Age 55 64 Age 65 Female × Child ≤ 6 Female × Age 35 Female × Age 35 44 Female × Age 55 64 Female × Age 65 Married IMG Urban Constant

Robust standard errors in parentheses. All regressions include a set of provincial dummies. Figures in square brackets are appropriate p-values. +Stock-Yogo weak identification test critical values: 10% maximal IV size = 19.93. * Significant at 10%. ** Significant at 5%. *** Significant at 1%.

mixed. On the one hand, to the extent that being married proxies for greater financial responsibilities because of the presence of additional family members, it may lead to longer work hours. On the other hand, if the spouse/partner works, being married may be picking up the availability of additional household income, which may result in fewer work hours. We find that marital status reduces direct patient care in settings other than office/clinic by about 9% in some specifications at the 10% level of significance. But, being married has no effect on hours worked on direct and

indirect patient care in all specifications.12 These results taken together suggest that marital status has a negligible effect on the labour supply of family physicians. Our analysis takes account of whether or not the physician earned his or her medical degree at an international institution: if the physician is an international medical

12 Note that we cannot separate married and living with partner as the NPS survey does not ask this question separately.

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graduate, then IMG takes the value 1, otherwise it is equal to zero. We find that international medical graduates work about 5–7% more hours on direct patient care in office/clinic than Canadian trained medical graduates. They are also likely to spend about 4–6% more hours per week on indirect patient care activities with the exception of NFFS sample. Whether or not the population being served by the practice is located in an urban or rural setting is represented by a dummy variable which takes the value one if located in an inner city or urban/suburban area. Urban areas are generally comprised of higher population density and hence higher demand for physician services than rural or remote regions. At the same time, the density of physicians is generally higher in urban jurisdictions. Our results show that physicians in urban areas spend about 6–13% fewer hours on direct patient care in other settings; no significant effect on hours worked on direct patient care in office/clinic; and no clear pattern emerges for hours worked on indirect patient care relative to physicians practicing in rural and remote areas.

Table 6 Family physician’s work activities: joint estimation.

Alternative Group Child ≤ 6 Dual-physician Female Age 35 Age 35 44 Age 55 64 Age 65 Female × Child ≤ 6 Female × Age 35

4. An extension

Female × Age 35 44

We extend the analysis of the preceding section to account for the fact that physicians may be choosing from amongst several alternatives—in particular, whether to be remunerated by a FFS, mixed, or non-FFS scheme.13 A multinomial logit model is utilized to account for the endogeneity of remuneration in the context of this multinomial choice setting. We achieved this by running a multinomial logit regression with all exogenous variables and instruments in the first stage, obtaining the predicted values, and then using these predicted values as instruments in the second stage.14 These IV regression results are reported in Tables A.4–A.5 and the corresponding results based on the joint model are presented in Tables 9 and 10. As seen from these tables the magnitude of these estimated coefficients are not greatly affected by the introduction of the multinomial procedure. Excerpted coefficients on the remuneration schemes from all model specifications are summarized in Table 11. As previously demonstrated, the OLS procedure consistently underestimates the estimated parameters.

Female × Age 55 64

5. Discussion Family physicians or general practitioners play an important role in organizing a practice and delivering primary health care services to the population. From a health and human resource perspective, physician resource planning is challenging: for instance, current concerns over shortages in physician services in Canada follow on the

13 We have 7931 valid observations with which to conduct multinomial choice analysis (4326 FFS, 2363 mixed and 1242 NFFS). The actual sample size in the estimation is smaller because of missing data on hours of work. The multinomial choice models are run only for the sample in which we observe H1 , H2 and H3 . 14 We thank Jeffrey Wooldridge for helpful discussion on this particular point.

Female × Age 65 Married IMG Urban Constant Sigma Rho Alternative H1 Rho Alternative H2 Rho Alternative H3 Rho H1 H2 Rho H1 H3 Rho H2 H3 Log Pseudo likelihood

(1) H1

(2) H2

(3) H3

−0.511*** (0.029) 0.035** (0.016) −0.095*** (0.027) −0.113*** (0.017) −0.134*** (0.020) −0.056* (0.034) −0.071*** (0.024) −0.046** (0.020) −0.198*** (0.032) −0.012 (0.035) 0.058 (0.053) 0.061* (0.033) 0.098*** (0.034) 0.073 (0.077) 0.004 (0.018) 0.048*** (0.016) −0.003 (0.014) 3.564*** (0.029) 0.477*** (0.008) 0.138*** (0.027) −0.040 (0.040) −0.202*** (0.029) −0.330*** (0.016) −0.134*** (0.017) −0.126*** (0.018) −22996.09

0.479*** (0.070) −0.284*** (0.028) 0.155*** (0.041) 0.010 (0.032) −0.292*** (0.043) 0.346*** (0.051) 0.214*** (0.041) −0.082** (0.040) −0.67 (0.065) −0.229*** (0.064) 0.046 (0.075) −0.029 (0.068) 0.010 (0.089) 0.461** (0.202) −0.055 (0.040) 0.034 (0.034) −0.067*** (0.025) 2.212*** (0.065) 0.837*** (0.007)

0.506*** (0.042) −0.071*** (0.019) −0.043 (0.031) −0.016 (0.021) −0.001 (0.026) −0.267*** (0.039) −0.077*** (0.029) −0.027 (0.027) −0.230*** (0.044) −0.104*** (0.042) 0.025 (0.051) −0.041 (0.042) 0.076 (0.048) 0.174 0.129 0.013 (0.024) 0.037* (0.021) 0.010 (0.017) 2.454*** (0.042) 0.664*** (0.007)

Robust standard errors in parentheses. All regressions include a set of provincial dummies. * Significant at 10%. ** Significant at 5%. *** Significant at 1%.

heels of oversupply concerns just a couple of decades earlier. An in-depth analysis of physicians’ work activities is clearly essential as small changes in the allocation of time at the individual physician level can have big effects at the macro level. We need to understand how physicians respond to various incentives facing them when they are setting up a practice and deciding how to allocate their time to direct and indirect patient care activities. This paper

S. Sarma et al. / Health Policy 98 (2010) 203–217 Table 7 Family physician’s work activities: joint estimation.

Mixed Group Child ≤ 6 Dual-physician Female Age 35 Age 35 44 Age 55 64 Age 65 Female × Child ≤ 6 Female × Age 35 Female × Age 35 44 Female × Age 55 64 Female × Age 65 Married IMG Urban Constant Sigma Rho Mixed H1 Rho Mixed H2 Rho Mixed H3 Rho H1 H2 Rho H1 H3 Rho H2 H3 Log Pseudo likelihood

213

Table 8 Family physician’s work activities: joint estimation.

(1) H1

(2) H2

(3) H3

−0.468*** (0.035) 0.024 (0.016) −0.084*** (0.026) −0.116*** (0.017) −0.154*** (0.019) 0.095*** (0.035) −0.081*** (0.023) −0.058*** (0.019) −0.210*** (0.032) −0.034 (0.035) 0.089** (0.044) 0.073** (0.033) 0.110*** (0.033) 0.085 (0.082) 0.019 (0.019) 0.054*** (0.016) 0.017 (0.014) 3.549*** (0.028) 0.446*** (0.009) 0.144*** (0.034) −0.054 (0.050) −0.207*** (0.038) −0.321*** (0.017) −0.079*** (0.018) −0.106*** (0.020) −18737.3

0.435*** (0.090) −0.251*** (0.030) 0.172*** (0.044) 0.023 (0.034) −0.307*** (0.047) 0.339*** (0.055) 0.203*** (0.044) −0.041 (0.042) −0.025 (0.068) −0.256*** (0.068) 0.020 (0.080) −0.007 (0.073) −0.034 (0.095) 0.432* (0.242) −0.083* (0.043) 0.019 (0.036) −0.138*** (0.027) 2.242*** (0.069) 0.817*** (0.008)

0.490*** (0.054) −0.027 (0.020) −0.034 (0.032) −0.027 (0.022) −0.002 (0.027) −0.208*** (0.042) −0.043 (0.030) −0.049* (0.027) −0.245*** (0.046) −0.105*** (0.043) 0.010 (0.050) −0.068 (0.043) 0.115** (0.049) 0.211 0.136 0.021 (0.025) 0.058*** (0.022) 0.042** (0.018) 2.401*** (0.043) 0.629*** (0.007)

NFFS Group Child ≤ 6 Dual-physician Female Age 35 Age 35 44 Age 55 64 Age 65 Female × Child ≤ 6 Female × Age 35 Female × Age 35 44 Female × Age 55 64 Female × Age 65 Married IMG Urban Constant Sigma Rho NFFS H1 Rho NFFS H2 Rho NFFS H3 Rho H1 H2 Rho H1 H3 Rho H2 H3 Log Pseudo likelihood

(1) H1

(2) H2

(3) H3

−0.579*** (0.046) 0.004 (0.016) −0.070** (0.030) −0.127*** (0.019) −0.111*** (0.021) −0.060 (0.039) −0.071*** (0.025) −0.022 (0.020) −0.193*** (0.033) −0.064* (0.039) −0.090 (0.050) 0.034 (0.036) 0.057* (0.034) 0.046 (0.079) −0.015 (0.020) 0.064*** (0.016) 0.010 (0.015) 3.560*** (0.030) 0.425*** (0.010) 0.107*** (0.036) −0.027 (0.053) −0.249*** (0.035) −0.352*** (0.021) −0.074*** (0.021) −0.112*** (0.025) −14303.6

0.504*** (0.115) −0.241*** (0.036) 0.113** (0.054) 0.038 (0.042) −0.279*** (0.054) 0.309*** (0.071) 0.223*** (0.055) −0.082* (0.050) −0.061 (0.078) −0.142* (0.084) 0.150 (0.100) −0.117 (0.087) 0.011 (0.102) 0.463* (0.217) −0.079 (0.050) 0.004 (0.041) −0.057* (0.032) 2.181*** (0.077) 0.854*** (0.009)

0.674*** (0.063) −0.072*** (0.022) −0.070* (0.039) −0.017 (0.025) −0.029 (0.030) −0.148*** (0.050) −0.036 (0.037) −0.051* (0.031) −0.238*** (0.048) −0.046 (0.052) −0.022 (0.065) −0.047 (0.051) 0.086 (0.056) 0.207 0.139 0.029 (0.028) 0.017 (0.024) −0.050** (0.021) 2.518*** (0.046) 0.664*** (0.009)

Robust standard errors in parentheses. All regressions include a set of provincial dummies. * Significant at 10%. ** Significant at 5%. *** Significant at 1%.

Robust standard errors in parentheses. All regressions include a set of provincial dummies. * Significant at 10%. ** Significant at 5%. *** Significant at 1%.

is particularly interested in how physician remuneration schemes influence these latter decisions. Our findings suggest that physicians who are remunerated by schemes other than fee-for-service reduce the number of hours worked on direct patient care in an office/clinic, causing reductions in physician labour supply at the intensive margin. However, these physicians also devote more hours to direct patient care in settings other than office/clinic—for example, through tracking

their patients elsewhere in the health care system. This finding may be indicative of better continuity of care and perhaps improved quality of care in some instances. Similarly, physicians in alternative modes of remuneration spend more hours on indirect patient care relative to their FFS counterparts, which again may lead to improved care for their patients because of the additional attention being paid to indirect patient care activities, like continuing medical education.

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Table 9 Family physician’s work activities: multinomial choice and joint estimation.

Mixed Group Child ≤ 6 Dual-physician Female Age 35 Age 35 44 Age 55 64 Age 65 Female × Child ≤ 6 Female × Age 35 Female × Age 35 44 Female × Age 55 64 Female × Age 65 Married IMG Urban Constant Observations Sigma Rho Mixed H1 Rho Mixed H2 Rho Mixed H3 Rho H1 H2 Rho H1 H3 Rho H2 H3 Log Pseudo likelihood

(1) H1

(2) H2

(3) H3

−0.467*** (0.035) 0.024 (0.016) −0.084*** (0.026) −0.116*** (0.017) −0.153*** (0.019) −0.095*** (0.035) −0.081*** (0.023) −0.058*** (0.019) −0.210*** (0.032) −0.034 (0.035) −0.089** (0.044) 0.072** (0.033) 0.110*** (0.033) 0.083 (0.082) 0.019 (0.019) 0.054*** (0.016) 0.017 (0.013) 3.548*** (0.028) 5964 0.446*** (0.008) 0.143*** (0.034) −0.060 (0.050) −0.182*** (0.038) −0.321*** (0.016) −0.076*** (0.017) −0.106*** (0.019) −18726.23

0.444*** (0.084) −0.252*** (0.031) 0.172*** (0.044) 0.022 (0.034) −0.306*** (0.047) 0.338*** (0.055) 0.203*** (0.044) −0.040 (0.041) −0.023 (0.069) −0.256* (0.068) 0.019 (0.080) −0.007 (0.073) −0.034 (0.095) 0.433* (0.242) −0.083* (0.043) 0.021 (0.036) −0.136*** (0.027) 2.238*** (0.068) 4275 0.817*** (0.008)

0.456*** (0.053) −0.025 (0.020) −0.035 (0.031) −0.026 (0.022) −0.005 (0.027) −0.206*** (0.041) −0.043 (0.030) −0.052* (0.027) −0.251*** (0.045) −0.106*** (0.043) 0.011 (0.053) −0.065 (0.043) 0.117** (0.049) 0.214 0.136 0.020 (0.025) 0.055*** (0.022) 0.038** (0.018) 2.416*** (0.043) 6640 0.626*** (0.007)

Table 10 Family physician’s work activities: multinomial choice and joint estimation.

NFFS Group Child ≤ 6 Dual-physician Female Age 35 Age 35 44 Age 55 64 Age 65 Female × Child ≤ 6 Female × Age 35 Female × Age 35 44 Female × Age 55 64 Female × Age 65 Married IMG Urban Constant Observations Sigma Rho NFFS H1 Rho NFFS H2 Rho NFFS H3 Rho H1 H2 Rho H1 H3 Rho H2 H3 Log Pseudo likelihood

(1) H1

(2) H2

(3) H3

−0.493*** (0.044) 0.010 (0.016) −0.071** (0.030) −0.127*** (0.019) −0.115*** (0.021) −0.062* (0.039) −0.071** (0.025) −0.018 (0.019) −0.186*** (0.033) −0.058 (0.038) −0.007 (0.049) 0.032 (0.036) 0.057* (0.034) 0.048 (0.079) −0.009 (0.020) 0.065*** (0.016) 0.020 (0.015) 3.530*** (0.030) 4693 0.423*** (0.009) 0.021 (0.038) 0.068 (0.050) −0.218*** (0.036) −0.354*** (0.020) −0.056*** (0.021) −0.119*** (0.023) −14273.79

0.423*** (0.108) −0.247*** (0.036) 0.116** (0.055) 0.038 (0.042) −0.274*** (0.054) 0.309*** (0.071) 0.223*** (0.055) −0.084* (0.050) −0.067 (0.078) −0.148* (0.084) 0.148 (0.100) −0.114 (0.087) 0.012 (0.102) 0.456** (0.217) −0.085* (0.050) 0.004 (0.041) −0.066** (0.032) 2.209*** (0.076) 3203 0.855*** (0.009)

0.622*** (0.063) −0.076*** (0.022) −0.070* (0.038) −0.017 (0.025) −0.027** (0.030) −0.147*** (0.050) −0.036 (0.036) −0.053* (0.031) −0.242*** (0.048) −0.050 (0.052) −0.023 (0.064) −0.045 (0.050) 0.086 (0.055) 0.207 0.138 0.026 (0.028) 0.017 (0.024) −0.056** (0.021) 2.536*** (0.045) 5518 0.660*** (0.008)

Robust standard errors in parentheses; All regressions include a set of provincial dummies. * Significant at 10%. ** Significant at 5%. *** Significant at 1%.

Robust standard errors in parentheses; All regressions include a set of provincial dummies. * Significant at 10%. ** Significant at 5%. *** Significant at 1%.

Aside from the mode of remuneration having distinct effects on direct and indirect patient care activities, our analysis suggests that how the practice is organized also matters. Working in a group has no effect on direct patient care in an office/clinic–hence group formation is not a solution to increasing this type of care. However, being part

of a group practice leads to reductions in direct patient care in other settings and indirect patient care when compared to solo practices. One explanation of this finding is that participating in a group practice facilitates the sharing of certain types of patient care among physicians, potentially reducing the per capita allocation of time to these activities. Moreover, collaborating with health care profes-

S. Sarma et al. / Health Policy 98 (2010) 203–217

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Table 11 (a) Family physician’s weekly hours worked on direct patient care in office/clinic (H1 ); (b) Family physician’s weekly hours worked on direct patient care in other settings (H2 ); (c) Family physician’s weekly hours worked on indirect patient care (H3 ). OLS

IV GMM

Joint estimation

Multinomial choice and joint estimation

(a) Alternative Mixed NFFS

−0.355*** (0.014) −0.327*** (0.015) −0.418*** (0.029)

−0.529*** (0.036) −0.528*** (0.044) −0.615*** (0.069)

−0.511*** (0.029) −0.468*** (0.035) −0.579*** (0.046)

−0.467*** (0.035) −0.493*** (0.044)

(b) Alternative Mixed NFFS

0.417*** (0.025) 0.338*** (0.026) 0.574*** (0.040)

0.460*** (0.076) 0.380*** (0.095) 0.456*** (0.124)

0.479*** (0.069) 0.435*** (0.090) 0.504*** (0.115)

0.444*** (0.084) 0.423*** (0.108)

(c) Alternative Mixed NFFS

0.247*** (0.016) 0.222*** (0.017) 0.289*** (0.025)

0.499*** (0.046) 0.528*** (0.060) 0.654*** (0.071)

0.506*** (0.042) 0.490*** (0.054) 0.674*** (0.063)

0.456*** (0.053) 0.622*** (0.063)

Robust standard errors in parentheses. The detailed results are available from the corresponding author upon request. * Significant at 10%; ** Significant at 5%. *** Significant at 1%.

sionals other than physicians could also contribute to this reduction. We gain important insights into the importance of the non-practice income opportunities of physicians through the presence of dual-physician family status—representing some 16% of our sample. Our finding shows that dualphysician family status leads to reductions in direct patient care in an office/clinic in the neighbourhood of 11%. By contrast, marital status in general has a negligible effect on the labour supply of family physicians. Taken together, our results suggest that it is not the fact of being married, per se, that influences the hours spent in direct patient care, rather it is whether or not the partner is also employed–consistent with the finding in economic theory that access to outside income usually leads to a reduction in own labour supply. While our data set cannot confirm this conjecture, it would certainly be interesting to look further into this phenomenon given the prevalence of two-income families especially among the younger population. Consistent with existing literature, we find that demographic play an important role in how physicians allocate their time to different professional activities. Although human capital theory predicts that older and more experienced physicians will earn higher incomes, the influence of experience on the number of hours worked is ambiguous. It depends upon whether the physician’s preference for income is greater than his/her preference for leisure: if the former effect dominates, then more experienced physicians would work longer hours. However, if the latter effect is stronger, then more experienced physicians would prefer to substitute leisure time at the margin over additional income, potentially leading to fewer hours worked. From the point of view of younger physicians, they may prefer to work longer and harder than older ones in order to establish their practices, build up capital and repay educational debts. By the same token, preferences for a more balanced life may prompt younger physicians not to work as many hours as their experienced counterparts. We expect younger physicians to work longer hours if the income effect dominates, and fewer hours if the

intergenerational effect dominates, relative to their older counterparts. Our results suggest that older physicians exhibit a much higher preference for leisure than do younger physicians. Moreover, we find that younger physicians tend to spend much more time caring for patients in settings other than an office/clinic compared to middle aged physicians. Younger and older physicians alike tend to spend fewer hours on indirect patient care activities compared to the middle-aged reference group. Taken together, it looks like younger family physicians spend less time on direct patient care in office/clinic and indirect patient care, and more time on direct patient care in settings other than office/clinic. These results reveal clear differences in the work patterns of family physicians across age groups. We find that female family physicians devote about 11–14% fewer hours per week to direct patient care in an office/clinic and about 24–26% fewer hours per week to direct patient care in settings other than office/clinic compared to their male counterparts. The gender differences in work activities may be explained by child bearing and rearing and other non-market activities of females–borne out by the finding that as female physicians become older, hours to direct patient care tends to increase. As younger female physicians are becoming a larger proportion of the total practicing physician population, more physicians are required to maintain the total number of direct contact hours with patients–clearly an important component of physician resource planning. Dependents may increase work hours because of greater financial responsibilities or may decrease work hours because of the need to provide personal care to them. A positive estimated coefficient on child ≤ 6 (child aged six or less) in the hours worked equation would suggest that the former argument holds while a negative sign suggests the latter is at work. The presence of children aged six or less leads to a reduction in direct patient care hours in office/clinic by about 7–9%. However, we find that the effect of this variable is to increase direct patient care hours in settings other than office/clinic in the range of 12–19%, sug-

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gesting that physicians shift into providing different types of services to their patients while caring for dependent children. IMGs are expected to exert more effort to establish their practice and be more motivated to succeed in their professional careers relative to Canadian graduates. By contrast, immigrant physicians may have to battle against adverse public perceptions and hence are more likely to service immigrant populations themselves, or high-need individuals who find themselves without a regular physician. Given the shortage of physicians and immigrants comprising significant proportion of the total Canadian population, we would expect that the former effect dominates. Indeed, this is what our results show: IMGs devote more hours to direct and indirect patient care activities. Since most of the international medical graduates are immigrants, the coefficient IMG will be largely picking up how immigrant physicians are behaving in Canada. This study corroborates much of the evidence on physician demographic factors on the labour supply behaviour of physicians found elsewhere. It extends the analysis to analyze how the mode of remuneration influences the physician’s allocation of time to direct and indirect patient care activities, after accounting for self-selection. Although the analysis of this paper is restricted to the self-reported hours worked on different activities, it sets the stage for future research with more and better data. As primary care reform unfolds in many developed countries, including Canada, an understanding of the labour supply behaviour of family physicians in a dynamic context is necessary for effective health policy formulation. 6. Conclusions and policy implications Recent years have witnessed a move away from the traditional fee-for-service approach towards alternative methods of remunerating physicians. The introduction of new modes of remuneration has the potential to alter physician work patterns. Although a literature exists that describes the association between work activities and demographic characteristics, a number of micro issues including the behaviour of physicians under different remuneration regimes are not well understood. We expand this sparse literature and examine the impact of the mode of remuneration on work activities of Canadian family physicians. Our analysis takes account of the fact that physicians self-select into the type of remuneration scheme which best suits their preferences. We show clearly that not taking into account this self-selection effect can severely underestimate the impact of remuneration schemes on hours devoted to direct and indirect patient care. Several findings are useful from a policy perspective. Across the board, the alternative schemes result in fewer hours per week devoted to direct patient care in office/clinic relative to fee-for-service arrangements. This result is not surprising given the way in which FFS physicians are remunerated and the resulting financial incentives that govern physician behaviour. Interestingly, however, is the fact that this finding does not mean that physicians in alternative arrangements work fewer hours: on the contrary, these

physicians are likely to work at least as many hours but with more hours devoted to direct patient care in other settings and indirect care, including research, administration, continuing medical education, co-ordination of care and the like. For instance, using the information from our regression analysis, an average physician in an alternative arrangement will devote about 13 h less per week on direct patient care in the office or clinic, but will work 7 more hours on direct patient care outside of the office and another 8 h on indirect patient care relative to his (or her) FFS counterpart. It seems clear that an efficient and effective primary care system needs to strike a balance between direct and indirect patient care: a system that focuses too much on the number of patients seen in office/clinic (i.e., FFS) may likely to under-provide important ancillary services; and one that disregards the link between remuneration and volume of patients seen in office/clinic may tend to provide excellent care to too few patients. Another interesting finding from the point of view of policy is that physicians who hold an international medical degree are likely to provide significantly more direct patient hours per week in office/clinic relative to those with a Canadian degree. Several explanations are possible—IMG physicians may have to work harder to “prove” themselves, or are being left with high-need patients once the other physicians have filled their practices. It seems that, with the shifting demographics of the population and the attendant shortages currently experienced and expected to worsen over the next decade or so, paying close attention to the behaviour of immigrant physicians is important as they may well make a difference to the sustainability of primary health care over the coming years. One limitation of our study is that it is unable to ascertain the extent to which reduced hours worked on direct patient care combined with increased hours worked on patient care in other settings affects the health and well-being of the population. Clearly, a richer analysis is necessary to determine the optimal mix of physician work hours which must be linked to understanding the relationship between the mode of remuneration and patient health outcomes. The analysis in this paper represents but one part of this intricate but important problem. Acknowledgements This study utilizes the 2004 National Physician Survey Database, part of the National Physician Survey project coled by the College of Family Physicians of Canada (CFPC), the Canadian Medical Association (CMA) and the Royal College of Physicians and Surgeons of Canada, and supported by the Canadian Institute for Health Information, and Health Canada. The NPS micro data are available under a special agreement for this research. We thank Sarah Scott of CFPC for facilitating the agreement and subsequent access to NPS data. We would like to thank Padmaja Ayyagari for commenting on an earlier draft of this paper as well as the many participants at the 43rd Annual Conference of the Canadian Economics Association held at the University of Toronto, May 21–31, 2009. The first author acknowledges a MOHLTC Career Scientist Award and funding from SSHRC Internal at the University of Western Ontario. The views

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expressed in this paper are those of the authors and do not necessarily reflect the views of any affiliated organization. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.healthpol.2010.06.019. References [1] Starfield B, Shi L, Macinko J. Contribution of primary care to health systems and health. Milbank Quarterly 2005;83:457–502. [2] Sarma S, Peddigrew C. The relationship between family physician density and health related outcomes: the Canadian evidence. Cahiers de Sociologie et de Démographie Médicales 2008;48:61–106. [3] Glazier RH, Moineddin R, Agha MM, Zagorski B, Hall R, Manuel DG, et al. The impact of not having a primary care physician among people with chronic conditions. ICES Investigative Report. Toronto: Institute for Clinical Evaluative Sciences; 2008. [4] Jegers M, Kesteloot K, De Graeve D, Gilles W. A typology for provider payment systems in health care. Health Policy 2002;60(3):255–73. [5] Devlin RA, Sarma S, Hogg W. Remunerating primary care physicians: emerging directions and policy options for Canada. Healthcare Quarterly 2006;9(3):34–42. [6] Fujisawa R, Lafortune G. The remuneration of general practitioners and specialists in 14 OECD countries: what are the factors influencing variations across countries? OECD Health Working Papers 2008, No.41. [7] Wendt C. Mapping European healthcare systems: a comparative analysis of financing, service provision and access to healthcare. Journal of European Social Policy 2009;19(5):432–45. [8] Wranik DW, Durier-Copp M. Physician remuneration methods for family physicians in Canada: expected outcomes and lessons learned. Health Care Analysis 2010;18:35–59. [9] Basu K, Mandelzys D. The modes of physician remuneration and their effect on direct patient contact. Cahiers De Sociologie et Démographie Médicales 2008;48:41–59. [10] Devlin RA, Sarma S. Do Physician remuneration schemes matter? The case of Canadian family physicians. Journal of Health Economics 2008;25:1168–81. [11] Kantarevic J, Kralj B, Weinkauf D. Enhanced fee-for-Service model and access to physician services: evidence from Family Health Groups in Ontario. IZA Discussion Paper 2010; No. 4862. [12] Sarma S, Devlin RA, Hogg W. Physician’s production of primary care in Ontario, Canada. Health Economics 2010;19(1):14–30. [13] Buske L. Younger physicians providing less direct patient care. Canadian Medical Association Journal 2004;170:1217. [14] Crossley TF, Hurley J, Jeon S. Physician labour supply in Canada: a cohort analysis. Health Economics 2009;18:437–56. [15] Canadian Medical Association (CMA). CMA Policy: Physician Resource Planning. Canadian Medical Association: Ottawa; 2001. [16] Canadian Institute for Health Information (CIHI). Supply, distribution and migration of Canadian Physicians, 2008. Ottawa: Canadian Institute for Health Information; 2009. [17] Chan B. From perceived surplus to perceived shortage: what happened to Canada’s physician workforce in the 1990s? Ottawa: Canadian Institute for Health Information; 2002. [18] Martin S. More hours, more tired, more to do: results from the CMA’s 2002 Physician Resource Questionnaire. Canadian Medical Association Journal 2002;167:521. [19] Slade S, Busing N. Weekly work hours and clinical activities of Canadian family physicians: results of the 1997/98 National Family Physician Survey of the College of Family Physicians of Canada. Canadian Medical Association Journal 2002;166:1407. [20] Canadian Institute for Health Information (CIHI). Physicians in Canada: the status of alternative payment programs, 2005-2006. Canadian Institute for Health Information: Ottawa; 2008. [21] Ferrall C, Gregory A, Tholl W. Endogenous work hours and practice patterns of Canadian physicians. Canadian Journal of Economics 1998;31:1–27.

217

[22] Watson DE, Slade S, Buske L, Tepper J. Intergenerational differences in workloads among primary care physicians: a ten-year, populationbased study. Health Affairs 2006;25:1620–8. [23] Woodward CA. When a physician marries a physician: effect of physician-physician marriages on professional activities. Canadian Family Physician 2005;51:850–1. [24] Saether E. Physicians’ labour supply: the wage impact on hours worked and practice combinations. Labour 2005;19:673–703. [25] Brown MC. Empirical determinants of physician incomes–evidence from Canadian data. Empirical Economics 1989;14:273–89. [26] Brown D, Lapan H. The supply of physician’s services. Economic Inquiry 1979;17:269–79. [27] Feldstein M. The rising price of physician’s services. Review of Economics and Statistics 1970;52:121–33. [28] Hu T, Yang B. Demand for and supply of physician services in the US: a disequilibrium analysis. Applied Economics 1988;20:995–1006. [29] Rizzo JA, Blumenthal D. Physician labour supply: do income effects matter? Journal of Health Economics 1994;13(4):433–53. [30] Thornton J. The labour supply behaviour of self-employed solo practice physicians. Applied Economics 1998;30:85–94. [31] Conrad DA, Sales A, Liang S, Chaudhuri A, Maynard C, Pieper L, et al. The impact of financial incentives on physician productivity in medical groups. Health Services Research 2002;37: 885–906. [32] Gaynor M, Pauly MV. Compensation and productive efficiency in partnerships: evidence from medical group practice. Journal of Political Economy 1990;98:544–73. [33] Gaynor M, Gertler P. Moral hazard and risk spreading in partnerships. Rand Journal of Economics 1995;26:591–613. [34] Baum CF, Schaffer ME, Stillman S. Enhanced routines for instrumental variables/generalized method of moments estimation and testing. STATA Journal 2007;7:465–506. [35] Kleibergen F, Paap R. Generalized reduced rank tests using the singular value decomposition. Journal of Econometrics 2006;127: 97–126. [36] Kleibergen F, Schaffer M. RANKTEST: Stata module to testing the rank of a matrix using the Kleibergen-Paap rk statistic, 2007. Available at: http://ideas.repec.org/c/boc/bocode/s456865.html. [37] Stock JH, Yogo M. Testing for weak instruments in linear IV regression. In: Andrews DWK, Stock JH, editors. Identification and inference for econometric models: essays in honor of Thomas Rothenberg. Cambridge: Cambridge University Press; 2005. p. 80–108. [38] Roodman D. Estimating fully observed recursive mixed-process models with cmp. CGD Working Paper 168. Washington, D.C.: Center for Global Development; 2009. Available at: http://www.cgdev.org/content/publications/detail/1421516/. [39] Canadian Institute for Health Information (CIHI). 2004 National Physician Survey response rates and comparability of physician demographic distributions with those of the physician population. Canadian Institute for Health Information: Ottawa; 2005. [40] Halvorsen R, Palmquist R. The interpretation of dummy variables in semilogarihmic equations. American Economic Review 1980;70:474–5. [41] Sobecks NW, Justice AC, Hinze S, Chirayath HT, Lasek RJ, Charen MM, et al. When doctors marry doctors: a survey exploring the professional and family lives of young physicians. Annals of Internal Medicine 1999;130:312–9. [42] Woodward CA, Williams AP, Ferrier B, Cohen M. Professional activity: how is family physicians’ work time changing? Canadian Family Physician 2001;47, 1441–1421. [43] Mitchell J. Why do women physicians work fewer hours than men physicians? Inquiry 1984;21:361–8. [44] Mitchell JM, Hadley J. Effects of managed care market penetration on physicians’ labour supply decisions. Quarterly Review of Economics and Finance 1999;39:491–511. [45] McMurray JE, Cohen M, Angus G, Harding J, Gavel P, Horvath J, et al. Women in medicine: a four-nation comparison. Journal of the American Medical Women’s Association 2002;57:185–90. [46] Bobula JD. Work patterns, practice characteristics, and income of male and female physicians. Journal of Medical Education 1980;55:826–33. [47] Freiman MP, Marder WD. Changes in hours worked by physicians. American Journal of Public Health 1984;74:1348–52.