estimating differences between male and female physician service provision using panel data

HEALTH ECONOMICSHealth Econ. 17: 1295–1315 (2008)Published online 10 April 2008 in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/hec.1344

ESTIMATING DIFFERENCES BETWEEN MALE AND FEMALEPHYSICIAN SERVICE PROVISION USING PANEL DATA

ALEXANDRA CONSTANTa and PIERRE THOMAS LEGERb,c,*aEconomist Health Policy Research Division, Health Canada, Ottawa, Ont., Canada

b Institute of Applied Economics, HEC Montreal, Montreal, Que., CanadacCIRANO and CIRPEE

SUMMARY

Using panel data, we estimate the impact of an increasing share of female physicians on the total output ofCanadian physicians. A micro-econometric model is developed specifically for the Canadian context and estimatedusing administrative data on all Canadian physicians paid on a fee-for-service basis from 1989 to 1998. Our resultssuggest that female physicians systematically provide fewer services than their male counterparts for almost allspecialities and provinces studied. Given that females account for an increasing share of the physician populationand that female physicians provide, on average, fewer services, potentially important future reductions in totalhealth-care service provision are likely. Copyright # 2008 John Wiley & Sons, Ltd.

Received 31 October 2005; Revised 12 December 2007; Accepted 13 December 2007

KEY WORDS: labour supply; gender differences

INTRODUCTION

The share of women attending Canadian medical schools has risen considerably during the past decade.In fact, by 1996, women accounted for approximately half of the graduating class (a 12% increase in theshare of female graduates since 1990) (Association of Canadian Medical Colleges, 2004). Furthermore,several American and Canadian studies find that female physicians behave differently than their malecounterparts – both in terms of the nature and the quantity of health-care services they provide(Frieman and Marder, 1984; Woodward et al., 1990; Lee and Mroz, 1991; Rizzo and Blumenthal, 1994;De Konnick et al., 1997). The possibility that women may provide fewer services than their malecounterparts is not necessarily an important issue. However, if, as it is Canada, the size of the enteringclass in medical school is capped, then the greater proportion of females in medical schools may haveimportant implications on the total number of services provided to a given population. Furthermore, ifthe cost of training new physicians is essentially born by the population (through, for example, tuitionsubsidies), then society may be receiving a lower return on its investment (in terms of service provision)when training a female physician rather than a male one. In light of these potential issues, the goal ofthis paper is to estimate the difference in service provision between female and male physicians using aunique data set that allows us to measure the total number of services provided by Canadian physiciansacross different specialties, over a 10-year period.

In order to examine potential differences in the total number of services provided by male and femalephysicians, we develop and estimate a simple model that which exploits an important feature of the

*Correspondence to: Institute of Applied Economics, HEC Montreal, 3000 chemin de la Cote-Sainte-Catherine, Montreal, Que.,Canada H3T 2A7. E-mail: [email protected], [email protected]

Copyright # 2008 John Wiley & Sons, Ltd.

Canadian health-care system.1 The Canadian system features province-specific fee-for-serviceremuneration schemes, where fees can be considered exogenous to both observable and unobservablephysician characteristics. In other words, fees are exclusively a function of the physician’s specialty,province, and year of practice. As a result, we can directly impute the number of services provided byeach physician using the physician’s total payments and the corresponding province-specific fee-for-service rates. Unlike the total number of hours worked and/or the total number of hours spent in directpatient care, a physician’s total number of services provide a clear, consistent and objective measure of aphysician’s productivity (and a better indication of the actual quantity of care provided to thepopulation). Furthermore, using the imputed number of services rather than the total payments isnecessary given that provincial fee-for-service schemes are often non-linear (due to province- and year-specific thresholds on annual income and corresponding reduced fee-for-service rates). Thus, differencesin total payments may not accurately reflect differences in the total number of services provided.

As mentioned above, physicians in certain provinces face non-linear fee schedules due to ‘incomethresholds’ and corresponding reduced fee-for-service rates. That is, physicians who reach a certainincome level before year’s end face reduced fee-for-service rates for all subsequent services provided.2

Although fee-for-service rates are theoretically non-linear, they may not necessarily be in practice. Thisis because physicians may be able to avoid reductions in the fee-for-service rates by practising in certaininstitutional settings and/or locations where they are not applied. Unfortunately, the data do not allowus to identify whether or not physicians avoided such reduced fee-for-service rates. As a consequence,we consider two different scenarios when imputing the total number of services provided by eachphysician. In Scenario 1, we assume that male and female physicians completely avoided the reducedfee-for-service rates by practising in designated institutional settings or locations. In Scenario 2, weassume that neither male nor female physicians avoided the reduced fee-for-service rates set by theirprovince (i.e. they faced non-linear fee-for-service schedules).

Using the imputed total number of services, we estimate a simple model in order to measuredifferences in male and female service provision across ages, specialties, provinces and time. Morespecifically, we estimate a separate model of service provision for General practitioners (GPs) and forseveral specialties, under the two different scenarios mentioned above. Among other things, oureconometric approach accounts for unobservable province and year characteristics (for each specialty)which may affect a physician’s total output. Caution, however, must be exercised in interpretingpotential differences in output, as unobservable quality differences may exist. Furthermore, differencesin service provision may reflect unobserved differences in patient mix.

Several studies have examined differences in practice behaviour between male and female physicians.Lee and Mroz (1991) study physician labour supply by specialty using a sample of married physicians inthe United States. Applying a 2SLS approach (to control for endogenous salaries) separately to eachspecialty group, they find that female physicians without children in family practice, obstetrics/gynecology and paediatrics, work fewer hours per week than their male counterparts, while those ininternal medicine work more. They also find that female physicians whose youngest child is under theage of six work less in every specialty (relative to their male counterparts) except in obstetrics/gynecology. Finally, they show that the greatest difference in hours worked per week occurs betweenmale and female GPs (irrespective of the presence of children). Rizzo and Blumental (1994) estimate theeffect of hourly earnings and non-wage income on the labour supply (in terms of hours worked inpatient care) of self-employed physicians 40 years of age or less in the United States. They also apply a2SLS approach to control for the endogeneity of salaries and control for a variety of physician-specificand regional characteristics. Although they find that wages are significantly lower for females, they donot find significant differences in the number of hours spent in patient care. Finally, using data on

1Because all Canadians are covered by the universal health-care system, we do not consider individual insurance status.2 In fact, they may face even smaller fee-for-service rates if they reach a second income threshold.

A. CONSTANT AND P. T. LEGER1296

Copyright # 2008 John Wiley & Sons, Ltd. Health Econ. 17: 1295–1315 (2008)

DOI: 10.1002/hec

Canadian physicians practising in 1990, Ferrall et al. (1998) estimate a model where group size, primarysource of income (fee for service or salaried position), weekly hours of direct patient care, and total weeklyhours of work are simultaneously determined. They find that female physicians in Canada see patients7.7 h less per week and work 6.74h less per week than their male counterparts. We differ from thesepapers in several respects, most notably: (i) by using panel data that allow for changes that might occurover time and that control for unobserved individual heterogeneity, (ii) by considering multiplejurisdictions (in our case, provinces) to account for unobserved differences that may affect the demand orsupply of services across specialties, across provinces and across time, and (iii) by using the total numberof services provided by each physician to measure physician output. Furthermore, unlike the previousliterature, we do not have to control for the endogeneity of wages given our fee-for-service setting.

Our results suggest that women systematically provide fewer services than their male counterparts foralmost all specialties and provinces studied, but to varying degrees. To cite but a few examples, femaleGPs in Ontario between the ages of 46 and 50 provide approximately 33% fewer services than their malecounterparts, whereas female GPs in Quebec of the same age group work 28% less than their malecounterparts. Among physicians in internal medicine, psychiatry and general surgery of the same agegroup in Ontario (Quebec), female physicians provide approximately 44% (34%), 28% (24%), and 36%(12%) fewer services than their male counterparts, respectively. Nonetheless, some interestingexceptions exist. For example, female surgeons in Quebec aged between 61 and 65 years provide38% more services per year than their male counterparts. Differences in service provision between maleand female physicians also vary across provinces. For example, female GPs less than 31 years of ageprovide approximately 21 and 39% fewer services than their male counterparts in Newfoundland andSaskatchewan, respectively.

The remainder of the paper is organized as follows. Section 2 presents the data. In Section 3, wedescribe how the number of services provided is imputed using the physician’s total payments and thecorresponding fee-for-service rates under two different scenarios. Summary statistics are presented inSection 4. The statistical model is presented in Section 5 with results presented in Section 6. Finally,conclusions and policy implications are presented in Section 7.

DATA

The data used in this paper come from three sources (see Appendix A for a detailed description of allvariables used in the estimation). The first, the National Physician Database (NPDB), is an institutionaldatabase that contains information on the entire population of Canadian physicians paid by fee forservice during the 1989–1998 period.3 Information about each physician’s demographic characteristics(age, sex, language, etc.), medical specialty, work location, province, postal code, medical schoolattended and year of graduation, current activity status (active, abroad, retired, semi-retired, deceased,etc.), and annual total payments received from the fee-for-service system are included.4

The second data set, provided by the Canadian Institute for Health Information (CIHI), includesannual province-specific adjusted average fee-for-service rates for services provided by generalpractitioners, non-surgical specialists, and surgical specialists. Fee-for-service rates are adjusted byCIHI since what constitutes a ‘service’ in a given specialty is likely to vary across provinces as well asacross time. We return to this issue in Section 3.

3Our sample does not include physicians who are paid uniquely by salary. If female physicians are both more likely to enter intosalaried work and likely to provide fewer services, then the true gap in service provision between male and female physicians willbe underestimated in our analysis. According to the Canadian Institute for Health Information (2001a,b), this group, however,constituted only 9% of all physicians in 1999–2000 (and likely less during our sample period).

4All provinces, except Quebec, submit payment data that reflect what was actually paid to the physician – not necessarily what thephysician billed. Thus, payments to physicians are simply the amount billed less any adjustments applied due to the non-linearfee-for-service scheme. Quebec, on the other hand, provides billing data that reflect the full amount the physician billed theprovincial Medical Services Plan and not necessarily what the physician received as payment.

ESTIMATING DIFFERENCES BETWEEN MALE AND FEMALE PHYSICIAN SERVICE PROVISION 1297


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Finally, the third source of data includes annual provincial data on the point after which physiciansface a lower fee-for-service rate (henceforth referred to as income thresholds), as well as thecorresponding reduced fee-for-service rates, for general practitioners and specialists. As noted in theIntroduction, reduced fee-for-service rates do not apply to all provinces and services.5 For example, insome provinces and for some specialties, services that are provided in hospitals, institutions, inemergency rooms, or in rural settings may be exempt. To illustrate how income thresholds andcorresponding reduced fee-for-service rates vary by province, we present in Table I the fee-for-serviceplans in effect in each province in 1992.

THE NUMBER OF SERVICES PROVIDED

In the following analysis, we define the dependent variable (serv Sit) as the number of services providedby physician i in year t in province j (for a given specialty S). Although the number of services is notincluded in the data set, it can be imputed using available information on each physician’s totalpayments (paymentsit), his or her specialty and corresponding adjusted average fee-for-service rates(ffssjt), rate reductions (rate1jt and rate2jt) and income thresholds (lim1sjt and lim2sjt).

6

Table I. Fee-for-service payment plans by province and specialty in 1992a

General practitioners Specialists

Province Payments ($K) Rate Payments ($) Rate

Newfoundland 0–300 1 0–400 1300–350 0.67 400–450 0.67350þ 0.33 450þ 0.33

Prince Edward Island 0–350 1 0–400 1350–375 0.75 400–425 0.75375–400 0.5 425–450 0.5400þ 0.25 450þ 0.25

Nova Scotia 0–(x+1.8s) 1 0–(x+1.8s) 1

x¼ average payment in specialty group;s¼ standard deviation of payment in specialty group

ðxþ 1:8sÞþ 0 ðxþ 1:8sÞþ 0

Quebec 0–180 1 No thresholds180þ 0.25

Ontario 0–400 1 0–400 1400–450 0.67 400–450 0.67450þ 0.33 450+ 0.33

Source: CMA, ‘A review of the current capping, dollar threshold and utilization formulae as of 31 July, 1992’.a Income thresholds do not exist in British Columbia, New Brunswick, Manitoba, Saskatchewan, and Alberta in 1992. New Brunswickeliminated its income thresholds in November 1990, whereas British Columbia set thresholds on visits per day in April 1997.

5 Income thresholds do not exist in New Brunswick, Manitoba, Saskatchewan, and Alberta for the sample period. New Brunswickeliminated its income thresholds in November 1990. Physicians in British Columbia face income thresholds (post April 1997) onthe number of visits per day not on their total income. In order to impute the number of services provided for physicians inBritish Columbia, we converted the visits-per-day thresholds into income thresholds by multiplying the former by 365 and thecorresponding adjusted fee-for-service rate.

6Specialty-specific fee-for-service rates are adjusted by CIHI to reflect any potential differences that may exist across provinces andacross time for a given specialty (see the National Grouping System Category Report for a detailed item-by-item description of theadjustments). Unfortunately, the fee-for-service rates provided to us by CIHI have been aggregated (from these adjusted fees) atthe province broad-specialty level (i.e. we use the same fee-for-service rate for all non-surgical (surgical) specialists). This isunlikely to cause any bias in our results as the same province–broad-specialty-specific fee-for-service rates (what we call theadjusted average province-specific fee-for-service rates) apply equally to men and women.



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Recall from the previous discussion that physicians in certain provinces may face reducedfee-for-service rates if their income exceeds some threshold. In practice, physicians may be able toavoid such reduced fees by choosing specific institutional settings and/or locations (i.e. incomethresholds and reduced fee-for-service rates are often non-binding). Because we do not haveinformation on whether physicians avoided such reduced fees, we consider two scenarios describedbelow.

Scenario 1: Both male and female physicians completely avoided reduced fee-for-service rates (i.e.income thresholds and corresponding reduced fee-for-service rates were avoided).

Under Scenario 1, the number of services provided by a physician i in year t in provincej (serv Sit) is calculated by simply dividing the physician’s annual total payments (paymentsit)by the corresponding adjusted average fee-for-service rate (ffssjt) (which are province–specialty–yearspecific).

It is important to recognize that our constructed variable (serv Sit) is unlikely to perfectly measurethe true number of services provided by physicians. In the event that physicians did not completelyavoid the aforementioned reduced fees, the number of services calculated under Scenario 1 willunderestimate the true number of services actually provided. Furthermore, if male physicians providemore services and are thus more likely to reach the income thresholds (i.e. are more likely to facereduced fee-for-service rates), then the imputed number of services is likely to underestimate the truenumber of services more for males than for females. As a result, differences in service provision betweenmales and females will also be underestimated.

Scenario 2: Neither male nor female physicians avoided reduced fee-for-service rates (i.e. incomethresholds and corresponding reduced fee-for-service rates always applied).

Under Scenario 2, the calculation of the annual number of services provided by physician i in year t inprovince j (serv Sit) can take several forms. The number of services depends on the specialty, the annualtotal payments, and the fee schedule used in the physician’s province. First, for physicians whopracticed in a province without income thresholds, the number of services provided is obtained, asbefore, by simply dividing annual total payments (paymentsit) by the adjusted province-specific averagefee-for-service rate (ffssjt).

7 The same formula is used for physicians whose income was insufficientlyhigh to be subject to reduced fee-for-service rates.8 Physicians with sufficiently high incomesmay face two different levels of income thresholds and corresponding reduced fee-for-service rates.More specifically, physicians face the full fee-for-service rate (ffssjt) until a first income threshold(lim1sjt).

9 Once the physician has reached this first income threshold, the fee-for-service rate is givenby a percentage of the original fee (given by ffssjt*rate1jt). If the physician subsequently reaches asecond income threshold (lim2sjt), he or she faces an even greater reduction in the fee-for-servicerate (given by ffssjt*rate2jt). As a result, the calculation of the total number of services provided isthe number of services provided at the full rate (ffssjt) plus the number of services provided atthe reduced rate (ffssjt*rate1jt) plus the number of service provided at the further reduced rate(ffssjt*rate2jt), where

serv Sit ¼paymentsit

ffssjtif the province does not have a non-linear payment system or

if the physician0s annual payments are below the income threshold

7Physicians who practice in Manitoba, Saskatchewan, Alberta, and New-Brunswick are not subject to Scenario 2 because there areno income thresholds in their fee-for-service payment plans. Physicians who practice in Quebec are also not subject to Scenario 2simply because Quebec reports billings data not payment data (which reflect the full amount physicians billed the provincialMedical Services Plan for a particular fee code item).

8A total of 99.4% (90.1%) of female (male) physicians who work under systems with income thresholds are unaffected by them(i.e. the thresholds are non-binding).

9 Income thresholds are indexed by s as they differ for GPs and specialists.



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or

serv Sit ¼lim1sjt

ffssjtþ

paymentsit � lim1sjt

ffssjt � rate1jtif the physician0s total payments are between the

first and second income thresholds

or

serv Sit ¼lim1sjt

ffssjtþ

lim2sjt � lim1sjt

ffssjt � rate1jtþ

paymentsit � lim2sjt

ffssjt � rate2jtif the physician0s

total payments are greater

than the second income threshold

Again, it is important to recognize that our constructed variable (serv Sit) is unlikely to perfectlymeasure the true number of services provided by each physician. If physicians were partially able toavoid reduced fee-for-service rates (by choosing institutional settings or locations that were not subjectto reduced rates), then our calculations will overestimate the number of services actually provided. Ifmales are more likely to reach income thresholds (i.e. provide more services), and are able to avoid thereduced rates, then the number of services imputed will be overestimated to a greater extent for malesthan for females. Consequently, we are likely to overestimate the difference in services provided bymales and females.

From the above, Scenarios 1 and 2 provide upper and lower bound estimates of the difference in thenumber of services provided by males and females.10

SUMMARY STATISTICS

From the above sources, we construct an unbalanced panel consisting of 388 178 observations.11 Thesample includes 13 147 female (25.5%) and 38 372 male (74.5%) physicians. Table II provides summarystatistics of male and female physician characteristics. Table III presents the number and share offemale physicians practising in Canada during the sample period. During this period, the share offemale physicians rose from 19 to 27% (although not uniformly across all provinces, with Nova Scotia,Newfoundland and Quebec experiencing the largest increases (see Table IV)).12,13

10 In the above analysis, we assumed that males and females behaved in exactly the same way with respect to their ability (or desire)to avoid fee reductions due to income thresholds. If, however, male physicians adopted a different strategy from their femalecounterparts (for example, male physicians avoided fee reductions by practising in certain institutional settings or locations,whereas female physicians did not), then Scenarios 1 and 2 will not provide realistic measures of the actual number of servicesprovided. As a result, we examined two alternative scenarios. In the first, we assumed that male physicians fully avoided ratereductions whereas females did not, and find results that are not statistically different than those found in Scenario 1. In thesecond, we assumed that male physicians were unable to avoid fee reductions whereas female physicians completely avoidedthem and find results that are not statistically different from those found in Scenario 2.

11Physicians who did not report their sex (35 observations), with resident status (7226 observations), or who were retired (4133observations), semi-retired (7183 observations), or deceased (481 observations) are excluded. We also excluded the 2% of thephysician population with the highest payments (8055 observations). We do not, however, exclude physicians without positivepayments because of frequent temporary exits by women (most likely due to the presence of young children). We do not estimatethe model for physicians who practiced in Prince Edward Island (which represents 0.3% of the observations in our sample) asfee-for-service schedules were unavailable. As a result of these exclusions, the number of physicians in our sample was reducedfrom 53 359 to 51 519, whereas the total number of observations was reduced from 421 465 to 388 178.

12The number and share of French-speaking physicians also increased over the same period.13 In our sample, the percentage of 30-year-old women increases annually, reaching 47.2% in 1998, which is quite similar to thosereported by the Association of Canadian Medical Colleges (ACMC). The ACMC reports that 51.6% of the graduating class inCanadian medical schools was female.



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Table II. Summary statistics

Male Female

Variables Average Std dev. Average Std dev.

Payments (real 2006 $) 234 482.59 150 612.41 142 752.88 96 306.61Serv1 (Scenario 1) 6193.67 3933.67 4048.76 2774.38Serv2 (Scenario 2) 6429.65 4707.76 4073.18 2878.52Variablesa Male Female

Number Share (%) Number Share (%)Fem 38 372 74.5 13 147 25.5Me 6178 17.5 1465 11.5Rural 4266 11.1 1233 9.4French 8173 21.3 3439 26.2Newfoundland 634 1.7 252 1.9Nova Scotia 1473 3.8 508 3.7New Brunswick 845 2.2 229 1.7Quebec 9142 23.8 3747 28.5Ontario 15 776 41.1 5132 39.0Manitoba 1407 3.7 420 3.2Saskatchewan 1038 2.7 303 2.3Alberta 3057 8.0 1031 7.8British Columbia 5000 13.0 1525 11.6Age 30� 5951 15.5 4649 35.4Age31 35 6964 18.2 3691 28.1Age36 40 6020 15.7 2277 17.3Age41 45 5283 13.8 1138 8.7Age46 50 3744 9.8 590 4.5Age51 55 3244 8.5 321 2.4Age56 60 2978 7.8 258 2.0Age61 65 2249 5.9 149 1.1Age66 70 1276 3.3 52 0.4Age71 75 487 1.3 21 0.2Age76 80 148 0.4 1 0.01Age81 85 26 0.07 0 0Age 86þ 2 0.01 0 0Sp 1 GP 19 795 51.6 8764 66.7Sp 2 Internal medicine 4044 10.5 873 6.6Sp 3 Dermatology 302 0.8 132 1Sp 4 Neurology 443 1.2 80 0.6Sp 5 Paediatrics 1101 2.9 648 4.9Sp 6 Physical medicine/rehabilitation 184 0.5 61 0.5Sp 7 Psychiatry 2124 5.5 834 6.3Sp 8 Public health 110 0.3 24 0.2Sp 9 Emergency medicine 182 0.5 25 0.2Sp 10 General surgery 1752 4.6 123 0.9Sp 11 Cardiology surgery 196 0.5 11 0.08Sp 12 Neurosurgery 177 0.5 10 0.08Sp 13 Obstetrics/gynaecology 1123 2.9 359 2.7Sp 14 Ophthamlmology 820 2.1 120 0.9Sp 15 Otoloaryngology 496 1.3 50 0.4Sp 16 Ortho. surgery 915 2.4 34 0.3Sp 17 Plastic surgery 321 0.8 31 0.2Sp 18 Urology 483 1.3 14 0.1Sp 19 Anaesthesia 1581 4.1 405 3.1Sp 20 Nuclear medicine 128 0.3 20 0.2Sp 21 Microbiology 106 0.3 49 0.4Sp 22 Pathology 503 1.3 152 1.2Sp 23 Radiology diagnostic 1263 3.3 267 2.0Sp 24 Radiology therapeutic 142 0.4 43 0.3Sp 25 Occupational medicine 3 0.01 5 0.04Sp 26 Biochemistry 36 0.09 8 0.06Sp 27 Medical scientist 38 0.1 3 0.02Sp 28 Medical genetics 4 0.01 2 0.02

aSummary statistics on the number and share are based on one observation per physician.



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Although our statistical analysis separates physicians into general practice and several specialties, forcompactness we present some summary statistics where we group physicians into three broad categories:(i) GPs, (ii) surgical specialists, and (iii) non-surgical specialists.14

Figures 1 and 2 show that females are more likely to choose general practice over specialization, andconditionally, less likely to choose a surgical specialty over a non-surgical specialty. Males, on the other

Table III. Number and share of female physicians from 1989 to 1998

Female

Year Number Share (%)

1989 6903 19.411990 7353 20.451991 8091 21.561992 8611 22.331993 9188 23.121994 9566 23.851995 10 096 24.771996 10 426 25.581997 10 692 26.371998 11 119 27.25

Table IV. Share of female physicians from 1989 to 1998 by province

Year NF PEI NS NB QC ON MA SAS AL BC

1989 22.02 0.00 21.94 15.74 21.09 18.89 17.50 18.16 19.58 18.211990 21.38 0.00 22.11 16.22 22.45 19.81 19.89 19.21 20.62 19.131991 23.68 15.00 22.61 17.74 23.51 20.89 19.78 20.04 22.04 20.461992 24.29 16.53 23.55 19.30 24.40 21.65 20.06 20.25 23.09 20.951993 23.98 16.67 24.74 20.77 25.23 22.35 19.97 20.65 23.66 22.181994 24.32 20.00 25.05 21.30 26.24 23.07 22.02 20.75 23.79 22.731995 27.35 18.90 25.77 21.95 27.08 23.84 23.04 22.25 25.34 23.611996 27.04 18.85 26.10 22.31 28.35 24.66 22.87 23.43 25.76 24.331997 27.69 15.97 27.28 22.67 29.70 25.35 24.05 23.08 26.23 24.741998 26.98 17.05 28.85 23.32 31.44 25.99 24.07 24.23 27.02 25.06

Figure 1. Distribution of women by specialty group from 1989 to 1998

14Physicians are classified in the non-surgical specialty group if their current specialty is internal medicine, dermatology, neurology,paediatrics, physical medicine and rehabilitation, psychiatry, public health, emergency medicine, anaesthesia, nuclear medicine,microbiology, pathology, radiology diagnostic and therapeutic, occupational medicine, biochemistry, medical scientist, medicalgenetics. Physicians are classified in the surgical specialty group if their current specialty is general surgery, cardiology surgery,neurosurgery, obstetrician and gynecology, ophthalmology, otolaryngology, orthopaedic surgery, plastic surgery, urology.



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hand, are more equally distributed across general and specialty medicine, and conditionally, across non-surgical and surgical specialties. Nevertheless, the share of females who do specialize has increased from33.3 to 37.3% over the 1989–1998 period, and there is an observable shift among male physicians fromgeneral practice towards non-surgical specialties. If female physicians are less likely to specialize (or areless likely to choose certain specialties) than their male counterparts, their increasing share of thegraduating class may have important effects on the GP-to-specialty ratio and, consequently, on thetypes (and quantities) of services available.

Summary statistics on total earnings suggest that female physicians provide fewer services than their malecounterparts. Recall that physician earnings in Canada reflect (subject to the aforementioned thresholds) thetotal number of services provided times the corresponding fee-for-service rates.15 Figure 3 presents the

Figure 2. Distribution of men by specialty group from 1989 to 1998

Figure 3. Evolution in average annual total payments for male and female physicians by specialty group

15 In a fee-for-service setting, lower total earnings do not reflect a lower ‘wage rate’ or fee-for-service rate for women, as men andwomen face the identical fee-for-service rates. Thus, issues of discrimination are unlikely to apply here (unless they are comingfrom the demand side).



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average annual total payments (in 2006 dollars) for male and female physicians over time, for each of thethree broad specialty groups.16 In our sample, female GPs, non-surgical specialists, and surgeons earned onaverage 32, 33, and 21% less than their male counterparts in 1998, respectively. These differencescorrespond to $70670 (in 2006 dollars) for GPs $76359 for non-surgical specialists, and $64512 forsurgeons. It is worth noting that over time the difference in average earnings decline in all three broadspecialty groups. More specifically, from 1989 to 1998, the gender gap in earnings drops to 26, 16, and 50%for GPs, non-surgical specialists, and surgeons, respectively.

Table V presents the average number of services (and standard deviation) by gender for 28 types ofproviders (i.e. 1GP and 27 specialties) in 1998 considering income thresholds and correspondingreduced fee-for-service rates, under Scenarios 1 and 2.

Summary statistics suggest that income thresholds are not important for most women as the averagetotal number of services calculated (for all specialties) are very similar under the two scenarios. This isnot surprising as closer examination of women’s total payments shows that they generally fall below theamount necessary to face reduced fee-for-service rates. This is not the case for men as the average totalnumber of services under the two scenarios can differ greatly. Without controlling for any individual orprovincial characteristics and pooling all of the data together, it appears that females provide fewer

Table V. Average number of services provided under the two scenarios by gender and specialty in 1998a

Female Male

Variables Scenario 1 Scenario 2 Scenario 1 Scenario 2

Sp 1 GP 4482 (2833) 4494 (2881) 6828 (3768) 7036 (4671)Sp 2 Intern medicine 3119 (2233) 3119 (2234) 5275 (3371) 5821 (4995)Sp 3 Dermatology 4995 (2272) 4995 (2272) 6286 (2828) 6554 (3620)Sp 4 Neurology 3375 (2217) 3376 (2222) 4303 (2867) 4501 (3657)Sp 5 Paediatrics 2690 (1956) 2690 (1956) 4193 (2818) 4322 (3346)Sp 6 Phys/rehabilitation 2514 (2322) 2514 (2323) 3821 (2869) 3942 (3225)Sp 7 Psychiatry 2517 (1533) 2517 (1533) 3474 (2157) 3517 (2446)Sp 8 Public health 2307 (2522) 2307 (2522) 2332 (2951) 2533 (3979)Sp 9 Emergency medicine 1532 (1370) 1532 (1370) 2861 (2052) 2898 (2267)Sp 10 General surgery 5880 (3195) 5881 (3196) 7326 (3939) 7617 (4898)Sp 11 Cardiology surgery 7441 (2755) 7447 (2767) 9756 (4671) 11 053 (7720)Sp 12 Neurosurgery 7257 (3315) 7278 (3355) 7021 (4348) 7350 (5351)Sp 13 Obstertics/gynaecology 6619 (3383) 6621 (3388) 7990 (4361) 8690 (6277)Sp 14 Ophthamology 6987 (3492) 6987 (3492) 9562 (4791) 10 766 (7582)Sp 15 Otoloaryngology 6449 (3049) 6449 (3049) 8836 (4781) 9808 (7152)Sp 16 Ortho. surgery 6049 (3321) 6049 (3321) 7443 (4004) 7697 (4833)Sp 17 Plastic surgery 5235 (2771) 5235 (2771) 6816 (3766) 6941 (4403)Sp 18 Urology 6120 (3860) 6120 (3860) 8519 (3893) 8990 (5333)Sp 19 Anaesthesia 3797 (1963) 3797 (1963) 4696 (2157) 4741 (2384)Sp 20 Nuclear med 5279 (3086) 5279 (3086) 6814 (3588) 7899 (6369)Sp 21 Microbiology 1798 (1147) 1798 (1147) 2520 (2179) 2520 (2179)Sp 22 Pathology 1451 (2173) 1451 (2173) 2427 (3250) 2659 (4157)Sp 23 Radiology diagnostic 4635 (2892) 4636 (2892) 6827 (4428) 8062 (7025)Sp 24 Radiology therapeutic 2682 (2025) 2682 (2025) 3234 (3214) 3455 (4233)Sp 25 Occupational medicine 1151 (2003) 1151 (2003) 1582 (2166) 1582 (2166)Sp 26 Biochemistry 1441 (2469) 1441 (2469) 2322 (2612) 2322 (2612)Sp 27 Medical scientist N/A N/A 3248 (4255) 3452 (4717)Sp 28 Medical genetics 1249 (1106) 1249 (1106) 1559 (1525) 1559 (1525)Group of non-surgical specialists 3030 (2177) 3030 (2177) 4653 (3220) 5003 (4452)Group of surgical specialists 6454 (3338) 6455 (3341) 8036 (4336) 8613 (6029)

aStandard deviations are in parentheses.

16Figure 3 is constructed using the sub-sample of physicians who practiced during the entire sample period. Consequently,observed variations do not reflect sampling bias.



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services than do their male counterparts in all specialties except neurosurgery and public-healthmedicine (irrespective of the scenario). Obviously, the calculated difference in average service provisionis greater under Scenario 2 than under Scenario 1, as the former assumes that neither male nor femalephysicians avoided reduced fee-for-service rates (which are more likely to bind for male physicians). Tocite but a few examples, female emergency-medicine physicians provide on average only 53% of theservices their male counterparts do (under both scenarios). Furthermore, female obstetricians/gynecologists provide, on average, 83% (76%) of the services their male counterparts do under Scenario1 (Scenario 2).

In the following section, we provide a more complete analysis of potential differences in male andfemale service provision while considering the two different scenarios discussed above.

THE STATISTICAL MODEL

In the standard labour-supply literature, individual labour supply is usually represented as hours orweeks worked (Killingsworth, 1983). For physicians who are paid by fee for service, the number ofservices provided during the year (denoted as serv Sit) represents a better measure of total output orwork effort than does total earnings or number of hours worked. However, what constitutes a ‘service’is not comparable across different specialties as they are heterogeneous in nature. Furthermore, whatconstitutes a service within a given specialty is likely to differ across provinces and may change overtime. As a consequence, we estimate the model for GPs and several specialties separately. Furthermore,consistency between provinces and across time for a given specialty is achieved by using adjusted fee-for-service rates which have been corrected to account for such potential inconsistencies (i.e. they havebeen constructed by the CIHI such that a service provided by a given type of specialist is consistentacross provinces for our sample years).17As a result, we estimate a model where the total number ofservices for physician i at time t (for a given specialty S) is given as

serv Sit ¼ aþ b1femi þ b2mei þ b3ðmei � femiÞi þ b4frenchi þ b5ruralit

þ b6ðruralit � femiÞit þX9

j¼2

jjðprov jÞit þX13

g¼2

lgðage gÞit þX13

g¼2

ogðage g � femiÞit

þX9

j¼2

sjðprov j � femiÞit þX9

j¼1

X1998

t¼1989

djtðprov j � year tÞit þ ðmi þ nitÞ ð1Þ

where S ¼ 1; 2; . . . ; 28 represents the physician’s area of practice (GP or one of 27 specialties),i ¼ 1; . . . ; n denotes the physician, t denotes the year from 1989 to 1998, g ¼ 2; . . . ; 13 denotes the agecategory, and j ¼ 1; . . . ; 9 denotes the province.

In (1), fem denotes a female-physician indicator variable. An indicator variable me, which accountsfor whether or not the physician graduated from a non-Canadian medical school, is included to capturepotential differences in service provision between Canadian and foreign-trained physicians. Suchdifferences may be due to differences in taste for leisure or differences in the demand for their services.This category is crossed with the female-physician indicator variable (me*fem) to allow for a gender-specific difference. An indicator variable french is included to capture whether the physician is anEnglish or French speaker. Again, differences may be due to differences in taste for work or the demandfor their services. Also, a rural indicator variable is included to capture the effect of geographical

17 In the model, the physician’s decision to participate and the number of services to be provided are simultaneously determined.Although participation and labour-supply decisions are often estimated separately (or in a two-step procedure), we do not optfor this approach given that most physicians in our sample are active over the sample period (i.e. 947 of the 389 125 observationsreporting a zero total payments).



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location (i.e. rural or urban) on services provided and is crossed with the female-physician indicatorvariable (rural*fem) to control for a gender-specific effect.18 We also include eight provincial indicatorvariables (excluding the province of Ontario as the reference category). Furthermore, we include 12 agecategory indicator variables age g (where g ¼ 2; . . . ; 13; the reference category is age group below 31years of age, g ¼ 1) to allow for a potential non-linear relationship between age and service provision.These age category variables are crossed with the female-physician indicator variable (age g*fem) toallow for female-specific age effects (female aged below 31 years is the excluded category). A series ofcross indicator variables (prov j*fem) are included to account for provincial-female effects (where theOntario*fem indicator variable is excluded). Finally, to allow for provincial-year specific effects whichmay affect service provision (including the fee-for-service rate and the demand for medical services), weinclude 89 cross indicator variables (prov j*year t) (we exclude the Ontario*1998 indicator variable asa reference category).19,20 All variables are described in detail in Appendix A.

In (1), we assume that the error term includes both an individual-specific component (mi) and an i.i.d.component vit�N(0,sv). We estimate the above model using random effects and report robust standarderrors. By using random effects (instead of fixed effects), we can estimate the effect of being female (thevariable of interest) on the total number of services provided. However, by doing so, we implicitlyassume that the unobserved individual component is orthogonal to all variables included in the model(including the specialty decision). Because we are interested in differences in service provision betweenmen and women, estimates of the parameters b1, og, and sj are of particular interest.

THE RESULTS

In this section, we present results from the estimation of (1) for several specialities. Because the modelincludes a very large set of indicator variables, it requires a large sample size (with sufficient variation inthe data). Unfortunately, many specialties do not have enough physicians to credibly estimate the modeland obtain precise estimates of the parameters of interest. As a result, we estimated the model only forthose specialties with a sufficient number of physicians.

Results from the estimation of Equation (1) for four specialties, for the two different scenarios, arepresented in Table VI(a) and (b). For reasons of compactness, we present and discuss the results forgeneral practice, internal medicine, psychiatry and general surgery. We present results for generalmedicine, internal medicine, and psychiatry because (i) they represent the three most important groupsin terms of number, and (ii) have a significant proportion of females in them (with 34.3, 18.2, and29.4%, respectively). We also present results for general surgery because, unlike the first three groups,the proportion of females is relatively small. We present results for this particular group to obtain asense of whether differences in male and female service provision are affected by the proportion ofwomen who practice in the specialty.21

Estimating the model under the two different scenarios allows us to establish an upper (Scenario 1)and lower (Scenario 2) bound on the estimated average difference in service provision between male andfemale physicians in provinces where income thresholds and corresponding reduced fee-for-service ratesmay apply. All estimates from Scenario 1 are not significantly different from those of Scenario 2 for

18This variable was constructed using the physician’s postal code. The definitions for urban and rural come from Statistics Canada(1997, pp. 24–25).

19We do not have data on the physician’s number of children, non-salary income, dependents, marital status, etc. Obviously, thisinformation would be useful to help explain differences in the number of services provided by males and females. Nonetheless,we assume that they are captured by the random effect.

20Because of sample-size restrictions, we do not allow for a province–year–gender-specific effect as such effects could not beestimated with precision.

21Results for the fourth largest specialty (anaesthesiology) are consistent with the other specialties. We do not present estimates forthis and other specialties for compactness. They are, however, available from the authors upon request.



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Table VI. Estimation resultsa,b,c

Specialty Sp 1 GP Sp 2 Internal medicine

Variables Scenario 1 Scenario 2 Scenario 1 Scenario 2R2=0.1813 R2=0.1717 R2=0.1476 R2=0.1416

(a)Fem �1781.05*** �1798.34*** �1086.22*** �1102.70***Age31 35 1491.40*** 1518.32*** 2744.02*** 3029.06***Age36 40 1949.41*** 2039.25*** 3655.87*** 4133.73***Age41 45 2082.06*** 2238.26*** 3900.57*** 4544.53***Age46 50 2030.14*** 2228.66*** 3795.75*** 4468.16***Age51 55 1787.40*** 2027.13*** 3506.29*** 4166.00***Age56 60 1347.52*** 1590.77*** 3207.23*** 3807.96***Age61 65 653.31*** 867.82*** 2585.21*** 3149.97***Age66 70 �499.78*** �356.50*** 1414.33*** 1716.49***Age71 75 �1730.00*** �1645.83*** 383.09*** 631.97***Age76 80 �2553.80*** �2547.24*** �717.94*** �558.02***Age81 85 �3453.30*** �3534.83*** �1388.04*** �1404.11***Age 86+ �4418.63*** �4504.43*** �1867.15*** �1693.63***Fem*Age31 35 �809.29*** �884.75*** �1321.52*** �1701.09***Fem*Age36 40 �1024.13*** �1217.79*** �1735.98*** �2460.46***Fem*Age41 45 �883.31*** �1195.02*** �1827.08*** �2868.88***Fem*Age46 50 �657.51*** �1074.98*** �1687.14*** �2972.69***Fem*Age51 55 �373.38*** �901.09*** �1326.79*** �2680.77***Fem*Age56 60 �97.90 �713.91*** �1173.56*** �2569.00***Fem*Age61 65 57.39 �589.93*** �1102.63*** �2607.00***Fem*Age66 70 494.11*** �72.73 �383.44 �1673.26***Fem*Age71 75 710.37*** 68.07 149.03 �943.18Fem*Age76 80 1166.13*** 564.11*** 798.29 �100.02Fem*Age81 85 1649.22*** 1155.74*** (dropped) (dropped)Nfld �1400.98*** �1695.65*** �235.12 �1533.63***Nova �1283.31*** �1544.38*** �3015.60*** �3924.10***Newbrun 321.74 �24.55 �586.16* �1629.86***Quebec �805.33*** �1323.49*** �907.86*** �2068.87***Manitob �788.21*** �645.80*** �1626.94*** �2687.00***Sask 305.43 �17.02 863.87* �870.17**Alberta �532.89*** �920.33*** �2158.14*** �3343.96***Bc �1314.83*** �1728.84*** �1727.46*** �2932.18***Fem*Nfld 938.71*** 1022.68*** 406.01 1137.67**Fem*Nova 799.06*** 947.85*** 1732.70*** 2543.11***Fem*Newbrun 377.46 561.60** �1040.39** �437.61Fem*Quebec 756.55*** 977.36*** 879.72*** 1599.59***Fem*Manitob 63.29 242.32 1616.47*** 2186.94***Fem*Sask �416.36 �186.55 �704.93 146.17Fem*Alberta 506.97*** 732.62*** 1007.92*** 1741.76***Fem*Bc 590.78*** 804.88*** 914.66*** 1616.96***Constant 5283.85*** 5593.07*** 2458.45*** 3213.65***

Specialty Sp 7 Psychiatric Sp 10 General surgery


(b)Fem �582.08*** �591.83*** �1866.53*** �1940.54***Age31 35 927.58*** 923.62*** 2454.89*** 2431.69***Age36 40 1169.41*** 1157.29*** 4047.47*** 4123.41***Age41 45 1313.45*** 1299.73*** 4338.78*** 4582.26***Age46 50 1379.10*** 1385.21*** 4376.05*** 4757.59***Age51 55 1318.55*** 1344.60*** 4097.15*** 4446.02***Age56 60 1171.71*** 1189.01*** 3423.01*** 3692.46***Age61 65 947.27*** 955.13*** 2512.60*** 2704.16***Age66 70 485.34*** 496.13*** 904.81*** 972.62***Age71 75 �98.44 �71.33 �889.84** �940.35***



DOI: 10.1002/hec

physicians in psychiatry and general surgery. However, Scenarios 1 and 2 do give different results forGPs and internal medicine specialists as incomes are sufficiently high to be affected by incomethresholds and corresponding reduced fee-for-service rates (except for female GPs less than 35 years ofage or older than 81 years of age, and for female internal medicine specialists 71 years of age or older).

The estimated (ceteris paribus) difference between male and female service provision by speciality fora given province and age group is given by the sum b1, sj, and og. By itself, b1 is the ceteris paribusdifference in the number of services provided by females and males aged 30 years of age or less, Englishspeaking, located in a urban area in Ontario in 1998 and graduated from a Canadian medical school. og

gives the marginal impact of age for females relative to females aged 30 years of age or less (holding theprovince constant). Finally, sj gives the differential impact of the province of practice for femalesrelative to females in Ontario (holding age constant). Given that our model allows for comparisons offemales across provinces, time and age-cohorts, for ease of presentation and compactness, we first focusour interpretations on (i) differences in male and female service provision across different age groupswithin a given province (Ontario) (or the sum of b1 and oj) and (ii) differences in male and femaleservice provision across provinces for a given age group (physicians who are less than 31 years of age)(or the sum of b1 and sj). We then present and interpret results for physicians of different age groups

Table VI. Continued

Specialty Sp 1 GP Sp 2 Internal medicine


Age76 80 �289.91* �279.19 �2224.74*** �2395.31***Age81 85 �718.89*** �712.57** �2574.36*** �2683.55***Age 86 + �3241.63*** �3231.32*** �3299.45*** �3481.40***Fem*Age31 35 �557.67*** �555.17*** �690.43 �667.80Fem*Age36 40 �672.58*** �668.43*** �1018.67** �1165.98**Fem*Age41 45 �561.84*** �564.58*** �885.42* �1332.21***Fem*Age46 50 �501.81*** �530.53*** �1332.55** �1966.80***Fem*Age51 55 �487.13*** �543.65*** �614.85 �1348.17**Fem*Age56 60 �480.76*** �536.15*** 718.31 �192.14Fem*Age61 65 �290.38* �341.50* 1968.28** 766.03Fem*Age66 70 18.34 �36.80 (dropped) (dropped)Fem*Age71 75 173.82 94.40 (dropped) (dropped)Fem*Age76 80 �191.26 �271.93 (dropped) (dropped)Fem*Age81 85 381.37 275.52 (dropped) (dropped)Nfld �2118.33*** 1623.99*** �3377.41*** �4124.15***Nova �1545.31*** �1988.25*** �1497.76*** �1870.08***Newbrun �686.62*** �1602.96*** �1665.59*** �2261.11***Quebec 387.38*** �723.62*** �1303.07*** �2090.56***Manitob �731.06* 738.27*** �1032.21*** �1566.98***Sask �211.51 �969.66* �37.72 �794.72**Alberta �1588.60 �271.40 �1270.06*** �1940.54***Bc �2118.33*** �1652.35*** �2881.93*** �3585.09***Fem*Nfld �2629.86*** �2606.54*** 1130.35 1418.31*Fem*Nova 115.84 148.33 2141.61** 2235.28***Fem*Newbrun 910.26* 947.12* 1129.68 1338.59Fem*Quebec 198.37 223.26 2251.43*** 2483.02***Fem*Manitob 117.75 140.85 �1293.97 �1052.94Fem*Sask 397.55 423.46 �669.02 �380.69Fem*Alberta 45.03 72.81 1093.56 1301.04Fem*Bc 742.68*** 776.30*** 1200.15* 1528.20**Constant 2491.17*** 2545.26*** 4477.55*** 5047.03***

aEstimated coefficients significant at *** 1%, ** 5%, * 10% levels.bThe excluded categories are: age below31, fem*age below31, Ontario, fem*Ontario and Ontario*1998.cCoefficients for other variables are not presented for reason of compactness.



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practising in Quebec, as well as for physicians 46–50 years of age (across provinces) to obtain a morecomplete picture of physician practice patterns.

In Table VII(a), we present the sum of the estimates of b1 and og (and their level of joint significance)for general practice, internal medicine, psychiatry, and general surgery. In parentheses, we present theestimated difference in service provision between male and female physicians as a percentage of thepredicted service provision by an English speaking male of specialty S, of age group g, who graduatedfrom a Canadian medical school and worked in an urban area of Ontario in 1998.

Estimates suggest that female GPs in Ontario aged 30 years or less provided 1781 and 1798 fewerservices than their male counterparts under both Scenarios 1 and 2 in 1998, respectively. In terms ofpercentage of services, they provide approximately 34% (32%) less services than their male counterpartsunder Scenario 1 (Scenario 2) (assuming Canadian medical training, English speaking and an urbanpractice). The gender gap in service provision increases with age in Ontario, reaching its peak for GPsbetween the ages of 36 and 40. For this age group, female physicians provide 2805 (3016) less servicesunder Scenario 1 (Scenario 2) or approximately 39% less services. It is not surprising that the genderservice gap widens during this period as women are likely to experience (i) temporary exits from theworkforce because of maternity leaves and (ii) reductions in hours work associated with having youngchildren. Although the gender gap in the number of service decreases afterwards, it nonetheless remainsimportant in terms of percentages of services.

When considering the number of services provided by physicians in internal medicine in Ontario in1998, the gender gap in service provision appears to first increase with age (reaching its peak for the 41–45 age group under Scenario 1 and the 46–50 age group under Scenario 2) and then appears to steadilydecrease. In percentage terms, however, the pattern is somewhat different (i.e. the difference is muchflatter (especially under Scenario 1) or presents more than one peak (Scenario 2)). Nonetheless, underScenario 2, female physicians in internal medicine under the age of 31 provide 34.3% fewer services thantheir male counterparts. This difference increases to 58.3% fewer services for the 61–65 age group. Thefact that differences are considerably larger under Scenario 2 than Scenario 1 is not surprising. This issimply because Scenario 2 assumes that neither female nor male physicians avoided reduced fee-for-service rates due to income thresholds. Given that males are much more likely to reach incomethresholds (and their corresponding reduced fee-for-service rates), the estimated difference in earnings(or payments) is likely to underestimate the true difference in service provision.

Although female psychiatrists and general surgeons provide fewer services (across all age groups)than their male counterparts, two notes are of particular interest. First, estimated differences (both interms of the number of services and the percentage of services) are not significantly different across thetwo scenarios. Thus, our results suggest that income thresholds and corresponding reduced fee-for-service rates are not particularly important for these types of physicians. Furthermore, the estimatedgender gap in service provision for psychiatrists is smaller in terms of number of services compared withother specialties, but it nonetheless remains important in terms of percentages of services. Finally, it isworth noting that female general surgeons in Ontario, 56 years old or more, do not provide significantlyless services than their male counterparts.

In order to present a more complete picture of practice patterns across males and female physicians inCanada, Table VII(b) reports the differences between male and female service provision by agecategories for English speaking physicians who practiced in an urban area of Quebec in 1998 andgraduated from a Canadian medical school. In parentheses, we present the estimated difference inservice provision between male and female physicians as a percentage of the predicted service provisionby an English speaking male of specialty S, of age group g, graduated from a Canadian medical schooland worked in an urban area of Quebec in 1998.

Estimates for Quebec in 1998 give similar practice patterns across age groups for GPs, internalspecialists, and psychiatrists to those for Ontario in 1998. That is, females in these three specialitieswork significantly less than their male counterparts. Also, the gender gap in service provision appears to



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Table VII. Estimated differences in female and male physician service provision by age category for the province of(a) Ontarioa,b,c,d,e and (b) Quebecf,g in 1998 for four specialties

GP Internal medicine Psychiatric General surgery

Age category Scenario 1 Scenario 2 Scenario 1 Scenario 2 Scenario 1 Scenario 1

(a)Age 30min �1781.05*** �1798.34*** �1086.22*** �1102.70*** �582.08*** �1866.53***

(�33.7%) (�32.2%) (�44.2%) (�34.3%) (�23.4%) (�41.7%)Age31 35 �2590.35*** �2683.09*** �2407.74*** �2803.79*** �1139.75*** �2556.95***

(�38.2%) (�37.7%) (�46.3%) (�44.9%) (�33.3%) (�36.9%)Age36 40 �2805.19*** �3016.13*** �2822.20*** �3563.16*** �1254.65*** �2885.20***

(�38.8%) (�39.5%) (�46.2%) (�48.5%) (�34.3%) (�33.8%)Age41 45 �2664.36*** �2993.36*** �2913.31*** �3971.58*** �1143.92*** �2751.94***

(�36.2%) (�38.2%) (�45.8%) (�51.2%) (�30.1%) (�31.2%)Age46 50 �2438.56*** �2873.32*** �2773.36*** �4075.39*** �1083.89*** �3199.08***

(�33.3%) (�36.7%) (�44.3%) (�53.1%) (�28.0%) (�36.1%)Age51 55 �2154.43*** �2699.43*** �2413.02*** �3783.47*** �1069.21*** �2481.38***

(�30.5%) (�35.4%) (�40.5%) (�51.3%) (�28.1%) (�28.9%)Age56 60 �1878.96*** �2512.25*** �2259.78*** �3671.70*** �1062.83*** �1148.21

(�28.3%) (�35.0%) (�39.9%) (�52.3%) (�29.0%) (�14.5%)Age61 65 �1723.67*** �2388.27*** �2188.86*** �3709.70*** �872.46*** 101.75

(�29.0%) (�37.0%) (�43.4%) (�58.3%) (�25.4%) (1.5%)Age66 70 �1286.95*** �1871.07*** �1469.66*** �2775.96*** �563.74***

(�26.9%) (�35.7%) (�37.9%) (�56.3%) (�18.9%) n.fAge71 75 �1070.69*** �1730.27*** �937.19 �2045.88*** �408.26**

(�30.1%) (�43.8%) (�33.0%) (�53.2%) (�17.1%) n.fAge76 80 �614.92*** �1234.23*** �287.93 �1202.72 �773.34***

(�22.5%) (�40.5%) (�16.5%) (�45.3%) (�35.1%) n.fAge81 85 �131.84 �642.61

(�7.2%) (�31.2%) n.f n.f n.f n.f.(b)Age 30min �1,024.50*** �820.98*** �206.50 496.89** �383.71** 384.90

(�26.2%) (�21.7%) (�11.1%) (35.5%) (�16.8%) (10.2%)Age31 35 �1833.79*** �1705.73*** �1528.02*** �1204.20*** �941.38*** �305.53

(�33.9%) (�32.2%) (�33.2%) (�27.2%) (�29.3%) (�4.9%)Age36 40 �2048.63*** �2038.77*** �1942.48*** �1963.56*** �1056.28*** �633.77

(�34.9%) (�35.0%) (�35.2%) (�35.5%) (�30.6%) (�8.1%)Age41 45 �1907.81*** �2016.00*** �2033.58*** �2371.99*** �945.54*** �500.52

(�31.8%) (�33.5%) (�35.3%) (�39.9%) (�26.3%) (�6.2%)Age46 50 �1682.01*** �1895.96*** �1893.64*** �2475.80*** �885.51*** �947.65*

(�28.3%) (�31.5%) (�33.5%) (�42.2%) (�24.2%) (�11.6%)Age51 55 �1397.88*** �1722.07*** �1533.29*** �2183.88*** �870.83*** �229.95

(�24.5%) (�29.6%) (�28.6%) (�39.3%) (�24.2%) (�2.9%)Age56 60 �1122.40*** �1534.89*** �1380.05*** �2072.11*** �864.46*** 1103.21

(�21.3%) (�28.6%) (�27.2%) (�39.8%) (�25.0%) (15.4%)Age61 65 �967.11*** �1410.90*** �1309.13*** �2110.11*** �674.09*** 2353.18***

(�21.2%) (�30.3%) (�29.5%) (�46.4%) (�20.9%) (37.5%)Age66 70 �530.39*** �893.71*** �589.94** �1176.37*** �365.37**

(�15.5%) (�26.1%) (�18.0%) (�37.8%) (�13.2%) n.f.Age71 75 �314.13** �752.90*** �57.47 �446.29 �209.89

(�14.4%) (�35.2%) (�2.6%) (�22.0%) (�9.6%) n.f.Age76 80 141.63 �256.87 591.79 396.88 �574.96**

(10.4%) (�20.8%) (51.9%) (47.3%) (�28.8%) n.f.Age81 85 624.72 334.76

(135.1%) (134.7%) n.f n.f n.f n.f.

aCoefficients are calculated using the estimates presented in Table VI(a) and (b). The excluded categories are age below31,fem*age below31, Ontario, fem*Ontario and Ontario*1998.bEstimated coefficients significant at ***1%, ** 5%, * 10% level (using (jointly) robust standard errors).cn.f. denotes ‘no females’ in the age category.dEstimated differences in service provision between genders as a percentage of service provision by a male in age in group g inOntario in 1998 who speak English, graduated from a Canadian medical school and works in an urban area are presented inparentheses.eResults under Scenario 2 are omitted for Psychiatrists and General Surgeons as they are not significantly different from thoseobtained under Scenario 1.fCoefficients are calculated using the estimates of Equation (1) under Scenarios 1 and 2 using the following excluded categories:age46 50, fem*age46 50, Quebec, fem*Quebec and Quebec*1998.gSee footnotes a–c.



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first increase with age, peak at the same age group as in Ontario and then decrease (Scenario 2 alsopresents a second peak at the end of the working life for GPs and physicians in internal medicine). It isworth mentioning that the gender differences in service provision (expressed in both the number andpercentage of services) are significantly smaller in Quebec than in Ontario across all age groups for GPs,internal specialists, and psychiatrists. Finally, estimated differences in male and female service provisionfor general surgeons in Quebec across most age groups are not significant (except for surgeons 46–50years of age where females work on average 12% less than their male counterparts, and for surgeons61–65 years of age where females work on average 38% more than their male counterparts).

Summarizing the findings from Table VII(a) and (b), results suggest that female physicians generallyprovide fewer services than their male counterparts across general medicine, internal medicine,psychiatry, and general surgery. For all four specialties, the largest differential effect of being femaleoccurs in the first half of the physician’s working life, and generally decreases afterwards. It is worthmentioning, however, that female general surgeons between 61 and 65 years of age in Quebec providesignificantly more services per year than their male counterparts.

In Table VIII(a), we report the sum of the estimates of b1 and sj (and their level of joint significance)for the same specialties. This sum represents the estimated differences in male and female serviceprovision across provinces for physicians who are less than 31 years of age, English speaking, graduatedfrom a Canadian medical school and located in an urban area in 1998. In parentheses, we present theestimated difference in service provision between male and female physicians as a percentage ofpredicted service provision (assuming that physicians are English speaking, practising in an urban area,graduates of a Canadian medical school and 30 years of age or less) for each specialty S in 1998. We donot report results for estimated differences for general surgeons in this age category as there areinsufficient numbers of females in each of the provinces to credibly do so. Also, we do not presentresults under Scenario 2 as they do not differ significantly from those under Scenario 1.

Our results suggest that female physicians less than 31 years of age provide fewer services than their malecounterparts in all provinces. However, the difference in service provision (in terms of percentage) variesgreatly from one province to another and from one specialty to another. For example, female GPs provide21.7% less services in Newfoundland and 39.3% less services in Saskatchewan than their male counterparts.

In Ontario, New Brunswick, and Saskatchewan, our results show that female internal medicinespecialists less than 31 years of age provide significantly fewer services than their male counterparts.Furthermore, no statistical difference is found in service provision in Newfoundland, Nova Scotia,Quebec, Manitoba, Alberta, and BC for this age-specialty group.

The estimated differences in service provision between male and female psychiatrists below 31 yearsof age are significant in Ontario, Newfoundland, Quebec, and Alberta. Although the differential effectin percentage terms is relatively small in Ontario, Quebec, and Alberta (where women provide less than24% fewer services than their male counterparts), it is relatively large in Newfoundland (where womenprovide approximately 77% fewer services than their male counterparts).

To provide a broader picture of potential difference in service provision across the life-cycle,Table VIII(b) presents the estimated differences in male and female service provision across provincesfor physicians who are between 46 and 50 years of age, English speaking, graduated from a Canadianmedical school and practiced in an urban area in 1998. The estimated difference in service provisionbetween male and female physicians as a percentage of predicted service provision (assuming thatphysicians are English speaking, practising in an urban area, graduates of a Canadian medical schooland between 46 and 50 years of age) for each specialty S in 1998 are presented in parentheses.

Our results suggest that differences in female and male service provision are much larger for physiciansbetween 46 and 50 years of age than they are for physicians 30 years of age or less in all provincesstudied. However, the estimated differences as a percentage of predicted male service provision are asimportant in both age categories for GPs, internal specialists, and psychiatrists. Table VIII(b) alsoreports that female general surgeons aged between 46 and 50 years of age provide significantly fewer



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Table VIII. Estimated differences in female and male service provision for physicians (a) 30 years of age or less byprovince in 1998 for three specialties under Scenario 1a,b,c,d,e,f,g and (b) aged between 46 and 50 years for four

specialities in 1998 under scenario1h,i,j

Province GP Internal medicine Psychiatric

(a)Ontario �1781.05*** �1,086.22*** �582.08***

(�33.7%) (�44.2%) (�23.4%)Newfoundland �842.34*** �680.21 �3,211.93***

(�21.7%) (�30.6%) (�77.1%)Nova Scotia �981.99*** 646.48 �466.24

(�24.5%) (}) (}})New Brunswick �1403.60*** �2126.62*** 328.18

(�25.0%) (}}) (�34.7%)Quebec �1024.50*** �206.50 �383.71**

(�22.9%) (�13.3%) (�21.3%)Manitoba �1717.77*** 530.25 �464.33

(�38.2%) (63.8%) (�16.1%)Saskatchewan �2197.42*** �1791.15*** �184.52

(�39.3%) (�53.9%) (�10.5%)Alberta �1274.08*** �78.31 �537.05**

(�26.8%) (�26.1%) (�23.6%)British Columbia �1190.27*** �171.57 160.61

(�30.0%) (�23.5%) (17.8%)

Province GP Internal medicine Psychiatric General surgery

(b)Ontario �2438.56*** �2773.36*** �1083.89*** �3199.08***

(�32.7%) (�50.7%) (�27.8%) (�37.5%)Newfoundland �1499.85*** �2367.35*** �3713.74*** �2068.73**

(�25.4%) (�40.0%) (�65.1%) (�35.6%)Nova Scotia �1639.50*** �1040.66*** �968.05*** �1057.47

(�27.2%) (�32.1%) (�55.3%) (�14.4%)New Brunswick �2061.11*** �3813.75*** �173.63 �2069.40**

(�27.6%) (�62.0%) (�7.5%) (�28.7%)Quebec �1682.01*** �1893.64*** �885.51*** �947.65*

(�28.3%) (�33.5%) (�24.2%) (�11.6%)Manitoba �2375.28*** �1156.89*** �966.13*** �4493.04***

(�36.4%) (�25.0%) (�22.7%) (�57.4%)Saskatchewan �2854.93*** �3478.29*** �686.33 �3868.09***

(�37.0%) (�51.6%) (�23.2%) (�44.2%)Alberta �1931.59*** �1765.45*** �1038.86*** �2105.52**

(�28.7%) (�43.8%) (�31.0%) (�29.4%)British Columbia �1847.78*** �1858.70*** �341.20** �1998.93***

(�30.8%) (�41.1%) (�15.0%) (�33.5%)

aCoefficients are calculated using the estimates presented in Table VI(a) and (b). The excluded categories are age below31,fem*age below31, Ontario, fem*Ontario and Ontario*1998.bEstimates coefficients are significant at ***1%, **5%, *10% levels (using (jointly) robust standard errors).cEstimated differences in service provision between genders as a percentage of predicted service provision by a male in speciality S,in province j in 1998, aged below 31 years who speaks English, works in an urban area and graduated from a Canadian medicalschool are presented in parentheses.dResults under Scenario 2 are not presented for reason of compactness and because they do not differ significantly from thoseunder Scenario 1 for most of the provinces.eWe do not report the estimated differences for general surgeons as there are insufficient number of females in this age category perprovince to credibly do so.fPredicted number of services is negative for men. As a result, we do not report the estimated difference in service provision.gThe predicted number of services for females is negative. As a result, we do not report the estimated difference is service provisionas a percentage the predicted services provided by men.hCoefficients are calculated using the estimates of Equation (1) under Scenarios 1 and 2 using the following excluded categories:age46 50, fem*age46 50, Quebec, fem*Quebec and Quebec*1998.iResults under scenario 2 are not presented for reason of compactness for GPs and internal medicine specialists. Results underScenarios 1 and 2 are not significantly different for psychiatrists and general surgeons.jSee footnote b and c.



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services than their male counterparts in all provinces, except for Nova Scotia where the difference is notsignificant. The province of Saskatchewan shows the largest gender gap (both in terms of the number andpercentage of services provided) for GPs and general surgeon, and is only second to New Brunswick inthe difference between male and female service provision for internal medicine specialists.

Summarizing the findings from Table VIII(a) and (b), estimation results suggest that femalephysicians 30 years of age or less and between 46 and 50 years of age always provide fewer services thantheir male counterparts in all provinces studied. Furthermore, the estimated difference in female andmale service provision (both in terms of number and percentage of services provided) for physicians lessthan 31 years of age and aged between 46 and 50 years differs from one province to another and fromone specialty to another.

CONCLUSION AND DISCUSSION

In this paper we examine differences in service provision between male and female physicians in theCanadian fee-for-service system during the 1989–1998 period. Using panel data and exploiting the fee-for-service setting, we control for physician specialty, individual characteristics, and local marketconditions in a random-effects framework. Unlike the previous literature, we impute each physician’stotal number of services provided. We then use the imputed annual service provision to estimatedifferences in female and male service provision by specialty. Because actual fees paid to physicians areuniquely a function of the physician’s specialty, year, and province of practice, they can be treated asexogenous (i.e. they are not of function of other physician observable and unobservable characteristics).

Results under the two scenarios studied suggest that female physicians in Ontario and Quebecgenerally provide fewer services than their male counterparts across general medicine, internal medicine,psychiatric, and general surgery. Only female surgeons in Quebec between 61 and 65 years of ageprovide significantly more services per year than their male counterparts. For all four specialties, thelargest differential effect of being a female occurs in the first half of the physician’s working life andgenerally decreases afterwards.

In all provinces studied, our results suggest that the estimated differences in female and male serviceprovision are much larger for physicians between 46 and 50 years of age compared with physicians 30years of age or younger, regardless of their specialty. However, the estimated differences as a percentageof predicted service provision are as important in both age categories.

The fact that the female physicians generally provide fewer services than their male counterparts suggeststhat the total quantity of health-care services provided to the Canadian population is likely to decrease asthe share of female physicians increases. Given that medical-school admissions are currently capped bygovernments and have not changed much over the period studied, and that females constitute an increasingshare of the graduating class, this trend is likely to continue. Consequently, increasing the incoming class inmedical schools may be necessary to guarantee an acceptable level of total service provision.

Our study also suggests several avenues for future research. First, it would be interesting to investigatewhy female physicians provide fewer services than their male counterparts. Furthermore, investigatingdifferences in service provision on other dimensions is likely important (for example, whether females spendmore time with each patient offer a better quality of care or see more severely ill patients). Finally, allowingfor specialty choice and the total number of services to be estimated simultaneously may provide furtherinsight. By doing so, we may be able to determine whether unobserved characteristics that affect physician’stotal number of services provided also affect their specialty decision.

ACKNOWLEDGEMENTS

Leger thanks HEC Montreal, the Social Sciences and Humanities Research Council of Canada(SSHRC), and the Fonds quebecois de la recherche sur la societe et la culture (FQRSC) for funding.



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Part of this research was undertaken while Leger was visiting Paris-Jourdan Sciences-Economiques(CNRS, EHESS, ENPC, ENS). He thanks them for their hospitality. The authors thank participants atHealth Canada, the CEA annual meeting (Hamilton), the IHEA Conference (Barcelona), and twoanonymous referees for their helpful suggestions. Opinions presented in this research are those of theauthors and do not reflect the official position of Health Canada. The usual caveats apply.

APPENDIX A: DESCRIPTION OF VARIABLES

Variable Description

Fem Indicator variable ¼ 1 if the physician is femaleSp S Indicator variables of physician specialty S; S ¼ 1 if the physician is in general

practice (GP), S ¼ 2 if in internal medicine, S ¼ 3 if in dermatology, S ¼ 4 if inneurology, S ¼ 5 if in paediatrics, S ¼ 6 if in physical medicine and rehabilitation,S ¼ 7 if in psychiatry, S ¼ 8 if in public health, S ¼ 9 if in emergency medicine,S ¼ 10 if in general surgery, S ¼ 11 if in cardio and thoracic surgery, S ¼ 12 if inneurosurgery, S ¼ 13 if in obstetrics and gynecology, S ¼ 14 if in ophthalmology,S ¼ 15 if in otolaryngology, S ¼ 16 if in orthopaedic surgery, S ¼ 17 if in plasticsurgery, S ¼ 18 if in urology, S ¼ 19 if in anaesthesia, S ¼ 20 if in nuclear medicine,S ¼ 21 if in medical microbiology, S ¼ 22 if in pathology, S ¼ 23 if in radiologydiagnostic, S ¼ 24 if in radiology therapeutic, S ¼ 25 if in occupational medicine,S ¼ 26 if in medical biochemistry, S ¼ 27 if in medical scientist, S ¼ 28 if in medicalgenetics

Age g Age-category indicator variables (g groups, each of a 5-year interval, where the 30years or less age group is the reference group)

Age g * Fem Crossed age category and female indicator variables (female below 31 years of agebeing the reference category)

Prov j Provincial indicator variables, j ¼ 129 (Ontario being the reference province)Prov j * Fem Province–female cross indicator variables (female in Ontario being the reference

dummy)Prov j*Year t Indicator variables for each province–year interaction. Prov j from 1 to 9 and year t

from 1989 to 1998. The reference group is Ontario *1998Me Indicator variable ¼ 1 if the physician graduated from a non-Canadian medical

schoolFrench Indicator variable ¼ 1 if physician’s current spoken language is FrenchRural Indicator variable ¼ 1 if physician works in a rural areaPayment The physician’s annual total payment receives from the fee-for-service payment planFfs Adjusted fee-for service rateLim1 First income thresholdLim2 Second income thresholdLim3 Third income thresholdRate1 Proportion of the fee-for-service rate applied after the first income threshold is

reachedRate2 Proportion of the fee-for-service rate applied after the second income threshold is

reachedServ1 Number of services calculated under Scenario 1 when ‘income thresholds’ do not

applyServ2 Number of services calculated under Scenario 2 when ‘income thresholds’ apply



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estimating differences between male and female physician service provision using panel data

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