© Allen C. Goodman, 1999 More on Physicians and Labor © Allen C. Goodman, 1999.

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Presentation transcript:

© Allen C. Goodman, 1999 More on Physicians and Labor © Allen C. Goodman, 1999

How do we look at productivity? May look at average product of labor, Q/L. Not easy, because there are lots of different kinds of output … and lots of different kinds of labor. A literature has also developed about explicit substitution between physicians, and physician-extenders. Let’s relate marginal product to the wage rate. Labor Wage rate W0W0 Marginal Product of Labor L0L0

© Allen C. Goodman, 1999 Table 19.2 Marginal Products and Efficiency of Input Use AllSoloGroup PhysiciansPhysiciansPhysicians InputMPMP/PMPMP/PMPMP/P Physician Secretary Reg. Nurse Practical Nurse Technician Phys. Ass't MP = Marginal Product MP/P = Marginal Product per Dollar Spent on Input Source: Brown (1988) Elasticity of Supply among Labor Inputs

© Allen C. Goodman, 1999 Labor Inputs Brown (1988) estimated marginal products of physician time and other inputs, calculated at mean values of the variables The marginal products of auxiliary workers are shown in the columns labeled MP for data from physician offices of various categories: all physicians, solo physicians, and group practice physicians. The columns labeled MP/P are of special interest. By dividing MP by the price of each input, to get the marginal product per dollar spent on each factor, we can draw inferences about whether physicians are underutitlizing or overutilizing various categories of workers.

© Allen C. Goodman, 1999 Labor Inputs The MP/P, the marginal product per dollar, is the relevant measure when determining which input to increase. To increase profits one should hire the extra input that has the greatest MP/P, the greatest bang for the buck. If this marginal product per dollar is not equal for each category of worker, the firm can always save money by trading a lesser producing worker per dollar for a higher producing one.

© Allen C. Goodman, 1999 Labor Inputs Brown concluded from these data that physicians were under- utilizing nursing inputs. Consider, for example, the data for practical nurses in all physicians offices. These practical nurses have a higher marginal product per dollar, 0.129, than do physicians, 0.114; thus the offices would become more profitable if one substituted practical nurses for physicians. In addition, Brown found that physicians in group practices were, on average, 22 percent more productive than those in solo practices. We can see that the marginal product of physician assistants, PAs, for solo practices was actually estimated to be negative; in contrast, PAs are very productive on the margin in group practices. Even so, group practices are underutilizing PAs.

© Allen C. Goodman, 1999 Manpower planning Term is sexist … but that’s what is used. How can we tell how many physicians we’ll need? Methods are pretty mechanical. Most important early work that employed a medical determination of health manpower needs was a study by Lee and Jones (1933). Their method calculated the # of physicians necessary to perform the needed # of medical procedures. The needed # of medical procedures, in turn, was based on the incidence of a morbidity (illness) in the population.

© Allen C. Goodman, 1999 Traditional Health Technology Analysis (2) Consider Condition A, which strikes 1 percent of the population in a given county. Suppose further that its treatment requires 6 hours of physician time, and that there are 250,000 residents in the county. How many physicians are needed, if a physician works 2,000 hours per year.

© Allen C. Goodman, 1999 Traditional Health Technology Analysis (3) a. 250,000 persons x (1 morbidity/100 persons) = 2,500 morbidities. b. 2,500 morbidities x (6 hours/morbidity) = 15,000 hours c. 15,000 hours x (1 physician/2,000 hours) = 7.5 physicians. Suppose that the county currently has 7.5 physicians, and that the county's population is projected to rise from 250,000 to 400,000. Without any adjustment for the morbidity rate, or for the technology of care, the need would be projected to rise to (400,000/250,000) x 7.5, or 12.0 physicians. If the projected (actual) total is less than 12.0 then a projected (actual) shortage is said to exist.

© Allen C. Goodman, 1999 Severe assumptions. Even if we supposed that there are only two factors of production, physicians L and some amount of capital or machinery K, the Lee-Jones approach tends to ignore the possibilities for substitution between inputs. Presumes: a. there is no substitution of other inputs for physician inputs, and, b. there is no projected technological change in the production of health care services.

© Allen C. Goodman, 1999 c. there is a single, unique answer to the question of how many medical procedures are appropriate given the illness data for a population, d. and prices and costs of various inputs are safely ignored. and additionally: e. manpower provided to the public will be demanded by the public or otherwise paid for, f. medical doctors are the appropriate body of people to determine population needs. Severe assumptions.

© Allen C. Goodman, 1999 Elasticity of substitution, .  = the % change in the factor input ratio, brought about by a 1% change in the factor price ratio. K L K/L 1 K/L 2 Clearly, planners must look at changes in technology and in factor costs. Look, for example, at dentists.

© Allen C. Goodman, 1999 Physician Training and Earnings Physicians must go to medical school. Limited number of medical schools. What role does licensure have? It has the impact of reducing entry. Do physicians have monopoly power? They have some, and they can price discriminate among clients. Why?

© Allen C. Goodman, 1999 Physicians, Dentists and Lawyers Burstein and Cromwell tried to compare internal rates of return. Incorporated adjustments, including: –Length of physician training –Length of working life –Earnings of medical students Also have to separate out the returns to labor from the “entrepreneurial” returns for self-employed physicians in family practice. Findings.

© Allen C. Goodman, 1999 Figure 16.4 Internal rates of return