Fig 1.
Economies of scale versus learning-by-doing.
The blue arrow refers to movement along the Average Quality (AQ 1) curve reflecting the effect of economies of scale. The red arrow refers to changes in productivity by experience or learning by doing, resulting in a new Average Quality curve AQ-2. The distinction between static economies of scale and dynamic learning-by-doing is important because if the volume-outcome operated entirely through movement along the economies of scale curve (AQ-1), such as by investments in infrastructure and research and development, then equating static marginal quality to marginal volume would be socially optimal. Consider the example of a transitory shock that raises short-term demand such as a pandemic, assuming demand does not exceed a supply threshold. In such a scenario the economies of scale mechanism predicts no long-run gains to quality when volume returns to baseline. In contrast, the learning-by-doing mechanism would predict a permanent.
Table 1.
Patient characteristics across quartiles of quarterly sepsis volume.
Table 2.
Probit regression results.
Table 3.
Probit regression model showing coefficients, the proportion of the contemporaneous volume to the total volume efcts and the ratio of lagged coefficients.
Table 4.
Sensitivity analysis of the Probit regression model showing coefficients with standard errors, the proportion of the contemporaneous volume to the total volume effects and the ratio of lagged coefficients with volume specified as monthly volume and by the square root of quarterly volume.