Table 5.

Association between physician‐industry transfers and prescribing behavior/costs 2014‐2015

	ln(Real annual prescription cost/Beneficiary)		ln(Real prescription cost/Day)		Percentage of costs from branded drugs		ln(Prescription costs/Day) for substitutable medications		P(High‐risk medication)		Percentage of 65+ part D beneficiaries with prescription for a high‐risk medication
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	(11)	(12)
	Coeff.	Coeff.	Coeff.	Coeff.	Coeff.	Coeff.	Coeff.	Coeff.	Coeff.	Coeff.	Coeff.	Coeff.
	(SE)	(SE)	(SE)	(SE)	(SE)	(SE)	(SE)	(SE)	(SE)	(SE)	(SE)	(SE)
Panel A: Association between receiving at least one transfer and prescribing behavior/Costs
Any transfers	0.358***	0.029***	0.113***	0.012***	0.041***	0.003***	0.174***	0.018***	0.053***	0.003***	0.007***	0.000
	(0.003)	(0.003)	(0.002)	(0.003)	(0.001)	(0.001)	(0.004)	(0.006)	(0.001)	(0.001)	(0.000)	(0.000)
	0.608	0.94	0.55	0.892	0.446	0.852	0.454	0.86	0.557	0.978	0.521	0.935
	734 216		741 659		507 918		533 405		516 856		328 347
Panel B: Association between the magnitude of transfers and prescribing behavior/Costs
ln($ Any transfers)	0.131***	0.030***	0.059***	0.013***	0.012***	0.003***	0.069***	0.014***	0.010***	0.001***	0.002***	0.001***
	(0.001)	(0.002)	(0.001)	(0.001)	(0.000)	(0.000)	(0.001)	(0.003)	(0.000)	(0.000)	(0.000)	(0.000)
R‐Squared	0.62	0.943	0.597	0.919	0.469	0.87	0.485	0.874	0.566	0.981	0.534	0.937
N	456 650		459 122		330 879		357 572		338 623		225 747
Fixed effects	County	Physician	County	Physician	County	Physician	County	Physician	County	Physician	County	Physician

Notes: Estimated using Ordinary Least Squares Multiple Regression with physician clustering using Stata 15. County fixed‐effect models were estimated with a full set of control variables including those listed in Table 4 as well as county fixed effects. Physician fixed‐effect models were estimated with time variant control variables listed in Table 4.

***P < 0.01.