Table 2.

Impact of DIP Payment Adoption Across the Entire Sample and When Subdivided into Tertiary and Secondary Hospitals: Difference-in-Differences Estimates

	Whole Sample		Tertiary Hospitals	Secondary Hospitals
	Unadjusted	Adjusted	Adjusted	Adjusted
	(1)	(2)	(3)	(4)
ln (total expenditure per case)
DIP Payment	−0.056***	−0.057***	−0.098***	−0.021*
	(0.013)	(0.012)	(0.020)	(0.011)
Sample size	423,631	423,631	189,086	234,545
In-hospital mortality
DIP Payment (*100)	−0.014	−0.019	−0.097**	0.032
	(0.026)	(0.026)	(0.047)	(0.026)
Sample size	423,631	423,631	189,086	234,545
All-cause readmission within 30 days
DIP Payment	−0.011***	−0.011***	−0.005	−0.014***
	(0.003)	(0.003)	(0.005)	(0.003)
Sample size	423,631	423,631	189,086	234,545
Severe patients
DIP Payment	0.017***	0.012**	0.025***	0.006
	(0.006)	(0.005)	(0.006)	(0.008)
Sample size	423,631	423,631	189,086	234,545
ln (Related weight per case)
DIP Payment	−0.005	0.000	−0.005	0.005
	(0.008)	(0.006)	(0.008)	(0.008)
Sample size	423,631	423,631	189,086	234,545

Notes: ***, **, and *Denoted significance at the 1%, 5%, and 10% levels, respectively. This table reported the estimators of the outcome variables based on equation (1) using case-level discharge data from cities W and Z. Each panel represented a separate regression analysis. The estimated coefficients were reported for the entire sample (Columns (1) and (2)), patients from tertiary hospitals (Column (3)), and patients from secondary hospitals (Column(4)). Column(1) reported the unadjusted results when no control variables were included. In Columns (2)–(4), all specifications included the full set of control variables (ie, indicators for age group, sex, insurance type, disease category, and CCI), except when we used severe patients as outcome variable (ie, indicators for sex, insurance type, and disease category). We also controlled for hospital and year–month fixed effects. Standard errors clustered at the hospital month level were shown in parentheses.