Efficacy of a numerical value of a fixed-effect estimator in stochastic frontier analysis as an indicator of hospital production structure

Table 2 Results of technical efficiency estimation (N = 127 for 3 years)

	Cobb–Douglas function		Translog function
	(equation 1)		(equation 2)
	Coefficient	Standard error	Coefficient	Standard error
ln Number of physicians	0.384***	0.019	2.898***	0.304
ln Number of beds	0.665***	0.034	−0.525**	0.211
Hospital standardized mortality ratio	−0.208***	0.022	−0.172***	0.020
ln Number of physicians (squared)			0.438***	0.108
ln Number of beds (squared)			0.691***	0.115
ln Number of physicians x ln number of beds			−0.711***	0.118
Efficiency score	0.610		0.586
Log likelihood	−66.852		−88.115
Likelihood ratio test value	365.405***		322.880***

***p < 0.01 **p < 0.05.
The results obtained with the TFEM show that all explanatory variables in the Cobb–Douglas function (equation 1) were statistically significant, and all sign directions were as expected. The inputs, the number of physicians and the number of beds, were both positive and statistically significant. The coefficient of the HSMR was significantly negative, suggesting that low mortality rates (or high quality of care) decreases output. The translog model (equation 2) yielded results similar to those obtained with the Cobb–Douglas model (equation 1).

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