Table 2.

Bootstrapping estimated efficiency and 95% CIs

Provinces	OTE				PTE				SE
	Estimated eff.	Bias corrected	Lower bound	Upper bound	Estimated eff.	Bias corrected	Lower bound	Upper bound	Estimated eff.	Scale efficient	RTS
Beijing	0.8091	0.7759	0.7284	0.8075	1.0000	0.9849	0.9667	0.9972	0.8091	Scale inefficient	DRS
Tianjin	0.9757	0.9356	0.8804	0.9729	1.0000	0.9868	0.9220	1.0000	0.9757	Scale efficient	MPSS
Hebei	0.9260	0.8601	0.7651	0.9137	1.0000	0.9945	0.9706	1.0000	0.9260	Scale efficient	MPSS
Shanxi	0.8637	0.7801	0.6799	0.8407	0.9999	0.9973	0.9790	0.9999	0.8638	Scale efficient	MPSS
Inner Mongolia	0.7836	0.7435	0.6817	0.7700	0.9999	0.9998	0.9998	0.9999	0.7837	Scale inefficient	DRS
Liaoning	0.7150	0.6441	0.5700	0.6893	1.0000	0.9998	0.9986	1.0000	0.7150	Scale inefficient	DRS
Jilin	0.8054	0.7566	0.6841	0.7945	0.9999	0.9999	0.9998	0.9999	0.8055	Scale inefficient	DRS
Heilongjiang	0.7818	0.6941	0.6033	0.7508	0.9999	0.9998	0.9992	0.9999	0.7819	Scale inefficient	DRS
Shanghai	0.8291	0.7952	0.7519	0.8264	1.0000	0.9743	0.9613	0.9859	0.8291	Scale inefficient	DRS
Jiangsu	0.8195	0.7840	0.7379	0.8126	1.0000	0.9930	0.9809	0.9992	0.8195	Scale inefficient	DRS
Zhejiang	0.8357	0.7885	0.7415	0.8245	1.0000	0.9877	0.9756	0.9964	0.8357	Scale inefficient	DRS
Anhui	0.9830	0.9031	0.8060	0.9660	0.9999	–	–	–	0.9830	Scale efficient	MPSS
Fujian	0.9471	0.8988	0.8317	0.9336	1.0000	0.9752	0.9371	0.9940	0.9471	Scale efficient	MPSS
Jiangxi	1.0000	0.8779	0.7628	0.9438	1.0000	–	–	–	1.0000	Scale efficient	MPSS
Shandong	0.8090	0.7458	0.6637	0.7890	1.0000	0.9991	0.9950	1.0000	0.8090	Scale inefficient	DRS
Henan	0.8873	0.7934	0.6873	0.8641	1.0000	0.9969	0.9820	0.9999	0.8873	Scale inefficient	DRS
Hubei	0.7577	0.6705	0.5829	0.7256	1.0000	0.9997	0.9980	1.0000	0.7578	Scale inefficient	DRS
Hunan	0.8835	0.7964	0.6920	0.8641	0.9999	0.9974	0.9812	0.9999	0.8836	Scale efficient	MPSS
Guangdong	1.0000	0.9345	0.8750	0.9770	1.0000	–	–	–	1.0000	Scale efficient	MPSS
Guangxi	1.0000	0.8911	0.7719	0.9673	1.0000	–	–	–	1.0000	Scale efficient	MPSS
Hainan	0.9666	0.9186	0.8530	0.9481	1.0000	0.9916	0.9485	1.0000	0.9667	Scale efficient	MPSS
Chongqing	0.8500	0.8097	0.7489	0.8453	1.0000	0.9995	0.9953	0.9999	0.8500	Scale inefficient	DRS
Sichuan	0.8068	0.7230	0.6260	0.7910	0.9999	0.9996	0.9964	0.9999	0.8069	Scale inefficient	DRS
Guizhou	0.9946	0.8963	0.7748	0.9810	0.9999	–	–	–	0.9947	Scale efficient	MPSS
Yunnan	0.9999	0.9225	0.8159	0.9937	0.9999	–	–	–	1.0000	Scale efficient	MPSS
Tibet	0.9724	0.9486	0.9099	0.9681	0.9991	0.9979	0.9880	0.9991	0.9733	Scale efficient	MPSS
Shaanxi	0.8101	0.7658	0.7045	0.8034	1.0000	0.9996	0.9987	1.0000	0.8102	Scale inefficient	DRS
Gansu	0.8947	0.8083	0.7136	0.8713	0.9999	0.9957	0.9691	0.9999	0.8948	Scale inefficient	DRS
Qinghai	0.7390	0.7022	0.6551	0.7314	0.9997	0.9997	0.9997	0.9997	0.7392	Scale inefficient	DRS
Ningxia	0.8160	0.7791	0.7285	0.8028	0.9998	0.9998	0.9998	0.9998	0.8161	Scale inefficient	DRS
Xinjiang	0.7081	0.6685	0.6097	0.7045	0.9996	0.9996	0.9996	0.9996	0.7084	Scale inefficient	DRS
Mean	0.8652	0.8022	0.7251	0.8492	0.9999	0.9947	0.9815	0.9988	0.8653	–	–

Means statistical inference cannot be provided for some provinces due to too few bootstrap replications where those observations lie within the bootstrap frontier.

DRS, decreasing returns to scale; MPSS, most productive scale size; OTE, overall technical efficiency; PTE, pure technical efficiency, SE, scale efficiency; RTS, returns to scale.