Figure 1.
Malmquist TFP index trend versus technological change and efficiency change during 2011 to 2015
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On the design of full duplex wireless system with chaotic sequences
August & September
2019, 12(4&5): 795-809.
doi: 10.3934/dcdss.2019053
Total factor productivity growth and technological change in the telecommunications industry
School of Economics and Management, Beijing University of Posts and Telecommunications, No.10 Xi Tu Cheng Road, Haidian District, Beijing 100876, China |
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The fast growing telecommunications industry in China has been experiencing dramatic technological change and substantial productivity growth. The actual productivity growth pattern in the sector, however, need to be empirically examined. In this paper, using input and output data at the provincial level, we employ DEA-based Malmquist productivity index to estimate productivity change, technological change and relative efficiency change in China's telecommunications industry for the period spanning the years from 2011 to 2015. The results show that based on our sample, the productivity improved by 22.9% per annum, which was exclusively due to an average of 25.5% technological progress in the industry production function, while the average efficiency change is slightly negative. Our results also indicate that regions with relatively low levels of telecommunications (and economic) development have a greater chance and ability of enhancing telecommunications productivity growth through technological catch-up. In addition, we find that the industry experienced significantly higher productivity growth and technological progress in the later sample period between 2013 and 2015 than in the early period between 2011 and 2013.
Keywords:
Telecommunications,
productivity,
technological change,
data envelopment analysis,
malmquist index.
Mathematics Subject Classification:
Primary: 90B30, 62P20; Secondary: 91B38.
Citation:
Xuchen Lin, Ting-Jie Lu, Xia Chen. Total factor productivity growth and technological change in the telecommunications industry. Discrete & Continuous Dynamical Systems - S,
2019, 12
(4&5)
: 795-809.
doi: 10.3934/dcdss.2019053
![]()
References:
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M. Abramovitz, Resource and output trends in the united states since 1870, in Resource and Output Trends in the United States Since 1870, NBER, 1956, 1–23. Google Scholar |
| [2] |
R. D. Banker, Z. Cao, N. Menon and R. Natarajan, Technological progress and productivity growth in the us mobile telecommunications industry, Annals of Operations Research, 173 (2010), 77-87. Google Scholar |
| [3] |
M. Calvo, J. I. M. Torcal and L. R. García, A new stepsize change technique for adams methods, Applied Mathematics and Nonlinear Sciences, 1 (2016), 547-558. Google Scholar |
| [4] |
D. W. Caves, L. R. Christensen and W. E. Diewert, The economic theory of index numbers and the measurement of input, output, and productivity, Econometrica: Journal of the Econometric Society, 1393–1414. Google Scholar |
| [5] |
A. Charnes, W. W. Cooper and E. Rhodes,
Measuring the efficiency of decision making units, European Journal of Operational Research, 2 (1978), 429-444.
doi: 10.1016/0377-2217(78)90138-8. |
| [6] |
H. W. Chesbrough and D. J. Teece, When is virtual virtuous, Harvard Business Review, 74 (1996), 65-73. Google Scholar |
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T. Coelli, A guide to deap version 2.1: A data envelopment analysis (computer) program, Centre for Efficiency and Productivity Analysis, University of New England, Australia. Google Scholar |
| [8] |
L. Correa, The economic impact of telecommunications diffusion on uk productivity growth, Information Economics and Policy, 18 (2006), 385-404. Google Scholar |
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A. Datta and S. Agarwal, Telecommunications and economic growth: A panel data approach, Applied Economics, 36 (2004), 1649-1654. Google Scholar |
| [10] |
M. Denny, M. A. Fuss and L. Waverman, The Measurement and Interpretation of Total Factor Productivity in Regulated Industries, with an Application to Canadian Telecommunications, Institute for Policy Analysis, University of Toronto, 1979. Google Scholar |
| [11] |
W. E. Diewert,
Exact and superlative index numbers, Journal of Econometrics, 4 (1976), 115-145.
doi: 10.1016/0304-4076(76)90009-9. |
| [12] |
A. Dutta, Telecommunications and economic activity: An analysis of granger causality, Journal of Management Information Systems, 17 (2001), 71-95. Google Scholar |
| [13] |
R. Färe and S. Grosskopf, Malmquist productivity indexes and fisher ideal indexes, The Economic Journal, 102 (1992), 158-160. Google Scholar |
| [14] |
R. Färe, S. Grosskopf, M. Norris and Z. Zhang, Productivity growth, technical progress, and efficiency change in industrialized countries, The American economic review, (), 66-83. Google Scholar |
| [15] |
R. Färe, S. Grosskopf and P. Roos, Productivity and quality changes in swedish pharmacies, International Journal of Production Economics, 39 (1995), 137-144. Google Scholar |
| [16] |
Y. Gao, M. Farahani and W. Gao, Ontology optimization tactics via distance calculating, Applied Mathematics and Nonlinear Sciences, 1 (2016), 154-169. Google Scholar |
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D. I. Giokas and G. C. Pentzaropoulos, Evaluating productive efficiency in telecommunications: Evidence from greece, Telecommunications Policy, 24 (2000), 781-794. Google Scholar |
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J. A. Hausman and W. E. Taylor, Partial deregulation in telecommunications: An update, Journal of Competition Law and Economics. Google Scholar |
| [19] |
E. Hisali and B. Yawe, Total factor productivity growth in uganda's telecommunications industry, Telecommunications Policy, 35 (2011), 12-19. Google Scholar |
| [20] |
H. Kang, Technology management in services: Knowledge-based vs. knowledge-embedded services, Strategic Change, 15 (2006), 67-74. Google Scholar |
| [21] |
F. Kiss, Productivity gains in bell canada, Economic Analysis of Telecommunications: Theory and Applications, Amsterdam: North-Holland. Google Scholar |
| [22] |
J.-J. Laffont and J. Tirole, Competition in Telecommunications, MIT press, 2001. Google Scholar |
| [23] |
P. Lall, A. M. Featherstone and D. W. Norman, Productivity growth in the western hemisphere (1978-94): The caribbean in perspective, Journal of Productivity Analysis, 17 (2002), 213-231. Google Scholar |
| [24] |
P.-L. Lam and T. Lam, Total factor productivity measures for hong kong telephone, Telecommunications Policy, 29 (2005), 53-68. Google Scholar |
| [25] |
P.-L. Lam and A. Shiu, Productivity analysis of the telecommunications sector in China, Telecommunications Policy, 32 (2008), 559-571. Google Scholar |
| [26] |
D. Lien and Y. Peng, Competition and production efficiency: Telecommunications in oecd countries, Information Economics and Policy, 13 (2001), 51-76. Google Scholar |
| [27] |
G. Madden and S. J. Savage, Telecommunications productivity, catch-up and innovation, Telecommunications Policy, 23 (1999), 65-81. Google Scholar |
| [28] |
S. K. Majumdar, Does new technology adoption pay? electronic switching patterns and firm-level performance in us telecommunications, Research Policy, 24 (1995), 803-822. Google Scholar |
| [29] |
S. Malmquist,
Index numbers and indifference surfaces, Trabajos de Estadística, 4 (1953), 209-242.
doi: 10.1007/BF03006863. |
| [30] |
MIIT, Statistical bulletin of china's telecommunications industry in 2016, http://www.miit.gov.cn/n1146290/n1146402/n1146455/c5471508/content.html. Google Scholar |
| [31] |
M. I. Nadiri and M. Schankerman, The structure of production, technological change, and the rate of growth of total factor productivity in the bell system, 1979. Google Scholar |
| [32] |
M. Nishimizu and J. M. Page, Total factor productivity growth, technological progress and technical efficiency change: dimensions of productivity change in yugoslavia, 1965-78, The Economic Journal, 92 (1982), 920–936. Google Scholar |
| [33] |
H. Oniki, T. H. Oum, R. Stevenson and Y. Zhang, The productivity effects of the liberalization of japanese telecommunication policy, Journal of Productivity Analysis, 5 (1994), 63-79. Google Scholar |
| [34] |
J. B. Quinn and M. N. Baily, Information technology: Increasing productivity in services, The Academy of Management Executive, 8 (1994), 28-48. Google Scholar |
| [35] |
R. W. Shepherd,
Theory of Cost and Production Functions, Princeton University Press, 1970. |
| [36] |
N. D. Uri, Measuring productivity change in telecommunications, Telecommunications Policy, 24 (2000), 439-452. Google Scholar |
| [37] |
N. D. Uri, Productivity change, technical progress, and efficiency improvement in telecommunications, Review of Industrial Organization, 18 (2001), 283-300. Google Scholar |
| [38] |
C.-H. Yoon, Liberalisation policy, industry structure and productivity changes in korea's telecommunications industry, Telecommunications Policy, 23 (1999), 289-306. Google Scholar |
show all references
References:
| [1] |
M. Abramovitz, Resource and output trends in the united states since 1870, in Resource and Output Trends in the United States Since 1870, NBER, 1956, 1–23. Google Scholar |
| [2] |
R. D. Banker, Z. Cao, N. Menon and R. Natarajan, Technological progress and productivity growth in the us mobile telecommunications industry, Annals of Operations Research, 173 (2010), 77-87. Google Scholar |
| [3] |
M. Calvo, J. I. M. Torcal and L. R. García, A new stepsize change technique for adams methods, Applied Mathematics and Nonlinear Sciences, 1 (2016), 547-558. Google Scholar |
| [4] |
D. W. Caves, L. R. Christensen and W. E. Diewert, The economic theory of index numbers and the measurement of input, output, and productivity, Econometrica: Journal of the Econometric Society, 1393–1414. Google Scholar |
| [5] |
A. Charnes, W. W. Cooper and E. Rhodes,
Measuring the efficiency of decision making units, European Journal of Operational Research, 2 (1978), 429-444.
doi: 10.1016/0377-2217(78)90138-8. |
| [6] |
H. W. Chesbrough and D. J. Teece, When is virtual virtuous, Harvard Business Review, 74 (1996), 65-73. Google Scholar |
| [7] |
T. Coelli, A guide to deap version 2.1: A data envelopment analysis (computer) program, Centre for Efficiency and Productivity Analysis, University of New England, Australia. Google Scholar |
| [8] |
L. Correa, The economic impact of telecommunications diffusion on uk productivity growth, Information Economics and Policy, 18 (2006), 385-404. Google Scholar |
| [9] |
A. Datta and S. Agarwal, Telecommunications and economic growth: A panel data approach, Applied Economics, 36 (2004), 1649-1654. Google Scholar |
| [10] |
M. Denny, M. A. Fuss and L. Waverman, The Measurement and Interpretation of Total Factor Productivity in Regulated Industries, with an Application to Canadian Telecommunications, Institute for Policy Analysis, University of Toronto, 1979. Google Scholar |
| [11] |
W. E. Diewert,
Exact and superlative index numbers, Journal of Econometrics, 4 (1976), 115-145.
doi: 10.1016/0304-4076(76)90009-9. |
| [12] |
A. Dutta, Telecommunications and economic activity: An analysis of granger causality, Journal of Management Information Systems, 17 (2001), 71-95. Google Scholar |
| [13] |
R. Färe and S. Grosskopf, Malmquist productivity indexes and fisher ideal indexes, The Economic Journal, 102 (1992), 158-160. Google Scholar |
| [14] |
R. Färe, S. Grosskopf, M. Norris and Z. Zhang, Productivity growth, technical progress, and efficiency change in industrialized countries, The American economic review, (), 66-83. Google Scholar |
| [15] |
R. Färe, S. Grosskopf and P. Roos, Productivity and quality changes in swedish pharmacies, International Journal of Production Economics, 39 (1995), 137-144. Google Scholar |
| [16] |
Y. Gao, M. Farahani and W. Gao, Ontology optimization tactics via distance calculating, Applied Mathematics and Nonlinear Sciences, 1 (2016), 154-169. Google Scholar |
| [17] |
D. I. Giokas and G. C. Pentzaropoulos, Evaluating productive efficiency in telecommunications: Evidence from greece, Telecommunications Policy, 24 (2000), 781-794. Google Scholar |
| [18] |
J. A. Hausman and W. E. Taylor, Partial deregulation in telecommunications: An update, Journal of Competition Law and Economics. Google Scholar |
| [19] |
E. Hisali and B. Yawe, Total factor productivity growth in uganda's telecommunications industry, Telecommunications Policy, 35 (2011), 12-19. Google Scholar |
| [20] |
H. Kang, Technology management in services: Knowledge-based vs. knowledge-embedded services, Strategic Change, 15 (2006), 67-74. Google Scholar |
| [21] |
F. Kiss, Productivity gains in bell canada, Economic Analysis of Telecommunications: Theory and Applications, Amsterdam: North-Holland. Google Scholar |
| [22] |
J.-J. Laffont and J. Tirole, Competition in Telecommunications, MIT press, 2001. Google Scholar |
| [23] |
P. Lall, A. M. Featherstone and D. W. Norman, Productivity growth in the western hemisphere (1978-94): The caribbean in perspective, Journal of Productivity Analysis, 17 (2002), 213-231. Google Scholar |
| [24] |
P.-L. Lam and T. Lam, Total factor productivity measures for hong kong telephone, Telecommunications Policy, 29 (2005), 53-68. Google Scholar |
| [25] |
P.-L. Lam and A. Shiu, Productivity analysis of the telecommunications sector in China, Telecommunications Policy, 32 (2008), 559-571. Google Scholar |
| [26] |
D. Lien and Y. Peng, Competition and production efficiency: Telecommunications in oecd countries, Information Economics and Policy, 13 (2001), 51-76. Google Scholar |
| [27] |
G. Madden and S. J. Savage, Telecommunications productivity, catch-up and innovation, Telecommunications Policy, 23 (1999), 65-81. Google Scholar |
| [28] |
S. K. Majumdar, Does new technology adoption pay? electronic switching patterns and firm-level performance in us telecommunications, Research Policy, 24 (1995), 803-822. Google Scholar |
| [29] |
S. Malmquist,
Index numbers and indifference surfaces, Trabajos de Estadística, 4 (1953), 209-242.
doi: 10.1007/BF03006863. |
| [30] |
MIIT, Statistical bulletin of china's telecommunications industry in 2016, http://www.miit.gov.cn/n1146290/n1146402/n1146455/c5471508/content.html. Google Scholar |
| [31] |
M. I. Nadiri and M. Schankerman, The structure of production, technological change, and the rate of growth of total factor productivity in the bell system, 1979. Google Scholar |
| [32] |
M. Nishimizu and J. M. Page, Total factor productivity growth, technological progress and technical efficiency change: dimensions of productivity change in yugoslavia, 1965-78, The Economic Journal, 92 (1982), 920–936. Google Scholar |
| [33] |
H. Oniki, T. H. Oum, R. Stevenson and Y. Zhang, The productivity effects of the liberalization of japanese telecommunication policy, Journal of Productivity Analysis, 5 (1994), 63-79. Google Scholar |
| [34] |
J. B. Quinn and M. N. Baily, Information technology: Increasing productivity in services, The Academy of Management Executive, 8 (1994), 28-48. Google Scholar |
| [35] |
R. W. Shepherd,
Theory of Cost and Production Functions, Princeton University Press, 1970. |
| [36] |
N. D. Uri, Measuring productivity change in telecommunications, Telecommunications Policy, 24 (2000), 439-452. Google Scholar |
| [37] |
N. D. Uri, Productivity change, technical progress, and efficiency improvement in telecommunications, Review of Industrial Organization, 18 (2001), 283-300. Google Scholar |
| [38] |
C.-H. Yoon, Liberalisation policy, industry structure and productivity changes in korea's telecommunications industry, Telecommunications Policy, 23 (1999), 289-306. Google Scholar |
Table 1.
Operating environments of 31 DMUs in 2015
| DMU | GRP (billion yuan) | Population (million) | Per capita GRP(yuan) | Pr1 | Pr2 | Pr3 |
| Eastern region | ||||||
| Beijing | 2301 | 21.7 | 106009 | 36.2 | 181.7 | 76.5 |
| Tianjin | 1654 | 15.5 | 76178 | 22.2 | 88.5 | 63 |
| Liaoning | 2867 | 43.8 | 132054 | 23.7 | 97.9 | 62.2 |
| Shanghai | 2512 | 24.2 | 115723 | 33 | 129.7 | 73.1 |
| Jiangsu | 7012 | 79.8 | 322968 | 24.7 | 100.2 | 55.5 |
| Zhejiang | 4289 | 55.4 | 197543 | 26.6 | 131.5 | 65.3 |
| Fujian | 2598 | 38.4 | 119668 | 23.2 | 108.2 | 69.6 |
| Shandong | 6300 | 98.5 | 290200 | 11.4 | 92.3 | 48.9 |
| Guangdong | 7281 | 108.5 | 335387 | 25.9 | 133.5 | 72.4 |
| Hainan | 370 | 9.1 | 17056 | 18.8 | 98.2 | 51.6 |
| Whole region | 37185 | 495 | 75157 | 24.5 | 116.2 | 63.8 |
| Central region | ||||||
| Hebei | 2981 | 74.3 | 137292 | 13.2 | 82.6 | 50.5 |
| Shanxi | 1277 | 36.6 | 58805 | 12.1 | 88.5 | 54.2 |
| Jilin | 1406 | 27.5 | 64777 | 20.8 | 91.2 | 47.7 |
| Heilongjiang | 1508 | 38.1 | 69478 | 15.6 | 87.4 | 44.5 |
| Anhui | 2201 | 61.4 | 101362 | 12 | 68.2 | 39.4 |
| Jiangxi | 1672 | 45.7 | 77033 | 12.5 | 66.4 | 38.7 |
| Henan | 3700 | 94.8 | 170438 | 10.7 | 79.5 | 39.2 |
| Hubei | 2955 | 58.5 | 136113 | 14.9 | 77.4 | 46.8 |
| Hunan | 2890 | 67.8 | 133129 | 11.6 | 69.2 | 39.9 |
| Whole region | 20590 | 505 | 40790 | 14 | 79 | 45 |
| Western region | ||||||
| Inner Mongolia | 1783 | 25.1 | 82135 | 12.9 | 94.7 | 50.3 |
| Guangxi | 1680 | 48 | 77398 | 9.2 | 75 | 42.8 |
| Chongqing | 1572 | 30.2 | 72396 | 18.6 | 90.8 | 48.3 |
| Sichuan | 3005 | 82 | 138430 | 16.5 | 82.9 | 40 |
| Guizhou | 1050 | 35.3 | 48377 | 8.9 | 83.3 | 38.4 |
| Yunnan | 1362 | 47.4 | 62732 | 8 | 78.9 | 37.4 |
| Tibet | 103 | 3.2 | 4728 | 10.8 | 82.9 | 44.6 |
| Shanxi | 1277 | 36.6 | 58805 | 12.1 | 88.5 | 54.2 |
| Gansu | 679 | 26 | 31277 | 12.5 | 81 | 38.8 |
| Qinghai | 242 | 5.9 | 11133 | 17.7 | 87.9 | 54.5 |
| Ningxia | 291 | 6.7 | 13412 | 12.6 | 95.3 | 49.3 |
| Xinjiang | 932 | 23.6 | 42952 | 21.2 | 86 | 54.9 |
| Whole region | 13976 | 370 | 37770 | 13.4 | 85.6 | 46.1 |
| Whole country | 71751 | 1370 | 52389 | 17.1 | 93.5 | 51.4 |
| Note: DMU = Decision-making unit, GRP = Gross regional product, Pr1 = Fixed-line penetration rate(per 100 persons), Pr2 = Mobile penetration rate(per 100 persons), Pr3 = Internet penetration rate(%). Source: China Statistical Yearbook 2016. | ||||||
| DMU | GRP (billion yuan) | Population (million) | Per capita GRP(yuan) | Pr1 | Pr2 | Pr3 |
| Eastern region | ||||||
| Beijing | 2301 | 21.7 | 106009 | 36.2 | 181.7 | 76.5 |
| Tianjin | 1654 | 15.5 | 76178 | 22.2 | 88.5 | 63 |
| Liaoning | 2867 | 43.8 | 132054 | 23.7 | 97.9 | 62.2 |
| Shanghai | 2512 | 24.2 | 115723 | 33 | 129.7 | 73.1 |
| Jiangsu | 7012 | 79.8 | 322968 | 24.7 | 100.2 | 55.5 |
| Zhejiang | 4289 | 55.4 | 197543 | 26.6 | 131.5 | 65.3 |
| Fujian | 2598 | 38.4 | 119668 | 23.2 | 108.2 | 69.6 |
| Shandong | 6300 | 98.5 | 290200 | 11.4 | 92.3 | 48.9 |
| Guangdong | 7281 | 108.5 | 335387 | 25.9 | 133.5 | 72.4 |
| Hainan | 370 | 9.1 | 17056 | 18.8 | 98.2 | 51.6 |
| Whole region | 37185 | 495 | 75157 | 24.5 | 116.2 | 63.8 |
| Central region | ||||||
| Hebei | 2981 | 74.3 | 137292 | 13.2 | 82.6 | 50.5 |
| Shanxi | 1277 | 36.6 | 58805 | 12.1 | 88.5 | 54.2 |
| Jilin | 1406 | 27.5 | 64777 | 20.8 | 91.2 | 47.7 |
| Heilongjiang | 1508 | 38.1 | 69478 | 15.6 | 87.4 | 44.5 |
| Anhui | 2201 | 61.4 | 101362 | 12 | 68.2 | 39.4 |
| Jiangxi | 1672 | 45.7 | 77033 | 12.5 | 66.4 | 38.7 |
| Henan | 3700 | 94.8 | 170438 | 10.7 | 79.5 | 39.2 |
| Hubei | 2955 | 58.5 | 136113 | 14.9 | 77.4 | 46.8 |
| Hunan | 2890 | 67.8 | 133129 | 11.6 | 69.2 | 39.9 |
| Whole region | 20590 | 505 | 40790 | 14 | 79 | 45 |
| Western region | ||||||
| Inner Mongolia | 1783 | 25.1 | 82135 | 12.9 | 94.7 | 50.3 |
| Guangxi | 1680 | 48 | 77398 | 9.2 | 75 | 42.8 |
| Chongqing | 1572 | 30.2 | 72396 | 18.6 | 90.8 | 48.3 |
| Sichuan | 3005 | 82 | 138430 | 16.5 | 82.9 | 40 |
| Guizhou | 1050 | 35.3 | 48377 | 8.9 | 83.3 | 38.4 |
| Yunnan | 1362 | 47.4 | 62732 | 8 | 78.9 | 37.4 |
| Tibet | 103 | 3.2 | 4728 | 10.8 | 82.9 | 44.6 |
| Shanxi | 1277 | 36.6 | 58805 | 12.1 | 88.5 | 54.2 |
| Gansu | 679 | 26 | 31277 | 12.5 | 81 | 38.8 |
| Qinghai | 242 | 5.9 | 11133 | 17.7 | 87.9 | 54.5 |
| Ningxia | 291 | 6.7 | 13412 | 12.6 | 95.3 | 49.3 |
| Xinjiang | 932 | 23.6 | 42952 | 21.2 | 86 | 54.9 |
| Whole region | 13976 | 370 | 37770 | 13.4 | 85.6 | 46.1 |
| Whole country | 71751 | 1370 | 52389 | 17.1 | 93.5 | 51.4 |
| Note: DMU = Decision-making unit, GRP = Gross regional product, Pr1 = Fixed-line penetration rate(per 100 persons), Pr2 = Mobile penetration rate(per 100 persons), Pr3 = Internet penetration rate(%). Source: China Statistical Yearbook 2016. | ||||||
Table 2.
Descriptive statistics of the input and output variables, 2011-2015 (n = 31)
| Telecom revenue(million) | Labour (person) | Cap 1 | Cap 2 | Cap 3 | Cap 4 | |
| 2011 | ||||||
| Mean | 37825 | 48776 | 390945 | 515413 | 14009 | 55366 |
| Median | 30775 | 43488 | 378032 | 433175 | 12154 | 44740 |
| S.D. | 30489 | 32707 | 254058 | 503184 | 10424 | 39246 |
| Minimum | 2386 | 1090 | 50642 | 34540 | 1270 | 2300 |
| Maximum | 161716 | 152384 | 1162101 | 2644348 | 47815 | 190767 |
| 2012 | ||||||
| Mean | 41879 | 49125 | 477203 | 508122 | 14112 | 59363 |
| Median | 34519 | 43579 | 441060 | 411477 | 11902 | 49244 |
| S.D. | 33254 | 32550 | 325007 | 497595 | 9840 | 42026 |
| Minimum | 3301 | 1090 | 63145 | 34540 | 1336 | 3420 |
| Maximum | 176638 | 152293 | 1567817 | 2587636 | 43606 | 203926 |
| 2013 | ||||||
| Mean | 50668 | 49600 | 563023 | 411595 | 13254 | 63406 |
| Median | 42938 | 44245 | 505633 | 342237 | 10255 | 52558 |
| S.D. | 40798 | 32412 | 372801 | 301962 | 9135 | 43518 |
| Minimum | 3965 | 1308 | 74047 | 16620 | 1337 | 3930 |
| Maximum | 217609 | 151377 | 1735687 | 1446394 | 40865 | 211481 |
| 2014 | ||||||
| Mean | 58511 | 50493 | 664920 | 315595 | 13069 | 66137 |
| Median | 52066 | 44681 | 584039 | 224364 | 8907 | 58580 |
| S.D. | 47187 | 33387 | 463481 | 281664 | 13534 | 44984 |
| Minimum | 4543 | 1635 | 88892 | 14310 | 536 | 3930 |
| Maximum | 249354 | 155175 | 2081008 | 1337449 | 74018 | 214181 |
| 2015 | ||||||
| Mean | 75311 | 50079 | 802043 | 268276 | 8530 | 70371 |
| Median | 69949 | 44354 | 656959 | 207480 | 7204 | 58941 |
| S.D. | 60863 | 32599 | 570614 | 209756 | 6017 | 47591 |
| Minimum | 5379 | 1617 | 115695 | 12870 | 115 | 4480 |
| Maximum | 315003 | 150432 | 2511543 | 851520 | 28447 | 220258 |
| Note: Cap 1 = the length of optical cable lines (in kilometres); Cap 2 = the capacity of long-distance telephone exchanges(in circuits); Cap 3 = the capacity of local office telephone exchanges(in thousand exchange lines); Cap 4 = the capacity of mobile telephone exchanges(in thousand subscribers). | ||||||
| Telecom revenue(million) | Labour (person) | Cap 1 | Cap 2 | Cap 3 | Cap 4 | |
| 2011 | ||||||
| Mean | 37825 | 48776 | 390945 | 515413 | 14009 | 55366 |
| Median | 30775 | 43488 | 378032 | 433175 | 12154 | 44740 |
| S.D. | 30489 | 32707 | 254058 | 503184 | 10424 | 39246 |
| Minimum | 2386 | 1090 | 50642 | 34540 | 1270 | 2300 |
| Maximum | 161716 | 152384 | 1162101 | 2644348 | 47815 | 190767 |
| 2012 | ||||||
| Mean | 41879 | 49125 | 477203 | 508122 | 14112 | 59363 |
| Median | 34519 | 43579 | 441060 | 411477 | 11902 | 49244 |
| S.D. | 33254 | 32550 | 325007 | 497595 | 9840 | 42026 |
| Minimum | 3301 | 1090 | 63145 | 34540 | 1336 | 3420 |
| Maximum | 176638 | 152293 | 1567817 | 2587636 | 43606 | 203926 |
| 2013 | ||||||
| Mean | 50668 | 49600 | 563023 | 411595 | 13254 | 63406 |
| Median | 42938 | 44245 | 505633 | 342237 | 10255 | 52558 |
| S.D. | 40798 | 32412 | 372801 | 301962 | 9135 | 43518 |
| Minimum | 3965 | 1308 | 74047 | 16620 | 1337 | 3930 |
| Maximum | 217609 | 151377 | 1735687 | 1446394 | 40865 | 211481 |
| 2014 | ||||||
| Mean | 58511 | 50493 | 664920 | 315595 | 13069 | 66137 |
| Median | 52066 | 44681 | 584039 | 224364 | 8907 | 58580 |
| S.D. | 47187 | 33387 | 463481 | 281664 | 13534 | 44984 |
| Minimum | 4543 | 1635 | 88892 | 14310 | 536 | 3930 |
| Maximum | 249354 | 155175 | 2081008 | 1337449 | 74018 | 214181 |
| 2015 | ||||||
| Mean | 75311 | 50079 | 802043 | 268276 | 8530 | 70371 |
| Median | 69949 | 44354 | 656959 | 207480 | 7204 | 58941 |
| S.D. | 60863 | 32599 | 570614 | 209756 | 6017 | 47591 |
| Minimum | 5379 | 1617 | 115695 | 12870 | 115 | 4480 |
| Maximum | 315003 | 150432 | 2511543 | 851520 | 28447 | 220258 |
| Note: Cap 1 = the length of optical cable lines (in kilometres); Cap 2 = the capacity of long-distance telephone exchanges(in circuits); Cap 3 = the capacity of local office telephone exchanges(in thousand exchange lines); Cap 4 = the capacity of mobile telephone exchanges(in thousand subscribers). | ||||||
Table 3.
Malmquist productivity change index
| DMU | Annual averages (2011-2015) | ||||
| EffCh | TechCh | PEffCh | SEffCh | TFPCh | |
| Eastern region | |||||
| Beijing | 1.000 | 1.110 | 1.000 | 1.000 | 1.110 |
| Tianjin | 0.919 | 1.158 | 0.986 | 0.932 | 1.064 |
| Liaoning | 0.872 | 1.175 | 0.871 | 1.001 | 1.025 |
| Shanghai | 0.998 | 1.137 | 0.999 | 0.999 | 1.134 |
| Jiangsu | 1.003 | 1.246 | 1.000 | 1.003 | 1.250 |
| Zhejian | 1.000 | 1.218 | 1.000 | 1.000 | 1.218 |
| Fujian | 1.019 | 1.289 | 1.017 | 1.002 | 1.314 |
| Shandong | 0.935 | 1.235 | 0.936 | 0.999 | 1.155 |
| Guangdong | 1.000 | 1.209 | 1.000 | 1.000 | 1.209 |
| Hainan | 1.016 | 1.309 | 1.000 | 1.016 | 1.330 |
| Central region | |||||
| Hebei | 0.921 | 1.221 | 0.924 | 0.996 | 1.124 |
| Shanxi | 0.929 | 1.272 | 0.931 | 0.998 | 1.182 |
| Jilin | 0.946 | 1.260 | 0.952 | 0.994 | 1.191 |
| Heilongjiang | 0.888 | 1.177 | 0.899 | 0.988 | 1.045 |
| Anhui | 1.040 | 1.360 | 1.068 | 0.974 | 1.415 |
| Jiangxi | 0.993 | 1.234 | 0.992 | 1.001 | 1.225 |
| Henan | 0.987 | 1.289 | 1.014 | 0.973 | 1.272 |
| Hubei | 0.996 | 1.296 | 1.003 | 0.993 | 1.291 |
| Hunan | 0.958 | 1.266 | 0.957 | 1.001 | 1.213 |
| Western region | |||||
| Inner Mongolia | 0.914 | 1.349 | 0.930 | 0.983 | 1.232 |
| Guangxi | 0.972 | 1.137 | 0.969 | 1.003 | 1.105 |
| Chongqing | 1.017 | 1.337 | 1.015 | 1.002 | 1.361 |
| Sichuan | 0.963 | 1.371 | 1.000 | 0.963 | 1.320 |
| Guizhou | 1.061 | 1.166 | 1.060 | 1.001 | 1.237 |
| Yunnan | 1.009 | 1.274 | 1.009 | 1.001 | 1.285 |
| Tibet | 1.000 | 1.351 | 1.000 | 1.000 | 1.351 |
| Shanxi | 1.016 | 1.268 | 1.022 | 0.994 | 1.288 |
| Gansu | 1.043 | 1.291 | 1.060 | 0.984 | 1.347 |
| Qinghai | 1.043 | 1.439 | 1.000 | 1.043 | 1.501 |
| Ningxia | 1.006 | 1.259 | 1.000 | 1.006 | 1.266 |
| Xinjiang | 0.932 | 1.275 | 0.947 | 0.983 | 1.188 |
| Eastern region | 0.975 | 1.207 | 0.980 | 0.995 | 1.177 |
| Central region | 0.961 | 1.263 | 0.970 | 0.991 | 1.214 |
| Western region | 0.997 | 1.291 | 1.000 | 0.997 | 1.287 |
| All regions | 0.979 | 1.255 | 0.985 | 0.994 | 1.229 |
| DMU | Annual averages (2011-2015) | ||||
| EffCh | TechCh | PEffCh | SEffCh | TFPCh | |
| Eastern region | |||||
| Beijing | 1.000 | 1.110 | 1.000 | 1.000 | 1.110 |
| Tianjin | 0.919 | 1.158 | 0.986 | 0.932 | 1.064 |
| Liaoning | 0.872 | 1.175 | 0.871 | 1.001 | 1.025 |
| Shanghai | 0.998 | 1.137 | 0.999 | 0.999 | 1.134 |
| Jiangsu | 1.003 | 1.246 | 1.000 | 1.003 | 1.250 |
| Zhejian | 1.000 | 1.218 | 1.000 | 1.000 | 1.218 |
| Fujian | 1.019 | 1.289 | 1.017 | 1.002 | 1.314 |
| Shandong | 0.935 | 1.235 | 0.936 | 0.999 | 1.155 |
| Guangdong | 1.000 | 1.209 | 1.000 | 1.000 | 1.209 |
| Hainan | 1.016 | 1.309 | 1.000 | 1.016 | 1.330 |
| Central region | |||||
| Hebei | 0.921 | 1.221 | 0.924 | 0.996 | 1.124 |
| Shanxi | 0.929 | 1.272 | 0.931 | 0.998 | 1.182 |
| Jilin | 0.946 | 1.260 | 0.952 | 0.994 | 1.191 |
| Heilongjiang | 0.888 | 1.177 | 0.899 | 0.988 | 1.045 |
| Anhui | 1.040 | 1.360 | 1.068 | 0.974 | 1.415 |
| Jiangxi | 0.993 | 1.234 | 0.992 | 1.001 | 1.225 |
| Henan | 0.987 | 1.289 | 1.014 | 0.973 | 1.272 |
| Hubei | 0.996 | 1.296 | 1.003 | 0.993 | 1.291 |
| Hunan | 0.958 | 1.266 | 0.957 | 1.001 | 1.213 |
| Western region | |||||
| Inner Mongolia | 0.914 | 1.349 | 0.930 | 0.983 | 1.232 |
| Guangxi | 0.972 | 1.137 | 0.969 | 1.003 | 1.105 |
| Chongqing | 1.017 | 1.337 | 1.015 | 1.002 | 1.361 |
| Sichuan | 0.963 | 1.371 | 1.000 | 0.963 | 1.320 |
| Guizhou | 1.061 | 1.166 | 1.060 | 1.001 | 1.237 |
| Yunnan | 1.009 | 1.274 | 1.009 | 1.001 | 1.285 |
| Tibet | 1.000 | 1.351 | 1.000 | 1.000 | 1.351 |
| Shanxi | 1.016 | 1.268 | 1.022 | 0.994 | 1.288 |
| Gansu | 1.043 | 1.291 | 1.060 | 0.984 | 1.347 |
| Qinghai | 1.043 | 1.439 | 1.000 | 1.043 | 1.501 |
| Ningxia | 1.006 | 1.259 | 1.000 | 1.006 | 1.266 |
| Xinjiang | 0.932 | 1.275 | 0.947 | 0.983 | 1.188 |
| Eastern region | 0.975 | 1.207 | 0.980 | 0.995 | 1.177 |
| Central region | 0.961 | 1.263 | 0.970 | 0.991 | 1.214 |
| Western region | 0.997 | 1.291 | 1.000 | 0.997 | 1.287 |
| All regions | 0.979 | 1.255 | 0.985 | 0.994 | 1.229 |
Table 4.
Malmquist TFP index summary of annual averages during 2011 to 2015
| Year | EffCh | TechCh | PEffCh | SEffCh | TFPCh |
| 2012 | 1.006 | 1.068 | 1.006 | 1.000 | 1.074 |
| 2013 | 0.916 | 1.293 | 0.934 | 0.981 | 1.185 |
| 2014 | 1.017 | 1.293 | 1.007 | 1.010 | 1.315 |
| 2015 | 0.982 | 1.390 | 0.993 | 0.988 | 1.364 |
| Year | EffCh | TechCh | PEffCh | SEffCh | TFPCh |
| 2012 | 1.006 | 1.068 | 1.006 | 1.000 | 1.074 |
| 2013 | 0.916 | 1.293 | 0.934 | 0.981 | 1.185 |
| 2014 | 1.017 | 1.293 | 1.007 | 1.010 | 1.315 |
| 2015 | 0.982 | 1.390 | 0.993 | 0.988 | 1.364 |
Table 5.
Comparison of the indexes between the period of 2011– 2013 and the period of 2013–2015
| EffCh | TechCh | PEffCh | SEffCh | TFPCh | |
| 2011-2013 | 0.960 | 1.175 | 0.970 | 0.990 | 1.128 |
| 2013-2015 | 0.999 | 1.340 | 1.000 | 0.999 | 1.339 |
| EffCh | TechCh | PEffCh | SEffCh | TFPCh | |
| 2011-2013 | 0.960 | 1.175 | 0.970 | 0.990 | 1.128 |
| 2013-2015 | 0.999 | 1.340 | 1.000 | 0.999 | 1.339 |
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