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Inspiration Versus Perspiration in the Former USSR Area and China, Ca

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Inspiration Versus Perspiration in the Former USSR Area and China, Ca Does economic change really create inspiration? Inspiration versus perspiration in the former USSR area and China, ca. 1920-2010 Dmitry Didenko Research Associate, Institute of Sociology, Russian Academy of Sciences. [email protected] Péter Földvári Associate Professor, University of Debrecen (Hungary). [email protected] Bas van Leeuwen Postdoc Researcher, Utrecht University (the Netherlands). [email protected] Abstract In this paper we extend our previous studies (Didenko et al., 2012; Foldvari et al., 2012; Van Leeuwen et al., 2011) on the role of conventional factors of production (fixed, or physical, and human forms of capital) and their productivity depending on their interrelations and economic development policies. Methodologically based on Solow (1956, 1957) and Mankiw, Romer and Weil (1992) we apply our theoretical models on the factors of economic growth to compare China with the republics of the former Soviet Union and, to this end, put together our new databases for both regions. Following Krugman (1994), we distinguish between perspiration (i.e. production factors) and inspiration (i.e. TFP, which consists in turn of technical efficiency of the production factors and a general production frontier) factors and find that in the socialist central-planning period economic growth was largely driven by physical and, to lesser extent, human capital accumulation. Moreover, at these times conventional TFP change was much more negative (1930s for the FSU, 1950s for China). This means that focusing mainly on physical capital increases the factors of production (hence increasing growth via perspiration) but reduces the technical efficiency of the factors of production strongly (hence lowers the growth via TFP, i.e. inspiration). After the economic transitions were launched (end 1970s in China and end 1980s in the FSU) the inspiration/perspiration pattern somewhat changed. China managed to keep technical inefficiency relatively moderate, largely by massively increasing its human capital (which made it easier to make use of physical capital). At the same time, they managed to increase their productivity frontier. In the FSU, however, the change in the human to physical capital ratio was primarily caused not by an increase of human-, but rather by a decrease of physical capital. This means that, even though technical efficiency relatively increased, the general productivity frontier remained stable or declined. This changed in the late 1990s and the start of the 21th century when the FSU started to recover somewhat, only to reach the 1990 level. 1. Introduction There is a lively debate on whether it is inspiration (i.e. technological development) or perspiration factors (i.e. factors of production - physical1- and human capital) that drive economic development (i.e. Krugman, 1994). Both in China (e.g. Chow, 1993; Li et al., 1993; Wang and Yao, 2003; 1 We employ national accounts statistics where ‘fixed capital’ is standard usage while in theoretical literature ‘physical capital’ is more preferred; we mean the same things using both. 1 Whalley and Zhao, 2010) and to, a lesser extent, Russia and the FSU2 (e.g. Easterly and Fischer, 1995; Meliantsev, 2004) it has been argued that economic development in the period up to the 1980s has been largely driven by perspiration factors while their economic transitions have increased their growth potential based on technical development. In this paper we try to analyze the development of modern economic growth from a production factor perspective. We are methodologically based on the growth model developed by Solow (1956, 1957), who introduced the level of technology into neoclassical production function, augmented with human capital accumulation by Mankiw, Romer and Weil (1992) – MRW hereinafter. However, instead of natural proxies used by MRW, we prefer a human capital cost- based monetary measure as proposed by Judson (2002), updated by Van Leeuwen and Földvári (2008). To this end, we put together our new databases on the factors of economic growth for both regions. In Section 2 we provide with a brief summary of the estimation of human-and physical capital, as well as an analysis of its spread over the USSR and China respectively. We find that until the reforms were launched both in the FSU and China both countries experienced a faster accumulation of physical capital compared to human capital, combined with a less rapid economic growth in terms of GDP. This situation changed after the reforms, but the changes were different in the FSU area compared to China. In section 3 we test the focus of both China and the FSU on physical capital accumulation before the reforms. This is done by using a one-sector model from Foldvari et al. (2012) in which the government can either prefer to maximize material output or consumption (or a combination of both). Using this model, we show that the high physical- to human capital ratio in the pre-reform are was largely policy driven. Since more physical capital leads to higher productivity, this increased GDP per capita, i.e. a higher physical- to human capital ratio causes a higher GDP per capita. However, when after the reforms the focus shifted to more consumption goods (which were human capital intensive), the growth of human capital became increasingly important for an increase in GDP/cap, i.e. a lower physical- to human capital ratio leads to a higher GDP per capita. However, so far our focus has just been on the perspiration factors. Therefore, in section 4 we discuss the factor contributions to growth and analyse the development of total factor productivity (TFP), technical efficiency, and general technical development. We find that, whereas technical efficiency went down stronger in China (partly due to faster growth in physical and human capital), its production frontier increased stronger than in the FSU area. This suggests that technical inefficiency (the use of the factors of production) was somewhat different in China compared to the FSU. Therefore, section 5 deals with economic development and spatial growth of human capital in the FSU comparing it with China. The emphasis of this section is made on inequality issues between the republics of the FSU and the provinces of China. We end with a brief conclusion. 2. Data This research topic requires data on both physical3 and human capital as well as GDP per capita. Data on human- and physical capital as well as GDP for the socialist countries are being extended quite rapidly these past years. GDP estimates for China are taken from Maddison (2007) as updated 2 ‘The former Soviet Union’ (the FSU or ex-USSR) is the mostly common term used hereinafter for all time periods and for all territorial coverage of both the USSR and the Newly Independent States (NIS) after its collapse. The terms ‘USSR’ or ‘Soviet Union’ are used for the period of 1922-1991 only when this state existed within its actual borders. The term ‘Newly Independent States’ (NIS) refers to multiple of existing states on the territory of the former USSR, both to the period after its dissolution and to the period when they were the Soviet republics, basically within their current borders. Russia refers to the territory basically within the borders of the contemporary Russian Federation, in various periods. 3 This refers to the gross fixed capital stock. 2 on his website (http://www.ggdc.net/MADDISON/oriindex.htm) and for the individual provinces from the National Statistical Bureau (1999). For the USSR and Russia GDP per capita is drawn from Didenko et al. (2012) based on the official statistics, secondary literature4 and, in the case of the other NIS, on the World Bank (2011). Physical capital is taken from Wu (2004, 2009) for China as well as its provinces, from Didenko et al. (2012) for the USSR and from CIS Stat (2011) and Extended Penn World Tables v. 4.0 to make our estimations in this paper for the NIS. The cost-based human capital measure for China is taken from Van Leeuwen et al. (2011), for the FSU up to 1989 from Didenko et al. (2012) and for the period after 1990 calculated in this paper based on the data from the UIS UNESCO (2012), UNSD (2012) and CIS Stat (2011). The results of our estimations are reported in below Table. They are reported by region in both the FSU and in China. Both Table 1: Per capita GDP, human- and physical capital in the FSU and China in 1990 GK dollars 1930s* 1980s** 2000s*** H/cap H/cap H/cap GDP/cap K/cap (cost based) GDP/cap K/cap (cost based) GDP/cap K/cap (cost based) USSR 1,787 1,547 1,649 6,753 30,432 10,127 6,013 20,319 12,866 of which Armenia 1,634 5,434 21,333 16,226 7,768 14,703 13,718 Azerbaijan 1,856 4,942 17,793 11,369 4,168 17,891 5,032 Belarus 1,298 5,554 27,216 12,580 8,969 22,755 23,236 Estonia 10,630 40,003 21,780 16,065 56,227 38,623 Georgia 1,930 9,355 22,933 17,423 4,484 10,013 5,299 Kazakhstan 2,517 8,104 30,300 15,670 7,996 15,873 8,857 Kyrgyzstan 1,730 3,184 16,114 12,202 2,439 7,680 4,091 Latvia 9,278 35,690 17,446 11,374 40,074 24,498 Lithuania 8,538 33,013 16,931 8,736 30,796 21,181 Moldova 5,679 21,148 12,747 3,095 17,503 8,959 Russia 1,931 7,308 36,218 11,458 6,943 23,913 13,384 Tajikistan 1,563 3,214 12,830 10,694 1,228 1,894 1,210 Turkmenistan 2,483 3,614 21,696 11,180 3,137 10,932 NA Ukraine 1,083 5,585 26,399 10,924 3,893 23,019 8,912 Uzbekistan 1,111 4,124 15,498 9,598 4,151 5,291 22,935 China 570 9.2 1,453 2,080 827 4,710 12,705 8,572 of which Hebei 26.1 1,892 2,571 1,863 9,404 50,447 15,784 Shanxi 53.9 1,349 1,693 1,511 5,941 20,971 7,371 Inner Mongolia 2.9 1,456 1,884 2,153 7,279 14,316 12,339 Liaoning 49.5 2,470 3,079 2,397 9,131 16,488 15,883 Jilin 25.9 1,583 1,753 1,833 6,667 9,455 12,423 Heilongjiang 21.8 1,897 3,026 1,741 6,960 12,552 11,765 Jiangsu 12.6 2,820 5,463 1,202 13,202 47,499 20,173 Zhejiang 6.2 1,804 1,917 899 12,014 23,315 15,928 Anhui 0.6 1,033 1,495 580 4,643 12,254 6,257 Fujian 3.4 1,299 1,708 827 9,612 17,978 10,201 Jiangxi 1.3 991 1,277 549 4,396 6,511 5,175 Shandong 5.8 1,420 1,729 827 9,111 15,952 11,322 4 Didenko et al.
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