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　　The two authors applied “regression towards the mean” to explain China ’sslowdown and claimed that such a statistical pattern was unavoidable. With aset of panel data, their argument overshadowed the protracted and varied growthpractices of numerous countries, overlooking the catch-up attributes ofdeveloping countries. Since this argument fails to explain the spectaculargrowth that took place in catching-up economies like Japan ,the Four Asian Tigers and China ,and since it does not provide any reasonable interpretation of China ’sslowdown, the “regression towards the mean” prediction is not convincing.Delineating the growth percentages of China ’s economy in the coming 20years following this methodology is like making a pair of “mean-sized” shoesfor everybody in the world, regardless of their gender, age and size.

　　Barro (2016) arrived at a similar prediction andconclusion, i.e., China ’seconomic growth rate will swiftly fall to the level of 3% to 4%, thus missingthe official growth targets of 6% to 7% set for the 13th Five-YearPlan (FYP) period (2016-2020). His argument is based on his copyrighted“conditional convergence” analytical framework. In his growth regression model,the determinants of growth rate are classified into the following two types:first, the convergence effect with initial per capita GDP (in logarithm) as anindependent variable; second, the group of explanatory variables that determinesthe steady state of growth (otherwise referred to as “X variables”). Afternumerous rounds of growth regression, he developed what he believed to be the“iron law of convergence”, i.e., it is impossible for a country to convergewith a more advanced economy or its own steady state with a growth rate thatdeviates from 2% over a long period of time; now that China’s growth hassignificantly outstripped what is predicted under this model, it is thusunlikely for China to maintain its current growth momentum in the future.

　　Granted, with a rising per capita income level andnarrowing potentials of convergence, slowdown is undoubtedly a natural outcome.However, even if Barro’s analytical framework of convergence is recognized,numerous X variables that affect growth still exist outside the law ofconvergence. Barro conceded that for a particular economy, there could beunique X variables or country-specific attributes that defy the so-called “ironlaw” or “mean”. For instance, Barro and his collaborator (Barro andSala-i-Martin, 1995) incorporated more than 100 explanatory variables intotheir growth regression model and discovered their significance. China ’seconomic growth story not only has general significance but is unique as well.Overlooking China ’suniqueness will cause China ’sgrowth potentials to be underestimated and the timing and extent of itsslowdown to be misjudged[1].

　　Eichengreen et al. (2011) do not admit any iron law ofeconomic slowdown. They made special efforts in identifying the country-specificfactors of slowdown in economy and TFP. According to their findings, an economywill encounter two rounds of deceleration when its per capita GDP in 2005purchasing power parity term is in the range between US$10,000 and US$11,000and between US$15,000 and US$16,000, respectively. By their definition, China has yetto reach the typical starting point of slowdown, i.e., US$10,000, but partiallymeets the definition of slowdown, i.e., its growth rate dropped from about 10%before 2012 to less than 8% afterwards, down two or three percentage points.Although comparison is conducted between the average growth rates of sevenyears before the slowdown and seven years after the slowdown, what we know forsure is that China ’seconomy indeed cannot return to its 10% growth rate.

　　In another paper, Eichengreen et al. (2013) identified afew common factors related to slowdown, including,inter alia, the “regression towards the mean” effect, diminishingdemographic dividends caused by population ageing, falling returns as a resultof high investment rates, as well as an underestimated exchange rate thatimpedes the upgrade of industrial structure. They also identified some factorsthat can reduce the probability of slowdown, such as better human capital accumulation.However, they did not present a clear account of the causal relationshipbetween certain factors and slowdown, nor did they distinguish cyclical factorsfrom growth factors. On the other hand, they laid no special emphasis on theimportant findings in their earlier paper (Eichengreen et al., 2011), i.e., TFPreduction can explain 85% of economic slowdown. Nevertheless, this omission wascompensated for in another paper by the same authors specifically on TFPslowdown (Eichengreen et al., 2015).