China’s economy has slowed significantly in the last five years. Some of this slow-down is a normal part of the convergence process of a developing economy. As a country reduces the gap in income per capita relative to advanced economies there is less scope for catch-up growth. China‘s income per capita is now around 25% of the US’. It is common for developing countries to stop growing quickly at a similar stage, getting stuck in a middle-income trap. However, catch-up growth can continue at a fast pace if a developing economy is on track to become developed. The main question in forecasting China’s future growth is whether over the next two or three decades it can make the transition to an advanced economy (following the growth experience of Asian economies such as South Korea or Taiwan) or whether it will stay a middle-income country, somewhere at 25-50% of GDP per capita in the US.
There are several factors suggesting that China may remain a middle income country in the long-term. These factors cast doubt on the Chinese government’s annual real GDP growth target for 2016-2020 of 6.5%, and point to a true growth rate closer to 4-5% in the next five years, declining to a 3-4% growth rate in the next decade.
Fast labour productivity growth in China mainly driven by capital accumulation
China’s economic development in the last 20 years is one of the fastest the world has ever seen. Output increased mainly due to rapid labour productivity growth (8.6% annually over 1995-2015), with a relatively small contribution from employment growth. According to OECD (2014) estimates employment should contract by 0.1% a year over the next 15 years, in comparison to annual employment growth of 0.5% over the last 15 years. However, China’s economy should suffer only moderately from this demographic slowdown if it can continue its fast labour productivity growth.
There are two sources of higher labour productivity: capital accumulation and greater efficiency in the use of existing capital and labour, also known as total factor productivity (TFP). China’s growth has been relatively unbalanced with around 75% of the increase in labour productivity coming from more capital per worker. In contrast higher TFP accounted for around 25% of growth, and has declined since 2007. TFP growth may have even become negative recently, meaning that labour productivity growth is now purely driven by capital accumulation with diminishing returns (Chang al, 2015, Wu, 2014).
The build-up in capital over 1995-2015 led to an increase in the investment to GDP ratio from 34.2% to 47.2%, and a decline in the consumption to GDP ratio from 46.4% to 38.2%, making China one of the most investment heavy economies in the world. The increase in capital has also relied in recent years on a fast expansion in lending to over 200% of GDP. Continuing the same pace of investment is likely to lead to a growing number of unprofitable projects, a higher proportion of non-performing bank loans or defaults and a greater risk of a credit crunch. There is also growing pressure on the government to redistribute more of the benefits of China’s economic expansion to workers and consumers. As a result, the Chinese government has started to promote a rebalancing of the economy towards consumption.
Maintaining fast economic growth in the future is likely to require a much greater reliance on total factor productivity increases. To understand the prospects for faster TFP growth we need to look more closely at three key structural trends that have shaped China’s economy in the last 20 years:
- The reallocation of production from the state to the private sector;
- State sector reforms;
- The government’s bias towards heavy industry.
Growing role of private Sector and first wave of state sector reforms boost productivity
The main factors behind China’s fast growth in the last 20 years are the successful transition to a mostly private sector economy and an increase in the productivity of the state sector. Starting in the 1990’s, The Communist Party officially encouraged a growing role for private business in the economy. As a result, the share of privately owned enterprises (POEs) in manufacturing employment increased from around 15% in 1998 to over 60% in 2011. The new POEs were typically significantly more efficient than state-owned enterprises (SOEs). As a result, the gradual reallocation of production to the private sector raised the overall productivity growth of the economy (Storesletten and Zilibotti, 2014).
SOEs also underwent major reforms in the late 1990’s. Inefficiently small SOEs were merged into bigger firms. Governance of SOEs improved, and they became more profit-oriented. Many SOEs were partially privatised, though local governments kept controlling shares in those companies. Less productive SOEs were liquidated or privatised. These reforms are estimated to have contributed 20% of manufacturing productivity growth in 1998-2007. However, TFP in the state sector is still on average about 30% lower than in the private sector (Song and Tai-Hsie 2015, Storesletten and Zilibotti, 2014).
The fast growth and reallocation to the private sector in 1995-2007 occurred despite significant inefficiencies in credit markets. External financing was and is still geared mostly to helping SOEs relative to POEs, with SOEs having much better access to loans from the dominant state-owned banks. POEs in contrast are much more reliant on self-financing. In 2011 almost 70% of the loans from the five largest banks went to SOEs, with only 3.5% going to private businesses (Matthews et al, 2013). Estimates for more recent years are hard to obtain, but the strong bias towards SOE lending is unlikely to have changed much. As a result of this bias, POEs are significantly more limited than SOEs in their ability to invest in capital and expand. The misallocation of external financing between the state and private sectors is likely to remain a major constraint on further productivity improvements. The flip-side of this is that financial sector reforms and a renewed emphasis on supporting the private sector could be the key to continuing China’s previous fast growth.
Bias towards heavy industry leads to high investment at lower efficiency
To understand the more negative trends of excessive investment and the recent decline in TFP growth we need to go beyond the usual distinction between the state and private sectors. Instead, we have to distinguish between more capital intensive sectors such as heavy industry, and more labour intensive sectors such as light industry and services. In 1996, the government launched a long-term strategy to boost the role of key sectors in the economy. These sectors include infrastructure (transportation, telecommunications), basic industry (electricity, coal and petroleum, natural gas production and refining, steel and other metal production, chemicals) and other pillar industries (electric machinery, automobiles, real estate and construction). Most of them could be classified as heavy industry.
Firms in these sectors are usually larger, more capital intensive and have lower TFP than firms in other industries. They get implicit government subsidies, mainly through cheaper government-guaranteed loans from state-owned banks. Even worse, there is evidence that lending to the larger firms in heavy industry is crowding-out lending to smaller firms in light industry and services due to the costs of expanding banks’ balance sheets. A symptom of this crowding- out is the negative correlation between short-term and long-term lending, a uniquely Chinese phenomenon. Recent model based analysis suggests that government policy favouring these sectors can explain most of the rise in the investment to GDP ratio, and the increasing dependence of growth on capital accumulation with a declining rate of return (Chang et al, 2015).
After the 2008 global financial crisis, government stimulus programmes have reinforced all these trends, leading to an unprecedented rise in the amount of credit, and continuing crowding-out of lending to more labour intensive but potentially more efficient sectors and firms. In the last two years, the government has become aware of these excesses and is now trying to rebalance the economy to be more consumer goods and services oriented. However, this rebalancing process is likely to progress slowly and may actually reduce growth in the short-term.
China’s growth prospects in 2016-2030
China’s wave of reforms in the late 1990s and early 2000s was enough to turn it into a middle-income country. But it is not necessarily enough to sustain its growth into an advanced economy. Distortions in credit markets, the bias towards lower productivity heavy industry and other problems such as pervasive corruption suggest that China could get stuck in its current-middle income country status for many decades.
The OECD (2014) uses a relatively sophisticated model to forecast 5% annual GDP growth in 2015-2030, declining to a 2.4% growth rate in 2030-2060. China’s recently announced reform plans are likely to give a moderate boost to this earlier projection. This leads us to a baseline forecast of 6.1% annual growth in 2016-2020, declining to 4.7% annual growth in 2021-2030.
Doubts about the accuracy of Chinese GDP numbers have increased recently, with many analysts suggesting the true growth rate of the economy is much lower than officially reported (1). China’s national accounts are still partially based on old Communist practices from the days of central planning. GDP statistics are much more reliant than in advanced economies on production reports from different firms, local governments and state agencies. There are strong political and economic incentives for local managers and officials to exaggerate production growth to hit certain targets. Price indices used to compute real growth rates are also subject to downward bias, tending to overestimate real growth.
A recent attempt by the Conference Board to adjust for these data problems and increase comparability with developed economies’ national accounting standards led to annual GDP growth estimates for 1978-2012 that are 2.6 percentage points lower than the official growth rates (Wu, 2014, Conference board, 2015). Assuming a similar bias going forward, our forecast for official GDP growth in 2016-2020 translates into an actual growth rate closer to 3.5%, declining to 2.1% annual growth in 2021-2030. The Conference Board estimate may have gone too far in the other direction, assuming for example that Chinese labour productivity only grew by 2% a year in the services sector after 1992 ( compared to the government’s estimate of 6% labour productivity growth in the sector). Using a more modest adjustment would still lead to a forecast of 4-5% annual GDP growth in 2016-2020, followed by 3-4% annual growth in 2021-2030.
Chun Chang, Kaiji Chen, Daniel F. Waggoner and Tao Zha. Trends and Cycles in China’s Macroeconomy . Working paper, 2015. https://www.frbatlanta.org/research/publications/wp/2015/05.aspx
Conference Board. Frequently Asked Questions on the Conference Board’s Alternative China GDP Series. Research note, 2015. https://www.conference-board.org/retrievefile.cfm?filename=FAQ-for-China-GDP-vs4_10nov15.pdf&type=subsite
Chang-Tai Hsie and Zheng Song2015. Grasp the Large, Let Go of the Small: The Transformation of the State Sector in China. Working paper, 2015. http://michaelzsong.weebly.com/uploads/4/8/1/4/48141215/cs_large_draft.pdf
Vo Phuong Mai Le, Kent Matthews, David Meenagh, Patrick Minford and Zhigui Xiao. Banking and the Macroeconomy in China: A Banking Crisis Deferred? Working paper, 2013. http://business.cardiff.ac.uk/sites/default/files/e2013_5_0.pdf
Growth Prospects and Fiscal Requirements over the Long Term. OECD Economic Outlook, Volume 2014/1, 2014. http://www.oecd.org/economy/Long-term-growth-prospects-and-fiscal-requirements.pdf
Kjetil Storesletten and Fabrizio Zilibotti. China’s Great Convergence and Beyond. Annual Review of Economics 6:14, 2014. http://folk.uio.no/kjstore/papers/China_Annual_Review_Economics.pdf
Harry X. Wu. China’s Growth and Productivity Performance Debate Revisited- Accounting for China’s Sources of Growth with a New Data Set. Working paper, 2014. https://www.conference-board.org/pdf_free/workingpapers/EPWP1401.pdf