Popular literature suggests a rapid narrowing of the technology gap between China and the US, based on large percentage increases in Chinese patent applications and equally large increases in college registrants and completed PhDs (especially in sciences) in China in recent years (Submaranian 2008). There is little research that attempts to measure the technology gap directly using estimates of country aggregate technologies. This gap is usually thought to be smaller than differences in GDP per capita.
We assess the technology gaps between China and the US and between China and India between 1979 and 2008 with a constant elasticity of substitution (CES) production framework.
- Our results suggest that although China has a higher growth rate of total factor productivity (TFP) than India over the period, the bilateral technology gap between China and India is still in India’s favour.
India had higher income per worker than China in the 1970s and China’s much more rapid physical and human capital accumulation has allowed China to move ahead, but a bilateral technology gap remains.
- Also, we find that the technology gap between China and the US is significantly larger than that between India and the US for the period before 2008.
- The pairwise gaps between China-US and India-US remain large while narrowing at a slower rate than GDP per worker.
The role of technology in Chinese economic growth
A major development in the world economy over the last quarter of the 20th century has been strong economic growth and poverty reduction in both China and India. The Penn World Tables (see Figure 1) show that the real GDP (or real GDP per worker) in 2008 was almost 14.6 (or 9.4) and 5.3 (or 2.9) times that in 1979 for China and India respectively, while the same number for the US was 2.3 (or 1.6).
Figure 1. Indices of real GDP/worker for China, India, and the US, 1979-2008
Note: Year of 1979=100; in Chain indexed constant 2005 purchasing power parity dollars.
Source: Authors’ calculations using Extended Penn World Tables v.4.0, Marquetti and Foley (2011).
China’s and India’s large economic size, combined with rapid growth, means that their economic rise has had large impacts on the global economy, although their absolute income levels are still quite low (the real GDP per worker of China and India in 2008 was about 12.9% and 9.2% of that of the US, respectively). Recent literature analyses China and India’s growing presence in the world economy (see Wang et al. 2011 for a related discussion), and also conducts comparative growth accounting studies for these two countries (e.g. Herd and Dougherty 2007, Bosworth and Collins 2008).
It is widely recognised that technology/efficiency is at least as important as physical and human capital accumulation in explaining income differences across countries. Since the Cobb-Douglas specification is the most widely used for the aggregate production function, technology gaps across countries can be simplified as TFP differences. Howitt (2000) and Klenow and Rodríguez-Clare (2005) show how large TFP differences can emerge in a world with slow technology diffusion from advanced countries to other countries, while Hsieh and Klenow (2009) estimate the effects of resource misallocation on China’s and India’s manufacturing TFP and find that if capital and labour are hypothetically reallocated to equalise marginal products to the extent observed in the US, TFP could be boosted by 30%–50% in China and by 40%–60% in India.
However, as Caselli (2005) emphasises, the Cobb-Douglas specification is key to the literature explaining income differences across countries, while a generalisation of TFP assumption from Cobb-Douglas to CES specification can lead to major changes in results. Notably, there has been increasing recent empirical evidence that rejects the Cobb-Douglas specification in favour of CES aggregate production functions.
It is also noteworthy that China’s real GDP per worker did not surpass that of India until the years 1998 to 2000 (as shown in Figure 2). We can conjecture naturally that the more rapid accumulation of physical capital in China (as shown in Figure 3) may suggest a lower technology level for China compared to that of India at least before the middle of the 1990s.
Figure 2. Real GDP per worker for China and India, 1979-2008
Note: In Chain indexed constant 2005 purchasing power parity dollars.
Source: Data from Extended Penn World Tables v.4.0, Marquetti and Foley (2011).
Figure 3. Indices of capital labour ratio for China, India, and the US, 1979-2008
Note: Year of 1979=100.
Source: Authors’ calculations using Extended Penn World Tables v.4.0, Marquetti & Foley (2011).
How to measure the technology gap
We measure the technology gap between two economies in terms of the ratio between actual output in Economy 1 using Economy 1’s technology and inputs, and hypothetical output using Economy 2’s technology with Economy 1’s inputs. The roles of Economies 1 and 2 in such a comparison can be reversed to yield an alternative pairwise measure. We assume that each of the two economies produces a single final good, Y, with two factors: capital and labour. However, the two economies can have different technologies in production, i.e. they may have different parameters in (or even have different structures of) production function. They can also have different factor endowments.
Our definition of technology gaps is related to but differs from the widely used Malmquist productivity index. An advantage of using the technology gap measures set out here rather than a Malmquist productivity index is that our concept is flexible in the structure of the aggregate production function, and can be conveniently generalised to include technological improvements embodied in other parts of the production function besides the multiplicative productivity factor.
For a constant elasticity of substitution production function with Hicks-neutral or factor-augmenting technological change, the key issues in calculating the technology gap are how to parameterise the corresponding production functions by the observed data of the two economies. In order to compare results from different forms of production function or different values of the elasticity of substitution, by considering the number of experiments that should be conducted, we adopt the calibration approach here. We specify as our benchmark a CES production function with Hicks-neutral technological change that also incorporates human capital.
The datasets we used combine variables from two different sources. The first is Version 4.0 of the Extended Penn World Tables (EPWT version 4.0, Marquetti and Foley 2011). We extract from the EPWT Version 4.0 data set for the countries we study data on labour productivity (output per worker, expressed in real GDP per worker in chain indexed 2005 purchasing power parity dollars), and capital-labour ratio (physical capital per worker, expressed in 2005 purchasing power parity dollars). To construct data on human capital, we use Barro and Lee (2011) estimates of the average years of schooling in the population over 15 years old (Barro and Lee v.1.2).
Technology gaps between China, India, and the US
Figure 4 presents the indices of technology/efficiency level between 1979 and 2008 for China, India, and the US, as well as the estimated parameters derived from a standard curve fitting procedure. The technology level index for the US in 1979 is 1, as all the time series of yit, kit and hit are normalised by the corresponding US data in 1979.
We can see from Figure 4 that the rate of technological change in China is much higher than India (as emphasised in literature on TFP growth rates), while the latter is still higher than that of the US. Notably, though both the technology levels of China and India are much lower than that of the US, at least before the year 2008, the technology level of India is higher than that of China. Although factor accumulation rates in China exceed those of India, and economic growth rates in China also exceed those of India, the technology gap is in favour of India before 2008 since the initial gap is in India’s favour and closing more slowly than relative GDP growth rates.
Figure 4. Technology levels with CES function for China, India, and the US, 1979-2008
Notes: Index of ‘CN’, ‘IN’ and ‘US’ denote China, India and the US; and Index of ‘cb’ and ‘ft’ denote calibrated and fitted data, respectively.
Source: Authors’ calculations.
The technology gaps of China and India both with the US and with each other are shown in Figure 5. The result suggests that if China and India’s input factors were hypothetically used with US technology, the hypothetical output would be much larger; if China’s input factors were hypothetically used with India’s technology, the hypothetical output would also be higher, and vice versa.
Figure 5. Pairwise technology gaps with CES function between China, India, and the US
Notes: Technology gaps denote ‘US-CN’ and ‘US-IN’ are China and India’s hypothetical output/worker with US technology divided by their estimated output/worker with own technology, and technology gap ‘IN-CN’ is China’s hypothetical output/worker with India’s technology divided by its estimated output/worker with own technology.
Source: Authors’ calculations.
Figure 5 also indicates that between 1979 and 2008, the technology gaps of China and India relative to the US decrease from about 7.79 and 3.59 to 3.86 and 3.37, respectively. These results confirm that the technology gap between China and the US is larger than that between India and the US, while both the gaps of China and India narrow at a slower rate than GDP per worker (consistent with the pattern shown in Figure 4). The technology gap of China relative to India between 1979 and 2008 decreases from 2.17 to 1.15; the technology gap is in favour of India before 2008, since the initial gap is in India’s favour.
For robustness purposes, we compare results from a Cobb-Douglas production function as well as different values of the elasticity of substitution and capital share. The results of these extensive experiments show that our previous inference on changes in the technology gaps of China and India both with the US and with each other seems robust.
These findings are noteworthy, since it seems that in the existing literature little attention is paid to the technology gap between China and India. Recent literature seems more inclined to emphasise the much higher growth rate of TFP (or technological change) in China than in India, and thus misses China’s comparatively lower aggregate efficiency or technology level (especially the initial gap) compared to India.
Authors’ note: We are grateful to the Ontario Research Fund (ORF) for financial support and to seminar participants at UWO for comments.
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