Induced innovation: evidence from China’s secondary industry

ABSTRACT

We investigate the effect of rising labour costs on induced technological change in China’s secondary industry. Building on insights developed in a rich literature, we propose a model linking changes in labour productivity to changes in labour costs and the predetermined availability of physical capital. Importantly, we derive testable hypotheses to distinguish induced innovation from standard substitution of capital for labour under fixed technology. Our empirical results support the hypothesis that rising wages have induced labour-saving innovation in China, at least in the decade of the 1990s, but less so or not at all after the middle of the next decade.

KEYWORDS:

• Induced innovation
• labour productivity growth
• secondary industry
• China

JEL CLASSIFICATION:

• O30
• D22
• D24
• D33

Disclosure of potential conflicts of interest

No potential conflict of interest was reported by the author(s).

Notes

¹ This is by far the most comprehensive annual survey of industrial firms conducted by the National Bureau of Statistics of China (NBS). It includes all state-owned enterprises and non-state-owned enterprises with sales over 5 million yuan. The only data base that has a larger sample size is the Economic Census, but that is only conducted once in several years. In 2004 when an Economic Census was conducted, this sample account for about 90% of total sales. This data base is widely used in the literature, including Song, Storesletten, and Zilibotti (2011), Hsieh and Klenow (2009), and Brandt, Van Biesebroeck, and Zhang (2012), to name just a few. They are primarily located in secondary industry and results are quite robust to the exclusion of all enterprises not located in this industrial category. Changes in sample definitions and variables measured limit our ability to use the full-time range of available ASIE surveys.

² We have also examined alternative rates for the two-tail trim: 1%, 2%, 5%, 10% and 15%. Our estimation results are quite robust to the trimming of implausibly extreme values.

³ The impact of accelerating wage growth in China has led to an immense literature that we cannot fully cite here. We note the insights in Yang, Chen, and Monarch (2010) and those in the collection of papers on whether China has passed the Lewis Turning Point in China Economic Review (Arthur 2010).

⁴ The theoretical framework can be easily modified to examine the impact of labour scarcity on induced innovation by assuming labour is supplied inelastically at a fixed level $\bar{L}$, as examined in Acemoglu (2010).

⁵ As explained in the Online appendix, to ensure the existence of wage-induced innovation, the wage level should be less than some threshold value that increases with A. We believe this is a realistic assumption in the sample period targeted in our study because of a relatively abundant supply of rural workers. The resulting technology level, ${\theta }^{\ast }$, should be greater than 0.5. This is consistent with China’s income share data: for the industry sector (that is relevant to our provincial and ASIE data), the labour income share was always less than 0.5 1978–2004 (Bai and Qian 2010 Table 4).

⁶ Exogenous technical changes (A), such as exogenous industrial upgrading, will impact labour productivity and wage rates equally, so their growth rates will remain the same.

⁷ It is not appropriate to use equation (4a)(4a) $ln\left(\frac{Y_{it}}{L_{it}}\frac{1}{W_{it}}\right)={\varphi }_t+{\eta }_{l(i)}+{\epsilon }_{it}$(4a) to identify ${\theta }_0$, so we use $\left(1-{\theta }_t\right)/\left(1-{\theta }_0\right)$ to examine how ${\theta }_t$ changes over time.

⁸ The optimal choice of technology can be affected by level of capital stock (this can be demonstrated using our theoretical model), so we include capital stock and B_it as additional control variables to capture non-wage-induced innovation.

⁹ $\bar{K}$ is assumed predetermined as an accumulation of prior investments as in Ge and Yang (2014) and thus is not endogenous with current W. If there is no wage-induced technical change, then a change in W will impact Y/L only through reducing the amount of labour per unit of $\bar{K}$ along the isoquants of an exogenously given production function.

¹⁰ Our key theoretical results shown in equations 5(5) $ln{\left(\frac{Y}{L}\right)}_{it}=B_{it}+\beta ln{W}_{it}+\delta ln{\bar{K}}_{it}+{\epsilon }_{it}.$(5) and 7(7) $ln{\left(\frac{Y}{L}\right)}_{it}=B_{it}+{\gamma }_{0}ln{\left(\frac{\bar{K}}{L}\right)}_{it}+{\gamma }_{1}ln(W_{it})ln{\left(\frac{\bar{K}}{L}\right)}_{it}+{\gamma }_{2}ln({\bar{K}}_{it})ln{\left(\frac{\bar{K}}{L}\right)}_{it}+{\epsilon }_{it}$(7) are conditioned on capital stock. However, the functional form of the conditioning is unknown because the cost function to produce technology $\theta$ can be specified in many different ways. We use a simple log linear function of capital stock in the main text, but we also conducted extensive robustness checks using fractional polynomials and splines. Specifically, we estimated the following specifications:

$ln{\left(\frac{Y}{L}\right)}_{it}=B_{it}+{\beta }_{t}ln{W}_{it}+m({\bar{K}}_{it}\mathrm{o}\mathrm{r}ln{\bar{K}}_{it})+{\epsilon }_{it}(5^{\prime })$ and

$ln{\left(\frac{Y}{L}\right)}_{it}=B_{it}+{\alpha }_{t}ln{\left(\frac{\bar{K}}{L}\right)}_{it}+{\gamma }_{t}ln{W}_{it}\cdot ln{\left(\frac{\bar{K}}{L}\right)}_{it}+m({\bar{K}}_{it}\mathrm{o}\mathrm{r}ln{\bar{K}}_{it})\cdot ln{\left(\frac{\bar{K}}{L}\right)}_{it}+{\epsilon }_{it}(7^{\prime }),$

where m(.) is either a fractional polynomial function or a spline function. This allows us to control for a wide variety of trends in capital stock, which is not an essential part of our analysis. Estimates of primary parameters, i.e. α, β, and γ, are very close to those in the baseline specifications. Estimation results are also robust to inclusion of county-specific fixed effects.

¹¹ Again, when the ASIE data are used, we allow ${\gamma }_0$, ${\gamma }_1$ and ${\gamma }_2$to vary by years.

¹² Rising labour cost is closely related to the dynamics of rural-urban migration (Golley and Meng 2011; Knight, Deng, and Li 2011; Wang et al. 2011; Zhang, Yang, and Wang 2011). An individual firm has very limited power to influence rural-urban migration decisions. In the regression models, we include county fixed effects and regional time trends to control for local common factors that could affect both local wage rates and labour productivity.

¹³ The time trend is defined as (current year – 1978), and the regions are defined as ‘coastal,’ ‘northeast,’ ‘west,’ and ‘far west.’ The provinces included in each region are specified in the notes to table 3.

¹⁴ Estimation results in tabular form are available on request. In particular, we pay special attention to biases and size distortions caused by potentially weak instruments, and we adopt the latest econometric tools, robust to heterogeneity, serial correlation, and clustering. Dealing with weak instruments along with heteroskedasticity and serial correlation when multiple endogenous regressors and instruments are involved is still an open econometric question. We used a Stata package, weakiv (Finlay, Magnusson, and Schaffer 2013), to conduct the Anderson-Rubin test: the null hypothesis is rejected at the 1% level in all regression models. The Anderson-Rubin test is a joint test of the structural parameters (all the coefficients of endogenous regressors are equal to one in equation 5(5) $ln{\left(\frac{Y}{L}\right)}_{it}=B_{it}+\beta ln{W}_{it}+\delta ln{\bar{K}}_{it}+{\epsilon }_{it}.$(5) , or zero in equation 7) and the exogeneity of the instruments.

¹⁵ Estimation results are robust to alternative specifications of the time period for the IV. Since our IV is lagged ten years behind our potentially endogenous independent variable, this approach also limits the years for which we are able to estimate the regression model. We are aware of the pitfalls involved when using lagged values in instrumental variable estimation as noted widely in the literature (e.g. Reed 2015). Nevertheless, we believe these results are of interest as a test of the hypothesis of wage-induced innovation in China, and they add robustness to our results based on individual firms’ data.

¹⁶ In the case with one endogenous regressor and one instrument, the Kleibergen-Paap rk Wald F statistic is equivalent to the Olea, Luis, and Pflueger (2013) effective F statistic.

¹⁷ Much of this literature is summarized in An (2017).

¹⁸ If the elasticity of substitution differs from one, the capital share parameter is not equal to the capital income share in general.

¹⁹ A_t could take a different value in a different time period. The qualitative conclusion of our simulation results is unchanged under different values of A_t.

²⁰ This is a simple shortcut to introduce wage-induced innovation without specifying a full structure of the model to endogenize the choice of technology. We have also explored alternative specifications ($\theta$ = 0.5 + x$\cdot (ln(W_t)+ln(K_t)$) and $\theta$ = 0.5 + x$\cdot ln(W_t)$), and the qualitative conclusion remains unchanged.

²¹ We note that our evidence of the timing of induced innovation is roughly consistent with Li et al. (2012)’s citation of Ceglowski and Golub (2007), that the ratio of Chinese wages to labour productivity declined substantially from the 1980s through the middle of the next decade.