The impact of product innovation on firm-level markup and productivity: evidence from China

This article empirically evaluates the impact of product innovation on firms’ markup and productivity. Based on a large sample of Chinese manufacturing firms, we estimate firm-level markup using the wedge between output elasticities of intermediate input and its cost share in total revenue. Firm productivity is measured as revenue productivity and adjusted with the estimated markup. The results suggest that product innovation increases firm-level markup and revenue productivity. However, the effect of product innovation on the adjusted productivity is mostly negative or insignificant. The observed relationships also vary in response to market structures. Our results indicate that the positive impact of product innovation on revenue productivity is mainly driven by price-cost markup changes rather than physical productivity improvements. Our study suggests the widely observed positive relationship between product innovation and revenue productivity should be interpreted with caution.     

Improving the estimation of total factor productivity growth: capital operating time in a latent variable approach

Measuring labor and capital services accurately is essential to obtaining reliable estimates of production functions and total factor productivity (TFP) growth. Using data on the operating time of capital, a series that exists for the French business sector, greatly improves the measurement of effective capital services in production. The ensuing estimation results are consistent with Cobb–Douglas technology under constant returns to scale, with the factor elasticities not statistically different from their income shares. In the same framework, TFP growth is estimated as a latent variable and found to be less volatile than accounting residuals, negatively correlated with employment, and free of cyclicality. It is statistically best estimated as a first-order autoregressive process, with an autoregressive coefficient of 0.95. Total factor productivity growth was estimated to have declined steadily between the mid-1970s and mid-1990s, but the rate of decline has diminished since then.     

How enterprise strategies are related to innovation and productivity change: an empirical study of Japanese manufacturing firms

This study analyzes the total factor productivity of 1067 Japanese manufacturing firms. In production estimation, we employ the directional distance function and Luenberger productivity indicator. Research and development strategy survey data are used to analyze the determinant factors related to improvements in innovation and productivity. Our results indicate that increasing technology and knowledge through a ‘black box’ process is related to an increase in productivity. Furthermore, the protection and management of production knowledge and expertise is a valid method of increasing global technical change.     

Economic liberalization and sources of productivity growth in Indonesian Banks: is it efficiency improvement or technological progress?

This article investigates the sources of productivity growth in the Indonesian banking sector during 23 years period from 1993 to 2015. The industry has gone through several episodes of policy reforms, starting from the radical deregulation in the late 1980s, the restructuring period following the 1997 Asian financial crisis, the consolidation period in the mid-2000s to the economic expansion in the 2010s. Using panel data of 98 commercial banks, we explore productivity growth using Malmquist indices complemented with bootstrapping technique to provide measures of the statistical precision of the results. The Malmquist index measures total factor productivity, efficiency change and technological change. Results show that productivity improves moderately and appears to be less volatile towards the end of the period. Furthermore, efficiency change tends to be the main source of productivity improvement rather than technological change.     

Panel‐data Estimates of the Production Function and the Revenue Function: What Difference Does It Make?*

The lack of individual firm information on output prices is a major problem in the econometrics of production. In particular, it may be expected to account for a significant share of the large discrepancies found between the cross‐sectional and time‐series estimates of capital and scale elasticities. However, taking advantage of two panel‐data samples for which we had such information, we find that estimating the revenue function (using a nominal output measure) or the production function proper (using a real output measure) makes very little difference for our results. The biases due to other sources of specification errors are probably more important.