Decline In Total Factor Productivity Growth Research Articles

Productivity and Economic Growth in South Asia and China (Distinguished Lecture)

This paper, begins with a discussion of the well-known issues involved in defining and measuring total factor productivity (TFP) and its contribution to growth (as well as the possible contribution of growth to productivity), the economic theory underpinning productivity, and policies that impact on and influence changes in productivity. It is followed by, first, a selective discussion of the studies on cross-country variation in productivity levels and growth, and then the experience of South Asia and China in a comparative perspective across the region and across the developing world. The share of South Asia in global GDP and its growth has remained stagnant since the early nineties. Disturbingly, except India, the rest of South Asia experienced a decline in TFP growth between 1989-95 and 1995-2003. The paper concludes that for achieving sustained productivity growth, well-functioning social and economic institutions are important, since through their incentive structure they influence, labour force participation, savings and accumulation of human and physical capital, risk-taking and innovation as well as efficiency of resource allocation. Public policies, particularly macro-economic, foreign trade and investment policies, matter a great deal.

Technical progress and induced innovation in China: a variable profit function approach

We propose a new methodology to estimate empirically the input price-induced technical change and total factor productivity (TFP) growth in China. Our primary goal is to test Hicks’ induced innovation hypothesis by examining whether technical change in China has been induced by sharp increase in input prices that have accompanied its rapid economic growth. Utilizing the idea of a firm’s two-stage optimization problem, we develop a new parametric form of the variable profit function wherein the derived input demand and output supply functions can be easily constrained to be regular, and the functional structure is parsimonious in the number of parameters. Applying this methodology to Chinese time series data for 1986–2017, we find that not only is wage-induced innovation significant and quantitatively important, but also that it substantially buffered a decline in TFP growth before 2006 that would otherwise be substantial. Overall, we conclude that China’s economic growth is predominantly driven by wage-induced innovation along with massive injection of heavily subsidized physical inputs in public works and huge investment in industrial sectors.

Measuring the productivity growth of Malaysia's water sector: Implications for regulatory reform

We apply the bootstrap Malmquist-Luenberger productivity index (MLPI) to measure the total factor productivity (TFP) growth for 14 water operators in Malaysia during the years 2000–2017. The analysis confirms that Malaysia's water sector experienced a deterioration in average productivity of 1.21% per year, attributed mainly to technological regression. Before the regulatory reform, the average productivity of water operators improved and was positively influenced by efficiency change. After the reform, the water operators suffered a decline in TFP growth due to decreased efficiency and technical change. The inefficient utilization of capital expenditure and aging water infrastructure are possible reasons for this effect. Regulatory reform is necessary but insufficient to improve the productivity growth of Malaysia's water sector.

The Impact of Uncertainty Shocks on State-Level Total Factor Productivity

Using U.S. data over the period 1961 - 2000 we estimate a structural factor-augmented vector autoregressive model and find that a one standard deviation shock to macroeconomic uncertainty generates declines in state-level total factor productivity (TFP) growth that range from -0.15 to -0.98 percentage points at their peak negative response. Cross-sectional regressions show that industry mix is an important channel through which uncertainty shocks affect TFP growth. In particular, a state with a larger manufacturing sector experiences a larger decline in TFP growth.

Components of Productivity Growth of the Manufacturing Industries of Petroleum and Coal Products in India: An Interstate Analysis

The study applies stochastic frontier approach to estimate and decompose the sources of total factor productivity growth (TFPG) of the 2-digit manufacturing industries of petroleum and coal products in fifteen major industrialized states in India as well as in All-India during the period from 1981-82 to 2010-11, during the entire period, during the pre-reform period (1981-82 to 1990-91) and post-reform period (1991-92 to 2010-11), and also during two different decades of the post-reform period, i.e., during 1991-92 to 2000-01 and 2001-02 to 2010-11. The components of TFPG are: technological progress (TP), technical efficiency change (TEC), economic scale change (SC) and allocation efficiency change (AEC). According to the estimated results, technological progress (TP) is the major contributing factor to TFPG of the organized manufacturing industries of petroleum and coal products in India and in its fifteen major industrializes states during 1981-82 to 2010-11. Further, TFPG of the 2-digit manufacturing industries of petroleum and coal products in India and in its fifteen major industrialized states declined during the post-reform period and the decline in TFPG of these 2-digit industries during that period is mainly accounted for by the decline in TP of the same during that period. However, allocation efficiency change (AEC) and economic scale change (SC) of them remain very negligible or even negative too in many states under study. Further, TEC of them remain unchanged or it is time invariant in nature as statistical tests suggest. So that increase in the combined effect of AEC and SC of them could not offset the decrease in their TP during that period. As a result TFPG of the 2-digit manufacturing industries of petroleum and coal products declined in India and its fifteen major industrialized states during the post-reform period.

Components of Total Factor Productivity Growth of the 2-Digit Manufacturing Industries in Karnataka: A Stochastic Frontier Approach

The study estimates the sources of total factor productivity growth (TFPG) of the 2-digit manufacturing industries in Karnataka during the period from 1981-82 to 2010-11, during the entire study period, during the pre & post reform period (1981-82 to 1990-91 and 1991-92 to 2010-11) and also during two different decades of the post-reform period, i.e., during 1991-92 to 2000-01 and 2001-02 to 2010-11 using stochastic frontier approach. Technological progress is found to be the major driving force of TFPG and the decline in TFPG of the state’s manufacturing industries during the post-reform period is mainly accounted for by the decline in technological progress (TP) of the same during that period.

REGIME-SWITCHING PRODUCTIVITY GROWTH AND BAYESIAN LEARNING IN REAL BUSINESS CYCLES

Growth in total factor productivity (TFP) in the USA has slowed down significantly since the mid-2000s, reminiscent of the productivity slowdown of the 1970s. This paper investigates the implications of a productivity slowdown on macroeconomic variables using a standard real business cycle (RBC) model, extended with regime-switching in trend productivity growth and Bayesian learning regarding the growth regime. I estimate the Markov-switching parameters using US data and maximum-likelihood methods, and compute the model solution using global projection methods. Simulations reveal that, while adding a regime-switching component to the standard RBC setup increases the volatility in the system, further incorporating incomplete information and learning significantly dampens this effect. The dampening is mainly due to the responses of investment and labor in response to a switch in the trend component of TFP growth, which are weaker in the incomplete information case as agents mistakenly place some probability that the observed decline in TFP growth is due to the transient component and not due to a regime switch. The model offers an objective way to infer slowdowns in trend productivity, and suggests that macroeconomic aggregates in the USA are currently close to their potential levels given observed productivity, while counterfactual simulations indicate that the cost of the productivity slowdown to US welfare has been significant.

Decomposition of Output and Productivity Growth in the Organised Manufacturing Industries in India: An Inter-State Analysis

This article examines and applies the theoretical foundation of the decomposition of output and total factor productivity growth (TFPG) of the aggregate manufacturing industries in 15 major industrialised states in India as well as in all-India during the period from 1981–1982 to 2010–2011, during the entire period, during the pre-reform period (1981–1982 to 1990–1991) and post-reform period (1991–1992 to 2010–2011), and also during two different decades of the post-reform period, that is, during 1991–1992 to 2000–2001 and 2001–2002 to 2010–2011. Output growth is decomposed into input growth effect and TFPG where the three attributes of TFPG are adjusted scale effect, technological progress (TP) and technical efficiency change. A stochastic frontier model with a translog production function is used to estimate the growth attributes of output and total factor productivity (TFP). The empirical results show that input growth is the major contributor to output growth, whereas TP is found to be the major contributor to TFPG and the decline in TFPG of the organised manufacturing sector in India and in its major industrialised states during the post-reform period is mainly due to the decline in TP of the same during that period. JEL Codes: C23, D24, L6, O47

Productivity and efficiency of labour intensive manufacturing industries in India

Purpose This paper aims to make an attempt to identify labour intensity of organized manufacturing industries in India using the Annual Survey of Industry (ASI) data at three-digit level. It estimates total factor productivity growth (TFPG) and technical efficiency for both labour intensive and all manufacturing industries during the pre- and post-reforms periods. Design/methodology/approach The study uses three approaches to estimate TFPG. They are growth accounting (GA) (non-parametric), production function with correction for endogeneity – Levinsohn-Petrin (LP) (semi-parametric) and stochastic production frontier (SPF) analysis (parametric). The study uses ASI data published by Central Statistical Organization, Government of India for the period 1980-1981 to 2007-2008 for the analysis. Findings The study finds that the rate of decline of the labour intensity is more pronounced in the case of labour-intensive industries than all the manufacturing industries. The results of GA method suggest that the TFPG of labour-intensive industries has declined continuously from the pre-reforms period to the post-reforms period. Similarly, LP method indicates a continuous decline in TFPG of labour-intensive manufacturing industries during the post-reforms period. Interestingly, the results of SPF method also corroborate the findings of earlier two methods at the aggregate level but vary at a certain degree at the disaggregated level. Originality/value This paper is useful in the context of India considering the importance given to labour-intensive industries by the present government in terms of reviving the sector and improving the productivity and output.

Measuring the Performance of River Basins: Application of Total Factor Productivity Approach to Andhra Pradesh, India

Total Factor Productivity (TFP) measures the growth in output which is not accounted for by inputs. Data Envelopment Analysis which forms the basis for the computation of Malmquist TFP index is used in this paper to study the performance of different river basins in Andhra Pradesh state, India. The results indicated that average technical efficiency of all the basins is only 66%. In all the basins there was a decline in growth of agricultural output during the first two decades, viz., 1979-1980 to 1988-1989 and it had picked in the last decade (1999-2000). All the river basins have TFP change greater than 1 indicating progress in agricultural productivity. Out of the 40 river basins, 14 river basins have technical efficiency change less than 1 indicating decline in TFP growth whereas all the basins have technical changes greater than 1 implying that there is shift in production frontier over years. In general, within the TFP, technical change contributed more than technical efficiency change. Looking at the future options for increasing the agricultural output in the river basins, it is important to focus on improving the TFP growth compared to increasing the quantities of physical inputs.

Banking Sector Deregulation and Productivity Change Decomposition of Indian Banks

The competition in the Indian Banking Industry has intensified since the financial liberalization, which started in the early 1990s. The paper tries to examine changes in bank productivity growth through employing the Malmiquist Total Factor Productivity (TFP) indices. The study also decompounds the TFP change of banks into technological progress, pure technical efficiency and scale efficiency change. Overall total productivity growth of banks was almost stagnant during the study period. The contribution of technical progress towards productivity is decline, however, technical efficiency and scale efficiency has been improved for all banks. The highest TFP growth has been observed among public sector banks, followed by old private banks (mostly in terms of increasing efficiency in catching up with new private and foreign banks), while both new private banks and foreign banks recorded decline in TFP growth.