Biased Technical Change Research Articles

SUBSTITUTION BETWEEN CLEAN AND DIRTY ENERGY WITH BIASED TECHNICAL CHANGE

AbstractThe elasticity of substitution between clean and dirty energy lies at the center of leading economic analyses of climate policy. Despite the importance, empirical assessments of this key parameter remain scarce. This article estimates the clean‐dirty elasticity of substitution using data on French manufacturing firms. Using the elasticity estimates, I then measure the bias in technical change at the firm level and find that technical change was largely biased toward dirty energy, despite a recent shift toward clean technologies. Finally, I recover the aggregate elasticity that combines substitution within firms and reallocation across firms from the micro elasticity estimates.

Explaining the decline of China's labor share: A wide replication of Oberfield and Raval (2021)

SummaryChina's labor share has declined since late 1990s. Using the methodology developed by Oberfield and Raval, this paper estimates China's aggregate capital‐labor elasticity of substitution, leveraging the estimated micro‐level elasticities. The findings indicate that China's aggregate capital‐labor elasticity falls within the range of 0.9 to 1. Utilizing this estimated aggregate elasticity for labor share decomposition, we find that the bias of technical change emerges as the predominant factor driving the decline in labor share.

Multiple directions for measuring biased technical change

The technological change component of the Malmquist productivity index is defined as the radial distance between isoquants along a single direction. Therefore, it relies on limited information to measure technological change, which is a naturally global phenomenon. This becomes problematic, particularly when technological change is not Hicks-neutral but biased towards certain inputs or outputs. To mitigate this problem, I propose a more global measure of technological change that uses two directions. When combined with existing measures of other contributing factors, this measure constitutes an alternative productivity index, which is the geometric mean of the Malmquist and Hicks–Moorsteen productivity indices based on the cone technologies.

Digital financial development, synergistic reduction of pollution, and carbon emissions: evidence from biased technical change.

Environmental pollution control and greenhouse gas emissions reduction have become the main ecological protection issues. The digital transformation of the financial sector provides a vital opportunity to holistically promote environmental governance. This article incorporates the synergistic reduction of pollution and carbon emissions into the environmental governance system of digital financial development. Using panel data from 280 cities in China between 2011 and 2018, we examine the impact of digital financial development on the synergistic reduction of pollution and carbon emissions. We find that (1) digital finance development can significantly improve the synergistic reduction of pollution and carbon emissions, effectively lowering carbon emissions while reducing pollution. External environmental concerns can further unleash the potential for digital finance development to enhance its synergistic reduction of pollution and carbon emissions. (2) The key transmission mechanism lies in the dual guidance of digital finance development toward biased technical change, i.e., toward energy-saving elements on the input side and toward reduced pollution output on the output side, thereby inducing the synergistic reduction of pollution and carbon emissions. (3) The synergistic effect of digital finance development on pollution and carbon reduction depends on the necessary regional development endowment, such as strong green technology innovation capabilities, lower traditional financial accessibility, and carbon sink reserves. This study expands the understanding of the environmental effects of digital finance development and offers crucial insights for exploring the optimal development path under green strategies.

Identifying the elasticity of substitution with biased technical change: a structural panel GMM estimator

Summary: This paper provides a structural panel GMM (P-GMM) estimator of the elasticity of substitution between capital and labour that does not depend on external instruments, and which can be applied in the presence of biased technical change. We identify the conditions under which P-GMM is a consistent estimator and compare it to a fixed effects estimator. Using a Monte Carlo study, we find that the P-GMM estimator is nearly unbiased provided the number of time periods (T) is not too small. We show analytically how the small-T bias is related to metrics of weak identification. In an application on manufacturing firms in Norway, we estimate the elasticity of substitution to be 1.9 using the P-GMM and 1.0 using the fixed effects estimator. Neglecting simultaneity may thus lead to the conclusion that capital and labour are complements or can be described by Cobb–Douglas technology, when, in fact, they are substitutes.

Dynamics of Employment and Accumulation of Capital in Colombia, 1965–2019: An Econometric Analysis

Following Marx’s general law of capitalist accumulation, the present article seeks to evaluate the impact of fixed capital stock, labor productivity, and the mass of profits on the dynamics of employment in Colombia from 1965 to 2019 using a Vector Error Correction Model. The results show that the level of employment exhibits a significant long-term relationship with both the fixed capital stock and labor productivity. In line with Marx’s theory, employment expands with fixed capital stock and contracts with labor productivity. In turn, the mass of profits has positive effects on both the capital stock and labor productivity. The empirical findings also support the existence of Marx’s biased technical change as well as a negative feedback effect from employment to profits. JEL Classification: J21, C21, C22, B51

Classical and Keynesian models of inequality and stagnation

This paper studies two formal models of long run growth with a medium-run distributive cycle, both of which feature causal links from the rise in inequality to a deterioration of long run macroeconomic performance. Both versions feature an endogenous income-capital ratio: one through the Keynesian notion of effective demand, the other building on induced bias in technical change. A key focus of the analysis is on the assumptions necessary in both frameworks to generate policy implications consistent with the observed decline of the labor share, the income-capital ratio, and labor productivity growth during the neoliberal era. Importantly, both theories: (a) provide space for mutually reinforcing pro-labor and pro-growth policies in the long run, although they differ in the mechanisms at play in these processes; (b) imply a potential tradeoff between pro-labor policies and growth on one hand, and long-run employment on the other; (c) are consistent with the evidence on the distributive cycle at business cycle frequency.

Biased technical change, capital deepening, and efficiency of environmental regulations: Evidence from the Chinese provinces

This study uses the Malmquist-Luenberger index to test how the capital-biased technical change affects the efficiency of environmental regulation in China using the provincial-level panel data from 2004 to 2018. It is found that there is a significant capital-biased technical change. The total factor productivity growth promotes the provincial-level efficiency of environmental regulation. However, capital deepening hinders the enhancement of the efficiency of environmental regulation. Finally, the Spatial Durbin model estimations indicate that the capital-biased technical change positively affects the provincial efficiency of environmental regulation, and its components exhibit remarkable spatial spillover effects.

The aggregate productivity slowdown: A system approach

I revisit the productivity slowdown debate by estimating the capital-labor elasticity and the bias of technical change for the U.S. economy under four different models of technical change. One with constant growth rates, one with a structural break in the constant growth rates, one in which growth is linear, and one with flexible time-varying growth rates. I find evidence in support of non-constant growth rates of factor-augmenting technical change. Labor-augmenting technical change growth rates are decelerating, while capital-augmenting technical change is non negligible but vanishes quickly.

Trade barrier decrease and environmental pollution improvement: new evidence from China’s firm-level pollution data

PurposeTrade and environment are essential issues closely related to the development of the national economy and the improvement of people’s livelihood in the new era. The Report to the 19th National Congress of the Communist Party of China (CPC) listed the construction of a strong trading power as an important part of building a modern economic system and pollution prevention and treatment as one of the three key battles to win the decisive victory of building a moderately prosperous society in all respects. However, the relationship between trade and environmental pollution is still very controversial in the existing literature, and there is a paucity of literature on the relationship between trade and environmental pollution based on micro data.Design/methodology/approachThis paper merged China’s Firm-Level Pollution Database with China’s Industrial Enterprise Database and China’s industry tariff rates. Additionally, by virtue of the quasi-natural experiment of China’s accession to the World Trade Organization (WTO), a difference in difference (DID) model was constructed to alleviate the endogeneity issue.FindingsAccording to the results, the trade barrier decrease (trade liberalization) significantly reduces the intensity of SO2 emissions, a major pollutant of enterprises, as the intensity of SO2 emissions decreased 2.16% for each unit decrease of the trade barrier. The analysis of the mechanisms shows that the SO2 emission intensity of enterprises is mainly due to the decrease of enterprises’ pollution emission rather than the decrease of output, and the decrease of enterprises’ pollution emission is mainly caused by the enterprises’ cleaner production process rather than the end treatment of pollution emission. The decrease of coal use intensity is an important mechanism of the decrease of SO2 emission intensity caused by the decrease of trade barriers. Among the technical effects of the change of the trade barrier affecting enterprises’ pollution emission, biased technical change rather than neutral technical change dominates.Originality/valueThe findings of this paper imply that expanding openness can enhance China’s social welfare not only through the economic growth mechanisms identified in the classical literature, but also through environmental improvements. This provides useful policy insights for promoting the construction of a strong trading power and winning the battle against pollution in the new era.

Whether the Directed Technical Change Promotes the Improvement of the Energy Structure in China

Optimizing energy structure is necessary to achieve full decarbonization. In this study, we introduced thermal power, clean energy, and traditional fossil energy as three different input factors into the translog production function. We considered three improvement paths of the energy structure, including replacing fossil energy with electricity and using thermal power to substitute for clean energy and then analyzed whether the directed technological change promotes the optimization of the energy structure in China. The results show that there is a substitution relationship between thermal power and clean energy during the internal transition process, and the biased technical change is thermal power-using, which suggests that technological change is not conducive to the improvement of electricity structure from thermal power to clean energy. In external transition, the biased technical change is fossil energy-using instead of thermal power-using or clean power-using, and there is a substitution relationship among three factors, which shows that technical change goes against the improvement of energy structure from fossil energy to electricity. We propose that the Chinese governments adopt energy price or carbon tax measures to adjust the directed technical change and thus optimize the energy structure. In addition, according to the results from China, different countries can improve the energy structure based on their characteristic production mode.

Investment Behavior Related to Automated Machines and Biased Technical Change: Based on Evidence From Listed Manufacturing Companies in China.

This paper studies the impact of a recent increase in the ratio of automated machines to ordinary capital (RAMOC) on the bias of technical change in the manufacturing industry and the mechanism influencing this. Using panel data of A-share listed manufacturing companies on the Shanghai and Shenzhen stock exchanges from 2012 to 2019, combined with the Xtfrontier model and trans-log production function, we measure the index of the bias of technical change of the manufacturing industry in China. Furthermore, we adopt a fixed effects model to test the impact of an increase of investment in automated machines on the bias of technical change. We also use an intermediary effect model to examine the intermediate mechanism from the perspectives of capital and skill matching. The results show that technical change in the manufacturing industry is biased toward automated machine capital. An incremental increase in RAMOC leads to technical change in the manufacturing industry becoming biased toward automated machine capital, wherein the intermediary mechanism is the labor structure effect. Based on industrial linkage, the investment in automated machines in the upstream (downstream) manufacturing industry increases, the technical change of the downstream (upstream) manufacturing industry is biased toward automated machine capital, and the forward linkage effect is greater than the backward linkage effect. This research enhances understanding of (1) the direction and characteristics of technical change in China, (2) how to improve the output efficiency of automated machines, (3) differences in factor revenue distribution, and (4) how new growth points in the economy can be cultivated. They show that we should encourage and support investment in automated machines, vigorously promote technical change to bias toward automated machine capital, improve the skill level of the labor force, and strengthen the match between automated machines and labor.

Carbon emission intensity and biased technical change in China's different regions: a novel multidimensional decomposition approach.

The decomposition analysis has been employed to discover the driving factors of carbon emission intensity, but the current studies assume that production functions are under the condition of the neutral technical change. Grounded on biased technical change production theory, this paper proposes a novel multidimensional decomposition approach which combines production-theory decomposition analysis (PDA) and index decomposition analysis (IDA). This novel approach can illustrate how energy structure effect, element substitution effect, efficiency change effect, input biased technical change, output biased technical change and magnitude of technical change affect carbon emission intensity of China's 30 provinces. The results indicate that during the 11th FYP and 13th FYP, output biased technical change and the magnitude of technical change are the critical factors in China's carbon emission intensity, while other four drivers increase carbon emissions. But, during the 12th FYP, the role of six drivers has been reversed contrasting 11th FYP and 13th FYP. In addition, we also explore the impact of each driver from the perspective of regional heterogeneity.

Effect of Feed-In Tariff with Deregulation on Directed Technical Change in the Energy Sector

Abstract In this study, we examine how a feed-in tariff (FIT) accompanied with deregulation in the energy sector affects the direction of technical change along the balanced growth path. A final good is composed of resource-saving (such as renewable) energy and traditional resource-intensive energy. The government introduces a FIT scheme for promoting resource-saving energy, while it deregulates the traditional energy sector for efficiency improvement. The implementation of the scheme positively affects directed technical change toward the resource-saving energy technology and economic growth. Meanwhile, the biased technical change leads to an upsurge in the surcharge. Associated deregulation not only accelerates the biased technical change but also drives the surge in the surcharge rate, unless the initial market structure of the traditional energy sector is highly concentrated.

Elasticity of Substitution in the Manufacturing Sector in the Czech Republic

The paper estimates the elasticity of substitution between capital and labour under biased technical change in the manufacturing sector in the Czech Republic. We use a dataset covering the period 1995-2019 and a 2-digit industry level, from NACE10 to NACE33. We find industry-level elasticities to be 0.19 and 0.94; therefore, labour and capital seem to be gross complements rather than substitutes across all manufacturing sectors. In the core industries of the Czech manufacturing sector, the elasticity is below average. Even if these core sectors have high and increasing labour costs (except NACE29, where the labour costs are below average), they do not replace labour with capital at a higher pace. This cannot be explained by the direction of technological bias because it is not capital-augmenting. On the other hand, our findings are in line with the literature, as most studies on transition countries report low estimates for the elasticity.

The effects of technical change on carbon intensity in China’s non-ferrous metal industry

China is the world’s largest producer and consumer of non-ferrous metals. With a characteristic of energy-intensive, China’s non-ferrous metal industry is under enormous pressure to reduce carbon emissions. Accelerating the process of technical change is an effective way to reduce the carbon intensity of the non-ferrous metal industry. Based on industry data from 30 Chinese provinces during 2005–2017, this study calculated the technical change and corresponding decompositions utilising the slacks-based measure method and the Malmquist-Luenberger index. Subsequently, the impact of technical change on carbon intensity in terms of magnitude and direction is discussed, and the mediation effects among three technical change indexes and carbon intensity are analysed. The research results show that (1) during 2005–2017, the technical change of China’s nonferrous metal industry presented an overall growth trend, mainly contributed by the magnitude technical change. Simultaneously, there was variation in the technical change among the provinces. (2) The progress of magnitude technical change could continuously reduce carbon intensity in China’s non-ferrous metal industry, whereas with a characteristic of energy-using, the biased technical change will weaken the positive impact of technical change. (3) The technical change can reduce the carbon intensity of the non-ferrous metal industry by reducing energy consumption and optimising the energy structure. These findings suggest that effective promotion of technical change requires more targeted polices to reduce regional heterogeneity. The government should also encourage non-ferrous metal enterprises to reduce overcapacity, accelerate the process of high-end industry, strengthen the secondary recycling of materials and promote the use of clean energy.

Elasticity of substitution and biased technical change in the CES production function for China's metal-intensive industries

To achieve sustainable and green development, it is imperative for China, as the world's major metal consumer, to improve utilization efficiency and conservation of metal resources. Thus, understanding the industrial endogenous growth patterns of China regarding metal utilization and technical change becomes the prerequisite. Therefore, this paper regards metal as one of production factors nested with capital and labor, employs normalized supply-side system approach to estimate the elasticity of substitution in constant elasticity of substitution (CES) function and analyzes technical change bias based on the analytical framework built in this paper. Results show that: the optimal nesting structure for most metal-intensive industries are metal-capital composite nested with labor. And the metal input is less efficient than capital or labor in all metal-intensive industries. The technical change biases for metal-intensive industries are diversified, but only metal mining industries and heavy machinery manufacturing industries tend to save metal when expanding the production effectively. Targeted suggestions are made per industry according to the heterogeneity of production factors allocation and technical change biases.

Measuring the bias of technical change of industrial energy and environment productivity in China: a global DEA-Malmquist productivity approach.

Thanks to the booming industry, China has made a huge economic achievement during the past several decades. However, it is suffering severe environmental and sustainable problems now. To find a sustainable development path, it is necessary to assess Chinese industrial energy and environment productivity and explore the contributing reasons. It is known that the technical change is the one power that drives the growth of the industrial productivity. Nevertheless, the technical change bias of Chinese industrial energy and environment productivity has rarely been analyzed, such that the secrets of Chinese industrial energy and environment productivity cannot be further uncovered. Thus, in this paper, we first propose a global DEA-Malmquist productivity index to evaluate the industrial energy and environment productivity of China and then figure out the Chinese industrial technical change biases by relaxing the Hicks' neutral assumption and decomposing the industrial technical change. We find out that both the global DEA-Malmquist productivity and the technical change are increased. Furthermore, the technical change drives the improvement of the global Malmquist productivity, but the technical progress is mainly driven by labor, energy consumption and CO2 emission biases. A multinomial logistic model is employed to find out the reasons for these biases. It finds that (1) the economic foundation has a significant positive impact on labor bias, while the infrastructures have negative impacts on labor bias. (2) CO2 emission bias is influence by energy prices positively. (3) The energy prices and the energy consumption structure have a negative influence on labor and energy bias, but the cost of curbing air pollutants and the size of the firm influence labor and energy bias positively. (4) The infrastructures and energy prices affect energy and CO2 emission bias positively, and the economic foundation and the size of the firm have negative impacts on energy and CO2 emission bias.

Direct and indirect effects of heterogeneous technical change on metal consumption intensity: Evidence from G7 and BRICS countries

To improve metal utilization efficiency, the effects of technical change cannot be ignored. However, existing researches often ignored the heterogeneous effects of different types of technical change on metal consumption intensity. Taking the G7 and BRICS countries as samples, this article first identifies the direct effects of heterogeneous technical change on the consumption intensity of iron ores, copper and aluminum from magnitude of technical change (MTC) and biased technical change (BTC). Then, industrial structure and metal consumption are introduced as mediating variables to identify the indirect effects of heterogeneous technical change. The results show the following: (1) there is a lag in the effect of TC on reducing metal consumption intensity and its effect on iron ore consumption intensity is the most significant. Reduction of iron ore consumption intensity mainly depends on MTC, whereas the reduction of copper and aluminum consumption intensity mainly depends on BTC. (2) The effects of heterogeneous technical change on metal consumption intensity in BRICS countries were consistent with the overall samples, while the effects of BTC on the intensity of copper and aluminum are positive in G7 countries. (3) Three types of technical change can reduce metal consumption intensity through the rationalization and advancement of industrial structure, and their effects are heterogeneous for different metals. (4) TC and BTC cannot reduce metal consumption intensity through reducing metal consumption due to a rebound effect, whereas MTC can significantly reduce iron ore consumption intensity by reducing iron ore consumption.

Micro Data and Macro Technology

We develop a framework to estimate the aggregate capital‐labor elasticity of substitution by aggregating the actions of individual plants. The aggregate elasticity reflects substitution within plants and reallocation across plants; the extent of heterogeneity in capital intensities determines their relative importance. We use micro data on the cross‐section of plants to build up to the aggregate elasticity at a point in time. Interpreting our econometric estimates through the lens of several different models, we find that the aggregate elasticity for the U.S. manufacturing sector is in the range of 0.5–0.7, and has declined slightly since 1970. We use our estimates to measure the bias of technical change and assess the decline in labor's share of income in the U.S. manufacturing sector. Mechanisms that rely on changes in the relative supply of factors, such as an acceleration of capital accumulation, cannot account for the decline.