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Technical change and total factor productivity growth: The case of Chinese provinces
Abstract
In the literature technical change is mostly assumed to be exogenous and specified as a function of time. However, some exogenous external factors other than time can also affect technical change. In this paper we model technical change via time trend (purely external non-economic) as well as other exogenous (external economic) factors (technology shifters). We define technology index based on the external economic factors which are indicators of 'technology'. Thus our definition of production function is amended to accommodate several technology shifters which are not separable from the traditional inputs. That is, these technology shifters allow for non-neutral shift in the production function. In doing so we are able to decompose technical change (a component of TFP change) into two parts. One part is driven by time (manna from heaven) and the other part is related to producer specific external economic factors. These exogenous technology shifters are aggregated (via hedonic aggregator functions) into several groups (technology indices) for parsimonious parametric specification. The empirical model uses panel data on Chinese provinces. We identify a number of key technology shifters and their effect on technical change and TFP growth of provinces.
... This is in line with Mincer's (1974) original work. Heshmati and Kumbhakar (2011) modeled TC via a time trend (external non-economic) and other exogenous factors (technology shifters). They used balanced panel data on output and inputs and production and technology characteristics for Chinese provinces for the period 1993 to 2003. ...
... The model used in our paper is drawn from Kumbhakar (2011, 2014) and Lien et al. (2017). Heshmati and Kumbhakar (2011) estimated technical change and total factor productivity growth in Chinese provinces. By using panel data on OECD countries during 1980-2006, Heshmati and Kumbhakar (2014) modeled technical change via a combination of time trend (purely neutral) and other observable exogenous factors that shift the technology and production functions. ...
This paper models and estimates total factor productivity (TFP) growth parametrically. The model is a generalization of the traditional production function model where technology is represented by a time trend. It decomposes TFP growth into an unobservable time trend induced technical change, scale economies and an observable technology shifter index’s components. The empirical results are based on unbalanced panel data at the global level for 190 countries observed over the period 1996–2013. It uses a number of exogenous growth factors in modeling four technology shifter indices to explore development infrastructure, finance, technology and human development determinants of TFP growth. Our results show that unobservable technical change remains the most important component of TFP growth. Our findings also show that technical changes and TFP growth are unexpectedly negative across all country income groups and years.
... The TFP growth which was categorised into several components was mainly due to technical efficiency change and not through technological changes. Heshmati and Kumbhakar (2010) TFP growth by utilising industry panel data of China region. Results showed that on the average, technical changes contributed between 13.7 percent and 22.3 percent to TFP growth; and it was the main contributor to TFP growth. ...
... Researchers often focus on the outside factors rather then TFP growth components when examining the determinants of TFP growth, such as economic openness, labour skills and R&D expenditure. For example, Heshmati and Kumbhakar (2010) further extended their study to examine the determinants of TFP growth. They found that TFP growth was determined mainly by human capital and economic changes. ...
In Malaysia, the contribution of TFP towards its economic growth is still small even though it is increasing over time and this reflects a low contribution at the firm level as well. This paper attempts to examine the contribution of TFP growth to palm oil-based industry output growth. As Malaysia stands the second largest oil palm producer in the world, the palm oil-based industry is one of the important subsectors of the manufacturing sector. The analysis in this paper is based on panel data of 13 years from 2000-2012 and eleven palm oil-based subindustries. The data are provided by the Depatment of Statistics, Malaysia. The TFP growth is obtained from the data envelopment analysis (DEA) of Malmquist index procedure and this variable is used as one of the independent variables in the growth model. Other variables include expenditure on training, information and communication technology (ICT) and research and development (R&D). The model is run using pooled ordinary least squares (POLS), fixed effect (FE) and random effect (RE) procedures. The results demonstrate that TFP growth positively and significantly contribute to industry's output growth. The contribution of TFP growth to output growth is higher in the non-food-based industry compared to food-based industry.
... However, Xia and Xu (2020) pointed out that by optimizing energy and industrial structure, enterprises can also increase productivity without sacrificing the environment [7]. Therefore, this paper examines the corporate total factor productivity of Chinese enterprises, intending to promote the rational allocation and reallocation of productive resources [8], and offers insights for sustainable corporate growth. ...
Corporate social responsibility (CSR) plays a vital role in facilitating sustainable long-term development. Despite its importance, the specific mechanisms through which CSR interacts with business productivity have not been extensively explored. This paper selects 4167 Chinese enterprises from 2011 to 2021 for study to elucidate this mechanism. The results of the study show that (i) CSR has a significant positive effect on enterprise productivity. (ii) Environmental regulation has a negative and significant moderating effect on the effect of CSR. After endogeneity and robustness tests, the findings of (i) and (ii) remain valid. (iii) There is heterogeneity in (i)–(ii) concerning corporate social responsibility, corporate shareholding structure, region, and degree of marketization. Based on these findings, sound recommendations are proposed for enterprise managers and governments.
... The complexities of estimating TFP growth in the context of the Chinese economy were analysed by (Heshmati and Kumbhakar 2011). More particularly, the work was concerned with technical change requirements and estimation using observable internal and external causes of technological change. ...
In this work, we develop a trade model that explains the pattern of trade between countries based on differences in total factor productivity (TFP) while also accounting for differences in relative factor endowments. The novelty stems from the introduction of production functions derived by combining the Ricardian and Heckscher-Ohlin-Samuelson (H-O-S) theories, with TFP differences serving as the basis of comparative advantage. To this end, a testable hypothesis is derived. For the empirical measurement of the TFP in each industry and country, a constant elasticity of substitution (CES)-type production function was employed, and the TFP was calculated as the Solow residual from the production function's fixed term. To offer a better understanding, the model was tested for the bilateral trade between Germany and Russia, and Germany and the Czech Republic. It was found that TFP differences can be used as a basis for explaining comparative advantages and, consequently, the bilateral pattern of trade between two countries.
... Embodiment means because of technological advance; the new inputs are more efficient than old the old ones. The growth rate of new inputs is dependable on embodied technology and old inputs as established in claim 5 which is alluded to by Hesmati & Kumbhakar; Mastromarco & Zago; Gordon and many others [35][36][37]. This also indicates that research firms are stimulated by the prospect of embodied technology that can be incarcerated when a successful technology is patented. ...
Purpose: We approach here the question of dynamical behavior of Total Factor Productivity (Hence TFP) and inputs of firms by constructing theoretical relations of economic parameters that considers economic growth. Results: This article develops a relationship of TFP and its dependent variables; a partial differential equation of TFP is established here and a set of relations of inputs are considered also. A recurrence relation and a second order differential equation of inputs are established here. Limitations: It is not proper setting to study TFP and inputs of a firm fully since all the parameters are not considered here. Contribution: The dynamical behavior of TFP and inputs of a firm found in this paper suggest that policymakers should seriously consider the investment, embodied technology and other factors of TFP for better business policy. The study of inputs focuses the additional valuable guidelines for policy reforms by establishing various relations between inputs and different economic parameters. Research Article Introduction Today, GDP per capita of Luxembourg, the richest country in the world, is 173 times of the poorest country Burundi. Economists around the globe have been trying to find out the inherent factors behind the differences since 1950s [1]. Consequent studies pointed out the importance of investments in the useful economic system and the effective embodied technology in generating progress of total factor of productivity (Now TFP) but different types of inputs (such as: human capital as it is characterized by skills, knowledge and experience acquired by an individual or group and research and development, fuelled by huge capital flows and trade booms) used to gear up the TFP have certainly been vital also [2, 3]. TFP is also influenced by labor and ecosystems negatively. All these factors except embodied technology are marked by other factors in this paper.
... Thus, we find no evidence that investments in healthcare R&D and multipurpose technological changes induced efficiency changes and productivity growth. Given that data is available now for a longer period one should be able to account for observable technology shifters and not rely on only an unspecified time trend as a source of technological change and the main source of productivity growth (see [48,49]). Observable technology shifters in this case are R&D investments in health-related technology development, number of researchers in the field, public and private R&D support, network and collaborative R&D programs, and the number of patents registered or sales share of new products and processes which are used for measuring R&D outcomes [50]. Appendix Figure A4, Panel B shows the development of technical changes over time. ...
Countries’ success in managing the Covid-19 pandemic was independent of their level of education or development and institutional quality. Similarly, different provinces in countries performed differently as compared to their neighboring provinces and the country. This study assesses the efficiency of Sweden’s different municipalities in managing the Covid-19 pandemic. Its aim is determining whether the relative efficiency of Sweden’s primary healthcare system during the Covid-19 pandemic was different in different in regions and whether the regions can learn from each other to improve their efficiency in the provision of healthcare in the future. Performance is measured as efficiency in keeping the number of infected cases and deaths low. We use a data envelopment analysis for measuring efficiency. Variations in the level of efficiency are attributed to the differences in the municipalities’ demography, population concentration, economic and industrial structures, labor markets, geographic locations, and national and local policies during the pandemic. The empirical part is based on a panel of the population of 290 Swedish municipalities observed on a weekly basis during April 2020 to February 2021. The results show large variations in municipalities’ performance that can, to some extent, be attributed to their observable socioeconomic, locational, and demographic characteristics.
... The continuous emergence of innovation provides the possibility for technologically backward countries or regions to catch up in technology and economic development with advanced countries (Heshmati and Kumbhakar 2010;Madsen, Ang, & Banerjee, 2010). Gancia and Zilibotti (2005) report the key role of technological progress in a country's long-term stable economic growth based on endogenous economic growth theory. ...
Innovation capability of manufacturing industry is one of the most important factors that determine a country’s competitiveness. This paper expounds the theoretical and empirical relationship among innovation capability (IC), capital enrichment (CE) and total factor productivity (TFP) by using the yearly panel data of 28 Chinese manufacturing segments from 2011 to 2018. The intermediary effect model and threshold model are used for empirical analysis, findings suggest that 1) IC not only directly promotes TFP but also indirectly through CE, indicating that CE has an intermediary effect on the relationship between IC and TFP. 2) The positive effect of CE on TFP is influenced by the “double threshold effect” of IC. Compared with industries having low IC, this positive effect in high IC industries is first enhanced then weakened and later re-enhanced. 3) There is a significant difference in IC and CE among different manufacturing segments.
... Also, recent studies based on stochastic frontier analysis have found that some external factors, such as development infrastructure, finance, and human capital, tend to affect technical change. For example, based on Chinese province-based data, Heshmati and Kumbhakar (2011) show that the impact of technical change on TFP growth steadily declined over time. document that importing intermediate input has a positive impact on the technical efficiency of Indonesian chemicals industry. ...
This article estimates the total factor productivity (TFP) growth of Chinese manufacturing firms and quantifies how international sourcing and trade liberalization affect TFP growth. To this end, we estimate stochastic frontier production that decomposes TFP growth into technical changes, returns to scale, and technical efficiency changes. Furthermore, we consider two channels through which external factors affect TFP growth: technical change and technical efficiency change components. We measure international sourcing and trade liberalization by foreign value-added shares and input tariffs, respectively. Several novel findings emerge. First, international sourcing and import tariff reduction positively impact TFP growth by improving technical changes and technical efficiency. Second, international sourcing has a negative effect on the technical changes of processing importers, while it has positive effects on the technical change and technical efficiency change of ordinary importers. Third, input tariff reduction has a substantial and positive impact on the technical efficiency change of processing importers and the technical change of ordinary importers.
... Fuglie and Rada (2012) employed multivariate regression analysis to examine factors, hypothesised to have an effect upon agricultural productivity, these included: investment in research and development; input subsidies; commodity price intervention; human capital development; investment in the education and health of the labour force; infrastructure investment; and political stability. Heshmati and Kumbhakar (2011) observed that investment in agricultural research through technical change provided a mechanism for TFP growth, while the other variables provided an enabling environment for economic growth by, for example, facilitating the access farmers have to new technologies and markets. ...
Background: A recent increase in the adoption of mobile phone technology generated a great deal of interest and optimism regarding its effect on economic development in sub-Saharan Africa (SSA), particularly on the enhancement of agricultural development.
Aim: In this study the impact of mobile phone technology on agricultural productivity in SSA is examined.
Setting: The empirical assessment uses a panel data set covering 41 countries over a period of 25 years.
Methods: We employed an econometric approach and panel data covering 41 countries and a 25 year-period (1990–2014) to investigate the effect of the adoption of mobile phone technology and other socio-economic variables on agricultural total factor productivity (TFP). The use of regression analyses allowed us to estimate and measure the contribution of certain variables to agricultural TFP growth in SSA.
Results: The results show that the uptake of mobile phone technology had a positive effect on agricultural TFP growth in SSA.
Conclusion: Mobile phone technology has been established to be one of the drivers of agricultural productivity in SSA.
Implication: The implications of this study are that governments, NGOs, and businesses working on improving agricultural productivity and food security in SSA need to continue endorsing mobile technology as a means to improve agricultural productivity.
... The maximum value of TFCP indicates the efficient decision unit locating at the production frontier. Now the translog transformation function, which is more flexible than Cobb-Douglas function (Coelli and Perelman, 1999;Heshmati and Kumbhakar, 2011;Lien et al., 2018), is adopted to express the Shephard distance function explicitly. ...
The total factor carbon emission performance has been largely used to investigate the effectiveness of climate policies and to support the design of carbon reduction strategies. Despite the important information that this indicator is providing in relation to historical and cross-country trends, no previous studies have been specifically devoted to analyse the persistent and the transient components of the total factor carbon emission performance. By disaggregating the time-variant and the time-invariant elements of the carbon dioxide emission changes, this paper adopts, for the first time, a new methodological approach to decompose the components of the total factor carbon emission performance indicator. Using panel data for selected 30 Chinese provinces for the time-period 1997-2017, this paper combines the environmental production technology, the Shephard distance function, and the stochastic frontier models to measure and investigate the spatio-temporal evolution of the total factor carbon emission performance and to evaluate the effectiveness of Chinese policies. By providing a better understanding of the main drivers of carbon dioxide emission changes, the proposed methodology, is suitable to be replicated across regions and countries, and provides an important opportunity for international comparisons and for the design of coordinated carbon reduction strategies.
... The green industrial policy (GIP), being the front-end pollution control approach, is just the right choice of pollution control policy. Also, it can not only save the high cost of "terminal pollution control" and avoid the "irreversibility of pollution", but also realize high-quality development and enhance the innovation capability of enterprises, thereby improving resource utilization and total factor productivity (TFP) growth of enterprises [5]. ...
This paper examines whether the national green industrial policy (GIP) can effectively optimize the enterprises’ structural transformation and upgrading, and improve production efficiency of enterprises. Using China’s firm-level data from 1998 to 2007, we take the 2003 “Cleaner Production Promotion Law of PRC” as the turning point of GIP implementation, and employ the difference-in-differences (DID) method to explore the policy effect on total factor productivity (TFP) of enterprises. The analysis shows that GIP can enhance the enterprise’s resource allocation capability and TFP growth. The implementation path of policy mainly relies on the compensation mechanism to incentivize innovation and the elimination mechanism of market selection. Specifically, GIP enhances TFP growth by accelerating the dynamic replacement (enterprise entry and exit) and elimination mechanism of the market, and by promoting innovative production to enhance the enterprise’s production efficiency. Further heterogeneity analysis reveals that state-owned enterprises are more susceptible to the influence of GIP, and GIP exerts more restrictive impact on high-pollution industries. Also, GIP has more significant net spillover effect on technology-intensive enterprises. The study provides a reliable factual basis for the market effect of GIP and the direction for green industry development.
... Zhang et al. (2018) indicated that the total factor energy efficiency and carbon emission performance of CDM host countries appear much lower than those of investment countries. Chang and Hu (2010) and Heshmati and Kumbhakar (2011) found that China's regional total factor energy efficiency increases progressively, but at a relatively low level. At the industry level, the total factor energy efficiency of the thermal power industry, the industry, the agriculture, and the iron and steel industry in China experiences an increasing trend, but at a relatively low level (Bi et al. 2014;Zhao et al. 2014;He et al. 2018;Chen et al. 2008;Sheng and Song 2013). ...
Pursuing innovation effect or efficiency is an important trade-off that Chinese local governments need to face in the process of developing economy and protecting the environment. From the perspective of the policy portfolio, we employ the industrial panel data of 30 provinces in China during 2000–2015 to analyze the impacts of effectiveness-based innovation and efficiency-based innovation on ecological total-factor energy efficiency (ETFEE), and further analyze the effects of command-and-control, market-based and voluntary environmental regulations on innovation. The findings reveal that (1) both effectiveness-based innovation and efficiency-based innovation have significant promoting effects on ETFEE. (2) Three types of environmental regulations have significantly inhibitory effects on effectiveness-based innovation and efficiency-based innovation. (3) The interaction term of command-and-control and market-based regulations plays a significant role in promoting effectiveness-based innovation and efficiency-based innovation, whereas the interaction term of market-based and voluntary regulations merely promotes efficiency-based innovation. The interaction term of three types of regulation only has a synergetic and positive effect on the efficiency-based innovation. Finally, this paper gives specific policy recommendations.
... Also, the finding shows the largest impact on the value added growth for the manufacturing sector in thirty-two countries, consists of low and middle income economies from the year 1965 to 1992. Another study by Almas and Subal (2010) confirmed that technology bring by FDI has a positive impact on the TFP in China. This result is verified by Ali et al. (2012) noted that technology increased the TFP level the manufacturing sector in Pakistan. ...
The process of globalization is happening so quickly thereby leading to rapid changes, particularly in communication and information technology. This situation has caused the economy to become
interdependent. Basically, the process of globalization will improve
efficiency and productivity of economic activities. However, the
process of globalization may have a positive or negative effect on the
TFP growth for different economic sectors. This paper aims to
investigate the effect of globalization on TFP growth of the
construction sector based on the data from the Construction Survey Malaysia collected by the Department of Statistics Malaysia. The analysis utilizes panel data of 1990-2009
for four construction subsectors, namely, residential buildings, non-
residential buildings, civil engineering and special construction activities. TFP growth is obtained from DEA-Malmquist approach and this variable is used as dependent. The globalization indicators like foreign direct investment (FDI), foreign workers and economic openness are used as independent variables in the TFP
model. The static panel data approach is adopted in running the models. The results show that all the globalization indicators positively and significantly affect the TFP growth
of the construction sector.
... For example, Hu (1997) and Arayama and Miyoshi (2004). Heshmati and Kumbhakar (2011) did that but they focused on an innovative way of modeling the technical change while addressing the TFP growth and its components using province level data from China. It is worth noting that none of the studies that we are aware of examine (1) whether there is a UKC and whether there exist economies of urbanization or not, (2) the contribution of urbanization to the GDP growth and/or TFP growth. ...
In this paper, we examine the effect of infrastructure financing (public expenditure) and urbanization on productivity using a new concept. We call this concept Urban Kuznets curve (UKC) and define it as an inverted U-shaped relationship between per capita GDP and urbanization. We examine the UKC relationship from several angles. First, we explore its existence from a data driven approach (the scatter plot of log per capita GDP against urbanization). We then examine it after controlling for the input factors and a time trend. The first approach is ad hoc because no formal economic model is used. The second approach is grounded in the microeconomic theory with state-of-the-art econometric tools instead of using some popular ad hoc regressions where (log) per capita GDP is regressed on public expenditure and urbanization along with some other controls. In our pursuit of understanding the various channels through which urbanization may affect productivity, we use parametric and semiparametric partially linear econometric approaches. In the parametric approach we treat urbanization in the same way as inputs (capital, labor, etc.) and compute optimum levels of urbanization to examine whether the benefits of urbanization are exhausted for any of the Chinese provinces during our study period. In the semiparametric partially linear models, the UKC is detected from the estimated elasticity of urbanization without calculating the optimal levels of urbanization. For this study we use the Chinese province level panel data over the period 2005–2014 from three sectors, viz., agriculture, manufacturing, and services.
... This model is similar in spirit to the hedonic model ofKumbhakar and Hjalmarsson (1998) andHeshmati and Kumbhakar (2011). ...
Empirical studies have often shown wide differences in productivity among firms. Although several studies have sought to identify factors causing such differences, only a few studies have examined the effects of risk and risk aversion on productivity. In this study, using Norwegian dairy farming data for 2009, we examined the effects of different aspects of risk on productivity. We used a range of variables to construct indices of risk taking, risk perception and risk management. These indices were then included as arguments in an input distance function which represents the production technology. Our results show that these risk indices did affect productivity. Regional differences in productivity, though small, were also found to exist, suggesting that unobserved edaphic factors that differ between regions also affected productivity.
... If this property does not hold, TFP growth becomes a mixture of technical change and scale effects. In the case of non-constant returns to scale technology, decomposition of TFP growth into its sources requires knowledge of scale effects, which require econometric estimation of parametric functions (Heshmati & Kumbhakar, 2010). ...
The Australian automotive industry has moved from a high protection environment to an increasingly open and competitive environment. This process of liberalisation has posed many challenges and opportunities to the industry. The industry outcomes have been heavily influenced by the protective policy calculus implemented by successive governments. Between 1983 and 1996, the industry went through major structural changes, fuelled by substantial tariff reductions, with an aim of improving industry competitiveness and making its orientation more outward.The purpose of this study is to investigate the effect of trade liberalisation on the competitiveness and trade performance of the Australian automotive industry. We do this using data from the motor vehicle and motor vehicle part manufacturing sector (ANZSIC 231) and the motor vehicle manufacturing sub-sector (ANZSIC 2311), given the limited automotive data in the consistent format.Our analysis indicates that since the introduction of reform in 1985, the Australian automotive industry is being increasingly rationalised and integrated creating profound changes in the relationships within. While the industry appears to have made some gains in terms of improved competitiveness and improved trade orientation, it continues to face several problems, such as increased competition and skill shortages. Improved productivity and export performance seem to have come from greater competition, and an increased access to large external market and superior technology brought about by foreign companies. Our findings do not confirm the hypothesis that the industry’s poor TFP growth was the result of protectionist policies of the past. Our analysis shows the importance of an in depth analysis of the link between trade policy reforms and its effects on trade and productivity performance of the Australian automotive industry, using disaggregated firm-level data. This will help provide better insights into the relationship between trade orientation and structural change in the Australian automotive industry.The present study draws the importance of developing a better data set by the Australian automotive industry and the Australian government for effective policy analysis and formulation for the sector.
... At the same time, at provincial and local levels one can observe a competition for investments and high growth indicators. According to economist John Lee from Sidney these domestic investments give 40% of growth while the export sector and FDI contribute about 30% (see Berthelsen, 2011; Heshmati and Kumbhakar, 2011: 577; Qian and Roland, 1998; Li and Zhou, 2005 4 ...
THIS IS THE FINAL PRINTED VERSION OF "WILL THE EXPLOSIVE GROWTH OF CHINA CONTINUE?" // Technological Forecasting & Social Change 95 (2015), pp. 294-308.
... This is consistent with the regional studies in China, e.g. [4], [5] and [6]. This needs to be address by better policy implementation in order to avoid the effects of inequality towards the economic development in China. ...
This paper intends to address the issue of the inequality in the economy development in China with the use of Total Factor Productivity in analyzing the productivity and efficient used of resources. The study focuses on regional analysis to determine the inequality patterns among the provinces in China with the objective to address the issues of inequality among the provinces in China which my dampened the economic development in the country. The sample of the study consist of 30 provinces in China starts from year 1978 to 2008 in order to investigate the economic efficiency and productivity change of the Chinese's economy after the China reformation in 1978. The results show that the Eastern region is relatively more efficient as compared to the Western and Central region in China. Results further found that the main contributor of economic inefficiency in China is mainly due to pure technical inefficiency. The technological change plays is found to play an important role in providing a road for sustainable economy development in China.
The success of the carbon emissions trading scheme (ETS) in fostering a mutually beneficial outcome for both the economy and the environment depends on its ability to enhance total factor productivity (TFP). We investigate the diverse implementation of this market-based environmental policy across Chinese provinces using a staggered Difference-inDifferences model. Our study delves into the mechanisms of the ETS, particularly focusing on financialization and innovation, utilizing listed companies in Chinese stock markets as our research sample spanning from 2009 to 2022. Our analysis shows several key findings: (1) ETS facilitates the growth of TFP in micro-firms, positively influencing other listed companies within the same sector and city. However, its impact on TFP is notably weaker in regions with robust environmental information disclosure and stringent environmental regulations. (2) The ETS helps alleviate resource misallocation stemming from excessive financialization of firms, prompting them to reallocate investments from financing activities to production and operation activities, thus enhancing TFP. (3) Contrary to expectations, the ETS does not directly influence TFP through enhancing firm innovation ability; instead, it encourages directed technical innovation toward green technology over digital technology. This study highlights the effectiveness of the ETS in promoting firm development and sheds light on resource management practices within firms. K E Y W O R D S carbon emission trading scheme, financialization, innovation, staggered difference-indifferences , total factor productivity
ntroduction: In this study, total factor productivity growth components in rough rice production are assisting by econometric approach and stochastic frontier function during 2005-2013 for several provinces (Mazandaran province, Guilan province, Golestan province, Fars province, Khoozestan province). According to Food and Agriculture Organization statics, Iran is the 3rd importer and 20th exporter of rice in the world. But, during the study years (2005-2013), Iran has been one of the 6 largest importers of these products that on average about 33 percent of domestic needs are provided by imports. Annual per capita consumption of rice during 1990-2012 had been changed from 38.6 to 43.9 Kilogram. So, it can be concluded that rice has a special place in the Iranian consumption bundle. But in the production sector, cultivation area has been decreasing 15 percent during 2005-2009 and was fixed during 2009-2013. These matters indicate that domesticate production cannot provide domesticate consumption. One of the suitable ways of increasing production is increasing in total factor productivity. This strategy is needed to identify components of TFP growth sources. So, the main goal of this study is the decomposition of rice TFP in Iran.
Materials and Methods: TFP decommission growth can be analytically by four approaches included econometric estimation of production and the cost function, TFP indices of Divisia and Turnqvist, Data envelopment approaches (DEA) such as Malmqvist and stochastic frontier analysis. This study uses a stochastic frontier analysis to decompose total factor productivity (TFP) growth into four components: technical change (TC), technical efficiency (TE) change, scale efficiency (SE) change, and allocative efficiency (AE) change. For this new approach, at first, Translog production function is estimated by gathering data. So, by estimation of Translog production function, total factor productivity growths are decomposed to TC and changes in TE, SE, and AE. For the total factor productivity decomposition, we employ the time-varying model for technical inefficiency. Firm inefficiency is assumed to be distributed as a generalized truncated–normal random variable which is distributed independently of the normally distributed random errors.
Results and Discussion: Results indicate technical efficiency have been 0.86, 0.79, 0.69, 0.73 and 0.86 for Mazandaran, Guilan, Golestan Khoozstan and Fars provinces, respectively for the year of 2012. That is, most technical efficiencies were for Mazandaran and Fars provinces. Also, technical efficiency has been 0.73, 0.75, 0.77, 0.81 and 0.81 for a farm with size less than 0.5 Ha, between 0.5 and 1 Ha, between 1 and 2 Ha, between 2 and 3 Ha and more than 3 Ha, respectively for years of 2012. That is, most technical efficiencies were from a farm with the size of more than 2 ha. The annual growth rate of technical efficiency changes during 2005-2013 have been 2.3, 1.6, 0.3, 0.9 and 1.6 percent for Mazandaran, Guilan, Golestan Khoozstan and Fars provinces, respectively. For Iran, also has been 1.5 percent. The annual growth rate of scale efficiency change during 2005-2013 have been 1.5, 1, 1, 1.2 and 2.3 percent for Mazandaran, Guilan, Golestan Khoozstan and Fars provinces, respectively. Also, for Iran it has been 1.9 percent. Annual growth rate of Allocative efficiency change during 2005-2013 have been 0.01, 0.6, 0.3, 0.5 and 0.5 percent for Mazandaran, Guilan, Golestan Khoozstan and Fars provinces, respectively. Also, for Iran it has been 0.8 percent. Finally, annual growth rate of TFP change during 2005-2013 have been 4.8, 3.8, 2.05, 3.1 and 4.7 percent for Mazandaran, Guilan, Golestan Khoozstan and Fars provinces, respectively. For Iran, also has been 4.3 percent. The most and least growth were for Mazandaran and Golestan Provinces. Differences in rough rice total factor productivity growth rates in the provinces were found to be explained primarily by differences in scale efficiency and technical efficiency. Scale elasticities for a year between 2005 and 2013 were between 1.13 and 1.12 for Mazandaran, between 1.12 and 1.13 (with fluctuation) for Guilan, between 1.14 and 1.13 for Golestan, between 1.18 and 1.19 for Khoozestan and between 1.14 and 1.18 for Fars. So, scale elasticities average between the sum of farms (between 1.12 and 1.18) shows that economics of scale exists in rough rice production technology. Scale elasticities for a year between 2005 and 2013 was between 1.24 and 1.27 for farm with size of less than 0.5 ha, between 1.17 and 1.22 for farm with size of between 0.5 and 1 ha, between 1.0.8 and 1.18 for farm with size of between 1 and 2 ha, between 1.05 and 1.13 for farm with size of between 2 and 3 ha and between 1.01 and 1.09 for farm with a size of more than 3 ha.
Conclusions: With the assumptions that rough rice production technology is similar in all provinces, approximately, differences between provinces in scale elasticities are about the size of the farm. That is, smaller farms in comparison with larger farms have more economics of scale. Finally, it can be noted that by increasing in size of farms, we can increase technical efficiency and TFP of rice production.
techWith the rise of emerging economies such as China, the research environment for the digital economy (DE) has changed significantly. However, our understanding of the productivity impact of DE development in Chinese firms remains in its infancy. The idiosyncrasies of the firm-specific contexts are closely related to further research on the this topic. As a baseline, we hypothesize a U-shaped DE-firm productivity (FP) relationship. We analyze the idiosyncratic influences of firm size and locality on the DE-FP relationship. The findings, which are based on a sample of Chinese firms from 2016 to 2019, show that (a) the U-shaped DE-FP relationship applies to Chinese firms; (b) this relationship is moderate for large firms, substantially steeper for medium firms, and inverted for small firms; (c) the U-shaped DE-FP relationship for eastern region firms is moderate, while the U-shaped relationship for central region firms is steep, but the transition is incomplete, and western region firms have experienced increasing productivity since the early stage of DE development. This study offers an alternative approach to understanding Chinese firms' strategic choices in DE development and provides a more nuanced explanation for the productivity paradox by emphasizing the significance of the firm-specific context. In this way, the study captures the sophisticated and constantly evolving relationships between DE and FP for heterogeneous Chinese firms.
This paper examines the impact of the technology frontier on the Total Factor Productivity (TFP) growth in African countries. The paper includes the human capital channel (i.e., low and high education) in the TFP growth function. The model also adds net foreign direct investment inflows and financial development as control variables. The long-run estimations indicate that the technology frontier hampers the TFP growth. The technology frontier adversely affects the TFP growth in a low education channel. Surprisingly, high education has a positive and insignificant impact on TFP growth. It is also found that financial development decreases the TFP growth while net foreign direct investment inflows have mixed effects. These findings suggest that educated labour embodied in rich technology frontier should also require the proper training for enhancing the TFP growth in African economies.
Purpose
The objectives of this study are to investigate the role of wages, skills development and R&D on the productivity of Thai manufacturing firms, using data from the 2017 Industrial Census of Thailand.
Design/methodology/approach
The paper uses two-stage least squares (2SLS) to examine the role of wages, skills development and R&D, as well as other vital factors, impacting productivity as measured by labour productivity and total factor productivity.
Findings
Thai manufacturing firms' technology in aggregate exhibits decreasing returns to scale. Increasing wages and skills development promote the labour productivity and total factor productivity (TFP) of Thai manufacturers. R&D is also shown to be vital in promoting the labour productivity and TFP of large firms, but not small firms. Foreign direct investment (FDI) and government support can significantly increase large and medium-sized firms' labour productivity and TFP. Financially constrained firms tend to perform more productively. However, older firms, larger firms, labour supply shortages and political instability adversely affect labour productivity and TFP.
Practical implications
Upskilling and improving HRD policies could move Thailand towards a knowledge-based and high-income country in the future. Intellectual property protection should be strengthened to boost the country's R&D. The government should consider lifting restrictions on FDI to encourage international openness. The Thai Board of Investment’s promotion should target Thai manufacturing firms and FDI.
Originality/value
This study is the first to examine in detail the role of wages, skills development and R&D on the productivity of Thai firms based on the 2017 Thailand Industrial Census.
We analyze the extent to which hospitality firms overuse staff using a production function model which considers firm heterogeneity and accounts for environmental variables in staff use. We decompose overall staff use inefficiency into transient and persistent inefficiency. To do this, we employ a state-of-the-art stochastic frontier model, which is estimated using daily data on 94 Norwegian hospitality firms from 2010 to 2014. The environmental variables, especially the annual time trend, seasonality, and days of the week are found to exert heterogeneous effects on staffing. The mean transient, persistent, and overall efficiencies of the hospitality firms are 69%, 67%, and 46%, respectively. We find that seasonality (days of the week) decreases (increases) transient inefficiency by about 4%, suggesting significant room for improvement in hospitality staff use.
We investigate long-run firm-level productive growth and technological changes and relate these to the formal and informal institutions and related factors. We conclude that persistency of easing of regulations through broader reforms, including privatization and perception of non-reversal of reforms, helped firms achieve prolonged productivity gains—led by technological changes alongside a socially desirable reduction in CO2 emissions due to energy saving, as well as increased labour usage, despite variations in the quality of formal and informal institutions. Broadly, both formal and informal institutions matter for all firms irrespective of productivity levels and technological gains. Government stability, the country's investment climate, socio-economic conditions, and corruption perception are essential in determining long-run productivity growth and technological changes. However, the role of law and order conditions, political constraints, and competitiveness of the political system/process in determining productivity gains and technological improvements varies by firms’ characteristics.
Economic performance is closely related with energy consumption, the major part of which still comes from non-renewable sources. While endeavoring to promote renewable energy, policy makers are interested in technological change that also increases energy efficiency. However, both growth models of directed technological change and microeconomic theories regarding innovation suggest that technological change is not necessarily biased towards energy. In order to investigate directed technological change at the micro level, this paper applies stochastic frontier analysis to firm data for 32 economic subsectors, with respect to output produced with four inputs: capital, labor, electricity and fuel. Subsectors demonstrate different levels of technical inefficiency, which could be induced by capital deepening and higher share of financial income in total revenue. Output elasticity of labor is generally high among the subsectors, emphasizing labor as the main driver for economic growth. Output elasticity of capital is low overall, although a few subsectors enjoy better marginal returns. In most subsectors, technological change is biased the most towards labor; between electricity and fuel, technological change has favored fuel in more cases. We infer that the market size effect is likely to overwhelm others in deciding the direction of technological change. Thus, policy should include tools in addition to the energy price in order to induce technological change.
Accurate carbon emissions forecasting plays a pivotal role in reducing global warming by providing references to formulate emission reduction policies. Although numerous studies have focused on forecasting China's carbon emissions, the results of different methods are contradicting, because they are based on different data and use different parameters. This paper aims to propose a hybrid carbon emissions forecasting model based on multi-factor identification to offer reliable forecasting results. First, association rule algorithm was applied to find influencing factors and analyse their joint effects on carbon emissions from the perspective of time and space. Energy consumption, economic growth, industrial structure, foreign direct investment, and urbanization are proven to be the five major factors that can cause an increase in carbon emissions. Second, a multivariate grey model optimized by firefly algorithm was utilized to conduct carbon emissions forecasting under different scenarios. Empirical results indicated that the proposed hybrid model had the best performance compared to other methods. If no effective measures are taken, it is difficult for China to realize its goal for carbon emissions reduction in 2020.
Reliance of modern economic activities on the use of energy, most of which still comes from non-renewable sources, provokes concerns regarding the most efficient utilization of energy inputs in production. While most theory expects directed technological change to be biased towards the non-renewable input, there is rare macro-level evidence that technological change is actually biased towards energy rather than other main inputs. To fill this gap, we apply stochastic frontier analysis to country data regarding output produced with capital, labor and energy, and estimate a set of indicators for technological change. Findings show that technological change is biased the most towards energy in general. In particular, although different groups of countries exhibit various patterns, there is strong evidence that technological change favors energy more than labor. This is in line with the theoretical expectation that technological change ought to be biased towards the non-renewable input rather than the renewable ones.
Abstract: Science and Technology (S&T) are the key contributing factors in shaping the economic growth of nations. Despite the fact that S&T impacts the growth processes, the assessment of this relationship is not simple, as there are also a number of other factors that impact growth. The present paper describes the findings of the study that analyzed the relationship between ‘science and technology (S&T) inputs’ and economic growth as represented by ‘Gross District Domestic Product (GDDP)’ at the district level. This distinctive study contributed to the identification of ‘S&T Indicators’ in three sectors of the economy viz; agriculture, industry and services and also developed ‘Index of S&T Indicators’ for five Indian districts. The empirical findings of the study clearly exhibited that technological interventions in all three sectors have a great impact on the growth of the districts. The study thus sets a new pathway in its approach to constructing the relationship between technology and economic growth. Keywords: Technology, GDDP, Relationship, Agriculture, Industry, Services, Index of Indicators
The projected rate of productivity growth is the critical determinant of the price cap in incentive regulation. However, the regulatory authorities generally lack sophisticated industry TFP studies to set an optimal cap. We thus estimate productivity growth in the Austrian gas distribution sector in a translog cost function framework. A key feature is our unique panel database on costs and outputs of regulated utilities for the period 2002–2013, covering six years prior and during incentive regulation as introduced in 2008. We find a modest TFP growth rate in the early sample years, followed by a decline to zero or even slightly negative rates in recent years. We also find a significant potential for returns to scale, which is left unexploited, indicating that utilities could significantly save on costs by merging. As essential investments have already been undertaken in the past, opportunities for technological progress seem to be limited in recent years.
Measurement of the productivity of firms is an important research issue in productivity literature. Over the years, various methods have been developed to measure firm productivity across the globe. But there is no unanimity on the use of methods, and research on the identification of factors which determine productivity has been neglected. In view of these gaps, this study aims to measure total factor productivity (TFP) and tries to identify firm-specific factors which determine productivity of Indian manufacturing companies. The study is based on data of 616 firms from 1998–99 to 2012–13. To measure TFP, the Levinsohn–Petrin (L-P) method has been employed, and the fully modified ordinary least squares (FMOLS) method has been used to identify factors that affect TFP. The results reveal that embodied and disembodied technology plays a crucial role in the determination of productivity overall in manufacturing and other sub-industries. Similarly, the size of firms and intensity of raw material imports are also important for the determination of productivity across the sub-industries.
JEL Classification: C14, C33, D24, L60
The purpose of this paper is to analyze technical efficiency of provinces in China and the ways the efficiency is affected by human capital factors using the stochastic frontier approach. The results of the study show that the Eastern region is the most efficient one as compared to the Western and Central region with average efficiency scores of 54.39%. The results also show that feminization, public investment in science and technology, and higher standard of living contribute to higher inefficiency level of the provinces in China. On the other hand, investment in education is positively related to efficiency level. Consequently, it is crucial to upgrade the education level of labour force, provide equal access to education irrespective of gender, and stabilize inflationary pressures as a result of higher standard of living to reduce inefficiency level of the provinces.
In this chapter the first group of econometric models the Cobb-Douglas production function and the Translog production function are estimated. The findings from estimating the Cobb-Douglas production function model reveal that (i) In general the South Korean industries are exhibiting increasing returns to scale
, (ii) There is a slight substitution pattern between energy and ICT capital, and (iii) There is a significant and positive impact of energy use on the production level in the South Korean industries.
This chapter introduces the second group of the econometric models estimation, namely the energy demand model. The model is constructed in two forms: The Cobb-Douglas and the Translog function
to allow for consistency and comparability. It is worthy of mentioning that the estimated energy demand is a derived demand, the variable of energy is considered as one of the input factors of production. The energy demand is, therefore, derived from the demand for the industry’s output. Since the demand for energy depends on the output level, the possible substitutability between energy and other inputs is allowed by production technology
and energy price.
This paper assesses sources of productivity spillovers in China’s electric and electronic manufacturing industry using a rich panel data-set of 25,360 firms observed over the period 2004–2007. This industry is characterized by its important reliance on technology. In particular, the paper focuses on the role of other firms’ productivity as well as productivity shifters in affecting own firm-level total factor productivity. In addition, this paper examines the possible difference between spillovers from foreign-owned units and from units which participate at global markets through exporting in comparison to domestically-owned and non-exporting units. We find evidence of stronger spillovers from exporting firms than from non-exporting firms. This is true for foreign-owned as well as domestic exporters. The strength of the spillover effects differ across subsectors.
A new functional form, the “transcendental logarithmic production possibility frontier”, or more simply the “translog production frontier”, is introduced. This functional form can be used in the empirical analysis of multiple-output and multiple-input technologies. It can provide a second-order approximation to any production possibility frontier and similarly to any price possibility frontier. When only one output is distinguished, the “translog” production frontier reduces to the “translog” production function.
Using the translog production frontier and its dual, the translog price frontier, and distinguishing two outputs--consumption and investment, and two inputs--capital and labor, the hypotheses implied by the theory of production under the assumption of profit maximization are statistically tested with aggregate time-series data for the U.S. private domestic economy for 1929-1969. We find that the data are consistent with the implications of the theory of production. Our tests of the theory of production are independent of the hypotheses of the production frontier being groupwise additive in outputs and in inputs because the “translog” production frontier is not necessarily groupwise additive or separately commodity-wise additive in outputs and in inputs. Conditional on the hypotheses of the theory of production, we also test the hypotheses of additivity and find that the data are inconsistent with either groupwise or commodity-wise additivity.
Both the constancy of the elasticity of substitution between inputs and the constancy of the elasticity of transformation between outputs imply the respective commodity-wise additivity. Our rejection of the hypotheses of additivity imply that the “Constant Elasticity of Substitution” and the “Constant Elasticity of Transformation” functional forms are not empirically viable.
In this paper our objective is to analyze technical change and the distribution of the value of output for thirty-six industrial sectors of the U.S. economy. Our most important conceptual innovation is to determine the rate of technical change and the distributive shares of productive inputs simultaneously as functions of relative prices. We show that the effects of technical change on the distributive shares is precisely the same as the effects of relative prices on the rate of technical change.
This paper investigates India's exceptional development pattern, specifically the major importance of information technology services (ITS), and compares it with China's development pattern. Both countries want to develop capabilities for carrying out the innovation of technologies that compete at the state-of-the-art in the world market. The paper posits that technological/economic success in the contemporary world market requires the ability to innovate complex technologies and complex technology-related services. The share of trade represented by complex technologies is compared with the “high-tech” share. The trading patterns of the two countries are compared using United Nations data. Two case studies of Indian ITS companies are then compared with two case studies of Chinese manufacturing companies. Historical and cultural differences appear to explain some of the differences in the development patterns of the companies located in the two countries.
China is the world's fastest growing economy and is also the second largest. However, the official estimates of the Chinese National Bureau of Statistics exaggerate GDP growth and need adjustment to conform to international norms as set out in the 1993 System of National Accounts (SNA). This paper presents and discusses the necessary adjustments. The two major contributions are new volume indices for the industrial sector and for "non-material" services. Finally, in order to measure the level of Chinese GDP in welfare terms, we use a measure of purchasing power parity (PPP)
China and India have both sought control over the exchange rate in order to maintain export competitiveness, manage current account balance, and pursue independent monetary policy. In this paper, we examine structural change in the Chinese and Indian de facto exchange rate regimes, focusing on the period from 1998 to 2007. With increasing capital account openness, exchange rate inflexibility has been associated with significant monetary policy distortions. In both countries, the short-term rate expressed in real terms dropped, and achieved very low values, in the unprecedented business cycle expansion of the early 2000s. In the Indian case, difficulties of sterilisation led to a modification of the exchange rate regime, moving towards greater flexibility. In China, in contrast, the exchange rate regime did not change.
We analyze the determinants of ICT investment and the impact of information technology on productivity and efficiency on a representative sample of small and medium sized Italian firms. In order to test the most relevant theoretical predictions from the ICT literature we evaluate the impact of investment in software, hardware and telecommunications of these firms on a series of intermediate variables and on productivity. Among intermediate variables we consider the demand for skilled workers, the introduction of new products and processes and the rate of capacity utilization. Among productivity measures we include total factor productivity, the productivity of labor, and the distance from the best practice by using a stochastic frontier approach. Our results show that the effect of ICT investment on firm efficiency can be more clearly detected at firm level data by decomposing it into software and telecommunications investment. We find that telecommunications investment positively affects the creation of new products and processes, while software investment increases the demand for skilled workers, average labor productivity and proximity to the optimal production frontier. We interpret these results by arguing that ICT investment modifies the trade-off between scale and scope economies. While software investment increases the scale of firm operations, telecommunications investment creates a flexibility option easing the switch from a Fordist to a flexible network productive model in which products and processes are more frequently adapted to satisfy consumers taste for variety.
This paper examines the growth of technological capability in the telecommunication industry in China. We apply a modified version of Lee and Lim's [Lee, K., Lim, C., 2001. The technological regimes, catch-up and leapfrogging: findings from the Korean industries. Research Policy, vol. 30.] model of technological learning and catching-up. Using the three cases of the Shanghai Bell, the CIT-led R&D consortium, and indigenous companies such as Huawei, we analyze how the catching-up in the telecommunication industry occurred. We find that the important factors in the catch-up are the strategy of “trading market for technology,” the knowledge diffusion from Shanghai Bell both to the R&D consortium and to Huawei, and industrial promotion by the government. As a condition for successful catch-up, the paper points out that the technological regime of the telephone switches is featured by a more predictable technological trajectory and a lower cumulativeness. These conditions and strategies helped the Chinese firms to achieve a stage-skipping catch-up, namely, by skipping the stage of analogue electronic switches to jump to digital electronic switches.
This paper provides empirical evidence on the links between infrastructure investment and economic growth in China. Using panel data from a sample of 24 Chinese provinces (excluding municipalities) throughout the 1985 to 1998 period, the estimation of a growth model shows that, besides differences in terms of reforms and openness, geographical location and infrastructure endowment did account significantly for observed differences in growth performance across provinces. The results indicate that transport facilities are a key differentiating factor in explaining the growth gap and point to the role of telecommunication in reducing the burden of isolation. J. Comp. Econ., March 2001 29(1), pp. 95–117. CERDI-IDREC, CNRS—Université d'Auvergne, 65, boulevard François Mitterand, 63 000 Clermont-Ferrand, France. Copyright 2001 Academic Press.Journal of Economic Literature Classification Numbers: C33, H54, O11, R11.
China's industrial policy regime has played an important role in the country's economic development and participation in the world economy. This paper examines the effect of this regime on cross-industry resource allocation in three aspects: incentives, openness of trade and investment, and allocative efficiency. The results indicate effectiveness of industrial policy in offering incentives and controlling market openness in line with the planned priority. Evidence in cross-industry resource allocation, however, suggests significant efficiency gains are yet to be obtained from further participation in globalization.
Resource misallocation can lower aggregate total factor productivity (TFP). We use microdata on manufacturing establishments
to quantify the potential extent of misallocation in China and India versus the United States. We measure sizable gaps in
marginal products of labor and capital across plants within narrowly defined industries in China and India compared with the
United States. When capital and labor are hypothetically reallocated to equalize marginal products to the extent observed
in the United States, we calculate manufacturing TFP gains of 30%–50% in China and 40%–60%.in India.
Incl. bibliographical notes and references, summary
China and India have successfully integrated into the world economy. Once specialised in textiles, they have developed new export-oriented sectors linked to the information and communication technology (ICT), taking advantage of the globalisation process which has enlarged access to new technology, capital and markets. China has become a global export platform for electronic goods and India a global centre for ICT services. They have followed different paths of specialisation. China is heavily involved in the international segmentation of production processes in manufacturing, which is not the case of India. China is heavily specialised in mass exports of cheap goods, while India focuses on niches. Both countries are in a process of technological catch-up but in different industries. By the middle of this decade, the pattern of development followed by each of them seemed to have reached its limits and even before the shock of the global crisis in 2008, there was a debate about the changes necessary to make growth sustainable. The crisis has made clear that their long term growth will depend on their ability to build on their large domestic markets.
The comparison of the periods of rapid economic growth in China since 1978 and India since 1992 markedly show different patterns of development and structural change. However, both countries experienced some advantages of "relative economic backwardness" and some aspects of the "fordist model of growth". China had an anticipated and deeper structural change, spurred mainly by economic reforms and the growth of the internal market in the 1980s, and, since the mid-1990s, by a very rapid penetration of its industrial products in the world market. However, a substantial part of China's exports in medium and high tech sectors are due to joint-ventures with foreign multinationals. India had a more balanced structural change and a slower insertion in the world market, although some sectors, such as software, steel, automotive and pharmaceuticals are recently increasing their share in the world markets. Owing to the huge number of micro-enterprises and the great size of the informal sector, India benefited much less than China from the economies of scale and from the third wave of the "fordist model of growth". Both countries, but in particular China, experienced negative externalities of this recent phase of rapid growth, such as higher inequalities, pollution and urban congestion.
Editors' Introduction to the symposium "Comparing China and India: Structural Change and Development"
The often-advocated view that the information technology revolution will change the world must stem from the basic premise that investment in IT has a visible impact on productivity and economic growth. But how can we measure this impact and how large is it? By surveying previous studies and by presenting new micro- and macroeconomic evidence, this collection of chapters shows that in recent years the use of IT in the production of goods and services has had a strong influence on productivity and economic growth in industrial and in newly industrialized countries. Yet developing countries seem neither to have invested in IT nor benefited from such investments to the same extent as industrial countries. There is concern that information is becoming a commodity, like income and wealth, by which countries are classified as rich and poor. The chapters in this volume argue that investment in infrastructure, physical capital, and education are key to economic development and that the IT content of these investments should be high. Besides providing citizens with access to IT and to IT education and training, governments should promote participation in the information society, thus generating a sufficiently strong demand base for information products. By developing advanced applications of IT, and by becoming a model for the private sector, governments can alter worker, firm, and consumer attitudes, and lower their costs of adopting IT. The use of IT, not necessarily its production, is what matters for economic development.
The current growth of the Chinese economy is of immense importance for the global economy. This book outlines the main characteristics of Chinese economic growth over the last two decades, and investigates in detail the key determinants of growth, especially capital formation and productivity issues. It goes on to examine the important related questions of employment and underemployment, regional disparity, and economic integration, exploring in detail how far economic integration has taken place in south China, including the economies of Hong Kong and Taiwan, and how far this integration has been a determinant of economic growth. The book makes comparisons with other East Asian economies, and concludes with a consideration of the prospects for continuing growth in the twenty-first century.
This article is concerned with the estimation of production functions, returns to scale, and measurement of the rate of technical change using panel data. Technical change is represented by single as well as multiple time trends. The underlying production technology is represented in translog functional form. A random effects model with heteroscedastic variances is used. The models are estimated using the generalized least squares method. The disturbances of cross-sectional units are assumed to be correlated over time. Empirically, our focus is on measuring technical change in Iranian manufacturing industries during the period 1971–1993. Empirical results show that single or multiple time trend representations yield different time behavior of technical change. In the multiple time trends model, we observe a sharp decline in the pattern of technical change in 1978 in relation to the political changes. In the single time trend, as expected, the sharp decline cannot be revealed due to the smooth pattern of technical progress during the entire period of study.
This paper investigates the simultaneous causal relationship between investments in information and communication technology (ICT) and flows of foreign direct investment (FDI), with reference to its implications on economic growth. For the empirical analysis we use data from 23 major countries with heterogeneous economic development for the period 1976–99. Our causality test results suggest that there is a causal relationship from ICT to FDI in developed countries, which means that a higher level of ICT investment leads to an increase inflow of FDI. ICT may contribute to economic growth indirectly by attracting more FDI. Contrarily, we could not find significant causality from ICT to FDI in developing countries. Instead, we have partial evidence of opposite causality relationship: the inflow of FDI causes further increases in ICT investment and production capacity.
This paper examines total factor productivity growth, technological progress, and technical efficiency change in postreform China. Production functions are estimated for three sectors, state industry, rural industry, and agriculture, using panel data from 1985–1991. The major issues considered include (1) introducing a model in which productivity growth is decomposed into two components, technological progress and changes in technical efficiency, and (2) comparing productivity and efficiency performance among the regions as well as across the sectors.
В статье производится анализ агрегированной производственной функции, вводится аппарат, позволяющий различать движение вдоль такой функции от ее сдвигов. На основании сделанных в статье предположений делаются выводы о характере технического прогресса и технологических изменений. Существенное внимание уделяется вариантам применения концепции агрегированной производственной функции.
This paper presents the parametric estimation of the rates of technical change and total factor productivity (TFP) growth of 7,462 Korean manufacturing firms for the period 1987 to 2007. Two alternative formulations of technical change measured by the time trend and the general index approaches are estimated with panel data models assuming flexible functional forms. Several extensions of each approach are also considered and their benefits and limitations are discussed. In addition to making estimates of the TFP growth and its decomposition, the paper compares the parametric TFP growth measure with the non-parametric Solow residual serving as a benchmark. Several hypotheses related to technology level, firm sizes, industrial sectors, skill biased technological change and macroeconomic and industrial policies are tested to explain the growth patterns and heterogeneity in technical change, input biases and TFP growth rates. Using second regression analysis, the paper explores the determinants of TFP growth and their policy implications.
Pundits point to the awesome growth of East Asia's economies and fret that the West cannot compete. But there is nothing miraculous about the successes of Asia's "tigers." Their rise was fueled by mobilizing resources--increasing inputs of machinery, infrastructure, and education--just like that of the now-derided Soviet economy. Indeed, Singapore's boom is the virtual economic twin of Stalin's U.S.S.R. The growth rates of the newly industrialized countries of East Asia will also slow down. The lesson here for Western policymakers is that sustained growth requires efficiency gains, which come from making painful choices.
We present in this paper a new method of estimating total factor productivity (TFP) and capital stocks simultaneously, and apply it to growth accounting in the Chinese economy at the national, regional, and provincial levels. Since our treatment of capital and labour does not allow for quality our TFP growth or residual also includes quality improvement of capital and labour, so that our findings can be summarized as follows. The rapid growth in China in the fifteen years since the Reform and Open-up Policy is mainly due to the high and stable level of capital input, which contributes around 50% of GDP growth. The contribution of labour is small at around 15% of GDP growth and has been declining steadily. The growth of TFP has been fairly high at around 3~4% and is tending to increase and contributes about 40% to GDP growth. The economic growth of regions and provinces in China also depends heavily on capital input. The higher growth in the East region is attributed also to the higher input of capital in the region compared with the Middle and West regions. The gap in GDP growth between the East and the other two regions, has widened recently during the period of the 8(th) five-year plan. This is partly due to additional promotion of capital input in the East region, but is mainly because TFP growth has accelerated for more quickly in the East than in the other two regions. There has also been a remarkable widening of the gap in per capita GDP, especially in the 1990s. The difference in income within rural households, especially between the West and the other two regions, is a crucial factor in explaining this regional income disparity. The level of TFP, together with the level of capital, is also important in explaining the income disparity in China between regions and provinces. The growth of TFP in each province in the 1990s is closely related to such common factors as the expansion of non-state enterprises, the increase in foreign direct investment and, to a lesser extent, the degree of human development, but it is still greatly dependent on the region-specific elements.
This paper looks into whether China's continuously high economic growth is mainly contributed by productivity growth after the mid-1990s when it has devoted more efforts to innovative activity. More importantly, I also systematically investigate how and to what extent various technological sources contribute to productivity growth in China. Identifying the internal and external technological sources, this paper adopts a newly developed technique of the stochastic metafrontier function to evaluate regional productivity growth in China and then analyzes the impacts of technological sources on moving up the technology ladder in China. The first-step estimation shows that the average productivity growth is 2.821% during 1996–2004, which is similar to that before 1995. However, the coastal and non-coastal regions witnessed an apparent difference in total factor productivity (TFP) growth, 4.567% vs. 1.718%. Concerning the effects of technological sources, in-house research and development (R&D), foreign direct investment (FDI) and technology import are all positive on significantly promoting productivity. Relative to R&D and FDI, the contribution through importing technologies seems to be larger, implying that China's technological progress relies heavily on more advanced foreign knowledge. However, neither technological source is found to have a larger influence on productivity growth among non-coastal regions.
Since the late 1970s, the economies of Taiwan, Hong Kong, Guangdong and Fujian together have formed one of the fastest growing regions in the world. Rapid growth in trade and investment flows among these economies has led to anincreasingly integrated sub-regional economic bloc. How has economic integration affected productivity performance among the economies involved? Has regionalintegration led to convergence in growth and efficiency? These are some of the questions that this paper attempts to deal with. Both time-series and panel-data models have been employed to examine productivity, technological progress and efficiency in the four economies.
Regional diversity in the process of economic growth is the major concern in this paper. We will try to identify its sources for growth and to specify production functions in each province by estimating translog production function. This paper clarifies the following four facts: First, capital accumulation was a major source for growth in the earlier stage of the Chinese economy, especially in the eastern coastal region. Unexpectedly, capital accumulation is losing its ground over the years. Second, the employment structure of the economy in the eastern region has changed significantly and the shares of workers in the secondary and tertiary industries increased until 1992. Since 1992, these figures have not changed significantly despite China's continuous economic high growth. Third, four distinguishable regional growth patterns have contributed to China's economic growth. Finally, production technologies in each province vary both in the direction of factor intensity and in the elasticity of substitution between inputs.
This paper studies a key driver of the demand for the products and services of the global IT industry---returns from IT investments. We estimate an intercountry production function relating IT and non-IT inputs to GDP output, on panel data from 36 countries over the 1985--1993 period. We find significant differences between developed and developing countries with respect to their structure of returns from capital investments. For the developed countries in the sample, returns from IT capital investments are estimated to be positive and significant, while returns from non-IT capital investments are not commensurate with relative factor shares. The situation is reversed for the developing countries subsample, where returns from non-IT capital are quite substantial, but those from IT capital investments are not statistically significant. We estimate output growth contributions of IT and non-IT capital and discuss the contrasting policy implications for capital investment by developed and developing economies.
ABSTRACT A widely entertained hypothesis holds that, in comparisons
among countries, productivity growth rates tend to vary inversely
with productivity levels. A century of experience in a group of presently
industrialized countries supports this hypothesis and the convergence
of productivity levels it implies. The rate of convergence, however,
varied from period to period and showed marked strength only during
the first quarter-century following World War II. The general process
of convergence was also accompanied by dramatic shifts in countries'
productivity rankings. The paper extends the simple catch-up hypothesis
to rationalize the fluctuating strength of the process and explores
the connections between convergence itself and the relative success
of early leaders and latecomers.
China has been growing at a spectacular rate in recent years, enabling per capita incomes to almost quadruple in only the last decade and a half. This paper identifies the sources of economic growth in China from 1952 to 1994. While capital accumulation played an important role in China's economic growth throughout the period, it is basically the sharp and sustained increase in total factor productivity that accounts for the unprecedented economic growth observed during the reform period. The productivity gains largely reflect market-oriented reforms, especially the expansion of the nonstate sector, as well as China's "open-door" policy, which brought about a dramatic expansion in foreign trade and foreign direct investment.
This paper examines whether the dominant sources of China's regional inequality have changed since the early 1980s. We adopt the decomposition method introduced by Tsui (1993) to facilitate comparisons with his results for 1982. The decomposition analysis shows that the dominant sources of overall regional inequality in output have shifted from the intraprovincial to interprovincial inequality, from the rural–urban to intrarural inequality, and also from the disparity within the coast to between the coast and the interior. In the case of consumption, however, the intraprovincial inequality, the rural–urban inequality, and the disparity within the coast are the major factors of the overall regional inequality.
This study investigates the geographic effects on regional economic growth in China under market reforms. We develop a model for the regional growth pattern of the Chinese economy during the period, characterized by foreign direct investment (FDI) and mobilization of rural surplus labor. The FDI and labor migration are directed by the differentials in the expected returns from the capital investment and in the wage rate. The differentials are, to a large extent, explained by geographic factors. In the context of market reforms and the open-door policy, the spatial and topographic advantages of the coastal provinces are realized. As a result, the returns to the capital investment in the coastal provinces are higher than in the rest of the country, thus attracting more FDIs and migrant labor into the region and causing the growth disparity. Our empirical test supports this hypothesis. It finds that geographic factors are statistically significant in explaining the regional disparity in China. This disparity is mainly a coast versus noncoast gap.
The People's Republic of China (PRC) has always emphasized a policy of achieving economic equality between regions. Yet regional disparity persists. During the 1980s and 1990s, government policies increased the disparities between regions, causing the eastern (coastal) region to advance at a greater rate than the western and central regions. Using 1985 and 1991 city-level data, this paper examines economic disparities across regions in the PRC and, within each region. It presents evidence on the magnitude and evolution of regional disparities, and estimates the response of regional per capita income (PCI) and per capita gross domestic product (PCGDP) both to specifics of the regions and to national policies.
This paper utilizes a unique county-level dataset to examine technical efficiency and technology gap in China's agriculture. We classify the counties into four regions with distinctive levels of economic development, and hence production technologies. A meta-frontier analysis is used. We find that although the eastern counties have the highest efficiency scores with respect to the regional frontier but the northeastern region leads in terms of agricultural production technology nationwide. Meanwhile, the mean efficiency of the northeastern counties is particularly low, suggesting technology and knowledge diffusion within region might help to improve production efficiency and thus agricultural output.
This paper examines the relationship between research and development (R&D) expenditure and productivity in China's enterprises. An empirical model that contains a system of three equations, i.e., the production function, a private R&D equation, and a government R&D equation, is estimated using a cross-sectional data set for Chinese enterprises of various ownership types. We find a strong link between private R&D and firm productivity. Although its direct contribution to firm productivity is insignificant, government R&D contributes indirectly to productivity by promoting private R&D. Hence, providing incentives for enterprises to invest in R&D may be a better alternative than providing R&D grants directly. J. Comp. Econ., March 2001 29(1), pp. 136–157. National University of Singapore, Singapore 119260, Singapore. Copyright 2001 Academic Press.Journal of Economic Literature Classification Numbers: L00, O31, O33, P31.
We investigate empirically whether foreign direct investment generates externalities in the form of technology transfer. Using data on 29 manufacturing industries over the period from 1993 to 1998 in the Shenzhen Special Economic Zone of China, we find that foreign direct investment has large and significant spillover effects in that it raises both the level and growth rate of productivity of manufacturing industries, and domestic sectors are the main beneficiaries. We also find that some domestic sectors benefit more than others from the external effects of foreign direct investment. The results are robust to a number of alternative model specifications. J. Comp. Econ., September 2002 30(3), pp. 579–602. State University of New York at Buffalo, Buffalo, New York 14260-1520. © 2002 Association for Comparative Economic Studies. Published by Elsevier Science (USA). All rights reserved.Journal of Economic Literature Classification Numbers: F2, O1, O3.
Based on a sample of about 600 state enterprises from 1980 to 1994, this paper investigates the productivity performance of SOEs using Data Envelopment Analysis and a Malmquist index. Our empirical results show that the average technical efficiency was low for these firms. Considerable productivity growth was found, but it was accomplished mainly through technical progress rather than through efficiency improvement. Regression analyses indicate that large SOEs were more likely to generate productivity growth than smaller ones. The best practice firms were most likely to be found among large enterprises located in the well-developed coastal province. Wage incentives and capacity utilization had positive impacts on productivity growth. Education had a significant effect on technical efficiency.
China's economic growth has been remarkable since the reform started in 1978. There is an ongoing debate about whether this performance is driven mainly by productivity growth or by factor accumulation. But few past studies taken human capital into account, and thus contained an omission bias. In this paper, we construct a measure of China's human capital stock over 1952–1999 and employ it in our growth accounting exercise. We find that, first, in China, the accumulation of human capital was quite rapid and it contributed significantly to growth and welfare; second, after incorporating human capital, the growth of total factor productivity (TFP) still played a positive role in GDP growth in the reform period, while it was negative in the prereform period. These results are robust changes in labor shares in GDP and in depreciation rates. An implication is that a high priority should be given to human capital accumulation and productivity growth, if China is to sustain its growth and welfare improvement in the next decade.
This study provides empirical evidence on China’s ICT industry development and diffusion in recent years. Although there is still a huge gap between China and the developed countries in the development of the ICT industry, the astonishing pace of its progress shows promise for the country’s New Economy. The ICT industry is becoming the most dynamic sector in China’s economy. There is, however, a clear digital divide among the nation’s three economic regions.
This paper analyzes the tariff structure and its determinants in China, with our research conducted under the rubric of endogenous policy theory. We study the tariff rates for 95 industries in China in 1996. The potential determinants of tariff rates are collected from an array of variables characterizing industries in 1995. A principal component method is used to reduce these variables into four major dimensions. The first component comprises the information on the composition of employees broken down by age, education, and job classification. The second component is underlined by the profitability of the industry. The third component consists of those variables not picked up with high salience in the first two components. More closely resembling those in the second component than the first, these variables include gross product, foreign capital, inventory, sales revenue, and total loss. The fourth component receives high loadings from two variables: the number of firms in the industry and the number of firms that incur net losses in their operation. Using variables identified by the principal component analysis and postulated by the variants of the endogenous trade theory, regression analysis finds that the trade policy in China is mainly defined by an industrial policy favoring high-tech industries and a social policy minimizing social instability. The implications for China's entry into the World Trade Organization (WTO) are also provided in the paper.
This paper investigates US multinational enterprises (MNEs) as a channel of international technology diffusion in 40 countries from 1966 to 1994. We use data on technology transfer to distinguish between the technology diffusion effect and other productivity-enhancing effects of MNEs. We find that the technology transfer provided by US MNEs contributes to the productivity growth in DCs but not in LDCs. We show that a country needs to reach a minimum human capital threshold level in order to benefit from the technology transfer of US MNEs; however, most LDCs do not meet this threshold requirement.
This study investigates whether ownership structure significantly affects the performance of publicly listed companies in China within the framework of corporate governance. A typical listed stock company in China has a mixed ownership structure with three predominant groups of shareholders—the state, legal persons (institutions), and individuals—each holding approximately 30% of the stock. Ownership is heavily concentrated. The five largest shareholders accounted for 58% of the outstanding shares in 1995, compared with 57.8% in the Czech Republic, 79% in Germany, and 33% in Japan. Empirical analysis shows that the mix and concentration of stock ownership do indeed significantly affect a company's performance. First, there is a positive and significant correlation between ownership concentration and profitability. Second, the firm's profitability is positively correlated with the fraction of legal person shares, but it is either negatively correlated or uncorrelated with the fractions of state shares and tradable A-shares held mostly by individuals. Third, labor productivity tends to decline as the proportion of state shares increases. These results suggest the importance of large institutional shareholders in corporate governance, the inefficiency of state ownership, and potential problems in an overly dispersed ownership structure.
This paper extends measures of total factor productivity (TFP) for China's state and collective industry to cover the period 1980–1992, analyzes issues raised by critics of previous studies, and evaluates the robustness of productivity results. TFP increased in both major segments of Chinese industry throughout 1980–1992. The analysis of measurement issues confirms earlier results showing moderate TFP growth for state industry. Data bias may undercut the common observation of differential TFP growth favoring collective industry. TFP growth declines after 1988, especially in the state sector, where partial productivity of fixed capital drops. Differential rates of capacity utilization and, in state industry, selection bias and an unresponsive system of investment finance may have retarded the measured growth of productivity during 1988–1992.
This paper examines several theories of regional industrial development in the case of China. It studies regional factors representing three broadly defined sources of regional growth: dynamic externalities arising from knowledge spillovers, natural advantage and local market conditions, and foreign trade and direct investment. Using provincial-level data on two-digit Chinese Industrial Classification industries over 1985–1993, we find that local competition is positively related to regional industrial growth. There is also strong evidence that provinces with a smaller state sector grow faster, and that a better transport system helps growth. Finally after controlling for various other regional factors, we find that exports and foreign direct investment have strong positive effects on regional industrial growth.
Using Chinese data which by standards of western industrial analysis is deficient but in other respects is quite rich, this paper corrects for these deficiencies and exploits the richness of the data set by (i) investigating the technological properties of China's iron and steel industry, (ii) identifying the sources of productivity variation among enterprises within that industry, and (iii) evaluating the industry's historical productivity performance. The paper explains over eighty percent of the variance in efficiency among 120 enterprises and finds that during the reform period productivity growth within this key industry has accelerated significantly.
