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Should intermediate consumption (IC), which historically accounts for around 50% of the value of production, be considered in growth accounting? The current growth accounting exercise does not model IC. This means that when IC experiences productivity gains, its capacity to transmit them to the whole economy is currently neglected. The rare current literature on this issue diverges substantially on the importance of the role of the IC as an explanatory factor of growth. We propose a bi-sectoral general equilibrium model that allows for an accounting where each productive sector contributes to growth in proportion to its weight in the economy. Our theoretical framework and the (proportional) growth accounting exercise it allows (i.e., calculating the contribution of technical change conveyed by IC and other usual key factors, such as: Global and sectoral TFP, Global and sectoral Capital deepening) are thoroughly presented. The proposed framework is autonomous and calibrated pedagogically. However, to illustrate empirically in a comparative way how it works, we have shown that it can also be reduced to a particular case of the literature and calibrated it on the US economy (1954–1990). Although this consistently improves the growth accounting accuracy, our results reveal that the IC has nevertheless contributed only 2% to US growth. We compared this result to the literature. The key factors influencing its magnitude were also highlighted and discussed.

The efficient and sustainable management of forestry resources is crucial in ensuring economic and societal sustainability. The Chinese government has invested significantly in regulations, afforestation, and technology to enhance the forest resource efficiency, reduce technological disparities, and boost productivity growth. However, the success level of this undertaking is unclear and worth exploring. To this end, this study applied DEA-SBM, meta-frontier analysis, and the Malmquist productivity index to gauge the forest resource efficiency (FRE), regional technology heterogeneity (TGR), and total factor productivity growth (MI) in 31 Chinese provinces for a study period of 2001–2020. Results revealed that the average FRE was 0.5430, with potential growth of 45.70%, to enhance the efficiency level in forestry resource utilization. Anhui, Tibet, Fujian, Shanghai, and Hainan were found to be the top performers in forestry utilization during the study period. The southern forest region was ranked highest, with the highest TGR of 0.915, indicating advanced production technologies. The average MI score was 0.9644, signifying a 3.56% decline in forestry resource productivity. This deterioration is primarily attributed to technological change (TC), which decreased by 5.2%, while efficiency change (EC) witnessed 1.74% growth over the study period. The Southern Chinese forest region, indicating an average 3.06% increase in total factor productivity, ranked highest in all four regions. Guangxi, Tianjin, Shandong, Chongqing, and Jiangxi were the top performers, with prominent growth in MI. Finally, the Kruskal–Wallis test found a significant statistical difference among all four regions for FRE and TGR.

This study assessed the change in productivity and environmental efficiency of agriculture for nine East Asian countries for the time period from 2002 to 2010. Data were collected and then analysed by data envelopment analysis (DEA) approaches, including Malmquist total factor productivity (TFP) index and slacks-based measure (SBM) with the consideration of undesirable outputs. The results showed that there existed relatively large differences in productivity growth and environmental performance in the agricultural sector between countries in the sample. Overall, the countries examined in the present study experienced a decline in TFP due to decreases in technical efficiency. Taiwan, Japan, and Korea were found to show growths in productivity and fully efficient environmental performances throughout the study period, while Thailand was identified as having the lowest environmental efficiency score. Therefore, agriculture production and operation models in Taiwan, Japan, and Korea could serve as good references for the other six countries.

PurposeThe present study reviews the theoretical and empirical literature about the significance of international migrants' remittance to empirically analyse the effect of remittance on the productivity growth of developing countries using a panel dataset from 1991 to 2021.Design/methodology/approachThe study utilised the data envelopment analysis (DEA)-based Malmquist Productivity Index (MPI) to measure nationwide production efficiencies. It first performed a unit root test, cointegration test and pool mean group autoregressive distributed lag (PMG-ARDL) technique. To assess the robustness of the findings, the study also uses dynamic ordinary least squares (DOLS) and fully modified OLS (FMOLS) estimators.FindingsThe results demonstrated that remittances are a significant source of funding that promotes innovation [i.e. technological progress (TEC)] and hastens the country's total factor productivity (TFP) growth. However, the study needed to have established the effect of inward remittances on the nation's technical efficiency (EFF).Research limitations/implicationsAs remittances encourage innovation and TFP growth (TFPG), the concerned governments must create favourable and enabling economic environments to increase remittance inflows, which will have far-reaching growth repercussions.Originality/valueThe present study emphasises the connection between remittances and productivity growth, the disintegration of TFP, advanced econometric techniques and contribution to research policy. Despite prior literature exploring the effect of remittances on economic growth, a dearth of literature exists on how remittances affect a country's productivity. The output-based MPI methodology used in this study offered a nuanced understanding of how remittances affect many facets of productivity growth in developing nations.

Since total factor productivity growth plays an essential role in China’s economic growth, the source of this growth has been a critical issue over the past decades. Hence, this paper applies an input slack-based productivity (ISP) index to investigate the contributors (i.e., labor and capital inputs) to China’s total factor productivity growth. The ISP index, combining the features of the directional distance function and Luenberger productivity index, can calculate the productivity change of each input factor under the total factor framework. According to the decomposition analyses, we find that China is confronting a dual challenge in total factor productivity growth: first, capital productivity growth exhibits a remarkable slowdown after the mid-1990s; second, although labor productivity continually expands, the relative labor efficiency among provinces has deteriorated since the 2000s. The results imply that the government should not only advocate upgrading industrial structure, but also consider balanced regional development policies for China’s sustainable growth.

The current paper ascertains the effectiveness of exports, foreign direct investment (FDI), R&D, and human capital on total factor productivity (TFP) growth in Pakistan. The Tornqvist expression of TFP growth has been applied in the first stage, whereas ordinary least squares (OLS) have been used in the second stage to search for possible determinants of TFP, in the long run, using data from 1991 to 2021. According to the results, physical capital and human capital have emerged as the most contributory factors toward output growth in Pakistan. The positive contributions of TFP in the growth of output have been observed during 2001–2005 and 2016–2021. Further, TFP growth is observed to be positively correlated with growth in physical capital, human capital, and FDI. According to the empirical results, physical capital, labor, and human capital have emerged as highly significant determinants of TFP, whereas exports possess weak significance albeit having favorable signs. Contrary to the finding of other studies in many other countries, R&D and FDI have no significant impact on TFP in Pakistan, which is mainly due to the stagnant level of R&D and fluctuating FDI during recent years. The study recommends the use of human capital as a strategic tool to promote growth in output as well as TFP on a sustainable basis in Pakistan.

This study investigates the impact of the digital economy (DE) on green total factor productivity (GTFP) and its transmission mechanism. Using panel data from 256 cities in China from 2009 to 2020, the study examines a directional distance function and the Malmquist–Luenberger productivity index to estimate the GTFP growth and constructs an ordinary least squares model to explore the impact effect and mechanism. Three findings are drawn from the estimation results: (1) The DE has significantly promoted GTFP. (2) Technological innovation has significantly aided in the promotion of GTFP. (3) By encouraging technological innovation, the DE further enhances the promotion of GTFP, verifying the DE → technology innovation → green conduction mechanism of total factor productivity.

This paper investigates total factor productivity growth (TFPG) and its determinants in the Indonesian palm oil sector industries. TFPG is estimated using a growth accounting method. This paper applies the fixed effects model to investigate the determinants of the TFPG. The data is sourced from a manufacturing survey of the Indonesian Bureau of Central Statistics (Badan Pusat Statistika/BPS) for the period 2000-2017. This paper finds that the TFPG of the Indonesian palm oil industry is relatively low. Moreover, output growth, output per worker, export activity, and wages per worker have significant effects on the TFPG. The effect of output growth, which is dominated by the large use of inputs, raises concerns in the aspect of environmental sustainability due to uncontrolled land expansion.

Optimizing the industry development system and implementing a high-quality development strategy in China’s coal industry require us to grasp the overall status and regional differences of industrial development. Measuring and decomposing the total factor productivity growth of the coal industry is a necessary prerequisite. In this study, we estimated the total factor productivity (TFP) growth of coal industry in 24 major coal-producing provinces in China by constructing a stochastic frontier analysis (SFA) model based on a translog production function and decomposed it into technological progress change (TC), technical efficiency change (TE), scale efficiency change (SE), and factor allocation efficiency (AE). After analyzing the temporal evolution characteristics of TFP growth and its decomposition terms, we also characterized the spatial characteristics by region and province. The results showed that TFP growth in China’s coal industry is on the rise, with TC growth being the main driving factor of this; additionally, the weak growth of SE and AE also plays a limited role in this increase, while the decrease in TE hinders this trend. There are also significant regional differences in the TFP growth of China’s coal industry, with a ranking of central > west > east > northeast. Drivers of TFP growth vary between regions or in different provinces within the same region.

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.