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Based on the analysis of the quantitative assessment of the influence of the factors of innovative activity on the dynamics of economic development and the endogenous determination of its contribution to the economic growth of the country, it is revealed that in foreign researches it is identified with the concept of the influence of scientific and technological progress. However, the impact of innovative factors on economic growth is not considered separately. Attempts of such a definition in Ukrainian researches are limited to well-known approaches: methods of integral assessment, methods of expert assessments, the method of multipliers, econometric models. Most of the approaches considered are focused on defining the innovative impact on economic development, rather than on economic growth. The main tool for assessing the role and innovative contribution to economic growth is the aggregate supply function model, usually based on the Cobb-Douglas production function. The analysis of the approaches used revealed a number of comments on econometric approaches. In contrast to this, an approach is proposed based on the neoclassical production function of Cobb-Douglas with a constant return to scale in the form of J. Tinbergen, with technical progress neutral according to J. Hicks, decreasing marginal productivity of macrofactors with limited interchangeability, which provides a causal functional (and not a statistical) relationship between input and output variables; does not require long time series. It is characterized by dynamic coefficients of elasticity, capital utilization rate and taking into account the innovation factor in each separate period. The innovation factor includes: the gross domestic expenditures on research and development, the expenditures on innovation, general expenditures on education. To define the contribution of formal innovation factor in economic growth applied "method of Solow residual", which by taking the logarithm and obtaining logarithmic derivatives in the production function, provides a formalized information about the contribution of each factor to economic growth and allows to develop the necessary regulatory measures. The method has been tested at the level of the country and old industrial regions of Ukraine: Dnepropetrovsk, Zaporozhye and Kirovograd regions of the Dnieper economic region of Ukraine. The role, importance and influence of the main factors of economic growth along with innovative ones on the part of the aggregate supply are considered and the cost-effectiveness of innovative factors is estimated.

We study technical change for multi-product technologies to examine its effect on economic growth for 27 OECD (Organization for Economic Cooperation and Development) countries during 1951–2014. We review the Malmquist index and reexamine von Neumann’s model of an expanding economy. We estimate the coefficient of uniform expansion via DEA (Data Envelopment Analysis) and use it to measure technical change via a Solow residual and an alternative von Neumann technical change index which equals the difference between the growth rates of the slowest growing output and the fastest growing input. We also exploit a property of constant returns to scale in order to examine technical change of the average technology. During 2005–2014 the Solow residual shows relatively fast technical change. In contrast, the Malmquist, average technology and alternative von Neumann technical change indexes show negative or stagnant technical change.

Purpose This study aims to estimate total factor productivity (TFP) growth for the post-2008 period for selected industries in the manufacturing sector at NIC 3-digit. Total factor productivity growth (TFPG) estimates are based on the theoretical framework provided by studies such Hall (1988), Abraham et al. (2009) and Crepon et al. (2005) that incorporate market imperfection in labour and product market, thereby modifying the traditional TFP estimation as Solow Residual. Design/methodology/approach Based on the theoretical model that incorporates market imperfections in labour as well as product market in modifying the TFP estimates using the Levinsohn–Petrin framework of empirical estimation, the authors have calculated industry wise TFPG for 62 industries at NIC 3-digit level. Findings The study finds three distinct trends: first, there are considerable industrial disparities in productivity growth in terms of TFP. The estimates have been found to be higher than the conventional Solow Residual for most industries, indicating the role played by market imperfections in affecting the conventional measure of productivity growth. Second, estimates of bargaining power are found to be lower than those compared to the earlier estimates in Maiti (2013) for the Indian organised manufacturing case for 1998-2005. This observation is commensurate with the observation in recent years of a falling share in labour wage in total output in organised manufacturing sector. Finally, the study also found a statistically significant contribution of greater mechanisation on TFPG while an adverse effect of the rising dependence of organised manufacturing on contractual labour. Originality/value The role of market imperfections in measuring TFPG has been undertaken, and it has been found to be an important factor, as the estimated measures vary from the conventional measures of TFPG. Moreover, the study has considered a very recent period from 2008-2015 in estimating TFPG, as well as analysing the factors behind the trends in TFPG at industrial level.

When capacity constraints limit the production of heterogeneous firms, demand shocks can endogenously generate a number of important business cycle regularities: recessions are deeper than booms, economic volatility is countercyclical, the aggregate Solow residual is procyclical and the fiscal multiplier is countercyclical. The model's main mechanism is that the share of firms at their production limit is strongly procyclical. A baseline calibration of a basic New Keynesian DSGE model with capacity constraints delivers more than 25% of the empirically observed asymmetry in output, 18% of the additional cross-sectional dispersion in recessions and around 25% of the additional aggregate volatility, and more than 50% of the fluctuations in the Solow residual. The model implies fluctuations in the fiscal multiplier of around 0.12 between expansions and recessions.

Energy plays no role in the standard Cobb-Douglas Production Function (CDPF), and a trivial role in a three-factor CDPF where it is treated as a third input, independent of labour and capital. Starting from an epistemological perspective, we treat energy as an input to both labour and capital, without which production is impossible. We then derive an energy-based CPDF (EBCDPF) in which energy plays a critical role. We argue for the redefinition and measurement of real GDP in terms of exergy. We conclude that the “Solow Residual” measures the contribution of exergy to growth, and that the exponents in the EBCDPF should be based on cross-country comparative data as suggested by Mankiw (1995) rather than the “cost-share theorem”.

Developing a de-capacity scheme that is economical, fair, and efficient, has become a key problem affecting the smooth achievement of coal de-capacity targets. It is also an important policy issue with regard to the overall implementation of China's supply-side structural reforms. In this study, we use the production function method as well as the panel variable coefficient model to calculate the 25 provinces' boundary production functions for the coal industry, which leads to the estimation of the coal capacity and capacity utilization rate of each province. Taking into account the labor resettlement cost and disposal cost of fixed assets, a total cost function of coal de-capacity is established. The growth rate function of total factor productivity (TFP) in the coal industry is constructed by the Solow residual value method. An allocation model of coal de-capacity is then proposed, based on multi-objective nonlinear optimization with constraint conditions on the total reduction amount and minimum output of coal in each region. Finally, using the relevant data of China's 25 coal-producing provinces in 1990–2015, the allocation of coal de-capacity is obtained under the goal of minimizing the total cost and maximizing the TFP growth rate. The results show that the capacity utilization rate of China's coal industry is much lower than the lower limit of the reasonable utilization ratio, while TFP shows negative growth over a long period. The comparative analytical results indicate that in terms of total cost, the optimal allocation scheme is 44.5335 billion yuan lower than the government allocation scheme. The disposal cost of fixed assets and labor resettlement costs decrease by 29.2749 billion yuan and 15.2586 billion yuan respectively. In terms of TFP growth, the optimal allocation scheme has a 1.54% higher TFP growth rate compared with the government allocation scheme. In terms of fairness, the Gini coefficients of the optimal scheme calculated by various indexes are all smaller than 0.3, placing the scheme within the category of considerable or absolute fairness. In addition, we calculate the optimal allocation ratio of coal de-capacity in the situation where cost preference and quality preference of central government are considered, to verify the intrinsic consistency of the model. In brief, the optimal allocation scheme proposed in this study effectively realizes the integration of economy, efficiency, and fairness. © 2018 Elsevier B.V.

Social capital has been credited with playing a role in many desirable economic outcomes. We analyze how these potentially beneficial effects translate into the macro-performance of economies by developing a dynamic stochastic general equilibrium (DSGE) model featuring the role of social capital in the explanation of the Solow residual. We then simulate and estimate the model with Bayesian techniques using Italian data. Our framework fits actual data better than a standard DSGE model, suggesting that social capital may improve the economic performance via its impact on total factor productivity.

This paper develops estimates of TFP growth adjusted for movements in unobserved factor utilization for a panel of 29 countries and up to 37 years. When factor utilization changes are unobserved, the commonly used Solow residual mismeasures actual changes in TFP. We use a general equilibrium dynamic multi-country multi-sector model featuring variable factor utilization to derive a production function estimating equation that corrects for unobserved factor usage. We compare the properties of utilization-adjusted TFP series to the standard Solow residual, and discuss the implications for international business cycle comovement generated by technology shocks. Unlike the Solow residual, utilization-adjusted TFP is virtually uncorrelated across countries, and as a result its direct contribution to GDP comovement is negligible. A general equilibrium model calibrated to the observed levels of international trade cannot generate much comovement through propagation of these TFP shocks.

One of the major limitations of the supply-driven input-output (I-O) Ghosh model concerns its linear production function. Using the I-O table, this paper replaces the linear production function with the Cobb-Douglas (CD) production function within the supply-driven model. The two models are compared both theoretically and empirically. Nonlinear production function, relative substitutability of primary factors, and variability of the proportion of intermediate inputs over product levels are the characteristics of the proposed model. The consideration of sectors' Solow residual as Total Factor Productivity (TFP) of sectors is yet another characteristics of the proposed model. The model is also plausible in value added and supply shock computations.

This article tests for the presence of monopolistic price markups across UK industrial sectors by testing for a non-zero covariance between the Solow residual and various instruments. This restrict...

In this paper we study the robustness of the results found recently by Guzzini and Palestrini (J Econ Interact Coord 11:35–55, 2016). Since the original analysis was carried out in a static setting, we perform a dynamic panel analysis by using the same dataset. The inclusion of the lagged value of the endogenous variable, missing in the original paper, could be justified for several reasons. Firstly, the statistical relationship may have itself a dynamical nature; secondly the inclusion of lagged-endogenous variable is a way to mitigate the possibility of an omitted variable problem. We find that the results are only qualitatively the same, and we discuss the quantitative differences.

We present a novel way to model technological shocks, with the feature that it can be biased towards more recently installed production units. We show that at one extreme, the shock is like a neutral technological shock, while at the other end of the spectrum, it resembles investment specific technological shocks, since it affects newly created machines the most. To make these ideas operational, we embed our proposed shocks in a model with putty-clay technology (where the notion of new and old firms is clear). We estimate the process for the shocks requiring that the model replicates the volatility properties of the Solow residual and the overshooting property of the labor share of output. Our estimates point to three factors to be capable of replicating well such overshooting property: putty-clay nature of technology, a time bias in the shocks towards new plants and competitive wage setting.

This paper discusses two simple decompositions for aggregate productivity analysis in the presence of distortions and in general equilibrium. The first is a generalization of Baqaee and Farhi (2017) and the second is due to Petrin and Levinsohn (2012). In the process, we propose a new "distorted'' Solow residual which, contrary to the traditional Solow residual, accurately measures changes in aggregate productivity in disaggregated economies with distortions. These formulas apply to any collection of producers ranging from one isolated producer to an industry or to an entire economy. They can be useful for empiricists and theorists alike. Potential applications of these formulas include: (1) decomposing aggregate productivity into its microeconomic sources, separating technical and allocative efficiency; (2) aggregating icroeconomic estimates (for example, from natural experiments) to assess macroeconomic effects; (3) constructing and interpreting aggregate counterfactuals. Despite their simplicity, the formulas are general, allowing for production networks, multi-product firms, and non-constant returns. They are also entirely nonparametric. They only assume market clearing and cost minimization.

This paper discusses two simple decompositions for aggregate productivity analysis in the presence of distortions and in general equilibrium. The first is a generalization of Baqaee and Farhi (2017) and the second is due to Petrin and Levinsohn (2012). In the process, we propose a new “distorted” Solow residual which, contrary to the traditional Solow residual, accurately measures changes in aggregate productivity in disaggregated economies with distortions. These formulas apply to any collection of producers ranging from one isolated producer to an industry or to an entire economy. They can be useful for empiricists and theorists alike. Potential applications of these formulas include: (1) decomposing aggregate productivity into its microeconomic sources, separating technical and allocative efficiency; (2) aggregating microeconomic estimates (for example, from natural experiments) to assess macroeconomic effects; (3) constructing and interpreting aggregate counterfactuals. Despite their simplicity, the formulas are general, allowing for production networks, multi-product firms, and non-constant returns. They are also entirely nonparametric. They only assume market clearing and cost minimization.

This paper selected the statistical data of the registration number of the scientific and technological achievements, the full-time equivalent of scientific researchers and the annual research funding investment from 2002 to 2016 in China. The production technical efficiency and contribution rate of the growth factors were analyzed by applying the DEA model and the Cobb-Douglas production function. The analysis showed that the number of researchers, the investment in research funding and the yields of scientific and technological achievements all increased year by year. However, the results of the calculation indicated that the yields of efficiency during the 15 years increased negatively with an annual average efficiency of 0.697 and a growth rate of -0.04. It revealed that the output of scientific and technological achievements (Q) had maintained a monotonous growth during the past 15 years, while, the growth rate had been declining year by year. By applying the Solow residual method, the analytic result indicated that it was the contribution rate of research funding growth played a more important role which nearly doubled the contribution rate of the growth of scientific researchers. The results derived from the above analysis models provided a useful reference for China's science and technology development in the aspects of policy formulation, performance management and talent incentives.

This study tests the extensive growth hypothesis, which would attribute the Soviet economic slowdown to low elasticity of substitution and over-investment rather than deteriorating productivity growth. To circumvent the low availability of data, widely applicable new methods for estimating productivity and elasticity of substitution were developed based on the normalized constant elasticity of substitution (CES) production function. One is an extended version of the Solow residual calculation, and the other is a direct estimation of the time-varying parameter normalized CES production function. The application of the methods to the Soviet data showed a decreasing trend in productivity growth and a low elasticity of substitution of approximately 0.25. The results neither reject nor support the extensive growth hypothesis because of uncertainty in the interpretation of the low elasticity of substitution. The results suggest that it is reasonable to assert that both deteriorating productivity growth and low ...

Development of energy policy is often informed by economic considerations via aggregate production functions (APFs). We identify a theory-to-policy process involving APFs comprised of six steps: (1) selecting a theoretical energy-economy framework; (2) formulating modeling approaches; (3) econometrically fitting an APF to historical economic and energy data; (4) comparing and evaluating modeling approaches; (5) interpreting the economy; and (6) formulating energy and economic policy. We find that choices made in Steps 1–4 can lead to very different interpretations of the economy (Step 5) and policies (Step 6). To investigate these effects, we use empirical data (Portugal and UK) and the Constant Elasticity of Substitution (CES) APF to evaluate four modeling choices: (a) rejecting (or not) the cost-share principle; (b) including (or not) energy; (c) quality-adjusting (or not) factors of production; and (d) CES nesting structure. Thereafter, we discuss two revealing examples for which different upstream modeling choices lead to very different policies. In the first example, the (kl)e nesting structure implies significant investment in energy, while other nesting structures suggest otherwise. In the second example, unadjusted factors of production suggest balanced investment in labor and energy, while quality-adjusting suggests significant investment in labor over energy. Divergent outcomes provide cautionary tales for policymakers: greater understanding of upstream modeling choices and their downstream implications is needed.

Development of energy policy is often informed by economic considerations via aggregate production functions (APFs). We identify a theory-to-policy process involving APFs comprised of six steps: (1) selecting a theoretical energy-economy framework; (2) formulating modeling approaches; (3) econometrically fitting an APF to historical economic and energy data; (4) comparing and evaluating modeling approaches; (5) interpreting the economy; and (6) formulating energy and economic policy. We find that choices made in Steps 1-4 can lead to very different interpretations of the economy (Step 5) and policies (Step 6). To investigate these effects, we use empirical data (Portugal and UK) and the Constant Elasticity of Substitution (CES) APF to evaluate four modeling choices: (a) rejecting (or not) the cost-share principle; (b) including (or not) energy; (c) quality-adjusting (or not) factors of production; and (d) CES nesting structure. Thereafter, we discuss two revealing examples for which different upstream modeling choices lead to very different policies. In the first example, the (kl)e nesting structure implies significant investment in energy, while other nesting structures suggest otherwise. In the second example, unadjusted factors of production suggest balanced investment in labor and energy, while quality-adjusting suggests significant investment in labor over energy. Divergent outcomes provide cautionary tales for policymakers: greater understanding of upstream modeling choices and their downstream implications is needed.

This paper aims to study a novel expansion discrete grey forecasting model, which could aggregate input information more effectively. In general, existing multi-factor grey forecasting models, such as one order and h variables grey forecasting model (GM (1, h)), always aggregate the main system variable and independent variables in a linear form rather than a nonlinear form, while a nonlinear form could be used in more cases than the linear form. And the nonlinear form could aggregate collinear independent factors, which widely lie in many multi-factor forecasting problems. To overcome this problem, a new approach, named as the Solow residual method, is proposed to aggregate independent factors. And a new expansion model, feedback multi-factor discrete grey forecasting model based on the Solow residual method (abbreviated as FDGM (1, h)), is proposed accordingly. Then the feedback control equation and the parameters' solution of the FDGM (1, h) model are given. Finally, a real application is used to test the modelling accuracy of the FDGM (1, h) model. Results show that the FDGM (1, h) model is much better than the non-homogeneous discrete grey forecasting model (NDGM) and the GM (1, h) model.

This paper tests the hypothesis in the revised endogenous dynamic Solow model that there exists dynamic convergence to the moving steady-state as a single economy grows. The convergence in the revised endogenous dynamic Solow model implies that the real interest rate and the growth rate of income per capita in an economy would move together, i.e., they would be cointegrated in empirical terms. Taking the U.S. economy as our research subject, we test this hypothesis by investigating the cointegration between the U.S. real interest rate and its growth rate of income per capita during a fifty-year period from 1951 to 2000. Our results show that the U.S. real interest rate and its growth rate of income per capita move together over time, providing strong evidence to support the dynamic convergence hypothesis.

In this paper we study the relevance of links among firms in explaining the mean and the auto-correlation property of the aggregate total factor productivity rate of growth (Solow residual). Our approach relies on the interaction between idiosyncratic shocks of firms and the network structure of firms. We study analytically this phenomenon using the adjacency matrix of a complete network and we present a simulation with more general random adjacency matrices. We also check empirically, using Italian data, the relationship between the network structure and the Solow residual. In particular, in the empirical part, we find two main results: Firstly, the relationship between the Solow residual and the measure of connectivity of firms is positive, in accordance with the analytical results. Secondly, we find that the measure of connectivity is pro-cyclical with the annual growth rate of industrial production.

Productivity catching-up among countries has been an important topic of interest for many researchers. In particular, one is willing to see how the productivity of countries evolves over time and how exogenous factors may influence the process. Many studies have analyzed the effect of Foreign Direct Investment (FDI) on catching-up over time, many by using restrictive parametric models. We use an appropriate nonparametric two-step approach on conditional efficiencies to investigate how FDI and time affect the process. By using a dataset of 44 countries over 1970-2007, we explore the channels under which FDI fosters productivity by disentangling the impact of this factor on production process and its components: impact on the attainable production set (input-output space), and the impact on the distribution of efficiencies. We extend existing methodological tools - conditional nonparametric efficiency measures - to examine these interrelationships. We emphasize the usefulness of smoothing over time to better analyze the potential dynamic influence of FDI on efficiency. We find that both FDI and time play an important role as influencing efficiency distribution and affecting, to a smaller extend, the production set. This effect of FDI does not seem to vary much over time. By the second stage nonparametric regression of the conditional efficiencies over FDI and time we identify clearly effect of time and FDI on conditional efficiency and we determine idiosyncratic efficiency, which represents ”Solow residual”, measured by looking to the unexplained part of the conditional efficiencies. By doing so, we are able to rank the productivity of countries at each time period, or on the average, when facing different environmental conditions.

This study uses quarterly data for a group of advanced and emerging market economies to study the role of TFP correlations between the US and the Rest of the World. Following Bosworth and Collins [1], Solow residuals are constructed to proxy for TFP shocks. Then, bilateral correlations with the US are calculated. Recent theoretical studies have emphasized the importance of TFP shock correlations for portfolio choice. The data suggests that the correlations are indeed very volatile and drop sharply during recessions. Arellano-Bond [2] regressions suggest that US Recessions are associated with a drop in cross-country productivity correlations in the medium and long term intervals.

Purpose – This paper deals with some structural indicators and their evolution, in China and its regions, over the period 1981-2010. Design/methodology/approach – The paper uses a quantitative approach. Linear programming and structural growth decompositions were used. The authors first produce estimates of the optimal productivities of incremental capital and the optimal incremental income elasticity of capital by means of a linear programming exercise. They then produce an accounting growth decomposition to assess the changes in the contribution of capital productivity, capital intensity and labour participation to the growth rate of output per capita. Finally, they combine an accounting growth decomposition with a standard production function, growth accounting, decomposition to assess the contribution of both capital productivity and capital intensity to total factor productivity (TFP). They also show in the Appendix the difference in the TFP growth contribution when marginal elasticities are assumed variable over time and when scale returns are assumed to be increasing rather than constant. Findings – The main conclusion of the paper is that capital intensity, rather than capital productivity or labour participation, has been the main growth contributor. Capital productivity has fallen, while capital intensity has increased significantly, but that does not mean that quantity in itself, rather than quality, is behind such growth, as total factor productivity, which is significantly more than engineering technical change, has been relatively important over the period. Originality/value – Both the use of linear programming to assess the evolution of incremental capital productivity and the decomposition of TFP.

In this paper we estimate the growth effects of human capital with country-specific time series data for Australia. Previous empirical studies, based on international data, have been inconclusive, in terms of the extent of the contribution of human capital to growth. We extend the Solow (1956) growth model by using educational attainment as a measure of human capital, as developed by Barro and Lee (2010). The extended Solow (1956) model performs well after allowing for the presence of structural changes. Our results, based on alternative time series methods, show that educational attainment has a small and significant permanent effect on the growth rate of per worker output in Australia. Alternative measures of human capital are also utilized to ensure robustness of results.

I estimate a Solow model augmented with human capital in 42 countries for 1910–2000. Estimated TFP growth is 0.3%/year, and the steady-state rate for GDP/capita is 1.0%/year. Implicitly for high-income countries maintaining growth above this rate will be increasingly difficult.

This paper proposes a dynamic multi-sector production network model in which firms receive news on the future product-specific demand of a representative household. Since production takes time and firms in the production sectors are connected via input-output links, news on the future final demand of an individual product changes firms' forecasts of their future sales, creating economy-wide effects named as forecast shocks. Forecast shocks are transferred upwards through the supplier-customer connections in the network, from the buyer of an input good to the producer. The model explains the asymmetry in the transmission of individual shocks in the network and how shocks to the expectations generate real, persistent effects. The equilibrium is analytically solved and calibrated to the U.S. economy. A preliminary estimation under the assumptions for the shock processes shows the importance of the forecast shocks.

This paper estimates the price–marginal cost markup for U.S. manufacturing using a new methodology. Most existing techniques of estimating the markup are a variant on the Hall (1988) framework that involves manipulating the Solow Residual. However, this paper argues that this notion is based on the unreasonable assumption that labor can be costlessly adjusted at a fixed wage rate. By relaxing this assumption we are able to derive a generalized markup index, which, when estimated using manufacturing data, is highly countercyclical and decreasing in trend since the 1960s. When we then seek to explain what causes the manufacturing markup to behave in this way, the most important determinant is the share of imported goods in the industry. Thus, increasing foreign competition in manufacturing has led to a decline in the industry's markup over time.

Since the reforming and opening up of China, domestic and foreign scholars have made deep researches on the total factor productivity of China, but the different estimation results led to much controversy. Herein, the new method is used to estimate the two key indexes of capital and labor input in the paper. Based on that, by extended Solow residual value Method to undertake an empirical study on the total factor productivity of Yunnan province and its contribution to economic growth. Result shows that since 2003 the growth rate of total factor productivity of Yunnan province is −0.96. The contributive rate of economic growth is −8.60%. The contributive rate of material capital is 64.63%, which indicated the economic growth of Yunnan province is mainly driven by capital investment. The contribution of technological progress to economic growth is negative; the contribution rate of human capital is 6%, the contribution to economic growth remains low.

Multi-input multi-output production technologies can be represented using distance functions. Econometric estimation of these functions typically involves factoring out one of the outputs or inputs and estimating the resulting equation using maximum likelihood methods. A problem with this approach is that the outputs or inputs that are not factored out may be correlated with the composite error term. Fernandez et al. (J Econ 98:47–79, 2000) show how to solve this so-called ‘endogeneity problem’ using Bayesian methods. In this paper I use the approach to estimate an output distance function and an associated index of total factor productivity (TFP) change. The TFP index is a new index that satisfies most, if not all, economically-relevant axioms from index number theory. It can also be exhaustively decomposed into a measure of technical change and various measures of efficiency change. I illustrate the methodology using state-level data on U.S. agricultural input and output quantities (no prices are needed). Results are summarized in terms of the characteristics (e.g., means) of estimated probability density functions for measures of TFP change, technical change and efficiency change.

Highlights? Computes the contribution rate of scientific and technological(S&T) progress to economic growth in Chinese 31 regions. ? Adopt the method of soft computing fusion with hard computing. ? For contribution rate, Shanghai is the highest, the second highest is Beijing and the lowest is Sichuan. According to the new economic growth theory, a new method of computing the contribution rate of scientific and technological (S&T) progress to economic growth based on the Cobb-Douglas production function and the Solow residual value method is proposed in this paper. This method includes three steps: Firstly, according to their levels of S&T progress, fuzzy soft clustering of thirty one Chinese regions is performed to obtain the membership degree of these places to the categories. Secondly, to calculate the contribution rates that different categories of levels of S&T progress contribute to economic growth. Thirdly, to multiply the obtained contribution rate of each category by the membership degree of the place belonging to this category, from which the contribution rate of S&T progress to economic growth in each place is obtained. Finally, this method is used to calculate the contribution rates of S&T progress to economic growth in thirty one Chinese regions during the period from 1998 to 2007. Last but not least, some reasonable suggestions and conclusions are proposed by analyzing the computing results.

This study investigates the sources of bank productivity growth in China over the period 2002–2009. In order to perform this research, we propose an advanced index – input slack-based productivity index (ISP) – a model that disaggregates total factor productivity growth into each input productivity change. Funds, capital, and employees are chosen as the inputs, whereas loans and other earning assets are outputs in this study. Our results show that technological gains transcend the efficiency regressions and result in total factor productivity growth. More specifically, technical progress in capital productivity reveals the dominant force behind the total factor technical change and productivity improvement. In addition, this paper uses these disaggregation terms to find out the competitive advantages and disadvantages of input usages for each Chinese bank. These findings indicate that the ISP index provides more insights than traditional total factor productivity indices.

We test whether the growth experience of a sample of 21 OECD countries over the past three decades is more consistent with the augmented Solow model or the Uzawa–Lucas model, by exploiting the different non-linear restrictions implied by them as regards the relationship between factor shares and speed of convergence. Using cross-country/time-series data, we specify our growth regression without imposing cross-country homogeneity restrictions on the speed of convergence and short-run parameters. Indeed, both theoretical models imply that the speed of convergence to the steady state differs across countries due to heterogeneity in population growth, technical change or progressiveness of income taxes. Our estimated speed of convergence is too fast to be compatible with the augmented Solow model, but is consistent with the Uzawa–Lucas model with constant returns to scale. Our main findings are robust to several robustness tests.

Summary This article presents multi–factor productivity (MFP) estimates using experimental measures of quality adjusted labour inputs and capital services. Using a growth accounting framework, output growth can be split into the relative contributions of labour and capital inputs, and a residual component called MFP. MFP is also described in the literature as ‘disembodied technical change’, the ‘Solow residual’ or total factor productivity. This approach complements traditional measures of productivity, which focus only on one input – labour – and take account only of the volume of labour and not changes in the composition of labour over time. The most recent data highlights how MFP has declined during the recession at both the whole economy and industry level. The article also backcasts the series to 1970 using the EU KLEMS dataset to create a longer run picture of MFP.

In this paper, we measure U.S. technology shocks by implementing a dual approach, which is based on price data instead of aggregate quantity data. By doing so, we find the relative volatility of technology shocks and the correlation between output fluctuation and technology shocks to be much smaller than those revealed in most real-business-cycle (RBC) studies. Our results support the findings of Burside et al. (Eur Econ Rev 40:861–869, 1996), who showed that the correlation between technology shocks and output is exaggerated in the RBC literature. This suggests that one should examine other sources of fluctuations for a better understanding of the business cycle phenomena.

In the extended Solow growth model of Mankiw et al. (1992), human capital has only permanent level and no growth effects. In the endogenous growth models human capital is a growth-improving variable. Human capital may have both a permanent level and a permanent growth effect, we show how both can be estimated with an extension to the Solow model.

This paper develops an integrated model of neoclassical and endogenous growth, which accounts for both income inequalities across countries and the convergence hypothesis, while all the growth stylized facts are satisfied. The model in this paper assumes that an economy industrializes in two stages. In the first stage, the economy starts industrialization through factor accumulation (the Solow stage); and after sufficient factor accumulation, it switches to the second stage of endogenous growth through innovation (the AK stage). Therefore, it becomes crucial to determine when switching from the Solow to the AK stages is implemented. We model this switching problem as a two-stage optimal control and show that the growth rate declines during the Solow stage, while in the AK stage it becomes constant. In addition, we draw several policy implications.

We demonstrate that a key empirical finding in environmental economics - The Environmental Kuznets Curve - and the core model of modern macroeconomics - the Solow model - are intimately related. Once we amend the Solow model to incorporate technological progress in abatement, the EKC is a necessary by product of convergence to a sustainable growth path. Our amended model, which we dub the Green Solow', generates an EKC relationship between both the flow of pollution emissions and income per capita, and the stock of environmental quality and income per capita. The resulting EKC may be humped shaped or strictly declining. We explain why current methods for estimating an EKC are likely to fail whenever they fail to account for cross-country heterogeneity in either initial conditions or deep parameters. We then develop an alternative empirical method closely related to tests of income convergence employed in the macro literature. Preliminary tests of the model's predictions are investigated using data from OECD countries.

Several studies make different prior assumptions on the magnitude of factor shares and scale of production when accounting for economic growth. The initial Solow estimations for instance assumed a capital share of 0.3 and constant returns to scale. Most authors have subsequently used the same restrictions just because they were used in previous studies even when production in the countries under study may not necessarily be taking place under constant returns to scale and capital share may be a value not any close to 0.3. This is likely to distort growth accounting estimation results. This study investigates whether these prior restrictions on factor shares and scale of production as commonly used in the literature are appropriate and whether the Solow assumptions are likely biased even in developed countries. Using Kenyan data and structural vector autoregressions, the main findings are; first, in all cases of the unrestricted estimations, the share of physical capital is less than 0.16. An estimation which imposes 0.3 as the share of physical capital in this case would therefore not be in line with the data generating process leading to biased results. Secondly, in all cases, the explanatory power of the model decreases when the restrictions are imposed implying that the restrictions are not appropriate in growth accounting exercises. The paper further disputes the “styled fact” in growth studies that the Solow assumptions may be relevant for developed countries but not developing countries and concludes that the Solow assumptions may be biased even in developed countries.

SUMMARYThis article presents multi–factor productivity (MFP) estimates using experimental measures of quality adjusted labour inputs and utilisation of capital services. Using a growth accounting framework, output growth can be split into the relative contributions of labour and capital inputs, and a residual component which is variously described as ‘disembodied technical change’, the ‘Solow residual’, total factor productivity or simply as MFP. This approach complements traditional measures of productivity, which focus only on one input, labour, and take account only of the volume of labour, measured in jobs or hours, and not on changes in the composition of labour over time. Estimates are produced at the market sector and whole economy levels.The article develops the work of Turvey (2009) by extending the back data to 1994 (1995 for the market sector) and forward to 2008, and by providing estimates at an enhanced level of industrial disaggregation, consistent with parallel developments in measuring labour and capital inputs.

The important contribution by Basu and Fernald (20021. Basu , S and Fernald , JG . 2002 . Aggregate productivity and aggregate technology . European Economic Review , 46 : 963 – 91 . [CrossRef], [Web of Science ®] View all references) shows that, in practice, there is a statistically significant gap between aggregate productivity and technology that can be attributed to inefficient product and labour markets. This is important, as it implies that the Solow residual is an imperfect index for aggregate technology change. We take a related approach and find that when we control for capacity utilization, time varying markup and account for externalities between industries, by employing a superior system estimator, the gap between the aggregate productivity and technology is shown to narrow considerably.

We estimate productivity growth for 33 industries covering the entire Chinese economy using a time series of input–output tables covering 1982–2000. Capital input is measured using detailed investment data by asset and labor input uses demographic information from household surveys. We find a wide range of productivity performance at the industry level. We then show how these industry growth accounts may be consistently aggregated to deliver a decomposition of aggregate GDP growth. For the 1982–2000 period aggregate TFP growth was 2.5 percent per year; decelerating from a rapid rate in the early 1980s to negative growth during 1994–2000. The main source of growth during the 1982–2000 period was capital accumulation, with a small negative contribution from the reallocation of factors across industries.

Most real business cycle models have a hard time jointly explaining the twin facts of strongly procyclical Solow residuals and extremely low correlations between wages and hours. We present a model that delivers both these results without using exogenous variation in total factor productivity (technology shocks). The key innovation of the paper is to add learning-by-doing to firms' technology. As a result, firms optimally vary their prices to control the amount of learning, which in turn influences future productivity. We show that exogenous variation in labor wedges (preference shocks) measured from aggregate data deliver around 50% of the standard deviation in the efficiency wedge (Solow residual) as well as realistic second moments for key aggregate variables, which is in sharp contrast to the model without learning-by-doing.

Multi-factor productivity (MFP), sometimes referred to as ‘total-factor productivity’ or ‘growth accounting’, is a method of analysing productivity which allows for a more in-depth assessment of performance at a whole economy or sectoral level. It apportions growth in output to contributions from capital, from labour and a residual MFP which represents the ‘productivity change’ not explained by the growth in either labour or capital inputs. This approach permits more detailed analysis of what is driving output growth compared with the traditional ‘headline’ measures of productivity, which use only labour as their factor input. This article presents multi-factor productivity results for 1998 to 2007 using an experimental quality-adjusted labour input measure and experimental estimates of capital services growth as inputs. The analysis has been produced for the whole economy, the market sector and some broad industry groupings. Economic & Labour Market Review (2009) 3, 33–38; doi:10.1057/elmr.2009.42

The aim of the study is to examine the long-run equilibrium of Solow growth model with a modified assumption for a rate of employment growth. In the first case, it is assumed that the number of workers changes in the trajectory defined by the logistic function. In the second case, it is assumed that the rate of growth of employment is a decreasing function of labor productivity (so called post-Malthusian growth path). The capital labor ratio and labor productivity with logistic growth path of the number of workers are defined by certain functions composed of hypergeometric function of Gauss. When we consider post-Malthusian growth path of employment, solution of the Solow equation depends on the value of the parameter α (production elasticity with respect to capital) – it may have no, one or two non-trivial steady-states. In performed numerical simulations, we calibrated elasticity of production with respect to capital at a level equal to 0.68216. In all variants of simulated investment rates, the standard, logistic and post-Malthusian trajectory of employment, labor productivity grows to a certain asymptote. Asymptotes of labor productivity for the logistic and post-Malthusian trajectory are located at a similar (slightly higher for logistic trajectory) level. Both are located far higher than the asymptotę of labor productivity function in the original Solow growth model. Celem opracowania jest zbadanie równowagi modelu wzrostu gospodarczego Solowa przy zmodyfikowanym założeniu dotyczącym stopy wzrostu liczby pracujących. W pierwszym wariancie przyjmuje się, że liczba pracujących zmienia się po trajektorii określonej przez funkcję logitową. Natomiast w drugim wariancie zakłada się, że stopa wzrostu liczby pracujących jest pewną malejącą funkcją wydajności pracy (tzw. post-maltuzjańska ścieżka wzrostu). Przy logitowej ścieżce wzrostu liczby pracujących trajektorie technicznego uzbrojenia pracy i wydajności pracy określone są przez pewne funkcje złożone z funkcją hipergeometryczną Gaussa. Natomiast przy post-maltuzjańskiej ścieżce wzrostu liczby pracujących rozwiązanie równania Solowa zależne jest od przyjętej wartości parametru α (elastyczności produkcji względem nakładów kapitału) – może ono nie posiadać żadnego punktu stacjonarnego, posiadać jeden lub dwa nietrywialne punkty stacjonarne. W przeprowadzonych w opracowaniu symulacjach numerycznych elastyczność produkcji względem nakładów kapitału skalibrowano na poziomie równym 0,68216. We wszystkich symulowanych wariantach stóp inwestycji, przy standardowej, logitowej oraz post-maltuzjańskiej trajektorii liczby pracujących, wydajność pracy rośnie do pewnej asymptoty. Asymptoty funkcji wydajności pracy dla trajektorii logitowej i post-maltuzjańskiej położone są na zbliżonym (nieznacznie wyższym dla trajektorii logitowej) poziomie. Obie położone są zdecydowanie wyżej niż asymptota funkcji wydajności pracy z oryginalnego modelu wzrostu gospodarczego Solowa. Opracowanie powstało w ramach grantu NCN pt. Cykle wzrostu – dynamiczne modele koniunktury i wzrostu gospodarczego nr OPUS8 UMO-2014/15/B/HS4/04264.