Endogenous Technical Change Research Articles

GOODWIN CYCLES, DISTRIBUTIONAL CONFLICT AND PRODUCTIVITY GROWTH

ABSTRACTA combination of an investment‐driven macroeconomy and a conflict‐determined income distribution gives cyclical behavior. Models of wage–price inflation can be nested in the Goodwinian tradition. Endogenous technical change has ambiguous effects on equilibrium: Kaldor–Verdoorn effects increase the wage share's responsiveness to changes in output, while labor‐saving technical change reduces it.

A unified theory of structural change

This paper uses dynamic general equilibrium and computational methods, inspired by the multi-sector growth model structure in Stephen Turnovsky's work, to develop a theory that unifies two of the traditional explanations of structural change: sector-biased technical change and non-homothetic preferences. The theory is based on an overlapping-generations growth model with endogenous technical change and non-homothetic preferences. An expanding-variety setup with two different R&D technologies, agricultural, and non-agricultural, is employed. The analysis, based on numerical simulations, shows that the biased technical change hypothesis finds most support in the data. It also points to production-side specific factors, such as asymmetries in cross-sector knowledge spillovers, as explanatory factors of the bias in technical change.

Economics of Endogenous Technical Change in CGE Models - The Role of Gains from Specialization

Computable general equilibrium models simulate the reaction of industries on carbon taxes. Their results differ strongly on the assumption of the underlying technologies. This paper compares two models and emphasizes the differences between their approaches to technology. The first model is the CITE model, which is the first model with endogenous growth based on gains from specialization so that growth dynamics result from investment incentives. The second model is a model with exogenous growth of endowments, which is the basis for many other CGE models. The results show that the CITE model unveils dynamics that cannot be obtained with the model based on exogenous growth. Reactions are stronger in the CITE model and industries need more time to approach the new balanced growth path.

A SCHUMPETERIAN GROWTH MODEL WITH EQUILIBRIUM UNEMPLOYMENT

We introduce efficiency-wage unemployment in a model of growth with endogenous technical change. Our research aim is twofold. First, we try to provide an analytically tractable model of growth with efficiency-wage unemployment that can be viewed as alternative to the standard models of growth and search unemployment. Second, we try to analyze the steady-state effects of some labor market policies on unemployment and growth. We find that a positive relationship between growth and unemployment exists and that the effectiveness of any labor market policy aimed at improving the performance of the labor market crucially depends on how individuals discount future income.

Modeling Biased Technical Change - Implications for Climate Policy

Climate-economy models aiming at quantifying the costs and effects of climate change impacts and policies have become important tools for climate policy decision-making. Although there are several important dimensions along which models differ, this paper focuses on a key component of climate change economics and policy, namely technical change. This paper tackles the issues of whether technical change is biased towards the energy sectors, the importance of the elasticity of substitution between factors in determining this bias and how mitigation policy is likely to affect it. The analysis is performed using the World Induced Technical Change model, WITCH. Three different versions of the model are proposed. The starting set-up includes endogenous technical change only in the energy sector. A second version introduces endogenous technical change in both the energy and non-energy sectors. A third version of the model embodies different sources of technical change, namely R&D and human capital. Although different formulations of endogenous technical change have only a minor influence on climate policy costs, the macroeconomic effects on knowledge and human capital formation can vary greatly.

Non-renewable resources, endogenous technical change and sustainable growth

Non-renewable resources are problematic for sustainable positive growth if they are essential for production, households solve an intertemporal Ramsey problem and population is growing. Second generation growth models predict that growth is positively related to growth in production factors. A growing population seems, therefore, beneficial for growth while the need for a steadily declining usage of the non-renewable resource due to sustainability seems not to be so. To elaborate on this issue, this paper develops a semi-endogenous growth model with two intermediate goods, one produced from labour and one from the non-renewable resource. The technology for producing these goods is partially endogenous and determined separately for both goods. The two intermediate goods are used to produce final output of the economy. Using this setup, this paper derives conditions for stable sustainable positive long-run growth in per capita production and consumption.

Factor-Augmenting Technical Change: An Empirical Assessment

This paper estimates factor-specific technical change and input substitution using a structural approach. It improves upon the existing literature by introducing technology drivers for factor productivities and by assessing the impact of endogenous technical change on the estimates of substitution elasticities. The empirical results suggest that factor-productivities are indeed endogenous. In addition, technology drivers are factor-specific. Whereas R&D investments and machinery imports are important determinants of energy and capital productivity, education is found to be statistically related to labour productivity. The rate of energy-augmenting technical change is larger than that of either labour or capital. By contrast, the productivity of these two factors grows at similar rates. Estimates of the elasticities of substitution are within the ranges identified by previous literature. In addition, our results suggest that endogenous technical change lowers the estimated values of the substitution elasticity.

Modeling Biased Technical Change; Implications for Climate Policy

Climate-economy models aiming at quantifying the costs and effects of climate change impacts and policies have become important tools for climate policy decision-making. Although there are several important dimensions along which models differ, this paper focuses on a key component of climate change economics and policy, namely technical change. This paper tackles the issues of whether technical change is biased towards the energy sectors, the importance of the elasticity of substitution between factors in determining this bias and how mitigation policy is likely to affect it. The analysis is performed using the World Induced Technical Change model, WITCH. Three different versions of the model are proposed. The starting set-up includes endogenous technical change only in the energy sector. A second version introduces endogenous technical change in both the energy and non-energy sectors. A third version of the model embodies different sources of technical change, namely R&D and human capital. Although different formulations of endogenous technical change have only a minor influence on climate policy costs, the macroeconomic effects on knowledge and human capital formation can vary greatly.

Factor-Augmenting Technical Change: An Empirical Assessment

This paper estimates factor-specific technical change and input substitution using a structural approach. It improves upon the existing literature by introducing technology drivers for factor productivities and by assessing the impact of endogenous technical change on the estimates of substitution elasticities. The empirical results suggest that factor-productivities are indeed endogenous. In addition, technology drivers are factor-specific. Whereas R&D investments and machinery imports are important determinants of energy and capital productivity, education is found to be statistically related to labour productivity. The rate of energy-augmenting technical change is larger than that of either labour or capital. By contrast, the productivity of these two factors grows at similar rates. Estimates of the elasticities of substitution are within the ranges identified by previous literature. In addition, our results suggest that endogenous technical change lowers the estimated values of the substitution elasticity.

On Population Growth and Technological Change: Selectivity Bias in Historical Analysis

This paper investigates the relationship between population growth and technological change. After an historical overview of the evolution of world population, alternative models of population growth are examined. They include a Malthusian model, a model of endogenous technological change, and a model of population growth that allows for switching regimes between Malthusian resource limitation and endogenous technological change. The regime-switching model stresses the potential for a biased interpretation of historical data. While there is strong empirical evidence supporting endogenous technical change, it is argued that the Malthusian scenario should not be overlooked even if the odds of facing it are low.

Variety of products, public capital, and endogenous growth

This paper develops an extension of the endogenous growth model with variety expansion presented in Romer [Romer, P.M., 1990. Endogenous technical change, Journal of Political Economy 98, part 2, S71–S102] by considering public capital accumulation. Characterizing the transitional dynamics, the growth rate of consumption traces (and available number of intermediate goods also might trace) an S-shaped converging path to the equilibrium growth rate, similar to a logistic growth curve, if the intensity of public capital is sufficiently high. We also show that public investment enhances economic growth because it stimulates demand for intermediate goods and raises the market interest rate.

Lump-Sum Taxes in a R&D Model

Is it possible to increase growth and welfare by raising taxes and disposing of the tax revenues? We show this may indeed be the case in a simple model with endogenous technical change, represented by an increase in the variety of intermediate goods.

Uncertainty and endogenous technical change in climate policy models

Until recently endogenous technical change and uncertainty have been modeled separately in climate policy models. In this paper, we review the emerging literature that considers both these elements together. Taken as a whole the literature indicates that explicitly including uncertainty has important quantitative and qualitative impacts on optimal climate change technology policy.

Introducing errors in progress ratios determined from experience curves

Progress ratios (PRs) derived from historical data in experience curves are used for forecasting development of many technologies as a means to model endogenous technical change in for instance climate–economy models. These forecasts are highly sensitive to uncertainties in the progress ratio. As a progress ratio is determined from fitting data, a coefficient of determination R 2 is frequently used to show the quality of the fit and accuracy of PR. Although this is instructive, we recommend using the error σ PR in PR, which can be directly determined from fitting the data. In this paper we illustrate this approach for three renewable energy technologies, i.e., wind energy, bio-ethanol, and photovoltaics.

Empirical challenges in the use of learning curves for assessing the economic prospects of renewable energy technologies

In bottom-up energy models endogenous technical change is introduced by implementing technology learning rates, which specify the quantitative relationship between the cumulative experiences of the technology on the one hand and cost reductions on the other. The main purpose of this paper is to critically analyze the choice of modeling and estimation strategies in learning curve analyses of power generation costs. We identify and discuss a number of theoretical and econometric issues involved in the estimation of learning curves. These include the presence of omitted variable bias and simultaneity, but also methodological problems related to the operationalization of theoretical concepts (i.e., learning-by-doing) and the associated use of data. We illustrate the importance of these issues by employing panel data for wind power installations in four western European countries, which are used to compare the results from different learning curve model specifications. The results illustrate that the estimates of learning rates may differ significantly across different model specifications and econometric approaches. The paper ends by outlining a number of recommendations for energy model analysts, who need to select appropriate energy technology learning rates from the empirical literature, or who choose to perform the empirical work themselves.

Growth, Development, and Technological Change

The theory of endogenous technical change has deeply contributed to our understanding of the fundamental sources of economic growth and development. In this chapter we survey important contributions in the field by focussing on the basic structure of endogenous growth models with horizontal as well as vertical innovation and emphasizing important implications for growth policy. We address issues like the scale effect problem, directed technological change to understand the evolution of wage inequality, long-run divergence between the innovating North and the imitating South due to inappropriate technology in the South, the relationship between trade and growth, competition and R&D, and the role of imperfect capital markets for R&D-based growth

Climate modelling with endogenous technical change: Stochastic learning and optimal greenhouse gas abatement in the PAGE2002 model

This paper looks at the impact of ETC on the costs and benefits of different abatement strategies using a modified version of the PAGE2002 model. It was found that for most standard abatement paths there would be an initial “learning investment” required that would substantially reduce the unit costs of CO 2 abatement as compared to a business as usual scenario. Furthermore, optimising an abatement program where ETC has been included leads to an increase in cost uncertainty during the period of widespread CO 2 abatements due to our lack of knowledge of the learning investments involved. Finally, the inclusion of ETC leads to a slightly deferred optimised abatement path followed by a rapid abatement program. Together, the results draw attention to the possibilities of ‘uncovering uncertainty’ through proactive abatements. ‘Learning about learning’ could become an important consideration for any plan to optimise future abatements.

06/01842 An economic perspective on experience curves and dynamic economies in renewable energy technologies: Papineau, M. Energy Policy, 2006, 34, (4), 422–432

In bottom-up energy models endogenous technical change is introduced by implementing technology learning rates, which specify the quantitative relationship between the cumulative experiences of the technology on the one hand and cost reductions on the other. The main purpose of this paper is to critically analyze the choice of modeling and estimation strategies in learning curve analyses of power generation costs. We identify and discuss a number of theoretical and econometric issues involved in the estimation of learning curves. These include the presence of omitted variable bias and simultaneity, but also methodological problems related to the operationalization of theoretical concepts (i.e., learning-by-doing) and the associated use of data. We illustrate the importance of these issues by employing panel data for wind power installations in four western European countries, which are used to compare the results from different learning curve model specifications. The results illustrate that the estimates of learning rates may differ significantly across different model specifications and econometric approaches. The paper ends by outlining a number of recommendations for energy model analysts, who need to select appropriate energy technology learning rates from the empirical literature, or who choose to perform the empirical work themselves.

Technology and technical change in the MIT EPPA model

Potential technology change has a strong influence on projections of greenhouse gas emissions and costs of control, and computable general equilibrium (CGE) models are a common device for studying these phenomena. Using the MIT Emissions Prediction and Policy Analysis (EPPA) model as an example, two ways of representing technology in these models are discussed: the sector-level description of production possibilities founded on social accounting matrices and elasticity estimates, and sub-models of specific supply or end-use devices based on engineering-process data. A distinction is made between exogenous and endogenous technical change, and it is shown how, because of model structure and the origin of key parameters, such models naturally include shifts in production process that reflect some degree of endogenous technical change. As a result, the introduction of explicit endogenous relations should be approached with caution, to avoid double counting.

Some foundations for Zipf's law: Product proliferation and local spillovers

This paper embeds the canonical model of endogenous growth with product proliferation developed by Romer [Romer, P.M., 1990. Endogenous technical change. Journal of Political Economy 98, S71–S102] into a simple urban framework. This yields a reduced form isomorphic to the popular statistical device developed by Simon [Simon, H., 1955. On a class of skew distribution functions. Biometrika 42, 425–440], which in turn can yield Zipf's law for cities. The stochastic outcomes of purposeful innovation and local spillovers can thus serve as foundations for random growth models.