Optimal growth in a two-sector model without discounting: a geometric investigation

We characterize optimal policy in a two-sector growth model with xed coeÆcients and with no discounting. The model is a specialization to a single type of machine of a general vintage capital model originally formulated by Robinson, Solow and Srinivasan, and its simplicity is not mirrored in its rich dynamics, and which seem to have been missed in earlier work. Our results are obtained by viewing the model as a specific instance of the general theory of resource allocation as initiated originally by Ramsey and von Neumann and brought to completion by McKenzie. In addition to the more recent literature on chaotic dynamics, we relate our results to the older literature on optimal growth with one state variable: speci cally, to the one-sector setting of Ramsey, Cass and Koopmans, as well as to the two-sector setting of Srinivasan and Uzawa. The analysis is purely geometric, and from a methodological point of view, our work can be seen as an argument, at least in part, for the rehabilitation of geometric methods as an engine of analysis.

Growth of the tambaqui Colossoma macropomum (Cuvier) (Characiformes: Characidae): which is the best model?

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Bayesian age and growth analysis of the shortfin mako shark Isurus oxyrinchus in the Western South Atlantic Ocean using a flexible model

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

A Model for comparing reduced impact logging with conventional logging for an Eastern Amazonian Forest

Using data from a logging experiment in the eastern Brazilian Amazon region, we develop a matrix growth and yield model that captures the dynamic effects of harvest system choice on forest structure and composition. Multinomial logistic regression is used to estimate the growth transition parameters for a 10-year time step, while a Poisson regression model is used to estimate recruitment parameters. The model is designed to be easily integrated with an economic model of decisionmaking to perform tropical forest policy analysis. The model is used to compare the long-run structure and composition of a stand arising from the choice of implementing either conventional logging techniques or more carefully planned and executed reduced-impact logging (RIL) techniques, contrasted against a baseline projection of an unlogged forest. Results from log and leave scenarios show that a stand logged according to Brazilian management requirements will require well over 120 years to recover its initial commercial volume, regardless of logging technique employed. Implementing RIL, however, accelerates this recovery. Scenarios imposing a 40-year cutting cycle raise the possibility of sustainable harvest volumes, although at significantly lower levels than is implied by current regulations. Meeting current Brazilian forest policy goals may require an increase in the planned total area of permanent production forest or the widespread adoption of silvicultural practices that increase stand recovery and volume accumulation rates after RIL harvests. Published by Elsevier B.V.

Stochastic structure and individual-tree growth models

The majority of past and current individual-tree growth modelling methodologies have failed to characterise and incorporate structured stochastic components. Rather, they have relied on deterministic predictions or have added an unstructured random component to predictions. In particular, spatial stochastic structure has been neglected, despite being present in most applications of individual-tree growth models. Spatial stochastic structure (also called spatial dependence or spatial autocorrelation) eventuates when spatial influences such as competition and micro-site effects are not fully captured in models. Temporal stochastic structure (also called temporal dependence or temporal autocorrelation) eventuates when a sequence of measurements is taken on an individual-tree over time, and variables explaining temporal variation in these measurements are not included in the model. Nested stochastic structure eventuates when measurements are combined across sampling units and differences among the sampling units are not fully captured in the model. This review examines spatial, temporal, and nested stochastic structure and instances where each has been characterised in the forest biometry and statistical literature. Methodologies for incorporating stochastic structure in growth model estimation and prediction are described. Benefits from incorporation of stochastic structure include valid statistical inference, improved estimation efficiency, and more realistic and theoretically sound predictions. It is proposed in this review that individual-tree modelling methodologies need to characterise and include structured stochasticity. Possibilities for future research are discussed. (C) 2001 Elsevier Science B.V. All rights reserved.

Use of confocal scanning laser microscopy to measure the concentrations of aerial and penetrative hyphae during growth of Rhizopus oligosporus on a solid surface

In order to develop a method for use in investigations of spatial biomass distribution in solid-state fermentation systems, confocal scanning laser microscopy was used to determine the concentrations of aerial and penetrative biomass against height and depth above and below the substrate surface, during growth of Rhizopus oligosporus on potato dextrose agar. Penetrative hyphae had penetrated to a depth of 0.445 cm by 64 h and showed rhizoid morphology, in which the maximum biomass concentration, of 4.45 mg dry wt cm(-3), occurred at a depth of 0.075 cm. For aerial biomass the maximum density of 39.54 mg dry wt(-3) occurred at the substrate surface. For both aerial and penetrative biomass, there were two distinct regions in which the biomass concentration decayed exponentially with distance from the surface. For aerial biomass, the first exponential decay region was up to 0.1 cm height. The second region above the height of 0.1 cm corresponded to that in which sporangiophores dominated. This work lays the foundation for deeper studies into what controls the growth of fungal hyphae above and below the surfaces of solid substrates. (C) Wiley Periodicals, Inc.

An Extension of Gompertzian Growth Dynamics Weibull and Frechet Models

In this work a new probabilistic and dynamical approach to an extension of the Gompertz law is proposed. A generalized family of probability density functions, designated by Beta* (p, q), which is proportional to the right hand side of the Tsoularis-Wallace model, is studied. In particular, for p = 2, the investigation is extended to the extreme value models of Weibull and Frechet type. These models, described by differential equations, are proportional to the hyper-Gompertz growth model. It is proved that the Beta* (2, q) densities are a power of betas mixture, and that its dynamics are determined by a non-linear coupling of probabilities. The dynamical analysis is performed using techniques of symbolic dynamics and the system complexity is measured using topological entropy. Generally, the natural history of a malignant tumour is reflected through bifurcation diagrams, in which are identified regions of regression, stability, bifurcation, chaos and terminus.

Unemployment, growth and fiscal policy: new insights on the hysteresis hypothesis

We develop a growth model with unemployment due to imperfections in the labor market. In this model, wage inertia and balanced budget rules cause a complementarity between capital and employment capable of explaining the existence of multiple equilibrium paths. Hysteresis is viewed as the result of a selection between these diferent equilibrium paths. We use this model to argue that, in contrast to the US, those fiscal policies followed by most of the European countries after the shocks of the 1970’s may have played a central role in generating hysteresis.

Knowledge, networks of cities and growth in regional urban systems

The objective of this paper is to measure the impact of different kinds of knowledge and external economies on urban growth in an intraregional context. The main hypothesis is that knowledge leads to growth, and that this knowledge is related to the existence of agglomeration and network externalities in cities. We develop a three-tage methodology: first, we measure the amount and growth of knowledge in cities using the OCDE (2003) classification and employment data; second, we identify the spatial structure of the area of analysis (networks of cities); third, we combine the Glaeser - Henderson - De Lucio models with spatial econometric specifications in order to contrast the existence of spatially static (agglomeration) and spatially dynamic (network) external economies in an urban growth model. Results suggest that higher growth rates are associated to higher levels of technology and knowledge. The growth of the different kinds of knowledge is related to local and spatial factors (agglomeration and network externalities) and each knowledge intensity shows a particular response to these factors. These results have implications for policy design, since we can forecast and intervene on local knowledge development paths.

Growth and Survival Determinants of Chinese Private Firms: Fieldwork evidence and econometric estimates

This paper reports on one of the first empirical attempts to investigate small firm growth and survival, and their determinants, in the Peoples’ Republic of China. The work is based on field work evidence gathered from a sample of 83 Chinese private firms (mainly SMEs) collected initially by face-to-face interviews, and subsequently by follow-up telephone interviews a year later. We extend the models of Gibrat (1931) and Jovanovic (1982), which traditionally focus on size and age alone (e.g. Brock and Evans, 1986), to a ‘comprehensive’ growth model with two types of additional explanatory variables: firm-specific (e.g. business planning); and environmental (e.g. choice of location). We estimate two econometric models: a ‘basic’ age-size-growth model; and a ‘comprehensive’ growth model, using Heckman’s two-step regression procedure. Estimation is by log-linear regression on cross-section data, with corrections for sample selection bias and heteroskedasticity. Our results refute a pure Gibrat model (but support a more general variant) and support the learning model, as regards the consequences of size and age for growth; and our extension to a comprehensive model highlights the importance of location choice and customer orientation for the growth of Chinese private firms. In the latter model, growth is explained by variables like planning, R&D orientation, market competition, elasticity of demand etc. as well as by control variables. Our work on small firm growth achieves two things. First, it upholds the validity of ‘basic’ size-age-growth models, and successfully applies them to the Chinese economy. Second, it extends the compass of such models to a ‘comprehensive’ growth model incorporating firm-specific and environmental variables.

Growth and Survival Determinants of Chinese Private Firms: Fieldwork evidence and econometric estimates

This paper reports on one of the first empirical attempts to investigate small firm growth and survival, and their determinants, in the Peoples’ Republic of China. The work is based on field work evidence gathered from a sample of 83 Chinese private firms (mainly SMEs) collected initially by face-to-face interviews, and subsequently by follow-up telephone interviews a year later. We extend the models of Gibrat (1931) and Jovanovic (1982), which traditionally focus on size and age alone (e.g. Brock and Evans, 1986), to a ‘comprehensive’ growth model with two types of additional explanatory variables: firm-specific (e.g. business planning); and environmental (e.g. choice of location). We estimate two econometric models: a ‘basic’ age-size-growth model; and a ‘comprehensive’ growth model, using Heckman’s two-step regression procedure. Estimation is by log-linear regression on cross-section data, with corrections for sample selection bias and heteroskedasticity. Our results refute a pure Gibrat model (but support a more general variant) and support the learning model, as regards the consequences of size and age for growth; and our extension to a comprehensive model highlights the importance of location choice and customer orientation for the growth of Chinese private firms. In the latter model, growth is explained by variables like planning, R&D orientation, market competition, elasticity of demand etc. as well as by control variables. Our work on small firm growth achieves two things. First, it upholds the validity of ‘basic’ size-age-growth models, and successfully applies them to the Chinese economy. Second, it extends the compass of such models to a ‘comprehensive’ growth model incorporating firm-specific and environmental variables.

Trade liberalization, competition and growth

Increasing evidence support the claim that international trade enhances innovation and productivity growth through an increase in competition. This paper develops a two-country endogenous growth model, with firm specific R&D and a continuum of oligopolistic sectors under Cournot competition to provide a theoretical support to this claim. Since countries are assumed to produce the same set of varieties, trade openness makes markets more competitive, reducing prices and increasing quantities. Under Cournot competition, trade is pro-competitive. Since firms undertake cost reducing innovations, the increase in production induced by a more competitive market push firms to innovate more. Consequently, a reduction on trade barriers enhances growth by reducing domestic firm's market power.

Growth, unemployment and wage inertia

We introduce wage setting via efficiency wages in the neoclassical one-sector growth model to study the growth effects of wage inertia. We compare the dynamic equilibrium of an economy with wage inertia with the equilibrium of an economy without wage inertia. We show that wage inertia affects the long run employment rate and that the transitional dynamics of the main economic variables will be different because wages are a state variable when wage inertia is introduced. In particular, we show non-monotonic transitions in the economy with wage inertia that do not arise in the economy with flexible wages. We also study the growth effects of permanent technological and fiscal policy shocks in these two economies. During the transition, the growth effects of technological shocks obtained when wages exhibit inertia may be the opposite from the ones obtained when wages are flexible. In the long run, these technological shocks may have long run effects if there is wage inertia. We also show that the growth effects of fiscal policies will be delayed when there is wage inertia.

An Analysis of Regional Economic Growth in the U.S. Midwest, April 2005

In this paper we examine some of the economic forces that underlie economic growth at the county level. In an effort to describe a much more comprehensive regional economic growth model, we address a variety of different growth hypotheses by introducing a large number of growth related variables. When formulating our hypotheses and specifying our growth model we make liberal use of GIS (geographical information systems) mapping software to “paint” a picture of where growth spots exist. Our empirical estimation indicates that amenities, state and local tax burdens, population, amount of primary agriculture activity, and demographics have important impacts on economic growth.

Transfers, social safety nets and economic growth

The role of social safety nets in the form of redistributional transfersand wage subsidies is analyzed using a simple model of criminal behavior. Itis argued that public welfare programs act as a crime--preventing ordisruption--preventing devices because they tend to increase the opportunitycost of engaging in crime or disruptive activities. It is shown that, in thepresence of a leisure choice, wage subsidies may be better than pure transfers. Using a simple growth model, it is shown that it is not optimal for the governmentto try to fully eliminate crime. The optimal size of the public welfare programis found and it is argued that public welfare should be financed with income(not lump--sum) taxes, despite the fact that income taxes are distortionary.The intuition for this result is that income taxes act as a user fee oncongested public goods and transfers can be thought of as {\it productive}public goods {\it subject to congestion}. Finally, using a cross-section of 75 countries, the partial correlation betweentransfers and growth is shown to be significantly positive.