Model Of Industry Dynamics Research Articles

R&D location in dynamic industry environments

Abstract We study firms’ optimal R&D location strategies in a dynamic industry model with competition in product quality. In light of potential future inwards and outwards spillovers firms make their location choices relying on heuristic strategies that are based on the expected present values associated with alternative location patterns. Using a simulation analysis, we show how the strategies of innovators and imitators differ and how they depend on whether firms operate in strongly or weakly innovative industry environments. We also characterize how firms’ location choices should account for the innovativeness of the competitors active in a location.

High-level simulation model of tourism industry dynamics

This article is devoted to the development of a high-level simulation model of tourism industry dynamics. The purpose of this study is to form recommendations for the recovery of the tourism industry from the effects of the pandemic. The resulting model considers domestic tourism from the point of view of the interdependence of the economic condition of the state, the contribution of the tourism industry to gross domestic product, the size of the tourist flow and the average income per tourist. In addition to describing the functional dependencies of the model elements, several experiments are proposed to test the logic of the elements’ relationships. System dynamics tools are used to develop the model. The study also examines the class of computable general equilibrium models as a tool for analyzing supply and demand in the market of tourist products.

Empirical Models of Industry Dynamics with Endogenous Market Structure

This article reviews recent developments in the study of firm and industry dynamics, with a special emphasis on the econometric endogeneity of market structure. The endogeneity of market structure follows from the presence of serially correlated unobservable shocks to the profitability of firms’ dynamic decisions, a feature common to many empirical settings. Methods that ignore endogeneity can lead to misleading parameter estimates and misleading counterfactual results. We pay particular attention to extensions of standard two-step methods that leverage instrumental variables to address endogeneity in both single-agent and oligopoly models. A first step set-identifies dynamic policy functions together with serial correlation parameters, and a second step quickly solves for profit function parameters using an extension of existing forward-simulation methods. We discuss how these new methods provide a general solution to initial-conditions problems and how they can yield practical estimation strategies.

Capital Buffers in a Quantitative Model of Banking Industry Dynamics

We develop a model of banking industry dynamics to study the quantitative impact of regulatory policies on bank risk‐taking and market structure. Since our model is matched to U.S. data, we propose a market structure where big banks with market power interact with small, competitive fringe banks as well as non‐bank lenders. Banks face idiosyncratic funding shocks in addition to aggregate shocks which affect the fraction of performing loans in their portfolio. A nontrivial bank size distribution arises out of endogenous entry and exit, as well as banks' buffer stock of capital. We show that the model predictions are consistent with untargeted business cycle properties, the bank lending channel, and empirical studies of the role of concentration on financial stability. We find that regulatory policies can have an important impact on banking market structure, which, along with selection effects, can generate changes in allocative efficiency and stability.

Empirical Models of Industry Dynamics with Endogenous Market Structure

This article reviews recent developments in the study of firm and industry dynamics, with a special emphasis on the econometric endogeneity of market structure. The endogeneity of market structure follows from the presence of serially correlated unobservable shocks to the profitability of firms’ dynamic decisions, a feature common to many empirical settings. Methods that ignore endogeneity can lead to misleading parameter estimates and misleading counterfactual results. We pay particular attention to extensions of standard two-step methods that leverage instrumental variables to address endogeneity in both single-agent and oligopoly models. A first step set-identifies dynamic policy functions together with serial correlation parameters, and a second step quickly solves for profit function parameters using an extension of existing forward-simulation methods. We discuss how these new methods provide a general solution to initial-conditions problems and how they can yield practical estimation strategies.

Monetary Policy, Productivity, and Market Concentration

This paper builds a New Keynesian industry dynamics model for the analysis of macroeconomic fluctuations and monetary policy. A continuum of heterogeneous firms populates the economy, markets are imperfectly competitive and nominal wages are sticky. An expansionary monetary policy shock triggers a response in labor productivity. By reducing borrowing costs, the shock initially attracts low productivity firms in the market. As a result, aggregate productivity decreases on impact. It then overshoots its initial level since, after the initial over-crowding, competition cleanses the market from low productivity firms. The overshooting amplifies the response of the main macroeconomic variables to the shock. A high ex-ante degree of market concentration partially impairs the transmission of monetary policy by disrupting the entry and exit mechanism.

R&D Location Strategies

We examine the profitability of different R&D location strategies of firms in a dynamic industry model. Firms engage in imitative and innovative activities in order to improve their products' quality, which determines their competitiveness. When choosing the set of locations in which to operate firms face a fundamental trade-off: co-locating with competitors' generates opportunities to improve product quality through imitation, but at the same time it increases the risk of losing one's competitive edge through outgoing spillovers. Being unable to fully predict competitors' moves, in making location choices firms rely on heuristics based on the expected present values associated with alternative location patterns. In a positive perspective, our model replicates key stylized facts highlighted in the pertinent empirical literature. On normative ground, we identify industry scenarios in which a firm should enter (not enter) a location even if the expected present value of doing so is negative (positive). Our key contribution is to provide a taxonomy of suitable firm location strategies depending on firm type and industry characteristics in a dynamic environment with endogenous cluster formation.

Genetic algorithms applied to an evolutionary model of industrial dynamics

• Implementation of a genetic algorithm in an evolutionary model of industrial dynamics using Netlogo. • The results corroborate with previous findings as the use of a machine learning method led to market dominance. • The results indicate that using a machine learning method do not necessarily improve the technological efficiency or the social welfare. • Actually, it was found a decrease in technological efficiency and social welfare when using a genetic algorithm, which contradicts previous works findings. In order to verify the effects of machine learning in a market structure, an evolutionary model containing firms that use a genetic algorithm to decide their investment in innovative R&D was developed. These firms share the market, with two other types of firms, those with a fixed rate of investment and those with random strategies. A model of industrial dynamics was implemented and simulated using several population distributions of the three types of firms. The availability of external credit and the length of learning periods were evaluated and their effects, in the market structure, analysed. The simulations results brought contrasting findings when compared to previous works, as it confirmed that machine learning led to market dominance, but the same did not occur when considering the improvement of technological efficiency and social welfare.

The industry supply function and the long-run competitive equilibrium with heterogeneous firms

In developing the theory of long-run competitive equilibrium (LRCE), Marshall (1890) used the notion of a representative firm. The identity of this firm, however, remained unclear. Subsequent theory either focused on the case where all firms are identical or else incorporated heterogeneity but disregarded the notion of a representative firm. Using Hopenhayn's (1992) model of competitive industry dynamics, we extend the theory of LRCE to account for heterogeneous firms and show that the long-run supply function can indeed be characterized as the solution to the minimization of a representative average cost function.

Information Technology and Returns to Scale

Disciplined by detailed firm-level data from France, we study the response of the macroeconomy to the dramatic fall observed in IT prices from 1990 to 2007. Our model of industry dynamics with heterogeneous firms accounts for the novel fact that demand for hardware and software, when compared to other inputs, is more elastic in firm size. We show that this nonhomotheticity has important consequences for firm-level returns to scale and for the aggregate response. In our calibration, falling IT prices quantitatively explain around half of the changes in concentration and in the composition of aggregate labor share in France.

The Increasing Presence of Large Firms and the Decline in US Startup Rates

While the significant decline in US firm formation rates over the past 30 years has raised concern about the health of the US economy, its causes are not yet fully understood. I argue that a significant part of this decline can be explained as an efficient response to size biased technological change. I document an increase in the size of large firms in the US since the mid-1980s contemporaneous to the decline in firm formation rates and show that large firms expanded by sharply increasing the number of establishments they operate. These changes in the organizational structure of large firms are consistent with improvements in information and communications technology which mainly benefited large firms by reducing monitoring, coordination and distribution costs. I construct a simple industry dynamics model in which an expansion by large firms due to reductions in the cost of managing many establishments crowds out smaller firms that are responsible for most of firm turnover. While generating a decrease in startup rates and an increase in the size of large firms through an increase in the number of establishments they operate, welfare improves. These results counter the popular opinion that declining firm formation rates are necessarily a bad sign.

Protocol invariance and the timing of decisions in dynamic games

We characterize a class of dynamic stochastic games that we call separable dynamic games with noisy transitions and establish that these widely used models are protocol invariant provided that periods are sufficiently short. Protocol invariance means that the set of Markov perfect equilibria is nearly the same irrespective of the order in which players are assumed to move within a period. Protocol invariance can facilitate applied work, and renders the implications and predictions of a model more robust. Our class of dynamic stochastic games includes investment games, research and development races, models of industry dynamics, dynamic public contribution games, asynchronously repeated games, and many other models from the extant literature.

Capital Requirements in a Quantitative Model of Banking Industry Dynamics

We develop a model of banking industry dynamics to study the quantitative impact of capital requirements on bank risk taking, commercial bank failure, and market structure. We propose a market structure where big, dominant banks interact with small, competitive fringe banks. The paper extends our previous work by letting banks accumulate securities like treasury bills and to undertake short-term borrowing when there are cash flow shortfalls. A nontrivial size distribution of banks arises out of endogenous entry and exit, as well as banks' buffer stocks of securities. We find that a 50% rise in capital requirements leads to a 45% reduction in exit rates of small banks and a more concentrated industry. Aggregate loan supply falls by 8.71% and interest rates rise by 50 basis points. The lower exit rate causes the tax/output rate necessary to fund deposit insurance to drop in half. Higher interest rates, on the other hand, result in a higher default frequency as well as a lower level of intermediated output. We also use the model to study the effect of lowering the rate different sized banks are charged when borrowing on their lending behavior studied by Kashyap and Stein (2001).

Information Technology and Returns to Scale

Relying on a novel dataset on hardware and software investments in the universe of French firms, we document a robust within-industry correlation between firm size and the intensity of IT demand. To explain this fact, we argue that the relative marginal product of IT inputs may rise with firm scale, since IT helps firms deal with organizational limits to scale. We propose a general equilibrium model of industry dynamics that features nonhomothetic production functions compatible with this mechanism. Estimating this production function, we identify the nonhomotheticity of IT demand and find an elasticity of substitution between IT and non- IT inputs that falls below unity. Under the estimated model parameters, the cross-sectional predictions of the model match the observed relationship of firm size with IT intensity (positive) and labor share (negative). In addition, in response to the fall in the relative price of IT inputs in post-1990 France, the model explains about half of both the observed rise in market concentration and the market reallocations toward low-labor-share firms.

A model of entry, exit, and plant-level dynamics over the business cycle

We build a general equilibrium model of plant-level dynamics to quantitatively analyze the entry and exit of manufacturing plants over the business cycle. The baseline model, which is a direct extension of Hopenhayn and Rogerson (1993) model of industry dynamics, can account for the macroeconomic rates of entry and exit over the business cycle observed in the U.S. data. It cannot, however, account for the microeconomic characteristics of entering plants. Assuming that entry costs are cyclical can resolve this inconsistency between the model and the data. We also show that this pattern of entry costs can arise from an endogenous mechanism. The business cycle results are robust when we extend the model to incorporate plant life cycle features.

INDUSTRY DYNAMICS AND THE MINIMUM WAGE: A PUTTY‐CLAY APPROACH

AbstractWe document two new findings about the industry‐level response to minimum wage hikes. First, restaurant exit and entry both rise following a hike. Second, there is no change in employment among continuing restaurants. We develop a model of industry dynamics based on putty‐clay technology that is consistent with these findings. In the model, continuing restaurants cannot change employment, and thus industry‐level adjustment occurs gradually through exit of labor‐intensive restaurants and entry of capital‐intensive restaurants. Interestingly, the putty‐clay model matches the small estimated short‐run disemployment effect of the minimum wage found in other studies, but produces a larger long‐run disemployment effect.

MULTIREGIONAL FIRMS AND REGION SWITCHING IN THE U.S. MANUFACTURING SECTOR

I use data on U.S. manufacturing establishments to study the spatial reallocation of resources that takes place within surviving firms as they open and close establishments in different regions. To motivate the empirical analysis, I extend existing models of industry dynamics to include production‐location decisions within firms. Consistent with the theory, the empirical results show that only a fraction of firms make the same product in multiple regions, that multiregional firms are larger and more productive on average compared to single‐region firms, and that “region switching” is pervasive among multiregional firms and correlated with changes in firm and firm‐region characteristics. (JEL L2, F12, F23)

Innovative entreprenuership under uncertainty

Purpose- This paper analyzes the economic activity of innovative entrepreneurship under uncertainty. We examine the profit maximization model of innovative firm under two kinds of uncertainties. The first kind of uncertainty pertains to market conditions. Erratic behavior of future prices creates market uncertainty. Methodology- This uncertainty is at least partly resolved by rational expectations hypothesis. But repercussions of market imperfections still loom large in the background of theoretical framework. The second kind of uncertainty is caused by technological changes and the patterns of innovations. We use the black-box approach methodology to production function and then improve it with a learning model. Findings- The pace and space of innovation adoption is retarded and narrowed by technological uncertainties. This problem is tackled by neoclassical innovation models to some extent. In particular, endogenous growth models and the industrial dynamics model attempt to endogenize the innovation and technology under rational expectations. Conclusion- But these models provide incomplete insights for they do not deal with learning behavior of innovative entrepreneurs. We propose an alternative learning model which takes into account the adaptive and imitative behavior of individual innovators whose economic actions matter in the general framework of real life economics.

Sunk Costs of R&D, Trade and Productivity: The Moulds Industry Case

Evidence suggests that trade improves industry productivity via (i) selection of the best …rms and (ii) within …rm productivity growth. While the selection eect has been well ex- plained by the literature, the productivity growth is not accounted for in standard models. Trade liberalization creates economies of scale in the R&D process that can explain the observed growth. I estimate a model of industry dynamics with endogenous productivity, capital accumulation and aggregate uncertainty. I use data from the Portuguese moulds industry which experienced an exogenous trade shock in 1993 (establishment of the Com- mon Market) and a signi…cant increase in foreign demand afterwards. Firms exploited the increase in demand from other European countries to increase R&D spending. The …nal results con…rm the trade-induced innovation eect.

A Framework for Dynamic Oligopoly in Concentrated Industries

In this article we introduce a new computationally tractable framework for Ericson and Pakes-style dynamic oligopoly models that overcomes the computational complexity involved in computing Markov perfect equilibrium (MPE). First, we define a new equilibrium concept that we call moment-based Markov equilibrium (MME), in which firms keep track of their own state, the detailed state of dominant firms, and few moments of the distribution of fringe firms’ states. Second, we provide guidelines to use MME in applied work and illustrate with an application how it can endogenize the market structure in a dynamic industry model even with hundreds of firms. Third, we develop a series of results that provide support for using MME as an approximation. We present numerical experiments showing that MME approximates MPE for important classes of models. Then, we introduce novel unilateral deviation error bounds that can be used to test the accuracy of MME as an approximation in large-scale settings. Overall, our new framework opens the door to study new issues in industry dynamics.