Daniel Shefer

R&D, firm size and innovation: an empirical analysis

Abstract Investment in R&D spawns innovations, which in turn, foster economic growth. In recent years, researchers have become increasingly aware of the role of industrial innovation in the rate of regional development and economic growth. In order to innovate, firms must invest in R&D (in-house or out-sourcing), and engage highly skilled labor that is able to cope with complex technological problems. The plethora of empirical studies on the determinants influencing R&D expenditure, and thus the rate of innovation, suggests that this investment is related, in different degrees, to firm size, organizational structure, ownership type, industrial branch and location. Large firms tend to invest more in R&D than do small ones. Numerous studies have found that R&D tends to be concentrated in large urban areas, and it plays a more vital role in creating innovation in central than in peripheral areas. This paper presents a model whose assumption is that expenditure on R&D is influenced by a firm’s characteristics—primarily its size, type of industrial branch, ownership type and location. The results obtained in the empirical analysis are based on data collected through personal interviews involving 209 industrial firms in the northern part of Israel.

A critical review of macro models for road accidents

This paper presents a critical review of state-of-the-art macro models for road accidents. Such a review is meant to identify and establish the significance of policy and socioeconomic variables affecting the level of road accidents. The aim is to identify those variables associated with effective policies and interventions to enable decision makers to improve the level of road safety. The variables that appear to affect the number of fatalities or injuries are: vehicle miles travelled (VMT), vehicle population, income (in its various forms), percentage of young drivers, intervention policies such as speed limits, periodic vehicle inspection, and minimum alcohol-drinking age. Viewed critically, the state-of-the-art models being used to explain and predict road accidents are still deficient. One possible approach to correcting this deficiency draws from consumer utility theory, using analytical models built on a newly constructed theoretical framework. Success in estimating such models may improve predictions of road accidents, thus demonstrating the comparative cost effectiveness of alternative intervention policies.

Congestion and Safety on Highways: Towards an Analytical Model

Congestion and accidents are important components of the externalities created by road users in metropolitan areas. In the present paper, we investigate the relationship between these two components. Among the factors which influence the number of fatalities on highways are: speed, speed differences and traffic composition. We pay special attention to the impact of congestion on the number of fatalities. The lower speeds which are caused by congestion would lead to lower numbers of fatal accidents. As a result, we expect a parabolic relationship between density and fatal accidents on highways. When densities increase, we would first have a positive relationship due to the increase in the numbers of cars in the system. However, when density becomes so high that speeds are influenced negatively, the number of accidents will decrease. The conclusion would be that in addition to the negative impact of congestion in terms of time losses, we also have a positive impact, since fatalities are reduced. Some supporting evidence is found for a number of countries where relatively low numbers of fatalities are observed during the morning peak.

NEURAL NETWORK ANALYSIS OF TRAVEL BEHAVIOR: EVALUATING TOOLS FOR PREDICTION

Abstract This article explores the application of neural networks to a behavioral transportation planning problem. The motivation for adding neural networks as a new modeling methodology stems from its apparent relevance to problems requiring large scale, highly dimensional, data analysis, such as travel related behavior. Neural networks provide a tool to analyze the data in which we can model our intuition, and they provide that capability without the complication of having to formalize all the complex causal variables and relationships which other models require. The transportation issue explored, upon which the neural network methodology is tested, is a comparison of travel demand patterns of men and women in Israel. The information base is the Traveling Habits Survey (Central Bureau of Statistics, Israel, 1984, Statistical Abstract of Israel , No. 35) commissioned by the Israel Ministry of Transport; combined with demographic and socioeconomic data of the 1983 Population and Housing Census. As extensive as such surveys are, the neural networks imply that additional categories of data are necessary to predict how these elements relate to travel behavior. This article concentrates on the extent to which neural networks can combine the relative simplicity of aggregate transportation models, with the theoretical advantages and level of detail of disaggregate transportation models, without the latters complexity. We describe the various directions we took in analyzing complex travel related data with feed forward, backpropagation trained, neural networks.

The Role of Location and Regional Networks for Biotechnology Firms in Israel

This article presents results of a study of the Israeli biotechnology sector. The findings of the study show that despite the small geographical size of the country, the Israeli biotechnology industry tends to a great extent to cluster around leading research institutes. Based on a survey of 109 high-tech projects, the study suggests that the relatively weak business background of the entrepreneurs, the fact that most of them have worked at research institutes and that most new biotechnology firms were originated from ideas that came out of universities or research institutes, can provide possible explanations for this geographical concentration. The research emphasizes the relative importance biotechnology entrepreneurs ascribe to networking, both in very early stages as well as in advanced stages of the firm development process. It is argued that although the focus of the network changes over time, both geographically and in content, its links to local research partners are maintained. It is suggested that earmarked regional support policies are crucial to the development of networks required by new biotechnology firms.

Public versus Private Technological Incubator Programmes: Privatizing the Technological Incubators in Israel

Abstract Private technological incubators began operating in Israel in 2000, and developed thanks to the rapidly growing private (venture) capital (VC) sector, which traditionally had not funded such projects. The present study examines the differences and similarities between two types of technological incubators—public and private. It addresses the question whether the need still exists for the Public Technological Incubators Programme (PTIP). Based on our empirical analysis and findings, the main conclusion is that private incubators cannot fully replace public incubators; even after the entry of the private sector into the area of technological incubator activity, there is still justification for the continuation of the PTIP. Private incubators tend to concentrate in selected fields, whereas public incubators sponsor a large variety of activities. The PTIP was found to provide answers to advancing national objectives, such promoting peripheral regions and providing special incentives to selected population groups (e.g. new immigrants) for whom such activities would otherwise be out of reach.

Modeling regional innovativeness and innovation

Advanced means of communication are necessary ingredients in the process of the dissemination of information and knowledge over space. Thus the spatial diffusion of innovation is contingent upon rapid, accurate transmission of knowledge and the ability to interact frequently and efficiently among different locations. This paper presents an extended empirical model for evaluating innovativeness and the innovation potential of various regions. The extended model is based on two basic models: the LOGIT model, a behavioral logistic model that is used to describe the diffusion process, and Bayesian statistical decision theory. A simplified example with synthetic data is presented to demonstrate the three steps involved in evaluating the probabilities of technology innovation in various regions with the extended model. Finally, the example is used to present possible way of employing the model to evaluate the effectiveness of public policy to attract firms to different regions.

The Demand for Housing, and Permanent Income, in Indonesia

This study reports the results and conclusions derived from a data analysis of urban household expenditure on housing in Indonesia. The urban households were stratified by income group, urban size and urban location; in these respects, this study is unique. Household expenditures on housing were computed for each income group and subgroup (by urban size and location). Expenditure on housing is increasing at a faster rate than income (except in the very low income groups). The proportion of income allocated by the average household to housing ranges from less than 15 per cent to close to 25 per cent (the latter being found in the upper income group). Similarly, significant variations in household expenditures on housing are observed for urban size and location. In Jakarta, for example, the average household expenditure on housing is almost twice as much as in the small urban localities (fewer than 20000 inhabitants). This observation, however, must be mitigated by income distribution: average household income in Jakarta is almost twice that of urban households elsewhere in Indonesia. Estimates of marginal propensities to consume (MPC) housing, and of income elasticities of demand for housing, reveal that the MPC varies between 0.15 and 0.40, depending upon urban size and location. Similarly, the income elasticities vary from 0.80 to 1.20, depending upon urban size and location (in Jakarta, for example, the estimated income elasticity was found to be 1.24).

Optimal plant location and air quality management under indivisibilities and economies of scale

Abstract The implications of indivisibilities and economies of scale on the optimal location of industrial plants and on the optimal choice of their pollution abatement technologies under given air quality standards are examined. Various optimization models, accounting for abatement, land development and plant relocation costs, as well as for locational economies of scale, are developed, using the integer and mixed-integer linear programming framework. A method for computing taxes and subsidies leading the system to the optimal pattern of decisions under decentralized decision-making is proposed. Finally, an application of the approach with real data of the Haifa region is implemented, illustrating the utility of the approach for environmental management.

Mathematical Models of Industrial Plant Location and Pollution Abatement Strategies

The present paper describes several mathematical models designed to control, through regulations, the locations and quantities of air pollution emissions. A Gaussian diffusion model is used in order to build an air-pollution transfer-coefficient matrix. Given the rate of increase in production and subsequently emission of air pollution, the model developed demonstrates how the permitted level of pollution emission can be set for each locality or industry, and how it is possible to assign methods of abatement and to measure their effectiveness. Finally the general abatement—location model is developed. This model simultaneously allocates industries to subareas and assigns control methods to the various industries so as to conform with the air quality standards set forth.