THE LOCATION OF NONMETROPOLITAN HIGH-TECH INDUSTRIES: A Comparison of Regional Determinants

A study of the location of three classifications of high-tech industries in rural areas was undertaken using ordinary least squares, Tobit, and a two-step estimation procedure suggested by Heckman (1976). The results indicate that the location decision for high tech firms is influenced primarily by the county population, adjacency to an SMSA, and the expenditures on public infrastructure (exclusive of education expenditures). However, location near interstate highways was generally not a significant factor. Results were similar between branch and unit (single ownership) plants. Some differences in signs of orders of magnitude occurred among the estimation procedures. Making a choice between Tobit and Heckman approaches depends upon the goal of the research.     

Diversification Categories in Investment Real Estate

This paper continues previous work evaluating the benefits of diversification and analyzes the various dimensions within the commercial real estate opportunity set. The database is large and extends through the 1982 downturn in property values. Due to the low levels of systematic risk, current distinctions by region and property type make little sense in a world of costly diversification. More exacting categories combining property type, SMSA growth rate and lease maturity offer promise for more efficient diversification within the real estate portfolio.     

The Atkinson Inequality Measure and its Sampling Properties: Bayesian and Classical Approaches

This paper examines several Bayesian methods of obtaining posterior probability density functions of the Atkinson inequality measure and its associated social welfare function, in the context of grouped income distribution data. The methods are compared with asymptotic standard errors. The role of the number of income classes is investigated using a simulated distribution. If only a small number of groups is available in published data, there is a clear gain from generating the posterior probability density function when using an explicit income distribution assumption. Even with a small number of groups, the Bayesian approach gives results that are close to the sample values obtained using the corresponding individual observations.     

Learning-by-doing and input demand of a rate-of-return regulated firm

The significance of learning by doing to input demand of a cost-minimizing rate-of-return-regulated firm is examined. Using a panel data, the results indicate that the firm's cost and input demand decisions are both influenced by learning-by-doing. The firm's cost and the rate-base (capital) input requirements decline as learning-by-doing measured by cumulative production expands. However, LBD may have different effects on the non-rate-base inputs (labor and fuel) considered in this study. While LBD ambiguously reduces fuel usage, it moderately increases labor employment. In addition to changing input intensity, LBD also influences returns to scale and elasticity of substitution.     

Potential Growth and Inflation: Estimates for Australia, the United States and Canada

Estimates of potential output growth for Australia, the United States and Canada are presented and analysed in this article. We define potential growth as that growth rate consistent with a steady (domestic component of the) inflation rate (SIRG). At around 4 per cent per annum, Australia's SIRG has been relatively stable for the past 30 years, which seems inconsistent with the view that wide‐ranging microeconomic reform in the 1990s raised growth potential. However, we show that the reduction in employment growth in Australia from the 1980s to the 1990s may account for the absence of a rise in potential growth. In Canada and the United States the SIRGs are closer to 3 per cent, and we explore the reasons why potential growth estimates are higher for Australia than for North America. We also discuss why Australia's growth averaged less than its potential in the 1980s and 1990s and the possible use of our estimates for monetary policy purposes.     

Making Financial Goals and Reporting Policies Serve

The main. nancial goal of Progressive Insurance, the third largest underwriter of auto insurance in the U.S., has remained the same since the late 1960s. Expressed in three words, "96 and grow," the goal tells the company's managers to pursue all growth opportunities while maintaining a "combined ratio" no higher than 96, or what amounts to a minimum 4% spread between revenues (premiums) and costs (including expected losses). Thanks in part to the clarity of mission provided by this goal, the company has produced an average 15% rate of growth in revenues and earnings, along with a remarkably stable 15% return for its shareholders, since going public in 1971.
Progressive's simplicity and clarity of mission is also partly responsible for another of the company's distinctive policies: product pricing that, while disciplined, is aggressive and highly decentralized. Having invested some $500 million per year developing statistical models for pricing individual customer risks and acquisition costs, the company was among the. rst in its industry to underwrite "non-standard" risks. And aided by sophisticated pricing models, each of Progressive's 100 or so local product managers are charged with adapting those models to come up with premiums for their own regions.
To go along with its strategic and organizational innovations, Progressive also has an innovative disclosure policy. Apart from SEC reports, the company's communications seldom mention earnings or earnings per share, and the company has never provided earnings guidance. With the passage of Reg. FD in late 2000, the company brie. y considered offering guidance. But in the spring of 2001, the board decided instead to provide monthly releases of its realized combined ratio. Since adoption of this new disclosure policy, Progressive has seen a 50% drop in the volatility of its stock price.