Jason Barr

Skyscrapers and the Skyline: Manhattan, 1895-2004

This article investigates the market for skyscrapers in Manhattan from 1895 to 2004. Clark and Kingston (1930) have argued that extreme height is a result of profit maximization, while Helsley and Strange (2008) posit that skyscraper height can be caused, in part, by strategic interaction among builders. I provide a model for the market for building height and the number of completions, which are functions of the market fundamentals and the desire of builders to stand out in the skyline. I test this model using time series data. I find that skyscraper completions and average heights over the 20th century are consistent with profit maximization; the desire to add extra height to stand out does not appear to be a systematic determinant of building height.

Preferences, the Agenda Setter, and the Distribution of Power in the EU

In this paper, we present a generalization of power indices which includes the preferences of the voters. Using a Multilinear Extension perspective (Owen in Manage Sci 18:p64–p72, 1972a) we measure the probability of the players’ voting “yes” for a particular political issue. Further, we randomize the issues and show the influence that the Agenda Setter can have on a player’s power. We demonstrate these results using data from the European Union to show how the power distribution may shift after enlargement and under the new Constitutional Treaty.

A computational theory of the firm

This paper proposes using computational learning theory (CLT) as a framework for analyzing the information processing behavior of firms; we argue that firms can be viewed as learning algorithms. The costs and benefits of processing information are linked to the structure of the firm and its relationship with the environment. We model the firm as a type of artificial neural network (ANN). By a simulation experiment, we show which types of networks maximize the net return to computation given different environments.

Organization, Learning and Cooperation

We model the organization of the firm as a type of artificial neural network in a duopoly framework. The firm plays a repeated Prisoners Dilemma type game, but also must learn to map environmental signals to demand parameters. We study the prospects for cooperation given the need for the firm to learn the environment and its rivals output. We show how a firms profit and cooperation rates are affected by its size, its rivals size and willingness to cooperate and environmental complexity.

SKYSCRAPERS AND SKYLINES: NEW YORK AND CHICAGO, 1885–2007†

This paper compares and contrasts the determinants of the market for skyscrapers in Chicago and New York from 1885 to 2007, using annual time series data. I estimate the factors that determine both the number of skyscraper completions and the height of the tallest building completed each year in the two cities. I find that each city responds differently to the same economic fundamentals. Also, regressions test for and find the presence of strategic interaction across the two cities. I also estimate the effects of zoning regulations on height. Compared to New York, Chicagos zoning policies significantly reduced the height of its skyline.

Skyscraper Height and the Business Cycle: Separating Myth from Reality

This paper is the first to rigorously test how height and output co-move. Because builders can use their buildings for non-rational or non-pecuniary gains, it is widely believed that (a) the most severe forms of height competition occur near the business cycle peaks and (b) that extreme height are examples of developers “gone wild.�? We find virtually no support for either of these popularly held claims. First we look at both the announcement and completion dates for record breaking buildings and find there is very little correlation with the business cycle. Second, cointegration and Granger causality tests show that height and output are cointegrated and that height does not Granger cause output. These results are robust for the United States, Canada, China and Hong Kong.

Depth to Bedrock and the Formation of the Manhattan Skyline, 1890–1915

New York City historiography holds that Manhattan developed two business centers—downtown and midtown—because the bedrock is close to the surface at these locations, with a bedrock “valley” in between. This article is the first effort to measure the effect of depth to bedrock on construction costs and the location of skyscrapers. We find that while depth to bedrock had a modest effect on costs (up to 7 percent), it had relatively little influence on the location of skyscrapers. null

Introduction to the Symposium on Agent-based Modeling

No abstract available.

The Dynamics of Subcenter Formation: Midtown Manhattan, 1861-1906 *

Midtown Manhattan is the largest business district in the country. Yet only a few miles to the south is another district centered at Wall Street. This paper aims to understand when and why midtown emerged. We have created a new data set from historical New York City directories that provide the employment location, residence and job for several thousand residents in the late 19th and early 20th centuries. We supplement this with data from historical business directories. The data allow us to describe how, when and why midtown emerged as a center of commerce. We find that midtown arose because of economies of scale related to shopping, rather than congestion in lower Manhattan or wage differentials across the city. Specifically, the evidence suggests that firms moved to midtown to be near retail businesses and other commercial activity in order to be closer to customers, who had been moving north on the island throughout the 19th century. Once several industries moved from lower Manhattan it triggered a spatial equilibrium readjustment in the 1880s, which then promoted the rise of skyscrapers in midtown around the turn of the 20th century, several years before the opening of Grand Central Station in 1913.