This paper tests the hypothesis that fear is a central factor in understanding human behaviour in the face of terror. This claim is addressed in the context of behaviour in the Jerusalem housing market over the terror-stricken years in the city, 1999—2004. Using a unique data source and the tools of spatial data analysis, the paper provides support for this hypothesis in three respects. First, patterns of terror in the city are shown to be increasingly deconcentrated over the period studied. Secondly, the types of terror having the sharpest effect on residential property prices are those most associated with randomness. Thirdly, the effect of terror is less on purchasing prices than on rental prices. The former represent revealed long-term behaviour less affected by fear and the latter, short-term behaviour more likely to be influenced by such disutility. The paper concludes with some of the policy implications arising from these findings.

A spatial vector autoregressive model (SpVAR) is defined as a VAR which includes spatial as well as temporal lags among a vector of stationary state variables. SpVARs may contain disturbances that are spatially as well as temporally correlated. Although the structural parameters are not fully identified in SpVARs, contemporaneous spatial lag coefficients may be identified by weakly exogenous state variables. Dynamic spatial panel data econometrics is used to estimate SpVARs. The incidental parameter problem is handled by bias correction rather than more popular alternatives such as generalised methods of moments (GMM). The interaction between temporal and spatial stationarity is discussed. The impulse responses for SpVARs are derived, which naturally depend upon the temporal and spatial dynamics of the model. We provide an empirical illustration using annual spatial panel data for Israel. The estimated SpVAR is used to calculate impulse responses between variables, over time, and across space. Finally, weakly exogenous instrumental variables are used to identify contemporaneous spatial lag coefficients.

The literature on regional growth convergence and economic disparities has tended to confound four interwoven measurement phenomena: 1) mean reversion (so-called beta convergence)—richer regions move towards the average from above and poorer regions from below; 2) diminishing inequality (so called sigma convergence)—the horizontal or spatial distribution of income becomes more equal; 3) mobility—the rank of a region in the overall distribution of income changes either upwards or downwards; and 4) leveling—the richer regions become poorer (leveling-down) or the poorer regions become richer (leveling-up). We use a new statistical methodology that treats these four phenomena on an integrated basis. The methodology is applied to Israeli regional earnings. We show that regional earnings are Gini divergent, but after adjusting earnings for regional cost-of-living differential, this picture is reversed. In the absence of genuine cost-of-living data, a simple and practical method is proposed, whereby regional house price data are used to proxy regional cost-of-living differentials.

This article introduces economic development planners to a new approach to evaluating local job creation efforts, an approach that explicitly considers the chains of employment vacancies that open up when new jobs are created. This “job chains” model is an analytic framework for assessing the employment impacts associated with economic development programs and the social value of those impacts. The approach focuses on measuring the wage gains to job changers and placing realistic values on jobs for those not previously employed in the area. It explicitly considers both efficiency and distributional effects of job creation. We discuss the simple mechanics of the technique and present an example relating to the establishment of a large auto plant in a major Midwestern city. We conclude with practical ground rules for planners carrying out a job chains analysis of an economic development effort.

Employment deconcentration has become a major issue on the policy and planning agenda in many metropolitan areas throughout the western world. In recent years, growing evidence indicates that in many developed countries, the deconcentration of employment - particularly of retail centres and offices - has become a key planning issue. This chapter uses the UrbanSim forecasting and simulation model in order to investigate some of the projected changes in land use, land value and sociodemographic characteristics of metropolitan areas undergoing employment deconcentration. The process of model application in the Tel Aviv metropolitan context is described. Two land-use scenarios of very different scales are simulated: a macro-level scenario relating to the imposition of an ‘urban growth boundary’ and a micro-level scenario simulating the effects of a shopping mall construction in different parts of the metropolitan area. The results are discussed in terms of the potential and constraints of microsimulation for analyzing metropolitan growth processes.