This article considers two instances of rapidly accelerating linguistic change in Glaswegian vernacular, th - fronting and l-vocalization , both typically associated with the Cockney dialect of London. Both changes have been underway for some time, but took off during the 1990s. In this article we consider a range of factors that are contributing to the rapid proliferation of these forms in the speech of inner-city Glaswegian adolescents. Our multivariate analysis shows very strong effects for linguistic factors, as well as strong positive correlations with social practices relating to local Glaswegian street style, some links with dialect contact with friends and family living in England, and—perhaps surprisingly—also positive correlations with strong psychological engagement with the London-based TV soap drama EastEnders . Our results suggest that the changes are being propelled by several processes: ongoing transmission and at the same time continuing diffusion through dialect contact; the local social meanings carried by these variants for these speakers; and strong engagement with a favorite TV drama. For this community at least, engaging with a favorite TV drama is an additional accelerating factor in rapid linguistic diffusion.

This paper uses data at English local authority district level to construct a simultaneous equation model of housing construction that compares elasticities of supply between two cross-sectional periods—1988 (boom) and 1992 (slump) — using the variable elasticity approach. Econometric issues raised by earlier supply studies are discussed and tested for. The paper also discusses the rationale for, and tests the existence of, a backward-bending supply relationship, and finds that supply is concave in both periods, and 'bends backwards' during the boom. Evidence of a structural break between boom and bust is found, producing average price elasticities of supply noticeably smaller in the boom (0.58) than in the slump (1.03), with considerable variation across disticts. Land supply elasticities are found to be more stable over time, and marginally greater in the boom (0.75) than in the slump (0.71). The paper also calculates second partial derivatives based on the whole demand-supply system to obtain estimates of the impact of land release on new house prices.

‘Social frontiers’ – places of sharp difference in social/ethnic characteristics between neighbouring communities – have largely been overlooked in quantitative research. Advancing this nascent field first requires a way of identifying social frontiers in a robust way. Such frontiers may be ‘open’ – an area may contrast sharply with a neighbourhood in one direction, but blend smoothly into adjacent neighbourhoods in other directions. This poses some formidable methodological challenges, particularly when computing inference for the existence of a social frontier, an important goal if one is to distinguish true frontiers from random variation. We develop a new approach using Bayesian spatial statistical methods that permit asymmetries in spatial effects and allow for spatial autocorrelation in the data. We illustrate our method using data on Sheffield and find clear evidence of ‘open’ frontiers. Permutations tests and Poisson regressions with fixed effects reveal compelling evidence that social frontiers are associated with higher rates of crime.

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We argue that the rush to apply multiple regression estimation to time on the market (TOM) durations may have led to important details and idiosyncrasies in local housing market dynamics being overlooked. What is needed is a more careful examination of the fundamental properties of time to sale data. The approach promoted and presented here, therefore, is to provide an examination of housing sale dynamics using a step-by-step approach. We present three hypotheses about TOM: (i) there is nonmonotonic duration dependence in the hazard of sale, (ii) the hazard curve will vary both over time and across intraurban areas providing evidence of the existence of submarkets and (iii) institutional idiosyncrasies can have a profound effect on the shape and position of the hazard curve. We apply life tables, kernel-smoothed hazard functions and likelihood ratio tests for homogeneity to a large Scottish data set to investigate these hypotheses. Our findings have important implications for TOM analysis.In the past 30 years, there have been over 20 published studies of time on the market (TOM) for residential properties. The number of papers doubled in the 1980s, 1 compared with the 1970s. 2 The number doubled again in the 1990s, 3 and there is a good chance that the number of papers will double again by the end of the current decade. 4 This bourgeoning of the literature is partly driven by the increasing popularity of survival analysis techniques per se (assisted by their incorporation into popular statistical software packages) and partly because of the emerging availability of suitable housing data. With respect to the second cause, the present study is a case in point: this is the 378 Pryce and Gibb first large-sample published analysis of residential time to sale in the United Kingdom. The only previous United Kingdom-based paper was the very first in the literature (Cubbins (1974), based on 83 sales in Coventry, England). To our knowledge, all other published studies have used U.S. data.We argue that the rush to apply multiple regression estimation to TOM may have led to important details and idiosyncrasies in local market dynamics being overlooked. What is needed is a more careful examination of the fundamental properties of time-to-sale data. The approach promoted and presented here, therefore, is to provide an examination of housing sale dynamics using a stepby-step approach. Kernel-smoothed nonparametric estimates of the aggregate hazard function (complemented by life table analysis and likelihood ratio tests) are applied to different subgroups of housing sales over different time periods. This anatomy of the selling process reveals insights and caveats previously unexamined in the housing literature-results that can inform future analysis of time to sale and related topics.The starting point for our article is that time to sale cannot be analyzed in the same way as other continuous variables. This is because it is a "duration" variable and hence subject to two crucial characteristics: time dependency and censoring. T...