Subsidized Housing, Emergency Shelters, and Homelessness: An Empirical Investigation Using Data from the 1990 Census

This paper uses data on the only systematic count of the homeless throughout the United States to estimate the effect on the rate of homelessness of a wide variety of potentially important determinants, including several major policy responses to homelessness that have not been included in previous studies. It improves upon estimates of the effect of previously studied determinants by using measures that correspond more closely to underlying theoretical constructs, especially by accounting for geographical price differences. It also conducts numerous sensitivity analyses and analyzes the consequences of the undercount of the homeless for point estimates and hypothesis tests. The paper's most important finding from a policy perspective is that targeting the current budget authority for housing assistance on the poorest eligible households will essentially eliminate homelessness among those who apply for assistance. Achieving this goal without concentrating the poorest households in housing projects and without spending more money requires vouchering out project-based assistance. The primary methodological finding of the paper is that the 1990 Decennial Census did not produce sufficiently accurate counts, especially of the street homeless, to permit very precise estimates of the effects of many factors which surely affect the rate of homelessness. The main exceptions are the price of housing and average March temperature. Plausible models of the undercount imply that in regressions with a rate of homelessness as the dependent variable estimators of the coefficients of explanatory variables will be biased towards zero. In regressions with the logarithm of a rate of homelessness as the dependent variable, only the estimator of the constant term will be biased downwards. The unknown magnitude of the undercount precludes predicting the effects of policy interventions on the number of homeless based on the results in this paper and previous studies.Homelessness

A Panel of Price Indices for Housing, Other Goods, and All Goods for All Areas in the United States 1982-2008

This paper produces a panel of price indices for housing, other produced goods, and all produced goods for each metropolitan area in the United States and the non-metropolitan part of each state from 1982 through 2008 that can be used for estimating behavioral relationships, studying the workings of markets, and assessing differences in the economic circumstances of people living in different areas. Our general approach is to first produce cross-sectional price indices for a single year 2000 and then use BLS time-series price indices to create the panel. Our geographic housing price index for 2000 is based on a large data set with detailed information about the characteristics of dwelling units and their neighborhoods throughout the United States that enables us to overcome many shortcomings of existing interarea housing price indices. For most areas, our price index for all goods other than housing is calculated from the price indices for categories of non-housing goods produced each quarter by the Council for Community and Economic Research. In order to produce a non-housing price index for areas of the United States not covered by their index, we estimate a theoretically-based regression model explaining differences in the composite price index for non-housing goods for areas where it is available and use it to predict a price of other goods for the uncovered areas. The overall consumer price index for all areas is based on the preceding estimates of the price of housing and other goods. The paper also discusses existing interarea price indices available to researchers, and it compares the new housing price index with housing price indices based on alternative methods using the same data and price indices based on alternative data sets. Electronic versions of the price indices are available online.Interarea price indices, interarea housing price indices, geographic cost-of-living differences, geographic price differences

Individualization as driving force of clustering phenomena in humans

One of the most intriguing dynamics in biological systems is the emergence of clustering, the self-organization into separated agglomerations of individuals. Several theories have been developed to explain clustering in, for instance, multi-cellular organisms, ant colonies, bee hives, flocks of birds, schools of fish, and animal herds. A persistent puzzle, however, is clustering of opinions in human populations. The puzzle is particularly pressing if opinions vary continuously, such as the degree to which citizens are in favor of or against a vaccination program. Existing opinion formation models suggest that "monoculture" is unavoidable in the long run, unless subsets of the population are perfectly separated from each other. Yet, social diversity is a robust empirical phenomenon, although perfect separation is hardly possible in an increasingly connected world. Considering randomness did not overcome the theoretical shortcomings so far. Small perturbations of individual opinions trigger social influence cascades that inevitably lead to monoculture, while larger noise disrupts opinion clusters and results in rampant individualism without any social structure. Our solution of the puzzle builds on recent empirical research, combining the integrative tendencies of social influence with the disintegrative effects of individualization. A key element of the new computational model is an adaptive kind of noise. We conduct simulation experiments to demonstrate that with this kind of noise, a third phase besides individualism and monoculture becomes possible, characterized by the formation of metastable clusters with diversity between and consensus within clusters. When clusters are small, individualization tendencies are too weak to prohibit a fusion of clusters. When clusters grow too large, however, individualization increases in strength, which promotes their splitting.Comment: 12 pages, 4 figure