The unemployment structure of the US States

This paper analyses the time series properties of the unemployment rates of the 50 US States, as well as the global rate of the USA. Our results, based on the use of ADF-type tests, show that the inclusion of some breaks is vital in order to reduce the persistence on these rates. Thus, we can reject the unit root null hypothesis versus a double mean-shifted stationary alternative for 46 States and for the US total rate. We also find that the behavior of this latter rate is not congruent with that of the States, implying the presence of some aggregation problems which have not been commonly accounted in the literature

New evidence on Gibrat’s law for cities

The aim of this work is to test empirically the validity of Gibrat’s law on the growth of cities, using data on the complete distribution of cities from three countries (the US, Spain and Italy) for the entire twentieth century. In order to achieve this, we use different techniques. First, panel data unit root tests tend to confirm the validity of Gibrat’s law in the upper-tail distribution. Second, when we consider the entire distribution, we find that Gibrat’s law does not hold exactly in the long term using nonparametric methods that relate the growth rate to the initial city size

Patterns in U.S. urban growth (1790–2000)

This paper reconsiders the evolution of the growth of American cities since 1790 in the light of new theories of urban growth. Our null hypothesis for long-term growth is random growth. We obtain evidence supporting random growth against the alternative of mean reversion (convergence) in city sizes using panel unit root tests. We also examine mobility within the distribution to try to extract growth patterns different from the general unit root trend detected. We find evidence of high mobility when we model growth as a first-order Markov process. Finally, using a cluster procedure we find strong evidence in favour of conditional convergence in city growth rates within convergence clubs, which we can interpret as “local” mean-reverting behaviours. Both the high mobility and the results of the clustering analysis seem to indicate a sequential city growth pattern

Patterns in U.S. urban growth (1790–2000)

This paper reconsiders the path of the growth of American cities since 1790 (the first census published) in light of new theories of urban growth. Our null hypothesis for long-term growth is random growth, but the alternative is not only mean reversion as is usual. We obtain evidence supporting random growth against the alternative of mean reversion (convergence) in city sizes using panel unit root tests, but we also examine mobility within the size distribution of cities to try to extract growth patterns different from the general unit root trend detected. We find evidence of high mobility when we model growth as a first-order Markov process. Finally, by using a cluster procedure, we find strong evidence in favour of conditional convergence in city growth rates within convergence clubs, which we interpret as “local” mean-reverting behaviours. We interpret the high mobility and the results of the clustering analysis as signs of a sequential city growth pattern toward a random growth steady state

Patterns in U.S. urban growth (1790–2000)

This paper reconsiders the path of the growth of American cities since 1790 (the first census published) in light of new theories of urban growth. Our null hypothesis for long-term growth is random growth, but the alternative is not only mean reversion as is usual. We obtain evidence supporting random growth against the alternative of mean reversion (convergence) in city sizes using panel unit root tests, but we also examine mobility within the size distribution of cities to try to extract growth patterns different from the general unit root trend detected. We find evidence of high mobility when we model growth as a first-order Markov process. Finally, by using a cluster procedure, we find strong evidence in favour of conditional convergence in city growth rates within convergence clubs, which we interpret as “local” mean-reverting behaviours. We interpret the high mobility and the results of the clustering analysis as signs of a sequential city growth pattern toward a random growth steady state

Patterns in U.S. urban growth (1790–2000)

This paper reconsiders the path of the growth of American cities since 1790 (the first census published) in light of new theories of urban growth. Our null hypothesis for long-term growth is random growth, but the alternative is not only mean reversion as is usual. We obtain evidence supporting random growth against the alternative of mean reversion (convergence) in city sizes using panel unit root tests, but we also examine mobility within the size distribution of cities to try to extract growth patterns different from the general unit root trend detected. We find evidence of high mobility when we model growth as a first-order Markov process. Finally, by using a cluster procedure, we find strong evidence in favour of conditional convergence in city growth rates within convergence clubs, which we interpret as “local” mean-reverting behaviours. We interpret the high mobility and the results of the clustering analysis as signs of a sequential city growth pattern toward a random growth steady state

Patterns in U.S. urban growth (1790–2000)

This paper reconsiders the path of the growth of American cities since 1790 (when the first census was published) in light of new theories of urban growth. Our null hypothesis for long-term growth is random growth, but the alternative is not only mean reversion as is usual. We obtain evidence supporting random growth against the alternative of mean reversion (convergence) in city sizes by using panel unit root tests, but we also examine mobility within the size distribution of cities to try to extract growth patterns different from the general unit root trend detected. We find evidence of high mobility when we model growth as a first-order Markov process. Finally, by using a cluster procedure, we find strong evidence in favour of conditional convergence in city growth rates within convergence clubs, which we interpret as "local" mean-reverting behaviours. We interpret the high mobility and the results of the clustering analysis as signs of a sequential city growth pattern toward a random growth steady state