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Abstract

Purpose – The purpose of this paper is to investigate area-level labour market dynamics from a spatial perspective. This analysis is aimed at better understanding what socio-economic actors are associated with shifts in unemployment rates across a major metropolitan city. Design/methodology/approach – Based on two waves of New Zealand census data, this paper combines a seemingly unrelated regression approach (allowing for relaxation of the assumption that residuals from models of different employment states are unrelated) with a spatial lag model. Findings – The key socio-economic drivers associated with intra-city employment dynamics were vehicle access, dependency rates and educational attainment. Importantly, the identification of spatial autocorrelation with respect to employment status patterns within this major New Zealand city motivates a case for heterogeneous employment policies across the city. Originality/value – This research improves the understanding of changes in labour market status rates within a city region. This is done by inclusion of two important considerations: a spatial perspective to labour market dynamics at an intra-city level; and formally modelling the interdependence across the four potential labour market outcomes (being full-time, part-time, unemployed or out of the labour force). Overall, there was clear empirical support for the need to include spatial considerations when using targeted policy to help lift areas out of unemployment.

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Shifts in job accessibility reflect, in part, the degree to whichland use and transportation decisions help bring job opportunities closer to labor forces. In this paper we argue for the wider use of accessibility indicators as part of the long-range transportation planning process. As a case example, changes in job accessibility indices are traced for the San Francisco Bay Area from 1980 to 1990, computed for 100 residential areas and the region's 22 largest employment centers. Indices are refined based on occupational match indicators that weigh the consistency between residents' employment roles and labor-force occupational characteristics at workplaces. The analysis reveals that peripheral areas tend to be the least job accessible. Moreover, employment centers that are home to highly skilled professional workers are generally the most accessible when occupational matching is accounted for. This is thought to reflect the existence of housing markets that are more responsive to the preferences of upper-income workers. Our analyses also show that residents of low-income, inner-city neighborhoods generally face the greatest occupational mismatches. Through a path analysis, the variable 'race' was found to be far more strongly associated with unemployment than was job accessibility, however, even after controlling for educational levels and other factors. We conclude that an important purpose of tracking changes in accessibility is to provide feedback on the degree to which resource allocation decisions in the urban transportation field are helping to redress serious inequities in accessibility to jobs, medical facilities, and other important destinations.
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In this note we test if unemployment has an effect on mortality using a large individual level data set of nearly 30,000 individuals in Sweden aged 20-64 years followed-up for 10-17 years. We follow individuals over time that are initially in the same health state, but differ with respect to whether they are employed or unemployed (controlling also for a number of individual characteristics that may affect the depreciation of health over time). Unemployment significantly increases the risk of being dead at the end of follow-up by nearly 50% (from 5.36 to 7.83%). In an analysis of cause-specific mortality, we find that unemployment significantly increases the risk of suicides and the risk of dying from "other diseases" (all diseases except cancer and cardiovascular), but has no significant effect on cancer mortality, cardiovascular mortality or deaths due to "other external causes" (motor vehicle accidents, accidents and homicides).
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This study examines the long-term unemployment rate and various health outcomes across Canadian communities to estimate employment-related health inequalities in these communities. The study uses cross-sectional community-level health data along with data on the long-term employment rate for various communities across Canada to quantify health inequalities among these communities. The health outcomes that are considered in this study include total and disease specific mortality rates; health conditions such as high blood pressure, diabetes, injuries, and self rated health; and life expectancies at birth and at age 65. Health inequalities are estimated using the concentration index, which is used to measure health inequalities along socioeconomic dimensions. The concentration index is estimated by a regression of weighted relative health (ill health) over weighted cumulative relative rank of the populations. All the estimates are provided separately for males and females. The findings of the study support the existence of inequalities in community health outcomes as related to the long-term employment rates in those communities. Communities with lower long term employment rates (higher unemployment rates) have poorer health outcomes in terms of higher mortality rates, worse health conditions, and shorter life expectancies. Health inequalities related to long-term employment have important policy implications. They call for policies that would increase and maintain long term employment rates as part of a broader socioeconomic approach to health. Long term employment ensures income security and prevents the psychosocial experiences leading to mental and physical ill health.
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By explicitly considering the spatial dimension of local regional labor markets, we develop a simple dynamic model that explains the spatial correlation between unemployment rates. We then test this model by using UK local data. Our evidence shows a significant spatial dependence that has been growing over time and characterized by a low distance decay. Highly localized effects are explained by commuting flows. These results are consistent with the theoretical model.
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This paper examines labour productivity in Auckland, New Zealand's largest city, using microdata from Statistics New Zealand's Prototype Longitudinal Business Database. It documents a sizeable productivity premium in Auckland, around half of which is due to industry composition. There is a cross sectional correlation between productivity and employment density, reflecting differences in both physical productivity and prices. This correlation is evident both within Auckland, and comparing Auckland with other areas. The relationship between changes in density and changes in productivity is less strong. The relationship between productivity and overall or own-industry employment density varies across industries, suggesting that the nature and extent of agglomeration benefits varies. Overall, localisation effects appear stronger than urbanisation, with productivity being more strongly related to own-industry density than to overall density.
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This paper examines the reflection problem that arises when a researcher observing the distribution of behaviour in a population tries to infer whether the average behaviour in some group influences the behaviour of the individuals that comprise the group. It is found that inference is not possible unless the researcher has prior information specifying the compisition of reference groups. If this information is available, the prospects for inference depend critically on the population relationship between the variables defining reference groups and those directly affecting outcomes. Inference is difficult to implossible if these variables are functionally dependent or are statistically independent. The prospects are better if the variables defining reference groups and those directly affecting outcomes are moderately related in the population.
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This paper uses a cross-sectionally hetero scedastic and time-wise autoregressive technique to examine the pooled suicide rates of 23 cities and counties in Taiwan from 1983 to 1993. A combination of economic and sociological variables were found to account for a significant proportion of the variations in suicide rates across regions and over time. Economic variables appeared to have a greater impact on regional suicide rates than sociological correlates. In particular, the level of income per capita in a region stood out as the most important predictor of the suicide rate. This study has also uncovered gender differences in the effect of some correlates on regional suicide rates, such as the proportion of the poverty population in the region and the presence of a local life-line center.