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The presence of cycles characterizes all economic systems, but economic cycles have differentiated spatial impacts. Some regions have broader cycles with respect to the country, while others tend to be less responsive to shocks and hence have narrower cycles. Being exposed to broader cycles, that is, greater volatility, may increase the strain on a regional economic system. This paper investigates the different responsiveness to cyclical forces and volatility of regions in the long run. It does so by using quarterly employment data for the Nuts2 Italian regions over almost 40 years before and during the period 1978–2016. Explored in particular are the cross‐regional variations in employment volatility and the reasons for the patterns observed, as well as whether they have changed the following different macroeconomic policy regimes. The paper identifies the break dates of different regimes, and these regime changes will be related to policy modifications, such as the implementation of the European Monetary Union. The determinants of this regional volatility appear to be quite stable, so that the changes in volatility are explained by how these determinants have changed overtime and how they are unevenly distributed in space. In particular, the lagging regions of the country suffer, in addition to lower production and income, from higher volatility due to a structure which is weaker and more unstable. Volatility can hence be an additional issue for lagging regions. [ABSTRACT FROM AUTHOR]

It is common in empirical regional economics to use total employment as an explanatory variable while investigating issues such as the level and distribution of income and migration. This paper argues that sector-specific changes in employment and labor market performance can have different effects on economic growth, the collection of tax revenue, migration, and the level and distribution of household income. As such, it is important to model sectors separately. We find that expansions in employment opportunities for a high-wage sector such as computer manufacturing or bioengineering, a medium-wage sector manufacturing, and the lower-wage sector of retailing have differing economic consequences for a small city. We use a data intensive computable general equilibrium model to obtain these results. [ABSTRACT FROM AUTHOR]