Abstract
This paper investigates the structural relation between the Italian weak macroeconomic performances and the productivity decline experienced over the last 15 years, estimating a Dynamic Stochastic General Equilibrium model. Modifying Ireland and Schuh’s (Rev Econ Dyn 11:473–492, 2008) two-sector RBC model in order to account for cointegration between consumption and investment, we interpret the unsatisfactory Italian economic dynamics in the light of a permanent negative shock to the component of productivity which is common across the consumption-good and the investment-good sector. In the light of our results, the common view that the Italian productivity problems involve only the Made in Italy sectors is only partially confirmed, since growth in the investment-good sector relies on the counterbalancing properties of its transitory sector-specific productivity component. Moreover, the model indirectly stresses the importance of the intermediate-good productions in the observed productivity decline. The short- and long-run implications of productivity dynamics for consumption, investment and hours worked are also discussed.
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Notes
Linearity in leisure is consistent with the view that the economy is compound by a large number of households, each including a potential employee who either works full time or not at all in each period (Hansen 1985).
Part-time contracts have been introduced for the fist time in 1984. The “pacchetto Treu” in 1997 and the Biagi’s reform in 2003 introduced and increased the variety of fixed-term labour contracts.
We computed \(\delta \) from the annual series of gross capital (\(K_{t}\)) and investment (\(I_{t}\)) in the non-farm sector, provided by ISTAT, using the steady-state property of the aggregate law of motion of capital, given by the sum of the Eqs. (4) and (5), so that \(I_{ss}/K_{ss}=\delta \). The annual depreciation rate, obtained as the average of the ratio I/K over the period 1981–2007, is then converted into the corresponding quarterly value.
We first computed the (simple) average of labour income shares \(\alpha \) for the period 1981–2007. The average capital income share is derived as \(1-\alpha \), exploiting the property of a Cobb–Douglas function with constant returns to scale.
Given our sample size, the choice to go through a maximum likelihood approach is preferable; indeed, the information contained in the data would have dominated the information in the priors in a Bayesian framework, and the results would have been not very different from those obtained by maximum likelihood. This is the same choice made by Ireland and Schuh (2008).
From Mario Draghi’s 2007 speech as former Governor of the Bank of Italy.
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This paper benefited from the comments and suggestions of Ulrich Woitek, Mathias Hoffmann and the participants at the Fifth Italian Congress of Econometrics and Empirical Economics (ICEEE 2013) in Genova.
Appendix: Equilibrium conditions in the common-trend model
Appendix: Equilibrium conditions in the common-trend model
The Social Planner’s problem consists of choosing
so that the social utility function is maximized, subject to the production and capital accumulation constraints. Denote by \(\Lambda _{ct}\) and \(\Lambda _{it}\) the non-negative Lagrange multipliers on the technology constraints (2)–(3), and by \(\Xi _{ct}\) and \(\Xi _{it}\) the non-negative Lagrange multipliers on the capital accumulation constraints (4)–(5), then the first-order conditions (FOC) derived from the Lagrangian of the planner’s problem are given by:
for \(t=0,1,2,\ldots ,\) where
and
The first-order conditions (21)–(31), along with the aggregation constraints (32)–(33) and the driving processes, as specified in (6)–(13) in the main text, are the equilibrium conditions of the model. This is a system of 21 equations in 21 variables—\(C_{t}\), \(H_{t}\), \(H_{ct}\), \(H_{it}\), \(I_{t}\), \(I_{ct}\), \(I_{it}\), \(K_{ct}\), \(K_{it}\), \(\Lambda _{ct}\), \(\Lambda _{it}\), \(\Xi _{ct}\), \(\Xi _{it}\), \(A_{t}\), \(a_{t}^{l}\), \(A_{t}^{g}\), \(Z_{ct}\), \(z_{ct}^{l}\), \(Z_{it}\), \(z_{it}^{l}\) and \(Z_{t}^{g}\). However, the equilibrium deriving from the solution of this system is not stable, since variables grow and some of them also may also be non-stationary. Following Ireland and Schuh (2008), this system can then be written in terms of the stationarized variables, i.e. variables that are constant in the steady state, obtained dividing the original ones by the sources of non-stationarity assigned to them by the model, so that
while \(a_{t}^{l}\), \(z_{ct}^{l}\), \(z_{it}^{l}\) remain the same, since already stable. The new system is obtained substituting the original variables with their stationary counterparts; however, in order to keep track of the three key original variables, \(C_{t}\), \(H_{t}\) and \(I_{t}\), which are our observables, we need to introduce three further variables, \(g_{t}^{c}\), \(g_{t}^{i}\) and \(g_{t}^{h}\), corresponding to their growth rates and defined as
The final system consists of 24 equations in 24 variables and can be log-linearized around the steady state of the lower-case variables and solved using Klein’s (2000) method.
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Marino, F. The Italian productivity slowdown in a Real Business Cycle perspective. Int Rev Econ 63, 171–193 (2016). https://doi.org/10.1007/s12232-015-0248-6
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DOI: https://doi.org/10.1007/s12232-015-0248-6