The Drivers of Italy’s Investment Slump During the Double Recession

Busetti, Fabio; Giordano, Claire; Zevi, Giordano

doi:10.1007/s40797-016-0028-9

Cited by 16 publications

(18 citation statements)

References 26 publications

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“…It is a measure of diagreement across firms concerning their economic outlook. Source: calculations on the European Commission Business Survey data using the methodology described in Busetti;Giordano and Zevi (2016).…”

Section: Cyclical Conditionsmentioning

confidence: 99%

“…Moreover, we introduce for the first time in the literature a comparable cross-country sector-specific survey-based uncertainty measure for sectors other than manufacturing. Following Fuss and Vermeulen (2008), Bachmann, Elstner and Sims (2014) and Busetti, Giordano and Zevi (2016), we proxy demand uncertainty with the cross-sectional dispersion in the expectations of firms interviewed in the monthly European Commission Business Survey. The measure is computed as � + + − − ( + − − ) 2 , where + and − are the fractions of firms with "increase" and "decrease" responses at time t, and varies between 0 and 1.…”

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confidence: 99%

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Capital and Labour (Mis)Allocation in the Euro Area: Some Stylized Facts and Determinants

2016

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. The paper is released in order to make the research of CompNet generally available, in preliminary form, to encourage comments and suggestions prior to final publication. The views expressed in the paper are the ones of the author(s) and do not necessarily reflect those of the ECB, the ESCB, and of other organisations associated with the Network. Terms of use: Documents in EconStor mayECB Working Paper 1981, November 2016 1 AbstractWe analyse the evolution of capital and labour (mis)allocation across firms in five euro-area countries (Belgium, France, Germany, Italy and Spain) and eight main sectors of the economy during the period 2002-2012. Three key stylized facts emerge. First, in all countries with the exception of Germany, capital allocation has worsened over time whereas the efficiency of labour reallocation has not changed significantly. Second, the observed increase in capital misallocation has been particularly severe in services as opposed to industry. Third, misallocation of both labour and capital dropped in all countries in 2009 and again for some country-sectors in 2011-2012. We next take stock of the possible drivers of input misallocation dynamics in a standard panel regression framework. Controlling for demand conditions and for the initial level of misallocation, heightened uncertainty, restrictive bank credit standards and tight product and labour market regulation are found to have boosted input misallocation, whereas the Great Recession per se exerted a cleansing effect. JEL codes: D24, D61, O47Keywords: total factor productivity, allocative efficiency, capital, labour, Great Recession ECB Working Paper 1981, November 2016 2 Non-technical summaryPoor total factor productivity (TFP) growth has been a key issue in the euro area over the last years and analysing its determinant is crucial from a policy perspective. At the sectorial level, TFP growth depends, almost equally, on technology improvements within firms (within-firm TFP growth) and on the efficiency with which production factors are (mis)-allocated across firms (between-firm TFP growth). The focus of this paper is on the latter and in particular on misallocation of labour and capital in eight macro-sectors (which include manufacturing and services) for five large euro-area countries (Belgium, France, Germany, Italy and Spain) during the period 2002-2012.Using the Hsieh and Klenow (2009) misallocation indicator, we uncover three stylized facts. First, in all countries, with the...

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Section: Cyclical Conditionsmentioning

confidence: 99%

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confidence: 99%

Capital and Labour (Mis)Allocation in the Euro Area: Some Stylized Facts and Determinants

2016

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“…These three series are found to be integrated of order one according to the two standard unit root/stationarity tests, the Augmented Dickey-Fuller test (Dickey and Fuller, 1979) and the Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test (Kwiatkowski et al, 1992), as shown in Appendix A.1. This result is quite standard in the empirical studies replicating the Hall and Jorgenson (1967) model, such as Bussière et al (2015), Busetti et al (2016) and Fatica (2018). 1…”

Section: Forecasting Modelsmentioning

confidence: 59%

“…Several studies (Bontempi et al, 2010;Busetti et al, 2016;Bacchini et al, 2018;Giordano et al, 2019) have focused specifically on Italy, an insightful case-study in that, after growing at a comparable rate to its peer euro-area economies since 1995 (the initial year of official national account data), Italy's GFCF then experienced a significantly more pronounced downturn during the double recession and a more sluggish recovery thereafter. In this country, non-financial corporations (which we loosely also refer to as "firms" in this article) alone undertake about half of total GFCF (Giordano et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Forecasting Corporate Capital Accumulation in Italy: The Role of Survey-Based Information

2021

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While there is a vast macroeconomic literature that singles out the main drivers of capital accumulation in advanced economies during and after the global financial and sovereign debt crises' recessionary phase, there is much less research seeking to identify both models and variables that possess out-of-sample forecasting ability for gross fixed capital formation. Moreover, micro-founded variables are scarcely employed in macroeconomic forecasting of real investment. We fill this gap by considering a battery of univariate and multivariate time-series models to forecast investment of non-financial corporations in Italy, an interesting case study due to its steep downturn during the two afore-mentioned crises. We find that a vector error correction model augmented with firm survey-based variables accounting for business confidence, demand uncertainty and financing constraints generally outperforms the autoregressive benchmark and a series of competing multivariate time-series models in various, alternative, evaluation samples that take into account the impact of both the global financial crisis and the sovereign debt crisis on forecast accuracy.

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“…Each sector can contribute to the investment-to-output ratio dynamics in two ways: by changes in investment within the sector (within-effect) and by changes in the share of the sector in aggregate output (between-effect, or structural shift). To decompose the contributions into within-and between-effects, we follow the shift-share methodology (see Busetti andothers 2016 andEC 2017, among others).…”

Section: With a Similar Downward Shift Observed In Individual Secmentioning

confidence: 99%