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Empirical Economics

Erschienen in:

19.01.2017

Short- and long-run heterogeneous investment dynamics

verfasst von: Fabio Bacchini, Maria Elena Bontempi, Roberto Golinelli, Cecilia Jona-Lasinio

Erschienen in: Empirical Economics | Ausgabe 2/2018

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Abstract

In this paper, we model the dynamics of business investment taking into account asset-specific characteristics potentially affecting the reactivity of aggregate and disaggregate capital accumulation over the business cycle. We estimate Information and Communication Technologies (ICTs) and traditional investment (non-ICT) determinants within a Vector Error Correction Model testing the assumptions of the flexible accelerator and neoclassical model as well as the role of financial constraints and uncertainty. We evaluate our model on Italian data over the period 1980–2012, and we check our results also with Spanish and UK data. Our findings support the assumption that capital is heterogeneous since short- and long-run determinants are significantly different across the assets. Traditional assets experience stock adjustment costs while ICT investment incurs flow adjustment cost. In the short run, liquidity is a key determinant of investment independently of the asset type. In the long run, uncertainty significantly affects ICT. Finally, the results of the counterfactual exercises support the idea that ICT is a key policy variable to foster economic growth.

Vorheriger Artikel The long-run effect of foreign direct investment on total factor productivity in developing countries: a panel cointegration analysis

Nächster Artikel Does human capital or physical capital constrain output in Japanese prefectures?

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Caivano et al. (2010) showed that in Italy financial factors may explain an investment decline of 9% points over the period 2008–2010. See also Gaiotti (2013).

Italian GDP accounts for 17 per cent of the Euro Area GDP.

Garicano and Steinwender (2013) looking at Spanish firms have shown the effects of the credit constraints on the composition of investments towards investments that take shorter time to yield output .

We keep UK and Spain as two reference countries for our comparative analysis since besides being fast and slow ICT adopters, they experienced different but superior productivity performances compared to Italy.

See Pessoa and Reenen (2014) for a deep analysis of the UK productivity puzzle.

Several empirical studies have been focused on traditional assets, such as machinery and equipment, to observe their relation with the business cycle (see, e.g. Lee and Rabanal 2010). However, to our knowledge, the empirical evidence about asset-specific investment determinants at macrolevel is scant. Very recently, Ketteni et al. (2015) investigate the impact of capital heterogeneity on productivity growth distinguishing between FDI, ICT, and non-ICT capital.

Among the macropapers, de Bondt and Diron (2008) find that financing constraints are relevant for aggregate investment, and Parigi and Siviero (2001) reveal the importance of business confidence (interpreted as a measure of uncertainty) to determine investment decisions.

Under the assumption of separate cointegration (Granger and Haldrup 1997), the estimation can be performed by three parsimonious subsystems (for j = me, nres and ict) which can be modelled by asset in analogy with the aggregate case (i.e. \(j = { agg}\)).

Once Eq (8) is solved for capital stocks, we can obtain the corresponding level of business investments adopting the perpetual inventory accounting identity: \(I_t^j \equiv \Delta K_t^j +\delta ^{j}K_{t-1}^j \) , where investments are defined as the difference between the changes in the levels of capital stock and the amount of past capital depreciation (\(\delta ^{j}\) is the depreciation rate specific to asset j).

The perpetual inventory method relating investment and capital stock, \(I_t^j /K_{t-1}^j =\Delta K_t^j /K_{t-1}^j +\delta _t \), implies that the investment ratios in Table 1, are linked to the growth of the capital stocks. Unreported unit root tests show that log-levels of capital stocks are I(1), as their first differences always reject the null of unit roots.

For software and R&D, this finding is consistent with Bloom (2007).

The data congruence of VAR models has been assessed through a number of residuals’ misspecification tests, which hardly ever reject the null of vector white noise errors. In the few cases of failure of the heteroscedasticity and/or the normality tests, the inclusion of one/two impulse dummies in the deterministic components prevents such rejections without qualitative changes in the results reported here without such dummies.

Columns 1 to 6 show the remarkable similarity of test results and parameter estimates in VAR5 and VAR3 models. In the trace tests, the cointegration rank is always one at least at 5%, and the weak exogeneity is never 1% significant.

In fact, the last two rows of Table 2 show that the ratio between desired and actual capital stock is relatively higher for non-residential building than for machinery and equipment.

This interpretation is also supported by opposite-signed and/or quite imprecise long-run \(\phi _{1}\) and \({\phi }_{2}\) estimates in the VAR5 where probably wrong restrictions to identify the long-run capital stock equation are imposed.

The slight difference between the long-run elasticities simulated in Table 5 and the corresponding estimates in Tables 2 and 4 is due to an approximation effect. In fact, the long-run estimates in Tables 2 and 4 are measured as ratios between changes in logs, while in Table 5 they are ratios between per cent deviations, i.e. \(\frac{\Delta \log A}{\Delta \log B}\approx \frac{\frac{\Delta A}{A}}{\frac{\Delta B}{B}}\).

Regarding the specific measurements in the contest of countries’ comparison, see the details in “Appendix 1”.

Note that these new proxies are necessary to be able to investigate the effect of credit conditions in Spain and the UK, for which a long time series of the degree of financial constraints indicator is not available.

We extend our sample period to 2013 to look more deeply to the effects of the financial crises on Italian investment dynamics.

MeMo-It is an annual model composed by 53 stochastic equations and 78 identities, and represents a New Keynesian economic system including households, firms, public administration, and a foreign sector. MeMo-It is structured into five main blocks such as supply side, labour market, demand side, prices, and government. For more details, see Bacchini et al. (2013) and the summary in “Appendix 3”. Of course, the three disaggregate investment equations above replace the pre-existing (aggregated) one.

We selected three Euro area countries as a benchmark to look as much as possible to countries with a comparable structure of the financial markets.

In particular, in the counterfactual the liquidity indicator is assumed to ignore the deep financing trough of 2012, by shifting back the observations for 2013 and 2014, as if the hole of 2012 never happened.

The projection for 2013 is still negative (\(-1.9\%\)); however, in Q4 2013, for the first time since Q2 2011, the growth rate has not been negative.

This section draws substantially from Bacchini et al. (2013).

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Titel: Short- and long-run heterogeneous investment dynamics
verfasst von: Fabio Bacchini
Maria Elena Bontempi
Roberto Golinelli
Cecilia Jona-Lasinio
Publikationsdatum: 19.01.2017
Verlag: Springer Berlin Heidelberg
Erschienen in: Empirical Economics / Ausgabe 2/2018
Print ISSN: 0377-7332
Elektronische ISSN: 1435-8921
DOI: https://doi.org/10.1007/s00181-016-1211-4

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