1. 1 Why Demand Uncertainty Curbs Investment: Evidence from a Panel of Italian Manufacturing Firms Maria Elena Bontempi (University of Ferrara) Roberto Golinelli (University of Bologna) Giuseppe Parigi (Bank of Italy) Workshop: MODELLING AND INFERENCE IN MICROECONOMICS Bologna, March 5, 2007

2. 2 The theoretical literature For a risk-averse firm with CRS technology PERFECT COMPETITION The effect of uncertainty on investment decisions is negative LABOUR FLEXIBILITY IMPERFECT COMPETITION + IRREVERSIBILITY Sign and dimension of the effect depend on the relevance of different hypotheses positive (or zero)

3. 3 The empirical literature In general empirical studies concentrate on the sign of the investment-uncertainty relationship Guiso e Parigi (1999) is the first paper where the role of irreversibility and of the degree of competition is analysed Main limitation is due to a lack of relevant data The result is that uncertainty has a negative effect on investment decisions The implications of adopting different theoretical frameworks are very rarely analysed Data about uncertainty, expected demand, investment plans and other indicators are needed...

4. 4 Aim of the paper To analyse the investment-uncertainty relationship in the light of alternative hypotheses, by using a panel of Italian manufacturing firms. The availability of a panel allows to:  reduce the risk of biased estimates due to the omission of unobservable variables varying among firms and almost fixed over time (i.e. risk aversion); varying over time and almost fixed among firms (i.e. macroeconomic shocks)  perform a deeper analysis of the effects of the hypotheses on the degree of irreversibility, of firm’s market power and of the flexibility of labour input  assess the time dynamics of the investment-uncertainty relationship and the stability of parameters in different subsamples

5. 5 Data Bank of Italy’s Survey on Investment of Manufacturing firms (SIM) is the main data source: an unbalanced panel of about 17,000 observations over 1996-2004. We use only the subsample of firms with more than 50 employees, replying to the question about demand uncertainty (about 8,000 observations). The source of data about capital stock; cash-flow, price-cost margins is Company Accounting Data Service (CADS, Centrale dei Bilanci). The merge of SIM-CADS firms leads to a loss of observations: our basic sample (unbalanced sample) has about 7,500 observations.

6. 6 The theoretical model Main problem: a closed-form solution of the general model for investments with uncertainty does not exist. A solution would be to impose restrictions about: adjustment costs specification; returns to scale; demand elasticity to prices. BUT this is exactly what we want to examine! Idea (Guiso-Parigi, 1999): if investments are in some way irreversible, the level of demand that drives investment is related to uncertainty, hence: investments elasticity to demand is negatively related to uncertainty.

7. 7 The empirical model t I it+1 investment plans in year t for t+1 I it realised investments in t t Y it+1 demand predicted in t for t+1 u( t Y it+1 ) uncertainty on future demand, obtained by using the expected growth rate of demand ( t g it+1 ) reported by SIM respondents: u( t g it+1 )Y it = ( t g max it+1 – t g min it+1 )Y it a i, λ t panel fixed effects by firm (i) and by year (t) The uncertainty effect depends on the sign of  2

8. 8 Cross-section estimates  1 estimate is always significantly positive; while  2 estimate is negative. Both effects decrease over time:  2 is not significant in the last two years of the sample. Preliminary estimates over repeated cross-sections: one estimation set for each year by imposing a i = a and λ t = 0

9. 9 Main finding with cross-sections If we had had data only for one year in the first half of the sample, before 2000, we would had estimated a negatively significant uncertainty effect, as in Guiso- Parigi; BUT … … if we had had data only for one year in the second half of the sample, after 2002, we would had estimated a close to zero (and not significant) uncertainty effect. In order to interpret such conflicting results, the PANEL approach may be useful

10. 10 Panel and sub-samples The pooling of cross-sections may be assumed: (1) for the whole sample (2) in sub-samples of firms selected on the basis of: (2a) high/low labour flexibility (the turnover is above-below the sector median in t) (2b) high/low market power (the PCM it is above-below the sector median in t) (2c) high/low reversibility, the indicator REV i =1 if operate at least two times in the second-hand market

11. 11 Panel estimates 0.10130.1827 0.14010.2443 4.642.35 11411000 52892353 0.0001 (0.0390) 0.0615 (0.1139) -0.0206 (0.0148) -0.0368 (0.0143) 0.0181 (0.0034) 0.0452 (0.0307) (3)(2) highlow Reversibility 0.11280.1900 0.13960.1974 2.682.54 13221400 35493555 -0.0898 (0.0911) 0.0591 (0.0822) -0.0493 (0.0215) -0.0157 (0.0085) 0.0226 (0.0060) 0.0422 (0.0256) (5)(4) highlow Market power 0.19330.1089 0.19120.1300 2.422.62 15601477 37753867 0.1087 (0.0486) 0.0199 (0.0426) -0.0151 (0.0105) -0.0673 (0.0241) 0.0445 (0.0251) 0.0188 (0.0076) (7)(6) highlow Labour flexibility

12. 12 Labour flexibility and PCM interaction

13. 13 Time-constancy of panel estimates The panel estimates of the three parameters of interest are constant at 5% in alternative sample splits in 1999, 2000, 2001, 2002 e 2003; using the sup-Wald statistic of Andrews (1993). Behind this overall constancy there are systematic and significant shifts of the uncertainty effect. Plot of the  2t deterministic shifts:

14. 14 Theoretical explanation Risk aversion reduction Empirical assessment Not identified (nested in the individual effects) Increase in capital reversibility Our indicators suggest a reduction Decrease in firms’ market power Other: labour flexibility and openness … Determinants of time-fluctuations

15. 15 Modelling uncertainty evolution The time evolution of  2 parameter can be modelled by a linear function with : where X it is a vector of variables (i.e. PCM) interacting, through  2 parameter, with the uncertainty effects on planned investments. The general model is:

16. 16 Significance of the interactions

17. 17 PCM interaction with… Openness Turnover yearPCM

18. 18 Main findings - 1 Overall, the finding about a negative effect of the uncertainty on investments is confirmed by our results. However, such effect weakens with less market power, more competitiveness and more labour flexibility. The economic environment in Italy has greatly changed:  Euro adoption  Stronger competition of new industrialised countries  More flexible labour market Together, these changes lead to a higher elasticity of investment plans to expected demand

19. 19 Main findings - 2 ΔGDP I/GDP the propensity to invest (non residential investments on GDP) … In fact, since the beginning of this century, it seems that the minimum level of expected demand necessary to trigger investment has lowered. At macroeconomic level … … during the last cycle did not fall, as it did in the previous two recessions.

20. 20 THANK YOU !