Empirical representation of firms' employment decisions by an (S,s) rule

We analyse the conditions under which an (S,s) rule may be derived and compare these with alternative rules. We consider the case of labour demand with fixed adjustment costs. The (S,s) rule implies a specific ordering of choices: downward adjustment, non-adjustment and upward adjustment with the decision of inaction lying crucially in the middle. We may model firms' decisions as an (S,s) rule only if it is possible to characterise unobserved heterogeneity as an exact negative relation between the choice-specific error terms. Assuming that these are normally distributed, the particular ordering of choices implied by the (S,s) rule may be estimated by an ordered probit. We test the (S,s) rule nesting the ordered probit within a multinomial model with correlated error terms. We find that restriction of univariate error distribution is rejected by the data

What do we gain by being discrete? An introduction to the econometrics of discrete decision processes

In this paper we analyse methods which allow us to estimate and identify the sources of censoring in dynamic models. We explicitly take into account the existence of corner solutions by considering a discrete-time-discrete-choice dynamic structural model. The availability of microeconomic datasets allows us to focus on decisions at the individual level and directly exploit the information contained in the corner solutions. We show how a discrete decision process (DDP) represents a natural framework within which to analyse agents' behaviour when optimal inaction generates censoring in observed decisions. A discrete decision process is characterised by a control variable which only takes a finite number of values. Some problems are naturally discrete, such as the optimal engine replacement or job the search problem in which the individual decides whether or not to accept a job offer. Other problems may be described very efficiently by a discrete decision problem. This is clear in the case of fixed costs of adjusting inputs which imply the discrete decision of whether or not to vary the production factor

Dynamic labour demand with lumpy and kinked adjustment costs

We analyse the dynamics of firms' employment decisions which underlie lumpy and kinked adjustment costs. We consider a dynamic structural model in which, in each period, firms face a choice of whether to vary the labour input or to postpone the adjustment to the future. By exploiting the first order condition for optimality, we derive a semi-reduced form in which firms' intertemporal employment are defined by a standard static marginal productivity condition augmented by a forward-looking term. In this way we obtain a marginal productivity equilibrium relation which takes into account the future alternatives of adjustment or non-adjustment that firms face as the result of the presence of fixed and linear adjustment costs. Linear costs amount to 35% of average labour costs and fixed costs are estimated to be about 3.65 times average unit labour costs

Non-convexities in the Adjustment of Different Capital Inputs: A Firm-level Investigation

Recent developments in investment research have highlighted the importance of non-convexities and irreversibilities in the firms’ adjustment of quasi-fixed inputs. However, aggregation across capital goods may smooth out the discontinuities associated with the adjustment of individual assets. The lack of suitable data is one of the reasons why empirical work has strongly relied on the assumption of capital homogeneity. In this paper we exploit a new data set of 1539 Italian firms which allows us to disaggregate capital and consider separately purchases and sales of assets. We disaggregate between equipment and structures and construct measures of fundamental Q to capture investment opportunities associated with each asset. To uncover the pattern of dynamic adjustment we use non-parametric techniques to relate each individual investment to own fundamental Q.Investment, heterogenous capital, non-convexities, fundamental Q, panel data

Investment and Time to Plan: A Comparison of Structures vs. Equipment in a Panel of Italian Firms

“Time to build” models of investment expenditures play an important role in many traditional and modern theories of the business cycle, especially for explaining the dynamic propagation of shocks. We estimate the structural parameters of a time-to-build model using firm-level investment data on equipment and structures. For equipment expenditures, we find no evidence of time-to-build effects beyond one period. For structures, by contrast, there is clear evidence of time to build in the range of 2-3 years. The contrast between equipment and structures is intuitively reasonable and consistent with previous results. The estimates for structures also indicate that initial-period expenditures are low, and increase as projects near completion. These results provide empirical support for including “time to plan” effects for investment in structures. More generally, these results suggest a potential source of specification error for Q models of investment and production-based asset pricing models that ignore the time required to plan, build and install new capital.Investment expenditures, Panel data, Italian firms, Time to build

La collezione Calderara Pino

La collezione Calderara Pin

Removal of acetaldehyde from saliva by mucoadhesive formulations containing cysteine and chlorhexidine diacetate: a possible approach to the prevention of oral cavity alcohol-related cancer

The aim of our work has been to develop buccoadhesive formulations (tablets) containing both L-cysteine and chlorhexidine diacetate and to verify their ability to reduce oral acetaldehyde produced after alcoholic drinks consumption

Investment and Time to Plan: A Comparison of Structures vs. Equipment in a Panel of Italian Firms

Time to build models of investment expenditures play an important role in many traditional and modern theories of the business cycle, especially for explaining the dynamic propagation of shocks. We estimate the structural parameters of a time-to-build model using firm-level investment data on equipment and structures. For equipment expenditures, we find no evidence of time-to-build effects beyond one period. For structures, by contrast, there is clear evidence of time to build in the range of 2-3 years. The contrast between equipment and structures is intuitively reasonable and consistent with previous results. The estimates for structures also indicate that initial-period expenditures are low, and increase as projects near completion. These results provide empirical support for including time to plan effects for investment in structures. More generally, these results suggest a potential source of specification error for Q models of investment and production-based asset pricing models that ignore the time required to plan, build and install new capital