Gruppo CNR di economia internazionale Torino, Febbraio 2007 “The decline in Italian productivity: new econometric evidence” by S. Fachin & A. Gavosto Discussion by Carlo Altomonte (Università Bocconi)

The main contributions of the paper The paper address the source of the productivity slowdown in Italy through panel cointegration methods Three relevant contributions: 1.The use of panel cointegration methods allows to relax the assumption of CRS and perfect competition in factor markets, typical of the growth accounting approach 2.The use of a flexible form for the production function (Kmenta linearisation of the CES) allows to discuss the relevance of the labour/capital elasticity of substitution 3.The results are comparable with other, more traditional studies, as in their Figure 6, and thus more prone to a generalisation

1. Panel cointegration methods - I The authors start from the labour productivity equation They assume that technical progress p it can be decomposed as: p it =  t +  i and thus they can write: where the industry-specific technological progress is incorporated in  ’ But if the shock is not entirely separable (i.e. not every industry follows the same growth rate of technological progress over time), then the term becomes: p it =  t +  i +  it and thus  it in (3) should incorporate the term  it, leading to an error term potentially correlated with the input choices (simultaneity bias).

1. Panel cointegration methods - II They implicitly discuss this issue by estimating Here they assume that the term φ in (4) captures not only the trend in the technical progress but also the effect of any other random shock. As a result, they have to impose an assumption on the distribution of these shocks: they are (log) additive and generated by a symmetric distribution, so that  s = E(φ s | t=s) and φ can be estimated non-parametrically However, again they implicitly assume that the shocks are entirely separable across industries, which in the panel structure is not likely  openly discuss the issue of simultaneity bias  elaborate more on the actual restrictiveness of the assumptions on , and the potential correlation of shocks across time/industries (e.g. by discussing the characteristics of the VCov Matrix of their panel estimation)

2. Capital-labour elasticity of substitution Their equation for log labour productivity (2) does not impose a unitary K/L elasticity of substitution  as in Cobb-Douglas And yet, they cannot reject but for one industry (Rubber) the null of , although the confidence intervals of the point estimates are rather large This result mimics Balistreri, McDaniel and Wang (2003) for the US economy ( , 28 industries), but the authors are still skeptical on  Large confidence intervals are really caused by different underlying elasticity of substitution, or by the model design ? One implication of aggregate equations like (2) is that, with , any redistribution of inputs across plants (i.e. any linear combination of  ’x) results in the same aggregate output, which is not true: if firms are heterogeneous in productivity levels and new inputs flow to the most productive firms, different linear combinations would yield different results => “noise” in the estimates Here the assumption on the elasticity of substitution is relaxed, but a potential bias from the aggregation problem persists => large confidence intervals could be due to the aggregation bias, while  holds.

If  (i.e. a Cobb-Douglas specification) cannot be excluded, what do we learn from the Kmenta specification ? Would the results dramatically change using a Cobb-Douglas ? And a translog ? The broad dynamics of TFP recovered through the panel cointegration are similar to standard growth accounting analyses, but the order of magnitude is rather different: more than double of the growth rate in the period and almost a third in : Is this due to the fact that TFP estimates in this paper are valid only for the long-run ? If this is the case, how big is the error incurred in trying to use these estimates in order to explain the business cycle ? If TFP estimates are ok also for the short run, how to explain the much higher volatility of the business cycle implied by these TFP figures ? 3. Relevance of the results