Temi di discussione del Servizio Studi

Employment, innovation and productivity: Evidence from Italian microdata by Bronwyn H. Hall, Francesca Lotti and Jacques Mairesse

Number 622 - April 2007

The purpose of the Temi di discussione series is to promote the circulation of working papers prepared within the Bank of Italy or presented in Bank seminars by outside economists with the aim of stimulating comments and suggestions. The views expressed in the articles are those of the authors and do not involve the responsibility of the Bank.

Editorial Board: D OMENICO J. M ARCHETTI , M ARCELLO B OFONDI , M ICHELE C AIVANO , S TEFANO I EZZI , ANDREA LAMORGESE, FRANCESCA LOTTI, MARCELLO PERICOLI, MASSIMO SBRACIA, ALESSANDRO SECCHI, PIETRO TOMMASINO. Editorial Assistants: ROBERTO MARANO, ALESSANDRA PICCININI.

EMPLOYMENT, INNOVATION, AND PRODUCTIVITY: EVIDENCE FROM ITALIAN MICRODATA. by Bronwyn H. Hall*, Francesca Lotti† and Jacques Mairesse‡ Abstract Italian manufacturing firms have been losing ground with respect to many of their European competitors. This paper presents some empirical evidence on the effects of innovation on employment and productivity, with the aim of investigating one of the possible causes of that poor performance. We use firm level data from the last three surveys on Italian manufacturing firms conducted by Mediocredito-Capitalia, covering the period 1995-2003. Using a modified version of the model proposed by Harrison, Jaumandreu, Mairesse and Peters (2005) - which separates employment growth rates into those associated with old and new products - we provide robust evidence that there is no employment displacement effect stemming from process innovation. The sources of employment growth during the period are equally split between the net contribution of product innovation and the net contribution from sales growth of old products. However, the contribution of product innovation is somewhat lower than in the four European countries considered by Harrison et al. JEL Classification: L60, O31, O33. Keywords: innovation, employment, productivity, Italy. Contents 1. Introduction .......................................................................................................................... 3 2. Theoretical and empirical underpinnings .............................................................................. 4 3. A model of innovation and employment .............................................................................. 6 3.1 The theoretical framework ........................................................................................... 6 3.2 Estimation strategy ........................................................................................................ 7 3.3 Measurement issues ...................................................................................................... 9 4. The data ............................................................................................................................... 11 5. Results ................................................................................................................................ 12 5.1 A rough comparison with France, Germany, Spain and the U.K............................... 15 5.2 A simple (but effective) employment growth decomposition..................................... 16 6. Conclusions ........................................................................................................................ 18 References ............................................................................................................................... 19 Tables ..................................................................................................................................... 21 Appendix ................................................................................................................................. 32

*

University of California at Berkeley and University of Maastricht. Bank of Italy, Economic Research Department. ‡ INSEE-CREST and UNU-MERIT. †

Introduction∗

1

Italian manufacturing firms have been losing ground with respect to many of their European competitors. This weak performance is not entirely attributable to the preponderance of traditional sectors, more exposed to competition from emerging countries: not only do the advanced sectors account for smaller shares of employment than in other countries, but they also display a significant negative productivity growth differential (see Lotti and Schivardi, 2005 and IMF, 2006). Also, many indicators of innovation activity, both in terms of input and output, signal that the Italian economy is lagging behind. Can this lower innovative activity account for slower productivity growth in Italian manufacturing? Or are other factors, such as labor market rigidity, at work? This paper presents some empirical evidence on the effects of innovation on employment growth and therefore on firms’ productivity, with the goal of contributing to our understanding the roots of such poor performance. We use a simple theoretical framework pioneered by Harrison et al. (2005) to disentangle the effects of innovation on employment and productivity growth applied to a panel of nearly 9,500 Italian firms observed over a nine year period (1995-2003). These data come from the last three surveys of Italian manufacturing firms conducted by Mediocredito-Capitalia (hereafter MCC), covering the period 1995-2003. These surveys contain balance sheets items and, more importantly, qualitative information on firm characteristics, with a strong focus on innovation activities. Using instrumental variable regressions to correct for the endogeneity of our innovation measures, we provide robust evidence that there is no employment displacement effect stemming from process innovation and that product innovation contributes about half the employment growth in these firms during the period. Sales growth of old products accounts for the other half of employment growth, although on average, old products do experience some efficiency gain in production so the growth comes from sales expansion. In the next section of the paper we discuss the prior empirical evidence on innovation and employment growth. We then present the model we use for estimation, and discuss ∗

We would like to thank Mediocredito-Capitalia research department for having kindly supplied firm level data for this project. We thank also M. Sbracia, M. Vivarelli, F. Zollino, and the participants at the Schumpeter Society Meetings (Nice, June 2006), at the Bank of Italy (Rome, November 2006), at Sant’Anna School of Advanced Studies (Pisa, February 2007) for useful comments. B. H. Hall gratefully acknowledges financial support from the Ente Luigi Einaudi. The views expressed by F.Lotti do not necessarily reflect those of the Bank of Italy.

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the measurement issues raised by the data that are available to us. This is followed by a presentation of the data and the results of estimating the model on our sample of firms. In the final sections of the paper we compare our results to those of Harrison et al. (2005) for France, Germany, Spain, and the U.K. and draw some conclusions.

2

Theoretical and empirical underpinnings

The debate about the impact of technological change on employment is a rather old one (Say, 1803; 1964 edition); since that time, scholars have been trying to disentangle the displacement and compensation effects of innovation both from a theoretical and an empirical point of view, often pointing out the different implications of process and product innovation. The introduction of a new or significantly improved product increases demand, and therefore an increase in the employment levels of innovating firms. Nevertheless, the innovating firm, enjoying temporary market power, may set profit-maximizing prices and reduce output enough so that the net effect after substitution to the new good is negative for the firm’s output. On the other hand, even though process innovation is typically labor-saving, its effect on employment is not straightforward. If the same amount of output can be made with fewer workers, the firm can share this efficiency gain with the consumers via lower prices, thereby increasing demand. Depending on market structure, the demand elasticity, and the elasticity of substitution between capital and labor, compensation mechanisms can counterbalance the labor saving effect of process innovation (for a detailed survey on the compensation mechanisms, see Spiezia and Vivarelli, 2002). From an empirical perspective and because firms are often involved in product and process innovation together, the identification of displacement and compensation effects becomes even more difficult. Nevertheless, the empirical literature on the effects of innovation on employment has been increasing since the 1990s, when micro-economic data on individual firms began to be widely available and econometric techniques which are robust to simultaneity and endogeneity problems were developed.1 While there is a widespread consensus on the positive impact of product innovation on employment at the firm-level, the evidence about process innovation is less clearcut. Using cross-sectional data for Germany, Zimmermann (1991) finds that technological 1

See Van Reenen (1997), Chennells and Van Reenen (2002), Hall and Kramarz (1998) and Lachenmaier and Rottmann (2006).

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progress was responsible for the fall of employment during the 1980s, while Entorf and Pohlmeier (1990) find no significant effects. Thanks to the availability of surveys with a time dimension, Brower et al. (1993) find a positive effect of product innovation on employment growth for the Netherlands in the 1980s, but a negative one for total R&D. Using the Community Innovation Survey (CIS) data for Germany, Peters (2004) finds a significantly positive impact of product innovation on employment, and a negative one for process innovation. In contrast, Blechinger et al. (1998) support the evidence of a positive relationship between both product and process innovation and employment growth in the Netherlands and in Germany. Blanchflower and Burgess (1998) and Doms et al. (1995) find a positive impact of process innovation on employment growth in the U.K. and in Australia, and in the U.S., respectively, whereas the study by Klette and Forre (1998) does not find a clear relation between innovation and employment in Norway. The paper by Harrison et al. (2005), which is closest to our work and serves as a model for it, uses CIS data for France, Germany, U.K., and Spain. These authors find that although process innovation displaces employment, compensation effects from product innovation seem to dominate, albeit with some differences across countries.2 Greenan and Guellec (2000), also combining firm-level panel data with innovation surveys, find that innovating firms (and industries) have created more jobs than non-innovating ones. Piva and Vivarelli (2005), combining different surveys by Mediocredito-Capitalia from 1992-1997, build a balanced panel of 575 Italian Manufacturing firms and find a small but significant positive relation between innovative investment and employment. However, they did not use the usual classification of innovation in product and process, but instead used investment aimed at introducing new innovative equipment, which corresponds to embodied technological change and is somewhat closer to process innovation. Summarizing these results, most studies have found positive effects of product innovation on employment, but the evidence on process innovation is mixed. For European firms, process innovation usually has a small negative or no effect on employment, although for non-European countries (the U.S. and Australia) it is more likely to be positive. However, the overall effect of innovation on employment is generally positive in these studies. 2

A comparison of our results with those in Harrison et al. is presented in Section 5.

5

3

A model of innovation and employment

3.1

The theoretical framework

The model presented here is the one described in the paper by Harrison et al. (2005, henceforth HJMP 2005), which is specifically tailored for the type of innovation data available to us. In this framework, a firm produces two kinds of products at time t: old or only marginally modified products (“old products”, denoted Y1t ) and new or significantly improved products (“new products”, Y2t ). Firms are observed for two periods, t = 1 and t = 2 and innovation occurs between the two periods (if it occurs at all). Therefore by definition, in the first period, only old products are available (Y11 ), so that Y21 = 0. We assume that each type of product is made with an identical separable production technology that has constant returns to scale in capital, labor and intermediate inputs. Each production technology has an associated efficiency parameter that can change between the two periods. New products can be made with higher or lower efficiency with respect to old products, and the firm can affect the efficiency of its productions over time through investments in process innovation. The production function for a product of type i at time t is the following: Yit = θit F (Kit , Lit , Mit ) ,

i = 1, 2; t = 1, 2

(1)

where θ represents efficiency, K, L and M are capital, labor and materials, respectively.3 The firm’s cost function at time t can be written as: C (w1t , w2t , Y1t , Y2t , θ1t , θ2t ) = c (w1t )

Y1t Y2t + c (w2t ) + FC θ1t θ2t

(2)

where c (w) is the marginal cost as a function of the factors price vector w, and F C represents the fixed costs. According to Shephard’s Lemma: Lit = cL (wit )

Yit θit

(3)

where cL (wit ) represents the derivative of the marginal cost with respect to the wage. The employment growth from period t = 1 to period t = 2 can be decomposed in two 3

We observe neither capital nor materials in our data so these factors are omitted in the rest of the paper and our measurement concerns labor productivity only.

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terms: the contribution to growth from the old products and the contribution from the new products.4 The decomposition looks as follows: ∆L L12 − L11 L22 − L21 L12 − L11 L22 = + = + L L11 L11 L11 L11

(4)

because there are no new products at time t = 1 and L21 = 0.5 We also assume that the derivative of the marginal cost with respect to wage does not change over time, i.e. cL (w11 ) = cL (w12 ) = cL (w1 ). Using the results of equation (3), the growth rate in equation (4) can be approximated as: ∆L '− L

µ

θ12 − θ11 θ11

¶

µ +

Y12 − Y11 Y11

¶ +

cL (w2 ) θ11 Y22 cL (w1 ) θ22 Y11

(5)

According to equation (5), employment growth is determined by three terms. The first is the rate of change in efficiency in the production of old products: it is expected to be larger for those firms that introduce process innovations related to old product production. The second term is the growth of old product production (i.e. a compensation effect after adjustment), while the third is the labor increase from expansion in production due to the introduction of new products. Assuming that the derivative of marginal cost with respect to the wage is equal for old and new products, that is, that cL (w1 ) = cL (w2 ), then the effect of product innovation on employment growth depends on the relative efficiency of the production processes of old and new products. If new products are made more efficiently than old ones, this ratio is less than unity, and employment does not grow at the same pace as the output growth accounted for by new products.

3.2

Estimation strategy

Equation (5) implies the following estimation equation: l = α0 + y1 + βy2 + u 4

(6)

As we show later, this decomposition corresponds to the share-weighted sum of growth rates when both products exist in both periods, but not when the new products only exist in the second period. 5 In the current setting, old products can either be produced more efficiently in the second period or stay the same, but their production is never ceased.

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where l is the growth rate of employment ³ ´between t = 1 and t = 2, y1 is the contribution 11 of old products to output growth Y12Y−Y , and y2 is the contribution of new products to 11 ³ ´ output growth YY22 . u is a random disturbance expected to have zero mean conditional 11 to a suitable set of instruments. In this specification, the parameter α0 represents the negative of the average efficiency growth in the production of the old product (i.e., labor productivity growth), while the parameter β measures the marginal cost in efficiency units of producing new products relative to that for old products. If β is equal to unity, efficiency in the production of old products at time t = 1 and new products at time t = 2 is the same; if β < 1, new products are produced more efficiently than old products in the previous period. Because process innovation can affect changes in the efficiency of both old and new products, equation (6) can be easily modified to take this feature into account as follows: l = (α0 + α1 d1 ) + y1 + (β0 + β1 d2 ) y2 + u

(7)

where d1 and d2 are dummy variables which take value one if the firm introduced process innovation related to the production of old and new products respectively. Because it is impossible to know from the survey what share of its process innovation the firm devotes to new versus old products, in the empirical exercise we will experiment with different alternatives (d1 = 1, d2 = 0 and d1 = 0, d2 = 1). Simply by rearranging equation (7), it is possible to obtain the usual labor productivity equation as: y1 + y2 − l = −α0 − α1 d1 + (1 − β0 ) y2 − β1 d2 y2 − u

(8)

which is helpful in interpreting the magnitude and the sign of the estimated coefficients (the dependent variable is the growth of real output per worker). Despite its simplicity, equation (6) can capture two effects of innovation. First, under the assumption that y2 is observable, it identifies the gross effect of product innovation on employment. Second, if process innovation related to old products is observed, it allows us to identify directly the productivity (or displacement) effect of process innovation on employment. It is worth noting that the variable y1 encompasses three different effects: an “autonomous” variation in the demand of old products, due to exogenous market con-

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ditions; a “compensation” effect induced by a price variation following process innovation, and a “substitution” effect stemming from the introduction of the new products. Unfortunately, without additional data on the demand side, it is impossible to disentangle these effects.

3.3

Measurement issues

In order to estimate equation (6), we must approximate real production (Y1 and Y2 ) with nominal sales, and this creates a measurement problem. Nominal sales encompass the effects of price changes, but real production as well is affected by price movements via demand adjustment mechanisms. Moreover, old and new products’ prices do not necessarily have the same patterns of change and, more importantly, they are unobservable in the data available to us. In this section of the paper we show that using nominal sales growth instead of real output growth in our equation implies that the coefficient of growth due to new products combines two effects: the relative efficiency of producing the new and old products and their relative price or quality differences. To show this, define the nominal growth rate of sales of old products g1 and the rate of increase of their prices π1 as follows: g1 =

P12 Y12 − P11 Y11 P11 Y11

π1 =

P12 − P11 P11

(9)

Then we can approximate y1 as (g1 − π1 ). Also define the nominal growth rate of sales of new products g2 and the difference in the prices of the new products with respect to the old products π2 as follows: g2 =

P22 Y22 P11 Y11

These definitions imply that y2 =

π2 =

g2 . (1+π2 )

P22 − P11 P11

(10)

Substituting g1 and g2 for y1 and y2 , which

are not observable, equation (6) becomes the following: l − (g1 − π1 ) = α0 + β

g2 +u 1 + π2

(11)

Unfortunately equation (11) is still not suitable for estimation, because neither π1 nor π2 are directly observed. What is observed are sectoral-level prices in two periods, where

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the price in the second period is in fact some unknown weighted average of old and new product prices. If we express these unobserved prices in terms of the observed prices, so that P21 ≡ (1 + ϕ1 ) P2 and P22 ≡ (1 + ϕ2 ) P2 , then we can show that the observed growth of prices π is related to π1 and π2 as follows:

π1 = π + ϕ1 (1 + π) π2 = π + ϕ2 (1 + π)

(12)

where ϕ1 and ϕ2 are the percent differences between the “true” price of the old and new products and the observed price obtained from the statistical agency. The observed price is some weighted combination of the two prices that probably does not include adjustments for all the quality change between the periods, since those indexes are not hedonic. Note also that in principle ϕ1 and ϕ2 vary across firms because the price deflators are available only at the sectoral level. Replacing π1 and π2 by π , the estimating equation becomes: l − (g1 − π) = α0 +

β g2 + [u − ϕ1 (1 + π)] 1 + ϕ2 (1 + π)

(13)

This equation expresses the growth in measured real labor productivity as a function of the growth in real new products, measured using the observed deflator. Compared to equation (11), there are two important differences: first, the coefficient of the new product term is the ratio of β, the relative efficiency of producing new versus old products, to (1 + ϕ2 ), the ratio of the quality-adjusted price of the new products to the share-weighted price of old and new products. If there is substantial quality improvement in the new product whose cost is passed on to consumers, ϕ2 will be greater than zero and the passthrough from its sales growth to real labor productivity will be moderated relative to the case of little quality change. On the other hand, if quality improvement leads to lower “effective” prices, ϕ2 will be less than zero, and new product sales will have an enhancing effect on real labor productivity. This result is analogous to one in Griliches and Mairesse (1984) for the production function: innovation and R&D can either improve efficiency (declines in β) or increase quality (increases in ϕ2 ). Without good information on quality-adjusted prices, we cannot separate the two effects. 10

The second difference in equation (13) is in the disturbance, which now contains a term ϕ1 (1 + π). We expect this term to be quite small, because the measured prices are likely to be close to the prices of old products, both because of statistical agency inertia and because old products make up a large share of sales on average, implying a ϕ1 that is near zero. Nevertheless, the term does introduce some more endogeneity into the equation, beyond that due to the simultaneous choice of labor input and firm output. The disturbance is now also correlated with measured deflation (via π) and with the share of new products (via ϕ1 ). There is little that can be done about the latter problem other than to point out that the impact of the new product share will be very small.

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The data

The data we use come from the 7th , 8th , and 9th waves of the “Survey on Manufacturing Firms” conducted by Mediocredito Centrale (MCC). These three surveys were carried out in 1998, 2001, and 2004 using questionnaires administered to a representative sample of Italian manufacturing firms. Each survey covered the three years immediately prior (19951997, 1998-2000, 2001-2003) and although the survey questionnaires were not identical in all three of the surveys, they were very similar. All firms with number of employees above 500 were included, whereas smaller firms were selected using sampling stratified by geographical area, industry, and firm size. We merged the data from the three surveys, excluding from the sample firms with incomplete information or with extreme observations for the variables of interest.6 The final sample is an unbalanced panel of about 13,000 observations on 9,500 firms, of which only 608 are present in all three waves.7 Simple statistics for both the unbalanced and balanced panels are presented in Tables 1 and 2. Tables 1 shows the characteristics of the sample for the three periods separately and then pooled together, whereas Table 2 shows various subsets of the sample: R&D6

We required sales per employee between 2000 and 10 million euros, growth rates of employment and sales of old and new products between -150 per cent and 150 per cent, and R&D employment share less than 100 per cent. We also replaced R&D employment share with the R&D to sales ratio for the few observations where it was missing. 7 An earlier version of this paper presented results using the balanced panel of 466 firms. There were few differences between those results and those presented here, so we prefer to present results for as large a sample as possible.

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doing firms only, innovating firms only, and firms in high and low technology sectors.8 . The first thing to note from these tables is that the balanced panel is in fact quite similar to the unbalanced panel. Although slightly more firms do R&D and innovate, the median R&D intensity for those who do R&D is actually higher in the unbalanced panel. The median firm in our unbalanced panel has 33 employees and sales of 154,000 euros per employee. 60 per cent perform R&D during the three years of the survey and 60 per cent innovate, either in processes or products. Those that do R&D have a median R&D intensity of 2.7 per cent and 81 per cent innovate at least once in the three years. The R&D-doing and innovating firms are somewhat larger than the other firms. Finally, although substantially fewer of the firms in low technology industries do R&D (29 versus 52 per cent), only slightly fewer innovate (56 versus 67 per cent). Equation (13) requires measures of g1 and g2 , the sales growth attributed to old and new products respectively. We observe g, the growth of nominal sales, and s, the share of sales in the second period that are due to new products. Given the definitions in equations (9) and (10), these two growth rates are given by the following formulas:

g1 = (1 − s) g − s g2 = s (1 + g)

(14)

Note that these two growth rates sum to g directly, without share weighting, so that strictly speaking, they should be interpreted as the contribution to growth from the two sources, rather than as growth rates themselves.

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Results

The results of estimating the models in equations (11) and (13) are shown in Tables 4 and 5. However, before discussing these results, we begin by presenting results for a simple descriptive regression of three-year employment growth on three-year real sales growth and dummies for innovation during the same three year period (process innovation only, product innovation only, and both process and product innovation). These results are 8

We classify as “innovating” those firms that do some process and/or product innovation, as reported in the questionnaire

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presented in Table 3, first for our three time periods separately, and then pooled over the three periods, but with separate intercepts for each period. Tests of slope and dummy coefficient equality over time are generally accepted. Price changes were approximated by a set of two digit industry price deflators and industry dummies at the two digit level were included in all the regressions. As we are interested in preserving the value of the intercept, we apply a linear constraint to these dummies so that the estimated sum of the coefficients is equal to zero (Suits, 1957) and the intercept corresponds to the overall mean effect. The coefficient of real sales growth is always significant and well below unity, suggesting that for non-innovating firms, employment growth is substantially dampened relative to the growth of real sales. However, the growth rate of employment for innovating firms is much higher. With the exception of process innovation in the first period, the coefficients of all three innovation dummies are positive in all waves and increase over the three periods, although they are rarely significantly different from zero. For the pooled estimates, if sales growth increases by one per cent, non-innovators’ employment increases 0.23 per cent. However, firms that introduce new processes but not new products have an average growth of employment that is 0.69 per cent higher than non-innovative firms whereas firms that introduce new products without new processes have an average growth of employment that is 1.10 per cent higher. Those that innovate in both ways have a growth of employment 2.13 per cent higher. Clearly innovation is associated with increases in employment. However, for the reasons described in section 2, all these estimates are likely to be downward biased. Table 4 contains OLS and IV estimates of the model described in equation (11), where the left hand variable is the employment growth rate minus the growth rate of the sales due to old products (l − (g1 − π)). The instruments for the sales growth due to new products are a dummy variable for positive R&D expenditures during the last year covered by the survey, its lagged value, the R&D employment intensity during the period, and a dummy variable for whether the firm assigned high or medium importance to developing a new product as the goal of its investment. Ideally, any suitable instrument would be correlated to growth in sales due to new products (s), but not to changes of the relative price of old and new products. In order to deal with the possible concerns about the true exogeneity of the instruments chosen, we test the validity of overidentifying restrictions

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in our specification. For the IV regressions, the coefficient of the sales growth due to new products is not significantly different from one, implying that no significant differences exist between the efficiency levels of production of old and new products. The negative of the constant term gives an estimate of the average productivity growth of the old products: 4.0% from 1995 to 1997, 5.8% from 1998 to 2000, and -1.7% from 2001 to 2003. In Tables 4a and 4b we show that the productivity slowdown in the latter period occurred equally in high tech and low tech industries, but also that there was a substantial higher productivity gain in the low tech sector during the middle (1998-2000) period. We also note that unlike the sample as a whole, the high tech sector exhibits evidence either of greater efficiency in producing new products (β < 1) or quality increases that are passed on to consumers in the form of higher prices for new products (ϕ2 > 0), or both. In Table 5 we extend the specification to take into account process innovation, in the spirit of model (13). It should be kept in mind that at this stage, it is impossible to quantify how much of the process innovation is devoted to old or to new products, and for this reason, alternative specifications will be tested. In the upper panel, it is assumed that all process innovation goes to the old products, since we consider only process innovation of those firms with no product innovation. In this framework, a negative coefficient for the variable process innovation only would indicate an increase in the productivity of manufacturing the old products and a displacement of employment. The results are rather contradictory, with both negative and positive coefficients. However, they are always statistically insignificant, which implies that process innovation has no impact on productivity. In the last two panels of Table 5 we add product innovation, trying to separate two different cases: in the central panel it is assumed that all process innovation of product innovators goes to the old products, while in the last panel it is assumed to be devoted to new products. Of course, these represent two extreme cases, and the true allocation of process innovation between old and new product lies somewhere in the middle. The results are rather disappointing - in all cases, the only variable that is significantly related to employment growth is the growth of sales of new products, with a coefficient of unity. The conclusion is that there is no difference in the efficiency with which old and new products are produced, regardless of whether the firm undertakes process innovation during the same three year period or not. In these specifications, the constant term (the estimate of

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the average productivity growth of the old products) displays the same pattern as in Table 4, showing that non-innovators did lose employment on average between 1995 and 2003. Results shown in the Appendix confirm these patterns for low and high tech industries separately (Tables A8 and A9).

5.1

A rough comparison with France, Germany, Spain and the U.K.

A similar analysis has been carried out by Harrison et al. (2005) for France, Germany, Spain and the U.K. using data from the third Community Innovation Survey, which covers the period 1998-2001. Even though the sample design and the questionnaire are slightly different from ours, it is still worthwhile comparing their estimates with the results obtained for Italy. Table 6 presents the results of estimating a model that is exactly the same as that used by Harrison et al. (2005): l − (g1 − π) = α0 + α1 d + βg2 + v

(15)

The results are very similar to those in the top panel of Table 5, although the intercept (the negative of the average productivity gain adjusted for industrial composition change) is slightly lower, which implies that the average productivity gain net of process innovation and growth in new product sales is higher when the new product sales are not adjusted for inflation. Table 7 contains a comparison of the results of Table 6 and the results of a corresponding specification from Harrison et al. (2005).9 The sample sizes are roughly comparable, although the instruments used are slightly different: the Harrison et al. paper uses only a dummy variable for the impact of innovation on increasing the range of products offered, as reported by the firm. Comparing the results for Italy with those for other countries, the coefficient of the sales growth due to new products is very similar and around one for all the countries, although significantly less than one for Italy, which implies that firms became more efficient in producing new products during the period. The coefficient of the process innovation dummy is negative and significant for Germany and the U.K., indicating an increase in productivity of the old products; for France and Italy it is insignificantly different from zero, while for Spain is 9

These results come from the first panel of Table 6 of that paper.

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positive and barely significant. Harrison et al. explain the Spanish result with a possible large pass-through of any productivity improvements to prices. For the period 1998-2001, the intercept is negative for all the countries, with the highest values for Germany, Italy, and Spain. Thus it appears that firms producing old but not new products that did no process innovation experienced declines in employment during the period, not surprisingly. Process innovation alone seems to have produced efficiency gains only in Germany and the U.K., whereas the employment effect of the growth in sales of new products was neutral except in Italy, implying neither greater nor lesser efficiency in their production than in that of old products in all countries. For Italy product innovation appears to have been negative for employment, but note from Table 6 that this is true only for the 1998-2001 period; for the other periods product innovation is neutral or positive for growth.

5.2

A simple (but effective) employment growth decomposition

Another way to summarize the results of the previous section is to decompose employment growth into several components:  P  ˆ0 + αˆ0j ) Dindj +  j (α       αˆ1 d+ [1 − 1 (g2 > 0)] (g1 − π ˆ) + l= ³ ´    ˆ g2 +  1 (g > 0) g − π ˆ + β 2 1  1+ˆ π    uˆ,

ind-specific productivity trend in old products; due to process innovation in old products; due to output growth of old products; due to product innovation (net of substitution); zero sum residual component. (16)

ˆ are the estimated coefficients of the specifiDindj are industry dummies, the α ˆ s and βs cation in the first panel of Table 6, and d is a dummy variable which takes the value one if the firm has introduced process innovation but not product innovation. Accordingly, for each firm, the first component accounts for the industry-specific productivity trend in the production of old products. The second component is the change in employment due to the net effect of process innovation in the production of old products, while the third is the change due to output growth of old products of those firms which did not introduce product innovation. The fourth term is the net contribution to employment growth of product innovation, after adjustment for any substitution effect of old and new products.

16

The last component is a zero-mean residual. The results of this decomposition for all industries are reported in Table 8, for each period separately and then pooled. We focus the discussion on the pooled analysis. Average employment growth during the whole period was 3.2 per cent. About half of this growth (1.7 per cent) is accounted for by new product innovations, net of the induced substitution away from old products, and the remainder (1.5 per cent) by changes in the efficiency of producing old products. Incremental process improvements in the production of old products reduce employment by a small amount (-0.2 per cent) whereas changes attributable to industry-specific deviations from the main trend are -2.1 per cent. These productivity enhancing effects are completely cancelled by the 4.0 per cent increase in employment associated with the production of old products by non-innovating firms. In other words, productivity among non-innovators fell enough to cancel all the employment growth in innovators during the period. Table 9 contains a comparison of the decomposition exercise sketched above based on the results of Table 6 with the results for France, Germany, Spain and the U.K. (drawn from Table 10 of the Harrison et al. 2005 paper). As in Table 7, the period considered is 1998-2000, to maintain comparability with the Harrison et al. paper. In that period, firmlevel employment growth in Italy is somewhat lower than in the other countries, as is the contribution of new product innovation to employment growth (2.4 per cent in Italy versus number ranging from 3.9 in the U.K. to 8.0 in Germany). Otherwise, the decompositions are rather similar. The sum of the contributions of old products to employment growth is quite positive in France and the U.K. (2.8 per cent), approximately zero in Italy and Spain, and negative in Germany (-2.1 per cent). However these effects are composed of a substantial decline due to increased average productivity and increases due to output growth of old products in firms not introducing product innovations. The conclusion from this comparison is that firm employment growth in Italy during this period is worse than that in the other countries primarily because there was lower net employment growth from the introduction of new products in the average firm.

17

6

Conclusions

In this paper we derived a simple model for employment growth, in which it is possible to disentangle the roles of displacement and compensation effects of innovation on employment growth at the firm level. Comprehending this mechanism is of primary importance: as Harrison et al. (2005) point out, the firm-level effects of innovation on employment are likely to determine the extent to which different agents within the firm behave with respect to innovation. Managers and workers have different incentives, and their behavior can foster or hamper innovation and technology adoption within the firm. Understanding how these mechanisms work at the firm-level is central for the design of innovation policy and for predicting how labor market regulation can affect the rate of innovation. Using data from the last three surveys on Italian manufacturing firms conducted by Mediocredito-Capitalia, covering the period 1995-2003, we estimate alternative models of employment growth and we provide robust evidence that process innovation does not have a displacement effect in Italian firms. Moreover, we find that the average productivity growth for existing products has been increasing until 2000 and declining thereinafter, signaling a widespread inability of Italian manufacturing firms to reallocate employment in order to fully exploit productivity gains stemming from process innovation. Comparing these results with the ones of Harrison et al. (2005) for France, Germany, Spain and the U.K. indicates that the displacement effect for process innovation in all countries is quite small, and significant only for Germany and the U.K. Although partial, this evidence suggests that Italian firms (and possibly French and Spanish firms) are not able to obtain productivity benefits from process innovation because of labor market rigidities. We also find that on net, about half of employment growth in Italy during the 19952003 period is contributed by product innovation and the other half by the sales growth of old products net of their productivity gains. Finally, although there are substantial productivity gains in the production of old products overall in Italy, these are more than cancelled by output growth in firms that did not introduce new products. As other researchers have found, the overall conclusion is that process innovation has little displacement effect in Italy and product innovation increases employment. However, the productivity decline during the period seems to come largely from non-innovating firms. In future, we hope to exploit the time dimension in our data further using a more structural model of innovation, employment and productivity in a panel data framework. 18

References Blanchflower, David, and Simon Burgess (1998) ‘New Technology and Jobs: Comparative Evidence from a Two-Country Study.’ Economic Innovations and New Technologies 5, 109–138 Blechinger, Doris, Alfred Kleincknech, Georg Licht, and Friedhelm Pfeiffer (1998) ‘The Impact of Innovation on Employment in Europe. An Analysis using the CIS Data.’ ZEW Documentation, 98-02 Brower, Erik, Alfred Kleincknech, and Jeroen-ON Reijnen (1993) ‘Employment Growth and Innovation at the Firm Level. An Empirical Study.’ Journal of Evolutionary Economics 3, 153–159 Chennells, Lucy, and John V. Reenen (2002) ‘Technical Change and The Structure of Employment and Wages: a Survey on the Microeconometric Evidence.’ Productivity, Inequality and the Digital Economy pp. 175–224. Greenan, N. and Y. L’Horty and J. Mairesse Editors Doms, Mark, Timothy Dunne, and Mark J. Roberts (1995) ‘The Role of Technology Use in the Survival and Growth of Manufacturing Plants.’ International Journal of Industrial Organization 13, 523–542 Entorf, Horst, and Winfried Pohlmeier (1990) ‘Employment, Innovation and Export Activity: Evidence form Firm-Level Data.’ Microeconometrics: Surveys and Applications pp. 349–415. Florens, J.P. and M. Ivaldi and J.J. Laffont and F. Laisney Editors Greenan, Nathalie, and Dominique Guellec (2000) ‘Technological Innovation and Employment Reallocation.’ Labour 14, 547–590 Griliches, Zvi, and Jacques Mairesse (1984) Productivity and R&D at the Firm Level in R&D, Patents and Productivity, Zvi Griliches ed. Hall, Bronwyn H., and Francis Kramarz (1998) ‘Effects of Technology and Innovation on Firm Performance, Employment, and Wages: Introduction.’ Economic Innovations and New Technologies 6(2-4), 99–107 Harrison, Rupert, Jordi Jaumandreu, Jacques Mairesse, and Bettina Peters (2005) ‘Does Innovation Stimulate Employment? A Firm-Level analysis Using Comparable Micro

19

Data from four European Countries.’ Mimeo, Department of Economics, University Carlos III, Madrid International Monetary Fund (2006) ‘Country Study: Italy.’ IMF Research Bulletin Klette, Tor Jacob, and Svein Erik Forre (1998) ‘Innovation and Job Creation in a Small Economy: Evidence from Norvegian Manufacturing Plants 1982-92.’ Economic Innovations and New Technologies 5, 247–272 Lachenmaier, Stefan, and Horst Rottmann (2006) ‘Employment Effects of Innovation at the Firm Level.’ IFO Working Papers Lotti, Francesca, and Fabiano Schivardi (2005) ‘Cross Country Differences in Patent Propensity: a Firm-Level Investigation.’ Giornale degli Economisti e Annali di Economia 64(4), 469–502 Peters, Bettina (2004) ‘Employment Effects of Different Innovation Activities: Macroeconometric Evidence.’ ZEW Discussion Papers, 04-73 Piva, Mariacristina, and Marco Vivarelli (2005) ‘Innovation and Employment: Evidence from Italian Microdata.’ Journal of Economics 86(1), 65–83 Say, Jean-Baptiste (1964) A Treatise on Political Economy or the Production, Distribution and Consumption of Wealth (New York: Kelley). First edition, 1803 Spiezia, Vincenzo, and Marco Vivarelli (2002) ‘Technical Change and Employment: a Critical Survey.’ Productivity, Inequality and the Digital Economy pp. 101–131. Greenan, N. and Y. L’Horty and J. Mairesse Editors Suits, Daniel B. (1957) ‘Use of Dummy Variables in Regression Equations.’ Journal of the American Statistical Association 52, 548–551 Van Reenen, John (1997) ‘Employment and Technological Innovation: Evidence from U.K. Manufacturing Firms.’ Journal of Labor Economics 2, 255–284 Zimmermann, Klaus F. (1991) ‘The Employment Consequences of Technological Advance: Demand and Labour Costs in 16 German Industries.’ Empirical Economics 16, 253– 266

20

Table 1: Descriptive statistics. All industries, cross section and pooled sample (unbalanced and balanced panel). UNBALANCED SAMPLE

1995-1997

1998-2000

2001-2003

1995-2003

Number of firms

4290

4618

4040

12948

% firms doing R&D

35.57

41.4

48.44

59.47

% firms doing innovation

73.10

46.51

59.80

59.57

R&D exp. over sales (%)

1.70

1.94

1.73

1.79

R&D exp. per employee (in th. euro)

2.69

3.22

3.16

3.05

185.74/139.29

189.63/143.76

247.06/187.98

206.26/154.08

5.39

9.99

9.62

8.33

116.30/34

88.24/25

142.43/49

114.45/33

66.27 30.02 43.08 23.19

37.31 24.82 21.70 15.61

42.65 41.63 18.17 24.48

48.57 31.79 27.68 20.89

1995-1997

1998-2000

2001-2003

1995-2003

608

608

608

1824

% firms doing R&D

37.99

58.88

49.51

48.79

% firms doing innovation

75.33

53.95

60.53

63.27

R&D exp. over sales (%)

1.54

1.92

2.10

1.88

R&D exp. per employee (in th. euro)

2.39

3.29

3.54

3.13

168.00/134.10

184.92/143.79

193.84/153.45

182.25/ 144.60

6.44

14.31

11.72

10.77

128.72/34

138.64/36

136.36/38

134.57/36

66.61 33.88 41.45 25.16

41.45 34.87 19.08 22.37

41.12 45.23 25.82 15.30

49.73 37.99 24.45 25.27

Sales/empl: mean/median (in th. euro) Share of innovative sales (%) Num. of employees: mean/median % % % %

of of of of

firms firms firms firms

with with with with

process innovation product innovation process innovation only process & product innov.

BALANCED SAMPLE Number of firms

Sales/empl: mean/median (in th. euro) Share of innovative sales Num. of employees: mean/median % % % %

of of of of

firms firms firms firms

with with with with

process innovation product innovation process innovation only process & product innov.

21

Table 2: Descriptive statistics for several groups of firms. Pooled sample (unbalanced panel and balanced panel). UNBALANCED SAMPLE

R&D firms

Innov. firms

High-tech ind.

Low-tech ind.

Number of firms

4638

7728

4068

8925

% firms doing R&D

100

48.72

51.65

28.51

% firms doing innovation

81.16

100

66.83

56.25

R&D exp. over sales (%)

2.71

2.35

4.41

2.25

R&D exp. per employee (in th. euro)

3.75

3.99

5.54

5.27

202.70/165.70

195.154.94

186.44/153.32

200.53/153.02

13.59

13.03

11.12

7.25

171.99/53

135.24/40

171.71/40

88.07/31

62.39 52.47 26.75 35.64

81.68 53.45 46.55 35.13

52.51 40.80 25.97 26.54

46.78 27.70 28.45 18.33

R&D firms

Innov. firms

High-tech ind.

Low-tech ind.

Number of firms

890

1154

600

1,224

% firms doing R&D

100

59.62

68.45

39.18

% firms doing innovation

77.30

100

73.29

58.37

R&D exp. over sales (%)

1.88

2.04

2.30

1.48

R&D exp. per employee (in th. euro)

3.13

3.38

3.81

2.48

188.32/ 153.27

178.17/ 145.17

173.07/143.46

186.75/145.25

15.25

15.70

14.78

8.86

175.38/51

160.3917 /43

192.10/39

106.44/34

58.76 55.84 37.30 21.46

78.60 60.05 38.65 39.95

53.26 52.25 32.22 21.04

48.00 31.02 20.65 27.35

Sales/empl: mean/median (in th. euro) Share of innovative sales (%) Num. of employees: mean/median % % % %

of of of of

firms firms firms firms

with with with with

process innovation product innovation process innovation only process & product innov.

BALANCED SAMPLE

Sales/empl: mean/median (in th. euro) Share of innovative sales Num. of employees: mean/median % % % %

of of of of

firms firms firms firms

with with with with

process innovation product innovation process innovation only process & product innov.

22

Table 3: Employment growth on real sales growth and innovation dummies (non-structural model). All industries, unbalanced panel. Robust standard errors in parenthesis (also clustered in the pooled estimate). Dependent variable: employment growth rate (in percentage, l)

ALL INDUSTRIES 1995-1997

1998-2000

2001-2003

1995-2003

OLS

OLS

OLS

OLS

Estimation method

23 Real sales growth (g − π) Process inno only Product inno only Process & product inno α0 test g − π = 1 N. obs

Coeff.

S.E.

P-value

Coeff.

S.E.

P-value

Coeff.

S.E.

P-value

Coeff.

S.E.

P-value

0.25 -1.25 0.54 1.49 2.01

0.01 0.91 0.59 0.76 0.56

0.000 0.168 0.364 0.049 0.000

0.17 1.50 1.01 1.94 0.30

0.01 0.63 0.40 0.48 0.26

0.000 0.017 0.011 0.000 0.126

0.26 1.49 1.54 2.84 1.26

0.02 0.69 0.68 0.63 0.44

0.000 0.031 0.023 0.000 0.002

0.23 0.69 1.10 2.13 1.05

0.01 0.43 0.32 0.36 0.24

0.000 0.105 0.001 0.000 0.000

0.000

3094.24

0.000

2285.27

0.000

8112.53

2807.36 4290

4618

4040

0.000 12948

Table 4: The effects of innovation on employment (basic model). All industries, unbalanced panel. Robust standard errors in parenthesis (also clustered in the pooled estimate). Dependent variable: empl. growth rate - real sales growth in percentage, l − (g1 − π)

ALL INDUSTRIES 1995-1997

1998-2000

2001-2003

1995-2003

OLS

OLS

OLS

OLS

Estimation method Coeff.

S.E.

P-value

Coeff.

S.E.

P-value

Coeff.

S.E.

P-value

Coeff.

S.E.

P-value

Real sales growth due to new prod (g2 / (1 + π)) α0

0.97 -3.73

0.02 0.57

0.000 0.000

0.96 -5.88

0.01 0.42

0.000 0.000

0.96 3.00

0.03 0.55

0.000 0.000

0.96 -2.27

0.01 0.30

0.000 0.000

test (g2 / (1 + π)) = 1

1.60

0.206

9.23

0.002

2.77

0.096

12.36

24

N. obs

Estimation method

0.000

4290

4618

4040

12948

IV

IV

IV

IV

Coeff.

S.E.

P-value

Coeff.

S.E.

P-value

Coeff.

S.E.

P-value

Coeff.

S.E.

P-value

Real sales growth due to new prod (g2 / (1 + π)) α0

1.02 -4.01

0.09 0.77

0.000 0.000

0.95 -5.81

0.04 0.59

0.000 0.000

1.11 1.71

0.07 0.80

0.000 0.016

1.01 -2.66

0.10 0.91

0.000 0.002

test (g2 / (1 + π)) = 1 Test of overident. restr.

0.06 1.05

0.800 0.789

1.76 0.84

0.185 0.841

1.97 8.41

0.160 0.043

0.00 12.46

N. obs

4290

4618

4040

0.948 0.086 12948

Note: R&D dummy, lagged R&D dummy, R&D intensity (R&D employment over total employment), and NEWPROD dummy used as instruments.

Table 4a: The effects of innovation on employment (basic model). High tech industries, unbalanced panel. Robust standard errors in parenthesis (also clustered in the pooled estimate). Dependent variable: empl. growth rate - real sales growth in percentage, l − (g1 − π)

HIGH TECH INDUSTRIES 1995-1997

1998-2000

2001-2003

1995-2003

OLS

OLS

OLS

OLS

Estimation method Coeff.

S.E.

P-value

Coeff.

S.E.

P-value

Coeff.

S.E.

P-value

Coeff.

S.E.

P-value

Real sales growth due to new prod (g2 / (1 + π)) α0

1.02 -5.13

0.04 1.19

0.000 0.000

0.96 -4.63

0.02 0.78

0.000 0.000

0.87 4.10

0.05 1.21

0.000 0.000

0.95 -1.84

0.02 0.62

0.000 0.002

test (g2 / (1 + π)) = 1

0.35

0.553

3.83

0.050

6.57

0.011

7.22

25

N. obs

Estimation method

0.007

1401

1394

1244

4039

IV

IV

IV

IV

Coeff.

S.E.

P-value

Coeff.

S.E.

P-value

Coeff.

S.E.

P-value

Coeff.

S.E.

P-value

Real sales growth due to new prod (g2 / (1 + π)) α0

0.96 -4.72

0.13 1.44

0.000 0.001

0.88 -3.31

0.06 1.21

0.000 0.003

1.13 1.34

0.16 1.97

0.000 0.247

0.84 -0.66

0.15 1.81

0.000 0.358

test (g2 / (1 + π)) = 1 Test of overident. restr.

0.12 0.07

0.734 0.995

4.11 0.28

0.043 0.964

0.72 8.41

0.397 0.038

1.03 9.36

N. obs

1401

1394

1244

0.309 0.228 4039

Note: R&D dummy, lagged R&D dummy, R&D intensity (R&D employment over total employment), and NEWPROD dummy used as instruments.

Table 4b: The effects of innovation on employment (basic model). Low tech industries, unbalanced panel. Robust standard errors in parenthesis (also clustered in the pooled estimate). Dependent variable: empl. growth rate - real sales growth in percentage, l − (g1 − π)

LOW TECH INDUSTRIES 1995-1997

1998-2000

2001-2003

1995-2003

OLS

OLS

OLS

OLS

Estimation method Coeff.

S.E.

P-value

Coeff.

S.E.

P-value

Coeff.

S.E.

P-value

Coeff.

S.E.

P-value

Real sales growth due to new prod (g2 / (1 + π)) α0

0.94 -3.00

0.03 0.60

0.000 0.000

0.95 -6.56

0.02 0.49

0.000 0.000

1.00 2.58

0.03 0.54

0.000 0.000

0.97 -2.49

0.01 0.31

0.000 0.000

test (g2 / (1 + π)) = 1

4.53

0.033

5.40

0.020

0.00

0.978

5.45

26

N. obs

Estimation method

0.020

2889

3224

2796

8909

IV

IV

IV

IV

Coeff.

S.E.

P-value

Coeff.

S.E.

P-value

Coeff.

S.E.

P-value

Coeff.

S.E.

P-value

Real sales growth due to new prod (g2 / (1 + π)) α0

1.08 -3.67

0.12 0.85

0.000 0.000

1.01 -7.02

0.05 0.65

0.000 0.000

1.10 1.86

0.08 0.75

0.000 0.007

1.08 -3.23

0.13 0.91

0.000 0.000

test (g2 / (1 + π)) = 1 Test of overident. restr.

0.42 2.50

0.516 0.475

0.03 1.46

0.870 0.691

1.85 2.37

0.173 0.499

0.39 3.30

N. obs

2889

3224

2796

0.530 0.856 8909

Note: R&D dummy, lagged R&D dummy, R&D intensity (R&D employment over total employment), and NEWPROD dummy used as instruments.

Table 5: The effects of innovation on employment; adding innovation dummies. All industries, unbalanced panel. Instrumental variables estimates. Robust standard errors in parenthesis (also clustered in the pooled estimate). Dependent variable: empl. growth rate - real sales growth in percentage, l − (g1 − π)

ALL INDUSTRIES

Estimation method

1995-1997

1998-2000

2001-2003

1995-2003

IV

IV

IV

IV

Coeff.

S.E.

P-value

Coeff.

S.E.

P-value

Coeff.

S.E.

P-value

Coeff.

S.E.

P-value

Real sales growth due to new prod (g2 / (1 + π)) Process inno only α0

1.01 -1.66 -3.20

0.10 1.25 1.23

0.000 0.184 0.005

0.95 0.28 -5.85

0.04 0.76 0.69

0.000 0.715 0.000

1.11 -0.56 1.84

0.07 1.05 0.87

0.000 0.596 0.018

1.01 -0.70 -2.49

0.10 0.93 1.11

0.000 0.450 0.012

test (g2 / (1 + π)) = 1 Test of overident. restr. N. obs

0.01 1.27

0.925 0.737

1.78 0.94

0.183 0.815

2.18 13.95

0.140 0.003

0.01 12.77

Estimation method

0.923 0.078

4290

4618

4040

12948

IV

IV

IV

IV

27

Coeff.

S.E.

P-value

Coeff.

S.E.

P-value

Coeff.

S.E.

P-value

Coeff.

S.E.

P-value

Real sales growth due to new prod (g2 / (1 + π)) Process inno only Process and product inno α0

1.04 -1.88 -1.27 -3.00

0.20 1.09 3.12 1.06

0.000 0.084 0.685 0.002

0.91 0.48 2.48 -5.91

0.07 0.72 2.14 0.70

0.000 0.507 0.246 0.000

1.19 -0.77 -1.92 1.69

0.11 1.04 1.49 0.86

0.000 0.459 0.199 0.025

1.01 -0.75 -0.12 -2.44

0.11 0.57 2.00 0.69

0.000 0.185 0.954 0.000

test (g2 / (1 + π)) = 1 Test of overident. restr. N. obs

0.05 1.36

0.825 0.714

1.57 1.41

0.210 0.703

3.10 12.26

0.078 0.007

0.01 12.80

Estimation method

0.943 0.077

4290

4618

4040

12948

IV

IV

IV

IV

Coeff.

S.E.

P-value

Coeff.

S.E.

P-value

Coeff.

S.E.

P-value

Coeff.

S.E.

P-value

Real sales growth due to new prod (g2 / (1 + π)) Process and product inno Sales gr. due to new prod * process inno α0

1.01 -1.67 -0.03 -3.18

0.11 1.30 0.13 1.24

0.000 0.197 0.785 0.005

0.95 0.49 -0.05 -5.84

0.04 0.85 0.08 0.69

0.000 0.566 0.493 0.000

1.10 -0.19 -0.09 1.90

0.08 1.24 0.10 0.89

0.000 0.881 0.367 0.017

1.00 -0.62 -0.05 -2.43

0.10 1.12 0.10 1.09

0.000 0.584 0.626 0.013

test (g2 / (1 + π)) = 1 Test of overident. restr. N. obs

0.00 1.29

0.944 0.732

1.81 1.03

0.179 0.794

1.79 14.49

0.181 0.002

0.00 12.80

4290

4618

4040

0.971 0.077 12948

Note: R&D dummy, lagged R&D dummy, R&D intensity (R&D employment over total employment), and NEWPROD dummy used as instruments.

Table 6: The effects of innovation on employment; same specification of Harrison et al (2005), for comparison purposes. All industries, unbalanced panel. Instrumental variables estimates. Robust standard errors in parenthesis (also clustered in the pooled estimate).

Dependent variable: empl. growth rate - real sales growth in percentage, l − (g1 − π)

ALL INDUSTRIES

28

1995-1997

1998-2000

2001-2003

1995-2003

IV

IV

IV

IV

Estimation method Coeff.

S.E.

P-value

Coeff.

S.E.

P-value

Coeff.

S.E.

P-value

Coeff.

S.E.

P-value

Sales growth due to new prod (g2 ) Process inno only α0

0.96 -1.82 -2.98

0.10 1.24 1.21

0.000 0.142 0.007

0.94 0.29 -5.88

0.04 0.76 0.69

0.000 0.699 0.000

1.07 -0.63 1.81

0.07 1.05 0.87

0.000 0.548 0.019

1.01 -0.46 -2.80

0.10 0.95 1.14

0.000 0.628 0.007

test g2 = 1 Test of overident. restr. N. obs

0.19 1.47

0.663 0.689

2.57 0.93

0.109 0.818

0.98 13.65

0.322 0.003

0.02 11.72

4290

4618

4040

0.887 0.110 12948

Table 7: The effects of innovation on employment: a comparison (1998-2000). Dependent variable: empl. growth rate - real sales growth in percentage, l − (g1 − π1 ) Sales growth due to new prod (g2 ) 29

Process inno only α0

MCC data Italy 0.94 (0.04) 0.29 (0.76) -5.88 (0.69)

CIS data France Germany Spain

U.K.

0.98 (0.06) -1.31 (1.57) -3.52 (0.78)

0.98 (0.05) -3.85 (1.87) -4.69 (0.88)

1.01 (0.07) -6.19 (2.92) -6.95 (1.86)

1.02 (0.04) 2.46 (1.78) -6.11 (0.90)

N. obs 4618 4631 1319 4548 2493 The first column is taken from the first specification of Table 6, while the others are from Harrison et al. (2005).

Table 8: The employment growth decomposition. All industries, unbalanced panel. ALL INDUSTRIES Employment growth decomposition

Year

Mean

Median

Std. Dev

Skewness

Kurtosis

1997 2000 2003 Pooled

5.052 2.537 2.134 3.245

0.000 0.000 0.000 0.000

17.954 11.502 16.705 15.577

1.957 2.140 1.828 2.053

13.807 22.153 16.364 17.420

0j ) Dindj j (αˆ0 + αˆ Industry-specific productivity trend in production of old products

1997 2000 2003 Pooled

-1.505 -5.572 1.133 -2.111

0.566 -6.027 2.483 -1.730

6.079 2.417 4.971 3.085

-1.547 0.037 0.450 -0.793

5.140 5.157 3.326 3.635

αˆ1 d Change in employment due to the net effect of process innovation in the production of old products

1997 2000 2003 Pooled

-0.784 0.064 -0.115 -0.177

0.000 0.000 0.000 0.000

0.901 0.121 0.243 0.287

-0.280 1.373 -1.651 -0.998

1.078 2.886 3.726 1.995

[1 − 1 (g2 > 0)] (g1 − π ˆ) Change due to output growth of old products of those firms which did not introduce product innovation

1997 2000 2003 Pooled

6.039 5.673 0.052 4.041

0.000 0.000 0.000 0.000

23.908 16.903 19.434 20.404

1.340 2.113 0.878 1.404

8.028 15.708 10.931 10.790

1997 2000 2003 Pooled

1.302 2.372 1.063 1.670

0.000 0.000 0.000 0.000

12.297 11.429 15.987 13.389

3.748 3.678 1.613 2.560

39.779 31.753 19.333 27.299

1997 2000 2003 Pooled

0.000 0.000 0.000 0.000

1.725 2.224 0.674 1.515

25.363 19.571 23.720 23.172

-0.522 -0.888 -0.218 -0.493

6.743 11.260 8.281 8.253

l Employment growth, in %

P

30   g2 1 (g2 > 0) g1 − πˆ1 + βˆ 1+π Net contribution to employment growth of product innovation, after adjustment for any substitution effect of old and new products

u ˆ Residual component

  P g2 This decomposition, l = ˆ ) + 1 (g2 > 0) g1 − π ˆ + βˆ 1+ˆ +u ˆ, is based on 0j ) Dindj + αˆ1 d + [1 − 1 (g2 > 0)] (g1 − π j (αˆ0 + αˆ π the coefficients reported in the first specification of Table 5. The skewness and the kurtosis are computed on the corresponding standardized distributions. Although not reported, in the pooled analysis, a further term was included in the decomposition, due to the presence of year dummies in the model specification.

Table 9: The employment growth decomposition: a comparison (1998-2000). Employment growth decomposition

MCC data Italy

France

2.5

8.3

5.9

14.2

6.7

j (αˆ0 + αˆ0j ) Dindj Industry-specific productivity trend in production of old products

-5.6

-1.9

-7.5

-5.7

-5.0

αˆ1 d Change in employment due to the net effect of process innovation in the production of old products

0.1

-0.1

-0.6

0.3

-0.4

[1 − 1 (g2 > 0)] (g1 − π ˆ) Change due to output growth of old products of those firms which did not introduce product innovation

5.7

4.8

6.0

12.2

8.3

2.4

5.5

8.0

7.4

3.9

24.8

45.2

48.4

32.4

28.5

4618

4631

1319

4548 ´

2493

l Employment growth, in %

CIS data Germany Spain

U.K.

P

31

³ ´ ˆ 2 1 (g2 > 0) g1 − π ˆ + βg Net contribution to employment growth of product innovation, after adjustment for any substitution effect of old and new products

Share of product innovators N. obs P

³

g2 βˆ 1+ˆ π

+u ˆ,is This decomposition, l = ˆ ) + 1 (g2 > 0) g1 − π ˆ+ j (αˆ0 + αˆ0j ) Dindj + αˆ1 d + [1 − 1 (g2 > 0)] (g1 − π based on the coefficients reported in the first specification of Table 6. The figures for France, Germany, Spain and the U.K. are from Harrison et al. (2005).

Appendix Table A.1: Employment growth on real sales growth and innovation dummies. All industries, balanced panel. Dependent variable: employment growth rate (in percentage, l)

ALL INDUSTRIES 1995-1997

1998-2000

2001-2003

1995-2003

OLS

OLS

OLS

OLS

Estimation method

Coeff. S.E. P-value Coeff. S.E. P-value Coeff. S.E. P-value Real sales growth (g − π) Process inno only Product inno only Process & product inno α0 test g − π = 1 N. obs

0.27 -0.06 -1.18 2.91 2.23

0.03 2.06 1.78 1.97 1.63

482.95 608

0.000 0.976 0.507 0.140 0.085

0.22 2.03 1.61 1.38 -0.13

0.000

268.48

0.05 1.20 0.99 0.91 0.67

608

0.000 0.090 0.104 0.128 0.422

0.27 2.75 2.86 3.07 -0.19

0.000

522.89

0.03 1.52 2.15 1.35 0.75

608

Coeff.

0.000 0.071 0.183 0.024 0.398

0.25 1.90 0.78 2.61 0.45

0.000

1369.51

S.E. P-value 0.02 0.91 0.88 0.81 0.58

0.000 0.037 0.377 0.001 0.217 0.000

1824

Table A.2: The effects of innovation on employment. All industries, balanced panel. Dependent variable: empl. growth rate - real sales growth in percentage, l − (g1 − π)

ALL INDUSTRIES 1995-1997

1998-2000

2001-2003

1995-2003

OLS

OLS

OLS

OLS

Estimation method Coeff.

S.E.

P-value

Coeff.

S.E.

P-value

Coeff.

S.E.

P-value

Coeff.

S.E.

P-value

Real sales growth due to new prod (g2 / (1 + π)) α0

1.09 -4.02

0.05 1.23

0.000 0.001

0.94 -5.54

0.02 0.81

0.000 0.000

0.96 5.34

0.05 1.05

0.000 0.000

0.98 -1.53

0.02 0.58

0.000 0.004

test (g2 / (1 + π)) = 1

3.74

0.054

7.45

0.007

0.72

0.397

0.88

0.349

N. obs

608

608

608

1824

Estimation method

IV

IV

IV

IV

Coeff.

S.E.

P-value

Coeff.

S.E.

P-value

Coeff.

S.E.

P-value

Coeff.

S.E.

P-value

Real sales growth due to new prod (g2 / (1 + π)) α0

1.03 -3.66

0.12 1.32

0.000 0.003

0.97 -5.96

0.04 1.00

0.000 0.000

1.03 4.60

0.11 1.55

0.000 0.002

0.84 -0.11

0.11 1.20

0.000 0.464

test (g2 / (1 + π)) = 1 Test of overident. restr.

0.08 0.04

0.779 0.980

0.55 1.15

0.459 0.564

0.07 5.60

0.786 0.061

2.33 2.59

N. obs

608

608

608

0.127 0.858 1824

Note: R&D dummy, lagged R&D dummy, R&D intensity (R&D employment over total employment), and NEWPROD dummy used as instruments.

Table A.3: The effects of innovation on employment; adding innovation dummies. IV estimates. All industries, balanced panel. Dependent variable: empl. growth rate - real sales growth in percentage, l − (g1 − π)

ALL INDUSTRIES

Estimation method

1995-1997

1998-2000

2001-2003

1995-2003

IV

IV

IV

IV

Coeff.

S.E.

P-value

Coeff.

S.E.

P-value

Coeff.

S.E.

P-value

Coeff.

S.E.

P-value

Real sales growth due to new prod (g2 / (1 + π)) Process inno only α0

0.97 -2.89 -2.21

0.14 2.43 1.95

0.000 0.234 0.129

0.98 1.48 -6.41

0.05 1.57 1.13

0.000 0.346 0.000

1.04 2.64 4.15

0.11 2.73 1.62

0.000 0.333 0.005

0.85 -1.88 0.25

0.11 1.58 1.48

0.000 0.235 0.433

test (g2 / (1 + π)) = 1 Test of overident. restr. N. obs

0.04 0.13

0.837 0.940

0.28 1.42

0.598 0.492

0.11 5.56

0.737 0.062

2.02 2.50

Estimation method

0.156 0.869

608

608

608

1824

IV

IV

IV

IV

Coeff.

S.E.

P-value

Coeff.

S.E.

P-value

Coeff.

S.E.

P-value

Coeff.

S.E.

P-value

Real sales growth due to new prod (g2 / (1 + π)) Process inno only Process and product inno α0

0.86 -2.17 4.19 -2.89

0.22 2.42 4.33 1.97

0.000 0.370 0.333 0.071

1.02 1.13 -2.89 -6.33

0.06 1.59 2.13 1.12

0.000 0.478 0.174 0.000

1.01 2.91 1.24 4.09

0.13 2.76 2.15 1.59

0.000 0.292 0.564 0.005

0.82 -0.90 3.52 -0.62

0.12 1.35 2.25 1.09

0.000 0.507 0.117 0.286

test (g2 / (1 + π)) = 1 Test of overident. restr. N. obs

0.39 0.11

0.533 0.946

0.09 1.13

0.759 0.568

0.00 5.61

0.970 0.061

2.33 2.37

Estimation method

0.127 0.883

608

608

608

1824

IV

IV

IV

IV

Coeff.

S.E.

P-value

Coeff.

S.E.

P-value

Coeff.

S.E.

P-value

Coeff.

S.E.

P-value

Real sales growth due to new prod (g2 / (1 + π)) Process and product inno Sales gr. due to new prod * process inno α0

0.97 -2.95 0.12 -2.20

0.14 2.50 0.15 1.98

0.000 0.238 0.420 0.133

0.98 2.07 -0.10 -6.48

0.05 1.74 0.05 1.17

0.000 0.235 0.074 0.000

1.04 4.75 -0.33 4.09

0.12 3.04 0.15 1.69

0.000 0.119 0.030 0.008

0.84 -2.11 0.04 0.39

0.10 1.84 0.11 1.46

0.000 0.251 0.726 0.394

test (g2 / (1 + π)) = 1 Test of overident. restr. N. obs

0.04 0.14

0.842 0.932

0.25 1.73

0.621 0.421

0.11 6.84

0.746 0.033

2.50 2.46

608

608

608

0.114 0.873 1824

Note: R&D dummy, lagged R&D dummy, R&D intensity (R&D employment over total employment), and NEWPROD dummy used as instruments.

Table A.4: The effects of innovation on employment; same specification of Harrison et al (2005), for comparison purposes. All industries, balanced panel. Instrumental variables estimates. Robust standard errors in parenthesis (also clustered in the pooled estimate).

Dependent variable: empl. growth rate - real sales growth (in percentage, l − g1 − π)

ALL INDUSTRIES 1995-1997

1998-2000

2001-2003

1995-2003

IV

IV

IV

IV

Estimation method Coeff.

S.E.

P-value

Coeff.

S.E.

P-value

Coeff.

S.E.

P-value

Coeff.

S.E.

P-value

Sales growth due to new prod (g2 ) Process inno only α0

0.92 -2.93 -2.03

0.13 2.42 1.91

0.000 0.226 0.144

0.97 1.57 -6.38

0.04 1.56 1.11

0.000 0.315 0.000

1.02 2.59 3.85

0.11 2.74 1.63

0.000 0.343 0.009

0.86 -1.57 -0.10

0.11 1.63 1.59

0.000 0.337 0.475

test g2 = 1 Test of overident. restr. N. obs

0.43 0.17

0.513 0.919

0.58 1.25

0.445 0.535

0.04 4.45

0.847 0.108

1.63 2.83

608

608

608

0.202 0.830 1824

Table A.5: The effects of innovation on employment: a comparison (1998-2000). Dependent variable: empl. growth rate - real sales growth (in percentage, l − g1 − π) Sales growth due to new prod (g2 ) Process inno only α0 N. obs

MCC data Italy

CIS data France Germany Spain

U.K.

0.97 (0.04) 1.57 (1.56) -6.38 (1.11)

0.98 (0.06) -1.31 (1.57) -3.52 (0.78)

1.01 (0.07) -6.19 (2.92) -6.95 (1.86)

1.02 (0.04) 2.46 (1.78) -6.11 (0.90)

0.98 (0.05) -3.85 (1.87) -4.69 (0.88)

608

4631

1319

4548

2493

The first column is taken from the first specification of Table A.4, while the others are from Harrison et al. (2005).

Table A.6: The employment growth decomposition. All industries, balanced panel. ALL INDUSTRIES Employment growth decomposition

Year

Mean

Median

Std. Dev

Skewness

Kurtosis

1997 2000 2003 Pooled

5.617 2.495 0.942 3.018

0.000 0.000 0.000 0.000

15.783 9.325 14.390 13.588

2.140 2.102 1.508 2.008

14.588 21.401 19.752 19.283

0j ) Dindj j (αˆ0 + αˆ Industry-specific productivity trend in production of old products

1997 2000 2003 Pooled

-0.032 -5.967 2.915 -1.469

1.942 -6.069 4.334 -1.549

6.062 2.926 6.839 3.319

-1.126 0.152 0.445 0.424

6.089 4.699 3.419 4.175

αˆ1 d Change in employment due to the net effect of process innovation in the production of old products

1997 2000 2003 Pooled

-1.188 0.301 0.391 -0.063

0.000 0.000 0.000 0.000

1.414 0.620 0.922 0.109

-0.347 1.574 1.928 -1.138

1.121 3.477 4.718 2.295

[1 − 1 (g2 > 0)] (g1 − π ˆ) Change due to output growth of old products of those firms which did not introduce product innovation

1997 2000 2003 Pooled

5.766 4.456 -1.442 2.927

0.000 0.000 0.000 0.000

20.470 12.096 15.894 16.798

1.361 1.688 0.621 1.265

8.545 10.385 8.556 10.141

1997 2000 2003 Pooled

1.072 3.705 -0.922 1.623

0.000 0.000 0.000 0.000

13.141 11.384 14.521 13.472

3.515 2.572 -0.777 1.403

34.208 17.064 11.635 20.217

1997 2000 2003 Pooled

0.000 0.000 0.000 0.000

l Employment growth, in %

P

  g2 1 (g2 > 0) g1 − πˆ1 + βˆ 1+π Net contribution to employment growth of product innovation, after adjustment for any substitution effect of old and new products

1.281 22.174 -0.353 7.262 1.082 14.673 -0.085 5.395 -0.549 19.710 0.120 6.849 0.680 19.607 -0.119 7.818   P g2 This decomposition, l = j (αˆ0 + αˆ0j ) Dindj + αˆ1 d + [1 − 1 (g2 > 0)] (g1 − π ˆ ) + 1 (g2 > 0) g1 − π ˆ + βˆ 1+ˆ +u ˆ,is based on the π coefficients reported in the first specification of Table A.3. The skewness and the kurtosis are computed on the corresponding standardized distributions.

u ˆ Residual component

Table A.7: The employment growth decomposition: a comparison (1998-2000). Employment growth decomposition

MCC data Italy

France

2.5

8.3

5.9

14.2

6.7

j (αˆ0 − αˆ0j ) Dindj Industry-specific productivity trend in production of old products

-6.0

-1.9

-7.5

-5.7

-5.0

αˆ1 d Change in employment due to the net effect of process innovation in the production of old products

0.3

-0.1

-0.6

0.3

-0.4

[1 − 1 (g2 > 0)] (g1 − πˆ1 ) Change due to output growth of old products of those firms which did not introduce product innovation

4.5

4.8

6.0

12.2

8.3

3.7

5.5

8.0

7.4

3.9

608

4631

1319

4548

2493

l Employment growth, in %

CIS data Germany Spain

UK

P

³ ´ ˆ 2 1 (g2 > 0) g1 − πˆ1 + βg Net contribution to employment growth of product innovation, after adjustment for any substitution effect of old and new products

N. obs

P

ˆ 2) + u This decomposition, l = j (αˆ0 + αˆ0j ) Dindj + αˆ1 d + [1 − 1 (g2 > 0)] (g1 − π ˆ ) + 1 (g2 > 0) (g1 − π ˆ + βg ˆ, is based on the coefficients reported in the first specification of Table A.3.

Table A.8: The effects of innovation on employment; adding innovation dummies. High-tech industries, unbalanced panel. Instrumental variables estimates. Robust standard errors in parenthesis (also clustered in the pooled estimate) Dependent variable: empl. growth rate - real sales growth in percentage, l − (g1 − π)

HIGH-TECH INDUSTRIES

Estimation method

1995-1997

1998-2000

2001-2003

1995-2003

IV

IV

IV

IV

Coeff.

S.E.

P-value

Coeff.

S.E.

P-value

Coeff.

S.E.

P-value

Coeff.

S.E.

P-value

Real sales growth due to new prod (g2 / (1 + π)) Process inno only α0

0.92 -2.21 -3.55

0.15 2.27 2.25

0.000 0.330 0.058

0.88 -2.01 -2.90

0.06 1.75 1.46

0.000 0.251 0.024

1.08 -1.41 2.14

0.16 2.42 2.13

0.000 0.561 0.158

0.78 -3.50 0.98

0.15 2.00 2.25

0.000 0.080 0.332

test (g2 / (1 + π)) = 1 Test of overident. restr. N. obs

0.25 3.84

0.617 0.279

3.80 2.29

0.051 0.515

0.25 7.53

0.617 0.057

2.12 9.84

Estimation method

0.146 0.198

1401

1394

1244

4039

IV

IV

IV

IV

Coeff.

S.E.

P-value

Coeff.

S.E.

P-value

Coeff.

S.E.

P-value

Coeff.

S.E.

P-value

Real sales growth due to new prod (g2 / (1 + π)) Process inno only Process and product inno α0

0.85 -1.42 3.28 -4.34

0.23 1.92 4.13 1.87

0.000 0.462 0.428 0.010

0.83 -1.94 3.19 -2.84

0.10 1.74 2.91 1.51

0.000 0.265 0.273 0.030

1.20 -2.10 -3.50 2.05

0.21 2.39 2.50 2.11

0.000 0.379 0.161 0.166

0.74 -2.60 3.53 0.25

0.16 1.34 2.78 1.54

0.000 0.053 0.204 0.435

test (g2 / (1 + π)) = 1 Test of overident. restr. N. obs

0.42 4.00

0.516 0.262

2.99 2.34

0.084 0.505

0.90 6.35

0.343 0.096

2.57 9.67

.

Estimation method

0.109 0.208

1401

1394

1244

4039

IV

IV

IV

IV

Coeff.

S.E.

P-value

Coeff.

S.E.

P-value

Coeff.

S.E.

P-value

Coeff.

S.E.

P-value

Real sales growth due to new prod (g2 / (1 + π)) Process and product inno Sales gr. due to new prod * process inno α0

0.93 -2.24 0.15 -3.57

0.15 2.30 0.20 2.24

0.000 0.331 0.441 0.056

0.88 -2.19 0.06 -2.91

0.06 1.89 0.12 1.45

0.000 0.247 0.593 0.023

1.04 -0.47 -0.18 2.54

0.16 3.00 0.19 2.14

0.000 0.877 0.365 0.118

0.77 -3.98 0.14 1.17

0.14 2.22 0.16 2.15

0.000 0.073 0.363 0.294

test (g2 / (1 + π)) = 1 Test of overident. restr. N. obs

0.24 3.85

0.624 0.279

3.81 2.29

0.051 0.514

0.07 8.26

0.790 0.041

2.63 9.32

1401

1394

1244

0.105 0.231 4039

Table A.9: The effects of innovation on employment; adding innovation dummies. Low-tech industries, unbalanced panel. Instrumental variables estimates. Robust standard errors in parenthesis (also clustered in the pooled estimate) Dependent variable: empl. growth rate - real sales growth in percentage, l − (g1 − π)

LOW-TECH INDUSTRIES

Estimation method

1995-1997

1998-2000

2001-2003

1995-2003

IV

IV

IV

IV

Coeff.

S.E.

P-value

Coeff.

S.E.

P-value

Coeff.

S.E.

P-value

Coeff.

S.E.

P-value

Real sales growth due to new prod (g2 / (1 + π)) Process inno only α0

1.12 -0.91 -3.47

0.13 1.46 1.40

0.000 0.532 0.006

1.01 1.07 -7.28

0.05 0.80 0.73

0.000 0.180 0.000

1.11 -0.33 1.90

0.08 1.12 0.83

0.000 0.769 0.011

1.07 -0.20 -3.17

0.12 0.92 1.07

0.000 0.827 0.002

test (g2 / (1 + π)) = 1 Test of overident. restr. N. obs

0.82 0.06

0.366 0.996

0.03 2.61

0.854 0.455

1.96 2.35

0.162 0.503

0.40 2.02

Estimation method

0.526 0.959

2889

3224

2796

8909

IV

IV

IV

IV

Coeff.

S.E.

P-value

Coeff.

S.E.

P-value

Coeff.

S.E.

P-value

Coeff.

S.E.

P-value

Real sales growth due to new prod (g2 / (1 + π)) Process inno only Process and product inno α0

1.37 -1.37 -6.85 -3.14

0.28 1.34 4.36 1.32

0.000 0.308 0.116 0.008

1.04 1.16 -0.47 -7.50

0.09 0.77 2.91 0.71

0.000 0.132 0.871 0.000

1.12 -0.34 -0.25 1.86

0.11 1.13 1.67 0.81

0.000 0.765 0.881 0.011

1.08 -0.47 -1.17 -2.95

0.14 0.59 2.60 0.67

0.000 0.432 0.652 0.000

test (g2 / (1 + π)) = 1 Test of overident. restr. N. obs

1.71 0.04

0.191 0.998

0.20 2.51

0.657 0.474

1.25 2.25

0.264 0.523

0.37 2.03

.

Estimation method

0.543 0.958

2889

3224

2796

8909

IV

IV

IV

IV

Coeff.

S.E.

P-value

Coeff.

S.E.

P-value

Coeff.

S.E.

P-value

Coeff.

S.E.

P-value

Real sales growth due to new prod (g2 / (1 + π)) Process and product inno Sales gr. due to new prod * process inno α0

1.13 -0.78 -0.18 -3.55

0.14 1.54 0.16 1.45

0.000 0.613 0.256 0.007

1.01 1.63 -0.13 -7.27

0.05 0.90 0.09 0.75

0.000 0.069 0.186 0.000

1.10 -0.30 -0.01 1.92

0.08 1.28 0.10 0.85

0.000 0.812 0.935 0.012

1.07 0.06 -0.11 -3.15

0.12 1.19 0.13 1.10

0.000 0.961 0.408 0.002

test (g2 / (1 + π)) = 1 Test of overident. restr. N. obs

0.85 0.06

0.357 0.996

0.01 3.06

0.915 0.382

1.75 2.38

0.186 0.497

0.35 2.11

2889

3224

2796

0.555 0.953 8909

RECENTLY PUBLISHED “TEMI” (*)

N. 599 – Bank’s riskiness over the business cycle: A panel analysis on Italian intermediaries, by Mario Quagliariello (september 2006) N. 600 – People I know: Workplace networks and job search outcomes, by Federico Cingano and Alfonso Rosolia (September 2006). N. 601 – Bank profitability and the business cycle, by Ugo Albertazzi and Leonardo Gambacorta (September 2006). N. 602 – Scenario based principal component value-at-risk: An application to Italian banks’ interest rate risk exposure, by Roberta Fiori and Simonetta Iannotti (September 2006). N. 603 – A dual-regime utility model for poverty analysis, by Claudia Biancotti (September 2006). N. 604 – The political economy of investor protection, by Pietro Tommasino (December 2006). N. 605 – Search in thick markets: Evidence from Italy, by Sabrina Di Addario (December 2006). N. 606 – The transmission of monetary policy shocks from the US to the euro area, by S. Neri and A. Nobili (December 2006). N. 607 – What does a technology shock do? A VAR analysis with model-based sign restrictions, by L. Dedola and S. Neri (December 2006). N. 608 – Merge and compete: Strategic incentives for vertical integration, by Filippo Vergara Caffarelli (December 2006). N. 609 – Real-time determinants of fiscal policies in the euro area: Fiscal rules, cyclical conditions and elections, by Roberto Golinelli and Sandro Momigliano (December 2006). N. 610 – L’under-reporting della ricchezza finanziaria nell’indagine sui bilanci delle famiglie, by Leandro D’Aurizio, Ivan Faiella, Stefano Iezzi, Andrea Neri (December 2006). N. 611 – La polarizzazione territoriale del prodotto pro capite: un’analisi del caso italiano sulla base di dati provinciali by Stefano Iezzi (December 2006). N. 612 – A neural network architecture for data editing in the Bank of Italy’s business surveys by Claudia Biancotti, Leandro D’Aurizio and Raffaele Tartaglia-Polcini (February 2007). N. 613 – Outward FDI and Local Employment Growth in Italy, by Stefano Federico and Gaetano Alfredo Minerva (February 2007). N. 614 – Testing for trend, by Fabio Busetti and Andrew Harvey (February 2007). N. 615 – Macroeconomic uncertainty and banks’ lending decisions: The case of Italy, by Mario Quagliariello (February 2007). N. 616 – Entry barriers in italian retail trade, by Fabiano Schivardi and Eliana Viviano (February 2007). N. 617 – A politicy-sensible core-inflation measure for the euro area, by Stefano Siviero and Giovanni Veronese (February 2007). N. 618 – Le opinioni degli italiani sull’evasione fiscale, by Luigi Cannari and Giovanni D'Alessio (February 2007) N. 619 – Memory for prices and the euro cash changeover: An alalysis for cinema prices in Italy, by Vincenzo Cestari, Paolo Del Giovane and Clelia Rossi-Arnaud (February 2007). N. 620 – Intertemporal consumption choises, transaction costs and limited participation in financial markets: Reconciling data and theory, by Orazio P. Attanasio and Monica Paiella (April 2007). N. 621 – Why demand uncertainty curbs investment: Evidence from a panel of Italian manufacturing firms, by Maria Elena Bontempi, Roberto Golinelli and Giuseppe Parigi (April 2007). (*) Requests for copies should be sent to: Banca d’Italia – Servizio Studi – Divisione Biblioteca e pubblicazioni – Via Nazionale, 91 – 00184 Rome (fax 0039 06 47922059). They are available on the Internet www.bancaditalia.it.

"TEMI" LATER PUBLISHED ELSEWHERE 2001 M. CARUSO, Stock prices and money velocity: A multi-country analysis, Empirical Economics, Vol. 26 (4), pp. 651-672, TD No. 264 (February 1996). P. CIPOLLONE and D. J. MARCHETTI, Bottlenecks and limits to growth: A multisectoral analysis of Italian industry, Journal of Policy Modeling, Vol. 23 (6), pp. 601-620, TD No. 314 (August 1997). P. CASELLI, Fiscal consolidations under fixed exchange rates, European Economic Review, Vol. 45 (3), pp. 425-450, TD No. 336 (October 1998). F. ALTISSIMO and G. L. VIOLANTE, The non-linear dynamics of output and unemployment in the US, Journal of Applied Econometrics, Vol. 16 (4), pp. 461-486, TD No. 338 (October 1998). F. NUCCI and A. F. POZZOLO, Investment and the exchange rate: An analysis with firm-level panel data, European Economic Review, Vol. 45 (2), pp. 259-283, TD No. 344 (December 1998). A. ZAGHINI, Fiscal adjustments and economic performing: A comparative study, Applied Economics, Vol. 33 (5), pp. 613-624, TD No. 355 (June 1999). L. GAMBACORTA, On the institutional design of the European monetary union: Conservatism, stability pact and economic shocks, Economic Notes, Vol. 30 (1), pp. 109-143, TD No. 356 (June 1999). P. FINALDI RUSSO and P. ROSSI, Credit costraints in italian industrial districts, Applied Economics, Vol. 33 (11), pp. 1469-1477, TD No. 360 (December 1999). A. CUKIERMAN and F. LIPPI, Labor markets and monetary union: A strategic analysis, Economic Journal, Vol. 111 (473), pp. 541-565, TD No. 365 (February 2000). G. PARIGI and S. SIVIERO, An investment-function-based measure of capacity utilisation, potential output and utilised capacity in the Bank of Italy’s quarterly model, Economic Modelling, Vol. 18 (4), pp. 525-550, TD No. 367 (February 2000). P. CASELLI, P. PAGANO and F. SCHIVARDI, Investment and growth in Europe and in the United States in the nineties, Rivista di politica economica, v. 91, 10, pp. 3-35, TD No. 372 (March 2000). F. BALASSONE and D. MONACELLI, Emu fiscal rules: Is there a gap?, in: M. Bordignon and D. Da Empoli (eds.), Politica fiscale, flessibilità dei mercati e crescita, Milano, Franco Angeli, TD No. 375 (July 2000). A. B. ATKINSON and A. BRANDOLINI, Promise and pitfalls in the use of “secondary" data-sets: Income inequality in OECD countries as a case study, Journal of Economic Literature, Vol. 39 (3), pp. 771-799, TD No. 379 (October 2000). D. FOCARELLI and A. F. POZZOLO, The patterns of cross-border bank mergers and shareholdings in OECD countries, Journal of Banking and Finance, Vol. 25 (12), pp. 2305-2337, TD No. 381 (October 2000). M. SBRACIA and A. ZAGHINI, Expectations and information in second generation currency crises models, Economic Modelling, Vol. 18 (2), pp. 203-222, TD No. 391 (December 2000). F. FORNARI and A. MELE, Recovering the probability density function of asset prices using GARCH as diffusion approximations, Journal of Empirical Finance, Vol. 8 (1), pp. 83-110, TD No. 396 (February 2001). P. CIPOLLONE, La convergenza dei salari dell'industria manifatturiera in Europa, Politica economica, Vol. 17 (1), pp. 97-125, TD No. 398 (February 2001). E. BONACCORSI DI PATTI and G. GOBBI, The changing structure of local credit markets: Are small businesses special?, Journal of Banking and Finance, Vol. 25 (12), pp. 2209-2237, TD No. 404 (June 2001). L. DEDOLA and S. LEDUC, Why is the business-cycle behaviour of fundamentals alike across exchange-rate regimes?, International Journal of Finance and Economics, v. 6, 4, pp. 401-419, TD No. 411 (August 2001). M. PAIELLA, Limited Financial Market Participation: a Transaction Cost-Based Explanation, IFS Working Paper, 01/06, TD No. 415 (August 2001). G. MESSINA, Per un federalismo equo e solidale: obiettivi e vincoli per la perequazione regionale in Italia,, Studi economici, Vol. 56 (73), pp. 131-148, TD No. 416 (August 2001). L GAMBACORTA Bank-specific characteristics and monetary policy transmission: the case of Italy, ECB Working Paper, 103, TD No. 430 (December 2001).

F. ALTISSIMO, A. BASSANETTI, R. CRISTADORO, M. FORNI, M. LIPPI, L. REICHLIN and G. VERONESE A real time coincident indicator of the euro area business cycle, CEPR Discussion Paper, 3108, TD No. 436 (December 2001). A. GERALI and F. LIPPI, On the "conquest" of inflation, CEPR Discussion Paper, 3101, TD No. 444 (July 2002). L. GUISO and M. PAIELLA, Risk aversion, wealth and background risk, CEPR Discussion Paper, 2728, TD No. 483 (September 2003). 2002 R. CESARI and F. PANETTA, The performance of italian equity fund, Journal of Banking and Finance, Vol. 26 (1), pp. 99-126, TD No. 325 (January 1998). F. ALTISSIMO, S. SIVIERO and D. TERLIZZESE, How deep are the deep parameters?, Annales d’Economie et de Statistique, (67/68), pp. 207-226, TD No. 354 (June 1999). F. FORNARI, C. MONTICELLI, M. PERICOLI and M. TIVEGNA, The impact of news on the exchange rate of the lira and long-term interest rates, Economic Modelling, Vol. 19 (4), pp. 611-639, TD No. 358 (October 1999). D. FOCARELLI, F. PANETTA and C. SALLEO, Why do banks merge?, Journal of Money, Credit and Banking, Vol. 34 (4), pp. 1047-1066, TD No. 361 (December 1999). D. J. MARCHETTI, Markup and the business cycle: Evidence from Italian manufacturing branches, Open Economies Review, Vol. 13 (1), pp. 87-103, TD No. 362 (December 1999). F. BUSETTI, Testing for (common) stochastic trends in the presence of structural break, Journal of Forecasting, Vol. 21 (2), pp. 81-105, TD No. 385 (October 2000). F. LIPPI, Revisiting the Case for a Populist Central Banker, European Economic Review, Vol. 46 (3), pp. 601-612, TD No. 386 (October 2000). F. PANETTA, The stability of the relation between the stock market and macroeconomic forces, Economic Notes, Vol. 31 (3), pp. 417-450, TD No. 393 (February 2001). G. GRANDE and L. VENTURA, Labor income and risky assets under market incompleteness: Evidence from Italian data, Journal of Banking and Finance, Vol. 26 (2-3), pp. 597-620, TD No. 399 (March 2001). A. BRANDOLINI, P. CIPOLLONE and P. SESTITO, Earnings dispersion, low pay and household poverty in Italy, 1977-1998, in D. Cohen, T. Piketty and G. Saint-Paul (eds.), The Economics of Rising Inequalities, Oxford, Oxford University Press, TD No. 427 (November 2001). E. GAIOTTI and A. GENERALE, Does monetary policy have asymmetric effects? A look at the investment decisions of Italian firms, Giornale degli economisti e annali di economia, v. 61, 1, pp. 29-60, TD No. 429 (December 2001). G. M. TOMAT, Durable goods, price indexes and quality change: An application to automobile prices in Italy, 1988-1998, ECB Working Paper, 118, TD No. 439 (March 2002). A. PRATI and M. SBRACIA, Currency crises and uncertainty about fundamentals, IMF Working Paper, 3, TD No. 446 (July 2002). L. CANNARI and G. D’ALESSIO, La distribuzione del reddito e della ricchezza nelle regioni italiane, Rivista Economica del Mezzogiorno, Vol. 16 (4), pp. 809-847, Il Mulino, TD No. 482 (June 2003).

2003 L. GAMBACORTA, Asymmetric bank lending channels and ECB monetary policy, Economic Modelling, Vol. 20, 1, pp. 25-46, TD No. 340 (October 1998). F. SCHIVARDI, Reallocation and learning over the business cycle, European Economic Review, Vol. 47 (1), pp. 95-111, TD No. 345 (December 1998). P. CASELLI, P. PAGANO and F. SCHIVARDI, Uncertainty and slowdown of capital accumulation in Europe, Applied Economics, Vol. 35 (1), pp. 79-89, TD No. 372 (March 2000). F. LIPPI, Strategic monetary policy with non-atomistic wage setters, Review of Economic Studies, v. 70, 4, pp. 909-919, TD No. 374 (June 2000).

P. ANGELINI and N. CETORELLI, The effect of regulatory reform on competition in the banking industry, Journal of Money, Credit and Banking, Vol. 35, 5, pp. 663-684, TD No. 380 (October 2000). P. PAGANO and G. FERRAGUTO, Endogenous growth with intertemporally dependent preferences, Contribution to Macroeconomics, Vol. 3 (1), pp. 1-38, TD No. 382 (October 2000). P. PAGANO and F. SCHIVARDI, Firm size distribution and growth, Scandinavian Journal of Economics, Vol. 105 (2), pp. 255-274, TD No. 394 (February 2001). M. PERICOLI and M. SBRACIA, A Primer on Financial Contagion, Journal of Economic Surveys, Vol. 17 (4), pp. 571-608, TD No. 407 (June 2001). M. SBRACIA and A. ZAGHINI, The role of the banking system in the international transmission of shocks, World Economy, Vol. 26 (5), pp. 727-754, TD No. 409 (June 2001). L. GAMBACORTA, The Italian banking system and monetary policy transmission: evidence from bank level data, in: I. Angeloni, A. Kashyap and B. Mojon (eds.), Monetary Policy Transmission in the Euro Area, Cambridge University Press, TD No. 430 (December 2001). M. EHRMANN, L. GAMBACORTA, J. MARTÍNEZ PAGÉS, P. SEVESTRE and A. WORMS, Financial systems and the role of banks in monetary policy transmission in the euro area, in: I. Angeloni, A. Kashyap and B. Mojon (eds.), Monetary Policy Transmission in the Euro Area, Cambridge, Cambridge University Press, TD No. 432 (December 2001). F. SPADAFORA, Official bailouts, moral hazard and the "Specialtiy" of the international interbank market, Emerging Markets Review, Vol. 4 ( 2), pp. 165-196, TD No. 438 (March 2002). D. FOCARELLI and F. PANETTA, Are mergers beneficial to consumers? Evidence from the market for bank deposits, American Economic Review, Vol. 93 (4), pp. 1152-1172, TD No. 448 (July 2002). E.VIVIANO, Un'analisi critica delle definizioni di disoccupazione e partecipazione in Italia, Politica Economica, Vol. 19 (1), pp. 161-190, TD No. 450 (July 2002). M. PAGNINI, Misura e determinanti dell’agglomerazione spaziale nei comparti industriali in Italia, Rivista di Politica Economica, Vol. 93 (3-4), pp. 149-196, TD No. 452 (October 2002). F. PANETTA, Evoluzione del sistema bancario e finanziamento dell'economia nel Mezzogiorno, Moneta e credito, v. 56, 222, pp. 127-160, TD No. 467 (March 2003). F. BUSETTI and A. M. ROBERT TAYLOR, Testing against stochastic trend and seasonality in the presence of unattended breaks and unit roots, Journal of Econometrics, Vol. 117 (1), pp. 21-53, TD No. 470 (March 2003). P. ZAFFARONI, Testing against stochastic trend and seasonality in the presence of unattended breaks and unit roots, Journal of Econometrics, v. 115, 2, pp. 199-258, TD No. 472 (June 2003). E. BONACCORSI DI PATTI, G. GOBBI and P. E. MISTRULLI, Sportelli e reti telematiche nella distribuzione dei servizi bancari, Banca impresa società, v. 2, 2, pp. 189-209, TD No. 508 (July 2004). 2004 P. ANGELINI and N. CETORELLI, Gli effetti delle modifiche normative sulla concorrenza nel mercato creditizio, in F. Panetta (eds.), Il sistema bancario negli anni novanta: gli effetti di una trasformazione, Bologna, il Mulino, TD No. 380 (October 2000). P. CHIADES and L. GAMBACORTA, The Bernanke and Blinder model in an open economy: The Italian case, German Economic Review, Vol. 5 (1), pp. 1-34, TD No. 388 (December 2000). M. BUGAMELLI and P. PAGANO, Barriers to Investment in ICT, Applied Economics, Vol. 36 (20), pp. 2275-2286, TD No. 420 (October 2001). F. BUSETTI, Preliminary data and econometric forecasting: An application with the Bank of Italy quarterly model, CEPR Discussion Paper, 4382, TD No. 437 (December 2001). A. BAFFIGI, R. GOLINELLI and G. PARIGI, Bridge models to forecast the euro area GDP, International Journal of Forecasting, Vol. 20 (3), pp. 447-460,TD No. 456 (December 2002). D. AMEL, C. BARNES, F. PANETTA and C. SALLEO, Consolidation and Efficiency in the Financial Sector: A Review of the International Evidence, Journal of Banking and Finance, Vol. 28 (10), pp. 24932519, TD No. 464 (December 2002). M. PAIELLA, Heterogeneity in financial market participation: Appraising its implications for the C-CAPM, Review of Finance, Vol. 8, 3, pp. 445-480, TD No. 473 (June 2003). F. CINGANO and F. SCHIVARDI, Identifying the sources of local productivity growth, Journal of the European Economic Association, Vol. 2 (4), pp. 720-742, TD No. 474 (June 2003).

E. BARUCCI, C. IMPENNA and R. RENÒ, Monetary integration, markets and regulation, Research in Banking and Finance, (4), pp. 319-360, TD No. 475 (June 2003). G. ARDIZZI, Cost efficiency in the retail payment networks: first evidence from the Italian credit card system, Rivista di Politica Economica, Vol. 94, (3), pp. 51-82, TD No. 480 (June 2003). E. BONACCORSI DI PATTI and G. DELL’ARICCIA, Bank competition and firm creation, Journal of Money Credit and Banking, Vol. 36 (2), pp. 225-251, TD No. 481 (June 2003). R. GOLINELLI and G. PARIGI, Consumer sentiment and economic activity: a cross country comparison, Journal of Business Cycle Measurement and Analysis, Vol. 1 (2), pp. 147-170, TD No. 484 (September 2003). L. GAMBACORTA and P. E. MISTRULLI, Does bank capital affect lending behavior?, Journal of Financial Intermediation, Vol. 13 (4), pp. 436-457, TD No. 486 (September 2003). F. SPADAFORA, Il pilastro privato del sistema previdenziale: il caso del Regno Unito, Economia Pubblica, 34, (5), pp. 75-114, TD No. 503 (June 2004). C. BENTIVOGLI and F. QUINTILIANI, Tecnologia e dinamica dei vantaggi comparati: un confronto fra quattro regioni italiane, in C. Conigliani (eds.), Tra sviluppo e stagnazione: l’economia dell’Emilia-Romagna, Bologna, Il Mulino, TD No. 522 (October 2004). G. GOBBI and F. LOTTI, Entry decisions and adverse selection: an empirical analysis of local credit markets, Journal of Financial services Research, Vol. 26 (3), pp. 225-244, TD No. 535 (December 2004). E. GAIOTTI and F. LIPPI, Pricing behavior and the introduction of the euro:evidence from a panel of restaurants, Giornale degli Economisti e Annali di Economia, 2004, Vol. 63, (3/4), pp. 491-526, TD No. 541 (February 2005). A. CICCONE, F. CINGANO and P. CIPOLLONE, The Private and Social Return to Schooling in Italy, Giornale degli economisti e annali di economia, v. 63, 3-4, pp. 413-444, TD No. 569 (January 2006). 2005 L. DEDOLA and F. LIPPI, The monetary transmission mechanism: Evidence from the industries of 5 OECD countries, European Economic Review, 2005, Vol. 49, (6), pp. 1543-1569, TD No. 389 (December 2000). D. J. MARCHETTI and F. NUCCI, Price stickiness and the contractionary effects of technology shocks. European Economic Review, v. 49, pp. 1137-1164, TD No. 392 (February 2001). G. CORSETTI, M. PERICOLI and M. SBRACIA, Some contagion, some interdependence: More pitfalls in tests of financial contagion, Journal of International Money and Finance, v. 24, 8, pp. 1177-1199, TD No. 408 (June 2001). GUISO L., L. PISTAFERRI and F. SCHIVARDI, Insurance within the firm. Journal of Political Economy, 113, pp. 1054-1087, TD No. 414 (August 2001) R. CRISTADORO, M. FORNI, L. REICHLIN and G. VERONESE, A core inflation indicator for the euro area, Journal of Money, Credit, and Banking, v. 37, 3, pp. 539-560, TD No. 435 (December 2001). F. ALTISSIMO, E. GAIOTTI and A. LOCARNO, Is money informative? Evidence from a large model used for policy analysis, Economic & Financial Modelling, v. 22, 2, pp. 285-304, TD No. 445 (July 2002). G. DE BLASIO and S. DI ADDARIO, Do workers benefit from industrial agglomeration? Journal of regional Science, Vol. 45, (4), pp. 797-827, TD No. 453 (October 2002). R. TORRINI, Cross-country differences in self-employment rates: The role of institutions, Labour Economics, V. 12, 5, pp. 661-683, TD No. 459 (December 2002). A. CUKIERMAN and F. LIPPI, Endogenous monetary policy with unobserved potential output, Journal of Economic Dynamics and Control, v. 29, 11, pp. 1951-1983, TD No. 493 (June 2004). M. OMICCIOLI, Il credito commerciale: problemi e teorie, in L. Cannari, S. Chiri e M. Omiccioli (eds.), Imprese o intermediari? Aspetti finanziari e commerciali del credito tra imprese in Italia, Bologna, Il Mulino, TD No. 494 (June 2004). L. CANNARI, S. CHIRI and M. OMICCIOLI, Condizioni di pagamento e differenziazione della clientela, in L. Cannari, S. Chiri e M. Omiccioli (eds.), Imprese o intermediari? Aspetti finanziari e commerciali del credito tra imprese in Italia, Bologna, Il Mulino, TD No. 495 (June 2004). P. FINALDI RUSSO and L. LEVA, Il debito commerciale in Italia: quanto contano le motivazioni finanziarie?, in L. Cannari, S. Chiri e M. Omiccioli (eds.), Imprese o intermediari? Aspetti finanziari e commerciali del credito tra imprese in Italia, Bologna, Il Mulino, TD No. 496 (June 2004).

A. CARMIGNANI, Funzionamento della giustizia civile e struttura finanziaria delle imprese: il ruolo del credito commerciale, in L. Cannari, S. Chiri e M. Omiccioli (eds.), Imprese o intermediari? Aspetti finanziari e commerciali del credito tra imprese in Italia, Bologna, Il Mulino, TD No. 497 (June 2004). G. DE BLASIO, Credito commerciale e politica monetaria: una verifica basata sull’investimento in scorte, in L. Cannari, S. Chiri e M. Omiccioli (eds.), Imprese o intermediari? Aspetti finanziari e commerciali del credito tra imprese in Italia, Bologna, Il Mulino, TD No. 498 (June 2004). G. DE BLASIO, Does trade credit substitute bank credit? Evidence from firm-level data. Economic notes, Vol. 34 (1), pp. 85-112, TD No. 498 (June 2004). A. DI CESARE, Estimating Expectations of Shocks Using Option Prices, The ICFAI Journal of Derivatives Markets, Vol. 2, (1), pp. 42-53, TD No. 506 (July 2004). M. BENVENUTI and M. GALLO, Il ricorso al "factoring" da parte delle imprese italiane, in L. Cannari, S. Chiri e M. Omiccioli (eds.), Imprese o intermediari? Aspetti finanziari e commerciali del credito tra imprese in Italia, Bologna, Il Mulino, TD No. 518 (October 2004). L. CASOLARO and L. GAMBACORTA, Redditività bancaria e ciclo economico, Bancaria, v. 61, 3, pp. 19-27, TD No. 519 (October 2004). F. PANETTA, F. SCHIVARDI and M. SHUM, Do mergers improve information? Evidence from the loan market, CEPR Discussion Paper, 4961, TD No. 521 (October 2004). P. DEL GIOVANE and R. SABBATINI, La divergenza tra inflazione rilevata e percepita in Italia, Bologna, Il Mulino, TD No. 532 (December 2004). R. TORRINI, Quota dei profitti e redditività del capitale in Italia: un tentativo di interpretazione, Politica economica, v. 21, pp. 7-42, TD No. 551 (June 2005). M. OMICCIOLI, Il credito commerciale come “collateral”, in L. Cannari, S. Chiri, M. Omiccioli (eds.), Imprese o intermediari? Aspetti finanziari e commerciali del credito tra imprese in Italia, Bologna, il Mulino, TD No. 553 (June 2005). L. CASOLARO, L. GAMBACORTA and L. GUISO, Regulation, formal and informal enforcement and the development of the household loan market. Lessons from Italy, in Bertola G., Grant C. and Disney R. (eds.) The Economics of Consumer Credit: European Experience and Lessons from the US, Boston, MIT Press, TD No. 560 (September 2005). S. DI ADDARIO and E. PATACCHINI, Wages and the city: The italian case, University of Oxford, Department of Economics. Discussion Paper, 243, TD No. 570 (January 2006). P. ANGELINI and F. LIPPI, Did inflation really soar after the euro changeover? Indirect evidence from ATM withdrawals, CEPR Discussion Paper, 4950, TD No. 581 (March 2006). 2006 C. BIANCOTTI, A polarization of inequality? The distribution of national Gini coefficients 1970-1996, Journal of Economic Inequality, v. 4, 1, pp. 1-32, TD No. 487 (March 2004). M. BOFONDI and G. GOBBI, Information barriers to entry into credit markets, Review of Finance, Vol. 10 (1), pp. 39-67, TD No. 509 (July 2004). LIPPI F. and W. FUCHS, Monetary union with voluntary participation, Review of Economic Studies, 73, pp. 437-457 TD No. 512 (July 2004). GAIOTTI E. and A. SECCHI, Is there a cost channel of monetary transmission? An investigation into the pricing behaviour of 2000 firms, Journal of Money, Credit, and Banking, v. 38, 8, pp. 2013-2038 TD No. 525 (December 2004). A. BRANDOLINI, P. CIPOLLONE and E. VIVIANO, Does the ILO definition capture all unemployment?, Journal of the European Economic Association, v. 4, 1, pp. 153-179, TD No. 529 (December 2004). A. BRANDOLINI, L. CANNARI, G. D’ALESSIO and I. FAIELLA, Household Wealth Distribution in Italy in the 1990s, In E. N. Wolff (ed.) International Perspectives on Household Wealth, Cheltenham, Edward Elgar, TD No. 530 (December 2004). A. NOBILI, Assessing the predictive power of financial spreads in the euro area: does parameters instability matter?, Empirical Economics, v. 31, 4, pp. , TD No. 544 (February 2005). L. GUISO and M. PAIELLA, The Role of Risk Aversion in Predicting Individual Behavior, In P. A. Chiappori e C. Gollier (eds.) Competitive Failures in Insurance Markets: Theory and Policy Implications, Monaco, CESifo, TD No. 546 (February 2005).

G. M. TOMAT, Prices product differentiation and quality measurement: A comparison between hedonic and matched model methods, Research in Economics, No. 60, pp. 54-68, TD No. 547 (February 2005). M. CARUSO, Stock market fluctuations and money demand in Italy, 1913 - 2003, Economic Notes, v. 35, 1, pp. 1-47, TD No. 576 (February 2006). R. BRONZINI and G. DE BLASIO, Evaluating the impact of investment incentives: The case of Italy’s Law 488/92. Journal of Urban Economics, vol. 60, n. 2, pag. 327-349, TD No. 582 (March 2006). A. DI CESARE, Do market-based indicators anticipate rating agencies? Evidence for international banks, Economic Notes, v. 35, pp. 121-150, TD No. 593 (May 2006). L. DEDOLA and S. NERI, What does a Technology Shock Do? A VAR Analysis with Model-Based Sign Restrictions, Journal of Monetary Economics, v. 54, 2, pp. 512 - 549, TD No. 607 (December 2006).

2007 F. LOTTI and J. MARCUCCI, Revisiting the Empirical Evidence on Firms' Money Demand, Journal of Economics and Business, v. 59, 1, pp. 51-73, TD No. 595 (May 2006).

FORTHCOMING S. MAGRI, Italian Households' Debt: The Participation to the Debt market and the Size of the Loan, Empirical Economics, TD No. 454 (October 2002). F. LIPPI and S. NERI, Information variables for monetary policy in a small structural model of the euro area, Journal of Monetary Economics TD No. 511 (July 2004). A. ANZUINI and A. LEVY, Monetary Policy Shocks in the new EU members: A VAR approach, Journal of Monetary Economics TD No. 514 (July 2004). A. DALMAZZO and G. DE BLASIO, Production and Consumption Externalities of Human Capital: An Empirical Study for Italy, Journal of Population Economics, TD No. 554 (June 2005). R. BRONZINI and G. DE BLASIO, Una valutazione degli incentivi pubblici agli investimenti, Rivista Italiana degli Economisti , TD No. 582 (March 2006). L. DEDOLA and S. NERI, What does a technology shock do? A VAR analysis with model-based sign restrictions, Journal of Monetary Economics TD No. 607 (December 2006).