DO INNOVATION INCENTIVES WORK? EVIDENCE FROM THE ITALIAN MANUFACTURING SECTOR

The main purpose of this study is to investigate upon the impact of fiscal incentives on firm’s innovative performance. We use data from the 7th, 8th and 9th waves of the “Indagine sulle Imprese Manifatturiere Italiane†by Unicredit (previously managed by Capitalia-Mediocredito Centrale), which contains information on both product and process innovation by manufacturing firms, on the amount of resources invested in R&D (if such amount is positive) and it is also informative of the existence of forms of fiscal incentive for R&D and investment in innovative activities. In our study we use different techniques. First we look at Average Treatment Effects, under the assumption of “selection on observablesâ€, implying that the econometrician has access to all the variables affecting the likelihood of being treated. In this part of the paper we verify whether -everything else constant (i.e. for a given value of the propensity score)- there is evidence that firms that have access to fiscal incentives tend to innovate more. In the second part of our study we cast some doubts on the plausibility of the “selection on observables†assumption and we look more in depth at one specific case of fiscal incentive: the one provided by Law 140/1999 to firms located in “depressed areas†(as defined by the law itself). We focus on this law because it is particularly important from a policy perspective within the Italian dual economy, but also because it allows us a more precise estimate of the treatment effect in a situation where treatment status (i.e. access to the incentive) is likely to depend to the same (unobserved) factors that affect the innovation outcome. In such a situation OLS estimated are biased and inconsistent and we have to use instrumental variable estimation. We choose to instrument treatment using the eligibility rules for treatment and we find the confirmation that indeed an endogeneity issue exists and that its effects are stronger the weaker is the impact of treatment on the outcome variable.

Earning Profiles for Italian Male Workers: Is There Evidence of a Premium for Education?

Are younger generations better off than older ones? Can younger cohorts starting with lower real wages catch up with previous generations? Are young or old generations becoming more unequal? In the last fifteen years these questions, of great interest for the policy maker, have motivated a considerable amount of research on changes in the wage structure, with a particular emphasis on North America and the U.K. first and other countries much later (see Acemoglu, 2002, Acemoglu 2003; Card et al., 1999; Goldin and Katz, 1996; Berman et al., 1998; Blau and Khan, 1996; Katz and Murphy, 1992). While some authors have documented an increase in inequality, however measured, which cannot be accounted for by observables like education, experience, sex, and age, others have concentrated their attention on how the earnings distribution (captured by its central location or other statistics) has changed through time. In our study we concentrate on the study of inter-generational and intra-generational patterns of earnings for Italian male dependent workers for the period 1987-2006. Using data from the Bank of Italy's Survey of Household Income and Wealth, we construct cohort-education-(macro) region-specific age profiles for mean real wages (the measure of central location here adopted) and for the 90-10 percentile differential (the inequality measure), allowing for region-specific price indexes. We verify how different cohorts have been doing comparatively and finally we test whether, with time, the (mean) returns to experience and education have increased. Our preliminary results indicate that, for the two education groups considered, each successive generation has benefited from higher entry wages. At the same time, we find that the wage age-profiles for both education groups have become flatter so that we cannot conclude that more recent cohorts are better off than their immediate predecessors. When looking at high/low education relative wages, we find that there is only scant evidence of positive cohort profiles (i.e. that the education premium has been rising across cohorts), while we notice that the relative wage tends to increase over the life-cycle. Finally, we find that inequality tends to increase with age, while we also find evidence of across-cohort variation, in the direction of increasing inequality. Our work is relevant under various aspects. On the one hand, it provides a clear framework in which between and within cohort comparisons are meaningful and easily interpretable. Moreover, it allows us to relate our results to those obtained by MaCurdy and Mroz (1995) and Beaudry and Green (2000) in their studies of the earning patterns of, respectively, American and Canadian workers.JRC.J.3-Information Societ

Technology, Skills and Retirement

In our work we study the role of Information and Communication Technology (ICT) skills and their utilization in the retirement decision. We provide empirical evidence based on Italian panel data in favour of the hypothesis that - ceteris paribus - better educated male employees with ICT know-how retire later. Such effect is stronger the longer the time horizon considered, and its magnitude is remarkably larger than the one observed in US and Germany in previous studies. We also document that ICT do not play a crucial role in the retirement decision of women. Our results are robust to the estimation strategy adopted.retirement, skill-biased technological change

Demographic and education effects on unemployment in Europe: economic factors and labour market institutions

We analyse the effects of demographic and education changes on unemployment rates in Europe. Using a panel of European countries for the 1980-2000 period - disaggregated by cohort, gender and education -, we empirically test the economic effects of two stylised facts that have occurred in recent decades: the baby bust and the education boom. We find that structural shifts in the population age structure play an important role and that a lot of variation is also attributable to educational changes, the latter usually neglected in aggregate studies. Results show that demographic and education shocks are qualitatively different for young (adult) workers as well as for more (less) educated people. While adult workers and more educated individuals, in general, experience lower unemployment rates, changes in the population age structure appear to be positively related to young workers' unemployment rates while they have no effect on adults. Conversely changes in the skill structure (education boom), even when controlling for skill-biased technological change, reduce the unemployment of the more educated. Labour market institutions also influence unemployment rates in different ways. Unemployment benefits are found to have a positive impact on unemployment, while bargaining coordination and employment protection reduce it

The evolution of income inequality and relative poverty in Italy: 1987-2010

In this paper we study the evolution of poverty and inequality in Italy in the period 1987-2010. Our data are from the Bank of Italy Survey of Household Income and Wealth and the variable of interest is real income (reference year is 2009), defined using price indexes that are allowed to vary by region and that allow us to make comparisons in levels of real incomes. We construct relative poverty and inequality indexes using equivalent income obtained by applying two types of equivalence scales widely used in the literature (square root of the number of household members and ISEE scale) in order to verify how our measures of poverty and inequality are sensitive to the adoption of the equivalence scale. While we do not intend to be innovative in the measurement of poverty or inequality (we rely on widely used indexes), our aim is to depict a complete picture of the evolution of poverty and inequality with a particular attention to their determinants. By using decomposable inequality and poverty indexes we look at five decompositions: by gender, geographical areas (North West, North East, Centre and South), class age (less than 30, between 30 and 40, 40 and 50, 50 and 60 and over 60), education (compulsory school or less, upper secondary and tertiary education) and employment condition (employee, self-employed and unemployed). Given the definition of non- overlapping groups we examine-as far as inequality is concerned- the relative weights of the “within” and of the “between” components while, for poverty, we look at “poverty risks”. These analyses allow us to understand weather inequality originates mostly from differences within each group or from differences across groups and how each group influences overall poverty (measured using both the headcount ration and the average squared normalised poverty gaps that embeds the poverty gap and its distribution among the poor). Finally we consider some counterfactual exercises as to find out the effect of the changes during the analysed period of the demographic composition of the groups, of the subgroups’ mean incomes (only for inequality) and of the subgroups’ specific inequality or poverty indexes. The results show that the main determinants of the inequality and poverty evolution in Italy can be traced to geographical and educational grouping, with the age grouping relevant for poverty only.JRC.J.3-Information Societ

Characterizing the evolution of the EU R&D intensity gap using data from top R&D performers

In this paper we look at the evolution of the R&D intensity gap between the EU and its major competitors using data from the Industrial Scoreboard covering the period 2002-2010. We focus on R&D intensity and we assess whether the gaps relative to major competitors arise from differences in industrial composition (structural component) or differences within sectors (intrinsic component). The paper is divided in two parts. In the first part of the paper we first present the evolution of the R&D intensity gap between the EU and its major competitors (US, Japan, BRIC, Asian Tigers) and then we look more closely at the role and evolution of the structural and intrinsic component for each pair-wise comparison, by looking at four basic macro-sectors defined in term of their R&D intensity. In the second part of our work we concentrate on the EU-US R&D intensity gap and, by applying firm level analysis, we test whether the results obtained by the statistical decomposition of aggregate R&D intensity are confirmed. In particular we test whether there is evidence of across-sector variability in R&D intensity and whether, within sectors, EU and US firms are performing differently, controlling for size, cyclical effects, common macroeconomic shocks and company's age. Age is important for at least two reasons. First, young companies might have more problems in finding access to funds necessary in order to invest in R&D. Second, young companies might have to be especially aggressive in terms of innovation if they want to enter and succeed in markets where incumbents already exist. Therefore, our aim here is also to document the age profile for R&D intensity and to verify whether the R&D intensity gap between EU and non-EU companies is related to age of the firm. Finally we check if R&D intensity is affected by the abundance of internal funds (as captured by the profit/sales ratio), if this relationship changes with the age of the company and if the latter shows across-regional variation. Our results from firm level analysis indicate that there is evidence of strong across-sector variation and some evidence of within-sectors-across-region variation, which –however- is not always in favour of the US. Moreover we find that R&D intensity tends to decrease as firm size increases (as measured by the number of employees), that the age profile for R&D intensity behaves very differently in the two regions and that young companies in the EU exhibit a much higher reactivity to lagged profits-to-sales ratio, when compared to their US counterpart. We believe that this is an indication that the conditions for accessibility and cost of funds differ significantly across the two regions

R&D intensity among top R&D perfomers: Implications for policy

In this report, we look at the evolution of Europe's R&D intensity gap relative to the US and its main competitors, using data from repeated waves (2002-2010) of the Industrial Scoreboard, which collects data from top R&D performers in Europe and in the rest of the world (US, Japan, BRIC, Asian Tigers). First we decompose the R&D intensity gap into a structural and an intrinsic component and, comparing the EU to its main competitors, we find that the gap is largely structural and that Europe's position relative to any of the other four regions, has worsened during the years 2005-2006. Since then, it has slightly improved relative to Japan and especially the Asian Tigers, but it has definitely worsened relative to the US and to the BRICS. In the second part of the paper, we focus on the EU-US comparison and, using firm-level data, we confirm the structural interpretation. We also find that European young companies seem to depend much more on their internal resources for the financing of R&D when compared to US young companies. This suggests that policies directed at financing young innovative companies might play a role in closing the EU-US R&D intensity gap.JRC.J.3-Information Societ

DO INNOVATION INCENTIVES WORK? EVIDENCE FROM THE ITALIAN MANUFACTURING SECTOR

The main purpose of this study is to investigate upon the impact of fiscal incentives on firm's innovative performance. We use data from the 7th, 8th and 9th waves of the "Indagine sulle Imprese Manifatturiere Italiane" by Unicredit (previously managed by Capitalia-Mediocredito Centrale), which contains information on both product and process innovation by manufacturing firms, on the amount of resources invested in R&D (if such amount is positive) and it is also informative of the existence of forms of fiscal incentive for R&D and investment in innovative activities. In our study we use different techniques. First we look at Average Treatment Effects, under the assumption of "selection on observables", implying that the econometrician has access to all the variables affecting the likelihood of being treated. In this part of the paper we verify whether -everything else constant (i.e. for a given value of the propensity score)- there is evidence that firms that have access to fiscal incentives tend to innovate more. In the second part of our study we cast some doubts on the plausibility of the "selection on observables" assumption and we look more in depth at one specific case of fiscal incentive: the one provided by Law 140/1999 to firms located in "depressed areas" (as defined by the law itself). We focus on this law because it is particularly important from a policy perspective within the Italian dual economy, but also because it allows us a more precise estimate of the treatment effect in a situation where treatment status (i.e. access to the incentive) is likely to depend to the same (unobserved) factors that affect the innovation outcome. In such a situation OLS estimated are biased and inconsistent and we have to use instrumental variable estimation. We choose to instrument treatment using the eligibility rules for treatment and we find the confirmation that indeed an endogeneity issue exists and that its effects are stronger the weaker is the impact of treatment on the outcome variable

INNOREG: A Comprehensive Dataset on Government Policies Affecting Innovation

The purpose of this report is to describe the methodology used to develop a comprehensive dataset, denominated INNOREG, which provides information on several potential drivers and barriers to firms’ innovation activity. All the examined drivers and barriers depend, in a more or less direct way, upon the decisions taken by national policy makers. By merging INNOREG with data on ICT use, innovation, productivity and employment it will be possible to investigate the effect of several policies (mainly concerning labour market and taxation) and of the efficiency of bureaucracy on measures of economic performance such as production, employment, innovation etc.. The data are of three main types: 1. reforms of labour market regulation, computed using the EU Commission LABREF database, and which gives us information on the direction and intensity of reforms affecting the labour markets of 27 EU countries from 2000 to 2012 (LABREF_DRF.DTA); 2. generosity of the tax treatment for R&D, as measured by the B-Index over the period 1990-2013 (not all years are available) for a set of EU countries (B_INDEX.DTA); 3. indices of business regulation, as measured by various indicators taken by the Wordbank DoingBusiness project, reported annually from 2004 to 2014 for all EU countries (DOINGBUSINESS.DTA). For each of the above three topics, we developed a specific dataset (name in parenthesis). The three resulting datasets were then merged to form the comprehensive INNOREG dataset (INNOREG.dta). In this report we also provide summary statistics on the three types of data mentioned above.JRC.B.4-Human Capital and Employmen

Equality of opportunity: theory, measurement and policy implications

In this report we present the Equality of opportunity approach, clarifying its theoretical foundations and empirical implications, and develop policy implications especially in the area of human capital investment. According to the equality of opportunity (EOp) approach, a primary goal of public policies is to insure that individuals develop their lives in a context where the playfield is levelled. The main idea behind EOp is that inequality in outcomes (e.g., income, wealth, human capital/education and health) is acceptable to the extent that it reflects the result of individual choices taken by individuals that share the same opportunities. According to the “equality of opportunities principle”, inequalities that are due to variables beyond individual’s control, called circumstances, (e.g. family socioeconomic and cultural background, ethnic origin, gender, age etc.), should be eliminated or compensated for by public intervention. Only those variables within the sphere of individual’s autonomy, called effort, (e.g., number of hours devoted to study or work, quality of the work supplied, occupational choices etc.) can justify a difference in the relevant outcome variable. This implies that the equality of opportunity approach is consistent with the notion of fair inequality, as long as it originates from effort.JRC.B.4-Human Capital and Employmen