Dynamics of micro Italian firms: the role of training

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Dynamics of micro italian firms:

the role of training

Candidate: Marco Rispoli

Supervisor: Prof. Federico Tamagni

Thesis submitted in fulfillment of the requirements of the

Master of Science in Economics

Department of Economics and Management

University of Pisa and Scuola Superiore Sant’Anna, Italy

Academic Year: 2018-2019

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Acknowledgements

I would first like to thank my thesis supervisor, Professor Federico Tamagni. The door of his office was always open whenever I ran into troubles or had a question about my dissertation. He consistently allowed this thesis to be my own work, but steered me in the right direction whenever he thought I needed it.

I would also like to thank all the experts who were involved in the obtainment of the data. First of all, Tommaso Rondinella and Roberta Palmieri of the National Institute of Statistics, that helped me to find the data and to be in contact with the different CNA. Secondly, all the Presidents and the workers of the regional CNA offices (Umbria, Toscana and Marche), that give me the authorization in order to use the micro data.

Moreover, I would like to thanks all my colleagues of the Master of Science of Economics for the continuous help during these two years, Professors Fabrizio Bientinesi, Angela Parenti and Alessio Moneta that encouraged and helped me in the choice of the dissertation, and all my family and my friends that constantly give me a support when I needed it. Finally thank to M. Imran who prepared the template of the thesis.

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List of Figures

2.1 Training effect on growth and productivity . . . 7

3.1 Comparison of productivity by class and sector in Italy and Europe. Source: The Multiprod project, OECD 2017 . . . 13

4.1 Training participation and training intensity in Europe. Source: Bas-sanini et al. (2005) . . . 19

4.2 Training participation in Italy and Spain. Source: Bassanini et al. (2005) . . . 20

4.3 Training participation in Finland, Portugal and Sweden. Source: Bas-sanini et al. (2005) . . . 20

4.4 Percentage of firms that do training in Italy over the years. Source: Unioncamere, Sistema Informativo Excelsior, 2017. . . 21

4.5 Distribution of training activity types for firm size class Source: Union-camere, Sistema Informativo Excelsior, 2014 . . . 23

4.6 Distribution of training activity types for firm size class. Source: Unioncamere, Sistema Informativo Excelsior, 2017 . . . 23

4.7 Percentage of micro-firms that do training over the years. Source: Unioncamere, Sistema Informativo Excelsior. . . 24

4.8 Percentage of micro-firms that do training across sectors and years. Source: Unioncamere, Sistema Informativo Excelsior. . . 25

5.1 Growth & D1.Training . . . 38

5.2 Growth & D2.Training . . . 38

5.3 Growth & Train . . . 39

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List of Figures vi

5.4 Prod & D1.Training . . . 39

5.5 Prod & D2.Training . . . 40

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List of Tables

4.1 Percentage of firms that provide training courses per firm size,

2007-2016. Source: Unioncamere, Sistema Informativo Excelsior. . . 21

4.2 Percentage of firms that do training per sector, 2007-2016. Source: Unioncamere, Sistema Informativo Excelsior. . . 22

4.3 Percentage of firms that do training per region, 2007-2016. Source: Unioncamere, Sistema Informativo Excelsior. . . 22

5.1 Number of the firms per region. . . 32

5.2 Number of the firms per macro sector. . . 32

5.3 Summary statistics of dependent variables . . . 33

5.4 Summary statistics of independent variables . . . 33

5.5 Distributional features of the variables . . . 34

5.6 Mean and Median of training across firms that perform it . . . 34

5.7 Number of observations with training and not, D1. . . 34

5.8 Number of observations with training and not, D2. . . 35

5.9 Descrpitive stats by D1 . . . 36

5.10 Descriptive stats by D2 . . . 36

5.11 Number of observations by D1 and regions . . . 37

5.12 Number of observations by D1 and years . . . 37

5.13 Number of observations by D1 and sectors . . . 37

5.14 Pairwise correlations . . . 41

6.1 Growth & D1. . . 45

6.2 Growth & D2 . . . 46

6.3 Growth & Train . . . 47 vii

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List of Tables viii

6.4 Prod & D1 . . . 49

6.5 Prod & D2 . . . 50

6.6 Prod & Train . . . 52

6.7 Training & firm growth: Marche . . . 53

6.8 Training & productivity: Marche . . . 54

6.9 Training & firm growth: Toscana . . . 55

6.10 Training & productivity: Toscana . . . 55

6.11 Training & firm growth: Umbria . . . 56

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Chapter 1 Introduction & research questions

In a globalized and fast-growing world, the role of the intangible assets is determi-nant; human capital, the set of skills and knowledge of the employees, is considered as one of these relevant factors. One of the main tool, in order to upgrade the skills of the workers and foster the accumulation of human capital, is training. A large theoretical literature demonstrates the role of training in enhancing the firm perfor-mance. Its increasing importance is stressed at the international level by the New Skill Agenda for Europe and by Europe 2020 for a sustainable and smart growth. The research on this topic is an important area of study, not only because company training covers a substantial part of the education after labour market entry, but because of the perception, sustained by the study of the EC (2005), that European employers do not sufficiently spend in upgrading the skills of their employees. This is especially true for what concerns micro and small firms, where the provision of training is even lower, as documented in Excelsior (2014) and SBRC (1992). In the literature, two possible explanations are presented. On the one hand, the ”igno-rance” explanation says that employers of small and micro firms consider training more as a cost than a value. On the other hand, the ”market” explanation attributes the lower provision of training to the lack of financial resources in this type of firms and to the greater market turbulence they have to deal with. Whatever the main reasons behind low training practices among micro-firms, training can be an instru-ment able to enhance the competitiveness and innovation capacity in this category of companies, that may be expected to suffer more from international competition.

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Chapter 1. Introduction & research questions 2 This perhaps particularly applies to countries like Italy, where small and micro firms are the highest percentage in terms of firm size and value added. Despite this, stud-ies about this kind of firms are rare, even in the small business literature, mainly due to the lack of data.

The aim of this dissertation is to analyze the impact of training on the perfor-mance of Italian micro firms. The analysis exploits data from the Trend Dataset, created within the collaboration between the National Statistical Office (ISTAT) and the National Craft Trades Confederation (CNA), which provides information about important economic variables for the period 2013-2017 for three Italian re-gions: Marche, Umbria and Toscana. The empirical analysis focuses on the effect of training on two measures of performance: firm sales and labour productivity. The methodology employed includes the OLS as the baseline model and the fixed effect estimator as the robust estimation technique to treat the unobserved heterogeneity. Overall, the results obtained through the analysis, show a positive and significant relationship between training and both the measures of firm performance.

The structure of the work is the following. Chapter 2 provides a review of the empirical and theoretical background about the effect of training on firm perfor-mance, with a focus on productivity and sales growth. Chapter 3 provides detailed information about the importance of small and micro firms and it analyzes the pro-vision of training and the empirical literature related to this firm size class. Chapter 4 presents data about training in the Italian context and the empirical literature available. Chapter 5 presents the Trend dataset, sources, variables used and some descriptive statistics. The core empirical analysis is presented in Chapter 6. Finally Chapter 7 presents the conclusions of this dissertation.

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Chapter 2 Training and firm performance:

theoretical and empirical

background

In this chapter is analysed the relevance of training for the dynamics of firm per-formance, both from a theoretical and an empirical point of view. The literature of the theoretical background is mainly based on human resources and human cap-ital theory. For what concerns the empirical literature, we focus on two particular measures of firm performance, that is to say, productivity and sales growth.

2.1 Theoretical background

2.1.1 Human resource theory

In the actual globalized and strongly competitive context, firms are looking for re-sources and factors, able to make them different from the others. Among the theories that describe the importance of internal resources of the firms for the business re-sults, there is the resource based theory. According to this theory, anything that can be thought as a strength or as a weakness specific to a given enterprise, is re-source. This definition includes tangible and intangible assets, skills, routines and processes, as discussed for instance, in Wernerfelt (1984) and Barney (1991). In this

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2.1. Theoretical background 4 wide range of resources, the main elements that distinguish one organization from the other, are not tangible. Rather, the intangible resources are the ones that are best able to meet the conditions for assuring a sustainable competitive advantage, as pointed out by Valle and Castillo (2009). Moreover, among intangible resources, the so called human resources, can be considered capable of generating a sustainable competitive advantage.

A resource, to be qualified as such of a competitive advantage, it has to fulfill four characteristics, (Barney, 1991):

• It must add value to the firm • It must be rare

• It must be inimitable

• There must be no adequate substitute for this resource

Human resources satisfy all of these characteristics. The first and the second features are related to the heterogeneity of people skills, these make them rare and capable of adding and providing value to the company. The third point is linked with the unique history of the firms. In fact, the unique nature of the human resources belonging to a specific firm, makes it hard for other firms to understand and to duplicate them. The last characteristic relates to the fact that human resources are not easy to transfer across markets, products and technologies.

One of the tools that firms have to modify human resources is training, that is, the pool of operations able to modify them in such a way that can better fit the organizational environment and so the business goals of the enterprises. Accordingly to Koch and McGrath (1996), the importance of training can be summarized as it follows: firstly, specific human resources are the more direct determinant of the firm competitive advantage, since they are harder to build and imitate. Secondly, training by increasing the skills of the employees, reduces the cost and risk of selecting, hiring and internalising people from labour markets, which again is beneficial for business results. Indeed, as in Hatch and Dyer (2004), developing human capital through training cannot be compared with the acquisition of the same via the recruitment

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2.1. Theoretical background 5 of employees with experience. Thirdly, apart from the benefits for the adaptation of new workers, an adequate training strategy can improve the day to day performance, guaranteeing the possibility to cope with change and using it as a positive force, through a continuous learning culture. Moreover, we have to take into account the capacity of human resource practices to influence indirectly company performance via their influence on the behaviour of the workers. The main contribution in this field, has been made by Akerlof and Kranton (2000). In their opinion, if workers identify themselves in a better way in the organization where they are involved in, they will need less monetary incentives to do their work well. Investing in the knowledge of the workers can be seen as a form of efficiency wage, that leads to increase the effort and full commitment. The final result is an improvement of firm performance since the workers act in the interest of the organization.

On the basis of these points, authors like Bryan (2006),Arag´on et al. (2006),Valle

and Castillo (2009),Vlachos (2009),Sala and Silva (2013), formulate a positive ex-pectation regarding the capacity of training to contribute in a positive manner to the results of the companies.

2.1.2 Human capital theory

Another theory relevant to understand the role of training relates to human capital theory. Human capital is an important factor in the economic literature both at macro level for the overall economic growth, (Barro, 2001; Lucas, 1993), and at micro level (Barney, 1991; Koch and McGrath, 1996; Pfeffer, 1994). Training is considered as one of the main tool for the accumulation of human capital, that is one of the inputs of the production function and therefore one of the possible channels of the increase of productivity. In fact, it is capable of increasing the firm specificity of employee skills, which is a determinant of productivity and efficiency (Huselid, 1995).

To understand the channels through which training influences productivity, an important element regards the different nature of training. Following Becker (1964), training can either be generic or specific, and the two types have different effects, thus creating different incentives to the firm to implement it. Specific training is

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2.1. Theoretical background 6 the pool of knowledge and skills directly applicable and useful in the firm context in which the worker is involved. Generic refers to the skills and knowledge that can be transferable outside the company specific context. In the case of specific training, the firm has the incentive to sustain the cost, since the skills learned are directly applicable and the risk of poaching is low, due to the low transferability. Differently, in the case of generic training, its characteristics made it easy to be used in other firms and so there is less incentive to pay for it. For these reasons, the cost of such a type of training may be sustained only through a reduction of the workers’ wage. In order to be valid, the conclusions presented by Becker (1964) should be asso-ciated with a competitive labour market. However such a vision is criticized by two different literatures. The first one is mainly based on the work by Acemoglu and Pischke (1998), where the authors define a model for training, introducing labour market imperfections. Through their introduction in the model, the authors were able to justify the investments of the firms into both generic and specific knowledge. In fact, the presence of imperfections in the labour market, assures a monopsony power to the firm able, to justify the incentive to invest also in the generic training. The other critique comes from the resource based view. The different explanation of training is drawn upon the concept of routines and path dependence as in Nelson and Winter (1992). Routines are usually understood to represent repetitive patterns of behaviour within the organizations. Moreover, they are built one upon the other in a path dependent way. Among many valuable assets, human capital exists just as a result of sustained investments over time. Based on this premise, the role of training is conceptually different compared to theories of human capital as in Becker (1964) and Acemoglu and Pischke (1998). The fundamental heterogeneity of the firms and the firm-specific routines, virtually never allow an employee trained in one firm to be of equal use at the same price in another, as stressed by Koch and McGrath (1996); the authors sustain that the corollary of this heterogeneity and firm specificity is that: any training that updates the skills of the workforce has a positive productivity effect. This of course, does not mean that there are not differences between the types of training that a firm decides to use for his workers; in particular, the more the company trains in the direction of the increase of firm

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2.1. Theoretical background 7 specificity skills, the higher the likelihood that the program brings a huge increase of productivity.

We can summarize the predictions coming from the literature in the two following points:

• First hypothesis: expectation about a positive impact of training on the growth of the firms.

• Second hypothesis: expectation of a positive impact of training on the pro-ductivity of the firms.

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2.2. Empirical literature 8

2.2 Empirical literature

We consider two different measures belonging to the broader concept of firm perfor-mance: sales growth that measures the success of the company on the market and productivity, which measures theoretically the efficiency and, thus, the competitive-ness of the firm. The empirical literature on training has been more concerned with the impact on inputs and so on productivity, rather than output like sales growth. This is stressed also by the report of Cedefop (2011), dedicated to the analysis of the impact of training on business performance. Even if the report finds out differences of the estimated coefficients of training across size and methodologies considered, the general expectation is a positive influence of training on business performance; Of the 62 studies analyzed in the report, 86.89% find out a positive effect of training at least in one estimation.

2.2.1 Training and firm growth

Even if the literature is scarce, some authors try to estimate the effect with con-trasting results. In fact, only some of them find out a positive and significant effect of training.

Bracker and Cohen (1992) study the effect of training and development on firm’s performance, including sales growth. Training and development correspond to any action with the final purpose of upgrading the skills of the workers. The expec-tation that a higher average sales growth should be associated with training and development, is not confirmed by data. In fact, using a sample of ninety-seven small firms belonging to the electronics industry, the authors failed to support the above hypothesis through an anova test. Only when the differential impact is studied in the group of firms that adopt structured strategy, a significant and positive effect is found. Using the same concept of training and development, Vlachos (2009) also studies the effect on sales growth. Differently from Bracker and Cohen (1992), the author defines various measures of performance. In particular, for what concerns the dependent variables, two different measures are considered: perceived sales growth and actual sales growth. Perceived sales growth correspond to the perceptions of

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2.2. Empirical literature 9 the HR managers and it is calculated by using a five point scale ranging from one to five. The sample includes 71 Greek firms belonging to the food sectors and the authors use a hierarchical regression model. The empirical results show a positive effect of training on perceived firm growth, while no significant effect is found for sales growth.

On the same line, Khatri (2000) considers a training measure made up of five items: employees of the company go through training programs every ten years, the company has formal training programs, training needs analysis, cost and benefits analysis of training and evaluation of training programs. Regarding the measure of firm performance, the authors consider sales growth, calculated as the percentage of growth of sales in the last three years. They use a sample of 915 largest companies in Singapore in order to test the hypothesis. In all types of the specifications, the amount of training has not a positive and significant impact on sales growth. Lastly, Cosh and Hughes (2003) analyze the relationship between training and business performance at firm level in the UK and find a positive and statistically significant effect of training on sales growth. However these results strongly depend on the type of proxy of training the authors use. A statistically and significant impact is found only for training expenditures per firm.

Most of the empirical works presented, do not find support for the expected positive association between growth and training.

2.2.2 Training and productivity

The empirical literature concerning the link between training and productivity is huge, since productivity is considered the most direct measure affected by training programs.

Bartel (1994), exploits a cross section coming from the 1986 Columbia Business Survey, regarding 495 business lines from the manufacturing sector. The author implements a Cobb-Douglas production function where the training provided to employee is taken into consideration. The training variable is calculated as the percentage of the firm’s occupational groups receiving formal training. The author implements a first difference model, where the training programs made after 1983,

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2.2. Empirical literature 10 are regressed on the change of productivity. A statistically significant and positive productivity gain of 18.6% over the three year period is found.

Barrett and O’Connell (2001) follow the empirical strategy of Bartel (1994), and apply it to enterprises in Ireland in the years between 1993 and 1996-1997. The sample, randomly selected, includes 1,000 enterprises employing more than ten people and covering sectors like manufacturing, construction and private services. Productivity is calculated as the ratio between sales and total employees, while the measures of training involve three different proxies: the ratio of total person trained to total employment,the number of total days of training to total employment and the ratio of the total expenditure of training to total payroll. By using a difference equation in order to treat unobserved heterogeneity, the authors find that two of the three measures of training have a positive and statistically significant relationship with productivity.

On the same line, the study of Black and Lynch (1996) find a difference in the significance and positiveness of the coefficient depending on the proxy of training used. The data comes from the National Employers Survey that provides infor-mation about sales, capital stock, cost of the materials and worker characteristics like education and training. In particular the total number of trained workers in 1990 and 1993 is available. A complex impact of training on the productivity of enterprises, calculated as sales per employee, is found: neither the level of trained worker in 1990 nor the level of 1993 have a positive influence on productivity. Only when other dimension of training are considered, they find positive and significant results. In detail, the greater the percentage of formal training outside the work-ing hours and computer trainwork-ing, the greater the productivity in the manufacturwork-ing and non manufacturing sector respectively. Lyau and Pucel (1995), analyzing the relationship between training and productivity in manufacturing firms in Taiwan, find out differences depending on the way in which productivity is calculated. No statistically significance impact is found when they use sales per employee, while when value added per worker is considered, both measures, direct training cost and total cost, have strong positive relationships with labour productivity.

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sec-2.2. Empirical literature 11 tional studies, some authors exploit panel data to estimate the effect of training on productivity. Among this, Dearden et al. (1996) analyzes the effect of training on productivity, using a panel data set of British firms. In order to control un-observed heterogeneity and endogeneity, the authors used a fixed effect estimator and a GMM estimator. When the proportion of trained workers is regressed on productivity, a statistically and significant effect is found both for the random and fixed effect model, even if the coefficient decreases when the authors use the second methodology; raising the training variable by 1%, it is associated with an increase of productivity of 0.7%. The further specification through the GMM, tell a similar story to the within estimator, even if the coefficient is a bit smaller.

Almeida and Carneiro (1996) investigate the productivity impact of training for 1500 large manufacturing firms in Portugal, in the period between the 1995 and 1999. Differently from the majority of the authors mentioned before, they consider the cost and the duration of training. The results show that the estimated coefficient of the OLS is similar to the one of the GMM, while the coefficient of the within estimator is larger than the two. An increase of the duration of training of 10 hours is linked with an increase in productivity between 0.6% and 0.7%.

Finally, Liu and Lu (2016) analyse the impact of on-the-job training on produc-tivity for China, by using a propensity score matching technique, where training is considered as the treatment variable. They measured training in two ways. Firstly a dummy, allowing to check if the firms using training, perform better than the firms that do not use it. Secondly, they use training expenditure to check whether firms that use training more intensively, perform better or not. The results of the within estimator are that training generally leads to a 1,31% and 2,99% increase in firm productivity when all control variables are included, While in the case in which they use the treatment effect model, an effect of 9,6% and 1,2% is found.

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Chapter 3 The role of training in small and

micro firms

In this chapter we focus our attention on the role of training in enhancing the performance of small and micro firms. In particular, the category of micro firms is relevant since it represents a huge percentage of the total of the firms, both in Europe and, in a stronger way, in Italy. Despite the importance, however, they are a neglected category both for what concerns the empirical analysis about the effect of training and for the available information.

3.1 Do small and micro firms deserve a particular

attention?

The large part of the firms in Europe is made by small and micro firms. With a total of 23 million enterprises, more than 99% of enterprises in the European Union are

SMEs1_{. In detail, micro enterprises, that are the ones with less than ten employees,}

constitute over the 90% of the total number. They alone employ more than one third of the European workforce and produce more than 20% of economic added value, as indicated in Farvaque and Voss (2009). Italy is the European country where the weight of micro firms, in terms of added value and firm size, is the greatest. In 2008

1_{Small and Medium Enterprises}

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3.1. Do small and micro firms deserve a particular attention? 13 the percentage of added value generated by micro firms was 33%, about 14% greater than the average of Europe, as highlighted in Mitri et al. (2013). In the words of the authors, the different percentage of the micro firms does not depend on the sector composition of the Italian production system, since the differences among Italy and Europe are explained in each sector by the composition of micro firms. Moreover, as Mitri et al. (2013) pointed out, in the period 2002- 2010, the growth of the turnover in micro firms results to be 7% higher, with respect to the other firm size classes. At the same time however, it is characterized by a huge heterogeneity and low persistence over time; only 38% of the firms remain in the same class of sales growth. Regarding the investments, they are characterized by a high variability and a strong discontinuity, where periods of huge investments are followed by periods without them. A recent study of Berlingieri et al. (2017), highlights more the specificity of Italian micro firms. As expected, as we can see from figure 3.1, micro firms are the majority both in Europe, that includes Austria, Belgium, Norway, Hungary, Denmark, Finland and Netherlands, and in Italy. However, the interesting feature is the difference in terms of productivity between Italian and European micro firms. In fact, in all the other firm size classes, the Italian firms have a level of productivity similar to the European ones and even bigger in the case of the largest enterprises. Instead, when we consider micro firms, a great difference of productivity is present both in the manufacturing and services sectors.

Figure 3.1: Comparison of productivity by class and sector in Italy and Europe. Source: The Multiprod project, OECD 2017

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3.2. Specificity of small and micro firms in the provision of training 14

3.2 Specificity of small and micro firms in the

pro-vision of training

Among the main challenges for small and micro firms, the intensified competition coming from foreign countries, leads to the need of improving the innovation capacity and upgrading the workers’ skills. As indicated in Farvaque and Voss (2009), recent surveys find out that the lack of skilled workers is a problem for more than one third of the SMEs in the European Union. One of the most serious problem for such type of firms is the lack of financial resources. Therefore is important to support SMEs owners, employees and managers when looking for public subsidies or European funding, as access for the information is often complex. Another crucial reason is linked to the perception of training in the SMEs companies: it is considered as not important since training is generally done informally on the job. A sense of resignation often stems from the poor information about the range of training opportunities, therefore, in this case, the role of the public authorities is to make as accessible as possible the information about training programs, in order to obtain a raise in the awareness with regard to them. This is exactly what in Westhead and Storey (1996a), is called the ”ignorance” reason for the less provision of training in small and micro firms. The authors provide two different types of explanations regarding the low level of investments in training in small firms with respect to larger ones:

• ignorance explanation: small firms are less likely to provide job-related formal training since the owners are not aware of the full potentiality of training. Since they are often opposed to formality, in principle this can lead to an important role of the government in fostering the investments in training programs. In this explanation of the ignorance of the small firms, the role of the government is only at the beginning, in order to show the results of such an investment. • market forces explanation: it is related to a variety of supply and demand

factors, which make the small and micro firms less likely to do training in comparison with the larger counterparts. The first one of these reasons is

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3.2. Specificity of small and micro firms in the provision of training 15

related to the fact that, as in the paper of Arag´on et al. (2006), the interest

of the small firm’s owners is strongly biased into short term results, while the nature of the investments in training involves long term and delayed effect on sales volumes and firm performance. The second one is related to the high probability that the employees trained in small firms will be poached by larger firms.

The fact that the provision of training is positively correlated with the size of the firms is a strong stylized fact, as stressed for instance in the study of SBRC (1992). For the authors of the report one of the relevant reason for which small firms provide less training with respect to larger ones, is the perception of training as a cost more

than a value. What employers generally perceive, is that the upgrading of the

skills reached by training is not necessary and far beyond the realistic need of the firms. Moreover, the less market power and the greater turbulence, make such expenditures more risky with respect to other companies. For what concerns the empirical literature, the results are different and subject to some critics. Among the study that points out the difficulties in assessing the impact of training on small firm performance, Patton et al. (2000), individuate three important issues that have to be taken into account: firstly the firm operating environment, for which the research undertaken to investigate any relationship between training and performance, must be addressed by taking into account the contextual environment in which training has been delivered. It is essential that other factors that could shape business performance are accounted for, if the objective is to establish the relative importance of training on performance. Secondly, the crucial differentiation between training as being part of the business strategy or training used to deal with difficulty, such as a crisis or a competition increase. It might be expected that training provision has a great effect on the firm performance in the former case. Thirdly, the characteristics of training: training provision varies in the knowledge or skills imparted, their duration, the type of employee involved and the mode of delivery.

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3.3. Training and firm’s performance in small and micro firms 16

3.3 Training and firm’s performance in small and

micro firms

The empirical literature about small and micro firms is quite scarce and moreover the studies that provide empirical results obtain different conclusions. Among the ones that obtain a weak consistency, Westhead and Storey (1996a,b) find out no substantial evidence to demonstrate that the provision of training in small firms leads to improvements for the company. Differently, Cosh et al. (1998) discover puzzling results, since the significance of training has been found to be dependent on the period of analysis. While a positive and significant relationship between training and growth performance in the period 1987-1990 has been discovered, it is not possible to find any significant effect in the five years after that period. This may reflect the severity of the recession after the 1990, which could have led many firms, with persistent training, to reduce or abandon it. A different impact of training given different external conditions, is demonstrated in the work of Youngsang and Ployhart (2014). Among the papers that focus on small firms, Bryan (2006), analyzes the impact of training on sales growth. Data about 114 manufacturing firms in Wales are collected through surveys and the enterprises that answer to the questionnaire are contacted two times: the first one in 1996 and the second one in 2003. From this data, sales growth in the period 1997-1993 and 2003-1997 is calculated and two dummies of training are used. The first one excludes firms which had not undertaken training in that period, while the second one tries to take into account the intensity of training by assigning 1 for the firms that have more than 50% of trained workers and 0 otherwise. The results, obtained through the use of the OLS methodology, show a strong significance and positiveness of the dummy variables of training on sales growth, respectively 8% and 2% marginal increase.

An important aspect, is the huge neglect of the micro firms in the small business literature, as highlighted in the book of Reid (2006). The simple reason for this, has to be attributed to the fact that data, for this kind of firms, are not available. In fact, the larger the firms, the more likely are its operations to be covered by the legislation on public disclosure. Despite the neglect, these types of firms are

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3.3. Training and firm’s performance in small and micro firms 17 important because they constitute the typical and modal firms in the large part of the European countries.

Two interesting papers try to estimate the impact of training focusing on a sam-ple of micro firms, one on Swedish micro firms (Yazdanfar, 2013) and the other on Irish micro firms, (Ipinnaiye et al., 2017). In the former case the authors define a variable called competence development, that includes all competences acquired through training and all courses that are able to make the employees to better deal with the business. Among the performance variable considered, there are job satis-faction, firm growth and organizational improvements. By using a SUR empirical methodology, the authors find out that the training of the managers has a positive and significant impact on the firm performance.

The second paper, analyses the determinants of the growth of micro and small firms by using a holistic approach. By considering the critique and suggestions made by Patton et al. (2000), we can see how the authors take into account all the various factors that influence firm growth. The work of Ipinnaiye et al. (2017) is interesting since it provides comprehensive coverage of the micro-sized firms, that following the classification of the European Commission, have less than ten employees. The au-thors analyse a sample of 7915 manufacturing firms in Ireland. In the definition of the dependent variables they consider three measures of firm performance: employ-ment growth, sales growth and productivity growth. The second one calculated as the logarithm difference of sales in consecutive years and the latter one as the loga-rithm difference of sales per employee in consecutive years. For what concerns the controls, they use firm characteristics variables, firm strategy variables and macroe-conomics variables. Training is one of the most important factor belonging to the firm strategy. In detail this is measured as a dichotomous variable that takes 1 if the firm has training costs and 0 otherwise. Due to the possible problem of endogeneity, the authors use the SYS-GMM methodology, that allows to consider as instruments, past values of the variables within the dataset.

The results of the econometric analysis are interesting and expected. For what concerns the econometric model that studies the impact of training on sales growth, the authors find out a negative relationship with size, with the lagged value of

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3.3. Training and firm’s performance in small and micro firms 18 productivity, the lagged value of growth and a positive and statistically significant coefficient for the training dummy. In particular, the marginal effect of training on sales growth estimated is 2% for what concerns the overall sample and 7.4% for what concerns the second sample period going from 2001 and 2007. Regarding the impact of training on productivity growth the results show: a positive and significant impact of the size, a negative and significant coefficient for the lagged value of productivity and a strongly significant effect of training on productivity growth, with a marginal effect of 2% in the overall and in the separated periods. Even if the literature concerning the impact of training on performance in small and micro firms is scarce and doesn’t show clear results, as evidenced by Cosh et al. (1998) and Kitching and Blackburn (2002), this does not justify a neglect of them. Conversely, the high percentage and the relevance of this firm size class, both in Europe and Italy, call for further analysis. It is important to understand if training could be or not, one of the driver capable of increasing the level of productivity in micro firms.

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Chapter 4 The Italian context

4.1 What we know about training in Italy

The specificity of Italy compared to other European countries emerges from a recent report by Bassanini et al. (2005), who examine workplace training in Europe in a comparative perspective. As show in figure 4.1, Italy has a lower level both in terms of the percentage of employees taking some training and in terms of hours of training per employee, with respect to most Central-EU and North-EU countries.

Figure 4.1: Training participation and training intensity in Europe. Source: Bas-sanini et al. (2005)

Another important feature that characterizes the provision of training in Italy, is the regional dispersion in terms of training participation rates. In fact, considering

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4.1. What we know about training in Italy 20 data at NUTS-2 level, it is possible to observe that the regional dispersion in Italy is higher with respect to the North-EU countries. Table 4.2 shows the comparison in the training participation among regions in Italy. With the national average equal to 100%, we can see that training participation is higher than the country average in the north part and lower in the centre and the south. Moreover taken into account the graphs together, we can observe a negative association between the variability of the training participation and its average in the country. In fact, countries like Sweden and Finland that in picture 4.1 have higher level of training participation, have a lower dispersion, while Portugal, Italy and Spain have lower percentage of training participation but higher regional differences.

Figure 4.2: Training participation in Italy and Spain. Source: Bassanini et al. (2005)

Figure 4.3: Training participation in Finland, Portugal and Sweden. Source: Bas-sanini et al. (2005)

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4.1. What we know about training in Italy 21 Given the Italian specificity, it is interesting to see more specific data about train-ing across firm size, sectors and regions. Data used come from the Excelsior (2014), Excelsior (2015), Excelsior (2016), Excelsior (2017), which analyses the behaviour of the firms that provide training to their employees.

As we can see from table 4.1, the percentage of firms that adopt training raises with firm size, with the lowest percentage in micro firms. Moreover, looking at the inter-temporal patterns about the percentage of firms that do training in figure 4.4, we can see a consistent growth from 2007 to 2011 (from 21,9% to 35%), and a fall in the following years until reaching a value similar to the one of 2007. This trend is confirmed in all firm size classes analysed.

Table 4.1: Percentage of firms that provide training courses per firm size, 2007-2016. Source: Unioncamere, Sistema Informativo Excelsior.

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 1-9 dip 18,8 23,3 28,8 29,4 31,1 24,3 18,6 19,1 16,5 21,6 10-49 dip 29,5 28,0 39,0 45,2 46,4 37,6 32,7 32,9 30,6 42,9 50-249 dip 44,7 49,8 64,5 68,0 68,9 60,2 48,3 56,4 NA NA >= 250 dip 75,2 80,8 83,2 83,7 82,0 85,1 82,4 NA NA NA Total 21,9 25,7 32,1 33,5 35,0 28,1 22,4 23,1 20,8 27,0

Figure 4.4: Percentage of firms that do training in Italy over the years. Source: Unioncamere, Sistema Informativo Excelsior, 2017.

Another interesting issue concerns the percentage of firms that do training across different sectors. Data presented in table 4.2 share the same temporal trend that we have found before. What we can notice is that Constructions and Services have a

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4.1. What we know about training in Italy 22 higher percentage with respect to Industry and Trade. This confirms the cross tab-ulation results find in the study by Guerrazzi (2016), where the highest percentage of trained workers is found in Constructions and Services. Further disaggregation of sectors within services shows clear differences among categories: in particular, financial services, services to the businesses, health services and services to people show more firms providing training.

Table 4.2: Percentage of firms that do training per sector, 2007-2016. Source:

Unioncamere, Sistema Informativo Excelsior.

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Industry 17,6 20,1 25,0 28,9 30,8 25,4 20,3 22,1 20,4 29,3 Constructions 24,1 30,1 34,0 37,5 38,2 28,5 22,5 24,4 21,7 36,1 Trade 21,2 23,8 30,1 29,8 30,8 25,1 19,6 21,3 16,5 21,2 Services 23,8 26,4 35,6 35,9 37,8 30,6 24,4 23,9 20,6 24,3 Total of Italy 21,9 25,7 32,1 33,5 35,0 28,1 22,4 23,1 20,8 27,0

The geographical differences in the provision of training by the firms are pre-sented in table 4.3. In line with Bassanini et al. (2005), we observe huge differences in the percentage of firm that do training among regions. In the North-East and North-West the percentage is always higher than in the Centre, and the figures for the Centre are always higher compared to the South.

Table 4.3: Percentage of firms that do training per region, 2007-2016. Source:

Unioncamere, Sistema Informativo Excelsior.

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

North-West 22,3 26,6 32,5 34,4 35,7 30,8 23,8 25,8 23,4 29,7

North-East 25,2 28,2 34,1 36,3 38,8 31,6 26,2 26,3 24,1 33,0

Centre 21,4 24,6 31,3 32,9 33,7 27,0 21,9 21,4 19,5 25,4

South and Islands 19,1 23,5 30,5 31,0 32,4 23,7 18,5 19,5 16,9 21,0

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4.1. What we know about training in Italy 23 Moreover data provide information for the comparison of the purpose of training across different firm size classes. As we can see in figure 4.5 the disparities among the different firm size classes are relevant in 2013. Smaller firms in fact have a greater percentage in the category related to improving workers in doing the same tasks and a corresponding lower value in the other two. Conversely, in 2016, the type of training activities made, seems to be more similar across firm size, with a few difference only in larger firms as in figure 4.6.

Figure 4.5: Distribution of training activity types for firm size class Source: Union-camere, Sistema Informativo Excelsior, 2014

Figure 4.6: Distribution of training activity types for firm size class. Source: Union-camere, Sistema Informativo Excelsior, 2017

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4.2. Some information about training in Italian micro firms 24

4.2 Some information about training in Italian

micro firms

Despite the relevance of micro firms, for what concerns training the lack of data is evident. In order to highlight this problematic for what concerns Italy, in the report realized by the Italian Institute of Statistics, ISTAT (2017), the information regarding training and firms does not cover the class of employees 1-9, that is to say micro firms. Fortunately, the Excelsior information system each year publishes a report about training in Italian firms, where the class of micro firms is taken into consideration. 18,8 23,3 28,8 29,4 31,1 24,3 18,6 19,1 16,5 21,6 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

Figure 4.7: Percentage of micro-firms that do training over the years. Source: Union-camere, Sistema Informativo Excelsior.

The evolution of the percentage of micro firms that do training, as in figure 4.7 shows an increase until 2011, when the percentage reach the 31.1%, followed by a huge decrease in the following years and a successive increase in the percentage in 2016. As we see for the firms in general, also micro firms present differences in term of sectors. The reports of Excelsior (2014, 2015, 2016, 2017) give the possibility to obtain a focus on this firm size class for what concerns the sectors. As we can see from figure 4.8, the sectors, in which there is the highest percentage of micro firms providing training, are the Services and Constructions. While the percentage related to the former one has been constant during the years going from 2013 and 2016, the percentage related to Constructions had a huge increase in 2016.

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4.3. Empirical literature 25 14,50 15,60 _13,90 20,40 20,50 22,30 19,70 32,90 19,10 19,20 16,40 19,50 2013 2014 2015 2016

Industry Constructions Services

Figure 4.8: Percentage of micro-firms that do training across sectors and years. Source: Unioncamere, Sistema Informativo Excelsior.

4.3 Empirical literature

The empirical literature about training in Italy is scarce. Moreover it is mainly focused on the relationship between training and productivity. The first study is by Conti (2005). By merging data of the Labour Force Survey that concerns individuals and the Aida archive, the study covers a panel of 176 industries analysed over the period 1996-1999. The author implements an OLS as the baseline model, followed by the within and GMM estimator. Considering the final model, the author concludes that an increase of 1% in the level of training is associated with an increase of 0.4% in productivity. Brunello (2007) analyses the effect of training on productivity for a panel of 150 Italian large firms, by using information coming from the AIDA dataset. Training is measured as the percentage of trained workers and also in terms of expenditures. Estimates through random and fixed effects, show a positive and statistically significant effect of the former measure of training that ranges from 0.6% to 1.3%, while for the latter no statistically significant effect is found.

Colombo and Stanca (2014) investigate the impact of training on workers’ pro-ductivity by using a large panel of Italian firms in the period 2002-2005. Three different methodologies are used: OLS, fixed effects and GMM estimator. Overall,

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4.3. Empirical literature 26 the results show a strong significant and positive effect of training, calculated both as the percentage of trained workers over the total number of employees and as the effective training intensity.

Finally, Guerrazzi (2016) considers a cross sectional dataset about Italian firms contained in the ASIA archive of ISTAT. Consistently with other contributions about Italian firms, the OLS regression shows a statistically significant and positive effect of training on productivity. An increase of 1% in the percentage of trained workers is associated with an increase in the level of productivity of 0.001%. Correcting for possible endogeneity of training, the two-stage least square estimates deliver a greater coefficient (0.0121).

Two general issues are tackled in these studies: unobserved heterogeneity using fixed and random effect estimators and endogeneity of training through the GMM estimator.

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Chapter 5 The Data

In this chapter we introduce the dataset by describing how it has been obtained, the structure and the relevant variables. Moreover we will present initial descriptive statistics.

5.1 Data sources & access

Data used in the present dissertation come from the TREND project, that is a project born from the collaboration of Italian Statistical Office (ISTAT) and the National Craft Trades Confederation (CNA). The project, started in 2006 in the Marche region, with the constitution of an observatory on small and micro enter-prises, later extended to Emilia Romagna, Umbria and Toscana. The focus of the project is on the collection of quarterly data about relevant economic variables of small and micro firms.

During the first year of my Master of Science in Economics, I had the possibility to do an internship in the offices of the National Institute of Statistics, situated in Florence. During this period, I worked on the reconstruction and modifications of time series of the TREND dataset, in order to make them comparable with other important sources of ISTAT (SBS and Rilevazione sul sistema dei conti delle imprese). Starting from this collaboration, we decided to continue the analysis of the dataset from a different point of view: using firm level data to conduct an analysis of the impact of training on the performance of micro firms. The final approval was

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5.2. Structure & cleaning & annualization 28 given for three of the four regions that I had requested for: Toscana, Umbria and Marche. The database covers the period 2013-2017 and it is based on administrative data on firms with less than 19 employees, collected by the regional office of CNA in collaboration with ISTAT. For what concerns the reliability of the dataset, the comparison of the dimensional structure of the firms of TREND and ASIA, shows a substantial alignment, endorsing the rightness of the sample used, (Palmieri, 2013). In addition data are disaggregated by region,(Marche, Toscana and Umbria), by province and by sector. The sector disaggregation is done until two digit of the Ateco code. For each region, a panel dataset of quarterly data is available and the identification of firms is possible through the Partita Iva code. The economics variables available, all expressed in euros, are the following ones:

• Sales: separated into sales in Italy, sales abroad, (sales in UE and extra UE) and sales under contract.

• Investments: further distinction into tangible, intangible assets and invest-ments in machinery.

• Salary.

• Expenditure for external cost. • Expenditure for insurances. • Expenditure for training. • Number of employees.

• Province and Region specification.

• Sector specification by the ATECO code.

5.2 Structure & cleaning & annualization

In order to obtain a reliable dataset, the first phase of the empirical analysis, has been devoted to the cleaning of the dataset of each region. The main cleaning involves

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5.2. Structure & cleaning & annualization 29 tackling negative values of the variables that shouldn’t be negative, by imputing a missing value. In particular we are referring to the variable sales, expenditure in training and salary. After, we have proceeded to the annualization of the dataset. We have generated the annual values as the sum of the quarterly values. This procedure has been done for all the variables selected, except for the case of number of employees, for which we have decided to take the mean, max and min value. In the annualized dataset we have replaced the negative values of salary and the values of training/sales greater than one, with missing values. Moreover, the last modification, made on the annualized dataset, involved the treatment of extreme values of the variables growth and productivity, that is to say the two dependent variables of the empirical analysis. After counting them, we found out a few quantity of such variables and a lack of impact on the graphical analysis, therefore we decided to exclude them from the estimation. We replaced all the growth values for which sales growth, calculated as (Sales(t)-Sales(t-1))/Sales(t-1), was greater than ten and the values of productivity greater than one million. In detail, the following process of cleaning in the three dataset has been made:

• Marche: The original quarterly dataset is a panel from 2013 to 2017. 22 negative values of training have been replaced with missing values. Missing values of Ateco 2007 have been filled through the use of the comparison table with Ateco 2002 code, available within the dataset. For what concern the four provinces: Macerata, Pesaro, Pescara and Ascoli, they have been reclassified by using the available province code. Moreover 63 negative values of salary and 2 values of training over sales greater than one, have been replaced with missing values. The final annualized dataset includes 6205 firms and a total of 21329 observations.

• Toscana: The original quarterly dataset is a panel from 2013 to 2016. 15 negative values of sales and 57 negative values of training have been replaced with missing values. The same treatment done for Marche has been done for what concerns the sectors and the provinces. Finally, 79 negative values of salary and 8 of the trainig/sales variable grater than one, have been substituted

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5.3. List of variables used 30 with missing. The final annualized dataset contains a number of firms equal to 13.067 and a number of observations equal to 38421.

• Umbria: The original quarterly dataset is a panel from 2013 to 2017. 5 neg-ative values of training have been replaced with missing values. The same treatment for Marche, has been done in correcting the missing Ateco and for the province. No change has been made for the training/sales variable while the 49 negative values of salary have been replaced with a missing. The final dataset includes 2072 firms and 6665 observations.

5.3 List of variables used

The final selection of the variables is the following one: sales, salary, investments in intangible assets, investments in tangible assets, expenditure in training, number of employees, macro sectors, regions and years.

For what concerns the dependent variables, we use two different measures of firm performance. The growth of sales (Growth), is calculated as the logarithm difference of sales into two consecutive years. Productivity, labeled as (Prod ), is calculated as the (log of) sales divided for the number of employees.

Regarding the main regressors we use three different proxies of training. The first one is a dummy variable, called (D1.Training), that takes a value of one when the firm has a positive training expenditure and a value of zero otherwise. The second one is also a dummy variable, labeled (D2.Training), which takes the value of one if the expenditure of training is greater than the median and zero otherwise. Finally, the third measure labeled as (Train), corresponds to the expenditure on training calculated as the (log of) training plus one.

Moreover, in order to make the results more robust, we use a set of control variables. In particular, we use the investments in intangible assets, labeled as (In-tangibles) and calculated as the (log of) intangible assets plus one, the investments in tangible assets that is to say machinery and equipment, called (Tangibles) and calculated as (log of) tangible assets plus one, the salary labeled as (Wage) that is calculated as (log of) the ratio between wages and number of employees plus

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5.4. Descriptive Statistics 31 one, and the size, labeled as (Size), and calculated as the (log of) the number of employees. Dummies for regions, sectors and years are also considered.

The following table sums up the variables used and the way in which they are calculated:

Variable Description of dependent variables

Growth logarithm difference of sales in consecutive years.

Prod logarithm of the productivity, measured as sales per

employee.

Variable Description of main regressors

D1.Training dummy that takes the 1 when the firms have a

pos-itive expenditure on training and 0 otherwise.

D2.Training dummy that takes 1 if the firm has expenditures of

training greater than the median of training and 0 otherwise.

Train calculated as log(training +1).

Variable Description of controls

Size logarithm of the number of employees.

Intangibles calculated as log(intangibles +1).

Tangibles calculated as log(tangibles +1)

Wage calculated as log(wage +1).

Sector dummies Two-digit ATECO 2007, classification (10-96).

Regional dummies Dummies for the three regions of Toscana, Marche

and Umbria.

Year dummies Dummies for the period 2013-2017.

Note: Variables definition

5.4 Descriptive Statistics

5.4.1 Numerosity

In this subsection we present some descriptive statistics about the number of firms considered, the sector in which they are involved and the regions.

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5.4. Descriptive Statistics 32

Regions Numerosity Percentage

Marche 6,205 29.07

Toscana 13,067 61.22

Umbria 2,072 9.708

Total 21344 100

Table 5.1: Number of the firms per region.

As we can see from table 5.1, the number of firms in the region of Marche is equal to 6205, the number of firms in the region of Tuscany is the greatest one and it is equal to 13.067, and the number of firms in Umbria is equal to 2.072. The total number of firms is equal to 21.334. Another interesting thing is the distribution of firms across sectors.

Macro-sectors Numerosity Percentage

Manufacturing 3,924 18.38

Construction 6,308 29.55

Trade 4,782 22.40

Services 6,330 29.66

Total 21344 100

Table 5.2: Number of the firms per macro sector.

As evidenced by the table 5.2 the majority of firms belong to the Constructions and the Services. This is important to know because of the influence of the sector specificity in the level of training.

5.4.2 Other tables

In this subsection we present some descriptive statistics about the variables within the dataset: at the beginning in a general way, and after focusing on the training variables and the distinction between firms that do or do not do training. In all the tables that we present we use size, intangibles, tangibles, expenditure in training and wage not in logarithm, as instead we have presented in the variable description of the previous section.

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Table 5.3: Summary statistics of dependent variables

Variable Mean Std. Dev. N

Growth -0.155 0.647 42294

Prod 10.12 1.129 63608

Table 5.4: Summary statistics of independent variables

Variable Mean Std. Dev. N

D1.Training 0.102 0.302 66415 D2.Training 0.053 0.224 66415 training 62.822 440.147 66415 intangibles 982.485 25252.882 66415 tangibles 14423.878 226303.677 66415 wage 2608.167 7973.043 66415 size 2.241 3.019 66415

The first table 5.3, refers to the summary statistics of the dependent variables. As we can see the average of the growth is equal to -0.155 and the average level of productivity is equal to 10.12. For what concerns the training proxies and the control variables, summary statistics are reported in table 5.4. The percentage of firms that do training considering the first dummy is 10%, while considering the second dummy it is 5.3%. Regarding the other control variables, the average of firm size is equal to 2.241 and in general 98% of the firms within the database have less or equal than 10 employees, the average investment in intangible assets is quite small and equal to 982.485, the average of tangible asset is equal to 14423.878 and the average of wage is equal to 2608.167.

As we can see from table 5.5, investments in training, intangible and tangible assets contain a lot of zeros; this fact evidences the discontinuous nature of such type of variables. Moreover all the distributions of the variable are far from being normal, evidencing a huge heterogeneity among firms. Now we focus on the main variable of interest, that is to say training. Three different measures have been considered

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Table 5.5: Distributional features of the variables

Variables

skewness

kurtosis

p10

p25

p50

p75

p90

Growth

-1.629

12.33 -0.842

-0.354

-0.0562

0.130

0.438 Prod

-1.175

8.473

8.780

9.570

10.22

10.83

11.36 training

26.26 1,321

0

40 intangibles

62.08 5,144

0

0 tangibles

40.31 2,101

0

414.6 5,500

wage

30.47 2,471

0

0 1,165

9,844

size

22.52 1,774

1

2.250

4.250

due to the nature of the dataset and to the usual differentiation made also in the literature: The first one is training. It is interesting to see statistics about training across the firms that perform it.

Variable Observations Mean Median Std. Dev.

training 6764 618.85 250 1249.26

Table 5.6: Mean and Median of training across firms that perform it

The average value of the expenditure in training is given by 618.85 euros while the median value is equal to 250 euros. This value is used after in order to calculate the second training dummy. The second measure of training used is a dichotomous variable. In particular it takes 1 when the firms has a positive expenditure of train-ing and 0 otherwise. The followtrain-ing table 5.7. presents the number of observations with values 1 and the ones with 0. As we can see in the final dataset the 10% of

D1.Training Freq. Percentage Cumulate

0 59,651 89,82 89,82

1 6,764 10,18 100

Total 66,415 100

Table 5.7: Number of observations with training and not, D1.

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5.4. Descriptive Statistics 35 of training is again a dichotomous variable. The purpose of using this further spec-ification is to take into account, in some sense, the intensity of training. Therefore the dummy variable is constructed in a way that takes a value of 1 when the expen-diture of training for the firms is greater than 250, that is to say the median value indicated in table 5.6, and 0 otherwise. The following table presents the numerosity of the variable:

D2.Training Freq. Percentage Cumulate

0 62,887 94,69 94,69

1 3,528 5,31 100

Total 66,415 100

Table 5.8: Number of observations with training and not, D2.

As we can see from the table above in this case the number of observations with 1 has dropped to 5.31% of the observations.

Once that the definition of the variables of training has been made, we can look at the different behavior of the firms that do training and the firms that do not do it. In the table 5.9 we differentiate among the firms that have training expenditures during the period 2013-2017 and the ones that do not have it. In order to do this comparison, we use both the mean and the median value, due to the high skewness of the distribution, as showed in the table regarding the distributional features of the variables. For what concerns the intangible assets, the two values are more or less equal, instead in the case of tangibles the value is greater for the observations associated with the zero value of the dummy. For all the other variables, firms with training evidence a greater value both for the mean and the median with respect to the firms that don’t have any training expenditures. In these group are included size, wage, productivity and growth. This is in line with the literature, since in general firms that do training are greater in size, with higher level of wages for the workers and have a better performance.

Moreover it is also interesting to show statistics about the different variables by using the second dichotomous variable of training, that is more concerned with the intensity of it. As showed in table 5.13 in this case all the mean and median values

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Table 5.9: Descrpitive stats by D1

D1.Training 0 D1.Training 1

Variables N mean p50 sd N mean p50 sd

Growth 37,642 -0.169 -0.0643 0.667 4,652 -0.0433 -0.0126 0.426 Prod 56,962 10.07 10.18 1.145 6,646 10.55 10.60 0.873 training 59,651 0 0 0 6,764 616.8 250 1,249 intangibles 59,651 981.5 0 26,248 6,764 991.2 0 13,633 tangibles 59,651 14,463 0 236,156 6,764 14,079 65.25 105,052 size 59,651 2.071 1 2.859 6,764 3.744 2.500 3.858 wage 59,651 2,132 0 6,173 6,764 6,807 3,626 16,387

related to the firms that do training are greater compared to the ones that do not perform it; moreover the difference, as expected, is greater compared to the first dummy.

Table 5.10: Descriptive stats by D2

D2.Training 0 D2.Training 1

Variables N mean p50 sd N mean p50 sd

Growth 39,943 -0.163 -0.0612 0.657 2,351 -0.0156 0,00005 0.415 Prod 60,128 10.09 10.20 1.137 3,480 10.61 10.64 0.849 intangibles 62,887 957.9 0 25,640 3,528 1,421 0 16,918 tangibles 62,887 14,216 0 230,391 3,528 18,121 265.0 133,893 training 62,887 6.558 0 31.07 3,528 1,066 602.5 1,603 size 62,887 2.126 1 2.889 3,528 4.292 3 4.275 wage 62,887 2,296 0 6,282 3,528 8,166 5,071 21,464

In addition, it is interesting to see the distribution of training among regions, sectors and years. The following tables present the number of observations and the percentage of them over the total in parenthesis. In table 5.10 we present the distinction in training for the different regions. The region with the highest number of observations of training, in percentage, is Marche.

For what concerns the years, the table 5.11 shows an increase in the training provision in the period between 2013 and 2016, with a drop in the last year due to the fact that data of Toscana are present until the year 2016.

Regarding the differences across sectors, table 5.12 shows that Constructions and Services have the greater percentage of investments in training.

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Table 5.11: Number of observations by D1 and regions

Marche Toscana Umbria D1.Training 0 18,882 34,494 6,275 (88.53) (89.78) (94.15) 1 2,447 3,927 390 (11.47) (10.22) (5.852) Total 21329 38421 6665

Table 5.12: Number of observations by D1 and years

2013 2014 2015 2016 2017 D1.Training 0 13,518 15,112 14,326 12,605 4,090 (90.37) (91.88) (89.86) (88.61) (84.52) 1 1,441 1,336 1,617 1,621 749 (9.633) (8.124) (10.14) (11.39) (15.48) Total 14959 16448 15943 14226 4839

Table 5.13: Number of observations by D1 and sectors

Manufacturing Constructions Trade Services

D1.Training 0 11,258 17,918 13,785 16,690 (90.94) (89.36) (91.52) (88.20) 1 1,121 2,134 1,277 2,232 (9.056) (10.64) (8.478) (11.80) Total 12379 20052 15062 18922

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5.4. Descriptive Statistics 38 We next look deeply into the relationship between the two dependent variables and the three different measures of training. The first variable we consider is Growth.

Figure 5.1: Growth & D1.Training

−10 −5 0 5 growth 0 .2 .4 .6 .8 1 dummF

Figure 5.2: Growth & D2.Training

−10 −5 0 5 growth 0 .2 .4 .6 .8 1 dummF2

As we can see from figure 5.1, the observations of the firms that do training expenditure are associated with greater value of growth. This is also true for the case of the second training dummy, as in figure 5.2. In the case of Train, we see a quite small positive association between growth and training, as evidenced by the fitted line in figure 5.3.

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Figure 5.3: Growth & Train

−10 −5 0 5 0 2 4 6 8 10 log_form

growth Fitted values

The second dependent variable considered, is Prod. For what concerns the figure 5.4 and 5.5 about the logarithm of productivity and the training dummy, the graphs show in both of the cases that firms doing training have greater value that the ones that do not perform it. Moreover, as we can see from picture 5.6, there is a positive association between Train and the logarithm of productivity.

Figure 5.4: Prod & D1.Training

−5 0 5 10 15 log_prod 0 .2 .4 .6 .8 1 dummF

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Figure 5.5: Prod & D2.Training

−5 0 5 10 15 log_prod 0 .2 .4 .6 .8 1 dummF2

Figure 5.6: Prod & Train

−5 0 5 10 15 0 2 4 6 8 10 log_form

Dynamics of micro Italian firms: the role of training