Master of Science in Economics
Master’s Thesis
Digitalization, Routine and Italian Wages
Supervisor:
Prof. Federico Tamagni
Candidate: Francesco Suppressa
Università di Pisa – Dipartimento di Economia e Management
Scuola Superiore Sant’Anna
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Abstract
This work investigates the level of Digitalization and routineness of the Italian professions in 2008 and 2013, by means of two specific indices, the Digitalization and Routine indices, that we have calculated starting from the Survey on Italian Occupations (ICP). The first index measures the utilization of digital devices during work-time and the digital skills of the professions, while the second one, which is similar to the well-known Routine task intensity index proposed in Autor, Levy and Murnane (2003), identifies the level of routineness of each kind of job. In the paper, by merging the ICP dataset with the Italian Labour Force Survey (ILFS), we have explored the relationship existing between these two indices, and their different impacts on Italian wages. There is a strong negative correlation between them, and their effects on wages are opposite. Indeed, the results show how workers of professions associated to a higher level of Digitalization tend to earn a positive premium on the level of wage on average, while, on the contrary, we observe lower wages for larger Routine index levels. The results are robust to a number of alternative estimation methods (OLS and selection-corrected Heckit Estimates) and specifications exploring variation of results by education level, and across sectors and geographical location.
Summary
1
Introduction
... 4
2
Literature ... 8
3
Data sources and variables ... 14
3.1 Data sources ... 14
3.2 Main variables ... 16
4
The DI and the RI Indices ... 20
4.1 The Digitalization Index ... 20
4.2 The Routine Index ... 23
4.3 Distribution of the two indices ... 25
4.4 Interesting correlations ... 26
5
Main regression analysis ... 34
5.1 Regression model ... 34
5.2 OLS results ... 35
5.3 Heckman procedure ... 38
6
Extended analysis ... 42
6.1 Pooled sample ... 42
6.2 Complementarity between the D.I. and the R.I. ... 44
6.3 Heterogeneity across Education levels, Geography and Sectors ... 47
7
Summary and final remarks ... 56
4
1
Introduction
In 1993 Krueger published an influential paper called “How computers have changed the wage structure” (Krueger, 1993), in which he analysed the impact of the “computer revolution” on the US wage structure.
Since then, technology has made great strides and today, when we talk about digital revolution, the diffusion of personal computers does not come to our mind, but we think directly about artificial intelligence, digital processes, robotics and other IT developments.
In parallel with these technological advances, the studies related to the relationship between digital innovation and employment have greatly increased, both from a quantitative and a qualitative point of view.
One of the most famous theoretical frameworks, the so-called Skill Biased Technical Change (SBTC), claims that with the spread of new technologies there seems to be a growth in the labour demand for high-skilled workers, while low-skilled employees tend to be replaced by new machinery (Katz and Murphy,1992).
However, the previous theory is not able to explain why in many countries we observe a drop in the percentage of the middle-skills occupations resulting in a growth in the high and low wage occupations. This empirical evidence is called “job polarization” (Goos and Manning, 2007).
The Routine Biased Technological Change (RBTC) theory of Autor, Levy and Murnane (Autor et al., 2003) seems to be able to explain this phenomenon. In this hypothesis, jobs are no longer categorised into different “skill” levels, but on the basis of a “task” classification. A decrease in the middle-wage occupations, which
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are characterised by a large number of routine tasks, is explained by a relatively easier replacement of these workers by new machines and technologies.
In this thesis, by analysing the tasks performed by each typology of Italian job, we have quantified the level of Digitalization and routineness across the workers, in a similar way to the paper by Cirillo, Evangelista, Guarascio and Sostero (Cirillo et al., 2019), in order to understand the effects of Digitalization on the Italian wage structure and, at the same time, to study the relationship between the extent of the digital tasks performed and the degree of routineness of a certain profession.
The data that we have used are taken from the Indagine Campionaria sulle Professioni (ICP) survey and the Italian Labour Force Surveys (ILFS). The ICP dataset is an O*Net-type dataset available for the Italian labour market and it gives us the opportunity to calculate two indices -the Digitalization Index (D.I.) and the Routine Index (R.I.)- related to the digitalization and the routine content of the Italian occupations (at the 3rd level ISCO) for 2007 and 2013. Then, we have used the ILFS
data, appropriately combined with the ICP survey, to obtain an approximate measure of the two indices for every Italian worker surveyed in 2008 and 2013. In the case of the ILFS, we have chosen the survey of 2008 since the ISTAT does not provide this data for 2007.
After computing the (D.I.) and the (R.I.) for each Italian occupation, we attached this information to the ILFS where for every worker the 3rd level ISCO classification of
job is available, and we created a working sample (for both 2008 and 2013). The result was a unique dataset in which for every worker (around 50,000 per year) we
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have a measure of the digital task and routine task levels in order to understand their impacts on the nominal wages.
Thanks to this precious information we can try to answer to many and various research questions.
Firstly, we want to empirically study the relationship between the Digitalization and the Routine content of a job. In the literature, they are considered as a sort of synonymous, following the idea that high routine jobs are more subject to being “digitalized”. By building two separate indices, we considered digitalization and routinization as two distinct phenomena. In this way, we can directly understand the existing connection and any correlation.
Based on that, our main research question is about the impacts of Digitalization and Routine on the level of wages. We want to understand which kind of relation there is between the wage and the levels of Digitalization and Routine tasks carried out by a worker.
Within this main aim, we also address a number of related issues. First, we ask whether these effects change over time, by investigating if there is a statistical difference between the results of 2008 and 2013. Second, we study the existence of a potential complementarity between the D.I. and the R.I. Finally, we explore whether the main effects of Digitalization and Routine change by education, regions and sectors.
In the econometric part, in order to answer the previous research questions, we estimate a series of wage equations where net monthly wages are the dependent variable, while the main regressors are alternatively the Digitalization and the Routine indices, properly combined with control variables accounting for factors that
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could be correlated to the indices taken into account. We present basic OLS estimates and then we test for selection bias by using a standard 2-step Heckman model.
The work is structured into eight chapters. In the next chapter, we provide a short review of the main theoretical frameworks related to the relationship between innovation and changes in the labour market. Section 3 describes the two datasets (the ILFS and the ICP) that we used to build our working sample, and we deeply analyse all the variables that we then use in the econometric part. In Chapter 4, we provide an analytical description of the D.I. and the R.I. focusing on the way we built them, and then we present some descriptive evidence directly linked to the level of Digitalization and Routine. Section 5 presents the baseline econometric analysis, in which we show the estimates of the wage equation built by using alternatively the D.I. and the R.I as the main regressor. In the 6th chapter we go beyond our main
analysis, and we try to answer other questions strictly linked to the effect of the indices on the Italian wages. Finally, in the last chapter we make a summary of our results and we discuss them.
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2
Literature
The relationship between new technologies and labour market outcome has always been central in the economic and public debate. A discussion about “technological unemployment” takes centre stage every time we observe a radical technological change. Nowadays, new discoveries in artificial intelligence, robotics and other related innovations have brought, once again, this discussion back.
In the economic literature scholars have extensively written about this topic, considering different critical perspectives.
The most popular issue is related to the impact of innovation in terms of possible job losses. A deep treatment of these effects has been analysed in Vivarelli (1995), who provided a dissection of the direct and indirect channels. The other fundamental topic concerns the effects of the new technologies on wages and skills. From this point of view, economists have developed many and different research questions (see, Pianta, 2005, for a review), such as how new technology has impacted the wage structure, what kind of jobs are destroyed by innovation and what are created, and how innovation has changed workers’ skills. In this chapter we want to briefly review the main contributions to all these debates.
Some of the first economists to express their concern about the impact of technological change on labour were Ricardo and Marx.
Ricardo added to the last edition of his Principle a whole chapter dedicated to the effect of innovation on the labour market, called “On Machinery” (Ricardo, 1821). In particular, he pointed out that new technologies can decrease the number of employees, reduce wages and reduce the aggregate output (Samuelson, 1989).
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Marx, whose ideas were certainly inspired by the previous studies of Ricardo (as we can see in Marx, Capital p. 475), in 1867 wrote “Once adopted into the production process of capital, the means of labour passes through different metamorphoses, whose culmination is the automatic system of machinery” (Marx, 1867). For many scholars the previous citation seems to be a prediction of the current process of automatisation of the labour market.
Although the idea of “technological unemployment” was already developed by Ricardo and Marx, the first author coining this notion was Keynes. According to him, this phenomenon derives from the fact that “the increase of technical efficiency has been taking place faster than we can deal with the problem of labour absorption” (Keynes, 1930). However, he was optimistic about this “new disease”. Indeed, he believed that the loss of jobs was only a “temporary phase of maladjustment”, and that the human condition would improve in the following one hundred years (Keynes, 1930).
Since the last quarter of the 20th century, the spread of Information and
Communication Technologies (ICT) re-opened the question of the effect of new technologies on the labour market. In the attempt to understand the digital revolution, many theories and empirical research projects have examined how this kind of innovation affects economic and productivity growth, the efficiency of firms and globalisation (for a summary see Van Reenen, 2007). More recently, another stream of the literature has focused on the wage, skills and tasks effects of the “computerization” and the “Digitalization”, focusing on the change occurring in the wage structure, in the skill requirements by firms and in the tasks of workers. In
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particular, studies concentrated on the asymmetric impacts, both in employment and in wage dynamics, of the “digital revolution” across high and low skill workers (Vivarelli, 2014).
The most popular theory belonging to this stream of the literature, the so-called “Skilled Bias Technological Change” (SBTC), has focused its attention on the complementarity between new technologies and skilled labour.
According to this theory, jobs can be classified through the skills required to the workers in order to perform them, distinguishing between high-skilled and low-skilled employees. In this context, innovation is generally considered as an exogenous process. For high-skilled workers it is easier than for low-skilled workers to get used to new technologies and they are more flexible in incorporating digital innovations in their job or, more generally, to master new technologies (Tinbergen, 1974; Machin and Van Reenen,1998).
This implies a positive relationship between demand for high-skilled workers and innovation, but also an increasing wage-differential between skilled and unskilled occupations (Krueger, 1993; Kats and Murphy, 1992; Acemoglu and Autor, 2011). In view of this, the SBTC seems to be successful in explaining a large part of the wage and employment pattern occurring over the last decades, such as the increasing proportion of highly qualified workers and the rising wage differential (Goos and Manning, 2007; Autor and Dorn, 2009).
However, the SBCT is not able to explain the evidence related to the loss of jobs in the middle skilled professions occurring simultaneously to the increase number of both high and low skilled occupations (Spitz-Oner, 2006; Goos and Manning, 2007; Fernàndez-Maciàs, 2012; Cirillo, 2016). This “polarization” in employment and wage
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structure is formally defined by Stiglitz as the phenomenon that occurs “when middle class jobs requiring a moderate level of skills, like autoworkers’ jobs seem to disappear relative to those at the bottom, requiring few skills, and those at the top, requiring greater skill levels” (Stiglitz, 2012).
In Italy (our case of study) in the period between 1995 and 2015 high-skilled and low-skilled jobs grew by around 4.5%, while medium-skilled occupations experienced a decrease of above 10 percentage points (OECD, 2017)
The reason why the SBCT fails in explaining this pattern could be intrinsic to the kind of categorization used in the SBCT models, in which workers are classified only by their skills (where the skill is merely identified through the level of education of an employee), without considering the actual tasks carried out on this job by each profession.
Responding to this failure of the SBTC framework, many scholars have started to focus their study not only on the impacts of digitalization and computerization across skilled and non-skilled workers, but also on the various effects of the digital diffusion on different task-content occupations. In particular, they have concentrated their attention on the relationship between innovation and the different tasks carried out by workers.
A new theory, the so-called Routine Biased Technical Change (RBTC), emerged where workers are no longer classified by skills, but by the degree of Routine of the tasks performed in each job (Autor et al., 2003; Autor and Dorn, 2013). A task is defined as “a unit of work activity that produces output” (Acemoglu and Autor, 2011). Therefore, the tasks are the actions carried out by workers during their job, and they can change due to technological innovation. In the theoretical model of this
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framework, labour and capital are the input of the production function, which is articulated in tasks. In this way, the tasks can be performed by capital or labour following different logics: economic convenience, the ease with which they are automatable, and their level of connection with other tasks (Sebastian and Biagi, 2018). Obviously, there is a close connection between the RBTC and the SBTC, since there is an inverse relationship between high skilled jobs and share of routineness of the occupation.
In addition, Autor, Levy and Murnane (2003) have suggested an innovative classification of the tasks: Routine Manual task, Routine Cognitive task, Non-Routine Cognitive task and Non-Non-Routine Manual task. In their studies, the results show that the first two are more subject to being replaced by machinery, the third one is strongly complementary to technology (in general these tasks are performed by high skilled workers), while the pattern of the last one is not so clear, because the impact of digital devices on them is minor.
All in all, the RBTC framework is seen as successful in accounting for the non-linear impact of Digitalization on labour demand. Indeed, since many routine tasks may be replaced by digital innovation of machines, and since many of the middle skills jobs are characterised by a high level of routine tasks, this could explain the polarization of both employment and wages.
One of the main problems of the existing literature is the quantification of the Digitalization of a task. In many papers, Digitalization is simply considered either as the investments made in ITC (Autor et al, 2003; 2013) or as the employment effects deriving from the introduction of robots (Acemoglu and Restrepo, 2017), but only
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few scholars used an indicator able to capture the level of ICT (see Bockerman et al, 2018).
This is probably due to the lack of data sources able to capture the inherent digital content of the tasks. In this dissertation, following Cirillo et al (2019) and, thanks to the access to the ICP dataset, we have the great opportunity of creating two different indices able to capture both the level of Digitalization and Routine of each occupation.
Differently from Cirillo et al. (2019) we do not focus on effects of Digitalization and Routine on employment dynamics, but we concentrate our efforts on the impacts of these two forces on wages.
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3
Data sources and variables
In this chapter we describe the data sources used for this thesis and the working sample we have built based on them. Then, we present all the variables we have used in the econometric part as dependent variable, main regressors and control variables.
3.1 Data sources
The following analysis focuses on two datasets: the Italian Labour Force Surveys (ILFS) and the The Indagine Campionaria sulle Professioni (ICP).
The ILFS is carried out by the Italian National Institute of Statistics (ISTAT). It is basically a quarterly survey aimed at monitoring the dynamics of the Italian labour market, ensuring data quality and being representative thanks to the size of the sample and to the sampling strategy behind it. In fact, the samples are made up of almost 150,000 individuals and the interviews are performed through a combined CAPI-CATI method. This survey gives information on about 300 variables, regarding both individual and occupational features. We use this dataset to gather information about wages, occupational status and standard workers characteristics.
The ICP is run by a collaboration between INAPP and ISTAT and it is the only European survey built on the basis of the American Occupational Information Network (O-Net).
The purpose of this dataset is to provide information about the characteristics of all the professions of the Italian labour market, with particular reference to the features of the jobs and to the organizational context in which the jobs are executed. It is
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representative with respect to sector, firm size and geographical location. The interviews were performed in accordance with the CAPI technique and they included twenty workers for each category of occupation, selected to gather information to the 5th digit ISCO.
In order to properly conduct our analysis, we have to properly merge the two datasets described above. In particular, we need to have a working sample in which for each worker we knew the net monthly wage and other individual and professional features. We merge the two datasets by using the kind of profession as the merging variable (the 3rd digit ISCO)
The ICP dataset was carried out by INAPP only for two years: 2007 and 2013. We would have liked to merge exactly the same years of the ILFS, but unfortunately the ILFS survey of 2007 is not available. So, we take the dataset relative to the first quarterly of 2008. As regards 2013 we took into consideration the ILFS survey relative to the first quarterly of 2013, hereinafter we refer to them simply as 2008 and 2013.
By merging the two data sources we obtain a unique dataset with more than 300,000 people (around 175,000 in 2008 and 126,000 in 2013), and about 84,000 observations of wages (approximately 45,000 in 2008 and 36,000 in 2013).
16 3.2 Main variables
Wage represents our dependent variable and, in the data, wages are reported as net monthly wages.
As concerns individual features, we have selected the following variables. Age is reported in the data as different age-classes a worker belongs to (15-24; 25-34; 35-44; 45-54; 55-64). We also include gender of workers, expressed as a dummy variable (GENDER) equal to 1 for female. Then, we have info about whether workers are married, identified by a dummy variable (MARITAL STATUS) taking value 1 if a person is married. Education attainments of workers are recorded in the data as a variable (EDUCATION) reporting the highest level of study reached by a person on a scale of 1 to 6 (1 No Qualification, 2 Primary school, 3 Middle school, 4 High school, 5 Bachelor’s degree, 6 Master’s degree). Finally, we also have info about whether the individual has children, codified as a dummy variable (CHILDREN) equal to 1 if with children, 0 otherwise.
Regarding occupational features, we have information about whether a worker is employed, as a dummy variable (EMPLOYED). Also, data report the sector of economic activity a worker is employed into, recorded as a variable (ECONOMIC SECTOR) reporting five categories of “macro-sectors” according to the Italian ATECO classification of activities (1 Agriculture, 2 Industry, 3 Construction, 4 Trade and 5 Services). Last, we know the geographical location of the place of work, recorded by a variable (LOCATION) reporting five Italian “Macro-regions” (1 North-West, 2 North-East, 3 Centre, 4 South, 5 Islands).
In Table 1 and Table 2 we report the basic descriptive statistics about all the variables in the two years under study.
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The average wage shows, on average, a rise of 4.76 percentage points, although simultaneously we observe an increase in the standard deviation.
The two samples for 2008 and 2013 are significantly homogenous when it comes to location, economic activities and gender. Indeed, in 2008 the percentage of women was around 54%, and this fraction is equal to the one observed in 2013. Regarding the location, the distribution by area is again easily comparable (Northwest shows 25% in 2008 and 26% in 2013; Northeast has 20% in both years; Centre shows 16% and 18%; South has 27% and 24%; and Islands accounts for 13% in both years). Moving to the classification of the economic activities, the 2008 and the 2013 samples have a five-level partition and show a similar distribution of workers by sector: Agriculture (5% and 4%, respectively in 2008 and 2013), Industry (21% and 20%), Construction (9% and 7%), Trade (15% and 15%) and Services (51% and 54%).
All the other variables show similar percentages for both years.
These variables are complemented by two indices of Digitalization and Routine that are the focal interest of this dissertation. We detail their description in the next chapter.
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vars n. mean sd median min max
Dependent variable WAGE 45,096 1,194 485 1,150 250 3,000
No Education 10,396 - - - - -Primary School 30,044 - - - - -Middle School 44,369 - - - - -High School 42,972 - - - - -Bachelor Degree 2,559 - - - - -Master Degree 10,951 - - - - -CHILDREN 132,685 0.39 0.49 0 0 1 GENDER 141,291 0.46 0.50 0 0 1 MARITAL STATUS 141,291 0.57 0.50 1 0 1 Age: 15-24 17,245 - - - - -Age: 25-34 18,854 - - - - -Age: 35-44 25,485 - - - - -Age: 45-54 23,577 - - - - -Age: 55-64 18,363 - - - - -Age: 65-70 18,974 - - - - -Age > 70 18,793 - - - - -EMPLOYED 141,291 0.41 0.49 0 0 1 North-West 35,169 - - - - -North-East 27,882 - - - - -Center 22,051 - - - - -South 38,193 - - - - -Islands 18,056 - - - - -Agriculture 2,861 - - - - -Industry 12,842 - - - - -Construction 5,405 - - - - -Trade 9,423 - - - - -Services 31,411 - - - - -D.I. 61,942 40.25 20.38 44.84 0.74 84.99 R.I. 61,942 52.70 19.32 54.10 0.66 100.00 Indeces Table 1
Basic Descriptive Statistics 2008
Individual Features
Occupational Features
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vars n. mean sd median min max
Dependent variable WAGE 38,276 1,250 521 1,200 250 3,000
No Education 7,473 - - - - -Primary School 24,078 - - - - -Middle School 39,255 - - - - -High School 41,594 - - - - -Bachelor Degree 3,059 - - - - -Master Degree 10,846 - - - - -CHILDREN 122,842 0.43 0.49 0 0 1 GENDER 126,305 0.46 0.50 0 0 1 MARITAL STATUS 126,305 0.54 0.50 1 0 1 Age: 15-24 14,250 - - - - -Age: 25-34 14,297 - - - - -Age: 35-44 21,128 - - - - -Age: 45-54 22,447 - - - - -Age: 55-64 17,080 - - - - -Age: 65-70 17,414 - - - - -Age > 70 19,689 - - - - -EMPLOYED 126,305 0.38 0.49 0 0 1 North-West 32,520 - - - - -North-East 25,666 - - - - -Center 22,223 - - - - -South 29,870 - - - - -Islands 16,026 - - - - -Agriculture 2,093 - - - - -Industry 10,302 - - - - -Construction 3,712 - - - - -Trade 7,666 - - - - -Services 27,880 - - - - -D.I. 51,653 36.80 17.47 34.56 3.07 76.20 R.I. 51,653 42.31 13.29 41.68 11.52 83.35 Table 2
Basic Descriptive Statistics 2013
Indeces Individual
Features
Occupational Features
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4
The DI and the RI Indices
As mentioned above, using the ICP dataset we have developed two fundamental indices, the Digitalization Index (D.I.) and the Routine Index (R.I.). Here we present how we have built them starting from the questions in the ICP survey and we provide some descriptive evidence about their properties and relationship with other variables.
4.1 The Digitalization Index
For every job category at the ISCO 3rd digit level, the Digitalization Index
summarises a number of factors about the utilization of, and the skills inherent to, digital technologies. Inspired by the work of Cirillo et al. (2019), we have calculated it by taking the average of two other indices, the Digital Use Index and the Digital Skill Index, which are exactly equal to the ones of the paper by Cirillo et al. However, differently from the previous paper we merge them in order to obtain a unique index able to capture the Digitalization level of each profession.
The Digital Use Index measures the intensity and the frequency with which digital technologies are used in the carrying out of the job. It is the average value between the results obtained in the following three ICP questions.
The selected questions for the Digital Use Index and their respective benchmarks are the following:
1) Working with computers
2: Entering information into a computer database 4: Writing software to manage an inventory
21 2) Documenting and reporting
2: Recording the cargo weight traveling on a highway 4: Documenting results about investigation of a crime scene
6: Saving data deriving from a satellite study for a private telecommunications company
3) Use of e-mail 1: Never
3: Once a month or more 5: Every day
For each of these questions we observe the normalized scores and we average them to obtain this first index.
It is important to bear in mind that in the ICP survey the respondent has to answer a question with a number from 1 to 7 (only for a small section of the survey this range become 1 to 5), in which the benchmarks of complexity level are reported.
As regards the Digital Skills Index, the level of knowledge of the digital technologies for every profession of the ICP dataset is summarized. The objective of this index is to analyse the ability of employees in understanding the background and the characteristics of the technology used at work. The Digital Skills Index is also calculated as the average value between some features of the worker that in this case are:
1) IT and electronics
1: Using a DVD or CD 3: Using a word processor
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6: Developing software to verify the existence of a virus on the computer 2) Telecommunication
1: Making a call
2: Installing a satellite dish
7: Designing a new worldwide telecommunication system 3) Scientific Method
2: Performing a test to determine soil quality
4: Testing the safety of a product, following written instructions 6: Analysing aerodynamic systems
4) Operation analysis
2: Choosing a photocopier for the office
4: Suggesting software changes to make it more user friendly 6: Identifying the correct control system for a new production line 5) Technological design
2: Adapting gym equipment for a client 4: Redesigning the handle grip of a tool
6: Defining a new technology to produce diamonds 6) Programming
2: Writing a program to order information in a database 4: Writing software for statistical analysis
6: Writing software to detect the existence of mineral deposits
In table 3 we report a summary with all the questions chosen to build the two indices. The Digitalization Index that we use in the analysis is obtained by taking the average value between the Digital Use and the Digital Skills Index.
23 4.2 The Routine Index
The Routine Index is computed in a very similar way to the Routine Task intensity index of Goos et al. (2014) taking the Routine dimensions considered by Autor et al. (2003). For each 3rd digit occupation K and for the two different periods T (2008 and
2013) it is defined as follows: (1) 𝑹𝑰𝒌,𝒕= 𝑹𝑪𝒌,𝒕+ 𝑹𝑴𝒌,𝒕 – 𝑵𝑹𝒌,𝒕
where RC is the task level of Routine Cognitive tasks, RM of Routine Manual tasks and NR of Non-Routine tasks.
The Routine Cognitive tasks are characterized by repetitive analytics actions. Jobs with a high-level of RC tasks can be easily replaced by machinery. Some examples are clerical, sales, and office occupations. The Routine Manual tasks are typical of occupations such as construction, transportation and repair. In general, RM tasks involve repetitive physical actions. Obviously, these kinds of tasks can also be automated and performed by robots. The Non-Routineness tasks can be divided between Non-Routine Manual (e.g. service occupations related to caring for others) and Non-Routine Cognitive tasks (e.g. management and professional occupations).
Table 3
Index Digital Use Index Digital Skills Index
Questions Used Use of Computer, Documenting and Reporting Information, Use of Email Computer Science knowledge, Telecommunications knowledge, Application of Scientific Method, Phase Analysis, Eco-Design, Programming The Digitalization Index
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For the former, the RBTC does not forecast either substitution or complementarity, since these kinds of tasks have not been directly affected by the ICT revolution. On the contrary, for the latter, according to Routinization Biased Technological Change there is not a high risk of replacement, and, in addition, new technologies play a complementary role for these work actions.
In Table 4 we report the ICP questions we have used to measure all these different categories of tasks. The Routine Index for each occupation is calculated by subtracting to the sum of the two routine-dimensions the non-routine level, as mathematical described in the equation(1).
After having calculated the D.I. and the R.I. for each occupation at the 3rd DIGIT
level and for both the years, we attached this information from the ICP to each individual observed in the ILFS, using the profession of each worker as the merging variable.
Dimensions Routine Manual Routine Cognitive Non-Routine
Questions Used
Controlling Machines and Processes, Repetitive Manual Activities, Dynamic Force, Speed in doing the job
Repetitive Mental Activities, Being
accurated, importance of Storing Information
Understanding of the Text, Listening, Public Speaking, Critical Thinking, Problem Solving, Developing Solutions, Give Direction
Table 4 The Routine Index
25 4.3 Distribution of the two indices
In Figure 1, we have plotted the distribution of the Digitalization Index and the Routine Index for both 2008 and 2013.
Concerning the D.I., even though there is a general drop in the mean value over time (see Table 1 and Table 2 above) we also see that in 2013 there was a decline in the number of the workers (from 8% to 5% of total workers) having a low level in the index (smaller than 10). At the same time, we observe a slight decrease in the number of high-digitalized workers (D.I. greater than 70), since they are around 4% in the 2008 sample, while in 2013 they represent only 3% of the total number of workers. There is also a significant reduction of the median value (see Table 1 and Table 2 above), which goes from 44.84 (2008) to 34.56 (2013).
The R.I. shows the same declining trend and in particular the mean value has a ten-point drop. Moreover, it is possible to observe a strong decrease in the extreme values of the distribution, and this evidence is confirmed by the reduction of the standard deviation, as reported in Tables 1 and 2.
26 Figure 1.
Distribution of the Digitalization Index (D.I.) and the Routine index (R.I) in 2008 and 2013
4.4 Interesting correlations
As a further point of the analysis, for the workers of our dataset, we have studied the correlation between, firstly, the two indices themselves and, then, between them and the level of wage, the educational background, the economic sectors and the geographical location.
First, the correlation between the two indices and the level of wage provides initial evidence about our main research question, regarding wage premium associated to Digitalization and Routine. In Figure 2,3,4 and 5 we can observe an inverse dynamic for Digitalization and Routine. Indeed, in 2008 the correlation is 0.42 for the former,
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and -0.42 for the latter, while in 2013 we observe a slightly increase in the coefficient of the D.I. (0.46) and a decrease in the correlation of the R.I. (0.3)
Moving to the correlation between Digitalization and Routine, as we said in chapter 2, many scholars have considered digitalization and routineness as synonymous, following the idea that tasks characterised by high level of routine are more likely to be digitalized. On the contrary, our analysis show as there exists an inverse relation between them. In fact, as we can see in Figure 6 and 7, in 2008 we obtain a correlation coefficient close to −1 (−0.92). In 2013 the correlation falls in intensity (−0.69), but it is still very significant.
Figure 2
Correlation between the Digitalization Index (D.I.) and the net monthly Wage (wage) in 2008
28 Figure 3
Correlation between the Routine Index (R.I.) and the net monthly Wage (wage) in 2008
Figure 4
Correlation between the Digitalization Index (D.I.) and the net monthly Wage (wage) in 2013
29 Figure 5
Correlation between the Routine Index (R.I.) and the net monthly Wage (wage) in 2013
Figure 6
30 Figure 7
Correlation between the Routine Index (R.I.) and the Digitalization index (D.I.) in 2013
Then, looking at the Figure 8, there seem to be strong linear relationships between D.I., R.I. and the highest level of education reached by a person (the categories are listed in chapter 3). In particular, we can notice a positive correlation between the Digitalization of a worker and his educational background, and a negative relationship between the level of Routine and the level of education.
Looking at the differences between the two years, the R.I. declines for all the highest level of education, although the largest percentage decrease is observed for the less educated categories (-28% for no education and -23% for the primary school). On the contrary, the intertemporal pattern of the D.I. is not homogenous. Indeed, it increases for no education and primary school, while it decreases for all the other educational categories.
31 Figure 8
The average value of the Digitalization and the Routine Index for each ATECO (5 classes)
Further, we explore the variation of the Digitalization and Routine indices across the five main economic sectors we can capture in our data (Agriculture, Industry, Construction, Trade and Services). In Figure 9 we plot average indices of the macro-sectors for the two years of interest.
On one hand, the average R.I. of the workers declines over time in all the five sectors, although the greatest decrease is observed in the Trade, where the decrease is around 27%. On the other hand, the intertemporal trend of the average D.I. is heterogeneous across sector. Indeed, the index shows a substantial boost (+50%) in agriculture, whereas the manufacturing and the industry sectors show stable value of the D.I., and a decline in Services (-14%) and Trade sectors (-12%).
0 10 20 30 40 50 60 70 80
D.I. 2008 R.I. 2008 D.I. 2013 R.I. 2013
No Education Primary school Middle school High school Bachelor degree Master degree
32 Figure 9
The average value of the Digitalization and the Routine Index for each ATECO (5 classes)
Lastly, in figure 8, we focus on average levels of the two indices across the Italian macro-regions (North-West, North-East, Center, South and Islands), and their changes in the considered time-period.
In 2008 Italy is perfectly split into two blocks. Workers of Northern and Central regions show the highest levels of Digitalization and, at the same time, the lowest levels of routineness. Employees of the Southern regions and of the Islands display exactly the opposite pattern. This negative relationship between the two indices does not emerge from the graphs of 2013. Northwest Italy is simultaneously the area with the highest level of Digitalization and routineness, and the overall net split between North and South is no longer evident.
0 10 20 30 40 50 60 70 80
R.I. 2008 D.I. 2008 R.I. 2013 D.I. 2013
33 Figure 10
The average value of the Digitalization and the Routine Index for each Macro-Region (5 areas)
34
5
Main regression analysis
In this chapter we report the main regression analysis aiming at studying the impact of Digitalization and Routine on the Italian wages in 2008 and 2013. We have implemented several econometric specifications. We present basic OLS analysis and complement that with a standard 2-step Heckman procedure to control for selection errors.
5.1 Regression model
Our baseline specification is a wage-earning equation of the individual worker level, in which the dependent variable is the wage, the main regressor is alternatively the Digitalization and the Routine Index, and a set of controls is added to avoid omitted variables bias. The regression equation is the following:
(2) 𝑾𝒊,𝒕= 𝜷𝟎+ 𝜷𝟏𝑰𝒊,𝒕+ 𝜷𝟐𝑿𝒊,𝒕+ + 𝜺𝒊,𝒕
where 𝑾 is the net monthly wage of worker in year
t
(2008 or 2013), 𝑰 is a continuousvariable referring alternatively to the D.I. and the R.I., and X is a set of control variables including both individual and occupational characteristics of each worker (gender, level of education, geographical location, age and economic sector of the profession). We perform separate estimates for the two years available (2008 and 2013).
Notice that variables that are categorical in the data (EDUCATION, LOCATION, AGE and ECONOMIC SECTOR) are recorded as dummy variables. As the reference comparison group in the estimates we have the following workers: no qualification, 15-24 years old, working in North-West and employed in agriculture.
35 5.2 OLS results
In table 5 we report the OLS results. In columns (1) and (5) there are the coefficients of the two regressions in which the level of Digitalization is the only right-hand side variable. Estimates show that each one-point increase of the D.I. implies on average an increase in wages of 10.06 Euros in 2008 and of 14.15 Euros in 2013. In columns (3) and (6), the level of routineness is the only regressor. We observe that a rise in the R.I. results always into a fall of the nominal wage (11.18 Euros in 2008 and 11.86 Euros in 2013). All the coefficients are statistically significant.
Next, we move to a “full” model, where we have also included the controls. Results are in columns (2), (4), (6), (8)
Obviously, the inclusion of these covariates reduces the coefficients of our two main indices, but the signs and significance are confirmed. Indeed, we have the following average variations for every one-level increase in the D.I. or in the R.I.: in 2008 a mean deviation of 7.17 Euros (D.I.) and -8.38 Euros (R.I.), while in 2013 we observe 9.72 Euros (D.I.) and -10.07 Euros (R.I.).
Looking at the control variables, we can say that the results are in line with the expectations. There seems to be an increase in wage for each level of study reached by a person. Only in the case of the primary school diploma there is no statistically difference with having no education, while having a master degree involves on average an additional wage earning between 460 Euros and 597 Euros (always compared to the no-education case).
On average, women tend to earn less than men: the gender gap is on average around 300 Euros for both the years.
36
The geographical location seems to be an important component in the determination of the wage. Indeed, using the Northwest as comparison group there is not statistically difference with the Northeast, while for all the other Italian macro-regions we can observe a significant differential. In addition, this geographical wage gap is increasing in 2013, where for example working in Sicily or in Sardinia involves on average a decrease in the earning around 145 Euros compared to working in the North-western regions.
Also, there seems to exist a non-linear relation between the age and the level of wage by using as comparison group the range 15-24. Indeed, initially getting older has a positive effect on the labour income, and this beneficial impact reaches its maximum level around 55-64 years old, and then it starts to fall in intensity. In particular for the last range (>70) this effect is no longer statistically significant. Finally, looking at the coefficients of the 5 ATECO economic sectors, workers in all sectors earn on average more than in the baseline Agricultural sector. The highest wages are in industry.
37 Variables (Intercept) 795.05 *** 878.49 *** 1806.43 *** 1625.60 *** 741.01 *** 713.39 *** 1754.36 *** 1602.60 *** (4.16) (20.30) (8.32) (24.08) (4.97) (22.79) (9.89) (28.56) D.I. 10.06 *** 7.17 *** 14.15 *** 9.72 *** (0.11) (0.11) (0.14) (0.14) R.I. -11.18 *** -8.38 *** -11.86 *** -10.07 *** (0.14) (0.13) (0.21) (0.23) Primary school 7.12 9.61 -18.56 3.07 (17.82) (18.35) (20.29) (21.23) Middle school 121.51 *** 125.73 *** 89.74 *** 131.86 *** (16.86) (17.37) (17.89) (18.77) High school 183.89 *** 200.60 *** 173.92 *** 258.10 *** (17.04) (17.50) (18.00) (18.83) Bachelor degree 274.62 *** 263.10 *** 253.59 *** 399.70 *** (19.88) (20.33) (20.65) (21.54) Master degree 477.25 *** 460.58 *** 465.67 *** 596.95 *** (18.53) (18.96) (19.63) (20.46) Gender -318.09 *** -328.68 *** -279.74 *** -360.28 *** (3.91) (3.90) (4.62) (4.85) North-East 2.29 -0.23 -16.82 ** -15.45 ** (5.11) (5.08) (5.74) (5.90) Center -54.17 *** -52.90 *** -79.32 *** -88.38 *** (5.59) (5.56) (6.21) (6.41) South -110.74 *** -112.26 *** -127.40 *** -132.58 *** (5.10) (5.08) (6.20) (6.40) Islands -115.23 *** -116.16 *** -142.59 *** -148.13 *** (6.43) (6.40) (7.79) (8.05) Age: 25-34 142.68 *** 152.02 *** 124.16 *** 131.06 *** (6.33) (6.40) (8.94) (9.01) Age: 35-44 285.84 *** 294.14 *** 268.02 *** 282.94 *** (6.22) (6.29) (8.66) (8.72) Age: 45-54 386.25 *** 393.61 *** 379.34 *** 396.75 *** (6.42) (6.47) (8.72) (8.77) Age: 55-64 454.23 *** 456.83 *** 446.53 *** 460.08 *** (8.63) (8.58) (10.00) (10.15) Age:65-70 345.50 *** 327.83 *** 335.35 *** 312.63 *** (40.82) (41.16) (38.06) (38.92) Age > 70 162.56 64.36 120.26 69.41 (145.04) (147.64) (198.43) (208.63) Industry 95.92 *** 119.06 *** 217.48 *** 193.02 *** (10.84) (10.83) (11.36) (12.04) Construction 93.87 *** 60.96 *** 235.43 *** 183.74 *** (11.54) (11.76) (13.50) (13.87) Trade 8.68 1.51 123.88 *** 9.28 (11.39) (11.44) (11.99) (13.08) Services 58.92 *** 52.57 *** 172.81 *** 69.17 *** (10.72) (10.80) (11.13) (12.14) N° 45096 45096 45096 45096 38276 38276 38276 38276 R2 0.18 0.40 0.18 0.40 0.23 0.40 0.09 0.36 (8)
Standard errors, in parenthesis, are heteroskedasticity robust. *** p < 0.001; ** p < 0.01; * p < 0.05.
Table 5
OLS estimate of model in equation (2)
2008 2013
38 5.3 Heckman procedure
A well-known issue with OLS estimates of wage equation is that they may deliver biased and inconsistent results due to sample selection, which exists since wages are only observed for employed individuals. The issue is that people who work may be (and often are) selected non-randomly from the population.
One standard solution to this problem consists in using the two-step Heckman correction (Heckman, 1974). In the first step, one runs a Probit regression of the employment probability for each individual in the dataset and uses Probit results to calculate the inverse Mills ratio.
In the second step, one estimates via OLS the main equation (2) correcting for selection by including the Inverse Mills ratio as an additional explanatory variable. We can apply this method in our case, since the ILFS reports info on whether an individual is unemployed.
A crucial point in practical application of the Heckman procedure is related to the exclusion restrictions. In practice, in order to avoid collinearity between the predicted Mill’s ratio and the other covariates of the wage equation, at least one variable used in the Probit selection equation needs to be excluded from the second step. Following a standard practice since Heckman (1974), we assume marital status and having children satisfy the exclusion restriction. That is, we assume being married and having a child directly impact on the decision to get a job, but they do not impact wages.
Looking at the results of the Probit selection equation. in table 6, the majority of the coefficients have the expected signs for both years. Indeed, compared to having no
39
qualification, each increasing level of education enhances the possibility of finding a job, and in particular having a master degree increases that probability a lot. Being a woman, especially in 2008, reduces the opportunity of working, but it is interesting to notice that this gender-difference is mitigated in 2013.
There is a non-linear relation between the age and having a job in which initially getting older enhances the chances to work (by comparing to the range 15-24). Then, after a certain age (around 55 years-old) this probability decreases.
In the Italian more developed regions is easier to enter in the job market, but there is not a statistically difference between Northeast and Northwest regions.
Finally, having a child leads to a higher probability of working, while being married decrease it.
In Table 7 we report the results of main equation (2) for both 2008 and 2013. As regard to the Digitalization Index, the Heckman procedure do not change the digital-premium on wages compared to the OLS regressions. Indeed, in 2008 for every increase in the level of the D.I. we notice, on average, a positive increase of 7.17 Euros (exactly the same as for the OLS). In 2013 the effect is slightly smaller (9.65 Euros vs. 9.72 Euros).
The results for the R.I. are substantially confirmed. Both in 2008 and 2013, a unit increase in the R.I. associates with a lower wage (respectively of -8.45 Euros and -9.92 Euros). Compared to the OLS regression, in 2013 the negative effect is more moderate.
40
All in all, our main analysis shows that Digitalization and Routine levels display a statistically significant and opposite relationship with wages. It also appears that their association with wages is stronger in 2013.
(Intercept) Primary School Middle School High Scool Bachelor degree Master Degree Gender North-east Center South Islands Children Married Age: 25-34 Age: 35-44 Age: 45-54 Age: 55-64 Age:65-70 Age > 70 N° 117615 *** *** *** *** *** *** (0.09) (0.02) 0.48 (0.02) -1.31 -2.24 (0.01) -0.05 (0.01) 1.04 (0.02) -0.09 (0.02) *** *** *** *** *** *** *** *** *** *** (0.05) (0.04) *** (0.02) -0.51 (0.01) *** *** *** 1.38 (0.01) -0.47 (0.02) *** 0.05 *** (0.02) (0.01) 1.34 *** (0.02) *** 0.37*** 0.85 (0.05) -0.04 (0.04) (0.04) -0.49 (0.04) 0.85 (0.05) 1.15 (0.04) -0.72 (0.01) 0.02 (0.01) *** *** Table 6 2008 *** *** *** -0.21*** *** ***
Heckman 1st step Probit
(0.01) 0.02 -0.05 (0.01) -0.14 *** (0.05) 0.21 (0.04) 0.69 (0.04) 0.80 1.02 (0.05) -0.51
Standard errors, in parenthesis, are heteroskedasticity robust. *** p < 0.001; ** p < 0.01; * p < 0.05.
2013 (0.03) -1.99 (0.09) 110561 1.49 (0.02) 0.94 (0.02) -0.98 (0.01) 1.06 (0.02) 1.41 (0.02) -0.49 0.06 *** -0.64 (0.05) -0.24
41 Variables (Intercept) 622.38 *** 289.12 *** 1394.25 *** 1173.83 *** (44.49) (72.23) (46.44) (77.17) D.I. 7.17 *** 9.65 *** (0.12) (0.15) R.I. -8.45 *** -9.92 *** (0.14) (0.24) Primary school -5.07 -87.79 *** -5.26 -59.86 * (19.04) (23.98) (19.60) (25.15) Middle school 175.20 *** 135.39 *** 173.25 *** 179.40 *** (19.76) (20.37) (20.23) (21.53) High school 298.00 *** 308.94 *** 303.65 *** 394.01 *** (25.85) (28.90) (26.05) (29.99) Bachelor degree 387.99 *** 410.57 *** 364.93 *** 554.35 *** (28.32) (33.31) (28.54) (34.42) Master degree 626.39 *** 658.73 *** 595.31 *** 789.92 *** (31.19) (37.22) (31.20) (38.33) Gender -409.87 *** -378.08 *** -414.60 *** -455.49 *** (15.43) (16.20) (15.22) (16.47) North-East 1.37 -14.71 * -0.93 -13.40 * (5.48) (6.05) (5.46) (6.22) Center -71.07 *** -102.36 *** -68.77 *** -111.31 *** (6.32) (7.75) (6.27) (7.94) South -174.49 *** -229.37 *** -171.95 *** -231.52 *** (12.02) (17.61) (11.86) (17.99) Islands -176.97 *** -239.10 *** -174.32 *** -242.39 *** (12.32) (17.67) (12.16) (18.01) Age: 25-34 304.34 *** 368.22 *** 303.66 *** 367.90 *** (26.28) (40.00) (26.05) (40.79) Age: 35-44 483.07 *** 574.32 *** 478.74 *** 580.47 *** (31.63) (49.68) (31.32) (50.65) Age: 45-54 590.85 *** 701.61 *** 585.02 *** 710.81 *** (32.66) (52.13) (32.32) (53.10) Age: 55-64 538.26 *** 670.19 *** 534.65 *** 677.08 *** (13.87) (35.49) (13.81) (36.17) Age:65-70 104.62 73.95 99.95 62.11 (65.30) (63.10) (65.41) (64.65) Age > 70 -205.58 -439.59 -267.02 -472.05 * (183.89) (227.31) (185.89) (236.34) Industry 107.12 *** 225.51 *** 128.57 *** 202.57 *** (11.42) (11.97) (11.41) (12.67) Construction 103.41 *** 244.90 *** 69.00 *** 198.06 *** (12.22) (14.19) (12.45) (14.56) Trade 18.32 126.16 *** 8.90 14.35 (11.98) (12.59) (12.05) (13.73) Services 66.12 *** 171.47 *** 59.08 *** 70.61 *** (11.28) (11.71) (11.37) (12.76)
Inv. Mill's Ratio 232.12 *** 321.91 *** 217.55 *** 312.35 ***
(37.17) (51.53) (36.77) (52.65)
N° 39834 34696 39834 34696
R2 0.40 0.40 0.40 0.37
Standard errors, in parenthesis, are heteroskedasticity robust. *** p < 0.001; ** p < 0.01; * p < 0.05. Table 7
Heckman 2nd step estimates of main equation (2)
Digitalization Index Routine Index
42
6
Extended analysis
In this chapter we present a series of additional regression analysis allowing to test the robustness of the main estimates and to gain further insights about the role of Digitalization and Routine on wages.
In all the analysis we proceed as above, providing OLS and Heckman procedure estimates.
6.1 Pooled sample
As a first exercise, we run a pooled sample analysis pooling together the observations of 2008 and 2013 to test more directly whether the coefficient estimates of the Digitalization and the Routine indices change over time.
We merge the two datasets (2008 and 2013) and estimate the following modification of the baseline equation:
(3) 𝑾𝒊,𝒕= 𝜷𝟎+ 𝜷𝟏𝒅𝟏𝟑+ 𝜷𝟐𝑰𝒊,𝒕+ 𝜷𝟑𝑰𝒊,𝒕∗ 𝒅𝟏𝟑+ 𝜷𝟒𝒙𝒊,𝒕+ 𝜺𝒊,𝒕
where, the Digitalization index and the Routine index enter alternatively alone and also interacted with a dummy identifying observations of the 2013 sample (𝒅𝟏𝟑). The coefficient of interest is 𝜷𝟑, telling whether in 2013 the association between
wages and the indices is stronger or weaker than in 2008.
The results are presented in Table 8. Not surprisingly, we observe that, as emerged from the baseline estimates, the two indices have an opposite relationship with wages: higher Digitalization associates with higher wages, while Routine displays negative coefficients. OLS estimates of 𝜷𝟑 clearly show that the strength of (positive or negative) associations increase over time. The Heckman results provide a quite consistent picture.
43 Variables (Intercept) 827.01 *** 522.97 *** 1579.72 *** 1288.59 *** (15.37) (41.31) (18.89) (43.44) D.I. 6.96 *** 6.98 *** (0.10) (0.11) R.I. -7.86 *** -7.89 *** (0.13) (0.14) DI_2013 2.83 *** 2.76 *** (0.16) (0.17) RI_2013 -2.43 *** -2.27 *** (0.21) (0.23) Dummy year -50.35 *** -46.05 *** 32.94 ** 26.90 * (5.76) (6.11) (10.93) (11.52) Primary school -4.80 -34.51 * 11.56 -15.89 (13.40) (14.69) (13.94) (15.38) Middle school 107.29 *** 157.66 *** 133.58 *** 182.91 *** (12.39) (14.66) (12.91) (15.31) High school 180.72 *** 297.62 *** 234.60 *** 348.66 *** (12.50) (20.15) (12.98) (20.64) Bachelor degree 266.41 *** 389.80 *** 340.12 *** 460.19 *** (14.42) (22.26) (14.96) (22.78) Master degree 474.46 *** 633.00 *** 536.58 *** 691.16 *** (13.59) (25.10) (14.07) (25.49) Gender -300.38 *** -388.75 *** -344.19 *** -429.63 *** (2.99) (11.69) (3.06) (11.72) North-East -6.74 -6.52 -7.06 -6.58 (3.82) (4.07) (3.87) (4.12) Center -66.64 *** -84.84 *** -70.31 *** -88.15 *** (4.16) (4.95) (4.23) (5.00) South -118.74 *** -191.23 *** -122.12 *** -191.98 *** (3.95) (10.36) (4.01) (10.41) Islands -128.17 *** -198.51 *** -131.51 *** -199.89 *** (4.98) (10.58) (5.07) (10.63) Age: 25-34 136.31 *** 316.85 *** 144.81 *** 318.47 *** (5.17) (23.43) (5.23) (23.58) Age: 35-44 279.98 *** 502.75 *** 292.02 *** 506.24 *** (5.05) (28.57) (5.10) (28.73) Age: 45-54 385.96 *** 618.40 *** 397.83 *** 621.86 *** (5.15) (29.70) (5.20) (29.85) Age: 55-64 453.94 *** 584.48 *** 461.56 *** 586.74 *** (6.41) (16.25) (6.47) (16.38) Age:65-70 344.59 *** 109.56 * 322.34 *** 96.98 * (27.71) (46.51) (28.22) (47.19) Age > 70 147.32 -276.95 63.61 -333.10 * (114.78) (142.31) (120.22) (146.63) Industry 150.14 *** 160.01 *** 154.46 *** 164.01 *** (7.97) (8.39) (8.08) (8.50) Construction 156.65 *** 165.86 *** 114.85 *** 125.21 *** (8.81) (9.29) (8.96) (9.45) Trade 61.07 *** 67.30 *** 7.43 13.45 (8.37) (8.80) (8.63) (9.07) Services 110.33 *** 113.61 *** 61.38 *** 65.20 *** (7.85) (8.26) (8.09) (8.51)
Inv. Mill's Ratio 250.06 *** 240.36 ***
(32.00) 250.06 *** (32.21)
N° 83372 74530 83372 74530
R2 0.40 0.40 0.38 0.40
Table 8
Pooled sample regressions
Digitalization Index Routine Index
OLS Heckman OLS Heckman
44
6.2 Complementarity between the D.I. and the R.I.
As a further exercise, we turn back to our baseline model estimated separately by year, but now we explore a different specification where the Routine Index and the Digitalization Index enter simultaneously as explanatory variables.
As we said in Chapter 2, digitalization and routineness are considered by many scholars as two proxiesof the same underlying phenomena. However, by studying them separately, we have seen that their effect on wages is opposite, which suggests that they do capture two distinct and non-overlapping factors. We want here to understand if there exists some complementarity between them.
To this purpose we specify the following model, where the indices enter together and interacted:
(4) 𝑾𝒊,𝒕 = 𝜷𝟎+ 𝜷𝟏𝑫. 𝑰.𝐢,𝐭+ 𝜷𝟐𝑹. 𝑰.𝒊,𝒕+ 𝜷𝟑𝑫. 𝑰.𝒊,𝒕∗ 𝑹. 𝑰.𝐢,𝐭+ 𝜷𝟒𝒙𝒊,𝒕+ 𝜺𝒊,𝒕
Table 9 reports OLS and Heckman results.
Starting from the OLS results, we see that the D.I. taken alone shows high positive coefficients when the R.I. is equal to zero, especially in 2013. On the contrary, the behaviour of the Routine index for low levels of the Digitalization index is not very clear. Indeed, in 2008 the coefficient is not statistically significant, while in 2013 it is positive.
More interestingly, the interaction coefficient 𝜷𝟑 turns out to be negative (although small in the magnitude) and significant for both years. This fact suggests that having simultaneously high level of Routine and Digitalization lead to decreasing wages. These findings are probably related to the high negative correlation between the D.I. and the R.I. (see Chapter 4).
45
All in all, we can say that the Digitalization and the Routine indices are not just two alternative proxy of the same phenomenon, but proxy for distinct features of the process of change in job characteristics.
46 Variables (Intercept) 348.61*** 39.96*** -298.88*** -868.80*** (29.04) (72.72) (37.77) (104.27) D.I. 11.48*** 11.37*** 21.02*** 21.00*** (0.34) (0.36) (0.62) (0.65) R.I. -0.37 -0.40 9.59*** 9.78*** (0.26) (0.28) (0.57) (0.60) D.I.*R.I. -0.16 *** -0.16 *** -0.27 *** -0.27 *** (0.01) (0.01) (0.01) (0.01) Primary school 22.67 14.57 -19.42 -81.78*** (17.49) (18.69) (19.99) (23.59) Middle school 143.50*** 181.51*** 97.08*** 138.66*** (16.51) (19.29) (17.61) (19.96) High school 203.68*** 283.86*** 184.13*** 306.95*** (16.68) (25.12) (17.71) (28.39) Bachelor degree 256.79*** 335.48*** 267.67*** 410.87*** (19.69) (27.77) (20.49) (32.81) Master degree 403.48*** 508.75*** 443.85*** 619.29*** (18.18) (30.41) (19.36) (36.80) Gender 310.85*** 375.70*** 266.93*** 355.02*** (3.84) (14.92) (5.04) (16.14) North-East 3.09 1.51 -14.73** -12.79* (5.01) (5.38) (5.71) (6.01) Center -50.91*** -63.83*** -78.61*** -99.24*** (5.47) (6.18) (6.16) (7.67) South -109.88*** -155.08*** -122.53*** -214.48*** (5.01) (11.61) (6.16) (17.43) Islands -115.58*** -159.27*** -137.15*** -223.78*** (6.28) (11.88) (7.76) (17.50) Age: 25-34 142.54*** 257.39*** 118.09*** 338.04*** (6.34) (25.55) (8.88) (39.52) Age: 35-44 279.40*** 419.66*** 261.56*** 537.79*** (6.23) (30.72) (8.60) (49.09) Age: 45-54 374.93*** 520.61*** 372.14*** 663.28*** (6.42) (31.68) (8.67) (51.48) Age: 55-64 432.74*** 494.71* 440.11*** 643.16*** (8.44) (13.71) (9.95) (35.10) Age:65-70 301.24*** 130.39 324.61*** 90.21 (39.58) (62.86) (36.84) (61.34) Age > 70 112.79 -120.81*** 115.38 -386.44 (145.04) (185.39) (191.48) (219.62) Industry 162.36*** 173.39*** 237.95*** 245.53*** (10.87) (11.46) (11.40) (12.04) Construction 118.06*** 126.71*** 214.29*** 224.52*** (11.52) (12.22) (13.36) (14.05) Trade 64.91*** 74.54*** 164.68*** 168.10*** (11.43) (12.06) (12.87) (13.54) Services 96.72*** 105.04*** 190.94*** 190.06*** (10.69) (11.28) (11.71) (12.34)
Inv. Mill's Ratio 162.63*** 289.72***
(35.94) (50.88)
N° 45096 39834 38276 34696
R2 0.42 0.42 0.40 0.40
Standard errors, in parenthesis, are heteroskedasticity robust. *** p < 0.001; ** p < 0.01; * p < 0.05.
Heckman OLS Heckman
Table 9
Comlplementarity between D.I. and R.I.
2008 2013
47
6.3 Heterogeneity across Education levels, Geography and Sectors
We finally explore whether the association between wages and indices varies by education and across regions or sectors.
First, in Table 10 and 11 we show the results of a modified baseline model where the D.I. and the R.I. indices are interacted with educational attainments of workers: (5) 𝑾𝒊,𝒕 = 𝜷𝟎+ 𝜷𝟏𝑯𝑬𝐢,𝐭+ 𝜷𝟐𝑰𝒊,𝒕+ 𝜷𝟑𝑰𝒊,𝒕∗ 𝑯𝑬𝐢,𝐭+ 𝜷𝟒𝒙𝒊𝒕
where 𝑯𝑬 is a dummy equal to 1 for Highly Educated workers, i.e. individuals with a bachelor or a master degree as highest level of study, and 0 otherwise.
We are interested in estimated 𝜷𝟑 coefficient, since it says whether for Highly
Educated workers the relationship between wages and indices is stronger than for Less Educated workers.
The OLS and the Heckman results confirm two different evidences. The D.I. shows an additional premium for H.E. workers (on average of 9.34 Euros in 2008, and of 7.60 Euros in 2013), while the R.I. associated to a highly level of education displays a negative extra-impact (-10.99 Euros in 2008 and -10.24 Euros).
These results are consistent with the correlation found in Chapter 4 between the two indices and the educational background.
48 Variables (Intercept) 984.34 *** 795.28 *** 1739.50 *** 1832.95 *** (13.00) (15.30) (16.99) (21.40) D.I. 7.35 *** 9.97 *** (0.10) (0.14) D.I.*H.E. 9.34 *** 7.60 *** (0.43) (0.44) H.E. -263.86 *** -107.30 *** 651.95 *** 661.70 *** (23.07) (20.29) (20.07) (26.60) R.I. -8.32 *** -11.30 *** (0.13) (0.22) R.I.*H.E. -10.99 *** -10.24 *** (0.46) (0.75) Gender -306.54 *** -266.95 *** -316.70 *** -356.46 *** (3.90) (4.62) (3.90) (4.95) North-East 5.13 -11.75 * 2.81 -9.81 (5.11) (5.77) (5.08) (5.95) Center -51.38 *** -77.00 *** -48.86 *** -87.36 *** (5.59) (6.23) (5.56) (6.45) South -110.21 *** -126.31 *** -113.93 *** -131.34 *** (5.10) (6.23) (5.08) (6.48) Islands -119.34 *** -146.15 *** -123.58 *** -153.16 *** (6.40) (7.81) (6.37) (8.16) Age: 25-34 141.28 *** 118.83 *** 156.78 *** 124.69 *** (6.35) (8.97) (6.38) (9.04) Age: 35-44 277.34 *** 263.13 *** 286.26 *** 272.93 *** (6.20) (8.66) (6.25) (8.71) Age: 45-54 369.78 *** 367.84 *** 372.85 *** 375.82 *** (6.35) (8.69) (6.40) (8.74) Age: 55-64 419.65 *** 428.35 *** 414.29 *** 429.33 *** (8.41) (9.93) (8.41) (10.10) Age:65-70 292.66 *** 296.56 *** 251.34 *** 232.78 *** (40.07) (38.32) (40.83) (39.41) Age > 70 180.52 62.56 -16.03 -54.55 (144.20) (204.30) (144.85) (216.99) Industry 119.64 *** 241.73 *** 149.84 *** 220.76 *** (10.90) (11.34) (10.86) (12.03) Construction 105.17 *** 254.15 *** 74.03 *** 203.72 *** (11.59) (13.45) (11.80) (13.83) Trade 37.98 *** 157.81 *** 37.35 ** 36.31 ** (11.45) (11.97) (11.49) (13.08) Services 96.09 *** 212.13 *** 96.38 *** 108.97 *** (10.78) (11.04) (10.84) (12.15) N° 45096 38276 45096 38276 R2 0.40 0.39 0.40 0.35
Standard errors, in parenthesis, are heteroskedasticity robust. *** p < 0.001; ** p < 0.01; * p < 0.05.
Table 10
OLS estimation of Equation (5)
Digitalization Index Routine Index
49 Variables (Intercept) 752.26 *** 296.62 *** 1478.71 *** 1255.49 *** (33.67) (65.39) (35.53) (69.49) D.I. 7.33 *** 9.95 *** (0.11) (0.14)
D.I. on H.E. worker 9.23 *** 7.19 ***
(0.46) (0.46)
H.E. -170.64 *** 54.35 749.31 *** 822.99 ***
(28.55) (29.56) (24.17) (32.82)
R.I. -8.32 *** -11.14 ***
(0.14) (0.23)
R.I. on H.E. worker -10.97 *** -10.26 ***
(0.49) (0.79) Gender -429.23 *** -406.45 *** -454.70 *** -513.01 *** (17.72) (18.07) (17.25) (18.43) North-East 6.87 -3.34 5.82 -0.32 (5.53) (6.19) (5.49) (6.38) Center -69.27 *** -108.66 *** -67.81 *** -124.23 *** (6.26) (7.89) (6.20) (8.10) South -202.82 *** -282.96 *** -218.39 *** -308.04 *** (14.34) (20.90) (14.00) (21.37) Islands -212.10 *** -299.76 *** -228.51 *** -327.38 *** (14.89) (21.37) (14.54) (21.81) Age: 25-34 369.32 *** 492.42 *** 414.08 *** 546.98 *** (32.28) (48.27) (31.55) (49.29) Age: 35-44 542.30 *** 716.75 *** 583.90 *** 785.58 *** (37.08) (58.22) (36.22) (59.42) Age: 45-54 635.41 *** 830.90 *** 670.73 *** 899.96 *** (36.85) (59.27) (36.01) (60.47) Age: 55-64 492.34 *** 715.85 *** 493.20 *** 753.01 *** (11.63) (36.38) (11.52) (37.16) Age:65-70 -87.54 -139.44 -181.80 * -258.53 *** (74.31) (72.32) (74.25) (74.02) Age > 70 -389.15 * -812.53 *** -666.19 *** -1042.33 *** (197.93) (234.54) (191.92) (243.52) Industry 130.26 *** 248.75 *** 159.20 *** 230.39 *** (11.52) (11.86) (11.49) (12.53) Construction 113.67 *** 261.28 *** 82.23 *** 216.76 *** (12.28) (14.07) (12.51) (14.41) Trade 46.81 *** 158.51 *** 44.64 *** 40.79 ** (12.08) (12.48) (12.14) (13.60) Services 102.37 *** 209.99 *** 102.71 *** 110.92 *** (11.38) (11.53) (11.47) (12.63)
Inv. Mill's Ratio 310.39 *** 466.88 *** 350.62 *** 528.00 ***
(43.23) (59.09) (42.22) (60.42)
N° 39834 34696 39834 34696
R2 0.40 0.40 0.41 0.35
Standard errors, in parenthesis, are heteroskedasticity robust. *** p < 0.001; ** p < 0.01; * p < 0.05.
Table 11
Heckman 2nd step estimation of equation (5)
Digitalization Index Routine Index
