4 Degrees of Separation: a Study of Social Mobility among Italian graduates, 1861-2010

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Universit´

a degli Studi di Pisa

Dipartimento di Economia e Management Master of Science in Economics

Four Degrees of Separation: a Study of

Social Mobility among Italian graduates,

1861-2010

Candidato:

Andrea Mattia

Relatore:

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The present work studies the persistence of educational attainment through generations from 1861 to 2010 in Centre-North Italy. The methodology employed mirrors the one developed by Clark, based on rare surnames. The dataset was built from scratch and reflects the peculiarities of Italian society. The result is a persistence rate equal to 0.41 in Centre-North Italy. Moreover, the paper studies the specific dynamics of some elite groups, and also looks for potential differences between regions.

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The six degrees of separation theory states that each person in the world may be connected to another random person through a network of acquaintances counting no more than five intermediaries. In this “friend-of-a-friend” sort of chain, everyone is no more than six steps away from everybody else. In a similar fashion, this paper concerns the distance between different people towards a hypothetical situation of equality of opportunities. Except that here they are separated by actual degrees, received by Universities.

In less metaphorical terms, the present work studies the persistence of educa-tional attainment through generations from 1861 to 2010 in Centre-North Italy. Thus, the aim is to assess the extent to which, in this framework, sons replicate their parents’ level of higher education. This concept will be mathematically represented by a single coefficient falling between 0 and 1, indicating the likelihood that the son of graduates will eventually get a degree himself or, conversely, that the son of non-graduates will still not be able to make it to the end of University. More broadly, this may serve as a more general social mobility index.

This kind of research has already been carried out by a number of scholars, most of whom employ what might be defined as a “conventional” methodology based on the analysis of father-son linkages. Here, instead, the estimation procedure mirrors the one developed by Clark (2014), who uses rare surnames, so that he does not need to identify paternal relationships anymore but he can still work on blood linkages because the number of same-surname bearers gets so small that they are all bound to be related. This choice, as will be thoroughly explained in the core of the paper, has significant effects upon the results but also requires a specific type of dataset to start with. Since Italy almost completely lacks historical data on both surnames and graduates, such a sample had to be created from scratch, in ways that will be explained in detail later on.

Hence, after the preliminary creation of the dataset, the analysis yields a persistence rate estimation equal to 0.41, which means that each person has around 40% possibility of replicating her parents’ attainment in terms of higher education. Or, from another perspective, that if someone’s parents are graduates, 4 generations need to go by before her offspring have the same chance of getting a degree as anybody else with no graduate ancestors. It appears that in Italy, along the path leading to equality of educational opportunities, up to four degrees of separation may come between any two randomly chosen people. In addition, some more specific estimations are carried out, namely to observe the dynamics of persistence within certain elite groups and on a region-by-region basis, to search

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1 Introduction

for any difference connected to geography.

Section 2 reviews the conventional literature about social mobility, with a paragraph devoted to research related to Italy only. Section 3 presents the main features of Clark’s methodology, the results it yields to the extent to which they differ from conventional ones, and describes how it was adapted to the Italian case in the present paper. Section 4 explains how the dataset was created, highlighting the points that had to be designed differently from Clark’s in order to reflect the peculiarity of Italian society. Section 5 shows the results, starting from the whole Centre-North sample and continuing to the more specific findings. Section 6 contains a final summary and some concluding remarks.

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In the first of the following two paragraphs, the general issue of social mobility will be introduced, explaining why it raises academically interesting questions and briefly mentioning the different research approaches found in literature, in order to place the present work accordingly. In the second, the focus of attention will zoom in on Italy only, which is the object of this paper, pointing out the specific features of the country that bear some relevance upon social mobility, and the related possible problems for the study.

2.1 Approaches to study social mobility

The concept of social mobility, according to the OECD definition,1 indicates the dynamic relationship between parents’ socio-economic position and their offspring’s when they have grown up. This means that the movement implied may be directed either up or down the social ladder, depending on whether children are improving their initial condition or viceversa. The more frequent and rapid this flow is, the more a society can therefore be deemed to be fluid, or mobile.

No wonder, then, that this dynamic draws a great deal of political interest, since it directly affects each individual’s social standing and it falls within the broader issue of inequality. Nevertheless, addressing social mobility is not just a matter of fairness or justice, but it makes a twofold contribution to economic efficiency: on the one hand, through a better allocation of resources; on the other hand, through the guarantee of equal initial opportunities which, in turn, enhances productivity.2 _{Moreover, Atkinson (1981) argues that the whole population would}

benefit, in terms of welfare, by a general increase in social mobility, when individual utility functions include offspring’s consumption possibilities in an overlapping generations framework.3 _{All these observations and implications certainly call for}

an adequate and scientific investigation of these dynamics.

Two basic assumptions are needed in order for this economic analysis to bear any significance: there must be a status disparity among members of a same society, and there must exist some kind of mechanism working to perpetrate, at least to a certain extent, such differences across generations. From the academic research point of view, though, both theoretical and practical problems arise which

1_{A Family Affair: Intergenerational Social Mobility across OECD Countries, OECD (2010).} 2_{OECD (2010), op. cit.}

3_{Causa and Johansson (2010), however, underline that there exists some trade-off between}

economic growth and policy decisions aimed at reducing inequality, hence having a beneficial effect on both sides would only be possible by implementing a balanced mix of the two measures.

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2 Literature review

do not allow for a thorough understanding of social mobility.

Firstly, one should determine which particular factors enjoy a positive causal relationship towards social mobility: for instance, one may argue that holding a degree, as a way of signalling ability and individual merit, could be the best asset for offspring to improve their condition relative to parents. Actually, in an ideal perfectly fluid society, education would be the only determinant of mobility, regardless of one’s family income or occupational class. In reality, as will be explained shortly, educational attainment is deeply intertwined with a series of other influencing factors, often unobservable, with blurred boundaries between them and the result of dimming the effectiveness of individual choices upon final achievement when family background is taken into account.

Secondly, one has to face practical measurement difficulties and lack of sufficient data, especially since multigenerational and historical statistics are often required in order for the study to yield significant results. For instance, and merely to mention a few of the possible hindrances, how can a person’s cognitive abilities be estimated? How can a family’s total assets in terms of money, culture, values and network be evaluated? And when it comes to assessing offspring as opposite to parents, how to compare statistics that may have been reported following different methodologies?

In the last few decades, academics have come up with different solutions to these issues, depending on the theoretical explanation they wished to provide, on their countries of interest and on the available data. So far research has focused on three main means of social mobility:

1. income;

2. occupational position; 3. educational attainment;

trying to estimate their so-called “intergenerational elasticity” according to the equation β in the equation yit+1 = α + βyit + εit. yit and yit+1, respectively,

represent individual i’s income, occupation or education at time t, and their offspring’s at time t + 1, α is the constant, β elasticity and εit the error term. In

other words, all the models developed by this stream of literature are designed to estimate the extent to which each of those three factors is passed on from parents to children, or the likelihood that the latter’s socio-economic condition will replicate the former’s. The same framework applies to the present work, where the coefficient β is going to represent the probability that graduates yit might give

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birth to future graduate offspring yit+1 for each individual i — that is, β indicates

intergenerational elasticity of academic degrees.

All three means of social mobility, however, show a deep mutual correlation, so that each, even when singled out, is a good predictor of the movement the other two might follow. Especially from a historical perspective, for instance, the most prestigious and elite professional positions often coincide with those that earn more income and with those that require higher qualifications; similarly, stronger educational attainment leads, on average, to better paying and eminent jobs.4

This notwithstanding, each factor accounts for a different aspect of individual standing in society, thus adding something peculiar to the analysis or turning out to be better suited to describe a country’s specific dynamic. Income, for example, is clearly the most straightforward in terms of historical or intergenerational comparisons; conversely, it may fail to pinpoint social prestige whenever the elite group is not also the richest one. The latter scope is perhaps better served by education and occupational status, even though they are more difficult to assess and may not always refer to those who enjoy the greatest spending capacity.

Thus, the final choice of the model also depends on which particular features of social mobility one is most interested in or regards as the most important. That is why some academics have decided to focus only on one of the three factors at a time, while others have attempted to estimate the contribution of each one to the total effect, as will be summarised below.5

The first approach, exemplified by Becker and Tomes (1986), Solon (1992 and 2002), Dearden et al. (1997), Ermisch and Francesconi (2002), Corak (2006) and Björklund and Jäntti (2009), addresses the issue of income persistence, that is to say, do richer people descend from richer parents? The model, lnyi = α+βlnyPi +ε,6

can be adjusted to measure life cycle earnings, or only certain ages, or salaries referring to a specific part of a person’s career. There is not, however, complete consensus around estimates of income elasticity obtained through this method, with coefficient values for Britain, for instance, spanning from 0.3 to 0.5.7

The second approach, actually more common in sociology, employs long-term

4_{Following the same line of reasoning, policies such as income redistribution, education and}

job market acts, which may sound as pointing to a single aim, actually have an effect on all the three dimensions involved in social mobility.

5_{A review of all different approaches to research in social mobility can also be found in}

D’Addio (2007).

6_{α, β and ε have the same meaning as in the general framework, whereas lny}

i and lnyPi

represent, respectively, offspring’s and parents’ income on a logarithmic scale.

7_{Blanden (2009) underlines that, while up to a few years ago considering solely male income}

did not induce significant estimation errors, now female work must enter the picture in order to gain a full understanding of the situation.

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2 Literature review

data on occupational classes and includes two different methods. Kerchkoff et al. (1985), Erikson et al. (1979), Long and Ferrie (2005 and 2006) and Ferrie (2010), for example, divide people into professional groups which are arranged in a square matrix according to the increasing level of ability they require. Rows and columns represent sons’ and fathers’ jobs respectively, but they are made up of the same list of occupation groups, so that along the principal diagonal are couples doing the same job. Ganzeboom et al. (1992) and Ganzeboom and Treiman (2007), instead, create a socio-economic status index based on the weighted average of income and educational attainment associated to each professional group; the analysis is then carried out according to a model very similar to that of income persistence.

The third approach, which the present work falls within, measures persistence of education through generations. Hertz et al. (2007) use years of schooling as an estimator and compute both elasticity — according to a regression model similar to the one already described — and simple correlation. Comparable as almost all their results may be, they find a major divergence for the UK, with the country appearing immobile from the elasticity perspective but mobile from the correlation one, due to the fact that most people left right at the end of compulsory schooling, thus exposing a possible problem related to this method. Causa et al. (2009), on the other side, examines the probability premium of achieving tertiary education that sons of graduate fathers enjoy across all European OECD Countries. Clark (2014) and Clark and Cummins (2014) have the same calculation aim but they come up with a very peculiar methodology, which will be explained thoroughly in the next section and applied, albeit after some due adaptations, in the present work.

Finally, some mixed approaches have been attempted, trying to show the compound effect of more than one factor of social mobility or the way one may affect the other. For Britain, Blanden and Machin (2008) link, at first, parental income to intermediate outcomes such as educational attainment, test scores and non-cognitive abilities, and then continue to obtain an estimate of the final intergenerational earnings correlations. Causa and Chapuis (2011), similarly, asso-ciate individual background with school performance in several OECD Countries, controlling for institute fixed effect in terms of socio-economic environment, so that the analysis yields both within- and between-school coefficients. Blanden and Gregg (2004), referring to the UK, and Clark-Kaufman et al. (2003) and Houston

et al. (2001), to the USA, employ various methods to disentangle the direct effect

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compute it accordingly.

Having devoted this paragraph to the general established methodology of research in social mobility and to the associated examples in literature, pointing out which group the present work might be a part of, we can now move on to the case of Italy, presenting specifically related contributions which the results of this paper may be compared to.

2.2 The Italian case

Although several different approaches have been used to study and interpret social mobility, Italy presents some further difficulties resulting from class structure as well as regional disparities, which one should be aware of before embarking in such research. In addition, the fairly young age of this country, together with often insufficient historical data, especially regarding income and father-son pairs, have hampered that expansion of literature about social mobility that has, instead, blossomed in the UK and in the US. These two broad issues concerning Italy, which we may classify as socio-cultural on the one hand and statistical on the other, will be explored in this paragraph.

In his 1974 Essay on social classes,8 _{Sylos Labini describes Italy as dominated}

by low-middle class or petty bourgeoisie, mainly made up of many economically eterogeneous, politically unstable and intellectually superficial small, local groups. This class’ increasing spending possibilities during and after the post-Second World War economic boom has, in turn, significantly widened the number of those who could afford and desired to enrol at university. It was, however, too slow a change in terms of “civic progress”, since the remaining semi-illiterate population could not keep up, so that Italy’s average education level has been — and still is9

— persistently lower than that of all other European countries;10 _{at the same time,}

though, the process was too fast with respect to national economic development, with qualified labour supply quickly and steadily exceeding demand in the so-called “intellectual unemployment” phenomenon. Thus, in the present situation the share of people holding a degree is rapidly growing and there certainly still is a cultural

8_{Original title: Saggio sulle classi sociali.}

9_{According to the 2014 Eurostat statistics, only 22.4% of people aged 30-34 in Italy currently}

holds a degree, the lowest percentage in the 28 European Union countries, against a 36.8% EU average. The 17% high school drop rate is the fifth highest in the same group, while 9 years after enrolling, on average, only 55% of Italian university students have actually completed their degree.

10_{Bratti et al. (2008), in fact, find that the expansion of higher education possibilities in}

Italy has increased the chances of enrolling in university but not those of graduating, thus only yielding limited effects in terms of social mobility.

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2 Literature review

advantage of higher education over the rest of the population, albeit this may not immediately translate into adequate working opportunities.11

Sylos Labini (1974) goes on to underline in the modernization process of social classes between the North, Center and South during the twentieth Century. While in all three the greatest movements happen inside the petty bourgeoisie groups, they appear much more prominent in the North, where private sector workers outnumber public sector ones, differently from what could be observed in the South, which is, for this reason, more prone to clientelism. It is, then, reasonable to expect higher dynamism and social mobility in the North rather than in the South throughout the whole last century. More recently, Checchi and Peragine (2005) find that, both looking at individual earnings and cognitive abilities, the South not only displays greater inequalities than the Center-North, but it also suffers from sharper incidence of disparity in opportunities, since parents’ education there plays a more important role in determining individual achievement.

On the part of historical statistics, Piraino (2007) and Mocetti (2007) lament lack of longitudinal data, and estimate parental income with similar methods from the Survey of Household Income and Wealth conducted by the Bank of Italy. The conclusions they get to, however, are strikingly different: the first obtains an intergenerational persistence in the order of 0.48, where only about 28% is directly mediated by higher education; the second, instead, finds an income correlation as high as 0.84, which is attributable to the university channel by a significant 60.7%. In general, Corak (2006) highlights that these estimations are very likely to be affected by measurement errors, which do not allow for a correct assessment of the direction and extent of the bias and, consequently, impede international comparisons. A notable exception in this stream of literature is embodied by Barone and Mocetti (2015), who link Florence inhabitants from 1427 to present ones in 2011 using surnames and find an earnings elasticity equal to only 0.04.

Nevertheless, there has been some research regarding social mobility in Italy through occupational classes and education persistence. Raitano (2011) applies a two-stage analysis to measure first the degree of association between fathers’ and sons’ professions, and then the latter’s income prospects, trying to calculate what share is inherited or influenced by external factors and what is strictly due to personal attainment.12 _{Gabriele and Kostoris (2006) draw their data from the 1998}

11_{According to the 2015 survey by Almalaurea, graduates show a 75.7% employment rate}

against 62.6% of non-graduates, and earn an estimated lifetime income equal to 1.5 times that of non graduates.

12_{Interestingly, Raitano (2011) finds, albeit with some eterogeneity, that the use of personal}

network to advance in one’s career is negatively correlated with higher education, making it a sort of last resort for less skilled workers.

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multiscope Survey by the ISTAT, Family, social agents and childhood condition,13 and divide their sample into six occupational classes in decreasing order according to associated earnings and specialization. They organize the observations in a square matrix, similarly to Long and Ferrie (2005), and calculate the odds ratio for each transition, always noting that offspring have way more chances of sticking to the same group as their parents than of changing it. Marzadro and Schizzerotto (2008), instead, employ statistics from the 2005 Longitudinal Survey on Italian

Households14 _{and, through a log-linear analysis, conclude that, during the second}

half of the twentieth century, middle-high classes have increased their chances of preserving their position or moving upwards, while low ones have decreased theirs.

Turning to education as a means of social mobility, Brunello and Checchi (2003) find that a decrease in pupil-teacher ratio, as a proxy for quality improvement, makes parental education less determinant especially in poorer backgrounds, thus reducing inequality. Checchi et al. (1999) also acknowledge that the Italian egalitarian school system tends to diminish income disparities when contrasted to the US; when it comes to mobility, though, the relationship is reversed, with the American lower classes more likely to be noticed for their talents and climb the social ladder. Tranferring the comparison to Germany, Checchi and Flabbi (2006) still find that family background is more important in Italy at all school levels and up to college choice. Finally, Checchi et al. (2008) analyse the persistence of education, and calculate that the correlation coefficient between fathers’ and sons’ attainment is significantly high but decreases from 0.575 for the 1910 cohort to 0.472 for the 1970 one.

Summarizing, despite a general lack of appropriate historical data, several studies of social mobility in Italy have been attempted, with different approaches and results that turn out all the more different, the more estimations have to be made based on the sample. In the next sections, a new methodology conceived by Clark (2014) to study income and education persistence will be presented, and its adaptation to the case of Italian graduates will be discussed.

13_{The original title is Famiglia, soggetti sociali e condizione dell’infanzia.}

14_{The original title is Indagine Longitudinale sulle Famiglie Italiane (ILFI), carried out by}

the Universities of Trento, Bologna and Milano Bicocca, by ISTAT and by the Istituto Trentino

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3 Clark’s methodology

In this section a new methodology based on rare surnames will be introduced, referring to Clark’s 2014 book The son also rises and other related papers.15

Before describing how this very peculiar approach performs in terms of measuring income and education persistence and how it could be applied to Italy, its features and advantages over other methods will be mentioned, as listed by its author.

3.1 Why rare surnames?

The major characteristic of Clark’s model is its simplicity, with the fundamental equation driving social mobility remaining:

yit+1 = α + βyit+ εit

where β indicates its intergenerational rate. The author names yi as “social

competence” for each individual i: it is the overall social status, made up of several measurable aspects such as income, occupational class and education,16

plus some random unobservable factors.17 Conventional studies employing the approaches listed in the previous section, therefore, can only take into account the first kind of elements, thus estimating the following:

xit+1 = α + bxit+ νit

where xi is the chosen observable mean of mobility. In other words, until now

researchers have only estimated the dynamics of income, occupational classes or education, which is not the true underlying social mobility process, but allegedly the way it appears as mediated by the effect of the random elements. Clark argues that the two variables xi and yi are linked by this relationship:

xit= yit+ uit

where uit is the general random component.

15_{Clark, G. (2014), “The Son Also Rises. Surnames and the history of social mobility”,}

Princeton University Press, Clark et al. (2012), Clark and Cummins (2013) and Clark and

Cummins (2014).

16_{Other factors could be residence, health or longevity.}

17_{Clark mentions luck, in the sense of chance, as the first element accounting for the existence}

of a random unobservable component. Another reason may be the fact that some people would rather sacrifice earnings and wealth in exchange for social or occupational prestige, as notably exemplified by University professors.

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Now, the question to be addressed is how similar the coefficient b is to the value

β which one should actually be interested in. Considering the three equations

listed above, it emerges that b = θβ, with θ = σ2y

σ2

y+σ2u < 1, i.e. b is only a fraction

of β.18 _{Hence, whatever measurable mean of social mobility is considered in any}

of the conventional studies, the result will invariably be an underestimation of the true underlying value β, since the observations will only account for a fraction of the total effect.

It is at this point that rare surnames enter the picture. In fact, the author argues that the random component is much more relevant at the individual level than it is when averaging over a whole group bearing the same last name. Simultaneously, considering only rare surnames, i.e. those that originally count no more than 50 bearers in the whole country,19 _{ensures that family-specific features,}

both observable and not, are actually all taken into account, since they are shared among people who are most likely related to one other.20 _{Mathematically,}

the expression associating measurable elements with social competence for each surname group becomes:

¯

xt= ¯yt+ ¯ut

where the bar symbolizes averaging. In this way, the mean value of ¯ut shrinks

towards zero together with its variance σ2

u, so that, on the one hand, ¯xt now

correctly tracks ¯yt and, on the other hand, θ approaches 1, making b an accurate

predictor of β.

Having established this particular new method, Clark turns back to applying it to income, occupational classes and education, such as in conventional studies, but obtaining different results, which will be summarized in the next paragraph.

18_{Knowing that b =} P xtxt+1 P_x2 t and that β = P ytyt+1 P_y2 t

, substituting for xt = yt+ ut, and

simplifying, the resulting θ is the one described above.

19_{Clark and Cummins (2014) actually work on progressively more common classes of surnames,}

up to those counting 301-500 bearers, but consistently find that higher status is associated with rarer surnames, so that the elite group is best represented by the 0-50 class. Since the numbers are so small, this choice even allows them to observe some father-son linkages, in order to compare their methodology to the conventional one.

20_{Other types of randomization, such as over race or minority groups, are clearly possible}

and, for the same reasons listed above, should give higher estimates than conventional studies, although they may neglect some relevant family-specific aspects.

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3 Clark’s methodology

3.2 Clark’s results

The last step in Clark’s methodology is picking one or more elite groups depending on the cases, be it attorneys, physicians, members of Parliament, Uni-versity professors, highly educated people or top-income earners, and checking how each surnames places relative to the total population average participation in that elite group. What has to be calculated is, therefore, the “relative representation” of every surname z, using the following equation:

Relative representation of z = share of z in elite group share of z in general population

Whenever the value obtained is higher or lower than 1, the meaning is that the status associated to that particular last name is, respectively, above or below the general population average according to that criterion. All the results thus computed, finally, must be normalized to have zero mean and constant variance in order to correctly estimate the intergenerational coefficient of social mobility. The latter is usually smaller than 1, indicating a pattern of regression towards the mean in society but, for some groups, it may be greater than 1, pointing out a divergent trend instead, likely increasing inequality.

Throughout The son also rises and in a series of previous papers, the new methodology has been applied to a number of countries with results different from conventional studies but surprisingly very similar to each other despite relating to very diverse societies. Moreover, in each case, rare surnames have been analysed both as a whole and in smaller groupings reflecting common origin, etymology or geographical settlement, to check if peculiar dynamics may arise.

Clark et al. (2012), for instance, find the following status persistence rates by occupation in Sweden from 1700 to 2012 (Table 1), as an example for all nordic countries. Although there are some missing values, the numbers look quite similar between different job classes and even between different epochs, with only a slight tendency towards the mean. The overall implied social mobility rate is between 0.7 and 0.8, way higher than the 0.25-0.4 found in conventional studies, which classified Sweden as a strongly equal society.

Clark (2014) includes the same kind of estimates for a few specific surname groups among doctors, displayed in Table 2, and among attorneys, not shown but very much alike. The first three groups listed here are found to have a higher than average relative representation, while the opposite is true for the last two; all, however, do not seem to have changed their position significantly during the last century. Strikingly, the general average values fall within the same 0.7-0.8

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1700-1900 1890-1979 1950-2012

Attorneys 0.73

Physicians 0.71 0.80

University students 0.80 0.67

Royal Academy members 0.88 0.75 0.83

Table 1: Estimates of status persistence rates by occupation in Sweden 1700-2012.

Source: Clark et al. (2012).

window as Sweden.

1920-49 1950-79 Average

to 1950-79 to 1980-2011 1970-2011

Ashkenazi Jews 0.88 0.75

1923-24 rich 0.78 0.84 0.94

Ivy League graduates 1650-1850 0.80 0.65 0.23

New France settlers 0.81 0.65 0.78

Black (English) 0.69 0.96

Average (all groups) 0.80 0.74 0.73

Table 2: Estimates of status persistence for various surname groups among doctors in the US

1920-2012.

Source: Clark (2014).

Moving to England, Clark and Cummins (2014) consider a much longer time span, from 1170 to 2012, and study the relative representation of some surname groups classified according to their status at the beginning of the period. The implied intergenerational correlation, around 0.9, is so high that three of them still show values above average after more than 800 years, both in terms of wealth at death and of graduation at Oxford and Cambridge. Fig. 1 shows this progression for Norman surnames, for the locative, i.e. indicating the ancestors’ birthplace,21 and for those appearing as landowners in the Inquisition Post Mortem (IPM) for the years 1236-99.

Estimates of wealth persistence in Clark (2014) provide, once more, average values over 0.7, far above the conventional 0.44 calculated for England. While the evidence presented so far regards three developed Western countries, Clark takes a step further and finds very similar intergenerational coefficients even among elite groups in States such as India, China, Japan, Korea and Chile, which all share

21_{Since travelling in the Medieval Age was not common at all, nobody could ever have the}

need to be named after his birthplace, except for a small group of very rich people, identified by this kind of surname.

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3 Clark’s methodology

Figure 1: Relative representation at Oxford and Cambridge for three surname groups from 1170

to 2012.

Source: Clark and Cummins (2013).

little with Sweden, England or the US. However, this model has not yet been tested for Italy, and a possible way to do so will be showed in the next paragraph.

3.3 Adaptations to Italy

In England, historical wealth estimates over the last two centuries are made easy by the availability of probate records and by the enforcement of a law stating that all estates below a minimum value would not be probated. This implies that the rich elite groups can be pinpointed with certainty and their assets evaluated with a reasonable degree of confidence. In Italy such estimates are not feasible, because very few people used to write wills, since heirs’ rights are anyway more protected than in England.

Another possible road may involve looking into professional elites, which would actually mirror pretty accurately some relevant features of Italian society. In fact, until recently, almost all jobs that required an university degree could not be legally practised before taking a bar exam and signing up on the official register. Some professions, such as attorneys, notaries and physicians, date back to just a few years after Italian Unification in 1861, while others were formally established only some time later, but they all clearly represent elite groups.22 _However,

22_{A very insightful and detailed discussion about the history of professions in Italy can be}

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the geographic fragmentation of each register into small district sections, paired with the restrictions imposed by privacy laws, make this kind of research, too, unrealistic.

The last and most suitable chance is, therefore, to follow Clark’s steps on the path of higher education. Italy has a strong tradition in terms of Universities: 19 had already been built before 1500 and 9 more had been created until 1850.23

The first problem, though, is that there is no such thing as Oxbridge on the south side of the Alps, meaning that there have never been two institutions so overwhelmingly superior to the others in terms of quality and prestige. The second issue is that the Unification happened only quite recently compared to other nations, so estimations of social mobility patterns are truly significant only after that date. Nonetheless, bearing these two caveats in mind, a group of about ten high quality Italian Universities can be put together, considering only those that are simultaneously among the most ancient, the biggest by number of students and the best throughout the period from 1861 to 2010; they will be listed in the next section. Moreover, since Italy has such a small number of graduates compared to other European countries, considering the group of people holding a degree as an elite is not far from being historically accurate.

Finally, differently from Clark, women will be taken into account, even though they can act as surname bearers but not givers, for the three following reasons. First, at least until after the Second World War when graduate figures by gender quickly started to converge,24 _{only daughters from rich and culturally enlightened}

families had the chance to go to University, so it is important to capture this feature because it may be relevant to social mobility. Secondly, especially in the last few decades, it has been shown that children’s final educational achievement depends more on their mothers’ than on their fathers’. Lastly, depending on how surname lists and University records are organized, it may prove impossible to tell men from women for some observations, hence considering all may avoid biases.

Every other methodology aspect, including the mathematical procedure, re-maining unchanged, all the necessary theoretical elements have been mentioned. The next section can thus be devoted to the description of how data have been collected, organised and built up into the final dataset used in the regression analysis.

23_{Each University states its establishment year on its official website. The complete list of}

Italian Universities is available on the official Ministry for Education website and is constantly updated.

24_{According to historical data by ISTAT, the share of women graduates on the total was}

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4 Building the dataset

This section illustrates how the dataset was built, the first fundamental step in this work, which would have otherwise proven unfeasible given the nearly complete dearth of digitised and ready to use statistics for Italian surnames and graduates. Since it would be both unfair to the reader and worthless to the scope to turn a most interesting research into a tedious list of details only to do justice to how time-consuming the process has been, the hope is that these few lines will suffice to serve that purpose properly. The core of the description, instead, focuses on the main features and choices that creating the dataset has required, in the mere attempt to make it as accurate as possible with respect to both Clark’s theoretical model and Italy’s own peculiar social structure. The first part outlines how the surname sample was built, while the second depicts how the historical data about graduates was gathered.

4.1 Rare surnames sample

According to Caffarelli and Marcato (2008), there are more than 330000 surnames in Italy, an immense variety that meets no equals in the rest of Europe and most other countries. Herein it would be impossible — and, even if one had the necessary knowledge to master the subject, beyond the scope — to scratch any deeper than the surface of pure curiosity into the linguistic and historical analysis of such a huge and multifaceted group. Still, some of these aspects need to be taken into account to the extent to which they affect the statistics and the sample composition.

In fact, for instance, an estimated 20% of all surnames were originated by spelling or reporting mistakes25_{, such as Abbiate and Abbiati or Accorinti and}

Acorinti, and may thus look as rare when they actually are not. In these cases, it is necessary to link them back to their more common initial form, since they do not constitute independent surnames. Other spelling variations may have happened on a regional basis due to the interaction with local dialects, as in Aguiari and Anguiari, which derive from two different pronunciations of the Venetian vernacular word for “eel catcher”. The solution is, again, to dismiss such alterations whenever a common origin can be found, although this is not always correct, as in Gioini and Gioino, which evolved from two distinct expressions.

Moreover, further information may be gathered by examining the meaning of surnames or of the words they originated from. For example, Italian surnames

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mainly derived from given names, places denominations, the forefather’s job or nicknames hinting at his physical appearance or some particular event in which a person was involved. From the Medieval Age onwards and during times when some parts of Italy were ruled by other countries, a few surnames of foreign descent, especially Spanish, French, German, Austrian and Slavic, were also introduced. Additional origins include derivations from Jewish,26 _{Latin, Greek and Arab, the}

latter two being much more common in Southern areas, whereas religious or well-wishing expressions often served as invented last names for foundlings while, finally, for a certain number of surnames the roots remain unknown.27 _{In general,}

this kind of analysis may prove useful to divide families on the basis of social class or of implied initial status.

The only available sources containing all the aforementioned information are dictionaries of Italian surnames, such as Bongioanni (1928), De Felice (1978) and Francipane (2005).28 _{Drawing from these volumes, considering also Caffarelli and}

Marcato (2008) and including spelling variations, when mentioned, the overall coverage amounts to about one third of the estimated total existing surnames in Italy.

Once the primary source has been identified, the next task is how to detect and isolate rare surnames, counting no more than 50 bearers in 1861, at the beginning of the chosen timespan. Since no general register of that sort whatsoever, whether electronic or printed, in 1861 or in 2010, is available in Italy, some other instruments for estimation have to be located.

The official “Pagine Bianche” website,29 _{the Italian white pages, provides a}

search service that associates each surname with the amount of people bearing it who have a landline telephone number; the database it refers to is made up of 11-12 million entries, corresponding to about one fifth of the whole population.30

Making a first simplifying assumption, then, the number obtained as an outcome when looking up a surname into the website should be equal, on average, to a fifth of the actual bearers.

Now, how much could a group of 0-50 people possibly have grown in Italy in

26_{For a list of Jewish surnames present in Italy, a possible source may be Schaerf (1925).} 27_{For a complete analysis of how Italian surnames first appeared and what sources can be}

used to determine their origins, see Addobbati et al. (2012) and Bizzocchi (2014).

28_{The already cited Caffarelli and Marcato (2008) also contains the same kind of information}

for 60000 surnames but focuses only on surnames born by at least 200 people, which do not qualify as rare.

29_{The website’s URL is www.paginebianche.it.}

30_{Considering that the database only includes landline telephone numbers and that each}

household rarely has more than one, the share of population actually covered is likely to be higher.

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4 Building the dataset

Year Italian Population Sample population

1861 26328000 50 1891 31785000* 60 1921 37856000 72 1951 47516000 90 1981 56557000 107 2011 59434000 113

Table 3: Italian population according to ISTAT census every 30 years from 1861 to 2011 and

corresponding replication over a 50 people sample.

* This number has been interpolated since no census was held in 1891.

the 150 years spanning from 1861 to 2010? A hint to what the progression might have looked like can be found in Table 3, where the evolution of a hypothetical 50 individuals sample over time replicates the one actually followed by the country’s population according to ISTAT census every 30 years. The growth rate was about 25% between 1921 and 1951, 5% between 1981 and 2011, and around 20% in all other periods. Hence, allowing for a slightly higher growth and making the second simplifying assumption that small groups of rare surname bearers have the same increase rate as the national population, the present day upper bound is set at 150 people, which translates into 30 contacts in the Pagine Bianche website.31

Surnames were therefore tested one by one and only those falling within the fixed 30 people limit were kept, until a sample of 2982 entries was built, with a total just below 45000 individuals. Each item was noted into a spreadsheet, together with its number of landline contacts, its initial social class, whenever explicitly stated by the dictionaries,32 _{and its alleged geographic origins up to a}

maximum of 3, in order to account for border areas between regions or uncertainty about the forefathers’ first settlement.33

One may now wonder whether the sample is in any way representative of the composition of the whole population: the issue can be addressed in two ways. As far as geographic origin distribution is concerned, Table 4 presents the region by

31_{Starting from 50 people in 1861, the growth rate required to get to 150 by 2011 would be}

about 24.5% every thirty years, which is only marginally above average until 1981 but definitely higher afterwards.

32_{In order to infer the initial social class within which a surname originated, dictionaries rely}

on written records, such as wills or signed papers, and on material sources, such as buildings bearing a family’s name or coat of arms. Hence, high class surnames can be identified with a considerable level of certainty, whereas for all the others the assessment is not straightforward and often remains undetermined.

33_{The geographic origin was double-checked using the website www.gens.info, which locates}

each surname on Italy’s map according to the districts it can be found in. Provided that most southern surnames also appear in the North due to internal migrations, in no circumstance this procedure yielded a different picture than the one described by the dictionaries.

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region list of percentage shares over total population computed for the sample and for the 1951 ISTAT census, a year when the Italian border was the same as today and the internal migration from the South towards the North had only just begun. In these calculations, surnames reported as having foreign or uncertain origin, which however account for less than 5% of the total, have been excluded. The figures look pretty similar, except for an overrepresentation of Sardinia and Friuli, and an underrepresentation of Tuscany and Emilia Romagna, which could be explained case by case as follows. The Sardinian dialect is extremely different from the Italian language and inhabitants of this region are more isolated from the rest of the country than their compatriots, which may have resulted in a higher likelihood of displaying rare surnames. Friuli, on the other hand, bordering Slovenia to the east and Austria to the north, may have been affected by this peculiar idiom mixture, therefore showing more variety and singularity in last names formation than other regions. Finally, regrettably no evidence could be found to justify the underrepresentation of Tuscany and Emilia Romagna, so a hypothesis may involve an excessive attribution of surnames native to their border areas to nearby regions. In general, however, the sample composition is pretty close to the actual one, and there is no literature stating that the two should be exactly equal to one another, although it also seems reasonable to expect them to be so.

To further corroborate the soundness of the selected surname group, they can also be assessed on the basis of percentage share of population in the North, Centre, South and Islands macroareas, according to the ISTAT classification.34 _In

this computation, surnames generically ascribed to the North, Centre or South, respectively amounting to 0.8%, 0.8% and 0.7% of the total, are now taken into account, and the resulting comparison with the 1951 ISTAT census is showed in

Fig. 2. The grounds for overrepresentation of the Islands are to be found in what

has already been pointed out regarding Sardinia, while the Centre is now only affected by a modest underrepresentation.

As far as initial social classes are concerned, instead, the matter is more complex and uncertain. On the one hand, patronimic surnames are very common and, on the other hand, there is still a significant share which dictionaries do not provide enough information about, so that, overall, the original status is known only for 15% of the sample. This notwithstanding, some significant results will be obtained in the next section using surnames of Jewish, noble, or rich class descent,

34_{According to this classification, Liguria and Emilia Romagna are in the North and Abruzzo}

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Region Population share over total 1951 ISTAT share

Abruzzo 3.4 2.7

Basilicata 2.1 1.3

Calabria 4.3 4.3

Campania 8.3 9.1

Emilia Romagna 4.3 7.5

Friuli Venezia Giulia 4.3 2.6

Lazio 6.3 7 Liguria 3.3 3.3 Lombardy 14.8 13.8 Marche 2.5 2.9 Molise 1.3 0.9 Piedmont 6.7 7.4 Puglia 6.2 6.8 Sardinia 5.8 2.7 Sicily 9.2 9.4 Tuscany 4.6 6.6

Trentino Alto Adige 1.8 1.5

Umbria 1.6 1.7

Valle D’Aosta 0.4 0.2

Veneto 8.5 8.2

Table 4: Region by region list of percentage shares over total population computed for the

surname sample and for the 1951 ISTAT census.

which respectively amount to 1.9%,35 _1.2%36 _{and 1.8% of the total.}

After creating and discussing the major features of the surname sample, the next paragraph will be devoted to the description of how the historical data was gathered.

4.2 Universities historical data

The major task at this point is to define a group of Italian Universities that compose a similar elite to Oxford and Cambridge — that is, institutions that provide their graduates with a prestigious degree, signalling their capabilities and serving as a means of social mobility. As anticipated in the second section, there

35_{This number refers to the number of people bearing an originally Jewish surname, but there}

is no way to determine how many of these are actually practicing Jews. The latter proportion was about 1 every 1000 people according to the 1938 Italian Jewish census, and is now estimated at 1 every 1200 according to the UCEI, the Union of Italian Jewish Communities.

36_{According to Banti (1994), although in the last decades of the Nineteenth Century in Italy}

aristocrats accounted for over 20% of Members of Parliament, over 30% of army generals and over 40% of diplomats, it is very difficult to estimate their total number, partly due to various reforms of nobility titles occurring in that period.

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Figure 2: Comparison between the percentage share over total population of Italy’s macroareas

in the sample and in the 1951 ISTAT census.

is not an equivalent but a group can be assembled so that it contains the biggest and most ancient Universities in Italy. These are listed in Table 5, together with the year they were established and the percentage share over total graduates in 2014.37 _{Genua, Siena and Urbino Universities, albeit all ancient, had to be left}

out due to data unavailability, whereas Macerata and Camerino Universities were neglected due to their very small size. The last six institutions in the second half of the table, instead, were momentarily left out but will hopefully be included in an extension of the present work. The final group herein considered is, therefore, made up of the first 11 listed Universities, where 28.7% of all graduates obtain their degree.

The last step in the dataset creation is now to find out and count how many rare surname bearers from the sample graduated from the selected elite colleges over the 1861-2010 timespan. Such information is contained in the yearbooks each University used to print every one or two academic years, where the full list of graduates appeared, often together with the individual’s major, final mark

37_{Since the listed Universities include almost all the ones existing before the Second World}

War, the purpose of this statistic is to underline that they still keep a certain relevance in terms of size. The number of Universities included in the Ministry for Education official data in 2014 is 89, among which only 35 account for at least 1% of total graduates each. 15 of these are also ancient and are thus listed in the table; in the remaining 20, only two were established before the First World War: one is specialized in East Asian studies exclusively and the other is located in L’Aquila, where the University historical archive is no longer accessible after the 2006 earthquake.

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University Establishment year Share (%)

Turin University 1404 3.64

Polytechnic University of Turin 1859 2.03

Pavia University 1361 1.46

Polytechnic University of Milan 1863 3.28

Padua University 1222 3.94

Venice Ca’ Foscari 1868 1.38

Parma University 962 1.70 Bologna University 1088 5.45 Ferrara University 1391 0.95 Pisa University 1343 2.15 Florence University 1321 2.72 Centre-North total 28.7

Rome University “La Sapienza” 1303 6.18

Naples University “Federico II” 1224 4.14

Catania University 1434 2.29

Palermo University 1805 2.55

Cagliari University 1607 1.31

Sassari University 1617 0.68

Total 45.85

Table 5: List of Italian Universities chosen to compose the elite group and relative share of

graduates over the national total.

Sources: establishment years can be found on each University’s official website. Percentage shares are available on the Ministry for Education official website.

and hometown.38 _{From the 1970s onwards, however, most institutions turned to}

digitized enrollment and graduation recording, usually replacing printed paper just shortly afterwards. Even though this made any related research enormously easier and faster, it paved the way for access limitation to statistics due to privacy regulation, so that it was possible to acquire data referring to after 1980 only for the Universities of Pisa, Bologna, Ferrara, Parma, Pavia, Padova and Venice and for the Polytechnic fo Milan.39

Again following Clark’s methodology, the whole timespan has to be divided into 30-year windows, each covering approximately one generation, with the figure of interest being the overall sum of graduates for each period. Since data is complete only until 1980, most results presented in this paper are computed over a 4-generation span, although a few merely tentative estimations over 5 generations will be attempted.

38_{Only 5-year degrees have been considered, whereas shorter diplomas have been ignored.} 39_{For a complete and detailed list of all the sources used to gather data about graduates,}

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Timespan Sample Overall sum Centre-North Centre-North (Generations) graduates of graduates graduates share (%)

1861-1890 99 18221 15824 86.84

1891-1920 237 42276 36041 85.25

1921-1950 332 88303 69143 78.30

1951-1980 708 286883 220058 76.71

Table 6: Number of Centre-North graduates in the sample and Centre-North share of graduates

in the selected Universities for each timespan.

Sources: data aggregated from all University yearbooks listed in Appendix A.

Finally, on the grounds that the selected Universities are all located in Centre-North Italy, the surname sample has to be reduced to those alone who, if choosing to pursue higher education, are most likely to enrol in this part of the country and not elsewhere, i.e. people originally from all Northern regions plus Tuscany, Umbria and Marche, which shrinks the number of entries from 2982 to 1509. Consequently, in order to correctly calculate relative representations, the population considered will be the sum of those regions’, and the total number of graduates for each generation will be decreased as well by a factor that drops Southern and foreign students. These values, except for demographic ones, are shown in Table 6 for each timespan for all the chosen Universities.40

One last remark to observe what Clark (2014) already states: the rarer the surname, the higher its relative representation. Considering progressively more common last names with bearers number classes ranging from 0-40 to 301-500, he finds that the initial social status constantly and steadily increases moving from the more widespread to the rarer surnames. Nonetheless, all have similar — albeit always somewhat larger whenever shifting towards the less common class — regression to the mean rates, so that their implied pattern is nearly the same.

Actually, this dynamic is perfectly consistent with the motivations leading to choose a methodology based on rare surnames, in order to avoid underestimation of the true underlying social mobility coefficient. The relative representation of the Centre-North portion of the sample, which follows a similar progression towards the mean value 1, is displayed in Fig. 3.

40_{The overall sum of graduates for the 11 selected Universities, even though it is not used}

in the calculations presented in the following section, was computed in the hope of a future expansion of this paper to the whole country.

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Figure 3: Relative representation of the Centre-North portion of the rare surname sample from

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After a brief summary of the regression framework and a few considerations that apply to all of the results, this section presents the outcomes that the model and dataset just previously outlined allow to obtain. The first paragraph shows the social mobility rate for the whole Centre-North sample, whereas the second and third limit the analysis, respectively, to some elite groups and to the individual regions considered. While up to that point all findings only refer to the 1861-1980 period, in the fourth paragraph an estimation over a 5-generation span is attempted. Finally, the last part is devoted to a result check, which is merely the first of the many possible ones, involving Members of the Italian Parliament.

As mentioned in previous sections, the model used in this paper is the following:

yzt= α + βyzt−1+ εzt

where z stands for each surname, yzt+1 and yzt, respectively, for offspring’s and

parents’ higher education attainment, α for the constant, εzt for the error term

and β for the true underlying social mobility rate. The latter represents how much more likely it is that the son of graduates will complete higher education relative to the son of non-graduates. Hence, β only refers to a one-generation gap, such as a father-son relationship, so that when the same equation is estimated over a longer timespan, β indicates the average mobility rate of all periods composing the whole one. Assuming, as in Clark (2014), that the likelihood of getting a University degree is passed on to descendants according to a first-order Markov process, the overall social mobility rate across n generations is equal to βn. For instance, if β = 0.5, 5 generations on average need to go by before no matter how educated parents are, all sons enjoy approximately the same chance of completing higher education, since β5 _{= 0.03125.}

Now, how to obtain the necessary values for y starting from the dataset? First, the relative representation of each surname z has to be computed for each 30-year period T according to the aforementioned expression:

Relative representation of z in T = share of z in elite group in T share of z in general population in T The share of z in the elite group is obtained by dividing the sum of graduates in period T bearing surname z by the total sum of Centre-North graduates in

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5 Results

period T . Similarly, the share of z in the general population in T is computed as the ratio between the estimated number of surname z bearers in period T and the total population of Centre-North regions in period T .

Finally, for each 30-year timespan, all of the values thus obtained are normalized to have mean zero and unitary standard deviation, and the resulting figures serve as y in the equation. The model regresses y on its first order lagged variable, which is done in this paper, almost in all cases, over a 4-generation period and using individual surname clusters.

In the next paragraphs, tables presenting regression results always refer to the procedure just outlined. Instead, graphs depicting progression towards the mean for elite groups are related to non-normalized relative representation, so each pattern has to be ideally compared to one decreasing to 1. However, whenever circumstances make it necessary, specific comments or ad-hoc modifications will be made.

5.1 Four degrees of separation

Running the model for the whole Centre-North sample, the general results may be found in Table 7. The β coefficient at 0.41 means that in Italy, across the 4 generations that compose the 1861-1980 period, on average, sons of graduates had just over 40% more chances of getting a degree themselves. Actually, since y in the regression equation more broadly assesses each family’s standing in terms of relative representation among the highly educated, β here indicates that offspring replicate parents’ attainment with a 40% probability. Moreover, to see it from another perspective, this extra chance of graduating persists for about 4 generations, because βn _{falls below the 5% threshold level only for n ≥ 4. In a sense, some}

metaphorical “four degrees of separation” come between sons of graduate parents and those of the non-educated, in terms of equality of opportunity.

How does this result compare with other estimations of social mobility regarding Italy and with Clark’s own outcomes?

To answer the first question, it generally looks like β = 0.41 may be consistent with or just somewhat smaller than what most of the literature has found up to now. Referring only to the last few decades, Mocetti (2007) gets a 0.54 persistence rate for degrees, similarly to Causa et al. (2009), who obtain 0.53 and 0.54, respectively, for graduate men and women, and 0.47 and 0.44 for men and women below secondary education level. Checchi et al. (2008) consider an historical dataset of cohorts born between 1910 and the 1970s, finding a steady albeit slow decrease in the elasticity rate from 0.58 to 0.44 between the beginning and the

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(1) Offspring’s attainment Parents’ attainment 0.409∗∗∗ (6.93) Constant -4.03e-10 (-0.00) Observations 4530 t statistics in parentheses ∗ _{p < 0.05,}∗∗ _{p < 0.01,}∗∗∗ _{p < 0.001}

Table 7: Social mobility coefficient over the whole sample in the period 1861-1980.

end of the period. On a slightly different note, Piraino (2007) shows that a son’s better educational attainment is positively correlated with a higher family income.

Since all of these results significantly point to a slower mobility of graduates in Italy than in other Western Countries, Ermisch and Francesconi (2001) and Checchi and Flabbi (2006) propose an explanation involving the peculiarity of the Italian school system, which relies on early tracking.41 _{In fact, family background}

not only affects children’s marks and outcomes in general, but it also plays a role upon which school offspring choose, which in turn may eventually boost or cripple their will and chances to go to University. Barone (2009) adds that the lack of any shorter form of higher education in Italy until the last decade or so may have discouraged major investment in it by lower classes, who regarded it as too risky.42

Accordingly with Sylos Labini’s (1974) prediction that social mobility should be lower in Southern Italy, Checchi and Dardanoni (2002) remark that there are significant differences between parts of Italy and even among individual regions. Moreover, they underline how this situation makes any comparison between different estimation methods, i.e. based on income, occupations or education, not very meaningful. Hence, here it will just be noted that the aforementioned Mocetti (2007), Piraino (2007), Causa et al. (2009) and OECD (2010) all find income elasticities between 0.33 and 0.54, a window which this paper’s result falls into, but no further discussion will be made on this point. Instead, a speculation might be that, since here the sample only takes into account the Centre-North

41_{In Italy, at 14 years of age students already have to choose between a Lycaeum type of high}

school and a technical or professional one. The latter two, though, usually do not provide pupils with a suitable set of knowledge and capabilities to perform well in University, so that one of the most relevant decisions regarding higher education is taken long before the actual time to go to college.

42_{He notes, however, that lower classes increasingly turn to higher education once middle ones}

have already saturated it, so that the growth of the number of graduates may either deprive degrees of their worth or force middle classes to get even better qualifications.

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5 Results

part of Italy, the β coefficient obtained might be smaller than what it supposedly should be on a national scale.

To address the second question, the model seems to yield significantly higher mobility rate for Italy than any other country Clark tested it for. In fact, all of his estimates are in the 0.6-0.8 range, which corresponds to about 7-9 “degrees of separation” rather than just 4. Since systematic underestimation of β is ruled out by the methodology design itself, a possible explanation of the results may involve the different concept of elite underlying each research. In fact, as previously stated, the lack of anything comparable to Oxbridge in Italy has led to the definition of a whole group of ancient and prestigious Universities, which is, as such, necessarily less exclusive. In other words, if Clark were referring to someone going to have dinner at a fancy haute cuisine restaurant, this paper might be talking about people eating maybe not at McDonald’s but just at that nice wine bar downtown. Needless to say, neither type is clearly at risk of starving but, certainly, only very few in the second group could afford to join the first one. Similarly, this paper considers a feature — graduation from one of the 11 selected Universities — which is definitely above average relative to the whole population but it is just not as exclusive as holding a degree from Oxford or Cambridge. The results presented in the next paragraph, referring to certain alleged elite groups, might serve as a partial reconciliation between this paper’s findings and Clark’s.

5.2 Elite groups analysis

Thanks to the information provided by the dictionaries, some surnames may be associated with the social class or condition they originated from. Since linkages need to be backed up by written records or some kind of material proof, higher class surnames are much more likely to be correctly and consistently identified. In this way, three separate elite groups could be assembled, namely Jewish descent families, nobles, and rich professionals such as judges, notaries, attorneys, bankers and merchants. Additionally, this time overlooking original social class, a fourth set of surnames can be considered, including only those who counted at least one graduate in the first period, 1861-1890.

The four results thus obtained will be shown hereafter one by one, together with an overall summary at the end as if they were all united under the label of a hypothetical high class. The methodology is the same as the general one for all groups, but the normalization of relative representation is now computed within each group rather than over the whole sample.

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(1) Offspring’s attainment Parents’ attainment 0.649∗∗∗ (11.11) Constant -0.0267 (-0.37) Observations 78 t statistics in parentheses ∗ _{p < 0.05,}∗∗ _{p < 0.01,}∗∗∗ _{p < 0.001}

Table 8: Intergenerational elasticity of degrees coefficient among Centre-North Italian Jews in

the 1861-1980 period.

5.2.1 Jews: seven degrees of separation

The first group is made up of 26 Jewish descent surnames, easily recogniz-able due to their derivation or resemblance to biblic characters and expressions. Emerging from Table 8 is their implied β coefficient equal to about 0.65, which translates into a 7-generation span needed to have complete regression to the mean. The value is not only significantly higher than the average sample one, but also quite near to Clark’s.

Since this ensemble is supposedly elitist, it should be worthwhile to inspect its relative representation pattern over the years, depicted in Fig. 4. The tendency seems one of convergence towards the mean value 1, but the presence of 2 major outliers in period 1891-1920, which could readily be identified due to the relatively small number of entries in this subsample, blurs the overall picture. A “cleaner” version of the graph is, therefore, provided in Fig. 5.

Even though the second pattern now looks neat, the impression is that there was anyway a pronounced acceleration — if not even an inversion in the trend, looking at the first graph — in the regression towards the mean process in the 1921-50 period. One obvious explanation for that lies in the persecution Italian Jews had to endure under the Fascist regime, which may have forced this formerly elite group to approach the average relative representation faster than it would otherwise have, even in the following 1951-80 years.

5.2.2 Nobles: a group of one-hit wonders?

In the general sample, 23 surnames of noble origin could be detected, most of which date back at least to the Fourteenth Century. Regrettably, the elasticity coefficient for this group is different from zero only at a 90% confidence level, as

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5 Results

Figure 4: Relative representation pattern for the Jewish descent surname group in the 1861-1980

period. Vertical axis: relative representation values, horizontal axis: time.

Figure 5: Relative representation pattern for the Jewish descent surname group without outliers

4 Degrees of Separation: a Study of Social Mobility among Italian graduates, 1861-2010

Universit´

a degli Studi di Pisa

Four Degrees of Separation: a Study of

Social Mobility among Italian graduates,

1861-2010

Contents

2.1

Approaches to study social mobility

2.2

The Italian case

3

Clark’s methodology

3.1

Why rare surnames?

3.2

Clark’s results

3.3

Adaptations to Italy

4

Building the dataset

4.1

Rare surnames sample

4.2

Universities historical data

5.1

Four degrees of separation

5.2

Elite groups analysis