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S
UPERIORE
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Master of Science in Economics
Spatial Wage Inequality:
Evidence from Italian Provinces
Author:
Alessandro C
ARLISupervisor:
Davide F
IASCHIM
ASTERT
HESISAcademic Year 2015/2016
Declaration of Authorship
I, Alessandro CARLI, declare that this thesis titled “Spatial Wage Inequality: Evidence from Italian Provinces” and the work presented in it are my own. I confirm that:
• This work was done wholly or mainly while in candidature for a research degree at this University.
• Where any part of this thesis has previously been submitted for a degree or any other qualification at this University or any other institution, this has been clearly stated.
• Where I have consulted the published work of others, this is always clearly attributed.
• Where I have quoted from the work of others, the source is always given. With the exception of such quotations, this thesis is entirely my own work.
• I have acknowledged all main sources of help.
• Where the thesis is based on work done by myself jointly with others, I have made clear exactly what was done by others and what I have con-tributed myself.
Signed: Date:
“The master-economist must possess a rare combination of gifts. He must reach a high standard in several different directions and must combine talents not often found to-gether. He must be mathematician, historian, statesman, philosopher - in some degree. He must understand symbols and speak in words. He must contemplate the particular in terms of the general, and touch abstract and concrete in the same flight of thought. He must study the present in the light of the past for the purposes of the future.”
Abstract
Alessandro CARLI Spatial Wage Inequality:Evidence from Italian Provinces
This research has the aim to analyse the inequality of wages and productiv-ity across the Italian provinces. This analysis is implemented on the ISTAT’s "Rilevazione sulle Forze di Lavoro" quarterly cross-sectional public data, in a temporal space that goes from I quarter of 2014 to IV quarter of 2016, which has been divided into four periods.
The research follows a two step procedure already known and used in litera-ture, in which at the first stage the focus of the analysis is on the relationship between the individual wage and the individual observed characteristics, the province of workplace, the industrial sector in which the individual works and a dummy variable that controls whether the individual moved her domicile because of the current job. In the second step, we try to describe the relation-ship between the Estimated Provincial Total Factor Productivity from the first step and provincial characteristics such as population, employment density, land area, administrative fragmentation, market potential, human capital and province’s industrial composition.
The main results from the first step concern, as expected, the positive and con-cave impact of the experience, the positive return on education and a positive impact on wage due to a change in the domicile.
The Estimated Provincial Total Factor Productivity shows persistence in its dif-ferentials during the four periods, with an increasing gap between the top and the bottom of the productivity distribution. The analysis on the determinants of Estimated Provincial Total Factor Productivity indicates positive impacts of population, land area, market potential, administrative fragmentation. While
viii
we found a negative impact of human capital inequality measured by Gini In-dex, meaning that greater inequality within the provincial human capital de-creases the provincial productivity. Finally the Herfindahl Index on industrial composition shows a negative coefficient, which means a negative impact on productivity due to the higher sectoral concentration. These results are slightly different from those founded in literature for Italy. The possible causes might be the use of different databases and different periods of analysis and the dif-ferent methodology followed.
According to our results, we are in presence of a spatial inequality among Ital-ian provinces’ Total Factor Productivity and we believe that it is due to differ-ences in population, administrative fragmentation and market potential. Hu-man Capital is another important factor that plays a very important role in provincial productivity differentials.
Acknowledgements
I would like to express my deep gratitude to my thesis supervisor Professor Davide Fiaschi for his insightful comments and suggestions, and his very im-portant advices.
I would like to thank Professor Angela Parenti for her constructive comments and her help on many technical issues and her patience.
I would like to thank my family and my friends for support during all these years.
Finally, my deepest thanks go to my girlfriend Elisa for always being there with her continuous and invaluable support.
Contents
Declaration of Authorship iii
Abstract vii
Acknowledgements ix
1 Introduction 1
2 Theories of Wage Differential at Local Level 5
2.1 A Theoretical Framework . . . 5
2.1.1 Equalization in Factor Prices . . . 5
2.1.2 The Provincial Total Factor Productivity . . . 7
2.2 From the Theoretical to Empirical Model . . . 11
3 The Estimate for Italian Provinces in 2014-2016 15 3.1 Descriptive Statistics of Variables . . . 15
3.2 The Estimate of Wage Equation at Provincial Level . . . 32
3.3 The Estimation of Determinants of Provincial TFP . . . 34
4 Concluding Remarks 53 A Appendix A 57 A.1 . . . 57
B Appendix B 59
List of Figures
3.1 Provincial Mean Wages in Nominal and Real Terms. . . 17
3.2 Provincial mean wages in nominal terms. . . 18
3.3 Provincial mean wages in real terms. . . 19
3.4 Experience frequency distribution by period. . . 21
3.5 Provincial observed mean nominal wage and population. . . 25
3.6 Provincial observed mean real wage and population. . . 26
3.7 Estimated Provincial TFP - (in nominal terms). . . 27
3.8 Estimated Provincial TFP - (in real terms). . . 27
3.9 Estimated Provincial TFP and Land Area - (in nominal and real terms). . . 29
3.10 Estimated Provincial TFP and Market Potential - (in nominal and real terms). . . 30
3.11 Estimated Provincial TFP and Administrative Fragmentation. . 31
3.12 Single Periods semi-parametric regressions (in nominal terms). Experience. . . 35
3.13 Single Periods semi-parametric regressions (in real terms). Ex-perience. . . 36
3.14 Pooled Periods semi-parametric regressions (nominal and real terms). Experience. . . 39
3.15 Estimated Provincial TFP distribution (in nominal and real terms). 39 3.16 Changes in the Estimated Provincial TFP. . . 40
3.17 Estimated Provincial TFP in nominal terms. . . 44
3.18 Estimated Provincial TFP in real terms. . . 45
3.19 Estimated Provincial TFP in nominal and real terms - Pooled Periods specification. . . 46
3.20 Second Step semi-parametric regression (in nominal terms): smooth terms’impacts. . . 51
xiv
3.21 Second Step semi-parametric regression (in real terms): smooth
terms’impacts. . . 52
B.1 Resident Population - Census 2011. . . 60
B.2 Population Density - Census 2011. . . 60
B.3 Land Area in Square Kilometre. . . 61
B.4 Administrative Fragmentation. . . 61
B.5 Market Potential. . . 62
B.6 Human Capital Density and Gini Index on Human Capital.. . . 63
List of Tables
3.1 Mean wages in each Period. . . . 17
3.2 Observations grouped by gender. . . . 20
3.3 Observations grouped by education category. . . . 20
3.4 Statistics on years of experience. . . . 21
3.5 Observations grouped by nationality. . . . 22
3.6 Observations grouped by marital status. . . . 22
3.7 Full-time contracts and part-time contracts. . . . 22
3.8 Observations grouped by sector according to ATECO 2007 clas-sification. . . . 23
3.9 Movers and stayers because current job. . . . 23
3.10 First Step Regression. Dependent Variable: individual monthly nominal wage (in natural log terms). . . 34
3.11 First Step Regression. Dependent Variable: individual monthly real wage (in natural log terms). . . 37
3.12 Semi-parametric regressions (GAM). Natural log of nominal wages on experience. . . 38
3.13 Semi-parametric regressions (GAM). Natural log of real wages on experience. . . 38
3.14 Second Step Regression. Dependent variable: Estimated Provin-cial TFP (in nominal terms). . . 47
3.15 Second Step Regression. Dependent variable: Estimated Provin-cial TFP (in real terms). . . 48
3.16 Semi-Parametric GAM regression. Dependent variable: Esti-mated Provincial TFP.. . . 50
B.1 Provincial Mean Wages (in nominal terms): Top and Bottom deciles. . . . 62
xvi
B.3 Education levels in Italian School System grouped according to the variable "TITSTUD" from Questionario Rilevazione sulle
Forze di Lavoro. . . . 65
B.4 Experience Average Years Top and Bottom Deciles. . . . 65
B.5 Resident Population in 2011 Top and Bottom Deciles. . . . 66
B.6 Counter-factual Level of Employment (2014) and Resident
Pop-ulation and Density (2011). . . . 67
B.7 Administrative Fragmentation Top and Bottom Deciles. . . . 68
B.8 Provincial Estimated TFP (in nominal terms): Top and Bottom
deciles. . . . 68
B.9 Provincial Estimated TFP (in real terms): Top and Bottom deciles.
. . . 68
B.10 Normalized Estimated Provincial TFP coefficients. Range val-ues. . . . 69
B.11 Correlation matrix. Second Step Variables (Pooled Periods
Specification). . . . 69
B.12 Descriptive statistics. Second Step Variables (Pooled Periods
Chapter 1
Introduction
Italian productivity levels exhibit a spatial inequality across provinces. This phenomenon might be caused by a variety of factors such as inhabitants char-acteristics, industrial composition and the presence of some local externalities. The inequality between provinces increased during the considered period, that goes from the first quarter 2014 to the fourth quarter 2016, and the estimated provincial Total Factor Productivity distribution shows a 40 percent gap be-tween the most and the least productive provinces.
The literature has tried to explain the existence of these wage differentials in many ways. The fundamental starting point lies on the presence of different stocks of Human Capital in the form of workers’ skills and abilities. These the-ories go back to Alfred Marshall’s ideas (Marshall, 1890) according to which, when the individuals work and live near each other, they undertake a learning process involving a spillover of skills. Marshall’s intuition has been formal-ized in a strand of literature developed by Edward Glaeser (Glaeser, 1999), who states that urbanization will rise when both the return to skills and the return to ability of learning by imitation will rise. The urbanization in turns, through the effect of density, will speed up the learning process between the individuals.
The simultaneous presence of both high and low skilled workers has been found to be related with an increase in Total Factor Productivity due to their complementarity effect as shown by Eeckhout, Pinheiro, and Schmidheiny, 2014. While Berry and Glaeser, 2005 found the increasing clustering of skill, due to the presence of skilled entrepreneurs who tend to hire skilled workforce. This behaviour creates agglomeration economies, in which skilled people are surrounded by other skilled people. A phenomenon that leads to a shift in labour demand with an increase in the urban wage premium. The urban wage
2 Chapter 1. Introduction gain in not only a static effect on the salary level, but as showed by Glaeser and Maré, 2001, it operates through a wage growth effect. The authors also affirm that the higher observed productivity (the higher wages) is not due just to the presence of high ability workforce, but it is also caused by the learning process. The urban density fosters the learning process, speeding up the skills growth, with a final result that consist in an higher productivity. Therefore we can see how the presence of both urbanization and localization is important in the explanation of productivity differentials.
It is important to highlight the complementary effect between skills and den-sity, as shown in Glaeser and Resseger, 2010: they found a strong connection between per worker productivity and metropolitan population, hence the ex-istence of agglomeration economies is compatible with the view that the ur-ban density both eases the spread of knowledge and increases workers’ skills, which in turn foster the productivity. They note that skilled places become more skilled over time, but at the same time they experiment the presence of two simultaneous issues: the quicker learning interactions and the faster growth of technological change. These two phenomena, when both present, lead to an ambiguous effect on the earning profile and the final result depends upon which one dominates.
The effect of population density (employment density), has been analysed by Ciccone and Hall,1996who found that more than half the variance of output per worker across United States’ counties can be explained by differences in the density of the economic activity. They also found that doubling the county employment density increases the average labour productivity by around 6 percent.
Similar findings that confirm the complementarity between skills and den-sity have been shown by De La Roca and Puga, 2016, in their analysis for Spain. They conclude that there are not considerable ability differences be-tween workers in big and small cities. The substantial divergence arises by working in cities with different sizes, and it is caused by a mixture of static gains and learning advantages added to the fact that higher ability workers benefits more from bigger cities. This phenomenon leads to higher mean and variance observed in city wages.
Having clear all these aspects and following the framework developed by Combes, Duranton, and Gobillon, 2008 we proceed with a two step analysis
that starts from the individual wage equation. In the first step we analyse the way individual wages are formed, controlling for individual observable char-acteristics plus economic activity charchar-acteristics, provincial workplace location and a dummy variable that controls whether the individual moved her domi-cile due to her current job. The main findings concern the important effect showed by the mover characteristic. Those who moved their domicile experi-ence a wage gain that ranges between 7 and 10 percent, according to the period. The other key finding concern the considerable role played by provincial TFP in the wage formation.
This part of wage variability, which remains after having controlled for both individual observable and economic activity characteristics, shows a signifi-cant spatial productivity inequality.
Due to the presence of these productivity differentials we continue our anal-ysis with the second step, in order to disentangle the determinants of the es-timated provincial Total Factor Productivity. The main findings concern the important role played by the population density (which in our case had also the function of proxy for the human capital density), the land area and the market potential which underlines the importance of spatial effects deriving from the density of neighbour markets. Another important role is played by the administrative fragmentation, which in our case, in contrast with some lit-erature findings, shows a positive impact on provincial productivity. Finally we find a negative impact of the inequality within the provincial human capi-tal, proxied by Gini Index. This variable confirms that higher inequality leads to lower productivity and point to the importance of localization effects, ac-cording to which productivity advantages arise because the presence of simi-lar individuals who live in the same location. The last variable we considered is the Herfindahl Index on physical capital inequality, which shows a negative impact on provincial TFP. This means that higher sectoral concentration leads to lower productivity.
Our results are in line with those found by Combes, Duranton, and Gobillon, 2008for France, by Ahrend et al.,2017for a group of OECD countries and by Ferranna, Gerolimetto, and Magrini,2016–2017for United States.
Slightly different conclusions have been found by Di Addario and Patacchini, 2008 for Italy, although their analysis follows a different methodology and is
4 Chapter 1. Introduction based on different data. They show how the urban wage premium is very lim-ited in its size, turning to be negative to some categories such as the college graduates.
In Chapter 2, we present the theoretical model of wage differentials at lo-cal level, starting from a Cobb-Douglas production function augmented by provincial externalities. In Chapter 3, we present the data we used for our analysis and we show the estimate results. While in Chapter 4 we conclude our discussion.
Chapter 2
Theories of Wage Differential at
Local Level
2.1
A Theoretical Framework
In this section we present a simple model for the determination of wages based on a Cobb-Douglas production function in presence of agglomeration external-ities. The production technology of firm q located at province j at period t is defined as:
Yqj = AjKqαH 1−α
q (2.1)
where Kqand Hqare respectively the level of physical capital and human
cap-ital employed in firm q located in province j. The parameter α measures the elasticity of output to physical capital. In addition to physical capital and hu-man capital the production depends on macro characteristics of province j measured by Aj, generally denoted in literature as total factor productivity.1
2.1.1
Equalization in Factor Prices
If we take the first derivative of Equation (2.1) with respect to the human cap-ital Hqj, according to the competitive markets framework, we obtain the real
wage paid to each unit of human capital employed by firm q in province j: wqjH = ∂Yqj
∂Hqj
= (1 − α)Ajκαqj (2.2)
6 Chapter 2. Theories of Wage Differential at Local Level with κqj ≡ Kqj Hqj (2.3) which is the capital intensity of firm q in region j with respect to the human capital employed.
Likewise the return on capital is given by
rqj = ∂Yqj ∂Kqj = αAjKqjα−1H 1−a qj = αAj Kqj Hqj α−1 = αAjκα−1qj . (2.4)
Under the assumption of free-allocation of capital among different firm in the same province rqj = rj ∀q, then taking two generic firms q1 and q2
αAjKqα−11j H 1−α q1j = αAjK α−1 q2j H 1−α q2j (2.5) we obtain: Hq1j Kq1j = Hq2j Kq2j (2.6) which can be written as
Kq1j
Hq1j
= Hq2j
Kq2j
= κj ∀q1, q2. (2.7)
Considering both Equations (2.4) and (2.7) the term κqj can be written as
κqj = rj αAj α−11 ∀q. (2.8)
If we combine Equation (2.2) and Equation (2.8) we get:
wqjH = (1 − α) αAj rj 1−αα Aj = (1 − α) α rj 1−αα A 1 1−α j (2.9)
taking the natural logarithm of both sides we obtain
log wHqj = log(1 − α) + log α rj
1−αα
+ 1
2.1.2
The Provincial Total Factor Productivity
Literature has advanced several explanations of the level of total factor pro-ductivity, we focused mainly on the explanations related to the importance of the human and the physical capital. Lucas,1988emphasised the human cap-ital accumulation through learning by doing, assuming that different sectors have different potential of growth for human capital and then differences in the comparative advantages. The comparative advantages will determine which good is produced where, directing the regions’ rate of human capital growth. This phenomenon will set the basis for the possibility of wide and sustained differences in productivity across regions. While Mankiw, Romer, and Weil, 1992found that the Solow’s model augmented with human and physical cap-ital better explains the differences among countries. Other explanations on total factor productivity concern the technological change, the accumulation of knowledge and the presence of natural resources.
In particular, we postulate that total factor productivity depends on some spe-cific characteristics of province j: the level of employment Lj, a composite
index of human capital eHj, a composite index of physical capital eKj, some
in-stitutional characteristics IN STj, the market potential M Pj and the land area
LAj. Aj ≡ A Lj, eHj, eKj, IN STj, M Pj, LAj , (2.11)
Both the composite index of human capital and the composite index of physi-cal capital follow Bénabou,1996specification:
• The human capital index eHj is expressed as
e Hj ≡ Hj X i∈Lj hij Hj ρ 1 ρ , (2.12) with Hj = X i∈Lj hij, (2.13)
where hij is the individual human capital index, ∀i ∈ {L1, .., Lj} and
8 Chapter 2. Theories of Wage Differential at Local Level different individuals. The second factor in the right hand side of Eq.2.12
is the inequality term.
• The physical capital eKj is expressed as
e Kj ≡ Kj X q∈Nj kqj Kj γ 1 γ (2.14) with kj = X q∈Nj kqj, (2.15)
where kqj is the physical capital index employed, ∀q ∈ {N1, .., Nj} and
γ ∈ (−∞, +∞) is the elasticity of substitution between physical capital of different firms. Like in Equation (2.12), the second factor in the right hand side is the inequality term.
When ρ > 1 and γ > 1 more inequality within human capital and phys-ical capital indexes leads to a positive effect of eH and eK, respectively. On the contrary ρ ≤ 1 and γ ≤ 1 more inequality within human cap-ital and physical capcap-ital indexes leads to a negative effect of eH and eK, respectively. It worth noting that when ρ = 2 and γ = 2 the inequality factors become the square root of the Herfindahl Index of the share of hu-man capital and physical capital, respectively. The least interesting case is when ρ = 1 and γ = 1 meaning perfect substitutability.2
Now we define the functional form of Aj, we choose the following
specifica-tion: Aj ≡ L φ1 j He φ2 j Ke φ3 j IN ST φ4 j M P φ5 j LA φ6 j (2.16) i.e.
log Aj = φ1log Lj+ φ2log eHj+ φ3log eKj+ φ4log IN STj+
+ φ5log M Pj + φ6log LAj.
(2.17)
We can express Equation (2.17) as: log Aj = φ1log Lj LAj + (φ2+ φ3) log Hj LAj + φ2log IN EQHj + φ3log Kj Hj + φ3log IN EQKj + φ4log IN STj+ φ5log M Pj + (φ6+ φ1+ φ2+ φ3) log LAj. (2.18) From Equation (2.8): κj = Kj Hj =rj α α−11 (Aj) 1 1−α = α rj 1−α1 (Aj) 1 1−α (2.19)
which logarithmic specification is
log Kj Hj = log α rj 1−α1 + 1 1 − α log (Aj) (2.20)
substituting Equation (2.20) into Equation (2.18) the latter becomes 1 − α − φ3 1 − α log Aj = φ1log Lj LAj + φ3log α rj 1−α1 + + (φ2+ φ3) log Hj LAj + φ2log IN EQHj + + φ3log IN EQKj + φ4log IN STj + φ5log M Pj+ + (φ6+ φ1+ φ2+ φ3) log LAj (2.21)
10 Chapter 2. Theories of Wage Differential at Local Level and then we obtain:
log Aj = 1 − α 1 − α − φ3 " φ1log Lj LAj + φ3log α rj 1−α1 + (φ2+ φ3) log Hj LAj + φ2log IN EQHj + + φ3log IN EQKj + φ4log IN STj+ + φ5log M Pj + (φ6+ φ1+ φ2+ φ3) log LAj # . (2.22)
This final specification of externalities term is based on competitive market equilibrium condition where labour demand equals labour supply and where all firms of province j have the same physical capital intensity and the same return on physical capital.
It is important to remark that both inequality terms:
φ2log IN EQHj = φ2 1 ρlog X i∈Lj hij Hj ρ = φ 0 2log X i∈Lj hij Hj ρ (2.23) and φ3log IN EQKj = φ3 1 γ log X q∈Nj kqj Kj γ = φ 0 3log X q∈Nj kqj Kj γ (2.24)
cannot be estimated directly. This problem arises because the unknown distri-butions of parameters ρ and γ, due to this issue we need to use proxy indexes for both (2.23) and (2.24) hence in the empirical analysis we are going to use Gini Index and Herfindahl Index, respectively.
If we assume that the real wage paid to worker i by firm q in province j is:
then we get the core Equation of the model: log wijR= log hij + log(1 − α) +
α + φ3 1 − α − φ3 log α rj + + 1 1 − α 1 − α 1 − α − φ3 " φ1log Lj LAj + + (φ2 + φ3) log Hj LAj + φ2log IN EQHj + + φ3log IN EQKj + φ4log IN STj+ + φ5log M Pj+ (φ6+ φ1+ φ2+ φ3) log LAj # . (2.26)
In this specification the natural logarithm of individual real wage paid by firm q in province j depends on the human capital characteristics, the output elas-ticity of physical capital, the intensity of physical capital, and the externalities term.3
2.2
From the Theoretical to Empirical Model
In this section we describe how the variables included in the Provincial TFP are specified for the use in the empirical analysis and we present an empirical model which follows the general framework designed by Combes, Duranton, and Gobillon,2008based on a two-step procedure.
• The population density is defined as the number of inhabitants per square kilometre living in the province j.
P OP.DEN Sj ≡
P OPj
LAj
(2.27)
• The human capital density is defined as the weighted sum of the human capital of workers in province j per square kilometre.
HC.DEN Sj ≡ P i∈Lj aihi LAj (2.28)
3In our specific case the term h
ij is composed by some observed variables such as the Education, Experience and the squared Experience.
12 Chapter 2. Theories of Wage Differential at Local Level with ai the sample weight.
• The institutional characteristic we include is the degree of administra-tive fragmentation, which is defined as the number of municipalities per 100.000 inhabitants in each province j.
IN STj ≡ F RAGj ≡
(N umber of municipalities)j P OPj
100,000
(2.29)
• The market potential index is our proxy for the goodness of access to the market and represent the local interaction among neighbouring provinces. The index is given by the spatial lagged population density. This vari-able has been obtained multiplying the spatial weights matrix W 4 and
the P OP.DEN Sj which in turn is defined as in Equation (2.27):
M Pj ≡ W · P OP.DEN Sj (2.30)
The first step specification is implemented at individual level on Italian work-ers public-use data (Rilevazione sulle Forze di Lavoro) collected by ISTAT, while the second step specification is implemented on provincial level in order to ex-plain the sources of variation of wage premium estimated in the first step. In more detail, the first step specification estimates the provincial wage pre-mium, as the part of individual nominal wage that remains to be explained after having controlled for observable worker characteristics, local industrial composition and non-random individual sorting across cities.
• I step specification:
At this step we use the Mincer Equation for wage (Mincer,1974), that can be viewed as the empirical version of the Equation (2.26),
log wRij = Ajgj + γXi+ dij + εij (2.31)
where wR
ij is the real wage of individual i who live in province j at time t,
gj is the dummy for the province of workplace and gathers the effects
of output elasticity of physical capital, intensity of physical capital and
all externalities from Equation (2.26). Hence, its estimated coefficient bAj
represents the provincial wage premium (the provincial TFP) that we ob-tain after controlling for the other observable covariates and it is used as dependent variable in the second step.
Xi is the matrix of individual observable characteristics, that includes
Gender, Education, Experience, Squared Experience, Nationality, Mari-tal Status, the type of job contract and the industrial sector where the individual works. dij is a dummy variable that assumes value 1 if the
individual did not moved her domicile due to her current job and 0 if she moved.
• II step specification:
The second step specification is required to explain the predictive power of all the factors embedded in the provincial dummy estimated coeffi-cient bAjt, which can be viewed as the estimated provincial total factor
productivity. b Ajt = const + β1log Lj LAj
+ β2log LAj+ β3log(F RAGj)+
+ β4log M Pj + β5log Hjt LAj + β6log(GIN I.IDjH)+ + β7log(HERF.IDGV Ajt ) + µjt (2.32)
The set of explanatory variables is composed by a constant term which embeds the output elasticity to physical capital and the return on capital, the employment density (population density), the provincial land area, the degree of administrative fragmentation, the market potential, the hu-man capital density, the index of huhu-man capital inequality proxied by Gini Index and Herfindahl concentration index as a proxy for physical capital inequality.
Chapter 3
The Estimate for Italian Provinces
in 2014-2016
3.1
Descriptive Statistics of Variables
In this section we present the data we used in our empirical analysis and a brief statistical description.
The data are extracted from the public-use data (Rilevazione sulle Forze di La-voro), collected by the Italian National Institute of Statistics (ISTAT) from the households’ members belonging to the selected sample. The survey collects data from all the households’ members older than fifteen. The households are selected from the General Register Office (Ufficio Anagrafe Comunale) accord-ing to a samplaccord-ing method which aims to build a statistical significative sample. Every year about 250 thousand households are interviewed. From year 2004 the survey is continuous and the collection of information is conducted in or-der to cover all the weeks of each quarters. The households belonging to the sample are interviewed four times (two consecutive quarters interviews with a pause of other two quarters) in a span of fifteen months. This survey is the base of the official statistical estimates of the Italian labour market macro ag-gregates, such as employment and unemployment, economic activity sectors, worked hours and contracts typology.
The extract we use covers all the interviewed employees who both declared the wage received in the month previous the interview and worked in Italy. The sub-sample covers a span that goes from the first quarter 2014 to the fourth quarter 2016. Since we used public data in which many characteristics are omitted we were not able to follow a household for its entire period in the
16 Chapter 3. The Estimate for Italian Provinces in 2014-2016 sample, so we re-sampled the data selecting the waves of appearance in or-der to obtain a sample in which a household is present just once. Following this method we obtained a repeated cross-section data set, divided into "Pe-riods" obtained by the aggregation of three consecutive quarters in order to avoid possible seasonal effects. Following this methodology we obtained four Periods data set:
• Period I: From First quarter 2014 to Third quarter 2014; • Period II: From Fourth quarter 2014 to Second quarter 2015; • Period III: From Third quarter 2015 to First quarter 2016; • Period IV: From Second quarter 2016 to Fourth quarter 2016.
The sample weights of each period have been rescaled in order to correctly rep-resent the number of Italian employees in the years 2014, 2015, 2016. Period I is composed by 49,785 observations representing 16,780,200 employees, Period II and Period III are composed by 43,422 and 45,399 observations, respectively. Both Periods represent 16,987,641 employees. Period IV is composed by 32,245 observations representing 17,310,452 employees.
The variables we selected for the estimate of the Mincer Wage Equation (2.31), (Mincer, 1974), further to individual wage, cover individual observable char-acteristics such as Gender (GEN), Education (EDU), Experience (EXP),
Na-tionality(NAT), Marital Status (MS), plus individual job characteristics such as Industrial activity sector (SECT), Full-Time or Part-Time contract (CON-TRACT), Province workplace location (PROV) and a dummy variable that states whether the individual had to move her domicile to the current province because her current job.
For the sake of completeness we will present both the nominal and the real terms specification results even though Combes and Gobillon,2015argue that since wages and prices are simultaneously determined in equilibrium, control-ling for the Consumer Price Index can lead to endogeneity biasses difficult to deal with. They conclude that as far the effect of agglomeration economies on productivity is our only concern, nominal wage is the only relevant dependent variable, with no need to control for the general level of prices.
TABLE3.1: Mean wages in each Period.
Nominal Terms Real Terms
Period I Period II Period III Period IV Period I Period II Period III Period IV Mean 1275.49 1285.90 1303.30 1303.57 1188.01 1197.67 1213.12 1213.45 Standard Deviation 523.20 527.38 527.87 527.64 487.52 491.38 491.51 491.45 6.8 6.9 7.0 7.1 7.2 7.3 0 1 2 3 4 5 6 7
Provincial Mean Nominal Wage
Provincial Mean Nominal Wage (in natural log terms)
Density Period I Period II Period III Period IV 6.8 6.9 7.0 7.1 7.2 7.3 0 1 2 3 4 5 6 7
Provincial Mean Real Wage
Provincial Mean Real Wage (in natural log terms)
Density
Period I Period II Period III Period IV
FIGURE3.1: Provincial Mean Wages in Nominal and Real Terms.
Individual wage variable has been censored by ISTAT and ranges from 250.00 Euro to 3000.00 Euro and Table3.1 shows their mean and standard deviation by periods of analysis. For more details see TableB.1. In Figure3.1we can see the changes in the distribution of provincial mean wages (in natural logarith-mic terms) across the periods. We can see a right-shift of the density functions and a simultaneous increase in the tails. This means an increasing gap between the richest and the poorest provinces, and must be noted that when we use the real terms this dynamic is still present, even though slightly mitigated. More details will be presented in the next section. The real wages have been obtained deflating the nominal wages by the Italian Consumer Price Index for 2014 and 2015.1 From both Maps 3.2 and 3.3 we can see the difference in mean wages across the Italian provinces in nominal and real terms, respectively. Table
3.2 shows the number of observation grouped by gender, it can be noted that
1The wages from periods I and II have been deflated by NIC 2014, while the wages from periods III and IV have been deflated by NIC 2015. NIC is the National Consumer Price Index for the community at large. Base year 2010.
18 Chapter 3. The Estimate for Italian Provinces in 2014-2016
Mean Nominal Wage − Period I
[ 989.14−1180.99 ) [ 1180.99−1239.09 ) [ 1239.09−1285.71 ) [ 1285.71−1473.87 )
Mean Nominal Wage − Period II
[ 949.54−1186.42 ) [ 1186.42−1262.18 ) [ 1262.18−1316.16 ) [ 1316.16−1473.87 )
Mean Nominal Wage − Period III
[ 716.08−1196.69 ) [ 1196.69−1276.01 ) [ 1276.01−1333.35 ) [ 1333.35−1473.87 )
Mean Nominal Wage − Period IV
[ 952.99−1184.42 ) [ 1184.42−1268.31 ) [ 1268.31−1335.83 ) [ 1335.83−1473.87 )
Mean Real Wage − Period I
[ 911.37−1093.06 ) [ 1093.06−1157.22 ) [ 1157.22−1202.2 ) [ 1202.2−1350.58 )
Mean Real Wage − Period II
[ 879.79−1107.99 ) [ 1107.99−1173.76 ) [ 1173.76−1229.65 ) [ 1229.65−1350.58 )
Mean Real Wage − Period III
[ 662.24−1108.86 ) [ 1108.86−1186.55 ) [ 1186.55−1244.82 ) [ 1244.82−1350.58 )
Mean Real Wage − Period IV
[ 854.59−1099.15 ) [ 1099.15−1184.15 ) [ 1184.15−1253.08 ) [ 1253.08−1350.58 )
20 Chapter 3. The Estimate for Italian Provinces in 2014-2016
TABLE3.2: Observations grouped by gender.
Period I Period II Period III Period IV
Male 26,123 22,698 23,941 16,901
Female 23,662 20,724 21,458 15,344
Total 49,785 43,422 45,399 32,245
TABLE3.3: Observations grouped by education category.
Period I Period II Period III Period IV
Low Education 15,937 13,922 14,162 10,156
Medium Education 24,811 21,388 22,657 16,039
High Education 1,790 1,637 1,776 1,351
Very High Education 7,247 6,475 6,804 4,699
the ratio between genders remains substantially unchanged across the period. The males slightly overcome the female being about the 52 percent of the total observations.
Education has been grouped into 4 categories, as shown in Table 3.3, ranging from Low Education Level to Very High Education Level. The first category is composed by those who either have no study title or Primary school or Mid-dle school title. Medium Education includes those who either completed 3 years cycle High school or 5 years cycle High school or those who attained Academia Diploma. The High Education category includes individuals who attained University Diploma or Bachelor’s Degree. The last category includes those who attained either a Master’s Degree or Professional Schools (such as Medicine, Law etc.).2
The variable experience has been created as the difference between the indi-vidual’s age and the presumed age the individual attained her education title, keeping into account the Italian child labour law and the law on compulsory years of schooling. These laws have been reformed few times, so we assigned different minimum ages necessary to enter in the labour market, according to the current individual’s age and education attainment. To the individuals who have either no study title, Primary school title or Middle school title have been assigned the minimum age required to enter in the labour market: four-teen years if the individual is older than thirty, fiffour-teen years if the individual is twenty-four or older and sixteen if the individual is younger than twenty-four
TABLE3.4: Statistics on years of experience.
Period I Period II Period III Period IV
Mean 24.40 24.64 24.63 24.72
Standard Deviation 11.53 11.58 11.68 11.89
Minimum 0.00 0.00 0.00 0.00
Maximum 61.00 61.00 61.00 61.00
Years of Experience Period I
Years of Experience Frequency 0 10 20 30 40 50 60 0 500 1000 1500
Years of Experience Period II
Years of Experience Frequency 0 10 20 30 40 50 60 0 200 400 600 800 1200
Years of Experience Period III
Years of Experience Frequency 0 10 20 30 40 50 60 0 200 400 600 800 1000 1400
Years of Experience Period IV
Years of Experience Frequency 0 10 20 30 40 50 60 0 200 400 600 800 1000
FIGURE3.4: Experience frequency distribution by period.
at the time of the interview. Table3.4 shows some statistics about experience in the four periods of analysis. From both Table 3.4 and Figure 3.4 can be noted how the mean, the standard deviation and the frequency distribution of experience remain substantially unchanged over time and the strong concave functional form exhibited by this variable. Table B.4 shows top and bottom decile of provincial mean experience.
Looking at the variable nationality in Table3.5we can see that in the four Peri-ods of analysis the amount of non-Italian employees is about 11 percent of the total employees interviewed. The last observable individual characteristics, marital status, is grouped into four categories: unmarried, married, divorced and widowed, Table3.6shows observations grouped by each category.
22 Chapter 3. The Estimate for Italian Provinces in 2014-2016
TABLE3.5: Observations grouped by nationality.
Period I Period II Period III Period IV
Italian 44,226 38,583 40,165 28,538
Non-Italian 5,559 4,839 5,234 3,707
TABLE3.6: Observations grouped by marital status.
Period I Period II Period III Period IV
Unmarried 15,566 13,679 14,796 10,828
Married 28,472 24,746 25,079 17,508
Divorced 4,862 4,268 4,747 3,383
Widowed 885 729 777 526
For what concerns the individual’s job characteristics we can see in Table3.7
the observations grouped by part-time jobs and full-time jobs, the former type of contract represents about the 20 percent of the total job positions. The other variable about the individual’s job characteristics concerns the industrial ac-tivity sector. The classification of Table 3.8 is based on ATECO 2007 which groups economic activities into twelve categories: Agriculture, Forestry and Fisheries; Manufacturing; Construction; Retail; Hotel and Restaurant; Trans-port and Storage; Media and Communication; Finance and Insurance; Real Es-tate and professional services; Public Administration; Instruction, Health and other social services; other generic services. One of the core variables of the entire analysis is the province workplace location, as showed in the previous chapter this variable has an important role in the determination of individual wage. Its estimate embeds all the determinants of provincial Total Factor Pro-ductivity (TFP) that we will analyse in the second stage.
Finally, the dummy variable about those who moved their domicile to the cur-rent province because their job shows a very limited mobility among Italian employees, as already noted by Di Addario and Patacchini,2008. They explain this phenomenon with two plausible reasons: the strong bond with the birth-place, so with the family’s place of residence and second, and the imperfection
TABLE3.7: Full-time contracts and part-time contracts.
Period I Period II Period III Period IV
Full-Time 39,864 34,615 36,226 25,764
TABLE3.8: Observations grouped by sector according to
ATECO 2007 classification.
Period I Period II Period III Period IV
Agriculture,Forestry and Fisheries 1,047 1,006 1,070 803
Manufacturing 11,863 10,347 10,824 7,949
Construction 2,641 2,123 2,145 1,562
Retail 5,525 4,716 5,019 3,588
Hotel and Restaurant 2,506 2,218 2,452 1,914
Transport and Storage 2,574 2,185 2,452 1,667
Media and Communication 1,087 903 965 676
Finance and Insurance 1,464 1,308 1,349 937
Real Estate and professional services 3,908 3,442 3,649 2,520
Public Administration 3,965 3,454 3,560 2,328
Instruction, Health and other social services 9,317 8,239 8,367 5,799
Others generic services 3,888 3,481 3,547 2,502
TABLE3.9: Movers and stayers because current job.
Period I Period II Period III Period IV
Moved Domicile 842 701 761 535
Did not move Domicile 48,943 42,721 44,638 31,710
of the Italian housing market which leads to high moving costs. According to them this phenomenon reduces the possible bias in the estimates due to the sorting into the largest labour market. It must be highlighted that, even though the stock of those who moved seems very limited, we are considering only few short temporal frames, while this phenomenon is a continuous flow in time. Then if we broad our temporal frame we could see the great magni-tude of the phenomenon.
Table3.9shows the number of people who moved their domicile because their current job, we can see that the figure seems very small with respect to the whole sample being about 1.6 percent of the interviewed employees.
As we will see in the next section, after controlling for all the other covariates, those who moved because their job earn more than those who did not. This can be a considerable support to the importance of non-observed abilities in the Wage Equation.
After the description of the variables of Mincer Wage Equation, we now present a brief analysis of the variables that are going to be used in the second stage where we focus on the possible determinants of Provincial TFP. We selected
24 Chapter 3. The Estimate for Italian Provinces in 2014-2016 some plausible externalities that contribute to the explanation of the produc-tivity differentials observed across Italian provinces. We consider the
resi-dent population (POP) and population density (POP.DENS), the provincial
land area (LA), the market potential(MP), the administrative fragmentation
(FRAG) and the contributes given by the Human Capital, both through its
density per square kilometre (HC.DENS) and through its inequality,
mea-sured by Gini Index (GINI HC). Lastly we control for industry heterogeneity using Herfindahl Index (HHI).
First of all we show the relationship between province population and both provincial observed mean wages and the estimated Provincial TFP. Our popu-lation data are based on "15◦ Censimento della popolazione e delle abitazioni 2011" collected by ISTAT, 2011. In Italy there are just ten provinces that over-come the million inhabitants. The most populous provinces are Rome, which almost reaches four million inhabitants, Naples and Milan that overcome three million inhabitants, followed by Turin which overcomes two million inhab-itants. (See Appendix B.5 for a more detailed list of the most and the least populous Provinces).
In Figure3.5we can see the positive relationship between provincial observed mean nominal wages (in logarithmic terms) and the resident population, while in Figure3.6nominal wages have been substituted by real wages. The provinces have been divided into three main cluster based on resident population, in or-der to unor-derline the differences in the number of inhabitants.
Looking at the first plot at the top of Figure3.7, we can see a positive relation-ship between provincial population (from Census 2011, as in Figures 3.5and
3.6) and the Estimated Provincial TFP. While the first plot at the bottom shows the same positive relationship, but this time with the 2014 actual employment3.
In all the four plots can be noted that, being equal the values of the variables on the x-axis, the estimated provincial TFP increases across the periods.
In Figure3.8we present the same relationships of Figure3.7, but in real terms, in all the cases the relationships remain valid.
In order to avoid possible sources of bias in the estimates, like the endogene-ity due to reverse causalendogene-ity problem in provincial employment level (or in provincial population), we followed the methodology exposed in Ferranna,
3The actual amount of Employees is based on Cambridge Econometrics, 2016 European Regional Database (ERD).
0e+00 1e+06 2e+06 3e+06 4e+06 6.9 7.0 7.1 7.2 Period I Population Pro
vincial Mean Nominal W
age − (in natur
al log ter ms) 0 − 500,000 500,000 − 1,500,000 1,500,000 − 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 1920 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 6264 63 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110
0e+00 1e+06 2e+06 3e+06 4e+06
6.9 7.0 7.1 7.2 Period II Population Pro
vincial Mean Nominal W
age − (in natur
al log ter ms) 0 − 500,000 500,000 − 1,500,000 1,500,000 − 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 4950 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106107 108 109 110
0e+00 1e+06 2e+06 3e+06 4e+06
6.6 6.7 6.8 6.9 7.0 7.1 7.2 7.3 Period III Population Pro
vincial Mean Nominal W
age − (in natur
al log ter ms) 0 − 500,000 500,000 − 1,500,000 1,500,000 − 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 1920 21 22 23 24 25 26 27 28 29 30 313233 3435 36 37 3839 40 41 42 43 44 45 46 47 48 4950 51 52 53 54 55 56 57 58 59 60 61 6264 65 63 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 9495 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110
0e+00 1e+06 2e+06 3e+06 4e+06
6.9 7.0 7.1 7.2 7.3 Period IV Population Pro
vincial Mean Nominal W
age − (in natur
al log ter ms) 0 − 500,000 500,000 − 1,500,000 1,500,000 − 1 2 3 4 5 6 7 8 9 10 11 13 12 14 15 16 17 18 19 20 21 22 23 24 25 26 2728 29 30 3132 33 34 3536 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 6667 68 69 70 71 72 73 74 75 76 77 78 7980 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110
FIGURE3.5: Provincial observed mean nominal wage and popu-lation.
26 Chapter 3. The Estimate for Italian Provinces in 2014-2016
0e+00 1e+06 2e+06 3e+06 4e+06
6.8 6.9 7.0 7.1 7.2 Period I Population Pro
vincial Mean Real W
age − (in natur
al log ter ms) 0 − 500,000 500,000 − 1,500,000 1,500,000 − 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 1920 21 22 23 24 25 26 2728 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110
0e+00 1e+06 2e+06 3e+06 4e+06
6.8 6.9 7.0 7.1 7.2 Period II Population Pro
vincial Mean Real W
age − (in natur
al log ter ms) 0 − 500,000 500,000 − 1,500,000 1,500,000 − 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 1617 18 19 20 21 22 23 24 25 26 27 28 29 30 31 3233 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 4950 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106107 108 109 110
0e+00 1e+06 2e+06 3e+06 4e+06
6.5 6.6 6.7 6.8 6.9 7.0 7.1 7.2 Period III Population Pro
vincial Mean Real W
age − (in natur
al log ter ms) 0 − 500,000 500,000 − 1,500,000 1,500,000 − 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 1920 21 22 2423 25 26 2728 29 30 313233 343536 37 3839 40 41 42 43 44 45 46 47 48 4950 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 676869 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110
0e+00 1e+06 2e+06 3e+06 4e+06
6.8 6.9 7.0 7.1 7.2 Period IV Population Pro
vincial Mean Reall W
age − (in natur
al log ter ms) 0 − 500,000 500,000 − 1,500,000 1,500,000 − 1 2 3 4 5 6 7 8 9 10 11 13 12 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 313233 3435 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110
FIGURE3.6: Provincial observed mean real wage and popula-tion.
0e+00 1e+06 2e+06 3e+06 4e+06 5.2 5.4 5.6 5.8 6.0
Estimated Provincial TFP VS. Resident Population
Resident Population Estimated Pro vincial TFP 1 2 3 4 5 6 7 8 911 1012 13 14 15 1617 18 1920 21 22 23 24 25 26 27 28 29 30 31 32 33343536 37 38 39 40 4142 43 44 45 46 47 48 4950 51 52 53 54 5556 57 58 59 60 61 62 63 64 65 66 6768 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 8889 90 91 92 93 9495 96 97 98 99 100 101 102 103 104 105106107 108 109 110 1 2 3 4 5 6 7 8 9 10 11 12 13 14 1617 15 18 19 20 21 22 23 24 25 2726 28 29 30 31323334 35 36 37 3839 40 41 42 43 44 45474946 48 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 8889 90 91 92 93 9495 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 1 2 3 4 5 6 7 8 911 1012 13 14 15 1617 18 19 20 21 22 23 24 25 26 27 28 29 30 3132 33343536 37 38 39 40 4142 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 9810099 101 102 103 104 105106 107 108 109 110 1 2 3 4 5 6 7 89 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 272826 29 30 3132 333435 36 37 383940 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 7371 72 74 75 76 77 78 79 80 81 82 83 84 85 8688 87 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 I Period II Period III Period IV Period 0 500000 1000000 1500000 5.2 5.4 5.6 5.8 6.0
Estimated Provincial TFP VS. Count. Employment
Counterfactual Employment Estimated Pro vincial TFP 1 2 3 4 5 6 7 8911 1012 13 14 15 16 17 18 1920 21 22 23 24 25 26 27 28 29 30 31 32 33343536 37 38 39 40 4142 43 44 45 46 47 48 4950 51 52 53 54 5556 57 58 59 6061 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 8889 90 91 92 93 9495 96 97 98 99 100 101 102 103 104 105106107 108 109 110 1 2 3 4 5 6 7 89 10 11 12 13 14 1617 15 18 19 20 21 22 2324 25 2726 28 29 30 31323334 35 36 37 3839 40 41 42 43 44 45474946 48 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 8889 90 91 92 93 9495 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 1 2 3 4 5 6 7 8911 1012 13 14 15 16 17 18 19 20 21 22 2324 25 26 27 28 29 30 3132 33343536 37 38 39 40 4142 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 9810099 101 102 103 104 105106 107 108 109 110 1 2 3 4 5 6 7 89 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 262728 29 30 3132 333435 36 37 383940 41 42 43 44 4546 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 7173 72 74 75 76 77 78 79 80 81 82 83 84 85 8688 87 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 I Period II Period III Period IV Period 0 500000 1000000 1500000 2000000 5.2 5.4 5.6 5.8 6.0
Estimated Provincial TFP VS. Actual Employment
Actual Employment Estimated Pro vincial TFP 1 2 3 4 5 6 7 8911 1012 13 14 15 16 17 18 1920 21 22 23 24 25 26 27 28 29 30 31 32 3334 35 36 37 38 39 40 4142 43 44 45 46 47 48 49 50 51 52 53 54 5556 57 58 59 6061 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 8889 90 91 92 93 9495 96 97 98 99 100 101 102 103 104 105106107 108 109 110 1 2 3 4 5 6 7 89 10 11 12 13 14 16 15 17 18 19 20 21 22 2324 25 2726 28 29 30 31323334 35 36 37 3839 40 41 42 43 44 45474946 48 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 8889 90 91 92 93 9495 96 97 9899 100 101 102 103 104 105 106 107 108 109 110 1 2 3 4 5 6 7 8911 1012 13 14 15 16 17 18 19 20 21 22 2324 25 26 27 28 29 30 3132 33343536 37 38 39 40 4142 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 9810099 101 102 103 104 105106107 108 109 110 1 2 3 4 5 6 7 89 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 262728 29 30 3132 333435 36 37 383940 41 42 43 44 4546 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 7071 72 73 74 75 76 77 78 79 80 81 82 83 84 85 868889 87 90 91 92 93 94 9596 97 98 99 100 101 102 103 104 105 106 107 108 109 110 I Period II Period III Period IV Period 0 500 1000 1500 2000 2500 5.2 5.4 5.6 5.8 6.0
Estimated Provincial TFP VS. Population Density
Population Density Estimated Pro vincial TFP 1 2 3 4 5 6 7 8 911 10 12 13 14 15 16 17 1819 20 21 22 2324 25 26 27 28 29 30 31 32 3334 35 3637 38 39 40 4142 434445 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 6465 66 67 68 69 70 71 72 73 74 75 76 7778 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105106107 108 109 110 1 2 3 45 6 7 8 9 10 11 12 13 14 16 15 17 18 19 20 21 22 2324 25 2726 28 29 30 31 32 3334 35 3637 38 39 40 41 42 43 44 4546 47 48 49 50 51 52 53 5455 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 7475 76 7778 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 9899 100 101 102 103 104 105 106 107 108 109 110 1 2 3 4 5 6 7 8 911 10 12 13 14 15 16 17 18 19 20 21 22 2324 25 26 2728 29 30 31 32 3334353637 38 39 40 4142 43 44 45 4647 48 49 50 51 52 53 5455 56 57 58 59 60 61 62 63 64 65 66 67 68 69 7071 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 9899 100 101 102 103 104 105106107 108 109 110 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 1819 20 21 22 2324 25 27 2826 29 30 31 32 333435 3637 38 3940 41 42 43 44 4546 47 48 49 50 51 52 53 5455 56 57 58 59 60 61 62 63 64 65 66 67 68 69 7071 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86898887 90 91 92 93 94 95 96 97 98 99 100 101102 103 104 105 106 107 108 109 110 I Period II Period III Period IV Period
FIGURE3.7: Estimated Provincial TFP - (in nominal terms).
0e+00 1e+06 2e+06 3e+06 4e+06
5.2
5.4
5.6
5.8
6.0
Estimated Provincial TFP VS. Resident Population
Resident Population Estimated Pro vincial TFP 1 2 3 4 5 6 7 8 9 10 11 13 12 14 15 1617 18 1920 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 4142 43 44 45 46 47 48 4950 51 52 53 54 5556 57 58 59 60 61 62 63 64 65 66 6768 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 8889 90 91 92 93 9495 96 97 98 99 100 101 102 103 104 105106107 108 109 110 1 2 3 4 5 6 7 8 9 10 11 12 13 14 16 15 17 18 19 20 21 22 23 24 25 26 2728 29 30 31323334 35 36 37 3839 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 8889 90 91 92 93 9495 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 1 2 3 4 5 6 7 8119 10 12 13 14 15 1617 18 19 20 21 22 23 24 25 26 2728 29 30 3132 33 343536 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 676869 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 9810099 101 102 103 104 105106 107 108 109 110 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 2728 29 30 3132 33 3435 36 37 383940 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 7371 72 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 I Period II Period III Period IV Period 0 500000 1000000 1500000 5.2 5.4 5.6 5.8 6.0
Estimated Provincial TFP VS. Count. Employment
Counterfactual Employment Estimated Pro vincial TFP 1 2 3 4 5 6 7 8 9 10 11 13 12 14 15 1617 18 1920 21 22 23 24 25 26 27 28 29 30 31 32 3334 35 36 37 38 39 40 4142 43 44 45 46 47 48 4950 51 52 53 54 5556 57 58 59 6061 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 8889 90 91 92 93 9495 96 97 98 99 100 101 102 103 104 105106107 108 109 110 1 2 3 4 5 6 7 8 911 10 12 13 14 16 15 17 18 19 20 21 22 2324 25 26 2728 29 30 31323334 35 36 37 3839 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 8889 90 91 92 93 9495 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 1 2 3 4 5 6 7 8911 10 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 2728 29 30 3132 33 343536 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 6869 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 9810099 101 102 103 104 105106 107 108 109 110 1 2 3 4 5 6 7 89 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 2728 29 30 3132 333435 36 37 383940 41 42 43 44 4546 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 7173 72 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 9596 97 98 99 100 101 102 103 104 105 106 107 108 109 110 I Period II Period III Period IV Period 0 500000 1000000 1500000 2000000 5.2 5.4 5.6 5.8 6.0
Estimated Provincial TFP VS. Actual Employment
Actual Employment Estimated Pro vincial TFP 1 2 3 4 5 6 7 8 9 10 11 13 12 14 15 1617 18 1920 21 22 23 24 25 26 2728 29 30 31 32 3334 35 36 37 38 39 40 4142 43 44 45 46 47 48 4950 51 52 53 54 5556 57 58 59 6061 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 8889 90 91 92 93 9495 96 97 98 99 100 101 102 103 104 105106107 108 109 110 1 2 3 4 5 6 7 8 9 10 11 12 13 14 16 15 17 18 19 20 21 22 2324 25 26 27 28 29 30 31323334 35 36 37 3839 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 8889 90 91 92 93 949596 97 98 99 100 101 102 103 104 105 106 107 108 109 110 1 2 3 4 5 6 7 8 9 10 11 13 12 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 3132 33 3435 36 37 38 39 40 4142 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 6869 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 9697 9810099 101 102 103 104 105106107 108 109 110 1 2 3 4 5 6 7 89 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 2728 29 30 3132 333435 36 37 383940 41 42 43 44 454746 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 7071 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 9596 97 98 99 100 101 102 103 104 105 106 107 108 109 110 I Period II Period III Period IV Period 0 500 1000 1500 2000 2500 5.2 5.4 5.6 5.8 6.0
Estimated Provincial TFP VS. Population Density
Population Density Estimated Pro vincial TFP 1 2 3 4 5 6 7 8 911 1310 12 14 15 16 17 1819 20 21 22 2324 25 26 2728 29 30 31 32 3334 35 3637 38 39 40 4142 434445 46484947 50 51 52 53 54 55 56 57 58 59 60 61 62 63 6465 66 6768 69 70 71 72 73 74 75 76 7778 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105106107 108 109 110 1 2 3 45 6 7 8 911 10 12 13 14 16 15 17 18 19 20 21 22 2324 25 26 2728 29 30 31 32 3334 35 3637 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 7475 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 1 2 3 4 5 6 7 8 9 10 11 13 12 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 3435 3637 38 39 40 4142 43 44 45 46 47 48 49 50 51 52 53 5455 56 57 58 59 60 61 62 63 64 65 66 6768 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 9899 100 101 102 103 104 105106107 108 109 110 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 1819 20 21 22 2324 25 26 2728 29 30 31 32 333435 3637 38 3940 41 42 43 44 454647 48 49 50 51 52 53 5455 56 57 58 59 60 61 62 63 64 65 66 67 68 69 7071 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101102 103 104 105 106 107 108 109 110 I Period II Period III Period IV Period
28 Chapter 3. The Estimate for Italian Provinces in 2014-2016 Gerolimetto, and Magrini, 2016–2017. They use the Bartik Instrument (Bartik, 1991) that allows to build a counter-factual level of employment which will be used as instrument for the actual population density.
For each province it is necessary to build a counter-factual rate of growth, as in Equation (3.1), which multiplied to the actual level of local employment at the beginning of the period gives the counter-factual level of employment. Using the Cambridge Econometrics’ European Regional Database (ERD) on employment grouped into six sectors (NACE Rev. 2) we compute the sector shares of workers in each Province in 1980, with respect to the provincial to-tal employment (3.2).4 Then we compute the imputed rate of growth of
em-ployment for each province, between 1980 and 2014, which is given by the sectoral nationwide rate of growth that would have been if the contribution of province j was equal to zero. Summing all these imputed rates of growth of employment weighted by the initial share of employment we obtain the counter-factual rate of growth of province j.
∆tBj≡ P s∈S h EM P.SHAREs,j,t−k· P z∈J 6=j(log(EM Ps,z,t) − log(EM Ps,z,t−k)) i (3.1) with EM P.SHAREs,j,t≡ EM Ps,j,t EM Pj,t (3.2) In our case, the counter-factual level of employment has been rescaled in or-der to correctly represent the sample’s proportions based on ISTAT’s data, while the Appendix Table B.6 shows the raw data, of both counter-factual growth rate and counter-factual level of employment (obtained using Cam-bridge Econometrics’ European Regional Database (ERD) ) and population and population density.5 The second plot at the top of both Figure3.7and
Fig-ure 3.8 shows the positive relationship between counter-factual employment and Estimated Provincial TFP.
While a strong relationship exists between Estimated Provincial TFP and pop-ulation or poppop-ulation density (or its instrument), from Figure 3.9 we can see that a strong and clear relationship between Estimated Provincial TFP and
4Data are grouped into 6 sectors: Agriculture, forestry and fisheries, Manufacturing and Energy, Construction, Wholesale and retail trade, transport, accommodation and food service activities, Financial and Business Services, Non-market Services.
0 1000 3000 5000 7000 5.2 5.4 5.6 5.8 6.0
Estimated nominal Provincial TFP VS. Land Area
Land Area (in Sq.Km)
Estimated Pro vincial TFP 1 2 3 4 5 6 7 8 9 10 1112 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 61 60 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 1 2 3 4 5 6 7 8 9 10 1112 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 353836 37 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 I Period II Period III Period IV Period 0 1000 3000 5000 7000 5.2 5.4 5.6 5.8 6.0
Estimated real Provincial TFP VS. Land Area
Land Area (in Sq.Km)
Estimated Pro vincial TFP 1 2 3 4 5 6 7 8 9 10 1112 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 5961 60 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 1 2 3 4 5 6 7 8 9 10 1112 13 14 15 16 17 18 19 20 22 23 24 25 26 27 28 29 30 31 32 33 34 353836 37 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 I Period II Period III Period IV Period
FIGURE3.9: Estimated Provincial TFP and Land Area - (in
nom-inal and real terms).
provincial land area does not exist. The relationship is almost flat in the nom-inal terms plot, while in the real terms case the relationship is slightly posi-tive. We will return on this issue in the next section, when we will control for other covariates. Among the externalities terms we include, as in Ferranna, Gerolimetto, and Magrini,2016–2017, the market potential (3.3), this variable is a proxy for the goodness of the access to the market. It is defined as the interac-tion between the Spatial Weights Matrix W , created according to the k-nearest neighbours method, setting k=10, and the population density. We think setting k=10 can be a good compromise to explain the possible interactions that occur among Italian provincial markets.
M Pj ≡ W · P OP.DEN S (3.3)
As shown in Figure 3.10 the relationship between the Estimated Provincial Productivity and the market potential is strongly positive both in nominal and real terms. As an institutional term, among the other externalities, we use the provincial administrative fragmentation (FRAG), defined as in Ferranna, Gerolimetto, and Magrini, 2016–2017and Ahrend et al., 2017 by the number of municipalities per 100,000 inhabitants, (3.4). (See AppendixB.7 for the list
