Milan - Spatial and Economic Analysis

1

MILAN Spatial and Economic Analysis POLITECNICO DI MILANO Master Degree in Management of the Built Environment Regional economics & Land Rent Theory Prof: Roberta Capello; Silvia Cerisola

GROUP 9 Caligari Elisa Cifarelli Dario Dealexandris Andrea Gaglione Giacomo Grimaldi Diana Rampino Giacomo

897812 899129 898372 899501 897591 905461

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TABLE OF CONTENT Introduction

3

Purpose of the report

4

CHAPTER 1

5

1.1 Specialization and Diversification

5

1.2 Employment Shares

5

CHAPTER 2

7

2.1 Location Quotient

7

2.2 Hirschmann-Herfindahl Index

8

CHAPTER 3 - Shift and Share Analysis

10

CHAPTER 4 - Regional Growth Patterns

12

CHAPTER 5 - Descriptive Statistics

14

CHAPTER 6

19

6. Econometrics

19

6.1 ols estimator

19

6.2 Multivariate regression model

22

Conclusion

24

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INTRODUCTION Milan is the county seat of Lombardy, the population is 1.366.037 million. It is recognised as an important economic area in Italy and is considered as the economic and financial capital of Italy. The geographical position of the city reinforces these characteristics; it is a strategic link between different urban realities and helps to strengthen Italy's role as a gateway to Europe. It is located at the intersection of regional, national and even international routes. Moreover, the transport network contributes to the economic reinforcement of the region.
 
 Some analysis data show the economic trend referred to the presence of economic companies in the area. This parameter shows the importance of Lombardy as the most important economic region of Italy. In 2006, there were more than 340,000 companies, ie approximately 40% of the companies in Lombardy and more than 6% of the Italian companies. Today, there are 69% of companies in the tertiary sector, 29% from industry and 2% in agriculture. 1 Today, Milan is the second largest city in Italy after Rome and is the largest economic centre. The region represents 10% of national GDP.

Fig.1 Map of Milan 2 1

Source: infocamere

Source: https://www.shutterstock.com/image-vector/vector-map-city-milan-capitallombardy-1055826023?src=pHFYwBZSR0fwa9B1q1n6Vg-1-13 2

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PURPOSE OF THE REPORT The purpose of the report is to represent a spatial economic analysis of the region of Milan, a city localised in the north of Italy. The analysis involves all the sectors and for each chapter it focuses the attention on the sectors which are more relevant for the area of Milan. The data collected were classified according to NUTS3. The NUTS provides a unique scheme of geographical distribution and is based on the population residing in each area. The classification has different levels, which are hierarchical, that is the unity of a level is 'daughter' of a single unit of the previous level. Finally, the NUTS 3 level considers 1303 territories, including the Italian provinces.3

Fig. 2 NUTS Code 4

3

Source: http://www.treccani.it/enciclopedia/nuts-nomenclature-des-unites-territoriales-

statistiques_%28Dizionario-di-Economia-e-Finanza%29/ 4

Source: https://en.wikipedia.org/wiki/NUTS_statistical_regions_of_Italy

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CHAPTER 1 1.1 Specialization and Diversification We have considered the employment rate of the various working sectors such as: agricolture, industry, manufacturing construction, whole sale and retail trade, information and communication, finance and public administration. We have analyzed the data present in the time interval between 2008 and 2015, focusing more on year 2015 as it is the most recent year with data. In particular, we have considered the data relating to Italy, Lombardy and Milan.

1.2 Employment Shares The review of the first indicator starts with the analysis of the employment in different industries; the first step is to calculate the employment shares. The formula of employment share is the ratio between the total employment and the industry employment. The employment shares is represented by:

where E represents employment, and indices i, j, and c refer to city (region) i, industry j, and country c, respectively.

By plotting a dispersion graph using the analyzed region and a reference area it is visible the difference in specialization of every industry in the two areas. The industries that appear above the bisecting line are industries whose relative specialization is higher and vice versa, while the distance from the origin is an indicator of how impactful is an industry compared to the total.

We have chosen to compare occupational data at national, regional and urban levels to highlight which sectors characterise the development of the city of Milan.

The Employment Shares is the ratio of employment in a given year in a given region divided by the total number of the employment. This value indicates the percentage of employment in a given sector in a given year. From the two graphs obtained through shift share analysis, Milan were compared with Italy and Lombardy. The data used refer to the year 2015. From the first graphical comparison (Fig.3) between Milan and Italy it emerges that the most developed sectors at the urban level are Financial, information and communication while the agricultural sector is underdeveloped. A similar situation is also reflected at the regional and urban level in the second chart (Fig.4).

6

0,6

Employement shares Milano

0,5

0,4

0,3

finance

trade

pa

0,2 industry 0,1information and communication

manufacturing construction

agriculture

0 0

0,1

0,2

0,3

0,4

0,5

0,6

0,4

0,5

0,6

Employement shares Italy

Fig.3

0,6

Employement sharesMilano

0,5

0,4

0,3

finance

trade pa

0,2 industry 0,1 information and communication

manufacturing

construction agriculture

0 0

0,1

0,2

0,3

Employement shares Lombardia

Fig.4

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CHAPTER 2 2.1 Location Quotient The location quotient is an indicator of relative specialisation of an area compared to a larger one (e.g. NUTS 3 relative to NUTS 2). Its based on employment data and compares the shares of a sector to the total, both at the area level and the the reference level. The location quotient is calculated as follows:

Therefore the interpretation is straightforward. If the LQ is greater than 1 the area has an higher relative specialisation than the reference one, if its equal to 1 then the specialisation is the same and naturally if the quotient is lower than 1 the specialisation is less.

Location quotients can be used to compare both in space and in time, Fig. 5 in fact is a representation of the location quotients in Milan in 2000 and 2014; this two representation shows the difference in a specific period of time. In those years [sectors] gained weight on the total employment in the area, while the other industries lost some shares. Location quotients radar graph is also used to identify which sectors are most concentrated compared to the others and to the reference region.

The biggest limitation of this system, despite its widespread use is the fact that its limited to representing one industry at a time, therefore to have a better identification of an area its necessary to use a system that provides data about the general specialisation or diversification.

In our case we have considered the shares of employment of the various sectors with respect to Italy, Lombardy and Milan. In the graphic elaboration we have chosen to use the data for the year 2015 because there were more recent information (Fig.5).

8 agriculture 2,5 2

pa

industry

1,5 1 0,5 finance

manufacturing

0

information and communication

construct ion

trade Italy

Milano

Lombardia

Fig.5 Radar graph for eight industries in Lombardy, Italy and Milan.

Consistent with shift share analysis, the most developed sectors remain those of finance, information and communication. While the least developed remains the agricultural sector.

2.2 Hirschmann-Herfindahl Index To answer the necessity of a general indicator of specialisation the HHI is widely used. Its calculated as the sum of the employment shares squared. The values range from 0 to 1, where 0 indicates perfect homogeneity and 1 perfect specialisation.

For this indicator we have analysed Italy, Lombardy and Milan as well as all the Italian provinces, whose values will be used in the exercise related to the Regional Growth Patterns. We have used the shares of each sector of Italy Lombardy and Milan of the year 2015, these values have been squared and then added together to obtain the HHI index.

In our case the Values range is 0.231 that means a more homogeneity value.

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0,25 0,245 0,24 0,235 0,23 0,225 0,22 Italy

Lombardia

Milano

Fig.6 HHIs for Italy, Lombardy and Milan

As can be seen from the graph also for Lombardy and Italy there is no perfect specialization, but both regions tend to perfect homogeneity.

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CHAPTER 3 3. Shift and Share Analysis Shift and share analysis is used to measure the growth of single industries compared to a reference region.

A city can have growth pattern equal to the ones of the reference region only if the productive structure is the same, when this does not happen the growth rate deviated from the reference region according to the equation: ∗ y =y +s r ∗ where y represents GDP growth in the city/region, y the growth rate the region should r achieve in order to match the growth rate of national income, and, finally s (the so called shift) represents the difference between the regional and the national growth rates.

The shift may be due to MIX or DIF effects, the former is the deviation in industrial rates from the reference region while the latter is due to differences in productivity in the single sectors.

The graphical interpretation of the data set is useful to understand the composition and the productive framework of a region in relation to the national level.

The sector analysed: • • • • • • • •

Agriculture Industry Manufactoring Construction Trade Information and communication Finance PA

What emerges from this graph (Fig. 7) is that during the period considered, from 2008 (when the economic crisis began) to 2015, the average growth in the employment of the sectors analysed both at the national level and at the Milan level area is actually negative.

Analysing each sector in more detail, the one with the greatest development at national level is the financial sector, with an increase of 8%, while in Milan the most developed sector is that of public administrations (about 8%). The information and communication and wholesale and retail trading sectors have not undergone significant changes at both levels. Agriculture has undergone a marked decline in the Milan area (which has never been specialised in this sector), while at the national level the most declining sector is that of construction.

The weaknesses of this model is that it is descriptive, not interpretative, therefore it’s able to provide a snapshot of the situation but does not try to explain the reasons.

To correctly interpret it is necessary to take into account also the initial specialisation of the industry in exam.

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Shift and Share Analysis -0,3

-0,2

-0,1

0

0,1

0,2

0,3 0,5

0,4

Milan sectoral growth rate 2008-2015

0,3

0,2

pa information and communication

0,1 finance

trade construction

0

-0,1 industry -0,2

manufacturing

-0,3

agriculture

Italy sectoral growth rate 2008-2015 Sectors

Average sectoral growth in Milan

Bisecting line

Rotated average growth in Italy

Fig.7 Shift-share analysis

-0,4

-0,5

Average sectoral growth in Italy

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CHAPTER 4 4. Regional Growth patterns The patterns of regional growth model analyses the process of deindustrialization that has been taking place in many advanced economies for decades now. Usually, deindustrialization is associated with an idea of almost melancholic decline; in fact, it is often approached to ideas of social and placement of the workforce.

At an urban level of analysis deindustrialization can be seen as relocation of productive plants to peripheral areas.

The two variables of interest are relative employment growth (REGr) and relative productivity growth (RPGr).

Analytically, the two variables are defined as:

and

Where r indicates the area of interest, while n represents the Italy as reference.

1 is the most recent time period, 0 the oldest one.

Theory of regional patterns of development's graph will show the differences in productivity and employment growth in manufacturing, from 2001 to 215, between two cities: Milan and Rome. We thought it would be useful to compare Milan with Rome in order to obtain a chart with significant values, because they are both vibrant cities, Rome is the most populous city of Italy, while Milan is the second one. At the same time these two cities are very different: Milan is more focused on the finance sector, but has also a quite good share of industries that deals with manufacturing (mostly focused on fashion).

The tertiary sector in Milan includes 72.5% of the total employed people, followed by the industrial one with 27%, the last one for agriculture with 0.5%.

Instead Rome’s economy is based on the public service sector, due to the presence of state and local public bodies. Indeed in Rome, 87% of total employees are employed in the service sector, only 7% in industry, in addiction to 5,4% in construction field.

So the expectations from this analysis are that Rome will show up as a city completely deindustrialized, with a low productivity growth in manufacturing sector, and Milan is expected to be more productive than Rome in manufacturing terms and with an higher employment rate for this sector.

Of course, both Milan and Rome are certainly two of the most specialized cities of Italy in tertiary sector, so employment and productivity growth will be probably lower than National values.

Looking at Milan's trend we can see that from 2001 to 2015 productivity and employment growth in manufacturing have changed a lot, in particularly the productivity. • In 2001-2003 Milan was in the restructuring stage, where productivity growth is higher than the average. But after few years its productive growth started collapsing, and the region started moving out from the restructuring stage and get into an industrial

13 conservatism stage; in fact with the increase of employment, the productivity decreases probably because is divided into more people. • From 2004 to 2006 the city started a huge productive growth, with a low decrease in employment. This increase in productivity made the region going through the industrial conservatism stage - in which an insufficient GDP performance is accompanied (and partly explained) by high employment growth, subsidized by state authorities - to the economic take-off stage. After that the productivity kept increasing, the region entered in the virtuous cycle stage, where it has generated a good performance in both employment and GDP, until 2007-2009. • In the next five years (2010-2015) productivity started a steep decrease, probably due to the effect of the crisis, that affect also the employment in manufacturing sector, and probably this is because many manufacturing industries went bankrupt; maybe also some employees have been fired or moved to different sectors during these years; this decline in manufacturing productivity and employment, caused the phenomenon of deindustrialization of the city. This caused a vicious cycle, where the high unemployment of the city due to the crisis caused low GDP growth and continued job losses.

RPGr

0,040

0,030

0,020 07-09

0,010

01-03

10-12

04-06 10-12

-0,040

-0,030

-0,020

0,000 0,000

-0,010

0,010

0,020

0,030

0,040

REGr -0,010 01-03

04-06

07-09 -0,020 13-15

-0,030 13-15

-0,040

Milan

Rome

DY=0

F i g . 8 Regional patterns of development for Milan and Rome

Looking at the chart is possible to see the differences between the two cities. Rome, compared to Milan, has much wider changes in manufacturing employment, and from 2001 to 2015 the capital city entered in the typical virtuous cycle moving from a period in which productivity growth generates a good performance in both employment and GDP, to a period characterised by the deindustrialization stage, where job cuts fail to recover competitiveness, causing low GDP growth and continued job losses, for two times. As expected Rome seems to be more deindustrialized than Milan, with both employment and productivity in manufacturing that are far below the National average.

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CHAPTER 5 5. Descriptive statistics Descriptive statistics is a branch of statistics that deals with the description of a set of data by highlighting some key features.

The report shows a possible use of the descriptive statistic for the values of some sector; shares of the employment of the finance, shares of the employment of the information and communication, the GDP values and HHI values. The analysis is articulated in a procedure of comparison between all the italian provinces. The attention is concentrated on the region of Milan.

To understand all this statistic measure used in this analysis it is necessary to analyse them.

•

where xi represent observations in the analysed sample while n indicates the sample size.

The arithmetic mean is 0,131 and it’s the share of the employment in the Italian provinces (represented by X1) divided by the number of them (110) (represented by the n).

This value indicates that about 13% of workers are employed in the finance sector in every Italian province. From this data, it can be said that the sector taken into consideration does not generally have a great consideration in the employment theme.

• STANDARD ERROR, STANDARD DEVIATION AND VARIANCE To understand the role of the Standard error it’s useful to know its meaning, that is the standard deviation of the sample distribution of that statistic. In our case the Standard error is circa 0.

The standard deviation is used to estimate the variance of a sample of data. It is a measure that is used to quantify the amount of variation or dispersion of a set of data values. It’s not a truly and precise value.

The variance is the expectation of the squared deviation of a random variable from its mean, and it informally measures how far a set of numbers are spread out from their mean.

• MEDIAN AND MODE Consider to have a list of number, the median is the number which divided the “higher” half and the lowest one. It’s necessary to organize the list of the number from the lowest to the higher one and then select the mean number, in the central position. If the list of the number is composed by a odd total it’s taken into consideration the mean between the two central value.

Mode represent the most repeated value in a set of data. If in the table the mode is not available, it means that there are no repeated values.

15 • KURTOSIS It is a descriptor of the shape of a probability distribution.

• SKEWNESS It is a measure of the asymmetry of the probability distribution of a real-valued random variable about its mean. The skewness value can be positive or negative, or even undefined.

• INTERVAL Interval consists of a range of values (interval) that act as good estimates of the unknown population parameter; however, the interval computed from a particular sample does not necessarily include the true value of the parameter. A larger size of the sample reduces the width of the interval around the mean, which lower the possibility of making a mistake in extracting information from the data.

• MINIMUM VALUE It is the smallest value in the data sample.

• MAXIMUM VALUE It is the biggest value in the data sample.

• SUM It is the simple sum of all the values of the sample.

• COUNT It is the number of values in the sample.

• LARGEST It is the second biggest value of the data sample • SMALLEST It is the second smallest value in the data sample • CONFIDENCE LEVEL A confidence level refers to the percentage of all possible samples that can be expected to include the true population parameter. A 95% confidence level implies that 95% of the confidence intervals would include the true population parameter.

FINANCE SECTOR MILAN

0,261281901

Mean

0,131663188

Standard error

0,002562968

16 Median

0,128599697

Mode

0,1

Standard deviation

0,027123904

Sample variance

0,000735706

Kurtosis

4,468389741

Skewness

1,265769416

Interval

0,182990086

Minimum value

0,078291815

Maximum value

0,261281901

Sum

14,74627705

Count

112

Largest

0,261281901

Smallest

0,078291815

Confidence level

0,005078692

Fig.9 Shares of the employment of finance sector in 2015.

As the introduction of the descriptive statistic said, the analysis involves the finance sector and through the process it is results the percentage of people, of each Italian provinces, involved in the Financial sector in comparison with the total employment.

According to the expectations the Milan value is higher than the average of the other Italian provinces, in particular is exactly the double of the mean of the other Italian provinces and corresponds to the maximum value. This result shows the highly specialization in the financial activities.

GDP MILAN

50.800

Mean

24646,42857

Standard error

640,916293

Median

24550

Mode

27200

Standard deviation

6782,82049

Sample variance

46006653,8

Kurtosis

0,751903409

Skewness

0,6019355

Interval

36400

Minimum value

14400

Maximum value

50800

17 Sum

2760400

Count

112

Largest

50800

Smallest

14400

Confidence level

1270,01842

Fig.10 GDP per capita in 2015.

In the analysis of GDP emerges the fact that the GDP per capita is approximately 50.800â‚Ź , almost double of the mean of the other Italian provinces.

Another important statistic value to observe is the standard deviation that shows the mean distance from which the values of the provinces differ from the national average The case of Milan shows a difference of 6,700 â‚Ź approximately from the mean.

The result value shows that the region is wealthier than the other in the Italian peninsula.

INFORMATION AND COMUNICATION MILAN

106,80

Mean

5,878571

Standard error

1,467688

Median

2

Mode

0,8

Standard deviation

15,53255

Sample variance

241,2603

Kurtosis

35,72069

Skewness

5,763123

Interval

112,5

Minimum value

0

Maximum value

112,5

Sum

658,4

Count

112

Largest

112,5

Smallest

0

Confidence level

2,908323

Fig.11 Share of employment of Info and communication in 2015.

This chart shows the analysis done with the value of the information and communication sector; it is analysed the percentage of people involve in this sector in comparison with the total employment. The region of Milan is second, after Rome. Its value is twenty times

18 higher than the average of the other Italian provinces. It is recognized that Milan has a central role in the national area in this sector.

HHI MILAN

0,231

Mean

0,25535879

Standard error

0,002687927

Median

0,251403017

Mode

#N/D

Standard deviation

0,028446345

Sample variance

0,000809195

Kurtosis

4,586497154

Skewness

1,699112103

Interval

0,170219103

Minimum value

0,204712625

Maximum value

0,374931728

Sum

28,60018449

Count

112

Largest

0,374931728

Smallest

0,204712625

Confidence level

0,005326307

Fig.12 HHI

The last analysis in term of descriptive statistic is that one on the HHI. The results show that the region of Milan is not specialized but it tend to a homogenues distribution. It reflects both the National and Lombardy level.

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CHAPTER 6 6. Econometrics Econometrics is the science that identifies and measures the relationships between socioeconomic using the ordinary least squares (OLS) estimator and the linear. The purpose is to identify factors that influence the GDP pc and to look at the ways in which they alter it.

For the analysis, it has been used the latest 2015 GDP pc data compared with the following socio-economic variables: - Total Employment - Patents in High Tech sector - Employment in finance - HHI The data of the above indicators refers to year 2011 and it is collected for NUTS3 regions of all the Italians’ provinces.

6.1 OLS Estimator The first approach to econometrics is OLS, a statistics tool used for estimating the unknown parameters in a multivariate regression model, with the goal of minimizing the differences between the observed responses in some ordinary dataset and the responses predicted by the linear approximation of the data.

It is necessary to define the trend line that it is useful to calculate the OLS estimator, that minimize the sum of the squares of the distances from the points to the linear interpolant.

From the equation of the trend line, it is possible to understand the slope of the line, the 2 value of the error term but also the value of R , that is a measure of the explaining power of the model.

The r squared is an indication of the goodness with which y and x are correlated. A perfect 2 correlation would give a value of r = 1; on the contrary, a value close to zero indicates a questionable correlation. The aim is to understand which independent variable, has a higher influence on the dependent variable of the model: the GDP.

The linear regression analysis is a technique that allows to analyze the linear relationship between a dependent variable and one or more independent variables. It is an asymmetrical methodology that is based on the existence of a relationship of cause-effect relationship between one or more independent variables and the dependent variable.

The study of this relationship may have a dual purpose: - Explanatory: Understand and weigh the effects of the independent variables on the dependent variable in function of a given theoretical model.

- Predictive: Identifying a linear combination of independent variables to predict optimally the value assumed by the dependent variable.

20 In the charts below, the red dot represents the region of Milan.

OLS GDP pc - Total Employment 60.000

50.000

GDP pc

40.000

30.000

y = 0,0108x + 22197 R² = 0,2076

20.000

10.000

0 0,00

500.000,00

1.000.000,00

1.500.000,00

2.000.000,00

2.500.000,00

Total Employment

Fig.13 OLS GDP pc - Total Employment

OLS GDP pc -Patent in High Tech Sector 60.000

50.000

GDP pc

40.000

30.000

20.000 y = 376,56x + 23290 R² = 0,1495

10.000

0 0

5

10

15

20

25

Patent in High Tech sector

Fig.14 OLS GDP pc- Patent in High Tech Sector

30

35

40

21

OLS GDP pc - Employment in Finance 60.000

50.000

GDP pc

40.000

30.000 y = 53,945x + 22856 R² = 0,2369

20.000

10.000

0 0

50

100

150

200

250

300

350

400

450

500

Employment in Finance

Fig.15 OLS GDP pc - Employment in Finance

OLS GDP pc - HHI 60.000

50.000

y = 75510x + 5385,3 R² = 0,1034

GDP pc

40.000

30.000

20.000

10.000

0 0,2

0,22

0,24

0,26

0,28

0,3

HHI

Fig.16 OLS GDP pc- HHI

0,32

0,34

0,36

0,38

22 The graphs show that each socio-economic indicator considered influence the GDP pc positively, ie when a variable increase, so does the other one. The region of Milan sets above the trend line in each plot. The HHI factor present the weakest correlation and the lowest r2 among the indicators chosen.

Milan is the Italian city with the second highest employment rate and highest GDP per capita. In particular the analysis has highlighted the role of the financial sector, which has fostered the economic growth of the region.

6.2 Multivariate regression model The multivariate linear regression model, that describes the relationship between p independent random variables (X1, ..., Xp) called predictors and the variable Y called response or dependent variable, in this case the GDP pc, is represented by the following regression equation: Y=β0+β1X1 +β2X2+...+βpXp +ε

- β0 is the intercept, that is the value Y assumed when all the Xj are equal to 0 (with j = 1, ..., p); - - βj is the slope of Y with respect to the variable Xj and then the regression coefficient of the predictor Xj, holding constant other variables; - ε is the random error.

The model is constructed using the same four predictors employed in the OLS indicator part and observing the Y response

Statistics of regression R multiple

0,621306853

R squared

0,386022206

R squared correct

0,362407675

Standard Error

5405,352349

Observations

109

Multiple r, the correlation coefficient, shows a good positive correlation of 62,1%.

R square is the Coefficient of Determination. It tells the variability percentage of Y that the model is able to explain; the closer it gets to 1, the more that model will be considered accurate. It indicates that 38.6% of the variations in the GDP can be explained by changes in the employment in finance, total employments, patents in high tech sector and HHI. In other words, 38% of the values fit the model.

VARIANCE ANALYSIS

23

gdl

SQ

MQ

4

1910473336

477618333,9

Residual

104

3038654738

29217834,02

Total

108

4949128073

Regression

F

Significatività F

16,34680838

2,0308E-10

Coefficien ts

Errore standard

Stat t

significativ ità

Inf. 95%

Sup. 95%

Inf. 95,0%

Sup. 95,0%

Intercetta

2765,6601 71

4658,659427

0,5936600 89

0,55402849 6

-6472,6 36

12003,95 7

-6472,6 36

12003,95 7

Total Empl.

80307,821 03

18169,14916

4,4200100 02

2,43007E-0 5

44277,7 16

116337,9 26

44277,7 16

116337,9 26

Patents

91,865662 27

37,88774427

2,4246801 72

0,01704621 7

16,733

166,998

16,733

166,998

Empl.

Finance

172,02317 26

85,86242524

2,0034744 19

0,04772740 6

1,755

342,292

1,755

342,292

HHI

-0,010148 607

0,007924953

-1,280588 885

0,20318672 2

-0,026

0,006

-0,026

0,006

This section shows very specific information about the components chosen to put into the data analysis and gives the linear regression equation associated to the model: Y = 2766 + (80307,8) X1 + (91,8) X2 + (172) X3 + (0,01) X4 The t-Stat value is the coefficients divided by the standard error. T-stat greater than 2, or smaller than -2, would mean that the coefficients found are significant and with more than 95% significance, i.e. they are quite reliable. This is true for each the β0, β1, β2 where tstat are large, which means the coefficients should be reliable.

The GDP per capita, the Y, should then be dependent on each of three indicators but less strictly related to HHI variable.

24

CONCLUSION The spatial and economic analysis carried out for the city of Milan has brought out some interesting aspects concerning this city and very often confirming expectations and forecasts.

From the analysis has emerged that the most developed sectors at the urban level are Financial, information and communication while the agricultural sector is underdeveloped, reflecting also the regional and national trend. Milan has the second highest employment rate and highest GDP per capita. In particular the analysis has highlighted the role of the financial sector, which has fostered the economic growth of the region. The analysis had underlined that Milan has a central role in the national area in sectors such as financial and administrative, acting as an examples for other major Italian cities.