LA CREAZIONE DI POSTI DI LAVORO LA CREAZIONE DI POSTI DI LAVORO NEL COMPARTO TURISTICO. NEL COMPARTO TURISTICO.

QUALE RILEVANZA PER LA STAGIONALITA’?QUALE RILEVANZA PER LA STAGIONALITA’?

III Workshop PRINIII Workshop PRIN““Frammentazione e Sviluppo Locale: Frammentazione e Sviluppo Locale:

Modelli Interpretativi e Scenari di Politica Modelli Interpretativi e Scenari di Politica Economica” Economica”

NovaraNovara28 giugno 200728 giugno 2007

G. Guidetti* e L. Zamparini**G. Guidetti* e L. Zamparini**

* Università degli Studi di Bologna* Università degli Studi di Bologna** Università del Salento ** Università del Salento

Struttura della presentazioneStruttura della presentazione

Theoretical introductionTheoretical introduction

The databasesThe databases

Descriptive statisticsDescriptive statistics

Dynamic labour demand with adjustment costs

Adjustment costs In addition to wages, labour costs

are given by hiring and firing expenses. The former include monitoring and training costs; the latter any outlay related to the procedure of lay-off of redundant employees.

The shape of the hiring costs curve

In standard models, symmetric convex adjustment costs are assumed, with the typical U-shaped profile. However, it seems more plausible (Nickell, 1986) to assume that the profile of hiring and firing curves are different.

For low levels of hiring it seems more reasonable to assume that marginal costs are not increasing, whereas when the hiring rates overcome a given threshold, increasing marginal hiring costs can be observed.

The shape of firing costs curve

As to firing costs, it seems reasonable to assume the same shape as hiring costs, although the non-convex initial profile might be smaller than the one noticeable for hiring costs.

The dynamic optimization of the employment level

The formal problem of optimization, when adjustment costs are relevant, is given as follows (Nickell, 1986):

Max

dttxCtNtwttNRtpe t ))(()()()),(()(0

)(

Results of the optimization problem (1)

Anderson (1993) shows that, when hiring, the following condition has to hold:

The marginal revenue product of labour has to outweigh the real wage plus hiring costs.

))t(r()t(w)t),t(N(R N

Results of the optimization problem (2)

On the other hand, when firing, the following condition has to hold:

The losses due to a decrease in revenues plus firing costs have to be offset by a decrease in wage costs.

))t(r()t(w)t),t(N(RN

What happens if these two inequalities hold ?

and

))(()()),(( trtwttNRN

))t(r()t(w)t),t(N(RN

If both the two previous inequalities hold, then neither hiring nor firing occurs. These two inequalities identify the so-called “no action area”. As a matter of fact, if both inequalities hold it is optimal for the employer to neither hire nor fire.

The higher adjustment costs (hiring and firing), the wider the “no action area”.

For our purposes, when product demand exhibits a highly seasonal profile, labour demand fluctuations can be assumed to be comparatively predictable, which implies that employers can exploit (decreasing average hiring costs) by implementing standardised and stable procedures to monitor the labour market and to find the suitable employees from both a quantitative and qualitative perspective.

Therefore, one can assume that average hiring and firing costs decrease as the level of both hiring and firing rate increases, at least until a given threshold. Non-convex adjustment costs prevail. The higher the degree of predictability of seasonal labour demand the higher the degree of volatility of the level of employment. The same considerations are worth for firing procedures and their related costs.

The databasesThe databases

Data about arrivals, tourist nights Data about arrivals, tourist nights and number of room in hotels have and number of room in hotels have been provided by local APT and, after been provided by local APT and, after their closing down, by local their closing down, by local Administrations.Administrations.

All data about employment have All data about employment have been provided by INAIL. been provided by INAIL.

Empirical analysis refers to the nine Empirical analysis refers to the nine Provinces of Emilia-RomagnaProvinces of Emilia-Romagna

Tourist nights (Quarterly data) 1

0

100.000

200.000

300.000

400.000

500.000

600.000

700.000

800.000

900.000

00-1

00-3

01-1

01-3

02-1

02-3

03-1

03-3

04-1

04-3

05-1

05-3

06-1

06-3

Bologna

Parma

Employment in tourism sectors (1)

0

2.000

4.000

6.000

8.000

10.000

12.000

14.000

16.000

18.000

20.000

00-1

00-3

01-1

01-3

02-1

02-3

03-1

03-3

04-1

04-3

05-1

05-3

06-1

06-3

Bologna

Parma

Tourist nights (Quarterly data)- 2

0

2.000.000

4.000.000

6.000.000

8.000.000

10.000.000

12.000.000

00-1

00-3

01-1

01-3

02-1

02-3

03-1

03-3

04-1

04-3

05-1

05-3

06-1

06-3

Rimini

Ravenna

Employment in tourism sectors (2)

0

5.000

10.000

15.000

20.000

25.000

30.000

35.000

00-1

00-3

01-1

01-3

02-1

02-3

03-1

03-3

04-1

04-3

05-1

05-3

06-1

06-3

Rimini

Ravenna

Tourist nights (coefficient of Tourist nights (coefficient of variation) variation)

00.20.40.60.8

11.21.41.61.8

2

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

Bologna

Ferrara

Forlì Cesena

Rimini

Modena

Parma

Piacenza

Ravenna

Reggio Emilia

Arrivals (coefficient of Arrivals (coefficient of variation)variation)

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

Bologna

Ferrara

Forlì Cesena

Rimini

Modena

Parma

Piacenza

Ravenna

Reggio Emilia

Gini Gini coefficientcoefficient - Arrivals - Arrivals

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

Bologna

Ferrara

Forlì Cesena

Rimini

Modena

Parma

Piacenza

Ravenna

Reggio Emilia

Gini coefficient – Tourist NightsGini coefficient – Tourist Nights

0

0.1

0.2

0.3

0.40.5

0.6

0.7

0.8

0.9

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

Bologna

Ferrara

Forlì Cesena

Rimini

Modena

Parma

Piacenza

Ravenna

Reggio Emilia

(Occupancy ratio – Quarterly (Occupancy ratio – Quarterly data) -1data) -1

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Bologna

Ferrara

Forlì Cesena

Rimini

(Occupancy ratio – Quarterly (Occupancy ratio – Quarterly data) -2data) -2

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Modena

Parma

Piacenza

Ravenna

Reggio Emilia

Ratio of change in FTE employees in hotels and in Ratio of change in FTE employees in hotels and in overall employment - 1overall employment - 1

Piacenza

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

1.4

2000

2001

2002

2003

2004

2005

2006

Parma

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

Reggio Emilia

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

Ratio of change in FTE employees in hotels and in Ratio of change in FTE employees in hotels and in overall employment - 2overall employment - 2

Modena

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

Bologna

-10

-8

-6

-4

-2

0

2

Ferrara

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

Ratio of change in FTE employees in hotels and in Ratio of change in FTE employees in hotels and in overall employment - 3overall employment - 3

Ravenna

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Forlì Cesena

0

0.2

0.4

0.6

0.8

1

1.2

Rimini

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

Coefficient of variation of FTE employees in the nine Coefficient of variation of FTE employees in the nine Provinces of Emilia-Romagna in tourist sectorsProvinces of Emilia-Romagna in tourist sectors

Piacenza ParmaReggio Emilia

Modena Bologna Ferrara RavennaForlì

CesenaRimini

2000 0.035 0.105 0.031 0.026 0.015 0.124 0.366 0.398 0.578

2001 0.057 0.095 0.027 0.040 0.013 0.152 0.361 0.341 0.535

2002 0.054 0.104 0.027 0.047 0.024 0.153 0.340 0.318 0.505

2003 0.006 0.079 0.027 0.022 0.012 0.138 0.307 0.274 0.414

2004 0.050 0.077 0.041 0.032 0.017 0.126 0.275 0.224 0.357

2005 0.007 0.066 0.013 0.017 0.011 0.122 0.290 0.220 0.357

Few preliminary conclusions The dynamic labour demand model

with adjustment costs is consistent with the higher degree of variability of employment in Provinces such as Rimini, Ravenna, Forlì-Cesena and Ferrara.

For these Provinces the higher degree of variability of the level of employment in tourism sectors depends on the higher level of seasonal variability, as far as tourist nights and arrivals are concerned

A high degree of seasonality in the demand for tourism services favours the implementation of formal and informal tools for monitoring the labour market, training the newly employees and, in general, smoothing transaction costs arising from the management of the workforce. These tools decrease the average costs of hiring and firing procedures, narrowing the “no action area”.

Very preliminary analysis of correlation seems to confirm our hypothesis.

After using the X11 technique to adjust the time series for both trend and seasonal components we find, at least for Rimini and Forlì-Cesena, a significant correlation between employment and tourist nights.

Furthermore, if one correlates change in employment for each quarter, with change in tourist nights lagged by one year, one finds that the higher coefficient of variation of employment, the higher the index of correlation. The provinces where employment shows the highest seasonal fluctuations exhibit the highest correlation between employment and tourist nights → high degree of labour market flexibility.