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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.