Climate and Tourism in Tuscany: Responsiveness of Tourist Inﬂows to Climate Variation

Climate and tourism in Tuscany: responsiveness of tourist inﬂows to climate variation

Mattia Cai*, Roberto Ferrise ✝, Marco Moriondo ✝ and Paulo A. L. D. Nunes+

June 21, 2010

Abstract The suitability of a location for a number of tourist activities is largely determined by its climate. Regions where tourism and related activities represent an important part of the economy can suffer potentially large economic damages as a result of climate change. This paper presents a fine-scale investigation of the effect of climate conditions on tourist flows to the municipalities of Tuscany, one of Italy's 20 administrative regions. In order to examine how tourist arrivals and the average length of their stay respond to variation in local weather conditions, an 8-year panel dataset of the regionʼs 254 municipalities was assembled. Using both static and dynamic model specifications, we conduct separate analyses for domestic and foreign tourists. We find evidence of a non-negligible effect of climate variation on tourist flows. In broad terms, warmer winters and hotter summers are associated with reduced tourist inflows, whereas higher temperatures in the intermediate seasons seem beneficial for tourism. These general results appear to hold regardless of the place of origin of the tourists, even though the observed statistical associations seem stronger for domestic than for foreign tourists, which may reflects their different ability to take the weather conditions into account at the time their travel arrangements are made. The effect of local climate on tourist inflow varies remarkably among destinations offering different types of tourism attractions (e.g. arts-and-business, hill-and-countryside, sea or mountain destinations).

FIRST DRAFT – PLEASE DO NOT CITE

* Department of Land and Agro-forestry Systems, University of Padova TeSAF, Padova, Italy ✝ CNR-IBIMET, via Caproni 8, 50145 Firenze, Italy + Fondazione Eni Enrico Mattei, Isola di San Giorgio Maggiore, 30124, Venezia, Italy 1 Introduction A location's suitability and attractiveness for a variety of tourist activities depends to a large extent on its weather. Climate change can have a significant impact on the economic outcomes of regions where tourism and related activities represent important sources of income. In this paper we take advantage of a rich climate dataset collected in the context of an agronomic research project to conduct a fine-scale empirical investigation of how variation in climate conditions influence tourist flows to the municipalities – i.e. the smallest administrative units – of Tuscany, one of Italy's 20 administrative regions. With annual tourist arrivals in excess of 10 million and growing, Tuscany accounts for more than one tenth of Italy's total (ISTAT 2010). The remainder of the paper is organized as follows: section 1 describes the dataset used in this study; section 2 sketches a picture of tourism flows to Tuscany and motivates the following analyses; section 3 and section 4 present the methodological aspects and the results of the analyses, respectively; section 5 summarizes.

1. Data The dataset used in the analysis is a panel of 254 municipalities (comuni) located in Tuscany which are observed annually over the period 2000-2007, amounting to a total of 2,032 observations1. Information on annual tourist inﬂows (arrivals and overnight stays) were obtained from Tuscany's ofﬁcial statistics (Sistema Statistico Regionale – Regione Toscana 2010). Tourism data are available separately for domestic and foreign visitors, even though the countries of origin of the latter are unknown. These data were combined with a detailed meteorological dataset produced by Chiesi et al. (2007), which consists of 1 Km interpolated daily observed data for maximum and minimum temperatures (Tmax and Tmin, respectively) and total daily precipitation (PP), for the period 1999-2007. For the purposes of this study, the meteorological data were aggregated at municipal level and then processed to produce climate indicators (e.g. seasonal temperature and precipitation) adopted in the following analyses.

1 In fact, Tuscany is comprised of 281 municipalities. Because some of them are very small, for privacy reasons in some cases ofﬁcial statistics are only released for aggregates of municipalities.

2 2. Tourism flows to Tuscany: a brief overview While its most celebrated spots are a handful of world-renowned arts-and-culture destinations such as Firenze, Pisa and Siena, Tuscany is quite varied in terms of tourist attractions, as it also hosts popular sea and mountain resorts. Tourist flows to hilly and rural areas are also significant. In a typical year, tourism accounts for about 8% of the region's GDP (Regione Toscana 2010). For comparison, over the period 2000-07 considered in our analysis, tourism's contribution to Italy's GDP varied between 9.9% and 11.8% (World Travel and Tourism Council 2010). Figure 1 displays the evolution of total tourist flows to Tuscany over the period of our study. In this interval, the arrivals of foreigners (which dipped between 2003 and 2005) and of domestic tourists (which appear to have grown steadily) respectively represented 50–55% and 45–50% of the total.

[FIGURE 1 HERE]

While no seasonal data are published at this spatial resolution, technical publications by Regione Toscana show that about a half of all overnight stays in the region take place in the four months from june to september (Regione Toscana 2007). For the purpose of tourism statistics, Tuscany classifies its municipalities into eight groups based on the prevalent type of tourism attraction (arts-and-business, sea, mountain, hill-and-countryside, lake, religious, spa, and other), as shown in the first panel of figure 2. The second panel represents the distribution over municipalities of average tourist density (total arrivals per unit of surface) for the period 2000-07. The most popular tourist destinations include a major part of the coastal zone, in addition to Firenze and Siena and the areas in between.

[FIGURE 2 HERE]

As one would expect, domestic and foreign tourists look remarkably different in terms of their tourism behavior. For a start, foreign tourists tend to stay longer (ﬁgure 4) and concentrate at fewer locations than domestic tourists (ﬁgure 5).

3 [FIGURE 4 HERE]

[FIGURE 5 HERE]

Furthermore, the two groups differ in the type of tourism destination they choose. Figure 3 shows the shares of foreign and domestic arrivals accounted for by each type of tourism destination, and their evolution over time. Remarkably, those shares remain relatively unchanged in spite of the trends and ﬂuctuations in total arrivals.

[FIGURE 3 HERE]

A dominant share of foreign tourists is accounted for by art-and-business destinations (58-59% of all arrivals). While art-and-business destinations draw a large chunk of domestic arrivals (39-40%) as well, tourists from Italy are also largely choosing sea destinations (33-34%), which are instead less signiﬁcant for foreigners (12-15%) . Mountain destinations also cater predominantly to tourists from Italy (5%).

[FIGURE 5 HERE]

3. Methodological framework Using the dataset described in section 1, we analyze the response of demand for tourism at a given location to variation in climate. We conduct our analyses in a standard panel data framework:

yit = xitʼβ + ui + εit (1) where i and t index municipalities and years, respectively; yit denotes the tourism outcome of interest; xit is a vector of variables relating to the climate; ui is an unobserved time-constant individual effect; εit is an idiosyncratic error; β is the vector of parameters that we aim to estimate. In some specifications (more details below), lags of the dependent variable are also included. Also, a full set of year dummies is included in all specifications. Two types of analyses are conducted. In the first, the dependent variable is the natural logarithm of tourist arrivals. Yet, tourists can respond to variation in climate not only by deciding whether or not to visit a given location, but also by choosing how long

4 to stay. In the second analysis, the tourism outcome we study is the (log of) average length of stay, computed as the ratio of overnight stays to tourist arrivals. Furthermore, it must be noted that changed climatic conditions can prompt tourists not only to switch to a different destination or to adjust the length of their stay (i.e. on the amount of tourism consumed), but also to shift their vacation in time (on the time when it is consumed). A detailed analysis of seasonality patterns would require data at a greater detail of temporal resolution than those available to us. Yet, we try to account for seasonality patterns by including seasonal measurements of key climate variables in the xit vector (e.g. average winter, spring, summer, and fall temperatures).

3.1 Climate change and tourist inﬂow

3.1.1 Direct versus indirect effects There are a number of mechanisms through which climate can inﬂuence demand for tourism at a given destination. A distinction can be made between direct and indirect effects, as in Simpson et al. (2008). Climate determines the suitability of a location for a number of tourist activities (snow and skiing, sun and sea,...). ʻGoodʼ or ʻ badʼ weather can by itself make a destination more or less attractive, which we refer to as direct effects. As the climate modiﬁes, the quality of the amenities available at a given location is also likely to be affected. For example, the visual appeal of landscapes may change as a result of changes in the vegetation – whether these are spontaneous or induced by farmer's production choices – or higher sea levels and beach erosion could reduce the attractiveness of a sea resort. The indirect effects of climate change on tourism are those that arise as a consequence of environmental change (e.g. biodiversity loss, beach erosion, agricultural production,...) induced by climate change. In general, indirect effects take place slowly and over a relatively long period of time, at least to the extent that would-be tourists can notice them. Attempting to measure such indirect effects would require longer time series. With 8 years of data, it is unlikely that any such effects can be detected, let alone separately measured. Furthermore, such long term changes would probably trigger some sort of adaptation on behalf of the tourism sector, and taking that into account would require a more sophisticated analysis than what we do here.

5 3.1.2 Tourism responsiveness to climate conditions On the other hand, direct effects should be observable in a short time. Such effects, however, will take place only if people can observe the climate when they choose their destination. In fact, it is plausible that a majority of people plan their major vacation trips ahead of time and take whatever climate is available once they get to the chosen destination. Perhaps people go ʻfar' away (e.g. abroad) predominantly for a major vacation. If so, many foreign tourists may be visiting Tuscany while on their major vacation (the relatively long length of stay observed suggests this may actually be the case), which was presumably planned well ahead of time and could not take short term weather variability into account. It is probably true that extreme weather conditions (e.g. severe heatwaves with significant death toll, widespread forest fires) get reported in foreign media and hence influence tourist departures. Yet variation within the bounds of the unexceptional may not have a large influence. On the contrary, weekend or other short trips – options that are only available to people living not very far away (domestic tourists) – are more likely to respond to short term changes in the weather. If this is the case, one would expect that the direct effect of climate change on trips by foreigners to be relatively small compared to the effect on domestic tourism. Possibly, foreigners are afforded more freedom to adjust the length of their stay at a specific location to favorable or unfavorable weather conditions, rather than in the choice of whether or not to visit. For these reasons, and because of the differences described in the previous section, we conduct separate analyses for domestic and foreign tourists.

3.2 Other determinants of tourist inflow Typically, empirical analyses of (international) tourist flows control for key economic variables such as income of potential tourists, travel cost, prices at destination relative to the prices at the place of origin, exchange rates, and quality of destination. Because such data are not available to us, we are not able to explicitly account for those variables in our analysis. Yet, in a given year, macroeconomic conditions and the prices of substitutes are the same for all municipalities in Tuscany. As long as they enter the model linearly, they can be accounted for through a set of year dummies. In addition to its climate and economic accessibility, factors that contribute to making a destination alluring include the presence of specific attractions (e.g. monuments, sea, sights, parks, events) and cultural appeal (history, tradition, cuisine, lifestyle, renown,...).

6 Because such variables are largely time-constant, at least in the short run, they can be dealt with in a standard panel data framework.

3.3 Econometric issues The discussion above makes it clear that it is necessary to allow for an individual effect ui in equation (1). Under these circumstances, ordinary least squares (OLS) estimation of β is at best inefficient. In fact, given all the unobservables that are being dumped in the error term, it is highly likely that ui is correlated with some of the variables in xit, which would cause OLS to be biased. For example, if places with nicer climates were developed as tourist destination earlier than places with less friendly climates, (unobserved) renown may be correlated with climate. Also, over centuries, historical settlements, culture or cuisine may have developed more in areas with higher agricultural productivity than in poorer areas. To the extent that agricultural productivity responds to changes in climate, then ʻattractionsʼ or ʻcultural appealʼ in ui are going to be correlated with xit. Perhaps more seriously, spatial variation in price levels could reflect the general pleasantness of local climates. All this suggests that a reasonable approach would be to use an estimator that does not require ui and the regressors to be uncorrelated, such as the fixed effects (FE) estimator. Furthermore, many unobserved economic variables – most notably prices – cannot be plausibly assumed to be time invariant. To control for such factors, we also estimate an augmented version of the model which also includes a lag of the dependent variable.

yit = ρyi,t-1 + xitʼβ + ui + εit (1)

By assumption, at time t+1 the lagged depend variable yi,t is correlated with the error term εit in the previous time period, so that the strict exogeneity assumption necessary for FE estimation is certainly violated. The standard approach to the estimation of equations such as (2) is to use the so-called Arellano-Bond (AB) system GMM estimator. This involves ﬁrst killing ui by ﬁrst differencing – i.e. lagging the model one period and subtracting it from (2) – and then estimating the differenced equation with a two-step GMM procedure, using older lags of y as instruments under a sequential exogeneity assumption. In addition to dealing with time varying unobservables, this approach enables us to compute both short-run and long-run elasticities of tourism to changes in climate.

7 4. Results Summary statistics for the variables that appear in the analysis are presented in table 1.

[TABLE 1 HERE]

The following subsections present the results of the analysis of domestic tourist arrivals (4.1), foreign tourist arrivals (4.2) and length of stay (4.3). Because in all estimated equations the dependent variable appears in logarithmic form whereas the climate variables (e.g. temperatures) appear in levels (degrees), the estimated coefﬁcients can be interpreted as semi-elasticities (i.e. each coefﬁcient represents the percent change in y that results from a unit change in the corresponding explanatory variable).

4.1 Domestic arrivals

In our first specification of the equation for domestic arrivals, the vector xit consists of four variables measuring the average daily of (average) temperatures in winter, spring, summer and fall, respectively. The model has no lagged dependent variables and is estimated by FE. The results are reported in the first column of table 2. In all seasons but spring, higher temperatures are associated with lower tourism inflows. Yet, climate variables are not significant either jointly (F4,253 = 1.30, p = .27) or individually, with the only exception of winter temperatures, which appear to negatively impact tourist arrivals.

[TABLE 2 HERE]

Column 2 presents estimates for the model that also includes a lag of the dependent variable and is estimated using the AB estimator. The coefficients on winter and summer temperatures display remarkably little change. What does change are the coefficients on the intermediate seasons that are now both positive and remarkably larger than before. While only winter and spring temperatures are significant individually, the four climate variables are jointly significant (χ2(4)= 9.44, p = .05). Neither Sargan's test (χ2(10)= 6.17, p = .80) nor the Arellano Bond test detect any model mis- specification.

8 Next we attempt to extent the basic model in two ways. First, we allow tourist arrivals to depend on (averages of) minimum and maximum daily temperatures, rather than on daily averages. As before, we use both a static specification (column 3) and a dynamic AB specification (column 4). In column 3, while individual significance levels are still pretty low, the eight climate variables are jointly highly significant (p < .01). The effect of temperature change is more significant both statistically and practically during the summer. Intuitively, this seems consistent with the known pattern of seasonality. The dynamic specification in column 4 produces broadly similar estimates on most coefficients and sign reversals are rare. The most notable difference between the estimates in columns 3 and 4 relates to the coefficient on spring minimum temperatures which changes sign between specifications but remains sizable in absolute terms. Next, we go back to the basic model of column 2 and extend it in a different direction. All the specifications presented so far implicitly imposed the assumption that, no matter what type of tourist attractions are available at a given destination, climate variation will have the same effect on the inflow of tourists. As different areas of Tuscany offer a diverse range of opportunities for recreation – from historical sites to sea and mountain destinations – this assumption does not seem entirely realistic. We now try to relax that assumption by allowing the coefficients on the climate variables to differ among groups of municipalities with similar characteristics. Aggregating from the eight-group classification used in the source data and displayed in figure 2, municipalities are partitioned into four groups: arts-and-culture (art/business, spa, religious, other – labelled CITY for convenience), countryside and hill (hill/ countryside, lake – HILL), sea (SEA), and mountain (MOUNTAIN). We then proceed to re-estimate the dynamic model of column 2 allowing for group-specific coefficients on each of seasonal average daily temperature. We do so by interacting the 4 seasonal temperatures with each of the 4 group dummies, which results in a total of 16 climate variables being included in the specification. Those 16 variables turn out jointly highly significant (χ2(16)= 35.25, p < .01). Furthermore, we test the restriction that the effect of each seasonal temperature is the same for all the municipalities (which leads back to the model of column 2) and reject it (χ2(12)= 23.29, p = .03). Rather than reporting the results from the fully unrestricted specification, however, we estimate a more parsimonious version of the model where restrictions that appear both theoretically plausible and empirically justified are imposed on the coefficients. The resulting specification (column 5) includes, in addition to the 4 variables that measure the seasonal average daily temperatures, selected interactions

9 of those variables with the dummies for tourism type. Indeed, these restriction are not rejected by the data (χ2(7)= 6.66, p = .46). As before, warmer springs appear to result in larger tourist inflows, but for sea municipalities the effect is much smaller, perhaps even absent. In general, average daily spring temperatures higher by one degree are associated with a 6.9% increase in the annual inflow of domestic tourist, but at sea destinations the effect is (partly) offset by the interaction term SPRING*SEA, which appears to imply a 5.4% decline. As a matter of fact, the hypothesis that the net effect at sea destinations is zero cannot be ruled out (χ2(1)= .14, p = .71). On the other hand, higher temperatures in the summer do not affect significantly the number of visitors in sea and mountain municipalities, but they reduce flows to the cities, the hills and the countryside. Vice versa, warmer falls appear to benefit hills, countryside and cities but have no effect on sea and mountain municipalities. That the effect of higher winter temperatures turns out negative and uniform over all municipalities is somewhat unexpected. While it appears plausible that warmer temperatures negatively influence winter tourism (for example because of lack of snow), it is somewhat counterintuitive that sea destinations should also be affected in any practically significant way. Looking at the patterns of seasonality could perhaps help clarify this issue, but unfortunately tourism data are only available with annual periodicity.

4.2 Foreign arrivals Analyses analogous to those of the previous section were also conducted for international tourist arrivals. In many respects, the results (in table 3) look remarkably similar to those obtained for domestic tourists.

[TABLE 3 HERE]

The estimates from FE estimation of the static specification are presented in column 1. Seasonal temperatures are jointly significant, the signs of the estimates are consistent with those obtained from the analysis of domestic arrivals, and the magnitudes of the coefficients are generally larger. Switching to the results from AB estimation of the dynamic specification in column 2, the signs of the coefficients on the climate variables remain unchanged, except for that on FALL, which becomes positive. The magnitudes become smaller and statistical

10 significance weaker. Only summer temperature comes close to statistical significance, and indeed the four climate variables are jointly not significant. Furthermore, the Sargan test of overidentifying restrictions suggests that there may be some issues with the specification of the model (χ2(20)= 29.51, p = .08). Even so, the estimates in column 2 provide some support for the notion that tourism responds negatively to higher winter and summer temperatures, and positively to higher temperatures in the intermediate seasons. Columns 3 and 4 report estimates produced using the same modeling approaches as columns 1 and 2, respectively, but using as explanatory variables the seasonal averages of daily minimum and maximum temperatures. The two sets of estimates are remarkably similar. The sign is the same in 6 cases out of 8, even though FE coefficients tend to have larger magnitudes. In both specifications, maximum temperatures seem to have a greater influence on tourism than minimum temperatures. Maximum summer and winter temperatures have a significant negative effect on tourist arrivals. Higher maximum spring temperatures are positively associated with tourist inflow. However, the eight climate variables are jointly significant only in the model of column 3, but not in that of column 4. In this case as well, we consider extending the AB model of column 2 to allow areas with different types of tourism attractions to display different responses to the weather conditions. As before, we let estimates and theory suggest plausible restrictions on which types of tourist destinations should have the same coefficient. The results are displayed in column 5. While the climate variables are jointly significant only at the 10% level (χ2(6)= 12.09, p = .06) and individual levels of significance are pretty high, the results suggest some interesting considerations. Most notably, warm falls seem to result in a large increase in foreign tourist arrivals at sea destinations (fall temperature higher by 1 degree causes annual arrivals to grow by more than 8%). Also, there are hints that higher temperatures in the spring positively influence visits to mountain municipalities.

4.3 Length of stay Similar analyses were conducted in order to assess to what extent domestic and foreign tourists adjust the length of their stay in response to changes in the weather. The results of the analysis are reported in table 4.

11 [TABLE 4 HERE]

For each origin of tourists, column 1 reports FE estimates for the simple model where all destinations are equally affected by weather variation, whereas column 2 displays estimates from the specification that allows for selected resource specific effects. Across all these models, seasonal temperatures are only weakly significant. Yet, it is encouraging that the corresponding AB dynamic specifications, despite having no clear causal interpretation, produce point estimates estimates (not reported in table 4) that are very close to those from FE. In general, it seems that for most destinations the weather conditions have relatively little influence on how long domestic tourists choose to stay. Hill and countryside areas, however, appear to represent exceptions. There is indeed evidence that stays at hill and countryside municipalities tend to be shorter in years with hot summers, and longer when the spring or the fall are warmer. How long foreign tourists stay at the chosen location appears relatively more responsive to changes in the weather conditions. On the one hand, higher temperatures in the fall are associated with longer stays. On the other, hot summers appear to make their stays shorter, but less so at sea destinations.

5. Final remarks The suitability of a location for a variety of activities is largely determined by its weather. Areas with sizable tourist sectors have grown increasingly concerned about the effects that impending climate change may have on their economies. This paper described an empirical investigation of how tourism flows to Tuscany, in central Italy, relate to fluctuations in climate conditions. An 8-year panel dataset of the regionʼs 254 municipalities was assembled and used to examine how tourist arrivals and the average length of their stays respond to variation in local weather conditions. While tourism is by its very nature a highly seasonal activity, our analysis is not able to fully take this aspect into account because tourism data are only available on an annual basis at the necessary spatial resolution. However, we try to allow for seasonality patterns by explaining annual observations of tourism outcomes in terms of winter, spring, summer and fall temperatures. Distinct but specular analyses were conducted for international and domestic tourists. We find evidence of a non-negligible effect of climate variation on tourist flows. In broad terms, warmer winters and hotter summers are associated with reduced tourist

12 inflows, whereas higher temperatures in the intermediate seasons seem beneficial for tourism. More specifically, high maximum temperatures in the summer seem to depress tourist arrivals. These general results appear to hold regardless of the place of origin of the tourists, even though the observed statistical associations seem stronger for domestic than for foreign tourists. Perhaps, this reflects their different ability to take the weather conditions into account at the time their travel arrangements are made. In fact, the influence of the climate on tourism outcomes seems to vary across municipalities depending on the type of attractions that they offer. For example, average summer temperatures higher by 1 degree appear to reduce annual arrivals of domestic tourists by 3 to 4% at arts-and-business and hill-and-countryside destinations but have little effect elsewhere. Correspondingly, such destinations appear to enjoy inflows of domestic tourists larger by 3 to 4% when falls are warmer by 1 degree on average. Springs with average temperatures higher by 1 degree appear to increase annual arrivals of domestic tourists by about 7% quite uniformly across municipalities, with the exception of sea resorts which seem to remain relatively unaffected. In a similar spirit, our estimates suggests that annual arrivals of foreign tourists at sea destinations are 3% higher if the fall is warmer by 1 degree on average. Compared to the arrivals, the average length of stay displays weaker associations with the climate for both foreigner and domestic tourists. Even so, the results are broadly consistent with those for the arrivals. For most sets of analyses, both types of econometric setups that we used (fixed effects estimation of a static specification and system GMM estimation of a dynamic model) produced estimates that are encouragingly close to each other. In principle, the estimates from the dynamic specification could be used to obtain estimates of the effect of changes in the climate on the long run equilibrium. However, we choose not to emphasize the long run implications of our estimates, as these can be reasonably expected to capture only the short-run direct effects of the weather on tourism. Indirect effects that work through the influence of climate on the quality of the local amenities are unlikely to show up in an 8-year panel.

13 References

Chiesi M., Maselli F., Moriondo M., Fibbi L., Bindi M., Running S.W.: Application of BIOME-BGC to simulate Mediterranean forest processes. Ecological Modelling, 206:179-190, 2007

ISTAT (2010), Capacità e movimento degli esercizi ricettivi, available at: http:// www.istat.it/dati/dataset/20100305_00/, accessed: June 2010.

Regione Toscana (2008), Toscana in cifre 2007, available at: http:// ius.regione.toscana.it/cif/pubblica/tic2007/indic2007.htm, accessed: June 2010.

Regione Toscana (2010), available at: http://www.regione.toscana.it/turismo/guida/, accessed: June 2010.

Sistema Statistico Regionale – Regione Toscana (2010), Toscana in cifre, available at: http://ius.regione.toscana.it/cif/stat/index-turismo.shtml, accessed: June 2010.

Simpson, M.C., S. Gössling, D. Scott, C.M. Hall, and E. Gladin (2008), Climate change adaptation and mitigation in the tourism sector: frameworks, tools and practices, UNEP, University of Oxford, UNWTO, WMO: Paris, France.

World Travel and Tourism Council Data Search Tool (2010), available at: http:// www.wttc.org/eng/Tourism_Research/Economic_Data_Search_Tool/, accessed: May 2010.

14 '(()*+,-./0,12 ! " # $% $& &%%% 56<()-7.+(()*+,-.&%%% Figure 1: Total tourist ﬂows to Tuscany 2000-07 Tuscany touristﬂowsto Total Figure 1: &%%& &%%! 34+( &%%" ! %= &%%# :604-7); 86(4)91 567+, 15 O Z Z E R A ! s l a v i A E O r Z N T r N E E I E S a S !

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A R A I R G ! O P m ! Figure 2: Tourism ﬂows to the municipalities of Tuscany T S s I i P ! r u A O o C t N C

R U f A O L ! S V o I I A

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16 Figure 3: Log average length of stay (2007): domestic versus foreign tourists Domestic vs. Foreign tourists 4 3 2 Foreign tourists 1 0 0 1 2 3 4 Domestic tourists

Figure 4: Tourist concentration in Tuscanyʼ municipalities Lorenz curves Foreign vs. Domestic arrivals, 2007 1 .8 .6 .4 .2 0 0 .2 .4 .6 .8 1

Foreign Domestic Perfect equality

17 spa religious mountain lake country or hill sea art and business other 2007 2006 2005 2004 Year Domestic 2003 2002 2001 2000 0% 90% 80% 70% 60% 50% 40% 30% 20% 10% 100% 2007 2006 2005 2004 Year 2003 Foreign Figure 5: Tourist arrivals by origin and type of destination (2000-07) 2002 2001 2000 0% 90% 80% 70% 60% 50% 40% 30% 20% 10% 100% 18 Table 1: Summary statistics

Mean Min Max SD SD (BG) SD (WG)

Log arrivals Domestic 8.42 0 13.6 1.69 1.66 0.34

Foreign 7.96 0 14.5 1.93 1.89 0.37

Winter temperature Average 6.8 -1.1 11.4 2.04 1.56 1.32

Min 2.6 -4 8.2 1.94 1.29 1.46

Max 10.9 1.6 15.8 2.38 1.98 1.33

Spring temperature Average 16.4 7.4 20.3 1.75 1.5 0.92

Min 10.9 4.6 15.1 1.42 1.14 0.85

Max 21.9 10.1 26.8 2.44 2.19 1.07

Summer temperature Average 21.1 12.2 25.8 1.81 1.61 0.83

Min 15.2 9.6 20.2 1.5 1.27 0.81

Max 27 14.9 32.3 2.53 2.31 1.03

Fall temperature Average 10.4 2.8 14.8 1.84 1.61 0.89

Min 6.6 0.1 11.8 1.66 1.32 1.02

Max 14.2 5.3 19.2 2.19 2 0.9

n = 254 T = 8 nT = 2,032 SD: standard deviation; BW: between groups; WG: within groups

19 Table 2: Regression results for domestic tourist arrivals (robust std. err. in brackets) Static Dynamic Static Dynamic Group interactions (1) (2) (3) (4) (5)

.0034 –.0273 min (.0241) (.0195) –.0486** –.0518** –.0456** WINTER (.0240) (.0212) (.0212) –.0279 -.0246 max (.0333) (.0258)

–.0493* .0569* min (.0275) (.0340) .0037 .0741** .0694* SPRING (.0481) (.0369) (.0358) .0377 .0165 max (.0369) (.0243)

.0505* .0190 min (.0257) (.0257) –.0321 –.0326 .0068 SUMMER (.0389) (.0299) (.0341) –.0687* –.0378* max (.0360) (.0219)

–.0321 –.0379 min (.0290) (.0231) –.0156 .0235 .0078 FALL (.0332) (.0218) (.0245) .0043 .0534** max (.0269) (0.0210)

SPRING*SEA –.0542*** (.0204)

SUMMER*CITY –.0317** (.0158)

SUMMER*HILL –.0441** (.0178)

FALL*CITY .0291* (.0155)

FALL*HILL .0376** (.0173)

yi,t-1 .6144*** .6282*** .6401*** (.0893) (.0900) (.0892)

N 2,032 1,524 2,032 1,524 1,524 p-value for zero 0.27 0.05 <0.01 0.03 <0.01 climate coefﬁcients legend: * p<.1; ** p<.05; *** p<.01

20 Table 3: Regression results for foreign tourist arrivals (robust std. err. in brackets) Static Dynamic Static Dynamic Group interactions (1) (2) (3) (4) (5) .0125 .0185 min –.1118*** –.0383 (.0296) (.0221) –.0428* WINTER (.0389) (.0257) (.0259) –.1207*** –.0539* max (.0402) (.0306)

–.0443 –.0083 min (.0466) (.0390) .0994** .0648 .0465 SPRING (.0489) (.0426) (.0413) .1347*** .0499* max (.0375) (.0265)

.0470 .0110 min (.0317) (.0295) –.0524 –.0586* –.0630* SUMMER (.0384) (.0337) (.0334) –.0859*** –.0593*** max (0.0319) (.0224)

–.0186 .0169 min (.0358) (.0255) –.0438 .0347 .0469* FALL (.0345) (.0259) (.0265) –.0177 .0265 max (.0330) (.0261)

SPRING*MOUNTAIN .0325* (.0195)

FALL*SEA .0389** (.0160) yi,t-1 .5925*** .5867*** .5868*** (.1377) (.1410) (.1341)

N 2,032 1,524 2,032 1,524 1,524 p-value for zero climate <.01 .18 <.01 .16 .07 coefﬁcients legend: * p<.1; ** p<.05; *** p<.01

21 Table 4: Regression results for average length of stay (robust std. err. in brackets) Domestic Foreign Static Static Static Static (1) (3) (1) (3) WINTER .0077 .0068 –.0150 –.0166 (.0119) (.0118) (.0194) (.0197)

SPRING –.0564*** –.0609*** .0242 .0268 (.0204) (.0208) (.0245) (.0247)

SUMMER .0292* .0333* –.0437* –.0438* (.0169) (.0172) (.0222) (.0223)

FALL .0148 .0111 .0383* .0363* (.0143) (.0146) (.0197) (.0200)

SPRING*HILL .0228** (.0089)

SUMMER*HILL –.0186** (.0091)

FALL*HILL .0099 (.0068)

SUMMER*SEA .0188 (.0127)

LAGGED STAY

N 2,032 2,032 2,032 2,032 p-value for zero climate .09 .06 .07 .06 coefﬁcients legend: * p<.1; ** p<.05; *** p<.01