Presentation by Chiara Dalle Nogare, Assistant Professor, Univerity of Brescia at the 15th Spatial Productivity Lab meeting of the OECD Trento Centre on 14 December 2021, Trento, Italy. More info https://oe.cd/SPL

1. Safety perception
at a tourist destination
in the COVID era:
Evidence from big data
Chiara Dalle Nogare
University of Brescia, Italy
Raffaele Scuderi
Kore University of Enna, Italy

2. Overview
• We want to investigate the factors associated to a higher degree of tourist satisfaction as to the anti-
contagion measures taken at a destination
• We exploit the database originated by the holders of a destination card (Trentino Guest Card)
• Period of reference: summer 2020, i.e. the first tourist season after the first dramatic wave of the COVID
pandemic in Italy
no vaccines available
soon after strict lockdown
• One of the first works to consider real tourists after the outburst of the COVID pandemic
• Large dataset! Ca. 27.000 Italian respondents
• We use logit and ordered logit models in our investigation
Dalle Nogare UNIBS and Scuderi UNIKORE (2021)

3. Dalle Nogare UNIBS and Scuderi UNIKORE (2021)
Dolomites
Garda
Trento
Dolomites

4. Data collected through the card:
 characteristics of the tourist group
 type of accommodation facility that issued the card and and tourist district in which it is
located
 date of arrival and departure
 typically, one online survey administered to TGC holders at the end of each season
(summer 2020 response rate: 19.1%)
Dalle Nogare UNIBS and Scuderi UNIKORE (2021)
Trentino Guest Card
TGC is offered to all tourists (excluding second homes) in all
tourist districts of Trentino, Italy. Its cost is included in the
tourist tax (excluding self-selection due to purchase). TGC is a
family card, i.e. one for each group of tourists. Attractions are
discounted or for free for TGC holders

5. Depvar
• Our depvar originates from the following question of the survey:
Considering the current global need to pay special attention to
health-related risks, how do you value the organisation and
hospitality you found in Trentino:
a) Not adequate, I expected more
b) Adequate
c) More than adequate
Dalle Nogare UNIBS and Scuderi UNIKORE (2021)

6. Focus of our analysis
Pandemic risk at place of residence
Evaluation of safety measures
Personal perception
of contagion risk
Characteristics of a tourist district
making it likely to be crowded
Characteristics of
accommodation facility
favouring contagion
Personal experience during the holiday
Personal experience before the holiday
≅
Dalle Nogare UNIBS and Scuderi UNIKORE (2021)

7. How this paper relates to existing literature
• Our paper contributes to the literature on health risk perception at a tourist destination,
a subset of the literature on tourism and the safety dimension. More precisely, it
contributes to the literature on contagion risk perception
• The focus on crowdedness at the destination comes from Kock, Norfelt, Josiassen, Assaf
and Tsionas (ATR 2020), who find that perceived infectability is positively correlated to the
perception of crowdedness
• The focus on contagion in one’s place of residence is present in Boto-Garcia and Leoni (TE
2021), who explore its association with travel intentions
• The idea that the accommodation facilities may make a difference comes from Naumov,
Varadzhakova and Naydenov (Anatolia, 2020)
Dalle Nogare UNIBS and Scuderi UNIKORE (2021)

8. Exploitable variability in our context
Pandemic risk at place of residence
Evaluation of
health safety measures
Rules-related determinants Tourist-related determinants
Type
of anti-contagion
measures
Effective
implementation of
anti-contagion
measures and
compliance to
anti-contagion
rules
Civicness at destination
Personal perception
of contagion risk
Characteristics of a tourist district
making it conducive of crowdedness
Personal/group
traits
Characteristics of an
accommodation facility
favouring contagion
DEPVAR

9. Empirical strategy.
• Where:
 𝑦 is the evaluation of the implemented anti-contagion measures at the destination by TGC holder
i (inverse of perception of contagion risk)
tourdistcar is a vector of the characteristics of the tourist district where the card was issued
accomcar is a set of dummies capturing the type of accommodation facility that issued the card
personalcar is a number of group characteristics of the TGC holder i
 𝑥 is a set of controls
 𝜀 is the error term
Dalle Nogare UNIBS and Scuderi UNIKORE (2021)
𝑦𝑖 = 𝛼 + 𝛽′ ∗ 𝑡𝑜𝑢𝑟𝑑𝑖𝑠𝑡𝑐𝑎𝑟𝑖 + 𝛾′ ∗ 𝑎𝑐𝑐𝑜𝑚𝑐𝑎𝑟𝑖 + 𝜃′ ∗ 𝑝𝑒𝑟𝑠𝑜𝑛𝑎𝑙𝑐𝑎𝑟𝑖 + 𝛿′ ∗ 𝑥𝑖 + 𝜀𝑖

10. Depvar and type of models.
• Distribution of answers:
a) Dissatisfied: 3.5%
b) Satisfied 53.9%
c) More than satisfied 42.6%
• It makes sense to first analyse our data after aggregating answers b) and c)
to isolate the determinants of dissatisfaction. We use logit models
• In order to exploit all information, we then consider b) and c) separately and
use ordered logit models
Dalle Nogare UNIBS and Scuderi UNIKORE (2021)

11. Tourist district characteristics
We want to test these propositions:
Proposition 1. Beaches are more conducive of crowdedness than mountains, hence
staying at a lake district is more likely to be associated with a higher perception of
contagion risk (a lower evaluation of the implemented anti-contagion measures).
=> We use dummies for lake tourist district (and city tourist district)
Proposition 2. Ceteris paribus, a strong tourist vocation for a district is conducive of
crowdedness. This increases the likelihood for a tourist not to be fully satisfied with
the safety measures implemented at the destination, i.e. to feel less safe than at
less touristic places, where crowding is not so common.
=> We use (log of) tourist beds as proxy for intensity of tourist vocation
Dalle Nogare UNIBS and Scuderi UNIKORE (2021)

12. Accommodation variables
Dalle Nogare UNIBS and Scuderi UNIKORE (2021)
Characteristics of an
accommodation facility
favouring contagion
hotel and self-catering dummies
for type of accommodation (ref.
catergory: other, i.e. camping
sites, hostels, mountain huts)
Proposition 3. A type of accommodation guaranteeing privacy (hotels and
apartments) increases a tourist’s likelihood to be fully satisfied with the safety
measures implemented, i.e., to perceive a lower health risk than at camping
sites, hostels and mountain huts.

13. Tourist-related variables.
Dalle Nogare UNIBS and Scuderi UNIKORE (2021)
Pandemic risk at
place of residence
COVID incidence in the province of
residence as proxy of salience of
contagion risk for the respondent
(source: National Protection
Department)
Other personal traits
number of members of the
tourist group
presence of children

14. Controls
• We consider:
 a weekly time trend (accounting for the distance from the peak of the pandemic)
 month dummies (to account for differences in occupancy rates)
 length of stay (the more you stay, the higher the probability to come across a
context in which you do not feel safe)
region of origin dummies (to account for differences in efficiency of local hospitals)
Dalle Nogare UNIBS and Scuderi UNIKORE (2021)

15. Logit
models
city lake Mountain hotel apartment camping
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
(Intercept) 5.341*** 7.389 54.208 5.228*** 5.028*** 8.106** 7.659
(1.030) (864.938) (680.371) (1.041) (1.171) (2.567) (4.638)
incidence 6.857 -10.307 -22.945 10.310 25.841 -33.323 -65.695
(14.711) (87.485) (53.676) (15.571) (18.093) (29.104) (51.935)
tweek -0.044 0.047 -0.005 -0.045 -0.054 -0.004 -0.120
(0.025) (0.148) (0.109) (0.026) (0.030) (0.056) (0.100)
mjul -0.064 0.908 -0.427 -0.056 -0.113 -0.022 -0.110
(0.102) (0.807) (0.464) (0.106) (0.125) (0.208) (0.422)
maug -0.205 0.798 -0.431 -0.214 -0.211 -0.300 -0.059
(0.121) (0.891) (0.485) (0.127) (0.140) (0.296) (0.449)
msep 0.800*** 1.219 -0.233 0.879*** 0.878*** 0.418 0.930
(0.194) (1.342) (0.698) (0.208) (0.227) (0.475) (0.691)
LoS -0.029** -0.006 -0.035 -0.030** -0.040** -0.021 -0.020
(0.010) (0.081) (0.037) (0.010) (0.013) (0.019) (0.030)
psize 0.051 0.372 0.005 0.044 0.053 0.022 0.102
(0.045) (0.305) (0.173) (0.047) (0.055) (0.091) (0.173)
children -0.052 -0.461 0.120 -0.056 -0.024 -0.131 -0.055
(0.094) (0.652) (0.387) (0.099) (0.114) (0.205) (0.363)
hotel 0.594*** 0.491 0.437 0.620***
(0.121) (0.795) (0.431) (0.128)
apartment 0.224 -0.312 0.426 0.250
(0.138) (0.992) (0.477) (0.148)
city -0.064 0.004 -1.005 0.332
(0.220) (0.245) (0.658) (0.849)
lake 0.136 -0.001 0.283 0.306
(0.149) (0.192) (0.311) (0.378)
logbeds -0.410* 1.654 -7.955 -0.409* -0.211 -0.815 -0.766
(0.204) (2.645) (12.247) (0.205) (0.231) (0.518) (0.972)
Regional
Dummies
Yes Yes Yes Yes Yes Yes Yes
AIC 8190.446 273.886 478.111 7501.888 6158.166 1396.141 611.300
Log Likelihood -4061.223 -105.943 -208.055 -3718.944 -3047.083 -667.070 -276.650
Num. obs. 27021 852 1507 24662 21940 3581 1339

16. city Lake mountain hotel apartment camping
Model 18 Model 19 Model 20 Model 21 Model 22 Model 23 Model 24
incidence -3.179 27.670 -13.957 -2.997 -2.466 -11.744 1.567
-5.206 -34.234 -21.552 -5.441 -5.757 -14.164 -26.643
tweek -0.030***
-0.004 -0.028 -0.031***
-0.028**
-0.025 -0.071
(0.008) (0.053) (0.035) (0.009) (0.009) (0.022) (0.040)
mjul -0.014 -0.058 -0.108 -0.005 -0.018 -0.014 0.060
(0.037) (0.267) (0.166) (0.038) (0.042) (0.088) (0.170)
maug -0.092*
-0.262 -0.188 -0.080 -0.102*
-0.123 0.060
(0.041) (0.238) (0.178) (0.043) (0.045) (0.120) (0.192)
msep 0.481***
0.271 0.202 0.510***
0.435***
0.699***
0.758**
(0.060) (0.338) (0.250) (0.063) (0.065) (0.181) (0.288)
LoS -0.013***
0.062*
-0.004 -0.016***
-0.015**
-0.010 -0.016
(0.004) (0.028) (0.015) (0.004) (0.005) (0.008) (0.014)
psize -0.009 -0.054 0.034 -0.013 -0.021 0.011 0.114
(0.017) (0.098) (0.064) (0.017) (0.019) (0.039) (0.074)
children -0.013 -0.041 -0.078 -0.010 -0.000 -0.006 -0.157
(0.035) (0.210) (0.142) (0.037) (0.039) (0.088) (0.160)
hotel 0.287***
0.664*
0.086 0.316***
(0.055) (0.303) (0.174) (0.060)
apartment 0.025 0.311 0.120 0.042
(0.063) (0.412) (0.192) (0.068)
city -0.127 -0.101 -0.316 -0.381
(0.075) (0.081) (0.311) (0.325)
lake -0.061 -0.165*
0.110 0.234
(0.054) (0.066) (0.117) (0.163)
logbeds -0.282***
1.250 -8.958*
-0.276***
-0.203*
-0.619**
-0.845*
(0.072) (0.876) -4.371 (0.072) (0.079) (0.206) (0.399)
Threshold (1->2) -5.079***
1.310 -45.688*
-5.018***
-5.106***
-6.507***
-7.349***
(0.366) -3.894 -20.421 (0.371) (0.398) -1.010 -1.923
Threshold (2->3) -1.446***
5.158 -41.862*
-1.397***
-1.427***
-2.926**
-3.921*
(0.364) -3.896 -20.415 (0.369) (0.395) -1.004 -1.914
AIC 43.742.896 1.384.700 2.442.557 39.957.267 35.309.700 5.905.328 2.294.165
Log Likelihood -21.836.448 -660.350 -1.189.278 -19.945.634 -17.621.850 -2.920.664 -1.117.083
Num. obs. 27021 852 1507 24662 21940 3581 1339
McFadden's R2
0.008 0.035 0.015 0.009 0.006 0.014 0.014
***
p < 0.001; **
p < 0.01; *
p < 0.05
Ordered
logit
models

17. Target variables evidence (1)
• Type of destination does not explain dissatisfaction
• Conditional to not being dissatisfied, lake increases the probability to
be very satisfied only of tourists staying at hotels
 Proposition 1: weak evidence
Dalle Nogare UNIBS and Scuderi UNIKORE (2021)

18. Why? Peculiarity of Garda Lake
• Garda lake is the most important lake destination
in Trentino
• It attracts both beach lovers and sports tourists
(climbers, cyclists, windsurfers)
• Sports tourists go to the beach only occasionally.
Notice: they are more likely to stay at camping
sites and apartments
Dalle Nogare UNIBS and Scuderi UNIKORE (2021)

19. Target variables evidence (2)
• Tourism vocation intensity of a district explains both dissatisfaction
and degree of satisfaction (negative sign)
 Proposition 2 confirmed
• Hotel accommodation also (positive sign), self-catering
accommodation not (less professional management?)
 Proposition 3 partially confirmed
Dalle Nogare UNIBS and Scuderi UNIKORE (2021)

20. The puzzling evidence on incidence
• COVID incidence in the province of origin never significant!
• Possible explanations:
 a province is too vast to capture a «close to home» effect
 antagonist effect: self-selection into the tourist status was different
here and there (in the provinces highly affected by COVID only the
least risk-averse took a holiday)
Dalle Nogare UNIBS and Scuderi UNIKORE (2021)

21. Interaction terms.
• Incidence: we checked further – interaction terms models
• Interaction between incidence and:
• (1) destination type;
• (2) accommodation type
• Interaction terms with accommodation types sometimes significant in
logit models; sign not always in line with expectations
Dalle Nogare UNIBS and Scuderi UNIKORE (2021)

22. Robustness checks
• Use of district’s cable car capacity in place of logbeds
• Still significant (even more significant than logbeds!) and with negative sign
• Lake significant also in ordered logit model on full sample
• Change of proxy for COVID impact in the province where the respondent
resides (death rates, absolute no. of cases)
• Number of children instead of dummy
• Partial specifications
• SE clustered at regional level and tourist district level
• OLS
Dalle Nogare UNIBS and Scuderi UNIKORE (2021)

23. Conclusions
• Strong tourist vocation (proxy: tourist beds) for a district is associated with higher
probability of dissatisfaction and, conditional on being satisfied, a lower degree of
satisfaction (lower safety perception)
crowdedness effect
marginal destinations have a competitive advantage
• Staying at a hotel is positively related with satisfaction/safety perception
professional management and communication of implemented safety measures
make a difference
• COVID incidence in tourists’ province of residence before the holiday does not
seem to matter, generally speaking, but more investigation needed
Dalle Nogare UNIBS and Scuderi UNIKORE (2021)

24. thank you
chiara.dallenogare@unibs.it
Dalle Nogare UNIBS and Scuderi UNIKORE (2021)