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  4. Nonlinear nexus between corruption and tourism arrivals: a global analysis
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Nonlinear nexus between corruption and tourism arrivals: a global analysis

Journal
Empirical Economics Journal
Date Issued
January 2022
Author(s)
Krambia-Kapardis, Maria  
Stylianou, Ioanna  
Dimitriou, Salomi  
DOI
10.1007/s00181-021-02193-2
Abstract
The relationship between corruption and tourism has been sporadically examined over
the years. According to the existing theory, there is an inverted U relationship which
implies that tourism demand initially increases as corruption increases (greasing the
wheels) and after a certain threshold level of corruption, tourism demand decreases
(sanding the wheels). Empirical studies so far concentrated on capturing the nonlinear relationship, by applying a simple linear model and by including corruption as
a quadratic term. In the current paper, the authors revisit the “greasing and sanding
the wheels” hypothesis by applying an advanced econometric technique, the threshold regression model, which deals with a key element of model uncertainty, namely
parameter heterogeneity. In particular, using a sample of 83 countries from 2001 to
2018, the authors firstly examine if there is a nonlinear relationship between corruption
and tourism, and then, they estimate the threshold value of corruption. According to
the results, the null hypothesis of a linear model against the alternative of a threshold
model with two regimes is strongly rejected. Furthermore, while the effect of corruption on tourism is positive in the low corruption regime and negative in the high
corruption regime, a heterogeneous relationship is also found between other politicosocio-economic variables and tourism demand in the low and high corruption regimes.
Subjects

Nonlinearities

Threshold regression

Corruption

Tourism demand

Affiliation
Cyprus University of Technology  
University of Central Lancashire (Cyprus)  
Handle
https://hdl.handle.net/20.500.14279/24162
File(s)
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s00181-021-02193-2.pdf

Size

335.73 KB

Format

Adobe PDF

Checksum (MD5)

94460618ca9a203cae2f1b41f54a16a1

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