Phillips curves are generally estimated under the assumption of linearity and parameter constancy. The conventional Phillips curve argues that there is a trade-off or negative relationship between unemployment and inffation or wage inffation. According to Lucas’s critique of econometrics’ empirics, however, there is no parameter stability in the forward–looking economic behaviour model of economic agents. Since the rational expectation theory revolution linear models of inffation have been criticised for their poor forecasting performance. Bai and Perron (1998), proposed the methodology which allows for an unknown number of breaks at unknown dates as a response to the mentioned problem. The purpose of this paper is to investigate the linearity and constancy assumptions of the augmented Phillips curve model using expected inffation variables and the methodology proposed by Bai and Perron and to validate the Phillips curve using Croatia as an example. Data used in this analysis was on quarterly basis from 1994 to 2010.

Sustainable tourism plays a dominant role in the economic well-being of some of the world’s countries, especially small ones. Tourism earnings account for a significant proportion of their GDP, and they have an overwhelming reliance on tourism as a source of service exports. The general trends in tourism earnings and volatilities in country risk ratings often go hand in hand, especially for small touristic countries in that region. The research presented in this paper provides a comparative assessment of the international country risk ratings and highlights the importance of their tourism earnings and tourism export. This study employs the ordered response and Poisson count panel data model for a sample of twenty-two countries most reliant on tourism, including Mediterranean countries. The aim of this study is to investigate whether the tourism determinants of sovereign credit ratings for those countries vary between different rating agencies (Standard & Poor’s, Moody’s and Fitch’s). The key finding is that an increase in tourism earnings as a proportion of GDP and as the main export share in the total country export impairs the sovereign risk rating and turns out to be robust across the different methodologies.

This paper investigates the impact of the transition from manufacturing to tourism on sectoral productivity, output prices, and labor costs. Using panel data econometric models for 15 selected EU countries from 2011 to 2023, the study confirms key dynamics predicted by Baumol’s cost disease (BCD) hypothesis. The findings reveal that higher productivity is positively associated with both implied prices and hourly labor costs across sectors, supporting the wage equalization mechanism central to BCD. However, the relationship between productivity and wages or prices is weaker in labor-intensive sectors like tourism, underscoring their structural vulnerability to wage-driven cost pressures. Additionally, the analysis captures the impact of major external shocks, including the COVID-19 pandemic and the Russo–Ukrainian war, treated as jointly sourced super-shocks. The regression results indicate significant price disruptions following these shocks, whereas no statistically significant trend in labor costs was detected in the post-treatment period. These results highlight the differential effects of external shocks on wages versus prices, emphasizing the challenges faced by low-productivity, labor-intensive sectors in managing cost dynamics. The findings provide valuable insights for policymakers addressing sectoral imbalances in the context of BCD and navigating the economic consequences of global disruptions.

The purpose of this study is to examine the relationship between tourism arrivals and the global incidence of biodiversity loss, measured by the probability of the extinction of species of plants, mammals, birds, and fish while controlling for other relevant covariates: protected areas, agrarian production's share in GDP, crop production, energy intensity, and population levels. This study is designed around cross-sectional data from 2018 in 178 countries across the world. The biodiversity loss–tourism arrivals nexus is assessed in this study by the count model estimation. We adopted negative binomial and hurdle negative binomial regressions. The findings showed the existence of a positive relationship between tourism arrivals and some species extinction variables. Tourism demand is responsible for an excessive degree of biodiversity loss in the case of mammals and birds. The findings of this study suggest that an overdispersion test due to count data constraints should be taken into account when examining the link between biodiversity loss and tourism in a cross-sectional analysis.