Agricultural export competitiveness is shaped not only by a country's own production capacity and scale but also by developments in other countries, as improvements elsewhere may alter relative market positions through competition and spatial interdependence. Foreign Direct Investment (FDI) represents one important channel through which such cross-country externalities may arise. However, much of the existing country-specific literature overlooks spatial interdependencies among countries, particularly in the agricultural sector. This study examines the impact of aggregate FDI on agricultural export competitiveness across 52 African economies, explicitly accounting for spatial spillovers and cross-sectional dependence. Employing a Spatial Autoregressive (SAR) model, the analysis finds that FDI exerts a positive direct effect on a host country's agricultural export competitiveness, but does not find strong evidence of negative externalities of FDI on neighbouring or nearby countries. Any observed negative spatial effects are largely attributable to disparities in the scale of agricultural production rather than to FDI-related displacement. Furthermore, the analysis finds no evidence of heterogeneous FDI effects on agricultural export competitiveness between landlocked and coastal countries, nor across major African regional economic groupings relative to the Southern African Development Community (SADC), while countries outside these groupings exhibit significantly stronger FDI effects. These results suggest that strengthening agricultural export competitiveness in Africa should prioritise attracting FDI at both national and regional levels, as its positive domestic effects are not offset by statistically significant losses in neighbouring countries, while regional coordination remains essential for managing scale-driven spatial competition.

This study examines the anchoring of inflation expectations in Sweden during the period characterised by volatile energy prices and elevated inflation. Using inflation expectations data from Swedish households (HH) and money market participants (MMP) through June 2024, as well as financial market participants (MB) up to April 2023, we employ a kernel-based regularised least squares model to estimate pointwise marginal responses. A Markov Regime Switching Autoregressive model is subsequently applied to identify structural breaks characterised by regime shifts in marginal responses to energy price and underlying inflation, respectively. Our findings indicate that recent turbulence has triggered multiple breaks, Specifically, MMP 1-year expectations oscillate between being anchored and unanchored in response to energy price inflation, whereas MMP 5-year expectations remain anchored to energy price shocks but exhibit shifts in persistence and volatility. Additionally, we find that all inflation expectations remain anchored to underlying inflation despite the turbulence. This suggests that recent fluctuations in inflation expectations can largely be attributed to energy price inflation. These results contrast with those derived from models that exclude structural breaks, underscoring the importance of incorporating dynamic features when assessing the anchoring of inflation expectations. 

The mention of the COVID-19 waves is as prevalent as the pandemic itself. Identifying the beginning and end of the wave is critical to evaluating the impact of various COVID-19 variants and the different pharmaceutical and non-pharmaceutical (including economic, health and social, etc.) interventions. We demonstrate a scientifically robust method to identify COVID-19 waves and the breaking points at which they begin and end from January 2020 to June 2021. Employing the Break Least Square method, we determine the significance of COVID-19 waves for global-, regional-, and country-level data. The results show that the method works efficiently in detecting different breaking points. Identifying these breaking points is critical for evaluating the impact of the economic, health, social and other welfare interventions implemented during the pandemic crisis. Employing our method with high frequency data effectively determines the start and end points of the COVID-19 wave(s). Identifying waves at the country level is more relevant than at the global or regional levels. Our research results evidenced that the COVID-19 wave takes about 48 days on average to subside once it begins, irrespective of the circumstances.

This study investigates empirically if land characteristics (especially forests and mountains) are of importance for the local ability to withstand the shock of the Covid-19 on domestic tourism demand during the summer of 2020. A second step of the analysis focuses on the recovery stage in the subsequent summer. Three tourism indicators are considered: arrivals, overnight stays and length of stay. Official data on land use characteristics of 2029 villages in the Federal state of Bavaria (South Germany) are employed for the analysis. Estimations using the Spatial Durbin model combined with the Heckman selection model reveal that there is a significant relationship between the proportion of forest within as well as surrounding the village and the demand for domestic tourism compared with the 2019 baseline. There is also a significant relationship with the altitude of the surrounding areas. The importance of mountains and forests is present in both the initial year of the pandemic and in the recovery year of 2021, although the magnitude is lower in the summer of 2021. Direct and spatial effects can also be found for lakes and rivers on overnight stays and length of stay.

This study investigates empirically how natural snow depth and permanent snow affect the number of new second homes in Norway. One out of four Norwegian municipalities is partly covered by glaciers and permanent snow. In the winter seasons of 1983–2020, there is a decline in snow depth from 50 to 35 cm on average (based on 41 popular second-home areas in the mountains). Results of the fixed effects Poisson estimator with spatial elements show that there is a significant and positive relationship between natural snow depth in the municipality and the number of second homes started. There is also a significant and negative relationship between the number of new second homes in the municipality and a scarcity of snow in the surrounding municipalities. However, the magnitude of both effects is small. Estimates also show a strong positive relationship between the proportion of surface covered by permanent snow or glaciers in the municipality and new second homes. This implies that a decline in permanent snow and glaciers may make these areas less attractive for the location of second homes.

We investigate the market performance of negatively screened environment social and governance (ESG) portfolio or sustainable investments prior to and during crisis. A general and simple method is developed under the ESG Capital Asset Pricing Model (CAPM) framework for the assessment. The novelty is that this method can be employed when the parent portfolio is not a market portfolio. In this situation, both coefficients, alpha and beta, in the reduced form of regression have special interpretations and are informative. This paper examines 24 negatively screened ESG indices from the S&P, DJSI and MSCI data across various regions, firm sizes, and criteria of screening, for 2017 to 2021. Markov Switching Autoregressive (MSAR) model is adopted to identify the crisis regime. Our results show that the negatively screened ESG indices provide positive investors’ surpluses for ESG-motivated investors during the crisis, when the corresponding parent indices are the market portfolios. For ESG investments where market portfolios are not their parent indices, half of ESG indices under consideration still provide positive surplus with similar systematic risks as their parent indices during the crisis. The remaining ESG indices under-performs but has relatively lower systematic risks, implying resilience as compared to the corresponding parent indices during the crisis. Furthermore, we demonstrate the sensitivity analysis of treating a parent index as a market portfolio.

This study investigates empirically how natural snow depth affects the number of downhill skiers. Data include the number of skier visits for the 32 largest ski resorts in Sweden from the 1998/1999 to the 2018/2019 seasons. Results of spatial dynamic estimations show that an increase in natural snow depth in the ski area has a significant negative impact on the number of skier visits in the short term, although the magnitude is small. This implies that a snow deficit leads to increased demand for downhill skiing both directly and indirectly (in the neighbouring areas). The variable snow depth in the neighbouring ski areas is not significantly different from zero, indicating that no spatial substitution takes place. There is, however, a strong positive relationship between skier visits to neighbouring areas, revealing that ski resorts are complements rather than substitutes. The long-term influence of snow depth is not significant, implying that the ski business is independent of variations in snow depth. Instead, the number of skier visits is mainly determined by past visits, revealing a high degree of persistence.

This study investigates two aspects of how an external shock in the guise of the Covid-19 pandemic affects domestic tourism demand: (1) If the impact varies across regions and over time or (2) whether a permanent change (hysteresis) occurs anywhere. By doing so, a presumptive change in domestic tourism demand during three summers is quantified based on timely official data for all Nordic countries (Denmark, Finland, Iceland, Norway and Sweden) and Estonia, encompassing a total of 76 NUTS3 regions. These regions are divided into five groups from large metropolitan to remote. Tourism demand is approximated by the number of domestic overnight stays in accommodation establishments in the summer months 2016–2022. Dynamic panel data estimations, including household consumption and hotel price index, reveal that all non-metropolitan regions experience a strong increase in domestic tourism flows in the first summer of the pandemic compared with the three years preceding 2020. In contrast, the largest metropolitan areas encounter a substantial decline. The surge in demand for non-metropolitan areas continues in 2021, while the large metropolitan regions return to their pre-pandemic level. After this, demand no longer deviates from its pre-2020 pattern across regions, confirming that the effects are temporary.

This study analyzes the relationship between self-employment, corruption, and property rights in 30 European countries, including 11 Central and Eastern Europe (CEE) economies, across the two decades of 1996–2016. In general, relatively little research has focused on the relationship between entrepreneurship and the protection of property rights. Furthermore, past findings show that corruption may have both negative and positive effects on the level of entrepreneurial activity, either “greasing” or “sanding” the wheels for entrepreneurship. Overall, research on how the informal institution corruption and the formal institution property rights are linked to entrepreneurship in post-socialist/transition economies has been limited. We find that stronger protection of property rights increases self-employment ratios, both in Europe in general and in CEE economies. The relationship between self-employment and the control of corruption is not significant. We conclude that neither higher nor lower levels of corruption control affect the share of self-employment. In comparative perspective, the ratio of self-employment in the group of CEE economies does not respond differently to these two key institutions.