Band 23, Heft 2

The objective is to characterize food inflation in general and, particularly, the food items and agrifood chains, according to the level of exposure to foreign trade, degree of processing, and inflation indicators in nine metropolitan areas of Brazil between 2007 and 2022. Basically, the data used were surveyed by the Brazilian Institute of Geography and Statistics for the Broad Consumer Price Index (Índice de Preços ao Consumidor Amplo, IPCA). The study fully characterized the food inflation in Brazil between 2007 and 2022, which was aggravated during the Covid-19 pandemic. Food-at-home inflation was generalized among its household components. Items associated with animal protein tended to put the most pressure on food-at-home prices. Among the agrifood chains, the lowest price variations were observed in products with greater exposure to foreign trade. A greater price variation was observed for fresh or minimally processed food products compared to more processed food products. Among the nine cities studied, from all regions of Brazil, no major differences were found in the variations of food prices of all groups considered.

I explore the relationship between incentive alignment and effort provision in the canonical multi-task principal-agent contract model. I rely on an experiment where human subjects act as agents making effort decisions based on contract structures providing both theoretically-correct and distorted incentives. Results suggest that distorted contracts, offering weaker incentives than theory recommends, may be less detrimental than expected when performance and quality are well-aligned. The experiment also confirms the theoretical prediction that strong incentives, when misalignment is severe, can increase effort but reduce the principal’s profit. The findings contribute to understanding the prevalence of contracts with weaker-than-optimal incentives, suggesting that such incentive schemes may be more prevalent in the real world when the cost of providing less-than-ideal incentives is relatively low.

The main goal of this study was to create a strong predictive model for forecasting olive oil prices. To do this, we applied four machine learning models in Python: Random Forest, Gradient Boosting, Decision Tree, and Support Vector Regression, using 164 monthly price observations along with factors like temperature, precipitation, consumer price index, IBEX35 stock market prices, EUR/USD exchange rate, and import/export quantities. The results showed that Random Forest and Gradient Boosting models performed the best. The Spearman correlation analysis revealed that the exchange rate had a strong negative correlation with prices, while the consumer price index and import quantity had moderate positive correlations. Random Forest highlighted the consumer price index as the most important factor in predicting olive oil prices. This study fills a gap in existing research and provides practical insights for companies in the olive oil industry to better monitor and forecast prices, helping with profitability, risk management, stock optimization, and investment decisions.

Structural economic transformation theories have emphasized the role of agricultural development in industrialization and vice versa. The question is whether industrial development and agricultural development were complementary. Using unique data from Sweden (1800–2020), we investigate the causal link between agriculture and industry. In the presence of structural shifts and breaks, the causal direction is established using the Fourier Granger causality technique. Based on the results, Sweden supports the growth complementarity hypothesis. This indicates that sectoral convergence tendencies are encouraged by the larger expansion of industry, which frequently spreads to agriculture and vice versa.

This paper discusses international trade prospects of the main wine-producing countries over different time horizons. Our framework is composed of two approaches and aims at renewing and extending the findings of the existing literature on the dynamics of major wine exporters. Firstly, we apply a Porter’s diamond analysis in order to disentangle competitive advantages in 16 countries from various features of the global wine market. Then, we assess short- and medium-term prospects from the evolution of past trends. Secondly, we use data from a survey of (57) wine experts as a robustness analysis to complete the prospective dimension over the long term. Compared with previous literature (prior to the Covid and Ukraine shocks), our results show that the international competitiveness of countries has significantly changed over the last five years. France and Italy should maintain their leadership, but Spain and Chile are slipping back, while China could collapse. New Zealand could eventually become a major leader. These results indicate that competitive advantages are very dynamic in the wine industry, despite static natural endowments (excluding climate). Investment incentives and innovation should therefore play a key role in the long term in this sector, which is often presented as being driven by tradition and natural factors.

The consumption of invasive species could be an opportunity to regulate these species to limit the negative environmental impacts. However, the commercialisation of an exogenous species raises several questions. We assess the acceptance of wels catfish, an invasive species in an alpine lake, and the willingness to pay (WTP) for environmentally friendly and locally produced wels catfish products. The results show that knowledge is an important dimension to explain consumption. Informing consumers about an environmental or local dimension increases WTP for wels catfish, but it does not increase product acceptance. There is no additional premium when both types of information are used together.