Kamil Makieła

W niniejszym artykule zbadano współzależność między cenami LPG i ropy naftowej, wykorzystując model szeregów czasowych Seasonal Auto-Regressive Integrated Moving Average ze zmiennymi egzogenicznymi (model SARIMAX). Rozważono trzy scenariusze uwzględniające indeksy cen dla indeksu zachodniego, indeksu wschodniego i indeksu całkowitego, który jest obliczany jako średnia ważona dwóch poprzednich indeksów. Badanie koncentruje się na Polsce, która w okresie analizy była silnie uzależniona od dostaw surowców energetycznych z Rosji. Wyniki analizy ekonometrycznej pokazują, że: 1) istnieje silna i statystycznie istotna zależność między zmianami cen ropy a zmianami cen gazu dla importu do Polski ze wszystkich kierunków; 2) zależność ta jest najsilniejsza w przypadku indeksu zachodniego; 3) tygodniowe zmiany cen LPG skorygowane o związek między cenami ropy naftowej a cenami LPG nie wykazują szczególnie długiej pamięci ani sezonowości; 4) indeks wschodni charakteryzuje się dość dużą elastycznością względem przeszłej (krótkookresowej) dynamiki aktualnych cen LPG. Tak więc konflikt na Ukrainie i wynikający z niego spadek importu LPG z Rosji prawdopodobnie nie spowodują długoterminowej niestabilności rynku w Polsce.

This study revisits the relationship between foreign direct investment (FDI) and total factor productivity (TFP) growth in two distinct ways. First, it employs a dynamic heterogeneous panel threshold model to examine potential threshold effects of FDI on TFP. Second, and more critically, it investigates whether the trajectory of FDI influences this relationship. Using a sample of 71 developed and developing countries over the period 1980–2014, the study reveals new insights. For developing countries, the findings provide some support for a threshold effect and strong evidence for the importance of the FDI trajectory. Notably, the results suggest that the trajectory of FDI is more influential than its level in promoting TFP growth. However, for developed countries, no threshold or trajectory effects are observed. The key policy implication is that maintaining a steady and sustained flow of FDI is essential for enhancing TFP growth, particularly in developing economies.

Objective: The article aims to compare the willingness to pay for energy from renewable sources with the willingness to pay for clean air in Poland and to establish factors that influence these phenomena. These topics are usually addressed in separate studies, making a credible comparison difficult. Research Design & Methods: We survey a representative group of single-family residents and apply advanced machine learning techniques based on Bayesian model selection (averaging) to construct a credible list of factors that determine willingness to pay for clean energy. Findings: We find that people are willing to pay more for clean air than for energy from renewable sources. Both aspects of willingness to pay are affected by the size of the electricity bills, the importance of the environment and the knowledge about renewable energy supplies. Willingness to pay for energy from renewable sources is also influenced by educational level and awareness of the need to increase the share of renewable energy in the domestic energy mix. Furthermore, we find that young people are more willing to pay for energy from renewable sources, whereas older people are more likely to pay for clean air. Implications & Recommendations: Arguably, a higher willingness to pay requires, first and foremost, greater environmental awareness and the promotion of its benefits among the population at large. Since willingness to pay studies are based on respondents’ declarations, their actual willingness to bear the costs of energy transition may be lower. A variety of initiatives should be taken to foster closer bonds between people and their environment to make them feel more responsible for the shared objective. Contribution & Value Added: The study bridges a significant gap by examining the willingness to pay for renewable energy and clean air in the same sample of respondents. We focus on single-family home residents. Using quantitative methods, we identify factors that are common to willingness to pay for clean air and renewable energy sources, as well as those that are specific to each type.

The study analyzes productive efficiency of crop farming in the EU. We use publicly available data on crop farming from FADN database. Standard efficiency measurement techniques based on frontier analysis indicate that the representative farms provided in the database are fully efficient, even though there is ample evidence in the literature that this is highly unlikely. We find that this is a consequence of overly restrictive assumptions about the compound error in standard SF models. The efficiency benchmark, based on the best model given data with generalized error specification, reveals substantial differences in crop farming efficiency in the EU.

The threat of the negative consequences of global warming makes the discussion about the relationship between economic growth, productivity, and increasing renewable energy involvement an important topic. Hence, the aim of this study is to analyze the impact of renewable energy and energy supply on economic growth and productivity at the national level using stochastic frontier analysis and the aggregate production function framework. In doing so, we analyzed a panel of annual data from 133 countries from 2008 to 2014. We apply a generalized stochastic frontier model, which allows us to differentiate between persistent and transient inefficiency, as well as individual effects. Our results indicate a threshold level in terms of a country’s development that needs to be obtained to benefit from increasing renewable energy involvement over time. However, if this threshold level is obtained, productivity gains are evident. We also found that the role of the energy supply in aggregate production is nontrivial. That is, its inclusion changes the relationship between key input factors (capital and labor) by decreasing their overall elasticities and increasing the observed economies of scale.

Advanced efficiency measurement methods usually fall within Stochastic Frontier Analysis (SFA), Data Envelopment Analysis (DEA), or their derivatives. Although SFA has some theoretical advantages, it has been criticized for relying on arbitrary and potentially restrictive assumptions about model specification. One strand of the literature suggests the use of nonparametric SF models to cope with the issue. We follow an alternative path and demonstrate that it is possible to deal with specification uncertainty and potentially restrictive assumptions while maintaining the advantages of the parametric approach. First, we develop a flexible stochastic model based on generalized t and generalized beta of second kind distributions, which encompasses virtually all known parametric SFA specifications. Second, we apply Bayesian inference methods, which are less restrictive than those used so far, and propose feasible approximate alternatives based on maximum likelihood. Third, we pool results from alternative specifications using model averaging. Our focus is on the distributional assumptions regarding the compound error in SFA since this aspect has not been addressed so far in a satisfactory way. However, extensions to other elements of specification uncertainty, like the choice of the frontier functional form, are straightforward. Finally, we show simulations results and analyze two well-researched datasets, for which we obtain probabilistic (density) estimates of efficiency scores that take into account the estimation error and model uncertainty in a formally justified manner.

The aim of this paper is to define the factors influencing the level of energy poverty among students. The analysis of these factors is based on the results of a survey conducted among a group of 937 students at the Cracow University of Economics. The study takes into consideration the changes in the attitudes and behavior of students resulting from the introduction of distance learning during the COVID-19 pandemic. The switch to distance learning resulted in a significant increase in the number of responses related to feeling ill or sick due to inadequate temperature (from 24% before a lockdown to 32% after the introduction of a lockdown). Students experienced temporary surges in their overall living costs due to the pandemic, especially during the first wave. The respondents who experienced inappropriate temperatures (inadequate heating) due to excessive costs felt ill or became sick more often than others. The study demonstrated that those who pay more for energy (defined as a surplus payment in excess of 10%) tended to be, on average, less energy-aware than others. The following indicators of energy poverty among the students were distinguished: high living costs, small degree of influence over the choice of living quarters, as well as concerns over energy efficiency and environment. The conclusions drawn from the conducted studies may be utilized to design public policies aimed at curtailing the phenomenon of energy poverty among students. This issue is particularly prominent in large urban agglomerations where the costs of living are high and result in the feeling of pressure regarding the need to save money on thermal energy consumption.

The objective of this paper is to investigate the impact of foreign direct investment (FDI) on economic growth and productivity in sectors of the Visegrad Group one decade after their accession to the EU. In order to account for sample heterogeneity, as well as productivity differences, we construct a generalized true random-effects model with varying efficiency distribution. We find that FDI has a positive impact on the Visegrad Group’s sectors and that its effectiveness depends upon the technological gap between the host and home economy. There are three sources of this positive impact: (i) sectoral output and labour productivity growth, (ii) more effective use of input factors, and via (iii) higher efficiency component of the total factor productivity (TFP). These sources form a three-way transmission mechanism through which FDI can impact economic growth conditioned upon FDI effectiveness due to the technological gap.

Contribution of this paper lies in the formulation and estimation of a generalized model for stochastic frontier analysis (SFA) that nests virtually all forms used and includes some that have not been considered so far. The model is based on the generalized t distribution for the observation error and the generalized beta distribution of the second kind for the inefficiency-related term. We use this general error structure framework for formal testing, to compare alternative specifications and to conduct model averaging. This allows us to deal with model specification uncertainty, which is one of the main unresolved issues in SFA, and to relax a number of potentially restrictive assumptions embedded within existing SF models. We also develop Bayesian inference methods that are less restrictive compared to the ones used so far and demonstrate feasible approximate alternatives based on maximum likelihood.

This paper discusses Bayesian model averaging (BMA) in Stochastic Frontier Analysis and investigates inference sensitivity to prior assumptions made about the scale parameter of (in)efficiency. We turn our attention to the “standard” prior specifications for the popular normal-half-normal and normal-exponential models. To facilitate formal model comparison, we propose a model that nests both sampling models and generalizes the symmetric term of the compound error. Within this setup it is possible to develop coherent priors for model parameters in an explicit way. We analyze sensitivity of different prior specifications on the aforementioned scale parameter with respect to posterior characteristics of technology, stochastic parameters, latent variables and—especially—the models’ posterior probabilities, which are crucial for adequate inference pooling. We find that using incoherent priors on the scale parameter of inefficiency has (i) virtually no impact on the technology parameters; (ii) some impact on inference about the stochastic parameters and latent variables and (iii) substantial impact on marginal data densities, which are crucial in BMA.