In this paper, the authors analyze trends in the Polish capital market from 1924 to 1944. The study is based on a newly developed database containing nearly 50,000 financial instrument quotations. Using this data, stock and bond market indices were calculated. The authors investigate the impact of macroeconomic factors and wartime events on financial instrument prices.
This study aims to identify the factors influencing farm households’ decisions to use family versus hired labour and owned versus rented land. The analysis is based on farm-level data obtained from the Farm Accountancy Data Network (FADN). The research objective is achieved using a bivariate probit model. The results indicate that decisions regarding the use of owned land and family labour are correlated. Among the factors that most strongly increase the probability of relying exclusively on owned land are payments for less favoured areas and the cost of land rental. The propensity to rely exclusively on family labour increases with higher hired labour costs and a greater share of cereals in the total cultivated area.
This paper examines the role of uncertainty in explaining the Uncovered Interest Parity (UIP) puzzle in Central and Eastern European (CEE) economies from 2008 to 2022. We investigate the UIP puzzle in four CEE economies: the Czech Republic, Hungary, Poland, and Romania. First, using the baseline Fama regression, we check if the UIP condition is satisfied. In the second model, regressions associated with uncertainty variables, such as Economic Policy Uncertainty and the World Uncertainty Index, are performed. Finally, using local projections models, we check how the UIP premium reacts dynamically to uncertainty shocks. The first finding is that the UIP condition holds better for the US dollar as the reference currency and that the UIP puzzle is more pronounced in a longer time horizon. Second, during periods of heightened uncertainty, for USD-based regressions, investors demand higher excess returns. Third, the dynamic responses of the UIP premium to uncertainty shocks prove that, for USD-based models, excess returns tend to increase in the short run before exhibiting a reversal effect after 12 months. In contrast, in EUR-based models, the UIP premium remains stable at around zero. In general, the results suggest that uncertainty has a significant impact on the UIP puzzle in CEE economies when the US dollar is used as the reference currency.
The social economy sector in the EU comprises 2.8 million entities, employing 13.6 million people and contributing 6-8% to GDP. In Poland, the sector included 97,400 entities in 2021, playing a key role in the economy. To achieve their social and economic goals, these entities require stable, tailored financing. Research into financing mechanisms is essential for addressing challenges and ensuring sustainable development. This study analyses the financial opportunities and barriers faced by Polish social economy entities (SEEs), based on 549 survey responses. The findings indicate that while SEEs generally maintain a stable financial situation, challenges persist, particularly in securing stable financing. Investment and expansion activities are common, yet limited capital access, complex regulations, and inadequate financial instruments pose significant barriers. These observations contribute to broader European discussions on financial sustainability in the social economy sector, particularly in post-transition economies.
This paper investigates the diversification properties of precious metals and international currencies as potential hedge and safe-haven assets for investors trading on the Polish capital market from 2007 to 2023. We apply two statistical models: the Multivariate Factor Stochastic Volatility (MFSV) model and a regression model with dummy variables, in which the residual term is modelled as a GARCH process. The results indicate that international currencies are generally more effective than precious metals in mitigating portfolio risk on the Polish capital market. Hedge properties are observed for all analysed currencies, whereas safe-haven properties are identified only for the British pound and the Swiss franc. Among precious metals, only platinum exhibits safe-haven characteristics, while gold appears to provide the strongest hedging properties against stock portfolios. In the case of bond portfolios, gold and silver offer both hedge and safe-haven benefits. Overall, precious metals are better diversifiers for Polish bond investors, while currencies are better for stock investors on average. The results of quarterly data analysis show that both precious metals and currencies can serve as safe-haven assets during periods of market turmoil. However, the safe-haven properties of the analysed assets were stronger during the Global Financial Crisis period and weaker during the Russia-Ukraine war.
This study evaluates methods for estimating total quarterly hours actually worked within enterprises, using known annual values obtained from a complete enumeration survey conducted by the Statistics Poland. Using data from Q1 2009 to Q4 2023, we compare the estimates across economic activity sections with official quarterly survey data from Statistics Poland. Our approach prioritises practicality, computational feasibility, and statistical integrity. The evaluated methods are classified using forecast accuracy metrics and taxonomic tools based on the distance from an abstract ideal solution. The analysis demonstrates that methods employing average paid employment as an auxiliary series are more effective than approaches focused on movement preservation. Litterman’s method, which minimises the weighted residual sum of squares, exhibits the highest forecast accuracy and the greatest resilience to external shocks such as the COVID-19 pandemic and the global energy crisis. Our findings provide useful insights for selecting optimal interpolation methods in labour market statistics from a complete enumeration survey by the Statistics Poland.
This study investigates the interdependence between LPG and crude oil prices by leveraging the Seasonal Auto-Regressive Integrated Moving Average time series model with exogenous variables (SARIMAX model). Three scenarios are considered, incorporating price indices for the Western Index, Eastern Index, and Total Index, which is calculated as a weighted average of the two preceding indices. The study focuses on Poland, which, during the analysed period, was heavily dependent on energy resource supplies from Russia. The findings of the econometric analysis show that: (1) there is a strong and statistically significant association between changes in oil prices and changes in gas prices for imports to Poland regardless of their source; (2) this relationship is strongest for the Western Index; (3) weekly LPG price changes adjusted for the relationship between oil and LPG prices do not exhibit a particularly long memory or seasonality; and (4) the Eastern Index is characterised by a fairly high elasticity with respect to the past (short-term) dynamics of current LPG prices. Thus, the conflict in Ukraine and the resulting decline in LPG imports from Russia are unlikely to result in long-term market in Poland.
Poland joined the European Union in 2004 and, in 2007, became the member state that benefited the most from the bloc’s Cohesion Policy, surpassing Spain. Over two financial perspectives, the EU allocated almost EUR 145 billion to Poland between 2007 and 2020. As a country with a significant share of agriculture in its economy, Poland also received substantial support from the Common Agricultural Policy, totalling EUR 50 billion over 14 years. The objective of this study is to assess the spillover effects of the Common Agricultural Policy and the Cohesion Policy on neighbouring NUTS 3 subregions in Poland from 2007 to 2020. Spatial panel methods were applied to evaluate the spillover effects of European funding. The findings suggest the existence of positive local spillover effects of the two policies, but they do not prove the existence of global spillover effects. Additionally, the empirical analysis indicates a positive relationship between European financial support and subregional growth.
Electronic commerce has disrupted traditional international trade categories, making it increasingly difficult to classify traded items as services, products or factors of production. This paper examines whether classic economic classifications can effectively capture the nature of e-commerce. The authors question the World Trade Organisation’s division between goods and services in frameworks such as the General Agreement on Tariffs and Trade (GATT), the General Agreement on Trade in Services (GATS), and regional trade agreements (RTAs), arguing that the concept of a “commodity” fails to fully represent digital trade. The analysis shows that e-commerce often combines multiple categories within single transactions. Consequently, traditional legal, economic and trade policy concepts are insufficient to address the complexities of digital trade. Without a precise definition of e-commerce, practical issues arise, including challenges in identifying trade barriers and implementing effective regulations. The paper outlines the limitations of the current regulatory framework, reviews the WTO’s trade classification system, and analyses the notion of “commodity” as an overly narrow term for e-commerce. Finally, the authors explore the characteristics of digital trade and recommend the introduction of new classification categories for electronically traded objects to help address these shortcomings.
Climate change is affecting agriculture worldwide, enabling wine production in regions previously considered unfavourable. Poland, including its central region encompassing the & Lstrok;& oacute;dzkie, Mazowieckie and & Sacute;wictokrzyskie provinces, is increasingly taking advantage of these conditions. An improved climate favours the establishment of vineyards and strengthens the region's position within the growing wine industry. The aim of this study was to assess the competitiveness of wineries in central Poland, with a focus on the role of marketing strategies and internal resources in shaping their market position. A qualitative approach was used, based on semi-structured interviews with 10 winery owners and an extended SWOT analysis. This made it possible to identify key strengths and weaknesses, as well as opportunities and threats affecting the industry. The results indicate that wineries are capitalising on local strengths, but face difficulties such as limited distribution channels, poor promotion and changing consumer preferences. Increasing competitiveness requires an expanded distribution network, more effective promotional activities and stronger regional cooperation.
The literature on estimating the tax gap in direct taxes generally emphasises that using a bottom-up method produces much better results than using a top-down method. The existing methods of estimating the PIT gap using the bottom-up approach rarely take advantage ofrisk-based audits, which can be successfully used when tax gap estimation based on random audits cannot be applied. An impor-tant issue with risk-based audits is the potential selection bias. To address this issue, we use the Heckman selection model. We estimate the size of the PIT gap in Poland in 2017 by using data from the tax filings of taxpayers earning income from business activities taxed at a flat 19-percent tax rate and risk-based audits. Our findings show that the predicted PIT gap corresponds to 4.5 percent of the total tax liability of this group of taxpayers. We also identify the regions and sec-tors with the highest predicted PIT gap per taxpayer. Our results show that the Heckman model can be used as a tool by tax authorities to improve the efficiency of risk-based audits.
Reducing carbon dioxide (CO2) and other greenhouse gas (GHG) emissions is of paramount importance because of their role in trapping atmospheric heat, a process that leads to global temperature increases commonly referred to as global warm-ing. Rising temperatures, in turn, catalyse climate change, significantly affecting ecosystems, public health, economic stability, and the global environmental bal-ance. Major corporations are thus increasingly expected to adopt policies aimed at minimising these adverse impacts. For publicly traded companies, this may influence financial metrics such as earnings per share (EPS). Recent regulatory changes have required many Polish companies to disclose Environmental, Social and Governance (ESG) metrics, particularly for 2023. This represents a historic shift, although implementation delays have led many companies to report some metrics on a voluntary basis. The relationship between air pollution indicators-including the percentage of fossil fuel-derived energy, GHG intensity, and overall GHG footprint-and EPS can now be assessed within the Pope-Wang analytical framework. Early findings indicate a negative, though statistically insignificant, relationship between emissions and current EPS, as confirmed by Huber regres-sion and quantile regression results. This suggests that emission-reduction meas-ures have not yet had a substantial impact on the profitability of Warsaw Stock Exchange-listed companies that voluntarily reported ESG data.
The study analyses the effects of fiscal devaluation on employment rates and finds that the significance and magnitude of this impact vary considerably by gender and age. The effect is substantially stronger for men than for women. For men, it is statistically significant across all age groups, with the strongest impact observed among the youngest cohort. For women, it is significant only in the 25-54 age group. Labour and product market regulations also influence the strength and variation of this effect, indicating that institutional quality significantly shapes the outcomes offiscal reforms. The findings support combining fiscal devaluation with structural reforms aimed at enhancing labour market.
Carbon taxation has emerged as an effective policy tool for combating global climate change. This study investigates the impact of carbon pricing on carbon emissions and the carbon footprint (CF) in selected countries that were among the first to adopt carbon taxation, using panel data from 1992 to 2021. We apply range of econometric methods to address specific data challenges, including cross-section dependence tests to explore interdependencies among countries; Delta homogeneity tests to check whether variables are homogeneous across the panel; second-generation panel unit root tests (robust against cross-section dependence) to assess the stationarity ofvariables; and the Gegenbach et al. [2016] panel cointegration test (robust to both cross-section dependence and heterogeneity) to identify long-term relationships. We also use the panel Dynamic Ordinary Least Squares Mean Group (DOLSMG) estimator to estimate long-run coefficients, considering both heterogeneity across the panel and cross-section dependence. Finally, the Dumitrescu and Hurlin [2012] panel causality test -which also accounts for heterogeneity and cross-section dependence - is employed to examine causal relationships. The results indicate that carbon pricing effectively reduces both carbon emissions and CF. Moreover, the findings reveal a cointegrating relationship among the variables, as well as a unidirectional causal relationship from carbon pricing to both carbon emissions and CF.
Unexpected events, such as financial crises, pandemics, or armed conflicts, gener-ate shocks that disturb economic mechanisms. For the events to be appropriately described and empirically analyzed, econometric models capable of considering the presence of structural changes in data-generating processes are needed, as is the continued development of existing methodologies. This article proposes a generalization of the threshold cointegrated VAR (TCVAR) model, along with a strategy for estimating its parameters and testing the hypoth-esis of asymmetric adjustments. It also presents an application of the TCVAR in the modeling of pricing of food commodities. Three stages of the supply chain were considered: the market for raw produce, the food processing market, and the retail market. The study shows that the prices of production sold of the food industry adjust asymmetrically to prices paid to producers and import prices. The main causes of these asymmetries are the strong market position of food produc-ers and the oligopolies in many industries. Across all long-run relationships that define the equilibrium prices at each of the three stages, structural changes driven by both domestic and global events play a significant role.
This paper investigates the relationships between the field of study mismatch, or horizontal educational mismatch, and the characteristics of employed individuals, as well as their satisfaction with various aspects of their career. Horizontal mismatch is defined as holding a degree in a field of study different from that required for one’s current job. Although understanding this phenomenon is important for shaping public policies, it has received considerably less attention than overeducation. The study draws on data from a local survey of 518 employed individuals in Poland and employs a series of logit models. The findings confirm the significant role of education-related variables and identify fields of study associated with higher and lower risks of mismatch. In contrast, variables such as gender, parenthood, and self-employment show no significant relationship with horizontal mismatch. The results further indicate that horizontal mismatch negatively affects satisfaction with education, employment, wages, career prospects, and job security. Mismatch also appears to be a gradable concept: individuals who are only partially matched report much higher satisfaction than those who are fully mismatched. The study additionally explores the relationship between horizontal and vertical mismatches and highlights the importance of distinguishing between these two forms of mismatch. The findings are discussed in the broader context of the voluntariness and persistence of mismatch.
Using a large sample of 160 economies observed over three decades since the mid-1990s, this paper documents patterns of trade specialisation in products embedding automation technologies. We draw on HS 6-digit trade data matched with product-level taxonomies identifying automation-related export lines to quantify the importance of such products in national export structures. Despite the rising value of global trade in automation products, their share in total exports remains small-negligible in low-income economies and not exceeding 2.5% in high-income countries. Between 1995 and 2019, Poland experienced a rise in automation-related exports, in terms ofboth value and as a share of total exports (reaching 2.5% in 2019). Export specialisation in products embedding automation technologies, measured by the revealed comparative advantage index, is positively correlated with income per worker. However, automation-related exports have not played a significant role in economic convergence. By contrast, technological trade in the broad sense is among factors driving global productivity convergence. The paper also discusses the limitations of trade data in capturing international trends in automation technology.
Foreign direct investment (FDI) plays a crucial role in the economic growth and development ofmost countries, generating significant interest among researchers and policymakers in its determinants. The institutional FDI fitness theory, which identifies four groups of institutions shaping investment inflows (socio-cultural, education system-related, market, and governmental), has not yet been addressed in the Polish-language literature. This paper outlines the core assumptions of the theory, reviews the relevant empirical studies, and tests its applicability to Central and Eastern European countries over 2004-2020. Using both cross-sectional data analysis methods (as in the original study) and fixed-effects panel data, we find that market institutions play a primary role in attracting FDI to the CEE region. FDI inflows are positively correlated with GDP per capita, total population, urban population, and tax revenues, while governmental, education system-related, and socio-cultural institutions appear to be ofmarginal significance. The estimates also show that time variables and lagged values of FDI inflows (in panel analysis) are statistically significant. Future research could refine the selection ofvariables rep-resenting all groups of institutions and examine the pace of institutional change as a moderator in the analysed relationship.
This study presents a global analysis of the potential effects of generative AI on employment. Using the GPT-4 model, we estimate task-level exposure scores and assess their potential employment impacts globally and across country income groups. We find that clerical work is the only broad occupational category highly exposed to the technology, while other occupational groups such as managers, professionals and associate professionals exhibit much lower exposure levels. Consequently, the primary impact of generative AI is likely to be the augmentation of work rather than the full automation of occupations. Due to different occupational structures, employment effects vary across countries. In low-income countries, only 0.4 percent of total employment is potentially exposed to automation effects, compared with 5.5 percent in high-income countries. The effects are also highly gendered, with women more than twice as likely as men to be affected by automation. We find that 10.4 percent of employment in low-income countries has the potential to be augmented, compared with 13.4 percent in high-income countries. However, these estimates do not consider infrastructure constraints, which may significantly limit adoption in lower-income contexts.
This paper investigates the relationship between artificial intelligence (AI) and global income growth, with a particular focus on the latest emerging category of digital technologies: generative AI (GenAI). GenAI introduces innovative meth-ods for content creation and can assist with both manual and cognitive tasks, potentially transforming productivity, output, and employment dynamics. By analysing patent data from a global sample of countries, this study aims to assess whether GenAI, even in its early stages, exhibits apositive correlation with income growth. Our findings reveal a statistically significant, albeit quantitatively mod-est, association between GenAI and GDP per capita growth. Specifically, we esti-mate a growth premium of approximately 0.02 percentage points over a decade for countries adopting this emerging technology domain-reflecting the extensive margin ofGenAI innovation. Additionally, when examining the scale ofresearch efforts in this field (the intensive margin), we find that GenAI has contributed between 0.009 and 0.013 percentage points to GDP per capita growth since 2009.
