This study analyses the state and evolution of Bulgaria’s social infrastructure – specifically health and educational facilities – using a share-shift and RS score methodology across municipalities between 2008/2009 and 2022/2023. The main objective is to assess the spatial distribution, accessibility, and performance of key social infrastructure indicators, with a particular focus on rural areas. By applying the RS framework, we quantify local impact, scenario stability, and the degree of convergence or divergence between rural and urban areas. The results reveal a dual trajectory. On the one hand, Bulgaria has achieved a structural shift toward near-universal access to healthcare, with self-reported unmet medical needs declining from 17% in 2009 to below 3% in 2024, and rural-urban disparities largely converging. On the other hand, the underlying health infrastructure remains unevenly distributed, with over two-thirds of municipalities classified as “constrict” or “adverse” in capacity and only 45% of rural residents within 15 minutes of a healthcare facility (compared to 60% in the EU). A similar pattern emerges in educational infrastructure. Urban areas maintain high levels of upper secondary and tertiary attainment, supported by denser networks of schools, whereas rural areas face school closures, longer travel times, and limited access to higher education. Over half of rural municipalities are rated “constrict” or “adverse” in educational infrastructure, reinforcing territorial disparities. Taken together, these findings depict Bulgaria’s social infrastructure as fragmented but improving. While access indicators have advanced, long-term sustainability requires structural investments in facilities, transport, and digital connectivity to ensure equitable, resilient social infrastructure nationwide.
The aim of this paper is to examine the price dynamics in the Bulgarian economy in the run-up to Euro Area accession within the framework of the supply chain, starting from the main production factors' prices, passing through producer and consumer prices, with a particular focus on the extent to which producer and consumer price dynamics can be explained by changes in the fundamental pricing factors. The analysis is based on a comprehensive author's data set of indicators for the prices of key production factors and a specifically designed Production Factor Price Index. The results show that in 2025 and early 2026, consumer price dynamics cannot, to any significant extent, be explained by the fundamental pricing factors of production. The final consumer prices are primarily formed by the interaction of domestic demand, market structure and consumer behaviour, while the role of traditional pricing factors is limited and strongly differentiated across the different sectors.
This article examines the desired and actual models of state administration in Bulgaria based on expert assessments measured on a three-point ordinal scale (1–3). Methods for paired (dependent) observations and analyses of association and agreement between the two assessments are applied. The results indicate statistically significant and substantively large differences between the desired and actual state for five of the six analysed criteria, while convergence is observed for one criterion. Overall, respondents express a preference for administrative arrangements associated with more contemporary models of public administration than those currently perceived in practice. The article discusses the evolution of public administration theories, their epistemological foundations, and the paradigms that have shaped the discipline. It further analyses contemporary trends in public administration as both a field of study and a practical activity, highlighting their relevance for the Bulgarian context. On this basis, the study seeks to identify which traditional or modern models of public administration most closely correspond to the current state of Bulgarian public administration.
This study analyses how savings-based bonus rules shape budgetary behaviour in public institutions using a mechanism-based institutional framework. Rather than estimating causal effects or evaluating policy impacts, the analysis identifies the incentive structure embedded in budgetary regulations and the behavioural adaptations it generates. The methodology combines institutional rule analysis with an indicator-based structural model that captures the key channels through which savings are generated and transformed into bonuses, including the retention of vacant positions, under-execution of procurement plans, compression of flexible expenditures, and the concentration of payments at the end of the fiscal year. The internal coherence of the mechanism is examined using a structural consistency model applied to synthetic data calibrated within institutionally plausible ranges. The findings suggest that these channels form a stable and internally coherent incentive system in which budgetary savings can be converted into discretionary bonuses within the existing regulatory framework. A comparison with Eastern European practices situates these findings within alternative approaches to incentive system design, demonstrating that pre-planned and normatively constrained bonus schemes are institutionally separated from mechanisms based on expenditure under-execution. The study contributes to the discussion on budgetary reforms by identifying institutional incentives and organisational constraints, and provides practical recommendations for policymakers and public managers seeking to enhance the efficiency of public financial management. The developed model, while holding behavioural assumptions and institutional parameters constant, generates an estimated annual savings of approximately USD 289.3 million under a fixed quarterly bonus configuration.
Consumers often rely on credible brands when evaluating product quality and deciding whether a price increase is acceptable. Credibility acts as a signal that indicates that a brand is consistently true to its word, therefore reducing information asymmetry and providing consumers with a measurable value of product quality. This signalling mechanism reduces consumer price sensitivity by decreasing the perceived risk of purchasing a product and subsequently increasing the level of consumer confidence in the product's performance. Given that the food category is typically the highest risk-sensitive category of frequently purchased products, this research aims to examine how brand credibility influences consumer reactions to price changes using Information Economic Theory, Signalling Theory, and current consumer behaviour research. The findings of this research highlight that consumers feel less concerned about changes in price when they are purchasing products from recognised, reputable brands due to the level of trust associated with the brand. Additionally, frequent or unexpected changes in price that are experienced by consumers purchasing food items result in an increased level of perceived risk regarding the value that they place on the products purchased. This study demonstrates that perceived brand trust can significantly influence how much cognitive effort consumers will use to make purchasing decisions, particularly in markets where they are unable to directly assess the quality, safety and freshness of an item prior to consumption. In conclusion, this study has important contributions for theory and practice. From the perspective of theory, this study supports and confirms that signalling mechanisms are significant in markets where uncertainty exists and provides an empirical basis for the moderating effect of brand trust within the food market. In terms of practice, the results from this study illustrate the importance of manufacturers investing in clear communication of price, improved quality assurance processes, and developing brand strategies that build trust over the long-term in order to reduce perceived risk.
This paper examines the impact of institutional quality and selected macroeconomic factors on foreign direct investment (FDI) inflows in 22 Central and South-Eastern European countries over the period 2007-2023. To account for endogeneity, unobserved heterogeneity, and the dynamic nature of FDI, the analysis employs dynamic panel data estimators, namely the Arellano–Bond difference GMM and the Blundell-Bond system GMM. Institutional quality is proxied by six Worldwide Governance Indicators: Control of Corruption, Government Effectiveness, Political Stability, Regulatory Quality, Rule of Law, and Voice and Accountability. In addition, the model controls for key macroeconomic determinants, including GDP per capita, trade openness, inflation, population size, and mobile subscriptions as a proxy for infrastructure development. The empirical results indicate that institutional quality indicators are generally not statistically significant across model specifications, with the exception of political stability, which consistently exhibits a positive association with FDI inflows. In contrast, macroeconomic variables – particularly population size and trade openness – emerge as robust determinants of FDI, while the lagged dependent variable confirms strong persistence in FDI inflows over time. Inflation is found to be positively associated with FDI in some specifications, suggesting that its effect may reflect short-term macroeconomic conditions rather than long-run stability. Overall, the findings suggest that, in Central and South-Eastern Europe, market size and economic openness play a more prominent role in attracting FDI than improvements in institutional quality. The study also highlights potential measurement limitations and structural rigidities that may obscure the observable impact of institutions on FDI, contributing to the ongoing debate on the relative importance of institutional reforms versus macroeconomic fundamentals in transition economies.
This study examines the role of digital technologies, particularly internet and mobile phone use, in increasing financial inclusion and reducing household poverty in rural Eastern Indonesia. The main objective of this study is to analyse the impact of internet and mobile phone use on financial inclusion and to measure their contribution to poverty reduction in areas with limited infrastructure. The research method uses data from the 2024 National Socio-Economic Survey (Susenas), focusing on rural households on four islands in Eastern Indonesia. The analysis employed OLS and Probit models to estimate the effects of digital technology and financial inclusion on poverty. Key findings suggest that internet and mobile phone use have a positive effect on financial inclusion, which in turn contributes to poverty reduction. However, the impact is not always optimal in regions with limited infrastructure, where the interaction between financial inclusion and digital technologies can sometimes exacerbate economic inequality. The study's implications highlight the need for policies that strengthen digital infrastructure, improve financial literacy and digital skills among rural communities, and maximise technology's potential to reduce poverty.
Market transition in Bulgaria experienced several periods during which a variety of political, social, and economic conditions have facilitated the processes for market reforms and social transformation since 1990. It was expected that EU accession processes (up to 2006) and especially the full integration of the country would substantially reduce the scope of grey economy operations. Nevertheless, the mainstream public opinion and scholarly research indicate the resilient nature of this phenomenon, which still keeps a notable share in economic activities, especially in particular business sectors. The paper presents empirical results from a questionnaire survey conducted in 2023 among business representatives in Bulgaria having expert, managerial, or ownership positions within the firm. The study provides evidence on selected issues related to the shadow economy in Bulgaria after two years of political instability with a series of temporary governments. For example, the business sectors with the greatest extent of the shadow economy spread indicated by the respondents are construction (83%); restaurants, coffee shops and similar establishments (81%); tourism, hotels and accommodation services (61%). The average share of underreported turnover is estimated at 38% for the overall economy, but when asked about their branch, respondents evaluate this share on average at 26%. Furthermore, the mean share of employees having an official labour contract but under “hidden clauses” (e.g. additional envelope wages) is evaluated at 19%, along with 7% on average for those employed without a contract. All results show that the phenomenon still has a significant impact on the economic activities in the country; however, further comprehensive exploration is necessary to reveal adequate alternatives for practical policies to be implemented towards limiting the scope of shadow economy operations.
The purpose of this paper is to evaluate forecasts of Albania’s Real GDP Growth and GDP Deflator under real-time operational constraints by conducting an operational backtest using the leakage-free rolling origin approach and mixed-frequency predictors. The main difficulty is the small number of observations in the quarterly data set, partially observed predictors, and a 90-day publication delay for GDP. In this study, we evaluate 19 models grouped into six families: baseline univariate rules, classical time-series models, dynamic regression/SARIMAX specifications, regularised linear models, bridge/factor-augmented models, and selected nonlinear/probabilistic machine-learning models. These approaches were applied individually to Real GDP Growth and the GDP Deflator to generate point forecasts and forecast intervals for 1-, 2-, and 4-quarter-ahead horizons, using metrics such as MAE, RMSE, interval accuracy and coverage, interval width, and CRPS. Robustness was assessed by conducting Diebold-Mariano tests, applying the block bootstrap, and treating the period after 2016 as an out-of-sample period. The three highest-ranking models are univariate rules that converge toward the expanding-window in-sample mean. This suggests mean reversion in GDP growth and persistence in the GDP deflator. Among the structured models, Bridge_PCA_Ridge is the most consistent option at the 1- and 2-quarter horizons, while SARIMAX is the strongest structured specification at H=4. Selected nonlinear machine-learning models did not improve upon the regularised linear alternatives in this sample.
The United States is one of the countries where the banking sector is well-developed, savings are high, and significant budgets are allocated to energy technologies; these factors could affect the sustainable development and environment. To this end, this paper aims to investigate whether green investments, banking sector development, capital formation and savings have an impact on sustainable development in the United States. This research employs cointegration and symmetric/asymmetric causality tests to analyse annual data from 1974 to 2023. The Hatemi-J (2008) cointegration test showed that there is no long-run relationship between load capacity factor and selected variables. Hatemi-J (2014) asymmetric causality test revealed that green investments and banking sector development have a positive impact on load capacity factor. Accordingly, it can be said that green investments and banking sector development are important factors in achieving sustainable development. The obtained results are expected to provide significant insights for policymakers.
The purpose of the article is to assess the direct and indirect impact of the discount rate on credit and deposit rates in the Ukrainian banking system, taking into account inflation dynamics and monetary transmission time lags. The methodological basis of the study is a vector autoregression (VAR) model, supplemented by impulse response analysis (IRF) and forecast error variance decomposition (FEVD), which made it possible to reproduce the time structure of monetary shock transmission through the interest rate channel. The results show that an increase in the policy rate raises lending rates with a lag of about two months, while deposit rates respond more gradually, and inflation exhibits a weaker and slower short-term reaction. It was found that the key channel for transmitting the monetary impulse to inflation is the deposit rate. The scientific novelty lies in the comprehensive combination of IRF and FEVD analysis to reconstruct the time architecture of monetary transmission and in substantiating the role of the deposit channel in shaping inflation dynamics. The practical significance of the results lies in the possibility of using them for macroprudential analysis and improving price stability policy.
This study examines the effects of Key Audit Matters, company operational complexity, audit committee gender, audit committee financial expertise, and audit committee work experience on audit report lag among Indonesian listed firms. Employing panel data regression analysis on 480 firm-year observations from consumer non-cyclicals, property, and real estate sectors listed on the Indonesia Stock Exchange during 2022–2024, the study adopts a situational approach by comparing firms with high versus low audit committee work experience. Results indicate that Key Audit Matters and audit committee work experience statistically significant negative effects on audit report lag in the full sample and low-experience subgroup, suggesting enhanced audit transparency and governance experience accelerate audit completion. Notably, audit committee financial expertise significantly reduces audit report lag only among high-experience firms, highlighting the synergistic role of accumulated expertise in leveraging financial literacy. Conversely, company operational complexity and audit committee gender demonstrate no consistent impact. These findings underscore the complementary interaction between audit transparency and governance capability in enhancing audit efficiency within emerging markets.
This study investigates the determinants of sustainable growth in the banking sector of the Western Balkan countries within a context of heightened regulatory pressure, financial transformation, and evolving risk profiles. While existing empirical evidence on bank sustainable growth largely relies on outdated datasets or focuses on advanced banking systems, evidence from emerging European markets remains limited. Using an unbalanced panel dataset of 152 commercial banks operating in Albania, Croatia, Kosovo, Bosnia and Herzegovina, North Macedonia, Montenegro, and Serbia over the period 2013-2022 (1,064 bank-year observations), the study employs panel econometric techniques to examine the role of operational efficiency, profitability, capital structure, balance-sheet growth, liquidity and credit risk, and risk-taking behavior in shaping banks’ sustainable growth capacity. Fixed-effects estimations are applied to control for unobserved bank-specific heterogeneity and common time effects, with robust standard errors clustered at the bank level. The results reveal that sustainable bank growth is primarily driven by internal performance and strategic choices. Operational efficiency, profitability, Financial Leverage, and controlled risk-taking exhibit a positive and statistically significant relationship with sustainable growth, whereas balance-sheet expansion, asset risk intensity, liquidity, and credit risk indicators do not exert a robust direct effect. Macroeconomic conditions play a limited role once bank-specific dynamics are taken into account. By adopting a thematic risk-channel approach and incorporating recent data, this study contributes to the banking and financial stability literature by offering new insights into how banks in emerging European markets balance growth objectives with long-term resilience. The findings provide relevant implications for bank managers and policymakers in designing strategies that support sustainable growth without compromising financial stability.
Environmental, Social, and Governance (ESG) performance has become a critical component of sustainable business strategy, yet its financial implications remain debated, particularly in emerging markets. While ESG engagement is often expected to enhance long-term resilience, evidence on its short-term market impact is inconclusive. The present study examines the dynamic relationship between ESG performance and firm market value across different economic conditions, focusing on the periods before and during the COVID-19 crisis. Using an unbalanced panel dataset of 411 firm-year observations from 2015 to 2023, the analysis employs fixed-effects, random-effects, and instrumental variable (IV) regression models to mitigate endogeneity. The results show that ESG performance negatively affects firm market value during stable periods but positively influences it during crises. These findings suggest that while ESG investments may impose short-term financial burdens, they serve as risk mitigation mechanisms that enhance resilience under uncertainty. The study contributes to the literature by providing context-specific evidence, highlighting the conditional nature of ESG’s financial effects across economic cycles. Practically, the results emphasise the need for investors to incorporate ESG into risk–return strategies, for managers to align ESG investments with financial stability goals, and for policymakers to design balanced regulations that promote sustainable growth without overburdening firms.
The aim of this article is to present dynamic estimates of the discount factor and the coefficient of relative risk aversion for the Bulgarian labour market, using an innovative quantitative approach. For this purpose, we use a modified version of McCall's job search model. Our estimates for the period 2004-2024 reveal four distinct, structural phenomena: (i) a pro-cyclical discount factor, (ii) a counter-cyclical coefficient of relative risk aversion, (iii) a marked divergence between the behavioural parameters of the mean and median worker, and (iv) relatively low estimates for the parameters of interest compared to other economies. Based on these results, the article draws conclusions about the basic characteristics of the labour market in Bulgaria and proposes relevant policies.
Intensifying competition in private higher education necessitates understanding how student experiences translate into brand outcomes. While prior studies focused on isolated interactions, limited research has examined how multi-stage brand touchpoints collectively shape student brand evangelism and its underlying mechanisms. Addressing this gap, this study examines how pre-admission, during-course, and student-perceived post-graduation touchpoints influence brand evangelism via institutional reputation. Data from 375 Indonesian private higher education institutions' students were analysed using PLS-SEM. Findings show all three touchpoints significantly influence evangelism, with pre-admission touchpoints having the strongest direct effect. Perceived post-graduation touchpoints contribute most to institutional reputation, which positively influences evangelism and mediates cumulative student experiences. The study extends customer-brand relationship theory by showing that relationship formation begins before enrolment, introducing a student lifecycle-based model, and positioning reputation as a key mechanism transforming experiential value into advocacy. Practically, private universities should strategically orchestrate coherent touchpoints to build reputational capital and foster student-driven advocacy in competitive emerging markets.
This paper investigates whether the diffusion of artificial intelligence (AI) is associated with gendered labour market outcomes at the macro level and whether gender-sensitive institutional reforms mitigate gender inequality. We constructed a cross-country dataset covering up to 50 countries over the period 2012–2022. Given the rapid acceleration of AI adoption after 2022, the estimates should be interpreted as evidence for the 2012–2022 period and may not reflect the most recent wave of AI diffusion. We then estimate three complementary econometric models. First, using 500 promotion-related observations pooled across countries, we estimate a logistic regression to test whether AI adoption and an AI bias proxy are associated with women’s promotion probabilities (RQ1; H1a–H1b). Second, using a balanced panel of 10 countries observed over 10 years (N = 100), we estimate country fixed-effects models for female labour force participation and the gender pay gap (RQ2; H2a–H2c). Third, we employ a difference-in-differences specification on the same panel to assess the impact of gender-sensitive institutional reforms on the gender pay gap (RQ3; H3). Across the logit and fixed-effects models, coefficients on AI adoption and the AI bias indicator are small, statistically insignificant, and imprecisely estimated, providing no support for the hypotheses that AI diffusion widens gender gaps in promotions, participation, or wages. By contrast, the reform indicator in the difference-in-differences model is negative, sizable, and highly significant, indicating that treated countries experience an average reduction of about 6.4 percentage points in the gender pay gap relative to non-treated countries. The findings suggest that AI adoption, as currently measured, is not a detectable structural driver of gender inequality in labour markets at the country level, whereas gender-sensitive institutional reforms are empirically associated with improved wage outcomes for women.
This paper looks at digital connectivity and the adoption of advanced technologies in five Southeast European EU countries, namely Bulgaria, Croatia, Greece, Romania, and Slovenia, using indicators from the European Commission’s Digital Decade/DESI platform and the 2024 country reports. The analysis combines descriptive comparisons and visualisations with PCA and k-means clustering to summarise how countries perform across connectivity (VHCN, 5G) and outcomes (SME digital intensity, cloud, AI use, and basic digital skills). To add a formal test, the study estimates a panel regression (random effects; Swamy – Arora) linking enterprise AI adoption to lagged connectivity, with model choice supported by a Hausman test and robustness checked using White heteroskedasticity-robust standard errors. The results point to a clear connectivity-adoption gap: Bulgaria and Romania pair strong infrastructure with weak skills and low cloud/AI uptake. Econometric evidence partially supports this link – lagged 5G is positively associated with AI adoption, while VHCN is not robust once 5G is included. Clustering identifies a catching-up group (BG, RO), an intermediate group (HR, SI), and Greece as a partial converger/outlier.
The paper studies the relationship between foreign trade and economic growth in Bulgaria, Estonia and Lithuania for the period 2000-2023 in the framework of post-Keynesian theoretical models. Main tasks are to examine the impact of exports, imports and net exports on the dynamics of GDP, to highlight its peculiarities over time and across countries, and to characterise the main determinants of exports and imports for the three countries overall. The research methodology includes the use of descriptive analysis of the dynamics in GDP and in exports, imports, trade openness and net exports, the interdependencies between them and their links to changes in domestic demand, as well as the econometric analysis of the exports and imports functions using R Statistics panel data tools. The empirical results show that trade openness has been trending upwards, with the greatest growth impact coming from increased exports. The econometric estimation of the exports function with the first difference estimator reveals a positive impact of EU GDP and a negative impact of real effective exchange rate, while that of the imports function indicates a positive impact of own GDP and a negative impact of real effective exchange rate. The dynamics of economic activity are highly sensitive to the performance of major trading partners, but there is also a significant external crowding-out effect. However, the rise in imports was constrained by the non-typical impact of increasing relative prices, contributing to an upturn in net exports and higher economic growth.
The rapid expansion of the digital economy has increased the need to understand how firm-level digital investment relates to operational efficiency in manufacturing sectors. While existing research primarily examines productivity and profitability outcomes, the relationship between digital investment and asset utilisation efficiency remains insufficiently explored, particularly when measured using direct financial indicators. This study examines the association between digital investment and asset utilisation efficiency in Chinese listed manufacturing firms. Using a balanced panel of 500 firms over the period 2018-2022, digital investment is measured as digital-related expenditure relative to operating income, and asset utilisation efficiency is proxied by the asset turnover ratio. Fixed-effects panel regression models are employed to control for unobserved firm-specific heterogeneity, with additional controls for firm size, leverage, ownership concentration, and research and development intensity. The results show a positive and statistically significant association between digital investment intensity and asset utilisation efficiency. Heterogeneity analysis indicates that this relationship is stronger among larger firms and firms in high-technology industries, while it is not statistically significant for smaller firms. The study contributes by providing firm-level panel evidence using a direct financial measure of digital investment and by identifying conditional efficiency effects across firms and industries. The findings suggest that policies supporting digital investment, particularly for smaller firms and lower-technology sectors, may enhance asset utilisation efficiency.
