Programme - The 6th Congress of Polish Statistics

Objective

The aim of this paper is to present the concept of a new methodology for the generalisation of results in the DG-1 survey using the sampling method. The current methodology, based on a generalisation factor relying on the number of employed persons, does not allow for the full application of the sampling method apparatus for assessing the quality of estimates. The new approach includes modifying the sampling scheme and sample allocation, applying the cut-off sampling method, implementing calibration estimation, and developing a method for assessing the quality of estimates.

Methods

The DG-1 survey covers non-financial enterprises (industry, construction, transport, trade, services) employing 10 or more persons. In January 2025, the population consisted of 113,361 units. Currently, all large units (employing at least 50 persons) are included in the sample, together with a minimum 10% sample of medium-sized enterprises (employing 10–49 persons), drawn with stratification by voivodeship, NACE section, division and group, and ownership sector. In order to evaluate the proposed methodological modifications, a simulation study was carried out using the Monte Carlo method (500 replications). The pseudo-population was constructed by linking the DG-1 register from January 2025 with unit-level data from the VAT register using the REGON identifier (103,028 units, 90.9% correctly linked). The study variable was sales. A total of 128 sampling schemes were tested, differing in sample allocation (proportional to stratum sizes and square-root allocation), minimum stratum sample size, and cut-off sampling thresholds, with a fixed sample size of 25,000 units. Calibration estimation was applied with the number of employed persons as the auxiliary variable, by voivodeship and NACE section. The quality of the estimates was assessed empirically using relative bias, relative standard error, and relative root mean square error.

Results

The analysis of the DG-1 population revealed a strong right-skewed distribution: units employing 10–49 persons account for 81.8% of the population but only 26.2% of employed persons, whereas units employing at least 1,000 persons (0.6% of the population) account for as much as 27.4% of employed persons. The problem of non-response mainly concerns the smallest enterprises – approximately 40% in the group employing 10–19 persons compared with approximately 2% in the group of the largest units. The results of the simulation study indicate that square-root allocation leads to a lower relative standard error than allocation proportional to stratum sizes, and that appropriately chosen cut-off sampling thresholds improve the precision of the estimates with an acceptable level of bias.

Conclusions

The generalisation of results in the DG-1 survey based on the sampling method will be possible only in selected domains in which an acceptable quality of estimates can be achieved. The introduction of the cut-off sampling method together with square-root allocation and calibration estimation makes it possible to improve the precision of the estimates while at the same time reducing the response burden, especially for the smallest enterprises, which are most affected by the problem of non-response. The proposed approach may find application in other business statistics surveys carried out within official statistics.

Keywords

DG-1 survey, sampling method, cut-off sampling, calibration estimation, business statistics

Objective

The objective of this study is to develop a quick-to-estimate, operational indicator of local content for Polish enterprises, based on administrative data sources and applicable across sectors. The research aims to quantify the domestic share of supply chains, incorporate multi-tier supplier structures, and provide a scalable tool for policy analysis, while addressing the limitations of existing international approaches.

Methods

The methodology combines administrative microdata from the JPK_VAT system with a directed-tree representation of supply chains. Suppliers network is identified through REGON identifiers. For each enterprise, the Local Content Score LCS(t1,t2,D) is computed for period (t1,t2) and depth D. For D=1, the score equals the share of domestic purchases in total purchases of goods and services. For D*1, the score incorporates weighted contributions of key domestic suppliers’ own LCS(D-1), where key suppliers are selected using statistical rules (e.g., top-N suppliers or suppliers generating x% of total purchases). The supply chain is modelled as a directed acyclic tree with a unique path from the focal firm to each supplier. When short supply chain is considered, the directed acyclic tree is a very precise model of real flows. The longer the supply chain, the more frequent non-unique paths from supplier to focal firm occur. The indicator is mathematically bounded in [0,1] and monotonically decreasing with depth. Two aggregation strategies: top-down and bottom-up, are evaluated for sector-level analysis. The approach emphasizes automation, scalability, and independence from survey-based data collection.

Results

The presentation will showcase illustrative Local Content Score (LCS) results for selected industries, highlighting how the indicator behaves across different supply chain depths (tiers). For each sector, LCS values will be reported for D=1, capturing direct domestic sourcing, and for higher tiers (D*1), which incorporate domestic inputs embedded in the operations of key suppliers. The examples will demonstrate how the score systematically declines with increasing depth, reflecting the diminishing visibility of domestic components further along the supply chain. Differences across industries will also be discussed, showing how sectors with concentrated supplier structures exhibit higher multi-tier domestic content, while more fragmented or import-intensive sectors display steeper declines.

Conclusions

The proposed LCS offers a practical and statistically robust tool for assessing domestic content in supply chains using existing administrative data. It supports evidence-based policymaking in areas such as industrial strategy, procurement, and economic security. While limited by the absence of product details and nationality of equity in JPK_VAT, the method enables scalable, automated monitoring of domestic participation across sectors. Its flexibility and interpretability make it a valuable contribution to the statistical toolkit for analyzing local content.

Keywords

local content: supply chains: administrative data

Objective

The aim of the paper is to identify and assess the dynamic relationships between producer price inflation and short-term receivables and credit adjustments in the manufacturing sector in Poland. The research problem focuses on whether changes in producer price inflation precede changes in receivables and short-term credit and loans, and whether significant dynamic relationships exist between these financial categories.

Methods

The study is based on quarterly data for Poland covering the period 2014–2025. Producer price inflation is measured by the producer price index. Short-term financial adjustments are captured by two variables derived from RF-01 (quarterly report on financial assets and liabilities) reports of Statistics Poland and analysed at the level of the manufacturing sector: receivables, proxied by the value of the item “other amounts receivable”, and credit and loans with an original maturity of up to one year. The analysis applies a vector autoregression (VAR) model, which makes it possible to examine dynamic interdependencies among three variables treated as endogenous. To identify the direction of relationships, Granger causality tests and the Toda–Yamamoto procedure are used: the latter allows inference under less restrictive assumptions regarding the orders of integration of the analysed time series. Given the relatively small sample size, a wild bootstrap procedure is also employed to strengthen the reliability of statistical inference. Prior to estimation, the time-series properties of the variables and the lag structure of the model are verified in order to reduce the risk of misspecification and improve the comparability of the results.

Results

The conducted analysis indicates that producer price inflation may be related to short-term receivables and credit adjustments in the manufacturing sector in Poland, although the strength and direction of these relationships may vary depending on the model specification. The obtained results provide a basis for assessing whether increases in producer prices precede changes in receivables, whether they are associated with changes in the scale of the use of short-term credit and loans, and whether significant dynamic relationships exist between receivables and short-term financing. The findings also make it possible to compare the relative importance of the two adjustment channels in the sector’s response to price pressure.

Conclusions

The contribution of the paper lies in combining a macroeconomic and a financial perspective in the analysis of short-term adjustment mechanisms in the manufacturing sector. The study fits into the stream of research on the transmission of price impulses to short-term financial positions and may serve as a reference point for further studies on the relationships between price changes and the short-term financial condition of the manufacturing sector in Poland. The findings may also be useful for institutions monitoring the condition of the enterprise sector, particularly manufacturing, in Poland.

Keywords

corporate finance, receivables, short-term credit, vector autoregression, Granger causality

Objective

This paper examines discount retail locations as outcomes of firm location choice and as indirect evidence of local consumer-facing market potential. Building on the consumer city perspective (Glaeser, Kolko, * Saiz, 2001), urban economics of retail allocation, retail agglomeration research (Sevtsuk, 2014: Piovani, Zachariadis, * Batty, 2017) and chain store location evidence (Jia, 2008: Holmes, 2011), it asks whether Lidl and Biedronka supermarkets’ geographies reveal transferable relationships between population, accessibility, amenities, barriers and private location suitability.

Methods

The study uses reproducible spatial sources: OpenStreetMap urban feature channels, an NSP 2021 Census Grid population channel from GUS at 250m granularity and observed supermarket store locations within Polish city boundaries. The empirical object is a rasterized urban surface. Layers are projected to EPSG:2180 and harmonized to a 50 m modelling grid: population is resampled and interpreted at the source-grid level, not as directly observed 50 m population. Lidl and Biedronka OSM tags are excluded from explanatory POI channels and used only as target locations. Separate models are trained for each brand. The explanatory tensor contains seven channels: population, roads, rail corridors, water barriers, point-of-interest density, mobility-access proxy and public-amenity density. To reduce spatial leakage, the design uses cross-city validation: Warszawa, Łódź and Gdańsk form the training set: Kraków, Poznań and Białystok the validation set: Wrocław, Szczecin and Bydgoszcz the test set. Following spatial machine-learning arguments about spatial dependence and transferability (Kopczewska, 2022), the model estimates a brand-specific store location suitability surface. The main estimator is a conditional GAN: the generator maps context rasters into predicted store-presence surfaces, while the discriminator evaluates their spatial plausibility given the same context. Evaluation combines pixel loss with count error, count bias, mass bias, cluster morphology, Moran’s I and Geary’s C.

Results

Preliminary diagnostics indicate that observed discount retail geography is not reducible to residential population alone. Economically informative store presence surfaces emerge where population support coincides with road accessibility, POI intensity, public amenities and limited barrier separation. The outputs therefore translate spatial prediction into measures of revealed local-market suitability: where demand-supporting context concentrates, where brand-specific retail mass is expected and where water or rail barriers weaken otherwise dense surroundings. Cross-city errors identify which components of the local market structure are transferable and which remain city specific.

Conclusions

The paper contributes a framework for measuring local urban market potential through revealed discount retail allocation. Predicted store presence surfaces are interpreted as indicators of private firm location suitability: areas that resemble observed store choices under the spatial structure of population, accessibility, amenities and barriers. The claim is empirical rather than normative: the model evaluates how existing retail networks encode local economic activity and how far this logic generalizes across cities. For urban economics and applied statistics, it links consumer city theory, OSM and Census Grid data, spatial ML and autocorrelation diagnostics.

Keywords

local market potential: retail allocation: consumer city: spatial machine learning: generative adversarial networks

Objective

The aim of this paper is to identify long-term trajectories of the spatial concentration of innovation activity in 30 European countries between 1980 and 2022, and to assess whether these changes reflect sustained convergence, persistent divergence, or rather nonlinear dynamics. The analysis addresses the question of whether the observed dispersion of innovation across regions was a permanent process or merely a temporary phase preceding a renewed concentration.

Methods

The study was based on two complete patent datasets rather than a statistical sample: therefore, no procedures were used to generalize results from the sample to the population or to assess random errors. The first dataset is the global geolocated patent database by Rassenfosse, Kozak, and Seliger (2019), covering the years 1980–2014: after restricting the data to Europe, 3,247,672 observations were used, each containing information on the year, region, and exact location (x,y) of the inventor. The second dataset is the OECD Regions and Cities Atlas for the years 1995–2022, containing regionally aggregated patent data for TL2 and TL3 levels. The analysis covered 30 European countries, divided into Western Europe and Central and Eastern Europe. Two complementary measures of spatial concentration were used: the Gini coefficient for regionally aggregated data and the ETA (Entropy–Tessellation–Agglomeration) index for point data, based on Voronoi tessellation and Shannon entropy. To limit the impact of short-term fluctuations, calculations were performed using five-year moving average windows. This comparative approach captures both inequalities between administrative regions and concentration independent of territorial unit boundaries, mitigating the MAUP problem. For the average trajectories of changes, a quadratic function fit was also tested, which allowed for the formal verification of the nonlinear, U-shaped pattern of concentration changes.

Results

The results indicate a U-shaped trajectory of spatial concentration of patents in Europe. Between 1980 and 2010, innovation activity became increasingly dispersed, followed by a subsequent re-concentration. In Central and Eastern European countries, the decline in concentration was more pronounced than in Western European countries, yet they remained more spatially concentrated. A possible explanation for this sequence of changes is a shift in the focus of public policy: earlier cohesion policy favored a broader diffusion of resources and a gradual dispersion of innovation activity, while later excellence policy concentrated support more strongly in centers already saturated with innovation.

Conclusions

The spatial equalization of innovation is temporary rather than permanent. Cohesion policy can expand the territorial reach of innovation, especially during the diffusion and catch-up phases, but it does not eliminate the agglomeration forces associated with innovation at the technological frontier, nor does it offset the effects of excellence policies, which once again concentrate funding and R*D activity in areas already heavily saturated with innovation. The study demonstrates the utility of combining point and regional data, as well as classical measures of inequality with entropy measures based on geolocated data.

Keywords

innovation: patents: spatial concentration: regional disparities: innovation policy

Objective

The aim of the presentation is to introduce a method for estimating the number of residents in Poland at the municipality level using integrated administrative registers. The starting point is the new regulation on European statistics on population and housing (ESOP), which defines requirements concerning the resident population and the use of administrative sources. The study aims to develop a methodology for determining the resident population for ESOP purposes, based on length of stay, place of residence, and traces of administrative activity in registers.

Methods

The study is based on administrative data, including registers used to identify a person’s presence in the country and to assign their place of stay over time. The research process includes the selection of registers and variables, data processing and cleaning, assessment of information completeness, and integration of sources. Records containing a PESEL identifier are linked deterministically, while records without this identifier are integrated probabilistically, using a blocking stage based on approximate nearest neighbours (ANN) and pair classification with machine learning algorithms. This is followed by the imputation of missing data using multiply robust imputation methods, identification of a person’s presence in the country based on signs of life, imputation of missing information on the municipality of stay in subsequent months, and estimation of length of stay. Taking the monthly dimension into account makes it possible to reconstruct changes in place of stay and the continuity of a person’s presence in the country. The final outcome is the classification of persons into length-of-stay categories: below 3 months, from 3 to 12 months, and above 12 months, with the last category corresponding to the definition of a resident.

Results

An assessment of the quality of administrative data used to determine the resident population will be presented, covering the completeness of identifiers, the scale of missing data, the availability of information on the municipality of stay, consistency of information between registers, and the usefulness of individual sources as signs of life. The analysis will be carried out in a cross-sectional and monthly perspective, taking into account successive stages of the research process and their importance for the final classification of persons into the resident population. The results will be presented at the municipality level.

Conclusions

The proposed approach may provide a useful extension of methods for estimating the resident population used in official statistics. Its value lies in the integration of administrative data, the imputation of missing values, and the modelling of length of stay. The results may be important not only from a methodological perspective but also in practical terms, as more accurate estimates of the number of residents affect the interpretation of socio-economic indicators, the planning of public services, and the assessment of demographic processes at the local level.

Keywords

residents: administrative registers: length of stay: signs of life

Objective

The aim of this article is to present a novel method for estimating statistical aggregates for sub-threshold entities that are excluded from mandatory reporting due to their small scale of operation. The research problem addresses the high costs and respondent burden associated with traditional data-gap filling methods, such as censuses and surveys. The thesis posits that leveraging the mathematical properties of truncated empirical distributions allows for the precise reconstruction of parameters for the entire population without the need to collect additional data from small and medium-sized

Methods

The methodology is based on the assumption that the distributions of financial microdata (e.g., income, loan values) conform to normal, log-normal, or other known statistical distributions. A key element of the method is the estimation of truncated distribution parameters using the method of moments and the implicit function theorem. This enables the determination of parameters for the unobservable (sub-threshold) portion of the distribution. The quality of the results and the stability of the estimators can be assessed by comparing the results with data from periodic full censuses (if available) or through sensitivity tests regarding the truncation threshold. Statistical inference includes the analysis of estimation errors of the cumulative distribution function and confidence intervals for the estimated aggregates, allowing for the verification of the reliability of this cost-free approach compared to methods based on random sampling. The method is illustrated with one-dimensional and two-dimensional examples. The study also utilizes simulated data from balance of payments statistics regarding assets and liabilities from trade credits. The study focuses on the population above the reporting threshold (3 million PLN), which by definition creates a truncated set. The authors propose a shift from costly census or survey methods toward stochastic modeling. Furthermore, considerations regarding the generalization of the method to cases requiring the analysis of more than two sta

Results

The primary result of the work is a formalized algorithm for reconstructing a full statistical distribution based on its truncated fragment. Simulations conducted on the problem of sub-threshold entities in trade credit within the balance of payments (using simulated data) demonstrated high precision in the estimates. It was observed that the method of moments combined with the implicit function theorem effectively determines the cumulative distribution function values for sub-threshold entities while maintaining a low mean squared error. The interpretation of the results indicates that the information gap created by reporting thresholds can be systematically closed with minimal statistical error, provided the stability of the distribution type in the population is maintained.

Conclusions

The application of the proposed method allows for a significant reduction in the operating costs of central banks and national statistical offices, as well as a decrease in the bureaucratic burden on the micro and small enterprise sector. The contribution to the development of statistical theory lies in the practical application of truncated distribution properties within the production process of official statistics. The method is universal and can be generalized to multidimensional cases and other areas of economic statistics where reporting thresholds are applied. It serves as a viable alternative to traditional survey research, supporting the concept of modern, cost-free statistics based

Keywords

official statistics. sub-threshold population, estimation, truncated distribution

Objective

The PESEL register provides the backbone of probability-based social research in Poland, including key projects of official statistics as well as academic and international surveys. The aim of this analysis is to empirically assess two layers of degradation of its address component – missing addresses in the register and outdated addresses among formally complete records – and to answer the question of the extent to which the resulting inaccessibility of the adult population is structurally non-random and whether it can be effectively corrected through standard post-stratification weighting.

Methods

The analysis draws on data from twelve monthly omnibus surveys conducted by CBOS each year between 2010 and 2025. Samples are drawn through a probability scheme directly from the PESEL register and cover the adult population of Poland. The total gross sample size over the analysed period amounts to approximately 540,000 sampled individuals (around 36,000 per year on average). Results are generalised to the population through post-stratification weighting by age, sex, locality size, and region, based on Statistics Poland`s current population estimates and the 2021 Polish Census. Inaccessibility was operationalised in two layers: (1) missing address in the list released for sampling – treated as a frame coverage error: (2) address-based inaccessibility confirmed during fieldwork – defined as cases in which the interviewer established that the sampled person did not reside at the PESEL address, and the invitation could not be effectively delivered even through alternative channels (CAWI, CATI, PAPI). This category is distinct from temporary noncontact and captures cases of structural unreachability. The structure of inaccessibility was analysed by age, sex, region, and locality size: its scale was compared against the resident population figures published by Statistics Poland. Confidence intervals for proportions were estimated, and the effectiveness of standard calibration in removing bias from estimates was examined.

Results

Total inaccessibility of the adult population through the PESEL frame currently stands at approximately 20%, comprising about 6% of records with no address and approximately 14–15% of records with an address that does not lead to effective contact even in mixed-mode designs. In earlier years, before CAWI and CATI channels were introduced into mixed-mode schemes, the proportion of fully unreachable persons was higher – mixed-mode mitigates the problem only partially, not fully. Inaccessibility is strongly non-random: it more often affects younger persons, residents of large urban centres, and regions of high mobility. Standard post-stratification weighting only partially reduces estimation bias.

Conclusions

Inaccessibility within the PESEL frame is no longer an operational issue of individual research projects – it is a systemic problem of official statistics and the social sciences in Poland. Mixed-mode designs reduce its impact but do not eliminate the hard core of structurally unreachable persons. Standard calibration is reaching its limits, since the inaccessible differ from the accessible also within weighting strata. The necessary direction of development is the integration of PESEL with other administrative registers, in line with established practices of European official statistics.

Keywords

PESEL register: sampling frame coverage error: address-based inaccessibility: post-stratification weighting:

Objective

The aim of the study is to explain the sharp fertility decline in Poland, Lithuania, Latvia, and Estonia between 2017 and 2024 by distinguishing between structural and behavioral determinants. The analysis seeks to assess to what extent the observed changes result from demographic shifts versus behavioral responses to overlapping crises—namely the COVID-19 pandemic and the war in Ukraine—and the associated economic and social uncertainty.

Methods

The study is based on demographic data for the period 2017–2024, including vital statistics, Eurostat data, and national statistical office records. The core methodological approach is the decomposition of changes in the total fertility rate (TFR) using the frameworks developed by Kitagawa (1955) and Das Gupta (1993), which allow the separation of structural components (changes in the age and parity composition of women) from behavioral components (changes in fertility rates within these groups). Additionally, tempo-adjusted fertility measures following Bongaarts and Feeney (1998) are applied to correct for distortions caused by the postponement of births. The analysis includes age-specific and parity-specific fertility rates, enabling the identification of shifts in first, second, and higher-order births. To evaluate the impact of crises, a counterfactual event-study design is employed, comparing post-2022 fertility trajectories with pre-pandemic trends and with selected Western and Central European countries that differ in institutional stability and proximity to the conflict. This approach allows capturing both the direct effects of shocks and their interaction with longer-term demographic processes.

Results

In Poland, the total fertility rate declined from approximately 1.45 in 2017 to around 1.10 in 2024, representing a drop of nearly 24%. Decomposition results indicate that about 80% of this decline was driven by behavioral factors, while only 20% can be attributed to structural changes. The most pronounced decreases were observed among women aged 25–34, particularly in first births, which account for the majority of the post-2021 decline. At the same time, the mean age at childbirth increased, indicating postponement of reproductive decisions. The combined impact of the pandemic and the war produced a “double shock” that reinforced lasting changes in fertility behavior.

Conclusions

The findings suggest that the fertility decline in Poland is primarily behavioral and reflects responses to rising uncertainty caused by the pandemic and the war in Ukraine, rather than long-term structural demographic changes. The postponement of first births plays a central role and may have lasting implications for future fertility levels. The study highlights the importance of economic and geopolitical factors in shaping reproductive behavior and points to limited demographic resilience in the face of compounded crises.

Keywords

fertility, uncertainty, COVID-19 pandemic, war in Ukraine, demographic decomposition

Objective

The aim of this paper is to present the findings of the European survey on gender-based violence (EU-GBV ) in Poland, as well as to address the scale, structure as well as variations of this phenomenon depending on gender and social context. The analysis focuses on identifying forms of violence and differences in how they are perceived and reported by respondents.The use of a common methodology across European Union countries makes it possible to obtain comparable results and assess the phenomenon of violence.

Methods

The EU-GBV survey was carried out as part of a project coordinated at European Union level and funded by Eurostat. In Poland, the survey was conducted by the Statistics Poland. As part of the project, a survey on gender-based violence (EU-GBV) was conducted at national level on a randomly selected sample of respondents (women only), using a questionnaire and methodology developed at European level. The data come from a representative survey conducted on a sample of adults (aged 18 and over), selected at random, allowing the results to be generalised to the country’s population. A standardised questionnaire covering various forms of violence (physical, psychological, sexual, economic and digital violence) was used as the basis for developing the Polish version of the questionnaire. The analysis utilised statistical weights to adjust the sample structure relative to the population. The quality of the results was assessed through an analysis of estimation errors and the application of basic statistical inference methods, including confidence intervals. Limitations arising from the underreporting of violence and differences in respondents’ willingness to disclose their experiences were also taken into account.

Results

The results indicate a significant scale of gender-based violence (EU-GBV) in Poland and its clear gender, age and context differences. The survey was conducted among a group of randomly selected female respondents across sixteen provinces in Poland. Most often, forms of psychological and economic violence are reported, with women more often declaring experience of violence in private relationships, and men less often revealing such events. There has also been a significant contribution of violence in the digital space and in the workplace. Interpretation of the results indicates the impact of social and cultural norms on the level of disclosure of violence.

Conclusions

The obtained results underline the need to further develop research on gender-based violence using methods of official statistics and to improve measurement tools. It is important to take into account differences in reporting violence and strengthen procedures to ensure the reliability of data. The results are important for shaping public policies, including preventive and intervention measures, and contribute to the development of methods for the analysis of social phenomena of a sensitive nature.

Keywords

gender-based violence, violence against women, mobbing, stalking, childhood violence

Objective

The study aims to examine how differences in the institutional design of long-term care (LTC) in Central and Eastern Europe, particularly in Poland and Czechia, are associated with older adults’ quality of life. Special attention is given to two regional LTC trajectories: the more family-based and subsidiarity-oriented Polish model and the Czech model of limited universalism. These are further contrasted with LTC regimes in the Netherlands, Germany, and Italy.

Methods

The analysis uses representative panel data from the SHARE study (Survey of Health, Ageing and Retirement in Europe), covering 28,025 older adults across five waves in Poland, Czechia, Germany, the Netherlands, and Italy. Country selection was purposeful and comparative. Poland and the Czech Republic are treated as two Central and Eastern European cases that, despite sharing a common regional classification, represent distinct institutional arrangements for long-term care. The Netherlands, Germany, and Italy, representing distinct LTC regimes: Nordic, mixed, and family, provide a comparative context. Quality of life is measured using the CASP index and its domains (control, autonomy, self-realization, pleasure), along with overall life satisfaction. Key explanatory variables include four LTC dimensions: formal institutional care, formal home care, receiving help, and providing help. Models control for age, gender, physical and mental health, financial situation, education, marital status, GDP per capita, income inequality, and the COVID-19 period, treated as a natural experiment (“stress test”) for LTC systems. Regression mixed models were applied to analyze the overall relationships between LTC forms and quality of life and their variation across countries.

Results

Receiving help is associated with lower quality of life, particularly in control and self-realization, with stronger effects in Poland and Czechia than in Germany and the Netherlands, suggesting a higher burden of dependency where LTC relies more on family or has weaker formal support. Formal home care is also linked to lower CASP, especially in control. Institutional care shows no clear negative association after controls. A notable contrast emerges: providing help is positively associated with quality of life in Poland but negatively in Czechia. The strongest predictors remain physical and mental health and perceived financial situation.

Conclusions

Central and Eastern Europe is not homogeneous in how LTC relates to older adults’ quality of life. Poland and Czechia, despite similar regional classifications, exhibit distinct patterns of relationships between receiving and providing care and the well-being of older adults, indicating that LTC outcomes depend not only on formal care types but also on institutional, familial, and cultural contexts, and on whether help is experienced as support or dependence. Similar LTC forms may thus have different consequences across care regimes.

Keywords

long-term care: older adults’ quality of life: Poland: Czechia: SHARE

Objective

The aim of this study is to examine the level and heterogeneity of job satisfaction among workers aged 50 and above in Europe in the post-COVID-19 period. The paper investigates whether disruptions to work during the pandemic, such as remote working and changes in working time, are associated with job satisfaction and how patterns of satisfaction differ across countries and socio-economic characteristics of older workers.

Methods

The analysis is based on data from Wave 9 (2021–2022) of the Survey of Health, Ageing and Retirement in Europe (SHARE), a cross-national, representative survey covering more than 20 European countries and Israel. The analytical sample includes 4,858 respondents aged 50 and above who were economically active at the time of the interview. Job satisfaction is assessed using a multidimensional set of indicators describing working conditions, including physical workload, time pressure, autonomy at work, job security, opportunities for skills development, recognition and support, as well as the perceived adequacy of earnings. All variables are harmonised across countries following SHARE methodological guidelines. In the first step, exploratory factor analysis is applied to identify latent dimensions underlying job satisfaction and to reduce the dimensionality of the observed indicators. Factor scores are subsequently used as inputs in a cluster analysis, which allows the identification of distinct and internally homogeneous job satisfaction profiles among older workers. In the final step, multinomial logistic regression models are estimated to examine the associations between cluster membership and individual characteristics such as age, gender, and educational attainment, as well as pandemic-related work disruptions, including the experience of remote working and changes in working hours during the COVID-19 period.

Results

Four job satisfaction profiles are identified: physically strained workers (26.8%), dissatisfied workers (27.0%), discouraged workers (25.2%), and satisfied workers (20.9%). Thus, around 80% of workers aged 50 and over experience limited job satisfaction, although for different reasons. The distribution of these profiles varies markedly across countries. Regression results indicate that pandemic-related work disruptions had a limited overall impact on job satisfaction, while remote working reduced the likelihood of belonging to the physically strained group. Higher educational attainment and greater perceived job security significantly increase the probability of being satisfied with one’s job.

Conclusions

The findings suggest that in the post-pandemic period the key challenge for extending working lives lies in job quality rather than in temporary pandemic-related disruptions. Low job satisfaction among older workers is widespread and strongly differentiated across countries. Policies aimed at improving working conditions, especially by reducing physical strain and strengthening job security and recognition, may play an important role in promoting longer and more sustainable employment among people aged 50 and above.

Keywords

job satisfaction: older workers: COVID‑19: SHARE: labour market

Objective

The aim of the study is to assess whether the transition toward a circular economy contributes to reducing regional disparities in separate municipal waste collection in Poland. The analysis focuses on identifying convergence processes across selected waste fractions and examining the role of socio-economic factors in shaping both the pace and direction of these changes across regions over time.

Methods

The study was based on a panel dataset covering Polish counties for the years 2017–2024, obtained from the Local Data Bank of the Central Statistical Office (GUS). The analysis was conducted in three complementary stages. First, dynamic panel models estimated using the system GMM approach were applied to test the hypothesis of ß-convergence and to capture regional heterogeneity as well as dynamic interdependencies between observations over time. Second, the non-parametric Phillips and Sul log-t test was employed to identify convergence clubs and to assess the existence of a common development path across units, allowing for heterogeneous transitional dynamics. Third, discrete choice models (logit and ordered logit) were used to examine the impact of socio-economic factors on the probability of counties belonging to specific convergence clubs. In panel models, the dependent variable was the volume of municipal waste in four fractions: paper and cardboard, glass, bio-waste, and bulky waste. Explanatory variables included income levels, unemployment rate, degree of urbanization, population density, demographic structure, and tourism intensity. All analyses were conducted using STATA and R software, ensuring robustness and comparability of the results.

Results

The results indicate the presence of conditional ß-convergence for paper and cardboard, glass, and bulky waste fractions which means that regions with higher initial waste levels experience lower rates of waste growth. In contrast, a divergence process was observed for bio-waste, indicating increasing regional disparities over time. The log-t test confirmed the absence of global convergence and revealed the existence of several convergence clubs characterized by clear spatial differentiation. The econometric results further show that socio-economic factors such as income levels, degree of urbanization, tourism intensity, and unemployment significantly influence the likelihood of counties belonging to specific convergence groups.

Conclusions

The transition toward a circular economy in Poland contributes to reducing regional inequalities only in selected waste streams. The absence of global convergence and the presence of convergence clubs indicate persistent heterogeneity in waste management systems across regions. These findings highlight the need for differentiated regional policies tailored to local socio-economic conditions rather than uniform national approaches. The study contributes to the literature by applying advanced convergence analysis methods at the subregional level, providing new insights into the spatial dynamics of waste management and circular economy transitions.

Keywords

waste, convergence, counties

Objective

This study aims to identify latent components of reporting behavior among micro and small enterprises and to assess whether data obtained from indirect survey questions, adjusted for behavioral factors, can be used in an econometric model to estimate the unobserved component of firm revenues. The analysis explores the potential of such data to approximate non-observable aspects of economic activity.

Methods

The study is based on primary survey data covering micro and small enterprises for the years 2022–2023, with more than 10,000 firms surveyed annually. The dataset is representative of the SME population. An indirect survey approach was applied, in which respondents acted as expert informants on conditions within their industries. Firms reported subjectively “satisfactory” levels of revenues, costs, and income, which were used to identify latent components of financial reporting behavior. To account for behavioral heterogeneity, a Sentiment Index was constructed, aggregating declared expectations, motivations, and perceptions of business conditions. This index was incorporated as a correction variable in the modeling process. A structural econometric model was then estimated, decomposing expected revenues into observable and latent components. Estimation was conducted using structural modeling techniques, and uncertainty was assessed through standard errors and parameter significance tests. The latent component is interpreted as a systematic deviation in subjective financial reporting, serving as a proxy for non-observable economic activity rather than a direct measure of the shadow economy

Results

The findings reveal significant differences between reported and adjusted financial performance in the analyzed population. Incorporating the Sentiment Index allows for the identification of an underlying structure in firms’ reporting behavior. The latent component is statistically significant and varies across sectors, indicating a systematic pattern in reporting deviations. The results are partially consistent with existing estimates of the shadow economy published by the Central Statistical Office (GUS), supporting the potential of the proposed approach as an approximate measurement tool for informal economic activity in the SME sector

Conclusions

The results show statistically significant differences between reported and behaviorally adjusted financial performance. The Sentiment Index reveals systematic patterns in reporting behavior, indicating that declarations are influenced by expectations and perceived conditions. The latent revenue component is significant and varies across sectors, suggesting structured rather than random deviations. The magnitude of these differences indicates that the model captures part of unobserved economic activity. The findings are partly consistent with estimates of the shadow economy published by the Central Statistical Office (GUS), supporting the validity of the approach.

Keywords

SMEs, shadow economy, survey data, econometric model, Sentiment Index

Objective

The aim of this article is to analyze changes in the higher education premium in Poland between 2002 and 2022. The research hypotheses are as follows: (1) The increase in demand for skills caused by technological progress translated into an increase in the higher education premium, (2) The strong increase in the labor supply of people with higher education, especially women, was a factor reducing the higher education premium.

Methods

The data source is individual data on individual wages and employer and employee characteristics from the Structure of Wages Survey by Occupation. The data are representative of the population working in firms employing 10 or more people. We use aggregated data across age groups, gender, and ownership sectors. We divided the sample into seven age groups, separately for men and women, and for the public and private sectors – a total of 28 age-gender-sector subgroups. We included fixed effects in the model to account for group-specific effects. The theoretical basis for the analyses is the canonical production function model of Card and Lemieux (2001), which we extend in two ways. First, we add a third dimension (besides age and gender) by varying the education premium between the public and private sectors. Second, we allow for the elasticity of substitution between production factors to vary over time. We are interested in estimating the impact of both the relative aggregate supply of workers with tertiary and secondary education on the size of the tertiary education premium, as well as assessing the role that changes in the relative labor supply across age groups, genders, and sectors have played in explaining the relative increase in returns to tertiary education. We test the hypothesis of imperfect substitution between workers with tertiary and secondary education, controlling for age, gender, and sector.

Results

The results indicate a significant role for changes in the relative aggregate labor supply of individuals with higher or secondary education, as well as a significant role for changes in the relative labor supply across age groups, gender, and sector in explaining changes in the higher education premium. The role of demand factors was small. The estimated values ??of the partial substitution elasticity are significantly higher for women than for men. Furthermore, sectoral differences play a significant role in explaining wage differences. The estimated values ??of the partial substitution elasticity are significantly higher for the public sector than for the private sector. Furthermore, the results indicate an increase in the partial substitution elasticity for individuals with higher or

Conclusions

The research shows that, unlike analyses for the US, where demand factors and technological changes played a significant role in shaping the premium for higher education, in Poland, changes in relative labor supply across age, gender, and ownership sector play a key role. The results show that women across age cohorts and sectors are treated by employers as a more substitutable production factor than men. The results also indicate a higher substitutability of workers with the same education in the public sector than in the private sector.

Keywords

Education premium, wages, labor supply, age cohorts.

Objective

The aim of this paper is to define functional urban areas. The research problem addresses the limitations of approaches used by, among others, Eurostat, which focus on one-way commuting flows into cities while ignoring internal linkages and outbound flows from urban centers. It is assumed that the application of graph methods enables a more comprehensive representation of actual functional structures and relationships between territorial units.

Methods

This study used public statistics data from the commuting survey conducted as part of the 2021 National Census. For comparative purposes, the commuting matrix was transformed to treat urban-rural municipalities as single units, without distinguishing between their urban and rural parts. A network approach was applied, in which municipalities were represented as graph nodes and flows of employed workers between them as weighted edges. Functional urban areas were identified using graph methods, particularly community detection algorithms (Leiden and Louvain), which allow the identification of structurally coherent groups of nodes with strong internal connections while taking bilateral relations into account. Centrality measures were also employed to identify centers playing key roles in the network structure and to determine their importance within the functional system. Additionally, an accessibility matrix developed as part of the experimental statistics of the Central Statistical Office (GUS) was used, containing travel times between municipalities. The obtained results were compared with other delimitations of functional urban areas, including those based on the Eurostat methodology. Differences were assessed using selected structural measures and local graphs.

Results

The results indicate that the use of graph methods leads to the identification of functional urban areas with greater internal cohesion than the traditional approach. The network approach enabled the capture of bilateral relationships, resulting in a more realistic representation of functional structures. Comparative analysis revealed differences from existing delimitations of functional urban areas, especially regarding boundary delineation. Furthermore, the inclusion of the accessibility matrix strengthened the identified connections between municipalities, contributing to greater cohesion of the identified structures and a better reflection of actual transport conditions.

Conclusions

The use of graph methods combined with commuting data provides an effective tool for delimiting functional urban areas. This approach allows for a better understanding of actual functional structures and spatial relationships. The results indicate that network analysis can significantly support spatial planning and regional policy by enabling a more precise definition of urban catchment areas. Furthermore, incorporating other types of flows, such as commuting to school, allows for a more comprehensive understanding of the multidimensional nature of connections between territorial units.

Keywords

functional urban areas, graphs, community detection, spatial accessibility, commuting to work, commuting to school

Objective

The study examines economic resilience from a micro-spatial perspective, focusing on how local industrial structures and firm dynamics shape variation within regions. Resilience is defined through four components: resistance, recovery, renewal, and reorientation. The paper introduces a grid-level measure - the Local Industrial Resilience Index (LIRI), to identify neighbourhood-level patterns and assess how diversity, specialisation and related variety are associated with local resilience.

Methods

The analysis is based on geo-referenced firm-level data from the REGON register for the Mazowieckie region (Poland), covering two cross-sections: 2012 and 2021. The study uses the full population of firms (approximately 989,000 in 2012 and 1.11 million in 2021), rather than a sample, which eliminates sampling-related uncertainty and the need for statistical inference based on sampling error. Firms are assigned to a regular 2×2 km grid (approximately 9,320 cells), enabling a fine-grained spatial analysis. The study introduces LIRI, a composite measure integrating four dimensions of resilience: resistance, recovery, renewal, and reorientation. The index combines information on firm survival and entry with indicators of local industrial structure, including diversity, specialisation, concentration, and related variety. Resistance and recovery are estimated as standardised residuals from econometric models (binomial model for firm survival and negative binomial model for firm births), comparing observed and expected values. Renewal and reorientation capture entries into related and unrelated sectors, based on measures of sectoral relatedness derived from co-occurrence patterns. This approach allows controlling for structural characteristics and identifying deviations from expected dynamics at the local level.

Results

The results show that economic resilience varies significantly across space and does not follow a simple core-periphery pattern. While the Warsaw metropolitan core exhibits high resilience, several medium-sized towns also demonstrate strong adaptive capacity. In contrast, many peripheral and mono-industrial areas remain structurally vulnerable. Related variety emerges as a key factor supporting both firm survival and renewal, whereas high sectoral concentration is associated with weaker resilience across all dimensions. These findings highlight the importance of local industrial structure in shaping resilience outcomes.

Conclusions

The results indicate that regional resilience is shaped by local industrial structures rather than scale alone. Areas characterised by greater diversity and related variety show higher adaptive capacity, while sectoral concentration increases vulnerability to shocks. The proposed LIRI measure provides a consistent framework for analysing resilience at a fine spatial scale. The findings contribute to empirical research on resilience and offer useful insights for place-based regional policy, particularly in supporting diversification and strengthening local economic linkages.

Keywords

composite measure: local resilience: firm resistance: firm renewal: spatial analysis

Objective

Categorical variables such as occupation (ISCO) or industry (NACE) classifications frequently change their encoding between survey waves, which precludes longitudinal comparisons. This study presents the cat2cat method for harmonizing inconsistently coded categorical variables and demonstrates its empirical application to the analysis of the relationship between age structure and wages across occupational groups using Polish Structure of Wages and Salaries Survey data (2006–2014).

Methods

The cat2cat method was originally proposed by Nasiński, Majchrowska, and Broniatowska (2020), subsequently formalized and extended by Nasiński and Gajowniczek (2023). The method has been further refined following expert review, including improvements in error handling and diagnostic transparency. The cat2cat algorithm uses a transition table for harmonization. The mapping is bidirectional, enabling harmonization from old encoding to new and vice versa. When encoding results from classification evolution, mapping of the new (more detailed) encoding to the old (less detailed) one is often ambiguous, whereas the reverse mapping may be more straightforward. The 2010 revision of the Polish Classification of Occupations (KZIS) affected approximately 85% of 4-digit codes, previously precluding the analysis of wages across specific occupational groups over time. Using the cat2cat method, comparability of SWZ data for 2006–2014 was restored and an augmented Mincer wage equation was estimated at the 3-digit occupational group level. The variable of interest is the share of workers aged 55–65 in a given occupational group. The empirical application uses 5 waves of the Structure of Wages and Salaries Survey (SWZ, 2006–2014, approx. 700,000 observations per wave). The design weight variable from SWZ was used to generalize results to the population.

Results

In the full-sample regression, the age structure coefficient changed significantly over time: before 2010 it was negative or near zero, whereas after 2010 it became positive, potentially indicating the decline of a *seniority premium* characteristic of the earlier period. At the aggregate level for Professionals, an unexpectedly strong negative correlation between wages and the share of older workers was observed. Disaggregation to the 3-digit level, however, revealed considerable internal variation — for example, wages of older physicians increase with experience, whereas the knowledge of older engineers and IT specialists may become obsolete.

Conclusions

The cat2cat method, progressively developed from an initial concept (2020) through formalization and open-source implementation (2023, R / Python) to further refinements informed by expert review, provides a complete tool for harmonizing evolving categorical classifications in longitudinal data. Its application to Polish wage data demonstrates both the practical utility of the method and novel findings on the heterogeneity of the relationship between age structure and wages across occupational groups. The methodology is applicable wherever categorical encodings change over time, including epidemiology (ICD) and economic activity statistics (NACE).

Keywords

data harmonization, categorical variables, longitudinal studies, machine learning, statistical software

Objective

The aim of the presentation is to present a methodological approach to using data from online job advertisements as a non-statistical source of labor market information in the study of skills requirement. In particular, we will discuss how the data were processed and analyzed. Additionally, the objective is to demonstrate how a properly designed methodological approach can enhance the usefulness of job advertisement data in labor market research, while taking into account limitations related to data quality, representativeness, and the specific nature of this type of source.

Methods

In the described model, a research approach combining qualitative and quantitative analysis was applied in order to comprehensively capture the structure and dynamics of skills requirement in selected occupations based on online job advertisements. The adopted perspective assumes the use of complementary analytical techniques, which increases the validity of inference and reduces the limitations arising from the specific nature of the data source. In the presented research approach, exploratory methods were used in the qualitative analysis, including content analysis and approaches specific to natural language processing (NLP), enabling, among others, the identification and standardization of skill category names. Categorization procedures were applied to develop a skills grouping framework, leading to the creation of a structured, original classification of skills used in further analysis. In the quantitative analysis, statistical description and elements of machine learning were employed, including clustering techniques and co-occurrence analysis, which made it possible to test an alternative method for identifying key skills in specific occupations. The data sources included information from online portals containing job advertisements. The analysis was conducted on a purposive sample of online job advertisements for selected occupations, within which more than 26 million instances of required skills were analyzed over the period from 2018 to 2025.

Results

The skills analytical model developed as part of this work is an example of how a properly designed methodological approach can increase the usefulness of job advertisement data in labour market research, while taking into account limitations related to data quality, representativeness, and the specific nature of this type of source. The OJA database as a data source can supplement analyses of labour demand and labour market changes, contributing to a better alignment between education and the labour market. It can also support the identification of skills gaps and supplement information derived from statistical data.

Conclusions

The skills analytical model developed as part of this work is an example of how a properly designed methodological approach can enhance the usefulness of job advertisement data in labor market research, while taking into account limitations related to data quality, representativeness, and the specific nature of such sources. The OJA database as a data source can support analyses of labour demand and labour market changes, contributing to a better alignment between education and labor market needs. It can also support the identification of skills gaps and supplement information derived from statistical data.

Keywords

online job advertisements, skills demand, new data sources, web scraping, ISCO

Objective

This study aims to provide a comprehensive global validation and extension of the entropy-marginal product model introduced by Bwanakare, Cierpiał-Wolan, and Rzeczkowski (2025), confirming their groundbreaking theoretical hypothesis that CO2 emissions are fundamentally driven by the interaction between thermodynamic entropy efficiency and energy marginal productivity. The research seeks to establish universal applicability of this framework across diverse economic systems and development stages, while demonstrating its superior predictive power compared to conventional energy intensity metrics

Methods

The study analyzes 127 countries over 23 years (2000-2022), representing 98.4% of global CO2 emissions. The theoretical framework utilizes nonextensive entropy based on Tsallis statistics with parameter (q), opposing conventional Boltzmann-Gibbs statistics. This aligns with Bwanakare`s theoretical predictions that economic-energy systems exhibit long-range dependencies inadequately captured by extensive thermodynamic frameworks. The entropy-to-marginal product ratio (?) serves as the key analytical metric. Analytical methods include spatial econometrics identifying cross-country spillover effects, panel regression with fixed and random effects controlling for heterogeneity, and dynamic modeling capturing temporal ? evolution. The study analyzes ? heterogeneity across developed economies, emerging markets, and developing nations. Comparative analysis evaluates explanatory power between the entropy-marginal product framework and conventional indicators including energy-to-GDP ratios and carbon intensity metrics. The research incorporates out-of-sample forecasting, temporal trend analysis, and optimization modeling for climate finance allocation. Robustness checks include sensitivity analysis for parameter q and cross-validation procedures ensuring reliability across contexts.

Results

The results confirm the main hypothesis, demonstrating that the ? ratio achieves explanatory power of R2 ranging from 0.847 to 0.923, surpassing conventional indicators (R2 = 0.521-0.687) with predictive improvement of 35-62%. Systematic heterogeneity of ? was documented: developed economies 0.847, emerging markets 0.789, developing countries 0.712. Temporal reinforcement amounts to +36% since 2000, with acceleration of +16.6% after the Paris Agreement. Spatial analysis reveals significant spillover effects (? = 0.156, p * 0.001). Entropy-optimized finance allocation achieves 26.3% higher cost-efficiency than GDP-based approaches. The parameter q exceeds unity (mean: 1.21, range: 1.12-1.31), confirming the necessity of Tsallis statistics.

Conclusions

The study represents a breakthrough in understanding CO2 emission mechanisms, empirically demonstrating the necessity of describing economic-energy systems within the framework of nonextensive thermodynamics. The entropy-marginal product metric enables identification of high-impact intervention points and optimization of resource allocation. The demonstrated 26.3% higher cost-efficiency suggests that policymakers should adopt the nonextensive entropy framework as the foundation for climate policy. Identified cross-country spillover effects underscore the importance of international coordination, while systematic ? heterogeneity indicates the need to differentiate policies according to countr

Keywords

CO₂ emissions forecasting: Tsallis entropy: marginal product of energy: thermodynamic efficiency: panel econometrics

Objective

The aim of the paper is to identify and analyse statistical disclosure control challenges for data presented as flow matrices (e.g. permanent internal migration, commuting to work, commuting to school). The main research hypothesis states that publishing such data — in particular with zero-valued flows disclosed — poses a serious risk of revealing cross-sections intended for suppression (cell suppression). The additional release of data at higher levels of territorial disaggregation may further increase this risk.

Methods

The study was conducted on real public statistics data concerning permanent internal migration, obtained from the resources of Statistics Poland (Demografia Database). The analysis covered flows at the level of communes, districts, subregions and voivodeships, allowing for a comprehensive assessment of how disclosure risk varies with the availability of different spatial aggregation levels. Methods from the field of Statistical Disclosure Control (SDC) were applied, including the analysis of sensitive cells based on the dominance rule (n, k), the analysis of zero cells as potential carriers of information, and the examination of interdependencies between cells within the matrix. A formalisation of the zero-flow problem was proposed, identifying the mechanism by which aggregation to higher spatial levels reveals additional information about flows at lower levels of data aggregation. The problem of dangerous zeros was treated separately — cells which, although formally absent from the set of sensitive cells, may in combination with an intruder`s auxiliary knowledge lead to a breach of data confidentiality at the level of individual flows. All analyses were carried out using custom programs written in the R language.

Results

The analysis conducted on data concerning inter-communal permanent migration, available in the Demografia database, demonstrated that zeros in the flow matrix are not informationally neutral — their presence unambiguously indicates the absence of specific relations between units, which in combination with additional external data may enable the indirect identification of suppressed cells. Importantly, access to complete data at a higher level of spatial aggregation (districts, voivodeships) increases the disclosure risk: the aggregated flows for larger territorial units allow for the additional reconstruction of data at the inter-communal level.

Conclusions

The results indicate the need to develop dedicated statistical disclosure control procedures for data presented in the form of flow matrices. Such procedures should account for both the informational role of zeros and the hierarchical dependencies between spatial aggregation levels. Existing confidentiality protection standards in official statistics do not sufficiently address the specific characteristics of data presented as flow matrices. The paper outlines possible directions for further research.

Keywords

statistical disclosure control, flow matrices, zero flows, primary suppression, aggregated data utilization.

Objective

The LabFam Individual Biographies project aims to enhance reproducibility and comparability in social science research by developing an open, harmonized database of family and employment biographies. It focuses on reconstructing consistent fertility, partnership, and employment trajectories across five long-running panel surveys. By integrating multiple data sources and aligning life-course information across countries, the project enables robust cross-national analyses of life-course dynamics.

Methods

The study is based on the harmonization of data from five long-running panel surveys: HILDA (Australia), SOEP (Germany), SHP (Switzerland), BHPS / UKHLS (United Kingdom), and PSID (United States). For each dataset, individual biographies were reconstructed in three domains: fertility, partnership, and employment. Data from core questionnaires, calendar modules, and retrospective components were used to recover events occurring between survey waves and prior to panel entry. The results are stored as dated spells with clearly defined starts and ends, enabling analyses of durations and transitions between states. Harmonization included standardizing variable definitions, resolving inconsistencies across sources, and prioritizing information closest in time to the observed episode. In the employment domain, a common classification of labor market statuses was applied, and overlapping information was resolved using a hierarchical ordering of states. The entire process was implemented in R as open, modular code that users can reproduce, customize by country, period, and domain, and link to other infrastructures such as CPF / CNEF. Validation procedures included comparisons with indicators derived from source surveys and external demographic and labor market statistics.

Results

The project resulted in an open data infrastructure enabling the reconstruction of comparable individual histories of fertility, partnership, and employment across five countries. Internal and external validation showed a high level of consistency between LIB-derived indicators and benchmark data, including measures of childlessness, age at childbirth, age at first marriage, and labor force participation of women and men. Minor discrepancies were mainly limited to specific cohorts and datasets, confirming the overall reliability and comparability of the harmonized biographies. The highest consistency was observed for SOEP, SHP, and BHPS / UKHLS, while larger deviations appeared primarily in PSID and in the youngest or oldest cohorts.

Conclusions

LIB reduces the costs of data preparation for comparative analyses and enhances transparency in life-course research. Its open-source and modular code ensures full reproducibility, makes harmonization decisions explicit, and allows users to adapt and extend the framework to their needs. By providing harmonized, spell-based histories, the project offers a tool that integrates family and employment perspectives in cross-national analyses and supports research on the interplay between employment uncertainty, partnership formation, childbearing, and labor market withdrawal.

Keywords

data harmonization, research reproducibility

Objective

The Polish Labour Force Survey (BAEL) is facing new challenges, expectations, and requirements. These stem from changing user needs and expectations, legal requirements (e.g., the new framework regulation on European social statistics), and the circumstances of fieldwork (e.g., the COVID-19 pandemic). The paper presents the changes to the way of calculation of estimation weights in the Polish LFS implemented from 2020 to the present, driven by all the factors mentioned above.

Methods

The presented actions, aimed at addressing the challenges mentioned above, involve changes in the methodology of weighting (estimation). The primary method is a calibration – calibration methods and conditions have been changed to more appropriate ones, or calibration has been introduced where it was not previously used. Regarding individual weights, the change primarily involved a shift from a simple post-stratification to a general calibration, which allowed for the inclusion of more calibration conditions to, among other things, better meet user needs. Calibration conditions have been expanded for regional estimates. Reference week conditions have been introduced, improving representativeness over time, which is particularly important in the case of unevenly distributed in time disturbances in the interviewing process (as was the case during the pandemic). In the case of household weights, a specific problem is the consequence of accepting partial unit non-responses in the survey, i.e., situations where the refusal to participate in an individual interview concerns some household members. Household weights were previously calculated as an average of household member weights and did not allow for estimates consistent with the individual estimates. The treatment of incomplete interviews in calculating household weights has been changed, and a calibration has been introduced to ensure consistency with the individual estimates.

Results

The introduction of general calibration for calculating individual weights allowed for controlled quality of estimates for a larger number of cross-sections meeting user needs, while also avoiding sample size problems in too small post-stratification strata, which negatively impacted the stability of the weights and the quality of estimates. The use of reference week based conditions ensures equal representation of all weeks of the quarter and eliminates the sensitivity of the results to variations in sample size across weeks (potential bias). Consistency was achieved between the estimates obtained using household weights and individual estimates for the basic cross-sections, including economic activity status.

Conclusions

The methodological changes allowed us to meet the requirements of the framework regulation and Eurostat`s expectations (primarily regarding weights and estimates for households), improve the quality of results, and enhance their usefulness for user needs. The change in the calculation of household weights facilitates analyses that require consideration of relationships between household members and improves the consistency of their results. The solutions for calibration with respect to reference week (time) or for dealing with incomplete household interviews may find wider application.

Keywords

Labour Force Survey, weighting, estimation, calibration

Objective

The aim of the study is to identify the latent factors determining Healthy Life Years (HLY) in selected European Union countries and to assess the extent to which the directions of maximum variability in the set of health determinants coincide with the directions of highest predictive power for HLY. The central research question is: does supervised dimension reduction (PLS) offer an advantage over unsupervised reduction (PCR) under conditions of strong multicollinearity and a limited number of panel observations?

Methods

Two dimension reduction methods are applied: Principal Component Regression (PCR) and Partial Least Squares Regression (PLS). PCR is an unsupervised procedure in which a spectral decomposition of the correlation matrix of explanatory variables precedes OLS estimation on the selected components. PLS, in contrast to PCR, maximises the covariance between X and Y (NIPALS algorithm), which makes it a supervised method. The sample comprises 120 panel observations: 6 countries (Austria, Denmark, Germany, Italy, Poland, Spain) × 2 sexes × 10 years (2015-2024). The dependent variable (HLY) and 10 health determinants (air pollution, education, material deprivation, social protection expenditure, population density, hospital beds, physicians, alcohol consumption, smoking, obesity) are drawn from Eurostat, the World Bank and WHO. The number of components is selected via the Kaiser criterion, Horn`s parallel analysis and Leave-One-Out Cross-Validation (LOOCV) minimising RMSEP. Sample adequacy for PCA is confirmed by KMO and Bartlett tests. The models are extended with country fixed effects and a binary COVID-19 dummy. Method comparison is based on: (a) LOOCV RMSEP, (b) a paired test on squared LOO residuals (Diebold–Mariano style), (c) the correlation between PCA loadings and PLS weights, (d) bootstrap confidence intervals (B=500). All computations are performed in R using the pls, psych, factoextra and car packages.

Results

PCR attains RMSEP=2.872 with 7 components, while PLS achieves a comparable RMSEP=2.856 with only 2 components. The paired test on squared residuals reveals no significant difference in predictive accuracy (p=0.925). The low correlation between PCA loadings and PLS weights (r=0.262) confirms that the directions of maximum variance in X do not coincide with the directions of highest predictive power. In the PCR model with fixed effects, only PC5 and PC6 are significant (not PC1–PC3, which absorb 73.9% of the variance). PCR+FE: R2=0.803 with 14 parameters: PLS+FE: R2=0.762 with 9. Bootstrap (B=500) confirmed the significance of 8 out of 10 PLS coefficients.

Conclusions

The results indicate that under strong multicollinearity and a limited n / p ratio, PLS offers a clear parsimony advantage over PCR, attaining comparable predictive accuracy with 3.5 times fewer components. The pooled panel approach eliminates the estimation instability typical of per-country analyses (n / p=1:1). Distinct profiles of HLY determinants are identified: behavioural-environmental factors dominate for men, while socio-economic factors prevail for women. The contribution to the literature is a formal comparison of PCR and PLS on panel HLY data and the demonstration that standard component selection criteria (Kaiser, Horn) may be suboptimal for predictive purposes.

Keywords

Healthy Life Years (HLY): Principal Component Regression (PCR): Partial Least Squares (PLS): dimension reduction: panel data

Objective

Characteristics of the measurement of economic poverty in Poland, taking into account the following issues: 1. Benchmarks for the objective measures of economic poverty used 2. Interpretation of the main indicators of economic poverty and privation 3. Data sources enabling the estimation of economic poverty and privation in Poland. 4. Trends in various types of economic poverty, privation and subjective assessments of financial circumstances 5. What scales are used in responses to questions regarding the subjective assessment of financial circumstances 6. Poverty and the social Cohesion

Methods

An analytical and synthetic approach to publications on research conducted by the Statistics Poland, from which the data used to estimate economic poverty are derived, as well as participant observation in the research process on poverty and social exclusion carried out by the Statistics Poland. The data sources on which the estimation of economic poverty is based are the results of the following representative surveys carried out by Statistics Poland: the annual Household Budget Survey (sample – approx. 30–35,000 households), the annual European Survey on the Living Conditions of the Population (approx. 14,000 households) and the periodic (every few years, with the latest edition of the survey in 2018) Social Cohesion Survey (current sample size – approx. 25–26,000 households). The research tools used in these surveys are questionnaires. The expenditure-based approach predominates in estimating the extent of the risk of economic poverty (this applies to the risk of extreme poverty, relative expenditure-based and legal poverty (which are estimated based on monthly expenditure on goods and services, including the renovation fund, using data from the Household Budget Survey) over the income-based approach (which is estimated based on annual income using data from EU-SILC and the Social Cohesion Survey).

Results

1. The ranges of risk associated with various types of economic poverty estimated in Poland, and the trends in these ranges over time, differ due to varying interpretations of the meaning of these types 2. The trends in extreme poverty and privation coincide (the constructs of both indicators are similar) 3. Trends in the prevalence of extreme poverty and assessments of a poor and very poor financial situation may show opposite dynamics 4. In subjective assessments for national purposes, a Likert scale is used instead of, for example, the 10-point scales used by Eurostat 5. Poverty was one of the elements of the analysis in the Social Cohesion Survey, which stemmed from the understanding of social cohesion adopted by Polish Statistics. UNECE ignores aspect of poverty in social cohesion.

Conclusions

1. In its study of poverty as a multidimensional phenomenon, Statistics Poland uses a relatively wide range of objective measures to estimate economic poverty and, as a supplement, also employs subjective assessments. 2. Each measure has a different interpretation and, from a methodological point of view, there is no basis for favouring any one of them. 3. In addition to the measures used to assess economic poverty, it would be worthwhile to introduce a measure that takes into account assets that can be quickly liquidated. The use of an indicator covering such assets would provide a more comprehensive picture of poverty. Legal poverty thresholds should be set more frequently than 3 years.

Keywords

the risk of extreme, relative and legal poverty: the risk of privation: the expenditure-based and income-based approaches:

Objective

The aim of this paper is to present the results of the estimation of the at-risk-of-poverty rate (AROP) in Poland at the subregion level (NUTS 3) for 2019–2023, using univariate (UFH) and multivariate (MFH) Fay-Herriot models. The study was carried out in cooperation between the Poznań University of Economics and Business, Statistics Poland, the Statistical Office in Poznań, and the World Bank. Small area estimation methods are applied to provide precise poverty estimates at a lower level of spatial aggregation than that previously published by official statistics.

Methods

The study covered all 73 subregions (NUTS 3) in Poland for the years 2019–2023. Direct estimates of AROP were obtained on the basis of the sample from the EU-SILC survey conducted by Statistics Poland, whose sample size and sampling design do not allow for the publication of reliable estimates below the regional level (provinces). In the Fay-Herriot models, the direct estimates of AROP were used as the dependent variable, while the auxiliary variables were taken from the Local Data Bank of Statistics Poland (BDL GUS) and described the demographic structure, the situation on the labour market, migration, income, and housing infrastructure (sets of 6 and 9 auxiliary variables were considered). In the UFH model, the empirical best linear unbiased predictor (EBLUP) was applied, which is a weighted combination of the direct and synthetic estimators. The multivariate Fay-Herriot (MFH) model extends the univariate approach by simultaneously taking into account a vector of correlated area-specific characteristics, which makes it possible to exploit the correlations between AROP and the auxiliary variables in order to improve the precision of the estimates. The model parameters were estimated by means of restricted maximum likelihood (REML), and the quality of the obtained estimates was assessed using the coefficient of variation (CV) and compared with the precision of the direct estimates.

Results

The poverty maps reveal a pronounced spatial variation of AROP in Poland: higher values are observed in central and eastern Poland, lower values in western Poland, and the lowest values in large cities and the subregions surrounding them. The estimates obtained on the basis of the UFH and MFH models are characterised by greater precision than the direct estimates: the coefficients of variation (CV) are noticeably lower throughout the entire analysed period of 2019-2023. The UFH models based on 6 and 9 auxiliary variables lead to similar results, while the application of MFH additionally improves the quality of the estimates by exploiting the correlations between area-specific characteristics, providing a coherent picture of the spatial differentiation of poverty in Poland.

Conclusions

The application of Fay-Herriot models makes it possible to estimate the AROP indicator at the level of 73 subregions (NUTS 3), for which results have not previously been published by official statistics in Poland. MFH constitutes an extension of the UFH approach and allows for the effective use of information derived from multiple correlated area-specific characteristics. The results of the study, carried out in cooperation with the World Bank, may be applied in the design of cohesion policy and the allocation of development funds. The proposed approach is transferable to other areas of official statistics in which there is a need for estimation at low levels of spatial aggregation.

Keywords

small area estimation, Fay-Herriot model, multivariate Fay-Herriot model, at-risk-of-poverty rate (AROP), poverty maps

Objective

The main objective of the research is to develop a methodology for a multidimensional analysis of energy poverty based on energy indicators and supplemented with non-energy data. The research problem focuses on creating an integrated set of indicators combining technical, economic, and social aspects, enabling a more precise diagnosis of the phenomenon than traditional single measures. The analysis covers the period 2018–2024.

Methods

The study uses data from the Household Budget Survey (HBS), conducted by Statistics Poland on a sample of approximately 32,000 households (2024 data). This source provides a representative database of information on expenditures, income, and living conditions, ensuring high reliability of the analysis. The study covers the entire surveyed population, which allows for drawing nationwide conclusions and identifying social and spatial disparities. Based on the source data, a set of energy poverty indicators was developed, reflecting both economic aspects and qualitative dimensions related to energy use and subjective assessment of housing conditions. The next stage involved an overlap analysis of indicators, enabling the identification of households affected by multidimensional energy poverty and the assessment of co-occurrence of different forms of energy deprivation. Subsequently, taxonomic methods were applied, including the Hellwig method, Weber method, and TOPSIS, as well as Principal Component Analysis (PCA). Non-energy data were incorporated into both approaches, enabling more precise classification of households, dimensionality reduction, and identification of the most relevant factors differentiating levels of energy poverty.

Results

The study showed that a change in the level of detail of questions in the HBS in 2020, affecting the LEAKS indicator, contributed to a noticeable decline in its national value. At the same time, observations from 2024 indicated an increase in selected non-financial indicators in regions affected by flooding, suggesting the sensitivity of these measures to sudden crisis events. The application of indicator overlap analysis enabled the visualization of the intensity of energy poverty. Taxonomic methods captured the direction and scale of changes in household conditions, while PCA enabled the identification of key factors influencing energy poverty levels. The results highlight the complexity of the phenomenon and importance of selecting appropriate analytical tools to assess its dynamics.

Conclusions

The developed synthetic index constitutes an innovative analytical tool enabling precise identification of areas requiring intervention in energy efficiency improvement. A key contribution to statistical methodology is the original combination of taxonomic methods with PCA, which enhanced the objectivity and stability of composite measures. The results indicate substantial regional disparities between voivodeships, which makes it necessary to analyse them individually rather than relying solely on national averages. This underscores the importance of a regional approach in diagnosing and monitoring energy poverty.

Keywords

energy poverty, taxonomy, principal component analysis (PCA), synthetic indicator

Objective

Within the economic literature, poverty is commonly conceptualized as a multidimensional phenomenon. One of these dimensions - in addition to the income dimension - is the housing situation of the population. The presentation aims to examine the relationship between income poverty and housing poverty across European countries, with particular emphasis on their co-occurrence and the extent of overlap between these dimensions in a comparative perspective.

Methods

The data used in the analysis come from the European Union – Statistics on Income and Living Conditions (EU-SILC) survey from 2020 for 30 European countries. EU-SILC provides harmonized and comparable microdata across European countries, making it a reliable source for analyzing social inequalities and living conditions. Its multidimensional approach, covering income, material deprivation, and housing conditions. The sizes of the datasets used is depend on the country and is e.g. 4192 for Cyprus and 19841 for Poland. The EU-SILC survey is essentially a representative survey, but the analyses used weights contained in the data set to correct deviations from the representativeness of the research sample.To identify housing poverty, the IFR (Integrated Fuzzy and Relative) method was used in a multidimensional non-monetary approach, taking into account technical, financial, and environmental dimensions. Monetarily poor households were identified using a poverty line defined as 60% of the median equivalized disposable income, calculated separately for each country. Next, the relationships between the risks of different types of poverty were examined using several measures of dependence, including Pearson`s linear correlation coefficient, Spearman`s rank correlation coefficient, distance correlation, mutual information, and normalized mutual information. Differences in housing poverty between monetary-poor and non-poor households were verified by a two-mean significance test.

Results

The results reveal substantial cross-country heterogeneity in both the level and structure of deprivation. The relationship between monetary and housing poverty is dimension-specific. A relatively strong and predominantly linear association is observed in the technical dimension, while the relationship in the financial dimension is weaker and less consistent. In contrast, the environmental dimension shows no clear relationship with monetary poverty. Correlation measures indicate near-zero dependence, and in some countries even suggest a reversed pattern, where higher-income households may experience greater environmental housing deprivation. An important result is the incomplete overlap between monetary and housing poverty – many households experience only one form of poverty.

Conclusions

The findings confirm that housing quality is only to a limited extent determined by household income and is instead strongly shaped by spatial, infrastructural, and policy-related factors. This points to the need for policy frameworks that move beyond income-based approaches and prioritize place-based interventions aimed at improving environmental quality and neighborhood conditions independently of redistribution mechanisms. Greater awareness of the risks associated with inadequate housing conditions can stimulate demand for high-quality, health-promoting housing and, consequently, strengthen public support for the development and implementation of policies that improve housing conditions.

Keywords

monetary poverty, housing poverty, multidimensional poverty measurement, fuzzy set approach (IFR), EU-SILC data

Objective

This study aims to examine how public transport accessibility affects the labour market in Poland. To this end, geographically weighted regression (GWR) will be used to examine the spatial variability of this relationship and to identify which areas of Poland could benefit most from increased accessibility. Based on the objective of the study, the following research hypotheses were formulated: 1. GWR models better describes the examined relationship than OLS models: 2. The relationship between the transport accessibility and the unemployment rate is significant and negative.

Methods

OLS and Geographically Weighted Regression (Brunsdon, Fotheringham * Charlton (1996)) were used to examine the impact of public transport availability on unemployment levels, using data from the Local Data Bank (Polish Statistics) for 2023 at the level of individual municipalities. The dependent variable in the study is the share of registered unemployed persons in the working-age population in the municipality. The main independent variable on which the analysis focuses is the developed transport accessibility index, which takes into account number of bus stops in the municipality per square kilometre, municipal expenditure on transport per capita, number of carriers registered in the voivodeship per 100,000 inhabitants, transport network density in the voivodeship, rail network density in the poviat. A key element of the analysis will be to examine the impact of this variable on the unemployment rate. A negative relationship between accessibility and the unemployment rate is expected. In addition, control variables, both demographic and economic, as well as a division into types of municipalities (urban, urban-rural, rural) were included in the model in order to avoid the error of omitting important variables. Two GWR models will be analysed: first, using an individual transport accessibility indicator for the municipality, and second, using a variable that determines the average transport accessibility of the poviat in which the municipality is located.

Results

The three tests proposed in Leung et al. (2000a) performed on GWR models showe that local models describe the relationships between variables much better than their global counterparts. Test based on Moran`s I statistic from Leung et al. (2000b) showed that spatial approach in modelling unemployment performs better than a-spatial. The spatial distribution of the regression coefficient values for the transport accessibility variable shows significant variation in both models analysed. In each model, the median coefficient is negative, which allows us to conclude that for most municipalities, improved transport accessibility is associated with a decrease in the unemployment rate.

Conclusions

Transport accessibility proved to be a significant variable explaining the level of unemployment, although its impact was not uniform across the country. In most municipalities, the regression coefficient for accessibility was negative, confirming the expected negative relationship between transport accessibility and unemployment levels. However, this variable was statistically significant only in selected regions, mainly on the peripheries of voivodeships, especially in border regions. This suggests that improving transport accessibility may be particularly effective in reducing unemployment in isolated areas most vulnerable to transport exclusion.

Keywords

transport accessibility, unemployment rate, geographically weighted regression, transport exclusion, spatial mismatch

Objective

The aim of this presentation is to discuss the properties of the Young–Balk–Mehrhoff–Dikhanov (YBMD) elementary price index, which, on the one hand, is not well recognized in the literature, but on the other hand has the potential to serve as a very good alternative to the Jevons index recommended in CPI and HICP manuals. In particular, the purpose of the paper is to identify those situations in which the statistical properties of the YBMD index outperform the corresponding properties of the Jevons index.

Methods

The research methods used to achieve this objective are: (1) an analytical approach, consisting of formal mathematical proofs of selected properties of the YBMD index, and (2) an empirical study based on data collected by statistical office interviewers (data from 207 regions in Poland concerning sugar and bicycles), as well as on web-scraped data (prices of rice, yogurt, sugar, and coffee scraped from the website of a retail chain in Poland). Within the analytical approach, formulas for the bias, variance, and mean squared error (MSE) of the sample YBMD index were derived, and its properties were examined under the assumption of log-normal prices, taking into account the correlation between base-period and current-period prices. In the empirical study, in the case of data from survey regions, 207 price observations for each of the aforementioned products were used for each month covered by the study. In the case of scraped data, depending on the product, from several to several dozen observations were collected daily, depending on the number of available EAN codes for a given product on the website of the scraped retail chain. The empirical study did not attempt to generalize the findings from the samples: instead, the focus was on identifying the relationships between the values of sample elementary price indices, including the YBMD index.

Results

The results obtained in the study clearly indicate a number of advantages associated with the use of the YBMD index at the elementary level of CPI data aggregation. In particular, the main theoretical findings are as follows: (a) under log-normal prices, the sample YBMD index is an asymptotically unbiased estimator of the population YBMD index: (b) the bias and mean squared error (MSE) of the sample YBMD index depend significantly on the direction and strength of the correlation between base and current prices: (c) it is demonstrated that there exist sets of base and current prices for which the bias and MSE of the YBMD index are always smaller than the corresponding characteristics of the Jevons index. The empirical results, in turn, indicate a close approximation between the CSWD, Jevons

Conclusions

The conclusions drawn from the analytical and empirical study support recommending the YBMD index as a very good alternative to the well-established Jevons index. This recommendation is based not only on the desirable axiomatic properties of the YBMD index, but also on its statistical properties (asymptotic unbiasedness, relatively low variance, and MSE), which is a new result in the literature. A practical implication of the analysis is also that the YBMD index—as a linear approximation of the Jevons index—is potentially less sensitive to the presence of extremely low prices compared to the Jevons index (which takes the form of a product of price relatives).

Keywords

elementary indices, Jevons index, Dikhanov index, Young–Balk–Mehrhoff–Dikhanov index, Consumer Price Index (CPI)

Objective

The COICOP classification (Classification of Individual Consumption According to Purpose) is an international classification standard published by the United Nations in 1999. It constitutes one of the key elements of modern socio-economic statistics, used to group household consumption expenditures according to their purpose. Its primary function is to ensure a consistent and comparable description of the structure of consumption across different statistical domains, in particular price statistics, national accounts, and household budget surveys.

Methods

The first version of COICOP was introduced in the early 1990s (1993) as part of the System of National Accounts. From the outset, its structure was based on a hierarchical division of expenditures into divisions, groups, and classes corresponding to different consumption categories (e.g. food, transport, health). Over time, the classification has been increasingly widely applied, becoming a foundation for the harmonisation of consumer price statistics, including CPI and HICP indices. In response to changes in consumption patterns and the development of new types of goods and services, the classification has been progressively updated. A key milestone in this evolution was the development of a new version—COICOP2018—adopted by the United Nations Statistical Commission in 2018. The updated classification better reflects contemporary consumption patterns, including the growing importance of services and the digitalisation of products. The implementation of COICOP2018 in consumer price statistics has been one of the most significant methodological undertakings in recent years. In Poland, this change has been introduced starting with data for 2026 and forms part of a broader process of modernising official statistics at the international level.

Results

The aim of this paper is to present the implementation of COICOP2018 as a systemic process encompassing methodological, organisational, and analytical dimensions. Particular emphasis is placed on the implications of this change for retail price surveys, which constitute the primary data source for CPI and HICP indices. The paper provides a detailed analysis of the challenges related to ensuring the continuity and comparability of time series. The impact of classification changes on the weighting structure and the potential implications for the level and dynamics of inflation indices are also examined.

Conclusions

The findings indicate that the implementation of COICOP2018 should be seen not only as an obligation resulting from international harmonisation, but also as an important opportunity to modernise price statistics. The change contributes to improving the representativeness of the consumer basket, enhancing cross-domain consistency, and enabling better use of new data sources. The paper falls within the thematic area of “Statistical surveys – methodology and applications”, presenting the implementation of COICOP 2018 as an example of a complex transformation process in official statistics in response to changing market conditions and increasing user demands.

Keywords

inflation, price indices, COICOP classification

Objective

The aim of the study is to present a new method – SDEA-INI (Spatial Data Envelopment Analysis with Independent Neighbours’ Inputs) – which integrates spatial interactions while simultaneously removing the restrictive assumption of controllability of spatial inputs within the DEA framework. The proposed approach enables the assessment of regional disparities in healthcare system efficiency across Europe, accounting for both spatial dependence and the exogeneity of resources in neighbouring regions.

Methods

The study introduces a novel DEA-based solution that incorporates both spatial interactions and the exogeneity of inputs. By applying the SDEA framework, spatial autocorrelation of inputs and outputs is explicitly included in the optimisation process. Furthermore, the SDEA methodology is extended so that the new optimisation model accounts for resources located in neighbouring regions that remain beyond the control of local decision-makers. We argue that ignoring spatial interactions or the exogeneity of neighbouring inputs in productivity analysis may lead to biased results. Monte Carlo simulations clearly demonstrate that spatial extensions of DEA outperform the classical approach under spatial dependence, while SDEA-INI performs best when inputs are non-controllable. The proposed method is applied to evaluate healthcare system efficiency using Eurostat data for 232 European NUTS 2 regions for the years 2011 and 2018. Inputs include the number of doctors and hospital beds per 100,000 inhabitants, as well as GDP per capita as a proxy for financial resources. Outputs are transformed survival indicators based on mortality rates by disease groups. A spatial weights matrix based on the three nearest neighbours is incorporated into the model.

Results

The results reveal significant spatial disparities in healthcare system efficiency across Europe, with a clear pattern of an “inefficient core” (including Germany and Austria) and a more efficient periphery (Central, Eastern, and Southern Europe). The SDEA-INI method shows greater variability than standard SDEA, reducing the smoothing effect and better reflecting real-world processes. Substantial differences in regional rankings are observed when compared with DEA and SDEA. To assess result quality, sensitivity analysis (for spatial weight matrices and expenditure measures) and statistical tests (Spearman’s rank correlation, Wilcoxon test, and Dunn’s test) were conducted.

Conclusions

Accounting for spatial interactions and the non-controllability of neighbouring resources is essential for a reliable assessment of regional efficiency. The proposed SDEA-INI method contributes significantly to the development of statistical and optimisation techniques by offering a more flexible analytical tool. The findings have practical implications for healthcare policy, particularly in terms of efficient resource allocation and interregional cooperation. The proposed approach can also be applied in other areas of regional analysis.

Keywords

regional efficiency: spatial analysis: DEA: healthcare system: SDEA-INI

Objective

This study provides a comparative assessment of the fiscal costs and distributional effects of a stylised Job Guarantee (JG) across all 27 EU member states. The central research question is the extent to which the policy`s net cost and redistributive impact depend on the structure of the existing tax-benefit system and the welfare-state regime in place. The working hypothesis is that a structural tension exists: countries where a JG is fiscally cheapest are precisely those where its redistributive reach is weakest.

Methods

The analysis uses EUROMOD version J2.0+, the EU-wide tax-benefit microsimulation model maintained by the Joint Research Centre (JRC) of the European Commission. The micro-level data are EUROMOD`s harmonised input files derived from EU-SILC, conducted by Eurostat with national statistical offices on representative random samples of households. National sample sizes range from a few thousand households in smaller member states to roughly 25–30 thousand in the largest, totalling more than 250 thousand households and about 600 thousand individuals. Population estimates use harmonised grossing-up factors aligned with Eurostat conventions. The Job Guarantee is operationalised as a full-time public employment offer for the long-term unemployed, paid at 60 percent of the country-specific median wage. For each country a baseline and a reform scenario are simulated: the fiscal cost is decomposed into gross cost, benefit savings, additional tax and social-contribution revenue, and net cost. Redistributive effects are measured by the Gini coefficient, the at-risk-of-poverty rate at 60 percent of median equivalised disposable income, and decile income changes. Quality assessment relies on sensitivity analysis of the JG wage (80, 100, 120 percent of the benchmark) and on significance tests of cross-country correlations. Results are interpreted as conditional projections from a static microsimulation without behavioural responses.

Results

Preliminary results show pronounced cross-country heterogeneity in the effects of a Job Guarantee within the EU. Estimated net costs span a wide range - from a small fraction to several percent of GDP - with a median well below 1 percent of GDP. The self-financing ratio, defined as the share of the gross cost recouped through reduced benefit spending and additional tax and social-contribution revenue, also varies substantially across countries and is positively correlated with the generosity of the existing social protection system. Redistributive effects - poverty reduction, the change in the Gini coefficient, and income gains in the lowest deciles - are largest in countries with weaker benefit systems and smallest in countries with extensive welfare states.

Conclusions

The findings reveal a structural redistribution-cost tension: the same institutional features that make a Job Guarantee fiscally attractive also limit its redistributive reach. Economic assessment of the policy must therefore be conditional - universal claims about its `affordability` are misleading and should give way to analysis that takes the national tax-benefit context seriously. The study delivers the first coherent comparative assessment of a Job Guarantee in the EU together with a fiscal-cost decomposition along two absorption channels: benefit savings and additional tax and social-contribution revenue.

Keywords

Job Guarantee, microsimulation, EUROMOD, welfare state, redistribution

Objective

The aim of the study was to identify and compare the profiles of older adults across the Visegrad Group countries regarding health, material conditions, and economic security. The study sought to determine whether the situation of older adults is homogeneous or whether it exhibits diverse patterns of characteristics, and to what extent these profiles are similar across countries. Understanding the profiles of older adults enables us to assess whether similar indicator values across countries reflect genuine similarities in their situation.

Methods

The analysis utilised EU-SILC data (European Union Statistics on Income and Living Conditions) for 2024 on individuals aged 65 and older, comprising over 27,000 observations for Poland, the Czech Republic, Hungary, and Slovakia. The data come from a representative public statistics survey conducted on a random sample, and the use of cross-sectional weights allowed the results to be generalised to the elderly population in the analysed countries. In the first stage, a factor analysis based on a tetrachoric correlation matrix was conducted to identify the latent structure among variables describing health, material conditions, and economic security. Subsequently, using the Two-Step Cluster method, profiles of older adults’ functioning were identified. In the subsequent stage, a structural similarity analysis was applied, along with tests of the significance of structural differences and a procedure to identify distinctive differences between the obtained functioning profiles. The analysis was supplemented by an estimation of a multinomial logit model, allowing for the assessment of determinants of membership in specific profiles, taking into account demographic and spatial variables. All analyses were conducted using SPSS and Statistica.

Results

Four profiles of older adults’ functioning have been identified, the largest of which is intermediate, situated between the extreme configurations of deficits and resources. The results clearly indicate that health, material conditions, and economic security do not form a single coherent dimension, but rather a system of interdependent yet partially autonomous components. The structure of these profiles is generally consistent across countries, though the proportions and intensities of individual characteristics vary, with the greatest variation in health and functional limitations. At the same time, the observed profiles indicate the existence of mixed configurations, in which a favourable situation in one area coexists with deficits in other dimensions of functioning.

Conclusions

The results confirm that the situation of older adults is complex and heterogeneous, and that the observed functional profiles, while common across the V4 countries, take on different significance in different national contexts. This points to the limited usefulness of analyses based solely on aggregated indicators and underscores the importance of a structural approach. The results suggest the need to design social policy in a differentiated manner, taking into account both cross-national differences in the structure of the elderly population and the internal heterogeneity of the elderly population. Effective interventions require simultaneous consideration of multiple aspects of living.

Keywords

older adults, EU-SILC, economic security, profiling, identification of significant differences

Objective

The paper analyses the economic integration of Ukrainian immigrants in Poland, comparing pre-war labour migrants with refugees who arrived after the Russian invasion in 2022. It examines whether labour market outcomes improve with duration of stay and whether refugee integration follows different trajectories than earlier migration cohorts. The study also assesses selected dimensions of broader social integration, including language skills and housing independence

Methods

The study uses repeated cross-sectional data from five nationwide waves of surveys of Ukrainian immigrants conducted by the National Bank of Poland in 2019 and annually from 2022 to 2025. Each wave covered approximately 3,000–4,000 respondents and provides detailed information on labour market status, earnings, migration history, legal status, language proficiency and housing conditions. The analytical framework follows the international literature on immigrant labour market integration, focusing on convergence over time. Two complementary measures of migration duration are applied: years since the first non-touristic stay in Poland and years since the beginning of the current stay. These indicators allow identification of both accumulated migration experience and stability of residence. Key dependent variables include employment rates, average net wages (estimated from income categories), contract type, self-employment, self-assessed command of Polish, and housing independence. The study compares pre-2022 immigrants with post-2022 refugee cohorts, including persons under PESEL-UKR temporary protection. In addition to cross-sectional comparisons, synthetic cohort analysis is used by grouping respondents according to year of arrival and tracing cohort outcomes across subsequent survey waves.

Results

The results show clear differences between refugees and earlier immigrants. Pre-war migrants reached very high employment rates, often above those of the native population, while refugees started from much lower levels but integrated relatively quickly. Within three years, refugee employment exceeded 60%, faster than in many European countries. Wage gaps remained substantial: earlier migrants earned about PLN 1,000 more on average than post-2022 arrivals. Longer stay was also associated with a higher share of permanent contracts and some growth in self-employment. Language proficiency and independent housing improved strongly with time spent in Poland.

Conclusions

Poland’s temporary protection regulations implemented in Poland enabled unusually rapid labour market entry of Ukrainian refugees. However, convergence in wages and job quality is slower than convergence in employment. Time spent in Poland remains a key driver of integration, not only economically but also socially through language acquisition and housing independence. The study contributes to migration research by combining cohort and cross-sectional approaches and offers evidence relevant for labour market, housing and integration policy design.

Keywords

Ukrainian migrants, refugees, labour market integration, Poland, migration cohorts

Objective

The main research question addressed in this article concerns whether and how the returns to education and work experience in Poland have changed over the last twenty years, and whether these changes vary according to gender, educational attainment and work seniority. The authors aim to determine whether traditional human capital factors continue to play a key role in shaping wages, or whether their significance is diminishing in the context of structural and technological changes in the economy.

Methods

This study applies a quantitative, longitudinal research design to estimate returns to education and work experience in Poland over 2004–2024. The empirical framework is based on an extended Mincer earnings function, where the logarithm of wages is explained by education level, work experience, its quadratic term, and time effects. The model is estimated on pooled cross-sectional data with time dummies, enabling the analysis of both average returns and their changes over time. Econometric regression analysis constitutes the main analytical tool, allowing estimation of marginal effects of education and experience. Additional specifications include gender-specific models and variants capturing heterogeneity across education and experience levels. Robustness checks involve testing model stability and including macroeconomic controls such as the unemployment rate. The analysis uses secondary data from the Structure of Earnings Survey conducted by Statistics Poland (GUS). The dataset is aggregated at the three-digit occupational level and covers firms employing at least ten workers, ensuring high reliability of wage data. The sample includes 13,914 observations across 11 waves. Education is assigned to occupations based on ISCO classification, adjusted to the Polish context, while experience is measured in intervals.

Results

The results indicate a decline in the rates of return on both education and work experience. The average rate of return on education fell from 17.2% in 2004 to 15.5% in 2024, with this decline being particularly pronounced among women. Significantly, a reversal in gender relations occurred in 2018. From 2018 onwards, women began to achieve lower returns on education than men. A similar downward trend applies to work experience, where the premium fell from 16.1% to 11.2%, and the decline was even more pronounced for women. An analysis by level of education shows that although the highest education premiums relate to higher education, a decline is being observed. At the same time, returns on vocational education remain relatively higher compared to general education at secondary level.

Conclusions

The findings of the study point to a significant transformation in the mechanisms shaping wages. The declining importance of education and experience suggests that traditional measures of human capital are losing their significance. The results support the hypothesis that the expansion of higher education leads to a compression of the wage distribution. From an economic policy perspective, this implies a need for greater balance in the education system, in particular the development of lifelong learning and the promotion of technical education. At the same time, the observed changes in gender differences indicate that equal access to education does not translate into equal outcomes in the la

Keywords

Mincer equation, returns to education, work experience, gender differences

Objective

We aim to ensure the confidentiality of information collected in censuses, particularly the National Population and Housing Census (NSP). The effective application of Statistical Disclosure Control (SDC) methods is essential to achieve an optimal trade-off between minimizing the risk of unit identification and maximizing the utility of disclosed data for potential users. The purpose of the presentation is to report results of applying SDC methods and tools to data from the 2021 census.

Methods

It follows and extends the work conducted and presented in previous years. At that time, the focus was on very limited 1-km grid data about the resident population. This time, we will focus on 1-km grid population data according to the national definition, broken down by sex, economic age groups, and binary employment status: the resident population at the level of census districts, broken down by sex, economic age groups, 10-year age groups and binary employment status: and the population according to the national definition at the level of census districts, with the same breakdowns. The main SDC method used in the study was Targeted Record Swapping (TRS), recommended by Eurostat. TRS required to define e.g. a hierarchy within data, variables used to assess similarity between rows, risk threshold, variables used to risk estimation and expected swaprate. Since the TRS method does not completely eliminate the risk of indirect identification, especial probabilistic approach was also employed. It was used when several sensitive cells had minimum size: in that case the cell to be swapped was sampled with probability proportional to the share of such cell in relevant total in the population.

Results

TRS accounts for the hierarchical structure of microdata (especially their geographic dimension) and consists in identifying groups of records with the highest disclosure risk at each level of the hierarchy and swapping values for specific levels of variables within groups of similar records based on distances determined using the values of specific variables (known as similarity variables or matching keys). Values are swapped between units at a higher level. A summary of results will include an assessment of their quality, particularly from the perspective of the trade-off between data protection and utility.

Conclusions

The obtained results which will be presented, show effectiveness and practical utility of the proposed approach for efficient protection of statistical confidentiality with maximisation of utility of released data. It will be illustrated by relevant conclusions concerning the disclosure risk and information loss occuring as a result of application of statistical disclosure control methods. We will also mention potential applications of our SDC solutions to protect output data in other statistical surveys.

Keywords

statistical disclosure control, censuses, resident population, population according to the national definition, targeted record swapping

Objective

The objective of the paper is to present the role of the Statistical Metadata System (SMS) as the central platform for managing metadata in official statistics. SMS supports the design of metadata model structures, their development, collection, versioning, and dissemination. The analysis examines the impact of SMS on the quality, consistency, and stability of statistical processes, as well as its importance for building a modern metainformation infrastructure within Statistics Poland.

Methods

SMS has been collecting metadata since 2013, storing them within metadata model structures that have been continuously adapted to evolving user needs and the requirements of IT systems operating in Statistics Poland. The paper is based on an analysis of SMS functionalities and documentation describing the mechanisms that have shaped metadata management practices over more than a decade. A functional review of key system components was conducted, including metadata approval workflows, a multi-level permissions model, metadata import and export, and the integration of SMS with statistical production systems. The study also examines how metadata are used in major statistical information products such as the Domain Knowledge Bases (DBW), the Public Services Monitoring System (SMUP), the Local Data Bank (BDL), the Programme of Statistical Surveys of Official Statistics, and the Statistics Poland Information Portal. In addition, the analysis incorporates insights from long-term system operation, highlighting both strengths and limitations related to performance, usability, and technological constraints. These observations provide a basis for identifying areas requiring modernization and for understanding how SMS has informed the design of the new Metadata Subsystem.

Results

The analysis confirms that SMS effectively supports metadata management by ensuring consistency, version control, and accessibility across multiple systems in official statistics. Workflow and permission mechanisms enable quality assurance and secure handling of metadata, while integration with publication and analytical platforms stabilizes metainformation processes and reduces the risk of inconsistencies. At the same time, growing technological constraints — including insufficient automation and an outdated architecture — increasingly limit the system’s development potential, highlighting the need for modernization and redesign.

Conclusions

SMS plays a key role in the standardization and management of metadata within Statistics Poland: however, further development requires a modern architecture and full integration with other components of the metainformation system. Experience gained from long-term operation of SMS has provided the foundation for designing the new Metadata Subsystem, intended to deliver higher performance, interoperability, process automation, and a more ergonomic and coherent working environment. The findings emphasize the strategic importance of a central metadata platform for the quality and reliability of official statistics.

Keywords

metadata: metadata lifecycle: metadata standardization: SMS: system integration

Objective

The aim of this work was to describe methodological changes and their impact on the measurement of labour market activity in Poland. The analysis covers discontinuities in time series arising from changes in the definitions of labour market status and the rotation scheme, the transition to computer-assisted telephone interviewing, and the update of the reference population following the 2021 National Census. Particular attention is paid to the consequences of changes introduced in 2020–2021, encompassing both the effects of the COVID-19 pandemic and the harmonisation of social surveys within t

Methods

The analyses were conducted on sub-samples of Polish LFS individual-level data for the years 1995–2024. The methods applied include descriptive statistics, linear and logistic regression, non-parametric classification models (random forest algorithm), post-stratification, and iterative proportional fitting (IPF raking). .

Results

The key factors affecting the measurement of the population and core labour supply indicators are presented. .

Conclusions

The Polish Labour Force Survey (PL-LFS), despite measurement disturbances and limited comparability of data over time, remains a key source of information on the size and structure of labour supply in Poland. It is used both for estimating the most important labour market indicators and for in-depth analyses. The identification and quantification of the effects of methodological changes are essential for the correct interpretation of labour market trends, particularly during periods of intensive change. The use of advanced weighting procedures can partially compensate for measurement disturbances.

Keywords

Polish Labour Force Survey, population, National Census, methodological changes

Objective

The European Innovation Scoreboard is a set of indicators measuring the innovation performance of the member states of the European Union, as well as some other countries, published for over 20 years by the European Commission. Based on these indicators, a ranking of EU countries is created. In this paper, we argue that while the EIS is useful as a database for comparative analyses of selected functions of innovation systems, it is not appropriate to draw conclusions solely from a country’s position in the ranking. In particular, it should not be used as an innovation policy evaluation tool.

Methods

The data source consists of detailed rankings from the European Innovation Scoreboard published since 2014. Based on these data, as well as basic data on countries’ levels of socio-economic development, we conduct a series of analyses to determine which variables are good predictors of a country’s position in the EIS. We begin with simple correlation and rank correlation, and then move on to machine learning techniques, including decision trees. The data source consists of detailed rankings from the European Innovation Scoreboard published since 2014. Based on these data, as well as basic data on countries’ levels of socio-economic development, we conduct a series of analyses to determine which variables are good predictors of a country’s position in the EIS. We begin with simple correlation and rank correlation, and then move on to machine learning techniques, including decision trees. The data source consists of detailed rankings from the European Innovation Scoreboard published since 2014. Based on these data, as well as basic data on countries’ levels of socio-economic development, we conduct a series of analyses to determine which variables are good predictors of a country’s position in the EIS. We begin with simple correlation and rank correlation, and then move on to machine learning techniques, including decision trees.

Results

The results indicate that a country’s position in the European Innovation Scoreboard is largely explained by a limited set of variables, including GDP per capita and total R*D expenditure as a percentage of GDP (i.e., GERD). The ranking also exhibits considerable stability over time. The results indicate that a country’s position in the European Innovation Scoreboard is largely explained by a limited set of variables, including GDP per capita and total R*D expenditure as a percentage of GDP (i.e., GERD). The ranking also exhibits considerable stability over time. The results indicate that a country’s position in the European Innovation Scoreboard is largely explained by a limited set of variables, including GDP per capita and total R*D expenditure as a percentage of GDP (i.e., GERD). The

Conclusions

In public debates, one can encounter opinions that Poland’s relatively low position in the European Innovation Scoreboard indicates the ineffectiveness of innovation policy, particularly financial support for firms’ innovation activities and tax incentives. Based on the obtained results, it can be argued that such reasoning is incorrect for at least two reasons. First, it ignores the efforts by other countries, and secondly, it ignores deeper convergence process that co-determine international country rankings.

Keywords

European Innovation Scoreboard, innovation, international rankings, machine-learning

Objective

The paper falls under the thematic area: History, education and dissemination of statistics. The aim is to introduce the figure and scientific achievements of Stefan Szulc, one of the distinguished Polish statisticians and demographers whose work played a significant role and gained recognition both in Poland and internationally. Professor Szulc, an outstanding scholar, educator, and President of the Central Statistical Office, is the patron of the Central Statistical Library, which in 2026 commemorates him in various ways by recalling his achievements on the 70th anniversary of his death.

Methods

Stefan Szulc left behind a rich body of scientific work as well as archival documentation related to his activities as a scholar and public official. The Archives of the Central Statistical Office hold his personal records, while the archives of the Polish Academy of Sciences contain documentation of his scientific activity and unpublished texts authored by him. The collections of the Central Statistical Library include all of his published books and articles in academic journals. In 1978, the Library published a bibliography of the Professor’s works, an updated edition is planned for 2026. Over the years, these materials have served as sources for studies on Szulc’s achievements, published in biographical works on the Polish scientific community, historical publications issued by the Central Statistical Office, and academic journals in the fields of statistics and library science, all of which are also available in the Library’s collections. Based on these sources, representatives of the Central Statistical Library aim to present the figure and accomplishments of the Professor as an important figure in the history of Polish statistics - one of the co-creators of Polish official statistics, an author and editor of significant statistical publications, a lecturer and mentor to many Polish statisticians, an author of educational publications in statistics, the organizer of the Central Statistical Office’s library, and the first post-war President of the CSO.

Results

The paper presents the academic and professional trajectory of Stefan Szulc, including: - his publishing and translation activities, - his work at the Central Statistical Office, which started in 1919 - initially as a junior officer, then as the first head of the CSO Library, followed by positions as head of the Publications and Scientific Resources Division, the Population Statistics Division, and Editor-in-Chief of CSO publications, - the initiation and organization of key publication series of the CSO that continue to this day, including the Statistical Yearbooks of the Republic of Poland, - his achievements as a long-standing university lecturer, - his impact on Polish statistics as the author of repeatedly reissued textbooks on which generations of Polish students were educated.

Conclusions

Stefan Szulc was one of the main co-creators and organizers of the statistical system in Poland, contributing to the thematic, organizational, and publishing achievements of Polish statistics. Over more than fifty years of scholarly work, he published 74 original scientific papers, one translation, and several dozen statistical publications of the Central Statistical Office, for which he served as Editor-in-Chief. His textbook Statistical Methods is still used in the teaching of statistics today. The 70th anniversary of Professor Szulc’s death provides a unique opportunity to recall the achievements of this outstanding statistician at the forum of the 6th Congress of Polish Statistics.

Keywords

history of statistics : historical demography : statistician`s biography

Objective

The aim of the presentation is to present modern approaches to promoting official statistics and disseminating knowledge about data, and to show how modern communication tools can increase access to statistical information, improve outreach to diverse key target groups, and strengthen public interest in data and its practical importance in everyday life, public life, and decision-making processes.

Methods

The presentation will showcase examples of communication and outreach activities using audio and video formats, webinars, educational initiatives, and other solutions supporting greater interest in statistics. It will discuss podcasts produced within the Office, externally created podcasts promoting survey research, short video formats published on social media, as well as webinars aimed at explaining statistical data in a simple and accessible way. Examples of modern internship programmes for students, activities engaging young audiences in discussions on the importance of data, and analytical games demonstrating the practical use of statistics in different environments will also be presented. The main point of reference will be the observation of changes in the ways statistical information is communicated and in the adaptation of language, format, and communication channels to the needs of contemporary audiences. The approach is of a review nature and is based on the analysis of examples drawn from information, educational, and promotional activities carried out with the use of modern communication tools. Their educational and promotional function will also be taken into account.

Results

The presented activities show that data and official statistics can be communicated in a way that is accessible, engaging, and tailored to the needs of contemporary audiences. Modern forms of communication increase the attractiveness of statistical content, facilitate its reception, and encourage greater interest in data, statistical surveys, and the role of official statistics. The use of podcasts, webinars, short video formats, and educational activities supports relationship-building with audiences and strengthens the presence of statistics in the social and information space. It also enhances public trust.

Conclusions

Modern communication channels can effectively strengthen the image of official statistics, foster dialogue with society, and build greater awareness of the importance of data in social and economic life. The diversity of communication formats supports better adaptation of messages to the needs of different target groups and increases the chances of sustaining interest in statistics and its practical use in everyday decision-making and in the informed interpretation of information across various areas of life.

Keywords

official statistics, data communication, podcasts, webinars, statistical education, modern

Objective

The aim of the study is to identify the minimum scale of homelessness and to estimate the volume of mortality intensity in this population based on available administrative data sources. This phenomenon is only partially recorded in statistical systems, which makes it difficult to fully capture and monitor it. Particular attention is given to identifying deaths among people experiencing homelessness and analyzing their mortality risk in relation to the general population.

Methods

The study employs an integrated approach based on three administrative data sources: (1) a dataset of social assistance beneficiaries for whom burial was provided at municipal cost, (2) an extended dataset of social assistance beneficiaries in which the number of deaths was estimated among individuals who ceased receiving benefits, and (3) a dataset of burials held by local government. A key element of the analysis was the use of proprietary algorithms enabling the identification of persons experiencing homelessness through the reconstruction of personal identification numbers (PESEL) and linkage of data across administrative registers. In cases of missing personal identification numbers, estimation approaches were applied to determine the most likely expected number of deaths in the group of individuals who disappear from the social assistance system. In the process of combining the sets, a deterministic approach was used, based on available identifiers and selected descriptive features, which enabled more precise matching of records and reduced the risk of counting the same persons multiple times. Crude mortality rates (CMR) and standardized mortality ratios (SMR) were calculated to enable international comparisons.

Results

Approximately 1,200 deaths per year were identified among persons experiencing homelessness who had received social assistance within the 12 months preceding their death. The average age of death in 2022 was 58.1 years (SD = 11.9), indicating earlier mortality compared to the general population. The crude mortality rate was 3.76, and the standardized SMR was 3.69, with the highest values observed among individuals under 50 years of age. Spatial analysis revealed significant variation—urban municipalities record approximately four times more municipal burials of people experiencing homelessness per capita than rural municipalities. These results represent a lower-bound estimate of the phenomenon.

Conclusions

Available administrative data sources allow to identify the minimum scale of homelessness and mortality in this population but do not enable for a full estimation of its true magnitude. The lack of systematic registration of homelessness and limited availability of personal identification numbers (e.g., PESEL) significantly hinder data linkage and analysis. The findings indicate more than a threefold higher risk of death among persons experiencing homelessness compared to the general population, particularly in younger age groups. Similar values have been reported in the studies conducted in other countries.

Keywords

homelessness: mortality: administrative data: SMR: social assistance

Objective

The objective of the paper is to present a new public statistics tool that produces the data on legally disabled people in Poland. The dashboard has been developed in collaboration with representatives of the Office of the Government Plenipotentiary for Disabled Persons at the Ministry of Family, Labour and Social Policy. The data recorded in the dashboard derived from the public statistics research “Legally Disabled People”, which is also discussed in the paper.

Methods

The research on legally disabled people is carried out annually, on a regular basis, by the Statistical Office in Kraków, with the use of the integrated data deriving from the registers and the administrative systems.Within the research, the data from five disability certification systems in Poland have been interconnected: the Electronic National System for Monitoring Disability Certification, the old pension and disability pension systems of the Social Insurance Institution (ZUS), the comprehensive pension benefits service system of the Agricultural Social Insurance Fund (KRUS), the Pension Office of the Ministry of the interior and Administration and the Information System of the Ministry of National Defence. The research covers the entire population of legally disabled people in Poland, which is related to the persons holding valid certificate issued by the Disability Assessment Board (regarding disability or level of disability) or equal certificate issued by Social Insurance Institution (referring to the level of incapacity for work), KRUS (regarding incapacity for work in the agricultural farm), the Ministry of the Interior and Administration and the Ministry of National Defence (on the level of disability, issued before 12 / 31 / 1997). In the research the auxiliary data source from the Civil Register recorded in the information systems of the Ministry of Digitalisation (on deceased persons) was used as a supplementary information to update the dataset.

Results

The paper deals with the resulting statistical information on legally disabled people visualised in the dashboard, broken down by place of residence, sex, age groups, level of disability and medical conditions. The options for filtering content, sample animated graphics, charts and maps, customising the view to users’ needs, and digital access are going to be presented. In addition, other publications including the results of the research will be discussed. They refer to release news and infographics. Some of the data are available on maps and charts in the Geostatistical Portal of Statistics Poland.

Conclusions

The dashboard is a practical instrument designed not only for decision-makers, public administration, analysts, researchers, journalists, members of non-government organisations and businessmen, but also for everyone interested in the issues affecting about four million legally disabled people in Poland. The data, available in the form of interactive maps and charts as well as in ready-to-download files, can be used by local government bodies, for example, in order to diagnose the regional needs or for the purpose of planning appropriate support for disabled people, including elders, e.g. within the framework of individual assistance programmes or supporting benefits.

Keywords

disability, disability certification

Objective

The aim of the study is to assess the impact of changes in the number of foreign nationals on the economic situation of selected counties in Poland in the context of the growing and spatially differentiated scale of migration after 2019. The analysis employs the concept of “matched counties,” based on pairing units with similar characteristics in the base year, which allows for reducing the influence of initial differences and enables more reliable comparisons over time.

Methods

The study uses data from the OBM system and the Local Data Bank of Statistics Poland (BDL GUS), including information on the socio-economic situation of counties as well as data on the number and distribution of foreign nationals in Poland. The analyses are currently ongoing and are being further developed in R, using tools for data processing, statistical analysis, and matching procedures. A key methodological element is the construction of “matched counties,” i.e., pairs of territorial units formed based on the similarity of their characteristics in 2019. The matching procedure is currently being implemented and tested. Similarity is measured using the Euclidean distance (with possible alternative specifications), with the objective of minimizing the overall distance between paired counties. Two groups of units have been preliminarily identified: 10 counties with the highest and 10 with the lowest number of foreign nationals. The final construction and validation of pairs are still in progress. This approach allows for controlling initial structural differences and provides a more precise assessment of changes over time, particularly in the period 2019–2024 (and partially 2025, depending on data availability).

Results

Preliminary findings indicate potential differences in the pace of changes in the economic situation of the analyzed counties depending on the level of foreign population presence. There are signals suggesting distinct trajectories of selected indicators between counties with high and low concentrations of foreign nationals: however, these results require further verification and more in-depth analysis. The “matched counties” approach is currently being evaluated in terms of its effectiveness in reducing the impact of initial differences and better capturing potential relationships between migration and socio-economic changes.

Conclusions

The applied approach has the potential to enable a more precise analysis of the impact of changes in the number of foreign nationals on local economies: however, at the current stage, the conclusions remain preliminary. Further work will involve extending the analysis and applying more advanced econometric methods. The final results may contribute to the literature on regional economics and migration studies, as well as inform public policy at the local level. The study is exploratory in nature and provides a foundation for further research

Keywords

spatial analysis, foreign nationals, matched counties

Objective

The main objective of the study was to assess the relationship between the functional linkage of rural and urban-rural municipalities with larger urban centers and the level of investments in the low-emission economy, co-financed by EU funds in the periods 2007–2013 and 2014–2020. The empirical analysis was conducted to verify the research hypothesis that “municipalities functionally linked to large urban centers (located within Functional Urban Areas – FUA) exhibit a higher level of investment in the development of a low-emission economy than municipalities located outside these areas.”

Methods

The study covered all rural and urban-rural municipalities in Poland, totaling 2,175 units. Data on EU co-financed projects in the field of the low-emission economy were obtained from the individual project database of the Ministry of Funds and Regional Policy. The analysis also used data from Statistics Poland (GUS), including the Local Data Bank and the “Delimitation of Rural Areas” (DOW). This typology allows municipalities to be classified according to their functional linkages with urban centers, distinguishing between agglomeration and non-agglomeration municipalities. In the first stage, the number and value of low-emission economy projects acquired by municipalities were analyzed (in absolute terms, per capita, and per km2), according to the DOW classification and voivodeships. Descriptive statistics and statistical inference methods based on significance tests were applied. In the second stage, correspondence analysis was used to graphically represent relationships between variable categories in a two-dimensional space. The input data took the form of a contingency table including two variables: (1) the type of municipality according to its affiliation with FUA and voivodeship (32 units in total), and (2) the level of investment in the low-emission economy. The quantitative variable describing the level of investment (per capita and per area unit) was categorized into four classes based on distribution quartiles.

Results

The functional linkages of the analyzed municipalities with large urban centers significantly differentiate their investment activity in the development of the low-emission economy. Correspondence analysis revealed a clear distinction between agglomeration and non-agglomeration municipalities, confirming the key role of the functional factor. Municipalities within FUAs more often implement investments at a moderate level (low or medium), whereas outside FUAs extreme patterns dominate—from no activity to high intensity. This pattern reflects center–periphery polarization mechanisms. The analysis of dispersion measures indicates that non-agglomeration municipalities are characterized by greater variability in the level of low-emission investments.

Conclusions

Development policy should be differentiated and tailored to the type and location of municipalities. It is particularly important to strengthen the institutional capacity of non-agglomeration municipalities, especially those less active in the energy transition, through financial support, advisory services, and simplified procedures. At the same time, agglomeration municipalities should be encouraged to undertake more ambitious pro-climate actions, while continued support for peripheral areas should be maintained.

Keywords

investments, energy transition, low-emission economy, Functional Urban Areas (FUA), Delimitation of Rural Areas (DOW)

Objective

The objective of this study is to identify the strength, direction, and temporal variability of the impact of producer and importer prices on the food price index in Poland. The analysis covers selected unprocessed plant and animal products. An additional objective is to determine during which periods food price inflation was driven by changes in producer or importer prices, as well as to assess the interrelationships and feedback loops between these price categories across different time horizons.

Methods

The study employed wavelet analysis as a tool enabling the simultaneous analysis of relationships in both time and the frequency domain. This approach allows for the identification of time-varying relationships between the dynamics of producer, importer, and consumer prices, as well as for determining the direction of price impulse propagation. In particular, continuous wavelet transform and wavelet coherence analysis were used to examine the strength of interdependence and phase shifts between the analyzed time series. The empirical data include monthly price indices for Poland from Eurostat’s “Food price monitoring tool” database (https: / / doi.org / 10.2908 / PRC_FSC_IDX) and cover the period from January 2005 to December 2025, with the number of observations varying depending on the product and data availability (from n=180 to n=240). The analysis covers selected unprocessed food products, both of plant and animal origin. The study is supplemented by a Granger causality test to confirm the results of the wavelet analysis. To ensure comparability, appropriate data preprocessing procedures were applied, including the removal of seasonal components.

Results

The results indicate significant variation in the relationships between producer, importer, and consumer prices depending on the type of product. The wavelet analysis enabled the decomposition of these relationships into short-term, medium-term, and long-term components, revealing the variability of the strength of these relationships over time. In many cases, periods of dominance by producer prices are observed, particularly in long cycles, while importer prices play a significant role in shorter time horizons. The results also indicate the presence of phase shifts, which allow for the identification of delays in the transmission of price shocks between the analyzed categories. This differentiation is particularly evident between plant and animal products.

Conclusions

The use of wavelet analysis allows for an in-depth assessment of the mechanisms underlying food price formation and the identification of time-varying relationships between producer, importer, and consumer prices. The results indicate that the price transmission process is multifaceted and depends on the time horizon and the specific characteristics of the product. Eurostat’s “Food price monitoring tool” database is a valuable source of information for this type of analysis, enabling the examination of interdependencies in a dynamic context. The results obtained can be used both in macroeconomic analyses and in the formulation of agricultural and food policies.

Keywords

wavelet analysis, inflation, HICP, food prices, price transmission

Objective

The study aims to assess to what extent the growth of technological startups operating in Warsaw is driven by their initial characteristics (initial capital endowment and its structure, number of employees, etc.) and by intra-urban location-related factors, both absolute (e.g. infrastructure accessibility) and relative (neighbourhood composition and local context). It addresses whether the impact of location is uniform, or varies depending on the initial profile of the firm.

Methods

The analysis is based on data on technological startups located in Warsaw, combining information on their initial characteristics (e.g. employee number, size, capital structure, initial financial resources and endowments) with detailed spatial indicators relating to their intra-urban location (here, both absolute and relative factors are considered, describing the neighbourhood environment, and absolute distance to the main city`s amenities). To reduce dimensionality and identify key components describing the spatial environment, Principal Component Analysis (PCA) is applied. Next, tree-based machine learning models (XGBoost) are used to capture non-linear relationships and interactions between variables. In contrast to standard econometric approaches, this framework allows for the identification of heterogeneous effects without imposing a predefined functional form. To support interpretation, Explainable Artificial Intelligence (XAI) tools are employed, including variable importance measures and partial dependence analysis. This enables the identification of both general patterns and differentiated effects across firm types, and allows to distinguish the role of absolute versus relative location factors in shaping startup growth.

Results

The results indicate that while absolute location factors, such as access to infrastructure and the main city`s amenities, are relevant for startup growth, relative factors linked to neighbourhood characteristics and local agglomeration play a more decisive role. At the same time, these effects are not uniform. A substantial degree of heterogeneity is observed: some startups benefit from being located in dense, competitive environments, while others perform better in less saturated areas. This suggests that the impact of spatial context depends strongly on initial firm characteristics and challenges approaches based on average effects.

Conclusions

The study contributes by integrating firm-level and intra-urban spatial perspectives within a single empirical framework and by explicitly addressing heterogeneity in location effects. The findings have implications for both policy and practice, suggesting that location strategies should be tailored to firm-specific profiles rather than based on general rules. From a methodological perspective, the study demonstrates the usefulness of machine learning and XAI tools in analysing complex spatial relationships and uncovering non-linear and context-dependent effects.

Keywords

technological startups, business location, agglomeration effects, machine learning, spatial analysis