Working Papers

The WNE Working Papers series has been published by the Faculty of Economic Sciences at the University of Warsaw since 2008.

The WNE Working Papers series provides a fast, open channel for disseminating research conducted at the Faculty of Economic Sciences, University of Warsaw. The papers hosted here are working versions (pre-prints) and may evolve as authors refine their analyses, incorporate feedback, or progress through formal peer-review. What you read is the current version released by the authors, timestamped and assigned DOI/ISSN identifiers to ensure precise citation and version tracking. Copyright remains with the authors, who may, at any time, upload a revised file or add a note directing readers to a later, peer-reviewed publication.

The Working Papers series accepts articles by research employees of the Faculty and publications from conferences organised at the Faculty of Economic Sciences at the University of Warsaw. Articles should be original research papers which have not been previously published, on the subject of economics.

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Number of Results 8

WP(23/2026)517. Using tweet phrases to predict the transition from migration intentions to real-life movements

Authors: Janicka Anna, Amirkhanyan Hayk
Understanding the transition from migration intentions to actual movements remains a challenge in demographic research, often hampered by a persistent intention-behavior gap. This research examines how linguistic variations in individuals' stated…

WP517. Using tweet phrases to predict the transition from migration intentions to real-life movements

Understanding the transition from migration intentions to actual movements remains a challenge in demographic research, often hampered by a persistent intention-behavior gap. This research examines how linguistic variations in individuals' stated migration intentions, particularly on social media, help predict actual movements. We hypothesize that the specific phrasing of the intention reflects the strength and proximity to realization. To verify this claim, we compiled a unique dataset based on migration-related tweets from Polish users posted between 2006 and 2021, identifying potential migrants via Polish keywords that indicated some form of migration intent. The collected statements were manually categorized for linguistic nuances and contextual factors. Actual migration was subsequently tracked by detecting significant country changes (minimum 30 or 180 days) in users' geotagged tweet histories, and logistic regression models were applied to assess the predictive power of different formulations. Our findings demonstrate that the linguistic phrasing of migration intentions correlates significantly with their realization: users employing “planning” keywords were most likely to migrate, while those expressing mere “preferences” were the least likely. Additionally, intentions communicated through multiple tweets, as well as those linked to concrete goals, such as education, positively predicted actual movement. Conversely, tweets with negative contexts, hypothetical conditions, or perceived obstacles were related to a reduced actual migration probability. This study empirically confirms that subtle linguistic variations and contextual elements within spontaneous social media expressions may be treated as indicators of migration intention strength and its likelihood of translating into actual behavior. Our results highlight the importance of differentiating intention forms to enhance migration predictions and refine future survey designs.


DOI: https://doi.org/10.33138/2957-0506.2026.23.517
Janicka Anna, Amirkhanyan Hayk
migration intentions migration behavior migration decision-making intention-behavior gap social media analysis Twitter data geolocated data linguistic cues migration prediction human mobility Poland

WP(5/2024)441. How stable and predictable are welfare estimates using recreation demand models?

Authors: Lloyd-Smith Patrick, Zawojska Ewa
Economic analysis of environmental policy projects typically use pre-existing welfare estimates that are then transferred over time to the policy relevant periods. Understanding how stable and predictable these welfare estimates are over time is impo…

WP441. How stable and predictable are welfare estimates using recreation demand models?

Economic analysis of environmental policy projects typically use pre-existing welfare estimates that are then transferred over time to the policy relevant periods. Understanding how stable and predictable these welfare estimates are over time is important for applying these estimates in policy. Yet, revealed preference models of recreation demand have received few temporal stability assessments compared to other non-market valuation methods. We use a large administrative dataset on campground reservations covering ten years to study temporal stability and predictability of recreation demand welfare estimates of lake water quality changes. Based on single-year models, our findings suggest welfare estimates are temporally stable across years in around 50% of the comparisons. Using an event study design, we find evidence that welfare estimates are stable within a year, that is, for weeks after a change in water quality. Our findings further reveal that having two years of data for predicting welfare estimates in subsequent years improves the prediction accuracy by 22% relative to using a single year of data, but further improvements in the prediction accuracy are modest when including additional years of data. Predictions of welfare estimates are not necessarily improved when using data closer in time to the prediction year. We discuss the implications of our results for using revealed preference studies in policy analysis.


Lloyd-Smith Patrick, Zawojska Ewa
recreation demand revealed preferences temporal stability prediction accuracy water quality welfare estimates

WP(3/2023)410. How Well Can Experts Predict Farmers’ Choices in Risky Gambles?

Authors: Schaak Henning, Rommel Jens, Sagebiel Julian, Barreiro-Hurlé Jesús, Bougherara Douadia, Cembalo Luigi, Cerjak Marija, Čop Tajana, Czajkowski Mikołaj, Espinosa-Goded María, Höhler Julia, Lagerkvist Carl-Johan, Rodriguez-Entrena Macario, Tensi Annika, Thoyer Sophie, Tomić Maksan Marina, Vecchio Riccardo, Zagórska Katarzyna
Risk is ubiquitous in agriculture and a core interest of agricultural economists. While farmers’ risk preferences are well studied, there is limited knowledge on the perspectives of other stakeholders on farmers’ risk preferences. We addr…

WP410. How Well Can Experts Predict Farmers’ Choices in Risky Gambles?

Risk is ubiquitous in agriculture and a core interest of agricultural economists. While farmers’ risk preferences are well studied, there is limited knowledge on the perspectives of other stakeholders on farmers’ risk preferences. We address this gap by eliciting predictions for a multiple-price-list task from 561 students, farm advisors, and experts from Italy, Poland, Croatia, Spain, France, Sweden, and the Netherlands. First, we investigate whether the risk preferences of farmers from different European production systems differ in terms of predictability for the experts. Second, we compare the predictions of different groups of experts, as well as their accuracy. Third, we evaluate whether the accuracy of predictions can be improved by changing incentive mechanisms. Overall, we find substantial variation in individual predictions. Yet, average predictions are close to the averages of the observed responses of farmers. We find that an international group of researchers in experimental economics provides more accurate predictions than farm advisors and other experts or students of agriculture. Differences in predictions by production systems are small. Incentivizing predictions by either a tournament scheme (the best prediction receives a reward) or high accuracy (randomly selected participants are paid depending on the quality of their prediction) do not strongly affect the accuracy, but may slightly reduce noise in the predictions.


Schaak Henning, Rommel Jens, Sagebiel Julian, Barreiro-Hurlé Jesús, Bougherara Douadia, Cembalo Luigi, Cerjak Marija, Čop Tajana, Czajkowski Mikołaj, Espinosa-Goded María, Höhler Julia, Lagerkvist Carl-Johan, Rodriguez-Entrena Macario, Tensi Annika, Thoyer Sophie, Tomić Maksan Marina, Vecchio Riccardo, Zagórska Katarzyna
Risk attitudes Expert predictions Expert forecasts Multiple prices lists Meta-science Experimental economics

WP(13/2021)361. Machine learning in the prediction of flat horse racing results in Poland

Authors: Borowski Piotr, Chlebus Marcin
Horse racing was the source of many researchers considerations who studied market efficiency and applied complex mathematic formulas to predict their results. We were the first who compared the selected machine learning methods to create a profitable…

WP361. Machine learning in the prediction of flat horse racing results in Poland

Horse racing was the source of many researchers considerations who studied market efficiency and applied complex mathematic formulas to predict their results. We were the first who compared the selected machine learning methods to create a profitable betting strategy for two common bets, Win and Quinella. The six classification algorithms under the different betting scenarios were used, namely Classification and Regression Tree (CART), Generalized Linear Model (Glmnet), Extreme Gradient Boosting (XGBoost), Random Forest (RF), Neural Network (NN) and Linear Discriminant Analysis (LDA). Additionally, the Variable Importance was applied to determine the leading horse racing factors. The data were collected from the flat racetracks in Poland from 2011-2020 and described 3,782 Arabian and Thoroughbred races in total. We managed to profit under specific circumstances and get a correct bets ratio of 41% for the Win bet and over 36% for the Quinella bet using LDA and Neural Networks. The results demonstrated that it was possible to bet effectively using the chosen methods and indicated a possible market inefficiency.


Borowski Piotr, Chlebus Marcin
horse racing prediction racetrack betting Thoroughbred and Arabian flat racing machine learning Variable Importance

WP(33/2020)339. The impact of the content of Federal Open Market Committee post-meeting statements on financial markets – text mining approach

Authors: Osowska Ewelina, Wójcik Piotr
This article examines the impact of FOMC statements on stock and foreign exchange markets with the use of text mining and modelling methods including linear and non-linear algorithms. Proposed methodology is based on calculating the FOMC statements&#…

WP339. The impact of the content of Federal Open Market Committee post-meeting statements on financial markets – text mining approach

This article examines the impact of FOMC statements on stock and foreign exchange markets with the use of text mining and modelling methods including linear and non-linear algorithms. Proposed methodology is based on calculating the FOMC statements' tone called as sentiment and incorporate it as a potential predictor in the modelling process. Additionally, we incorporate the market surprise component as well as two financial indicators namely Purchasing Managers' Index and Consumer confidence index that gauge for corporate managers and retail customers assessment of the economic situation and potential fluctuations. Eight event windows around the event are considered: 60-minute and 20-minute windows before the event and also 15-minute, 20-minute, 25-minute, 30-minute, 60-minute and 120-minute windows after the event. Research has shown that given linear models the sentiment of FOMC statements does not generate a significant response in any of the analyzed event windows neither for the S&P 500 Index nor for the spot price on the EUR/USD currency pair. However, significant predictors occurred to be market shock in case of both S&P 500 Index and EUR/USD spot price, PMI in case of EUR/USD spot price and also CCI in case of EUR/USD spot price. Given non-linear models, the negative relation of statement's sentiment score and the model prediction is observed for EUR/USD spot price.


Osowska Ewelina, Wójcik Piotr
FOMC Statements event arbitrage sentiment analysis financial markets prediction

WP(30/2020)336. Impact of using industry benchmark financial ratios on performance of bankruptcy prediction logistic regression model

Authors: Heba Mateusz, Chlebus Marcin
The phenomenon of companies bankruptcy is crucial for business partners and financial institutions due to the fact that business failure might be the cause of huge losses. Researchers has continually been aimed for improving models performance in the…

WP336. Impact of using industry benchmark financial ratios on performance of bankruptcy prediction logistic regression model

The phenomenon of companies bankruptcy is crucial for business partners and financial institutions due to the fact that business failure might be the cause of huge losses. Researchers has continually been aimed for improving models performance in the prediction of companies bankruptcy. Some authors of scientific papers claim that the process of evaluation of the companies situation requires comparison of its characteristics defined as financial ratio with situation of whole sector in order to obtain reliable conclusions. In this paper, a hypothesis that usage of the industry benchmarks (transformation of raw financial ratios values into sectoral deciles groups numbers) improves results of bankruptcy prediction logistic regression model is verified. Based on empirical results for Polish market, it turns out that although models estimated on different types of data have similar discriminatory power, logistic regression using raw financial ratios obtained a bit better results than its industry equivalents defined as sectoral deciles groups numbers. It is worth emphasizing that empirical part of paper uses information about 109K companies what is the rarity in bankruptcy prediction papers – researchers usually use small datasets that include less than several hundred records.


Heba Mateusz, Chlebus Marcin
bankruptcy prediction financial ratios industry financial ratios sectoral financial ratios logistic regression financial econometrics

WP(12/2020)318. How do managers actually choose suppliers? Evidence from revealed preference data

Authors: Manucharyan Hayk
Supplier selection plays a pivotal role in the success of any organization as it significantly reduces purchasing costs and increases corporate competitiveness. At the same time, it is a very challenging task as decision-makers have to tradeoff among…

WP318. How do managers actually choose suppliers? Evidence from revealed preference data

Supplier selection plays a pivotal role in the success of any organization as it significantly reduces purchasing costs and increases corporate competitiveness. At the same time, it is a very challenging task as decision-makers have to tradeoff among different supplier attributes. In this paper, a discrete choice model of supplier selection is developed, based on revealed preference data collected from an electrical equipment manufacturer in Poland. We explore the importance of different attributes for the initial choice and subsequent switching of suppliers. The proposed logit model is proceeded by a nonparametric analysis conducted through the Chi-square Automatic Interaction Detector (CHAID) framework, which serves exploratory purposes. We find that delivery and reliability play a crucial role in decision-making with regards to choosing suppliers and switching them if necessary.


Manucharyan Hayk
supplier selection purchasing supply chain management revealed preferences discrete choice analysis logit model CHAID prediction model

WP(1/2016)192. One-Day Prediction of State of Turbulence for Portfolio. Models for Binary Dependent Variable.

Authors: Chlebus Marcin
This paper proposes an approach to predict states (states of tranquillity and turbulence) for a current portfolio in a one-day horizon. The prediction is made using 3 different models for a binary variable (LOGIT, PROBIT, CLOGLOG), 4 definitions of a…

WP192. One-Day Prediction of State of Turbulence for Portfolio. Models for Binary Dependent Variable.

This paper proposes an approach to predict states (states of tranquillity and turbulence) for a current portfolio in a one-day horizon. The prediction is made using 3 different models for a binary variable (LOGIT, PROBIT, CLOGLOG), 4 definitions of a dependent variable (1%, 5%, 10%, 20% of worst realization of returns), 3 sets of independent variables (untransformed data, PCA analysis and factor analysis). Additionally an optimal cut-off point analysis is performed. The evaluation of the models was based on the LR test, Hosmer-Lemeshow test, GINI coefficient analysis and KROC criterion based on the ROC curve. 
Six combinations of assumptions have been chosen as appropriate (any model for a binary variable, the dependent variable defined as 5% or 10% of worst realization of returns, untransformed data, 5% or 10% cut-off point respectively). Models built on these assumptions meet all the formal requirements and have a high predictive and discriminant ability.


Chlebus Marcin
prediction state of turbulence regime switching risk management risk measure market risk.
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