Projekty badawcze

From Top to Tails: Decoding the Hidden Patterns in Cultural Consumption

UMO-2024/55/I/HS4/02760 - OPUS LAP

The digital revolution has profoundly transformed cultural and creative industries, reshaping how content is produced, distributed, and consumed. This project, led by researchers from the University of Warsaw and the University of Lausanne, explores how these changes affect individual cultural consumption patterns across different industries, such as music, books, films, and video games.
Digitisation lowered barriers for creators, while a simultaneous shift from physical formats to online distribution opened the way for niche products with audiences scattered around the world. The combination of these changes contributed to a rapid increase in the number of available products and a simultaneous growth of both the sales concentration at the top (a superstar effect) and of the share of sales comprised by the huge number of low-selling titles (the long tail). Beyond that, digitisation also affected the diversity of what is produced, discovered and eventually consumed. Prior research showed remarkable shifts in the characteristics of most popular titles, changes in representation of minorities across creative content, growing roles of artist collaborations, brands, franchises and much more.
As the markets evolved, new intermediaries have gained an increasing say in what gets to be produced, promoted and recommended to audiences. At the same time, new technologies equalised access to content across regions to an unprecedented level – raising concerns of globalization in culture. In response, many policymakers responded in initiatives aimed at promoting local culture and creators – whether through implementation of quotas or direct support to national creators. Importantly though, most of these phenomena and trends have been studied at aggregate market levels, based on information on sales of different titles, often from top charts or bestseller lists. However, far less is known about how individual consumption behaviours have evolved. Does the broader availability of content translate to diverse personal habits, or are consumers confined to narrower niches? Do observed trends reflect evolving
preferences within generations, or are they driven by new cohorts entering the market? This project aims to address these questions by shifting the focus to individual patterns and exploring how they contribute to broader market dynamics. We believe that such perspective is crucial for understanding cultural trends, as well as for the development of comprehensive policies targeting consumption beyond its aggregate levels.
This project investigates these hidden patterns in cultural consumption, addressing four key questions:
1. How is cultural consumption distributed at the individual level?
2. How do individual consumption patterns contribute to aggregate cultural consumption trends?
3. What is the role of age and cohort effects in the overall trends in cultural consumption?
4. How do patterns identified in questions 1-3 differ for different cultural and creative goods?
The study adopts an innovative approach, by analysing huge datasets on individual consumption histories for several creative industries. These datasets include music listening events from Last.fm, virtual bookshelves from Goodreads users, gaming activity from Steam and movie picks from Letterboxd or IMDb users. These sources will allow us to analyse millions of consumption events from hundreds of thousands of individuals. By focusing on the audiences instead of products, the project moves beyond traditional market analyses to provide a deeper understanding of the actual consumer experience of digitisation.

Use of remote sensing for management of blue-green infrastructure in urban climate change adaptation (LIFECOOLCITY)

LIFE21-CCA-PL-LIFECOOLCITY/101074553 - LIFE

The ambition of the “Use of remote sensing for management of blue-green infrastructure in urban climate change adaptation” (LIFECOOLCITY) project is to support the management of blue-green infrastructure (BGI) in 10,000 cities in the European Union.

This will be achieved through the implementation of innovative systems utilizing Geographic Information Systems (GIS) and remote sensing technologies, including satellite and aerial imagery.

As a result, city administrators and residents will gain access to four informational products grouped into two computer systems. These tools will help identify key adaptive needs, develop BGI management strategies, and monitor the effectiveness of implemented actions.

The EUROPE system

The EUROPE system, based on satellite data, will identify areas with intensified environmental challenges in European cities and include two products:

• EU Cities Ranking of BGI – a free report published periodically from 2025 until the end of the project, assessing the state of BGI in at least 10,000 cities across the European Union.
• BGI SAT-MONITORING Report for city – a paid report offering detailed spatial analysis of environmental issues within a specific city, available from 2025.

The CITY system

The CITY system will operate using data obtained from aerial surveys and will function as a decision-support tool for implementing Nature-Based Solutions (NBS) at the city level.

• BGI AUDIT Report for city – identifying key intervention areas and proposing optimal nature-based solutions.
• BGI AIR-MONITORING Report for city – assessing the condition and effectiveness of implemented BGI solutions.

The first city selected for the development of a BGI reconstruction concept is Wrocław.

During the project, an informational and educational platform titled “Life in Cool City” will be developed to provide access to the project’s informational pr

An agent-based integrated assessment of climate and social tipping points

UMO-2022/46/E/HS4/00023 - SONATA BIS

Recently, climate-economy models have intergated climate tipping points into the climate policy assessment. It has been shown that their inclusion increases the estimates of economic losses due to climate change. A climate tipping point is a critical value in the temperature or other environmental variable, after which it is crossed, the catastrophic event may occur. However, current models are still not well equipped to study multiple social tipping points. A social tipping point describes a critical number of adopters of a new technology, after which its further adoptions are driven by self-reinforcing positive feedback mechanisms. Such points can have synergic, complementary or antagonistic impacts on the carbon dioxide emissions, thus may slow down or accelerate climate tipping points. For instance, digitalization and electrification of transport, if adopted by a critical mass, would increase electricity use.

This would require massive investments in renewable energy to support the low carbon transition. However, investments in renewable energy will be mineral intensive. As a result, the process may be undermined by the scarcity of rare metals and minerals, unless a critical mass of rare metals embodied in consumer products is recycled. The net effect of these mechanisms has not been assessed quantitatively, which we will do in this project. Our goal is to identify the positive social tipping cascades so as to avoid negative tipping cascades in climate.

To this end, in this project, we will develop models for integrated assessment of climate change impacts using an agent-based modelling method. Agent-based models have been suggested as a new wave of climate change modelling. They offer a behavioural alternative to mainstream economic models. In particular, each aggregate equation in traditional economic models, e.g. describing capital accumulation or total consumer spending, is replaced by the network of interacting agents, namely: heterogeneous consumer, firms and investors. This allows for modelling diversity of behaviours, bounded rationality and social interactions. As a result, ABMs offer a better starting point for integrating social tipping points than traditional models. The project will results in novel agent based intergrated assessment models to study multiple social and climate tipping points.

Behavioral anomalies in non-market valuation – New solutions based onbehavioral welfare economics

UMO-2023/51/D/HS4/00598 - SONATA

Public goods, such as clean water in rivers and public safety, bring benefits to society, but providing public goods also generates costs. To figure out the appropriate amount of spending on their provision, it is essential to calculate the benefits they bring in monetary terms. The monetary benefits associated with public goods can be compared against the costs of their provision in benefit -cost analysis to assess which goods it pays off to provide —that is, which ones bring larger benefits than costs.

The challenge lies in calculating the monetary benefits of public goods. The benefits can be expressed by how much people value these goods. Economists approximate values of goods with the amount s that people are willing to pay for them. But public goods are not bought or sold in markets and so lack prices that could indicate their value. This makes the measurement of the benefits that people derive from public goods challenging. Economists have proposed several methods for assessing the value of goods that are not traded in markets.

One of the commonly used techniques is stated preference (SP) methods. SP methods rely on data collected from specially designed surveys, in which respondents are asked about their preferences toward public goods. The querying about the preferences always involves some monetary trade-offs (e.g., providing a good with specific characteristics at a given cost), which helps researchers calculate the monetary value (implicit price) of the goods. 

The key objective of the project is to propose new practical measures and tools for SP studies —guided by insights from behavioral welfare economics —to improve the validity of SP-based

value estimates in the face of anomalous behaviors. Behavioral welfare economics has put forth various ways to measure values that take into account that people might not always make rational decisions to maximize their utility. Surprisingly, SP studies have not used these insights to address issues with non-standard behaviors in SP surveys. Our goal is to apply the approaches grounded in behavioral welfare economics to SP research and examine how well they work in practice. We aim to study if incorporating these approaches can improve the validity of SP value estimates.

Non-Gaussian Structural Dynamic Factor Models in Macroeconomics: Identification Theory, Inference Algorithms, and Applications

UMO-2024/55/B/HS4/00110 - OPUS

This project develops a new methodology for structural dynamic factor models (SDFMs) without the restrictive assumption of Gaussian (normal) shocks. While traditional Gaussian SDFMs are widely used in macroeconomics to assess the impact of economic policies and shocks, they fail to capture key empirical features of real-world data.

We aim to build the theoretical foundations for non-Gaussian structural dynamic factor models (N-GSDFMs)—including a general identification theory and efficient Bayesian inference algorithms that scale to large datasets. The project will also provide automated tools for practitioners.

From a practical standpoint, the project will improve how we measure the effects of economic shocks via impulse response functions, including uncertainty bands—currently a major challenge in non-Gaussian models.

Finally, we will apply our framework to re-examine the influential claim by Angeletos et al. (2020) that a single demand-type shock explains most of GDP volatility, aiming to provide a more nuanced view based on richer models and data.

Impact of Globalization, financial development and economic structures on energy diversification: Implications for European Green Deal

UMO-2024/53/B/HS4/01399 - OPUS

Global energy markets have been significantly affected by the Russia-Ukraine war and has exposed the risks faced by European nations that source their energy from Russia. This crisis points to the fact that Europe must move to reduce its reliance on Russian energy in order to meet the goals of the European Green Deal. The goal of the proposed project is to investigate and compare the energy diversification strategies of European countries through the lens of globalization, financial development, economic structure and European green deal. Therefore, through the analysis of these factors, we propose a course of action aimed at endeavoring a safer and more stable energy future for Europe. The specific research questions that guide this research include; first, what kind of policy frameworks exist across Europe to diversify their energy sources? Second, how does globalization, financial development, and economic structure affect energy diversification? Third, what is the relationship between energy diversification and environmental sustainability in the presence of other determinants like globalization, financial development, natural capital, structural changes and economic growth? 

This project is based on mixed methodology: qualitative method that will consist of literature, policy desk reviews; interviews with key policy and academic experts, secondary data sources whereby we will check interconnections and impact of independent variables on energy diversification and environmental sustainability in the European countries.

The European Green Deal is a package of strategic initiatives presented by the European Commission to achieve environmental objectives by 2050. Our work aims to respond to current energy security challenges while supporting the long-term objectives of environmental protection and climate change mitigation, thus paving the way for meaningful advancements in the development of a more secure, sustainable, and resilient energy future for Europe.

A life course microsimulation perspective on multi-dimensional well-being for five European countries

UMO-2024/06/Y/HS4/00029 - CHANSE

Well-being is a multi-dimensional concept that spans a variety of life domains and time scales, affected by variable determinants across the life course. As such, it eludes simple analytical frameworks and calls for an integrated perspective. Data limitations, however, significantly constrain empirical analyses. The relationship between subjective and objective measures of well-being is also challenging to analyse in the absence of retrospective information on individual histories and prospective information reflecting expectations for the future, all of
which bear upon a broad interpretation of well-being.
The WELLSIM project aims to advance the analysis of well-being and its inequalities in Europe by using a dynamic, life course microsimulation approach, integrating insights from various
fields such as economics, demography, psychology, public health, epidemiology, and sociology. Focusing on five European countries, WELLSIM plans to go beyond typical crosssectional
studies to provide a more nuanced understanding of how well-being changes over the course of a person’s life, influenced by both personal circumstances and broader societal
factors.
At the core of WELLSIM’s investigation is the desire to uncover the determinants of subjective well-being and how individual and societal crises affect its development. The project will study
the impact of life events like job loss, health challenges, and changes in family dynamics on well-being, as well as the role of welfare policies in buffering these effects. It also aims to assess
the implications of social processes such as labour market downturns, demographic ageing, and immigration on well-being and the capacity of societies to adapt. The overall well-being will
be measured multi-dimensionally considering both objective measures (income, work quality, housing, education level) and subjective ones (self-assessed health, happiness). The project will
measure how multi-dimensional well-being is affected by individual life events and general social and economic forces like labour market crises or population ageing. In addition, the
impact of these forces on inequality in well-being will be studied.
Methodologically, WELLSIM builds on the existing microsimulation framework, enhancing it to include dimensions like subjective well-being, chronic pain, and skill levels, alongside macroeconomic scenarios. This enriched model will be developed for the UK (liberal welfare regime), Germany (conservative welfare regime), Spain (Mediterranean welfare regime), Poland (post-communist welfare regime), and Sweden (Nordic welfare regime), providing insights into how different welfare systems contribute to improving well-being. A key focus of research interest will be the efficacy of policy interventions designed to enhance individual and social well-being resilience to considered events and crises. WELLSIM’s approach to modelling well-being through the life course stands out for its
comprehensive, dynamic perspective that captures the intricate interactions between individual life events and broader economic and societal trends. The project is set to contribute significantly to our understanding of well-being dynamics and offer a strong basis for policymaking aimed at enhancing societal well-being and resilience in Europe.

Automation exposure and human capital investments in European Union countries

UMO-2024/53/N/HS4/01844 - PRELUDIUM

Advancing technologies change the way we perform job tasks and transform labour markets. We are witnessing the emergence of new occupations, the decline of others, and significant changes
to existing ones. Such processes require individuals, firms and institutions to adapt, blurring the vision of a predictable career path. That evokes the need for lifelong learning, so frequent investments in human capital. Technology can both automate and support job tasks. In each case, investing in human capital can be beneficial. On one hand, employees exposed to automation may need new or enhanced skills to maintain their position in the labour market. At the same time, companies may be reluctant to train employees whose work they intend to automate. On the other hand, using implemented technologies may require specific skills, compelling companies to upskill their workforce. Moreover, recent advancements in artificial intelligence have significantly expanded the group of
individuals at risk of automation. This group now includes not only workers performing routine tasks but also highly skilled professionals who were previously considered "automation-proof."
Consequently, the exposed group has become very diverse, varying not only in qualifications and tasks performed but also in their investments in human capital.
This project aims to enhance the understanding of how exposure to automation technologies impacts human capital investments and to identify motivations and barriers behind them. The project focuses on employees in the European Union, distinguishing between routine exposure and AI exposure. Research findings can prove valuable for organisations and policymakers striving to provide tailored training that minimises the negative impact of technology on the situation and prospects of employees in the evolving labour market

Exploring the Accuracy of Travel Cost Method for Nonmarket Valuation: Econometric Challenges and the Impact of Behavioral Factors on Self-Reported Data Quality

UMO-2024/53/N/HS4/04303 - PRELUDIUM

The need to assign "prices" to public goods, the value of which cannot be determined through demand and supply relationships, is not new. Although the benefits derived from goods such as a clean lake, a forest clearing from which one can watch sunsets or a playground can be described as priceless, the costs associated with their creation and maintenance are usually clearly defined and must be regularly incurred. Therefore, it is important to estimate what portion of public funds should be allocated for this purpose. Economists have developed various methods for valuing non-market goods, many of which are based on surveys. Over the years, non-market valuation methods have been extensively studied for their accuracy, but little attention has been paid to revealed preference methods. One of these methods is the travel cost method, which is the focus of this project. The method allows for the estimation of the benefits that non-market goods provide to society based on people's past choices (the number of trips to certain places and their costs). The project aims to make the travel cost method more reliable and accurate by addressing problems related to
the quality of data reported by respondents, examining the stability of estimates over time, and proposing new ways to incorporate the value of time into modeling. The improved method will help decision-makers and researchers better understand the value of public goods and better allocate public funds. The quality of data reported by respondents can depend on many factors, such as memory. People often do not remember the exact number of trips and overestimate them, leading to an overvaluation of the public goods. Various tools, such as reminders of previously given answers and scripts encouraging the use of calendars or photos, will be tested in the study to improve memory. The study will also focus on previously unexamined factors that may affect the quality of data reported in surveys, such as behavioral factors related to motivation. Currently, the travel cost method studies assume that respondents try to provide the most accurate and truthful answers, but this might not always be true. Surveys can be tiring, leading respondents to rush through them or give less thoughtful answers, a behavior known as “satisficing."

The project will examine how the order of questions, survey length, and rewards for participation in the survey influence this behavior. Techniques such as "trap questions" will be used to check respondents' attentiveness. Additionally, the study will try to differentiate between respondents who try to provide truthful answers and those who deliberately provide false information to maximize their gain. This problem arises because respondents are often materially rewarded for the time spent participating in surveys, provided they are in the group targeted by the survey. In travel cost method studies, this usually applies to people who have made trips to the surveyed places within a specified period (e.g., the last 12 months). Unfortunately, this may encourage some to lie about their past behavior, making the data they provide useless. The study will propose new tools to solve this problem, such as allowing respondents to skip some survey questions while still receiving full rewards. This practice will align respondents' motivations with the research objectives of scientists and ensure better data quality.
The project will also examine the stability of travel cost method estimates over time by comparing data collected at different times, controlling for time-varying factors that may influence people's travel (such as weather). This will help determine whether the method can be effectively used for long-term decision-making. Finally, the project will improve the way the value of travel time is considered in modeling, taking into account individual differences in how people value their time.
The research will be conducted in several phases: developing the project structure, developing and testing the survey, and then conducting it in collaboration with a research firm. The collected data will be analyzed using econometric models and statistical tests. The results and conclusions will be published in scientific articles in high-quality journals and presented at international conferences.

The economic value of health – scope effects and risk preferences in mortality and morbidity valuation

UMO-2023/49/B/HS4/01655 - OPUS

The research project aims to investigate the economic value of health by studying preferences and valuation methods in the context of mortality and morbidity. The project focuses on addressing scope sensitivity issues, which occur when respondents' stated preferences or willingness to pay (WTP) do not accurately reflect the true value they place on a specific attribute. Insufficient scope sensitivity can lead to biased estimates of economic values, potentially underestimating or overestimating the true value of the attribute.
The project proposes several strategies to mitigate the scope sensitivity problem, including range selection, attribute bundling, anchoring and calibration, sensitivity analysis, and accounting for other
factors such as experimental designs, cognitive and behavioral factors, and alternative elicitation formats. Additionally, the project aims to investigate the influence of risk preferences on valuation
and explore the heterogeneity and domain-specificity of risk preferences in relation to observed WTPs and health policy indicators.
The significance of the project lies in its potential to improve the reliability and validity of stated preference-based value estimates of health effects. It aims to bridge the gap between stated preference
methods used in health economics and state-of-the-art research in other fields, such as environmental economics and transport economics. The research findings could have implications for shaping efficient health-related public policies and may also be relevant for other fields like psychology or behavioral sciences.
The project proposes three empirical studies using discrete choice experiments (DCEs) to investigate scope effects in health effects valuation. The surveys will be designed based on state-of-the-art recommendations for stated preference studies and will include carefully chosen treatments to verify research hypotheses and gain insights into the research questions. The studies will cover important topics related to public health policies and involve representative samples of the target populations. Advanced models accounting for preference heterogeneity will be applied to the data.
The expected outcomes of the project include publishing at least five papers in leading journals, presenting results at international conferences, and establishing an integrated research group dedicated to this area of economics in Poland. The project aims to make a significant contribution to the validity of preference models and value estimates in health economics and improve the reliability of stated-preference valuation of health effects.