Working Papers

This paper examines the effects of asset purchase programs (APPs) that were implemented in a number of countries during the COVID-19 pandemic in concert with large fiscal stimulus packages. We identify APP shocks for 14 advanced and emerging market economies using high-frequency identification techniques. We next estimate panel local projections, finding that APPs tend to stimulate output, but decrease prices. By using a Kitagawa-Blinder-Oaxaca decomposition, we demonstrate that these responses significantly depend on the magnitude of the simultaneously applied fiscal stimulus. Remarkably, higher government purchases during that period crowded in private consumption and had a large effect on inflation. We show that these empirical findings, some of which are inconsistent with a standard New Keynesian framework, can be rationalized in a simple general equilibrium model with segmented asset markets and fiscal dominance.

We estimate the impact of railway construction on local populations in Russian Poland in the 19th century. The initial wave of railway expansion outpaced economic demand. From the late 1860s onward, locations connected to the railway network experienced significantly higher population growth. The economic effects of the connection to the railway network increased over time. State-funded military railway lines generated a smaller impact than private-owned lines. We also study the impact of the railway connection on social mobility proxied through a number of notable people born in a given city. However, we do not identify any robust impact.
 

This paper estimates the role of six shocks originating in the United States in driving the international business cycle. To this end, it employs impulse response functions and forecast error variance decomposition from panel local projections. We find that key macroeconomic shocks originating in the United States contribute significantly to business cycle synchronization between the US and other economies. These shocks also account for a substantial part of output fluctuations in these economies. Considering individual shock contributions, we document that technology and monetary policy innovations are of the highest relevance.

This paper investigates the domestic and international transmission of U.S. fiscal news shocks emphasizing the importance of the sentiment channel for the global economy. We identify these shocks using federal government spending forecasts from the Survey of Professional Forecasters. Employing the local projection method, we find  that anticipated increases in U.S. government spending are expansionary domestically, leading to improved sentiment and enhanced financial conditions. On the other hand, the U.S. dollar appreciates, and the U.S. trade balance deteriorates when future fiscal expansion is expected. In the international context, we apply panel local projection models across a broad set of countries and show that positive sentiment and improved financial conditions driven by U.S. fiscal news spill over, stimulating demand and output growth in other economies. However, we find no significant effect of currency depreciation on net exports in a broad sample as rising domestic demand tends to boost imports. In turn, in a subsample of countries with high trade exposure to the U.S., the trade channel becomes significant, while financial channel diminishes in importance. At the same time, sentiment channel appears to play a significant role in all subsamples. Finally, we find that positive fiscal news shocks have strong stimulating effects (both domestic and international) during US recessions but not in expansions.

The theory of disruptive innovation has gained significant interest from the academic and business communities, investigating the reasons for inadequate or delayed responses of incumbents to innovation which challenges their established markets. Inadequate responses were explained by the concept of innovator’s dilemma, which postulates how incumbents tend to satisfy their consumers on the established markets, while overlooking opportunities with other consumers. However, the optimal incumbents’ response to disruptive innovation in the normative sense has not been researched. We investigate the choice of the payoff-maximizing strategy in response to the observed market disruption as a choice between different management approaches. Our study shows the importance of the speed of innovation adoption for the choice of the optimal response to innovation. We also show that the response can be delayed in some cases, reflecting the exploitation-exploration dilemma. This new insight complements the reasons of delayed response to disruptive innovation threats.

We provide the first estimate of the Polish national, regional and sectoral GDP in the interwar period. We find that the Polish economy's performance in the interwar period was much better than it was assumed before. In the years 1924 – 1938, the real GDP per capita increased by almost 40% or by 2.3% annually. As economic growth was stronger in the poorer regions significant regional convergence was achieved. Our results challenge the dominant narrative about the weak performance of the Polish economy in the interwar years.

We measure the value added in agriculture in Poland during the interwar period. Our calculation is based on the bottom-up methodology. We provide estimates on the national and regional levels. Cultivated area, yields and yields per hectare increased during the investigated period. Significant regional convergence, both in the case of prices and value added occurred. In the years 1924 -38 value added increased by 5.35% annually, resulting 4.01% per capita growth rate. However, the yields per hectare grew less than in a majority of other European economies. While less developed eastern regions caught up with more economically advanced western Poland, the leading west lost compared to European peers. Therefore, our assessment of the development of agriculture in Poland in that period remains mixed.

In the 21st century, advancements in technologies such as industrial robots have raised concerns about their impact on employment and wages, prompting extensive research. However, their effects on workers’ subjective well-being remain underexplored. This study addresses this gap ¬by examining whether workers experience a decline in well-being due to a loss of agency or maintain it by leveraging human skills to adapt to automation. Using data from the International Federation of Robotics, Eurostat, and the European Social Survey (2002–2018), we link robot density at the country-industry-year level to workers’ life satisfaction, happiness, job influence, and health. Employing an instrumental variables approach, we find that robot adoption negatively affects medium-educated workers’ well-being, particularly its eudaimonic dimension, supporting the decreasing agency thesis. In contrast, low- and highly educated workers experience positive effects. These impacts are more pronounced among women and weaker in countries with robust compensatory social policies.

The article investigates the usage of Informer architecture for building automated trading strategies for high frequency Bitcoin data. Three strategies using Informer model with different loss functions: Root Mean Squared Error (RMSE), Generalized Mean Absolute Directional Loss (GMADL) and Quantile loss, are proposed and evaluated against the Buy and Hold benchmark and two benchmark strategies based on technical indicators. The evaluation is conducted using data of various frequencies: 5 minute, 15 minute, and 30 minute intervals, over the 6 different periods. Although the Informer-based model with Quantile loss did not outperform the benchmark, two other models achieved better results. The performance of the model using RMSE loss worsens when used with higher frequency data while the model that uses novel GMADL loss function is benefiting from higher frequency data and when trained on 5 minute interval it beat all the other strategies on most of the testing periods. The primary contribution of this study is the application and assessment of the RMSE, GMADL and Quantile loss functions with the Informer model to forecast future returns, subsequently using these forecasts to develop automated trading strategies. The research provides evidence that employing an Informer model trained with the GMADL loss function can result in superior trading outcomes compared to the buy-and-hold approach.

This study investigates the distributional impacts of carbon taxes, traditionally examined through simulation studies on the regressivity of hypothetical tax scenarios. However, the dy-namic influence of actually implemented carbon taxes on consumption/income poverty and inequality in a cross-country setting has been less scrutinised. This paper assesses the effect of carbon taxes introduced in the past three decades in 15 European countries on consumption shares of the lowest decile groups, poverty rates and inequality indices. The analysis shows that a $40/ton CO2 tax covering 30% of emissions leads to a consumption share increase of up to 4% for the bottom 20% and 40% of the population, a trend that persisted for five years post-implementation, particularly in nations that efficiently redistribute carbon tax revenues. This resulted in a modest reduction in consumption inequality over three years. In contrast, the impact of carbon taxes on income poverty and inequality is not statistically significant. These findings suggest that concerns about poverty and inequality due to carbon taxes can be miti-gated by implementing a moderate tax combined with a strategically efficient revenue redis-tribution mechanism.

This paper examines Eurobarometer survey data from 27,438 individuals across 28 EU Member States in 2019 to evaluate the awareness and impact of EU Energy Labels. Specifically, we analyze the role of socioeconomic characteristics such as age, gender, education, financial stability, and political engagement. Our results suggest that individual characteristics have a greater effect on the influence of labels on purchase decisions than on label awareness. However, significant heterogeneity across countries persists even after controlling for individual characteristics. Using our model, we conduct three exercises in which we assume a policymaker can either increase label awareness among all unaware individuals or target those with specific characteristics, and we demonstrate the resulting impact on the share of people whose purchases are influenced by the label. The findings reveal that even when label awareness is at its highest level, it does not necessarily translate into substantially higher influence on purchasing decisions in some countries. Additionally, at the country level, certain socioeconomic and political variables are positively correlated with label awareness.
 

In this paper, we analyze the determinants of individual’s willingness to pay higher prices and taxes and to reduce their standard of living to support environmental protection. Using data from the 2020 International Social Survey Programme (ISSP), Environment IV module from 26 countries on about 29,000 individuals, we investigate the influence of socio-demographic factors, consumer behavior, environmental beliefs, opinions, and attitudes. The findings reveal significant variations in willingness to bear financial burdens for environmental protection across different countries and socio-economic groups. Our analysis highlights the critical role of education, religion, political affiliation, and trust in institutions in shaping environmental attitudes and behaviors. Moreover, after controlling for individual characteristics, significant international disparities persist, with countries like India showing exceptionally high willingness across all measures, while many European countries, despite their progressive environmental policies, show lower willingness for higher taxes due to possibly already high tax burdens. These findings underscore the importance of tailoring policy communications to different socio-economic groups, emphasizing both the immediate and long-term benefits of environmental protection to enhance acceptance among various demographic segments.

We study substitution between fixed and mobile broadband services in South Africa using survey data on 134,000 individuals collected between 2009 and 2014. In our discrete-choice model, individuals choose fixed or mobile voice and data services in a framework that allows these services to be considered substitutes or complements. We find that there is substantial heterogeneity in the perception of these services as substitutes/complements. We use our model to simulate the uptake of fixed and mobile broadband across various demographic groups under different policy interventions, including: (i) a reduction in mobile data prices; (ii) an expansion in fixed-line coverage; (iii) a widespread distribution of computers; and (iv) broader internet access in schools and workplaces. Our results suggest that, when applied in isolation, these interventions do not significantly increase internet access among poorer households. In particular, the uptake of fixed broadband would remain limited, even if accessible to all households. This is because many households prefer mobile internet access, perceiving it as a substitute for fixed broadband.
 

In this paper, we investigate the effects of non-exclusive agreements between networks of mobile money agents on mobile network operator choices, using survey data from Tanzania conducted in 2017. By combining survey responses with geo-location data and information on agent proximity, we employ discrete choice models to analyze consumers’ decisions in subscribing to mobile network operators and their corresponding mobile money providers. Our findings highlight the significant influence of the distance to mobile money agents on consumers’ subscription choices. To explore the impact of interoperability (non-exclusivity) at the mobile money agent level, where consumers can use the nearest agent from any mobile money provider, we assess its effects on market shares of mobile network operators. Our results indicate that interoperability at the agent level has only a minor impact on market shares. Smaller operators experience marginal gains as their consumers can now utilize agents of larger providers, which are often closer in proximity. In conclusion, we find that interoperability at the agent level does not considerably alter the market structure in the context Tanzania during the period under consideration.
 

In this paper we analyse whether having a mobile phone impacts chances of getting employed. We use five waves of panel data from the National Income Dynamic Survey (NIDS), which was conducted in South Africa between years 2008 and 2017. In the estimation we include a vector of observable individual and household characteristics and account for unobserved heterogeneity amongst individuals. The estimation results suggest that mobile phone ownership has a positive impact on the change in employment status from unemployed to employed. On the other hand, ownership of a computer by a household and computer literacy do not increase the likelihood of getting employed. The average probability of becoming employed increases from 54.2% when no one among unemployed adults has a mobile phone to 57.4% when all of them have a mobile phone, which is an increase of 5.9%.

In this paper, we utilize survey data collected in 2017 from 12,735 individuals across nine Sub- Saharan African countries. We merge the survey data with geographic information related to the proximity of mobile network towers and banking facilities, based on the geo-locations of the respondents. Our estimation approach comprises a two-stage model. In the first stage, consumers make choices between adopting a feature phone or a smartphone. In the second stage, they make decisions regarding the use of mobile money services. Our findings reveal that network coverage significantly influences the adoption of mobile phones. Moreover, we observe that mobile money services are more favored by younger and relatively wealthier individuals for sending money, while older individuals and those with lower incomes tend to use mobile wallets for receiving money. Consequently, mobile money services facilitate younger migrant workers residing in areas with better infrastructure in providing support to their older relatives in less developed regions.

A large literature in behavioral science suggests that people’s emotional condition can have an impact on their choices. We consider how people’s emotions affect their stated preferences and willingness to pay for changes in environmental quality, focusing on the effects of incidental emotions. We use videos to induce emotional states and test the replicability of the results reported in Hanley et al. (2017). Additionally, we employ Face Reader software to verify whether the intended emotional states were successfully induced in our experimental treatments. We find that our treatments succeed in implementing the predicted emotional condition in terms of self-reported emotions, but had a variable effect on measured (estimated) emotional states. We replicate the key result from Hanley et al. (2017): induced emotional state has no significant effect on stated preference estimates or on willingness to pay for environmental quality changes. Moreover, we confirm that, irrespective of the treatment assignment or emotional state - be it self-reported or measured - we observe no significant effect of emotion on stated preferences. We conclude that stated preference estimates for environmental change are unaffected by changes in incidental emotions, and that preference estimates are robust to the emotional state of the responder.

This study investigates the impact of investor sentiment on stock returns and trading volume, challenging the efficient market hypothesis. Using CRSP data from May 1998 to March 2022, methods like Fama-MacBeth and quantile regression were applied to analyze sentiment indicators such as the VIX, AAII Investor Sentiment Survey, Consumer Confidence, and Baker-Wurgler Index. The findings reveal that investor sentiment significantly influences stock returns and trading volume, especially during uncertain times. Sentiment also affects financial metrics like SMB, HML, RMW, and CMA uniquely. This research provides new insights and practical implications for investors and analysts, emphasizing the importance of considering sentiment in investment strategies to better anticipate market movements and manage risks.

Research background: The number of research regarding employment effects of minimum wages is enormous. Another problem examined by prior studies is the impact of minimum wage increases on the wages. The evidence shows that minimum wage increases compress the wage distribution. The same literature brings conflicting evidence regarding minimum wage spill-over effects.
Purpose of the article: The study analyses the effects of a minimum wage increase on the wage distribution of low- and high-wage sectors and possible spill-overs. The analysed period 2014-2018 is characterized by relatively stable economic conditions, while the minimum wage increased by 25%. 
Methods: We follow case study method and as example Poland, the EU country with high share of minimum wage workers. We use individual data on wages and worker characteristics from the Structure of Earnings Survey in Poland for 2014–2018. We use reweighting and decompose counterfactual wage distribution.
Findings & value added: In low-wage sector, a wage increase in the left tail of the distribution is almost entirely due to the increase in the minimum wage level and spill-over effects are present throughout the distribution. In high-wage sector the role of the minimum wage growth is weaker and also the workers’ characteristics have substantial impact on wages; no spill-over effects of a minimum wage increase are observed. We demonstrate that the conflicting evidence on the effects of minimum wage changes on the wage distribution may occur because the effects differ across the low- and high-paid economic sectors. They depend on sector productivity and openness.

This study utilizes machine learning algorithms to analyze and organize knowledge in the field of algorithmic trading, based on filtering 136 million research papers to 14,342 articles ranging from 1956 to Q1 2020. We compare previously used practices such as keyword-based algorithms and embedding techniques with state-of-the-art dimension reduction and clustering for topic modeling method (BERTopic) to compare the popularity and evolution of different approaches and themes. We show new possibilities created by the last iteration of Large Language Models (LLM) like ChatGPT. The analysis reveals that the number of research articles on algorithmic trading is increasing faster than the overall number of papers. The stocks and main indices comprise more than half of all assets considered, but the growing trend in some classes is much stronger (e.g. cryptocurrencies). Machine learning models have become the most popular methods nowadays, but they are often flawed compared to seemingly simpler techniques. The study demonstrates the usefulness of Natural Language Processing in asking intricate questions about analyzed articles, like comparing the efficiency of different models. We demonstrate the efficiency of LLMs in refining datasets. Our research shows that by breaking tasks into smaller ones and adding reasoning steps, we can effectively address complex questions supported by case analyses.