The urgency surrounding environmental sustainability has triggered an innovation of financing channels for climate and environmental projects. Green bond as one such channel has garnered immense interest from investors, with an implicit view that this fixed-income instrument is a relatively safer choice as an investment portfolio. Yet, the uncomfortable spread of greenwashing as a marketing spin has subjected green bonds to significant market volatility, at least as much as other financial assets or sectoral indices if not more. Whether green bonds as a financial instrument may incur losses to the extent of the loss in various sector indices, can be gauged by studying the nature of their contemporaneous growth. In this paper, we use daily data on green bonds and several S&P sectoral indices and a fractionally cointegrated vector autoregression framework (FCVAR) to study the extent to which green bonds dynamically co-move with various sectoral indices. Such a co-movement, if any, would elicit the extent to which a variation of uncertainty would determine an investors inclination to the diversification of a portfolio between an investment in a sectoral index and a green bond. The identifying mechanism is the shock-dissipation speed, which also informs a policymaker before choosing the right instrument to stabilise the system. We show that the system-wide shocks indeed dissipate slower than could be predicted by a conventional cointegrated VAR system. Further, the property of the slow error correction within the dynamic system of Green Bond and sectoral S&P indices, for instance, may demonstrate the speed of adjustment of the global economy to sudden shocks. Rigorous predictions exercises complement our baseline conclusions.

BACKGROUND. The stomach-derived hormone ghrelin stimulates appetite, but the ghrelin receptor is also expressed in brain circuits involved in motivation and reward. We examined ghrelin effects on decision making beyond food or drug reward using monetary rewards. METHODS. Thirty participants (50% women and 50% men) underwent 2 fMRI scans while receiving i.v. ghrelin or saline in a randomized counterbalanced order. RESULTS. Striatal representations of reward anticipation were unaffected by ghrelin, while activity during anticipation of losses was attenuated. Temporal discounting rates of monetary reward were lower overall in the ghrelin condition, an effect driven by women. Discounting rates were inversely correlated with neural activity in a large cluster within the left parietal lobule that included the angular gyrus. Activity in an overlapping cluster was related to behavioral choices and was suppressed by ghrelin. CONCLUSION. This is, to our knowledge, the first human study to extend the understanding of ghrelins significance beyond the canonical feeding domain or in relation to addictive substances. Contrary to our hypothesis, we found that ghrelin did not affect sensitivity to monetary reward anticipation, but rather resulted in attenuated loss aversion and lower discounting rates for these rewards. Ghrelin may cause a motivational shift toward caloric reward rather than globally promoting the value of reward. TRIAL REGISTRATION. EudraCT 2018-004829-82.

We study the co-movement between innovative financial assets (i.e., FinTech-related stocks, green bonds and cryptocurrencies) and traditional assets. We construct a co-movement mode transmission network and discuss the network topology during the pre-COVID-19 and COVID-19 periods. We extract network topology information to predict the co-movement mode by machine learning algorithms. We further propose dynamic trading strategies based on the co-movement mode prediction. The empirical results show that (i) the evolution of co-movement is dominated by some key modes, and the mode transmission relies on intermediate modes and shows certain periodicity; (ii) the co-movement relationships are influenced by the ongoing COVID-19 outbreak; and (iii) the novel approach, which combines complex network and machine learning, is superior in co-movement mode prediction and can effectively bring diversification benefits. Our work provides valuable insights for market participants.

The academic literature on green energy equity markets has increased extensively over the last decade due to growing concerns about climate change and the substantial flow of investments into alternative energy markets. This study contributes by investigating the effect of climate risk on the return and volatility of green energy assets. This is one of the first papers to assess such effects using the recently developed climate policy uncertainty index as an indicator of climate risk. In particular, we seek to answer the following research questions. Firstly, does rising climate risk lead to a significant increase in green energy asset returns? Secondly, does climate risk affect the volatility of green energy assets negatively? Employing various models, we provide statistical evidence in favour of our hypotheses. Rising climate risk seems to encourage investment in alternative energy, which leads to an upward demand for green energy, which in turn increases the prices of green energy investments and decreases their volatility levels. Our analysis further shows that when climate risk increases, the correlation between crude oil and green energy returns decreases. Furthermore, green energy assets are more effective than gold for hedging oil market risk, without ignoring the hedging ability of technology stock investment.

We scrutinise the role of institutional, market, and financial freedoms within the occurrence of wealth and income inequalities, thus attempting to corroborate the Kuznets curve hypothesis by using general and decomposed measures. To this end, we apply an auto-regressive fixed effect framework with Driscoll Kraay standard errors to analyse the panel time series data for twelve Post-Communist economies. Our empirical results highlight that the overall economic growth provides two different implications for the income and wealth inequalities. Economic growth fosters income inequality up to a threshold point, afterwards it declines with further economic growth, thereby validating the Kuznets curve hypothesis. The decomposed analysis confirms that further economic growth surpassing the threshold level re-distributes income from the top 10% class to the bottom 50% and middle 40% classes.

Players often fail to coordinate on the efficient equilibrium in laboratory weak-link coordination games. In this paper, we investigate whether such coordination failures can be mitigated by increasing the number of rounds or altering per-period stakes. We find that neither time horizon nor stakes affect equilibrium selection. In contrast to previous findings, players are not more likely to play above the previous periods minimum choice when the horizon is longer or per-period stakes lower. We also investigate which socio-demographic factors and behavioral traits correlate most strongly with play both in the first round and in subsequent rounds. Cognitive ability as measured by a cognitive reflection test stands out as the characteristic that is most strongly associated with efficient coordination.

The biggest benefit of dual-process theory lies in its role as a benchmark theory that, regardless of its empirical plausibility, serves as a starting point for better and more domain-specific models. In this sense, dual-process theory is the Barbapapa of psychological theory - a blob-shaped creature that can be reshaped and adapted to fit in the context of any human behavior.

This paper considers the dynamic features of housing prices in metropolises that are characterised by a high degree of internationalisation. Using the wavelet coherency procedure, the degree of comovement and causality between housing, stock markets, and macroeconomic uncertainty are investigated. In addition, the existence of volatility spillover across housing markets is assessed in the time-frequency domain using a novel procedure that involves combining the wavelet decomposition with a time varying parameter vector autoregression model. The results highlight that the clustering of global business in a limited number of metropolises that act as "global hubs" leaves the local housing markets exposed to international shocks and volatility spillover. The empirical analysis suggests that the correlations between real estate and stock markets from one side, and real estates and uncertainty on the other side, intensify during the turmoil periods. Causality and co-movement relationships appear predominately in the medium and long run periods. The evidence presented in this paper suggests that policymakers cannot ignore the possibility that international shocks to housing markets may affect the domestic markets. In this respect, macroprudential policy tools may target tapering off the unintended effects of housing market globalisation such as house price shock synchronisation, especially when these shocks take place in cities that are also major financial centres.

Financial assets are volatile, and volatility becomes more intense in terms of size and rate of recurrence when markets are uncertain and growing rapidly. The fact that the recurrence rate increased during crisis periods, such as the IT bubble in the early 2000 and the global financial crisis that started in 2007, is a key finding in the literature. Estimating these results requires modeling a time series that can consider volatility clustering. However, the prominent model in finance and economics estimates that the average volatility increases when uncertainty increases. This modeling process needs to consider the asymmetry that financial assets and economic outcomes, such as gross domestic product (GDP) exhibit, which tend to fall drastically in a short period and increase steadily over a long period. To model these different behaviors, one must consider the asymmetric nature of the return, for example, when a stock has extremely low or extremely high returns in a day. 

To model this behavior, I used several methods in settings that could better explain what happens during market periods when there is higher uncertainty. The general finding is that correlations are higher when returns are in the lower quantiles, called the left tails. Thus, financial assets are positively correlated, especially during periods of increased uncertainty. It is not only clustering that one would try to explain, but another issue is the prediction of one asset’s effect on another. The effect of one asset on another asset is called the spillover effect. We tried to distinguish between events that happen during the same time that affect all assets. These events are called systematic risk, and the effects that one asset has on another asset is called systemic risk. Explaining the systemic risk typically has higher priority from a policy perspective, as systemic risk can be a driver for risk transmission from one asset to another, creating a chain of risk or a spiral of risk. Hence, the approaches I used can model that chain of risk and predict risk transmission while controlling for external factors that increase uncertainty. The results of this research show the connection between energy assets and renewable energy stocks in Papers 1 and 2. For instance, we found that there is a possibility of adjusting the European carbon emission cap and that renewable energy stocks positively correlate with energy commodities in the tails. Thus, renewable energy stocks follow a macroeconomic cycle. The findings of Paper 3 show the systemic and systematic nature of cross-country spillovers between emerging and developed financial markets, and that the spillover is time-varying with increasing spillovers in crisis periods. Paper 4 examines the Nordic banking sector. The results show that banks’ spillover to their local markets is due to their systemic importance and the strength of the spillover is related to the bank’s characteristics. In the final Paper, I studied the upside and downside movement asymmetry of stocks and found that betting on upside volatility is better than a portfolio perspective but comes at the cost of increased pricing errors. The empirical findings of this thesis significantly contribute to policymakers and institutional investors in portfolio diversification and risk management. 

In this study, we explore the critical demand drivers of electricity consumption in Thailand based on monthly data from 2002 to 2020. Using Autoregressive Distributed Lag (ARDL), cross-quantile correlation (CQC), Generalized Method of Moments (GMM), and Granger-causality-in-quantile approaches, we find that industrial production and oil production positively contribute to next months aggregate and provincial energy consumption in Thailand, both in the short and long run. We also find that industrial production positively affects current electricity consumption, whereas electricity prices negatively affect current electricity consumption. Oil production, however, has no effect on current electricity consumption. Moreover, the CQC analysis finds evidence of cross-predictability running from industrial production and electricity prices to next months electricity consumption at the extreme and median quantiles of the distribution. Further, industrial production, electricity prices, and oil production Granger-cause energy consumption at the extreme and median quantiles of the distribution. Nevertheless, we show that the Thai governments energy policies are ineffective for reducing electricity consumption. Our findings have crucial policy implications for the electricity market efficient allocation and its reform.

This study examines the impact of crude oil volatility jumps on the realized volatility (RV) of green and dirty stocks in India. In doing so, we first estimate the time-varying jumps in oil market implied volatility index (OVX) and then augment the heterogeneous autoregressive (HAR) process with the information on such jumps. Our sample runs from December 2012 to April 2022, which includes 2328 data points. Comparing a range of HAR-type models, we find that crude oil volatility jumps provide additional information, which is not contained even in the OVX index itself. In particular, the HAR-RV model that considers both leverage effects and the information on volatility jumps produces superior forecasts compared with the existing approaches. The economic significance of these results is also supported by a simple value-at-risk analysis.

We analyse time-varying risk spillover and dependence to assess the systemic risk benefits of ethical, sustainable, and green investments. Our data comprise sustainable investments from ethical, environmental, social and governance (ESG), and green bonds. We investigate the link to major asset classes, including equity, commodity, and currency markets. We find evidence of close connection between the major asset classes and sustainable assets, except green bonds. We also explore the improvement in hedging efficiency from combining ethical and ESG investments with commodities and currencies over investment horizons. Our analysis based on systemic risk measures indicates that there is evidence of lower time-scale systemic risk connectedness in the case of commodities and currencies combined with ethical and ESG assets. These findings have significant implications for portfolio managers, policymakers, and market participants.

Climate change is an increasing problem, with more extreme weather conditions and rising temperatures. To fulfill the temperature goals of the Paris agreement a societal change is needed, a change that requires a shift of lifestyle from all of us. If we want to change our behaviors to more sustainable ones, we need to sacrifice substantial things today to improve a future, which often seems distant and abstract. People with high level of self-control have been shown to have a better ability to visualize future events, which makes self-control an interesting trait to look at in relation to pro-environmental behavior. The aim of this study was to examine how self-control correlates with environmental well-being and environmental behavior. An internet-based survey was sent to a representative Swedish sample (n = 602). The respondents were asked to fill out a newly developed scale measuring their anxiety and security regarding environmental matters (environmental wellbeing), as well as indicate how often they engage in six different pro-environmental behaviors (e.g., turning lights off when leaving the room). Additionally, data on the respondents gender, age, political orientation, and self-control was collected. Our results suggest a positive correlation between self-control and environmental wellbeing and a weaker, but still positive, correlation between self-control and some pro-environmental behaviors. Additionally, respondents who identified themselves as politically left had lower environmental wellbeing, while men had higher environmental wellbeing, but behaved less environmentally friendly. Thus, our results suggest that political orientation was a better predictor of sound environmental behavior than subjective self-control was.

This paper proposes a novel interconnected multilayer network framework based on variance decomposition and block aggregation technique, which can be further served as a tool of linking and measuring cross-market and within-market contagion. We apply it to quantifying connectedness among global stock and foreign exchange (forex) markets, and demonstrate that measuring volatility spillovers of both stock and forex markets simultaneously could support a more comprehensive view for financial risk contagion. We find that (i) stock markets transmit the larger spillovers to forex markets, (ii) the French stock market is the largest risk transmitter in multilayer networks, while some Asian stock markets and most forex markets are net risk receivers, and (iii) interconnected multilayer networks could signal the financial instability during the global financial crisis and the COVID-19 crisis. Our work provides a new perspective and method for studying the cross-market risk contagion.

We exploit exogenous variation arising from the historical rollout of the Swedish railroad network across municipalities to identify the impacts of lowered interaction costs on innovative activity. A network connection led to a surge in local innovation due to an increased entry, productivity, and specialization of independent inventors. As the railroad network expanded, it further led to the emergence of a national market for ideas: inventors in connected areas began to develop ideas with applications outside the local economy, which were subsequently sold to firms along the network. Our findings suggest that the reduced interaction cost between firms, intermediaries, and inventors was a key driver of the historical emergence of a market for ideas.

We study the impact of different sources of global, regional and local uncertainty on the seven largest Scandinavian banks over the period 2005-2018. Using a spillover approach and network analysis, we find that Swedish banks are the main source of contagion in the region and spillovers tend to increase in times of heightened uncertainty. Global economic policy uncertainty, global financial uncertainty and local housing market uncertainty affect the Scandinavian banking sector the most. By contrast, geopolitical risk spillovers are limited.

There is no doubt that oil price shocks significantly affect oil-producing countries macroeconomic fundamentals and financial stability, mainly in crisis times. The recent oil price shocks, coupled with the COVID-19 pandemic, motivated us to investigate the connectedness and risk transmission among oil shocks and banking sectors in the Gulf Cooperation Council (GCC) economies from June 30, 2006, to September 9, 2021. Thus, we construct multilayer information spillover networks between oil price shocks and GCC banking sectors. The empirical results show that the Bahrain banking sector depicts the highest connectedness and risk transmission with oil price shocks on the extreme risk spillover layer. In addition, Kuwait and the United Arab Emirates are highly connected to oil demand shocks. Furthermore, we find a substantial increase in extreme risk spillover and volatility spillover layers during the COVID-19 period. The results of this paper have some important implica-tions for regional portfolio risk management, alleviating systemic risk, and developing hedging and investment strategies.

In light of the COVID-19 outbreak and the recent Russian war in Ukraine, this paper explores the asymmetric and nonlinear interconnectedness between financial and commodity markets using high-frequency intraday data. We employ cross-quantilograms (CQ), paired vine-based copulas, and copula vine-based regression analysis to examine the heterogeneous and asymmetrical connectedness among various assets. Our study presents several key findings: (1) connectedness among assets increases sharply during the COVID-19 pandemic and intensifies with the Russia-Ukraine war; (2) stronger tail dependence is observed in the lower tail, indicating asymmetric connectedness among the assets; (3) the S & P 500 and natural gas have a predictive influence on the crude oil market; and (4) increased uncertainty and volatility in global markets due to these events impact the interconnectedness of the assets in our study, particularly the dependence between crude oil and the other assets in the sample. These results have important implications for governmental agencies, policymakers, investors, and portfolio managers, emphasizing the need for a non-linear framework to capture heterogeneous and asymmetric connectedness dynamics under extreme market conditions.

In a couple of decades, the immense interest of scholars and policymakers is to identify the factors that support environmental sustainability without sacrificing economic well-being. Nordic countries with huge natural resource reserves are known as the top countries of the "European GDP per capita league table." However, with the lofty climate targets, these countries are still responsible for environmental impairments through their incessant contribution to the global energy supply network. Therefore, to defeat this predicament, the study investigates the role of natural resources (NTR), green finance (GFN), green energy (GEC), and economic growth (GDP) in Nordic countries from 1990 to 2018. The study employed the novel quantile-based econometrics approach of "Method of the Moments Quantile Regression" (MMQR), which provides the direction and magni-tude of the asymmetric association of NTR, GFN, GEC, and GDP with the ecological footprint. The results of this test revealed that the NTR and GDP have a significantly positive influence, while GEN and GEC have significantly negative associations with an ecological footprint across all quantiles. This has implied that green finance and green energy work as the solution, whereas natural resources and economic growth are key drivers of envi-ronmental degradation. Further, the "Augmented Mean Group" (AMG) and "Common Correlated Effect Mean Group" (CCEMG) are used as the robustness check, which endorsed the same association of NTR, GFN, GEC, and GDP with the EFP suggested by MMQR. Based on these findings, the study suggests policy implications for controlling the ecological footprint in Nordic countries.

In a seminal paper, Frederick et al. (J Consum Res 36:553-561, 2009) showed that peoples willingness to purchase a consumer good declined dramatically when opportunity costs were made more salient (Cohens d = 0.45-0.85). This finding suggests that people normally do not pay sufficient attention to opportunity costs and as a result make poorer and less efficient decisions, both in private and public domains. To critically assess the strength of opportunity cost neglect, we carried out a systematic review and a meta-analysis including published and non-published experimental work. In total, 39 experimental studies were included in the meta-analysis (N = 14,005). The analysis shows a robust significant effect (Cohens d = 0.22; p < 0.001) of opportunity cost neglect across different domains, albeit the effect is considerably smaller than what was originally estimated by Frederick et al. (2009). Our findings highlight the importance of meta-analyses and replications of initial findings.

We approximate the spatial and temporal distribution of the Pigouvian kilometre tax for road traffic in the most urbanized part of Sweden, with four million inhabitants and a similar "degree of urbanization" to the Netherlands and the UK, in a future scenario where most vehicles are electric. We apply the national transport model and include all links and four time-of-day periods. We find that roughly half of the vehicle kilometres travelled in Ma center dot lardalen has a marginal external cost (congestion and other external costs included) below 0.04 euro/km which is below the fuel tax in 2019). The mean marginal external cost is higher, at 0.09 euro/km. Our focus is not the exact numbers but the magnitudes and the vast variation across links in a country-like region: 90 percent of the revenue is collected on 10 percent of the road network. Hence, a nation-wide kilometre tax, implying high enforcement cost, is likely not the best option. Instead, the marginal external cost could probably be internalized fairly accurate by a congestion tax in the big cities in combination with for instance an ownership tax. We find that the Pigouvian tax would cover the public costs for our target road system. We relate our findings to the mainstem fiscal tax literature.

Predicting that a stimulus is painful facilitates action to avoid harm. But how distinct are the neural processes underlying the prediction of upcoming painful events vis-a-vis those taking action to avoid them? Here, we investigated brain activity as a function of current and predicted painful or nonpainful thermal stimulation, as well as the ability of voluntary action to affect the duration of upcoming stimulation. Participants performed a task which involved the administration of a painful or nonpainful stimulus (S1), which predicted an immediately subsequent very painful or nonpainful stimulus (S2). Pressing a response button within a specified time window during S1 either reduced or did not reduce the duration of the upcoming stimulation. Predicted pain increased activation in several regions, including anterior cingulate cortex (ACC), midcingulate cortex (MCC), and insula; however, activation in ACC and MCC depended on whether a meaningful action was performed, with MCC activation showing a direct relationship with motor output. Insulas responses for predicted pain were also modulated by potential action consequences, albeit without a direct relationship with motor output. These findings suggest that cortical pain processing is not specifically tied to the sensory stimulus, but instead, depends on the consequences of that stimulus for sensorimotor control of behavior.

This study evaluates extreme uncertainty connectedness among top global energy firms. The sample comprises of 68 firms from four energy-related subsectors (oil & gas, oil & gas related equipment and services, multiline utilities, and renewable energy). To provide an overview of tail connectedness, we construct a high-dimensional network between firms by utilizing a generalized error decomposition and a sparse vector autoregression framework with a latent common factor. Our empirical results indicate that between the four subsectors, the renewable energy subsector exhibits the highest uncertainty transmission to other underlying subsectors, primarily credited to an increased within-subsector idiosyncratic uncertainty before the COVID-19 crisis. After the burst of the COVID-19 pandemic, due to the higher connectedness, the role of the renewable energy companies in the spillover network is further intensified. The uncertainty connectedness demonstrates a time-varying trait. While the oil and gas subsector exhibits greater long-term linkages with the oil and gas related equipment and services subsector, the long-run dynamics exhibit a lower interconnectedness as compared to the short-run. Finally, there is an increased connectedness among companies operating in the same subsector with similar size, attributing to similarity and competition.

The COVID-19 pandemic has affected all domains of human life, including the economic and social fabric of societies. One of the central strategies for managing public health throughout the pandemic has been through persuasive messaging and collective behaviour change. To help scholars better understand the social and moral psychology behind public health behaviour, we present a dataset comprising of 51,404 individuals from 69 countries. This dataset was collected for the International Collaboration on Social & Moral Psychology of COVID-19 project (ICSMP COVID-19). This social science survey invited participants around the world to complete a series of moral and psychological measures and public health attitudes about COVID-19 during an early phase of the COVID-19 pandemic (between April and June 2020). The survey included seven broad categories of questions: COVID-19 beliefs and compliance behaviours; identity and social attitudes; ideology; health and well-being; moral beliefs and motivation; personality traits; and demographic variables. We report both raw and cleaned data, along with all survey materials, data visualisations, and psychometric evaluations of key variables.

This paper examines whether family firms outperform compared to nonfamily firms. We utilize both accounting-based and market-based performance measures in the present study. Secondly, we test whether operating performance and market valuation are higher for family firms with international diversification compared to locally operated family firms. We employ a unique setting where more than 65% of publicly listed companies are family firms to test our conjectures. We use ordinary least square regression models to test our hypotheses. Using a large dataset, we find that nonfamily firms perform significantly (both operating and market based performance) higher than counter family firms. However, we find that family firms outperform non-family firms when they have international diversifications. This study contributes to family business literature, international diversification literature. It provides policy implications in that regulators understand the importance of ownership structure and how it affects firms valuation, particularly in emerging economies where family firms dominate the markets.

Developing sustainable energy systems through system integration and sector coupling may involve permeating previous isolated and idiosyncratic reactions through the whole system. This study examines supply side dynamics of how uncertainty and spot prices influence electric power supply in an internationally connected electricity system. By applying quantile methods, the study reveals highly nonlinear and asymmetric responses in supplied electricity given a change in uncertainty or system spot prices. The findings show that while the total supply of electricity is largely unaffected by uncertainty and spot price, both supply level and changes in power supply for specific dispatchable power sources are influenced differently depending on the type of uncertainty (domestic or global). The findings also show that the response structure induced by uncertainty and spot price seems to be largely uniform and power source specific. This has implications for creating, amongst other things, integrated systems, and sector coupling, where there is a need for inexpensive excess power supply. (c) 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Social drinking is common, but it is unclear how moderate levels of alcohol influence decision making. Most prior studies have focused on adverse long-term effects on cognitive and executive function in people with alcohol use disorders (AUD). Some studies have investigated the acute effects of alcohol on decision making in healthy people, but have predominantly used small samples and focused on a narrow selection of tasks related to personal decision making, e.g., delay or probability discounting. Here, we conducted a large (n = 264), preregistered randomized placebo-controlled study (RCT) using a parallel group design, to systematically assess the acute effects of alcohol on measures of decision making in both personal and social domains. We found a robust effect of a 0.6 g/kg dose of alcohol on both moral judgment and altruistic behavior, but no effects on several measures of risk taking or waiting impulsivity. These findings suggest that alcohol at low to moderate doses selectively moderates decision making in the social domain, and promotes utilitarian decisions over those dictated by rule-based ethical principles (deontological). This is consistent with existing theory that emphasizes the dual roles of shortsighted information processing and salient social cues in shaping decisions made under the influence of alcohol. A better understanding of these effects is important to understand altered social functioning during alcohol intoxication.

We study the effects of experimental manipulation of decision mode (rational "brain" vs. affective "heart") and individual difference in processing styles (intuition vs. deliberation) on prosocial behavior. In a survey experiment with a diverse sample of the Swedish population (n = 1,828), we elicited the individuals processing style and we experimentally manipulated reliance on affect or reason, regardless of subjects preferred mode. Prosocial behavior was measured across a series of commonly used and incentivized games (prisoners dilemma game, public goods game, trust game, dictator game). Our results show that prosocial behavior increased for the affective ("heart") decision mode. Further, individual differences in processing style did not predict prosocial behavior and did not interact with the experimental manipulation.

Affect fluctuates in a moment-to-moment fashion, reflecting the continuous relationship between the individual and the environment. Despite substantial research, there remain important open questions regarding how a stream of sensory input is dynamically represented in experienced affect. Here, approaching affect as a temporally dependent process, we show that momentary affect is shaped by a combination of the affective impact of stimuli (i.e., visual images for the current studies) and previously experienced affect. We also found that this temporal dependency is influenced by uncertainty of the affective context. Participants in each trial viewed sequentially presented images and subsequently reported their affective experience, which was modeled based on images normative affect ratings and participants previously reported affect. Study 1 showed that self-reported valence and arousal in a given trial is partly shaped by the affective impact of the given images and previously experienced affect. In Study 2, we manipulated context uncertainty by controlling occurrence probabilities for normatively pleasant and unpleasant images in separate blocks. Increasing context uncertainty (i.e., random occurrence of pleasant and unpleasant images) was associated with increased negative affect. In addition, the relative contribution of the most recent image to experienced pleasantness increased with increasing context uncertainty. Taken together, these findings provide clear behavioral evidence that momentary affect is a temporally dependent and continuous process, which reflects the affective impact of recent input variables and the previous internal state, and that this process is sensitive to the affective context and its uncertainty.

This paper examines how different uncertainty measures affect the unemployment level, inflow, and outflow in the U.S. across all states of the business cycle. We employ linear and nonlinear causality-in-quantile tests to capture a complete picture of the effect of uncertainty on U.S. unemployment. To verify whether there are any common effects across different uncertainty measures, we use monthly data on four uncertainty measures and on U.S. unemployment from January 1997 to August 2018. Our results corroborate the general predictions from a search and matching framework of how uncertainty affects unemployment and its flows. Fluctuations in uncertainty generate increases (upper-quantile changes) in the unemployment level and in the inflow. Conversely, shocks to uncertainty have a negative impact on U.S. unemployment outflow. Therefore, the effect of uncertainty is asymmetric depending on the states (quantiles) of U.S. unemployment and on the adopted unemployment measure. Our findings suggest state-contingent policies to stabilize the unemployment level when large uncertainty shocks occur.

This paper studies intertemporal changes in the value of travel time (VTT) and investigates whether the change of VTT over time can be studied based on national VTT data, collected at two points in time. We use repeated national VTT data from the Netherlands and Sweden, collected 13 and 14 years apart. The results show mostly a declining VTT for a given income level. The results show also a large within-country heterogeneity across modes and purposes, in the cross-sectional income elasticity of the VTT, and in its development over time. The explanation most consistent with our results and those of others is that the VTT has in fact increased due to income increases, but that the repeated stated choice data cannot detect this given the data, methodology and population changes. In particular, it seems that the response rate has dropped considerably in the later surveys partly due to a higher share of (busy) respondents declining to be recruited. The main contribution of this paper is to document the differences between the studies carried out in different years, indicating the reasons why it is difficult to identify temporal changes in the VTT.

This paper compares the tail dependence and risk spillovers from the oil and gas to high-yield (HY) and investment grade (IG) bond markets. We use time-varying optimal copula framework to examine the dependence and further quantify upside and downside risk spillovers. We also explore how energy futures can be used to hedge risk of HY and IG bond portfolios. Our results show that the bond returns are more sensitive to risk shocks in the oil market compared to gas market. We find both negative and positive tail dependence between the bond and energy pairs and the relationship is stronger during the oil-crunch period. The dependence however is asymmetric across the tails. Finally, compared to oil futures, gas futures are found to be better hedge for the bond investment. These results can help in managing portfolio risk and designing optimal asset allocation strategies. These might also assist in formulating policies and regulations to manage the effects of cross-market risk transmissions.

This paper studies the connectedness among energy equity indices of oil-exporting and oil-importing countries around the world. For each country, we construct time-varying measures of how much shocks this country transmits to other countries and how much shocks this country receives from other countries. We analyze the network of countries and find that, on average, oil-exporting countries are mainly transmitting shocks, and oil-importing countries are mainly receiving shocks. Furthermore, we use panel data regressions to evaluate whether the connectedness among countries is influenced by economic sentiment, uncertainty, and the global COVID-19 pandemic. We find that the connectedness among countries increases significantly in periods of uncertainty, low economic sentiment, and COVID-19 problems. This implies that diversification benefits across countries are severely reduced exactly during crises, that is, during the times when diversification benefits are most important.

We develop a model for empirical evaluation of the social costs and benefits of street parking charges. From the model, we derive an expression for optimal parking charges and occupancy levels: in optimum, parking search costs are balanced against the loss of consumer surplus from unused parking spaces. Contrary to rules-of-thumb common in practice, optimal occupancy levels are not constant but depend on parking turnover rates and parking search costs. We demonstrate the models applicability in a case study from Stockholm, where parking charges were recently introduced in suburban residential areas. The charges had considerable effects on parking de-mand, but our analysis shows that the overall welfare effect was a substantial welfare loss. Using parameters and demand functions estimated from the case study, we calculate optimal parking charges and occupancy levels, and show that the welfare loss arises because the introduced charges were considerably higher than the optimal ones.

Background. During the COVID-19 pandemic, the world witnessed a partisan segregation of beliefs toward the global health crisis and its management. Politically motivated reasoning, the tendency to interpret information in accordance with individual motives to protect valued beliefs rather than objectively considering the facts, could represent a key process involved in the polarization of attitudes. The objective of this study was to explore politically motivated reasoning when participants assess information regarding COVID-19. Design. We carried out a preregistered online experiment using a diverse sample (N = 1500) from the United States. Both Republicans and Democrats assessed the same COVID-19-related information about the health effects of lockdowns, social distancing, vaccination, hydroxychloroquine, and wearing face masks. Results. At odds with our prestated hypothesis, we found no evidence in line with politically motivated reasoning when interpreting numerical information about COVID-19. Moreover, we found no evidence supporting the idea that numeric ability or cognitive sophistication bolster politically motivated reasoning in the case of COVID-19. Instead, our findings suggest that participants base their assessment on prior beliefs of the matter. Conclusions. Our findings suggest that politically polarized attitudes toward COVID-19 are more likely to be driven by lack of reasoning than politically motivated reasoning-a finding that opens potential avenues for combating political polarization about important health care topics.

This study investigates the impact of COVID-19 on the US equity market during the first wave of Coronavirus using a wide range of econometric and machine learning approaches. To this end, we use both daily data related to the US equity market sectors and data about the COVID-19 news over January 1, 2020-March 20, 2020. Accordingly, we show that at an early stage of the outbreak, global COVID-19s fears have impacted the US equity market even differently across sectors. Further, we also find that, as the pandemic gradually intensified its footprint in the US, local fears manifested by daily infections emerged more powerfully compared to its global counterpart in impairing the short-term dynamics of US equity markets.

We examined customer and worker discrimination against gay and lesbian business owners using a web-based experiment conducted at a Swedish university campus. Participants (N = 1,406) were presented with a prospective restaurant establishment on the campus. They then stated whether they would be positive to such an establishment, whether they would be interested in working at the restaurant, and what their reservation wage would be if they were interested in the job. Owners sexual orientation was randomized across participants. Results showed that participants were less positive to a restaurant opening if the owners were lesbians, and they were less interested in an available job if the owners were gay. The participants had higher reservation wages if the owners were lesbians. In fact, the participants increased their wage demands when the number of women among the owners increased. Our study underlines that gay and lesbian people face various inequalities in society.

Decision-making competence is a skill that is associated with numerous positive life outcomes. Even though multiple cognitive abilities have been shown to predict decision-making competence, few studies have incorporated a large test battery tapping into several cognitive abilities concurrently in the same models. The current paper presents a study that sought to investigate which cognitive abilities predicted overall decision-making competence in adults using hierarchical regression analysis. A cognitive test battery, comprising abilities such as general intelligence, executive functions, numeracy, visuospatial ability, and time perception, was administered to 182 participants. Results indicate that both general intelligence, which was consistently the strongest predictor, and numeracy contributed independently to overall decision-making competence. Executive functions did predict overall decision-making competence, while all predictors were included in the models. A novel finding concerns the relationship between time perception and decision-making competence. The complementary roles of these cognitive abilities are discussed.

We study the dependence of renewable energy production-related critical metal futures and producer equity returns, compared to the non-renewable energy (oil and natural gas) and some other globally relevant commodity markets. We find different asymmetric and symmetric dependencies in these commodity markets. The dependence is asymmetric in the most important critical metal markets, i.e., of silver, copper, and platinum. Still, surprisingly, for example, in the oil market, the relationship is symmetric, and no relationship is found in the natural gas market. Furthermore, the oil and agricultural markets have homogenous dependence structures in most market conditions, so the information transmission channels in these markets seem to be highly efficient. Still, the critical metal markets seem inefficient in this respect. The short-term speculation effects from the precious metals-related stock market segment towards critical metals futures markets are strong compared to others. We suggest that the future regulation of the precious metals producer stock market sector should be tighter to reduce speculative spillovers from this market segment to the futures markets of these metals.

Being an environmentally friendly fuel obtained from rapeseed oil, biodiesel is used extensively in Europe. However, the dependence structure between global crude oil prices and the European prices of biodiesel and rapeseed oil is understudied and unclear. In this paper, we address this gap by utilizing asymmetric copulas and cross-quantilogram approaches on daily data. The results of the DCC-Student-t copula indicate that during bearish periods the conditional connectedness between crude oil prices and biodiesel (rapeseed oil) prices are stronger than during bullish periods, indicating increased co-movement with a decline in crude oil prices. The application of cross-quantilogram indicates that an increase in crude oil price positively influences biodiesel prices reflecting an asymmetric dependence structure among the assets. There is evidence of shifts in the dynamics of quantile dependency during periods of financial and economic turmoil. Overall, the results show a significant dependence between the global crude oil market and the European markets of biodiesel and rapeseed oil in specific periods and under specific market conditions, which have important implications for policymakers and investors.

Hiring discrimination is illegal, morally distasteful, and seen as incommensurate with modern societal ideals. From an economic perspective, if employers hire based on anything other than an applicant’s expected productivity they are behaving inefficiently. If group markers are imperfect signals of productivity, discrimination is also inefficient. Measuring discrimination is a substantial practical challenge but indispensable to policy development and theory evaluation. This thesis focuses on correspondence testing, experiments where researchers create fictitious applicants who apply for real jobs, and then analyze differences in responses between groups to arrive at credible estimates of discrimination. 

In chapter I, "Do ethnicity and sex of employers affect applicants' job interest? An experimental exploration," co-authored with Ali Ahmed and Niklas Ottosson and published 2020 in Journal for Labour Market Research, we present the findings of a survey experiment. We tested the novel hypothesis that job seekers may discriminate against employers based on ethnicity or gender when they are choosing jobs to apply to. Ultimately, we concluded that the survey experiment provided no evidence of such discrimination. 

In chapter II, "Hiring discrimination against transgender people: Evidence from a field experiment," co-authored with Per A. Andersson and Ali Ahmed and published 2020 in Labour Economics, we present the findings of a correspondence experiment that tested for hiring discrimination against transgender applicants. We found that transgender applicants were indeed discriminated against in hiring, but that there were some important nuances. For example, transgender men seemed to be discriminated against in male-dominated occupations because they were transgender and in female-dominated occupations because they were men. 

In chapter III, "Gender discrimination in hiring: An experimental reexamination of the Swedish case," co-authored with Ali Ahmed and Shantanu Khanna published 2021 in Plos One, we present the findings of a study that combined data from three previously published correspondence experiments. Although these experiments were originally designed to test other hypotheses, we used the data to test for gender discrimination in hiring. We found discrimination against males, largely driven by female-dominated occupations. 

In chapter IV, "An assessment of the correspondence testing methodology," I describe and analyze the methodology and ethics of correspondence tests. I do this by reviewing the 199 correspondence studies published between 2005 and 2020, focusing on methodological choices and the ethical implications of those choices.

This study analyses the impact of different uncertainties on commodity markets to assess commodity markets hedging or safe-haven properties. Using time-varying dynamic conditional correlation and wavelet-based Quantile-on-Quantile regression models, our findings show that, both before and during the COVID-19 crisis, soybeans and clean energy stocks offer strong safe-haven opportunities against cryptocurrency price uncertainty and geopolitical risks (GPR). Soybean markets weakly hedge cryptocurrency policy uncertainty, US economic policy uncertainty, and crude oil volatility. In addition, GSCI commodity and crude oil also offer a weak safe-haven property against cryptocurrency uncertainties and GPR. Consistent with earlier studies, our findings indicate that safe-haven traits can alter across frequencies and quantiles. Our findings have significant implications for investors and regulators in hedging and making proper decisions, respectively, under diverse uncertain circumstances.