Contributions in Merlin

The flows to equity method is commonly used in leveraged buyouts and other highly levered transactions. These flows are hybrid flows, mixing expected operating cash flows with promised debt payments under a planned debt schedule. Because of this, it is difficult to accurately estimate the appropriate discount rate, a difficulty that is compounded by the typically changing leverage over time under the planned debt schedule. We show how the flows to equity approach works and discuss its benefits and drawbacks as compared with other, ‘more standard’ methods.

Central-bank money provides utility by serving as means of exchange for virtually all transactions in the economy. New reserves (money) are issued to banks in exchange for collateral such as government bonds. An asset's degree of direct convertibility into fresh reserves may affect its utility and, consequently, its market price. We show the existence of a government-bond reserves convertibility premium, which tapers off at longer maturities. Essentially, there is a pure monetary component to some asset prices. Our findings have implications for our understanding of liquidity premia, the term structure of interest rates, and the impact of central-bank collateral policy.

We propose an efficient, reliable, and interpretable global solution method, the Deep learning-based algorithm for Heterogeneous Agent Models (DeepHAM), for solving high dimensional heterogeneous agent models with aggregate shocks. The state distribution is approximately represented by a set of optimal generalized moments. Deep neural networks are used to approximate the value and policy functions, and the objective is optimized over directly simulated paths. In addition to being an accurate global solver, this method has three additional features. First, it is computationally efficient in solving complex heterogeneous agent models, and it does not suffer from the curse of dimensionality. Second, it provides a general and interpretable representation of the distribution over individual states, which is crucial in addressing the classical question of whether and how heterogeneity matters in macroeconomics. Third, it solves the constrained efficiency problem as easily as it solves the competitive equilibrium, which opens up new possibilities for normative studies. As a new application, we study constrained efficiency in heterogeneous agent models with aggregate shocks. We find that in the presence of aggregate risk, a utilitarian planner would raise aggregate capital for redistribution less than in absence of it because poor households do more precautionary savings and thus rely less on labor income.

The current knowledge system of macroeconomics is built on interactions among a small number of variables, since traditional macroeconomic models can mostly handle a handful of inputs. Recent work using big data suggests that a much larger number of variables are active in driving the dynamics of the aggregate economy. In this paper, we introduce a knowledge graph (KG) that consists of not only linkages between traditional economic variables but also new alternative big data variables. We extract these new variables and the linkages by applying advanced natural language processing (NLP) tools on the massive textual data of academic literature and research reports. As an example of potential applications, we use it as the prior knowledge to select variables for economic forecasting models in macroeconomics. Compared to statistical variable selection methods, KG-based methods achieve significantly higher forecasting accuracy, especially for long run forecasts.

Primary stressors are direct outcomes of extreme events (e.g., viruses, floodwater) whereas secondary stressors stem from pre-disaster life circumstances and societal arrangements (e.g., illness, problematic pre-disaster policies) or from inefficient responses to the extreme event. Secondary stressors can cause significant long-term damage to people affected but are also tractable and amenable to change. In this study we explored the association between secondary stressors, social identity processes, social support, and perceived stress and resilience. Pre-registered analyses of data from the COVIDiSTRESS Global Survey Round II (N = 14,600; 43 countries) show that secondary stressors are positively associated with perceived stress and negatively associated with resilience, even when controlling for the effects of primary stressors. Being a woman or having lower socioeconomic status (SES) is associated with higher exposure to secondary stressors, higher perceived stress, and lower resilience. Importantly, social identification is positively associated with expected support and with increased resilience and lower perceived stress. However, neither gender, SES, or social identification moderated the relationship between secondary stressors and perceived stress and resilience. In conclusion, systemic reforms and the availability of social support are paramount to reducing the effects of secondary stressors.

In this study, we tested the validity across two scales addressing conspiratorial thinking that may influence behaviors related to public health and the COVID-19 pandemic. Using the COVIDiSTRESSII Global Survey data from 12,261 participants, we validated the 4-item Conspiratorial Thinking Scale and 3-item Anti-Expert Sentiment Scale across 24 languages and dialects that were used by at least 100 participants per language. We employed confirmatory factor analysis, measurement invariance test, and measurement alignment for internal consistency testing. To test convergent validity of the two scales, we assessed correlations with trust in seven agents related to government, science, and public health. Although scalar invariance was not achieved when measurement invariance test was conducted initially, we found that both scales can be employed in further international studies with measurement alignment. Moreover, both conspiratorial thinking and anti-expert sentiments were significantly and negatively correlated with trust in all agents. Findings from this study provide supporting evidence for the validity of both scales across 24 languages for future large-scale international research.

Parsimony is a desirable feature of economic models but almost all human behaviors are characterized by vast individual variation that appears to defy parsimony. How much parsimony do we need to give up to capture the fundamental aspects of a population’s distributional preferences and to maintain high predictive ability? Using a Bayesian nonparametric clustering method that makes the trade-off between parsimony and descriptive accuracy explicit, we show that three preference types—an inequality averse, an altruistic and a predominantly selfish type—capture the essence of behavioral heterogeneity. These types independently emerge in four different data sets and are strikingly stable over time. They predict out-of-sample behavior equally well as a model that permits all individuals to differ and substantially better than a representative agent model and a state-of-the-art machine learning algorithm. Thus, a parsimonious model with three stable types captures key characteristics of distributional preferences and has excellent predictive power.

Motivated by a popular perception that Roth accounts are welfare-improving for most people, this paper compares the effects of mandated Traditional (tax-deferred) or Roth (taxprepaid) retirement policies in a controlled laboratory setting. Selection effects, which complicate analyses of observational data, are avoided by random assignment to policies. Subjects receive exogenous incomes during “working” periods, followed by no-income “retirement” periods. In each period, subjects decide how many lab dollars to convert into “takehome pay,” akin to consumption with diminishing returns. Subjects’ decisions determine retirement savings and tax payments. Flat income and tax-rate profiles facilitate the analysis of behavioral factors like present-period tax avoidance, while optimal consumption and after-tax savings are identical for both treatments. Our results show that observed savings are suboptimal in both treatments and are influenced by gender, patience, and risk aversion measures. In contrast to conventional wisdom, there are no significant differences between policies; if anything, the Traditional treatment leads to marginally higher post-retirement consumption.

Several developments in Western democracies over the past decade have sparked worries about political stability. Standing out are the rise of radical political parties, heated polarization around questions of immigration, nationalism, or social liberalism, and – in some cases – attacks on democratic institutions. However, conflict and choice between clearly distinctive alternative ideas of how societies and economies should be governed are at the heart of democracy. Democracy needs competition and conflict. But where is the line between healthy and harmful conflict and polarization? In this paper, Silja Häusermann and Simon Bornschier explain that an interpretation of today’s state of democratic conflict as chaotic, fragmented, or volatile is misleading. Rather, Western democracies are in a process of a fundamental restructuring of the main political dividing lines. Over the past decades a new social cleavage has been emerging between universalistic and particularistic ideas of social, economic, and political organization, between openness and closure. This conflict is rooted in social groups defined by education, occupation, and territory. It relates to underlying collective identities on both sides, and it will dominate democratic party competition for the foreseeable future. It is not per se harmful to democracy but reflects genuinely different visions of desirable social order. However, under certain conditions, it can turn on democracy itself. The authors thus examine the functional and dysfunctional implications of polarized political conflict for democracy. To what extent is conflict and polarization healthy and under what conditions is it likely to endanger the very legitimacy and institutional stability of democracies? Building on existing knowledge about the dynamics of polarization, they discuss political and institutional means to contain polarization and to protect democratic stability.

Profit shifting by multinational corporations is thought to reduce tax revenue around the world. While transfer pricing regulations are meant to curtail profit shifting, there have been rising concerns that a sophisticated tax advisory industry can limit their effectiveness. This paper provides a comprehensive analysis of how firms and tax advisors respond to the introduction of standard regulations aimed at limiting profit shifting. Using administrative tax and customs data from Chile in difference-in-differences event-study designs, we find that the reform was ineffective in reducing multinationals’ transfers to lower-tax countries and did not significantly raise tax payments. At the same time, interviews with tax advisors reveal a drastic increase in tax advisory services. The qualitative interviews also allow us to identify and then quantitatively confirm a common tax planning strategy in response to the reform. These results illustrate that when enforcement can be circumvented by sophisticated tax planning, it can benefit tax consultants at the expense of tax authorities and taxpayers.