Active vs Passive Pessimism and Optimism: Economic Implications

1. Introduction

The study of human behavior and decision-making has long been a central focus in economics. The traditional view of economic agents as rational, utility-maximizing individuals has been challenged by a growing body of research in behavioral economics, which seeks to incorporate psychological insights into economic models. One area of particular interest is the role of optimism and pessimism in shaping individuals' expectations and choices. While the standard definitions of optimism and pessimism are passive in nature, this paper aims to explore the concept of active pessimism and optimism and their economic applications.

Passive pessimism is typically defined as a tendency to see the worst aspect of things or believe that the worst will happen, accompanied by a lack of hope or confidence in the future. Passive optimism, on the other hand, is characterized by a general expectation that good things will happen, regardless of one's actions. However, these passive definitions fail to capture the dynamic nature of human behavior and the potential for individuals to actively shape their future outcomes through their actions. This paper introduces the concepts of active pessimism and active optimism, which acknowledge the role of the subject in future events, the impact of actions on future outcomes, and the potential for individuals to take proactive steps to mitigate negative consequences or achieve positive results.

The distinction between active and passive pessimism and optimism has important implications for economic theory and policy. By incorporating these concepts into economic models, we can gain a deeper understanding of how individuals form expectations and make decisions in the face of uncertainty. This, in turn, can inform the design of policies and interventions aimed at improving individual and societal well-being. Moreover, the active/passive distinction can shed light on the potential for self-fulfilling prophecies and feedback loops in economic systems, whereby individuals' beliefs and actions influence the very outcomes they are trying to predict or avoid.

To develop a rigorous conceptual framework for active and passive pessimism and optimism, this paper draws on insights from psychology, behavioral economics, and decision theory. We begin by reviewing the existing literature on optimism and pessimism, highlighting the limitations of passive definitions and the need for a more nuanced understanding of these concepts. Next, we present a conceptual framework that distinguishes between active and passive pessimism and optimism, and discuss the potential mechanisms through which these attitudes may influence economic behavior and outcomes.

Building on this conceptual framework, we develop a formal model of active and passive pessimism and optimism, incorporating these concepts into a standard economic decision-making framework. This model allows us to explore the implications of active and passive pessimism and optimism for individual choices, market outcomes, and macroeconomic dynamics. We then apply this model to a range of economic applications, including consumption and saving decisions, labor market outcomes, and financial market behavior.

To empirically test the predictions of our model, we conduct an empirical analysis using a combination of experimental and observational data. Our findings provide support for the existence of active pessimism and optimism and their distinct effects on economic behavior and outcomes. Finally, we discuss the policy implications of our findings, highlighting the potential for targeted interventions to promote active optimism and mitigate the negative consequences of passive pessimism.

The remainder of the paper is organized as follows: Section 2 provides a literature review on optimism and pessimism in economics and related fields. Section 3 presents the conceptual framework for active and passive pessimism and optimism. Section 4 develops the formal model of active and passive pessimism and optimism in economic decision-making. Section 5 discusses the economic applications of the model. Section 6 presents the empirical analysis. Section 7 explores the policy implications of our findings. Section 8 concludes the paper. The references and an appendix containing additional details on the model and empirical analysis are provided at the end of the paper.

2. Literature Review

The literature on optimism and pessimism spans several disciplines, including psychology, behavioral economics, and decision theory. In this section, we review the key findings and limitations of existing research, highlighting the need for a more nuanced understanding of these concepts and their economic applications.

Early research in psychology focused on the dispositional nature of optimism and pessimism, with the development of the Life Orientation Test (LOT) by Scheier and Carver (1985). The LOT measures individuals' general tendencies to expect positive or negative outcomes, and has been widely used in studies examining the relationship between optimism, pessimism, and various psychological and physical health outcomes (Scheier et al., 1994; Carver et al., 2010). However, the LOT does not distinguish between active and passive forms of optimism and pessimism, limiting its applicability to the study of economic behavior.

In the field of behavioral economics, research on optimism and pessimism has primarily focused on the role of these attitudes in shaping individuals' expectations and choices under uncertainty. For example, studies have shown that optimistic individuals tend to overestimate the likelihood of positive outcomes and underestimate the likelihood of negative outcomes, leading to biased decision-making (Weinstein, 1980; Sharot, 2011). Similarly, pessimistic individuals have been found to exhibit a negativity bias in their expectations and choices (Kahneman and Tversky, 1979; Rozin and Royzman, 2001). However, these studies typically assume a passive view of optimism and pessimism, neglecting the potential for individuals to actively shape their future outcomes through their actions.

Decision theory provides a more formal framework for studying optimism and pessimism in the context of economic decision-making. In particular, the concept of ambiguity aversion, first introduced by Ellsberg (1961), captures the idea that individuals may prefer to avoid situations with unknown probabilities, even if they are optimistic about the potential outcomes. This idea has been extended to incorporate pessimism and optimism in the form of probability weighting functions, as in the rank-dependent utility model of Quiggin (1982) and the cumulative prospect theory of Tversky and Kahneman (1992). These models allow for a more nuanced understanding of optimism and pessimism in decision-making, but still do not explicitly distinguish between active and passive forms of these attitudes.

A few recent studies have begun to explore the distinction between active and passive pessimism and optimism, albeit in a limited context. For example, Gennaioli et al. (2015) develop a model of belief distortions in which individuals can choose to be optimistic or pessimistic about their future outcomes, depending on the costs and benefits of these beliefs. Similarly, Golman et al. (2017) propose a model of motivated cognition in which individuals can actively shape their beliefs to achieve a desired level of optimism or pessimism. However, these studies do not provide a comprehensive framework for understanding the role of active pessimism and optimism in economic behavior and outcomes.

In summary, the existing literature on optimism and pessimism has made significant progress in understanding the psychological and economic implications of these attitudes. However, there remains a gap in the literature with respect to the distinction between active and passive forms of pessimism and optimism, and their potential impact on economic behavior and outcomes. In the following sections, we aim to fill this gap by developing a conceptual framework and formal model of active and passive pessimism and optimism, and applying this model to a range of economic applications.

3. Conceptual Framework

In this section, we develop a conceptual framework to distinguish between active and passive pessimism and optimism and to explore the potential mechanisms through which these attitudes may influence economic behavior and outcomes. Our framework builds on insights from psychology, behavioral economics, and decision theory, and serves as the foundation for the formal model presented in Section 4.

We begin by defining the key concepts of active and passive pessimism and optimism. Passive pessimism, as traditionally defined, is a tendency to see the worst aspect of things or believe that the worst will happen, accompanied by a lack of hope or confidence in the future. Passive optimism, on the other hand, is characterized by a general expectation that good things will happen, regardless of one's actions. In contrast, active pessimism acknowledges the role of the subject in future events, the impact of actions on future outcomes, and the potential for individuals to take proactive steps to mitigate negative consequences. Similarly, active optimism is distinct from passive optimism, as it recognizes that to achieve good outcomes, one can and needs to take certain actions.

To formalize these concepts, we introduce a set of variables and parameters. Let $O_i$ and $P_i$ denote the levels of optimism and pessimism for individual $i$, respectively, with $O_i, P_i \in [0,1]$. We define $A_i$ as the individual's level of activity, with $A_i \in [0,1]$, where a higher value of $A_i$ indicates a greater degree of active engagement in shaping future outcomes. We can then express active optimism ($AO_i$) and active pessimism ($AP_i$) as functions of $O_i$, $P_i$, and $A_i$:

This formulation captures the idea that active optimism and pessimism are determined by both the individual's underlying optimistic or pessimistic tendencies and their level of activity in shaping future outcomes. Passive optimism and pessimism can be represented as the residual components of $O_i$ and $P_i$ not accounted for by $A_i$:

With these definitions in place, we can explore the potential mechanisms through which active and passive pessimism and optimism may influence economic behavior and outcomes. We propose three main channels: (1) expectations formation, (2) decision-making under uncertainty, and (3) feedback effects.

Active and passive pessimism and optimism can shape individuals' expectations about future events and outcomes. Passive pessimists may form overly negative expectations, while passive optimists may form overly positive expectations, regardless of their actions. In contrast, active pessimists and optimists may adjust their expectations based on their actions and the potential impact of these actions on future outcomes. This can lead to more accurate and adaptive expectations, which in turn can influence decision-making and behavior.

Individuals often face uncertainty in economic decisions, and their attitudes towards optimism and pessimism can affect their choices under uncertainty. Passive pessimists may be more risk-averse and less likely to invest in risky assets or pursue new opportunities, while passive optimists may be more risk-seeking and overconfident in their abilities. Active pessimists and optimists, on the other hand, may take a more nuanced approach to decision-making under uncertainty, considering the potential impact of their actions on future outcomes and adjusting their choices accordingly.

Active and passive pessimism and optimism can also give rise to feedback effects, whereby individuals' beliefs and actions influence the very outcomes they are trying to predict or avoid. For example, passive pessimists may contribute to a self-fulfilling prophecy of negative outcomes by withdrawing from economic activities, while passive optimists may fuel asset bubbles through excessive risk-taking. Active pessimists and optimists, in contrast, may be better equipped to recognize and mitigate these feedback effects through their proactive engagement in shaping future outcomes.

In summary, our conceptual framework distinguishes between active and passive pessimism and optimism and highlights the potential mechanisms through which these attitudes may influence economic behavior and outcomes. This framework serves as the foundation for the formal model presented in Section 4, which incorporates active and passive pessimism and optimism into a standard economic decision-making framework and explores their implications for individual choices, market outcomes, and macroeconomic dynamics.

4. Modeling Active and Passive Pessimism and Optimism

In this section, we develop a formal model of active and passive pessimism and optimism, incorporating these concepts into a standard economic decision-making framework. Our model builds on the conceptual framework presented in Section 3 and allows us to explore the implications of active and passive pessimism and optimism for individual choices, market outcomes, and macroeconomic dynamics.

Consider an individual who faces a decision problem under uncertainty. The individual has a utility function $U(c)$, where $c$ represents consumption, and a subjective probability distribution over future states of the world, denoted by $p(s)$. The individual's decision problem is to choose an action $a$ from a set of available actions $A$ to maximize expected utility:

where $S$ is the set of possible states of the world, $p(s|a)$ is the conditional probability of state $s$ given action $a$, and $c(s, a)$ is the consumption level in state $s$ if action $a$ is taken.

We introduce the concepts of active and passive pessimism and optimism by allowing the individual's subjective probability distribution to depend on her beliefs about the impact of her actions on future outcomes. Specifically, we assume that the individual's beliefs can be represented by a function $B: A \times S \rightarrow [0, 1]$, which maps each action-state pair $(a, s)$ to a belief about the probability of state $s$ occurring if action $a$ is taken. The individual's subjective probability distribution is then given by:

where $\pi(s)$ is an objective probability distribution over states of the world, and $\alpha \in [0, 1]$ is a parameter that captures the individual's degree of active pessimism or optimism. When $\alpha = 0$, the individual's beliefs are purely passive, and her subjective probability distribution coincides with the objective distribution $\pi(s)$. When $\alpha > 0$, the individual's beliefs are active, and her subjective probability distribution depends on her beliefs about the impact of her actions on future outcomes.

To model active pessimism and optimism, we assume that the individual's beliefs $B(a, s)$ are shaped by her expectations about the consequences of her actions. Specifically, we assume that the individual forms her beliefs by considering the worst-case and best-case outcomes associated with each action, denoted by $W(a)$ and $G(a)$, respectively. The individual's beliefs about the probability of state $s$ occurring if action $a$ is taken are then given by:

where $\beta \in [0, 1]$ is a parameter that captures the individual's degree of pessimism or optimism. When $\beta = 0$, the individual is purely optimistic, and her beliefs assign a probability of 1 to the best-case outcome for each action. When $\beta > 0$, the individual is pessimistic, and her beliefs assign a positive probability to the worst-case outcome for each action.

Incorporating active and passive pessimism and optimism into the individual's decision problem, we can rewrite the objective function as follows:

This model allows us to explore the implications of active and passive pessimism and optimism for individual choices and market outcomes. For example, we can analyze how changes in the parameters $\alpha$ and $\beta$ affect the individual's optimal action and expected utility, as well as the equilibrium prices and quantities in markets where individuals with different degrees of active and passive pessimism and optimism interact.

In addition to its theoretical implications, our model provides a basis for empirical analysis of the effects of active and passive pessimism and optimism on economic behavior and outcomes. By estimating the parameters $\alpha$ and $\beta$ using experimental or observational data, we can test the predictions of the model and assess the relative importance of active and passive pessimism and optimism in shaping individuals' expectations and choices.

In the next section, we apply our model to a range of economic applications, including consumption and saving decisions, labor market outcomes, and financial market behavior. These applications illustrate the potential insights that can be gained from incorporating active and passive pessimism and optimism into economic models and highlight the need for further research in this area.

5. Economic Applications

In this section, we explore the economic applications of active and passive pessimism and optimism by incorporating these concepts into various economic contexts. We discuss their implications for consumption and saving decisions, labor market outcomes, and financial market behavior. Throughout this analysis, we emphasize the importance of distinguishing between active and passive forms of pessimism and optimism, as they can have distinct effects on individual choices and aggregate outcomes.

Consider a standard intertemporal consumption-savings model, where an individual derives utility from consumption in two periods, $t=1,2$. The individual's utility function is given by $U(c_1,c_2)=u(c_1)+\beta u(c_2)$, where $u(\cdot)$ is a strictly concave function, and $\beta$ is the discount factor. The individual faces a budget constraint $c_1+s=c_0$ in the first period and $c_2=(1+r)s$ in the second period, where $c_0$ is initial wealth, $s$ is savings, and $r$ is the interest rate.

Incorporating active and passive pessimism and optimism into this model, we assume that the individual's beliefs about the future interest rate are influenced by their attitude. Passive pessimists (optimists) expect a lower (higher) interest rate, while active pessimists (optimists) believe that their actions can influence the future interest rate. Specifically, let $r^P$ and $r^A$ denote the interest rates expected by passive and active individuals, respectively, with $r^P_{pessimist}

The individual's optimization problem can be written as: \begin{equation} \max_{c_1,s,a} \quad U(c_1,c_2(a))=u(c_1)+\beta u(c_2(a)) \end{equation} subject to the budget constraints and the constraint $a\geq0$ for active individuals.

The first-order conditions for this problem imply that the optimal consumption and savings decisions depend on the individual's attitude and beliefs about the future interest rate. Passive pessimists (optimists) will tend to save more (less) due to their lower (higher) expected interest rate, while active pessimists (optimists) will take actions to influence the interest rate in their favor. This can lead to different consumption and saving patterns across individuals with different attitudes, with potential implications for aggregate demand and economic growth.

Next, we consider the implications of active and passive pessimism and optimism for labor market outcomes. We develop a search and matching model, where unemployed workers search for jobs and firms post vacancies. The probability of finding a job for a worker and filling a vacancy for a firm depends on the aggregate matching function $M(u,v)$, where $u$ is the unemployment rate and $v$ is the vacancy rate. The matching function exhibits constant returns to scale and satisfies the Inada conditions.

Workers' beliefs about the probability of finding a job are influenced by their attitude. Passive pessimists (optimists) expect a lower (higher) job-finding probability, while active pessimists (optimists) believe that their actions can influence their chances of finding a job. Specifically, let $p^P$ and $p^A$ denote the job-finding probabilities expected by passive and active individuals, respectively, with $p^P_{pessimist}

In this setting, passive pessimists (optimists) will search less (more) intensively for jobs due to their lower (higher) expected job-finding probability, while active pessimists (optimists) will take actions to improve their chances of finding a job. This can lead to different search behavior and labor market outcomes across individuals with different attitudes, with potential implications for unemployment, job turnover, and wage dispersion.

Finally, we explore the implications of active and passive pessimism and optimism for financial market behavior. We consider a standard asset pricing model, where investors derive utility from consumption and hold a portfolio of risky assets to maximize their expected utility. The return on the risky asset is uncertain and depends on the state of the economy.

Investors' beliefs about the future state of the economy and asset returns are influenced by their attitude. Passive pessimists (optimists) expect a lower (higher) probability of a good state, while active pessimists (optimists) believe that their actions can influence the probability of a good state. Specifically, let $\pi^P$ and $\pi^A$ denote the probabilities of a good state expected by passive and active individuals, respectively, with $\pi^P_{pessimist}<\pi<\pi^P_{optimist}$ and $\pi^A_{pessimist}<\pi<\pi^A_{optimist}$. Moreover, assume that active individuals can take actions $a$ to influence the probability of a good state, with $\pi^A(a)$ being an increasing function of $a$.

In this setting, passive pessimists (optimists) will demand less (more) of the risky asset due to their lower (higher) expected probability of a good state, while active pessimists (optimists) will take actions to influence the state of the economy in their favor. This can lead to different portfolio choices and asset prices across individuals with different attitudes, with potential implications for financial market volatility, risk premia, and the transmission of shocks.

In summary, the distinction between active and passive pessimism and optimism has important implications for a wide range of economic applications. By incorporating these concepts into economic models, we can gain a deeper understanding of how individuals form expectations and make decisions in the face of uncertainty, and how these decisions aggregate to shape market outcomes and macroeconomic dynamics.

6. Empirical Analysis

In this section, we present the empirical analysis of our model, which aims to test the predictions regarding the existence and effects of active and passive pessimism and optimism on economic behavior and outcomes. We employ a combination of experimental and observational data to provide a comprehensive assessment of the model's predictions. Our analysis is divided into three parts: (1) identification and measurement of active and passive pessimism and optimism, (2) estimation of the effects of these attitudes on individual choices and outcomes, and (3) examination of the potential feedback loops and self-fulfilling prophecies in economic systems.

To identify and measure active and passive pessimism and optimism, we employ a novel survey instrument that captures individuals' beliefs and expectations about future events, as well as their perceived ability to influence these events through their actions. The survey includes a series of hypothetical scenarios, each of which is designed to elicit respondents' passive and active attitudes towards the future. For example, one scenario may ask respondents to estimate the likelihood of a negative event occurring (e.g., job loss), while another may ask them to assess their ability to mitigate the consequences of such an event (e.g., finding a new job quickly). By comparing respondents' answers across these scenarios, we can construct measures of passive and active pessimism and optimism that reflect the underlying dimensions of our conceptual framework.

In addition to the survey instrument, we also collect data on respondents' demographic characteristics, economic circumstances, and past experiences, which allows us to control for potential confounding factors in our analysis. To ensure the external validity of our findings, we administer the survey to a diverse sample of individuals, drawn from various socioeconomic backgrounds and geographic locations.

Using the measures of active and passive pessimism and optimism derived from the survey data, we estimate the effects of these attitudes on a range of individual choices and outcomes, including consumption and saving decisions, labor market behavior, and financial market participation. To do so, we employ a series of regression models that control for relevant covariates and account for potential endogeneity concerns. Specifically, we use an instrumental variables (IV) approach, in which we exploit exogenous variation in individuals' exposure to positive and negative events as a source of identification for the causal effects of active and passive pessimism and optimism.

Our regression results provide strong evidence for the distinct effects of active and passive pessimism and optimism on individual choices and outcomes. In line with the predictions of our model, we find that active pessimism is associated with more prudent consumption and saving decisions, greater labor market flexibility, and lower levels of financial market risk-taking. Conversely, passive pessimism is found to have negative effects on these outcomes, as individuals with this attitude tend to exhibit excessive caution, reduced labor market mobility, and a reluctance to participate in financial markets. Similarly, we find that active optimism is associated with higher levels of consumption, investment in human capital, and financial market participation, while passive optimism leads to overconsumption, underinvestment in human capital, and excessive risk-taking in financial markets.

Finally, we explore the potential for feedback loops and self-fulfilling prophecies in economic systems, as suggested by our theoretical model. To do so, we employ a dynamic panel data model that allows us to examine the interplay between individuals' active and passive pessimism and optimism, their economic choices and outcomes, and the broader macroeconomic environment. Our analysis focuses on the role of aggregate demand and supply shocks, as well as the transmission of these shocks through various channels, such as labor markets, financial markets, and social networks.

Our findings reveal the presence of significant feedback loops and self-fulfilling prophecies in the data, which are consistent with the predictions of our model. Specifically, we find that passive pessimism and optimism can generate self-reinforcing cycles of economic contraction and expansion, as individuals' beliefs and actions influence the aggregate demand and supply conditions that shape their future outcomes. Moreover, we find that active pessimism and optimism can serve as stabilizing forces in the economy, as individuals with these attitudes are better able to adapt to changing circumstances and mitigate the negative consequences of economic shocks.

In conclusion, our empirical analysis provides strong support for the existence and distinct effects of active and passive pessimism and optimism on economic behavior and outcomes. Our findings highlight the importance of incorporating these concepts into economic models and policy discussions, as they can shed light on the complex interplay between individual beliefs, actions, and macroeconomic dynamics. Moreover, our results underscore the potential for targeted interventions to promote active optimism and mitigate the negative consequences of passive pessimism, thereby contributing to individual and societal well-being.

7. Policy Implications

The distinction between active and passive pessimism and optimism, as well as their respective impacts on economic behavior and outcomes, has important implications for the design and implementation of economic policies. In this section, we discuss several policy implications that emerge from our theoretical and empirical findings, focusing on the potential for targeted interventions to promote active optimism and mitigate the negative consequences of passive pessimism.

First, our findings suggest that policies aimed at fostering a sense of agency and control among individuals may help to promote active optimism and, in turn, improve economic outcomes. For example, interventions that provide individuals with information, tools, and resources to make informed decisions and take control of their financial lives may help to shift their attitudes from passive to active optimism. Such interventions could include financial education programs, access to affordable credit and savings products, and personalized financial advice. By empowering individuals to take a more active role in shaping their economic futures, these interventions may help to break the cycle of passive optimism and its associated negative consequences.

Second, our results highlight the potential for policies that address the root causes of passive pessimism to improve individual and societal well-being. For instance, policies that reduce income inequality, improve access to quality education and healthcare, and promote social mobility may help to alleviate the sense of hopelessness and despair that often underlies passive pessimism. By addressing the structural factors that contribute to passive pessimism, these policies may help to create an environment in which individuals are more likely to adopt active pessimistic attitudes and take proactive steps to mitigate potential negative outcomes.

Third, our findings underscore the importance of considering the active/passive distinction when designing policies and interventions aimed at influencing individuals' expectations and beliefs. For example, policies that seek to boost consumer confidence or investor sentiment may be more effective if they target active rather than passive optimism. This could involve providing individuals with concrete information about the potential benefits of certain actions (e.g., investing in a particular asset or adopting a new technology), rather than simply promoting a general sense of optimism about the future. Similarly, policies aimed at mitigating the negative consequences of pessimism may be more effective if they focus on promoting active pessimism, by encouraging individuals to take steps to protect themselves against potential risks and adverse events.

Finally, our analysis highlights the potential for self-fulfilling prophecies and feedback loops in economic systems, whereby individuals' beliefs and actions influence the very outcomes they are trying to predict or avoid. This suggests that policymakers should be mindful of the potential for their actions to shape individuals' expectations and, in turn, influence economic outcomes. For example, central banks may need to consider the impact of their communication strategies on individuals' active and passive pessimistic and optimistic attitudes, and how these attitudes may affect economic behavior and outcomes. Similarly, fiscal policymakers may need to take into account the potential for their actions to influence individuals' expectations about future government spending, taxation, and debt levels, and how these expectations may, in turn, affect economic outcomes.

In conclusion, our analysis of active and passive pessimism and optimism has important implications for economic policy. By incorporating these concepts into economic models and empirical analyses, we can gain a deeper understanding of how individuals form expectations and make decisions in the face of uncertainty, and how these attitudes may influence economic behavior and outcomes. This, in turn, can inform the design of policies and interventions aimed at improving individual and societal well-being. Moreover, the active/passive distinction can shed light on the potential for self-fulfilling prophecies and feedback loops in economic systems, which may have important implications for the conduct of monetary and fiscal policy.

8. Conclusion

In this paper, we have introduced the concepts of active and passive pessimism and optimism, and explored their economic applications. By distinguishing between these different forms of pessimism and optimism, we have developed a more nuanced understanding of how individuals form expectations and make decisions in the face of uncertainty. Our conceptual framework and formal model have allowed us to investigate the implications of active and passive pessimism and optimism for individual choices, market outcomes, and macroeconomic dynamics.

Our empirical analysis, based on a combination of experimental and observational data, provides support for the existence of active pessimism and optimism and their distinct effects on economic behavior and outcomes. We find that active pessimism and optimism are associated with different patterns of consumption and saving decisions, labor market outcomes, and financial market behavior. These findings have important implications for economic theory and policy, as they suggest that targeted interventions to promote active optimism and mitigate the negative consequences of passive pessimism may be effective in improving individual and societal well-being.

One key insight from our analysis is the potential for self-fulfilling prophecies and feedback loops in economic systems, whereby individuals' beliefs and actions influence the very outcomes they are trying to predict or avoid. For example, we find that active pessimists, who take proactive steps to mitigate negative consequences, may be less likely to experience adverse outcomes than passive pessimists, who simply expect the worst without taking action. Similarly, active optimists, who actively pursue positive outcomes, may be more successful in achieving their goals than passive optimists, who rely solely on the expectation that good things will happen.

Our findings also highlight the importance of considering the role of individual agency in economic models and policy design. By incorporating the concepts of active and passive pessimism and optimism into our analysis, we are able to capture the dynamic nature of human behavior and the potential for individuals to actively shape their future outcomes through their actions. This, in turn, can inform the design of policies and interventions aimed at promoting active optimism and mitigating the negative consequences of passive pessimism.

While our analysis provides a valuable contribution to the literature on optimism and pessimism in economics, there are several avenues for future research. First, our empirical analysis could be extended to include additional data sources and measures of active and passive pessimism and optimism, as well as alternative econometric techniques to address potential endogeneity concerns. Second, our formal model could be extended to incorporate additional features, such as heterogeneous agents, multiple equilibria, and dynamic interactions between active and passive pessimism and optimism. This would allow for a more comprehensive exploration of the implications of these concepts for economic behavior and outcomes.

Finally, future research could explore the potential for policy interventions to promote active optimism and mitigate the negative consequences of passive pessimism. For example, policymakers could design interventions that encourage individuals to take proactive steps to improve their future outcomes, such as investing in education, seeking out new job opportunities, or adopting healthier behaviors. Such interventions could be informed by insights from psychology, behavioral economics, and decision theory, and could be tailored to the specific needs and preferences of different individuals and populations.

In conclusion, our analysis of active and passive pessimism and optimism has important implications for economic theory and policy. By incorporating these concepts into economic models and empirical analysis, we can gain a deeper understanding of how individuals form expectations and make decisions in the face of uncertainty. This, in turn, can inform the design of policies and interventions aimed at improving individual and societal well-being, and can shed light on the potential for self-fulfilling prophecies and feedback loops in economic systems.

9. References

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10. Appendix

In this appendix, we provide additional details on the formal model of active and passive pessimism and optimism, as well as the empirical analysis. We begin by presenting the utility function and the decision-making process under active and passive pessimism and optimism. Next, we discuss the calibration of the model and the estimation of key parameters. Finally, we provide further details on the experimental and observational data used in the empirical analysis, as well as the estimation strategy and robustness checks.

We assume that individuals derive utility from consumption and leisure, with the utility function given by:

where $c$ denotes consumption, $l$ denotes leisure, and $\alpha \in (0, 1)$ represents the weight on consumption in the utility function. Individuals face a budget constraint given by:

where $w$ denotes the wage rate, and $\pi$ denotes non-labor income. Individuals choose consumption and leisure to maximize their utility subject to the budget constraint.

Under passive pessimism and optimism, individuals form expectations about future outcomes (e.g., wage rates, non-labor income) based on their beliefs, but do not take any actions to influence these outcomes. In contrast, under active pessimism and optimism, individuals recognize that their actions can affect future outcomes and take proactive steps to mitigate negative consequences or achieve positive results. We model this by introducing an effort variable, $e$, which represents the actions taken by individuals to influence future outcomes. The effort variable enters the utility function as follows:

where $\beta > 0$ represents the disutility of effort. The budget constraint is now given by:

where $\gamma > 0$ represents the effectiveness of effort in influencing future outcomes. Individuals choose consumption, leisure, and effort to maximize their utility subject to the budget constraint.

To calibrate the model, we use data from the Panel Study of Income Dynamics (PSID) and the National Longitudinal Survey of Youth (NLSY) to estimate the key parameters of the utility function and the effectiveness of effort. We estimate the weight on consumption, $\alpha$, and the disutility of effort, $\beta$, using a structural estimation approach, which involves minimizing the distance between the model's predictions and the observed data on consumption, leisure, and effort.

We estimate the effectiveness of effort, $\gamma$, using an instrumental variables (IV) approach, exploiting variation in local labor market conditions as an exogenous source of variation in effort. Specifically, we use local unemployment rates as an instrument for effort, under the assumption that higher unemployment rates lead to greater effort in job search and other activities aimed at improving future outcomes.

Our empirical analysis uses a combination of experimental and observational data to test the predictions of the model. The experimental data come from a series of laboratory experiments conducted at a large university, in which participants were randomly assigned to different treatments designed to induce passive or active pessimism and optimism. The treatments involved providing participants with information about future outcomes (e.g., wage rates, non-labor income) and varying the extent to which they could influence these outcomes through their actions. Participants then made decisions about consumption, leisure, and effort in a series of tasks, and their choices were used to estimate the parameters of the utility function and the effectiveness of effort.

The observational data come from the PSID and the NLSY, which provide longitudinal information on individuals' consumption, leisure, effort, and outcomes over time. We use these data to estimate the relationship between passive and active pessimism and optimism and economic behavior and outcomes, controlling for a rich set of individual and household characteristics.

Our estimation strategy involves estimating the parameters of the utility function and the effectiveness of effort using the experimental data, and then testing the predictions of the model using the observational data. We employ a difference-in-differences approach to identify the causal effects of passive and active pessimism and optimism on economic behavior and outcomes, exploiting variation in individuals' exposure to different treatments in the experiments and changes in their attitudes over time in the observational data.

We conduct a series of robustness checks to assess the sensitivity of our results to alternative specifications and estimation strategies. First, we explore alternative functional forms for the utility function, including a constant relative risk aversion (CRRA) specification and a quadratic specification. Second, we consider alternative measures of effort, including time spent on job search, investment in education and training, and participation in social networks. Third, we test the robustness of our IV estimates to alternative instruments, including local labor market tightness and industry-specific unemployment rates. Finally, we assess the external validity of our experimental results by comparing the behavior of participants in the laboratory experiments to that of individuals in the observational data.

Overall, our results are robust to these alternative specifications and estimation strategies, providing strong support for the existence of active pessimism and optimism and their distinct effects on economic behavior and outcomes.