This document discusses a study that examines how loss aversion affects household portfolio choices. The study uses survey data from Dutch households that includes direct measures of each household's loss aversion coefficient, derived from questions involving hypothetical payoffs. The study finds that higher loss aversion is associated with a lower probability of participating in equity markets. It also finds that higher loss aversion reduces the probability of direct stockholding more than the probability of owning mutual funds. After controlling for other factors, the study does not find a significant relationship between loss aversion and portfolio allocations.
This document discusses using extreme value theory (EVT) to model policyholder behavior in extreme market conditions using variable annuity lapse data. EVT allows predicting behavior in the extremes based on nonextreme data. The paper applies EVT by fitting bivariate distributions to lapse and market indicator data above a large threshold. This provides insights into policyholder behavior in extreme markets without direct observations. The goal is a dynamic lapse formula capturing different characteristics than traditional methods.
The subjective well-being (SWB) approach to environmental valuation, also known as the life satisfaction approach (LSA), has both pros and cons compared to traditional preference-based valuation techniques. A key pro is that it avoids assumptions of market equilibrium and strategic responses that can bias other methods. However, a major con is that studies often produce implausibly high valuation estimates. Additionally, its assumption of interpersonal comparisons of life satisfaction is controversial but may not be a significant limitation. The LSA continues to evolve and its advantages of using large, representative datasets and providing differentiated valuations could increase its usefulness for policymakers.
The author conducted an experiment to analyze the disposition effect among 58 student subjects. In a simulated financial market, subjects decided whether to hold or sell assets over 20 periods as values fluctuated. The majority of subjects exhibited the disposition effect by selling winning assets too early and holding losing assets too long. Correlations between the disposition effect and variables like gender, age, major, and risk aversion were explored but inconclusive due to small sample sizes for some groups. Overall, the experiment provided evidence that the disposition effect is present among most investors.
Deal or No Deal, That is theQuestion The Impact of Increasi.docxtheodorelove43763
Deal or No Deal, That is the
Question: The Impact of Increasing
Stakes and Framing Effects on
Decision-Making under Risk
ROBERT BROOKS, ROBERT FAFF, DANIEL MULINO AND
RICHARD SCHEELINGS
Department of Accounting and Finance Faculty of Business and Economics,
Monash University, Melbourne, Australia
ABSTRACT
In this paper, we utilize data from the Australian version of the TV game
show, ‘Deal or No Deal’, to explore risk aversion in a high real stakes setting.
An attractive feature of this version of the game is that supplementary
rounds may occur which switch the decision frame of players. There are four
main findings. First, we observe that the degree of risk aversion generally
increases with stakes. Second, we observe considerable heterogeneity in
people’s willingness to bear risk – even at very high stakes. Third, we find that
age and gender are statistically significant determinants of risk aversion,
while wealth is not. Fourth, we find that the reversal of framing does have a
significant impact on people’s willingness to bear risk.
I. INTRODUCTION
The analysis of decisions under uncertainty is fundamental to modern
economics and finance. This paper contributes to a recently developing
empirical literature that adopts the central research question: How risk averse
are individuals? Subsidiary questions regarding risk aversion that we address
include its heterogeneity and how it varies with individual demographic
characteristics (especially age, wealth and gender). While the theoretical
literature on risk aversion and expected utility theory is large and long-
standing, the literature explicitly testing for risk aversion is comparatively
small. Such empirical tests as exist, either in laboratory or field experiments
involving real stakes, have mostly been confined to small cash values. There has
been a recent debate doubting the applicability of such estimates when
extrapolated to high real stakes (see Rabin 2000). Our paper exploits an
Australian game show dataset to explore the nature of risk aversion of
contestants who face an environment of very high stakes.
r 2009 The Authors. Journal compilation r International Review of Finance Ltd. 2009. Published by Blackwell
Publishing Ltd., 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA.
International Review of Finance, 9:1–2, 2009: pp. 27–50
DOI: 10.1111/j.1468-2443.2009.01084.x
‘Deal or No Deal’ is a half-hour TV game show in which contestants make a
series of choices between a sure thing and a lottery.1 It is ideal for studying a
range of issues relating to economic decision making. The show consists of a
chosen contestant faced with 26 suitcases, randomly containing amounts
ranging from 50 cents to US$200,000 dollars. There are up to nine ‘normal’
rounds in the main stage of the game. Unlike other versions of the show, the
Australian version of ‘Deal or No Deal’ also involves the potential for one of two
extra rounds – the Chance round and the S.
Although there exists a vast literature on aid efficiency (the effect of aid on GDP), and that aid allocation determinants have been estimated, little is known about the minute details of aid allocation. This article investigates empirically a claim repeatedly made in the past that aid donors herd. Building upon a methodology applied to financial markets, this article finds that aid donors herd similarly to portfolio funds on financial markets. It also estimates the causes of herding and finds that political transitions towards more autocratic regimes repel donors, but that transitions towards democracy have no effect. Finally, identified causes of herding explain little of its overall level, suggesting strategic motives play an important role.
Behavioral Public Choice The Behavioral Paradox of Gove.docxtaitcandie
Behavioral Public Choice:
The Behavioral Paradox of
Government Policy
W. Kip Viscusf & Ted Gayer”
I. Overview
W hat are the economic justifications for governm ent inter
vention in the economy? In a m arket economy, prices coordi
nate the activities of buyers and sellers and convey inform ation
about the strength of consum er dem and for a good and the
costs of supplying it. Because trade is voluntary, buyers and
sellers only m ake exchanges w hen both parties benefit. Under
ideal m arket conditions, this process leads to an efficient alloca
tion of goods w ithout governm ent intervention.
However, economics has long recognized instances in which
markets can fail to lead to an efficient outcome. The long-standing
view is that either market power or the nonexistence of markets
causes market failures. Market power is present w hen some indi
viduals or firms are price makers (for example, monopolists) ra
ther than participants in a perfectly competitive environment.
Such situations typically lead to the production of a less than effi
cient quantity of goods. The problem of market power is the pur
view of industrial organization economics and antitrust policy. * 1
The nonexistence of markets, or the failure of a robust market to
arise, can occur for a num ber of reasons, such as asymmetric in
formation (when one party in a transaction has information that is
not available to another) and public goods (when a good is non
rival and nonexcludable in consumption and thus likely to be un-
* University Distinguished Professor of Law, Economics, and Management,
Vanderbilt Law School, 131 21st Ave. South, Nashville, TN 37203. [email protected]
vanderbilt.edu. We are grateful to the Mercatus Center for their support.
**Vice President and Director, Economic Studies, Brookings Institution, 1775
Massachusetts Ave. NW, Washington, DC 20036. [email protected]
1. H arvey S. Rosen & Ted Gayer, Public Fin a n c e 46-48 (10th ed. 2013).
mailto:[email protected]
974 Harvard Journal of Law & Public Policy [Vol. 38
dersupplied by the market). Another cause for the nonexistence of
markets is externalities, which occur when transactions impose
costs or benefits on a third party that are not considered in the
market exchange. A classic example is when a factory produces
and sells a good to a consumer to their m utual advantage, but the
pollution generated by the production of the good has a negative
impact on the health of nearby residents. A market for the clean
air in the affected area would not emerge if high transaction costs
of organizing the pollution victims prevented the parties from
negotiating.2 The market system will fail to internalize the health
costs imposed by the factory's operations and lead to inefficiently
high production and health consequences.
For about a century, economists have argued that policymakers
should rely, when possible, on market-based principles in design
ing regulations to address thes.
Joanna R. Pepin University of Texas–AustinBeliefs About Mo.docxvrickens
Joanna R. Pepin University of Texas–Austin
Beliefs About Money in Families: Balancing Unity,
Autonomy, and Gender Equality
Objective: This study provides the first nation-
ally representative data on U.S. adults’ percep-
tions of income sharing within families.
Background: Modern couples confront tensions
between ideals of mutual interests and values
of economic autonomy, a departure from fit-
ting themselves into culturally expected family
arrangements of the past. This study teases apart
the conditions under which people might priori-
tize one cultural value over another.
Method: The author conducted a nationally rep-
resentative survey experiment (N = 3,986 indi-
viduals). The respondents selected an income
allocation arrangement for a fictional couple
with varied relationships investments (i.e., mar-
riage, parenthood, length of relationship) and
earning disparities.
Results: Although stronger relationship invest-
ments were associated with greater support
for sharing all income, the most commonly
selected income allocation arrangement was
a hybrid arrangement of sharing some income
and keeping the rest separate. When respondents
preferred some amount of financial autonomy,
the primary earner was expected to maintain
ownership of a greater amount of the total
household income. The preferred level of with-
holding income was slightly larger in magnitude
when women were shown as the primary earner
when compared with men shown as the primary
earner.
Population Reasearch Center, 305 E. 23rd Street, Austin,
Texas 78712-1699 ([email protected]).
Key Words: couples, culture, family economics, family
resource management, gender roles, money management.
Conclusion: The pursuit of economic auton-
omy, in combination with beliefs about gender,
are important dimensions of gender inequality
located within families.
Introduction
Two contradictions related to the ways couples
share money are commonly studied. First, cou-
ples must reconcile the conflict between their
commitment to a collective family unit and their
desire for individual autonomy (Treas, 1993;
Vogler, Brockmann, & Wiggins, 2008). Sec-
ond, many couples struggle to create equality
in the home given inequalities prevalent in the
labor market (Blumstein & Schwartz, 1983; Bur-
goyne, 1990; Pahl, 1989; Treas, 1993; Vogler
et al., 2008). To understand how these contradic-
tions are reconciled within American families,
researchers initially focused on grouping sys-
tems of money arrangements into analytical cat-
egories (Ashby & Burgoyne, 2008; Pahl, 1995).
Thereafter, many have searched for possible
explanations for the prevalence of various cat-
egories and sought to identify the consequences
of these arrangements (for an overview of prior
research, see Bennett, 2013).
Drawing conclusions about how people rec-
oncile these competing cultural values based
on couples’ behavioral practices may be par-
tial or even misleading (Ashby & Burgoyne,
2008, 2009). Behaviors tend to reflect a mi ...
This document examines investment behavior when firms face costs in accessing external funds. It finds that standard investment regressions predict cash flow is important for investment only without considering q. Conversely, it also obtains significant cash flow effects even without financial frictions. These findings support the argument that cash flow effects in regressions are due to measurement error in q and identification problems. The document then provides context on empirical investment patterns and the relationship between investment and financing. It describes a model of heterogeneous firms facing costly external finance to understand these stylized facts. Using this model, it finds cash flow effects in regressions are not solely due to financial constraints and are sensitive to measurement error.
This document discusses using extreme value theory (EVT) to model policyholder behavior in extreme market conditions using variable annuity lapse data. EVT allows predicting behavior in the extremes based on nonextreme data. The paper applies EVT by fitting bivariate distributions to lapse and market indicator data above a large threshold. This provides insights into policyholder behavior in extreme markets without direct observations. The goal is a dynamic lapse formula capturing different characteristics than traditional methods.
The subjective well-being (SWB) approach to environmental valuation, also known as the life satisfaction approach (LSA), has both pros and cons compared to traditional preference-based valuation techniques. A key pro is that it avoids assumptions of market equilibrium and strategic responses that can bias other methods. However, a major con is that studies often produce implausibly high valuation estimates. Additionally, its assumption of interpersonal comparisons of life satisfaction is controversial but may not be a significant limitation. The LSA continues to evolve and its advantages of using large, representative datasets and providing differentiated valuations could increase its usefulness for policymakers.
The author conducted an experiment to analyze the disposition effect among 58 student subjects. In a simulated financial market, subjects decided whether to hold or sell assets over 20 periods as values fluctuated. The majority of subjects exhibited the disposition effect by selling winning assets too early and holding losing assets too long. Correlations between the disposition effect and variables like gender, age, major, and risk aversion were explored but inconclusive due to small sample sizes for some groups. Overall, the experiment provided evidence that the disposition effect is present among most investors.
Deal or No Deal, That is theQuestion The Impact of Increasi.docxtheodorelove43763
Deal or No Deal, That is the
Question: The Impact of Increasing
Stakes and Framing Effects on
Decision-Making under Risk
ROBERT BROOKS, ROBERT FAFF, DANIEL MULINO AND
RICHARD SCHEELINGS
Department of Accounting and Finance Faculty of Business and Economics,
Monash University, Melbourne, Australia
ABSTRACT
In this paper, we utilize data from the Australian version of the TV game
show, ‘Deal or No Deal’, to explore risk aversion in a high real stakes setting.
An attractive feature of this version of the game is that supplementary
rounds may occur which switch the decision frame of players. There are four
main findings. First, we observe that the degree of risk aversion generally
increases with stakes. Second, we observe considerable heterogeneity in
people’s willingness to bear risk – even at very high stakes. Third, we find that
age and gender are statistically significant determinants of risk aversion,
while wealth is not. Fourth, we find that the reversal of framing does have a
significant impact on people’s willingness to bear risk.
I. INTRODUCTION
The analysis of decisions under uncertainty is fundamental to modern
economics and finance. This paper contributes to a recently developing
empirical literature that adopts the central research question: How risk averse
are individuals? Subsidiary questions regarding risk aversion that we address
include its heterogeneity and how it varies with individual demographic
characteristics (especially age, wealth and gender). While the theoretical
literature on risk aversion and expected utility theory is large and long-
standing, the literature explicitly testing for risk aversion is comparatively
small. Such empirical tests as exist, either in laboratory or field experiments
involving real stakes, have mostly been confined to small cash values. There has
been a recent debate doubting the applicability of such estimates when
extrapolated to high real stakes (see Rabin 2000). Our paper exploits an
Australian game show dataset to explore the nature of risk aversion of
contestants who face an environment of very high stakes.
r 2009 The Authors. Journal compilation r International Review of Finance Ltd. 2009. Published by Blackwell
Publishing Ltd., 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA.
International Review of Finance, 9:1–2, 2009: pp. 27–50
DOI: 10.1111/j.1468-2443.2009.01084.x
‘Deal or No Deal’ is a half-hour TV game show in which contestants make a
series of choices between a sure thing and a lottery.1 It is ideal for studying a
range of issues relating to economic decision making. The show consists of a
chosen contestant faced with 26 suitcases, randomly containing amounts
ranging from 50 cents to US$200,000 dollars. There are up to nine ‘normal’
rounds in the main stage of the game. Unlike other versions of the show, the
Australian version of ‘Deal or No Deal’ also involves the potential for one of two
extra rounds – the Chance round and the S.
Although there exists a vast literature on aid efficiency (the effect of aid on GDP), and that aid allocation determinants have been estimated, little is known about the minute details of aid allocation. This article investigates empirically a claim repeatedly made in the past that aid donors herd. Building upon a methodology applied to financial markets, this article finds that aid donors herd similarly to portfolio funds on financial markets. It also estimates the causes of herding and finds that political transitions towards more autocratic regimes repel donors, but that transitions towards democracy have no effect. Finally, identified causes of herding explain little of its overall level, suggesting strategic motives play an important role.
Behavioral Public Choice The Behavioral Paradox of Gove.docxtaitcandie
Behavioral Public Choice:
The Behavioral Paradox of
Government Policy
W. Kip Viscusf & Ted Gayer”
I. Overview
W hat are the economic justifications for governm ent inter
vention in the economy? In a m arket economy, prices coordi
nate the activities of buyers and sellers and convey inform ation
about the strength of consum er dem and for a good and the
costs of supplying it. Because trade is voluntary, buyers and
sellers only m ake exchanges w hen both parties benefit. Under
ideal m arket conditions, this process leads to an efficient alloca
tion of goods w ithout governm ent intervention.
However, economics has long recognized instances in which
markets can fail to lead to an efficient outcome. The long-standing
view is that either market power or the nonexistence of markets
causes market failures. Market power is present w hen some indi
viduals or firms are price makers (for example, monopolists) ra
ther than participants in a perfectly competitive environment.
Such situations typically lead to the production of a less than effi
cient quantity of goods. The problem of market power is the pur
view of industrial organization economics and antitrust policy. * 1
The nonexistence of markets, or the failure of a robust market to
arise, can occur for a num ber of reasons, such as asymmetric in
formation (when one party in a transaction has information that is
not available to another) and public goods (when a good is non
rival and nonexcludable in consumption and thus likely to be un-
* University Distinguished Professor of Law, Economics, and Management,
Vanderbilt Law School, 131 21st Ave. South, Nashville, TN 37203. [email protected]
vanderbilt.edu. We are grateful to the Mercatus Center for their support.
**Vice President and Director, Economic Studies, Brookings Institution, 1775
Massachusetts Ave. NW, Washington, DC 20036. [email protected]
1. H arvey S. Rosen & Ted Gayer, Public Fin a n c e 46-48 (10th ed. 2013).
mailto:[email protected]
974 Harvard Journal of Law & Public Policy [Vol. 38
dersupplied by the market). Another cause for the nonexistence of
markets is externalities, which occur when transactions impose
costs or benefits on a third party that are not considered in the
market exchange. A classic example is when a factory produces
and sells a good to a consumer to their m utual advantage, but the
pollution generated by the production of the good has a negative
impact on the health of nearby residents. A market for the clean
air in the affected area would not emerge if high transaction costs
of organizing the pollution victims prevented the parties from
negotiating.2 The market system will fail to internalize the health
costs imposed by the factory's operations and lead to inefficiently
high production and health consequences.
For about a century, economists have argued that policymakers
should rely, when possible, on market-based principles in design
ing regulations to address thes.
Joanna R. Pepin University of Texas–AustinBeliefs About Mo.docxvrickens
Joanna R. Pepin University of Texas–Austin
Beliefs About Money in Families: Balancing Unity,
Autonomy, and Gender Equality
Objective: This study provides the first nation-
ally representative data on U.S. adults’ percep-
tions of income sharing within families.
Background: Modern couples confront tensions
between ideals of mutual interests and values
of economic autonomy, a departure from fit-
ting themselves into culturally expected family
arrangements of the past. This study teases apart
the conditions under which people might priori-
tize one cultural value over another.
Method: The author conducted a nationally rep-
resentative survey experiment (N = 3,986 indi-
viduals). The respondents selected an income
allocation arrangement for a fictional couple
with varied relationships investments (i.e., mar-
riage, parenthood, length of relationship) and
earning disparities.
Results: Although stronger relationship invest-
ments were associated with greater support
for sharing all income, the most commonly
selected income allocation arrangement was
a hybrid arrangement of sharing some income
and keeping the rest separate. When respondents
preferred some amount of financial autonomy,
the primary earner was expected to maintain
ownership of a greater amount of the total
household income. The preferred level of with-
holding income was slightly larger in magnitude
when women were shown as the primary earner
when compared with men shown as the primary
earner.
Population Reasearch Center, 305 E. 23rd Street, Austin,
Texas 78712-1699 ([email protected]).
Key Words: couples, culture, family economics, family
resource management, gender roles, money management.
Conclusion: The pursuit of economic auton-
omy, in combination with beliefs about gender,
are important dimensions of gender inequality
located within families.
Introduction
Two contradictions related to the ways couples
share money are commonly studied. First, cou-
ples must reconcile the conflict between their
commitment to a collective family unit and their
desire for individual autonomy (Treas, 1993;
Vogler, Brockmann, & Wiggins, 2008). Sec-
ond, many couples struggle to create equality
in the home given inequalities prevalent in the
labor market (Blumstein & Schwartz, 1983; Bur-
goyne, 1990; Pahl, 1989; Treas, 1993; Vogler
et al., 2008). To understand how these contradic-
tions are reconciled within American families,
researchers initially focused on grouping sys-
tems of money arrangements into analytical cat-
egories (Ashby & Burgoyne, 2008; Pahl, 1995).
Thereafter, many have searched for possible
explanations for the prevalence of various cat-
egories and sought to identify the consequences
of these arrangements (for an overview of prior
research, see Bennett, 2013).
Drawing conclusions about how people rec-
oncile these competing cultural values based
on couples’ behavioral practices may be par-
tial or even misleading (Ashby & Burgoyne,
2008, 2009). Behaviors tend to reflect a mi ...
This document examines investment behavior when firms face costs in accessing external funds. It finds that standard investment regressions predict cash flow is important for investment only without considering q. Conversely, it also obtains significant cash flow effects even without financial frictions. These findings support the argument that cash flow effects in regressions are due to measurement error in q and identification problems. The document then provides context on empirical investment patterns and the relationship between investment and financing. It describes a model of heterogeneous firms facing costly external finance to understand these stylized facts. Using this model, it finds cash flow effects in regressions are not solely due to financial constraints and are sensitive to measurement error.
Existing evidence of inequality aversion relies on data from class-room experiments where subjects face hypothetical questions. This paper estimates the magnitude of inequality aversion using representative survey data, with questions related to the real-economy situations the respondents face. The results reveal that the magnitude of inequality aversion can be measured in a meaningful way using survey data, but the estimates depend dramatically on the framing of the question. No matter how measured, the revealed inequality aversion predicts opinions on a wide range of questions related to the welfare state, such as the level of taxation, tax progressivity and the structure of unemployment benefits.
This document analyzes the effects of education, financial literacy, and cognitive ability on financial market participation using a dataset of over 14 million observations from the 1980, 1990, and 2000 US Censuses. It finds that one additional year of schooling increases the probability of participation by 7-8%, even after controlling for income. While cognitive ability increases participation, financial literacy education received in high school does not affect participation decisions. The document explores other ways education may influence financial behavior through personality, borrowing, discount rates, risk aversion, and social connections.
This document summarizes a journal article that examines how stale prices impact the performance evaluation of mutual funds. The article introduces a model to estimate "true alpha" based on the true returns of underlying fund assets, independent of biases from stale pricing. Empirical tests show true alpha is about 40 basis points higher than observed alpha and remains positive on average. The difference between the two alphas consists of three components - a small statistical bias, dilution from long-term fund flows, and a large and significant dilution effect primarily from short-term arbitrage flows exploiting stale prices.
You are on the right track. Here are a few sugg.docxtarifarmarie
You are on the right track. Here are a few suggestions:
1. I would work on making each slide more visually appealing. Here is
a before/after example:
Before:
After (kept title only in the orange section, numbered the four step,
centered the text):
2. You can tell me more in each of your notes section, you are a little
brief.
3. Please make sure you tell me where you are getting your information
on each slide.
Behavioral Public Choice:
The Behavioral Paradox of
Government Policy
W. Kip Viscusf & Ted Gayer”
I. Overview
W hat are the economic justifications for governm ent inter
vention in the economy? In a m arket economy, prices coordi
nate the activities of buyers and sellers and convey inform ation
about the strength of consum er dem and for a good and the
costs of supplying it. Because trade is voluntary, buyers and
sellers only m ake exchanges w hen both parties benefit. Under
ideal m arket conditions, this process leads to an efficient alloca
tion of goods w ithout governm ent intervention.
However, economics has long recognized instances in which
markets can fail to lead to an efficient outcome. The long-standing
view is that either market power or the nonexistence of markets
causes market failures. Market power is present w hen some indi
viduals or firms are price makers (for example, monopolists) ra
ther than participants in a perfectly competitive environment.
Such situations typically lead to the production of a less than effi
cient quantity of goods. The problem of market power is the pur
view of industrial organization economics and antitrust policy. * 1
The nonexistence of markets, or the failure of a robust market to
arise, can occur for a num ber of reasons, such as asymmetric in
formation (when one party in a transaction has information that is
not available to another) and public goods (when a good is non
rival and nonexcludable in consumption and thus likely to be un-
* University Distinguished Professor of Law, Economics, and Management,
Vanderbilt Law School, 131 21st Ave. South, Nashville, TN 37203. [email protected]
vanderbilt.edu. We are grateful to the Mercatus Center for their support.
**Vice President and Director, Economic Studies, Brookings Institution, 1775
Massachusetts Ave. NW, Washington, DC 20036. [email protected]
1. H arvey S. Rosen & Ted Gayer, Public Fin a n c e 46-48 (10th ed. 2013).
mailto:[email protected]
974 Harvard Journal of Law & Public Policy [Vol. 38
dersupplied by the market). Another cause for the nonexistence of
markets is externalities, which occur when transactions impose
costs or benefits on a third party that are not considered in the
market exchange. A classic example is when a factory produces
and sells a good to a consumer to their m utual advantage, but the
pollution generated by the production of the good has a negative
impact on the health of nearby residents. A market for the clean
air in the affected.
Essay About Overpopulation. Descriptive essay: Essay overpopulationRocio Garcia
Essay on Overpopulation | Overpopulation Essay for Students and .... Overpopulation: Not What You Think - Free Essay Example | PapersOwl.com. Overpopulation in the World - Free Essay Example | PapersOwl.com. Persuasive essay: Causes of overpopulation essays. Effects of overpopulation in developing countries essay in 2021 | Essay .... (DOC) Overpopulation - Problems and Solutions | Anh Le - Academia.edu. Overpopulation Essay In English || Effects and Causes of Overpopulation .... 008 Over Population Cause And Effect Of Overpopulation Essay ~ Thatsnotus. Overpopulation Effects on Health and the Environment - Free Essay .... History Essay: Overpopulation research paper. ⇉Overpopulation conclusion Essay Example | GraduateWay. Overpopulation essay in english.
This document summarizes a research paper that develops a dynamic stochastic general equilibrium (DSGE) model to explain how monetary policy affects risk in financial markets and the macroeconomy. The key feature of the model is that asset and goods markets are segmented because it is costly for households to transfer funds between the markets. The model generates endogenous movements in risk as the fraction of households that rebalance their portfolios varies over time in response to real and monetary shocks. Simulation results indicate the model can account for evidence that monetary policy easing reduces equity premiums and helps explain the response of stock prices to monetary shocks.
This document provides background and motivation for a study examining determinants of stock market participation and risky asset allocation among Canadian households. Specifically, it discusses how previous literature has studied factors like age, gender, education level, and others that may influence an individual's willingness to hold stocks. The author develops a simple economic model framing the decision between investing in risky stocks versus less risky assets. The goal is to better understand individual investor behavior and stock market participation based on analysis of a Canadian household survey.
Information can be found in the attachment.pdfbkbk37
- The document provides guidance on writing academic papers, including how to summarize arguments, structure paragraphs, avoid vague statements, cite sources, and get feedback on drafts.
- It recommends summarizing arguments in the present tense, avoiding starting paragraphs with quotes, keeping paragraphs focused on the topic sentence, and specifying what is being agreed or disagreed with.
- The document also provides formatting guidance for long quotes and advises re-reading papers multiple times before submission.
This document summarizes a study on the relationship between firm investment and financial status. The study uses a sample of 1,317 firms from 1987 to 1994 to examine how investment decisions differ across financially constrained and unconstrained firms. It finds that investment is most sensitive to internal funds for firms that are least financially constrained, consistent with the findings of Kaplan and Zingales (1997). Statistical tests show this difference is statistically significant. Additionally, firms that reduced dividends exhibited traditional signs of greater financial constraints such as lower current ratios and profitability compared to firms that increased dividends. The study uses multiple discriminant analysis and regression analysis to classify firms and compare investment-cash flow sensitivities between financially constrained and unconstrained groups.
This paper presents a model to value cash holdings for all-equity financed firms with growth opportunities. The model considers the tradeoff between agency costs of free cash flow and costs of external financing. It derives the optimal dynamic cash retention policy and shows that firms optimally retain only a fraction of cash flows. The model implies that high cash flow volatility decreases the value of cash and that optimal cash retention can delay investment timing. Empirical tests on US firm data from 1980-2010 confirm these implications, finding a negative relationship between cash value and volatility in the context of growth options.
This document provides information about a study conducted on the efficiency of 38 Hamilton County Judges in Ohio. The study looked at the number of cases disposed, appealed, and reversed for each judge over a three year period. In total, there were 182,908 cases disposed of, with 2,368 being appealed and 320 decisions being reversed. The report analyzes and ranks the judges based on these metrics to determine their individual and overall efficiency. It is presented to Dr. Norman Lewis for a statistics course.
The Psychology and Neuroscience of Financial Decision MakingTrading Game Pty Ltd
Financial decisions are among the most important life-shaping decisions that people make. We review facts about financial decisions and what cognitive and neural processes influence them. Because of cognitive constraints and a low average level of financial literacy, many household decisions violate sound
financial principles. Households typically have underdiversified stock holdings and low retirement savings rates. Investors overextrapolate from past returns and trade too often. Even top corporate managers, who are typically highly educated, make decisions that are affected by overconfidence and personal history. Many of these behaviors can be explained by well-known principles from cognitive science.
A boom in high-quality accumulated evidence–especially how practical, low-cost ‘nudges’ can improve financial decisions–is
already giving clear guidance for balanced government regulation
Decision makers often face powerful incentives to increase risk-taking on behalf of others either through bonus contracts or competitive relative performance contracts. Motivated by examples from the recent financial crisis, we conduct an experimental study of risk-taking on behalf of others using a large sample with subjects from all walks of life. We find that people respond to such incentives without much apparent concern for stakeholders. Responses are heterogeneous and mitigated by personality traits. The findings suggest that lack of concern for others’ risk exposure hardly requires “financial psychopaths” in order to flourish, but is diminished by social concerns. We believe the research reported here is the first to experimentally investigate the effects of incentives on risk-taking on behalf of others, and to do so on a large scale using a random sample of the general population.
By Ola Andersson, Håkan J. Holm, Jean-Robert Tyran and Erik Wengström
To read more research articles, please visit https://www.hhs.se/site
The real cost of homelessness: Can we save money by doing the right thing?TheHomelessHub
The document summarizes research on the real costs of homelessness. It finds that relying on emergency services like shelters is an expensive way to address homelessness that fails to improve health and outcomes. Research shows that the annual costs of supporting someone who is homeless through emergency services ranges from $30,000 to over $100,000, more than the costs of providing housing and supports. Housing interventions can save tens of thousands per person per year and have been shown to be more cost effective than emergency responses for reducing health costs and utilization of prisons, hospitals, and emergency rooms over time.
This document summarizes an experiment by Grether and Plott on preference reversals. In the experiment, subjects were asked to choose between two gambles and then state prices for the gambles. For many subjects, their stated preferences from choices and prices were reversed, violating assumptions of economic theory. Grether and Plott found preference reversals occurred even when addressing economists' concerns about incentives and language. This suggests a fundamental flaw in economic theories assuming perfectly rational choice. Later research found preference reversals reduced but not eliminated by higher stakes, awareness of consequences, or market pressures. Failures of transitivity and procedural invariance appear to explain many preference reversals.
American Economic AssociationAmerican Economic Association.docxgalerussel59292
American Economic Association
American Economic Association
http://www.jstor.org/stable/4132805 .
Your use of the JSTOR archive indicates your acceptance of the Terms & Conditions of Use, available at .
http://www.jstor.org/page/info/about/policies/terms.jsp
.
JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of
content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms
of scholarship. For more information about JSTOR, please contact [email protected]
.
American Economic Association is collaborating with JSTOR to digitize, preserve and extend access to The
American Economic Review.
http://www.jstor.org
ARE CONCERNS ABOUT RELATIVE INCOME RELEVANT
FOR PUBLIC POLICY?t
Positional Externalities Cause Large and
Preventable Welfare Losses
By ROBERT H. FRANK*
In traditional economic models, individual
utility depends only on absolute consumption.
These models lie at the heart of claims that
pursuit of individual self-interest promotes ag-
gregate welfare. Recent years have seen re-
newed interest in economic models in which
individual utility depends not only on absolute
consumption, but also on relative consumption.
In contrast to traditional models, these models
identify a fundamental conflict between individ-
ual and social welfare.
The conflict stems from the fact that concerns
about relative consumption are stronger in some
domains than in others. The disparity gives rise
to expenditure arms races focused on positional
goods-those for which relative position mat-
ters most. The result is to divert resources from
nonpositional goods, causing welfare losses.
Compelling theoretical and empirical evi-
dence confirms the importance of relative con-
sumption in individual valuations. In light of
this evidence, we must question the wisdom of
economic policy recommendations stemming
from models that ignore relative consumption.
I. Positional and Nonpositional Goods
To help fix ideas, consider two simple
thought experiments. In each, you must choose
between two worlds that are identical in every
respect except one. The first choice is between
world A, in which you will live in a 4,000-
square-foot house and others will live in 6,000-
square-foot houses; and world B, in which you
will live in a 3,000-square-foot house, others in
2,000-square-foot houses. Once you choose,
your position on the local housing scale will
persist.
If only absolute consumption mattered, A
would be clearly better. Yet most people say
they would pick B, where their absolute house
size is smaller but their relative house size is
larger. Even those who say they would pick A
seem to recognize why someone might be more
satisfied with a 3,000-square-foot house in B
than with a substantially larger house in A.
In the second thought experiment, your
choice is between world C, i.
Version March 13, 2015 – Please contact authors for an updated version before citing
Randomized Controlled Trials (RCT) are considered the gold standard in empirical social sciences and have been increasingly used in recent years. While their internal validity is in most cases beyond discussion, RCTs still need to establish external validity. External validity is the crucial determinant for a study’s policy relevance and might be at stake because of potential general equilibrium effects, Hawthorne effects,
or representativeness problems that compromise generalizing results beyond the studied population. For this paper, we reviewed all RCTs published in leading economic journals between 2009 and 2012 and scrutinized them for the way in which they treat external validity. Based on a set of objective indicators, we find that the RCT literature does not adequately account for potential hazards to external validity. A large part of published RCTs does not discuss potential limitations to external validity or provide the information that is necessary to assess potential problems. We conclude by calling for a more systematic approach to designing RCTs and to reporting the results.
Bridging the Gap between Psychology and Economics: The Role of Behavioral Fin...inventionjournals
This article is a descriptive presentation of how behavioral finance plays key role in providing insight into how individuals’ investment behavior typically deviates from traditional economic theories. The efficient market hypothesis (EMH) and capital asset pricing model (CAPM) theories have gained prominence in modern finance platform. The adequacy of these popular, rational-based behavior theories has however, remained skeptical among many scholars including Daniel Kahneman, Amos Tversky, and Richard H. Thaler. While the EMH and CAPM theories have contributed significantly to the investment world, some scholars contend the theories fail to fully explain certain inconsistent behaviors exhibited in the investment world. Behavioral finance is a new theory that attempts to fill the void between psychology and economics by providing a better understanding of investor behavior through the theories of psychology. Investment decisions are impacted by an array of irrational behavioral biases. The article identifies some finance and economic theory anomalies such as the January effect, equity premium puzzle, and others, which shift away from the traditional economic theories. Understanding these anomalies not only would assist individuals have a sense of how investors generally behave in the investment arena but also would help in efficient capital allocation.
Superior performance by combining Rsik Parity with Momentum?Wilhelm Fritsche
This document examines different strategies for global asset allocation between equities, bonds, commodities and real estate. It finds that applying trend following rules substantially improves risk-adjusted performance compared to traditional buy-and-hold portfolios. It also finds trend following to be superior to risk parity approaches. Combining momentum strategies with trend following further improves returns while reducing volatility and drawdowns. A flexible approach that allocates capital based on volatility-weighted momentum rankings of 95 markets produces attractive, consistent risk-adjusted returns.
This document discusses a study on the effects of introducing punishment in a public goods game where individuals can endogenously choose their effort level. The key points are:
1) When effort is endogenous, punishment can depend on both contribution deviations and effort deviations from others.
2) With heterogeneous endowments, there is no clear norm to guide contributions, and a proportional contribution norm may emerge but discourage high effort.
3) The authors hypothesize that while punishment typically increases welfare with exogenous effort, it may be less robust or even reduce welfare when effort is endogenous due to downward pressure on contributions and incentives to reduce effort over contributions.
How To Shift Consumer Behaviors to be more sustainable; a literature review a...Nicha Tatsaneeyapan
This article presents a literature review and framework for understanding how to shift consumer behaviors to be more sustainable. The framework is called SHIFT and proposes that consumers are more likely to engage in pro-environmental behaviors when messages or contexts leverage social influence, habit formation, individual identity, feelings and cognition, and tangibility. The review identified these five factors as the most common ways discussed in the literature to encourage sustainable consumption. The article then provides an in-depth discussion of each of these five factors and how they can shape consumer decision making and behaviors related to sustainability.
This document summarizes key aspects of data privacy protection based on a journal article. It discusses how data privacy can be achieved through technical and social solutions, as well as complying with relevant laws and regulations. International Data Privacy Principles are proposed that draw from standards in Asia, Europe, the US and internationally. Hong Kong's Personal Data Privacy Ordinance is used as an example, outlining its six data protection principles. Protecting data privacy is seen as urgent and complex due to issues like socio-techno risks from technology use and the need to balance various parameters in contractual agreements regarding data use.
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This document analyzes the effects of education, financial literacy, and cognitive ability on financial market participation using a dataset of over 14 million observations from the 1980, 1990, and 2000 US Censuses. It finds that one additional year of schooling increases the probability of participation by 7-8%, even after controlling for income. While cognitive ability increases participation, financial literacy education received in high school does not affect participation decisions. The document explores other ways education may influence financial behavior through personality, borrowing, discount rates, risk aversion, and social connections.
This document summarizes a journal article that examines how stale prices impact the performance evaluation of mutual funds. The article introduces a model to estimate "true alpha" based on the true returns of underlying fund assets, independent of biases from stale pricing. Empirical tests show true alpha is about 40 basis points higher than observed alpha and remains positive on average. The difference between the two alphas consists of three components - a small statistical bias, dilution from long-term fund flows, and a large and significant dilution effect primarily from short-term arbitrage flows exploiting stale prices.
You are on the right track. Here are a few sugg.docxtarifarmarie
You are on the right track. Here are a few suggestions:
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Behavioral Public Choice:
The Behavioral Paradox of
Government Policy
W. Kip Viscusf & Ted Gayer”
I. Overview
W hat are the economic justifications for governm ent inter
vention in the economy? In a m arket economy, prices coordi
nate the activities of buyers and sellers and convey inform ation
about the strength of consum er dem and for a good and the
costs of supplying it. Because trade is voluntary, buyers and
sellers only m ake exchanges w hen both parties benefit. Under
ideal m arket conditions, this process leads to an efficient alloca
tion of goods w ithout governm ent intervention.
However, economics has long recognized instances in which
markets can fail to lead to an efficient outcome. The long-standing
view is that either market power or the nonexistence of markets
causes market failures. Market power is present w hen some indi
viduals or firms are price makers (for example, monopolists) ra
ther than participants in a perfectly competitive environment.
Such situations typically lead to the production of a less than effi
cient quantity of goods. The problem of market power is the pur
view of industrial organization economics and antitrust policy. * 1
The nonexistence of markets, or the failure of a robust market to
arise, can occur for a num ber of reasons, such as asymmetric in
formation (when one party in a transaction has information that is
not available to another) and public goods (when a good is non
rival and nonexcludable in consumption and thus likely to be un-
* University Distinguished Professor of Law, Economics, and Management,
Vanderbilt Law School, 131 21st Ave. South, Nashville, TN 37203. [email protected]
vanderbilt.edu. We are grateful to the Mercatus Center for their support.
**Vice President and Director, Economic Studies, Brookings Institution, 1775
Massachusetts Ave. NW, Washington, DC 20036. [email protected]
1. H arvey S. Rosen & Ted Gayer, Public Fin a n c e 46-48 (10th ed. 2013).
mailto:[email protected]
974 Harvard Journal of Law & Public Policy [Vol. 38
dersupplied by the market). Another cause for the nonexistence of
markets is externalities, which occur when transactions impose
costs or benefits on a third party that are not considered in the
market exchange. A classic example is when a factory produces
and sells a good to a consumer to their m utual advantage, but the
pollution generated by the production of the good has a negative
impact on the health of nearby residents. A market for the clean
air in the affected.
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To read more research articles, please visit https://www.hhs.se/site
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American Economic AssociationAmerican Economic Association.docxgalerussel59292
American Economic Association
American Economic Association
http://www.jstor.org/stable/4132805 .
Your use of the JSTOR archive indicates your acceptance of the Terms & Conditions of Use, available at .
http://www.jstor.org/page/info/about/policies/terms.jsp
.
JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of
content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms
of scholarship. For more information about JSTOR, please contact [email protected]
.
American Economic Association is collaborating with JSTOR to digitize, preserve and extend access to The
American Economic Review.
http://www.jstor.org
ARE CONCERNS ABOUT RELATIVE INCOME RELEVANT
FOR PUBLIC POLICY?t
Positional Externalities Cause Large and
Preventable Welfare Losses
By ROBERT H. FRANK*
In traditional economic models, individual
utility depends only on absolute consumption.
These models lie at the heart of claims that
pursuit of individual self-interest promotes ag-
gregate welfare. Recent years have seen re-
newed interest in economic models in which
individual utility depends not only on absolute
consumption, but also on relative consumption.
In contrast to traditional models, these models
identify a fundamental conflict between individ-
ual and social welfare.
The conflict stems from the fact that concerns
about relative consumption are stronger in some
domains than in others. The disparity gives rise
to expenditure arms races focused on positional
goods-those for which relative position mat-
ters most. The result is to divert resources from
nonpositional goods, causing welfare losses.
Compelling theoretical and empirical evi-
dence confirms the importance of relative con-
sumption in individual valuations. In light of
this evidence, we must question the wisdom of
economic policy recommendations stemming
from models that ignore relative consumption.
I. Positional and Nonpositional Goods
To help fix ideas, consider two simple
thought experiments. In each, you must choose
between two worlds that are identical in every
respect except one. The first choice is between
world A, in which you will live in a 4,000-
square-foot house and others will live in 6,000-
square-foot houses; and world B, in which you
will live in a 3,000-square-foot house, others in
2,000-square-foot houses. Once you choose,
your position on the local housing scale will
persist.
If only absolute consumption mattered, A
would be clearly better. Yet most people say
they would pick B, where their absolute house
size is smaller but their relative house size is
larger. Even those who say they would pick A
seem to recognize why someone might be more
satisfied with a 3,000-square-foot house in B
than with a substantially larger house in A.
In the second thought experiment, your
choice is between world C, i.
Version March 13, 2015 – Please contact authors for an updated version before citing
Randomized Controlled Trials (RCT) are considered the gold standard in empirical social sciences and have been increasingly used in recent years. While their internal validity is in most cases beyond discussion, RCTs still need to establish external validity. External validity is the crucial determinant for a study’s policy relevance and might be at stake because of potential general equilibrium effects, Hawthorne effects,
or representativeness problems that compromise generalizing results beyond the studied population. For this paper, we reviewed all RCTs published in leading economic journals between 2009 and 2012 and scrutinized them for the way in which they treat external validity. Based on a set of objective indicators, we find that the RCT literature does not adequately account for potential hazards to external validity. A large part of published RCTs does not discuss potential limitations to external validity or provide the information that is necessary to assess potential problems. We conclude by calling for a more systematic approach to designing RCTs and to reporting the results.
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This document discusses a study on the effects of introducing punishment in a public goods game where individuals can endogenously choose their effort level. The key points are:
1) When effort is endogenous, punishment can depend on both contribution deviations and effort deviations from others.
2) With heterogeneous endowments, there is no clear norm to guide contributions, and a proportional contribution norm may emerge but discourage high effort.
3) The authors hypothesize that while punishment typically increases welfare with exogenous effort, it may be less robust or even reduce welfare when effort is endogenous due to downward pressure on contributions and incentives to reduce effort over contributions.
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2. Following Tu (2004) we measure loss-aversion from 16 survey questions involving the speed-up and delay of payoffs, both gains
and losses. With standard utility functions, people will have a single discount rate across gains and losses, and across speed-up and
delay. This prediction is strongly violated in the responses to these questions. Most people require much higher compensation to
delay receiving a sure gain than the amount they are willing to pay to expedite receipt of the same gain. Loewenstein (1988),
Loewenstein and Prelec (1992), and Thaler (1981) show that a loss-averse individual who does not integrate payments with
existing consumption plans, but rather frames the payments as gains and losses relative to a reference point, can have multiple
discount rates. The predictions of these models are supported by experiments conducted by Benzion et al. (1989), Loewenstein
(1988), Shelley (1993), and Thaler (1981) among others. Frederick et al. (2002, p. 370) explain the intuition behind this result as
follows: “Shifting consumption in any direction is made less desirable by loss-aversion, since one loses consumption in one period
and gains it in another. When delaying consumption, loss-aversion reinforces time discounting, creating a powerful aversion to
delay. When expediting consumption, loss-aversion opposes time discounting, reducing the desirability of speed-up…”.
Clearly a key issue for this paper is the reliability of our measures of loss-aversion. We test the internal consistency of our
measures in two ways. First, as we have multiple questions designed to measure the same set of latent variables, we use
Cronbach's alpha — the standard psychometric test for reliability in these cases — and find strong evidence to support the notion
that similar questions are consistently measuring the same underlying concepts. Second, there are simple, logical relations which
must hold between many of the responses. We find that only a small percent of household responses violate these relations.
Further we show that the responses to the DNB Household Survey are both qualitatively and quantitatively similar to a large
number of experimental studies. Finally, we discuss other potential explanations for the pattern of responses and show that the
data reject alternative explanations.
We hypothesize that our empirical measure of loss-aversion is a proxy for the level of loss-aversion that households experience
when investing in actual markets. Using our loss-aversion measure we show that households with higher loss-aversion are
significantly less likely to participate in equity markets. For an average household if their estimated loss-aversion coefficient
increases from the 25th to the 75th percentile, the probability of owning stocks decreases by 7%, relative to the sample mean.
These results are robust to controlling for a direct survey measure of risk-aversion and a wide variety of other variables used in
previous studies, such as age, education, income, financial wealth, and unsecured debt. Although loss-aversion predicts household
equity market participation, after controlling for sample selection we do not find a significant relationship between loss-aversion
and portfolio allocations.
If investors are loss-averse and exhibit narrow framing, meaning they evaluate gains and losses on securities in isolation rather
than after integrating their entire portfolio, then the bundling of returns will affect the relative attractiveness of mutual funds and
individual stocks. Consistent with this idea, we find that loss-aversion affects the type of equity that households hold. Households
with higher loss-aversion avoid investing in individual stocks to a greater extent than they avoid mutual funds.
This paper provides direct empirical evidence on the importance of loss-aversion for household decision making. To our
knowledge it is the first paper to empirically measure the heterogeneity in loss-aversion across a representative sample of
households and use this information to explain household portfolio choice.
This paper is related to a branch of the literature on household portfolio choice that shows how psychological factors measured
through survey questions can predict household equity market participation. Barsky et al. (1997) show that hypothetical
questions designed to measure risk-aversion are significantly related to household stock market participation. Hong et al. (2004)
show that more social households are more likely to participate in the equity market. Guiso et al. (2008) show that households
that are more trusting of others have higher participation rates and allocations to equity. Puri and Robinson (2007) show that
optimism and equity market participation are related.
The remainder of this paper is organized as follows. Section 2 outlines the theories and hypotheses tested in this paper.
Section 3 describes the data source and the variables. Section 4 presents and discusses our measure of loss-aversion. Section 5
presents the results. Section 6 concludes.
2. Theory and hypotheses
Heaton and Lucas (1997) calibrate a model of a representative household's portfolio choice using standard utility functions and
parameter values drawn from the US economy. They find that all households should participate in equity markets and that, in the
absence of market frictions, they should allocate all of their financial wealth to equity. However, numerous empirical papers have
shown that many households do not participate in the equity market, and many participants own only small amounts of equity.2
There are two broad streams of research attempting to explain these two puzzles: models based on market frictions such as
participation costs, credit constraints,3
and background risk such as risky labor income4
; and models based on non-standard
preferences. Note that these two classes of explanations are not mutually exclusive. While our focus is on non-standard
preferences, we acknowledge that market frictions play a role in determining household portfolios. However, as discussed by
2
See for example Bertaut (1998) and Vissing-Jorgenson (2002).
3
Davis et al. (2006) show that if there are frictions in the credit markets it is possible to get non-participation and realistic portfolio allocations. Vissing-
Jorgenson (2002) presents evidence that some of the participation puzzle can be explained with fixed entry costs, which could include costs such as learning,
fees, taxes etc.
4
Cocco et al. (2005), Heaton and Lucas (1997, 2000) and Viceira (2001) show that if a household's labor income has a high correlation with equity or a high
standard deviation it is optimal to hold a safer portfolio. Guiso et al. (1996) and Vissing-Jorgenson (2002) empirically demonstrate that households with high
background risk are less likely to participate and hold less equity.
442 S.G. Dimmock, R. Kouwenberg / Journal of Empirical Finance 17 (2010) 441–459
3. Barberis et al. (2006) market frictions cannot explain non-participation among the wealthy. Haliassos and Bertaut (1995)
document substantial non-participation at all income levels, including the top 1%. Thus, while we do not dispute that market
frictions are relevant and affect participation, they offer an incomplete explanation.
This paper empirically tests the effect of one particular type of non-standard preferences, loss-aversion, on the portfolio choices
of households. Prospect theory, introduced in Kahneman and Tversky (1979), differs from the standard expected utility model in
four ways: 1) Individuals frame events as gains and losses relative to a reference point, representing the status quo or an aspiration
level. 2) Individuals are loss-averse meaning that losses are weighted about twice as heavily as gains.5
3) Individuals are risk
averse in the region of gains and risk seeking in the region of losses. 4) Individuals use subjective probability weights that
overweight small objective probabilities. This paper is concerned with the first two features of prospect theory — loss-aversion
relative to a reference point. Many authors use the term loss-aversion to mean a combination of features one, two and three,
including the convex–concave shape of the utility function. In this paper loss-aversion refers only to the sudden increase in the
slope of the value function for payoffs that fall below the reference point.
Ang et al. (2004), Barberis et al. (2006), Gomes (2005), and Polkovnichenko (2005) show that stock market non-participation
can be explained by loss-aversion. If households are loss-averse over stock market fluctuations the potential pain from stock
market declines outweighs the pleasure from gains even with a high equity premium.6
These theoretical papers lead to a clear,
one-sided hypothesis for our study; greater levels of loss-aversion will result in a lower probability of participating in the equity
markets. Ang et al. (2004), Benartzi and Thaler (1995) and Berkelaar et al. (2004) further show that loss-aversion will lead to
lower portfolio allocations to equity. The increased sensitivity to losses decreases the benefit of owning equity and as a result loss-
averse households allocate less of their wealth to equity.
We argue that loss-aversion will also affect the type of equity that households choose to hold. Barberis and Huang (2001)
derive a model in which investors are loss-averse and frame narrowly, deriving utility directly from the gains and losses of
individual equity securities. With these preferences households with higher loss-aversion will prefer mutual funds over individual
stocks, as mutual funds integrate the returns of many securities into a single package. This encourages investors to frame returns at
a broader level than does ownership of individual stocks, making mutual fund ownership relatively more attractive for loss-averse
investors.
Consider the following simple example. An investor with a loss-aversion coefficient of 2.5 has an equally weighted portfolio of
three equity securities. Suppose that in a given year two of the stocks increase in value by 10% and one stock decreases in value by
10%. The value of the portfolio has increased by 3.3% and an investor who frames at the level of the portfolio will have a gain from
investing in stocks. However, if the investor frames at the level of individual stocks the pain from the single loss outweighs the
pleasure of the two gains. Since mutual funds package equities in a way that encourages framing at the portfolio level, loss-averse
investors will prefer to own mutual funds rather than individual stocks.
Even if investors do manage to integrate all portfolio gains and losses, i.e. if investors frame at the portfolio level, we would still
expect loss-averse individuals to prefer mutual funds over portfolios of individuals stocks. The reason is that in practice individual
investors typically hold under-diversified portfolios, see for example Ivkovic et al. (2008). The excessive unsystematic risk in a
typical under-diversified individual stock portfolio will lead to occasional large losses, which are very painful for loss-averse
investors.
3. Data
The data source for this paper is the CentERdata DNB Household Survey, a household survey conducted by CentERdata at
Tilburg University in The Netherlands.7
We use this dataset because it contains information about household wealth, income, and
financial assets, as well as a set of questions we can use to extract a measure of loss-aversion. This paper uses data from the 1997–
2002 waves of the DNB Household Survey as the questions used to measure loss-aversion are unavailable in other years. The DNB
Household Survey is conducted entirely online. To avoid the obvious sample selection effect of limiting the survey to households
with internet access, CentERdata provides all households with a set-top box, which allows internet access through a television and
phone lines.
Alessie et al. (2002) and Das and van Soest (1999) provide an excellent introduction to this data. Comparing the DNB
Household Survey results to national accounts data and microdata on household wealth published by Statistics Netherlands they
find that it is generally representative of Dutch households. Although no household survey can ever be entirely free of potential
biases caused by non-response, their findings suggest that this problem is limited in the DNB Household Survey. As Das and van
Soest (1999) and Tu (2004) discuss, there is considerable attrition in this dataset. However, there is no clear reason why attrition
would be correlated with loss-aversion and so it seems unlikely to bias our results. 8
To be included in this study in a given year, the household must have answered the General Information section of the
Household module, the Assets and Liabilities module, the Health and Income module, and the Economic and Psychological
5
For experimental evidence see: Kahneman and Tversky (1979), Loewenstein (1988), and Thaler et al. (1997). Tversky and Kahneman (1991) provide a good
review of the experimental evidence.
6
Barberis et al. (2006) show that loss-aversion alone is not sufficient to explain stock market non-participation. Investors must also narrowly frame stock
market gains and losses, meaning that they evaluate stock returns in isolation rather than after integrating stocks with their other sources of wealth.
7
For more information see http://www.centerdata.nl/en/TopMenu/Projecten/DNB_household_study/.
8
The number of observations per household is uncorrelated with our measure of loss-aversion. However, as will become clear in the next section, it is possible
that the precision of our estimate of loss-aversion is increasing in the number of observations.
443
S.G. Dimmock, R. Kouwenberg / Journal of Empirical Finance 17 (2010) 441–459
4. Concepts module. For all monetary variables, such as income and asset ownership, we aggregate within households and we adjust
for inflation to 1997 prices. For non-monetary variables we take the response of the self-identified household head.9
If the
household head's answer is not available we use the spouse's response. If neither the head, nor the spouse, responds, we drop the
household from the sample.
Summary statistics of the control variables used in this paper are presented in Table 1. This table shows results for all
respondents, equity owners, and non-owners. Following Alessie et al. (2004) summary statistics are computed using sampling
weights based on information from Statistics Netherlands. We refer to Donkers and van Soest (1999) and Alessie et al. (2004) for
an analysis of equity ownership in the earlier DNB Household Survey waves 1993–1998, using a similar set of control variables.
3.1. Household wealth and income
Numerous authors have shown that wealth is an important determinant of portfolio allocation, e.g. Bertaut (1998), Vissing-
Jorgenson (2002). As Panel A of Table 1 shows, equity owners in this sample have greater wealth than non-participants. Vissing-
Jorgenson argues that this is consistent with fixed costs of market entry, such as minimum investments or investment advising
fees, as it is easier for wealthy households to pay these costs.
As in Alessie et al. (2002), total financial assets is defined as the sum of: all savings and checking accounts, bonds, stocks, mutual
funds, money market funds, single-premium annuity insurance policies, cash value of life insurance, employer sponsored savings
plans, money lent to friends and family, and other savings or investments. To avoid having outliers drive our results we winsorize
total financial assets at the 99th percentile by replacing all observations in the top percentile with the value of the 99th percentile
of total financial assets.
We define income as total income before taxes less dividends and interest income. This variable is highly skewed and is
winsorized at the 1st and 99th percentiles. Several studies have shown that equity ownership increases with income (e.g. Bertaut,
1998; Haliassos and Bertaut, 1995; Vissing-Jorgenson, 2002). Alessie et al. (2004) and Donkers and van Soest (1999) find a
significant positive relation between equity ownership and wealth, as well as income, in the DNB Household Survey. As can be
seen in Panel A of Table 1 equity owners have considerably higher incomes than non-owners.
3.2. Demographic and other control variables
The DNB Household Survey contains the standard demographic variables used in studies of household portfolio choice: age,
employment status, and education. Summary statistics of these variables are presented in Table 1.
9
When data is missing we use imputed amounts based on the method of Alessie et al. (2002). CentERdata provides this imputed data on its website. Our
equity ownership variables are never imputed.
Table 1
Summary statistics.
Variable Full sample Equity owners Non-owners
Panel A — control variables
Total financial assets 67,007 134,268 35,534
Income 50,505 60,665 45,750
Age 49.9 52.5 48.7
Panel B — employment status
Regular employment 65.4% 60.5 67.6
Unemployed 1.4% 1.2 1.5
Retired 15.6% 19.9 13.6
Disabled 8.9% 10.5 8.1
Self-employed 1.9% 1.9 1.9
Other 6.9% 6.0 7.4
Panel C — education
Low education 5.4% 4.5 5.8
Intermediate/low education 11.5% 10.1 12.2
Intermediate/high education 10.3% 11.8 9.6
Vocational 1 31.8% 26.4 34.3
Vocational 2 24.5% 27.8 22.9
University education 16.5% 19.5 15.1
Panel D — other control variables
Risk-aversion 5.0 4.8 5.1
Home owner 56.2% 66.9 51.2
Unsecured debt to total financial assets 105.6% 21.8 144.8
This table contains summary statistics for variables used in this paper. Means are shown for the full sample as well as for equity owners, and non-owners. The
means are pooled across households and time periods. All monetary values are inflation adjusted to 1997 levels. The summary statistics are sample weighted
following the method of Alessie et al. (2002).
444 S.G. Dimmock, R. Kouwenberg / Journal of Empirical Finance 17 (2010) 441–459
5. We include the measure of risk-aversion used in Alessie et al. (2002). This variable is the response to a seven point Likert scale
question asking to what extent the household head agrees with the statement “I think it is more important to have safe
investments and guaranteed returns, than to take a risk to have a chance to get the highest possible returns”. A high value indicates
greater risk-aversion. Bertaut (1998), Haliassos and Bertaut (1995), and Puri and Robinson (2007) use a similar measure from the
survey of consumer finances.
We include indicator variables to control for the household head's employment status. Employment is measured with a series
of indicator variables where the default is regular paid employment. The definitions that we use follow Alessie et al. (2002):
Regular Employment, Unemployed, Retired, Disabled, Self-Employed, and Other.
In studies using American data, the number of years of education is typically used as a control variable. This is not appropriate
for this data set as The Netherlands has a Germanic education system in which the education stream is more important than the
length of education. At a young age children are divided into different education streams depending on the results of standardized
tests and input from parents. Lower education streams are designed for individuals who will not seek higher education.
Accordingly, we use indicator variables for different education streams where the default is college education. Table 1 Panel C
shows that university educated households and vocational education level 2 households (white collar vocational education such
as accounting and actuarial science) are more likely to own equities.
Cocco (2005) and Yao and Zhang (2005) argue that home ownership has an important effect on equity ownership. Home
ownership can crowd out investment in equities, particularity for younger, credit constrained households. Panel D of Table 1
shows that there is extensive homeownership in the sample and equity owners and wealthier households are more likely to own
homes.
Davis et al. (2006) and Cocco et al. (2005) argue that credit constraints are an important factor in household portfolio choice. To
control for credit constraints we use the debt to total financial assets ratio.10
This ratio varies widely across households and is
markedly higher for non-equity owners.
3.3. Equity ownership
The key dependent variable in this paper is publicly traded equity, which includes publicly traded stocks and mutual funds.
Mutual fund ownership includes balanced funds, so this variable will include fixed income ownership for some households.11
To
study household portfolio allocations we use the ratio of publicly traded equity to total financial assets.
The first column of Table 2 shows the proportion of the population holding equity over time. Column two shows portfolio
allocations to equity, conditional on equity market participation. Allocations generally follow the market's rise and fall during this
time period.
4. Measuring loss-aversion
In addition to demographic information, income, and wealth, the DNB Household Survey also contains an “Economic and
Psychological Concepts” module. This module includes a series of questions based on work by Thaler (1981) and Loewenstein
(1988), showing that loss-aversion affects intertemporal choice. Thaler shows that individuals discount gains and losses at
different rates. Loewenstein shows a related result; individuals will demand more to defer receipt of a payment than they will pay
to expedite receipt. This pattern of responses implies intransitivity and is thus incompatible with standard preferences, but these
authors show it is consistent with a loss-averse value function. Donkers and van Soest (1999) provide an excellent discussion of
these questions in the DNB Household Survey.
10
Debt is the sum of: private loans, extended lines of credit, debt with mail-order firms, loans from family and friends, student loans, credit card debt, and other
debt not reported elsewhere. We have tried a version of this where debt is defined simply as credit card debt. This version is never significant in the regressions
and the results on loss-aversion are unaffected.
11
The Netherlands has a system of employer pensions that cover the vast majority of employees. For legal reasons over 99% of these pensions are defined
benefit plans, and thus tax deferred equity investment in retirement accounts is not a significant issue during this time period.
Table 2
Equity ownership.
Year Proportion of sample holding equity Equity/TFA
Pooled 33.1% 37.1%
1997 30.7% 35.3%
1998 30.8% 38.0%
1999 34.5% 38.4%
2000 31.0% 41.6%
2001 35.9% 39.1%
2002 35.7% 31.3%
This table shows the proportion of the sample population holding equity pooled across all years and by each year in the first column. In the second column it shows
the average allocation of total financial assets (TFA) to equity among households that hold equity, both pooled across all years and by year.
445
S.G. Dimmock, R. Kouwenberg / Journal of Empirical Finance 17 (2010) 441–459
6. 4.1. The questions
The DNB Household Survey includes 16 questions about intertemporal choice which vary across four dimensions: delaying
versus speeding up a payment, gains versus losses, the timing of the decision, and the size of the payment. For example:
1. Imagine that you win a prize of ƒ 1000 (€454)12
in the National Lottery. The prize is to be paid out today. Imagine however, that
the lottery asks if you are prepared to wait A YEAR before you get the prize. There is no risk involved in this wait.
How much extra money would you ask to receive AT LEAST to compensate for the waiting term of a year? If you agree on the
waiting term without the need to receive extra money for that, please type 0 (zero).
2. Imagine that you receive notice from the National Lottery that you have won a prize worth ƒ 1000 (€454). The money will be
paid out after A YEAR. The money can be paid out at once, but in that case you receive less than ƒ 1000 (€454).
How much LESS money would you be prepared to receive AT MOST if you would get the money at once instead of after a year? If
you are not interested in receiving the money earlier or if you are not prepared to receive less for getting the money earlier,
please type 0 (zero).
The first question frames the decision as the delay of a gain while the second question frames the decision as the speed-up of a
gain. The survey asks similar questions for a three-month decision period and for 100,000 guilders, for a total of eight questions
involving a gain. These eight questions are then repeated asking for compensation and payments required to delay or speed-
up a loss, where the loss comes from a tax assessment.13
The questions are repeated in each year of the survey from 1997 to
2002.14
4.2. Loss-aversion and intertemporal choice
In this section, we derive equations that link the answers to the 16 survey questions about intertemporal choice to the
parameters of the value function of prospect theory. The approach for deriving the equations follows Loewenstein (1988) and Tu
(2004). Based on the available empirical and experimental evidence in Loewenstein (1988), Loewenstein and Prelec (1992), and
Thaler (1981), we assume that individuals do not integrate the payoffs mentioned in the 16 questions with existing consumption
plans, but rather evaluate them as gains and losses relative to a reference point. The value of a payoff sequence offering X0 at time 0
and XT at time T is expressed as:
VðX0; XT ; RÞ = vðX0–RÞ + δðTÞvðXT –RÞ ð1Þ
where R designates the reference point, δ(T) denotes the individual's discount factor for a period of length T and v(·) is the value
function used to evaluate payoffs. For convenience, we assume v(0)=0.
We first consider the case where an individual will receive a gain of amount X, in the present at time 0. The individual is willing
to delay the receipt of X to time T, if the payment is increased by the amount PDG.15
This implies that the individual is indifferent
between receiving (X, 0) and (0, X+PDG), and hence
VðX; 0; RÞ = Vð0; X + PDG; RÞ ð2Þ
vðX–RÞ + δðTÞvð–RÞ = vð0–RÞ + δðTÞvðX + PDG–RÞ ð3Þ
where R denotes the individual's reference point for payments at time 0 and at time T, subject to 0bR≤X.16
We use the value function of prospect theory for v(·), but to simplify the analysis, we follow Barberis and Huang (2001) and
Barberis et al. (2001) and set the curvature parameter of the value function equal to one:
vðxÞ =
x; if x≥ 0
λx; if x b 0
ð4Þ
where λN1 implies loss-aversion. Using this specification of the value function, Eq. (3) can be written as:
X–R–δðTÞλR = –λR + δðTÞðX + PDG–RÞ ð5Þ
PDG = ½ð1–δðTÞÞðX–RÞ + ð1–δðTÞÞλR= δðTÞ ð6Þ
12
In 1997–2001 the question asks for a response based on ƒ (Dutch guilders) 1000. This is approximately $500 US dollars through most of the sample period. In
2002, after The Netherlands switched to the euro, the question is asked as given above, including both guilders and the equivalent amount in euros. The
capitalization of certain words follows the original questionnaire.
13
In all cases the counterparty is the government of The Netherlands. This is done to eliminate counterparty risk from the decision.
14
Donkers and van Soest (1999) test the relationship between the implied discount rates from similar questions in the 1993 and 1995 waves of the survey and
risky asset holdings. They find a significant positive relationship in one year and an insignificant relationship in another.
15
Throughout this paper the subscript DG refers to delay of gain, DL refers to delay of loss, SG refers to speed-up of gain, and SL refers to speed-up of loss.
16
The individual has either completely (R=X), or partially (0bRbX), adjusted to receiving the amount X.
446 S.G. Dimmock, R. Kouwenberg / Journal of Empirical Finance 17 (2010) 441–459
7. To simplify the exposition, we consider the special case of complete reference point adjustment (R=X). Let pDG =PDG /X and
r=R/X=1, then the following equation expresses the relative premium pDG demanded in return for delaying the gain as a
function of the loss-aversion parameter and the discount rate:
pDG = ð1–δðTÞÞ½ð1–rÞ + λr= δðTÞ = λð1–δðTÞÞ= δðTÞ ð7Þ
Given λN1 and 0bδ(T)≤1, the premium is positive and bounded. Following similar steps, we derive expressions for the three
other types of questions (SG, DL and SL):
pSG = ð1–δðTÞÞ ð8Þ
pDL = ð1= λÞð1–δðTÞÞ= δðTÞ ð9Þ
pSL = ð1–δðTÞÞ ð10Þ
We refer to Appendix A for derivations and more general expressions with a variable reference point r (with 0br≤1).
As a small numerical example, consider λ=2.25, T=1 and δ(T)=0.90. The equations above then predict pDG =0.250,
pDL =0.049, pSG =0.100 and pSL =0.100, quite close to the actual mean survey response of pDG =0.246, pDL =0.039, pSG =0.038
and pSL =0.100. The mean absolute deviation between the four actual means and the predictions is 0.019, and the model only has
difficulty replicating the mean response to the speed-up of a gain (pSG) questions.
As an alternative approach, suppose that individuals use the single constant rate p to answer all four questions, as is the case in
a standard utility framework with unlimited borrowing and lending at the same risk-free rate (p). The rate p=0.077 gives the
lowest possible mean absolute deviation of 0.050. When we use different borrowing and lending rates, pB (for the DG and SL
questions) and pL (for the DL and SG questions), the best fit is found at pB =0.181 and pL =0.038, with a mean absolute deviation
of 0.037, which is still about twice as large as the approximation error of the loss-aversion model (7)–(10).
Above we follow Loewenstein (1988) in assuming that the individual uses a single reference point R for payoffs at time 0 and
time T, when evaluating whether to delay the gain. Implicitly, we assume that the person feels a loss in the period without a payoff
(for example, when not choosing to delay, a loss at time T). Loewenstein (1988, p. 204) points out that this does not mean that
when time T actually arrives, the person will really experience a loss; only that when evaluating the choice between receiving the
gain at time 0 or at time T, the period without a payoff is assigned a loss for sake of comparison.17
Analyzing the same data, Tu (2004) assumes that the respondent's reference point is equal to zero at the time when no
payment was initially expected. This implies a zero reference point at time T when considering delay of a gain (or loss) and a zero
reference point at time 0 when considering speed-up. We derive alternative estimates of loss-aversion under this reference point
assumption and let the data reveal whether the Loewenstein (1988) or Tu (2004) reference point specification fits better.
Following Tu (2004), indifference between receiving (X, 0) and (0, X+PDG) implies:
vðX–RÞ + δðTÞvð0Þ = vð0–RÞ + δðTÞvðX + PDGÞ ð11Þ
Substituting the specification of the value function given in Eq. (4) and using pDG =PDG /X and r=R/X, Eq. (11) can be written
as (see Appendix B):
pDG = ½ð1–δðTÞÞ + ðλ–1Þr= δðTÞ ð12Þ
Given λN1 and 0bδ(T)≤1, the delay premium is positive and bounded. Following similar steps, we can derive an equation for
speed-up of gains:
pSG = ð1–λÞδðTÞr + ð1–δðTÞÞ ð13Þ
Note that pSG can easily become negative, for example when λN1 and δ(T)=1, because loss-averse individuals with a
reference point equal to X will experience a considerable loss at time T in case of speed-up and will actually demand compensation
(i.e. PSG b0). The speed-up question in the DNB Household Survey explicitly asks respondents to type zero when they are not
willing to pay for speed-up.18
In line with the framing of the question, we therefore define the relative speed-up payment as in
Eq. (13) as long as it is positive, and zero otherwise:
pSG = maxfð1–λÞδðTÞr + ð1–δðTÞÞ; 0g ð14Þ
17
The assumption that people have a single reference point to compare payoffs at different points in time is analogous to the assumption in prospect theory
that people use a single reference point to evaluate the different possible payoffs from a gamble. People will tend to update their reference point once one
particular outcome actually occurs. Similarly, in the current setting as time passes the individual may eventually update his or her reference point.
18
“If you are not interested in receiving the money earlier or if you are not prepared to receive less for getting the money earlier, please type 0 (zero).”
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S.G. Dimmock, R. Kouwenberg / Journal of Empirical Finance 17 (2010) 441–459
8. Following similar steps, we derive the following expressions for pDL and pSL:
pDL = maxf½ð1–λÞð1 = λÞr + ð1–δðTÞÞ= δðTÞ; 0g ð15Þ
pSL = ðλ–1Þð1= λÞδðTÞr + ð1–δðTÞÞ ð16Þ
As a small numerical example, consider r=R/X=1, λ=2.25, T=1 and δ(T)=0.90. The equations above then predict
pDG =1.5, pDL =0, pSG =0 and pSL =0.6, quite far from the actual mean survey response of pDG =0.246, pDL =0.039, pSG =0.038
and pSL =0.100, with a mean absolute deviation of 0.537. It turns out that with the reference point specification of Tu (2004) the
data can be fitted much closer when the reference point does not adjust completely to the promised payments (RbX) and loss-
aversion is less than two (λb2). For example, with r=R/X=0.25, λ=1.50, T=1 and δ(T)=0.90, the predicted values are
pDG =0.25, pDL =0.019, pSG =0 and pSL =0.175, and the mean absolute deviation is 0.0345. Later on we will inspect the
estimation results to see which model specification best fits the data, using various levels of reference point adjustment.
4.3. Descriptive statistics and reliability
4.3.1. Descriptive statistics
Before beginning estimation we check the answers to the 16 questions for two types of errors. First, some respondents answer
zero to all questions in a particular year. In total 345 of the 7590 (4.5%) responses are all-zero. We discard these observations, as
they probably indicate that the respondent did not want to spend time and thought on answering the questions.19
Second, we
check the data for implausibly high responses. Fortunately, the frequency of implausibly large answers is relatively small. We
winsorize the data at X for the delay of gain category, and at 50% of X for the other categories, as this amounts to approximately 2%
of the responses for each category.20
Table 3 Panel A shows descriptive statistics of the winsorized responses. Generally, respondents require high compensation to
delay gains. On average respondents require 252 guilders additional compensation to delay a gain of 1000 guilders for one year.
Only 4.7% of the respondents agree to delay the payment without any compensation. On the other hand, respondents are willing to
pay only 43 guilders to speed-up a 1000 guilders gain due in one year. Further, about 63.3% of the respondents are not willing to
pay to speed-up a gain of 1000 guilders. The large divergence in answers to the questions about delay and speed-up of gains
indicates intransitivity and is a clear violation of the traditional discounted expected utility framework, which predicts that the
discount rates for all 16 questions are either equal (assuming borrowing and lending at the same rate) or very similar (without
borrowing).
In general respondents are unwilling to pay much to delay losses. For example respondents will pay 35 guilders on average to
delay a loss of 1000 guilders for one year, with 56.7% of the respondents not willing to pay at all. On the other hand, respondents
require a loss reduction of 99 guilders on average to accept speeding up a loss of 1000 guilders by one year, while 23.7% of the
respondents do not require a reduction.
The results in Table 3 Panels B and C are based on households' average discount rate within each category of questions. Below
the diagonal in Panel B are the correlations between the average discount rates. If respondents have standard utility we would
expect all correlations to equal one. Although all of the correlations are significantly positive, they are considerably lower than one.
The percentages above the diagonal in Panel B show how often the discount rate identified in the row is greater than the discount
rate in the column. Panel C shows p-values of significance tests of the differences in medians between different categories of
questions.
4.3.2. Reliability
The validity of our measures of loss-aversion and reference points is crucial for this study. Fortunately, the DNB Household
Survey consists of four sets of closely related questions and we can check the consistency of answers to these sets of related
questions. For example, there are four questions about delay of gains, with a gain of either 1000 or 100,000 guilder, and with a
horizon of either three months or one year. Although we expect some variation in the relative compensation pDG as a function of
the amount at stake and the horizon, the four answers should satisfy certain basic constraints. For example, if a respondent asks for
no compensation to delay a payment of 1000 guilders for one year, but demands 500 guilders to delay the same amount for three
months, this would indicate a judgment error or lack of concentration. Table 4 Panel A displays the frequency of responses where
the answer to a question with a horizon of three months is larger than the answer to the same question with a horizon of
12 months (related questions are compared in pairs of two, and frequencies averaged). Table 4 also displays the frequency of
responses where the answer to a question with a payoff of 1000 guilder is larger than the answer to the same question with a
payoff of 100,000 guilder. The overall frequency of both types of errors is 2.9% and 2.4%, respectively, indicating that the majority of
respondents avoid these kinds of judgment errors. To reduce measurement error we delete these observations.
19
Within our preference framework an all-zero response is only possible when the individual is not loss-averse (λ=1) and has a discount rate of zero (δ=1).
20
The payments asked for in the speed-up questions reduce the payoff X (gain or loss), and therefore theoretically the answer should be less than or equal to X.
Only 0.1% of the respondents violate this constraint in their answers to speed-up questions. We decided to set the cutoff point for winsorizing the answers at the
level 0.5X, as this roughly coincides with the 98th percentile for the two speed-up questions and the delay of losses question. However, the answers for delay of
gains questions are typically much larger compared to the other questions, and there the 98th percentile lies roughly at 1×X.
448 S.G. Dimmock, R. Kouwenberg / Journal of Empirical Finance 17 (2010) 441–459
9. In survey-based research, Cronbach's alpha (Cronbach, 1959) is frequently used to measure the reliability of a set of questions
intended to measure the same underlying latent variable. Cronbach's alpha is a function of the average cross-sectional correlation
of the respondents' answers. Most researchers consider empirical values of alpha above 0.70 as an acceptable level of reliability,
see, e.g. Nunnally (1978). We expect an individual's answers to the four questions about speed-up of gains to reflect the
individual's underlying latent preference for speeding up gains, apart from some small variations caused by changes in time
horizon and the payoff amount. Table 4 Panel B reports Cronbach's alpha for the four sets of related questions (DG, DL, SG and SL)
for each survey year (1997–2002), as well as the average over six years. All alpha estimates are above 0.70 and the average alpha
for each set of questions is above 0.80. Hence, it is clear that the responses to related questions are not random, but closely related
with acceptable levels of reliability.21
4.3.3. Discussion of alternative explanations
It is important to establish that not only are the data consistent with preferences based on loss-aversion, but that other
potential explanations fail. If households have standard preferences and there is a single market interest rate for both borrowing
and lending, then all households should report a rate equal to the market interest rate for each of the 16 questions. Equality of
discount rates within households is overwhelmingly rejected by pairwise signed rank tests. If there is a wedge between borrowing
21
Interestingly, if we group variables into groups by time or the amount of money the Cronbach's alphas are lower. For example, the Cronbach's alphas of
questions with a time period of three months and twelve months are 0.701 and 0.706 respectively. The Cronbach's alphas of the 1000 guilder questions and
100,000 guilder questions are both 0.753. These results suggest that grouping the questions by the question frame produces grater response consistency than
grouping by the time period or amount of money.
Table 3
Descriptive statistics of the payments for delay/speed-up of gains/losses.
Question Horizon Amount Type of payment Mean Median Std. dev. % Zero answers
Panel A: Descriptive statistics
DG 3 m 1000 Receive extra 10.2% 3.5% 20.6% 18.3%
DG 12 m 1000 Receive extra 25.2% 10.0% 30.6% 4.7%
DG 3 m 100,000 Receive extra 7.8% 2.0% 20.1% 6.4%
DG 12 m 100,000 Receive extra 17.0% 10.0% 26.1% 1.6%
DL 3 m 1000 Lose more 1.2% 0.0% 3.6% 69.5%
DL 12 m 1000 Lose more 3.5% 0.0% 6.9% 56.7%
DL 3 m 100,000 Lose more 0.6% 0.0% 1.2% 48.9%
DL 12 m 100,000 Lose more 2.2% 0.5% 3.6% 42.7%
SG 3 m 1000 Receive less 1.3% 0.0% 5.4% 76.8%
SG 12 m 1000 Receive less 4.3% 0.0% 13.6% 63.3%
SG 3 m 100,000 Receive less 1.4% 0.0% 6.5% 59.3%
SG 12 m 100,000 Receive less 3.8% 0.0% 12.9% 48.8%
SL 3 m 1000 Loss reduction 4.6% 2.5% 5.6% 27.8%
SL 12 m 1000 Loss reduction 9.9% 7.5% 11.3% 23.7%
SL 3 m 100,000 Loss reduction 2.7% 1.0% 4.5% 21.6%
SL 12 m 100,000 Loss reduction 6.4% 5.0% 7.9% 20.8%
DG DL SG SL
Panel B: Correlations and proportion of discount rates in ColumnNRates in row
DG – 97.2% 89.0% 63.0%
DL 0.203 – 69.5% 28.5%
SG 0.102 0.192 – 37.9%
SL 0.242 0.181 0.165 –
Panel C: Significance tests of differences in medians
DG –
DL 0.000 –
SG 0.000 0.000 –
SL 0.000 0.000 0.000 –
Panel A shows summary statistics of the responses to the 16 questions about the speed-up and delay of gains and losses, after winsorizing 2% of the extreme
observations in the right tail of the distribution. “DG” refers to questions about the delay of a gain (lottery prize), “DL” to questions about the delay of a loss (tax
assessment), while “SG” and “SL” refer to speed-up of a gain and speed-up of a loss, respectively. The time-period mentioned in the questions — i.e. for delay and
speed-up — is either three months or one year, as indicated in the 2nd column. The size of the payoff mentioned in the questions is either 1000 Dutch guilders or
100,000 guilders, as indicated in the 3rd column. In the DNB Household Survey households are asked to indicate the minimum amount they want to receive to
accept delay of a gain (DG) and speed-up of a loss (SL), and instructed to write “0” if they do not require compensation to accept DG or SL. Households are asked to
indicate the maximum amount they are willing to pay to speed-up gains (SG) and delay losses (DL), and instructed to write “0” if they do not want to consider SG
or DL at any price. The last four columns of Panel A show descriptive statistics of the winsorized household answers to the 16 questions, including the proportion of
households that give “0” as their answer to a particular question. Panel B shows correlations between average discount rates below the diagonal. The numbers
above the diagonal show the percentage of observations for which the average discount rate for the type of question indicated in the row is larger than the average
discount rate for the question type in the column. Panel C shows p-values from tests of the equality of discount rates. The p-values are based on a Wilcoxon
matched pairs signed rank test. The null hypothesis is that across households the median difference between the rates is zero.
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S.G. Dimmock, R. Kouwenberg / Journal of Empirical Finance 17 (2010) 441–459
10. and lending rates then all households should report a single rate for lending transactions (as all households can lend at the same
risk-free interest rate), but possibly households will report heterogeneous borrowing rates. However, with standard preferences
each household should be internally consistent and report a single borrowing rate. Again, this is overwhelmingly rejected by
pairwise t-tests. The average within household difference in borrowing rates is significantly different from zero,22
implying that on
average the households' responses are intransitive.
If there are borrowing constraints that prevent households from accessing credit markets it is possible to get some variation
across rates for a given individual due to the concavity of the utility function. Concavity predicts that households will report higher
rates for losses than gains, as the utility function is steeper for losses than gains. However, the largest rates are reported for the
delay of gain questions, not loss questions, in direct violation of this prediction. The effect of concavity should be stronger for the
100,000 guilder questions than the 1000 guilder questions, as most households will be close to risk neutral once the 1000 guilders
is integrated with current wealth. There is no evidence to support this prediction.
Concavity of the utility function also fails to explain the magnitude of the intra household range of reported rates. For a
calibrated power utility function a household with median wealth, no access to credit markets, and a risk-aversion coefficient of 3
would have only a 0.47% annualized difference in rates across responses to the 1000 guilder questions. Even assuming a risk-
aversion coefficient of 50, this implies at most a difference across rates of 2% for a given household. In the data there is an average
difference across rates of over 20%, implying that the variation in responses predicted by a standard power utility function is too
small by many orders of magnitude.23
Benzion et al. (1989) discuss another potential explanation they call the implicit risk approach. Perhaps respondents believe
that there is some counterparty risk and thus wish to accelerate gains while delaying losses. However, implicit risk should affect
delay and speed-up of gains questions symmetrically and delay of losses and speed-up of losses symmetrically. This is flatly
rejected by the data.
There are two additional issues sometimes raised in response to survey questions: that households respond at random to
survey questions, or that households respond to hypothetical questions differently than they would respond to real situations. The
questions asked in the DNB Household Survey have been asked in numerous prior studies, such as Benzion et al. (1989),
Loewenstein (1988), Shelley (1993) and Thaler (1981) all of whom find similar results to the DNB Household Survey. This suggests
responses are not random but measure some systematic characteristic of preferences. Loewenstein (1988) asks questions similar
to those in the DNB using both real and hypothetical rewards, and finds similar results in both cases.
Finally we note that the relevance of hypothetical behavior for real behavior is jointly tested along with the hypothesis that
loss-aversion matters. Since our loss-aversion measures come from hypothetical questions, our results can be viewed as tests of
the joint statement: 1. Loss-aversion matters and, 2. Loss-aversion can be reliably measured from hypothetical questions. If either
part is wrong we will find coefficients indistinguishable from zero. Since we find significant coefficients, with the signs as
predicted, we can reasonably interpret this as evidence that household responses to hypothetical questions do contain relevant
information about preferences.
Table 4
Frequency of suspect responses and reliability estimates.
Panel A: Average frequency of suspect responses
P (1000)NP (100,000) P (3 m)NP (12 m)
DG 0.015 0.011
DL 0.035 0.016
SG 0.021 0.032
SL 0.045 0.037
Panel B: Cronbach's alpha
Average 1997 1998 1999 2000 2001 2002
DG 0.919 0.888 0.896 0.921 0.967 0.948 0.893
DL 0.894 0.909 0.936 0.847 0.891 0.914 0.868
SG 0.885 0.885 0.832 0.871 0.878 0.925 0.916
SL 0.821 0.798 0.763 0.839 0.781 0.875 0.870
This table shows the reliability of the responses to the survey questions used to extract our measure of loss-aversion. Panel A shows the frequency that answers to
1000 guilder questions are larger than for 100,000 guilder questions and the frequency of answers for delay/speed-up questions with a three month horizon
exceeding the answers for equivalent questions with a 12 month horizon. Panel B shows Cronbach's alpha for related questions.
22
Even assuming households face different borrowing rates for 1000 guilders and 100,000 guilders, there is strong evidence that households report different
intra household borrowing rates across 1000 guilder questions, and across 100,000 guilder questions, depending on the framing of the question.
23
As a further check that our results are inconsistent with standard preferences we derive a system of equations similar to Eqs. (7)–(10), but using a power
utility function that integrates the payoffs with household wealth. We estimate risk-aversion for each household based on their responses to the 16 survey
questions and their wealth. The sum of squared errors is, on average, two times as large for these estimates as for estimates based on a loss-averse utility
function.
450 S.G. Dimmock, R. Kouwenberg / Journal of Empirical Finance 17 (2010) 441–459
11. 4.4. Estimation
4.4.1. Notation and assumptions
To simplify the discussion in this section we begin by introducing the following extended notation to describe the survey
data: PDG,i,t(X, T) denotes the payment that individual i wants to receive for the delay of a gain of size X (1000 or 100,000 guilders)
over a period of time T (three months or one year), measured in survey year t (1997–2002). We define the relative amount as
pDG,i,t(X ,T)=PDG,i,t(X, T)/X.
Our aim is to keep the specification of the empirical preference model parsimonious, as for each survey participant we have a
limited number of data points. We assume that the preference parameters vary from individual to individual, but do not depend on
the survey year t. For the discount rate δi(X, T) we use the specification δi(X, T)=δi
T
. For the reference point we will initially
assume that it fully adjusts to the gain or loss initially expected (r=R/X=1). We will then re-estimate the model with reference
point adjustment of 50% and 25% (r=0.50 and r=0.25), respectively, and compare model fit.
Much of the experimental work in the area of intertemporal preferences was designed not just to show that loss-aversion
affects intertemporal choice, but also that individuals' time-preferences are dynamically inconsistent. Because of this, to avoid any
possibility that dynamic inconsistency is driving our measure, we report results using only the eight questions where the time
choice is over one year. Both the loss-aversion estimates and all of the results in this paper are very similar if we derive a measure
using all 16 questions, or if we use only the three-month questions.
As the panel is not balanced, not every household has answered the questions in each of the six years from 1997 through 2002.
Further, we eliminate all pairs of suspect answers reported in Panel A of Table 4. We use nDG,i to denote the number of years that
individual i provided answers to the delay of gain question (for T=1) and the answer did not violate the conditions in Panel A of
Table 4. From here onwards summation over time t, denoted by Σt, is presumed to include only years with non-suspect data
available for individual i for that particular question.
4.4.2. Estimation
Our aim is to estimate the loss-aversion parameter (λi), and discount rate (δi) of individual i from his or her indicated speed-up
and delay payments (PSG,i,t(X,T), PDG,i,t(X,T), PSL,i,t(X,T) and PDL,i,t(X,T)). The preference parameters should satisfy the following
feasibility conditions: λi N0, 0bδi ≤1. This leaves us with two parameters to estimate from eight equations, one for each survey
question (T=1 only). As an example, below we show the equations for the Loewenstein (1988) specification with full reference
point adjustment, i.e. r=1:
ΣtpDG;i;tðX; TÞ= nDG;i–½λið1–δT
i Þδ−T
i = 0;
ΣtpSG;i;tðX; TÞ= nSG;i–½ð1–δT
i Þ = 0;
ΣtpDL;i;tðX; TÞ= nDL;i–½ð1 = λiÞð1–δ
T
i Þδ
−T
i = 0;
ΣtpSL;i;tðX; TÞ= nSL;i–½ð1–δT
i Þ = 0;
ð17Þ
with Xa{1000; 100,000}, T=1 and t {1997, 1998, …., 2002} and subject to λi N0, 0bδi ≤1. We estimate system (17) for each
individual i separately using the Generalized Methods of Moments (GMM).24
Let uSG,i(X,T), uDG,i(X,T), uSL,i(X,T) and uDL,i(X,T)
denote the errors in the eight moment equations, for a given set of preference parameters, for individual i. We minimize the sum of
the squared errors to estimate the parameters25,26
:
ΣXΣT ½uSG;iðX; TÞ
2
+ uDG;iðX; TÞ
2
+ uSL;iðX; TÞ
2
+ uDL;iðX; TÞ
2
:
We derive and estimate two similar systems as in Eq. (17) for the case of partial reference point adjustment with r=0.50 and
r=0.25, respectively. Further, we also derive a system to estimate λi and δi under the reference point assumptions of Tu (2004),
with r=1, r=0.5 and r=0.25. Overall, six different specifications are estimated under alternative assumptions the about
reference points. Our aim is to compare model fit and to use the estimates from the model with the lowest sum of squared errors.
24
The number of unknown parameters in system (17) is two, while the number of moment conditions is eight. The mean of each moment condition is
estimated with n=1 up to n=6 yearly observations. On average the two parameters are estimated with 21 answers to speed-up and delay questions, as 2.6
survey years are on average available per household. The number of observations ranges from eight, for households with only one year of survey data available,
to 48 for households with six years of data. Due to the small number of observations and the non-linearity of the system, consistency and unbiasedness of the
estimates cannot be guaranteed.
25
We set the weighing matrix for the errors equal to an identity matrix. We do not attempt to estimate the covariance matrix of the errors, e.g. as part of a 2-
stage GMM estimation procedure, as this implicitly involves estimating (8×9)/2=36 additional unknown parameters in the covariance matrix.
26
As constrained optimization in practice is limited to equality constraints and inequality constraints (≤ and ≥), we model the strict inequality constraint λi N0
as λi ≥0.1, and δi N0 as δi ≥0.1. The lower bound for λi and δi is set at 0.1 — away from zero — because the inverse of these parameters (1/λi and 1/δi) occur in
the moment equations, and near-zero values might lead to error propagation and numerical instability.
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12. 4.4.3. Estimation results
Table 5 displays the estimation results, using each household's mean response to the questions, averaged over the survey years
1997 through 2002, as the dependent variable. Panel A displays descriptive statistics of the loss-aversion estimates λi, for six
different reference point specifications: following Loewenstein (1988) and Tu (2004), respectively, with r=1, r=0.5 and r=
0.25. Panel B shows statistics of the estimated discount factor δi and panel C displays model fit (sum of the squared estimation
errors).
Under the assumption that households use a single reference point when making a trade-off between payoffs at time 0 and
time T, as in Loewenstein (1988), the loss-aversion estimates are relatively high (median between 1.7 and 2.6), while the median
discount rate is about 5% annually. Under the assumption that households have a zero reference point at times when no payment
was expected initially, as in Tu (2004), the loss-aversion estimates are relatively small (median between 1.1 and 1.4), while the
estimated discount rates are close to zero (median of 0%). Hence, the two different reference point setups lead to considerably
different estimates, both qualitatively and quantitatively.
We assess model fit by inspecting the average sum of squared errors in Panel C. The Loewenstein specification fits best when
the reference point adjusts completely to the payoff (r=1, with mean squared error of 0.043), while fit deteriorates considerably
in case of partial adjustment (r=0.5 and r=0.25). The Tu specification achieves best fit with low reference point adjustment, at
r=0.25 (the mean squared error is 0.045).
Overall, the best fit is achieved by the Loewenstein setup with full reference point adjustment (r=1). For this specification the
average discount factor δi is equal to 0.95, implying a discount rate of 5% per year. The median loss-aversion estimate λi across the
2526 households is 2.47, which is close to the loss-aversion estimate of 2.25 reported by Tversky and Kahneman (1992). However,
the distribution of loss-aversion estimates is skewed to the right and the average is considerably higher at 5.61, driven by a
number of households with great reluctance to intertemporal trade-offs.
In the subsequent sections we will use two sets of loss-aversion estimates while analyzing equity ownership: estimates derived
from the Loewenstein specification with r=1 and estimates from the Tu setup with r=0.25. We choose these two specifications
because they have the best fit based on mean squared error. Further, by using two sets of loss-aversion estimates that are
substantially different (correlation=0.658), we aim to establish the robustness of our results with respect to the reference point
assumptions.
5. Results
In this section we estimate the relation of loss-aversion and estimated reference points with equity ownership. We show that
households with higher reported loss-aversion are less likely to participate in the equity market and avoid direct stockholding to a
greater extent than mutual funds. After controlling for sample selection, we do not find a significant relation between loss-
aversion and allocations to equity.
Table 5
Household loss-aversion and time-preference estimates.
Mean Median Std. dev. 5th% 95th%
Panel A: Loss-aversion estimates
Loewenstein, r=1 5.61 2.47 8.22 1.00 25.11
Loewenstein, r=0.5 3.50 1.68 6.50 0.47 10.76
Loewenstein, r=0.25 4.96 2.61 6.57 0.25 17.92
Tu, r=1 1.17 1.11 0.19 0.99 1.60
Tu, r=0.5 1.33 1.17 0.41 1.01 2.26
Tu, r=0.25 1.64 1.36 0.76 1.03 3.23
Panel B: Discount factor estimates
Loewenstein, r=1 0.95 0.96 0.043 0.86 0.99
Loewenstein, r=0.5 0.93 0.95 0.050 0.83 0.99
Loewenstein, r=0.25 0.93 0.95 0.053 0.82 0.99
Tu, r=1 1.00 1.00 0.006 0.98 1.00
Tu, r=0.5 0.98 1.00 0.040 0.90 1.00
Tu, r=0.25 0.97 1.00 0.054 0.85 1.00
Panel C: Estimation error
Loewenstein, r=1 0.043 0.011 0.084 0.0005 0.201
Loewenstein, r=0.5 0.098 0.026 0.161 0.0012 0.525
Loewenstein, r=0.25 0.106 0.026 0.186 0.0012 0.592
Tu, r=1 0.071 0.018 0.124 0.0012 0.343
Tu, r=0.5 0.055 0.015 0.099 0.0005 0.231
Tu, r=0.25 0.045 0.013 0.086 0.0004 0.198
This table shows descriptive statistics of the estimated preference parameters and estimation error. All parameters are estimated subject to the constraints:
0.1≤λi ≤40 and 0.1≤δi ≤1. Panel A shows summary statistics of the estimates of households' loss-aversion parameters. Panel B shows summary statistics of the
estimates of households' discount factors. Panel C summarizes estimation error.
452 S.G. Dimmock, R. Kouwenberg / Journal of Empirical Finance 17 (2010) 441–459
13. 5.1. Participation
To test if loss-aversion affects households' equity market participation decisions we estimate a random-effects panel probit
model on the full unbalanced panel. Table 6 shows the estimation results where the dependent variable equals one if the
household owns equity. In the first two columns, loss-aversion is measured using the assumptions of Loewenstein (1988) and full
reference point adjustment (r=1). In the third and fourth columns, loss-aversion is measured using the assumptions of Tu (2004)
and partial reference point adjustment (r=0.25).
In all columns loss-aversion has a significant negative relation with equity market participation, however the loss-aversion
measure based on Loewenstein (1988) is considerably more significant, and the Akaike information criterion suggests this
measure provides a better fit. In all specifications we include the estimated discount rates, but they are not significantly related to
equity participation and their inclusion has little effect on the loss-aversion coefficients.
The estimates also indicate that loss-aversion is economically significant. After setting all other variables equal to their mean, if
loss-aversion changes from the 25th percentile to the 75th percentile this results in a change in the probability of owning stocks of
1.7% points for the Loewenstein loss-aversion measure and 2.0% points for the Tu loss-aversion measure. While this may seem low,
this represents approximately a 10% to 15% increase relative to the mean probability of owning equity.27
This is economically
comparable to changing wealth by 5% points.
In columns two and four we include our measure of risk-aversion. It is highly significant, but its inclusion has little effect on the
magnitude of the loss-aversion estimates, and the significance of loss-aversion actually increases. Lagged participation is highly
significant, indicating considerable persistence in equity market participation. Coefficients on the other control variables are
generally consistent with existing findings in the household portfolio literature and results reported for the 1993–1998 waves of
the DNB Household Survey in Donkers and van Soest (1999) and Alessie et al. (2004). We also include equity market participation
in 1996 to handle the initial conditions problem, as suggested in Wooldridge (2005). Wealthier, home owning, higher income
Table 6
Random-effects probit model of the participation decision.
Variable 1 2 3 4
Loewenstein Loewenstein Tu Tu
Loss-aversion −0.014** −0.015** −0.093* −0.103*
[−2.38] [−2.55] [−1.66] [−1.84]
Discount rate −0.677 −0.564 −0.124 −0.12
[−0.65] [−0.55] [−0.16] [−0.15]
Risk-aversion −0.110*** −0.110***
[−4.71] [−4.70]
Initial participation 0.249*** 0.246*** 0.251*** 0.247***
[9.68] [9.54] [9.73] [9.60]
Lagged participation 1.152*** 1.154*** 1.157*** 1.159***
[12.60] [12.60] [12.65] [12.66]
TFA/1000 0.017*** 0.017*** 0.017*** 0.017***
[17.14] [17.06] [17.15] [17.07]
TFA/1,000,000 Squared −0.019*** −0.019*** −0.019*** −0.019***
[−12.37] [−12.38] [−12.35] [−12.37]
Homeowner 0.242*** 0.245*** 0.245*** 0.247***
[3.05] [3.09] [3.08] [3.12]
Debt to TFA −0.052*** −0.053*** −0.052*** −0.053***
[−3.22] [−3.23] [−3.22] [−3.22]
Income/1000 0.003** 0.003** 0.004** 0.004**
[2.13] [2.16] [2.24] [2.28]
Income/1,000,000 squared −0.008 −0.009 −0.009 −0.009
[−0.93] [−1.01] [−1.00] [−1.07]
Age −0.038* −0.039* −0.039* −0.040**
[−1.92] [−1.95] [−1.95] [−1.99]
Age squared 0.328 0.346* 0.326 0.345*
[1.64] [1.74] [1.63] [1.74]
Employment effects Yes Yes Yes Yes
Education effects Yes Yes Yes Yes
Constant Yes Yes Yes Yes
Log likelihood −1997.9 −1986.9 −2000.5 −1989.6
Akaike I.C. 4053.9 4033.8 4059.1 4039.1
*,**,*** Significant at the 10%, 5%, and 1% level respectively. N=5810.
This table shows random-effects probit estimates of the participation decision. Marginal effects are reported rather than coefficients. The dependent variable
equals one if the household owns equity. Financial variables are measured across all members of the household. Other variables, such as age, employment, risk-
aversion, and loss-aversion, are measured as the value given by the household head. Z-scores are shown in parentheses below the marginal effect estimates. The
last two rows show the log likelihood and the Akaike information criterion.
27
While the unconditional probability of owning equity is around 25%–30%, the probability of owning equity is quite low for households with total financial
assets near the mean value. This is caused by the fact that equity ownership is concentrated among wealthier households.
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S.G. Dimmock, R. Kouwenberg / Journal of Empirical Finance 17 (2010) 441–459
14. households are more likely to participate in equity markets. Unlike in many other empirical studies, age is not very significant. This
may be because age is highly correlated with several other variables in the employment effects category, notably the retirement
indicator variable. The estimated coefficient of the ratio of unsecured debt to total financial assets is negative and significant. This is
intuitively reasonable as for indebted households paying off consumer debt is their best investment opportunity.
Overall, the results in this section support the hypothesis that loss-aversion has a significant impact on household participation.
Households with a higher level of loss-aversion are unwilling to risk the pain that comes from declines in equity prices and so
avoid equity.
5.2. Portfolio allocations
Having examined the effect of loss-aversion and reference points on equity market participation, our next step is to test their
effect on portfolio allocation. Miniaci and Weber (2002) and Vissing-Jorgenson (2002) argue some households that desire to hold
equity are unable to, because of fixed costs of participating in equity markets, and as a result it is important to control for sample
selection effects when examining household portfolio allocations. Because of the possibility of fixed participation costs we
estimate portfolio allocations using the panel sample selection model of Wooldridge (1995). We estimate the inverse Mill's ratio
for each household using the panel probit regressions presented in the previous subsection. As independent variables we include
each households' average wealth, income, and age, and the squares of each of these averages, with the averages taken across all
years the household is in the panel. These averages function as quasi-fixed effects and remove any unobserved household level
effects which are correlated with these variables.
Table 7 shows the sample selection model results. In columns one and two loss-aversion is measured based on Loewenstein
(1988) and in columns three and four loss-aversion is based on Tu (2004). In all cases loss-aversion is not significant. It appears
that loss-aversion affects households' participation decision, but conditional upon participation loss-aversion does not affect
allocations. If we estimate these regressions on the sample of participants without including the Mill's ratio we find qualitatively
similar results.
The combination of a significant effect on participation but no effect on allocation is surprising. However, participation
represents the result of a clear decision by a household. It is less clear that allocations are the result of conscious decisions. For
example, Agnew et al. (2003) find that in an average year only 12.5% of investors rebalance their equity holdings.
Table 7
Sample selection model estimates of portfolio allocations.
Variable 1 2 3 4
Loewenstein Loewenstein Tu Tu
Loss-aversion 0.002 0.002 0.019 0.017
[1.31] [1.26] [1.54] [1.45]
Discount rate −0.110 −0.113 −0.023 −0.032
[−0.52] [−0.53] [−0.14] [−0.20]
Risk-aversion −0.007* −0.007*
[−1.79] [−1.76]
TFA/1000 0.000 0.000 0.000 0.000
[−1.13] [−1.00] [−1.14] [−1.01]
TFA/1,000,000 squared 0.000 0.000 0.000 0.000
[−0.33] [−0.45] [−0.33] [−0.45]
Homeowner −0.022* −0.022 −0.022 −0.022
[−1.65] [−1.60] [−1.64] [−1.59]
Debt to TFA 0.009** 0.009** 0.009** 0.009**
[2.08] [2.08] [2.09] [2.09]
Income/1000 0.000 0.000 0.000 0.000
[0.18] [0.23] [0.16] [0.22]
Income/1,000,000 squared 0.000 0.000 0.000 0.000
[−0.12] [−0.14] [−0.13] [−0.14]
Age 0.016 0.016 0.016 0.016
[1.07] [1.07] [1.07] [1.07]
Age squared −0.001 0.001 0.000 0.002
[−0.01] [0.01] [0.00] [0.02]
Mill's ratio −0.058*** −0.055*** −0.058*** −0.055***
[−4.20] [−3.99] [−4.24] [−4.01]
Employment effects Yes Yes Yes Yes
Year effects Yes Yes Yes Yes
Education effects Yes Yes Yes Yes
Quasi-fixed effects Yes Yes Yes Yes
Constant Yes Yes Yes Yes
*,**,*** Significant at the 10%, 5%, and 1% level respectively. N=1921.
This table shows sample selection model estimates of the asset allocation decision. The dependent variable is the proportion of total financial assets allocated to
equity. Financial variables are measured across all members of the household. Other variables, such as age, employment, risk-aversion, and loss-aversion, are
measured as the value given by the household head. T-statistics shown in parentheses below parameter estimates.
454 S.G. Dimmock, R. Kouwenberg / Journal of Empirical Finance 17 (2010) 441–459
15. 5.3. Equity type
In this subsection we examine the effect of loss-aversion and reference points on households' investment choices between
mutual funds and direct stock holdings. If a household is loss-averse and frames at the level of each individual stock, as in Barberis
and Huang (2001), the pain from losses on individual stocks may outweigh the pleasure from gains, even if the overall portfolio
return is positive. A mutual fund effectively integrates the gains and losses from individual stocks into a single reported return.
Thus loss-averse households subject to narrow framing and loss-aversion will place a higher value on owning a mutual fund than
on directly owning the component securities.
In Table 8 we show the results of an ordered probit regression28
where households are divided into four categories: no equity,
mutual funds only, mutual funds and individual stocks, and individual stocks only. No equity ownership is the default category and
the other categories are coded as one, two, and three respectively. Standard errors are clustered by household.
Consistent with our hypothesis, the results in Table 8 show that loss-aversion has a significant negative coefficient. These
results indicate that not only does loss-aversion reduce the probability of a household owning equity: loss-aversion affects the
type of equity participants select.29
Loss-aversion increases households' preference for having individual stock returns integrated
into a single mutual fund return. In columns two and four we include risk-aversion as a control variable. Risk-aversion is highly
significant, and its inclusion increases the significance of loss-aversion.
6. Conclusion
Despite the high equity premium many households choose not to participate in the equity markets, and across participating
households there is great heterogeneity in allocations to equity. These empirical facts are difficult to reconcile with normative
results obtained from frictionless models using standard utility functions. One proposed explanation for these facts is that
Table 8
Ordered probit model of the equity type decision.
Variable 1 2 3 4
Loewenstein Loewenstein Tu Tu
Loss-aversion −0.011 −0.012 −0.059 −0.068
(3.19)*** (3.42)*** (1.82)* (2.08)**
Discount rate −0.483 −0.391 −0.663 −0.676
(0.84) (0.68) (1.44) (1.48)
Risk-aversion −0.092 −0.092
(6.68)*** (6.67)***
TFA/1000 0.008 0.008 0.008 0.008
(17.68)*** (17.63)*** (17.80)*** (17.77)***
TFA/1,000,000 squared −0.008 −0.009 −0.009 −0.009
(12.93)*** (12.86)*** (13.02)*** (12.99)***
Homeowner 0.114 0.123 0.114 0.123
(2.44)** (2.63)*** (2.44)** (2.62)***
Debt to TFA −0.037 −0.038 −0.037 −0.038
(3.04)*** (2.96)*** (3.07)*** (2.97)***
Income/1000 0.001 0.001 0.001 0.001
(1.04) (1.09) (1.24) (1.29)
Income/1,000,000 squared 0.001 0 0 −0.001
(0.11) (0.07) (0.04) (0.22)
Age 1.326 1.308 1.337 1.319
(27.03)*** (26.56)*** (27.20)*** (26.74)***
Age squared −0.025 −0.024 −0.024 −0.024
(2.03)** (2.01)** (2.01)** (1.99)**
Employment effects Yes Yes Yes Yes
Year effects Yes Yes Yes Yes
Education effects Yes Yes Yes Yes
Constant Yes Yes Yes Yes
*,**,*** Significant at the 10%, 5%, and 1% level respectively. N=5810.
This table shows results from an ordered probit model. The dependent variable equals zero if the household does not own equity, one if the household owns
mutual funds but not individual stocks, two if the household owns mutual funds and individual stocks, and three if the household owns only individual stocks.
Standard errors are clustered by household.
28
The results are similar if we use a multinomial probit model.
29
We have also estimated a probit model making a direct comparison between households owning mutual funds only and households owning individual
stocks only (i.e. excluding households holding no equity and households holding both mutual funds and stocks). The direct comparison reduces the sample size
from 5810 to 1364, so statistical power is probably lower. The coefficient of the Loewenstein loss-aversion measure is significantly negative at the 5% level, as
predicted, while controlling for risk-aversion and other household characteristics. For the Tu measure the coefficient is negative as well, but insignificant.
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S.G. Dimmock, R. Kouwenberg / Journal of Empirical Finance 17 (2010) 441–459
16. households do not in fact have standard utility functions but are loss-averse. In this paper we empirically test how loss-aversion
affects household portfolio choice.
We use a unique dataset from The Netherlands which contains data on household portfolios and as well as a series of questions
which allow us to directly estimate each household's loss-aversion coefficient. Following the methods in Tu (2004), we derive our
behavioral measures from a series of questions asking for rates of time-preference across gains versus losses and speeding up
versus delaying transactions. These questions are based on experimental work by Loewenstein (1988) and Thaler (1981) who
show that loss-averse individual's discount rates will vary depending on the framing of intertemporal choices.
We then use this measure to test how loss-aversion affects household portfolio choice. We find that households with higher
loss-aversion are less likely to participate in equity markets and avoid direct stockholding to a greater extent than mutual funds.
However, after controlling for sample selection we do not find a significant relation between loss-aversion and household portfolio
allocations to equity. Overall, the results indicate that loss-aversion is an important feature of households' investment decision
making process with the ability to explain puzzling features of empirical household financial behavior.
Acknowledgement
We would like to thank Jeffrey Brown, Louis K.C. Chan, Bing Han, Terry Odean, Joshua Pollet, Thierry Post, Allen Poteshman,
Joshua White, William Ziemba, three anonymous referees, and seminar participants at Case Western Reserve University,
Michigan State University, Tulane University, University of Alberta, University of Illinois at Chicago, University of Illinois at
Urbana-Champaign, and the People and Money Conference at DePaul University for helpful comments. This paper uses data from
the DNB Household Survey. We are grateful to CentERdata at Tilburg University for providing this data. The usual disclaimer
applies.
Appendix A
A.1. Delay of losses, Loewenstein specification
Individuals select the delay payment such that they are indifferent between both alternatives:
vð–ðX–RÞÞ + δðTÞvð–ð–RÞÞ = vð–ð–RÞÞ + δðTÞvð–ðX + PDL–RÞÞ ðA1Þ
Using the specification of the value function, Eq. (A1) can be written as:
–λðX–RÞ + δðTÞR = R–δðTÞλðX + PDL–RÞ ðA2Þ
δðTÞλPDL = ð1–δðTÞÞλðX–RÞ + ð1–δðTÞÞR ðA3Þ
Let pDL =PDL /X and r=R/X, then we find:
pDL = ð1–δðTÞÞ½ð1–rÞ + ð1 = λÞr= δðTÞ ðA4Þ
Given λN1 and 0bδ(T)≤1, the payment for delay of losses is non-negative.
In the special case r=1, we find:
pDL = ð1= λÞð1–δðTÞÞ= δðTÞ ðA5Þ
A.2. Speed-up of gains, Loewenstein specification
Individuals have chosen the speed-up payment such that they are indifferent between both alternatives:
vð0–RÞ + δðTÞvðX–RÞ = vðX–PSG–RÞ + δðTÞvð0–RÞ ðA6Þ
Please note that (X−PSG −R) is positive when R≤X−PSG, and negative when RNX−PSG. We substitute the piece-wise linear
value function of prospect theory v(·):
–λR + δðTÞðX–RÞ = ðX–PSG–RÞ–δðTÞλR; if R≤ X–PSG
–λR + δðTÞðX–RÞ = λðX–PSG–RÞ–δðTÞλR; if R N X–PSG
ðA7Þ
PSG = ð1–δðTÞÞðX–RÞ + ð1–δðTÞÞλR; if R≤X–PSG
PSG = ð1–ð1 = λÞδðTÞÞðX–RÞ + ð1–δðTÞÞR; if R N X–PSG
ðA8Þ
456 S.G. Dimmock, R. Kouwenberg / Journal of Empirical Finance 17 (2010) 441–459
17. Let pSG =PSG /X and r=R/X, then we find:
pSG = ð1–δðTÞÞ½ð1–rÞ + λr; if r≤1–pSG
pSG = ð1–ð1 = λÞδðTÞÞð1–rÞ + ð1–δðTÞÞr; if r N 1–pSG
ðA9Þ
Further we can prove that r≤1−pSG is equivalent to λ≤δ(T)(1−r)/[(1−δ(T))r], so that the expression for pSG in Eq. (A9)
becomes fully exogenous.30
Given λN1 and 0bδ(T)≤1, the payment for speed-up of gains is non-negative.
In the two special cases r=1 and r=0, Eq. (A9) reduces to:
pSG = ð1–δðTÞÞ ðA10Þ
A.3. Speed-up of losses, Loewenstein specification
Individuals will select the speed-up premium such that they are indifferent between both alternatives:
vðRÞ + δðTÞvð–ðX–RÞÞ = vð–ðX–PSL–RÞÞ + δðTÞvðRÞ ðA11Þ
Please note that −(X−PSL −R) can be positive (e.g. when R=X) or negative (when RbX−PSL), depending on the premium PSL
and the value of the reference point. Using the specification of the value function, Eq. (A11) can be written as:
λPSL = ð1–δðTÞÞλðX–RÞ + ð1–δðTÞÞR; if PSL b X–R;
PSL = ð1–δðTÞλÞðX–RÞ + ð1–δðTÞÞR; if PSL ≥ X–R:
ðA12Þ
Let pSL =PSL /X and r=R/X, then we find31
:
pSL = ð1–δðTÞÞ½ð1–rÞ + ð1 = λÞr; if rbλδðTÞ = ð1 + ðλ–1ÞδðTÞÞ
pSL = 1–λδðTÞ + ðλ–1ÞδðTÞr; if r≥λδðTÞ= ð1 + ðλ–1ÞδðTÞÞ
ðA13Þ
Given λN1 and 0bδ(T)≤1, the premium demanded for speed-up of losses is non-negative. In the two special cases r=1 and
r=0, Eq. (A13) reduces to:
pSL = ð1–δðTÞÞ ðA14Þ
Appendix B
B.1. Delay of losses, Tu specification
Individuals select the delay payment such that they are indifferent between both alternatives:
vð–ðX–RÞÞ + δðTÞvð0Þ = vð–ð0–RÞÞ + δðTÞvð–ðX + PDLÞÞ ðB1Þ
Using the specification of the value function, Eq. (B1) can be written as:
–λðX–RÞ = R–δðTÞλðX + PDLÞ ðB2Þ
δðTÞλPDL = ð1–λÞR + λXð1–δðTÞÞ ðB3Þ
Let pDL =PDL /X and r=R/X, then we find:
pDL = ½ð1–λÞð1 = λÞr + ð1–δðTÞÞ= δðTÞ ðB4Þ
Note that the “indifference” payment pDL for the delay of losses can become negative, for example when λN1 and δ(T)=1. A
negative solution means that the individual does not want to delay the loss and needs to be compensated to do so. The survey
question about delay of a tax assessment explicitly instructs respondents to answer zero in this case: “If you are not interested in
getting an extension of payment or if you are not prepared to pay more for the extension of payment, please type 0 (zero).” In line
with the framing of the question, we therefore define the relative delay payment pDL as Eq. (B4) when it is positive, and zero
otherwise:
pDL = maxf½ð1–λÞð1 = λÞr + ð1–δðTÞÞ= δðTÞ; 0g ðB5Þ
30
Given λN0, it is easy to show that the condition pSG =(1−δ(T))[(1−r)+λr]≤1−r is equivalent to (1−δ(T))λr≤δ(T)(1−r). Similarly, pSG =(1−(1/λ)
δ(T))(1−r)+(1−δ(T))rN1−r is equivalent to (1−δ(T))λrNδ(T)(1−r).
31
Given λN0, it is easy to show that the condition pSL =(1−λδ(T))(1− r)+(1−δ(T))r≥1−r is equivalent to r≥ λδ(T)/(1+(λ−1)δ(T)). Similarly,
pSL =(1− δ(T))[(1−r)+(1/ λ)r]b1− r is equivalent to rbλδ(T)/(1+(λ−1)δ(T)).
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S.G. Dimmock, R. Kouwenberg / Journal of Empirical Finance 17 (2010) 441–459
18. B.2. Speed-up of gains, Tu specification
Individuals have chosen the speed-up payment such that they are indifferent between both alternatives:
vð0Þ + δðTÞvðX–RÞ = vðX–PSGÞ + δðTÞvð0–RÞ ðB6Þ
We substitute the piece-wise linear value function of prospect theory v(·):
δðTÞðX–RÞ = ðX–PSGÞ–λδðTÞR ðB7Þ
PSG = ð1–λÞδðTÞR + Xð1–δðTÞÞ ðB8Þ
As mentioned in the main text, PSG can become negative: in that case respondents are not willing to speed-up the gain and
instructed to write down zero. Hence:
PSG = maxfð1–λÞδðTÞR + Xð1–δðTÞÞ; 0g ðB9Þ
pSG = maxfð1–λÞδðTÞr + ð1–δðTÞÞ; 0g ðB10Þ
B.3. Speed-up of losses, Tu specification
Individuals will select the speed-up premium such that they are indifferent between both alternatives:
vð0Þ + δðTÞvð–ðX–RÞÞ = vð–ðX–PSLÞÞ + δðTÞvð–ð0–RÞÞ ðB11Þ
Using the specification of the value function, Eq. (B11) can be written as:
–λδðTÞðX–RÞ = –λðX–PSLÞ + δðTÞR ðB12Þ
λPSL = ðλ–1ÞδðTÞR + λXð1–δðTÞÞ ðB13Þ
Let pSL =PSL /X and r=R/X, then we find:
pSL = ðλ–1Þð1= λÞδðTÞr + ð1–δðTÞÞ ðB14Þ
Given λN1 and 0bδ(T)≤1, the premium demanded for speed-up of losses is non-negative.
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