DOCSLIB.ORG

The Relationship of Expected Value-Based Risky Decision Making Tasks

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
The Relationship of Expected Value-Based Risky Decision Making Tasks University of Cincinnati Date: 5/2/2011 I, Andrew B Brown , hereby submit this original work as part of the requirements for the degree of Master of Arts in Psychology. It is entitled: 7KH5HODWLRQVKLSRI([SHFWHG9DOXHEDVHG5LVN\'HFLVLRQ0DNLQJ7DVNV WR$WWLWXGHV7RZDUG9DULRXV.LQGVRI5LVNV Student's name: Andrew B Brown This work and its defense approved by: Committee chair: Chung-Yiu Chiu, PhD Committee member: Frank Kardes, PhD Committee member: Gerald Matthews, PhD 1434 Last Printed:5/2/2011 Document Of Defense Form The relationship of expected value-based risky decision making tasks to attitudes toward various kinds of risks A thesis submitted to the Graduate School of the University of Cincinnati in partial fulfillment of the requirements for the degree of Master of Arts In the Department of Psychology of the College of Arts and Sciences by Andrew B. Brown B.A., Kalamazoo College 2007 Committee Chair: C.-Y. Peter Chiu, Ph.D. ABSTRACT Laboratory-based risky decision making paradigms developed by researchers in behavioral economics (e.g., Kahneman & Tversky, 1979) ask participants to choose between two options with varying expected values (EV). Very few studies have explored the relationships between these expected-value tasks and risk perceptions and intentions in other life domains. Five studies were conducted to explore these relationships. Study 1 (N = 345) used a survey format to examine the relationship between a hypothetical two-choice EV risky decision making task and perceptions of financial risk climate. Findings suggested that the number of risky choices remained positively correlated with our measures of consumer confidence, even after controlling for income level and self-reported stress. Study 2 (N = 213) expanded the scope of Study 1 by examining the relationship between risky choices on a two-choice EV task and intentions to engage in risky activities in a variety of behavioral domains. Results suggested that risky choices were positively correlated with risk taking intentions from behavioral domains such as: aggressive and illegal behavior, risky sexual behavior, heavy drinking, risky academic/work behaviors, high risk sports, betting, investing, and recreational risk taking. Study 3 (N = 138) examined whether the pattern of results found in Studies 1 and 2 might be the result of a change in attitude caused by the current economic recession. No statistically significant differences between the two samples were detected, although the number of risky choices for our sample in 2010 was numerically smaller for both gain ii and loss framed trials compared to participants from 2006 collected by Lauriola, Levin, and Hart (2007). Study 4 (N = 144) was conducted to elucidate the nature of the loss aversion findings from Studies 1 and 2 (where the ratio of risky loss to risky gain choices was close to 1:1) and those of Study 3 (where the ratio of risky loss to risky gain choices was close to 2:1). Results suggested that outcome magnitude appeared to be the strongest influence on loss aversion. Study 5 (N = 116) was a pilot study conducted to determine whether consumer confidence and risk taking could be experimentally manipulated by asking participants to consider hypothetical positive or negative financial events. Results indicated that those in the positive condition reported greater consumer confidence, and also took more risks on a two-choice EV task compared to those in the negative condition. There were no statistically significant group differences in positive or negative affect, income level, optimism, or self-reported stress. Overall, these results indicate that risk propensity measured by two-choice EV tasks does appear to be related to perceptions of financial risk climate, intentions to engage in risk taking in other behavioral domains, and that it may be possible to experimentally manipulate both risk taking and perception of financial risk climate at a macro-level (e.g., consumer confidence). iii This page is intentionally left blank. iv TABLE OF CONTENTS TABLE OF CONTENTS………………………………………………………….. v LIST OF TABLES………………………………………………………………… vi LIST OF FIGURES………………………………………………………………. vii CHAPTER 1: Introduction……………………………………………………….. 1 CHAPTER 2: Study 1…………………………………………………………….. 22 CHAPTER 3: Study 2……………………………………………………………... 37 CHAPTER 4: Study 3……………………………………………………………... 55 CHAPTER 5: Study 4……………………………………………………………... 60 CHAPTER 6: Study 5……………………………………………………………... 70 CHAPTER 7: General Discussion………………………………………………… 81 REFERENCES……………………………………………………………………. 89 v LIST OF TABLES 1. Summary of key findings from representative studies utilizing two-choice expected value tasks and other paradigms related to risk………………………………………… 13 2. Two common measures of consumer confidence……………………………………… 19 3. Reliability and descriptive statistics by trial type for participants (N =345) in Study 1.. 27 4. Component loadings of the four consumer confidence items………………………….. 30 5. Pearson correlations for climate variables of interest (N = 345)……………………….. 31 6. Descriptive statistics of four groups with different types of self-reported personal experiences…………………………………………………………………………… 31 7. Pearson correlations between variables of interest (N =345)…………………………... 33 8. Pearson partial correlations between variables of interest, controlling for income level (N =345)………………………………………………………………………………. 34 9. Descriptive statistics and Cronbach’s alpha coefficients for Study 2………………….. 43 10. Spearman partial correlation coefficients between self-reported stress, risky choices, and self-reported intentions to engage in risk taking on the CARE subscales…………. 47 11. Spearman partial correlation coefficients between self-reported stress, risky choices, and self-reported intentions to engage in risk taking on the DOSPERT subscales…….. 48 12. Component loadings for overall risky choices and the DOSPERT and CARE subscales…………………………………………………………………………….... 49 13. Component loadings for overall risky choices and the DOSPERT subscales……… 51 14. Comparison of descriptive statistics from Lauriola et al. (2007) and our 2010 study…................................................................................................................... 57 15. Comparison of descriptive statistics from our 2010 study, and our Study 4……… 63 16. Comparison of descriptive statistics from Study 3 and Study 4 for trials similar in magnitude to those used in our Studies 1 and 2…………....................................... 64 17. Comparison of descriptive statistics from Study 3 and Study 4 for trials with larger EVs than those used in our Studies 1 and 2…………………………………………… 65 18. Descriptive and inferential statistics comparing mean number of risky choices for loss domain trials to those in gain domain trials by task conditions………………………. 66 19. Comparison statistics for the two conditions in Study 5……………………………… 74 20. Component loadings for economic climate items…………………………………….. 75 21. Descriptive statistics for variables potentially responsible for the differences in economic outlook or risky choices, (N = 116)……………………………………….. 76 22. Spearman correlations for variables of interest in Study 5, (N = 116)……………….. 78 vi LIST OF FIGURES 1. Example of a multi-choice expected value risky decision task. Figure taken from Lopes & Oden, (1999), The role of aspiration level in risky choice: A comparison of cumulative prospect theory and SP/A theory. Journal of Mathematical Psychology, 43, p. 293……………………………………………………………………………... 6 2. Risk preference for each trial type (RA = risk advantageous, EQEV = equal expected value, RD = risk disadvantageous). Risk preference indicates the proportion of the time participants selected the probabilistic or risky option over the certain option for that trial type. Error bars indicate standard errors………………… 29 3. Risk preference for each trial type (RA = risk advantageous, EQEV = equal expected value, RD = risk disadvantageous) and each outcome magnitude. Risk preference indicates the proportion of the time participants selected the risky option over the certain option for that trial type. Error bars are standard errors……………. 45 4. The proportion of risky choices for gain and loss trials by condition. Error bars are standard errors………………………………………………………………………... 67 vii CHAPTER 1 Introduction Researchers in the field of risky decision making have taken much interest over the years in discovering the factors affecting choice and preference (Fox & Poldrack, 2008; Weber & Johnson, 2008, 2009). Although most early research in this area focused primarily on the generation of a “list of phenomena that show[ed] deviations from the predictions of normative models,” Weber and Johnson (2009) note that research involving judgment and decision making has gradually transformed into a field interested in “developing and testing hypotheses about the psychological processes that give rise to judgments and choices and about the mental representations used by these processes” (p. 22.3). The research described in this manuscript is largely a product of this more nuanced view of decision making, and our goal was chiefly one related to discovering potential relationships between constructs involving risk and uncertainty throughout a broad realm of decision making contexts. In the pages that follow, we will begin with a review of the literature most relevant to the five studies that we conducted; which will include an introduction to risky decision making and its measurement from four primary perspectives: developmental, neuropsychological, personality and individual
Load more

Recommended publications
  • What Role Does Consumer Sentiment Play in the U.S. Economy?
    The economy is mired in recession. Consumer spending is weak, investment in plant and equipment is lethargic, and firms are hesitant to hire unemployed workers, given bleak forecasts of demand for final products. Monetary policy has lowered short-term interest rates and long rates have followed suit, but consumers and businesses resist borrowing. The condi- tions seem ripe for a recovery, but still the economy has not taken off as expected. What is the missing ingredient? Consumer confidence. Once the mood of consumers shifts toward the optimistic, shoppers will buy, firms will hire, and the engine of growth will rev up again. All eyes are on the widely publicized measures of consumer confidence (or consumer sentiment), waiting for the telltale uptick that will propel us into the longed-for expansion. Just as we appear to be headed for a "double-dipper," the mood swing occurs: the indexes of consumer confi- dence register 20-point increases, and the nation surges into a prolonged period of healthy growth. oes the U.S. economy really behave as this fictional account describes? Can a shift in sentiment drive the economy out of D recession and back into good health? Does a lack of consumer confidence drag the economy into recession? What causes large swings in consumer confidence? This article will try to answer these questions and to determine consumer confidence’s role in the workings of the U.S. economy. ]effre9 C. Fuhrer I. What Is Consumer Sentitnent? Senior Econotnist, Federal Reserve Consumer sentiment, or consumer confidence, is both an economic Bank of Boston.
    [Show full text]
  • Consumer Behaviour During Crises
    Journal of Risk and Financial Management Article Consumer Behaviour during Crises: Preliminary Research on How Coronavirus Has Manifested Consumer Panic Buying, Herd Mentality, Changing Discretionary Spending and the Role of the Media in Influencing Behaviour Mary Loxton 1, Robert Truskett 1, Brigitte Scarf 1, Laura Sindone 1, George Baldry 1 and Yinong Zhao 2,* 1 Discipline of International Business, University of Sydney, Sydney, NSW 2006, Australia; [email protected] (M.L.); [email protected] (R.T.); [email protected] (B.S.); [email protected] (L.S.); [email protected] (G.B.) 2 School of Economics, Fudan University, Shanghai 200433, China * Correspondence: [email protected] Received: 24 June 2020; Accepted: 19 July 2020; Published: 30 July 2020 Abstract: The novel coronavirus (COVID-19) pandemic spread globally from its outbreak in China in early 2020, negatively affecting economies and industries on a global scale. In line with historic crises and shock events including the 2002-04 SARS outbreak, the 2011 Christchurch earthquake and 2017 Hurricane Irma, COVID-19 has significantly impacted global economic conditions, causing significant economic downturns, company and industry failures, and increased unemployment. To understand how conditions created by the pandemic to date compare to the aforementioned shock events, we conducted a thorough literature review focusing on the presentation of panic buying and herd mentality behaviours, changes to discretionary consumer spending as defined by Maslow’s Hierarchy of Needs, and the impact of global media on these behaviours. The methodology utilised to analyse panic buying, herd mentality and altered patterns of consumer discretionary spending (according to Maslow’s theory) involved an analysis of consumer spending data, largely focused on Australian and American markets.
    [Show full text]
  • CONSUMER CONFIDENCE and the ECONOMY by Jay Wortley, Senior Economist
    STATE NOTES: Topics of Legislative Interest March/April 2001 CONSUMER CONFIDENCE AND THE ECONOMY by Jay Wortley, Senior Economist There are a number of key economic variables that are closely watched because they help identify the current state of the economy and provide some clues as to where the economy is headed. One of these indicators is the University of Michigan’s Index of Consumer Sentiment. This index is designed to measure the level of confidence consumers have in the economy. This article explains why this index is closely watched, describes how the index is created, and presents what the index is revealing about the current state of the economy. Why is Consumer Confidence Important? Consumer confidence is important because it is a key factor that helps determine how much consumers are going to spend on goods and services, which is one of the major driving forces in the economy. From 1980 to 2000, expenditures by consumers accounted for 67% of total economic activity. Due to the fact that consumer spending is a major source of overall economic activity, most of the time it is safe to say that as consumer spending goes, so goes the overall economy. For example, in 1991, personal consumption expenditures, adjusted for inflation, declined 0.2% and overall economic activity, as measured by real Gross Domestic Product (GDP) fell 0.5%, but in 2000, real consumer spending increased 5.3% and total economic activity grew 5.0%. Consumer confidence is a good indicator of consumer spending because the more confident consumers are about future economic conditions, the more likely they are to purchase consumer goods, particularly the relatively high- priced major purchases such as motor vehicles, houses, and household furnishings.
    [Show full text]
  • The-Great-Depression-Glossary.Pdf
    The Great Depression | Glossary of Terms Glossary of Terms Balanced budget – Government revenues equal expenditures on an annual basis. (Lesson 5) Bank failure – When a bank’s liabilities (mainly deposits) exceed the value of its assets. (Lesson 3) Bank panic – When a bank run begins at one bank and spreads to others, causing people to lose confidence in banks. (Lesson 3) Bank reserves – The sum of cash that banks hold in their vaults and the deposits they maintain with Federal Reserve banks. (Lesson 3) Bank run – When many depositors rush to the bank to withdraw their money at the same time. (Lesson 3) Bank suspensions – Comprises all banks closed to the public, either temporarily or permanently, by supervisory authorities or by the banks’ boards of directors because of financial difficulties. Banks that close under a special holiday declaration and remained closed only during the designated holiday are not counted as suspensions. (Lesson 4) Banks – Businesses that accept deposits and make loans. (Lesson 2) Budget deficit – When government expenditures exceed revenues. (Lesson 4) Budget surplus – When government revenues exceed expenditures. (Lesson 4) Consumer confidence – The relationship between how consumers feel about the economy and their spending and saving decisions. (Lesson 5) Consumer Price Index (CPI) – A measure of the prices paid by urban consumers for a market basket of consumer goods and services. (Lesson 1) Deflation – A general downward movement of prices for goods and services in an economy. (Lessons 1, 3 and 6) Depression – A very severe recession; a period of severely declining economic activity spread across the economy (not limited to particular sectors or regions) normally visible in a decline in real GDP, real income, employment, industrial production, wholesale-retail credit and the loss of overall confidence in the economy.
    [Show full text]
  • Financial Stability Or Instability? Impact from Chinese Consumer 2
    FINANCIAL STABILITY OR INSTABILITY? IMPACT FROM CHINESE CONSUMER 2. CONFIDENCE Shi-Qi LIU1 Xin-Zhou QI2 Meng QIN3 Chi-Wei SU4 Abstract This paper attempts to study the dynamic causal relationship between Chinese consumer confidence and financial stability by using a sub-sample time-varying rolling window test. Through empirical research, it is proved that consumer confidence improves the stability of the financial market, ensure the smooth operation of the financial system, and reduce the possibility of financial risks. Similarly, financial stability has a positive impact on consumers. Due to the government's intervention in the economy, the financial market is relatively stable, thus consumers are full of confidence in the market. Therefore, we find that the causal relationship between consumer confidence and financial stability is consistent with financial system volatility model, which contributes to the stability of financial markets and the reduction of financial crises. This finding helps monetary authorities maintain financial stability by increasing consumer confidence and making the most effective decisions based on economic trends. Keywords: consumer confidence; financial stability; bootstrap; rolling window; causality; time-varying JEL Classification: C32, D81, E32 1 Qingdao University, School of Economics, No. 308 Ningxia Rd., Qingdao, Shandong, China; E- mail: [email protected] 2 Corresponding Author. Qingdao University, School of Economics, No. 308 Ningxia Rd., Qingdao, Shandong, China; E-mail: [email protected] 3 Party School of the Central Committee of the Communist Party of China (National Academy of Governance), Graduate Academy, No. 100 Dayouzhuang, Beijing, China; E-mail: [email protected] 4 Corresponding Author. Qingdao University, School of Economics, No.
    [Show full text]
  • Redalyc.Is the Consumer Confidence Index a Sound Predictor of The
    Estudios de Economía Aplicada ISSN: 1133-3197 [email protected] Asociación Internacional de Economía Aplicada España Loría, Eduardo; Brito, Luis Is the Consumer Confidence Index a Sound Predictor of the Private Demand in the United States? Estudios de Economía Aplicada, vol. 22, núm. 3, diciembre, 2004, pp. 795-809 Asociación Internacional de Economía Aplicada Valladolid, España Available in: http://www.redalyc.org/articulo.oa?id=30122317 How to cite Complete issue Scientific Information System More information about this article Network of Scientific Journals from Latin America, the Caribbean, Spain and Portugal Journal's homepage in redalyc.org Non-profit academic project, developed under the open access initiative E STUDIOS DE ECONOMÍA A PLICADA VOL. 22 - 3, 2004. PÁGS. 727 (14 páginas) Is the Consumer Confidence Index a Sound Predictor of the Private Demand in the United States? LORÍA , EDUARDO(*) Y BRITO, LUIS(**) (*)School of Economics, National Autonomous University of Mexico (UNAM).(**)Journal CIENCIA ergo sum, Autonomous University of the State of Mexico (UAEM) (*) Edificio Anexo, Tercer Piso, Cub. 305 Circuito Interior S/N, Ciudad Universitaria Del. Coyoacán, México, D. F. 04510 Telf.: +52 (55) 5622 2142 - E-mail: [email protected] (**) Av. V. Gómez Farías No. 200-2 Ote. Toluca, México, 50000 E-mail: [email protected] - Telf. & fax: +52 (722) 213 7529 ABSTRACT The United States (U.S.) Consumer Confidence Index (CCI) has been widely and unambiguously regarded as a good ‘predictor’ of the U.S.’s private investment and consumption. In order to demonstrate such with statistical rigor, co-integration and Granger causality tests were applied for 1978:Q1-2003:Q1.
    [Show full text]
  • Summary of IS-LM and AS-AD
    Summary of IS-LM and AS-AD Karl Whelan September 19, 2014 The Goods Market • This is in equilibrium when the demand for goods equals the supply of goods. • Higher real interest rates mean there is less demand for spending. – Consumers choose to save instead of spend. – Businesses discouraged from borrowing for investment. • So higher real interest rates mean the goods market equilibrium (demand = supply) occurs at a lower level of supply, i.e. lower GDP> The IS Curve Shifts in the IS Curve • Anything that leads to higher demand for spending that is NOT real interest rates will shift the IS curve to the right. This includes – Improvements in consumer\business sentiment. – Higher government spending. – Lower taxes. – Good news about the future of the economy. • The opposite type of developments (e.g. lower consumer sentiment) will shift the IS curve to the left. A Fall in Consumer Confidence The Money Market: Demand • You have to keep all of your assets in one of two forms – Money, which bears no interest but can be used for transactions. – Bonds, which pay an interest rate of r. • Three factors determine the demand for money – GDP: More GDP means you need more money for transactions. – Prices: Doubling prices means you need double the money for transactions – Interest Rates: Higher interest rates means less demand for money and more demand for bonds. An Equation for Money Demand An equation to describe this would look like this: Where Money Supply • This is set by the central bank. • In reality, the central bank only controls the monetary base (currency and reserves at the central bank).
    [Show full text]
  • Consumer Spending
    October 2, 2020 Introduction to U.S. Economy: Consumer Spending Consumer spending is a key driver of short-run economic result of business closures, social distancing, and other growth in the U.S. economy. During the initial months of measures taken to limit the spread of the virus. the Coronavirus Disease 2019 (COVID-19) pandemic, consumer spending dropped at a rapid and historic rate. Figure 1. Real Personal Consumption Expenditures This In Focus provides an overview of consumer spending, Q1 2005 - Q2 2020 summarizes recent trends, describes its relationship with the business cycle, and discusses policy that can impact and be affected by consumer spending. How Consumer Spending Is Measured As defined by the Bureau of Economic Analysis (BEA), consumer spending, also referred to as personal consumption expenditures (PCE), is the value of the goods and services purchased by, or on the behalf of, persons (households and nonprofit institutions serving households) living in the United States. PCE comprises roughly two- thirds of gross domestic product (GDP) and is therefore typically a large component of short-run economic growth. BEA provides PCE data monthly and measures these expenditures in relation to personal income and prices. Transactions included in the calculation of PCE consist Source: Bureau of Economic Analysis (BEA). largely of the purchases of new goods and services by Note: Seasonally adjusted at annual rates. households, among others. BEA measures the values of expenditure transactions, including sales and excise taxes. Consumer Spending and the Economy Measuring consumption expenditures against income Patterns of consumer spending can be linked closely with allows for a comparison of how much consumers spend broader economic conditions, specifically the business versus save.
    [Show full text]
  • Confidence and the Propagation of Demand Shocks∗
    Confidence and the Propagation of Demand Shocks∗ George-Marios Angeletosy Chen Lianz July 9, 2021 Abstract We revisit the question of why shifts in aggregate demand drive business cycles. Our theory com- bines intertemporal substitution in production with rational confusion, or bounded rationality, in consumption and investment. The first element allows aggregate supply to respond to shifts in aggregate demand without nominal rigidity. The second introduces a “confidence multiplier,” that is, a positive feedback loop between real economic activity, consumer expectations of permanent income, and investor expectations of returns. This mechanism amplifies the business-cycle fluctu- ations triggered by demand shocks (but not necessarily those triggered by supply shocks); it helps investment to comove with consumption; and it allows front-loaded fiscal stimuli to crowd in pri- vate spending. ∗This paper subsumes earlier versions, entitled “A (Real) Theory of the Keynesian Multiplier” and “On the Propagation of Demand Shocks,” that contained somewhat different frameworks. We are grateful to the editor, Veronica Guerrieri, and three anonymous referees for extensive feedback that helped improve and reshape the paper. For useful comments, we also thank Susanto Basu, Jesse Benhabib, Marty Eichenbaum, Pierre-Olivier Gourinchas, Cosmin Ilut, Jianjun Miao, Emi Nakamura, Jon´ Steinsson, and seminar participants at BU, Berkeley, CREI, Cornell, Goethe University, NYU, MIT, FSU, SF Fed, the 2018 AEA meeting, the 2018 SED meeting, the 2020 NBER Summer Institute, and the 2020 NBER Monetary Economics Fall Meeting. Angeletos acknowledges the financial support of the National Science Foundation, Award #1757198. Lian acknowledges the financial support of the Alfred P.Sloan Foundation Pre-doctoral Fellowship in Behavioral Macroeconomics, awarded through the NBER.
    [Show full text]
  • 15.012 Lecture 3, the IS-LM Model
    Simple Notes on the ISLM Model (The Mundell-Fleming Model) This is a model that describes the dynamics of economies in the short run. It has million of critiques, and rightfully so. However, even though from the theoretical point of view it has some loopholes, it continues to be an excellent way of analyzing and understanding the behavior of economies. Robert Solow once was asked: “Professor Solow when were at the Counsel of Economic Advisors, what of all your knowledge did you use the most?” He answered something like this: “Well you just need the Dornbusch and Fischer, but you have to use it right.” The Dornbusch and Fischer is essentially the ISLM model. In practical policy making the ISLM dominates at least 50 percent of discussions. The most important assumption required for this model to work is that prices (and in particular wages) are fixed or predetermined in the short run. This model has two schedules that reflect the equilibrium in two markets: goods and money. In other words, one schedule represents the market in which the supply of goods is equal to the demand of goods, and the other schedule represents the market in which the supply of money is equal to the demand of money. Now let us see what happens when we have a model that has these ingredients… Model for a closed economy. First assume that the economy is closed. This will help us better understand the basic model; later we will proceed with the more complicated version when there are international transactions on goods and capital.
    [Show full text]
  • Learning to Classify the Consumer Confidence Indicator (In Portugal)
    economies Article Learning to Classify the Consumer Confidence Indicator (in Portugal) António Bento Caleiro Departamento de Economia, Universidade de Évora, 7004-516 Évora, Portugal; [email protected] Abstract: The literature paid some attention, some time ago, to the relationship that, supposedly, should exist between the level of consumer confidence and the unemployment rate. This relationship is interesting, both from a scientific point of view, given the inherently subjective character of that level of confidence, but also from the point of view of economic policy, given the importance of the unemployment rate. In this article, that relationship is revisited, using learning models, namely regression and classification trees. Using, for example, the case of Portugal, the unemployment rate presents itself as an adequate classifier of the consumer confidence level. The use of classification trees shows that the separation between low and high values of the consumer confidence indicator is made from an adequate threshold value of the unemployment rate. The use of regression trees shows that the levels of consumer confidence are inversely related to the levels of the unemployment rate. In terms of policy lessons, this confirms that, in the face of economic crises, such as the one we are experiencing, in which confidence levels tend to fall and the unemployment rate increases, the relationship between these two variables cannot be ignored. Keywords: classification trees; consumer confidence; learning models; regression trees; unemployment rate Citation: Caleiro, António Bento. 2021. Learning to Classify the Consumer Confidence Indicator (in 1. Introduction Portugal). Economies 9: 125. https:// The severity of the current economic crisis, that many associate with the pandemic doi.org/10.3390/economies9030125 outbreak, is generally affecting all countries throughout the world.
    [Show full text]
  • Consumer Confidence
    US Economic Briefing: Consumer Confidence Yardeni Research, Inc. September 17, 2021 Dr. Edward Yardeni 516-972-7683 [email protected] Debbie Johnson 480-664-1333 [email protected] Mali Quintana 480-664-1333 [email protected] Please visit our sites at www.yardeni.com blog.yardeni.com thinking outside the box Table Of Contents TableTable OfOf ContentsContents Consumer Confidence & Sentiment 1-2 Consumer Optimism 3 Consumer Confidence 4-6 Consumer Confidence & Unemployment 7-9 Consumer Confidence & Employment 10-12 Consumer Confidence By Region 13-15 Consumer Optimism Index 16 Jobs Plentiful & Wage Inflation 17 Consumer Confidence & Wage Inflation 18-21 September 17, 2021 / Consumer Confidence Yardeni Research, Inc. www.yardeni.com Consumer Confidence & Sentiment Figure 1. 200 200 CONSUMER CONFIDENCE INDEX (1985=100, sa) 180 180 160 160 Total Index (113.8) Current Conditions (147.3) 140 Expectations (91.4) 140 120 120 Aug 100 100 80 80 60 60 40 40 20 20 yardeni.com 0 0 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 Source: The Conference Board. Figure 2. 130 130 CONSUMER SENTIMENT INDEX 120 (Q1-1966=100) 120 110 110 100 100 90 90 80 Aug 80 70 70 60 60 50 50 Consumer Sentiment Index Total (70.3) 40 Current Conditions (78.5) 40 Expectations (65.1) yardeni.com 30 30 67 69 71 73 75 77 79 81 83 85 87 89 91 93 95 97 99 01 03 05 07 09 11 13 15 17 19 21 23 25 Note: Shaded areas are recessions according to the National Bureau of Economic Research.
    [Show full text]