Research Lounge 2013/14: Catalogue

For details on Research Lounge sessions in 2013/14, please visit the following links:

Research Lounge: Megan Crawford-Grime, “The Crisis of Suggestion: Propaganda and Risky Behaviour”

RL 13/14: Megan Crawford-Grime

Date: Wednesday, 22 Jan 2014
Location: PG Hub 1, Senate House
Topic: The Crisis of Suggestion: Propaganda and Risky Behaviour
Speaker: Megan Crawford-Grime

About the Speaker: Megan earned a B.A. in Psychology (Cognitive) and a B.A. in Philosophy (Logic) at Texas A&M University-Corpus Christi before attending the Behavioral & Economic Science, MSc program at the University of Warwick. Her main research focus for the last 3 years has been investigating the various aspects of risky behaviour and rational decision-making. She plans to expand her work into the employment and job-seeking arena while living in Coventry.

Overview: The United States (U.S.) housing market crash in late 2007 was considered by the U.S. Secretary of the Treasury as “the most significant current risk to our economy” (Andrews, 2007, para. 2). By 2008 it was difficult to find a media outlet not reporting shock, dismay, and fear over the quick economic downturn. My question is to what extent does the frequency and salience of these dire descriptions about our economic atmosphere prime our rational investment decisions?

One factor that may play a role is repetition priming. This tells us that frequent exposure to a message eases its retrieval and recognition, also known as the availability heuristic (Glanzer & Bowles, 1976; Kahneman, 2011; Leicht, 1968; Zielske, 1959). It is in the ease of retrieval and recognition which can bring about the illusion of a message’s truth (Tulving & Pearlstone, 1966; Tverksy & Kahneman, 1973). The illusion of truth resulting from frequent exposure is the basis of the propaganda effect (Begg, Anas, & Farinacci, 1992; Chomsky, 1997; Jowett & O’Donnell, 1992). In other words, it is not the truth of the message that will eventually permeate people’s mindset and influence rational investment decision, but rather the frequency. If message frequency can influence decisions, then repeated messages of economic stability should permeate the investors’ mindset, which then should influence investment decisions. But how does this frequency influence investment decisions?

We have a suggestion of how the link between the investor’s mindset and investor’s decisions occurs, from Kahneman & Tversky’s prospect theory (PT; 1979). One principle of PT suggests that consistent positive messages of economic growth & stability, like what the US experienced in the 1980’s, should lead to perception of an economic state that is more certain. Perceptions of stability should then lead to decisions that are more risk-seeking.

Conversely, PT suggests that consistent negative messages of economic recession & instability, like the one we began to experience in 2008, should lead to perceptions of an uncertain economic environment. Perceptions of uncertainty should then lead to decisions that are more risk-averse.

Experiment 1: My research began by priming participants (N = 128) with economic messages that either suggested positive growth, neutral stagnation, or a negative recession. Participants were randomly assigned to one of the conditions. My prediction was that through repetition priming of messages perhaps participants’ risk-taking decisions could be changed. Using prospect theory’s framework, I hypothesized that repeated positive messages of economic growth would lead to a more risk-seeking mindset and result in this group being more willing to invest their money. By contrast repeated negative messages of an economic recession would lead to a more risk-averse mindset and result in this group being less willing to invest their money. The stagnant economic messages were neither of growth nor recession and I hypothesized that repeated neutral messages of economic stagnation would prime a mindset somewhere between the extreme risky mindsets and result in investment willingness that falls between the extreme investment behaviours.

Task #1: Each participant read three articles modeled after real-world articles from popular publications. That is, a single participant read three articles that shared perspectives on a single economic state: either all positive economy articles, all stagnant economy, or all recession economy articles. The articles reported false events, with falsified evidence, about real cities in the state of Texas (e.g. Austin, Houston, Dallas).

The articles retained the same skeletal framework and differed only in strategically placed trigger words. Trigger words were matched between and within by frequency, letter & syllable count, part of speech (Wilson, 1988), and scores from the Rubin & Friendly goodness & emotional scale (1986). To help illustrate, trigger words from three cross-message articles, one from each message condition, will be compared. Article A, from the positive growth message condition, contains the trigger words OPPORTUNITY and PROSPERITY in the beginning of the article. Each word has a high score in both goodness (5.4 and 5.9, respectively) and emotionality (5.4 and 5.9, respectively), measured on a 7-point scale. In article A, from the neutral stagnant message condition, the trigger words INVESTIGATE and ECONOMIC hold the same word position as OPPORTUNITY and PROSPERITY, but have neutral goodness scores (3.0 and 3.2, respectively) and neutral emotionality scores (3.1 and 3.0, respectively). Finally, article A, from the negative recession message condition, contains the trigger words SICKNESS and POVERTY in the same word position, but have low goodness scores (both 1.6) and high emotionality scores (both 6.9). After reading three articles that reflected one of the economic conditions, participants performed a lexical decision task.

Task #2: To help determine whether the repetition priming of each condition successfully manipulated the mindsets of the participants, a lexical decision task was used as a manipulation check. In a lexical decision task, participants quickly decide whether a letter string is a word or not a word. The stimuli for the task were 45 words and 45 pronounceable non-words (e.g., BINT). Fifteen of the words consisted of bad words (HORRIFIC), 15 consisted of good words (HAPPY), and 15 consisted of neutral words (DOOR). The non-words were generated from the ARC Nonword database (Rastle, Harrington, & Coltheart, 2002). The words selected for the task were sampled from emotionally good, bad, and neutral lists (Ruben & Friendly, 1986), and matched by the same standards to the article trigger words as well as within (Wilson, 1988).

If priming was successful then participants in the positive growth condition should have faster reaction times (RTs) to the good words compared to the bad words. Conversely, participants in the negative recession condition should be faster at recognizing the bad words compared to the good words. To control for extraneous variables, RTs that were ±2.5 standard deviations from the overall mean were removed from the analysis. Participants primed with positive growth messages averaged a 17ms priming effect for recognition of good words relative to bad words. Participants primed with negative recession messages averaged a 21ms priming effect for recognition of bad words relative to good words. Finally, participants primed with intermediate stagnant messages had average RTs that fell between both groups on all word-types (see Figure 1). The results support the repetition priming predictions. In other words, the first task priming successfully manipulated the mindset of the reader with respect to the differing emotional messages of the economy. The next step is to determine whether this priming effect carries the type of influential weight required to alter risk-taking decisions of the primed participants.

Figure 1Figure 1

Experiment 2: Risk-taking decisions in past literature usually involved participants evaluating hypothetical scenarios. For example, participants would be asked, “If you had $10 and you encountered an investment scenario with a more risky choice and a less risky choice, what do you think you would do?” In an effort to increase the ecological validity of the results, a separate group of participants (N = 100) first received a real payment of $10 when they entered the lab, then performed the same priming task (Task #1). Primed and paid participants were then provided with an opportunity to invest their $10 for a chance to receive more money. As well, participants were offered a chance to invest another valued commodity, their personal time. That is, each participant was offered a chance to invest their future time for a chance to lessen their time in the lab.

Task #3: First the participants were required to decide whether to invest their money or time. “Investing” in this sense, meant they paid in their $10 for the chance at more money and risked a longer lab time for the chance at leaving early. If they chose to take one of the investment options, they were presented with a investment choices that offered different levels of riskiness as well as different payouts.

Half the participants were randomly assigned to a dual investment scenario. In the monetary domain, the less risky options offered a 63% chance to receive $5 more and the more risky option offered a 60% chance to double their money (+5, .63; -10, .37 or +10, .60; -10, .40). In the temporal domain, the less risky option offered a 65% chance to shorten their time in the lab by 3 minutes, and the riskier option offered a 60% chance to end their lab time immediately (-3, .65; +10, .35 or -10, .60; +10, .40).

The other half of the participants were randomly assigned to a three investment scenario. In the monetary domain, a higher probability option was added, 65% chance to receive 3 more dollars (+3, .65; -10, .35 or +5, .63; -10, .37 or +10, .60; -10, .40). In the temporal domain, a moderate option was added, 63% chance to shorten their participation time by 5 minutes (-3, .65; +10, .35 or -5, .63; +10, .37 or -10, .60; +10, .40). Adding a third option allows for a deeper exploration of the priming effect. Often, risk assessment research offers two gambling options, and conclusions are drawn from scenarios that require only one acceptance and one rejection. Perhaps by comparing investment behaviors, more subtle preference differences can be detected that are not possible to discover with only two options. Adding a third option also allows for a more in depth look at both the superordinate and subordinate levels. The superordinate level of risk-seeking behavior is initially choosing to invest their money and/or time. The subordinate level of risk- seeking behavior is the particular probability chosen in the investment.

Results: Overall, at the superordinate level, participants who read messages promoting a growing economy exhibited the most risk-seeking behavior by being the most willing to invest both their money and time (p̂ = .42, .42, respectively). Those that read messages conveying a neutral tone of a stagnant economy exhibited a slightly smaller proportion of willing investors in both domains (p̂ = .32, .35, respectively). Finally, participants who read economic messages which highlighted a failing economy, exhibited the most risk-averse behavior by having the smallest proportion of willing investors in both domains (p̂ = .26, .23, respectively; see Figure 2). Though there is a trend in both domains which supports the original hypothesis, there is no significant investment preference between the conditions in either the monetary domain, χ2 (2, n = 19) = .75, p > .05, or temporal domain χ2 (2, n = 26) = 1.48, p > .05. At the superordinate level of investment behavior, the trend of the results support the hypothesis that frequent exposure to a message can implicitly influence perceptions of truth. When this influence is based on the stability or volatility of their economy, people can quickly translate that state to their immediate valued commodities.

RL 13/14: Megan Crawford-Grime (2)Figure 2

Staying at the superordinate level, of the participants given two monetary investment options, the growth message condition yields the highest proportion of willing investors
(n = 17, p̂ = .24), the recession message condition yields a smaller proportion of willing investors (n = 15, p̂ = .07), and no one primed with a stagnant economic message choose to invest (n = 18, p̂ = .00)*. The trend reverses and is magnified when three monetary investment options are offered. The stagnant message condition yields the highest proportion of willing investors (n = 13, p̂ = .46), followed by the recession (n = 13, p̂ = .31), then growth message conditions (n = 14, p̂ = .29; see Figure 3). By increasing the number of monetary investment options, taking an initial investment risk appears to be most attractive to participants primed with neutral messages about a stagnant economy, χ2 (1, n = 31) = 9.07, p < .05, V = .54, but regarded as equivalently attractive to participants primed with either growth or recession economic messages, though the recession condition did approach significance, χ2 (1, n = 28) = 3.04, p > .05.

RL 13/14: Megan Crawford-Grime (3)Figure 3

Of the participants given two temporal investment options, the growth message condition yields the highest proportion of willing investors (p̂ = .24), than either the stagnant (p̂ = .17) or recession (p̂ = .13) message conditions. This investment behavior is also magnified when given three temporal investment options. The growth message condition yields the highest proportion of willing investors (p̂ = .50) than either the stagnant (p̂ = .46) or recession (p̂ = .31) message conditions (see Figure 4). The difference in proportions within the economic message conditions is significant for the growth message condition, but not the others, χ2 (1, n = 31) = 4.33, p < .05, V = .38. It appears that participants primed with positive messages about the economy are more effected by the temporal investment change than the participants primed with other economic messages. The three superordinate measurements so far show that participants’ investment decisions may be reflecting the manipulation of the different emotional priming messages of economic certainty.

RL 13/14: Megan Crawford-Grime (4)Figure 4

Curiously, there were more willing investors, overall, when an extra option was offered in either domain. The increased attractiveness of more investment options could be a reflection of an illusion of choice. The increased number of choices may have the effect of increasing the participant’s feelings of autonomy, or freedom of choice (Katz & Assor, 2007). This increased feeling of autonomy can increase the participant’s certainty that they can win, even though this belief has no rational base (Langer, 1975). This increased certainty of winning can, then, make a risk appear more attractive (Kahneman & Tversky, 1979).

At the subordinate level, within the monetary domain, all the investing participants given two options exclusively choose the higher probability. Of the investing participants given three investment options, the growth message condition evenly divides between the moderate and lower probabilities (p̂ = .50, .50, respectively) favoring riskier preferences; the stagnant message condition evenly divides between higher, moderate, and lower probabilities (p̂ = .33, .33, .33, respectively) exhibiting equal risky preference; and the recession message condition favors the lower over the moderate and higher probabilities (p̂ = .50, .25., .25, respectively) favoring riskier preferences overall, but not as extreme as the growth message condition (see Figure 5). Participants primed with a positive message about a growing economy are more likely to take riskier monetary investments. Furthermore, participants primed with stronger emotional messages about the economy are more likely to be riskier with their monetary investments than participants primed with neutral messages, once they have taken the first step to invest at all.

RL 13/14: Megan Crawford-Grime (5)Figure 5

At the subordinate level, within the temporal domain, all the investing participants given two options exclusively choose the higher probability, just as in the monetary domain. Of the investing participants given three investment choices, the growth message condition favors the lower over the higher probability (p̂ = .71, .29, respectively), and the stagnant message condition also favors the lower over the higher probability (p̂ = .83, .17, respectively). Both conditions exhibit stronger risk-seeking behaviour. The recession message condition evenly divides between the lower and the higher probabilities (p̂ = .50, .50, respectively) reflecting an equivalent preference for both the risky-seeking and risk-averse behaviour (see Figure 6).

RL 13/14: Megan Crawford-Grime (6)Figure 6

Concluding Thoughts: This experiment helps to expose the persuasive power of the media. Results show that repeatedly reading messages about a particular economic state can influence short-term investment behavior. Experiment 1 shows that message priming is effective with respect to emotionally charged words. Experiment 2 shows that messages of economic stability can affect the desire to invest across different domains. Participants who read messages promoting a growing economy exhibited the most risk-seeking behavior by being the most willing to invest both their money and time. Conversely, participants who read economic messages which highlighted a failing economy, exhibited the most risk-averse behavior by being the least willing to invest either their money or time. At the superordinate level of investment behavior, the results support the hypothesis that frequent exposure to a message will implicitly influence perceptions of truth. When this influence is based on the stability or volatility of their economy, people can quickly translate that state to their immediate valued commodities.

It is important to understand the impact messages like this have on the everyday consumer. Perhaps a shopper is less likely to wait in line to purchase a basket-full of groceries and housewares after reading the negative headlines on the newsstands than one who has read positive headlines. Further, how would gambling practices change if patrons had to sit next to newsstands at every slot machine?

Interestingly, adding an extra investment option showed that risky behavior did not translate equivalently within, nor between, the monetary and temporal domains. Investors in both domains showed an increase in willingness to invest. However, the investment trend reversed for money, but not for time. Personal values placed on money appear to be more susceptible to various influences than values of time. Perhaps this is symptomatic of the different inherent relationships people have with money verses time. Money can be gained, spent, lost, and found. These experiences can be planned or unplanned. Time, on the other hand, is a fixed linear experience. Time cannot be saved for later, or lost without knowledge in the same manner as money. The stability of risky investment behavior with personal time may be a reflection of the stability of our relationship with time. Conversely, the instability of risky investment behavior with personal money may also be a reflection of the instability of our relationship with money.

As much as investment behavior did not translate equivalently from one domain to another, neither did it from the superordinate to the subordinate level. However, investing participants primed with a positive message about the economy were consistently more risk- seeking than investing participants primed with a recession message. The inconsistency of subordinate investment behavior by neutrally primed participants exposes an interesting element of emotionally neutral information. Without an emotionally charged message to prime the reader, risk decisions can become influenced by other factors.

References:
Begg, I. M., Anas, A., & Farinacci, S. (1992). Dissociation of processes in belief: Source recollection, statement familiarity, and the illusion of truth. Journal of Experimental Psychology: General , 121, 446-458.

Chomsky, N. (1997). Media control: The spectacular achievements of propaganda. (G. Ruggiero, & S. Sahulka, Eds.) Seven Stories Press.

Glanzer, M., & Bowles, N. (1976). Analysis of the word-frequency effect in recognition memory. Journal of Experimental Psychology: Human Learning & Memory , 21-31.

Jowett, S., & O’Donnell, V. (1992). Propaganda and Persuasion (2nd ed.). Newbury, CA: Sage Publications.

Kahneman, D. (2011). Thinking Fast and Slow. NY: Farrar, Straus and Giroux.

Kahneman, D., & Tversky, A. (1979). Prospects theory: An analysis of decision under risk. Econometrica , 47, 263-291.

Katz, I., & Assor, A. (2007). When choice motivates and when it does not. Educational Psychology Review , 19.4, 429-442.

Langer, E. (1975). The illusion of control. Journal of Personality and Social Psychology , 32, 311-328.

Leicht, K. L. (1968). Recall and judge frequency of implicitly occurring words. Journal of Verbal Learning & Verbal Behavior , 7, 918-923.

Rastle, K., Harrington, J., & Coltheart, M. (2002). 358,534 nonwords: The ARC nonword database. Quarterly Journal of Experimental Psychology , 55A, 1339-1362.

Ruben, D. C., & Friendly, W. (1986). Predicting which words get recalled: Measures of free recall, availability, goodness, emotionality, and pronunciability for 925 nouns. Memory & Cognition , 14, 79-94.

Tulving, E., & Pearlstone, Z. (1966). Availability versus accessibility of information in memory for words. Journal of Verbal Learn- ing & Verbal Behavior , 5, 381-391.

Tverksy, A., & Kahneman, D. (1973). Availability: A heuristic for judging frequency and probability. Cognitive Psychology , 5, 207-232.

Wilson, M. (1988). The MRC psycholinguistic database: Machine readable dictionary, version 2. Behavioural Research Methods, Instruments and Computers , 20, 6-11.

Zielske, H. A. (1959). The remembering and forgetting of advertising. Journal of Marketing , 23, 239-243.

* The number of participants in each condition (n) do not sum to the overall number (N) because some participant information had to be removed from the final analysis. This was due to extremely fast article reading times (< 10 seconds) or incomplete participation. However, no participant included in the final analysis had data from more than one article removed due to short reading time.

 

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Research Lounge 2012/13: Catalogue

For details on Research Lounge sessions in 2012/13, please visit the following links:

Research Lounge: Jennifer Ljungqvist, “UK Fuel Poverty: Is the liberalisation of energy markets to blame?”

Date: Monday 17 June, 2013
Location: PG Hub 7, Senate House
Topic: UK Fuel Poverty: Is the Liberalisation of Energy Markets to Blame?
Speaker: Jennifer Ljungqvist

About the Speaker: Jennifer Ljungqvist previously did an MA in Economics and International Relations at University of Aberdeen before commencing her MSc in Economics at Warwick. She has in the past done IT Risk, IT Assurance and Health Consultancy work with private and public bodies in the Utilities, Retail, and Aviation, Telecoms, Financial Services and Healthcare sectors. Jen has also long held an interest and done economics research work in Health Economics at e.g. the Health Economics Research Unit in Aberdeen and Environmental Economics. Her undergraduate dissertation was titled ‘The Lack of Corporate Environmental Responsibility Environmental Management in Context’ and her recent research on Energy Poverty for Dr. Monica Giulietti, Associate Professor of Global Energy at Warwick Business School, was the foundation for her 17 June presentation.

Presentation: Though a relatively recent topic in terms of academic literature, with Belinda Boardman leading the way in the 1980s, the subject of ‘Fuel Poverty’ [FP] or ‘Energy Poverty’ is not a recent phenomenon. Three of the main definitions of FP can be outlined as follows:

  • ‘Need to Spend’: Households who would spend >10% of their income on fuel to achieve recognised heating standards (rhs) – Used in the UK
  • ‘Actual Spend’/ ‘Expenditure Fuel Poor’: Households who actually spend > ‘specified % of their income’ on fuel (often 10% mark used) – Used in Ireland, Australia, New Zealand
  • ‘Feel Fuel Poor’: Households who report difficulties in affording sufficient energy for their needs

There is yet to be a commonly agreed definition of the concept, thus comparisons of FP statistics across countries and the value of FP targets is subject to the definition used. The ‘Need to Spend’ definition for instance, unlike ‘Actual Spend’, is promoted for taking into account both those who under-heat their homes and those with high fuel bills but is does not take into account consumer attitudes, perceptions or preferences the way that the ‘Feel Fuel Poor’ definition does.

So why should we care about FP?

First, looking at the UK as an example of a country with colder winters, if vulnerable energy consumers such as older adults, families with young children or the ill do not adequately heat their homes because they cannot afford it or because it costs too much due to rising energy prices and poor insulation then this could have direct adverse effects on their health. The World Health Organisation recommends minimum indoor temperatures of 18C or 20-21C for more fragile elderly adults, but if temperatures are below this there are risks of small adverse health effects (16-19C), increased probability of respiratory or cardiovascular conditions (<16C) or even hypothermia (<10C).

Second, if households do spend enough to heat their homes but this expenditure is a significant proportion of their income then this will affect those consumers ability to spend money elsewhere in the economy and their living standards in general. Research has shown that a large number of fuel poor are also often income poor.

Third, long term solutions to tackle FP such as improved insulation, more cost-efficient use of energy, more educated energy consumers and utilizing waste heat from renewables and other industries (e.g. Combined Heat and Power [CHP]) also tie into today’s climate change targets.

The problem in the UK is a prominent one, there was a rise in the number of fuel poor in England to 2.8 million (13.2%) in 2004-2007 of which 2.3 million were vulnerable households and 56% were in the lowest income percentile. The problem peaked in 2009 (see graph) at a total of 5.5 million households and has since decreased to 4.5 million in 2011, possible correlated to improved insulation and wider use of more cost-effective heating mechanisms such as district heating using combined heat and power (CHP); the efficient use of waste heat from renewable energy sources.

The title of the presentation was whether liberalized energy markets are to blame for FP in the UK. Some of the existing arguments for and against are as follows:

Since the 1990s de-regulation of UK gas and electricity markets the initial rise in prices among prepayment consumers (mostly low income households) as a form of cross-substitution for the decrease in prices for direct debit payments and the prevalence of market dominating energy suppliers (the ‘big six’ in the UK) and distributors (only 7 in the UK) the reform has been criticized for allowing natural monopolies and possible cartels to form and focusing energy companies’ attention on energy pricing rather than efficient supply and use of energy.

On the other hand, more recent data does not support the fear that prices for prepayment users is rising faster. Also, many of the large energy suppliers and distributors argue that the consistent transparency of price changes maintained by watchdogs Consumer Focus, Ofgem and Energy UK as well as the UK Government’s 2000 and 2004 Fuel Poverty targets are providing unofficial pressure on prices, keeping them from increasing significantly.

Final considerations…
Whatever conclusion you reach on the advantages and disadvantage of a de-regulated market Waddams Price (2005) cautions that re-imposing price caps would bring new costs and disturbances in the market. Furthermore, it should be noted that British history of cheap access to coal for heat starting during the industrial revolution, which required large and open air vented buildings and resulted in poorly constructed and insulated buildings, the country’s cultural trend to admire and maintain old buildings and the bas past experiences with energy efficient solutions such as district heating have also played their part in today’s fuel poverty numbers. Whereas the much earlier and wider use of energy efficient solutions in continental Europe in large part grew out of necessity.

The problem of fuel poverty, though prominent, does not only apply to the UK or cold climate countries. The recent summer heat wave of 2013 where a number of fragile energy consumers have died shows that too hot temperatures and the lack of access to or inability to afford cooling facilities such as air conditioning can be equally detrimental to one’s health.

Thus, lessons learned in tackling fuel poverty alongside climate change through more energy- and cost-efficient means such as renewable energy, district heating and/or CHP can be applied to developing as well as developed countries. This will become more and more important as energy use and costs look to continue to increase; see below graphs.

 

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Research Lounge: Grace Chang, “What is wrong with immigration? Immigration economics as a way of thought”

Date: Monday 17 June, 2013
Location: PG Hub 7, Senate House
Topic: What is wrong with immigration? Immigration economics as a way of thought
Speaker: Grace Chang

About the Speaker: Grace Chang previously attained her BSc (Hons) Economics from the University of Warwick and has continued on in the University to pursue Masters in Economics this academic year. She previously was interested in microeconomic behaviour and her previous work focused largely on Life Satisfaction where she wrote her dissertation on the “Happiness of Working Women”. Later on, she delved heavily into labour economics and settled on immigration as her topic of interest. She is currently writing her dissertation on “The impact of skilled migrants on skilled natives: a study in the US” where she hopes to investigate any displacement effects, if any, and the mobility of natives from the influx of migrants.

The main aim of the talk was to encourage the way of thinking about a typical academic subject in Economics. In this particular case, Grace decided to focus on immigration, a large topic in labour economics that is still under scrutiny by academics and in public debates.

Firstly, it is important to assess the importance of focusing on the issue. Why is it important to still discuss issues on immigration? Besides the various results in academic literature, there are still on-going differences in public policies in countries worldwide today. The issue at hand is why do we see the policies enacted today, why do they vary and how are we to judge it ourselves?

Thus, we see a transformation in perspective over time. Using a rough guideline, she finds that the literature first had the investigation of the pressing issues during that time. The first “headlining” literatures in the 90s are focused largely on mass emigrations onto the respective countries such as seen in Card (1990) and Friedberg (2001). Here, there was a large interest in the substitutability of natives and migrants – that is, if natives and migrants are perfect substitutes, the migrants would displace natives of their jobs, or saturate the labour market to then put downward pressure on native wages and employment.

However, more recent studies then had a transformation in their perspective. Recent work by Hunter and Gauthier-Loiselle (2010) find that educated migrants have positive effects on native workers and on the native economy through increased patenting. So in her perspective, the accumulation if literature today has led to the exhaustion of the popular question of “Is immigration bad?” The bias of each “native” country will always exist and the question will always be asked. However, at least academically, the argument of substitutes and complements may be a weak one. As labour becomes more mobile, it is perhaps more interesting to question: 1) Do migrants add productive value to the native economy? Who are they and how can we encourage their contribution? 2) Do restrictive policies help native workers to better contribute to the economy? 3) How well do migrants (first and second-generation) assimilate into the native country and how can we encourage this?

The idea is to think ahead of the current literature, and to question the old. Perhaps substitutability in labour is not as strong as we assumed it to be such as its comparison to substitutability in goods. Maybe the migration policies enacted today are in such a variety because we have been holding on to such old assumptions that lead us to contrasting results. Thus, the talk concluded as a food for thought about the way we think about an argument, especially when there is always a natural bias to one side.

 

© The content is provided by the speaker Grace Chang to present the opinions and findings completely and accurately. Reference and data source may not be fully displayed but can be requested from the author. The Full content is considered as intellectual or academic work and strictly protected by copyright law. All rights reserved.

Research Lounge: Katharina Allinger, “Happiness Economics: Key findings from several decades of happiness research and their implication for economics”

Date: Monday 10 June, 2013
Location: PG Hub 7, Senate House
Topic: Happiness Economics: Key findings from several decades of happiness research and their implication for economics
Speaker: Katharina Allinger

In my research lounge talk I attempted to discuss a variety of interesting findings from the research on “happiness economics” *. I chose an interactive format where we tried to use common sense and economic intuition to approach questions and answers from the field of happiness economics. For this reason I want to thank all of my amazing colleagues for their participation and, as usual, very insightful comments. This article cannot possibly reflect all the issues we touched upon which is why I focus on two topics instead: the relationship between income and happiness and happiness equations.

Income and Happiness
What economists ultimately care about is welfare and not GDP, but GDP and welfare maximization have often been equated in the public debate. Whether GDP and well-being are indeed necessarily positively related has been questioned on various accounts and happiness research has made important contributions to the discussion. One of the earliest academic contributions in the field was a book chapter written by Richard Easterlin in 1974.

What Easterlin discovered was that when you looked at time series data for the United States, GDP per capita in the period of analysis had increased rapidly, but there had been no increase in life-satisfaction over the same time period. If you believed that GDP growth was sufficient for increasing life satisfaction, then the logical conclusion was to name the finding “Easterlin paradox”. As usual, when conventional wisdom gets challenged, people reacted strongly to the findings and tried to prove them wrong. Almost 30 years later, the consensus among economists is that Easterlin’s results are robust and indeed, do not only apply to the United States, but to some other developed countries as well. His findings have been confirmed and extended in numerous studies. On the other hand, there are also some stylized facts on the impact of income on happiness: Individual-based cross-country studies, experimental evidence and macro studies largely show a positive correlation between measures of happiness and income.

So how can we reconcile the Easterlin Paradox that shows no increase in happiness combined with large increases in GDP over time with these stylized facts that suggest that GDP does lead to higher happiness? Many researchers have attempted to answer this question and found two main explanations: social comparison and adaptation. The former refers to the fact that for individuals it does not only matter what they have, it also matters what others - our reference groups - have. An impressive amount of studies has focused on different reference groups and in many studies the effects on happiness of a personal income increase are exactly neutralized if the income of the reference group also increases by the same amount. In addition, there is a certain adaptation effect, meaning that a one-off increase in income may only increase happiness temporarily. Studies have shown that income’s long-run effect is only 40% of it’s short-run effect. **

If we wanted to translate these findings into economic intuition we could say that increasing income for an individual has two effects: a consumption effect and a status effect. When we assume that happiness rises with income we normally invoke the idea of a positive but decreasing marginal utility of consumption. Therefore in developed countries the additional benefit from more income and consumption may become relatively small. On the other hand, if the income of the reference group is held constant, the individual gets a happiness boost because its relative income rises: we can call this a status effect. If we assume that status is not a zero-sum game, then we can, for example, hypothesize that increasing income inequality might have negative effects on happiness even though it might raise average income. There are certainly many different combinations of consumption and status effects that can lead on aggregate to very different effects of an increase in average income on average happiness.

Happiness Equations
Most of the second half of our discussion was then devoted to another interesting question. Obviously, income is not the only factor that causes people to be happy (or in this case maybe better: content with their lives). After some brain storming we were able to use common sense to figure out the main determinants of happiness and stumbled upon one important distinction: the difference between “being happy in the moment”, which could be triggered by eating chocolate, and “being generally happy with one’s life”. This distinction is mainly one between “affective” and “evaluative” measures which have yielded different results in empirical studies. While every study follows a slightly different approach and focuses on different measures we could generally say that the factors that are most important for evaluative happiness are: income, employment (unemployed, employed, housewife, job satisfaction, …), family (marital status, children, …), community and friends, subjective health, freedom and religion (which has yielded ambiguous results in empirical studies).

In addition to these “happiness equations” that try to explain the variation in happiness through different individual variables, a number of authors, e.g. Helliwell, have argued that we need to include societal variables as well in our happiness equations and that including purely individual determinants without societal controls leads to confounding results. For this reason many studies in recent years have attempted to distinguish between individual and national determinants of well-being.

Footnotes:
* To avoid confusion I subsequently use the term happiness even though it is generally assumed that happiness is a more „affective“, short-run measure while subjective well-being is a more „evaluative“ and long-run measure and that both are strongly linked but not identical.
** If one cares to dig a little deeper into the literature on happiness, we can find that what happiness economists have proven with the means of elaborate econometrics, Bernard Mandeville already discussed as early as 1705 in his book „Fable of the Bees“.

 

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Research Lounge: Justus Timmers, “Income Inequality in the UK and beyond”

Date: Monday 4 March, 2013
Location: PG Hub 7, Senate House
Topic: Income Inequality in the UK and beyond
Speaker: Justus J.H.P. Timmers

Note on the author: Justus Timmers was at arms length from core of the 99% protest at St. Paul’s Cathedral during his internship over the summer of 2011 when the nationwide riots and looting sparkled among others the income inequality debate in the UK. Income inequality is traditionally often a secondary consideration at best in the hierarchy of considerations in economics. As a student Economics and Geography at UCL and currently of Behavioural and Economic Sciences at Warwick, this is core to Justus’ interests and found the national divide on the issue striking. At the Warwick Economics Research Lounge Justus sought to discuss further the causes of income inequality.

Presentation: Justus started his presentation with providing an overview and definitions of inequality, to continue with some traditional sociological and economic theories on the causes of inequality. His own empirical research is based on the 2002 and 2010 Labour Force Survey data for a course at UCL titled Scales of Inequality. Possibly a quirk of history, the protest witnessed nearby was marked by little structure, no identifiable leader and no demands: yet it might have been very successful in its goal, which is to stipulate debate in this unprecedented social and somewhat ignored academic territory.

The economic terrain is indeed unprecedented in terms of scope and depth, as both the universality and severity of the economic crisis and levels of inequality have not been seen since founding of the OECD in 1961. The level of income perceived as typical is often severely biased by surrounding, neither rich nor poor often realise the scale of the differences, and I refer you for the pre-tax hourly job wages and the coefficients of inequality to the presentation, please click here for the slides.

The graph below summarises the key facets of increase of income inequality over the last decade adequately: income inequality is a feature of the top earnings monopolising most economic gains. This requires some additional comments.

The bottom 90th percentile of earners are more egalitarian. It is indeed neither at the 99th percentile – i.e. those just making the 1% category – nor even the 99.9th percentile that capture the situation to any justifiable extend. It is the top-of-the-top earners.

To translate this in the more protest’s banker bonus terminology: it is not all those greedy bankers, it is the one banker who earned a £40mm bonus opposite to me in his glass office responsible for corporate and financial engineering, as dicing and slicing ABN-Amro in a €72bn deal. The consequent bailouts might have nearly bankrupted governments, but enriched a few grossly over the crisis for those who manage to benefit from their connections.

Ascribed characteristics / SBTC.

It should therefore indeed not come to a surprise that these ascribed characteristics are not key in making these individuals so privileged in society. These super-earners are typically male (gender however is the fastest closing gap and accounts for about 4% of inequality in 2010), reside around London (again about 4% of inequality can be explained by region) and are white-European (0.3% of inequality is explained by ethnicity). Yet, these characteristics barely account for the discrepancies in earnings.

The inequality within any group you can imagine is far greater than that between different groups and increasingly so. It is indeed very hard to guess in advance exactly who the individuals are by characteristics they are endowed with.

Instead of ascribed characteristics, maybe these people are more skilled, more experienced, more future oriented, and educated? Skill-Biased Technological Change (SBTC) is a popular economic theory based on these premises and actually does a better job than the ascribed characteristics mentioned above.

To summarise the SBTC theory predicts that technological change caused some workers to become very productive (think designers, IT engineers, bankers) and other workers (think catalogue keepers, toy makers, etc.) to become replaceable. Dependent on skill level some groups gained and others lost out. The high skilled are the predicted winners. Slightly counter-intuitive those losing out most are not the lowest skilled, as non-routine work is hard to replace, but pretty a university education to adapt to take advantage of the newest advances in technology has become a prerequisite.

Yet the capacity of education and experience to explain income is diminishing. Differences in education and age accounted for about a fourth of inequality in 2002, but diminished in explanative power to a fifth in 2010.

Intuitively it makes sense that these explanations do not bring us very far. For Joe on the street to Warren Buffet there is a large realization that income is greatly affected by serendipity. For an economist this is very dissatisfying, as lucky few by definition are impossible to identify a priori, nor does this say much about the underlying causes of inequality.

Alternative explanations.

Therefore people have increasingly been looking to alternative explanations, often involving politics, institutions, globalisation and norms. Prominent thinkers include Stiglitz, Acemoglu, or Pogge and all believe that the political aspect is undeniable. It is the interaction between economic and political power that create this vicious spiral of increased inequality. Put very simply: (political, economic) connections matter and are becoming more profound. What empirical evidence can we present here?

Justus focuses in his presentation on parent-child earning elasticities. Previous research, by e.g. Björklund, shows that unequal countries often have very high father-son earning elasticities. This nears parity for the top 0.1% earners. Given the low accountability of income personal characteristics have, it can only be concluded connections – if not outright nepotism – play a main role in income inequality. Why is it right now that income inequality is increasing? For instance, in his presentation Justus Timmers presents evidence of his research that the institution of marriage is very recently becoming less universal and dependent on incomes. This effect is not significant yet in 2002. However, post anno 2010, if you have two parents from which connections you can benefit: they are most likely financially well off. We find hereby evidence for an increasing polarisation of those not connected and those connected in a bubble providing unlimited financial opportunities.

N.B. My current MSc. Research Project (eq. dissertation) is on the topic of assortitative matching in couples.

N.B. The analysis is based on hourly earnings from the main job before deductions. As Lukas Mergele noted in the discussion, the inclusion of capital gains would most likely exacerbate the effect of income inequality.

 

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Research Lounge: Konrad Gudjonsson, “Fisheries economics: How Iceland made professional fisheries profitable”

Date: Monday 25 Feb, 2013
Location: PG Hub 7, Senate House
Topic: Fisheries economics: How Iceland made professional fisheries profitable
Speaker: Konrad Gudjonsson

About the Speaker: Konrad earned his B.Sc. in Economics at the University of Iceland before going to Warwick. Coming from a tiny fishing village in a rural part of Iceland, he has learned to understand the industry and how efficient it can be if the right framework is provided. He wrote his undergraduate dissertation on profitability in mackerel fisheries in Iceland and concluded that there was a great capacity for higher profits without increasing the catch.

Why are commercial fisheries so special?
The short answer is simply: The tragedy of the commons. Free and wild fish in the seas are per se not owned by anyone, which gives fishermen incentives to overfish and not consider the effect of their own actions on other fishermen and the fishstocks. This very often leads to unsustainable fisheries, higher costs per unit of catch and low, or even zero, profits in the industry.

Commercial Fisheries around the world.
Around the world, fisheries are generally unsustainable, with small stocks and small profits. Furthermore, fisheries get large subsidies, which are estimated to account for 30-35% of revenues (Milazzo, M. 1998. Subsidies in World Fisheries: A Reexamination). The economic loss has then been estimated by the World Bank to be around $50 billion, which is equivalent to the GDP of Cameroon or Uruguay.

How Iceland solved the problem:
Gradually, from 1970′s until the 1990′s, Iceland implemented what is called “Individually Transferable Quotas” (ITQ). In its simplest version the system works like this: The Fisheries Institute decides each year how much fish can be caught, “Total Allowable Catch” (TAC), based on stock size and looking at maximizing the size of fish stocks in the long run. Then each vessel, or company, is given a fixed percentage (quota) in the TAC which they can fish during that year. These quotas, if they are permanent property, can then be traded between companies.

Why can ITQs work in theory?
First, if the quotas can be traded, those who have the best technology or knowledge are likely to buy quotas from those who are less profitable. Second, ITQs eliminate the competition to catch the fish and give fishermen better incentives to protect their resource, especially if the quotas are permanent property. That essentially eliminates the “Tragedy of the Commons” problem. Finally, the fishermen have incentives to make better long-term investments and invest in R&D.

How has this worked out for Iceland?
- Profits have increased dramatically with net income increasing by 170% in real terms, while catch has decreased slightly.
- Productivity in fisheries has increased more than in other industries and capital utilization has improved with smaller and more efficient fleet.
- Positive impact on the environment with less oil being used for unit of catch and less chance of overfishing.
- The incentives to look at the long-run have increased the prices and brought Iceland to the “best markets” with vertical integration, long-term supply contracts and marketing.
- Has provided a platform for Icelandic companies that service the fishing industry, such as Marel hf.

Challenges ahead:
Although Iceland has overall done well in managing its fisheries in the past two decades, there is still a lot of work to be done. For instance, there are still disputes ongoing over the initial allocation, and the Fisheries laws don’t state clearly how permanent the quotas are.

As mentioned before, the total catch has not increased, which is a bit disappointing but could be caused by other factors. Also, with quotas moving away from some towns and villages has caused negative socio-economic effects. That, along with unhappiness over how few people have earned most of the profits from the industry, has caused the fisheries management to be in center of the debate for some years now.

However, the EU, with its seriously flawed and heavily subsidized fisheries, and other countries should seriously consider ITQs. They could without a doubt both learn from the mistakes Iceland has made, and more importantly, how well Iceland has done relatively to most other countries in managing its fisheries.

 

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