Tag Archives: Income

34. Inflation Predictions, Elites vs Regular Joes

meThesis: Consumers with higher education and income can predict next year’s inflation rate better than their counterparts, i.e. the average consumer.

On last week’s blog post, in an effort to match up Michigan’s survey of inflation expectations, the Survey of Professional Forecasters (SPF), and a RW no-change forecast, all forecasting one-year ahead, I was unable to conclude beyond a reasonable doubt which forecast model would be the best for predicting inflation, but in this post I hope to redeem myself.

As we consider a consumer’s level of income and education, what should we expect to happen to their predictive power? Some intuition would tell us that as a consumer’s level of income rises, she may have a higher propensity to save, because she will have more excess funds. Naturally, she may invest these funds, and hold a portfolio comprised of stocks, bonds, and other securities, in order to grow her nest egg for retirement, or set money aside to cushion against economic downturns. Thus it would be important for her to frequently keep track of the inflation rate, to make sure that her investment is not eroded by price increases.

Poor-Man-Rich-ManGiven education, it is plausible that higher education on average leads to higher income for the consumer, which goes back to our previous argument on income. However we can also infer that more educated consumers are more likely to read the newspaper and be more informed about fluctuations in the inflation rate, a claim made by Christopher Carrol. Lastly it may be the case that more educated consumers are simply more likely to know what inflation is in the first place, as opposed to their counterparts. To test this claim, we can sum up the number of survey participants according to their income group, from the Michigan Survey, who did not know what to say when asked about their inflation expectation. We see there is evidence that indeed people with lower education were not able to provide an answer more frequently than individuals with a graduate degree. This is looking at the far ends of the education spectrum, but the relationship holds nonetheless, as we increase education, people less frequently fail to give an answer for their inflation prediction. Interestingly it seems that consumers with less than a high school education know less, on average, about inflation now than they did back in the 80s.

Knowledge of inflation

My claims above would have no ‘oomph’ to them if I didn’t back them up with any evidence. So are we right in assuming that higher education and income lead to better inflation predictions? The answer is a resounding yes!
Let’s take different education and income subgroups, using the Michigan Survey one more time, and lets take the median inflation expectation at each quarter for each subgroup. We use Median, because, this measure is more robust against outliers. (Note choosing mean expectation gives weaker results, in that I am able to statistically reject more often). Finally lets compare each subgroup against one another and see how well they do in comparison at predicting the median-CPI in our experiment. (Reason for this specific inflation measure is explained on last week’s blog). I also use the same Diebold-Mariano test used on my previous post, to test for statistical significance of predictive superiority amongst subgroups. The time period I analyze is 1982-2014, because 1981Q3 is when the SPF began.

So let’s look at the results! Below I report values for income, as the probability that the highest income group performs better than the alternative income groups. Likewise for education, I display the probability that those with graduate level education do better than their counterparts. Finally we put the highest education and income groups against the SPF, and settle the forecasting battle once and for all. In all of the following results, we should interpret them as probabilities in repeated trials.

Note: These are actually p-values, but I report them this way to make the results more intuitive.

Note: These are actually p-values, but I report them this way to make the results more intuitive.

The above table demonstrates that survey participants grouped in the top 25% bracket, consistently beat their counterparts with about 99% probability. However when the predictions of the top income group were tested against those from the SPF, there was only a 34.31% probability that the high income group would predict better. Similarly, there is large evidence that the group with graduate school education beats every other education group’s predictions with a 99% probability, except for those with a college Bachelor’s degree, but still beating them with a 92.08% probability. The high education group also lost miserably to the SPF, with a probably of fairing better than the SPF of 27.69%. We get very similar, statistically significant results, when we compare the alternative income groups against a lower levels of income and education against the lowest income and education levels. From this we gain two conclusions:

1. As income and education increase, predictive ability for inflation increases unambiguously.
2. There is no subgroup from the Michigan survey that can predict the inflation better than the SPF, at least to a statistically significant level like.

Here is a graph comparing graduate level education vs less than high school, and the top 25% income group against the bottom 25% (For those more visually inclined 🙂 ) What we see is that the results are consistent with those in the table above. One thing to note is that inflation expectations for low education and income groups are higher  than their counterparts for most of the time period analyzed (1982-2014).
High vs low income High vs low education

One final note, I find out conclusively that the SPF does better than any income group, except the Top 25%, although the SPF probably does better, we cannot tell beyond a reasonable doubt. This result would satisfy the claim in the beginning of the post that those with higher incomes may have a higher predisposition to know the inflation rate,  because it is only those in the top 25% income bracket that are able to make predictions somewhat as close to the SPF’s. This claim would be consistent with the fact that about half of Americans hold any assets, hence only individuals in the highest income groups would have any interest in the inflation rate.

A Crucial Factor in any Analysis of Inequality

Thesis: Properly implemented cost-of-living adjustments are often missing from analyses of inequality data, which often leads to overstated or outright incorrect results.

As the income gap between the wealthy and everybody else grows wider and wider in the United States, the search for the underlying causes has brought more and more economists, think tanks, and academics alike to analyzing data on inequality.  But, as with any statistical analysis, researchers must be careful with their underlying assumptions and variables before coming to conclusions.  One key variable that must be controlled for when comparing income levels across the country is cost of living: in different parts of the country, income levels will vary as employers must compensate labor differently based on the market they’re in, to compete with employers in lower cost of living markets.  For example, the mean annual wage of a nurse in California is $90,860 versus $67,140 in Pennsylvania (source: Bureau of Labor Statistics).  The map below, created by the Tax Foundation, provides a visualization of how cost of living varies across the country.  

$100bystateDespite the large variation in cost of living across the country, many studies seem to neglect controlling for the variable and the effect it has on nominal income.  For example, a paper written by David Autor and cited in the Wall Street Journal argues that the biggest societal factor for explaining inequality is that of education, pointing to the fact that the earnings gap between non-college educated households and those with a college education has grown at a faster pace than the gap between the 1% and the rest of the country.  But Autor’s study is missing a key piece: it doesn’t control at all for cost of living across earners.  It simply compares earnings by education level, with no regard for location – even though there is definitely a correlation between education level and cost of living.  More educated households are more likely to be located in areas like California and New England, where not only is the cost of living higher, but so are income levels as employers compensate for that cost of living.  The result is that Autor’s claims are overstated: while there is still certainly truth to his study, it is also the case that we would expect there to be a correlation between education and nominal income that is actually due simply to variation in cost of living.

On the flip side, there are cases where arguments about income inequality have been made that seem to overstate the cost of living effect.  An example that should be well known to economics students on this campus by now is the highly controversial Michigan Daily article Relative Wealth, which argues that one can be considered to be a middle class American even with an annual household income of $250,000, due to of cost of living variation.  While I won’t argue for or against the premise of this article, I will point out that Klein overestimates the degree to which higher cost of living areas dilute one’s income, and that weakens her argument.  While it is certainly true that higher major expenses (mainly rent/home prices) should correspond to a higher income in certain areas of the country, many discretionary expenses become relatively cheaper when one lives in a high cost of living area.  Goods which do not vary in price geographically (e.g. practically anything you can buy on the internet) cost a smaller portion of one’s income if they live in a higher cost of living area, giving them more purchasing power for certain goods.  This somewhat offsets the nominal wage differential of different cost of living areas.  And so those who wish to study income inequality must tread carefully: there are many confounding variables at play.  I don’t know what the best way to control for cost of living is, but those who ignore it will come to conclusions that may be over or understated.

The Lottery: An Unfair Gamble

The relationship between the lottery and the economy is a complicated one. Although the $2 price tag is relatively small to pay for the enjoyment of fantasizing one’s life with a vast amount of wealth, where and from whom those $2 come makes a difference.

When profiling the individuals who purchase lottery tickets compared to those who do not there is a strong class divide. The majority of people who buy lottery tickets come from low-income, less educated, communities of color. Each year state lotteries take away about 9% of the take home income from households making less than $13,000 a year. For majority of these households a $2 ticket each week does make a difference financially in comparison to the chance of actually winning. According to the Multi-State Lottery Association the chances of winning the grand prize is about 1 in 175 million. With a chance of winning so low, is it really worth the fantasy?

In addition to preying on lower income households, the lottery takes money away from local businesses, exactly what a struggling economy does not need. In 2012, Americans spent about $65.5 billion on lottery tickets and this number has been steadily increases since the first state lottery in 1965. However, if that same amount of money were redistributed into the local economy instead of into the lottery, an economic boost would be inevitable. In addition to taking billions out of the economy, the lottery redistributes money up the economic ladder, which is exact opposite of what a struggling economy needs.

In addition to preying on local businesses and low-income households, the lottery is a hypocritical statement in comparison to anti-drug policies. State drug laws are in place in order to reduce harm to individuals, families, and the society as a whole from the adverse effects of addiction. But how is playing the lottery any different from gambling? Gambling is a highly addictive behavior that has the potential to cause the same adverse effects such as violence, money troubles, and deception.

Best-case scenario if an individual does win the story is not always happily ever after. The biggest winners often see their lives unravel after millions are deposited into their bank account. The enormous lifestyle change can destroy families, disrupt friendships, violence and more. Many of the big winners struggle to manage this enormous amount of money due to lack of experience and therefore end up spending the money quickly and frivolously.

Although the lottery provides governments basic services for schools, police force, and roads, the consequences outweigh the benefits, especially when considering the population the consequences affect the most.

Blog 6: Income Distribution in an Economy with 4.5 Million Dollar Raises

The type of income distribution a country faces can provide insight on its economic and social environment. Optimally, the distribution will display a strong middle class to disseminate the negative externalities created by concentrated wealth. When wealth is concentrated unequally, issues such as access to education, are amplified and the future economy is likely to suffer. According to the US Census in 2013, we see this breakdown of income:

Screen Shot 2015-01-24 at 8.55.08 PM

In 2012, the Census shows that 50% of Americans were earning an income below $27,000 each year. Using this income calculator, you would need $36,700 to live in a modest single family home, drive a Honda Civic, not travel, rarely buy clothing and have no kids. This is a pretty meager existence and considering half of the US was making roughly $10,000 less than this in 2012, I would say this is a major problem.

The situation is even more problematic as it continues with the rich getting richer. Perpetuating the unequal distribution, the Wall Street Journal notes, “For the second consecutive year, Morgan Stanley’s chief executive got a raise.” The 4.5 million dollar “raise” was accompanied by raises to other executives as well. When our economy is allegedly growing and improving in this post-recession era, are the benefits universal, or merely being reaped by those heading distribution?

During my Freshman year, (Fall of 2011), here at the University of Michigan, the Grad Student Instructor in one of my discussions proposed an income distribution of students at the University of Michigan in comparison to that of the US in general. It was a surprise to me that the distribution on campus was so heavily skewed towards higher income families. This alone began to open my eyes to the real effects of unequal distribution. Enrollment at this notably ranked school was limited to the pockets with enough depth. This study by Stanford, done in 2014, analyzed 2004 graduates from a variety of schools including the University of Michigan.

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We see that for the people with family incomes below $35,000, less than 10% are able to go to college. Granted, this study is relatively dated and since its publication there have been more programs created to aid students with loans. However, it is still a large problem that will take much effort to correct. If the rich keep getting richer, the country as a whole is not advancing. Moving forward together is what helps alleviate social problems such as poverty and economic issues such as debt. A healthy economy lies hand in hand with a healthy income distribution.