Author Archives: Boykin

Final Revision -Inequality in Household Wealth: A Long Time in the Making

The economic inequality between racial and ethnic groups has risen markedly over the past half century. The graph below illustrates a sharp divergence in average family wealth by race/ethnicity over the period from 1963- 2013. How can such a large difference be accounted for? Disparity in income and education readily spring to mind as explanations. While these variables certainly account for some of the difference, wealth disparity is three times greater than income disparity by some measures, and the wealth of college-educated minority groups still lags behind that of the majority counterpart. There is an additional explanation.  In this post I attempt to argue that current racial wealth disparity is partially explained by one of the largest components of wealth: housing. To be sure, early FHA policies set in motion a housing valuation scheme based on neighborhood demographics, and the legacy of these policies are seen in the current wealth disparity.

New Picture (5)

Source: datatools.urban.org

 Why housing is worth considering:

The most valuable item on many household balance sheets and a significant component of net worth is home value. According to the Federal Reserve Board, primary residences account for 30% of household balance sheets. Home equity is used by many families to finance education and save for retirement. As one of the most valuable privately-owned assets, it is a nontrivial factor in calculations of household net worth. Given the positive effect of net worth on consumption and output trends, a substantial amount of attention is owed to the political and social forces that made and continue to make wealth accumulation difficult for some.

A history of federal housing policy sheds light:

As part of the New Deal Housing program The Federal Housing Administration instituted racial assessment in rating property values. In doing so, the FHA redlined integrated and minority neighborhoods giving them lower (red) ratings than exclusively white neighborhoods. The government cited financial risk as the rationale behind their ratings system. This lead to the governments underwriting of over $100 billion in new housing loans from 1934-1962 (where graph above begins) almost exclusively to white home buyers. Alternatively, federal housing projects were built in central cities which housed high concentrations of non-white minorities. The result of such actions was to suburbanize America racially.

When the FHA ended its policy of racial risk assessment and non-white families were able to move into suburban neighborhoods, their neighborhood property values declined. Original residents of these subdivisions faced incentives to sell off their properties in anticipation of lowering house values. This reinforced the FHA’s initial policy, creating a dual housing market: one which subsidized home ownership for families of the racial majority and one that divested minority families of the same opportunities for wealth accumulation. Although the government eventually turned to more progressive policies, it had already laid the groundwork for economic disparity into the future.

A 2010 report from John R. Logan and Brian J. Stults on “The Persistence of Segregation in the Metropolis” illustrates the current legacy of such policies:

“In 367 metropolitan areas across the U.S., the typical white lives in a neighborhood that is 75% white, 8% black, 11% Hispanic, and 5% Asian.”

“The basic message here is that whites live in neighborhoods with low minority representation. Blacks and Hispanics live in neighborhoods with high minority representation, and relatively fewwhite neighbors. ”

Clearly, the effects of government redlining did not reverse course when public policy changed.

Much of wealth is inherited. The role of inheritance in passing on wealth suggests that the structure of residential segregation established in the mid-1900s in part contributes to present position of net wealth. I.e., if transfers of wealth are generally familial, then the effects of discriminatory housing in the fifties, for instance, are likely show up in the current racial wealth gap. This is further compounded by current residential preferences and incentives (to protect property value by living away from minority groups) reminiscent of those in existence when neighborhoods first opened to integration, a claim supported by the neighborhood statistics cited above.

Remedial policies must address the effect of housing discrimination on wealth if they are to unlock a more robust consumer base and consequently drive up the growth rate of output. This post takes a further look at one of the root causes of racial economic disparity in the U.S., the effects of which still linger. This is a first step in the process of ensuring full participation of all U.S. citizens in the American economy and its recovery.

 

Pension Fund Managers Should Engage EMT

Corporate pension fund managers should heed advice given to casual investors: don’t try to beat the market.

On the topic of retirement savings: It’s widely proposed that private casual investors invest in a diversified portfolio and just let their earnings grow over time without moving money around or giving in to market frenzies. As the story goes, those who are duped by their own psychologies into maneuvering in the stock market more closely are likely to lose. Those who don’t lose in this manner are merely lucky. (Unless you’re Warren Buffet -who may just be lucky enough to be good at stock-market valuations).

As Burton Malkiel illustrated in “A Random Walk Down Wall Street”, most investment managers don’t subscribe to this advice. Indeed, they’ve made careers out what they and their clients believe to be a competitive advantage in investment management. Still, academics remain skeptical. Malkiel writes that “Much of the academic community… believes that professionally managed investment portfolios cannot outperform randomly selected portfolios of stocks with equivalent risk characteristics”. By their line of logic, our efficient market simply won’t allow anyone to beat it because new information is priced in so quickly.

If the academic claim is accepted at value, then a clear imperative arises for investors with a lot more on the line than individual retirement funds. These are the pension fund managers who are responsible for managing billions of dollars in corporate pension assets. The imperative is as follows: instead of trying to pick winners, invest pension assets in index funds and let them alone to grow in value. If, statistically, this is good advice for casual investors with singular interests, then how much more so for professional investment managers who are accountable for a much wider scope of interests, for people’s livelihoods into retirement?

A recent article in the Wall Street Journal details the trouble now facing retailer Sears, a company with over $1 billion in pension assets. Sears Holdings Corp. has spent several billion dollars to prop up their pension plan in recent years. Some of the shortfall in assets is due to changes in actuarial assumptions (such as lower interest rates), but as the headline reads “Bad Picks Hem in Sears Pension Plan”. In other words, some of its trouble is explained by its funds managers’ choice of investments. As a managing director close to the firm revealed, the firm had higher than average weighting in investment such as commodities. Given the plummet in oil prices, this weighting had a negative impact on returns.At 1.5%, Sears pension assets yielded less than the 2014 median for similar companies.

As the accounting adjusts to the presumably higher interests rates ahead, underfunded plans may turn around. However, the operating cash that could have been used to seize better business opportunities, will have been tied up in the meantime due to underperforming investments. In cases like these, the opportunity cost of picking hopeful winners depresses business opportunities, a lose-lose that could be avoided with more well-rounded investing.

Pension fund investment becomes especially important considering the demographic shift underway in the U.S. As Baby Boomers reach retirement age and the demand for social security benefits exceed the supply, payments to retirees will at the very least be less than is currently promised. Coupling this with shaky corporate benefits plans (and managers who gamble with funds set aside for pension obligations) heightens the risk of a retirement crisis. Although companies have incentive to seek out higher returns, pension funds should be handled with caution, and wisdom about diverse investment strategies is especially important for large scale (collectively earned) investments. Investment of pension funds in diversified securities will hedge against the risk of an all out retirement benefits meltdown.

Home Buyers Beat to the Punch by Institutional Investors

Thesis: Investors exposed to housing markets have more protection than before, but an increase in institutional investment in housing markets is bad for American tenants.

A new asset class has emerged from the rubble of our last decade’s financial collapse: REO-to-rental securities. Everyone who isn’t living under a rock knows something about the central role housing markets played in the financial crisis. The 2001-2002 U.S. housing price boom gave way to innovation in the mortgage markets. Too much innovation and too little appreciation for fundamental housing values gave way to financial chaos when the bubble burst. This, of course, is a rough accounting of how the financial world unraveled around us not too long ago. Given the scope of the fallout, it’s a wonder more noise isn’t being made about the structural changes currently sweeping through the housing market. While investors exposed to housing markets have more protection than before, an increase in investor ownership through REO-to-rental arrangements and a decrease in resident ownership could destabilize communities.

The Wall Street Journal  reports that in 2009 corporate investors bought nearly half of all homes in foreclosure through short sale. Essentially, wall street seized on the huge pool of distressed homes and acquired profitable assets, housing it would never live in. While the number of single family and condo purchases by institutional investors has reportedly fallen in recent quarters, the trend, as shown in the graph below, is upward. 2014 U.S. residential sales went 4% to residential investors. This is a fourfold percentage point increase compared to 1% sales in 2001.

Ins. Investor

Increasingly, institutional investors are becoming America’s landlords, and the financial vehicle driving this housing market makeover is REO-to-rental or Real Estate Owned assets.  In 2013, multinational private equity firm The Blackstone Group offered the first bonds ($479 million) backed by rental income of single family homes. The bonds were secured by individual mortgage liens against the underlying property.  Essentially, the firm is selling off streams of single-family home rental earnings  under the assumption that rental rates will outstrip interest rates and yield high returns. This way they can extract value from tenants, but will add little to the surrounding neighborhood. The problem is that these institutional landlords do not have much at stake in the communities that their rental properties are in.

Comparing REO-to-rental assets to the mortgage backed securities of the early 2000’s can lead to some optimism. For one, the government has tightened its scrutiny as have lenders. This has obvious advantages for today’s investors. A climate of tighter lending standards is a ballast for investor confidence. Also, investor demand for housing adds to aggregate demand in the housing market and stabilizes prices. In some sense consumers who may have been in over their head with too high mortgage payments are protected.

On the flip side, however, consumers who can not make all-cash deals are being outbid by institutional investors. As home ownership is a big part of wealth accumulation, consumers who are redirected to rent rather than buy suffer an economic loss. Furthermore, the externalities of homeownership are lost to the residential community at large. That is, If it is assumed that renters do not have as much invested in the community as owners who are tied to an illiquid asset. Due to these reasons, the government has a role to play and a vested interest in promoting homeownership. It should ensure that REO policies do not crowd out individual home buyers by regulating REO arrangements.

Stock Prices an Ally in the Fight to Raise Minimum Wage?

If the Efficient Market Theory is a good approximation of how the stock market operates, then market response to minimum wage hikes predicts a different outcome than opponents who argue that a rise in minimum wage will result in much lower profits. The effect of labor-saving technology and other expected gains in efficiency should be considered when projecting future earnings.

According to the efficient market theory (EMT), “the price of every stock tends to reflect all of the information that is publicly known”. Following this logic, federal mandates to raise minimum wage are priced into stocks as information about new wage policies becomes public. Given a higher price floor for wages, stock prices will move in the direction of projected profits. If the profits of minimum wage employers are expected to decline because of higher labor costs, then according to EMT, we should see a corresponding decline in the market value of firms in low-wage industries. Significant stock price declines, however, have not been observed in past instances of federal minimum wage hikes.

Avoiding higher prices and lower profits is a common argument used to justify keeping the minimum wage low. For example, conservative think tank The Heritage Foundation, concluded in response to minimum wage proposals, that prices will rise by 38% for fast-food restaurants and that profits will fall by 77%, absent any major operational changes. Looking at a time series of stock prices for fast-food market leader McDonalds however shows that, in the past, stock prices did not respond much to minimum wage hikes when they were implemented in 1997 and again in 2007.

mcdonalds-close-1997

mcdonalds-close-2007

(Minimum wage went into effect September 1, 1997 and July 24, 2007). Source

For McDonalds, for instance, a 77% fall in profits is completely inconsistent with the effect on future earnings investors projected less than a decade ago. In both 1997 and 2007, prices did not fall by any more than 15% for the entire period shown. Another similar example is Walmart’s recent wage hike. When the retail giant announced that it would respond proactively to minimum wage increases, its share prices remained flat at $86.69. Although a leader in a low-wage industry, their market value did not plummet by anything close to 77%. There was no major investor sell-off to drive stock prices down; no fundamental devaluation of shares. It should be stated that empirically, a problem with these measures may be that we do not observe the effect of new information on stock prices in isolation of other factors, and that new information may be incorporated in stock market prices before official announcement.

However, taking the discrepancy between wage hike alarmists and stock market participants for what it’s worth, one of several things must be true:  1) the efficient market theory does not accurately predict the effect of new information on stock prices 2) given EMT is a good approximation of how the stock market operates, traders do not foresee a big hit to future earnings in low-wage industries due to minimum wage increases. And this means that predictions like that of the Heritage Foundation defy those whose money is actually on the line in the stock market. I tend to think that the second case is true for a reason that is hinted to in The Heritage Foundation’s conclusion.

The Heritage Foundation’s caveat is that their predictions are valid absent “major operational changes”. However, the absence of operational changes is unlikely given the recent pace of technological progress. Old manual processes and dated technology can be a costly source of inefficiency in low-wage industries (a problem that Walmart has begun to address through logistics innovation, for example). As technology catches up to these inefficiencies, one worker effectively becomes many. Ultimately, labor-saving technology gives firms the ability to absorb higher input costs like rising cost of labor while maintaining positive profit margins. Because the business environment is ripe for adoption of labor-saving technology, minimum wage hikes are not the bane to profits that opponents say they are. Stock market responses to minimum wage hikes in the past seem to be consistent with this idea.

If Walmart Didn’t Accept My Visa, I’d Shop Target

When one company accepts a new form of payment (to capture more of the market), there is incentive for competitors to mimic.

A careful look at Walmart’s balance sheet turns up something rather surprising for a Fortune 1 company: despite sizable profits, the retail giant has very low return on sales. Walmart’s net profit margin (net profit ÷ total revenue) is  under 3% and has been for the past five years (at least). This conundrum is one in which the question asks itself. How did Walmart land a number one spot on the Fortune 500 list with such a mean profit margin? What is Walmart’s secret? Ultimately, the answer is sales volume. In 2104 Walmart Stores brought in more revenue than any other U.S. company; its revenue totaled over $476 billion. It is by this measure, total revenue, that Fortune racked and stacked the famous listing of the top 500 U.S. companies.

What has all this to do with the price of tea in China?

Nothing, actually. However, I believe the success of Walmart illuminates something about the importance of market share in explaining firm behavior. According to this article, Walmart enjoys a leading U.S. grocery market share (30%), has a strong market position in apparel, and is a top seller of prescription drugs. In its 2014 annual report, Walmart emphasizes the value of market leadership along with its widely known pricing strategy “EDLP” or “Everyday Low Prices”. From this emphasis, it is clear that Walmart is focused on preserving its place in the market by keeping prices down.  And as a $476 billion sales figure and low profit margins might suggest, this is because its market share is a key component to its success. When Walmart entices price sensitive consumers to buy retail goods from their stores, it translates low markups into ever-ringing cash registers. Pennies on the dollar add up. Walmart is like a man who becomes rich by picking up pennies, except because of its market position Walmart picks up pennies around the world, all the time.

How can this case inform the way widespread credit card acceptance in American stores is understood? The following line of reasoning comes to mind:

For companies in competitive markets, imperfect as they may be, sales volume can account for a large share of profits (over and above markups). This is clearly illustrated in the case of Walmart. If just one large retailer decided that instead of accepting only cash/debit/check, they would now accept credit card, effectively they could expand their customer base and increase market share. (Alternatively, if Walmart stopped accepting credit cards today, its customer base would shrink.) If word got out to cash-strapped customers, or customers who prefer credit cards for their cash back deals, etc. , that a store with a large selection of consumables now accepts credit, that store would likely see an influx in business.

Effectively, the store’s new payment policy relaxed the budget constraints of potential customers, resulting in increased demand (read higher sales volume). To protect themselves from losing market share to their clever competitor, more and more stores might adopt similar payment policy. Ultimately this would result in an environment like we see today, where most stores accept payment by credit card and willingly pay fees to credit card companies because to do not do so would mean to forfeit more revenue than is offset by the absence of the credit card expense. Again, if this decision makes sense economically, it is because the tradeoff between market share (revenue) and cost management (credit card expenses) favors more market share.

Walmart, and similarly successful business (particularly retail), bring the importance of sales volume to life. As a proxy for market share, sales volume can account for more of profit than the spread between price and input cost. Using this concept, it is possible to understand why American businesses generally accept credit card payments and incur the associated fees.

Big Data Will Give Wall Street an Edge

Thesis: New trading technology that uses big data to mark trends has potential in the hands of experts.

I recently read a blog post by a fellow classmate that got me thinking in a new direction about innovation on Wall Street. His post highlighted a group of P.H. D. scientists who programmed machines to collect information from various internet sites presumed to be relevant to firms’ performances.  The scientists then wrote algorithms to make stock picks based on signals in the data. As Israel Diego pointed out, some of the buy and sell signals generated by these algorithms were found to be based on trivial correlations. This is problematic, but can be resolved by human input. I propose that the potential of big data algorithms is in their usefulness to expert analysts, not in their autonomous operation.

When first reading over the WSJ article about the work of the scientists at the Two Sigma Company, my initial thoughts were returned to something I’d been reading on the topic of growth theory and technology. Similar to a Cobb-Douglas production function with capital and labor augmented technology, I though the trading algorithms could be considered as a type of labor augmenting technology in a Wall Street production function(capital gains = output?). A slightly more intense Solow/growth model includes human capital and incorporates features of Wall Street trading even better. Solow’s framework can model the differential talent of professional investors. The more human capital an investor has, the greater the return (output) he wins for his stakeholders.

The introduction of new technology into this system increases the efficiency of the investing professional by identifying potential market signals more quickly and systematically than human limitations allow. Investor discernment does not need to be lost from the relationship, however. Rather it is a critical component of trading operations. An investor’s human capital can be enhanced by the new technology of financial algorithms if the technology is used to complement analyst input.

A concrete example of this concept can be found in the book, “The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies” by Andrew McAfee and Erik Brynjolfsson. The authors give the insight that “people and computers don’t approach the same tasks the same way”. When observing the results of chess matches, a game once thought to be fit only for human reason, the authors find that neither a computer nor a master chess player are a match for a computer/human team. The computer contributes “technical acuity” to the game while the human provides “strategic guidance”. The combination of computing power and human ability grant the tech/human team a competitive advantage that is unparalleled by either one alone.

While trading is not chess, trading is arguably a type of contest which involves calculation of opponents’ moves, and this makes the above analogy useful.  This story is not a unique one. The pairing of quick algorithms with deep human discernment has transformed our world in many ways already and this combination will continue to emerge in the financial industry.

 

Divided We Govern (Ineffectively)

Thesis: Republicans and Democrats have disincentive to cooperate which leads to ineffective governance.

Has our two-party political system created perverse incentives?

Like characters from the movie “Groundhogs Day”, every day we wake up to a reality of dysfunction in Washington. It has become almost redundant to mention. One needs to look no further than the recent government shutdown to get a sense of the extent of this dysfunction. The Wall Street Journal recently posted a video clip with political author P. J. Rourke giving the following advice: “…[millennials] are highly skeptical of politics and not terribly interested in politics and we should all be that.” His advice is a long cry from a government designed to be “by the people”, but his words succinctly capture popular sentiment.  Something is wrong with the incentives in our two-party system.

Method to the Madness

Fields such as game theory likely come to mind when looking for explanations on the current state of congressional politics. But no game theory model can accurately account for Capitol Hill-scale antics without allowing for the role of human psychology on governance. In psychology, the theory of economic perspective says that as people compete for limited resources, intergroup bias is likely to arise.  It is certainly true that today’s politicians are a highly polarized group of people. Although their competition isn’t directly for material resources, they explicitly compete for tenure and the vote.

Theory further asserts that groups under economic difficulty glorify their own group while vilifying others. Consistent with this idea, politicians, who must always have in mind the political expediency of their actions, draw ingroup/outgroup lines in the sand. Like in the prisoner’s dilemma, such partisanship often leads to political outcomes that are not socially optimal. Each party, and certainly the public, would be better off if parties cooperated. The problem, however, is that their cooperation might work to the benefit of the other side. This dilemma leads to political obstructionism. An instance of this is the government shut down and failure of Congress to pass the budget in 2013.  Legislators looked at the payoffs of their actions and found inaction a superior alternative to negotiation. The outcome of their political dance was obvious inefficiency. Both sides lost in the court of public opinion.

Clearly,  political incentive schemes need upending.

Superordinate Goals

If the problem with our two-party system is partly psychological in nature, then the solution is too. The famous Robber’s Cave experiment, in which a group of young subjects must work together to achieve a common goal after being divided into opposing teams, offers hope for our broken political system:

Unlike author Rourke suggested, Americans should maintain interest  in politics and vote in elections; that is, if we want to incent government officials to avoid the situations of gridlock and obstructionism that have come to characterize current politics. Instead of voting solely on fringe issues, voters would be wise to base votes on how well incumbents (and new candidates) have worked (and our expected to work) within the existing Congress to address relevant public issues. In this way, voters ensure that Congress is rewarded for working together and across the aisle on issues that matter most to them. Greater voting consideration of this kind would shift the political paradigm and push government officials to look at the common good they serve.

Revision – Inequality in Household Wealth: A Long Time in the Making

The most valuable item on many household balance sheets and a significant component of net worth is home value. According to the Federal Reserve Board, primary residences account for 30% of household balance sheets. This estimate is illustrated in the graph below:

New Picture (43)

Home equity is used by many families to finance education and save for retirement. As one of the most valuable privately-owned assets, it is an nontrivial factor in calculations of household net worth. Given the positive effect of net worth on consumption and output trends, a substantial amount of attention is owed to the political and social forces that made and continue to make wealth accumulation difficult for some. I argue that the federal government’s discriminatory housing policies of the mid-1900s created a cumulative disadvantage for many non-white Americans and that this helps to explain the widening racial wealth gap today.

The following chart depicts the disparity in average family wealth by race/ethnicity from 1963- 2013.

New Picture (5)

How can this be accounted for?

A history of federal housing policy helps to address this question:

As part of the New Deal Housing program The Federal Housing Administration instituted racial assessment in rating property values. In doing so, the FHA redlined integrated and minority neighborhoods giving them lower (red) ratings than exclusively white neighborhoods. The government cited financial risk as the rationale behind their ratings system. This lead to the governments underwriting of over $100 billion in new housing loans from 1934-1962 (where graph above begins) almost exclusively to white home buyers. Alternatively, federal housing projects were built in central cities which housed high concentrations of non-white minorities. The result of such actions was to suburbanize America racially.

When the FHA ended its policy of racial risk assessment and non-white families were able to move into suburban neighborhoods, their neighborhood property values declined. Original residents of these subdivisions faced incentives to sell off their properties in anticipation of lowering house values. This reinforced the FHA’s initial policy, creating a dual housing market: one which subsidized home ownership for families of the racial majority and one that divested minority families of the same opportunities for wealth accumulation. Although the government eventually turned to more progressive policies, it had already laid the groundwork for economic disparity into the future.

A 2010 report from John R. Logan and Brian J. Stults on “The Persistence of Segregation in the Metropolis” illustrates the current legacy of such policies:

“In 367 metropolitan areas across the U.S., the typical white lives in a neighborhood that is 75% white, 8% black, 11% Hispanic, and 5% Asian.”

“The basic message here is that whites live in neighborhoods with low minority representation. Blacks and Hispanics live in neighborhoods with high minority representation, and relatively fewwhite neighbors. “

Clearly, the effects of government redlining did not reverse course when public policy changed.

Author Judith R. Blau argues that in terms of the economic gap:  “the deepest gulf is in holdings of property, such as real estate, including home ownership, and of financial assets, such as stocks and bonds.”  “The key to the racial wealth gap is the gulf in capital resources acquired via inheritance, which is now the major source of wealth (Blau and Graham, 1990)”. Her findings support the argument that the wealth gap is in part reinforced by differential rates of homeownership. Also, the role of inheritance in the wealth gap suggests that the structure of residential segregation established in the mid-1900s contributes to present position of net wealth. I.e., if transfers of wealth are generally familial, then the effects of discriminatory housing in the fifties, for instance, are likely show up in the current racial wealth gap. That is, via the mechanism of inheritance, economic advantage and disadvantage is accumulated along racial lines.

Remedial policies must address the effect of housing discrimination on wealth if they are to unlock a more robust consumer base and consequently drive up the growth rate of output. This post takes a further look at one of the root causes of racial economic disparity in the U.S., the effects of which still linger. This is a first step in the process of ensuring full participation of all U.S. citizens in the American economy and its recovery.

Use of Statistical Controls Can Mislead

In introductory economics we are taught the importance of the notion ceteris paribus: keeping other factors equal or alternatively, holding other factors constant. In econometrics, as in the intro courses, we discuss the economist’s goal to infer that one variable has a causal effect on another variable (e.g.  that education has a causal effect on wages, or that current stock yields predict future returns). It is in econometrics that the notion of ceteris paribus appears in more explicit form. Linear regression models allow us to simulate ceteris paribus experiments by controlling for factors that simultaneously affect the dependent variable.  Through the methods of regression analysis, we create a condition in which other relevant factors are held constant, and this in turn makes causal inferences more reliable.

Related to this idea of ceteris paribus and its significance in regression analysis is the topic of model specification (and its inverse -model misspecification). The textbook we used in our econometrics course had a section dedicated to the discussion of model misspecification of two types:

  • Including irrelevant variables in regression models – This is a case in which OLS beta estimates remain unbiased since no important regression assumptions are violated and the coefficient on the irrelevant variable is zero.
  • Omitted variable bias – A more problematic (and well understood) case in which OLS estimators are generally biased by the exclusion of relevant variables, i.e. the expected values of the estimated betas do not equal the true effect of the independent variable on the dependent variable.

However, there is another important model specification problem that merits attention because of its recurring incidence in economics (econometrics) studies and the resulting ideology it engenders. This is the problem of included (relevant) variable bias. For example, my econometrics book included a passage about the relationship between loan approval rates and percentage of minorities in a neighborhood. Suggested variables to include in a regression model to isolate the effect of discrimination on loan approvals included factors like housing value, income, and creditworthiness. While certainly including these additional variables would satisfy the ceteris paribus condition, their inclusion may also be controlling for discrimination itself.

One of the most compelling pieces I read on this issue was article on Vox.com by Ezra Klein. She gives an example related to gender discrimination “…research around the gender wage gap, which tries to control for so many things that it ends up controlling for the thing it’s trying to measure.”

“Take hours worked, which is a standard control in some of the more sophisticated wage gap studies. Women tend to work fewer hours than men. If you control for hours worked, then some of the gender wage gap vanishes. As Yglesias wrote, it’s ‘silly to act like this is just some crazy coincidence. Women work shorter hours because as a society we hold women to a higher standard of housekeeping, and because they tend to be assigned the bulk of childcare responsibilities.’

Controlling for hours worked, in other words, is at least partly controlling for how gender works in our society. It’s controlling for the thing that you’re trying to isolate.”

The concept of included relevant variable bias is also explicated in a paper by Ian Ayres for Yale Law School. In his paper, Ayres explains the difference between legal statutes that address both disparate treatment and disparate impact (reminiscent of the discourse around the idea that equality of opportunity doesn’t equal equality of outcome). However, legal statutes that forbid intentional discrimination against protected classes of people (age, gender, race, country of origin, etc.) also regulate employer policies that result in disparate impact to these classes of people. This is despite whether the policy is race-neutral; it is illegal if it’s unnecessary and disproportionately restrictive.

The statistical methods he proposes to test for disparate impact can be extended to studies that involve a similar type of included variable bias:

“Under disparate impact theory, it is necessary to intentionally omit nonracial
variables from a regression to test whether those variables produce a disparate
racial impact.”

In other words, in cases in which we are testing for discrimination or similarly broad variables that influence control variables, nonracial, non-gender, etc. factors can be purposefully excluded to reveal a more telling (upper bound) of the effect being researched.

Why U.S. Economic Recovery is Uneven

“Consumers have made strides on both sides of the net worth equation, reducing their liabilities through a deleveraging process and seeing their assets appreciate. Indeed, household wealth in the fourth quarter was more than 22 percent above its pre-crisis peak, leaving families better positioned to consume and invest. Although household wealth is substantially higher on an aggregate basis, many have yet to participate fully in the recovery, and we have more work to do to boost incomes for middle-class Americans.” – Jason Furman

New Picture (3)

The above quotation is one of the key points of a 2014 Q4 GDP estimate report from the Bureau of Economic Analysis. Key points from the report were posted to the White House blog   by Chairman of the Council of Economic Advisers, Jason Furman. Mr. Furman’s synopsis of the report underscores the fact that personal consumption expenditure accounts for more than two thirds of output, and last quarter personal consumption expenditure grew at a higher rate than overall output (2.2%) at 4.4% annually. The excerpt above also draws a natural link between financial position and consumption expenditure: higher levels of household wealth enable families to increase consumption and investment. Moreover, the bolded section reflects a reality that is sometimes hidden in summary metrics of our economic recovery.  In the sentence beginning “household wealth is substantially higher [post-recession] on an aggregate basis”, the operative word is “aggregate”. A substantial amount of attention is due to the forces that make full participation in the recovery more difficult for certain demographics of the population.

It is worth mentioning at this point that the most valuable item on most household balance sheets and the most significant contributor to net worth is home value. Home equity is used by many families to finance education and save for retirement. It is a major factor in the calculation of household wealth and is used to estimate such figures as the household net worth reported above. The following chart shows the racial divide in the distribution of American wealth.

New Picture (41)

The history of federal housing policy helps to explain the cumulative disadvantage experienced by non-white minorities in acquiring household wealth:

As part of the New Deal Housing program The Federal Housing Administration instituted racial assessment in rating property values. In doing so, the FHA red-lined integrated and minority neighborhoods giving them a lower red rating and white neighborhoods a higher green rating. The government cited financial risk as the rationale behind their ratings system. This lead to the governments underwriting of over $100 billion in new housing loans from 1934-1962 almost exclusively to white home buyers. Alternatively, federal housing projects were built almost entirely in the central city which housed high concentrations of non-white minorities.  The result of such actions was to suburbanize America racially.

When the FHA ended its policy of racial risk assessment and non-white families were able to move into suburban neighborhoods, their neighborhood property values declined. Original residents of these subdivisions faced incentives to sell off their properties in anticipation of lowering house values. This reinforced the FHA’s initial policy, creating a dual housing market: one which subsidized home ownership for families of the racial majority and one that divested minority families of the same opportunities for wealth accumulation. The racial structure of American housing first set in motion by federal policies is what we live with today and helps explain the wealth gap illustrated in the second chart above. (Discussed in previous post here.)

 

To compound matters, the housing bubble of the mid 2000’s disproportionately affected this same demographic. As articulated by The Atlantic correspondent Ta-Nehisi Coates (I believe his emphasis can be extended to the larger demographic of American minorities):

“Black home buyers—even after controlling for factors like creditworthiness—were still more likely than white home buyers to be steered toward subprime loans. Decades of racist housing policies by the American government, along with decades of racist housing practices by American businesses, had conspired to concentrate African Americans in the same neighborhoods.”

Remedial policies must address the effect of housing discrimination on wealth if they are to unlock a more robust consumer base and consequently drive up the growth rate of output. This post does not offer a remedy to the bureau’s claim that “many have yet to participate fully in the recovery”. Instead, it takes a further look at those unidentified “many” along with some of the roots of racial economic disparity in the U.S. This is the first step in ensuring full participation of all U.S. citizens in the economic recovery.