The Patchwork Quilt Becomes More Complex and Colorful

Published on by Brynne VanHettinga Category: Patchwork Nation, Public Policy, Voting Behavior

A lot of research has attempted to correlate demographic differences with political preferences. Generalizations (sometimes backed by data) are that younger people and minorities tend to prefer Democrats, and older white people tend to prefer Republicans. Besides the more traditional divisions of age (generational cohort), race, gender and income, other social divisions have been identified based on “cultural” and community factors; i.e., rural vs. urban, higher education (college and above) or not, religious/churchgoing or not, and use of technology or not (the digital divide).

patchwork-america-map

Dante Chinni and James Gimpel have divided the United States into 12 distinct community types based on patterns in census and voting data. They have made these distinctions based on demographics such as population, age, race, income and education as well as voting behaviors over time. Rather than attempting to predict who is more likely to vote Republican or Democrat, these researchers look at how people vote, then analyzed their demographics and community lifestyles—in essence constructing community typologies based on historical voting patterns rather than attempting to predict voting patterns based on the characteristics of individual voters. In descending order of population, these communities are:

Monied Burbs:  286 counties and 69.1 million people. These communities enjoy higher than average incomes and educational levels, and tend to be areas of job (and population) growth. Monied Burbs have the highest percentage of college graduates as well as post-graduate degrees. Other than higher incomes and monied-burbseducational levels, however, Monied Burbs are statistically “average” with respect to the national composition of age, race and religion.  These communities tend to weather recessions better than the rest of the country and also help to “pull the country out of it.”

Boom Towns:  384 counties and 59.3 million people. These are areas which had been growing and relatively wealthy, but were also hardest hit by the Great-Recession housing collapse. They still tend to have higher median incomes, with residents employed in professional and executive positions as well as in the more stable occupations of education and government. They also tend to be younger than the nation as a whole, and slightly more diverse (84% white as opposed to the national average of 87%).

Industrial Metropolises: Only 41 counties, but 53.9 million people. These communities are densely populated, younger, and more racially diverse. They tend to have greater income divergence (very high and very low incomes, with an average that is close to the national average), which is correlated with race.  metropolisIn spite of the “industrial” label, occupations in these areas have bifurcated into professional jobs and service jobs. Industrial Metropolis residents attend college at a higher than national rate (25% versus 16% nationally).

Service Worker Centers: 663 counties and 31 million people. These areas are frequently mid-size towns, neither really “rural” nor really “urban.” Jobs are impermanent, low-paying and lack employee benefits. However, the cost of living is not necessarily low, and many workers here live paycheck-to-paycheck.  Service Worker Centers are slowly losing population—no one really moves here service-workersfor work because of higher-than-average unemployment rates, and the jobs that exist are unappealing. They are concentrated in the Northeast, the Northwest, and upper Midwest, and racially are mostly White. These communities often have a higher-than-average proportion of “tourist” income, so their economic welfare depends to a larger extent on how the rest of the country is doing (and spending). Voters tend to lean Republican, but not by large margins.

Immigration Nation:  204 counties with 20.7 million people.  These areas are concentrated in the Southwest (Arizona, California, New Mexico, and South Texas). They have higher than average Hispanic populations, with lower than average incomes and higher rates of poverty. These areas are hit hardest by “boom and bust” cycles in housing and construction. The authors predict that, “the tensions over labor…will likely intensify if economic times stay tough [and] high unemployment endures in the long term.”

Evangelical Epicenters:  468 counties and 14.1 million people. These communities are concentrated in the mid-South-West (Arkansas, Oklahoma, Alabama, evangelicalsMissouri and East-Central Texas). They are characterized by young families and evangelical Christians. Incomes are lower than national average.  The authors suggest that these are the so-called “values voters,” who tend to believe abortion should be illegal (21%) and seek communities with “strong family values.”

Minority Central:  364 counties and 13.5 million people. These communities contain much larger than average percentages of African Americans or Native Americans. They are located primarily in the Deep South or in connection with Native Nation reservations (the Arizona-New Mexico border, North and South Dakota). They have lower than average incomes and higher than average poverty rates, along with high unemployment and low college enrollment. However, there is a distinct disparity in the incomes of minority and white populations, reflecting vestiges of racial segregation.

Campus and Careers:  71 counties with 13.1 million people. These are so-called “college towns” that are scattered throughout the country. As the name implies, campusthese communities have a lot of college students and young people starting careers.  While incomes are not particularly high, neither are poverty rates. Jobs pay decently and employment tends to be stable. These communities also tend to have higher rates of Internet and technology usage.

Empty Nesters:  250 counties and 12.1 million people.  These communities are located primarily in the upper Midwest (Iowa, Indiana, Illinois, Ohio, Wisconsin, Michigan, and Minnesota) and Florida. Populations are older, many who are living on fixed incomes. Empty Nesters are “solidly white and solidly middle class,” many who had been (or are still) employed in manufacturing.  Although these people are not wealthy, they enjoy slightly higher than average incomes and lower poverty rates. Many of them grew up in an era of strong unions, a healthy middle class, and secure retirements.

Military Bastions:  55 counties and 8.4 million people. These are a small number of communities scattered throughout the country that are located near military bases—which are the major source of jobs in these areas. The soldiers (and their militarypaychecks) are usually welcomed by the regular residents, although the neighboring towns experience disruption when soldiers are deployed overseas for long periods of time. In addition to the soldiers, these communities also attract high-dollar military contractors, which the authors allege could make them “Republican-leaning versions of Campus and Career communities.”

Tractor Country: 311 counties with 2.3 million people. Tractor Country is sparsely populated, and residents earn their livelihoods primarily in farming and agribusiness. They are concentrated in the upper Midwest and middle of the country (North and South Dakota, Montana, Idaho, Colorado, Wyoming, Nebraska and Kansas), with a few counties scattered in Washington, Oregon and Texas. In addition to the Deep South, these are the quintessentially “red” counties. After Mormon communities, it is thtractor-countrye least diverse, being mostly (96%) white. Incomes are lower than national average, but so is the cost of living.  The authors call Tractor Country residents “Progressive Conservatives.” These communities are conservative in their distrust of Washington, their fiscal frugality, and their appreciation for hard work. Yet, they also are strong supporters of their local communities and are willing to help out neighbors in need. Tractor Country was relatively unaffected by the housing collapse and financial crisis—mainly because most of the banks are small and locally owned. However, family-owned farms are increasingly being replaced by industrialized agriculture, which the authors predict will “change the nature of life in these communities.” Tractor Country is also losing about 2% of its population per year, and 40% of its residents are over age 50.

Mormon Outposts: 44 counties with 1.7 million people.  These communities have high proportions of members of the Church of Jesus Christ of Latter-Day Saints. Although Mormons are a religious minority (comprising about 2% of the U.S. population), they have settled in a finite area mainly in the states of Utah, Southern Idaho and Western Wyoming, where their insular communities allow them to maintain their religion and culture. The vast majority are white (96%).  About 11% of Mormon communities work in agriculture, making them the second (after Tractor Country) most rural community group. They are, however, economically better off than their rural counterparts in Tractor Country and their fellow churchgoing Evangelicals, with slightly higher than average incomes and slightly lower than average poverty rates.

The strongest support for Democrats is in Industrial Metropolises, followed by Campus and Careers. Surprisingly, Service Workers tend to support Republicans by a small degree, and both Immigrant and Minority communities were about equally divided in past elections, but the Democrats have been gaining in all three of these groups more recently. The authors allege that Service Worker Centers are “the economy’s canaries in the coal mine”…  When things go bad, they go bad here first.”

The strongest Republican support is in Mormon, Evangelical and Tractor Country communities, which suggests a cultural rather than an economic bias.  Boom Towns, Empty Nesters, and Military communities have favored the Republicans by small amounts in the past, but Democratic support in these communities has also been increasing.

The truest “swing” communities have been the Monied Burbs—favoring Republicans in one election and Democrats in the next, with support for Democrats apparently growing since 1980. Monied Burb constituents are thus the most sought-after voter, both for their higher incomes and for their general lack of diehard party loyalty. Because of their sheer numbers and shifting electoral preferences, Monied Burbs, Boom Towns and Service Worker communities offer the greatest potential for electoral change.

The Patchwork Quilt of Voting Patterns

Published on by Brynne VanHettinga Category: Patchwork Nation, Public Policy, Voting Behavior

Having just been through a national election, many of us have seen those maps that show states as being either red (Republican) or blue (Democratic). However, when voting patterns are displayed at the county level, the states do not appear so monochromatic. For example, Texas is a quintessentially “red” state, but there is a string of “blue” counties along its southern border.  Conversely, California is mostly blue along its West coast and red in the interior.  The typical “swing” states of Wisconsin, Michigan, and Florida show a “patchwork” pattern of red and blue. These maps get even more interesting when they have been mathematically distorted based on population density and shadings of purple based on how close the electoral split is at the state and county levels.

2016-election-results-by-county

Some studies have identified divisions that are not necessarily “red vs blue,” but may impact political leanings. These divisions are based on demographics (age, race and income) as well as “cultural” factors such as church attendance and where people shop. Income (and related wealth) is certainly one of the more salient factors, but there is some debate about whether the wealthy favor Republicans or Democrats.  Some of us intuit that wealthy people will favor Republicans based solely on policy preferences. Yet there exists a persistent media-fueled stereotype of opera-season-ticket-holders and chardonnay-sipping Democrats versus NASCAR-attending “Joe six-pack” Republicans.

In Red State, Blue State, Rich State, Poor State, a group of political scientists first present data that supports a correlation between higher income and voting Republican at the individual level, along with increasing stratification of voting patterns. Paradoxically, the richer states tend to vote Democratic. The authors then exhaustively analyze the data in an attempt to answer the following research question:  Why do rich people vote Republican and rich states vote Democratic?  After meticulous parsing (a statistical multi-level modeling) of the data, the authors find that rich people in poor states are more likely to vote Republican (i.e., in accordance with their economic interests), while in rich states, income has almost no correlation with voter preference—suggesting that voter preferences in wealthier states are based more on cultural factors than economics.

Data confirmed a historical pattern of rich counties traditionally supporting Republicans, although this has been steadily declining over the past forty years. Moreover, rich voter support for Republicans varied by region, with the strongest correlation between income and Republican support in the most Republican-leaning of the Southern states. The authors acknowledge that income is also correlated with race, particularly in the South, so preferences might be race-based as much as income-based. Alternatively, there are correlations between higher income and higher education.  Higher education is also correlated with preferences for Democrats, which could result in a higher-income Democratic preference (the so-called “liberal elites”). However, even outside of the South, voting patterns are predictable in the same manner:  higher individual income predicts support for Republicans and living in a wealthier state (regardless of individual income) predicts support for Democrats.

This group of political scientists next analyzed whether “cultural” factors might provide an explanation. They look at trends in “issue polarization” on the basis of economic, civil rights, moral and foreign policy issues.  Party identification has indeed become more associated with economic issues in poor states and with civil rights and moral issues everywhere, with moral issues especially becoming “increasingly correlated with liberal-conservative ideology and with each other.”  One counterintuitive result was that that low-income voters—who also tend to have less education, interest, and participation in politics—are “surprisingly complex,” exhibiting “no clear left-right opinion clusters.”

The next question to be addressed was whether or not there was a religious correlation; e.g., religious observers voting Republican and secular voters voting Democratic. For low income voters, there was little difference in voting patterns when comparing religious vs secular voters. Only among high-income voters does religious observance predict Republican voting. Religious and secular voters are increasingly different in their voting pattern higher up the income scale, which is also manifested in their attitudes on economic and social issues.  Thus it is richer Americans in richer parts of the country—more than the poor and rural—who are creating the cultural divide based on “God, guns and gays.”

The authors then asked whether income inequality may be affecting voting patterns, especially since it has “changed in recent decades,” but conclude (without much analysis) that income inequality does not explain the differences in voting patterns either. However, while income inequality (as far as the authors could establish) does not per se affect voting patterns, it “keeps the economic issues relevant and allows the polarization to continue.” Data at the international level indicates that countries with the biggest differences in voting preferences between rich and poor are those where the left and right parties are furthest apart on economic issues.

Although the authors could not offer an explanation for these results, they were confident in saying that a “typical Republican voter” was a rich person in a poor state and a “typical Democratic voter” was a lower-income person in a rich state. Further discrete findings were:

  • The red-blue divide is sharpest among the richer and more politically influential voters.
  • The voting gap between rich and poor is larger in republican states.
  • The division between rich and poor voters is highest in poor states.
  • Divisions between religious and secular voters are highest among high income voters.
  • The strongest rich-poor divides are those in which the major parties are furthest apart on issues of redistribution.
  • The data does NOT support generalizations that rich people vote based on economic issues while lower-income people are “more likely to be swayed by emotions.”

Here are my own possible explanations for these results:

1. The votes of higher income voters have a more significant impact than the votes of moderate and low income voters. Not only do wealthy people vote at higher rates, they are more likely to be politically active and support political issues and causes (as well as framing them) in public forums other than strictly voting. In summary, the concerns of wealthier voters are more likely to drive overall voter motivation at the polls.

2. When voters are more communitarian (“we’re all in this together”) on both social and economic issues, everyone in the aggregate is better off. This a reverse cause-and-effect explanation. That is, the Democratic-leaning values are what makes the state itself better off as a whole, so economic issues are neither as salient nor as divisive.

Which leads to the suggestion for additional testing for correlations between Gini coefficients, social welfare policy preferences, and voting patterns. Also not addressed by this study (but suggesting subject material for a future study) is the extent to which stricter state voting regulations might be discouraging lower-income individuals from voting.

Toward the end of their findings, the authors pose the question why Democrats have not been more successful than they have been. The economy is a key issue for many voters, not just low income ones, and there is more than ample evidence that the economy has generally performed better during Democratic administrations. The authors allude to some anomaly created by the confluence of electoral and economic cycles as well as deliberately gerrymandered redistricting, but it is not entirely clear, nor does it explain the phenomenon they are looking at. Perhaps it is because neither party has either coalesced or captured the debate about economic angst:  “Conservatives argue that the Democrats have lost relevance and authenticity, while liberals accuse the Democrats of selling out.”

As for predictions, the authors predict that increasing voter polarization could increase the probability of extremely close elections, and that “it will take more to swing voters away from their usual attachments.” Indeed, the recent election—with its extremely close results and sharp partisan rhetoric aimed at increasingly disaffected voters—seems to support this prediction.

Fighting Over Scraps

Published on by Brynne VanHettinga Category: Job Market, Underemployment

Recent articles in The Washington Post describe co-worker hostility faced by veterans working in federal agencies, purportedly because of federally mandated hiring preferences.  Some ascribe this hostility to cultural differences between military and civilian workers.  Combat vets, who are used to making and responding to rapid decisions, have little patience for the bureaucratic process, where decisions travel up and down the chain of command for submission and approval.  Alternatively, civilians accuse the vets of “blind deference to authority” and their only real skill is knowing how to kill people.  However, like many workers elsewhere, the vets complain that they are accepting jobs where they do not make full use of their talents and abilities.

A big problem is the perception that the veteran workers may actually be unqualified and were hired only because of the federally-mandated preferences.  In essence, the dynamic is similar to the conflict raised by arguments about racial and gender discrimination and the backlash of so-called “reverse” discrimination.  Women and racial minorities tend to be more sensitive to traditional patterns of discrimination, while white males are more sensitive to patterns of reverse discrimination (aka affirmative action).  While these groups have historically been mutually suspicious of hiring and promotion decisions based on “illegal” motives, it is also not historically unusual for these groups to be suspicious of each other.  But veterans?  When did “thank you for your service” become “what are you doing in my job”?

Suspicious Workers

What many people do not realize is that persons who are hired pursuant to preferences must nonetheless be minimally qualified.  That is, they must possess the skills, education, and experience necessary to perform the essential functions of the job.  Where the ambiguity arises is whether or not they are the “best” qualified.  This judgment often depends on more subjective criteria which cannot be either proven or disproven objectively.  These types of decisions involve less measurable concepts such as person-job fit and cultural compatibility.  That is, even the person making the hiring decision really has no way of knowing with absolute certainly who the “best” candidate actually is without giving each and every qualified candidate a chance to prove themselves.  When there are literally dozens, if not hundreds, of minimally qualified applicants for a single position, the hiring authority may welcome preferences as a way to simplify the decision process.

Michael Sattinger, a professor of economics at the University at Albany, has developed a model of the job market represented by a group of n dogs presented with a batch of n bones delivered by a dump truck.  Assuming that each dog can only receive one bone and that the bones can be assigned a value, equilibrium is established when every dog has a bone that is not desired by any other dog that could take it away, and each dog prefers its own bone to any bone it could take away from another dog.  In this model then, the “value” of any dog’s bone is a function of both the assortment of bones available and the individual dog’s ability to compete for them.

Analogized to the job market, an individual’s wages (and by extension, job quality) are thus a function of both the jobs that are available and the individual’s ability to compete in the market. However, in the “real world” job market, there are many more dogs than bones, and many of the bones are of lower quality than even a junkyard dog would be willing to accept under more normal circumstances. The predictable outcome is that the dogs will fight each other over the bones rather than question the adequacy of the source—a source which is out of their sight and beyond their ability to effect. At least, not without coordinated, strategic, collective action.

Instead of suspiciously eyeing our co-workers or consulting an attorney about suing someone who has made a hiring or promotion decision against us, perhaps we should direct some of that energy to finding out why there aren’t enough decent jobs to go around. All of us are fighting each other over the scraps that seem to get more meager and distasteful as time goes on. This is not only unsustainable in the long run, it can destroy our sense of common humanity.