Left Behind: Characteristics of Low Labor Participation Counties

In my last post, I examined labor market participation in New York State counties.  I found that most New Yorkers, both upstate and downstate, live in counties where labor market participation differs only slightly from national levels, but upstate counties with small populations in many cases have labor participation rates that are significantly below the average.  In this post, I look more closely at the ways that low participation counties differ from those with higher labor market participation.  In particular, I examine how income compositions, and industry compositions differ.

The data shows that employment has decreased between 1970 and 2015 in upstate counties with low levels of labor participation, largely because weak service sector employment growth was insufficient to offset losses in non-service industries.  Earnings per employee were weak in low participation counties as well.  In 2015, per employee earnings overall were 16% lower in  upstate low labor participation counties than in high participation counties, and 47% lower than those in the New York city metropolitan area.  Differentials between per employee earnings were particularly large in relatively high paying service industries, such as information, financial services, and professional and business services.  Government transfer payments make up a larger part of personal income in counties with low labor participation because of low employment levels in these counties.  Government employment made up a larger part of total employment in low participation counties because of the low level of private sector employment in these counties.

Data from this post is from the U. S. Bureau of Economic Analysis – Local Area Personal Income and Employment tables.  Much of the BEA data referenced was taken from the data presentations found at the Headwaters Economics Economic Profile System.

County Population Size

On average, the fifteen upstate counties with the lowest levels of labor participation had populations that were much smaller than those with high levels of participation, or those in the New York State metropolitan area.  On average, low participation upstate counties populations were only 22% of high participation counties, and 6% of counties in the New York metro area.

Populations of both the top and bottom 15 participation counties upstate have been essentially stagnant since 1970.  While high participation counties showed a small amount of growth between 1990 and 2015, and low participation counties have declined since that year, populations in both have only increased by about 2% since 1970.  The populations of New York metropolitan area counties have increased by 12% since 1970, and by 18% since 1980.

Employment

Not surprisingly, full and part-time employment as the percentage of the population of low labor participation counties was lower than counties where labor participation was higher.

Low participation counties had lower levels of employment throughout the period than either high participation counties, or counties in the New York metropolitan area.  Significantly, the gap in employment levels between low participation counties and other counties has increased over time – from six to eight percent less in 1970 to 14% to 16% less in 2015.

Income

Average wages and salaries have consistently been highest in counties in the New York Metropolitan area, followed by high participation upstate counties, with low participation upstate counties having the lowest average wages and salaries.  While the difference between low and high participation counties upstate has been relatively constant since 1970 – about 15% – the gap between average wages and salaries in the New York City Metropolitan area and in high participation areas upstate has grown from 7% in 1970 to 24% in 2015.

The Bureau of Economic Analysis income data is average, not median, income.  In contrast, inflation adjusted median incomes have not risen significantly for the past 15 years, and have risen much less than average incomes over the period since 1970.  Median incomes have increased less than average incomes because most real income growth over the past several decades nationally has been received by high income workers, while typical workers have had much smaller income growth.  See, for example, the chart below, from “Distributional National Accounts: Methods and Estimates for the United States,” by Thomas Piketty, Emmanuel Saez, and Gabriel Zucman

Components of Personal Income

Labor income (work related compensation) is a smaller part of personal income in low labor participation counties than in high participation counties upstate and in the New York City metropolitan area, while transfer payments (such as Social Security benefits, medical benefits, veterans’ benefits, unemployment insurance benefits, liability payments for personal injury and corporate gifts to nonprofit institutions)  make up a larger part of personal income in these counties than elsewhere.  Transfer payments are 26.6% of total personal income in low participation counties upstate, compared with only 15.9% in the New York metropolitan area and 19.9% in high participation counties upstate.  Dividends, interest and rent are 14.6% of total personal income in low participation counties, compared with 21.8% in the New York City metropolitan area.

The fact that transfer payments make up more than 26% of per capital personal income in low participation counties compared to 16% in the New York metropolitan area might lead readers to conclude that the amount of government transfer payments per capita is greater in low participation counties  upstate than in high participation counties or in the New York City metropolitan area.  In fact, that is not the case – per capita transfer payment levels per capita differ little.  Instead, they are more important in low participation counties because wage, salary and benefit levels are lower than elsewhere.

Employment

Employment patterns in high and low labor participation counties upstate and in the New York City metropolitan areas differ. Low labor participation counties had much larger percentages of total employment in non-service sector industries and in government.

Employment in service industries was 79% of the total in the New York City metropolitan area in 2015, while in low labor participation counties upstate, service sector employment was only 56% of the total.  Service sector employment in high labor participation counties upstate was 70% of total employment.

Several relatively high paying service industries – information services, financial activities, professional and business services and education and health services were  concentrated in counties in the New York City metropolitan area, with 48% of total employment in the region in 2015. In high participation counties upstate, these industries were also relatively important, having 38% of total employment.  In low participation counties upstate, however, these industries only accounted for 24% of total employment. In contrast, relatively low paying service industries, like trade, transportation and utilities and leisure and hospitality accounted for between 27% and 28% of all employment in each region.

Non-Services related employment (primarily manufacturing and construction) was 19% of the total in low labor participation counties upstate, while it was only 7% of the total in the New York City metropolitan area.  Again, high participation counties upstate were between low participation counties and the New York City metropolitan area on this measure, with 13.2% of total employment in non-service related industries.  Manufacturing was 8.7% of total employment in high labor participation upstate counties, 13.3% in low participation upstate counties, and 2.9% in counties in the New York City metropolitan area in 2015.

In low participation counties in upstate New York, more than one-quarter (25.4%) of all employment is in government, while in the New York metropolitan area, government employment is only 13.5% of the total.  In high participation counties, upstate, government employment was 17% of total employment. Local government employment is a larger part of total employment in low participation counties upstate than elsewhere – 16% of the total.  State government employment is also a larger part of total employment in low participation areas than elsewhere – 8%.

Why is government employment a much larger percentage of total employment in low labor participation counties?  The table above shows that government employment is about the same percentage of the total population in high and low participation counties upstate – between 8% and 9%, while in New York City, government employment is about 6% of the population.  But, the percentage of the population that is employed in the private sector is much lower – 26% in low participation counties upstate, compared with 40% in upstate high participation counties and in counties in the New York metropolitan area.

Overall, total employment in low labor participation counties is only slightly more than one-third of the total population – 35%, compared with nearly half – between 46.4% and 48.5% in counties in the New York City metropolitan area and in high participation counties upstate.

Change in Employment by Industry Sector Since 1970

Low labor participation counties upstate differed from high labor participation counties upstate and those in the New York City metropolitan area in the fact that these counties lost employment between 1970 and 2015.  Employment decreased in these counties by 6.3% during the period.  Employment in the New York metropolitan area increased by 6.7%, while in upstate high participation counties, it increased by 12.2%.

While non-services employment declined slightly less in low labor participation counties in upstate New York than other counties studied — 52% vs. 56% and 66%,  service sector employment grew much more slowly than elsewhere – 26% vs. 35% and 58%.

Employment and Population Change

Employment in low labor participation counties upstate decreased as a percentage of the counties’ populations by 2.9 percent between 1970 and 2015.  In the New York metropolitan area, employment as a percentage of counties’ populations decreased by 2.1%. Only in upstate counties with high levels of labor participation did employment as a percentage of population increase – by 4.1%.

In low participation counties, non-services employment  as a percent of county populations in industries like manufacturing has decreased slightly less than elsewhere – by 7.2% compared with 7.5% and 8.5% between 1970 and 2015, but service sector employment as a percentage of population  in upstate counties with low labor participation grew slower than elsewhere in those years.  In low participation upstate counties, service sector employment as a percentage of population grew by 3.8% compared to 11.9% percent in high labor participation counties, and 6.4% in counties in the New York City metropolitan area.

Per Employee Earnings by Industry

Per employee earnings are the sum of wages and salaries, supplements to wages and salaries, and proprietors’ income divided by employment.  Per employee earnings overall, in the private sector, and in government were lower in low participation counties upstate than they were in counties in the New York City metropolitan area and in high participation counties upstate.  Overall, earnings per employee were 16% lower in low participation counties upstate than in high participation counties upstate.  Compared to counties in the New York City metropolitan area, earnings per employee were 47% lower in low participation counties upstate.

Per employee earnings in the service sector in low participation counties show the greatest weakness compared to high participation counties upstate  (25% lower) and counties in the New York city metropolitan area (58% lower).   Per employee earnings in non-service sector industries, such as manufacturing, in low participation counties upstate were 16% lower than in high participation counties upstate, and 26% lower than in counties in the metropolitan area.  Differences in government earnings per employee between low and high participation counties upstate and counties in the New York City metropolitan area. Per employee earnings in low income counties upstate were 14% lower than in high income counties upstate, and 25% lower than in counties in the New York City metropolitan area.

A closer look at per employee earnings by industry sector in 2015 shows that low labor participation rate counties upstate were at a greater disadvantage to others in some industries than others.  For information services, per employee earnings were 31,536 in low participation counties, compared with 69,604 in high participation counties upstate, and $131,767 in counties in the New York City metropolitan area.  For professional and business services, per capita employee earnings were $39,004 in low labor participation counties upstate, compared with $60,015 in high labor participation counties, and $99,132.  For financial activities, per capita earnings for low participation counties were $48,746, compared with $66,689 in high labor participation counties upstate, and $200,727 in the New York City metropolitan area.  The industries with the greatest differentials between low participation areas and other locations were industries with relatively high per employee earnings.   Industries with lower per employee earnings, like leisure and hospitality and education and health services had smaller differences in earnings per employee between upstate low and high participation counties and counties in the New York metropolitan area.

Conclusions

The data supports the notion that counties with low levels of labor participation have weak labor markets characterized by weak employment growth in service industries that have seen growth nationally, and relatively low earnings per employee in service industries that have comparatively high earnings per employee.  Both of these factors support the conclusion offered in my earlier post on the subject that, “Differences in participation within groups between counties that have low and high labor participation levels at least partially reflect the hypothesis that people have been unable to find jobs and have left the labor force.”  

But, in my earlier post, based on low levels of participation by low skilled workers, I argued that, “The low levels of participation of these groups in low participation counties probably reflect reinforcing factors, like lower average levels of education among minority group members, the decline of manufacturing industries that employed low skilled men, and low levels of demand for workers without college educations in places where there are relatively few jobs available.”  Instead, this data shows that labor market growth in low labor participation counties has been particularly weak in those industries in the service sector, like information, financial services and professional and technical services that have relatively high earnings per employee.

In my earlier post, I concluded that, “The ability of low participation counties in small metropolitan areas and rural locations to create the resources needed to diversify their economies to include occupations that match the qualifications of residents are likely to be limited, because of the significant advantages offered by locations that have larger labor pools.”  The data presented in this post does nothing to contradict that conclusion.  And, because these labor markets are typically small and lack concentrations of workers with skills that are in high demand in growing industries, reversing their decay would present substantial challenges to remedial policy efforts.

Government actions can give residents the opportunity to increase their social mobility, though, even if the way mobility can be achieved is to move to locations with greater opportunities.  In “Where is the Land of Opportunity? The Geography of Intergenerational Mobility in the United States,” by Raj Chetty, Nathaniel Hedren, Patrick Kline, and Emmanuel Saenz, the authors found that higher mobility is associated with higher quality primary and secondary education, greater family stability, and less income inequality.  But, more economic mobility also is related to geographic mobility from low income locations – those places that had higher levels of economic mobility also had higher levels of out-migration.

In a recent study by Eleanor Krause and Richard V. Reeves, “Rural Dreams, Upward Mobility in America’s Countryside” the authors conclude that several actions could be taken to increase mobility.  These include “increase the number of Advanced Placement courses and improve career and college counseling.” and providing better funding for rural schools.  Another option might be to encourage charter schools to open in low labor participation areas.  Extending the availability of high speed fiber networks could benefit schools that lack access to online curricula, internet-based research and online testing.

Krause and Reeves also point to the need to encourage family stability.  Discouraging teen pregnancy is one approach to helping teens to make decisions that are more likely to lead to stable families with two parents.  Increasing access to long-acting reversible contraceptives is particularly important. They argue,  that residents of rural areas face obstacles to receiving health care that urban residents do not.  “Efforts to reduce these disparities are likely to include the protection or expansion of health coverage (particularly publicly-funded health insurance for low-income families), supporting publicly funded clinics, and promoting the availability of the full range of contraceptive options, including LARCS. School-based health centers and online programs could also help to reduce transportation barriers in more isolated areas. (Of course, online platforms will only prove effective if complemented by expanded access to broadband in some regions.)”




The Income Gap between Men and Women: 2015 vs. 1970

Since 1970,  inflation adjusted wage income growth has been almost nonexistent – only five percent over the 45 year period ending in 2015.  Income change in metropolitan areas in New York State has differed little from the nation.  Rochester and Buffalo were two exceptions – both had lower median real wage incomes in 2015 than in 1970.  Because of the declines in upstate’s two largest metropolitan areas, upstate real wage incomes overall were 5% lower in 2015 than they were in 1970.

In an earlier post examining inflation adjusted wage income changes in New York State Metropolitan areas, I found that although incomes have stagnated since 1970’s, education and age have become increasingly important in determining the changes that have occurred.  Overall, people aged between 25 and 29 with high school educations or less had median wage incomes in 2015 that were 38% lower than in 1970.  College graduates aged 50 or older had income gains averaging 6%

In a second post, I examined the gap in real wage incomes between black and white residents and found that the gap had not decreased over the 45 year period.  In fact, in upstate metropolitan areas, the gap was somewhat larger in 2015 than it was in 1970.

This post examines changes in inflation adjusted wage income as they affect men and women in New York State and the nation.  While real wages have been relatively stable since 1970,  wage incomes for men, particularly younger men with high school educations or less, have shown sharp declines, while those of women have substantially increased.  Nevertheless, a wage gap between the sexes remains.

The Data

The Census Bureau defines personal wage income as total pre-tax wage and salary income – that is, money received as an employee – for the previous year. Annual wage income includes the amount of wage income received by all people having wage income in a year, including those who worked full or part-time, and those who worked only part of a year as well as those who worked all year

Data, as in my earlier posts, is from Public Use Microdata samples made available by the U. S. Census Bureau (Steven Ruggles, Katie Genadek, Ronald Goeken, Josiah Grover, and Matthew Sobek. Integrated Public Use Microdata Series: Version 6.0 [dataset]. Minneapolis: University of Minnesota, 2015. http://doi.org/10.18128/D010.V6.0) Public Use Microdata Sample files (PUMS) are a sample of the responses to the American Community Survey and the Decennial Census and include most population and housing characteristics.  Because the data is from samples of households in metropolitan areas, sampling error is possible, particularly for smaller metropolitan areas.

Inflation Adjusted Wage Income – 1970

In 1970, real wage incomes of men were substantially higher than those of women.

In upstate metropolitan areas, women’s median wage incomes averaged between 40% and 50% of those of men.  Income differentials between men and women upstate were near the national average of 44%.  In the New York City metropolitan area, median wage incomes for women were higher, but were still only about 62% of those for men.  This is particularly significant, because the data here only reflects those women who reported earning wages during the year. The lower median incomes of female wage earners were not related to the fact that less than half of women were in the labor force in that year.

  • Male residents of the Rochester metropolitan area had the highest median incomes in 1970 – almost $60,000, which was substantially higher than the national median for men – $48,013.
  • In that year, the real median real wage income of men in the New ‘York City metropolitan area – $48,000  – only exceeded that in the Utica-Rome metropolitan area – $46,820.

For women, the picture was different – the real median income of women in the New York metropolitan areas was $29,500 – substantially higher than in any upstate metropolitan area or nationally.

Education

Here again, median wage incomes were strongly related to educational attainment, with college graduates earning at least 50% more than those with less than those who have less than four years of high school in most cases. For women, the differences in median wage incomes were even greater than for men — the median wage income of young women aged 25-34 was only $14,016, while that for those with 4 years of college was $33,698.  Because the effect of education on women’s income was stronger than for men in 1970, women and men with less education generally had larger median income gaps than those with more education.  But, even for women with four years of college, median incomes were substantially lower than for men – ranging from 40% to 60% of median male incomes at the same level of education.

In the New York City metropolitan area, the same relationships were present.  Women had wage incomes that were substantially lower than men, and less educated workers had much lower incomes than higher income workers.  At all levels, the gap in incomes between men and women was smaller in the New York City metropolitan area than it was in upstate metropolitan areas.   The difference between upstate metros and the New York City metropolitan area was particularly large for less educated women.

  • Women with less than four years of high school in the New York City metropolitan area had 55% to 60% of the median income of men with the same level of education, while in upstate metropolitan areas ranged from 33% to 47% of men’s median incomes, depending upon age.

For men, the picture was different – less educated male residents of upstate metropolitan areas had higher median incomes than those in the New York metropolitan area in 1970.  For college educated men, incomes were similar.

Inflation Adjusted Wage Income – 2015

By 2015, the income gap between men and women was much smaller than in 1970, but was not gone.  As in 1970, the income gap was smaller in the New York metropolitan area than it was in upstate metros.  But, the gap between men’s and women’s incomes was much smaller in both regions.

  • The median wage income for women upstate was 76.1% of the men’s median, while in New York City, the comparable percentage was 80%.
  • Two upstate metropolitan areas had the same gender wage gap as the New York City metropolitan area – Albany-Schenectady-Troy and Binghamton.

Education and the Gender Wage Gap

Though there has been progress in wage income equity since 1970, the progress has been uneven.

  • In upstate New York metropolitan areas, women with four or more years of college education have come closest to achieving equity with men – attaining median incomes of between 70% and 80% of men’s median wage incomes.
  • In contrast, women with less than four years of high school education have median wage incomes that are between 47% and 68% of men’s median incomes.
  • And, median incomes for young men and women with less than four years of high school in upstate metropolitan areas were very low in 2015- only $19,000 for men, and $9,450 for women under 35 years old.

In 2015, for men under 35 in upstate metropolitan areas, the median wage income for those with four or more years of college was $45,000 compared with $19,000 for those with less than four years of high school.  For women, the comparable numbers were $38,000 and $9,450.

In the New York metropolitan area the median wage gap between women and men was 20% – the median wage income for women in the metropolitan area was $40,000 compared with $50,000 for men.  For people with less than four years of high school, median wage incomes were more equal for men and women in the New York City metropolitan area in 2015 than they were in upstate metropolitan areas.

High levels of education were associated with large wage premiums for both women and men – those with four or more years of college had median incomes that were at least three times those with less than four years of high school education for all age groups.

The income gap between those with four years or more of college education and those with high school education or less has increased sharply since 1970.  The difference between median incomes for men with a high school education or less was 60% less than for men with four or more years of college in 1970.

By 2015, the gap had increased to 142%.  For women, the gap was 70% in 1970 and 155% in 2015.  This increase in the wage gap reflects the greater inequality of incomes in our society in 2015 compared to 1970.

Change in the Gender Gap Overall

Overall, the median wage gap between men and women decreased substantially between 1970 and 2015 – by 32% in upstate metropolitan areas and by 18% in the New York City metropolitan area.  While the decrease in the New York city area was smaller than that upstate, the New York City had a smaller wage gap than upstate metros in 1970, and continued to have the smallest gap in the state in 2015.

Upstate metropolitan areas and the New York metropolitan area also had smaller wage income gaps than the median for metropolitan areas nationally in 2015.

  • Nationally the difference between men’s and women’s median wage incomes was 29% in 2015.
  • For upstate metropolitan areas, the difference was 24%,
  • For the New York metropolitan area, the difference was 20%.

Inflation Adjusted Wage Income Gains and Losses – 2015 vs. 1970

Real median wage incomes have followed sharply different paths for men and women since 1970.  In most metropolitan areas, men’s median wage incomes have declined.  In a few, like Rochester, Binghamton, and Utica, the declines have been significant – 15% or more.  For women, the path has been up.

Median incomes for women were up sharply in 2015 compared with 1970.

  • Nationally, and in upstate metropolitan areas, the increase was slightly more than 50%.
  • In New York City, starting from a higher base in 1970, the increase was 35.5%.
  • Women’s median wage incomes were far lower in 1970 than men’s, and the gains made by women have not been large enough to erase the wage gap that existed.

In my earlier posts, I showed that inflation adjusted median incomes for people with less education were much lower in 2015 than they were in 1970.

  • The median income for those with high school educations or less was 38% lower, compared to 21% lower for those with four years of college or more.
  • In contrast, workers over fifty years old, at all educational levels had median incomes in 2015 that were close to those in 1970.

The table above shows the impact of education and age upon the difference in median wage incomes for men and women between 1970 and 2015.  It shows that for both genders, young people have fared less well than older people and that less educated workers have fared worse than more educated workers.

For young men, the decline in real median incomes between 1970 and 2015 is very troubling.

  • Young people with less than four years of high school education had real median incomes in 2015 that were 44% lower in the New York metropolitan area than in 1970, while in upstate metropolitan areas, median incomes were 53% lower.
  • The median income for men under 34 with some college experience was 37% lower in upstate metropolitan areas in 2015 than in 1970, while in the New York metropolitan area, the median real income was 43.2% lower in 2015.
  • The median income for young  workers with four or more years of college in upstate metropolitan areas was 24% lower in 2015 than 1970, while in the New York metropolitan area the decrease was 2%.

The income premium for those with four or more years of college remained substantial for young men, but in upstate New York, median incomes, even for college educated young people were substantially lower in 2015 than in 1970.

  • For less educated workers, the decline in median incomes extends to older workers as well, ranging from 35% to 45%, from 1970 to 2015.
  • But, workers with four or more years of college aged 35 or older saw only small decreases in median incomes.
  • In New York City, these workers had significant median income gains – 15% for those aged 35 to 49, and 24% for those from 50 to 64.

Less educated women – particularly those with less than four years of high school were not immune to the declines in median real incomes between 1970 and 2015.  losses ranged from 6% to 32%, and were not strongly related to age.  In fact, the gains that this group made compared with men in wage income simply reflected the declines in real wage income were smaller for women with low levels of educational attainment than they were for men.

In upstate metropolitan areas, women with four years of high school or more education had median income gains in most cases, with generally greater gains at higher educational levels.  In the New York metropolitan area, however, women in most age groups who had less than four years of college had lower median incomes in 2015 than in 1970.

The data points to the conclusion that but for the increase in the percentage of wage earners with four or more years of college, wage earners in metropolitan areas would have had much lower median wage incomes in 2015 than they did in 1970.  Clearly, the labor market has substantially less demand for workers with limited educational backgrounds than it did in 1970.

The erosion of median incomes of workers in the 25-34 year age group, especially in upstate metropolitan areas, should be of particular concern, because it may point to a long-term trend to lower wage incomes for workers who were in that age group in 2015.  As the table above shows – the performance of median incomes for men and women aged 25-34 between 1970 and 2015 was substantially weaker than it was for older workers.

  • The inflation adjusted median income of male workers between 35 and 64 in upstate metropolitan areas increased by 7.4%, while the median for those aged from 25-34 decreased by 29%.
  • The median income of women between 35 and 64 in upstate metropolitan areas was 90% higher in 2015 than in 1970.  For women aged 25-34 the increase was 40%.

Conclusions

The Pay Gap Between Men and Women

The narrowing of the wage gap between men and women is good news, although the fact that women’s median wage incomes continue to be lower than men’s is not.  In 1970, the median wage income for women was less than half of men in upstate metropolitan areas, and about 60% of that for men in the New  York metropolitan area.  In 2015, the gap between median incomes for men and women was between 20% and 30% in upstate metros, and 20% in the New York metropolitan area.

By controlling for education and age, the data shows that women with less educational attainment face larger income gaps than men, particularly upstate, where women with less than four years of  high school earn between 48%  and 68% of what comparably educated men in the same age groups earn.  Even so, the gap between college educated men’s and women’s real incomes ranged from 15% to 35%.

The data used in this analysis does not, in itself give explanations for the continuing wage gap.  While the effect of age and educational levels are controlled for here, the analysis does not compare people with the same jobs in the same industries to determine the extent of differences between women’s and men’s incomes.  If women participate more in lower paying occupations with similar educational qualifications than men, that could account for some of the difference in wage incomes.

The data from this study is consistent with other studies that do compare incomes of men and women in the same occupations.

This chart, reprinted from “The Simple Truth about the Gender Pay Gap (Spring 2017), published by the American Association of University Women, shows that weekly earnings of men in the same occupations as women are ten to twenty percent higher than women’s.  So, it is reasonable to ask, as in the case of the lower incomes of black/African-American workers, whether existing prohibitions against wage discrimination are being effectively enforced.  It is important to note that discrimination may or may not be explicit and intentional – but may also reflect the relative absence of women in high level management positions who could offer the equivalent of  “old boy networks” to help women get good jobs and higher pay.  Thus, enforcement could be very difficult.

A recent article in The Atlantic, “How Do We Close the Wage Gap in the U.S.?” by Bourree Lam suggests some solutions.  One part of the gap is believed to be caused by what is called “the motherhood penalty.”  Lam argues that, “There are two ways to go about fixing this huge part of the gender wage gap. The first is for companies or the government to implement policies that enable women to be both moms and workers, such as paid family leave and supported childcare. But there’s also a cultural shift that needs to happen: The assumption that mothers are not as good at their jobs, not deserving of promotions, or won’t work as hard is discrimination. Employers need to do their part in seeing women who are mothers as valued as employees.”

The second major part of the solution, according to Lam is pay transparency.  The Obama administration, in its last year proposed a regulation requiring “companies with 100 employees or more to report to the federal government how much they pay their employees broken down by race, gender, and ethnicity.”  Unfortunately, this action was revoked by President Trump, in one of his first executive orders this year. Trump’s executive order also revokes other worker protections contained in Obama’s regulation.

Large Decreases in Less Educated Workers’ Real Income 

Men and women with four years of high school education or less have seen large decreases in median real income since 1970.  The decrease is particularly severe for men with high school educations.

  • Men with less than four years of high school saw median income losses between 1970 and 2015 of between 35% and 53%.
  • Men under 50 years old lost between 26% and 42% of real income.
  • Women under 50 years old lost between 23% and 33% of real income.
  • In the New York metropolitan area, women with four years of high school lost between 22% and 40% of income.

At the same time, men with four years of college did not see losses as large  as those with lower educational attainment, and gained real income in some cases:

  • In metropolitan areas in upstate New York, median income declines for men under 35 with four years of college were less than half of the losses of people with less than four years of high school (25% vs 53%).
  • Older men in upstate metros saw small income losses – 5% to 8%.
  • Male residents of the New York metropolitan area saw gains in most cases, ranging from 15% to 24% for workers 35 years old or older.
  • Women with four or more years of college saw significant income gains, ranging from 11% to 66%, but started from much lower income levels in 1970 than did men.

The large real income losses of workers with low levels of educational attainment contrast with the better performance of median incomes for workers with more education.  This is one cause of the greater income inequality that has developed in the United States since 1970.

In earlier posts, I have described the loss of manufacturing employment in the rust belt, and its consequences for worker earnings.  The loss of millions of manufacturing jobs in the rust belt has resulted in the substitution of jobs in service industries that are on the average lower paying.  Unlike the manufacturing jobs that were lost, most service employment is not unionized, so workers have less negotiating power than more unionized manufacturing employees.

The causes of manufacturing employment losses have been debated in the political arena, but most researchers have concluded, as I did here, that the great majority were victims of automation and process improvements, not imports. But both increased automation in manufacturing production and importation of manufactured products resulted from the desire of owners and managers of manufacturing businesses to cut costs to gain or retain market share in competitive marketplaces.  Since labor has been a major element of manufactured product costs, the substitution of automation and lower cost foreign labor has been an attractive strategy to reduce costs.

To date, service employment has continued to grow in the United States, in the rust belt, and in New York State.  But, if anything, labor costs are a larger share of service industry costs than in manufacturing industries.  As a result, high labor costs per service unit make local labor employment vulnerable to substitution by lower cost labor elsewhere or by automation.  In many cases, services businesses have been unable to find substitutes for local labor that work well enough to prevent service employment growth, but in the future, technology may make more substitution possible.

Government could play a positive role, by taking regulatory actions that help low income workers, such as increasing the minimum wage and facilitating union representation of employees.  Though actions that increase labor costs might encourage substitution of automation or foreign labor for local labor, most studies show that the benefits of government assistance are likely to outweigh potential employment losses, as long as the actions do not dramatically increase unit costs.

Declines in Real Incomes of Young Workers

One of the more significant findings of this research was the disparate impact of real income decreases on workers under 35 years old.  This problem was particularly pronounced for male workers, particularly those with high school educations or less.  Overall, men between 25 and 34 had 21% lower real wage incomes in 2015 than they did in 1970.  Young male workers in at all educational levels, except for those with four or more years of college, had median real incomes that were more than 40% lower overall than in 1970.  The income losses extended to young women in the New York Metropolitan area, who also saw large real income losses compared with 1970.

A recent working paper by Fatih Guvenen, Greg Kaplan, Jae Song, and Justin Weidner of the Federal Reserve Bank of Minneapolis , “Lifetime Incomes in the United States over Six Decades,” presents two important findings:  First, “From the cohort that entered the labor market in 1967 to the cohort that entered in 1983, median lifetime income of men declined by 10% to 19%.  We find little to no rise in the lower three quarters of the percentiles of the male lifetime income distribution during the period…For women, lifetime income increased by 22%-33%, from the 1957 to 1983 cohort, but these gains were relative to very low lifetime incomes for the earliest cohort…Second, we find that inequality in lifetime incomes has increased substantially in each gender group.  However, the closing lifetime gender gap has kept overall lifetime inequality virtually flat.  The increase in inequality within gender groups is largely attributed to an increase in increase in inequality at young ages.”

The finding in my research that the 25-34 year old age group in 2015 had much lower incomes than older workers leads to the conclusion that the trend towards increased lifetime income inequality noted in young age cohorts ending in 1983 by Guyvenen, Kaplan, et. al. is likely to continue.  Options available to government to counter this negative trend could involve the Fed continuing to provide low federal fund rates, reducing the cost of borrowed capital, or stimulative federal fiscal policies that focus on things like infrastructure projects that could employ large numbers of construction workers, or increased income supports for low-income workers.  However, stimulative monetary or fiscal policy could, if used to excess, lead to inflation that would in itself erode workers’ purchasing power.

Robert Samuelson argues in “Trumps Low Growth Trap,” “We have entered a new era of low economic growth and high political disappointment. Our democratic system requires strong-enough economic growth to raise living standards and support activist government. These expectations, present in most advanced democracies, are no longer realistic, because the global economy has changed in ways that reduce growth….

Quoting an article by Ruchir Sharma that appeared in Foreign Affairs, he points out, ““The causes of the current slowdown can be summed up as the Three Ds: depopulation, deleveraging and deglobalization. Between the end of World War II and the financial crisis of 2008, the global economy was supercharged by explosive population growth, a debt boom that fueled investment and boosted productivity, and an astonishing increase in cross-border flows of goods, money and people. Today, all three trends have begun to sharply decelerate: families are having fewer children . . . , banks are not expanding their lending [as before] . . . , and countries are engaging in less cross-border trade.”

President Trump’s argument that by implementing tax cuts and regulatory relief, economic growth would increase from the current rate of slightly over two percent to four percent is simply unrealistic, given the headwinds that it faces.  And, anti-trade and immigration policies could actually slow economic growth.

Greater income inequality threatens the existence of a large and secure middle class, and creates greater economic and social distress for those whose incomes are below the median.  If the bottom half of the income distribution includes more people whose incomes are towards the extreme low-end of the income distribution, government will be faced with the choice of paying larger fiscal costs to support those whose wages are inadequate to provide a reasonably secure life, or of ignoring the needs of those who lack resources.   Recent political history also shows that people who sense that their well-being is declining are vulnerable to political appeals based on resentment to “undeserving” beneficiaries of government assistance, and towards other factors, such as international trade or immigration, that may be portrayed as the primary causes of income declines, despite the fact that they are not.

Reasonable policy options to counter the wage declines faced by less educated workers involve trade-offs like potentially increased inflation and greater substitution of automation and foreign labor for domestic workers.  Approaches that address claimed causes like immigration and trade that have played at most a small role in the decreases in income will be ineffective and have potentially large negative consequences.  As a result, they will increase cynicism beyond already high levels.  Leaders should not argue that “magic bullet” solutions exist.  Instead, they should promote reasonable expectations and acknowledge both benefits and costs.




The Persistent Gap Between White and Black Incomes in New York

There has long been a substantial gap between the incomes of white Americans and those who describe themselves as African/American or black.  As early as 1964, with the enactment of the Civil Rights Act, the Federal and state Governments began passing laws aimed at preventing discrimination in the workplace.  Has New York seen significant progress in reducing wage inequality between blacks and whites since 1970?  This post examines the changes in wage incomes of black and white New Yorkers between 1970 and 2015, and considers the impact of education levels on incomes.  This analysis does not consider the experience of other non-white groups, because small sample sizes would not yield reliable results.

The Census Bureau defines personal wage income as total pre-tax wage and salary income – that is, money received as an employee – for the previous year. Annual wage income includes the amount of wage income received by all people having wage income in a year, including those who worked full or part time, and those who worked only part of a year as well as those who worked all year.

Data, as in my last post, which examined the relationship between age, education and inflation adjusted wage income is based on Public Use Microdata Samples made available by the U. S. Census Bureau (Steven Ruggles, Katie Genadek, Ronald Goeken, Josiah Grover, and Matthew Sobek. Integrated Public Use Microdata Series: Version 6.0 [dataset]. Minneapolis: University of Minnesota, 2015. http://doi.org/10.18128/D010.V6.0.) Public Use Microdata Sample files (PUMS) are a sample of the actual responses to the American Community Survey and the Decennial Census and include most population and housing characteristics.  Because the data is from samples of households in metropolitan areas, sampling error is possible, particularly for smaller metropolitan areas.

This post compares inflation adjusted wage income differences between African-American and white New Yorkers in Upstate metropolitan areas and in the New York City Metropolitan area.  Since upstate New York has not historically been racially diverse, relatively few black/African-American wage earners are in the Census sample.  For that reason, data from the Upstate Metropolitan areas (Binghamton, Buffalo, Rochester, Syracuse, and Albany-Schenectady-Troy) examined in my earlier post on real wage income changes has been combined.  Even so, only 657 respondents identified themselves as black or African American in 1970 – 4.7% of the population of those metropolitan areas.  By 2015, the percentage of respondents identifying themselves as black or African-American in Upstate Metropolitan areas had increased to 8.6%.

The population of the New York City Metropolitan area has been consistently more diverse than that upstate. As a result, black/African-American residents comprised 21.5% of the sample in 2015, compared with 14.3% in 1970.

Differences in Real Wage Income – No Substantial Progress

The table above shows two significant things.  First, inflation adjusted wage income increased  between 1970 and 2015 for both white and African-American residents of the New York City Metropolitan area, but declined for blacks/African Americans in Upstate metropolitan areas.   White residents of upstate metropolitan areas saw stable incomes over the period.

Second, the percentage difference in incomes between whites and African-Americans/blacks decreased from 47% to 35% in the New York City Metropolitan area, but increased from 40% to 50% in Upstate metropolitan areas.

The data shows that overall, adjusted wage incomes did not substantially converge over the 45 year period. In Upstate metropolitan areas, the gap increased from 40% to 50%, while in New York City, white residents had more than one third more wage income than blacks/African-Americans in 2015.

Educational Disparities Remain

In my earlier post, the data showed that wage earners with more education fared better than those with less.  People with high school educations or less saw significant real declines in wage incomes over the 45 year period, in many cases.  But, because the educational backgrounds of New York residents have substantially improved, fewer people today have high school educations, or less, than in 1970.  This has offset some of the income decrease that would have otherwise occurred.

This data offers several important findings.  First, in 1970, in most cases people with less than four years of high school education were the largest single group – both in the New York City metropolitan area and Upstate.  The only exception was that the percentage of people with four years of high school was slightly larger than those with less than four years for white wage earners in Upstate metropolitan areas.  For black/African-American residents, more than half of New York City Metropolitan area wage earners had less than four years of high school, while upstate, nearly two-thirds had less than four years.

Second, in 1970, those with four or more years of college comprised less than 20% of the total.  For black/African-Americans, only 6% in the New York City metropolitan area, and 4% upstate had four or more years of college.

By 2015, the situation had changed dramatically.  Seventy percent of white wage earners in the New York Metropolitan area had some post-secondary education. More than half had four or more years of college.  In Upstate metropolitan areas, 67% had some post-secondary education, while 40% had four or more years of college.  For black/African-American wage earners in the New York City Metropolitan area, 58% had some post-secondary educational experience, while 31% had four or more years of college.   In Upstate metropolitan areas, 51% had had some post-secondary experience, while 19% had four or more years.

Third, by 2015, the percentage of New York City Metropolitan area wage earners with four or more years of college exceeded the percentage of Upstate metropolitan area wage earners with four or more years of college by more than 10%.  This may reflect the concentration of headquarters jobs in the New York City area, and the area’s greater ability to pull higher skilled workers into it compared with Upstate metropolitan areas.

While black/African-American wage earners have much higher levels of education than in 1970, the percentage increase in black/African American wage earners who have four or more years of college has lagged that of white wage earners. In 1970 about 12% more white wage earners had four or more years of college than black/African-American wage earners.  By 2015, the gap was 20%.  The increasing gap in highly educated wage earners between white and black/African-American wage earners contributes to the continuing disparities in wage incomes.  But, it is not the only reason why the gap persists.

Income Differences for Wage Earners with Similar Educations

While the differing educational levels of the populations of black/African American and white wage earners provide a partial explanation of the difference in wage income between the groups, they do not totally explain it.  In fact, black/African wage earners continue to have median wage incomes that are less than white wage earners with the same levels of educational attainment.

In 1970, at each educational level, differences in median wage income between Black/African-American and White wage earners were 20% or more.  In that year, wage differences between races did not appear to be related to educational level.  In 2015, results were more varied, with black/African-American workers with less than four years of high school in the New York City Metropolitan area having median wage incomes that were about 10% less than white wage earners.  But, at other wage levels, disparities were larger.  White wage earners with four or more years of college in the New York Metropolitan area earned more than one-third more than black/African-American workers.

In Upstate metropolitan areas, median wage disparities between black/African-American wage earners and white workers were larger in most cases than in the New York Metropolitan area.  For example, the median wage income for white wage earners with four years of high school was 56% higher than similar black/African-Americans.  The larger gap between white and black/African-American incomes in upstate areas leads to speculation that weak employment growth and the lack of racial diversity upstate may be related to larger racial income differentials.

Changes in disparities between white and African-American/black wage earner median incomes between 1970 and 2015 were inconsistent, though it is notable that black/African-American wage earners with four or more years of college fell further behind white wage earners in 2015 than they were in 1970.

Note that in the table above, overall, both whites and blacks did better than they did at specific educational levels.  This was the result on the large improvements in educational attainment that occurred between 1970 and 2015.  Much larger percentages of workers in 2015 had some college or four or more years of it than they did in 1970.

Comparing educational levels, white and black/African-American workers with less education fared worse than those with more education.  In New York City, white and black/African-American workers with four or more years of college saw gains in median inflation adjusted wage earnings.  Overall, black/African-American workers saw slightly larger median income gains than white wage earners.

Upstate, workers at all levels saw decreases, but wage earners at higher educational levels saw smaller median income losses.   Overall, however, black/African-American workers lost more wage income between 1970 and 2015 than did white wage earners.

Conclusions

The data shows that increasing educational levels in both white and African-American communities have offset much of the wage erosion that has occurred among those who have high school educations or less.  But, whites continue to to have higher levels of educational attainment than blacks/Afro-Americans, and that the gap has increased for those with four or more years of college education.

Differences in average educational attainment provide a partial explanation of the continued gap between median wage earnings of white and black/African-American workers. But, the fact that differences in median incomes between white and black/African Americans exist at similar educational levels points to the reality that policies promoting higher levels of education will not erase the median income deficit that this group faces.  While programs that “raise all boats” are more politically salable than those that address the needs of particularly disadvantaged groups, they cannot erase the income gap faced by African-American wage earners.

Because the wage gap between white and black wage earners persists, even among those at the same educational level, it is clear that the enactment of laws, like the Civil Rights Act of 1964, that have been aimed at erasing employment discrimination, are not in themselves enough to eliminate it.   The data from this analysis cannot provide the answer to the question of why this is true.  It does not show whether local, state and federal agencies need to do a better job of enforcing the laws, or whether the causes of the gap lie outside their purview. For example, it may be that white and black/African-American job candidates are often seen differently because many black candidates are products of central city school systems with high levels of poverty and low levels of academic achievement.

The racial gap in incomes remains a significant component of the inequalities that exist within our society.  If the wage gap between white and African-American workers is to be ameliorated, local, State and Federal governments must confront the realities that a better understanding of the causes of the gap is needed and that compensatory policies must have an implicit or explicit component that addresses racial differences in wage incomes.




Education, Age and Declines in Real Income Since 1970

The economic malaise that has affected small and medium sized rust belt cities since 2000 has been widely noted.  Most have seen little or no real household income growth since then.  Much of the weak performance has been associated with the long-term decline of manufacturing employment in the region – a trend that is largely the result of increased productivity; imports have played a smaller role.  But, even before 2000, incomes in rust belt metropolitan areas were weakening and employment growth had been slower than in other places.

This post looks closely at the relationship between age, education and inflation adjusted personal wage income over the period from 1970 to 2015 to get a better understanding of the economic changes that have taken place during that period.  The data shows that while income stagnation has been widespread, some age groups and educational levels have seen sharp declines in real income per capita since 1970 – nearly 50% in some cases.  Younger workers and those without college educations have been hardest hit.

The data for this post is from Public Use Microdata Samples made available by the U. S. Census Bureau.[1] Public Use Microdata Sample files (PUMS) are a sample of the actual responses to the American Community Survey and the Decennial Census and include most population and housing characteristics.  Because the data is from samples of households in metropolitan areas, sampling error is possible, particularly for smaller metropolitan areas.  

For this post, six metropolitan areas were examined – Albany-Schenectady-Troy, Binghamton, Buffalo, New York City, Rochester and Syracuse.  These metropolitan areas have shown differing levels of performance in past analyses.  The New York City and Albany, Schenectady, Troy metropolitan areas have been among the best performing areas in the region for earnings and employment growth since 2000.  These areas have historically had relatively low levels of manufacturing employment.  The remaining upstate areas have shown
relatively little employment or earnings growth.  Binghamton has been particularly hard hit.

While all the metropolitan areas are in New York State, their recent economic performance differs relatively little from others in the region. Some, like the New York metropolitan area and the Albany-Schenectady-Troy metropolitan area had relatively high median household incomes compared to other areas in the region.  Others, like most upstate metropolitan areas, had median incomes that were typical of the rust belt.  Consequently, it is likely that other rust belt metropolitan areas performed similarly.

 

Inflation Adjusted Median Wage Income – 1970-2015

The Census Bureau defines personal wage income as total pre-tax wage and salary income – that is, money received as an employee – for the previous year. Annual wage income includes the amount of wage income received by all people having wage income in a year, including those who worked full or part time, and those who worked only part of a year as well as those who worked all year.

Inflation adjusted median wage incomes[2] for most groups declined between 1970 and 2015 – in some cases, substantially.   Younger people and people with lower levels of educational attainment saw bigger losses than older and highly educated people.

On average in the six metropolitan areas, the real median wage income for people with a high school education or less declined by 38.3% between 1970 and 2015.  In that age group, even college educated people saw significant declines – about 21%.  The only group that did not see a decrease between 1970 and 2015 was workers between 50 and 64 who had a college degree or more.  In general, however, having a college education reduced by more than half, the size of median income losses.

Age was also strongly related to the decline in median wage income.  Median incomes for workers aged 25-29 were at least 21% lower in 2015 than they were in 1970.  Older age groups were less affected than younger people, with those in the 50-64 age group seeing relatively small median income losses.

A comparison of the inflation adjusted median wage incomes of people with high school educations or less shows that younger people had much lower median incomes in 2015 than they did in 1970 in each metropolitan area studied.  In Buffalo-Niagara Falls, the median for the 25-29 age group decreased from $35,000 $18,600 (47%).    The median for 25-29 year olds decreased from $36,000 to $20,000 (44%) in the New York City Metropolitan area.  In 2015, inflation adjusted median wage incomes for all age groups, with the exception of those aged 50-64, were 5% to 40% lower in each city than they were in 197o.  While the New York City and Albany-Schenectady-Troy metropolitan areas had greater employment growth and higher household incomes than most of their peers the declines in real wage income for people with four years or less of high school were not smaller than those in slower growing and less affluent places.

For college educated people, the picture was better, though younger age groups saw real income decreases.  With the exception of the Binghamton metropolitan area, people aged 25-29 with four years or more of college had median real wage incomes that decreased by 30% or less, with the exception of Binghamton.  New York City metropolitan area inflation adjusted incomes were higher in 2015 than in 1970, with the exception of those aged 25-29. In other metropolitan areas, median personal incomes for most age groups were lower in 2015 than they were in 1970.The relatively strong performance of the New York metropolitan area may point to the relative strength of the labor market for people with four years of college or more there.

In contrast, the median income of people aged 25-29 with four years of college in Binghamton was $37,000 in 2015, compared with $60,000 in 1970, while in Rochester, the comparable decrease was from $50,000 to $35,000.  Both areas saw employment at important businesses, like Kodak,Xerox and IBM, collapse or substantially decline between 1970 and 2015.

Change in Inflation Adjusted Median Wage Income by Time Period

The data shows that the period from 1970 to 1980 was the worst in the past 45 years for wage incomes.  Non-college educated people aged 25-29 saw declines of more than 40% in that decade in the median real wage income in the metropolitan areas studied. Those with four or more years of college aged 25-29 had declines of more than 20%.  Only workers with four or more years of college aged 50-64 did not see declines in real income during the period.  The 1970’s were notable for large increases in energy prices, beginning with the Arab oil embargo, beginning in 1973.  The resulting inflation overwhelmed nominal increases in wages for almost all workers.

Between 1980 and 2000, most age and education groups saw some recovery from the 1970-1980 period. People with four or more years of college in all age groups saw the biggest largest increase in median real wage income.  By 2000, medians largely recovered from the losses seen in the 1970’s.  While median incomes for people with four years of education recovered slightly in most cases during the 1980 to 2000 period, the increases for people with high school educations or less fell far short of erasing the losses of the 1970’s.  And, for the 29-29 year age group with high school or less education, median real incomes continued to decrease.

Between 2000 and 2015, median incomes again generally weakened.  Once again, declines for people with high school educations or less were significantly greater than for other groups — more than 10% in most cases during the 15 year period. Median inflation incomes for people with four years or more of college generally
increased by five percent or less.

the 2008 recession, the labor market has tightened – unemployment is relatively low, though labor participation rates remain somewhat depressed compared to the past decades (Source – U. S. Bureau of Labor Statistics – Current Population Survey). Consequently, median wage incomes increased between 2010 and 2015.  While the increases of the current decade trimmed the losses of 2000 to 2010, gains have been relatively small.

Implications

The declines in median wage income in the past forty five years point to a number of problems in the labor market in rust belt areas.  Much of the decline can be attributable to events that took place in the 1970’s related to increases in energy prices and economy-wide inflation.  But, the decades following the 1970’s have seen uneven economic performance.  The 1980’s and 1990’s saw some median wage income growth, but wage earners with high school educations or less did less well than those with four years of college or more.  The 2000’s and 2010’s saw small gains for those with college educations, and significant losses for those with high school educations, or less.

Government actions to increase aggregate labor demand would certainly benefit workers with all levels of education.  Similarly, governmental actions to enhance the bargaining power of less educated workers in low skill jobs, such as higher minimum wage requirements, and easier union certification, would help.

At the same time, the persistent gap in median incomes and income changes between workers with college educations and those with four years of high school or less demonstrates the continuing importance of good educational preparation. Despite the fact that the supply of college educated workers has increased substantially since 1970, the wage premium that they enjoy has increased, especially for worker in younger age groups.   In 1970 and 1980, median wage incomes of workers under 30 with four or more years of college were less than 40% more than those with a high school education or less.  In 2015, the difference was 76%.   For most other age groups, median wage incomes for people with four years of college or more were more than double those of people with high school degrees or less.

Given the durability of the income benefits of higher education, government policies aimed at increasing access to post-secondary education would be beneficial. For example, programs assisting groups that face barriers to higher education, such as  economically disadvantaged children, could improve opportunities.   Similarly, adults who do not have post-secondary training should be assisted in gaining access to college level studies.

[1] Steven Ruggles, Katie Genadek, Ronald Goeken, Josiah Grover, and Matthew Sobek. Integrated Public Use Microdata Series: Version 6.0 [dataset]. Minneapolis: University of Minnesota, 2015. http://doi.org/10.18128/D010.V6.0.

[2] Median per capita income was computed for all cases showing income of more than $0.  Income was adjusted for inflation by the Consumer Price Index for all Urban Workers and Clerical Workers.  See:  https://fred.stlouisfed.org/series/CWUR0000SA0.  All values rounded to $1,000.




Response to Lost Manufacturing Jobs – The Effects of Imports and Increased Productivity

I’d like to thank Kay Wilkie, who serves on the United States Trade Representative’s Intergovernmental Policy Advisory Committee for offering useful comments concerning my post, “Lost Manufacturing Jobs – The Effects of Imports and Increased Productivity”  Kay points out that “It would be worthwhile to carefully examine and review the aspects of international trade and investment agreements, and US tax code, which favor the interests of Multinational Corporations over those of small and mid-sized US manufacturers and service providers.  Such provisions, and the paucity of trade development assistance to US exporting firms compared to OECD competition, serve to encourage US offshoring activities.”  

Kay also argues that “the emphasis should be on mechanisms to improve pay in poorly paying service sector jobs, in providing educational opportunity for everyone, on creating apprenticeship programs on a larger scale in sophisticated manufacturing, by better aiding dislocated workers and by assisting domestic firms impacted by adverse trade and technology trends.

Note that US trade policy, since the post WWII era, has been premised on foreign policy rather than trade balance interests: making our marketplace available to other countries’ exports to win adherents to the US vision of a world market economy.  Hence, US trade policy typically has not focused on bolstering the competitiveness and exporting interests of domestic industry.  Programs to offer technical and financial assistance to US-based firms have been paltry compared to our major trading partners, as US global dominance was assumed.

In the current context, trade in general, and imports are blamed for all job losses and economic ills.  Protectionist impulses abound, with false promises that implementing ‘Buy America’ policies and shredding trade pacts will magically restore jobs from a 19th or 20th century industrial economy – notably in coal, steel and heavy manufacturing. 

Missing from scrutiny has been any assessment of the comparative costs and benefits to the federal budget and US employment of the over-allocation of resources and trade protections to agricultural commodities, rather than technologies, manufactured products, and services sectors.  Arguably, US agri-businesses need less taxpayer support for their commodities exports than do our SMEs.

Another area worth examination: the impact of provisions in international agreements, notably investor-state dispute settlement, that protect the interests of multinational corporations over US-based employers.  Investment agreement provisions in some international agreements extend greater investor rights to foreign investors than those available to US investors via US federal, state and local courts, have redefined and constrained government regulation in the public interest at federal, state and local levels.   

Rather than rhetoric that misleads and harms US workers and firms, improvements to a Technology and Trade Adjustment Assistance Program could make a difference.  Since its inception by President Kennedy in 1962, when the US was running trade surpluses, Trade Adjustment Assistance (TAA) efforts have assisted US trade expansion objectives by mitigating the injuries to workers, firms and communities facing import competition.  TAA programs, however, have been supported in a reluctant and miserly way, with the program’s extension usually linked to approval of ‘fast track’ trade promotion authority.

The Trade Adjustment Assistance Program merits a significant redesign and relabeling as the “Technology and Trade Adjustment Program,” building on the expansion accomplished under ARRA in 2009-10 and providing sufficient funding to conduct nonpartisan research on the workforce impacts of technological advances confronting manufacturing and services industries in an increasingly competitive global context.   At a minimum, programmatic flexibility needs to adapt to varying states’ needs, and there needs to be effective outreach to impacted workers, employers and communities. A reinvigorated program would better redistribute a small portion of the national gains from technology and trade growth to dislocated workers and communities, might foster more public understanding of, and support for, investments in education, research, technology, and engagement in the world economy.”

 




Lost Manufacturing Jobs – The Effects of Imports and Increased Productivity

The decline in manufacturing employment in the United States has caused a wrenching economic adjustment, as one path to relatively well paying jobs has narrowed, particularly for workers without college educations.  As the percentage of workers in our society who work in manufacturing industries decreases, and lower paying service employment has increased, wages have stagnated.

The causes of the decline in manufacturing employment have been hotly debated. President Trump promoted the idea that product imports are a major cause of the shift away from manufacturing employment.  There is certainly common-sense evidence to support this position.  Clothing that was sold in the 1960’s with labels like Levi’s and L. L. Bean were largely made in America.  Major television manufacturers, like RCA and Zenith also produced most of their products in this country.  And, most people drove Chevrolets, Fords, and Plymouths that were made in America.

But, there is a significant counter-argument.  Several studies have shown that increases in worker productivity from automated production processes have significantly reduced the number of employees required to produce manufactured products.  One recent New York Times headline proclaimed, “The Long-Term Jobs Killer is Not China.  It’s Automation.”[1]  The article goes on to argue, “Donald J. Trump told workers…that he would bring back their jobs by clamping down on trade, offshoring and immigration.  But economists say the bigger threat to their jobs has been something else:  automation.”

This post examines industry based employment and shipment data along with trade data to evaluate the import and automation arguments.

The Impact of Imports on Manufacturing Employment

 To understand the impact of imports on manufacturing employment, it is first necessary to know the value of goods shipped by manufacturers located in the united states, and the value of manufactured goods imported during the period examined.   In this post, I examine data from 1963 to 2015.

Manufactured Imports

 The data shows that manufacturing imports have significantly increased since 1963 to 2015, from 2.6% of the total of domestic plus imported manufacturing goods to 25.3%.  But the extent of import penetration varies significantly among manufacturing industries – from a low of 6.9% of food products, to 63.9% of textiles and apparel.  For apparel alone, 86.3% of goods shipped or imported were imported in 2015.[2]

 Manufacturing Employment

In 1963, manufacturing employment was 17,035,000.  By 1987, the number had increased to 20,935,000.   But since then, manufacturing jobs have consistently declined, reaching 12,320,000 in 2015.  Total displacement of domestic manufacturing employment associated with import penetration grew gradually over the 50+ year period, reaching 4,179,000 by 2015 – one third of current manufacturing employment in the United States.

The impact of import penetration varied significantly by industry.  Materials related industries, like wood products, foods, chemicals and petroleum were relatively unaffected by import penetration, while durable product industries, like machinery, transportation equipment and electronics saw high employment impacts.  Of the sectors examined, clothing and apparel lost the most domestic employment – nearly 1.8 million jobs.  Electrical and electronic products,  including computers and other high technology devices, had the greatest increase in foreign jobs for import production – almost one million jobs between 1963 and 2015.

The Impact of Productivity on Manufacturing Employment

Several studies have shown that automation and process improvements have made manufacturing significantly more efficient than in the past.  Thus, many manufactured products are significantly less expensive today than in the past, in real terms.  How large an impact has the substitution of imported manufactured goods had on manufacturing employment in the United States?

The data shows that productivity increases have had a very large impact on manufacturing jobs.  The value of domestically produced manufacturing shipments more than doubled between 1963 and 2015, in real dollars.  Thus, without productivity gains we would expect employment to substantially increase.  But, it hasn’t – in fact manufacturing employment is only 58% of what it was in the peak year – 1987.  About 4,000,000 of the lost jobs can be accounted for by import substitution, but many more (28,000,000) were estimated to be lost as the result of increased productivity.[3] Thus, imports have caused the loss of only 13% of total manufacturing employment losses, compared with 87% for productivity growth.

Full size table:  Click here:

Implications

The data shows that both import substitution and productivity gains have cost manufacturing jobs, but that increased productivity has been a far more significant cause of the decline in manufacturing employment.  Without the effects of import substitution and productivity gains, an estimated 44,000,000 domestic employees would be required to produce the goods made and imported to the United States in 2015.[4]  Productivity gains are estimated to have led to the loss of 28,500,000 jobs based on productivity at 1963 levels, and import substitution is estimated to have cost 4,179,000 jobs.

It is important to note that imports and productivity increases have substantially benefitted most U. S. residents, because they have resulted in significantly lower prices than would have been the case without the substitution of automation and more efficient processes for labor, and of lower cost foreign workers for better compensated American workers.  Since 152,000,000 people are employed full and part-time in the United States, and only 12,000,000 are employed by manufacturers, most people are better off than they would have been without productivity gains and import penetration.

There is no overall benefit, or practical method, to reverse the job losses that have occurred because of manufacturing automation and process improvements.  But, there is some potential benefit to addressing trade imbalances, though the benefits would be quite small in the scheme of things – an increase in the share of the workforce employed in manufacturing industries of a few percent, at most. A recent McKinsey Report, Manufacturing the Future:  The Next Era of Global Growth and Innovation,[6] points out that

“Policy makers must also be realistic about what they can achieve with manufacturing industry strategy…. Manufacturing has changed in ways that make approaches that are aimed primarily at large scale job creation in advanced economies increasingly ineffective and costly at a time of tight government budgets…. Manufacturing can continue to grow and contribute to value added and export growth…[and] create new jobs – but not in the volumes or at the same skill levels as seen in previous decades.”

Some approaches, such as the imposition of tariffs carry trade-offs that most likely exceed their potential benefits, both because they increase the prices of imported goods, and because they invite retaliation.  Others, like the proposed Border Adjustable Tax on businesses bear closer examination, since they would have the effect of shifting the net burden of taxes from exporters to importers.[5]  Also worth considering are questions of currency manipulation, and whether American producers have fair access to markets in nations that export to the United States.

Government policies to promote manufacturing should look beyond trade balances if they are to have significant impacts. Government has several tools available to encourage manufacturing, ranging from setting the regulatory environment, including labor, capital market and general business regulations, to enabling growth ‘with hard and soft infrastructure investments, educating and training a skilled workforce, supporting R&D and basic research and upgrading highways and ports.”[7] It can also provide investment support and shape demand through public purchasing and regulation.

Finally, given the reality that manufacturing employment is likely to continue to stagnate, most future employment growth will be in the service sector.  For that reason, emphasis should be on mechanisms to improve incomes in low paid service sector jobs. Here, government can use its regulatory powers to provide low skilled workers who lack power in the job market with tools like minimum wage policy and rules that promote collective bargaining rights.[8]  It can also provide better access to relevant post-secondary education for young people, and for workers needing retraining, and by providing direct aid to displaced manufacturing workers.

[1] https://www.nytimes.com/2016/12/21/upshot/the-long-term-jobs-killer-is-not-china-its-automation.html

[2] Data Sources:  Import Data – Statistical Abstract of the United States, various years: http://www.census.gov/library/publications/time-series/statistical_abstracts.html

Exports & Imports by NAICS Commodities:  https://usatrade.census.gov/data/Perspective60/Dim/dimension.aspx?ReportId=7028

Manufacturing Shipments:  Statistical Abstract of the United States- Various years:  http://www.census.gov/library/publications/time-series/statistical_abstracts.html,  U.S. Census Bureau – “Value of Manufacturers Shipments for Industry Groups,” various dates: https://www.census.gov/manufacturing/m3/index.html

[3] Producer Price Index data is from the Federal Reserve Bank of St. Louis:  https://fred.stlouisfed.org/series/PIEAMP01USA661N

Employment data is from the Statistical Abstract of the United States, various years, and from the U. S. Department of Labor, Bureau of Labor Statistics – Current Employment Series –  Table B-1a. Employees on nonfarm payrolls by industry sector and selected industry detail, seasonally adjusted – https://www.bls.gov/web/empsit/ceseeb1a.htm, various years.

[4] Note that without the lower prices that result from import substitution and productivity increases, prices would be significantly higher, and actual product demand significantly lower than estimated.

[5]For discussions see:  https://taxfoundation.org/house-gop-s-destination-based-cash-flow-tax-explained/

and pp. 10-11 of:  https://www.americanprogress.org/wp-content/uploads/issues/2010/12/pdf/auerbachpaper.pdf

and,https://www.washingtonpost.com/posteverything/wp/2016/12/30/my-take-on-the-republicans-new-interesting-corporate-tax-plan/?utm_term=.570520155032

[6] For a comprehensive review of manufacturing policy issues, see “Manufacturing the Future:  The Next Era of Global Growth and Innovation,” McKinsey Global Institute – McKinsey Operations Practice:  http://www.mckinsey.com/business-functions/operations/our-insights/the-future-of-manufacturing

[7] McKinsey, Ibid.

[8] Like other approaches, these policies carry trade-offs, since they can encourage employers to substitute automation for human labor.




Poverty in Upstate Metropolitan Areas – Characteristics and Change: 1999-2013

A paper, based, in part, on data previously presented on this blog site.

This paper examines the incidence of poverty in upstate New York cities, compared to the surrounding suburbs.  The data shows that while residents of upstate suburbs enjoy incomes that are substantially higher than the national average, and poverty rates that are substantially lower, upstate cities have higher levels of poverty and lower incomes than the nation, and it shows that the level of poverty in upstate cities is growing more quickly. Compared with other rust belt cities, the economic separation of central cities and suburbs is greater in upstate New York.

  • The data shows that poverty levels are particularly high for families with children under 18 – more than 50% in some cases.
  • The ratio of families with children living in poverty in upstate cities to those living in poverty in suburbs is greater than the average of rust belt cities outside New York State – as much as twice as great in some cases.
  • The residents of upstate cities are becoming increasingly economically segregated from those outside them. While nearly half of families with children in upstate cities are poor, only 5% to 15% of those in suburbs live in poverty.
  • Residents living in poverty in upstate central cities are less educated and less likely to work than people not in poverty outside those cities.
  • Households in poverty are far more likely to be headed by a single householder – usually a woman.
  • Minority group members are greatly over-represented among those living in poverty.

Read more here:




The Shrinking Middle Class in New York State – Cities and Suburbs

Pew Research has been releasing a series of studies showing that the percentage of Americans who have middle class incomes has been declining.  The most recent of these is  America’s Shrinking Middle Class:  A Close Look at Changes Within Metropolitan Areas.  The report received extensive coverage in many newspapers, including the New York Times.  It concluded that in nine of ten metropolitan areas, the middle class lost ground – from 61% of the population in 1971 to 49.5% in 2014.

The Pew findings are a result of the widely reported increase in income inequality that has developed in the United States since about 1980.

household-incomes-mean-real

Source:  Doug Short – U. S. Household Incomes: A 47 Year Perspective.

The data shows that income gains were concentrated among those with higher incomes. In fact, middle to low income households have seen no significant real (inflation adjusted) income gains since 1967. And, since 2000, real household incomes have stagnated at all levels.  Because income gains between different income groups have diverged, the percentage of Americans who live in the middle class has declined.

The Shrinking Middle Class in New York State’s Metropolitan Areas

Pew found that in New York Sate, each metropolitan area studied saw a decline in the percentage of residents whose incomes were classified as middle class.  For the purpose of their study, middle class was defined as the range between two thirds of the median household income and twice the median.  Albany-Schenectady-Troy showed the largest decrease- 5%.  On average, the percentage or residents with middle class incomes decreased by 3.9%.

Five of the seven metropolitan areas saw increases in the percentage of residents with high incomes – Albany-Schenectady-Troy, Glens Falls, New York-Newark-Jersey City, Syracuse and Utica. Four of seven metropolitan areas saw increases in the percentage of low income residents, with Buffalo-Niagara Falls showing the largest increase – 8.3%.

Change In Income Distribution
New York State Metros 2000-2014
Low Middle High
Albany-Schenectady-Troy  (1.90)  (5.00)  7.00
Buffalo-Niagara Falls  8.30  (7.40)  (0.90)
Glens Falls  0.10  (3.30)  3.00
New York-Newark-Jersey City  (0.10)  (2.60)  2.70
Rochester  3.00  (2.90)  (0.20)
Syracuse  (1.20)  (2.10)  3.30
Utica  0.50  (3.80)  3.30

Source:  Pew Research Center – America’s Shrinking Middle Class:  A Close Look at Changes within Metropolitan Areas.  

The 3.9% average decrease in middle class residents in upstate metropolitan areas was very close to the 4% decrease that Pew found nationally.  But the Pew data does not examine the way the increase in income inequality affects city residents compared to residents of suburban areas around them.  This is a significant issue because cities in New York state have become increasingly separated economically from their suburbs.

For example, in upstate New York in 1969, cities had rates of poverty that were only slightly higher than for the state as a whole.  But, by 2013, most upstate cities had rates of poverty that were at least two times the state rate.

Percent of Residents Living in Poverty
1969 1989 1999 2013
Albany 14.20% N/A 21.50% 25.30%
Buffalo 15.20% 25.60% 26.60% 31.40%
Rochester 12.40% 23.50% 25.90% 33.90%
Syracuse 14.10% 22.70% 27.30% 36.50%
Schenectady N/A N/A 20.80% 24.80%
Troy N/A N/A 19.10% 27.30%
Utica N/A N/A 24.50% 31.70%
New York State 11.10% 13.00% 14.60% 15.60%

(Data for cities with populations of less than 100,000 were not available for years before 1999).

Inflation Adjusted Median Household Income Change in Cities and Suburbs

The same dynamic played out with respect to household incomes in cities and their suburbs.  Between 1999 and 2014, the median inflation adjusted household income for residents of 14 New York cities declined, while those of households outside those cities in the counties within which they were located increased in all but two cases. Moreover, those cities with poorer populations saw greater income losses on average, while those suburbs with higher incomes saw larger income gains.

median income

(Income adjusted by CPI-U for 1999 and 2014 – Northeast Urban Class B&C Metropolitan Areas).

Several cities saw particularly large adjusted median income declines between 1999 and 2014.  Adjusted household income declined by 23% in Elmira and Newburgh, and 20% in Rochester.  Overall, households in poorer cities lost 15% of inflation adjusted income, while those in wealthier cities lost 9.2% of income between 1999 and 2014.

In contrast, suburban areas saw gains, on average, with suburban areas around wealthier cities seeing increases of 6.3% on average, while those around poorer cities saw increases of 4.5% on average.  Household income in suburbs outside Elmira increased by 13%, compared to the 23% decline in the city.  In suburbs around Syracuse, adjusted household income increased by 10%, while in the city, it decreased by 9%.

Chautauqua County outside Jamestown, Duchess County, outside Poughkeepsie, Monroe County, outside Rochester, and Ulster County outside Kingston saw declines in inflation adjusted median household income.  But, even here, central cities far worse than suburbs.  In Rochester, adjusted median household income declined by 20%, while Rochester suburbs decreased by 3.8%.  In Poughkeepsie, real median household income declined by 11.4%, while in the rest of Dutchess County, median income declined by 3.5%. Jamestown saw a 15% decline, while the remainder of Chautauqua County saw a decrease of 2.3%.

Inflation Adjusted Income Change in New York City

nyc household

Three boroughs in New York City saw declines in inflation adjusted household income between 1999 and 2014 – Bronx with a decline of 15%, Queens with a decrease of 7.7%  and Staten Island with a decrease of 7.9%. Manhattan saw an increase, while Brooklyn’s income was stable.

Middle Income Shrinkage in Cities and Suburbs

 

middle income

Both cities and suburbs had smaller percentages of middle income residents in 2014 than in 1999, but started from different positions.  In cities in 1999, on average only 43% of residents were middle class, compared with 55% in suburban areas.  In 2014, 38% of city residents on average were middle class compared with 50% in suburbs.  So, both cities and suburbs lost the same percentage of middle income residents.

Implications

The decline of middle income households is a significant concern, but, even more significant are the overall growth of inequality, and the overall decline in real household income that has taken place this century.

For the United States as a whole, by 2014, inflation adjusted median household income had decreased by 8% from 1999.  While more recent data suggests that incomes have recovered since 2014, the lack of growth in median household incomes is a significant concern.

adjusted income

Source:  Federal Reserve Bank of St. Louis

But the impact of income stagnation has been unequal.  The chart and table below show that the impact of recent income declines has been greatest on lower income groups.

household-incomes-growth-real-annotated

household-income-real-decline-from-peak-table

Source:  Doug Short – U. S. Household Incomes: A 47 Year Perspective

Because of the concentration of low income residents in cities, city households saw significant declines in inflation adjusted income over the past fifteen years – averaging a decrease of 12%, compared with an increase of 6% in suburban areas.  As a result, the average difference in household incomes between cities and suburbs increased from 46% in 1999 to 74% in 2014.

Our society has become increasingly divided economically over the past 35 years.  More recently, the U. S. economy has not provided income growth for the country’s residents.  In New York State, the impact of these changes has been significantly different for suburban residents, who have been largely insulated from these economic problems, and for city residents who have suffered from them.

 

 




More on Race, Income and Student Achievement

A few months ago, I wrote about the link between economic disadvantage and poor student performance.  I looked at the performance of students on the State’s annual student assessment for grades 3 to 8, and found that the percentage of economically disadvantaged students in schools and school districts accounted for about three quarters of the difference in performance.

performance vs disadvantaged

The data showed that a ten percent increase in students who were economically disadvantaged was associated with a six percent decrease in performance on the statewide assessment.

The New York Times, on April 29, published an article “Money, Race and Success: How Your School District Compares” that reports on a study that was based on national level data from the National Assessment of Educational Progress.  The article points out that:

“We’ve long known of the persistent and troublesome academic gap between white students and their black and Hispanic peers in public schools.

We’ve long understood the primary reason, too: A higher proportion of black and Hispanic children come from poor families. A new analysis of reading and math test score data from across the country confirms just how much socioeconomic conditions matter.

Children in the school districts with the highest concentrations of poverty score an average of more than four grade levels below children in the richest districts.”

The data for the analysis comes from “The Geography of Racial/Ethnic Test Score Gaps” by Sean F. Reardon, Demetra Kalogrides and Kenneth Shores of Stanford.

In “School Segregation and Student Performance Gaps,” Reardon argues:

“Although it is clear that racial segregation is linked to academic achievement gaps, the mechanisms underlying this link have been debated since Coleman published his eponymous 1966 report. In this paper, I examine sixteen distinct measures of segregation to determine which is most strongly associated with academic achievement gaps. I find very clear evidence that one aspect of segregation in particular—the disparity in average school poverty rates between white and black students’ schools—is consistently the single most powerful correlate of achievement gaps, a pattern that holds in both bivariate and multivariate analyses.”

My own research showed that upstate cities have increasingly high poverty rates – in Syracuse and Rochester, for example, more than 50% of children under 18 live in poverty, and that minority group members living in upstate cities have far lower median incomes than white city residents or of white suburban residents.

Median Family Income – 2014
Upstate Cities
  Black Hispanic White
Albany $39,077 $29,268 $84,422
Buffalo $29,155 $21,803 $55,516
Rochester $28,752 $23,717 $56,178
Utica $22,975 $18,149 $51,043
Syracuse $27,902 $23,438 $57,246
Troy $21,563 $20,061 $60,843
Schenectady $27,338 $25,111 $62,818
Median Family Income – 2014
Outside Cities
  Black Hispanic White
Albany $67,400 $78,594 $91,693
Buffalo $39,001 $44,463 $77,996
Rochester $44,716 $44,179 $81,432
Utica $50,785 $34,792 $70,457
Syracuse $48,187 $57,778 $80,714
Troy $47,521 $67,381 $84,992
Schenectady $65,062 $52,505 $88,674

Median family incomes for blacks in most upstate cities were between $20,000 and $30,000 in 2013, while white families in suburbs around most upstate cities had median incomes of between $80,000 and $90,000.

The increasing separation of residents of upstate and suburbs by income is one of the most dramatic changes in the past half century in the way upstate residents live.  In 1969, 14.2% of the residents of the City of Rochester, and 14.1% of the residents of Syracuse lived in poverty, compared with 11.1% for New York State as a whole.  By 2013, 33.9% of Rochester residents lived in poverty, while in Syracuse, 36.5% lived in poverty.  The poverty rate for New  York State was 15.6%.  So, New York State saw in increase in the percentage of residents living in poverty of 4.5%, while residents of Rochester had an increase of 19.7%, and 25.4%.

Percentage of Residents Living in Poverty
1969 1989 1999 2013
Albany 14.2%  N/A 21.5% 25.3%
Buffalo 15.2% 25.6% 26.6% 31.4%
Rochester 12.4% 23.5% 25.9% 33.9%
Syracuse 14.1% 22.7% 27.3% 36.5%
Schenectady N/A N/A 20.8% 24.8%
Troy N/A N/A 19.1% 27.3%
Utica N/A N/A 24.5% 31.7%
New York State 11.1% 13.0% 14.6% 15.6%

(Data for cities with populations of less than 100,000 were not available for years before 1999).

Recent studies, like the national study by Reardon and other Stanford researchers, show strong associations between the concentration of black and hispanic residents in areas with high concentrations of poverty and poor student performance.

Unless we address the factors that lead to the growth of concentrated poverty in upstate central cities, and the continued separation of white, hispanic and black residents in upstate metropolitan areas, how can we effectively combat the persistent economic differences between black, Hispanic and white residents of upstate metropolitan areas, and the cycle of intergenerational poverty?




Income Divisions in Upstate Metropolitan Neighborhoods

In earlier posts, I wrote about income and racial separation between the residents of upstate cities and suburbs.  The data showed that residents of upstate cities saw sharp increases in poverty levels between 2000 and 2013, while city populations became increasingly diverse, primarily because of the loss of white residents.  The data also showed that minority group members living in cities had median incomes that were less than half of those of white residents living in suburban areas.

Income and racial residential patterns can be viewed through a different lens – one which focusses on the differences between residents of neighborhoods, not cities and suburbs.  While my earlier study examined differences between residents of seven upstate cities (Albany, Buffalo, Rochester, Schenectady, Syracuse, Troy and Utica, and their suburbs, this study reviews the differences in economic characteristics of residents living in approximately 800 neighborhoods within the counties where the upstate cities are located.  The data comes from the United States Census Bureau which divides the nation into census tracts, the most detailed level publically tabulated. Overall, there are 73,000 census tracts nationally, averaging 4,200 residents each.

The data shows that while there are significant differences in concentrations of incomes, unemployment and poverty among upstate urban neighborhoods, but that income differences are not strong enough to characterize most of these neighborhoods as truly segregated by income.

Income Divisions – Neighborhood Types

Low Income Census Tracts

Upstate Metropolitan Census Tracts – 2014
Sorted by Percentage of Low Income Households
High Concentration Census Tracts Average Concentration Census Tracts Low Concentration Census Tracts
  30% of all Low Income Households 40% of all Low Income Households 30% of all Low Income Households
 
Low Income Households 66.4% 39.9% 20.9%
Medium Income Households 29.1% 47.2% 49.3%
High Income Households 4.5% 12.9% 29.8%
Low Income Households  123,900  166,461  125,022
Total Households  185,296  417,704  598,295
% Black Residents 40.8% 12.1% 3.0%
%Hispanic Residents 14.3% 5.6% 2.7%
%White White Residents 35.8% 76.4% 90.0%
Mean Household Income $34,938 $57,855 $89,876
% Unemployment 16.9% 8.2% 5.5%
% Poverty 37.2% 11.7% 3.7%

This section looks at low income households in neighborhoods (census tracts) that had median neighborhood incomes that were less than 67% of the national household median ($53,000 in 2014).  The average household income in these neighborhoods was  $34,398 – less than twice the poverty level for a family of three.

Neighborhoods with high concentrations of low income people have higher percentages of residents who identify as black/African American than White (not Hispanic) – 41% vs. 36%.   Overall, 77% of residents of upstate metropolitan neighborhoods were white in 2014, and 12% black. Blacks are more than three times as likely to live in neighborhoods with high concentrations of low income residents as their overall population, while whites are half as likely to live in poor neighborhoods as their population averages in upstate urban counties.

Residents of neighborhoods with high concentrations of low income residents had much greater chances (17%) in 2014 of being unemployed than the average (5.5%) for all members of the workforce in upstate metropolitan census tracts.

Finally, the likelihood that residents of neighborhoods with high concentrations with low incomes lived in poverty (37%) was much higher than it was for all upstate metropolitan residents (14%).

Chart 1.

low med hi income

Chart 1 shows the distribution of low income households compared with middle and high income households.  The chart shows that half of low income households live in neighborhoods where low income residents constitute 40% or more of all residents.   Forty percent of low income residents live in neighborhoods where they are a majority of all households.

However, most low income households are located in neighborhoods where there are significant numbers of middle and high income households.  Only about 15% of low income households are located in census tracts where middle income households are less than 30% of the total.

High Income Census Tracts

Upstate Metropolitan Census Tracts – 2014
Sorted by Percentage of High Income Residents
High Concentration Census Tracts Average Concentration Census Tracts Low Concentration Ceusus Tracts
  30% of  all High Income Households 40% of all High Income Households  30% of High Income Households
 
High Income Households 43.4% 27.5% 10.6%
Medium Income Households 42.1% 48.9% 44.4%
Low Income Households 14.5% 23.6% 45.0%
High Income Households 71890 96128 72361
All Households 165451 349901 685943
%Black 2.3% 3.2% 19.3%
%Hispanic 2.2% 2.9% 7,8%
%White 90.1% 89.7% 66.5%
Mean Household Income $117,693 $83,309 $52,016
% Unemployment 4.7% 5.6% 10.1%
% Poverty 2.4% 4.6% 17.3%

Residents of typical neighborhoods with high concentrations of high income households (households with incomes of more than twice the median), would most likely live in a neighborhood with almost as many middle income households as high income households (43% high income vs. 42% middle income).  Also, almost 15% of the households in these neighborhoods have low incomes (less than 67% of the median income).  So, in upstate New York, households with high incomes are less separated from other households than those who live in neighborhoods with high concentrations of low income households.

Ninety percent of the residents of a typical upstate metropolitan neighborhood with a high concentration of high income households are white, compared with 77% of all upstate metropolitan neighborhoods.  These neighborhoods have very few blacks and Hispanics – each group has only 2% of households in neighborhoods with concentrations of high income residents.

Unemployment and poverty in neighborhoods with high concentrations of high income residents were very low – about 5% of the people in the labor force were unemployed in 2014, while 2.4% lived in poverty.

Chart 2.

high income pic 2

About 90% of high income households are in neighborhoods with more middle class households than high income households.  About 40% of high income households are in census tracts with more low income households than high income households, so there is little segregation of high income households from others, overall.

Families Living in Poverty

Upstate Metropolitan Census Tracts – 2014
Sorted by Percentage of Families in Poverty
High Concentration Census Tracts Average Concentration Census Tracts Low Concentration Census Tracts
  30% of all Residents 40% of all Residents 30% of all Residents
  % in Poverty % in Poverty % in Poverty
% Families in Poverty 47.1% 22.4% 4.2%
Low Income Households 69.8% 50.7% 25.7%
Medium Income Households 26.1% 40.9% 25.4%
High Income Households 4.1% 8.4% 48.9%
%White 28,1% 57.2% 88.7%
%Black 43.4% 26.4% 3.9%
%Hispanic 17.7% 8.9% 3.0%
Families in Poverty  23,564  30,812  22,657
All Residents  50,021  137,252  539,318
Mean Household Income $31,869 $47,385 $81,463
% Unemployment 19.2% 11.5% 5.8%

In neighborhoods with high concentrations of families in poverty, about half the families lived in poverty in 2014.  Given that about 11% of all families of upstate counties lived in poverty, these poor residents are highly concentrated.  But, 70% of all families in poverty lived in neighborhoods where 75% or more of the families did not live in poverty.

Neighborhoods with high concentrations of poverty had high concentrations of low income families – 70%.  Black/African-American families were also overrepresented in census tracts with high concentrations of families living in poverty, with 43.4% of families, compared with 12% for the counties overall. Hispanic families were 17.7% of those in neighborhoods with high poverty concentrations, compared with 5.5% overall.  Not surprisingly, unemployment was also high in these neighborhoods, at 19%.

Chart 3.poverty familiesChart 3 shows a strong relationship between the percentage of families in poverty and the percent of low income households.

Upstate Urban Neighborhoods Compared to the Nation

A 2012 study by the Paul Taylor and Richard Fry of the Pew Research Center, “The Rise of Residential Segregation by Income”[1] examined changes in patterns of residence by income  in 942 metropolitan and micropolitan areas between 1980 and 2010 at the census tract level.  The report found that over that period of time, there was a significant increase in residential income segregation, with particularly large increases in the percentage of upper income families living in upper income census tracts.

The Pew study found that in 2010, 28% of lower income households lived in majority low income census tracts.  In 1980, 23% lived in low income census tracts, and 25% lived in them in 2000.   Eighteen percent of high income families lived in majority upper income census tracts  compared with 9% in 1980 and 16% in 2000.[3]

In upstate metropolitan counties 38% of low income residents lived in majority low income census tracts, compared with 37% in 2000.  In 2014, 6% of high income residents lived in majority upper income census tracts, compared with 7% in 2000.  In all, there was relatively little change in the percentage of low and high income residents living in majority same income census tracts between 2000 and 2014. Neighborhoods in upstate metropolitan areas did not see the increase in the separation of poor and wealthy residents that was found by Taylor and Fry in national data.

Overall, in 2014, upstate metropolitan counties had about 10% more low income residents living in majority low income census tracts than the average for all 948 metropolitan and micropolitan areas studied by Taylor and Fry, and 12% fewer high income residents living in majority high income census tracts.  These differences are relatively large – they point to the fact that low income residents are substantially more likely live in low income census tracts in upstate New York than in the nation as a whole.  With regard to high income residents, the reverse is true in upstate metropolitan areas – upstate high income residents are more likely to live in middle class census districts than is true nationally, and significantly less likely to live in high income census tracts.

The change in the distribution of low and high income residents by census tracts median incomes can be seen in the following chart:

% of Group Living in Majority Same Income Census Tract
Upstate Metropolitan Areas vs. Nat’l Metro/Micro Areas
Year/Region Low Income High Income
2000- National 25% 16%
2000- Upstate 37% 7%
2010- National 28% 18%
2014- Upstate 38% 6%

Chart 4.

income distribution

Chart 4 shows that while there is a concentration of low income residents on the left of the chart – the portion showing census tracts having low median incomes, the largest number of low income residents live in census tracts with near average median incomes.  In fact, using the Taylor-Fry middle income group (from 67% of median household income to 2 times median income), 61% of low income residents lived in middle income census tracts.  In 2000, the comparable figure was 63%.  For high income residents, 89% lived in middle income census tracts in 2014, compared with 90% in 2000.  So, most low and high income residents live in middle income census tracts.

All of this points to the reality that the use of the term “segregation” by Taylor and Fry overstates the reality of the differences in living patterns between high and low income residents – both in upstate New York and in the nation.

Distribution of Households by Income Groups

 Taylor and Fry found that between 1980 and 2010, the percentage of households with middle incomes declined from 54% to 48% of the total.  The percentage of low income households stayed steady at 32%, while the percentage of upper income households increased from 15% to 20%.  Taylor and Fry found little change between 2000 and 2010, with the only change in the distribution of incomes being an increase in the percentage of high income households from 19% to 20%.

Distribution of Households by Income Group
Upstate Metropolitan Areas vs. Nat’l Metro/Micro Areas
Year/Region Low Income Middle Income High Income
2000- National 32% 50% 19%
2000- Upstate 33% 50% 17%
2010- National 32% 48% 20%
2014- Upstate 35% 45% 20%

Upstate metropolitan counties saw a larger shift between 2000 and 2014. Neighborhoods in upstate urban areas had increases in the percentages of low and high income households, while the percentage of middle income households decreased from 50% to 45%.

Implications

In earlier posts, I pointed out disparities in poverty and income between upstate cities and their suburbs, and between white, black and Hispanic residents.  This research extends the analysis to the neighborhood level.  Because this neighborhood analysis does not treat cities as separate units from communities in the rest of counties outside cities, it finds less residential segregation than the city/outside city analysis that was the subject of my earlier posts.  In fact, because cities are not treated separately, the dire condition of some city neighborhoods gets lost in the overall picture.

Nevertheless, there are significant differences in the residential patterns of many poor neighborhoods, compared to the population as a whole.  The data shows that about 30% of them live in neighborhoods where low income households are a majority of all households.  These low income neighborhoods have high concentrations of minority residents, high levels of unemployment and poverty.

Similarly, while poverty was present in most neighborhoods, about thirty percent of families living in poverty lived in neighborhoods where poverty was highly concentrated, with about half the families in those neighborhoods living in poverty.  These neighborhoods had high concentrations of black and hispanic residents.

My next post will look at race in upstate urban neighborhoods, an area where there is more separation

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[1] “The Rise of Residential Segregation by Income,” Paul Taylor and Richard Fry, Pew Social and Economic Trends, Washington, D. C., August 1, 2012.  http://www.pewsocialtrends.org/files/2012/08/Rise-of-Residential-Income-Segregation-2012.2.pdf

[2] Ibid, p. 1

[3] The Taylor-Fry study defined low income as having less than two thirds of the national median income (34,000 in 2010) and high income as more than double the national median income (104,000).