Misconceptions About People in Poverty: Are Work Requirements Effective?

This is an expanded version of the essay, “False Stereotypes Harm People in Poverty” that appeared in the Rochester Beacon, containing additional information relating to the five largest upstate metropolitan areas.


Misconceptions about people in poverty appear to drive proposed changes in social welfare policy, particularly the work requirements either being promoted by the Trump Administration and discussed or implemented in several states. A fuller understanding of factors underlying the problem of poverty suggests that these policies will be counterproductive, neither reducing the incidence of poverty nor helping those individuals and families targeted by these new policies.

The concentration of poor black and Hispanic people in central cities has allowed politicians to characterize the poor as “others”- people unlike white suburban majorities in upstate metropolitan areas, and to disparage them as dishonest, lacking in ambition and willingness to work.

For example, Ronald Reagan claimed the existence of a “welfare queen”, who supposedly cheated the government of $150,000.  Josh Levin, in “The Welfare Queen” describes Reagan’s line of argument, “When he ran for president in 1976, many of Reagan’s anecdotes converged on a single point: The welfare state is broken, and I’m the man to fix it. …. In Chicago, they found a woman who holds the record,” the former California governor declared at a campaign rally in January 1976. “She used 80 names, 30 addresses, 15 telephone numbers to collect food stamps, Social Security, veterans’ benefits for four nonexistent deceased veteran husbands, as well as welfare. Her tax-free cash income alone has been running $150,000 a year.”

The story was untrue.  The real welfare queen had defrauded the government of $8,000 using a few false identities.

Reagan was not alone in stoking resentment against poor beneficiaries of government assistance.  Donald Trump more recently claimed that, “I know people that work three jobs and they live next to somebody who doesn’t work at all. And the person who is not working at all and has no intention of working at all is making more money and doing better than the person that’s working his and her ass off.”  Trump also claimed in 2011, the existence of “a food stamp crime wave.”  Neither of these claims was supported by evidence.

Like much of the public, Trump believes that most black people are poor.  The New York Times reports that, “Trump also said to black voters: “You’re living in poverty; your schools are no good; you have no jobs.”

Those who see people in poverty as “others” are more likely to ascribe their condition exclusively to lack of personal responsibility, ignoring other factors, like education, disability, racial discrimination and family structure.  These beliefs often lead to support for coercive government assistance policies for the poor, such as work requirements for medical, food and housing assistance.

Poverty in Upstate Metropolitan Areas

More than one in seven (540,000) residents of five upstate metropolitan areas – Albany-Schenectady-Troy, Buffalo-Niagara Falls, Rochester, Syracuse and Utica-Rome – lived in poverty in 2016, roughly comparable to the national average of 15%.[1]

Most people view poverty as primarily a problem of the central cities.  Concentrations of poverty in upstate New York cities are far higher than in suburban areas. But the concentration of poverty in upstate cities does not tell the full story.  In each metropolitan area, about half of people in poverty live outside central cities.  In the Rochester and Syracuse Metropolitan areas, more than half lived outside central cities.

For example, in the Syracuse metropolitan area, 34% of city residents live in poverty, while 11% of those living outside the city are poor.  But, the population of Syracuse is only 21% of the metropolitan area total.  As a result, more than half (55%) of the people living in poverty in the Syracuse metropolitan area live outside the city of Syracuse.

Other metropolitan areas show similar patterns.  55% of poor people in the Rochester MSA live outside the City of Rochester.  49% of poor residents of the Albany-Schenectady-Troy MSA live outside central cities.  47% and 43% of poor residents in the Utica-Rome and Buffalo-Niagara Falls MSA’s live outside central cities.

Because about only one in ten people living outside central cities in upstate metropolitan areas is poor, the suburban poor are relatively invisible despite their relatively large numbers, while those living in central cities, with higher poverty concentrations, are much more visible.


Many people believe that people living in poverty are primarily members of racial minorities, even though overall, in most upstate metropolitan areas most poor people identify as “white, non-Hispanic or Latino. Overall, 64.6% of all residents of upstate New York[2] living in poverty identify as white, not Hispanic or Latino.

  • In upstate New York, only in the Buffalo-Niagara metropolitan area did non-white residents make up most people living in poverty.
  • In the Albany-Schenectady-Troy, Syracuse and Utica-Rome metropolitan areas, more than six in ten people in poverty identified as “white alone, not Hispanic or Latino.”

Because of the high concentration of poor people in central cities, city residents living in poverty are more visible than those living in suburbs.  In upstate metropolitan areas, more than 60% of people in poverty outside central cities identify as “white alone, not Hispanic or Latino.”  In central cities, the picture differs. Only about 25% of central city residents in poverty identify as “white only.”

In the Rochester metropolitan area, 19% of city residents living in poverty identify as white, non-Hispanic or Latino, while outside Rochester, 81 percent of poor residents identify as non-Hispanic/Latino whites.  In the Syracuse metropolitan area, 25% of city residents living in poverty identify as non-Hispanic or Latino white, while outside Syracuse, 76% of poor residents identify as white alone, not Hispanic or Latino.

Similarly, there is a commonly held belief that most minority group members are poor.  But, in the United States, and in upstate New York metropolitan areas, most black and Hispanic residents do not live in poverty.  More than six in ten lived above the poverty line in 2016.

  • About nine of ten white, non-Hispanic or Latino households do not live in poverty.
  • The fact that a much larger percentage of minority residents of upstate metropolitan areas live in poverty than people who identify as white is a real problem, but the common perceptions that most poor people are minority group members, and that all (or most) blacks are poor are not true.


In upstate metropolitan areas, a much smaller percentage of people over 16 living in poverty than those not in poverty worked full or part-time during 2016.  More than 80% of people between 16 and 64 not in poverty worked in 2016 in upstate metropolitan areas, while between 40% and 50% of those in poverty worked. Note that this data includes people who are disabled, caregivers and those in schools and colleges.

A Brookings Institution report notes that most of those who do not work nationally are caregivers (15%), students (13%), disabled (22%), or early retirees (6%).

Poor people who work in upstate metropolitan areas were much more likely to work part-time than full-time in 2016.  In contrast, most workers who were not in poverty worked full-time. About 80% of working people in poverty in upstate metropolitan areas reported working part-time in 2016.  In contrast, only about one-third of working people not on poverty reported that they worked part-time.

People in poverty work part-time for a variety of reasons, but the largest number – 33% — worked part-time involuntarily in 2016.  Caregivers (23%) and students (21%) were the next largest groups of part-time workers in poverty in 2016.

The high percentage of people who work less than full-time year around is the result of several factors.  A study by the Center for Budget Priorities points out that these include:

  • Low wage jobs often have irregular work schedules.
  • These jobs often lack paid sick leave or other paid leave.
  • Job turnover is high among low paid workers.
  • Many low paid workers are unable to find affordable child care arrangements.
  • Some low paid workers lack stable housing arrangements.

Work Requirements for Safety Net Programs

Stereotypes about people in poverty and their relatively low participation in the labor market have spurred policy proposals that make SNAP (food stamp) and Medicaid benefits conditional on beneficiaries participating in worker training or gaining employment.

President Trump’s Council of Economic Advisors (CEA) claims that employment levels of people on major government assistance programs are low: 60% of Medicaid recipients, 60% of SNAP (food stamps) recipients, and 52% of housing assistance recipients who were working age and not disabled worked less than 20 hours a week, or not at all.

The Council argues that “expanding work requirements, similar to those in place in TANF, to the three non-cash welfare programs discussed here (Medicaid, SNAP and housing assistance) would affect the majority of program recipients and require major increases in the work effort of non-disabled working-age adults, potentially helping recipients and their families.”

The CEA substantially overstates the number of poor people who receive government assistance who do not work. A Center for Budget and Policy Priorities analysis of the workforce participation data from the Council of Economic Advisors concluded it was based on a significant methodological error.  The CEA report looked at “whether an individual receiving assistance worked in a single month (December 2013), ignoring the fact that many workers have unstable jobs and receive help when they are between jobs.

A report by the Brookings Institution, “Work Requirements and Safety Net Programs” shows  42 percent being out of the labor force and roughly 11 percent unemployed in the one-month snapshot – leading to more than half of the group being labeled “not working” in the one month snapshot – [but] roughly 29% are out of work and just one percent are persistently unemployed over two years, meaning fewer than one third are not working consistently.”

Many people in poverty work less than full-time year around involuntarily.  If their work is seasonal or they have been subject to layoffs and recalls to employment, they would be likely to be subject to potential denials of benefits at those times when they were out of work – precisely the times that assistance would be most needed.  The Center for Budget Priorities report points out that

“SNAP participants often experience periods of joblessness and are more likely to participate in SNAP when they are out of work. Individuals who participated in SNAP at any point over a 3.5-year period from 2009 through 2013 worked most months over this period but were more likely to participate when they were out of work.

  • They participated in SNAP in over two-fifths of the months that they were working (44 percent).
  • They participated in SNAP in 62 percent of the months in which they were not working, a time when their income was lower and their need for help affording food was higher.”[4]

To be sure, personal responsibility can be a factor in poverty. Single parents are much more likely to encounter poverty, for example. It certainly makes sense to implement policies that educate and remind people of the difficulties faced by single parents, encourage family planning, and hold absent fathers responsible for a share of the cost of raising children.

Instituting additional work requirements for participation in programs like SNAP and Medicaid would likely reduce participation levels and would increase administrative costs associated with compliance requirements.  Many of those who could lose benefits would lose assistance when they most need it.  Brookings found that For those who qualify for exemptions, satisfy waiver requirements, or work enough to meet the requirements, there are still significant informational and administrative barriers to compliance. Program participants must understand how the work requirement policy relates to them, obtain and submit documentation, and do so at the frequency prescribed by the state (Wagner and Solomon 2018) …. These continuing roadblocks to participation, with attendant informational and transactional costs, are likely to result in lower take-up among the eligible population and disenrollment (Finkelstein and Notowidigdo 2018).”

An analysis of the implementation of work requirements for Medicaid in Arkansas by the Kaiser Family Foundation found that recipients lost benefits because “Many Medicaid enrollees [were] still not aware of program changes despite substantial outreach.  In addition, an online reporting requirement is proving difficult for many enrollees due to limited knowledge of the requirements as well as lack of computer literacy and internet access,”

The fundamental question that these approaches raise is whether the government should condition access to essentials for human life, such as government provided food or health care coverage for unemployed poor people, on participation in training programs or gaining employment.  If such an approach is an acceptable incentive, why not limit public schools, police and fire services to those who work as well?

Because of false stereotypes about people in poverty that emphasize their differences from white suburban majorities, attitudes of much of the public to programs like SNAP and Medicaid and the poor people who receive assistance from them are negative.  Politicians have fostered these attitudes by promoting ideas like the “welfare queen” who supposedly abused the system, and people who don’t work who do better than those who “work his and her ass off.”  They use these stereotypes to promote punitive approaches to policies that help people in poverty pay for food, receive medical care and find housing.  These coercive policies are not responsive to the practical obstacles  confronted by individuals and families in poverty. Instead, they will further harm some who need assistance.

[1] Note that the official measure of poverty does not reflect the market value of food stamps or tax benefits like the earned income tax credit.

[2] Residents of counties outside the New York City metropolitan area.

Misconceptions About People in Poverty: The Push for Work Requirements

Misconceptions about people in poverty appear to drive proposed changes in social welfare policy, particularly the work requirements either being promoted by the Trump Administration and discussed or implemented in several states. A fuller understanding of factors underlying the problem of poverty suggests that these policies will be counterproductive, neither reducing the incidence of poverty nor helping those individuals and families targeted by these new policies.  Read more at:  https://rochesterbeacon.com/2018/11/30/false-stereotypes-harm-people-in-poverty/

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:

New York’s Ineffective Business Tax Incentives

In 1987, New York State enacted legislation to create an Economic Development Zones Program, modelled after the enterprise zones concept, championed by Congressman Jack Kemp.  Proponents argued that by reducing taxes in specific geographic areas with high concentrations of poverty and unemployment, existing firms would be more likely to create jobs, and other firms would be encouraged to locate in the areas and create jobs.

Enterprise Zones programs were attractive to policy makers, in part because they were “off budget.”  The programs provided financial benefits to companies that, unlike incentive grants, did not require the appropriation of state budget dollars to pay for them.

There is scant evidence that Enterprise Zones programs have been effective.  See, for example, this GAO report,[1]  which concluded that evaluations of Federal Empowerment Zones and Enterprise Communities could not demonstrate effectiveness, and this study,[2] which was did not show any impact as a result of Enterprise Zones programs in Florida and California.  Evaluations of the New York State program found significant administrative problems, but did not find significant benefits.[3]  The major problem with the Enterprise Zones concept was that because the tax advantages provided by the program were insufficient to offset the perceived disadvantages of inner city locations, the program did not result in job creation within the zones.

The program generated a cottage industry of consultants who advised businesses on how to take advantage of the benefits, by reorganizing in to new organizations so that existing jobs could be counted as new ones and by modifying zone boundaries, creating gerrymanders, to incorporate specific businesses.

But, over the years, the program was expanded and the benefits deepened.  It was renamed the Empire Zones program.  More areas were made eligible, yet the areas that the program was initially intended to benefit – distressed inner city communities in New York State – did not see improved conditions.  In fact, 20 years after the program’s enactment, they were in significantly worse economic condition.  In 1969, upstate cities had poverty rates that were slightly higher than the average for the state.  By 2013, most upstate cities had poverty rates that were more than double the state’s.

poverty cities

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

In the end, Economic Development Zones/Empire Zones became an embarrassment to successive governors and Empire State Development because of the difficulties in policing the abuses of the overly complex program, and its lack of success in inducing job creation.  Successive legislative efforts to “clean the program up” were met with continued creative approaches to exploit it by businesses.  The program was ended in 2010.

Despite the failure of the tax benefits contained in the Economic Development Zones and Empire Zones programs to induce job creation, and despite the administrative difficulties associated with administering the programs, Governors Paterson and Cuomo continued to rely on tax incentives as key elements of their economic development efforts.  Governor Paterson initiated the “Excelsior Jobs” tax credit that focuses on providing benefits to companies in industries that make capital investments and/or create new jobs in manufacturing and other sectors of the economy.  Governor Cuomo proposed the Start-Up NY program that offered tax-free benefits to certain businesses in selected locations connected to universities and colleges.  Both programs were promoted as major initiatives that would significantly improve New York’s economy.  But like the Enterprise/Economic Development/Empire Zones, the programs have failed to create significant numbers of jobs.  And, the job creation figures reported for them contain many jobs that would likely have been created without the loss of tax revenues.

Problems with Business Tax Incentives

Business tax incentives are similar to incentives provided to buyers of electric cars or insulation for their homes, in that people or companies become eligible by doing something that government considers desirable – conserving energy or creating jobs. But, they contain no “but for” test – users of the credits are not required to show that without the credits they would not do what is being incentivized.  As a result, some credits are always wasted on “free riders” — people or companies that would have acted if the credit was not available.

The use of tax policy to incentivize behavior is widespread, and if large enough, credible arguments can be made for their effectiveness.  For example, the available federal tax credit for the purchase of a Nissan Leaf, an electric car, is $7,500.  The advertised price of a Leaf begins at about $30,000.  Similarly, federal credits for energy conserving improvements in households have been as much as a quarter of the cost.  While we do not know how many of the people who purchased Nissan Leafs or weatherized their homes did so because of the availability of financial incentives from government, it is likely that some did.

But, while energy conservation incentives have been designed to be large enough to change people’s purchase decisions, state taxes are too small as components of business revenues to make a significant difference in most cases, particularly given the large differences in wage rates, which are a larger portion of business costs, between the United States and competitive locations.

The Tax Foundation published[4] a comparative analysis of total state and local tax costs for representative businesses in seven industries, including manufacturing, distribution, corporate headquarters, research and development, call centers, and retail.  From their data, I calculated total state and local tax costs as a percentage of firm operating costs, and compared New York with national medians, and with nearby states.[5]  The data shows that state and local tax costs are a very small percentage of total firm operating costs, and that differences between states are even smaller.


New York State had higher total state and local tax costs than the national median for most types of businesses, but the differences ranged from 0.7% more for call centers, to 1.8% more for research and development facilities.  For manufacturers, New York’s total tax costs were lower than the national median, but again the difference between state and local tax costs for manufacturers in New York State and for manufacturers in other states was less than 1% of operating costs.

comparable states

Compared to neighboring states, the picture was similar.  New York state and local tax costs were higher for some businesses, but lower for others.  But, in most cases, variations in other factors in the cost of production could be large enough to significantly change the relative advantage of differing locations.

In many cases, state taxes are too small a component of business revenues to make a significant difference in comparative location costs.  Fifty years ago, American businesses competed with businesses in other locations in the United States.  Differences in wages, construction and transportation costs were relatively small.   Today, with globalization, for manufacturers and other businesses that can move operations offshore, the large difference in wage costs between any state in the United States and in low wage locations outside the United States would swamp differences in company operating costs resulting from differences in state and local taxes.

While manufacturing cost structures vary widely,[6] on average, labor is estimated to account for 21% of manufacturing costs.[7]  In an article examining China’s manufacturing cost advantage, Peter Navarro, Professor of Economics at the University of California, Irvine, estimates that the cost of labor in China, adjusted for productivity differences, is 18% of that in the United States.  As a result, Navarro estimates that manufacturers would save 17% of manufacturing costs by producing in China, compared to the United States.

Business locations are not based solely on cost factors – labor availability, site quality, transportation, quality of life and other factors come into play.  But, available evidence shows that differences in state and local tax levels are relatively small factors in business costs, and that adjusting state tax structures to reduce business tax burdens has limited impact.

Repeating Failed Policies:  The Excelsior Jobs Program

When the Excelsior Jobs program was created in 2010, Governor David Paterson said “I’m pleased that the Excelsior Jobs Program, a streamlined economic development effort that will support significant potential for private sector economic growth, is now available in the marketplace to encourage businesses to grow and invest in New York.[8]

Eligible industries are those that could create net new jobs in New York State, not those, like most retail jobs, that simply move jobs from one company in New York State to another in the State.  The program description states, “The Program is limited to firms making a substantial commitment to growth – either in employment or through investing significant capital in a New York facility…The Job Growth Track comprises 75% of the Program and includes all firms in targeted industries creating new jobs in New York.”  While the program requires a commitment to job growth and/or investment, it does not limit program benefits to firms that would not expand or locate in New York State without the assistance.

The program requires that participants receving credits for job creation or investment have a positive benefit/cost ratio, defined as “total investment, wages and benefits divided by the value of the tax credits, or 10 to 1 or greater.  The program provides a refundable credit equal to 6.85% of new employee wages, or two percent of qualified capital investment, or 50% of the Federal Research and Development Credit.  Various employment and investment thresholds along with an aggregate benefit cap limit eligibility.[9]  Aggregate benefits were initially limited to $250 million annually.

Though the program had a generous dollar allotment for credits, credits actually issued never came near the $250-million-dollar annual limit.  In its best year, it provided $18.4 million in credits. Activity decreased to $745,000 in 2015.  The program has had a small job creation impact.  Empire State Development reports that companies receiving credits during that time period created 15,582 net new jobs, at a cost to the state of $47,357,602. The program’s impact has decreased in each of the last two years, with only 531 jobs credited as being created by companies receiving the tax credit in 2015.[10]


Because the program does not have a “but for” requirement, ESD’s job figures certainly overstate the program’s true job impact.  While the exact percentage of “free rider” jobs is not known, one study estimated that nine of ten jobs created by companies receiving business tax incentives would be created without them.[11]  If that is true for the Excelsior Jobs program, the true program impact would be only about 1,500 jobs,  three tenths of one percent of New York’s private sector employment growth during the period.

Additionally, a recently issued audit from the State Comptroller’s office points to issues with ESD’s administration of the Excelsior Jobs Program.  The describes weaknesses in ESD’s processes in evaluating applications in in confirming job creation claims (note that the agency disputes a number of the audit’s findings.)[12]  In particular, the audit noted that “ESD generally authorizes tax credits based on the job numbers and investment costs that businesses self-report without corroborating support….[13]

Why has the program failed to have a significant impact?  The evidence points to the fact that because much of its emphasis is on manufacturers and other companies that could locate outside the United States, it does not offer benefits that are sufficiently large to offset the cost disadvantages of creating jobs in New York State, or anywhere else in the United States.[14]

Repeating Failed Policies:  Start-Up NY

In announcing the Start-Up NY program, Governor Cuomo said, “Upstate New York has seen too many years of decline, and our communities have lost too many of their young people,… We desperately need to jumpstart the Upstate economy and these new tax-free communities will give New York an edge like we’ve never had before when it comes to attracting businesses, start-ups, and new investment. Today’s agreement on the START-UP NY legislation is a major victory for our Upstate communities as we are now set to launch what will be one of the most ambitious economic development programs our state has seen in decades.”[15]

 The promotional materials for the program advertise tax free benefits, and give the impression that the program is relatively easy to access:

“START-UP NY offers new and expanding businesses the opportunity to operate tax-free for 10 years on or near eligible university or college campuses in New York State.

 Partnering with these schools gives businesses direct access to advanced research laboratories, development resources and experts in key industries. 

To participate in START-UP NY, your company must meet the following requirements:

  • Be a new business in New York State, or an existing New York business relocating to or expanding within the state
  • Partner with a New York State college or university
  • Create new jobs and contribute to the economic development of the local community”[16]

The State Comptroller found[17] that between October 2013 and October 2014, ESD committed $45.1 million to advertise the program, generating more than 15,000 applications during the period.  However, despite the heavy advertising for the program, which continued after the period examined in the Comptroller’s report, the program has had almost no job creation impact.

Empire State Development has issued two reports on the program’s progress.  In 2014, companies assisted by the program created 76 jobs, while in 2015, assisted companies created 332 jobs.  The state tax benefits provided per job through the program were even smaller than those offered by the Excelsior Jobs Program, averaging $1,121 per job created (not including local property tax exemptions).[18]  And, because Start-Up NY has no requirement limiting assistance to companies that would not create jobs in New York without the tax credits offered, it is likely that the jobs reported substantially overstates the program’s actual impact on job creation.


The reality is that Start-up NY is extremely complex, the value of benefits to participating companies is small, the program is available in very small areas, and its requirements are difficult to meet.  One economic development professional described it as “the worst program I ever saw.  I was glad I never had to explain it to a client.”[19]

Effective Approaches to Job Creation and Retention

 The tax incentive based approaches used by the State in its Empire Zones, Excelsior Jobs, and Start-Up NY programs have not met the claims made by the governors that championed them.  But, other economic development efforts of state and local governments have been shown to be effective.  Among them are:

  • Regional Economic Development Councils: Regional councils are required to create strategic plans, set clear goals, and disclose progress in meeting established goals as a condition to receive funding for proposed projects.  While Regional Council strategies and reports vary in quality, some are well grounded and provide good disclosures of project performance.[20]
  • Project Based Assistance: Assistance from the State and localities for plant and equipment capital costs and for customized job training that employs a “but for” test can be effective in inducing companies to create and retain jobs because the amount of assistance offered may be large enough in relation to project size to affect company decisions.  ESD, for example, uses “but for” tests in making grants, employs benefit/cost benchmarks, and monitors company performance in meeting performance goals.
  • Develop Long Term, Well Integrated Industry Development Strategies: For example, New York, through Empire State Development and other agencies, provided substantial assistance to the development of nanotechnology research and development capacity at the College of Nanoscale Science and Engineering, and with local partners, significant financial assistance to the development of the Global Foundries chip-fab facility.  In Buffalo, the State has assisted in the region’s effort to enhance its bioinformatics and life sciences concentration at Roswell Park and related institutions.  Efforts like these take an integrated approach to industry development.
  • Recognize that Retaining Existing Jobs Should be as High a Priority as Job Creation: Because decisions of existing businesses about expansion, contraction or closing can have large effects on a state’s economy, state and local economic development agencies need to focus on understanding the needs of local business and assisting them, where appropriate.
  • Support Entrepreneurship: Evidence shows that entrepreneurial training programs increase business startups.[21]  New York has an existing program, the Entrepreneurial Assistance Program, that focuses on minorities, women, dislocated workers, public assistance recipients, disabled persons and public housing residents.  While the focus on disadvantaged workers is commendable, broader availability could increase the program’s reach.

New York State’s Economic Condition

 There has been longstanding concern about the impact of the decline of manufacturing, particularly in upstate New York.  The region’s population growth has been very slow, while its central cities have seen significant population declines.  Compared to thirty years ago, the residents of upstate central cities are far more likely to live in poverty.  These are all significant concerns.  But, even upstate, the region’s overall economic health is as good as, or better than the average for nearby states.[22]


Each of the Metropolitan areas in New York State, including those in upstate New York had greater growth in real gross domestic product per resident than the average for metropolitan areas in nearby states.  But, the growth of poverty in New York metropolitan areas was below the average for nearby metropolitan areas.


Private sector wage growth in New York State presented a more mixed picture – Buffalo, Albany, and New York City did better than regional average, while Syracuse and Rochester did worse.



While the economic condition of metropolitan areas in New York State, including those in upstate New York, improved relative to nearby areas, in most cases, some places in the state are in very poor economic condition.  Upstate cities continue to lose population and have increasingly great concentrations of low income populations.  Upstate downtowns have large amounts of vacant commercial space, and upstate cities suffer from blighted, abandoned housing.  Minority group residents of upstate cities have average household incomes that are about one third of white suburban residents.

If the lives of residents of central cities are to be improved, New York must address the factors that create concentrations of economically disadvantaged people.  These include:

  • Schools with high concentrations of economically disadvantaged children. Evidence demonstrates that children from disadvantaged families perform substantially better in schools that have higher percentages of students who are not disadvantaged:  http://policybynumbers.com/why-critics-of-upstate-city-school-performance-miss-the-largest-cause
  • Single parent families face significant obstacles to success, that also damage the prospects for their children: http://policybynumbers.com/category/single-parent-families.
  • Racial segregation is highly related to poverty and poor student performance. http://policybynumbers.com/income-student-achievement.
  • Cities have high concentrations of low income residents living in blighted neighborhoods, because most cannot afford to live in better quality housing. More housing vouchers, additional income supplementation, particularly for part-time workers, and increased job accessibility for low skilled workers would help central city residents find better places to live.
  • Cities need help in tearing down vacant housing, cleaning up and reclaiming vacant industrial sites and rehabilitating blighted neighborhoods.

But, the focus of highly publicized and expensively marketed economic development initiatives in New York State has been on ineffective programs that have led to negligible job creation.  By all accounts they have not succeeded in “supporting significant potential for private sector economic growth” nor do they “give New York an edge, like we’ve never had before.”  While many existing economic development efforts at the state and local level produce tangible results, few of them focus on the places in New York State that have done the worst, from an economic perspective. Given the growing bifurcation of the economic conditions of city and suburban residents, more attention should be given to them.

[1] http://www.gao.gov/products/GAO-10-464R,

[2] http://edq.sagepub.com/content/23/1/44.short

[3] Findings of many of these studies are summarized here:  http://www.cbcny.org/sites/default/files/report_ez_12012009.pdf

[4] http://taxfoundation.org/sites/default/files/docs/location%20matters_0.pdf

[5] The Tax Foundation calculated state and local tax costs as a percentage of net profits.  But since companies seek to minimize overall costs, I compared taxes to total costs. (operating expenses, interest, taxes and preferred stock dividends, but not common stock dividends).

[6] Depending on the capital or labor intensiveness of a manufacturing process, the productivity of labor and labor demand and supply factors.

[7] Peter Navarro, “The Economics of the China Price,” https://chinaperspectives.revues.org/3063, p. 3.

[8] “Governor Paterson Announces Excelsior Jobs Program Launch”  http://readme.readmedia.com/Governor-Paterson-Announces-Excelsior-Jobs-Program-Launch/1717095

[9] https://www.tax.ny.gov/pit/credits/excelsior.htm

[10] http://esd.ny.gov/reports.html

[11] http://www.upjohn.org/publications/upjohn-institute-press/state-enterprise-zone-programs-have-they-worked

[12] http://www.osc.state.ny.us/audits/allaudits/093016/15s15.pdf

[13] Ibid., p. 7.

[14] Manufacturing firms continue to operate in New York and the United States because of other kinds of location advantages, such as labor productivity, the need to be close to markets, or insensitivity to production costs.

[15] https://www.governor.ny.gov/news/governor-cuomo-and-legislative-leaders-announce-agreement-start-ny-legislation-will-implement

[16] http://startup.ny.gov

[17] “Marketing Service Performance Monitoring” Audit 2014-S-10. http://osc.state.ny.us/audits/allaudits/093015/14s10.pdf

[18] The small benefits provided by the program may reflect the fact that many of the firms participating in the program are start-ups, and have little taxable income.

[19] Communication with this writer.

[20] See for example:  http://regionalcouncils.ny.gov/themes/nyopenrc/rc-files/centralny/final%20CNY%20REDC%20plan%20single%20pages.pdf

[21] Benus, J. M., Wood, M. and Glover, N. “A Comparative Analysis of the Washington and Massachusetts UI Self-Employment Demonstrations,” Report prepared for the U. S. Department of Labor by Abt Associates.

[22] Source for this and following tables: U. S. Cluster Mapping Project. http://www.clustermapping.us/region/economic/syracuse_auburn_ny


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%.


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


[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).



The Minimum Wage Debate – Part II

The Albany Times Union carried an article on March 24 detailing the connections between researchers who produced the reports for and against a minimum wage increase that I discussed in my post “A $15 Minimum Wage for New York – Benefits and Risks.”  The article points out that one of the authors of the study favoring the minimum wage, Ken Jacobs, was closely connected with the campaign to increase the minimum wage.

“In May 2014, an advocate for hiking the minimum wage in New York emailed a University of California labor economist with a list of talking points “we’d love you to cover.”

The economist, Ken Jacobs, was set to testify before the New York state Senate’s Labor Committee about the benefits of municipal minimum wage hikes in California.

“That works for me,” replied Jacobs, chair of the Berkeley Center for Labor Research and Education. “I will work on it tomorrow.”

During his trip to New York, a progressive public relations firm working for higher wages set up a meeting for Jacobs at The New York Times editorial board. Jacobs assisted an advocate rounding up New York union support, according to emails.”

“In one April 2014 email, the relationship between academic and funder seemed explicit: Jacobs explained he was seeking grant money to support his unit’s research “for local groups engaged in work to raise the minimum wage” in California. Jacob added that his Center would provide “testimony/media work.”

The article also points out that

“Two officials at the business-backed American Action Forum, another Washington, D.C.-based group, penned a November study on the $15 wage in New York. That nonprofit’s funders, according to tax records, include the U.S. Chamber of Commerce Foundation, which paid the Action Forum $129,000 in 2014 to produce policy research.”

The Times-Union article points to a fact that has long been recognized, that the funding of public policy research is rarely truly independent.  None of this proves that the research intentionally intends to mislead, but it does illustrate the connections between political and financial interests and those who study important policy issues.

The kind of financial support that has been provided by labor and business interests in this case is found in the financing of other kinds of research studies.  Pharmaceutical and food safety research are examples.  There has been widespread publicity about the financial connections between researchers and the drug companies that could benefit from positive findings about the effectiveness of a drug.

Why is this pattern so prevalent?  The first reason is that the stakes attached to the outcomes of policy decisions, like those of decisions about food safety or drug efficacy, are high.  For employers, a hike in the minimum wage could cut profits, or, for some small businesses, threaten their existence.  For labor, a hike in the minimum wage could improve the lives of low wage workers.

The second reason is that the entities that do research operate like businesses.  Many years ago, I taught at a college, and one of the things that was made clear to me was that colleges and universities have limited resources to support research, and that if research is costly, faculty should look to outside entities to help pay the cost.  From the institutional perspective, outside funding provides the resources for additional personnel and needed equipment.

Similarly, consulting firms are driven by the same logic. In the end, someone has to pay the cost of salaries and facilities.  Very often the funding needed by these firms is most available from groups in society that have policy agendas.  Some, like American Action Forum, appear to have been created to serve particular interest groups.

None of this means that the results reported by researchers on each side are falsified.  They represent real differences of opinion among economists who understand the impact of policy changes differently.  But the funding of policy research by competing interests can lead to the exaggeration of differences in conclusions about policy outcomes.

In my earlier piece, I pointed out that the Congressional Budget Office (one entity that does not receive funding that comes from a group for or against the minimum wage increase) in its research presented a range of possible outcomes – in the case of a federal minimum wage increase, they projected the possible loss of a few to a million jobs, with a center point of 500,000 jobs.

But neither the American Action Forum, or the the Center for Wage and Employment Dynamics at Berkeley presented a range of possible outcomes.  Instead, each presented point estimates of impacts leading to sharply different conclusions about the employment costs of a minimum wage increase, suggesting a greater degree of certainty about conclusions than may be warranted.

Finally, it should be noted that readers might conclude from the Times Union piece that the competing studies presented by business and labor interests “fog” the real answer to the employment impact question, in the way that tobacco companies funded studies in an attempt to shed doubt on data that showed that smoking is harmful to health.

In fact, that conclusion would be incorrect.  The reality is that there is no consensus, and that in this case the competing studies represent real differences of opinion between experts.

A $15 Minimum Wage for New York: Benefits and Risks

Recently, a friend and colleague from the time when I worked at Empire State Development suggested that I take a look at Governor Cuomo’s proposal to raise New York’s minimum wage to $15 from $9.00.  Like others, I’m sure that he wanted to cut through the competing claims about the impact of the proposed increase.

A columnist for the Albany Times-Union, Fred LeBrun, expressed the confusion felt by many, writing, “The truth is I don’t really know what the impact will be. I’m not sure anybody does. Predictions vary wildly. Nor are the Cuomo administration and the Democratic Assembly making any serious effort to find out.”  The reason for LeBrun’s confusion and frustration is that there is no certain answer to his question, nor can there be at this point in time, given the complexity of the factors involved in estimating the benefits of a minimum wage increase, and the lack of solid data available at the state level.

As with many political issues, there are sharply divergent perspectives to the costs and benefits of raising the minimum wage.  A well known Albany think tank, the Empire Center for Public Policy, released a report late last fall, “Higher Pay, Fewer Jobs,” written by Douglas Holtz-Eakin and Ben Gitlis of the American Action Forum, the policy arm of the American Action Network, a group that has provided substantial support for Republican candidates for Congress.  The report presents three models of the impact of the proposed increase in the minimum wage to $15, and finds that the proposal would reduce employment in the state by “at least 200,000 jobs, with proportionately larger employment decreases in upstate regions.”  The report also estimates that the proposal would increase wage earnings by $4.6 billion.

On the other side, the Center for Wage and Employment Dynamics (CWED), at the Institute on Labor and Employment at the University of California, Berkeley issued a report, “The Effects of a $15 Minimum Wage in New York State,” by Michael Reich, Sylvia Allegretto, Ken Jacobs and Claire Montialoux.  CWED has received funding from the Fiscal Policy Institute, a union funded think tank.  That report concluded that “a $15 statewide minimum wage would generate a 23.4% average wage increase for 3.16 million workers in the state, with a net value of $14.4 billion and would create an increase in jobs of 3,178.

Finally, Governor Cuomo, through the State Department of Labor issued a report in support of his proposal entitled “Built to Lead – Analysis: Raising New York’s Minimum Wage to $15.”  The report claims a benefit from increased wages of $15.7 billion and argues that, “A review of 70 studies on minimum wage increases found no discernible negative effect on employment.”

Problems Estimating Number of Employees Affected

Perhaps a good place to begin understanding how difficult it is to understand what impact an increase in the minimum wage might have is by looking at the question of how many people might be affected by the proposed change.  This is important, because the number of people affected impacts both the amount of wage benefits received in aggregate, and the number of people who might be affected by layoffs that could result from the proposed increase.  Here, there are differing estimates.
• Governor Cuomo’s report argues that 2.4 million people would benefit from a minimum wage increase.
• The Empire Center report estimates 3.1 million workers would be directly affected by the increase.
• The CWED report estimates that 2.4 million workers would be directly affected, with an additional 1.2 million indirectly affected.

How can there be such a large disparity in the estimates of the number of people affected?  The answer is that researchers seeking information about the number of people who would be receiving less than $15 per hour at the time of the proposed increase could not find data that directly answers the question, and had to develop estimates using other data that does not directly measure wage distributions at the state level.  In both cases, the authors used data from the Census Bureau’s American Community Survey, and because they used different techniques to estimate the percentage of the employed population from the available data, they arrived at significantly different answers.

Problems Estimating Possible Job Losses

The bigger problem associated with evaluating the effects of an increase in the minimum wage involves estimating the impact of the change on employment.  Until about 20 years ago, there was near unanimity among economists that there was a trade-off between employment and minimum wage increases, particularly for young and low skilled workers.  For example, a number of studies found that for a 10% increase in the minimum wage, teenage employment decreased by 1%-3%.  For adult workers, the impact was estimated to be smaller – perhaps 1% for a 10% increase.  Since almost 90% of minimum wage workers are 20 years old or older, the largest impact of a minimum wage increase is on adult workers, even considering the fact that a larger portion of teenage workers are paid at the minimum wage rate.

From the perspective of these studies, a minimum wage increase of $9 to $ 15, or 60%, as has been proposed by the Governor, would have a relatively large negative impact on jobs. In New York’s case, with roughly 9,000,000 workers, about 550,000 could be expected to lose their jobs, if the estimate is correct.

The report from the Empire Center presents three study models, one which is consistent with an analysis by the Congressional Budget Office, that estimates a loss of 200,000 jobs, a second by two economists, Jonathan Meer and Jeremy West, that estimates a loss of 432,500, and a third by economists Jeffrey Clemens and Michael Wither, that projects a loss of 588,800 jobs.

How is it possible that the Center for Wage and Employment Dynamics could conclude that increasing the minimum wage could result in a small increase in jobs?  The answer is that some more recent research has found no significant employment effect from increases in the minimum wage.  For example, Alison Wellington in “Effects of the Minimum Wage on the Employment Status of Youths: An Update.” found that a 10% increase in the minimum wage reduced teenage employment by only 0.6%.  In 1992, David Card and Alan Krueger studied the impact of a minimum wage increase in New Jersey on fast food restaurants by comparing their employment with those in nearby Eastern Pennsylvania and found that the wage increase was associated with slightly increased employment.  They also examined a set of more recent studies of a 1988 increase in the California minimum wage and the 1990 increase in the federal minimum wage and found no impact.  Subsequent studies have shown mixed results.  Some have shown employment decreases with increases in the minimum wage, others have not.

A better approach than providing a single estimate of job losses associated with increasing the minimum wage would recognize a variety of possible outcomes.  The Empire Center study does this to an extent, by presenting the outputs of several models.  But the study only presents one set of possible outcomes, reflecting the views of economists who believe that minimum wage increases are associated with job losses.  And, while the Empire Center presented a single estimate for job losses for the approach used by the Congressional Budget Office, the CBO itself said that a range of outcomes is possible.  In its study of a possible federal minimum wage increase from $7.25 to $10.10, it predicted a very slight job loss to one million jobs, with a central point of 500,000.  From my perspective, the best approach would recognize the uncertainty of any job loss estimate, and present a broader range of possibilities.

So, unfortunately for my friend, and for Fred LeBrun, who wanted to know what the impact of an increase to the minimum wage would be, there is no definite answer.  We do know that the proposal does have a positive economic impact on workers affected – estimates range from about $5 to $15 billion.  And, we know that it is not true that most beneficiaries would be teenagers flipping hamburgers at fast food outlets – in fact, they represent a small minority of workers who would be affected.  What we don’t know is whether there would be a significant trade off in lost jobs.

But, there are some significant reasons to be cautious about the impact of a proposal as large as the one that has been proposed by Governor Cuomo.  Many economists are concerned about the size of the proposed increase – an increase from $9 to $15 is much larger than previous increases, and is more likely to impose worker dislocations than a smaller increase – to $12 for example. Alan Krueger, former Chair of President Obama’s Council of Economic Advisors, and the author of the New Jersey study that found no negative impact of a minimum wage increase, wrote,

But $15 an hour is beyond international experience, and could well be counterproductive. Although some high-wage cities and states could probably absorb a $15-an-hour minimum wage with little or no job loss, it is far from clear that the same could be said for every state, city and town in the United States…Although the plight of low-wage workers is a national tragedy, the push for a nationwide $15 minimum wage strikes me as a risk not worth taking”

Failing Schools – Bill Hammond’s follow up discussion

Bill Hammond’s piece may be found here:

Bill Hammond: Why New York’s ‘Failing Schools’ Fail — and How We Can Turn the Tide

Income Inequality and Minority Group Status in Upstate Metropolitan Areas

In an earlier post, I pointed out that residents of upstate metropolitan areas actually have incomes that are somewhat higher than the average for other cities in the so called “rust belt” – cities located in the old manufacturing regions of the Northeast and Midwest. But, the largest upstate cities – Buffalo, Rochester and Syracuse have greater concentrations of poverty than average, while their suburbs have lower levels of poverty than comparable cities, creating a high degree of economic segregation.

Minority group status and location within an upstate central city are strongly related to economic disadvantage. Large income differences exist between minority group residents of central cities, and white residents.  Nationally, the median[1] family income was $68,426 in 2014.  Nationally, black/African-American families averaged $42,711, while families identifying as Hispanic or Latino averaged $44,013.  Families identifying as white (not Hispanic or Latino) averaged $73,974.[2]  But in one upstate city, the median income for Hispanic and Latino families was only $18,149, while in the suburbs of another upstate city, white[3] families averaged $91,693.

Median Family Incomes in Upstate Cities

Median Family Income – 2014
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

There is a large gap between the median incomes of minority families in upstate cities and those of minorities nationally, and a huge gap between minority incomes in upstate cities and those of white residents of those cities – the median income of whites in upstate cities is two to three times that of blacks and Hispanics.  To give a sense of just how poorly minority families are doing in upstate cities, in every city, except for black families in Albany, minority median family incomes are below what would be earned by a worker making the minimum wage proposed by the Governor – $15 per hour, working 40 hours each week.

In five of seven upstate cities, the median incomes of white families were more than twice those of Black and Hispanic residents.  In Buffalo and Rochester, white median incomes were 1.9 times those of black residents, and more than two times those of Hispanics.

In each upstate city, except Albany, the median income of black families was less than half of the national median.  In two cities – Utica and Troy, the median income for black families was one-third of the national median.  For Hispanic families in Utica, the median family income was only one quarter of the national median for all races/ethnicities.  In Buffalo and Troy, the median family income for Hispanics was less than one third of the national median for all races.  White families in upstate cities had median incomes that were below the national median for all races in most cases (except Albany, which was above the national median.  But in each case, median incomes of white city families ranged from 75% to 90% of the national median.

Compared to the median income for all black families in America ($42,711), black families in upstate cities had substantially lower median incomes.  The average black family income median in upstate cities was $28,019.  In Troy, black families had a median income of $21,563, only half of the national median. In Utica, the median income for black families ($22,975) was only 54% of the national black family median.

The picture was just as grim for Hispanic families living in upstate cities.  For Hispanic families, the average upstate median income was $22,047.  In Utica, the the median income for Hispanic families – $18,149 – was only 40% of the national median for Hispanic families, and only 26.5% of the national median for all races and ethnicities.  In Buffalo and Troy, median Hispanic family incomes were less than half of the median income for all Hispanic families in the nation. Hispanic families in Troy and Buffalo had median incomes that were less than one third of the median income for all races and ethnicities.

Median Family Incomes Outside Upstate Cities

 Families living outside central cities in upstate counties had substantially higher median incomes than central city residents – regardless of racial or ethnic background.  In the case of blacks and Hispanics, suburban families had median incomes that were approximately twice those of black and Hispanic families in cities.  But, racial and ethnic differences were significant in suburban areas as well – minority families had median incomes that were substantially lower than those of white families.

Note that the estimates of median family incomes outside central cities have been estimated from available county and city median income data.[4]  Most residents of the counties where upstate cities are located live outside the cities.  Even outside the cities, there are significant disparities between the median incomes of minority and white families.  However, median incomes for minority and white families within counties outside central cities are significantly higher than those in the cities.

Median Family Income – 2014
Outside City 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

Black families in outside of central cities in upstate counties had median incomes ranging from $39,001 in Erie County, outside of Buffalo, to $67,400 in Albany County outside of Albany, averaging $51,810.  While these incomes were substantially below those of white suburban residents – for example the median income for white families in Albany County outside Albany was $91,693, and $77,996 in Erie County outside Buffalo, they were substantially above the median incomes for black families in central cities.  For example, the median income for black families in the city of Albany was $39,077, while in Buffalo, it was $29,155. On average, the median incomes of white families living outside central cities in upstate counties was 62.8% higher than that of black families.

The median family income of black families living outside upstate cities was lower than that of all families nationally – ranging form 57% of the national median in Buffalo to 99% of the median in Albany.  Compared to the national median income for black families ($42,711) black families living outside central cities median incomes were higher in all upstate counties, except Erie County outside Buffalo.

For Hispanics, the pattern was similar.  Hispanic family median incomes averaged $54,342 in counties outside upstate central cities. Hispanic families in Utica had a median income of $18,149, but Hispanic families outside Utica had a median income of $34,792. The median income of white families living in Oneida County outside Utica was $70,457.  On average, the median incomes of white families living outside central cities in upstate counties was 59.1% higher than for Hispanic families.  Hispanic families living in upstate counties outside central cities, other than in Oneida County outside Utica, had median incomes that were higher than the national median for Hispanics.

Where Minority and White Families Live

 Given that most residents of upstate metropolitan counties live outside central cities; a reader might conclude that because minority families living in suburban communities have substantially higher incomes than minority families, there are many minority families who have relatively high incomes.  In fact, the level of residential segregation is very high in suburbs outside upstate central cities.  Minority families make up very small percentages of suburban populations in upstate metropolitan areas.

Percent of Population
Outside City Black Hispanic
Albany 2.7% 2.7%
Buffalo 3.3% 2.0%
Rochester 4.3% 3.0%
Utica 0.9% 1.0%
Syracuse 2.3% 1.8%
Troy 0.9% 2.0%
Schenectady 1.4% 1.4%

In the counties surrounding upstate cities, minority families make up substantially less than 10% of families – in Oneida County, less than 2% of all families.  Outside Syracuse, only 2.3% of families identified themselves as black/African-American, while 1.8% identified themselves as Hispanic. Outside Rochester, 4.3% of families identified themselves as black and 3% of families were Hispanics.  These numbers stand in stark contrast to the percentage of minority families in upstate cities.

Percent of Population
Central City Black Hispanic
Albany 35.2% 8.0%
Buffalo 40.5% 10.4%
Rochester 44.8% 18.2%
Utica 13.6% 10.0%
Syracuse 33.4% 8.0%
Troy 16.6% 8.8%
Schenectady 19.0% 9.7%

While 7.4% of families living in Monroe County, outside Rochester were blacks or Hispanics, 63% of Rochester families were members of these minority groups.  In Syracuse, 41% of families were black or Hispanic, while in Onondaga County, outside Syracuse only 4.2% of families were black or Hispanic.


 Upstate New York metropolitan areas are not post-racial communities. White families living outside of central cities in upstate counties are the majority of county families. Minority group members constitute a tiny fraction of suburban populations.  Median incomes of white families living in suburban communities are substantially higher than the medians for all families and for white families nationally.

Minority families are concentrated in central cities – few enjoy the benefits of suburban housing and school systems. Typical black and Hispanic families bear a heavy burden of economic inequality. Median family incomes for minority city residents are very low – only one quarter to one third of the median incomes of white suburban residents.

The contrast between the relative affluence of suburban families and minority residents of central cities is extreme.  Consider that in Rochester, the median black family income was $28,752 in 2014 and of Hispanic families $23,717 while white families in Monroe County outside Rochester had a median income of $81,432.  In Schenectady, the median income for black families was 27,338 and for Hispanic families $25,111, but the median for white families in Schenectady County was $88,674.

The causes of minority group members’ privation have been discussed elsewhere – weak educational backgrounds that lead to limited job-skills, single parent families that can’t get good jobs, high levels of incarceration, and limited access to public transportation, among others.  There are no easy solutions to these problems, but there are many approaches to helping low income people in central cities.  Among them are:

  • Strengthen early child development interventions that promote better parenting, and provide more resources to help low income families access early childhood education options.
  • Help low income parents access better child care options by providing access to all disadvantaged children.
  • Consider adopting classroom management approaches used by successful charter schools in center city public schools.
  • Reduce the concentration of economically disadvantaged students in central cities though strategies like inter-district magnet schools.
  • Employ “dual-generation” assistance models for low income families that integrate a range of health, social and other services in local schools.
  • Leaders should emphasize to targeted audiences how difficult it is to raise children without a committed co-parent.
  • Offer a range of birth control measures, including long acting forms for free.
  • Strengthen SNAP (food stamp) administration. Because of wide variability in food stamp usage rates among eligible populations, consider state administration and increase the number of sites for in-person verification.
  • Consider providing a state supplement to the SNAP program to ensure more adequate support for nutrition.
  • Enhance support for community college programs that provide industry specific skills in high demand fields.
  • Support efforts to provide community college training with flexible class scheduling, and short modules outside traditional AA or certificate programs.
  • Increase or supplement the Earned Income Tax Credit – the program is an effective work incentive. Increasing benefits would provide more adequate support for low income families.
  • Expand or supplement the Child Tax Credit – make it fully refundable.
  • Consider increasing the minimum wage. Though trade-offs are likely in the form of increased unemployment if the minimum wage is raised to $15, a more moderate increase would provide a better balance between assisting low income workers, and potential lost jobs.
  • Address the negative impacts of high levels of incarceration in the minority male population. Consider ways to reduce the impact of “stop and frisk” policing strategies, reduce penalties for non-violent crimes, reduce barriers to employment for those who have completed prison sentences.
  • Focus on developing better public transit access to work sites and community college locations for central city residents.

Despite the stark reality of the economic and residential segregation of minority groups in upstate metropolitan areas, little attention has been paid to this problem at the state level.  The question is why political leaders haven’t made the economic deprivation of minority residents of central city residents a top policy priority, and how the needs of low income inner city residents can become a priority for them.

[1] The income at which half the families have greater incomes and half the families have incomes that are lower.

[2] Source:  U. S. Census Bureau, American Community Survey – 2014-2010, 5 year average data.

[3] Henceforth I will refer to voters who identify as “white, not Hispanic or Latino” as “white.”

[4] The products of median incomes and the number of families were calculated for counties and cities.  City totals were subtracted from county totals, and the result divided from the number of outside city families to derive median income estimates for outside city families.