School Segregation is Increasing in New York’s Cities and Suburbs

Recent articles in the New York Times and The Nation have focused on efforts to resegregate schools in the South, by carving new predominantly white school districts out of larger county-wide school districts that are predominantly black and Hispanic.  The articles examined a recent federal court decision that permitted the creation of the Gardendale School District near Birmingham, Alabama.  The new district is 75% white, in a county school district that has a majority of black and Hispanic students.

In 1954, the United States Supreme Court, in Brown vs. Board of Education, outlawed the creation of segregated school systems by law.  While first efforts to combat segregation focused on legally created barriers to integration in the South, later, courts ordered busing to combat segregation in northern school districts, like Boston.  These efforts were met with fierce resistance from parents who did not want their children to be bused to schools that had large minority student populations outside their neighborhoods .

Resistance to school integration has been has been widespread.  While legally created separate schools in the same school system for white and black students have been eliminated, opposition to efforts to combat segregation based on residential patterns has been widespread and largely successful.  Today, the schools attended by black and Hispanic students typically have far higher concentrations of minority students than those attended by white students.  While segregation in the South was the result of laws that created separate school systems for white and black students, today much of the segregation results from the concentration of black and Hispanic students in cities with majority black and Hispanic populations.

In an earlier post, I examined the growth of segregation of black and Hispanic students in metropolitan areas in New York State.  In this post, I compare the concentration of black and Hispanic students with white students in schools in cities and suburbs in New York metropolitan areas.

Changes in School Enrollment 

In upstate metropolitan areas, and in the suburbs in the New York metropolitan area, enrollments of black and Hispanic students have increased substantially between 1990-91 and 2014-15 – by more than 50,000 upstate and by 140,000 in the New York suburbs.

  • Black student enrollments increased in upstate metropolitan areas grew by 23,000, while Hispanic enrollments grew by 32,000.
  • In Westchester, Orange and Rockland counties in the New York metropolitan area, black student enrollments grew by 15,000 and Hispanic enrollments grew by 45,000.
  • In New York City, black student enrollments decreased by 113,000 while Hispanic enrollments increased by 68,000.

White student enrollments decreased significantly both upstate (by 125,000) and in the New York metropolitan area (by 113,000).  Nationally, enrollments of black and Hispanic students increased by 7.4 million, between 1994 and 2014 (1990 data is not available) while white student enrollments decreased by 4.2 million.

Overall, school enrollments increased in New York City and its suburbs between 1990-91 and 2014-15, while they decreased in upstate metropolitan areas. Nationally, enrollments increased 12.6% between 1995 and 2014.

In percentage terms, school enrollments nationally were 49.2% white and 42.1% black and Hispanic in 2014-15.

  • Upstate metropolitan areas (66.3% white) and New York City suburbs (55.2% white) had higher percentages of white student enrollments than the nation, while New York City had higher percentages of black and Hispanic students.
  • National level data for 1990 showed a student population of 27.2% black and hispanic students, and 69.4% white students.

By 2014-15 the composition of student populations in schools had changed significantly from the 1990’s, nationally, in upstate New York metropolitan areas and in the New York metropolitan area, with large increases in the percentage of black and Hispanic students. New York City was the only exception – black and Hispanic students decreased as a percentage of the total.

Increasing Minority Student Concentrations in City Schools

In cities in upstate metropolitan areas, black and Hispanic student populations grew substantially as a percentage of the total – by nearly 25% on average.  Black and Hispanic student populations as a percentage of the total grew in suburbs as well, but the growth was much smaller – only 6.4% on average.  In the Orange-Rockland-Westchester portion of the New  York City metropolitan area, the growth of black and Hispanic students as a percentage of the total was about equal in cities and suburbs – 16% on average.

Most upstate cities have student populations that are majority black and Hispanic, while most suburban areas in upstate metropolitan areas have student bodies that are less than 10% black and hispanic.  On average, the gap in black and hispanic student percentages between upstate cities and suburbs grew from 44% to 63%.

Schools attended by Typical Black and Hispanic Students Differ from those attended by Typical White Students

This section compares the racial and ethnic composition of schools attended by typical black and Hispanic students with those attended by white students in 2014-15.  It does so by finding the percentage of black/Hispanic students at schools for a median student in each racial/ethnic group.  Computing the median involves sorting all the students in a group (black/Hispanic or white) in a metropolitan area by the percentage of minority students in the schools that they attend, and finding the percentage of black/Hispanic students in the school attended by a student who is at the exact middle of the sort.  Half of the white or Hispanic/black students would be attending schools with an equal or higher percentage of Hispanic/black students, while half would have an equal or lower percentage.

The data shows that in both cities and suburbs upstate, black and Hispanic students typically attend schools with higher concentrations of black and Hispanic students than do white students.

  • For example, in the Buffalo-Niagara Falls MSA, black and Hispanic students living in cities typically attend schools where 78% of the students are black or Hispanic.
  • White students in those cities typically attend schools whose student bodies are 46% black – a difference of 32%.
  • In other upstate Metropolitan areas, the concentration of black and Hispanic students in city schools ranges from no higher in the city of Binghamton to 20% higher in Utica-Rome.

Within suburban school districts in New York’s metropolitan areas, black and Hispanic students typically attend schools that have higher percentages of black and Hispanic students.

  • In the Rochester metropolitan area, black and Hispanic students living outside Rochester typically attend schools with 24% black and Hispanic students, while white students typically attend schools with 9% black and Hispanic students.
  • In other upstate metropolitan areas, the differences ranged from 2% to 11%.

Since most black and Hispanic students in metropolitan areas live in cities, while most white students live outside them, it is useful to compare the percentage of black and Hispanic students in schools typically attended by black and Hispanic students in cities with the percentage of black and Hispanic students in schools attended by typical white students outside cities.  Here, the contrast is stronger.

  • For example, In the Albany-Schenectady-Troy metropolitan area, a typical black or Hispanic student living in a city would attended a school that had 67.5% black and Hispanic students.
  • In contrast, typical white students living outside Albany, Schenectady and Troy attended schools that had 5.1% black and Hispanic students, a difference of 62.4%.

Differences were large in other upstate metropolitan areas, as well.  The difference in the percentage of black and Hispanic students in city schools attended by typical black and Hispanic students and schools outside cities attended by typical white students was 80.6% in the Rochester MSA, and 73% in the Buffalo-Niagara Falls MSA.

Conclusions

Since the 1954 Brown vs. Board of Education Supreme Court decision, it has been illegal to maintain separate schools for minority students and white students in a school district.  But, efforts to create racial balance in schools in cities and metropolitan areas in New York state and elsewhere have largely been unsuccessful.

In fact, the data shows that over the past 25 years, changes in living patterns have seen large increases in black and Hispanic populations in central cities in New York state, but relatively little change in areas outside them.  As a result, because school districts in New York State often follow city and town boundaries, black and Hispanic students are increasingly concentrated in city schools.

  • School districts in cities in upstate metropolitan areas have seen substantial increases in the percentage of students who are black and Hispanic – from 47.6% to 72.4% between 1990-91 and 2014-15.
  • In contrast outside Upstate cities, the average percentage of black and Hispanic students only grew from 2.8% to 9.2%.

The increasing concentration of black and Hispanic students within cities is not the full explanation of their increasing segregation.  Within cities and outside them, black and Hispanic students are likely to attend schools with higher percentages of black and Hispanic students than are whites.  This is largely the result of residential housing segregation within communities in our metropolitan areas.  As a result, the difference between the racial and ethnic composition of schools typically attended by black and Hispanic students and white students has grown larger – in five of seven metropolitan areas typical black and Hispanic students attended schools that had 65% or more black and Hispanic students, while in five of seven metropolitan areas typical white students attended schools whose populations had 5.1% or less black and Hispanic students.

The growth of racial segregation in New York schools is paralleled by its growth nationwide:  The U. S. General Accounting Office found in 2016 that “Over time, there has been a large increase in schools that are the most isolated by poverty and race. From school years 2000-01 to 2013-14 (most recent data available), both the percentage of K-12 public schools that were high poverty and comprised of mostly Black or Hispanic students (H/PBH) and the students attending these schools grew significantly. In these schools 75 to 100 percent of the students were eligible for free or reduced-price lunch, and 75 to 100 percent of the students were Black or Hispanic.”

Although there are significant potential benefits from schools that are more representative of the diversity of the population as a whole, the barriers to change are substantial.  While New York state has not seen the creation of white enclave school districts carved out of larger majority minority districts, the existing structure of local school districts has a similar effect.

There is no silver bullet that will remedy the growth of segregated schools in New York state, or elsewhere.  Remedies tried in the past, like school busing, have been very unpopular, and have generally failed.  Historically, federal housing policies in the 20th century supported racial segregation.  Similarly, suburban zoning laws and resistance to low and moderate income multi-family housing continue to play a role in preventing minority residents from living in them.  In the current political environment, with an administration in Washington, D. C. that is not supportive of federal intervention to promote integration, segregation in our schools is likely to continue to increase.

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Note:  For the Orange-Rockland-Westchester portion of the New York City Metropolitan Area, cities are:  Mount Vernon, New Rochelle, White Plains and Yonkers.




President Trump to Upstate Residents: Move to Wisconsin

Recently, in an interview with the Wall Street Journal, President Trump suggested that upstate New York residents should leave the state for Wisconsin, where a new Foxconn LCD display panel manufacturing plant will be located, creating at least 3,000 jobs.  President Trump said, “I said, you know, Gary, you go to certain sections and you’re going to need people to work in these massive plants that we’re getting, that are moving in. Where do we have the people? You know where we have the people? In New York state that can’t get jobs, in many other places that can’t get jobs. And people are going to have to start moving. They’re going to move to Colorado and they’re going to move to Iowa and Wisconsin and places where – like if Foxconn goes to Wisconsin, which is one of the places they’re very strongly considering – but if Foxconn goes to Wisconsin and they have a very low rate and the governor’s done an excellent job, you’re going to have a situation where you got to get the people. But they’re going to start moving. And I’m going to start explaining to people when you have an area that just isn’t working – like upper New York state, where people are getting very badly hurt – and then you’ll have another area 500 miles away where you can’t – you can’t get people, I’m going to explain you can leave, it’s OK, don’t worry about your house.” Source, “Full transcript: Trump’s Wall Street Journal interview” Politico, August 1, 2017.

It is true that upstate employment performance has been weak, with most upstate metropolitan areas seeing decreases, while a few, like Buffalo, Glens Falls and Albany-Schenectady-Troy had small increases (Source – Bureau of Labor Statistics – Local Area Statistics). Many of the region’s smaller metropolitan areas had relatively large losses:  Binghamton, Elmira and Utica-Rome each lost more than 10% of its population.

On the other hand,   Median household incomes upstate were near the average for rust belt states, and the unemployment rate for upstate counties was the same as the national average in 2016: 4.9% in 2016 (Source: U. S. Department of Labor, Bureau of Labor Statistics, Local Area Unemployment Statistics).

The fact that the average unemployment rate in upstate counties is near the national average shows that the President’s statement, “You know where we have the people? In New York state that can’t get jobs…when you have an area that just isn’t working – like upper New York state, where people are getting very badly hurt,” is unfounded, given that unemployment upstate is no higher than the national average and that median household incomes are near it.  The labor force in upstate New York is stagnant or shrinking in most cases, but few labor force members are unemployed.  Upstate’s problem is not that its residents cannot find jobs, it is that the region’s population and workforce are stagnant or shrinking.

E. J. McMahon, in a recent New York Post op. ed., “Trump’s right, Cuomo wrong about the woes of Upstate” pointed out that many upstate New York counties are losing population.  McMahon argues, “From mid-2010 to mid-2016, nearly 194,000 people moved out of the 50 counties north of the New York City metro region — a net out-migration rate exceeded only by four states. Births and foreign immigrants made up some of the difference, but the total upstate population still dropped by nearly 60,000 people.”  McMahon’s statement is correct – many areas upstate have lost population since 2010 – in fact, 40 of 62 counties in New York State lost population between 2010 and 2016.

New York State is not unique in seeing population declines in some areas.  In Wisconsin, one of the places that the President said “they’re going to move to,” 36 of 70 counties saw population declines between 2010 and 2015.  In Ohio, included for comparison as another rust belt state which claims to have more business friendly policies than New York State, county populations decreased in 62 of 90 counties.

Since counties differ substantially in size within states, a better measure of the economic weakness of an area is the percentage of residents living in counties that are losing population.   In that respect, New York and Wisconsin performed similarly – in 2016, 13.2% of New Yorkers lived in areas with declining populations, while 9.2% of Wisconsin residents lived in declining areas.  In Ohio, 55.5% lived in declining population areas. Reflecting New  York’s regional divide, 61.3% of upstate residents lived in counties with declining populations, while none of the counties in the New York City Metropolitan area had declines.

(Table with full listing of counties is here:)

 

 

 

The data shows that population changes between 2000 and 2015 at the county level within New York, Wisconsin and Ohio varied significantly.  Like New York, Wisconsin and Ohio had counties that had significant population increases, and others that had large losses. Saratoga, Orange and Rockland Counties all had population increases between 2000 and 2016 that were greater than 10%.  New York’s least populous county, Hamilton, lost 15% of its population – a decrease of 834 residents. Wisconsin and Ohio saw similar variations. One county in Wisconsin had a 38% increase, while another lost 16% of its population.  In Ohio,  One county gained 75%, while another lost 10.5%.

“Business Friendly” Policies and Job Growth

E. J. McMahon argues in his New York Post piece that, “Trump, in effect, was simply prodding upstaters to act in their own best economic interests. …So, taxes aside, what advantages does Wisconsin offer over New York?….While Wisconsin Gov. Scott Walker has been an aggressive deregulator, New York’s regulatory climate in general is notoriously hostile to businesses. The 1970s-era State Environmental Quality Review Act, which has no equivalent in most states, hands a potent weapon to anti-development activists.”

Looking at New York, Wisconsin and Ohio from 2000 to 2015,  there is no evidence of consistent differences in performance that would reflect the effect of “business friendly” policies on job growth.  Instead, it shows that population and job growth vary substantially from local labor market to local labor market within New York State, and in Wisconsin and Ohio.  In each state, some areas are suffering, while others are doing relatively well.  New York had by far the strongest job growth overall between 2000 and 2015, but employment growth in New  York’s rural areas was the weakest of the three states.  Wisconsin’s performance was in the middle in both metropolitan areas and non-metropolitan areas, and Ohio’s was weakest in metropolitan areas, but stronger than New York’s in rural areas.

In a recent post, “Government Policies and Job Growth in the Rust Belt,” I showed that the relative performance of metropolitan areas over the rust belt differed substantially across time periods between 1990 and 2015.  If government policies, like “business friendliness” determined the economic performance of regions we would expect to see consistent advantages for states with states with business friendly attributes like low taxes or lax environmental regulation.  But, we do not.

Upstate’s relatively weak economic performance may be attributed to several factors  – most importantly, its past reliance on manufacturing employment.  In 1970, manufacturing employment was more than 40% of the private sector total in the Rochester and Binghamton metropolitan areas, and more than 35% of the total in Buffalo-Niagara Falls.  Today, in these areas, manufacturing employment is about 10% or less of the total.  In contrast, metropolitan areas that have had stronger growth recently, like New York City and the Albany-Schenectady-Troy metropolitan area, were less dependent on manufacturing.

Manufacturing Mega-Projects and Job Creation

Large manufacturing attraction projects, like the Foxconn plant in Wisconsin, the Solar City project in Buffalo,  and Global Foundries near Saratoga Springs cannot, in themselves be successful approaches to significantly improving the employment rate at the state level.

To encourage Foxconn to locate its facility in Wisconsin with a promise to create 3,000 jobs, the state agreed to provide three billion dollars in tax incentives and to waive environmental regulations  to allow Foxconn, without permits, to discharge dredged materials, fill wetlands, change the course of streams, build artificial bodies of water that connect with natural waterways and build on a riverbed or lakebed.Foxconn would also be exempt from having to create a state environmental impact statement, something required for much smaller projects.” Source: The Washington Post, “The Latest: Wisconsin Foxconn deal waives regulations,” July 28, 2017.

Projects that involve expenditures of as much as one million dollars per job are simply too expensive to replicate on a scale that would be large enough to meaningfully change  a regional economy.  New York’s employment was about 9,100,000 in 2016.  Increasing the state’s employment by even one percent – 91,000 – would cost ninety-one billion dollars at the cost of one million dollars per job for recent projects, assuming that enough large new job attractions were possible to enable that large an employment increase.  In fact, most job creation occurs at existing businesses, not at new facilities attracted because of government subsidies, while very few large manufacturing investments take place in a given year.

At the same time, the focus on attracting manufacturing is largely misguided.  Although manufacturing jobs are important, because they have higher average wages than jobs available to people without college educations in other sectors, manufacturing has been hemorrhaging jobs for forty years.  Mostly because of automation and productivity improvements, and less so because of import competition, manufacturing employment has sharply declined in the United States – from 20,000,000 in 1980 to 13,000,000 in 2016.   Between 2000 and 2015, New York lost 239,000 manufacturing jobs, while gaining 1,878,000 service sector jobs.  Ohio and Wisconsin also lost manufacturing employment, while gaining service sector employment. Because the growth of New York’s already strong service sector was particularly large – 25%, the state’s percentage job growth was much larger than the other states.

Because potential job growth continues to be likely to occur almost entirely in the service sector, focusing state resources on attracting manufacturing employment has a high opportunity cost.  Instead, policies and programs to support existing manufacturers in a region can be useful.

Upstate’s relative economic weakness is partly explained by the changing factors that drive location decisions in manufacturing and service industries. For manufacturers, upstate New York is a less attractive location than it once was because of factors including its location relatively far from the country’s population center, relatively high labor costs, difficult environmental permitting processes and relatively small and tight labor markets.  But, because manufacturing provides only about 10% of jobs upstate and nationally, manufacturing employment is a less significant economic driver than employment in other sectors is.

For high value added service industries, upstate New York suffers from relatively shallow labor markets, its relatively low percentages of college graduates compared to places like New York City and Boston, and the increasing concentration of industries in a few large companies headquartered in major cities.  Although the region has some significant strengths in higher education and health care, it has lost a number of corporate headquarters in financial services, because of the increasing concentration of the industry.

None of the problems faced by upstate New York, or for that matter, those parts of Ohio and Wisconsin that have stagnant economies, are easily resolvable.  But, leaders should recognize that the resurgence of these areas will not result from a policy of attracting manufacturing jobs to them – there are just too few opportunities to attract companies like Solar City, Foxconn or Global Foundries, and the cost is exorbitant.  Instead, leaders need to do what they can to anchor the companies in their area that have the potential to grow.  In most cases, those are service industries.  For these businesses, robust labor pools with appropriate skill sets are far more critical than the financial incentives or permitting issues that were critical to attracting large manufacturing facilities.




More Regional Diversity but a Larger Racial/Ethnic Divide in New York Schools

This post examines changes in the ethnic and racial compositions of kindergarten through twelfth grade schools in New York State metropolitan areas over the past 25 years.  During that period, the student population, like the general population has become more diverse, with the percentage of students identified as white decreasing, while minority group members, particularly Hispanics, have become a larger proportion of the total population.  But, has the increased diversity of the overall student population been reflected in individual schools?  The data show that although New York’s metropolitan regions are more diverse than they were in the past, the gap in racial and ethnic composition in our schools is larger.

New York Metropolitan Areas and the Nation as a Whole are Racially and Ethnically more Diverse than in 1990

Between 1990 and 2015, the percentage of the national population that identified itself as white decreased from more than 75% to 62%, while Hispanics increased from 9% to 17%.  The number of people who identified as Asian increased from 2.8% to 5.1%. (Source: U.S. Census Bureau, 2011-2015 American Community Survey 5-Year Estimates, 1990 Decennial Census.) Upstate metropolitan areas are much less diverse than the nation as a whole.  Smaller metropolitan areas had very small minority group populations – the Binghamton metropolitan area was 87% white in 2015, while the Utica area was 86% white. Larger upstate metropolitan areas were also less diverse than the nation in 2015.  Buffalo was 78.5% white, while Rochester was 77.4% white.  In contrast, in the New York City metropolitan area, white residents were less than half of the population – 44.4% – while Hispanics were nearly one-quarter of the total.

Student Populations in the United States and in New York Metropolitan Areas are more Diverse than in 1990

The kindergarten through grade twelve population has shown a greater demographic shift than the population as a whole at the national level between the 1990-1991 school year and 2014-2015.  The percentage of students who identified as white decreased by 17.3%, from 69.4% to 52.1%.  The Hispanic population increased by 13.1%, while the percentage of students identified as Asian increased from 3.2% to 5.1%. Source:  National Center for Educational Statistics – Elementary and Secondary Information System.

Compared to the nearly even split between white and minority group students nationally, upstate metropolitan areas are relatively less diverse – particularly smaller metropolitan areas, like Binghamton, Utica and Syracuse.  But even these areas have seen significant increases in the percentage of students who are black, Hispanic and Asian since 1990.

  • Small metropolitan areas, like Binghamton and Utica-Rome, were about 80% white in 2014-2015, and had black and hispanic populations that each comprised less than 10% of the total.  In 1990-1991, these metropolitan areas were more than 90% white.
  • Medium sized metropolitan areas had k-12 student populations that were between 65% (Buffalo) and 75% (Syracuse) white in 2014-2015.  In 1990-91 the percentage of white students was 15% to 20% higher in each of these metropolitan areas.  In these metropolitan areas, the percentage of students who identified as black and Hispanic increased by about 10%.
  • In New York City, in 1990-91, the school population was 18.9% white in 1990-91, 37.6% black and 34.5% Hispanic.  By 2014-15 the white population had decreased to 15.5% of the total, while the black population decreased to 25.1%. The Hispanic population in New York schools increased to 40.9%, while the Asian population grew from 7.8% to 18.1%.
  • Suburban areas around New York City on Long Island, and Westchester, Orange and Rockland Counties had large decreases in the percentage of white students in school populations – more than 20%.  These areas saw large increases in Hispanic students, who increased by 15% to 17% as a percentage of school populations.

In the next section, I look at the question of whether the increased ethnic and racial diversity of student populations in metropolitan areas is associated with decreased racial and ethnic segregation in schools.

The Racial and Ethnic Composition of Schools attended by Whites, Blacks and Hispanics Differed more in 2015 than in 1990

This section compares the racial and ethnic composition of schools attended by typical black and Hispanic students with those attended by white students in 1990 and 2015.  It does so by finding the percentage of black/Hispanic students at a school for a median student in each racial/ethnic group.  Computing the median involves sorting all the students in a group (black/Hispanic or white) in a metropolitan area by the percentage of minority students in the schools that they attend, and finding the percentage of black/Hispanic students in the school attended by a student who is at the exact middle of the sort.  Half of the white or Hispanic/black students would be attending schools with an equal or higher percentage of Hispanic/black students, while half would have an equal or lower percentage.

The difference between the percentage of minority students in schools attended by typical white students compared to typical black/Hispanic students increased between 1990-1991 and 2014-2015, with one exception (New York City).  Despite the increase of Hispanic and black students from less than 10% (18% in Rochester) to between 20% and 30% of the student population in upstate metropolitan areas, typical white students attend schools with Hispanic/black populations that make up about 5% of the total.  In contrast, black and hispanic students typically attended schools in 2015 where black and Hispanic students make up more than 50% of the population.

Also of concern is the fact that the racial/ethnic gap between schools that typical white and Hispanic/black students attend increased between 1990-1991 and 2014-2015.

 

  • In the Albany-Schenectady-Troy metropolitan area, in 1990-1991, typical white students would have attended schools with 2% black and Hispanic students. Typical black and Hispanic students attended schools with 38% black students in that year.
  • In 2014-2015 typical white students attended schools with 5.8% black/Hispanic students, while typical black and Hispanic students attended schools with 55% black/Hispanic students.
  • The gap between the schools attended by typical white and typical black/Hispanic students increased from 36% to 49%.

Other upstate metropolitan areas had increases in the gap between white and black/Hispanic percentages as well:

  • In the Binghamton MSA, in 1990-1991 median white students attended schools that were 1.6% black/Hispanic while median black and Hispanic students attended schools that were 10.4% black/Hispanic.  In 2015, whites typically attended schools that were 4.7% black/Hispanic, while blacks and Hispanics typically attended schools that were 31.4% black/Hispanic.  The gap increased from 8.8% to 26.7%
  • In Buffalo-Niagara Falls, in 1990-91 median white students attended schools that were 1.4% black/Hispanic. Typical black/Hispanic students attended schools that were 54.9% black/Hispanic. In 2014-15 typical white students attended schools that were 5.3% black/Hispanic, while median black and Hispanic students attended schools that were 63% black/Hispanic. The gap increased from 53.3% to 57.7%
  • In the Rochester MSA in 1990-1991, typical white students attended schools that were 4.2% black/Hispanic, while black and Hispanic pupils attended schools that were 67.3% black/Hispanic.  In 2014-2015 the comparable numbers were 9.2% and 82.7%.  The gap increased from 63.1% to 73.5%
  • In Syracuse in 1990-1991 median white students attended schools that were 1.6% black/Hispanic, while typical black and Hispanic students attended schools that were 43.4% black/Hispanic.  In 2014-15 the percentages were 5.5% and 52.7%.  The gap increased from 41.8% to 47.2%
  • In Utica-Rome, in 1990-1991, typical white students attended schools that were 1.1% black/Hispanic, while median black and Hispanic students attended schools that were 23.3% black/Hispanic.  In 2014-15 the numbers were 3.3% for white students and 61.7% for black and Hispanic students.  The gap increased from 22.2% to 58.4%

Suburban counties in the New York City Metropolitan area saw similar changes:

  • In Nassau and Suffolk Counties in 1990-1991, typical white students attended schools at which black and Hispanic students made up 5.8% of the population.  In 1990-91, black and Hispanic students attended schools which were 50% black/Hispanic.
  • In 2014-15, white students typically attended schools that had 14.5% black/Hispanic students.  Typical Black/Hispanic students schools that were 64.8% black/Hispanic
  • The racial/ethnic gap increased by 6.1%.
  • In Westchester, Orange and Rockland Counties, in 1980-81, typical white students attended schools that were 7.1% black/Hispanic, while typical black/Hispanic students attended schools that were 49.9% black/Hispanic.
  • In 2014-2015, in those counties, typical white students attended schools that were 19.4% black/Hispanic, while typical black/Hispanic students attended schools that were 71.6% black/Hispanic.
  • The racial/ethnic gap in Westchester, Orange and Rockland counties increased by 9.4%.

New York City differed from other locations in New York state in that the differences in the racial/ethnic composition of schools attended by white students and black/Hispanic students has stayed constant at about 60% in 1990-91 and 2014-15.

Conclusions

Although our nation is often characterized as a melting pot that has absorbed many ethnicities and races, black/Hispanic students increasingly attend schools that are primarily black/Hispanic, while white students continue to attend schools that are overwhelmingly white.  Over the period between 1990-1991 and 2014-15, the gap between the schools typically attended by whites and those attended by blacks and Hispanics increased – in schools attended by median black and Hispanic students, the percentage of black and Hispanic students increased more than it did in schools attended by median white students.

This is important because black and Hispanic residents and whites have relatively little interaction in their neighborhoods, and in the schools that their children attend.  Cities and the schools within them continue to see large decreases in white populations.  City minority populations are stable or increasing, while suburban communities remain overwhelmingly white.  Housing segregation is reinforced by the fact that average incomes of blacks and Hispanics are substantially lower than white residents, so that relatively high suburban housing costs are a barrier to suburban housing choices.

Economic and residential separation is strongly rooted in New York and the United States. Causes include discriminatory government housing policies and non-governmental practices that denied black people access to home ownership in the growing suburbs of the post-World War II era,  historically lower average levels of minority educational attainment, and lower black and Hispanic incomes at the same levels of educational attainment as whites.

The concentration of minority students in schools with high percentages of minority students has negative consequences for minority students, largely because these schools typically have high concentrations of low-income students.  Economically disadvantaged students who attend schools with relatively few disadvantaged students do better on evaluations of student performance than those who attend schools with high concentrations of disadvantaged students (see this, and this).   The fact that black and hispanic students increasingly attend schools with high concentrations of minority students should be of concern.

Because white students in New York typically attend schools that have few black and Hispanics attending them, they do not gain the benefit of exposure to other cultures. Researchers have found that diverse schools promote reductions in levels of racial and ethnic prejudice, stereotyping and fears of “others” and that all students, including white students attending diverse schools, show “higher achievement in mathematics, science, language and reading.”

My next post will compare changes in minority and white student populations in cities and suburbs in New York state.




The Decline of Manufacturing in New York and the Rust Belt

In a recent post I looked at employment changes in New York’s metropolitan areas and compared their performance with other metropolitan areas in the rust belt.  I found that change was inconsistent between cities in each state, and over different time periods.  I argued that industry mix probably was the primary cause of the differing results.

Here, I look at the decline of the manufacturing sector and its impact on employment change in New York State metropolitan areas.  Overall, rust belt metropolitan areas in this study have 4,500,000 less manufacturing jobs today than they did in 1970, compared with 28.4 million private sector workers in that year.  Overall, 1.2 million fewer people were employed in manufacturing in New York State in 2014 than in 1970, equal to 12.8% of the private sector employment total in 1970.  In two metropolitan areas (Binghamton and Utica-Rome), manufacturing job losses were about one-quarter of private sector employment in 1970, while in Buffalo and Rochester the manufacturing losses were about 20% of the total.

The loss of manufacturing jobs created a significant drag on job growth in the rust belt, and explains much of the growth of income inequality in the United States since the middle part of the last century.  Manufacturing jobs provided working class people with relatively high incomes.  Today, the opportunities that manufacturing provided to people with high school educations have sharply declined.

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Rochester provides a good example of the impact of the decline in manufacturing.  The chart above shows that in 1970, 152,000 people in the Rochester worked in industries in the manufacturing sector, with average earnings of $68,000 (in today’s dollars), compared with the regional average private sector earnings of $53,200.  In 2014, 61,800 people worked in manufacturing industries in the area, with average earnings of $74,500, compared with regional average private sector earnings of $51,400.  The loss of nearly 100,000 jobs paying significantly more than the regional average has large impact on Rochester and other rust belt metropolitan areas.

State Level Changes

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In this section, aggregate data for all metropolitan areas each rust belt state is examined.[1]  The data shows that while overall employment change in metropolitan areas was inconsistent over time, that between 1970 and 2014, manufacturing showed a larger decline in New York State than in metropolitan areas in other rust belt states.  New York metropolitan areas have lost 75% of the manufacturing jobs that existed in 1970.  Other rust belt states lost between 35% and 63%.  (Note that in the data, there is a discontinuity between the years 2000 and 2001, reflecting the change from the Standard Industrial Code Classification System and the North American Industry Classification System, which removed some industries from the manufacturing sector. As a result, the long-term data charts and tables exaggerate the change that took place between 2000 and 2001.  For that reason, shorter term charts and tables exclude the 2000-2001 data).

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(In the chart above, blue shaded cells performed better than the median for metropolitan areas)

Dividing that data into periods reflecting economic boom-bust cycles, there were significant differences in the relative performance of manufacturing in state metropolitan areas across economic cycles.  However, manufacturing employment in New York metropolitan areas decreased more than most metropolitan areas in other rust belt states in most periods. Only in the 2007-2009 recession did it outperform the rust belt median.[2]  Between 1970 and 1976, between 1992 and 2000, and between 2001 and 2007 manufacturing employment performance in New York metropolitan areas was the worst of the seven rust belt states.

Because more than two-thirds of New York residents live in the New York City Metropolitan area, the very large decrease in manufacturing employment in that area has had a disproportionate impact on the decline of manufacturing in the state.  But, while upstate metropolitan areas had smaller percentage decreases in manufacturing employment that the New York Metropolitan area, they were more dependent on manufacturing.  As a result, the loss of manufacturing jobs in those areas did more economic harm to them than the losses in the New York City area.

Despite the large losses in manufacturing employment, each metropolitan area in New York State has shown some private sector employment growth since 1970, but the growth has been uneven.  This data also makes clear that changes in manufacturing jobs are not the only factor driving employment change in metropolitan areas.   Because so much employment is now in service sector industries, the performance of industries within the service sector has had substantial effects on the relative ability of metropolitan area employment to withstand the declines in manufacturing employment.

Manufacturing Employment in New York’s Metropolitan Areaspicturea

As in other rust belt metropolitan areas, manufacturing employment in New York State metropolitan areas decreased during most periods.  The patterns of the declines varied, with some metropolitan areas, like Rochester and Binghamton, doing quite well in the 1970’s and 1980’s but going into steep declines in the late 1980’s and 1990’s.  Others, like Utica-Rome performed quite poorly in the 1970’s and 1980’s but performed better than other New York MSA’s in more recent periods.  New York City’s manufacturing employment losses were consistently larger in percentage terms than average.  In all the periods, every metropolitan area in New York State lost manufacturing employment, with the exception of the 2009-2015 period, where Albany-Schenecady-Troy gained 16%, and Buffalo-Niagara Falls gained 3.1%.pictureb1

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While there were losses in manufacturing employment in each metropolitan area in each decade except the present one, the patterns of losses varied.  New York City, Utica-Rome  and Buffalo-Niagara Fall had losses that were greatest between 1970 and 1990.  Binghamton and Rochester saw the largest losses between 1990 and 2010.  Syracuse’s losses were largest between 2000 and 2010.  Employment changes in non-manufacturing sectors in different decades led to sharply varying results.  For example, despite losing 36,600 manufacturing jobs between 1980 and 1990, Buffalo-Niagara Falls had a net gain of 48,400 jobs during the period, because non-manufacturing employment increased by 85,000.  From 2001 to 2010, Buffalo-Niagara Falls lost 30,600 manufacturing jobs, but gained only 24,600 non-manufacturing jobs.  As a result, the area lost private sector employment in that decade.  Rochester and Syracuse also performed well during the 1970 to 1990 period but did poorly during the first decade of this century.  In contrast, The New York City metropolitan area lost employment during the 1970’s, but has steadily gained strength since then.

Since 2001, two New York metropolitan areas have shown significant private sector employment growth – New York City and Albany-Schenectady-Troy.  Buffalo, Rochester and Syracuse did not do well between 2001 and 2010, but showed significant recoveries from 2010 to 2014.  Binghamton and Utica-Rome had employment losses in the 2001 to 2014 period.

Percentage of Private Sector Employment in Manufacturing

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Overall, 1.2 million fewer people were employed in manufacturing in New York State in 2014 than in 1970, equal to 12.8% of the private sector employment total in 1970. New York’s metropolitan areas each had substantial declines in manufacturing employment between 1970 and 2014.  Binghamton lost the highest percentage (26.66%) of manufacturing jobs compared with its private sector employment in 1970.  Albany-Schenectady-Troy, which lost 8.1%, was the least affected.

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Over the 44-year period between 1970 and 2014, manufacturing employment in New York State metropolitan areas both declined and converged.  Four metropolitan areas had significantly higher percentages of manufacturing employees compared to private sector employment in 1970 than the rust belt average:  Binghamton, Buffalo-Niagara Falls, Rochester, and Utica-Rome.  In 1970, more than four of ten private sector employees in the Rochester and Binghamton metropolitan areas were in manufacturing.  More than 35% worked for manufacturers in Buffalo-Niagara Falls and Utica-Rome.  Each of these metropolitan areas had larger decreases in the percentage of manufacturing employment than the average.  New York, and Albany-Schenectady-Troy had the lowest percentages of manufacturing employment in 1970 – 21.5% and 24.7% respectively, and had the smallest long-term declines – 18.7% and 19%. Note, however, that when metropolitan areas with similar concentrations of manufacturing employment are compared (see below), much of the difference in performance between New York metropolitan areas and other rust belt locations disappears.

In 2014, the areas with the highest percentages of manufacturing employment – Binghamton and Rochester – had only 11.4% and 10.9%. Only 2.9% of private sector employees in the New York metropolitan area and 5.8% of those in the Albany-Schenectady-Troy metropolitan area were employed in Manufacturing.  By 2014, only Binghamton, Rochester and Buffalo-Niagara Falls had higher percentages of manufacturing employment than the rust belt average.  The percentage of manufacturing employment in these two metropolitan areas exceeded the rust belt average by less than 2%, compared with 7% to 9% in 1970.

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Manufacturing and private sector employment change each varied substantially from decade to decade, but the relationship between the two was not constant.  Clearly, the decade from 2001 to 2010 was the worst decade for employment change in upstate New York, both for the private sector and for manufacturing.  On the average, more nearly one-third of manufacturing employees were lost during that decade, while overall, private sector employment declined by 1.7% on average.  From the perspective of manufacturing employment, 1980 to 1990 was the second worst decade in the period, but private sector employment had the second highest growth of the five time periods.  Rochester and Syracuse had the strongest private sector growth between 1970 and 1990, but showed little growth after 2000. New York City’s employment growth was the weakest in the state between 1970 and 1990 but among the strongest since 2001. 

Decreases in Concentration of Employment in Manufacturing Industries  

picturerrOverall, metropolitan areas[3] in the rust belt that had relatively greater percentages of private sector employment in manufacturing in 1970 lost a greater share of manufacturing employment than other areas with lower initial manufacturing employment concentrations. The data shows that metropolitan areas in New York State performed similarly to others with similar concentrations of employment in manufacturing industries. Buffalo, Rochester, Binghamton and Utica-Rome had both the highest concentrations of manufacturing employment and the greatest declines in the share of private sector employment in manufacturing.

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Between 2001 and 2014, the relationship between manufacturing’s share of private sector employment and the decline in the manufacturing share of employment was weaker, but still present.  In general, areas that had higher concentrations of manufacturing employment in 2001 had greater decreases in the concentration of manufacturing employment than those with lower concentrations.  Once again, metropolitan areas in New York State generally performed in a similar manner to those in the rust belt outside New York having similar concentrations of manufacturing employment.

The data in both periods points to the steep decline in manufacturing employment from an average of more than three in ten private sector jobs to an average of one in seven.  With the decline came a convergence of manufacturing employment in metropolitan areas, with the range in the percentage of private sector employment in manufacturing ranging from about 20% to 40% in 1970, compared with 5% to 20% in 2014. 

Decreases in Manufacturing Employment and Concentration of Employment in Manufacturing Industries

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Here, the percentage decrease in manufacturing employment is compared with the initial share of private sector employment in manufacturing industries.  The data shows little relationship between these two factors.  Over the 1970-2014, and in the 2001 to 2014 period, metropolitan areas in New York State performed relatively poorly compared to others in the rust belt.  However, over the more recent period from 2001 to 2014, New York metropolitan areas, other than New York City saw percentage decreases in manufacturing employment that were closer to other rust belt cities with similar concentrations of employment in manufacturing.

picture1zzManufacturing Employment Concentration vs. Private Sector Employment Change 

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In this section, the percentage of total private sector employment in manufacturing industries is compared with private sector employment change.  Between 1970 and 2014 overall, Albany-Schenectady-Troy had better performance than metropolitan areas with similar concentrations of manufacturing employment in 1970.  Syracuse and Rochesster were near the average.

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Most metropolitan areas in New York State performed better in the 2001 -2014 period relative to other rust belt metros than they did in the longer term period, Binghamton being a notable exception.  The New York City metropolitan area had the best job creation performance of the rust belt metropolitan areas studied. Note also that the charts above show that when New York’s metropolitan areas are compared with other rust belt areas with similar concentrations of manufacturing employment, much of the apparent worse employment performance of New York metropolitan areas described in an earlier section disappears.

Over the 1970 to 2014 period, percentage decreases in manufacturing employment did not show an association with private sector employment change for the rust belt . However, metropolitan areas in New York State performed somewhat differently:  Areas with higher concentrations of manufacturing employment in 1970 showed less private sector employment growth than those with lower concentrations.  Similarly, in 2014, for the rust belt overall, there was not a significant relationship between the concentration of employment in manufacturing industries and private sector growth.  In that period, in New York State, areas with lower concentrations of manufacturing had greater private sector growth.   New York City had the greatest percentage growth in private sector employment during the period along with a low percentage of manufacturing employment.  Albany-Schenectady-Troy was another metropolitan area with relatively little manufacturing employment in 2001 and relatively high private sector employment growth.
  

Implications 

Since 1970, New York and the rust belt region have seen a substantial transition from high concentrations of manufacturing employment to lower ones.  In 1970, one third of all private sector jobs in the rust belt outside New York State, and more than 40% of private sector jobs in Rochester and Binghamton were in manufacturing.  In 2014, manufacturing employment in New York State metropolitan areas ranged from 2.8 to 11.4% of private sector jobs.  Since 2010, manufacturing employment has levelled off.  Whether this is a lasting change or a temporary stabilization after the very large manufacturing employment losses between 2000 and 2010 is not known.

This data shows that much of New York’s relatively large manufacturing employment loss resulted from the fact that a number of upstate cities had higher concentrations of manufacturing than average for the rust belt.  In New York, unlike metropolitan areas elsewhere in the rust belt, private sector employment growth appeared to be negatively related to the level of employment in the manufacturing sector.

All of the metropolitan areas in the rust belt were hurt by technological change, factory automation and the movement of manufacturing off-shore.  These trends reflect the continuing attempt of manufacturers to cut costs to be competitive.  In addition, the New York and the rust belt are no longer as good location to serve markets as they were when manufacturers in the United States primarily served domestic markets.  For those manufacturers that find it advantageous to serve domestic markets from the United States, the center of population has continued to move South and West.

Manufacturing employment losses in New York State had differing causes.  In Rochester, Kodak was initially threatened from foreign competition by Fuji, then saw its cash cow (film production) killed by the introduction of digital cameras.  In Syracuse, New Process Gear was closed by Fiat/Chrysler because of high labor costs.  Production continued at factories in Indiana and Tennessee, locations with lower labor costs and better geographic locations.  Carrier moved production of air conditioners from Syracuse to Tennessee, Texas, and Indiana (now being transferred to Mexico) for the same reasons.

Given transportation costs, the need for quick delivery of some products, and in a few cases technological leadership, some manufacturing continues in the United States.  In the competition to retain manufacturing, New York may continue to be handicapped by its location in the Northeast, its relatively high labor costs, and congestion in the New York metropolitan areas.

Future losses of manufacturing jobs have a smaller potential to harm regional economies because manufacturing employment is now only a small portion of private sector employment in the rust belt and New York State.  But, the loss of millions of relatively high paying jobs in manufacturing industries has had significant negative consequences for New York and rust belt metropolitan areas.

In New York, the decline of manufacturing has been a cause of private sector employment declines in places like Binghamton and Utica-Rome, and slow growth in Rochester, Syracuse and Buffalo-Niagara Falls.  And, though employee earnings are not the primary subject of this post, data from Rochester showed that the loss of 93,000 manufacturing jobs contributed to the stagnation in average private sector earnings in that metropolitan area, as well as greater earnings inequality.

In future posts I will examine employment change in service industries, and implications for metropolitan area wages.

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[1] The data for this post is from the Economic Profile System at www.headwaterseconomics.org  and the U. S. Department of Commerce, Bureau of Economic Accounts, Regional Economic Accounts.

[2] Periods were broken between 2000 and 2001 because of the change from the SIC to NAICS classification system, which creates a discontinuity because of changes in firms classified as manufacturers.

[3] Metropolitan areas included in rust belt comparison:  Illinois:  Champaign-Urbana, Chicago, Peoria, Rockford, Springfield; Indiana:  Elkhart, Evansville, Fort Wayne, Gary, Indianapolis; Massachusetts:  Boston, Springfield, Worcester; Michigan:  Ann Arbor, Detroit, Flint, Grand Rapids, Kalamazoo, Lansing; New York:  Albany-Schenectady-Troy, Binghamton, Buffalo-Niagara Falls, New York City, Rochester, Syracuse, Utica; Ohio:  Akron, Canton, Cincinnati, Cleveland, Columbus, Dayton, Toledo, Youngstown; Pennsylvania:  Allentown-Bethlehem-Easton, Erie, Harrisburg, Lancaster, Philadelphia, Pittsburgh, Reading, Scranton-Wilkes-Barre, York.




Government Policies and Job Growth in New York State and the Rust Belt

A recent Washington Post article, “As senator, Clinton promised 200,000 jobs in Upstate New York. Her efforts fell flat.”[1] points out that during Senator Clinton’s tenure between 2001 and 2009, Upstate New York saw job growth of only 0.2%, far from what Clinton claimed could be achieved.  While the article neglects to point out that the nation as a whole actually lost jobs during the period, since Clinton’s term ended near the low point of the recession of 2008-2009, it is clear that her claim was unfounded.

But, Senator Clinton’s emphasis on economic development and job creation is not unique.  Politicians in New York state and elsewhere regularly claim that their policies lead to job creation, often using statistics to tout their arguments.  In 1994, a significant element of Governor George Pataki’s first campaign for Governor focused on his claim that the state’s loss of jobs in the period immediately prior to the campaign was a result of Governor Mario Cuomo’s tax and regulatory policies.  Governor Pataki was fortunate, initially, because following the recession that took place in the early 1990’s, the national economy, and New York’s, improved.  Each month for the first five years of Pataki’s terms of office, his Administration pointed to the creation of thousands of jobs in New York State.

Then, in 2000, the nation again entered recession, which was exacerbated by the 9/11 attack on the World Trade Center. Not surprisingly, New York stopped seeing job growth, and the frequent press releases ceased.

More recently we have seen Governor Andrew Cuomo point to continued job growth during his administration.  In his 2016 State of the State speech, the Governor said, “We limited the state’s new spending to less than 2% a year. We passed a 2% property tax cap that has brought welcome relief to the citizens of our state and we have cut income, corporate and estate taxes. In total, we have reduced the tax burden on New Yorkers by $114 billion dollars. Why is that important? Because reducing taxes is part of our strategy to create jobs.”

During Governor Cuomo’s administration, like the first years of Governor Pataki’s administration, New York State has seen significant job growth.  But can governors or senators rightfully take credit for employment growth during their administrations?  Is New York’s relative job creation performance primarily the result of State and local tax and spending policy?  This post will examine patterns of job growth in New York, and will attempt to provide some answers.

Employment Change in New York State and the Nation

Many analyses of employment change focus on comparisons between New York State and the national average.  Between 1990 and 2015, private sector employment grew by 18.7 percent, compared with 33.5% for the nation (note, data in this report, unless otherwise noted, is from the U. S. Department of Labor, Bureau of Labor Statistics, Current Employment Statistics).  When New York is broken into regions – the New York City metropolitan region, and the rest of the state (Upstate) – there is a considerable difference in performance.  New York metropolitan employment grew by 24.5%, while Upstate employment grew by 6.1%.

NYS V US

 

But, a closer examination of the state’s performance shows significant variations in performance across different economic cycles.  Since 1990, the nation has experienced three significant growth periods, broken by recessions in 1990-1992, 2000-2002, and 2007-2009.  In the first growth period, 1992-2000, New York’s performance lagged the nation’s – private sector employment in the state as a whole grew by 15.2%, compared with 23.5% for the nation.  The difference in performance between upstate New York and the New York metropolitan area was substantial – downstate employment grew by 18.2%, while upstate job growth was 8.2%.

Percent Employment Change  – 1990-2015
    United States New York NYC Metro Upstate
 Recession   1990-1992  -0.08% -5.24% -6.84% -1.83%
 Growth   1992-2000  23.52% 15.18% 18.70% 8.06%
 Recession   2000-2002  -2.55% -4.38% -4.56% -3.97%
 Growth   2002-2007 6.39% 5.10% 6.43% 2.14%
 Recession   2007-2009  -7.54% -3.97% -4.04% -3.81%
 Growth   2009-2015 12.88% 12.65% 15.54% 6.00%

During the second growth period – from 2002-2007, New York’s performance again lagged the nation’s, but by significantly less than in the 1990’s.  Nationally, private sector employment grew by 6.4% compared with 5.1% for New York State.  Employment in the New York portion of the New York metropolitan area grew by 6.4%, which was greater than the national growth, while upstate employment grew by only 2.1%.

During the third growth period, from 2009-2015, private sector job growth in New York State about equaled the growth in the nation – 12.7% in New York vs 12.9% in the nation.  Growth in the New York portion of the New York Metropolitan area exceeded the nation’s – 15.54%, while that in upstate New York was again sub-par, at 6%.

New York Compared to Rust Belt States

Population growth in the United States has continued to shift south and west.  That factor alone contributes to regional variations in employment change.  Additionally, regions vary in “industry mix,” the relative proportions of their populations employed in different industries.  Given the historic importance of manufacturing in the rust belt, states in the Northeast and Midwest have suffered more than the rest of the nation.  For thirty years, manufacturing employment was stayed constant, at 18 million jobs, as service employment grew.  But, the decade from 2000 to 2010 saw one in every three manufacturing jobs disappear in the United States – from 17.3 million to 11.5 million.[2]

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Not surprisingly, employment growth in rust belt states in the first decade of this century reflected the weak performance of the manufacturing sector.  Even before the great recession of 2007-2009 rust belt states saw little or no private sector job growth.  For the rust belt, both the decade between 1990 and 2000 and that between 2010 and today saw much better economic performance.

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Employment Change in Rust Belt States – 1990-2015

 

 

Illinois Indiana Massachusetts  Michigan New York Ohio Pennsylvania
1990-1992 -0.18% 2.45% -3.93% 0.81% -5.26% -0.34% -1.19%
1992-2000 15.78% 17.93% 21.10% 20.55% 14.56% 17.18% 13.50%
2000-2007 -1.62% -1.04% -2.91% -11.85% 0.52% -5.22% 1.46%
2007-2009 -9.09% -10.31% -5.11% -12.44% -4.48% -9.78% -5.55%
2009-2015 9.00% 13.41% 11.50% 14.45% 12.73% 10.91% 7.67%

State and Local Tax Policy and Job Creation

Does the data support the argument that state economic performance is related to tax policy?  We have often seen arguments that New York, as a relatively high taxed state, is at a disadvantage to regional competitors with lower tax burdens.  The data shows that some states with relatively high tax burdens – Massachusetts and New York – did better than states with significantly lower burdens – Michigan and Ohio, for example. (source -State & Local Government Finance Data Query System. http://slfdqs.taxpolicycenter.org/pages.cfm. The Urban Institute-Brookings Institution Tax Policy Center. Data from U.S. Census Bureau, Annual Survey of State and Local Government Finances, Government Finances, Volume 4, and Census of Governments (1977-2013)).  It also shows that the relative performance of states varied from period to period.  For example, Michigan was one of the strongest performers in the rust belt from 1992 to 2000, but was among the weakest in the recessions of 2000-2002 and 2007 to 2009.

State and Local Taxes Per Capita

Region and State 2013
United States ……………………………………………………………………… $4,599
Massachusetts……………………………………………………………………… $5,723
New York……………………………………………………………………… $8,047
Pennsylvania……………………………………………………………………… $4,627
Illinois……………………………………………………………………… $5,374
Indiana……………………………………………………………………… $3,793
Michigan……………………………………………………………………… $3,750
Ohio……………………………………………………………………… $4,275

The Upstate Downstate Divide

For the past half century, Upstate New York has consistently grown more slowly than downstate, largely because of its historical dependence on manufacturing.  Even so, the chart below shows that there have been significant differences in private sector employment growth between New York’s metropolitan areas.  The New York City metropolitan area had the greatest employment growth – more than 25% – among those studied in New York State between 1990-2015.  The Albany Schenectady Troy metropolitan area was second, with about 20% private sector job growth.

NY Metros Jobs2

 

But other metropolitan areas upstate had little private sector employment growth, or in some cases, losses.  Rochester’s employment grew by about 5%, and Buffalo’s by 3%. Binghamton’s employment declined by more than 15% during the period.

The job creation performance of New York metropolitan areas compared to other metropolitan areas in the rust belt varied substantially during different periods of growth and recession, even within relatively short time periods.  Relative to other rust belt metropolitan areas, New York metropolitan areas showed the weakest performance in the 1990-1992 recession, and the strongest in the 2007-2009 recession.  These kinds of shifts can reflect the effects of differing economic environments as they relate to metropolitan areas’ industrial bases.  For example, in 2007-2009,  metropolitan areas in Michigan, highly dependent on the auto industry, were particularly hard hit while New York’s metropolitan areas generally did relatively well.  Syracuse and Buffalo’s performance was weak between 1990 and 2000, but did relatively well between 2000 and 2009.

upstate employment change rank

Is the large variation in private sector employment change between metropolitan areas in New York State found in other states?  A look at employment change in other rust belt states shows that it is.

Ohio

Ohio Employment

Michigan

michigan

Pennsylvania

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The differences in employment change between cities within each state were substantially larger than those between states.  For example, Columbus, Ohio metropolitan area private sector employment grew more than 40% between 1990 and 2015, while Youngstown saw a decline of nearly 10%.  In Michigan, Grand Rapids private sector employment grew by more than 40%, while Flint’s dropped by nearly 20%.  The high level of dispersion between the economic performance of individual cities within states points to the fact that in these historically relatively undiversified metropolitan areas, the performance of a dominant industry or company can significantly affect metropolitan area private sector employment change.  Both Detroit and Flint suffered signficantly from the woes of the domestic auto industry, while the Rochester area saw Eastman Kodak employment decrease from nearly 50,000 in 1988 to a small fraction of that today.

Implications

There is clear evidence that federal policies, whether relating to labor and environmental regulations, taxes, trade, or the use of fiscal and monetary policy, can have a significant impact on corporate decision making and job growth.  But, former Senator Clinton’s claims about growing the upstate economy foundered on several realities.  First, the Senator failed to recognize that the region’s job creation would largely depend on national economic conditions.  When the national economy contracted from 2007 to 2009, any chance that 200,000 jobs would be created in upstate New York disappeared.  And, it must be recognized that  as a junior senator in a body of 100 members, Senator Clinton’s influence on federal economic policy was very limited.

Policy claims about employment change in New York often center around the notion that New York’s high taxes have retarded the state’s growth.  These claims are rooted in historical experience.  Beginning in the 1960’s New York State began to see its manufacturing base erode, as textile manufacturers, appliance makers and others sought locations with lower labor costs and taxes, and easier regulatory policies.

But it is important to remember that even then, other factors influenced location decisions.  While some people and businesses moved south and west for lower living costs, quality of life was a factor as well, probably a more important one than tax costs.  People chose to locate in the sunbelt to avoid cold winters and snow, and to access new opportunities found in these areas. As the nation’s population grew in the South and West, New York and other rust belt states were no longer as competitive as locations to serve national markets as they had been.  Metropolitan areas in the rust belt stagnated as areas in the South and West grew.  Areas that were heavily dependent on manufacturing saw  the greatest losses.

The data shows that New York’s employment change over the past 25 years has been similar to that in other rust belt states.  The relatively small differences in performance at the state level do not show an association with state and local tax levels.  There were  large differences in relative job creation performance between metropolitan areas within states overall, and significant variations in the relative performance of metropolitan areas over relatively short time periods.

Both of these findings are inconsistent with the argument that state and local tax differences are a primary explanation of state economic performance, since state and local tax burdens within states do not significantly differ, and New York’s state and local tax burden relative to other rust belt states has not shifted significantly over time.  If tax levels were a significant factor influencing job growth, we would expect to find more consistent patterns of performance within states  and across time periods, and differences in job growth between states that would be consistent with differing tax burdens.  Instead, the data points to the fact that job creation in metropolitan areas depends mostly on their industry mix – the performance of the companies within the industries that make up their economies.

These findings reflect the fact that today, state and local tax costs are a very small percentage of total firm operating costs, and that differences between states are even smaller.  In earlier research, based on data from the Tax Foundation,  I found that state and local taxes amounted to less than 4% of business operating costs (less than 2% for manufacturing businesses), on average, and that differences between New York  taxes and  taxes in other states were less than 2% of operating costs. These relatively small differences pale compared with the large differences in labor costs between locations in the United States and in low wage countries.

One study, by Professor Peter Navarro, estimated that differences in labor costs between the United States and China could amount to 17% of the cost of production – more than ten times the impact of state and local taxes on manufacturing operating costs.  For manufacturers, the large differences in labor costs and the growth of global markets have led to the movement of manufacturing operations to locations outside the United States.

While New Yorkers might legitimately question whether the services they receive are good enough to justify paying state and local taxes that are 80% higher than the average for the nation, and substantially above the average for the rust belt, the data does not support the notion that high taxes have hurt employment levels in New York State.

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Metropolitan areas included in rust belt comparison:  Illinois:  Champaign-Urbana, Chicago, Peoria, Rockford, Springfield; Indiana:  Elkhart, Evansville, Fort Wayne, Gary, Indianapolis; Massachusetts:  Boston, Springfield, Worcester; Michigan:  Ann Arbor, Detroit, Flint, Grand Rapids, Kalamazoo, Lansing; New York:  Albany-Schenectady-Troy, Binghamton, Buffalo-Niagara Falls, New York City, Rochester, Syracuse, Utica; Ohio:  Akron, Canton, Cincinnati, Cleveland, Columbus, Dayton, Toledo, Youngstown; Pennsylvania:  Allentown-Bethlehem-Easton, Erie, Harrisburg, Lancaster, Philadelphia, Pittsburgh, Reading, Scranton-Wilkes-Barre, York.

[1] “As senator, Clinton promised 200,000 jobs in Upstate New York. Her efforts fell flat.” Jerry Markon, Washington Post, August 7, 2016.

[2] Economic Policy Institute, “The Manufacturing Footprint and the Importance of U. S. Manufacturing Jobs,” Robert E. Scott.  January 22, 2015.  http://www.epi.org/publication/the-manufacturing-footprint-and-the-importance-of-u-s-manufacturing-jobs/

 




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”




Should Teachers be Evaluated by Student Performance on Standardized Tests?

In January 2015, Governor Cuomo proposed changing the state’s teacher evaluation system to increase reliance on measures of student progress on statewide standardized tests, using a so called “Value Added Model.” In his 2015 State of the State address, he said:

“Now 38% of high schools students are college ready. 38%. 98.7% of high school teachers are rated effective. How can that be? How can 38% of the students be ready, but 98% of the teachers effective? 31% of third to eight graders are proficient in English, but 99% of the teachers are rated effective. 35% of third to eighth graders are proficient in math but 98% of the math teachers are rated effective. Who are we kidding, my friends? The problem is clear and the solution is clear. We need real, accurate, fair teacher evaluations.

We asked the State Department of Education for their ideas and they gave us their feedback and we accept their recommendation. To reduce the over-testing of students we will eliminate local exams and base 50% of the evaluation on state exams. Second, the other 50% of the evaluations should be limited to independent classroom observations. Teachers may not be rated effective or highly effective unless they are effective in the test and the observation categories. We will stop local score inflation, which is resulted in virtually all teachers being rated by setting scoring bans in the state law.”

The proposal was very unpopular with teachers and the unions that represent them.  Their opposition led to a boycott of the testing by significant numbers of students in many school districts.  Because of the controversy, the Board of Regents, apparently at the Governor’s behest, changed the rules to delay implementation of the rules for four years.

Some critics of the change in direction have argued that the Governor “caved in” to the unions.  For example, the New York Post, in an editorial on November 29th, titled “Did the teachers unions just break Andrew Cuomo” said:

For years, Cuomo has been hitting his head against the wall on getting a real state teacher-rating system. Every time he seems to make progress, it’s followed by delays, postponements and revisions that ensure nothing meaningful happens.

Now he’s reportedly set to give up — abandoning the effort to use student scores on state tests to help judge teacher performance. If so, teachers will be judged subjectively, probably by their own peers. Count on every teacher to rank as just peachy — and incompetents to keep on “teaching.”

Just as the teachers unions have demanded all along.

All of us want our children to have good teachers.  As a child, while most of my teachers were competent, I was was taught by a few individuals who had no business being teachers.  One of my teachers used test questions found in the text books that we used, and permitted us to use the answer keys in the back of the books to find the correct answers.  A music teacher gave my class “study hall” on a number of occasions, and put his head on his desk, to sleep.  That kind of “teaching” cheats children, by denying them the opportunity to learn.

But, is the use of student progress on standardized tests an accurate way to measure teacher effectiveness?  Unfortunately, the answer is no.

There are two basic statistical problems involved in the use of student performance on standardized tests to measure teacher performance.  The first involves the question of whether the students in a given classroom are representative of the entire student population in a school district.   Unless students are assigned in a random fashion across the district, variations in student abilities could affect their performance in systematic ways that are unrelated to the effectiveness of teachers.  The American Statistical Association points out:

VAM [the test based teacher evaluation method] scores are calculated from classroom-level hererogeneity that is not explained by the background variables in the regression model. Those classroom-level differences may be due in part to other factors that are not included in the model (for example, class size, teaching “high-need” students, or having students who receive extracurricular tutoring). The validity of the VAM scores as a measure of teacher contributions depends on how well the particular regression model adopted adjusts for other factors that might systematically affect, or bias, a teacher’s VAM score.

The form of the model may lead to biased VAM scores for some teachers. For example,gifted” students or those with disabilities may exhibit smaller gains in test scores if the model does not accurately account for their status.

Similarly, the Educational Testing Service, the developers of the College Board exams and others, says:

The fundamental concern is that, if making causal attributions is the goal, then no statistical model, however complex, and no method of analysis, however sophisticated, can fully compensate for the lack of randomization. The problem is that, in the absence of randomization, it is hard to discount alternative explanations for the results that are found. (This explains why many consider randomized experiments the gold standard in scientific work.

 Specifically, teacher effects based on statistical estimates may actually represent the combined contributions of many factors in addition to the real teacher contribution we are after. Thus the estimate could be fundamentally off target.

 Further, it is usually difficult to determine how off target an estimate is. Clearly, substantial discrepancies would seriously undermine the utility of inferences made on the basis of the analysis.

The second statistical problem stems from the small number of students that most teachers work with.  For example, elementary school teachers, with classes of twenty or thirty students, see only a small sample of all the students in a school district.  Because of sample variability, those small samples are unlikely to be accurately represent typical students in a school district.  Consider the idea of forecasting the result of an election from a sample of 25 voters – the likelihood of an accurate result is small.  For that reason, researchers seek large sample sizes to ensure accuracy.  ETS describes the problem this way:

With a relatively small number of students contributing to the estimated effect for a particular teacher, the averaging power of randomization can’t work for all teachers in a given year. Suppose, for example, that there are a small number of truly disruptive students in a cohort. While all teachers may have an equal chance of finding one (or more) of those students in their class each year, only a few actually will — with potentially deleterious impact on the academic growth of the class in that year. The bottom line is that even if teachers and students come together in more or less random ways, estimated teacher effects can be quite variable from year to year.

Teacher performance is important.  As parents, we want our children to have every opportunity to succeed  Incompetent teachers can limit that opportunity, so it is important that the people who teach our children are capable of teaching effectively.  Teacher evaluation is an important way in which administrators can help ensure that teachers are competent.  But the mindless use of unreliable teacher evaluation methods cannot ensure teacher competency.

So, while it is easy to characterize the Board of Regents decision to postpone implementation of the proposal as a political decision that reflects the power of teacher unions, in fact, the decision reflects the reality that the use of student performance on standardized tests as the primary way to evaluate teachers is not a good way to measure their effectiveness.

 




Can Charter Schools break the Poverty-Poor Student Performance Link?

In an earlier post, I argued that school based solutions to the problem of the poor performance of students in central city schools were not likely to succeed because they ignored the impact of the concentration of disadvantaged students on student achievement.  The data showed that 79% of the variation in performance in school performance in upstate New York metropolitan areas was related to the concentration of economically disadvantaged students within them.

Discussions about the benefits of charter schools tend to be heated – inflamed by ideological differences.  But whatever one’s feelings are about the virtues of preserving public education, or of competition in improving educational opportunity, before making judgements, we should examine the available data about their effectiveness.

At the outset, it should be noted that evaluating the true impact of charter schools is difficult.  Ideally, the performance of charter and public schools should be compared by selecting and assigning students at random and following their progress over a period of years.  But, in reality, students in charter schools are not selected at random, and matched samples of public school students are not available for comparison. Published analyses on the subject have pointed out the need to adjust performance comparisons of students at public and charter schools for selection bias, because charter school students are to a large degree self-selected.

Where competent analyses comparing charter and public schools have been done, the findings have been mixed. One review of the available studies concluded:

“Taken in the aggregate, the empirical evidence to date leads one to conclude that we do not have definitive knowledge about the impacts of public charter schools on students and schools. But in reviewing the existing evidence, one is also struck by the fact that the impacts of charter schools appear to be very contextual. Some public charter schools are better than others. Some are very successful in meeting student needs, and others are not very successful…. Consequently, the impacts of public charter schools should not be painted with one broad brush stroke. Each should be judged on its own evidence and performance.”

Other studies  have found significant advantages for charter schools in central cities. Atila Abdulkadiroglu, Joshua Angrist, Susan Dynarski, Thomas J. Kane and Parag Pathak, in “Accountability and Flexibility in Public Schools: Evidence from Boston’s Charters and Pilots” found:

“A consistent pattern has emerged from this research. In urban areas, where students are overwhelmingly low-achieving, poor and nonwhite, charter schools tend to do better than other public schools in improving student achievement. By contrast, outside of urban areas, where students tend to be white and middle class, charters do no better and sometimes do worse than public schools.”

My research is based on a reanalysis of state education data on the performance of students on the 2015 Statewide Student Assessment.  It cannot provide a controlled analysis of the performance of charter school students, compared with those in public schools.  For that reason, the data available to me cannot produce conclusive evidence about the effectiveness of charter schools.

Because publicly available data is cross-sectional, it provides information about the performance of students at a given point in time, but unlike longitudinal studies, it does not directly measure their gains over a year or years.  For that reason, when a  cross-sectional study finds out-performance, or under-performance, there is the danger of making an attribution error, because we don’t know whether the out-performance or under-performance was a characteristic of the student population that was unrelated to the effectiveness of the schools being evaluated.  For example, the students at out-performing schools might have characteristics related to their selection that would predispose them to perform better than other students.

With those limitations in mind, it is worth looking at the New York State Education Department data on student performance from the 2015 Statewide Student Assessment, controlling for the concentration of poverty in schools, to see whether students at charter schools do significantly better than those at public schools with similar concentrations of disadvantaged students.  The chart below shows the performance of students in public and charter schools in all counties in metropolitan areas, except for the City of New York:

Public Charter Outside NYC

Note that data was available for only 33 charter schools outside New York City, so conclusions from this group of schools must be regarded as tentative.  Still, a few things stand out.  First, the performance of charter schools was quite varied – several charter schools were among the worst performers compared to schools with similar concentrations of disadvantaged students, while a number of others, particularly those with high concentrations of disadvantaged students performed better.  Second, for charter schools, unlike public schools, student performance was not related to the concentration of poverty.

As a group, students at charter schools did slightly better than at public schools with the same concentrations of disadvantaged students. However, the fact that 24% (8 of 33) schools exceeded the percent of students predicted to pass by 20% or more, based on the concentration of poor students, is significant.  Only 1.9% of public schools outside New York City had student performance reaching that level.   And, as Abdulkadiroglu, et. al. found, the benefit from charter schools was most significant for students in schools with high concentrations of poor students.

The performance of the better charter schools in urban counties outside New York City was significantly better than average schools with high concentrations of disadvantaged students, but not as good as at schools with few poor students.  Most of the better performing charter schools had about 40% of students passing the Statewide Assessment, compared with as many as 60% in schools with few disadvantaged students.

School Performance in New York City

The concentration of disadvantaged students in New York City schools is associated with 52% of the variation in student performance between the schools.  Compared to public schools in urban counties outside New York City economic disadvantage is a less powerful predictor of student performance in City schools – 52% vs. 79%.  For charter schools, the relationship between the concentration of poverty and student performance was very weak – explaining only 8% of the difference in student performance.  As with other counties, the performance of charter schools was quite heterogeneous. Students at charter schools in New York City as a group did better than those at public schools with similar concentrations of disadvantaged students.  At the same time, a number of Charter schools performed less well than the average of public schools with the same concentration of poor students.

The weaker relationship between the concentration of poverty and student performance in New York City schools appears to be in part a consequence of the city’s policy of creating specialized schools with selective admission criteria.  For example, the Medgar Evers College Preparatory School includes questions about student performance on the Statewide assessment in its application form.  Another example is the TAG Young Scholars School, which describes its admission policy this way:  “Prospective students must be tested by The New York City Department of Education to determine whether they qualify for a seat in one of the City’s Gifted and Talented programs.” Note that while charter schools often use lotteries to select students, they are not permitted to use test performance as a selection criterion.

These selective public schools raise the issue of causal attribution, since unlike schools that do not choose students based on test scores, it is likely that student bodies enter the selective public schools at higher levels of performance than students at other public and charter schools, and that their better performance may primarily be a result of selection criteria, rather than teaching at the schools.

Public Charter NYC

Some charter schools and public schools in New York City did as well as schools with low percentages of disadvantaged students.  Some of the best performing public schools with high concentrations of disadvantaged students use test performance as one criterion for admission.  Since charter schools are not permitted to exclusively serve high performing populations, the performance of the best charter schools is more remarkable.  At 34 of 148 (23%) of charter schools, 20% or more students than were expected to pass based on the concentration of disadvantaged students passed the statewide assessment. Among public schools in New York City, including those that have selective admissions, 8.9% of schools exceeded their predicted performance level by 20% or more.

While this data cannot prove that the excellent performance of some charter schools was the result of the schools themselves, rather than some other factor, it is consistent with studies that have shown charter schools to be advantageous for disadvantaged students in central cities.

Implications

Much of the discussion about the performance of schools, and how to improve outcomes, has focused on the common core and its testing requirements.  The purpose of these requirements was to provide a universal set of assessment tools that would provide comparable data about student progress across systems.

The results of the testing have been disappointing to many, since, as the figures above show, large percentages of students did not achieve passing grades.  For example, Governor Cuomo’s 2015 The State of New York’s Failing Schools report stated, “It is incongruous that 99% of teachers were rated effective, while only 35.8 percent of our students are proficient in math and 31.4 percent in English language arts. How can so many of our teachers be succeeding when so many of our students are struggling?”

Governor Cuomo’s proposal to improve student performance included the creation of a teacher evaluation system that relied more heavily (50%) on the performance of students in standardized tests, a process to make it easier to remove substandard teachers, and a process to place under-performing schools in receivership.  Several of the proposals have problems.  Teacher evaluation systems that rely heavily on the progress of students on standardized tests suffer from statistical defects that result in low reliability of results – a subject for a future blog post.  The process for identifying under-performing schools does not effectively identify schools that are under-performing relative to the concentration of students in poverty within them.

Most significantly, by focusing almost exclusively on accountability for under performing teachers and schools, the proposal does not offer a strategy for overall improvement of New York’s schools.  Accountability focused methods focus on remedying or removing the worst five or ten percent of schools and teachers in the system, but do nothing to help the great majority achieve better results.

If New York’s education system is to make strides in improving student outcomes, it must encourage schools and teachers to adopt known classroom teaching strategies and effective curriculum choices that have the potential to improve overall outcomes.  Since a significant number of charter schools have achieved excellent student outcomes, it would be helpful if the strategies they use could be considered for adoption in schools that do not perform well.  The state should focus on finding ways to encourage the use of effective strategies, by disseminating information and incentivizing their adoption.

Considerable research has been done on the strategies employed by effective charter schools in improving student performance.  For example, “Getting Beneath the Veil of Effective Schools: Evidence from New York City,” by Will Dobbie and Roland G. Fryer of Harvard University found that:  “traditionally collected input measures – class size, per pupil expenditure, the fraction of teachers with no certification, and the fraction of teachers with an advanced degree – are not correlated with school effectiveness.  In stark contrast…an index of five policies…explains approximately 45% of the variation in school effectiveness.”  They are consistent with the approaches used by “no excuses” model charter schools that emphasize selective teacher hiring, extensive teacher feedback, increased instructional time, and a focus on discipline and academic achievement.

For most schools in cities with high concentrations of disadvantaged students in central cities, academic performance remains poor. In some of these schools less than 10% of students received passing grades on the statewide assessment, and the overwhelming majority of schools with concentrations of disadvantaged students of 90% or more had less than 20% of students passing.

But almost one quarter of charter schools and a few public schools have broken the link between poverty and poor school performance.  At these schools, more than 40% of students passed the statewide assessment, despite very high concentrations of poverty within them.

Accountability based approaches aimed at weeding out ineffective teachers, or taking control of schools from boards of education will benefit only a small minority of students statewide.  Instead, we should focus on making use of what works in improving student performance at the best charter schools, encouraging poor performing schools to adopt effective techniques.

 




New York’s “Failing Schools” – The Wrong Diagnosis and a Misguided Solution

For the past several years, Governor Cuomo’s office has issued a report, “The State of New York’s Failing Schools.” The 2015 report contends that “Despite the fact that districts with failing schools receive more state funding than other districts, these schools are delivering unacceptable results…The statistics and facts contained in this report and its Appendix expose a public education system badly in need of change.” The report identifies 178 schools, 69 of which are located upstate, that have been identified as “failing.” The report identifies several ways in which the “failing” schools perform poorly. According to the report, these schools are among the worst performing in the state in ELA (English Language Arts) and mathematics performance, or have graduation rates of less than 60%.failing schools chartThe report goes on to state, “Of the 178 schools, 77 have been failing for a decade. More than 250,000 students have passed through these 77 schools in the past ten years. That represents 250,000 students who did not have access to the high quality public education that they deserved. Moreover we are failing the kids who need us most. Ninety three percent of students in failing schools are students of color and 82 percent of these students are eligible for free or reduced price lunch.”

The report benchmarks the “failing” schools against average schools throughout the state, even though the students at the failing schools do not have the same backgrounds and advantages.  The “failing” schools are concentrated in communities with high levels of poverty. And, high concentrations of poverty are strongly associated with poor student performance. My post, “What Critics of Central City Schools Ignore” shows that 79% of the variation in performance between schools in upstate metropolitan counties is accounted for by the percentage of economically disadvantaged students attending them. Given that fact, it is worth examining how well students at the “failing” schools do on the Statewide Assessment compared with students at other schools with similar concentrations of disadvantaged students.

The chart below shows that students at the “failing” schools did badly on the exam. On average, only six percent of the students passed. But, note that students at other schools with high concentrations of disadvantaged students did poorly as well – in fact the average performance at the similar schools that were not labeled “failing” was only 9.8% – only 3.8% better than students at the “failing” schools. And, note that the classification scheme is inconsistent with the performance of schools on this year’s test. A number of the “failing” schools were at or above the average performance of all schools having similar concentrations of disadvantaged students. The reality is that almost all schools with high percentages of disadvantaged students had very low passing rates.

failingschoolsvsnot
Disadvantaged students face challenges not faced by students in privileged communities- they often come from single parent families, their parents are often poorly educated, they may not be native English speakers, and they may face discrimination as minority group members. Because of these factors, it is not surprising that students at schools with high concentrations of economically disadvantaged students do not do well on statewide exams.

Throughout the report, there is an emphasis on the notion that the remedy for the problem of poor student performance in central city schools is to find more competent teachers and administrators. In another part of the report, the authors argue, “It is incongruous that 99% of teachers were rated effective, while only 35.8% of our students are proficient in math and 31.4$ in English language arts. How can so many of our teachers be succeeding when so many of our students are struggling?” To remedy these problems, the governor proposes reforms to “teacher preparation, certification, evaluation and tenure and the transformation of our failing schools. Our students deserve nothing less.” To transform the “failing” schools, he proposes close supervision and replacing school management, in some cases. The Albany Times-Union reports, “State receivership would allow the state Education Department to appoint a person or organization like a charter school or nonprofit to come in and reorganize a district school without the oversight of the school board, superintendent or other administrators.”

But the report’s emphasis on the failings of the so-called “failing” schools is misplaced The major causes of poor central city school performance lie outside the classroom. Addressing school performance without remedying the problem that is the primary contributor to poor student performance — poverty — is unlikely to significantly improve it.

The report stigmatizes city schools as “failing,” with the implication that if they were run better, or if their teachers were more competent, that students would perform significantly better. The “Failing Schools” program risks damaging schools by attributing power to them that they do not have, alienating faculty and damaging reputations. A more realistic approach would recognize the challenges faced by central city schools, and address them in the context of their socioeconomic environments, by focusing on combating poverty in our central cities .