A Closer Look at Student Performance in Upstate City Schools

Parents in central cities seeking good educations for their children face the disconcerting reality that relatively few city school children pass standardized tests required by New York State, suggesting that the schools are failing.  In Rochester, Buffalo, Syracuse and Schenectady, less than 20% of students passed state required English Language Arts and Mathematics exams in 2016 and 2018.

A considerable body of research shows that student performance is strongly related to socioeconomic status. Parents’ incomes and educational status predict almost three-quarters of the variation in performance between schools and school districts where parents are relatively affluent and well educated and those who are not.  In Buffalo, Syracuse, Utica and Schenectady, more than 80% of students were disadvantaged. In Rochester, more than 90% were.

Socioeconomic status and student performance on tests are strongly related to economic status once students become young adults. Only 23% of students from low-income families whose math scores were below average had above average socioeconomic status as young adults, according to a study by Anthony P. Carnevale, Megan L Fasules, Michael C. Quinn and Kathryn Peltier Campbell (“Born to Win, Schooled to Lose”). Eighty percent of students from high income families whose performance was above average on math tests in 10th grade had above average SES as adults.

But socioeconomic status (SES) does not tell the whole story. There are variations in performance that are not accounted for by economic and educational status. Adjusted for SES, students in Albany, Rochester and Syracuse performed below expectations.  In Rochester, only 9% of students in grades three through eight passed the state required exams in 2018. Based on the percentage of economically disadvantaged students and district educational levels, 17% were expected to pass.  In Syracuse, only 13% passed.  18% were expected to pass.  In Albany 18% passed, compared to 31% predicted by the two factors.

Although we might infer that unexplained performance differences are the result of differences in school quality, we cannot be certain that it is the cause. Because the available data has a limited number of variables, we cannot know why students in Albany, Rochester and Syracuse performed more poorly than expected. Poor performance could be the result of differences in classroom instruction, but it might also be the result of other factors, such as student differences that existed in pre-school years. We know that as early as third grade – the lowest grade level at which the tests are given – students in the poorly performing school districts did less well than would be predicted by their educational and economic statuses. This suggests that differences in early childhood might be important.

There are better measures of the effect of schools on student learning than comparing achievement on tests at a single point. One approach is to compare the educational growth of students in different schools and school districts. This approach reduces the effects of non-school related differences.

When student educational growth is compared different patterns appear. Differences between urban school districts are smaller using the educational growth measure than the SES/Education model. But differences within school districts are large in some cases. And charter schools are more successful in some cities than in others in providing better settings for student educational growth.

Charter Schools

Given the bleak academic performance of central city school students, it is not surprising that charter schools were established. The fundamental promise of these schools was to offer learning environments that would be more conducive to student success than existing district operated schools.  Supporters of the movement believed that charter schools, freed of bureaucratic rules and limits on school districts ability to incentivize teachers to help students perform better, could produce better results for city children.

Charter schools have been controversial.  Teachers unions have argued that they divert needed resources from existing public schools, and that they produce little real benefit for schools.  Assessed in aggregate across the nation, evaluations of them have been mixed, some showing benefits, others showing that students at charter schools do less well than at comparable schools operated by school districts.

Defenders point to consistent differences in charter school performance by state, reflecting differences in the way charter schools are authorized and evaluated.  For example, a recent study, “Charter School Performance in New York” issued in 2017 by the Center for Research on Educational Outcomes (CREDO) at Stanford University found that overall, students at charter schools performed significantly better than those at comparable school district operated schools.  CREDO compares the educational growth of students at charter and district operated schools to evaluate differences in outcomes.  The chart (reproduced above from the CREDO report) shows that overall, students at charter schools in New York State performed significantly better in mathematics than those in schools run by school districts, by about 1/10 standard deviation (one tenth of a standard deviation is equivalent to a move from the 50th percentile to the 54th percentile.)  In English, the difference amounts to a change from the 50th percentile to the 51st percentile.

The difference in performance in New York state was largely driven by charter school performance in New York City.  Upstate differences between charter school and district school performance were not statistically significant.  However, CREDO data shows that Uncommon Schools in Rochester was associated with a positive difference of 0.11 standard deviation in English compared with district operated schools – a difference of 4.37 percentiles.  In math, the difference was larger – 0.26 standard deviation, or 10.26 percentiles.

Not all charter schools outperformed their traditional public school counterparts. There were large variations in charter school performance.  Nearly half significantly outperformed district schools, but slightly more than half performed at levels that were not significantly different from schools operated by school districts or performed worse.. (p. 11).

New York State has recently begun publishing data on student growth at schools administering the state’s third through eighth grade exams. The state defines student growth as follows: “[A] Student Growth Percentile (SGP)…measure[s] a student’s improvement or growth relative to other students, considering the students’ prior academic histories…The SGP indicates whether a student grew more than or less than students with similar test histories in the state. New York defines Elementary/Middle-Level (EM) Growth as “three years of student-level growth in ELA and mathematics combined.”

Analyzing student progress to assess the quality of schools and school districts offers significant advantages over simple comparisons of student test scores. Student progress comparisons exclude the effect of differences in student performance that result from differences that are not related to school quality. Even so, there are weaknesses in the student growth measure unless some additional control measures are used. In New York’s case, three additional controls are included in constructing the measure: percentage of English Language Learners in the classroom, percentage of students with disabilities, and percentage of students in poverty.

Educational Growth in School Districts

Although Albany, Rochester and Syracuse performed relatively poorly compared with what the SES/education model predicted, the State’s student educational growth data did not show the same performance deficit. Large upstate cities except Binghamton showed student educational growth that was within two percentiles of the average for all school districts in the counties within which they were located. Utica did best, with the average student in the district being in the 52nd percentile in the state, 2.95 percentiles higher than the county average (49%).

In the upstate metropolitan counties studied, the districts that performed the best were a mix of large and small, suburban and rural places. In the best performing district – Whitney Point — the average student’s performance growth placed her or him in the 55th percentile.

The weakest performing districts were also a mix of district types. In the worst performing district, the average student’s performance growth placed him or her in the 41st percentile.

Although the gap between the best performing district and the worst is large – 15 percentiles – overall differences between districts in the upstate metropolitan counties studied were small – 64% of the districts were within plus or minus 2% of the average for all districts.

As a practical matter, although the data does not show that city school districts like Albany, Rochester and Syracuse are doing a particularly poor job of educating students, most in these districts perform poorly, and high percentages drop out before completing high school. And, as the following section shows, there are large variations in student educational growth among schools within the same school districts.

Educational Growth in Schools

The following section is based on student growth data published by New York state for 2017-2018. Charter schools from three counties – Albany (Albany), Monroe (Rochester) and Erie (Buffalo)– were included in this analysis because data was available for five or more charter schools in each of these counties.

This chart shows the average EM growth percentile at each school in the three cities compared with other schools. Three quarters of the schools had EM growth percentiles that were within a range of only six points – between the 46th and 52nd percentiles.

Charter school performance varied in the 2017-2018 New York State data. The best (Buffalo Academy of Science – 69th percentile) and worst performing school (Charter School of Inquiry in Buffalo – 39th percentile) were 30 percentiles apart in student growth. Similarly, there were large variations in district operated schools in each of the cities. For example, at the best performing district operated school (the Montessori School in Albany), average EM student growth was in the 60th percentile, while at the worst school (PS 82 in Buffalo), the average student was in the 38th growth percentile compared to students state-wide.

School sizes are relatively small. Random variations in performance could occur because of the small number of test subjects in each. To prevent misinterpretations of differences in school performance because of sample variability, I use the method applied in the CREDO study. For schools to be labeled better or worse performing than average, a statistical test was employed. For schools to be considered better or worse than average, the statistical test had to show with 95% confidence that the school’s student growth percentile was different from the average (49th percentile) of school districts in the areas examined.

NYSED data from 2017-2018 shows that in the three cities and counties studied, charter schools did perform better overall than district operated schools. 37% of charter schools had average EM growth percentiles that were significantly above average, compared with 23% of district operated schools. Even so, 63% of charter schools’ average EM growth percentiles were average or below average. For district operated schools, 77% were average or significantly below average. But there were large variations in the performance of district operated and charter schools in each of the three cities studied.


Buffalo’s district operated schools had the highest percentage of significantly above average district operated schools and the lowest percentage of above average charter schools in the three cities.

For students in Buffalo, in many cases choosing to attend a charter school offers no real benefit. Thirteen district operated schools had EM growth percentiles that were significantly above average, compared with three charter schools. For students at average or above average district operated schools, there are few charter school alternatives where students on average show significantly higher educational growth. Eight district schools had growth percentiles that were significantly below average. Students at these schools might benefit from seeking to enroll in a charter school that had above average or average student growth scores.


The overall performance of district operated schools in Albany was significantly weaker than charter schools in the city and was the weakest of the three cities studied. Two of five charter schools had EM growth percentiles that were significantly above average, while three were average. Almost half (47%) of district operated schools had average growth percentiles that were significantly below average, while only 13% were significantly above average. There were exceptions, however. At the city’s Montessori Magnet School, the average EM growth percentile was 60%, while at William S. Hackett middle school, the EM growth profile was 55% — also above average.

Many Albany students in district operated schools might benefit by seeking to enroll in charter schools, particularly those attending the seven schools with below average growth percentages. For students at district operated schools with average EM growth percentiles, relatively little might be gained. For example, students at the Delaware Community school would be moving from a school with an average EM growth percentile of 51.8 to a charter school that would at best have an average growth percentile that is 3% higher.


Rochester’s district operated schools overall performed slightly better than Albany’s, with 29% having EM growth percentages that were significantly below average, compared with 47% in Albany. But, as in Albany, only 13% of district schools were significantly above average. As a group, charter schools in the Rochester area performed the best among those in the three cities, with 55% having significantly above average EM growth percentiles.

Six of nine schools with significantly above average EM growth percentiles were charter schools, compared to only three district operated schools. Among schools that had EM growth percentiles that were significantly below average, seven of nine were district operated schools.

Most schools operated by the Rochester school district had EM growth percentiles that were statistically average. For many of these students, seeking to transfer to a school ranking higher might not offer a significant advantage, given the amount of variation in school performance that could be associated with statistical sample “noise.” For students in below average schools operated by the Rochester School district, moving to a better performing school could provide greater educational opportunity.


Although differences in student growth between city school districts were not large, within school districts there were relatively large differences between the best and worst performing schools. In two of three cities – Albany and Rochester – charter schools as a group outperformed district operated schools. But in those cities some charter schools performed poorly, and some district operated schools performed well.

For students, avoiding schools whose performance is significantly below average could be beneficial to student growth. But, geographic accessibility and realities of competition for limited seats in better performing schools can make it difficult to get into them.

For policy makers there are several challenges. Attempts to turn around poorly performing schools do not have a promising track record. Brian Backstrom of the Rockefeller Institute at the State University of New York writes, Over the past half-century, billions of dollars have been spent across the nation on efforts to transform persistently low-performing public schools — most of them urban, most of them low-income, and most of them disproportionately enrolled with students of color — into models of success. It hasn’t worked.”

School turnaround efforts are often frustrated by forces that contribute to organizational inertia, ranging from unions that fear that members will lose their jobs to uneven implementation because of differences in senior and middle level managers’ commitment and ability and insufficient long term financial commitments.

Backstrom points out that the most effective approach to improving schools that perform poorly has been to close them. But doing so faces practical impediments – most notably that better alternatives must be available to displaced students if efforts are to succeed. This can be difficult, because existing, better performing schools usually have enrollment constraints that limit the number of additional students that they can accommodate.

Where city school districts have seen improved results, charter schools often play an important role. The challenge here is two-fold. One issue is that scalability is a problem. Evidence suggests that some charter school operators with proven track records – KIPP and Uncommon Schools are two examples – are more likely to provide statistically superior results than independently operated charter schools, but the successful organizations are constrained by their organizational capacity to grow while maintaining quality. Charter schools take time to establish and, in many cases, need substantial private financial resources to support their efforts. Quality control is a concern as well. Student performance at some charter schools is significantly worse than at most district operated schools. These schools do not add meaningful choice to those seeking greater educational growth. Here, the State Education department should be vigilant in monitoring charter school performance.


An abridged version of this post appears in the Rochester Beacon.


Education, Economic Status and Student Performance in New York School Districts

Note:  A more recent, closer look at this subject may be found here:  https://policybynumbers.com/a-closer-look-at-student-performance-in-upstate-city-schools

In 1966, James S. Coleman and associates wrote the report, “Equality of Educational Opportunity,” for the United States Department of Health Education and Welfare, as required by the Civil Rights Act of 1964.  The report was commissioned to examine the causes of differences in educational outcomes experienced by minority group students, particularly African-American children compared to majority group whites.   Initially, the report’s sponsors had expected that differences in resources available to black, Hispanic and white students would explain the differences.

Coleman, however, found that another factor was far more important.  “The first finding is that the schools are remarkably similar in the way they relate to the achievement of their pupils when the socioeconomic background of the students is taken into account….When these factors are statistically controlled, however, it appears that differences between schools account for only a small fraction of differences in pupil achievement.” (pp. 21-22).  Coleman’s report has led to the notion that factors like parents’ education and incomes determine the performance students in schools.

But Coleman went on to do further research.  In 1982 he published, “Cognitive Outcomes in Public and Private Schools.”  Coleman found “strong evidence that there is, in vocabulary and mathematics, higher achievement for students in Catholic and private schools than in public; the results are less consistent in reading.” In large part, Coleman attributed this to his finding that “Private schools provide. a safer, more disciplined, and more ordered environment than do public schools.”  Coleman’s study is controversial. Critics have argued that his findings could be attributed to other factors, particularly the likelihood that self-selection may be an unmeasured but important factor in these schools’ outperformance.

In New York State, as in other places, students in central cities are much less likely to pass the state’s standardized tests than those in other, more affluent, locations.  One of the consequences of the long-term problem of high failure rates in these schools has been the call for reforms, such as the establishment of charter schools to provide alternatives – presumably better ones – to schools operated by public school districts.  Others have called for state takeovers of underperforming school systems.

What does the data show?  How strongly does socioeconomic status influence performance in school districts in New York State?  Do some districts outperform or underperform expectations?

Upstate Central City Student Performance

Few students in upstate central city school districts do well on New York’s standardized examinations. Fewer than one in five students in these cities passed the state’s English and mathematics examinations given between third and eighth grades in 2016 and 2018. Utica was the only large upstate city school district in which more than 20% of grade 3-8 students passed the state’s English Language Arts and Mathematics exams in 2016 and 2018. In Rochester, only 9% passed, while in Syracuse, 13% passed. In some selected nearby suburban districts, the picture was quite different – in most cases, more than 60% of students passed the state exams.

Economic Disadvantage and Parents’ Education as Predictors of Student Performance

Economic Disadvantage

New York State defines economically disadvantaged students and family as those who take part in assistance programs “such as the free or reduced-price lunch programs, Social Security Insurance (SSI), Food Stamps, Foster Care, Refugee Assistance (cash or medical assistance), Earned Income Tax Credit (EITC), Home Energy Assistance Program (HEAP), Safety Net Assistance (SNA), Bureau of Indian Affairs (BIA), or Family Assistance: Temporary Assistance for Needy Families (TANF).” When two years of data were combined to reduce random variation associated with small sample sizes, 72% of the variation in student performance was associated with the percentage of students defined as economically distressed. The chart below shows the strong relationship between the percentage of school district students passing New York’s Grades 3-8 ELA and Math exams and the percentage of disadvantaged students in school districts.


We cannot directly measure the educational levels of parents in the school districts studied, but Census data permits us to examine the relationship between the percentage of adult district residents who have college degrees and student performance.   Separately, the percentage of college graduates is associated with 48% of the variation in student performance in school districts. The chart below shows the relationship.

Although economic disadvantage proved to be more closely related to student test outcomes at the school district level, the percentage of adults in a district with at least a four-year college degree increased the percentage of variation explained by a small amount – about 2%. Consequently, a linear model that includes both variables was created to understand the combined relationship between economic disadvantage, adult education levels and student performance. Including both variables in the model increased the percentage of variation explained by the model to 74% – nearly three-quarters.

Although the relationship between student performance and economic status and district educational levels is very strong, about one-quarter of the differing performance of school districts is not explained. One factor that could be associated with this unexplained variance is the instructional efficacy of schools. Note, however, that this possible explanation is inferential. Other unexamined factors could be in play as well.

Characteristics of School District Populations

In upstate central cities, high concentrations of disadvantaged students combine with adult populations that have relatively low educational levels. More than 85% of students in Rochester, Syracuse, Schenectady and Utica were economically disadvantaged in recent years. Only about one-quarter of adults were college graduates.

The characteristics of student bodies in large communities surrounding upstate cities are quite varied. In some communities less than 20% of students were economically disadvantaged. In almost all these communities, 50% or more of the students passed the state examination. In a few cases, more than 70% passed. In other communities outside upstate central cities, more than 50% of students were economically disadvantaged. In many cases, less than 40% of students passed the state’s ELA and Mathematics exams. But in only three cases (Lackawanna, Solvay and Watervliet) was the percentage of economically disadvantaged students as high as it was in the central cities. In these communities, as in upstate central cities, more than 70% of students failed the state exams.

As in the case of economic disadvantage, in most cases, cities had lower percentages of people with at least a four-year college degree than did most residents of communities outside them. But, the difference between the educational levels of city residents and residents of other communities was less clear-cut. In fact, about 10% of communities outside central cities had lower percentages of college graduates than did central city residents. And, in general students in school districts in those communities  performed better on state tests than central city residents. Overall, the relationship was not as strong as the relationship with economic disadvantage.

Which School Districts Outperformed and Underperformed?

Calls for reform of central city school districts, such as state school district takeovers and the charter school movement, rest on the notion that the poor performance of many students attending urban schools is at least in part caused by poor school management and instruction. How well do students at schools in central city school districts perform given the percentage of economically disadvantaged students and people without college degrees within them?

Overall, economic disadantage and education are very important predictors of school district performance.  But, there were significant differences in district performance after controlling for socioeconomic factors.  In Utica, student performance exceeded expected performance by 7% – 24% passing vs 17% predicted. Performance in Buffalo was close to model predictions – 19% passed vs 22% predicted. Schenectady’s performance was also close to model predictions – 17% passed, compared to the prediction of 20%. The Albany school district fell short of expectations by the largest amount.  Had Albany students performed as expected, nearly one-third (31%) would have passed.  Only 18% did. In Syracuse, 21% were predicted to pass but only 13% did.  In Rochester 17% were predicted to pass.  Only 9% did.

The next tables show the fifteen districts within the counties within which Albany, Buffalo, Rochester, Syracuse, Schenectady and Utica are located where student performance exceeded expectations and lagged expectations to the greatest degree.

The performance of school districts outside central cities was quite varied. Among the fifteen school districts where student performance was the lowest compared to the model’s prediction, twelve of fifteen were outside central cities. Among those that exceeded expectations, all but one were outside central cities.

Three upstate cities – Albany, Rochester and Syracuse were in the group of poor performers. Among the school districts where students performed the best compared with the model’s prediction, only one – Utica – was a city school district.

Coleman’s finding continues to be true.  Socioeconomic factors explain most of the difference in performance between school districts.  But even after controlling for these factors, there are differences in performance between school districts.  Some are relatively large.

This data is not a causal analysis. We cannot draw conclusions as to the causes of differences in student performance in school districts compared with model predictions. Other unmeasured variables might explain the differences in performance. However, residents of school districts where performance is lower than predicted by the model might question whether their school district is performing its job effectively.

For residents of three large city school districts – Albany, Rochester and Syracuse, this question is particularly important because the percentage of students passing the state exams is lower than in almost all other school districts, and because controlling for known factors associated with poor performance, they did less well than expected.

(a version of this post also appeared in the Rochester Beacon).


Rochester’s Broken School System

Kent Gardner argues forcefully in the Rochester Beacon that Rochester’s school system is broken and in need of radical change. Gardner’s post highlights the efforts of a local organization, ROC the Future, to bring about reform of the city’s school system. Gardner is correct – students in the city’s schools do poorly compared to others in the state, and the state designated Distinguished Educator Jaime Aquino’s recent report shows a series of failures of leadership, communication and implementation in the school district.   Here I take a close look at the dimensions of student performance in Rochester compared with other districts in Monroe County and the state outside New York City and look at what my findings suggest for possible reforms.

The Effect of Economic Disadvantage on Performance

Economic disadvantage is the strongest predictor of student performance among the socio-economic variables available for review in the data available from the state test database. The percentage of economically disadvantaged students in school districts is associated with 70% of the differences in performance on the 2018 Grade 8 New York State English Language Arts exam among school districts outside New York City.  

New York State defines economically disadvantaged students and family as those who take part in assistance programs “such as the free or reduced-price lunch programs, Social Security Insurance (SSI), Food Stamps, Foster Care, Refugee Assistance (cash or medical assistance), Earned Income Tax Credit (EITC), Home Energy Assistance Program (HEAP), Safety Net Assistance (SNA), Bureau of Indian Affairs (BIA), or Family Assistance: Temporary Assistance for Needy Families (TANF).

Grade 3 Results 

Third graders in the Rochester school system had the lowest percentage – 17% – of students passing the English Language Arts Exam of school districts in New York State outside New York City.  Among large school districts, Rochester had the highest percent of third grade students were economically disadvantaged – 93%.  Students in Syracuse performed nearly as badly – 20% of Syracuse third grade students passed. Ninety percent of Syracuse third grade students were economically disadvantaged.

Grade 8 Results

Eighty-seven percent of eighth grade Rochester School District students were economically disadvantaged in 2018, among the highest percentage in the state, and only 11.4% of 8th graders received a passing grade on the test – the lowest among the school districts studied. For districts in Monroe County outside the City of Rochester, an average of 37% of students were economically disadvantaged, and 50% passed the eighth grade ELA exam, receiving a grade of 3 or 4.

Other upstate cities also had relatively high percentages of disadvantaged students and small percentages of students passing the state exam, but Rochester’s case was the most extreme.

Five of the nine upstate cities in the chart above had a lower 8th grade passing rate than would be expected based on the relationship between student performance and economic disadvantage.  But, even if the schools in these cities had performed as well as expected based on the model, student performance would still have been poor.  In the case of Rochester, 17% of eighth graders would have passed and not 11%.  For Syracuse, 19% would have passed, not 15%.

The strong association between economic disadvantage and poor student performance found in Rochester and other upstate cities reflects other studies that find that student achievement at the school district level is strongly related to family income. For example, a group of Stanford University researchers in a study reported on by the New York Times in “Money, Race and Success: How Your School District Compares” found that sixth grade students in the richest districts are four grade levels ahead of those in the poorest districts.

How the Performance of Rochester Schools Compares with Others in Monroe County

The performance of schools within school districts varied, but the overall relationship between economic disadvantage and student performance continued to be strong. In this case, test results from grades 3 and 4  were combined, as were results from grades seven and eight, because small numbers of students attend typical elementary schools. By combining grades, random sampling variation is decreased.

Third and Fourth Grade Results

Rochester School District third and fourth grade students were both less likely to pass the ELA exam than students in other Monroe County school districts, and more likely to be economically disadvantaged.  The data for grade three and four shows that overall, more than 70% of students at all Rochester City schools were disadvantaged. At three schools, all of the tested students were disadvantaged. The performance of third and fourth grade Rochester School District students varied, ranging from six percent passing at one elementary school to 37.5% at another.

Of 17 Rochester School district schools studied, only four had passing rates that equaled or exceeded the percentage that would be expected based on a linear model of the relationship between student performance and the percentage of disadvantaged students. Among those four schools, the amount by which performance exceeded expectations was not large. Four of the 17 were in the bottom 20% of schools based on performance when the percentage of disadvantaged students was considered.

As a group charter schools had a lower percentage of disadvantaged students (78%) in third and fourth grade than schools in the Rochester School District (91%).  Overall, performance of third and fourth grade students at charter schools was better than at schools in the Rochester School District when the concentration of poverty was considered.  Of the six charter schools examined, performance was near what would be expected based on the percentage of disadvantaged students at three schools. Performance at two charter schools relative to the percentage of disadvantaged students was in the top 3% of all schools.

Readers should not conclude from this finding that charter school students at some charter schools performed better than at most Rochester City schools solely because of charter school teaching methods. Other studies have shown that self-selection accounts for some of the better performance of some charter schools. Charter schools are likely to be seen as more challenging than public school alternatives and may attract parents and students seeking a more rigorous alternative. See this study for aa review the issue. The kind of analysis employed here cannot account for self-selection.

Seventh and Eighth Grade Results

Smaller percentages of eighth grade students in Rochester schools passed the 8th grade ELA exam than in other Monroe County school districts.  Between 0% and 24% of students passed the state’s ELA exam. At the same time, concentrations of economically disadvantaged students in Rochester were much higher than in other Monroe County school districts.  73% to 94% of eighth grade Rochester City School students were economically disadvantaged. Performance at five of the ten schools in the group was in the bottom 20% of schools based on the relationship between performance and economic disadvantage. Performance at the other five schools was near average.

As with third and fourth grades, a smaller percentage of seventh and eighth grade charter school students was economically disadvantaged (75%) than students in the Rochester School District (88%). Charter school performance was mixed, though overall it was significantly better than at Rochester School District schools.

Performance at one school was slightly below what was predicted.  At two charter schools, performance was average. Performance at two schools was in the top 20% of schools based on the model. Performance at the remaining charter schools was in the top two percent. Cautions again apply about the causes of the relatively good performance at two of the charter schools – self-selection may have influenced the outcomes.


Rochester’s school system is at a crossroads. Student performance in Rochester schools is poor, even accounting for effect of the high percentage of students who are disadvantaged. The recent report by distinguished educator Jaime Aquino documents shortcomings in leadership, communications and policy execution. These factors all call for substantial changes in current practice.

The data about charter schools is somewhat equivocal. While student performance at several charter schools was substantially better than at public schools with similar percentages of disadvantaged students, the performance of others was not above average. Because charter school students apply for admission, selection bias is a possible explanation of better performance.  It should also be noted that though most students at charter schools were economically disadvantaged, the concentration of disadvantaged students in Rochester School District schools was 13% higher than at charter schools as a group.

In the best performing schools in the region, more than 70% of students pass the state’s ELA exams, compared to 15% for third graders and 16% for eighth graders in Rochester City Schools. But, only 20% to 30% of students in the best performing districts were economically disadvantaged compared with about 90% in Rochester.

The pervasiveness of the relationship between economic insecurity and poor student performance points to the need reduce the concentration of poverty in the City of Rochester. As a city, Rochester is highly dependent on other levels of government – primarily the Federal government — for assistance in combatting poverty. The city/county anti-poverty initiative – RMAPI operates with involved entities on strategy development and coordination. It is important that RMAPI and its partners develop clear strategies implemented at a scale that is large enough to have meaningful impacts. Rochester’s Center for Governmental Research developed a series of options for policy initiatives for RMAPI in 2014, some of which the organization followed.

The most recent progress report posted on the RMAPI website describes its 2017 accomplishments. The most recent press release is dated January 2018. Although the organization made some programmatic initiatives in the 2015-2017 time-frame, its website lacks information on the current status of its efforts.

There are potential federal actions that could benefit low income families. Because poor families cannot invest in their children as much as wealthier parents, solutions like proposed refundable family tax credits could help the already existing Earned Income Tax Credit reduce income insecurity. Increasing food stamp benefits, particularly for families with children might be another approach. Because there is evidence that disadvantaged students perform better in schools that have lower concentrations of disadvantaged students, efforts to provide lower income families with access to housing in wealthier neighborhoods has been proposed as a remedy.

In part, the organizational failures in the Rochester City School District may reflect the difficult conditions within which they operate. But, students at city schools do not perform well, even considering the concentration of economically insecure students who attend them. Given the lasting consequences of student failure, community expectations demand better. The Distinguished Educator’s report demonstrates that the district must make a sharp course correction if it hopes to meet the needs of Rochester families.

At the same time, as long as the concentration of poverty in Rochester continues to be very high, efforts to substantially improve student performance at city schools are likely to be only marginally successful.  The performance of students in Rochester City school reflects the nexus of income inequality and organizational failures. Ameliorating the problem will require a multi-pronged approach that addresses both problems.

New York’s Local Revenue Sharing Aid Program is Broken:  How to Fix It

Most New Yorkers are aware that the state has a cap on local property taxes that has effectively slowed their growth.  But few know that residents of a few large cities benefit from a multi-million-dollar infusion of state dollars that limits property taxes, while residents of smaller cities, towns and villages get far less help.  Because of this, residents of smaller cities pay a substantial property tax penalty compared with their suburban neighbors, one that residents of Buffalo, Rochester and Syracuse do not pay.

Among New York’s 25 most highly taxed municipalities, the State’s $714 million AIM revenue sharing program has a highly variable impact on property tax burdens. Without AIM aid, the property tax on a $150,000 home would be $4,500 in Buffalo and $4,827 in Binghamton.  With AIM aid, the Binghamton homeowner would save about a thousand dollars while savings to the owner of a similarly valued home in Buffalo are nearly three times that. Within the Rochester MSA, AIM saved owners of a $150,000 Rochester home $2,043 while Genevans with a comparable home—and a higher tax rate than Rochester without AIM—save only $735. Why should the tax break vary so much?

Residents of villages with high tax burdens fared even worse.  In Ellenville, the tax on a $150,000 home would have been $4,155.19, but AIM only provided a benefit of $38, bringing the property tax bill down to $4,118.

Note the data in this report is from the New York State Comptroller’s Office, Financial Data for Local Governments, 2017.

City taxes, except for Rochester, Syracuse and Buffalo are typically far higher than in surrounding suburbs.  In the Albany metropolitan area, for example, residents of the City of Albany owning a house valued at the metropolitan area median price would pay $1,788 more than in the neighboring town of Colonie.  Schenectady residents pay $2,624 more than in the neighboring Town of Niskayuna.  Residents of Utica pay $1,786 more than New Hartford homeowners.  In effect, the state’s property tax structure imposes a substantial penalty on people who chose to live in most of its cities.

The AIM program provides enough aid to even out disparities in property tax burdens between large cities like Buffalo, Rochester and Syracuse and their surrounding suburbs, but falls far short of providing enough help for smaller cities like Albany, Troy and Geneva.    The gap in property tax rates between Rochester, Syracuse and Buffalo and their suburbs is about $2 or less.  For most cities, AIM does relatively little to reduce the gap between cities with high tax rates and lower tax rates in towns and villages surrounding them. For most cities the gap is $10 or more – $1,500 on a $150,000 home.  The gap between Buffalo’s property tax rate and neighboring towns was $1.22.  For neighboring Niagara Falls, the gap was $14.63 per thousand of full value.  The gap between Syracuse’s property tax rate and neighboring towns was fifteen cents.  For nearby Utica, the gap was $13.48.

In a recent post “AIM Aid Needs an Overhaul” in the Beacon, Kent Gardner argues that “We would think that AIM aid would be driven by a formula based on community need. If that’s the case, the formula seems to work poorly.” The existing AIM program reflects a series of past legislative bargains, responding to perceived needs that were identified many years ago that may no longer exist.  The practice of allocating aid based on a combination of prior program funding and additional criteria results in a jumbled funding pattern.

AIM As a Source of Municipal Revenues

AIM is an important source of revenue for cities.  In a few places, particularly large cities like Rochester, Buffalo and Syracuse, state revenue sharing through AIM generates far more revenue than property taxes — in the case of Buffalo and Syracuse, about twice as much.  For many others, AIM aid is important, but is a smaller contribution than are local property taxes.  In Albany, AIM provides revenues equal to 22% of what local property taxes generate.  In Binghamton, AIM generates 25% of what local property taxes provide.

For towns and villages, AIM aid is less significant, averaging less than 3% of local property tax revenues.

Differences in Local Tax Burdens

Municipal taxes vary substantially in New York State. Homeowners in the first quartile paid about $550 in municipal taxes in 2017 (not including school or county taxes).  Those in the highest quartile paid about $4,100.  AIM revenue sharing aid did reduce taxes in high tax municipalities more than in low tax locations, but the assistance was not large enough to substantially reduce the difference in taxes.

Much of the difference in property tax rates between communities reflects regional differences in property values.  Home values in the Albany-Schenectady-Troy metropolitan area are about 50% higher than those in other metropolitan areas.  Values in Nassau and Westchester Counties are about double those in Albany-Schenectady-Troy and triple those in other upstate metros.

Because of the large differences in regional housing prices, tax equity between municipalities should be addressed regionally – within counties, rather than statewide.  Statewide comparisons overwhelm differences in tax rates within housing markets, distorting our understanding of tax impacts within them.  It should be noted that housing prices vary significantly even within counties.  City housing prices in most cases are lower than in suburban areas.
Property taxes are based on wealth derived from property ownership.  But, residents typically pay taxes from their incomes.  Median household incomes vary across the state, but not as much as housing prices.  Incomes in Albany-Schenectady-Troy are about 20% higher than in Utica-Rome and Buffalo, and about 13% higher than in Rochester and Syracuse.  Household incomes in Nassau County are 60% higher than in Albany-Schenectady-Troy, while incomes in Westchester area about 33% higher.  Municipal (City, Village and Town) municipal property taxes per household are similar in Binghamton, Syracuse and Rochester but taxes in Buffalo-Niagara Falls were about 30% higher than in most other upstate metros.   Per household municipal property taxes in Nassau and Westchester were about almost twice as has as in most upstate metros.  Overall, property tax per resident by cities, towns and villages tracks household income more closely than home values.

Within metropolitan areas, the difference in household incomes is much greater than the differences between metros would suggest.  In the Rochester metropolitan area, median incomes range from $31,700 in the Village of Penn Yan to $110,544 in the Town of Pittsford.  In the Albany-Schenectady-Troy metropolitan area, the range is from $34,495 for the Village of Cobleskill to $105,398 for the Town of Niskayuna.
All upstate cities, except for Saratoga Springs had median incomes per household that were lower than the upstate average.  But large cities like Buffalo, Yonkers, Rochester, Syracuse get far more than can be justified based on median household income relative to other cities.

Although Buffalo, Rochester and Syracuse each have about the same household incomes, Buffalo gets more aid per household ($1,458) than either Rochester ($1,024) or Syracuse ($1,290).  Niagara Falls ($835), Lackawanna ($844), Rome ($708), Utica ($686), Troy ($614) and Binghamton ($463) also get less aid per household, despite having similar median household incomes.  Jamestown, which is the city with the lowest median household income gets $358 per resident compared to Buffalo’s $1,458, even though its median household income is lower.

On average, AIM benefits per household were much smaller for villages and towns than for cities, averaging $17 compared with $756 for cities.  But, like AIM assistance for cities. AIM assistance for towns and villages with similar median household incomes varied substantially.  For example, the Village of Kaser in Rockland County had a median household income of $17,564 and received $17 per household in AIM assistance.  Two towns in the Adirondack forest preserve received much more.  The Town of Newcomb, with a median household income of $46,500 received $752 for each household, and the Town of Long Lake which had a median household income of $55,795 received $766.

Reforming AIM Revenue Sharing

Reducing the large disparity in tax rates between cities, towns and villages with high property taxes and those with lower tax rates should be a priority for reform of the current AIM revenue sharing program. Cities have been coping with losses of population for many years.   Public concerns about public safety, deteriorating housing stock and school quality as well as racial, ethnic and religious fears can discourage home buyers from considering city locations.  Attaching a significant property tax penalty to cities and other high tax municipalities further deters housing consumers.

This table compares municipal tax rates for cities and towns (including special districts) in metropolitan areas.  It also shows the 2017 municipal property tax based on a median priced home (source: www.zillow.com) in the metropolitan area.  (Note that school and county taxes are not included here.  Village taxes were also not included because village taxpayers also pay town taxes, which would have made calculations much more complex.)   For example, in the Buffalo metropolitan area, the municipal tax on a median priced home in Buffalo, Erie County would have been $296 more than the average for towns in the Burralo-Niagara Falls MSA.  But, home owners in smaller cities in the MSA saw much larger differences.  Residents in Niagara Falls would have paid $1,868 more, while residents of Tonawanda would have paid $1,598 more than average town residents.

To make the state’s AIM revenue sharing program more effective, the State legislature could consider increasing assistance to high tax municipalities.  As a hypothetical example, the legislature could provide enough additional revenue sharing aid to reduce the maximum tax rate differential to thirty percent more than the average for towns (including fire districts) within a metropolitan area or county.

This table shows that increasing AIM aid to ensure that the municipal property tax rate for every locality in a county to no more than 30% more than the average for towns, villages and special districts  in a metropolitan area or county would even out the burden on in different localities.  The most dramatic case is that of the City of Schenectady, which would see taxes on a median priced home decrease from $3,504 to $1,274. Troy taxpayers would have substantial savings as well – about $1,800 on a median priced home.   Utica taxpayers would save nearly $1,500.  Again, note that there are variations in median home values within counties.  Home values in most cities are lower than in most suburbs.


Currently, New York property taxes impose a substantial penalty on residents of municipalities with high taxes.  Cities, other than Rochester, Syracuse and Buffalo, face a significant disadvantage in attracting homebuyers because property taxes in cities are typically thousands of dollars higher than in surrounding communities.  High property tax rates can reduce home values in a municipality because tax rates factor into their affordability.

Remedying the problems with AIM would be simple, though politically difficult.  A uniform approach to cities, towns and villages that provides enough funding to reduce the tax penalty for living in high tax cities and other localities to a few hundred dollars for a typical taxpayer would go a long way to resolving the problem.  The approach should focus on differentials within housing markets – metropolitan areas or counties, not against statewide averages, since home buyers and owners are primarily interested in tax differentials within the areas that they might consider choosing.

The cost of making AIM more effective would be $510 million.  But the important point is that an effective revenue sharing reform would not add to overall state and local spending. Instead, by reducing local tax bills and moving costs to the state, it would even out tax burdens paid by residents for local government services.  $510 million is a significant amount of money for state government to raise.  But the cost should be viewed in context.  Last year, state school aid increased by $995 million.  And, a reform to AIM need not be implemented all at once.  Instead, it could be introduced gradually.

The State’s AIM revenue sharing does not address another large inequality that results from local government reliance on property taxes to pay the cost of local services.   Because property taxes tax the value of homes and other real property in a community, they do not reflect ability to pay, which, for households, depends on income.  Because there are large income variations within every municipality in the state, some homeowners face a substantial burden in paying property tax bills.

The state does offer one program, the STAR tax credit, that provides property tax relief to homeowners.  The program has one income sensitive element, available only to seniors, that provides additional assistance to homeowners with incomes below $86,300. Additionally, homeowners who itemize deductions receive larger deductions if they have larger property tax bills.  But the state could consider additional mechanisms to aid low income householders with high property tax burdens by extending property tax deductions to non-itemizers or by structuring the STAR tax credit to be more progressive.

Times Union Op-Ed: State’s Unfair City Aid Formula Needs Revising

I’ve been looking at how well the State’s local revenue sharing works.  It doesn’t work well.  I wrote an Op-Ed that appears in the Albany Times-Union this weekend:  It may be found here:  https://www.timesunion.com/opinion/article/State-s-unfair-city-aid-formula-needs-revising-13709884.php

I’ll have a longer piece that takes a closer look at this on my blog next week.

How the State Senate Gerrymander Ultimately Hurt Upstate Residents

The November election brought a marked change in the composition of the New York State Senate that will have significant implications for upstate New York residents.  Tom Precious in the Buffalo News noted, “Majority party rules and minority party lawmakers are left with table scraps when it comes to funding and policy matters.  As a result, millions of upstate residents could see themselves lose the sole remaining seat at the table in closed door Capitol talks…”[1]

Kent Gardner wrote in the Rochester Beacon, “A New York State Legislature wholly controlled by downstate interests won’t stop sending education aid Upstate or close the state parks…. Complete control of the NYS Legislature by downstate reps will change things over time. Party aside, upstate has a lot at stake in this election.”

This post will seek to answer two questions:  Why did Republicans lose control of the State Senate, and why does upstate have so little majority party representation?

Republicans had controlled the State Senate from 1966, with only a brief break in 2012, when Democrats gained a majority and then splintered into two factions – one of which joined the Republicans to form a majority.  But in 2018, voters decisively gave control to Democrats, who will hold 40 of 63 seats.  The loss of Republican control will significantly reduce upstate’s representation in the party controlling the Senate, since only three seats outside the New York metropolitan area are held by Democrats.  In the most recent legislative session, 18 upstate Senators were in the Republican/Reform Democrat Senate Majority.

For many years, the Republican Senate majority was composed of a coalition of upstate, Long Island and Hudson Valley members.  In 2000, Republicans maintained control in the State Senate by winning 35 of 61 districts in all parts of the State – 17 in upstate New York, nine on Long Island, five in New York City, and four in the Hudson Valley portion of the metropolitan area.

In 2018, Senate Republicans won only three seats on Long Island, one in New York City and one in the Hudson Valley portion of the metropolitan area.  Only in upstate New York did the party increase its strength – to 18 seats.

The regions that moved towards the Democratic party – Long Island, New York City and the lower Hudson Valley — had substantial Democratic party representation by 2018.  Two-thirds of Senators from Long Island will be Democrats, all but one Senator in New York City and all but one in the Hudson Valley portion of the metropolitan area will be Democrats.

In upstate New York, only three of 21 State Senators will be Democrats.  Ironically, had the Senate’s legislative district apportionments more accurately represented voter party preferences, about half of upstate’s Senators would be Democrats.

Upstate’s majority party representation in the State Assembly will now be stronger than in the Senate.  Twenty-three of 48 upstate seats in the Assembly will be held by Democrats.[2]

Differences in Regional Party Affiliations

In New York City and in the lower Hudson Valley, Democrats and allied parties hold commanding registration advantages in 2018. Of those affiliated with a political party, 87% were Democrats in New York City and 62% in the lower Hudson Valley.  Long Island and Upstate New York are competitive – Democrats and allied parties are 52.5% of Long Island voters who were affiliated with a political party in 2018, and in upstate New York, 52.7% were affiliated with Democrats or allied parties.

Upstate party registrations have shifted toward the Democratic party – from 47.3% to 52.7% between 2000 and 2018.  In 2000, in most upstate metropolitan areas Republicans and Conservatives were a majority of voters affiliated with a political party.[3]  By 2018, Republicans were in the majority in Binghamton and Utica, but Democrats (with Green Party, Working Family Party and the Women’s Equality Party) had majorities of party affiliated voters in the Albany-Schenectady-Troy, Buffalo-Niagara Falls, Rochester, and Syracuse metropolitan areas and in Tompkins County.

Within upstate New York, there are significant differences in the political affiliations of voters in metropolitan areas and outside them.  About one-third of upstate voters live outside the six metropolitan areas.  Almost 60% of these non-metropolitan residents who are affiliated with a party are Republicans.  But, residents of upstate’s larger metropolitan areas – Buffalo-Niagara Falls, Rochester, Albany-Schenectady-Troy and Syracuse lean Democratic.  Even the area’s smaller metropolitan areas – Utica-Rome and Binghamton show close to an even division between parties.

The significance of the split between registered Republicans and Democrats is somewhat weakened by the large number of upstate voters who are unaffiliated with a political party. 890,000 of the 3.84 million registered voters in upstate New York fall into this category.

Also note that the Independence Party was not included in this analysis because its candidate affiliations have been inconsistent.  Initially, In New York State, the party supported Tom Golisano, a wealthy Rochester businessman who ran on conservative a platform in 1994, 1998 and 2002.  More recently, the party has supported Democratic Gubernatorial candidates Eliot Spitzer and Andrew Cuomo.  The Independence Party is by far the largest splinter party in upstate New York, with 213,000 members.

Republican and Democrat Voting Strength in Upstate New York

This map, above (from Wikipedia), of the 2016 Presidential Election result suggests that Republican voters (in red) dominate upstate New York.  Other than metropolitan counties like Albany, Onondaga, Monroe and Erie, and a few counties in the Hudson Valley, most of upstate New York voted for Donald Trump, the Republican nominee.

This map looks like Republicans dominate upstate, but most of the red colored Republican Counties are sparsely populated, while the blue Democratic areas are population centers. And, the map is based on a single race. Voter preferences are more complex than can be captured in the results of a single face-off.

Contests for Congress and for Governor show that voting in elections in upstate New York is closely divided between Republicans and Democrats.  Republicans had more votes than Democrats upstate in the 2016 election for President and the 2018 election for Governor, but more votes were cast for Democrats in the 2012 Presidential election and the 2018 Senate and House of Representatives elections.

Why Upstate State Senate Representation Does Not Reflect Voter Preferences

Upstate representation in the State Senate does not reflect underlying party preferences as measured by party registration or voting behavior.  Democrats only hold three of twenty-one upstate State Senate seats, even though races at the State and Congressional levels have been highly competitive, with Democrats winning 5 of 9 upstate congressional districts in 2018, and 23 of 48 Assembly seats as well as gaining more votes in one of two presidential elections and in the most recent U. S. Senate election.  Why is the Senate unrepresentative of upstate party preferences and election performance?

When I began researching this question, I assumed that the primary cause of the difference in electoral results in upstate State Senate districts and other elections had to be legislative district gerrymandering.  After all, what else could explain the fact that Republicans control 18 of 21 districts, even though party preferences are about evenly split between the parties?  Though there is clear evidence that the controlling parties in the Legislature structured districts to favor members of their own parties, another factor appears to be important.  Republicans continue to succeed in electing candidates in upstate Senate districts where there are more Democrats than Republicans.


The State Legislature has historically controlled the redistricting process, creating gerrymandered districts that favored the parties in control.  In 2010, Republicans controlled the State Senate, and Democrats controlled the Assembly.  The result was a series of gerrymandered districts.

For example, State Senate District 50 in the Syracuse Metropolitan Area was carefully constructed to include suburban Republican votes, while excluding Democratic votes in the city of Syracuse.  The seat was held for many years by Senator John DeFrancisco until his retirement this year.  In the 2018 election, another Republican, Bob Antonacci, was the victor.

Assembly District 101, which looks like a worm that connects the town of Montgomery, just west of Newburgh with New Hartford – a suburb of Utica – is another example. The district, which carefully avoids nearby cities (with higher Democratic enrollment), is about 150 miles long and includes parts of Oneida, Herkimer, Otsego, Delaware, Ulster and Orange Counties! It is held by Republican Brian Miller.

Gerrymandering in the legislative apportionment process attempts to make one party waste as many votes as possible by concentrating voters in that party in overwhelmingly one-party districts, while spreading votes in the party benefiting from the gerrymander across districts in smaller, but safe majorities.

Since the mere requirement of population equality does not necessarily result in the creation of legislative districts that come close to reflecting the political preferences of residents, political parties within legislatures have taken advantage of the opportunity to maintain control, often creating legislative majorities that are much stronger than voter preferences would indicate, or in some cases, maintaining control despite the fact that only a minority of voters prefer them. Misalignments between voter preferences and public policy result from partisan gerrymanders.

Additionally, upstate Senate districts on average have fewer residents than downstate districts in order to strengthen upstate’s presence.  The difference in district sizes increased the region’s representation from 20 to 21 seats.  Under the law, legislative district populations may vary by 10%.

In upstate New York, State Senate districts were engineered to break up metropolitan areas into a series of districts that also contained large numbers of non-metropolitan residents, predominantly Republican voters.  The result has been to weaken the ability of democratic leaning metropolitan area voters to elect representatives who shared their political preferences.

For example, the Albany-Schenectady-Troy metropolitan area has 887,000 residents.  With 63 Senators, each Senate District should have approximately 315,000 residents.  The area has enough residents for almost three State Senate districts.  But the most recent Senate Reapportionment in 2016 split the metropolitan area into pieces of four State Senate districts that encompass large rural areas outside the metropolitan area.  In the process, three of the area’s primary cities – Troy, Schenectady and Saratoga Springs were each split into two Senate districts, disregarding their boundaries.

Similarly, the Rochester MSA, with a population of 1.1 million has enough residents to populate three and one third Senate districts but has been split into five Senate Districts including substantial non-metropolitan populations.

An analysis by Jeremy Creelan and Allison Douglas of the Rockefeller Institute, “New Tools to Challenge Partisan Redistricting in New York State”[4]  measured the extent of partisan gerrymandering in New York and examined the reviews of other authors and came to the conclusion that the extent of partisan gerrymandering in New York favoring Republicans was substantial, quoting an analysis by Simon Jackman of Stanford University that concluded that New York’s districts were “the most Republican favoring out of any state…”[5]

Democratic Underperformance

Although State Senate districts were structured to disadvantage Democratic party candidates by mixing Democratic majorities in metropolitan areas with rural, Republican voters, Democrats have not elected as many State Senators as would be expected from upstate Senate districts’ partisan makeups.  Eight of 21 upstate Senate districts have more Democratic voters than Republicans, but only three districts are represented by Democratic State Senators.  Several factors may account for Democrats’ underperformance.

  • The natural advantage conferred by incumbency is reinforced by gerrymandering. In 2018, 18 of 21 Senate contests involved Republican incumbents. The Democrats didn’t even field a candidate in 7 of these races. The three winning Democrats were incumbents. (A similar dynamic plays out in the Assembly. Eighteen of 48 Assembly contests were uncontested.)
  • There is a large fall-off in voting between statewide candidates, and those for the State Senate – 15% of those who voted in the election for U. S. Senate did not vote for a State Senate candidate. This may reflect Democratic voters’ decision to abstain from voting when the race is uncontested or manifestly uncompetitive.


Viewed purely with “sectional” eyes, upstate New York’s influence in Albany may have been greater because Republican gerrymandering granted upstate control of one of the Legislature’s two houses.  But that power meant that the views of about half of upstate’s residents – Democratic voters who make up the majority of residents of metropolitan areas – were underrepresented in the State Legislature.  The upstate Republican leadership of the State Senate primarily represented the interests of conservative rural and suburban residents.  Upstate city residents and people whose policy views corresponded with Democratic party positions received little Senate attention.

With Democratic party control of the State Senate, upstate now has less majority party representation than it would if Senate districts had not been gerrymandered in favor of Republicans, weakening the region’s voice in the State Capitol.

To be sure, fairer legislative districts would not ensure particular outcomes in State Senate races, because voters do not vote for parties in legislative elections any more than they do in elections for Governor or the U. S. Senate.  The large difference in the percentage of upstate voters who favored Andrew Cuomo (46%) against Republican Marcus Molinaro compared with the support given Kristin Gillibrand (57%) against Republican Chele Farley illustrates this point.  In fact, in much of upstate New York, party affiliations are relatively evenly divided, with electoral results in Statewide elections favoring each party in different races.

Reapportionment after the 2020 Census

Reapportionment after the 2020 Census is likely to create legislative districts that more accurately represent the partisan preferences of New York residents than past apportionments.  In 2012, New York voters passed a Constitutional amendment that reforms the apportionment process and institutes new Constitutional requirements for district representativeness.  The process is a mixed bag, containing features that could work against accurate voter representation, and others that could work for it.

The amendment sets up a ten-member redistricting commission with four members appointed by each of the major parties’ legislative leaders plus two additional members selected by the eight.  The amendment also requires that any plan developed receive the support from members nominated by both political parties.  The State Legislature must vote upon the plan submitted by the Commission without amendment. If two successive plans from the Commission fail to receive Legislative and gubernatorial approval, the Legislature is then empowered to present its own plan. As legislative approval is required, there will be an unnamed “third party” involved in the redistricting effort, the “Party of the Incumbency.” Logrolling and trading to preserve the prerogatives of incumbents and secure the required approvals may work against representation of voter preferences within legislative districts.[6]

The Constitutional amendment includes a prohibition against partisan gerrymandering, “[d]istricts shall not be drawn to discourage competition or for the purpose of favoring or disfavoring incumbents or other particular candidates or political parties.  This important provision is intended to prevent the redistricting commission from manipulating district boundaries to advantage particular parties and candidates and  creates a constitutional basis for a court challenge to a legislative districting plan that is gerrymandered.

Because of the statewide shift in voter preferences to the Democratic party, Republicans are unlikely to retake control of the State Senate, at least in the near future.  Upstate New York is unlikely to have as much influence as it had in the past in the State legislature.  But if the  passage of the 2012 constitutional amendment achieves the goal of representational equity,   the Senators who represent it will be more likely to represent the policy preferences of upstate residents.

(Note:  An abridged version of this post appears in the Rochester Beacon, titled, “The Cost of Gerrymandering”)

[1]Potential State Senate Power Shift Offers Big Implications for Upstate,” Tom Precious, Buffalo News, October 7, 2018.

[2] Upstate districts outside the New York metropolitan area are districts 101-103 and 106-150.

[3] The Independence Party is excluded from the analysis.

[4] Jeremy Creelan and Allison Douglas, New Tools to Challenge Partisan Redistricting in New York State?”, Rockefeller Institute of Government, August 31, 2017, https://rockinst.org/issue-area/new-tools-challenge-partisan-redistricting-new-york-state/

[5] Simon Jackman, “Assessing the Current Wisconsin State Legislative Districting Plan,”  July 7, 2015, http://www.campaignlegalcenter.org/sites/default/files/Jackman-WHITFORD%20V.%20NICHOL-Report_0.pdf

[6] Creelan and Douglas, op. cit.

Misconceptions About People in Poverty: Are Work Requirements Effective?

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


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

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

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

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

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

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

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

Poverty in Upstate Metropolitan Areas

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

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

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

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

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


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

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

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

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

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

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


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

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

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

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

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

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

Work Requirements for Safety Net Programs

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Misconceptions About People in Poverty: The Push for Work Requirements

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

How Amazon Could Help Upstate New York

One of the Thanksgiving meal discussions at my house involved Amazon’s new HQ2 and why Governor Cuomo didn’t get the company to locate its new facility in upstate New York.  And, though my argument that upstate metropolitan areas lacked technology focused labor pools of sufficient size to be seriously considered by a technology based firm creating 25,000 jobs was received with skepticism, that is one of the real difficulties that upstate metropolitan areas faced in the competition.

But, my former colleague and friend from Empire State Development, Dave Catalfamo points out that with some effort from the Cuomo Administration, some of the benefits offered by Amazon might be more widely shared.  So, I’d encourage you to read the “Put Amazon to Work” on his blog, Exiled in Albany, for some ideas.

The Finger Lakes – Economic Challenges and Strategic Response: An Assessment

This research is a case study of employment challenges facing the Rochester Metropolitan area and the Finger Lakes region, and an analysis of the region’s strategic economic development plans and reports, developed in response to Governor Cuomo’s challenge to regions seeking economic development funding.  While the report finds reasons why the region’s recent weak economic growth may improve, there are real concerns about the focus of its strategic economic development plans, and the absence of meaningful outcome/impact measurement and analysis.

Employment History

The Rochester area, among all the metropolitan areas in New York State, was historically most dependent on a few large manufacturing employers.  Rochester companies like Kodak and Xerox were industry leaders that pioneered dominant imaging technologies in the 20th Century.  The 2011 Finger Lakes Strategic Plan[1] reported that in 1983, Eastman Kodak had 60,400 employees in the region, and that “Kodak, Xerox, Bausch + Lomb, and General Motors employed nearly one fifth of the local workforce and indirectly had an impact on half of the region’s economy.”  Today, according to Greater Rochester Enterprise,[2] Kodak employs 1,750, and Bausch and Lomb 985, while Xerox, according to the Rochester Democrat and Chronicle, employs 3,400.[3]  General Motors employment in Rochester is 880.[4]  Together, these companies now account for about 1% of regional employment.

Over the 47 years between 1970 and 2017, manufacturing employment in the Rochester MSA declined by 63% – from 152,511 to 56,546.  The number of manufacturing jobs decreased in every decade, except the 1970-1980 decade. In 2017, 11% of Rochester MSA employment was in manufacturing industries, compared with 10% nationally.

The presence of large manufacturers in the 20th Century was good for employee incomes as well.[5]  In 1970, average worker earnings were 12% higher than the national average.  Because of the shift of employment from manufacturing to lower paying service sector occupations, Rochester area average worker earnings in 2016 were 8% lower than the nation.[6]

Like other rust belt areas, and neighboring Buffalo-Niagara Falls and Syracuse, Rochester’s employment growth since 2000 has been nearly non-existent. Rochester area employment grew by less than one percent by 2017, while Buffalo grew 1.4%, and Syracuse lost 2%.[7] Employment growth for the nation was 12% during the period.

Manufacturing employment declined substantially between 2000 and 2017 in the United States, upstate New York and the Rust Belt. In upstate New York,  Rochester and Syracuse were most affected, each losing more than 40% during the period.  Buffalo lost more than 35% of its manufacturing jobs.  These locations and most rust belt metros lost greater percentages of manufacturing jobs than the United States, which lost 28%.

Automobile manufacturing industries experienced a sharp employment decline with the bankruptcies of GM and Chrysler in 2009.  But, the federal bailout led to a substantial increase in employment after that.  As a result, automobile manufacturing employment grew by 25% between 2010 and 2016, compared with 5% for other manufacturing industries.  Metropolitan areas like Detroit, and others in Michigan and Ohio that have high concentrations of manufacturing had stronger manufacturing employment recoveries than those in New York.

Since the recession of 2008-10, manufacturing employment in Rochester, Buffalo and Syracuse has seen smaller declines or has stabilized.  Buffalo’s manufacturing employment has been stable for the past four years, while Syracuse has seen a rebound after losing New Venture Gear/Magna in 2012, and Rochester’s decline has slowed.  In Rochester, continued job losses in chemical manufacturing (primarily Kodak) accounted for 4,402 jobs lost between 2010 and 2017.[8]

While manufacturing employment declined significantly after 2000, employment in other private sector industries continued to grow in Rochester, Buffalo, Niagara-Falls, other rust belt metropolitan areas and the nation.  Nationally, private sector non-manufacturing employment grew by 19%, the Rochester MSA grew by 13%, Buffalo-Niagara Falls by 9.7% and Syracuse by 5.8%.

Overall, the Rochester MSA’s performance since 2000 is near the middle of the group of rust belt metropolitan areas, even though only Flint and Ann Arbor, Michigan and Youngstown, Ohio lost greater percentages of manufacturing employment.  Rochester’s weak manufacturing performance was offset by relatively strong employment growth in non-manufacturing industries, compared to other rust belt metropolitan areas.

Industry Performance from 2010-2017

Industry Sectors

Most service industries in the Rochester MSA saw growth between 2010 and 2017.  Employment in health care and social assistance increased by more than 8,000, professional and technical services grew by 3,861 and educational services increased by 3,250.  Nine industry sectors had employment growth of more than 10%.  Industry sectors  with significant employment decreases were management of companies and enterprises, losing 1,148, Information, losing 1,318 and manufacturing, losing 4,165.

Manufacturing losses were concentrated in two industries – chemical manufacturing, largely Kodak, which lost 4,402 jobs between 2010 and 2017, and machinery manufacturing, which lost 1,720.  The losses in chemical manufacturing were larger than the overall employment losses in manufacturing.

The performance of industries within the manufacturing sector varied.  The region saw significant employment growth in computer and electronic product manufacturing (1,268), and two food related industries – food manufacturing and beverage and tobacco product manufacturing (these industries grew by 1,661 employees).  But, other manufacturing industries each lost more than 500 employees:  machinery manufacturing, chemical manufacturing, miscellaneous manufacturing, transportation equipment manufacturing, and electrical equipment and appliances.

Industries with Largest Employment Gains

The industry showing the largest employment growth (5,453) in the Rochester area between 2010 and 2017 was professional and technical services.  Employment within the industry sector was primarily in four sub-sectors:  engineering services, information technology services, management, scientific, and technical consulting services and scientific research and development services.

Education and health related industries, so-called “eds and meds” also had relatively large employment growth. Hospital employment grew by 4,445 (18%) while educational services employment grew by 3,250 (13%).   Nursing and residential care facilities and ambulatory health care services each grew by more than 1,500 employees.  It should be noted that a large portion of the growth in employment in the health care industries is from health care use by area residents, not from service users from outside the region.

Fast Growing Companies

The Rochester metropolitan area had 22 companies on the Inc. 5000 list of fastest growing privately held companies for 2017.  The number of companies is 34% higher than would be expected from the area’s population.  Twenty of the Inc. 5000 growing companies in the area were service businesses, two were manufacturers.  Information technology services, particularly software services, was about one-third of the total, suggesting that the area has potential strength in this industry.

Finger Lakes Regional Economic Development Strategy

In 2011, Governor Andrew Cuomo restructured the delivery of economic development assistance to create a formal role for regional economic development councils in the decision-making process.  The regional economic development awards temporarily replaced the process of state legislative grant-making though so-called “member items.”[9]  That process required local organizations to request funding from individual state legislators.  Funding was allocated by party leaders within the legislature.  The new, executive branch-controlled process involves regional council members in the decision-making process, though the project scoring system allocates 80% of points to the state agencies administering projects, and only 20% to the regional councils.

As a part of the restructuring, in 2011 regions developed strategic plans.  The 2011 guidance document, “A New State Strategy for Economic Growth” required public involvement, an assessment of current conditions, issues and opportunities, creation of a vision statement, development of strategies and implementation agendas and monitoring and evaluation of effectiveness.[10]

In 2015, Governor Cuomo created a regional competition for $1.5 billion of state funding, with $500,000,000 awarded to each of three regions.  The purpose was to “create and maintain high-paying permanent private sector jobs and to lure private sector investments in amounts that are significant to the region.”[11]  The initiative required regional applicants to develop transformative regional strategies to create significant employment and income growth.  The Finger Lakes Regional Economic Development Council, along with Central New York and the Southern Tier received funding through the 2015 round.

The Finger Lakes 2017 Progress report describes the region’s strategies,

    • Three industry clusters, or pillars, that will act as the core drivers of job and output growth: (1) Optics, Photonics, and Imaging (OPI); (2) Agriculture and Food Production; and (3) Next Generation Manufacturing and Technology.
    • Three core enablers that will facilitate economic growth within the key pillar industries: (1) Pathways to Prosperity: Workforce Development; (2) Entrepreneurship and Development; and (3) Higher Education, Research, & Healthcare.
    • And four foundational strategies that will support the pillar and enabler strategies enhance the region’s quality of life and help the region attract and retain jobs: (1) Global NY; (2) Tourism & Arts; (3) Infrastructure & Transportation; and (4) Sustainability.[12]

Although some funding through the regional strategies involves traditional economic development assistance to business such as capital projects and worker training, most of the region’s effort is focused on developing broad based initiatives in support of economic growth – ranging from support for industry related research and manufacturing process improvements to entrepreneurial assistance, institutional capacity for workforce development and civic improvements.

Only 17% of regional “priority project” grants – projects designated by the Finger Lakes Regional Economic Development Council as important elements of its strategic plan went to businesses.  Most grants were given to non-profit organizations (60%), followed by universities and colleges (19%).


At the outset it must be acknowledged that the Rochester metropolitan area’s economic performance since 2011 continues to be weak.  In fact, the Rochester MSA, along with Buffalo and Syracuse, had less employment growth between 2011 and 2018 than rust belt metropolitan areas in Ohio and Michigan, except for Youngstown.[13]  Also of concern is the difference in average wages between growing and declining industries.   The average wage in growing industries in the Rochester MSA was $45,752 in 2017, while in industries with declining employment it was $53,786.[14]

The regional strategies are works in progress.  Many of the projects initiated through the regional strategy have not yet been completed.  But, if the Governor’s approach is to have significant employment benefits, they are yet to be realized.

In this section, I examine strengths and weaknesses in the Finger Lakes Regional economic development strategy and in the overall strategic planning and implementation process as implemented by New York State.

Industry Targeting

The 2017 Finger Lakes Progress Report states that “Three industry clusters, or pillars, that will act as the core drivers of job and output growth: (1) Optics, Photonics, and Imaging (OPI); (2) Agriculture and Food Production; and (3) Next Generation Manufacturing and Technology.”

Much of the region’s funding reflected its stated priorities.  Photonics/Optics and Imaging received the largest amount of funding, $148,155,000 through 2016.[15]  $147,000,000 was granted to the AIM photonics facility.  Advanced manufacturing projects received the second largest amount – $69,457,000.  Higher Education, Research and Health Care received $39,498,000

The Finger Lakes regional strategies between 2011 and 2016 supported several of the region’s fast-growing industries, particularly education and research.  But, the emphasis overall in the strategic plans was on support of manufacturing related initiatives.  59% of regional funding went to advanced manufacturing and optics, photonics and imaging.  Manufacturing is important to the Finger Lakes economy – average wages are high, and the region benefits from the income that results from the sale of manufactured products outside it.  Consequently, regional efforts to help local manufacturers improve their competitive position should be key elements of the region’s employment retention strategy.

But, to identify next generation manufacturing and optics, photonics and imaging as two of the region’s three pillars driving job and output growth is unrealistic given the weakness of the area’s (and the nation’s) manufacturing performance in the past forty years.  Though it is true that the Finger Lakes region is far less dependent than it was on a few large manufacturers like Kodak and Xerox, without the employment losses at those businesses, the region’s manufacturing employment growth during the recovery since 2010, like that of the nation, continues to be slow, and has lagged service sector growth.  Manufacturing, including photonics, optics and imaging may grow, but because of its size and long-term growth, the fate of the region’s service sector is most important to future job creation.

In one significant way, the practice of grant making differs from the conception of the 2011 and 2015 strategic plans.   Higher education and health care received about 11% of all funding – nearly $40 million.  The plans view higher education and research as enablers – ways to develop the workforce to encourage employment growth in photonics/optics/imaging, advanced manufacturing and technology and agriculture/food processing.  But, in fact, higher education and research are among the region’s largest industries and are also among those that have shown the most employment growth since 2010.  Future planning efforts could benefit if they viewed growing traded service industries, including higher education and research and professional and technical services as employment drivers.

Optics, Photonics and Imaging

Optics, photonics and imaging is by far the largest manufacturing initiative in the Finger Lakes strategy.  Greater Rochester Enterprise lists optics, photonics and imaging companies in the greater Rochester region.  Companies on the list in related industries employed about 11,000 workers in the region in 2018.[16]  Because the industry grouping contains Kodak and Xerox, employment has declined substantially over the past 40 years.  To support future industry growth, the Finger Lakes region has made substantial efforts to support photonics initiatives at universities and other research and development institutions.

The most significant funding initiative through the regional strategies has been the American Institute for Manufacturing Integrated Photonics partnership.  The AIM Photonics initiative is a $600 million public-private partnership that will receive $250 million of state support and $110 million from the Federal Government.  The initiative is the region’s largest single bet for future employment growth.

The largest portion of AIM Photonics Initiative funding in the region – $147 million – went to create a photonics test, assembly and packaging facility in Rochester for industry partners.  The objective of the effort is “invest in nurturing start-ups, enable small businesses and attract large enterprises” in order to “create thousands of jobs.”[19]  The Finger Lakes regional strategy includes some significant efforts to support entrepreneurs and small businesses in the field.

To attract major industry companies to build large manufacturing plants, the region must develop industry intelligence, relationships with key industry players and build a long-term marketing campaign.  In addition, appropriate sites for manufacturing facilities should be evaluated, permitted and basic site infrastructure development should be undertaken.  Finally, attracting such a facility would require a large grant from New York State.  Even so, because there is intense competition for major manufacturing plants, because electronics manufacturing has largely moved off-shore, and because the region does not have growing major regionally based stakeholders in photonics, success could be elusive.

A Strategic Plan, or a Strategic Planning and Implementation Process?

Strategic plans are of no real value if they do not guide actions, effectively measure outcomes against goals, and use knowledge gained in the implementation process to guide future action.  Though the 2011 guidance for regional strategic plans required “monitoring and evaluation of effectiveness,”[20] regional plans have focused primarily on describing the funding status of projects, rather than outcomes.

Finger Lakes 2017 Annual Report – Progress

The 2017 Finger Lakes Progress Report section titled “Progress” begins, on pages 5-9 with a series of charts showing economic conditions in the region.  Though the section is labelled “progress,” the charts do not examine the impacts of actions taken by the regional councils and the state in funding projects.  Instead, we cannot know whether the conditions described are a result of regional initiatives or would have occurred in their absence.

The second section of the “Progress” section is a listing of funded projects.  In this section, projects are identified with the amount of state funding received, total project cost, and project status (i. e., progressing on schedule, complete, etc.)

The final section provides aggregated data about project funding and status.  In this section, there is a table on page 17 labelled “job creation to date”.  But the data is provided without information about its source.  Does the table show the estimated number of jobs created and retained based on contractual commitments to the state or are they actual jobs as measured by Empire State Development in its post-funding employment compliance reports?[21] Equally important, the measures of actual or projected job creation and retention are not tied to specific goals, project categories, or projects.  As a result, the chart is of no value in assessing progress against program or project goals.

Finger Lakes 2017 Annual Report –“Implementation Agenda”

The following section of the 2017 Annual Report labelled “Implementation Agenda” looks at specific regional strategies.  The sections detail activities undertaken by regional entities in support of major initiatives.  For example, the first portion of the section, “Workforce Development” explains how workforce development is delivered in the Finger Lakes region, describing the steps being taken to address the need to help workers acquire marketable job skills.  The quantitative data provided shows intermediate program activity but does offers little information about how many workers were able to get jobs because of the assistance provided.  Here is an example:

“In the past program year, the three workforce development boards

and their community partners have achieved a record of success in several areas. Collectively they:

      • Hosted 125 recruitments and job fairs attended by 6,000 job seekers.
      • Funded skills training for 800 workers and job seekers.
      • Served 20,000 customers at their career centers throughout the region.
      • Helped 14,700 job seekers find employment (latest data from the 2016 program year).
      • Served more than 1,000 youth with employment and job readiness services.
      • Hosted multiple special events and programs such as Finger Lakes Works with their Hands, Health Care Career Day,
      • Rochester Work’s Criminal Justice Partnership, Agricultural Career Day, and del Lago Casino & Spa recruitment days.”[22]

 The section then provides a number of vague assertions about program effectiveness.

“Employers are moving the Finger Lakes forward.

  • Through initiatives such as the Young Adults Manufacturing & Training Employment Program (YAMTEP).

Economic developers are moving the Finger Lakes forward.

  • In partnership with Monroe Community College, Monroe County is leading the charge to train the local workforce with the new LadderzUP program. The program recruits, trains, and places workers into the most in demand careers in the region.

Secondary educators are moving the Finger Lakes forward.

  • The Hillside Work – Scholarship Connection has been a consistent beacon of hope for at risk youth in the region for thirty years.
  • Students that fully participate in the program maintain employment and graduate at a rate of over 90%.

Post-secondary educators are moving the Finger Lakes forward.

  • Rochester Institute of Technology (RIT) Electronic Assembly Training program trained 10 veterans and 10 dislocated workers in 20 days to enter the electronic assembly industry.

The community is moving the Finger Lakes forward.

  • Catholic Family Center, Action for a Better Community, and Community Place of Greater Rochester have partnered to offer two adult mentoring programs to the community.”

The data provides no useful information to evaluate the effectiveness of the region’s workforce development efforts because it does not measure outcomes/impacts of specific programs or kinds of assistance, (i. e, how many training participants were able to secure employment in the field for which they received training).  Given this, it isn’t possible to know how much impact the initiatives have had on worker skill deficits in the region or do any measurement of costs and benefits.

Another portion of the Implementation Agenda section, “Higher Education, Research, and Healthcare Work Team presents a series of charts described as performance indicators.  Several of the charts[23] appear to show that the region is not meeting goals but offer no discussion of the data or how the region intends to address the failures. In  other cases charts present data that is not relevant to the goal in the chart title.  For example, The first chart presents “Goal 1 – increase enrollment” in regional colleges and universities:

The data shows that enrollments have declined by about 5,000 over the 2011-2015 period.  Clearly, this goal is not being achieved.  But, the text provides no discussion of this issue, or how it might be addressed. Instead, the reader is provided with six pages of project descriptions, like the one below. None of the items provide specific information about which priority goal is being addressed, what specific strategic goal related benefit is to be derived, or a status description showing progress to a specific goal.  For example:

The Chart, “Goal 2: Increase R&D” similarly shows that R&D has been declining in the Finger Lakes Region since 2011.  Again, no discussion of the data is presented, nor is a specific strategy to remedy the shortfall identified.



The chart labelled, “GOAL 4: Increase STEM Graduates” is supposed to show area progress in increasing the number of STEM (Science, Technology, Engineering and Mathematics Graduates), But, the chart doesn’t measure change in the area’s ranking, or in the number of STEM graduates over time.  Even the static data that is presented doesn’t show which year the data is taken from.

Evidence Based Policy Implementation

The Governor’s regional planning and grant program could potentially provide a rich source of data for regional and state decision-makers to measure project impacts, evaluate what works and what doesn’t, and to use the information to guide future actions.  The Evidence Based Policy Making Collective[24] provides useful guidance as to how an effective implementation and evaluation could work.  The Collective argues for this process:

Monitor program delivery

      • To clearly define the key components of a program model and track the inputs, activities, outputs, and outcomes of program service delivery through process evaluation and performance management
      • To check whether services are delivered as intended, in terms of both quantity and quality, and whether programs are meeting their goals

Use impact evaluation to measure program effectiveness

      • The systematic collection of information about a program to identify (or estimate) the specific contribution of that program to intended outcomes
      • That specific contribution, in the language of evaluators, is known as a program’s impact5…

A process by which measurable goals are specified, and relevant data is collected and evaluated is essential to understanding the real impacts of programs and to making informed decisions about program continuation or termination.  Unfortunately, that process does not exist in the Governor’s regional planning and funding competitions.

Since State and regional decision-makers are operating in the dark about the specific contribution to the intended strategic outcomes of the projects that received more than $4 billion in funding provided by state taxpayers, to strengthen future decisions, much improved definitions of project goals and appropriate impact measures are absolutely necessary.  If the Governor’s regional partnership initiative is to continue, it is imperative that the state mandate substantial improvements in data reporting, performance analysis and evaluation.


The Finger Lakes, since 2000, has been hard hit by the collapse of employment at several large manufacturers.  Because relatively few jobs remain at these companies, the region has less concentrated risk of losing jobs at one or a few companies.   At the same time, the Finger Lakes region has had faster service sector growth than other upstate metropolitan areas west of Albany-Schenectady-Troy, available data suggests a strong entrepreneurial culture, and strong knowledge-based industries, all of which could strengthen its future employment growth.

The region’s strategic economic development plans and annual reports are concerns, though.  The plan has an unrealistic view of the potential for manufacturing employment, particularly optics, photonics and imaging, to create regional employment growth, while giving short shrift to those service industries that are growing.

Of equal concern is the total absence of any kind of outcome/impact measurement or program evaluation.  This weakness is not unique to the Finger Lakes strategies and reports.  Several regional economic development strategies and reports have the same weakness. For example, see the Central New York Region Report:

Given the $4 billion that has been spent of the regional economic development grant competitions, the Governor and legislature should mandate effective impact measurement and evaluation as a condition of participation in the next round of funding.


[1] “NEWYORK OPEN FOR BUSINESS – Seneca County CCE.” 14 Nov. 2011, http://senecacountycce.org/resources/finger-lakes-redc-strategic-plan. Accessed 20 Jun. 2018.

[2] “Top Optics, Imaging & Photonics Companies – Greater Rochester ….” http://www.rochesterbiz.com/Portals/0/Top%20Optics%2C%20Photonics%2C%20and%20Imaging%20Companies%20in%20the%20Greater%20Rochester%2C%20NY%20Region%2C%202018.pdf. Accessed 20 Jun. 2018.

[3] “Fate of local jobs uncertain after Xerox, Fujifilm announce merger.” 31 Jan. 2018, https://www.democratandchronicle.com/story/money/business/2018/01/31/fuji-xerox-fujifilm-merger-cost-savings-jeff-jacobson/1081916001/. Accessed 20 Jun. 2018.

[4] “GM Corporate Newsroom – United States – Company – GM Media Site.” 4 Jun. 2018, http://media.gm.com/media/us/en/gm/company_info/facilities/component-fac/rochester.html. Accessed 20 Jun. 2018.

[5] United States Department of Commerce, Bureau of Economic Analysis, Regional Economic Accounts, https://www.bea.gov/itable/iTable.cfm?ReqID=70&step=1#reqid=70&step=1&isuri=1

[6] Author’s calculations from BEA data.

[7] Department of Labor, Bureau of Labor Statistics, Current Employment Statistics.  https://www.bls.gov/data/

[8] Source:  U. S. Census Bureau, County Business Patterns.

[9] The practice of making grants directed by the legislature later returned, see:  https://www.empirecenter.org/publications/inside-albanys-secretive-slush-fund/ and https://www.politico.com/states/new-york/albany/story/2015/10/partisan-spoils-persist-in-new-legislative-earmark-program-026317

[10]“A New State Government Approach to Economic Growth” https://regionalcouncils.ny.gov/sites/default/files/2017-12/2011-redc-guidebook.pdf

[11] “Competition Guidelines:  New York Urban Revitalization Initiative, April 2015” https://regionalcouncils.ny.gov/sites/default/files/2017-12/2015-uri-guidebook.pdf

[12] Finger Lakes Progress Report 2017, p. 8.  https://regionalcouncils.ny.gov/sites/default/files/2017-12/2017ProgressReportFingerLakes.pdf

[13] Department of Labor, Bureau of Labor Statistics, Current Employment Statistics.  https://www.bls.gov/data/

[14] Employment change from 2010-2017.

[15] Most recent available data from 2017 Finger Lakes Progress Report, op cit.

[16][16] “Top Optics, Photonics and Imaging Companies in the Greater Rochester, NY Region, 2018”  http://www.rochesterbiz.com/Portals/0/Top%20Optics%2C%20Photonics%2C%20and%20Imaging%20Companies%20in%20the%20Greater%20Rochester%2C%20NY%20Region%2C%202018.pdfyujg8hni6rthuj76i  Number adjusted to reflect more recent Xerox employment from Rochester Democrat and Chronicle, op. cit.

[17] https://en.wikipedia.org/wiki/Photonics

[18] http://www.aimphotonics.com/what-is-integrated-photonics/

[19] “Finger Lakes 2015 Upstate Revitalization Initiative Plan,” https://regionalcouncils.ny.gov/sites/default/files/2017-12/2015-finger-lakes-uri-plan.pdf

[20] “A New State Government Approach to Economic Growth,” op. cit.

[21] See, for example, “2016 Annual Jobs Report on ESD’s Loan and Grant Programs,”  https://esd.ny.gov/sites/default/files/2016-Annual-Jobs-Report-Final.pdf

[22] 2017 Finger Lakes Progress Report, p. 25.

[23] 2017 Finger Lakes Progress Report, p. 31

[24] https://www.evidencecollaborative.org/principles-evidence-based-policymaking