Wayne State, UofM and MSU draw most students from local region

September 15, 2014

There are three universities in the state of Michigan that make up the University Research Corridor, an alliance committed to transforming and diversifying the state’s economy. These three universities are the only public universities in the state to have their governing bodies appointed by the voters of the State of Michigan. These universities are Wayne State University (WSU), the University of Michigan (UofM) and Michigan State University (MSU). This post aims to show where students who attend these universities come from within the state, country and across the nation.

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In looking at all three maps, it becomes obvious that WSU’s population is largely representative of residents from the tri-county area (Wayne, Oakland and Macomb Counties). As WSU is historically a commuter school centered in Detroit, this reflects what one would expect. In fall of 2013, about 7,900 of the students who enrolled at WSU lived within Wayne County. During that same time, there were about 6,000 students from Oakland County and about 4,900 from Macomb County. Although Washtenaw County is still within the Southeastern Michigan region, only 507 students were from there; Washtenaw County residents represented the fourth largest population in the state.

Just as geographic representation decreased the farther away one got from Wayne County within the state, the same continued for states outside of Michigan. Ohio and California were the two states mostly highly represented in fall of 2013 with 107 and 97 students, respectively, coming from each. These two states, individually, had more representation at WSU than some counties in Michigan, such as Jackson and Ionia to name a few.

When looking at the geographic makeup of WSU on a global scale, aside from the United States, Canada had the largest population with 576 students and China had the second largest representation with 332 students. There are 26,020 students, including both graduate and undergraduate students, who attended Wayne State in fall of 2013 who were from the U.S.

Overall, enrollment in fall of 2013 was recorded at 27,897 students. Of that, 25,043 (89%) were from within the state of Michigan, 977 (4%) were from another state and 1,877 (7%) were from another country.

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Similar to Wayne State University, much of the University of Michigan’s student population came from Wayne, Oakland, Washtenaw or Macomb Counties. For UofM, however, the representation of Washtenaw County residents, where UofM is located, was five times higher than those who attend WSU. Conversely, WSU had more than twice the number of Wayne County residents than UofM.

Although both universities largely drew from the same geographic locations in state, UofM had a much greater overall representation of students from across the state. At WSU, there were some counties with no representation, but at UofM, every Michigan county was represented. Keeweenaw and Oscoda Counties had the lowest in-state representation at 1 student.

When looking at the representation from across the country, UofM out-did both WSU, and as you will see below, Michigan State University. In fall of 2013, UofM enrolled 15,704 students from across the country (not including Michigan); this represented 36 percent of the student population. Illinois was the state with the largest representation; 1,918 students from there attended UofM in fall of 2013. Only nine students from the state of North Dakota enrolled in UofM at the state time, making it the state with the least representation.

On an international scale, China was the most represented with 2,334 students enrolled at UofM for fall of 2013. The international population at UofM during this time represented about 14 percent of the student body.

Overall enrollment at UofM during this time was 43,710; 37,651 of those students were from the U.S.

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Unlike UofM and WSU, where the largest geographic representation comes from the universities’ home counties, Michigan State University drew the majority of students from outside of the region it is located in (Ingham County). Like its sister schools, Wayne, Oakland, Macomb and Washtenaw counties were heavily represented. From in-state, Oakland County was the most represented with 8,558 students. There were 4,937 students from Wayne County who attended MSU in fall of 2013, 2,764 from Macomb County and 1,364 from Washtenaw County. There were 3,130 students from Ingham County, where MSU is located, who attended the university; this was more than those sent from Macomb and Washtenaw Counties. Kent County was also highly represented with 2,348 students attending MSU in fall of 2013.

When looking at enrollment from out-of-state residents, Illinois again had the highest representation with 1,308 students. West Virginia had the lowest with one student. Overall, the out-of-state student population at MSU in fall of 2013 represented 11.6 percent of the student body.

In 2013, 4,419 students from China attended MSU, making it the country with the highest representation, aside from the U.S. The international population at MSU during fall of 2013 represented about 15 percent of the student body.

Overall, in fall of 2013 enrollment at MSU was 49,292; the number of full-time students from the U.S. was 41,950.

For this data set, MSU only counted all full-time students.

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In comparison, above is a map that shows where students who attended Temple University in Philadelphia, Pennsylvania in fall of 2013 originally resided. This university was chosen because it is located in a similar environment as WSU and typically has similar enrollment numbers.

Temple University had 38,148 students enrolled in fall of 2013, of whom 22,318 were from Pennsylvania. The state of New York had the highest out-of-state representation with 564 students.

Overall, the Temple student population of only undergraduate students was 26,454 and the overall student undergraduate population was 27,514.

For the purpose of this post, Temple was the only school to only count undergraduates for its student population.

Detroit’s unemployment experiences two month increase

September 7, 2014
  • From June 2014 to July 2014, the unemployment rate across the state and the city of Detroit increased (monthly);
  • The Purchasing Manager’s Index for Southeast Michigan decreased from June 2014 to July 2014 (monthly), but increased over the last month;
  • The Commodity Price Index increased from June 2014 to July 2014 for Southeast Michigan (monthly);Standard and Poor’s Case-Shiller Index declined for June.
  • Data also indicate a decline in the rate of year-to-year increases in the prices of homes in the Detroit Metropolitan Statistical Area;
  • Wayne and Macomb counties experienced increases in the number of monthly building permits pulled.

Slide02According to the most recent data provided by the Michigan Department of Technology, Management and Budget, from June to July the unemployment rate for the state of Michigan increased from 7.9 to 8.6 percent. The city of Detroit experienced a more severe unemployment rate increase — from 16.4 percent in June to 17.7 percent in July. The unemployment rate in Detroit has decreased 1.2 points since July of 2013.

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From June to July the number of people employed in the City of Detroit decreased by about 250, leading to a total of to 284,497 people employed.

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The above chart shows the number of people employed in the auto manufacturing industry in the Detroit Metropolitan Statistical Area (MSA) (Detroit-Warren-Livonia) from July 2013 to July 2014. During the period under consideration, the highest employment levels in the auto manufacturing and auto parts manufacturing industries occurred in June 2014, when there were 99,100 people employed in the Detroit MSA. That number dropped by 7,500 people to a total of 91,600 people employed in July.

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The Purchasing Manger’s Index (PMI) is a composite index derived from five indicators of economic activity: new orders, production, employment, supplier deliveries, and inventories. A PMI above 50 means the economy is expanding. 

According to the most recent data released on Southeast Michigan’s Purchasing Manager’s Index, the PMI for July was 60, a positive increase of 12.9 points from the prior month and 16.9 points from a year ago.

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The Commodity Price Index, which is a weighted average of selected commodity prices, was recorded at 59.1 points in July, which was 2.6 points higher than the previous month.

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The above charts show the Standard and Poor’s Case-Shiller Home Price Index for the Detroit Metropolitan Statistical Area. The index includes the price for homes that have sold but does not include the price of new home construction, condos, or homes that have been remodeled.

According to the index, the average price of single-family dwellings sold in Metro Detroit was $97,260 in June 2014. This was an increase of approximately $8,790 from the average price in June 2013.

The percent changes in price from the year prior decreased from 18.2 percent in June of 2013 to 10.2 percent in June of 2014. This shows that the prices of area dwellings are not increasing as much as they were at this time last year.

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The above charts show the number of residential building permits obtained each month in Wayne, Oakland, and Macomb counties from January 2013 until July 2014. These numbers are reported by local municipalities to the Southeastern Michigan Council of Governments and include single-family units, two-family units, attached condos, and multi-family units.

Oakland County showed a slight increase from June 2014 to July 2014, while the other two showed more substantial increases. Oakland saw 201 permits pulled in June, while in July 205 were pulled. Macomb County experienced the largest increase; in June 153 permits were pulled and in July 274 were pulled.

African Americans most likely to leave Wayne County

September 1, 2014

This post breaks down the top locations of out-migrants and in-migrants by race, age, educational attainment and income from years 2006-2010 and 2007-2011 (depending on the data). As you will see, there are certain groups that are much more likely to leave Wayne County. Slide03 Wayne County has lost a large share of its residents to out-migration. The high out-migration is reinforced by the chart above, which shows the ratios per category for those coming into Wayne County, compared to those leaving. African Americans had the largest disparity; for every one African American who moved into Wayne County, 27 left. The age group of 60-69 had the smallest disparity; for every one person that age who moved in, two left.

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Slide06   Of the top 10 destination counties for out-migrants, seven of them were in Michigan. In total, 13,417 residents left Wayne County to move elsewhere. For White in-migrants to Wayne County, only four of the top sources were other Michigan counties. In total, 897 White in-migrants moved to Wayne County between 2006 and 2010.Slide09 Slide08   Five of the top 10 destinations for Latino out-migrants were other Michigan counties, with Kent County ranking first with a total of 273 people; in total there were 682 Latinos that left Wayne County between 2006 and 2010. Only three of the top 10 sources of in-migrants were in Michigan. The county that provided the most in-migrants (238) was Cook County, Illinois. Cook County is the home of Chicago, which is a major entry point for Latinos into the Midwest. The Cook County numbers were 41.5 percent of the Latino population that entered Wayne County between 2006 and 2010. Slide11 Slide12   A large number of African Americans left Wayne County for areas immediately around it—Oakland, Macomb and Washtenaw Counties. Far fewer African Americans moved into Wayne County. This is consistent with the substantial increases in African Americans throughout the inner suburbs of Detroit, which commentators suggest is driven by the search for better schools and safer communities. Of the top 10 locations African American in-migrants left to move to Wayne County from, only three counties were in Michigan (St. Clair, Alger, and Ottawa counties) from 2006 to 2010. In total there were seven counties in Michigan in which African Americans migrated to Wayne County from. There was a total of 197 African American residents who moved from Dakota County, Minnesota, to Wayne County; this location had the most African American in-migrants come from one place during this period. This location represented 22 percent of the total African American population that moved into Wayne County between 2006 and 2010. Slide14 Slide15
  For the three charts above and the three below, only certain age groups were examined to show the movement of families, young people and the elderly. We wanted to see if the assumption that the elderly would have a higher rate of out-migration, as they typically move to retirement communities, was true. It was not. For all age groups represented above, Oakland and Macomb counties had the highest number of Wayne County residents move within their boundaries. Of the three groups, children were the largest (5,375), followed by young adults (1,309). Only a total of 258 Wayne County residents from the 60-69 year old age group moved to those two counties. Slide18

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Slide19 Slide20   Few children between the ages of 5 and 17 moved to Wayne County from elsewhere in the state. There were 32 in-migrants from Ottawa County and 30 children from Ingham County. Those between the ages of 25 and 30 from within the state of Michigan represented larger in-migration numbers. For example, 326 residents moved from Washtenaw County to Wayne County, 91 from Genesee County, 82 from Ingham County and 53 from Kent County. Slide22 Slide23 Slide24   Wayne County is losing many residents of all education levels to the out counties and beyond. Of those with bachelor’s degrees, from 2007 to 2011, 1,566 went to Oakland County and 256 went to Macomb County; in total 3,417 residents with bachelor’s degrees left Wayne County. Oakland and Macomb counties also received the highest number of Wayne County residents with less than a high school degree and a graduate or professional degree. Slide26 Slide27 Slide28   At each educational level, immigrants represented just a fraction of out-migrants. There were 424 residents with a bachelor’s degree from outside Wayne County that moved in. There was a net total of 272 people with a graduate or a professional degree who moved to Wayne County and 499 people without a high school diploma who moved in. A Michigan county ranked number one for each education level represented here, in terms of residents leaving to move to Wayne County. Slide30 Slide31   Three times as many residents who made $150,000 a year moved out of Wayne County than moved to it. Of those who left between 2007 and 2011, 253 residents went to Oakland County and another 209 went to Wake County, North Carolina. In total there were 1,869 residents who earned $150,000 or more and left Wayne County between 2007 and 2011. When examining the in-migrants, more (228) moved from Washtenaw County than any other county. Clark County, Nevada ranked second on that list with a total of 105 residents leaving there for Wayne County. Overall, 644 people who earned $150,000 a year or more moved to Wayne County.

Birmingham has highest rate of income inequality in Southeast Michigan

August 25, 2014

The GINI Index is a statistical representation of income equality that is measured between 0 and 1. The closer a place is to 1, the higher income inequality is; with 0 signifying that total income equality is 0 (for a more in-depth explanation on the GINI Index and its background please click here). Just because income inequality is high in one area does not mean its income levels are low, or high. Rather, it represents the disparities between any and all income levels in a certain place. In this post we compare the seven counties, along with the municipalities and Census Tracts in the region, to the state and national GINI Index. In 2012, the GINI Index in Michigan was .4554 and the nation’s was .4712.

 

Thus, the GINI Index measures differentials in income levels in some area. Therefore it may also provide useful perspective to examine the income levels in areas across the region. These are explained in an earlier blog post here.

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Wayne County, which is home to the city of Detroit, had a GINI Index of .4789 which was higher than the index for both the state, at .4554, and nation, at .4712. This score, according to the World Bank, is comparable to nations like Kenya, Nigeria and Zimbabwe.

Oakland and Washtenaw Counties’ indices were above that of the state but lower than that of the nation at .4644 and .4689, respectively.

Livingston County had the lowest GINI Index in the region at .3898. On a national scale, this index is similar to those of Israel, Lithuania, Turkey and Thailand.

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When looking at all the communities that make up the region, there were only a few with a GINI Index above (higher income differentials) that of the state and nation. In Wayne County, these communities were Detroit (.4923), Hamtramck (.474) and Highland Park (.4996). In Oakland County, these communities were Bloomfield (.5057), Bloomfield Hills (.5258), Orchard Lake Village (.5112) Birmingham (.5088). The four communities in Washtenaw County with a GINI Index above that of the state and nation were Ann Arbor (.4996), Barton Hills (.5002), Superior (.4905) and Ypsilanti (.4813). The only community in Monroe County with this characteristic was the township of Monroe (.4723).

Oakland and Washtenaw counties both had four communities with a GINI Index above national and state averages. All of the communities in Oakland County with such a characteristic had a GINI Index above .5; Birmingham had the highest index in the region. Such high GINI Index numbers in places like Birmingham and Bloomfield show that disparity is not isolated to just poor communities.

Again, the high GINI Indices of places like Birmingham and Bloomfield show that income inequality is not only prevalent in lower income areas. By clicking here, you can see what income levels are in cities a high or a low GINI Index. For example, in 2009 Birmingham’s median household income was about $96,000, the city of Monroe’s was about $45,000 and the city of Detroit’s was about $34,000.

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When focusing in on Census tracts in the tri-county region, it becomes clear that there were more areas that had a GINI Index above that of the state and nation than the aggregated data indicates. The above map shows that higher rates of income inequality existed in some sections of a community, but not others. For example, the northwest portion of Oakland Township had a higher rate of income inequality than the aggregated data indicates. There were also several neighborhoods in Detroit where income inequality was below that of the state and nation. Areas with substantial income inequality in Detroit were found in the Downtown, Midtown and Riverfront areas.

Not only does income inequality exist in Detroit and the surrounding areas, but also across the country. In an article from The Atlantic, it’s described how the top 10 percent of America’s wealthiest residents earned more than half of the country’s income. This is particularly exemplified in places such as New York, L.A. and San Francisco. In 2012 there were more than 19 metro-areas with a GINI coefficient above .50.

 

SEMCOG: Property taxable values and state equalized values increase throughout region

August 18, 2014

According to information recently released by the Southeast Michigan Council of Governments, majority of the communities in the region have experienced an increase in their property tax values and state equalized values. In total, 207 communities gained SEV. To read more click here

Percent Change SEV

Wayne County loses more residents than gains

August 11, 2014

From 2007 to 2011, Wayne County consistently decreased in population, according to the U.S. Census Bureau’s American Community Survey 5-year estimates for the period. The results indicate a net total of more than 45,000 people left the county, with 53,000 residents leaving and 8,000 new residents moving into Wayne County during that period.

This week, we will explore the overall domestic migration patterns. In a later post, we will examine the data behind Wayne County migration to better understand who is coming into and departing.

First, we will examine the net numbers. From 2007 to 2011, Wayne County had a net loss of residents to the rest of Michigan and to 41 other states.   There was also a net gain of residents from 8 states. Across all states, a total of 813 counties gained residents from or lost residents to Wayne County. The majority (566 counties) gained residents from Wayne County. The map below shows the rate of loss or gain in the counties that had residents relocate to or from Wayne County during that period.

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From 2007 to 2011, a total of 28,252 Wayne County residents chose to relocate within their home state of Michigan. No other state gained more than 2,000 Wayne County residents. The most popular relocation states for Wayne County residents are listed below with their total of out-migrants from Wayne County. Neighboring Ohio gained the second highest number of out-migrants from Wayne County, followed by eight southern states. Except for California, Arizona and Texas, these states are largely those Southern states from which the Great Migration came into Michigan came in the early part of the 20th Century.

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Out-migration also had three distinct patterns, as listed in the chart below. Neighboring counties received the largest numbers of Wayne County out-migrants. Just under half of all departing residents chose Oakland, Macomb or Washtenaw for their destination. Noted retirement centers in the West and South also drew a fair amount of Wayne residents, as did smaller municipalities in other parts of Michigan.

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From 2007 to 2011, there were eight states that were sources of in-migration of residents to Wayne County. These states, listed in the table below, were more rural with predominantly cooler weather conditions. Sparsely-populated Alaska was the largest source of in-migration to Wayne County from 2007-2011, relocating 680 residents to the Detroit area.

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When considering the counties that were the source of the most in-migrants to Wayne County, as shown in the chart below, three patterns emerged. First, other large cities contributed many residents to the area – including Anchorage, the Bronx, Chicago and Minneapolis. Wayne County also had in-migrants from some of the most rural areas in Michigan, including Berrien County, Eaton County and Newaygo County. Military outposts such as Annapolis and Fort Payne are also contributors to the Wayne County population, possibly representing residents returning to the area after service.

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The next map takes a closer look at the regional patterns. Wayne County had a net gain of residents from counties that border the Great Lakes and other larger cities, including Chicago, St. Louis, Buffalo and the New York metro area. It is still a net-loser of residents to most of the region’s small cities and interior rural areas.

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Next week we will continue to look at migration as relates to Wayne County. The upcoming post will drill down on the sex, age and income levels of residents leaving and moving to the region.

 

Gap exists between pre-k and kindergarten

August 4, 2014

A quick glance at the numbers seems to state the obvious: pre-kindergarten (pre-k) numbers are highest in areas with the highest population. However, a closer look shows in certain circumstances, this is not the case. Rather, the larger issue appears to be the gap that exists between the number of children enrolled in pre-k versus the number of children enrolled in kindergarten.

It should also be noted there are several school districts throughout the region that do not offer pre-kindergarten through the public school district. This occurs not only in the region, but throughout the state because Michigan does not mandate pre-k, despite the positive effects shown by participation in the program.

In this post we examine the number of students enrolled in pre-k classes and kindergarten classes across the region to show where gaps exist.

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As noted above, there are several districts in the region that do not offer pre-kindergarten classes. The majority of these districts are located in the more rural areas, such as Monroe and Livingston counties. St. Clair County, which is also rural, has low participation in pre-k. The Port Huron and East China school districts are the only districts within St. Clair County with more than 50 children enrolled in pre-k. The Port Huron School District covers both the city of Port Huron and Port Huron Township, while the East China School District welcomes students from Marine City, the city of St. Clair, St. Clair Township, China Township, East China Township and Cottrelleville Township. Even though the East China district covers so many communities, it only had about 25 more children participate in pre-kindergarten than larger single community school districts like Dearborn City Schools. In Dearborn, 30 students were enrolled in pre-kindergarten from 2012-13 and in East China 56 students were enrolled.

The Village of New Haven, which has a smaller population than the City of Dearborn and many of the townships encompassed by the East China School District, had 85 students enrolled in pre-kindergarten. The Great Start Readiness Program, which is a larger feeder for pre-k programs, is based on income eligibility. According to the guidelines, households trying to enroll children in the pre-k through this program need to be at at least 100 percent of the poverty level. This shows why districts such as the New Haven Schools enroll more students per capita than places such as Anchor Bay School District (both are located in Macomb County).

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Both the chart and map above show how large the gap is between pre-k and kindergarten enrollment. Even in the Detroit City Public Schools, which had the highest pre-k enrollment in the region at 409, kindergarten enrollment (4,144) was 90 percent higher.

The importance of pre-k enrollment cannot be overstated. Research has shown that it has effects on students’ readiness to learn in elementary school and beyond. According to the Center for Public Education, children who participated in pre-k, rather than being in daycare, scored better on math and reading exams later in life. As noted before, pre-k is not mandated in the State of Michigan.

In 2012, The Bridge Magazine wrote a series of stories for their feature piece “The Forgotten 30,000.” These articles detail the importance of pre-k education and discuss the gap between pre-school and kindergarten attendance. Even with Michigan’s $65 million reinvestment in the Great Start Readiness Program, it is clear that hundreds of children in Southeast Michigan are not receiving the early education that many feel is necessary for greater academic success later in life.

Catholics represent largest religious population in Southeastern Michigan

July 27, 2014

In the seven-county region there are several different religious denominations represented. We examine the rate of religious membership to some of the most prevalent denominations in the region. In the following maps,  the percentage of residents who claim active membership with these groups will be shown using data from the Association of Religious Data (ARD) and the 2010 American Religious Census; the  definitions for these groups are provided below by the ARD.

Catholicism: A form of Christianity where there is a hierarchy of bishops, priests and the pope. In the Catholic Church priests must also be celibate.

Mainline Protestantism :Typically moderate and liberal theological denominations that include the United Church of Christ, Methodist Church and the Episcopal Church.

Evangelical Protestantism: A more conservative movement in the Protestant Church that emphasizes the importance of sharing ones’ faith with non-believers, which is focused on a strong personal relationship with Christ. Examples include Baptists, Anabaptists, Church of God and Pentecostals among others

Black Protestantism: A branch of Protestantism that is similar to Evangelical Protestant denominations. African American protestants focus on the importance of justice and freedom. Such denominations include  the National Convention Baptist of America and the African Methodist Episcopal Zion; there are seven major denominations.

Islam: Muslims follow the text of the Koran and focus on the “oneness of God”. There are five pillars practiced in Islam: praying, fasting, almsgiving, pilgrimage and testimony of faith. The two divisions of Islam are Sunni and Shi’a.

Judaism: This is based on texts in the Hebrew Bible

Orthodox Christianity: This includes both Eastern Orthodox Christians and Oriental Christians. In these forms of Christianity there is no central form of leadership.

Latter Day Saints: Also known as Mormonism, this religion is guided by the Book of Mormon and aspects of the Bible. They seek to restore the New Testament.

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Those who identify themselves as Catholics in the region represent the largest religious based population. The state percentage of Catholics was 17.4 percent. Two counties in the region (Wayne and Washtenaw) where the Catholic population was below that percent. Macomb County had the largest population of Catholics in the region at  29.7 percent.

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Macomb County had the lowest percent of residents (2.5%) who identified themselves as Mainline Protestants. With the state average being 6.6 percent, Washtenaw (7.5%), Monroe (6.6%) and St. Clair (6.7%)counties were the only ones in the region that were above the state average.

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Wayne County had the largest percent of Black Protestant practitioners in the region at 6.6 percent. The county with the second highest population of Black Protestant practitioners was Washtenaw County and that percentage (1.6) was 5 percentage points below Wayne County and .6 percentage points below the state average (2.2%). The City of Detroit is in Wayne County, where the largest African American population resides.

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Overall, Evangelical Protestants represent more of the state’s and region’s population than do Mainline or Black Protestants. According to the data, state average for Evangelical Protestants is 12.9 percent. In Monroe County, 17.5 percent of the population is comprised of residents who identify themselves as Evangelical Protestant practitioners; this is the only county in the region above the state average. Washtenaw County had the lowest representation at 7.3 percent.

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The state average for Judaism practitioners was 0.4 percent and five the seven counties in the region were below this average with 0.0 percent of their population being affiliated with this religious group. Oakland County had the highest population at 3.0 percent. The Holocaust Memorial Museum of Michigan is located in Oakland County, as is the Jewish Federation of Metropolitan Detroit.

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Those who are members of the Muslim religious group in Southeast Michigan were most heavily represented in Wayne County at 3.6 percent, above the state average of 1.2 percent; the City of Dearborn is located in Wayne County. The only other county above the state average was Washtenaw County where 1.3 percent of the population identified as being a practitioner of Islam.

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The state average for those who practice Orthodox Christianity was 0.5 percent; Wayne (0.9%), Oakland (1.1%) and Macomb (1.1%) were the only counties in the region above the average. Livingston County was the only one in the region where 0.0 percent of the population stated that they did not affiliate with this religious group.

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The Church of Latter Day Saints is one of the religious groups in the region that has a lower representation. Washtenaw County was the only county in the region above the state average of 0.6 percent; with practitioners representing 0.9 percent of the population.

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This map shows the percentage of people in the region who either don’t affiliate with a religion or who aren’t associated with the most common religions discussed in this post. The percentages represented in the map above are not directly correlated with irreligion or atheism. According to the data, in the state of Michigan, 57.9 percent of the population did not identify with one of the common religious groups or with one at all. St. Clair, Washtenaw, Livingston and Monroe Counties were above  the state average with Washtenaw County having the largest population at 67.2 percent.

A closer look at the National Assessment of Education Progress (NAEP)

July 21, 2014

We noted in a previous post that students in Michigan and Detroit post weaker performances on the National Assessment of Educational Progress (NAEP) than states across the country, particularly Minnesota. For many years, researchers have attempted to identify factors associated with NAEP scores, which would be of considerable interest to stakeholders who want to address Michigan and Detroit’s NAEP performance. Here, we will briefly summarize some of these factors and selected research addressing them.

For several reasons, NAEP scores in mathematics and reading have been of primary interest to researchers. Much of the research on NAEP score predictors, therefore, focuses on performance in these two subject areas.

Given the primacy of demographic factors such as race, ethnicity, and gender in education research, researchers have also asked whether these variables might predict students’ NAEP performance. For example, Vanneman et al. (2009) and Hemphil & Venneman (2011) noted achievement gaps in NAEP mathematics scores between African-American and White students and between White and Hispanic students. A number of peer-reviewed studies also identify race as a factor in NAEP results (Tate, 1997; Fuchs & Reklis, 1994; Thomas & Stockton, 2003). Some studies explore this factor at a greater depth; for example, Card & Rothstein (2007) attribute the race/ethnicity gap (though using SAT, not NAEP scores) to racial segregation of particular geographic areas, while Lubienski (2006) finds that varying test modes for NAEP mathematics appears to have little or no impact on performance.

There is less evidence for the influence of gender on NAEP scores (Abedi & Lord, 2001; Tate, 1997; Hyde & Linn, 2006; Guthrie et al., 2001), though Thomas and Stockton (2003) identify a small positive relationship between female students and NEAP reading scores and McGraw and colleagues (2006) find a negative relationship between female students and NAEP mathematics scores.

The results are also fairly consistent for socioeconomic status (SES). Biddle (1997) and McQuillan (1998) find a negative relationship between poverty and NAEP scores while Abedi & Lord (2001) and Nelson et al. (2003) find a negative relationship between Free lunch/Aid to Families with Dependent Children (AFDC) status and NAEP scores. Byrnes (2003) and Fuchs & Reklis (1994) find a positive association between parental education levels and students’ 12th and 8th grade NAEP math scores, respectively. Using 1996 NAEP data, Lubienski (2002) finds that SES factors such as parent education and number of literary resources in the home do not explain the African-American/White achievement gap discussed above. Inherent in these studies is, of course, the selection and validity of individual-level or school-level (e.g., Title I designated school) definitions of SES (Thomas & Stockton, 2003).

Some researchers have also considered other literacy-related factors and their possible effect on NAEP scores. For instance, Abedi et al. (2001) and Abedi and Lord (2001) find that English Language Learner (ELL) and Limited English Proficiency (LEP) statuses are negatively related to NAEP mathematics performance. Length of stay in the United States appears to be positively associated with NAEP mathematics performance (Abedi et al., 2001). Access to printed reading material (McQuillan, 1998) and access to school and public libraries (Krashen et al., 2012) also appear to be positively associated with NAEP reading scores.

In general, coursework and related preparation seem to be consistent predictors of NAEP scores. Tate (1997), Abedi & Lord (2001), and Abedi et al. (2001) find that advanced mathematics preparation and coursework are positive predictors of NAEP math scores. Guthrie et al. (2001) and Pinnell et al. (1995) find that reading opportunities and reading prosody, respectively, are positively associated with NAEP reading performance. Abedi et al. (2001) find evidence of a positive association between students’ overall grades since 6th grade and NAEP mathematics performance.

Some authors have considered more systemic or institutional factors in their NAEP research, though this research is less consistent and (less?) extensive. Lubienski (2006) finds a positive association between NAEP math scores and (1) collaborative problem-solving instruction, (2) teacher knowledge of National Council of Teachers of Mathematics (NCTM) standards, and (3) certain ‘reform-oriented’ teaching practices such as non-number math strands. Guthrie (2001) finds that balanced reading instruction is positively associated with Grade 4 NAEP Reading Comprehension in Maryland. Grissmer et al. (2000) and Fitzpatrick (2008) find that greater levels of Kindergarten and pre-Kindergarten participation are positively associated with NAEP scores. Carnoy & Loeb (2002) find a positive association between gains in NAEP mathematics results and strength of state accountability (based on high-stakes testing to sanction and reward schools), but no effect on 9th grade retention rates. In a study supported by the American Federation of Teachers (AFT), Nelson et al. (2003) find that charter school attendance, especially in autonomous charter schools in urban areas, are negatively associated with NAEP math and reading test scores. Nevertheless, institutional factors such as these are not definitive in the literature, and their results should be viewed with caution.

Those who are interested in understanding why Michigan and Detroit students lag behind the rest of the nation in NAEP scores might explore some of the variables discussed above. There is not, however, any one variable or combination of variables that appears to serve as a sole and consistent predictor of NAEP performance, and this will pose a challenge for both understanding and devising solutions to the matter.

 

Bibliography

Abedi, J. & Lord, C. (2001). The language factor in mathematics tests. Applied Measurement in Education 14(3), 219-234.

Abedi, J., Lord, C., & Hofstetter, C. (2001). Impact of selected background variables on students’ NAEP math performance. Center for the Study of Evaluation, University of California, Los Angeles.

Biddle, B.J. (1997). Foolishness, dangerous nonsense, and real correlates of state differences in achievement. Phi Delta Kappan 79(1), 8-13.

Byrnes, J.P. (2003). Factors predictive of mathematics achievement in white, black, and Hispanic 12th graders. Journal of Educational Psychology 95(2), 316-326.

Card, D. & Rothstein, J. (2007). Racial segregation and the black-white test score gap. Journal of Public Economics 91(11) 2158-2184.

Carney, M. & Loeb, S. (2002). Does external accountability affect student outcomes? A cross-state analysis. Educational Evaluation and Policy Analysis 24(4), 205-331.

Fitzpatrick, M.D. (2008). Starting school at four: The effect of universal pre-kindergarten on children’s academic achievement. The B.E. Journal of Economic Analysis & Policy 8(1) 1-38.

Fuchs, V.R. & Reklis, D.M. (1994). Mathematical achievement in eighth grade: Interstate and racial differences. National Bureau of Economic Research. Working Paper No. 4784.

Grissmer, D., Flanagan, A., Kawata, J., & Williamson, S. (2000). Improving Student Achievement: What state NAEP test scores tell us. RAND Corporation.

Guthrie, J.T., Schafer, W.D., & Huang, C.W. (2001). Benefits of opportunity to read and balanced instruction on the NAEP. Journal of Educational Research 94(3), 145-162.

Hemphil, F.C. & Vanneman, A. (2011.) Achievement gaps: How Hispanic and white students in public schools perform in mathematics and reading on the national assessment of educational progress. Statistical Analysis Report. NCES 2011-459. National Center for Education Statistics.

Hyde, J.S. & Linn, M.C. (2006) Gender similarities in mathematics and science. Science-New York Then Washington 314(5799), 599.

Krashen, S., Lee, S., & McQuillan, J. (2012). Is the library important? Multivariate studies at the national and international level. Journal of Language & Literacy Education 8(1), 27-36.

Lubienski, S.P. (2002). A closer look at the black-white mathematics gaps: Interactions of race and SES in NAEP achievement and instructional practices data. Journal of Negro Education 71(4), 269-287.

Lubienski, S.P. (2006). Examining instruction, achievement, and equity with NAEP mathematics data. Education Policy Analysis Archives 14(14), 1-33.

Mcgraw, R., Lubienski, S.P., & Strutchens, M.E. (2006). A closer look at gender in NAEP mathematics achievement and affect data: Intersections with achievement, race/ethnicity, and socioeconomic status. Journal for Research in Mathematics Education 37(2), 129-150.

McQuillan, J. (1998). The literacy crisis: False claims and real solutions. Portsmouth, NH: Heinemann.

Nelson, F.H., Rosenberg, B., & Van Meter, N. (2003). Charter school achievement on the 2003 National Assessment of Educational Progress. American Federation of Teachers, AFL-CIO.

PInnell, G.S., Pilulski, J.J., Wixson, K.K., Campbell, J.R., Gough, P.B., & Beatty, A.S. (1995). Listening to children read aloud: Data from NAEP’s integrated reading performance record (IRPR) at grade 4. National Center for Education Statistics.

Thomas, J. & Stockton, C. (2003). Socioeconomic status, race, gender, & retention: Impact on student achievement. Essays in Education 7.

Tate, W.F. (1997). Race-ethnicity, SES, gender, and language proficiency trends in mathematics achievement: An update. Journal for Research in Mathematics Education 28(6), 652-679.

Vanneman, A., Hamilton, L., Anderson, J.B., & Rahman, T. (2009). Achievement gaps: How black and white students in public schools perform in mathematics and reading on the National Assessment of Education Progress. Statistical Analysis Report. NCES 2009-455. National Center for Education Statistics.

Minnesota easily reins in carbon emissions

July 20, 2014

According to the New York Times,  Minnesota continues to mandate strict energy regulations, a fete that residents easily comply with. The article showcases how the state uses more wind energy than all but four states in the country and has reduced its carbon emissions by about 33 percent since 2003. To read more click here.

 


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