The Farm Bill’s odd couple: SNAP and agriculture

In January, I spent some time discussing SNAP in Virginia here and here; at the time, there was a lot of hypothesizing about what kinds of changes were in store for the program.

In early February, the Farm Bill was passed by Congress and signed into law by President Obama. This bill reauthorized Federal funding to the SNAP program, and included an estimated funding reduction of about $8 billion that is projected to influence hundreds of thousands of SNAP recipients.  Virginia remained almost entirely unaffected by changes to the program, as did many other states.  For details about the changes the Agriculture Act of 2014 made to SNAP, check out this article, or this synopsis of the Farm Bill conference agreement.

Maybe you’re not all that interested in the outcome, or maybe you’re the type to review the summaries, ponder the formal text of the final act, or even pore over helpful timelines to figure it out.  Either way, you might still be wondering: why are food stamps included in an agriculture bill, in the first place? Continue reading

What are the young people up to these days?

Much has been made of the living preferences and economic situation of millenials.  In the current economy, most localities can expect to lose almost all of their brightest young people to college towns.  Whether these localities are able to lure these college graduates back is another story, and an important one since (many argue) it’s during the free-and-easy years after college that most young people will start businesses, launch careers, and develop regional networks and allegiances.

In this post, I’ll take a closer look at the people who were in their 20′s during the 2010 census.  That’s people born between 1980 and 1990.  As one might expect, those 80′s babies were reasonably well-distributed when the prior census was taken in 2000.  At this point, the millennials were anywhere from 10 to 19 years old.  There was an uptick in college towns (18 and 19 year-olds), but it wasn’t huge.  In fact, that uptick helps to balance out the number of millenials who were undergraduates during the 2010 census (20 and 21 year-olds).

Ten years later, some of those kids are still in college or graduate school, some are young professionals, some are in the military, some are in prison, and some have young families with several kids.

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Virginia’s 2013 Metro Areas

Metropolitan Statistical Areas (MSAs) or Metro Areas are perhaps the most common way to define an urban region. Because many urban areas cross into multiple localities, such as in Hampton Roads, MSAs are frequently used in the public and private sector to understand an urban area and its suburbs. Despite the widespread usage of MSAs, it is actually very difficult to find an up-to-date map of Virginia’s MSAs, which is why I created this updated map following the 2013 definitions from the Office of Management and Budget.

Virginia 2013 Statistical Areas

MSA Map 2

(Click on the map for a larger version)

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Virginia Population Estimates: Growing More Slowly

Every year, the Cooper Center produces the official population estimates for the commonwealth of Virginia. The estimates are based on changes since the 2010 census in housing stock, school enrollment, births, deaths, and drivers’ licenses. The estimates are used by state and local government agencies in revenue sharing, funding allocations, planning and budgeting.

Slower Growth Overall
This year’s estimates show that Virginia’s population grew by less than 1 percent between July 2012 and July 2013 to 8,260,405, the slowest growth since before the recession began. Between 2010 and 2013, Virginia’s population has grown 3.2 percent. While the number of births has fallen in recent years, the recent decline in Virginia’s growth rate was caused largely by fewer people moving into the state.

                                    Change in population 2010 – 2013
2013 Change

 

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A quick glance at School Enrollment Projections

Being primarily an Economist, and the newest member of the group, I still have a lot to learn about the demographic changes affecting Virginia and the US. So attending the Applied Demography Conference 2014 was a very educative experience for me. One of the subjects I found particularly interesting was school level projections, so here are some thoughts on the subject.

School enrollment projections are crucial for staffing, budgeting and classroom allocations as school districts rely on these numbers to anticipate future needs and plan accordingly. It is reasonable to assume that number of students in a particular grade will depend upon the class-size of this cohort when they were in the immediately preceding grade. Consider a batch of students moving from 9th to 10th grade between 2012 and 2013.

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If everything remained constant, all 110 9th graders from 2012 should progress to 10th grade in 2013 and so forth. However the numbers are not always the same which could be due to several reasons. As we go from 9th to 10th grade, 5 more students could have joined the cohort in 2013 so the class-size would grow to 115 as some new children may have moved into the school district from elsewhere. Alternatively in 2013 when we follow these 9th graders into 10th grade there may be 5 fewer students, making the class size 105 for the next year. Some of these children could have remained in the previous grade to repeat a year, they may have left to join a different school or may have dropped out of school altogether causing the class-size to shrink. Hence all students from a grade may not automatically advance to the next higher grade and we need a method for estimating future class-sizes. Grade Progression Ratio (GPR) is the standard go-to for forecasting school enrollment. To see how this works, suppose you are a school administrator who needs to know how many students to expect in the 10th grade in 2014.

From the example above we find that GPR9th-10th  = 10th grade Enrollment in 2013/9th grade Enrollment in 2012 = 105/110 = 0.95, which implies that we expect 95% of students in 9th grade to move on to 10th grade. To calculate the current enrollment for grade 10 in 2014, we can apply the progression rate from last year: 10th grade Enrollment in 2014 = 0.95 * 97 = 92.59. Therefore approximately 93 students are projected for the upcoming year. This is a simplified illustration of how we may predict the expected number of students in different grades in the future.  In practice, we use data from multiple years to build ratios in order to minimize randomness and several other elements must be incorporated into the calculations to get greater reliability.

Here are some other updates and advances about enrollment projections:

  • One way to calculate future student enrollment in rapidly expanding areas is to track new residential developments (historical trends, current construction, home sales etc. are indicators of single-family and multi-family presence in the school districts) for creating different area-specific yield factors.
  • For each new house that is constructed, there are several pre-existing homes that exchange hands; so neighborhoods could evolve even though number of housing units remains steady. New families come into ownership of these resold properties changing the population composition which in turn may change the demand for schooling.
  • Migration alters the prevailing age structure and family type of a locality which will determine schooling needs. Number of school age children in a household fluctuates over time and migrating households could contain elderly individuals with adult offspring or they may be young families planning for or already with children. For example, a 3rd grader moves with the whole household while a college student moves alone and movement of empty-nesters will not add new students to an area.
  • Geo-spatial analysis has become an indispensable tool for understanding modern demography. Families move and household composition changes, therefore the geographic distribution is useful for identifying trends in student yield with variation in housing tenure. Plotting child-densities on a map helps to visualize concentration of students in a school district and could improve the accuracy of projections.
  • Public school enrollment rates may be affected by presence of private schools among others; the odds of attending a private school significantly depend upon household income, race and neighborhood of residence. The economic climate also plays a significant role as in times of prosperity more families can afford to send their children to private institutions. Public schools will receive more funding during economic booms as opposed to times of recession when the financial downturn percolates into both household and administrative schooling decisions.

All trend projections and estimates are speculative in nature which means that there is a constant need for dynamically updating the statistics. Here at the Weldon Cooper Center, every year we conduct school enrollment projections under contract with individual school divisions. We apply Grade Progression Rates for general analyses and implicitly take account of net effect from migration, dropout, deaths, retention, and school transfers. For more customized analyses, we include further nuances into the methodological design such as housing development, family structure, differential fertility rates by race and ethnicity etc. to incorporate location specific characteristics. For more details please visit our School Enrollment Projections page.

Official Poverty Estimates, in the US and the Commonwealth

This past weekend, The New York Times published an interactive map visualizing recently released Census data on poverty in America.  The NYT map gives information down to the census tract level; this level of precision allows the viewer to see poverty rates of not just counties and cities but, in fact, neighborhoods.

As for Virginia, poverty rates in Southwest, Southside and Hampton Roads far exceed the poverty rates of localities closer to DC.  According to these small area estimates, Falls Church County has the lowest poverty rate of around 3 percent, while Radford City and Harrisonburg City have the highest rates (34.2 and 37.5 percent, respectively).

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Retirement patterns: Anywhere but cities

Virginia is often ranked as one of the top states for retirees, according to lists published by groups like AARP or Bankrate. Temperate climate, good healthcare, and standard of living are all listed among Virginia’s advantages. During the past decade, however, few retirees moved to Virginia, and more 65- to 74-year-olds moved out of the state than in. Additionally, despite both college towns and cities being promoted as attractive retirement spots, in Virginia’s major cities more retirees moved out than in and there was no great influx of retirees moving in to college towns such as Blacksburg or Charlottesville. Instead rural and suburban regions were the only areas in Virginia to experience growth due to in-migration of retirees.

Regions

 

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Breadwinner Moms in Virginia: A closer look at single mothers

Asain Mother

Applying findings to Virginia from a Pew Social & Demographic Trends report, two previous blog posts examined breadwinner mothers in Virginia.  In the first post we found differences between married and unmarried breadwinner moms:

  • Households where the breadwinning mom was married had higher income levels
  • Married breadwinning moms had higher educational attainment
  • Even with the same educational attainment, married breadwinning moms earned more than unmarried moms (and worked more hours on average).

In the second post we examined differences between two groups of unmarried breadwinning moms – those who are single and those who are cohabiting with a partner.  We found that, between these two groups, a greater proportion of cohabiting moms and their children live in poverty, and that lower earnings among cohabiting moms are found even when we hold age and educational attainment constant.

In this post, we will wrap up by focusing on single mothers, the group that makes up the largest share (54%) of breadwinning mothers.[1] 

In light of stereotypes about single mothers represented in popular media, findings from the American Community Survey are particularly important to describing single motherhood in Virginia.

Single mothers in Virginia

The data about single mothers in Virginia points to an important finding: the lives of single mothers who have never been married is quite different from those who have been married before, even when holding constant age, educational attainment, or age of the children. For example, in Virginia, single mothers have median household incomes of about $28,000.  But when we examine marital history, we find some variation around that number. Continue reading