We are now 17 weeks into the 2017-18 FAFSA Cycle and about 1.27 million high school seniors have completed the form, according to FSA’s data.

Below is a chart comparing this cycle to last year’s (which began on Jan 1). We are far above where we were last January and about 150,000 behind where we were in April, when most states’ filing deadlines have passed.  The solid black line will continue to slope upward, and when April comes we will be able to make a better comparison between the two cycles.

But in some states, we can already start to make preliminary comparisons since their filing deadlines have already passed. In Tennessee, for example, Jan 17 was the deadline for the Tennessee Promise program and we now have data that spans this deadline for both cycles (last year’s deadline was in February, so this chart shows “weeks from deadline” instead of calendar weeks).

In the current cycle, we see a sharper spike in the weeks leading up to the deadline, whereas last cycle was not quite as pronounced. Last year, the deadline was followed by a plateau, so we will be monitoring other states with deadlines to see whether similar patterns hold.

Regardless, nearly 50,000 Tennessee high school seniors filed the FAFSA this cycle compared to the nearly 38,000 who filed prior to last year’s deadline. This 12,000 differential is a large bump and likely due to a number of factors including greater awareness of the Tennessee Promise program, outreach efforts, the number of high school seniors, FAFSA changes, etc. We are unable to say with this data, but it sure is a promising trend to see!

But several states do not have filing deadlines and instead use a first-come, first-serve basis for awarding financial aid. Wisconsin is one example and, unlike Tennessee, we do not see a surge in filing. Instead, we see more of a parallel filing trend that slowly converges.

Here 17 weeks into the current cycle, we have about the same number of filers as we saw last April (Week 14 of that cycle). In states without filing deadlines, we might expect to see flatter growth but we are unsure if this will plateau or steadily slow its growth rate until the FAFSA cycle ends.

We are thinking through the consequences of using hard (TN) versus soft (WI) filing deadlines and will eventually be able to see whether overall filing rates differ by state policy design. Maybe the won’t vary, but it’s worth a look!

For example, it seems possible a hard-deadline state should have higher overall filing rates since students may be more aware of the “use it or lose it” consequences of missing the deadline. But it is also possible hard-deadline states end up with lower overall filing rates for the exact same reason — students who miss the deadline may be out of luck and missing out will result in lower overall filing rates. We don’t know yet, and maybe we don’t detect any patterns, but this project is one step in that direction to dig into new areas of financial aid research.

Data:

week 17 summary.xlsx

I have been digging into the new income mobility paper, just trying to wrap my head the details while not losing the forest for the trees. This is a remarkable study and the authors provide institution-level public data files here. There are 11 different files, so it takes some time getting familiar with their layout and to see which ones they used in the Upshot.

This post hopefully helps other folks navigate the data or offer me some insights into what you’ve found as you work through it. But here goes, I’ll replicate findings for Washington University in St. Louis and for California State University since these two got a lot of attention in the NYT article.

Replicating family income

According to Wash U’s Upshot page, the median family income of students is $272,000 and 84% come from the top quintile of the income distribution. To show just how lopsided their enrollments are in terms of economic diversity, consider this: about 22% of their student body comes from the top 1% of the income distribution, while less than 1% come from the bottom 20%!

Table 5 is where to find this data. Wash U’s identification number is “2520” and the Upshot uses the 1991 birth cohort, so be sure to restrict the panel to those criteria. These variables measure median parental household income (par_median_age20), along with the fraction of students enrolled by income quintile (par_q5_age20 and par_q1_age20) and top 1% (par_top1pc_age20) according to where the student was enrolled at age 20:

Replicating upward mobility rates

The Upshot also displays two different mobility rate metrics:

The first is “moving up two or more quintiles,” and about 9 in 100 students come from the bottom and end up at least two quintiles higher as adults (30-somethings).The Upshot calls this the overall mobility rate.

The second is the same rate used in the Chetty et al paper, which is “moved from the bottom to top income quintile.” About 1 in 100 students come from the bottom and end up at the top quintile of the income distribution.

If I had to choose one of these measures, I would prefer the first since upward mobility doesn’t always have to be a rags to riches story. Moving from the bottom 20% to the top 40% is still upward mobility and wouldn’t be captured in the second measure.

Table 2 is where to find this mobility rate data. The paper explains how mobility rate is calculated as the product of access (fraction of students from bottom income quintile) and success rate (fraction of such students who reach the top income quintile as adults).

In the data, it’s easy to get bottom-to-top mobility rates (mr_kq5_pq1) that are used in the paper. But if you want to measure overall mobility (the first definition above), then you need to jump through some hoops — unless I overlooked a variable that does this already. You have to calculate each of the six possible ways a child can jump up two income quintiles: they can go from the bottom to the top three, or from the second-lowest to the top two, or from the third-lowest to the top.

Between Tables 2 and 5, you can pretty easily replicate some of the main data elements of the Upshot page. It took a little bit of a learning curve to search across all these files and figure out which variable to use and when, but hopefully this brief discussion helps validate your efforts if you’re trying to use this data for research. And of course, if you see I missed something, please let me know!

Using this data for research

Now that we know how to extract two different mobility rates and the fraction of students from various income quintiles for each college, we might be inclined to use this data in our own research. There is still a lot to learn about this data and it will take time to fully understand the strengths, limitations, and opportunities this dataset provides researchers. It’s a massive amount of information generated from sources that haven’t been used to date. So researchers will have a learning curve and I know I’m still trying to make sense of what’s in here and how it might be used for research. Here are a few helpful things I’m still working through:

1) Parent-child relationships

Each student is connected to a college via the 1098-T tax form and NSLDS Pell Grant reports. Colleges must report tuition payments for all students using the 1098-T form, and if a student paid no tuition the NSLDS record picks them up. The tax form includes the employer ID number (EIN) corresponding to where the student paid tuition, creating a “near complete roster of college attendance at all Title IV institutions in the US,” which is remarkable.

However, this does not mean the college where the student enrolled is available in the dataset. In Table 11, Chetty et al begin with 5,903 institutions. About 2,960 of these colleges have “insufficient data” to report in the final analysis. Another 576 colleges end up getting clustered with at least one more institution.

For the University of Wisconsin, all 13 public four-year universities and UW Colleges (the system’s community colleges) are all lumped together as a single observation. Indiana University, University of Pittsburgh, University of Massachusetts, University of Maine, University of Maryland, University of Minnesota, University of South Carolina, West Virginia University, Miami-Dade Community College, the University of Tennessee, and several more colleges are in a similar boat as Wisconsin: the data does not reflect individual institutions. Considering the heterogeneity within systems, these system-level mobility rates will not reflect the mobility rate of each individual campus.

Here’s a better way to see it. Below is the total number of institutions (this time using unitid from IPEDS) by each sector. Two-year colleges include those that offer less-than two-year degrees. All are Title IV degree-granting institutions. Mapping Chetty et al’s data to this, we can see the data captures about 480 out of 684 (70%) public four-year universities but only half of non-profit (805 out of 1,568) and about 12% of for-profit four-year universities (78 of 645). Similar patterns occur in the two-year sector where about 77% of community/technical colleges are accounted for, but far lower rates in the private sectors:

It is hard to tell the extent to which this matters, and maybe it doesn’t if we ascribe to the “some data is better than no data” argument (which I’m pretty sympathetic to in this case). But about 3.17 million students attend a collapsed institution, which is about 20% of the nation’s total enrollment. So given “University of Wisconsin System’s” bottom-to-top mobility rate of <1% and its overall mobility rate of 13%, I don’t know how to interpret this finding. Are the results driven by Madison’s campus? Milwaukee? Any of the others?

Let’s take the Cal State system as an example. Each campus is listed separately in the Chetty database and their mobility rates (bottom-to-top and overall) are below:

We can see here the range of overall upward mobility (the right column) ranges from a low is 14% at CSU-Channel Islands to a high of 47% at CSU-Los Angeles. This is quite a bit of variation within a single system. If I lump these 15 universities together into “Cal State System” then our overall upward mobility score would be 26%, which doesn’t represent either campus’ experience very well:

All of this is to say: be cautious working with parent-child relationships and realize this study only looks at about half of all colleges in the U.S. (depending on how we classify them).

2) Transfer and enrollment

The research team had to make some tough decisions about how to link students to their college. With nearly 40% of freshmen transferring within six years, should a student be matched to their original institution, the one they attended most often, the one they last enrolled in, or the one they earned a degree from? The paper and data files focus on the most-often attended college and the college last-attended at age 20 (they don’t give a lot of details, so you really have to read between the lines with some of this).

It is unclear whether or why one institution should be credited for upward mobility if their students attended multiple colleges. It is also unclear what mechanisms are at play when a student experiences upward mobility simply by attending (not completing) a college. Yet, that’s exactly what this study concludes — simply enrolling yields upward mobility. I don’t know how to make sense of this quite yet, but I found Figure IXC to be provocative. Despite the upward mobility gains documented throughout the paper, this figure shows how fewer low-income students are enrolling in high-mobility-rate colleges. The downward-sloping line shows how access is slowly (and shallowly) falling at the colleges that seem to be doing the best in terms of advancing opportunity. Again, I haven’t wrapped my head fully around this, but it’s certainly stuck in my head when thinking about where this trend is heading:

3) Graduation rates

The paper and data files do not address or measure degree attainment. All the mobility gains discussed in the NYT and elsewhere are associated with attending, not completing, college.

We can look at the correlation between each college’s graduation rate (from Table 10)  and merge this with the mobility data (Table 2) and see another side of the story the authors tell in the paper: broad-access institutions (which often have low graduation rates) are those that have the highest upward mobility. Below uses the “up two income deciles” measure of mobility and only looks at four-year institutions, but the negative relationship is pretty clear — even if a college has a low graduation rate, they are correlated with having have high overall upward mobility rates.

I’m happy to share more about how I ran these descriptives, and of course please chime in or email me if you see that I’m overlooking something here. The data set is new and I’m still getting oriented. And in that process, I found it helpful to document some of the steps I took so other researchers interested in this data might be able to have a sounding board. Good luck and I’ll be looking forward to seeing how this data is used, interpreted, and otherwise dealt with in the future. It will take a bit of community learning in order to get up to speed on what we can and can’t do with this new treasure or truly remarkable data.

The new FAFSA timeline added 14 weeks to the filing cycle. Instead of filing on January 1, students could file as early as October 1. As shown below, about 1.13 million high school seniors took advantage of the earlier and easier form:

We are still about 300,000 behind “Week 14” of last year’s cycle. But “Week 14” used to be in April, so we have plenty of time to catch up. The old cycle was pretty condensed and the new cycle is elongated: the gray line should soon flatten out and the black line will catch (and hopefully surpass) it. We’ll keep monitoring to see when that moment occurs.

Data:

Week	2016-17	2017-18
2	135,387	196,736
3	239,605	328,607
4	334,837	448,958
5	444,951	563,560
6	591,914	672,694
7	711,858	740,843
8	852,752	842,620
9	990,403	882,778
10	1,198,080	976,563
11	1,269,537	1,023,711
12	1,332,608	1,067,470
13	1,380,565	1,099,324
14	1,425,995	1,130,204

Week 14 is a significant milestone in the new FAFSA timeline because that’s the number of weeks early FAFSA added to the 2017-18 filing cycle. We’ve been keeping track of the weekly total number of forms submitted and completed, shown below.

The “week is cycle” approach is a little tricky to interpret since Week 1 of each cycle starts at different times of the calendar year. This chart tells us between Oct 1 and Dec 30, 2016, about 1.13 million high school seniors (18 or younger) filed for the first time. The number was about 1.43 million for last year’s cycle, which ran from Jan 1 to April 1, 2016.

Last year, the weekly growth in FAFSA completions started to slow down around April. We still have about 10 weeks to go until then, so we anticipate the gaps in the two lines (about 300,000 students, or 26%) should start to close and eventually the black line will cross back over the gray. But if that doesn’t happen, or if we end up where we were last year in terms of overall completion, then I will be curious to see what folks point to as the underlying reason(s). And I’ll be even more curious if the elongated filing cycle brought with it any unintended consequences, like reducing the amount of state aid late-cycle filers receive.

FAFSA completion by state and week:

	2	3	4	5	6	7	8	9	10	11	12	13	14
AK	154	320	446	612	764	873	1,064	1,126	1,286	1,352	1,436	1,478	1,524
AL	1,611	3,054	4,555	5,810	7,159	8,068	9,249	9,677	10,956	11,582	12,127	12,426	12,716
AR	1,377	2,567	3,726	4,738	5,743	6,395	7,368	7,685	8,383	8,811	9,143	9,393	9,639
AZ	4,143	6,061	7,914	9,734	11,405	12,508	14,398	15,009	16,068	16,832	17,543	18,034	18,742
CA	17,987	32,495	46,924	60,863	73,850	82,765	95,905	100,917	113,508	119,063	124,161	127,823	131,153
CO	2,991	5,466	7,766	9,703	11,551	12,638	14,427	14,951	16,303	16,910	17,473	17,873	18,323
CT	2,351	4,227	5,896	7,879	9,766	10,806	12,429	13,090	14,442	15,136	15,780	16,332	16,857
DC	234	421	572	738	926	1,045	1,237	1,310	1,496	1,585	1,670	1,746	1,804
DE	416	785	1,123	1,452	1,841	2,072	2,449	2,606	2,882	3,070	3,232	3,343	3,460
FL	11,419	17,703	23,769	28,665	33,335	36,247	40,033	41,657	45,760	48,037	50,730	51,993	53,227
GA	3,056	5,701	8,000	10,268	12,438	13,992	16,337	17,269	19,358	20,613	21,816	22,703	23,718
HI	1,162	1,787	2,342	2,841	3,276	3,533	3,905	4,087	4,407	4,593	4,787	4,932	5,064
IA	1,634	3,094	4,331	5,580	6,710	7,646	8,975	9,712	11,672	12,195	12,587	12,894	13,179
ID	562	1,128	1,741	2,463	3,190	3,594	4,259	4,449	5,204	5,443	5,636	5,759	5,895
IL	23,489	34,065	42,338	49,007	54,998	57,738	61,774	63,228	66,707	68,223	69,645	72,491	73,460
IN	2,684	4,879	7,061	9,250	11,509	12,751	15,012	15,841	17,701	18,651	19,411	19,979	20,736
KS	1,694	2,877	3,956	5,175	6,231	6,876	7,690	8,016	8,790	9,145	9,420	9,606	9,841
KY	9,186	12,388	14,689	16,386	17,654	18,342	19,277	19,688	20,611	21,078	21,499	21,711	21,910
LA	1,456	2,829	4,132	5,312	6,535	7,428	8,667	9,048	9,957	10,610	11,219	11,550	11,931
MA	2,995	6,003	8,763	12,027	15,643	17,903	21,119	22,251	24,700	26,005	27,268	28,367	29,443
MD	3,595	6,108	8,329	10,415	12,536	13,801	15,609	16,347	17,905	18,733	19,659	20,267	21,000
ME	1,111	1,941	2,693	3,340	3,944	4,312	4,806	4,970	5,308	5,501	5,694	5,836	5,941
MI	6,033	10,376	14,731	19,198	23,179	25,449	28,477	29,909	33,034	34,547	35,764	36,641	37,570
MN	2,302	4,232	6,032	8,344	10,429	11,721	13,636	14,595	16,635	17,505	18,322	18,908	19,642
MO	3,553	6,177	8,573	10,833	12,951	14,235	16,338	17,059	18,831	19,694	20,461	20,977	21,516
MS	717	1,521	2,413	3,233	3,871	4,499	5,351	5,636	6,392	6,875	7,382	7,587	7,779
MT	291	662	949	1,292	1,780	2,075	2,548	2,717	3,443	3,564	3,652	3,691	3,736
NC	5,452	9,189	12,307	15,103	18,120	19,933	22,953	24,269	26,853	28,155	29,297	30,151	31,084
ND	108	209	324	467	608	701	824	896	1,066	1,165	1,241	1,297	1,370
NE	1,119	2,071	2,990	3,858	4,672	5,314	6,090	6,463	7,107	7,502	7,835	8,085	8,312
NH	533	1,058	1,512	2,039	2,613	3,015	3,556	3,750	4,177	4,427	4,683	4,867	5,041
NJ	4,875	8,957	12,808	16,693	20,837	23,361	27,646	29,477	34,024	35,722	37,390	38,654	39,879
NM	595	1,126	1,743	2,328	2,842	3,208	3,806	4,030	4,533	4,791	5,012	5,131	5,283
NV	2,587	3,676	4,542	5,319	6,256	6,653	7,178	7,335	7,790	8,054	8,278	8,440	8,579
NY	8,152	15,264	22,293	30,150	37,996	43,111	51,361	54,919	61,595	65,697	69,653	72,782	75,545
OH	6,320	11,462	16,247	20,779	25,197	27,933	32,022	33,919	39,110	40,844	42,290	43,241	44,304
OK	2,377	3,989	5,335	6,557	7,755	8,358	9,174	9,477	10,212	10,651	11,139	11,339	11,584
OR	5,327	7,491	9,346	10,884	12,267	13,064	14,088	14,475	15,287	15,745	16,133	16,326	16,572
PA	6,098	11,082	15,412	19,755	24,102	26,863	31,075	32,865	36,120	38,293	40,208	41,680	43,366
RI	344	690	998	1,323	1,683	1,912	2,337	2,500	2,844	3,084	3,300	3,454	3,605
SC	1,844	3,108	4,437	5,726	6,944	7,720	8,783	9,198	10,315	10,913	11,462	11,809	12,207
SD	174	425	615	804	1,000	1,134	1,320	1,440	1,660	1,774	1,867	1,954	2,085
TN	8,765	12,746	16,642	20,089	23,132	24,692	26,780	27,564	29,369	30,999	32,686	33,529	34,412
TX	19,353	32,086	43,102	52,796	60,926	66,364	74,612	77,268	84,555	88,291	91,565	93,687	95,749
UT	707	1,316	1,985	2,712	3,524	4,192	5,291	5,698	6,887	7,305	7,606	7,781	8,030
VA	3,820	6,963	9,625	12,469	15,031	16,678	19,325	20,414	22,734	24,049	25,412	26,317	27,390
VT	242	537	838	1,188	1,554	1,673	1,883	1,938	2,101	2,178	2,268	2,336	2,421
WA	6,011	9,283	12,183	14,686	16,940	18,198	20,026	20,755	22,529	23,379	24,091	24,587	25,118
WI	2,985	5,327	7,431	9,449	11,521	12,963	14,910	15,813	17,990	19,058	19,945	20,711	21,446
WV	572	1,249	1,825	2,370	2,948	3,349	3,916	4,075	4,427	4,668	4,926	5,117	5,265
WY	173	436	654	858	1,012	1,142	1,325	1,390	1,539	1,614	1,666	1,709	1,751

Source: https://studentaid.ed.gov/sa/about/data-center/student/application-volume/fafsa-completion-high-school

In anti-tax states like Missouri, it’s a hard political bargain to just give universities new money. Legislators, particularly those on the right, ask “what did colleges do to earn it?” Perhaps the only politically feasible way to get new money for Missouri public higher education is to play the performance funding game. The chart below suggests this may be the case:

The vertical line is when the state’s new PBF system began. And it is precisely the same year we see the first large uptick in funding since the early 1990s. As it turns out, Missouri operated an old version of PBF from 1993 to 2002, which corresponds with the largest growth period on this 40-year chart.

There are several plausible explanations behind these correlations, but this context helps me think about an alternative goal of performance funding: providing political cover for justifying public investment. I have heard this logic in Wisconsin, which is considering reinvesting $42.5 million in the UW-System. Legislators say they can’t go back to their district and tell constituents, “we just gave universities more money.” Instead, it plays a lot better politically to say, “we reinvested because universities showed us they earned it.” The politics of resentment and the strategic dismantling of public institutions is alive and well in Wisconsin, so performance funding is a convenient political tool for justifying reinvestment.

Tying just a small share of a budget to performance measures may give enough political cover to build a broad coalition of support, even if few funds are actually based on performance. In Missouri, about 3% of funds flow through its performance funding model and advocates would like to see that raised to at least 25%. So let’s look at which Missouri universities benefit from this new era of finance.

A recent audit report provides data we rarely see from PBF states by documenting how much money each institution has received from PBF over time. Since 2014, the state has appropriated a total of $109 million through the model and all four-year universities have received budget increases. We could stop there and say, “everybody’s a winner, so there’s nothing to see here.” But we need to look a bit closer if we’re at all concerned about an equitable funding model for higher education.

The charts below merge Missouri’s audit data with IPEDS to show overall budgetary gains due to PBF (x-axis)  according to the percent of students receiving Pell and the percent of students who are Black (y-axis).

The chart above shows how campuses receiving the largest budgetary gains are those enrolling the smallest share of Pell Grant students. Harris-Stowe State University and Lincoln University, the state’s two public HBCUs, are in the upper-left corner of this chart. They enroll the highest share of Pell Grant recipients, yet they have received the smallest budgetary boost from the new funding model. They did gain money, but not as much as other institutions in the state.

The chart below shifts attention to the share of Black students and finds a similar negative relationship, where colleges enrolling the highest share of Black students tend to have the smallest budgetary growth. Harris-Stowe and Lincoln University are again the two dots in the upper-left quadrant.

These two campuses enroll about 3% of the state’s total student population, but nearly 20% of the state’s entire Black undergraduate population in the public four-year sector. In a similar back-of-the-envelope analysis, I found nearly identical patterns in Tennessee.

Some might look at these charts and say HBCUs are outliers. If you just removed them from the analysis, then these relationships wouldn’t exist. But that argument misses the entire point and, aside from the dog-whistle politics it evokes, is precisely what we should care most about. Just for the sake of argument, I’ve dropped the “outliers” and replicated the charts below and find the same negative relationship.

There is a lot of work to be done with respect to understanding and explaining the causes of funding inequities in state higher education finance. Even more work is in store if we want to evaluate their consequences and discover new solutions to age-old problems in higher education finance. This will be hard to do when political expedience is the preferred strategy guiding state higher education finance policy. This approach may very well do more to reinforce rather than reverse inequality.

Data:

	Total enrollment	Budget change from PF	Black enrollment	Pell enrollment	Percent Black	Percent Pell
Harris-Stowe	1,280	9%	1,058	981	83%	77%
Lincoln University	2,977	10%	1,179	1,617	40%	54%
Missouri Southern	5,561	11%	341	3,131	6%	56%
Missouri State	18,517	13%	742	5,931	4%	32%
Missouri Western	5,650	10%	590	2,413	10%	43%
Northwest Missouri	5,491	12%	356	1,960	6%	36%
Southeast Missouri	10,848	12%	973	3,698	9%	34%
Truman State	5,910	12%	212	1,187	4%	20%
Central Missouri	9,838	12%	771	3,654	8%	37%
U of Missouri System	51,969	13%	5,659	13,013	11%	25%
U of Missouri-Columbia	27,642	n/a	2,268	5,756	8%	21%
U of Missouri-Kansas City	10,453	n/a	1,376	3,285	13%	31%
U of Missouri-St Louis	13,874	n/a	2,015	3,972	15%	29%

There are two ways to track FAFSA completions: week-in-cycle or week-on-calendar. The good folks at the US Department of Education report both, so it’s up to the researcher to decide how best to use this data. Each approach has merits and below are a couple of examples that show why.

The “week-in-cycle” approach seems preferable when states do not have hard filing deadlines. This is because these states use a first-come first-serve approach, where funds are available until they run out. October 1 marked the first day funds were available for the current cycle and January 1 for last cycle. This means “Week 1” is when the state rings the bell to start the financial aid race.

The figure below shows how this plays out in IL and TX. Here we compare last year’s and this year’s cycles and can see nearly identical patterns: there was a nice boost in the first several weeks, but this boost cools down around Weeks 10 or 11. In these states (which have two of the nation’s largest aid programs) we are seeing fewer completed FAFSAs this year:

The “week-on-calendar” approach seems best to use in states that have hard filing deadlines. Missing this deadline means missing out on aid. This matters when comparing trends in the two filing cycles because “Week 13” corresponds with different calendar dates: March 25, 2016, versus December 23, 2017. If I am in a state with a March deadline (as many have) then I want to see how many people have filed relative to that deadline.

California and Tennessee are good examples. California’s deadline is March 2nd and the Tennessee Promise’s deadline is January 17, so let’s set those deadlines at the value of zero below. The weeks on the x-axis tell us how many weeks we are out from the deadline:

California is still several weeks out from its deadline, but Tennessee is only a few weeks away. In both states, FAFSA completions are below last year’s levels, but we should expect a bump in the weeks immediately prior to the deadline. Once those deadlines pass, we should expect to see things flatten out as the states’ main programs targeted to high school seniors close their books for the year.

The next trick is verifying the priority dates and deadlines for each state. The Office of Federal Student Aid provides a database here and NASFAA has one here, which will be handy as we analyze and share this data.

 

 

Every week, the U.S. Department of Education releases data on the number high school seniors who have submitted and completed the FAFSA form. Two UW doc students and I are downloading, archiving, and analyzing this data each week and below is a chart summarizing completions for each state and week of the current cycle (2017-18) and last year’s cycle (2016-17).

Tracking completions by this “week-in-cycle” approach is a first step in monitoring progress and tracking the rhythms of the filing cycle. Notice how there is an early boost in several states, where completions are above last year’s levels. But then notice in most states how that boost seems to flatten out when compared to last year’s cycle.

Nationally, the number completions is down by about 20% from the same time in last year’s cycle. This could simply be because the two FAFSA cycles do not match up on the calendar: Week 13 of the current cycle spans from October 1st to December 23rd, 2016, while Week 13 of last year’s cycle spans from January 1st to March 25th.

For example, California’s deadline is March 2nd and it didn’t change from last year. Week 13 spanned that deadline last year, but not this year. As state filing deadlines pass, we will take a closer look at each one to see whether there is a bump in filing. We are looking closely at Tennessee since the TN Promise deadline is Jan 17 (Feb 15 last year).

Ultimately, we want to know whether earlier and easier filing increases overall FAFSA filing rates. We won’t be able to answer that definitively with our data, but we think we’re taking a step in the right direction.

This is a similar public policy question asked by fellow UW faculty members, who were curious if early voting brings more voters to the polls. This is a complicated question to answer, but they found the short answer was “no” as shown below.

Surprisingly, early voting may actually depress overall voter turnout. However, when states have election-day registration or couple election-day registration with early voting, they see higher turnout. The authors’ conclusion has stuck with me and is occupying my attention, “election reform has goals other than increasing voter turnout, including minimizing cost and administrative burden…”

We don’t have to squint too hard to see the similarities between these two contexts: making a bureaucratic process easier and earlier does not necessarily increase overall participation. It very well may (and I hope/think it will) with the FAFSA. Drawing lessons from other areas of social science can help us think about higher education in new and creative ways. Doing so might also help us stumble upon new solutions to age-old problems.

If we find that overall filing rates didn’t tick up (or didn’t tick up as much as anticipated) then perhaps we need the equivalent of “election-day registration” for the FAFSA. I don’t even know what that might look like, but it’s something we might need to anticipate if the trends in the chart above don’t pick back up in March when many states’ deadlines are up.

The following four charts highlight a critical and often overlooked consequence of Tennessee’s performance-based funding model. The first two are taken from the Tennessee Higher Education Commission’s recent report on the “cumulative change in funding due to the formula.” The left chart includes four-year universities; the right includes community colleges:

All campuses started at zero in 2010-11 (when the new funding model was first adopted), and by 2016-17 we can see a handful of colleges emerge as winners and losers under the funding model. Austin Peay State University has benefited the most among four-year institutions: APSU funding has increased by 35% since the policy was adopted. In the community college sector, Chattanooga State Community College’s budget has grown by about 27%.

On the other hand, several colleges are hovering right around the 0% line, meaning the formula has not rewarded them with any financial benefits. Tennessee State University, the state’s only public HBCU, stands out as having not received any new money under the formula. This is despite being one of the few campuses experiencing steady enrollment growth during this same period, rising 5% while most other campuses (including APSU) enroll fewer students.

The two charts above include a solid black “total funding” line representing the state average. Four universities are above this line, but these four universities only enroll 6,165 Black undergraduates. The five universities below this line enroll 18,869. In other words, three in four Black undergraduates attend a college receiving the below-average budgetary growth under the state’s model. Exactly the same pattern holds in the chart on the right that looks at community colleges. We could look also look at Pell grant enrollments at these institutions and a similar pattern would persist.

The two charts below look at this in a slightly different way. In these charts, the blue dots are community colleges (n=13), black dots are board of regents universities (n=6), and orange dots are campuses in the UT system (n=3). The horizontal line represents the cumulative budgetary change and vertical lines represent Black and Pell enrollment share.

Austin Peay State University is the black dot furthest to the right since it received the greatest budgetary gain. Tennessee State University and Southwest Tennessee Community College are the two left-most dots since they received the greatest budgetary loss. In both charts, we see a negative relationship between budgetary gains and enrolling traditionally under-served students. Less racially and socioeconomically inclusive colleges tend to receive more state support.

These are only back-of-the-envelope calculations using the Tennessee Higher Education Commission report and the most recent IPEDS data. More research is needed to understand the causes and consequences of these trends. But given what we know about college access and student success, and the role state subsidies play to those ends, it seems that Tennessee’s funding model may be poised to reinforce rather than reverse educational inequality.

Raw data:

			Budget change	Black enroll	Pell enroll	Total undergrad enrollment					% Black	% Pell
						2010-11	2011-12	2012-13	2013-14	2014-15
1	APSU	4yr	35%	2077	5008	11343	11564	11381	11432	10782	19%	44%
2	Chatt.	2yr	27%	2065	4566	13549	13606	13530	13104	12661	16%	35%
3	Pellissippi	2yr	24%	941	4194	15585	15625	15015	15244	14494	6%	28%
4	UTC	4yr	24%	1255	3520	10106	10941	11197	11265	11232	11%	31%
5	Walters	2yr	21%	171	2550	9093	8744	8390	8010	7601	2%	32%
6	UTK	4yr	20%	1666	6366	23168	22963	22958	23202	23302	7%	27%
7	Roane	2yr	18%	184	2811	8900	8861	8505	8183	7765	2%	34%
8	Dyersburg	2yr	17%	671	1583	4753	4765	4613	4143	3627	19%	38%
9	Columbia	2yr	15%	486	2201	7537	7393	7229	7070	6933	7%	31%
10	Northeast	2yr	15%	206	2697	8785	8468	8427	7676	7556	3%	35%
11	UTM	4yr	14%	1167	3368	9118	8581	8311	7942	7471	16%	42%
12	Nashville	2yr	12%	4181	5021	14305	14329	14688	14573	14396	29%	34%
13	Volunteer	2yr	11%	970	3029	12351	11894	11503	11482	10725	9%	26%
14	ETSU	4yr	11%	812	5119	13752	14194	13960	13455	13196	6%	38%
15	UM	4yr	9%	7203	8057	20299	20650	20443	19939	19850	36%	40%
16	MTSU	4yr	8%	4743	9465	27196	26958	25850	24521	23955	20%	39%
17	Cleveland	2yr	5%	294	1681	4944	4986	4739	4830	4648	6%	35%
18	Jackson	2yr	5%	1169	2026	7412	6752	6172	6180	6616	18%	33%
19	TTU	4yr	5%	409	3640	10352	10940	11005	11252	11331	4%	32%
20	Motlow	2yr	3%	581	1411	6878	6689	6391	6434	6299	9%	22%
21	TSU	4yr	0%	5702	4409	7816	8231	7909	7869	8168	70%	56%
22	Southwest	2yr	-8%	9023	6282	19989	19592	17949	16180	15335	59%	39%