Ryan KingMatt NowlinPOLI 405April 30, 2015

STUDENT LOANS AND FINANCIAL AID POLICY

INTRODUCTION

In the 2012 State of the Union Address, President Obama asserted, “Getting the best pos-

sible education has never been more important than it is right now.” In the most recent State of

the Union, President Obama amended, “By the end of this decade, jobs will require some higher

education, [but] striving Americans are priced out of the education they need.” Scholars and

economists agree as the global economy and workforce expand, competition will intensify and

higher educational attainment will become increasingly paramount. And Americans seek higher

education for higher and more competitive wages and happiness; and to improve the quality of

life. However, tuition for private and public institutions continues to rise and students struggle

with student loans and debt upon graduation. According to financial aid experts, the average stu-

dent loan debt exceeded $33,000 (Fortenbury). With skyrocketing debts and recent changes to fi-

nancial aid, political scientists have shifted focus to financial aid policy.

Financial aid policy has evolved since the passage of the Higher Education Act of 1965.

Seeking career and job opportunities and managing loans and debt remain the primary concerns

for many middle class and lower income students and families. However, policymakers and re-

searchers focus on student loan default rates, financial aid tied to academic achievement, con-

gressional hearings, debates, field hearings, public opinion concerning loans and grants, and the

implications of such factors on the financial aid policy process. Despite previous research and

scholarly articles on the financial aid policy process, minimal experimentation has been carried

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out to determine the variables, which stimulate new financial aid policies or amendments. As a

result, we find ourselves asking, “What causes Congress to address financial aid policies?”

The following research design provides an empirical and categorical investigation of the

financial aid policymaking process. In addition, the research seeks to determine whether finan-

cial aid policy constitutes punctuated equilibrium or incrementalism or gradualism. A number of

factors relate to financial aid policy including public opinion, state funding and grants, the pref-

erences of policy makers, and reauthorizations. However, evidence suggests student loan default

rates play a more significant role in increased congressional hearings; and action or inaction on

financial aid policy. Jacob P.K. Gross, Osman Cekic, Don Hossler, and Nick Hillman from the

Indiana and Perdue Universities propose a direct correlation exists between the levels or rate of

student loan default and increased congressional hearings and actions regarding financial aid pol-

icy. Correlation does not imply causation. Therefore, the following research seeks to determine

the factors or variable(s) that influence Congressional attention to financial aid issues. In other

words, “What factors influence Congressional attention to financial aid?”

LITERATURE REVIEW

As higher education plays a significant role in the global economy, workforce and soci-

ety, the financial burden and costs increase as well. Prior the passage of the Higher Education

Act of 1965 under President Lyndon B. Johnson, the earliest financial aid policy began in 1944

with the GI Bill; and Congress frequently revisits financial aid policy. Political scientists con-

tinue to conduct studies and experiments to investigate the efficacy of the current financial aid

policies. Research also investigates the factors that influence Congressional attention to financial

aid. Recent studies show ideology, political party identification, the student default rate, and

party of power play important roles. However, researchers concur the student default rate and the

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party in power appear to influence Congressional attention to financial aid policy significantly.

The Executive and Legislative Branches must review the budget annually and financial aid and

education fall among the top appropriation bills. According to the Congressional Budget Office,

the federal government spent $30.9 billion in Pell Grants and student aid in 2009 and 2010; how-

ever, as the number of students who default on loans increases, Congress must amend and

reevaluate financial aid more frequently. In addition, the agenda of the party of power may focus

on education and employment thus increased attention to financial aid. The following literature

review explores existing research on the factors that influence Congressional attention to finan-

cial aid.

In relationship to analysis and the influences, which lead to reauthorizations of the Higher

Education Act Dr. Robin L. Capt from Texas A&M University, utilizes categorical analysis. Dr.

Capt explores the “key elements in the federal financial aid policy process, policy-decision, and

policy implementation” (Capt). Recent enrollment trends, student loans, and existing legislation

force researchers and Congress to analyze HEA reauthorizations and related policies in order to

adjust appropriations and increase accessibility. Dr. Capt argues increasing tuition costs decrease

state funding but dramatically increase federal loans.

The driving theory states, as higher education remains a federal concern and the govern-

ment continues to support research and development, Congress must oversee three areas: Equal

Protection under the Fourteenth Amendment, research appropriations, and fund matching of

loans for postsecondary students (Capt). Regulations and accountability also require the continu-

ous federal oversight to distribute funds. As Baumgartner and Jones argue punctuated equilib-

rium dictates most public policy, Dr. Capt concurs “structural fragmentation and the preferences

of influential participants in the policy outcomes” (Capt). However, financial policy is character-

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ized as incremental due to the vast number of participants. Currently, House and Senate commit-

tees and subcommittees, the Department of Education, the OMB, the Executive Office Policy

Development, firms and financial organizations representing educational institutions and pro-

grams, which benefit from student aid programs play significant roles in the policymaking

process (Capt). The key players dictate the agenda in a slow evolving political environment

based on existing policies. Adjusting policy is also a direct response to public opinion and con-

stituents. The increased demands of middle and lower class families has resulted in increased ac-

tion with various legislation including the reauthorizations of HEA and Higher Education Recon-

ciliation Act of 2005, which reduced the loan rate. Current proposed bills include FAST, (Finan-

cial Aid Simplification and Transparency Act and the Higher Education Affordability Act. As in-

terest groups, lawmakers, and committees continue proposing recommendations, Congress is

forced to address the issue of affordable higher education. Unfortunately, Capt discusses theories

and factors relating to increased policy attention without statistical or numerical data. The en-

closed research design seeks to incorporate continuous data to determine the significance of mul-

tiple factors.

In Journal of Student Financial Aid, Seymour, Zimmerman, and Donato discuss early

policies dictating the distribution of financial aid policy. In the late 1970s and 1980s, political

scientists did not fully investigate the variables that influence the outcomes of the financial aid

policy process. Previous research theorized granting financial aid revolved around loan repay-

ments and academic performance. However, Seymour et al. investigate the relationship of stu-

dent variables to the assignment of financial aid – a specific aspect of financial aid policy. The

variables utilized in the experiment could be applied in the proposed research design as control

variables. Academic achievement and major were among the top variables, and the Chi-squared

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test measured statistical significance indicating the variables are expected to influence financial

aid distribution. Major produced varied results. Business and Public Administration students re-

ceived less financial assistance than Education, Art and Sciences, and Agriculture. Clearly, the

field of study reflected the interests of the federal government, the party in power, and economy

during the late 20th century. The p-values of Agriculture (.06) and A&S (.45) compared to B&PA

(34.20) indicate the significance of the former. Therefore, specific majors may determine or in-

fluence financial aid policy and distribution. In addition, academic achievement produced a posi-

tive relationship between financial assistance. However, a correlation could not be determined.

While the research omitted policymaking variables, Seymour et al. reveal the complex nature of

financial aid.

Michael Mumper and Pamela Vander Ark analyze the Stafford Student Loan Program

and the challenges to loan reform. The challenges and lack of reform in loans may indicate finan-

cial aid policy remains incremental; however, financial aid policy also incorporates grants and

intuitional aid. While current proposed legislation deals with lower loan interest rates, the basic

structure of the loan program remains unchanged. Mumper and Ark concluded the Stafford Loan

Program remains a major political matter; however, conditions prevent any major structural re-

form characterized as punctuated equilibrium. The initial discussion supports the party in power

theory. The Johnson Administration and Congress ushered and prioritized college student assis-

tance programs with the Higher Education Act as an “integral part of the Great Society legisla-

tion” (Mumper and Ark 63). As the programs grew, Congress could not control the short-term

costs; as a result, Congress addressed financial program appropriations with the budget. As the

volume of loans increased, the expansion broadened political support increasing attention to vari-

ous student aid programs. While the program is considered a success, the expansion resulted in

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various problems – specifically drastic costs – that currently press members Congress and the

President. With budget cuts and increased tuition costs and loans, reform proposals are compli-

cated with high default rates. While reform presses Congress, choices “must be made over objec-

tions of primary constituencies: middle-income students, banks, and expensive private college”

(Mumper 75). In turn, policymakers must focus on spending policy in the budget cycle, which

may bring strong political opposition and minimal support from low-income students and local

colleges. It would appear the student default rate and general increasing expenditures have in-

creased Congressional attention. Translating the student default rate into measurable data still re-

mains a common deficiency among the later research. In Congress and the Politics of Financial

Aid, Katherine A. Ozer argues drastic budget cuts and increases in maximum grant and aid funds

with increases in tuition complicate financial aid policy. However, the reauthorizations of the

Higher Education Amendments “reaffirm governmental commitment in financing and supporting

post secondary education” (Ozer 27). While Dr. Dongbin Kim researches the effect of financial

aid on choice; and choice refers to priorities of students, which may conflict with the agendas of

policymakers. As policymakers focus financial aid policy on research universities and students

who seek degrees in the sciences, math, or engineering. Kim argues financial aid ought to pro-

vide equal educational opportunities especially for minorities and low-income families (Kim 47).

While the subject matter of the research may not be applicable to the proposed design, the tests

are applicable including the chi-square test to test null hypotheses. It appears categorical data

would be more appropriate to measure and test; however, continuous data would permit the use

of the t-test, p-values, and linear regression models.

Gross, Cekic, Hossler, and Hillman analyze the effect of student loan default on financial

aid policy in “What Matters in Student Loan Default: A Review of the Research.” According to

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Gross et al. policymakers monitor student loan default rates to track progress. Student loan de-

fault influenced the HEA reauthorizations; Representatives Bishop (D-NY) and Grijalva (D-AZ)

introduced an amendment, which requires a federal study of default rates in 2008. The high de-

fault rates raised a series of questions for policymakers including the following:

“Is default a function of the characteristics of students or of the institutions they attend?

Do the types of loans influence the probabilities of default? Do life circumstances—like

the types of jobs and income levels of students after they graduate—have an impact on

default rates?” (Gross et al. 20)

Policymakers must address these issues in order to produce the most efficient financial aid pol-

icy. Educational attainment, academic preparation (achievement), and program of study resur-

faced in the study. However, evidence supporting the relationship between financial aid policy

and default remained mixed (Gross et al. 27). Fiscal constraints and rising student debt forced

Congress to provide loans to provide access to higher education. Gross and his colleagues agree

loans and default rates are cornerstones of federal higher education policy.

Neil S. Seftor and Sara E. Turner discuss a brief history of the Pell Grants in the research

article. In the early 1970’s, policymakers sought a universal and level finance plan for all stu-

dents. Constituents and interest groups pushed for equal access and Congress developed the Pell

Grant. However, as incomes fluctuate, policymakers must amend eligibility requirements (Seftor

and Turner 339). Between 1970 and 2000, Pell Grants gradually increased; however, loans have

replaced a majority of grants. The research designed showed “changes in available federal finan-

cial aid significantly affect the enrollment of nontraditional students” (Seftor and Turner 349).

Seftor and Turner address an issue policymakers will face in the near feature. With increasing

expenditures on aid to students; will financial aid policymakers continue to fund both grants and

loans as students default?

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The following scholarly publications focus solely on the politics of financial aid policy.

The general consensus among preceding researchers reiterates student loan default rates, increas-

ing costs, and field of study influence financial aid policy in various capacities. The major chal-

lenge analyzing the influences of Congressional attention to financial aid policy involves the lack

of continuous or numerical data.

Dr. Susan Dynarski of the University of Michigan and Dr. Judith Scott-Clayton of Co-

lumbia University several issues of student aid policy in Financial Aid Policy: Lessons from Re-

search. Since HEA, “financial aid programs have grown in scale, expanded in scope, and multi-

plied” (Dynarski and Clayton 67). While a majority of research focuses on the impacts of finan-

cial aid on student achievement and opportunity, policymakers continue to seek new ways to

control costs. The authors reiterate previous themes – aid now assists nontraditional students and

accessibility has increased. In addition, the Department of Education, the Treasury as well as

Congress, the President, and state governments must work together to pass cohesive policy. “As

government expenditures increase, investors, taxpayers, and policymakers seek to determine the

return” (Dynarski and Clayton 68). As a result, attention to financial aid policy is increasing. The

evolving nature of financial aid also requires increased attention and legislative action. On the

other hand, policy remains even more complex as financial aid policy and FAFSA involve the

IRS, taxes, and credits as prescribed with the passage of the Hope Credit and American Opportu-

nity Tax Credit. Dynarski and Clayton reinforce the theory as “state and federal budgets face in-

creasing pressures and politicians look for ways to control spending and financial aid programs

will be vulnerable to cutbacks if evidence is lacking on their effectiveness” (86). Again costs

play a significant role in financial aid policy and attention.

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Dan Madzelan discusses similar changes in student aid policy in The Politics of Student

Aid. Madzelan sheds light on the party in power. However, administrations regardless of party

“pursue more explicit higher education policy and goals” (3). While the President typically

drives the policy agenda and student aid policy, Congress and interest groups ultimately shape

aid packages and policies.

Interestingly, Dr. William R. Doyle of Vanderbilt University seeks “to explain different

levels of financial aid and the importance of the ideology of state legislatures and the influence

of public and private institutions” (Abstract). Doyle posits the following research question, “To

what extent do state policy makers’ preferences affect levels of tuition and financial aid in the

states.” While Doyle focuses on state financial aid policy, the variables could be used with cau-

tion in a federal analysis. The fact student aid policy remains on political agendas means various

players impact legislation. In order to determine the influence of ideology, Doyle proposes the

following hypotheses:

H1: Holding other variables constant, appropriations in any state will depend on the level of liberalism in state government.

H2: Holding other variables constant, tuition will decrease as state government becomes more liberal. Tuition will decrease more with liberalism in states with higher public enrollments and less in states with higher private enrollments.

H3: Holding other variables constant, financial aid will in- crease as state gov-ernment becomes more liberal. Financial aid will increase more with liberalism in states with higher private enrollment and less in states with lower private en-rollment.

The dependent variables involved: state tax appropriations for high education, tuition and re-

quired fees at all public four year colleges and universities, and the total amount of state student

financial aid on a per-student basis. The independent variables include the level of state govern-

ment liberalism and the size of private enrollment in the state (630-632). Liberalism is based on

the range of 0 (most conservative) to 100 (most liberal). A series of control variables were uti-

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lized. Doyle ran several models. “The results suggest ideological positions of policymakers influ-

ence tuition levels and private institutions influence financial aid”(645). The complex research,

however, does not suggest ideology influences the frequency of attention. Ideology, costs (fed-

eral appropriations and tuition), and the rate of student default clearly play significant roles in at-

tention towards financial aid policy. The research design and data methods will explore these

variables to determine the most statistically significant factors.

RESEARCH DESIGN

Researches have identified policymaking as a political phenomenon of high interests as

new policies and legislation consist of far-reaching effects on the state and federal level. As men-

tioned previously, higher education has become increasingly important for the evolving global

economy; however, the costs of such education continue to rise. I have chosen to focus on the

student debt/loan default rate, the number of congressional hearings concerning higher education

funding, party control, and possibly the New York Times Index (the number of articles published

concerning the subject matter) to indicate media attention. To build upon the theories presented

in the literature review, the following research design seeks to operationalize the independent

and dependent variables for analysis in R Studio and to test the proposed hypotheses.

THEORY

Previous research and current studies indicate a relationship between student loan rates

and congressional attention concerning financial aid policy. As college and university tuition

costs continue to increase, students require more assistance; thus, Congress must appropriate

funds for such expenses in the budget. As a result, budgetary action requires more congressional

attention. Political scientists also concur party control influences agenda as well as the impor-

tance or lack thereof of education. Traditionally, Democrats and Republicans both support higher

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education; however, the parties approach different goals. According the article, Democrats typi-

cally seek “to make college affordable for students of all backgrounds and confront the burden

of loans” while Republicans seek to “address rising college costs while shifting federal student

aid onto a sustainable path” (UW). Regardless, the party in control drives the agenda and con-

gressional attention to higher education and financial policy fluctuates.

I will also include media attention towards financial aid and higher education. As publi-

cations increase in prominent titles such as the Wall Street Journal, The New York Times, and

the Washington Post, increased hearings may occur due the fact members of Congress not only

pursue individual agendas, but represent and listen to the constituents which may influence atten-

tion towards financial aid policy.

In order to test the theories, to determine the nature of financial aid policymaking, and to

determine the factors, which influence congressional attention towards financial aid policy, I will

test the following hypotheses:

H1: As the student loan default (i.e. rate of delinquent loans) increases, Congressional at-tention towards financial aid policy increases.

H0: The likelihood of Congressional attention towards financial aid policy does not in-crease with increasing student loan default rates.

H2: If the Democrats control the House, Congressional attention towards financial aid policy increases.

H0: The likelihood of Congressional attention towards financial aid policy does not in-crease with party control.

H3: If the number of publications concerning higher education and financial aid in-creases, Congressional attention towards financial aid policy increases.

H0: The likelihood of Congressional attention towards financial aid policy will not in-crease with an increase in newspaper articles or publications concerning financial aid & higher education.

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DATA & METHODS

Several constraints including time and lack of resources prevented the conduction of a

time series analysis. However, the research design incorporates a bivariate test, simple linear

OLS regression as well as multiple regression models, and in-depth descriptive statistics. The

Policy Agendas Project, sponsored by the University of Texas at Austin, provided a majority of

the data used in the research. Utilizing the trend analysis function, I downloaded the respective

data sets for Congressional Hearings on Higher Education and the New York Times Index. I will

determine Party Control through external research corresponding with the Congressional Hearing

data provided by the Policy Agendas Project. The student loan default rate will be analyzed as a

percentage over 27 years (1987 – 2014). The New York Federal Reserve of New York supplied

the dataset providing percent changes in student loan debt from Q1: 1987 through Q1: 2014. As

the data regarding student loan default rate for previous years is not available in either dataset,

the research design will only focus on Congressional Hearings on financial aid and higher educa-

tion policy from 1987 though 2014.

The research design operationalizes the dependent variable, Congressional attention to-

wards financial aid policy, as the number of Congressional hearings held concerning financial

aid and educational assistance programs for students and families for each year between 1987

and 2014. The number of hearings in ascending order (1987 to 2014) follows as: 14, 24, 29, 16,

27, 55, 9, 17, 20, 12, 25, 21, 32, 13, 11, 26, 6, 12, 12, 17, 18, 20, 22, 24, 28, NA, NA, NA.

The independent variable, student loan default rate, will be tested and coded as a per-

centage from the lowest percentage to the highest percentage. The student default rate is mea-

sured as percentages in order relationship to year and is scaled as followed: 17.6, 17.2, 21.4,

22.4, 17.8, 15, 11.6, 10.7, 10.4, 9.6, 8.8, 6.9, 5.6, 5.9, 5.4, 5.2, 6.13, 6.34, 6.03, 6.39, 6.85, 7.38,

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7.88, 8.36, 9.12, 8.69, 11.19, 11.01. The coding of the student loan default rate operationalizes

the independent as a continuous variable. Provided the continuous nature of the variables, the

correlation coefficient (Pearson’s R) can be used in the bivariate hypothesis testing and the test-

ing of the directional and null hypotheses can occur.

The independent control variable, party control of Congress will be coded as a 1 or 0,

with 1 as Democrats and 0 as Republicans or Split as the two major parties split control of Con-

gress. The independent control variable, media attention, will be represented via the New York

Times Index dataset provided by the Policy Agendas Project. Media Attention will be measured

by the number of publications regarding financial & educational policy per year and will be

scaled from 1987 to 2014: 2, 3, 2, 6, 7, 4, 3, 2, 4, 5, 2, 3, 2, 3, 2, 2, 6, 2, 2, 1, 4, 0, NA, NA, NA,

NA, NA, NA. The ascending scale operationalizes media attention in order to test the variable in

R.

The following research focuses primarily on the student loan default and party control.

We should be able to avoid spurious relationships and observe if any relationship exists between

the independent and control variables and the dependent variable, Congressional attention to fi-

nancial aid policy. Furthermore, the data concerning control variables influencing Congressional

attention has been scarce as the topic is relatively new. Hopefully, we will see a positive relation-

ship between the variables and the number of Congressional hearings towards financial aid pol-

icy for higher education.

DATA ANALYSISDESCRIPTIVE STATISTICS

Table 1: Rate_Hearings Descriptive Statistics

Variable Name Mean sd Min. Max.

Rate** 10.245 4.938 5.2 22.4

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Hearings*** 20.4 NA* 6 55

Party Control .429 .504 0 1

Media Attention 3.045 NA* 0 7

NOTES: **IV; ***DV; *R - responds with NA due to unavailable data (3 & 6 NA’s)

The descriptive statistics in Table 1 display the mean, minimum, and maximum of each

variable indicative of the available data between 1987 and 2014 as well as the standard deviation.

The descriptive statistics present the data concerning the student default rate and congressional

hearings on financial aid model in meaningful manner. We must avoid any premature conclu-

sions or assumptions regarding the hypotheses based on the statistics. The mean and standard de-

viation show the expected average values and the degree to which values deviate.

Rate represents the student default rates as a percentage with a minimum of 5.2% (low)

and a maximum of 22.4% (high). The mean indicates the average default loan rate over 27 years

was approximately 10.245%. The standard deviation from 10.245% is ±4.938%, indicating a dif-

ference between student loan default rates. The default rates are not completely consistent, and

the sample population experiences volatility; student default rates varied. The lowest default rate

measured at 5.2% while the highest default rate measured 22.4%. Hearings represent congres-

sional attention towards financial aid and higher education funding (grants, etc.) with a mean of

20.4. In other words, the average number of congressional hearings on financial aid policy and

funding between 1987 and 2011 is 20.4 hearings. Unfortunately, there is no data available for

2012 through 2014; however, R accounts for NA responses. The minimum and maximum values

for hearings indicate a wide range of the number of hearings 6 to 55. While controlling for Party

Control over Congress and Media Attention, we also see the (mean), (standard deviation),

minimum and maximum values for each respective variable. For party, of party control (Re-

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publican and Democrats) was .429 while the spread (or deviation) measured .504, which is rela-

tively low. For Media Attention, the measures 3.045 articles.

RESULTS

BIVARIATE TEST

The bivariate hypothesis test involved continuous dependent and dependent variables

with the number of Congressional Hearings concerning higher education financial aid as the DV;

student default loan rate as the IV. The results of the Pearson’s Correlation Coefficient between

Rate and Hearings are as followed:

Rate and Hearingst = 1.3645, df = 23, p-value = 0.1856alternative hypothesis: true correlation is not equal to 095 percent confidence interval: -0.1361947 0.6035325sample estimates: cor 0.2736633

According to the results of Pearson’s Product-Moment Correlation test, the p-value

is .1856 and not considered statistically significant. Kellstedt and Whitten, authors of The Foun-

dations of Political Science Research, state, “most political scientists use the standard p-value

of .05, and scientists consider relationships with p-values less than .05 statistically significant

(149). With a p-value greater than .05, the relationship between the two variables – student de-

fault loan rate and congressional hearings – is not systematic or occurs with random chance. In

addition, we cannot reject the null hypothesis that indicates the student default loan rate does not

cause an increase in the number of Congressional hearings on higher education grants and finan-

cial aid policy. In addition, the standard critical t-value of 1.96 is greater than the calculated t-

value of the bivariate test (1.3645), which does not indicate statistically significance. Further-

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more, the probability of observing the relationship between student default rates and congres-

sional hearings due to random chance is 0.186 or 93 in 500. A clear covariation between rate and

congressional hearings does not exist.

In order to control the experiment, the relationships between the dependent variable, Con-

gressional Hearings, and party control of Congress and Media attention were considered and

measured. The relationship between Party and Hearings revealed a slight more correlation based

on the p-value of .1065 compared to .1856 (Rate). The results of Pearson’s Correlation Coeffi-

cient test are below:

Pearson's Product-Moment CorrelationParty and Hearingst = 1.6798, df = 23, p-value = 0.1065alternative hypothesis: true correlation is not equal to 095 percent confidence interval: -0.07426399 0.64185997sample estimates: cor 0.3305671

However, just the Rate~Hearings Model did not satisfy the characteristics of statistical

significance, the relationship Party~Hearings fails to produce the standard critical and p-values.

In addition, the p-value for Media~Hearings measures .9924, which indicates no relationship,

correlation, or covariance. I can be confident that there is no covariance among the variables and

I must consider the proposed null hypotheses.

REGRESSIONS

In addition to the bivariate tests, the simple OLS regression concerning Congressional

Hearings and the student default loan rate produced the following results:

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SIMPLE REGRESSION lm(formula = Hearings ~ Rate)Residuals: Min 1Q Median 3Q Max -12.260 -6.369 -0.484 2.829 32.121 Coefficients: Estimate Std. Error t-value Pr(>|t|) (Intercept) 15.0676 4.3686 3.449 0.00218 **Rate 0.5208 0.3817 1.365 0.18560 ---Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1Residual standard error: 9.764 on 23 degrees of freedom (3 observations deleted due to missingness)Multiple R-squared: 0.07489, Adjusted R-squared: 0.03467 F-statistic: 1.862 on 1 and 23 DF, p-value: 0.1856

The simple regression model produces inconclusive data; and evidence to support the primary di-

rectional hypothesis – H1: As the student loan default (i.e. rate of delinquent loans) increases,

Congressional attention towards financial aid policy increases – remains nonexistence. The t-

value (1.365) is less than the standard critical value and the Beta coefficient as a result is in-

significant even though the estimated value measures .5208. In other words, while (X) Rate may

have a positive influence on (Y) Hearings and the number of hearings increases by .5208 for one

change in the default rate, the model indicates the relationship is no based on covariance or pure

chance. The p-value also measures 0.1856, which does not indicate statistical significance. The

adjusted R-squared, which indicates the percentage of the response variable variation explained

by the fitted regression line is low — .03467 or 3.4%. The model explains none of the variability

of the response data around the mean.

MULTIPLE REGRESSION

Figure 1.1 and Table 1.2 display the results of the multiple regression:

FIG.1.1lm(formula = Hearings ~ Rate + Party + Media)***

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*** includes lagged or lead values to simulate simple Time-Series test***Residuals: Min 1Q Median 3Q Max -12.528 -5.610 -1.373 3.262 32.419

Coefficients: Estimate Std. Error t-value Pr(>|t|) (Intercept) 15.7772 5.9045 2.672 0.0155 *Rate 0.5068 0.8148 0.622 0.5418 Party 1.8840 8.3300 0.226 0.8236 Media -0.6705 1.5704 -0.427 0.6745 ---Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1Residual standard error: 10.65 on 18 degrees of freedom (6 observations deleted due to missingness)Multiple R-squared: 0.1074, Adjusted R-squared: -0.04131 F-statistic: 0.7223 on 3 and 18 DF, p-value: 0.5517

Table 1.2OLS Regression Estimates for Voter Frequency in Primary Elections

Variable Model 1(Intercept) 15.8

(0.015)Rate 0.51

(0.54)Party 1.88

(0.82)Media -0.67

(0.6)

R2 0.11

Adj. R2 -0.04Num. obs. 21***p < 0.001, **p < 0.01, *p < 0.05

According to the multiple regression results controlling lagged, or lead variables, a single

increase in the default rate (the primary independent variable) results in a .507 increase in the

number of congressional hearings on financial aid policy. The default rate is not statistically sig-

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nificant even controlling for party control of Congress and media attention. The model indicates

the following t-values for the variables in descending order: .622 (Rate); .226 (Party); -0.427

(Media) none of which are greater than the critical value of 1.96. In addition, the p-value (proba-

bility) measures 0.5517 confirming the lack of covariance and evidence against the null hypothe-

ses. Surprisingly, all of the variables are not statistically significant even incorporating the time

lag. The adjusted R2 value of -.04 indicating the model while controlling for party and media at-

tention accounts for 0.0% percent of the variance of the relationship between Rate and Hearings.

The model also indicates an inclusive relationship between the student default loan rate and the

frequency of congressional hearings on federal grants and financial aid.

DISCUSSION & CONCLUSION

With careful, extensive and appropriate and accurate methods and tests, and knowledge

of current research and literature, I hoped to find a stronger evidence to support the directional

hypothesis. However, the data and models indicate no relationship exists between the indepen-

dent variables and the dependent variable – the number of Congressional hearings on financial

aid, grants, and expenses related to government aid in higher education. My theory cannot ex-

plain congressional attention to such an increasingly important social issue and public policy.

The results of the multiple regression fail to support both the proposed theory and hypotheses;

additional variables must be responsible and tested. Based on the literature review and prelimi-

nary research, the data concerning the student default loan rate and congressional attention re-

mained sparse even though conclusions arose with positive results indicating a positive relation-

ship. The default rate, however, cannot stand as a sole influence of congressional attention.

While the student default rate has been used as an indicator to determine the level of con-

gressional in previous research, the tests proved that the rate does not have a significant effect on

POLI 405 4/22/2319 King

congressional attention; thus, another variable or variables are influential. In relationship to

punctuated equilibrium theory and the concepts of public policymaking, the Rate~Hearings

model combines characteristics of bounded rationality and disproportionate attention. While the

student default loan rate is on the rise again, policymakers cannot consider budgetary amend-

ments to coincide with default changes continually and solutions cannot be pursued at all times.

In terms of disproportionate attention, the lack of attention or input will explain less action and

vice versa. In The Politics of Attention, Jones and Baumgartner highlight, “policymakers are un-

willing to shift focus on certain issues for ideological and pragmatic reasons.” In other words,

policy outcomes play a critical role in policy decisions or inaction. Hopefully, future research

will pose serious questions concerning why the policymakers, institutions and venues prompt

particular solutions or attention of issues that should be addressed or ignored. We can also the

data to identify patterns – based equilibrium, stability, continuity disrupted by change – regard-

ing higher education aid policies.

While my hypothesis and theory appear extremely weak, the results of the research de-

sign will prove beneficial to future political scientists seeking to understand the drivers of con-

gressional attention towards financial aid. Future research could incorporate cross-sectional or

case study and a true time-series analysis. And perhaps the number of legislation or bills passed

and proposed to deal with student loans should be considered in future tests.

POLI 405 4/22/2320 King

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