Henry Peng Thesis

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Do Labor Market Conditions Affect College Graduation Rates? United States as a Case Henry Peng 1  [email protected] Senior Honors Thesis Department of Economics Washington University in St. Louis February 25, 2011 Abstract: This paper investigates the impact of labor market conditions on college graduation rates. Using panel data of 436 four-year public colleges across the fifty states spanning five years, I examine the impact of both national and state unemployment on the difference between a school’s four year and six year graduation rate by cohorts. I use different models to compare the impact of both the national unemployment rate and the state unemployment rate on the graduation rates. A robust model controlling for both time and school fixed effects result in a positive relationship between both the national and state unemployment rate by bachelor’s degree and higher and the difference between a school’s four year and six year graduation rate. Credible assessment of the impact of labor market conditions on college graduation rates can provide important insight into what factors college students consider when graduating.  I would like to thank my thesis advisor, Professor Juan Pantano, for his valuable time, patience, and guidance. I would also like to thank Professor Sebastian Galiani, Professor Bruce P etersen, and Professor Dorothy Petersen for their support and assistance throughout the honors thesis process. I thank Andrew Mary from the Department of Education and Peter Horner from the Bureau of Labor Statistics with their assistance with the data collection.

Transcript of Henry Peng Thesis

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Do Labor Market Conditions Affect College Graduation Rates?

United States as a Case

Henry Peng1 [email protected]

Senior Honors Thesis

Department of EconomicsWashington University in St. Louis

February 25, 2011

Abstract: This paper investigates the impact of labor market conditions on college graduation rates.Using panel data of 436 four-year public colleges across the fifty states spanning five years, I examine

the impact of both national and state unemployment on the difference between a school’s four year andsix year graduation rate by cohorts. I use different models to compare the impact of both the national

unemployment rate and the state unemployment rate on the graduation rates. A robust modelcontrolling for both time and school fixed effects result in a positive relationship between both the

national and state unemployment rate by bachelor’s degree and higher and the difference between a

school’s four year and six year graduation rate. Credible assessment of the impact of labor marketconditions on college graduation rates can provide important insight into what factors college studentsconsider when graduating.

 I would like to thank my thesis advisor, Professor Juan Pantano, for his valuable time, patience, and guidance. I would

also like to thank Professor Sebastian Galiani, Professor Bruce Petersen, and Professor Dorothy Petersen for their support

and assistance throughout the honors thesis process. I thank Andrew Mary from the Department of Education and Peter

Horner from the Bureau of Labor Statistics with their assistance with the data collection.

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I.  Introduction

During times of high unemployment many people worry about their jobs, either holding on to their

current job or finding a new job. For the 1.5 million job seeking college students that graduate this year

into a job market with national unemployment hovering around double-digits, their employment

situation seems bleak. The high unemployment rate is not the only factor that is plaguing college

graduates since many have little or no job experience and are competing with experienced workers.

Research has shown during poor labor market conditions the job matching process deteriorates because

of the difficulty of finding a job that fits. (Oreopoulos, von Wachter, and Heisz 2006) This results in

individuals accepting jobs less preferred and lower salaries, only to leave when labor market

conditions improve (Kahn 2006). What is even more critical is that recent research shows that a

college graduate who enters the labor market during a bad economy negatively impacts that

individual’s long-run wages (Kahn 2006).

On the other hand, poor labor market conditions mean that the opportunity cost of attending school

decreases which is why from the fall of 2007 to the fall of 2008 there was a 6% surge in the freshmen

enrollment rate in post-secondary schools across the U.S, making it the largest increase since 1968. In

this sense, college can be seen as a “safe haven” for incoming students. However, for the college

students whom are about to graduate and do not see graduate school as a viable option, can college still

 be a “safe haven”? Given the studies that show long-term consequences in entering the labor market

during a bad economy, one wonders if college students have thought about waiting to enter the labor

market until the economy picks up. This would mean that they would forgo one to two years of earning

but that might be the smarter decision in the long-run.

I am specifically interested in this question and examine the effect of labor market conditions on

college graduations rates. I look at the national and state unemployment rate as a proxy for the labor

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market conditions since a higher unemployment rate usually means unfavorable job conditions for

students about to graduate.

I follow students from five cohorts, starting with the incoming class of 1999 and the next four

cohorts stopping at the incoming class of 2003. I look at the labor market conditions during the

cohort’s fourth year which is when many students have the option of graduating. For example, I look at

1999’s cohort during 2003 which is the cohort’s four -year graduate time and also 2005 which is the

cohort’s six-year graduation time. The logic is if college students notice the high unemployment during

their fourth year, they may be eligible to graduate but decide to postpone and stay in college longer in

hopes the labor market improves.

I use panel data to control for unobserved school level effects that are likely to be correlated with

the main variable of interest. For example, colleges that have always been sensitive to the state

unemployment rate have a larger difference between the four year and six year graduation rates within

the given year. Since a cross-sectional specification may produce biased estimates for the impact of the

unemployment rate on the difference between the graduation rates, I use a panel data model with

school and period fixed effects to avoid this problem.

I examine 436 U.S public four year bachelor’s degree granting institutions from 2003 to 2007,

collecting data from the Bureau of Labor Statistics and the Department of Education.

II.  Prior Research

There has been an increasing amount of evidence that shows the impact of labor market conditions

at graduation affecting long-term wages and subsequent employment opportunities (Ohtake and Inoki,

1997 for Japan; Oreopoulos, von Wachter and Heisz, 2006 for Canada; Brunner and Kuhn 2009 for

Austria; Kahn 2006 for the United States). The standard neo-classical model theorizes that labor

markets function in perfectly completive spot markets however other career models based on frictions

in the labor market and human capital accumulation suggests that shocks in the labor market may have

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long-run effects on wages (Oreopoulos, von Wachter, and Heisz 2006). Using regional unemployment

rates at the time of graduation Orepoulos, von Wachter and Heisz find that young graduates entering

the labor market during a recession suffer large initial earnings losses which slowly faded away only

after 8 to 10 years. Murphy and Welch (1990) estimate that around 80% of all wage increases accrue

within the first ten years of an individual being in the labor market. Other studies have found that

individuals who face poor local labor market conditions at the time of graduation are at higher risk of 

unemployment not only upon graduation but subsequent years also (Raaum and Roed 2006).

Kahn’s work on the impact of labor market conditions on long-run wages show that there is

evidence of negative and persistent effects on wages in the United States. Kahn’s study shows that

using national unemployment as a proxy for labor market conditions result in a negative and

statistically significant relationship with wages both in the first year after graduation and in the long-

run. Interestingly, when the state unemployment rate is used as the proxy variable for labor market

conditions the results are weak and statistically insignificant in most cases. Kahn believes that state

rates only provide support for the national wage results. The case can be made that state

unemployment rates might not result a large effect because highly educated workers may be less

sensitive to local labor market conditions since they are more likely to migrate (Wozniak 2006).

These negative findings on labor market conditions impact on short-term and long-term may be

explained by variations in human capital accumulation at the firm level (Jovanovic 1979a) or in

general, (Neal 1999) developed between graduates during good and bad economies. Other studies

show that workers who enter firms during a recession on average have shorter employment within a

firm which might be indicative of the poor match quality (Bowlus 1995). These conclusions line-up

with studies that show early training/human capital development has positive effects on future wages

(Gardecki and Neumark 1998).

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While all of the studies mentioned above have made large contributions to understanding the

impact of labor market conditions on wages, the dependent variable they used as the proxy for labor

market conditions for graduating students may not be the best specification. Using the national

unemployment rate may not be the best indictors for the labor market conditions college students face

since a study by the Department of Education shows that 69 percent of college graduates stay within

the state they earned their degree and 87 percent are working one year after graduation (Department of 

Education 2000). Although my investigation does not look into the impact of labor market conditions

on wages, I believe the state unemployment rate and specifically the state unemployment rate for

individuals with a bachelor’s degree and higher would be a better proxy for the labor market conditions

college graduates face. That is why I will investigate the impact of both the national and state

unemployment rates (with and without educational attainment) on college graduation rates.

III.  Data and Model

The data was obtained from the Bureau of Labor Statistics (BLS) and the Department of 

Education. The Current Population Survey (CPS) conducted by the BLS provided the overall state

unemployment rates and the state unemployment rates for individuals with a bachelor’s degree and

higher from 2003 to 2007. The BLS also provided the national unemployment rates and the national

unemployment rates by bachelor’s degree and higher from 2003-2007. I collected the four-year and

six-year college graduation rates and tuition data from the Department of Education from 2003 to

2007. I chose only to look at public four-year colleges granting Bachelor’s degree or higher due to the

higher tendency of those students staying within the state they obtained their bachelor’s degree post-

graduation (Department of Education 2000). I further narrowed down my universe of school by

eliminating the schools that had gaps in their data. Further, military schools were not included due to

the unlikelihood of those graduates entering the labor market.

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Dependent Variable 

 Difference in Graduation Rate

The difference between the six-year and the four-year graduation rate at a college for a

particular cohort (same incoming class) is the key dependent variable. Both the four-year and six-year

graduation rates are by cohort so 2002’s incoming class would be measured at 2006 to obtain that

cohort’s four -year graduation rate and 2008 to obtain that cohort’s six-year graduation rate. By

subtracting the four-year graduation rate from the six-year graduation rate we get the percentage of 

students that graduated not within four years but within five or six years. Both graduation rates account

for transfer students, college dropouts, and joint degree programs.

= –

 

Four-Year Graduation Rate

I also use the four-year graduation rate as another dependent variable. Looking at the four-year

graduation rate by itself allows us to extend our examination to not only students who postpone their

graduation but look at other possibilities such as the inability of parents to pay for college which might

also be influencing the four-year graduation rate.

Independent Variables 

My explanatory variables can be divided into two categories: the national unemployment rate

and the state unemployment rate. Within both of these categories I further look at the overall

unemployment rate and the unemployment rate by bachelor’s degree and higher.

 National Unemployment Rate

The national unemployment rate is very general since it includes all fifth states and the District

of Columbia and all individuals currently out of work but actively seeking work out of the total

See Table I in Appendix for Summary Statistics

See Table I in Appendix for Summary Statistics

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number of employable individuals. Kahn uses the national unemployment rate as a proxy for labor

market conditions and found this measure to have a negative impact on wages and to be statistically

significant.

 National Unemployment Rate by Educational Attainment 

I believe the national unemployment rate by bachelor’s degree and higher would be a better 

proxy compared to the overall national unemployment rate since college graduates are most likely

looking for high-skilled work. Therefore the overall unemployment rate might encompass too wide of 

a net since the labor market conditions for high skilled vs. unskilled jobs might vary.

State Unemployment Rate

I look at the overall state unemployment rate since prior studies have also used this as the proxy

for labor market conditions. The rationale in using the state unemployment rate is that many college

graduates tend to stay within the state they earned their bachelor’s degree post-graduation. Logically, it

is easier for firms to recruit at colleges close by. Also since all of the colleges used in our sample are

four-year public institutions a majority of the student population live within the state.

Although previous literature has indicated that highly educated workers may be less sensitive to

local labor markets conditions because of the option to migrate, a study conducted by the Department

of Education of Bachelor’s Degree recipients show that 69 percent of graduates and 74 graduates from

a public college reside in the same state they received their bachelors from one year later (Department

of Education 2000).

State Unemployment Rate by Educational Attainment 

The state unemployment rate by educational attainment is for individuals with bachelor’s

degree and higher helps us narrow down the unemployment rate even further. I decided to use this

more narrow definition as a proxy for the labor market conditions because logically college students

look for high-skilled jobs to match their skill level. This is my main variable of interest because I

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believe it is the best proxy for the labor market conditions college graduates face immediately upon

graduation. This measure is defined by the amount of employable residents in a given state with a

 bachelor’s degree or higher currently out of work but actively seeking work out of the total number of 

employable individuals in the state with a bachelor’s degree or higher.

Tuition

I look at tuition as a control variable because tuition cost for colleges can vary within and

between states. As the price to college increases the opportunity cost of attending college becomes

higher. I use in-state tuition because most at most state schools the majority of the students are in-state

students. Theoretically tuition should have a negative relationship with the difference between the

four-year and six-year graduation rate since the longer an individual stays in school the more money

that individual will have to pay.

Model

The following equations summarize the two models I use to estimate the impact of labor

market conditions on college graduation rates.

(1)

(2)

Substitutes for : , ,  

where is the overall national unemployment rate and is the national unemployment rate by

 bachelor’s degree and higher, that corresponds to college i in year t . is the overall state

unemployment rate and is the state unemployment rate by bachelor’s degree and higher both which

also corresponds to college i in year t . is the log of tuition for college i in year t . is the

difference between the four-year and six-year graduation rate and is the four-year graduation rate.

and are school-level and year-level fixed effects, respectively. The key variable of interest is in

all four models.

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IV.  Results

I look at two sets of regressions, one for each model. I start with the simplest model and build

in the appropriate controls. All of the regressions control for tuition and use clustered robust errors.

I start by looking at the impact of the national unemployment rate on college graduation.

Model 1  Labor Market Proxy Variable Coefficient

1 Random Effect National Unemployment Rate () .3912113***

2 Random Effect w/ Year

Fixed EffectNational Unemployment Rate () .3328469**

3 College & Year Fixed

EffectNational Unemployment Rate () .4043632***

The national unemployment rate is significant in all three regressions. Using the random effects

panel regression to explain the difference between the four and six year graduation rates shows a

highly significant positive coefficient for national unemployment (0.3912113, p<0.01). However, this

coefficient is flawed in that it is not accounting for upward time trends that might positively bias the

coefficients.

In regression two I add in year fixed effects which slightly lowers the national unemployment

coefficient (0.3328. p<0.05).Yet this model is also flawed in that each school may have inherent

differences that are specific to all and only that particular school which would cause different

differences between the four and six year graduation rate.

For my final regression using this model I add in school fixed effects to the regression. Using

both school and time controls allows my model to look at the data more precisely since it looks at 436

schools over a five year period. This model indicates that with all else held constant with a one

percentage point increase in national unemployment rate we see a ..4 percentage point increase in the

difference between the four year and six year graduation rate (p=0.009). Interestingly, these results

complement prior studies that show the impact of the national unemployment rate on wages.

See Table 2 for regression results.

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Model 1  Labor Market Proxy Variable Coefficient

6 School & Year Fixed

Effect

National Unemployment Rate:

Bachelor’s Degree & Higher ()

.5146441***

7 School & Year Fixed

EffectState Unemployment Rate () .0889683

8 School & Year Fixed

Effect

State Unemployment Rate: Bachelor’s

Degree & Higher ()

.3122881*

For my second regression, I use the national unemployment rate by bachelor’s degree and higher () 

and get similar results. Controlling for school and year effects the impact on the difference between the

four year and six year graduation rate increase to .5146. Using the overall state unemployment rate

() as my main independent variable, controlling for the year and school effects, the coefficient is

insignificant.

Finally, I use the state unemployment rate for individuals with a bachelor’s degree and higher 

() as my main dependent variable and notice stay significant even when school and year effects

are controlled for. When controlling for school and year effects my model indicates that with all else

held constant with a one percentage point increase in state unemployment for individuals with a

 bachelor’s degree and higher we see a .312 percentage point increase in the difference between the four

year and six year graduation rate (p=0.68).

Model 2  Labor Market Proxy Variable Coefficient

1 School & Year Fixed Effect State Unemployment Rate () -.3345804*

2 School & Year Fixed Effect State Unemployment Rate:

Bachelor’s Degree & Higher ()

-.6339153***

Looking at just the four-year graduation rate we find similar results. However, due to

collinearity, both national rates had to be dropped in my results. However, interestingly both state rates

are significant and for state unemployment by bachelor’s degree and higher our coefficient doubled.

See Table 3 for regression results.

See Table 4 for regression results.

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When controlling for school and year effects my model indicates that with all else held constant with a

one percentage point increase in state unemployment for individuals with a bachelor’s degree and

higher we see a .633 percentage point decrease in the four-year graduation rate. This may indicate that

some students are not only prolonging their stay in college but other students and their parents may not

be able to afford college and may have to leave school.

These results indicate that college students seem highly sensitive to the national labor market

conditions and local labor market conditions for individuals with bachelor’s degrees and higher. Both

the national and state unemployment by bachelor’s degree are significant which logically makes sense

in that college students care the most about the labor market conditions in the industries and type of 

 jobs they plan to work in.

I use the Hausman test on model 1 regression 4 to verify that using the fixed effect model is the

preferred model. We reject the null hypothesis that there is no systematic difference between the two

models and therefore determine that it is better to use a fixed effects model for our purposes

(p=0.0113).

In all of four models with school and year fixed effects, all else held constant log of tuitions has a

negative coefficient as predicted but is insignificant. Therefore the impact of tuition on the difference

between the four year and six year is not useful however it is still an important control variable.

V.  Conclusion

The economic conditions college students face upon graduation can be very different and research

shows that students who plan to enter the labor market during a bad economy may face long-term

consequences, especially negative impacts on their wages. The lucky may find themselves in the job

they desire with relatively high wages and the not so lucky may be forced to a less promising job and

lower earnings potential. So does luck matter? The short answer is yes but the results of this paper

support the hypothesis that college students may be prolonging their stay in college in hopes that labor

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market conditions improve. Since the opportunity cost of attending school decreases if potential long-

run earnings for working decreases, forgoing a year or two of earnings might not be a bad idea if it

means a smaller net loss to long-run wages.

The results indicate that a sizable number of college students have not let the business cycle control

their luck and the labor market conditions they graduate into. Both the overall national unemployment

rate and the national unemployment rate by bachelor’s degree and higher appear to influence the

difference between a cohort’s four year and six year graduation rate. The state unemployment rate by

 bachelor’s degree and higher is significant but does not appear to be as strong of an indicator compared

with the national rate. These results complement Kahn’s findings that the national unemployment rate

has a strong negative relationship with both short and long-run wages. College students may be aware

of the negative relationship between the labor market conditions at graduation and wages and trying to

avoid exiting school during poor labor market conditions. The results in this study are limited since the

data does not cover a recession period and the time horizon is relatively short. Once the data from the

great recession becomes available it will add further clarification to this investigation.

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VI.  References

Bowlus, Audra J. (1995): “Matching Workers and Jobs: Cyclical Fluctuations in Match Quality.” Journal of Labor Economics, Vol. 13, No. 2. pp 335-350.

Bureau of Labor Statistics (2011):” Geographic Profile of Employment and Unemployment (2003-

2007)” Department of Labor. 

Gardecki, Rosella and David Neumark (1998), “Order from Chaos? The Effects of Youth Labor Market Experiences on Adult Labor Market Outcomes,” Industrial and Labor Relations Review,

pp. 299-322.

Jovanovic, Boyan (1979a). "Job Matching and the Theory of Turnover." Journal of 

Political Economy 87 (October): 972-90.

Kahn, Lisa B. (2008): "Job Durations, Match Quality and the Business Cycle: What

We Can Learn from Firm Fixed Effects." print, Harvard University

Kahn, Lisa B. (2009): “The Long-Term Labor Market Consequences of Graduating from College

in a Bad Economy.” print, Yale School of Management.

Murphy, K. & Welch, F., 1990. "Wage Differentials In The 1980s: The Role Of The InternationalTrade," Papers 23, California Los Angeles - Applied Econometrics.

 National Center for Education Statistics, (2003): “A Descriptive Summary of 1999– 2000Bachelor’s Degree Recipients 1 Year Later.” print, Department of Education. 

 National Center for Education Statistics, (2011): ““Four Year and Six Year College Graduation

Rates”.” print, Department of Education. 

Oreopoulos, Phil, von Wachter, Till and Andrew Heisz (2006). "The Short- and LongTerm CareerE§ects of Graduating in a Recession: Hysteresis and Heterogeneity in the

Market for College Graduates." print, Columbia University.

Ohtake, Fumio, and Takenori Inoki. 1997. “Cohort effects in the labor market.” Asako, N. Yoshino

and S. Fukuda, 297 – 320. Tokyo: University of Tokyo Press.

Raaum, Oddbjørn and Knut Røed, 2006: Do Business Cycle Conditions at the Time of Labour

Market Entry Affect Future Employment Prospects?, The Review of Economics and Statistics , 

88(2) May, 193-210

Wozniak, Abigail (2006), "Why are College Graduates More Responsive to Distant

Labor Opportunities?," print, Notre Dame.

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VII.  Appendix

Table I: Variables, Definitions, Summary Statistics

Name Definition Mean / 

(Standard Deviation)

Min/Max

Difference The difference between a school’s four 

year and six year graduation rate.

23.81927

(6.822364)

3/48

ur-Year Graduation Rate The four-year graduation rate by cohort 27.05497

(14.91399)

2/85

onal Unemployment Rate The overall national unemployment

rate for each year.

5.16

(.5390114)

4.6/6

onal Unemployment Rate

y Bachelor’s Degree and

Higher

The national unemployment rate by

educational level: bachelor’s degree

and higher.

2.42

(.4262433)

2/3.1

ate Unemployment Rate The overall state unemployment ratefor each year.

5.06578(1.002893)

2.5/8.1

e Unemployment Rate by

helor’s Degree and Higher 

The state unemployment rate by

educational level: bachelor’s degreeand higher.

2.282294

(.6763278)

.9/4.8

Tuition The Log of tuition cost or the change in

the tuition cost from year to year.

9.301638

(.3557994)

1,714/28,94

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Table II: Regressions using equation 1 (Overall National Unemployment Rate) and clustered-robust

errors 

(1) (2) (3)

DEPENDENT VARIABLES Difference (D) Difference (D) Difference (D)

National Unmployment Rate (N) 0.391*** 0.333** 0.404***(0.124) (0.146) (0.155)

LogP (P) 0.256 0.309 -0.568

(0.452) (0.453) (0.521)

t1 0

(0)

t2 -0.176 -0.0737

(0.155) (0.156)

t3 -0.0119 0.161

(0.155) (0.152)

t4 0.245

(0.182)t5 -0.274 0

(0.176) (0)

Constant 19.42*** 19.32*** 26.96***

(4.548) (4.559) (5.227)

Year Fixed Effects No Yes Yes

School Fixed Effects No No Yes

Observations 2,178 2,178 2,178

Number of id 436 436 436

R-squared 0.009Robust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

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Table III: Regressions using equation 1 (National Unemployment Rate by Bachelor’s Degree andHigher) and clustered-robust errors

(1) (2) (3)

DEPENDENT VARIABLES Difference (D) Difference (D) Difference (D)

National Unemloyment Rate Bachelor'sDegree and Higher (H)

0.483*** 0.424** 0.515***

(0.158) (0.186) (0.197)

LogP 0.241 0.309 -0.568

(0.454) (0.453) (0.521)

t1 0

(0)

t2 -0.173 -0.0700

(0.155) (0.155)

t3 0.0274 0.208

(0.156) (0.155)

t4 0.245

(0.182)

t5 -0.274 0

(0.176) (0)

Constant 20.41*** 20.01*** 27.80***

(4.443) (4.422) (5.063)

Year Fixed Effects No Yes Yes

School Fixed Effects No No Yes

Observations 2,178 2,178 2,178

R-squared 0.009

Number of id 436 436 436

Robust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

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Table IV: Regressions using equation 1 (Overall State Unemployment Rate) and clustered-robust

errors

(1) (2) (3)

DEPENDENT VARIABLE Difference (D) Difference (D) Difference (D)

State Unemployment (U) 0.346*** 0.216 0.0890(0.0991) (0.147) (0.154)

LogP (P) 0.0918 0.290 -0.575

(0.434) (0.451) (0.520)

t1 0

(0)

t2 -0.124 -0.119

(0.100) (0.0995)

t3 -0.0509 -0.0460

(0.0775) (0.0771)

t4 -0.0505 -0.0532

(0.0691) (0.0718)t5 -0.0928 -0.0905

(0.0566) (0.0589)

Constant 21.22*** 20.24*** 28.93***

(4.283) (4.305) (4.867)

Year Fixed Effects No Yes Yes

School Fixed Effects No No Yes

Observations 2,178 2,178 2,178

R-squared 0.010

Number of id 436 436 436Robust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

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Table V: Regressions using equation 2 (State Unemployment Rate by Bachelor’s Degree and Higher)and clustered-robust errors

(1) (2) (3)

DEPENDENT VARIABLES Difference Difference Difference

State Unemployment Rate Bachelor's Degree and Higher (E) 0.490*** 0.423*** 0.312*

(0.122) (0.164) (0.171)LogP (P) 0.123 0.273 -0.592

(0.427) (0.448) (0.517)

t1 0

(0)

t2 -0.107 -0.0907

(0.0969) (0.0949)

t3 -0.00893 0.00215

(0.0762) (0.0754)

t4 -0.0200 -0.00922

(0.0634) (0.0641)

t5 -0.0664 -0.0531(0.0517) (0.0528)

Constant 21.56*** 20.44*** 28.72***

(4.117) (4.192) (4.799)

Year Fixed Effects No Yes Yes

School Fixed Effects No No Yes

Observations 2,178 2,178 2,178

R-squared 0.011

Number of id 436 436 436

Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

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Table VI: Regressions using Four-Year Graduation as Dependent Variable

(1) (2)

VARIABLES Four-Year Four-Year

State Unemployment (U) -0.335*

(0.196)State Unemployment Rate Bachelor's Degree and Higher (E) -0.634***

(0.201)

t1 0 0

(0) (0)

t2 0.418*** 0.397***

(0.119) (0.117)

t3 0.416*** 0.359***

(0.0922) (0.0909)

t4 0.481*** 0.439***

(0.0824) (0.0734)t5 0.584*** 0.548***

(0.0699) (0.0638)

Constant 27.37*** 27.24***

(1.163) (0.621)

Year Fixed Effects Yes Yes

School Fixed Effects Yes Yes

Observations 1,910 1,910

R-squared 0.162 0.166Number of id 382 382

Robust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

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Table VII: Hausman test for Model 4

Table VIII: Comparison of the Different Unemployment Rates for a Random State

0

1

2

3

4

5

6

7

2003 2004 2005 2006 2007

   U   n   e   m   p    l   o   y   e   n   t   R   a   t   e

Labor Market Conditions (Alabama)

State Unemployment Rate

National Unemployment Rate

National Unemloyment Rate

Bachelor's and Higher

State Unemployment Bachelor'sDegree and Higher