Econ Senior Experience

Senior Experience for Economics

Qianyu Chen

Content

-Research Question

-Literature Review

-Methodology

-Result & Discussion

-Conclusion

Research Question

What factors affect people’s decisions to enter the labor force in US major metropolitan areas?

-Motivations -Final project for Econ 380 -Different theories predict different results— “added-worker hypothesis” vs. “discouraged-worker hypothesis”

Literature Review

A lot of pertinent studies were focusing on female issues

• Compton & Pollak(2013) found that family proximity affects the labor force attachment of married women with young child

Most studies were done at the national level• Hornstein(2013) identified negative relationship

between Civilian LFPR and the unemployment rate

Literature Review(cont.)

Gap

• Almost no studies have been done at the metropolitan level using cross sectional data

-The only related one was about married women, done in 1950

• Most studies were trying to find relationships between the LFPR and one single factor

Methodology

Part I• Conduct regression analysis to find

relationships between LFPR and selected variables using cross sectional data

Part II• Case study: New york metro area(NY-NJ-PA)

vs. Los Angles metro area(LA-Long Beach-Riverside)

Methodology Part I

Linear RegressionDependent Variable: • Y: the LFPR (by metro areas) Independent Variables:• X1:Income(Personal income as a portion of US metropolitan

areas)• X2:unemployment rate (by metropolitan area )• X3:education attainment(percentage of population holding a

bachelor’s degree or higher by metropolitan area)• X4:aged population (the percent of population of age 65 or

older by metropolitan areas)

Methodology Part I (cont.)

Data:• Cross Sectional data• Obtained from credible online sources including Bureau of

Labor Statistics, Bureau of Economic Analysis, National Center for Educational Statistics, and the U.S. Census Bureau.

• Sample size: n=46• Metro areas with population more than one

million• Year: 2010

Methodology Part I (cont.)

• Model:

Y= β0 + β1X1 + β2X2 + β3X3 + β4X4 + ε

Methodology Part II

• NY metro area vs. LA metro area • Time series plots• Data:

– Time series data– Sample size:

• For LFPR and UR: n=288 (Jan 1990-Dec 2013, monthly)• For Income: n=23 (1990-2012, yearly)• For education attainment: n=8 (93, 95, 00, 02, 08-11)• For aged population: n=5 (06-10, yearly)

Methodology Part II (cont.)

• The LFPR Data– Purchased from Moody’s Analytics – Their economists group calculated the data using a

11-step method including • Finding the non-institutionalized population by county

by month• Finding the break-adjusted labor force• Calculating the LFPR by metro by month

Result & Discussion Part ISTATA/R Output:Check linearity:

Laborforce

participationrate

unemploymentrate

personalincome(percent

of US metroportion)

percentof 65

years andover

percentwith

bachelor'sor higher

60 65 70 75

5

10

15

5 10 15

0

5

10

0 5 10

10

15

20

10 15 20

20

30

40

50

Result & Discussion Part I (cont.)

• Unemployment rate:

46

810

1214

Aug

men

ted

com

pone

nt p

lus

resi

dual

6 8 10 12 14unemployment rate

0.0

5.1

.15

.2D

ensi

ty

6 8 10 12 14 16unemployment rate

Kernel density estimate

Normal density

kernel = epanechnikov, bandwidth = 0.7135



• Personal Income

-6-4

-20

24

Aug

men

ted

com

pone

nt p

lus

resi

dual

0 2 4 6 8 10personal income(percent of US metro portion)

0.2

.4.6

.8D

ensi

ty

0 2 4 6 8 10personal income(percent of US metro portion)


Normal density




• Percentage of age population:

-6-4

-20

24

Aug

men

ted

com

pone

nt p

lus

resi

dual

8 10 12 14 16 18percent of 65 years and over

0.0

5.1

.15

.2D

ensi

ty

8 10 12 14 16 18percent of 65 years and over


Normal density




• Education attainment

510

1520

Aug

men

ted

com

pone

nt p

lus

resi

dual

20 30 40 50percent with bachelor's or higher

0.0

2.0

4.0

6.0

8D

ensi

ty

20 30 40 50percent with bachelor's or higher


Normal density




Income Log transformationCheck:

0.2

.4.6

Den

sity

-1 0 1 2 3log-10 of personalincomepercentofusmetropo


Normal density



-50

5A

ugm

ente

d co

mpo

nent

plu

s re

sidu

al

-1 0 1 2 3log-10 of personalincomepercentofusmetropo


Check normality of residual

0.0

5.1

.15

.2D

ensi

ty

-5 0 5Residuals


Normal density



-50

5R

esid

uals

62 64 66 68 70 72Fitted values


Check homoscedasticity

Prob > chi2 = 0.8351 chi2(1) = 0.04

Variables: fitted values of laborforceparticipationrate Ho: Constant varianceBreusch-Pagan / Cook-Weisberg test for heteroskedasticity

. estat hettest

Total 21.18 19 0.3269 Kurtosis 2.50 1 0.1135 Skewness 3.59 4 0.4636 Heteroskedasticity 15.08 14 0.3726 Source chi2 df p

Cameron & Trivedi's decomposition of IM-test

. estat imtest


Check multicollinearity

Mean VIF 1.23 percentof6~r 1.13 0.886528unemployme~e 1.20 0.832854 lgPI 1.23 0.812945percentwit~r 1.36 0.737091 Variable VIF 1/VIF

. vif


• Output:

_cons 64.73122 4.2566 15.21 0.000 56.13484 73.32759 lgPI -1.213894 .5430952 -2.24 0.031 -2.310697 -.1170908percentwit~r .2551002 .0723993 3.52 0.001 .1088868 .4013135percentof6~r -.5118291 .1847824 -2.77 0.008 -.8850048 -.1386533unemployme~e -.0392647 .2116215 -0.19 0.854 -.466643 .3881137 laborforce~e Coef. Std. Err. t P>|t| [95% Conf. Interval]

Total 432.099323 45 9.60220718 Root MSE = 2.4708 Adj R-squared = 0.3642 Residual 250.296513 41 6.104793 R-squared = 0.4207 Model 181.80281 4 45.4507025 Prob > F = 0.0001 F( 4, 41) = 7.45 Source SS df MS Number of obs = 46


• All the independent variables are statistically significant except for X1:Unemployment Rate

• Evidence shows positive relationship between education attainment and the LFPR• Evidence shows negative relationship between

unemployment, income, aging and the LFPR

Result & Discussion Part I (cont.)-5

05

e( la

bor

forc

epar

ticip

atio

nra

te |

X )

-4 -2 0 2 4e( unemploymentrate | X )

coef = -.03926466, se = .21162154, t = -.19

-50

5e(

lab

orfo

rcep

artic

ipat

ionr

ate

| X

)

-4 -2 0 2 4 6e( percentof65yearsandover | X )

coef = -.51182906, se = .18478244, t = -2.77

-10

-50

5e(

lab

orfo

rcep

artic

ipat

ionr

ate

| X

)

-10 -5 0 5 10 15e( percentwithbachelorsorhigher | X )

coef = .25510016, se = .0723993, t = 3.52

-50

5e(

lab

orfo

rcep

artic

ipat

ionr

ate

| X

)

-1 0 1 2e( lgPI | X )

coef = -1.2138938, se = .5430952, t = -2.24


Why isn’t the variable Unemployment Rate significant?• Potential heteroscedasticity• The UR is a complex variable which is affected by

numerous other factors besides the LFPRThe higher income, the lower LFPR—counterintuitive? • If leisure is normal good—the income effect says an

increase in income will tend to cause workers to supply less labor in order to "spend" the higher income on leisure


The trend of aged people re-join the labor force?• Still not strong enough to reverse the negative

relationship between aging and the LFPREducation attainment?• The more educated, the higher LFPR rate!• May be affected by the easiness of finding a

job

Result & Discussion Part II (cont.)4

68

10

12

1990m1 1995m1 2000m1 2005m1 2010m1 2015m1month

LaUR NyUR

Result & Discussion Part II (cont.)

• Time series plot: LFPR vs. UR– LFPR: LA> NY– UR: no big differenceConsistent with the regression result: UR not

significant.But…Over years—strong negative correlation!

Result & Discussion Part II (cont.)20

00030

00040

00050

00060

000

1990 1995 2000 2005 2010year

incomeLA incomeNY


• Time series plot: LFPR vs. Income– LFPR: LA> NY– Income: LA<NYConsistent with the regression result: cross areas,

income and the LFPR have negative correlationsOver time: no evidence on correlations.

Result & Discussion Part II(cont.)

20

25

30

35

40

1995 2000 2005 2010Year

LA NY


• Time series plot: LFPR vs. education attainment– LFPR: LA> NY– Education attainment: LA<NYNot consistent with the regression result!!!

WHY???Over time: no evidence on correlations.


10

11

12

13

2006 2007 2008 2009 2010year

LA NY


• Time series plot: LFPR vs. percentage of aged population – LFPR: LA> NY– percentage of aged population :LA<<NY– Consistent with the regression result and explains

the previous “WHY”: offsetOver time: positive correlation

Conclusion

• Unemployment rate: complicated issue, no single answer

• Education attainment: positively correlated• Income, portion of aged population:

negatively correlated• Case study: consistent with the general result!

Conclusion(cont.)

Policy implications• Better health care ,more job opportunities for

retired people• Policies encourage college(and higher level)

enrollment -more financial aids, etc.

• Increasing wage rate?—not always a good thing, depending on whether income effect or substitution effect dominates

Conclusion(cont.)

Potential problem of the study• data problem• Sample size

Documents

Econ Senior Experience