1

Reducing New Jersey Poverty: Job Growth is Not Enough!!!

William M. Rodgers III

Heldrich Center for Workforce Development

Rutgers, The State University of New Jersey

March 2015

2

Summary

During the economic recovery from 2009 to 2013, New Jersey’s poverty rate increased

from 9.4 to 11.4 percent. Most disturbing was the increase in the state’s childhood poverty rate

from 13.3 to 16.5 percent. Why did New Jersey’s poverty rate increase? There are three leading

explanations. First, the state’s macroeconomic growth has been anemic at best, causing the labor

market to severely lag the national recovery. Second, Governor Christie cut the state’s Earned

Income Tax Credit (EITC) from 25 to 20 percent in 2009 and vetoed its restoration to 25 percent

in 2014, thus removing an important source of income for many low and moderate income

residents. Third, during the recovery, the inflation-adjusted value of the state’s minimum wage

fell by 6.6 percent.

This paper first presents estimates of the relationship between New Jersey poverty and

economic growth. I then use this empirical relationship to show that during the current recovery,

the drop in the state’s unemployment rate cannot offset the poverty rate’s actual growth. From

2009 to 2013, actual New Jersey poverty increased by 2.0 percentage points, while the state’s

jobless rate fell by 0.8 percentage points. Based on the statistical relationship between poverty

and the unemployment rate, the state’s poverty rate would have been predicted to fall by 0.3

percentage points.

The final set of estimates show that counties with larger actual increases in poverty have

greater numbers of households and persons that lost use of the EITC. The models also reveal that

the funds lost associated with the state EITC are larger in counties that had the greatest increases

in poverty.

The paper concludes with a discussion of the short-term prospects for the state’s low and

moderate income families. The good news is that the state’s minimum wage automatically

increases with the Consumer Price Index each year. However, the bad news is that these modest

annual adjustments alone will not be large enough to offset the continued loss in income

associated with the state EITC remaining at 20 percent of a family’s federal credit. The wages of

low and moderate income New Jersey workers will continue to stagnate and the growth in

employment opportunities will continue to be modest.

Thus, when the 2014 poverty data is published this fall, we should not be surprised to see

that poverty rose last year.

We have choices though. To reduce poverty now, increasing the state EITC to 30 percent

will end the five-year erosion in the well-being of our state’s most challenged residents.

Doing so would set New Jersey’s credit equal to its peers: Connecticut and New York.

Further, setting the state credit at 30 percent would be consistent with Governor Christie’s

message in his January 2015 Iowa Freedom Summit speech.

The Governor said that the rich are doing fine and that we should not cater to them at the

expense of middle-income workers and the working poor. “Every policy we advance, every

decision we make should be focused on making their lives better, renewing their future,

renewing their faith, their belief in this country.”

Increasing the state EITC to 30 percent of the federal credit would honor the sacrifices

that these families have made since 2009, and serve as a down payment for ensuring their

financial security. It would also begin to restore their belief in New Jersey and the nation.

3

Introduction

After increasing during the first few years of the recovery, the U.S. poverty rate dropped

for the first time in 2013. However, for New Jersey, the state’s poverty rate jumped upward two

percentage points, from 9.4 to 11.4 percent. Most disturbing was the increase in the state’s

childhood poverty rate from 13.3 to 16.5 percent.1

Why did New Jersey’s poverty rate increase during the recovery? There are three leading

explanations. First, the state’s macroeconomic growth has been anemic at best, causing the labor

market to severely lag the national recovery. The state’s private sector employment increased 3.3

percent from June 2009 to December 2014, compared to a national growth rate of 9.5 percent.

Because of this slower pace, the state’s private sector employment has not returned to pre-

recession levels, while the national employment exceeds its pre-recession level. Average hours

worked in New Jersey remain below the start of the recovery. Average hourly earnings adjusted

for inflation are 1.8 percent below the start of the recovery, and remain 1.2 percent below their

level at the start of the recession. Second, Governor Christie cut the state’s Earned Income Tax

Credit (EITC) from 25 to 20 percent in 2009 and vetoed its restoration to 25 percent in 2014,

thus removing an important source of income for many low and moderate income residents.

NJPP estimates that New Jersey’s low-income families have lost about $300 million in income

boosting and poverty reducing tax credits.2 Third, during the recovery, the inflation-adjusted

value of the state’s minimum wage fell by 6.6 percent.

This paper first presents estimates of the relationship between New Jersey poverty and

economic growth. To do this, I create a panel data set of New Jersey’s 21 counties from 1998 to

2013 and regress a county’s poverty rate (youth poverty rate) on its “official” unemployment

rate. Second, I use this empirical relationship to show that during the current recovery, the drop

4

in the state’s unemployment rate cannot offset the poverty rate’s actual growth. From 2009 to

2013, actual New Jersey poverty increased by 2.0 percentage points, while the state’s jobless rate

fell by 0.8 percentage points. Based on the statistical relationship between poverty and the

unemployment rate, the state’s poverty rate would have been predicted to fall by 0.3 percentage

points.

The final set of estimates show that counties with larger actual increases in poverty have

greater numbers of households and persons that lost use of the EITC. The models also reveal that

the funds lost associated with the state EITC are larger in counties that had the greatest increases

in poverty.

Based on the state’s current economic growth, what are the short-term prospects for the

state’s low and moderate income families? The good news is that the state’s minimum wage

automatically increases with the Consumer Price Index each year. However, the bad news is that

these modest annual adjustments alone will not be large enough to offset the continued loss in

income associated with the state EITC remaining at 20 percent of a family’s federal credit. The

wages of low and moderate income New Jersey workers will continue to stagnate and the growth

in employment opportunities will continue to be modest. Thus, when the 2014 poverty data is

published this fall, we should not be surprised to see that poverty rose last year.

We have choices though. To reduce poverty now, increasing the state EITC to 30 percent

will end the five-year erosion in the well-being of our state’s most challenged residents.

The New Jersey Recovery

To build my narrative, I first compare the paths of U.S. Gross Domestic Product (GDP)

and New Jersey Gross State Product (GSP). Since the start of the recession, the state’s economic

5

growth has severely lagged U.S. economic growth. Cumulative real U.S. GDP growth has been

11 percent, compared to 6 percent GSP growth through the fourth quarter of 2013, the latest

available GSP data (Figure 1). Although 2014 NJ GSP data is not available, based on wage and

employment growth comparisons, the state’s economic growth has not caught up to national

growth.

An examination of cumulative growth in real GSP by industry provides some clues as to

why state economic growth lags the nation. There are seven key points. The lagging growth is

broad based. No single sector is the source of the anemic growth. In an effort to solve the state’s

fiscal woes, the Governor and legislature have removed a nontrivial growth component of Gross

State Product. Public sector GSP has fallen by 6.8 percent, a much larger drop than the 1.5

percent at the national level. However, the lagging growth is primarily due to the slow growth in

the state’s private sector. New Jersey’s private sector GSP growth was 6.3 percent compared to

the national level of 11.0 percent. Growth in New Jersey’s manufacturing sector severely lags the

nation’s growth. While the cumulative U.S. durable goods GDP component surged by 28.5

percent, New Jersey durable manufacturing grew by an anemic 2.8 percent. The performance of

the state’s nondurable manufacturing sector was worse. During the recovery, the state component

of nondurable manufacturing contracted by 1.5 percent, while the U.S. component increased by

1.9 percent.

New Jersey’s growth in sectors that move goods and information lags U.S. growth. GSP

in retail trade, transportation and warehouse, and information are much smaller in these sectors.

The state’s wholesale trade sector is the only one in this group that comes closest to experiencing

growth similar to its national sector. Only two of the three service sectors experienced GSP

growth greater than their national counterpart. New Jersey’s finance and insurance sector

6

expanded by a robust 17.4 percent, compared to a modest 2.7 percent at the national level.

Management of companies and enterprises and arts, entertainment and recreation were the other

two sectors that experienced faster growth at the state level.

How do these cumulative growth patterns translate into changes in the state’s poverty

rate? Even though the state’s economic growth during the recovery has been modest, it should

have still put downward pressure on the state’s poverty rate. Yet, this is not the case. Figure 2

plots poverty rates from 1980 to 2013. The chart shows that prior to the “Great Recession”, the

state’s poverty rate was always significantly below the U.S. poverty rate. The only exception was

the 1981-82 recession, which at the time was the worst economic downturn since WWII. This

state-national poverty difference was maintained during the “Great Recession”; however, during

the current recovery, the difference appears to have vanished. Further, while the U.S. rate began

to trend downward in 2013, the New Jersey poverty rate appears to have at a minimum remained

elevated. The latter is surprising even with the tepid economic growth that the state has

experienced. Something must be going on within New Jersey to offset the benefits that economic

growth has in reducing poverty.

How Much Should the State’s Economic Growth Contributed to a Drop in Poverty?

To answer this question, I need an estimate of the relationship between poverty and

economic growth? I create a panel data set of New Jersey’s 21 counties from 1998 to 2013 and

regress a county’s poverty rate (youth poverty rate) on its “official” unemployment rate, which I

use to proxy for local macroeconomic conditions. Included in the regression are year and county

dummy variables. Including both enables me to interpret the unemployment rate’s coefficient as

follows. A one percentage point increase in a county’s unemployment rate is associated with an

7

X percentage point increase in the county’s poverty rate. The regressions are weighted by the

county’s 2010 decennial census population.

The estimated coefficients indicate that a 1 percentage point increase in a county’s

unemployment rate is associated with a 0.371 (robust standard error of 0.104) percentage point

increase in the county’s poverty rate, and a 0.682 (robust standard error of 0.201) percentage

point increase in the county’s child poverty rate.

I use these empirical relationships to show that during the current recovery (2009 to

2013), the drop in the state’s unemployment rate cannot offset the poverty rate’s actual growth.

From 2009 to 2013, actual New Jersey poverty increased by 2.0 percentage points. During this

period, the state’s jobless rate fell by 0.8 percentage points. Based on the statistical relationship

between poverty and the unemployment rate (the above regression coefficients), the state’s

poverty rate would have been predicted to fall by 0.3 percentage points (Figure 3).

To illustrate that the current recovery is weaker than the 1990s recovery, I generate a

prediction of economic growth’s impact on the state’s poverty rate from 1992 to 1996 (four years

into the recovery). During the first four years of the recovery, the state’s poverty rate actually fell

by 1.0 percentage point, while the state’s jobless rate fell by 2.3 percentage points. Based on the

relationship between poverty and the unemployment rate, the poverty rate would have been

predicted to fall by 0.9 percentage points (Figure 3).

What is the difference between the two recoveries? During the 1990s recovery, the

improvement explains almost the entire decline in the poverty rate, while during the current

recovery; the economy’s improvement explains virtually none of the decline in the poverty rate.

In fact, actual poverty increased.

8

The evidence for New Jersey youth is much more disturbing. The economy’s

improvement only generates a predicted 0.5 percentage point decline in the youth poverty rate.

Yet, the actual youth poverty rate rose by 3.2 percentage points. The economy’s improvement

didn’t make a dent in reducing poverty. On the other hand, during the 1990s recovery, the

economy’s improvement (2.3 percentage point drop in the unemployment rate), explains almost

the entire actual 1.9 percentage point decline in youth poverty (Figure 4).

Before attempting to answer what explains this result, Table 1 presents estimates of the

predicted and actual changes in poverty for each New Jersey County. The main takeaway from

these county-specific estimates is that even counties that saw sizeable drops in the

unemployment rate (e.g., more than 1.0 percentage point), the economy’s improvement could not

offset the actual increase in the poverty rate. For example, Salem County’s unemployment rate

fell by 1.7 percentage points. But the implied reduction in the poverty rate of 0.6 percentage

points can’t offset the actual 4.8 and 7.6 percentage point increases in overall and youth poverty.

The counties that warrant the greatest concern are Essex, Camden, Atlantic, Cumberland, and

Hudson. During the recovery, these counties had larger increases in poverty that economic

growth in those areas could not offset. For several, such as Essex and Camden, they started the

recovery with persistently high poverty rates.

If Economic Growth Can’t Explain the Increase in Poverty, What Does?

What happened in New Jersey from 2009 to 2013 that put upward pressure on poverty

such that economic growth could not offset its increase? There are three leading explanations.

The first is the cut in the state’s Earned Income Tax Credit. The timing of poverty’s increase is

consistent with the tax increase that New Jersey’s low and moderate income families faced in

9

2009. Governor Christie cut the state’s Earned Income Tax Credit from 25 to 20 percent of the

federal credit. Second, from 2009 to 2013, the state’s minimum wage was only increased once.

Thus, for the full period, the inflation-adjusted value of the state’s minimum wage fell by 6.6

percent. Collectively, these policy choices weakened two key tools for fighting poverty.

To illustrate the effects of the tax increase, which I think explains the bulk of the gap

between the predicted and actual increases in poverty rates; I compare the difference between the

predicted decline in the poverty rate and the actual increase in the poverty rate to several

measures of EITC usage. The first are estimates developed by New Jersey Policy Perspective.

They measure in 2011-12, the number of households, persons, and dollars lost associated with

the tax increase. Each of these indicators is positively correlated with the difference between the

predicted and actual change in both overall and childhood poverty. Counties with larger

increases in poverty have greater numbers of households, persons, and actual dollars lost. Table

2 reports coefficients from regressions of the poverty “gap” on each indicator. They show the

positive correlation between the adverse impacts of the tax increase and the growth in poverty

that can’t be offset by the economy’s improvement.

Specifically, I regress the difference between a county’s actual and predicted change in

poverty from 2009 to 2013 on its lost use of the state EITC in 2011-12. Table 2 of Column 1 of

Panel A reports the coefficient for the number of households that lost use of the state EITC.

Column 2 of Panel A uses the number of persons that lost use of the EITC, and Column 3 of

Panel A uses the dollar amount of lost funds. Panel B repeats the same model, but uses the

logarithms of the number of households, people and dollars lost. All regressions have 21

observations. All regressions are weighted using the county’s 2010 decennial census population.

Robust standard errors are reported in the parenthesis. The key take away from these estimates is

10

the existence of a strong correlation between lost use (tax increase) of the state EITC and the

increase in poverty that can’t be explained by the state-wide economy’s improvement.

I conclude from these estimates that the tax increase and also declining real value in the

state’s minimum wage explain why poverty rose during the recovery.3 Another notable feature of

the table is that the coefficients for the youth equations are larger than the overall estimates. This

indicates that the tax increase and falling real minimum wage had a harsher impact on families

with children.

I also show that counties that subsequently experienced the larger increases in poverty

have a greater reliance on the EITC.4 They have larger average credits. A larger percentage of

their tax returns are comprised of EITC returns. Table 3 reports regression estimates to support

these claims. The entries are estimates of the relationship between a county’s actual change in

poverty from 2009 to 2013 and its use of the state EITC in 2011-12. Column 1 of Panel A

regresses the “unexplained” (difference between actual and predicted increase in the poverty

rate) change in poverty on a constant and the percentage of a county’s total returns that are EITC

based. Column 2 uses the average EITC credit, and Column 3 uses the natural logarithm of the

average credit as the predictor variables. Estimates are presented for all and youth poverty. All

regressions have 21 observations. All regressions are weighted using the county’s 2010

decennial census population. Robust standard errors are reported in the parenthesis.

The estimates support my claim that counties that experience the largest increases in

poverty that can’t be offset by the economic recovery had a greater reliance on the EITC.

Positive coefficients are obtained for all models. They are all measured with precision. Counties

with a larger percentage of EITC returns have larger poverty increases. They have larger average

11

EITC credits too. For example, a 100 dollar increase in the average EITC credit is associated

with a 0.43 percentage point increase in the poverty rate’s growth from 2009 to 2013. For youth,

the estimate suggests an increase of almost two-thirds of a percentage point.

What does the future hold for New Jersey’s low and moderate income families?

The next few years will bring significant challenges to the state’s low and moderate

income families. Until robust job and wage growth return, many New Jersey households will

continue to struggle. Things will be made worse because the Governor and legislature continue

to be against restoring the state’s EITC to 25 percent. Some relief will occur because the state

minimum wage automatically increases each year; however, this is not enough support.

Strengthening the EITC will yield a more efficient and effective approach to poverty reduction.

Further, refusing to reset the state EITC to 25 percent affects families above the poverty

line who also utilize the EITC. They use it to keep from falling into poverty. Who are these New

Jersey households? Several years ago, the United Way of Northern New Jersey introduced us to

A.L.I.C.E, where A.L.I.C.E. stands for Asset limited, Income constrained, and Employed.5

These are New Jersey households that do not earn or receive enough assistance to afford:

Housing, Child care, Food, Transportation, and Health care. Who is ALICE? Almost 40 percent

(38% of NJ Households, 10.5% in poverty, 27.5% ALICE) or 1.2 million New Jersey households

are ALICE. New Jersey’s ALICE households experience a 24% resource shortfall. Without the

25 percent level of assistance, this shortfall most likely expanded during the recovery and has the

potential of expanding in the future.

So, simply put, if the Governor and state legislature wanted to quickly reduce poverty,

they could restore the state credit to its 2009 level. NJPP estimates that “restoring the EITC

12

would reverse a de facto tax hike of about $50 million a year on approximately 500,000 New

Jersey families…” 6 Better yet, to offset the hardship that they created over this five year period,

they could not only restore the credit but raise it to 30% of the federal. This would set New

Jersey’s credit equal to its peers: Connecticut and New York.

I conclude with the Governor’s words from his January 24, 2015 Iowa Freedom Summit

speech. The Governor said that the rich are doing fine and that we should not cater to them at the

expense of middle-income workers and the working poor. “Every policy we advance, every

decision we make should be focused on making their lives better, renewing their future,

renewing their faith, their belief in this country.” Increasing the state EITC to 30 percent of the

federal credit would honor the sacrifices that these families have made since 2009, and serve as a

down payment for creating future financial stability. Doing so would begin to restore their belief

in New Jersey and the nation.

13

Table 1: Predicted and Actual Changes in Poverty from 2009 to 2013

All Youth

Change in

Unemployment Rate

Change in Poverty Rate Change in Poverty Rate

County Predicted Actual Predicted Actual

Atlantic 0.2 0.1 6.2 0.1 8.2

Bergen -0.6 -0.2 1.5 -0.4 2.4

Burlington -0.5 -0.2 0.1 -0.3 1.3

Camden -0.7 -0.3 3.4 -0.5 5.1

Cape May 0.9 0.3 0.1 0.6 1.8

Cumberland -0.2 -0.1 3.7 -0.1 4.7

Essex -0.8 -0.3 3.3 -0.5 3.9

Gloucester -0.7 -0.3 1.7 -0.5 1.5

Hudson -1.4 -0.5 4.8 -1.0 7.7

Hunterdon -0.7 -0.3 -0.2 -0.5 0.4

Mercer -0.8 -0.3 1.0 -0.5 2.1

Middlesex -1.1 -0.4 1.5 -0.8 2.5

Monmouth -0.8 -0.3 0.9 -0.5 1.5

Morris -0.7 -0.3 1.1 -0.5 1.2

Ocean -1.0 -0.4 2.3 -0.7 5.6

Passaic -1.0 -0.4 0.1 -0.7 0.6

Salem -1.7 -0.6 4.8 -1.2 7.6

Somerset -0.9 -0.3 1.3 -0.6 1.3

Sussex -0.7 -0.3 0.7 -0.5 0.3

Union -0.9 -0.3 2.0 -0.6 1.6

Warren -1.7 -0.6 1.7 -1.2 2.4

Notes: These estimates come from a panel data set of New Jersey’s 21 counties from 1998 to 2013. First, I

regress a county’s poverty rate (youth poverty rate) on its “official” unemployment rate. Included in the two

regressions are year and county dummy variables. The regressions are weighted by the county’s 2010

decennial census population. I find that a 1 percentage point increase in the NJ unemployment rate is

associated with a 0.37% point increase in the poverty rate, and a 0.68% point increase in the child poverty rate.

Second, I use these coefficients to generate the predicted change in poverty rate associated with the actual

change in the county’s unemployment rate. I multiply the coefficient by the county’s actual change in its

unemployment rate.

14

Table 2: The Relationship between the Growth in “Unexplained” Poverty and Lost Use of the EITC

Number Lost EITC

(1,000s)

Amount of Funds Lost

($1 million)

Panel A. Level Households Persons Funds

All 0.042 0.014 0.204

(0.015) (0.005) (0.074)

Constant 1.004 1.004 1.004

(0.465) (0.465) (0.465)

Youth 0.055 0.018 0.266

(0.029) (0.010) (0.142)

Constant 1.898 1.898 1.898

(0.811) (0.811) (0.811)

Panel B. Log Specification

All 0.887 0.887 0.887

(0.376) (0.376) (0.376)

Constant -6.684 -7.681 -11.409

(3.665) (4.086) (5.665)

Youth 1.252 1.252 1.252

(0.601) (0.601) (0.601)

Constant -9.090 -10.496 -15.760

(5.836) (6.510) (9.034)

Notes: Entries are estimates of the relationship between a county’s actual change in

poverty from 2009 to 2013 and its lost use of the state EITC in 2011-12. Column 1 of

Panel A regresses the “unexplained” (actual minus predicted change due to economic

growth) change in poverty on a constant and the number households that lost use of

the state EITC. Column 2 of Panel A uses the number of persons that lost use of the

EITC, and Column 3 of Panel A uses the dollar amount of lost funds. Panel B repeats

the same models, but uses the logarithms of the number of households, people and

dollars lost. All regressions have 21 observations. All regressions are weighted using

the county’s 2010 decennial census population. Robust standard errors are reported in

the parenthesis.

15

Table 3: The Relationship Between “Unexplained” Increases in Poverty and EITC Usage

Average EITC ($100's)

Panel A: %EITC Returns Dollars Logarithm

All 17.144 0.430 8.744

(6.316) (0.156) (3.056)

Constant -0.143 -6.717 -24.258

(0.699) (3.049) (9.054)

Panel B:

Youth 21.629 0.610 12.472

(10.088) (0.231) (4.514)

Constant 0.513 9.190 34.286

(1.177) (4.435) (13.308)

Notes: Entries are estimates of the relationship between the differences

in a county’s actual and predicted changes in poverty from 2009 to

2013 and its use of the state EITC in 2011-12. Column 1 of Panel

regresses the “unexplained” change in poverty on a constant and the

percentage of a county’s total returns that are EITC based. Column 2

uses the average EITC credit, and Column 3 uses the natural logarithm

of the average credit. Estimates are presented for all and youth poverty.

All regressions have 21 observations. All regressions are weighted

using the county’s 2010 decennial census population. Robust standard

errors are reported in the parenthesis.

16

ENDNOTES

1 New Jersey Policy Perspectives showed how the cut in the state EITC from 25 to 20 percent of the federal EITC

adversely impacted low and moderate income New Jersey families. See, for example,

http://www.njpp.org/blog/restoring-the-eitc-will-help-working-families-and-new-jerseys-economy .

2 Jon Whiten, Published in “Economic Opportunity, NJPP Blog: As a Matter of Fact ...,”

http://www.njpp.org/blog/new-jersey-should-build-on-minimum-wage-increase-by-restoring-earned-income-tax-

credit, accessed September 9, 2014.

3 The minimum wage conclusion is based on the timing of the changes in poverty and the erosion in the real value of

the minimum wage. The minimum wage is a state level measure. Because of that, it is not possible to estimate a

similar model as I do for poverty and the unemployment rate.

4 The data on usage comes from the Brookings Institution’s analysis of IRS data.

http://www.irs.gov/Individuals/States-and-Local-Governments-with-Earned-Income-Tax-Credit

5 See, for example, http://www.unitedwaynnj.org/ourwork/alice.php.

6 Jon Whiten, Published in “Economic Opportunity, NJPP Blog: As a Matter of Fact ...,”

http://www.njpp.org/blog/new-jersey-should-build-on-minimum-wage-increase-by-restoring-earned-income-tax-

credit, accessed September 9, 2014.