The E¤ects of Poverty on Marital Separation:Accounting for Dynamic Feedback

David ZimmerDepartment of Economics

Western Kentucky University

May 8, 2019

Abstract

This paper explores the extent to which poverty leads to maritalseparation. A dynamic nonlinear panel model with correlated ran-dom e¤ects suggests that poverty increases the likelihood of maritaldisruption by approximately 60 percent. However, accounting for thepossibility that marital troubles, once present, might feed back tofuture poverty states, that estimate shrinks by about one-third, toapproximately 40 percent. Consequently, poverty appears to remainan important determinant of marital instability, but a sizable por-tion of the observed link between the two owes to dynamic feedback.These �ndings suggest an unfortunate loop in which poverty leads tomarital separation, but then marital separation, in turn, increases thelikelihood of future poverty.JEL Codes: C33; C51Keywords: average treatment e¤ects; numerical integration; randomintercepts

1 Introduction

This paper seeks to investigate whether poverty increases the likelihood of

marital separation. In addition to the usual problems of unobserved het-

erogeneity in non-experimental data, the observed link between poverty and

marital separation might be contaminated if marital troubles, once present,

feed back to probabilities of future poverty. The estimation method adopts a

dynamic random intercept panel model developedWooldridge (2000) that ac-

commodates both unobserved heterogeneity and feedback e¤ects. The main

�nding is that, although poverty does, indeed, appear to increase the like-

lihood of marital separation, some of that link stems from feedback. Once

that feedback is taken into account, the actual a¤ect of poverty on marital

disruption, while still positive, shrinks in magnitude by about one-third.

A large swath of research, scattered across many academic disciplines, has

attempted to estimate whether poverty a¤ects various socioeconomic out-

comes, including subjective wellbeing (Clark, D�Ambrosia, and Ghishlandi,

2015), social relations (Mood and Jonsson, 2016), childhood development

(Guo and Harris, 2000), and teenage pregnancy (Harding, 2003). However,

the speci�c e¤ects of poverty on marital separation have received surprisingly

little attention.

1

Estimating the e¤ects of poverty on marital disruption must confront sev-

eral econometric challenges. First, unobserved factors might simultaneously

push a person into poverty while at the same time destabilizing his or her

marriage. For example, the arrival of a health problem could simultaneously

deplete a person of his or her �nancial resources, while also causing mari-

tal stress. In this example, it is the health problem, rather than �nancial

troubles per se, that causes marital separation.

A second econometric challenge, and the central focus of this paper, is

that marital troubles, once present, might �feed back� to future poverty

status. There are several reasons to suspect the presence of such feedback.

First, not only does marital separation often require hiring attorneys, but the

separation process itself also might require signi�cant time, possibly reducing

a person�s number of available work hours. Second, marital separation might

cut a person o¤ from spousal income sources and subject the person to the

vagaries of the legal process involving spousal support. Add these reasons to

the possible psychological toll of divorce, and there is signi�cant reason to

suspect that marital troubles might lead to �nancial stress. Indeed, a sizable

literature documents the extent to which marital disruption leads to �nancial

troubles (Colletta, 1979; Amato, 2000; Andreßand Bröckel, 2007; Leopold,

2

2018).

Wooldridge (2000) shows that, in dynamic panel models, feedback ef-

fects, if present in the data but ignored during estimation, tend to impart

bias on estimates of key model parameters of interest. This paper adopts

his proposed solution, which involves specifying the joint distribution of the

outcome and the control variable to which the outcome feeds back, and then

using that joint distribution as the basis for likelihood-based estimation. The

method, despite being fairly intuitive statistically, has not seen widespread

use, though it has appeared in studies of poverty (Biewen, 2009), health

insurance (Zimmer, 2010), and education (Welsch and Zimmer, 2015).

This paper produces several �ndings. First, after accounting for observed

and unobserved heterogeneity, poverty increases the probability of marital

separation by approximately 60 percent. However, that estimate does not

allow marital troubles to feed back to future poverty, raising the possibil-

ity that that 60 percent number is contaminated by feedback. Indeed, the

feedback e¤ect appears to be relatively important, with marital separation

increasing the probability of future poverty by approximately 6 percent. Af-

ter allowing for such feedback, the estimated e¤ect of poverty on marital

dissolution shrinks by about one-third, to 40 percent. Consequently, poverty

3

still appears to lead to martial troubles, but a sizable part of the observed

link owes to feedback.

These �ndings point to an unfortunate loop in which poverty tends to

destabilize marriages, but then marital troubles, once present, in turn in-

crease probabilities of falling into future poverty. Policy makers can short

circuit that loop by lessening poverty in the �rst place, or by reducing the

onerous aspects of divorce that, themselves, appear to lead to future poverty.

The estimation approach also produces evidence that certain person-speci�c

traits that are di¢ cult or impossible to measure in household survey data

tend to simultaneously raise the contemporaneous probabilities of marital

separation and poverty. Although the econometric model does not help iden-

tify what those unobserved factors might be, the suggestion of their existence

raises the possibility that government survey administrators might be able

to shed light on them, perhaps through more detailed survey questionnaires.

2 Data

Data for this study come from the 1979 National Longitudinal Survey of

Youth (NLSY), sponsored by the Bureau of Labor Statistics, a unit of the

U.S. Department of Labor. The NLSY provides a nationally-representative

4

sample of 12,686 men and women who were between the ages 14-22 in 1979.

The survey has tracked those individuals annually until 1994 and biennially

since. The survey remains active to this day.

This study considers biennial data from the years 1994, 1996, 1998, 2000,

2002, 2004, 2006, and 2008. Over the course of those eight years, respondents

were between the ages 29-51. The estimation sample considers all respon-

dents who were present during each of those eight years, and who reported

being married during the initial year (1994). (The sample does not include

any respondent married to another respondent.) The �nal estimation sample

includes 3,456 unique individuals, each observed for eight years, for a total

of 27,648 person/year observations.

This study considers two key variables. Keeping in mind that all re-

spondents were married in 1994, the �rst variable is a time-varying binary

indicator for whether the person reports not being married (for any rea-

son) during the survey year. (Approximately 31 percent of respondents re-

port marital separation at some point after 1994.) The second variable is a

time-varying binary indicator for whether the person�s family falls below the

federal poverty level during the survey year. (Approximately 21 percent of re-

spondents spent at least one survey year in poverty.) The sample correlation

5

between those two measures across all person/year observations (after 1994)

is 0.22 (standard error < 0.01), suggesting a strong positive contemporaneous

link between the two.

The top row of Table 1 further suggests that marital separation strongly

correlates with poverty, with approximately 44 percent of person/year obser-

vations in poverty reporting marital separation, compared to only 12 percent

of observations not in poverty. However, that relationship cannot be inter-

preted as causal, because, as demonstrated in the remainder of Table 1, other

socioeconomic factors also appear to vary across poverty status. Notably, fe-

males, blacks, and Hispanics are all more likely to fall into poverty, compared

to their male and nonblack/nonHispanic counterparts. Subjects with health

limitations also are more likely to fall below the poverty level. Finally, and

perhaps least surprisingly, lower levels of educational attainment also appear

to correlate with poverty.

The fact that certain observable socioeconomic traits appear to correlate

with poverty raises the possibility that unobservable characteristics also link

to poverty. And if those unobserved traits also relate to marital status, then

the true e¤ect of poverty on marital separation becomes muddled. Further,

and the main focus of this paper, if marital separation feeds back to future

6

poverty, then the relationship becomes further confounded.

The following section outlines an econometric approach that aims to ad-

dress those concerns, with particular emphasis on the feedback problem. But

the appropriateness of that method hinges on the two key variables �poverty

status and marital separation �showing su¢ cient intra-person variation over

time. Although little guidance exists on what constitutes �su¢ cient,�one in-

formal check involves calculating intra-person coe¢ cients of variation (intra-

person standard deviations divided by overall means), and checking that

those numbers are �large enough.�

Table 2 reports those intra-person coe¢ cients of variation. Fortunately,

poverty status and marital separation, shown near the top of the table, ap-

pear to show relatively large intra-person variation, larger, in fact, than other

control variables, save health limitations. Consequently, the two key variables

should o¤er su¢ cient variation to permit precise estimates of key parameters.

(Indeed, some of the precise links between marital separation and poverty,

reported below, seem to con�rm the presence of su¢ cient time variation in

those measures.)

7

3 Dynamic Panel Model

This section presents a dynamic panel model for marital separation. The

model seeks to accommodate both observed and unobserved heterogeneity,

as well as dynamic patterns in marital disruption. The model does not ac-

commodate feedback; the following section addresses that concern.

Let yit denote a binary indicator for whether person i (i = 1; : : : ; n) was

separated from his or her spouse in time period t (t = 1; : : : ; T ). Let xit be a

binary indicator for whether the person was below the federal poverty level

in period t. The probability of marital separation follows

Pr(yit = 1) = �(�1yi;t�1 + �2xit + Z0it + ci); (1)

where the symbol � represents the cumulative distribution of the standard

normal distribution, and where the vector Zit includes control variables with

estimable coe¢ cients . The central focus of this study involves the para-

meter �2, which captures the extent, if any, to which poverty a¤ects marital

stability.

The probability in (1) is �dynamic�in the sense that the previous-period

outcome, yi;t�1, appears on the right-hand side as a conditioning variable.

Dynamic speci�cations account for the possibility that, following a change

8

in the outcome variable, its value returns partly, but not entirely, to its orig-

inal state. That type of incomplete mean reversion applies to many micro-

level economic measures, including, as indicated in results reported below,

marital separation. A lagged dependent variable setup also captures (poten-

tially time-varying) unobserved heterogeneity, to the extent that such het-

erogeneity a¤ected the previous-period outcome (Angrist and Pischke, 2009,

pp. 243-246). For example, previous-period marital in�delity likely a¤ects

previous-period marital stability, which, in turn, in�uences current-period

marital stability. Thus, lagged outcomes indirectly control for unobserved

confounders, like marital in�delity.

Dynamic panel models require one to confront the �initial conditions�

problem, in that, in most household surveys, the �rst-period value of the

dependent variable, yi1, does not provide the starting point for yit, but rather

the �rst realization of an already-ongoing process (Wooldridge, 2005). The

most popular solution to this problem involves adding yi1 as an additional

control variables, e¤ectively treating the initial state of the outcome variable

as exogenous. However, the sample construction described in the previous

section, itself, circumvents that concern, in that every subject is married in

period 1, meaning that every cross-sectional unit has the same value for yi1.

9

Consequently, the models estimated in this paper do not control for initial

marital status.

Equation (1) includes a person-speci�c, time-invariant random intercept,

ci, which is treated as a �random e¤ect� in the sense that it remains un-

correlated with control variables. That random e¤ects assumption, which is

imposed primarily for computational simplicity, is likely to be violated in the

current context. For example, marital �contentment,�which is unobserved

and therefore absorbed into the random intercept ci, might correlate with

educational attainment, which is included in xit. But the random e¤ects as-

sumption prohibits such correlation, potentially leading to biased estimates

of key parameters of interest.

Fortunately, that assumption can be relaxed by including intra-cross sec-

tional time averages of (time varying) control variables as additional controls

(Mundlak, 1978). In fact, adding Mundlak terms to a linear random ef-

fects model � often referred to as �correlated random e¤ects� � produces

estimates identical those obtained from a �xed e¤ects speci�cation. That

same equivalency does not carry over to nonlinear settings, such as the one

employed in this paper. Nonetheless, Mundlak terms allow one to harness

some of the bene�ts of ��xed e¤ects,�while remaining within a random ef-

10

fects framework. (The model does not include time averages of xit, only Zit.

Endogeneity of xit is handled in the following section.) Consequently, the

term ci is speci�ed as

ci = Z0i�� (2)

where Zi� represents a vector of intra-person time averages of (time varying)

control variables, and � denotes a vector of estimable parameters.

Consistent estimation of the parameters in equation (1) requires strict

exogeneity of Zit and xit, which, formally, holds that

E(yit j yi;t�1; xi;Zi; ci) = E(yit = 1 j yi;t�1; xit;Zit; ci);

where on the left-hand side xi = (xi1; : : : ; xiT ) and Zi = (Zi1; : : : ;ZiT ). In

words, strict exogeneity states that, after controlling for lagged y and the

random intercept, the outcome variable in period t may not correlate with

control variables in rounds other than t (Wooldridge, 1997). Most problem-

atic in the current setting, that assumption forbids marital separation from

feeding back to future poverty. The following section relaxes that assumption

by permitting y to feed back to future values of x.

Maximum likelihood estimation requires the joint density of (yi2; :::; yiT ),

with the �rst period omitted to accommodate lagged y. That joint density

11

follows

fy(yi2; :::; yiT ) =

TYt=2

�((2yit � 1) � (�1yi;t�1 + �2xit + Z0it + ci)): (3)

The main estimation hurdle involves the presence of the random intercept

ci, which is unobserved. The typical approach in nonlinear settings, and

the one adopted here, is to numerically integrate the random intercept out

of the density (3) by drawing pseudo-random numbers from an assumed

distribution, such as ci � N(0; 1), and then averaging expression (3) across

those draws. Logging the resulting integrated density and summing over all

i cross-sectional units produces the log likelihood function, which is then

maximized with respect to the estimable parameters.

Honore and Tamer (2006) explore the behavior of dynamic nonlinear

panel estimators like the one shown in equation (3). They show that, in

general, such models do not produce point-identi�ed parameter estimates,

especially for the coe¢ cient attached to the lagged outcome. However, they

also show that, in most cases, in addition to correctly capturing signs, the

bounds of parameter estimates fall in very tight regions around �true�val-

ues, such that lack of point identi�cation is of little practical concern. Con-

sequently, this paper interprets the main parameters estimates as if they are

properly point identi�ed, while acknowledging caveats pointed out by Honore

12

and Tamer.

4 Dynamic Panel Model with Feedback

Any feedback from marital separation to future poverty status will violate

the strict exogeneity assumption, potentially resulting in biased parameter

estimates obtained from the model outlined in the previous section. To elimi-

nate bias caused by dynamic feedback, Wooldridge (2000) suggests explicitly

modeling that feedback via a second probability,

Pr(xit = 1) = �(�1yi;t�1 + �2xi;t�1 + Z0it� + �ci); (4)

where the random intercept ci is speci�ed identically to equation (2), in-

cluding time averages of (time varying) controls. The estimable �loading

parameter��, absent in the probability of marital separation given in equa-

tion (1), allows the random intercept to exert separate in�uences on y and

x. A positive value for the loading parameter would indicate that, aside

from any causal e¤ect of poverty on marital separation, unobserved person-

speci�c factors simultaneously increase the contemporaneous probabilities of

both marital separation and poverty. Note that, similar to the probability for

y, this probability also introduces a dynamic pattern, in that current-period

x depends, in part, on its previous-period value. Though not a necessary part

13

of the model, allowing for such dynamics in poverty status seems reasonable.

But the main parameter of interest in (4) is �1, which captures the extent, if

any, to which y feeds back to future x.

Combining the probabilities given in (1) and (4), the (unlogged) likelihood

contribution for cross-sectional unit i isf(yi2; : : : ; yiT ; xi2; : : : ; xiT ) =TQt=2

f�((2yit � 1) � (�1yi;t�1 + �2xit + Z0it + ci))g

�f�((2xit � 1) � (�1yi;t�1 + �2xi;t�1 + Z0it� + �ci))g:

(5)

Estimation follows a similar approach to that described in the previous sec-

tion, with the random intercept ci numerically integrated out of (5), and

with the log likelihood function formed similarly to model presented in the

previous section. The main empirical point of focus is whether accounting

for potential feedback alters the impact of poverty on marital separation in

a substantively important way.

5 Parameter Estimates

Appendix Table 1 presents estimates from the dynamic panel model without

feedback, shown in equation (3). Shown near the top of the table, marital

separation, not surprisingly, shows strong serial persistence across time pe-

riods, indicating that being separated from one�s spouse last period tends

to correlate with also being separating during the subsequent period. That

14

intertemporal persistence highlights the importance of using a dynamic ap-

proach.

The coe¢ cient of poverty is positive and highly statistically signi�cant,

regardless of the inclusion of controls and correlated random e¤ects, indicat-

ing that poverty seems to lead to marital instability. Unfortunately, being

buried inside nonlinear functions, the coe¢ cients of poverty are di¢ cult to in-

terpret, and also nearly impossible to compare across the three speci�cations

reported in Appendix Table 1. The following section attempts to address

that.

The estimated coe¢ cients of control variables appear, for the most part,

to corroborate a priori expectations. Focusing on the middle panel, age

negatively correlates with marital separation. Females, blacks, and Hispanic

appear more likely to separate than their male and nonblack/nonHispanic

counterparts. Higher educational attainment and the presence of children

appear to reduce the likelihood of marital separation. Those same patterns

remain in the third column after including time averages of controls, although

the coe¢ cient of age �ips in sign, while the e¤ect of educational attainment

loses statistical signi�cance.

Appendix Table 2 takes the correlated random e¤ects setup and appends

15

a feedback mechanism from marital separation to future poverty. The main

�nding, shown in the right-hand panel, is that the coe¢ cient of lagged marital

separation, indeed, does appear to increase the probability of future poverty,

suggesting the presence of statistically signi�cant feedback. Nonetheless, even

after taking into account that feedback, poverty still appears to correlate with

marital instability, as shows in the left-hand panel.

Finally, shown near the bottom of the table, the loading parameter, de-

noted by � in equation (5), is positive and statistically signi�cant. The impli-

cation is that, aside from any causal e¤ect of poverty on marital separation,

unmeasured traits that increases a person�s likelihood of being in poverty also

increase the contemporaneous probability that a person is separated from his

or her spouse. A more naive model that ignores those contemporaneous con-

founding factors would impart upward bias on this paper�s main relationship

of interest: the e¤ect of poverty on marital separation.

6 Average Treatment E¤ects

Being buried inside nonlinear functions, the main coe¢ cients of interest are

di¢ cult to interpret, and nearly impossible to compare across the various

speci�cations reported in Appendix Tables 1 and 2. To aid interpretation,

16

Table 3 reports average treatment e¤ects (ATE), which aim to simplify those

hard-to-interpret coe¢ cients. The ATE of poverty on marital separation is

calculated as

ATE =

PNi=1

PTt=2�(

b�1yi;t�1 + b�21 + Z0itb )� �(b�1yi;t�1 + b�20 + Z0itb )N � (T � 1) :

(6)

Circum�exes denote converged parameter estimates. The denominator re-

�ects that there are (T � 1) estimates for each person, one for each time

period used in estimation. The standard error for the ATE is obtained using

a Bayesian simulation method, where, on each replication of the approach,

parameter �2 is randomly perturbed by drawing it from a normal distrib-

ution centered on its estimated value and with standard deviation equal to

its converged standard error estimate. The ATE in (6) is then recalculated

(Radice, Marra and Wojtys, 2016). The standard deviation of several hun-

dred replications of that process serves as the standard error of the ATE.

Table 3 shows that, according to the model that ignores feedback, poverty

increases the probability of marital separation by approximately 18.2 percent-

age points. Considering that 30.9 percent of subjects in the estimation sam-

ple experience marital separation, that ATE suggests that poverty increases

the likelihood of marital separation by approximately 59 percent, relative to

17

the sample mean. Including controls and correlated random e¤ects expands

the ATE to 18.7 percentage points (approximately 61 percent relative to the

sample mean of marital separation).

Shown at the bottom of Table 3, accounting for feedback shrinks the ATE

to 12.8 percentage points (approximately 41 percent relative to the sample

mean of marital separation). Therefore, the main �nding is that, although

poverty appears to correlate with marital troubles, a sizable portion of that

relationship derives from the �nding that marital troubles, once present, seem

to increase the likelihood of future poverty. Once that feedback is taken into

account, poverty still a¤ects marital stability, but the e¤ect shrinks by about

one-third.

Table 4 attempts to quantify that feedback e¤ect by calculating the ATE

of marital separation on future poverty following a very similar set of cal-

culations to those described above, but based on the feedback probability

equation. The estimate shows that marital troubles, once present, increase

the probability of future poverty by 1.2 percentage points (approximately

6 percent relative to the sample mean of poverty). The implication is that

the feedback e¤ect, while not large in magnitude, is important enough to

sizably distort the main quantity of interest: the e¤ect of poverty on marital

18

separation. It is this small, but statistically signi�cant, feedback e¤ect that

exerts upward bias on the non-feedback estimates reported in Table 3.

7 Conclusion

This paper explores the extent to which poverty leads to marital separa-

tion, while allowing for some of the intricate econometric patterns that likely

muddle the observed link between the two. A dynamic nonlinear panel model

with correlated random e¤ects suggests that poverty increases the likelihood

of marital disruption by approximately 60 percent. However, accounting for

the possibility that marital troubles, once present, might feed back to future

poverty states, that estimate shrinks by about one-third, to approximately

40 percent. Consequently, poverty appears to remain an important determi-

nant of marital instability, but a sizable portion of the observed link between

the two owes to dynamic feedback.

From a policy perspective, these �ndings suggest an unfortunate loop in

which poverty leads to marital separation, but then marital separation, in

turn, increases the likelihood of future poverty. But that loop, while unfor-

tunate, also points to an opportunity for policy makers to simultaneously

reduce poverty and marital instability by short circuiting that loop. The re-

19

sults of this paper suggest several possible options. First, policy makers can

initiate policies that reduce poverty in the �rst place, an admittedly large

and di¢ cult-to-achieve aim that already occupies a huge amount of govern-

ment policy attention. Alternatively, policy makers could seek to ease the

onerous legal process of obtaining marital separation, as that process appears

to some extent to contribute to future poverty states.

Finally, the �loading factor� included in the econometric approach sug-

gests that poverty and marital instability are simultaneously in�uenced in

the same direction by unobserved factors. Although the model employed in

this paper is not designed to identify what those factors might be, policy

makers could perform such an investigation, perhaps by adding more de-

tailed questions to census-type forms, in an e¤ort to identify and address

such factors.

20

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22

Table 1: Sample meansBelow poverty Above poverty

n = 1,510 n = 26,138

Separated from spouse 0.44 0.12

Age 40.4 40.0

Female 0.63 0.52

Black 0.28 0.18

Hispanic 0.31 0.18

Education...

Less than high school (omitted) - -

High school 0.48 0.41

Some college 0.16 0.24

College 0.05 0.27

Any children 0.92 0.89

Health limitation 0.27 0.08

Subjects are drawn from the 1994, 1996, 1998, 2000, 2002, 2004, 2006, 2008 waves of the

1979 NLSY. All respondents are married in 1994.

23

Table 2: Within-person coe¢ cients of variation(within-person standard deviation divided by overall mean)

CV

Marital separation 1.71

Poverty 3.26

Age 0.11

Female 0.00

Black 0.00

Hispanic 0.00

Education...

Less than high school (omitted)

High school 0.30

Some college 0.56

College 0.37

Any children 0.13

Health limitation 2.14

24

Table 3: ATE estimates of poverty on marital separation(Mean marital dissolution = 0.309)

Percentage compared

APE St. Err. to mean marital dissolution

Constant only 0.182 0.012 59%

Including controls 0.162 0.012 52%

Random e¤ects 0.187 0.016 61%

Random e¤ects with feedback 0.128 0.019 41%

Table 4: ATE estimates of feedback from marital separation to futurepoverty(Mean poverty = 0.207)

Percentage compared

APE St. Err. to mean marital dissolution

Random e¤ects with feedback 0.012 0.007 6%

25

AppendixTable1:Estimationresults(withoutfeedback)

Dependentvariable:Maritalseparation

Constantonly

Includingcontrols

Randome¤ects

Coe¤.

St.Err.

Coe¤.

St.Err.

Coe¤.

St.Err.

Laggedseparation

2.720��

0.033

2.713��

0.033

2.113��

0.042

Below

poverty

0.956��

0.045

0.893��

0.048

1.173��

0.064

Age

�0.009��

0.003

0.017��

0.004

Female

0.045�

0.026

0.064

0.050

Black

0.221��

0.032

0.423��

0.062

Hispanic

0.084��

0.035

0.158��

0.066

Education:lessthanhighschool(omitted)

��

��

Education:highschool

0.051

0.048

0.100

0.234

Education:somecollege

�0.017

0.051

�0.060

0.273

Education:college

�0.158��

0.054

0.239

0.316

Anychildren

�0.170��

0.042

�0.670��

0.176

Healthlimitation

0.015

0.044

�0.011

0.079

Timeaveragesoftime-varyingcontrols?

nono

yes

Constant

�1.672��

0.015

�1.205��

0.131

�2.558

0.198

*p<.10;**p<.05

26

AppendixTable2:Estimationresults(withfeedback)

Maritalseparation

Below

poverty

Coe¤.

St.Err.

Coe¤.

St.Err.

Laggedseparation

2.093��

0.042

0.141��

0.072

Poverty

0.895��

0.092

��

Laggedpoverty

��

1.174��

0.043

Age

0.017��

0.004

�0.006�

0.004

Female

0.070

0.050

0.194��

0.033

Black

0.435��

0.062

0.313��

0.040

Hispanic

0.169��

0.066

0.269��

0.040

Education:lessthanhighschool(omitted)

��

��

Education:highschool

0.114

0.233

0.055

0.188

Education:somecollege

�0.051

0.272

0.048

0.236

Education:college

0.244

0.314

�0.096

0.299

Anychildren

�0.661��

0.176

�0.031

0.219

Healthlimitation

�0.001

0.078

0.154��

0.064

Timeaveragesoftime-varyingcontrols?

yes

yes

Constant

�2.499��

0.198

�1.293

0.166

Loadingparameter

��

0.197��

0.052

*p<.10;**p<.05

27