Recent panel data is used to reconsider the determinants of interstate differences in the ratio of insured to total unemployment. We conclude that previous research on the influence of replacement rates, duration of jobless spells and female labor force participation is robust, but find that political affiliations and attitudes could be more important, and unionization rates less important, than once believed.insured unemployment, work norms, replacement rates, unionization

Bradford Maxwell

Ivan T. Kandilov

Peter Matthews

English

Research Papers in Economics

“Interstate Differences in Insured Unemployment:
Some Recent Evidence”
by
Peter Hans Matthews, Ivan T. Kandilov and Bradford Maxwell
June, 2002
MIDDLEBURY COLLEGE ECONOMICS DISCUSSION PAPER NO. 02-16
DEPARTMENT OF ECONOMICS
MIDDLEBURY COLLEGE
MIDDLEBURY, VERMONT 05753
http://www.middlebury.edu/~econInterstate Differences in Insured Unemployment:
Some Recent Evidence
Peter Hans Matthews (*) Ivan T. Kandilov Bradford Maxwell
Department of Economics Department of Economics Law School
Middlebury College University of Michigan Cornell Univ
Middlebury, Vermont Ann Arbor, Michigan Ithaca, New York
Current Version October 2001
Forthcoming, Applied Economics Letters
JEL Classification Number:  J65
Abstract:  Recent panel data is used to reconsider the determinants of interstate
differences in the ratio of insured to total unemployment.   We conclude that previous
research on the influence of replacement rates, duration of jobless spells and female
labor force participation is robust, but find that political affiliations and attitudes could
be more important, and unionization rates less important, than once believed.
Keywords:  insured unemployment, work norms, replacement rates, unionization
(*) corresponding author:  Peter Hans Matthews, Department of Economics, Munroe
Hall 309, Middlebury College, Middlebury Vermont 05753.  e-mail:
peter.h.matthews@middlebury.edu.1.  Introduction
*
The behavior of what is sometimes called the “recipient rate” RECIP, the ratio of the
rates of insured to total unemployment, is one of the most remarkable but least studied
features of American labor markets.
1  With the notable exceptions of Burtless (1983),
Blank and Card (1991), Anderson and Meyer (1997) and Rejda (1999), the decline in
RECIP since the 1960s has received little published attention. For the United States as a
whole, the value of RECIP peaked in the late 1940s, at close to 100 percent, and reached
its nadir of between 40 and 50 percent in the mid-1980s, with sharp declines over the
first halves of the 1960s and 1980s, before levelling off around 50 percent.
The absence of comprehensive data on the number of workers who are either eligible for
and/or collect UI has proven a serious obstacle for researchers.  In an influential paper
on the “fraction of insured unemployment” or FIU,
2 Blank and Card (1991) infer the
number of eligible workers from CPS data and state UI laws for the critical period
between 1977 and 1987.  Of the three possible explanations for the substantial decline in
FIU over this time – changes in state UI laws, changes in the “eligibility determining
characteristics” of workers, and changes in “takeup” or collection rates – the last of these
is identified as the most important.  In particular, their estimated collection rate series
exhibits a sharp decline between 1980 and 1982, behavior that Anderson and Meyer
(1997) have since attributed to the inclusion of UI benefits in the tax base, which were
phased in between 1979 and 1987.
It is not just the behavior of state and national RECIP values over time that calls for
attention, however.  As Table 1 demonstrates, there are also substantial and persistent
                                                
*  We wish to thank Middlebury College and UC San Diego for generous leave support
(Matthews), the Christian A. Johnson summer internship program at Middlebury (Kandilov) and
Carolyn Craven for her comments on a previous draft.  The results here do not reflect the views
of either Middlebury College or Centrulo & Capone LLP.
1  The word “recipient” can be misleading, however, inasmuch as the insured unemployment rate
is the ratio of UI claims to covered employment.  Some claims (25 percent, more or less) are
disqualified, of course, and about 10 percent of all workers are not covered.
2  The FIU is defined to be the fraction of jobless workers who collect regular UI benefits.  The
decline in RECIP has been sharper than that in FIU, which Blank and Card (1991: 1161) attribute
first and foremost to the increase in the coverage rate over their sample period. The FIU is
perhaps the more “intuitive” measure, but it is variations in RECIP that trigger extended benefit
programs.disparities across states, from more than 85 percent (Rhode Island, in 1996) to around 20
percent (South Dakota, in 1994).  These differences are mirrored in Blank and Card’s
(1991) estimated collection rates for 1987, which range from 85 percent for New England
as a whole, to less than 50 percent in both the Mountain and West South Central census
regions.
[Insert Table 1 Here]
There is, furthermore, an important connection between these interstate differences and
the behavior of the national rate over time:  some of the decline in the latter has been
attributed to the migration of workers from high RECIP areas (New England, for
example) to low RECIP states in the South and West, and to slow(er) labor force growth
in the former.
In the penultimate section of their paper, Blank and Card (1991) estimate a cross-
sectional model of collection rates, and we undertake a similar exercise here:  a simple
and distinct model of RECIP differences across states is estimated using pooled annual
data over a small, but more recent, sample period, from 1992 to 1996.
2.  Data and Model
Our dependent variable is the aforementioned RECIPi,t the recipient rate for state i in
period t, the ratio of the insured unemployment rate, as reported at the ITSC
Employment Insurance web site (www.itsc.state.md.us), to the overall unemployment
rate, available in the Statistical Abstract of the United States (hereafter, SAUS).
The first of our independent variables is the replacement rate REPLACEi,t, the ratio of the
average benefit to the average wage in the covered sector.  The coefficient is expected to
be positive, of course:  the more generous the UI program relative to local economic
conditions, the greater the claimant rate, other things equal.  The second is DURATi,t, the
mean duration in weeks of jobless spells, the effects of which are uncertain:  if job losers
in state i have reason to believe more time will be required to find another “match” than
those in state j, the proportion who submit claims should rise, but as mean duration
rises, the proportion of those without work who will exhausted their UI benefits rises,
too.  Common sense suggests, however, that the first effect will dominate the second in
practice, in which case the coefficient should be positive.  From a somewhat differentperspective, it is well known that state and national RECIP rates tend to be
countercyclical, and to the extent that duration is an alternative measure of labor market
slack, this behavior could manifest itself in our cross-sections.  The values of both
REPLACE and DURAT are those reported in the ITSC database.
The third of our “economic” determinants is UNIONi,,t, the proportion of the workforce
in state i that was unionized in period t, as reported in various issues of SAUS.  If, as
Blank and Card (1991: 1174) find, unions “increase their members’ awareness of UI
programs or expedite their applications,” this coefficient should also be positive and
significant.
We wanted to separate unionization effects, however, from possible “industrial” and
“urban” influences, and to this end, included both MANUFi,t, the proportion of the labor
force engaged in manufacturing, and METROPi,t, the proportion of the population in
metropolitan areas, as regressors, as reported in various issues of Morgan et al’s State
Rankings.  On the basis of previous research, we expected a positive, if not significant,
coefficient on MANUF, but were ambivalent about METROP:  it seemed to us that the
relevant “neighborhood effects” could cut in either direction.
We also consider two other demographic variables, POVi,t, the proportion of the
population in state i classified as poor in period t, and FEMALEi,t, the female labor force
participation rate, as reported in various issues of State Rankings.  There are at least two
reasons to introduce the former, even if these pull RECIP in different directions.  On the
one hand, one would expect that the poorer a state’s population and labor force, the
higher its claimant and collection rates.  On the other, to the extent higher POV values
reflect the increased presence of workers who have exhausted their UI benefits or
differential access to public services, the predicted RECIP values could well be smaller.
The correlation of POV with other variables we use – in particular, FEMALE, MANUF
and METROP – further complicates estimation of its direct influence.  The expected sign
of the FEMALE coefficient is also uncertain.  If interstate differences in female labor force
participation reflect differences in the proportion of households with multiple wage-
earners, and if “second,” often female, participant submits fewer UI claims and is
disqualified more often, the coefficient should be negative.  It will be positive, however,
if the differences in FEMALE correspond to differences in the proportion of single
female-headed households, or if increased participation is associated with more durablelabor force attachment. All of this said, previous empirical research suggests that the
sign tends to be positive.
Last, Blank and Card (1991) were perhaps surprised to find that their measure of the
“political climate” in each state, the fraction of its Congressional delegation that was
Democratic, was insignificant.  We consider a pair of alternative measures:  GOVi,t, equal
to “1” if state i had a Democratic governor at the start of period t, and “0” otherwise; and
RTWI,t, equal to “1” if i was a “right to work” state in period t, and “0” otherwise.  The
rationale for the former is that UI is, and is perceived to be, an ensemble of state
programs with federal minimum standards, in which case the relevant political
affiliations are perhaps local, not national.  The second is premised on the notion that
traditional political affiliations will sometimes fail to capture durable social and/or
political attitudes vis-à-vis labor markets.  In this context, the residents of right to work
states might be supposed to hold more “non-interventionist” views.  If so, the coefficient
on the former should be positive and negative on the latter.
In the absence of fixed effects, then, our  benchmark model is:
     
RIU i,t = 0 + 1REPLACEi,t + 2DURATi,t + 3UNIONi ,t + 4MANUFi,t
              5METROP i,t + 6POVi,t + 7FEMALEi,t + 8GOV i ,t + 9RTWi,t + ei,t
where eI,t is an error term that is almost certain to be heteroskedastic. For this reason,
GLS with cross-section weights are used below.  Furthermore, we allowed for period
effects, measured relative to 1992, in all of the models we estimated, and considered two
sorts of cross-sectional fixed effects, state and regional.
3
3.  Estimation Results
Table 2 reports the estimates for four versions of the model, one without fixed effects,
one with regional effects, and two with state effects. We note first that despite the
substantial difference in goodness of fit, the introduction of regional effects does not
much influence our the estimates of “core” parameters:  the coefficients on duration,
                                                
3 The nine census regions are New England, Middle Atlantic, East North Central, West North
Central, South Atlantic, East South Central, West South Central, Mountain and Pacific.unionization, female labor force participation and labor in manufacture are all positive
and significant, both in the economic and statistical senses, and the coefficient on
metropolitan population is negative and significant.  Furthermore, the coefficients on the
replacement rate and governorship have the “correct” signs but appear to be
insignificant.  The signs on two coefficients “flip” between the two models, however:
the estimated coefficient on the poverty rate switches, from negative and significant to
positive and insignificant, while that on right to work status moves from negative and
(almost) significant, to positive and insignificant.  The last of these comes as no surprise,
however, inasmuch as right to work status exhibit a pronounced regional concentration.
4
[Insert Table 2 Here]
The substitution of state for regional effects does exert a substantial effect on our results,
however.  The coefficient on duration remains positive and significant, but much smaller
in absolute terms:  a one week difference in mean duration is now predicted to push
RECIP upward about one third of a percentage point.  Consistent with intuition, the
coefficient on the replacement rate is now positive, significant and much larger (0.62)
than before, which implies that the generosity (relative to local economic conditions) of
state UI programs does have an important influence the behavior of potential claimants.
Surprisingly, perhaps, the estimated influence of unionization is small in both the
economic and statistical senses, a result that contradicts Blank and Card’s (1991)
evidence on collection rates:  a one percent difference in unionization rates between
states is associated with no more than a 0.21 percent difference in recipient rates.
The coefficients on the poverty rate and proportion of the labor force engaged in
manufacture are significant but negative in the extended fixed effects model, both
provocative results: it is unfortunate, but perhaps not surprising, that holding the
replacement rate fixed, jobless workers in poorer states still make fewer UI claims, but
the inference that a substantial manufacturing presence exerts a similar effect is
unexpected, and more difficult to rationalize.  The coefficient on female labor force
participation has the “right” sign, on the other hand, but is now small (0.10) and
                                                
4 Three of the four states in the West South Central region (Arkansas, Louisiana and Texas) are
right to work, for example, and the current governor of the fourth (Oklahoma) has advocated it.
Likewise, three out of four states in the East South Central region, five of eight in each of the
Mountain and South Atlantic, and four of eight in the West North Central have passed right to
work legislation.  There are no right to work states, on the other hand, in the Middle Atlantic,
New England, East North Central and Pacific regions, on the other hand.insignificant.  The influence of a metropolitan population is also estimated to be small
and negative.
Last, the two “political” variables exert substantial, if not quite significant, effects now:
other things equal, the value of RECIP is estimated to be a full percentage point lower in
right to work states, and three quarters of a point higher in states with Democratic
governors.  At the least, this suggests that Blank and Card’s (1991) conclusions
concerning the role of political affiliation merit reconsideration.
The truncated model includes fixed state and period effects, but omits the three “least
significant” variables, UNION, FEMALE and GOV .  None of the remaining coefficients,
except for that on RTW, which increases from –1.00 to –1.42, is much affected.
4.  Conclusion
Our results suggest that previous work on the effects of interstate differences in
replacement rates, duration of joblessness and female labor force participation on UI
claimant behavior is robust with respect to sample period and model specification.  We
also find, however, that the roles of unions and political attitudes are perhaps less so,
and that the effects of a “poor” labor force require further attention.
References
Anderson, P. M., Meyer, B. D. 1997. Unemployment insurance takeup rates and the
after-tax value of benefits, Quarterly Journal of Economics 112: 913-937.
Blank, R. M., Card, D. E. 1991. Recent trends in insured and uninsured unemployment:
is there an explanation? Quarterly Journal of Economics 106: 1157-1189.
Burtless, G. 1983. “Why is insured unemployment so low? Brookings Papers on
Economic Activity: 225-249.
Morgan, K. O., Morgan, S., Quinto, N. (Eds.).  various. State rankings. Lawrence,
Morgan Quinto.
Rejda, G. 1999. Social insurance and economic security, 6th edition. New York, Prentice
Hall.
Tannenwald, R., O’Leary, C. J., Huang, W.-J. 1999. New ways of evaluating state
unemployment insurance, New England Economic Review, March/April: 15-40.Table 1.  Descriptive Statistics 1992-1996
Minimum Maximum Mean Standard Deviation
Recipient Rate 21.21 86.27 40.41 12.29
(NH, 93) (RI, 96)
Unemployment 8.40 27.30 15.83 4.31
Duration (Weeks) (NC, 95) (MA, 92)
Replacement Rate 25.10 52.80 36.85 5.03
(CA, 96) (HI, 95)
Unionization Rate 5.00 31.90 15.93 5.97
(NH, 95) (HI, 92)
Poverty Rate 5.30 26.40 13.42 4.07
(NH, 95) (LA, 93)
Female Labor Force 43.70 69.80 60.18 4.62
Participation (WV, 92) (MN, 94)
Labor Force in 3.13 26.58 15.37 5.75
Manufacturing (HI, 96) (NC, 92)
Metropolitan 23.5 100.0 66.98 21.31
Population (MT, 96) (NJ, 96)Table 2.  Regression Analysis of State Ratios of Insured Unemployment, 1992-1996
(Dependent Variable: RECIPi,t x 100, t statistics in parantheses)
Model 1 Model 2 Model 3 Model 4
Fixed Effects? No Yes Yes Yes
Regions States States
Duration (Weeks) 1.20 0.67 0.38 0.34
(6.69) (3.97) (4.63) (3.84)
Replacement (x 100) 0.03 0.07 0.62 0.60
(0.38) (0.65) (3.84) (4.19)
Union Rate (x 100) 0.88 0.83 0.21
(9.71) (6.95) (1.03)
Poverty Rate (x 100) -0.39 0.04 -0.26 -0.27
(-2.84) (0.28) (-2.13) (-2.36)
Female LPR (x 100) 0.36 0.96 0.10
(2.95) (6.28) (0.57)
Manufacture (x 100) 0.34 0.48 -1.57 -1.50
(5.12) (4.04) (-3.73) (-3.69)
Metropolitan (x 100) -0.15 -0.14 -0.12
(-7.01) (-5.45) (-0.45)
Right to Work (1=Yes)-1.54 0.61 -1.00 -1.42
(-1.62) (0.60) (-1.92) (-2.52)
Governor (1=Dem) 0.92 0.53 0.70
(1.20) (0.73) (1.53)
R
2 0.45 0.59 0.91 0.91

(-5.45) (-0.45) Right to Work

(0.28) (-2.13) (-2.36) Female

(0.65) (3.84) (4.19) Union Rate (x

(6.95) (1.03) Poverty Rate (x

Female Labor Force 43.70 69.80 60.18 4.62 Participation (WV, 92) (MN, 94) Labor Force

Fixed Effects? No Yes Yes Yes Regions States States Duration (Weeks)

Unemployment insurance takeup rates and the after-tax value of benefits,

Interstate Differences in Insured Unemployment: Some Recent Evidence

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Interstate Differences in Insured Unemployment: Some Recent Evidence

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