Health insurance coverage and the macroeconomy

Journal of Health Economics 24 (2005) 299315

Health insurance coverage and the macroeconomyJohn Cawley, Kosali I. Simon

Department of Policy Analysis and Management, College of Human Ecology, Cornell University,134 Martha Van Rensselaer Hall, Ithaca, NY 14853-4401, USA

Received 1 November 2003; received in revised form 1 August 2004; accepted 1 September 2004Available online 23 December 2004

Abstract

This paper investigates the relationship between the macroeconomy and health insurance coveragefor non-elderly Americans. We find that, for men, state unemployment rate is positively correlated withthe probability of health insurance coverage in general and through an employer in particular, and thatthese correlations are only partly explained by changes in employment status. In contrast, the insurancecoverage of women and children appears to be insulated from fluctuations in the unemployment rateby public health insurance programs like Medicaid and State Childrens Health Insurance Program(SCHIP). We estimate that 984,000 Americans, nearly all of whom were adult men, lost healthinsurance due to macroeconomic conditions alone during the 2001 recession. 2004 Elsevier B.V. All rights reserved.

JEL classication: I10; J3; J6; E32

Keywords: Health insurance; Medicaid; SCHIP; Recession; Unemployment

1. Introduction

In March 2001, the longest economic expansion in U.S. history ended, and a recession be-gan that lasted until November 2001 (Business Cycle Dating Committee, 2003). This, com-bined with recent policy interest in the uninsured (Committee on the Consequences of Unin-surance, 2001, 2004), raises the question: What is the relationship between macroeconomicclimate and health insurance coverage among the non-elderly U.S. population? This paperanswers that question, plus these others: How does the effect of the macroeconomy on insur-

Corresponding author. Tel.: +1 607 255 0952; fax: +1 607 255 4071.

0167-6296/$ see front matter 2004 Elsevier B.V. All rights reserved.doi:10.1016/j.jhealeco.2004.09.005

300 J. Cawley, K.I. Simon / Journal of Health Economics 24 (2005) 299315

ance coverage differ for men, women and children? What aspect of the macroeconomy mat-ters: state unemployment rate or real per capita gross state product (GSP)? Does the macroe-conomic climate primarily affect rates of uninsurance through changes in employment?

Policymakers should be concerned about the loss of health insurance coverage for severalreasons. First, some who lose employer-provided health insurance will join the rolls of publichealth insurance programs such as Medicaid and State Childrens Health Insurance Program(SCHIP), increasing the strain on the budgets of those programs. Second, uninsured personsmay receive less medical treatment than the insured (Doyle, 2001). Third, uninsured personsmay impose costs on the health care system by receiving their care in relatively inefficientways, such as using the emergency room for conditions that could have been treated withan office visit (Weissman et al., 1992). Fourth, uninsured individuals are at risk of severefinancial loss, including bankruptcy, in the event of illness (Jacoby et al., 2000).

Only a few studies focus on the link between macroeconomic conditions and healthinsurance coverage. Gruber and Levitt (2002), a Kaiser Family Foundation brief, studiedaggregate March Current Population Survey (CPS) data for 19802000 and found thatevery percentage point rise in unemployment was associated with an increase of 1.2 millionuninsured persons. Holahan and Garrett (2001) estimate that a percentage point increasein unemployment is associated with a rise in Medicaid enrollment of 1.5 million. Marquisand Long (2001) find mixed evidence that county unemployment rates are correlated withemployer offers of health insurance and employer contributions to health insurance. Gliedand Jack (2003) study state-level CPS data and find that unemployment rates are morestrongly correlated with insurance coverage for well-educated than less-educated workers,in part because workers with less education are at all times less likely to be offered employer-provided health insurance.

A limitation of several of these previous studies is their use of the CPS data. The CPSrecords whether the respondent was covered by health insurance at any point in the last 12months; thus, one cannot use the CPS to determine health insurance coverage in a specificmonth. A contribution of this paper is to provide estimates derived from reports of healthinsurance coverage in a specific month matched with macroeconomic conditions duringthat month.

The previous literature is also limited by its use of cross-sectional data and inability toremove unobserved time-invariant heterogeneity. This paper contributes to the literature byanalyzing longitudinal data and controlling for person-specific fixed effects.

Our results indicate that increases in unemployment rate lower the probability of healthinsurance coverage for adult men. A substantial fraction, but not all, of this correlation isexplained by changes in employment status. In contrast, the insurance coverage of womenand children appears to be insulated from fluctuations in the unemployment rate by publichealth insurance programs like Medicaid and SCHIP. For men, women, and children, realper capita gross state product is uncorrelated with the probability of coverage.

2. Conceptual framework and methods

We utilize two measures of the macroeconomy in our empirical work. In using stateunemployment rate as our primary measure of the macroeconomy, we follow the literature

J. Cawley, K.I. Simon / Journal of Health Economics 24 (2005) 299315 301

on the effect of the macroeconomy on welfare use (Grogger, 2003), substance abuse (Ruhm,1995), and health (Ruhm, 2003, 2000). We also use real per capita gross state product as ameasure of aggregate income and production.

Our conceptual framework of how health insurance coverage is affected by these macroe-conomic variables is based on the literature that establishes the relationship between, on theone hand, employment, unemployment rates, and income, and, on the other hand, employeroffers of health insurance coverage, employee take-up, and individual insurance purchases.In short, high unemployment and/or low GSP are associated with a lower probability thatemployers offer insurance, and with greater cost shifting to employees in existing plans,reducing coverage through employers. They have also been found to result in state cutbacksin eligibility or generosity of public health insurance programs like Medicaid and SCHIP,reducing coverage through public sources.

When thinking of how unemployment rate and GSP may affect the probability of healthinsurance coverage, it is useful to consider the ways in which Americans receive healthinsurance coverage. Data from the 2001 CPS indicate that 50% of adult Americans receivehealth insurance through their employer, and an additional 19% receive it through the em-ployer of a parent or spouse. Six percent of Americans purchase individual health insurancecoverage, 4% are covered by Medicaid, 4% receive it through some other source, and 18%are uninsured (Lambrew, 2001). Unemployment and GSP may affect the probability ofcoverage through each of the major sources.

There are several ways that a poor economy may result in the loss of employer-providedcoverage. A high state unemployment rate is, naturally, associated with a higher probabilitythat a resident of that state is unemployed, and unemployment is correlated with a lack ofhealth insurance. When those who were previously covered by employer-provided healthinsurance lose their jobs, they (and any dependents on the same policy) are likely to losecoverage from the former employer. Although the Consolidated Omnibus Budget Reconcil-iation Act of 1985 (COBRA) allows eligible unemployed workers to temporarily purchasehealth insurance through their former employers, take-up rates are low (Rice, 1999), mostlikely because of cost (Lambrew, 2001). Some who lose their jobs switch to coverage pro-vided by the employer of their spouse. However, 44% of those who lose their job spend atleast 1 month in the next 36 without health insurance coverage (Bennefield, 1998).

High unemployment rates may lower the probability of employer-provided coverageeven among those who remain employed. When labor demand shifts in because of a poormacroeconomy, total labor compensation will fall. If wages are costly to renegotiate, em-ployers may reduce compensation by shifting health insurance costs to employees. Higherpremia increase the probability that workers decline coverage; it has been estimated thatfor every 1% increase in real health insurance premiums, 300,000 fewer people take upemployer offers of health insurance (Sheils, 1998). Due to decreased labor demand, previ-ously full-time workers may have their hours cut back to the extent that they are no longereligible for health insurance benefits (Rowland, 2002). A more extreme method of reducingtotal compensation is for employers to cease offering health insurance (Marquis and Long,2001). Small firms are particularly sensitive to cost in their decision to offer health insurance(Feldman et al., 1997).

Low gross state product may lead to lower state tax revenue, leading state governmentsto reduce the generosity of publicly provided health insurance, increasing rates of unin-


surance. Medicaid represents 20.8% of total state spending (National Association of StateBudget Officers, 2004), so when state tax revenues fall because of an economic downturn,there is increased pressure to cut Medicaid budgets, potentially increasing the number ofMedicaid-eligible individuals left without coverage (Kaiser Family Foundation, 2001). Forexample, in response to fiscal pressure since the 2001 recession, 34 states have reducedor restricted Medicaid eligibility (National Association of State Budget Officers, 2004); inthese states, those who lose their employer-provided health insurance may be more likelyto become uninsured. In addition, since the 2001 recession, 35 states have reduced Medi-caid benefits and 32 have increased beneficiary copayments (National Association of StateBudget Officers, 2004), both of which may reduce take-up among the eligible.

The lower labor income that accompanies higher unemployment rates and low GSP mayalso affect the number of uninsured if those who previously purchased private health insur-ance become unable to afford it. On the other hand, some people might gain health insurancecoverage during bad macroeconomic times if their incomes fall to a level that qualifies forMedicaid. Holahan and Garrett (2001) estimate that an increase in unemployment of onepercentage point would expand eligibility for Medicaid by 400,000 non-disabled adults and1 million children.

Based on this framework and the existing literature, we hypothesize that an increase inunemployment rate decreases the probability of coverage through any source and decreasesthe probability of coverage through ones own employer. We hypothesize that an increasein gross state product has the opposite effect, increasing the probability of coverage throughany source or through ones own employer.

We do not have unambiguous predictions about the relationship between these macroe-conomic variables and public health insurance coverage. High unemployment and low GSPmay increase the probability of coverage through the government if a persons income fallsto a level that qualifies for Medicaid, or may lead state legislatures to tighten eligibilityrequirements or reduce the generosity of benefits, leading to lower take-up rates among theeligible population.

We estimate logit models in which the dependent variables are: an indicator variable forwhether one has health insurance coverage through any source, an indicator for whether onereceives health insurance coverage through ones own employer, an indicator for whetherthe individual is covered by Medicaid or SCHIP, an indicator for whether the individualis covered by any government-provided health insurance, an indicator for whether onescurrent employer offers health insurance, and an indicator for whether a worker offeredhealth insurance by an employer has accepted that offer (i.e. health insurance take-up).The regressors of interest are state unemployment rate and real gross state product. Modelsalso control for respondent age, marital status, education, and family size.

All models control for both individual-specific and year-specific fixed effects; our iden-tification of the effect of macroeconomic conditions on the probability of health insurancecoverage comes from variation within people over time in deviations from the nationalmean for that year. The following example illustrates how controlling for individual fixedeffects is potentially important. If a booming state economy attracts into the state labor forceworkers with a greater demand for fringe benefits, using cross-sectional data may lead toan overestimate of the effect of low unemployment on the probability of health insurancecoverage. By controlling for individual fixed effects, the fact that the new workers have


always been more likely to have health insurance is captured by the person-specific indica-tor variables, and we generate a more consistent estimate of the effect of macroeconomicconditions on insurance coverage.

We estimate models separately for men and women because labor force participationand attachment differs by gender (Blau et al., 2002). In addition, eligibility for publiclyprovided health insurance sometimes differs by gender; for example, low-income pregnantwomen are eligible for Medicaid coverage.

We cluster standard errors to account for the fact that individual outcomes are regressedon macroeconomic measures that vary at the state level. When a micro outcome is regressedon an aggregate regressor, unadjusted standard errors will be biased downwards, perhapsdramatically (Moulton, 1990).1 A related concern is serial correlation in standard errors forobservations within states over time (Bertrand et al., 2004). We adjust for these issues bybootstrapping the standard errors, selecting with replacement all observations in a particularstate.2 As expected, this adjustment considerably increases the standard errors.

3. Data

The relationship between state and national economic climate and individuals healthinsurance status is measured using data from two nationally representative samples: theSurvey of Income and Program Participation (SIPP) and the National Longitudinal Surveyof Youth (NLSY).3 Each is well suited for a study of health insurance and the macroeconomybecause each follows the same individuals over a considerable period of time, permittingus to control for individual fixed effects. An advantage of the SIPP is its large sample size(we have samples of roughly three quarters of a million observations each for men, women,and children), and an advantage of the NLSY is its richer set of questions about healthinsurance. The SIPP serves as the primary dataset in this study, but when the SIPP lackscertain health insurance information, we use that contained in the NLSY.

3.1. The survey of income and program participation

The Survey of Income and Program Participation is a nationally representative sampleof Americans over the age of 154 that consists of a series of panels that are up to 4 yearsin length with sample sizes ranging from approximately 12,00040,000 households. TheSIPP, which started in 1984, interviews households at 4-month intervals (collecting data

1 Donald and Lang (2001) and Bertrand et al. (2004) discuss this problem as it applies to the standard errors ofdifference-in-differences estimators.

2 We conduct 50 replications to estimate the bootstrap standard errors, which is in the range recommended byEfron and Tibshirani (1993).

3 The CPS is commonly used to assess the health insurance coverage of Americans. The advantages of the SIPPand NLSY over the CPS are that the SIPP and NLSY track individuals for long periods of time and that theyrecord health insurance coverage at a particular point in time whereas the CPS is cross-sectional and records onlywhether the individual had health insurance coverage at any time in the past year. Bennefield (1996) finds thatCPS respondents tend to underreport health insurance coverage relative to SIPP respondents.

4 There are also records for children in the household, based on parents reports.


on the current month and, retrospectively, each of the 3 months between interviews); thus,there exist up to 12 interviews for each individual.

Each wave contains information on the respondents insurance coverage and the sourceof their coverage, for a particular month. We study the following outcomes in the SIPP:an indicator variable for whether one has health insurance coverage through any source,an indicator for whether one receives health insurance coverage through ones own em-ployer, an indicator for whether the individual is covered by Medicaid or SCHIP, andan indicator for whether the individual is covered by any government-provided healthinsurance.5

The SIPP also contains information on job status and demographic characteristicsthat may influence insurance status (e.g. age, race, gender, education, marital status,and family size). This paper uses data from the 19901996 panels of the SIPP cov-ering the period 19902000. In order to avoid recall bias (Marquis and Moore, 1990;Kalton and Miller, 1991), we follow recent convention (e.g. Grogger, 2003) and do notuse the retrospective data; we instead focus exclusively on current data. As a result, wehave up to 12 monthly observations (each 4 months apart) for each individual in theSIPP. The set of regressors used in each regression includes: highest grade completed,age, number of children in the family, marital status, indicator variables for each indi-vidual, and indicator variables for each year. We exclude income from the set of re-gressors because wages and salary are determined simultaneously with fringe benefitssuch as health insurance. Summary statistics of the SIPP data appear in Table A.1 inAppendix A.

3.2. The National Longitudinal Survey of Youth

The National Longitudinal Survey of Youth contains data from interviews of 12,686respondents conducted annually from 1979 to 1994 and every 2 years from 1994 to 2000.We study the following outcomes in the NLSY: an indicator variable for whether onescurrent employer offers health insurance (which is available for 19832000) and an indicatorfor whether the worker accepted (took up) that offer of coverage (which is available for19892000).

The set of regressors used in each regression includes: indicator variables for individ-ual, indicator variables for year, highest grade completed, age, family size, and indicatorvariables for marital status. Summary statistics of the NLSY data appear in Table A.2 inAppendix A.

3.3. Data on macroeconomic conditions

The key explanatory variables that reflect the economic climate are monthly state un-employment rate and annual real per capita gross state product. State-level variables are

5 For adults in the SIPP, government-provided coverage includes Medicare, Medicaid, other free or subsidizedpublic assistance health insurance, or Armed Forces related health insurance provided by the government. Forchildren, the list excludes Medicare since the SIPP did not ask about childrens Medicare status.


merged with the NLSY using a restricted-access geocode, and with the SIPP using publiclyavailable state identifiers.6

We also include in our set of regressors a vector of indicator variables for year.The coefficients on these indicator variables capture the correlation of nationwide, year-specific macroeconomic conditions with changes in the probability of health insurancecoverage.

The Bureau of Labor Statistics Local Area Unemployment Statistics Series is the sourcefor monthly unemployment rates at the state level. We control for individual and year fixedeffects, so our identifying variation of unemployment on health insurance coverage is withinpeople over time of deviations from the national mean for that year.

Data on gross state product are derived from the Regional Accounts Data collected bythe Bureau of Economic Analysis of the U.S. Department of Commerce. We convert GSPto year 2000 dollars using the annual CPI-U. Real GSP is divided by Census estimates ofthe state population in that year. Since we control for individual and year fixed effects, ouridentifying variation of real per capita GSP on health insurance coverage is within peopleover time of deviations from the national mean for that year.

3.4. Additional state-level data

We also include three regressors that control for heterogeneity at the state level. The firstregressor is the percent of the workforce that is unionized in that state; Hirsch et al. (2001)is the source of this data. Unionization is relevant because unions are likely to negotiatehealth insurance coverage for their members.

Second, the Medicare hospital wage index is used to proxy for differences in the cost ofhealth insurance. We use the statewide rural area measure of the index because we knowthe state, but not county, of residence in the SIPP.

Finally, we control for variation across states and over time in the generosity of pub-lic health insurance programs like Medicaid and SCHIP using a simulated measure ofpublic health insurance eligibility as in Currie and Gruber (1996). Specifically, we sim-ulate the fraction of children under age 18 who would have been eligible for publichealth insurance had their families lived in a given state in a given year (after adjustingfinancial variables for inflation), using the 1990 Public Use Micro Sample (5%) of theCensus.

4. Empirical results

We initially estimate the probability that an individual has health insurance coverageas a function of macroeconomic conditions and basic demographic characteristics whileexcluding employment status, and then we re-estimate our models controlling for employ-ment status in order to determine the proportion of the correlation explained by changes in

6 Several of the least-populous states are grouped for identification and it is not possible to merge state-levelvariables to respondents in such states, so these respondents are dropped from the sample.


Table 1SIPP men whether covered by health insurance as a function of macroeconomic conditions logit fixed-effectscoefficients, standard errors and marginal effects

Macroeconomic variable or statistic Any source Employer coverage Government provided

State unemployment rate 0.0284** 0.0509* 0.0251(0.0126) (0.0091) (0.0291)[0.007] [0.013] [0.006]

Per capita real GSP 0.0194 0.0269 0.0073(0.0194) (0.0192) (0.0474)[0.005] [0.007] [0.0018]

Mean of dependent variable 0.83 0.55 0.09Number of observations 731742 731742 731742

Notes: (1) Cells of the table contain: coefficient, bootstrapped standard errors in parentheses, and marginal effectsin italics.(2) Superscripted notations next to the coefficients indicate the level of statistical significance from a two-tailedt-test. * Denotes the 1% level and ** denotes the 5% level.(3) Bootstrapped standard errors are clustered at the state level.(4) Data: pooled 19901996 waves of the SIPP. Sample includes all men between the ages of 17 and 64 years ofage regardless of employment status.(5) Dependent variablescolumn 1: indicator variable that equals 1 if individual covered by health insurance fromany source and 0 otherwise; column 2: indicator variable that equals 1 if individual is covered by employer healthinsurance in own name and 0 otherwise; column 3: indicator variable for any type of government-provided healthinsurance.(6) Other regressors: individual fixed effects, year-specific fixed effects, Medicare hospital wage index, unioncoverage rate in the state, childrens Medicaid generosity index of the state, highest grade completed, maritalstatus, presence of children in the family, and age.

employment. Each cell of each of our tables contains, from top to bottom, the logit fixed-effects coefficient, the standard error in parentheses, and the marginal effect italicized inbrackets.7

Table 1 contains results for males in the SIPP. In the first row, our prediction that higherunemployment rates will be associated with a lower probability of coverage is confirmed forboth coverage through any source and that through an individuals employer. The marginaleffects indicate that a one-percentage point increase in state unemployment rate is associatedwith a 0.7 percentage point decrease in the probability of any coverage for men. While thismay seem like a small effect, we show in Section 6 that this implies that more than 900,000adult men lost coverage during the 2001 recession.

The second row of Table 1 does not confirm our prediction that higher GSP is associatedwith a higher probability of coverage. While the point estimates of the coefficients on GSPare positive, they are not statistically significant.

7 We evaluate the marginal effect at the sample means, which measures the effect for a person with characteristicsequal to the sample averages. As an alternative, we calculated the average marginal effect across individuals; thetwo methods produce very similar marginal effects. For example, the marginal effect of a one-percentage pointincrease in the unemployment rate on the probability that men have any health insurance was0.0070 percentagepoints using the first method and 0.0065 percentage points using the second method.


Table 2SIPP women whether covered by health insurance as a function of macroeconomic conditions logit fixed-effectscoefficients, standard errors and marginal effects

Macroeconomic variable orstatistic

Any source Employercoverage

Medicaid Governmentprovided

State unemployment rate 0.0007 0.0316** 0.0308 0.0064(0.0106) (0.0115) (0.0205) (0.0169)[0.000] [0.007] [0.005] [0.002]

Per capita real GSP 0.0096 0.0030 0.0228 0.0074(0.0185) (0.0161) (0.0375) (0.0291)[0.002] [0.001] [0.004] [0.002]

Mean of dependent variable 0.86 0.37 0.09 0.14Number of observations 800659 800659 800659 800659

Notes: (1) Cells of the table contain: coefficient, bootstrapped standard errors in parentheses, and marginal effectsin italics.(2) Superscripted notations next to the coefficients indicate the level of statistical significance from a two-tailedt-test. ** Denotes the 5% level.(3) Bootstrapped standard errors are clustered at the state level.(4) Data: pooled 19901996 waves of the SIPP. Sample includes all women between the ages of 17 and 64 yearsof age regardless of employment status.(5) Dependent variablescolumn 1: indicator variable that equals 1 if individual covered by health insurance fromany source and 0 otherwise; column 2: indicator variable that equals 1 if individual is covered by employer healthinsurance in own name and 0 otherwise; column 3: indicator variable for Medicaid coverage; column 4: indicatorvariable for any type of government-provided health insurance.(6) Other regressors: individual fixed effects, year-specific fixed effects, Medicare hospital wage index, unioncoverage rate in the state, childrens Medicaid generosity index of the state, highest grade completed, maritalstatus, presence of children in the family, and age.

We have no unambiguous prediction for the sign of the effect of the macroeconomyon government-provided coverage; the last column of Table 1 indicates that neither thecoefficient on unemployment nor that on GSP is statistically significant.

Table 2 presents the analogous results for women. While coverage through any sourceis not sensitive to macroeconomic conditions for women, coverage through an employeris negatively correlated with state unemployment rate. A one-percentage point increasein unemployment rate decreases the probability of coverage through an employer by 0.7percentage points. The coefficient on unemployment in the Medicaid regression is notstatistically significant, but its marginal effect is of similar magnitude to, and opposite signthan, that for unemployment in the employer-provided insurance regression. Results forchildren are presented in Table 3. Unemployment and real GSP are not correlated with theprobability of coverage through any source, but unemployment rate is positively correlatedwith the probability of Medicaid coverage. Specifically, a one-percentage point increase inunemployment is associated with a 1.04 percentage point increase in the probability a childis covered by Medicaid or SCHIP.8 These results likely reflect the counter-cyclical nature

8 The 1996 SIPP does not specifically ask about the SCHIP because of the timing of the survey versus SCHIPenactment, but children on SCHIP should be recorded under the publicly provided/Medicaid question in the latterpart of the 1996 SIPP Panel.


Table 3SIPP children whether child has health insurance coverage as a function of macroeconomic conditions logitfixed-effects coefficients, standard errors and marginal effects

Macroeconomic variable or statistic Any source Medicaid Government provided

State unemployment rate 0.0008 0.0318# 0.0323(0.0130) (0.0177) (0.0182)[0.000] [0.0104] [0.008]

Per capita real GSP 0.0149 0.0199 0.0027(0.0154) (0.0320) (0.0280)[0.000] [0.005] [0.001]

Mean of dependent variable 0.86 0.19 0.22Number of observations 703094 703094 703094

Notes: (1) Cells of the table contain: coefficient, bootstrapped standard errors in parentheses, and marginal effectsin italics.(2) Superscripted notations next to the coefficients indicate the level of statistical significance from a two-tailedt-test. # Denotes the 10% level.(3) Bootstrapped standard errors are clustered at the state level.(4) Data: pooled 19901996 waves of the SIPP. Sample includes all children under the age of 18.(5) Dependent variablescolumn 1: indicator variable that equals 1 if child covered by any health insurancefrom any source and 0 otherwise; column 2: indicator variable that equals 1 if child covered by Medicaid and 0otherwise; column 3: indicator variable that equals 1 if child has government-provided health insurance coverageand 0 otherwise (excluding Medicare).(6) Other regressors: individual fixed effects, year-specific fixed effects, Medicare hospital wage index, unioncoverage rate in the state, childrens Medicaid generosity index of the state, and age.

of Medicaid and SCHIP; when unemployment rates are high and children lose employer-provided health insurance because their parents have lost their jobs, SCHIP and Medicaidexpand their coverage of children and the net effect is that the probability a child is coveredthrough any source is uncorrelated with the unemployment rate.

The NLSY contains certain information about health insurance options that is not avail-able in the SIPP. For example, the NLSY asks respondents whether their employer of-fered them health insurance coverage. It also allows us to measure take-up of employeroffers.

The correlation of employer offers of health insurance coverage with macroeconomicconditions for the sample of employed NLSY respondents is presented in Table 4.9 Inaddition to controlling for the earlier set of regressors, we also add an indicator for whetherthe employee is a part-time worker (defined as 20 hours a week or less). Employer offers tomen are more sensitive to state unemployment rate than those to women; a one-percentagepoint increase in unemployment rate is associated with a decrease in the probability thatones employer offers health insurance coverage of 0.16 percentage points for males and0.11 for women. This difference is not necessarily due to employers having different policies

9 Note that the data on employer offers are at the employee level. As a result, large employers are likely to beover-represented in the data, biasing our estimates of the willingness of employers to offer health insurance.


Table 4NLSY whether current employer offers health insurance as a function of macroeconomic conditions logit fixed-effects coefficients, t statistics, and marginal effects

Variable or statistic Men Women

State unemployment rate 0.0652* 0.0357#(0.015) (0.021)[0.0016] [0.0011]

Per capita real GSP 0.0069 0.0076(0.010) (0.019)[0.0002] [0.0002]

Part-time worker 1.184* 0.8713*(0.162) (0.205)[0.0508] [0.0401]

Mean of dependent variable 0.76 0.76Number of observations 33206 27759

Notes: (1) Cells of the table contain: coefficient, bootstrapped standard errors in parentheses, and marginal effectsin italics.(2) Superscripted notations next to the coefficients indicate the level of statistical significance from a two-tailedt-test. # Denotes the 10% level and * denotes the 1% level.(3) Bootstrapped standard errors are clustered at the state level.(4) Data: 15 pooled years of the NLSY. Sample includes only those currently employed.(5) Dependent variable equals 1 if current employer offers health insurance coverage and 0 otherwise.(6) Marginal probabilities are computed using the sample means of the regressors.(7) Other regressors: individual fixed effects, year fixed effects, Medicare hospital wage index, highest gradecompleted, age, family size, and indicator variables for marital status.

toward the two genders; it may be due to differences in occupation and sector or industryof occupation.

We test for changes in take-up rates of employer-offered health insurance during periodsof high unemployment. Specifically, we regressed an indicator variable for whether one re-ceives health insurance coverage through ones own employer on macroeconomic variablesfor the sample of NLSY respondents who were both employed and offered health insurancecoverage by their employer. The results, presented in Table 5, offer no support for the hy-pothesis that take-up is sensitive to the macroeconomy. The coefficients on unemploymentrate and GSP all fall short of statistical significance.

One important way that the macroeconomy affects individuals health insurance status isthrough their employment status (Bennefield, 1998). For this reason, we measured the extentto which macroeconomic conditions are correlated with insurance status conditional onemployment status. Specifically, we added indicators for current employment and part-timeemployment status to the set of regressors. Recall that without controlling for employmentstatus, a one-percentage point rise in unemployment was associated with a 0.7 percentagepoint decrease in the probability of health insurance coverage for men. After controllingfor employment status, the associated decrease is 0.4 percentage points. Employment isclearly one major pathway through which macroeconomic conditions affect the probabilityof health insurance coverage.


Table 5NLSY whether employee takes up employer offer of health insurance as a function of macroeconomic conditionslogit fixed-effects coefficients, t-statistics, and marginal effects

Variable or statistic Men Women

State unemployment rate 0.0267 0.0602(0.038) (0.054)[0.0043] [0.0087]

Per capita real GSP 0.0012 0.0106(0.023) (0.032)[0.0002] [0.0015]

Part-time worker 0.1207 1.1705*(0.624) (0.452)[0.0203] [0.1131]

Mean of dependent variable 0.85 0.76Number of observations 8992 8669

Notes: (1) Cells of the table contain: coefficient, bootstrapped standard errors in parentheses, and marginal effectsin italics.(2) Superscripted notations next to the coefficients indicate the level of statistical significance from a two-tailedt-test. * Denotes the 1% level.(3) Bootstrapped standard errors are clustered at the state level.(4) Data: 15 pooled years of the NLSY. Sample includes only those currently employed.(5) Dependent variable equals 1 if current employer offers health insurance coverage and 0 otherwise.(6) Marginal probabilities are computed using the sample means of the regressors.(7) Other regressors: Individual fixed effects, year fixed effects, Medicare hospital wage index, highest gradecompleted, age, family size, and indicator variables for marital status.

5. Sensitivity analyses

We conduct several sensitivity checks to gauge the robustness of our findings. We ex-tended our analysis by investigating whether lagged measures of the macroeconomy affectinsurance coverage. If an employer wished to shift the costs of health insurance to employ-ees, or an employee wanted to change their decision to decline or take up coverage, theywould have to wait until the next open enrollment period, suggesting that coverage mayhave a delayed response to the macroeconomy. One-year lags of GSP were, like currentGSP, consistently not statistically significant. One-year lags of unemployment rate tendedto be statistically significant, but due to multicollinearity, the magnitude of the main effectfell by roughly the magnitude of the lagged effect. We tested using the Akaike InformationCriterion (AIC) (Judge et al., 1985) whether the fit of the model was improved by includ-ing a 1-year lag of the unemployment rate. The AICs of the model without a lag and themodel with a lag were extremely close and, in most cases, the test statistic of the modelwithout a lag was lower, indicating that the model without a lag is preferable. This is likelya reflection of the fact that current and lagged values of unemployment rate are highlycollinear, so the current value alone does roughly as well as the current and lagged valuestogether.


We experimented with controlling for the employment rate instead of the unemploy-ment rate. The first, but not the second, denominator includes people who are out ofthe labor force. We find that the coefficient on employment rate is statistically signifi-cant in the same regressions in which that on unemployment rate is statistically signifi-cant and that the absolute values are similar, although, predictably, the two have oppositesigns.

We have also estimated our regressions controlling for fixed effects at the state, in-stead of the individual, level. We find very similar coefficients on unemployment rateand GSP, perhaps in part because most people stay in the same state throughout thesample.

We also investigated using gender-specific rather than overall unemployment rates.We do not use gender-specific unemployment rates in our primary regressions be-cause the gender-specific rates are reported only annually, and thus we are unable tomatch insurance coverage in a particular month to the macroeconomic conditions in thatsame month. Exploiting our ability to match insurance status to macroeconomic con-ditions in the SIPP and NLSY is a major innovation of this paper over the previousliterature, which was unable to do so using the CPS. However, as a sensitivity anal-ysis, we also pursue the use of gender-specific unemployment rates. Gender-specificand overall unemployment rates are highly correlated; for men the correlation coeffi-cient is 0.934 and for women it is 0.855. Given such a high correlation, it is not sur-prising that when we use gender-specific unemployment rates, the point estimates ofthe coefficients are similar to those resulting from the use of overall unemploymentrates.

In our primary results, we use state-level unemployment rate. The restricted-accessgeocode for the NLSY allows us to merge county unemployment rates to the indi-vidual observations and thereby determine whether our results differ when we use ameasure of unemployment from a smaller geographic area. We find very similar re-sults when we use county rather than state unemployment rate in the NLSY regres-sions.

6. Discussion

We find significant gender differences in the relationship between macroeconomic con-ditions and the probability of health insurance coverage. For men, the probability of anycoverage is negatively correlated with unemployment rate; a one-point increase in stateunemployment rate is associated with a decrease in the probability of health insurance cov-erage through any source of 0.70 percentage points. In contrast, the unemployment rate isnot significantly correlated with coverage through any source for women or children. Thisdifference in results between men, women, and children is likely due to Medicaid and SCHIPworking counter-cyclically to enroll women and children who lose other types of cover-age during periods of high unemployment. We find that unemployment rate is negativelycorrelated with coverage even controlling for employment status.

Our prediction that real per capita gross state product would be positively correlatedwith the probability of coverage was not supported by the data. Unemployment rate is


apparently the most relevant measure of the macroeconomy for determining the risk ofuninsurance.

From March to November of 2001, the U.S. experienced an economic recession. Weuse our estimates of the correlation between health insurance coverage and unemploy-ment rate and GSP to predict the number of Americans who lost health insurance dur-ing the recession. During the 2001 recession, the national unemployment rate rose from4.3% in March to 5.6% in November and real per capita GDP fell from US$ 34,764 inthe first quarter to US$ 34,499 in the fourth quarter (U.S. Department of Labor, 2004;U.S. Department of Commerce, 2004). Based on these changes, our regression results,and Census estimates of the U.S. population of men aged 1864, women aged 1864,and children under age 18 in the year 2001 (U.S. Census Bureau, 2004), we estimatethat roughly 984,000 Americans lost health insurance during the 2001 recession. Our es-timates indicate that almost all of those who lost health insurance were adult men; specif-ically, that 908,000 of the 984,000 were men. The remaining 75,000 were virtually allwomen; our estimates indicate that virtually no children lost health insurance during therecession.

Our estimate of the total number losing health insurance during the 2001 recession isless than that of Families USA (2002), which estimated that 2 million Americans lost healthinsurance due to increased unemployment between March and December of 2001. Threedifferences between the Families USA study and the present one should be kept in mindwhen comparing the overall estimates: first, Families USA was based on an extra month(December 2001); second, the Families USA study is based on the CPS rather than SIPP;third, the Families USA study did not take into account the loss in health insurance coveragethat occurred as a result of the slight decline in real per-capita GSP.

Our estimates also indicate that more than enough Americans have gained coverageduring the current recovery to fully offset the loss of coverage during the recession. Be-tween the end of the recession in November 2001 and March 2004, the national unem-ployment rate rose from 5.6 to 5.7% and real per capita GDP rose from US$ 34,499 to36,567 (U.S. Department of Labor, 2004; U.S. Department of Commerce, 2004). Basedon these figures, and holding constant population at the 2001 levels in order to avoid con-fusing an increase in population with an increase in the probability of insurance cover-age, we estimate that roughly 1.246 million Americans gained health insurance coverageduring the current recovery. Of the 1.246 million who gained health insurance, roughly806,000 were men, 440,000 were women, and virtually none were children. We empha-size that our estimates cover only those who lost (or gained) health insurance becauseof changes in the macroeconomy. Because of other changes in health insurance mar-kets, labor markets, or society, additional people may have lost (or gained) health insur-ance.

Acknowledgements

We thank Shailesh Bandhari, David Cutler, Alan Garber, Jeanne Lambrew, LindaLoubert, Catherine McLaughlin, Mark Pauly, and conference and seminar partici-


pants for their helpful comments. We thank Justine Lynge for editorial assistance.We gratefully acknowledge financial support from the Economic Research Initia-tive on the Uninsured and the Bronfenbrenner Life Course Center Innovative Re-search Program. Simon gratefully acknowledges support from a W.E. Upjohn Institutemini-grant.

Appendix A

See Tables A.1 and A.2.

Table A.1Summary statistics of SIPP Data

Variable Number ofobservations

Mean Standarddeviation

Minimum Maximum

Indicator: covered by ownemployer HI

1532401 0.460 0.498 0 1.0

Indicator: covered by any HI 1532401 0.845 0.36 0 1.0Indicator: covered by Medicaid 1532401 0.068 0.251 0 1.0Indicator: covered by government

HI1532401 0.115 0.319 0 1.0

State unemployment rate 1532401 5.942 1.704 1.9 12.80Medicare hospital wage index 1329634 8231.92 952.01 4080 12456Per capita real gross state product 1532401 24.08 6.33 11.54 105.01State Medicaid generosity 1532401 0.24 0.11 0.074 0.82Union coverage 1532401 17.22 6.70 3.8 31.89Indicator: female 1532401 0.522 0.499 0 1.0Year 1532401 1994.4 2.84 1990 2000Indicator: high-school dropout 1532401 0.163 0.369 0 1.0Indicator: high-school graduate 1532401 0.333 0.471 0 1.0Indicator: some college 1532401 0.281 0.449 0 1.0Indicator: college graduate 1532401 0.128 0.334 0 1.0Age 1532401 38.666 12.48 18 64Indicator: no children in family 1532401 0.57 0.494 0 1Indicator: employed 1532401 0.726 0.446 0 1.0Indicator: employed part time 1532401 0.294 0.456 0 1.0Indicator: married 1532401 0.596 0.490 0 1.0Indicator: widowed 1532401 0.022 0.148 0 0.0Indicator: separated or divorced 1532401 0.13 0.334 0 1.0Indicator: child covered by any HI 703094 0.86 0.34 0 1.0Indicator: child covered by gov-

ernment HI703094 0.217 0.412 0 1.0

Notes: (1) The sample for all but the last two items consists of adults (age 1864).(2) The sample for the last two items is all children under age 18.


Table A.2Summary statistics of NLSY data

Variable Number ofobservations

Mean Standarddeviation

Minimum Maximum

Indicator: employer offers HI 102135 0.743 0.44 0 1Indicator: took up employer offer of HI 56179 0.592 0.49 0 1State unemployment rate 102135 6.44 2.21 1.7 21.6Medicare hospital wage index 81080 8409.09 958.67 4089 14870Per Capita Real Gross State Product 102135 18.52 8.41 5.9 105State Medicaid generosity 102135 17.86 7.46 4.4 36.3Union coverage 102135 0.231 0.115 0.028 0.769Indicator: female 102135 0.469 0.50 0 1Indicator: black 102135 0.267 0.44 0 1Indicator: Hispanic 102135 0.173 0.38 0 1Year 102135 199.48 4.81 1983 2000Highest grade completed 102135 12.94 2.33 0 20Age 102135 29.42 5.27 18 44Family size 102135 3.05 1.67 1 15Indicator: employed 102135 0.930 0.26 0 1Indicator: married, spouse present 102135 0.481 0.50 0 1Indicator: other marital status 102135 0.154 0.36 0 1

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Health insurance coverage and the macroeconomyIntroductionConceptual framework and methodsDataThe survey of income and program participationThe National Longitudinal Survey of YouthData on macroeconomic conditionsAdditional state-level data

Empirical resultsSensitivity analysesDiscussionAcknowledgementsAppendix AReferences

Health insurance coverage and the macroeconomy

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