ECONOMIC FREEDOM AND LABOR MARKET CONDITIONS: EVIDENCE FROM THE STATES

ECONOMIC FREEDOM AND LABOR MARKET CONDITIONS: EVIDENCEFROM THE STATES

LAUREN R. HELLER and E. FRANK STEPHENSON∗

Using 1981–2009 data for the 50 states, this article examines the relationshipbetween economic freedom and the unemployment rate, the labor force participationrate, and the employment-population ratio. After controlling for a variety of state-levelcharacteristics, the results from most specifications indicate that economic freedom isassociated with lower unemployment and with higher labor force participation andemployment-population ratios. (JEL J68, K31, O43)

I. INTRODUCTION

Labor market conditions are one of the mostpolitically salient dimensions of public policy.For example, the change in the unemploymentrate is strongly related to presidential electionoutcomes (Fair 1978). That such a relationshipexists is not particularly surprising—people outof work or anxious about being able to findor retain a job are, ceteris paribus, probablyless inclined to think existing policies shouldbe continued.

Beyond the electoral fortunes of politicians,U.S. labor market conditions are especiallyprominent in current public discourse becauseunemployment reached levels not seen forthree decades during the sharp recession of2007–2009 and has receded only lethargically.The attention placed on national labor mar-ket aggregates, however, obscures the disparatelabor conditions that exist across states. Forexample, in December 2012 (the most recentmonth available at the time of writing), stateunemployment rates ranged from 3.2% in NorthDakota to 10.2% in Nevada and Rhode Island.This variation in labor market conditions acrossstates is the topic of our article.

We posit that the variation in labor marketconditions across states is related to interstatedifferences in economic freedom. There are a

∗We thank Hillary Anderton and Kelly Hastings forvaluable research assistance and the anonymous referees forhelpful comments.Heller: Assistant Professor, Department of Economics,

Berry College, Mount Berry, GA. Phone 706-290-2688,Fax 706-238-7854, E-mail [email protected]

Stephenson: Professor, Department of Economics, BerryCollege, Mount Berry, GA. Phone 706-238-7878, Fax706-238-7854, E-mail [email protected]

number of channels through which economicfreedom might be associated with labor mar-ket outcomes. The most direct is through labormarket regulations. For example, there is anextensive, although not unanimous, literaturefinding a negative relationship between the min-imum wage and employment (Neumark andWascher 2007). Consistent with the possibilitythat labor market regulations stifle labor marketoutcomes, Garrett and Rhine (2011) find thatless labor market regulation (as well as moreeconomic freedom in general) is associated withhigher rates of employment growth among U.S.states over the period 1980–2005.

Beyond a direct link between labor mar-ket regulation and employment conditions, thereare other channels through which economicfreedom might also be associated with favor-able labor market outcomes. There is an exten-sive literature finding that economic freedomis positively related to economic growth (Daw-son 2003; Doucouliagos and Ulubasoglu 2006;Gwartney, Lawson, and Holcombe 1999; Heller2009). Increases in economic output presumablyincrease the demand for labor (and other inputs)unless the entire increase in output arises solelyfrom productivity gains.

Entrepreneurship is another channel throughwhich economic freedom might foster robustlabor market outcomes. Using different measures

ABBREVIATIONS

EFNA: Economic Freedom of North AmericaGDP: Gross Domestic ProductLMO: Labor Market OutcomeOLS: Ordinary Least SquaresRTW: Right to Work

56Contemporary Economic Policy (ISSN 1465-7287)Vol. 32, No. 1, January 2014, 56–66Online Early publication July 1, 2013

doi:10.1111/coep.12031© 2013 Western Economic Association International

HELLER & STEPHENSON: ECONOMIC FREEDOM AND LABOR MARKET CONDITIONS 57

of new firm creation, Kreft and Sobel (2005),Campbell, Rogers, and Heriot (2007–2008), andCampbell and Rogers (2007) find that eco-nomic freedom is positively related to inter-state differences in new firm formation. If suchentrepreneurial activity leads to job creation,then there would be a positive relationshipbetween economic freedom and favorable labormarket outcomes.

Another channel through which economicfreedom could be associated with more favor-able labor market outcomes is net immigration.Ashby (2007) finds that economic freedom ispositively related to net immigration among U.S.states. If the decision to migrate is based, at leastin part, on seeking out better labor market pos-sibilities then net immigration would be relatedto job creation in states attracting immigrants.

In addition to the theoretical reasons whyinterstate variation in economic freedom shouldbe associated with cross-state differences inunemployment and other labor market out-comes, extensive international comparisons sup-port such a conclusion. Cross-country studies byFeldmann (2006, 2007, 2010) find that economicfreedom is associated with lower unemploy-ment. Our article extends this line of researchto the U.S. states.

II. EMPIRICAL FRAMEWORK

We use annual state-level data to examinestate labor market conditions over the period1981–2009. In the following model, the labormarket outcome for a state i in year t is givenby:

LMOi,t = β0 + β1EFi,t(1)

+ β2DEMOGRAPHICi,t

+ β3RESOURCEi,t + αt + εit

LMO is the labor market outcome underconsideration. It is well known that the unem-ployment rate is an imperfect indicator of labormarket conditions because of movements in andout of the labor force. Hence, as in Feldmann(2009), we also consider the labor force par-ticipation rate and the employment to popula-tion ratio. Examining the labor force partici-pation rate and the employment to populationratio in addition to the unemployment rate pro-vides a more complete picture of labor marketconditions. It is hypothesized that more eco-nomic freedom will be associated with a lower

unemployment rate but a higher labor force par-ticipation rate and employment to populationratio. Data for the three labor market outcomescomes from the U.S. Bureau of Labor Statistics.

Our primary variable of interest is EFi,t , ayearly measure of each state’s level of economicfreedom. This measure is obtained from theEconomic Freedom of North America (EFNA)index (Ashby, Bueno, and McMahon 2011).The EFNA index is available for 1981–2009,hence our choice of sample period. The EFNAdefines economic freedom as “minimal govern-ment interference, relying upon personal choiceand markets to answer the basic economic ques-tions such as what is to be produced, howit is to be produced, how much is produced,and for whom production is intended” (Ashby,Bueno, and McMahon 2011, 4). The index isconstructed to give each state a rating between 1(least free) and 10 (most free). In addition to theaggregate index of economic freedom, three sub-categories (size of government, takings and dis-criminatory taxation, and labor market freedom)are constructed. Each of the sub-components isbased on several factors. For example, the sizeof government sub-component is based on gov-ernment consumption as a percentage of grossdomestic product (GDP) and transfer paymentsand Social Security payments as a percentageof GDP. Our empirical analysis uses both theaggregate index and the sub-components.

The vector DEMOGRAPHICi,t containsmeasures of population characteristics that varyacross states and over time. These data areobtained from the U.S. Census Bureau. Thesevariables include the percentage of the popula-tion that is black, female, or over age 65, thepercentage of the population that lives in anurban area, and the percentage of the popula-tion over age 25 that has earned a Bachelor’sDegree, as well as the rate of population growth.Controlling for the demographic compositionof the population is necessary to obtain cleanestimates of the relationship between economicfreedom and labor market conditions becauselabor market outcomes may vary systemati-cally across demographic groups. For example,African Americans have systematically higherunemployment rates than whites. In addition,a high percentage of females in the populationcould affect labor market outcomes due to exitfrom the labor force for child rearing.

RESOURCEi,t is a vector containing vari-ables measuring each state’s energy produc-tion. These measures include variables for

58 CONTEMPORARY ECONOMIC POLICY

TABLE 1Unemployment, OLS with Year Fixed Effects

Dependent Variable: State Unemployment Rate

(1) (2) (3) (4)

Economic freedom measuresSummary measure −0.7814∗∗∗

(4.37)

Area 1: Size of government −0.7137∗∗∗

(6.34)

Area 2: Taxation −0.4249∗∗∗

(2.91)

Area 3: Labor market freedom −0.3781∗∗

(2.03)

Percentage of the state population who is:African American 0.0304∗ 0.0221 0.0097 0.0228

(1.87) (1.51) (0.58) (1.18)

Female 0.3555 0.2711 0.4861∗∗ 0.4173∗∗

(1.59) (1.32) (2.18) (2.03)

Over 65 −0.1693∗ −0.1655∗ −0.2021∗∗ −0.1560∗

(1.81) (1.98) (2.19) (1.70)

A college graduate −0.1274∗∗∗ −0.1235∗∗∗ −0.1330∗∗∗ −0.1341∗∗∗

(4.76) (4.73) (4.84) (5.00)

An urban resident 0.0176∗∗ 0.0191∗∗∗ 0.0186∗∗ 0.0170∗∗

(2.61) (2.83) (2.65) (2.31)

Population growth (%) −0.0200 −0.0180 −0.0464 −0.0344(0.86) (0.96) (1.23) (1.03)

Resource controlsCoal production (trillions of BTUs) 0.0002 0.0001 0.0002 0.0002

(1.48) (1.05) (1.47) (1.39)

Ethanol production (millions of barrels) −0.0071 −0.0004 −0.0134 −0.0211(0.47) (0.03) (0.81) (1.46)

Natural gas production (trillions of BTUs) −0.0002∗ −0.0001 −0.0004∗∗∗ −0.0003∗∗∗

(1.70) (0.97) (2.80) (2.83)

Oil production (trillions of BTUs) 0.0006∗∗ 0.0004∗ 0.0008∗∗∗ 0.0008∗∗∗

(2.47) (1.68) (3.16) (3.41)

Constant −0.0353 3.5292 −8.2621 −9.4451(0.00) (0.38) (0.82) (0.98)

R2 65% 68% 62% 61%

N 1,450

Notes: T -statistics are reported in parentheses. All regressions include year fixed effects, and standard errors are clusteredat the state level.

∗p < .1; ∗∗p < .05; ∗∗∗p < .01.

coal, natural gas, oil, and ethanol production.These data are obtained from the U.S. EnergyInformation Agency’s State Energy Data System.Resource market conditions (e.g., the price ofoil) and resource endowments are often citedas factors influencing state economic condi-tions; for example, North Dakota’s recent eco-nomic growth and its low unemployment rateare thought to result from the large increase inoil production in the western part of the state(Davey 2010). More generally, Holtz-Eakin

(1993) finds that natural resources increaseworker productivity.

Finally, αt represents a vector of year-specificfixed effects, which are included to controlfor factors such as business cycle conditionscommon to all states.

III. RESULTS

With 50 states and 29 years of data, oursample is a fully balanced panel of 1,450observations. Estimation is via ordinary least


TABLE 2Labor Force Participation, OLS with Year Fixed Effects

Dependent Variable: State Labor Force Participation Rate

(1) (2) (3) (4)

Economic freedom measuresSummary measure 1.5210∗∗∗

(3.16)

Area 1: Size of government 1.1227∗∗∗

(3.65)

Area 2: Taxation 1.3630∗∗∗

(3.42)

Area 3: Labor market freedom 0.7000(1.28)

Percent of the state population who is:African American −0.1256∗∗∗ −0.1021∗∗ −0.0950∗∗ −0.1088∗∗

(3.12) (2.55) (2.46) (2.15)

Female −1.1601∗ −1.0829∗ −1.3911∗∗∗ −1.2886∗∗

(1.99) (1.78) (2.37) (2.05)

Over 65 −0.4210∗∗ −0.4197∗∗ −0.3410∗ −0.4439∗∗

(2.13) (2.30) (1.87) (2.22)

A college graduate 0.3825∗∗∗ 0.3807∗∗∗ 0.3858∗∗∗ 0.3961∗∗∗

(4.85) (4.98) (5.30) (4.94)

An urban resident −0.0369∗∗ −0.0398∗∗ −0.0384∗∗ −0.0359∗

(2.02) (2.17) (2.32) (1.73)

Population growth (%) −0.0978∗∗∗ −0.0877∗∗∗ −0.0578∗∗ −0.0678∗∗

(2.84) (2.78) (2.86) (2.34)

Resource controlsCoal production (trillions of BTUs) −0.0007 −0.0007 −0.0008 −0.0007

(1.32) (1.16) (1.40) (1.29)

Ethanol production (millions of barrels) 0.1751∗∗∗ 0.1704∗∗∗ 0.1755∗∗∗ 0.2024∗∗∗

(5.29) (5.61) (4.44) (5.55)

Natural gas production (trillions of BTUs) −0.0000 −0.0001 0.0002 −0.0002(0.05) (0.27) (0.74) (0.66)

Oil production (trillions of BTUs) 0.0006 −0.0003 −0.0010∗ −0.0009(1.02) (0.58) (1.76) (1.51)

Constant 112.94∗∗∗ 111.85∗∗∗ 124.08∗∗∗ 129.18∗∗∗

(4.27) (4.33) (4.66) (4.31)

R2 65% 65% 65% 61%

N 1,450


∗p < .1; ∗∗p < .05; ∗∗∗p < .01.

squares (OLS) with standard errors clustered bystate.

Table 1 contains the results for the unem-ployment rate. The estimates in the first columnare based on the aggregate EFNA index; theremaining three columns report results derivedfrom the sub-categories of the EFNA. In allcases the relationship between the measure ofeconomic freedom and unemployment is neg-ative and statistically significant. The magni-tude of the estimated coefficients suggests thatthe relationship is economically meaningful; a

one point increase in economic freedom (about1.33 SD) is associated with a 0.78 percent-age point decrease in the unemployment ratefor the overall EFNA measure. For the sub-categories of the EFNA, a one point increasein the rating for area 1 (size of government)is also associated with a 0.7 percentage pointreduction in the unemployment rate. For areas2 (taxation) and 3 (labor market regulations),the estimated effect of a one point increase inthe value of the economic freedom index is aroughly 0.4 percentage point reduction in the


unemployment rate. Turning to the other regres-sors, the estimated relationship between the per-centages of the population that are college grad-uates or are over age 65 and the unemploymentrate are significantly negative for both the aggre-gate EFNA regression and the equations usingthe EFNA sub-categories. By contrast, the esti-mated coefficient for the urban percentage ofthe population is positive and statistically sig-nificant in all four equations and the percent-age of the population that is female is positivein all equations but statistically significant inonly two. As for the resource controls, naturalgas production has a negative relationship withthe unemployment rate and oil production hasa positive relationship with the unemploymentrate.

Estimation results using the labor force par-ticipation rate as the dependent variable arereported in Table 2. The point estimate is pos-itive for the aggregate index and the sub-components indicating a positive relationshipbetween economic freedom and the labor forceparticipation rate. A one point increase in theoverall index is estimated to increase the laborforce participation rate by 1.5 percentage points.Similarly, a one point increase in the sub-components for size of government (area 1) anddistortionary taxation (area 2) are estimated toincrease the labor force participation rate by1.1 and 1.4 percentage points, respectively. Thecoefficient for the third area, labor market regu-lations, is both smaller (0.7 percentage points)and imprecisely estimated. Using labor forceparticipation instead of the unemployment rateas the dependent variable gives more consis-tent results for the control variables: popula-tion growth and the percentages of the popu-lation that are urban residents, black, female, orover 65 have negative coefficient estimates andare statistically significant in all four columns.Conversely, ethanol production and the percent-age of the population holding college degreesare both positive and statistically significant inall four estimations. Other resource controlsshow no systematic relationship with labor forceparticipation.

Table 3 reports results using the employmentto population ratio as the dependent variable.The results are similar to the labor force par-ticipation rate regressions in Table 2. The eco-nomic freedom measure is positive in all fourcolumns but statistically significant in only thefirst three. As with the labor force participation

results, the magnitude of the estimated coeffi-cients is largest when using the aggregate EFNAas the explanatory variable and smallest whenusing the labor regulation sub-category (area3). The other covariates mostly perform in asimilar manner as in Table 2. The percentages ofthe population that are urban, black, or femalehave negative coefficients and are statisticallysignificant; ethanol production and the percent-age of the population that has graduated collegehave positive coefficients and are statisticallysignificant. There is no evidence that the otherresource controls are related to the employmentto population ratio.

Our findings show a strong relationshipbetween economic freedom and labor mar-ket outcomes, with more economic freedombeing associated with lower unemployment ratesand higher labor force participation rates andemployment-population ratios. While it mayseem counterintuitive that labor market regu-lations (area 3) tend to have smaller effectsthan the other sub-components (particularly area1, the size of government sub-component), weattribute this finding to the construction of theEFNA index. One of the factors comprisingthe labor regulations sub-component (area 3)of the index is the minimum wage. The min-imum wage typically applies to a small fractionof the labor force and it has modest disem-ployment effects; hence the effect of the mini-mum wage on aggregate labor market outcomesshould be small. By contrast, the larger esti-mated effects for the area 1 sub-component sug-gest that overall government size has a greatereffect on labor market conditions.

IV. EXTENSIONS AND ALTERNATIVESPECIFICATIONS

In this section, we discuss four additionalspecifications performed as robustness checks.1

1. We also performed two robustness checks that areomitted for brevity. One is adding a vector of geographicvariables to control for any effects associated with a state’sbeing located on the Mexican border, on the Canadianborder, or on one of the coasts. These factors might berelated to labor market outcomes if border states havelarger immigrant inflows (likely the case for states borderingMexico) or if coastal states have greater international tradelinkages. The second robustness check that we do notreport in the paper is re-estimating the model omittingAlaska and Hawaii, in the event that these states areintrinsically different in terms of institutions and culture thanthose within the contiguous United States. Neither of theunreported robustness checks has a qualitative effect on theresults.


TABLE 3Employment Population Ratio, OLS with Year Fixed Effects

Dependent Variable: State Employment Population Ratio

(1) (2) (3) (4)


(3.85)


(4.72)


(3.69)


Percentage of the state population who is:African American −0.1391∗∗∗ −0.1113∗∗ −0.0959∗∗ −0.1179∗∗

(3.14) (2.54) (2.22) (2.09)

Female −1.3265∗∗ −1.1951∗ −1.6341∗∗ −1.4906∗∗

(2.24) (1.96) (2.65) (2.34)

Over 65 −0.2746 −0.2761 −0.1768 −0.3051(1.17) (1.30) (0.80) (1.30)

A college graduate 0.4465∗∗∗ 0.4420∗∗∗ 0.4535∗∗∗ 0.4639∗∗∗

(5.27) (5.41) (5.79) (5.35)

An urban resident −0.0487∗∗ −0.0525∗∗∗ −0.0509∗∗∗ −0.0474∗∗

(2.52) (2.71) (2.85) (2.08)

Population growth (%) −0.0775∗∗∗ −0.0695∗∗∗ −0.0216 −0.0392(2.98) (2.90) (0.65) (1.26)

Resource controlsCoal production (trillions of BTUs) −0.0008 −0.0007 −0.0009 −0.0008

(1.35) (1.15) (1.43) (1.31)

Ethanol production (millions of barrels) 0.1763∗∗∗ 0.1672∗∗∗ 0.1810∗∗∗ 0.2116∗∗∗

(4.75) (5.01) (3.93) (5.11)

Natural gas production (trillions of BTUs) 0.0001 −0.0000 0.0005 0.0004(0.38) (0.02) (1.34) (1.21)

Oil production (trillions of BTUs) −0.0009 −0.0006 −0.0015∗∗ −0.0014∗∗

(1.60) (0.95) (2.34) (2.16)

Constant 110.02∗∗∗ 106.50∗∗∗ 126.19∗∗∗ 131.82∗∗∗

(4.19) (3.90) (4.62) (4.39)

R2 68% 68% 66% 62%

N 1,450


∗p < .1; ∗∗p < .05; ∗∗∗p < .01.

First, we include state fixed effects.2 Table 4reports the estimated coefficients for the EFNAvariables; results for other variables are omit-ted for brevity but are available upon request(in other words, each coefficient reported inthe table represents a separate regression).

2. Sargan-Hansen tests of fixed vs. random effects wereconducted for each empirical specification under considera-tion. All of the test statistics unambiguously suggest rejec-tion of the null hypothesis of the consistency of randomeffects estimates, indicating that fixed effects is the preferredestimation method in this case.

The unemployment rate results indicate thateconomic freedom is statistically significant forthe overall measure and for each of the sub-categories. Moreover, the estimated effect ofaggregate economic freedom is more than 50%larger (−1.38 vs. −0.78 percentage points) inmagnitude with the inclusion of the state fixedeffects. The estimated coefficient on the labormarket freedom category (area 3) shows a sim-ilar increase, going from −0.38 to −0.61 per-centage points. The estimated effect for the sizeof government sub-category is also somewhat


TABLE 4Regressions with State Fixed Effects



(−5.97)


(−5.78)

Area 2: Taxation −0.4369∗∗

(−2.09)

Area 3: Labor market freedom −0.6079∗∗

(−2.01)



(3.23)

Area 1: Size of government 0.4201∗∗

(2.26)

Area 2: Taxation 0.2645(1.34)




(5.64)


(4.11)

Area 2: Taxation 0.5202∗∗

(2.04)

Area 3: Labor market freedom 0.9432∗

(1.85)

larger with the fixed effects model while theestimated effect for the distortionary taxationsub-category is essentially unchanged. By con-trast, the inclusion of state fixed effects tendsto reduce the estimated magnitude of the eco-nomic freedom coefficients in the labor forceparticipation rate and employment-populationratio regressions. In some of the fixed effectsequations the estimated coefficients are less thanhalf as large as their counterparts in Tables 2and 3; however, all the estimated coefficientscontinue to have the expected sign and most areprecisely estimated.

Second, we consider right to work (RTW)laws. RTW laws have been adopted by 22 statesas of 2009, thereby granting workers in thosestates the right to be employed without beingrequired to join a union (about 42% of stateshad RTW laws at a given point in time in thesample). RTW laws allow greater workplace

freedom; however, the labor market regulationcomponent (area 3) does not include RTW lawsas one of its indicators of economic freedom.(We presume RTW laws are omitted from theindex because they do not exist in Canada andthe EFNA includes both Canadian provincesand American states.) Since one of the factorsincluded in the labor market sub-component ofthe EFNA is unionization density for each state,it is not clear that adding an RTW law variablewill add any additional explanatory power to themodel. If the main effect of RTW laws is a lessunionized workforce (Ellwood and Fine 1987),then the inclusion of unionization density in theEFNA would be sufficient.

To assess the effect of RTW laws being omit-ted from the EFNA, we add a dummy variabletaking a value of one if state i has an RTW lawin year t . The results are reported in Table 5;for brevity the table includes only the EFNA


TABLE 5Regressions with Right to Work Laws Included


Economic freedom measureSummary measure −0.6923∗∗∗

(−3.85)


(−6.20)


(−2.30)

Area 3: Labor market freedom −0.1517(−0.71)

State right to work laws −0.3063 −0.2509 −0.7122∗∗ −0.6702∗∗

(−1.22) (−1.14) (−2.68) (−2.09)



(3.26)


(3.60)


(3.39)


State right to work laws 0.2656 0.4455 0.9645 0.9732(0.38) (0.61) (1.37) (1.18)



(3.85)


(4.70)


(3.51)


State right to work laws 0.4659 0.5950 1.4033∗ 1.3870(0.63) (0.79) (1.84) (1.52)

variables and the RTW dummy variable butcomplete results are available upon request. Theestimated coefficients on the RTW dummy havethe anticipated signs—negative in the unem-ployment rate regressions and positive in thelabor force participation rate and employmentto population ratio regressions—but are statisti-cally significant in only 3 of 12 cases. The effectof including the RTW dummy is negligible forthe regressions using the aggregate EFNA, thesize of government sub-component (area 1),and the distortionary taxation sub-component(area 2). Unsurprisingly, including the RTWvariable has an appreciable effect on the labormarket regulation sub-component (area 3). The

coefficients on the area 3 EFNA variable arereduced by roughly half.

Third, we consider the possibility that theconstruction of the EFNA creates a simultane-ity bias that leads to the findings in Tables 1–3.This concern is particularly germane for the sizeof government measures included in the index.Simultaneity bias would arise in the EFNAif the size of government increases and labormarket outcomes worsen even if there is nocausal relationship between them. For example,a recession might increase both the unemploy-ment rate and the size of government because ofincreased unemployment benefits being paid out.To guard against the possibility that simultaneity


TABLE 6Regressions with Lagged Economic Freedom Measures


Economic freedom measures, lagged by 1 yearSummary measure −0.6085∗∗∗

(−3.34)


(−5.08)


(−2.09)

Area 3: Labor market freedom −0.2140(−1.12)


Economic freedom measures, lagged by 1 yearSummary measure 1.6397∗∗∗

(3.45)

Area 1: Size of Government 1.2177∗∗∗

(3.95)


(3.44)



Economic freedom measures, lagged by 1 yearSummary measure 1.9686∗∗∗

(3.78)


(4.61)


(3.47)


bias creates spurious correlations between theEFNA and the labor market outcomes underconsideration, we repeat the estimations reportedin Tables 1–3 using lagged EFNA values ratherthan contemporaneous measures. The resultsfor the lagged EFNA variables are reported inTable 6; as with Tables 4 and 5 complete resultsare available upon request. Comparing Table 6to Tables 1–3 indicates that using lagged EFNAvalues has a minimal effect on the results. Thecoefficients on the economic freedom variablestend to be a bit smaller in the unemploymentrate regressions and essentially unchanged in thelabor force participation rate and employment-population ratio regressions. The similarity ofthe results obtained using lagged values ofthe EFNA and contemporaneous values of theEFNA suggest that simultaneity bias is nota concern.

Fourth, the federal government’s tax andspending policies systematically redistribute

funds across states. For example, California res-idents earn, on average, higher incomes thanMississippi residents so the progressive federalincome tax places a heavier burden on Cali-fornians than on Mississippians. Likewise, fed-eral spending varies systematically across statesbecause some states are home to more militaryfacilities than others while some states receivegreater benefits than others from social pro-grams. To assess whether our estimated rela-tionship between economic freedom and labormarket outcomes is biased by not accounting forfederal tax and spending policy, we re-estimatethe models in Tables 1–3 with each state’s ratioof federal spending received to taxes paid as anexplanatory variable.3 This ratio is greater thanone for states that receive more federal spending

3. These data are obtained from the Tax Founda-tion: http://taxfoundation.org/article/federal-taxes-paid-vs-federal-spending-received-state-1981-2005.


TABLE 7OLS Regressions with Federal Spending Per Tax Dollar Included



(−4.23)


(−6.12)

Area 2: Taxation −0.4814∗∗∗

(−3.04)

Area 3: Labor market freedom −0.3749∗

(−1.94)

Federal spending per tax dollar 0.0536 −0.0814 0.0819 0.4053(0.12) (−0.18) (0.18) (0.92)



(2.74)


(2.91)


(2.98)


Federal spending per tax dollar −2.9543∗∗∗ −2.9039∗∗∗ −2.7298∗∗ −3.5327∗∗∗

(−2.84) (−2.71) (−2.65) (−3.51)



(3.39)


(3.98)


(3.31)


Federal spending per tax dollar −2.8265∗∗ −2.6867∗∗ −2.6384∗∗ −3.5981∗∗∗

(−2.37) (−2.18) (−2.23) (−3.08)

than they pay to Washington in taxes and lessthan one for states that are net tax donors. Cau-tion must be used in thinking about the relation-ship between the federal spending per tax dollarratio and labor market outcomes because causa-tion could run in both directions. If being a netrecipient state improves labor market outcomesthen the federal spending to tax ratio shouldhave a negative relationship with unemploymentand positive relationships with the labor forceparticipation rate and the employment to popu-lation ratio. However, the structure of many fed-eral programs might mean that states with poorlabor market conditions receive more federalfunds relative to the tax dollars that their citizens

pay. In this case, a higher federal spending to taxratio would be associated with increased unem-ployment and decreased labor force participationand employment to population ratio.

Table 7 contains the regression results withthe federal tax to spending ratio added to thebasic model. Note that the results in this tableare based only on the years 1981–2005 becausewe were unable to locate federal spending pertax dollar data for 2006–2009. In the unemploy-ment rate regressions, the federal spending to taxratio is both small and not statistically differentfrom zero and its inclusion has little effect onthe estimated EFNA coefficients. The estimatedcoefficients on the EFNA variables in both the


labor force participation rate and the employ-ment to population ratio models are a bit smallerwith the inclusion of the federal spending to taxratio variable but their statistical significance isunchanged. However, in these regressions, theestimated coefficients on the federal spendingto tax ratio variable are negative thereby sug-gesting that more federal spending might flowto states with weaker labor market conditions.4

V. CONCLUSION

Our findings have clear implications for pol-icymakers. Adopting policies that increase eco-nomic freedom by one point in the EFNAindex would reduce the unemployment rate byas much as 1.3 percentage points. Likewise aone point increase in economic freedom wouldincrease the labor force participation rate by upto 1.9 percentage points and the employment-population ratio by as much as 2.3 percentagepoints. Moreover, the estimation results forthe EFNA sub-components offer policymakerssome guidance about the most beneficial ways toimprove state EFNA ratings. The effect of area1 (size of government) is generally larger thanthe effect for areas 2 or 3. Hence, our resultssuggest that reducing the size of governmentwould have the largest effect on labor marketoutcomes.

REFERENCES

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4. Recall that the regressions include the share of thepopulation over age 65 so the negative relationship betweenfederal spending per tax dollar and the labor participationrate and employment-population ratio should not be causedby some states have more retirees who are drawing SocialSecurity retirement benefits.

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ECONOMIC FREEDOM AND LABOR MARKET CONDITIONS: EVIDENCE FROM THE STATES

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