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Education and economic growth in the United States: cross-national applications for an intra-national path analysis

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Abstract

Do the leading predictors of economic growth found in the cross-national research have a capacity to predict economic growth at the state level in the United States (US)? Are the effects of education spending on economic growth underestimated because research fails to examine the indirect effects of spending on economic growth? This article presents the findings from a study investigating the relationship between education and economic growth in US states while controlling for the effects of the leading predictors of economic growth from the cross-national research. It also utilizes a path model to examine direct and indirect relationships between education spending and economic growth measured as per capita income growth. The results indicate that spending on higher education and highway expenditures demonstrate a positive association with growth in per capita income, while K12 (kindergarten through 12th grade) spending and K12 pupil–teacher ratios demonstrate a negative association with income growth from 1988 to 2005. Moreover, K12 spending and population growth indirectly affect income growth through their relationship with K12 pupil–teacher ratios, and spending on higher education indirectly affects income growth through college attainment rates. Overall, all but one variable from the cross-national research demonstrates a significant direct or indirect relationship with income growth during at least one time-period investigated. Treating K12 pupil–teacher ratios and college attainment as mediating variables also enhances our understanding of the dynamics that explain growth in per capita income at the sub-national level in the US. However, some unexpected findings emerge when the data are analyzed on the basis of two eight-year sub-periods.

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Notes

  1. Developing a theoretical model of education policy from beginning to end, especially one that resolves the disputes over the placement of educational variables, is beyond the scope of this article. However, we believe that efforts to acknowledge that education policy can be depicted as a causal process are productive and that it is time to begin sequencing processes in our empirical models involving the relationship between education and economic growth. So, after our first-cut analysis, which includes K12 spending, spending on higher education, pupil–teacher ratios, and high school and college attainment rates as simultaneous predictors of economic growth, we move to a more realistic path analysis that depicts education spending as a causal antecedent of pupil–teacher ratios and high school and college attainment rates. Even if our path analytic approach does not rehabilitate educational spending as a predictor of economic growth, we think its estimates are more reliable because the specification is more realistic.

  2. Hefner (1990) found that regional GSP is not cointegrated with national GDP, nor is there an equilibrating mechanism that prevents regions from drifting apart from each other. Moreover, given that Nijkamp and Poots' (2004) meta-analysis demonstrates an absence of discernable effects of national versus regional data, we suspect that the cross-national predictors of economic growth are likely to be relevant to the political subdivisions of geographically large and economically diverse federations such as the US.

  3. The US demonstrates regional patterns in manufacturing and retail trade, and various regional patterns emerge as NAIC classifications narrow (US Census Bureau 2002a). Various state economies are also quite similar with respect to social assistance, health care assistance, and professional, scientific, and technical services. However, no region of the US dominates in the major industrial classifications of wholesale trade, utilities, or construction.

  4. Morgan et al. (2004) find that, as bank linkages between two states increase, the fluctuation in a state’s economic growth becomes less volatile and converges with the growth fluctuations of the state in the paired linkage.

  5. States with a high level of urban poverty may have a difficult time demonstrating impressive high school attainment rates because of the high drop-out rates of poor students in poorly funded inner city public school districts. Recent academic research supports the idea that inequalities in the distribution of funding within the state (Roscigno et al. 2006) and/or inefficiencies in the education bureaucracy (Ragkumar and Swaroop 2007) can disrupt the aggregate correlation between spending and high school attainment. A recent study funded by the Bill and Melinda Gates Foundation and Colin Powell’s America’s Promise Foundation highlights the alarmingly high dropout rates in many of the US’ largest urban school districts (Swanson 2008). That many of these high-dropout districts are located in states with traditions of relatively high state funding for education (e.g., Michigan, Ohio, Minnesota, and New York) would seem to cast doubt on the face validity of the hypothesis that spending is associated with higher attainment rates.

  6. Hanushek et al. (1996) argue that aggregate, state-level analyses of the impact of educational resources on various outputs and outcomes exaggerate the effects of school resources to a much greater extent at the state level than at the school level because of omitted-variable bias. Just because state-level spending might be correlated with state-level growth does not mean that the impact of spending is causal. Omitted parental or community variables—correlated with both school resources and economic growth—could possibly be distorting our aggregate-level results. However, we conducted an aggregate-level analysis because we mirrored the pattern of most scholars to investigate state-level data and because state-level data for a large number of years are much more readily available than district-level, school-level, classroom-level, or student-level data.

  7. Per capita income is measured in current dollars instead of constant dollars because the data sources (Statistical Abstracts and The Survey of Current Businesses) provided 2000–2005 per capita income data in 2000 constant dollars, while providing 1999 and 2001 per capita income data in 1996 constant dollars.

  8. We assume that educational spending enhances the content of what is learned, which has an eventual payoff in terms of economic growth. However, if education systems do not adjust to the nature and demands of the economy, then the explanatory value of expenditure variables that are lagged based on how long it takes to complete a degree will have limitations. For example, if K12 and higher education systems in the Rustbelt states of the Midwest are slow to adjust their curricula to accommodate the shift from a manufacturing-based economy to a more service-based economy, then the lagged effects of education spending will be limited. More appropriate but less identifiable lags might be the number of years that it takes K12 and higher education personnel to adjust their thinking about curricular needs sufficiently enough to override the “politics of inertia” and the lingering appeal of popular programs that no longer work.

  9. For example, in determining K12 expenditures per capita for the 2004–2005 school year, we divided the 2004–2005 K12 expenditures by the state population in 2005 versus 2004.

  10. For example, the effect on income growth of spending on higher education is the sum of the beta coefficient for its direct effect on income growth plus the beta coefficient for the relationship between expenditures on higher education and college attainment rates multiplied by the beta coefficient for the relationship between college attainment rates and income growth.

  11. The path coefficients in Fig. 2 for the relationships between the independent and intervening variables are the significant standardized Beta coefficients from Table 3. The path coefficients for the relationships between the independent variables and income growth are the significant standardized coefficients from Appendix 1.

  12. This conclusion does not negate the importance of investing in K12 education. High school drop-out rates in the US range from 3.3 to 4.7% between 2000 and 2005, and the earnings of high school drop-outs are substantially lower than those of high school and college graduates (US Census Bureau 2008, p. 171, 453). Spending aimed at preventing high school attrition is undoubtedly a worthwhile effort for enhancing per capita income. Moreover, even when educational spending translates into economic growth, concerns over how dollars are spent and whether those dollars optimize returns to human capital persist. To optimize learning experiences that transfer to contemporary work environments, concerns over investments in curriculum, teaching methods, classroom technology, and methods of assessment are legitimate.

  13. These speculative explanations focus more on the recent positive association between college attainment and state income growth. They are less adept at explaining the negative association between college attainment and income growth from 1988 to 1996. And, they are not meant to imply that, prior to the years investigated (pre-1988), college attainment has always had a negative association with economic growth in US states because of slower technological progress, a smaller service sector, and a less global economy.

  14. A general examination of the data reveals that three Rustbelt state—Ohio, Michigan, Illinois—have particularly low income growth rankings, especially during the latter period investigated where they represent three of the four lowest ranking states in per capita income growth (see Appendix 2). No Rustbelt states ranked in the top 10 states for income growth from 1988 to 1996 or from 1997 to 2005. Further, Minnesota is the only Rustbelt state to rank among the top ten states (number eight) in income growth for the entire period investigated. If regional dummy variables are added to the model tested in Table 2 using the Rustbelt states as the referent variable, the South and the West also have significantly larger associations with per capita income growth than do the Rustbelt states from 1997 to 2005. However, from 1988 to 2005, the West emerges as the only regional variable with a significantly more positive relationship with income growth than the Rustbelt states.

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Authors and Affiliations

  1. Univerisity of Alabama, Tuscaloosa, AL, USA

    Norman Baldwin & Stephen A. Borrelli

Authors
  1. Norman Baldwin
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Correspondence to Norman Baldwin.

Appendices

Appendix 1

Re-specified multiple regressions predicting per capita income growth over three periods

Predictors

Income 1988–2005

Income 1988–1996

Income 1997–2005

Beta

SE

Beta

SE

Beta

SE

K12 expends

−.264**

.000

    

HEd expends

.485***

.000

  

.308**

.000

Hway expends

.297**

.000

  

.382***

.000

Pupil–teacher ratio

−.331**

.001

  

−.238**

.000

% College graduates

  

−.608***

.000

.243**

.000

% HS graduates

  

.264*

.000

  

R 2

.388

 

.239

 

.420

 
  1. * p < .10; ** p < .05; *** p < .01
  2. n = 48

Appendix 2

Ten lowest ranking states in per capita income growth

Rank

1988–2005

1988–1996

1997–2005

State (% growth)

State (% growth)

State (% growth)

39

Delaware (4.0%)

Florida (3.9%)

Washington (3.7%)

40

Rhode Island (4.0%)

Massachusetts (3.9%)

Nevada (3.7%)

41

Illinois (4.0%)

Delaware (3.9%)

North Carolina (3.5%)

42

New Hampshire (4.0%)

Virginia (3.9%)

Missouri (3.5%)

43

New York (4.0%)

New Hampshire (3.8%)

Oregon (3.5%)

44

Arizona (4.0%)

Maine (3.8%)

Georgia (3.5%)

45

Michigan (3.9%)

Rhode Island (3.7%)

Illinois (3.4%)

46

Ohio (3.9%)

Arizona (3.7%)

Ohio (3.3%)

47

Louisana (3.9%)

Maryland (3.6%)

Michigan (3.3%)

48

California (3.8%)

California (3.3%)

Louisiana (2.3%)

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Baldwin, N., Borrelli, S.A. Education and economic growth in the United States: cross-national applications for an intra-national path analysis. Policy Sci 41, 183–204 (2008). https://doi.org/10.1007/s11077-008-9062-2

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  • DOI: https://doi.org/10.1007/s11077-008-9062-2

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