The Education/Growth Relationship: Evidence from Real State Panel Data

I. INTRODUCTION State and local policy makers face a myriad of competing demands for the scarce resources available for allocation. Among programs competing for funding that the authors are interested in here are primary and secondary education expenditures as well as programs promoting research and development (R & D). State and local policy makers will often base their decisions on the return of such expenditures to state-level income. Moreover, state and local policy makers must also consider whether they are the only source of funding or whether it is more appropriate for a particular program to seek funding from another level of government. Thus making appropriate budgetary decisions involves understanding the relationship between these competing programs and state-level income. This article examines the real state-level relationship between education expenditures and income and that between R & D expenditures and income. This is the first study in this literature to appropriately deflate state-level data by individual state-level price data. Although the authors consider the above-mentioned relationships, others in the literature have considered the important relationships between education expenditures and student performance, (1) how educational characteristics within the United States played a significant role in U.S. economic growth, (2) and the importance of the degree of centralization of educational and fiscal decisions. (3) However, although the relationship between education expenditures and income attracts continued interest in the literature, a consensus does not exist as to what that relationship is. For example, Fischer (1997) surveyed 19 studies of the relationship and found that although some showed a counterintuitive negative (and significant) relationship between these variables, only six of the studies considered could establish a significant positive relationship. Moreover, five of the studies produced results that "are highly variable within each study" (Fisher, 1997, p.57). In summarizing the literature, Fischer notes that this relationship remains "quite cloudy." (4) An examination of this literature suggests three issues that need to be addressed. As Fischer (1997, p.59) pointed out, the first issue is that of establishing the direction of causality between education expenditures and income (also see Garcia-Mila and McGuire, 1992). For example, it has been argued in the endogenous growth literature that educational expenditures cause income: a higher-educated population translates to increased productivity and, hence, increased output and income (Barro, 2001; Morrison and Siegel, 1997). (5) Others argue that causality runs in the opposite direction: higher income leads to larger tax revenues and hence an improved ability for a state to spend more on education. Though such arguments present an interesting and lively debate, there is a clear need to establish the direction of causality within this literature. Unfortunately, as pointed out in Evans and Karras (1994) and Garcia-Mila and McGuire (1992), if causality is mentioned at all in a particular study, it typically occurs only as an afterthought. The authors address this issue by performing a series of bivariate regressions between education spending and income to ascertain whether the information between these variables runs from education spending to income or from income to education spending. (6) The second issue of concern to this body of literature involves using data in level form versus using first-differenced data, or data relative to an aggregate counterpart. There are two benefits from using data in level form. First, a production function argument can be used to motivate the analysis. Second, via log transformations, coefficient estimates can be interpreted as elasticities--which are useful for policymaking decisions. However, problems exist with the use of level data. First, as Evans and Karras (1994) note, studies using level data are likely to be misspecified. …