[Correction added on June 27, 2008, after online publication: Equation (7) was incorrectly stated as AICc = T x ln([SSE/T]) + T x (T+k - 1/T - k -1). The correct statement is AICc = T x ln([SSE/T]) + T x ([T + k/T - k - 2).]
It is now well established that economic growth studies reach different conclusions depending on model specification. This has been documented repeatedly in the literature on cross-country growth regressions (1) and in studies of growth in U.S. states. (2) In response, attempts have been made to identify "robust" variables, the "best" model specification, or ways of combining alternative model specifications (e.g., Crain and Lee 1999; Fernandez et al. 2001; Granger and Uhlig 1990; Hendry and Krolzig 2004; Hoover and Perez 2004; Levine and Renelt 1992; Sala-i-Martin 1997; Sala-i-Martin, Doppelhofer, and Miller 2004). While not intended as a substitute for economic theory, these approaches can be useful when the theory is sufficiently broad such that a large number of variables are potential regressors.
This study follows in this line of research by attempting to identify robust determinants of U.S. economic growth from 1970 to 1999. I innovate on previous studies by developing a new approach for addressing "model uncertainty" issues associated with estimating growth equations. My approach borrows from the "extreme bounds analysis" (EBA) approach of Learner (1985) while also addressing concerns raised by Granger and Uhlig (1990), Sala-i-Martin (1997), and others that not all specifications are equally likely to be true. I then apply this approach by sifting through a very large number of explanatory variables in order to find robust determinants of state economic growth. My analysis confirms the importance of productivity characteristics of the labor force and industrial composition of a state's economy. I also find that policy variables such as (1) the size and structure of government and (2) taxation are robust determinants of state economic growth.
The paper proceeds as follows: Section II develops a framework for specification of the empirical growth models. Section III describes the full set of variables used in this study. Section IV presents my approach for identifying robust determinants of economic growth. Section V describes my data and discusses details about the estimation procedure. Section VI presents the empirical results. Section VII concludes.
II. A FRAMEWORK FOR SPECIFICATION OF THE EMPIRICAL GROWTH MODELS
I assume that state income ([Y.sub.t]) is determined by the following generalized Cobb-Douglas production function:
(1) [Y.sub.t] = [A.sub.t][K.sup.[alpha].sub.t][([L.sub.t][Q.sub.t]).sup.[beta]] = [A.sub.t][Q.sup.[beta].sub.t][K.sup.[alpha].sub.t][L.sub.[beta].sub.t],
where [L.sub.t] and [K.sub.t] are labor and capital, [Q.sub.t] is the efficiency of labor, and [A.sub.t] is a time-varying parameter that represents other variables that can influence state income (e.g., human capital variables). The textbook Solow model and the augmented human capital model of Mankiw, Romer, and Weil (1992) are both special cases of Equation (1). (3)
Dividing both sides by population, [N.sub.t], produces the following per capita expression:
(2) [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII].
This can be expressed in log form as:
(3) [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII],
where [y.sub.t] = [Y.sub.t]/[N.sub.t], [k.sub.t] = [K.sub.t]/[N.sub.t], and [l.sub.t] = [L.sub.t][N.sub.t].
Differentiating Equation (3) with respect to time yields:
(4) [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII].
It follows that:
(5) [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII],
where [C.sub.t] = [ln([A.sub.t]) - ln([A.sub.t-L])] + [beta][ln([Q.sub.t]) -ln([Q.sub.t-L])] and L = the length of the time period minus 1 (i. …