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US Public Infrastructure and Its Contribution to Private Sector BLS WORKING PAPERS U.S. DEPARTMENT OF LABOR Bureau of Labor Statistics OFFICE OF PRODUCTIVITY AND TECHNOLOGY U.S. Public Infrastructure and Its Contribution to Private Sector Productivity Aklilu A. Zegeye, U.S. Bureau of Labor Statistics Working Paper 329 June 2000 The views expressed are those of the author and do not necessarily reflect the policies of the U.S. Bureau of Labor Statistics or the views of other staff members. U. S. Public Infrastructure and Its Contribution to Private Sector Productivity Aklilu A. Zegeye Bureau of Labor Statistics e-mail: [email protected]. May 15, 2000 The views expressed are those of the author and do not reflect the policies of the U. S. Bureau of Labor Statistics or the views of other staff members. The author is deeply indebted to Larry Rosenblum for his very useful discussions and comments during the preparation of this paper. I would also like to thank Michael Harper and Marilyn Manser for their helpful comments. Any errors are, of course, a nontradable liability of the author. U. S. Public Infrastructure and Its Contribution to Private Sector Productivity By Aklilu A. Zegeye* Abstract: The study examines the impact of public infrastructure capital on the productivity of the manufacturing sector for a sample of over 1500 counties and the 50 U. S. states using a translog production function approach. The study also examines productivity convergence across states and across counties. The county level data are chosen as a unit of analysis in order to minimize the impact of the macro-economy on the estimates. The study finds a positive correlation between infrastructure and output at both the state and local levels. The evidence also seems to suggest that the elasticity of public capital on private sector output rises with the level of aggregation. The estimates further show that convergence is occurring faster at the state level than at the county level which has a similar implication of increasing spillover with the level of aggregation. However, the study finds that even though public infrastructure does have an impact on output and productivity at both the state and county levels, its influence on productivity is small. (JEL CODE: H54 &R00) * The Bureau of Labor Statistics, Division of Productivity Research, 2 Massachusetts Avenue NE, Washington, DC 20212. The author is deeply indebted to Larry Rosenblum for his very useful discussions and comments during the preparation of this paper. I would also like to thank Michael Harper and Marilyn Manser for their helpful comments. Any errors are, of course, a nontradable liability of the author. U. S. Public Infrastructure and Its Contribution to Private Sector Productivity The study examines the impact of public infrastructure capital on the productivity of the manufacturing sector for a sample of over 1500 counties and the 50 U. S. states using a translog production function approach. The study also examines productivity convergence across states and across counties. The county level data are chosen as a unit of analysis in order to minimize the impact of the macro-economy on the estimates. The study finds a positive correlation between infrastructure and output at both the state and local levels. The evidence also seems to suggest that the elasticity of public capital on private sector output rises with the level of aggregation. The estimates further show that convergence is occurring faster at the state level than at the county level which has a similar implication of increasing spillover with the level of aggregation. However, the study finds that even though public infrastructure does have an impact on output and productivity at both the state and county levels, its influence on productivity is small. (JEL CODE: H54 &R00) I. INTRODUCTION A number of studies have examined the relationship between public sector infrastructure capital and its contribution to private sector productivity.1 There is little doubt that public sector infrastructure affects private sector production by increasing aggregate demand and by augmenting productivity and output.2 One important source of disagreement in the literature arises from choices in the level of aggregation. By and large, the reported elasticities of infrastructure on output at different levels of aggregation are mixed. The findings frequently imply under-investment in infrastructure, but the evidence is far from firm and thus hardly conclusive. At the national and state levels, the macro economic effects of any spending including infrastructure may dominate the positive 1 Using national, regional, metropolitan, state and industry level data, studies by Eberts (1986), Aschauer (1989), Munnell (1990), Garcia Mila and McGuire(1992), Morrison and Schwartz (1992), and Nadiri and Mamuneas (1994) have shown a significant contribution of public capital to private sector productivity. On the other hand, using regional and state data, Hulten and Schwab (1993), Evans and Karras (1994), Holtz-Eakin (1994) find no evidence that public capital growth leads to greater productivity growth. Hulten and Schwab (1984, 1993), Eisner (1991), and Munnell (1990) did regional studies by breaking down states into four regions (northeast, north central, south, and west) while Meira (1975) divided the 48 states into 9 US census regions. Eberts (1986), Deno and Eberts (1989) using a translog production function, and Deno (1988) using a translog profit function estimated the effects of some of the components of public sector capital stock on regional manufacturing output for 38 metropolitan areas, while Nadiri and Mamuneas (1991) estimated a cost function at an industry level using twelve two digit US manufacturing industries. Deno and Eberts (1989) estimated for 28 SMSAs for the first half of the 1980s using 2SLS rather than OLS to avoid simultaneity bias that can arise between private income and public investment. See Gramlich’s (1994) survey article for a range of estimates and some of the issues. 2 See Tatom (1991) and Holtz-Eakin (1993b) who have examined a number of issues such as fixed effects, specification of error structure, endogeneity bias, restrictions on the coefficients to satisfy constant returns to scale, and the effects of aggregation. 3 externalities of spending on production. Specifically, public spending may increase aggregate demand and provide stimulus to the economy. However, this result may not be unique to infrastructure. In such cases, the resultant correlation between public spending and private sector output may not be the result of the public good nature of public capital. Second, public sector spending may be a normal good. That is, as income rises the demand for public infrastructure increases so that the correlation between infrastructure and output may reflect the marginal propensity to consume public goods rather than any productivity enhancing effects of infrastructure. This paper seeks to address these concerns by examining manufacturing production at the county level. While state output has its own components that are not tied to the aggregate economy, large states are clearly affected by national trends. In general, counties are the smallest geographical areas for which significant amounts of data are available. Since output is less correlated across counties than across states and regions, analysis of infrastructure at the more disaggregated levels are more likely to measure the impact of infrastructure on output rather than the marginal propensity to spend tax revenues on infrastructure.3 We will estimate fixed effect models to eliminate unobservable productivity differences such as natural resource endowments and air and water quality as well as further test their differences using more broadly defined regional categories (north, south, east and west) as dummy variables. The study will also address the issue of productivity performance and productivity convergence across U. S. counties. Furthermore, to examine the possible existence of differences of productivity of manufacturing due to the degree of urbanization, the study uses Beale's codes for U. S. counties 3 At the county level, national influences are considerably muted and the correlation between county level growth and national growth are much weaker. 4 (which divide counties into 10 demographic regions depending on the degree of urbanization and nearness to a metro area) as dummy variables. 4 Because there are no comprehensive measures of private and public capital stock, to my knowledge, available at the county level, this study will construct a measure of public and private capital stocks based on the perpetual inventory technique. This approach improves on the use of current capital outlays or adding up a short series of past capital expenditures. It further weakens any correlation between infrastructure spending and aggregate stimulus. In section 2, we begin by sketching the model which provides the basis for analysis, discussing appropriate estimation techniques. The sources and description of data are discussed in section 3, while section 4 presents the empirical results of these estimates and their effects on productivity. In section 5, we discuss productivity performance and productivity convergence across U. S. counties, while the final section is a summary. II. METHOD OF ANALYSIS Aggregate production relates the gross state or county manufacturing output (Q) to four inputs: private capital (K), workers (L), intermediate materials (M), public sector capital (G) and the level of technology, and thus Q = F(L, M, K, G, t). A Translog production function in its unrestricted form is:5 1 2 1 2 ln Q = b + b ln L + b ln K + b ln M + b ln G + b + b 0 L K M G 2 LL ln L 2 KK ln K 1 2 1 2 + b + b + b ln K ln L + b ln M ln L + b ln M ln K + (1) 2 MM ln M 2 GG lnG LK LM KM ln G ln L + ln M ln G + ln K ln G b GL b GM b KG 4 See Butler (1990) for details of Beale’s code grouping.
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