Public Infrastructure Investment, Costs, and Inter-State Spatial Spillovers in U.S. Manufacturing: 1982-96

Abstract

The size and significance of public infrastructure investment impacts on costs and productivity of private enterprise, and thus on economic health and growth, has proven nebulous to empirically substantiate. Various studies using alternative theoretical and econometric methodologies, and for different time periods, sectors, and countries, have tentatively established that such a productive impact exists and is statistically significant. It also seems smaller and more variable over time, space, and sector than was implied by initial studies on the "public capital hypothesis". One piece of the puzzle that has received little attention, however, is the role of spatial spillovers in driving infrastructure investment benefits. Such spillovers are not only conceptually important, but could also shed light on discrepancies between studies for different data, and particularly aggregation levels. In this study we apply a cost-based model to state-level U.S. manufacturing data, for capital, production and non-production labor, and materials inputs, and for the 1982-96 time period, in an attempt to untangle the private cost-saving contributions of inter-and intra-state public infrastructure investment. We carry out two kinds of spatial adaptations -a spatial autocorrelation adjustment and a spatial spillover theoretical modification -to the estimating system consisting of a Generalized Leontief cost function and input demand equations, to address this issue. We find that intra-state public infrastructure benefits appear larger in magnitude when inter-state spillovers are directly recognized, as well as being invariably statistically significant. Inter-state spillovers are also directly beneficial to manufacturing firms, although their contribution appears smaller in size when temporal serial correlation is recognized in addition to spatial correlation.

The impacts of education spending and finance reform on manufacturing property shadow values: a cost function approach

“…The results were that the estimated elasticities all had the same signs and significance, except for e D,t , which is marginally insignificant when allowing d ED to be non-zero but subsequently becomes significant. Thus, due to the similarity of results in both cases, the elasticities presented here allow for d ED to be nonzero.11 As noted byCohen and Paul (2004), this is consistent with a linear demand curve.12 The state fixed effect (F i ) parameter estimates are omitted fromTable 2for ease of presentation.…”

“…Since, one might hypothesize that E and D affect manufacturing costs but are outside of the control of firms, they are modeled as external shift variables. 9 The short run cost structure may be approximated by a fully flexible Generalized Leontief (GL) variable cost (VC) function similar to that used by Cohen and Paul (2004). The GL form is desirable for our purposes because it incorporates optimizing behavior on the part of manufacturing firms, which allows for representation of firms' behavior in response to school finance reform and changes in education spending through first-and second-order cost elasticities.…”

“…In addition to this reason, the analysis here is conducted at the state level opposed to the school district level, due to the availability of the production data at this level of aggregation, as mentioned above. 8 See Cohen and Paul (2004) for a description of the output, materials, production labor, and non-production labor price, and quantity data. The rest of the variables are explained in the data appendix of this paper.…”

“…To accommodate profit maximization and test for imperfect competition, which has been shown to be present in other recent manufacturing cost studies (Cohen and Paul 2004), we incorporate the standard output supply expression equating marginal costs (MC) and revenues (MR),…”

The productive effect of transport infrastructures: does road transport liberalization matter?

“…Afraz et al (2006) and Romp and de Haan (2007) provide excellent surveys of this literature, which generally suggests the existence of small but not-negligible effects of public infrastructures expenditure on production. 3 Limiting our discussion to some of the most recent works which are more closely related to the relationship between production and the road network, we mention Cohen and Morrison (2004) who estimated a variable cost function for the manufacturing sector using US states panel data, and found that an increase of the motorway capital stock of 10% tends to be associated with a reduction of variable costs of about 1.5%. In turn, Bronzini and Piselli (2009) estimated a production function for a sample of Italian regions and found an elasticity of output with respect to the road stock of about 0.15, which is a very similar value to that reported in Boarnet (1998) for a sample of Californian counties.…”