Abstract
Rising inequality in the US, and resulting economic and wage stagnation, has been attributed to the growing productivity-compensation gap. Academics and policy makers have puzzled over the source of the gap since the 1970s. We hypothesize that the observed gap is misleading because productivity spillovers only partially translate into increased compensation. We analyze productivity spillovers with a spatial dimension, such that the industrial productivity of one state spills over to that of contiguous states. To do so, we employ a unique data set and spatial econometric techniques, including a spatial two-stage least squares instrumental variable approach and the spatially lagged X framework. The data set includes state-level industry panel data on employment, compensation, and gross industrial output for sixty-six industries within the forty-eight continental US states from 1998 to 2017. Once we account for productivity spillovers within industries, the gap between labor compensation and productivity narrows significantly.
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Availability of data and material
1997–2017 IO Snap data is available for purchase through the Regional Research Institute (RRI) at West Virginia University.
Code availability
Code is available at http://shishirshakya.github.io/. Code does not include data resources since those must be purchased through RRI.
Notes
IO-Snap is a user-friendly graphical-user-interface software that uses input-output data from the US national Supply and the Use tables, available from the US Bureau of Economic Analysis and the US Bureau of Labor Statistics (BLS). This software produces regionalized input-output accounts and coefficients for state economies (Regional Research Institute 2020) based on the Jackson (1998) input-output regionalization method and the Jackson and Court (2015) cross-hauling regionalization method.
We exclude Alaska and Hawaii from the analysis because they do not border any states.
Stansbury and Summers (2017) state that options such as the Annual Average Consumer Price Index Research Series (CPI-U-RS) and Personal Consumption Expenditures (PCE) yield similar results, but they prefer to deflate using CPI- U-RS rather than the PCE because CPI-U-RS deflates consumption of individuals/households; Lawrence (2016) favors PCE because it deflates consumption by nonprofits and some purchases of health care for individuals by government or employers.
National welfare data are maintained through the Center for Poverty Research at the University of Kentucky. These data contain annual measures of population, employment, unemployment, welfare, poverty, and politics.
The unionization database was created by Barry Hirsch of Georgia State University and David Macpherson of Trinity University. This data set provides information on private and public sector labor union memberships, coverage, and density compiled from monthly BLS reports. We use the total union membership within a state for the purposes of our sample.
µs and νt are states- and year-level fixed effects that absorb state characteristics and omitted effects common across all states that occurred during the period, respectively.
\(\Delta {\rm lnComp}_{{{\rm st}}} = \alpha + \gamma \Delta {\rm lnProd}_{{{\rm st}}} + \theta {\mathbf{W}}\Delta {\rm lnProd}_{{{\rm st}}} + {\mathbf{X}}_{st} \Gamma + \mu_{s} + \nu_{t} + e_{st}.\)
\(\Delta {\rm lnComp}_{{{\rm st}}} = \alpha + \rho {\mathbf{W}}\Delta {\rm lnComp}_{{{\rm st}}} + \gamma \Delta {\rm lnProd}_{{{\rm st}}} + \theta {\mathbf{W}}\Delta {\rm lnProd}_{{{\rm st}}} + {\mathbf{X}}_{{{\rm st}}} \Gamma + \mu_{{\rm s}} + \nu_{{\rm t}} + e_{{{\rm st}}}.\)
\(\Delta {\rm lnComp}_{{{\rm st}}} = \alpha + \gamma \Delta {\rm lnProd}_{{{\rm st}}} + \theta {\mathbf{W}}\Delta {\rm lnProd}_{{{\rm st}}} + {\mathbf{X}}_{{{\rm st}}} \Gamma + \mu_{{\rm s}} + \nu_{{\rm t}} + e_{{{\rm st}}} ,{\text{where}},u_{{{\rm st}}} = \lambda {\mathbf{W}}u_{{{\rm st}}} + e_{{{\rm st}}}.\)
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We would like to thank Akshiti Modi, Randall Jackson, and Roger Bivand.
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Shakya, S., Plemmons, A. & Sayago-Gomez, J.T. Spatial spillovers and the productivity-compensation gap in the United States. Ann Reg Sci 68, 669–689 (2022). https://doi.org/10.1007/s00168-021-01099-2
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DOI: https://doi.org/10.1007/s00168-021-01099-2