A Study of the Role of Regionalization in the Generation of Aggregation Error in Regional Input –Output Models

Abstract

ABSTRACT.Although the need for aggregation in input-output modeling has diminished with the increases in computing power, an alarming number of regional studies continue to use the procedure. The rationales for doing so typically are grounded in data problems at the regional level. As a result many regional analysts use aggregated national input-output models and trade-adjust them at this aggregated level. In this paper, we point out why this approach can be inappropriate. We do so by noting that it creates a possible source of model misapplication (i.e., a direct effect could appear for a sector where one does not exist) and also by finding that a large amount of error (on the order of 100 percent) can be induced into the impact results as a result of improper aggregation. In simulations, we find that average aggregation error tends to peak at 81 sectors after rising from 492 to 365 sectors. Perversely, error then diminishes somewhat as the model size decreases further to 11 and 6 sectors. We also find that while region-and sector-specific attributes influence aggregation error in a statistically significantly manner, their influence on the amount of error generally does not appear to be large.

The impact of occupational injury reduction on the U.S. economy

“…It also comes equipped with 37 construction-related impact vectors. Lahr and Stevens [2002] shows that such extra sector detail is critical to maintaining accuracy in impact estimates. The techniques used to produce Rutgers' software, unlike similar software which often seem ''black box'' in nature, are detailed in Treyz and Stevens [1985] and Lahr [2001].…”

Balance issues in input–output analysis: A comment on physical inhomogeneity, aggregation bias, and coproduction

“…The rich literature on aggregation bias has explored how inhomogeneous aggregations have a systematic effect on EEIO analysis and can cause systematic errors in analysis results (Morimoto, 1970;Olsen, 1993Olsen, , 2000Lahr and Stevens, 2002).…”

“…Using a single, aggregated battery production process would therefore lead to systematic deviations in these lifecycles: emissions from lead mining in the lifecycle of laptops and emissions from lithium mining in the lifecycle of passenger cars would both be systematically overestimated. This phenomenon has been thoroughly documented and is known as aggregation bias (Morimoto, 1970;Olsen, 1993Olsen, , 2000Lahr and Stevens, 2002).…”

Stochastic key sector analysis: an application to a regional input–output framework

“…Lahr and Stevens (2002) argued that aggregation continues to be a problem even given the massive increases in computing power over the last four decades that enable large matrices to be quickly inverted. They conclude that with high levels of aggregation could come increasing error in aggregated regional models that have derived technical coefficients from national input-output frameworks.…”

“…However, they realise that available data is rarely in the disaggregated form required, and crucially they suggest this is something that will always be the situation (see also De Mesnard and Dietzenbacher 1995). Lahr and Stevens (2002) further note that few attempts have been made to explore the effects of aggregation on error in regional model applications, but argue against unnecessary aggregation. Unfortunately, as they recognise in their contribution, aggregation of sectors in a regional model might be constrained by the availability of socioeconomic data.…”