What is Add-On Density Analysis? A Reference Guide

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Add-on density analysis is the practice of describing where the establishments in an industry are located across geography — how the universe of physical locations concentrates by place, and how that footprint is drifting over time — rather than describing how many establishments exist in aggregate. The reference question is not “how large is this industry” but “where, specifically, are its establishments, and where is the base filling or thinning.” For a buy-and-build investor, geography is the question of where the acquisition pipeline actually sits.

Add-on density analysis maps the geographic structure of an industry's establishment universe: where physical locations concentrate today, how dense they are relative to the surrounding local economy, and how the count has drifted across recent years (Ellison & Glaeser, 1997; Rosenthal & Strange, 2001). The unit is the establishment — a physical location — because the location of physical capacity is what a geographic roll-up reasons about, and because the establishment is the unit federal data resolves cleanly at fine geography.

It is descriptive and spatial. It reports, for a given industry code, where establishments are, how concentrated that footprint is, and which markets are gaining or losing locations — measured consistently over time so that genuine geographic drift can be separated from statistical noise. The construction is held fixed across editions so that observed movement reflects real change in the footprint, not methodological drift.

Add-on density analysis is not a target list. It maps the density of the establishment universe; it does not identify named companies, owners, or transactions. Establishment counts are physical locations, not firms — a multi-location operator appears as multiple establishments — so the map describes where capacity sits, not who owns it.

It is not a forecast. A trend layer describes where the base has filled or thinned over a defined historical window; it does not predict where it will move next, and slow geographic drift is explicitly not momentum.

It is not advice. It does not recommend where to locate, expand, or acquire. It describes where the universe of establishments is; the reader supplies the judgment about what that means for a specific strategy.

An aggregate count tells an investor how big an industry is; it says nothing about where the targets are. Two industries of identical national size can have entirely different geographies — one broadly national, appearing in hundreds of counties, the other concentrated in a few dozen markets (Ellison & Glaeser, 1997; Duranton & Overman, 2005). That difference is decisive for consolidation strategy: a dispersed footprint supports a national platform assembled market by market, while a clustered one concentrates both the opportunity and the competition in a handful of places (Krugman, 1991; Glaeser et al., 1992).

Density relative to the local economy matters as much as raw count. A market with many establishments may simply be a large market; a market where a trade is over-represented relative to all local business activity signals a genuine cluster — a place where the industry is structurally rooted (Marshall, 1890; Ellison, Glaeser, & Kerr, 2010). And because geographic structure shifts over years rather than quarters, a multi-year drift layer shows where the base is durably filling or thinning in a way an aggregate count cannot reveal (Holmes & Stevens, 2002).

The bolt-on pipeline is, concretely, a map. A platform pursuing geographic roll-up needs to know which markets hold independent establishments to acquire, how concentrated those establishments are, and whether the footprint in a target region is stable, growing, or eroding. Add-on density analysis answers the pipeline question directly: where the candidate universe sits, market by market, for a specific NAICS code.

It also disciplines the footprint-extension thesis. A platform claiming it can roll up a region needs that region to contain enough establishments to consolidate; a thinning base in a target market is a structural headwind that an aggregate national figure would hide. Reading the geography before committing to a regional thesis is cheaper than discovering it in the field.

Operators use geographic density to situate their own footprint: where their locations sit relative to where the industry's establishment universe concentrates, which adjacent markets are dense enough to support expansion, and where the surrounding base is filling or thinning. The same map a buyer reads as an acquisition pipeline, an operator reads as a competitive and expansion map.

At the board level, geography supports the “where could we grow” conversation in descriptive terms — where the establishment universe is concentrated, where it is drifting — without prescribing a specific move.

Add-on density analysis describes the location of establishments; it does not value markets or identify deals. A dense county is a place with many establishments, not necessarily a good place to acquire — density says nothing about price, quality, or willingness to sell. Counts are the published universe of physical locations, not firms, so a market's establishment count overstates the number of distinct ownership groups available. And small geographic cells are withheld in federal data for disclosure reasons, so the mapped universe is the published portion, not the entire population; sparse codes are mapped at coarser geography or deferred where an honest county map is not possible.

These are the limits of any descriptive spatial reference. The discipline is to read density as the starting point for pipeline work, not as a conclusion about where to deploy capital.

The Industrial Patterns Add-On Density Atlas maps the geographic structure of a fixed US industry peer set, computed from the U.S. Census Bureau County Business Patterns at the county level. The mapped measure is the establishment count, which County Business Patterns publishes exactly at county granularity; employment and payroll, which the source noise-infuses, are not mapped. Each edition presents a current-year cross-section — where establishments concentrate, with intensity shown relative to all local business activity — and a five-year trailing drift layer computed on a comparable-cell panel, so that disclosure-threshold flicker is never mistaken for real movement. Each code is mapped at the finest geography meeting a published-coverage floor; codes too sparse for an honest county map are shown at their subsector level or deferred to the Industry Structure Reference. Figures are point-in-time at the edition's data vintage and are not revised once an edition ships.

The complementary Operating Benchmarks module reports how firms in the same peer set perform, and the Industry Structure Reference describes the population-level structural condition of the same peer set. Each module's methodology is documented in detail on the methodology page and in the appendix of every edition. Free samples and current editions are on the editions page.

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Duranton, G., & Overman, H. G. (2005). Testing for localization using micro-geographic data. Review of Economic Studies, 72(4), 1077–1106. https://doi.org/10.1111/0034-6527.00362

Ellison, G., & Glaeser, E. L. (1997). Geographic concentration in U.S. manufacturing industries: A dartboard approach. Journal of Political Economy, 105(5), 889–927. https://doi.org/10.1086/262098

Ellison, G., Glaeser, E. L., & Kerr, W. R. (2010). What causes industry agglomeration? Evidence from coagglomeration patterns. American Economic Review, 100(3), 1195–1213. https://doi.org/10.1257/aer.100.3.1195

Glaeser, E. L., Kallal, H. D., Scheinkman, J. A., & Shleifer, A. (1992). Growth in cities. Journal of Political Economy, 100(6), 1126–1152. https://doi.org/10.1086/261856

Holmes, T. J., & Stevens, J. J. (2002). Geographic concentration and establishment scale. Review of Economics and Statistics, 84(4), 682–690. https://doi.org/10.1162/003465302760556495

Krugman, P. (1991). Increasing returns and economic geography. Journal of Political Economy, 99(3), 483–499. https://doi.org/10.1086/261763

Marshall, A. (1890). Principles of economics. Macmillan. https://www.econlib.org/library/Marshall/marP.html

Rosenthal, S. S., & Strange, W. C. (2001). The determinants of agglomeration. Journal of Urban Economics, 50(2), 191–229. https://doi.org/10.1006/juec.2001.2230