Applying organizational density to local public service performance: separating homeless service outcomes from outputs

Andrew Sullivan, Saerim Kim, David Lee

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


What happens to local services’ performance when service-provider density increases in a community? The answer is difficult. To explore how density relates to multiple aspects of performance, this study aims to examine the effects of service-providers’ density on service outputs and policy outcomes. Using a panel dataset of local homeless service planning bodies, the Continuum of Care Programme, we found that service outputs improved; however, the prevalence of homelessness did not decrease. Drawing upon organizational density theory, our findings contribute to the extant knowledge on public management by exploring how service-provider density relates to service outputs and policy outcomes separately.

Original languageEnglish
Pages (from-to)262-285
Number of pages24
JournalPublic Management Review
Issue number2
StatePublished - 2023

Bibliographical note

Funding Information:
To measure service-provider density, which is the main explanatory variable in our model, we use the number of organizations providing homeless services in a CoC included in the HUD’s raw housing inventory count reports. Although beds or housing units for those experiencing homelessness are not solely financed by HUD, the housing inventory count reports provided by HUD include almost all service providers within a CoC, as the HUD requires CoCs to report all services and beds funded by federal, state, local, or private funds (HUD ). Thus, organizational density and bed counts used in the study include all federal, state, local, and private service providers, as well as beds available to homeless individuals within a CoC regardless of funding source. Further, the data on housing inventory counts contains both service providers’ names and the names of programmes under which housing is offered, as most providers have multiple programmes. For example, the Department of Homeless Services in New York City has a programme for households with children which is operated separately from the one serving adults. For our analyses, the number of provider names was converted into a per-10,000 CoC population rate to measure density. This approach ensured that, even if one provider has multiple programmes, it is only counted once in the measure of that particular CoC’s organizational density. Similarly, when measuring the number of beds provided, we included the previously delineated six service types (emergency shelter, transitional housing, permanent supportive housing, safe haven, rapid rehousing, and other permanent housing).

Publisher Copyright:
© 2021 Informa UK Limited, trading as Taylor & Francis Group.


  • Organizational density
  • local homeless services performance
  • two-way fixed effects model


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