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Efficiency of local public spending in Cameroon: Does population size matter?

  • Published In: African Development Review / Revue Africaine de Développement, 2024, v. 36, n. 2. P. 362 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Ongo Nkoa, Bruno Emmanuel; Wangbara, Djondandi; Tchoffo Tameko, Gautier 3 of 3

Abstract

Promoting decentralization is currently high on the global agenda. To date, the existing literature in the African context, and more specifically in Cameroon, has not established a link between the size of local authorities and the efficiency of their infrastructure spending. This study therefore attempts to fill this gap in the literature by empirically examining the effect of population size on the efficiency of local public spending in Cameroon. Using the two‐stage Data Envelopment Analysis model on a sample of 100 communes for the period 2017–2020 to estimate composite efficiency scores and the censored Tobit model to determine the effect of population size on the efficiency of local public spending, the results show that population size and density positively and very significantly affect the efficiency of local public spending. In light of these results, we recommend that the state make population size the main allocation key for transfers and subsidies to local authorities and that communes organize themselves into inter‐municipalities to benefit from economies of scale and curb spillover effects. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:African Development Review / Revue Africaine de Développement. 2024/06, Vol. 36, Issue 2, p362
  • Document Type:Article
  • Subject Area:Economics
  • Publication Date:2024
  • ISSN:1017-6772
  • DOI:10.1111/1467-8268.12764
  • Accession Number:178071464
  • Copyright Statement:Copyright of African Development Review / Revue Africaine de Développement is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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