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Neighbourhood effects and the incidence of child labour


In spite of the growing interest in factors driving the incidence of child labour, little is known of the relationship between neighbours’ decisions and a child’s propensity to engage in paid work, i.e., the neighbourhood effect. This paper examines this relationship using the spatial autoregressive linear probability model. We find a positive and highly significant relationship. Using several subsample analyses, we find that the relationship is stronger for males and for children in rural areas. Contrary to earlier studies, the association between poverty and the incidence of child labour is relatively weak in the presence of neighbourhood effects. We also find that the propensity to engage in child labour is increasing in the level of employment at the community level.

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Fig. 1

Availability of data and material

Data used in this project are publicly available on the website of Ghana Statistical Services.

Code availability

All estimation was conducted using the open-source software R. All codes are available on the authors’ websites.


  1. 1.

    The website is

  2. 2.

    A total of 2804 observations with missing values were dropped.

  3. 3.

    The poverty rate in the entire population is 24.2% (Ghana Statistical Services 2013).

  4. 4.

    Employment is constructed using the employment status of 33,267 18–60 year-old individuals.

  5. 5.

    LEAP is a cash transfer programme to Ghanaian households in poverty (MOGCSP 2020).

  6. 6.

    Only the Brong Ahafo, Central, Volta, and Western Regions have at least two ecological zones; Table 5 therefore only focuses on these.

  7. 7.

    Estimates of \(\rho\) in the Upper West and Volta regions in Table 4, and the Central-Forest and Volta-Coastal zones in Table 5 fall outside the natural [0, 1) bound of the spatial coefficient. Results from these specifications hence need to be interpreted with caution.


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We are grateful to the Editor and an anonymous referee for comments which helped to immensely improve the paper. Our thanks also go to Viviane Sanfelice, Rhiannon Jerch, Clara-Christina Gerstner, Boubacar Diallo, and Mohammed Jabir for valuable comments.


The authors did not receive any funding for the execution of this project.

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Correspondence to Emmanuel S. Tsyawo.

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Gaku, S., Tsyawo, E.S. Neighbourhood effects and the incidence of child labour. Lett Spat Resour Sci 14, 247–259 (2021).

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  • Child labour
  • Poverty
  • Neighbourhood effect
  • Spatial autoregressive model
  • Linear probability model

JEL Classification

  • C21
  • I32
  • J22