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External Monitoring and Enforcement and the Success of Collective Property Rights Regimes

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Abstract

In this paper, we analyze how public monitoring and enforcement (M&E) efforts affect the success of a collective property right. We develop a bioeconomic model to generate several theoretical predictions, which we test empirically by assembling and analyzing novel data on public patrolling and fishing activity in the Chilean abalone fishery. Consistent with our model, we find robust evidence that patrolling increases abalone stocks and harvest for nearby fishers’ organizations. In our preferred (conservative) specifications, a 10% increase in patrolling increases stock density by 0.95% and harvest by 1.2%, which translates roughly to an increase in annual revenues of 6770 USD on average within a port captainship jurisdiction. Our work provides new empirical evidence on the determinants of success for collective property rights regimes, revealing the pivotal role that public M&E can play in helping sustain these institutions.

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Notes

  1. Monitoring a common pool resource can include monitoring stock levels for conservation and administrative purposes, monitoring compliance of property rights owners, and monitoring illegal activity by outsiders. In this paper, we focus on the latter as we are concerned about deterrence. Enforcement entails the imposition of penalties for illegal use. Monitoring and enforcement can be local or external. Local M&E is conducted by resource users, whereas external or government M&E is made by a government authorities.

  2. Another approach has been to recover the probability of detection using surveys (Furlong 1991; Kuperan and Sutinen 1998; Ali and Abdullah 2010). However, this subjective probability is most likely to be biased as agents will report higher probabilities of detection where more violations occur (Hatcher et al. 2000).

  3. In fisheries, the rights-based approach can be space-based rights (e.g., TURFs) or species-based rights. According to Wilen et al. (2012), under a space-based rights system, it is expected that property right owners can coordinate and solve different space externalities such as metapopulation externalities (i.e., heterogeneity in population distribution across space), predator and prey linkage, and habitat destruction externalities (e.g., gear impact on ecosystem habitat). Moreover, they can coordinate a single species’ harvests over time and space to achieve optimal extraction and stock levels. Conversely, an example of species-based rights is Individual Transferable Quotas (ITQ). Under ITQ, an individual can harvest a share of the total allowable catch assigned to a particular species determined by the authorities. The main drawback of species-based systems is that users cannot internalize space externalities (Wilen et al. 2012).

  4. Coves are called caletas in Spanish. Fishers’ communities are located close to coves, and we will use these terms interchangeably.

  5. There is heterogeneity in TURF success depending on the targeted species. For instance, a TURF system for the surf clam Mesodesma donacium in Chile failed after three years of its implementation due to its high stock variability (Aburto and Stotz 2013; Aburto et al. 2014).

  6. Illegal fishing includes extraction within a TURF by outsiders, unreported extraction by TURF members, extraction under minimum legal size (\(<10\)cm for loco), extraction during fishing closures and, in the case of the loco, extraction in open-access areas (Oyanedel et al. 2018). According to Gonzalez et al. (2006), illegal harvest might account for at least 50 percent of total Chilean catch of loco. However, it seems that illegal fishing is higher in non-TURFs areas (where loco harvesting is banned) than in TURFs (Jarvis and Wilen 2016).

  7. Other explanations for why fishers might stop protecting their area are lack of the capacity to monitor or that fishers may underestimate the benefits from M&E (Gelcich et al. 2017). Using a simulation-based approach, Davis et al. (2015) found that M&E of TURFs have benefits exceeding costs. These benefits are attributed to the prevention of poaching in the area, which increases stock abundance. However, fishers might not be attributing this increase in abundance to monitoring.

  8. Stock assessment reports also contain stock abundance (units) and stock biomass (kilograms). Stock abundance for a particular species is estimated using both density and effective area (m2) of the resource in a TURF. After obtaining stock abundance, consultants use the estimated relationship between size (mm) and weight (kilograms) together with the average size of the resource to compute stock biomass. The distribution of sizes obtained from sampled units is used by consultants to determine the stock available for extraction, which is a fraction of stock abundance and stock biomass.

  9. Molinet et al. (2008) note that salinity profiles are important to loco distribution in Chile, while Fernandez et al. (2007) speak to the importance of temperature to Chilean loco. Salinity is also a useful covariate because it is correlated with dissolved oxygen, which is an important primary production factor, but one that we did not have data for. Prior work by Axbard (2016) uses chlorophyll as a control in an analysis of pelagic fish in Indonesia. For robustness, we reproduce our primary specifications with a chlorophyll control in the Online Appendix.

  10. Loco have an annual reproductive cycle in which eggs hatch to larvae and then develop to juveniles in three to four months (Bustos and Navarrete 2000). Therefore, we consider that using annual panel data is adequate to capture any changes in reproduction levels that extraction may have on stock density.

  11. We conduct a Hausman test to determine whether to use a fixed effects or random effects model. The test was rejected with a p-value of 0.000. Therefore, we estimated our models using fixed effects.

  12. In prior analysis, we also included number of active areas managed by a fishers’ organization, number of active species harvested in the area, and a binary variable that took the value of one if an area was neighboring another one within a predetermined radius. These variables were included to capture different harvest pressure, and their exclusion does not change our main results. Results are available in Tables C3 and C4 (Online Appendix) to demonstrate robustness to such modeling variations.

  13. The first difference of the lagged term is correlated with the first differences of the error term through the period \(t-1\).

  14. Moreover, simultaneity between patrolling effort and density might be present as patrolling is likely to be allocated to highly productive areas.

  15. The sample size was reduced from 2131 to 1621 because we use density in \(t+1\) as outcome variable.

  16. Our results suggest that Proposition 3 in Subsection B.3, Online Appendix B, holds. Even when M&E is endogenous, the effect is negative on outsider effort (Proposition 3), and then positive on stock (Proposition 6).

  17. Shortest topographical distances were computed using the R package topoDistance (R Core Team 2013; Wang 2020). We used the elevatr package (Hollister et al. 2017) to extract raster elevation layers with a zoom of 4 (approximately 8 km resolution).

  18. Note that if certain TURFs are more susceptible to internal poaching (e.g., if they have weak governance, have fundamentally uncooperative members, or are more likely to bribe consultants), such time-invariant characteristics should be captured by our TURF fixed effects. However, our TURF fixed effects will not difference out time-varying internal poaching, e.g., if external M&E directly mitigates or magnifies these behaviors.

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Acknowledgements

We would like to thank Rodrigo Lepe Zamora for his responsiveness and insights on patrolling conducted by the Chilean Navy. We are also grateful to John Stranlund and Marta Vicarelli for feedback on an early draft.

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  1. Institute of Marine Sciences, University of California Santa Cruz, 1156 High Street, 95064, Santa Cruz, CA, USA

    Felipe J. Quezada

  2. Department of Resource Economics, University of Massachusetts Amherst, 80 Campus Center Way, 01003, Amherst, MA, USA

    Nathan W. Chan

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Correspondence to Nathan W. Chan.

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Quezada, F.J., Chan, N.W. External Monitoring and Enforcement and the Success of Collective Property Rights Regimes. Environ Resource Econ 87, 605–628 (2024). https://doi.org/10.1007/s10640-023-00828-9

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