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General

Study Overview

Title:

On the long-term impacts of Marine Protected Areas: A 18-years follow-up study in Tanzania

Study is 3ie funded:

No

Study ID:

RIDIE-STUDY-ID-61d2b5b8150cf

Initial Registration Date:

01/03/2022

Last Update Date:

11/23/2021

Study Status:

Ongoing

Location(s):

Tanzania

Abstract:

Marine protected areas (MPAs) are the cornerstones of today’s marine conservation strategies, promoted by international conservation organizations and implemented around the world to prevent biodiversity loss. If well managed, they can at the same time promote local economic development through different channels, including increased fish stocks and captures for fishers, or tourism. We conduct among the first long-term study of the socio-economic effects of MPAs by focusing on Tanzania. Using data collected in 2003, Tobey and Torell (2006) found that MPAs had limited economic impacts. Eighteen years after, we conduct a follow-up study in the same communities. In addition, we collect in-situ biodiversity measurements using environmental DNA methods next to the villages to measure how current socio-economic outcomes correlates with biodiversity.

Registration Citation:

On the long-term impacts of Marine Protected Areas: A 18-years follow-up study in Tanzania (Leblois A., Desbureaux S., Girard S., Devillers R. et al.)

Categories:

Agriculture and Rural Development
Environment and Disaster Management

Additional Keywords:

Marine Protected Areas

Secondary ID Number(s):

Principal Investigator(s)

Name of First PI:

Antoine Leblois

Affiliation:

INRAE, Center for Environmental Economics-Montpellier

Name of Second PI:

Sébastien Desbureaux, Julia Girard

Affiliation:

CNRS, Center for Environmental Economics-Montpellier

Study Sponsor

Name:

WIOMSA and MUSE

Study Sponsor Location:

Kenya

Research Partner

Name of Partner Institution:

ZaMaCos

Type of Organization:

NGO (local) or other civil society organization

Location:

Tanzania

Intervention

Intervention Overview

Intervention:

Marine protected areas or locally managed marine areas (all called MPAs thereafter) are the main conservation tools used to curb the multiple threats on species and ecosystems (Grorud-Colvert et al. 2021). MPAs are geographical spaces intended to protect biodiversity and conserve marine resources, where regulations are set to limit human impacts, in particular through gear restrictions, fishing quotas, and minimizing destructive activities.

Tanzania has over 1400km of coastline, representing a large number of critical habitats for marine wildlife. Tanzania’s coastal and marine resources are under increased pressure partly because of anthropogenic factors (increasing costal population, resource dependence, illegal harvesting techniques). The first MPA were created in the 1970s but without much effect on the ground (WIOMSA 2021). Conservation efforts were increased in the mid-1990s following the 1994 law for marine conservation was enacted. It notably led to the creation of a first Marine Park in 1995 (Mafia Island Marine Park) which remains today Tanzania’s flagship park. As of 2021, there are 18 formal MPAs in Tanzania, comprising 3 Marine Parks and 15 Marine Reserves. In total, they cover about 1 percent of the country’s waters. There is also one National Park (Saadani NP) and a number of mangrove forest reserves extending along the five coastal regions of Tanga, Coast, Dar es Salaam, Lindi and Mtwara. The conservation approach is participatory. It relies on the involvement of local communities in planning; decision-making and implementation of conservation activities; benefit sharing and evaluation. As such, it is expected that MPA in Tanzania can translate not only in biodiversity conservation but also poverty reduction.

Theory of Change:

The relationship between MPA and socioeconomic outcomes is a long-running debate in academic and policy circles (Mascia et al. 2010). On the one hand, MPAs restrict access or exploitation of coastal resources to local users who depend on them to sustain their livelihood and food security, so can be seen as negative.  On the other hand, MPAs preserve a natural capital that can bolster fisheries yields through the exportation of larvae and adults towards fished areas.  By regulating activities and preventing unsustainable practices, MPAs are expected to better protect marine wildlife within its boundaries (both alpha diversity: species richness; and beta-diversity: abundance). This includes stocks of species with commercial values. Fishes being a mobile resources, this diversity is expected to increase outside core-conservation zones, notably in areas where fishers can operate.

Hence, MPAs are expected to provide social and economic outcomes to local inhabitants, notably through increasing catch in surrounding fishing areas and providing income from recreational and touristic activities (Schratzberger et al. 2019). On the balance, empirical evidence is mixed between studies showing some socioeconomic benefits of living close to MPAs and others reporting no effect or even negative impacts.

The paper of Tobey and Torell (2006) constitutes a unique baseline survey for which we obtained the raw data. Twenty-four villages were surveyed in 2003 in the vicinity of 6 MPAs. Socio-economic data were collected on treated (~2/3) and control (~1/3) households. As there is currently only one observation in time (2003), findings of the 2006 study relied on simple differences of revealed (perceived) recent improvements or deterioration of outputs. Our project tries to overcome this limitation by comparing the 2003 survey with the follow-up survey that would ideally take place in the summer of 2021.

Multiple Treatment Arms Evaluated?

No

Program Funder

Name of Organization:

Diverse

Type of Organization:

Public Sector, e.g. Government Agency or Ministry

Intervention Timing

Intervention or Program Started at time of Registration?

Yes

Start Date:

01/01/1995

End Date:

Evaluation Method

Evaluation Method Overview

Primary (or First) Evaluation Method:

Regression with controls

Other (not Listed) Method:

Additional Evaluation Method (If Any):

Difference in difference/fixed effects

Other (not Listed) Method:

Method Details

Details of Evaluation Approach:

We will pool micro data from 2003 and 2021. Through regressions analyses, we will measure the evolution of outcomes between 2003 and 2021 (temporal dummy variable), the difference between inside and outside MPA and an interaction variable for the few sites that have switched status in a difference-in-differences design. We do not expect our sample will be powered enough to detect significant results for the interaction term (too few sites have switched from control to treated), and therefore we will pay attention to temporal (2003 vs 2021) and geographical variations (inside outside) of the outcomes and interpret the results as correlations. We will complete these regressions by focusing on data from 2021 only for outcomes that were not measured in 2003 and because of possible quality issues with some data collected in 2003. Coefficient will need to be interpreted as correlations. We will correlate socio-economic outcomes with biodiversity outcomes from the environmental DNA analysis. We intend to measure our treatment variable in several ways, including a dummy variable for whether the village will be inside or outside the MPA, the distance from a village to the border of a MPA. We intend to run extensive robustness checks notably to control for bias that could arise from certain enumerators and weather variables: Dropping one enumerators, flagging suspicious surveys, controlling for rainfall and wind, exploring the robustness of results for fishers with a dow (a sort of boat) as they can go fish far away from MPAs.

Results will be compared with the results of an expert survey. To be considered an expert, a respondent will need to be an academic or practitioner having worked in fisheries or conservation in Tanzania. We identify experts through our literature review. A snow-ball sampling will be used to extend the sample.

Outcomes (Endpoints):

Primary outcomes of interest in bold. Note: standard errors in later regressions will be corrected for multi-hypothesis testing. 

Livelihoods: Main economic activity of household members between 18 and 70 years old during the last seven days, Secondary economic activity of household members between 18 and 70 during the last seven days, Activity that brings in more money to the household, Employment in tourism as the main economic activity, Farming as the main economic activity, Number of days fishing in the last 30 days Note: Tobey and Torell collected data in June. Our project will collect data between September and October. To account for possible seasonality in fishing practices, we will ask the additional following questions: Are the last 30 days representative of a normal month regarding fishing activities, or are there months during which you fish more or fish less for any type of reason?  How many days a month you fish in June?, Index of durable goods (excluding fishing / professional equipment), Index of food security

Fishing practices: Average captures in a month, Average monthly revenues from fishing, 5 main species of fish captured, Ownership of fishing equipment (index), Problems faced by fishers (3 main)

Perceptions of MPAs: People know about MPAs?, MPAs have any goodness? Badness ? more goodness than badness?, Standardized score of the effect of MPAs (for households knowing MPAs : To what extent the establishment of MPA have influence your fish catches in your area?, What is the impact of establishment of MPA on employment in your householdn, People in the household belong to an environmental organisation

Unit of Analysis:

Household

Hypotheses:

By combining socio-economic data from 2003 and 2021, we want to test the overall evolution of our primary and secondary outcomes over the period (period dummy variable), whether socio-economic outcomes are now better in MPA than outside (MPA dummy variable), and for a couple of villages which have changed status between 2003 and 2021 the more causal effect of MPAs (in the spirit of difference in differences models).

 

By combining our socio economic data and biodiversity data, we want to assess the correlation between current fish catches, wealth and fishing practices; and the diversity of wildlife species and presence of key species (including endangered / iconic ones).

Unit of Intervention or Assignment:

Number of Clusters in Sample:

24

Number of Individuals in Sample:

768 (32 per village) for the socio-economic survey. A secondary sample of spouses of the head of the household when the head is a male will be collected to assess gender differences. eDNA = 20

Size of Treatment, Control, or Comparison Subsamples:

2/3 treated, 1/3 control

Supplementary Files

Analysis Plan:

Other Documents:

Main Questionnaire: Main_Survey _ KoboToolbox.pdf

Data

Outcomes Data

Description:

Household survey combined with eDNA data on marine biodiversity.

Data Already Collected?

Yes

Data Previously Used?

Yes

Data Access:

Not restricted - access with no requirements or minimal requirements (e.g. web registration)

Data Obtained by the Study Researchers?

Yes

Data Approval Process:

Approval Status:

Treatment Assignment Data

Participation or Assignment Information:

Yes

Description:

Data Obtained by the Study Researchers?

Data Previously Used?

Data Access:

Data Obtained by the Study Researchers?

Data Approval Process:

Approval Status:

Data Analysis

Data Analysis Status:

No

Study Materials

Upload Study Materials:

Questionnaire: Main_Survey _ KoboToolbox.pdf
Kobo and Qfield guide: Kobo_Qfield_install_training_final.pdf
Protocol main survey: PROTOCOL MAIN SURVEY.docx

Registration Category

Registration Category:

Prospective, Category 3: Data for measuring impacts have been obtained/collected by the research team but analysis for this evaluation has not started

Completion

Completion Overview

Intervention Completion Date:

Data Collection Completion Date:

Unit of Analysis:

Clusters in Final Sample:

Total Observations in Final Sample:

Size of Treatment, Control, or Comparison Subsamples:

Findings

Preliminary Report:

Preliminary Report URL:

Summary of Findings:

Paper:

Paper Summary:

Paper Citation:

Data Availability

Data Availability (Primary Data):

Date of Data Availability:

Data URL or Contact:

Access procedure:

Other Materials

Survey:

Survey Instrument Links or Contact:

Program Files:

Program Files Links or Contact:

External Link:

External Link Description:

Description of Changes:

Study Stopped

Date:

Reason: