Disclaimer:
Please be aware that the content herein has not been peer reviewed. It consists of personal reflections, insights, and learnings of the contributor(s). It may not be exhaustive, nor does it aim to be authoritative knowledge.
Overview
Prepared by (Name of the experimenter)
Javier Brolo and Juan Pablo Rustrián
On date (Day/Month/Year)
August 31th, 2022
Current status of experimental activity
Completed
What portfolio does this activity correspond to? If any
Co-creation of waste management solutions
What is the frontier challenge does this activity responds to?
How to improve the collaboration between society and public institutions to increase resilience to climate change
What is the learning question(from your action learning plan) is this activity related to?
How to improve waste management
Please categorize the type that best identifies this experimental activity:
Pre Experimental (trial and error, prototype, a/b testing), Quasi Experimental (Analytical, observations, etc)
Which sector are you partnering with for this activity? Please select all that apply
United Nations agency, Public Sector, Private Sector
Please list the names of partners mentioned in the previous question:
The environmental programmatic unit from the UNDP Office, Local Governments, Municipalities, The Ministry of Environment and Natural Resources
Design
What is the specific learning intent of the activity?
We wanted to test whether positive deviance was a viable method to be used with available data and to prioritize municipalities in the Motagua River basin in which activities are to be carried out within the framework of learning cycles on how to promote the adoption of good solid waste management practices. It's hoped that theses good practices came from the DPPD analysis as positive deviation from other municipalities.
What is your hypothesis? IF... THEN....
If we use DPPD, then we will prioritize locations with improved waste management practices despite unfavorable circumstances
Does the activity use a control group for comparison?
Yes, a different group entirely
How is the intervention assigned to different groups in your experiment?
Non-random assignment
Describe which actions will you take to test your hypothesis:
Data Powered Positive Deviance (DPPD) is a technique which assumes that in every community there are individuals or groups who develop unconventional practices that help them deal better with challenges than their peers and consists of five stages. For this experiment, we focused only in the first two stages (Assess problem-method fit and determine positive deviants). During the first stage we defined the problem and the scope of the intervention. Checked if DPPD is a suitable and feasible method by assessing required data and capabilities to ensure that potential outcomes are desirable for the target group. Then, for the second stage, an analysis was made to determine which Municipalities (positive deviations) have an exceptional performance based on people burning or burying waste considerably less than expected. To do so, we identified those municipalities located along the Motagua River (whose activities have a strong influence in waste management in the river), then a linear regression model was made, based on the XII National Population Census and VII of Household from 2018, Secretary of Planning and Programming of the Presidency (SEGEPLAN for its acronym in Spanish) and Noack 2015 (water governance in the municipalities of the Upper Motagua River Basin) data, to compare the observed results with the expected results.
What is the unit of analysis of this experimental activity?
Municipalities
Please describe the data collection technique proposed
Secondary data sources collected from open data sources provided by official national institutions.
What is the timeline of the experimental activity? (Months/Days)
About one week.
What is the estimated sample size?
50-99
What is the total estimated monetary resources needed for this experiment?
Less than 1,000 USD
Quality Check
This activity is relevant to a CPD outcome, The hypothesis is clearly stated, This activity offers strong collaboration oportunities, This activity offers a high potential for scaling, This activity has a low risk
Please upload any supporting images or visuals for this experiment.
Please upload any supporting links
What are the estimated non- monetary resources required for this experiment? (time allocation from team, external resources, etc) If any.
We need access to data, software for analysis, and skills to analyze the data.
Results
Was the original hypothesis (If.. then) proven or disproven?
Proven. The use of DPPD allowed us to identify municipalities (5) with exceptional performance in final waste disposal practices, which could provide solutions based on good local management practices, those solutions could be adopted by another municipalities.
Do you have observations about the methodology chosen for the experiment? What would you change?
Using secondary data possess challenges on the validity of the interpretations. Implementing this exercise required a considerable amount of knowledge on how to analyze different types of data. In addition, the DPPD does not tell us the specific local solution, this makes it necessary to verify this information on site.
From design to results, how long did this activity take? (Time in months)
Less than one month.
What were the actual monetary resources invested in this activity? (Amount in USD)
US$0.00
Does this activity have a follow up or a next stage? Please explain
Yes. The DPPP method has 3 more stages: (3) Discover factors underlying outperformance (4) Design and implement interventions (5) Monitor and evaluate. These stages along with the positive deviants might be relevant for the Local Governments and UNDP to do fieldwork those local solutions for waste management, and thus inform actions in other territories (municipalities) to increase good local practices.
Is this experiment planned to scale? How? With whom?
No.
Please include any supporting images that could be used to showcase this activity
Please add any supporting links that describe the planning, implementation, results of learning of this activity? For example a tweet, a blog, or a report.
Considering the outcomes of this experimental activity, which of the following best describe what happened after? (Please select all that apply)
This experiment led to partnerships, This experiment led to adoption of new ways of working by our partners
Learning
What do you know now about the action plan learning question that you did not know before? What were your main learnings during this experiment?
We know that DPPD might be a viable methodology using available and open access data, now we have learned that some municipalities could have solutions for waste management based on people burning or burying waste considerably less than expected.
What were the main obstacles and challenges you encountered during this activity?
The main obstacles were the skills needed to process and integrate the data from our sources. Also, it's not clear if our model for estimating expected performances for waste management is sound. Yet, the most difficult part is creating interest in the results and using them to scale in other territories.
Who at UNDP might benefit from the results of this experimental activity? Why?
The environmental programmatic
unit might find it relevant to understand which municipalities could have
solutions for waste management.
Who outside UNDP might benefit from the results of this experiment? and why?
Local Governments, Municipalities, The Ministry of Environment and Natural Resources. All of them could use the DPPT analysis to identify good waste management practices and after that might be possible to scale those solutions in other municipalities.
Did this experiment require iterations? If so, how many and what did you change/adjust along the way? and why?
It required a bit of iterations in the data processing and analysis techniques.
What advice would you give someone wanting to replicate this experimental activity?
Look at documentation on DPPD, identify people with data processing and analysis skills. Be ready to use messy data. Understand the technical limitations of the audiences to understand the process. DPPD is a very counterintuitive idea to traditional projects. Traditional projects seem to operate under a "hero" mentality, believing they have the answer on how to rescue people in most need. It is difficult to sell the idea that projects could be facilitators of peer-to-peer learning at the local level, where some local solutions exist that work in the context where needs emerge, but aren't being used by everyone yet.
Can this experiment be replicated in another thematic area or other SDGs? If yes, what would need to be considered, if no, why not?
Absolutely. We could use positive deviance to identify unexpectedly high performance in access to services, good practices, or participation other than waste management topics.
How much the "sense" and "explore" phases of the learning cycle influenced/shaped this experiment? In hindsight, what would you have done differently with your fellow Solution Mapper and Explorer?
We received the training together as a LAB, sensing and exploring informed the topic to be prioritized.
What surprised you?
It's surprising that DPPD's analysis has the capacity to show you (visually talking) those municipalities that don't fit your model (or positive deviations). In this case we can see those municipalities with a higher burning (a lot) but also our positive deviations (lower burning than expected).
11Sustainable cities and communities
13Climate action
17Partnerships for the goals
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