Local Governance and Crisis Response in Mali

Last registered on May 09, 2024
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Pre-Trial

Trial Information

General Information

Title
Local Governance and Crisis Response in Mali
RCT ID
AEARCTR-0013515
Initial registration date
April 26, 2024

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published
May 09, 2024, 1:48 PM EDT

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Locations

Country
Mali
Region

Primary Investigator

Name
Jordan Kyle
Affiliation
IFPRI
Contact Primary Investigator

Other Primary Investigator(s)

PI Name
Jessica Gottlieb
PI Affiliation
University of Houston
Contact Investigator
PI Name
Jaimie Bleck
PI Affiliation
University of Notre Dame
Contact Investigator
PI Name
Katrina Kosec
PI Affiliation
IFPRI
Contact Investigator

Additional Trial Information

Status
On going
Start date
2024-03-15
End date
2024-12-31
Keywords
Behavior, Governance
Additional Keywords
anticipatory action, political economy, aid transparency, humanitarian aid, local governance
JEL code(s)
D78, D72, D73
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Mali faces increasingly severe conflict and crises, with important consequences for food security, water availability, poverty, and well-being. Within Mali and in other settings affected by recurring crises, there is growing interest in the potential of anticipatory action programming (AA) to provide early intervention before the onset of crisis. Anticipatory action (AA) is a relatively new type of aid distribution mechanism. Whereas humanitarian aid responds to shocks \textit{after} they occur, AA relies on early warning systems to identify impending threats such as floods, droughts, and other natural disasters. When an early warning is received, aid would be released \textit{after} the early warning but \textit{before} a shock occurs in order to mitigate effects of shocks. Its central motivation is the recognition that ``an ounce of prevention is worth a pound of cure.'' In order to set up AA programming, actors agree to set aside funding to support AA programming, which is then released upon an agreed-upon trigger. Often, formal pacts are signed laying out how triggers work and when and what types of aid funding would be released upon an early warning. In the Malian context, we flag that nascent AA programming is typically associated with flood shocks, and so the focus of our research is on aid preferences related to flood shocks. This has also been the focus of early donor interest on developing AA programming.

Implementing and scaling AA programming necessitates collaboration and engagement with various local leaders; in fragile contexts, local leaders play crucial roles in overcoming ``last mile'' delivery challenges; resolving disputes over aid distribution; and providing insights into local conditions, vulnerabilities, and sources of resilience. Their involvement is vital for addressing any errors in early warning systems and supporting resilience where AA fails to protect vulnerable households. Little rigorous evidence exists globally on the views that these local leaders have on AA programming and what type of early aid could be useful to communities under what conditions, nor on how different ways of designing such programming might condition their support. Moreover, little rigorous evidence exists within the Malian context specifically on trust in and capacity of early warning systems for crises, how aid delivery systems might adapt to an AA approach, nor how local leaders who are currently involved in the delivery and distribution of humanitarian aid might react to reforms to the aid system. Considering how local leaders think about aid more broadly, there is also limited evidence on how they view transparency in the logistics around allocating aid, and discretion in decision-making over aid.

This project contributes to these evidence gaps by implementing an original survey of ~2,500 local leaders (including civil society representatives) across 125 communes in Mali. Our sample draws in a diverse sample of local leaders, deliberately selecting leaders who may currently participate in the delivery and distribution of humanitarian aid and those who may currently consider themselves to be more ``outsiders'' of the current aid distribution system. We also varied the sample geographically, by sampling from a wide range of regions in Mali, including those that have been more and less affected by shocks and conflict. These comprise local leaders at both the commune- and village-levels to understand how anticipatory action (AA) and humanitarian aid distribution works in practice from their perspectives; their preferences and priorities over AA vs. other forms of emergency assistance (hereafter, humanitarian aid); their views of discrepancy and transparency in aid distribution; as well as sources of local resilience present in their communities. Understanding local leaders' views about AA is important given increasing recognition in the donor community that provision of AA, compared to humanitarian aid, can reduce the overall need for aid, and thus is a critical component of crisis management policy. Our study analyzes the political economy of AA to shed light on what factors may serve as barriers to its wide acceptance in local communities. Given the importance of these questions for effective aid in Mali and in other fragile contexts, there is significant potential for insights from this research to inform donor policies and priorities.
External Link(s)

Registration Citation

Citation
Bleck, Jaimie et al. 2024. "Local Governance and Crisis Response in Mali." AEA RCT Registry. May 09. https://doi.org/10.1257/rct.13515-1.0
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Experimental Details

Interventions

Intervention(s)
To answer our research questions, we implement two experiments. The first is related to anticipatory action programming and implements an experimental game with local leaders in Mali, varying key features of how anticipatory action would be implemented to understand how these differences might change support for anticipatory action vs. humanitarian aid among local leaders. The second experiment focuses on aid transparency and discretion. This is designed as a discrete choice exercise, asking respondents to choose between aid profiles that vary along different parameters in order to understand how local leaders value transparency (or lack thereof) over aid distribution and local discretion over aid distribution.
Intervention Start Date
2024-03-15
Intervention End Date
2024-04-30

Primary Outcomes

Primary Outcomes (end points)
For the anticipatory action game: Allocation to humanitarian aid vs. anticipatory action programming; support for anticipatory action programming (4-pt scale) (AA7); satisfaction with initial allocation decision (AA4); desire to switch allocation (binary, AA5); new allocation decision to humanitarian aid vs. anticipatory action (AA6)

For the aid profiles experiment: Chooses high transparency profile (binary); Chooses high discretion profile (binary); Aid profile rating (1-10)
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The study is being conducted in approximately 125 communes in rural Mali across 7 regions. In each commune, we aim to recruit the mayor and four other leaders at the commune level: the state-appointed civil servant - the Secretary General, an opposition politician, and 2 civil society leaders. Within each commune, we will also sample village-level leaders in four villages: the village chief, the women's leader, the youth leader, and another resource person or key interlocutor for the village on whom the chief relies. With approximately 16 village-level leaders and approximately 5 commune-level leaders per commune, we aim to recruit about 2500--2700 respondents in total from across the 125 communes. Each respondent will receive both of the experiments described here.

Experimental Design Details
Not available
Randomization Method
For the Anticipatory Action (AA) game, we randomly assign two separate features of the setup. We use simple randomization separately for each. In the first experiment, respondents have a 50% probability of being assigned to one of the following conditions:(1) High accuracy (or an 80% chance of the early warning sign being correct) or (2) Low accuracy (or a 50% chance of the early warning sign being correct). In the second treatment, respondents have a 50% probability of being assigned to one of the following conditions: (1) High discretion (the commune's own aid committee distributes aid) or (2) Low discretion (the donor distributes aid).

For the discrete choice experiment, we randomly assign aid type with 1/3 probability at the level of the donor profile pairing so that each profile that is being compared is holding aid type constant. For the attributes of discretion and transparency, we do not use simple randomization. We are only interested in a subset of the possible combinations of these two features. In particular, we are interested in the below four contrasts:
(1)The effect of transparency conditional on discretion
(2) The effect of transparency conditional on discretion
(3) The effect of discretion conditional on no transparency
(4) The effect of discretion conditional on transparency

As a result, we have four distinct pairings of donor profiles where only one of the two attributes -- transparency and discretion -- is different across the two profiles, holding the other one constant. Because we also want to randomize aid type at the level of the donor pairing, this leaves us with 4x3 = 12 possible donor pairings.

Each respondent receives 3 of these 12 possible pairings. However, we want each respondent to be constrained to receive only one of each aid type and, at most, one of each of the four contrasts of interest with respect to discretion and transparency. This leaves us with four unique sets of profile pairs.

Each respondent is thus assigned one of the four unique sets of three profiles with equal probability. To avoid order effects, the order of the three profiles in each set is randomly assigned. And to avoid order effects within each profile pair, the treatment of interest (whether transparency or discretion is High) is randomly assigned to occur in Donor profile 1 and Donor profile 2 with each probability.

This randomization protocol is equivalent to blocking. The assignment of each of the four contrasts is blocked on the assignment of aid type.
Randomization Unit
individual level randomization
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
2500 leaders
Sample size: planned number of observations
2500 leaders
Sample size (or number of clusters) by treatment arms
For the AA game: 1,250 in low accuracy, 1,250 in high accuracy, 1,250 in low discretion, 1,250 in high discretion
For the transparency / discretion exercise: 1,875 high vs. low transparency when discretion is high; 1,875 high vs. low transparency when discretion is low; (3) 1,875 high vs. low discretion when transparency is low; and (4) 1,875 high vs. low discretion when transparency is high.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
AA game: In our sample of 2,500, we have 0.99 power to detect an effect size of 0.2 standard deviations with a p value of 0.01667 (to account for our three primary hypotheses). We have a power of 0.87 to detect an effect size of 0.1 standard deviations. Transparency / discretion vignettes: Because each participant gets three of four contrasts and thus for 2500 respondents, N=1875 for this test. For the expected minimum detectable effect size, we consider the effects of attributes in conjoint experiments as a reasonable guide. In a meta-analysis of highly cited forced-choice conjoint experiments, the median average causal mediation effect size was 0.05. Using DeclareDesign, we find that we have a power of 0.99 to detect an effect size of this magnitude, or to detect a difference between an estimate of Y=0.55 and 0.5. We have about 0.73 power to detect a difference between an estimate of Y=0.53 and 0.5. If we implement conservative multiple comparison corrections for our four hypothesis tests in this theory family by dividing our p value of interest by 4 (p<= 0.0125), then we have a power of 0.97 to detect a difference of 0.05 and a power of 0.54 to detect a difference of 0.03. We expect that we will have more power to detect Hypotheses 4, 6 and 7 than Hypothesis 5 because the latter is about the difference between two estimates (preference for discretion when transparency is high vs. low; we expect both will themselves be different than 0.5) rather than the estimate being different from 0.5.
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
University of Houston
IRB Approval Date
2023-11-15
IRB Approval Number
STUDY00004487
Analysis Plan

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