Slack and Development

Last registered on March 21, 2024


Trial Information

General Information

Slack and Development
Initial registration date
March 17, 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
March 19, 2024, 5:22 PM EDT

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

Last updated
March 21, 2024, 9:50 PM EDT

Last updated is the most recent time when changes to the trial's registration were published.


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Primary Investigator

Harvard University

Other Primary Investigator(s)

PI Affiliation
Oxford University
PI Affiliation
University of Zurich
PI Affiliation
UC Berkeley
PI Affiliation
UC Berkeley

Additional Trial Information

On going
Start date
End date
Secondary IDs
Prior work
This trial is based on or builds upon one or more prior RCTs.
Slack -- the under-utilization of factors of production -- varies systematically with development and predicts a substantial share of the differences in productivity across countries. Using novel and detailed measures of the utilization of labor, capital, and input factors overall from a large representative sample of firms in rural and urban Kenya, we show that utilization is increasing in firm size, market access, and local GDP.
We argue that indivisibilities of inputs are a key driver of capacity under-utilization in poor economies. We present a monopoly model of capacity choice in which one of the inputs is subject to an integer constraint and show how pricing and investment are non-trivial functions of firm size. Embedded in spatial general equilibrium, the model rationalizes the endogenous emergence of slack in steady state, and accounts for differences in productivity in the cross-section.
We validate the model using reduced-form estimates of the general equilibrium effects of cash transfers from a large-scale RCT in Western Kenya. Consistent with our model, the data show that (1) Supply curves are highly elastic, (2) output responses to demand shocks are substantially larger for low-utilization firms, (3) aggregate inflation in response to the fiscal shock is low, but there exists some inflation in high-demand regions, and (4) slack declines in the medium-run in response to a demand shock.
We then derive the aggregate implications of the high share of small firms, and low market access in poor economies for overall productivity and structural transformation, and the impacts of macroeconomic and development policies such as cash transfers and social protection. Our findings rationalize large demand multipliers in poor economies.
External Link(s)

Registration Citation

Egger, Dennis et al. 2024. "Slack and Development ." AEA RCT Registry. March 21.
Experimental Details


The NGO GiveDirectly is responsible for the intervention; GiveDirectly provides large, unconditional cash transfers to poor households in rural Kenya. GiveDirectly identifies villages in which they are willing to work, and in order to facilitate research on cash transfers, these villages are randomly assigned to treatment or control status. Within treatment villages, GiveDirectly then identifies all households that meet their eligibility criteria, enrolls and verifies the eligibility of eligible households, and sends cash transfers to all eligible households via the mobile money system M-Pesa. Eligible households receive a one-time of around USD 1,000 made in a series of three payments.

This intervention will serve as the basis for the current study on general equilibrium effects of slack.
Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
Associations of slack and underutilisation with firm size, market size, and response to the cash transfer.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The original experimental design was described in AEA Trial 505. We now seek to calibrate a structural model by matching key moments from this experiment.
Experimental Design Details
Not available
Randomization Method
The original experiment was randomised in office using a computer.
Randomization Unit
In the original experiment, randomization to treatment status was conducted at the village level. High versus low saturation was randomly assigned to groups of villages based on their sublocation, an administrative unit above the village.
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
The original experiment had 653 villages
Sample size: planned number of observations
The original experiment had 7,836 households (12 per village), 6,530 enterprises (this assumes an average of 10 enterprises per village; the exact number will depend on the number of enterprises in the study area).
Sample size (or number of clusters) by treatment arms
328 treatment, 325 control.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number
Analysis Plan

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