Incentivizing the Uptake of Renewable Technologies

Last registered on May 30, 2022

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Pre-Trial

Trial Information

General Information

Title

Incentivizing the Uptake of Renewable Technologies

RCT ID

AEARCTR-0009498

Initial registration date

May 27, 2022

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 30, 2022, 8:08 AM EDT

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

Locations

Country

Pakistan

Region

Sargodha

Primary Investigator

Name

Alexandra Avdeenko

Affiliation

World Bank

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Other Primary Investigator(s)

PI Name

David Mueller

PI Affiliation

University of Mannheim

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PI Name

Mikaela Cochran

PI Affiliation

University of Bath

Contact Investigator

PI Name

Laura Peters

PI Affiliation

c4ed

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Additional Trial Information

Status

On going

Start date

2021-12-15

End date

2022-09-30

Keywords

Finance & Microfinance, Firms & Productivity

Additional Keywords

Renewable energy, sustainability, mitigation, microfinance, adaptation

JEL code(s)

Secondary IDs

Prior work

This trial does not extend or rely on any prior RCTs.

Abstract

National energy supply in Pakistan is characterized by excessive demand that cannot be sufficiently supplied by its national grid. As a result, power cuts are frequent and prolonged throughout the country. Rural small-medium sized entrepreneurs are particularly susceptible to this reduction in energy access as it risks impacting their productive capacity. To address this concern, renewable energies have been introduced to SMSEs in rural areas. Demand for these technologies, however, needs to be understood further. Therefore, we evaluate an intervention which focuses on understanding how the demand and uptake of RE loans and RE technologies can be incentivized through subsidies. A randomized control trial is conducted using a lottery allocated cash prize subsidy to assess uptake behavior. Primary outcomes of interest are uptake, with a focus on heterogeneity w.r.t. baseline characteristics of the entrepreneurs.

External Link(s)

Registration Citation

Citation

Avdeenko, Alexandra et al. 2022. "Incentivizing the Uptake of Renewable Technologies." AEA RCT Registry. May 30. https://doi.org/10.1257/rct.9498-1.0

Sponsors & Partners

Interventions

Intervention(s)

The intervention focuses on the demand for renewable energy (RE) technologies. Demand for RE loans and uptake behavior will be measured through response to price changes. Therefore synthetic price changes will be randomly introduced to clients in the form of small subsidies through a lottery system. The eligible applicants to the RE loan will be automatically enrolled in the lottery to win the subsidy cash prize. Winners of the lottery can keep the subsidy cash prize contingent upon taking up the loan. Using administrative data, we will monitor the number of applications to the RE technologies and retention rates of applicants after the results of the lottery are announced.

Intervention Start Date

2021-12-15

Intervention End Date

2022-06-30

Primary Outcomes

Primary Outcomes (end points)

Uptake of RE loans (incl. by type of client, e.g. new vs old clients; gender; loan size, loan purpose, loan duration, business type, # HH members, education)
Uptake/adoption of RE technologies

Primary Outcomes (explanation)

Uptake and adoption of loans can be measured with the available MIS data from NRSP. Uptake of RE technologies will be confirmed with satellite imagery data (conditional on availability of this data).

Secondary Outcomes

Secondary Outcomes (end points)

Uptake of RE technology of neighbors within the villages
Crowding out on other investments (in size and type)
Withdrawal of application if individual does not win the lottery

Secondary Outcomes (explanation)

Experimental Design

Randomized Control Trial (RCT)

We conduct an RCT that operates at two levels:
First, a series of 18 MFI field offices randomly allocated to treatment and control in the Sargodha region of Pakistan. Among these, 9 are allocated to treatment, and the other 9 to control. In treated field offices, loan officers from local NRSP branch offer a loan for renewable energy products to small-medium sized business owners (to be used for productive services). This loan has not been offered previously to this clientele.

Second, those who apply for the renewable energy loan, are assessed for eligibility. If eligible, they are entered into a lottery through which applicants can win a cash prize subsidy that is applied to the total loan amount. This second level of treatment therefore takes place at the individual level, where we will allocate 250 cash prize grants of $86 USD among 500 applicants. Those who lose the lottery can still take up the loan, however they are also allowed to withdraw their application.

Experimental Design Details

Randomization Method

Lottery at the entrepreneur level

Randomization Unit

Level 1 = field offices
Level 2 = individuals (applicants)

Was the treatment clustered?

Yes

Experiment Characteristics

Sample size: planned number of clusters

Total field offices = 18 (9 treated, 9 control)

Sample size: planned number of observations

Among 18 field offices, 500 individuals (250 treated – winners, 250 control – losers)

Sample size (or number of clusters) by treatment arms

As there is only one treatment arm at each level, see above.

Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

For differences in uptake of the loan and the product, we focus on the effect of the lottery: Hence, we have a sample size of 500 (250 control and 250 treatment). Moreover, we use standard assumptions of a power of 0.8 and a 0.05 significance level. Furthermore, we assume the largest possible standard deviation for a binary variable (uptake is a binary variable) and an intra-cluster correlation of 0.3, because the outcomes of the individuals within one field office are unlikely to be largely correlated as one field office operates over a wide areas with several villages. Then, we find a minimum detectable effect size of 0.0471 in a two-sample mean test.

Supporting Documents and Materials

IRB