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Marginal propensity to consume out of liquidity

Last registered on February 02, 2015

View Trial History

Pre-Trial

Trial Information

General Information

Title

Marginal propensity to consume out of liquidity

RCT ID

AEARCTR-0000608

Initial registration date

February 02, 2015

Last updated

February 02, 2015 2:57 PM EST

Location(s)

Country

Turkey

Region

Primary Investigator

Name

Deniz Aydin

Affiliation

Stanford

Contact Primary Investigator

Other Primary Investigator(s)

Additional Trial Information

Status

On going

Start date

2014-07-15

End date

2016-09-01

Keywords

Finance & Microfinance

Additional Keywords

consumption, debt, borrowing constraints, precautionary saving, household finance, permanent income hypothesis, field experiment

JEL code(s)

C93, D12, D14, D91, E21, E44, E51, G21

Secondary IDs

Abstract

This experiment uses exogenous variation in borrowing capacity to test competing models of inter temporal consumption behavior. I estimate the marginal propensity to consume (MPC) out of liquidity, the debt response to a change in borrowing capacity, using changes in credit card limits in a randomized controlled trial. I analyze the magnitude, duration and the heterogeneity of the MPC, as well as where the additional liquidity is spent. I test apart theories that predict a high MPC, such as myopia or liquidity constraints/precautionary savings.

External Link(s)

Registration Citation

Citation

Aydin, Deniz. 2015. "Marginal propensity to consume out of liquidity." AEA RCT Registry. February 02. https://doi.org/10.1257/rct.608-4.0.

Former Citation

Aydin, Deniz. 2015. "Marginal propensity to consume out of liquidity." AEA RCT Registry. February 02. http://www.socialscienceregistry.org/trials/608/history/3513.

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Experimental Details

Interventions

Intervention(s)

Intervention Start Date

2014-09-01

Intervention End Date

2016-09-01

Primary Outcomes

Primary Outcomes (end points)

The main outcome variable of interest is the response of credit card debt to a change in credit card limits. Credit card limit consists of revolving credit card balances, and balances in installments. Secondarily, I am interested in the consumption response in different sectors and other balance sheet effects, such as savings accounts.

Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)

Secondary Outcomes (explanation)

Experimental Design

The assignment of credit line is done in three steps. First, the credit sales group pre-selects customers according to a set of profitability criteria. These criteria include the expected value added from limit increase, as well as macro prudential criteria imposed by the banking regulation authority preventing line increases, such as having pre-existing unpaid balances exceed half the card limit. The pre-selected individuals are then filtered by the bank’s risk group, according to a set of risk criteria. Finally, the remaining customers are pushed into the central bank’s credit limit clearing system to check if they are eligible for a credit limit extension, i.e. if their current limit is below four times their income. The randomization is done after the final step, therefore the control group consists of individuals that pass all criteria for being assigned an increased credit limit, but are not.

Experimental Design Details

Randomization Method

Randomization is done in STATA.

Randomization Unit

Individuals are grouped on the basis of credit card utilization, defined as the ratio of end-of-month credit card balances to credit limit. I first estimate a distributed lag-model on the observational data for each utilization decile. The standard errors of the MPC estimates are higher for high utilization individuals, therefore I under-sample individuals that have a low credit card utilization, proportional to the standard errors.

Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters

54524 individuals

Sample size: planned number of observations

54524 individuals

Sample size (or number of clusters) by treatment arms

13438 control, 13416 treatment, 27670 treatment (undersampled)

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

Supporting Documents and Materials

IRB

INSTITUTIONAL REVIEW BOARDS (IRBs)