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Opting For A Preferential Tax Regime – The Role Of Accountants And Long Term Incentives On Small Businesses’ Behaviour
Hungarian small businesses have access to a preferential small business tax regime (SBT). SBT offers show term benefits with lower tax rates and easier administration relative to the general corporate tax regime. Additionally it has long term benefits through its cash-flow design, favouring firms who invest their profits. Despite their eligibility and potential gains, many firms do not opt for SBT. Previous trials showed that the tax authority can increase take-up rate by directly providing information on SBT to eligible firms. In this randomized field experiment we investigate two questions. Is targeting this messaging to firms, or their accountants effective; and is emphasizing long term benefits over short term benefits more effective. The results will shed light on small businesses' decision-making.
External Link(s)
Citation
Horváth, Katalin, Tibor Keresztély and András Svraka. 2021. "Opting For A Preferential Tax Regime – The Role Of Accountants And Long Term Incentives On Small Businesses’ Behaviour." AEA RCT Registry. March 02. https://doi.org/10.1257/rct.7259-1.0.
The trial consisted of two subtrials with similar interventions but different experimental design. We sent informational letters on the benefits of SBT to the recipients official eGovernment inboxes. The first subtrial aimed at the accountants’ role on firms choices. Here, in one treatment arm firms received the informational letter, and in the other arm their registered accountant received the letter. The second subtrial aimed at the effects of short, or long term benefits on firms’ behaviour. In the first arm letters emphasized immediate gains (lower taxes and easier administration), in the second letters emphasized long term benefits (profits are not taxed until paid out as dividend, raising capital is not taxed).
Intervention Start Date
2020-12-04
Intervention End Date
2021-01-31
Primary Outcomes (end points)
Switching to the preferential SBT tax regime by early 2021 at the firm level
Primary Outcomes (explanation)
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
In the first subtrial we used a block-clustered design. As firms can share accountants, treatments had to be assigned based on accountant clusters, and therefore randomization was also done at accountant level. Blocks were generated based on cluster size (number of eligible firms in the target population of the current trial, who share the same accountant); the share of firms already in SBT and among an accountant’s clients in the current target population (SBT firms did not receive any treatment, they were only used in the construction of clusters); a measure of the intensity of communication to the accountant’s clients in previous SBT related informational campaigns; and a proxy measure of the size of the accountancy based on its VAT filing status.
The second subtrial was a block randomized design, with blocks based on the number of SBT related informational letters received in previous years and the amount of estimated immediate absolute tax gains when choosing SBT. The target population of the second subtrial does not have an overlap with the population of the first subtrial through common accountants.
Experimental Design Details
Randomization Method
Computer (randomizr package in R)
Randomization Unit
Clusters of firms who share an accountant (1st subtrial), firms (2nd subtrial)
Was the treatment clustered?
Yes
Sample size: planned number of clusters
10,478 in the first subtrial, not available in the second subtrial
Sample size: planned number of observations
52,631 firms (30,201 in the first, 22,430 in the second subtrial)
Sample size (or number of clusters) by treatment arms
First subtrial: in 5,237 clusters 15,217 firms were treated, and in 5,237 clusters accountants were treated.
Second subtrial: 11,215 firms in both treatment arms (short term, long term)
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)