The effectiveness of public support for high-potential businesses – The Innovation Vouchers Programme

Last registered on June 11, 2025

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Pre-Trial

Trial Information

General Information

Title

The effectiveness of public support for high-potential businesses – The Innovation Vouchers Programme

RCT ID

AEARCTR-0016166

Initial registration date

June 05, 2025

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

June 11, 2025, 7:00 AM EDT

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

Locations

Country

United Kingdom of Great Britain and Northern Ireland

Region

Primary Investigator

Name

Albert Bravo-Biosca

Affiliation

Innovation Growth Lab

Contact Primary Investigator

Other Primary Investigator(s)

PI Name

Maria Brackin

PI Affiliation

Innovation Growth Lab

Contact Investigator

PI Name

James Phipps

PI Affiliation

Innovation Growth Lab

Contact Investigator

PI Name

Rob Fuller

PI Affiliation

Innovation Growth Lab

Contact Investigator

PI Name

Marco Kleine

PI Affiliation

University of Groningen

Contact Investigator

PI Name

Laura Rosendahl Huber

PI Affiliation

Erasmus University Rotterdam

Contact Investigator

Additional Trial Information

Status

On going

Start date

2015-04-01

End date

2025-10-30

Keywords

Finance & Microfinance, Firms & Productivity

Additional Keywords

Industrial policy, SMEs, Collaboration, Subsidy

JEL code(s)

H43, O25, O31, O36

Secondary IDs

Prior work

This trial is based on or builds upon one or more prior RCTs.

Abstract

We study the long-term effects of subsidized R&D collaboration on business performance of small and medium-sized enterprises (SMEs). In particular, we analyze the effects of a nationwide Innovation Vouchers Programme in the United Kingdom (UK) that sought to encourage SMEs to work with external knowledge providers, with the goal to increase knowledge and capacity within those businesses and in turn to increase innovation. By means of a randomized controlled trial (RCT), the programme provided SMEs with vouchers of up to £5,000 towards the cost of working with an external knowledge provider. The primary goal of this project is to assess whether there is an impact of the Innovation Vouchers Programme on business performance several years after the award of the innovation vouchers. The key business performance measures (e.g. turnover, employment and productivity) of the SMEs participating in the programme will be retrieved from the Longitudinal Business Database of the UK’s Office for National Statistics. As a secondary goal, we advance the understanding of the reliability of quasi-experimental methods vs. RCTs by comparing estimates of the two methods.

External Link(s)

Registration Citation

Citation

Brackin, Maria et al. 2025. "The effectiveness of public support for high-potential businesses – The Innovation Vouchers Programme." AEA RCT Registry. June 11. https://doi.org/10.1257/rct.16166-1.0

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Experimental Details

Interventions

Intervention(s)

The Innovation Vouchers Programme was an RCT run in 2015 (across 3 waves). The purpose of the programme was to encourage SMEs to work with external knowledge providers, with the goal that this would lead to increased knowledge and capacity within those businesses and in turn to increased innovation. The programme provided innovation vouchers to companies randomly selected into the treatment group.

Intervention (Hidden)

Intervention Start Date

2015-04-01

Intervention End Date

2016-04-30

Primary Outcomes

Primary Outcomes (end points)

A previous analysis of the Innovation Vouchers Programme (https://www.socialscienceregistry.org/trials/1556) analysed short-term outcomes, primarily from surveys. In this analysis, we consider long-term outcomes of programme participation, using data from the UK Office for National Statistics’ Longitudinal Business Database (LBD).

The primary outcomes for our analysis are computed as follows, using data from the ONS Longitudinal Business Database (LBD):

Cumulative turnover, the sum of annual turnover between the programme year and the programme year + 4, in £0,000. Turnover for years in which a business is marked as “inactive” will be counted as zero. Each annual turnover value will be adjusted for inflation by using the GDP deflator value in that year.
Cumulative employment (“job years”), defined as the sum of people employed by the company between the programme year and the programme year + 4. Employment for years in which a business is marked as “inactive” will be counted as zero.
A proxy measure of productivity, defined as turnover per employee in the programme year + 4. If employment is reported as zero or is missing in a particular year, we will consider productivity that year to be zero.

Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)

The secondary outcomes for our analysis are computed as follows, using data from the LBD, Intellectual Property Office (IPO) and Innovate UK:

Turnover in the programme year + 4, in £0,000.
Employment in the programme year + 4.
Survival, defined as whether the business is marked as “active” in the LBD in the programme year + 4, as a binary measure (0 or 1).
Number of patents, defined as the sum of patents issued to the business between the programme year + 1 and 2018.
Awards of R&D funding between the programme year + 1 and the programme year + 4, defined as the sum of grants received from Innovate UK, in £0,000.

Secondary Outcomes (explanation)

Experimental Design

Participants in the Innovation Vouchers Programme were randomly assigned to the control or treatment group. Participants in the control group were not allocated to receive an innovation voucher (up to £5,000), while participants in the treatment group were allocated to receive a voucher.

Participants were subject to eligibility checks, which reduced the number of participants from 2,149 to 1,463. Out of 1,463 participating SMEs, 356 were in the control group and 1,107 in the treatment group. Random assignment to the treatment group was done based on available budget, which explains why the size of the treatment group exceeds the size of the control group. 66.5% of the firms in the treatment group actually redeemed the voucher.

Experimental Design Details

Randomization Method

Randomly allocated by innovation vouchers portal/Innovate UK

Randomization Unit

Firm

Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters

Out of 1,463 participating SMEs, we expect to be able to find matches with the important databases (most notably the ONS Longitudinal Business Database) for about 1,100 firms.

Sample size: planned number of observations

1,463 SMEs participated in the programme. We anticipate a final total sample size of approximately 1,100 companies.

Sample size (or number of clusters) by treatment arms

About 850 firms in treatment group, about 250 firms in control group

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

Our power calculations are based on an initial ingest of data for the IVP control group only. All calculations assume an alpha level of 5% and 80% power. For cumulative turnover, based on a mean of 4201.5 thousand pounds, a standard deviation of 13172.2 thousand pounds, and an R-squared of 97.4% for covariates, our minimum detectable effect size is 354.8 thousand pounds, or a difference of 8.4% between the treatment and control groups. For cumulative employment (“job years”), based on a mean of 37.0 job years, a standard deviation of 78.5 job years, and an R-squared of 84.8% for covariates, our minimum detectable effect size is 5.1 job years, or a difference of 13.8% between the treatment and control groups. For productivity, based on a mean of 52.1 thousand pounds per employee, a standard deviation of 77.2 thousand pounds per employee, and an R-squared of 49.7% for covariates, our minimum detectable effect size is 9.15 thousand pounds per employee, or a difference of 17.6% between the treatment and control groups.

Supporting Documents and Materials

IRB