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Chapter One outlined the market failure arguments, as an explanation of why policy is concerned with providing assistance to small firms. A large number of schemes exist to support small firms to overcome these perceived market failures in the economy. Policy-makers’ intentions for specific interventions are often not well defined, leaving it uncertain as to what specific criteria support policy should be assessed against (as discussed in Chapter Two). Generally small business policy considers the array of options governments have at their disposal to support small firm development (Smallbone and Welter, 2001). It is a logical assumption that one of the intended outcomes of policy is to enhance the survival prospects of assisted firms. Unless firms survive, they would be unable to provide the job and other economic effects hoped for in the policy design (as concluded in Section 4.2 – keeping in mind that support could possibly also extent inefficient market contenders’ lifes). In fact, Coad et al. (2013) make the argument that a firm’s resource base influences the likelihood for its survival,

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improving the odds of a (random) growth event. So if a support intervention adds to the firms’ resource base, this would not translate into growth, which is perceived as a random event, but instead would only increase the odds for growth given that longer survival51_.

Some literature considering business support and its impact on the survival of firms exists, usually as an addition to their actual defined evaluation objectives. Wren and Storey (2002) found in their assessment of marketing consultancy to SMEs that advice had a considerable impact on firm performance. Overall, support was not found to be of significance on firm survival. However, when mediating by firm size, survival effects depending on firm size (and stage of the economic cycle) were identified. Support benefited mid-range SMEs, defined by Wren and Storey as having 6-80 employees and £300k-£2m sales, with four percent higher survival rates in the longer run, and for the survivors up to ten percent higher growth rates. Supported firms were low-growth but high-survival, without taking the selection bias into account the survival and growth impact would be about half. This was considered as “strong support” for the then recent changes to the UK’s business support framework delivered through Business Link, with a stronger focus on those mid-range small businesses.

Fuentes and Dresdner (2013) considered the impact of support on micro-enterprise survival in Chile as a developing nation. There were two assessed dimensions to the support – firstly the amount of funds granted to the micro-enterprise; and secondly the characteristics of the so- called sponsors52 _{providing soft support to complement the financial assistance. The study was}

based on a relatively small sample of 76 firms covering nine years, and the authors conclude their paper with the call for “a more complete database”, to be “continuously pursued”53_{. It was}

found that the amount of funds granted was linked to longer firm survival, reducing their hazard rate minimally but significantly. Different sponsors also impacted the survival chances across

51 _{Coad et al. (2013) are not without criticism, Chapter 5 looks at that specific debate in more detail.} 52 _{The concept of ‘sponsors’ is interesting in that it represents a non-financial support product.}

Generally, the limited evidence around the impact of support on firm survival focusses on financial support (such as grants).

53 _{This links back to Chapter Two, which discusses how data availability presents one of the major}

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the firms. One obvious concern, raised by the authors, is the uncertainty around potential selection issues here. Have firms really survived due to higher grants and a more effective sponsor, or were those firms with a high probability to succeed singled out for higher grant awards and the “more effective” sponsors? A literature review by Schwartz (2009, p. 406) would suggest that substantial selection bias is highly likely in the context of such programme. It was not controlled for in Fuentes and Dresdner (2013).

Drawing on a far larger sample, an earlier study of the effects of government grants on survival was undertaken by Girma et al. (2007), studying the case of Ireland. Whilst also investigating financial support, their study is of interest due to the use of official micro-data. Their final sample of 3,095 firms was the result of linking national Irish databases, comprised of firm-level performance data and covariates with a universe of assisted firms, and therefore achieving a far greater sample size than the Chilean case reviewed above. Propensity score matching was undertaken to address selection issues (which were found to be clearly present). Kaplan-Meier survival functions suggest a higher likelihood of survival for those firms in receipt of grants compared to those that were not. After ten years, the probability of survival for firms in receipt of a grant was 85 percent, compared to 70 percent for those firms in the non-assisted control group. A subsequent Cox proportional hazards model confirms a significant difference between the two groups, with the grants linked to firms with a higher chance of survival. The results also underline the important role of size for firm survival, significant at one percent level across all presented models54_{, whilst none of the other controls such as foreign ownership}

were found to be of significance.

Financial support will quite naturally help a small business more immediately than any non- financial support such as advice, by instantly impacting a firm’s cash flow. Overall, the small amount of evidence that does exist for the impact of public support on firm survival would point

54 _{An interesting additional observation is the clearly limited evidence that exists with regards to the}

effect of business support on survival. Only Wren and Storey (2002, as presented above) and Jarmin (1999) are highlighted as previous survival studies by Girma et al. (2007). As survival is a long-term evaluation matter, this again points towards the lack of evidence accounting for the longer term.

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towards some effect on survival rates for those firms in receipt of assistance. No survival studies other than Wren and Storey (2002) and Jarmin (1999) focussing on pure non-financial assistance through means of business advice appear to exist, however. Given the more immediate nature of the impact of financial support, care should be taken to avoid generalising the results and considering the impact of financial and non-financial support to be comparable. There is a clear need to add to the rather scarce evidence on the impact of non-financial business support on firm survival.

Given the scarce evidence in existence suggests a positive impact of support in survival rates, this chapter’s analysis, therefore, seeks to test the following hypotheses:

Hypothesis H1 – Business support impacts firm survival rates.

Assistance is split into Intensive and Other Assistance. A reasonable assumption would be for Intensive Assistance to have a greater impact on survival rates than Other Assistance:

Hypothesis H2 – Business support impact on firm survival rates will differ by intensity of

support.

Survival is impacted by a variety of factors, most notably age. It appears unlikely that a soft support intervention would make a lasting measurable impact on survival working against those broader trends.

Hypothesis H3 – Assisted firms will face the same long-term survival prospects as non-assisted

firms.

The hypotheses will be tested in the following analysis.

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