How to judge accelerator outcomes — a literature review

Feb 20 · 21 min read

As part of my PhD on startup accelerators, I reviewed the academic literature around the topic of accelerator outcomes. The first, obvious area was investment returns, but the literature clearly pointed to the importance of other outcomes too. This literature review looks at how accelerator outcomes can be measured, and to what effect. The academic literature presents an unclear picture of investment outcomes in accelerators, and suggests that other outcomes may also be important but equally hard to measure.


As exemplified in the research carried out by Christiansen (2009) the early, traditional, accelerators invested in the startups on their program. The investment is relevant in different ways for the different stakeholders associated with the accelerator. For startups, it provides funding for the founders to cover their living costs during the program, and therefore not be distracted by earning money elsewhere. For the accelerator it offers a way to realise a return on the money they invest in running the program, and for investors it allows them to become involved in the board of a startup very early on, with a view to making later rounds of investment.

The investment is referred to as a ‘stipend’ by Hochberg (2015), and while it is in return for equity, she argues it should be seen as part of a package of value, including mentoring, education, office space, and brand association.

Christiansen (2009) argued in favour of accelerators investing in the startups as it brings the accelerator’s incentives in line with those of the startups. Both parties want the startup to succeed, to raise more money, and to exit. This is similar to an angel investor’s incentives, but different to an incubator, which is incentivised to keep the startup as a tenant.

Bliemel et al. (2014) observed that an aspect of investing that differentiates accelerators from angel investors is that angels tend to make investments on a case-by-case basis, carrying out extensive due diligence and term sheet negotiations for each deal. Accelerators, however, focus on an average of 10 deals at any given time, standardising the term sheet, and funnelling the due diligence into an application process for the program. They suggest that this allows accelerators to make more investments more quickly.

In examining accelerator investments, Hallen (2016) found the average investment by accelerators into startups was $26,000 for an average of 6% equity in the startup, referencing, but acknowledging that the amounts had risen between the time of the study and its publication, supporting the observation by Bone et al. in 2017 that research had trouble keeping up with accelerators. Hathaway (2016) found that between 2005–2015, 172 US based accelerators invested a median of $100,000 in over 5000 US based startups. Bone et al. (2017) found that the investment in UK accelerators ranged from £10,000-£50,000.

Exploring further the attraction of this approach to investing, Shane (2015) pointed to the problem investors face when trying to diversify their investments, and to access deals at an earlier stage. In particular suggesting that conventional angel and VC investors are not suited to finding, processing, and investing in large numbers of startups, and that the cost of sourcing and carrying out due diligence is prohibitive. He observed that accelerators resolve this problem because they have the structure, process, and brand in place to find and sort larger numbers of startups, providing a benefit to other investors and to corporates.

Hochberg (2015) added that accelerators are a leaner type of organisation, and their fund structures are less complicated than a conventional VC investor, so they can make a larger number of smaller investments more quickly than a larger fund.

This translates into the larger investors both coming into the initial investment group formed by the accelerator and positioning themselves as follow-on investors into the startups graduating from the program (Shane 2015). The investors quite often either do not expect to see a return on their accelerator investment or are not making that investment just for the return (Hochberg 2015). The aim of investing in an accelerator is more usually to get an oversight of the startups and deal flow, and an opportunity to carry out slower and more in-depth due diligence in the startups by mentoring them (Shane 2015).

Fehder & Hochberg (2014) pointed out that investors can become involved not only by investing into the accelerator ‘fund’ itself, but also as mentors. Mentoring allows investors to get to know the startups, and the demo day brings investors to the location because of the efficiency of meeting the clustered startups all at once. They observed that investors also arranged meetings with other startups near the accelerator if they travelled in for a demo day or mentoring sessions, with the benefit of their involvement spreading more widely into the ecosystem.

Consequently, Hochberg (2015) found that the arrival of an accelerator in a given location tended to increase the proportion of local investment made into earlier stage companies, and therefore the investment aspect of accelerators has a positive impact on the wider ecosystem. By connecting angel investors around the accelerator, and promoting the idea of angel investing, the accelerator improves the culture of investing, across the ecosystem, so this benefit is not restricted to those startups in the program.

Does accelerator investing work?

In the typical investment model accelerator, or ‘deal-flow maker,’ Hochberg (2015) argued that investors either do not expect to see a return on their investment into the accelerator fund at all, or not for some time. He found that a typical early stage investment would take over 9 years to realise a return, and that accelerators which cannot invest in follow-on rounds become too diluted to make a viable return over the longer term.

In aiming to understand how returns on accelerator investments may work, Christiansen (2009) put forward a simple financial model to explore the viability of investing by accelerators. He based it on an investment of $20,000 for 5% equity spread over 20 companies, so a total investment of $400,000. He assumed one company exits with a $100m valuation, earning the accelerator $500,000, then 5 companies exit at $10m, 5 companies break even, and 10 companies fail and bring no return. Based on this calculation, the accelerator returns $1.4m on its investment of $400,000. At the time of writing, he was able to suggest that both Y Combinator and TechStars were already starting to prove this model.

Hathaway (2016) found that the companies he studied raised $19.5 billion between 2005–2015, representing an average of over $3.7m each, which he suggested points to the investor model of accelerator succeeding, and having strong prospects for the future. However, this author considers that averaging money raised across a number of startups produce misleading conclusions as to the benefit of accelerators to startups. As Christiansen’s figures suggest, in reality most startups fail, and only a few have large exits. Averaging money raised might give the impression that most startups raised a moderate amount of investment, which would be inaccurate if in fact very few had raised large amounts, and most had raised little or no money.

Lehman (2013) pointed to anecdotal evidence and data reported by accelerators and startups, which therefore may suffer from a bias of reporting, which suggests that 60–70% of startups in accelerators attracted follow-on funding. However, as accelerators are investors in the startups, they require big wins to offset the losses of the under-performing, or failing startups. He therefore concludes that an exit is the only real measure of success, being when the accelerator realises a return on its investment.

Supporting this view, Pauwels et al. (2015) concluded that deal-flow makers are only likely to succeed with a pure investment business model if they are located in very dense ecosystems like Silicon Valley. This concern was first predicted by Christiansen (2009) and transpired to be accurate.

In examining the questions around accelerator investing, Kim & Wagman (2014) argued that a potential obstacle in understanding whether the deal-flow maker model works is that traditional deal-flow maker accelerators only make a profit when their portfolio firms raise further investment. This may incentivise them to be selective in the information they disclose about their portfolio, with a bias towards positive information in order to help the company raise money and the accelerator to exit their investment.

Another concern with deal-flow makers and signalling was first raised by Christiansen (2009), who cautioned that when an accelerator is solely funded by a VC firm with considerable available funds, if the accelerator does not make a follow-on investment into a startup on their program it sends a danger signal to other investors. For this reason, he suggested startups avoid such programs. The same could apply to accelerators that have evolved into seed funds, and many of the European accelerators which received European Investment Fund grants for follow-on investing. These accelerators consequently have the resources to make follow-on investments and send danger signals if they do not (Hochberg 2015).

Another potential problem with deal-flow makers is that some startups see them primarily as investors, rather than support programs that also offer a stipend. Bliemel et al. (2014) cautioned that this can lead to them undervaluing the mentoring, networking, and co-location, and not becoming actively involved members of the program. It is also a problem because the value of the financial investment alone is not then perceived to justify the equity stake being asked by the program, which reflects the whole package of value. The author of this thesis has also observed that it is important for programs to demonstrate that equity is in return for funding, support, office space, and a combined package of benefits in order to avoid negotiating with startups who try to reduce the amount of equity they give up.

The investment model should be judged both by whether it produces a return for the investors, and whether it benefits the startups. Gonzalez-Uribe & Leatherbee (2017) found that the funding provided by accelerators is not on its own a factor in the future success of startups on accelerator programs, but that the “entrepreneurial capital,” and related features of the program combined with funding are key for a successful outcome. This supports the argument above that neither party should focus solely on the investment being provided.

Cohen (2013) suggested that more research is needed to determine whether accelerators work as an investment vehicle, and Bone et al. (2017) argued that it is not clear whether most venture backed accelerators will ever break even, and suggest that most will not. Furthermore, they suggest, with the growth in accelerators and increased competition for good quality startups, the venture-backed model will become more difficult as more accelerators chase a finite group of high quality startups that are capable of delivering a return on investment.

Brunet et al (2016) found that due to the difficulty for accelerators to succeed as a business from the return on investments made into the cohort alone, fewer programs are focussing on investment as their main form of income. They found that (in 2016) only 62% of accelerators were pursuing the investment business model, and this approach was declining. They identified just 52 reported exits in their research of startup investments, suggesting that this route is not bringing in enough revenue to support accelerators. As a consequence, they found that 35.8% of accelerators in Europe received some form of public funding. The main sources of revenue for accelerators were corporate sponsorship (32%), with exit of startup investments representing just 8%. However, the accelerators reviewed predicted the share of revenue from exits would rise to 32% in the future, presumably reflecting that they had not yet seen many exits but assumed more as the startups matured.

Golomb (2015) suggested that whilst the top tier accelerators can work, lesser accelerators would end up accepting the startups which leading accelerators reject. Consequently, they will struggle to profit if their business model is investment outcomes alone. The founders on these programs come away with excellent educational benefits and networks and will probably succeed in a future startup, but the accelerator would fail to benefit from this. He argued that the rapid growth in accelerators saw programs established by people who did not have enough experience as entrepreneurs setting up and scaling technology companies, or finding and funding the best startups as investors. He anticipated that the rapid explosion of accelerators, most of which he thought would fail, would be looked back upon as a learning phase out of which a new way to educate entrepreneurs would emerge.

Clearly investment was the main catalyst for the birth of startup accelerators (Christiansen 2009, Tarani 2010), but as they have evolved, and spread beyond the main startup clusters that have enough investors in the ecosystem to make that model work, investment has become less important.

Fehder & Hochberg (2014) concluded that accelerators have a role in encouraging the emergence of local angel and VC communities, which in turn helps support the development of greater entrepreneurial activity. Therefore, investment activity and outcomes should in fact be measured across the whole ecosystem, not just in relation to the startups on the program.

Startups still require funding, in the form of the ‘stipend’ as it was originally designed, and many stakeholders funding accelerators are looking for a return on their investment. However, as it is becoming clear that accelerators can do more than just create a return on investment, other outcomes are becoming key performance indicators, such as economic development and sourcing innovation. Equally, accelerators are looking to other forms of income that is more stable and predictable than investing, such as sponsorship, grants, and fees from corporates to run a program. It appears from the literature that under the right circumstances, investing into startups in an accelerator can still lead to returns, but this no longer applies to all accelerators. Therefore, in terms of defining an accelerator, investing should no longer be seen as a mandatory activity for a program to be considered an accelerator, and should not be the only, or the main measure by which a program is judged.

Evaluating other outcomes

This examination of investment outcomes, and the suggestion that other outcomes should also be valued suggests that evaluating whether accelerators work poses many challenges. It is difficult to make meaningful comparative studies between startups that did and did not attend programs because no two startups are the same. Various studies (e.g. Hochberg 2015), have looked at the success of accelerated startups in raising further investment compared to those which did so without the support of a program. However, the author of this thesis argues that ascribing to the accelerator whether a startup raises, for example $1m or does not, or does so more quickly, seems too specific a measure for something that is so complex and nuanced as a disruptive new business.

Bone et al. (2017) acknowledged that there is no agreed set of criteria for measuring the performance of accelerators, but posited that commonly used metrics include:

· The number of applicants to a program

· The number of startups supported by the program

· How much further investment the startups raised

· The survival rate of the startups

· The number of people employed by the startups

They observed that as not all accelerators have the same goals, it is hard to find a common set of metrics to measure success. They recognised the value of measuring impact on the founder not just the venture, and whether social impact should be measured as well as just financial outcomes.

Looking at the wider impact of accelerators, Fehder & Hochberg (2014) examined the effect of accelerators on their regional entrepreneurial ecosystems specifically relating to the availability of seed and early stage venture capital funding. They carried out a comparative analysis of US Metropolitan Statistical Areas (MSAs) that have an accelerator with similar areas that do not. They compared these MSAs from 2005–2012 and found that a new accelerator was associated with an 104% increase in the number of seed and early stage VC investments made that year, and a 97% increase in the number of investors active in the region. They proposed that this was not just due to direct investment into accelerator startups, but also to the way an accelerator attracts investors to engage with the wider ecosystem and promotes an investment culture.

The research by Pauwels et al. (2015) identified that accelerators in Europe specifically fall into different typologies, with different aims and objectives. They argued that accelerators should not be evaluated using the single criterion of investment raised, but instead new criteria needed to be developed that acknowledge the distinction between a deal-flow maker, ecosystem builder, and welfare stimulator. Only the deal-flow maker should be judged using the traditional criteria relating to investment outcomes. The other types of accelerator need a more refined set of KPIs, including impact on the ecosystem, on the corporate and government funders, and on the wider economy.

Obstacles to evaluation of accelerators

The literature has shown that it is not clear how to evaluate accelerators, and that simply judging them on investment returns risks overlooking other valuable outcomes or judging them on the wrong criteria. However, there are many obstacles to evaluating their outcomes, and that observation is woven into the discussion in this chapter. One reason is identified by Bone et al. (2017), who found that as many accelerators are startups themselves, they often do not have the time or resources to engage in complicated measuring and reporting, so there is a dearth of data on outcomes across the sector.

Kim & Wagman (2014) also found that the difficulty in evaluating accelerators is aggravated by the lack of available information. They looked at the incentives accelerators have to disclose only partial information about their portfolios, and to supress negative information. This partial disclosure may also tie in with incentives to exit portfolio investments early. They cautioned that accelerators can cause ‘valuation bubbles’ for startups, and other misleading data and signalling because of their natural internal bias.

By contrast to these arguments, Bliemel et al. (2014) proposed that it is in fact simple to measure the success of accelerators, because the only metric that matters is follow-on funding. Analysing smaller operational metrics is misguided because accelerators are not there to make marginal changes to startups. However, their conclusion predated the research by Pauwels et al. (2015) and with the benefit of their more detailed categorisation of accelerators should be seen only to apply to deal-flow makers that set out primarily to achieve investment outcomes.

However, this observation assumes that the only stakeholders interested in outcomes are the startups, investors, and the accelerator manager, being the three parties that would benefit from follow-on investments or exits. Bone et al. (2017) examined in more depth the motivations of people founding accelerators and suggested that the aims of the accelerator should define how outcomes should be measured. They suggest that, despite the proliferation of accelerators, by the time of their report, there was still relatively little evidence about what works, and in particular whether accelerators create success or select for success. The bias of evidence, as referenced above by Kim & Wagman (2014), makes for a very complicated set of criteria that are hard to measure given the lack of available data sets, and the potential biases in that data.

Another problem with the data and how it is analysed can be observed in the report by Fox (2014) for Telefonica, where data is broken into averages, yet startups by nature are rarely average. Startups are generally expected to grow exponentially or fail. She found, for example, that 17 programs had supported 1655 startups which went on to raise £112.83m. She then proposed that this worked out as an average of £68,176. Whilst in London 10 programs had supported 279 startups, which raised £46.3m, or an average of £165.949. It is not clear how useful such average numbers are when presumably they do not reflect the reality of the startups. In reality it is likely that a small number of the startups raised a large proportion of the money, whilst many raised small amounts, and most failed.

As Bone et al. (2017) observed in their research, to evaluate the effectiveness of accelerators would require a comparison of the same metrics across different programs, and with a control set of non-accelerated startups. Neither exist, because accelerators are not all the same, and therefore do not have easily comparable data points. Startups are also so different that trying to find a control group against which to compare accelerated startups would run into the same problem.

Not only are startups different to each other, but also the factors that decide their success are so various. Such a comparison would have to take into account the team’s education and past experience, their networks, the location of the startup, the sector, and much more. Ultimately, Bone et al. (2017) conceded that it is not possible to evaluate whether accelerators create success, or select for it. Equally, Bliemel et al. (2014) pointed out that measuring the survival of startups graduating from accelerators is a weak metric because it should also be deemed a successful outcome of an accelerator to help a startup fail quickly, freeing up the founder and team to pursue a better startup, and preventing investors from losing more money.

They further add that there is another problem when discussing average outcomes on startups from a wide range of accelerators. This is due to the possibility that any positive effect of the top accelerators on their startups is diluted by the negative effect of the larger number of average or bad accelerators on their cohorts, creating a flat average where one cancels out the other.

Bone et al. (2017) went on to observe that examining the state of the accelerated startups 2–3 years after they graduate is too soon to ascertain whether they have become a success, and to calculate the wider impact on the ecosystem around them. There is a dearth of longitudinal data due to the relative newness of the sector.

Frimodig & Torkkeli (2013) proposed that the approach of evaluating accelerators by follow-on funding raised by their startups does not accurately reflect ‘success’ because startups that raise money may fail, and startups that do not raise money may succeed. Therefore, this measure is possibly more of a marketing tool, to attract future startups into the program. They suggested that a true indicator for a for-profit accelerator should be its own Return on Investment (ROI) after 5–10 years. However, this would restrict any attempt to measure progress before then, and therefore limit how accelerators raise funding and attract startups in the short and medium term.

Bliemel et al. (2014) add to the doubts over using follow-on funding as a metric of success asking whether in the longer term is it better for a startup to become profitable and self-sustaining, or to keep raising multiple rounds of investment? It is not clear whether the current phase of startups, especially in Silicon Valley, that have raised very large rounds of investment in fact represent a bubble, so this question cannot yet be answered.

Concentrating the discussion into other forms of funding, such as government grants, and how this can be justified, Bone et al. (2017) argued that the difficulty in evaluating outcomes makes it hard to assess whether public funding of accelerators is an appropriate use of resources, and whether there is a long term economic impact from, for example, attracting foreign startups to the city or country. This point relating to the deployment of public money is also explored by Bliemel et al (2014) who concluded that the metric of job creation, common in public sector evaluation, is not a good way to measure the benefit of accelerators because startups typically operate on a lean budget and automate or outsource functions rather than employ people.

Non-investment outcomes

Observations by Fehder & Hochberg (2014) suggested that accelerators do bring a benefit to the wider ecosystem. They found that the increase in investment amounts, numbers of investors and investee companies in an ecosystem with an accelerator that they identified in their research, showed that the presence of an accelerator increases interest in the ecosystem from nearby investor groups, rather than from new outside investors entering the market. They proposed that the increased investments into startups from the wider ecosystem as well as into graduates of the accelerator, indicated that the presence of the accelerator positively affected investment across the region and not just within the accelerator. However, the benefit is specific to the sector that is the focus of the accelerator, rather than more widely across other sectors too.

The type of program being evaluated also impacts the level of benefit. Hathaway (2016) identified a clear distinction in outcomes between startups that enter the top programs, and startups that enter lesser programs. The top programs noticeably accelerated the startups’ ability to raise money, exit, and gain customers, whereas he found that lesser accelerators do not have any impact, or even impede the startups. He also found that startups that receive funding from accelerators are more likely to raise further funding, be acquired, or fail, sooner than startups that raised money from leading angel investor groups. He argued that this distinction is important, and the emphasis is on outcomes happening ‘sooner’ rather than what the outcome is, confirming that helping a bad startup fail quickly is also a positive outcome. The implication is that startups that are backed by angel investors lack the subjective feedback and advice of those in accelerators and may therefore be kept alive longer than is beneficial.

Hathaway (2016) concluded that the learning in accelerators is of genuine value to the startups, which suggests that the benefit to the startups on a program is not restricted to credential signalling to future investors, or selection bias. Echoing this finding, in examining how these other outcomes are supported, Frimodig & Torkkeli (2013) proposed three preconditions for a successful accelerator. First, access to deep and tacit knowledge sources. Second, the ability to transfer that knowledge effectively. For this to work, they argue that the accelerator has to know how to transfer the knowledge effectively, but also startups have to have the desire for knowledge, and the humility to accept advice from the mentors, acknowledging that they do not already have all the knowledge and experience they require.

Thirdly they suggested that the ownership of the accelerator is important in defining whether it can succeed. Their observation was that if the owner is subject to rules, restrictions, bureaucracy or internal organisational culture from a supporting corporate this could inhibit the owner’s ability to execute in the style and speed expected of an accelerator.

Beyond the impact of an accelerator on its startups, and the investors associated with it, an accelerator can also be judged by the impact it has on the wider ecosystem around it. Hochberg (2015) argued that accelerators may have a positive effect on outcomes for all startups in an ecosystem, including those not on the accelerator program.

Fehder and Hochberg (2014) found that an accelerator can lead to an increase in startups being developed in a region, and they can act as network aggregators across an ecosystem by organising open events like demo day s, networking events, and involving mentors. Bliemel et al. (2014) added that accelerators facilitate their startups to connect other stakeholders in the ecosystem, so overall having the effect of creating multiple new network connections across the ecosystem, and in particular cross-sector connections that may not otherwise traverse typical hierarchies and sectoral divisions.

However, Hochberg (2015) observed that it can be hard to know whether an accelerator attracted investment to a region, and then developed the entrepreneurial ecosystem, or whether the underlying policy preferences that led to the accelerator also led to the attraction of investment and improved ecosystem.

Given the suggestion that accelerators help develop the ecosystem, and in particular entrepreneurship, venture creation, and angel investment, the question of whether accelerators could be used as economic development tools in regions not already strong in tech entrepreneurship was explored by Miller & Bound (2011). They suggested that examples like The Difference Engine, in North East England, indicate that accelerators may be an efficient way to create new businesses and jobs, but in these lower density ecosystems would need to be supported with public money to make up for the lack of private sector funding in the earlier stage ecosystem.

They suggested that finding the right balance for public sector support is a challenge. If accelerators are fully paid for, or even run by public sector organisations, there is a risk that they become disconnected from the investment community and the most innovative ventures. Miller & Bound (2011) concluded that any public-sector investment should be matched with private sector money, whether that be investment or corporate sponsorship, in order to ensure strong ties to the private sector. Despite this potential for public sector involvement in accelerators, and their role in regional economic development, there is still very little data or research into this topic.


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Tobias Stone @ Newsquare

Written by is an innovation agency founded by Tobias Stone.

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