- The study examines how SME participation and success rates in public procurement can be improved. It analyzes data on over 11,000 bids from different sized firms to model participation and probability of winning. - The results show that evaluating quality increases participation from micro, small, and large firms. However, it decreases the probability of small firms winning contracts compared to large firms. - Including many smaller part contracts increases participation from small and micro firms and their probability of winning contracts, but does not significantly affect medium sized firms.

1. SMEs participation and success
in Public Procurement
Johan Stake
Södertörn University
IPPC
2014/08/14

2. Summary
• SMEs stressed as important by the EU Commission and by
local governments – procurement area of improvement
• Model SME participation by using count data model,
estimating the number of bids by SMEs
• Model SME success in bidding by multinomial logit model
• Guidelines issued – do they have any effect?
– Including many part contracts increase participation by small and
micro firms
– Evaluating quality (+) all firms participation, (-) probability of
winning for small firms compared to large firms
– Value (+) participation for all except micro firms, (-) probability of
winning for micro and small firms
2

3. Background
• SMEs
• 0-9 employees – proprietorships and micro firms
• 10-49 employees – small firms
• 50-249 employees – medium-sized firms
• >249 employees – large firms
• Account for 99 percent of all firms in EU
– 52 % of total turnover
– Secured 33 % of total procurement value 2006-2008 (SBA 2011)
• European Commission adopted ”Small Business Act” to
recognize SME’s local key players and employers
• Public procurement one addressed area – intention of
increasing SME participation
3

4. Background
• All firms should compete on equal terms (Directive 2004)
• Several countries use set-asides and quotas: USA, Canada,
India, South Africa.
• Reasons for non-participation
– Too complicated - economies of scale in bidding
– Time-consuming – administrative capacity constraints
– Contracts too large
• EU Commission issued guidelines on measures to increase
SME participation (2008)
4

5. ”Best practices”
• EU Commission and SCA have published similar
documents of best practices
• Gathering market information pre advertisement
• Rapidly answering questions when procuring
• Advertising early on
• Avoiding too large and extensive contracts
– Low administrative costs vs more bidders?
– Divide procurements into smaller lots where possible
• Evaluating economically most preferential bid
– SME proposed sector of innovation and growth
5

6. Previous research
• Myerson (1981) and Lafonte and Tirole (1987) on optimal
auction design attracted research in public procurement
auctions
• Manelli and Vincent (1995) addresses the problem where
the quality is unknown ex ante
• Using mechanism-design, Morand (2003) concludes set-
asides are not optimal for preferential treatment
• Report by GHK (2010) found that higher value decreases
SME’s probability to win, and that evaluating quality
surprisingly decreases the probability of SME’s winning
• Krasnokutskaya & Seim (2011) on SME probability of
winning in highway auctions when preferential
treatment is used
6

7. The data
• 20 procurements (if applicable) collected from 40
weighted and randomly selected authorities, counties and
municipalities during 2007-2008
• 652 procurements, 11 236 bids
• 121 procurements use 1067 part contracts, total of 1610
contracts
• Many different goods and services, heterogenous dataset
• Mean value of contracts 21 million SEK
• Median value 1.5 million SEK
7

8. 8
58 % (937) contracts use
evaluation of quality
Value ranges from 30 000 SEK
to 4.35 billion SEK

9. Bidding statistics
TABLE 1. STATISTICS ON ENTERPRISE SIZE AND BIDS
Enterprise Employees
No. of
bids
Percent
of bids
Winning
bids
Percent of
winning bids
Winning
probability
Proprietorships 0-1 2 912 25.92 579 20.35 19.88
Micro 2-9 2 206 19.63 443 15.57 20.08
Small 10-49 2 342 20.84 787 27.65 33.60
Medium 50-249 1 310 11.66 462 16.23 35.27
Large >249 2 466 21.95 575 20.20 23.32
Total -- 11 236 100 2 846 100 --
9
1% of firms win 20% of procurements

10. Modelling participation
• Estimate number of SME bids (count data) using a
negative binomial model
• Possible endogeneity due to unobserved variables
influencing SMEs decision to submit bids
• Use coarsened exact matching to improve causal
inference
– Finds matches to improve analysis of treatment effect
• Estimation will focus on evaluation of quality
𝜆𝑖 = 𝑒 𝐵 𝑋
= (𝛽1 + 𝛽2 𝑷𝒂𝒓𝒕 + 𝛽3ln(𝑉𝑎𝑙𝑢𝑒) + 𝛽4 𝑄𝑢𝑎𝑙 + 𝛽5 𝑇ℎ𝑟𝑒𝑠 + 𝛽7 𝑀𝑊 + 𝛽8 𝑿 + 𝜀𝑖)
CPV codes are used as controls
10

11. Micro firms Small firms
Medium-
sized firms All firms Large firms
Part procurements 0.989*** 1.009*** 0.986** 0.998 0.990
(0.00345) (0.00250) (0.00657) (0.00260) (0.00684)
ln(Value in 100000 SEK) 0.953 0.963 1.051 1.029 1.178***
(mean=0)
(0.0407) (0.0388) (0.0468) (0.0260) (0.0499)
Threshold 0.862 1.089 1.212 1.019 1.201
(0.125) (0.152) (0.204) (0.0832) (0.220)
Evaluation of quality 1.285* 1.378** 1.078 1.290*** 1.578***
(0.173) (0.183) (0.175) (0.106) (0.239)
Multiple winners 1.788*** 1.810*** 2.041*** 1.921*** 2.083*
(0.315) (0.360) (0.499) (0.348) (0.873)
Constant 1.232e+12 0 0 2.129e+13 155,690
(2.348e+14) (0) (0) (2.323e+15) (3.348e+07)
Alpha 0.100*** 0 0 0.0503*** 0.0491
(0.0454) (0) (0) (0.0231) (0.148)
(Not concave)
Observations 816 816 816 816 816
CPV controls Y Y Y Y Y
Year control Y Y Y Y Y
chi2 - - - - -
p - - - - -
Coefficients in incidence rate ratios ; seEform in parentheses; *** p<0.01, ** p<0.05, * p<0.1 11
Results for
participation
Note:
coefficients are
in incidence-
rate ratios
(multiplicative)

12. Modelling probability to win
• Estimate probability of winning using MNL model
• Modelled as procurer choosing between different firms to
maximize utility
• Four different outcomes, choice of firm is:
• Micro
• Small
• Medium
• Large
• All firms have the same basic probability of winning (1/n)
• Same variables as participation estimation except
multiple winners
• Observation=contract, clustered on procurement
12

13. 13
Probability of
winning
Coefficients in
relative risk
ratios
Negative effects
Positive effects
Multinomial logit Micro Small Medium
(default)
Large
2-4 part procurements 1.171 1.499 0.490 -
(0.520) (0.737) (0.292)
5-10 part procurements 1.925* 1.632 1.315 -
(0.764) (0.856) (0.570)
>10 part procurements 2.964** 4.815** 0.943 -
(1.518) (2.997) (0.556)
ln(Value) 0.862* 0.865* 1.055 -
(0.0760) (0.0761) (0.101)
Threshold 0.517 0.625 0.584* -
(0.239) (0.241) (0.191)
Evaluation of quality 0.626 0.323*** 0.715 -
(0.181) (0.106) (0.279)
Bidratio micro firms 1 (0) - - -
Bidratio small firms - 1 (0) - -
Bidratio medium firms - - 1 (0) -
Observations 1,006 1,006 1,006 1,006
CPV controls X X X X
Log-Likelihood -843.8 -843.8 -843.8 -843.8

14. Summary
• Evaluating quality significantly increases participation for
micro, small and large firms
– More firms are willing to submit bids because they might know
that they are not the cheapest but have a chance due to good
quality
• Including relatively many part procurements increases
probability of micro and small firms to win contracts
– No significant effect on medium-sized firms
• A larger procurement value decreases micro and small
firms probability to submit a winning bid
• Evaluation of quality decreases small firms probability to
win
– Micro firms significant at 89% level
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