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Firm Participation and Competition in
Public Procurement Jani Saastamoinen*
6th International Public Procurement Conference, 14th – 16th August 2014, Dublin, Ireland
*University of Eastern Finland
Outline
1) Background
2) Research Problem
3) Data and Methods
4) Results
5) Conclusion
27.8.2014 Saastamoinen 2
27.8.2014 Saastamoinen 3
1) BACKGROUND
– Contracting authority (CA) mandated to exploit competitive environment in the markets when carrying out public procurement
• Efficient allocation of resources
• Attracting bidders crucial to obtain the efficient outcome
– Winner’s Curse
• Aggressive bidding leads to negative or lower than expected profit for the winning bidder
• Less likely in competitive markets (Wilson 1977; Thaler 1992)
• Contract options may influence the level of competition in a tender request
– Open vs. restricted tendering
– Competitive dialogue
– Electronic auctions
– Dynamic procurement
– Aggregated vs. lots
– Contract size
– Framework agreements
– Pure price competition vs. Economically most advategeous tender (EMAT)
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• Usually public prorcurement is carried out by first-price sealed bid common value reverse auctions
• In theory, the winning bid corresponds to the true value of the auctioned object (Wilson 1977)
• Competition intensifies with the number of bidders (Milgrom and Weber 1982)
• Biased estimation may lead to the Winner’s Curse (Milgrom 1989)
• Potential consequences for procurer: higher costs, lower quality, order delays, legal expenses, externalities of business failure (Bilginsoy 2000)
• Attempts to avoid the Winner’s curse may lead to cautious bidding resulting in a higher costs (Hong and Shum 2002)
• Empirically studied with bid dispersion which measures the difference between the winning bid and the second highest bid
• Substantial bid dispersion may be an indicator of the Winner’s Curse (Dyer and Kagel 1996)
• Bid dispersion decreases with the number of bidders (Dougherty and Lorenz 1976; Saidi and Marsden 1993; Dyer and Kagel 1996; Bilginsoy 2000)
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2) MOTIVATION AND RESEARCH PROBLEM
•Reseach is scarce on the impact of contracting options on the number of bidders and competition
•Provides a wider perspective to competition in public procurement
•Research questions:
– How different contracting options affect the number of bidders?
– How the number of bidders impacts competition?
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3) DATA AND METHODS
•Regional CA from Eastern Finland
– Annual purchasing volume € 150 million
– e-procurement only
•363 procurement contracts (including lots) awarded in 2011
•Contract values usually above the national or the EU threshold
•Statistical analysis carried out with OLS, logit and tobit regressions
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4) RESULTS
Descriptive statistics (categorical variables)
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Variable Frequency Relative Frequency (%)
Contract value above the EU threshold (eu_d) 126
[156]
47.4
[43.0]
Contract value above the national threshold (nat_d) 102
[153]
38.3
[42.1]
Lots contract (lots_d) 200
[278]
75.2
[76.6]
Service contract (serv_d) 106
[127]
39.8
[35.0]
Supplies contract (sup_d) 158
[231]
59.4
[63.6]
Restrictive tendering (res_d) 40
[53]
15.0
[14.6]
Framework agreement (frame_d) 172
[221]
64.7
[60.9]
Single bid tender (onebid_d) -
[96]
-
[26.4]
Contract awarded to multiple bidders (multi_d) 144
[183]
54.1
[50.4]
Notes: Figures in parentheses include the tender calls where only a single bid was submitted.
Descriptive statistics (continuous variables)
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Mean Median Standard
Deviation
Minimum Maximum Number of
Observations
n 4.30
[3.42]
3
[2]
4.13
[3.82]
2
[1]
42
[42]
266
[363]
N 21.24
[18.61]
12
[12]
30.59
[26.92]
2
[1]
175
[175]
266
[363]
B2 – B1 7170.67 332.73 35208.60 0 457450 266
ln(B2 – B1 + 1) 5.65 5.81 2.98 0 13.03 266
(B2 – B1)/B1 .43 .13 2.10 0 32.9 266
ln(B2) – ln(B1) .23 .13 .34 0 3.52 266
CV .24 .19 .23 0 1.61 266
Sd. 9697.77 611.59 40604.73 0 517791.2 266
B 68353.04
[52561.33]
5225.41
[3546.87]
421979.60
[351557.1]
.93
[.71]
6320431
[6062820]
266
[363]
p 80.47
[81.76]
90
[90]
22.98
[23.06]
40
[40]
100
[100]
225
[284]
Notes: The figure in parentheses includes also the tenders where only a single bid was received.
n: Number of bidders.
N: Number of interested bidders.
B2 – B1: The difference between the two lowest bids.
ln(B2 – B1+1): A logarithmic transformation of the difference between the two lowest bids.
(B2 – B1)/B1: A relative difference of the difference between the two lowest bids.
ln(B2)-ln(B1): A logarithmic transformation of the relative difference between the two lowest bids.
B : The average bid size (Euro).
p: The proportion of price in percentage points [0, 100] in the applied EMAT criterion.
Single bidder tenders (logit regression on a dummy dependent variable)
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Estimated
Coefficient
Marginal Effect
constant -.457
(.653)
-
eu_d .108
(.477)
.026
(.116)
nat_d -.391
(.425)
-.090
(.092)
sup_d 1.058***
(.302)
.228***
(.055)
frame_d -.055
(.310)
-.014
(.078)
lots_d .822***
(.301)
.164’**
(.047)
multi_d -1.400***
(.405)
-.357***
(.099)
p -.004
(.005)
-.001
(.001)
inv_N 2.447***
(.885)
.604***
(.225)
)ln(B -.109***
(.036)
-.027***
(.009)
Wald Chi2 43.96*** -
Pseudo-R2 .134 -
Number of Obs. 279 279
Notes: * p-value < 0.1; ** p-value < 0.05; *** p-value < 0.01.
Standard errors in parentheses. Robust standard errors used.
Tenders with several bidders (dependent variable = inv(n))
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Variable Estimate
(OLS)
Estimate
(Tobit)
constant .535***
(.127)
.599***
(.159)
lots_d .158***
(.045)
.194***
(.058)
sup_d .226***
(.040)
.251***
(.049)
frame_d -.033
(.057)
-.040
(.073)
eu_d .053
(.100)
.056
(.126)
multi_d -.278***
(.063)
-.325***
(.083)
res_d -.025
(.088)
-.071
(.112)
nat_d -.043
(.095)
-.066
(.126)
p -.001
(.001)
-.001
(.001)
inv_N .733***
(.148)
1.011***
(.254)
)ln(B -.027***
(.007)
-.031***
(.009)
F-statistic 21.20*** 12.76***
R2 .263 -
Pseudo-R2 - .231
Observations 279 279
Censored
observations
- 57
Average VIF 3.68 -
Notes: * p-value < 0.1; ** p-value < 0.05; *** p-value < 0.01.
Standard errors in parentheses, robust standard errors used.
Competition in tenders (dependent variable relative bid dispersion)
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A1 A2 A3
(OLS)
A4
(Tobit)
A5
(OLS)
A6
(Tobit)
constant .117**
(.052)
.100*
(.053)
.139
(.150)
.135
(.151)
.105
(.189)
.089
(.189)
lots_d .026
(.062)
.022
(.063)
.077
(.076)
.070
(.076)
sup_d -.033
(.052)
-.033
(.052)
-.008
(.066)
-.012
(.067)
frame_d -.099
(.079)
-.090
(.080)
-.130
(.089)
-.120
(.090)
eu_d .013
(.117)
.008
(.117)
.105
(.142)
.097
(.143)
multi_d .118
(.088)
.110
(.088)
.081
(.105)
.078
(.105)
res_d -.042
(.106)
-.046
(.107)
-.030
(.127)
-.039
(.127)
nat_d -.024
(.107)
-.018
(.107)
.053
(.131)
.057
(.131)
p - - -.001
(.001)
-.001
(.001)
inv(n) .348**
(.147)
.380**
(.150)
.414**
(.171)
.443**
(.172)
.423**
(.191)
.453**
(.192)
)ln(B -.004
(.009)
-.005
(.009)
-.003
(.189) -.003
(.010)
F-statistic 5.63** - 1.31 - 1.22 -
LR Chi2 - 6.38** - 11.94 - 11.87
R2 .021 - .045 - .055 -
Pseudo-R2 - .032 - .059 - .067
Observations 266 266 263 263 222 222
Censored
observations
8 - 8 - 8
Average VIF 1.00 - 3.43 - 3.66 -
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Competition in tenders (dependent variable absolute bid dispersion)
B1 B2 B3
(OLS)
B4
(Tobit)
B5
(OLS)
B6
(Tobit)
constant 5.733***
(.475)
5.643***
(.500)
-1.906
(.648)
-1.916
(.646)
-3.291
(1.074)
-3.334
(1.088)
lots_d .212
(.335)
.201
(.336)
.490
(.348)
.473
(.346)
sup_d -.148
(.292)
-.147
(.293)
-.069
(.334)
-.075
(.333)
frame_d -.186
(.399)
-.170
(.398)
-.471
(.466)
-.452
(.465)
eu_d .272
(.561)
.263
(.557)
.770
(.780)
.754
(.785)
multi_d -.185
(.352)
-.200
(.349)
-.114
(.390)
-.119
(.385)
res_d -.410
(.485)
-.419
(.477)
-.408
(.546)
-.427
(.541)
nat_d .417
(.502)
.430
(.496)
1.037
(650)
1.045
(.646)
p - - .009
(.009)
.009
(.009)
inv(n) -.242
(1.322)
-.076
(1.364) 2.545***
(.886)
2.606***
(.892)
2.143**
(1.051)
2.210**
(1.060)
)ln(B
.822***
(.034)
.820***
(.035)
.847***
(.040)
.845***
(.040)
F-statistic .03 .00 110.94*** 111.98*** 91.68*** 92.94***
LR Chi2 - - - - - -
R2 .000 - .675 - .667 -
Pseudo-R2 - .00 - .212 .204
Observations 266 266 263 263 222 222
Censored
observations
- 8 - 8 - 8
Average VIF 1.00 - 3.43 - 3.66 -
Graphical interpretation (relative bid dispersion)
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Bid dispersion Number of bidders
Contract size
Lots
Supplies
Multiple suppliers
Interested bidders
+
+
+
- - -
Summary of results
•Single bidder tenders
– A single bid is more likely in supplies and lots contracts
– When a contract is awarded to multiple suppliers and there is more general interest toward a request for tenders, receiving only one bid is less likely
•Tenders with several bidders
– Supplies and lots contracts attract less bidders
– Contracts awarded to multiple suppliers, larger contracts by value, and a general interest toward a request for tenders attact more bidders
•Competition
– Bid dispersion decreases when the number of bidders increases
• Intensifies competition
• Winner’s Curse less likely
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5) CONCLUSION AND IMPLICATIONS
•This study showed that the choice of contracting options affects the number of bidders a request for tenders attracts
• In turn, the number of bidders is positively connected with the level of competition and potential Winner’s Curse in public procurement
• Interest toward a request for tenders translates into a higher number of submitted bids
– CAs should advertise upcoming invitations to tender could increase the number of received bids
•CAs should consider using multi-supplier contracts to instigate competition
•CAs should carefully consider when a contract should be divided in lots and an aggregated contract should be preferred in light of economic benefits arising from competition
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Thank you for your attention!
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