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Other than the backlog variables, it is found that larger road projects would attract fewer bidders. It is because firms that can undertake large-scale projects are limited due to their inevitable resource boundaries; the coefficients of _LENG_ , _LANE_ and _COST_ are broadly negative and significant for the bidder’s entry decisions _d_ 1 and _d_ 2 _b_ . It is also because auctioneers have a propensity to put more emphasis on bidders’ technical capabilities and past experiences when contracting out a large project. Thus, the probability that an auctioneer disqualifies an applicant would increase with the size of contract holding everything else constant. In the model for _d_ 2 _a_ , again, _LENG_ , _LANE_ and _COST_ have negative coefficients.

<u>Table 2. Binary Response Model for Bidders’ Participation</u>

|Dependent variable|_d_1|_d_1|_d_2_a_|_d_2_a_|_d_2_b_|_d_2_b_|
|---|---|---|---|---|---|---|
|ln_LENG_|–0.046|–0.371 <sup>**</sup>|–0.254 <sup>**</sup>|–0.560 <sup>**</sup>|–0.856 <sup>***</sup>|–0.713 <sup>***</sup>|
||(0.094)|(0.152)|(0.109)|(0.287)|(0.321)|(0.274)|
|ln_LANE_|–1.019 <sup>**</sup>|–0.194|–0.498|–1.613 <sup>**</sup>|–1.627 <sup>*</sup>|–1.910 <sup>**</sup>|
||(0.402)|(0.538)|(0.450)|(0.790)|(0.903)|(0.850)|
|ln_COST_|–0.068|–0.323 <sup>**</sup>|–0.192 <sup>*</sup>|–0.187|–0.115|–0.329|
||(0.105)|(0.133)|(0.119)|(0.149)|(0.185)|(0.302)|
|ln_MONTH_|–0.308|1.317 <sup>***</sup>|0.163|0.850|–1.448|3.220|
||(0.299)|(0.456)|(0.358)|(0.763)|(1.118)|(3.684)|
|ln_BKLG_|||0.024 <sup>***</sup>|0.019 <sup>**</sup>|0.005|0.013|
||||(0.007)|(0.008)|(0.012)|(0.012)|
|ln_CAID_|–0.093|0.059|–0.104|0.005|–0.781 <sup>**</sup>|–3.144 <sup>*</sup>|
||(0.081)|(0.138)|(0.104)|(0.506)|(0.317)|(1.673)|
|Constant|3.113 <sup>***</sup>|–0.291|2.225 <sup>**</sup>|3.119 <sup>*</sup>|15.508 <sup>***</sup>|13.678 <sup>***</sup>|
||(0.844)|(1.279)|(1.025)|(1.846)|(4.676)|(4.300)|
|Obs.|252|252|184|184|117|117|
|Wald-chi2|68.23 <sup>***</sup>|145.38 <sup>***</sup>|27.48 <sup>***</sup>|57.98 <sup>***</sup>|21.46 <sup>***</sup>|42.03|
|No. of country dummies|(0)|(3)|(0)|(4)|(0)|(2)|
|No. of bidder nationality dummies|(0)|(0)|(0)|(7)|(0)|(5)|
|Chi2 test statistics:|||||||
|H0: Coef. of country and bidder<br>nationalitydummies_=_0||61.89 <sup>***</sup>||43.24 <sup>***</sup>||8.38|

Note: The dependent variable is the bidder's entry selection at each stage. The robust standard errors are shown in parentheses. *, ** and *** indicate the 10%, 5% and 1% significance levels, respectively.

Source: Author’s calculation.

Turning back to the formal selection model we analyze, two main decision nodes are focused on: _d_ 1 and _d_ 2. _d_ 2 _a_ and _d_ 2 _b_ can be considered to have been merged. Notably, however, 2 is _d_ supposed to represent mainly the former decision by auctioneers ( _d_ 2 _a_ ), because the number of bidders that were qualified but did not participate in the price competition is much small in our data. It is also noteworthy that when estimating a censored bivariate probit model for _d_ 1 and _d_ 2 as the first stage regression, the two error terms can be correlated with one another unlike the above sequential response model. Table 3 shows the results, which are broadly