00:00 Start by introducing our uh presenters.
00:03 Uh,
00:03 first,
00:03 it will be Michelle Cheng.
00:06 She's a research economist in the Development Impact evaluation department,
00:10 DIME at here at the World Bank.
00:13 She earned her PhD in agricultural
00:16 and Research Economics from the University of California,
00:19 Berkeley
00:20 in 2024,
00:22 and her research focuses on exploring the intricate
00:25 relationship between policies and their impacts on firms
00:29 and the broader economy.
00:31 In particular,
00:31 she is interested in public procurement policy in developing countries.
00:36 Now this is the formal presentation.
00:38 For a more personal
00:39 note,
00:40 I would add that I worked with Michelle.
00:42 Uh,
00:43 she is really an awesome mind,
00:45 a brilliant mind,
00:47 and
00:48 I would add to all these skills the ability
00:50 to make a very complicated things look extremely easy.
00:55 Uh,
00:55 I was impressed since I had the honor to start working with her.
00:59 Hi,
01:00 everyone.
01:00 Uh,
01:00 thanks for,
01:01 uh,
01:02 thanks for uh Alex for this warm introduction of my paper.
01:06 Um,
01:06 yeah,
01:06 this is actually,
01:07 uh,
01:08 like,
01:08 uh,
01:08 from my PhD thesis,
01:10 um,
01:10 so I'm trying to study what's their,
01:13 like,
01:13 how we can use the granularly
01:16 big data that to detect corruption
01:18 and throw like a really data-driven perspective,
01:21 and I'm trying to
01:23 go through,
01:23 OK.
01:24 So we already like learned,
01:25 uh,
01:26 heard a lot of things like about how important And the public procurement is like,
01:30 what's the percentage of GDP and what's the percentage of government expenditure.
01:34 So,
01:35 um,
01:35 I don't need to like address that more
01:37 and particularly for the corruption and also mentioned
01:41 in some like keynote speakers like corruption in
01:43 public procurement is like more than half of
01:46 those corruption in the whole public sector.
01:49 So that's why I think like
01:51 trying to use the more and more arising data
01:54 sets to tangle this question is really important.
01:58 OK.
01:59 And um
02:00 so I think there's several um consensus like among all over
02:04 the world that what's the best practices we should adopt,
02:07 so one of them is like we need open auction.
02:10 That's the open bidding,
02:11 we encourage the competition and encourage the participation from
02:15 all over the country or like a region.
02:18 However,
02:20 Actually,
02:20 what I'm showing you is here,
02:22 open auction is not uh a key like that to address all the corruption,
02:27 and actually in anyway,
02:29 it's like a lot of firms can play around with these tools.
02:32 And one of the open auctions format,
02:35 now today I'm discussing is the open scoring auction.
02:38 And it's pretty general and pretty commonly used all over the world.
02:42 And what's the open scoring auction?
02:44 Um,
02:45 we learned some
02:46 first auction,
02:47 first prize auction,
02:49 2nd prize auction,
02:50 or any kind of price auction in textbook,
02:53 and that auction only bidding on the price.
02:56 However,
02:56 as a government,
02:57 we also care about quality,
02:59 right?
02:59 We,
03:00 we don't want just like,
03:01 OK.
03:01 They use the
03:02 uh either chases uh product but like with really bad quality.
03:08 So that's why the open scoring auction is kind of popular is it can give
03:12 uh government a formula that we can like summarize,
03:15 we can sum up the
03:17 quality score and the price score together,
03:20 that's all the contracts going to the firms winning the highest score.
03:25 And for sure,
03:26 there's a lot like description,
03:27 you can think,
03:28 there's like different ways you put on different things.
03:31 For example,
03:32 like here,
03:32 I show you the equations used in Chinese context,
03:36 we use uh two ways,
03:37 large weights,
03:38 one is on the price,
03:39 one is on the quality,
03:41 and the weight in the quality,
03:42 you definitely have a lot of other requirements,
03:45 you can break down to really detailed requirements there.
03:50 Here is a case study,
03:52 and this is a public procurement project happening in 2013 in countywide.
03:57 And that's our agriculture department trying to,
04:00 this is under a large
04:02 agriculture promotion activity to develop the rural area
04:06 and to distribute the fertilizer.
04:08 And what we can see in this call for tinder file,
04:12 we can see first,
04:13 they listed the budget,
04:14 so what's the maximum price they use for this product.
04:18 Second,
04:18 they list some like basic qualifications
04:21 and stating here,
04:22 we can.
04:23 See something weird,
04:24 right?
04:25 First,
04:25 they ask for registration capital should be like larger than 5 million RMB.
04:30 Back to 2013,
04:32 that's not a normal,
04:33 uh,
04:34 like size for firms,
04:35 for,
04:36 especially for like standardized uh fertilizer firms.
04:39 Second,
04:40 they ask for the product with some really
04:42 weird criteria bacteria embedded in the fertilizers,
04:46 and you can ask for like fertilizer.
04:48 uh
04:49 can be like more uh like highly qualified,
04:52 but not like some like special
04:54 bacteria like within us.
04:57 And then like we go into the scoring rule,
04:59 then there's a more interesting thing we can find
05:02 for the business and the technical rules,
05:04 they ask for,
05:05 you need to provide proof of the previous contracts that
05:10 those contracts also have thresholds
05:12 that at least 3 minutes.
05:14 RMB
05:15 for those contracts and also within really uh short time lengths,
05:19 like 22 years pass.
05:21 Second,
05:21 like to ask for a plan for the after service,
05:23 after sale services.
05:25 And I didn't show more details here.
05:27 In the plan for the after-sale services,
05:30 they ask for those firms can respond to their request within
05:34 30 minutes,
05:35 which means you need to relocated close to the uh local agency.
05:40 And uh in addition,
05:42 um,
05:42 there's a regulation in Chinese public procurement system is
05:46 they require at least 3 bidders in open auctions that you make the all,
05:51 all the auctions valid.
05:53 That's not uh like uh uncommon thing for a lot of contacts,
05:57 uh,
05:57 especially for like,
05:58 uh,
05:59 Canada,
05:59 India,
06:00 they all have this kind of like a requirement,
06:02 like minimum number of bidders to make this like
06:05 open auctions really valid for next step contracting stuff.
06:10 OK.
06:11 Then,
06:11 here's the results of this,
06:13 uh,
06:13 uh,
06:13 like auction.
06:14 And first thing,
06:15 we can see,
06:16 OK,
06:16 that's exactly 3 bidders,
06:18 uh,
06:18 good,
06:18 like we qualify for the like the next step contracting stuff.
06:23 Second,
06:24 we will see that if we look into the final score,
06:27 uh,
06:28 that's the
06:28 last second column,
06:30 we can see,
06:31 OK,
06:31 firm A,
06:32 this winner
06:33 got really high scores,
06:34 that's almost
06:36 100 out of 100.
06:38 And then like,
06:39 let's look into the B and C.
06:42 They are super close and super bad,
06:45 which means like,
06:46 why they even participate.
06:48 And then like we looked into the price score,
06:51 Amazingly,
06:52 a lot of those
06:53 price,
06:53 a lot of those like final score discrepancy
06:57 come from
06:58 not the price part,
06:59 but from the first part,
07:01 the business technical part we call like in total,
07:03 like quality part.
07:05 And then like,
07:07 this is
07:08 actually busted.
07:09 This is a real corruption case
07:11 that was documented in the judge documentation.
07:15 And firm A confessed in the documentation that,
07:18 like,
07:19 they got connection.
07:20 They know there's a large agriculture promotion program and
07:23 there are a lot of demand for the fertilizers.
07:26 So firm A got connection with the government,
07:29 uh,
07:29 with the,
07:30 uh,
07:30 director of the agriculture department.
07:32 And then like,
07:33 the director say like,
07:35 I can help you win,
07:36 and but I need like 5% kickback of the total,
07:39 uh,
07:39 like budget.
07:40 And firm B and C,
07:42 they are just like players,
07:44 and because like when you kind of like design all the rules as like call for tenders,
07:49 I just show you,
07:51 if you are like a pure or like innocent firm,
07:54 when you saw those documents,
07:55 what's in your mind is,
07:56 OK,
07:57 that's not my
07:58 game.
07:59 I just don't want to waste my time,
08:01 waste my money to participate in the game.
08:03 So
08:04 then like they still need to satisfy the three.
08:06 Bidders requirements.
08:07 What the firm A do is like they just invite firm B and C.
08:11 They use their name,
08:12 and also like prepare all the documentation for
08:15 firm B and C and like submit something
08:18 just like qualify for the 3 million bidders requirements.
08:22 And finally,
08:22 uh,
08:23 the,
08:23 the director was sent to jail for 7 years.
08:26 But
08:27 unfortunately,
08:28 like,
08:28 fortunately,
08:29 they find the case,
08:30 but unfortunately,
08:31 they,
08:32 when they found the case,
08:33 like the case already passed,
08:34 uh,
08:35 it's like
08:36 5 years later on,
08:37 so
08:38 It's only because
08:39 there's another anti-corruption investigation,
08:41 they can like check back and find this case.
08:44 So
08:45 it's,
08:45 there's a really no like real-time checking
08:48 or auditing things that can find those things
08:51 in the later,
08:52 in the earlier stage of the procurement.
08:54 OK.
08:55 Um,
08:56 so my question for the paper is too.
08:58 First,
08:59 how prevalent is corruption in open scoring auction within the Chinese context?
09:05 Second,
09:05 what kind of policy can help us to reduce the corruption
09:08 and also to reduce those distortions?
09:14 And before I move to,
09:15 uh,
09:15 like deeper into the paper,
09:17 I want to show you two stylized facts.
09:20 First,
09:20 uh,
09:20 I want to show you
09:21 what the distribution looks like when we look into like the winning margin.
09:26 How I define the winning margin is kind of follow the auctions of paper.
09:31 They,
09:31 when they measure the winning margin is
09:33 my score,
09:34 I'm the competitor,
09:35 my score minus the maximum score among other people.
09:40 So,
09:40 if I am the winner,
09:42 so I am the highest score,
09:43 and I'm minus the 2nd-highest score,
09:46 so that's the gap.
09:47 If I'm the loser,
09:48 so my score minus the winner's score.
09:50 So it's like how much,
09:52 what's the distance I compare to the winner.
09:55 So if I'm the loser,
09:56 I've got like a negative uh scores.
09:58 OK.
10:00 And if we look into the previous case,
10:02 and uh like to put them into the scale,
10:05 we will see,
10:05 OK,
10:05 firm A located
10:07 uh in the positive side,
10:09 firm BC and located in the negative side,
10:12 and B and C really close to each other.
10:14 And there's no data points between them.
10:18 And then we pull all those things together.
10:22 What looks like
10:24 in that by naturally,
10:25 we want to see something really smooth,
10:27 like the pic picture I show you,
10:29 we want some smooth,
10:30 there's no any like manipulations or like kind of like smooth distribution.
10:35 However,
10:36 when we look into 3 bidder cases,
10:39 then
10:40 things cause weird.
10:41 It's like there's
10:42 a big missing mass around the,
10:44 the 0,
10:45 which means
10:46 a lot like bidding
10:48 process or the outcomes.
10:49 It's like,
10:50 I'm the winner.
10:51 I just like to run further
10:53 really quick,
10:54 and all of the other people are just like thousands decent like meters away from me.
11:00 So that's why we saw this kind of a lot,
11:02 a lot missing mass around the zero.
11:05 But for sure,
11:05 I would not see like for 4 bidder on 4 or more bidder cases,
11:09 there's no corruption.
11:10 There's still corruption.
11:11 I can like later on show you.
11:13 It's just,
11:13 it's not kind of systematic things as compared to 3 bidder cases.
11:20 And then,
11:21 like,
11:21 let's look into the distribution of number of bidders,
11:24 and I compare the US data with Chinese data.
11:28 So,
11:28 one thing is like,
11:29 there's no restriction on number of bidders for US.
11:32 So we'll see like,
11:33 OK,
11:33 there's kind of like distribution
11:35 uh smoothly,
11:36 and also there's a lot of cases.
11:38 really competitive.
11:39 There's more than 7 bidder cases.
11:41 However,
11:42 for Chinese cases,
11:43 wow,
11:44 that's all around 3.
11:46 And like,
11:47 uh,
11:47 it's that's like a magic number that makes the distribution look like this.
11:51 And really less competition in the like uh 7 or more cases.
11:57 OK.
11:58 So,
11:58 uh,
11:58 then,
11:59 the
11:59 main context of paper has two parts.
12:01 First part is the Marri part.
12:03 Second,
12:03 I'm talk about what's the economic implications and the policies.
12:08 For the measurements part,
12:10 I collect a really rich and novel data set,
12:13 um,
12:13 from one province of China.
12:15 So covers,
12:16 uh,
12:16 11 years and
12:17 including all the speeding details about like what's their
12:21 uh final scores,
12:22 was their quality score and price score,
12:24 and then I link those procurements auction data
12:28 to firm registration data.
12:30 That's.
12:31 That way it can help us to know what's the identity of the firm,
12:35 what's their basic information,
12:37 and then I link those data to a firm uh tax survey,
12:41 so then that we can help us to better understand what's the productivity level,
12:45 what's the employee size of the firm.
12:48 So I kind of like a link all those data together
12:50 that you have a bigger picture about what's happening here.
12:54 Then I design a model base,
12:57 so it's come from like scoring auction theory,
13:00 that's,
13:00 but like,
13:00 there's not a lot like empirical strategies can really leverage those theories.
13:05 So,
13:06 I can try to link the theory with data together,
13:09 to distinguish or like to screen all this,
13:12 those,
13:13 those behaviors I just showed you.
13:16 OK.
13:16 For the statistic tests,
13:18 um,
13:18 I don't want to go to details about like this kind
13:21 of like auction theory and how game players play around,
13:25 but I just want to show you the basic,
13:27 uh,
13:27 equilibrium,
13:28 like in the auction,
13:29 the scoring auction model is,
13:31 when there's a winner,
13:33 They have the score.
13:35 They actually,
13:36 what their bidding strategy is looking all the competitors behind,
13:39 or like the expectation at least.
13:42 So what they need to try to do is not like,
13:44 I try my best on the 100%
13:47 because that way you just like put too much effort and
13:51 like your
13:52 uh profit is like so low.
13:54 And what you need to do is like look back to those competitors.
13:57 I only need to beat
13:59 the second best,
14:00 then I can win.
14:01 So,
14:02 it's always like,
14:04 I'm the winner,
14:05 and try to look the second best,
14:06 then
14:07 I'm done.
14:07 I'm just cut it and done.
14:09 But if you put too much effort,
14:11 you got like so,
14:13 so large score gaps,
14:14 which means
14:15 you just like put
14:16 waste a lot of effort,
14:17 money or time that make you
14:20 even win,
14:21 but like it's less uh like a profitable,
14:24 OK.
14:25 So,
14:25 uh,
14:26 what I did here is like,
14:28 uh,
14:28 pull all those data together,
14:30 and based on this,
14:31 um,
14:32 prediction of the model,
14:34 what I,
14:34 if for all the competitive scenario,
14:36 we should like see one distribution of those gaps,
14:40 I mean,
14:40 the gap between like the,
14:42 the,
14:43 um,
14:43 first,
14:43 uh,
14:44 the winner score and what's his
14:46 projection of the second best,
14:48 the highest score.
14:49 When we see that if there's all the compact cases,
14:52 we should see a,
14:53 a uniform distribution.
14:55 Uh,
14:55 I mean,
14:55 the one distribution.
14:57 However,
14:57 when you put all the data together,
14:59 we'll see,
14:59 OK,
14:59 clearly there are two distributions.
15:01 And this kind of separation can help us to estimate,
15:05 OK,
15:05 what's the percentage of the
15:07 auctions
15:08 has this kind of a huge gap that's non-reasonable behaviors
15:12 and And that is one
15:14 tool I use.
15:15 The second tool is more go into the auction level details,
15:18 like,
15:19 see,
15:19 OK,
15:20 given firm A,
15:21 we see like the scores are 98.
15:24 Then his projection is,
15:26 I'm competing with really strong competitors that pushed me to work really hard.
15:30 And then use his score as a benchmark to look back to the B and C.
15:35 We'll see,
15:35 OK,
15:36 B and C,
15:37 there is just really low,
15:39 and compared to
15:41 what projection on the A.
15:43 The distribution is to look like,
15:44 should look like what I just applied the distribution.
15:47 However,
15:48 B and C all form the really lower tile
15:51 and also clustered together,
15:53 then that's kind of like a reject the,
15:55 their main uh direct test.
15:57 So that's like a rejection of this non-hypothesis.
16:00 I label as there's like potential corruption here.
16:03 OK.
16:04 So I compare,
16:05 put them two,
16:07 those two text results together.
16:09 They kind of like mimic to each other
16:11 and all of those,
16:13 it just like gave us a huge number of corruption proportion
16:17 and 90
16:18 65% of the auctions
16:21 kind of like
16:23 feel the uh reject the
16:25 test and show some evidence of the corruption.
16:28 OK.
16:29 And also,
16:30 that number even like,
16:31 also really high for 4 bidder cases.
16:34 And I do some like,
16:36 uh uh textes about why that is possible,
16:38 but we didn't show this kind of gap in the 4 bidder cases.
16:42 And actually,
16:42 you separate those uh passing the test and not passing test.
16:46 For the non-passing set,
16:47 you still see this kind of like really huge missing gaps there.
16:51 OK.
16:54 And then
16:55 I,
16:56 you are like wondering,
16:57 OK,
16:57 well,
16:58 like all the task tests like huge gaps because corruption,
17:02 maybe not because they are all some other complicated things will happen,
17:06 right,
17:07 because some people maybe study the collusion,
17:09 maybe it's just.
17:10 Collusion like firm like play with them and not about the government side
17:14 or people will think,
17:15 OK,
17:16 there's not really competitive firms in the local market.
17:19 That's
17:20 so that's results in this kind of like
17:23 really bad like heroes.
17:25 But
17:26 to like further exclude those concerns,
17:29 what I did is I did all this study.
17:31 And
17:32 what I did is to hire 5
17:34 public procurement uh scoring experts.
17:36 So why I did that?
17:38 One thing is when I talked to the people,
17:40 it's like
17:41 all,
17:41 a lot of firms,
17:42 when they saw those call for tender files,
17:44 uh,
17:44 they actually have like since like,
17:46 OK,
17:46 there's a signal they sent to us,
17:48 the contract was already locked.
17:51 And then that tells us like really a lot like.
17:53 Like
17:54 local uh industry
17:56 policy,
17:57 or like a lot of experts,
17:58 they actually know what happens.
17:59 So what I did is I randomly sample 500 call for Tinder files from all those files,
18:06 and
18:07 without letting them know what's the outcome,
18:08 and let them just read it all the
18:11 rules and highlight anything they think that's suspicious,
18:15 or they think there's like non-necessary rules,
18:19 and they will put explanation on the uh uh margins.
18:23 And then,
18:24 here's the like example I show you,
18:26 that's the expert audio survey.
18:28 And this is one particular um example.
18:32 This is try to procure the circulated television
18:36 system for one city.
18:38 And it's a huge contrast,
18:40 and
18:41 in the business score we can say the first category
18:43 they ask for software development and level 2 and involved,
18:48 and then like they have some like standards they ask for,
18:51 and experts see like
18:52 those rules are not.
18:54 There's industry requirements
18:57 and they think it's kind of like specific for suppliers
19:00 and the second they ask for a lot of certificates
19:03 and they're not,
19:04 not really spacious about like the certificate but expert put they say like
19:09 usually there are 3 come together.
19:15 The expert,
19:15 yeah,
19:16 that's a good question.
19:16 I didn't talk more about like the what's the scoring evaluation process,
19:21 because
19:22 um
19:23 without this kind of like uh scoring process,
19:26 previously,
19:26 they just like,
19:27 OK,
19:28 I
19:28 can find 5 experts to stay there,
19:31 give score for each person.
19:33 And now they have like a random selection of this uh like expert.
19:37 They just like those experts are like in
19:39 every industry or sectors that have their own
19:42 uh like uh expertise,
19:44 like engineer or are they kind of like
19:46 um manufacturer,
19:48 uh,
19:48 like their expertise,
19:49 those persons will be randomly selected to stay there to
19:52 give the score for each proposal.
19:54 So I am like invite those experts to do it.
19:58 OK.
19:59 Um,
20:00 then the second group of requirements,
20:02 it's just like a purchase like the circulated television,
20:05 but they ask for,
20:06 OK,
20:07 the manager should have at least a master's degree
20:10 and ask for some like information system,
20:13 project manager,
20:15 a certificate,
20:16 any like a lot of a certificate that's
20:20 actually not necessary at all,
20:22 and like that's like expert on market here.
20:25 And interestingly,
20:26 when they finished all this,
20:27 all this study,
20:28 I asked the expert,
20:29 like the expert told me,
20:31 he exactly know
20:32 what's the supplier of this project is
20:35 and told me the name,
20:36 and then I checked back with the contract is exactly the that firm.
20:39 So,
20:41 And then like I pull all those 500 copies together
20:45 and do some uh kind of like 2x2 table
20:48 on the diagonal you can see that's like
20:50 kind of consistent of my model prediction and the
20:54 uh
20:54 expert there judge
20:56 and on the other side there's some like uh mismatch there,
21:00 but
21:00 we kind of see like expert evaluation is ground truth,
21:03 right.
21:04 Right,
21:04 because they also have their own bias in sometimes.
21:07 So,
21:07 but this is basically want to provide some evidence to see like at least most of
21:12 what the model captures
21:14 is really like due to the corruption or like this kind of customized scoring rule.
21:20 OK
21:21 Then,
21:22 uh,
21:22 I will move to the,
21:23 um,
21:24 economic implications of policies.
21:27 And,
21:27 um,
21:27 for sure,
21:27 people want to say,
21:28 OK,
21:29 what those corrupt firms look like.
21:31 And I didn't put the results here,
21:33 but I just like to do the lasso,
21:35 just put all those firm characteristics to say,
21:37 OK,
21:37 what can explain like their corrupt label.
21:41 And what I find is,
21:42 most of them are local,
21:43 local to the buyer agency.
21:45 Second is they have like
21:48 state-owned connected in some layers of their ownership.
21:52 Third is they are like less productive.
21:56 Then I asked,
21:57 uh,
21:57 ask,
21:58 can we just really,
22:00 uh,
22:00 reduce the corruption through any policies we can like to try to reduce this.
22:05 And I,
22:05 using the anti-corruption campaign,
22:07 that's a,
22:07 a huge campaign in China starts from 2012.
22:11 It targets a lot like different
22:13 Officials in different departments and different levels,
22:15 just like look back into their cases and to find who is corrupt or not.
22:20 And what I find is
22:21 exactly after the corruption or like the person was under investigation,
22:25 we saw more bidders or more firms come into competition
22:29 and
22:31 And also we saw this like reduce of the probability of the corruption,
22:35 but it's only like kind of delayed because we have like
22:38 a long time like a planning of the procurement process.
22:40 It's not like direct effect you will
22:43 expect after the person was under investigation.
22:46 And then like we break down to the investigation to a higher level
22:50 officials and the lower level officials.
22:52 What we find is
22:53 most of the results come from higher-level officials.
22:57 If you adjust like a front desk,
22:59 uh,
22:59 police to do something,
23:01 not to have the powerful
23:03 influence on who you want to allocate the contract,
23:06 then you have less influence on this kind of allocation process.
23:12 But,
23:12 uh,
23:12 one thing I need to like,
23:13 uh,
23:14 the point out is
23:15 a lot,
23:16 all of those effects are pretty much like really short term.
23:20 And if we talk to people,
23:21 it's like if there's no systematic change in the system,
23:25 then
23:26 um people just like let people out and new people came,
23:28 and all new connection just like formed uh within the new people.
23:33 So there's not really
23:35 deeply changes of those behaviors.
23:38 And
23:39 then like,
23:39 what kind of policy can we really think to tangle this?
23:43 One experiment,
23:44 uh,
23:44 not experiment,
23:45 the counterfactual structural estimation I did is,
23:47 oh,
23:47 how about just like we
23:49 invite those experts to do another round of evaluation.
23:53 If they think those
23:55 rules are not necessary,
23:55 we just take them out.
23:57 And I did this counterfactual analysis just based on those 500 audit study I did.
24:02 So what I did is,
24:03 OK,
24:04 let's remove all those rules,
24:05 rescale their.
24:07 Quality scores
24:08 and then like say,
24:09 OK,
24:09 there's no unfair rules.
24:12 Let them compete together.
24:13 What's the new results.
24:15 In addition,
24:16 I add more entry because we see that if there's less corrupt
24:20 um requirements,
24:21 then there are more entries.
24:23 So I combine those two together
24:25 and what I show you is
24:26 if we just remove those non-necessary rules,
24:30 the price can decrease by 12%
24:33 and the quality can increase.
24:35 And we introduce more entry,
24:37 even though it's like uh the price maybe like in decrease more,
24:42 but uh the overall like
24:44 all the major contribution comes from the
24:46 competitive like without like really unfair requirements.
24:51 OK.
24:52 Uh,
24:52 so I'm trying to wrap up.
24:53 Um,
24:54 the takeaway of this paper,
24:56 first,
24:56 the merriment part.
24:57 I think also like mentioned by a lot of people,
25:00 we now have a lot of like really rich data sets,
25:03 and that's really provide our opportunities to
25:06 think about what kind of indicators,
25:08 what about like a methodology,
25:11 we can capture
25:12 those kind of misbehaviorals from the data set.
25:15 Second,
25:15 I want the,
25:16 uh,
25:17 a lot of those,
25:18 um,
25:18 methodology can be generally applied to other contexts if using the same
25:23 uh auction format or,
25:25 uh,
25:25 there's uh any like similar um context there.
25:30 In terms of economic implications and the policies,
25:34 what we really want is
25:35 not just like anti-corruption,
25:37 that's like,
25:38 OK,
25:38 we
25:39 Investigates person,
25:41 but more about thinking about what's what's wrong,
25:44 what procedure we need to target,
25:46 and what's their logic and the incentive to do that.
25:49 So
25:50 as long as we can figure out which procedure
25:53 we can like really put effort on,
25:55 then like we can like change it so we can target more accurately
25:58 to change those regulation
26:00 and change the people's behaviors,
26:02 um.
26:03 For sure,
26:03 I think there's more and more research can be done
26:05 in this area,
26:06 especially has like large language model and AI powerful things
26:10 we can
26:11 use,
26:12 and I think public procurement is really perfect setting
26:15 for those tools because we have a lot of documents
26:18 that one people or like data,
26:20 the,
26:21 the traditional data structure cannot capture those things,
26:24 but AI can help us do a lot of things to do like.
26:26 Like tax analysis
26:28 or and even like we can help us to identify which rules that are
26:32 more like necessary as core for this competition and the words we cannot put them
26:37 so it can help those audits we who do not have background
26:41 knowledge to do better regulation and also put firms to do a real
26:46 uh competition induction.
26:47 Yeah,
26:48 thank you all.
26:48 That's my presentation.
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