00:01 Um,
00:02 welcome,
00:03 everyone.
00:03 Thanks for coming on,
00:04 uh,
00:04 on a Friday afternoon to,
00:06 uh,
00:07 uh,
00:07 to hear a very interesting paper,
00:08 Elite Capture and Foreign Aid by our colleague Bob Rikers,
00:12 who is a,
00:13 uh,
00:13 senior economist in the Development Research Group in DEC in the World Bank,
00:17 co-authored with Jurgen Andersson and Niels Johansson.
00:20 Um,
00:21 you know,
00:21 this is a paper that's been in the news a lot lately,
00:24 and we all know that it's perilous to get information from headlines,
00:27 and so we thought that it would be very useful to have
00:30 a seminar in which,
00:31 uh,
00:32 Bob could actually present the entire paper
00:34 as opposed to sort of the,
00:36 the,
00:36 the headlines that have been making the rounds in,
00:38 uh,
00:39 in,
00:39 in Twitter and in the news and so on.
00:41 I,
00:42 I wanted to say,
00:42 uh,
00:43 just one other quick thing about the paper.
00:45 So,
00:45 The papers,
00:46 uh,
00:46 in the World Bank's working paper series and it has,
00:49 uh,
00:49 garnered 21,000,
00:51 20,
00:52 you're right,
00:53 21,000 downloads,
00:54 um,
00:54 since last Tuesday when it was posted.
00:57 This makes it the,
00:58 uh,
00:58 fourth most downloaded World Bank working paper,
01:01 um,
01:02 uh,
01:03 sort of in terms of lifetime downloads since 2014 when we started tracking it.
01:07 Now you might
01:08 wonder what the most downloaded World Bank working paper is.
01:11 And for those of you who doubt that the World
01:13 Bank has a well-developed sense of both humor and irony,
01:16 the most downloaded paper is a paper by
01:18 our colleagues Dorta Dumelund and James Trevino,
01:20 which is called Which World Bank Reports Are Most Widely Read.
01:25 Uh,
01:26 I,
01:26 I'm not making this up.
01:27 That is actually,
01:29 so,
01:30 anyhow,
01:30 so this all fits very nicely together and it's a,
01:32 it's a great pleasure to have,
01:33 um,
01:33 Bob here to present,
01:35 uh,
01:35 um,
01:36 uh,
01:36 to present about the paper.
01:37 Um,
01:38 because it's a large room,
01:39 we won't do our regular sort of seminar format of questions along the way,
01:43 but rather we'll hear first from Bob,
01:45 then we'll hear from our discussion,
01:46 uh,
01:47 Justin Sandifer.
01:48 Justin is the co-director of Education Policy and
01:50 senior fellow at Center for Global Development,
01:53 which is,
01:53 despite being a broad title,
01:55 extremely narrow compared to what Justin's.
01:57 Interests are and what his influence is in the developmental policy discussions.
02:01 He's worked extensively also on aid effectiveness,
02:04 which makes him a particularly apt,
02:06 uh,
02:06 discussant for this talk,
02:07 and he's really been,
02:08 uh,
02:09 a valued colleague in the Greater Washington area who I
02:11 always enjoy talking with and having him criticize my research.
02:14 So,
02:15 hopefully you'll do Bob the same courtesy.
02:17 And,
02:18 uh,
02:18 so Bob will talk for about 40 minutes,
02:20 maybe just in 10,
02:21 and then we'll open up for,
02:22 uh,
02:23 uh,
02:23 for Q&A.
02:24 So thank you again and over to you,
02:25 Bob.
02:34 Oh,
02:34 OK.
02:38 Good afternoon.
02:39 Thanks very,
02:40 very much for coming and,
02:41 uh,
02:41 for having me.
02:42 I'm here to present my paper,
02:43 Elite Capture
02:44 of Foreign Aid Evidence from Offshore Bank Accounts,
02:48 which is joint work with Niels Johannesen and Jurgen Juel Andersen.
02:51 But I would also like to use the opportunity
02:53 to thank uh our research assistant Nicholas Gomez-Para.
02:56 I'm not sure whether,
02:57 where he is
02:58 there,
02:59 like,
03:00 he's excellent and he's really made like
03:02 big contributions.
03:04 I also want to use the opportunity to thank many of you uh for providing comments.
03:08 If you feel that we haven't addressed your comment,
03:10 it's not because we're not taking it seriously,
03:12 it's probably because somebody else made a comment
03:14 suggesting to do the exact opposite.
03:17 So,
03:18 uh,
03:18 with that,
03:19 um,
03:19 I would like to start.
03:20 So,
03:20 a question that concerns
03:23 Certainly everybody in this room,
03:24 but I would argue the entire development community
03:27 is does aid reach its intended beneficiaries?
03:32 And the concern is that it may not,
03:33 that some part of it
03:35 may be captured by
03:36 those who need it least,
03:38 the elites.
03:40 And those concerns are certainly consistent
03:42 with economic theories of rent seeking.
03:45 And empirical evidence suggesting
03:49 That aid
03:49 doesn't necessarily flow to countries that are less corrupt.
03:53 And this
03:54 in some sense is pretty
03:56 intuitive
03:57 because if countries were amazingly well governed,
04:00 perhaps they wouldn't need so much aid
04:02 in the first place.
04:04 But of course,
04:06 this phenomenon is very difficult
04:08 to measure.
04:10 And this is
04:11 typical in the literature
04:12 on corruption.
04:14 One often has to rely on very indirect evidence,
04:16 and this paper is
04:18 no exception.
04:20 So the basic question.
04:22 We ask in this paper.
04:25 To address this concern
04:26 is
04:27 do World Bank aid disbursements
04:29 trigger increases
04:31 in foreign deposits in bank accounts,
04:33 he
04:34 in havens?
04:37 The
04:38 So what are havens?
04:40 Havens are
04:40 offshore banking centers
04:42 known for secrecy
04:44 and asset protection.
04:46 Such as
04:46 Switzerland,
04:48 Luxembourg,
04:49 the Cayman Islands.
04:52 And our main sample is going to comprise
04:54 22 countries.
04:56 That are really reliant
04:58 on foreign aid,
04:59 but World Bank aid
05:01 alone.
05:02 Accounts for more than 2% of GDP.
05:05 Each year
05:07 over our sample period,
05:08 which is from 1990
05:10 until 2010,
05:11 and our overall
05:13 aid.
05:14 is on average 10% of GDP.
05:19 But for context,
05:20 I want to emphasize from the beginning
05:23 that
05:23 these countries actually only absorb approximately 10% of all World Bank lending.
05:30 And an extensions
05:32 of the paper,
05:34 I'm going to show you sort of what has happened
05:36 in the period since 2009
05:39 when
05:40 there's been a significant crackdown
05:42 on,
05:43 on tax havens.
05:44 And what our results are for less aid dependent countries,
05:47 and I'm going to show you that in those countries we do not see this pattern.
05:51 Um,
05:52 that we see
05:53 in the most independent countries.
05:56 So the premise of the paper
05:58 is that haven deposits belong to elites.
06:02 How do we know this?
06:03 Well,
06:03 evidence from Scandinavia suggests
06:05 that
06:06 the top 0.01% richest households.
06:11 Own 50%
06:13 of the wealth
06:14 held in havens
06:15 and the richest 0.1%.
06:19 Own
06:19 80%
06:21 of that wealth.
06:22 And so there are similar studies in Colombia that sort of confirm.
06:26 That this offshore world is extremely concentrated
06:29 at the top of the
06:30 distribution.
06:32 By contrast,
06:34 The poor are typically
06:36 unbanked.
06:37 So thanks to the work of Ashley
06:39 and our colleagues,
06:40 uh,
06:41 in the finance group and DC.
06:44 We know that
06:45 in our sample of 22 aid dependent countries
06:47 in 2011,
06:49 Among the bottom 40% poorest households,
06:53 only 9%
06:55 had a domestic bank account,
06:56 so it's entirely implausible,
06:58 I would argue
06:59 that
07:00 the poor
07:01 are controlling
07:02 the money flowing to havens.
07:04 So that's the premise.
07:07 So what's our main finding?
07:08 Our main finding
07:10 is that
07:10 World Bank aid disbursements.
07:13 Do these highly aid dependent countries
07:16 coincide
07:18 with significant increases.
07:20 And haven deposits
07:21 within the same quarter.
07:26 And
07:27 How,
07:27 how big is this increase?
07:29 Well,
07:31 We define something we call the leakage rate,
07:33 which is the aid induced increase in haven deposit.
07:37 And that
07:38 Equals
07:39 roughly 75%
07:41 of aid inflows
07:42 for the average country.
07:45 But
07:46 because most of the aid actually flows to,
07:48 to countries
07:49 where
07:50 the stock of having deposits to GDP is relatively low.
07:53 As a share of all aid
07:55 going.
07:57 To these 22 countries,
07:58 this number is roughly 5%
08:01 and intriguingly,
08:04 This leakage rate tends to increase
08:07 with the ratio of aid to GDP
08:10 as I will show you later.
08:13 By contrast,
08:14 We do not find any evidence
08:16 that a disbursements trigger flows to non-havens.
08:20 Like
08:21 New York.
08:22 Or London.
08:24 Or
08:25 Gothenburg.
08:27 And
08:28 there's also no
08:30 evidence that this correlation
08:32 we document between
08:34 aid disbursements and the accumulation of haven deposits
08:37 has changed
08:38 over the last decade.
08:43 So I hope that by the end of the talk,
08:44 I'll,
08:45 I'll,
08:46 I've managed to convince you that sort of this,
08:47 this correlation exists and is very real.
08:50 What I think is,
08:51 is much harder to figure out
08:53 is what is driving
08:54 this correlation that we document.
08:58 And that's because we simply don't know
08:59 who owns these deposits because we have macrodata
09:02 and because we cannot track individual financial flows,
09:05 it's very difficult to attribute
09:07 agency.
09:10 Nonetheless,
09:11 I'm going to argue that elite capture
09:13 is an explanation that's certainly consistent
09:17 with the totality of the patterns
09:19 we observe,
09:20 but
09:21 Other explanations are possible
09:24 with our data,
09:24 unfortunately,
09:25 you're not going to be able to prove or disprove
09:27 any of them.
09:28 And if you've any ideas or suggestions,
09:30 I would very,
09:31 very,
09:31 very much welcome them.
09:34 Uh,
09:35 but before proceeding,
09:35 I think it's important to highlight
09:38 some of the important caveats.
09:40 Uh,
09:41 of our analysis
09:42 and
09:43 you know some misperceptions that,
09:45 that I think have made headlines.
09:47 So
09:48 for instance,
09:49 for instance,
09:49 according to the Times,
09:51 Billions of foreign aid disappears into tax havens.
09:54 Yes,
09:55 quite possibly based on our analysis,
09:58 but correlation doesn't necessarily imply causation,
10:01 so we cannot be confident
10:03 that
10:04 this is what is driving our results.
10:06 And on top of that,
10:07 money is fungible,
10:08 so it's not clear that
10:10 if diversion is taking place,
10:12 that diversion is out of aid flows as opposed to,
10:16 say,
10:17 money that was earmarked
10:18 for the Ministry of Education.
10:21 Second,
10:22 some newspapers have,
10:23 have sort of
10:24 made
10:25 what I
10:26 deem relatively grandiose
10:28 claims about sort of the,
10:30 the magnitude of leakage we document.
10:32 So for instance,
10:33 according to Forbes,
10:35 as much
10:36 as like 15%
10:39 of foreign aid
10:40 flows into uh tax havens.
10:43 Uh,
10:43 I think this is a wild exaggeration
10:45 because again,
10:47 Our main south pole
10:48 comprises 22 countries that together absorb
10:51 10%
10:52 of all World Bank lending.
10:54 We find a leakage rate of roughly 5%.
10:56 We don't find.
10:58 Leakage sort of in less aid dependent countries,
11:01 so expressed as a share of the World Bank's overall portfolio,
11:05 the true number is
11:06 much likely to be closer to 0.5%.
11:09 So,
11:09 so it's not the case that sort of
11:11 this pattern
11:13 applies across the board,
11:14 which I think is very important
11:16 to bear in mind when we discuss
11:18 our results.
11:20 So this concludes the introduction of the paper.
11:22 Next I'll tell you a little bit about our data,
11:24 our empirical strategy,
11:26 uh,
11:26 the various robustness tests we've done,
11:29 and I would like to spend more time on the interpretation and
11:32 this is also something where,
11:33 you know,
11:34 I would welcome your,
11:35 your,
11:36 uh,
11:37 feedback because many of you have,
11:38 have expertise that I think is extremely relevant to interpreting
11:42 these findings
11:43 and then I'll wrap up.
11:45 So
11:45 our main data set is a data set of cross-border
11:48 bank deposits,
11:50 sorry,
11:51 non-bank deposits,
11:52 so it excludes
11:53 financial
11:55 institutions,
11:56 banks,
11:57 including the central bank.
11:59 Uh,
12:00 so you can think of these as privately held account
12:03 firms and individuals,
12:05 but we only observe
12:06 immediate ownership
12:08 of these.
12:09 This data is confidential and comes from the
12:12 locational banking statistics
12:14 of the Bank for International Settlements,
12:17 which covers,
12:18 uh,
12:19 bank deposits and roughly 43 financial centers
12:21 owned by residents of around 200 countries.
12:25 And so we use confidential data that covers
12:28 all these uh
12:29 financial centers
12:31 for the period 1919 to 2010.
12:33 But then we also replicate our analysis with the publicly available BAS data
12:38 which allows us to also examine what happened
12:41 over the last
12:42 decade.
12:43 But these data have much more partial
12:45 coverage because they're based on sort of voluntary reporting of,
12:48 of countries.
12:50 Um,
12:51 and our key dependent variable is going to be
12:54 the sum of all haven deposits owned by residents
12:57 of a particular country
12:59 in the 17 havens in a particular quarter.
13:01 So think about sort of
13:03 All the money
13:05 that
13:06 residents of Kyrgyzstan
13:09 have
13:09 in Switzerland,
13:11 that they have in
13:12 Luxembourg,
13:12 that they have in Belgium,
13:13 like any of these 17 havens,
13:15 and they were going to add
13:17 these up.
13:19 For foreign aid,
13:20 we use information
13:22 on
13:23 project level disbursements
13:24 from the World Bank project database.
13:27 Now there's a real irony in this work because the reason we focus on the World Bank
13:31 is that it is in fact the only
13:34 major donor
13:35 for which we can construct
13:37 a series at the quarterly level
13:40 for a long period of time.
13:41 So
13:43 because of the transparency,
13:45 In our institution,
13:46 I think we're now under scrutiny.
13:48 Um,
13:50 so,
13:50 so in this data,
13:51 we know for each project,
13:53 project when it was approved,
13:54 how much money was committed,
13:55 what sector,
13:57 uh,
13:59 The project was in,
14:00 the type of lending instrument
14:02 and the timing of each disbursement,
14:04 and this is going to be extremely useful
14:06 when we try to
14:07 Address potential endogeneity of it because we can use this
14:11 to predict.
14:13 How much aid
14:14 we
14:14 would expect to flow to these countries
14:17 if
14:18 Aid would flow sort of typical
14:20 patterns.
14:21 So,
14:23 Our measure of aid is simply the sum of
14:24 all these project level disbursements on iodine IBRD projects
14:28 in a given country in a given quarter.
14:32 So to be clear,
14:32 this excludes
14:34 debt relief and it also excludes
14:36 uh
14:37 trust funds.
14:40 In addition,
14:40 we,
14:41 we,
14:41 uh,
14:41 use a number of,
14:42 uh,
14:42 other data sets,
14:43 so
14:44 we're going to exclude periods of war,
14:46 coups,
14:47 crisis,
14:47 disasters,
14:48 uh,
14:49 we look at how the results vary with petroleum,
14:52 rents,
14:52 financial sector development,
14:54 the capital account,
14:56 and disclosure requirements for politicians.
14:59 Uh,
15:00 you're also gonna look how it varies with
15:01 democracy,
15:02 uh,
15:03 corruption,
15:04 and
15:05 importantly with sort of the share
15:08 of aid that is procured domestically
15:10 versus
15:11 internationally.
15:12 Because
15:12 you might think that sort of these patterns that we're documenting
15:16 are just an artifact
15:17 of,
15:18 of contractors who execute a sponsored contracts.
15:24 Depositing some of their profits
15:26 in in havens,
15:27 and
15:28 I'm going to argue that,
15:29 yeah,
15:29 maybe that's,
15:30 maybe that's happening,
15:32 but it's quite unlikely.
15:35 So,
15:37 Just to give you a sense that these are distributions of aid
15:40 as a share of GDP
15:41 and,
15:42 and
15:42 even deposits growth.
15:44 As you can see,
15:44 there's quite a lot of variation in in aid inflows,
15:47 which is very useful for identification.
15:49 And you also see,
15:51 like,
15:51 when you look at the distribution of haven deposits growth,
15:54 that in many quarters,
15:55 they actually go down.
15:56 So,
15:56 so there is this saving,
15:58 which I think is relevant to bear in mind.
16:01 So,
16:02 and some descriptive statistics on,
16:04 on,
16:04 on the sample.
16:06 So World Bank aid in the sample accounts for roughly 2.7% of GDP on average.
16:11 Overall aid,
16:12 uh,
16:14 It's roughly 10% of GDP.
16:16 So these are economies in which
16:17 aid is really
16:18 a big deal.
16:21 Uh,
16:22 when we look at the level of haven deposits,
16:24 we see that
16:25 It stands on average around 61 million
16:29 and the level of haven deposits stands around 136 million.
16:33 So this I think is an extremely
16:35 relevant descriptive statistic
16:38 because it suggests that
16:39 On average,
16:42 It would seem that residents of these countries
16:44 have a strong preference for depositing money in non-hans
16:48 relative to havens,
16:49 so that makes it all the more puzzling
16:51 that when aid flows in,
16:52 we only see outflows.
16:54 To havens and not to non-hans,
16:56 so it's not simply not true
16:58 that,
16:59 you know,
16:59 residents of these countries routinely
17:02 Prefer depositing money
17:04 in havens.
17:07 Uh
17:11 So,
17:12 our empirical strategy is extremely
17:14 simple.
17:15 We're going to
17:16 regress the growth in haven deposits measured in logs
17:20 on aid
17:21 as a share of GDP.
17:23 Controlling
17:25 for
17:26 GDP growth
17:27 and in robustness checks,
17:28 we also had
17:29 controls for
17:32 Um,
17:32 the mechanical
17:34 appreciation
17:35 in
17:36 deposits that would result from,
17:38 from exchange rate
17:39 movements alone.
17:41 As well as commodity price shocks because you might
17:43 expect that sort of some of the wealth that's going into havens
17:47 is the result of
17:49 these commodity price,
17:50 price movements.
17:52 Uh
17:54 We're going to control also for country and time fixed effects.
17:57 So effectively we're asking whether
18:00 a country
18:01 when it receives aid.
18:03 Accumulates deposits more rapidly than in times when it doesn't.
18:09 Uh,
18:09 standard errors are going to be clustered at the country level,
18:11 which is conventional,
18:13 and we're going to Windsorize our deposit and aid measures,
18:16 uh,
18:17 at the 1% level
18:18 because there are some extreme outliers,
18:20 but these are this Windsorization
18:22 is not
18:24 driving the results as I'm gonna.
18:26 show you.
18:27 And then in alternative specifications,
18:28 we also look at the growth in non-haven deposits
18:31 and the differential growth of haven
18:33 deposits relative to non-haven deposits.
18:35 So if you thought that,
18:36 you know,
18:36 this was just a story about sort of
18:39 Aid sort of stimulating
18:41 economic performance and
18:43 leading to capital
18:44 outflows,
18:46 that wouldn't necessarily explain why there's differential,
18:48 uh,
18:49 growth
18:50 outflows to havens versus non-havens.
18:52 But of course,
18:52 a major problem here is aid is deeply endogenous.
18:57 So it's quite likely that,
18:59 you know,
18:59 a country receives aid at a time
19:01 when it needs it most,
19:02 for instance,
19:03 when it's experiencing
19:05 A natural disaster
19:06 or a financial crisis,
19:08 when I would argue it's also particularly appealing to
19:10 take your money out of the country and park it
19:13 somewhere else.
19:15 Um
19:16 So
19:19 We're going to address this in 180 in three ways.
19:21 First,
19:22 we're just going to augment our specification with both
19:25 lags
19:26 and leads of our aid variable
19:28 to make sure that
19:29 it's really
19:30 aid
19:31 that is driving
19:32 the surge in haven deposits
19:34 so that there are no pre-trends.
19:36 Secondly,
19:36 we're going to use an instrumental variable
19:38 strategy,
19:39 drawing on an excellent paper by Art who had a simple but powerful insight that
19:44 how much aid a country receives in any given quarter
19:47 is to a very large extent determined by past commitments
19:51 that cannot respond
19:53 to sort of contemporaneous events,
19:54 contemporaneous
19:56 shocks.
19:56 And so if we take those past commitments
19:59 and sort of we apply sort of the typical disbursement
20:02 schedule that applies to World Bank loans.
20:04 Then
20:05 we can come up with sort of a synthetic measure of predicted a disbursements
20:09 that's plausibly exogenous that we can use as an instrument.
20:13 And the third,
20:14 we're going to exclude
20:15 the episodes of turmoil.
20:18 Um,
20:19 such as war,
20:19 coups,
20:20 uh,
20:20 conflict,
20:21 and financial crisis.
20:23 So,
20:24 these graphs show you the first stage of our
20:27 instrument with aid
20:29 residualized on the left hand side and predicted aid on the right hand side.
20:33 And as you can see,
20:33 the correlation is,
20:34 is really strong.
20:36 So,
20:37 as I already sort of explained,
20:38 it's based on sort of predicting project level quarterly disbursements
20:43 based on
20:45 sort of the typical
20:46 disbursement profile
20:48 for
20:49 Projects in the same sector,
20:51 in the same region,
20:52 executed in the same time period.
20:55 And then simply aggregated those predicted disbursements
20:58 to get a measure of sort of
21:00 country level
21:01 predicted World Bank
21:03 aid.
21:05 Um
21:07 So on to the main result,
21:09 so.
21:12 This table shows you our,
21:13 our main results,
21:14 so.
21:15 It shows that
21:17 When
21:19 It
21:20 increases.
21:22 The growth in haven deposits accelerates.
21:25 In fact,
21:26 if aid goes up by 1%,
21:27 the growth of in haven deposits
21:29 accelerates by roughly 3.4%,
21:34 and we do not see a similar
21:36 response
21:38 of non-haven
21:39 deposits.
21:41 So
21:42 the differential which is presented in the 3rd column.
21:45 Between haven versus non-haven deposits growth is also statistically
21:49 significant.
21:51 I would also like to use the opportunity to point out
21:53 that when we look at the coefficients on GDP growth,
21:56 those are much more similar
21:57 for both haven and non-haven deposits growth.
21:59 So
22:00 they're 0.12 and
22:01 0.15.
22:03 So it seems that both haven deposits and non-haven deposits responded relatively.
22:08 Symmetric proportional
22:10 fashion
22:11 to,
22:11 to sort of general increases and,
22:13 and or decreases in economic
22:16 um
22:16 activity.
22:18 So sort of this,
22:19 this asymmetric response of haven deposits
22:22 seems to be very particular
22:24 to,
22:24 to aid.
22:28 But you may be concerned that perhaps sort
22:29 of there were earlier events that triggered both
22:32 uh
22:33 aid inflows
22:34 and,
22:34 and the accumulation of having deposits.
22:37 So what this graph here does,
22:38 it
22:39 sort of shows you.
22:41 Um,
22:41 the results of
22:43 a specification in which we add lags and lead.
22:46 And so
22:47 the quarter relative,
22:49 the,
22:49 the
22:51 Dots are sort of point estimates
22:53 for
22:55 the coefficient estimates of the lags and leads
22:58 and the,
22:58 the bars are the confidence intervals.
23:01 And as we can clearly see,
23:02 sort of we see a big spike.
23:05 On
23:06 At the time the aid arrives,
23:08 but no evidence sort of
23:09 of responses to
23:12 aid received in earlier quarters or to
23:15 aid that's about to,
23:16 to come in.
23:18 Uh,
23:19 this is the same graph for non-haven deposits which don't seem to significantly
23:23 respond
23:24 to aid inflows,
23:25 although they're in a small,
23:26 insignificant dip,
23:28 uh.
23:29 At times 0
23:31 and of course then the difference.
23:34 Between
23:35 the accumulation of haven deposits and non-haven deposits
23:38 is also
23:39 Yeah.
23:41 Exhibiting a very similar pattern
23:43 as the one we saw two slides ago.
23:47 Yeah
23:50 And
23:50 how robust is this result?
23:52 Well,
23:52 when we instrument aid,
23:54 What we see,
23:55 which we do here in columns 2 and 3,
23:57 where the difference between
23:59 the 2nd and the 3rd column is that we
24:01 exclude sort of more of the initial
24:03 quarters
24:04 to,
24:05 to have sort of uh ar more exogeneity.
24:08 And you see that sort of the coefficient drops
24:10 somewhat,
24:12 as you would expect,
24:13 but it remains.
24:16 Similar in magnitude,
24:17 I would argue,
24:18 and
24:19 Strongly statistically.
24:22 Significant.
24:22 So this should sort of
24:24 assuage a lot of the concerns about
24:26 endogeneity.
24:28 Nonetheless,
24:28 we also examine what happens when we exclude periods of wars,
24:32 coups,
24:33 disasters,
24:33 and financial crisis,
24:35 uh,
24:35 and you can see that
24:36 our main finding.
24:38 holes in the coefficient
24:41 remains similar in size and also strongly statistically
24:44 significant.
24:44 And it's also true when we sort of exclude all of these
24:47 at the same time,
24:48 which I'm not presenting here.
24:51 Then we also run a number of alternative specifications.
24:53 So
24:54 crucially,
24:55 in column 5,
24:56 we
24:57 include country year fixed effects.
24:59 So this is an
25:00 extremely demanding test,
25:01 I would argue,
25:02 because now all the variation.
25:04 is coming from
25:06 looking at what happens
25:07 to that same country.
25:10 Uh
25:13 In the same year,
25:14 Uh
25:16 And
25:16 as expected,
25:17 sort of the results are slightly less significant.
25:19 They remain significant to the 10% level,
25:21 but the coefficient estimate is very,
25:22 very similar.
25:24 In column 6,
25:25 we
25:26 include exchange rate control.
25:28 So some people have said,
25:29 oh,
25:29 perhaps
25:30 this is just something to do with sort of how exchange rates move.
25:33 Well,
25:33 here we
25:35 sort of control for the mechanical appreciation
25:38 of deposits that would
25:40 eventuate because of exchange rate movements
25:43 alone.
25:44 This
25:45 does not seem to meaningfully alter
25:48 our estimated coefficient.
25:50 Then in column 7 we control for resource dependence and
25:54 fluctuations
25:56 in the price of oil.
25:58 Uh
25:59 By interacting
26:01 A dummy for being a resource,
26:03 uh,
26:03 being an oil producer
26:05 with,
26:06 uh,
26:06 our time dummies,
26:07 and the reason for doing this is that my co-authors have a similar paper
26:12 where they examine what happens to
26:15 resource windfalls
26:17 and they show that
26:18 in autocratic countries roughly 15%
26:21 of
26:22 um
26:23 oil wealth
26:24 seems to be flowing
26:25 into
26:26 tax havens
26:27 but
26:28 controlling sort of for this doesn't really
26:31 Impact,
26:32 uh,
26:32 our estimates of the impact of aid.
26:35 And then finally,
26:36 in the final column,
26:37 we show what happens
26:38 when we just use the raw data and don't wins our eyes and
26:42 as you can see,
26:42 the results are strikingly similar
26:45 to uh our main results.
26:49 So I hope that by now you're
26:51 somewhat convinced that sort of this pattern is real,
26:54 but to understand the mechanism,
26:55 I think it's important sort of to look
26:56 at
26:57 how this
26:58 pattern
26:59 varies
27:01 across countries.
27:02 Now,
27:03 it's important to bear in mind that our sample is extremely small.
27:06 We only have 22 countries,
27:07 so
27:08 naturally we don't expect sort of
27:10 Huge differences,
27:12 but nonetheless sort of to see whether
27:14 at least sort of
27:15 qualitatively these,
27:16 these parents could be suggestive.
27:18 What we do is sort of we split the sample.
27:21 Every time
27:22 into countries that sort of
27:24 have above and below the median.
27:27 Control of corruption,
27:28 for instance.
27:29 So here we see that.
27:32 Outflow is slightly larger,
27:34 but certainly not significantly larger
27:36 in countries where the
27:38 control of corruption
27:40 is lower.
27:41 We also see that outflows are slightly larger,
27:44 but again not significantly larger
27:46 in countries that require their politicians to disclose their assets,
27:50 i.e.,
27:51 countries
27:51 where
27:53 You could argue politicians should have an incentive to,
27:56 to use
27:57 havens.
27:59 The fact is also slightly
28:01 higher in countries that are less democratic.
28:04 Uh,
28:05 and
28:07 In countries
28:10 With better developed financial systems.
28:13 And last but not least,
28:16 The effect
28:17 is slightly higher
28:18 in countries.
28:20 That
28:21 Procure
28:23 more
28:25 of
28:27 They
28:28 um.
28:30 A larger share of the 8 contracts
28:32 from foreign firms.
28:33 So if you had expected that sort of this was a story
28:36 about sort of domestic contractors depositing.
28:39 They're legitimately earn profits
28:41 in havens.
28:43 You might not have expected.
28:45 The,
28:46 the opposite and that's not what we see.
28:48 Now you could argue that,
28:49 well,
28:49 you know,
28:50 these contractors must subcontract,
28:51 so
28:52 you know,
28:53 whether you give it to a foreign firm or
28:55 uh a domestic firm in the end,
28:57 like they're all going to rely on the same domestic subcontracts
28:59 that I think is like a legitimate contract critique sort of to,
29:02 to this
29:03 point.
29:03 So,
29:03 so I don't think like,
29:04 you know,
29:05 anything I'm showing is sort of
29:06 uh conclusive,
29:07 but I do think it,
29:08 it raises questions.
29:11 And third,
29:12 I want to look at some of the type of aid,
29:14 so.
29:15 Uh
29:17 Based on a lot of conversations sort of with country economists,
29:19 my prior had been that
29:21 if we believe that these patterns are the product of diversion,
29:23 we should perhaps expect to see
29:25 larger effects for development policy lending,
29:28 where there's relatively less
29:30 oversight,
29:31 and that's actually not what we see
29:33 in the data.
29:34 We see,
29:34 if anything,
29:34 slightly stronger effects for projects.
29:37 But then,
29:38 uh,
29:38 a colleague in the integrity vice president,
29:40 he suggested that this is exactly precisely what he would have expected
29:44 because his argument was that,
29:45 well,
29:45 most of the money is actually going through projects
29:48 and if you want to capture these funds,
29:49 you typically do it through politically connected firms.
29:52 Now,
29:52 whether that's a,
29:53 uh,
29:55 plausible argument,
29:55 I think it's a matter of,
29:56 of debate,
29:57 but
29:58 I frankly do not have a
30:00 completely compelling explanation as to why
30:03 we see these differences,
30:03 but I should emphasize that these differences are extremely small and not
30:07 Statistically
30:08 significant,
30:08 so I think it's also important not to overinterpret them.
30:13 Uh.
30:16 So
30:18 then we replicate our analysis using publicly available data
30:22 to see whether the crackdown on tax havens that we've been seeing in the past
30:25 10 years since 2009.
30:28 has sort of had an impact
30:30 on the patterns we see
30:31 in the data.
30:33 And
30:34 basically the coefficient estimates we get for
30:37 The period
30:38 from 2010 to 2019 relative sort to a free period.
30:43 Coefficient estimate is very strikingly similar in magnitude.
30:47 But as you can see like the last period like has a much wider confidence interval
30:52 so
30:53 you know I don't think we can
30:54 conclusively conclude sort of that transparency hasn't,
30:57 uh,
30:58 helped.
30:59 But
31:01 The point estimates
31:03 suggest that the change
31:05 has been very limited.
31:09 OK,
31:09 so
31:10 how big is sort of the aid induced
31:13 increase in offshore deposits,
31:14 what we refer to
31:15 as leakage?
31:16 Well,
31:17 this is not something our model uh
31:19 delivers directly.
31:20 So we have to rely on a simple
31:22 transformation.
31:23 So the coefficient we get on aid is 3.4.
31:26 So it means that if aid goes up by 1%,
31:29 growth in haven deposits accelerates by 3.4%.
31:35 On average,
31:36 The stock of haven deposits
31:39 express as a share of GDP in our sample
31:41 is 0.22.
31:44 So
31:45 when we multiply 3.4
31:47 with 0.22,
31:49 we get 7.48 or
31:50 let's say roughly 7.5%
31:53 uh.
31:54 Is the implied leakage leakage rates because if
31:56 aid goes up by $1
31:58 haven deposits
31:59 should go up
32:00 at the sample mean by
32:02 $7.5.
32:04 But
32:05 this
32:07 Calculation applies to
32:09 the average country,
32:10 but it happens to be the case that most of the aid is actually flowing to countries
32:14 with a relatively low
32:16 stock of haven deposits to GDP.
32:18 So if we do the same calculation using the same 3.4%
32:22 number applying,
32:23 assuming it applies uniformly.
32:26 But
32:27 now you sort of the weighted mean of haven deposits
32:30 to GDP where with weights coming from the share of aid
32:33 uh
32:34 in the total sample that these countries receive.
32:37 Then actually
32:38 this new weighted mean is significantly lower.
32:41 It's 0.014
32:43 and so the implied leakage rate would be 3.4 times 0.014
32:48 is roughly
32:50 5%.
32:51 So this is why we claim that sort of the aggregate leakage rate
32:54 is roughly
32:55 5%,
32:56 but of course,
32:57 There's a lot of uncertainty going into this calculation,
33:01 uh,
33:01 including statistical
33:02 uncertainty,
33:03 but I think
33:04 As a ballpark estimate,
33:05 probably we sort of get the order of magnitude.
33:08 Correct.
33:11 Uh.
33:12 Then
33:13 you may wonder,
33:14 OK,
33:14 why did you focus on these 22 highly a dependent countries?
33:18 Were you cherry picking?
33:20 So what we do in this graph is where we show how our point estimates,
33:23 which are depicted in blue,
33:25 change
33:26 when we change this threshold.
33:28 So when you move from the left to the right,
33:31 so we include progressively fewer,
33:33 fewer.
33:34 So,
33:35 have we done this analysis just for countries where World Bank aid exceeds 1% of GDP.
33:40 And actually we would have concluded that there is no significant correlation
33:44 between aid
33:45 and haven deposits.
33:47 But
33:48 as we move up,
33:50 we see that this coefficient estimate increases.
33:53 So in then we report
33:55 these estimates.
33:56 We we to go further,
33:58 we would typically find even higher.
34:01 Go fishing.
34:03 And
34:04 the implied leakage rate,
34:05 which is
34:07 depicted in red.
34:09 Also increases because these countries also
34:11 are made more aid dependent also seem to have an average sort of
34:15 higher ratios of having deposits to,
34:18 to GDP.
34:20 So
34:21 When looking at sort of this graph,
34:23 I think it's important to bear in mind that sort of
34:27 OK,
34:27 so yeah,
34:27 it's important to bear in mind that.
34:29 Particularly as you move towards the tail end of this distribution,
34:33 so we have fewer and fewer and fewer countries.
34:35 So,
34:37 and the data are relatively noisy,
34:38 so
34:39 perhaps we shouldn't make
34:40 too much of this,
34:41 but if you were asking me like what could be driving this pattern,
34:44 that sort of
34:45 the most intuitive explanation is that perhaps
34:48 Countries that sort of
34:50 receive the most aid are also countries where governance challenges are,
34:53 are greatest.
34:55 So,
34:56 now I want to conclude by sort of discussing
34:59 potential mechanisms.
35:01 So,
35:02 first I want to discuss some mechanisms that are almost surely not
35:05 driving what we see
35:07 in the data.
35:08 So
35:09 one explanation that some people
35:11 have sort of alluded to is that
35:13 oh this could be just central banks or
35:16 uh
35:17 regular banks
35:18 adjusting their portfolios.
35:19 Well,
35:20 those are not part of the data so
35:21 that really cannot be driving
35:24 what we're seeing.
35:25 The second explanation is that this could reflect profit shifting by
35:29 multinationals,
35:29 but we only observe
35:31 immediate
35:31 ownership.
35:33 Um,
35:33 so
35:34 this is also not part of our data.
35:36 Third,
35:37 you could say,
35:37 well,
35:37 this might just reflect general economic stimulus,
35:40 right?
35:40 So
35:41 aid tends to be good for economic activity,
35:44 um,
35:44 but in our estimates,
35:45 GDP growth is already
35:48 accounted for,
35:49 plus
35:50 the output response
35:51 of economies to
35:52 economic stimulus tends to be relatively more
35:55 protracted.
35:56 And this wouldn't explain why
35:59 money only flows
36:00 to havens
36:01 and not
36:02 to non-hans.
36:06 A second possible explanation is
36:08 elite capture.
36:10 So
36:10 this is definitely consistent with money flowing to havens but not to non-havens.
36:16 It could also rationalize why
36:18 aid inflows and capital outflows
36:21 concur
36:22 within
36:23 the same quarter.
36:25 And
36:28 To the extent you want to take sort of heterogeneity analysis seriously,
36:32 we,
36:32 we do find marginally higher
36:34 uh estimates
36:36 in countries with higher levels of corruption and,
36:38 and where disclosure requirements are present,
36:40 but I think we should,
36:41 you know,
36:42 take
36:42 those results with
36:44 uh
36:45 A grain of salt because.
36:48 Uh,
36:49 they're not statistically significantly different from another.
36:53 Then perhaps one of the most salient alternative explanation is that
36:57 this is a story about contractors executing,
37:00 uh,
37:00 a sponsored,
37:02 um,
37:06 Project.
37:08 And this is definitely consistent with sort of
37:09 finding slightly larger effects when the financial sector
37:13 is small,
37:13 so it could be the case that
37:15 Contractors prefer to use sort of foreign banking systems relative to their own
37:20 relatively underdeveloped
37:22 banking systems.
37:23 But again,
37:24 there's differences.
37:25 Not statistically
37:27 significant.
37:28 But
37:29 it's inconsistent with not finding larger effects when a
37:32 greater share of procurement goes to foreign firms,
37:35 and it also doesn't explain why money only flows to havens and not to non-hans,
37:39 which we've seen
37:42 are
37:43 what residents seem to be preferring
37:46 on average.
37:47 So
37:48 to conclude,
37:49 I hope that
37:50 uh
37:51 I've convinced you that this correlation exists,
37:53 that
37:54 World Bank
37:54 aid disbursements.
37:57 To coincide
37:59 with more rapid accumulation
38:00 of haven deposits.
38:03 But we only see this correlation in extremely aid dependent
38:07 countries.
38:08 It does not apply
38:10 across the board
38:11 and I would argue that these patterns are consistent with elite capture,
38:14 but
38:15 other explanations are
38:16 certainly possible.
38:34 All right.
38:51 All right,
38:52 uh,
38:53 thanks,
38:53 everybody.
38:54 Um,
38:56 So
38:59 I just wanna,
39:00 I know everybody's here to
39:02 um throw their hardest questions at Bob,
39:04 so I'll try to be brief,
39:05 uh,
39:06 and get out of the way so you can do that.
39:07 Um,
39:08 but
39:10 I just want to say,
39:11 and I don't know who needs to hear this,
39:12 but I think it's important to kind of kick off
39:14 by saying kind of the nature of the conversation that I think
39:17 we're having here and that we need to have,
39:18 which is that
39:19 the paper,
39:20 in my view,
39:21 um,
39:22 Rigorously establishes a new fact about the world,
39:25 and it's a fact
39:26 that we all need to kind of wrestle with.
39:29 And so most of my comments here are gonna be kind of taking,
39:31 I'll talk a bit about maybe a few concerns,
39:34 but taking Bob's core result at face value.
39:36 Um,
39:37 and there's lots of room for arguments about interpretation,
39:40 and that's kind of what I'm going to do,
39:42 but I think it's an extremely careful paper on an important topic
39:45 that to me
39:46 is quite persuasive,
39:48 um,
39:48 in its main contention here and that main kind of
39:51 fact about the world that I think we're all.
39:52 He wrestling with and as Bob pointed out,
39:54 media around the world are wrestling with what it means
39:57 is that aid disbursements to highly aid dependent countries
40:00 cause,
40:00 and I'm going to be less shy about the causal language
40:03 than the authors are,
40:04 um,
40:06 cause increased deposits
40:07 in tax havens.
40:09 And I think the strength of that analysis
40:11 comes off of,
40:12 you know,
40:12 the unique access to this BIS locational banking services data so they can see
40:17 who owns or from what country
40:20 the owners come for these deposits in different
40:22 tax havens and non-tax havens quarter by quarter.
40:26 I really I think commendable,
40:28 simple and transparent kind of differences and differences
40:31 frameworks so we're not lost in having to buy into a structural
40:34 model that we can't understand and you can
40:36 see exactly where the results are coming from,
40:39 and it's corroborated by this more flexible event study
40:41 um analysis.
40:43 And this IV estimation building on arts,
40:47 you know,
40:47 QJE paper
40:49 that addresses sort of our standard causality concerns,
40:52 you know,
40:52 is correlation causation.
40:54 You know,
40:55 we can never be 100% sure,
40:56 but this paper goes a long ways
40:58 towards establishing,
40:59 I think,
40:59 the causal chain
41:00 up there in,
41:01 in Blue Font.
41:03 Um,
41:03 the paper's full of myriad robustness checks,
41:06 um,
41:06 and Bob
41:07 walked us through several of those
41:09 already.
41:10 And I think kind of most importantly in the write-up and,
41:12 and certainly today in the presentation of the paper
41:14 is kind of a transparent recognition of what the paper can and can't show.
41:18 When you read the paper,
41:19 and most of what I'm gonna
41:21 comment on here in terms of
41:22 ah my questions of interpretation are things that are already noted
41:26 in the text of the paper,
41:27 and I would like to say that,
41:28 oh wow,
41:29 I thought of something clever and new,
41:30 but for the most part,
41:31 anything that I thought up,
41:32 I'm gonna say here,
41:33 you'll find on page 16 and,
41:35 and,
41:35 you know,
41:35 and so on.
41:37 Um,
41:38 so just 3 comments that I do want to focus on,
41:40 and I'm in the weird position
41:42 of
41:42 kind of standing here at the World Bank and trying to give you a more optimistic spin,
41:46 I think,
41:47 on some of the
41:48 results,
41:48 which for those of you who know me is not,
41:50 you know,
41:50 often the role,
41:51 um,
41:52 that I play,
41:53 but I've one comment on the sample,
41:54 the choice of sample,
41:56 one on kind of what the magnitudes mean and what we should make of this 5%
42:00 number,
42:00 um,
42:01 and then probably most significantly on the,
42:03 on the proposed mechanism that's advanced here.
42:06 Um,
42:07 this,
42:08 and I wasn't sure exactly what Bob was gonna show when I made these slides,
42:10 so this has kind of been
42:12 covered perfectly in Bob's presentation.
42:14 Um,
42:15 I think it is worth pointing out,
42:16 maybe mostly if we're,
42:17 you know,
42:18 discussing the paper in public,
42:19 is,
42:20 you know,
42:20 there's this question of,
42:21 is the average leakage of World Bank aid distinguishable from zero,
42:24 and I think the results
42:25 possibly imply that it's,
42:27 it's not.
42:27 So the focus here is on 22 highly aid dependent economies.
42:31 Um,
42:32 and I would have liked to see a little bit more justification.
42:34 I think I would have come in ex ante saying like,
42:37 oh,
42:37 I expect to see more aid in more corrupt places,
42:39 sorry,
42:40 more leakage in more corrupt contexts,
42:42 but I'm not sure I necessarily would have come in ex ante expecting to see
42:45 higher leakage rates
42:47 in more aid dependent.
42:49 economies,
42:50 um,
42:51 but that's what we find.
42:52 And so the paper focuses on those 22 countries where the effects are relatively
42:57 larger,
42:58 you know,
42:58 is that real heterogeneity or is it just easier for us to pick up
43:01 this leakage
43:03 when there's more signal to noise because
43:05 aid flows are a bigger share of what's going on in the economy?
43:07 I'm not sure.
43:09 But if we go to the lowest threshold,
43:10 as Bob already noted,
43:11 which becomes more representative and comprehensive of
43:14 the coverage of all World Bank lending,
43:16 then the results become indistinguishable
43:18 from zero.
43:19 And so it might be
43:21 reasonable to say
43:22 for overall bank lending,
43:23 the best evidence we have so far is that there's no statistical evidence
43:27 of leakage.
43:30 OK,
43:31 2,
43:32 on absolute versus relative magnitudes of the coefficient.
43:35 Um,
43:37 The absolute magnitude.
43:39 What do we make of this 5% number?
43:40 Well,
43:40 it's greater than 0,
43:41 and I think that's the most important thing.
43:43 That's,
43:43 you know,
43:44 that took a lot of work to get that.
43:45 So,
43:46 um,
43:47 that's an important point.
43:48 But the 5%,
43:49 I'm less convinced on how informative
43:51 that number is.
43:53 Um,
43:54 Again,
43:54 this is all on the paper.
43:55 That number is potentially too high if World Bank aid crowds in
43:59 other aid.
44:00 If a $50 million World Bank disbursement is matched
44:03 with a $50 million US aid or DFID disbursement.
44:06 Then the given leakage dollars
44:08 are compared to a bigger denominator and that leakage rate is smaller.
44:12 There's some counter-evidence provided in the paper,
44:14 but there's only so far you can go because
44:16 the other donors don't give us quarterly disbursement data.
44:19 Probably a bigger concern
44:21 is that this is an underestimate,
44:23 um,
44:24 because
44:25 without being able to see through shell corporations and
44:27 all the other ways this money is held,
44:29 um,
44:29 there could be a lot more movement to tax havens going on
44:32 than is captured
44:34 in this part of the BIS data,
44:36 in which case,
44:37 you know,
44:38 we could be looking at a bigger number.
44:39 So I don't know what exactly to make of the number and its absolute magnitude.
44:44 I think what's particularly interesting about the paper
44:46 is we can talk about relative magnitudes in a meaningful
44:51 and apples and apples way for once,
44:54 and there
44:55 we find that the leakage
44:56 is not significantly declining despite reforms.
44:59 I think that's news and kind of bad news.
45:03 I'm going to take statistical significance,
45:05 you know,
45:06 uh,
45:06 as a real filter here and say that it's
45:09 leakage is not significantly associated with corruption,
45:11 disclosure rules,
45:12 capital account openness,
45:13 democracy,
45:14 credit market debt.
45:15 Lots of the things we expected to predict this leakage rate
45:19 aren't predicting it significantly.
45:21 But maybe,
45:22 you know,
45:23 if I was working in World Bank
45:25 press,
45:26 um,
45:27 most notably is I could make a pitch that a big
45:29 finding of the paper is that aid is not oil.
45:33 And there's been this literature which has fretted for years and years.
45:37 Paul Collier,
45:37 you know,
45:38 literally a paper is aid oil.
45:40 Janko Vidal in 2008,
45:42 Steve Knack's papers
45:44 worried about aid as a sovereign rent,
45:46 which is going to have a
45:47 corrupting influence and undermine domestic institutions.
45:51 But what's nice about this is,
45:52 you know,
45:54 Bob can compare his results to earlier work by co-authors Anderson et al,
45:57 this 2017 GEO paper,
45:59 which is saying 15 to 20% of oil rents are flowing to these same tax havens.
46:04 One reading of the results would be,
46:06 wow,
46:06 the controls in place on bank disbursements are so good
46:10 that the majority of the leakage that's happening
46:12 through other transfers to developing countries are not happening
46:15 with World Bank loans.
46:16 Again,
46:17 glass 2/3 full,
46:18 but it is
46:19 another reading of the same result.
46:23 Finally,
46:23 on the proposed
46:25 mechanism,
46:26 um,
46:28 The proposed mechanism in the paper
46:30 is that this comes from corruption and
46:31 embezzlement by ruling politicians and bureaucrats.
46:34 I agree with Bob that I think this is a plausible mechanism.
46:37 Maybe if I had to put money on,
46:39 you know,
46:39 we were going to find out with the god's eye view what it was.
46:42 Maybe that's where I put my money,
46:44 but I do think we've got to be
46:46 fairly open to alternative.
46:48 Mechanisms and here I'm mostly kind of serving as
46:51 rapporteur on the active Twitter conversation going on
46:55 around this paper,
46:56 um,
46:56 including lots of bank veterans chiming in
46:59 with their experience about what could be going on here.
47:01 Um,
47:02 one being aid disbursements leading to currency appreciation,
47:05 triggering capital flight.
47:07 Um,
47:07 disbursements to Ethiopia,
47:08 I mean the bur appreciates and if you were in Ethiopia looking to get money out,
47:12 now's a good time,
47:13 um,
47:14 when the bur is strong.
47:16 Um,
47:18 Alternatively,
47:19 to avoid appreciation,
47:21 for some people suggest that policymakers might
47:23 actually loosen capital controls momentarily and allow
47:26 Forex to escape
47:28 in the window around the World Bank disbursement.
47:31 So again,
47:32 people looking to move money overseas,
47:33 taking advantage of this window
47:35 right around the time of disbursement to do so.
47:39 Um,
47:40 and 3rd.
47:42 Something that Bob referred to as well,
47:43 that vendors choose to hold
47:45 potentially illicit and legal
47:47 revenues offshore
47:49 for for possibly illicit reasons.
47:52 I think
47:53 a key to all of these alternative mechanisms is
47:56 there's still big news here.
47:58 We still have a tax havens problem
48:00 where people are motivated to put money in tax havens,
48:03 quite possibly for tax evasion reasons,
48:06 but these stories don't necessarily begin with corruption and diversion
48:10 of World Bank monies.
48:12 Hat tip to all the former World Bank economists on Twitter,
48:15 um.
48:18 And just kind of continuing on that point,
48:20 I'm
48:21 probably.
48:24 Least solved by
48:25 what the empirics can give us
48:27 and what continued regressions can give us
48:29 to distinguish between
48:31 these different
48:33 mechanisms.
48:34 So,
48:35 for the corruption and embezzlement mechanism,
48:37 the paper offers corroborating evidence,
48:38 you know,
48:39 that there are bigger effects in more corrupt countries.
48:42 Yes,
48:42 but not significantly so,
48:45 um,
48:45 that the timing of the effect is,
48:46 you know,
48:47 T equals 0,
48:49 which would be
48:50 consistent with a politician skimming off of the transfer,
48:53 but could be
48:54 consistent with alternative stories.
48:56 I'd argue
48:57 the effect is only present for tax havens.
49:00 I think that's
49:01 a big important part of the paper,
49:03 um.
49:04 But here I want to be particularly careful because I think there's an
49:07 established assumption in this literature,
49:09 like in the Zuman,
49:11 Gabriel Zucman's work,
49:12 to say that deposits in tax havens
49:15 are sort of prima facie evidence of tax evasion.
49:18 That's one step,
49:19 but that's,
49:20 it's a big step further to say that deposits in
49:23 tax havens are evidence that those were ill-gotten gains.
49:26 That's a
49:26 different assertion,
49:27 and I don't think the previous tax havens literature has made that assertion,
49:31 assertion.
49:32 So I'm not sure we can use this
49:34 as evidence that the gains themselves were ill-gotten.
49:37 Um,
49:38 and then.
49:40 You mentioned different people had different priors here.
49:42 I guess my prior would have been
49:44 that if this was skimming off of World Bank transfers,
49:46 I would have expected to see bigger
49:48 effects for the DPLs than for the investment projects.
49:51 That's
49:52 More fungible money,
49:54 um,
49:54 and we,
49:55 we just don't
49:56 see that here.
49:58 Um,
49:59 To wrap up,
50:00 I mean,
50:01 I think rather than trying to tease out
50:04 more of this story with heterogeneous effects,
50:07 at some points the samples,
50:08 you know,
50:09 we're talking about a fairly small number of countries,
50:11 and I'd be really interested to see
50:13 some triangulation of the
50:15 great econometric evidence you have on the core result,
50:18 with some qualitative support about can you find,
50:21 Evidence,
50:22 uh,
50:22 you know,
50:22 we have,
50:23 I was asking former bankers,
50:25 you know,
50:25 the Padma Bridge scandal in Bangladesh or the Lesotho Highlands Water project,
50:29 like,
50:30 do the known cases of diversion of bank monies
50:33 fit into the mechanism that you're describing,
50:36 and can you do some sort of like,
50:38 you know,
50:38 you can look for influential observations,
50:40 we can do the kind of aeronau and Sammy stuff to say,
50:43 who are the observations that are driving the results.
50:46 And then I don't know,
50:47 send it out to the 1818
50:49 H Street Club,
50:50 what is it called,
50:50 and say,
50:51 you know,
50:52 do people have stories about those influential observations?
50:54 But I,
50:55 I think a little more qualitative work
50:57 to back up that mechanism would make the paper a lot,
50:59 would be fun to read,
51:00 um,
51:01 and would make me feel better,
51:02 um,
51:03 about this uh
51:04 corruption mechanism.
51:05 So in the end,
51:06 On the sample,
51:07 if I was going to be super optimistic here,
51:09 average leakage of World Bank aid is indistinguishable from zero.
51:12 On the magnitudes,
51:13 the main takeaway is,
51:14 hey,
51:15 aid's a lot better than oil in terms of our leakage rate,
51:17 and on the proposed mechanism,
51:19 are we 100% sure that this is really corruption and not something else
51:22 going on?
51:24 Let me stop there,
51:25 but overall,
51:26 really fun paper,
51:27 really persuasive main result.
51:28 Thanks a lot.
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