00:02 OK,
00:02 um,
00:03 very,
00:04 uh,
00:04 warm welcome to this,
00:06 uh,
00:06 second session on,
00:08 uh,
00:08 growth and convergence.
00:11 Um,
00:12 we have,
00:12 uh,
00:13 3 papers and they're all terrific.
00:15 Uh,
00:16 I applaud both the authors and the,
00:18 uh,
00:18 organizers of this conference for selecting them.
00:21 They're really outstanding papers,
00:24 um,
00:24 and I very selfishly,
00:26 I teach a course on
00:28 economic growth at Georgetown and I'm.
00:30 Already looking at what I can cannibalize from these lovely papers,
00:33 so,
00:34 uh,
00:34 each,
00:35 uh,
00:35 presenter has,
00:36 uh,
00:37 20 minutes,
00:38 uh,
00:38 and I've been told to,
00:39 uh,
00:40 uh,
00:40 be rigorous in enforcement.
00:42 So we will start with,
00:44 uh,
00:44 Chamiro and,
00:45 uh,
00:46 and then,
00:46 uh,
00:47 move on from there.
00:48 Thank you.
00:51 Is there
00:56 So sorry.
01:07 All right,
01:07 uh,
01:08 uh,
01:08 thanks a lot to the organizers for including our paper in today's,
01:11 uh,
01:12 agenda.
01:12 Um,
01:13 so this is joint work with Elissa Genone who is at CRE in Barcelona and Can Cuno,
01:18 our PhD student at Penn State,
01:20 um.
01:21 Uh,
01:22 before I begin,
01:22 I must say that parts of this project are sort of still work in progress.
01:26 This has been a huge kind of data collection exercise,
01:28 and I'm going to point out to those pieces,
01:31 uh,
01:31 where we should be
01:32 slightly careful about interpreting the quantitative numbers.
01:35 But just to begin,
01:37 let's,
01:37 I mean,
01:38 what do we know about.
01:39 Economic growth in the last 30 years,
01:41 we know that there has been a lot of growth
01:44 amongst the poorer countries in the world and on an
01:46 average poorer countries have caught up with richer countries.
01:49 Um,
01:50 and then there's a little bit of evidence of what has happened within countries,
01:53 uh,
01:54 um,
01:54 mostly the US and,
01:56 and some in the UK.
01:58 What we really don't know is what has
02:00 happened to the evolution of spatial inequality,
02:03 uh,
02:03 uh,
02:04 within
02:05 countries over time in these last 30 years which has
02:08 seen a lot of economic growth in the world,
02:10 right?
02:11 And,
02:11 and with the evolution of spatial inequality in particular
02:16 what has been the role of um um structural.
02:19 Change
02:19 in shaping this,
02:21 this evolution.
02:22 So that's going to be the focus of our paper,
02:25 um,
02:25 uh,
02:25 a key contribution of this project is kind of building this new data set
02:30 on the GDP per capita at subnational units over time
02:35 so both kind of states and provinces of countries,
02:38 but also at the city level.
02:40 And then more importantly,
02:42 something that did not exist at all
02:44 is the labor force composition by sectors within
02:48 each region of various countries of the world,
02:51 right?
02:51 So using this data set,
02:52 we first kind of document three facts.
02:55 One is for the average country in the world in the last 30 years,
02:59 um,
03:00 convergence between regions within a country has been falling over time.
03:05 Um,
03:05 the second is that the,
03:06 the,
03:07 the,
03:08 this,
03:08 this stall in uh spatial convergence is related to growth of
03:13 services as the key driver of development in the last,
03:17 um,
03:17 um,
03:17 20 years or so.
03:19 Um,
03:19 and the,
03:20 and the last fact,
03:21 Is that
03:22 much more than happened during the manufacturing phase in the services phase,
03:27 um,
03:27 especially kind of professional and business services,
03:30 employment is getting very,
03:32 very concentrated in few regions of,
03:35 of,
03:35 of countries.
03:36 So,
03:38 Then we were,
03:38 so once we had the facts,
03:39 we were thinking like,
03:40 could,
03:41 could a sort of canonical structural change model with space built in it
03:45 explain these facts,
03:46 or do we need some other ingredients?
03:48 And in terms of the theory,
03:49 I'm going to show you a
03:50 very,
03:51 very simple kind of canonical model of structural
03:53 change with kind of space built into it,
03:56 um,
03:56 which is going to at least qualitatively match,
03:59 um,
03:59 um,
04:00 our facts,
04:01 um,
04:01 very well.
04:02 Um,
04:03 we're contributing sort of to two large literatures.
04:06 Uh,
04:06 uh,
04:06 uh,
04:07 one is just the literature on the evolution of spatial income disparities,
04:12 um,
04:12 and the second,
04:13 which is a more recent literature,
04:14 which takes kind of structural transformation and space,
04:17 uh,
04:18 uh,
04:18 more seriously,
04:19 um,
04:20 um.
04:20 Um,
04:20 on the first one,
04:22 we are just expanding the evidence to,
04:25 to many,
04:25 many more countries than existed before.
04:28 And in the second one,
04:29 we are kind of highlighting,
04:31 um,
04:31 the role of agglomeration and services,
04:34 uh,
04:34 uh,
04:34 which is going to be kind of a key feature of our,
04:37 of our theoretical framework.
04:39 So just to spend a little bit of time on,
04:41 on the data,
04:42 we are building on an existing data set by Giannoli,
04:45 LaPorta Schleifer,
04:47 uh,
04:47 uh,
04:47 but we have added many,
04:49 many more countries that,
04:50 that data set did not have.
04:52 We have also went on and corrected some of the data.
04:55 So for example,
04:56 the data for India in that data set
04:58 was missing for many,
04:59 many years.
05:00 Uh,
05:01 uh,
05:01 uh,
05:02 uh,
05:02 so we have updated it to the most recent year possible for our sample of countries.
05:07 Um,
05:08 from the Economist,
05:08 we also have Data on the GDP at the level of cities,
05:12 um,
05:12 in the world.
05:13 So sort of combining these two data sets,
05:15 we have like a balanced panel on GDP per capita by region within a country,
05:20 um,
05:20 for 34 countries between 1980 and 2017,
05:25 but we also have an unbalanced panel that starts much further back
05:28 and essentially the reason is that for the advanced countries you,
05:32 you,
05:32 you can go back further,
05:34 but for the developing countries you don't get data,
05:36 um,
05:36 going back far in time.
05:38 We have added to this.
05:39 This data on years of schooling by subnational region by year,
05:43 um,
05:43 in the world,
05:44 um,
05:44 and,
05:45 um,
05:45 the labor force composition by,
05:47 by kind of broad sectors.
05:49 We are,
05:49 we are,
05:50 so this summer we are making a,
05:51 a,
05:51 a new effort to kind of,
05:53 so right now we have the labor force composition by just broad sector agriculture,
05:57 manufacturing and services.
05:59 Now we are making an effort to break down services into further subsectors so
06:03 that we can get the tradable high skill part of the services in,
06:07 in much more,
06:08 um,
06:08 richer detail.
06:10 In terms of coverage,
06:11 well,
06:11 you can see that we're doing sort of a pretty good job in most continents,
06:16 um,
06:16 um,
06:16 except for Africa and since I'm standing at the World Bank,
06:20 um,
06:21 here is a call for help,
06:22 uh,
06:22 uh,
06:23 if any of you can help us add more countries in the African continent,
06:27 that would be terrific.
06:27 We have
06:28 tried a lot.
06:29 Like we write to like national statistical agencies in various countries.
06:33 We sometimes get replies and other times don't.
06:36 But,
06:36 but,
06:36 but Africa is one continent that is kind of,
06:39 so we have done some robustness with night lights,
06:41 etc.
06:42 but it would still be useful to have
06:45 actual employment and GDP figures at the subnational level.
06:49 So let me start with the facts and so
06:51 this is kind of your familiar convergence regression,
06:54 except now we are doing it at the sub,
06:55 uh,
06:56 uh,
06:56 uh,
06:56 within countries.
06:57 So you regress kind of
06:59 growth in GDP per capita,
07:01 uh,
07:01 uh,
07:02 for the next 10 years,
07:03 um,
07:04 for a given state or province
07:06 on the initial level of GDP per capita,
07:09 um,
07:09 at time T0.
07:11 So for a particular country.
07:13 Uh,
07:13 uh,
07:14 and as must be familiar to all of you,
07:15 if this beta is kind of less than 0,
07:18 then that points towards convergence,
07:19 and if it's positive,
07:20 then that means the
07:22 poorer regions are not catching up with the richer regions.
07:25 So we run this regression
07:27 for all the countries in our sample,
07:29 country by country.
07:30 So we have one beta C,
07:32 uh,
07:32 uh,
07:33 at a given point in time.
07:35 Uh,
07:35 uh,
07:36 uh,
07:36 for each country and then for the figure I'm going to show you,
07:39 we're going to average this across all countries,
07:42 right?
07:42 So,
07:42 what this,
07:43 so what this point is showing is
07:46 the average beta C for all the 34
07:49 countries in our sample and essentially showing that
07:52 in the 1980s,
07:54 there was a lot of within-country convergence on an average in the world.
07:58 And then over time there has been a sharp decline
08:01 in the within-country convergence rates and by the 2000s,
08:04 the within-country convergence process has basically come to a complete stall.
08:09 Um,
08:09 these are unweighted regressions.
08:10 If you do population weighted regressions,
08:13 the,
08:13 the,
08:13 the key fact that in the,
08:15 in the 2000s,
08:16 there is no within-country convergence.
08:18 It's still true except that the decline happened,
08:21 uh,
08:21 much faster.
08:23 Um,
08:23 this is true for like 56% of our countries,
08:26 but,
08:27 but this covers like for,
08:28 for most of the large countries by economic size or population,
08:33 um,
08:33 the within-country convergence rate is lower in,
08:36 in,
08:36 in the 2010s than it was in the 1980s.
08:41 Um,
08:41 um,
08:41 this is also true if you do the same convergence regressions,
08:44 not at the level of provinces or states,
08:47 but by cities.
08:48 Uh,
08:49 uh,
08:49 it's also true for conditional convergence,
08:52 not only,
08:52 um,
08:53 unconditional convergence.
08:55 It's true if you exclude India and China.
08:58 Um,
08:59 the
08:59 one sort of big challenge is accounting for regional price differences,
09:02 so we are collecting data we now have.
09:04 Have it for India,
09:05 China,
09:05 Britain,
09:06 uh,
09:06 and the US,
09:08 and for all these four countries,
09:09 our facts are quite robust.
09:12 We are trying to get it for more countries and hopefully we'll,
09:14 we'll be able to say more.
09:16 You can
09:17 cut this by
09:18 any dimension that you want,
09:19 but like the big fact that in the 2000s for most countries of the world,
09:24 there is
09:25 very little within country convergence kind of holds.
09:28 So,
09:29 for the main story,
09:30 let me just tell you,
09:31 tell this to you through the lens of like two countries.
09:34 So what I'm plotting here is GDP per capita of
09:36 India and China relative to the US over time,
09:39 right?
09:39 So what this graph is showing
09:41 is the enormous catch-up
09:43 that US,
09:44 uh,
09:44 India and China have had with the US.
09:47 But the key thing is that when you look
09:49 what is happening within these countries,
09:51 so now I'm plotting GDP per capita for
09:54 regions within these countries arranged by deciles,
09:57 right?
09:57 So
09:58 the orange line is the top decile regions in India and China,
10:01 and the,
10:02 the blue lines are the bottom decile,
10:04 uh,
10:05 regions within India and China.
10:06 They used to be very close to each other in the 1990s and over time,
10:10 um,
10:11 um,
10:11 the divergence has just increased,
10:13 right?
10:14 What we think is this is related to is,
10:16 is a growing concentration of tradable high-skilled services.
10:21 So,
10:21 if you just focus on the national employment
10:25 in sectors that is concentrated in the top regions
10:29 for manufacturing in India,
10:31 that has declined.
10:32 That's consistent with deindustrialization in India,
10:35 right?
10:35 The top regions are de-industrializing.
10:37 China has relatively been stagnant.
10:40 Um,
10:40 services overall,
10:42 India has been stagnant.
10:43 China has increased a little bit,
10:45 but if you look at professional and business services,
10:47 which is this high skill services,
10:49 uh,
10:49 India has gone from something like 39% to 47%.
10:53 So 47% of the national high skill services in India.
10:57 In terms of employment is just concentrated,
11:00 uh,
11:01 uh,
11:01 uh,
11:01 in these five states
11:03 and you see sort of similar patterns,
11:05 um,
11:05 um,
11:06 kind of in China.
11:07 So if I,
11:07 if I look,
11:08 if I take this to the entire sample
11:10 and if I regress,
11:12 uh,
11:12 the internal convergence rate on the services
11:15 employment shares of course with development,
11:17 the internal convergence rate is falling,
11:20 but what it is falling,
11:21 uh,
11:22 uh,
11:22 starkly with is the growth of services within these countries.
11:25 So ideally once we have the full.
11:27 Data set with the professional business services.
11:29 I want to replace the x axis
11:31 not by broad services but just by
11:34 uh professional and business services which we have done for 22
11:37 countries where we have the data and the thing holds,
11:40 uh,
11:40 uh,
11:40 but we want to sort of um do it for the full,
11:42 full sample.
11:44 Here's the same thing in regression so the Y variable is the
11:49 within country convergence rates.
11:51 All regressions have country fixed effects,
11:53 so we are tracking the evolution of within
11:56 country convergence rate over time within a country.
12:00 It's of course rela related to development.
12:02 So as development progresses,
12:04 as employment moves out of agriculture,
12:07 the within-country convergence rate falls,
12:10 but
12:11 it's not so much related to manufacturing than it is,
12:15 um,
12:15 um,
12:15 to the growth of services,
12:17 um,
12:17 within these,
12:18 within these countries.
12:19 Now,
12:19 one valid question to ask is maybe with development.
12:23 There is no regional,
12:25 there is no sizable regional inequality left and so
12:28 this might just be reflecting kind of the solo lesson
12:31 that once your regional inequality is down a threshold,
12:34 uh,
12:34 uh,
12:35 there is,
12:35 there are no gaps left to close,
12:39 uh,
12:39 uh,
12:39 that is not fully true either.
12:41 So now on the Y axis we.
12:43 Have just the coefficient of variation in GDP per capita between regions,
12:48 um,
12:48 within a country.
12:50 Of course,
12:50 with the development,
12:51 this
12:52 sort of fell sharply,
12:54 but then it kind of,
12:55 when services hit kind of the 30% mark,
12:58 uh,
12:58 uh,
12:59 this kind of stabilized at around 18% or 17%.
13:02 And since then,
13:04 um,
13:04 these gaps between GDP per capita between regions haven't,
13:08 haven't been closing.
13:10 Um,
13:12 So here is where we should be a little bit careful.
13:14 So it turns out that even if you look at the EU,
13:17 uh,
13:17 they don't have a,
13:19 they don't have
13:20 good data on,
13:21 uh,
13:21 labor force composition by sectors before 2000.
13:25 It depends on the country.
13:26 For example,
13:26 Italy,
13:27 you don't get this data,
13:28 which is why.
13:30 I don't want you to focus on anything before 2000s,
13:32 but
13:33 after 2000,
13:34 so what this,
13:35 uh,
13:35 chart is plotting is the,
13:37 um,
13:38 employment in,
13:39 in each sector in the top region versus the bottom region.
13:43 So lesson number one,
13:45 services is much more concentrated than manufacturing or agriculture,
13:50 right?
13:51 The second is
13:52 the growth in concentration in services,
13:55 which is the slope of this line.
13:57 Um,
13:57 is much more for services than manufacturing or agriculture.
14:01 So,
14:02 not only is it more agglomerated in,
14:04 in,
14:04 in services,
14:05 the rate of increase,
14:07 um,
14:07 in concentration,
14:09 um,
14:09 is,
14:09 is,
14:10 is also much higher.
14:12 Now,
14:12 we can't do,
14:12 we,
14:13 in terms of testing what other factors might be involved,
14:16 we can't do much because you don't get a lot of
14:18 sort of time varying controls by region within a country,
14:22 but we can look at cross-country relationships.
14:24 So if you put a time fixed effect
14:26 and compare
14:27 two different countries,
14:29 one which has a lot of internal convergence and the other that does not,
14:33 and see what factors would explain this,
14:35 and I'm only showing some factors here.
14:38 So this is like a kitchen sink regression.
14:40 It turns out that
14:42 it's services,
14:43 employment share and the growth of productivity in services
14:47 that seems to be the leading predictor of why certain countries have high
14:51 internal convergence,
14:53 um,
14:53 than others and in particular,
14:55 uh,
14:55 uh,
14:56 growth of services associated with less,
14:58 um,
14:58 internal,
14:59 internal convergence.
15:01 So,
15:03 I'm going to sort of
15:05 To see,
15:06 to see whether this,
15:06 all these facts can be rationalized by a simple sort of structural change model.
15:11 I'm gonna explain this in words.
15:12 The model is not complicated at all,
15:14 right?
15:14 So you,
15:14 you take the most canonical structural change model
15:17 where structural change happens because of non-hommothetic preferences,
15:21 right?
15:21 And you put it in a standard model of economic geography.
15:25 So there are multiple regions,
15:26 people choose which region they want to locate in.
15:29 And they produce stuff and they,
15:31 and,
15:31 and they consume.
15:32 The,
15:33 the only departure from standard models is going
15:36 to be so in standard structural change models,
15:38 you have,
15:38 so this is the sectoral
15:40 growth rate of TFP
15:42 in agriculture and manufacturing.
15:45 Uh,
15:45 uh,
15:45 uh,
15:45 it's all exogenous and,
15:47 and,
15:48 uh,
15:48 uh,
15:49 uh.
15:49 Uh,
15:49 uh,
15:49 as a standard in the structural change models,
15:52 what we have done for services is we have put in an additional kick
15:56 that comes from agglomeration and services.
15:58 So,
15:59 growth rate of services productivity is,
16:01 is due to an exogenous factor,
16:04 but also because there are more people located in a particular region,
16:09 um,
16:09 um,
16:10 in services.
16:11 So
16:12 the calibration is preliminary and,
16:13 and pretty standard.
16:14 So in the interest of time,
16:15 I'm not going to focus much,
16:17 but just show you this.
16:19 Um,
16:20 um,
16:20 the,
16:20 the red dots with the dashed lines is what
16:24 the,
16:24 the,
16:25 the,
16:25 the data says about the evolution of within-country convergence.
16:29 That very simple model just matched to the
16:32 initial concentration of employment shares in different regions
16:37 can pretty much predict
16:38 the decline in the uh,
16:40 the decline in the within country convergence rate over time,
16:44 um,
16:44 um,
16:44 at least qualitatively.
16:46 And we can,
16:47 of course,
16:47 use the model to see what role this agglomeration force is playing,
16:51 right?
16:51 Right.
16:52 So,
16:52 what role is,
16:53 is,
16:54 is,
16:54 is,
16:54 is this factor playing here?
16:57 So in the model,
16:58 if you do this decomposition where you choose like some agglomeration force
17:02 that is calibrated to the data versus setting it to zero,
17:06 it turns out that you still get some decline in beta convergence,
17:10 even if there was
17:11 no agglomeration.
17:13 But then the effect just multiplies,
17:16 uh,
17:16 uh,
17:16 if there is agglomeration in services.
17:18 So if I,
17:19 if I excluded
17:20 the agglomeration factor from the model,
17:22 there are two pieces of the data that I won't be able to match.
17:26 One is
17:27 the speed of this decline,
17:28 like it would be much slower without the agglomeration kick,
17:32 and the second is increase in concentration,
17:35 uh,
17:35 uh,
17:35 of services,
17:36 employment in,
17:37 in,
17:37 in,
17:38 in particular regions.
17:40 So,
17:41 kind of the big picture message of our,
17:43 of our,
17:43 of our research is this.
17:44 So,
17:44 one is,
17:45 of course,
17:45 like putting out the data set,
17:46 which,
17:46 of course,
17:47 I'm assuming would be useful for many of you.
17:50 Uh,
17:50 but also to say this,
17:51 I mean,
17:51 the World Bank had this fantastic report showing that
17:54 future growth is going to be driven by services.
17:57 And if that is true,
17:58 and,
17:58 and,
17:58 uh,
17:59 and,
17:59 and assuming that it's all sort of professional tradable high skill services,
18:04 then spatial inequality is going to be a free gift of it,
18:07 um,
18:07 um,
18:08 that we should be,
18:09 um,
18:09 kind of cognizant about.
18:11 So thank you.
18:13 Amazing.
18:17 Thank you so much,
18:18 Micro,
18:18 and you,
18:19 uh,
18:19 and you,
18:20 uh,
18:20 are great on time.
18:21 So,
18:21 uh,
18:22 let's move right on to,
18:23 uh,
18:24 Marta for,
18:25 uh,
18:25 the second paper.
18:32 Great.
18:32 So thank you so much for,
18:34 to the organizers for inviting me to present this project
18:37 that's called the Global Race for Talent,
18:39 Brain Drain,
18:40 Knowledge Transfer and Growth.
18:43 And in this paper,
18:44 I studied the effects
18:46 of migration on innovation and growth
18:49 and the starting point is that
18:51 uh inventors are very highly mobile individuals.
18:55 22
19:05 Great.
19:06 So to illustrate this,
19:07 I want to start with this example
19:09 where Jean Calvignac here is a prolific French inventor
19:13 that appears in my data from the European Patent Office.
19:17 And this is an example of a real patent that he filed in 1991
19:22 and from this we learned that at this time
19:24 Calvignac lived in the city of Lagos in France.
19:28 But also most of his collaborators at this time,
19:31 that is the other co-inventors that contributed to
19:34 this technology were also based in France.
19:38 Then in 1998,
19:39 Calvin Jaak moved to the US.
19:42 And we know this thanks to his later patents such as this one from 2004,
19:47 which shows that first at this time he lived in North Carolina.
19:51 Second,
19:52 most of his collaborators at this time were also based in the US.
19:56 But most interestingly,
19:57 even after moving to the US,
19:59 Kaviac continued to work with some inventors based in France,
20:04 and this is not an isolated example.
20:06 In fact,
20:07 there is a systematic flow of inventors across countries.
20:11 To give a sense of this using patent data between 2001 and 2010,
20:16 I observed that about 6%
20:18 of European national inventors live in the US in any given year on average,
20:23 but only about 0.4% of American inventors live
20:27 in Europe in any given year on average,
20:30 and the difference between these two numbers,
20:31 the net migration is what I refer to as the brain drain.
20:35 It has been a cause of concern
20:36 and policy interventions both on the European and the American side.
20:41 So in this project,
20:41 I first ask
20:43 how does inventors migration affect migrants' productivity,
20:46 their collaboration networks,
20:48 and their knowledge spillovers on local inventors.
20:51 And second,
20:52 I study the positive implications of some real world policies
20:55 that have been implemented to manage its migration flows.
20:58 So I asked what is the role of tax and migration policy
21:01 in shaping migration flows,
21:02 the innovative capacity of the economy,
21:05 productivity and output.
21:08 And in this paper,
21:09 I answered these questions in 3 steps.
21:11 The first step is to bring a theoretical framework to
21:14 a literature that had so far been mostly empirical.
21:18 So I built a two country dynamic general equilibrium innovation based growth model
21:22 that has two important new features.
21:25 One is that inventors in this model are allowed to move
21:28 across countries and to return to their country of origin.
21:31 And second,
21:32 inventors here accumulate human capital by learning from others,
21:36 but this learning happens inside endogenous interaction networks that
21:40 produce knowledge spillovers both within and across countries.
21:44 Now the key takeaway from this model
21:46 is that migration has ambiguous effects on innovation and
21:50 output in the short and in the long run,
21:52 and this is because it produces several forces that have opposite signs.
21:57 And the two main forces that come out of this model are on the one side,
22:00 the effect of the brain drain,
22:02 as it is partly offset on the other side
22:05 by the positive benefits for knowledge spillovers.
22:10 Now,
22:10 given this ambiguous theoretical result,
22:12 the next question is how can we quantify these different forces.
22:16 And this takes me to the 2nd part of the project where
22:19 I build a micro-level data set of migrant inventors between Europe
22:22 and the US using data from the European Patent Office.
22:26 Now here one of the challenges is that
22:29 there is very scarce availability of data
22:31 that consistently track individuals across countries,
22:34 which is why here I focus on Europe and the US
22:37 but more generally the theoretical insight from this model
22:40 apply more broadly not just to individuals that
22:42 invent technologies but also individuals that adopt technologies
22:46 and so this model could apply more generally not
22:48 just to advanced economies but also to developing countries.
22:52 Now,
22:53 the key findings from my data are that first,
22:56 after migration,
22:57 migrants,
22:57 both European and Americans
22:59 tend to increase their patent applications by 42% per year on average.
23:05 Next,
23:05 what I think is even more interesting in this data is to look at
23:09 what happens to the collaboration network of these migrants.
23:12 And here I find that after migration,
23:14 migrants actually continue to work with inventors in their country of origin,
23:19 but less frequently
23:20 as they work more often with investors and
23:22 desti inventors at destination than other migrants.
23:26 So then the last question is what happens to these inventors in the country of origin
23:31 after somebody that they work with moves away.
23:34 And here what I find in the last point is that the local inventors at Origin
23:38 also increase their patent applications by about 15% per year
23:42 after their collaborator moves away.
23:45 And this is what I will interpret through the lens of
23:47 my model as the effects of the knowledge spillovers channeled by migrants
23:51 on the productivity of the local inventors at origin.
23:55 Finally,
23:56 the last step in this paper is to bring together the
23:58 theory and the data by calibrating the parameters of this model
24:02 to match his empirical result
24:03 and finally to study counterfactual policy
24:06 exercises that resemble real world policies implemented
24:09 either by European countries or the US
24:11 to address these migration flows.
24:15 So let me start with a brief overview of this model.
24:20 On the human capital side,
24:22 here,
24:22 the population is exogenously split between
24:24 inventors that produce technologies or ideas
24:27 and workers that produce the final good.
24:30 On the production side,
24:32 these technologies or ideas are used to improve the quality of intermediate goods
24:36 that are then sold to the final good aggregator.
24:39 So that overall innovation here drives
24:42 aggregate productivity growth through the improvement in intermediate quality,
24:46 as is common in the innovation-based growth models.
24:50 Now,
24:50 the novelties that I bring to this framework are the following.
24:54 I consider a two country version of this economy with two countries,
24:57 A and B.
24:59 And the goal is to describe the endogenous evolution
25:01 of TFP ABC in each of the two economies.
25:05 At the heart of the process of growth are inventors.
25:09 And inventors here produce innovation,
25:12 but on top of that,
25:12 they are allowed to move across countries and to return to their country of origin.
25:17 And second,
25:18 they meet others in the economy and they learn from them.
25:21 And this interaction and learning process generates
25:23 knowledge spillovers both within and across countries.
25:28 Finally,
25:28 in this model,
25:29 I also allow for some additional exogenous technology diffusion across countries
25:34 to capture other sources of ideas flows such as foreign direct investment
25:38 or trade in intermediate goods.
25:40 And finally,
25:41 these countries also trade the final good
25:43 and share a common exogenous interest rate.
25:46 So let me give you some details on this process of migration for these inventors.
25:52 Inventors are born in a given country in this model,
25:56 and they have heterogeneity across two key dimensions.
25:59 The first,
26:00 as Ufuk mentioned in his presentation,
26:02 is that
26:02 inventors have heterogeneous talent that I call Z.
26:06 But in this model inventors also have
26:08 heterogeneous idiosyncratic opportunities in different countries,
26:12 and this is what I call their foreign productivity differential epsilon.
26:16 And together these two variables determine how productive an inventory is.
26:20 So an inventor in every period produces a certain bundle of ideas or technology
26:25 Q that is equal to their talent when they live in their home country.
26:29 But this is equal to the talency plus the
26:31 foreign productivity differential epsilon if the inventor moves abroad.
26:36 Now this epsilon here is meant to capture idiosyncratic reason why a
26:40 certain inventor could be more productive in one place versus another,
26:43 such as
26:44 their individual field expertise that could be a good match
26:47 with a firm in the other country in this period.
26:50 So the important point here is that you can see that
26:52 inventors will choose to move when they have a high draw
26:55 for these idiosyncratic opportunities in the other location.
26:59 But also these opportunities epsilon are allowed to move over time
27:03 and if they become low enough,
27:05 the inventor will choose to return to the country of origin.
27:08 So here migration decisions are endogenous
27:10 to the idiosyncratic
27:12 productivity opportunities of these inventors in different countries.
27:16 The second important piece here to the right of these
27:19 slides is that their return to innovation for these inventors
27:23 are also proportional to the aggregate TFP ABC in each location.
27:27 And this means that a 2nd reason for inventors to move here is also
27:31 that they may want to go to the country with the highest TFP,
27:33 the technology frontier,
27:35 where they earn more from their innovations.
27:40 Finally,
27:41 the third point is that inventor silent Z also
27:44 evolves over time thanks to this learning process.
27:48 And this works in a way such that
27:50 with some probability lambda an inventor meets another individual
27:54 in the economy and the result of this
27:56 meeting is that both individuals become more productive.
28:00 However,
28:01 a novelty here is that the probability of meeting
28:03 different individuals is different for locals and for immigrants,
28:07 and this is due to matter frictions.
28:09 And this liter frictions means that
28:11 a certain inventor that is born in a certain country C and lives in some
28:17 other country D has different probabilities of
28:19 meaning inventors that live in different places and
28:22 come from different countries.
28:24 The important point of this structure is that first of all,
28:27 individuals have a 3rd reason to move because
28:29 when they move they can change their interaction network
28:32 and look for better learning opportunities.
28:35 But also the other important point is that
28:37 when individuals move with some probability these migrants
28:40 can still interact with inventors in the country of origin
28:44 and with this meaning they transfer knowledge that
28:46 makes the locals of origin more productive.
28:48 So this is the way in which the model captures the idea that
28:52 the
28:53 knowledge spillovers channeled by migrants make the
28:55 local inventors at origin more productive.
28:59 So as a summary of the effects of migration in this model,
29:02 if we think about the migration flow from country A to country B,
29:05 which is the frontier.
29:08 What we find is that there are now more inventors in B
29:10 who are also more productive when they move because they're positively selected,
29:14 although some of these migrants may crowd out
29:17 some of the local inventors,
29:19 and on,
29:19 on the other side in Country A,
29:21 there are fewer inventors because of the brain drain,
29:24 but the migrants transfer knowledge to the locals at origin,
29:26 making them more productive.
29:28 And on top of that,
29:29 the Country A benefits from higher innovation through the
29:32 front at the frontier also through exogenous technology diffusion.
29:36 Which is why overall the net effect of migration in this economy is ambiguous.
29:41 And this motivates the need for an
29:43 empirical and quantitative analysis in this model.
29:47 So to do that I focus on an application of this model where location A is Europe
29:52 and location B is the US
29:53 and again this is due to data availability,
29:56 but what could think of a different application and calibration of this
29:59 model where one country is the US and the other country is,
30:01 for example,
30:02 a developing economy.
30:04 Now,
30:04 I use my patent data to calibrate the parameters of this model.
30:09 And to do that,
30:09 I first show this fact that represents the
30:12 collaboration network of local and migrant inventors.
30:16 So what I'm doing here is to
30:18 group the inventors in my data into 4 categories that are
30:21 locals and migrants of European and American origin.
30:24 And here I plot on the y axis the fraction of collaborators of these inventors
30:29 that belong to each of the four categories.
30:32 So for example,
30:33 the first bar on the left
30:35 here shows that when I look at European local inventors,
30:39 in orange,
30:39 this means that they mostly work with other local European inventors.
30:43 However,
30:44 the second bar looking at the European inventors that move into the US
30:48 shows in green that
30:50 these migrants are much more likely to work with American inventors
30:54 as well as other migrants,
30:56 and the similar pattern is true for American inventors.
31:00 Now,
31:00 this is helpful to think about the network frictions in my model,
31:03 and it says that inventors are more likely to meet
31:06 and interact with other inventors that either live in the same country
31:10 or also they have their same nationality.
31:16 Now the next step here is to illustrate the evolution of patenting activity
31:20 both for the migrant inventors as well as for their local collaborators.
31:25 So to do this,
31:25 the strategy here is to match every migrant in my sample with one placebo or
31:31 control inventor who is not a migrant and not a direct collaborator of a migrant,
31:35 but this control inventor comes from the same country of origin,
31:39 appears for the same first year in the sample,
31:41 and also has the same cumulative patent stock
31:44 by the time of migration as the migrant as a proxy of productivity.
31:49 And finally,
31:50 as the last step,
31:51 I collecting the data,
31:52 the full network of collaborators in the country of origin,
31:55 both for the real migrants
31:57 and for the control group.
32:00 And here is what I find.
32:02 In this first figure I'm plotting here on the X axis,
32:05 times 0 is the time when the real migrants move away.
32:08 And the Y axis is showing the mean of patent applications per year.
32:13 The blue line is the dependent applications of the real migrants,
32:16 and the red line is for the control group.
32:19 So what we see here is that the key point is that after migration,
32:22 migrants tend to increase their patent
32:25 applications relative to the control group.
32:28 The next question is what happens then to the local
32:31 collaborators of these migrants in the country of origin.
32:35 That's what I show in the second figure where the
32:38 X axis still shows the time 0,
32:40 the time when the real migrants move away,
32:42 but now in the blue line I'm tracking the patent applications of
32:46 the collaborators of the real migrants in the country of origin,
32:49 whereas the red line are the patent applications
32:51 of the collaborators of the control group.
32:55 And the key message here is that also
32:56 the local collaborators of the migrants at origin
33:00 see an increase in their patent applications after the migrant moves away
33:05 relative to the patent applications of the collaborators of the control group.
33:10 Now this,
33:11 this fact is very important to calibrate the parameters of my model.
33:14 Why is that the case?
33:16 That is because
33:17 I can replicate these two event studies
33:20 from a simulated sample of inventors in my model.
33:23 And I calibrate my parameter in such a way that the model simulated sample in red
33:28 produces the same magnitude as the data event studies
33:31 that we've just seen that here is in green.
33:34 And the key figure here is the one for the local inventors on the right
33:38 because in the model,
33:39 the reason why the local collaborators of the migrants at origin
33:43 see an increase in their patent applications
33:45 is thanks to the knowledge spillovers
33:47 that the migrants abroad channel
33:49 to these inventors back in the country of origin.
33:54 Now in the last few minutes,
33:55 I want to show some policy exercises.
33:58 Today I'm going to talk about a tax on inventors' profits
34:02 that is allowed to be heterogeneous across countries
34:05 and yet in my my calibration is initially higher in Europe and lower in the US.
34:10 And in the paper I talk more also about migration policy in the US.
34:15 Now the policy exercise that I do here is to study a
34:19 a tax cut in the European Union
34:22 for American immigrants and European returned migrants,
34:25 and this is motivated by the fact that several European countries
34:29 have implemented policies of this flavor starting from the Netherlands,
34:32 Denmarks,
34:33 more recently also Italy and other countries
34:36 with the goal of trying to revert this brain drain.
34:40 In my model this means solving the transitional dynamics from
34:43 the initial equilibrium where the tax rate is higher in Europe
34:47 to the new equilibrium where the tax rate in
34:49 Europe is lower for Americans and returned migrants.
34:53 Now in the next few figures,
34:55 I'm ploying the results of this policy,
34:57 so the X axis is always going to be
34:58 the number of years since the policy implementation.
35:02 This first figure here on the y axis is plotting
35:04 the stock of migrants as a fraction of domestic inventors.
35:08 So what we see here in terms of allocation of talent
35:11 is that
35:12 the green line shows that the stock of
35:14 American migrants in Europe increases and this is
35:17 because Americans know that now if they go to Europe they'll have a lower tax rate,
35:22 but at the same time the stock of European migrants in the US,
35:25 the red line
35:26 is decreasing,
35:27 and this is because European migrants know now that if they
35:31 return to Europe they will have this lower tax rate.
35:34 And the dashed line here is showing that
35:36 uh the magnitude of this policy is such that
35:39 the level of net migration that is the dash line
35:42 is going down to zero in the long run.
35:45 Now,
35:45 what is the,
35:46 the effect of this reallocation of talent on innovation?
35:50 That's what we see in this figure,
35:51 where now the y axis is an innovation plotted as a percentage of GDP.
35:56 And what we see is that this reallocation of inventors
36:00 towards Europe implies that there is an increasing innovation in Europe
36:03 and a decline in innovation in the US.
36:06 Now the last question is,
36:08 what is the impact of this change in innovation on output in the two economies?
36:14 So in this last figure I'm plotting outputs in Europe and the US
36:18 relative to the counterfactual equilibrium
36:20 where there is no policy change.
36:23 So what we see here in the green line is that output in the US is declining,
36:27 and this is because innovation in the US is declining and
36:31 the US is the innovation and technology frontier to start with.
36:35 But more interestingly,
36:36 here in the red line we see that output in Europe
36:39 increases for a few decades,
36:41 but eventually,
36:41 and this is thanks to higher innovation in Europe,
36:44 but eventually at some point output in Europe starts to decline.
36:48 And to understand why we need to look at the table of the right,
36:51 where I provided the composition of the different
36:54 forces that I discussed in the theoretical section,
36:57 quantify the two different time horizons.
36:59 And the one that I want to highlight here is given by
37:02 the knowledge spillovers that are at the center of this model.
37:06 And what's happening here is that this policy
37:08 by reducing net migration flows to zero,
37:11 it also reduces knowledge spillovers across countries
37:14 and eventually this has a sizable negative effect on output in Europe,
37:18 but this is also a force that takes a long time to become
37:21 effective because it operates on the distribution of human capital in the economy
37:25 that is a slow moving object.
37:29 So
37:30 to summarize,
37:31 this paper presents a framework to study migration decision,
37:35 knowledge networks and innovation
37:36 and some new empirical evidence to think about
37:39 the magnitude of these different forces.
37:43 More importantly to me,
37:44 the key takeaway from these policy exercises are the following.
37:48 First is that
37:49 um policies that try to change migration flows and reduce the brain drain.
37:55 Actually generate several forces on the economy that have opposite sides
38:00 and so they even
38:02 they may even end up in backfiring and reducing output in the long run.
38:06 On the other hand,
38:07 I also highlight that knowledge spillovers are very important.
38:10 They have a sizable quantitative effect on output.
38:14 So perhaps if anything rather than
38:16 focusing on policies that affect migration flows,
38:19 one could think about policies that
38:21 foster these knowledge spillovers across countries.
38:24 And finally,
38:24 I think we still have some open questions,
38:27 especially relevant for this audience.
38:29 Due to data availability,
38:31 this,
38:31 this paper focuses on Europe and the US
38:34 and we need to have more research to understand,
38:36 for example,
38:37 how sizable these knowledge spillovers are
38:39 for other countries,
38:40 especially the developing countries.
38:42 Thank you.
38:47 OK,
38:47 great.
38:48 Very,
38:48 very nicely done and,
38:49 and right on time,
38:50 uh,
38:51 as well.
38:52 Uh,
38:52 let's now have the 3rd,
38:54 uh,
38:54 paper.
38:55 Alison,
38:55 uh,
38:55 20 minutes.
38:59 Um,
38:59 great.
39:00 Thank you for having,
39:01 um,
39:01 our paper as part of the session.
39:03 Um,
39:04 this paper is with Sam Asher,
39:05 Paul Novisad,
39:06 and Doug Galen,
39:07 who you'll hear more from later
39:09 on the long-run development impacts of canal irrigation in India.
39:13 So the motivating question of this paper is
39:15 one of the oldest in development economics.
39:17 How do gains to agricultural productivity
39:20 affect broader growth and structural transformation?
39:23 This question naturally arises from the observation that um
39:26 Lewis put in 1954
39:28 that economies in which agriculture is stagnant
39:31 do not show uh industrial development.
39:33 So there's been a lot of theory,
39:35 um,
39:36 and exploration of exactly why and how,
39:38 uh,
39:38 gains in agricultural productivity are translated into,
39:42 uh,
39:42 into industrial development.
39:44 Um,
39:45 and more recently,
39:46 there's been an empirical literature trying to quantify this effect,
39:50 uh,
39:50 much of it looking at the green revolution
39:52 and the introduction of high yielding seed varieties,
39:55 um,
39:55 through the 20th century.
39:57 Um,
39:57 and in both the theory and in the empirics,
39:59 um,
40:00 the relationship is,
40:01 is complicated,
40:02 and we want to understand more about
40:04 exactly when,
40:05 um,
40:06 technical advancement in agriculture
40:08 translates into growth in the non-farm sector as opposed
40:11 to crowding out growth in the non-farm sector.
40:14 So in this paper,
40:14 we add what we think is a unique
40:16 angle by looking at the generally equilibrium effects
40:19 of,
40:20 uh,
40:20 a massive agricultural productivity shock at high spatial resolution.
40:25 So that allows us to look at exactly how and where,
40:28 um,
40:28 structural change might take place in the long run.
40:32 So we're looking at the canal network in India,
40:34 which is very large.
40:35 It runs for 300,000 kilometers and serves,
40:38 uh,
40:38 about 1/5 of all villages in India.
40:41 What's interesting about canals as a,
40:43 as a shock to agricultural productivity
40:45 is that unlike other technical advancements
40:48 like seeds or fertilizers or mechanization.
40:51 Um,
40:51 canals are fixed in space.
40:53 So when you build a canal,
40:54 you introduce a long-run differential,
40:56 um,
40:56 in productivity for places that get access to canal water versus those that don't.
41:01 Um,
41:01 they don't diffuse across space through time like many other technologies do.
41:05 Um,
41:06 these canals were mostly built,
41:07 uh,
41:08 through the 19th and 20th century,
41:09 and the majority of the canals in our data set were completed over 40 years ago.
41:13 So we're going to be looking at outcomes in,
41:16 uh,
41:16 the long run in the modern cross section from this,
41:18 uh,
41:18 canal network
41:20 that was built to deliver water primarily during the dry season,
41:23 the rubby growing season.
41:25 Um,
41:25 to allow farmers to have,
41:27 uh,
41:28 better,
41:28 more production in a year.
41:30 So you're getting more production per hectare per farmer,
41:32 and they're more resilient to the annual weather seasonality.
41:37 Um,
41:38 so as I mentioned,
41:38 the canals,
41:39 uh,
41:40 are an old technology.
41:41 They were invested heavily in by the British Raj and
41:43 then also by the early independent government in India.
41:46 They were actually the dominant source of irrigation before
41:48 the rise of groundwater in the last 40 years,
41:51 and they're still a very significant,
41:52 um,
41:53 source of irrigation for Indian farmers.
41:56 Just so you have a picture of what these canals look like.
41:58 These are not the Suez Canal or the Erie Canal.
42:00 They're not built for massive transport.
42:02 You can see here there's a boy standing in the middle of this canal,
42:05 so it's fairly shallow,
42:06 and the water level actually fluctuates such that sometimes these canals are
42:10 empty,
42:11 um,
42:11 and the water level will go up and down as,
42:13 as the,
42:14 as the season changes.
42:16 So don't picture big transport canals.
42:18 These are really built for,
42:19 uh,
42:19 irrigation purposes for agriculture.
42:22 So our goal in this paper is to estimate the impact of these,
42:26 uh,
42:26 getting access to irrigation from these canals in India.
42:29 And we're going to do that in 3 ways,
42:31 estimating 3 different local average treatment effects.
42:34 The first is the direct treatment.
42:35 So,
42:35 in villages that get access to canal water versus those that don't,
42:39 what are the agricultural and
42:41 non-agricultural outcomes we see?
42:43 Then we'll look at spillovers from those directly treated,
42:46 um,
42:46 areas into other villages that are proximal.
42:50 And lastly,
42:50 we'll look at the timing of canal construction
42:53 to understand regional urbanization.
42:55 So our gains from canal irrigation being invested in the region,
42:59 um,
42:59 into urban centers.
43:02 To give you an idea of where we're going,
43:03 uh,
43:04 to cut to the chase,
43:05 we're going to show that
43:06 this increased access to canal irrigation,
43:09 we can actually measure an increase
43:11 in canal irrigated agricultural land that
43:13 leads to increased agricultural productivity,
43:16 that's been sustained,
43:17 um,
43:17 into the modern day.
43:19 And that increase in agricultural productivity leads to an increase in migration.
43:24 So,
43:24 the population density in irrigated areas drastically increases,
43:28 but we don't see any long-run,
43:30 um,
43:31 uh,
43:31 gains in consumption.
43:33 So,
43:33 for the general population,
43:35 places that are irrigated versus not irrigated,
43:37 uh,
43:38 their consumption are about equal,
43:40 except for landowners.
43:41 So landowners,
43:42 um,
43:42 who get their land irrigated have sustained gains,
43:45 um,
43:45 in the modern data.
43:47 And we see no effects on rural industrialization,
43:50 but we do see regional urban growth.
43:52 So basically,
43:52 in the long run,
43:53 um,
43:54 the story we're going to try to show here is that productivity
43:56 gains introduced by these canals are equilibrated by labor flows across space,
44:01 and any structural change they,
44:03 uh,
44:03 create is concentrated as,
44:05 as urban growth.
44:07 So to walk you through our empirical strategy in more detail,
44:09 just consider two,
44:11 villages that are similar,
44:12 except for their elevation.
44:13 So we have one that's slightly higher,
44:14 maybe 10 m higher in elevation than the other.
44:17 When the canal is constructed through the area,
44:19 because canals require gravity to deliver water to their endpoint,
44:23 the canal will only be able to feed the village that lies below the canal.
44:27 So this is going to introduce a discontinuity,
44:29 um,
44:30 that allows us to do a regression discontinuity design based on
44:32 the elevation of the villages relative to the canal placement.
44:36 So in our first stage,
44:38 we're going to see that access to this canal water is going to give,
44:40 um,
44:41 these irrigated villages higher agricultural productivity,
44:44 and we'll test for any other non-farm outcomes.
44:48 We'll then look at spillovers into those,
44:50 uh,
44:50 villages that lie above the canal that weren't directly treated,
44:53 but are proximal to this boost in agricultural productivity
44:56 by comparing to a third set of distant villages that are much farther from the canal.
45:01 And then lastly,
45:02 we'll look at,
45:03 uh,
45:03 regional urbanization to see if these gains
45:05 from productivity in the treated villages,
45:07 uh,
45:08 result in growth of urban population.
45:11 The data we're going to use for this,
45:12 um,
45:13 I won't go into detail here,
45:14 but it's all administrative,
45:15 publicly available data from India between 2011 and 2013.
45:20 The one thing I'll highlight is that we don't have,
45:22 um,
45:22 a,
45:23 a direct measure of crop yields at the village level.
45:25 So we're going to use a satellite proxy,
45:27 which is the enhanced vegetation index.
45:30 So that essentially gives us,
45:31 uh,
45:32 a measure of the green up in every village during every season.
45:35 So that's what we're going to use for our productivity measure.
45:38 If you want to know any more about this data,
45:40 you can check out the Shrug open data product that's,
45:43 um,
45:43 made available through Development Data Lab.
45:45 There was just a second version of this
45:47 data platform released last week,
45:48 which was very exciting.
45:50 Um,
45:50 there's a lot of documentation there and happy to talk about it later.
45:54 All right,
45:54 so for our first,
45:55 um,
45:56 uh,
45:56 identification strategy,
45:57 we have a standard regression discontinuity,
45:59 uh,
45:59 specification where our running variable
46:02 is elevation of villages relative to the nearest canal.
46:06 So what I'm showing here on the,
46:07 in the map,
46:08 you'll see each polygon is a village,
46:10 and the villages colored purple lie,
46:13 um,
46:13 below the nearest canal and the villages
46:15 colored orange lie above the nearest canal.
46:18 The gray villages are far enough away from
46:20 a canal that they're excluded from our sample.
46:22 And just for this district,
46:23 I'm plotting in,
46:24 um,
46:25 on the Y axis of that plot,
46:27 you see,
46:27 it's the share of agricultural land that's irrigated by canals.
46:31 So this is just to,
46:32 to show you the discontinuity we were describing,
46:35 um,
46:35 the orange villages have much lower levels of irrigation than the,
46:39 uh,
46:39 purple villages.
46:40 So that's the discontinuity that we want to exploit in this,
46:43 um,
46:43 design.
46:44 So looking at a result with our full sample,
46:46 um,
46:46 here's this,
46:47 uh,
46:47 regression discontinuity,
46:49 again with canal,
46:50 um,
46:50 irrigation as the outcome variable.
46:53 And we see on the right-hand side,
46:54 um,
46:55 those are the villages that are below the canal that get access to irrigation,
46:58 and on the left-hand side,
46:59 those are above the canal that do not get access.
47:01 So we see a jump at this boundary,
47:04 um,
47:04 and I'm going to show you a bunch more results here
47:06 that just show you that jump at the boundary is on coefficient plots,
47:10 but that's what we're measuring,
47:11 um,
47:11 with the,
47:11 the,
47:12 uh,
47:13 design.
47:14 So here
47:15 is a coefficient plot showing irrigation outcomes.
47:17 So the second one you'll see there is
47:19 the same one we just looked at.
47:20 It's percent of your agricultural area that is irrigated by canals.
47:24 And we're plotting this as a normalized treatment effect.
47:27 So all of these,
47:28 uh,
47:28 coefficients are in terms of standard deviation of their outcome variable,
47:32 so that we can look,
47:33 compare them all together.
47:35 And we see that the villages that lie below the canals really do have,
47:38 um,
47:39 increased irrigation that's driven by canals.
47:41 And
47:42 we notably don't see,
47:43 um,
47:44 too well,
47:44 which is groundwater irrigation or any other source of irrigation.
47:47 There's no difference,
47:48 um,
47:49 at the boundary of in our
47:50 regression discontinuity.
47:52 And these irrigation outcomes lead to agricultural productivity gains.
47:56 So villages below the canal
47:58 have a larger share of,
47:59 uh,
47:59 their area that's cultivated.
48:02 And they also have higher productivity,
48:04 and that's going to be concentrated
48:06 in the rubi,
48:07 uh,
48:08 season,
48:08 the dry season,
48:09 versus the Kari harvest season.
48:11 So,
48:12 that's what we would expect because these canals really
48:14 were designed to deliver water during that dry season,
48:16 and that's where we're detecting,
48:17 uh,
48:17 productivity gains.
48:19 And these villages are also more likely to grow water-intensive crops.
48:22 So we're pretty confident that this illustrates a first stage of,
48:25 of,
48:25 uh,
48:26 what we're hypothesizing here is that
48:28 the,
48:28 um,
48:29 canals really are bringing additional irrigation,
48:31 which is creating long-run,
48:33 uh,
48:33 sustained differences in agricultural productivity.
48:35 So the big question is,
48:36 do these translate into any local non-farm economic outcomes?
48:41 And the answer is,
48:42 uh,
48:43 there is one major one which is population density.
48:45 So
48:46 areas that are irrigated by canals have much higher population densities,
48:50 but notably don't have,
48:52 um.
48:53 Any increase in non-farm employment.
48:56 So we don't see an increase in industrialization,
48:58 so no
48:59 increase in manufacturing or services or even agro-processing employment
49:03 directly in the villages that are serviced by the canals.
49:06 Um,
49:06 this increase in population density,
49:08 I won't show the details here,
49:09 but we have suggestive evidence that this really is driven by
49:11 migration as opposed to changes in fertility or mortality patterns.
49:17 Um,
49:17 just to dig in on one result we showed here is there's,
49:20 there's also no difference in consumption,
49:21 but if we desegregate that by landowners and landless,
49:24 so in the bottom graph here,
49:26 you'll see,
49:26 um,
49:27 landowning households on the top and landless households on the bottom.
49:30 So if you compare landowning households that have
49:32 access to irrigation versus those that don't,
49:34 they have,
49:35 uh,
49:35 higher consumption
49:36 in the modern day,
49:38 while landless households have about equal consumption across that,
49:41 that discontinuity.
49:42 And on the top,
49:42 we just break it out by,
49:44 um,
49:44 the percentile of,
49:46 uh,
49:46 the size of landholdings.
49:48 So the wealthiest households that own the largest land
49:50 get the best returns.
49:52 So the gains from this,
49:53 uh,
49:53 the increase in agricultural productivity are accruing to the fixed factor.
49:57 So landowners are benefiting while landless laborers,
49:59 um,
50:00 are the,
50:01 the gains from the productivity increase are,
50:03 are eaten up by the population density increase.
50:07 Um,
50:08 these results are,
50:09 are robust to several different specifications and checks.
50:12 I won't go into detail for the sake of time,
50:13 but happy to talk more about these later.
50:16 Um,
50:17 so the first question we have is if you have,
50:20 you know,
50:20 an increase in agricultural productivity in these
50:23 local villages,
50:24 there may be many reasons why this would spill over into other villages in the area.
50:29 Um,
50:29 the first could just be that as these villages are,
50:31 are taking water from the canals,
50:33 that allows the water table to run.
50:35 And that groundwater recharge might lead to positive agricultural effects in
50:39 nearby villages that don't get access to the canal water.
50:42 Also,
50:42 labor and goods could be flowing between these,
50:44 um,
50:45 villages and we might see,
50:46 uh,
50:47 a null result in the regression discontinuity.
50:49 That just means the entire area proximal to a canal
50:52 may be,
50:52 uh,
50:52 experiencing,
50:53 um,
50:54 economic gains.
50:56 So we're going to test this with a regression where we hold out the
51:00 above canal tree,
51:01 uh,
51:01 group.
51:02 So those are the villages that are near the canal,
51:04 but don't get treated by,
51:06 um,
51:06 getting access to the canal.
51:07 And we'll compare those
51:09 again to the villages that are below the canal and then to a third
51:12 group of distant villages that are more than 15 kilometers away from the canal,
51:16 and we're confident are not affected
51:18 by the introduction of the canal.
51:21 To create that,
51:22 um,
51:22 comparison group,
51:23 we use an entropy balance weighting so that we ensure that characteristics,
51:27 uh,
51:27 like these geographic fundamentals are matching.
51:29 So we try to match the,
51:30 the distant
51:32 village group to the villages that are treated by the canals.
51:36 Um,
51:36 now,
51:36 these are a little bit hard to,
51:37 hard to see,
51:38 but I just want you to focus on the right-hand side.
51:40 That's the coefficient that's estimating the spillover effect.
51:43 So it's directly comparing
51:44 the above,
51:45 uh,
51:46 villages that lie above the canal,
51:47 but proximal to the canal to distant canals.
51:50 And we mostly see,
51:51 No spillovers in agricultural and irrigation outcomes.
51:55 So there's no difference in agricultural productivity
51:58 or percent of the land that's irrigated.
52:00 There's a small spillover where villages near the canal are more likely to,
52:04 um,
52:05 grow water-intensive crops.
52:07 And moving on to non-agricultural outcomes,
52:10 again,
52:10 we see mostly no spillover.
52:12 So our,
52:12 our villages that are near the canal look very
52:15 similar to the villages that are far away,
52:17 except for population density.
52:18 So there's a small increase in population density,
52:21 um,
52:21 in the areas right near the canal,
52:22 which
52:23 mirrors the influx in population density we saw,
52:26 um,
52:26 from the regression discontinuity.
52:29 But the,
52:29 the major takeaway here is we don't see spillovers in rural manufacturing.
52:33 So there's no increase
52:34 in non-farm employment,
52:36 in service sector employment,
52:37 or manufacturing employment
52:38 in areas that are in,
52:40 that are proximal to the canal but not treated.
52:42 So that leaves us to conclude that we
52:44 don't have much evidence of rural industrialization.
52:48 So our final,
52:49 uh,
52:49 set of results is going to look at
52:52 regional urban growth.
52:53 So our,
52:53 our first two strategies really relied on the spatial distribution
52:57 of the canal network and the villages,
52:59 both the distance and the elevation and how those are all related.
53:02 But we know that the gains in agricultural productivity
53:05 might be
53:06 invested into urban growth in a way
53:08 that's totally orthogonal to that spatial relationship.
53:11 So we could see over time as canals are,
53:13 um,
53:14 constructed in the,
53:15 uh,
53:16 in the region,
53:17 that those gains are invested into town growth.
53:19 So what we do is we have a panel of towns.
53:21 So we have every town from the 2011 population census,
53:25 and we have the population going back every decade until 2001.
53:28 And so for each town,
53:30 we draw a catchment area of a 20 kilometer radius around the town,
53:33 and we look at the area.
53:34 So that's what's
53:35 in yellow,
53:36 and then in blue,
53:37 we have the area serviced by canals,
53:39 um,
53:39 over time as,
53:40 as those change.
53:41 So for every town,
53:42 we can define a share of the area that's treated by canals
53:46 and look at that change over time in a difference in difference analysis.
53:50 So in this,
53:51 uh,
53:51 first result,
53:52 we see that time 0 is when a town gets
53:54 treated by canals in the catchment area surrounding it,
53:58 and we see a significant increase in urban population.
54:01 Um,
54:02 for the periods after the town received access
54:05 to canal water in those outlying areas.
54:08 Um,
54:08 we can also look at an outcome that is town appearance by size.
54:12 So we can threshold town size and say,
54:14 as soon as this town hits 5000 or 10,000 or,
54:17 or 50,000,
54:18 so at what,
54:19 at what part of the size distribution
54:22 is town growth sensitive to the,
54:24 uh,
54:25 canal construction?
54:26 And what's important here is that we see it's concentrated in smaller towns.
54:30 So,
54:30 the smaller town sizes are more sensitive to,
54:33 um,
54:33 increases in canal irrigation in the areas surrounding it.
54:36 So this tells us that small regional towns that might
54:39 be very reliant on agro-processing or trade from agricultural goods
54:43 are sensitive to growth when
54:45 there's more productivity
54:47 around them,
54:48 while large cities uh
54:50 that might be more diversified aren't going to be as affected by,
54:53 by,
54:53 um,
54:53 the canal construction.
54:56 So,
54:57 kind of in summary,
54:58 what we've seen from both the,
55:00 uh,
55:00 the regression,
55:01 discontinuity and spillover analysis as well as this,
55:04 uh,
55:04 town panel of urban
55:06 population data,
55:07 is that there are large movements of people both
55:09 from into rural areas that are treated by canals
55:12 and into urban areas that are in the region.
55:14 So
55:15 the first question you might have is how big are these population movements.
55:18 So if we take these coefficients and do a back of the envelope calculation,
55:22 uh,
55:22 we see that
55:23 the effect is about.
55:23 5 million people living in urban areas and 29 million people living in rural areas,
55:28 um,
55:28 due to this canal network.
55:30 So just for an order of magnitude,
55:32 the most,
55:33 um,
55:34 significant migration event in Indian history is obviously the,
55:37 the partition,
55:37 which
55:38 displaced about 17 million people.
55:40 So we're talking about a large scale movement of people,
55:43 um,
55:43 obviously over a long period of time,
55:45 but it's quite economically significant,
55:47 um,
55:48 to think about the spatial,
55:49 uh,
55:49 mobility of people,
55:50 uh,
55:51 just from these rural infrastructure projects.
55:55 So to conclude,
55:56 um,
55:56 we'd see that these canals introduce
55:58 a long-run difference in agricultural productivity.
56:00 Again,
56:00 these were built,
56:01 um,
56:02 through the 19th and 20th century,
56:04 and we're now looking at the long-run effects in the modern cross section,
56:07 and we still see sustained differences in agricultural productivity.
56:11 Um,
56:12 but those differences
56:13 mostly were equilibrated by movements of labor,
56:16 um,
56:17 across space rather than between sectors in a highly localized way.
56:21 So,
56:21 the structural change that we can detect is not directly in rural areas,
56:24 but rather in,
56:25 in regional urban centers,
56:27 um,
56:28 and any sustained living standard changes
56:30 we see are concentrated among landowners,
56:32 uh,
56:33 as opposed to landless laborers.
56:36 So the implications for these findings are that,
56:38 um,
56:39 you know,
56:39 generally in the long run,
56:40 development entails substantial movement of people across space.
56:44 Um,
56:45 and as we rural infrastructure projects might not
56:48 lead to the kind of rural industrialization.
56:50 But rather relieving barriers to uh mobility and allowing people to move to cities
56:56 might be an effective way to bring about structural change.
56:59 Um,
57:00 and one other thing I'll add is that,
57:02 you know,
57:02 this paper we,
57:03 we deal with uh a shock to agricultural productivity that's driven by
57:07 agricultural technology,
57:08 but we also know agricultural productivity will change in the future,
57:11 especially with,
57:12 with climate change.
57:13 Changing the patterns of what places are most productive,
57:16 um,
57:17 and which are less productive.
57:18 So
57:18 we can expect other shocks to agricultural productivity
57:21 to inspire similar movements of people,
57:24 um,
57:24 across space.
57:25 So it's important,
57:26 uh,
57:26 not definitely not the first paper to point to,
57:28 to mobility due to climate change,
57:30 but,
57:30 um,
57:31 to just in line with those other,
57:32 other findings.
57:33 So,
57:33 um,
57:34 that's all,
57:34 and,
57:34 uh,
57:35 thank you very much.
57:40 OK,
57:40 that was,
57:41 that was awesome as well.
57:42 Thanks very much,
57:43 uh,
57:43 Allison.
57:43 And so Chris Papageorge's going to,
57:45 uh,
57:45 discuss the three papers.
57:54 Alright,
57:54 thanks everyone.
57:55 I'd like to thank the organizers for
57:58 um
57:59 the generous invitation to be part of this great event
58:03 and for for taking a look at these three fantastic papers.
58:07 I can really not do justice to,
58:10 you know,
58:10 the depth of these papers in 15 minutes,
58:13 so
58:14 I would be sending bilateral detailed comments to the authors
58:18 and what I'll be doing here is just,
58:20 just,
58:20 uh,
58:21 present to you what are my top highlights from my readings of these papers.
58:25 I'd like to spend a few minutes in the end of
58:29 zooming out and looking at country convergence and some of my own thoughts
58:34 about some of the emerging challenges uh to developing economies that I see.
58:39 Um,
58:40 so let's start with,
58:41 uh,
58:42 Sumiro's,
58:42 uh,
58:43 paper.
58:45 Which is a fantastic paper uh
58:48 on specifically on the data side
58:52 because it fills a gap,
58:53 a major gap in the literature we have done work on convergence
58:57 looking um
58:59 at country level where the unit of information is uh the country a lot of work there.
59:05 We have also some work that is at the individual level
59:08 but we have very little at the regional level and this
59:11 paper uh
59:12 exactly uh
59:14 fills that gap
59:15 so
59:16 the paper is very novel because it it constructs 600
59:21 regional income level data.
59:24 Um,
59:24 and it spans 34 countries
59:27 for the period 1980 to 2015,
59:30 so for those of you who have done
59:32 work with data,
59:33 this is an incredible achievement.
59:35 Um,
59:36 in the paper you can see how
59:38 the authors are homogenizing,
59:41 uh,
59:41 different regions,
59:42 and we are talking about Tanzania,
59:43 Peru,
59:43 and the US,
59:44 very difficult,
59:45 uh,
59:45 task.
59:46 Then,
59:47 as,
59:47 uh,
59:48 Schmiro have shown
59:49 they have
59:50 used a,
59:50 a very standard spatial
59:52 structural transformation model,
59:54 uh,
59:55 to explain some of the,
59:56 uh,
59:56 main findings.
59:57 Really the key finding of the paper is that there is a sharp slowdown,
1:00:02 uh,
1:00:02 within in within country regional convergence.
1:00:05 Uh,
1:00:06 and,
1:00:06 and then they are using their model to provide a mechanism
1:00:11 of why that's why they observe this,
1:00:13 this fact,
1:00:14 and the way they describe it then in the
1:00:16 model is that this is basically due to the transition
1:00:20 to services
1:00:22 and uh and the economic activity
1:00:25 being specially concentrated
1:00:27 which results in this decline in regional convergence.
1:00:31 Uh,
1:00:31 so I have a few comments.
1:00:33 Definitely the first one is on data,
1:00:36 um,
1:00:37 you know,
1:00:38 uh,
1:00:38 the devil is in the details on these constructions,
1:00:41 and,
1:00:41 uh,
1:00:41 I have seen that the authors
1:00:44 relied quite a bit on interpolations,
1:00:46 and I've never been
1:00:47 a fan of,
1:00:48 uh,
1:00:49 interpolating data.
1:00:50 There is a lot of work.
1:00:51 Uh,
1:00:52 for example,
1:00:53 from Angus Deaton and others that show that
1:00:55 when we finally figure out actual data and we replace the interpolated data,
1:01:01 many of the results
1:01:02 are overturned.
1:01:03 So I would
1:01:04 say in my bilateral comments,
1:01:05 uh,
1:01:06 Schmitra,
1:01:06 basically I guide you to some
1:01:08 new,
1:01:09 uh,
1:01:09 now casting machine learning,
1:01:11 uh,
1:01:12 uh,
1:01:13 empirics where basically they take into
1:01:16 account all of the information that you have out there.
1:01:19 To feel uh this data,
1:01:21 basically the signal to noise that you will be obtaining
1:01:24 from,
1:01:25 uh,
1:01:25 what I suggest I think is is higher than what you have right now.
1:01:29 Um,
1:01:29 the second issue that I wanted to bring up is,
1:01:32 um,
1:01:33 sort of,
1:01:33 you know,
1:01:34 I take a little bit of an issue of how quickly
1:01:36 the authors come to the conclusion
1:01:38 about country convergence and that
1:01:40 the evidence is that we have actually country convergence,
1:01:44 um,
1:01:44 I have done quite a bit of work.
1:01:46 I have a gel paper in 2020 with Paul Johnson.
1:01:49 And we had looked at,
1:01:50 we looked at the the evidence very carefully,
1:01:53 um,
1:01:54 and
1:01:54 what we came out with is that
1:01:56 the jury is still out so
1:01:58 basically the way we explain some recent papers which
1:02:02 focus this convergence because
1:02:03 they look at shorter periods we call these episodes of convergence if you'd like
1:02:08 I'll relate this comment actually to my final points about
1:02:12 kind of zooming out and looking at
1:02:14 uh cross country uh convergence.
1:02:16 It's a fantastic paper let's move on to the next paper
1:02:19 next paper
1:02:20 takes us somewhere else on growth the
1:02:23 one of the key
1:02:25 production functions,
1:02:26 the human capital
1:02:28 specifically on talent and uh
1:02:30 innovation migration basically the paper
1:02:33 does a fantastic job in offering a new model
1:02:36 of brain train
1:02:37 brain gain
1:02:38 it's a very complicated question.
1:02:40 Um,
1:02:42 the,
1:02:42 the paper,
1:02:44 um,
1:02:44 complements the model with
1:02:47 fantastic micro level data.
1:02:50 Um,
1:02:50 they look at basically patterns in the EU US migrant,
1:02:55 uh,
1:02:55 corridor.
1:02:57 And finally they have they don't stop with positive analysis they do
1:03:00 also
1:03:01 some policy experiments,
1:03:02 some key policy experiments one tax cuts
1:03:06 for uh the migrant innovators and the second
1:03:09 in increasing the cap of H-1B visas in the United States.
1:03:14 So
1:03:14 main findings,
1:03:15 and I'll stick only to the policy here
1:03:17 is not surprising the tax cuts
1:03:21 have positive effects in the short run,
1:03:23 negative effects in the long run.
1:03:25 The increase in the cap of H-1B visas is good for everyone,
1:03:29 the US,
1:03:31 the,
1:03:31 the European Union,
1:03:32 the global growth.
1:03:34 So two main comments I think,
1:03:36 uh,
1:03:36 Martha has already
1:03:38 picked up on the first one.
1:03:40 Um,
1:03:40 you know,
1:03:41 this is a fantastic tool,
1:03:42 a framework,
1:03:43 a template,
1:03:44 and we look forward to having the published version
1:03:48 and taking this and applying it to developing economies like
1:03:51 the corridor being
1:03:53 EU,
1:03:54 I don't know,
1:03:54 Africa or Asia.
1:03:56 Uh,
1:03:57 Europe,
1:03:57 uh,
1:03:58 so those would be very exciting
1:04:00 ways by which we can bring in developing economies in this.
1:04:03 I understand the
1:04:05 reason why they went to EU US for,
1:04:08 for,
1:04:08 for data availability,
1:04:09 but I think this is a very huge opportunity.
1:04:12 Have
1:04:12 uh to go in different corridors with developing economies
1:04:16 then of course we can go further we can look
1:04:19 at refugee uh innovators we can also you look at
1:04:22 more specialized innovators
1:04:24 uh with all the new reality of AI.
1:04:28 The second,
1:04:29 uh,
1:04:29 comment I have is for
1:04:31 to get some clarity from the authors,
1:04:33 uh,
1:04:33 to get them to think a little bit
1:04:35 further on
1:04:36 the assumptions made is a complicated model
1:04:40 and assumptions are very important.
1:04:42 One that I have a little bit difficulty understanding.
1:04:46 Is
1:04:47 whether that assumption made about the inventor's experience
1:04:51 a productivity boost when they are moving abroad
1:04:55 kind of
1:04:56 pinning down the results and and whether
1:04:58 uh I'm interpreting this correctly,
1:05:00 you know,
1:05:01 basically that's the conclusion of the model
1:05:03 and policy implications depend on that assumption,
1:05:06 something that we can discuss further
1:05:08 in the Q&A,
1:05:09 but again a fantastic uh
1:05:11 uh paper Martha
1:05:13 so finally we come to the last paper.
1:05:15 Uh,
1:05:15 Allison's paper,
1:05:17 uh,
1:05:17 on,
1:05:17 uh,
1:05:18 the process by which we grow the structural transformation,
1:05:21 especially early stages on agricultural productivity,
1:05:24 a fantastic paper,
1:05:26 you know,
1:05:27 very rich,
1:05:28 uh,
1:05:28 it took me forever to read,
1:05:29 it's like 78 pages,
1:05:31 so,
1:05:32 uh,
1:05:32 but,
1:05:32 uh,
1:05:33 but,
1:05:33 uh,
1:05:33 wonderful,
1:05:34 so
1:05:34 it careful,
1:05:36 carefully documents how investment in irrigation using the India example,
1:05:41 the canal example.
1:05:43 Um,
1:05:43 results in this positive return on productivity,
1:05:47 but to me the most exciting part of the paper bear is the disaggregation,
1:05:51 right?
1:05:52 So
1:05:53 you go from
1:05:54 looking at the results at impact,
1:05:56 which is the irrigated villages.
1:06:00 You know then you have the
1:06:02 connection
1:06:03 with rural areas and then you go to
1:06:06 regional urban economies and then you have possibly
1:06:10 the global economy.
1:06:10 This is a fantastic way
1:06:12 of mapping out
1:06:13 the impact
1:06:15 and the ripple.
1:06:15 Effects the total economy so I,
1:06:17 I
1:06:18 for me that was the most exciting part of the
1:06:21 of the paper and for for that to happen of course
1:06:24 the authors use different empirical methods,
1:06:26 uh,
1:06:27 a multi-sector location uh model by
1:06:31 you know I think motivated by boosters and and so forth
1:06:34 um
1:06:35 so
1:06:36 the findings are clear
1:06:38 for me the biggest finding is on population movement.
1:06:42 Uh,
1:06:42 being so central to
1:06:45 productivity,
1:06:46 uh,
1:06:46 gains and,
1:06:47 uh,
1:06:47 the key mechanisms for structural transformation,
1:06:50 a lot of papers have been talking about this,
1:06:53 but this paper documents,
1:06:55 uh,
1:06:55 quantitatively that impact.
1:06:57 Uh,
1:06:57 the numbers I had was 9 million,
1:06:59 uh,
1:07:00 drawn to cities and,
1:07:01 uh,
1:07:02 32 million in rural areas.
1:07:03 I think the numbers changed a little bit now
1:07:05 is 5 and 29,
1:07:07 but still,
1:07:08 uh,
1:07:08 massive,
1:07:09 uh,
1:07:09 impacts.
1:07:10 Um,
1:07:11 on my main comments is for future work mainly,
1:07:14 uh,
1:07:14 one,
1:07:15 I understand the limitation of the paper
1:07:17 to draw
1:07:19 implications,
1:07:20 um,
1:07:21 at the aggregate level,
1:07:22 and,
1:07:23 uh,
1:07:23 I understand that the limitation comes from measuring labor flows directly.
1:07:28 I was wondering there and this is what I'm sending in my bilateral,
1:07:31 uh,
1:07:31 comments whether.
1:07:33 You,
1:07:33 you could do surveys to fill those gaps and we have come a long way on this.
1:07:37 The World Bank is an expert on this
1:07:39 um the fund is catching up.
1:07:41 I think
1:07:42 actually this surveys can be done and can be done effectively and
1:07:46 go that extra distance to say something about aggregate
1:07:50 effects,
1:07:50 uh,
1:07:51 which now I think you
1:07:52 you are you come a little short.
1:07:54 Um,
1:07:55 the second I think is something that
1:07:57 Alison picked up at the very end of the presentation which is
1:08:01 exactly these shocks how do major shocks impact agricultural productivity,
1:08:07 both economic
1:08:08 but also climactic,
1:08:09 and uh I'll come to this
1:08:11 in my discussion in a minute.
1:08:14 OK,
1:08:14 so
1:08:15 I'll spent the last 5 minutes I have on kind of zooming out and talk
1:08:19 about um
1:08:20 country convergence and
1:08:22 before I get to my
1:08:24 uh to the challenges that I worry most about uh facing developing economies
1:08:28 I'll start with the current context.
1:08:31 Most of the papers that have been written
1:08:33 and trying to calculate country convergence stop at 2019,
1:08:38 2018,
1:08:39 even before that 2015.
1:08:41 Uh,
1:08:42 a lot has happened and not in the right direction for developing economies.
1:08:46 Uh,
1:08:47 some people can call it transitional.
1:08:49 There are all these shocks,
1:08:49 the pandemic shock,
1:08:51 Ukraine war,
1:08:52 um,
1:08:53 of
1:08:53 financing tightness in the,
1:08:55 in the global markets.
1:08:56 But we also know that,
1:08:59 you know,
1:08:59 uh,
1:09:00 impacts that seem transitory or are transitory for
1:09:03 emerging markets and advanced economies are permanent for
1:09:06 developing economies that,
1:09:07 that worries me.
1:09:09 But also this is in the back of
1:09:11 what we think is a shift in the global economy
1:09:14 for many decades we have been thinking about convergence
1:09:18 when the economy,
1:09:18 the global economy was doing fantastically maybe because of China
1:09:22 and other factors but now we are getting into an era where
1:09:25 the projections from the World Economic Outlook elsewhere show
1:09:30 a truly a real
1:09:33 global slowdown mainly because of China
1:09:35 so.
1:09:36 You know there is this the the so the initial condition for
1:09:39 many of the developing economies given the shocks and given the new phase
1:09:44 of,
1:09:44 uh,
1:09:45 global slowdown
1:09:46 in growth
1:09:48 is not very promising
1:09:50 but this is not really what worries me mostly for developing economies are these
1:09:54 emerging challenges and here I I'd like to talk about 3
1:09:59 and I have to move on here.
1:10:03 OK this is it
1:10:04 um climate AI and fragmentation you all know about climate,
1:10:09 uh we have seen it in the fund
1:10:11 uh
1:10:11 how many of the countries come to us
1:10:14 for financial assistance because of the shocks
1:10:17 this demand is increasing at an increasing rate,
1:10:20 um,
1:10:21 so
1:10:22 you know
1:10:23 you wonder for example Alison how
1:10:27 your work would change when you take into account
1:10:30 this climatic changes both
1:10:31 increasing,
1:10:32 you know.
1:10:33 Uh,
1:10:33 frequency,
1:10:34 intensity,
1:10:35 duration of all these,
1:10:36 uh,
1:10:37 major shocks we had
1:10:38 floods in India and so forth.
1:10:40 I think,
1:10:40 I think that that would be a very nice natural experiment for you guys.
1:10:44 Um,
1:10:45 second is AI,
1:10:46 um,
1:10:47 conceptual frameworks show that.
1:10:50 You know robots,
1:10:51 especially the low end robots,
1:10:53 substitute for low skilled workers,
1:10:56 and
1:10:57 many of you know worked in
1:10:59 developing economies there was this demographic dividend
1:11:02 dream in Africa,
1:11:03 right,
1:11:03 that in 20 years,
1:11:05 you know,
1:11:05 we'd have the youngest population.
1:11:07 You know this new robots that keep work
1:11:11 basically in China
1:11:13 uh where it should have been in Africa by real people
1:11:17 it's something that worries me quite a bit so.
1:11:20 Uh,
1:11:20 Martha,
1:11:20 I think it is very interesting to think about
1:11:22 your work with
1:11:24 AI migrants,
1:11:26 of course,
1:11:26 for
1:11:27 your,
1:11:28 uh,
1:11:28 work is EU and US,
1:11:30 but I mean you can extend it for
1:11:32 developing economies as well.
1:11:33 Finally,
1:11:34 fragmentation is,
1:11:35 I think something that we don't take very seriously yet,
1:11:38 but we see it happening.
1:11:39 There will be a trade war between China and the US.
1:11:42 And where does that leave our discussion of uh
1:11:46 country convergence?
1:11:47 Should we be thinking about club convergence going back to that world
1:11:51 um uh so Shimito I mean that actually this is even
1:11:54 more important for you when you consider regional uh convergence.
1:11:57 Let me stop here.
1:11:58 Thank you.
1:12:04 OK,
1:12:05 terrific thank you Chris.
1:12:06 Um,
1:12:07 so we're gonna open it up,
1:12:08 uh,
1:12:09 in a moment for,
1:12:10 um,
1:12:11 some Q&A,
1:12:12 um,
1:12:12 and I'll,
1:12:13 uh,
1:12:14 give
1:12:14 each,
1:12:15 uh,
1:12:15 each presenter an opportunity to address Chris's
1:12:18 comments and the questions that come up.
1:12:20 Let me,
1:12:20 let me just,
1:12:21 uh,
1:12:22 put.
1:12:23 One or two,
1:12:24 general issues,
1:12:25 uh,
1:12:26 that came to my mind listening to the three excellent presentations.
1:12:30 Um,
1:12:31 Sumitra's central
1:12:32 finding about,
1:12:33 uh,
1:12:34 slowing,
1:12:35 uh,
1:12:35 regional convergence,
1:12:37 um,
1:12:38 is that a policy problem and what,
1:12:40 what are the policy remedies,
1:12:42 uh,
1:12:42 in your opinion if it is a policy problem?
1:12:46 Uh,
1:12:46 second,
1:12:47 for,
1:12:48 um,
1:12:48 uh,
1:12:49 for Marta,
1:12:50 um.
1:12:52 You have very strong results about short run uh gains to output and,
1:12:56 uh,
1:12:56 longer run costs
1:12:58 again,
1:12:58 sort of from a,
1:12:59 from a political economy perspective,
1:13:01 would you use this kind of a model,
1:13:03 uh,
1:13:04 to advocate certain policies whether on
1:13:06 uh visa reform or tax reform?
1:13:09 Uh,
1:13:09 how,
1:13:09 how do you think,
1:13:10 uh,
1:13:10 you would sell these very long run results
1:13:13 from a,
1:13:13 from a political economy perspective,
1:13:16 uh,
1:13:16 and,
1:13:16 uh,
1:13:17 third,
1:13:17 Alison,
1:13:18 um.
1:13:19 Your your results about,
1:13:21 uh,
1:13:22 the gains accruing uh
1:13:24 to the fixed factor,
1:13:25 uh,
1:13:26 entirely,
1:13:27 uh,
1:13:27 and the fact that,
1:13:28 um,
1:13:29 uh,
1:13:29 you know,
1:13:30 the non-farm sector doesn't really,
1:13:32 uh,
1:13:32 doesn't really benefit at all,
1:13:34 um,
1:13:34 uh,
1:13:35 what,
1:13:35 what policy remedies,
1:13:37 uh,
1:13:37 do you see as,
1:13:39 uh,
1:13:39 potential levers to,
1:13:40 uh,
1:13:41 to address those,
1:13:42 um,
1:13:42 uh,
1:13:42 those issues?
1:13:44 So let me,
1:13:44 let me turn it over,
1:13:45 uh,
1:13:46 now to questions and I'll,
1:13:47 I'll,
1:13:48 I'll turn back to the speakers.
1:13:49 Uh,
1:13:50 do people want to,
1:13:51 um,
1:13:51 uh,
1:13:52 intervene at this point?
1:13:58 Yeah.
1:14:08 Uh,
1:14:08 thanks,
1:14:08 Jonathan.
1:14:09 I'm Art Cray at the World Bank.
1:14:11 Um,
1:14:11 you,
1:14:12 Jonathan,
1:14:12 you asked the question I was going to ask Jomiro,
1:14:14 so I'll skip that one,
1:14:15 but I also have questions for Marta and for Alison.
1:14:19 So for Marta,
1:14:19 two quick questions on the paper.
1:14:21 The one is that,
1:14:23 um,
1:14:23 I think in the real world a lot of migration of inventors is because they already
1:14:28 know who their collaborators are in the other country and they move to where they are
1:14:32 and I wonder to what extent that changes the mechanisms and the model.
1:14:35 The second question has to do with the policy experiments that.
1:14:38 You look at,
1:14:39 so you showed us
1:14:40 a policy experiment in which one part of the world taxes innovation
1:14:44 and so in the end it's,
1:14:46 you know,
1:14:46 not that surprising that there's negative effects in the long term.
1:14:49 It'd be interesting to see what happens if you say had
1:14:52 fully integrated patent protection between the US and the EU.
1:14:56 You know,
1:14:56 how,
1:14:56 how much more positive would a policy like that be.
1:14:59 And for Alison,
1:15:00 two questions.
1:15:01 One is just a little one,
1:15:02 which is why don't people pump uphill out of canals
1:15:04 if there's such a high return to doing so?
1:15:06 The technology obviously is there because of all the groundwater pumping in India.
1:15:11 The second question is,
1:15:12 can you say more about the
1:15:14 history
1:15:15 of the placement of canals because I was thinking about this
1:15:18 in the sort of slightly cynical way and you think about,
1:15:21 you know,
1:15:21 these are big public works,
1:15:23 uh,
1:15:24 projects when they were built,
1:15:25 they were probably built under the influence of landowners and it's not
1:15:28 very surprising that they lined up in places that benefit the landowners.
1:15:32 Is that oversimplifying?
1:15:33 Is there something to that history that matters for the findings today?
1:15:38 Thanks,
1:15:39 Art.
1:15:39 And,
1:15:39 and I apologize for not recognizing your face.
1:15:42 The light completely blinds me from where I'm,
1:15:44 uh,
1:15:45 where I'm sitting.
1:15:45 Other,
1:15:45 other questions?
1:15:53 Are there any,
1:15:53 uh,
1:15:54 questions in the chat that are,
1:15:56 uh,
1:15:56 that are bubbling up?
1:15:58 OK,
1:15:59 so,
1:15:59 um,
1:16:00 why don't we,
1:16:00 uh,
1:16:01 give,
1:16:01 uh,
1:16:01 each speaker a chance to,
1:16:03 uh,
1:16:04 to respond,
1:16:05 Sumitra,
1:16:06 uh,
1:16:06 thanks,
1:16:06 Chris,
1:16:07 uh,
1:16:07 for all your kind and generous comments.
1:16:09 Um,
1:16:10 both points sort of well taken.
1:16:11 Um,
1:16:12 I'll just say that,
1:16:13 I mean,
1:16:13 we have to interpolate one way or the other to look at the trends.
1:16:17 Um,
1:16:17 the validation exercise that we did with the interpolation
1:16:20 was that we only interpolated the within country data.
1:16:24 And then we aggregated it up
1:16:26 to see whether or not we are matching the aggregate GDP that
1:16:30 you get from the Pen World table of the World Bank.
1:16:33 But if there are ways to kind of improve it further,
1:16:35 of course,
1:16:35 um,
1:16:36 um,
1:16:36 I would look for the,
1:16:37 um,
1:16:37 um,
1:16:38 detailed comments.
1:16:39 Um,
1:16:39 the cross-country things I I'm aware of the debate between sort of your paper,
1:16:44 and now there's a paper by Michael
1:16:45 Kramer that is claiming convergence and the Sandifer
1:16:48 Subramani and Patel paper.
1:16:50 Uh,
1:16:51 I thought it got resolved one way,
1:16:52 but maybe not,
1:16:53 and my reading is incorrect.
1:16:55 The thing is,
1:16:55 it's not even central to our paper.
1:16:57 Like,
1:16:58 I can rewrite the framing and say there has been
1:17:00 a lot of growth in the developing world and,
1:17:02 and,
1:17:03 and just focus on within-country convergence.
1:17:05 Um,
1:17:06 um,
1:17:06 but again,
1:17:07 would look,
1:17:07 um,
1:17:08 forward to talking more.
1:17:09 Um,
1:17:10 on the policy side,
1:17:11 um,
1:17:12 I mean,
1:17:12 let me even sort of extend that question
1:17:15 on the policy and politics,
1:17:17 um,
1:17:17 um,
1:17:17 problem of this.
1:17:19 Um,
1:17:19 I think that would depend on country by country,
1:17:22 like,
1:17:22 so I,
1:17:23 I mean the issue is that the,
1:17:24 the,
1:17:24 the regions that are getting left behind.
1:17:27 Uh,
1:17:27 uh,
1:17:28 uh,
1:17:28 uh,
1:17:29 what is happening to kind of people there and are people able to migrate to,
1:17:33 to regions with opportunities?
1:17:35 And of course,
1:17:35 like,
1:17:36 when you focus on countries like India and markets here,
1:17:39 there are migration restrictions.
1:17:40 So,
1:17:41 Bihar and UP,
1:17:42 for example,
1:17:43 tend to be the most unproductive states,
1:17:45 but also the
1:17:47 states with the most population and population density.
1:17:50 Um,
1:17:50 um,
1:17:50 and that's kind of part of the policy challenge.
1:17:53 But if you look at the US,
1:17:54 we are like,
1:17:55 I guess,
1:17:55 facing a completely different story here where the Midwest,
1:17:58 which is kind of falling behind,
1:18:00 um,
1:18:00 there are not too many people here,
1:18:02 but they do have an equal representation in terms of the politics.
1:18:06 And,
1:18:06 and so this,
1:18:08 this unequal sort of spatial growth in
1:18:10 countries depending on political structures will.
1:18:13 Um,
1:18:13 um,
1:18:14 kind of have impact
1:18:15 on political outcomes as well.
1:18:17 So,
1:18:18 of course,
1:18:18 we don't get into those issues in the paper,
1:18:20 but that's kind of what we have at the back of our minds.
1:18:25 Thank you,
1:18:25 um,
1:18:26 Mara.
1:18:30 Yeah thank you so much for the comments and the question and
1:18:34 uh
1:18:34 to start with uh from the comments by Chris I would I would say that
1:18:39 it is certainly an important assumption in this model that migrants
1:18:43 tend to
1:18:44 increase their productivity when they move and this is.
1:18:46 Because
1:18:47 these are migrants motivated by economic reasons,
1:18:50 so they move when they have good opportunities in the model
1:18:53 and that's sort of like reflects what I see
1:18:55 in the data that on average these migrants tend to
1:18:58 find more patterns after they move
1:19:00 but of course one could think of different settings where.
1:19:03 People including inventors move for different reasons like
1:19:07 refugees or as we were talking about the war in Ukraine that caused a
1:19:11 huge reallocation of people or as we also talked about
1:19:14 uh migration motivated by climate change
1:19:17 and so in this case it might be it might not
1:19:19 be necessarily the case that people actually increase productivity when they move
1:19:24 so 11 channel of this one looks at the effects
1:19:26 that come sort of like from the reallocation of people.
1:19:30 But on the other side,
1:19:31 there is also this important channel that comes from the knowledge
1:19:33 spillovers from the destination country back to the origin country.
1:19:37 And here again it really depends on the nature of migration.
1:19:41 This type of econo economic migrants that move for economic opportunities
1:19:46 are able to maintain a network in the country of origin,
1:19:49 but when we think about
1:19:50 refugees,
1:19:51 it might be the case that
1:19:52 even if they move to a different country and they may become more productive,
1:19:55 it might be very difficult for them to keep a network,
1:19:58 a professional network in their home country.
1:20:00 And so this connects to the questions about policy
1:20:03 where what I've done so far in this work is to
1:20:06 do a positive evaluation of some policies that these countries have implemented
1:20:11 to really answer the question about policy recommendations more deeply.
1:20:14 I think this would require
1:20:16 sort of solving
1:20:17 a planning problem to think about what the optimal policies are
1:20:21 and so in that sense I think
1:20:23 this is a great comment to think more carefully about
1:20:26 other types of policy completely for example instead of tax.
1:20:29 Where tax cuts,
1:20:30 what about,
1:20:31 uh,
1:20:31 a fully integrated patent protection or what
1:20:34 about temporary migration programs that could be,
1:20:37 could foster these knowledge spilovers
1:20:39 and this is,
1:20:40 this is again something that requires even uh
1:20:42 uh further analysis of these,
1:20:44 this
1:20:45 type of models
1:20:47 and,
1:20:47 and,
1:20:48 and related I think to to the other question about the fact that very often
1:20:51 people move
1:20:53 when they already know some people in their destination country.
1:20:55 I think this is,
1:20:56 this is true.
1:20:57 And this is something that I've seen the data where
1:21:00 actually most of these migrants move within
1:21:03 foreign branches of the same multinational that they already work for
1:21:06 but for the
1:21:08 um main channels of this model this is fine uh
1:21:11 for for.
1:21:12 For simplicity in my model I use sort of random matching of people in the economy,
1:21:17 but one can think about
1:21:19 uh directed matching where people know who they want to meet
1:21:22 and if anything I think this would make
1:21:24 the knowledge spilovers in the model even stronger.
1:21:28 Thank you.
1:21:30 Thank you,
1:21:30 Marta and Allison.
1:21:33 Uh,
1:21:33 great,
1:21:33 thank you all for those questions.
1:21:35 Um,
1:21:35 I guess
1:21:36 on the first point about kind of the effects of climate
1:21:38 on agricultural productivity,
1:21:40 I think one interesting,
1:21:41 um,
1:21:42 thing to think about in our paper is the result that the of the town growth,
1:21:46 the regional town growth as a result of the increase in agricultural productivity,
1:21:49 and
1:21:50 I think an interesting implication is that not only
1:21:52 are the outlying areas that might be experiencing drought.
1:21:55 Um,
1:21:55 going to be affected,
1:21:56 but that these towns that might be supported
1:21:58 by,
1:21:58 um,
1:21:59 the productivity and the demand of people in rural areas.
1:22:01 So thinking about I guess the,
1:22:03 the policy,
1:22:04 um,
1:22:04 ramifications of,
1:22:05 of needing of people
1:22:07 needing to move not just from extreme weather events,
1:22:09 uh,
1:22:09 disasters but also just changing,
1:22:11 uh,
1:22:12 economic realities of what what populations are able to be supported in what areas,
1:22:16 um.
1:22:17 On the,
1:22:18 the question about policy,
1:22:19 uh,
1:22:19 ramifications,
1:22:21 um,
1:22:21 so there's some other work by our co-authors Paul Novasad and Sam Asher looking at,
1:22:26 uh,
1:22:26 rural roads also in India
1:22:28 and looking at the places that get access to roads,
1:22:31 and they find that similarly,
1:22:32 um,
1:22:33 the roads essentially facilitate people to migrate to
1:22:35 other areas to search for other opportunity.
1:22:37 So in a lot of these,
1:22:38 um,
1:22:38 areas,
1:22:38 we're seeing
1:22:39 these infrastructure projects just leading to migration.
1:22:42 So I think,
1:22:42 um,
1:22:43 understanding more of those,
1:22:44 uh,
1:22:44 incentives to migrate,
1:22:45 like a lot of the work in Bangladesh,
1:22:47 um,
1:22:48 running
1:22:48 trials to incentivize people to move during the dry seasons,
1:22:52 or giving people vouchers to move to cities and understanding
1:22:54 what are the impediments to,
1:22:56 uh,
1:22:56 migration would be interesting to,
1:22:58 to understand more about,
1:22:59 um,
1:23:00 especially
1:23:00 making the destinations more appealing to actually migrate to and not,
1:23:04 um,
1:23:04 deter people from moving to cities and,
1:23:06 and living in,
1:23:06 in bad conditions.
1:23:08 Um,
1:23:08 and then lastly,
1:23:09 I'll just address the questions.
1:23:11 I,
1:23:11 I should know more about the answer of why don't people pump uphill from canals.
1:23:14 Um,
1:23:15 I imagine part of it has to do with the fact that these are,
1:23:18 many of these canals were built before that was a viable
1:23:21 option.
1:23:21 And I wonder how the,
1:23:22 um,
1:23:23 kind of gains accrue to landowners and how,
1:23:25 what capital is there to invest in those,
1:23:27 um,
1:23:27 you know,
1:23:27 how,
1:23:27 how expensive is it to,
1:23:28 to pump that water uphill and who's choosing to make that investment or not.
1:23:32 Um,
1:23:33 but it's a good question.
1:23:34 We should probably,
1:23:34 I should probably have a better answer for that.
1:23:36 Um,
1:23:37 and then lastly,
1:23:38 just on the,
1:23:38 the placement of canals,
1:23:39 we've we've definitely thought a lot about how,
1:23:41 how that could be endogenous.
1:23:43 Um,
1:23:43 there's actually another,
1:23:44 uh,
1:23:45 regression discontinuity strategy we use,
1:23:47 um,
1:23:48 where we look at
1:23:49 the,
1:23:49 the,
1:23:49 the government puts out polygons drawn around the canals called command areas,
1:23:53 which is the area that the canal should service.
1:23:56 And we do a spatial discontinuity looking
1:23:57 at places just inside the boundary versus.
1:23:59 Outside,
1:24:00 but we actually thought
1:24:01 those might be more likely to be,
1:24:03 um,
1:24:03 influenced by politics or,
1:24:05 or whoever is drawing those maps.
1:24:07 So we think that the elevation strategy is,
1:24:10 um,
1:24:10 more dependent on geographic fundamentals rather than
1:24:13 maybe other incentives.
1:24:14 Um,
1:24:15 and then we also do a test
1:24:16 where we look at
1:24:18 the longest and straightest canals.
1:24:19 Um,
1:24:20 this test has been done with some road construction,
1:24:22 um,
1:24:23 Uh,
1:24:23 as well to say if they needed to get water from point A to point B,
1:24:27 which places were treated along the way,
1:24:29 and especially when you're limited geographically and,
1:24:31 and where the canal can go to get from A to B,
1:24:34 it's more likely that those are,
1:24:35 uh,
1:24:36 treated
1:24:36 kind of by circumstance as opposed to endogenous to interest preconstruction.
1:24:41 Um,
1:24:42 yeah.
1:24:44 OK,
1:24:44 thank you to Shamitro,
1:24:45 to Martha,
1:24:46 to Alison,
1:24:47 and to Chris.
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