00:01 All right.
00:02 Good morning and good day,
00:03 everyone.
00:04 Uh,
00:04 my name is Sho McLal.
00:05 I'm leading the 2024 WDR and I'm very happy to welcome
00:11 all of you
00:12 to the third in our series of,
00:15 uh,
00:15 uh,
00:16 seminars
00:17 that we have started this fall.
00:19 Uh,
00:20 the first two seminars were,
00:22 uh,
00:22 uh,
00:23 have been recorded and I put on the WDR website.
00:27 And this morning,
00:28 I'm very pleased to introduce and invite uh
00:32 Li Ming,
00:32 who is an assistant professor at the Chinese University of Hong Kong in Shenzhen.
00:38 To present her work on labor market integration,
00:40 entrepreneur sorting,
00:42 and regional inequality in China.
00:44 Uh,
00:45 as part of the WDR consultations,
00:47 um,
00:49 Uh,
00:49 we,
00:50 uh,
00:50 had,
00:51 uh,
00:51 a series of meetings in China back in August,
00:55 where we met researchers and policymakers,
00:59 uh,
00:59 in Beijing,
01:00 in Shanghai,
01:01 and Shenzhen.
01:03 And at that time,
01:04 uh,
01:05 Li Ming and her colleagues had kindly hosted the WDR team,
01:09 telling us about the breadth of research,
01:11 uh,
01:12 and,
01:12 uh,
01:12 and applied policy work that they were doing.
01:15 Uh,
01:16 and we thought it would be really useful for
01:18 our team and the broader audience in our institution
01:21 to learn about this paper because it speaks
01:25 to one of the bigger themes of the World Development report,
01:30 which is how mobility restrictions
01:33 exacerbate misallocation,
01:35 both in terms of talent,
01:37 but also in terms of entrepreneurship.
01:39 Uh,
01:40 and she has some amazing empirical evidence.
01:44 From China that covers,
01:45 uh,
01:46 uh,
01:46 I think,
01:47 um,
01:48 Uh,
01:48 a large time frame going all the way to 2019.
01:52 So,
01:52 Li Ming,
01:53 we will give you 35 minutes to present your paper,
01:57 and after that,
01:58 I'm very happy that Nancy Lozano has agreed to be a discussant
02:04 to,
02:04 uh,
02:05 to this paper.
02:06 Uh,
02:06 as many of you know,
02:08 Nancy is the lead economist for sustainable development in Latin America,
02:12 and she's also the global lead for spatial development solutions.
02:18 But more importantly,
02:19 Nancy has done
02:20 a huge amount of applied policy and research work looking at spatial frictions
02:25 across the world,
02:26 including in Latin America,
02:28 East Asia,
02:29 Africa,
02:30 and the Middle East and North Africa.
02:32 So,
02:33 Nancy,
02:33 after that,
02:33 we'll turn to you for your uh comments,
02:36 and then we'll open it up for questions and answers.
02:40 So Lim Ming,
02:41 over to you.
02:43 Thank you so much,
02:44 So Mei,
02:45 for the really nice introduction and also for the very kind invitation.
02:49 And let me first uh uh share my screen.
02:53 You see my screen,
02:54 right?
02:56 Yeah,
02:56 and thank you Nancy for uh being my discussion and thank
02:59 you everyone for joining the meeting in the early morning.
03:02 And today I'm gonna talk about our recent
03:04 preliminary work on labor market integration and entrepreneurship
03:08 and it's a joint work with Haim Ming from PEN
03:11 and Wei Ling from uh uh XiXi Shenzhen who is my colleague here.
03:16 And
03:17 this research is motivated by the following several observations from China.
03:23 Uh,
03:23 we observing the past several decades that there has been
03:26 a great regional variation in terms of the entrepreneurial activities.
03:31 And this great regional variation in entrepreneurial activities
03:34 is largely driven by the flow of entrepreneurs.
03:38 We observed recently in the data that more than 50% of the firms
03:42 are established by the entrepreneurs who
03:44 actually operate outside their home city,
03:46 and more than 30 of them are uh operating outside their home provinces.
03:51 And on the labor market side,
03:53 despite the very well known,
03:55 uh,
03:55 very,
03:56 uh,
03:56 uh,
03:56 restrictive fuku restrictions in China,
03:59 actually we have seen a large increase in internal migration in the last 30 years.
04:04 And overall migrant population grew from 21 million in 1990
04:09 to 253 million in 2015.
04:12 And in accounting with this uh migration of
04:16 entrepreneurs as well as migration of laborers,
04:18 uh there is a large variation in local hukou policies.
04:23 Let me give you a few examples of what I
04:24 mean by the variations in the local hukou policies.
04:28 So here I'm displaying some slogans or policies from 3 1st tier cities in China
04:35 which are Shenzhen,
04:36 Beijing and Shanghai.
04:38 And there are 3 well known,
04:39 uh,
04:40 very developed,
04:40 uh,
04:41 uh,
04:41 first tier cities.
04:43 And you can see even within the first tier,
04:45 uh,
04:45 3 cities,
04:45 uh,
04:46 even within the first,
04:47 uh,
04:47 tier cities,
04:49 there are still great regional variations in their local hukou policies.
04:52 Let me show you.
04:53 So on the left hand side,
04:55 the two panels or two
04:57 several slogans from Shenzhen,
04:58 and where is the city where I come from.
05:01 And basically this is saying whoever comes to Shenzhen is a Shenzhenner.
05:05 So Shenzhen is a city that is very well known
05:07 for its friendliness to all kinds of migrant workers.
05:11 And on the right hand side we see two other examples.
05:14 On the right upper panel,
05:16 which is the city of Beijing.
05:18 And you see the slogan basically it says evict uh
05:21 everyone is responsible for the eviction of the low-end population.
05:26 And that is what makes Beijing a very unfriendly city to the migrant workers.
05:31 Basically,
05:31 uh,
05:32 if you see later on in the data that Beijing still
05:34 uh remains very restrictive in their hukou policies across years.
05:38 And somewhere in the middle is the city of Shanghai,
05:42 uh,
05:42 which they have a kind of credit system
05:45 where they award points or credits to the uh people with higher level of education
05:50 as well as people with higher level of skill,
05:53 skill certificate.
05:55 And only for people who have accumulated certain points of skills,
05:59 uh,
05:59 they are eligible.
06:00 for local hukou.
06:01 So actually,
06:03 uh,
06:03 these three kinds of,
06:04 uh,
06:05 policy,
06:05 uh,
06:06 policy types actually correspond to our later on
06:09 empirical studies in which we define as policy,
06:12 uh,
06:12 uh,
06:12 uh,
06:12 hukou policies.
06:14 Beijing is one extreme
06:16 where they always stick to their baseline policy,
06:19 a very strictive,
06:20 uh,
06:20 a restrictive one.
06:21 And Shanghai is somewhere in the middle where there's this
06:24 uh uh uh uh hukou policy that rewards a local hukou
06:28 to the high skilled labor.
06:30 And Shenzhen is the other extreme where uh basically there
06:33 has no restrictions at all to all the migrant workers.
06:36 So you're gonna see later on that
06:38 these three kinds of uh different policy designs have very
06:41 different implications on the regional distributions of economic activities.
06:45 And that inspires us to
06:47 ask the following research question.
06:49 So in this paper uh we want to ask,
06:52 how does the reduction
06:54 in the institutional migration barriers,
06:56 especially the heterogeneous reduction in the institutional barriers,
07:00 shape the landscape of human capital flow
07:03 as well as entrepreneurship in China?
07:06 And in answering this question,
07:07 we'll find that
07:08 the skill bias of Hukou policy where you can
07:10 think of the Hukou policy as Shanghai's policy,
07:13 that attracts migrant entrepreneurs,
07:16 but at the cost of crawling out local entrepreneurs.
07:19 So overall we see a now effect in terms of the total number of entrepreneurs there
07:23 uh induced by the hukou policies.
07:26 And on the other hand,
07:27 the non restrictive hukou policy where you can
07:29 think of the hukou policy as Shenzhen's policy,
07:31 that spurs overall entrepreneurship,
07:34 both the migrant entrepreneurs as well as the local entrepreneurs.
07:37 And then we want to understand why.
07:40 So in exploring the underlying channels of these findings,
07:44 um,
07:44 we're examining the labor market side as well as the firm performances,
07:49 uh,
07:49 in response to the changes in the hukou policies.
07:52 On the labor market side,
07:54 we see that the low skilled labor,
07:55 they only respond to this low,
07:57 uh,
07:57 non uh non-restrictive hukou reforms.
08:00 Well,
08:00 the high skilled labor,
08:02 they're responding to both the non-restrictive reforms
08:04 as well as the buyers of reforms.
08:07 And that will yield very different implications
08:09 on the firms in the low skilled sector
08:12 and the firms in the high skill sector.
08:14 Basically,
08:15 you can think that
08:16 uh the firms in the low skill industries,
08:19 they're gonna be hurt by the buyers who policies.
08:21 Mainly because of the lack of the abundant labor supply in the low skilled sector.
08:27 And on the other hand,
08:28 they gonna be benefit from the non-restrictive
08:31 reforms.
08:32 On the other hand,
08:33 the firms in the high skill industries,
08:35 they're gonna benefit from both the skill,
08:37 uh,
08:38 high skill indus uh high skill,
08:39 uh sorry,
08:40 skilled buyers huo reforms as well as the non-restrictive hukou reforms.
08:44 And they're gonna benefit more from the,
08:46 uh,
08:46 actually they're gonna benefit more from the non-restrictive huku reforms.
08:51 And after we document these findings,
08:54 we further want to understand
08:56 what is the equilibrium effects of the Hukou reforms and its overall welfare
09:00 implications in terms of the uh distribution
09:03 of economic activities across the country.
09:06 The reason why we really need to understand the equilibrium in fact is that
09:10 simply think of this,
09:11 uh,
09:12 whenever a city attracts one entrepreneur,
09:14 that means the other city is losing an entrepreneur.
09:16 So in this kind of empirical design,
09:18 there's not a clearly defined and contaminated control group.
09:23 So,
09:24 uh,
09:24 that is why we really need to employ a spatial equilibrium model
09:28 to understand the general equilibrium effect of this Hukou reforms.
09:32 First of all,
09:33 uh,
09:33 we build an equilibrium model to illustrate the key,
09:36 uh,
09:36 mechanism understanding,
09:37 uh,
09:38 and,
09:38 uh,
09:38 underlying our findings.
09:40 Uh,
09:40 the key mechanism we document in the model is that
09:43 the labors are gonna sort like different kinds of labors,
09:46 they're gonna sort in response to the reduction in in mobility cost.
09:50 And firm will sort with the labor in response to the change in the uh local labor base.
09:57 We try to contribute to the
09:59 like both the literature on the firm side as well as the uh labor side.
10:03 by,
10:04 by combining the two strands of literature together
10:07 and introduce heterogeneous worker type and policy induced worker types,
10:11 worker type specific labor mobility costs,
10:14 as well as to the labor uh
10:17 uh literature on the uh
10:19 internal migration literature,
10:21 uh,
10:21 by incorporating the firm location choice in
10:24 which we can endogenize the labor demand.
10:27 And with this,
10:29 uh,
10:29 uh,
10:29 structural model
10:31 that allows us to conduct a lot of more uh interesting counterfactual analysis.
10:36 So,
10:36 uh,
10:37 basically with those counterfactual analysis
10:39 and welfare implications we found that
10:41 the relaxation of local restrictions.
10:45 Will contribute,
10:46 may contribute to greater regional inequality,
10:49 but
10:49 that will improve overall efficiency as well as welfare.
10:53 And more importantly,
10:55 non-restrictive fuh reforms,
10:57 they're gonna yield larger welfare gains as
10:59 compared to the biased uh huco policies.
11:01 And they are also gonna yield uh more equal
11:04 gains across different individuals and across different regions.
11:09 I'll briefly talk about my,
11:11 uh,
11:11 our contributions to literature.
11:13 So we try to uh speak to three strands of literature.
11:16 The first is on the firm's location choice,
11:19 um,
11:20 in which we specifically account for the role of interregional labor
11:24 mobility restrictions and its changes in shaping the firm's location choice.
11:29 Basically,
11:30 understanding the firm's location choice,
11:32 we specifically take into account
11:34 what is the role of the labor mobility.
11:37 And in terms of the literature on internal migration,
11:40 previous literature mostly overlooked the role of the labor demand
11:44 side which is shaped by the firm's location choice.
11:47 And we explicitly account for this firm location choice
11:50 in this internal migration,
11:53 and we consider firm and labor market effects of internal migration jointly
11:57 and that allows us to estimate the welfare effect in equilibrium.
12:01 Of course there's a lot of literature on the hukou system in China,
12:05 which I cannot list all of them here.
12:07 But we'd like to claim our contribution to be
12:10 the first to distinguish different kinds of hukou policies
12:14 and study its heterogeneities and its effects on,
12:17 uh,
12:17 its equilibrium effects on the distribution of entrepreneurial activities.
12:23 So let me now go into a little bit more uh institutional details
12:28 uh that uh uh correspond to what I'm already talked
12:30 about a little bit uh at the very beginning.
12:34 So here,
12:35 I'm plotting,
12:36 oh sorry,
12:37 here I'm plotting the regional distributions of entrepreneurial activities
12:41 in year 2000 as well as year 2015.
12:44 As you can see,
12:45 as early as the year 2000,
12:47 the regional distribution of new firm entry in the year is pretty even,
12:52 right?
12:53 So most of these uh except for the uh few uh cities on the coastal area,
12:58 most of the firms,
12:59 uh,
13:00 most of the cities has a very even distribution of entrepreneurial activities.
13:03 Most of the cities have a new firm entry of less than 500.
13:08 However,
13:08 on the other hand,
13:10 if we go to the time of 2015,
13:12 you can see there is a great regional variation in terms of the new firm entry.
13:17 Well,
13:17 some cities have a new firm entry of less than 500,
13:21 some other cities in the coastal area or in the southern area
13:24 has a new firm entry of more than 10,000 firms per year.
13:28 And actually this kind of regional distribution uh
13:31 regional and even regional distribution of entrepreneurial activities
13:35 is largely driven by the entrepreneurial flows.
13:39 Let me show you this graph.
13:41 So on the left hand side panel,
13:43 I'm plotting the ratio of the uh uh
13:46 firms that are established by the migrant entrepreneurs,
13:50 which are operating outside their home city.
13:53 So as you can see,
13:54 although the coastal area uh despite the fact that the
13:57 coastal area and the southern area has more new firms,
14:01 the majority of it is established.
14:03 By the migrant entrepreneurs,
14:04 which means the cities are attracting the entrepreneurs from other cities.
14:09 And on the right hand side panel,
14:10 I'm plotting the percentage of firms that are established by the movers.
14:15 I define a mover as an entrepreneur who has originally operated
14:21 in other cities but now currently moved to this particular city.
14:25 And you can see also in the more like richer southern area and coastal area,
14:31 the majority of the firm is established by the movers.
14:34 Jointly,
14:35 the graphs are trying to tell us that
14:38 in like in understanding the
14:40 regional distribution of entrepreneurial activities,
14:42 it is really important
14:44 to understand what causes the flow of migrants and uh what causes
14:48 the flow of firms and what causes the flow of entrepreneurs.
14:53 And as you can see over time,
14:55 the flow of entrepreneurs are increasing over time.
14:58 Originally in 19995,
15:00 less than 10% of them were operating outside their home province.
15:05 Well,
15:05 recently,
15:05 we see the percentage of micro entrepreneurs operating outside
15:09 their home province increased to more than 30%,
15:12 and the percentage that are operating outside their home city,
15:16 uh,
15:16 increased to more than 50%.
15:18 And on the right hand side panel,
15:19 I'm plotting the distribution,
15:21 the histogram
15:22 of the uh destination or region GDP difference.
15:27 Basically the graph is trying,
15:28 you can see the distribution is really skew.
15:40 Did we lose Ming?
15:42 Can you hear her?
15:43 I cannot hear either.
15:46 We can't hear you,
15:46 Mingli.
15:54 Where,
15:54 uh,
15:54 I plot the histogram distribution of the destination origin log GDP difference
15:59 for the low skilled labor as well as the high skilled labor.
16:02 You see for different kinds of labor,
16:04 the,
16:04 the distributions both uh skilled to the right,
16:07 which means that uh both the high skilled labor and the low skilled labor,
16:11 they are migrating to the larger cities.
16:13 And this pattern is more salient for the low skilled labor.
16:17 And on the policy side,
16:19 actually we have seen great variations in terms of the Hukou policy restrictions.
16:25 There have been 4 weeks of Hukou policy changes over time.
16:29 And the very uh early wave was from 1984 to 1997.
16:34 Before that,
16:34 uh,
16:35 no one was allowed to work outside of their uh uh registration place.
16:40 And starting from the first wave,
16:42 a particular group of entrepreneurs were the people
16:45 who are like really the high-end people,
16:47 they're allowed to acquire the uh uh urban hukou in this first uh uh wave of reform.
16:53 And the second wave of reforms starting from 1997,
16:56 basically it enabled migrants to uh work or uh permanently reside
17:02 in certain cities
17:03 and most of them are smaller cities.
17:06 And the third wave expanded the uh this kind of policy
17:10 to more larger cities,
17:12 about 100 larger cities.
17:14 And the largest wave of policy reform starting from,
17:17 uh starting from 2014.
17:19 And it was actually national policy trying
17:22 to push forward the Hukou reforms nationwide,
17:26 but later on you can see actually each city has their
17:29 own practice in the uh uh in in real practice.
17:33 And although you have seen there are several national wave policies,
17:37 but each city may carry out their own policies.
17:41 And the policy details can are really heterogeneous across cities
17:45 and can distinguish by the workers or the migrants specific characteristics.
17:50 And this is one of our very important contributions is that we collect data on all
17:54 the migration related policy reforms from all kind
17:57 of policy platforms that we can reach.
18:00 And we define huku reform as a deviation from the baseline restrictive huku policy.
18:05 For each document,
18:06 we summarize all the requirements into 6 categories including education degree,
18:10 skill,
18:11 investment,
18:12 employment,
18:12 purchase of housing unit,
18:13 and others.
18:14 And this allows us to further classify
18:18 all the hukou reforms into three broad categories.
18:22 The first broad category is what we define as a skill bias of Hukou reform
18:26 in which uh we included the reforms that require an education degree or skill,
18:32 uh certificate or a business investment.
18:36 And secondly,
18:37 we define the uh Huku reform with other requirements
18:40 as the uh uh reform with other requirements.
18:43 And thirdly,
18:44 uh,
18:44 if there is no requirement at all or only very minimum requirement
18:49 on employment such as 3 months of employment in the destination city,
18:52 we're gonna code it as a non-restrictive huku reform.
18:56 And here I'm um uh I'm showing you a
18:59 regional distribution of the hukou reform over time.
19:02 This was the distribution of the hukou policies in year 2000.
19:07 We will see nearly any like uh only a few cities are relaxing their hukou uh policies.
19:12 The majority of the cities are sticking to the baseline policy,
19:15 which is very restrictive.
19:18 And this is the uh distribution of the policies in year 2015,
19:23 which you see about half of the cities
19:26 in China has already relaxed their hukou restrictions,
19:29 but to different extent.
19:31 And this exhibit this kind of policy change exhibits great regional variations.
19:35 Some cities completely relax their hukou restrictions,
19:38 while some other cities
19:40 uh
19:40 uh still uh have a lot of requirements on the skills or others.
19:46 And
19:47 this is the policy data.
19:48 We are also combining,
19:49 uh,
19:50 uh,
19:50 in order to do the empirical analysis on
19:51 the entrepreneurial side and the labor market side,
19:54 we combine this with several other other data sets.
19:57 On the entrepreneurial data,
19:58 uh,
19:58 uh,
19:58 on the entrepreneur side,
20:00 we use a comprehensive data set covering the universe of
20:03 the firms that ever registered in China in the history.
20:06 And from the sample period that we're looking at 1995 to 2019,
20:11 there are altogether 30 million firms.
20:14 And for every firm registered,
20:16 we have very detailed information on the establishment date,
20:18 exit date,
20:20 industry,
20:20 registration
20:21 place,
20:22 registration capital,
20:23 so on and so forth.
20:24 And very luckily,
20:25 we get access to the entrepreneur's information
20:29 for every registered firm.
20:31 Basically,
20:31 we define the entrepreneur as the largest shareholder of each registered firm.
20:36 And for each entrepreneur,
20:37 we have a unique identifier so that we can
20:40 track the entire investment history of the entrepreneur.
20:43 And we observe some basic demographic information including their birthplace,
20:47 birth year,
20:48 uh uh uh uh uh including their birthplace and birth year.
20:54 And uh to measure the firm's performance,
20:56 we combined the data set with the comprehensive
20:58 administrative administrative data from 2008 to 2016.
21:03 It's administrative tax record data collected by
21:07 uh the Chinese State Administration of Tax.
21:10 It although it's not covering the full sample,
21:11 but it's actually quite a representative sample,
21:14 uh,
21:15 using the stratified sampling of and covers more than
21:18 500,000 firms per year.
21:21 And we use this data set to measure the firm's performance,
21:24 where we know the firm's revenue,
21:26 profit,
21:26 value added,
21:27 so on and so forth.
21:29 And the uh like labor side,
21:32 we construct the migration flows from 1996 to 2005 2015
21:37 from China population census of four waves of China population census.
21:42 We identify individual to be a migrant.
21:44 If he or she report a move
21:46 within 5 years of each census year.
21:49 So we denote the original city as the city of registration huku registration,
21:54 and we denote the destination city as a city of his current living or working place.
22:00 And we aggregate this to the city pair level so
22:03 that we know that city pairer uh migration flows.
22:07 And now it's time to show you some reduced form evidence
22:09 that are uh like will later on motivate the uh the,
22:13 the,
22:13 the uh the model.
22:15 So first of all,
22:15 we want to see what is the overall effect
22:17 of uh Huku reform on the evolution of entrepreneurship.
22:21 So
22:22 we did like as we talked about before,
22:24 we classify the hukou reforms into three broad categories,
22:28 the skilled by one
22:29 and the reform with other requirement as well as
22:32 the reforms that are not restrictive at all.
22:35 And we also call one general hukou policy which is turning on the treatment
22:40 as long as one of the other,
22:41 uh,
22:42 the above 3
22:43 treatment happens.
22:45 So here's the result.
22:46 We I'm regressing the number of new firm entry each year on the Huku reforms.
22:51 So as you can see in the first column,
22:53 if we simply regress the number of new firms on a general huku reform,
22:57 we see a null effect,
22:58 which means the general huku reform doesn't really change
23:00 the number of new entrepreneurships overall in the market.
23:03 However,
23:04 if we further decompose the huku reform into three categories,
23:07 that is yielding some uh uh interesting uh patterns,
23:11 we see that,
23:12 the non-restricted huku reform significantly contribute to a
23:17 flourish of local entrepreneurship in the local market.
23:20 Well,
23:20 the bias of huko policy doesn't really have the same virtuous effect.
23:25 And you further decompose the uh entrepreneurs or firms uh uh
23:29 by the sample of migrant entrepreneurs and the local entrepreneurs.
23:33 And that is telling us why this pill bias reform doesn't have an effect on overall.
23:38 Basically we see that.
23:40 The skill biashuku reform increase significantly increases the number
23:44 of firms established by the angry migrant entrepreneurs,
23:48 which means they are attracting entrepreneurs from other cities.
23:51 However,
23:52 at the cost of crowding out local entrepreneurs.
23:55 On the other hand,
23:56 the non restricted huku reforms as coloring in red
24:00 uh is boring overall local entrepreneurship.
24:03 So now we want to understand
24:05 uh who are actually responding to this hukou reforms.
24:10 So if we believe that the better entrepreneurs are responding,
24:13 if the entrepreneurs from the poorer regions are more likely to respond,
24:17 and if the entrepreneurs are more likely to
24:19 respond to policy changes in larger cities,
24:22 we will expect that this kind of policy
24:24 reform will induce a sorting of the entrepreneurs,
24:27 which means the better ones are moving to the larger cities.
24:31 In order to understand this heterogeneity in the response,
24:34 we request at the firm level
24:36 which the dependent variable equals to one if the
24:39 firm is established by an entre uh migrant entrepreneur.
24:43 And on the right hand side,
24:44 we regret or we include all the policy damages as well as interacting
24:48 the policy damage with the entrepreneurs uh
24:50 uh characteristics or the city's characteristics.
24:54 And this is a result.
24:55 As you can see in the first column.
24:58 So when we are interacting the policy reform dummies with the destination cities's
25:04 GDP we see significantly positive effect as I colored in in red,
25:09 which is saying
25:10 the larger like the entrepreneurs are more likely
25:13 to respond to the uh hukou reforms.
25:16 In the larger cities.
25:18 In the 2nd column and the 3rd column,
25:21 when we are interacting with uh the Huku
25:23 reform domains with the entrepreneurs' home uh GDP
25:27 or with their uh the city GDP of their previous firm's operation,
25:32 we see a negative effect which is saying
25:35 the entrepreneurs from the smaller cities were more likely to respond.
25:39 And in the last column,
25:40 we interact to the uh uh huku reform dummies
25:43 with the uh measures for the entrepreneur's performance
25:47 uh with their previous uh firm's performance percentile.
25:50 We see exactly that the better entrepreneurs like
25:53 with the when they are better performing entrepreneurs,
25:56 they are more likely to respond to this huu policy changes.
25:59 The results are very robust whenever we,
26:01 uh,
26:01 whether we include the person dummies or not.
26:06 And
26:07 so we want to understand why,
26:09 why there's a such a heterogeneous response to heterogeneous policy uh changes.
26:14 And we try to understand the mechanism from the labor
26:17 side as well as from the uh firm performance side.
26:21 On the labor side,
26:23 we see like we regress the number of migrant inflows on the policy changes,
26:28 where we see that
26:29 the high skill,
26:30 the skill buyers who reforms
26:32 is only able to attract those high skilled labor,
26:36 as I'm coloring in blue here.
26:38 While on the other hand,
26:40 the non-restrictive policies are able to attract both
26:43 the low skilled labor
26:45 and the high skilled labor,
26:46 which I'm
26:47 Coloring in red here.
26:48 So this is explain exactly explaining that uh uh
26:51 our mechanism why there is a heterogeneous response.
26:55 Because
26:55 the skill buyers who could reform is only able to attract the high skilled labor
27:00 and the coming of the high skilled labor will benefit those high skilled firms.
27:04 However,
27:04 at the same time,
27:05 they were gonna increase the local labor cost of the low skilled labor,
27:08 which hurts the low,
27:10 uh,
27:10 the firms in the low skill sector.
27:12 On the other hand,
27:13 the non-restricted co policy uniformly attract both the high
27:17 skilled labor as well as the low skilled labor.
27:19 And that is going to be beneficial to,
27:20 to firms in both the high skill sector as well as in the low skill sector.
27:24 And we try to confirm this uh uh uh hypothesis with firm performance data,
27:29 which we see
27:30 is actually when we're seeing this uh blue blue ones.
27:34 The the coefficient that the color in blue
27:36 uh is exactly telling us the bias of reforms are hurting
27:41 the firm's performance in the low skill industries.
27:45 And
27:46 however,
27:47 the non-restrictive fuku reform,
27:49 they are benefiting the firms in the low skill industries as well.
27:52 And when I'm interacting the policy re domains with the firm's skills,
27:57 uh sorry,
27:58 when I'm interacting the policy domains with the firm's skills uh measure,
28:02 we see that
28:03 both uh like both the high uh the high skilled firms are
28:07 benefiting from both the bias reform as well as the non-restrictive reform.
28:11 So enlightened by this reduced form findings.
28:16 We're gonna like,
28:17 let me briefly recap on the reduced form findings.
28:19 Basically we find that the skill by
28:21 Go policy change the local change the composition
28:23 of the entrepreneurial market,
28:26 but not the total number of the entrepreneurs.
28:30 And the better performing entrepreneurs are moving from
28:33 the smaller cities to the larger cities,
28:34 which is uh uh inducing regional redistribution of entrepreneurial activities.
28:39 And the reason is because
28:41 the entrepreneurs in low skill industries are hurt by the skill biased policies,
28:46 but benefit from non-restrictive policy changes.
28:48 Well,
28:49 the entrepreneurs in high skill industries,
28:51 they benefit from both,
28:52 and they benefit more from the non-restrictive ones.
28:55 And inspired by this,
28:56 uh,
28:56 uh,
28:57 reduced form findings,
28:58 we're gonna construct our,
28:59 uh,
29:00 uh,
29:00 uh,
29:00 structural model.
29:02 We build this spatial equilibrium model following the existing urban literature.
29:06 However,
29:07 we try to incorporate a little bit more features,
29:10 uh,
29:10 uh,
29:11 to fit our setting.
29:12 Specifically,
29:13 we incorporate two additional features build on existing literature.
29:17 Uh,
29:17 we incorporate heterogeneous worker type as well
29:20 as policy induced type-specific labor mobility cost.
29:24 Uh,
29:25 on the labor side and also on the firm side,
29:28 we allow firms to respond endogenously to the uh local labor supply
29:33 and thus to the endogenously to the changes in the local,
29:36 uh,
29:36 labor mobility restrictions.
29:39 So the model is going to illustrate
29:40 our key mechanism in that the labor is going to
29:43 start sort in response to the reduction in mobility cost,
29:46 and the firms is gonna sort with labor.
29:49 And due to time constraint,
29:50 I cannot go to the full model,
29:52 but let me introduce the model setup,
29:54 which are already including all the key elements.
29:57 And this is a simplified version of the model
29:59 which is uh simply for the illustration purpose.
30:02 And in the model there is uh there are N
30:04 cities indexed by the original O or destination D.
30:08 There's a mass of high tech workers,
30:10 high skilled workers,
30:11 and denoted by MH and a mass of low skilled workers denoted by ML.
30:16 The workers are born in a particular origin city index by O
30:19 and they're gonna receive a location-specific and
30:22 employer-specific India Indian uh Indiosyncratic preference shocks
30:27 and,
30:27 and they're gonna sort across uh destinations
30:30 according to the wage in the destination,
30:33 uh,
30:34 uh,
30:34 um,
30:34 and the migration costs.
30:36 The migration costs are relative to the birth location,
30:39 the destination and destination
30:41 destination location,
30:43 and it's gonna be type-specific.
30:45 So we allow the high skilled labor and low skilled
30:47 labor to face different uh uh uh migration costs.
30:51 And the workers gonna consume two types of products,
30:54 which we denote as
30:56 uh product QH in the H sector
30:59 and product QL in the L sector.
31:01 Basically,
31:02 uh uh next slide I'm gonna tell you what are the two sectors name.
31:07 And the local regional supply is going to
31:09 be determined by the labor sorting decision.
31:13 On the firm side,
31:14 the firms are owned by the mobile entrepreneurs.
31:18 And the firms are going to hire this high tech labor and the low tech labor.
31:23 And as I just mentioned,
31:24 there are two types of firms which are using different production technologies.
31:29 The firms in the H sector
31:31 employs more high skilled labor,
31:33 and they're producing high skill product.
31:36 And the firms in the low L sector,
31:38 they're gonna
31:39 Employ only the low skilled labor and they're
31:42 going to produce the low skill products.
31:45 All the firms,
31:46 they're gonna sort across cities
31:48 in response to the changes in the local labor supply
31:52 as well as the changes in the local labor cost.
31:55 And all the goods are freely traded in the baseline model.
32:00 So to to be more specific,
32:02 the firms in the high skilled sector.
32:05 Sure.
32:06 The firms in the high skill sector,
32:07 they're gonna employ both the high skilled labor and the low skilled labor.
32:10 And specifically there's a uh agglomeration force
32:14 which is uh the productivity of the firm depends on the mass of the high skill,
32:19 high tech firms in this location.
32:21 And this role captures the agglomeration effect.
32:24 And to be simple,
32:25 the IT sector firms only employ the low skilled labor.
32:29 And the firms is gonna uh uh sort uh uh according to their labor cost.
32:33 In equilibrium,
32:34 the firms make their optimization decision,
32:36 workers make their optimization decision,
32:39 uh,
32:39 final goods market clears,
32:41 labor market clears.
32:43 So due to time constraint,
32:44 I have to skip this identification of the parameters,
32:47 but just just to give you a key intuition here,
32:50 um we are identifying the key parameters on the labor side using the census data,
32:55 on the firm side using the tax data to calibrate
32:59 the wage distribution as well as the production function distribution,
33:03 and using the firm registration data to capture the firm's mobility.
33:07 So that are the key information we are using in order to identify the model.
33:12 So after we identify the model,
33:13 we can conduct some uh very interesting counterfactual analysis.
33:17 So let me spend my last 5 minutes introducing
33:20 uh the uh talking about this counterfactual practices.
33:23 So the first counterfactual practice,
33:25 we randomly choose 2 Chinese cities
33:27 and reduce their mobility cost.
33:30 And when we say we reduce mobility cost,
33:32 we reduce the mobility cost by 50%.
33:35 So we tried two kinds of policy practice.
33:39 On the left panel,
33:40 we biasedly relax the uh the mobility cost
33:44 only for the high skilled labor,
33:46 but not for the low skilled labor.
33:48 On the right hand side panel,
33:50 we unrestrictedly uh we uh we unrestrictedly
33:53 unbiasedly relax all the mobility costs for both
33:57 the high skilled labor and the low skilled labor in this treated 20 cities.
34:01 And here is the distribute uh this is the uh uh uh uh welfare effect.
34:07 You can see from the first comparison
34:10 of the unrestricted policy versus the biased policy
34:13 of uh there's a larger welfare gain from the non-restrictive hukou reform,
34:17 uh,
34:17 hukou relaxations.
34:19 And this is spec specifically like the,
34:22 uh,
34:22 welfare gain for the high skilled labor is kind of the same,
34:25 a little bit more from from the non-restrictive cities.
34:28 Uh,
34:29 non-restricted reforms,
34:30 but the majority of that is from,
34:32 uh,
34:33 another,
34:33 another proportion of that is from the low skilled labor.
34:36 Where the low skilled labor hurt a little bit
34:38 in the biased policies while they benefit a lot,
34:41 uh,
34:42 from the non-restrictive reforms.
34:45 More interestingly is this result is that
34:48 uh as you can see on the right hand side panel,
34:51 basically
34:52 the wage in the original city,
34:54 in the unrelaxed city,
34:55 in the city that are untreated,
34:58 they are,
34:58 they also increase.
34:59 This is mainly driven by uh uh uh uh reduce in the local labor supply.
35:04 So even in the unretreated cities where they do
35:07 not have any change in their local policy,
35:09 the people who stay,
35:11 they also have a welfare gain.
35:12 And for people who move,
35:14 of course they benefit from the reduction in the mobility cost.
35:17 So basically on the right hand side panel,
35:19 you can tell that
35:20 the non-restrictive relaxation of the hukou reforms,
35:23 uh hukou uh huko restrictions are going to benefit everybody.
35:27 For people who stay,
35:28 they benefit from higher wage.
35:29 For people who move.
35:30 They benefit from higher wage as well as lower mobility cost.
35:34 On the other hand,
35:35 there's the salary changes for the buyers whoo policy.
35:38 So for the best huco policy,
35:40 people who stay high,
35:41 high skilled labor who stay are benefiting,
35:43 but low skilled labor who stay are gonna be hurt
35:45 by uh by by the policy changes in other cities.
35:49 And uh of course for uh the treaty city,
35:52 they are going to benefit from the inflow of the
35:54 labor as well as the reduction in the mobility cost.
35:58 And we also try this uh policy counterfactual in the uh uh
36:04 sorry.
36:05 Oh,
36:05 also here is the uh uh the results
36:07 that are corresponding to our reduced form findings
36:10 in which we find for the bias focal policy
36:12 they attract the firms in the high skilled sector,
36:15 but crowd out firms in the low skill sector.
36:18 On the other hand,
36:19 for the unre received policy,
36:20 they attracted both firms from the high skill sector as
36:22 well as the firms from the low skilled sector.
36:26 And we also tried the policy experiments in the largest
36:29 20 cities as well as in the smallest 20 cities.
36:31 The findings are pretty similar to the random ones,
36:34 but the uh the,
36:35 the message here is that of course people are responding much
36:38 more to the policy changes in the largest 20 cities.
36:41 And this is a,
36:42 a,
36:42 a large proportion of this is driven by the agglomeration effect.
36:46 And
36:48 so let me briefly conclude.
36:50 So I believe we're the first one to really provide a full picture of the dynamics
36:54 of Fuko policies in the past three decades
36:56 and document its distributional effect on entrepreneurial activities.
37:00 The reduced form findings inform us that
37:02 the importance of this policy heterogeneity.
37:05 We know that skill biased policies change the
37:07 composition but not the overall uh entrepreneurship,
37:10 while the,
37:11 uh,
37:11 non-restrictive policies,
37:12 the changes that this.
37:14 For the overall entrepreneurship.
37:15 Entrepreneurs in low skill sectors who are going to be hurt by the pious policies,
37:19 where entrepreneurs in both sectors are going
37:21 to benefit from the non-restrictive ones.
37:23 In the model,
37:24 we illustrate the key mechanism
37:26 uh that labor sort in response to the changes in the
37:29 mobility cost and the firms are going to sort with labor.
37:32 And this explains the human capital foundation
37:35 of entrepreneurship in China across the country.
37:38 And we see that the relaxation of
37:40 restrictions may contribute to greater regional uh
37:43 uh inequality where they're gonna benefit the traded firms more.
37:47 However,
37:47 it indeed improves overall efficiency and welfare,
37:51 especially for the non-restrictive fuku reforms.
37:53 And,
37:54 um,
37:54 in another related project we try to document a long term.
37:57 of this inequality,
37:59 uh,
37:59 rising inequality,
38:00 where we're starting to see the better
38:02 performing entrepreneurs are returning to their hometowns.
38:04 And this is another force
38:06 where we see,
38:07 uh,
38:08 a hope that that,
38:09 uh,
38:09 uh,
38:09 uh,
38:10 uh,
38:10 kind of inequality will decrease in the long run.
38:12 So that is pretty much for,
38:14 uh,
38:14 my talk and thank you so much for listening.
38:16 I really look forward to the discussion.
38:19 Thank you very much,
38:20 uh,
38:20 Li Ming.
38:21 That was,
38:21 that was fabulous.
38:22 I think your conclusions.
38:24 Uh,
38:25 stress the importance of openness,
38:27 uh,
38:28 as a mechanism for reducing misallocation.
38:32 Nancy,
38:32 let me turn it to,
38:33 to you for your discussion comments.
38:37 Excellent.
38:38 Thank you so much,
38:39 Shome and L for an excellent presentation.
38:43 I think this is
38:44 a very important paper because uh it really brings together several branch,
38:50 um branches
38:52 of the literature,
38:53 as you pointed out the,
38:55 the literature,
38:56 of course,
38:57 on
38:58 Uh,
38:59 firm
38:59 firm and entrepreneurial location decisions,
39:02 but also the literature on migration and that
39:04 growing literature on the impacts of migration on,
39:08 on inequality.
39:10 Um,
39:11 my
39:12 first,
39:12 the first point that I want to make is,
39:14 of course,
39:15 there is also room to bring another set
39:18 of,
39:19 uh,
39:19 another branch of literature that I think may,
39:23 uh,
39:23 enrich the discussion in the paper.
39:25 So a little bit of,
39:26 um,
39:27 um,
39:27 I,
39:27 I would say the
39:29 Uh,
39:30 urban economics literature,
39:32 and let me give you one example where I think,
39:34 uh,
39:35 that I think could be useful.
39:36 There's,
39:36 uh,
39:37 in their paper of 2017,
39:40 uh,
39:40 de la Roca and Puga are looking at,
39:43 they see higher wages in large cities.
39:47 In Spain,
39:48 and they build us a spatial equilibrium model
39:52 to look at the different channels through which,
39:54 or that could explain this uh higher wages in,
39:58 in cities,
40:00 uh,
40:00 that whether there are static advantages
40:04 of large cities
40:07 or whether
40:09 Cities,
40:09 in large cities,
40:12 worker workers accumulate more valuable experience,
40:15 which is tied to,
40:16 to the discussion in the paper,
40:18 or whether it's sorting and you just have
40:21 workers that have,
40:23 uh,
40:23 that are more productive moving to bigger cities.
40:27 And what they find for the case of Spain is that there are actually
40:31 very large static and dynamic advantages uh from working in bigger cities.
40:38 Um,
40:39 and that it's not really coming from the unobserved ability of workers.
40:46 It is actually,
40:47 uh,
40:48 a combination of,
40:49 of static gains and learning advantages received or,
40:54 uh,
40:56 that,
40:56 that workers receive from moving to bigger cities.
41:01 Uh,
41:02 that explained the difference in wages.
41:04 And interestingly,
41:05 they find that these differences,
41:08 uh,
41:08 does not
41:10 disappear
41:11 immediately or after workers move out from these larger cities,
41:16 but rather,
41:17 this is,
41:17 this is a game that workers,
41:20 let's say,
41:21 um,
41:22 Maintained through time even if they move out of these bigger cities.
41:26 I think this is important for your,
41:29 for your paper
41:30 uh because it may
41:32 be uh that beyond heterogeneity of skills,
41:37 uh,
41:38 that you are already accounting for,
41:40 there may be some heterogeneity.
41:42 Meeting the attractiveness of different places
41:46 because of the added benefits that these entrepreneurs would receive
41:51 if they move in this case to larger cities.
41:55 So,
41:55 maybe thinking about how to incorporate that into the
41:58 model can actually uh add some uh additional uh
42:03 Insights to an already very rich paper.
42:07 You,
42:07 you already have
42:08 agglomeration economies
42:11 included in the model,
42:12 but I was thinking whether some of that
42:14 additional heterogeneity could bring some additional insights.
42:19 And,
42:20 second,
42:21 I think this,
42:22 this paper is very important from,
42:24 from the policy perspective.
42:26 Uh,
42:27 I think,
42:28 of course,
42:28 um,
42:32 I,
42:32 I think mainly 3 reasons.
42:34 Let me,
42:35 let me put forward mainly 3 reasons why
42:38 I think it's important from a policy perspective.
42:40 First,
42:41 because it stresses the fact that policies that have a spatial focus
42:47 have impacts
42:49 beyond that are not spa their impacts are not spatially contained.
42:54 And that I think that's very important.
42:56 Here,
42:56 we're seeing that the relaxation of constraints in one area
43:00 have impacts beyond the select the selected area,
43:04 and I think that's very important
43:07 uh as we from,
43:09 for example,
43:09 from our side engage with our clients
43:12 to stress the importance of carefully designing
43:17 uh place-based policies.
43:19 Anything that's spatially containing the design is will not
43:23 Necessarily be spatially contained in its effects,
43:27 and I think that's a very important message.
43:30 Second,
43:31 the link to inequality that you stressed.
43:34 I think that's also from the policy perspective,
43:37 very important because
43:39 it um
43:41 It raises the question of
43:44 What,
43:45 what to do about it.
43:46 So,
43:46 you were putting it into your,
43:48 in your finance slides.
43:49 There are improvements in efficiencies,
43:52 but there are also increases in inequality.
43:55 So that
43:56 it brings us back to the discussion of the trade-offs between
43:59 efficiency and inequality and what to do about it.
44:03 And I think there,
44:04 um
44:05 there is a very,
44:08 um there is a growing literature looking at,
44:10 at
44:11 the
44:13 How to tackle that spatial inequality and poss and how it is possible actually
44:20 to think in some cases about uh place-based redistribution
44:25 schemes that
44:27 do not or that can actually improve
44:30 um or that do not affect uh equity.
44:35 There is a recent uh paper by Gobert and colleagues and NBER.
44:40 Working paper actually that looks at
44:43 the conditions uh for the US
44:46 under which uh place-based redistribution schemes can
44:50 actually improve the equity efficiency trade-off.
44:54 So,
44:54 I think this also ties to that literature,
44:57 uh,
44:58 and,
44:58 and points at an area that requires more research
45:02 and where we really need to understand how we can
45:06 improve,
45:06 um,
45:08 Or reduce those trade-offs.
45:11 And third,
45:12 um,
45:14 I think it,
45:15 it,
45:15 uh,
45:16 uh,
45:16 the,
45:17 the final point of why I think this from the policy perspective is
45:20 so important ties to a message that you give in the paper,
45:25 which is entrepreneurs and firms do not make decisions regardless
45:29 of market conditions.
45:32 You're stressing it in terms of uh migration policy,
45:36 migration constraints.
45:38 But in the same line,
45:39 I think it raises the question of what
45:42 other policies may actually interact with migration policies.
45:47 And in some recent work that,
45:49 that we've done at the bank,
45:50 we're showing that it's,
45:51 uh,
45:52 the interaction between
45:54 urban development policies and migration policies is very important for
45:59 integration of migrants into city in a broader context.
46:04 But here,
46:04 it made me think,
46:06 About whether there are important interactions,
46:09 for example,
46:10 with any industrial policies that have been placed in China
46:14 in,
46:15 um,
46:15 at the same time.
46:17 My understanding is,
46:19 at least recently,
46:21 uh,
46:22 there is a,
46:23 there are
46:25 Uh
46:27 Tax,
46:28 how do you say,
46:29 uh,
46:29 reductions in,
46:30 in tax,
46:31 tax incentives for firms that would locate to the western areas.
46:37 I wonder if in at that time
46:39 that you're studying something similar could have happened
46:42 that could have interacted with migration policies.
46:46 Uh,
46:47 and similarly,
46:48 other urban policies that would make some places more attractive to firms,
46:53 to workers
46:54 that,
46:55 as I was mentioning on my first point,
46:57 would ultimately,
46:59 uh,
47:00 have an impact on the productivity of entrepreneurs or
47:04 Our firms,
47:05 uh,
47:06 think about,
47:07 uh,
47:07 land policies,
47:08 restrictions or housing availability in these places.
47:12 Um,
47:13 so,
47:13 maybe thinking about the interaction with other policies could be also,
47:18 uh,
47:19 another area for future extensions.
47:22 Let me stop here so that we have some time for a question.
47:26 Right,
47:26 thanks a lot,
47:27 Nancy.
47:27 Those are very
47:29 useful points.
47:30 Uh,
47:31 Liming,
47:31 here's what we do.
47:32 Let's open it up for a few more questions
47:35 and then we turn it back to you to provide reflections.
47:38 Then you can kind of reflect on
47:40 Nancy's questions as well as the ones we received from the colleagues joined in.
47:44 Folks,
47:45 if you have any questions,
47:47 please come in at this time.
47:48 You could raise your hand and if I can
47:51 then
47:52 ask you to,
47:53 to
47:54 speak.
47:55 Uh
47:57 Anyone have any questions?
48:03 Well,
48:06 OK,
48:07 I don't see any right now,
48:08 so let me go to you.
48:12 Thank you,
48:12 Sami,
48:12 and thank you very much,
48:13 Nancy,
48:14 for really insightful discussion.
48:16 I learned a lot.
48:17 And let me,
48:19 uh,
48:19 briefly,
48:20 uh,
48:21 reflect on what you have already discussed.
48:23 Like your first point I really like is the dynamic learning effect,
48:27 uh,
48:27 introduced by the large cities,
48:29 and actually,
48:30 um,
48:30 uh,
48:30 uh,
48:31 unfortunately,
48:32 this paper is using a static model which we do not account for those dynamic effects,
48:37 but what you said is really interesting and really worth,
48:40 uh,
48:40 for the investigating
48:42 like the advantages of the larger cities have a dynamic learning effect that may,
48:47 uh,
48:47 Contribute more to this discussion.
48:50 And on this point you talked about the policy relevance.
48:55 Uh,
48:55 I,
48:56 I really appreciate you pointed out the three
48:59 policy relevance for the paper
49:01 and I'm sure I'm gonna include them in the right of the paper.
49:05 And specifically I want to echo your point on the second one,
49:09 regarding the trade-off of inequality and the efficiency.
49:14 And I think that actually echoes the uh important.
49:17 im port ance of our findings in that
49:19 you see the non-restrictive policies that actually they benefit all.
49:23 So
49:24 even though there is a rising inequality,
49:26 but that rising inequality is mainly driven by the ones who are getting better off.
49:30 And even the ones who are left untreated,
49:33 they also get getting better off to some extent.
49:36 So this is like,
49:37 like this point really echoes the point like the non-restrictive policy changes
49:42 is gonna uh be more beneficial in terms of
49:45 simultaneously improve.
49:48 everyone's welfare as well as taking control of the regional inequality.
49:52 While on the other hand,
49:53 the skill biased ones,
49:55 there is some more nuance of findings like someone gets better,
49:58 someone gets a little bit worse off
50:00 and may not benefit everybody.
50:02 So I,
50:02 uh,
50:02 that is what I especially uh uh appreciate your second point.
50:07 And yes,
50:08 exactly for your first point regarding the spatial uh uh uh uh spatial uh
50:13 uh uh contamination is exactly what we try to capture with the Spatial model
50:18 and the third one is actually there are a lot of super more
50:22 important policies going on in China across the like across recent decades,
50:27 industrial policies,
50:28 place-based policies,
50:29 hukou policies.
50:30 In this project we are focusing on the hukou policies and we are trying to study the
50:34 industrial policies in a separate project.
50:37 But where you point out is really interesting is that looking at
50:39 the interactions of those policies which I actually I haven't think of,
50:43 but I will definitely think about more in the future.
50:45 Thank you so much,
50:46 Nancy.
50:50 Great,
50:50 uh thank you,
50:51 Li Ming.
50:52 Uh,
50:52 others,
50:53 any more questions?
50:54 Omar,
50:54 you wanna come in?
51:02 Hi,
51:02 can you hear me?
51:03 Yep,
51:03 hi Omar.
51:05 Hi,
51:05 how are you?
51:06 Sorry,
51:06 I was trying to mute for a while,
51:08 so I decided to post the question in the,
51:10 in the chat.
51:11 Um,
51:12 thank you very much.
51:12 Very,
51:13 very interesting model.
51:14 I remember looking at this,
51:15 uh,
51:17 issue of,
51:17 uh,
51:17 mobility in China,
51:19 um,
51:20 a few years ago,
51:21 and,
51:21 you know,
51:21 clearly a lot has been happening since then.
51:24 So I was wondering whether you thought at some point
51:27 of,
51:28 uh,
51:29 building into the model.
51:31 Uh,
51:31 the role of universities,
51:33 as,
51:34 you know,
51:34 the location,
51:35 the special location of universities,
51:37 that's obviously
51:38 units of production of,
51:39 uh,
51:40 human capital,
51:41 well,
51:41 both
51:42 the work,
51:42 you know,
51:42 worker skills and entrepreneurial
51:45 skills
51:46 and whether there's,
51:47 uh,
51:48 you know,
51:48 obviously policy implications in terms of the things that,
51:51 uh,
51:52 we were just discussing,
51:53 um,
51:54 uh,
51:54 uh,
51:55 arising from the model if it were to be modified in that way.
51:59 Thanks.
52:00 Thank you so much,
52:01 Omar.
52:02 And actually,
52:02 you know what,
52:03 so at the very beginning of this project,
52:05 we were thinking about this role of
52:07 universities and in generating the local effect as
52:10 well as the spillover effect in attracting the
52:12 high skilled labor from the some people,
52:14 some places to the uh to the large cities.
52:17 And
52:18 in the end we didn't really model that in,
52:20 but that is indeed an interesting angle to look at.
52:25 And especially when you mention the uh
52:27 production of entrepreneurial skills in the universities,
52:30 yes,
52:30 that is something we,
52:31 we,
52:32 we have not done for now,
52:33 but that is something really worth trying in the future.
52:41 Great.
52:41 Uh,
52:42 anyone else has any more comments,
52:44 uh,
52:45 going once.
52:48 Twice,
52:48 OK.
52:49 Uh,
52:50 well,
52:50 colleagues,
52:51 thank you very much for joining this,
52:52 uh,
52:53 seminar that Li Ming gave this morning,
52:55 and Nancy,
52:56 thank you very much
52:57 for the excellent feedback.
52:59 Uh,
53:00 the seminar slides and the recording will be posted on the WDR website.
53:08 w.worldbank.org/WDR2024.
53:14 Uh,
53:15 we also have two,
53:18 very exciting
53:20 presentations coming up in October.
53:24 On October 18th,
53:26 Roberto Fatal,
53:27 who is a core team member of the report,
53:30 will present his work on distortions and firm dynamics in developing countries.
53:35 So I would encourage everyone to join as it is a preview
53:40 of the findings that are reported in the chapter
53:43 for the WDR that he is writing.
53:46 That's on October 18th,
53:48 and on October 25th,
53:50 we have a seminar on higher education
53:53 for social mobility and talent development
53:56 looking at the case of Chile.
53:58 So colleagues,
53:58 thank you very much again for joining,
54:01 and,
54:01 uh,
54:02 and I,
54:02 I,
54:03 I hope some of these
54:05 seminars are of use and kind of spreading the,
54:09 the knowledge that we're trying to put together in the report.
54:12 Thanks very much.
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