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Good. Okay, welcome, everybody. And this session. I'm looking at housing. Probably. What is the session. Put it. Sorry

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in the

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right.

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apologies.

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Okay, looking at housing inequality in China.

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obviously a very major issue. And before I so this like to do acknowledgment of country on behalf of everyone gathered. I pay respect to the spirit of country. Yeah. In Narum, where we are meeting, I acknowledge the past and present

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Klynnation. Wow, we're in dangerary war one as the sovereign people. Yeah. The cultural knowledge holders who maintain and promote the non aboriginal law customs and custodianship, the responsibility of a country and the people. That country continues to sustain generation of the generation into the future of all of us.

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Goodness, I recognize the important contribution that first nations think as I made to sociology, including the challenges to our dominant ways of thinking, we thank all first nations delegates in the room today for their contributions to knowledge in the discipline.

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Right? Thanks very much.

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so we have quite a bit of time. So if people want to go a bit longer. I don't think that's a problem.  so we'll finish the I mean, we'll follow the order.

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And so, Jenning, you want to start. thanks very much. And we will also. I just want to welcome people on. Zoom, Flavia. Thank you. There.

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And anybody else I know.

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Right? Okay, thanks very much.

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Yes.

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all right.

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Well, thank you for the opportunity to share our research here. My name is Jenning Lee. I'm with the University of Texas at Arlington.

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I'm representing my co-author, Dr. Year, who is from Guangzhou? Who

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the Southern China culture University?

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so

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I what's

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does it look like? It's working with the

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no, I have to do that.

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here we go.

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I thought, I'm going back. Yes, all right. Thank you.

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All right. I would like to start with some background information. Relate to our research.

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Then I'm going to describe our research design

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and present the fighters.

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and finally conclude with a few remarks.

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 Tods, I don't know. our audience here. I assume everybody understand? Tods, right?

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No, quite all right. transit oriented development. basically talking about the developments around real stations.

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here, Tood is a big thing worldwide, especially. I'm a planner, and I don't know if other people know about that. But this is a big deal right now in the field of urban planning.

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and, according to the most reason, statistics.

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the

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493 cities, their own real urban rail in

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the total mileage for a while is 26 kilometers, with

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about 27,000 stations. Right now. so many cities who own

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urban rails, implemented tods around rail stations and tood toodes are kernel

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characterized with high density and mixed land use development, and Tod has been used as a strategies for place branding

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for quite a while.

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and

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Well, a waters here are all for China, right? So you probably know that urban and real development in China has experience quite rapid

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 developments. urban population and urban land use had expanded. about 4 time and 8 time, respectively, between

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1,918 in in 2,018,

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currently 43 cities own rail system and more than 30 cities plan to build one

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here.

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It is expanded. That urban rail and toodes have many benefits. One

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big ones is urban, is expanded, urban rail will reduce traffic congestion

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and promote real estate development and generate tax revenues

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for local governments.

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And it's also expanded

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 real urban rail. And to these will bring changes in the build and social environment

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 around them.

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I mean, it's a good deal. So it's a big deal. A lot of studies have been

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focusing on

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the impact of urban rail and tods in specific and some studies focusing on the impact on transportation and environment.

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And it has been found that urban rail has been able to expand.

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travel, wrench, and shorten travel time. and also increase public transit ridership.

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It's also found that the passenger fro has been  dominated by the commute between the Cbds and suburban areas.

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 and of course, when you attract people from

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driving that will reduce vehicle emissions.

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Other studies also focus on housing and land use. effect. The study in this area is enormous.

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empirical studies have been using data from many cities around the world.

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Some of them in here are focusing on us cities, some in other cities, for example, Canada, Italian, Italy, and also even Taiwan, and even in China there are also significant studies on

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those impacts. for several cities, especially Beijing. tending since those are 2 big urban systems in there.

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 so those studies,

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have used a variety of methodology there simply, you know, some simple ones, including

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and over other sophisticated methodology, include spatial regression models, difference in differences and also regression.

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difference in difference

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and the results are not quite and

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assistance. some find some impact and some find no impact. But even with the within, the studies who have

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find positive impact. they also find some, you know, varieties

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in terms of value, prize intensity, and they are very by location, development stage and also time.

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So

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what does that mean to me? To us?

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That means there are plenty of opportunities for research in here.

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So that bring in

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to our study here. So what we'd like to do is we like to investigate the relationship

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between will development and

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housing supply the theories underneath. Under this kind of a study usually

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is assuming that increased access to transportation infrastructure will increase the demand.

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As we know that travel cost is part of.

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you know this. So increase ss, to transportation would increase the demand for housing

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and because the increase in

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demand in housing that potentially drive up the price of poverty around the proximities around rail stations.

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And something I should have put it further is

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because the price increase that potentially drive some people out of those areas. That's what we call, I think, if you all are sociologists in here, probably you have the gentrification effects in here.

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So this is where it's relevant to here. So but on the other hand, we do know that the price economic 101 tells us that the price of housing or any good has something to do, not only determined by the demand, but also the supplies. So that's what

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we are going to focus on. We were focusing on how that

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you know.

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the surprise in here. Specifically, we want to focus our research question. This is, we ask if there is a real induced change in supply of affordable housing

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and affordable housing in our study is measured by the percentage of low price rental housing units in here.

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We want to use Guangzhou as a case study.

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You guys probably wonder why, Guancho? A couple of reasons. First of all, is the

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it's your location of Guanto. We have one soup

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presenter here is from Guangzhou, and you probably know that Guangzhou is now, as the South. 8 of China is a main city in the Sutton China, and it's the gateway for

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economic and trading with countries around the war here.

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and also Wong Jo built its first rail system. in the nineties. So

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if we are looking at impacts.

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I think that it has enough history which you allow us to look at the changes over time.

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And the third reason we choose this as our case study, because that's the data we can get, we're limited by the data. And also, although there are quite a few studies on ground to real but very few. As far as we know, probably no one has been looking at how its impact on the affordable housing.

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So as reason that we choose this as our case study here so mainly, we focusing on the central city area. so if you look at the map in here, those are the dark area. the whole map. In the index map there is the metropolitan area

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and the dark area.

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This is the area that we are studying.

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and those are also the main area that covered by the real system here. And we use as we mentioned, we use the 22,02010 Chinese census data.

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And our unit of data is.

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I use qiui here. This is basically equivalent to the census track

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in the United States. And I'm not quite sure census data unit in other countries. But this is equivalent to

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 the census track. In the United States. We have about 2,400 cases in here. We plan on using the linear mix effect regression model to answer our research questions, we think this is the most appropriate model for our data. because it's appropriate for repeat measurements and data dependency.

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And also this model allows us to

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estimate the fixed in random effects.

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So this slide here basically shows the variables we include in our model. And obviously the key variable is the real.

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because we're looking at will induce impacts in here. So for neighborhoods that contains real stations, we call them as one and the rest of them.

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0. And then we include

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social demographic variables that are listed in there. And we also control for real real estate development variables.

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And finally, we can put the temporal effect in here.

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Those are the extended relationship with the dependent variable which is the affordable housing here.

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All right. first of all, we wanted to see the changes.

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as we know, I may

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like the rest of the cities in China, it has experienced rapid developments.

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So we wanted to see Gundo as a whole. How that has changed between the year of 2,02010, and then we also look at the changes

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in the real neighborhoods between those 2 years and

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and then look at the difference in here. I highlight this one here, and those are pretty small here, but I will report the results in there. But this is what we are looking at. so basically over time. the affordable housing has been increased, and the difference is significant.

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 and but

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in the

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well neighborhoods we also see increase here. But the increase. The difference is not significant there.

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So basically, this is what we found from the previous slide there. basically, we see.

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age population has been age, education level has increased.

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And we also see increase of population engaging in research and technology. We also see increase in commercial housing. And

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like what I just.

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and we also see some changes increase in low price rental housing unit as well.

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Obviously new housing development has been quite a few

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over there.

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 on the other hand, we see some decrease in immigrants.

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There can also decrease in labor, force.

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and also decrease in population

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that engage in agriculture and manufacturing activities.

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the changes

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for the city

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pretty much is significant except the changes in immigrants. But for the real neighborhoods all the changes are significant

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except for the immigrants and also

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low price rental housing. Here. All right. So

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between the city and real neighborhoods we see similar directions in terms of changes.

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However, we do see that for

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a number of variables to change in the city as a whole is greater than the changes in neighbor, real neighbor

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neighborhoods.

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But

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you know those are.

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The changes in city are greater than those in the real neighborhoods, but

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the for the rest of them the changes in real neighborhoods are greater than the city as a whole here.

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So that's some kind of a basic and description about.

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you know what's going on in there.

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and

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Those are the few observations here.

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Population has age, education, increase

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and industrial structure has transferred towards research and technology, and housing has become

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commercialize.

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Well, the thing is.

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this is what we are interested in, what the real developments there has some kind of impact. The description before only tells us about what has been changing.

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but with that has some

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determinant impact in there.

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that where we have to relied on models to

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 to to to allow us to see the results here. But first thing first, before we run our model, we want to

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check on the mountain

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collinarities in here. So basically, this table here presents the pairwise correlation between the independent variables, and here the highest Corey correlation relation, coefficient, is point 6 4 here, which

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is below the

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stressful of point 7. So

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I guess we are safe

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in here.

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So this is the results of the mix effect model. And I want to highlight the 3 different ones in here, if we control everything else. We found that the we we do not find the

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The rail induced effect in here, because it's not statistically significant at the point 0 5 level. Here, however, we do find the temple effects in here

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a photo for housing.

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the percentage of

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of of low price rental housing unit.

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And this is something that I found interesting. Random effect has been quite large in here. So what does that tells me is that probably we, this model, have not increased

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other variables that might have has some impact on that as well.

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So in some.

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This is what we found significant changes in social, economic, industrial structure and real real estate developments in Gundos Central City. Between those

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 you know, to

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point in time. And

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Also we find some differences between the rail and also the not real neighborhoods

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and the findings shows a indication of Upward Chan.

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A. Trans. Here, however, we do not find the metric-induced effect on

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low price housing supply here. So that's basically the key findings

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we have in here.

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So in conclusion, and there are studies on

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transit ridership housing, land use in gentrification.

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They are mostly focusing on the demand-driven effect.

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however, very few are focusing on the supply side like this. So our research, both

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in our opinion, field the gap in there.

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we're using a different methodology, which we believe is more appropriate for our data.

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for our research questions, and we provide some empirical evidence in the debate regarding the rail and Dod effect in here.

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Of course, this study is with a lot of the limitations. As you have already know, we use secondary data from the Chinese census in 20100, by the way, I forgot to mention you probably ask.

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You know it's 2,023 now, and the 2,020 data is out. Yes, it is true.

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But the 2020 day. The do not have the detail, not the disagreement. Data is not out yet, so we have to like on the previous census

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data here.

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and also from our results there. we have the random effects. It's quite large in there. So that tells us there are quite a few unexplored factors. Now.

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we' not include in our study here.

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Of course, this is a case study. is

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you know validity. External validity is in question, because we are all based on Guanto's data, and we don't know if that would apply or be able to generalize in other places.

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So but we do have a couple of takeaways in here and number one. This issue of real induced effects are quite complex, as we find that for this particular one here, we did not find that induced by a lot of the studies do find some in this effect in here. So it's quite complicated in here. And

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one might ask, What does that mean to research? And for my feel of I'm planning.

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what does that means? What is, you know what we can learn from this study here? I mean, obviously from the limitations in their

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I mean, one area that the research community can do is to explore what are additional factors that might have something to do with the real induced effects in here. I mean, this is probably an open area that allowed a lot of studies in the future.

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and also for planning.

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We all know that China has very different political system, and even in urban planning

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systems.

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probably quite different

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from the planning in many Western countries.

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one thing that

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I do know that the Chinese planning organization they actually planning. And they set specific ratios in terms of our housing. What kind of housing can be developed

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in each communities or neighborhoods?

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Would that be the reason? A difference that they're able to capture the likewise

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housing in those sensitive area, because in Western countries, especially in American, a lot of those there are France gentrifications, because people who cannot afford

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the expensive housing in

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close proximities to rail systems has been priced out from those areas, and that creates what we call inequality

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in China, in America. So with

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the central control in China, planning China and Barbara, development policy would not be the country builder for this fighting here. I don't know, because we were not able to, you know, capture that effect in there.

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But if that's really is the reason, then what planning feel can take away from this is us. does have some federal control policies and also programs that can provide some incentive

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for controlling the unintended effect on a real development. So

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I'm done. Thank you for your time.

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Right? Thanks very much.

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And

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okay.

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I'll just

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thank you.

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I'm sorry. I have a great question. Yeah. I was wondering if I'm just thinking about the implications of

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what you found.

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and

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I, is it?

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It' be a fair interpretation. To say that increases in supply of housing

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by transit or in development haven't increased

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an increase that has been an increase of supply

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is that a reasonable interpretation of what you found or not. Really.

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So your question is, where? Actually, I think it's okay.

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Can you repeat that the interest?

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I think that I'll do this for the people, is it on? Yeah. My question was, I'm just thinking about how to interpret your finding.

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So you said that metro

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developments having it have not on affordable housing supply right? But they have brought about increases in the supply of housing.

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Is that correct? Increase? Well, it According to the data it. Yeah, I'm just interested in this, because in Australia there's a lot of people that argue that increasing the supply of housing will necessarily increase. housing affordability?

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Oh, yeah. Yeah. And in your case this doesn't appear to happen. It has not increased that. Yeah. we have not found that. this is not statistic, the significance. Yeah, yes.

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Thank you.

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Okay.

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So I'd like to take the advantage of asking one question as well as consider we have pretty plenty amount of time here. so actually, my question is, I'm not that surprised to see, you know, if that because this on the descriptive fund. results. Yeah, not not the regression results. We always see the pattern right? So to to compare to the overall increase. Yeah, increase in the Metro rail area is not the.

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It's not very different from the over-oriented. So even from the descriptive results, I think the influence of the natural development of wouldn't insult that significant influence on the provision of affordable housing.

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And this is one of my guests, because it is partly related to my research not directly related. I'm thinking about the influence because your data compares 2,000 2,010 right?

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And that's a interesting comparison. I mean, the years are pretty

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special, because it covers the year 2,008, and before and after 2,008 there was a dramatic change in the urban housing. We are provision in China because the Chinese Government's decision of using the house and market as a booster to save

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its economy from the 2,000 and economic crisis, and that will induce the increase of the supply.

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That's exactly compatible with the pattern of the increased silver of the supply. So that's an important issue that I have in one.

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Which means that

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no matter what, if I build that background in mine, there will be an increase in the supply in whatever housing units

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from 2,000 to 2,010,

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and that will that may contribute to your funding regarding the increase of the supply. And and and even though you are focusing on the affordable housing units, it's part of the housing market provision, right?

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So with that background in one, I'm thinking, if you really want to ascending the influence of the Metro, the, the, the, the, the, the the railway, induced

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an increase of

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affordable housing you like.

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Is it possible, or would it be better for you to change a timeframe for analysis rather than covering the time of 2,008,

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because you're comparing before and after 2,008. And that's a significant change. And you can, if you use this data, it is just not possible for you to part out that, in fact.

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Well, the time effect. All right.

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we do. We are aware of the time effects there. So that's why, one of the

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variables in there is the time we do control the time there.

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the temporal effects in there. So you're talking about 2,008. Right?

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 is what you were talking about. Yes, okay. So I will. Base 2,010 2,000. Okay, so 2,000

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compared to 2,010.

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So

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otherwise

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constantly.

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Hmm.

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I can think about that.

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But again, we are limited by the data that's only available for that

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But I think if this is a good question here that we can think about it, I mean, obviously, this is work in progress in here, and we definitely will. You know this is the reason for the presentation here that here what other people will have

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or some idea in terms of what can be done or doing better. So this is something that we can certainly take into consideration, and thank you very much for that

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holy bows. I think a boncho compared to a lot of other countries.

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the private sector.

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 you know, especially in the house, and I think it's quite dawn, and then? so you know, and especially if you're looking at the like, the commercialized housing. And the one thing that we do see is the commercial housing has become very, you know, obvious there and

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But is it something that the government control? I mean the policy in terms of they said, the ratio? Of what kind of a housing should be developed in each neighborhood.

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Would that be the reason that contribute to this?

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Maybe that's my suspicion. But again, we don't have the data for policy, but we don't have a measure for that policy. So I cannot say that for sure. But that's my suspension. Yeah, thanks very much. Thank you. Another question from the audience to you. Oh, come in as you chat. So.

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he said.

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I take the assumption that with the arrival of self-driving, and it might be possible that the

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yes, I'm sure. Yes, thank you. Good point.

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And then this kind of a sheer

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make a line to work involvement from any point, a to be

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from buses in jail. In addition to this.

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your house and developments do not hold expectations in densities. Yes.

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her. Yes, definitely, very much. Yeah. And this is the

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right. Thank you.

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So you want to.

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Thanks very much.

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I'll just put this down

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this

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come out this week, if you want

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right. Our next speaker.

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I'm sure. When? Why, I hope why, I'm on her list in China.

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 so the structure I'm just a response from gender.

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Thanks very much.

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Thank you. Thank you so much for having me, and thank you so. thank you. Everyone for coming today. So my name is Fanshiwen. So previously I was working at the Australia National University, and starting in this August, I will start as an assistant professor at the Department of Sociology at the Ohio State University.

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So, together with my course at Chung-hong today, I'm going to talk about why women own less in China and the role of in and the role of sip-trip structure.

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So assisting literature has well documented that

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a gender wells gap widely exists.

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just as in most other societies. Women in China own less wells than men, which affects their rights and welfare within and beyond household.

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So what are the factors that explain the gender wells gap

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trivia studies on Western developed countries generally attributes the gender wells gap to gender disparities

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in individual characteristics, such as lifetime, earnings, and education.

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In our study, we introduced a new demographic angle to examine how siphon structure affects gender wells gap in China, where the patriarchal conditions remain strong

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on the broader literature on gender inequality. There is a line of research focusing on the relationship between fertility, decline, and gender inequality

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which is particularly relevant to our study

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as he has been argued

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as fertility declines, women have become increasingly more likely to grow up in families without brothers.

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That's suffering less from unequal in parental investments.

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studies on educational attainment provide very robust support to this hypothesis.

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therefore, it is reasonable to speculate that a smaller S

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would also lead to a narrower gender wealthscape

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when it comes to the intergenerational resource transfer.

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Prior research has considered it as a cause of gender inequality

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in wealth. and there are 2 related processes going on.

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On the one hand, according to the general resource dilution theory. If we consider that parents, monetary and non-monetary resources are more or less constant.

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more children will inevitably result in a smaller share for each child within the family.

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On the other hand, in societies with strong some preference, parents make unequal resource, transfers to their sons and daughter.

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Taking these 2 processes together.

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we should also expect that sipshot size might have a more negative effect for women than men.

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However, prior research has really conceded the role of inter-sibling resource transfer in explaining the gender wells gap

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inter-sibling resource transfers are actually very common in East Asian context.

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Previous studies have documented that families may sacrifice the educational opportunities of their older daughters and use their remedies to support younger children's education.

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In addition due to the persistence of patriarchal marriages.

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siblings might have are more likely to help their brothers buy housing so as to increase their competitiveness in the marriage market.

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As a consequence, the sex and age compositions of siblings would also have differential effect from women and them.

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So on the basis of the multiple lines of research discussed above

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in our study, we focus on the gender gap in home ownership in particular, whether or not one's name is listed on the property deed

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after decades of market reforms. Housing assets have already constituted the largest share of household wells in China today.

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So, having the name on the deed, grants want control over selling the assets or pledging them for loans.

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and it often offers advantages in asset division, appoint divorce.

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How ownership does enables us to identify individual holdings and distinguish wealth separately owned by men and women.

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It also allows us to identify each partner's wealth even after marriage.

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In other words, we can measure the well, the gender wells Gap. Among both the merit and the emerit populations.

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We propose the following research questions, the sipshot size, or the number of siblings.

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and the subject composition

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explain the gender inequality in home ownership, in contemporary China.

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If yes, what are the potential mechanisms?

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We have 4 hypotheses. The first 2 hypotheses are derived from the general resource, dilution, theory, and the gendered process of intergenerational resource transfer

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so due to the resource dilution.

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So we hypothesize that compared to only children. Not only children are less likely to own housing assets.

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and due to the persistence of patriarchal traditions compared to only child sons, only child daughters are less likely to own housing assets.

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In addition due to the inter-sibling resource transfer. we expect that

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the effect of citrus on home ownership is opposite for women and men.

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While having more siblings, decreases women's probability of owning housing assets, it increases men's probability.

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and compared to having older siblings, women's a more disadvantaged when, having younger siblings.

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we employed data from the 2,010 to 2,018 5 waves of the China family panel studies as our primary data source

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and our key. Independent variables include gender.

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The number of siblings and the sex and age gap of siblings.

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Previous research has well documented that cipher size is most likely to be endogenous to your outcome variables.

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To alleviate these issues we adopted the newly developed, covariant, balancing, generalized propensity school method to balance individuals with fewer siblings, and those with more siblings in terms of observed characteristics.

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Compared to the more conventional propensity. School method. This new approach is more robust to miss specification to the of the propensity score model.

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In the first step

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we apply the Cbgps method to generate a

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propensity scored as well as the corresponding weights

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to

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to balance like observe characteristics across separate size.

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So we use respondents, ethnicity, rural or urban origin, parents, education, and their province of birds to predict their exercise. and in the second step we apply the generative weights to a linear probability model specified as below.

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So the main, the main effect. The coefficients of the main effect of the female dummy

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size, and the interaction effects of these 2 variables are our primary focuses.

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as they are shown after balancing

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which are denoted by the red triangles. Here

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the coefficients of all peers and correlations drop to almost 0,

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suggesting that the Cbgps method has successfully minimized the associations between the covariates and the number of siblings. So we actually try to make people with more siblings and fewer siblings as similar as possible.

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Here are our main results. In our unweighted sample on the left we show that

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gender

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gap in home ownership increased as the number of siblings increased.

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and in the weighted sample on the right.

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We show that after balancing

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the

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the widening gap was less steep, and it was only present among individuals with at least one sibling or more.

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suggesting that

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the gender inequality in home ownership is partially explained by selections into different sections size.

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Our findings also suggest that after balancing the rise of gender inequality

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in wells across such a size is mainly driven by my men's increasing advantages rather than women's disadvantages

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by plotting the predicted probability of home ownership by gender and the age gap with the oldest brother or sister. We show that men particularly benefited from having a much older brother or sister.

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which is the evidence of receiving monetary support from all the siblings for men.

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In contrast, when having a much older brother or sister did not help women

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portrays housing

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when it comes to younger siblings.

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for women

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having a much younger brother or sister severely harm their interest, suggesting that women's sacrifice for their younger brothers or sisters

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by receiving less intergenerational transfer, and or providing more inter-sibling resources.

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support

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our patterns, remain largely similar for respondents of urban and rural origin.

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But

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the the evidence of intersibling resource transfer from men from urban areas only exists since the third birds, which

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it accounts for a relatively small proportion of men of this background

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we we.

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We also show that our main findings are mainly driven by the 1,900 seventys, and the 1,900 eightys cohorts.

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suggesting that inter-sibling resource transfer is more likely to occur in a competitive housing market rather than in a redistributive Socialist regime.

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Since the effect of secret size on home ownership only provide indirect evidence of inter-sibling resource transfer.

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We use data from the China Household Finance Survey to provide additional descriptive evidence as they are shown. Siblings were the second largest long source for housing only after banks.

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and among households who borrow from non-banking sources.

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Almost half of them borrow from their siblings

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these findings highlight the important role of inter-sibling resource transfer in the acquisition of housing assets in contemporary China.

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So in our analytical sample.

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The average number of siblings drop from 3.6 to 1.6 before between 1,950 and 1,980,

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and they accounted for around 7.8 decline in gender inequality, in home ownership

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for individuals of urban origin, fertility decline between 1950 and 1980 accounted for around 20% decline in gender inequality

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and for individuals of ruler origin, fertility, decline accounted for around sixtys.

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6.7% decline

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considering, because, considering that in our observation period most respondents grew up from

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from rural areas, so the overall contribution of fertility decline to gender equality was moderate.

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However, our findings still demonstrate that

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the gender gaps, substantially narrow, due to a fertility decline

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for respondents from urban areas.

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although it might not further decline in the future, because the number of sibling had already become very small for the 1,980 cohorts.

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So in the context of fertility decline. Our study suggests that gender inequality in long-term outcomes can be much, much more persistent. while the gender gap in educational attainment in China is disappearing, drastic gender inequalities persist in housing and wells.

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and the process of intergenerational and inter-sibling resource transfers remain highly gendered.

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So under the current pro Natalist

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population policies in China, in the absence of drastic changes in norms and culture, Chinese women might continue to be disadvantaged in wealth.

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So thank you so much for coming, and I look forward to your comments.

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Yeah.

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any questions?

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Okay.

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yes.

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Very nice work here.

420
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 does it make any difference?

421
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Oh, for the online audience, yeah. Okay. You know the family.

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If you have control family resources and beyond settlements

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and use it.

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I was wondering.

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You know, they're not needed for the speeches.

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Families have to have such

427
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 yeah, so so because this is more of what the the matching massive work so in the first stage I'm just trying to like match the respondents with more siblings and sewer siblings as

428
00:55:32.840 --> 00:55:59.870
similar as possible. In various observed characteristics, including their parental characteristics. And they are a characteristic. Actually, yeah, so like a parents education as well as the place where they grow up, those things like ethnicity and rural urban origins of things like that and trying to make the comparisons. like, sensible. Yeah, yeah, yeah.

429
00:56:00.030 --> 00:56:04.400
Yes.

430
00:56:05.360 --> 00:56:32.290
Yeah. So okay, also, the second one. let's thank you for this great presentation. I really enjoy it. So my question is more the clarification question regarding the matching process, because, as we all know, most of the matching are made are matching binary variables here you always mention. So I I just. I'm just curious here whether you are exactly matching the number of siblings

431
00:56:32.290 --> 00:56:35.499
of of different groups, or.

432
00:56:36.000 --> 00:56:52.019
as you said, more and fewer, exactly the number of siblings. So this is like a very newly developed matching method which support the continuous continuous treatment variable. Yeah, that's great. Yeah. Good to learn that. Thank you. Thank you.

433
00:56:52.530 --> 00:56:53.320
These

434
00:56:53.860 --> 00:56:56.669
on one I didn't share.

435
00:56:57.390 --> 00:56:58.090
Yeah.

436
00:56:58.730 --> 00:57:14.030
so access to birth control allows women to delay and create breaks between birds, bringing women up for work, and inducing smaller families in which parents are able to devote more time to health and education for their children.

437
00:57:14.030 --> 00:57:38.459
Would you consider this is the case in China? So, of course, so of course, this is definitely so. If we look at the first stage regressions. You can see their highly educated parents, especially highly educated women. They tend to have fewer children. So that's the reason why it is very important to apply the matching methods and make the make people with fewer or more siblings comparable with each other's.

438
00:57:38.580 --> 00:57:39.320
Yeah.

439
00:57:41.950 --> 00:57:42.910
thank you.

440
00:57:45.740 --> 00:57:47.280
This is not good.

441
00:57:47.860 --> 00:57:52.660
 I have. I have a few questions, but I'll try, and I

442
00:57:52.790 --> 00:57:59.589
I'll focus on one. I wondered if you could say a little bit. I that really interesting the

443
00:58:00.750 --> 00:58:04.739
the Ch. You put off the shine, that

444
00:58:05.190 --> 00:58:09.529
outside of bank lines the most common sauce!

445
00:58:10.520 --> 00:58:16.319
What a reliance! And I wonder if you could say anything about how.

446
00:58:16.430 --> 00:58:18.570
if there is any kind of

447
00:58:18.630 --> 00:58:26.480
is there a cultural or an economic logic behind that? And I did just see a piper in the asset vice

448
00:58:26.900 --> 00:58:28.830
comparing the Us. And

449
00:58:29.640 --> 00:58:35.039
China, and actually said that in the Us. Home ownership wealth

450
00:58:35.090 --> 00:59:01.560
trends upwards with age. So they all populations have more. But that's not the case in China because of the point at which counselling was privatized. Actually, yeah, yeah, it's it's simply my point is because the younger generation. They, if they want, they want to buy housing, they have to buy it from the private market. But for the older generation it it was possible that they

451
00:59:01.560 --> 00:59:26.549
they get their housing through the post Socialist transition. So actually, because they are in the Socialist era. Actually, the State allocate housing and during the economic reform. Actually, the government privatised those housing. So that's the way how the older generation acquired their housing. So usually they actually got their housing at a very low price, but for the younger generation, because they have to buy their housing for

452
00:59:26.550 --> 00:59:51.490
like a competitive housing market, so usually they they need much, much more money in order to get their own housing. So that's the reason why. So we can see that. So our pattern is mainly driven by a younger cohort like cohort born in the 19 seventies and eighties, because those are the people they actually need a lot of money to acquire housing. So I want to clarify a little bit here, so the

453
00:59:51.640 --> 00:59:57.500
I won't say so. In the descriptive paper we show that actually

454
00:59:57.930 --> 01:00:22.729
I won't say that the siblings are more important than parents in terms of resource transfer, because parents usually just give the money to their children. So it's very hard to reflect here, but because now we are so, the table actually only shows like the long sauce, but siblings. Sometimes you still need to pay back your siblings. Yeah.

455
01:00:22.730 --> 01:00:40.500
So so that's a but but that actually provides some very important like descriptive evidence about like how people get their mortgage money. So and actually, siblings are very, very important. They actually provide a lot of monetary support

456
01:00:40.500 --> 01:01:04.039
for for their siblings to acquire housing. And if If our series is correct, So they actually help their brothers more than their sisters because of the marriages. Patriarchal! So men are expected to have housing in order to get married. So that's the reason why, if you are not helping your brother, they cannot get married, but if you are not helping your sisters, they might still

457
01:01:04.040 --> 01:01:20.320
be able to get married. So that's the and gender inequality. part there. So, so due to this dynamic, we can see, like an unequal gender trend in providing the inter-sibling resource transfer.

458
01:01:20.400 --> 01:01:23.209
This is the expectation that

459
01:01:24.360 --> 01:01:26.060
access has.

460
01:01:26.820 --> 01:01:51.299
Yeah. I was, of course, this, of course, like social change, also happened, but I would say that as a whole, as a whole, even at the point of today, maybe, like there is still like a stronger norm for men to provide housing before marriage. But women, it is women. The the expectation for women to have housing before marriage is lower. Yeah, yeah.

461
01:01:53.520 --> 01:02:08.360
there's another. so show that domestic violence have negatively have affected housing. Tenure security among women. Have, you considered, can encounter these situations

462
01:02:08.760 --> 01:02:11.140
in China?

463
01:02:13.640 --> 01:02:15.060
The

464
01:02:15.870 --> 01:02:33.709
yeah. So so if I understand your question correctly. So actually, I would say that like like home ownership, is, of course, related to like many different outcomes, including domestic violence. So that's a reason why like

465
01:02:34.010 --> 01:02:45.570
that's the reason why, having your name on the property deed is very important for women. So even though you can share the housing with your partner. But but actually.

466
01:02:45.680 --> 01:03:02.430
there are some like evidence showing that like women's home ownership, actually decreased domestic violence. So so so. So that's the reason why we think the our outcome measure is very important, and we should distinguish the the names on the property deed

467
01:03:06.250 --> 01:03:11.620
we can always come back to? Yes, yeah, definitely. Yeah. Thank you.

468
01:03:21.130 --> 01:03:22.250
In China.

469
01:03:22.350 --> 01:03:26.080
from an 8 year of Federal analysis.

470
01:03:31.720 --> 01:03:34.479
Thank you. Hannah. so

471
01:03:34.660 --> 01:03:45.190
good morning. Everyone. Thank you very much for having this chance to present to you my recent work about house inequality in China. Any issue

472
01:03:46.080 --> 01:03:46.750
this?

473
01:03:47.110 --> 01:04:13.649
Okay? so as you can see from the screen. Actually, I changed my title a little bit so as to capture the core same. And the core findings of this study better, and the new title is, has it become easier or harder to be homeowners in contemporary urban china? The divergence of 2 tests of home ownership rates since 2,008. So here I emphasize 2,008 again. now let's

474
01:04:13.680 --> 01:04:14.880
start it.

475
01:04:15.060 --> 01:04:29.210
so first of all, I would like to briefly introduce to you. The research background of this study. Urban housing has always been a vocal issue in understanding the economic inequality, in transitional economies, such as China.

476
01:04:30.010 --> 01:04:43.150
And since the housing reforms which were initiated in China in the late 1990 S. The urban housing has developed rapidly and become an important engine of economic growth in urban China.

477
01:04:43.650 --> 01:05:12.499
and nowadays China has already transitioned into an ownership society with rapidly increasing urban homelessness rates. So, for example, according to the statistics provided by Fu in his 2,016 paper, while the urban home ownership rates in China were only about 50% in 1,989 already high, but it increased to nearly 90% in 2,011, based on C. And S.

478
01:05:12.610 --> 01:05:25.250
And based on the statistics, official statistics provided by the central bank of China. The urban home, omniscient rate has reached as high as 96% in 2,020. So very high.

479
01:05:25.340 --> 01:05:35.730
based on this statistics, we may conjecture that it has become easier and easier for people in urban China to acquire housing units right.

480
01:05:36.750 --> 01:05:48.409
But the company, with the spectacular achievement of urban homeowners. Rates are the right thing, rapidly rising housing prices with depending on the marketization

481
01:05:48.440 --> 01:06:03.710
which may have made it harder for new entrance of the housing market to assess urban housing units. and there have been prevailing media reports about the growing difficulties for Chinese young people to attain home ownership nowadays.

482
01:06:04.060 --> 01:06:27.489
then, in the 2,021 report on the work of the Government, the former Chinese Premier, Li Kiyan, even included in this report about the importance of addressing urban housing issues, particularly the hardship faced by the young people and new immigrants. Those are the new entrance of the housing market, so based on this background.

483
01:06:27.530 --> 01:06:37.269
Our puzzle is whether it has become easier or harder for the Chinese urban residents to be homeowners over the past 2 decades.

484
01:06:38.690 --> 01:06:56.750
Well, I'm not the first one to raise this puzzle. Actually, there has been many scholars considering this issue before, and with this consideration there has been an emerging literature about the investigation of young adults housing difficulties in urban China.

485
01:06:56.950 --> 01:07:03.400
However, this literature has also provided contradictory findings in this regard.

486
01:07:04.590 --> 01:07:06.490
Some people found that

487
01:07:06.520 --> 01:07:29.650
the older cohorts actually have higher home ownership rates. For example, in Foo's paper, in 2,016, based on the C. And S. 1989 to 2,011, he found that home ownership raid pick for the London cystic cohort, the older cohort, and declined for those who won afterwards for the younger cohort.

488
01:07:29.650 --> 01:07:38.640
Similarly, Krog, I all published the paper published in 2,021 based on the 2,017 C. H. Fs.

489
01:07:38.710 --> 01:07:53.039
They also found that among the Chinese young adults the but millennials, those who are one later are less likely to be home homeless, so which, suggesting that it has become harder for people to get to become homeowners.

490
01:07:53.830 --> 01:08:21.699
But some other scholars found the contradictory funding, and they are actually documented that younger cohorts have higher home ownership rates rather than lower home ownership rates, for example, Woo and Moves paper published in 2,019 and 2,021 respectively. They found that the 1980 S. Cohort were more likely to transit into home ownership at the younger State, and at the younger age, compared to the older cohorts.

491
01:08:21.790 --> 01:08:32.859
So this foundence suggests that it is not Kalia empirically regarding whether it has become easy or hard for Chinese urban residents to be homeowners.

492
01:08:34.330 --> 01:08:47.550
and in this study we argue that a key factor contributing to this inconsistence is that previous studies have employed different definitions of home ownership.

493
01:08:47.870 --> 01:08:52.270
Specifically, there are 2 major different types of home ownership.

494
01:08:52.280 --> 01:09:06.770
One we refer to it as the broadly defined home ownership, which is a more encompassing one that indicates the ownership of an urban housing unit, regardless of the location of that urban housing unit and the function of that urban housing unit.

495
01:09:06.840 --> 01:09:27.119
And the second definition, we've we've we term it as the narrowly defined home ownership, which is a more restrictive one that indicates the ownership of the housing unit that one currently lives in. So, using the terms developed by fun you had seen at all in in their 2,020 paper the narrow defiance ownership

496
01:09:27.120 --> 01:09:39.440
actually in only include those who are owner occupants, but those broadly defined home ownership actually includes owner, renters, and owner occupants, a different group of people.

497
01:09:40.370 --> 01:10:07.049
and these 2 definitions have been widely used by the previous studies to measure the same concept of home ownership and without careful differentiation. For example, in Foo and Clark's paper, when they measure home ownership. They are actually referring to the ownership of the apartments that the respondents currently live in, which is the narrowly defined home ownership.

498
01:10:07.120 --> 01:10:23.879
But for war and newspaper. When they measure home ownership, they are actually capturing the ownership of the respondent's first apartment, which may or may not be the one that respondents currently live in. So they are actually capturing the broadly defined home ownership.

499
01:10:25.040 --> 01:10:46.470
Well, this practice is relatively reasonable before 2,008, because at that time. Housing units are expensive goods. And this is this situation all around the world whose major function is to provide dwelling right? So it was pretty rare that people own housing units but live in the rented one.

500
01:10:46.600 --> 01:10:51.710
So which means that have the broadly defined housing unit, but do not have the narrowly defined housing unit.

501
01:10:52.040 --> 01:11:03.029
But so under this situation, these 2 definitions actually are relatively comparable. Their gaps are very small. And so it was reasonable to use these 2 definitions interchangeably.

502
01:11:03.480 --> 01:11:12.099
however, something happened in 2,008, and there was a significant divergence of this 2 type of home ownership since then.

503
01:11:13.070 --> 01:11:17.839
and this divergence was elicited by the global economic crisis.

504
01:11:17.970 --> 01:11:26.989
While the global economic crisis has led to the initial downturn of the housing market in China also, all around the world in the late 2,008.

505
01:11:27.040 --> 01:11:41.509
But rapidly the Chinese Government decided to use the house market again as the economic booster, just like the measure and practice they have adopted after the Asian financial crisis in 1,997,

506
01:11:42.180 --> 01:12:03.849
and this practice has increased the demand side and the supply side of the housing market simultaneously. Firstly, from the demand side, the central government initiated a stimulus package with 4 trillion R and B, and this stimulus package has included major investments in infrastructure projects.

507
01:12:03.850 --> 01:12:28.969
This has led to the increased rapid increase in the land supply, and also the central government has injected capital liquidity with a reduction of 1% in the band deposit requirement. And this practice has led to the release of 4 50 billion R and B for investment which further release the capital, constraints for real estate developers to for for for developing new housing units.

508
01:12:29.280 --> 01:12:41.380
and also the minimum capital requirement for commodity housing projects was reduced from 30% to 20% since the late 2,008. So orders contributed to the increase of the supply side of the housing market.

509
01:12:42.030 --> 01:12:57.670
and from the demand side the down payment for house purchase was also reduced from 30% to only 20%. From October 2,008, and this has increased the demand of purchasing commercial housing units in China since 2,008,

510
01:12:57.750 --> 01:13:00.349
and orders in combine

511
01:13:00.570 --> 01:13:19.099
has led to a transition of the function of housing units in urban China that is, housing units in urban China has become a liquid asset, and people increasingly use them, not as a place to leave to dwelling, but use them as an investment. Good. So this

512
01:13:19.280 --> 01:13:25.160
transition has further led to the divergence of the 2 types of homeless rates in China.

513
01:13:25.950 --> 01:13:35.550
So regarding going back to our original puzzle, whether it has become easier or harder for Chinese urban residents to be homeowners over the past 2 decades.

514
01:13:35.610 --> 01:13:39.820
Our to our key proposition is actually the answer

515
01:13:39.830 --> 01:13:54.450
depends on whether we are focusing on the broadly defined one or the narrowly defined one, and there are systematic differences of the variation of these 2 types of home ownership rates end up in China over the periods, age groups, and cohorts.

516
01:13:54.470 --> 01:14:02.730
and here we provide 3 sets of hypotheses regarding this divergence across periods, age groups, and cohorts.

517
01:14:02.810 --> 01:14:23.630
So the first hypothesis is about the peerious effect our first hypothesis. in this regard is that while the difference between the broad-defined and narrow-defined homage rates in up in China was more before 2,008. They have enlarged significantly afterwards, and specifically the second hypothesis in this aspect.

518
01:14:26.100 --> 01:14:47.229
after 2,008, while it becomes increasingly easier for one to own private housing units in urban China which indicates the increasing. broadly defined home ownership rates. It becomes increasingly harder for people to own housing units that they can live in, which refers to the decreasing, narrowly defined, homeless rates.

519
01:14:48.240 --> 01:15:04.550
and regarding the age effect. Our first hypothesis is that age effects should be significant for both type of homelessness rates. It is less likely for the young people to access those type of housing units than the older people, because they have less assets compared to the older people.

520
01:15:04.690 --> 01:15:29.500
however, there is a divergence of age, in fact, of this 2 type of home ownership rates since 2,008. Specifically, the age effect of these 2 types of homeless rates are similar before 2,008, but they diverge, since 2,008 and younger people's disadvantage become more stadium for the narrow, define home ownership rather than the broadly defined homeless rates. After 2,008

521
01:15:29.900 --> 01:15:39.789
and third, the cohort effects we differentiate 2 types of cohort aspects, both cohort and marriage cohort. Because marriage is an important factor of getting housing unit.

522
01:15:39.910 --> 01:15:46.160
So regarding the birth cohort, we hypothesize that controlling for age effect

523
01:15:46.200 --> 01:16:07.690
younger cohorts, as documented by the previous study, they should have advantage in. In some studies they found that younger cohorts have advantage in getting urban housing units. We hypothesized that if this advantage indeed exists, they will show up more in the broadly different homeless rates rather than the narrow, different, homeless rates

524
01:16:07.860 --> 01:16:34.469
and regarding the marriage cohort effect. We hypothesized that marriage increased people's likelihood of becoming home owners, but compared to those who got married before 2,008. Those who got married after 2,008 are more likely to own a housing unit, but less likely to live in self-owned housing units, so more likely to become the narrative of homeless.

525
01:16:35.260 --> 01:16:56.560
Okay? So the data we use for empirical analysis is the Chinese journal. Social Survey waves between 2,003 and 2,017. We restrict our study in urban area because we focus on urban housing and we only Include respondents aged between 18 years old to 65 years old.

526
01:16:56.560 --> 01:17:07.190
and the dependent variable. Here have we have 2 dependent miracles here. The first is the narrow, defined home that rates home ownership status, and the second is the broadly defined, homeless status

527
01:17:07.190 --> 01:17:19.899
For the periods we differentiate before 2,008, and after 2,008 for the age groups we differentiate non H groups, first cohorts. We differentiate 4 different purpose cohorts

528
01:17:19.900 --> 01:17:31.070
and marriage cohorts. We differentiate those who got married before 2,008, and after 2,008. We also control a series of demography and geography variables.

529
01:17:31.070 --> 01:17:56.069
and regarding the method here. Actually, since we need to examine both the age, area, and cohort effects, it will be more reasonable to use hierarchical HP. A real cohort model. But regarding the data issue, we found that sometimes this model just cannot be converged. So we decided to present and study our and present our keyfundance that is based on the logistic regression. But we have our. We have. We have also do some

530
01:17:56.070 --> 01:18:00.200
robustness chess using hierarchical HP, a real cohort analysis.

531
01:18:00.670 --> 01:18:04.080
So let's go to our findings.

532
01:18:04.110 --> 01:18:26.240
for easier presentation. I would like to first present to you the descriptive patterns of our findings. So in this sorry. So here we present to you the divergence of this 2 type of home ownership rates over periods. So the red lines here indicate the broadly defined homeless rates, and the blue line here indicate the narrowly different homeless rates

533
01:18:26.280 --> 01:18:36.729
from this figure. It is pretty clear that before 2,008, actually the broadly defined and narrow, different homeless rates have very narrow gap, so they are close to each other.

534
01:18:36.740 --> 01:18:48.819
But this gap enlarged significantly after 2,008, and specifically it has increased for the broadly-defined, homeless rates. But decline for the narrow, defined, homeless rates.

535
01:18:49.950 --> 01:19:11.469
And how about this divergence across regions? Here we contrast. The broad, narrow gap before 2,008, and after 2,008, and the darker color of the red colors indicate the greater gap between the broadly defined and the between the broad-defined and narrow, different homeless rates.

536
01:19:11.470 --> 01:19:27.589
And here you can see that before 2,008, actually the gap was pretty small across all provinces in China, but after 2,008 they have increased significantly across almost all provinces in China.

537
01:19:27.880 --> 01:19:48.489
and to further show the change of narrow, define homeless rates and broadly-defined homelessness rates before and after 2,008, we provide these 2 figures. So this figure is for the narrow-defined home leadership rates change before and after 2,008. The

538
01:19:48.530 --> 01:19:51.790
a blue color indicates the

539
01:19:51.970 --> 01:20:00.920
the clim and the yellow color indicate increase. The darkness of the colors increase the the level of that gap.

540
01:20:01.140 --> 01:20:11.280
So here you can see that for narrowly defined homeless rates it has declined for the majority of provinces in China before and after 2,008,

541
01:20:11.310 --> 01:20:16.659
that especially for those more developed regions or promises in China.

542
01:20:17.670 --> 01:20:24.189
But for the broadly defined homeless rates they have increased for the majority of the provinces in China.

543
01:20:24.350 --> 01:20:29.299
Ok, so this is the contrast. And this is the information about the periods. Effect.

544
01:20:29.320 --> 01:20:52.500
then how about the age effects here? For it's your presentation. We only show you the contrast of those older group, over 40 years old and younger group on the 40 years old. But actually we have this plot for all the 9 age groups, and the patterns are pretty consistent. And here you can see that for both age groups before 2,008,

545
01:20:52.500 --> 01:21:13.519
the broadly defined homeless rates and the narrow define homage race has a very narrow gap right? But after 2,008. They have in this gap in large. Significantly and specifically, the gap is much larger for the younger group under 40 years old than the older group before over 40 years old.

546
01:21:14.160 --> 01:21:24.819
And how about the divergence across both cohorts? In this graph we brought the variation of home ownership rates

547
01:21:25.040 --> 01:21:38.599
for different cohorts represented by different lines across different ages. And this graph is for the narrow, defined, homeless rates. And this graph is for the broadly defined homeless rates. The red line is the

548
01:21:38.650 --> 01:21:42.489
Oh, this group and the blue line is the

549
01:21:42.530 --> 01:21:44.829
youngest group, youngest cohort.

550
01:21:44.870 --> 01:21:57.000
From this 2 graph you can see that for the narrative and homeless rates at the same age. Actually, the younger cohort are less likely to own the narrative and homeless.

551
01:21:57.150 --> 01:21:58.969
narrow define housing units

552
01:21:59.620 --> 01:22:27.569
that, regarding the broadly defined housing units. The younger co-hosts cohort actually at the same age, are more likely to own the broadly defined housing units. So this also confirms our previous hypothesis, that if younger people, younger co-hosts, advantage of acquiring housing units as documented by the previous studies, actually exist, they only exist in the broadly defined house, and Hornage rates rather than the narrow, define homeless rates

553
01:22:27.980 --> 01:22:34.959
and across marriage cohort. Here we provide the contrast between the

554
01:22:34.990 --> 01:22:37.140
world. If I, home ownership rates

555
01:22:37.420 --> 01:22:56.389
and the narrow, different home rates for those who do not get married married before 2,008, and marry after 2,008. Again, you can see that marriage have important influence of increased one's likelihood of becoming homeowners. So this one have the lowest bound for for all the 3 groups.

556
01:22:56.460 --> 01:23:18.660
and before 2,008. Actually, these 2, the broadly defined and the narrow, define homeless rates are very close to each other. However, this gap increased significantly after 2,008. And specifically, the gap is much larger for those who married after 2,008.

557
01:23:18.990 --> 01:23:42.879
Okay, these are all the descriptive patterns. that is relevant with our hypothesis, and we have also provide the regression results here. But for the sake of time I will just skip this results because, all of them are actually confirming our hypothesis. So I'm going to skip all this regression results if there is still time. And if you have questions, we can definitely come back to this coefficients.

558
01:23:43.640 --> 01:23:44.760
So

559
01:23:44.910 --> 01:23:55.140
going to rally to our conclusion. Since the massive housing privatization, there has been a significant improvement in various aspects of urban housing. In China.

560
01:23:55.690 --> 01:24:22.330
however, it remains a puzzle whether it has become easier or harder for the Chinese urban residents to be home homeless. According to the official data and some research article reports, there has indeed been substantial increase in urban homeless rates, but there has also been prevalence, studies and media reports about the growing difficulties of young people and new entrants of the housing market to attend home ownership in urban China.

561
01:24:22.430 --> 01:24:35.859
and in this study we propose to differentiate 2 types of homeowners rates the ownership of an urban housing unit, the broadly defined one versus the ownership of the housing unit that one can currently live in

562
01:24:36.260 --> 01:24:37.699
a narrow, defined one.

563
01:24:37.860 --> 01:24:59.229
and the trends of these 2 types of homeless rates has diverged since 2,008, while it has become easier for Chinese urban residents to achieve the former one, the broadly defined one, it has become harder for new entrance of the housing market. To achieve the latter one, the narrowly defined one. A new entrance disadvantage is more stadium

564
01:24:59.300 --> 01:25:14.170
in leaving cell phone housing units rather than only a housing unit, and this is the key information and take away message we want to convey here, and this advantage, disadvantage has significantly enlarged since 2,008,

565
01:25:14.480 --> 01:25:25.899
and but but not least. This study contributes to the emerging literature about the housing inequality in urban China, particularly about the growing housing difficulties for Chinese young adults.

566
01:25:26.020 --> 01:25:27.189
Thank you very much.

567
01:25:29.940 --> 01:25:31.180
Thanks very much.

568
01:25:31.780 --> 01:25:39.009
I was getting a question. Yeah. So thank you for a very, very interesting talk. And actually, this is very

569
01:25:39.490 --> 01:25:49.819
so. Actually, this is very consistent with my details about the the context. So. But but I'm thinking about because

570
01:25:49.930 --> 01:25:55.940
I feel that like I'm this is for like a suggestion to.

571
01:25:55.950 --> 01:26:04.239
because I think the paper can be a frame like in a more important way. So because because I think

572
01:26:04.240 --> 01:26:29.239
but can I understand, in a sense that because the the the

573
01:26:29.240 --> 01:26:55.140
they can find, like housing in smaller city or in their hometown and a lower price. So that's the reason why they have to rent housing in bigger cities, but but they can still be considered as homeowner, because they find, like a buy housing in places that were there were. So so in this sense, can we understand? You're finding in a way that because there is like a growing one

574
01:26:55.620 --> 01:27:21.030
number of domestic migration. in China. So maybe during the past 20 years. And also there is maybe there's like an increasing inequality in terms of job opportunities between the bigger city and smaller city. So that's the reason why we can observe a phenomenon you just describe. So so in this regard I'm thinking about. So if you can like.

575
01:27:21.030 --> 01:27:41.429
you can highlight this kind of like trends in your front end and make the and and make the paper more important, because right now I feel that it's very interesting finding, but I feel that the friend like there's a lack of introduction about this kind of other social forces, other social changes, happy

576
01:27:41.440 --> 01:28:00.170
at the same time, and I think they are very, very relevant to the phenomenon you describe. And a related question is that so? Even that there's a discrepancy between the narrowly and defined ownership. And broadly they find ownership. So where we are talking about housing and inequality, income Henry China today. So is it

577
01:28:00.170 --> 01:28:16.399
more meaningful to talk about like the absolute housing value a person owned instead of whether or not a personal housing. So because because, yeah. So so, because if you can get the like, the value of housing

578
01:28:16.400 --> 01:28:34.699
for each respondent, so maybe you can still like part the very like that product inequality as you describe, and maybe in even the discrepancy. So maybe nowadays the absolute value that's the home value is more important than whether or not you all my heart.

579
01:28:35.080 --> 01:28:42.489
Yeah, thank you very much. Fancy for the great questions I I enjoy your question.

580
01:28:42.490 --> 01:29:05.929
first of all, but what you talk about about the migration issue and some other social changes in China, whether I should present it, if up front, to give the readers the audience more background, I definitely agree, and because of the consideration of the time, I think I only have 15 min, and as I have so many interesting results to present, I just squeeze the introduction part. Yeah.

581
01:29:06.110 --> 01:29:11.220
probably they find how much we are very, about as much about the key concept of your

582
01:29:11.540 --> 01:29:36.309
yes, yes, yeah. And one important reason I did not include it. Here is.

583
01:29:36.310 --> 01:29:47.330
I will definitely include it in my paper, but I do not include it in my presentation, and as the main information of this study is because actually on the renting problem is not.

584
01:29:47.330 --> 01:30:11.659
I'm not the first one to notice the owner renting problem. The owner ran the increasing owner, renting phenomenon. And actually Professor Hong Kong, the organizer of our session today. She and her group actually has a paper came out in 2,020 that is specifically talking about the phenomenon of owner renting. And that's the first key. Reason I did not

585
01:30:11.660 --> 01:30:22.869
included it as the main dish in this study, because it has already been raised and pinpointed out by the by other scholars. And the second reason is here.

586
01:30:23.440 --> 01:30:49.759
if we take that pay into consideration about migration. So here, what I emphasize is actually another aspect. That is the function. The transition of the function of housing units from a place of dwelling to an investment grid rather than migration, that this to a couple, but not quite the same, if you think it carefully. And if I take into migration into consideration carefully, actually, our hypothesis, regional disparity.

587
01:30:50.100 --> 01:31:14.530
right? Right? Yeah. So actually, I have. It's broad that I've tried to frame this paper better and more interesting. So I have its broader. So if that is in my consideration, I will speculate that there will be a regional disparity of the gap, the broadly defined, the narrow-defined ownership rate gap. But the interesting funding is that

588
01:31:14.650 --> 01:31:28.099
we do not find that regional variation that's the most beautiful part to some extent. And why would that be the case? And to sub up to to the opposite? Actually, those east coastal regions.

589
01:31:28.670 --> 01:31:41.369
those most expensive cities. The gap especially stays in the middle. but in the middle of China. which is not the second. Here cities.

590
01:31:42.180 --> 01:31:43.820
The gap is larger.

591
01:31:43.860 --> 01:31:57.010
and I try to when I try to conceive that I immediately realized that oh, it is not a simple migration issue. It is something related to the investment issue. Why? It is because, for example.

592
01:31:57.120 --> 01:32:18.990
everyone wants to live in self-owned apartment, right? If we have this capability to own a housing unit where we live and work, and where our kids go to school, we want to and own a housing unit there. But in the largest city it will be hard for them to do that, but if they want to do that they want, they will need to accumulate enough money.

593
01:32:19.140 --> 01:32:25.790
and if they want to accumulate enough money they wouldn't have money to purchase another housing unit in other places.

594
01:32:25.970 --> 01:32:38.339
You are not treating the housing unit as an investment. Good. You wouldn't do that right, the sir. Yes.

595
01:32:38.630 --> 01:32:57.770
yeah, yes, exactly. So. Here is. There are 2 sense. There is both the demand side issue and the supply side issue. So in the big city, even though people would love to buy some housing unit in the cheaper price. But if they want to first buy a housing unit that they can live in.

596
01:32:58.040 --> 01:33:20.570
Then they want to accumulate safe money, first to allow them to to allow for the down payment for the place, the expensive place, the expensive housing units in those specificities. And that's why the regional disparity do not show up in what I have imagined and hypothesis at the beginning. We can discuss that more. Yeah

597
01:33:20.620 --> 01:33:50.459
province province. That's another data issue. So the the data, the the data in China just cannot afford for more final level of regional variation analysis. And your second question is actually relates to housing, pricing issue. And I definitely agree with you. And actually, in my previous paper, I always use housing. housing units market value as the dependent variable, because I think it's a more

598
01:33:50.600 --> 01:34:11.260
accurate indicator of housing stratification in China rather than home ownership per se, and Here it is because I want to dialogue with a bit literature that use home ownership as the dependent variable. And that's the first reason I use home actually. And second, if I want to do this cross-on analysis.

599
01:34:11.370 --> 01:34:20.480
 housing that the market value of housing units is not that available

600
01:34:20.510 --> 01:34:47.189
for this kind of comparison, because I can see the definition of the market price of the housing unit change all the time. Sometimes it is capturing. So most of the time in this service they only have one questions asking, what's the market price of your Pr. Of your safely owned housing units? Sometimes they're asking, What's the market price of the of of this housing unit? You are currently living.

601
01:34:47.410 --> 01:35:09.470
And sometimes they're asking, What's the market value of the housing of the housing units that you own. So the the definition change all the time, and that's making the comparison the analysis even harder. If I use the housing price as the dependent variable rather than home ownership. Yeah. But thank you very much for the questions. Yes.

602
01:35:10.900 --> 01:35:29.129
yeah, very interesting funding. I I very much agree with your argument about differentiating these 2 types of home home ownership. I have one question and one comment. The comment is that your your title is about how easy it is to get home ownership. But I think easy is as an imprecise term. It's

603
01:35:29.130 --> 01:35:49.300
it means it can be becoming different things. Affordability is one thing, and easiness to to buy. Housing, you know, in terms of other restrictions. Right? The housing may be affordable in one city, but there are other restrictions. For example, you need that city, you, you know, down payment and things. So how easy it is. It's it's not very clear. And you also use

604
01:35:49.300 --> 01:36:14.300
the the age when they got their home ownership. That doesn't mean easier right? The house may be more expensive, but because of the urgency, because of the importance of getting a housing, they may still fall out of a lot of money, as the previous paper is saying, that family contribute to that. So they get the housing at a younger age. But that doesn't mean it's easier to get in. So I think you wanted to make it more precise either to say affordability.

605
01:36:14.300 --> 01:36:24.709
We also say the home ownership rate or prevalence of home ownership. It's not about how easy. So that's the comment. The question is related with the previous question, that's

606
01:36:24.710 --> 01:36:36.599
you. You described 208 as the watershed year, and then what happened in 2,008? You were saying that it's the global financial crisis, the stimulus package and all that. However, 108

607
01:36:36.730 --> 01:36:58.550
those those developments don't directly link with the discrepancy. The discrepancy ultimately is people working the city. They don't where they don't own their homes right? Maybe in the same city. That's another thing. I'll I'll come back to them later. So it really, it's about migration all about another thing which I think relates with what you describe what happened to a that is

608
01:36:58.550 --> 01:37:18.930
the supply of housing, commercial housing in small cities rapidly because of the 208 stimulus package. So a lot of housing construction in county level cities, so commercial housing become easily available in those cities, therefore, migrants working in big cities, they

609
01:37:19.010 --> 01:37:34.300
become it. It becomes possible for them to buy a commercial customer service whole city, right? So that's why this discrepancy increase. So basically, it's people who previously don't own home. Now, after 2,008. Because of all these governments, as you mentioned.

610
01:37:34.380 --> 01:38:00.510
they can't afford a commercial housing in their home, county, or city, where they still why rise? They still work in in big cities. Right? So that's one thing I think you you can. You can look into that and possibly discuss that. I think that more directly connects with the issue of the discrepancy. Yes, another issue is that, how do you count the you showed all the map of regional variation of the gap. But if I

611
01:38:00.520 --> 01:38:24.269
let's say I own the home in to try, and somewhere in to try. But I'm working south high. So there is a discrepancy. I but where do you count me coming to trial account being Shanghai.

612
01:38:24.690 --> 01:38:43.259
then probably will go back to that chart to to to to try to understand the the information, and then, just in in the previous answer to the question, you mentioned another possibility, which is that for someone living in Shanghai, where housing is expensive, but I want to get into the housing market. So what do I do? Is I buy a house in chin

613
01:38:43.380 --> 01:38:55.609
right. It's cheaper. Yes, I can. It's the house of market accumulate asset. But I still work in Juan to district, so I rent a housing example. But I own a housing chimple is that counted into your data?

614
01:38:55.750 --> 01:39:04.079
Yes, that's kind of into our data. I think maybe it's you. Can. You can possibly try to separate this from the Shanghai Sutran

615
01:39:04.090 --> 01:39:27.620
type of you know, discrepancy, because that tells a different mechanism, right? If I own a homing in to try. But I work in Shanghai. That's a necessity. I can't live. Oh, no, no, no, no, no, no, no! No! So you are. You are. You are saying that you are. You are. You are Shanghai, resident. You own a house in in Temple, but you work, but you work in, but you live in Temple.

616
01:39:27.620 --> 01:39:47.619
no. I'm working. Well, I rent a housing farm. Because, okay, okay, okay, okay, in the same city, right in the San city, people living in what we own a house somewhere, you know, in the far out suburb, but they work in the central city, so they may rent a apartment, a small, crappy apartment in in the central city.

617
01:39:47.720 --> 01:40:03.450
because the community is too long, and they also want to accumulate asset in the housing market. So That seems to describe the mechanism you're discussing more accurately, that is, housing is really a liquid asset. They want to accumulate asset in the housing market.

618
01:40:03.590 --> 01:40:16.900
but they're not migrants per se. Right? They. But you know, in this central district of the city the housing is too expensive. So they buy a housing unit somewhere else, you know, within the same city, but cheaper.

619
01:40:17.120 --> 01:40:43.529
so I think that would be an interesting you know possibility to further investigate. Yes, for for sure. Actually, we have examined the variation of the migrant status of the housing gap and so so we differentiate 4 types of who cause for 4 types of who call and migrants did, because it's an interaction between hockey and migrant status. We differentiate local

620
01:40:43.590 --> 01:40:45.350
locomotive.

621
01:40:45.540 --> 01:41:07.549
The urban local rural local. urban migrant and rural migrant. And I I did not include I I think your comment is actually in my mind, and we are actually is boring that dimension. But for this presentation I just do not want the presentation to be too scatter and to sporad it like like like, like, like involving too many

622
01:41:07.550 --> 01:41:21.270
floating parts, and I want to make it concrete and concise, and therefore I do not show the results here. But we indeed I we we indeed do. Some do that contrast, and that's in our paper. And and and thank you very much for that. Yeah.

623
01:41:21.330 --> 01:41:41.539
and also for the easy and her issue. I agree with you. I I think you. You remind me, and I think if you I will, I would definitely think about whether I can come up with better terms rather than easier or harder, because, as an easier, hardest is the most

624
01:41:41.540 --> 01:42:00.000
communionable. term that I can commute with people easily to to to convey the information. But this may invite some confuses. I I totally agree. So if you have but better terms, I I definitely hope you can provide me, and we can

625
01:42:00.000 --> 01:42:14.490
also be more careful. when we really write a paper and send the paper out about easier and harder. I can definitely understand what you talk about, the confuses the audiences may have. Yeah. And I also see 2 questions here. So can I

626
01:42:14.510 --> 01:42:30.899
attend those questions. So with the decision of building ghost cities in China, highly publicized worldwide in 2,009 be relevant as the 2,008 us crisis. Yes. So the answer of this of this. Yes.

627
01:42:30.940 --> 01:42:34.180
it is not lighted decision of building.

628
01:42:34.430 --> 01:42:52.210
So. I I I would not say the decision of building ghost cities. I would rather say it is a decision of investing in the housing market of the Chinese Government to boost the economy, since the 2,008 crisis, and this decision has lead to the

629
01:42:52.980 --> 01:43:16.519
very significant building of many ghost cities in China, and that also leads to the phenomenon of the gap of the broadly and narrow-defined home national rates. So I think for the first question definitely, yes, and for the second question with the millenniums. They live in mom and par later than their forefathers. With

630
01:43:16.540 --> 01:43:33.310
this affect the life Circle cycle to become a houseowner. Hmm! Interesting. I did not explore this fatter. So the timing, the millennians, the younger cohorts to leave their mom. And

631
01:43:33.400 --> 01:43:48.309
but if we imagine this is indeed happening, that millenniums, millenniums, are leaving their mom and power later than their forefathers. Actually, we will predict that they will become homeowners at the later age, right?

632
01:43:48.680 --> 01:43:52.350
But regarding the properly defined home ownership

633
01:43:52.530 --> 01:44:04.300
status. Actually, the Younger Age cohorts are getting up, becoming broadly different homeowners at the younger age rather than older age than their older

634
01:44:04.850 --> 01:44:20.630
cohorts or the counterparts. So I think even if this effect the the the the time living effect. matter, it would then influence our results significantly.

635
01:44:21.059 --> 01:44:21.799
Yeah.

636
01:44:22.219 --> 01:44:28.250
yeah, that's my any final question. And you

637
01:44:31.350 --> 01:44:56.809
thank you very much. Another really interesting paper. I wanted to ask about So a a key kind of cohort of actors in the Australian housing market, and people that not only are in their own home, but they also invest in property and our private landlords for others. And this group is multiple, homeless ship, like, yeah, multiple home ownership in this group here is particularly seen as particularly

638
01:44:56.809 --> 01:45:12.309
effectual in the housing market because they are able to draw on their existing housing to accumulate more housing. Well, and this is what contributes to younger people in particular, who don't have that well getting into. I wondered if this group is present in China, and if they play.

639
01:45:12.450 --> 01:45:35.590
what kind of role that they play. yes, this group is especially in those big cities, those really super rich people. They usually own many housing units in China, and especially after housing units have become investment goods. but we have our data actually allow for some examination of those groups influences. And we found

640
01:45:35.590 --> 01:45:46.610
in our example those groups also account for a very small group of the population, so it wouldn't affect our funding significantly. Yeah. yeah, thank you.

641
01:45:46.830 --> 01:45:47.790
Yes.

642
01:45:49.980 --> 01:45:55.020
Private rental group.

643
01:45:55.120 --> 01:45:58.009
Now, what proportion of households

644
01:45:58.250 --> 01:46:00.000
of private branches.

645
01:46:00.420 --> 01:46:06.410
So those who those are purely renters rather than homeless. Right?

646
01:46:06.790 --> 01:46:10.109
 Can I show my slide?

647
01:46:10.540 --> 01:46:32.610
We have that number? So it's actually the opposite of the broadly-defined homenership rates. So nowadays in our data, After waiting, you can see that the broad, defined, homeless rates is pretty consistent. The number is pretty consistent with the prior funding. It's about above 80%. So the the pure renters.

648
01:46:32.860 --> 01:46:37.070
So here you can see. So the probability for homeless is

649
01:46:37.590 --> 01:47:00.350
I send this one is more so here nowadays. You can see that. Oh, okay. Here. Here we also excluded some. some some missing values, some, some data issues. But overall, you can see the the broader, different homeless rates is around 80%. So for those pure renters, it may be 20%.

650
01:47:22.180 --> 01:47:31.799
yeah. So so actually, according to the official statistics, it's about, it's about 90%, even for the urban home ownership rates. It's above

651
01:47:32.070 --> 01:47:37.200
here at the beginning. Actually, we provide these other statistics

652
01:47:37.310 --> 01:47:58.180
provide by the prior studies. So full statistics is nearly 90% based on C. Hns in 2,011, and based on the central bank of China. This is official status. It's 96%. And this is the urban home ownership rates, household level. And yes, I sent this house unravel, and also, I think.

653
01:47:58.180 --> 01:48:18.820
but after I check in this, that is, I think they do not exclude urban residents on rural housing units this population, and therefore it has this large number 93. But if we is crude, those population, I think my my estimate, my estimate, may be more reasonable. It's about 80%.

654
01:48:18.880 --> 01:48:19.740
Yeah.

655
01:48:19.880 --> 01:48:24.369
yeah, thank you very much for all this. You this very interesting session.