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That's shopify.com/ Tech. And

1:02

what is modernity anyway? I'm

1:06

Ryan McDermott, host of Genealogies of

1:08

Modernity, and I'm here to tell

1:10

you that it's complicated. No,

1:12

just kidding. In this show, we get

1:14

a bunch of academics to actually venture answers

1:17

to some really tough questions. What

1:19

is genealogy? What are the sources

1:21

of racism and anti-racism? You

1:24

might disagree with our answers, but you

1:26

can find them on Genealogies of Modernity,

1:29

a limited series from Ministry of Ideas.

1:37

Welcome to The New Books Network. I'm

1:42

Caleb Zacron, assistant editor of The New Books

1:44

Network. Today I'm speaking with Yawen Lei,

1:46

professor of sociology at Harvard University.

1:49

We're discussing her recently published book, The

1:52

Gilded Cage, Technology, Development and

1:54

State Capitalism in China. The

1:56

Gilded Cage examines the economic transformations

1:59

in China. over the past two

2:01

decades. China has started to shift

2:03

from an export-oriented industrial economy

2:06

to one focused on developing technology companies in

2:08

the vein of Silicon Valley. This

2:11

rapid change has been both nurtured and

2:13

constrained by China's authoritarian government. Economic

2:16

transformations inevitably lead to

2:18

social reconfigurations. China's

2:20

economic strategy has implications for everyday

2:22

Chinese citizens and the neglected manufacturing

2:25

sectors and the newly upheld tech

2:27

sectors. There are also tremendous implications

2:29

for the global economy as China

2:31

charts a course of development that both competes

2:34

with and serves as a potential model for

2:36

other nations. Yauan, thanks for joining me

2:38

today on the New Books Network. Thank

2:40

you so much for the introduction, Kayla, and thank you

2:42

for having me. Of course, this

2:44

is I think an extraordinarily

2:46

fascinating book. I think, you

2:49

know, I feel like many people, like it's very

2:51

hard and difficult to understand what's

2:53

going on in China's economy.

2:57

At a broad scale, I try and read the news and

2:59

keep up, but I

3:01

really needed a guide. And I think that this book really

3:04

was a very helpful guide for helping

3:06

paint a broader picture and

3:09

giving some background on what's

3:11

going on with China's

3:13

economic transformation. But

3:15

before jumping into the book, I was wondering if you could just tell

3:17

us a little bit about yourself and your background. So

3:20

I am a sociologist. I

3:23

was trained in both law and

3:26

sociology. So before

3:28

I came to the US, I

3:30

practiced law in Taiwan. And

3:34

at one point, I decided to come to

3:36

the US and I had a low degree

3:40

GSD from Yale Law School. But in

3:42

my second year at

3:44

law school, I decided to pursue

3:46

a sociology PhD at Michigan at

3:48

the same time. So basically, I

3:50

wrote two dissertations, one in law,

3:52

one in sociology. So the

3:55

PhD training was more intellectual property

3:57

rights. But then I feel

4:00

in love with sociology and now

4:02

I identify myself as a sociologist

4:05

and don't really have a strong

4:07

like low person or legal scholar

4:09

identity. And for

4:11

that you know particular background it's

4:13

obviously a you know a very

4:17

it's a very interesting background in terms

4:19

of you know your depth of skills.

4:22

I'm wondering you know how it is that

4:24

through that course of study that eventually came

4:26

around to writing this book. Where did the

4:29

idea first germinate for you? Yeah

4:31

that's a great question. So as

4:34

I mentioned I had two degree and I

4:37

wrote two doctoral

4:39

dissertations. So my

4:41

low school dissertation at Yale

4:43

was about the

4:46

copycat mobile phone industry in China.

4:48

So at one point of time

4:50

so I think you heard of

4:53

like Huawei and other

4:55

brand name Chinese mobile phone but

4:57

at one point of time in

5:00

the early 2000s they

5:02

had some kind of copycat mobile

5:06

phone industry and then I

5:08

was interested in the like

5:10

intellectual property related issues. And

5:15

then but then I

5:17

switched to my PhD dissertation

5:19

which is about how the

5:22

internet has impacted

5:24

the political development in China. So

5:26

these are totally too like

5:28

unrelated not that related a project.

5:31

One is more on intellectual property

5:33

right the other is on political

5:36

sociology especially if you are familiar

5:38

with Habermas idea of the public

5:40

sphere. So I was

5:42

really fascinated by the internet

5:45

the development of the internet in China

5:47

when I was doing my PhD studying

5:50

sociology but after

5:53

I finished my first book based

5:55

on my PhD dissertation I

5:57

was thinking about what kind of project I

6:00

I want to, I would like to pursue

6:02

for my second book, because

6:05

we need to have two books to get in. And

6:07

so, and then I was really

6:10

around that time, so in

6:13

the beginning when the internet was developing

6:16

in China, people were very excited by

6:18

the political potential. And then

6:20

gradually you saw the Chinese government

6:22

actually began

6:25

to kind of like be able to find

6:27

a way to really contain their

6:30

public fear mediated by the internet.

6:34

But then at the same time, you

6:36

also kind of observed

6:39

the rapid rise of the

6:41

internet related business in China,

6:45

so the rise of this kind of big tech

6:48

companies. And there was

6:50

a point in time, I really, every time

6:52

when I went to China, between

6:54

2008 to 2015, I

7:00

observed like a rapid economic transformation.

7:03

It just seems to me that

7:05

everyone was kind of fascinated by

7:07

the possibility brought by this new

7:10

technology, especially the information technology, the

7:12

kind of potential it can make

7:16

everyone, improve everyone's life. And

7:18

at the same time, I also

7:21

observed things

7:23

that, didn't look that

7:25

rosy, for example, around

7:27

the same time I saw the great

7:29

transformation of Chinese economy, for example, I

7:32

went to some places in China and

7:34

I found the local governments really wanted

7:37

to upgrade the economy and they want

7:39

to have everything that's cutting edge and

7:42

they want to actually replace,

7:44

for example, human workers with

7:46

robots and the hedge, what

7:48

they consider as obsolete industry

7:50

or business. They didn't like

7:53

people or citizens without

7:55

like high level of human

7:58

capital. So, I

8:00

am, people were so excited

8:02

in China, but I was

8:04

kind of, I feel ambivalent about

8:06

this development because I know

8:10

the consequences of the

8:12

industrialization in other parts of

8:14

the world, like in the US, some people benefit

8:16

from technology-oriented development, but

8:19

some people suffer from the

8:21

process. So, and

8:23

then when I began the project,

8:25

that's what was the time. So that's around

8:27

like 20. So because

8:30

of my previous study on

8:32

the cell phone sector, so

8:35

I had been doing research in

8:37

Guangdong Province, that's where

8:39

China's economic development began. And

8:42

they had, they are the kind of, if you want

8:44

to observe industrial

8:46

operating and then move to

8:49

a tech-oriented industry, that's the

8:51

most important problem to observe.

8:54

So because of my

8:57

continuous kind of fieldwork

9:00

based on my GSD dissertation in that

9:03

province, and then

9:05

my, so I just, looking

9:07

at the development of the tech

9:09

sector, I was really fascinated, but

9:12

I'm also thinking about the different

9:14

dimension who actually benefited

9:16

and who actually didn't

9:18

benefit that much. So this kind

9:20

of thinking motivated

9:22

my study, and I want

9:24

to know, especially because

9:26

I began to do my GSD

9:29

dissertation after the 2008 financial

9:32

crisis, just immediately after.

9:35

So I was able to kind of observe

9:37

the rapid transformation in

9:41

China. So I'm wondering how we

9:43

should understand, how I should

9:45

make sense of the rapid transformation, that

9:48

kind of like a very zealous

9:50

pursuit of technology-oriented development.

9:52

So that's why, and also

9:54

a lot of book on

9:56

China don't really focus on

9:58

the post-2008. financial

10:01

crisis period. So

10:04

that's what motivated my

10:06

book. When you look

10:08

at the roots of today's transformation to

10:10

a more tech-oriented

10:13

economy, where do

10:15

you see the ideas germinating

10:18

from? At what point

10:20

was China's either government leaders

10:22

or people in the tech

10:24

sector saying that China needs to

10:26

begin to start this transition? And

10:29

how did that first begin? I

10:34

think there has been a long-standing

10:36

of wanting to use science and

10:38

technology to rejuvenate China. So they

10:40

have been having this kind of

10:43

thinking and ideology. I actually show

10:45

this in the book since

10:48

the late 19th century, basically

10:50

the Qing dynasty,

10:52

because it's probably because of the

10:54

imperialism, the Western imperialism, the Chinese

10:57

empires interacting with

11:00

the Western powers. So

11:03

there has always been this kind of

11:05

thinking and like science

11:08

and democracy were also

11:10

very important thinking

11:12

in the ROC period, Republic of

11:15

China, so before the Chinese revolution.

11:17

And this thinking continued to exist. But

11:20

in my book, I think I argue that

11:22

only in the mid 2000s,

11:25

but that's before the financial

11:27

crisis, a local government in

11:30

China and also to some

11:32

extent central governments began to kind

11:34

of see the limitation of their

11:37

developmental model based on

11:39

export oriented and labor

11:41

intensive manufacturing. So

11:46

they were kind of, they

11:48

were concerned about the problem of

11:50

scarcity, especially the scarcity of land

11:52

because there is a limited supply

11:55

of land. And sometimes when I

11:57

tell people who

11:59

actually Don't. Have

12:01

a lot of knowledge about china

12:03

day was tell me okay turn

12:05

I so huge right? So. Nice

12:07

a country with a huge huge

12:09

territories. Why? there is a problem

12:12

of the scarcity of lend you

12:14

always have lived. For. Them.

12:16

On. We saw China has

12:18

a very and even on economic

12:21

development so ah the economic developments

12:23

or begin the from close to

12:25

time i just feel province he

12:27

starts the richest a problem in

12:29

china and then I'm and different

12:32

provinces are difference of and it's

12:34

this kind of reach province these

12:36

are in close to try not

12:38

that began to. experience

12:40

the problem of a lacking ah

12:43

enough land and in the past

12:45

or when investors or go to

12:47

china for example a lot of

12:49

the hong kong based on manufacturers

12:51

are a lot of or taiwanese

12:53

manufacturers for example one of the

12:56

best example he thought com o

12:58

that produce that the manufacturer that

13:00

produce a lot of alcohol related

13:02

products they assemble thing to get

13:04

up so when these people least

13:07

company went to china basically the

13:09

work week a local governments. And

13:11

low garments basically gave them obe

13:13

large piece of land and done

13:15

and provide cheap labor right and

13:18

bottom of the local governments realized

13:20

at that time they the exit

13:22

don't really have are they didn't

13:24

have a nurse lens who provides

13:26

investor the are more and more

13:28

it or steal a lot of

13:30

people a lot of business who

13:32

still want to invest or in

13:34

coastal china but then the cult

13:36

of those cities especially on in

13:38

the municipality cdc these ah. It

13:41

in this province to realize. They. Don't

13:43

have a men's announcement. And

13:45

addison time because the real estate

13:47

markets also began to grow very

13:50

rapidly. So and of the land.

13:52

on either belong to this but governments

13:54

are belong to the village is so

13:56

if you mlk more land to manufacturing

13:59

that means that And

14:01

the government can profit from land

14:03

selling. So that means that

14:05

the government would actually make less money. So they

14:08

just think they had to actually

14:10

kind of reorganize their economy in

14:12

another way that don't require so

14:14

much land. And then

14:16

so then technology like became

14:19

a solution because if you think about

14:21

like having or some kind of like

14:24

internet based company, they don't really

14:26

require that much land compared

14:29

with like manufacturers. So when we

14:31

talk about like labor intensive manufacturing,

14:34

we often forget

14:36

that it actually it's kind

14:38

of land extensive manufacturing. So

14:41

today if you go to like you go

14:43

to Detroit, right, even the US or you

14:45

go to Phoenix, right, there are

14:48

like a Taiwanese semiconductor company

14:50

is building a fab

14:53

in Phoenix and you can see

14:55

how large the land is. So

14:58

they really require a lot of land. So

15:00

the mid 2000 is the period in which

15:02

they didn't make the low governments in China

15:04

see, okay, we just don't have enough land

15:07

to continue the existing model. And

15:09

then in the situation was

15:12

worsened by the 2008 financial crisis

15:14

because when the export the markets

15:17

in the US and EU and

15:19

Europe were hit severely by the

15:21

financial crisis, then they just saw

15:23

that they have to have something

15:25

else that can rely more on

15:28

domestic consumption and that didn't really

15:30

need so much land. So

15:32

yeah, you mentioned the 2008 global financial

15:35

crisis. You know, I imagine

15:38

in the US, of course, and in

15:40

the West, you know, severely

15:43

it impacted incomes and, of

15:45

course, you know, lowered the demand, you

15:48

know, demand in the West for manufactured

15:50

goods. You know, how did

15:52

China adapt to

15:55

this new economic challenge? So

15:58

they did. And they were

16:00

really faced by this challenge.

16:03

So the major, the

16:06

main measure taken by the

16:08

government was to come up

16:10

with a huge stimulus package.

16:12

I forgot a specific number.

16:15

And they just, the government had to

16:17

put a lot of money into their,

16:20

the financial system. And

16:22

then they, so that actually went to

16:24

the, a lot of money went to

16:26

the real estate sector. And

16:28

also the tech sector and also

16:30

the infrastructure building.

16:34

So, so that's

16:36

the way they actually deal with the

16:38

financial crisis. And but

16:40

then this kind of strategy and

16:42

also actually led to a lot

16:45

of unintended consequences

16:47

because, so I

16:49

think there are some major

16:51

transformations after the financial crisis.

16:54

So the first is, as I

16:56

mentioned, that's the focus of my book

16:58

is the rise of the internet sector.

17:01

And then, and then

17:03

you can also see the really

17:05

the continuum, the continuum in

17:08

growth of the real estate sector.

17:10

And that's collapsed today. Yeah.

17:14

And, and also really the huge

17:16

investment in infrastructure. That's the way

17:18

they boost their GDP. And

17:21

but then the problem today that, so

17:24

they spend so much money, a lot

17:26

of investments, but then the return has

17:28

been declining over time. And

17:30

it's really, it's one problem

17:32

with China's developmental model is that it's

17:35

so investment oriented,

17:37

rely on investment. So they don't really, and

17:39

they don't really know about whether the return

17:41

is good or not. And it's

17:44

just not sustainable. But

17:46

then I think one very

17:49

important development is the rise

17:51

of the internet sector because

17:53

it's the,

17:55

I mean, the proportion of

17:58

the digital economy. the

18:00

entire GDP has grown from very low

18:02

around 18 or 15, dropping around 2010.

18:08

I forgot the exact number, but then to almost

18:10

40% in 2022. So the digital-related economy became

18:16

really a very important pillar

18:18

of China's economy. And

18:20

you can imagine that when this

18:22

sector was cracked

18:24

down by the Chinese

18:26

government, what kind of impact it has

18:29

on the Chinese economy. So

18:32

when it comes to the Chinese

18:34

government, how exactly do

18:36

they involve themselves?

18:45

At what level of just the business

18:47

process of building a company, do

18:50

they get involved? Is it at the ground

18:52

level? Or is it more so they come

18:54

in once a company has

18:56

already reached a certain stature? Yeah,

18:59

so basically both

19:04

China's economic development relates

19:07

to a very large stench

19:10

on local governments. So after

19:13

China's economic reform, many local

19:15

government officials behave as if

19:17

they are a CEO of

19:19

a company because their performance

19:22

evaluation is linked

19:24

to or was linked to

19:27

directly linked to the GDP growth

19:29

rate. So they really try very

19:31

hard to attract investors

19:33

and to decide who has what

19:35

kind of land and in which

19:37

prices and they can decide utility

19:40

prices and give people different rates.

19:43

So they played like local government's official

19:45

played a very central role in deciding

19:47

who got what. And

19:50

then so after the mid-2000s in

19:54

coastal China, so this global government

19:57

official, when they began to really

19:59

decide to pursue

20:01

a more technology-driven developmental

20:04

model. As I mentioned

20:06

in my book, they began to crack

20:08

down on this kind of obsolete, like

20:10

small traditional manufacturers

20:13

because they think these

20:16

businesses were not worthy,

20:19

were not worthy and they shouldn't deserve

20:21

on the land and the resources bank

20:23

loan. So they would decide

20:25

like which businesses, what kind of

20:27

business to crack down. So basically

20:29

they can enforce the law. They

20:31

usually initiate a lot of like

20:34

legal enforcement, law enforcement can

20:36

tend in a very, very

20:38

arbitrary way. And based on

20:40

a lot of excuses like

20:42

environmental regulation or safety regulation,

20:44

there could be a lot

20:46

of different kinds of regulations.

20:49

But the legal enforcement is

20:51

very selective. When they

20:53

wanted to promote some kind

20:55

of industrial businesses, they don't really

20:57

implement the law. But then when

20:59

they decided that these are not

21:01

the sectors, the businesses they want

21:03

to promote, they began to enforce

21:05

law very severely,

21:09

very strict way. So that's how,

21:11

and they can also decide, like

21:14

local governments also decide like bank,

21:16

the allocation of bank credits, right?

21:18

So for example, if you are

21:21

considered a high-tech company, then they

21:23

allocate you to some kind of

21:26

science park zone with very good

21:28

price. You have land and

21:30

like, and you also have better prices

21:32

in terms of utility, you get a

21:35

lot of subsidies. So these are the

21:37

instrument which they can influence

21:39

the local economy. And

21:42

also they, it's

21:44

not only about the kind

21:46

of resources for a

21:49

business actor, it's also

21:51

about resources for workers,

21:53

ordinary citizens, because in China they

21:55

have this household registration

21:58

system, or you can consider it. consider

22:00

that as some kind of local

22:02

citizenship. So not everyone has a

22:04

citizenship in a specific locality.

22:06

For example, when we think about the

22:08

situation in the US, if you move

22:10

from one state, like Massachusetts to New

22:13

York, you have to get a local

22:15

citizenship in New York. Like if

22:18

this, according to Chinese institution,

22:20

otherwise it's very likely that

22:23

your children cannot have access

22:25

to a local public

22:27

school. So there is this kind

22:29

of uneven citizenship. And

22:33

local government can decide who deserves

22:35

local citizenship. So they come up

22:37

with some kind of application, like

22:39

a point system. And they

22:41

can decide, in my book, I kind of documented

22:44

the kind of measures,

22:46

metrics in which some

22:48

rich local government dislocate,

22:53

decide, forget what. So

22:55

basically, if

22:57

you have very good

22:59

education, you are a property owner, and

23:02

you have high income, and

23:06

if you are a patent owner,

23:08

if you can invent things, and

23:10

if you are not too old, like if

23:13

you are below 40 years old,

23:16

and these things give you more points. And

23:20

you can imagine that. So basically, they

23:22

classify people based on

23:24

those people's perceived contribution

23:27

to a more technology-oriented development.

23:29

So they link things together.

23:32

So the local government, in

23:35

this way, they just don't

23:38

want to include people

23:41

who cannot contribute to technical

23:43

development. If they were

23:46

factory workers, they provide,

23:48

these workers provide low-skill

23:52

work, then those people are now welcome.

23:54

But today, if you are an engineer

23:56

in a high-tech company, then you can

23:58

get more points, and your tools are

24:00

more likely to be included in the local

24:02

public school system. So this is

24:05

also one way that they can

24:07

allocate resources. And then the

24:09

central government is also very powerful. They kind

24:11

of came up with more central level policy,

24:14

industrial policy. They began to develop a

24:16

lot of industrial policy things around

24:19

2006. And

24:22

that's what the US government has. I

24:24

think, for me, I think it's a

24:26

big invitation. So the US

24:28

has also began to develop a lot

24:30

of industrial policy. That was

24:33

kind of some similar, based on

24:35

some similar thinking. So

24:39

these are the way both central governments

24:41

and also the central government can decide,

24:44

can kind of give a kind of more

24:47

overarching guidance. They

24:49

have a clear indication about

24:52

what kind of industry they

24:54

want to prioritize. Then

24:56

the local governments would actually follow that

24:58

and came up with their own specific

25:01

metric to incentivize a

25:03

different kind of business actor.

25:06

So you mentioned that for

25:08

manufacturing workers, they're not

25:10

necessarily as considered as

25:12

welcome. But what happens

25:15

to manufacturing workers?

25:17

What ends up being the

25:19

kind of life experience? What

25:23

do they end up doing with their lives? So

25:26

this problem has existed

25:28

in China for a

25:30

long time, this kind of

25:33

institutional discrimination. So based

25:35

on local citizenship, because a lot of

25:37

workers, they are from villages,

25:39

like rural areas. And

25:42

then, so usually for this kind

25:44

of manufacturer, so they

25:47

leave. So you might have heard

25:49

of a term called left behind children.

25:51

And because of their institution, Parents

25:54

cannot bring their children to the

25:56

place where they work because children

25:58

don't have... A

26:00

lot of opportunity to go to

26:02

public school in a private school

26:04

sometimes is and to be more

26:06

expensive and some time to quality

26:08

is also not very good and

26:10

as some point of time ah

26:12

some local governments montana even crack

26:14

on on private school. For a

26:16

migrant workers dull these people. These

26:18

walker can only leave their children

26:20

behind and when they become olds

26:22

and with a safe more money

26:24

they go back to their village.

26:27

Village is still so you can

26:29

see how family sacrifice. So

26:31

that creates a lot of family problem

26:33

because of the separation. Appearance

26:35

and also and the kids

26:37

are but them. Intense.

26:40

Abilities element that information the in

26:42

the manufacturing sector So I was

26:44

the ones who mentioned that there

26:47

have been changes in different generations

26:49

of affect You will curse. Though.

26:52

Turn up again soon or begin

26:54

is economically from in ah noting

26:56

in late nineteen seventies and you

26:59

can imagine like the are different

27:01

generation the ready decade different generations

27:03

of workers so in the past

27:06

when chan hours for own. People.

27:08

Actually feel proud to be

27:11

a factory worker. but then

27:13

today. A. Young people. On.

27:16

Don't want to become of sexy walk

27:18

her anymore because lives on. His

27:20

are obviously very boring and

27:23

could be miserable in factories.

27:25

The work conditions are not

27:27

the best cell phone so

27:29

when I was our understanding

27:31

that impact of ah some

27:34

the comments initiated to replace

27:36

a walk or sweet. Robots.

27:39

And. i found that in fact a

27:41

lot of young workers don't really care

27:44

about this because they're the ones who

27:46

be like sexual curve in the long

27:48

term and they just want to find

27:51

do something else many of them once

27:53

you become like small business owners but

27:55

then it's kind of there is a

27:57

gap between aspiration and the reality some

28:00

I also know, for

28:02

example, several female factory workers, they hate

28:04

factory life. But they just simply couldn't

28:06

find a better job in the service

28:09

sector. And they kind

28:11

of went back and forth between

28:13

like service sector and the factory.

28:16

And for people with children, they

28:19

kind of began to really appreciate

28:21

the stability of kind of

28:23

factory job because usually

28:26

the labor protection in the service sector is

28:29

really not good. And when I

28:31

was doing the interview, because young

28:34

people really don't like

28:36

the factory job. And

28:39

when I was doing field work, many

28:41

of my interviewees actually left

28:43

their jobs in the factory and

28:45

moved to China's platform economy.

28:48

They became like food delivery

28:50

workers. And

28:53

that's actually related to the rights

28:55

of China's internet sector.

28:57

So I think from the

28:59

government's perspective, they also

29:01

think like the central governments in

29:03

China also think kind

29:06

of this kind of transformation to

29:08

a more tech oriented economy might

29:12

not kind of undermine like

29:15

factory kind of low skill

29:17

workers so much because first,

29:19

these people don't want to become

29:21

factory, don't want to remain as

29:23

a factory worker anymore. And then

29:25

they want to have more interesting

29:27

thing. And second, they expected that

29:30

the kind of platform economy, the

29:32

internet sector can accommodate it and

29:34

absorb this kind of like surplus

29:36

labor. So that's the calculation. You

29:40

look at Daniel Bell's 1973 book, The

29:42

Coming of the Post-Industrial Society. This

29:45

is a book that, you know, where Daniel Bell, it

29:48

looks at the economic transformations that are

29:50

going on in America, the process of

29:52

de-industrialization, the rise of the service sector.

29:55

And you kind of demonstrate how what Daniel

29:57

Bell is predicting about the future of America.

30:00

kind of come true in China. So

30:02

you know, can you talk about Daniel Bell's predictions

30:05

for his book and what you see about

30:07

how his ideas about what would occur in

30:10

America maybe maps a little

30:12

bit better on to present-day China? Yeah,

30:15

so I think Daniel Bell's

30:18

work is super interesting and

30:22

I think it's unfortunate that because,

30:24

I don't know, because of a lot

30:28

of people's perception of him because of

30:30

his cultural view and

30:32

also because of the politics in

30:34

the late 80s, late

30:37

60s and he was

30:39

considered as kind of very culturally conservative

30:42

perhaps by a lot

30:44

of intellectuals today. So I don't

30:46

find people, a

30:49

lot of people are reading his

30:51

book anymore but I do find

30:53

his book very important and a

30:56

lot of things we are talking about

30:58

today like algorithm, big

31:00

data and computing, he was talking

31:03

about this kind of thing in

31:05

his book. He was trying to

31:08

think about the future but

31:10

I think he didn't really

31:12

predict, I wouldn't

31:14

say that he predicted things like

31:17

correctly but then I think I

31:20

mean his predictions for me are not

31:22

important but I think he really lay

31:24

out kind of his soul, he kind

31:26

of pre-soul, very

31:29

important social transformations because

31:32

of technology change

31:34

and the pursuit of a technology called

31:37

driven development. I think that kind of

31:39

thinking is very important and

31:42

in a lot of, for example, like

31:44

discipline, for example, in sociology and also

31:46

in political science sometimes we tend to

31:49

over-lack this kind of material dimension of

31:51

change. I'm not

31:53

talking about like, I'm not making

31:55

a generalizable argument but I think in

31:57

some situation we tend to kind of

32:00

of neglect, this kind of aspect. And

32:03

I think his description,

32:06

he didn't really expect what's going to

32:09

happen in the US because in

32:11

his time, that was the

32:14

period of

32:16

the welfare state, right? So in

32:19

which, like still the

32:21

New Deal order.

32:23

So that's before the rise

32:25

of the neoliberalism. So

32:29

that's a very, very interesting period.

32:31

The 60s and also

32:34

the 70s, then you saw the transformations

32:36

in the UK and also in the

32:38

US. So basically, he

32:40

thought that, he kind of thought

32:42

that the government would become very

32:45

important, would play a very important

32:47

role in planning the

32:49

economic development based on technology. So

32:51

he saw that. And

32:54

so, but that actually

32:56

is important because I think

32:58

still in the US, I

33:00

mean, and actually what he

33:03

understand about

33:05

the role of the government

33:07

is very similar to what

33:10

a lot of scholars study

33:12

about, we call developmental states

33:15

in the world. The most

33:17

classical examples are

33:19

like the Japanese government, the Taiwanese

33:21

government, the Korean government, right? They

33:23

plan things and then try to

33:26

kind of upgrade

33:28

the economy based on technology. And the

33:30

government play a very important role. But

33:32

he emphasized the role of the government.

33:34

But in the US, I think the

33:36

government still played a very important role,

33:38

for example, in terms of the development

33:40

of the internet, the

33:42

investment of the military, the

33:44

Ministry of Defense. But

33:47

then you can see like the dwindle

33:50

of the withering of the state in

33:53

the US, that people didn't like the

33:55

idea of the big states anymore, with

33:57

the rights of the neoliberalism. So. So

34:00

I think he didn't really expect the

34:02

rise of the neoliberalism, at least at

34:04

a discursive level. And

34:08

also, I think what he didn't

34:10

see, but then he's actually

34:12

understanding of the role of the

34:14

government really kind of

34:16

mapped very well in the Chinese

34:18

context, because the Chinese government really

34:22

kind of really take that kind of

34:24

leadership in terms of guiding

34:26

the development of economic

34:29

and social development based on technology.

34:32

So then the second

34:34

thing about Daniel Beall's

34:36

book is the kind

34:38

of stratification, like a social transformation

34:40

as an outcome of

34:42

technology driven development, like new

34:45

kind of stratification in society.

34:47

So he wrote about the development

34:50

of kind of the

34:53

professional class, people

34:55

who actually have the cultural, have

34:57

the human capital to understand technology.

35:01

So he saw the rise of this kind

35:03

of people and also the rise of service

35:05

sector. And

35:07

I think one thing he kind of, he

35:10

didn't really predict and he kind

35:12

of regretted when he

35:15

was old was the

35:17

increasing equality in the US. So

35:20

he didn't expect that the

35:22

technology kind of driven development would

35:24

lead to so a high level

35:27

of inequality in the US. He thought

35:29

that technology kind of

35:32

would contribute to like

35:34

a high level of abundance and

35:37

everyone actually benefits from that

35:39

kind of development. And

35:42

then so no one is left behind. So

35:46

he didn't really think about, he didn't

35:48

really write about like the industrialization in

35:50

his book. So his picture was like,

35:52

is very, very rosy. But

35:55

then we see that in that aspect,

35:57

his prediction, he didn't

35:59

really see, he He

36:01

was very, very surprised and

36:03

very, very bad about the

36:06

consequences of the industrialization in the

36:08

US and the increasing level of

36:10

inequality. So I think he wrote

36:13

an essay perhaps around 2000 and

36:16

in which he kind of reflected on

36:18

his own thinking in the past. And

36:20

then I think this in

36:23

terms of the inequality, I

36:27

think what happened in China is very similar

36:30

to what happened in the US because

36:33

now I think US and China are

36:35

both one of the most

36:38

unequal countries in the world

36:40

even though China has socialism

36:43

as its official principle written

36:45

in constitution. Yeah,

36:49

it's really, really interesting and I'm sure

36:51

that like you said, part of the

36:53

problems that take a while to foresee

36:55

neoliberalism is still kind of in that

36:57

Keynesian frame of we're

37:00

just right around the corner from the

37:03

society where we won't need to work

37:05

as many hours and people can enjoy

37:07

their lives and of course that hasn't

37:09

quite happened. That's

37:12

very shocking. Yeah,

37:15

and one can understand why he

37:18

would have thought that considering how much growth and

37:20

development occurred in the 20th century. But

37:24

I'm also really interested in what

37:26

it's like for the tech elite in China.

37:30

What is their perception of

37:32

the economic shifts? Are they

37:34

through the roof with excitement

37:37

or is there maybe a

37:39

sense of concern about what's

37:41

been going on as well? So

37:44

they were super, super excited. I

37:47

think they only began to concern

37:51

after or around the time when the Chinese

37:53

government began to crack down on the tech

37:55

sector in 2020. So

37:58

there was a golden age for

38:01

them to make

38:03

money. So these entrepreneurs,

38:05

many of them were

38:07

from like, not

38:09

like extraordinary. They didn't

38:12

have extraordinary family

38:14

background, but they were able to get

38:17

rich. And there is

38:19

a really high level of upward

38:22

mobility in the tech sector. So

38:24

people were really full of hope.

38:27

And they were also

38:29

very inspired by the development of

38:32

Silicon Valley. So they saw that

38:34

the entrepreneurs in China saw this

38:36

kind of Silicon Valley, the

38:39

American tech companies, as some

38:42

of their kind of models. But

38:44

it's also very interesting that, in

38:47

fact, even though China was a

38:50

late comer, a late comer in terms

38:52

of industrial development. So

38:55

in the late 90s, I

38:57

think you are young, but then before you

38:59

were born, there was

39:01

like a.com boom, right? And

39:04

at that time, so in the late 90s, so

39:07

several US tech companies

39:10

were founded at that time.

39:12

And then at the same time,

39:15

several, like a lot of very

39:17

big China tech companies like Alibaba,

39:20

Tencent were founded around the same

39:22

time. And so

39:24

I think the future, I think, and

39:27

also they really have big connection

39:30

with the US. They were inspired

39:32

by the US, the Silicon Valley,

39:34

and they got money

39:36

from the US. So they

39:38

got a lot of capital

39:40

from the US, like the

39:42

market, the financial capital market.

39:44

So it's really kind of

39:46

the two, the

39:49

two like tech sectors, the tech

39:51

sector in the two countries are

39:54

really, were really, really connected. And

39:56

in fact, I began my book

39:58

by writing about President Clinton's visit

40:00

in China in 1998, that

40:02

was the first time an

40:05

American president visited China after the

40:07

1989 Tiananmen

40:10

massacre. And so,

40:12

and in that, and they had,

40:14

and when Clinton, when

40:17

President Clinton went to China, he

40:19

went to Shanghai Library, and then

40:21

he was talking to all kinds of

40:23

leaders like tech leaders and other

40:26

kind of like scholars

40:29

in China, they had like

40:32

a panel discussion. And the topic

40:34

they were discussing were about like

40:36

technology and cooperation between China and

40:39

the US. And both sides thought

40:41

that the internet related sector is

40:44

where the two countries can cooperate

40:46

and they can they

40:48

can actually mutually benefit it. And

40:51

Clinton also said that, okay, like by

40:53

working with China, so that's in his

40:56

speech. So around that time, so the

40:58

trip was for preparation for for

41:02

US-China's bilateral

41:04

trade agreements and China's

41:06

subsequent accession to the

41:09

WTO, that's in 2001. So at that

41:11

time, so Clinton think by working with

41:13

China, that that benefit the US, that

41:16

US workers, so their life can be

41:18

improved. So you can see the thinking

41:20

at that time. So that was the

41:23

heyday of the neoliberalism that

41:25

before the financial crisis.

41:29

So you can see how it works. And

41:32

then today, you think about who you, I

41:34

mean, I think

41:36

the Biden administration and the Trump, they

41:38

were very similar, actually very similar in

41:41

terms of their policy toward China and

41:43

their trade policy. And

41:45

both like both Trump and Biden

41:48

administration are talking about like how

41:50

American workers, the working class suffer

41:53

from the trade arrangements. But

41:55

that but then if you look

41:57

at Clinton's speech at that time, so the

42:00

thinking was that would benefit the working

42:02

class in the

42:04

USA. So

42:06

I think in my book, it's like, things

42:10

happening in my book occurred in

42:12

the heyday of the neoliberalism. And then

42:15

in the end, the crackdown of

42:17

the tech sector in China also

42:20

happened in a context

42:22

where the neoliberalism declined and

42:24

we are entering into a

42:26

new stage of

42:29

globalization. Yeah.

42:31

Your book, it's almost, even though

42:33

you're writing on such recent topics and

42:36

recent occurrences, it's almost a history in the

42:38

sense that it kind of ends

42:40

by looking at this, whatever

42:42

the period of time that led to

42:44

this, the rise of the tech sector

42:46

in China is, there's

42:50

changes afoot. Do you think

42:52

that this crackdown, is

42:54

it going to lead to broader

42:58

shifts? Or what do

43:00

you sort of see as the kind of the

43:02

present day circumstances and challenges for China when it

43:04

comes to transitioning their economy

43:06

and reigning in the tech sector?

43:09

Yeah. So I

43:12

think the crackdown is really

43:15

unfortunate. But I do

43:17

believe that there need to be some

43:19

kind of regulation like both in the

43:21

tech sector, both in the US and

43:23

in China, and in many parts of

43:25

the world. And the US, I think, really,

43:28

because of the lobbies, the

43:31

regulation on the tech sector, I

43:34

think for me, was not adequate.

43:36

But then I think people have different

43:38

kinds of thinking today. But

43:42

I think in China, I think the problem

43:44

is really that, I think there

43:46

need to be some kind of regulation, but then

43:49

they actually don't do this kind of

43:51

regulation through the framework of the rule

43:53

of law. So they do this

43:55

kind of regulation through a very arbitrary legal

43:57

system. And that's a very, that's a very,

44:00

That's very bad for everyone. It

44:02

may be good for the government because the government can

44:04

decide what they want. They are not constraints. But

44:08

it's really unfortunate, unfortunate because no

44:10

one really can challenge the decision

44:12

made by the government. For example,

44:15

all of these tech companies in China, like

44:17

major tech companies like Alibaba, and

44:20

they receive fines, like a

44:22

punishment from the Chinese government. But no

44:24

one can sue the Chinese government or

44:26

suing the government has no meaning. It's

44:29

not meaningful. So after

44:33

the government punish them, their

44:35

response is, I appreciate the government's

44:37

guidance. That's it. And

44:40

so I think without

44:42

this kind of rule of law, people

44:45

don't have trust

44:47

in the system, don't have confidence. And that

44:49

really gives a very bad signal. And

44:52

no one would – very few people would want to

44:54

invest in China, like domestic

44:56

– I mean, even regardless,

44:58

they are like domestic investors

45:00

or foreign investors. And

45:03

then that's very bad, I think, institutionally. That's

45:05

not good for development – I mean, for

45:07

the development. I mean, they

45:09

want to pursue – like they want to

45:11

have – the government

45:13

has trying to kind of

45:15

like promote foreign invest kind of

45:18

to attract foreign investors. But then you

45:20

can see there has been a capital

45:22

flight. And so

45:25

institutionally, that's very bad.

45:27

And also, the economy

45:30

really rely on China's

45:32

tech sector, this kind of internet-related sector, because

45:35

they really play a lot of role in the

45:37

GDP. And so

45:39

that's – and this company really

45:41

– they really kind of

45:43

profit. They really kind of operate

45:45

quite well and provide a

45:48

lot of opportunity for different kind of

45:50

social groups, like employment opportunity.

45:53

So that's bad for – that's kind

45:55

of you – the government is killing

45:57

one of the pillar of the economy.

46:00

So now, but then the

46:02

government, but they have to, I

46:05

think the reason why they correct down of

46:08

the tech sector is that the government

46:10

feels threatened by the tech sectors because,

46:12

so usually in China they

46:15

have something called like national champions.

46:17

So basically national champion

46:19

control, they are usually

46:22

state-owned enterprises. They control

46:24

very important infrastructure in

46:26

China, like electricity,

46:29

railway, bank, but

46:31

then the internet. So

46:33

people who study the like communication,

46:35

they kind of, or platform, they

46:38

argue that this kind of platform

46:40

or internet company are

46:42

really, they really kind of

46:44

control infrastructure like the digital

46:46

infrastructure, right? The cloud system

46:49

and like in China

46:51

the online payment. And I think for

46:54

the Chinese government, it's threatening because these

46:56

are private companies in the end and

46:59

then the government

47:01

doesn't have direct control. They

47:03

are not state-owned companies. And

47:08

the government also kind of worry about

47:10

the financial problems, stability,

47:13

I mean brought by this company because

47:15

these companies, a lot of tech companies

47:17

actually lend money to a

47:20

lot of internet users. So they

47:22

also exist like banks

47:24

in some way. So they're different from

47:26

the US counterparts. So

47:28

they have to, they correct down on them

47:31

and they feel threatened by them. Also, this

47:33

company has data and they

47:35

go IPO, they went IPO in the

47:37

US. So it's really unclear the

47:39

US government like want to have data from

47:42

them, what this company would do. And

47:44

the Chinese government feels insecure

47:46

about this. So, but

47:49

then really they destroy, they

47:51

kind of try to regulate the sector but they

47:54

regulate in a way that kind of,

47:57

that really make the sector very, very bad.

48:00

stable and no one

48:02

can know what the government is going

48:04

to do, no predictability, so that's bad

48:06

for businesses. And at

48:09

the same time, the government now

48:11

thinks these internet-related companies are not

48:13

really high-tech. So in my book,

48:15

I talk about like they have this kind

48:17

of – they want to select birds, like new birds.

48:20

But I think now the calculation is that

48:22

these internet companies are not really deserving

48:25

new birds because their

48:27

technologies are soft. They

48:29

are not like semiconductor or energy,

48:33

so they have kind of –

48:35

they kind of have their new target. They

48:37

– for example, they will continue

48:39

to invest in kind of semiconductor industry

48:42

and industry that kind of – they

48:45

have developed their new thinking and who

48:48

are deserving and what kind of industry are

48:50

deserving and what are not. But

48:52

the problem is that – so

48:55

it's very difficult for

48:57

the newly emerging sector

49:00

to replace the internet sector

49:02

in terms of their role

49:04

in economic development in

49:06

GDP and their role in

49:08

providing employment. For example,

49:11

a lot of people are crazy

49:13

about the electric cars, the new

49:15

energy sectors in China. But if

49:17

you look at the numbers, they

49:20

only account for 9

49:22

percent of China's GDP. And

49:24

in digital-related economy, accounts for 40

49:26

percent. And the real estate sector

49:28

account for around – related

49:30

account for 30 percent. So

49:33

even they have this kind

49:35

of small new engine, but

49:38

then the extent to which they

49:40

can really support economy is

49:42

not that large. Yeah.

49:44

Is there any attempt

49:47

at all to try and shift

49:49

to a more consumer-oriented economy, or is that

49:52

to unwieldy, hard to control?

49:54

Yeah. I think they really want

49:56

to kind of shift to a

49:58

more consumer-oriented economy. And in

50:00

fact, the internet related to the internet,

50:03

the previous model, like the

50:05

internet, the e-business, they

50:10

contribute to that. But

50:14

then there

50:16

is a limitation because

50:18

in China, because

50:20

they don't like the US, they have very kind

50:23

of different from

50:27

the US, but it's always similar

50:29

to the US in terms of the

50:32

very weak welfare state. So

50:34

they need to actually save money for

50:37

unpredictable things. And

50:44

also, really, if you look at

50:47

GDP per capita, or

50:50

the average income, it's really not high.

50:52

There is a huge difference between China

50:54

and also the US in

50:57

terms of GDP per

50:59

capita. So I think there is

51:01

a difficulty in terms of relying

51:04

on a more consumer-driven economy, even

51:06

though the Chinese government wanted to

51:08

do that. And now, the real

51:10

estate sector has been

51:13

having so many problems. And

51:15

many Chinese people already put so much money

51:18

in real estate sector because for

51:20

Chinese, they don't have a lot of

51:22

channels to investments. So usually,

51:25

for them, the real estate sector is one

51:27

of the very few ways in which they

51:29

can invest. And usually,

51:31

the family, they collect a

51:33

lot of money. They just put the money

51:35

in the real estate sector. And now, the

51:37

housing prices have went

51:40

down so much. And they

51:42

still have to pay the loan. The

51:44

housing prices are crazy in

51:47

major Chinese cities. It's really crazy.

51:49

And so if you think about the

51:51

fact that the housing prices are depreciating

51:55

and then you still have to pay the

51:57

loan, and the future is unpredictable, And

52:00

you have to take care of yourself because

52:03

of the weakness of the welfare

52:05

state. And then I think these

52:07

kind of uncertainty wouldn't

52:10

be good for developing

52:12

a consumer kind of consumption-oriented

52:16

economy. Yeah,

52:18

that's fascinating. And

52:21

certainly, I think

52:24

you've given myself and lots of people

52:26

many things to pay attention to, to

52:28

look at, to consider. Yawen,

52:31

thank you so much for being guest on the New Books Network.

52:33

It was great to talk to you about The Gilded Cage. It's

52:36

really a really fascinating book where you

52:38

cover so many

52:40

different topics and do a great job of blending

52:43

social theory and sociology with

52:45

kind of contemporary political

52:48

and economic analysis. Thank you so

52:50

much for being a guest. Thank you for

52:52

having me. Thank you.