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Learning in Chinese Cities: Do Rural Migrants Benefit from
Labor Market Agglomeration Economies？
Shihe Fu
Fulbright Visiting Scholar at CRE, MITSouthwestern University of Finance and
Economics
STL China Talk Series October 17 2016
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Outline
Background: Why do cities exist • business agglomeration economies• labor market agglomeration
economies Research questions and motivation Data and methodology Results Policy implications and future research
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Why Do Cities Exist? An Economics Approach
Cities are areas with high-density population (or concentration of people and firms in limited geographic areas) The benefits of such concentration are called agglomeration economies The reason why cities exist
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Firm Side: Business agglomeration economies
Localization Economies: the benefit from the concentration of same-industry firms in a city
• Silicon Valley, Route 128, Detroit Urbanization Economies: the benefit from the concentration of different-industry firms in a city
• New York CityHoover (1937) (Location Theory and the Shoe and Leather Industries)
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Micro-foundations of Localization Economies
Sharing• sharing inputs: highways, public utility,
airport Pooling
• concentration of firms and workers facilitates matching and reduces search costs
Learning • information or knowledge spillovers
Specialization; Competition
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Dynamic Localization Economies
Industries with strong localization economies tend to grow fast (Marshall, 1920)
In the dynamic context, localization economies is dubbed Marshallian externalities
• Marshallian-Arrow-Romer (MAR) externalities (Glaeser et al., 1992) (Growth in cities, JPE)
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Urbanization Economies Benefits from the general level of city economy. Measured by city size (population). (Hoover, 1937, 1971) , Henderson (1986) Benefits from overall local urban scale and diversity (Henderson et al., 1995) Benefits from industrial diversity In dynamic context: Jacobs externalities,
• Glaeser et al. (1992)• Jacobs (1961,1969): The Death and Life
of Great American Cities
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Micro-foundations of Urbanization Economies
Sharing Pooling Learning
• Jacobs: Cross-industry fertilization promotes innovation and urban growth
Economies of scope
Worker Side: Labor Market Agglomeration Economies
Benefit from the concentration of employment Labor market localization economies:
• Benefits from concentration of workers in the same industry (occupation) in a city.
• In dynamic context, Marshallian externalities in labor markets
Labor market urbanization economies: • Benefits from concentration of workers in
different industries (occupations) in a city.
• In dynamic context, Jacobs externalities in labor markets.
Urban Wage Premium Labor market agglomeration economies can improve workers’ matching and learning, therefore help enhance skills and accumulate human capital
Workers’ productivity will be higher in larger cities
Wages will be higher in larger cities: urban wage premium
Micro-foundations of Labor Market Agglomeration
Economies Labor market pooling:
• Improve matching between workers and firms; reduce search friction; increase labor mobility
Knowledge spillovers (human capital externalities) through social interactions
• Formal communications (Charlot and Duranton, 2004)
• Informal social interaction (social networking)
• Poaching• Peer effect
When an industry has thus chosen a locality for itself, it is likely to stay there long: so great are the advantages which people following the same skilled trade get from near neighbourhood to one another…if one man starts a new idea, it is taken up by others and combined with suggestions of their own; and thus it becomes the source of further new ideas. And presently subsidiary trades grow up in the neighbourhood, supplying it with implements and materials, organizing its traffic, and in many ways conducing to the economy of its material.
Marshall (1920): Principles of Economics, Book IV, Chapter 10 The Concentration of Specialized Industries in Particular Localities
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Most of what we know we learn from other people. We pay tuition to a few of these teachers, either directly or indirectly by accepting lower pay so we can hand around them, but most of it we get for free, and often in ways that are mutual - without a distinction between student and teacher. … We know this kind of external effect is common to all the arts and sciences - the 'creative professions'. All of intellectual history is the history of such effects. But, as Jacobs has rightly emphasized and illustrated with hundreds of concrete examples, much of economic life is 'creative' in much the same way as is 'art' and 'science‘…What can people be paying Manhattan or downtown Chicago rents for, if not for being near other people? Lucas (1988): On the mechanism of economic development
Empirical Evidence for Labor Market Agglomeration Economies Extensive empirical evidence on urban wage premium: Glaeser and Mare (2001), Moretti (2004), Rosenthal and Strange (2006) mostly from developed countries mostly on effect of city size (urbanization economies) mostly on urban workers Testing whether cities make workers more productive or productive workers move to cities
Research Questions
Do Marshallian externalities exist in Chinese cities?
And if so, how large is the magnitude?
Do rural migrants benefit from urban labor market agglomeration?
Research Motivation Massive rural-urban migration of low-skilled workers. Regulations on urban growth: institutional barriers preventing free migration (hukou system); cities are relatively small (Au and Henderson, 2005) Global competition; manufacturing industry upgrading City growth and human capital (Glaeser and Saiz, 2004) How to make Chinese cities become skilled?
Why Focus on Labor Market Marshallian Externalities?
Mitigate the problem “productive workers select into cities” Agglomeration economies are very localized—decaying with distance Very limited empirical evidence so far
Main Findings There exist Marshallian externalities in the urban labor market in China Rural migrants also benefit from Marshallian externalities, but benefit much less than do local workers, urban workers, or local workers with an urban hukou “Double discrimination” (based on hukou and migration status)
Data 2004 Manufacturing Census data: total employment in each firm, by education 2005 inter-census population survey (one-fourth of the 1% sample) Merge by city-industry link (two-digit industries) (Moretti, 2004)
Key Variables of Agglomeration
log(Emp): total employment in a city-industry, measuring labor market pooling effect CollegeShare: number of workers with a college degree or above in a city-industry cell divided by total employment in that city-industry cell (human capital externality)
Model
effect fixedindustry :effect fixedcity :
city in industry in share college :
city in industry in employment total:..)education. age, (gender, attributes individual :
city in industry in worker of wage:
)log(log
3
21
j
k
jk
jk
i
ijk
ijkjk
jkijkijk
kj reCollegeSha
kjEmpX
kjiW
reCollegeSha
EmpXW
Causal Identification Two observationally identical workers (A
and B) working in the same industry in two identical cities (CA and CB), the only difference is that in one city (CA) there are more workers and more highly-educated workers in that industry, does this increase worker A’s wage?
How to make two workers observationally identical? Include many observed worker characteristics: gender, age, marital status, education, hukou status, migration year, type of employers, type of labor contract, industry, occupation
Existence of labor market agglomeration economies baseline industry occupation occuinduUrbanhukou 0.0297*** 0.0375*** 0.0166*** 0.0217***
2.51 3.49 2.20 2.93Highschool 0.1330*** 0.1322*** 0.1008*** 0.1004***
23.57 22.47 26.64 26.91Associate 0.4284*** 0.4250*** 0.3226*** 0.3200***
31.01 31.02 33.12 33.56College 0.7560*** 0.7522*** 0.6032*** 0.6005***
26.67 26.81 28.84 29.53Masterabove 1.3198*** 1.3097*** 1.1195*** 1.1132***
29.55 29.48 31.21 31.45log(Emp) 0.0052* 0.0015 0.0019 0.0029
1.72 0.36 0.66 0.74CollegeShare 0.5044*** 0.3431*** 0.4621*** 0.3598***
9.97 5.36 10.03 6.10R2 0.39 0.40 0.43 0.44sample size 172,002
Weak evidence from labor market pooling Subsamples: sorting bias not serious Significant human capital externalities (0.2-0.4 in USA)
Robustness check occuindu local migrants <=2.5year >2.5year <=33 >33Log(Emp) 0.0029 0.0111** 0.0015 -0.0018 0.0147*** 0.0018 0.0071
0.74 1.91 0.47 -0.51 3.40 0.50 1.49CollegeShare 0.3598*** 0.3473*** 0.2812*** 0.2285*** 0.3454*** 0.3228*** 0.3847***
6.10 5.06 3.87 3.05 3.22 5.26 5.63
R2 0.44 0.44 0.43 0.39 0.46 0.44 0.45obs. 172002 97478 74524 34975 39549 91426 80576
Rural migrants benefit from Marshallian externalities baseline
Migrant year<=2.5
Migrant year>2.5 Age<=26 Age>26
Log(Emp) 0.0123*** 0.0128** 0.0115*** 0.0181*** 0.0104***
2.70 2.20 2.26 3.02 2.19CollegeShare 0.2095** 0.3551*** 0.1119 0.2209* 0.2381**
1.93 2.64 0.93 1.67 2.07
R2 0.30 0.25 0.33 0.26 0.35obs. 49916 23302 26614 25260 24656
Rural migrants benefit less from agglomeration economies
1 2 3 4 5 6 7 full
sampleRural
migrantsUrban hukou
Local hukou
Local urban
Urban migrants All
Log(Emp) 0.003 0.012*** 0.018*** 0.011** 0.020*** 0.018** 0.012**log(Emp) *Migrant*Urban
0.009**
log(Emp) * Local *Rural
-0.014***
log(Emp) *Rural *Migrant
-0.024***
CollegeShare 0.360*** 0.210** 0.348*** 0.347*** 0.329*** 0.513*** 0.554***CollegeShare*Migrant*Urban
0.037
CollegeShare*Local*Rural
-0.615***
CollegeShare*Rural* Migrant
-0.588***
Possible Interpretation
Work in informal job sectors that have fewer spillovers?
Low-skilled, low absorptive capacity? (education categories)
Rural migrants lack of social network? (information asymmetry)
Discrimination?
High-skilled workers benefit less if they are rural1 2 3 4 5 6
Low skilled
High skilled
Low skilled
U/R
High skilled U/R
Low skilled L/M
High skilled L/M
Log(Emp) 0.003 0.014** 0.010* 0.017*** 0.005 0.017**
Log(Emp)*Rural -0.013*** -0.038**Log(Emp)*Migrant -0.009** -0.010
CollegeShare 0.311*** 0.504*** 0.537*** 0.517*** 0.322*** 0.499***CollegeShare* Rural -0.556*** -0.577***CollegeShare*Migrant -0.074 0.035
Low / high-skilled worker sampleFull sample Low-skilled High-skilled
Log(Emp) 0.012* 0.008 0.018***
Log(Emp)*urban*migrant 0.009** 0.002 -0.004Log(Emp)*local*rural -0.014*** -0.007* -0.019Log(Emp)*rural*migrant -0.024*** -0.014** -0.046***
CollegeShare 0.554*** 0.538*** 0.509***
CollegeShare*urban*migrant 0.037 -0.093 0.053CollegeShare*local*rural -0.615*** -0.580*** -0.584**
CollegeShare*rural*migrant
-0.588*** -0.500*** -0.503(-1.49)
There may exist two types of discrimination: local bias and urban bias
Other Studies Suggest Double Discrimination
Zax (2016): returns to education vary significantly and persistently across provinces and years, suggesting mobility barriers across provinces
Chen et al. (2015): rural migrants are more likely to search jobs through informal social network but receive lower wages if they do so.
Other Studies Suggest Double Discrimination
Liu et al. (2016): rural migrants are residentially segregated in Shanghai, based on Census 2010
(.9,1](.8,.9](.65,.8](.5,.65](.4,.5][0,.4]
Local Ratio in Shanghai(2010)(0.750,1.000](0.550,0.750](0.350,0.550](0.250,0.350](0.200,0.250][0.000,0.200]
Migrant Ratio in Shanghai(2010)
Conclusion
Labor market agglomeration economies exist in Chinese cities
Rural migrants benefit from labor market agglomeration economies, but benefit much less than do local, urban residents
Double discrimination towards rural migrants
Implications What drives rural-urban migration
and urbanization? Cities facilitate learning
Learning in cities through social interactions, alternative to school education
Barriers to learning
How Can Chinese Cities Attract Skilled People?
Make cities safe Make cities clean: air quality Make cities accessible: public transit,
walkable streets Make cities livable: affordable housing,
open space… Make cities open, tolerant: remove
mobility barriersFortunately, China is reforming the hukou system.
Future Research Identify how people socially interact
in cities Test how relaxing or removing mobility
barriers enhances social interactions Urban public policies that promote
social interactions and learning in cities
