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High-tech Clusters, Labor Demand, and Inequality:
Evidence from Online Job Vacancies in China
Qin Chen Klint Mane Geunyong Park Ande Shen*
February 24, 2022
AbstractThis paper evaluates the impact of high-tech clusters on inequality by focusing on a re-
cent Chinese placed-based industrial policy called “Made in China 2025.” Exploiting the
staggered roll-out of the policy and the representative online job posting data in China,
we conduct an event-study analysis to investigate the heterogeneous effects of high-tech
clusters on labor demand, wages, and living costs across occupations and regions. We
find that targeted cities experience a significant increase in labor demand measured by
online job vacancies relative to non-targeted cities, partially due to an increase in new
firm entry. The gap in the offered wages between non-routine and routine jobs grows at
the same time. This policy also causes labor demand and wages to drop in neighboring
areas of the targeted cities. Combining the labor market effects with increasing living
costs in the pilot cities, the welfare of non-routine job workers in the targeted cities dis-
proportionately declines by construction of high-tech clusters. Our results suggest that
policymakers should be cautious about occupational and regional inequalities when con-
structing high-tech clusters.
Keywords: Made in China 2025, Inequality, Labor Demand, Industrial Policy, Vacancy
Postings
*Qin Chen: Business Big Data Company; Klint Mane: University of Rochester (email:[email protected]); Geunyong Park: University of Rochester (email: [email protected]);Ande Shen: University of Rochester (email: [email protected]). We are grateful to Travis Baseler,Lisa Kahn, Ronni Pavan, John Singleton, and Kegon Tan for their guidance and support. We thank GeorgeAlessandria, Victor Hernandez, Cameron LaPoint, Arvind Sharma, and seminar participants at RochesterApplied Reading Group, Rochester Student Seminar, 15th North America Meeting of Urban EconomicsAssociation, 92nd International Atlantic Economic Conference, and 2021 Jinan-SMU-ABFER Conference onUrban and Regional Economics for their valuable comments. All errors are ours.
1
1 Introduction
Many countries recently have set up high-tech clusters to foster the adoption of modern
technologies into their production.1 The implications of high-tech clusters, however, are am-
biguous and not well understood, especially on inequalities. When it comes to inequalities
across workers, advanced technologies such as robots and AI newly adopted by high-tech
clusters might replace some types of workers, while raising labor demand for high-skilled
workers (Acemoglu et al., 2020; Acemoglu and Restrepo, 2020). On the contrary, high-tech
clusters may improve the overall productivity of the workers in the treated areas as tradi-
tional place-based industrial policies (Busso, Gregory, and Kline, 2013; Kline and Moretti,
2014a; Ehrlich and Seidel, 2018; Criscuolo et al., 2019). When it comes to spatial inequal-
ities, high-tech clusters might have negative spillover effects on nearby areas by draining
their resources (Hanson and Rohlin, 2013). On the contrary, the technology spillover from
high-tech clusters may lead to increased productivity in neighboring areas.
The implications are highly important for the governments of developing countries to boost
inclusive economic growth by technology upgrading. However, it is far from conclusive on
the impacts across different economic players due to three challenges. First, construction of
high-tech clusters and technological adoption usually happen in a gradual and cumulative
way, which leads to the difficulty of estimating their causal effects. Second, the locations
of high-tech clusters are endogenous in most of cases. High-tech firms intentionally choose
their locations to maximize their profit and a high-tech cluster is naturally built as more firms
decide to locate in the same advantageous place. For example, high-tech companies and IT
professionals choose to locate in San Francisco Bay Area to take the exclusive advantages
of the location. Lastly, the empirical evidence on the impacts of technological adoption
and constructing high-tech clusters usually comes from developed countries due to data
restrictions. Unlike developed countries, it is enormously hard to get detailed information
across individuals and regions for developing countries.
In this paper, we tackle these challenges by evaluating a modern industrial place-based pol-
icy called “Made in China 2025” (henceforth MIC25). The goal of MIC25 is to make China
a global leader in the manufacturing of high-technology products. From 2016 to 2017, 30
1For example, South Africa’s Inter-ministerial Committee on Industry 4.0, The Republic of Korea’s I-Koreastrategy in 2017, Indonesia’s Making Indonesia 4.0, and Uganda’s National 4IR Strategy in 2020.
2
pilot cities of different levels were chosen to be the front runners for this technological ad-
vancement. MIC25 targeted ten high-tech fields, including next-generation IT, robots, and
electric vehicles. With this policy, the government provided firms in the pilot cities’ targeted
industries with financial instruments such as tax incentives and direct subsidy. We empir-
ically explore the policy’s impact on labor demand, wages, and living costs across regions
by utilizing representative online job postings data and housing rent records. Combining
the reduced-form estimates, we examine the overall welfare implications of such industrial
policies which induced high-tech clusters.
In our setting, there are three key advantages that help elevate the aforementioned issues.
First, in MIC25, the decisions to upgrade technology and build high-tech clusters in specific
regions are made by the central government, leading to a plausibly exogenous shock to firms
and individuals around the targeted areas. Second, 30 cities were selected as pilots out of
more than 300 cities in China, which leaves a way to identify the causal impact by comparing
each pilot city with a non-pilot but similar city and to evaluate the inequality issues across
cities induced by the policy. The implementation date of the policy also varies across the
pilot cities. The regional and time variations created by this policy are useful in solving the
identification challenges. Finally, the representative online job-posting data collected since
2015 provide detailed information on labor demand and wages at a high frequency, which
is not the case for most of Chinese official data. This enables us to cleanly identify the labor
market dynamics induced by MIC25 across occupations and regions both before and after
the policy.
By utilizing the place-based implementation and staggered roll-out of MIC25, we conduct
an event-study analysis to identify the causal impacts of high-tech clusters on labor de-
mand, employment, firm entry, and housing costs. To deal with the potential selection bias
of governments’ choice on pilot cities, we compare the 30 pilot cities to the 30 control cities
matched based on observable characteristics before the implementation of MIC25. Our re-
sults are robust to using different control groups. We further compare the neighborhood
cities of pilot cities to the neighborhood cities of non-pilot cities to examine the spillover ef-
fects and inequalities across cities. By comparing wage and rent changes across occupations
and places, we identify who are the winners and losers of the high-tech clusters. We also
explore the mechanisms behind the welfare implications by analyzing the location choices
of both firms and workers.
3
We find that the number of job openings in pilot cities has significantly increased by 24%
compared to control cities for 3 years after the implementation of MIC25. During the same
period, the policy does not have a statistically significant effect on offered wages in the job
postings, but we could observe an slightly increasing trend of offered wages in the pilot cities
toward the latter periods. These results are supported by city-level yearly employment and
wage data. On the other hand, the neighboring cities of pilot cities suffer 25% fewer job
openings for 3 years after MIC25 compared neighboring cities of control cities. Neighbor-
ing cities also suffer wage decreases of 3%. The negative effects on the neighboring areas
are larger than the positive impact of MIC25 on the pilot cities. In pilot cities, wages are
increasing for non-routine occupations and targeted industries. At the same time, housing
rent also increases by 7% in the pilot cities during the same period, reducing the welfare of
residents in the pilot cities. The main mechanisms behind the results are inflow of workers
and firms to the pilot cities. Using China’s firm registration data, we provide evidence that
10% more firms enter the pilot cities and that 32% less firms enter the neighboring cities for
3 years after MIC25. We also observe that the population of the pilot cities increases by 2%
even with migration restrictions in place.
To quantify the welfare impact of MIC25 and disentangle the channels behind it, we con-
struct a theoretical model inspired by Kline and Moretti (2014b). Workers’ utilities depend
on wages, local amenities, and rents and vary across locations and occupations. Given that
wage increases for any type of workers do not catch up with the hike in rent prices in the
pilot cities, the welfare of workers is worse off there. In the neighborhood cities, wages
significantly decline for any type of workers due to the reduction in labor demand though
housing costs do not change. So, the welfare of workers in the neighborhood cities is also
worse off. The back-of-the-envelope calculation weighing both wage reductions and rent
hikes suggests that average workers would lose around $260 per year due to the implemen-
tation of MIC25 for 3 years after the policy, which accounts for around 3% loss in yearly
wages, and the total welfare loss of the workers accounts for around 0.15% ($ 19 billion) of
China’s annual GDP in 2017. Moreover, the heterogeneity in the response of labor demands
and wages indicates that the welfare loss is more significant for routine job workers than
non-routine-job workers in the pilot cities. Readers, however, should be cautious to inter-
pret our results because we only focus on the first 3 years of the policy. Long-run effects
could be different from our estimates.
4
This paper is related to several strands of the literature. First, our study is related to the
growing literature on the local effects of high-tech facilities. The majority of these stud-
ies focus on identifying productivity spillovers on knowledge production or R&D (Bloom,
Schankerman, and Van Reenen, 2013; Carlino and Kerr, 2015; Moretti, 2021), but some of
the studies present the effects on local economies. For instance, Kantor and Whalley (2014)
report the long-term positive effects of research university activity on their local economies.
They find that the spillover effects grow over time as the composition of local industries ad-
justs to the comparative advantage of the location. Qian and Tan (2021) identify the mixed
welfare effects of the entry of a large high-skilled firm on local residents. Our idea is closely
related to this study, but we focus on national-level high-tech clusters rather than a single
high-tech firm.
This paper is also part of a much larger literature on place-based industrial policies. Busso,
Gregory, and Kline (2013) and Criscuolo et al. (2019) identify strong positive employment
and wage effects due to subsidies in disadvantaged areas in the US and UK, respectively.
Both studies find that there is not much negative spillover effect of the traditional place-
based industrial policies. In China, Alder, Shao, and Zilibotti (2016) find a positive spillover
effect of Special Economic Zones(SEZs) along with a 20% increase in GDP in SEZs. On the
contrary, Kline and Moretti (2014a) focus on the Tennessee Valley Authority (TVA) imple-
mented in the 1930s of the US, and find that a positive effect of subsidies for disadvantaged
areas is offset by the negative effect on nearby areas. We contribute the literature by in-
vestigating a modern version of place-based industrial policies, building high-tech clusters,
which focus on adopting and distributing advanced technology as a national strategy rather
than developing disadvantaged areas.
Lastly, this study follows the ideas in the literature on the adoption of recent technologies
and inequality. Acemoglu and Autor (2011) provide a theoretical framework where recent
technological progress could have the opposite effects on workers based on replaceability
and complementarity of their tasks. Graetz and Michaels (2018) is the first paper to report
that industrial robots are reducing the labor share of low-skilled workers at national level.
Acemoglu and Restrepo (2020) also find that robots’ impact is distinct from traditional capi-
tal and technologies, having negative effects on wages and employment in local economies.
Using establishment level data on online job vacancies in the US, Acemoglu et al. (2020)
report a rapid growth in AI-related job postings from 2010 and find that AI-exposed estab-
5
lishments are reducing hiring in non-AI positions while they expand AI-related hiring. Our
study complements this literature by investigating such a composition change in labor force
induced by national-level high-tech clusters.
The remainder of this paper unfolds as follows. We first review the background of China’s
place-based industrial policy MIC25 in Section 2. In Section 3, we introduce the data used
in our analysis. Section 4 demonstrates our matching and estimation strategies. Section 5
presents the results and analyze the mechanisms. In Section 6 we build a conceptual frame-
work and investigate the welfare impact of building a high-tech cluster by combining the
estimation results. Section 7 concludes.
2 Background
This section provides a detailed description on the policy “Made in China 2025” and rele-
vant institutional background. After about 40 years of development since the opening and
reform in 1978, China has gained tremendous development in the manufacturing sector.
China’s industrial production, however, is still backward compared to industrial countries.
Unlike technology-leading countries, such as the US, Germany and Japan, where there is
intensive use of production lines and management processes based on modern information
technology and highly automated machines, China’s manufacturing production is still very
labor-intensive and in a rudimentary level of automation compared to advanced countries
(Hanson, 2020). The data from the International Federation of Robotics (IFR) demonstrate
that on average Chinese enterprises utilize just 19 industrial robots per 10,000 industry em-
ployees in 2015, which is much lower than 531 in South Korea, 301 in Germany, or 176 in the
US.
In May 2015, the Chinese government issued the national strategic plan “Made in China
2025” — as part of the Thirteen and Fourteen Five-year Plans — aimed to further develop
the manufacturing sector in China and transform China from a labor-intensive workshop
to a more technology-intensive powerhouse by the first half of the century. China used
to be a producer of cheap low-technology goods by utilizing the advantage of low labor
costs. This plan is intended to develop the production of high-technology goods, with a
goal of increasing the Chinese-domestic content of core materials to 40% by 2020 and 70%
by 2025.
6
To concentrate resources and fulfill the goal of MIC25 more efficiently, the Chinese gov-
ernment selected ten key industries to focus on: Information Technology, Robotics, Green
energy and green vehicles, Aerospace equipment, Ocean engineering and high tech ships,
Railway equipment, Power equipment, New materials, Medicine and medical devices, and
Agriculture machinery. All of these industries are regarded as crucial technology sectors,
which can lead China to a strong position in the Fourth Industrial Revolution.
The Chinese government implemented MIC25 in a “pilot-and-expansion” way which took
a step-by-step procedure of implementation following the experience of the other reforms
such as the Special Economic Zone (Lu, Wang, and Zhu, 2019).2 In August 2016, Ningbo, a
city in Eastern coast of China, was selected as the first pilot city of MIC25. In the following
couple of months, the other 29 cities were selected, which makes a total of 30 pilot cities
among 335 cities. In 2018, the pilot cities were upgraded to National Demonstration Zones
(NDZs) to lead the MIC25 initiative under centralized guidance. These cities are widely
located across regions in China, as shown in Figure 1.
Figure 1: The Spatial Distribution of Pilot Cities
Notes: The map shows the boundaries of all cities (both province-level and prefecture-level) in China. The highlighted regions are 30 pilot cities of MIC25.
2Xin Guobin, the vice-minister of the Ministry of Industry and Information Technology (MIIT) states thatthese pilot cities were chosen to prevent the trend of local governments rushing in implementation and there-fore causing inefficiencies. See https://www.chinadaily.com.cn/business/2016-08/19/content 26536807.htm.
7
After the Ministry of Industry and Information Technology (MIIT) of the Chinese govern-
ment approved a city’s application to be a pilot city, then the city announced its own de-
velopment plan for MIC25 later. Table 1 shows the quarter when each pilot city published
its implementation plan for MIC25. Before the announcements, firms and workers have
no way of knowing which cities are going to be selected or how the policy will be imple-
mented, which is consistent with our results showing no anticipation effects. All these pilot
cities focus on their own advantageous industries, rather than being involved in all targeted
industries of MIC25.
Table 1: The Policy Starting Dates of Pilot Cities in MIC25
Quarter Number of cities City name
2016Q2 1 Ningbo
2017Q1 8 Shenyang, Changchun, Nanjing, Wuxi, Changzhou, Suzhou, Zhenjiang,Quanzhou
2017Q2 15 Huzhou, Zhengzhou, Luoyang, Xinxiang, Changsha, Zhuzhou, Xiangtan,Hengyang, Zhuhai, Foshan, Jiangmen, Zhaoqing, Yangjiang, Zhongshan,Chengdu
2017Q3 3 Hefei, Wuhan, Wuzhong
2017Q4 3 Ganzhou, Qingdao, Guangzhou
Notes: The starting date is the time when the city published its own development plan for MIC25 after being se-lected as a pilot city. Usually the starting date is a few month after the approved date. All the date informationis collected from government announcements, reports, official newspapers, and policy documents database.
Though MIC25 was inspired by the other modern industrial plans in other countires such as
“Industry 4.0” of Germany, its scale is incomparable with the other policies in many aspects
(Li, 2018; Malkin, 2018; Zenglein and Holzmann, 2019). First, the president Xi Jinping made
MIC25 his signature project, reflecting how crucial it is in the central government side. Sec-
ond, both central and local governments provide a wide range of policy benefits including
protecting intellectual property, allocating more lands, and simplifying government review
and approval procedures. Third, the governments offer huge amount of financial support.
Firms with MIC25-related projects can get higher subsidies, lower loan interest and higher
tax benefits. For example, MIIT and China Development Bank promised $45 billion in di-
rect loans, bond sales and leasing to support major MIC25 projects. The Special Construc-
tive Fund provided an estimated $270 billion funding for numerous MIC25-related projects.
Shanxi MIC25 Fund offers $117 billion of financial support for approximately 100 MIC25-
related projects. This huge scale makes MIC25 the best example of “big push” development
strategy in developing countries and may generate huge increases in welfare (Rosenstein-
Rodan, 1943; Murphy, Shleifer, and Vishny, 1989; Kline and Moretti, 2014a).
8
3 Data
Our empirical analysis draws on four main data sources: (1) online job posting data; (2)
publicly-available city-level statistical yearbooks; (3) housing rent database; and (5) firm
registration database. This section describes each of these data sources and their represen-
tativeness.
We begin with the online job posting data from a research company called Business Big
Data. The company collects job postings from six major job hunting platforms in China.3
To avoid duplicate job postings, the company only keeps the first appearance of a specific
job from a specific company. The data span from 2015 to 2020, covering periods before
and after the adoption of MIC25. The job postings from the six online platforms can cover
more than 80% of all online job postings in China.4 For each job posting, the company
scraps detailed information about the job position including job title, job description, offered
wages, required education and specific skills, etc. It also collects the information about the
employer such as name, industry, location, size, founding year, etc.
Online job posting data is necessary in our research for two reasons. First, it is a unique data
source for investigating labor demand and wages across detailed occupations and locations
in China. For instance, the China City Statistical Yearbooks only report city-level wages and
China Census does not report wages at all. So, Chinese job postings data have been used
in literature to infer detailed aspects in the labor market of China (Kuhn and Shen, 2013;
Fang et al., 2020; Helleseter, Kuhn, and Shen, 2020; He, Mau, and Xu, 2021). Second, it also
provides high-frequency data of job vacancies. This feature is helpful for the identification
of policy effects because we utilize the staggered adoption across the pilot cities in differ-
ent quarters. Considering the short time window (2015 - 2019) of our research, it is also
important to expand the number of periods by using quarterly data.
Our online job posting data is most close to the US job posting data set assembled by Burning
Glass Technologies (BG). It has been widely applied in literature (Hershbein and Kahn, 2018;
Acemoglu and Restrepo, 2020; Forsythe et al., 2020). The Chinese job posting data set, how-
ever, provides two additional advantages. First, we can observe the job posting itself and
351job, zhaopin.com, liepin.com, lagou.com, 58.com, and ganji.com451job and liepin.com have more than half of the market share in 2019 and 51job, 58.com
and zhaopin.com account for over 80% share of the online-hiring market in 2018. Seehttp://report.iresearch.cn/report/202004/3572.shtml.
9
Figure 2: Representativeness of Job Postings Data
(a) Industry Composition (b) Employment-Job Postings Share Deviations
Notes: In Panel (a), industry composition of employment are from the City StatisticalYearbooks and job ads are from the main data of online job postings for the periods from2015 to 2018. In Panel (b), we plot the deviation of the online job ads share from theyearbook employment share in 2015 on x-axis and that in 2018 on y-axis. Each pointrepresents a city-industry pair. If a point (city-industry) is on the 45-degree line, then therelative representativeness of that city-industry pair in the online job postings does notchange from 2015 to 2018.
job openings, which refers to how many workers the firm intends to hire in each job post.
The US data does not report how many workers an employer would like to hire for a job
vacancy. So, we can better capture the changes of labor demand in response to the policy by
using the number of job openings rather than the number of job postings. More interestingly
and importantly, roughly 80% of the job postings have salary information in contrast with
the US data where salary information is usually unavailable. This unique wage information
constitutes a major component in our empirical study on inequality across occupations, and
regions.
The main concern of using online job vacancy data is that online job ads may be biased
towards certain types of occupations among all job vacancies. Following Hershbein and
Kahn (2018),5 we investigate the representativeness of online job postings by comparing our
data with employment from the China City Statistical Yearbooks for the periods from 2015
to 2018. Figure 2a compares industry composition of employment from the City Statistical
Yearbooks (blue bars) and that of job ads from the online job posting data (red bars). The
5Hershbein and Kahn (2018) compare the online job vacancies in the US with public data sets including JobOpenings and Labor Turnover Survey (JOLTS), Current Population Survey (CPS), and Occupational Employ-ment Statistics (OES) and find that the changes of the job distributions across time fit well with other publicnationally representative data, though the online postings overrepresent high-skill occupations and industriesoverall.
10
online job ads over-represent several industry groups such as business, retailing, and IT,
while under-represent manufacturing, construction, and education. These results are not
surprising considering that firms in business, retailing, and IT sectors are more likely to hire
their employees via internet than other sectors.
What matters more for our analysis is whether the representativeness changes over time. If it
is the case, our results could be deteriorated by the composition changes of the collected job
postings across industries or locations. Figure 2b shows each city-industry pair’s deviation
of the online job ads share from the employment share in 2015 (on the x-axis) and that in
2018 (on the y-axis), as well as the 45-degree line. If a point (an industry in a city) is on the
45-degree line, then the representativeness of that city-industry pair does not change from
2015 to 2018. The figure indicates that the representativeness of the online job ads for each
city-industry is very stable over the periods of interest because most of the dots lie around
the 45-degree line. Thus, we believe that our results are not driven by a growing importance
of certain cities or industries in the job vacancy data.
Due to the fluctuation and seasonality issue, we aggregate the firm-specific data to a city-
quarter-industry level, starting from the first quarter of 2015 and covering all cities (pre-
fectures) in China. We use the 2-digit industry code (18 in total) due to data availability.
This paper examines two main variables related to job postings: the number of job openings
in each city-quarter-industry cell and the weighted average wage where the weight is the
number job openings in each posting.
Second, we assemble publicly-available data including the China City Statistical Yearbooks
(2010-2018) and the China City Development Yearbooks (2010-2018) to construct annual eco-
nomic outcomes at the city level. The integrated data include city-level characteristics such
as GDP, employment, wage, land area, population, government expenditure, and others.
These data provides the measure on the similarity of cities, which serves to construct our
matching sample. We also investigate the effect of MIC25 on city-level employment from
this data set to make sure that the changes of job vacancies generate corresponding changes
of employment in local labor market.
To account for the impact of MIC25 on housing market, we employ monthly housing rent
from the Wind Financial Terminal.6 The data set includes average housing rent of 100 cities
6See https://www.wind.com.cn/en/wft.html.
11
out of 335 cities in China, but 26 out of 30 pilot cities are included. The change in rent also re-
veals the migration pattern of the labor force. Pilot cities aim to develop their manufacturing
sector and attract workers from non-pilot cities, which will lead to the inflow of migrants
and increase in housing rent. To get a more detailed picture of migration induced by the
policy, we also applied the China Migrants Dynamic Survey data, which is an yearly cross-
sectional data covering around 160,000 households in all provinces in China from 2010 to
2018.
In addition to workers’ sorting across locations, the behavior of firms is another channel
through which the policy could affect welfare. We gathered the registration information of
all firms from China’s Administrative Registration Database (CARD). These data include
all firms in China starting in 1980, including firm name, registration place, operation place,
registration asset, and registered industry (4-digit). Using these data, we can analyze the
effect of building a high-tech cluster on the inflow of new firms, which could be a main
mechanism behind the changes in labor demand.
4 Empirical Strategy
Our empirical analysis’ main objective is to estimate the causal effects of MIC25 on labor
demand and housing market in the targeted and neighboring cities. As described above,
30 cities were selected as pilot cities by the Chinese central government. The main obstacle
for the causal inference is that the pilot cities are not randomly selected, which generates
biases in a difference-in-difference strategy. Ideally we would have chosen cities that were
candidates to become pilot cities, but were not chosen. Unfortunately, such information is
not available on MIC25. Given that we do not have the details on how pilot cities are se-
lected, we rely on observed characteristics to create a comparable sample of control cities.
To create the control sample, we estimate propensity scores of being chosen as a pilot city
using logistic regression. The propensity score matching (PSM) has been widely used in ap-
plied research and Dehejia and Wahba (1999) find that the PSM approach can be powerful to
eliminate the biases coming from the observable differences between the treated and control
groups.
The propensity score, P(D = 1|X), gives the predicted probability of being a pilot city
(D = 1) under the assumption of selection on observables ((Y0, Y1) ⊥ D|X). The regres-
12
Figure 3: The Maps of the Cities of Interest
(a) Pilot and Neighboring Cities (b) Control and Neighboring Cities
Notes: The control cities in Panel (b) are chosen by the matching algorithm based on thecity-level characteristics in the base year 2015. The neighbor cities are the closest 5 citiesfor each pilot and control cities except for the pilot and control cities.
sors of the logit model include population, employment, GDP per capita, land area, number
of industrial firms, government tax income, GDP growth, and employment shares of main
industries in the base year 2015 from the Chinese City Statistical Yearbook. After getting
the predicted propensity scores from the logistic model, we pick the 30 cities with the clos-
est propensity scores to the treated cities to be in the control group. Note that we are not
selecting one control city for each pilot city for pairwise comparison. We compare the 30
pilot cities versus the 30 control cities. Based on our predicted scores, selecting 30 cities
corresponds to selecting the 90th percentile of the most similar cities in terms of propensity
scores.
The neighboring cities of pilot cities are not allowed to be chosen as control cities since if
treatment has positive or negative spillover effects to the nearby regions, then we would be
under- or over-estimating the impact of MIC25. We define neighboring cities as the closest
5 cities in terms of distance between the city centers. Since there are some clusters of pilot
cities, there are way less than 150 neighboring cities. Four different groups of interest are
depicted in Figure 3. On the left, we can see the pilot cities in red and the neighboring cities
of pilot cities in blue. On the right, you can see the control cities in red and the neighboring
cities of the control cities in blue. The maps imply that the pilot and control cities are not
concentrated in some particular region of China.
13
It is important to note that the results shown in Section 5 do not depend on our choice of
control cities. Choosing a control sample of 60 cities instead of 30, or using all other cities
as controls yield very similar results. As a further robustness check, we estimate propensity
scores using random forest rather than the logistic regression. There are several reasons why
we consider a random forest approach. Firstly, a parametric logistic model is prone to model
misspecification, which results in the inaccurate or imprecise estimation of the propensity
score (Zhao et al., 2016). Secondly, using a random forest algorithm allows for non-linearities
and interactions which are not captured by linear models. Finally, the literature suggests that
using logistic regression leads to subpar performance compared to random forests or other
machine learning methods (Zhao et al. (2016); Lee, Lessler, and Stuart (2010); Setoguchi et al.
(2008)).
Table A.1 shows the summary statistics of the treated and control samples from using 2015
data from the yearbook. Panel (a) presents the main outcomes in our empirical analysis
and panel (b) are the matched variables in our matching algorithms. Average values are
shown throughout the table. Column (1) shows the averages for treated cities. Columns
(2) and (4) show the averages for the control sample selected using logistic regression and
random forest, respectively. Columns (3) and (5) show the difference between the control
samples and the treated sample. We run a t-test to see if the differences are statistically
significant. The random forest algorithm does slightly better than the logistic regression
in balancing observables. There are still significant differences for a few variables, which
are to be expected given the small and limited number of cities. In general, both matching
samples are much similar to the treated sample than the sample where all non-pilot cities
are included, as shown in column (6).
To validate that all cities are evolving similarly if there were no MIC25 and gain confidence
in the causal effect of MIC25, we conduct an event study approach to compare the treated
pilot cities to the control cities constructed above. We exploit the variation in the timing of
the start date across the pilot cities. The data is aggregated to a city by quarter by industry
level. Our main specification is given by:
Yict = α +14
∑k 6=−1;k≥−4
βk · 1{t=t∗c+k,Dc=1} + γic + δit + εict. (1)
14
where Yict is the outcome (log of job openings, offered wage, rents, etc.) at city c and quarter
t and industry i. Dc is the indicator of being chosen as a pilot city, so 1{t=t∗c+k,Dc=1} = 1 if
city c is a pilot city and quarter t is k quarters before/after the start date (t∗c ) and 0 otherwise.
We control for industry-by-city (γic), and industry-by-time (δit). γic is included to control for
any time-invariant unobservable heterogeneity of city-specific industry and δit additionally
takes away the industry-specific time trends. We weight observations by the number of job
postings during 2015, the year before the policy was implemented. This weighting scheme
allows to up-weight cities with larger number of postings to help with the precision of our
results.
If our assumption of selection on observables are satisfied or the fixed effects well control for
the possible unobservable heterogeneity (1{t=t∗c+k,Dc=1} ⊥ εict|γic, δit), then our coefficients
of interest
βk = E[Yc,k −Yc,−1|Dc = 1]− E[Yc,k −Yc,−1|Dc = 0] (2)
should capture the causal effects of MIC25 on the outcome variables at k quarters after the
policy change. Based on our conceptual framework in Section 6, we also utilize βk on log
wage and log rent to investigate the welfare incidence of MIC25. The coefficients on firm
entry and population would give the evidence on the mechanism of MIC25.
As has been recognized by Goodman-Bacon (2021), the two-way fixed effects models and
event studies could lead to biased estimates if the policy had heterogeneous impacts across
cities (i.e., treatment heterogeneity) and the treatment effects evolved over time (i.e., dy-
namic treatment effects). To mitigate this concern, we use the modified approach following
Callaway and Sant’Anna (2020). We also compare to approaches following Borusyak and
Jaravel (2017) and Sun and Abraham (2020), the results are similar.
15
Tabl
e2:
Sum
mar
ySt
atis
tics
(1)
(2)
(3)
(4)
(5)
(6)
Var
iabl
esPi
lotC
itie
sM
atch
ing
(Log
it)
Mat
chin
g(R
ando
mFo
rest
)A
llN
on-P
ilotC
itie
sM
ean
Mea
nD
iffer
ence
Mea
nD
iffer
ence
Mea
n
Pane
lA:M
ain
outc
omes
(not
targ
eted
inm
atch
ing)
Num
ber
ofjo
bpo
stin
g(1
0,00
0)32
.67
22.5
3-1
0.14
22.6
6-1
0.01
4.80
Num
ber
ofjo
bop
enin
g(1
0,00
0)10
3.12
70.2
1-3
2.91
71.5
3-3
1.59
14.4
7#
Wag
e(m
onth
ly,y
uan)
5353
.69
5085
.52
-268
.17
4866
.12
-487
.645
79.4
6
Pane
lB:M
atch
edva
riab
les
Popu
lati
on(1
0,00
0)57
1.48
725.
9715
4.49
775.
0720
3.59
443.
97Em
ploy
men
t(10
,000
)12
9.67
130.
921.
2513
9.93
10.2
664
.00
GD
P(b
illio
nyu
an)
580.
3346
8.11
-112
.22
480.
71-9
9.62
247.
41La
ndar
ea(k
m2 )
1031
9.10
1923
1.77
8912
.67*
*13
662.
0033
42.9
016
460.
93N
umbe
rof
indu
stri
alfir
ms
2978
.10
3382
.33
404.
2320
51.9
3-9
26.1
7**
1425
.47
Tax
profi
t(bi
llion
yuan
)55
.09
61.7
46.
6448
.55
-6.5
523
.87
GD
Pgr
owth
rate
(%)
8.42
7.99
-0.4
38.
30-0
.12
7.58
Empl
oym
ents
hare
(sec
onda
ryse
ctor
,%)
55.2
047
.80
-7.4
0**
51.5
5-3
.65
45.9
2Em
ploy
men
tsha
re(t
hird
sect
or,%
)44
.47
51.7
77.
30**
47.4
52.
9851
.64
GD
Psh
are
(sec
onda
ryse
ctor
,%)
49.1
449
.92
0.78
48.0
2-1
.12
46.5
2G
DP
shar
e(t
hird
sect
or,%
)44
.84
42.0
3-2
.81
42.4
9-2
.35
41.0
3Em
ploy
men
tsha
re(m
anuf
actu
ring
,%)
38.4
29.5
1-8
.89*
*31
.05
-7.3
5**
25.1
0Em
ploy
men
tsha
re(I
T,%
)1.
591.
860.
271.
850.
261.
33Em
ploy
men
tsha
re(b
usin
ess,
%)
2.08
1.99
-0.0
92.
490.
411.
74Em
ploy
men
tsha
re(s
cien
ce,%
)1.
962.
500.
541.
91-0
.05
1.70
Num
ber
ofC
itie
s30
3030
290
Not
es:
This
tabl
epr
esen
tsth
eav
erag
esac
ross
the
pilo
tci
ties
and
the
two
cont
rol
sam
ples
crea
ted
usin
glo
git
and
rand
omfo
rest
mat
chin
g.D
iffer
ence
colu
mns
show
whe
ther
the
diff
eren
ces
betw
een
the
cont
rol
sam
ples
and
trea
ted
sam
ple
are
sign
ifica
nt.
Star
s**
show
sign
ifica
nce
atth
e5%
leve
l.A
llin
dust
ries
are
clas
sifie
din
toth
ree
broa
dse
ctor
s(p
rim
ary,
seco
ndar
y,an
dth
ird)
.Ea
chbr
oad
sect
orin
clud
esso
me
gran
ular
sect
ors,
for
exam
ple,
the
seco
ndar
yse
ctor
incl
udes
the
man
ufac
turi
ngin
dust
ry,t
heco
nstr
ucti
onin
dust
ry;t
heth
ird
sect
orin
clud
esth
ein
form
atio
ntr
ansf
er,c
ompu
ter
serv
ice
and
soft
war
ein
dust
ry(n
amed
asIT
inth
eta
ble)
.Mor
ede
tails
can
befo
und
here
.
16
It is also important for us to capture the announcement date of each pilot city along with
the start date considering that economic agents can respond right after the announcement
of policies if they have any incentive to do that (Gruber and Koszegi, 2001). For instance, a
firm in a neighboring city of a pilot city that decides to move to the pilot city or to construct
a branch in the pilot city may reduce labor demand at the neighboring city in advance to
minimize labor cost. Home owners in the pilot cities may expect inflow of migrants search-
ing for jobs to the pilot cities and raise the housing rent right after the announcement of
designation. The official announcement dates are not available for many pilot cities, so we
reasonably define two quarters before the start date as the announcement date for each pilot
city based on the available information. To be specific, we use the announcement dates to
analyze labor demand in the neighborhood cities and housing market in pilot and neighbor-
hood cities following the above logic and find that they do respond to the announcement
date before the start dates of MIC25.
5 Results
Our results unfold in four parts. First, we use the online job posting data to explore the effect
of MIC25 on labor demand and wages. We investigate labor demand and wage changes in
pilot cities. We provide support for our job posting results using yearly employment and
wage data from City Statistical Yearbooks. We discuss the inequalities across industries or
occupations, as well as the spillover effect to nearby cities. Secondly, we investigate the
impact of MIC25 on the housing market in pilot and neighboring cities. Thirdly, we dig into
the sorting behaviors of firms and workers, to instruct the analysis on the policy’s welfare
implications. Lastly, using the results above, we derive the welfare impact of MIC25.
5.1 Labor Demand and Wages
5.1.1 Pilot Cities
We begin by analyzing the results for pilot cities. Figure 4 plots the coefficients of interest
from Equation (1). The outcome on the left-hand side of the figure is the log of job openings
and on the right log wages. Wage information is attached to the job posting. 60 cities are in-
cluded in the regression, 30 pilot cities, and 30 control cities. The number of job openings for
17
Figure 4: The Impacts on Job Postings in the Pilot Cities
(a) Number of Job Openings (b) Monthly Wages
Notes: Figures present coefficients and 95% confidence intervals on the interaction be-tween the indicator of relative quarterly periods from the policy start date and the indi-cator of the pilot cities. Industry-by-city and industry-by-calendar quarter fixed effectsare controlled and each city-by-industry observation is weighted by the total number ofpostings in 2015.
pilot cities increase significantly after the policy compared to control cities. The lack of a pre-
trend is assuring that the impact on the job openings is coming from MIC25. The increase
is quite large and reaches a level of about 50% after 2 years since MIC25 was implemented.
Overall, we find an increase of 24% in job openings in pilot cities. The results are shown
in columns (1) and (2) of Table 3 where we run a difference in difference specification. This
results is comparable to literature estimates for the effect of place based policies on employ-
ment in China of about 35% (Lu, Wang, and Zhu, 2019), and the US and UK at about 10-15%
(Kline and Moretti (2014a); Criscuolo et al. (2019)). The event study shows that it takes about
a year for the positive effect to arise. Then it increases significantly to 25% for one year after
the policy and 50% after two years. We believe that this pattern is reasonable, considering
that it should take time to apply and receive funding, built the proper production facilities
and new firms to emerge.
Offered monthly wages, on the other hand, appear to be constant over time with an in-
creasing trend towards the latest periods. This pattern is partly consistent with the lagged
response in the number of job openings. Overall, we observe an increase of about 2% in
wages shown in columns (3) and (4) of Table 3. However, the effect is not statistically signif-
icant. We believe that the event study is more informative as it shows the increasing trend
towards the end of the sample period. The relatively muted effects of MIC25 on wages at-
18
Table 3: Estimation Results of Labor Demands in the Pilot Cities
(1) (2) (3) (4)
Log(Openings) Log(Openings) Log(Wage) Log(Wage)
Post×Pilot Cities 0.244** 0.243*** 0.025 0.020
(0.122) (0.093) (0.026) (0.022)
Year FE Y Y
Industry-Year FE Y Y
Industry-City FE Y Y Y Y
Observations 20,160 20,160 20,160 20,160
Notes: The estimates are calculated based on the aggregated treatment effects version of figures 4. The controlcities are based on the propensity matching method. Each city-by-industry observation is weighted by thetotal number of postings in 2015.
tached to the online job postings could indicate two different patterns in the labor market of
the pilot cities. First, firms may not need to increase the wages if there are enough inflows of
job seekers from the other cities even though they need to hire more workers in response to
the policy change. Second, there could be composition changes in online job postings. The
increase in the number of postings in the early periods may be accounted for by increasing
labor demand for low-skilled jobs, which also results in static wages. Then, the increase in
labor demand more concentrates on high-skilled jobs, in turn generating the growing wage
effects in the later periods.
These results indicate that MIC25 generates regional inequality between pilot cities and
other cities. Inequality arises in two dimensions. First, the pilot cities are already cities
with good industrial and technological foundations. Thus, MIC25 further increases the in-
equality by creating these high-tech clusters. This is opposite to the place-based industrial
policies in the developed countries where the government selects disadvantaged areas to re-
duce spatial inequalities. Second, the migration of workers in China is restrained by Hukou
system. So, Chinese workers face much larger moving costs if they move out of their home
town to get better job opportunities. Given this restrictions, MIC25 generates inequality
between residents of the selected pilot cities versus other cities.
The main drawback of using job vacancy data is that one cannot see how many of the post-
ings turn into realized employment. To tackle this issue, we use formal employment data
from City Statistical Yearbooks in China. The yearbook for 2019-2020 is not yet available, so
the furthest we can go is 2018-2019. On the positive side, we have data available even before
2015 which is when our sample starts for the job postings data. Hence we use data going
19
Figure 5: The Impacts on Employment
(a) Employment (b) Monthly Wages
Notes: Figures present coefficients and 95% confidence intervals on the interaction be-tween the indicator of years and the indicator of the pilot cities. City and year fixedeffects are controlled and each city is weighted by population in 2015.
back until 2013. The data is yearly instead of quarterly. Since we have pilot cities being an-
nounced in 2016 and 2017, we decide to choose 2016 as the policy start year. The event study
for formal employment is shown in Figure 5. We observe a large and significant increase in
formal employment for pilot cities close to 20%. The lack of a pre-trend is assuring that what
we are capturing is the effect of MIC25. Similar to the job postings, we find an increase in
wages of about 3%. Overall, the formal employment results support the representativeness
of the job postings data. Having established robust effects on the pilot cities, we move on to
explore the impact of MIC25 in neighboring areas.
5.1.2 Spillover Effects
The main criticism of place-based policies is that they might just draw resources from neigh-
boring places, resulting in no overall effects in the end. Therefore, in the next exercise, we
compare job openings and wages between neighboring cities of the treated group and neigh-
boring cities of the control group. Visually, we are comparing the blue cities on the left of
Figure versus the blue cities on the right of Figure 3. The results are shown in Figure 6.
Neighboring cities of the treated group suffer a sharp decrease in job openings after the pi-
lot city announcement. The decrease is large and significant, reaching about 50% after two
quarters. After the first year, the decreasing effects start fading away and become close to
zero towards the end of our sample period. Similar effects can be observed on wages shown
20
Figure 6: The Impacts on Job Postings in the Neighboring Cities
(a) Number of Job Openings (b) Monthly Wages
Notes: Figures present coefficients and 95% confidence intervals on the interaction be-tween the indicator of relative quarterly periods from the policy announcement dateand the indicator of the neighborhood cities around the pilot cities. Industry-by-cityand industry-by-calendar quarter fixed effects are controlled and each city-by-industryobservation is weighted by the total number of postings in 2015.
in panel (b) of Figure 6. The neighboring cities of the treated group suffer a 5% decrease in
wages about a year from the announcement. The decrease in wages is persistent, though it
gets smaller towards the end of the sample period.
Therefore, MIC25 has negative spillover effects on labor demand at least in the short term.
Moreover, the spillover effect on wages remains negative throughout the sample period.
Overall, the results provide support that high-tech clusters generated by MIC25 are draining
resources from the neighboring cities. These results are particularly interesting in the context
of China. The Hukou system in China restrains the migration of workers who want to seek
better job opportunities in the other cities, while there are no particular restrictions on the
reallocation of firms.
We have shown that pilot cities experience large growth in job openings and an increasing
trend in wages and their neighboring cities suffer in the short term from the decrease in
job openings and wages. Which of the effects dominates in the short term? To provide an
answer, we compare the sample of pilot cities and their neighbors to the sample of control
cities and their neighbors. Visually, we are comparing the red plus blue cities on the left of
versus the red plus blue cities on the right of Figure 3. We call these two samples composite
cities. The aim of this exercise is to figure out if the positive effect on pilot cities outweighs
the negative effect on neighboring cities or vice versa. The results are shown in Figure 7. We
21
Figure 7: The Impacts on Job Postings in the Composite Cities
(a) Number of Job Openings (b) Monthly Wages
Notes: Figures present coefficients and 95% confidence intervals on the interaction be-tween the indicator of relative quarterly periods from the policy announcement date andthe indicator of the composite cities around the pilot cities. The composite cities of thepilot cities include both pilot cities and their neighboring cities and the composite citiesof control cities include both control cities and their neighboring cities. Industry-by-cityand industry-by-calendar quarter fixed effects are controlled and each city-by-industryobservation is weighted by the total number of postings in 2015.
find that the negative effects on neighboring cities dominate the positive effects of the pilot
cities. This is not surprising as there are 5 neighbor cities experiencing negative effects for
each pilot city experiencing large positive effects.
To get a sense of the overall effects after the policy, we run a difference in difference specifica-
tion. The results are shown at Table 4. Columns (1) and (3) of Table 4 compare neighbor cities
of pilot cities versus neighbor cities of control cities before and after the policy. Columns (2)
and (4) compare composite cities of pilot cities versus composite cities of control cities be-
fore and after MIC25. The coefficient in column (1) shows that neighboring cities suffer a
decrease of about 25% overall. Column (2) shows that the increase in pilot cities mitigates
slightly for the decrease. However, the overall effect is still negative at about 17% in the
composite cities. Similarly for wages. The decrease in wages for the neighboring cities dom-
inates the slight increase in pilot cities. The overall decrease in wage in composite cities is
about 2%. Thus, we conclude that in the short-term, the construction of high-tech clusters in
the pilot cities increases labor demand and wages in pilot cities, but the negative spillovers
in neighboring areas dominate the overall effects of MIC25.
22
Table 4: Estimation Results of Labor Demands around the Pilot Cities
(1) (2) (3) (4)
Log(Openings) Log(Openings) Log(Wage) Log(Wage)
Post×Pilot Cities -0.248*** -0.171** -0.032** -0.020**
(0.086) (0.085) (0.013) (0.010)
Unit Neighborhood Cities Composite Cities Neighborhood Cities Composite Cities
Observations 98,612 119,997 95,398 116,368
Notes: The estimates are calculated based on the aggregated treatment effects version of figures 6 and 7. Thecontrol cities are based on the propensity matching method. The treatment group in columns (2) and (4)include both pilot cities and their neighboring cities and the control cities include both control cities and theirneighboring cities. Industry-by-city and industry-by-calendar quarter fixed effects are controlled and eachcity-by-industry observation is weighted by the total number of postings in 2015.
5.1.3 Heterogeneity Analysis
We have shown the dynamics between pilot and neighboring cities in terms of job openings
and wages. What about the heterogeneity within the high-tech clusters? The next step is to
investigate the heterogeneity of effects between industries and types of occupations. Given
MIC25 is establishing high-tech clusters, one might expect the benefits to be concentrated
on targeted industries and related occupations. Therefore, we run the event studies across
industries and occupations.
We plot the event study coefficients for target and non-targeted industries in Figure B.1.
Targeted industries are defined as manufacturing, IT, science, and business. The number of
job openings does not vary. The large increase in job openings is evident for targeted and
non-targeted industries. Thus, MIC25 enlarges job opportunities not only in the targeted
high-tech industries, but also the other industries which could have complementary with
the targeted industries. The other reason why the targeted and non-targeted industries have
the similar increasing patterns in job openings would be that the targeted industries are more
capital-intensive. Though the government’s investment subsidies would concentrate in the
targeted industries, the effective labor demand increase in those industries could be smaller
than that in the non-targeted industries. So, the direct effects in the targeted industries could
be similar to the spillover effects in the non-targeted industries.
On the right panel of Figure B.1, wages in targeted and non-targeted industries appear to co-
move perfectly around the policy period. Interestingly, after one year of the implementation
of MIC25, we observe a diverging pattern on wages for targeted and non-targeted industries.
While not significant, the point estimates hint that wages in targeted industries are 2-3%
23
Figure 8: The Impacts on Job Postings in the Pilot Cities by Industry
(a) Number of Job Openings (b) Monthly Wages
Notes: Figures present coefficients and 95% confidence intervals on the interaction be-tween the indicator of relative quarterly periods from the policy start date and the indi-cator of the pilot cities. The targeted industries of MIC25 include manufacturing, IT, sci-ence, and business sectors. Industry-by-city and industry-by-calendar quarter fixed ef-fects are controlled and each city-by-industry observation is weighted by the total num-ber of postings in 2015..
higher and on an increasing trend. The point estimate for wages in targeted industries in
the end of our sample suggests an 8% increase. The divergence in wages arises at the same
time when the job postings start increasing. Given this result, the next natural step is to
investigate whether there is heterogeneity across occupation types.
If building high-tech clusters induces routine-biased technological change as reported in
the developed countries, then we would observe the disproportionate impacts of MIC25 on
the demand for non-routine positions. So, we classify the occupations based on the tasks
they perform following Acemoglu and Autor (2011): routine versus non-routine. On the
left panel of Figure 9, after about 6 quarters of the policy, we observe that job openings for
non-routine jobs appear to be 10-15% higher than routine jobs. Thus, the hiring patterns are
consistent with the hypothesis that MIC25 results in routine-biased technological change
and upskilling as the policy targets technological upgrading of high-tech industries. The co-
movement before the policy across occupations and initially after the policy is convincing
that MIC25 is behind the divergence. Similar to all previous results, the divergence arises
after 1 year the policy has been announced.
On the right panel of Figure 9, wages for routine and non-routine jobs are quite similar up
to 1 years after the policy. After 1 years, we notice the same diverging pattern as before
24
Figure 9: The Impacts on Job Postings in the Pilot Cities by Occupation
(a) Number of Job Openings (b) Monthly Wages
Notes: Figures present coefficients and 95% confidence intervals on the interaction be-tween the indicator of relative quarterly periods from the policy start date and the in-dicator of the pilot cities. The classification of occupations into non-routine and routinejobs follows Acemoglu and Autor (2011). Industry-by-city and industry-by-calendarquarter fixed effects are controlled and each city-by-industry observation is weighted bythe total number of postings in 2015. Each line is estimated by a separate regression foreach occupational group.
where wages for non-routine jobs increase more. While the differences are not significant,
the coefficient estimates provide evidence that MIC25 is opening more jobs for non-routine
occupations with higher wages after 1 year of the implementation date. This means that the
wage gap among occupations are disproportionately raised within the pilot cities compared
to the control cities after building high-tech clusters.
To summarize the before and after effects of MIC25 by occupation we run a difference in
difference specification by occupation group. The results are shown in table 5. Columns (1)
and (2) show the results of job openings between non-routine and routine occupations. The
coefficients are both positive and significant. The job openings for non-routine occupations
increase by about 27% while the job openings for routine occupations increase by about
19%. There is 8% more job openings created by MIC25 for non-routine occupations. Similar
results for wages are shown in columns (3) and (4). Wages for non-routine occupations
show an increase of about 3% and is statistically significant. The effects on wage for routine
occupations are close to zero.
We conclude that aside from regional inequality, MIC25 is causing inequality in terms of
labor demand across occupations as well as wages across occupations and industries. Non-
25
Table 5: Estimation Results of Labor Demands in the Pilot Cities by Industry and Occupation
Panel A: By Industry
(1) (2) (3) (4)
Log(Openings) Log(Openings) Log(Wage) Log(Wage)
Post×Pilot Cities 0.259** 0.235* 0.009 0.025
(0.128) (0.133) (0.027) (0.021)
Industry Targeted Non-targeted Targeted Non-targeted
Observations 5,025 15,135 5,025 15,135
Panel B: By Occupation
(1) (2) (3) (4)
Log(Openings) Log(Openings) Log(Wage) Log(Wage)
Post×Pilot Cities 0.266*** 0.192*** 0.027*** 0.013
(0.042) (0.056) (0.009) (0.008)
Job Type Non-routine Routine Non-routine Routine
Observations 145,323 97,609 139,358 93,340
Notes: The estimates are calculated based on the aggregated treatment effects version of figures B.1 and 9. Thecontrol cities are based on the propensity matching method. In Panel A, the targeted industries of MIC25 in-clude manufacturing, IT, science, and business sectors and industry-by-city and industry-by-calendar quarterfixed effects are controlled. In Panel B, the classification of occupations into non-routine and routine jobs fol-low Acemoglu and Autor (2011) and occupation-by-city and occupation-by-calendar quarter fixed effects arecontrolled. Each observation is weighted by the total number of postings in 2015.
routine occupations and targeted industries observe increased wages as expected given
the high-tech nature of the policy. The divergence in wages is increasing more and more
throughout the sample. While we investigate short-run effects, inequality could potentially
be higher in the long-run given the trends. Equipped with the wage results, we turn our
attention to the highest cost of living in China, the housing market.
5.2 Housing Market
The importance of looking at the housing market is twofold. First of all, rent and housing
are some of the largest costs of living for China. It can give us an idea of whether the policy
is making it more expensive to live in the pilot cities. Secondly, increasing house and rent
prices might indicate higher demand and migration. Given we cannot observe between city
migration or seasonal migration, the housing market serves as a proxy for it. The results
from the event studies are shown in Figure 10. Note that the specification used here differs
slightly from the main specification. Firstly, we only have data available for around 100
cities. We have rent price data for 24 pilot cities. Since we do not have all of the control cities,
26
Figure 10: The Impacts on Monthly Rents
(a) Pilot Cities (b) Neighboring Cities
Notes: Figures present coefficients and 95% confidence intervals on the interaction be-tween the indicator of relative quarterly periods from the policy announcement dateand the indicator of the pilot cities (or the indicator of the neighborhood cities in Figure(b)). City and calendar quarter fixed effects are controlled and each city is weighted bypopulation in 2015.
we decide to use all other cities we have data on as control cities except for neighboring
cities. Ideally, we would want to have data on all cities, but since most of the pilot cities are
available the exercise is still informative.
Figure 10 shows that pilot cities experience significant increases on rent prices relative to
other cities. The effects jump quickly to around 5% immediately after the policy. After 1
year the effects remain persistent at 10% until the end of the sample period. The lack of
pre-trends is assuring that the housing market is responding to the announcement of pilot
cities. To investigate the overall effects, we also run a difference-in-differences specification,
the results are shown in Table A.3. The average effect of the policy is a 7% increase in rent
prices in pilot cities. The average monthly rent is around 360$ for our sample.7 Thus, the
policy results in 25$ increase in monthly rent (302$ per year). MIC25 causes the residents in
pilot cities to pay higher rent and housing prices. In contrast, the neighboring cities do not
experience any increase or decrease in monthly rent prices as shown in panel (b).
Given wages between pilot and control cities did not change for the first two years after the
policy, people in the pilot cities were worse off due to the housing market price increase.
In Figure 9 we see that wages increase after 2 years. The increase in wages for non-routine
occupations is steeper and statistically significant compared to routine occupations. Since
7We use 1 USD = 7 CNY as the approximate conversion rate.
27
Table 6: Estimation Results of Monthly Rent
(1) (2) (3)
Log(Rent) Log(Rent) Log(Rent)
Post×Pilot Cities 0.071** -0.018 0.051*
(0.032) (0.059) (0.029)
Unit Pilot Cities Neighborhood Cities Composite Cities
Observations 1,010 951 1,247
Notes: The estimates are calculated based on the aggregated treatment effects version of figures 10. Column(1) includes 24 pilot cities and 56 controls cities, which are not the neighborhood cities. Column (2) includes26 neighborhood cities and 56 controls cities, which are not the pilot cities. Column (3) includes 50 pilot andneighborhood cities and 56 controls cities, which are not the pilot cities.
the rent and houses are now more expensive, workers in routine occupations are worse off
compared to non-routine occupations. Both occupation groups are worse off in the first two
years as the housing market reacts as soon as the policy is enacted. Wages are slower to react
and they pick up around 2 years after the policy which corresponds to pilot cities becoming
National Demonstration Zones and gaining even more support from the government. Since
the policy is still on going, the increasing trend in wages would have to persist in order to
overcome the increase in living expenses.
In addition to living expenses, the housing market speaks to migration patterns. We know
from economic models of place based policies that if workers are mobile and housing supply
is inelastic workers are going to move and rent and housing prices will increase (Kline and
Moretti, 2014b). The event studies in Figure 10 show that the housing market responds
even earlier than the labor demand. We interpret this as evidence that workers are reacting
quickly to the policy change and moving to the pilot cities as soon as they are announced.
Similarly, Alder, Shao, and Zilibotti (2016) find that Special Economic Zones in China attract
large populations. Hence, the limited housing supply and increased demand can explain
the increase in the price of the housing market. This scenario would make landowners in
the area better off (Kline and Moretti, 2014b), while having a negative effect on the average
worker whose rent or house price would increase. In summary, we observe increasing rent
prices and flat wages in the short term with an increasing trend towards the end of the
sample period. Our conclusion is that in the short term, the average worker is worse off
as the living expenses increase and the wage does not. This is more severe for workers in
non-targeted industries and workers in routine occupations. The increasing trend in wages
for non-routine and target industries may worsen inequality in the long-run.
28
5.3 Mechanisms
We have shown evidence of regional and wage inequality caused by the high-tech clusters
formed by MIC25. The aim of this section is shed light on the mechanisms behind the results.
Specifically, we focus on firm and worker location choices. Firstly, we want to investigate
whether the large labor demand increase is due to incumbent firms expanding of new firm
creation. Secondly, the housing cost increase suggests potential migration in order to fill the
labor demand increase.
Using job postings data we cannot distinguish whether a firm posting for the first time is
a new firm or it might be an old firm posting online for the first time. Therefore to answer
the question we leverage new firm registration data. Similarly, we run event study and a
difference in difference specification shown in Figure 11 and Table 7 respectively.
Panel (a) of Figure 11 compares firm entry in pilot versus control cities. The pre-trend is
flat and the firms react as soon as the pilot city is announced as expected. The increase is
significant and larger right after the announcement period. The increase in the number of
firms early on could explain why labor demand is slower to pick up as it might take some
time for firms to establish and receive funding on their projects before hiring. Panel (b)
shows the results for neighboring areas. Right after the policy, neighboring cities suffer large
and persistent firm entry decreases. The timing is reasonable. As soon as an entrepreneur
learns about the high-tech clusters, they would rather locate there compared to neighboring
ares. This impact of MIC25 could be troublesome for neighboring cities in the long run
as firms would rather locate in the pilot cities. In order to explain the increasing trend in
labor demand in the neighbor cities found in section 6.1, it must be that incumbent firms are
hiring more in neighboring cities in the later periods. The intensive margin appears to play
an important role in affecting labor demand for neighboring cities.
After understanding the timing of the reaction of the new firm to MIC25, we investigate
the overall effects for pilot, neighboring and composite cities as before. Columns (1)-(3) of
Table 7 presents the difference in difference results for firm registration. Overall, pilot cities
experience about 10% increase in new firm entry after MIC25 compared to control cities.
That corresponds to about 8,000 more new firms per year in pilot cities versus the control
cities. For comparison, (Lu, Wang, and Zhu, 2019) find Special Economic Zones (around
2004-2008) experience an increase of 29% in the number of firms.
29
Figure 11: The Impacts on Firm Entry
(a) Pilot Cities (b) Neighboring Cities
Notes: Figures present coefficients and 95% confidence intervals on the interaction be-tween the indicator of relative quarterly periods from the policy start date and theindicator of the pilot cities (or the indicator of the neighborhood cities in Figure (b)).Industry-by-city and industry-by-calendar quarter fixed effects are controlled and eachcity-by-industry observation is weighted by the number of registered firms in 2015.
Even more staggering, the estimates for neighboring cities how a 32% decrease in new firm
entry. When we compare composite cities as before, the negative effects suffered by neigh-
boring areas outweigh the positive effects in the high-tech clusters. There are simply more
neighboring cities undergoing lack of firm entry. The overall effects are large and negative.
One caveat here is that we cannot distinguish how much of the labor demand increase is
from incumbents and how much from new firms. However, the results on firm entry sug-
gests a significant labor demand impact from the creation or lack of new firms. (Lu, Wang,
and Zhu, 2019) find that almost 80% of the increase in employment is due to the creation
of new firms when studying Special Economic Zones in China. Similarly, (Criscuolo et al.,
2019) find that most of the positive effect on employment in place based policy in UK comes
from small or new established firms. While we cannot conclude the degree of extensive ver-
sus the intensive margin, we believe that the establishment of new firms plays a key role in
increasing labor demand for pilot cities and decreasing in neighboring areas.
After establishing that MIC25 caused firm reallocation, we shift our attention to worker lo-
cation decisions. In order to do so, we run the main specification event study and difference
in difference using City Statistical Yearbook population variable. A caveat is that in the City
Yearbook the population measure corresponds to the end-of-year number of persons whose
Hukou (house-hold registration) are registered in the city. As a result, this measure poten-
30
Figure 12: The Impacts on Population
(a) Pilot Cities (b) Neighboring Cities
Notes: Figures present coefficients and 95% confidence intervals on the interaction be-tween the years and the indicator of the pilot cities (or the indicator of the neighborhoodcities in Figure (b)). The policy is implemented in 2016 at all the pilot cities. City andyear fixed effects are controlled and each city is weighted by population in 2015.
tially excludes migrant workers who work in the city but do not have their Hukou registered
in the same city. Moreover, the City Yearbook is not available for 2019-2020. On the other
hand, we do more data to observe pre-trends. The results are shown in Figure 12. After
MIC25 we observe an increase in population. The trend is also increasing, which would
lead to larger population increase in the future for pilot cities. The effect is not significant
and close to zero for neighboring ares.
Similar to other results, we show the difference in difference specification on columns (4)-(6)
of Table 7. The overall increase in population is significant at 2%. Given the measure for
population excludes a portion of migrants, we consider the result to be a lower bound. In
contrast, neighboring ares have close to zero effects. In column (5) we see that the composite
cities experience population growth driven by pilot cities. We conclude that workers are
moving into pilot cities along with new firms. This result is consistent with the higher la-
bor demand and wages in the pilot cities. The increase in population would also result in
higher rent prices due to increased demand. While our measure is not perfect, it shows evi-
dence that one of the mechanisms of MIC25 is worker reallocation even though the Hukou
restriction. This channel would potentially be even more important for countries with no
movement restrictions.
31
Table 7: Estimation Results of Firm and Worker Sorting
(1) (2) (3) (4) (5) (6)
Log(FirmEntry)
Log(FirmEntry)
Log(FirmEntry)
Log(Pop) Log(Pop) Log(Pop)
Post×Pilot Cities 0.100* -0.317*** -0.298*** 0.021*** 0.006 0.009***
(0.059) (0.084) (0.080) (0.007) (0.004) (0.003)
Unit Pilot Cities NeighborhoodCities
CompositeCities
Pilot Cities NeighborhoodCities
CompositeCities
Observations 22,858 110,594 133,452 345 1,642 1,987
Notes: The estimates are calculated based on the aggregated treatment effects version of figures 11 and 5. Ob-servations in columns (1) through (3) are city-industry pairs and each city-by-industry observation is weightedby the number of registered firms in 2015. Industry-by-city and industry-by-calendar quarter fixed effects arecontrolled in columns (1) through (3). Observations in columns (4) through (6) are cities and each city isweighted by population in 2015. City and year fixed effects are controlled in columns (4) through (6). Thetreatment group in columns (3) and (6) include both pilot cities and their neighboring cities and the controlcities include both control cities and their neighboring cities.
6 Welfare Analysis
The empirical results indicate a variety of impacts of MIC25 on different economic variables
within and around the pilot cities where the high-tech clusters were built: (1) Labor de-
mand and offered wages increase in the pilot cities especially for non-routine jobs; (2) Labor
demand and offered wages decrease in the neighboring cities of the pilot cities; (3) Hous-
ing cost rises in the pilot cities; (4) Firms and workers are more likely to move to the pilot
cities.
6.1 Conceptual Framework
To combine all those estimates in Section 5 and learn the welfare implication of all those ef-
fects, we present a conceptual framework consistent with our reduced-form estimates.
Following the literature, we rely on three main channels driven by workers’ and firms’ be-
haviors. First, since the policy provides extra investment incentives to firms in pilot cities,
firms’ hiring decision (extensive margin) changes in a heterogeneous way among the pilot
and neighboring cities. Second, firms could change their labor demand across occupations
(i.e., intensive margin) within the pilot cities because the adoption of technologies induced
by high-tech investment has heterogeneous effects on the productivity of different occupa-
tions. Third, workers’ migration and sorting for jobs to pilot cities raise demand in the local
housing market, increasing the rent therein.
32
Following Kline and Moretti (2014b), we construct a parsimonious spatial equilibrium model
to highlight all the aforementioned channels by extending conventional spatial equilibrium
models of Rosen (1979) and Roback (1982). In the model, we allow for two types of la-
bor inputs and heterogeneity in capital-labor complementarity. We assume a continuum of
workers of measure one and each of them resides and works in a city j ∈ J = {1, 2, · · · , J},
which is either a pilot city (a) or a neighboring city (b). A typical worker demands a unit of
housing which is rent at local market rates. Each worker elastically supplies a unit of labor
of one type, routine (R) or non-routine (NR).8 To account for the migration constraint in
China (i.e., hukou or household registration system), we assume a moving cost if the worker
moves out of her hukou city. The indirect utility of worker i, from city j0 ∈ J (hukou city),
choosing to live in city j ∈ J and work in occupation s ∈ {R, NR}9 is:
Vsi,j = ws
j − rj − bs1(j0 6= j)− Asj + σsεi,j ≡ vs,j0
i,j + σsεi,j, (3)
where wsj is the nominal wage in city j and occupation s, rj is the rent of housing, As
j is a
measure of local amenities. bs is the moving cost, which depends on workers’ skills. The
heterogeneity in individual preference for a city and an occupation is important to generate
heterogeneous welfare effects of the policy change, as some workers are infra-marginal and
can take economic rents of policy changes. We assume that σsεi,j is independently and iden-
tically distributed across individuals and follows a type-I extreme value (T1EV) distribution
with mean zero and scale parameter σs (occupation specific). The scale parameter governs
the strength of idiosyncratic preferences for cities and occupations and in turn affects the
sorting of workers. The larger σs is, the larger shocks to wages and rents are required to
induce workers to switch.
Firms in each city j produce a single good Y using capital (K), routine labor (LR), and non-
routine labor (LNR) following a constant return to scale production technology that incorpo-
rates capital-skill complementarity
Yj = L1−βNR,j
(αRLµ
R,j + αkKµj
) βµ . (4)
8Note that here we assume workers are of two types: high skilled and low skilled. High-skilled workerschoose to work in non-routine occupations while low-skilled workers work in routine occupations.
9To characterize the welfare impact of MIC25, we assume that workers cannot switch their occupationacross cities. This assumption is consistent with our focus on short-term effects.
33
In this production function, capital is a complement to non-routine labor and a substitute
for routine labor.10 This form has been widely used in the literature to investigate the ef-
fect of the decrease in capital cost on the composition of labor force (Autor and Dorn, 2013;
Karabarbounis and Neiman, 2014). In our setting, the implementation of MIC25 dispropor-
tionately lowers capital cost for the pilot cities. The labor market is perfectly competitive
and firms set wages equal to the marginal product of labor. A friction-less capital market
supplies capital perfectly elastically at local-level price ρj.11
We can derive routine-non-routine wage gap in city j:
log
(wNR
j
wRj
)= log
(1− β
β
)+ log
(LR,j
LNR,j
)− log
(1−
αkKµj
αRLµR,j + αkKµ
j
)
' log(
1− β
β
)+ log
(LR,j
LNR,j
)+
αkKµj
αRLµR,j + αkKµ
j.
(6)
The second row of Equation (6) gives a linear decomposition of the sources of location-
specific policy changes in wage inequality incorporating two particular channels. First, in-
equality can increase as a result of an increase in the proportion of routine labor compared
to non-routine labor (i.e., LR,j/LNR,j). Second, the share of capital in the factor of produc-
tion that combines capital and routine labor raises the wage gap as shown in the third term,
which reflects routine-biased technological change induced by high-tech clusters. So, the ef-
fect of MIC25, a decline of capital cost in pilot cities, on the wage gap in pilot cities depends
on the composition change of inputs and worker’s distribution over the preference for a city
and an occupation.
For simplicity, we assume housing supply is upward sloping in each city to capture the land
10The elasticity of substitution between capital or routine labor and non-routine labor is β1−β and that be-
tween capital and routine labor is 11−µ with β < 1 and µ < 1.
11Firms’ inverse demands for capital and labor at city j are given by:
ρj = βL1−βNR,jQ
βµ−1j
(αkKµ−1
j
),
wNRj = (1− β)L−β
NR,jQβµ
j ,
wRj = βL1−β
NR,jQβµ−1j
(αRLµ−1
R,j
),
(5)
where Qj = αRLµR,j + αkKµ
j . Note that the capital cost is set at city level to account for the fact that the taxbenefits and subsidies of MIC25 lowers the capital cost for the firms in the pilot cities.
34
supply restrictions in China. The inverse supply function is given by equating local rents
as in Busso, Gregory, and Kline (2013), Kline and Moretti (2014b), and Liang, Song, and
Timmins (2020):
ln(rj) = αj + κj ln(Nj), (7)
where the aggregate housing demand is determined by the number of workers in the city
regardless of their occupation. We assume that the elasticity of housing supply (κj) is exoge-
nously determined by geography and local land use regulations.
We can write the expected utility of workers from city j0 and working in occupation s
as:
Vs,j0 = Eε
[max
j{vs,j0
i,j + σsεi,j}]= σsNs,j0 ln
(1 + ∑
j∈Jexp
(vs,j0
i,j /σs))
, (8)
where Ns,j0 is the population of workers working in occupation s and with hukou city j0.
From the T1EV assumption on the idiosyncratic term σsεi,j, we can derive the number of
workers of types (s, j0) who choose to work and live in city j as
Ns,j0j = Pr
(Vs,j0
i,j = maxj′∈J
Vs,j0i,j′
)=
exp(vs,j0i,j /σs)
1 + ∑j′∈J exp(vs,j0i,j′ /σs)
, (9)
where the first equality is from our assumption on a continuum of workers.
Note that MIC25 lowers the capital cost in pilot cities (ρa) by granting subsidies and tax
deductions to the firms in the city.12 So, we model the implementation of MIC25 as a de-
cline in ρa and the welfare incidence of MIC25 on the workers from city j0 and working in
occupation s can be represented by:
dVs,j0
dX=
dVs,j0
dvs,j0i,j
dvs,j0i,j
dX= Ns,j0
[∑j∈J
Ns,j0j
(dws
j
dX−
drj
dX− dbs1(j0 6= j)
dX−
dAsj
dX
)]
' Ns,j0
[∑j∈J
Ns,j0j
(dws
j
dX−
drj
dX
)],
(10)
where Ns,j0j represents the population of workers of type (s, j0) who work in city j prior
to the implementation of the policy. The second row of Equation (10) holds because we
12In estimation, we cannot directly observe the change in capital cost. So, the policy shock (dX) can beviewed as the one reducing the capital cost in the pilot cities (dρa/dX < 0)).
35
focus on short-run effect of the policy and migration cost and local amenities are not likely
to change dramatically in the short run. Thus, the welfare incidence of MIC25 on workers
in each occupation group boils down to the change in wages and rents weighted by their
population shares across hometown j0 and their choice probabilities across the cities before
the policy shock.13
As shown in Equation (6), the wage gap between routine and non-routine jobs is widened
as a result of decreasing capital cost in the pilot cities: d(wNRa /wR
a )dX = d(wNR
a /wRa )
dKadKadρa
dρadX > 0.
Because of in-migration of workers from other cities, the rents in the pilot cities increases:dradX > 0. These model-based predictions are well aligned with our estimation results in
Section 5.
Consequently, the welfare gap between routine and non-routine in the pilot cities is also
widened because the incidence of MIC25 on the rents is equally shared by both types of
workers in the pilot cities. However, the welfare of routine-job workers in the pilot cities
gets worse if the in-migration of workers from the neighbor cities is large enough to offset
the wage increase for them: d(wNRa −ra)dX > 0 > d(wR
a −ra)dX . It depends on how many workers are
induced to move to the pilot cities after MIC25, which depends on worker’s distribution of
preference across cities and the moving cost induced by hukou constraint.
We do not explicitly model firm’s resource allocation across cities, but we can investigate
the impact of MIC25 on the neighboring cities by assuming the substitutability between
the production facilities in the two places. This assumption is not implausible because the
benefits of MIC25 are conditional on investment in the pilot cities and are not shared within
the firms’ internal networks across regions. So, investment subsidies in the pilot cities reduce
investment in the neighboring cities: dKbdX < 1. From Equation (5), we can observe that
wages for both occupational groups can be lowered if the decline of labor force does not
fully absorb the capital decrease: dwsb
dX < 0 ∀s.
However, housing cost in the neighboring cities can be reduced in response to building
high-tech clusters in the pilot cities if the outflow of workers from the neighboring cities to
the pilot cities is large enough. Our empirical results confirm that the decreases in capital
13Of course, after the shock, some workers who were initially “marginal” workers will re-optimize andmove. However, the Envelope Theorem suggests agents will only have small gains in utility by switchingtheir choices in response to marginal changes in prices. Hence, these behavioral effects due to price changescould only have secondary effects on worker welfare (Qian and Tan, 2021).
36
Table 8: Migrants in Pilot Cities and Neighboring Cities
Occ. j0 j Ns,j0 (%) Ns,j0j (%) ws
j ($)dws
jdX (%) rj ($)
drjdX (%) Ns,j0
jdv
s,j0i,j
dX ($) dVs,j0dX ($)
1 NR a a 14.98 73.90 10241.64 2.7 4853.4 7 -46.71 -7.00
NR a b 26.10 9450.25 -3.2 4572.48 0 -78.93 -11.82
2 R a a 9.54 27.46 9069.46 0 4853.4 7 -93.30 -8.90
R a b 72.54 8359.92 -3.2 4572.48 0 -194.04 -18.51
3 NR b a 13.75 33.38 10241.64 2.7 4853.4 7 -21.10 -2.90
NR b b 66.62 9450.25 -3.2 4572.48 0 -201.46 -27.70
4 R b a 61.72 42.98 9069.46 0 4853.4 7 -146.03 -90.13
R b b 57.02 8359.92 -3.2 4572.48 0 -152.53 -94.14
Total 100 -261.11
Notes: The annual total welfare change in the table is calculated for one representative worker. All the sharesof workers are calculated from the 2017 wave of China’s migrants dynamic survey. People with college degreeor above are assumed to work in NR occupations. Only people in working age (16-65) and with hukou cityin either a pilot city (a) or a neighboring city (b) are included. The total number of migrants is 51,021, whichaccounts for 30% people in the survey (169,989 people are surveyed in the 2017 wave). The annual wages ws
jare calculated from averaged monthly wages in 2016, the annual housing rents rj are calculated from averagedmonthly rents for a 100 m2 house in 2020.
(Figure 11) is large enough to lower wages in the neighboring cities. On the contrary, the net
outflow of workers cannot be observed in Figure 12 and housing cost also does not respond
to MIC25 much as shown in Figure 10.
6.2 Welfare
From the model, we can predict the effects of MIC25 on wages and rents change for those
four subgroups (s, j0) of population: {(NR, a), (R, a), (NR, b), (R, b)}.14 As has been argued
in the previous subsection, the change in utilities of people switching their location choices
has only secondary effects on worker welfare, so we don’t need to specify people’s locations
after the policy. Then we can calculate the welfare incidence on those four subgroups of pop-
ulation by combining the shares of migrants and natives across the pilot and neighboring
cities.
We utilize the 2017 wave of China’s migrants dynamic survey to calculate the share of mi-
grants in each city, then we aggregate them to the four subgroups and calculate the welfare
change by combining Equation (10) and estimates from Section 5.
Table 8 shows the welfare change for workers in each subgroup and aggregated welfare
14Note that a represents pilot cities and b refers to neighboring cities.
37
change. Only people of high skills (i.e., working in NR occupations) and choosing to work
in pilot cities could gain from the policy. People of low skills (i.e., working in R occupa-
tions) and choosing to work in neighboring cities suffer the most from the policy due to the
significant wage drop and their large share of among all migrants.
The back-of-the-envelope calculation in Table 8 suggests that on average a migrant worker
from either a pilot city or a neighboring city would lose around $260 due to the policy,
which accounts for around 3% loss in annual wages. In 2017, there are 244 million migrants
in China, 30% of them are in pilot cities or neighboring cities, so the welfare loss for all
migrants accounts for around 0.15% of the annual GDP ($ 19 billion/$12.31 trillion).
7 Conclusion
We evaluate the impact of high-tech clusters on labor demand and inequality between re-
gions and occupations. The place-based feature and staggered implementation of the policy
in China provide a suitable setting to study the causal effects. We see a significant increase
in labor demand in the pilot cities using the detailed job posting data. Offered wages are
increasing for non-routine occupations, while there is no significant change in wage for rou-
tine occupations. These results suggest that high-tech clusters are causing inequality across
different occupations. On the other hand, labor demand and wage decrease significantly for
neighboring areas, leading to inequality across regions.
Living costs sharply increase in the pilot cities right after the implementation of MIC25, in-
dicating that the policy results in the inflow of workers. In the neighboring cities, we could
not observe any significant changes in rent. So, building a high-tech cluster has dispropor-
tionate effects in the housing markets across regions. To calculate the overall welfare effects
of MIC25 across subgroups of population, we construct a simple conceptual framework.
The overall welfare effects depend on the population shares across the pilot and neighbor-
ing cities and across non-routine and routine positions along with the effects of the policy on
wages and rents. The back-of-the-envelope from our conceptual framework calculation sug-
gests that on average a migrant worker from either a pilot city or a neighboring city would
lose around $260 per year due to the policy, which accounts for around 3% loss in yearly
wages, and the total welfare loss for migrants accounts for around 0.15% ($ 19 billion) of
China’s annual GDP in 2017. More importantly, the welfare loss concentrates in routine jobs
38
and neighboring cities.
As far as we know, this study is the first to evaluate the consequence of building national-
level high-tech clusters. Given several developed and developing countries are implement-
ing similar policies, we believe our findings would provide helpful policy insight. This is an
essential first step in understanding this new wave of industrial policies. Given that China
has restrictions on migration due to the Hukou system, the potential impact of labor real-
location and wage inequality might be even more pronounced in other countries. Since the
policy is still ongoing, we focus more on the short-term effects in this paper. Although it
might be hard to evaluate long-run effects due to the disruption caused by Covid-19 right
after our sample period, including post-pandemic periods could be a sensible next step and
constructive for assessing the long-term impact.
Building national-level high-tech clusters provide an exceptional tool for policymakers to
adopt modern technology and to upgrade production capability of a nation. Our findings,
however, imply that policymakers may need to be cautious in at least two dimensions. Cre-
ating high-tech clusters appears to have negative short term effects in neighboring areas as
well as longer lasting firm entry decrease. Therefore, undesired regional inequalities may
arise. Finally, high-tech clusters create wage inequality between occupations while increas-
ing living costs. We believe further research is required to see whether the long term pro-
ductivity effects of the high-tech clusters might outweigh the negative short term effects in
inequality.
39
References
Acemoglu, Daron and David Autor. 2011. “Skills, Tasks and Technologies: Implications for
Employment and Earnings.” Handbook of Labor Economics 4:1043–1171.
Acemoglu, Daron, David Autor, Joe Hazell, and Pascual Restrepo. 2020. “AI and Jobs: Evi-
dence from Online Vacancies.” Tech. rep., Mimeo. Massachusetts Institute of Technology.
Acemoglu, Daron and Pascual Restrepo. 2020. “Robots and Jobs: Evidence from US Labor
Markets.” Journal of Political Economy 128 (6):2188–2244.
Alder, Simon, Lin Shao, and Fabrizio Zilibotti. 2016. “Economic Reforms and Industrial
Policy in a Panel of Chinese Cities.” Journal of Economic Growth 21 (4):305–349.
Autor, David H and David Dorn. 2013. “The Growth of Low-Skill Service Jobs and the
Polarization of the US Labor Market.” American Economic Review 103 (5):1553–1597.
Bloom, Nicholas, Mark Schankerman, and John Van Reenen. 2013. “Identifying Technology
Spillovers and Product Market Rivalry.” Econometrica 81 (4):1347–1393.
Borusyak, Kirill and Xavier Jaravel. 2017. “Revisiting event study designs.” Available at
SSRN 2826228 .
Busso, Matias, Jesse Gregory, and Patrick Kline. 2013. “Assessing the Incidence and Effi-
ciency of a Prominent Place Based Policy.” American Economic Review 103 (2):897–947.
Callaway, Brantly and Pedro HC Sant’Anna. 2020. “Difference-in-differences with multiple
time periods.” Journal of Econometrics .
Carlino, Gerald and William R. Kerr. 2015. “Agglomeration and Innovation.” In Handbook
of Regional and Urban Economics, Handbook of Regional and Urban Economics, vol. 5. 349–404.
Criscuolo, Chiara, Ralf Martin, Henry G Overman, and John Van Reenen. 2019. “Some
Causal Effects of an Industrial Policy.” American Economic Review 109 (1):48–85.
Dehejia, Rajeev H. and Sadek Wahba. 1999. “Causal Effects in Nonexperimental Studies:
Reevaluating the Evaluation of Training Programs.” Journal of the American Statistical As-
sociation 94 (448):1053–1062.
Ehrlich, Maximilian and Tobias Seidel. 2018. “The Persistent Effects of Place-Based Policy:
40
Evidence from the West-German Zonenrandgebiet.” American Economic Journal: Economic
Policy 10 (4):344–374.
Fang, Hanming, Chunmian Ge, Hanwei Huang, and Hongbin Li. 2020. “Pandemics, Global
Supply Chains, and Local Labor Demand: Evidence from 100 Million Posted Jobs in
China.” Working Paper 28072, National Bureau of Economic Research.
Forsythe, Eliza, Lisa B. Kahn, Fabian Lange, and David Wiczer. 2020. “Labor Demand in
the Time of COVID-19: Evidence from Vacancy Postings and UI Claims.” Journal of Public
Economics 189.
Goodman-Bacon, Andrew. 2021. “Difference-in-differences with variation in treatment tim-
ing.” Journal of Econometrics .
Graetz, Georg and Guy Michaels. 2018. “Robots at Work.” Review of Economics and Statistics
100 (5):753–768.
Gruber, Jonathan and Botond Koszegi. 2001. “Is Addiction “Rational”? Theory and Evi-
dence.” The Quarterly Journal of Economics 116 (4):1261–1303.
Hanson, Andrew and Shawn Rohlin. 2013. “Do Spatially Targeted Redevelopment Pro-
grams Spillover?” Regional Science and Urban Economics 43 (1):86–100.
Hanson, Gordon H. 2020. “Who Will Fill China’s Shoes? The Global Evolution of Labor-
Intensive Manufacturing.” Working Paper 28313, National Bureau of Economic Research.
He, Chuan, Karsten Mau, and Mingzhi Xu. 2021. “Trade Shocks and Firms Hiring Decisions:
Evidence from Vacancy Postings of Chinese Firms in the Trade War.” Labour Economics 71.
Helleseter, Miguel Delgado, Peter Kuhn, and Kailing Shen. 2020. “The Age Twist in Em-
ployers’ Gender Requests: Evidence from Four Job Boards.” Journal of Human Resources
55 (2):428–469.
Hershbein, Brad and Lisa B Kahn. 2018. “Do Recessions Accelerate Routine-Biased
Technological Change? Evidence from Vacancy Postings.” American Economic Review
108 (7):1737–72.
Kantor, Shawn and Alexander Whalley. 2014. “Knowledge Spillovers from Research Uni-
versities: Evidence from Endowment Value Shocks.” The Review of Economics and Statistics
96 (1):171–188.
41
Karabarbounis, L. and B. Neiman. 2014. “The Global Decline of the Labor Share.” The
Quarterly Journal of Economics 129 (1):61–103.
Kline, Patrick and Enrico Moretti. 2014a. “Local Economic Development, Agglomeration
Economies, and the Big Push: 100 Years of Evidence from the Tennessee Valley Authority.”
The Quarterly Journal of Economics 129 (1):275–331.
———. 2014b. “People, Places, and Public Policy: Some Simple Welfare Economics of Local
Economic Development Programs.” Annual Review of Economics 6 (1):629–662.
Kuhn, Peter and Kailing Shen. 2013. “Gender Discrimination in Job Ads: Evidence from
China*.” Quarterly Journal of Economics 128 (1):287–336.
Lee, Brian K, Justin Lessler, and Elizabeth A Stuart. 2010. “Improving propensity score
weighting using machine learning.” Statistics in Medicine 29 (3):337–346.
Li, Ling. 2018. “China’s Manufacturing Locus in 2025: With a Comparison of “Made-in-
China 2025” and “Industry 4.0”.” Technological Forecasting and Social Change 135:66–74.
Liang, Wenquan, Ran Song, and Christopher Timmins. 2020. “Frictional Sorting.” Tech. rep.,
National Bureau of Economic Research.
Lu, Yi, Jin Wang, and Lianming Zhu. 2019. “Place-based policies, creation, and agglomer-
ation economies: Evidence from China’s economic zone program.” American Economic
Journal: Economic Policy 11 (3):325–60.
Malkin, Anton. 2018. “Made in China 2025 as a Challenge in Global Trade Governance:
Analysis and Recommendations.” .
Moretti, Enrico. 2021. “The Effect of High-Tech Clusters on the Productivity of Top Inven-
tors.” American Economic Review 111 (10):3328–75.
Murphy, Kevin M., Andrei Shleifer, and Robert W. Vishny. 1989. “Industrialization and the
Big Push.” Journal of Political Economy 97 (5):1003–1026.
Qian, Franklin and Rose Tan. 2021. “The Effects of High-skilled Firm Entry on Incumbent
Residents.” .
Roback, Jennifer. 1982. “Wages, rents, and the quality of life.” Journal of political Economy
90 (6):1257–1278.
42
Rosen, Sherwin. 1979. “Wage-based indexes of urban quality of life.” Current issues in urban
economics :74–104.
Rosenstein-Rodan, P. N. 1943. “Problems of Industrialisation of Eastern and South-Eastern
Europe.” The Economic Journal 53:202–211.
Setoguchi, Soko, Sebastian Schneeweiss, M Alan Brookhart, Robert J Glynn, and E Francis
Cook. 2008. “Evaluating uses of data mining techniques in propensity score estimation: a
simulation study.” Pharmacoepidemiology and Drug Safety 17 (6):546–555.
Sun, Liyang and Sarah Abraham. 2020. “Estimating dynamic treatment effects in event
studies with heterogeneous treatment effects.” Journal of Econometrics .
Zenglein, Max J and Anna Holzmann. 2019. “Evolving Made in China 2025.” Mercator
Institute for China Studies 12.
Zhao, Peng, Xiaogang Su, Tingting Ge, and Juanjuan Fan. 2016. “Propensity score and prox-
imity matching using random forest.” Contemporary Clinical Trials 47:85–92.
43
Tabl
eA
.1:B
alan
ceC
heck
:Nei
ghbo
rsof
Pilo
tCit
ies
vsN
eigh
bors
ofC
on-
trol
Cit
ies
Var
iabl
esN
eigh
bors
ofPi
lotC
itie
sN
eigh
bors
ofC
ontr
olC
itie
sD
iffer
ence
P-V
alue
s
Num
ber
ofjo
bpo
stin
g(1
0,00
0)2.
844.
67-1
.83
0.48
62N
umbe
rof
job
open
ing
(10,
000)
7.85
11.9
6-4
.11
0.53
85W
age
(mon
thly
,yua
n)45
22.5
945
59.0
4-3
6.45
0.70
46Po
pula
tion
(10,
000)
497.
4841
5.44
82.0
40.
0969
Empl
oym
ent(
10,0
00)
63.7
266
.15
-2.4
30.
8784
GD
P(b
illio
nyu
an)
238.
5325
0.44
-11.
910.
8072
Land
area
(km
$2$)
1510
3.89
1117
2.16
3931
.74
0.02
93N
umbe
rof
indu
stri
alfir
ms
1160
.22
1519
.24
-359
.03
0.08
49Ta
xpr
ofit(
billi
onyu
an)
26.9
325
.47
1.46
0.84
82G
DP
grow
thra
te(\
%)
7.62
7.57
0.05
0.89
94Em
ploy
men
tsha
re(s
econ
dary
sect
or,\
%)
45.8
450
.67
-4.8
30.
0179
Empl
oym
ents
hare
(thi
rdse
ctor
,\%
)52
.72
48.2
64.
460.
0237
GD
Psh
are
(sec
onda
ryse
ctor
,\%
)47
.90
48.5
0-0
.59
0.63
57G
DP
shar
e(t
hird
sect
or,\
%)
40.7
839
.88
0.90
0.48
22Em
ploy
men
tsha
re(m
anuf
actu
ring
,\%
)23
.61
28.9
2-5
.31
0.00
43Em
ploy
men
tsha
re(I
T,\%
)1.
361.
210.
150.
2880
Empl
oym
ents
hare
(bus
ines
s,\%
)1.
691.
640.
060.
7901
Empl
oym
ents
hare
(sci
ence
,\%
)1.
651.
350.
290.
0614
Not
es:T
his
tabl
epr
esen
tsth
eav
erag
esac
ross
the
neig
hbor
sof
pilo
tcit
ies
vers
usth
ene
ighb
ors
ofco
ntro
lcit
ies
in20
15be
fore
MIC
25ha
dbe
enim
plem
ente
d.C
olum
n4
show
sth
edi
ffer
ence
betw
een
the
aver
ages
,and
colu
mn
5sh
ows
the
p-va
lues
fora
t-te
stde
sign
edto
test
sw
heth
erth
eav
erag
esof
the
two
grou
psar
est
atis
tica
llydi
ffer
ent
from
one
anot
her.
Not
eth
atne
ighb
orci
ties
are
chos
enpu
rely
onge
ogra
phic
alch
arac
teri
stic
san
dva
riab
les
show
she
rear
eno
tta
rget
edby
usto
besi
mila
rbe
twee
nth
egr
oups
.
45
Table A.2: Average Treatment Effects with the Random Forest Matching Algorithm
Panel A: Pilot Cities
(1) (2) (3) (4)
Log(Openings) Log(Wage) Log(Firm Entry) Log(Pop)
RF100 0.249*** 0.020 0.110* 0.019***
(0.089) (0.026) (0.063) (0.007)
RF200 0.250*** 0.018 0.111* 0.016**
(0.086) (0.026) (0.063) (0.007)
RF500 0.250*** 0.018 0.114* 0.016**
(0.086) (0.028) (0.062) (0.007)
RF1000 0.248*** 0.018 0.120** 0.017**
(0.084) (0.026) (0.061) (0.007)
RF5000 0.247*** 0.018 0.117* 0.018***
(0.083) (0.026) (0.061) (0.007)
Panel B: Neighbor Cities
(1) (2) (3) (4)
Log(Openings) Log(Wage) Log(Firm Entry) Log(Pop)
RF100 -0.224** -0.034** -0.359*** 0.000
(0.113) (0.014) (0.082) (0.003)
RF200 -0.177 -0.026* -0.342*** 0.000
(0.118) (0.014) (0.086) (0.003)
RF500 -0.172 -0.026* -0.349*** 0.000
(0.121) (0.014) (0.082) (0.003)
RF1000 -0.174 -0.026* -0.343*** 0.000
(0.123) (0.013) (0.083) (0.003)
RF5000 -0.225* -0.033** -0.348*** 0.000
(0.116) (0.013) (0.082) (0.003)
Panel C: Composite Cities
(1) (2) (3) (4)
Log(Openings) Log(Wage) Log(Firm Entry) Log(Pop)
RF100 -0.143 -0.019* -0.291*** 0.004
(0.114) (0.011) (0.077) (0.003)
RF200 -0.127 -0.019* -0.276*** 0.004
(0.111) (0.011) (0.080) (0.003)
RF500 -0.117 -0.019* -0.281*** 0.003
(0.111) (0.010) (0.076) (0.003)
RF1000 -0.119 -0.019* -0.276*** 0.004
(0.112) (0.010) (0.077) (0.003)
RF5000 -0.148 -0.020* -0.282*** 0.004
(0.113) (0.010) (0.077) (0.003)
Notes: The aggregated treatment effects are calculated following Callaway and Sant’Anna (2020). The obser-vations, controls, and weights are corresponding to the parallel parts of the main analysis. To make sure ourresults do not depend on the parameters of the random forest model, we use a different number of trees ineach row of the table. i.e. RF100 means that a random forest with 100 trees is used.
46
Table A.3: Average Treatment Effects for Neighboring Cities by Distance
(1) (2) (3)
Distance Log(Openings) Log(Wage) Log(Firm Entry)
100km -0.321** -0.055*** -0.288***
(0.140) (0.011) (0.107)
200km -0.268* -0.017** -0.168***
(0.144) (0.009) (0.053)
300km -0.247** -0.011* -0.056
(0.102) (0.006) (0.040)
400km -0.187** -0.010* -0.027
(0.090) (0.006) (0.026)
500km -0.149* -0.007 -0.012
(0.079) (0.005) (0.020)
Notes: The aggregated treatment effects are calculated following Callaway and Sant’Anna (2020). The observa-tions, controls, and weights are corresponding to the parallel parts of the main analysis. Distances are betweencity centers and a non-pilot city within each distance of a pilot or a control city is defined as a neighboring city.
47
Figure B.1: The Impacts on Job Postings in the Pilot Cities by TargetedIndustry
(a) Job Openings in IT (b) Monthly Wages in IT
(c) Job Openings in Science (d) Monthly Wages in Science
(e) Job Openings in Manufacturing (f) Monthly Wages in Manufacturing
(g) Job Openings in Business (h) Monthly Wages in Business
Notes: Figures present coefficients and 95% confidence intervals on the interaction be-tween the indicator of relative quarterly periods from the policy start date and the indi-cator of the pilot cities. City and calendar quarter fixed effects are controlled and eachobservation is weighted by the total number of postings in 2015.
49
Figure B.2: Leave One Pilot City Out: The Impacts on Job Postings in thePilot Cities
(a) Job Openings
(b) Monthly Wages
Notes: Figures present the impact of MIC25 on job postings in pilot cities with each timeleaving out one pilot city, indexed from 1 to 30. The solid horizontal line represents theimpact without leaving any city out. The dashed lines show the 95% confidence intervalsfor each estimation where one city is left out.
50
Figure B.3: Leave One Pilot City Out: The Impacts on Job Postings in thePilot Cities (Targeted Industries)
(a) Job Openings
(b) Monthly Wages
Notes: Figures present the impact of MIC25 on job postings in targeted industries in pilotcities with each time leaving out one pilot city, indexed from 1 to 30. The solid horizontalline represents the impact without leaving any city out. The dashed lines show the 95%confidence intervals for each estimation where one city is left out.
51
Figure B.4: Leave One Pilot City Out: The Impacts on Job Postings in thePilot Cities (Non-targeted Industries)
(a) Job Openings
(b) Monthly Wages
Notes: Figures present the impact of MIC25 on job postings in non-targeted industriesin pilot cities with each time leaving out one pilot city, indexed from 1 to 30. The solidhorizontal line represents the impact without leaving any city out. The dashed linesshow the 95% confidence intervals for each estimation where one city is left out.
52