Discussion Paper No. 137
Agglomeration and Selection Effects in
Privatized-SOEs: The Role of SOE Reforms
Yikai Zhao and Jun Nagayasu

October, 2023

Data Science and Service Research
Discussion Paper

Center for Data Science and Service Research

Graduate School of Economic and Management
Tohoku University27-1 Kawauchi, Aobaku

Sendai 980-8576, JAPANAgglomeration and Selection Effects in
Privatized-SOEs: The Role of SOE Reforms*
Yikai Zhao† Jun Nagayasu‡
October 25, 2023

Abstract
While privatizing state-owned enterprises (SOE) has been a global trend, its successas an economic policy remains controversial. Focusing on agglomeration and selection
effects in firms with different types of ownership, we examine the impact of SOE privatization on firm productivity and how these effects alter during the SOE privatization
process. We first show that both Always-private-owned enterprises, or Always-POEs, andAlways-SOEs benefit from marked agglomeration effects, highlighting the extensive influence of urban concentration. However, the positive selection effects are present only
among Always-POEs; privatized firms only marginally benefited from selection effects
after SOE privatization. Moreover, while proximity to the government may be advantageous for improving productivity during their state-owned tenure, it can become an
inhibitor after privatization.
Keywords: Agglomeration Effect, Selection Effect, Productivity, Privatization, Government Ties
JEL Classification: D24, O18, O47, P25* An earlier version of this paper was circulated under the title ‘’Privatization’s Influence on Agglomerationand Selection Effects: Evidence from China’s Manufacturing Industry” (July 2022)† Corresponding author, Tohoku University, Graduate School of Economics and Management, 27-1 Kawauchi,
Aoba-Ku, Sendai-city, Miyagi 980-8576 JAPAN; Tel: +81 22 795 6265; Fax: +81 22 795 6270; Email:zhao.yikai.p7@dc.tohoku.ac.jp.
‡ Tohoku University, Graduate School of Economics and Management, 27-1 Kawauchi, Aoba-Ku, Sendai-city,
Miyagi 980-8576 JAPAN; Tel: +81 22 795 6265; Fax: +81 22 795 6270; Email: jun.nagayasu.d8@tohoku.ac.jp.

11Introduction
Privatizing public enterprises has been a global trend. While China’s approach, marked

by its caution and gradualism, stands out due to its magnitude and scope (Estrin and Pelletier,2018), evidence suggests that privatized state-owned enterprises (SOEs) rarely catch up withprivate firms (Boardman et al., 2016). Therefore, minimizing the negative effect of possible
shocks in the early stages of the privatization process and quickly reaping the outcomes ofreforms are vital considerations for the government in implementing reforms. This study emphasizes that agglomeration economies and market selection can help address the challenges
faced by privatized SOEs.1 Specifically, we are motivated by the following three questions: 1)
Are there agglomeration and selection effects among SOEs and private enterprises? 2) Does
the SOE reform influence the enterprise’s agglomeration and selection effect? 3) To what
extent is this influence affected by the local government?
To address the first and second questions, we estimate the differentials in agglomerationand selection effects between large and small cities for privatized SOEs using a quantile specification proposed by Combes et al. (2012) and micro-data on Chinese manufacturing firms.
An appropriate benchmark is required to validate our results to control the disturbance fromchanges in regulatory conditions, and other factors that impact agglomeration and selection
effects during the SOEs’ reform. For this, we follow Boardman et al. (2016) to demonstrate
the unique behavior of privatized SOEs by constructing five comparable firm groups by ownership type: Always-SOEs, Always-COEs, Always-POEs, SPs, and CPs. COE and POE are theabbreviations for collective- and private-owned enterprises, respectively. The prefix Alwaysdenotes firms whose ownership does not change during the sample period. SPs are firms where
ownership changes from state- to private-owned. CPs are firms where ownership changes from
1 Studies (Syverson, 2004; Marshall, 2009) demonstrate that firms in large cities consistently outperform
those in smaller cities, which can be attributed to the inherent advantages of agglomeration. This urban-centricadvantage suggests that privatizing SOEs in densely populated urban areas can be a strategic move to counteract
potential vulnerabilities, especially during the initial phases of privatization, by tapping into positive externalities.
Furthermore, the intense market competition in urban areas can push firms to enhance productivity and eliminate
underperforming entities, ensuring that the remaining privatized SOEs are likely to improve overall productivity.

2

collective to private ownership.2
The third question arises from the notable change in the relationship between SOEs andthe government before and after privatization. In the pre-privatization phase, the government’s role in appointing SOE managers provides these enterprises with preferential access toresources, industry licenses, and bank credit (Faccio, 2010). However, this privilege often dissipates after privatization. Hayek (1945), Aghion and Tirole (1997), and Huang et al. (2017)advocated bolstering SOEs’ interactions with the government, leveraging local information todrive performance improvements. Echoing this sentiment, we posit that the tight relationshipbetween SOEs and the government becomes a linchpin for them to obtain agglomeration benefits while simultaneously sidestepping market-driven selection pressures. Interestingly, thesebenefits are absorbed with increased geographic separation from the government. Huang et al.
(2017) underscored that proximity to the oversight government is crucial to ensure the readyavailability of local insights. Similarly, Duchin et al. (2020) used the geographic distance between collective firms and local governments to explore the role of government involvement
since the distance is largely exogenous due to the adjustment costs associated with relocating
the firms. Building on this premise, we use the physical distance to classify our sample into
distinct sub-groups, and our subsequent analyses quantify each subgroup’s agglomeration andselection impacts.
We make four contributions by extending previous empirical works using a similar quantile approach (Arimoto et al., 2014; Accetturo et al., 2018; Ding and Niu, 2019; Adachi etal., 2021). First, we focus on Chinese SOEs and compare their agglomeration and selection
effects with those of firms with different ownership types. This differs from studies (Ding andNiu, 2019), which investigated the heterogeneity among different industries. Second, while
research focuses on the static nature of these effects (see Section 2), we study the role playedby agglomeration and selection effects during the ownership transition period. The salient
drop in general agglomeration benefits from pre- to post-privatized SOEs implies an adverse
2 Section 3.2 elaborates the definition of each group.

3

impact of privatization in the short term. In contrast, the gradually increasing agglomeration, which only benefits productive reformed SOEs, has a positive impact. This empiricalevidence enriches existing research on agglomeration effects and partially explains why privatized firms struggle to improve productivity in the short term. Third, we use nightlight satellite
data to proxy city density, which differs from Ding and Niu (2019), who used population or
population density. As indicated by Duranton and Puga (2020), nightlight data can measure
crowding more directly. Finally, based on the oversight agency conjecture (Hayek, 1945) thatgovernments have the motivation to decentralize distant SOEs, we study if SOEs with fewer
ex-ante government connections suffer less from the loss of agglomeration effects during thetransition period.
The remainder of this article is organized as follows. Section 2 reviews the literature onindustrial agglomeration and selection effects. Section 3 describes our data. The theoreticalmodel and empirical results of the various specifications for pre- and post-privatization fordifferent groups are explained and discussed in Section 4. Section 5 conducts robustness
checks. Finally, Section 6 presents the conclusions of this study.

2

Literature Review and Hypothesis Development

2.1

Agglomeration and Selection Effects

Large cities consistently attract more firms, and exhibit higher productivity and innovation
than their smaller counterparts (Puga, 2010; Naz et al., 2015). A key reason is the advantagesof aggregation, which encapsulates the trade-offs between increasing returns and costs of urban congestion (Marshall, 2009). Urban productivity also increases due to the selection effect. This mechanism removes inefficient “zombie” firms from the market, thereby enhancing
overall productivity (Melitz, 2003; Melitz and Ottaviano, 2008; Combes et al., 2012). Besides
their dwindling productivity, these firms occupy crucial market spaces, often thwarting private

4

investments and genuine competition (Lam et al., 2017).
Agglomeration and selection operate through distinct channels while helping elevate average productivity. Agglomeration promotes productivity through shared resources, optimized
matches, and knowledge dissemination; meanwhile, the selection process realigns market resources, ensuring dominant roles for efficient firms, fostering competitive dynamism, andcontributing to aggregate productivity (Melitz, 2003; Melitz and Ottaviano, 2008). Moreover,
Combes et al. (2012) argued that the synergy of agglomeration and selection manifests itself
in a unique productivity distribution in larger cities: while agglomeration influences the mean
and upper parts of this distribution, selection targets its bottom rungs, ensuring that urban
productivity remains dynamic and efficient.
Several studies compare the magnitude of agglomeration advantages and selection gains.
Some find that firm selection outweighs agglomeration in the Chilean food industry (Saito and
Gopinath, 2009) and Japanese silk-reeling industry (Arimoto et al., 2014). In contrast, others
find that productivity improvement is primarily due to agglomeration. Combes et al. (2012)
developed a theoretical model and used a quantile regression to estimate the differentials in
agglomeration and selection effects between large and small cities. Using data from Frenchindustries, the authors found that the selection process could not explain spatial productivity
differences. Applying a similar model to Italian manufacturing firms, Accetturo et al. (2018)
showed that while agglomeration effects play a significant role, the importance of the selection effect also increases in several sectors. Ding and Niu (2019) used the same method to
examine China’s manufacturing and construction industries, and reported strong evidence forthe agglomeration effect, while the selection effect contributed less to productivity.
On SOEs, some researchers emphasized the growth-driving role of state capitalism, suchas balancing social stability and economic performance (Bai et al., 2006). Meanwhile, others
criticized their inefficiencies arising from policy burdens, agency problems, and monopolistic
privileges (Xu et al., 2005; Liu and Zhang, 2018; Zhang et al., 2020). These arguments suggest

5that SOEs can enjoy agglomeration effects, given that their close ties with the state could
attract resources, talent, and knowledge concentration. However, state protection can hinder
the natural evolution of market forces. Instead of fostering a competitive environment where
the most efficient thrive, these conditions may preserve and even protect many inefficient“zombie” firms, allowing them to linger despite their non-competitive performance (Chang etal., 2021). Thus, we propose the following hypotheses on the first question:
Hypothesis 1: Both Always-POEs and Always-SOEs enjoy significant agglomeration effects.
Hypothesis 2: Always-POEs enjoy significantly positive selection effects, while Always-SOEs
do not.

2.2

Privatization and Firm Productivity in China

China initiated SOE privatization in the mid-1990s, although economic reforms started in1979 (Bai et al., 2006). SOEs mainly rely on central and local governments. A hallmark ofprivatized SOEs is their diminished political ties, which is crucial in societies with frail institutional structures (Huang et al., 2019). Studies on government ties indicate that SOE managers
maintain stronger governmental connections than their non-SOE counterparts (Li et al., 2011).
Through an extensive meta-analysis, Luo et al. (2012) found that government relationships are
more vital for SOEs. These ties provide SOEs with financial benefits, improved regulatory understanding, richer information, and specialized protection (Guo and Miller, 2010; Luo et al.,2012). These benefits shape the agglomeration effect. A key component of this effect isknowledge spillover, especially tacit knowledge (Howells, 2002). Therefore, the proximity of
SOEs to local governments fosters rapid information exchange. Moreover, Huang et al. (2017)highlighted a distance-decay effect: SOEs distant from government centers tend to be more
decentralized and face financial constraints.
Furthermore, firms with different types of ownership generally may not experience information exchange and technological diffusion (Zhu et al., 2019). Cognitive proximity and6technological relatedness can explain this because SOEs and non-SOEs are subject to various
operating codes and confront distinct threats and opportunities (Peng et al., 2004; Liao, 2015).
Therefore, one may reasonably assume that after privatization, SOEs enjoy fewer agglomeration benefits from local governments. Meanwhile, Zhu et al. (2020) suggested that comparedto Always-SOEs, reformed SOEs benefit more from knowledge spillovers from non-SOEs.
This beneficial effect amplifies year by year after SOEs successfully transition and redefine
their identities. Such knowledge spillovers can be a critical component of agglomeration effects, as they can foster innovation and improve business processes and overall productivity.
However, realizing these benefits and the resultant growth in agglomeration effects for privatized SOEs heavily depends on the ability of their workforce to adapt and efficiently incorporate this new knowledge. Thus, the magnitude of the agglomeration effect observed after
privatization is intrinsically tied to the efficiency and adaptability of SOEs’ employees. Accordingly, we propose the following hypotheses to explore our second and third questions
regarding agglomeration effects:
Hypothesis 3: SOEs’ reform negatively influences the agglomeration effect.
Hypothesis 4: The post-privatization change in the agglomeration effect is associated withthe firm’s relationship with the government.
Compared with the agglomeration effect, the evolving selection effect for privatized SOEs
is complex. While they might face selection stress as they assimilate into the private sector, theimmediate implications for them may be obscure. Oi (2005) indicated that certain privatizedfirms are restricted from declaring bankruptcy. Boubakri et al. (2008) observed that privatized
SOEs receive preferential treatment compared to native private firms. This suggests that the
influence of the selection effect might differ between privatized SOEs and completely private
enterprises. Given this background, we propose the following hypotheses to answer the second
and third questions regarding selection effects:

7

Hypothesis 5: SOEs’ reform improves selection effects.
Hypothesis 6: The post-privatization change in the selection effects is associated with thefirm’s relationship with the government.

3

Data

3.1

Firm Data

Our estimation of total factor productivity (TFP) extensively uses data from Chinese firms
from 1998 to 2007. While three industries (mining, manufacturing, and public utilities) are
included in the data set, we study only manufacturing firms because most sampled firms belong to this industry; moreover, the production behaviors of mining and public utilities differ.
The Annual Survey of Industrial Firms Database (ASIF), maintained by the National Bureauof Statistics (NBS), covers all SOEs and non-SOEs with sales exceeding RMB 5 million in
mainland China.3 This data set has detailed information on firm balance sheets, income statements, and basic information, such as ownership, established time, and registration address.4
After matching firms each year following the method proposed by Brandt et al. (2012), cleaning data, and extracting our basic sample according to Section 3.2, we obtain unbalanced panel
data with 461,642 firms from 1998 to 2007 (1,653,782 observations in total) covering 28 twodigit manufacturing industries across 31 provinces and 287 prefecture-level cities. (Section 6
details how we obtain the basic sample, Figure B.1 displays the distribution of firms with dif3 In the ASIF, a firm is defined as a legal unit. It means that a qualified subsidiary can be considered as anothercompany and counted separately in the database. Fortunately, according to the Census, most sampled companies(96.6% in 2007) do not have subsidiaries (Brandt et al., 2014). Therefore, we can justifiably assume that an
observation is a unique single-plant firm. In 2011, the designated size for non-state firms changed from RMB 5
million to 20 million.
4 Table B.1 compares our original data set with that of Brandt et al. (2014). As the database has been reviewed
several times (mainly to remove duplicate reports), the original data used here slightly differ from these authors’
but are still very close to the statistics summarized in the Statistical Yearbook. The database has been updated
until 2013 from 1998; however, the data after 2007 are generally considered debatable due to the small numberof variables and large discrepancy with the Statistical Yearbook contents. Therefore, we only consider the period
from 1998 to 2007.

8

ferent ownership every year, and Figure B.2 demonstrates the trends of employment, capitaland value-added.)5

3.2

State-Owned and Private Firms

Many studies (Brandt et al., 2017; Khandelwal et al., 2013) find that Chinese SOEs havedistinct operational characteristics that cannot be ignored. First, the production function andgrowth trajectory of SOEs differ from those of private firms. Second, because China is implementing large-scale SOE reforms during the sample period, the type of firm ownership is an
important factor.
The ASIF reports the officially registered company structure but does not properly reflect
the type of owner controlling the firm. This is because firms rarely modify their registration
status even if the controlling shareholder changes (Dougherty et al., 2007). Instead, we examine the structure of the shareholding by capital share to understand the de facto firm owner.
Following Dougherty et al. (2007), we define a firm as an SOE when it directly reports that itis held by the state. Among non-SOEs, COEs are those with a collective capital share greaterthan 50%, while the remainder are POEs.6 Finally, we divide the firms into five categories
based on changes in ultimate control. In line with Boardman et al. (2016), we extract groupsof Always-POEs, Always-SOEs, and Always-COEs; these firms have no changes in ultimate
control during our sample period. SPs refer to privatized firms that have changed from SOEs
to POEs, while CPs are those that have transition from COEs to POEs. Overall, we focus
5 China has four de facto tiers of local governance: provinces, prefecture-level cities, county-level cities, andtownships. This article focuses on the second tier but includes four municipalities from the first tier (but without
being broken into sub-areas): Beijing, Shanghai, Tianjin, and Chongqing. Henceforth, we collectively refer tothem as “prefecture-level” cities.
6 Private firms can be controlled by private companies (non-state legal persons), individuals, non-mainland
agents, or other shareholders, depending on which capital exceeds 50%. 50% is a common threshold to distinguish controlling ownership, as seen in other studies (Liao et al., 2014). Unlike simply dividing firms into
SOEs and non-SOEs, we also distinguish COEs. This is because compared with SOEs, COEs are viewed as acompetitive organizational form with remarkable performance under China’s partial reform; however, they still
suffer from policy burdens and agency problems as local governments oversee them. However, many COEs
transitioned from collective ownership to private ownership during the SOE reform period (Xia et al., 2009).

9

on firms whose ultimate control has never changed and those privatized only once; importantly, we exclude firms whose ownership has changed multiple times or those that have beennationalized.

3.3

The Distance from the Oversight Government

We regard SOEs’ decentralization as losing agglomeration advantages because they canno longer leverage local information. Huang et al. (2017) used the physical distance between
SOEs and the corresponding oversight governments to proxy the availability of local information exchange. This was based on Hayek (1945), who argued that one way to improve SOE
performance is by taking advantage of local information when the government urges themto improve efficiency. Intuitively, firms may be able to access more local information when
they are closer to local governments. This points to more relation-based government ties,
strengthening the agglomeration advantages from the interaction between SOEs and government officials, and the inextricable connection among SOEs through the relationship with thegovernment.
Thus, following Huang et al. (2017), we consider three levels of affiliation: central, provincial, and municipal governments. Using Google Maps API, we obtain each government’s geolocation (WGS84). For the central level, we use the location of the China State Council. For
others, we use the location of the People’s governments of provinces and cities. We classify
firms at each level from the smallest to the largest based on distance and compare the top
50% and bottom 50% (top-1/2 versus bottom-1/2), and top and bottom third (top-1/3 versus
bottom-1/3). The geographically close and distant groups are denoted as C- and F-Groups,
respectively.

10

3.4

Market Size

Market size is essential to measure the effects of agglomeration and selection in our setting. Two questions arise regarding this issue: the choice of the index (e.g., population, employment, or nightlight data) and spatial unit (e.g., provinces or cities). Agglomeration always
occurs on a local scale. Therefore, we take the prefecture-level city as the spatial unit in ourbenchmark model.7 Regarding the first question, instead of using population or employment
data that are popularly used in previous studies (Ding and Niu, 2019), we employ nighttime
light data to measure the market size; these data have been demonstrated to reflect and project
the trajectory of urban development directly (Ma et al., 2012; Duranton and Puga, 2020). Chan
(2007) stated that in many Chinese statistical publications, the the National Bureau of Statistics’ (NBS) definition of urban areas — an average population density of at least 1,500 per sq
km or contiguity of the built-up area — can be an appropriate criterion to reflect the de facto
population density. This is because the population of a city administrative unit (shi) includes
both an urbanized core and extensive rural areas (which are primarily agricultural areas andsometimes quite broad). Thus, using city (shi) population or population density is not ideal
for the size or urbanization of the market. Meanwhile, the hukou system in China does notcount workers who migrate from rural areas to urban centers. Therefore, using the hukou
population of urban areas (labeled as chengzhen renkou) may underestimate the actual population. According to Chan (2007), approximately 150 million people in Chinese cities belong
to this category in 2005. Therefore, remotely sensed nightlight datasets are more appropriate
for measuring the market size. We use employment data as a proxy for urban population.8
7 Provinces with low average population density also have densely-populated capital cities.

Therefore, we
use prefecture-level city data. Intuitively, the city boundaries may inhibit the impact of agglomeration caused bymutual interaction even within a province. As Puga (2010) noted, the agglomeration effect generally operates
within restricted spatial boundaries. The indicators we use to classify cities are at the prefecture level, different
from Ding and Niu (2019), who used local data. Hence, our results may differ, especially when identifying
dilation effects.
8 DMSP-OLS nighttime lights nighttime data are downloaded from https://ngdc.noaa.gov/eog/
download.html. To verify the reliability of light brightness, we also use other measures of city size: the numberof employees in the secondary industry (employment density) and population (population density) in urban areas(shi xia qu). These data are collected from city statistics yearbooks (Table A.1 presents the five main and last

11

3.5

Total Factor Productivity Estimation

Based on the standard Cobb–Douglas production function for firm i at time t, TFP can be
estimated using: ln TFPit = ln(value-added)it − βk,s ln(Capital)it − βl,s ln(Labor)it . The coefficients are estimated by Equation (1) as follows:

ln Yisct = β0,s + βk,s ln X1,isct + βl,s ln X2,isct + γs + θc + µt + εisct(1)

where Y, X1 , and X2 stand for industrial value-added, capital, and labor, respectively. s and cdenote industry and city, respectively. γs , θc , and µt represent industry, city, and time effects,
respectively. We calculate TFP for firms by the Olley-Pakes (OP) method on an industry-byindustry basis (Olley and Pakes, 1992). Table 1 presents the descriptive statistics for the key
variables by subgroups.
[TABLE 1 about here.]

4

Methodology

4.1

Agglomeration, Selection, and Dilation Effects

Based on the firm data and estimated TFP, we investigate the discrepancies between firms
in large and small cities by identifying agglomeration, dilation, and selection effects; theseeffects can improve productivity through various channels (Combes et al., 2012).9
Agglomeration effect (Ai ): Each worker is more productive in large cities by interactingwith each other, represented as Ai = ln[a(Ni + δ ∑i6=i N j )], where δ is a decay parameter andNi is the size of city i.10
cities according to different criteria).
9 STATA codes are based on Kondo (2017), who also elaborated on the correctness of the estimation process.
10 δ ∈ [0, 1] δ = 0 means that workers in city i can only interact with people in the same place. δ = 1 meansthat workers enjoy interactions with workers from everywhere with the same intensity; in this case, the dilationor agglomeration effects between cities do not differ: Di = D j , Ai = A j

12

Dilation effect (Di ): Workers are more productive when they work for a more efficient
firm, expressed as Di = ln[d(Ni + δ ∑i6=i N j )]. Then, φi (h) = Ai − Di ln(h) where h stands forlabor requirement per output (or marginal cost); a higher h means lower productivity φi . Thiseffect suggests that the benefits of agglomeration are also related to individual productivity
and not just to city size.
Selection effect (Si ): This effect is estimated as the probability that a firm will exit the local
market. It is represented as Si = 1 − G(h̄i ),11 where G(.) is the cumulative density function(CDF) from which a firm randomly draws its h and is assumed to be the same across cities;
and h̄ is the price threshold such that only firms with h ≤ h̄ can sell their products.12 We caneasily understand that a lower h̄i (higher φi ) leads to a higher Si . Entry barriers keep potential
entrants out of the market due to high sunk costs or productivity pressures.
Then, the CDF of the city i can be written as a function of the CDF (F̃(.)) without Ai , Di ,or Si :
i F̃ φ −A−SiDi
Fi (φ ) = max 0,
1 − Si(2)This equation cannot be estimated because the underlying distribution F̃(.) is unknown. Nevertheless, Combes et al. (2012) showed that by comparing the distribution of log productivity
across two cities of different sizes i and j, F̃(.) can be eliminated. That is:

D≡

Di
,
Dj

A ≡ Ai − DA j ,S≡

Si − S j
1−Sj(3)Then, Equation (2) can be arranged as follows:
11 Low-efficiency firms may also relocate to smaller cities; however, we do not consider these dynamic sorting

effects. We analyzed the sample and found that only 2% (10326/513500) of the companies have changed theirlocations (prefecture-level city level) during 1998–2007.
2
R
N j R h̄ j /τ
12 h̄ is the function of Ni h̄i h̄ − h 2 g(h)dh +h̄ j − τh g(h)dh = s. This means that city size,∑ j6=i 4γ 0i
4γ 0marginal distribution (g(.)), sunk entry cost s, and degree of product differentiation parameter γ influence h̄, andthus, the selection effect. If selling in other cities has no additional cost (τ = 1), then the selection density does
not differ: Si = S j .

13






 Fj φ −A−S
D
Fi (φ ) = max 0,


1−S
(
Fj (φ ) = max 0,−S
Fi (Dφ + A) − 1−S

Si > S j

if

(4)

)

−S
1 − 1−Sif

Si < S j(5)

Finally, we rewrite these two equations in quantiles and estimate the following function:

λi (rS (u)) = Dλ j (S + (1 − S)rS (u)) + Aforu ∈ [0, 1]

(6)

 

−S
−S
where λi (u) ≡ Fi−1 (u) is the uth quantile of Fi and rS (u) = max 0, 1−S+ 1 − max 0, 1−Su.
Using this method, we can obtain the relative shift parameter A, relative dilation parameter D,
and relative truncation parameter S. Returning to Equation (3), the hypothesis of no agglomeration, dilation, or selection effect between two cities can be denoted as follows:

H0 : A = 0,

D = 1,

S=0(7)

Figure 1 displays the simulated CDF of productivity between large and small cities withspecific A, D, and S. In summary, densely populated cities have a dual impact on enterprises.
First, the agglomeration effects enhance productivity. This enhancement can be further dissected into two distinct facets: a universal increase in TFP across all firms, quantified as A, andan additional increase in TFP specifically observed among higher performing companies, denoted by D. Concurrently, a selection mechanism exists in which productivity is left truncated
due to the market’s natural inclination to phase out less efficient firms.
[FIGURE 1 about here.]

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4.2

Empirical Results

4.2.1

Agglomeration and Selection Effects under Different Ownership

Estimates of agglomeration (A and D) and selection effects (S) using light brightness forgrouping cities are reported in Table 2. Note that R2 often exceeds 0.90, indicating that theseeffects can explain most of the productivity divergence between large and small cities. Moreover, agglomeration positively affects productivity for all firms controlled by various types ofownership. Based on bootstrapped standard errors, estimates of A differ significantly fromzero, validating Hypothesis 1 that both Always-POEs and Always-SOEs enjoy significant agglomeration effects. Furthermore, we observe different trajectories of productivity on agglomeration effects over time (column (1) in Table 2). Specifically, A gradually declines in
Always-POEs and drops slightly among Always-SOEs, while Always-COEs still enjoy more
agglomeration effects over time.
Meanwhile, due to an insignificant S at most times, no evidence of stronger firm selection
is detected for Always-COEs in larger cities. Conversely, Always-SOEs unsurprisingly havenegative values of S, corresponding instead to greater truncation in less dense areas. With
the government’s extensive support, SOEs are less likely to exit the local market, even on theverge of a breakdown. Better access to credit markets may explain why growing firms, suchas Chinese SOEs with low productivity, survive (Hu et al., 2015). Typically, urban cities tend
to have the ability to provide more bank credit.
Nevertheless, we find evidence of fierce selection in larger cities for Always-POEs, where
S is always positive and statistically significant. This result is similar to Arimoto et al. (2014),
who investigated the Japanese silk-reeling industry, Accetturo et al. (2018), who controlled formarket access, and Ding and Niu (2019), who examined Chinese manufacturing industries andfound that 15 out of 29 industries exhibit a significant S using provincial population density
as the standard. Thus, without considering the selection effect, productivity gains from agglomeration advantages may be overestimated. Still, the effect of selection on productivity is15much less than the agglomeration effect. As Brandt et al. (2012) reckoned, although the Chinese market exhibits market selection, limited efficiency-enhancing input re-allocations may
curtail this function. In general, Table 2 supports Hypothesis 2 that Always-POEs have statistically significant selection effects; however, positive selection effects are not found among
Always-SOEs.
[TABLE 2 about here.]

4.2.2

Post-privatization: TFP

Before investigating the extent to which privatization impacts productivity through agglomeration and selection effects, we first check changes in TFP before and after privatization. Figure 2 displays the productivity trends of different groups. The left graph shows thatat the point of privatization, t = 0, the TFP of the SOEs undergoing privatization substantially
increases. The post-privatization trajectory is also positive, exhibiting a consistent increase in
their productivity. When juxtaposed with the Always-SOEs group from the right graph, the privatized SOEs exhibit a considerably larger increase in TFP. The productivity trend for COEs
during their privatization phase remains relatively stable. Unlike SOEs, privatized COEs do
not experience a similar boost in productivity during or after privatization. In particular, evenwith the observed surge in TFP for privatized SOEs, their average TFP, when compared to theright graph, remains below that of Always-POEs.
In other words, at least in the short term, the TFP of recently privatized enterprises does
not seem to match up to entities that have always operated in the private sector. This suggeststhat while privatization can be beneficial for productivity growth, privatized SOEs have aperiod of adjustment and transition before they can reach the productivity benchmarks set
by their always-private counterparts. Considering these findings, to further investigate theagglomeration and selection effects during SEO privatization, we estimate the values of A, D
and S for privatized SOEs.
16

[FIGURE 2 about here.]

4.2.3

Post-privatization: Agglomeration Effect

Table 3 reports A, D, and S before and after privatization (Panel A), and according to
the distance from the corresponding oversight government (Panel B, top-1/2 versus bottom1/2). In Table A.2, we demonstrate results grouped by top versus bottom one-third of the
distance from the corresponding oversight governments. The agglomeration effects A and Dwith confidence intervals (CI) are plotted in Figure 3 and Figure 4, respectively. Here, t = 0
stands for the year when a SOE changed its ultimate control from state to private ownership,
while t = −1 is one year before the reform and t = +1 is one year after.
[FIGURE 3 about here.][FIGURE 4 about here.][TABLE 3 about here.]
In column (1) of Table 3, the estimates of A are always significantly positive before the
SOE reform. However, A drops after t = 0 and starts becoming insignificant two years after
privatization. This shows that productivity improvements in all firms due to agglomeration
advantages gradually fade away once SOEs are privatized. For example, according to PanelA, in the year preceding privatization (t = −1), the value of A is 0.181. In the year following
privatization (t = +1), A decreases to 0.090. This indicates that if a firm moved to a denser
area before its privatization, its TFP would increase by 19.8% (e0.181 − 1 = 0.198).13 Yet,when the same firm moves to a denser area after privatization, its TFP would only increase by
9.4% (e0.090 − 1 = 0.094), exhibiting a difference of 10%. In particular, this decline is even
13 Following Combes et al. (2012), we have φ (h) = A −D ln(h) and A = A −DA = φ (h)−D ln(h)−φ (h)+
i
i
i
i
j
i
i
j
D j ln(h). If D = DDij = 1, then A = φi (h) − φ j (h), where φi (h) refers to ln TFPi . Thus, we rewrite this equation as
FPieA − 1 = ( TT FP− 1). This implies an increase in mean TFP if a firm relocates from a small city to a large cityjdue to a general agglomeration effect A.

17

more pronounced for firms near the local government (C-Group), exceeding 20%. In contrast,
for those in the F-Group, the drop is approximately 6%.
According to Siegel (2007), political networks burdened companies after a change in thepolitical regime in Korea because the way they accessed information and resources in the past
was outdated in the new environment. Song et al. (2022) likewise demonstrated that during
China’s transition, firms’ close ties with the pre-reform institutions impeded new activities asfirms’ operations were constrained by the connections established in the past and employees
continued following old business norms. As the environment evolves, privatized SOEs mightfind that these historical ties, rather than providing them with leverage, often act as shackles.
Their organizational structures, corporate cultures, operational models, and decision-making
processes are formulated during their time as SOEs. This makes them considerably different
from emerging private companies. Furthermore, the close ties maintained by privatized SOEs
with previous institutional systems often make their integration with the private sector challenging. This assertion is supported by the fact that C-Group firms’ A becomes insignificant
earlier (since t = +1) than that of F-Group firms (since t = +3).
Then, why does the average productivity of private firms substantially increase after privatization? We hypothesize that this could be attributed to the driving force behind theincrease in the productivity of high-quality enterprises with fewer government connections(Hypothesis 3). Comparing column (2) of C-Group with that of F-Group in Panels B of Table 3, D is insignificantly less than one pre-privatization but substantially rises above one forpost-privatized SOEs located far from their oversight governments. A value of D above one
demonstrates that more productive firms benefit more from being in denser areas, while D
smaller than one indicates that more productive firms benefit less from being in denser areas.
Figure 3 and Figure 4 complement this finding, and show a steady increasing D starting fromt = 0. This holds significantly only for post-privatized SOEs, which are far from the oversightgovernment. For post-privatized SOEs close to the oversight government, except for estimates18

at t = +1, the estimates of D are insignificantly above one. Briefly, according to columns (1)and (2) in Table 3, after transitioning to a private firm, the agglomeration benefits that comefrom a shift to the right of the productivity distribution (represented by A) decline, while theagglomeration benefits of dilation (represented by D) increase. That is, only pre-privatized
SOEs with high quality (e.g., hiring efficient workers) can improve productivity through theagglomeration effect after the reform; this is especially true for F-Group firms. These discoveries support Hypothesis 3 and Hypothesis 4.
Next, we ask whether agglomeration effects (or, more generally, productive advantages)
shared by post-privatized SOEs weaken after they lose the privileged treatment. Specifically,
we weigh the gain (due to dilation D) and loss (due to shift A) of the agglomeration economies
at different quantiles of the logarithmic productivity distribution. Figure 5 depicts ln TFP
differences in the 25th, 50th, and 75th quantiles between large and small cities. The sample
is grouped by the distance from the corresponding oversight government, where thick arrows
represent C-groups (top half) and thin arrows represent F-groups (bottom half). The directionof each arrow is from small to large cities.
[FIGURE 5 about here.]
We have two observations. First, productivity increases substantially when SOEs are privatized (t = 0). However, thereafter, the 25th and 50th percentiles of the ln TFP distribution ofthese privatized SOEs do not exhibit a trend of substantial productivity increases; only distantfirms in the 75th percentile productivity group show a steady increase (as shown by F-Group
p=75 in Figure 5). Second, the size of productivity increases firm relocation from small tolarge cities, as a response to agglomeration benefits, differs between large and small cities.
After privatization, the difference in production efficiency between large and small cities narrows significantly, as shown by the most paired-coordinate plots in Figure 3. Thus, the scaleeffect of large cities is not efficient or accretive for mediocre privatized SOEs.14 Nonetheless,
14 Here, mediocre firms are those with productivity below the 50th percentile.

19

the advantages of agglomeration effects still exist for more efficient and distant firms (F-Groupp=75), especially between the [t = −3] and [t = +3] windows. Regarding the agglomeration
effect, most SOEs instantly lose this advantage after privatization, which may further affectproductivity growth. Instead, high-productivity SOEs with weak government relations maintain or even enhance the agglomeration benefit. However, this effect does not last long anddisappears four years after SOE reform (as shown by F-Group p=75 ). However, the outstanding privatized SOEs (such as firms in the 95th percentile) that are far from the oversightgovernment demonstrate a lasting agglomeration effect, which is accompanied by a steadyincrease in efficiency (see F-Group p=95 in Figure 6). This discovery is interesting because
privatized SOEs absorb the advantages of large cities through the agglomeration effect, which
implies a relatively straightforward possibility of improving firm productivity.
[FIGURE 6 about here.]

4.2.4

Post-privatization: Selection Effects

Column (3) reports the estimates of the selection effects S. Unlike the agglomeration effectshown by A and D, the values of S are insignificant and negligible in all periods. This suggeststhat privatization does not significantly improve the selection process. However, by linking
column (3) of Always-SOEs in Table 2 to column (3) in Table 3, S among Always-SOEs issignificantly negative but insignificant among SOEs soon to be privatized. This suggests thatgovernments in large cities tend to protect SOEs on the brink of bankruptcy. Conversely, asreported in Panel A, the pre-privatization values of S are often negative; meanwhile, S has
positive values after privatization. Residual government connections within newly privatized
SOEs may also explain the poor performance (Boubakri et al., 2008). Harrison et al. (2019)
discovered that privatized SOEs are still favored for low-interest loans and government subsidies relative to Always-POEs in China. This favoritism for resources means that privatized
SOEs continue to operate even if their performance is not outstanding. Thus, without creating20

a competitive market environment, privatizing SOEs may have little effect on their productivity (Konings et al., 2005).
However, regarding the negative to positive change in S, as shown in Panel A of Table 3,
we optimistically expect that privatization can help improve firm efficiency in large cities if thegovernment can also change their behavior (e.g., less credit); however, this may take longer.
Unlike Always-POEs, SPs barely have significantly negative selection effects. The positive S(although statistically insignificant) partially indicates that SOE reform contributes to market
selection. Thus, even though we cannot strongly support Hypotheses 5 and 6 statistically, theimproved competitive market for privatized SOEs remains noteworthy.

4.2.5

Summary of Findings

Without finding a similar pattern among CPs as reported in Table A.3, we see a characteristic unique to CPs. Our results shed light on several key hypotheses. Evidently, both
Always-POEs and Always-SOEs experience considerable agglomeration effects, suggesting awide-reaching impact of urban concentration. Furthermore, Always-POEs display significant
selection effects, while Always-SOEs do not.
Interestingly, while SOE reform adversely influences agglomeration effects, this negative
trend can be offset by the quality of the firm (e.g., the efficiency of a firm’s employees). This isbecause high-efficiency privatized SOEs are often better equipped to adapt to market changes
and face external challenges. Consequently, they are more likely to attract a wider network ofpartners, suppliers, and clients, thus amplifying the effects of agglomeration. This finding is
consistent with previous studies. Combes et al. (2012) found that workers are more productive
in a more efficient firm, which is the origin of the dilation effect. Zhu et al. (2020) found thatprivatized SOEs can benefit from increased knowledge spillover effects through interactions
with the private sector, hinting at the ability to learn from private firms. Furthermore, Chonget al. (2011) underscored the adverse outcomes of pre-privatization retrenchment initiatives

21

and emphasized that skill-biased retrenchment strategies stand out as the only approaches
correlated with elevated privatization valuations. Therefore, this also supports our finding
and highlights the beneficial influence of the dilation effect on privatized SOEs that boast ahigh-efficiency workforce.
Another significant observation is the clear correlation between changes in agglomeration
effects after SOE privatization and firm ties to the government. Specifically, if privatized SOEs
are located closer to the local government, their post-privatization agglomeration benefits diminish more significantly. Thus, while proximity to governmental entities may be beneficial
during their state-owned phase due to preferential treatment or easier access to resources, it
becomes a liability after privatization. Close physical and operational proximity could expose
these firms to bureaucratic inefficiencies, policy volatilities, or other challenges that dilute theadvantages typically associated with economic clustering.
Lastly, contrary to our initial expectations, the results do not significantly corroborate thehypothesis that SOE reforms positively affect selection effects, nor do they provide a link
between changes in selection effects after SOE privatization and the firm’s governmental ties.

5

Robustness checks
Our primary variables are the division of ownership and firm productivity.15 Here, we

use productivity and labor productivity (industrial value-add divided by employment) to test
the robustness of our results. Following Liao et al. (2014), we distinguish firm ownership by
the ratio of state-owned equity to total equity. Firms are assigned by their state ownership into
four groups: Non-SOEs, L-SOEs, M-SOEs, and H-SOEs. Non-SOEs include firms without any
state-owned equity. The rest are ranked ascendingly by low, medium, and high firm ownership
15 There may be endogeneity concerns due to the government’s preference for the privatization of highefficiency or loss-making firms. However, the dilation effect (D in Equation (7)) already addresses this issue.
Specifically, the agglomeration effect consists of two parts: the increase in the average agglomeration effect (AEquation (7)) of SOEs after privatization and benefits that only high-quality SOEs can obtain (D Equation (7)).

22as L-SOEs, M-SOEs, and H-SOEs, respectively. Privatization is equivalent to selling stateowned equity; that is, the transition from H-SOEs to Non-SOEs directly (only one change).
Firms that have undergone multiple transitions are not included in the robustness test because
the preceding section (Section 4.2) only incorporates firms that have been privatized once.
Table 4 uses a different firm productivity estimation method, and Table 5 uses another firmownership classification. These tables show that our results hold, and suggest that market
competition and withdrawal mechanisms have gradually improved after the SOE reform.
[TABLE 4 about here.][TABLE 5 about here.]

6

Conclusion
Employing a large firm-level dataset and the generalized firm selection model (Melitz,

2003; Melitz and Ottaviano, 2008), we examine the impact of SOE privatization on Chinese
firms’ productivity through two transmission channels: agglomeration and selection effects
and report significant heterogeneities in productivity changes by ownership type. Using extensive data from manufacturing firms with a quantile specification (Combes et al., 2012),
we establish that agglomeration explains a large part of the productivity differences between
cities in China’s manufacturing industry for each type of ownership. In contrast, a statisticallysignificant selection effect is present only among Always-POEs. The remaining sub-samples(Always-SOEs, Always-COEs, Always-POEs, SPs, and CPs) exhibit insignificant (or evennegative) selection effects.
In addition, the transformative journey of SOE reforms is focused on agglomeration effects. High-quality firms, especially those endowed with efficient and competent human capital, demonstrate remarkable resilience after privatization. Their TFP withstands the adverse
consequences of reforms and even increases. This underscores the crucial role of workforce
23efficiency in harnessing and increasing the benefits of agglomeration, regardless of ownershiptransitions. However, the picture becomes more complex when we consider the geographical proximity to government centers. Privatized SOEs closer to these centers, regardless offirm quality, face a paradoxical challenge. Although such proximity may have been a boon
during their state-owned tenure, offering them preferential treatment and resource access, it
is a double-edged sword after privatization. For instance, these firms might grapple with bureaucratic entanglements and policy volatility, eroding some of their post-privatization TFP
gains.
Intriguingly, our data do not offer a conclusive endorsement of the assumptions that SOE
reforms unequivocally amplify selection effects or that these effects’ post-reform trajectory issignificantly influenced by firm proximity to the government. This may be one reason for thelimited evidence supporting privatization. One possible explanation for the limited evidence
might be the temporal nature of market competition: the formation and maturation of a morecompetitive market might be much longer than our study period. Nonetheless, despite theabsence of stark significance, subtle indications point towards improved selection effects. Thissuggests that while the changes might be incremental now, more pronounced changes may beobserved in the long run.
Given our findings, the government may consider reassessing its associations with preprivatized SOEs. This may help privatized SOEs maintain their inherent agglomeration advantages and embark on sustained productivity improvement. Encouraging such enterprises tonavigate a market characterized by cooperation and competition could be beneficial. Furthermore, giving precedence to SOEs which demonstrate efficiency and have minimal government
dependencies may be beneficial. This can position them better to take advantage of privatization. Moreover, our data point towards the possible merits of nurturing a market environment
rich in genuine competition. A reflective approach might involve credit support policies forprivatized SOEs, ensuring that the policies are balanced and fair. Such refinements could

24

pave the way for more discernible selection effects driven by market competition, potentiallyboosting firm productivity.

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Figures

FIGURE 1. M ONTE C ARLO S IMULATIONS , D ISTRIBUTIONS IN L ARGE AND S MALL C ITIES
Notes: Created by the authors. In panel (a), Ai = 0, A j = 0.2, Di = D j = 1, Si = S j = 0 and thus A = 0.2, D = 1, S = 0, it corresponds to thedifferences in agglomeration that are required to move the entire (only right-shift); in panel (b), Ai = 0, A j = 0.2, Di = 1, D j = 1.2, Si = S j = 0,
it additionally dilates the distribution by stretching out the productivity especially at the right-side tail (right-shift and dilation); in panel (c),Ai = A j = 0, Di = D j = 1, Si = 0, S j = 0.1, it corresponds to the differences in selection required to left truncate the productivity by taking
probability away from the left of the distribution (left-truncation); the panel (d) identifies right-shift, dilation and left-truncation by visualcomparison of two distributions of ln TFP distributions in large cities and small cities.

30

FIGURE 2. T REND IN AVERAGE LnT FP BY D IFFERENT G ROUPS
Notes: This figure consists of two side-by-side graphs, both showing the group average LnT FP on the y-axis; The left graph represents the trend
around the time of privatization of SOEs or COEs; The x-axis uses a relative time scale, with t = 0 denoting the year of privatization, t = +1
indicating one year post-privatization; The x-axis of the right graph is the actual years. The groups (SPs, CPs, Always-POEs, Always-SOEs
and Always-COEs are described in Section 3.2.

31

FIGURE 3. PARAMETERS OF SP S AGGLOMERATION E FFECTS BY D ISTANCE F ROM OVERSIGHTG OVERNMENT, TOP -1/2 VS . BOTTOM -1/2, C ITIES ARE G ROUPED BY M EDIAN VALUE OF L IGHT
B RIGHTNESS
Notes: SPs are firms that change their ownership from state-owned to private-owned, as Section 3.2; t = 0 denotes the year when a firm isprivatized from an SOE to a POE, and t = −1 is one year before the privatization while t = +1 one year after, and so forth; Firms are ranked
from nearest to farthest by distance from the corresponding oversight government in Section 3.3, and thus, top-1/2 are the nearest 50% firms
denoted by prefix C-, while bottom-1/2 are the farthest 50% firms denoted by prefix F-; The null hypothesis H0 is A = 0, D = 1; Bootstrap
replication (50); Estimated values with 95% confidence intervals are plotted.

32

FIGURE 4. PARAMETERS OF SP S AGGLOMERATION E FFECTS BY D ISTANCE F ROM OVERSIGHTG OVERNMENT, TOP -1/3 VS . BOTTOM -1/3, C ITIES ARE G ROUPED BY M EDIAN VALUE OF L IGHT
B RIGHTNESS
Notes: SPs are firms that change their ownership from state-owned to private-owned, as Section 3.2; t = 0 denotes the year when a firm isprivatized from an SOE to a POE, and t = −1 is one year before the privatization while t = +1 one year after, and so forth; Firms are ranked
from nearest to farthest by distance from the corresponding oversight government in Section 3.3, and thus, top-1/3 are the nearest 33% firms
denoted by prefix C-, while bottom-1/3 are the farthest 33% firms denoted by prefix F-; The null hypothesis H0 is A = 0, D = 1; Bootstrap
replication (50); Estimated values with 95% confidence intervals are plotted.

33

FIGURE 5. P RODUCTIVITY D IFFERENCE OF SP S AT 25 TH , 50 TH AND 75 TH P ERCENTILE BY
D ISTANCE F ROM OVERSIGHT G OVERNMENT, TOP -1/2 VS . BOTTOM -1/2
Notes: SPs are firms that change their ownership from state-owned to private-owned, as Section 3.2; t = 0 denotes the year when a firm isprivatized from an SOE to a POE, and t = −1 is one year before the privatization while t = +1 one year after, and so forth; Firms are ranked
from nearest to farthest by distance from the corresponding oversight government as described in Section 3.3, and thus, top-1/2 are the nearest
50% firms denoted by prefix C- (deep color), while bottom-1/2 are the farthest 50% firms denoted by prefix F- (light color); The pairedcoordinate arrows point from small cities to large cities; The y-axis is lnT FP at different percentiles. The length of each arrow is explained by
the combined effects of agglomeration and selection. Within the context of our study, an observed increase in the length of the arrow implies
an increase in these effects.

34

FIGURE 6. P RODUCTIVITY D IFFERENCE OF SP S AT 95th P ERCENTILE BY D ISTANCE F ROM
OVERSIGHT G OVERNMENT, TOP -1/2 VS . BOTTOM -1/2
Notes: SPs are firms change their ownership from state-owned to private-owned, as Section 3.2; t = 0 denotes the year when a firm is privatizedfrom an SOE to a POE, and t = −1 is one year before the privatization while t = +1 one year after, and so forth; Firms are ranked from nearest
to farthest by distance from the corresponding oversight government as described in Section 3.3, and thus, top-1/2 are the nearest 50% firms
denoted by prefix C- (deep color), while bottom-1/2 are the farthest 50% firms denoted by prefix F- (light color); The paired-coordinate arrows
point from small cities to large cities. The y-axis is lnT FP at different percentiles. The length of each arrow is explained by the combined
effects of agglomeration and selection. Within the context of our study, an observed increase in the length of the arrow implies an increase inthese effects.

35

Tables
TABLE 1. D ESCRIPTIVE S TATISTICS OF K EY VARIABLES
Groups
Always-SOEs

Mean
Std

LnTFP LnAsset-1.058
9.790
0.986
1.857

LnLabor
4.980
1.453

LnOutput
8.760
2.349

LnCapital
3.905
2.062

Obs.
164,487

Percentage
9.95%

Always-COEs

Mean
Std

0.303
0.888

9.279
1.176

4.701
1.046

9.549
1.194

3.246
1.396

81,818

4.95%

Always-POEs

Mean
Std

0.072
0.876

9.553
1.255

4.616
1.024

9.979
1.175

3.493
1.475

1,200,095

72.57%

CPs

Mean
Std

0.312
0.839

9.622
1.215

4.872
1.003

10.049
1.186

3.618
1.414

144,184

8.72%

SPs

Mean
Std

-0.2630.946

10.410
1.419

5.392
1.158

10.180
1.519

4.489
1.650

63,198

3.82%

266,442
1,387,340
120,314
1,249,737

16.11%
83.89%
7.28%
75.57%

Small Cities
Big Cities
Small Cities
Top-1/3 vs. bottom-1/3
Big Cities
Top-1/2 vs. bottom-1/2

Notes: Always-SOEs, Always-COEs, Always-POEs, CPs, and SPs are defined in Section 3.2; Cities are divided by the median value of lightbrightness to top-1/2 (top-1/3) and bottom-1/2 (bottom-1/3).

36

TABLE 2. I NTER - TEMPORAL E STIMATION R ESULTS FOR D IFFERENT U LTIMATE C ONTROLF IRMS , C ITIES ARE G ROUPED BY M EDIAN VALUE OF L IGHT B RIGHTNESS(1)
Agglomeration Effects

(2)

(3)
Selection Effects

A

D

S

(4)
R2

(5)
Obs.

(6)

Big City

Small City

Always-SOEs
1998-2000
1999-2001
2000-2002
2001-2003
2002-2004
2003-2005
2004-2006
2005-2007

0.143***
0.162***
0.168***
0.176***
0.182***
0.196***
0.164***
0.135***[0.1142,0.1730][0.1325,0.1920][0.1336,0.2028][0.1418,0.2107][0.1473,0.2182][0.1513,0.2409][0.1107,0.2181][0.0731,0.1979]

1.010
1.046***
1.073***
1.062***
1.066***
1.082***
1.069***
1.082***[0.9844,1.0352][1.0218,1.0702][1.0418,1.1046][1.0262,1.0983][1.0343,1.0970][1.0478,1.1153][1.0236,1.1144][1.0378,1.1261]-0.014***-0.012***-0.009**-0.013***-0.013***-0.013***-0.014**-0.030***[-0.0186,-0.0098][-0.0164,-0.0068][-0.0150,-0.0036][-0.0197,-0.0071][-0.0176,-0.0085][-0.0179,-0.0074][-0.0227,-0.0043][-0.0398,-0.0199]

0.950
0.938
0.924
0.925
0.943
0.948
0.926
0.976

27,61924,553
21,837
17,173
17,664
15,29812,94010,196

11,455
10,053
8,300
6,129
5,768
4,596
3,484
2,576

Always-COEs
1998-2000
1999-2001
2000-2002
2001-2003
2002-2004
2003-2005
2004-2006
2005-2007

0.125***
0.166***
0.135***
0.182***
0.140***
0.234***
0.181***
0.180***[0.0977,0.1541][0.1308,0.2025][0.0914,0.1798][0.1277,0.2377][0.0823,0.1990][0.1641,0.3053][0.1084,0.2546][0.1252,0.2362]

0.960***
0.985
1.056***
1.019
1.092***
1.065***
1.116***
1.101***[0.9347,0.9856][0.9441,1.0278][1.0152,1.0975][0.9618,1.0768][1.0439,1.1409][1.0087,1.1213][1.0517,1.1805][1.0430,1.1604]

0.001-0.012**-0.004-0.0050.003
0.009-0.002-0.002[-0.0044,0.0062][-0.0196,-0.0042][-0.0119,0.0041][-0.0148,0.0046][-0.0043,0.0104][-0.0020,0.0195][-0.0123,0.0078][-0.0158,0.0122]

0.981
0.964
0.981
0.970
0.982
0.963
0.960
0.968

18,105
15,728
12,170
9,149
9,370
8,106
6,498
5,603

3,577
2,778
2,249
1,675
1,572
1,244
847
870

Always-POEs
1998-2000
1999-2001
2000-2002
2001-2003
2002-2004
2003-2005
2004-2006
2005-2007

0.130***
0.124***
0.132***
0.153***
0.094***
0.102***
0.079***
0.034***[0.1109,0.1504][0.1015,0.1478][0.1187,0.1465][0.1394,0.1684][0.0835,0.1058][0.0902,0.1150][0.0686,0.0909][0.0232,0.0454]

0.984
1.012
1.000
1.017***
1.031***
1.016***
1.017***
1.027***[0.9609,1.0080][0.9872,1.0368][0.9852,1.0156][1.0004,1.0348][1.0190,1.0430][1.0052,1.0281][1.0089,1.0267][1.0184,1.0368]

0.006**
0.009***
0.003*
0.008***
0.005***
0.003**
0.002*
0.003***[0.0015,0.0107][0.0049,0.0147][0.0002,0.0068][0.0050,0.0113][0.0021,0.0081][0.0014,0.0057][0.0001,0.0050][0.0014,0.0057]

0.949
0.951
0.946
0.965
0.931
0.930
0.875
0.613

55,581
72,32785,717
102,966
176,519
193,043
214,605
235,966

7,208
9,666
11,715
14,884
22,26026,59830,96638,033

Notes: The prefix Always- denotes firms without changing ultimate control during the sample period as Section 3.2; The null hypothesis H0 is
A = 0, D = 1, S = 0; Bootstrap replication (50); 95% confidence intervals in brackets; ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001.

37

TABLE 3. E STIMATION R ESULTS OF SP S BY D ISTANCE F ROM OVERSIGHT G OVERNMENT, ALL
SAMPLE AND TOP -1/2 VS . BOTTOM -1/2, C ITIES ARE G ROUPED BY M EDIAN VALUE OF L IGHT
B RIGHTNESS(1)
Agglomeration Effects

(2)

A

D

Panel A: all samplet<=-5 0.228*** [0.1728,0.2825]
t=-40.178*** [0.1120,0.2435]
t=-30.182*** [0.1301,0.2334]
t=-2
0.153*** [0.0807,0.2245]
t=-10.181*** [0.1034,0.2579]
t=00.120*** [0.0976,0.1433]
t=+10.090*** [0.0486,0.1306]
t=+20.088*** [0.0382,0.1374]
t=+3
0.066
[-0.0134,0.1451]
t=+4
0.011
[-0.0786,0.1013]
t>=+5 0.013[-0.2570,0.2840]
Panel B: top-1/2 vs. bottom-1/2
C-Group (top-1/2)
t<=-5 0.249*** [0.1850,0.3140]
t=-40.219*** [0.1561,0.2811]
t=-30.256*** [0.1906,0.3224]
t=-20.215*** [0.1241,0.3063]
t=-10.247*** [0.1767,0.3168]
t=00.159*** [0.0890,0.2292]
t=+1
0.062
[-0.0016,0.1257]
t=+2
0.078
[-0.0102,0.1660]
t=+3
0.080
[-0.0232,0.1827]
t=+4
0.059
[-0.1195,0.2373]
t>=+5 0.006[-0.1233,0.1348]
F-Group (bottom-1/2)
t<=-5 0.193*** [0.1202,0.2663]
t=-40.158*** [0.0653,0.2499]
t=-30.130** [0.0497,0.2100]
t=-20.122*** [0.0646,0.1799]
t=-10.165*** [0.0964,0.2344]
t=00.118*** [0.0613,0.1739]
t=+10.110** [0.0429,0.1766]
t=+20.103** [0.0278,0.1785]
t=+3
0.053
[-0.0525,0.1579]
t=+4-0.020[-0.1237,0.0830]
t>=+5 -0.047[-0.1477,0.0534](3)
Selection Effects
S

(4)
R2

(5)
Obs.

(6)

Big City

Small City

1.029
0.998
1.003
1.011
1.012
1.036***
1.029*
1.027**
1.069**
1.048
1.053

[0.9779,1.0796][0.9182,1.0782][0.9391,1.0664][0.9771,1.0457][0.9751,1.0483][1.0156,1.0573][ 1.0075,1.0606][1.0027,1.0510][1.0118,1.1259][0.9163,1.1804][0.8669,1.2395]-0.001-0.011-0.001-0.029-0.007
0.008
0.012
0.010
0.020
0.000
0.007

[-0.0140,0.0123][-0.0317,0.0093][-0.0268,0.0241][-0.0635,0.0064][-0.0181,0.0038][-0.0029,0.0198][-0.0055,0.0291][-0.0074,0.0264][-0.0316,0.0726][-0.1257,0.1252][-0.1204,0.1348]

0.874
0.925
0.929
0.923
0.924
0.918
0.913
0.889
0.901
0.806
0.902

4,034
2,493
3,477
4,774
6,050
6,575
4,805
3,841
3,186
2,196
3,533

1,584
1,065
1,447
1,976
2,402
2,643
1,975
1,616
1,300
927
1,299

1.011
0.914
1.008
1.003
0.985
1.039
1.077***
1.104
1.057
1.040
1.023

[0.9318,1.0910][0.8089,1.0195][0.9166,1.0997][0.9356,1.0697][0.9157,1.0550][0.9643,1.1133][1.0131,1.1409][0.9982,1.2088][0.9735,1.1397][0.8600,1.2208][0.9153,1.1308]-0.003-0.004-0.002-0.003-0.009-0.008-0.0030.001-0.015-0.014-0.004[-0.0156,0.0104][-0.0390,0.0309][-0.0194,0.0146][-0.0143,0.0085][-0.0189,0.0017][-0.0206,0.0040][-0.0117,0.0063][-0.0134,0.0162][-0.0302,0.0000][-0.0641,0.0371][-0.0710,0.0634]

0.966
0.968
0.955
0.978
0.970
0.958
0.842
0.857
0.846
0.721
0.474

2,326
1,409
1,931
2,628
3,267
3,592
2,634
2,095
1,752
1,221
1,858

787
530
710
971
1,182
1,300
963
795
622
444
622

0.973
0.993
0.985
0.957
1.029
1.082***
1.169***
1.098***
1.197***
1.142***
1.152***[0.9062,1.0401][0.9070,1.0789][0.9053,1.0654][0.9040,1.0103][0.9625,1.0946][1.0334,1.1297][1.0743,1.2630][1.0177,1.1781][1.1202,1.2729][1.0432,1.2412][1.0611,1.2428]

0.001-0.0040.003
0.000-0.0040.001
0.002-0.002
0.003
0.003
0.003

[-0.0200,0.0225][-0.0212,0.0126][-0.0122,0.0173][-0.0116,0.0107][-0.0161,0.0084][-0.0089,0.0112][-0.0128,0.0177][-0.0188,0.0138][-0.0140,0.0207][-0.0165,0.0221][-0.0160,0.0211]

0.979
0.965
0.924
0.890
0.964
0.949
0.938
0.911
0.937
0.823
0.836

1,708
1,084
1,546
2,146
2,783
2,983
2,171
1,746
1,434
975
1,675

797
535
737
1,005
1,220
1,343
1,012
821
678
483
677

Notes: SPs are firms change their ownership from stated-owned to private-owned, as Section 3.2; t = 0 denotes the year when a firm is privatizedfrom an SOE to a POE, and t = −1 is one year before the privatization while t = +1 one year after, and so forth; Firms are ranked from nearest
to farthest by distance from the corresponding oversight government in Section 3.3, and thus, top-1/2 are the nearest 50% firms denoted by
prefix C-, while bottom-1/2 are the farthest 50% firms denoted by prefix F-; The null hypothesis H0 is A = 0, D = 1, S = 0; Bootstrap replication(50); 95% confidence intervals in brackets; ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001.

38

TABLE 4. E STIMATION R ESULTS OF SP S BY D ISTANCE F ROM OVERSIGHT G OVERNMENT,TOP -1/2 VS . BOTTOM -1/2, C ITIES ARE G ROUPED BY M EDIAN VALUE OF L IGHT B RIGHTNESS ,P RODUCTIVITY ARE E STIMATED BY OLS M ETHOD AND L ABOR P RODUCTIVITY(1)
Agglomeration Effects

(2)

(3)
Selection Effects

(4)

A

D

S

Panel B: LnTFP, Labor Productivity
C-Group (top-1/2)
t<=-5 0.312*** [0.2329,0.3903]
t=-40.299*** [0.2017,0.3954]
t=-30.336*** [0.2565,0.4163]
t=-20.304*** [0.2391,0.3682]
t=-1
0.340*** [0.2745,0.4046]
t=00.221*** [0.1639,0.2772]
t=+10.120** [0.0481,0.1918]
t=+20.133** [0.0494,0.2156]
t=+30.093*[0.0112,0.1746]
t=+4
0.098
[-0.0096,0.2053]
t>=+5 0.022[-0.0727,0.1174]

1.081*** [1.0083,1.1528]
0.922[0.7973,1.0464]
1.058[0.9816,1.1354]
1.055[0.9891,1.1210]
1.009[0.9324,1.0856]
1.073** [1.0172,1.1292]
1.047[0.9874,1.1072]
1.106** [1.0391,1.1730]
1.04[0.9472,1.1319]
1.108[0.9700,1.2451]
1.038[0.9519,1.1232]

0.000
0.001
-0.0010.004-0.0100.000-0.003-0.001-0.0110.001-0.002[-0.0119,0.0120][-0.0473,0.0498][-0.0152,0.0122][-0.0077,0.0150][-0.0229,0.0021][-0.0087,0.0083][-0.0144,0.0078][-0.0115,0.0102][-0.0375,0.0152][-0.0307,0.0332][-0.0246,0.0213]

0.983 2,326
0.984 1,409
0.990 1,931
0.984 2,628
0.986 3,267
0.982 3,592
0.957 2,634
0.950 2,095
0.635 1,752
0.759 1,221
0.317 1,858

787
530
710
971
1,182
1,300
963
795
622
444
622

F-Group (bottom-1/2)
t<=-5 0.278*** [0.2032,0.3521]
t=-40.205*** [0.1158,0.2938]
t=-30.174*** [0.1021,0.2459]
t=-2
0.199*** [0.1274,0.2701]
t=-10.201*** [0.1418,0.2596]
t=00.145*** [0.0702,0.2199]
t=+10.142*** [0.0754,0.2079]
t=+20.113*[0.0135,0.2121]
t=+3
0.087
[-0.0125,0.1864]
t=+4
0.002
[-0.1072,0.1111]
t>=+5 -0.01[-0.1001,0.0809]

1.027[0.9535,1.1011]
1.057[0.9439,1.1709]
1.049[0.9335,1.1650]
1.005[0.9528,1.0571]
1.067** [1.0101,1.1244]
1.093** [1.0424,1.1427]
1.179*** [1.1034,1.2549]
1.105** [1.0375,1.1721]
1.149*[1.0415,1.2572]
1.126*** [1.0126,1.2398]
1.153*** [1.0656,1.2401]

0.004
0.001
0.010
-0.002
0.001
0.003
0.002
-0.001-0.001
0.002
0.005[-0.0129,0.0210][-0.0324,0.0350][-0.0132,0.0340][-0.0144,0.0113][-0.0107,0.0136][-0.0069,0.0132][-0.0098,0.0147][-0.0136,0.0113][-0.0287,0.0274][-0.0151,0.0186][-0.0085,0.0188]

0.958 1,708
0.918 1,084
0.878 1,546
0.922 2,146
0.977 2,783
0.960 2,983
0.958 2,171
0.956 1,746
0.871 1,434
0.862 975
0.838 1,675

797
535
737
1,005
1,220
1,343
1,012
821
678
483
677

R2

(5)
Obs.

(6)

Big City

Small City

Notes: SPs are firms that change their ownership from state-owned to private-owned, as Section 3.2; t = 0 denotes the year when a firm isprivatized from an SOE to a POE, and t = −1 is one year before the privatization while t = +1 one year after, and so forth; Firms are ranked
from nearest to farthest by distance from the corresponding oversight government in Section 3.3, and thus, top-1/2 are the nearest 50% firms
denoted by prefix C-, while bottom-1/2 are the farthest 50% firms denoted by prefix F-; The null hypothesis H0 is A = 0, D = 1, S = 0; Bootstrap
replication (50); 95% confidence intervals in brackets; ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001.

39

TABLE 5. E STIMATION R ESULTS S NON S BY D ISTANCE F ROM OVERSIGHT G OVERNMENT,TOP -1/2 VS . BOTTOM -1/2, C ITIES ARE G ROUPED BY M EDIAN VALUE OF L IGHT B RIGHTNESS(1)
Agglomeration Effects

(2)

(3)
Selection Effects

A

D

S

C-Group (top-1/2)
t<=-5 0.991**
t=-4
0.422
t=-3
0.452***
t=-2
0.451***
t=-1
0.371***
t=00.264***
t=+1
0.115*
t=+20.152**
t=+3
0.155
t=+4
0.011
t>=+5 0.047[0.2388,1.7427][-0.0393,0.8833][0.3308,0.5723][0.3030,0.5997][0.2828,0.4596][0.1860,0.3417][0.0195,0.2113][0.0492,0.2555][-0.0025,0.3117][-0.1437,0.1659][-0.0752,0.1685]

F-Group (bottom-1/2)
t<=-5 0.191[-0.1828,0.5647]
t=-4
0.398
[-0.2512,1.0470]
t=-3
0.482*** [0.3345,0.6287]
t=-2
0.328*** [0.1961,0.4603]
t=-1
0.261*** [0.1824,0.3395]
t=00.131*** [0.0583,0.2044]
t=+10.122*[0.0212,0.2221]
t=+20.120*[0.0108,0.2284]
t=+3
0.096
[-0.0348,0.2261]
t=+4
0.028
[-0.1429,0.1991]
t>=+5 -0.027[-0.1354,0.0809]

0.481*
0.860
1.016
1.120**
1.002
1.044
1.142**
1.149**
1.099
1.141*
1.058[0.0818,0.8802][0.5016,1.2176][0.8913,1.1403][1.0167,1.2228][0.9373,1.0672][0.9747,1.1138][1.0323,1.2518][1.0138,1.2843][0.9624,1.2363][1.0032,1.2795][0.9468,1.1701]

1.323*** [1.0078,1.6376]
1.167[0.4987,1.8360]
1.181[0.9932,1.3684]
1.077[0.9068,1.2478]
1.05[0.9640,1.1362]
1.106** [1.0182,1.1935]
1.232*[1.0844,1.3791]
1.188** [1.0804,1.2961]
1.214** [1.0563,1.3718]
1.188** [1.0220,1.3548]
1.158*** [1.0540,1.2620]

(4)
R2

(5)
Obs.

(6)

Big City

Small City

-1.014[-2.1164,0.0885]-0.031[-0.6569,0.5957]-0.013[-0.0469,0.0212]-0.016[-0.0396,0.0084]
0.002[-0.0129,0.0160]-0.017* [-0.0307,-0.0028]
0.014* [0.0005,0.0276]
0.008[-0.0128,0.0298]-0.004[-0.0438,0.0365]-0.002[-0.0298,0.0261]-0.015 [-0.0338,0.0042]

0.932 748
0.970 541
0.988 882
0.960 1,707
0.994 3,225
0.970 3,584
0.947 2,366
0.906 1,831
0.853 1,402
0.879 1,081
0.819 2,055

182
162
274
525
917
1,049
659
506
381
298
520

0.045
-0.017-0.001
0.003-0.002
0.015
0.012
0.008
0.014
0.005
0.018

0.923 367
0.945 327
0.969 630
0.951 1,381
0.944 2,661
0.897 2,984
0.938 1,881
0.914 1,463
0.945 1,1260.870 892
0.752 1,829

151
135
235
503
928
1,056
670
501
369
308
520

[-0.0871,0.1778][-0.7279,0.6937][-0.0609,0.0588][-0.0562,0.0622][-0.0168,0.0127][-0.0047,0.0339][-0.0072,0.0312][-0.0099,0.0250][-0.0100,0.0383][-0.0439,0.0545][-0.0038,0.0399]

Notes: SnonS are firms that transform from H-SOEs to Non-SOEs as discussed in Section 5; t = 0 denotes the year when a firm is privatizedfrom an SOE to a POE, and t = −1 is one year before the privatization while t = +1 one year after, and so forth; Firms are ranked from nearest
to farthest by distance from the corresponding oversight government in Section 3.3, and thus, top-1/2 are the nearest 50% firms denoted by
prefix C-, while bottom-1/2 are the farthest 50% firms denoted by prefix F-; The null hypothesis H0 is A = 0, D = 1, S = 0; Bootstrap replication(50); 95% confidence intervals in brackets; ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001.

40

Appedix
Rank

Province

City

Employment(secondary industry)

Employment
Density

Population(hukou)Population
DensityLight

Sort by employment of the secondary industry in the urban areas
1
2
3
4
5

Tibet
Yunan
Gansu
Yunan
Qinghai

Lasa
Lincang
Longnan
Lijiang
Haidong

695
1,600
2,600
3,100
3,100

13
42
397
102
389

181,991
288,307559,049
153,023
416,148

3,321
7,500
85,351
5,057
52,214

8,545
6,913
2,964
6,016
5,749

283
284
285
286
287

Shandong
Helongjiang
Tianjin
Shanghai
Beijing

Guangzhou
HarbinTianjin
Shanghai
Beijing

763,900
913,500
928,400
1,434,800
1,651,700

1,256
3,021
1,905
2,611
1,400

5,882,553
4,754,753
7,912,385
13,091,515
11,453,643

9,676
15,723
16,232
23,821
9,706

231,834
110,809
276,413364,622383,236

Sort by employment density of the secondary industry in the urban areas
1
2
3
4
5

Tibet
YunnanInner Mongolia
Inner Mongolia
Yunnan

Lasa
Lincang
UlanqabHulunbuir
Lijiang

695
1,600
6,700
10,400
3,100

13
42
60
66
102

181,991
288,307298,887
263,005
153,023

3,321
7,500
2,681
1,662
5,057

8,545
6,913
16,04836,640
6,016

283
284
285
286
287

SichuanJiangxi
Henan
FujianHenanPanzhihua
Xinyu
Pingdingshan
PutianPuyang141,500
125,100
188,000
125,600
152,600

3,369
3,475
3,547
3,873
4,348

664,700
821,919
1,104,000
2,016,300
516,600

15,826
22,831
20,83062,174
14,718

16,8478,301
41,15633,33537,515

Sort by population (hukou) in the urban areas
1
2
3
4
5

Yunan
Tibet
Helongjiang
Gansu
Jiangxi

Lijiang
Lasa
Heihe
Jinchang
Yintan

3,100
695
9,200
42,000
5,400

102
13
497
1,712
284

153,023
181,991
191,440
198,401
201,787

5,057
3,321
10,348
8,088
10,6206,016
8,545
15,207
7,241
7,049

283
284
285
286
287

Tianjin
Hubei
BeijingShanghai
Chongqing

Tianjin
Wuhan
BeijingShanghai
Chongqing

928,400
621,800
1,651,700
1,434,800
677,400

1,905
2,824
1,400
2,611
1,293

7,912,385
8,282,137
11,453,643
13,091,515
15,260,234

16,23237,608
9,706
23,821
29,139276,413
105,769
383,236
364,622
113,443

Sort by population density (hukou) in the urban areas
1
2
3
4
5

Inner Mongolia
Guangdong
Inner Mongolia
Inner Mongolia
Guangdong

Hulunbuir
Zhaoqing
Ordos
UlanqabShenzhen

10,40052,80025,9006,700
576,600

66
271
284
60
809

263,005
477,428
243,429
298,887
2,168,453

1,662
2,453
2,669
2,681
3,041

36,64037,04035,097
16,048
176,437

283
284
285
286
287

Guangxi
FujianGuizhou
SichuanGansu

LaibinPutianBijie
Bazhong
Longnan

12,700125,600
12,70022,700
2,600

698
3,873
605
1,437
397

1,023,100
2,016,300
1,415,638
1,332,929
559,049

56,21462,17467,411
84,36385,351

7,364
33,335
6,633
2,524
2,964

Sort by light brightness
1
2
3
4
5

SichuanSichuan
Gansu
Shaanxi
Hunan

Yaan
Bazhong
Longnan
Shangluo
Zhangjiajie

20,10022,700
2,600
5,400
5,300

1,267
1,437
397
470
277

350,987
1,332,929
559,049
552,900
497,957

22,11684,36385,351
48,07826,071

2,431
2,524
2,964
3,418
3,998

283
284
285
286
287

JiangsuGuangdong
Tianjin
Shanghai
BeijingSuzhouGuangdong
Tianjin
Shanghai
Beijing249,600
763,900
928,400
1,434,800
1,651,700

1,675
1,256
1,905
2,611
1,400

2,940,849
5,882,553
7,912,385
13,091,515
11,453,643

19,737
9,676
16,232
23,821
9,706

226,203
231,834
276,413364,622383,236

TABLE A.1. C ITY R ANKS ACCORDING TO D IFFERENT C RITERIA

41

TABLE A.2. E STIMATION R ESULTS OF SP S BY D ISTANCE F ROM OVERSIGHT G OVERNMENT,TOP -1/3 VS . BOTTOM -1/3, C ITIES ARE G ROUPED BY M EDIAN VALUE OF L IGHT B RIGHTNESS(1)
Agglomeration Effects

(2)

(3)
Selection Effects

A

D

S

Panel B: top-1/3 vs. bottom-1/3
C-Group (top-1/3)
t<=-5 0.269*** [0.1795,0.3593] 1.065
t=-4
0.183** [0.0724,0.2938] 0.835
t=-3
0.230*** [0.1451,0.3146] 0.959
t=-2
0.167*** [0.0776,0.2562] 0.924
t=-1
0.204*** [0.1368,0.2713] 0.946
t=00.136** [0.0438,0.2290] 1.001
t=+1
0.029
[-0.0784,0.1363] 1.049
t=+2
0.050
[-0.0351,0.1356] 1.089
t=+3
0.046
[-0.0822,0.1750] 1.062
t=+4-0.004[-0.1688,0.1609] 1.064
t>=+5 -0.112[-0.2553,0.0307] 1.025

(4)
R2

(5)
Obs.

(6)

Big City

Small City

[0.9571,1.1720][0.6552,1.0139][0.8279,1.0911][0.8398,1.0075][0.8648,1.0279][0.9315,1.0708][0.9603,1.1371][0.9750,1.2030][0.9519,1.1721][0.8346,1.2928][0.9073,1.1426]

0.013 [-0.0059,0.0317]-0.009 [-0.0648,0.0462]-0.005 [-0.0277,0.0186]-0.010 [-0.0243,0.0038]-0.012 [-0.0270,0.0035]-0.007 [-0.0189,0.0051]-0.007 [-0.0288,0.0156]
0.006 [-0.0145,0.0264]-0.015 [-0.0325,0.0021]-0.003 [-0.0524,0.0467]
0.002 [-0.0316,0.0357]

0.979
0.960
0.928
0.960
0.961
0.913
0.806
0.851
0.814
0.467
0.812

1,696
1,035
1,381
1,839
2,291
2,526
1,855
1,486
1,257
860
1,257

538
350
477
637
779
865
629
520
408
296
432

F-Group (bottom-1/3)
t<=-5 0.149*** [0.0679,0.2303] 0.917[0.7907,1.0428]
t=-4
0.082
[-0.0292,0.1926] 0.956[0.8125,1.0997]
t=-3
0.096*[0.0079,0.1847] 0.980[0.8736,1.0855]
t=-2
0.088*[0.0047,0.1704] 0.946[0.8891,1.0025]
t=-1
0.119** [0.0455,0.1931] 0.985[0.9107,1.0586]
t=00.081*[0.0001,0.1616] 1.053[0.9824,1.1243]
t=+1
0.073
[-0.0217,0.1674] 1.147*** [1.0403,1.2539]
t=+2
0.043
[-0.0721,0.1578] 1.104*** [1.0187,1.1883]
t=+3
0.001
[-0.1265,0.1295] 1.171*** [1.0356,1.3074]
t=+4-0.105[-0.3549,0.1444] 1.161[0.9480,1.3731]
t>=+5 -0.102[-0.6336,0.4301] 1.183[0.7627,1.6023]-0.012 [-0.0438,0.0199]-0.010 [-0.0913,0.0718]
0.003 [-0.0158,0.0211]-0.004 [-0.0208,0.0128]-0.005 [-0.0245,0.0136]
0.000 [-0.0133,0.0142]
0.005 [-0.0153,0.0246]
0.001 [-0.0178,0.0194]
0.013 [-0.0229,0.0489]
0.016 [-0.0505,0.0825]
0.004 [-0.1874,0.1955]

0.967
0.796
0.821
0.823
0.933
0.877
0.892
0.847
0.876
0.740
0.821

1,178
750
1,079
1,454
1,848
1,985
1,428
1,152
942
631
1,078

570
380
528
701
846
931
699
572
462
323
449

Notes: SPs are firms change their ownership from stated-owned to private-owned, as Section 3.2; t = 0 denotes the year when a firm is privatizedfrom an SOE to a POE, and t = −1 is one year before the privatization while t = +1 one year after, and so forth; Firms are ranked from nearest
to farthest by distance from the corresponding oversight government in Section 3.3, and thus, top-1/3 are the nearest 33% firms denoted by
prefix C-, while bottom-1/3 are the farthest 33% firms denoted by prefix F-; The null hypothesis H0 is A = 0, D = 1, S = 0; Bootstrap replication(50); 95% confidence intervals in brackets; ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001.

42

TABLE A.3. E STIMATION R ESULTS OF CP S BY D ISTANCE F ROM OVERSIGHT G OVERNMENT,TOP -1/2 VS . BOTTOM -1/2, C ITIES ARE G ROUPED BY M EDIAN VALUE OF L IGHT B RIGHTNESS(1)
Agglomeration Effects

(2)

A

D

C-Group (top-1/2)
t<=-5 0.042
t=-4
0.140
t=-3
0.154***
t=-2
0.097*
t=-1
0.106***
t=00.086**
t=+10.117***
t=+20.141***
t=+3
0.158
t=+4
0.109*
t>=+5 -0.014(3)
Selection Effects
S

(4)
R2

(5)
Obs.

(6)

Big City

Small City[-0.0549,0.1380][-0.0733,0.3524][0.0761,0.2316][0.0179,0.1763][0.0440,0.1681][0.0319,0.1402][0.0640,0.1706][0.0646,0.2169][-0.0481,0.3638][0.0247,0.1932][-0.0906,0.0625]

1.144***
1.074
1.06
1.011
1.048
1.063*
1.058*
1.027
1.006
1.061
1.050

[1.0184,1.2689][0.8810,1.2680][0.9726,1.1466][0.9205,1.1011][0.9927,1.1030][1.0122,1.1136][1.0031,1.1127][0.9546,1.1001][0.8506,1.1609][0.9813,1.1406][0.9947,1.1051]

0.015-0.0130.010-0.003
0.010
0.002
0.001
-0.012-0.012-0.0010.008[-0.0022,0.0330][-0.1085,0.0831][-0.0076,0.0278][-0.0196,0.0134][-0.0000,0.0201][-0.0087,0.0122][-0.0084,0.0099][-0.0259,0.0024][-0.0913,0.0673][-0.0177,0.0155][-0.0053,0.0212]

0.960 2,802
0.974 2,150
0.969 3,365
0.851 5,621
0.934 9,457
0.906 9,944
0.916 7,353
0.944 5,863
0.885 4,515
0.865 3,492
0.611 6,259

418
334
549
851
1,455
1,509
1,082
844
635
476
857

F-Group (bottom-1/2)
t<=-5 0.050[-0.0397,0.1392]
t=-40.136*[0.0089,0.2637]
t=-30.152*[0.0226,0.2815]
t=-20.128** [0.0484,0.2084]
t=-1
0.184*** [0.1343,0.2342]
t=00.092*** [0.0471,0.1359]
t=+10.105** [0.0270,0.1821]
t=+2
0.039
[-0.1444,0.2219]
t=+3
0.075
[-0.0243,0.1739]
t=+4
0.036
[-0.5926,0.6651]
t>=+5 -0.015[-0.1215,0.0909]

1.157**
1.02
1.031
1.014
0.994
1.045**
1.01
1.074
1.063
1.108*
1.181*[1.0699,1.2446][0.9074,1.1334][0.9322,1.1304][0.9328,1.0954][0.9358,1.0516][1.0011,1.0892][0.9404,1.0800][0.9301,1.2171][0.9746,1.1512][0.6922,1.5242][1.0821,1.2809]

0.012
0.005
0.004
0.012
-0.002-0.001-0.011
0.010
0.003-0.0020.029[-0.0049,0.0292][-0.0222,0.0323][-0.0261,0.0350][-0.0013,0.0244][-0.0120,0.0084][-0.0083,0.0065][-0.0335,0.0109][-0.0546,0.0746][-0.0254,0.0321][-0.2117,0.2078][-0.0002,0.0588]

0.911 2,586
0.951 2,160
0.959 3,507
0.955 5,864
0.984 10,156
0.943 10,580
0.871 7,879
0.603 6,368
0.747 4,831
0.781 3,691
0.787 6,833

485
341
545
891
1,617
1,684
1,222
958
719
547
889

Notes: CPs are firms that change their ownership from collective-owned to private-owned, as Section 3.2; t = 0 denotes the year when a firm isprivatized from an SOE to a POE, and t = −1 is one year prior to the privatization while t + 1 one year after, and so forth; Firms are ranked fromnearest to farthest by distance from the corresponding oversight government in Section 3.3, and thus, top-1/2 are the nearest 50% firms denotedby prefix C-, while bottom-1/2 are the farthest 50% firms denoted by prefix F-; The null hypothesis H0 is A = 0, D = 1, S = 0; Bootstrap
replication (50); 95% confidence intervals in brackets; ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001.

43

Data Cleaning Process
The firm linkages over time are made using the methods of Brandt et al. (2012, 2014) and
Brandt et al. (2017), who provided do-files online, including programs for matching firms overyears and tables of industrial concordance codes.16 After matching firms, we follow standard
procedures documented in the previous literature to clean the data (Wei and Liu, 2006; Brandt
et al., 2012, 2014; Yang, 2018):
1. Only keep the manufacturing industry;
2. Exclude the tobacco industry;
3. Use observations with positive industrial value-added, intermediate inputs, and net fixed
assets;4. Keep observations with no less than eight employees;
5. Drop observations not under accounting principles: liquid, fixed, or net fixed assets
larger than total assets;
6. Make 4-digit industrial numerical codes uniform across the entire period following
Brandt et al. (2014). Further, update the renamed or merged city to the latest cityname.17
7. Firms that changed their locations are deleted.18
8. Observations without population data, another critical variable collected from city statistical yearbooks, are deleted.
9. According to the method for distinguishing the ownership of the firm instructed in Section 3.2, we exclude firms that have changed ownership many times and have beennationalized.
we finally obtain unbalanced panel data with 461,642 (specifically, 50,041 Always-SOEs,
30,854 Always-COEs, 347,056 Always-POEs, 23,717 CPs, 756 SCs, and 9,218 SPs) uniquefirms from 1998 to 2007 (totally 1,653,782 observations) covering 28 two-digit manufacturingindustries across 31 provinces and 287 prefecture-level cities.19
16 Online website:https://feb.kuleuven.be/public/u0044468//CHINA/appendix/, accessed March

13th, 2021.
17 During the analysis period from 2000 to 2007, industrial standard classification for national economic activities was revised in 2002 from GB/T 4754—1994 to GB/T 4754—2002, where some 4-digit industries weremerged while others were divided.
18 Firms whose addresses have changed accounted for only 0.16% of the whole sample. Hence, we can assume
that the firm does not have the possibility of relocation; it can only go bankrupt and cannot change its official
location.
19 SCs are those who changed from SOEs to COEs; however, we do not analyze this group’s firms due tolimited observations.

44

45

1998
1999
2000
2001
2002
2003
2004
2005
2006
2007

Year

165,118
162,033
162,833
169,030
181,557
196,222
279,092
271,835
301,961
336,768

165,116
162,033
162,885
171,256
181,557
196,222274,886
270,043
301,961
336,768

Number of firms
Brandt et al. (2014) Dataset

2
0
-52
-2226
0
0
4206
1792
0
0

Diff.
6.41
6.99
8.42
9.24
10.95
14.32
20.43
24.69
31.36
39.97

0.00
0.00
0.00
-0.13
0.00
0.00
0.55
-0.13
-0.06
-0.09

Diff.
6.77
7.27
8.57
9.41
11.08
14.23
20.16
25.16
31.66
40.52

6.77
7.27
8.57
9.54
11.08
14.23
20.10
24.99
31.66
40.51

OutputBrandt et al. (2014) Dataset
0.00
0.00
0.00
-0.13
0.00
0.00
0.06
0.17
0.00
0.01

Diff.
1.94
2.16
2.54
2.79
3.30
4.20
6.62
7.22
9.11
11.70

1.94
2.16
2.54
2.83
3.30
4.20
5.67
7.14
9.11
11.70

Value added
Brandt et al. (2014) Dataset
0.00
0.00
0.00
-0.04
0.00
0.00
0.95
0.08
0.00
0.00

Diff.
56.44
58.05
53.68
52.97
55.21
57.49
66.27
68.96
73.58
78.75

56.44
58.05
53.68
54.41
55.21
57.49
66.26
69.09
73.58
79.27

Employment
Brandt et al. (2014) Dataset
0.00
0.00
0.00
-1.44
0.00
0.00
0.01
-0.13
0.00
-0.52

Diff.

4.41
4.73
5.81
5.45
5.95
6.61
7.97
8.95
10.58
12.34

4.41
4.73
5.18
5.54
5.95
6.61
7.96
10.53
10.58
12.34

0.00
0.00
0.63
-0.09
0.00
0.00
0.01
-1.58
0.00
0.00

Net value of fixed assets
Brandt et al. (2014) Dataset Diff.

TABLE B.1. O RIGINAL DATA VS. TABLE .4 IN B RANDT ET AL . (2014)

6.41
6.99
8.42
9.37
10.95
14.32
19.88
24.82
31.42
40.06

Sales
Brandt et al. (2014) Dataset

1.08
1.16
1.46
1.61
2.01
2.69
4.05
4.77
6.05
7.34

1.08
1.15
1.46
1.62
2.01
2.69
4.05
4.77
6.05
7.34

ExportBrandt et al. (2014) Dataset

0.00
0.01
0.00
-0.01
0.00
0.00
0.00
0.00
0.00
0.00

Diff.

FIGURE B.1. P ERCENTAGE OF FIRM WITH DIFFERENT ULTIMATE OWNERSHIP PER YEARFIGURE B.2. AVERAGE F IRM PERFORMANCE GROUPED BY ULTIMATE OWNERSHIP

46

OP Method
We start with the production function:ββ

Yit = Ait Kit k Litl

(.8)
β

β

β

where Yit stands for value-added; the revenue production function looks like: Yit = Ait Kit k Litl Mit m ,where Yit is gross output. Taking the natural logarithm of Equation (.8) results in a linear production function:
yit = β0 + βk kit + βl lit + εit(.9)
and εit includes two parts ωit + uit , where ωit represents firm-level productivity and uit is i.i.d.
A direct estimation using OLS will cause endogeneity problems and selection bias, and leadto biased productivity results.
Olley and Pakes (1992) assumed that investment decisions at the firm level can be shownto depend on capital (Iit = Kit+1 − (1 − δ )Kit ) and productivity (higher expectation, higher
investment decision):ωit = ht (kit , iit )(.10)
Since this is a monotonically increasing function, it can be written as ht (.) = it−1 (.):
yit = βl lit + β0 + βk kit + ht (kit , iit ) + uit(.11)

The first term on the right side of the equation represents the contribution of labor. The latterterm represents the contribution of capital and can be further written as:
ϕ (iit , kit ) = β0 + βk kit + ht (iit , kit )(.12)

yit = βl lit + ϕ (iit , kit ) + uit(.13)

An unbiased labor coefficient can be obtained by estimating Eq.(.13). Then, the estimatedcoefficient is used to fit the polynomial term (ϕ (iit , kit )) formed by investment and capitalstock:
yit+1 − βl lit+1(.14)
q=β0 + βk kit+1 + g (φit , βk kit ) + ξit+1 + uit+1
The second stage of the estimation includes the estimation of high-order polynomials. The
current and lag periods of the capital stock exist simultaneously, which needs to be completed
by the nonlinear least square method.

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