Does the All-China Federation of Industry and
Commerce Align Private Firms with the Goals
of the People’s Republic of China’s Belt
and Road Initiative?
Jeffrey B. Nugent and Jiaxuan Lu∗

This paper demonstrates that the largest business association of private firms
in the People’s Republic of China (PRC), the All-China Federation of Industry
and Commerce (ACFIC), has induced its members to help achieve the goals
of the PRC’s extremely ambitious but risky Belt and Road Initiative (BRI)
since its inauguration in 2013. Through its newspaper, the ACFIC has drawn
the attention of its member firms to countries participating in the BRI, cual
has led to increased trade between provinces in the PRC and BRI-participating
countries emphasized by the ACFIC’s newspaper. The results show that the
PRC’s exports have been encouraged substantially more than its imports, cual
could be a cause for concern for the sustainability of the BRI. The results were
obtained through various specially designed versions of the gravity model and
have shown to be robust to the use of various methods for mitigating possible
estimation biases.

Palabras clave: Belt and Road Initiative, business association, People’s Republic of
Porcelana, private firm, comercio
JEL codes: D23, F14, F21, L22, O53

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I. Introducción

When the initiatives that countries take to achieve certain objectives are
massive, multinational, and laden with serious challenges, coordination among the
relevant parties, both public and private, can be difficult since each participating
firm or agency has its own objectives. The Belt and Road Initiative (BRI) de
the People’s Republic of China (PRC), inaugurated by President Xi Jinping in
Kazakhstan in 2013, includes more than 50 partner countries across the continents

∗Jeffrey B. Nugent (Autor correspondiente): Departamento de Economía, University of Southern California, United
Estados. Correo electrónico: nugent@usc.edu; Jiaxuan Lu: Departamento de Economía, University of Southern California, United
Estados. Correo electrónico: jiaxuanl@usc.edu. We would like to express our thanks for all the comments received at the Asian
Development Bank–International Economic Association Roundtable held in July 2019 in Tokyo and at the Applied
Microeconomics Conference held in December 2019 at the University of Hawaii, and also to Junjie Xia, Terrie
Walmsley, the managing editor and the anonymous referee for helpful comments and suggestions. The Asian
Development Bank recognizes “China” as the People’s Republic of China, “Russia” as the Russian Federation, y
“Vietnam” as Viet Nam. The usual ADB disclaimer applies.

Asian Development Review, volumen. 37, No. 2, páginas. 45–76
https://doi.org/10.1162/adev_a_00149

© 2020 Asian Development Bank and
Asian Development Bank Institute.
Publicado bajo Creative Commons
Atribución 3.0 Internacional (CC POR 3.0) licencia.

46 Asian Development Review

of Asia, Europa, and Africa. It is one of the largest projects ever attempted and
will remain of great importance to the world for decades to come. Todavía, con
uncertainties concerning its real objectives—and with participating countries of
different sizes, development levels, and political orientations—achieving sufficient
intra-BRI coordination is especially challenging.

Lei and Nugent (2018) made the case that the PRC’s government-controlled
business association, the All-China Federation of Industry and Commerce (ACFIC),
had served well as a coordinating device between the Government of the PRC and
the country’s private firms from 2007 a 2011. This was when Beijing sharply
changed its economic objectives from “Going Outward” to “Going Inward” to
escape the adverse effects of the 2008–2009 global financial crisis on important
exporting countries. Todavía, that experience had nothing to do with the BRI and the
PRC’s leadership of it.

Given the BRI’s ambitious goals,

the many countries involved, y
uncertainties about the extent to which coordination among all governments, firms,
and agencies can be successful, ongoing analysis of the BRI’s progress and the
problems confronted will be needed and will require a wide variety of research
perspectives. Sin embargo, given the serious concerns about its political and financial
viability for some BRI countries identified in the following section, we deem it
crucial to examine the initiative’s early experience to identify the magnitude of
the risks involved and how it might be improved, and possibly even to reconsider
whether the BRI is still worth pursuing. This paper’s objective is, por lo tanto, a
undertake an analysis of the extent to which the ACFIC has been successful in
aligning its member firms with the government’s BRI objectives and the need for
possible reforms.

The remainder of the paper is organized as follows. Section II provides
background on business associations in general and the ACFIC in particular,
as well as on the BRI and some of its challenges. Section III outlines the
steps to be followed in our overall evaluation of the ACFIC as a coordination
device in achieving the BRI’s goals. Section IV develops the econometric models,
including the methods designed to deal with potential estimation biases. Sección
V describes the data used and displays the results from the regression analysis.
Section VI conducts robustness checks to resolve selection, heteroskedasticity,
and “confounding” issues. Section VII evaluates the extent to which the ACFIC’s
trade-promoting effects may differ between BRI and non-BRI countries. Finalmente,
section VIII concludes.

II. Background on Business Associations, the All-China Federation of Industry

and Commerce, and the Belt and Road Initiative

Can business associations be counted upon to help guide private firms to
exert healthy influences on the economy to achieve the desired objectives of the state

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Does the ACFIC Align Private Firms with the Goals of the PRC’s BRI? 47

and society? One cannot help but be skeptical about this since business associations
are often believed to hurt the local economy by offering monopolistic protection,
triggering corruption and criminal groups, reducing bureaucratic efficiency, y
encouraging cartels that raise prices for consumers and reduce allocative efficiency
(Doner and Schneider 2000). Además, they may use their collective power to
influence local politics to attain benefits for themselves and undermine good
governance (Bräutigam, Rakner, and Taylor 2002). Por otro lado, negocio
associations can benefit the general public by introducing regulations favoring
market development and protecting businesses from default, criminal activity,
insolvency, and unreasonable governmental interference (bennett 1998, Önis and
Türem 2001). They may be especially helpful in assisting new and small firms
to adopt innovations and break into value chains. As experiences in Eastern
Europe showed during and after the 1980s, nuevo, freestanding, bottom-up, privado-
sector-oriented business associations came to play an essential role in the region’s
transition from central planning to free markets (Sukiassyan and Nugent 2011).
En efecto, they can serve as a coordination device between governments and their
private sectors by sharing information and encouraging mutual understanding and
sustainable economic growth (Johnson, McMillan, and Woodruff 2000).

Sin embargo, the ACFIC is very different from most business associations in
that it is entirely government controlled, being run by the Communist Party of China
(CPC), and yet its member firms are private. All private firms are eligible to become
ACFIC members, and large ones are especially encouraged to join by national and
local governments, and CPC officials. While the membership fees for province-level
ACFIC are not high (alrededor $3,000 per year), the largest cost to members is in terms of the time required to attend the association’s meetings, use the services offered, and connect to national and local government offices, and other firms. By the end of 2016, Había 3 million ACFIC member firms, accounting for about 10% of all Chinese private firms. Taking advantage of the ACFIC, member firms have lobbied for more favorable government policies, especially those concerning private property rights. The association has also assisted its members to be better informed of new government policies to facilitate connections between business owners and government officials. Given the controversy concerning business associations in general and the uniqueness of the situation in the PRC, we endeavor to contribute to the literature by exploring ACFIC’s effectiveness in achieving coordination between government agencies and firms in this new and especially challenging BRI context. No es sorprendente, there is disagreement in the economics literature over how helpful the PRC’s top-down ACFIC has been to private firms and the extent to which it has succeeded in inducing private firms to attain the government’s economic objectives. Por ejemplo, Jia (2014) and Ma, Rui, and Wu (2015) employed standard econometric techniques, including propensity score matching, to suggest that the ACFIC’s most useful function is to allow owners and managers of private firms to win positions in the CPC or government, but not to boost the performance of l D o w n o a d e d f r o m h t t p : / / directo . mi t . / e d u a d e v / a r t i c e – pdlf / / / / / 3 7 2 4 5 1 8 4 6 8 0 5 a d e v _ a _ 0 0 1 4 9 pd . f por invitado 0 7 septiembre 2 0 2 3 48 Asian Development Review their firms. Todavía, taking advantage of some surveys that compare ACFIC member firms with nonmember firms in multiple respects, Lei and Nugent (2018) employed various estimation techniques to show quite robustly that the ACFIC did play a significant role in helping its member firms change their focus rather radically from the government’s earlier goal of promoting outward-looking exports to the subsequent goal of prioritizing inward-looking investments between 2007 y 2011 (es decir., before the BRI’s inauguration). The reason for the sudden change was to prevent the PRC’s economy from falling victim to the 2008–2009 global financial crisis that did serious damage to the firms and economies of other exporting countries. Their study also identified the mechanism behind the success in achieving a sharp change in objectives by providing information to member firms about both the new government objectives and possible means of attaining them. Sin embargo, due to limited information on the geographical destination of firm- level sales and investments available in the Chinese Private Enterprise Survey, which was the dataset utilized in previous studies, and the absence of any recent survey results, it was not possible to use that data to examine the role of the ACFIC in recent years. As explained below, as an alternative source of relevant data, we use data published by the PRC’s province-level statistical agencies to see if the ACFIC has succeeded in encouraging its member firms to trade with countries favored by the association in a way that would be consistent with the government’s BRI objectives. As noted above, the BRI is of enormous importance, not only to the PRC but also to the rest of the world. Announced by President Xi Jinping while visiting Kazakhstan in September 2013, the initiative is designed to develop transportation, logistics, and other infrastructure to link the PRC with BRI-identified countries across the world. By sharply reducing the cost of exporting and importing goods and services across this enormous network of countries, the BRI is expected to stimulate industrial production and technological improvements, not only in the PRC but also throughout Eurasia and Africa (Dunford and Liu 2019). To help accomplish this, the Asian Infrastructure Investment Bank, the China Development Bank, and the Export–Import Bank of China were formed, and they have all been growing rapidly since their establishment (Yu 2017). Por ejemplo, by the end of 2018, 152 countries had joined the BRI in some capacity and 96 of them had joined the Asian Infrastructure Investment Bank as members. The accumulated investments of these institutions amounted to at least $1 trillion by the end of 2018, and this total is
expected to grow to more than $2 trillion to finance the BRI’s infrastructural needs
(Hillman 2018). If the BRI develops as expected, it will perhaps become the largest
international investment project ever created and serve as an integrating force for
Eurasia and much of the world.

Sin embargo, the BRI faces enormous challenges. One is that many developed
countries, especially the United States (US) and the United Kingdom, seem
to be moving in directions less friendly to global trade. These trends toward

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Does the ACFIC Align Private Firms with the Goals of the PRC’s BRI? 49

de-integration could possibly spread to Asia and Africa, negatively affecting their
initially positive attitudes toward the BRI. Además, as large numbers of Chinese
workers have been moving into BRI countries to facilitate local construction and
other infrastructural activities, resentment has arisen among the nationals of host
countries like Pakistan (Solangi 2018) and Viet Nam (Elmer 2018). Newspaper
artículos (p.ej., Sotavento 2019) have also called attention to the concerns of the US, India,
and Japan over the PRC’s recent establishment of military bases in Djibouti, dónde
each of these countries had already established its own base. Other studies have
claimed that without addressing the different needs of various BRI countries for
importing or exporting labor over time, or facilitating internal labor mobility, el
BRI could contribute to rising geographic and income inequalities (Gill, Lall, y
Lebrand 2019; Bruni 2019). In light of these challenges, the future of the PRC’s
involvements in these countries is increasingly uncertain. Concerning the allocative
efficiency of different regions within the PRC, Gibson and Li (2018) empleado
data envelopment analysis and other statistical tools to demonstrate that distributing
too much effort and resources to low productivity areas in the western PRC along
transport routes to other BRI countries could jeopardize the overall efficiency of the
PRC’s economy and the sustainability of its remarkable growth.

Given both its great economic potential and substantial political and
economic risks, multiple studies focusing on the BRI’s trade and investment
facilitation mechanisms have been conducted. Herrero and Xu (2017); Kohl (2019);
and Baniya, Rocha, and Ruta (2019) have employed gravity models to argue
that the initiative has sharply increased trade volumes between most participating
economies since 2013. Bird, Lebrand, and Venables (2019) have constructed spatial
equilibrium models for BRI regions suggesting that the initiative could substantially
improve the real incomes of the participating developing economies. Wiederer
(2018) and de Soyres et al. (2018) have collected firsthand data from countries
involved in the initiative and find that logistical costs have, as intended, been falling
rapidly since 2013. Todavía, since most logistical and infrastructural activities have been
those of the public sector, these studies have done little to determine whether private
firms have been participating sufficiently for the BRI to be successful.1

It is the combination of the importance of the private sector’s involvement
to make BRI successful and uncertainty about whether the PRC’s growing private
sector will become sufficiently engaged in the prioritized activities that motivates
our primary research question: “Has the ACFIC yet come to play a significant role
in assuring sufficient participation of the PRC’s private firms in alignment with the
country’s BRI objectives?"

1While some studies have shown that the private sector is involved in trade and investment with BRI countries

(cheng 2018, ACFIC 2018, Zhai 2018), other analysts, such as Hillman (2018), have doubted this.

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50 Asian Development Review

III. Steps for Evaluating the All-China Federation of Industry and Commerce’s

Role in the Belt and Road Initiative

To answer our central question, we break our analysis into two parts. Primero,
we seek to determine if the ACFIC has been successful in increasing exports and
imports primarily with the countries it seems to prioritize. Then we determine if,
since the BRI’s inauguration in 2013, the ACFIC has increased its priority toward
BRI countries in general. In view of the “one-way road” argument raised by US
Vice President Mike Pence at the 2018 Asia–Pacific Economic Cooperation summit
(Tarabay and Choe 2018) and echoed in several other countries, we also investigate
whether the BRI has mainly benefited the PRC’s exports to, rather than its imports
de, BRI countries.2 This would seem especially important as Hurley, morris, y
Portelance (2018) concluded that the negative outcomes for individual BRI partners
are so large as to raise their debt levels enough to trigger defaults by up to eight
participating BRI countries.

The focus of the paper is, por lo tanto, on testing the validity of the following

three hypotheses:

(i) The ACFIC’s promotion of trade activities with any non-PRC country is
positively related with the extent to which the ACFIC calls attention to that
country in its newspaper, the China Business Times, which acts as a proxy
for the ACFIC’s policy direction.

(ii)

Since the inauguration of the BRI in 2013, the ACFIC has emphasized
BRI countries to a larger extent than non-BRI countries in its mostly trade-
encouraging news reports, thus implying that the ACFIC encourages its
member private firms to trade with BRI countries, though not necessarily
equally.

(iii) The ACFIC promotion of trade with BRI countries has resulted in greater

exports from the PRC than imports to the PRC.

IV. Econometric Models

A.

Province-Level Gravity Model of International Trade

Since the pioneering efforts of Jan Tinbergen (1962), gravity models have
served as the most common means of analyzing bilateral trade patterns, cuales son

2The analyses to date have been mixed on this. Chen and Lin (2018) and Dunford and Liu (2019) deny
él, while tending to confirm it are Huang (2016) for BRI countries in general; Irshad, Xin, and Arshad (2015) para
Pakistán; Yu (2017) for Myanmar; and Kohl (2019) for Europe.

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Does the ACFIC Align Private Firms with the Goals of the PRC’s BRI? 51

essential to the BRI. According to his specification, bilateral trade volumes from
region i to region j, Ti j, could be expressed as

Ti j ≡ K

b

GDPi

aGDPj
C
Di j

(1)

where GDPi is a proxy for the economic size (gross domestic product [PIB]) de
region i, GDPj is a proxy for that of region j, Di j is the distance (a proxy for trading
costo) between i and j, and K is a positive constant.

anderson (1979) provided a theoretical framework for the model by
incorporating a Cobb–Douglas utility function. Anderson and van Wincoop (2003)
extended that model by using a constant elasticity of substitution utility function,
whereby the exports from region i to region j, xi j, could be expressed as

xi j = yiy j
yW

(cid:3)

1−σ

(cid:2)

ti j
PiPj

(2)

where yW is the economic size of the world, measured by GDP, yi and y j are the
GDPs of regions i and j, respectivamente, ti j is the trading cost between regions i and j,
Pi and Pj are the relative consumer prices of regions i and j, and σ is the elasticity
of substitution in the constant elasticity of substitution utility function.

Since this paper’s primary concern is the economic influence of the ACFIC
on cross-border trade, we conduct province-level analyses by treating region i as a
PRC province and region j as a country or region outside of the PRC. This allows
us to detect variations in the ACFIC’s influence on both exports and imports across
different province–country pairs. While the ACFIC can do little to directly affect
the economic size of a province or country, it can reduce the information and other
trading costs (or levels of distrust) between PRC provinces and the countries it
prioritizes in its official newspaper. Por lo tanto, unless otherwise noted, asumimos
that the ACFIC affects the relationship in equation (2) only by lowering the trading
cost ti j.

Following the insight provided by Maurel and Afman (2010) in their
examination of the effect of establishing foreign missions on trading activities, nosotros
specify trading cost ti j as

(cid:4)

ti j ≡

ACFICiCBT j

(cid:5)
kdi j

ρ

(3)

es el
where ACFICi is the number of ACFIC members in province i, CBTj
frequency of the name of country j appearing in the China Business Times, a
newspaper entirely controlled by the ACFIC, and di j
is the distance between
province i and country j. The larger CBT j, the more favorable country j should
be in the ACFIC’s eyes. Respectivamente, ACFICiCBT j could be perceived as a proxy
for the magnitude of the ACFIC’s influence on the bilateral trade between the
province–country pair i j, with ACF ICi by itself reflecting ACFIC’s power over

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52 Asian Development Review

private firms in province i. ρ is expected to be positive as geographical distance
increases transportation costs, and k should be negative as the ACFIC’s influence
on bilateral economic interactions, weighted by CBT j, should be positive.

Plugging equation (3) into equation (2) and transforming it into logarithmic

forma, we obtain
(cid:3)

(cid:2)

ln

xi j
yiy j

= (1 − σ ) k ln

(cid:4)
ACFICiCBT j

(cid:5)

+ (1 − σ ) ρ ln

(cid:5)

(cid:4)
di j

− (1 − σ ) ln(yW )

(cid:4)
− (1 − σ ) ln(Pi) − (1 − σ ) ln

Pj

(cid:5)

(4)

Transforming equation (4) into a format suitable for empirical estimation and

(cid:5)

ln

(cid:4)
xi j,t+1

(cid:4)
= β1 ln

adding both time subscripts and control variables, we obtain
(cid:4)
+ β2 ln(GDPit ) + β3 ln
GDPjt
+ β5Borderi j + β6Religioni j
+ β7 ln(Populationit )
(cid:4)
(cid:5)
+ β9 ln(Areait ) + β10 ln
Area jt

ACFICitCBT jt
(cid:5)

(cid:5)

(cid:5)

(cid:5)

(cid:4)
+ β4 ln
Distancei j
(cid:4)
+ β8 ln
Population jt
(cid:4)
+ β11SF I jt + β12ln

TCPj,t−1

+ β0 + πi jt + εi jt

(5)

(cid:5)

where the dependent variable, xi j,t+1, could alternatively represent exports from i to
j or imports from j to i in year t + 1; ACFICit is the number of ACFIC members in
province i in year t; CBTjt is the frequency of the name of country j appearing in the
China Business Times in year t; GDPit and GDPjt are the GDPs of province i and
country j in year t, respectivamente; Distancei j is the geographical distance between
province i and country j; Borderi j and Religioni j are dummy variables indicating
whether province i and country j share a common border or a common dominant
religion as suggested by Lewer and Van den Berg (2007) (given that some province-
level administrative districts in the PRC are Muslim); Populationit and Population jt
are the populations of province i and country j, respectivamente, in year t; and Areait
and Area jt are the geographic sizes of provinces i and country j, respectivamente, en
year t. To broaden the analysis from a traditional version of the gravity model, nosotros
also include (i) SF I jt, the state fragility index of country j in year t, as an indicator
of the country’s level of political instability in that year; y (ii) TCPj,t−1, the total
turnover of Chinese-contracted projects in country j in year t − 1. El restante
terms include β0, the intercept; πi jt, the interacted fixed effect for the region in the
PRC in which province i is located (Eastern PRC, Central PRC, or Western PRC),
for the continent where country j is located, and for year t, and finally, εi jt, el
residual. From equation (4), β1 ≡ (1 − σ )k and β4 ≡ (1 − σ )ρ.

If the model yields the expected results, the treatment effects quantified by
β1, β2, and β3 should be positive, but β4 should be negative to be consistent with
the gravity model. β5 and β6 should be positive because commonality in border and
religion should reduce trading costs. β7 and β8 can be either positive or negative

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Does the ACFIC Align Private Firms with the Goals of the PRC’s BRI? 53

since the relevance of population sizes and their effects after controlling for GDP
are ambiguous. β9 and β10 are expected to be negative because larger land areas
increase the distance between the centers in the two regions. β11 should be negative
because the insecurity of a country may serve as a hidden cost (Blomberg and
Hesse 2006), and β12 should be positive since the reduction in trading costs through
logistics, transportation improvements, and information dissemination has been a
major thrust of the BRI (Rehman and Ding 2019). Además, we allow for fixed
effects for year and for both province and country to capture unobserved effects.
Finalmente, the dependent variable, xi j,t+1, is designated to be 1 year after the year in
which the independent variables are measured so as to mitigate simultaneity and/or
reverse causality problems.

B.

Two-Stage Least Squares Strategy Based on the Province-Level
Gravity Model

While equation (5) provides a suitable econometric model, it may be subject
to endogeneity bias. Por ejemplo, it could be possible that CPC membership affects
both ACFICit and trade but with no direct connection between them. To alleviate this
type of imprecision, we devise a two-stage least squares (2SLS) estimation with an
instrumental variable. Using equation (5) as the second stage, following the method
employed by Lei and Nugent (2018) for the ACFIC’s coordination effects prior to
the BRI’s inauguration, the first stage for ln(ACF ICitCBTjt ) becomes

(cid:4)

(cid:5)

ln

ACF ICitCBTjt

+ α2 ln(GDPit ) + α3 ln

(cid:5)

(cid:4)
GDPjt

(cid:5)

(cid:5)

(cid:4)
= α1 ln
PrivateFirmitCBTjt
(cid:4)
+ α4 ln
Distancei j
+ α7 ln(Populationit ) + α8 ln
(cid:4)
(cid:5)
+ α10 ln
Area jt
+ α0 + πi jt + εi jt

+ α5Borderi j + α6Religioni j

(cid:5)

(cid:4)
Population jt
(cid:4)
TCPj,t−1

(cid:5)

+ α11SF I jt + α12ln

+ α9 ln(Areait)

(6)

where PrivateFirmit is the number of private firms in province i in year t; él
corresponds to ACFICit, the number of private firms that are members of the
ACFIC in province i. We use ln(PrivateFirmitCBTjt ) as the instrumental variable in
equation (6) because the number of private firms in a province, which has no direct
link with trade, provides a reasonable proxy for the number of ACFIC member
firms in that province and, hence, for the ACFIC’s potential influence on the trade
of province i with country j. By including all exogenous variables in the second
stage along with the instrument, any correlation between the error term and other
independent variables that could bias the estimates can be reduced (lanaridge
2010, 89–90).

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54 Asian Development Review

C.

Exports-versus-Imports Comparison by Incorporating
an Interaction Term

To compare the magnitude of the ACFIC’s effects on exports with that
on imports, following Dunlevy (2006), we incorporate an interaction term in the
regression model of equation (5):

(cid:4)
Exporti j,t+1

(cid:5)

ln

(cid:4)
= γ1 ln

(cid:5)

(cid:5)

ACF ICCBTi jt
(cid:5)
+ γ5 ln

(cid:4)
+ γ2 ln
ACF ICCBTi jt
(cid:4)
(cid:4)
+ γ3 ln(GDPit ) + γ4 ln
Distancei j
+ γ6Borderi j + γ7Religioni j + γ8 ln(Populationit )
(cid:4)
+ γ9 ln
Population jt
(cid:4)
+ γ12SF Ii jt + γ13ln

+ γ10ln (Areait ) + γ11 ln

+ γ0 + πi jt + εi jt

TCPi j,t−1

GDPjt

Typei jt
(cid:5)

(cid:5)

(cid:5)

(cid:4)
Area jt

(cid:5)

(7)

where i represents the origin and j the destination. If Typei jt = 1, i is a PRC
province and j a non-PRC country, and ACF ICCBTi jt is the number of ACFIC
members in province i times the frequency of the name of country j appearing in
the China Business Times in year t; if Typei jt = 0, i is a foreign country and j a
PRC province, and ACF ICCBTi jt is the number of ACFIC members in province
j multiplied by the frequency of the name of country i appearing in the China
Business Times in year t. Other variables are the same as those in preceding
ecuaciones. If γ2 > 0 and is statistically significant, this would support the “one-way
road” argument.

V. Data Sources and Statistical Analysis

A.

Data Sources

To carry out econometric analysis for the ACFIC’s effects on trade, we rely
on bilateral trade data from province-level statistical yearbooks between 2010 y
2017. The values from 2010 a 2017 are used for the dependent variable and those
de 2009 a 2016 for the lagged trade variables appearing as explanatory variables.
Combining all available trade data for the 8-year interval allows us to construct a
dataset with more than 20,000 observaciones.

The greatest data collection challenge is with respect to the measures
of ACFICit and CBTjt. For ACFICit, we utilize the yearbooks published by the
ACFIC since 2009. In each yearbook, each province-level ACFIC branch has an
annual report on its membership, though it does not in every case disclose the
precise number of members in that year. After inspecting these reports, we found
information to be missing for 40 out of 248 province–year observations. For CBTjt,
programming techniques were used to identify all country names on the website of
the China Business Times, the entries were then read, and their numbers recorded

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Does the ACFIC Align Private Firms with the Goals of the PRC’s BRI? 55

for each country and year from 2009 a 2016. Many of these news notes include the
kind of information that should encourage member firms to consider activities in
these countries, and hence, these newspaper-oriented trends could serve as a valid
proxy for the policy direction of the ACFIC’s influence over its member firms in
general. En otras palabras, the ACFIC should affect its member firms in the same way
as its newspaper influences its readers. Apéndice 1 presents an English translation
of a typical news article in China Business Times. Data sources for the control
variables are indicated in Table 1, which also reports descriptive statistics for all
variables.

B.

Exports and Imports

We report the ordinary least squares (OLS) results of our regression analysis
for xi j,t+1, being the log of exports from province i to country j in year t + 1 en
columnas (1) y (2) de mesa 2. Similarmente, columnas (3) y (4) report the OLS
results for imports. The odd-numbered columns contain no fixed effects, mientras que la
even-numbered columns include interacted fixed effects for year, PRC province,
and non-PRC country to capture the time-invariant unobserved variables. Missing
values are omitted because they could represent unrecorded, en vez de 0, valores.
The possible selection bias caused by this truncation will be addressed in section VI.
As shown in the first row, the coefficient of ln(ACF ICitCBTjt ) is positive
and statistically significant in all columns, indicating the ability of the ACFIC
to influence its member firms to increase exports to and imports from the non-
PRC countries frequently covered in its mouthpiece, the China Business Times.
Además, as expected, the parameter estimates of the GDP terms, border and
religion dummies, and the lagged turnover of contracted projects are all positive
and statistically significant, while those for the distance and state fragility index are
negative and statistically significant. The large R-squared values also demonstrate
the model’s relatively high explanatory power. Following the method employed by
Lei and Nugent (2018), but in this quite different context, we test for the stability of
the coefficient of ln(ACF ICitCBTjt ) by calculating the ratio of the R-squared value
obtained with ln(ACF ICitCBTjt ) as an independent variable to the R-squared value
obtained in equation (5) without it (es decir., when β1 = 0 for equation [5]). As suggested
by Altonji, Elder, and Taber (2005) and Oster (2017), this ratio, represented by δ,
measures how large the impact of unobserved variables must be to invalidate the
identified treatment effect of ln(ACF ICitCBTjt ) en mesa 2. For this to be so, δ ought
to be at least 1. As shown in the last row of Table 2, all specifications yield greater-
than-one values of δ, demonstrating that unobserved variables are unlikely to nullify
our statistical results.

Despite the strong statistical significance of most of the explanatory variables
displayed in Table 2,
to various endogeneity
these results could be subject
prejuicios. Por lo tanto, en mesa 3, using equation (6) as the first stage equation and

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56 Asian Development Review

Dependent
Variables

Mesa 1. Estadísticas descriptivas

No. de

Estándar

Observations Mean Deviation Min

máx.

Fuente

Log of exports

13,605

10.317

3.061

0.000

19.385 Provincial-level

yearbooks

Log of imports

9,065

10.181

3.504

0.000

17.695 Provincial-level

yearbooks

Independent
Variables

ln(ACF ICit )
ln(CBTjt )

ln(GDPit )

ln(GDPjt )

ln(Distancei j )

Borderi j

Religioni j

No. de

Estándar

Observations Mean Deviation Min

máx.

Fuente

203
1,229

11.328
2.558

0.961
1.800

7.688
0.000

248

19.121

1.013

15.681

12.682 ACFIC yearbooks

7.974 China Business Times
website
20.920 National Bureau of

Statistics of China

1,438

17.320

2.315

10.368

23.552 World Bank World

1,878

1,861

1,861

8.832

0.650

4.716

0.007

0.083

0.000

Desarrollo
Indicators

9.899 Google Maps and
CEPII
1.000 Google Maps and
CEPII

0.028

0.166

0.000

1.000 Organization of

Islamic Cooperation

ln(Populationit )

248

17.319

0.847

14.901

18.516 National Bureau of

Statistics of China

ln(Population jt )

1,438

15.574

2.116

9.253

21.004 World Bank World

ln(Areait )

ln(Area jt )

SF I jt

ln(TCPj,t−1)

Instrumental
Variables
ln(PrivateFirmit )

Desarrollo
Indicators

12.016

1.225

9.031

14.305 National Bureau of

Statistics of China

11.110

2.682

0.693

16.611 World Bank World

8.309

6.225

0.000

7.142

2.509

−1.204

Estándar

Desarrollo
Indicators
25.000 Quality of Government
database

13.445 National Bureau of

Statistics of China

31

298

1,325

1,426

No. de

Observations Mean Deviation Min

máx.

Fuente

186

11.934

1.244

7.312

14.253 National Bureau of

Statistics of China

ACFIC = All-China Federation of Industry and Commerce, CEPII = Centre d’Etudes Prospectives et d’Informations
Internationales.
Notas: The time range of the dependent variables is from 2010 a 2017; those for the independent and instrumental
variables, except the log of CBTjt , are from 2009 a 2016. The time range of the log of CBTjt is from 2009 a 2017
because it becomes a dependent variable in section V. The variables measured by currency values are in thousand
current United States dollars. Land area is measured in square kilometers.
Fuente: Authors’ calculations.

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Does the ACFIC Align Private Firms with the Goals of the PRC’s BRI? 57

Mesa 2. Ordinary Least Squares Estimates

Exports

Imports

Explanatory Variables

(1)

(2)

(3)

(4)

ln(ACF ICitCBTjt )

ln(GDPit )

ln(GDPjt )

ln(Distancei j )

Borderi j

Religioni j

ln(Populationit )

ln(Population jt )

ln(Areait )

ln(Area jt )

SF I jt

ln(TCPj,t−1)

Fixed effects

No. of observations
F-statistic
R-squared
δ

0.263***
(0.029)
0.861***
(0.057)
0.417***
(0.046)
−0.315***
(0.060)
3.572***
(0.606)
1.897***
(0.223)
0.591***
(0.075)
0.266***
(0.046)
−0.650***
(0.031)
−0.079***
(0.021)
−0.038***
(0.011)
0.126***
(0.020)

No

8,504
443.889
0.717
1.012

0.265***
(0.026)
1.716***
(0.062)
0.479***
(0.044)
−0.431***
(0.056)
3.451***
(0.575)
2.568***
(0.223)
−0.448***
(0.078)
0.205***
(0.046)
−0.488***
(0.023)
−0.078***
(0.020)
−0.038***
(0.011)
0.129***
(0.019)

Province
Country Year

8,504
961.344
0.685
1.015

0.280***
(0.058)
1.366***
(0.110)
0.761***
(0.098)
−0.620***
(0.111)
3.155***
(0.842)
1.443***
(0.536)
−0.084
(0.154)
−0.279***
(0.095)
−0.970***
(0.060)
0.194***
(0.038)
−0.065***
(0.024)
0.090**
(0.035)

No

6,599
194.546
0.552
1.010

0.257***
(0.054)
1.762***
(0.111)
0.867***
(0.090)
−0.586***
(0.105)
3.686***
(0.810)
1.856***
(0.500)
−0.604***
(0.146)
−0.270***
(0.090)
−0.798***
(0.046)
0.179***
(0.036)
−0.079***
(0.022)
0.092***
(0.032)

Province
Country Year

6,599
361.034
0.514
1.011

Notas: Standard errors clustered at the level of province–country pair are included in parentheses.
Significance level = *p < 0.1, **p < 0.05, ***p < 0.01. Source: Authors’ calculations. equation (5) as the second stage, we report 2SLS estimates. Panel A of the table displays the second-stage results, and panel B displays the fist-stage results. The setting of fixed effects in Table 3 is the same as in Table 2. The treatment effect of ln(ACF ICitCBTjt ) is again statistically significant and slightly larger than in Table 2, while the effects of the other explanatory variables are similar. At the bottom of Table 3, following Stock, Wright, and Yogo (2002), we also report the Cragg–Donald statistics, which are the same as the F-statistics testing the significance of the instrumental variable in the first-stage equations given that there is only one such variable. Since the Cragg–Donald statistics are much larger than their corresponding critical values shown in parentheses, our use l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / / 3 7 2 4 5 1 8 4 6 8 0 5 a d e v _ a _ 0 0 1 4 9 p d . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 58 Asian Development Review Table 3. Two-Stage Least Squares Estimates Exports Imports Explanatory Variables (1) (2) (3) (4) A. Second stage; dependent variable: ln(Export i j,t+1) and ln(Import i j,t+1) 0.288*** ln(ACF ICitCBTjt ) (0.066) 2.389*** (0.125) 0.837*** (0.104) 0.378*** (0.032) 1.721*** (0.066) 0.399*** (0.048) ln(GDPjt ) ln(GDPit ) 0.381*** (0.068) 1.581*** (0.129) 0.672*** (0.103) 0.336*** (0.034) 0.827*** (0.072) 0.372*** (0.050) −0.236*** −0.271*** −0.543*** −0.740*** (0.063) (0.060) (0.115) 3.513*** 3.335*** 3.288*** (0.597) (0.586) (0.829) 1.991*** 2.838*** 1.582*** (0.541) (0.256) (0.233) 0.576*** −0.508*** −0.379** (0.084) (0.091) (0.175) 0.226*** −0.273*** −0.320*** 0.267*** (0.098) (0.049) (0.048) −0.645*** −0.507*** −0.953*** −0.732*** (0.033) −0.087*** −0.086*** (0.021) (0.022) −0.033*** −0.026** (0.012) (0.012) 0.117*** 0.113*** (0.020) (0.021) (0.062) 0.187*** (0.038) −0.059** (0.024) 0.093*** (0.035) (0.115) 3.287*** (0.834) 1.463*** (0.467) −1.333*** (0.154) (0.046) 0.170*** (0.037) −0.070*** (0.024) 0.108*** (0.035) (0.100) (0.025) 1.015*** (0.009) 0.988*** (0.010) 0.975*** (0.010) 1.001*** (0.009) −0.896*** −0.976*** −0.898*** −1.031*** (0.019) (0.020) (0.018) 0.034** 0.030*** 0.008 (0.013) (0.011) (0.011) 0.074*** −0.029 −0.025 (0.019) (0.016) (0.016) −0.093 −0.029 −0.067 (0.103) (0.097) (0.100) −0.115*** −0.173*** −0.003 (0.087) (0.038) (0.043) 0.603*** 0.518*** 0.592*** (0.031) (0.024) (0.027) −0.006 −0.002 0.021* (0.015) (0.013) (0.012) 0.138*** 0.162*** 0.135*** (0.013) (0.008) (0.010) 0.001 0.000 0.007 (0.006) (0.007) (0.006) −0.008*** −0.002 −0.001 (0.003) (0.003) (0.003) (0.024) 0.072*** (0.014) −0.145*** (0.019) −0.237* (0.131) −0.074 (0.083) 0.746*** (0.028) −0.040*** (0.015) 0.097*** (0.010) −0.002 (0.007) 0.001 (0.003) Continued. ln(Distancei j ) Borderi j Religioni j ln(Populationit ) ln(Population jt ) ln(Areait ) ln(Area jt ) SF I jt ln(TCPj,t−1) ln(GDPit ) ln(GDPjt ) ln(Distancei j ) Borderi j Religioni j ln(Populationit ) ln(Population jt ) ln(Areait ) ln(Area jt ) SF I jt B. First stage; dependent variable: ln(ACFICitCBT jt ) ln(PrivateFirmitCBTjt ) l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / / 3 7 2 4 5 1 8 4 6 8 0 5 a d e v _ a _ 0 0 1 4 9 p d . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 Does the ACFIC Align Private Firms with the Goals of the PRC’s BRI? 59 Table 3. Continued. Exports Imports Explanatory Variables (1) ln(TCPj,t−1) Fixed effects No. of observations Cragg–Donald statistic (Critical value) First-stage R-squared Second-stage R-squared 0.008* (0.005) No 6,203 25,938 (16.38) 0.957 0.702 (2) 0.004 (0.005) (3) 0.013** (0.005) (4) 0.030*** (0.006) Province Country Year 6,203 23,083 (16.38) 0.947 0.786 No 4,870 19,117 (16.38) 0.950 0.549 Province Country Year 4,870 14,459 (16.38) 0.932 0.640 Notes: Standard errors clustered at the level of province–country pair are included in parentheses. Significance level = *p < 0.1, **p < 0.05, ***p < 0.01. The critical value of the Cragg–Donald statistic in each parentheses corresponds to the 10% maximal IV size. Source: Authors’ calculations. of ln(PrivateFirmitCBTjt ) as the instrumental variable seems validated and the conclusion from Table 2 confirmed. In summary, through its official newspaper’s country-specific news coverage, the ACFIC has encouraged its member firms to increase exports and imports with the countries it prioritizes. According to Tables 2 and 3, if the OLS and 2SLS estimates are unbiased as assumed, a 1% increase in the frequency a country’s name appearing in the China Business Times would be expected to increase the PRC’s trade activities with that country by around 0.3%. In view of the large volumes of PRC exports and imports across the globe, this level of magnitude of the impact on trade is impressive. Therefore, this result convincingly demonstrates the large influence of the ACFIC on the trading destinations of its member firms. C. Exports versus Imports Next, we conduct a comparative analysis between exports and imports based on the technique articulated in equation (7). These estimates are reported in Table 4.3 Those in columns (1) and (2) contain no fixed effects, and those in columns (3) and (4) contain interacted fixed effects for year, province, and country. For comparison purposes, columns (1) and (3) are the baseline regressions without the interaction term with Typei jt, while columns (2) and (4) estimate coefficients with the interaction term included as in equation (7). The primary objective in this comparison is to examine the likelihood of a positive γ2, the coefficient of the interaction term, to determine whether the effects of ACF ICitCBTjt on exports are greater than those on imports. 3Since the log of ACF ICitCBTjt appears twice in equation (7), greatly complicating matters, we choose not to carry out 2SLS estimation in this case. l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / / 3 7 2 4 5 1 8 4 6 8 0 5 a d e v _ a _ 0 0 1 4 9 p d . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 60 Asian Development Review Table 4. Estimates of Exports-versus-Imports Comparison Explanatory Variables (1) (2) ln(ACF ICCBTi jt ) ln(ACF ICCBTi jt )Typei jt ln(GDPit ) ln(GDPjt ) ln(Distancei j ) Borderi j Religioni j ln(Populationit ) ln(Population jt ) ln(Areait ) ln(Area jt ) SF Ii jt ln(TCPi j,t−1) 0.251*** (0.036) 0.945*** (0.052) 0.792*** (0.053) −0.218*** (0.080) 3.268*** (0.716) 0.707** (0.306) 0.176*** (0.068) −0.076 (0.053) −0.321*** (0.027) −0.138*** (0.022) 0.022* (0.013) 0.094*** (0.022) 0.193*** (0.036) 0.100*** (0.005) 1.167*** (0.055) 0.666*** (0.052) −0.209** (0.081) 3.540*** (0.733) 0.563* (0.308) −0.139** (0.067) 0.261*** (0.054) −0.177*** (0.027) −0.323*** (0.022) 0.019 (0.013) 0.099*** (0.023) (3) 0.193*** (0.032) 1.253*** (0.050) 1.082*** (0.051) −0.214*** (0.070) 3.626*** (0.739) 1.480*** (0.284) −0.013 (0.065) −0.229*** (0.053) −0.362*** (0.025) −0.185*** (0.020) 0.055*** (0.013) 0.078*** (0.022) (4) 0.135*** (0.033) 0.102*** (0.005) 1.485*** (0.054) 0.954*** (0.050) −0.212*** (0.072) 3.884*** (0.757) 1.255*** (0.282) −0.322*** (0.065) 0.119** (0.054) −0.231*** (0.023) −0.381*** (0.021) 0.054*** (0.013) 0.087*** (0.022) Fixed effects No No Province Country Year Province Country Year No. of observations F-statistic R-squared δ 15,103 317.004 0.522 1.012 15,103 308.884 0.562 1.090 15,103 678.995 0.479 1.009 15,103 614.109 0.521 1.099 Notes: Standard errors clustered at the level of province–country pair are included in parentheses. Significance level = *p < 0.1, **p < 0.05, ***p < 0.01. Source: Authors’ calculations. Consistent with hypothesis (3) stated in section III, γ2 is positive and statistically significant in columns (2) and (4). The coefficient of ln(ACF ICitCBTjt ) is also positive, indicating that the ACFIC promotes both exports and imports with the specific countries it prefers, albeit exports more than imports, and that the coefficients of the economic size variables are positive, while the coefficient of bilateral distance is negative. The coefficients of most control variables are also consistent with those found in the previous tables except that most of the coefficients on SF Ii jt are positive and statistically significant. We also compute the value of δ to test the stability of the coefficient of ln(ACF ICitCBTjt ) and that of the interaction term. In columns (1) and (3), the l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / / 3 7 2 4 5 1 8 4 6 8 0 5 a d e v _ a _ 0 0 1 4 9 p d . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 Does the ACFIC Align Private Firms with the Goals of the PRC’s BRI? 61 definition of δ is the same as that displayed in Table 2. δ is slightly different in columns (2) and (4) because it is defined as the ratio of the R-squared value with γ1 (cid:4)= 0 and γ2 (cid:4)= 0 to that with γ1 = γ2 = 0. Simply put, δ compares the R-squared values before and after incorporating any term related to ACF ICitCBTjt or Typei jt. Since all values of δ are larger than 1, the estimates of the treatment effects are statistically valid. In summary, the estimation results support hypothesis (3) in section III and show that the ACFIC seems to have the effect of encouraging its member firms to conduct export activities to a greater extent than import activities with the countries it prioritizes in the China Business Times. Numerically, while a 1% increase in ACF ICitCBTjt is expected to increase exports from province i to country j by 0.24%, it is only expected to increase the imports of province i from country j by 0.14%. Consequently, this 0.1 percentage points difference could lead to a trade surplus in province i and a trade deficit in country j. VI. Robustness Checks As pointed out when discussing our treatment of missing data for exports and imports in the previous section, omitting observations for trade with zeroes can evade the problem arising when the log of zero is undefined, but it cannot assure econometric validity. To resolve this issue in a rigorous manner, we use two alternative methods: Heckman (1979) selection and Poisson pseudo-maximum- likelihood (PPML) models. Furthermore, based on the fixed-effects model and the Arellano–Bond estimation, we add the lagged dependent variable as an independent variable to help deal with confounding and endogeneity biases. A. Heckman Selection Model The Heckman selection model is an econometric maneuver to correct for bias caused by nonrandomly selected samples. In the context of trade, independent variables with missing observations could possess properties different from those with nonmissing observations. Consequently, omitting them might have led to considerable imprecision. Following the earlier applications of the Heckman selection model to the gravity model by Bikker and de Vos (1992) and Head and Mayer (2010), we construct a Heckman-augmented, two-step gravity model. In the first step, the probability of a trade interaction being recorded between province– country pair i j is estimated by using a probit model: l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / / 3 7 2 4 5 1 8 4 6 8 0 5 a d e v _ a _ 0 0 1 4 9 p d . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 Any_xi j,t+1 = φ ⎛ ⎝ (cid:5) (cid:4) (cid:4) , ln ACF ICitCBTjt ln GDPjt (cid:4) Borderi j, Religioni j, ln(Populationit ) , ln Population jt (cid:4) (cid:4) , SF I jt, ln ln(Areait ) , ln TCPj,t−1 Area jt (cid:4) , ln(GDPit ) , ln Distancei j (cid:5) (cid:5) (cid:5) (cid:5) (cid:5) , , ⎞ ⎠ (8) 62 Asian Development Review where Any_xi j,t+1 = 1 if there is a record of the relevant cross-border economic imports, or exports-versus-imports, depending on the activity (i.e., exports, definition of xi j) between province i and country j, and Any_xi j,t+1 = 0 if such a record does not exist. The symbol φ indicates that this is a probit- estimating operation rather than a linear function. In short, the first step could be understood as a selection process, detecting the commonalities among those with missing observations and preparing to correct for the biases resulting from these commonalities. Then, in the second step, we use an estimating equation similar to the combination of equations (5) and (7), but without fixed effects to avoid excessive complexity. Also, the inverse Mills ratio, λi jt, computed for each observation based on the first stage is added as an additional regressor because if β13, the coefficient of the inverse Mills ratio λi jt, is statistically significant, then the OLS estimations might well be subject to selection biases (Heckman 1979; Helpman, Melitz, and Rubinstein 2008). Thus, the new equation for this second step is expressed as (cid:5) (cid:4) xi j,t+1 ln (cid:4) = β1 ln (cid:5) Typei jt (cid:5) (cid:5) (cid:4) ACF ICCBTi jt (cid:4) + β4 ln + η ln (cid:5) ACF ICitCBTCBTjt (cid:4) + β2 ln(GDPit) + β3 ln GDPjt + β6Religioni j + β7 ln(Populationit ) + β8 ln (cid:4) + β9 ln(Areait ) + β10 ln + β13λi jt + β0 + εi jt Area jt (cid:5) Distancei j (cid:4) Population jt + β5Borderi j (cid:5) (cid:4) + β11SF I jt + β12 ln TCPj,t−1 (cid:5) (9) where η = 0 except when the equation is employed for the exports-versus-imports comparison. Table 5 presents the regression results based on equation (8) for the first stage and equation (9) for the second stage for each of the different measures of xi j (exports, imports, or exports-versus-imports). Columns (1) and (2) show the results for exports and imports, respectively. Columns (3) and (4) show the results of the exports-versus-imports comparison without and with the interaction term, respectively. As shown, the inverse Mills ratio is only statistically significant in columns (1) and (4), implying that our earlier estimates for imports can be trusted at least from the perspective of selection bias. After incorporating the inverse Mills ratio as an additional regressor, the implications drawn from the results in section V still hold true for both exports and the exports-versus-imports comparison even though there no longer remains strong statistical evidence to support some components of the gravity model, especially in column (1). Since the treatment effects represented by the parameters β1 and η remain positive and hover around 0.25 in all specifications, the results robustly confirm all findings in section V. l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / / 3 7 2 4 5 1 8 4 6 8 0 5 a d e v _ a _ 0 0 1 4 9 p d . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 Does the ACFIC Align Private Firms with the Goals of the PRC’s BRI? 63 Table 5. Heckman Selection Model Exports Imports Exports versus Imports Explanatory Variables (1) (2) (3) (4) ln(ACF ICitCBTjt ) ln(ACF ICCBTi jt )Typei jt 0.290*** (0.022) 0.279*** (0.031) 0.245*** (0.019) ln(GDPit ) ln(GDPjt ) ln(Distancei j ) Borderi j Religioni j ln(Populationit ) ln(Population jt ) ln(Areait ) ln(Area jt ) SF I jt ln(TCPj,t−1) Inverse Mills ratio 0.917*** (0.034) 0.762*** (0.027) 1.367*** 1.420*** (0.104) (0.096) 0.766*** 0.084 (0.082) (0.055) −0.618*** −0.151*** −0.072 (0.058) (0.074) (0.054) 3.111*** 3.165*** 2.701*** (0.237) (0.401) (0.304) 0.678*** 1.444*** 2.015*** (0.125) (0.240) (0.135) −0.087 −0.052 0.147*** (0.114) (0.042) (0.163) 0.154*** −0.264*** −0.096** (0.036) (0.038) (0.048) −0.878*** −0.968*** −0.347*** (0.040) (0.038) 0.185*** −0.152*** −0.058*** (0.020) (0.016) −0.064*** 0.007 (0.015) (0.009) 0.085*** 0.082*** (0.015) (0.021) −2.303*** −0.011 (0.348) (0.309) (0.016) 0.038** (0.016) 0.086*** (0.013) −0.449 (0.332) (0.019) No. of observations No. of observations (Selected) No. of observations (Nonselected) 20607 8420 12187 19819 6516 13303 40426 14936 25490 0.190*** (0.017) 0.090*** (0.005) 1.116*** (0.036) 0.640*** (0.025) −0.120** (0.053) 3.321*** (0.227) 0.520*** (0.120) −0.151*** (0.033) 0.198*** (0.047) −0.226*** (0.024) −0.325*** (0.014) 0.043*** (0.014) 0.087*** (0.012) −0.605** (0.302) 40426 14936 25490 Notes: Standard errors are included in parentheses. Significance level = *p < 0.1, **p < 0.05, ***p < 0.01. Source: Authors’ calculations. B. Poisson Pseudo-Maximum-Likelihood Estimation However, since the coefficients of economic size and bilateral distance variables in the gravity model augmented by Heckman selection were not always statistically significant, we implement PPML estimation to reexamine the suitability of the gravity model. First introduced by Silva and Tenreyro (2006), the PPML method estimates the gravity equation in its multiplicative form to simultaneously solve the problem of zero flows and to mitigate the presence of heteroskedasticity. Mathematically, the estimating equation for PPML is l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / / 3 7 2 4 5 1 8 4 6 8 0 5 a d e v _ a _ 0 0 1 4 9 p d . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 64 Asian Development Review xi j,t+1 = exp (cid:10) (cid:5) (cid:4) + η ln (cid:5) (cid:4) β1 ln (cid:4) GDPjt (cid:5) ACF ICitCBTCBTjt (cid:4) + β3 ln + β4 ln Distancei j (cid:4) + β7 ln(Populationit ) + β8 ln Population jt (cid:4) (cid:4) + β11SF I jt + β12 ln + β10 ln TCPj,t−1 Area jt (cid:5) (cid:5) ACF ICCBTi jt + β5Borderi j + β6Religioni j + β9 ln(Areait ) + β0 + πi jt + εi jt (cid:5) (cid:5) Typei jt + β2 ln(GDPit) (cid:11) (10) where η = 0 except when the equation is employed for the exports-versus-imports comparison. Table 6 reports the PPML estimation results. As in Table 5, columns (1) and (2) correspond to exports and imports, respectively. Columns (3) and (4) contain the results for the exports versus imports comparison. Unlike the OLS or 2SLS estimations using R-squared values to quantify the percentage of the variance explained by the independent variables, Table 6 employs pseudo R-squared, a proxy for the regular R-squared, the estimates of which are displayed at the bottom of the table. Accordingly, δ becomes the ratio of the pseudo R-squared value with β1 (cid:4)= 0 to that with β1 = 0 in columns (1) through (3), and the ratio of the pseudo R-squared values with β1 (cid:4)= 0 and β2 (cid:4)= 0 to that with β1 = β2 = 0 in column (4). According to Table 6, when this somewhat more rigorous variant of the gravity model is used, we find that the corresponding treatment effect of the ACFIC and its newspaper on the PRC’s exports is around 30% higher than that in the OLS estimates obtained from Table 2, and the treatment effect on the PRC’s imports remains at roughly the same level. In addition, although the estimated coefficient on the interaction term, η, in Table 6 is smaller than the OLS estimates from Table 4, it is still positive and statistically significant. The features of the gravity model also seem to hold, and the pseudo R-squared values are larger than the R-squared values in the previous tables. Thus, despite some changes in magnitudes, the directions of all the findings regarding the ACFIC’s treatment effects on trade in section V are confirmed by Table 6. C. Are Past Economic Interactions Confounders? Thus far, our statistical analysis has confirmed that the hypothesized correlations between the ACFIC’s pair-wise (province–country) influences exerted by ln(ACF ICitCBTjt ) on both exports and imports between the same pairs in the next year are both statistically significant and free of selection and heteroskedasticity biases. We have also dealt with the potential endogeneity of ACFICit with an instrumental variable approach. Yet, these discovered relationships might still not be causal if the assumptions used to eliminate biases are incorrect and/or if there exists any other variable linking the dependent and any of the independent variables, such as CBTjt, in our models. For example, some previous province–country economic interactions might have impacted both the ACFIC’s l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / / 3 7 2 4 5 1 8 4 6 8 0 5 a d e v _ a _ 0 0 1 4 9 p d . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 Does the ACFIC Align Private Firms with the Goals of the PRC’s BRI? 65 Table 6. Poisson Pseudo-Maximum-Likelihood Estimates Exports Imports Exports versus Imports Explanatory Variables (1) (2) (3) (4) ln(ACF ICitCBTjt ) ln(ACF ICCBTi jt )Typei jt 0.388*** (0.055) 0.294*** (0.079) 0.191*** (0.064) ln(GDPit ) ln(GDPjt ) ln(Distancei j ) Borderi j Religioni j ln(Populationit ) ln(Population jt ) ln(Areait ) ln(Area jt ) SF I jt ln(TCPj,t−1) 0.845*** (0.099) 0.946*** (0.110) 1.618*** 1.132*** (0.216) (0.338) 0.515*** 0.509*** (0.136) (0.084) −0.414*** −0.630*** −0.456*** (0.164) (0.090) 3.448*** 3.529*** (0.732) (0.339) 1.811*** 2.919*** (0.621) (0.414) 0.302 0.477 (0.409) (0.261) −0.153 0.028 (0.122) (0.069) −0.673*** −0.830*** −0.230*** (0.051) (0.111) (0.069) −0.176*** −0.093*** 0.069** (0.038) (0.028) (0.029) −0.056*** −0.123*** −0.020 (0.022) (0.033) (0.018) 0.133*** 0.197*** 0.191*** (0.041) (0.060) (0.037) (0.154) 2.711*** (0.485) 1.699*** (0.509) 0.013 (0.111) −0.081 (0.107) Fixed effects No. of observations Pseudo R-squared δ Province Country Year 20607 0.864 1.049 Province Country Year 19819 0.775 1.063 Province Country Year 40426 0.768 1.061 0.184*** (0.066) 0.015* (0.008) 0.900*** (0.113) 0.897*** (0.103) −0.456*** (0.154) 2.713*** (0.486) 1.699*** (0.509) −0.051 (0.121) −0.028 (0.100) −0.209*** (0.053) −0.195*** (0.039) −0.020 (0.022) 0.133*** (0.041) Province Country Year 40426 0.769 1.062 Notes: Standard errors clustered at the level of province–country pair are included in parentheses. Significance level: *p < 0.1, **p < 0.05, ***p < 0.01. Source: Authors’ calculations. current influence on that pair and that pair’s future economic interactions. If so, this would imply that the correlations identified above could be spurious. To address this type of potential threat, we use a fixed-effects model and a dynamic panel data approach by including ln(xi j,t ), the lagged value of the dependent variable, in the set of independent variables. Mathematically, the new econometric equation can be expressed as (cid:4) ln xi j,t+1 (cid:5) = α1 ln (cid:5) (cid:4) xi j,t (cid:4) + α2 ln ACF ICitCBTjt (cid:5) + W (cid:5) i jt ξ + πt + πi j + εi jt (11) l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / / 3 7 2 4 5 1 8 4 6 8 0 5 a d e v _ a _ 0 0 1 4 9 p d . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 66 Asian Development Review where xi j,t+1 represents the economic interactions (exports or imports) between province i and country j in year t + 1, xi j,t is the lagged value of xi j,t+1 in year t, Wi jt is the set of other control variables identified in equation (5), πt is the fixed effect for year t, and πi j is the interacted fixed effect for province i and country j (not just for their corresponding PRC region and continent). There is only one lagged value of the dependent variable because the timeliness of news is assumed. Based on equation (11), we endeavor to employ various estimation techniques to examine whether the coefficient of ln(ACF ICitCBTjt ), α2, is positive, statistically significant, and not overridden by the presence of ln(xi jt ), so that the results of this exercise would at least be indicative of the robustness of the conclusions from the previous sections. We first use standard fixed-effects models to estimate equation (11), the results of which are reported in the first four columns of Table 7. Columns (1) and (3) exclude the control variable set Wi jt, and columns (2) and (4) include it. While xi j represents the exports from province i to country j in columns (1) and (2), in columns (3) and (4) it represents the imports by province i from country j. According to the four columns, adding the 1-year lagged value of the dependent variable does not override the finding that the coefficient of ln(ACF ICitCBTjt ) is positive and statistically significant, implying that the ACFIC has managed to exert substantial effects on the trading activities between province i and country j even after controlling for the influence of past economic interactions. Moreover, we truncate our dataset into a balanced panel data and conduct a Harris–Tzavalis unit root test designed for samples with short time periods but many cross-sectional units (Harris and Tzavalis 1999). This test helps ensure that the premise of stationarity for the practice of including lagged dependent variable is not violated as suggested by Keele and Kelly (2006). As shown at the bottom of the table, all the p-values from the Harris–Tzavalis unit root tests for the dependent variables (exports or imports) are far smaller than 5%, thereby rejecting the null hypothesis of the existence of unit roots. Thus, the dependent variables are stationary, and the use of their lagged values legitimate. While the results from the first four columns in Table 7 are quite satisfying, they could be exposed to the Nickell bias (Nickell 1981) in that the difference between each dependent or independent variable and its mean across years within a cross-sectional unit could create a correlation between the independent variables and the error term. To mitigate this imprecision, Arellano and Bond (1991) devised a dynamic panel data approach, which takes the first differences of the dependent, lagged dependent, and independent variables, utilizes the first differences of the lagged dependent and lagged independent variables as instruments, and estimates the entire system with the generalized method of moments. Since our panel dataset contains a small number of time periods and a large number of cross-sectional units, which is of the type for which this Arellano–Bond estimator was designed, we believe that its use is appropriate in this context and serves to minimize l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / / 3 7 2 4 5 1 8 4 6 8 0 5 a d e v _ a _ 0 0 1 4 9 p d . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 Does the ACFIC Align Private Firms with the Goals of the PRC’s BRI? 67 Table 7. Dynamic Panel Data Analysis (Dependent Variables: Exports and Imports in Year t + 1) Fixed-Effects Estimation (2) xi j,t as Export 0.800*** (0.009) 0.069*** (0.012) 0.422*** (0.052) 0.073*** (0.017) −0.032 (0.032) 0.658*** (0.130) 0.431*** (0.076) −0.132** (0.059) 0.068*** (0.018) −0.122*** (0.015) −0.026*** (0.008) −0.011*** (0.004) 0.027*** (0.008) Province Country Year 6,685 0.159*** (3) xi j,t as Import 0.855*** (0.008) 0.116*** (0.013) Province Country Year 4,958 0.083*** (4) xi j,t as Import 0.806*** (0.009) 0.074*** (0.020) 0.242*** (0.093) 0.060* (0.031) −0.074 (0.050) 0.776*** (0.219) 0.449*** (0.171) −0.001 (0.118) −0.006 (0.031) −0.217*** (0.034) 0.016 (0.013) −0.025*** (0.008) 0.015 (0.014) Province Country Year 4,958 0.083*** Dependent Variables ln(xi j,t ) ln(ACF ICitCBTjt ) (1) xi j,t as Export 0.896*** (0.007) 0.109*** (0.009) ln(GDPit ) ln(GDPjt ) ln(Distancei j ) Borderi j Religioni j ln(Populationit ) ln(Population jt ) ln(Areait ) ln(Area jt ) SF I jt ln(TCPj,t−1) Province Country Year 6,685 0.159*** Fixed effects No. of observations Harris–Tzavalis statistics Hansen test (p-value) Serial correlation of order 1 (p-value) Serial correlation of order 2 (p-value) Arellano–Bond Estimation (5) (6) xi j,t as Export 0.273** (0.139) 1.140*** (0.212) xi j,t as Import 0.440*** (0.152) 0.992** (0.475) l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / / 3 7 2 4 5 1 8 4 6 8 0 5 a d e v _ a _ 0 0 1 4 9 p d . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 Province Country Year Province Country Year 6,229 4,535 [0.529] [0.000] [0.004] [0.000] [0.274] [0.127] Notes: Standard errors clustered at the level of province–country pair are included in parentheses. Significance level = *p < 0.1, **p < 0.05, ***p < 0.01. Source: Authors’ calculations. 68 Asian Development Review the endogeneity bias driven by past trading activities in our panel data, thereby enhancing the credibility of the findings of the substantial effects exerted by the ACFIC. The last two columns of Table 7 display the results of the Arellano–Bond estimation on equation (11). Following Arellano and Bond (1991), we perform serial correlation tests to determine whether this estimator’s assumption that the differenced error term is first-order, but not second-order serially correlated, is satisfied. As shown at the bottom of the table, both p-values for the first-order serial correlations are smaller than 1%, and both for the second-order are larger than 10%, so the serial correlation tests are passed. Moreover, to avoid overidentification caused by having too many strong instruments, we collapse the generalized method of moments style instruments and restrict the lagged periods to year t − 6 and year t − 7 to be sufficiently far away from year t, as suggested by Wintoki, Linck, and Netter (2012). These procedures help us eventually obtain the greater-than- 10% p-value of the Hansen test for exports, but the p-value of the Hansen test for imports is still smaller than 1%. Thus, though the overidentifying condition for exports is satisfied, that for imports is still violated. These statistical tests ensure the appropriateness of the Arellano–Bond estimation for exports but cast doubt on this practice for imports. In summary, although the estimates of α2 are both positive and statistically significant for exports and imports, we are only confident that the estimates are free of possible bias in the case of exports. Numerically, we find that the estimates of α2 in both columns are larger than 0.5, which suggests that the values of the coefficient on ln(ACF ICitCBTjt ) in previous tables might be underestimated. As there could still be other estimation biases, the implications drawn from this subsection do not guarantee that the ACFIC’s influences on the province– country pair and that pair’s future trade activities are causal. Yet, the results based on this practice of including the lagged value of the dependent variable and applying the Arellano–Bond estimation still increase the credibility of both the estimates presented and the econometric methods used throughout this study, especially in the case of exports. VII. Has the ACFIC Promoted Relations between the PRC and BRI Countries? Difference-in-Differences Analysis Even after the discussion above of how the ACFIC influences the PRC’s foreign trade with the countries it seems to favor, hypothesis (2) about the BRI remains untested. This section concentrates on whether since 2013 the ACFIC has come to prioritize BRI countries in the China Business Times. Only if BRI countries have indeed become the ACFIC’s increasingly favored targets since 2013 may we safely conclude that the ACFIC has encouraged its member firms to trade more with BRI countries. l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / / 3 7 2 4 5 1 8 4 6 8 0 5 a d e v _ a _ 0 0 1 4 9 p d . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 Does the ACFIC Align Private Firms with the Goals of the PRC’s BRI? 69 To identify the change in the global distribution of the ACFIC’s frequently the BRI’s initiation, we combine an mentioned targets before and after autoregressive model of order one (AR[1]) with the difference-in-differences (DiD) method to determine whether the BRI or related geographical information has a causal relationship with CBTj.4 Formally, CBTjt = ϕ1 CBTj,t−1 + ϕ2D j + ϕ3Postt + ϕ4D jPostt + ϕ5 ln (cid:5) + ϕ7g jt + ϕ8n jt + ϕ0 + εi jt (cid:4) + ϕ6 ln Population jt (cid:5) (cid:4) GDPjt (12) where D j is the dummy for country j being a BRI member, Postt = 0 if t ≤ 2013 or Postt = 1 if t > 2013, g jt is the GDP growth rate of country j in year t, and n jt is
the population growth rate of country j in year t. As alternatives to the dummy for
the BRI as a whole as the dependent variable, we also create dummy variables for
D j for four different subregions of the BRI: Central Asia and the Caucasus, África,
Eastern Europe, and Southeast Asia. Applying the model to the four subregions
separately, we can determine whether there is any difference between these regions
in terms of ϕ4, the coefficient on D jPostt. As in Card and Krueger (1994) and other
DiD empirical studies, if ϕ4 is positive, this would indicate that the ACFIC has
increased its reports about the countries defined by the dummy variable D j since
the inauguration of the initiative in 2013, or the opposite if ϕ4 is negative.

The regression estimates based on equation (12) are reported in Table 8.5
Each column reports the results for the dummy D j and its interaction with Postt for
a different set of BRI countries. Columna (1) is for BRI membership as a whole,
columna (2) is for BRI countries in Africa, columna (3) for those in Central Asia and
the Caucasus, columna (4) for those in Eastern Europe, and column (5) for those in
Southeast Asia.

The entries in the first row of the table represent the effects of the CBTj in
the previous year, which are indeed all positive and strong, revealing considerable
persistence of this AR(1) modelo. The estimate of the parameter ϕ2 is negative in
most specifications although not always statistically significant, suggesting that
BRI countries have received smaller amounts of attention from the ACFIC than
other large trading partners of the PRC such as the US and Japan. This is a
reasonable finding because most BRI countries are developing countries. El resultado
that the estimated values of ϕ3 are also negative indicates that the ACFIC has
decreased its overall news reports about non-PRC countries, consistent with its
“Going Inward” strategy since 2009 as documented by Lei and Nugent (2018).
In none of the columns are the coefficients of GDP growth or population growth
statistically significant, indicating that the ACFIC’s attention to a specific country
does not necessarily depend on that country’s economic or demographic status.

4See Wooldridge (2010, 197) for explanations and examples of the AR(1) model and Angrist and Pischke

(2008, 227–46) for the DiD model.

5Appendix Figure A2 confirms that our DiD model satisfies the parallel trend assumption.

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70 Asian Development Review

Mesa 8. Difference-in-Difference Estimates of the All-China Federation of Industry and
Commerce’s Prioritization in the China Business Times

Explanatory Variables

BRI
(1)

África
(2)

Central Asia
and Caucasus
(3)

Eastern
Europa
(4)

Southeast
Asia
(5)

CBTj,t−1

D j

Postt

D jPostt

ln(GDPjt )

ln(Population jt )

g jt

n jt

No. of observations
F-statistic
R-squared

0.940***
(0.003)
−13.959*
(5.624)

0.940***
(0.003)
−8.959***
(2.406)

−49.487*** −23.822***
(14.585)
40.931**
(14.698)
1.098*
(0.452)
0.234
(0.319)
−21.676
(22.204)
−53.893
(37.050)
985
42,047.26
0.968

(5.582)
20.559***
(5.702)
0.549
(0.497)
0.473
(0.280)
−21.035
(21.361)
−51.396
(42.369)
985
47,125.74
0.967

0.940***
(0.003)
−7.371***
(2.006)
−20.761***
(4.716)
21.019***
(4.846)
0.729
(0.447)
0.407
(0.256)
−23.616
(22.109)
−58.668
(39.428)
985
46,984.37
0.967

0.939***
(0.003)
−7.317*
(3.089)

0.940***
(0.003)
4.752
(4.281)

−21.134*** −19.086***

(4.991)
14.874*
(6.225)
0.655
(0.448)
0.464
(0.255)
−25.908
(20.835)
−69.335
(47.293)
985
44,900.18
0.967

(4.746)
−2.503
(8.091)
0.688
(0.432)
0.355
(0.239)
−25.899
(20.527)
−51.615
(39.036)
985
50,166.27
0.967

BRI = Belt and Road Initiative.
Notas: Standard errors clustered at the country level are included in parentheses. Significance level = *p < 0.1, **p < 0.05, ***p < 0.01. Source: Authors’ calculations. Most importantly, however, ϕ4 is positive in all columns except the two for Southeast Asia, which demonstrates that since 2013 the ACFIC has indeed boosted its relative attention to the BRI in general and to Africa, Central Asia and the Caucasus, and Eastern Europe (but not Southeast Asia) in particular. Combining this finding with the conclusion drawn from previous sections, it would appear that the ACFIC has induced its member firms to engage in more trade with BRI countries since 2013. This statistical implication persuasively demonstrates that the ACFIC has substantially helped the central government to align its member firms with the national objective of developing the BRI, at least based on the information disseminated by the ACFIC’s newspaper. However, this impact has been quite unequal across different groups of BRI countries. VIII. Conclusion The results presented in sections V, VI, and VII have demonstrated that the ACFIC has managed to induce its member firms from the private sector in the PRC’s different provinces to engage in both exports and imports with the countries that the ACFIC has stressed in its newspaper, the China Business Times. On average, a 1% increase in the newspaper’s level of dissemination of the positive opportunities l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / / 3 7 2 4 5 1 8 4 6 8 0 5 a d e v _ a _ 0 0 1 4 9 p d . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 Does the ACFIC Align Private Firms with the Goals of the PRC’s BRI? 71 in a non-PRC country has increased the PRC’s trade activities in that country by around 0.3% (and perhaps more as indicated in Tables 6 and 7). The results have also been quite robust to different model specifications and means of dealing with possible econometric problems, although the implications for exports are likely to be more reliable than those for imports based on the Arellano–Bond estimates. The last step in the analysis showed that, although the ACFIC has been posting fewer news articles about other countries in recent years, reflecting the continuation of its “Going Inward” strategy, its focus on news about BRI countries has not decreased. In addition, from the use of the interaction term that compares the ACFIC’s effects on exports with those on imports, we find fairly strong evidence that the ACFIC’s influence on the PRC’s exports to BRI countries has been substantially larger than on its imports from those countries. Given the vulnerability of such a massive program as the BRI to so many different risks, especially with regard to debt default risks that have been rising in several BRI countries, the Government of the PRC and the ACFIC might do well to be concerned by the evidence presented here of the unequal balance of payment effects between the PRC and many of its BRI partners. The results suggest that some attention should be given to policies that could increase imports into the PRC from these BRI countries to prevent them from defaulting on loans or experiencing other macroeconomic crises. In cases where business associations in other BRI countries appear to have some potential to act as a coordinating entity, it may also be useful to see if the ACFIC can coordinate with, or even train members of, such business associations in other BRI countries to increase their ability to coordinate with member firms. 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Zhai, Fan. 2018. “China’s Belt and Road Initiative: A Preliminary Quantitative Assessment.” Journal of Asian Economics 55: 84–92. Appendix 1. English Translation of a Sample Article in the China Business Times Dedicated to the Construction of the Interconnected Information Infrastructure in Africa “We believe that more than 150 thousand kilometers of optical cables will be laid in the next 15 to 20 years, and the consumption of cable-related goods in Africa will be greater than 100 billion US dollars.” In the eyes of Wang Jianyi, the chairman of Zhejiang’s Federation of Industry and Commerce as well as the chairman of Futong Group’s board of directors, Africa is a continent full of hope. He l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / / 3 7 2 4 5 1 8 4 6 8 0 5 a d e v _ a _ 0 0 1 4 9 p d . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 Does the ACFIC Align Private Firms with the Goals of the PRC’s BRI? 75 is very optimistic about the future of the interconnected information infrastructure in Africa. Founded in 1987 and headquartered in Hangzhou, Zhejiang, Futong Group is a Chinese private firm focusing on high-tech manufacturing. Its industrial specializations include optical fiber communication and electric power transmission, and its research specializations include energy storage, high- temperature superconductor and submarine photoelectric composite cable. Today, Futong Group has 1 international headquarters, 3 regional headquarters, 31 factories, 15 national high-tech subsidiaries and more than 12,000 registered employees. In recent years, following the Belt and Road Initiative, hundreds of Chinese companies have been participating in the construction of foreign interconnected information infrastructure. Futong Group is one of the participants as well as the beneficiaries. Futong’s development in Africa exemplifies recent globalization. In countries such as Kenya, Nigeria, Seychelles, and Angola, Futong’s products have been widely applied to local telecommunication, electrical transmission, automobile manufacturing, mobile terminal, and household electrical appliances. the company’s According to chairman Wang, Chinese private firms are very competitive in fields such as optical fiber transmission and terminal equipment. Given these advances, Chinese firms are able to lead the construction of the interconnected information infrastructure in Africa. Futong’s long-term goal is to become an international cable manufacturing sustainable conglomerate respected by the society and promoting global development. African continent is a wonderful market from chairman Wang’s perspective. Following the “Made in China 2025” strategy, Futong has been actively participating in the construction of information infrastructure in multiple African countries to realize the upgrade of local optical communication industry and build a world-class cluster of advanced manufacturing. “The industrialization in Africa and the manufacturing reform in (the province of) Zhejiang are highly complementary, and there is a perfect synergy between them.” According to chairman Wang, the industrialization in Africa should rely on Zhejiang’s advances in manufacturing, automotive and information technology. As the chairman of Zhejiang’s Federation of Industry and Commerce, he expresses that Zhejiang’s Federation of Industry and Commerce is very willing to advocate the economic cooperation between China and Africa and accelerate the industrialization of African countries. Chairman Wang also argues that Chinese private firms need to agglomerate together when they are developing their business in Africa. In other words, taking advantage of constructing industrial parks, Chinese private firms should develop orderly industrial chains instead of doing business on their own. l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / / 3 7 2 4 5 1 8 4 6 8 0 5 a d e v _ a _ 0 0 1 4 9 p d . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 76 Asian Development Review Wang’s proposal is inspired by his conglomerate’s recent experiences of together with other Chinese private firms, has globalization. Futong Group, developed an eight-squared-kilometer high-end industrial park in Mexico. Through combining each company’s advantages, they together formed orderly industrial chains and competed with other countries’ firms. As a Chinese poem goes, the immense sea allows fish to leap at liberty, and the vast sky allows birds to fly at liberty. 2018 is the fifth anniversary of the Belt and Road Initiative. As the cornerstone of information interconnection, information infrastructure is an important component in the development of the Belt and Road Initiative. Following the Belt and Road Initiative and develop industrial parks in foreign countries, Chinese firms such as Futong Group obtain a greater amount of opportunities for their business development. Futong Group’s Official Website (in English): http://www.futonggroup.com .cn/en/ Source: Li, Renping. 2018. “Futong Group Wants to Become an International Cable Manufacturing Conglomerate.” China Business Times, September 18. http: //epaper.cbt.com.cn/epaper/uniflows/html/2018/09/18/01/01_68.htm. [In Chinese] Appendix 2 Figure A2. Parallel Trend Test for Difference-in-Difference Estimates of the All-China Federation of Industry and Commerce’s Prioritization in the China Business Times l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / e d u a d e v / a r t i c e - p d l f / / / / / 3 7 2 4 5 1 8 4 6 8 0 5 a d e v _ a _ 0 0 1 4 9 p d . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 BRI = Belt and Road Initiative, CBT = China Business Times. Notes: The vertical axis represents CBTj,t , the frequency of the name of country j appearing in the China Business Times in year t. The horizontal axis represents year t. The dashed line represents the average CBTj,t of all non-BRI countries. The solid line represents the average CBTj,t of all BRI countries. The vertical line represents the threshold when the BRI intervention began to take effect. The dotted line represents the counterfactual average CBTj,t of all BRI countries if the BRI did not exist. Source: Authors’ calculations.
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