CAES Working Paper Series

Fixed Investments and Profits in China’s Wholesale and Retail Sectors

Qiujing Li and Junmin Wan

Fukuoka University, Japan

WP-2024-003

Center for Advanced Economic Study
Fukuoka University(CAES)
8-19-1 Nanakuma, Jonan-ku, Fukuoka,
JAPAN 814-0180+81-92-871-6631

Fixed Investments and Profits in China’s Wholesale and Retail Sectors1

Qiujing Li and Junmin Wan2

Graduate School of Economics, Fukuoka University, Japan

July 16, 2024

1This research was partially supported by a JSPS KAKENHI Grant (#22K01589). The second author gratefully
acknowledges the fund’s support.
2
The authors thank Ko Nishihara and Sheng Li for their beneficial comments. Any remaining errors here are theauthors’ responsibility. Correspondence: Nanakuma 8-19-1, Jounan Ward, Fukuoka City, Fukuoka 8140180, Japan;(e-mail) wan@fukuoka-u.ac.jp; (tel) +81-92-871-6631(ext.4208); (fax) +81-92-864-2904.
1

Highlights

1. Using panel data from 2006 to 2020 for above-scale wholesale and retailenterprises in 31 Chinese provinces, we estimated fixed investment and foundthat it was significantly and positively correlated with profits. Consequently, we
did not observe the investment–profit puzzle.

2. We calculated Tobin’s marginal q values for the wholesale and retail sectors byprovince and year. The marginal q value of the wholesale sector exceeded that of
the retail sector, potentially due to differences in intangible assets, such as
business right concessions.

3. Panel regression analysis indicated that the q value positively influences theinvestment rate in both the wholesale and retail sectors. Additionally, thewholesale sector exhibits higher implicit adjustment costs compared with theretail sector.

4.

Investments in the wholesale and retail sectors decreased as the q value declined
due to reduced profits, which may be attributable to the unfavorable economic
environment in China.

2

Abstract
Using panel data from 31 Chinese provinces from 2006 to 2020, we initiallyestimated fixed investments for the wholesale and retail sectors. Fixed investments
were positively and significantly correlated with profits; thus, we did not observe theinvestment–profit puzzle. Subsequently, we calculated Tobin’s marginal q values forthe wholesale and retail sectors and discovered a decreasing trend, whereby thewholesale sector’s marginal q value exceeded that of the retail sector. This disparity
may arise from differences in intangible assets, such as business right concessions.
Based on a structural model with internal adjustment costs, panel regressions
demonstrated that q values significantly and positively impacted investments; thewholesale sector exhibited higher implicit adjustment costs compared with the retailsector. Finally, the decline in q values may be attributed to profit reductions resulting
from the unfavorable economic environment in China.

JEL Classification: E22; E32; L81
Keywords: Fixed Investment, Profit, Investment–Profit Puzzle, Marginal q,
Wholesale Sector, Retail Sector, China

3

1 Introduction
1.1 Current Status of China’s Wholesale and Retail Sectors
Fixed investments in China’s wholesale and retail sectors have recently been
declining (Figure 1). This paper examines the reasons for this decline from multiple
perspectives, potentially including profit reductions in both sectors that likely resulted
from the ongoing economic downturn in China.
In recent years, China’s economic landscape has undergone profound changes,
significantly impacting the wholesale and retail sectors, which constitute a vital
component of the macroeconomy. First, the U.S.–China trade war has substantiallyaffected China’s economy since 2018 (Wan 2021c). Tariffs and trade restrictions havedirectly impacted Chinese manufacturing and exports, indirectly affecting the domestic
market’s supply–demand balance and thus influencing the development of wholesale
and retail sectors. The uncertainty engendered by the trade war has weakened
investment confidence and altered market expectations, posing challenges forwholesale and retail sectors from both supply and demand perspectives.
Second, as reported by the People’s Daily (April 20, 2021), the onset of thecoronavirus disease 2019 (COVID-19) pandemic in early 2020 severely impacted theglobal economy. Although China managed to control the outbreak relatively early,
preventive measures and interruptions to international logistics posed significant
challenges to the wholesale and retail industries. Offline retail, in particular,considerably suffered, with numerous brick-and-mortar stores closing due to a dramatic
decline in foot traffic. For example, the Wanda Group has closed several stores in recent
4years, reflecting not only internal management and market strategic issues but also theoverall economic downturn and consumer downgrade trends (People’s Daily, March 5,2020). Despite the growth of online retail, the industry as a whole remains in a recovery
phase. The long-term impact of the pandemic has not only altered consumer shopping
habits but also accelerated the digital transformation of the retail sector.
Specifically, the prolonged impact of the U.S.–China trade war and recurring
fluctuations of the pandemic have eroded consumer confidence, resulting in morecautious spending patterns. The demand for high-end consumer goods has decreased,
whereas the demand for daily essentials has remained stable or even increased. Offline
retail experienced significant challenges, and the rapid growth of online retail
introduced new competitive pressures. E-commerce platforms, such as Alibaba and
JD.com, continuously captured market share through technological innovation andmarket promotion, compelling traditional wholesale and retail enterprises to accelerate
their transformation to adapt to the new market landscape (People’s Daily, May 10,2022).
Furthermore, the significant financial risk caused by excessive real estate
investments is notable. Due to China’s severe housing bubble (Wan 2015, 2018a, b,
2021b, 2024a), the massive expansion of the real estate industry led to severe
overinvestments in all housing-related sectors during the bubble era (Wan and Qiu 2020,2023; Wan 2024b). This overinvestment in real estate has also affected the developmentof the wholesale and retail industries. High returns in the real estate market attracted
substantial capital, resulting in relatively reduced investments in other industries. As
5capital-intensive and highly liquid industries, the wholesale and retail sectors were
influenced by this capital flow, with some enterprises struggling to obtain sufficient
funds for expansion. The potential risks of the real estate bubble intensified the financial
market’s volatility, further affecting the financing environment of the wholesale andretail sectors. In the context of the "abnormally high marginal q during the period of
explosive housing prices" (Wan and Qiu 2023, p. 850), the government implemented
strict regulations on speculative housing purchases and bank loans to housing-related
enterprises (Wan 2015, 2018a, b), which triggered the reversal of the real estate market
and caused the housing bubble to burst. Consequently, investment in the real estateindustry has recently decreased (Wan and Qiu 2023; Wan 2024b). According to reports
by Qiu and Wan (2021a, b) and Huang and Wan (2022), investments by listed residential
companies in China have declined as a consequence of the sluggish housing market. As
the real estate market gradually cools, capital may flow back to the wholesale and retailindustries, but this process involves considerable uncertainty.
China’s rapid economic development has also positively affected the wholesale
and retail sectors. The increase in residents’ income levels and the shift in consumption
patterns have propelled consumption upgrades, providing new growth momentum forthe wholesale and retail sectors. Against the backdrop of rapid development in thedigital economy, particularly e-commerce, the wholesale and retail sectors haveembraced new growth opportunities. The rise of e-commerce giants such as Alibaba
and JD.com has not only altered the retail industry’s competitive landscape but alsooffered consumers more diverse shopping options (People’s Daily, March 5, 2020).
6

These new growth opportunities have generated numerous potential profits in thewholesale and retail industries, attracting investors and promoting development in these
sectors.
As argued in the literature, fixed investment cannot be influenced by profit, acondition known as the investment–profit puzzle (Stockhammer 2006; Orhangazi 2019;
Ogawa et al. 2019). Therefore, studies of fixed investments in China’s wholesale andretail sectors not only aid in understanding the current economic environment’s impact
on these sectors but also provide essential references for future policy-making andcorporate strategies, as well as for promoting the industry’s healthy and sustainable
development.

1.2 Related Literature
Our search for relevant news articles on China’s tertiary industry, particularly thewholesale and retail sectors, via the People’s Daily identified the following four topics:
1. Promotion of high-quality development in the tertiary industry (People’s Daily, June15, 2023); 2. Digital transformation in the wholesale and retail industry to enhancecompetitiveness (People’s Daily, May 10, 2022); 3. The response to the COVID-19
pandemic and its impact on the tertiary industry (People’s Daily, April 20, 2021); 4.
Discussion of consumption upgrading and downgrading (People’s Daily, March 5,2020). Our objective was to understand the current development status and potential
future trends within China’s wholesale and retail industry. To conduct a more intuitive
analysis, we utilized data from the "China Statistical Yearbook of The Tertiary Industry"7released by the National Bureau of Statistics of China. Scholars have argued that the
explosive growth in housing prices in China has led to severe overinvestment in realestate-related industries and implicit underinvestment in non-housing industries, suchas the service sector (Wan and Qiu 2023, Wan 2024a, b). Based on investments in theservice sectors and housing prices in China, Wan and Yin (2019) reported that China’s
service industries, including healthcare and education, suffer from severe
underinvestment, primarily due to overinvestment in real estate. To our knowledge, noresearch specifically focusing on the wholesale and retail sectors has been published todate. Therefore, the present paper concentrates on these two sectors.

1.3 Contributions of this Research
Using panel data from above-scale wholesale and retail enterprises in 31 Chinese
provinces derived from the China Statistical Yearbook of The Tertiary Industry 2006–
2021, we estimated the investment rate by year and region using fixed assets andinventories. We then identified a positive correlation between fixed investments andcorporate profits (coefficient = 0.1279, p-value = 0.0001). Thus, we did not observe theinvestment–profit puzzle, whereby fixed investments remain low even with high profit
levels (Orhangazi 2019; Ogawa et al. 2019).
Considering the incomplete data regarding China’s wholesale and retail sectors,
we used overall data for the tertiary sector from 31 Chinese provinces from the China
Statistical Yearbook of The Tertiary Industry 2017–2023 to estimate the proxy variableof depreciation rate. Subsequently, we calculated Tobin’s marginal q for the wholesale8and retail sectors by province and year. Panel regression analysis revealed a positive
correlation between investment and Tobin’s marginal q. As Tobin’s marginal q
decreases, investment also declines. This study’s empirical findings indicate thatinvestments in the wholesale and retail sectors decreased with declining profits. The
results further highlight the difficulty in enhancing profits within the wholesale andretail sectors, considering the need to improve the macroeconomic environments of the
primary, secondary, and tertiary sectors.

1.4 Organization of the Paper
The structure of this paper is as follows: Section 2 presents the research questions
and hypotheses. Section 3 describes the data sources for the investment rate and details
the methods used to derive the proxy of depreciation rate, Tobin’s marginal q value, theinvestment function, and the investment rate. Section 4 summarizes the empiricalresults, whereas Section 5 presents the conclusions and policy implications.

2 Research Question and Hypotheses
Assuming that businesses in the wholesale and retail sector utilize technology that
follows the neoclassical fixed capital theory (Jorgenson 1963; Tobin 1969; Ogawa et al.
1994; Wan 2021c; Wan and Qiu 2023; Wan 2024b), we hypothesize that a positive
correlation exists between corporate fixed investment and returns. Therefore, a
reduction in profits in the wholesale and retail sectors may induce a decline in fixed
investments for these sectors in China.
9However, considering the limited and incomplete data available for China’s
tertiary sector, we obtained relevant data from various sources for the wholesale andretail sectors. We also encountered theoretical challenges. The tertiary industry is
unique, distinguished from the secondary industry by its relatively low proportion of
fixed assets, estimated at approximately 10% based on information from the China
Statistical Yearbook of the Tertiary Industry. This fact likely contradicts the hypothesisthat profit is primarily derived from fixed assets. Based on these considerations, we
propose the following null hypothesis.
Null Hypothesis: The fixed investment behavior of the wholesale and retailenterprises in China cannot be elucidated through Tobin’s marginal q theory.
Consequently, corporate fixed investments are not positively correlated with Tobin’s
marginal q values (Jorgenson 1963; Tobin 1969; Ogawa et al. 1994; Wan and Qiu 2023).
We will use panel data to test whether the above null hypothesis should be rejectedin favor of the alternative hypothesis that fixed investment can be explained by Tobin’s
marginal q theory.

3 Data, Methods, and Empirical Specifications
3.1 Panel Data and Estimation of Investment Rate
We obtained data on the wholesale and retail sectors for 31 Chinese provincesfrom the China Statistical Yearbook of The Tertiary Industry 2006–2021, collecting
information regarding the "Main Indicators of Wholesale and Retail Enterprises Above
10

Designated Size" by province and year. These yearbooks exclude information about
fixed asset depreciation and interest payments. Therefore, we used overall tertiaryindustry data from the China Statistical Yearbook of The Tertiary Industry 2017–2023
for the years 2016 to 2022 to estimate proxy variables for depreciation rates and interest
payments in the wholesale and retail sectors.
For the investment rate of the wholesale and retail industries, we focused on fixed
asset data and total profits from the China Statistical Yearbook of The Tertiary Industry
2006–2021, along with balance sheet data from the China Tertiary Industry Statistical
Yearbook 2017–2023. We ultimately obtained balanced panel data comprising 930
observations for the period 2006–2020 (31 provinces * 15 years * 2 sectors). Investment
data for 2005 were unavailable due to the lack of fixed asset information for 2004, andeach yearbook included data only up to the year prior to its publication (e.g., up to 2005
for the yearbook published in 2006).
We define new fixed investment at time t as the fixed assets at time t minus
previous fixed assets, and the investment rate at time t as the ratio of new fixed
investment at time t to fixed assets at time t-1. Tables 1a and 1b show the estimatedinvestment rates for the wholesale and retail sectors by province and year.

3.2 Estimation of Tobin’s Marginal qFirst, we estimate average proxy values for the depreciation rate. We then calculate
the marginal q values for the wholesale and retail sectors. According to Tobin (1969),
Hayashi (1982), Abel and Blanchard (1986), Ogawa (2003), and Wan and Qiu (2023),
11

under specific conditions, the marginal q can be expressed as follows:
𝑀𝑞 =

𝜋 1+𝑟
𝑝 𝑟 +𝛿

where
𝑀𝑞

is the before-tax marginal q of the industrial firm in province 𝑖 at time 𝑡;𝜋

is the ratio of total before-tax profit to fixed assets plus inventories of theindustrial firm in province 𝑖 at time 𝑡;𝑝

is the price index of investment of the industrial firm in province 𝑖 at time 𝑡;and 𝛿is the average depreciation rate of total assets of the industrial firm inprovince 𝑖 at time 𝑡.
Using the China Statistical Yearbook of The Tertiary Industry 2017–2023 andfollowing Wan and Qiu (2022), we obtained two average values. The first is 0.0761,
calculated as annual depreciation divided by previous fixed assets; the second is 0.0494,
calculated as annual depreciation divided by previous fixed assets plus inventories. We
use the second value in this analysis.
𝑟is the average interest payment of the industrial firm in province 𝑖 at time 𝑡.

In accordance with the approach of Wan and Qiu (2023), we use the adjustment
coefficient to estimate the interest burden for debt.
Tables 2a and 2b show the average marginal q values calculated using fixed assets
plus inventories for the wholesale and retail sectors by year.

3.3 Empirical Specification of Investment Equation
Based on the works of Abel (1980), Chirinko (1993), Ogawa (2020), Ogawa and12

Kitasaka (1999), Ogawa et al. (1994, 2019), Chen and Wan (2022), Huang and Wan(2022), and Wan and Qiu (2023), we assume the following empirical specification of
the fixed investment function with internal adjustment cost:
𝐼
𝐾

=𝜏+

1
𝑝
(𝑞 − 1)+𝜇 +𝛾 +𝜀
𝛼
𝑝

where
𝐼

is the fixed assets investment of the industrial firm in province 𝑖 at time 𝑡;

𝐾

is the fixed assets of the industrial firm in province 𝑖 at time 𝑡 − 1;𝑞is the before-tax marginal q of the industrial firm in province 𝑖 at time 𝑡;τ is a constant term implying the long-term equilibrium of the investment;is the impact of the marginal q on investment, which is expected to have apositive sign—a smaller coefficient indicates a larger adjustment cost;
𝑝

and 𝑝

are the investment goods price index (2006 = 1) and the retail priceindex (2006 = 1), respectively;
and 𝜇 , 𝛾 , and 𝜀

are constants related to province-specific effects, time effects,

and random errors, respectively.
We performed panel estimation with fixed effects and robust standard errors to
obtain the coefficients in the above equation.

4 Empirical Results
Tables 1a and 1b report the investment rates for each province from 2006 to 2020in the wholesale and retail sectors, respectively. Figure 1 illustrates these two13

investment rates’ contributions to the national average, demonstrating similar
volatilities and decreasing trends. They reached their lowest values in 2009, likely due
to the impact of the 2008 financial crisis in the U.S. and the Wenchuan earthquake in
China that same year. The q value of the wholesale sector prior to 2014 exceeded 3, anabnormally high value potentially associated with housing-related overinvestments
during the bubble economy (Chen and Wan 2022; Huang and Wan 2022; Huang and
Wan 2023; Wan and Qiu 2023).
Tables 2a and 2b report Tobin’s marginal q values for the wholesale and retailsectors by province and year. Figure 2 illustrates the national average q values by year
for these two sectors. The q values of the wholesale sector consistently exceed those of
the retail sector, despite exhibiting decreasing trends. This disparity may be attributed
to higher intangible assets, such as business right concessions, in the wholesale sector
compared with the retail sector (Crouzet and Eberly 2023). The values rapidly
decreased in 2015 and after 2016, potentially due to the "Double Eleven" e-commerce
promotions and the subsequent U.S.–China trade war in 2018. Live e-commerce, anascent industry within the internet sector ten years ago, was not traditionally classified
under wholesale and retail sectors in the conventional sense. Therefore, the declining
trends in investment rates and Tobin’s marginal q visible in Figures 1 and 2 may result
from the increasingly challenging economic environment in China.
According to the panel regression results in Tables 4a, 4b, and 4c, the q values[(𝑞 − 1)

] exerted significant positive effects on investment rates in the wholesale

sector, retail sector, and a pooled sample of both sectors, regardless of whether the14

analysis controlled for year variables or annual dummies. The null hypothesis—that
investment is not affected by q values—was thus rejected. This finding suggests thatmarginal q theory can explain the fixed investment behavior of the wholesale and retailsectors. This result is somewhat unexpected, considering that the ratio of fixed assets
to total assets is approximately ten percent, a situation presumably far from thenecessary condition of fixed capital investment theory.
A comparison of q value coefficients for the wholesale and retail sectors reveals
that the coefficient is substantially lower in the wholesale sector than in the retail sector.
According to investment theory, this indicates that the implicit adjustment cost should
be significantly higher in the wholesale sector than in the retail sector. This disparity
may be attributed to the acquisition of intangible assets, such as business rightconcessions in the wholesale sector, which incur substantial internal or non-market
costs.

5 Conclusions and Policy Implications
This study analyzed fixed investment in the wholesale and retail sectors across 31
Chinese provinces from 2006 to 2020. First, fixed investment was positively andsignificantly correlated with profits, thus refuting the investment–profit puzzle. Second,
we estimated Tobin’s marginal q values by province and year. The marginal q value ofthe wholesale sector exceeded that of the retail sector, potentially due to differences inintangible assets, such as business right concessions. Third, based on neoclassicalinvestment theory with adjustment costs, we conducted panel regressions. The results
15

supported the alternative hypothesis that fixed investment is guided by marginal q.
Considering the low ratio of fixed assets to total assets in the wholesale and retailsectors, this empirical result is somewhat unexpected because it diverges fromneoclassical investment theory. We also found that implicit adjustment costs in thewholesale sector exceeded those in the retail sector, possibly due to the acquisition ofbusiness right concessions. Finally, declining q values may be attributed to profit
reductions resulting from the unfavorable economic environment in China.
Regarding policy implications, increases in fixed investments within wholesale
and retail sectors will require recovery of the entire macroeconomy. This is because
demands and profits for these two sectors are derived from the primary, secondary, andtertiary sectors of the economy. Conversely, at the microeconomic level, in the context
of current economic challenges, Chinese wholesale and retail firms can enhance profits
by developing new marketing methods, such as e-commerce or live shopping. For
example, the recent emergence of e-commerce and live streaming industries, coupledwith annual promotional activities, can help companies reduce costs, expand sales
channels, and improve market competitiveness. Moreover, the adoption of innovative
discount physical store models may offer a viable option to attract more consumers andincrease sales and profits.
Furthermore, the government could implement policies to enhance the economic
environment of the wholesale and retail sectors. These policies might include offering
tax incentives or financial subsidies, providing platforms to support small retailer
development, promoting industry diversification and competition, strengthening16

supervision to prevent market monopolies and protect consumer rights, and reducing
administrative approvals and corporate tax burdens to encourage business investment
and expansion.
This study has some important limitations. First, concerns arise regarding thesuitability of our methodologies: the wholesale and retail sectors, in particular, possess
small proportions of fixed assets, which may significantly deviate from the assumptionsof fixed investment theory. Second, theories derived from the U.S. context may notalign with China’s social and economic circumstances. Future research should addressthese crucial issues.

17

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Table 1a: Ratio of new fixed investment to fixed assets of the wholesale sector by province in China, 2006-2020
Year

Beijing

Tianjin

Hebei

Shanxi2006

0.05740.1070-0.0739-0.00172007

0.27700.20690.0326-0.03402008

0.20100.41260.44340.10402009-0.0373-0.1395-0.29982010

0.16160.15832011-0.0987

2012

Inner

Liaoning

Jilin

Heilongjiang

Shanghai

Jiangsu

Zhejiang

Anhui

FujianJiangxi

Shandong

Henan

0.0430-0.0025-0.2013-0.0085-0.0140-0.0483-0.0342-0.1850

0.01300.0000-0.0427-0.0955-0.20850.12950.22800.0018-0.02550.22020.15170.13280.03130.00780.55770.15020.89190.3856

0.8476

0.27800.73400.63170.22540.56870.3816-0.0182

0.6335-0.0866-0.1213-0.1806-0.2244-0.5134-0.4129-0.4152-0.3331-0.2878-0.1357-0.3197-0.1923-0.3141-0.3106

0.22780.3056-0.0333-0.07470.22870.10510.21200.13030.08630.04970.13620.51640.20630.08590.17340.1441-0.14020.89060.31970.02040.24940.29960.11160.0794-0.13510.1609-0.1204

0.15280.15910.02300.1646

0.09360.1051-0.2463-0.0157

0.2232

0.09610.04960.20740.08830.05130.20590.06600.06290.04092013

0.12210.14040.54000.09690.06540.7444-0.02710.14740.18780.37260.32540.16330.42650.21780.4696

0.37692014

0.0779-0.0038-0.19960.12140.19370.2796

0.25820.0251-0.0153-0.1096-0.00220.26640.06740.22010.07210.33492015

0.01520.0551-0.2271

0.42160.1711-0.2290-0.16810.22320.07740.00200.0738-0.10140.10900.0366-0.0300-0.25092016

0.00940.17430.25320.16500.1242-0.33420.12630.0757-0.00160.0062-0.13390.05930.05030.22650.03410.72242017

0.0595-0.1400

0.52320.1251-0.20660.22430.1314-0.02080.11990.05170.1881

0.04650.07030.3078-0.1066-0.02942018

0.0017-0.0902-0.4155-0.1716-0.0804-0.1953

0.0736-0.1697

0.03710.0283-0.0361-0.0435-0.0971-0.1472-0.1232-0.43682019

0.1294-0.0846

0.1631-0.3068-0.2235-0.0835

0.06550.18150.18970.00880.04733.32270.1262-0.0124-0.28142.82882020

0.0601-0.1501-0.2759

0.06370.41180.0926-0.1185-0.0282

0.00640.24850.1395-0.78160.03540.0377

0.2711-0.7573

Avg

0.0706

0.0656

0.06200.04880.10750.06780.07830.0495

0.09610.10190.06070.2186

0.09310.07640.10410.1821

Mongolia

Source: Authors’ estimation based on China Statistical Yearbook of The Tertiary Industry, 2006-2021.

Table 1a: Ratio of new fixed investment to fixed assets of the wholesale sector by province in China, 2006-2020 (cont.)
Year

Hubei

Hunan

Guangdong

2006

0.01230.12700.01352007-0.00640.30270.11812008

0.2473-0.19160.10162009-0.1820-0.18492010

0.2385

2011

Guangxi

HainanChongqing

Sichuan

Guizhou

Yunnan0.01390.2857-0.0205

0.0014-0.0939-0.0457-0.4387

0.11040.07170.20330.06770.58880.0858-0.1554-0.2800-0.2902-0.3349-0.0971

0.0683-0.01100.25770.13990.05180.25370.19660.20382012

0.12340.16830.77142013

0.65940.49262014

0.09992015

Shannxi

Gansu

Qinghai

Ningxia-0.0587

0.07550.0111-0.2729-0.1877-0.0861

0.0618-0.0195-0.0595-0.27540.31620.42590.33560.0809-0.13120.09250.16560.45730.5860-0.01410.65700.25510.43000.15510.3249-0.2981-0.3751-0.3017-0.2898-0.2315-0.3479-0.2651-0.2251-0.2330-0.2637

0.14130.19920.37800.0031-0.26220.3628-0.30050.40090.22430.11510.14380.00290.39080.2059-0.0565-0.02290.37010.16850.19020.20940.6506-0.0463

0.1624-0.0372-0.0742-0.09190.1286

0.03460.1283-0.05490.08830.17240.0216

0.33680.16000.09970.12220.16820.04870.23150.11640.23880.09850.42960.07730.2579-0.0135-0.03200.13750.23880.1098-0.04640.0675-0.1901-0.0184

0.34320.3531

0.04290.18360.13654.34150.36860.01380.24850.2465-0.0909-0.09890.0487-0.0632

0.23480.0187-0.2555-0.2610-0.0411-0.1551-0.1201-0.8128-0.2693-0.0136-0.1990-0.06132016

0.1544-0.0181-0.1108

0.54110.7498-0.0295

0.25500.49710.30840.29040.3086

0.67930.80220.19210.43490.21332017-0.0299

0.22220.0678-0.05560.00050.18060.08520.1961-0.1986

0.57210.1385

0.1982-0.21620.16340.06780.08832018

0.0765-0.1272-0.03260.17630.0399-0.11800.0444-0.2023-0.1240-0.1999-0.1877-0.0651-0.4566-0.2181-0.1035-0.10852019-0.1170-0.1029-0.0518

0.06100.5400-0.0986-0.04900.1596-0.1016-0.1860-0.1632-0.11930.75000.03470.28410.22292020-0.3901-0.0026

0.01660.03550.04730.13330.1280-0.2156

0.08780.20190.33440.1517-0.1288-0.0878

0.0284-0.0131Avg

0.05650.06790.0616

0.0734

0.10930.0546

0.05470.06810.01450.08570.08170.34370.1071

0.0976

0.06530.0911

Source: Authors’ estimation based on China Statistical Yearbook of The Tertiary Industry, 2006-2021.

Xinjiang

National

XizangaverageTable 1b: Ratio of new fixed investment to fixed assets of the retail sector by province in China, 2006-2020
Year

Beijing

2006-0.03082007

0.00402008

0.21472009

Tianjin

Inner

Hebei

Shanxi

Jilin

Heilongjiang

Shanghai

Jiangsu

Zhejiang

Anhui

FujianJiangxi

Shandong

Henan

0.01290.0500-0.00030.1525-0.0061-0.0067

0.15520.3084-0.04710.00140.1516-0.05290.00830.0236

0.05670.07280.0948-0.0438

0.15380.20700.1426

0.03120.10580.08210.01490.1670-0.1149

0.04560.21470.01510.44210.35970.75500.18970.05030.46020.08680.07130.5296

0.35520.35500.51140.95000.46070.5700-0.2229-0.1976-0.1420-0.2724-0.0582-0.1826-0.2736-0.2021-0.3443-0.2730-0.1888-0.0650-0.0024-0.1941-0.1737-0.28562010

0.00080.06370.14770.17820.36620.27700.29650.3056

0.05180.11350.15570.22700.17860.13340.19690.14242011

0.27110.11710.15080.20020.16960.23800.50240.14970.19370.24210.10490.29530.35270.0051

0.06180.01102012-0.11880.25780.14100.0592

0.06180.09520.06960.04000.00080.17240.15370.24550.06090.16610.14120.06922013

0.01610.38970.18440.36320.21200.19590.16320.09980.08810.34350.17040.2101

0.09610.43130.25900.44242014-0.0435-0.0606

0.22370.12500.10970.02270.08170.32040.15250.08030.09040.12770.14150.15130.02310.23822015

0.1142-0.0773

0.06650.0394

0.1093-0.09750.23520.1251-0.01100.07230.06230.11310.01270.15330.00600.1931

2016-0.04860.02040.10272017-0.0419-0.0869

0.08300.07620.0766-0.0096

0.05510.03080.00780.07100.09630.05100.0797

0.4666-0.0067

0.1609-0.0523-0.1142-0.20210.18410.00130.0284-0.0162-0.0976

0.09190.1129-0.2414-0.0717-0.04642018-0.0595-0.0978-0.1635-0.0261-0.0848-0.0892-0.0425-0.0670-0.0834-0.1199-0.0715-0.11020.0375-0.0862-0.1932-0.2158

2019-0.1040-0.1143-0.0318-0.2732-0.1575

0.0335-0.3594-0.3694-0.0322-0.0829-0.0283-0.1390-0.2120-0.3082-0.3023-0.29942020

0.0012-0.11620.0283-0.11810.0022-0.1353-0.0375

0.0881-0.0169-0.0783

0.0232-0.0969

0.33680.22750.03680.0233Avg-0.0032

0.03650.07960.08060.08960.02010.08560.04750.00580.08430.07370.10770.10820.13370.04900.0689Mongolia

Liaoning

Source: Authors’ estimation based on China Statistical Yearbook of The Tertiary Industry, 2006-2021.

Table 1b: Ratio of new fixed investment to fixed assets of the retail sector by province in China, 2006-2020 (cont.)
National

Year

Hubei

Hunan

Guangdong

Guangxi

HainanChongqing

Sichuan

Guizhou

YunnanXizangShannxi

Gansu

Qinghai

Ningxia

Xinjiang

2006

0.08510.1142-0.1121-0.0053

0.29240.02380.00580.11080.13810.12090.0829-0.03700.00140.0501-0.00360.04982007

0.1081-0.23030.20270.08801.7403-0.1876-0.0182

0.07750.05050.49300.17110.0015-0.0560

0.0848-0.1443

0.1185

2008

0.37490.92240.22220.28270.13420.6842

0.9496

1.11440.85090.12460.2005-0.02640.26870.16510.20450.41402009-0.1718-0.2102-0.2391-0.1694-0.2919-0.14780.0402-0.1778-0.0077-0.3046

0.3848-0.1376-0.3142-0.0727-0.2737-0.16682010

0.34890.31880.0668-0.12390.13440.14380.20080.05230.2127-0.0071

0.0387-0.09470.10350.10110.17370.14532011

0.18380.51080.02980.29260.04580.1943

1.0487

0.27610.11420.2782-0.03330.53020.11030.04310.14940.22062012

0.3522-0.19900.17790.11810.32000.1348-0.19050.21030.40590.06680.42010.24690.15450.09230.30460.13652013

0.25970.32180.09600.15590.09980.3181

0.19800.23560.13290.1856

0.21920.4332

1.26890.22800.41780.2657

2014

0.18400.14530.01190.16280.04820.1452

0.1172

0.06310.02930.39700.06945.24810.0506-0.0059

0.11880.27642015-0.0179

0.0896-0.0353

0.08680.10500.05740.09040.31940.05500.12080.0940-0.7626

0.14230.00800.07600.04982016

0.02820.26140.11340.0168-0.02100.14120.01170.17460.0750-0.06110.1596-0.05530.10000.06800.08730.07522017-0.1006

0.0576-0.0701

0.1496-0.0229-0.0629-0.03130.4705-0.0206

0.0586-0.02200.01300.0377

0.09330.05060.00422018

0.0897-0.0148-0.0650-0.0494-0.0434

0.0622-0.0139-0.2303-0.0737

0.0186-0.1381-0.0737

0.04350.16500.0381-0.05672019-0.3224-0.25220.2909-0.08070.0186-0.2314-0.2323-0.1199-0.1992

0.0224-0.2092-0.2542-0.2582-0.0903

0.8326-0.12472020-0.2485-0.0131-0.24200.05440.08990.02200.09190.11720.04510.16380.11230.23200.09270.0035-0.44240.0080Avg

0.07690.12150.02990.06530.17660.08650.15120.17960.12060.11180.10330.35090.11640.06220.10600.0944

Source: Authors’ estimation based on China Statistical Yearbook of The Tertiary Industry, 2006-2021.

averageTable 2a: Marginal q of fixed assets plus inventories of the wholesale sector by province in China, 2006-2020
Year

Beijing

Tianjin

Hebei

Shanxi2006

1.63634.76742.21532.06342007

2.21174.76322.50252.74022008

2.11475.99723.44022009

1.71942.30932010

1.9904

2011

Inner

Liaoning

Jilin

Heilongjiang

Shanghai

Jiangsu

Zhejiang

Anhui

FujianJiangxi

Shandong

Henan

7.40501.7774

1.21881.75764.49893.62622.88154.29582.89324.53342.29981.65427.8281

3.06632.30443.44765.18154.30452.90534.46203.39474.36243.21042.09672.878412.2588

4.87244.43766.04945.747310.10612.57045.27812.87445.48516.91122.88881.46612.2786

7.24392.3192

2.10953.67132.8332

3.98732.00142.88351.9024

3.99813.48082.64492.67902.29912.36275.87881.87143.11134.8831

3.87834.36452.00132.50822.11454.44444.29252.7003

2.18343.72832.06061.6522

4.94372.70582.29244.49624.35493.74092.02462.65162.3052

3.52524.44873.66172012

1.33772.3922

1.4376

1.06272.36572.03463.08343.53093.65912.86991.48683.31191.69394.62564.49472.81432013

1.22212.00761.03780.82981.55542.04112.17822.35463.67255.7852

1.69832.62401.79534.9056

6.18094.5002

2014

0.8644

1.66400.82050.61270.73200.89192.24832.84552.66092.54431.38072.3842

1.2716

3.22383.38834.71032015

0.6832

1.22220.75440.28150.54090.80581.90072.33872.03492.5394

1.23701.6784

1.64612.71532.50204.6061

2016

0.8401

1.09141.02500.2296

0.97210.65273.3642

3.18312.29082.93101.37571.64111.27972.00263.06202.3831

2017

0.72590.43301.29330.32710.46051.00881.94820.70482.35682.93161.4343

1.9352

1.48352.86001.69462.73852018

1.02000.69461.75340.46701.12540.68841.04470.76222.14004.20371.46792.55132.13602.77171.29022.85532019

1.00420.6542

1.49600.62720.9116

1.19551.03611.22312.48172.18381.55822.13781.16063.46401.28422.66602020

0.99920.7412

1.01650.76550.69700.58541.50170.84102.36881.75911.47371.8176

1.06632.30520.85591.7945Avg

1.37022.34301.6412

1.2786

3.66131.76782.25202.80593.34403.8585

1.83312.81071.93453.68153.29312.9810

Mongolia

Source: Authors’ estimation based on China Statistical Yearbook of The Tertiary Industry, 2006-2021.

Table 2a: Marginal q of fixed assets plus inventories of the wholesale sector by province in China, 2006-2020 (cont.)
National

Year

Hubei

Hunan

Guangdong

Guangxi

HainanChongqing

Sichuan

Guizhou

YunnanXizangShannxi

Gansu

Qinghai

Ningxia

Xinjiang

2006

2.90713.35092.61292.63812.39931.35933.75556.81883.56042.9553

1.9513

1.63665.28683.22770.66473.0532

2007

3.56773.1791

3.53952.39292.22191.98394.01275.68795.62940.70976.92322.20933.03133.49780.98833.49542008

6.43374.56253.73034.69986.35823.81203.543010.4327

3.54574.829312.0086

3.40445.06846.54561.64645.30752009

3.37653.41392.77242.08933.51744.02213.14599.2992

3.61562.002012.4000

3.0075

1.86322.73482.30663.43272010

3.93732.62662.97822.53354.26084.07624.0626

11.2540

3.7966

1.887517.64211.48734.13052.94962.17673.90902011

3.59913.12093.31722.17583.3822

3.19785.055514.6290

4.71210.3177

6.03703.86583.89391.97501.27363.59122012

2.94413.4512

3.33522.50013.26002.55444.264517.4887

2.99092.05094.17992.68303.23412.08202.64853.28612013

3.32343.70032.81241.35518.47534.34463.227510.4432

3.37624.89092.53071.10601.14441.38211.51713.16192014

2.48362.69481.98611.0036

1.66183.65561.90832.8016

3.20865.29391.96044.13551.44220.40430.88282.18602015

2.36872.16442.0646

0.98762.17964.50271.94447.14551.91964.1402

1.47790.12731.50460.21400.1127

1.94652016

2.64443.18682.41471.16131.4003

13.5963

4.34017.22443.35505.17062.2277

3.2225

1.77850.37711.23372.63412017

2.10132.50601.82500.8222

1.81584.05981.97529.6506

1.73103.21732.34651.09521.23590.19910.8085

1.92662018

3.43392.67571.94480.84541.01103.7396

3.068619.56233.30192.62171.93430.66551.25180.23001.10482.39882019

2.1646

3.25341.51040.93273.51513.10153.062115.41392.49784.88272.27430.91550.10840.25200.53812.24212020

1.55612.58631.36190.66392.12193.00382.221011.4682

1.32447.37271.40880.27080.34280.22010.3433

1.8340Avg

3.12283.09822.54701.78683.17204.06733.305810.6213

3.23773.48955.1535

1.98882.35451.7527

1.21642.9603

Source: Authors’ estimation based on China Statistical Yearbook of The Tertiary Industry, 2006-2021.

averageTable 2b: Marginal q of fixed assets plus inventories of the retail sector by province in China, 2006-2020
Year

Beijing

Tianjin

Hebei

Shanxi2006

0.89790.51830.14440.32402007

1.12531.50280.22650.77002008

0.84942.19750.70522009

1.06972.27512010

1.3004

2011

Inner

Liaoning

Jilin

Heilongjiang

Shanghai

Jiangsu

Zhejiang

Anhui

FujianJiangxi

Shandong

Henan

2.42040.52010.2501-0.0533

1.18091.17980.69090.70911.03940.38430.98940.52403.04520.75590.46950.4443

1.50831.0207

0.97680.7676

1.27820.3152

1.4036

0.79191.13974.1546

1.8946

1.66530.88520.83402.6076

1.17001.38151.7092

3.73673.42861.98080.46320.6913

3.63301.14000.91391.20881.12531.6214

1.11741.1616

1.9271

1.58242.04431.56412.92320.78631.27444.29281.43231.55482.01481.57491.76941.21011.67892.02181.63392.54521.88071.89181.18490.8782

0.69892.20400.99701.25861.80811.9834

1.69151.1601

1.7171

1.8452

0.78462.26752.12132012

0.69450.36510.55210.37531.27560.91951.14422.80321.12031.01010.50661.3131

1.25751.33432.5191

1.88712013

1.08520.37120.70140.3084

1.09791.16331.1251

1.86730.93601.96560.83351.67491.32281.8837

3.1951

3.24432014

1.19010.08040.7886

0.03110.98680.46460.99955.43910.2686

1.06920.44620.82041.30031.24112.55132.54782015

1.2195-0.0227

0.4898-0.0492

0.8916

0.40660.88091.25670.60381.24790.54000.83951.36791.34902.06882.42612016

1.02040.28860.72530.07100.54770.38690.70511.20180.85171.31980.92050.89322.02231.64312.10637.8442

2017

0.8497-0.2987

0.4724-0.0862

0.4834

1.58570.6126

1.21411.49920.99680.82270.73582.41191.0702

1.24231.99092018

0.6862-0.2504

0.3133-0.2576

0.04790.08990.14110.57281.44671.27700.84001.67652.80601.38620.71761.74532019

0.5144-0.15200.3739-0.7053

0.09510.33490.13350.33851.77950.84140.92680.95181.7572

1.13720.5292

1.68872020

0.1871-0.24320.1501-0.71200.00000.01420.01410.03392.14080.78751.02110.8474

1.3936

1.11500.28281.2553Avg

0.97210.71600.51810.2583

1.67840.80700.7912

1.4023

1.25691.36040.87881.1446

1.69741.3731

1.85942.2328

Mongolia

Source: Authors’ estimation based on China Statistical Yearbook of The Tertiary Industry, 2006-2021.

Table 2b: Marginal q of fixed assets plus inventories of the retail sector by province in China, 2006-2020 (cont.)
National

Year

Hubei

Hunan

Guangdong

Guangxi

HainanChongqing

Sichuan

Guizhou

YunnanXizangShannxi

Gansu

Qinghai

Ningxia

Xinjiang

2006

0.7593

1.5563

1.36730.67081.49550.10491.52131.8302

1.51971.38640.47620.26700.13090.47530.19190.82172007

1.05500.8386

1.9812

1.17892.97570.76541.8112

1.1992

1.41833.31240.86601.21280.47910.43210.38781.17152008

1.84123.5006

3.4634

1.92351.29214.0231

3.44771.94262.75912.46472.61720.83730.89341.00901.24702.05172009

1.34441.60102.77532.22581.23382.32712.1464

1.49371.48532.9253

2.95981.57651.31901.5113

1.5665

1.67842010

1.4851

1.7165

3.21161.90171.95272.46662.4082

1.85791.69884.23463.68261.62271.9392

1.82491.93772.05922011

1.69151.46913.1256

2.1235

1.99652.58301.88872.22301.78974.6751

2.97872.16591.9571

1.38182.3045

1.89832012

2.06451.3194

1.8523

1.33962.07882.10880.91851.59991.40865.36972.66091.82351.50650.6392

1.5873

1.52762013

2.51711.59152.27541.3653

1.78294.13751.3233

2.78722.18764.26322.25371.81751.3871

0.97871.33581.76712014

1.69671.34871.45551.04441.79073.29491.14371.75651.1607

1.78431.64491.70400.60981.19150.82401.37662015

1.31891.09701.60770.83401.45542.53341.07050.68171.53380.5475

1.17280.47820.75130.53290.74711.02832016

1.24401.16282.5342

1.4933

1.55632.48861.30171.17881.55320.5652

1.6641

1.18310.63030.48920.74161.36562017

1.06931.20472.80161.50991.9386

1.4192

1.1035

1.14290.95590.41301.12250.72950.89620.44330.37171.05562018

2.06800.49761.9322

1.20892.1316

1.58831.47031.90571.36900.5513

1.19810.86791.5456

0.36990.59371.04962019

1.05981.07561.44831.23232.38371.24411.22492.14250.97253.77030.89970.29940.44280.63650.58350.96652020

0.85250.88850.51190.98054.45890.93950.94791.27110.79552.15040.8496

0.66490.49150.26470.27440.7945Avg

1.4712

1.39122.1562

1.40222.03492.13501.58181.66751.50722.56091.80311.15000.99870.81200.97961.3741

Source: Authors’ estimation based on China Statistical Yearbook of The Tertiary Industry, 2006-2021.

averageTable 3: Summary statistics for main variables
Variable(the wholesale sector)
year
i/k
marginal q
I(marginal q -1)*P /P

Obs

Mean

MedianStd. Dev.

Min

Max

465
465
465
465

2013
0.09112.9603
1.95142013
0.06102.41471.44144.325147
0.37172.47862.47292006-0.81280.1084-0.92592020
4.341519.562318.8546

465
465
465
465

2013
0.09441.37410.36872013
0.06681.23230.23384.325147
0.3331
0.96350.96162006-0.7626-0.7120-1.80282020
5.2481
7.8442
6.7890(pooled sample of the wholesale and retail sectors)
year
930
2013
i/k
930
0.0928
marginal q930
2.1672I
930
1.1601(marginal q -1)*P /P

2013
0.0637
1.69270.68914.325147
0.35272.04012.03552006-0.8128-0.7120-1.80282020
5.2481
19.562318.8546(the retail sector)
year
i/k
marginal q
I(marginal q -1)*P /P

Source: Authors’ calculations.

Table 4a: Determinants of investment rate, panel regression with fixed effect and robust standard errors(the wholesale sector)
VARIABLES

i/k

i/k

i/k (without robust standard errors)i/k(q -1)*PI/P

0.0294**(0.0142)0.0339*(0.0183)

0.0225**(0.0102)0.1026(0.0887)
0.2953***(0.0913)-0.2527***(0.0886)
0.1446(0.0890)0.1708*(0.0887)
0.1153(0.0886)0.2563***(0.0886)0.2857***(0.0890)-0.0168(0.0893)0.2426***(0.0887)
0.1327(0.0893)-0.0747(0.0888)0.2596***(0.0889)
0.0329(0.0894)

0.0225(0.0162)0.1026**(0.0492)
0.2953***(0.0566)-0.2527***(0.0246)0.1446***(0.0411)0.1708***(0.0441)0.1153***(0.0374)
0.2563***(0.0395)
0.2857*(0.1601)-0.0168(0.0355)
0.2426***(0.0594)
0.1327***(0.0428)-0.0747*(0.0374)
0.2596(0.1537)
0.0329(0.0564)0.0338(0.0277)0.0052(0.0044)-10.3624(8.8396)-0.0658(0.0660)-0.0658(0.0533)465
0.184731465
0.1847
31

y2007
y2008
y2009
y2010
y2011
y2012
y2013
y2014
y2015
y2016
y2017
y2018
y2019
y2020
year
Constant

Observations 465
465
R-squared
0.02110.0243
Number of id 31
31
Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

Table 4b: Determinants of investment rate, panel regression with fixed effect and robust standard errors(the retail sector)

VARIABLES

i/k

i/k

i/k

(q -1)*PI/P

0.1131***(0.0251)0.1010***(0.0253)0.0758***(0.0253)
0.0423(0.0490)0.2724***(0.0616)-0.2816***(0.0304)
0.0025(0.0420)0.0904*(0.0459)0.0342(0.0283)
0.1447***(0.0463)0.1848(0.1615)-0.0156(0.0345)-0.0156(0.0258)-0.0634**(0.0287)-0.1239***(0.0237)-0.1854***(0.0462)-0.0390(0.0291)

0.0527***(0.0092)-0.0091***(0.0027)
18.3234***(5.3397)

0.0633***(0.0144)465
0.090331465
0.2405
31

y2007
y2008
y2009
y2010
y2011
y2012
y2013
y2014
y2015
y2016
y2017
y2018
y2019
y2020
year
Constant

Observations
465
R-squared
0.0768
Number of id
31
Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

Table 4c: Determinants of investment rate, panel regression with fixed effect and robust standard errors(pooled sample of the wholesale and retail sectors)

VARIABLES

i/k

i/k

i/k

(q -1)*PI/P

0.0430***(0.0149)0.0403**(0.0158)

0.0258*(0.0131)0.0805**(0.0353)
0.3105***(0.0410)-0.2464***(0.0184)
0.1028***(0.0280)
0.1563***(0.0306)
0.0917***(0.0219)0.2238***(0.0287)
0.2505**(0.1135)-0.0092(0.0215)0.1277***(0.0338)
0.0423(0.0259)-0.0925***(0.0204)
0.0420(0.0835)-0.0020(0.0305)

0.0429**(0.0173)-0.0029(0.0027)
5.7853(5.4492)-0.0091(0.0247)930
0.031362930
0.1753
62

y2007
y2008
y2009
y2010
y2011
y2012
y2013
y2014
y2015
y2016
y2017
y2018
y2019
y2020
year
Constant

Observations
930
R-squared
0.0302
Number of id
62
Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

Figure 1: Ratios of new investments to fixed assets of the wholesale and retail sectors in China, 2006-2020(average value for 31 provinces)
0.5
wholesaleretail

0.4

0.3

0.2

0.1

0
2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

2018

-0.1

-0.2

-0.3

Source: Authors’ estimation based on China Statistical Yearbook of The Tertiary Industry 2006-2021.

2019

2020

Figure 2: Marginal q values of fixed assets plus inventories of the wholesale and retail sectors in China, 2006-2020(average value for 31 provinces)6
wholesaleretail

5

4

3

2

1

0
2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

2018

Source: Authors’ estimation based on China Statistical Yearbook of The Tertiary Industry 2006-2021.

20192020