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Economic Research-Ekonomska Istraživanja
ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/rero20
Research on the economic effect of employment
structure change in heterogeneous regions:
evidence from resource-based cities in China
Qiushuang Ren, Guofeng Gu, Yinan Zhou & Zhiyu Zhang
To cite this article: Qiushuang Ren, Guofeng Gu, Yinan Zhou & Zhiyu Zhang (2022): Research
on the economic effect of employment structure change in heterogeneous regions: evidence
from resource-based cities in China, Economic Research-Ekonomska Istraživanja, DOI:
10.1080/1331677X.2022.2048199
To link to this article: https://doi.org/10.1080/1331677X.2022.2048199
© 2022 The Author(s). Published by Informa
UK Limited, trading as Taylor & Francis
Group.
Published online: 15 Mar 2022.
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Research on the economic effect of employment
structure change in heterogeneous regions: evidence
from resource-based cities in China
Qiushuang Ren
a
, Guofeng Gu
a
, Yinan Zhou
b
and Zhiyu Zhang
c
a
School of Geographical Sciences, Northeast Normal University, Changchun, China;
b
School of
Economics and Management, Zhejiang Sci-Tech University, Hangzhou, China;
c
School of Arts and
Social Sciences, Hong Kong Metropolitan University, Hong Kong, China
ABSTRACT
The Report on the Work of the Chinese Government in 2021
emphasised that stable employment is the foundation of national
development. Therefore, adjustment of the employment structure
is one of the main routes for sustainable development of
resource-based cities. However, the impact of employment struc-
ture on sustained economic growth, particularly in heterogeneous
regions, has not yet been determined. This study analyses China’s
employment structure’s spatial evolution, using panel data from
2004 to 2018 of 115 prefecture-level resource-based cities. It
explores the driving factors and spatial effects of employment
structure changes on economic growth through an extended
two-sector economic growth model and spatial econometric
model, and proposes solutions for heterogeneous regions. The
results show that the labour productivity of the employed popula-
tion in the secondary industry is the most important factor affect-
ing economic growth, but the spatial effects of employment
structure adjustment on economic growth are different in hetero-
geneous regions. They further reveal that improving the product-
ivity of the employed population in the secondary industry and
building an industrial system according to regional advantages
are the top priorities for developing the sustainable economy of
resource-based cities.
ARTICLE HISTORY
Received 7 May 2021
Accepted 24 February 2022
KEYWORDS
Employment structure;
economic growth;
heterogeneous regions;
spatial effects; China
JEL CLASSIFICATIONS
O15; R11; R12
1. Introduction
Resource-based cities are an essential strategic support base for China’s energy
resources and an important support for sustained and vigorous development.
However, the historical problems of resource-exhausted cities are serious and need to
be solved. The problems of shantytown renovation, subsidence area management,
CONTACT Guofeng Gu gugf@nenu.edu.cn
Supplemental data for this article is available online at https://doi.org/10.1080/1331677X.2022.2048199.
ß2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http://
creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any
medium, provided the original work is properly cited.
ECONOMIC RESEARCH-EKONOMSKA ISTRAŽIVANJA
https://doi.org/10.1080/1331677X.2022.2048199
employment of unemployed miners, and protection of urban low-income people need
to be addressed. Emerging industries in resource-based cities are at the initial stage,
and the development of alternative industries lags, making it difficult to sustain eco-
nomic growth, and easy to fall into the ‘resource curse’. Innis (1999), a famous
Canadian economic historian and economic geographer, pioneered research on such
problems as early as the 1930s. The existing comprehensive theoretical research (e.g.,
Liu et al., 2011, 2013; Zhu, 2014; Ruan et al., 2020 ) and empirical analysis (
Copi
c
et al., 2014; He et al., 2017; Li et al., 2016;2021) have been relatively rich and covered
aspects such as resource-based city planning, transformation models and sustainable
development. Research on resource-based cities in Chinese academia began after 1949
and focused on the national overall development strategy prepared by the government
during the planned economic period. After the economic reform and opening up of
China, it focused on all aspects of the development of resource-based cities, especially
transformation performance, coordinated and integrated development, reemployment
of urban labour force, and industrial structure adjustment (Cai & Wu, 2005;Gu
et al., 2013; Jing & Wang, 2020; Liu et al., 2020).
Lewis (1954), a pioneer in the study of economic problems in developing coun-
tries, put forward a dual economic model to explore the impact of sectoral structure
changes on economic growth. Chenery et al. (1986), Kuznets (1965), Rostow (1990),
etc. determined that the change in department structural composition brought about
economic growth through research. After World War II, the digital revolution began,
and the tertiary industry developed rapidly. From the 1980s to the 1990s, with the
adjustment of China’s economic structure and the transformation of the economic
growth model, Chinese scholars began to pay attention to the relationship between
employment structure and economic growth. Many studies have shown that the
transfer of labour from agriculture to industry and service will promote national eco-
nomic growth (Brandt et al., 2008; Cai & Wang, 2010; Echevarria, 1997; Fan et al.,
2001; Ghose,1990; Sposi, 2019).
In 2013, the State Council of China issued ‘The Plan of Sustainable Development
for Resource-based Cities in China (2013–2020)’(hereinafter referred to as ‘the Plan’)
to guide the sustainable development of various resource-based cities across the coun-
try, yielding remarkable achievements. After the initial capital accumulation, the
urban industrial structure was transformed, and the employment structure adjusted
accordingly. Against the background of actively implementing the employment prior-
ity strategy in China, employment capacity continued to expand. As part of China’s
urban system, resource-based cities studied the characteristics of their employment
structure and its impact on economic growth, which helps analyse issues related to
sustained economic growth.
2. Literature review
Previous studies have revealed the economic effects of changes in the employment
structure from different perspectives. The first is the interactive effect. Fan et al.
(2001) show that with rapid economic growth, great changes have taken place in
China’s industries and employment structure since 1978. Zhang et al. (2020) postulate
2 Q. REN ET AL.
that the interaction between economic growth and employment structure has both
positive and negative aspects. The performance varies in different periods of develop-
ment, and the overall relationship is an inverted ‘U’. Since 1978, the agricultural
employment population has continued to shift to non-agricultural industries, and the
temporary demographic dividend has greatly promoted rapid economic growth. After
2011, rural surplus labour was gradually absorbed by the industrial sector, the demo-
graphic dividend began to disappear, and China’s economic growth rate declined.
The second is the relation effect. Changes in the employment structure are trig-
gered by economic development, which significantly increases the economic growth
rate. Chenery et al. (1986) and Solow (1970) confirm that economic growth drives
structural adjustment. Adjusting the employment structure, which is the reallocation
of resources, begets further economic development. Liu et al. (1999) demonstrate that
rapid economic growth is characterised by significant changes in the employment
structure of various industries in China, and the changes have obvious regional differ-
ences. In brief, rapid economic growth leads to significant changes in the national
economic structure, which are closely related to changes in the employ-
ment structure.
Another view is about the elastic effect. Cai (2010) shows that full employment is
a source of economic growth and maximising employment is the key to maintaining
the population’s role in promoting economic growth. Cai and Wang (2010) provide
detailed statistics on the problems, and confirm that China’s forceful economic
growth has been accompanied by the simultaneous expansion of employment, and
the diversification of the employment structure has also been significantly reflected
since 1978. Kuznets and Murphy (1966), and Ray et al. (2017) show that a rational-
ised employment structure can drive the transformation of the economic
growth model.
Additionally, there is a view of the intermediary effect. Romer (1986) and Lucas
(1988) established a theoretical model of endogenous economic growth by taking
technological progress as an endogenous variable, which theoretically proves that
technological progress can promote industrial structure adjustment and transform
economic growth patterns, thereby affecting employment structure and ultimately
promoting economic growth. He and Lan (2020) found that the interaction effect
between research and development intensity and the proportion of the employment
population in the tertiary industry has a significant effect on promoting economic
growth, while the interaction effect with the proportion of the employment popula-
tion in the primary and secondary industries does not.
Existing studies are also more comprehensive on countries at different stages of
development, and mainly focus on the coordinated development of employment
structure and economic growth. Studies have been conducted in developed countries
(e.g., Dietrich, 2012; Hartwig, 2012; Lains, 2006; Sposi, 2019) and developing coun-
tries (e.g., Dobrescu, 2011; Francois & Abimbola, 2019; Jula & Jula, 2013; Yu & Xu,
1999). Although most existing studies have examined the positive influence of
employment structure on economic growth in different regions, debates and disagree-
ments persist (Cheong & Wu, 2014). Research on evolution characteristics has grad-
ually changed from qualitative to quantitative descriptions. As for the influence
ECONOMIC RESEARCH-EKONOMSKA ISTRAŽIVANJA 3
mechanism, as the spatial model can reflect the spatial difference of influencing fac-
tors more accurately, the research method has changed from the traditional to a spa-
tial statistical method (Gu & Wu, 2019).
However, studies on the impact of employment structure on economic growth
using spatial models with spatial weights are rare. The research on economic growth
and employment structure is mostly carried out from the macroscopic national per-
spective, while the research on employment structure and economic growth in the
process of transformation and development of resource-based cities is less from the
prefecture-level scale. And existing studies do not classify cities according to resource
types, so it is difficult to provide specific policy recommendations for cities with dif-
ferent resource types.
Although the population and economic scale of resource-based cities are not large,
what urgently needs to be solved is the surplus of labour and reemployment caused
by the inability of the original pillar industries to meet the development needs of the
new era during the transformation process. Therefore, it is more practical to study
the population and economic development of resource-based cities from the perspec-
tive of employment structure.
3. Theoretical framework
3.1. Basic model
Accelerated economic growth has led to a transformation of underemployed primary
sector labour to the higher-productivity secondary and tertiary sectors, resulting in
structural changes in employment. With increased capital investment, technological
progress, or greater openness to international trade, changes in employment structure
have affected economic growth. The practice of Temple and W€
oßmann (2006) can be
used to establish an economic growth model based on dual economic structure the-
ory. Assuming that the total employed population is L,L
a
represents the employed
population of primary industry (traditional sector), L
b
represents the employed popu-
lation of the secondary and tertiary industries (modern sector).
L¼LaþLb(1)
Assuming that the output of the traditional sector is Y
a
, the output of the modern
sector is Y
b
. Interestingly, the productivity of the traditional sector is U
a
, the product-
ivity of the modern sector is U
b
. The factors of production in both sectors contain
capital investment K, the level of technological progress A, and labour supply L.
When capital and labour are equally intensive, technological progress is Hicks-neu-
tral, Fis the rule of function correspondence, and the production function can be
expressed as follows.
Ya¼AaFK
a,La
ðÞ Yb¼AbFK
b,Lb
ðÞ (2)
It is assumed that the wages of the employed population in both sectors are set
according to the marginal output, and the wage differential is the determining factor
4 Q. REN ET AL.
influencing the transfer of employment. Lower labour productivity implies lower
wages, and the two are positively correlated. Compared with the secondary and ter-
tiary industries, the labour productivity of the employed population in the primary
industry is lower (U
a
<U
b
). When the economy is in long-run equilibrium, the ratio
of labour productivity q, and the probability of labour migration pcan be expressed.
q¼Ub
Ua
p¼
xUb
qUa1
1þxUb
Ua1
(3)
The parameter xin Equation (3) is the speed of adjustment of the economic sys-
tem to long-run equilibrium. Thus, the quantitative relationship between labour prod-
uctivity in the two sectors can be expressed as Equation (4).
Ub¼q1þ1
x
p
1p
Ua(4)
Assuming that capital flows freely between sectors, the capital profit rate is also
determined by marginal output. Thus, without considering the rate of depreciation of
capital, the total output of the economy Ycan be expressed.
Y¼UaLaþUbLbþraKaþrbKb(5)
ris the capital profit rate and r
a
¼r
b
.sis the share of traditional sector output in
total economic output. gis the share of labour output in total economic output, from
which the economic growth rate can be determined.
Y
Y¼sYa
Ya
þ1s
ðÞ
Yb
Yb
¼sAa
Aa
þ1s
ðÞ
Ab
Ab
þ1g
ðÞ
Ka
KþKb
K
þUaL
YLa
LþUbL
YLb
L(6)
By substituting Equation (1) and (3) into Equation (6),Equation (7) can be
obtained.
Y
Y¼sAa
Aa
þ1s
ðÞ
Ab
Ab
þ1g
ðÞ
K
KþUaLa
YL
LþLb
Lq1þqp
x1p
ðÞ
(7)
The last term of Equation (7) captures the economic increment caused by the
transformation of the employed population. The analysis shows that when the incre-
ment of L
b
is greater than zero, the employed population will shift from the primary
industry to the secondary and tertiary industries, and the adjustment of the employ-
ment structure will promote economic growth.
ECONOMIC RESEARCH-EKONOMSKA ISTRAŽIVANJA 5
3.2. Further development and explanation
The theoretical model reflects that economic growth is driven by three effects: total
factor productivity, capital and labour input, and employment structure changes. In
the long run, technological progress (A) is a source of economic growth. To acceler-
ate economic growth, cities will first invest in new capital and develop until economic
growth stagnates. Thus, it is necessary to invest in new technologies to promote
innovation and improve economic development. Fan et al. (2017) showed that gov-
ernment support for innovation significantly impacted on economic growth in
labour-intensive and capital-intensive state-owned sectors.
In the short term, capital investment (K) significantly impacts social output and
employment. Strengthening capital investment can promote urban capital accumulation,
accelerate the construction of supporting infrastructure, and promote large-scale produc-
tion enterprises. Subsequently, as the employment structure is adjusted, productivity is con-
centrated and distributed in different regions, and economic strength is greatly improved.
When the proportion of investment increases, the per capita capital stock increases as the
per capita output growth rate. Labour input (L) can be influenced by human capital input,
the level of human capital, and labour supply. Barro and Martin (1995) emphasised the
importance of human capital input or the efficiency of the education sector in increasing
the rate of consumption growth and economic growth. Improving the level of human cap-
ital will effectively increase the labour supply by increasing labour productivity, thereby
promoting urban economic growth (Stankovi
cetal.,2021). Population growth is also a
source of economic growth (Cai, 2010). In an atmosphere of steady economic growth, as
the labour force increases, the total output also increases at the same rate. Therefore, sus-
tained economic growth is affected by population growth.
The ‘structural dividend’, generated when factors of production are transferred
from low productivity growth sectors to the high sectors, is an important source of
rapid economic growth due to the significant differences in productivity growth rates
between sectors (Peneder, 2003). The changes in employment structure brought about
by market-oriented reforms have made outstanding contributions to economic
growth. However, as the ‘demographic dividend’gradually disappears, the benefits of
adjusting the employment structure may decrease. He (2018) confirms that market
openness positively affects economic growth and employment, and there is a two-way
causal relationship between employment and economic growth. There are differences
in the development of resource-based cities, but the employment structure must be
adjusted to maintain a high economic growth rate. Given this background, this study
explores the economic effects of employment structures in heterogeneous regions and
provides suggestions for the sustainable development of society and economy in the
process of transformation of resource-based cities.
4. Materials and methods
4.1. Study area
Resource-based cities are cities developed using natural resources that are dominated
by resource-based industries. Resource-based cities were defined at the end of 2015,
6 Q. REN ET AL.
according to the Plan and related academic research (Yu et al., 2019). There are 116
prefecture-level resource-based cities in China, accounting for approximately 39% of
the total number of cities above the prefecture level in China. This study chose 2004
to 2018 as the sample period. The samples were categorised into four types of hetero-
geneous regions based on the differences in the development stages of resource-based
cities. The research objects were 62 mature, 23 recessionary, 16 regenerated and 14
growing resource-based cities, including 115 prefecture-level resource-based cities
(Bijie was not included). There are 20, 37, 39, and 19 administrative divisions in the
eastern, central, western, and north-eastern regions of China, respectively (Figure 1).
4.2. Variable selection
As an intuitive indicator of economic growth, GDP can reflect the economic develop-
ment level and comprehensive economic strength of resource-based cities. This study
uses real GDP growth rate to represent economic growth (Y), the response variable.
Analysis shows that a correlation between economic growth and the proportion of
employment in various industries and their increments. Existing research also con-
firms that employment structure is closely related to economic growth. The flow of
labour among industries impacts the output efficiency in each sector, as the speed of
economic growth. Therefore, employment structure change (U
1
,U
2
,U
3
), the explana-
tory variables, can be expressed by the growth rate of labour productivity of each
department. Explanatory variables can be obtained by calculating the ratio of value
added of output to year-end employment in three industries.
Control variables are as follows: Regardless of whether economic growth is in a
stable state, technological progress (tec) has a decisive influence on the per capita out-
put growth rate. In this study, the expenditure on scientific undertakings was used.
Capital investment (inv) is a main production input factor, affecting output. The total
investment in fixed assets of the whole society can be selected, and capital investment
can be expressed by accounting for fixed capital stock. Educational expenditure (edu)
is expressed as a proportion of GDP spent on education, with a higher proportion
Figure 1. Overview map of the study area.
Note: Drawing review number of base-maps is GS (2019) No.1719 (Supervised by the Ministry of Natural Resources
of China).
Source: Drawn by authors.
ECONOMIC RESEARCH-EKONOMSKA ISTRAŽIVANJA 7
indicating that the government attaches greater importance to education. Human cap-
ital is a key factor influencing economic growth, increasing productivity and promot-
ing employment restructuring by improving managerial and innovation efficiency.
This study used the average years of schooling to express population quality (hum).
Population growth rate (pop) is also a source of economic growth and can be
expressed as the natural population growth rate. Market openness (ope) is the actual
utilisation of foreign direct investment which facilitates increased investment and
technology introduction, thus affecting economic growth. It is measured by the pro-
portion of actual use of foreign direct investment in each region to GDP.
4.3. Methods
4.3.1. Exploratory spatial data analysis
Exploratory Spatial Data Analysis can detect whether the spatial distribution of an elem-
ent in a region has spatial correlation with its neighbouring regions, and intuitively reflect
the distribution model and influence characteristics of the element. Global spatial auto-
correlation was used to test whether the employment structure and economic growth of
different cities were spatially correlated. It reflects the degree of similarity in the attribute
values of spatially contiguous or spatially adjacent regional units and can characterise the
spatial distribution of elements within the whole system, usually measured as the
Moran’sI(Anselin, 1988). The calculation formula is expressed as follows.
I¼Pn
i¼1Pn
j6¼iWijðXi
XÞðXj
XÞ
S2Pn
i¼1Pn
j¼1Wij
(8)
S
2
is the variance of the sample, X
i
and X
j
represent the observations of cities i
and jrespectively. nis the total number of resource-based cities, 115 in total. W
ij
rep-
resents the weight matrix of queen space based on adjacency relation; it is equal to 1
when region iand region jhave an edge or a point in common, otherwise, it equals
0. The value range of Moran’sIis 1 to 1. The closer the absolute of Moran’sIis to
1, the stronger the spatial correlation is. If the Moran’sIis equal to 0, it indicates
that the regional variables are random in their spatial distribution.
4.3.2. Empirical models
First, the likelihood ratio test (LR test), robust LR test, Lagrange multiplier test (LM
test) and robust LM test were carried out. The panel data without spatial interaction
effect was estimated to determine whether the spatial model should choose random
effect or time fixed effect, a spatial fixed effect, or bidirectional fixed effect. The
model was set as follows:
Yit ¼aþqWYit þbXit þhWXit þeit (9)
The subscript irepresents a resource-based city, and the subscript trepresents a
year within the period of study 2004 to 2018; a,b, and hare fixed estimated parame-
ters; qrepresents the spatial autoregressive coefficient or spatial autocorrelation
8 Q. REN ET AL.
coefficient; and e
it
represents the random error term. Wrepresents the row random
space weight matrix of queen contiguity. WY
it
represents the endogenous interaction
effect between the explanatory variables. WX
it
represents the exogenous interaction
effect between the explanatory variables.
When selecting the optimal model, the spatial lag model (SAR) or spatial Durbin
model (SDM) was selected when h¼0. The spatial error model (SEM) or SDM was
selected when h¼±qb. The maximum likelihood estimation (ML) method was used
to estimate the model parameters. The partial differential estimation method (LeSage
& Pace, 2008) can explain the influence of variable changes in the model. Spatial
effects can be divided into direct effects (the influence of regional explanatory varia-
bles on local regions) and spillover effects (the influence of regional explanatory vari-
ables on neighbouring regions). The optimal model is determined, which can
estimate the total, direct, and spillover effects of the change in the population’s
employment structure in the resource-based cities on economic growth. In conclu-
sion, the spatial econometric model can be further set as follows.
Yit ¼aþqWYit þbXitþhWXit þeit
bXit¼b1U1it þb2U2it þb3U3it þb4tecit þb5invit þb6eduit
þb7humit þb8popit þb9opeit
hWXit¼h1WU1it þh2WU2it þh3WU3it þh4Wtecit þh5Winvit þh6Weduit
þh7Whumit þh8Wpopit þh9Wopeit
(10)
Figure 2. Employment structure of resource-based cities in selected years.
Note: Drawing review number of base-maps is GS (2019) No.1719 (Supervised by the Ministry of Natural Resources
of China).
Source: Drawn by authors.
ECONOMIC RESEARCH-EKONOMSKA ISTRAŽIVANJA 9
4.4. Data sources
The data used in this study were obtained from the China Urban Statistical
Yearbook, China Fixed Assets Investment Statistical Yearbook, and CEIC Data from
2004 to 2019. Individual missing data were interpolated. To eliminate the influence of
inflation and improve the accuracy of the results, the GDP deflator was used to
deflate the price-related data based on 2003 report figures. Using the calculation
method proposed by Ke (2009) for reference, the fixed capital stock was calculated by
using the fixed asset investment price index in the China Statistical Yearbook.
5. Results and discussion
5.1. Spatial pattern analysis
Figures 2 and 3reflect the population employment structure’s spatial distribution
characteristics in resource-based cities. Based on extreme value and mean value, tak-
ing standard deviation as the interval, ArcGIS was used to create classification inter-
vals proportionally and spatial distribution maps.
The characteristics of time evolution postulate that from 2004 to 2018, the propor-
tion of employed population in the primary industry (P
1
) of resource-based cities
decreased yearly falling by approximately 41% over the 15 years. The proportion of
employed population in the secondary industry (P
2
) has remained at around 50%.
The proportion of employed population in the tertiary industry (P
3
) fluctuated and
increased by approximately 8%. The concentrated distribution areas of resource-based
cities have a relatively high employment population. Primary industry is in Northeast
China. Secondary industry is situated near the Yellow Sea and the Bohai Sea, which
are mainly regeneration and recession types. Tertiary industry is in the central and
western regions of China (Figure 2).
From the perspective of spatial heterogeneity, P
1
in a few maturity-type cities in
Northeast China and West China has significantly changed. P
2
in recession-type cities
in Northeast and Western China has been dramatically reduced. P
2
in eastern, cen-
tral, and western growth-type cities has increased, and P
2
in the regeneration-type cit-
ies around the Yellow Sea was still relatively high by 2018. P
3
in recession-type cities
Figure 3. Changes in proportion of employment population from 2004 to 2018.
Note: Drawing review number of base-maps is GS (2019) No.1719 (Supervised by the Ministry of Natural Resources
of China).
Source: Drawn by authors.
10 Q. REN ET AL.
in Northeast China remained at a low level during the study period. However, P
3
of
regeneration-type cities in the eastern region and of growth-type cities in the western
region decreased. P
3
of growth-type cities in eastern Inner Mongolia and most cities
in Northeast China increased significantly (Figure 3).
5.2. Descriptive statistics and correlation analysis
Table 1 shows the summary statistics for the variables of the spatial econometrics
model. Among the core explanatory variables, the standard deviation of labour prod-
uctivity growth in the primary sector (U
1
) was the largest due to the considerable
variation in P
1
over the study period. The growth rate of labour productivity in the
secondary sector (U
2
) was also relatively large. Using the county-level database from
1997 to 2010, Cheong and Wu (2014) proved that the root of the difference in
regional economic growth rate in China was the difference in the degree of develop-
ment in the secondary industry among regions. The growth rate of labour productiv-
ity in the tertiary sector (U
3
) was relatively low, which indicated that the high-end of
the tertiary industry chain of resource-based cities had a relatively low proportion.
Among the control variables, the mean values of population quality and natural
population growth rate are 8.43 and 5.30, respectively, which indicated that the qual-
ity of population in resource-based cities had been improving and the total popula-
tion had been increasing.
As the prerequisite for constructing a spatial panel model, the core variables must
satisfy the spatial correlation. This study used Moran’sIto determine whether the eco-
nomic growth and employment structure variables of resource-based cities had spatial
autocorrelation characteristics (Table 2). The Moran’sIof GDP from 2004 to 2018 was
in the range of 0.18 to 0.60, and passed the significance test at 5%. This suggested that
both economic growth and employment structure had a significant spatial correlation.
Spatial externality is an important factor affecting economic growth and should be
incorporated into spatial econometric models with its spatial lag terms.
5.3. Results of resource-based cities
The effect of population employment structure change on regional economic growth
in resource-based cities from 2004 to 2018 was estimated using MATLAB. First, the
Table 1. Descriptive statistics.
Mean Std.Dev Median Min Max
Y 11.092 4.928 11.600 19.380 37.690
U
1
26.755 108.301 10.792 970.344 813.070
U
2
2.327 3.642 2.026 23.262 50.840
U
3
1.920 2.079 1.564 15.781 20.536
tec 0.002 0.002 0.001 0.000 0.063
inv 0.001 0.002 0.000 0.000 0.023
edu 0.033 0.018 0.029 0.002 0.142
hum 8.503 0.550 8.429 6.779 10.718
pop 5.482 4.928 5.300 16.270 39.180
ope 0.014 0.016 0.009 0.000 0.118
Source: Authors’calculations.
ECONOMIC RESEARCH-EKONOMSKA ISTRAŽIVANJA 11
Hausman test was set, and the estimated value of the test result was 50.25 (p <0.01),
which indicated that the fixed effect model could be chosen. The non-spatial panel
model test showed that the two-way fixed effect’s Log-L value was the largest (Table
3). It passed the (robust) LM test and the LR test of spatial (or temporal) fixed effects
at a significance level of 1%. Consequently, SAR or SDM with two-way fixed effects
should be selected for estimation, based on the test results in Table 4.
Table 4 shows that the qvalues of SAR and SDM models all pass the test at the
1% significance level. The degree of fit of the parameters was high—R
2
values were
all greater than 0.6, and all Log-L values were small. The spatial lagged LR test and
Wald test rejected the original hypothesis at the significance level of 1%, which
showed that SDM could not be simplified as SAR. From the results of parameter esti-
mation, SDM with the two-way fixed effect was the optimal model. Most of the
explanatory variables in SDM were not at the zero-significance level after the spatial
lag term was added, which showed that these explanatory variables have significant
spatial effects on resource-based cities’economic growth. It can be determined that
SDM had a systematic deviation in the estimation of spillover effects. Table 5 decom-
poses each explanatory variable to determine the influence coefficients of its direct
effects and spillover effects.
The coefficient of U
2
in SDM was 0.21, which was significantly positive. This
shows that accelerated labour productivity in the secondary industry promotes eco-
nomic growth. Resource-based cities are small and medium-sized cities. The key to
improving economic growth in small cities lies in developing industries according to
local conditions rather than productive services (Deng & Zhang, 2020). Therefore,
increasing labour productivity in the secondary industry positively impacts economic
growth. Furthermore, edu and tec played an irreplaceable role in promoting the eco-
nomic growth of resource-based cities. Moreover, pop had no significant impact on
the economic growth of resource-based cities. This conclusion is consistent with
Gregory (1980); that is, there is no evidence that population growth hinders economic
growth.
Table 2. Spatial correlation tests.
YU
1
U
2
U
3
Year Moran’sIZ(I) Moran’sIZ(I) Moran’sIZ(I) Moran’sIZ(I)
2018 0.249 2.783 0.162 3.134 0.169 1.936 0.297 3.310
2017 0.464 5.145 0.0601.077 0.1251.544 0.154 1.795
2016 0.597 6.749 0.0640.967 0.370 4.267 0.369 4.286
2015 0.330 3.690 0.002 0.345 0.171 1.984 0.0701.274
2014 0.582 6.657 0.068 2.523 0.530 5.905 0.380 4.316
2013 0.221 2.534 0.009 2.742 0.194 2.236 0.138 1.802
2012 0.298 3.409 0.008 2.947 0.1361.623 0.453 5.011
2011 0.285 3.329 0.0521.144 0.023 0.530 0.434 4.828
2010 0.368 4.163 0.1101.399 0.0931.284 0.441 4.910
2009 0.488 5.440 0.1211.613 0.143 1.663 0.550 6.098
2008 0.280 3.136 0.0571.286 0.303 3.722 0.196 2.251
2007 0.193 2.203 0.1031.354 0.172 2.166 0.1021.332
2006 0.311 3.449 0.0981.379 0.1281.656 0.1321.609
2005 0.277 3.206 0.1261.615 0.181 2.176 0.247 2.913
2004 0.182 2.139 0.231 3.570 0.229 2.578 0.232 2.648
Note: The numbers in parentheses are t-statistics. Asterisks means the figure is statistically significant. ,, and
denote significance at the 10%, 5%, and 1% levels, respectively.
Source: Authors’calculations.
12 Q. REN ET AL.
Table 3. Results of non-spatial panel model test.
Effect
Total Growth type Maturity type Recession type Regeneration type
OLS Spatial-fixed Time-fixed Two-way fixed Two-way fixed Two-way fixed Two-way fixed Two-way fixed
Log-L 4950.000 4750.000 4540.000 4360.000 518.018 2160.000 920.814 646.422
LMLAG 441.021 444.657 155.203 150.740 3.843 32.188 22.129 8.645
R-LMLAG 95.691 200.248 44.404 37.409 3.78111.151 6.557 4.236
LMERR 361.760 279.196 126.156 125.673 10.143 26.272 16.577 5.064
R-LMERR 16.391 34.787 15.357 12.342 10.080 5.235 1.005 0.655
Note: The numbers in parentheses are t-statistics. Asterisks means the figure is statistically significant. ,, and denote significance at the 10%, 5%, and 1% levels, respectively.
Source: Authors’calculations.
ECONOMIC RESEARCH-EKONOMSKA ISTRAŽIVANJA 13
Employment structure with the spatial lag term was significantly different from 0.
After decomposition, the spatial effect on regional economic growth was also significantly
positive. Namely, U
2
had a significant positive impact on both the local and surrounding
cities’economic growth. The decline in U
1
and U
3
inhibited the economic growth of sur-
rounding resource-based cities, which had been caused by technology spillovers, invest-
ment in education and optimisation of employment structures.
5.4. Result of heterogeneous regions
The influence coefficient of U
2
in growth-type cities was 0.10, the direct effect coeffi-
cient was 0.09, both of which passed the significance test. The results show that
increasing U
2
can promote local economic growth. U
1
and U
3
had no significant
Table 4. Estimation result of the spatial econometric model with spatial and time fixed effect.
Variables
Total Growth
type
Maturity
type
Recession
type
Regeneration
type
OLS SAR SDM SDM SDM SDM SDM
U
1
0.001 0.001 0.001 0.004 0.001 0.001 0.005
(0.391) (0.245) (0.040) (1.483) (-0.271) (0.696) (-1.534)
U
2
0.258 0.225 0.214 0.104 0.251 0.963 0.506
(8.883) (7.851) (7.730) (2.281) (6.138) (6.009) (3.694)
U
3
0.036 0.015 0.003 0.011 0.071 0.058 0.323
(0.670) (0.280) (-0.171) (0.059) (1.171) (0.291) (1.999)
tec 125.473 106.238 96.028 55.767 137.574 476.285451.220
(3.111) (2.653) (2.469) (1.041) (2.473) (1.852) (-1.445)
inv 16.589 8.681 3.930 90.548 89.425 90.406 122.730
(-0.223) (-0.118) (-0.049) (0.362) (-0.975) (-0.385) (0.764)
edu 44.188 33.065 27.496 28.464 15.406 15.768 137.949
(3.222) (2.519) (2.178) (1.013) (1.006) (0.373) (2.844)
hum 0.036 0.002 0.085 7.231 0.684 0.020 0.731
(0.123) (0.006) (0.296) (6.750) (-2.142) (0.024) (-0.689)
pop 0.084 0.068 0.036 0.058 0.031 0.1320.043
(3.628) (2.885) (1.637) (1.226) (1.077) (1.789) (-0.658)
ope 15.074 14.868 22.759 61.34717.92219.740 14.607
(2.013) (2.012) (3.024) (1.652) (2.016) (0.942) (0.782)
WU
1
0.0020.005 0.001 0.062 0.005
(1.685) (0.368) (0.216) (-1.122) (-0.750)
WU
2
0.152 0.155 0.137 0.678 0.273
(3.714) (1.274) (2.171) (2.767) (2.032)
WU
3
0.192 0.260 0.156 0.364 0.273
(2.678) (0.771) (1.312) (-0.938) (1.100)
Wtec 76.192 81.328 214.222 886.655 866.778
(1.572) (-1.339) (2.228) (0.962) (1.254)
Winv 58.135 31.510 42.305 356.534 2.550
(2.649) (-0.090) (-0.561) (0.797) (-0.011)
Wedu 45.891 111.780 40.382 13.609 120.814
(2.649) (-2.271) (2.194) (-0.209) (-1.389)
Whum 0.005 5.284 0.290 5.297 2.748
(-0.031) (3.150) (0.663) (-3.476) (-1.611)
Wpop 0.085 0.104 0.016 0.293 0.068
(2.930) (1.121) (-0.431) (2.119) (0.840)
Wope 23.460 39.432 34.650 21.987 6.419
(-2.279) (0.727) (-2.723) (0.362) (0.277)
q0.260 0.227 0.236 0.131 0.236 0.236
(11.407) (9.641) (-3.064) (3.896) (-3.630) (-3.657)
R
2
0.080 0.659 0.664 0.748 0.672 0.646 0.660
Note: The numbers in parentheses are t-statistics. Asterisks means the figure is statistically significant. ,, and
denote significance at the 10%, 5%, and 1% levels, respectively.
Source: Authors’calculations.
14 Q. REN ET AL.
impact on the economic growth of growth-type cities. Obviously, significant problems
exist, such as the long distance from provincial capitals, unbalanced economic devel-
opment, and irregular order of resource development. In the process of development,
the population can be transferred to employment in secondary and tertiary industries
to promote economic transformation and upgrading. This type of urban natural
resource reserve can be exploited for a long time, so the government can undertake
orderly planning, raise the threshold, and develop rationally. Moreover, it accelerates
the cultivation of the resource industry chain (Sun & Zheng, 2019) and continues to
absorb the population and transfer it to the secondary industry for employment.
The influence coefficient of U
2
in maturity-type cities was 0.25, the direct effect
coefficient was 0.26, and the indirect influence coefficient was 0.19, both of which
were significantly positive. This shows that increasing U
2
has a positive effect on the
economic growth of a city and its surrounding cities. U
1
and U
3
had no significant
influence on economic growth. Maturity-type cities can stably develop more intense
resources. Mining, transportation, and processing systems are also relatively mature.
The proportion of the employed population in extractive industries is still increasing
(Yu et al., 2019). Therefore, a higher proportion of the secondary industry’s employed
population is beneficial for regional economic growth. However, the problems of
environmental governance are prominent, and there are many contradictions in the
distribution of various interests, so the speed of transformation and development is
relatively slow. In transformation and development, maturity-type cities should
lengthen the industrial chain, promote development of the urban economy, improve
the utilisation of resources by combining new technologies, cultivating enterprises
with deep processing of resources, and build new brands made in China to promote
sustained economic growth.
The influence coefficient of U
2
in recession-type cities was 0.96, the direct effect
coefficient was 0.93, and the indirect influence coefficient was 0.40, both of which
were significantly positive. Increasing U
2
and vigorously developing alternative
Table 5. Spatial effect decomposition of SDM based on panel data.
Variables Direct Indirect Total
Total U
1
0.001 (0.317) 0.002(1.675) 0.003 (1.537)
U
2
0.235 (8.344) 0.238 (5.203) 0.473 (8.299)
U
3
0.016 (0.309) 0.226 (2.646) 0.242 (2.334)
tec 105.735 (2.706) 117.949 (2.062) 223.684 (2.900)
edu 32.274 (2.481) 61.741 (2.970) 94.015 (3.489)
pop 0.046 (1.992) 0.112 (3.312) 0.156 (3.806)
ope 21.259 (2.918) 22.180(-1.862) 0.921 (-0.068)
Growth type edu 36.881 (1.295) 105.636 (-2.480) 68.755 (-1.334)
hum 6.950 (6.677) 3.207 (2.311) 10.157 (6.072)
Maturity type U
2
0.260 (6.115) 0.187 (2.737) 0.446 (5.456)
tec 149.127 (2.767) 251.319 (2.392) 400.446 (3.301)
edu 17.485 (1.148) 46.864 (2.308) 64.349 (2.475)
ope 16.358(1.798) 35.698 (-2.558) 19.341 (-1.111)
Recession type U
2
0.9299 (5.873) 0.397(1.795) 1.327 (5.954)
hum 0.399 (0.361) 4.688 (-3.299) 4.289 (-3.112)
pop 0.117 (1.477) 0.220(1.758) 0.336 (3.141)
Regeneration type edu 153.188 (3.113) 142.627(-1.860) 10.561 (0.128)
Note: The numbers in parentheses are t-statistics. Asterisks means the figure is statistically significant. ,, and
denote significance at the 10%, 5%, and 1% levels, respectively.
Source: Authors’calculations.
ECONOMIC RESEARCH-EKONOMSKA ISTRAŽIVANJA 15
industries can accelerate economic transformation and speed of development. The
rate of technological progress in recession-type cities was lower than the rate of
resource depletion. The original pillar industries still contribute a greater proportion
of economic growth, while upgrading U
2
will increase the possibility of technology
spillovers, which is beneficial to the economic development of surrounding cities.
Affected by the decline of resource development, the economic development of reces-
sion-type cities was sluggish, and hidden unemployment was widespread. Recession-
type cities have a high industrial concentration, and strengthening the endogenous
power of urban development could reduce its dependence on resources. And raising
the natural population growth rate, promoting the quality of the employed population
to match productivity could solve historical problems such as the reemployment of
miners, which lead to green economy development.
The impact coefficients for the change in employment structure in regeneration-
type cities were 0.01, 0.51 and 0.32, with the spatial effects mainly direct, which
shows that transferring the employed population in the primary industry to the sec-
ondary and tertiary industries, and upgrading U
2
and U
3
can promote economic
growth. Most resource exploitation activities in renewable cities have stopped, and
their dependence on resource-based economies is not high. The emission intensity of
water and gas is also low, and the economy and society gradually develop. Therefore,
increasing U
2
leads to a series of economic, social, and ecological problems caused by
resource exploitation which still negatively impact economic growth. Improving the
efficiency of tertiary output, especially high-tech industries, can effectively solve these
problems. S
avoiu et al. (2015) confirm that the service industry is an important factor
in economic growth, and its role in the economy is increasing. Compared with the
other three types of cities, regeneration-type cities have an enormous comprehensive
economic scale. Transformation and development should promote urban functions,
increase investment in education to enhance development, and establish a long-term
mechanism for sustainable development.
Table 6. Estimation results of robustness tests.
Variables
Total Growth
type
Maturity
type
Recession
type
Regeneration
type
OLS SAR SDM SDM SDM SDM SDM
U
1
0.001 0.001 0.001 0.004 0.001 0.001 0.004
(0.391) (-0.116) (0.038) (1.136) (-0.077) (0.626) (-1.176)
U
2
0.257 0.241 0.228 0.125 0.251 1.025 0.558
(8.883) (8.333) (8.217) (2.735) (6.380) (6.514) (4.429)
U
3
0.036 0.015 0.013 0.126 0.051 0.213 0.285
(0.670) (0.276) (-0.251) (-0.679) (0.837) (1.115) (1.849)
WU
1
0.018 0.006 0.019 0.008 0.005
(2.054) (0.692) (2.066) (0.672) (0.623)
WU
2
0.2140.009 0.572 3.086 0.482
(0.651) (-0.081) (2.148) (3.379) (1.771)
WU
3
0.6310.645 0.086 0.398 0.085
(-0.986) (-1.537) (0.133) (0.330) (-0.209)
q0.798 0.423 0.2310.290 0.2750.324
(23.204) (5.072) (-1.725) (2.945) (-1.845) (-2.227)
R
2
0.080 0.653 0.663 0.728 0.687 0.713 0.667
Note. The numbers in parentheses are t-statistics. Asterisks means the figure is statistically significant. ,, and
denote significance at the 10%, 5%, and 1% levels, respectively.
Source. Authors’calculations.
16 Q. REN ET AL.
5.5. Robustness test
To test the robustness of the economic growth effect of employment structure adjust-
ment, the results were retested by constructing a spatial weight matrix of geographical
distance instead of the spatial weight matrix of adjacency relation (Table 6). The
most significant difference between the robustness test results and the previous results
is that the coefficients of the variables, spatial spillover coefficients and significance
have been somewhat increased or decreased. However, the estimation results of the
core variables are basically consistent with the above conclusions. This result shows
that the effect of changes in the employment structure on economic growth is reliable
and robust.
6. Conclusions
Following the concept of sustainable development, the Chinese government pro-
mulgated the Plan to prevent resource-based cities from falling into the ‘resource
curse’. In transformation, resource-based cities strive to develop a green economy
and actively promote population transfer to non-agricultural industries. In this
context, this study analysed the relationship between employment structure
adjustment and economic growth in 115 prefecture-level resource-based cities in
China from 2004 to 2018. The results show differences in the economic effects of
employment structure adjustment in heterogeneous regions. Overall, increasing
labour productivity in the secondary industry and building an industrial system
according to local advantages are the top priorities in developing the economy of
resource-based cities. Moreover, it is necessary to improve labour productivity in
the tertiary industry, improve the comprehensive service function of cities, and
promote the return of high-tech talent. Simultaneously, increasing investment in
education and technology and expanding market openness can significantly boost
the sustained economic growth of resource-based cities. From the perspective of
regional heterogeneity, promoting the proportion of employed people in the three
industries in resource-based cities to be reasonable is an appropriate strategy.
Increasing labour productivity in the secondary industry in all types, not only the
tertiary industry, and reducing the labour input in the primary in regeneration-
type, is conducive to the rational development and utilisation of natural resources
in resource-based cities.
This study confirms differences in the impact of heterogeneous regional employ-
ment structure adjustments on economic growth. As in previous studies, this study
inevitably has several limitations. Future research can further explore the non-linear
factors of the adjustment of employment structure to economic growth or the rela-
tionship between the adjustment of employment structure and the rate of economic
growth. Second, whether the research results of this study apply to resource-based cit-
ies at the same developmental stage in other regions needs further discussion. In
future research, international samples from South Africa and the Middle East may
expand the current research.
ECONOMIC RESEARCH-EKONOMSKA ISTRAŽIVANJA 17
Notes
Average years of schooling ¼(number of primary school students 6þnumber of
ordinary secondary school students 10.5þnumber of higher school students 16)/
total number of students enrolled.
P
1
,P
2
, and P
3
were calculated by the ratio of employed population in the primary,
secondary, and tertiary sector industries to the total employed population.
In the two-way fixed effects test results, the estimated value of the LM test for
SAR was 150.74, and for SEM was 125.67. The estimated value of the robust LM test
for SAR was 37.41, and for SEM was 12.34.
Acknowledgements
We are grateful to the editor and the anonymous reviewers for constructive comments. All
remaining errors are our own.
Disclosure statement
No potential conflict of interest was reported by the authors.
Funding
This research was funded by the Chinese National Funding of Social Sciences (16BJL032).
References
Anselin, L. (1988). Spatial econometrics: Methods and models. Kluwer Academic Publishers.
Barro, R., & Martin, X. S. (1995). Economic growth (1st ed.). McGraw-Hill.
Brandt, L., Hsieh, C. T., & Zhu, X. (2008). Growth and structural transformation in China.
In Loren Brandt & Thomas G. Rawski (Eds.), China’s great economic transformation
(pp. 683–728). Cambridge University Press. https://doi.org/10.1017/CBO9780511754234.018
Cai, F. (2010). Demographic transition, demographic dividend, and Lewis turning point in
China. China Economic Journal,3(2), 107–119. https://doi.org/10.1080/17538963.2010.
511899
Cai, F., & Wang, M. (2010). Growth and structural changes in employment in transition
China. Journal of Comparative Economics,38(1), 71–81. https://doi.org/10.1016/j.jce.2009.10.
006
Cai, F., & Wu, Y. W. (2005). China’s population and labor report on employment and social
security in resource-based cities. Social Science Literature Press. (in Chinese).
Chenery, H. B., Robinson, S., & Syrquin, M. (1986). Industrialization and growth: A compara-
tive study. Oxford University Press.
Cheong, T. S., & Wu, Y. (2014). The impacts of structural transformation and industrial
upgrading on regional inequality in China. China Economic Review,31, 339–350. https://doi.
org/10.1016/j.chieco.2014.09.007
Copi
c, S., et al. (2014). Transformation of industrial heritage: An example of tourism industry
development in the Ruhr area (Germany). Geographica Pannonica,18(2), 43–50. https://doi.
org/10.5937/GeoPan1402043C
Deng, Z. L., & Zhang, K. Y. (2020). Why does the spatial differentiation of China’s economic
growth exist? An interpretation of spatial economics. Economic Research Journal,55(4),
20–36. in Chinese).
18 Q. REN ET AL.
Dietrich, A. (2012). Does growth cause structural change, or is it the other way around? A
dynamic panel data analysis for seven OECD countries. Empirical Economics,43(3),
915–944. https://doi.org/10.1007/s00181-011-0510-z
Dobrescu, E. (2011). Sectoral structure and economic growth. Romanian Journal of Economic
Forecasting,3,5–36. https://ssrn.com/abstract=1950990
Echevarria, C. (1997). Changes in sectoral composition associated with economic growth.
International Economic Review,38(2), 431–452. https://doi.org/10.2307/2527382
Fan, S. M., Yan, J. J., & Sha, J. H. (2017). Innovation and economic growth in the mining
industry: Evidence from China’s listed companies. Resources Policy,54,25–42. https://doi.
org/10.1016/j.resourpol.2017.08.007
Fan, J. Y., Yan, Y., & Wang, J. S. (2001). Changes in employment structure and its contribu-
tion to economic growth since the reform. Macroeconomics,9,43–47. in Chinese).
Francois, M. D., & Abimbola, S. K. (2019). A causality analysis of the relationships between
gross fixed capital formation, economic growth, and employment in South Africa. Studia
Universitatis Babes-Bolyai Oeconomica,64(1), 33–44. https://doi.org/10.2478/subboec-2019-
0003
Ghose, A. K. (1990). Economic growth & employment structure: A study of labour outmigration
from agriculture in developing countries. International Labour Organization.
Gregory, P. (1980). An assessment of changes in employment conditions in less developed
countries. Economic Development and Cultural Change,28(4), 673–700. https://doi.org/10.
1086/451211
Gu, J. J., Guo, P., Huang, G. H., & Shen, N. (2013). Optimization of the industrial structure
facing sustainable development in resource-based city subjected to water resources under
uncertainty. Stochastic Environmental Research and Risk Assessment,27(3), 659–673. https://
doi.org/10.1007/s00477-012-0630-9
Gu, G. F., & Wu, Y. Z. (2019). The impact of population age structure change on regional
economy. Economic Geography,39(1), 47–55. in Chinese).
Hartwig, J. (2012). Testing the growth effects of structural change. Structural Change and
Economic Dynamics,23(1), 11–24. https://doi.org/10.1016/j.strueco.2011.09.001
He, Y. (2018). Foreign direct investment, economic growth and employment: Evidence from
China. International Research in Economics and Finance,2(1), 12–25. https://doi.org/10.
20849/iref.v2i1.320
He, F., & Lan, D. X. (2020). R&D intensity, employment structure and economic growth.
Reform of Economic System,4,72–77. in Chinese).
He, S. Y., Lee, J. W., Zhou, T., & Wu, D. (2017). Shrinking cities and resource-based economy:
The economic restructuring in China’s mining cities. Cities,60,75–83. https://doi.org/10.
1016/j.cities.2016.07.009
Innis, H. A. (1999). The fur trade in Canada: An introduction to Canadian economic history.
University of Toronto Press.
Jing, Z. R., & Wang, J. M. (2020). Sustainable development evaluation of the society–econo-
my–environment in a resource-based city of China: A complex network approach. Journal
of Cleaner Production,263, 121510–121517. https://doi.org/10.1016/j.jclepro.2020.121510
Jula, D., & Jula, N. M. (2013). Economic growth and structural changes in regional employ-
ment. Romanian Journal of Economic Forecasting,16(2), 52–69. https://doi.org/10.1515/rne-
2012-0001
Ke, S. Z. (2009). Spread-backwash and market area effects of urban and regional growth in
China. Economic Research Journal,44(08), 85–98. in Chinese).
Kuznets, S. S. (1965). Economic growth and structure selected essays. Norton.
Kuznets, S., & Murphy, J. T. (1966). Modern economic growth: Rate, structure, and spread
(Vol. 2). Yale University Press.
Lains, P. (2006). Growth in the ‘Cohesion Countries’: The Irish tortoise and the Portuguese hare,
1979-2002. Departamento de Economia, Gest~
ao e Engenharia Industrial, Universidade de
Aveiro, Working Papers de Economia (Vol. 37), pp. 1–37.
ECONOMIC RESEARCH-EKONOMSKA ISTRAŽIVANJA 19
LeSage, J. P., & Pace, R. K. (2008). Spatial econometric modeling of origin-destination flows.
Journal of Regional Science,48(5), 941–967. https://doi.org/10.1111/j.1467-9787.2008.00573.x
Lewis, W. A. (1954). Economic development with unlimited supplies of labour. The
Manchester School,22(2), 139–191. https://doi.org/10.1111/j.1467-9957.1954.tb00021.x
Li, L., Lei, Y., Pan, D., & Si, C. (2016). Research on sustainable development of resource-based
cities based on the DEA approach: A case study of Jiaozuo, China. Mathematical Problems
in Engineering,2016(3), 1–10. https://doi.org/10.1155/2016/5024837
Li, H. J., Long, R. Y., & Chen, H. (2013). Economic transition policies in Chinese resource-
based cities: An overview of government efforts. Energy Policy,55, 251–260. https://doi.org/
10.1016/j.enpol.2012.12.007
Liu, B., Wang, J. M., Jing, Z. R., & Tang, Q. (2020). Measurement of sustainable transform-
ation capability of resource-based cities based on fuzzy membership function: A case study
of Shanxi Province, China. Resources Policy,68, 101739–101714. https://doi.org/10.1016/j.
resourpol.2020.101739
Liu, A. Y., Yao, S. J., & Zhang, Z. Y. (1999). Economic growth and structural changes in
employment and investments in China, 1985–94. Economics of Planning,32(3), 171–190.
https://doi.org/10.1023/A:1003775414092
Liu, Z., Zhou, W. S., & Yao, H. (2011). Progress of studies abroad on development and transi-
tion of resource-based Cities. China Population, Resources and Environment,21(11),
161–168. in Chinese).
Li, Q. Y., Zeng, F. E., Liu, S. H., Yang, M., & Xu, F. (2021). The effects of China’s sustainable
development policy for resource-based cities on local industrial transformation. Resources
Policy,71, 101940. https://doi.org/10.1016/j.resourpol.2020.101940
Lucas, R. E. (1988). On the mechanics of economic development. Journal of Monetary
Economics,22(1), 3–42. https://doi.org/10.1016/0304-3932(88)90168-7
Peneder, M. (2003). Industrial structure and aggregate growth. Structural Change and
Economic Dynamics,14(4), 427–448. https://doi.org/10.1016/S0954-349X(02)00052-8
Ray, D. M., MacLachlan, I., Lamarche, R., & Srinath, K. P. (2017). Economic shock and
regional resilience: Continuity and change in Canada’s regional employment structure,
1987–2012. Environment and Planning A: Economy and Space,49(4), 952–973. https://doi.
org/10.1177/0308518X16681788
Romer, P. M. (1986). Increasing returns and long-run growth. Journal of Political Economy,
94(5), 1002–1037. https://doi.org/10.1086/261420
Rostow, W. W. (1990). The stages of economic growth: A non-communist manifesto. Cambridge
University Press.
Ruan, F. L., Yan, L., & Wang, D. (2020). The complexity for the resource-based cities in China
on creating sustainable development. Cities,97, 102571. https://doi.org/10.1016/j.cities.2019.
102571
S
avoiu, G., Dinu, V., & T
achiciu, L. (2015). Services and structural patterns of a post-transition
Romanian economy. Economic research-Ekonomska Istra
zivanja,28(1), 788–806. https://doi.
org/10.1080/1331677X.2015.1087326
Solow, R. M. (1970). Growth theory: An exposition. Clarendon Press.
Sposi, M. (2019). Evolving comparative advantage, sectoral linkages, and structural change.
Journal of Monetary Economics,103,75–87. https://doi.org/10.1016/j.jmoneco.2018.08.003
Stankovi
c, J. J., Marjanovi
c, I., & Drezgi
c, S. (2021). Urban magnetism in the global city frame-
work: Exploring the link between urban functions and population growth. EþM Ekonomie
a Management,24(4), 4–21. https://doi.org/10.15240/tul/001/2021-4-001
Sun, X. H., & Zheng, H. (2019). Economic transformation mode in resource-based regions:
International comparison and reference. Economist,11, 104–112. in Chinese).
Temple, J., & W€
oßmann, L. (2006). Dualism and cross-country growth regressions. Journal of
Economic Growth,11(3), 187–228. https://doi.org/10.1007/s10887-006-9003-x
Yu, J. H., Li, J. M., & Zhang, W. Z. (2019). Identification and classification of resource-based
cities in China. Journal of Geographical Sciences,29(8), 1300–1314. https://doi.org/10.1007/
s11442-019-1660-8
20 Q. REN ET AL.
Yu, S., & Xu, G. Q. (1999). Economic transition and the changing occupational structure of
China’s population. Chinese Journal of Population Science,5,38–44. in Chinese).
Zhang, S., Liu, Y., & Huang, D. H. (2020). Contribution of factor structure change to China’s
economic growth: Evidence from the time-varying elastic production function model.
Economic Research-Ekonomska Istra
zivanja,33(1), 2919–2942. https://doi.org/10.1080/
1331677X.2019.1697722
Zhu, L. (2014). A review of the studies on the development of China’s resource-based cities.
Management Science and Engineering,8(4), 120–126. https://doi.org/10.3968/5999
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