Does the Regional Development Policy Promote Industrial Structure Upgrading? Quasi-experimental Evidence from China

“Guiding Opinions on Relying on the Golden Waterway to Promote the Development of the Yangtze River Economic Belt (YREB)”, the “YREB Development Policy”, is a regional Chinese policy aiming to promote industrial structure upgrading and high-quality development in the YREB. To test the effects of this policy, we used 2009-2019 data from 283 cities to examine whether its implementation promoted regional industrial structure upgrading. The YREB Development Policy positively impacted industrial structure advancement but scarcely benetted industrial structure rationalization. Moreover, the impacts indicated a temporary, unsustainable inuence on industrial structure advancement, presenting a clear U-shaped development trend. The YREB Development Policy can more signicantly improve industrial structure upgrading in cities with large populations. The effects of this policy on industrial structure upgrading in the middle and lower reaches of the YREB are almost ve times that in the upper reaches. In addition, the policy more greatly impacts the tertiary industry than the primary and secondary industries, especially in the lower reaches. These ndings have policy-making implications, enrich the research regarding the YREB Development Policy impacts on industrial structure upgrading, and provide an empirical reference to improve subsequent policies.


Introduction
Although brilliant achievements have been made in the economic development of China since the reform and opening up, the extensive economic development mode carries high costs that cannot be ignored (Pang et al., 2019). The Chinese government has realized that upgrading the industrial structure is an essential way for the Chinese economy to achieve high-quality development at the present stage (Liang & Yang, 2019). To effectively reverse the high costs associated with the current economic development model, the Chinese government is working extensively to eliminate the negative monetary impacts by upgrading the industrial structure. In essence, upgrading industrial structure involves transforming the economic growth mode from a production function to a service and consumption function (Li & Lin, 2017). This transformation can not only stimulate the internal driving forces and vital development of economic growth but can also effectively promote economic development into a virtuous circle .
To ensure the smooth transformation and upgrading of the industrial structure, the Chinese government has formulated ambitious development plans. For example, during the 12th Five-Year Plan period, the Chinese government clari ed the direction with which key industries are to be restructured. At the same time, it also proposed requirements regarding the closing of outdated production facilities and the reduction and lessening of excess capacity (Wang, 2021). During the 13th Five-Year Plan period, the Chinese government paid more attention to the real changes occurring in relevant factor conditions and adopted market-oriented policies to guide the optimization and upgrading of industry (Jiang et al., 2019). After drawing on the useful experiences of the past, the Chinese government identi ed four key issues related to the industrial structure during the 14th Five- Year Plan period, that is, solving overcapacity, cultivating emerging industries, reforming the manufacturing industry and optimizing the service industry (Ren, 2021). These development practices indicate that China could not achieve such extraordinary economic achievements without effective guidance associated with policy planning. depth evaluation of the effectiveness of the YREB Development Policy on industrial structure upgrading in this area is of great practical signi cance.
Policy evaluations represent very important links in the process of public policy analysis and are also effective methods used to test the effects of public policies (Hill, 2021). In fact, a policy evaluation is a process involving estimating the causal effects of policy intervention measures (Costa, 2021). Traditional policy evaluations have obvious shortcomings in causal inference, and it is di cult to establish a de nite relationship between a policy and its related effects using traditional techniques (Chernozhukov, 2021). In contrast, choosing a method that precisely quanti es policy effects can obviously generate more realistic test results.
As a powerful policy evaluation tool, the difference-in-differences (DID) method can be used to avoid endogeneity problems. This method also mitigates biases introduced by missing variables and allows more accurate measurements to be obtained (Wang & Watanabe, 2019). Regarding the subsequent policy-making implications, this method is conducive to furthering policy revisions and improvements.
The YREB Development Policy provides us with a quasi-natural experimental environment. Thus, this policy is taken as the research basis in this study. The DID method is used to identify the impacts caused by the implementation of the YREB Development Policy on the upgrading of different types of industrial structures in different regions in the YREB. Based on panel data obtained for 283 cities in mainland China from 2009 to 2019, the industrial structure upgrading of 107 cities in the YREB was evaluated based on industrial structure advancement and industrial structure rationalization. Then, a series of robustness tests were carried out, including parallel trend tests, placebo tests and sample size adjustments. Finally, an in-depth exploration of the effects of the implementation of the YREB Development Policy was conducted from three perspectives, namely, urban population size heterogeneity, regional heterogeneity, and industrial structure changes. This study is of great practical signi cance and can serve as a reference in China and even in other developing countries with similar global economic development trends.

Literature Review
The industrial structure usually refers to the proportions of the three major industries in a country (Dasgupta & Stiglitz, 1980). According to the international general classi cation standards, the industries in a country can be divided into three major categories. The primary industry usually includes the agriculture, forestry, animal husbandry and shery industries; the secondary industry comprises industry and construction; and all other activities belong to the tertiary industry. The upgrading of the industrial structure not only represents the core variable in understanding economic development differences among developed and developing countries but is also the essential requirement by which economically underdeveloped countries can accelerate their economic development (Thirlwall et al., 1989). Kuznets (1973) found that under modern economic growth conditions, the industrial structure is constantly adjusted as the economy develops. The speci c performance of the share of the primary industry in the total economic output continuously declines, the share of the secondary industry initially maintains growth and then gradually stabilizes, and the share of the tertiary industry in the total economic output continuously increases.
Previous studies have shown that the different dimensions of in uencing factors can affect the upgrading of the industrial structure. Many scholars have attempted to explore these factors from the perspective of economics. For example, Liu et al.
(2017) proved through empirical tests that scal decentralization and nancial e ciency played positive roles in promoting the regional industrial structure.  investigated the factors in uencing China's provincial industrial structural upgrading from nancial development and foreign trade perspectives. He et al. (2021) examined the relationships among marketization, energy e ciency, and the upgrading of the industrial structure. Some scholars have explored the impact of technological innovation on industrial structure upgrading. For example, Wang et al. (2020) found that technological innovation is an important channel by which industrial structure upgrading can be promoted. Lu (2019) clari ed that technological innovations can induce great development potential, which, in turn, drives the upgrading of the industrial structure. A few scholars have concluded that the urbanization level and higher education development are closely related to the upgrading of the industrial structure. For example, Murakami (2015) empirically analysed the relationship between industrial structure changes and urbanization by using Japanese county data after World War II and found a two-way causality between the industrial structure and urbanization. Wu (2021) examined the relationship between higher education and the industrial structure and found that higher education has a signi cant positive and indirect effect on the upgrading of the industrial structure. Furthermore, some other research has indicated that social needs (Krammer, 2015), the level of infrastructure construction (Wang & Zhong, 2021), social reforms (Dekle & Vandenbroucke, 2010;Han et al., 2017), and institutional changes (Krugman, 1993;Kergroach, 2019) may signi cantly impact the upgrading of the industrial structure.
In recent years, to ensure that the Chinese economy can smoothly move towards a high-quality development stage, the Chinese government has actively proposed a series of macro policies to promote the upgrading of the industrial structure. To date,  Kim (2000) found that the construction of high-speed rails accelerates the interregional ow of factor resources, thereby optimizing the regional resource allocation e ciency and increasing regional industrial output e ciency. Other researchers have given Although previous research into the effects of macro policies on the upgrading of China's industrial structure has provided us with valuable information, some de ciencies still exist. First, most studies have focused on changes in explained variables within a certain period or after a policy has been issued, while the differences before and after policy implementations have not been accurately assessed . Second, most studies have focused on the in uence of a single factor on the explained variable while ignoring the in uence of some unobservable factors; this kind of oversight leads to errors in the results (Fang et al., 2021). Third, previous studies have given little attention to the effects of spatial heterogeneity, leading to less explicit and speci c assessments (Jia et al., 2021). Compared with other comprehensive evaluation methods, DID can not only effectively avoid the endogeneity problems caused by viewing policy as an explanatory variable but can also control the in uence of unobservable variables that change over time . The DID method also mitigates biases in missing variables and improves the accuracy of measurements. The YREB Development Policy aims to promote the balanced development of China's regional economy and narrow the regional development gap. The policy has great potential to promote the industrial structure upgrading of the YREB. First, it is conducive to promoting the industry along the Yangtze River from a factor-driven industry to an innovation-driven industry and exerts a guiding role in the rational layout and orderly transfer of industries. Second, it is bene cial for leading the development of emerging industries and accelerating the transformation and upgrading of traditional industries. In addition, this policy also helps foster an international level of industrial clusters and enhance industrial competitiveness in the YREB. Based on this, this study regards the YREB Development Policy as a policy shock event and uses the DID method to identify the impact of the policy implementation on the upgrading of the industrial structure in the YREB. The purpose of this study is to ll this research gap in the literature and provide a reference for the improvement and adjustment of follow-up policies.

Data source
Our data were mainly obtained from the China Urban Statistical Yearbook, the China Statistical Yearbook and provincial statistical yearbooks. This study adopts the data of 283 cities above the prefecture level (the Chinese mainland has 295 cities above the prefecture level) in Mainland China from 2009 to 2019 to investigate the impact of the YREB Development Policy on the upgrading of the industrial structure in the YREB. The speci c geographical distribution of the YREB in China is shown in Figure 1. A portion of missing data were lled in by consulting o cial websites and using the average interpolation method. We excluded some prefecture-level cities to the serious lack of data. Considering that the YREB Development Policy was implemented in 2014, we set the starting time of the sample to 2009 to ensure the collection of a su cient number of samples before the implementation of the policy. Finally, this study obtained a total of 3113 observation samples.

Econometric model
This study used panel data from a total of 283 cities in Mainland China from 2009 to 2019 as a research sample. We adopted the DID method to investigate whether the YREB Development Policy promoted the upgrading of the industrial structure in the YREB region. The setting method of the DID model compares the differences between the experimental group and the control group before and after the policy is implemented by controlling for other factors. The DID model is set up as follows Upgrading it = α 0 +α 1 treat i × time t + γX it + μ it + π it + ε it (1) where i represents the city and t represents the year. is an explanatory variable that represents the industrial structure upgrading of the i-th city in the t-th year. This article measures industrial structure upgrading from two dimensions, namely, industrial structure rationalization and industrial structure advancement. The rst layer of in uence comes from the city, and the second layer of in uence comes from the year. Therefore, this study sets two dummy variables. is the dummy variable set for the experimental group. When treat i is 1, city i is one of 107 cities in the YREB. Similarly, if treat i is 0, city i is not included among the 107 cities of the YREB. Time t is a dummy variable representing the experimental period. When the time t is 0, the policy has not yet been implemented. When the time t is 1, the policy has been implemented. Treat i ×time t is the interaction term between treat i and time t and is also known as the treatment effect. When treat i ×time t is 1, city i is one of the 107 cities in the YREB, and the policy has been implemented in city i in year t. When treat i ×time t is 0, either city i is not among the cities in the YREB or the policy has not yet been implemented. Moreover, X it is the control variable, μ it is the city xed effect, and π it is the year xed effect. It is worth noting that α 1 is the core estimation parameter and represents the net effect of the YREB Development Policy on the upgrading of the industrial structure. If α 1 is greater than 0, it means that the implementation of the YREB Development Policy promoted industrial structure upgrading in the YREB. If it is less than 0, it means that the implementation of the YREB Development Policy had a reverse impact on the upgrading of the industrial structure in the YREB.

Dependent variable
Our dependent variable is the upgrading of the industrial structure, and this variable can be divided into industrial structure advancement and industrial structure rationalization. The former refers to a transition between the economic growth mode and the development model (Jin, 2007). According to Petty Clark's theorem, with an increase in the per-capita national income, the centre of the national economy shifts from the primary industry to the secondary industry and then to the tertiary industry (Zhou et al., 2019). Therefore, this study adopted an industrial structure hierarchy coe cient (AIS) to measure industrial structure advancement. The calculation formula of this coe cient is as follows: where y imt stands for the proportion of the m industry in the regional GDP in city i in the t-th year. Equation (2) is the weighted sum of the proportion of the primary industry, the secondary industry and the tertiary industry, and the weights (3, 2, or 1) of the three industries are assigned according to the hierarchy level. When is larger, the hierarchical coe cient of the industrial structure is larger and the level of the industrial structure is higher.
Industrial structure rationalization refers to the process by which industrial synergy is improved through the adjustment of unreasonable industrial structures (Lin et al., 2021). Using the Theil index to measure industrial structure rationalization can effectively retain its economic meaning; the speci c calculation is shown in equation (3): where represents the output structure, that is, the proportion of the m-th industry in city i to the regional GDP; represents the employment structure, that is, the proportion of employed persons in the m-th industry in city i to all employed persons; and represents the employment structure, that is, the proportion of employment personnel in the m-th industry in city i to all employment personnel. Since the Theil Index is an inverse indicator, when the Theil Index is positive, the policy has inhibited the rationalization of the industrial structure, and vice versa.

Independent variable
The independent variable in this article is the product of the experimental group dummy variable and experimental period dummy variable (. This variable can be estimated based on the implementation time of the YREB Development Policy and whether the city under consideration is one of the 107 cities in the YREB. This is also the core explanatory variable in our article.

Control variables
Our control variables include the level of economic development, the process of urbanization, the level of opening up to the outside world, the level of infrastructure construction, the level of governmental nancial support and the level of technological innovation; these factors involve economic, urban, open, infrastructure, innovation, government, and innovation dimensions, respectively. Descriptions of all control variables are listed in Table 1. The descriptive statistics of all variables are shown in Table  2. The process of urbanization The proportion of the urban population to the total regional population Open The level of opening up The proportion of foreign capital to the total regional GDP Infrastructure The level of infrastructure construction The ratio of the built-up area to the total regional population Innovation The level of technological innovation Patents per capita

Government
The level of governmental support The proportion of nancial expenditure to the total regional GDP

Information
The level of informatization The ratio of post and telecommunications per capita to the GDP per capita An important prerequisite for the use of the DID model in policy evaluations is that the experimental group and the control group must have common development trends before the policy is implemented. To verify this condition, we used the parallel trend tests proposed by Beck and Jacobson. As shown in Figure 2, before 2014, the parameter estimation coe cient of the advancement of the industrial structure in the YREB uctuated around approximately 0, and the con dence interval of the coe cient included 0. This indicates that before the YREB Development Policy was implemented, there was no signi cant difference between the experimental group and the control group. Therefore, the advancement of the industrial structure passed the parallel trend test. In the three years following the implementation of the policy, AIS showed a clear "U-shaped" development trend, indicating that the implementation of the policy did, in fact, impact the advancement of the industrial structure. Only the year when the policy was implemented and the following two years showed a signi cant negative impact; subsequently, the policy promoted the industrial structure upgrading process. Then, it returned to the nonsigni cant state, indicating that the impact of the YREB Development Policy on industrial structure advancement was temporary and unsustainable.
It can be seen from Figure 3 that before the policy was implemented, the estimated parameter coe cients of the rationalization of the industrial structure all uctuated around approximately 0, and the con dence intervals of the coe cients all contained 0. This indicates that there was no signi cant difference between the experimental group and the control group before the implementation of the policy, and the industrial structure rationalization thus passed the parallel trend test. However, following the implementation of the policy, many years of parameter estimates still uctuated around approximately 0, and the con dence intervals of the coe cients also contained 0. This result suggests that the YREB Development Policy did not play a role in promoting industrial structure rationalization after its implementation. Only in the third year following the implementation of the policy did the policy have a signi cant inhibitory impact.

Base regression analysis
Columns (1) and (2) in Table 3 show the base regression results representing the impact of the YREB Development Policy on industrial structure advancement. In column (1), the coe cient of Treat×Time representing the AIS treatment effect was 0.027, which was signi cant at the 5% level. This means that after the implementation of the YREB Development Policy, the industrial structure advancement in the YREB increased by 2.7%. After adding a series of control variables, the estimated value of the treatment effect coe cient was reduced to 0.017, which was still signi cant at the 5% level. This result con rmed that the YREB Development Policy did indeed signi cantly promote industrial structure advancement.
Columns (3) and (4) in Table 3 show the base regression results of the impact of the YREB Development Policy on industrial structure rationalization. The results indicate that before the control variables were added, the coe cient of Treat×Time representing the Theil treatment effect in column (3) was -0.007; this value was not statistically signi cant. After a series of control variables were added, the Treat×Time coe cient in column (4) was still not statistically signi cant. This result con rmed that the YREB Development Policy did not signi cantly affect industrial structure rationalization in the YREB.
In addition, columns (2) and (4) in Table 3 report the impacts of the control variables on the upgrading of the industrial structure.
In column (2), except for the level of technological innovation, the other six control indicators all positively impacted industrial structure advancement. In column (4), four control variables signi cantly affected industrial structure rationalization, namely, the level of economic development, the process of urbanization, the level of opening up, and the level of infrastructure construction.

Placebo test
A series of robustness tests were carried out to ensure the robustness of the results. First, a placebo test was performed by adjusting the time window to construct a dummy policy implementation year. The implementation year of the policy was assumed to be 2008, and data from 2005 to 2011 were selected to examine the impact of the policy on the upgrading of the industrial structure in the YREB. Table 4 shows the results of this test obtained after adjusting the time window. Column (1) shows that before the control variables were added, the AIS coe cient was -0.006, which was not statistically signi cant. After adding the control variables, the results listed in column (2) were still not statistically signi cant, contrary to the results listed in Table 3. In addition, the test results of the Theil index showed opposite results. The above results con rm that the study passed the placebo test.  Table 5 shows the test results obtained after adjusting the sample size. Since non-random problems may exist in central cities, the "peripheral cities" method was adopted: the samples of all provincial capitals and four municipalities were eliminated, and the DID test was performed again. We found that after removing these cities, the impacts of the YREB Development Policy on the AIS and Theil index were consistent with the regression results listed in Table 3. This indicates that the inclusion of provincial capitals and municipalities did not cause measurement errors in the test results. Therefore, the results of this test con rm the robustness of our ndings.

Analysis of population heterogeneity
Due to China's vast territory and large population, great developmental differences exist among regions. Therefore, in this study, we divided the research samples according to the size of the urban population. If the urban population was greater than 5 million,    In the middle reaches of the YREB, the policy signi cantly positively impacted industrial structure advancement (AIS coe cient of 0.025, p<0.1) but had a nonsigni cant impact on industrial structure rationalization (Theil coe cient of -0.009, p 0.1). In the lower reaches of the YREB, the policy signi cantly positively impacted industrial structure advancement (AIS coe cient of 0.027, p<0.05) but had a nonsigni cant impact on industrial structure rationalization (Theil coe cient of 0.009, p 0.1). It can be seen from these results that the policy more obviously impacted economically active areas and even had restrictions on the transformation and upgrading of the upper and middle reaches.

Analysis of industrial structure change
The above results indicate that the implementation of the YREB Development Policy more signi cantly impacted industrial structure advancement than industrial structure rationalization. Therefore, this study considered the proportions of the primary, secondary, and tertiary industries in the regional GDP as proxy variables representing industrial structure changes to explore the changes induced by the policy on the industrial structure of the YREB. Columns (1), (2), and (3) in Table 8 successively represent the impacts of the YREB Development Policy on the primary, secondary and tertiary industries in the YREB. In columns (1) and (2), the proportions of the primary and secondary industries decrease following the implementation of the policy, but these decreases are not signi cant. In contrast, the YREB Development Policy promoted the growth of the tertiary industry (coe cient=0.013, p<0.05). Consequently, this result implies that the development focus of the industrial structure tends to be service-oriented, and this focus bene ts the development of the advanced industrial structure. Note: *** p<0.01, ** p<0.05, and * p<0.1.
As clari ed above, the YREB Development Policy promoted the development of the tertiary industry in cities in the lower reaches of the YREB and signi cantly reduced the primary industry proportion in the middle reaches and the secondary industry proportion in the lower reaches. To further determine the roles these control variables play in the upgrading of the industrial structure among different regions, we conducted a deeper analysis of the regional industrial structure changes (see Table 9).
1. Economy. In the middle and upper reaches of the YREB, the development speed of the secondary industry is almost two to three times that of the tertiary industry. However, in the lower reaches of the YREB where the economic strength is robust, the development of the secondary and tertiary industries does not differ extensively and is relatively balanced.
2. Urban. Accelerating the process of urbanization in the YREB can restrict the development of the primary and secondary industries and can play a sustained and signi cant role in promoting the tertiary industry. Under this policy shock, the impacts of urbanization on the middle and upper reaches of the YREB were more pronounced than that on the lower reaches. The impacts on the secondary and tertiary industries were obviously more signi cant than that on the primary industry.
3. Opening up. The results show that the impacts of the level of opening up on the three industries in the upper and middle reaches of the YREB were relatively consistent. An increase in the level of opening up signi cantly reduced the primary and secondary industry proportions and promoted the development of the tertiary industry. Moreover, under the current policy shock, the development of the tertiary industry in the upper reaches of the YREB is better than that in the middle reaches, while the policy has no signi cant impact on the lower reaches. Table 9, the higher the level of infrastructure construction is, the more constrained the primary and secondary industries are. The level of infrastructure construction can promote the vigorous development of the tertiary industry. For China, accelerating the infrastructure construction level means promoting the trans-regional ow of resources, and this promotion helps accelerate the regional integration process and is conducive to driving the rapid development of the regional economy. . Governmental support. In the process of promoting the transformation of the industrial structure, the government has been vigorously developing the primary and tertiary industries. The YREB Development Policy has played an active role in guiding regional modern agriculture and developing the modern service industry. However, the government has signi cantly reduced its funding support for the secondary industry, which contains a large number of highly energy-consuming industries with underdeveloped production capacities.

Infrastructure. As shown in
7. Informatization. One of the intentions of the implementation of the YREB Development Policy was to make full use of the new generation of information technology to transform and upgrade traditional industries and cultivate emerging industries. As shown in Table 9, the positive impact of improved informatization on the industrial structure is embodied in the tertiary industry. Informatization also greatly weakened the primary industry and secondary industry proportions. Table 9 Test results obtained when considering regional industrial structure changes

Discussion
Currently, the transformation of the economic development mode has become a top priority in China, and this transformation is closely tied to the upgrading of the industrial structure. Based on panel data characterizing 283 cities in Mainland China from 2009 to 2019, this article explored the changes in the industrial structure of the YREB from the perspectives of industrial structure rationalization and industrial structure advancement by using the DID method. To further explore the implementation effects of the YREB Development Policy, an in-depth exploration of the policy implementation effects was conducted from three perspectives, namely, the urban population size heterogeneity, regional heterogeneity, and industrial structure changes.
First, this study revealed that the YREB Development Policy signi cantly promoted the industrial structure advancement of the YREB, but its impact on industrial structure rationalization has been nonsigni cant. Our results show that the industrial structure advancement of the experimental group was 2.7% higher than that of the control group, and this result was statistically signi cant at the 5% level. At the same time, industrial structure rationalization increased by 0.07%, but this result was not statistically signi cant. This nding implies a hysteresis effect exerted by the YREB Development Policy on the upgrading of the industrial structure. At present, the development of China's tertiary industry basically goes hand-in-hand with the development of the secondary industry, and the tertiary industry is even developing faster than the secondary industry (Dewi & Pratama, 2021).
As the tertiary industry gradually replaces the dominant position held by the secondary industry, the succession characteristics of the industrial structure are gradually emerging (Zhu et al., 2019). After controlling some latent in uencing factors and conducting a series of robustness tests, our results were still reliable. This nding was consistent with the results reported by Zheng et al.
(2021), who found that pilot programmes in low-carbon cities positively impacted industrial structure supererogation but had little effect on industrial structure rationalization.
Second, this study found that the YREB Development Policy imposed a short-lived and unsustainable impact on industrial structure advancement, presenting an obvious U-shaped development trend. On the one hand, the macroeconomic situation changed due to the time lag, causing an opposite effect to be observed following the policy implementation in that year (Shinagawa & Tsuzuki, 2019). It was not until the third year following the implementation of the YREB Development Policy that a signi cant positive impact on industrial structure advancement was observed. On the other hand, depending on the policy itself, certain lag periods occur regarding the manifestation of policy effects (Wang et al., 2016). Since the policy was fully enacted in the YREB region, the policy ignores differences in the responses of different regions to policy shocks, resulting in the measured temporary and unsustainable effects of the policy.
Third, this study con rmed that cities with larger populations in the YREB were more conducive to industrial structure upgrading; additionally, the YREB Development Policy more signi cantly impacted the tertiary industry than the primary and secondary industries. Due to the vast territory of China, many signi cant differences among cities exist in terms of their economic

Research implications
Our potential contributions to the literature are re ected in the following aspects. First, this study organically links the YREB Development Policy with the development of the regional industrial structure and accurately examines the effects of this policy from the perspectives of industrial structure rationalization and industrial structure advancement. The ndings ll the research gaps regarding the impacts of national macro policies on the upgrading of the regional industrial structure and contribute to the further improvement of follow-up policies. Second, this study further compared the differentiated impacts of the YREB Development Policy among regions from the perspectives of the urban population scale and regional heterogeneity. We found that the YREB Development Policy more signi cantly impacted cities with larger urban populations. Additionally, we revealed the generally positive impacts of a series of control variables on the development of the tertiary industry across the upper, middle and lower reaches of the YREB. These ndings provide an empirical reference with which more effective development policies with local characteristics can be formulated in the future. Third, this study proposes a more scienti c policy-evaluation method to  Parallel trend test for AIS Figure 3 Parallel trend test for Theil