(2) Focused issues

Whether it is the (1) factor allocation efficiency of micro-enterprises; (2) construction industrial organization efficiency at the meso-level; or (3) changes in the macro-level demand structure, technological progress, and the quality of economic growth. RM has caused a decline in economic growth. The RM problem's research and exploration can be divided into such different aspects as the effect analysis and organization management. Table 3 shows the result created from analyzing the data relating to *focused issues*.

**Table 3.** Key literature relating to *focused issues*.



**Table 3.** *Cont.*

The focused issues node has a total of 168 reference points, which is the largest of all the first-level nodes, and also the most important in the entire research field. The effect analysis node is also the main node with more related publications. It has the most reference points of the three second-level nodes included in focused issues (a total of 83). Financial constraints and output efficiency also have a particularly important impact of RM from the three-level nodes included. Without fair, open, and transparent market rules and strict compliance supervision, the party with less information is very likely to be seriously disadvantaged. In this situation, the market mechanism can hardly allocate funds effectively, as effect analyses are markets with extremely asymmetric information. Moreover, the development of effect analysis can lead to more investment in growing industries and the withdrawal of capital from declining industries to improve capital allocation efficiency [34]. Therefore, more research into the misallocation of resources in effect analysis is also needed.

Furthermore, there are a total of 50 upgrading of construction industry structure nodes, which contain more reference points. Since upgrading construction industry structure can identify the industries in need of the Chinese government's intervention currently [49], it also plays an important role in reducing enterprises' RM and improving production efficiency. The organization management node is a minor node with a small number of publications, but still contains 34 reference points. The analysis shows that, in many developing countries, the influence of RM caused by construction market distortions, such

as the unemployment rate or enterprise labor costs, is still relatively large [45]. Table 4 shows the focused issues node at all levels and coding reference points.

**Table 4.** Number of nodes and coding reference points in the focused issues node.


Table 5, Figures 6 and 7 show the results of a grouping word frequency query applied to the 124 selected publications, identifying the most popular research foci in different periods. Figure 8 also shows the keyword cluster analysis results obtained by VOS viewer software based on keyword classification.

**Table 5.** Most popular research focuses.


**Figure 6.** Word cloud for the 2010–2015 research papers.

**Figure 7.** Word cloud for the 2016–2020 research papers.

**Figure 8.** Keyword cluster analysis based on keyword classification.

The core issue is to manage the relationship between the government and the market in such a way that the market can play a decisive role in resource allocation and better reflect the government's role. Therefore, the most popular research focuses range from simply studying economic benefits, TFP, resource allocation, and other issues, to more in-depth research on the effect of market distortions on RM, as well as that of resource mismatching on TFP.

At the same time, there is an urgent need to strengthen the research and exploration of "extension misallocation". Both extensional RM defined by Banerjee and Moll (2010) or the net entry effect calculated by Brandt [50] are based on the difference in the literature between the TFP entering and exiting the enterprise. The effective dynamic replacement of enterprises should comprise three ways to realize the effective allocation of resources: (1) the entry of potentially high TFP companies and the exit of existing low TFP companies (selection effect); (2) after entering, the potentially high TFP companies achieve rapid

growth of their own TFP and narrow the gap with incumbents or even surpass incumbents through acquired learning (learning effect); and (3) the process of enterprise entry and exit creates competitive pressure on the incumbents and forces them to improve their TFP (competitive effect).

These three effects together constitute the connotation of the process of optimal allocation of external resources. Distortion of the normal entry and exit process of enterprises will directly lead to the misallocation of extended resources. Therefore, it is necessary to overcome technical defects, exhaustively consider various distortive policies, further analyze the causes of RM and market distortion, and make a more accurate prediction of efficiency loss.

#### (3) Methods of misallocation degree measurement

RM leads to a loss of production efficiency. It is of theoretical and practical relevance to measure the degree of misallocation and its influence on productivity accurately [4]. It is necessary to clarify the mechanism and degree of efficiency loss caused by RM before attempting its rectification, while, a comprehensive understanding of the practical problems involved is needed to formulate policies to solve the RM problem, and thus help improve the economic benefits of enterprises and promote the economy and society's sustainable development. Table 6 shows the current methods used for measuring the efficiency loss caused by RM.


**Table 6.** Key literature for resource misallocation degree measurement and characterization.

There are 67 reference points in total, including three second-level nodes: the variable substitution method, simple proportion method, and growth rate decomposition method. Of these, the variable substitution method is the main node with more related publications and 52 nodes. It contains the most reference points and occupies an important position in the Misallocation degree measurement and Characterization nodes. This, in turn, indicates that it is more suited for use when conducting a robustness test in RM measurement. The growth rate decomposition node contains the relatively small number of 10 reference points. In addition, the simple proportional method has a total of 5 reference points, with the least number of points and the least impact. This means that the simple proportional method is seldom used in the literature and is often ignored (or needs further study). A simplified

study of problems related to resource allocation from the perspective of misallocation degree measurement and characterization can better reflect the extent of specific RM and effectively solve complex allocation problems.

Here, we considered the relationship of various dimensions and believe that misallocation degree measurement and characterization have a strong correlation with the sources and concepts and focused issues nodes. We conducted the measurement of the specific allocation of related resources and a quantitative study of their level of correlation (Table 7).

**Table 7.** Number of nodes at all levels and coding reference points included in the misallocation degree measurement and characterization node.


The simple proportion method assumes a perfectly competitive market and uses productivity dispersion between firms to describe the degree of RM. The common indicator is the ratio of 90/10 firms' TFP. The greater the difference in TFP among firms, the more serious the degree of RM. This method has simple steps and is convenient to use for measurement. However, solely relying on the TFP ratio is insufficiently representative and may be distorted by endogeneity and selectivity biases.

With the gradual deepening of the analysis of corporate TFP and economic growth, a model of monopolistic competition was built with the variable substitution method used for its misallocation degree measurement and characterization. The TFP variance was used to measure the degree of distortion of resource allocation across companies. This method allowed us to demonstrate that the greater the difference in TFP between companies, the smaller is the total industry TFP. The assumption of constant returns to scale was gradually relaxed. The Levinsohn-Petrin semiparametric estimation method was used to estimate the cross-industry capital and labor output elasticity of China's construction industry. In addition, the marginal product returns of production factors were used as a tool for measuring the resource allocation distortion and the potential growth of manufacturing TFP after the Pareto improvement of a single factor allocation.

Furthermore, with the continuous improvement of data acquisition methods and the increasing availability of micro data, RM research has gradually moved from the macro to the micro level. At present, the general way of measuring RM-caused efficiency loss in many countries is the growth rate decomposition method. For instance, it is possible to decompose TFP growth into the industry's own TFP growth and the allocation effect of factors among industries. This operation allows the impact of structural changes in a country's production efficiency to be analyzed. It also enables the reallocation of resources between industries when we decompose it into the simply increasing industry share input effect and the factor price distortion effect.

Finally, with the continuous updating and maturity of theoretical models, the misallocation degree measurement and characterization have changed from a simple proportion to such other current mainstream methods as growth rate decomposition, DSEG, variable substitution, and the Aoki method. These methods not only measure RM between industries and within companies, but also can measure the impact on RM of different ownership systems, among regions, and even gradually measure the RM level of a whole national economy. All of these are relevant applications that can prevent negative effects on TFP and avoid hindering economic growth [55]. Therefore, the impact path of intermediate inputs should be considered when measuring construction industrial outputs. Intermediate inputs should also be included in the growth accounting framework when measuring the impact on the overall TFP. This will allow the impact of industrial TFP on overall TFP to be more

accurately estimated, and the misallocation degree measurement and characterization to be continuously improved, to eventually solve specific RM-related problems.

#### *3.4. Saturation Test*

The saturation test involved randomly reserving 10 journal papers before coding, encoding the remaining 114 papers, and then recoding the 10 papers again. Upon performing this operation, it was found that all existing codes and categories coincided. That is, there were no new codes related to the study's topic, which was taken to mean that theoretical saturation had been achieved [56].

#### **4. Discussion and Implications**

#### *4.1. Discussion*

This study adopts a bottom-up qualitative research method with grounded theory to compare, analyze, condense, and summarize the main dimensions and hierarchical structure of RM concepts:


#### *4.2. Implications*

This study develops a theoretical model to study the dimensions of the resource misallocation problem and shows that certain elements play a dominant role in the process (Figure 9).

More studies are urgently needed to explore the effects and influences of different market distortions on TFP. This will allow the implementation of more effective industrial policies and improved enterprise production efficiency while optimizing the country's economic structure. At the same time, it is important to identify effective methods for optimal resource allocation and reducing operating costs. For example, we suggest establishing market-oriented and rule-of-law mechanisms for the 'survival of the fittest' and match scientific technological progress paths to meet a substantial proportion of social production needs. Moreover, attracting investment that follows equal national treatment and pre-entry national treatment, and respects intellectual property rights and other principles, provides a significant way to develop the local economy, and provides development motivation

and experience (such as capital, talents, technology, products, markets, and management methods for open areas).

**Figure 9.** Dimensional model of resource misallocation.

Further improving management system design can also help achieve mutual benefits and complementary resources. When a higher resources efficiency is achieved, it will be possible to solve other core problems of the national economic system and promote the transformation of an economy from the traditional extensive growth model to a new sustainable version.

Overall, future studies would benefit from focusing on "core categories" and "supported categories". This can be achieved by concentrating on endorsing market-oriented reforms, transforming government functions, adjusting industrial structure, and implementing income distribution methods. At the same time, it is important to understand that RM is all-pervasive in many industries, companies, and national economies. Nevertheless, the value of marginal output—the increased revenue of a firm's additional use of a unit factor under conditions of perfect competition—still needs more in-depth empirical research on whole markets, industries, and companies from macro and micro perspectives to verify the conclusions of this study. This will allow the enrichment of the hierarchical dimensions and RM model structural elements proposed here.

#### **5. Conclusions**

RM-related research is at the core of economic theory, but usually involves regression analysis with a single explanatory variable. Consequently, there are many limitations, gaps, and deficiencies both in the RM analysis framework and its explanatory action mechanisms. For example, although studies have found that intermediate products are extremely important factor inputs in addition to capital, labor, and other basic factors, it is difficult to obtain data from intermediate inputs and purchased intermediate services from enterprises due to the data availability restrictions. Nevertheless, this influence path of intermediate input needs to be considered when calculating industry output, which means it is necessary for inputs to be also included in the RM growth accounting framework. In this regard, measuring the impact of intermediate input itself on the overall TFP, for example, could more accurately predict the impact of the industry's TFP on the overall country's TFP.

Similarly, RM-related research in practice tends to explore the influence of individual factors in one dimension. That is, it neglects the potential mutual (interrelated) effects of these factors. Currently, the impact of market distortions (such as RM finance and labor) and changes in the process of TFP need to be resolved. The existence of RM is reflected in the excess of return on capital of individual companies by observing simple cross-sectional data at the micro level. But this excess of return on capital is the driving force behind most companies when innovating and improving productivity. Hence, RM's effect on the economy's overall productivity needs to be explored in both theory and practice. Additionally, errors of measurement and the existence of "externalities" in the production process may affect the estimation of efficiency loss. In particular, the (unobtainable) changes in the input of production factors will always lead to the inaccurate estimation of an enterprise's productivity. However, the literature has not yet considered any of this.

Finally, there is still a lack of countermeasures to solve RM-related problems. In real settings, concrete feasible improvement measures from governments or enterprises remain undeveloped. To address this gap, the cooperation between governments, scholars, disciplines, and institutions would be ideal—the aim being to build a high-level, comprehensive, and pioneering academic exchange platform to further develop more RM-related research with a special application to practical settings.

**Author Contributions:** J.Z. contributed to the study's conception, performed the experiment, F.D. and H.L. contributed significantly to analysis and manuscript preparation, F.D. performed the data analyses and wrote the manuscript together with M.S. and P.B.-P. helped perform the analysis with constructive discussions. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research is supported by the National Social Science Fund projects (No. 20BJY010); National Social Science Fund Post-financing projects (No. 19FJYB017); Sichuan-Tibet Railway Major Fundamental Science Problems Special Fund (No. 71942006); List of Key Science and Technology Projects in China's Transportation Industry in 2018-International Science and Technology Cooperation Project (No. 2018-GH-006 and No. 2019-MS5-100); Emerging Engineering Education Research and Practice Project of Ministry of Education of China (No. E-GKRWJC20202914).

**Conflicts of Interest:** The authors declare that they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper.

#### **References**

