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Article

Understanding the Impact of Land Supply Structure on Low Consumption: Empirical Evidence from China

by
Yating Dai
1,
Jian Cheng
1 and
Daolin Zhu
1,2,*
1
College of Land Science and Technology, China Agricultural University, Beijing 100193, China
2
Center for Land Policy and Law, Beijing 100193, China
*
Author to whom correspondence should be addressed.
Land 2022, 11(4), 516; https://doi.org/10.3390/land11040516
Submission received: 6 March 2022 / Revised: 29 March 2022 / Accepted: 31 March 2022 / Published: 2 April 2022
(This article belongs to the Section Land Socio-Economic and Political Issues)
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Abstract

:
In studies of low consumption in China, there is a lack of consideration of land policy, which may be an important factor contributing to the industrial structure, thus impacting consumption. This paper explores the relationship between local governments’ distorted land supply strategies and final consumption and its mechanism of action based on panel data for 31 provinces in China from 2002 to 2017, using a fixed-effects panel model and a mediating-effects model. The results show that (1) the ratio of industrial land supply area to the land supply area of commercial and residential significantly suppresses the final consumption rate, and the results remain significant after robustness tests; (2) the effect of land supply structure on final consumption is related to the development strategy adopted by local governments and the urban–rural inequity, thus showing heterogeneity, with regions with high economic growth and large urban–rural income gaps further contributing to the suppression of consumption rates; (3) the intermediation effect suggests that the structure of land supply affects consumption through the industrial structure. As land supply favors the development of industrial enterprises, it increases the ratio of gross capital formation to GDP and can have a crowding-out effect on the income of the household sector, thus reducing the rate of final consumption. Under the Chinese decentralization system, in order to achieve regional economic development, local governments intervene in the allocation of land resources among different industries through differentiated land supply strategies, resulting in an industrial structure dominated by the secondary industry, which has an important impact on consumption. Therefore, the reform of the land supply structure should be accelerated to restrain the excessive intervention of local governments in the land supply structure and promote the transformation of the economic development model to enhance consumption.

1. Introduction

In the last 20 years, the final consumption rate (as a percentage of GDP) has fluctuated around 80% in countries around the world, with higher final consumption rates in upper-middle-income countries and lower final consumption rates in high-income countries [1]. The problem of low consumption in China has existed since the late 20th century during which the Chinese economy achieved rapid growth. From 2001 to 2010, China’s final consumption rate fell from 61.93% to 48.91%, which is far lower, compared with both developed countries in Europe and the United States and developing countries in East Asia, and China’s final consumption rate is currently only around 55%. With China’s economy entering the “new normal”, the ongoing trade disputes between China and the United States and US sanctions on China’s high-tech companies pose serious threats and challenges to cross-border FDI. The powerful shock triggered by COVID-19 has also affected China’s foreign trade and investment, and the pulling effect of external investment demand on China’s economy is significantly weaker [2]. In recent years, China has adopted a “dual circulation” economic development model, with the main domestic cycle and the domestic and international cycles promoting each other, in order to solve the problem of low consumption.
China’s low consumption rate is the result of a combination of the household, government, and corporation sectors, but past studies have explored it only in terms of the household savings rate—for example, the life-cycle hypothesis [3,4], liquidity constraints [5], precautionary savings [6,7,8,9], and personal income inequality [10,11]. Although household consumption can have an important impact on final consumption, non-household saving has grown steeply, rising above 32% since 2000 [12]. Thus, it is difficult to fully explain the fact that China’s final consumption rate is severely lower than that of major world countries by focusing on the household sector, and policies adopted by the government to stimulate domestic demand are hardly effective [13]. The impact on consumption has been analyzed separately in terms of demographics and financial disincentives, but it is also difficult to adequately explain the fact that China’s final consumption rate was declining before 2010. Some studies have noticed this shortcoming, finding that the inequitable distribution of national income between the household and non-household sectors has an impact on the final consumption rate. Since national income is allocated to a greater extent to the non-household sector, unlike the household sector, disposable income in this sector is mainly used for business and infrastructure investment rather than consumption, leading to a decrease in final consumption [14,15,16,17,18]. China, Germany, and Japan had large current account surpluses, and the falling share of the national income dedicated to wages appears to have weakened consumption demand [19].
The literature examining the decline in the labor share of income has grown considerably in the past few years, and the main driving factors include price supercycles in the energy and mineral sectors [20], superstar firms reaping rising shares of profits and value added [21], capital deepening and automation [22], and increased trade competition [23,24]. Numerous studies have shown that as the industrial structure shifts to the industrial enterprises and capital-biased technical change raises the marginal product of capital relative to that of labor [25], the share of labor income will decrease, and inequality in the distribution of national income factors will increase [26,27,28,29]. In China, there is strong evidence that land policy has played an important role in the transformation of the industrial structure [30]. With considerable autonomy regarding land supply, local governments can collude with entrepreneurs to interfere with the land supply structure [31]. In order to achieve high economic growth, local governments use distorted land supply strategies as an important tool to attract investment, which stimulates the development of secondary industries [32,33,34]. The land supply policies favoring industrial enterprises will result in an increase in capital formation rates, a decrease in the share of labor compensation in the distribution, and insufficient spending on public services, which will have suppressive effects on consumption demand from the perspective of the corporate sector, the household sector, and the general government sector. Thus, we found a transmission mechanism of “land supply–industrial structure–consumption”. There exists a special land system in China, and, as one of the most basic factors of production, the allocation of land resources can have a series of effects on the industrial structure, but little attention has been paid to this point in the literature [35]. Our research is based on a three-sector perspective of households, government, and corporations to address this gap by exploring the root causes of China’s low consumption in terms of land elements.
This article establishes empirical evidence of the land supply structure as a determinant of consumption in China. As the main aspect of land resource fact allocation, the land supply structure refers to the proportion of land allocated to various types of industries in China’s socialist market economy system, which will directly affect the industrial structure and, in turn, result in different national income distribution ratios across sectors. Households, the general government, and corporations have different consumption and investment preferences, which will affect the final consumption. We designed an econometric model with the final consumption rate as the explained variable, the land supply structure as the core explanatory variable, and other economic, demographic, and social factors as the control variables, all of which affect final consumption to some extent. A two-stage least squares (2SLS) estimator was used to deal with the endogenous variables in the land supply structure in order to improve the robustness of the model. Second, a subsample regression was used to compare the differences in the effects of land supply structure on consumption rates under different economic development strategies and urban–rural income inequality. Finally, a mediating mechanism was used to demonstrate the dampening effect of the land supply structure on the final consumption rate through the industrial structure. We found that, in China, the distorted land supply policy is a major cause of low consumption. The results of the study provide a new perspective on the allocation of land resources to fully understand the root causes of the special phenomenon of the “Chinese consumption puzzle”, which has important implications regarding how to improve consumption effectively and efficiently.

2. Literature Review

With an explosion in China’s high saving ratio and low consumption rate, various studies have attempted to identify its root causes and effects. Moreover, most of them have started from household savings and attempted to explain the low and declining consumption rate in China for a long time with theories such as the life-cycle hypothesis, precautionary savings, and liquidity constraints.
The life-cycle hypothesis was adopted to discuss the impact of income growth, population structure, gender ratio, age distribution, and population policies on household consumption. Using a household life-cycle savings decision model to quantify the impact of demographic changes on the aggregate household savings rate in Japan, China, and India, the age distribution was found to help explain the savings patterns across time in these three countries [36]. Applying the same model, scholars have studied the existence of a relationship between the demographic structure of households and the savings rate, showing that the savings rate varies with age and tends to be higher for households with more workers, higher education, better health, and more assets [37]. Many studies also have shown that the risks of old age, family insurance, demographic changes, and productivity growth rates can generate changes in the national saving rate in China [38,39]. However, other research found that the life-cycle hypothesis can explain only 35% of the surge in the Chinese household saving of the 1980s, and discussion that the public pension reforms and uncertainty in public services also cannot fully explain the Chinese saving puzzle [40].
Preventive savings theory suggests that a high level of individual risk, related to health costs, retirement, and the financing of education, causes people to save for risk prevention. Based on this theory, scholars argue that there is still a large shortage in China’s social security system, and residents still have strong incentives to preventively save for housing, retirement, health care, and education, thus curtailing current consumption. In the context of rising house prices, residents save to invest in housing, thus limiting consumption demand and exacerbating their saving behavior as income inequality rises [41]. In addition, both the 1988 national housing reform and the removal of home purchase restrictions in China may have had an impact on consumption as residents invested in housing [42]. Contrary to the general view that rising house prices increase the household saving rate, some scholars use a consumer saving model to prove that rising house prices do not explain China’s high household saving rate [43].
The liquidity constraint theory suggests that China’s underdeveloped financial markets lead residents and firms to save more and consume less, lowering the consumption rate [13]. China’s financial system tends to allocate resources to inefficient state-owned enterprises, while the private sector, which has a much higher demand for loans, is unable to obtain loans of sufficient amounts [44]. In the case of the private sector and households, these sectors have to save for a long time in order to purchase large commodities, thus suppressing consumption. Further development of financial markets would reduce the number of credit-constrained people and small businesses, and reduce the impact of firms’ short-term solvency and profitability on their savings rates [45]. In addition, some scholars have explained the saving behavior of the population in terms of individual behaviors such as cultural habits and consumption preferences [46].
However, based on the above literature analysis, these theories are biased and unable to fully explain the actual changes in the final consumption rate, which is why researchers are still discussing the Chinese consumption puzzle today. Admittedly, these theories can help us to understand part of the low consumption in China, but there is a need to probe further in order to unveil additional factors. Studies by several scholars in recent years have shown that the distribution of national income in favor of the non-household sector is a key factor in determining China’s high aggregate savings [17]. Moreover, a decline in the share of household income in the national income plays a much larger role than an increase in the household savings rate [13]. The decline in the share of residents’ income in the national income, brought about by distortions in factor markets (including financial, labor, and land markets), may be the main cause of low consumption, and structural reforms on the supply side of the economy are the long-term mechanism to effectively stimulate consumption demand. The financial factor and labor factor have been discussed in the liquidity constraint and life-cycle hypothesis, respectively [47]. Regarding the land factor, a few studies have attempted to assess the impact of total land supply on the consumption rate from the perspective of the primary land market, showing that a decrease in the area of land supply and an increase in the ratio of land grant revenue to financial revenue reduce the consumption rate [48].
Since the economic reform and opening, under the socialist market economy system, China has developed a unique land system in which local governments intervene and control the primary land transfer market to promote local industrialization and urbanization and pursue high economic growth [49,50]. Local governments, not simply as the sole supplier of the primary land market but also as city managers, interfere with the land allocation structure. In pursuit of the goal of regional economic growth, local governments have adopted distorted land supply policies of oversupplying industrial land and tightening the supply of commercial and residential land (especially residential land). As one of the most basic factors of production, the misallocation of land resources will bring a series of negative effects on the industrial structure, national income distribution pattern, enterprise production efficiency, etc. [35]. However, the impact of land supply structure on China’s consumption rate has not received much attention. Therefore, this paper will explore how the land supply structure affects the consumption rate and the mechanism behind it, to fill the existing research gap and explore the root causes of China’s low consumption rate in the long term.

3. Theoretical Hypothesis

3.1. China’s National Accounts

In China’s national accounts, the expenditure measure of GDP is a method of calculating the final outcome of production activities from the perspective of the end-use of goods and services and includes three components: final consumption expenditure, gross capital formation, and net exports of goods and services [51]. The final consumption rate in this paper refers to the ratio of final consumption expenditure to GDP, which can reflect the degree of contribution of consumption demand to GDP. The final consumption expenditure is divided into the final consumption expenditure of households, the final consumption expenditure of the general government, and the final consumption expenditure of non-profit institutions serving households (NPISH) by the main body. Household final consumption expenditure refers to expenditure on consumer goods and services borne by permanent households, while general government final consumption expenditure refers to expenditure on public services and specific personal goods or services borne by government departments, both of which account for the majority share of final consumption expenditure. Gross capital formation is the accumulation of productive assets formed through transactions and the acquisition of productive assets incurred in fixed capital formation, inventories or valuables, and fewer disposals. Net exports of goods and services are the difference between exports of goods and services and imports of goods and services, which has less impact, as it accounts for only about 4% of GDP. Thus, the final consumption rate is closely related to final consumption expenditure, gross capital formation, and the interrelationship between the two.
The reliability of China’s national accounts data has always been the focus of debate [52]. It is undeniable that there are certain differences between China’s GDP and actual economic growth, and there are deficiencies in the collection scope of household consumption statistics [53]. However, a study by Holz (2014) found that official Chinese GDP data exhibit few statistical anomalies (thereby conforming to Benford’s Law) [54]. Moreover, Mehrotra and Pääkkönen (2011) evaluated the dynamics of GDP growth for China against alternative indicators, discovering that China’s real GDP data are not likely to be systematically biased, and are, in fact, rather reliable [55], which is consistent with Chow’s 2006 study [56]. Therefore, in this study, we believe that China’s official statistics are effective and in the process of continuous improvement and that they play an irreplaceable role when analyzing China’s economic law.

3.2. Theoretical Mechanism of Land Supply Structure Affecting Final Consumption

Investment demand and consumption demand are important demand drivers for a country’s economic growth, but the differences in their essential attributes often lead to an imbalance in their relationship. On the one hand, this imbalance originates from the automatic evolution mechanism within the economic system, but on the other hand, it can be caused by the impact of external factors such as economic policies. Since 2000, China’s economy has been growing at a high rate; the contribution of investment demand to economic growth has seen increasing fluctuations, while the contribution of consumption demand to economic growth has seen decreasing fluctuations [57]. In 2000, the contribution of investment demand to economic growth was 21.7%, and the contribution of consumption demand to economic growth was 78.8%; by 2010, the contribution of investment demand to economic growth was 63.4%, while the contribution of the consumption demand to economic growth was 47.4% (China Statistical Yearbook, 2001–2011). During this period, the supply of production capacity in the market under the long-term high investment rate exceeded the demand, while the sharp decline in net export demand due to the global financial crisis in 2008 made the problem of overcapacity and low domestic consumption demand prominent.
The miracle of China’s economic growth over the past 30 years or so has come from a process of industrialization triggered by a cheap “labor–capital” special interest mechanism. It has been shown that China’s high investment rate has been greatly influenced by central government policies that have promoted rapid industrialization and economic growth [58,59,60]. These policies include a number of investment tax incentives, issuance of licenses to firms with annual land supply plans, and provision of more production-related services such as infrastructure [61].
Land is an important resource and factor of production, and the Chinese government has the right to intervene and allocate land resources. In order to compete for external scarce capital for economic growth, local governments promote park expansion through low prices and a high proportion of industrial land allocation, leading to an industrial structure tilted toward secondary industries. The expansion of industrial enterprises will increase investment demand, which correspondingly weakens the contribution of consumption demand to economic growth, reflected in the national accounts as an increase in the share of gross capital formation and a decrease in the share of final consumption. The above is based on the perspective of the corporate sector, while—from the perspective of the household sector—the local government’s subsidies to the secondary industry will create a market environment that emphasizes capital over labor. Cheap labor costs will reduce the share of labor compensation in the national income distribution and squeeze consumption demand [62,63]. From the perspective of the general government sector, along with the development of industrialization, in order to create a better investment environment, local governments invest heavily in production-oriented public goods, such as infrastructure construction, crowding out the space for social-oriented public goods such as education, health care, and social security. Final consumption expenditures by the government sector, therefore, decrease [47,64,65,66]. The insufficient public service expenditures reinforce the household sector’s propensity to save preventively and suppress local consumption [67]. In terms of long-term economic growth, along with changes in the industrial structure, the labor–capital income distribution pattern is deteriorating, resulting in an irrational pattern of the national income distribution, which both affects the rise in consumption demand and leads to excess capacity across society as a whole. Therefore, this paper hypothesizes that a distorted land supply structure will have a negative influence on consumption demand by affecting the industrial structure.

3.3. Descriptive Analysis

In this section, we provide some descriptive evidence of the causal relationship between the land supply structure and the final consumption rate. From China’s statistical data, the ratio of final consumption expenditure to GDP tends to decrease, while the ratio of gross capital formation tends to increase; additionally, the ratio of household final consumption expenditure tends to decrease, and the ratio of general government final consumption expenditure also decreases slightly between 2000 and 2010 (Figure 1). After 2011, the final consumption rate, household final consumption rate, and general government final consumption rate all increased. It can be seen that, before 2010, China’s final consumption experienced a clear downward trend and was mainly associated with the decline in household final consumption; only after 2010 did final consumption start to slowly rise.
Since the 1990s, the focus of the government’s structural transformation has been industrialization, that is, establishing both industrial parks and attracting investment through land supply. According to the land supply data at the national level, the supply area of industrial land rose rapidly after 2002, and by 2006, the ratio of industrial land supply area to the land supply area of commercial and residential had increased from 1.07:1 to 2.28:1, expanding by more than two times. As the State Council restricted agreements for the sale of industrial land from 2006, the ratio of industrial land to commercial and residential land supply gradually declined to 1.05:1 in 2017. It can be seen that the ratio of industrial land to commercial and residential land has a clear trend of inverse movement with the final consumption rate.
We can further analyze the relationship between the land supply structure and the final consumption rate. From 2002 to 2009, the ratio of industrial land supply area to the land supply area of commercial and residential represents the land supply structure (LSS), which increased from 1.07 to 1.30, while the final consumption rate (FCR) decreased from 48.16% to 39.41% (Figure 2). The fitted trend line shows that the land supply structure has a negative relationship with the consumption rate. After 2009, the consumption rate slowly increased as the land supply structure decreased; by 2017, the ratio of land supply area fell back to 1.05 and the consumption rate gradually increased to 44.5%. Based on the above basic facts, it can be inferred that the land supply structure moves inversely with the final consumption rate and has some inherent causal relationship. Therefore, in the following section, the causal relationship between the land supply structure and final consumption is examined mainly through panel data of China’s provinces, and the heterogeneity of the effect and the transmission mechanism behind it is further investigated, in an attempt to provide a fundamental explanation for the low consumption in China.

4. Model and Data

4.1. Model Settings

To test the theoretical logic assumed in the second part and to investigate the effect of land supply structure on consumption and the transmission mechanism of its effect, we constructed a mediating-effects regression model using data from 31 Chinese provinces (excluding Hong Kong, Macao, and Taiwan) from 2002 to 2019. For all the estimates shown throughout the paper, we used several commands in Stata 15.0. Mediation analysis can help us to understand the specific process in which the independent variable affects the dependent variable by using the causal stepwise regression method combined with the Sobel test [68].
F C R i t = β 10 + β 11 L S S i t + ∑ c 1 k X k . i t + ε i t
I S i t = β 20 + β 21 L S S i t + ∑ c 2 k X k . i t + ε i t
F C R i t = β 30 + β 31 L S S i t + β 32 I S i t + ∑ c 3 k X k . i t + ε i t
where subscripts i and t refer to province and time, respectively. The dependent variable F C R i t represents the final consumption rate, L S S i t represents the land supply structure; I S i t is the industrial structure and is a mediating effect variable; X k . i t represents the control variable; ε i t is the error term. In the econometric Equations (1)–(3), a “stepwise regression method” was used to analyze the effect of land supply structure on consumption and the possible mediating effects.

4.2. Specification of Variables

Low consumption refers to the low share of consumption in the GDP, so we adopted the final consumption rate (FCR) in the GDP by expenditure approach as the explained variable to measure the level of consumption in the provinces. The GDP by expenditure approach includes the three components of final consumption expenditure, gross capital formation, and net exports of goods and services, thereby reflecting the use and composition of GDP produced during the period. The land supply structure strategy of local governments is mainly reflected in the oversupply of industrial land and the tight supply of commercial and residential land, so the ratio of industrial land supply area to the land supply area of commercial and residential was selected to represent the land supply structure (LSS) as the core explanatory variable, whose coefficient was expected to be negative.
Based on existing research, in this paper, we also controlled for a set of variables that may affect consumption in the regression model to mitigate the bias of omitted variables as much as possible. The following variables were considered: (1) The level of economic development (LnGDP), expressed as the logarithm of GDP per capita in each province. Based on the theory of absolute income hypothesis, the consumption rate is closely related to economic development, so GDP per capita is used to control the effect of economic development on the consumption rate [69]. (2) The urbanization rate (UR) is the proportion of the resident population in urban areas to the total population. Due to the high uncertainty of agricultural income, rural residents tend to save a large portion of their income, so higher urbanization rates are beneficial to improve consumption. (3) Population dependency ratio (PDR), which is expressed as the proportion of the total population that needs to be supported (children and the elderly). According to the life-cycle hypothesis, if the proportion of a society’s population composition changes, the marginal propensity to consumption also changes. If the proportion of children and the elderly in society increases, the propensity to consumption increases, and if the proportion of the middle-aged population increases, the propensity to consumption decreases [3]. (4) Regional fiscal expenditures (RFE), expressed as the ratio of regional fiscal expenditures to GDP. The implementation of an accommodative fiscal policy can provide more public services and reduce the incentive for residents to save, thus increasing consumption demand. (5) The degree of economic nationalization (DEN), measured by the share of employed persons in state-owned units in total urban employment. Denationalization of urban enterprises will increase the overall income level of the population, which will boost consumption [6].
Thus, according to the previous theoretical analysis, the land supply structure influences consumption by affecting the industrial structure, so the ratio of tertiary industry GDP to secondary industry GDP was chosen to represent the industrial structure (IS) as a mediator variable. The local government’s behavior of giving priority to industrial land supply promotes the rapid development of regional industry and has a certain extrusion effect on the development of the service industry [70]. Industry and services are capital-intensive and labor-intensive, respectively. The distorted industrial structure, on the one hand, squeezes the labor income share and reduces the income share of the household sector, thus suppressing household consumption demand; on the other hand, high investment in industrial enterprises drives the share of gross capital formation in GDP, thus reducing the final consumption rate [47].

4.3. Data Collection

The land supply data were obtained from the “China Land and Resources Statistical Yearbook”. There are no data related to land supply for industrial use, commercial use, and residential use until 2009. Therefore, we used panel data on land supply by use from 2002 to 2008, compiled from the China land market (https://www.landchina.com/landSupply, 3 March 2022), which is a website dedicated to recording China’s land transaction information [71]. Data on the urbanization rate were obtained from the “China Population and Employment Statistical Yearbook (2003–2018)”, while other data were sourced from the “China Statistical Yearbook (2003–2018)”. Table 1 shows the description of each variable, and Table 2 shows the descriptive statistics of each variable.

5. Results and Discussion

5.1. Basic Estimation Results

Table 3 reports the regression results for the ordinary least square (OLS) model, fixed-effects (FE) model, and random-effects (RE) model, respectively. The large individual differences among Chinese provinces and the F test also indicate that the individual effects of the panel data are significant and the OLS estimates are strongly rejected. The Hausman test (HT) indicates that the FE model fits the statistical characteristics of the data better than the RE model, and therefore, this analysis is based on the fixed-effects regression results.
According to the FE regression, for every one-unit increase in land supply structure, the final consumption rate will decrease by 0.5%, indicating that a land supply structure biased toward industrial land reduces the final consumption rate, supporting the theoretical hypothesis.
Among the control variables, the degree of economic nationalization (DEN) is significantly negative, while the urbanization rate (UR) and the regional fiscal expenditures (RFE) are positive and pass significance at 1%, 5%, and 10%, respectively, which is consistent with the expectations of this paper and the studies of related scholars [69]. A possible reason for the significant negative relationship between GDP per capita (LnGDP) and consumption rate is that, in the early stage of economic development, along with the process of industrialization, the share of labor income in the national income gradually decreases, and therefore, the consumption rate decreases. When the economy grows to a certain level, the share of labor income starts to rise and the consumption rate will increase. To further verify the non-linear relationship between the level of economic development and consumption, the squared term of GDP per capita was added for regression analysis, the coefficient of which was 0.002 and was significant at 1%, indicating a U-shaped relationship between the level of economic development and the consumption rate of the population, which is consistent with the previous findings [72]. This is because the first period constituted the industrialized development phase, and as GDP per capita increased, the share of labor income declined and so did the consumption rate. Finally, a further increase in GDP per capita represents a move into a service-led development phase, where the share of labor income again increases and consumption, therefore, also increases. The significance level of the population dependency ratio (PDR) in Model 3 is exactly 0.1, which indicates that its effect on the consumption rate is weak and that an increase in the share of the dependent population in the total population promotes consumption. Possible reasons for this are that the dependent population requires more consumption expenditures on education and health care, while the corresponding share of the labor force population with higher motivation to save is smaller in the same period, which may have an overall consumption-boosting effect.

5.2. Robustness Tests

The basic regression results support the conclusion that the land supply structure inhibits consumption; however, the result may be influenced by the variable measure, sample selection, and endogeneity. To ensure the validity of the conclusions, several robustness tests were conducted based on the basic regressions. First, considering the existence of measurement error and omitted variables, the ratio of industrial land supply area to commercial and residential land supply area as a proxy variable for land supply structure may have endogeneity problems. Therefore, the logarithm of the area involved in illegal land and the second-order lagged term of the land supply structure were chosen as instrumental variables, and the two-stage least squares (2SLS) method was used for robustness testing. The reason for choosing these two instrumental variables is that (1) there may be a large amount of illegal land in the process of supplying a large amount of industrial land at low prices by local governments to attract investment. Local governments may establish and expand development zones without permission, adjust the overall land use plan without permission, and illegally encroach on arable land, resulting in a large amount of illegal land use. Thus, the area of illegal land involved can reflect the land supply structure to a certain extent without affecting consumption, as described in the “China Land and Resources Statistical Yearbook”. (2) For a continuous economic process, the second-order lagged term of land supply structure is strongly correlated with the current period’s land supply structure but not with the current period’s error term. The results of the instrumental variables test in Table 4 indicate that the coefficient of the core explanatory variable of land supply structure is significantly negative, and the absolute value of the regression coefficient is larger than the absolute value of the coefficient of Model 3, suggesting that the endogeneity problem causes the fixed-effects model to underestimate the effect of the land supply structure on the consumption rate (Model 4). The other control variables remain largely consistent with the basic regression results. The choice of instrumental variables is also justified. The p-value of the Davidson-MacKinnon test statistic is 9.5 × 10−9, which is less than 0.01, rejecting the original hypothesis that “land supply structure is an exogenous variable”; therefore, the study required the introduction of instrumental variables; the Cragg-Donald Wald F value is 14.206, which is greater than 10; the p-value of Hansen’s test is 0.8557, which is greater than 0.1; the weak instrumental variable test and overidentification test were passed.
Second, the core explanatory variables may be subject to certain measurement errors, so the land supply structure was tested for robustness. The land supply structure is mainly reflected in the oversupply of industrial land, so the proportion of industrial land (IL) was used as a proxy explanatory variable for testing (Table 4). The regression results show that the use of different metrics does not affect the negative correlation between land supply structure and consumption rate (Model 5). In addition, all control variables are lagged by one period for robustness testing (Model 6), and the regression results further support the conclusions of the basic model. Finally, considering that the economic development level, urbanization rate, and fiscal expenditure scale of the four province-level municipalities differ significantly from those of other provinces, the regression analysis was conducted by excluding the four province-level municipalities (Beijing, Shanghai, Tianjin, and Chongqing), and the regression results of Model 7 indicate that the relationship between land supply structure and consumption remains robust.

5.3. Heterogeneity Analysis

The results of the basic regressions suggest that land supply biased toward industrial land has a dampening effect on final consumption. However, this relationship may have different effects on the pattern of economic development between regions and income disparity within regions. Therefore, we further explore the heterogeneity of the causal effects.
  • Heterogeneous impact of economic catch-up degree. The degree of economic catch-up is expressed as the multiplication of the ratio of the highest GDP per capita of neighboring provinces to the GDP per capita of the province and the ratio of the highest GDP per capita of any province in the country to the GDP per capita of the province. Local economic growth is an important criterion for government officials to be promoted [73]. Due to the high mobility of manufacturing industries, local governments in China sell industrial land at lower prices in order to attract real investment and boost regional economic growth, which creates competition for growth among local governments. Therefore, the greater the economic gap between the region and the neighboring regions or regions with faster economic development nationwide, the greater the intensity of economic catch-up. The intersection term of the degree of economic catch-up variable (ECD) and land supply structure was constructed and added into the regression equation after centralized processing (Model 8). The results in Table 5 show that the marginal effect of the land supply structure on the final consumption rate is −0.008, whose absolute value is larger than the absolute value of the fixed-effects model in the baseline regression results (Model 3); the coefficient of the intersection term is significantly positive, which indicates that the greater the degree of economic catch-up, the more obvious the restraint effect of land supply structure on consumption. This is mainly because the pursuit of economic catch-up will prompt the local government to expand the supply of industrial land to achieve rapid economic growth, which will further promote the development of secondary industries, thus causing a decrease in the share of the household sector in the national income and lowering the consumption rate. This suggests that distorted land supply strategies are in part a result of China’s pursuit of high economic growth, which is consistent with the new structural economics view that governments can promote structural change [74].
  • Heterogeneous effects of urban–rural inequality. The urban–rural inequality is the ratio of the per capita income of urban residents to the per capita income of rural residents. Since the reform and opening up, in the process of China’s economic development, the inadequate market system, the “dual-track system” between urban and rural areas in the household registration system, and the monopoly of resource industries have caused the widening of the income gap between urban and rural areas. The urban–rural inequality variable (URI) and its intersection term with land supply structure were input into Model 9 for regression analysis, and the intersection term was also centralized. The results from model 9 show that the coefficient of the intersection term is significantly positive, indicating that as the income gap between urban and rural areas widens, the inhibiting effect of land supply structure on consumption becomes more severe, which is consistent with the study by Lichtenberg and Ding [49]. On the one hand, local governments monopolize the market for converting agricultural land into construction land, which undermines farmers’ land property rights and interests and causes the income gap between urban and rural areas to widen, thus reducing the consumption rates of households. On the other hand, in areas with large urban–rural income disparities, this implies an unequal distribution of resources, a high level of government participation in economic activities, mobility barriers, and market distortions. The government tends to have a disproportionate supply of industrial land, which in turn can exacerbate the disincentive to consumption by affecting the income of the household sector [75].
It can be seen that both local competition mechanisms and urban–rural inequality have an impact on consumption rates, and there are significant differences between regions. In the context of the current transformation of economic strategy from high growth to high quality, the land system also presents an opportunity for reform, and China must adjust the land system in a timely in order to meet the requirements of high-quality development. Therefore, is necessary to change the competitive mechanism of local economic catch-up while reducing government intervention in the supply of different land types, narrowing the income gap between urban and rural areas, and giving full play to the role of the market.

5.4. Intermediate Mechanism

In the previous theoretical analysis, we found that a land supply policy that favors industrial land leads to an increase in the share of the secondary industry. Due to the capital-intensive nature of manufacturing, it will, on the one hand, have a crowding-out effect on household sector income and dampen consumption demand; on the other hand, it will increase the ratio of gross capital formation to GDP, thus reducing the final consumption rate. To test this argument, we first tested the effect of land supply structure on industrial structure based on Equation (2) (first-stage regression) and then tested the effect of industrial structure on final consumption rate based on Equation (3) (second-stage regression), and the regression results are shown in Table 6.
The results show that the land supply structure significantly suppresses the ratio of the tertiary industry to secondary industry, and for every one-unit increase in the ratio of industrial land to commercial and residential land, the ratio of tertiary to secondary industry will decrease by 2.6%, indicating that the industrial structure is indeed an important channel through which the land supply structure suppresses consumption. Moreover, the industrial structure significantly affects the final consumption rate—with each one-unit increase in the tertiary-to-secondary industry ratio, the final consumption rate increases by 7.7%.
The results of the Sobel tests show that the value of the test statistic for industrial structure is −1.69, which is significant at the 10% level, indicating the presence of a mediating effect. The coefficient of land supply structure is approximately −0.003 after adding the ratio of tertiary to secondary industries, while the regression coefficient of land supply structure without the mediating variable is −0.005 (Model 3); after adding the ratio of tertiary to secondary industries, the core explanatory variable is no longer significant, which indicates that the land supply structure affects the consumption rate mainly through the industrial structure, and the mediating effect is 42.41%. Therefore, based on the existing land supply structure, the redevelopment of idle and inefficient industrial land can be used to optimize the industrial structure and thus increase the consumption level. In the future, land supply can be adjusted according to a reasonable industrial layout, giving full play to the role of the market to make it more scientific and efficient.

6. Conclusions

Since the financial crisis in 2008, the structural contradiction of insufficient domestic demand has always plagued China’s economy and has become an important factor limiting the sustainable and healthy development of China’s economy. How can one establish a long-term mechanism to expand consumption demand? Most of the existing studies are limited to the traditional consumption theory, which considers the causes of China’s low consumption in terms of a single sector. China’s special land system, on the other hand, can cause an uneven distribution of national income across sectors, which ultimately affects consumption [12,17,76]. This study bridges this research gap in terms of the land element, arguing that the distorted land supply strategies of local governments lead to an industrial structure change, thus triggering a lack of consumption. Based on the existing literature, in this paper, we first provided a summary of the theoretical mechanism by which the land supply structure suppresses consumption demand and then empirically investigated the theoretical hypothesis by using panel data econometric regression. For a long time, local governments in China have tended to prioritize the supply of industrial land, resulting in the rapid development of industrial industry, which, on the one hand, raises the investment rate of the corporate and government sectors, while, on the other hand, suppressing the consumption demand of the household sector, thus lowering the final consumption rate. The impact of the land supply structure on consumption is related to the development strategy adopted by local governments and the urban–rural inequity, thus exhibiting inter-regional heterogeneity.
Unlike previous studies of consumption, which have been limited to the household sector, the findings of this paper illustrate the fact that the final consumption rate in China is low. This study examined the impact of China’s particular land supply strategy on consumption demand in terms of the land element, bridging the gap from traditional consumer theory research. Furthermore, the transmission path of “land structure–industrial structure–final consumption” was verified. The results of this study help to further explain the underlying causes of the particular phenomenon of the “consumption puzzle” in relation to China’s special land policies and institutions and provide important guidance on how to effectively boost consumption demand. First, in the context of improving the market allocation of factors in China, the government should reduce the supply of inefficient industrial land, eliminate backward production capacity, focus on revitalizing abandoned and idle industrial land, and improve policies such as long-term leasing and flexible tenure supply, to optimize the industrial structure, increase the proportion of income in the household sector, and raise consumption by addressing the root cause [77]. Second, on the premise of complying with the spatial planning and use control requirements of the land, they should promote the reasonable conversion of different industrial land types, and release the normal price level of various types of land concessions, so as to reverse the incentive to develop high value-added industries and seek new paths for truly expanding domestic demand. Third, they should avoid excessive and inefficient investment in infrastructure construction, strengthen the proportion of social welfare fiscal spending on education and medical care, eliminate people’s concerns about preventive savings, and effectively raise the level of consumption. Last but not least, the government should promote the transfer of land revenue from government departments and cities to the household sector and rural areas. The main purpose of this would be to increase the expenditure of land proceeds on society and people’s livelihoods, including education, housing security, and social security, such as medical care and pensions. The expenditure of land proceeds should also be increased in rural areas to support rural infrastructure construction and public services, which would reduce the urban–rural income gap and stimulates effective demand. However, this is only a preliminary study in the area of land elements, and our findings have some limitations. The expected relationship between the land supply structure, industry structure, and consumption rate in this paper was based on China’s unique land system, but whether it can be applied to countries with different land-use rules and different economic systems remains to be determined. In China’s current development model, the land supply structure has an impact on the consumption rate, and it remains to be found whether this relationship will change as China enters a phase of high-quality development. Thus, in order to solve the problem of low consumption in China at the root, follow-up studies should further focus on the impact of China’s special system and some institutional changes (e.g., market-oriented reform of land factors, supply-side structural reform, etc.) on consumption.

Author Contributions

Conceptualization, Y.D. and D.Z.; methodology, Y.D. and J.C.; software, Y.D. and J.C.; validation, Y.D., J.C. and D.Z.; formal analysis, Y.D.; investigation, Y.D.; resources, Y.D.; data curation, Y.D.; writing—original draft preparation, Y.D.; writing—review and editing, Y.D. and D.Z.; visualization, Y.D.; supervision, D.Z.; project administration, Y.D.; funding acquisition, D.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Key Projects of Philosophy and Social Sciences Research, Ministry of Education, China (19JZD013).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Publicly available datasets were analyzed in this study. These data can be found here: https://www.landchina.com/landSupply, http://www.stats.gov.cn/tjsj/ndsj/, https://data.cnki.net/yearbook/Single/N2020030130, accessed on 6 March 2022.

Conflicts of Interest

The authors declare no conflict of interest.

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Land 11 00516 g001 550
Figure 1. Final consumption rate (%) and capital formation rate (%) in China, 1998–2020. Source: The National Bureau of Statistics.
Figure 1. Final consumption rate (%) and capital formation rate (%) in China, 1998–2020. Source: The National Bureau of Statistics.
Land 11 00516 g001
Land 11 00516 g002 550
Figure 2. The ratio of industrial land area to commercial and residential land area and final consumption rate (%), 2002–2017. Sources: The National Bureau of Statistics and the “China Land Resources Statistical Yearbook”.
Figure 2. The ratio of industrial land area to commercial and residential land area and final consumption rate (%), 2002–2017. Sources: The National Bureau of Statistics and the “China Land Resources Statistical Yearbook”.
Land 11 00516 g002
Table
Table 1. Variable description.
Table 1. Variable description.
Variable TypeVariablesCalculation MethodsSource
Explained variableFinal consumption rate (FCR)The final consumption rate in the GDP by expenditure approach (%)China Statistical Yearbook
(2003–2018)
Core explanatory variableLand supply structure (LSS)Industrial land transfer area/commercial and residential land transfer areahttp://www.landchina.com (accessed on 3 March 2022)
China Land and Resource Statistical Yearbook (2009–2017)
Control variableThe level of economic development (LnGDP)The logarithm of GDP per capita (CNY per capita, at 2000 constant price)China Statistical Yearbook
(2003–2018)
Urbanization rate (UR)The resident population in urban areas/the total population (%)China Population and Employment Statistics Yearbook (2003–2018)
Population dependency ratio (PDR)The population of children and the elderly/the total population (%)China Statistical Yearbook
(2003–2018)
Regional fiscal expenditures (RFE)The regional fiscal expenditures/GDP (%)China Statistical Yearbook
(2003–2018)
The degree of economic nationalization (DEN)Employed population in state-owned enterprises/total urban employed population (%)China Statistical Yearbook
(2003–2018)
Mediator variableIndustrial structure (IS)The tertiary industry GDP/the secondary industry GDPChina Statistical Yearbook
(2003–2018)
Table
Table 2. Descriptive statistics for the variables used.
Table 2. Descriptive statistics for the variables used.
Variable NameObs.MeanStd. DevMin.Max.
FCR4960.3620.0630.2170.621
LSS4851.2090.7990.0265.603
LnGDP4969.9320.6858.04811.453
UR4960.4970.1510.1390.896
PDR4960.3720.0680.1930.576
RFE4960.2320.1800.0791.379
DEN4960.3250.1350.0700.654
IS49611.6850.35610.56312.548
Table
Table 3. Land supply structure and consumption rate.
Table 3. Land supply structure and consumption rate.
Model 1Model 2Model 3
(OLS)(RE)(FE)
LSS−0.0112 ***−0.00439 **−0.00466 **
(0.00260)(0.00210)(0.00208)
UR0.241 ***0.162 ***0.138 **
(0.0271)(0.0250)(0.0522)
LnGDP−0.113 ***−0.107 ***−0.117 ***
(0.00865)(0.00849)(0.0176)
RFE−0.008490.02380.209 **
(0.0133)(0.0243)(0.0812)
PDR0.251 ***0.223 ***0.242
(0.0490)(0.0510)(0.142)
DEN−0.247 ***−0.314 ***−0.311 ***
(0.0339)(0.0400)(0.0791)
Constant1.367 ***1.367 ***1.431 ***
(0.103)(0.104)(0.217)
R20.490-0.408
F/Wald76.64 ***310.20 ***51.51 ***
N485485485
The data in the table excluding those in parentheses, are coefficients; t values (FE) and z values (RE) are presented in parentheses; ** and *** represent significance at the 5%, and 1% levels, respectively; FE(RE) stands for fixed (random)-effects model.
Table
Table 4. Robustness tests.
Table 4. Robustness tests.
Model 4Model 5Model 6Model 7
(FE IV)(FE)(FE)(FE)
LSS−0.0477 *** −0.00498 **−0.00343 *
(0.0177) (0.00227)(0.00197)
IL −0.0379 ***
(0.0112)
Control variablesYESYESYESYES
Constant1.886 ***1.356 ***1.286 ***1.438 ***
(0.297)(0.215)(0.224)(0.230)
R20.4020.4260.3240.438
F/Wald168.58 ***55.72 ***35.84 ***50.39 ***
Davidson-MacKinnon test9.5 × 10−9
Cragg-Donald Wald F value14.206
Hansen’s test0.856
N423487485421
The data in the table, excluding those in parentheses, are coefficients; t values are presented in parentheses; *, **, and *** represent significance at the 10%, 5%, and 1% levels, respectively.
Table
Table 5. Heterogeneity analysis.
Table 5. Heterogeneity analysis.
Model 8Model 9
(FE)(FE)
LSS−0.0080 *−0.0297
(0.0026)(0.0139)
ECD0.0130 ***
(0.00345)
ECD × LSS0.0012 ***
(0.0003)
URI −0.0208
(0.0152)
URI × LSS 0.0085 *
(0.0045)
Control variablesYESYES
Constant1.136 ***1.554 ***
(0.216)(0.230)
R20.4570.471
F46.84 ***39.95 ***
N485485
The data in the table, excluding those in parentheses, are coefficients; t values are presented in parentheses; * and *** represent significance at the 10% and 1% levels, respectively.
Table
Table 6. Mediation effect test.
Table 6. Mediation effect test.
Model 10Model 11
LSS → ISLSS and IS → FCR
(FE)(FE)
LSS−0.0257 *−0.00268
(0.0150)(0.00171)
IS 0.0770 ***
(0.00538)
Control variablesYESYES
Constant2.925 ***1.206 ***
(0.771)(0.0892)
R20.2180.594
F20.84 ***93.50 ***
N485485
The data in the table, excluding those in parentheses, are coefficients; t values are presented in parentheses; * and *** represent significance at the 10% and 1% levels, respectively.
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Dai, Y.; Cheng, J.; Zhu, D. Understanding the Impact of Land Supply Structure on Low Consumption: Empirical Evidence from China. Land 2022, 11, 516. https://doi.org/10.3390/land11040516

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Dai Y, Cheng J, Zhu D. Understanding the Impact of Land Supply Structure on Low Consumption: Empirical Evidence from China. Land. 2022; 11(4):516. https://doi.org/10.3390/land11040516

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Dai, Yating, Jian Cheng, and Daolin Zhu. 2022. "Understanding the Impact of Land Supply Structure on Low Consumption: Empirical Evidence from China" Land 11, no. 4: 516. https://doi.org/10.3390/land11040516

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