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Promoting or inhibiting: The role of housing price in entrepreneurship
Technological Forecasting & Social Change 148 (2019) 119732
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Promoting or inhibiting: The role of housing price in entrepreneurship a
b
Mingzhi Hu , Yinxin Su , Wenping Ye a b c
c,⁎
T
Department of Economics, College of Economics, China Research Center for Economic Development and Innovation Strategy, Jinan University, Guangzhou 510632, China Business School, Central South University, Changsha 410083, China Department of Business Administration, School of Management, Jinan University, Guangzhou 510632, China
A R T I C LE I N FO
A B S T R A C T
Associate Editor: Steven Walsh
Housing prices have been soaring in China since the past decade. Rising housing prices indicate good opportunities in the labor and housing markets, which can discourage the entrepreneurial investment decisions of would-be entrepreneurs. However, high appreciation in housing prices can also relax credit constraints in setting up nascent businesses and thereby encourage entrepreneurship. This study investigates whether rising housing prices have a pulling or pushing effect on entrepreneurial activities. We find that housing price has a diminishingly negative effect on entrepreneurial activities using data from China's Urban Household Survey and China Statistical Yearbook for Regional Economy for the period 2002–2009. The mechanisms underlying why housing price affects entrepreneurship—labor market opportunities, relaxation of credit constraints, and housing market opportunities—are also investigated. Overall, this study offers new insights into entrepreneurial activities and highlights the negative externalities of overheated housing market to entrepreneurship in developing countries.
JEL classification: J23 R21 O18 Keywords: Housing price Entrepreneurship Labor market Housing market China
1. Introduction Entrepreneurial dynamism is broadly recognized as the driving force of innovation and the engine for economic growth (Acs et al., 2008; Carree and Thurik, 2010; Gries and Naudé, 2010; Schumpeter, 1951). Hence, understanding the determinants of entrepreneurial ventures is important for public policy analysts, economic forecasters, and business managers. Research on the factors that influence entrepreneurship in the social sciences can be generally divided into three categories: economic, political, and legal institutions; sociological variables such as values and social networks; and individual characteristics (Djankov et al., 2006). Nonetheless, the phenomenon of entrepreneurship and especially the regional factors that determine entrepreneurial activities are still not well understood in the literature. Most nascent entrepreneurs are deterred by credit constraints of startup financing (Evans and Jovanovic, 1989; Evans and Leighton, 1989) and owner-occupied housing is the single biggest asset in most households' balance sheets (Chen and Hu, 2019). Hence, the relationship between housing price and entrepreneurship rate deserves in-depth investigation. The present study contributes to the burgeoning literature of entrepreneurship in two main respects. First, the role of housing price has only recently been discussed in a paucity of papers despite extensive studies investigating the
⁎
determinants of entrepreneurship from economic, institutional, social, and individual perspectives (Cagetti and De Nardi, 2006; Chen and Hu, 2019; Evans and Jovanovic, 1989; Johnson et al., 2002; Liu et al., 2019; Paulson and Townsend, 2004; Yueh, 2009). Moreover, existing research is far from reaching a consensus over whether housing price promotes or inhibits entrepreneurship. On the one hand, findings suggest that housing price appreciation raises the likelihood of entry into entrepreneurship because housing wealth helps overcome financial constraints for would-be entrepreneurs through collateralized housing assets (Adelino et al., 2015; Corradin and Popov, 2015; Harding and Rosenthal, 2017). On the other hand, Disney and Gathergood (2009) find limited evidence of house price shocks unbinding liquidity constraints with which potential entrepreneurs are confronted. Hurst and Lusardi (2004) also find that households located in regions with higher growth of housing prices are no more likely to enter into entrepreneurship compared with households in other regions. The present study regards the association between housing price and entrepreneurship rate in post-reform urban China. The result offers useful information to explain entrepreneurship in China better and in general contexts. The wealth portfolio of urban China's households has substantially changed with soaring housing prices and high home ownership rate after the housing reform in the 1990s (Chen et al., 2019; Li and Wu, 2014). We can understand how the correlations between
Corresponding author. E-mail addresses: [email protected] (M. Hu), [email protected] (Y. Su), [email protected] (W. Ye).
https://doi.org/10.1016/j.techfore.2019.119732 Received 12 March 2019; Received in revised form 6 July 2019; Accepted 28 August 2019 0040-1625/ © 2019 Elsevier Inc. All rights reserved.
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3,500
housing price and entrepreneurship that are found in the literature are sensitive to market and institutional features that differ between emerging and advanced economies by understanding how housing price affects entrepreneurship in an emerging economy like China. Second, we investigate the mechanisms underlying why housing price may influence entrepreneurship. The literature has suggested several mechanisms explaining the effect of housing price on entrepreneurship. These mechanisms are labor market opportunities (Sieg et al., 2002), relaxation of credit constraints (Adelino et al., 2015), and housing market opportunities (Li and Wu, 2014). Nonetheless, disentangling the effects of these mechanisms in the empirical analysis is challenging as they are confounding together, and one effect may offset another. A possible reason for the controversial finding on the relationship between housing price and entrepreneurship is that the dominated mechanism differs across countries that feature different financial and housing market conditions. We consistently find that housing price has a negative effect on entrepreneurial activities using data from China's Urban Household Survey (CUHS) and China Statistical Yearbook for Regional Economy (CSYRE) for the period 2002–2009. The marginal effect of housing price decreases with the increase of housing price. Rising housing prices have a positive and diminishing effect on employment rate and wage level (labor market opportunities), housing price appreciation inhibits entrepreneurship (housing market opportunities), and housing asset appreciation relieves the negative effect of high prices on the entrepreneurial engagement of homeowners (relaxation of credit constraints). These findings suggest that the negative effects (opportunities in the labor and housing markets) should outweigh the positive impacts (relaxation of credit constraints) with regard to the effects of housing price on entrepreneurship. The remaining parts of this paper are structured as follows. Section 2 briefly introduces China's housing market and housing price trend. Section 3 reviews the relevant literature regarding the linkage between housing price and entrepreneurship. Section 4 describes the data. Section 5 reports the empirical results. The final section concludes the paper by discussing the implications of our findings.
3,000 2,500
2,198
2,000 1,500
1,642 1,419
2,841
3,302
2008
2009
2,608
1,937
1,481 1,000 500 0 2002
2003
2004
2005
2006
2007
Fig. 1. National average house prices. Data source: China statistical yearbook for regional economy (CSYRE 2003–2010).
3. Literature review: mechanisms and empirical evidence A fairly extensive body of literature discusses how regional entrepreneurial activities are influenced by economic and financial factors, social institutions, demographics, and economic environments (Brunello and Langella, 2016; Cagetti and De Nardi, 2006; Chen and Hu, 2019; Dutta and Sobel, 2016; Liang et al., 2018). However, analysis of the effect of housing price on the aggregate level of entrepreneurial engagement remains limited in the literature. We summarize the mechanisms underlying why housing price will impact entrepreneurship into three groups: (i) labor market opportunities, (ii) relaxation of credit constraints, and (iii) housing market opportunities. First, rising housing prices reflect more opportunities in the labor market. Traditional theories argue that potential economic opportunities, such as job opportunities, welfare payments, expected income, and unemployment insurance, are critical factors that appeal to migrants (Fields, 1979; Sjaastad, 1962). Local public goods, such as hospitals, subway access, public schools, and public parks, are non-tradable in conventional markets. Recently, the literature pays increasing attention to the effects of amenities and local characteristics on the flow of migration (Brueckner et al., 1999; Clark et al., 2003). Public expenditure can be regarded as the cost of improving the surrounding environment of houses in the entire city. Hence, local public goods are positively correlated with housing prices (Zheng et al., 2014). The comovement of housing prices, local public goods, and potential economic opportunities (Sieg et al., 2002) suggests that high housing prices mean high expected incomes and job opportunities for individuals. Second, rising housing prices relax credit constraints and free up financial resources to start a business. Entrepreneurial ability and private assets greatly affect individuals' entrepreneurial decisions (Cagetti and De Nardi, 2006; Chen and Hu, 2019; Gentry and Hubbard, 2004; Rosti and Chelli, 2005). Access to adequate capital is critical for entrepreneurship in an imperfect credit market. An ample empirical evidence shows a positive relationship between individual assets and the likelihood of starting a business (Djankov et al., 2006; Paulson and Townsend, 2004). Owner-occupied housing is the single biggest asset for the majority of households. A positive relationship between the ownership of home assets and small business startups is naturally expected (Adelino et al., 2015), considering the vast substantial and flexible potential of the collateral value of an owned home in easing credit constraints for venture financing (Bernanke and Gertler, 1989). Third, rising housing prices increase the attractiveness of investment in the housing market relative to the venture market. People will believe that buying homes is one of the safest ways of investing in a housing market with an increase in the housing price over a long period of time (Li and Wu, 2014). The soaring housing prices subsequent to the
2. Background: China's housing market and housing price trend In Maoist China, nearly all housing stocks were provided by the government and allocated to employees in the state work units for free. Public housing was essentially a form of compensation for low salaries (Huang, 2004). China's housing reform in 1994 was part of the nationwide market-oriented economic reform. A considerable amount of housing stock in urban China was sold to sitting tenants at heavily subsidized prices within a few years after the housing system reform (Wang, 2012). Real housing prices in China's urban areas have been increasing since the housing reform (Li and Song, 2016). China's housing market started to heat up after 2004 when the open auction and listing policy was implemented in the transfer of state-owned land use rights for all types of urban land (Chen et al., 2019). Fig. 1 plots the average housing prices from 2002 to 2009. The price per square meter increased by 1883 yuan from 1419 yuan in 2002 to 3302 yuan in 2009 with an annual average growth rate of 18.96% during the period. Surging housing prices and increasing unaffordability of housing in post-reform urban China have attracted wide attention in the recent literature (Adelino et al., 2015; Chen et al., 2010; Chen et al., 2019a; Chen and Wen, 2017; Yang et al., 2018; Zhang et al., 2016). Great efforts have been made to discuss the extent of the housing bubble and the social and economic consequences brought by the continuous rising housing prices in recent years. However, the extent of the concern about the effect of surging housing prices on regional economic performance, especially on entrepreneurial activities, has received limited attention in research.
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recent years. Hence, the entrepreneurship rate observed in the present study is lower than the figure reported in the work of Chen and Hu (2019).
housing reform in urban China have substantially changed the wealth portfolio of Chinese households (Chen et al., 2019). Thus, individuals opt for investment in housing rather than in business ventures when housing investment yields higher returns than entrepreneurial investment, a situation that dampens people's entrepreneurial zeal. Fig. 2 depicts the logic relations between regional housing prices and entrepreneurship rate. In summary, negative effects (labor and housing market opportunities) and positive effects (relaxation of credit constraints) of housing prices on regional entrepreneurial activities exist. Whether housing price positively or negatively affects the entrepreneurship rate depends on whether positive effects or negative effects dominate. This question will be answered empirically in this paper.
4.2.2. Independent variable The independent variable in our analysis is housing price. Data on housing price are obtained from CSYRE. Housing prices have increased substantially in urban China during the past decade, whereas housing prices' appreciation varied substantially across cities largely due to different land transfer and land supply policies in local markets (Chen et al., 2019a; Gyourko et al., 2013). The average housing price is roughly 2186.9 yuan per square meter of the sample period. 4.2.3. Control variables Individual characteristics, such as age, gender, and education, are suggested to be correlated with personal entrepreneurial engagement (Chen and Hu, 2019; Li and Wu, 2014; Yueh, 2009; Zhang and Pan, 2012). Thus, we control for the proportion of young, male, and highly educated people in the analysis of city-level entrepreneurship rate. We also control for context factors that affect entrepreneurial activities, including wage (opportunity cost of starting up business ventures), proportion of small enterprises, population density, and proportion of tertiary industry (entrepreneurial environment) (Chen and Hu, 2019; Glaeser et al., 2010).
4. Data source and variable description 4.1. Data source Our empirical analysis is mainly based on CUHS (2002–2009) and CSYRE (2002–2009).1 CUHS is an official nationwide household survey administrated and conducted annually by the National Bureau of Statistics of China (NSBC). Sampling bias can be largely mitigated through a stratified random sampling design adopted by this survey. The dataset contains comprehensive information on household and individual characteristics and especially the occupation status of individuals. We define an entrepreneur according to the occupational information and calculate the aggregate entrepreneurship rate at the city level. A total of 979,933 observations across 143 cities remain after excluding missing values and outliers. The NBSC uses the CUHS data as a basis for producing official, annual aggregate statistics on the employment and income of Chinese urban residents. Thus, the quality of these data is high. A growing number of CUHS-based research papers have been published in decent international journals in recent years (the CUHS is described in detail in these papers: Chamon and Prasad, 2010; Chen and Hu, 2019; Chen et al., 2019a; Fan and Yavas, 2018; Li et al., 2015). The second dataset is acquired from China Statistical Yearbook for Regional Economy (CSYRE 2002–2009). CSYRE is a massive statistical publication produced by the NBSC. This yearbook comprehensively records information on China's regional economy and social development status at the city level. We use several context factors that affect personal entrepreneurial preference, including wage, proportion of small enterprises, population density, and proportion of tertiary industry.
5. Empirical results In this section, we first estimate the baseline regression model to investigate the effect of the city's housing price on entrepreneurial activities. We find that housing price has a negative effect on entrepreneurial activities, whereas the marginal effect of housing price decreases with the increase in housing price. In the second stage, we further investigate the mechanisms underlying why housing price is associated with entrepreneurial investment. We finally estimate the heterogeneous relationship between housing price and entrepreneurship among regions with different demographic characteristics. 5.1. Baseline estimations: housing price and entrepreneurship rate We first turn to econometric models for the estimation of housing price effects on city-level entrepreneurial activities. The regression with continuous outcome of entrepreneurship rate is first estimated by the fixed-effects model for controlling for unobserved time-invariant city characteristics and time trends. The form of baseline regression model is set as follows:
4.2. Definitions of variables
Entrepreneurship rateit
We focus on the effect of housing price on regional entrepreneurial activities. Thus, all variables are defined at the city level. The key variables are described and defined as follows.
= β0,1 + β1,1 Ln (Housing price )it + β2,1 Ln (Housing price )it2 + δ1 Xit + ui + γt + εit
(1)
where Entrepreneurship rateit represents city i's proportion of entrepreneurs in year t. The proportion of entrepreneurs is a function of the log value of housing price (Ln(Housing price)it) and its squared term (Ln(Housing price)it2), control variables (Xit), city dummies (ui), year dummies (γt), and an error term (εij). Housing priceit is the key explanatory variable of interest. The values of β1, 1 and β2, 1 measure the curvilinear relationship between housing price and entrepreneurship. Xijt is a vector of control variables, including Proportion of young people, Proportion of male people, Proportion of highly educated people, Proportion of small enterprises, Wage, Population density, and Tertiary industry as percentage to GDP. Table 3 presents the results of the estimation of the effect of housing price on entrepreneurial activities by using the fixed-effects model. Columns (1) to (3) in Table 3 display a series of different specifications by gradually increasing the number of controlled variables. We begin with the simplest specification by controlling for housing price and its
4.2.1. Dependent variable We classify a person as an entrepreneur if his/her employment status in the survey is either “self-employment” or “employer” following Chen and Hu (2019) and Li and Wu (2014). Table 1 reports the summary statistics of the variables for the sample. Based on this definition, the entrepreneurship rate is approximately 4.53% of the sample population during the period 2002–2009. Chen and Hu (2019) show that the fraction of entrepreneurs was approximately 8% in urban China in 2009. The entrepreneurship rate in urban China has been growing in 1
CUHS is a longitudinal survey that can be traced back to 1955. This survey became regularized and professionalized in 1986, but the survey before 2002 is conducted in several provinces merely as pilot experiments. The 2009 survey was the last year of data collection. Therefore, we choose the pooled CUHS data from 2002 to 2009 in our analysis. 3
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Labor market opportunities (–)
Housing Price
Housing market opportunities (–)
Entrepreneurship Rate
Relaxation of credit constraints (+) Fig. 2. Theoretical framework for housing price and entrepreneurship rate.
squared term only. Column (1) of Table 3 shows the results. The coefficients of the log value of housing price and its squared term are −0.0837 and 0.0055, respectively. Without controlling for other observables, cities with high housing price are significantly associated with weak entrepreneurial activities, whereas the inhibiting effect of housing price on entrepreneurship decreases with the increase of housing price. In Specification 2, we control for city demographic characteristics, such as the proportion of young, male, and educated people, as a first step toward measuring the association between housing price and entrepreneurship. Column (2) of Table 2 shows the results. The coefficients of housing price and its squared term remain negative and positive, respectively (with both being statistically significant at the 5% level). In Specification 3, we further control for city socioeconomic characteristics, and column (3) of Table 2 shows our results. Housing price is still nonlinearly associated with entrepreneurship after controlling for city demographic and socioeconomic characteristics. Coefficients of other control variables in Table 2 are as expected by economic theory and are consistent with findings in most previous literature. For example, previous literature on entrepreneurship in China has already reported the high possibility of young people (Chen & Hu, 2019; Li and Wu, 2014) and the low propensity of female residents (Li and Wu, 2014; Zhang and Pan, 2012) declining odds that rise with educational level (Li and Wu, 2014; Yueh, 2009). Wage is negatively associated with entrepreneurial engagement because it measures the opportunity cost of leaving a stable job. The proportion of small enterprises, population density, and proportion of tertiary industry reflect the entrepreneurial environment (Chen & Hu, 2019; Glaeser et al., 2010). We choose to skip the discussion of these variables because their effects are not the central focus of this paper.
Table 1 Summary statistics. Data source: Data of Entrepreneurship rate, Proportion of young people, Proportion of male people and Proportion of highly educated people are obtained from the China's Urban Household Survey (UHS 2002–2009); Data of other variables are from China Statistical Yearbook for Regional Economy (CSYRE 2002–2009). Variable
Mean
Std. Dev.
Min
Max
Entrepreneurship rate Housing price Proportion of young people Proportion of male people Proportion of highly educated people Proportion of small enterprises Wage Population density Tertiary industry as percentage to GDP Observations
0.0453 0.0281 0.0000 0.3241 2186.9 1306.7 676.16 8361.0 0.2381 0.0387 0.1292 0.3864 0.4953 0.0157 0.4122 0.5574 0.0581 0.0280 0.0025 0.1925 0.8755 0.0561 0.6957 0.9718 1.7992 0.7826 0.7319 4.3350 0.0486 0.0342 0.0052 0.2175 0.3539 0.0783 0.1403 0.5841 1120
Note: Housing price is measured as # yuan per square meter; Wage is measured as # 10,000 yuan per year; Population density is measured as # 10,000 person per square kilometer. Table 2 Housing price and entrepreneurship rate. (1) Ln(Housing price) Ln(Housing price)-Squared
(2) ⁎⁎⁎
−0.0837 (0.0274) 0.0055⁎⁎⁎ (0.0018)
Control variables Proportion of young people Proportion of male people Proportion of highly educated people Proportion of small enterprises
(3) ⁎⁎⁎
−0.0850 (0.0283) 0.0055⁎⁎⁎ (0.0019)
−0.0749⁎⁎⁎ (0.0290) 0.0048⁎⁎ (0.0019)
0.0300 (0.0193) 0.0565 (0.0360) −0.1313⁎⁎⁎ (0.0318)
0.0300⁎ (0.0196) 0.0561 (0.0361) −0.1326⁎⁎⁎ (0.0320) 0.0300⁎⁎ (0.0123) −0.0058 (0.0060) 0.0032 (0.0027) 0.0004 (0.0187) 0.2818⁎⁎ (0.1146) 1120 0.1448 140
Wage Population density Tertiary industry as percentage to GDP Constant Observations R-squared Number of cities
0.3564⁎⁎⁎ (0.1031) 1120 0.1181 140
0.3324⁎⁎⁎ (0.1094) 1120 0.1372 140
5.2. Generalized methods of moments (GMM) estimations: housing price and entrepreneurship rate A central challenge of estimating Eq. (1) is that the possible endogeneity problem of the key variable Housing priceit is due to two issues: (1) omitted variable bias in which some unobservable factors may simultaneously affect housing price and entrepreneurial activities, and (2) action of inertia in which current entrepreneurial activities may depend on past entrepreneurial investments. For example, Audretsch et al. (2015) and Mikelbank (2004) documented that regions with advanced infrastructures have high housing price and strong entrepreneurial activities. One commonly used strategy is to implement a GMM model based on dynamic panel data to address this potential endogeneity (Lokshin et al., 2008; Santos-Paulino, 2002). We also conduct a series of models within the GMM framework out of concern that housing price is endogenous to entrepreneurship rate. We choose the GMM system because it can improve the efficiency of estimation compared with differenced GMM and level GMM (Blundell and Bond,
Note: Robust standard errors in parentheses. ⁎⁎⁎ p < 0.01. ⁎⁎ p < 0.05. ⁎ p < 0.1.
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entrepreneurs in the last year of t. Other variables are defined as the same as those in Eq. (1). Columns (1) to (2) in Table 3 display the results estimated by using one-step and two-step system GMM, respectively. The results indicate that the coefficient of Ln(Housing price)it is negative, whereas that of Ln (Housing price)it2 is positive. Both coefficients are significant at the 5% level. In summary, these results are consistent with previous findings that housing price is curvilinearly associated with entrepreneurship rate. The rate for the negative effects of housing price on entrepreneurial activities is decreasing.
Table 3 GMM: housing price and entrepreneurship rate. (1)
(2)
One-step system GMM Ln(Housing price) Ln(Housing price)-Squared Lag1(Entrepreneurship rate) Constant Hansen test: chi2 Prob > chi2 Control variables Observations Number of cities
⁎⁎
−0.0716 (0.0313) 0.0041⁎⁎ (0.0020) 0.7802⁎⁎⁎ (0.0427) 0.2551⁎ (0.1478) 114.07 0.326 Yes 980 140
Two-step system GMM −0.0746⁎⁎ (0.0352) 0.0045⁎⁎ (0.0021) 0.7942⁎⁎⁎ (0.0454) 0.2598⁎ (0.1451) 114.07 0.326 Yes 980 140
5.3. Mechanisms We have observed in the data that housing price has a decreasing inhibiting effect on entrepreneurship. In this subsection, we attempt to unveil the mechanisms that housing price works on entrepreneurship rate. Specifically, we investigate labor market opportunities, investment opportunities in housing market, and relaxing credit constraints in setting up nascent businesses brought about by the rapid rise of housing prices.
Note: Robust standard errors in parentheses. Control variables include Proportion of young people, Proportion of male people, Proportion of highly educated people, Proportion of small enterprises, Wage, Population density, and Tertiary industry as percentage to GDP. ⁎⁎⁎ p < 0.01. ⁎⁎ p < 0.05. ⁎ p < 0.1.
5.3.1. Labor market opportunities We first explore whether housing price can bring opportunities in the labor market. Using several proxies of labor market opportunity, we investigate the association between housing price and labor market opportunities using panel data over the period 2000 to 2014. The regression is first estimated using the fixed-effects model and further estimated using the GMM model as a robustness check. The form of the fixed-effects model is set as follows:
1998). The two-step system GMM is important in excluding the effects of heteroscedasticity and serial autocorrelation (Wooldridge, 2002). Hence, it is asymptotically more efficient than the corresponding onestep GMM. We report results from the one-step and two-step GMM in consideration of robustness. The coefficients of interest β1, 1 and β2, 1 in Eq. (1) can be re-estimated by using the following dynamic panel data model:
Labor market oppurtunityit = β0,3 + β1,3 Ln (Housing price )it + β2,3 Ln (Housing price )it2
Entrepreneurship rateit = β0,2 + β1,2 Entrepreneurship rateit − 1
+ δ3 Xit + ui + γt + εit
+ β2,2 Ln (Housing price )it + β3,2 Ln (Housing price )it2 + δ2
(3)
where the dependent variable Labor market opportunityit represents city i's labor market opportunities in year t and takes two outcomes, which are employment rate and average wage that reflect labor market opportunities in different aspects. Columns (1) and (2) of Table 4 report the estimation results from
Xit + ui + γt + εit (2) where Entrepreneurship rateit−1 represents city i's proportion of Table 4 Housing price and labor market opportunities. (1)
(2)
Fixed-effects
Ln(Housing price) Ln(Housing price)-Squared
(3) One-step system GMM
(6)
Two-step system GMM
Ln(Wage)
Employment rate
Ln(Wage)
Employment rate
Ln(Wage)
0.2896⁎⁎⁎ (0.0761) −0.0195⁎⁎⁎ (0.0051)
0.6787⁎⁎⁎ (0.1537) −0.0437⁎⁎⁎ (0.0103)
0.3944⁎⁎⁎ (0.1007) −0.0254⁎⁎⁎ (0.0065) 0.5531⁎⁎⁎ (0.0596)
0.4942⁎⁎ (0.1991) −0.0262⁎ (0.0138)
0.3879⁎⁎⁎ (0.1029) −0.0251⁎⁎⁎ (0.0068) 0.5614⁎⁎⁎ (0.0504)
0.4936⁎⁎⁎ (0.1868) −0.0260⁎⁎ (0.0130)
−1.1617⁎⁎⁎ (0.3898) 121.56 0.159 Yes 980
0.8340⁎⁎⁎ (0.0536) −2.0378⁎⁎⁎ (0.7645) 118.69 0.207 Yes 980
−1.1323⁎⁎⁎ (0.4123) 121.56 0.159 Yes 980
0.8352⁎⁎⁎ (0.0547) −2.0443⁎⁎⁎ (0.7108) 118.69 0.207 Yes 980
140
140
140
140
Lag1(Ln(Wage))
Hansen test: chi2 Prob > chi2 Control variables Observations R-squared Number of cities
(5)
Employment rate
Lag1(Employment rate)
Constant
(4)
−0.0759 (0.3021)
−2.2414⁎⁎⁎ (0.6099)
Yes 1120 0.6266 140
Yes 1120 0.9563 140
Note: Robust standard errors in parentheses. Control variables include Proportion of young people, Proportion of male people, Proportion of highly educated people, Proportion of small enterprises, Wage, Population density, and Tertiary industry as percentage to GDP. ⁎⁎⁎ p < 0.01. ⁎⁎ p < 0.05. ⁎ p < 0.1. 5
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5.3.3. Relaxing credit constraints in setting up nascent businesses Home ownership is conducive to the wealth accumulation of owners. The residential property market is one of the most profitable and stable investments in many countries (Goodman and Mayer, 2018; La Grange and Pretorius, 2000). The housing market in China started to boom after the implementation of the housing reform in 1994 (Chen and Wen, 2017). This housing reform made China a country with one of the highest home ownership rates in the world (Chen et al., 2019b). The home ownership rate increased by approximately 20% during the reform period from 1994 to 1998 and continued to rise after the reform period. The current home ownership rate in urban China reaches > 80% (Chen et al., 2019b). Having access to adequate capital is critical for entrepreneurship in an imperfect credit market. Ample empirical evidence showing a positive relationship between individual assets and the likelihood of starting a business is limited (Djankov et al., 2006; Paulson and Townsend, 2004). The collateral value of an owned home is significant and has flexible potential in easing credit constraints for venture financing (Bernanke and Gertler, 1989). Hence, a positive relationship between the ownership of home assets and small business startups is observed (Adelino et al., 2015). We commence empirical investigations on whether rising housing prices increase the aggregate level of entrepreneurial engagement of home owners by using econometric approaches. We estimate the following regression to verify this assumption:
Eq. (3). The estimated relationship between housing price and labor market opportunities in these two specifications is consistent with our previous assumption. The positive coefficient of Ln(Housing price)it and the negative coefficient of Ln(Housing price)it2 indicate that housing price has a positive and diminishing effect on labor market opportunities, namely, employment opportunity and wage level. We estimate the specification in Columns (3) to (4) and Columns (5) to (6) by using one-step and two-step system GMM, respectively, as a robustness check on measuring the housing price effect on entrepreneurial activities. Again, we find that housing price is curvilinearly associated with labor market opportunities. Rising housing prices can improve the employment rate and wage level, whereas this promoting effect decreases with increasing housing prices. 5.3.2. Investment opportunities in housing market The appreciation of housing prices generates real estate investment opportunities with high returns, which stimulate firms to enter the real estate industry (Rong et al., 2016). Investors also opt to invest in housing rather than in business ventures when housing investments yield higher returns than entrepreneurial investments (Chen & Hu, 2018). We now move to investigate the role of housing price on investment opportunities in the housing market by studying the crowding-out effect of rising housing prices on regional entrepreneurial activities. In the empirical exercises, we estimate the effect of housing price appreciation on entrepreneurship rate by using the regression in the following form:
Entrepreneurship rateit = β0,5 + β1,5 Homeownershipit × Ln (Housing price )it
Entrepreneurship rateit = β0,4 + β1,4 Housing price appreciationit + δ4 Xit + ui + γt + εit
+ β2,5 Homeownershipit + β3,5 Ln
(4)
(Housing price )it + β4,5 Ln (Housing price )it2
where Housing price appreciationit represents city i's growth rate of housing prices in year t. Column (1) of Table 5 displays the estimation results from Eq. (4) based on the fixed-effect model. We find evidence that housing price return is negatively associated with the engagement rate of entrepreneurship. The negative statistically significant coefficient for Housing price appreciationit indicates that housing price appreciation inhibits entrepreneurship. Columns (2) and (3) present the results estimated by using the one-step and two-step system GMM, respectively. The coefficients of Housing price appreciationit remain negative, which further confirm the crowding-out effect of rising housing prices on entrepreneurial activities.
+ δ5 Xit + ui + γt + εit
where Homeownershipit represents city i's aggregate home ownership ratio in year t. β1, 5 is the coefficient of interest. Column (1) of Table 6 displays the estimation results based on the fixed-effect model, and Columns (2) and (3) report results from system GMM. As expected, the coefficients of the interaction term Homeownershipit × Ln(Housing price)it remain broadly the same in signs and Table 6 Housing price and relaxation of credit constraints.
Table 5 Housing price returns and investment opportunities in housing market.
Housing price appreciation
(1)
(2)
(3)
Fixed-effects
One-step system GMM
Two-step system GMM
−0.0043⁎⁎ (0.0021)
−0.0104⁎ (0.0060) 0.7484⁎⁎⁎ (0.0597) 0.0000 (0.0000) 88.8 0.211 Yes 980
−0.0124⁎ (0.0064) 0.6976⁎⁎⁎ (0.0373) 0.0000 (0.0000) 88.8 0.114 Yes 980
140
140
Lag1(Entrepreneurship rate) Constant Hansen test: chi2 Prob > chi2 Control variables Observations R-squared Number of cities
−0.0140 (0.0235)
Yes 1120 0.1421 140
(5)
Homeownership × Ln (Housing price) Homeownership Ln(Housing price)
(1)
(2)
(3)
Fixed-effects
One-step system GMM
Two-step system GMM
0.0186⁎⁎⁎ (0.0063) −0.1112⁎⁎ (0.0484) −0.0093⁎⁎ (0.0039)
0.0248⁎⁎ (0.0114) −0.1555⁎ (0.0856) −0.0090 (0.0063) 0.6799⁎⁎⁎ (0.0540) 0.0963 (0.0678) 87.79 0.258 Yes 980
0.0187⁎⁎ (0.0083) −0.1259⁎ (0.0650) −0.0053 (0.0052) 0.7443⁎⁎⁎ (0.0535) 0.0385 (0.0440) 87.79 0.258 Yes 980
140
140
Lag1(Entrepreneurship rate) Constant Hansen test: chi2 Prob > chi2 Control variables Observations R-squared Number of cities
Note: Robust standard errors in parentheses. Control variables include Proportion of young people, Proportion of male people, Proportion of highly educated people, Proportion of small enterprises, Wage, Population density, and Tertiary industry as percentage to GDP. ⁎⁎⁎ p < 0.01. ⁎⁎ p < 0.05. ⁎ p < 0.1.
0.0609 (0.0375)
Yes 1120 0.1645 140
Note: Robust standard errors in parentheses. Control variables include Proportion of young people, Proportion of male people, Proportion of highly educated people, Proportion of small enterprises, Wage, Population density, and Tertiary industry as percentage to GDP. ⁎⁎⁎ p < 0.01. ⁎⁎ p < 0.05. ⁎ p < 0.1. 6
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respondent's employment status is self-employed or owner of private enterprise to define entrepreneurship. A productive area for further research can employ additional data to investigate the relationship between housing price and these two types of entrepreneurship. Second, we have not investigated whether the relationship between housing price and entrepreneurship changes in different financial and housing market conditions. Third, we do not consider the role of housing price in affecting entrepreneurial success, such as profits and survival. Despite these limitations, the study offers preliminary insight into the relationship between housing price and entrepreneurial engagement and implies several important areas for future research.
magnitudes in Columns (1)–(3). Throughout Columns (1)–(3), the regression results consistently indicate that high housing price and homeownership rate are both detrimental to entrepreneurship, whereas housing asset appreciation can relieve the negative effect of high prices for home owners by relaxing their financial constraints when embarking on entrepreneurial activities. 6. Conclusions A series of market-oriented housing reforms since the 1980s has successfully transformed China from a renter economy with a serious housing shortage and poor living conditions into a country with one of the highest rates of home ownership (Chen et al., 2019). This reform also triggers a major housing boom in China (Li and Wu, 2014). Empirical evidence had shown that rising property rights and housing price appreciation due to the housing reform of the 1990s enabled entrepreneurship via alleviative credit constraints and increased labor mobility (Wang, 2012). However, rising housing prices in post-reform urban China have had a generally negative effect on entrepreneurship because of the attractive returns of property augmented by the tradition of treasuring housing assets, thereby diverting interest from investment ventures (Li and Wu, 2014). The ambiguity of housing price effect in theory and the dominant position of housing in total household wealth in contemporary China inspire us to investigate the relationship and mechanisms between real estate prices and entrepreneurship. The present study presents new insights into the economic implications of housing price dynamics using data from CUHS for the period 2002–2009. Housing price has a negative effect on entrepreneurial activities, whereas the marginal effect of housing price decreases with the increase in housing prices. Theoretically, the effect of housing price on entrepreneurship works mainly through three mechanisms: (i) labor market opportunities, (ii) housing market opportunities, and (iii) relaxation of credit constraints. Evidence shows that the appreciation of housing prices has a positive and diminishing effect on employment rate and wage level (labor market opportunities). Housing price appreciation inhibits entrepreneurship (housing market opportunities), and housing assets appreciation relieves the negative effect of high prices on the entrepreneurial engagement of home owners (relaxation of credit constraints). These findings suggest that the negative impacts (housing market opportunities) should outweigh the positive impacts (labor market opportunities and relaxation of credit constraints). The result of the negative effect of housing price on entrepreneurship stands in contrast to findings from existing studies based on the US experience (Adelino et al., 2015; Corradin and Popov, 2015). This finding carries broad policy implications. First, credit markets may fail to provide sufficient financial resources for would-be entrepreneurs in China. This scenario calls for innovation in the financial and credit market to alleviate credit constraints regarding the increase in entrepreneurial ventures. Second, our results show the structural limits on investing which favor real estate ownership in China. Appropriate policies are necessary to avoid declining house prices and make real estate investments less attractive. Third, extracting equity from home owners' properties and repositioning them to other investments in China than in developed countries is less likely for homeowners. Thus, policymakers should make the cash-out option for housing wealth easily available when potential entrepreneurs invest in small businesses. Our study also has limitations. First, individuals are either pushed into entrepreneurship out of necessity or pulled into entrepreneurship out of opportunity (Kautonen and Palmroos, 2010; Xavier-Oliveira et al., 2015). These two types of entrepreneurship differ in many aspects, such as entrepreneurial purpose, requirements for the capacity of management, and entrepreneurial assets. As a result housing price may have different impacts on these two types of entrepreneurship. Unfortunately, we cannot determine such distinction due to limited data. In the CUHS, we can only use a one-question survey that asks whether a
Acknowledgements This study was funded by the Fundamental Research Funds for the Central Universities (Jinan University, No. 19JNQM19), the Program for China Postdoctoral Science Foundation (No. 2019M651511), the Provincial Foundation for Excellent Young Talents of Colleges and Universities of Anhui Province (No. gxyq2017089), the Program for Scientific Research at Chuzhou University (No. 2018qd12), and the National Natural Science Foundation of China (No. 71902072; No. 71810107002; No. 71872193). References Acs, Z., Desai, S., Hessels, J., 2008. Entrepreneurship, economic development and institutions. Small Bus. Econ. 31 (3), 219–234. https://doi.org/10.1007/s11187-0089135-9. Adelino, M., Schoar, A., Severino, F., 2015. House prices, collateral, and self-employment. J. Financ. Econ. 117 (2), 288–306. https://doi.org/10.1016/j.jfineco.2015.03.005. Audretsch, D.B., Heger, D., Veith, T., 2015. Infrastructure and entrepreneurship. Small Bus. Econ. 44 (2), 219–230. https://doi.org/10.1007/s11187-014-9600-6. Bernanke, B., Gertler, M., 1989. Agency costs, net worth, and business fluctuations. Am. Econ. Rev. 79 (1), 14–31. Retrieved from. http://www.jstor.org/stable/1804770. Blundell, R., Bond, S., 1998. Initial conditions and moment restrictions in dynamic panel data models. J. Econ. 87 (1), 115–143. https://doi.org/10.1016/S0304-4076(98) 00009-8. Brueckner, J., Thisse, J.-F., Zenou, Y., 1999. Why is central Paris rich and downtown Detroit poor? Eur. Econ. Rev. 43 (1), 91–107. https://doi.org/10.1016/S00142921(98)00019-1. Brunello, G., Langella, M., 2016. Local agglomeration, entrepreneurship and the 2008 recession: evidence from Italian industrial districts. Reg. Sci. Urban Econ. 58, 104–114. https://doi.org/10.1016/j.regsciurbeco.2016.03.004. Cagetti, M., De Nardi, M., 2006. Entrepreneurship, frictions, and wealth. J. Polit. Econ. 114 (5), 835–870. https://doi.org/10.1086/508032. Carree, M., Thurik, R., 2010. The impact of entrepreneurship on economic growth. In: Acs, Z., Audretsch, D. (Eds.), Handbook of Entrepreneurship Research. Springer, New York, NY, pp. 557–594. https://doi.org/10.1007/978-1-4419-1191-9_20. Chamon, M., Prasad, E., 2010. Why are saving rates of urban households in China rising? Am. Econ. J. Macroecon. 2 (1), 93–130. https://doi.org/10.1257/mac.2.1.93. Chen, J., Hu, M., 2019. What types of homeowners are more likely to be entrepreneurs? The evidence from China. Small Bus. Econ. 52 (3), 633–649. https://doi.org/10. 1007/s11187-017-9976-1. Chen, K., Wen, Y., 2017. The great housing boom of China. Am. Econ. J. Macroecon. 9 (2), 1–44. https://doi.org/10.1257/mac.20140234. Chen, J., Hao, Q., Stephens, M., 2010. Assessing housing affordability in post-reform China: a case study of Shanghai. Hous. Stud. 25 (6), 877–901. https://doi.org/10. 1080/02673037.2010.511153. Chen, J., Hardin, W., Hu, M., 2019. Housing, wealth, income and consumption: China and homeownership heterogeneity. Real Estate Econ. https://doi.org/10.1111/15406229.12245. (Forthcoming). Chen, J., Hu, M., Lin, Z., 2019a. Does housing unaffordability crowd out elites in Chinese superstar cities. Journal of Housing Economics (Forthcoming). https://doi.org/10. 1016/j.jhe.2018.03.003. Chen, J., Hu, M., Lin, Z., 2019b. Housing reform in China and labor market participation. The Journal of Real Estate Finance and Economics (Forthcoming) Accepted. Clark, D., Herrin, W., Knapp, T., White, N., 2003. Migration and implicit amenity markets: does incomplete compensation matter? J. Econ. Geogr. 3 (3), 289–307. https:// doi.org/10.1093/jeg/3.3.289. Corradin, S., Popov, A., 2015. House prices, home equity borrowing, and entrepreneurship. Rev. Financ. Stud. 28 (8), 2399–2428. https://doi.org/10.1093/rfs/hhv020. Disney, R., Gathergood, J., 2009. Housing wealth, liquidity constraints and self-employment. Labour Econ. 16 (1), 79–88. https://doi.org/10.1016/j.labeco.2008.05. 002. Djankov, S., Qian, Y., Roland, G., Zhuravskaya, E., 2006. Who are China's entrepreneurs? Am. Econ. Rev. 96 (2), 348–352. Retrieved from. http://www.jstor.org/stable/ 30034671. Dutta, N., Sobel, R., 2016. Does corruption ever help entrepreneurship? Small Bus. Econ.
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Mingzhi Hu is a lecturer at the Department of Economics of the College of Economics, Jinan University. His research focus is entrepreneurship, labor market, and urban and housing– related studies. He is particularly interested in examining and evaluating housing inequality and the effects of homeownership, housing price, and housing unaffordability. His research publications include articles in Technological Forecasting & Social Change, Small Business Economics, Journal of Housing Economics, Journal of Real Estate Finance and Economics and Real Estate Economics. Yinxin Su is a master student at the Business School of Central South University. Her research is mainly focused on urban renewal, urban governance, real estate economics and housing market dynamics. She has participated in several national fund projects. She has published several publications in academic journals in China. Wenping Ye is a lecturer at the Department of Business Administration of the School of Management, Jinan University. His research interests mainly include entrepreneurship and family business. He has participated in several national fund projects. Her research publications include articles in the top academic journals in China, such as Economic Research Journal, Management World and Nankai Business Review.
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Contents lists available at ScienceDirect
Technological Forecasting & Social Change journal homepage: www.elsevier.com/locate/techfore
Promoting or inhibiting: The role of housing price in entrepreneurship a
b
Mingzhi Hu , Yinxin Su , Wenping Ye a b c
c,⁎
T
Department of Economics, College of Economics, China Research Center for Economic Development and Innovation Strategy, Jinan University, Guangzhou 510632, China Business School, Central South University, Changsha 410083, China Department of Business Administration, School of Management, Jinan University, Guangzhou 510632, China
A R T I C LE I N FO
A B S T R A C T
Associate Editor: Steven Walsh
Housing prices have been soaring in China since the past decade. Rising housing prices indicate good opportunities in the labor and housing markets, which can discourage the entrepreneurial investment decisions of would-be entrepreneurs. However, high appreciation in housing prices can also relax credit constraints in setting up nascent businesses and thereby encourage entrepreneurship. This study investigates whether rising housing prices have a pulling or pushing effect on entrepreneurial activities. We find that housing price has a diminishingly negative effect on entrepreneurial activities using data from China's Urban Household Survey and China Statistical Yearbook for Regional Economy for the period 2002–2009. The mechanisms underlying why housing price affects entrepreneurship—labor market opportunities, relaxation of credit constraints, and housing market opportunities—are also investigated. Overall, this study offers new insights into entrepreneurial activities and highlights the negative externalities of overheated housing market to entrepreneurship in developing countries.
JEL classification: J23 R21 O18 Keywords: Housing price Entrepreneurship Labor market Housing market China
1. Introduction Entrepreneurial dynamism is broadly recognized as the driving force of innovation and the engine for economic growth (Acs et al., 2008; Carree and Thurik, 2010; Gries and Naudé, 2010; Schumpeter, 1951). Hence, understanding the determinants of entrepreneurial ventures is important for public policy analysts, economic forecasters, and business managers. Research on the factors that influence entrepreneurship in the social sciences can be generally divided into three categories: economic, political, and legal institutions; sociological variables such as values and social networks; and individual characteristics (Djankov et al., 2006). Nonetheless, the phenomenon of entrepreneurship and especially the regional factors that determine entrepreneurial activities are still not well understood in the literature. Most nascent entrepreneurs are deterred by credit constraints of startup financing (Evans and Jovanovic, 1989; Evans and Leighton, 1989) and owner-occupied housing is the single biggest asset in most households' balance sheets (Chen and Hu, 2019). Hence, the relationship between housing price and entrepreneurship rate deserves in-depth investigation. The present study contributes to the burgeoning literature of entrepreneurship in two main respects. First, the role of housing price has only recently been discussed in a paucity of papers despite extensive studies investigating the
⁎
determinants of entrepreneurship from economic, institutional, social, and individual perspectives (Cagetti and De Nardi, 2006; Chen and Hu, 2019; Evans and Jovanovic, 1989; Johnson et al., 2002; Liu et al., 2019; Paulson and Townsend, 2004; Yueh, 2009). Moreover, existing research is far from reaching a consensus over whether housing price promotes or inhibits entrepreneurship. On the one hand, findings suggest that housing price appreciation raises the likelihood of entry into entrepreneurship because housing wealth helps overcome financial constraints for would-be entrepreneurs through collateralized housing assets (Adelino et al., 2015; Corradin and Popov, 2015; Harding and Rosenthal, 2017). On the other hand, Disney and Gathergood (2009) find limited evidence of house price shocks unbinding liquidity constraints with which potential entrepreneurs are confronted. Hurst and Lusardi (2004) also find that households located in regions with higher growth of housing prices are no more likely to enter into entrepreneurship compared with households in other regions. The present study regards the association between housing price and entrepreneurship rate in post-reform urban China. The result offers useful information to explain entrepreneurship in China better and in general contexts. The wealth portfolio of urban China's households has substantially changed with soaring housing prices and high home ownership rate after the housing reform in the 1990s (Chen et al., 2019; Li and Wu, 2014). We can understand how the correlations between
Corresponding author. E-mail addresses: [email protected] (M. Hu), [email protected] (Y. Su), [email protected] (W. Ye).
https://doi.org/10.1016/j.techfore.2019.119732 Received 12 March 2019; Received in revised form 6 July 2019; Accepted 28 August 2019 0040-1625/ © 2019 Elsevier Inc. All rights reserved.
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3,500
housing price and entrepreneurship that are found in the literature are sensitive to market and institutional features that differ between emerging and advanced economies by understanding how housing price affects entrepreneurship in an emerging economy like China. Second, we investigate the mechanisms underlying why housing price may influence entrepreneurship. The literature has suggested several mechanisms explaining the effect of housing price on entrepreneurship. These mechanisms are labor market opportunities (Sieg et al., 2002), relaxation of credit constraints (Adelino et al., 2015), and housing market opportunities (Li and Wu, 2014). Nonetheless, disentangling the effects of these mechanisms in the empirical analysis is challenging as they are confounding together, and one effect may offset another. A possible reason for the controversial finding on the relationship between housing price and entrepreneurship is that the dominated mechanism differs across countries that feature different financial and housing market conditions. We consistently find that housing price has a negative effect on entrepreneurial activities using data from China's Urban Household Survey (CUHS) and China Statistical Yearbook for Regional Economy (CSYRE) for the period 2002–2009. The marginal effect of housing price decreases with the increase of housing price. Rising housing prices have a positive and diminishing effect on employment rate and wage level (labor market opportunities), housing price appreciation inhibits entrepreneurship (housing market opportunities), and housing asset appreciation relieves the negative effect of high prices on the entrepreneurial engagement of homeowners (relaxation of credit constraints). These findings suggest that the negative effects (opportunities in the labor and housing markets) should outweigh the positive impacts (relaxation of credit constraints) with regard to the effects of housing price on entrepreneurship. The remaining parts of this paper are structured as follows. Section 2 briefly introduces China's housing market and housing price trend. Section 3 reviews the relevant literature regarding the linkage between housing price and entrepreneurship. Section 4 describes the data. Section 5 reports the empirical results. The final section concludes the paper by discussing the implications of our findings.
3,000 2,500
2,198
2,000 1,500
1,642 1,419
2,841
3,302
2008
2009
2,608
1,937
1,481 1,000 500 0 2002
2003
2004
2005
2006
2007
Fig. 1. National average house prices. Data source: China statistical yearbook for regional economy (CSYRE 2003–2010).
3. Literature review: mechanisms and empirical evidence A fairly extensive body of literature discusses how regional entrepreneurial activities are influenced by economic and financial factors, social institutions, demographics, and economic environments (Brunello and Langella, 2016; Cagetti and De Nardi, 2006; Chen and Hu, 2019; Dutta and Sobel, 2016; Liang et al., 2018). However, analysis of the effect of housing price on the aggregate level of entrepreneurial engagement remains limited in the literature. We summarize the mechanisms underlying why housing price will impact entrepreneurship into three groups: (i) labor market opportunities, (ii) relaxation of credit constraints, and (iii) housing market opportunities. First, rising housing prices reflect more opportunities in the labor market. Traditional theories argue that potential economic opportunities, such as job opportunities, welfare payments, expected income, and unemployment insurance, are critical factors that appeal to migrants (Fields, 1979; Sjaastad, 1962). Local public goods, such as hospitals, subway access, public schools, and public parks, are non-tradable in conventional markets. Recently, the literature pays increasing attention to the effects of amenities and local characteristics on the flow of migration (Brueckner et al., 1999; Clark et al., 2003). Public expenditure can be regarded as the cost of improving the surrounding environment of houses in the entire city. Hence, local public goods are positively correlated with housing prices (Zheng et al., 2014). The comovement of housing prices, local public goods, and potential economic opportunities (Sieg et al., 2002) suggests that high housing prices mean high expected incomes and job opportunities for individuals. Second, rising housing prices relax credit constraints and free up financial resources to start a business. Entrepreneurial ability and private assets greatly affect individuals' entrepreneurial decisions (Cagetti and De Nardi, 2006; Chen and Hu, 2019; Gentry and Hubbard, 2004; Rosti and Chelli, 2005). Access to adequate capital is critical for entrepreneurship in an imperfect credit market. An ample empirical evidence shows a positive relationship between individual assets and the likelihood of starting a business (Djankov et al., 2006; Paulson and Townsend, 2004). Owner-occupied housing is the single biggest asset for the majority of households. A positive relationship between the ownership of home assets and small business startups is naturally expected (Adelino et al., 2015), considering the vast substantial and flexible potential of the collateral value of an owned home in easing credit constraints for venture financing (Bernanke and Gertler, 1989). Third, rising housing prices increase the attractiveness of investment in the housing market relative to the venture market. People will believe that buying homes is one of the safest ways of investing in a housing market with an increase in the housing price over a long period of time (Li and Wu, 2014). The soaring housing prices subsequent to the
2. Background: China's housing market and housing price trend In Maoist China, nearly all housing stocks were provided by the government and allocated to employees in the state work units for free. Public housing was essentially a form of compensation for low salaries (Huang, 2004). China's housing reform in 1994 was part of the nationwide market-oriented economic reform. A considerable amount of housing stock in urban China was sold to sitting tenants at heavily subsidized prices within a few years after the housing system reform (Wang, 2012). Real housing prices in China's urban areas have been increasing since the housing reform (Li and Song, 2016). China's housing market started to heat up after 2004 when the open auction and listing policy was implemented in the transfer of state-owned land use rights for all types of urban land (Chen et al., 2019). Fig. 1 plots the average housing prices from 2002 to 2009. The price per square meter increased by 1883 yuan from 1419 yuan in 2002 to 3302 yuan in 2009 with an annual average growth rate of 18.96% during the period. Surging housing prices and increasing unaffordability of housing in post-reform urban China have attracted wide attention in the recent literature (Adelino et al., 2015; Chen et al., 2010; Chen et al., 2019a; Chen and Wen, 2017; Yang et al., 2018; Zhang et al., 2016). Great efforts have been made to discuss the extent of the housing bubble and the social and economic consequences brought by the continuous rising housing prices in recent years. However, the extent of the concern about the effect of surging housing prices on regional economic performance, especially on entrepreneurial activities, has received limited attention in research.
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recent years. Hence, the entrepreneurship rate observed in the present study is lower than the figure reported in the work of Chen and Hu (2019).
housing reform in urban China have substantially changed the wealth portfolio of Chinese households (Chen et al., 2019). Thus, individuals opt for investment in housing rather than in business ventures when housing investment yields higher returns than entrepreneurial investment, a situation that dampens people's entrepreneurial zeal. Fig. 2 depicts the logic relations between regional housing prices and entrepreneurship rate. In summary, negative effects (labor and housing market opportunities) and positive effects (relaxation of credit constraints) of housing prices on regional entrepreneurial activities exist. Whether housing price positively or negatively affects the entrepreneurship rate depends on whether positive effects or negative effects dominate. This question will be answered empirically in this paper.
4.2.2. Independent variable The independent variable in our analysis is housing price. Data on housing price are obtained from CSYRE. Housing prices have increased substantially in urban China during the past decade, whereas housing prices' appreciation varied substantially across cities largely due to different land transfer and land supply policies in local markets (Chen et al., 2019a; Gyourko et al., 2013). The average housing price is roughly 2186.9 yuan per square meter of the sample period. 4.2.3. Control variables Individual characteristics, such as age, gender, and education, are suggested to be correlated with personal entrepreneurial engagement (Chen and Hu, 2019; Li and Wu, 2014; Yueh, 2009; Zhang and Pan, 2012). Thus, we control for the proportion of young, male, and highly educated people in the analysis of city-level entrepreneurship rate. We also control for context factors that affect entrepreneurial activities, including wage (opportunity cost of starting up business ventures), proportion of small enterprises, population density, and proportion of tertiary industry (entrepreneurial environment) (Chen and Hu, 2019; Glaeser et al., 2010).
4. Data source and variable description 4.1. Data source Our empirical analysis is mainly based on CUHS (2002–2009) and CSYRE (2002–2009).1 CUHS is an official nationwide household survey administrated and conducted annually by the National Bureau of Statistics of China (NSBC). Sampling bias can be largely mitigated through a stratified random sampling design adopted by this survey. The dataset contains comprehensive information on household and individual characteristics and especially the occupation status of individuals. We define an entrepreneur according to the occupational information and calculate the aggregate entrepreneurship rate at the city level. A total of 979,933 observations across 143 cities remain after excluding missing values and outliers. The NBSC uses the CUHS data as a basis for producing official, annual aggregate statistics on the employment and income of Chinese urban residents. Thus, the quality of these data is high. A growing number of CUHS-based research papers have been published in decent international journals in recent years (the CUHS is described in detail in these papers: Chamon and Prasad, 2010; Chen and Hu, 2019; Chen et al., 2019a; Fan and Yavas, 2018; Li et al., 2015). The second dataset is acquired from China Statistical Yearbook for Regional Economy (CSYRE 2002–2009). CSYRE is a massive statistical publication produced by the NBSC. This yearbook comprehensively records information on China's regional economy and social development status at the city level. We use several context factors that affect personal entrepreneurial preference, including wage, proportion of small enterprises, population density, and proportion of tertiary industry.
5. Empirical results In this section, we first estimate the baseline regression model to investigate the effect of the city's housing price on entrepreneurial activities. We find that housing price has a negative effect on entrepreneurial activities, whereas the marginal effect of housing price decreases with the increase in housing price. In the second stage, we further investigate the mechanisms underlying why housing price is associated with entrepreneurial investment. We finally estimate the heterogeneous relationship between housing price and entrepreneurship among regions with different demographic characteristics. 5.1. Baseline estimations: housing price and entrepreneurship rate We first turn to econometric models for the estimation of housing price effects on city-level entrepreneurial activities. The regression with continuous outcome of entrepreneurship rate is first estimated by the fixed-effects model for controlling for unobserved time-invariant city characteristics and time trends. The form of baseline regression model is set as follows:
4.2. Definitions of variables
Entrepreneurship rateit
We focus on the effect of housing price on regional entrepreneurial activities. Thus, all variables are defined at the city level. The key variables are described and defined as follows.
= β0,1 + β1,1 Ln (Housing price )it + β2,1 Ln (Housing price )it2 + δ1 Xit + ui + γt + εit
(1)
where Entrepreneurship rateit represents city i's proportion of entrepreneurs in year t. The proportion of entrepreneurs is a function of the log value of housing price (Ln(Housing price)it) and its squared term (Ln(Housing price)it2), control variables (Xit), city dummies (ui), year dummies (γt), and an error term (εij). Housing priceit is the key explanatory variable of interest. The values of β1, 1 and β2, 1 measure the curvilinear relationship between housing price and entrepreneurship. Xijt is a vector of control variables, including Proportion of young people, Proportion of male people, Proportion of highly educated people, Proportion of small enterprises, Wage, Population density, and Tertiary industry as percentage to GDP. Table 3 presents the results of the estimation of the effect of housing price on entrepreneurial activities by using the fixed-effects model. Columns (1) to (3) in Table 3 display a series of different specifications by gradually increasing the number of controlled variables. We begin with the simplest specification by controlling for housing price and its
4.2.1. Dependent variable We classify a person as an entrepreneur if his/her employment status in the survey is either “self-employment” or “employer” following Chen and Hu (2019) and Li and Wu (2014). Table 1 reports the summary statistics of the variables for the sample. Based on this definition, the entrepreneurship rate is approximately 4.53% of the sample population during the period 2002–2009. Chen and Hu (2019) show that the fraction of entrepreneurs was approximately 8% in urban China in 2009. The entrepreneurship rate in urban China has been growing in 1
CUHS is a longitudinal survey that can be traced back to 1955. This survey became regularized and professionalized in 1986, but the survey before 2002 is conducted in several provinces merely as pilot experiments. The 2009 survey was the last year of data collection. Therefore, we choose the pooled CUHS data from 2002 to 2009 in our analysis. 3
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Labor market opportunities (–)
Housing Price
Housing market opportunities (–)
Entrepreneurship Rate
Relaxation of credit constraints (+) Fig. 2. Theoretical framework for housing price and entrepreneurship rate.
squared term only. Column (1) of Table 3 shows the results. The coefficients of the log value of housing price and its squared term are −0.0837 and 0.0055, respectively. Without controlling for other observables, cities with high housing price are significantly associated with weak entrepreneurial activities, whereas the inhibiting effect of housing price on entrepreneurship decreases with the increase of housing price. In Specification 2, we control for city demographic characteristics, such as the proportion of young, male, and educated people, as a first step toward measuring the association between housing price and entrepreneurship. Column (2) of Table 2 shows the results. The coefficients of housing price and its squared term remain negative and positive, respectively (with both being statistically significant at the 5% level). In Specification 3, we further control for city socioeconomic characteristics, and column (3) of Table 2 shows our results. Housing price is still nonlinearly associated with entrepreneurship after controlling for city demographic and socioeconomic characteristics. Coefficients of other control variables in Table 2 are as expected by economic theory and are consistent with findings in most previous literature. For example, previous literature on entrepreneurship in China has already reported the high possibility of young people (Chen & Hu, 2019; Li and Wu, 2014) and the low propensity of female residents (Li and Wu, 2014; Zhang and Pan, 2012) declining odds that rise with educational level (Li and Wu, 2014; Yueh, 2009). Wage is negatively associated with entrepreneurial engagement because it measures the opportunity cost of leaving a stable job. The proportion of small enterprises, population density, and proportion of tertiary industry reflect the entrepreneurial environment (Chen & Hu, 2019; Glaeser et al., 2010). We choose to skip the discussion of these variables because their effects are not the central focus of this paper.
Table 1 Summary statistics. Data source: Data of Entrepreneurship rate, Proportion of young people, Proportion of male people and Proportion of highly educated people are obtained from the China's Urban Household Survey (UHS 2002–2009); Data of other variables are from China Statistical Yearbook for Regional Economy (CSYRE 2002–2009). Variable
Mean
Std. Dev.
Min
Max
Entrepreneurship rate Housing price Proportion of young people Proportion of male people Proportion of highly educated people Proportion of small enterprises Wage Population density Tertiary industry as percentage to GDP Observations
0.0453 0.0281 0.0000 0.3241 2186.9 1306.7 676.16 8361.0 0.2381 0.0387 0.1292 0.3864 0.4953 0.0157 0.4122 0.5574 0.0581 0.0280 0.0025 0.1925 0.8755 0.0561 0.6957 0.9718 1.7992 0.7826 0.7319 4.3350 0.0486 0.0342 0.0052 0.2175 0.3539 0.0783 0.1403 0.5841 1120
Note: Housing price is measured as # yuan per square meter; Wage is measured as # 10,000 yuan per year; Population density is measured as # 10,000 person per square kilometer. Table 2 Housing price and entrepreneurship rate. (1) Ln(Housing price) Ln(Housing price)-Squared
(2) ⁎⁎⁎
−0.0837 (0.0274) 0.0055⁎⁎⁎ (0.0018)
Control variables Proportion of young people Proportion of male people Proportion of highly educated people Proportion of small enterprises
(3) ⁎⁎⁎
−0.0850 (0.0283) 0.0055⁎⁎⁎ (0.0019)
−0.0749⁎⁎⁎ (0.0290) 0.0048⁎⁎ (0.0019)
0.0300 (0.0193) 0.0565 (0.0360) −0.1313⁎⁎⁎ (0.0318)
0.0300⁎ (0.0196) 0.0561 (0.0361) −0.1326⁎⁎⁎ (0.0320) 0.0300⁎⁎ (0.0123) −0.0058 (0.0060) 0.0032 (0.0027) 0.0004 (0.0187) 0.2818⁎⁎ (0.1146) 1120 0.1448 140
Wage Population density Tertiary industry as percentage to GDP Constant Observations R-squared Number of cities
0.3564⁎⁎⁎ (0.1031) 1120 0.1181 140
0.3324⁎⁎⁎ (0.1094) 1120 0.1372 140
5.2. Generalized methods of moments (GMM) estimations: housing price and entrepreneurship rate A central challenge of estimating Eq. (1) is that the possible endogeneity problem of the key variable Housing priceit is due to two issues: (1) omitted variable bias in which some unobservable factors may simultaneously affect housing price and entrepreneurial activities, and (2) action of inertia in which current entrepreneurial activities may depend on past entrepreneurial investments. For example, Audretsch et al. (2015) and Mikelbank (2004) documented that regions with advanced infrastructures have high housing price and strong entrepreneurial activities. One commonly used strategy is to implement a GMM model based on dynamic panel data to address this potential endogeneity (Lokshin et al., 2008; Santos-Paulino, 2002). We also conduct a series of models within the GMM framework out of concern that housing price is endogenous to entrepreneurship rate. We choose the GMM system because it can improve the efficiency of estimation compared with differenced GMM and level GMM (Blundell and Bond,
Note: Robust standard errors in parentheses. ⁎⁎⁎ p < 0.01. ⁎⁎ p < 0.05. ⁎ p < 0.1.
4
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entrepreneurs in the last year of t. Other variables are defined as the same as those in Eq. (1). Columns (1) to (2) in Table 3 display the results estimated by using one-step and two-step system GMM, respectively. The results indicate that the coefficient of Ln(Housing price)it is negative, whereas that of Ln (Housing price)it2 is positive. Both coefficients are significant at the 5% level. In summary, these results are consistent with previous findings that housing price is curvilinearly associated with entrepreneurship rate. The rate for the negative effects of housing price on entrepreneurial activities is decreasing.
Table 3 GMM: housing price and entrepreneurship rate. (1)
(2)
One-step system GMM Ln(Housing price) Ln(Housing price)-Squared Lag1(Entrepreneurship rate) Constant Hansen test: chi2 Prob > chi2 Control variables Observations Number of cities
⁎⁎
−0.0716 (0.0313) 0.0041⁎⁎ (0.0020) 0.7802⁎⁎⁎ (0.0427) 0.2551⁎ (0.1478) 114.07 0.326 Yes 980 140
Two-step system GMM −0.0746⁎⁎ (0.0352) 0.0045⁎⁎ (0.0021) 0.7942⁎⁎⁎ (0.0454) 0.2598⁎ (0.1451) 114.07 0.326 Yes 980 140
5.3. Mechanisms We have observed in the data that housing price has a decreasing inhibiting effect on entrepreneurship. In this subsection, we attempt to unveil the mechanisms that housing price works on entrepreneurship rate. Specifically, we investigate labor market opportunities, investment opportunities in housing market, and relaxing credit constraints in setting up nascent businesses brought about by the rapid rise of housing prices.
Note: Robust standard errors in parentheses. Control variables include Proportion of young people, Proportion of male people, Proportion of highly educated people, Proportion of small enterprises, Wage, Population density, and Tertiary industry as percentage to GDP. ⁎⁎⁎ p < 0.01. ⁎⁎ p < 0.05. ⁎ p < 0.1.
5.3.1. Labor market opportunities We first explore whether housing price can bring opportunities in the labor market. Using several proxies of labor market opportunity, we investigate the association between housing price and labor market opportunities using panel data over the period 2000 to 2014. The regression is first estimated using the fixed-effects model and further estimated using the GMM model as a robustness check. The form of the fixed-effects model is set as follows:
1998). The two-step system GMM is important in excluding the effects of heteroscedasticity and serial autocorrelation (Wooldridge, 2002). Hence, it is asymptotically more efficient than the corresponding onestep GMM. We report results from the one-step and two-step GMM in consideration of robustness. The coefficients of interest β1, 1 and β2, 1 in Eq. (1) can be re-estimated by using the following dynamic panel data model:
Labor market oppurtunityit = β0,3 + β1,3 Ln (Housing price )it + β2,3 Ln (Housing price )it2
Entrepreneurship rateit = β0,2 + β1,2 Entrepreneurship rateit − 1
+ δ3 Xit + ui + γt + εit
+ β2,2 Ln (Housing price )it + β3,2 Ln (Housing price )it2 + δ2
(3)
where the dependent variable Labor market opportunityit represents city i's labor market opportunities in year t and takes two outcomes, which are employment rate and average wage that reflect labor market opportunities in different aspects. Columns (1) and (2) of Table 4 report the estimation results from
Xit + ui + γt + εit (2) where Entrepreneurship rateit−1 represents city i's proportion of Table 4 Housing price and labor market opportunities. (1)
(2)
Fixed-effects
Ln(Housing price) Ln(Housing price)-Squared
(3) One-step system GMM
(6)
Two-step system GMM
Ln(Wage)
Employment rate
Ln(Wage)
Employment rate
Ln(Wage)
0.2896⁎⁎⁎ (0.0761) −0.0195⁎⁎⁎ (0.0051)
0.6787⁎⁎⁎ (0.1537) −0.0437⁎⁎⁎ (0.0103)
0.3944⁎⁎⁎ (0.1007) −0.0254⁎⁎⁎ (0.0065) 0.5531⁎⁎⁎ (0.0596)
0.4942⁎⁎ (0.1991) −0.0262⁎ (0.0138)
0.3879⁎⁎⁎ (0.1029) −0.0251⁎⁎⁎ (0.0068) 0.5614⁎⁎⁎ (0.0504)
0.4936⁎⁎⁎ (0.1868) −0.0260⁎⁎ (0.0130)
−1.1617⁎⁎⁎ (0.3898) 121.56 0.159 Yes 980
0.8340⁎⁎⁎ (0.0536) −2.0378⁎⁎⁎ (0.7645) 118.69 0.207 Yes 980
−1.1323⁎⁎⁎ (0.4123) 121.56 0.159 Yes 980
0.8352⁎⁎⁎ (0.0547) −2.0443⁎⁎⁎ (0.7108) 118.69 0.207 Yes 980
140
140
140
140
Lag1(Ln(Wage))
Hansen test: chi2 Prob > chi2 Control variables Observations R-squared Number of cities
(5)
Employment rate
Lag1(Employment rate)
Constant
(4)
−0.0759 (0.3021)
−2.2414⁎⁎⁎ (0.6099)
Yes 1120 0.6266 140
Yes 1120 0.9563 140
Note: Robust standard errors in parentheses. Control variables include Proportion of young people, Proportion of male people, Proportion of highly educated people, Proportion of small enterprises, Wage, Population density, and Tertiary industry as percentage to GDP. ⁎⁎⁎ p < 0.01. ⁎⁎ p < 0.05. ⁎ p < 0.1. 5
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5.3.3. Relaxing credit constraints in setting up nascent businesses Home ownership is conducive to the wealth accumulation of owners. The residential property market is one of the most profitable and stable investments in many countries (Goodman and Mayer, 2018; La Grange and Pretorius, 2000). The housing market in China started to boom after the implementation of the housing reform in 1994 (Chen and Wen, 2017). This housing reform made China a country with one of the highest home ownership rates in the world (Chen et al., 2019b). The home ownership rate increased by approximately 20% during the reform period from 1994 to 1998 and continued to rise after the reform period. The current home ownership rate in urban China reaches > 80% (Chen et al., 2019b). Having access to adequate capital is critical for entrepreneurship in an imperfect credit market. Ample empirical evidence showing a positive relationship between individual assets and the likelihood of starting a business is limited (Djankov et al., 2006; Paulson and Townsend, 2004). The collateral value of an owned home is significant and has flexible potential in easing credit constraints for venture financing (Bernanke and Gertler, 1989). Hence, a positive relationship between the ownership of home assets and small business startups is observed (Adelino et al., 2015). We commence empirical investigations on whether rising housing prices increase the aggregate level of entrepreneurial engagement of home owners by using econometric approaches. We estimate the following regression to verify this assumption:
Eq. (3). The estimated relationship between housing price and labor market opportunities in these two specifications is consistent with our previous assumption. The positive coefficient of Ln(Housing price)it and the negative coefficient of Ln(Housing price)it2 indicate that housing price has a positive and diminishing effect on labor market opportunities, namely, employment opportunity and wage level. We estimate the specification in Columns (3) to (4) and Columns (5) to (6) by using one-step and two-step system GMM, respectively, as a robustness check on measuring the housing price effect on entrepreneurial activities. Again, we find that housing price is curvilinearly associated with labor market opportunities. Rising housing prices can improve the employment rate and wage level, whereas this promoting effect decreases with increasing housing prices. 5.3.2. Investment opportunities in housing market The appreciation of housing prices generates real estate investment opportunities with high returns, which stimulate firms to enter the real estate industry (Rong et al., 2016). Investors also opt to invest in housing rather than in business ventures when housing investments yield higher returns than entrepreneurial investments (Chen & Hu, 2018). We now move to investigate the role of housing price on investment opportunities in the housing market by studying the crowding-out effect of rising housing prices on regional entrepreneurial activities. In the empirical exercises, we estimate the effect of housing price appreciation on entrepreneurship rate by using the regression in the following form:
Entrepreneurship rateit = β0,5 + β1,5 Homeownershipit × Ln (Housing price )it
Entrepreneurship rateit = β0,4 + β1,4 Housing price appreciationit + δ4 Xit + ui + γt + εit
+ β2,5 Homeownershipit + β3,5 Ln
(4)
(Housing price )it + β4,5 Ln (Housing price )it2
where Housing price appreciationit represents city i's growth rate of housing prices in year t. Column (1) of Table 5 displays the estimation results from Eq. (4) based on the fixed-effect model. We find evidence that housing price return is negatively associated with the engagement rate of entrepreneurship. The negative statistically significant coefficient for Housing price appreciationit indicates that housing price appreciation inhibits entrepreneurship. Columns (2) and (3) present the results estimated by using the one-step and two-step system GMM, respectively. The coefficients of Housing price appreciationit remain negative, which further confirm the crowding-out effect of rising housing prices on entrepreneurial activities.
+ δ5 Xit + ui + γt + εit
where Homeownershipit represents city i's aggregate home ownership ratio in year t. β1, 5 is the coefficient of interest. Column (1) of Table 6 displays the estimation results based on the fixed-effect model, and Columns (2) and (3) report results from system GMM. As expected, the coefficients of the interaction term Homeownershipit × Ln(Housing price)it remain broadly the same in signs and Table 6 Housing price and relaxation of credit constraints.
Table 5 Housing price returns and investment opportunities in housing market.
Housing price appreciation
(1)
(2)
(3)
Fixed-effects
One-step system GMM
Two-step system GMM
−0.0043⁎⁎ (0.0021)
−0.0104⁎ (0.0060) 0.7484⁎⁎⁎ (0.0597) 0.0000 (0.0000) 88.8 0.211 Yes 980
−0.0124⁎ (0.0064) 0.6976⁎⁎⁎ (0.0373) 0.0000 (0.0000) 88.8 0.114 Yes 980
140
140
Lag1(Entrepreneurship rate) Constant Hansen test: chi2 Prob > chi2 Control variables Observations R-squared Number of cities
−0.0140 (0.0235)
Yes 1120 0.1421 140
(5)
Homeownership × Ln (Housing price) Homeownership Ln(Housing price)
(1)
(2)
(3)
Fixed-effects
One-step system GMM
Two-step system GMM
0.0186⁎⁎⁎ (0.0063) −0.1112⁎⁎ (0.0484) −0.0093⁎⁎ (0.0039)
0.0248⁎⁎ (0.0114) −0.1555⁎ (0.0856) −0.0090 (0.0063) 0.6799⁎⁎⁎ (0.0540) 0.0963 (0.0678) 87.79 0.258 Yes 980
0.0187⁎⁎ (0.0083) −0.1259⁎ (0.0650) −0.0053 (0.0052) 0.7443⁎⁎⁎ (0.0535) 0.0385 (0.0440) 87.79 0.258 Yes 980
140
140
Lag1(Entrepreneurship rate) Constant Hansen test: chi2 Prob > chi2 Control variables Observations R-squared Number of cities
Note: Robust standard errors in parentheses. Control variables include Proportion of young people, Proportion of male people, Proportion of highly educated people, Proportion of small enterprises, Wage, Population density, and Tertiary industry as percentage to GDP. ⁎⁎⁎ p < 0.01. ⁎⁎ p < 0.05. ⁎ p < 0.1.
0.0609 (0.0375)
Yes 1120 0.1645 140
Note: Robust standard errors in parentheses. Control variables include Proportion of young people, Proportion of male people, Proportion of highly educated people, Proportion of small enterprises, Wage, Population density, and Tertiary industry as percentage to GDP. ⁎⁎⁎ p < 0.01. ⁎⁎ p < 0.05. ⁎ p < 0.1. 6
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respondent's employment status is self-employed or owner of private enterprise to define entrepreneurship. A productive area for further research can employ additional data to investigate the relationship between housing price and these two types of entrepreneurship. Second, we have not investigated whether the relationship between housing price and entrepreneurship changes in different financial and housing market conditions. Third, we do not consider the role of housing price in affecting entrepreneurial success, such as profits and survival. Despite these limitations, the study offers preliminary insight into the relationship between housing price and entrepreneurial engagement and implies several important areas for future research.
magnitudes in Columns (1)–(3). Throughout Columns (1)–(3), the regression results consistently indicate that high housing price and homeownership rate are both detrimental to entrepreneurship, whereas housing asset appreciation can relieve the negative effect of high prices for home owners by relaxing their financial constraints when embarking on entrepreneurial activities. 6. Conclusions A series of market-oriented housing reforms since the 1980s has successfully transformed China from a renter economy with a serious housing shortage and poor living conditions into a country with one of the highest rates of home ownership (Chen et al., 2019). This reform also triggers a major housing boom in China (Li and Wu, 2014). Empirical evidence had shown that rising property rights and housing price appreciation due to the housing reform of the 1990s enabled entrepreneurship via alleviative credit constraints and increased labor mobility (Wang, 2012). However, rising housing prices in post-reform urban China have had a generally negative effect on entrepreneurship because of the attractive returns of property augmented by the tradition of treasuring housing assets, thereby diverting interest from investment ventures (Li and Wu, 2014). The ambiguity of housing price effect in theory and the dominant position of housing in total household wealth in contemporary China inspire us to investigate the relationship and mechanisms between real estate prices and entrepreneurship. The present study presents new insights into the economic implications of housing price dynamics using data from CUHS for the period 2002–2009. Housing price has a negative effect on entrepreneurial activities, whereas the marginal effect of housing price decreases with the increase in housing prices. Theoretically, the effect of housing price on entrepreneurship works mainly through three mechanisms: (i) labor market opportunities, (ii) housing market opportunities, and (iii) relaxation of credit constraints. Evidence shows that the appreciation of housing prices has a positive and diminishing effect on employment rate and wage level (labor market opportunities). Housing price appreciation inhibits entrepreneurship (housing market opportunities), and housing assets appreciation relieves the negative effect of high prices on the entrepreneurial engagement of home owners (relaxation of credit constraints). These findings suggest that the negative impacts (housing market opportunities) should outweigh the positive impacts (labor market opportunities and relaxation of credit constraints). The result of the negative effect of housing price on entrepreneurship stands in contrast to findings from existing studies based on the US experience (Adelino et al., 2015; Corradin and Popov, 2015). This finding carries broad policy implications. First, credit markets may fail to provide sufficient financial resources for would-be entrepreneurs in China. This scenario calls for innovation in the financial and credit market to alleviate credit constraints regarding the increase in entrepreneurial ventures. Second, our results show the structural limits on investing which favor real estate ownership in China. Appropriate policies are necessary to avoid declining house prices and make real estate investments less attractive. Third, extracting equity from home owners' properties and repositioning them to other investments in China than in developed countries is less likely for homeowners. Thus, policymakers should make the cash-out option for housing wealth easily available when potential entrepreneurs invest in small businesses. Our study also has limitations. First, individuals are either pushed into entrepreneurship out of necessity or pulled into entrepreneurship out of opportunity (Kautonen and Palmroos, 2010; Xavier-Oliveira et al., 2015). These two types of entrepreneurship differ in many aspects, such as entrepreneurial purpose, requirements for the capacity of management, and entrepreneurial assets. As a result housing price may have different impacts on these two types of entrepreneurship. Unfortunately, we cannot determine such distinction due to limited data. In the CUHS, we can only use a one-question survey that asks whether a
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Mingzhi Hu is a lecturer at the Department of Economics of the College of Economics, Jinan University. His research focus is entrepreneurship, labor market, and urban and housing– related studies. He is particularly interested in examining and evaluating housing inequality and the effects of homeownership, housing price, and housing unaffordability. His research publications include articles in Technological Forecasting & Social Change, Small Business Economics, Journal of Housing Economics, Journal of Real Estate Finance and Economics and Real Estate Economics. Yinxin Su is a master student at the Business School of Central South University. Her research is mainly focused on urban renewal, urban governance, real estate economics and housing market dynamics. She has participated in several national fund projects. She has published several publications in academic journals in China. Wenping Ye is a lecturer at the Department of Business Administration of the School of Management, Jinan University. His research interests mainly include entrepreneurship and family business. He has participated in several national fund projects. Her research publications include articles in the top academic journals in China, such as Economic Research Journal, Management World and Nankai Business Review.
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