Urban Quality of Life and Production Amenity in Chinese Cities

Abstract

Do firms and households like the same cities? Can the quality of the business environment be compatible with the quality of life? We develop a new methodology based on the Rosen–Roback general equilibrium model for answering these questions and apply it to newly collected and manually matched data from Chinese cities. In order to overcome the challenges that arise when measuring the desirability of cities, we set up indexes of production amenities as well as the urban quality of life, and use wages and housing costs to estimate the implied prices of cities, i.e., the residents’ and firms’ willingness to pay for urban features. Our examination of the dynamic trends and influencing factors shows that firms and households differ in preference over urban features, many cities which are attractive to firms are unattractive to households, and vice versa. More specifically, in China, households prefer cities with better leisure conditions, entertainment, culture, and education resources, while firms are willing to allocate production in cities with less sunshine, more rainfall, better infrastructure, and fewer environmental restrictions. Our paper provides a unified perspective on the measurement of urban quality of life and production amenity. We also give policy suggestions to get a better grip on the functions and roles of cities; this is of practical significance for sustainable urban development.

1. Introduction

1.1. Background

Urban quality of life (QOL) refers to the comfort and convenience brought by urban characteristics to residents, such as a beautiful living environment, reliable social security, convenient transportation facilities, and varieties of recreation and entertainment services (Luo et al., 1995 [1]). It is the weighted sum of individuals’ living quality and is reflected in the living costs that residents are willing to pay for living in a city and enjoying its urban characteristics (Glaeser et al., 2001; Gabriel and Rosenthal, 2004 [2,3]). Therefore, different from residents’ QOL and people’s well-being, urban QOL focuses on the convenience and comfort brought by urban features, regardless of people’s feelings, such as happiness and satisfaction. As an important part of urban comprehensive competence and core value, urban QOL has attracted extensive attention during the transformation of China’s economy from high-speed growth to high-quality development. On the one hand, it measures the level of urban development, identifies cities that are lagging behind, and narrows the imbalance in development (Bauer, 1966 [4]). On the other hand, through its influence on location decisions such as population migration and site selection of firms, urban quality of life helps cities attract talents and capitals to improve cities’ sustainable development capacity (Mulligan, 2004; Lu, 2018 [5,6]). China has been experiencing rapid urbanization with more production and population reallocating to cities. However, there have been very few studies on both the urban quality of life and the quality of the business environment in a unified framework. This paper aims to fill in the gap by developing a new methodology based on the Rosen–Roback general equilibrium model and providing empirical evidence with manually collected and matched data from Chinese cities.

Corresponding to urban QOL (the convenience brought by urban characteristics to residents), this paper puts forward an index of urban “production amenity”, that is, the production advantages and efficiency introduced to firms by urban characteristics. Cities with high production amenities are characterized by good business environments, proper industrial structure, and fewer trade restrictions, creating conditions for enterprise production, thus improving their total production and efficiency. It is worth noting that the definition of “production amenity” is firstly constructed in this paper, while it reflects the firm productivity from a different standpoint. Though Glaeser (2010, 2011), Zheng et al. (2011), and Albouy (2013, 2016) used similar measures such as “firm productivity”, “trade productivity” and “local productivity” to represent the urban business environment [7,8,9,10,11], our definition of “production amenity” is broader. Unlike “total factor productivity (TFP)”, production amenity examines the production advantage and the output increase brought by urban characteristics rather than production factors. For this reason, to distinguish these two different concepts, “urban production amenity” is used herein to describe the production conditions and production efficiency provided by urban characteristics.

Both urban QOL and production amenity are important variables in urban development that represent the amenity urban features bring to residents and firms for living and producing, respectively. Since residents and firms are the main subjects of a city, improving their convenience and giving them better living and production conditions is the goal of urban development. Through analyzing them, we can get a better grip on the function and roles of cities and develop a better understanding of whether firms and residents have different preferences for urban features. In addition, we give a theoretical basis for the subsequent discussion of the trade-off between urban “livability” and “production suitability”, which is of practical significance to study the sustainable development path of cities.

We develop a new methodology based on the Rosen–Roback general equilibrium model to measure urban QOL and production amenity simultaneously. We focus on wages and housing costs to calculate the implied prices of urban features for both residents and firms in a unified model, and further investigate the dynamic trends and influencing factors of urban QOL and production amenity with Chinese metropolitan data. We reveal the possible factors that may lead to incomparability between urban QOL and production amenity in many cities and provide policy implications for sustainable urban development.

1.2. Literature Review

To date, thousands of publications have studied the urban quality of life and they are mainly divided into two streams. One is the quantification and the measurement. Most scholars combine objective urban environments with subjective personal feelings to establish a multi-dimensional index system, using comprehensive indexes to measure the urban quality of life. Galbraith (1958) first proposed the concept of “Quality of Life” in The Affluent Society, regarding it as people’s comfort and convenience of living, as well as spiritual enjoyment and pleasure [12]. Bauer (1966) then took the quality of life as an indicator to measure the degree of social development [4]. From 1970, western developed countries began to discuss the connotation and definition of urban quality of life and gradually formed some authoritative indicator systems (shown in

Table 1. Representative Index and Influencing Factors of Urban QOL in Existing Studies.

Panel A: Representative Index of Urban QOL (Domestic and Abroad)
Index	Institution	Year	Indicators
Gross National Happiness (GHI)	Kingdom of Bhutan	1970s	4 aspects: good governance, economic growth, cultural development, environmental protection (Zurick, 2006) [32]
Human Development Index (DHI)	United Nations Development Program	1990	3 indicators: life expectancy at birth, adult literacy and comprehensive enrolment rate, real per capita GDP (UNDP, 1990) [33]
National Happiness Index (NHI)	Kahneman, Krueger	2006	4 aspects: social health index, social welfare index, social civilization index, ecological environment index (Kahneman, Krueger, 2006) [34]
Better Life Index (BLI)	Organization for Economic Cooperation and Development (OECD)	2013	11 areas: housing conditions, household income, work, community environment, education, natural environment, civic engagement, health, life satisfaction, safety, work-life balance (OECD, 2013) [35]
China’s Livelihood Development Index	National Bureau of Statistics of China	2011	6 indicators: economic development, improvement of people’s livelihood, social progress, ecological civilization, scientific and technological innovation, public evaluation (Tang, 2011) [36]
Quality of Life Index in Chinese Cities	Chinese Academy of Social Sciences, Capital University of Economics and Business	2011	16 indicators: income status, income expectation, cost of living, human capital, medical security, security, the pace of life, life convenience, income level, living improvement, cost of living, human capital, social security, living convenience, ecological environment, perceived income gap (Zhang et al., 2011) [37]
China balanced Development Index	China Economic and Social Data Research Center, Tsinghua University	2019	4 aspects: economy, society, ecology, people’s livelihood (Xu et al., 2019) [38]
Panel B: Influencing Factors of Urban QOL in Past Studies
Studies	Objects	Year	Main Factors Influencing Urban QOL
Gabriel et al. (2003)	U.S. cities	2003	Infrastructure, less traffic congestion, and less air pollution [3].
Rappaport (2007)		2007	Climate condition [25].
Albouy (2008)		2008	Climate, safety, recreational facilities, and schools [26].
Berger et al. (2008)	Russian cities	2008	Health care, favorable climate, low crime rate, and racial conflict [39].
Kahn (2010)		2010	Less crime, air pollution, and commuting costs [40].
Zheng et al. (2011)	84 Chinese cities	2011	Education resources, a green environment, and less congestion [9].
Albouy (2013)	Canadian metropolitans	2013	Climate and culture atmosphere [10].
Albouy (2016)	U.S. metropolitans	2016	Coastal, sunny, warm cities and education resource [11].
Xiao (2016)	Provincial capitals in China	2016	Less environmental pollution [41].
Wang et al. (2019)	Tianjin	2019	The distance to CBD, better urban service facilities, and environment [21].
Barreira (2020)	Portugal	2020	Geographical location, population size and density, and the dependence on government public expenditure [22].

panel A). Therefore, urban QOL is a complex multidimensional concept with extensive connotations, covering all aspects of residents’ lives, and it is usually measured by a comprehensive evaluation index of social and economic development. Such a method is mostly subjective and used in sociological and psychological research, such as by Zhang et al. (2019), Patil (2020), and Zhang et al. (2021) [13,14,15]. However, in urban economics, a hedonic model is usually used to quantify the implicit value of QOL based on housing price and wage level. Rosen (1979) was the first to use wages to calculate the implicit price of QOL according to the utility and the profit maximization of consumers and producers [16]. Roback (1982) then added housing prices into the model to establish a spatial equilibrium system and reflect the intrinsic value of QOL with wage and housing price [17]. Blomquist et al. (1988) used cross-sectional data from 185 metropolises in the U.S. to study the relationship between households, enterprises, and city structure, and found that cities compensate households with low quality of life for higher wages or lower land rent [18]. Zhou (2009) empirically analyzed Chinese cities from 1999 to 2006 and concluded that urban livability poses a significantly higher impact on wages and housing prices in the east than in the western part of China [19]. Albouy (2013) measured the differences in the quality of life and productivity in metropolitan areas of Canada and found that Canadian residents are more concerned about the climate and cultural environment. He later added tax rates and non-traded local goods to the general equilibrium model, and found that the coastal, sunny, warm cities abundant in educational resources are usually most productive and valuable in the U.S. (Albouy, 2016) [10,11]. Emilio et al. (2014) measured the QOL in Italian cities and evaluated their compensating differentials in climate, environment, service, and society [20]. Wang et al. (2019) measured the QOL in the Chinese city Tianjin and concluded that higher urban QOL is correlated with a shorter distance from the residential place to the central business district and the better urban facilities and environment [21]. Barreira (2020) analyzed the urban QOL in Portugal, and found that the geographical location, population size and density, and the dependence on government public expenditure significantly affected the urban QOL [22]. Chen (2019) and Shi (2021) quantified and ranked the QOL in Chinese cities based on the Rosen–Roback model using both the China Urban Household Survey Data and the 1% Census Data in 2005 [23,24]. Different from previous literature, our paper uses the updated data from 2013 for quantitative analysis and expands the data to a 2005–2018 panel data to discuss the evolution of urban QOL. In addition, we construct the production amenity corresponding to urban QOL and calculate both in a unified model, providing a theoretical basis for studying the trade-off relationship between city livability and production suitability.

Another stream of research discusses the impact of QOL on urban development and holds that QOL represents urban livability and affects the decision-making of population migration and site selection of firms. Good urban QOL attracts high-tech industries and talents, brings human capital and technological innovation, thereby promoting urban development and competitiveness (Lu, 2018 [6]). Glaeser et al. (2001) brought real estate developers, labor force, and urban livability into the Rosen–Roback model and put forward the “compensating differential”, concluding that urban livability is positively correlated with housing prices and negatively correlated with real wages in open economies [2]. Since then, scholars have conducted empirical analyses of this theory, and believe that climate, safety, recreational facilities, and schools have a great influence on urban QOL, while the negative effects of high density in big cities can be compensated by their higher livability, such as abundant resources and opportunities (Rappaport, 2007; Albouy, 2008 [25,26]). Zheng et al. (2011) found that the balance between housing costs and land prices affects QOL and productivity, and suggested that high-skilled laborers are willing to pay higher living costs for conveniences such as educational resources, a green environment, and less traffic congestion [9]. Zong (2015) analyzed the relationship among urban amenities, housing price, and wage in 35 megalopolises in China, and found that improving urban amenities attracts laborers and expands city size [27]. Xiang and Yang (2017) discussed the impact of city amenities on urban population growth in 213 cities in China and found that high wage is the main reason for rising urban populations, and QOL and living cost are important factors in population movement and location choice [28]. Zhang and Fang (2019) argued that artificial comfort and natural comfort are important in attracting labor forces. In addition, they showed relatively high heterogeneity among labor forces of different ages and education levels [29]. Shen and Zhang (2021) measured the income level, QOL, and population concentration in Chinese cities, and tested the impact of the gap in income level and QOL on city size [30]. Goerlich (2021) ranked the QOL in Spanish cities and held that high-quality life attracts human capital, promotes economic growth, and people’s well-being, and shows higher heterogeneity in the relationship between the QOL and the size of the urban population scale [31]. In Table 1 panel B, we have shown the influencing factors of urban QOL in some of the existing studies.

Compared with existing literature, we extend the measure of urban QOL to construct the first-ever measure of production amenity, the value that considers the amenities in a metropolitan area. Although there is a great number of quantitative studies on firm productivity, which usually measure the total factor productivity (TFP). The TFP does not accurately explain the production amenity or the production suitability of cities, whereas this paper will focus on the urban features instead of production factors to reflect the production efficiency of firms as well as cities. In addition, few studies use a hedonic model, the method quantifying urban QOL, to measure production amenity. Therefore, we are the first to build a unified model measuring both urban QOL and production amenity, analyzing the convenience urban characteristics bring to residents and firms.

To sum up, the existing studies on urban QOL are developing, but they have several limitations. First, most studies build a subjective index system or questionnaire survey to quantify urban QOL. Second, none of the studies have measured urban production amenity in Chinese cities, that is the production convenience brought by urban features. Third, existing studies on urban QOL in China mostly take small samples of major cities or metropolitan areas, while a large number of small and medium-sized cities are barely examined.

In this paper, we construct measurements of urban QOL and production amenity based on the Rosen–Roback general equilibrium model and apply them to data from Chinese cities to analyze the dynamic trends and influencing factors. This paper makes contributions to the related literature as follows: (1) we adopt a general equilibrium model and use observable price variables to quantify urban QOL, which has a reliable economic theoretical basis and is more objective and effective; (2) we propose a concept of urban “production amenity” corresponding to urban QOL and quantify it using a hedonic model. Therefore, we measure the convenience for both residents for living and firms for producing in a unified model; (3) we manually collect data from 111 cities above the prefecture-level with different economies and population sizes (According to the 2013 China Household Income Survey, the samples are distributed in 15 provinces in 126 cities. After deleting missing values and outliers, there are 111 sample cities around the country. A spatial map of these 111 cities is shown in Appendix D). We compile and match up micro-information on individuals’ income (e.g., household registration, education level, work experience) and housing information (e.g., floor, orientation, age) with macro-city features, and provide a large sample size. Moreover, we expand the data to a 2005–2018 panel data to discuss the evolution and influencing factors on urban quality of life and production amenity.

The remainder of this paper is organized as follows: Section 2 establishes the models and describes the variables; Section 3 calculates and analyzes urban QOL and production amenity; Section 4 extends the cross-sectional data into panel data from 2005–2018 for further analysis and discussion; Section 5 explores the urban factors that affect urban QOL and production amenity, and gives suggestions based on the results; Section 6 concludes.

2. Methods

2.1. Model

We establish the model based on Rosen’s (1979) and Roback’s (1982) [16,17] work, and incorporate the framework of Albouy (2008, 2013, 2016) [10,11,26]. Assume that residents in city j consume tradable goods x (price normalized to 1) and non-tradable local goods y (priced at $p^j$, measured by housing cost), and firms produce goods using land L (priced at $r^j$ and is immovable and homogeneous within the city), capital K (completely movable and supplied at a fixed price $\overline{\iota }$) and labor N (flows freely among cities, providing one unit of labor and getting wage income ${\omega }^j$). Resident N also obtain non-labor income R and I generated by the land and capital, which is irrelevant to urban characteristics. Thus, the total income of residents is:

$$m^j={\omega }^j+R+I$$

and a personal income tax $\tau \left(m^j\right)$ is paid in a lump sum. Cities are different in urban quality of life $Q^j$, production amenity of traded goods $A_X^j$ (urban features such as geographical location, openness, traffic construction, road density, communication and logistics facilities, and relevant national policies that may affect productivity), and production amenity of local goods $A_Y^j$ (urban features such as industrial structure, technology preference, management system, and per capita income that affect productivity). Furthermore, all of these three depend on a vector of urban features $Z^j=\left(Z_1^j,\dots ,Z_K^j\right)$. Therefore, under the general equilibrium conditions, the prices ($r^j,{\omega }^j,p^j$) are correlated with urban variables ($Q^j,A_X^j,A_Y^j$), so that the urban QOL and production amenity can be accurately identified when the prices are obtainable.

In an open economy, residents and firms have different preferences for the urban features of each city j, and through free mobility and site migration decisions, they maximize their utility or profit. For residents, their preference is expressed by the utility function $U\left(x,y;Q^j\right)$, and the consumption cost to obtain utility u is measured by the expenditure function:

$$e(p^j,u;Q^j)\equiv mi{n}_{x,y}\left\{x+p^{j}y:U\left(x,y;Q^j\right)\ge u\right\}$$

(1)

Since the residents’ preferences for different cities have no differences, they obtain the same utility ${\overline{u}}^{*}$ when the economy is balanced. Therefore, firms in cities with low livability or high living cost should provide higher wages to workers:

$$e(p^j,\overline{u};Q^j)=m^j-\tau \left(m^j\right)$$

For firms, the perfect competition is reached when the economy is in equilibrium, and the production functions are $X=A_X^j{F}_X\left(L_X,N_X,K_X\right)$ and $Y=A_Y^j{F}_Y\left(L_Y,N_Y,K_Y\right)$, and the unit cost for producing the tradable good x is:

$$c_X\left(r^j,{\omega }^j,\overline{\iota };A_X^j\right)\equiv mi{n}_{L,N,K}\left\{r^{j}L+{\omega }^{j}N+\overline{\iota }K:X=1\right\}=c_X\left(r^j,{\omega }^j,\overline{\iota }\right)/A_X^j$$

(2)

Similarly, we have the unit production cost of local good $c_Y$. In perfect competition, the profit of a firm is zero, so under a certain output price, cities with high production suitability will have higher productivity and output, reflected in the higher production costs. Therefore, from the zero-profit condition, we have: $\frac{c_X\left(r^j,{\omega }^j,\overline{\iota }\right)}{A_X^j}=1,c_Y\left(r^j,{\omega }^j,\overline{\iota }\right)/A_Y^j=P^j$.

By logarithmic linearizing Equations (1) and (2), the percentage change between the variable $z^j$ of city j and the national average $\overline{z}$ is expressed by ${\widehat{z}}^j=dln{z}^j=d{z}^j/\overline{z}\cong \left(z^j-\overline{z}\right)/\overline{z}$. In this way, the following first-order conditional equations can be obtained from the free mobility condition of residents (i.e., no difference in the preference for cities), and the zero-profit condition of firms in the equilibrium:

$${\widehat{Q}}^j=-s_{\omega }\left(1-{\tau }^{\prime }\right){\widehat{\omega }}^j+s_y{\widehat{p}}^j$$

(3)

$${\widehat{A}}_X^j={\theta }_L{\widehat{r}}^j+{\theta }_N{\widehat{\omega }}^j$$

(4)

$${\widehat{A}}_Y^j={\varphi }_L{\widehat{r}}^j+{\varphi }_N{\widehat{\omega }}^j-{\widehat{p}}^j$$

(5)

where the “^” represents the percentage change of the variable relative to the national average. The explanatory variables ${\widehat{r}}^j$, ${\widehat{\omega }}^j$ and ${\widehat{p}}^j$ represent the percentage change of land rent, wage income, and housing cost. The coefficient $s_{\omega }$ is the ratio of residents’ labor income, $s_y$ is the ratio of the expenditure on local goods y, ${\theta }_L$ and ${\theta }_N$ are the shares of land and labor costs for firms to produce tradable goods x, and ${\varphi }_L$, ${\varphi }_N$ denote the shares of land and labor costs used to produce the local non-tradable good y.

The above equations show that the relative value of urban QOL and production amenity is indirectly measured by the willingness-to-pay of residents or firms: Equation (3) shows that the urban QOL ${\widehat{Q}}^j$ is reflected in the higher living cost $s_y{\widehat{p}}^j$ that residents are willing to pay than their nominal income $s_{\omega }\left(1-{\tau }^{\prime }\right){\widehat{\omega }}^j$, and this high living cost compensates for the urban livability. Equation (4) shows that the production amenity of traded goods ${\widehat{A}}_X^j$ is reflected in the labor cost ${\theta }_N{\widehat{\omega }}^j$ and land cost ${\theta }_L{\widehat{r}}^j$ that firms are willing to invest in a certain city to compensate for the production advantages provided by urban features. Equation (5) suggests that the production amenity of local goods ${\widehat{A}}_Y^j$ is reflected in the higher labor cost ${\varphi }_N{\widehat{\omega }}^j$ and land cost ${\varphi }_L{\widehat{r}}^j$ that firms are willing to pay than the price ${\widehat{p}}^j$. In this way, the city variables ${\widehat{Q}}^j$, ${\widehat{A}}_X^j$ and ${\widehat{A}}_Y^j$ can be calculated from prices ${\widehat{\omega }}^j$, ${\widehat{p}}^j$ and ${\widehat{r}}^j$. It is assumed that the production suitability for local goods in each city is the same, i.e., ${\widehat{A}}_Y^j=0$, so we have the expressions of ${\widehat{Q}}^j$ and ${\widehat{A}}_X^j$ as follows by assigning parameters to the formula (see Appendix A for details):

$${\widehat{Q}}^j=0.3{\widehat{p}}^j-0.67{\widehat{\omega }}^j$$

(6)

$${\widehat{A}}_X^j=0.05{\widehat{p}}^j+0.88{\widehat{\omega }}^j$$

(7)

In this way, when the differentials of wage income ${\widehat{\omega }}^j$ and the housing price ${\widehat{p}}^j$ of each city can be observed, we have the differentials for the urban livability and production suitability, to calculate their coupling coordination degree and to make a horizontal comparative analysis.

2.2. Regressions of Wage Differentials and Housing Costs Differentials

According to Equations (6) and (7), the hedonic price model is first used to estimate the differentials of wages ${\widehat{\omega }}^j$ and housing costs ${\widehat{p}}^j$.

We divide variables affecting wage differentials into individual characteristics $X_i^j$ (such as education level, work experience, working industry, gender, and other micro factors) and urban characteristics $Z_j$ (such as natural environment, infrastructure, medical education, and other macro factors). By referring to Mincer’s earning equation (Mincer 1958 [42]), a semi-log hedonic price model is established as follows:

$$ln{\omega }_i^j={\beta }_0+{\beta }_1{Z}_j+{\beta }_2{X}_i^j+\epsilon$$

(8)

Among them, the dependent variable ${\omega }_i^j$ is the wage income, the independent variables $Z_j$ and $X_i^j$ are the urban and individual characteristics, and their coefficients represent the percentage change in wage when a certain urban characteristic $Z_j$ changes one unit while other characteristics remain unchanged. Here, the wage differential is denoted by the coefficient of urban characteristic ${\beta }_1$, which represents the wage growth rate introduced by urban characteristics, namely, the wage changes that an individual is willing to give up or accept in pursuit of one unit of a certain urban characteristic.

Similarly, variables affecting housing costs are also divided into housing characteristic $Y_i^j$ (such as house area, house age, floor, number of rooms, orientation, and other micro factors), and urban characteristic $Z_j$:

$$ln{p}_i^j={\gamma }_0+{\gamma }_1{Z}_j+{\gamma }_2{Y}_i^j+ϵ$$

(9)

The dependent variable $p_i^j$ is the housing price, and independent variables $Z_j$ and $Y_i^j$ are the variables of urban and housing characteristics. The housing cost differential is denoted by the coefficient ${\gamma }_1$, which represents the increasing rate of sale price introduced by urban characteristics, that is, the change in the housing price that individuals are willing to give up or accept in pursuit of one unit of a certain urban characteristic.

Through regressions, the wage differentials ${\widehat{\omega }}^j$ and housing cost differentials ${\widehat{p}}^j$ among cities are calculated (see Appendix B for detailed calculation and results). It can be observed that almost all small cities with a population of no more than 3 million have lower wages and lower housing costs than the average level, while megalopolises with a population of over 5 million generally have higher housing prices, especially in provincial capitals and eastern cities, where housing prices are more than double the average.

2.3. Variables and Data

In this paper, the wage income is calculated from the logarithm of hourly wages, obtained by dividing the annual wage by the number of working hours in a year. A total of eight variables are selected as the individual characteristic $X_i^j$, including gender, average education years, work experience, industry, marital status, nationality, household registration, and health condition. The housing price is calculated from the total listing price of second-hand houses, and nine variables are selected for the house characteristic $Y_i^j$, including the house age, house type, room numbers, living rooms, toilets, house area, orientation, floor, and decoration degree. The urban characteristics $Z_j$ are divided into three categories: natural features, social features, and cultural features, and 28 influencing factors are investigated, such as geographical conditions, infrastructure security, ecological environment, and medical education (see

Table 2. Variables and Data.

Explained Variable	Explanatory Variable
Individual characteristics $X_i^j$ (Data comes from the 2013 China Household Income Survey)
the logarithm of Hourly Wage	Gender, education year, work experience, work industry, marital status, nationality, household registration, health condition
Housing characteristics $Y_i^j$ (Data is collected and arranged from the Anjuke website by the author)
the listing price of second-hand housing	Age, room numbers, living rooms, toilets, area, orientation, building types, floors, decorations.
City characteristics $Z_j$ (Data comes from the 2013 China City Statistical Yearbook, the Environmental Statistical Yearbook, the Urban Construction Statistical Yearbook, the National Meteorological Administration and the Atmospheric Composition Analysis Group)
Natural features: Annual average temperature, Daily peak sunshine hours, Annual precipitation. Social features: Buses per 10,000 people, Urban road area per capita, Number of mobile phone users, Number of internet broadband access users, Water penetration rate, Gas penetration rate, Drainage pipeline density, Park green area per capita, Green coverage in the built area, Industrial wastewater discharge, Industrial sulfur dioxide emissions, Centralized sewage treatment rate, Harmless waste treatment rate, Proportion of urban unemployed, Population density, Employment rate 1, PM2.5 concentration. Cultural features: Numbers of theaters, Library collections per 100 people, Number of hospitals, Hospital beds per 10,000 people, Doctors per 10,000 people, Numbers of college students per 10,000 people, Numbers of teachers in universities per 10,000 people, Proportion of employees in the tertiary industry.

¹ The employment rate is calculated by the ratio of the number of employed persons (including urban units, private and individual employed persons) to the total number of employed persons at the end of the year.

).

The data calculate ${\widehat{\omega }}^j$ from the 2013 China Household Income Project Survey (CHIP2013), which involves urban households, rural households, and the floating population (The sample of CHIP2013 comes from the large sample base of the 2013 Routine Household Survey of Urban-rural Integration of National Bureau of Statistics. The CHIP project team stratified according to east, middle and west, and extracted CHIP samples according to the systematic sampling method. Urban household refers to the household head with non-agricultural household registration. Rural household refers to the household head who has agricultural registered permanent residence and the registered permanent residence is located in the current township (street). Floating population refers to households whose heads have agricultural registered permanent residence and whose registered permanent residence is outside the current township (street). See “China Institute for Income Distribution—CHIP dataset (ciidbnu.org)” for details. (http://www.ciidbnu.org/chip/chips.asp?year=2013 (accessed on 3 August 2021))). All the questionnaires are included in the study objects, and full-time workers (working at least 15 days a month for least 6 months a year) aged 18–60 were selected. After eliminating missing values and outliers, 20,030 samples were taken from 111 prefecture-level cities in 14 provinces (cities) including Beijing, Shanxi, Liaoning, Jiangsu, Anhui, Shandong, Henan, Hubei, Hunan, Guangdong, Chongqing, Sichuan, Yunnan, and Gansu. The data calculate ${\widehat{p}}^j$ is taken from the Anjuke Website (see https://cs.anjuke.com/ (accessed on 10 August 2021) for details) using web crawler software. We collected prices and house characteristics of 7400 second-hand houses in 111 prefecture-level cities in the above 14 provinces (cities). New houses and renting houses were not included because new houses cannot be accurately priced before opening and the renting data is affected by factors such as the unfixed cohort, unstable lease terms, and unavailability of data. The data on urban characteristics come from the 2013 China City Statistical Yearbook, the China Statistical Yearbook of Environment, the China Statistical Yearbook of Urban Construction, the China Meteorological Administration, and the Atmospheric Composition Analysis Group of Dalhousie University (see http://fizz.phys.dal.ca/~atmos/martin/?page_id=140 (accessed on 12 August 2021) for details).

3. Measurement of Urban Quality of Life and Production Amenity

3.1. Measurement of Urban Quality of Life

The differentials of urban quality of life among cities ${\widehat{Q}}^j$ can be obtained by substituting the calculated ${\widehat{\omega }}^j$ and ${\widehat{p}}^j$ in Equation (6). Figure 1 is a scatter plot of ${\widehat{\omega }}^j$ and ${\widehat{p}}^j$, and the two lines respectively describe the price combination (${\widehat{\omega }}^j, {\widehat{p}}^j)$ when urban QOL ${\widehat{Q}}^j$ and production amenity ${\widehat{A}}_X^j$ are 0, that is, at the national average level. Among them, the oblique dotted line is the “iso-QOL line”, describing cities with national average urban quality of life ${\widehat{Q}}^j=0$. Its positive slope inferred from the free mobility condition of residents in the equilibrium (that is Equation (3)), $s_{\omega }\left(1-{\tau }^{\prime }\right)/s_y>0$, indicates how living costs (housing prices) increase with wage income to maintain the same real consumption level and willingness to pay. The vertical distance from each city to the iso-QOL line indicates the QOL differentials. To be specific, cities at the upper-left of the line have higher urban QOL than the average, and their residents have to pay a higher consumption premium than the wage income, indicating their positive willingness to pay for the city. Whereas cities at the lower-right have a lower-than-average urban QOL and their residents have a lower willingness to pay for urban features. In addition, the urban QOL in this paper, according to our definition in Equation (3), refers to the living cost that residents are willing to pay for urban features relative to their wage income. Therefore, cities with a larger gap between living cost (housing price) and wage income have a higher QOL, while cities with lower QOL have a similar wage income and housing cost.

From the measurements, 72 of the 111 sample cities have higher-than-the-average QOL, especially provincial capitals and municipalities. Among them, Beijing, Jinan, and Guangzhou are the top three, with QOLs more than 20% higher than the average. In addition, QOL in the economically strong provinces Shandong and Henan is generally high, and some small cities such as Dezhou, Puyang, Yantai, Luoyang, and Kaifeng perform well. However, Jiangsu, which is a developed province with higher wages and housing prices, has a poor urban QOL and only Nanjing and Xuzhou are in the top 20. Furthermore, the QOL in Shenzhen, a well-developed first-tier city, is not at the forefront either. This may be because the high wage level in Shenzhen lowers the housing price-income gap and lowers residents’ willingness to pay for the city.

Table 3. Calculation of Variables in Major Cities and Some Areas.

Main Cities	Observed Prices		City Attributes		City Description
	Wage	Housing Cost	Production Amenity	QOL	Aver. Population	ln(GDP)	Areas
Beijing	0.25	1.71	0.30	0.35	1288	19.00	16,411
Shenzhen	0.49	1.30	0.50	0.06	278	18.68	1997
Nanjing	0.21	1.04	0.24	0.17	637	18.09	6587
Guangzhou	0.13	1.01	0.17	0.21	818	18.72	7434
Suzhou	0.26	0.90	0.28	0.09	645	18.60	8488
Chengdu	0.09	0.66	0.11	0.14	1168	18.21	12,121
Jinan	−0.11	0.59	−0.07	0.25	608	17.69	8177
Wuhan	0.09	0.63	0.11	0.13	825	18.20	8494
Qingdao	0.04	0.45	0.05	0.11	768	18.11	11,282
Zhengzhou	0.04	0.43	0.05	0.11	763	17.83	7446
Hefei	0.05	0.42	0.07	0.09	708	17.54	11,445
Shenyang	−0.07	0.33	−0.04	0.14	724	18.01	12,980
Lanzhou	−0.15	0.28	−0.12	0.19	322	16.57	13,086
Taiyuan	−0.15	0.23	−0.12	0.17	365	16.96	6977
Changsha	0.04	0.25	0.05	0.05	659	17.97	11,816
Dalian	−0.04	0.23	−0.02	0.10	589	18.06	12,574
Kunming	−0.05	0.12	−0.04	0.07	544	17.22	21,012
Chongqing	0.04	0.01	0.03	−0.02	3337	18.55	82,374
The average by population 1							Obs.
Small Cities	−0.11	−0.21	−0.11	0.01	206	15.87	32
Medium Cities	−0.08	−0.11	−0.08	0.02	381	16.48	30
Large Cities	−0.04	0.07	−0.03	0.05	635	16.97	37
Megacities	0	0.35	0.02	0.11	1180	17.61	12
The average by GDP 2
Underdeveloped Areas	−0.14	−0.26	−0.13	0.01	304	15.62	34
Developing Areas	−0.08	−0.17	−0.08	0.00	482	16.40	38
Developed Areas	0.01	0.32	0.03	0.09	693	17.62	39

¹ Small cities have a population of less than 3 million; Medium cities have 3–5 million population; Large cities have a 5–8 million population; Megacities have a population of more than 8 million. ² Underdeveloped areas have GDP less than 100 billion; Developing areas have 100–200 billion GDP; Developed areas have GDP more than 200 billion.

lists the QOL results of some major cities and these sample cities are also classified according to urban population and the total economic aggregate. We find that areas with large populations and high GDP have a large gap in housing cost and income, thus a high QOL. Residents in these cities pay high costs for urban features, among which economic agglomeration, employment opportunities, medical and educational resources, and human capital in megacities have significantly improved residents’ quality of life, as some are even double that of other cities.

3.2. Robustness Test

3.2.1. Comparison with the Results Using the U.S. Parameters

We recalculated QOL using the U.S. parameters in Albouy (2016) and compared our results to analyze the robustness of the model. The results showed that the QOL differentials calculated using U.S. parameters are more volatile and the number of cities with QOL above the average reduced to 59. However, the ranking has not changed much, as the provincial capitals and municipalities are still at the forefront.

Figure 2 compares the results of QOL measured under the two parameters. It demonstrates that the QOL in medium or small-sized cities with a population of less than 5 million is higher in our results, and for large cities with a population of more than 5 million, the two results are roughly the same. Since the tax rate selected in our model is only 9%, much lower than the 36% of the U.S. parameters, the wage coefficient in Equation (3) is 20% higher in our result when calculating ${\widehat{Q}}^j$ and this leads to a lower value for the living cost residents are willing to spend compared to the U.S. results. Therefore, in most small and medium-sized cities that have a lower-than-the-average wage ${\widehat{\omega }}^j<0$, a higher wage coefficient will amplify ${\widehat{Q}}^j$ and makes the result larger, whereas in megacities where housing cost is much higher than the average compared to wage level, a large wage coefficient will narrow the housing price-income gap, thus reducing ${\widehat{Q}}^j$ and making the results similar to those using the U.S. parameters.

3.2.2. Comparison with Popular Rankings

To date, many institutions in China have led research on urban livability and have ranked urban QOL, comprehensive strength, and other similar aspects. We selected some popular rankings to compare and analyze the QOL rankings calculated in our study and carry out a robustness test.

Table 4. Correlations with QOL Rankings and Public Popular Rankings.

	Correlation	${\widehat{\mathit{Q}}}^{\mathit{j}}$	$\mathbf{A}\mathbf{l}\mathbf{b}\mathbf{o}\mathbf{u}\mathbf{y} {\widehat{\mathit{Q}}}^{\mathit{j}}$	Obs.
1	$\mathrm{Albouy}{\widehat{Q}}^j$	0.9571		111
2	2005 China Urban Quality of Life Report ①	0.7043	0.6901	47
3	Comprehensive Analysis of the Quality of Life in 287 Cities in 2006 ②	0.6023	0.5689	111
4	2013 Chinese Urban Competitiveness Blue Book ③	0.6005	0.4576	111
5	2019 Top 100 Cities in China ④	0.7668	0.6348	49

^① The “2005 China Urban Quality of Life Report” was released by the Beijing Summit of the China Urban Forum, as the result of an analysis and online survey of 100 cities in China by the Beijing International Urban Development Institute. It is the first research report on the quality of life in China. ^② The “Comprehensive Analysis of the Quality of Life in 287 Cities in 2006” was released by the Beijing Summit of the China Urban Forum. This ranking quantitatively analyzed 12 subsystems including consumer income, education and medical care, public safety, and environmental employment, and introduced an Internet public survey to enable citizens to rate the city where they live. ^③ The “2013 Chinese Urban Competitiveness Blue Book” was jointly released by the Chinese Academy of Social Sciences Institute of Financial Strategy, the Social Science Literature Publishing House, and the China Social Science Cities and Competitiveness Research Center. The sustainable competitiveness ranking of each city includes eight aspects including livability, business ability, harmony, ecology, knowledge, global scope, information, and culture. ^④ The “Top 100 Chinese Cities in 2019” was compiled by the Wharton Economic Research Institute. The cities with the top 100 GDP were selected as finalists, and then comprehensive rankings were conducted in terms of economic indicators, science, education, culture, and health.

compares the ranking of our ${\widehat{Q}}^j$ and some popular rankings and shows their correlations. The higher the correlation, the more consistent our result is with the popular rankings, and the more robust the results, while the lower the correlation, the greater the difference in rankings.

Table 4 (1) shows that the correlation between the ${\widehat{Q}}^j$ calculated here and Albouy ${\widehat{Q}}^j$ calculated by the U.S. parameters is 0.96, indicating that the two results are consistent except for some cities. Following this, we selected 47 samples from the “Report on the Quality of Life in Chinese Cities (2005)” to compare with the calculated rankings, resulting in a correlation up to 70% (Table 4 (2)). This demonstrates the consistency of our QOL rankings calculated from the housing price and wage differentials and public ranking. Due to the limited samples in 2005, this paper compares the QOL rankings of 287 cities in 2006 (Table 4 (3)). The correlation of 60% shows that after increasing sample size, the ranking changes slightly but still maintains a relatively concordant order. Next, to match the year of 2013, this paper compared the livability competitiveness in the “Blue Book of Urban Competitiveness” in 2013 (Table 4 (4)), and obtained a correlation of 60%. Finally, a comparison was made using the latest “Ranking List of China’s Top 100 Cities (2019)” (Table 4 (5)). Although the list emphasizes a city’s economic and comprehensive strength and does not analyze them from the perspective of urban QOL, urban economic indicators have a great influence on housing costs and wage levels, and soft economic indicators such as science, education, culture, and health are also important factors in the urban QOL. Therefore, compared with the city samples, the correlation of 77% shows that the QOL calculated here is closely related to the urban economic indicators, and demonstrates that housing price and wage level also have a great impact on the comprehensive strength of cities.

To sum up, the ranking of urban QOL calculated from the ${\widehat{\omega }}^j$ and ${\widehat{p}}^j$ is consistent with and more than 60% correlated with the popular ranking, resulting in robust results. Considering that subjective evaluations and personal feelings are contained in popular rankings, our ranking results are more objective as they use calculation methods derived from theoretical models and data.

3.3. Measurement of Production Amenity

Production amenity, as the advantage and convenience provided by urban characteristics to firms, reflects firm productivity and is embodied in the production costs that firms are willing to pay. In equilibrium, firms have zero profit under perfect competition, so higher production cost reflects higher output and production efficiency. The production amenity ${\widehat{A}}_X^j$ of each city is calculated by Equation (7) and is shown in Table 3. The diagonally downward dotted line in Figure 1 is the “iso-production amenity line” representing cities with average production amenity ${\widehat{A}}_X^j=0$. According to the zero-profit condition in Equation (4), the slope of this line is ${\varphi }_N-{\varphi }_L{\theta }_N/{\theta }_L<0$, indicates the land cost (calculated by housing cost) a firm needs to reduce to balance its wage level. The production cost in cities above this line is higher than the average, indicating higher production amenities in these cities. Most small and medium-sized cities are below this line, showing that cities with small populations have low labor costs and poor production amenities due to the lack of labor.

In the 111 sample cities, only 26 have higher-than-the-average production amenities, among which Shenzhen, Beijing, Suzhou, Nanjing, and Wuxi are the top 5 and 20% higher than the average (50% higher in Shenzhen). Most provincial capitals, municipalities, and cities in eastern provinces like Jiangsu and Guangdong have higher production conditions and amenities, whereas cities in the western provinces like Shanxi and Gansu have lower production amenities. This is consistent with the unbalanced development between the east and the west in China as developed cities with high GDP have a better production environment and convenience, thus higher productivity, while developing cities with low GDP have lower production amenities and productivity. It can also be seen from Table 3 that the production amenity in well-developed areas is above the average and is significantly higher than those in other areas. These cities provide urban characteristics that are conducive to production activities and bring high production efficiency. Moreover, production amenity has a positive relationship with population size and is significantly better in megalopolises with a population of over 8 million than in other cities. This is probably because a large population brings higher human capital and larger labor input, thus improving the production efficiency of firms.

Although both the urban QOL ${\widehat{Q}}^j$ and production amenity ${\widehat{A}}_X^j$ are calculated from the housing costs and wage incomes, production amenity is the sum of the two and reflected in the input costs of trade production factors, while the urban QOL is the difference between the two and reflected in the additional living cost that residents are willing to pay for a city relative to their wage income. By combining the two, we find that: firstly, there is no significant linear relation between them, as the correlation is only 0.09. Secondly, the production amenity in most cities is lower than the average, and only a few large-scale cities and megalopolises have higher productivity than the average, showing unbalanced production amenities among cities in China. Thirdly, cities, where both variables are lower than the average, are mostly small and medium-sized, as they have lower housing prices and wages, resulting in less input in firm production and lower willingness-to-pay for residents. Finally, high QOL accompanied by low production amenities is common in most cities with high housing prices and low wages, and this shows a higher willingness to pay for residents but insufficient input for firm production.

4. Characteristic Facts of Urban Quality of Life and Production Amenity

For better analysis of the distribution and evolution of urban QOL and production amenity, the data are simplified and expanded into the panel data of 281 prefecture-level cities from 2005 to 2018 for further discussion.

Considering that the data in the “2013 China Household Income Survey” cannot provide the detailed income and housing information of more than 300 prefecture-level cities in China in the last ten years, we directly employ macro-data from the urban level to calculate urban QOL and production amenity. Specifically, “the average wage of fully-employed workers in a city (yuan)” from the China City Statistical Yearbook is selected as wage income, and “the average selling price of commercial housing in a city (yuan/m²)” from the Macroeconomics and Real Estate Database of State Information Center (SIC) as the housing price. Moreover, the national annual data are set as the average to calculate the percentage change of both wage and housing cost relative to the average among cities, namely wage differential ${\widehat{\omega }}^j$ and the housing cost differential ${\widehat{p}}^j$.

Table 5. Descriptive Statistics of Simplified Panel Data.

	$\mathbf{Urban}\mathbf{Quality}\mathbf{of}\mathbf{Life}{\widehat{\mathit{Q}}}^{\mathit{j}}$				$\mathbf{Production}\mathbf{Amenities}{\widehat{\mathit{A}}}_{\mathit{X}}^{\mathit{j}}$
	2005	2010	2015	2018	2005	2010	2015	2018
Mean	−0.024	0.030	0.033	0.045	−0.157	−0.151	−0.143	−0.151
Obs. ①	277	281	281	281	277	281	281	281
# Positive Obs.	134	182	197	204	54	55	42	39
Max.	0.217	0.799	1.011	1.348	0.813	0.916	0.810	0.820
Min.	−0.471	−0.319	−0.362	−0.315	−0.609	−0.574	−0.835	−0.472
Top 5	Yichun Zhoukou Sanya Heze Xinzhou	Sanya Shenzhen Wenzhou Zhuhai Hangzhou	Shenzhen Sanya Dingxi Xiamen Zhuhai	Shenzhen Sanya Zhuhai Xiamen Dongguan	Beijing Guangzhou Shenzhen Shanghai Hangzhou	Shanghai Beijing Shenzhen Guangzhou Tianjin	Beijing Shanghai Shenzhen Tianjin Guangzhou	Beijing Shanghai Shenzhen Guangzhou Nanjing

^① In 2005, the data on wage income and housing prices were missing for some cities and the sample number was reduced to 277.

presents the statistical descriptions. In this paper, 281 prefecture-level cities in China were divided into seven regions: East China, Northeast China, North China, Central China, South China, Southwest China, and Northwest China, as detailed in Appendix D.

According to the simplified panel data, the values of urban QOL and production amenity are between −0.8 and 1.3, which are relatively balanced at the national level. The mean value of urban QOL is positive, showing that Chinese residents are willing to pay a certain living cost for their living cities. However, the average production amenity is negative, indicating that firms are less willing to pay for urban characteristics and these characteristics provide insufficient production efficiency. These panel data results are roughly the same as those previously calculated from cross-sectional data, and the nationwide distribution is relatively consistent. Therefore, we consider this simplified macro-data to be effective and suggest that they can replace the cross-sectional data for higher quality comprehensive research and analysis. A detailed calculation was made based on the years of 2005, 2010, 2015, and 2018, which represent the nodes of the 11th Five-Year Plan, the 12th Five-Year Plan, and the 13th Five-Year Plan respectively, to study the characteristic facts of the development of urban QOL and production amenity in Chinese cities.

4.1. Characteristic Facts of Urban Quality of Life

In recent years, the urban QOL has been significantly improved with a gradually widening gap among cities in China. This is mainly reflected in three aspects: the sample mean turns from negative to positive and increases gradually, the number of cities above the average is increasing, and the maximum value is increasing significantly. In 2005, the 277 sample cities had an average of only −0.024, a lower value with small differences among cities. In 2010, the average became positive, and in 2018, it reached 0.045 and 70% of cities were above the average, with Shenzhen, which had the highest QOL, being 1.3 times higher than the average, indicating that cities with high QOL are much better than other cities. However, the minimum value did not increase and remained 30% lower than the national average level, indicating that the gap among cities is still widening despite the improvement of urban QOL in recent years.

With regard to the distribution, the urban QOL in east and south China is higher than in the western and northern parts, among which the southeast coastal areas have the most remarkable development. Figure 3 shows the distribution of differences in urban QOL among cities in four years. Although the urban QOL in each city is alike, there are regional differences. In 2005, cities were similar in QOL, with Zhejiang and Fujian along the southeast coast only reaching the national average and beginning to improve from 2010. The differences among cities have shown up by 2018, with Shenzhen, Sanya, Zhuhai, Xiamen, and Dongguan among the top five, all of which are southern coastal cities. Alternatively, Inner Mongolia, Gansu has a lower urban QOL, indicating a correlation between urban QOL and the external geographical environment. Figure 4 depicts the evolution of urban QOL in seven regions of China. From 2005 to 2018, the urban QOL has improved as a whole, among which the southern region experiences the most significant improvement, rising linearly from −0.04 in the penultimate place in 2005 to over 0.2 in the first place in 2018. The urban QOL in the northwest part is not much improved and relatively backward, basically between −0.03 and −0.02. Therefore, the urban QOL is high in the east and south, low in the west and north, triggering a need to attach importance to and strengthen the urban QOL in western cities.

4.2. Characteristic Facts of Production Amenity

In recent years, urban production amenity has changed slightly but developed unevenly according to the results, and Chinese firms have a stable willingness to pay for urban features. Less than 20% of cities have higher-than-the-average production amenities, while others remain at a lower level. Although the average and maximum values of the variables do not fluctuate greatly from 2005 to 2018, the minimum increased gradually while the number of cities with high production amenities gradually decreased, indicating the unbalanced development of the production efficiency of firms with a narrowing gap among cities. In addition, regions with high production amenities are all well-developed first-tier cities, and Beijing, Shanghai, Shenzhen, and Guangzhou rank among the top five continuously in recent years.

As for the distribution (Figure 5), Jiangsu, Zhejiang, and the Pearl River Delta Region have high production amenities, and some cities in Northeast China and Inner Mongolia, such as Dalian, Daqing, Shenyang, and Baotou, also performed well. In contrast, firms in the central region have a lower willingness to pay. In 2005, there were large differences and polarization in production amenities among cities. By 2018, this gap narrowed. Although the southeast cities remain ahead of others, the production amenity in most cities fell within the range of −0.2 to −0.1, a slight rise compared to 2005. Figure 6 is a diagram of production amenity by regions and shows that, except for cities in the southwest, the firms’ willingness to pay is relatively stable in recent years. This may be because cities begin to attach importance to urban QOL, thus resulting in fewer production costs and stagnant production. Interestingly, the production amenity in the north and northeast has declined, while the urban QOL in these areas has improved. In addition, the production amenity in the south, where the urban QOL improved most significantly, has declined in recent years. On the contrary, the production amenity in the southwest and northwest has improved but the urban QOL has declined slightly. On the whole, the production amenity in the eastern and southern cities is slightly higher than that in other regions, while that in the central and northeast regions is relatively lacking. Therefore, the central and northeast regions need to focus on increasing inputs in production and improving production efficiency.

To sum up, China has experienced a significant improvement in urban QOL and the gaps among cities have expanded in recent years, showing higher values in the east and south but lower values in the west and north. The production amenity is relatively stable and develops unevenly. It is higher in Jiangsu, Zhejiang, and the Pearl River Delta Region, but lower in the central region, with a narrowing gap among cities. Finally, it has been found that the production amenity in areas with improved urban QOL is low or stagnant, while the urban QOL in cities with better production conditions declines, indicating a degree of substitution relation between the urban QOL and production amenity.

5. Influencing Factors

This paper uses a hedonic price model and urban characteristic vector $Z^j=\left(Z_1^j,\dots ,Z_K^j\right)$ to analyze the influencing factors affecting both the urban quality of life and production amenity, to figure out different preferences of urban features for residents and firms, and to study urban features and functions that are conducive to the sustainable and coordinated urban development under a united theoretical framework. In addition, we classify sample cities into different types by economic aggregate and population size to better explore urban development in terms of city heterogeneity. After the correlation test of independent variables, three variables with high correlation (correlation coefficient of more than 0.8) including the number of Internet broadband, the number of beds in hospitals, and the number of college students were eliminated, retaining 25 urban characteristics. The determination coefficient R² showed that these characteristics explain more than 80% of the changes in urban QOL and production amenity, among which the natural environment, transportation, communication, green environment, leisure and entertainment, culture, and education are closely related to urban QOL and economic development.

5.1. Influencing Factors of Urban Quality of Life

Table 6. Regression Results of Influencing Factors.

Urban Features			$\mathbf{Urban}\mathbf{QOL}{\widehat{\mathit{Q}}}^{\mathit{j}}$			$\mathbf{Production}\mathbf{Amenity}{\widehat{\mathit{A}}}_{\mathit{X}}^{\mathit{j}}$
			Full-Samples	Sub-Sample by Population Size		Full-Samples	Sub-Sample by Population Size
				Large Cities	Small or Medium Cities		Large Cities	Small or Medium Cities
			(1)	(2)	(3)	(4)	(5)	(6)
Natural Features	Average annual temperature	X1	0.045 ***	0.042	0.063 ***	0.02	0.048	0.008
			(0.014)	(0.037)	(0.016)	(0.019)	(0.03)	(0.018)
	Sunshine duration	X2	0.19 ***	0.165 *	0.152 *	−0.181 **	−0.163 **	−0.27 *
			(0.049)	(0.084)	(0.077)	(0.069)	(0.077)	(0.133)
	Precipitation rate	X3	−0.087 ***	−0.079 ***	−0.106 ***	0.073 ***	0.064 ***	0.071 **
			(0.013)	(0.019)	(0.022)	(0.018)	(0.021)	(0.034)
Social Features	Bus numbers	X4	−0.011	−0.003	−0.023 **	0.031 ***	0.035 ***	0.044 ***
			(0.007)	(0.011)	(0.009)	(0.011)	(0.012)	(0.016)
	Mobile phones numbers	X6	0.017 *	−0.013	0.017	0.03 **	0.035	0.026
			(0.01)	(0.015)	(0.012)	(0.013)	(0.022)	(0.021)
	Water penetration rate	X8	0.149 ***	0.088 *	0.249 ***	0.015	0.148 **	0.053
			(0.036)	(0.048)	(0.08)	(0.058)	(0.07)	(0.105)
	Drainage pipe density	X10	0.002	−0.037 **	0.008	0.039 ***	0.064 ***	0.041 ***
			(0.007)	(0.015)	(0.008)	(0.009)	(0.019)	(0.014)
	Green space per capita	X11	0.073 ***	0.089 ***	0.056 ***	−0.031 *	−0.046 *	−0.027
			(0.013)	(0.019)	(0.018)	(0.018)	(0.026)	(0.023)
	Wastewater discharge rate	X13	−0.003	−0.01	−0.011 *	0.012 **	0.041 ***	0.013
			(0.005)	(0.013)	(0.006)	(0.006)	(0.012)	(0.008)
	Sewage treatment rate	X15	0.113 ***	0.149 ***	0.122 ***	−0.119 ***	−0.177 ***	−0.099 ***
			(0.013)	(0.03)	(0.017)	(0.021)	(0.044)	(0.021)
	Population density	X18	0.021 ***	0.051 ***	0.023 ***	−0.012	−0.023	−0.015
			(0.006)	(0.013)	(0.008)	(0.008)	(0.017)	(0.013)
	Employment rate	X19	−0.031	0.165 ***	−0.026	0.172 ***	0.216 ***	0.156 ***
			(0.027)	(0.033)	(0.018)	(0.02)	(0.048)	(0.023)
Cultural Features	Cinema numbers	X21	0.011 **	0.024 ***	0.016 **	0.02 ***	0.028 **	0.009
			(0.005)	(0.006)	(0.006)	(0.005)	(0.011)	(0.007)
	Library collections	X22	0.022 ***	0.026 *	0.001	0.003	−0.02 *	0.008
			(0.008)	(0.014)	(0.012)	(0.008)	(0.011)	(0.015)
	Hospital Health Center numbers	X23	0.001	0.034	−0.01	−0.018 *	−0.068 **	−0.014
			(0.007)	(0.021)	(0.008)	(0.011)	(0.025)	(0.013)
	Doctor numbers	X25	0.021	−0.01	0.035 *	−0.056 ***	−0.027	−0.057 **
			(0.015)	(0.018)	(0.018)	(0.016)	(0.025)	(0.027)
	College teacher numbers	X27	0.019 ***	−0.005	0.025 ***	0.013 **	0.038 **	0.005
			(0.004)	(0.01)	(0.005)	(0.006)	(0.014)	(0.006)
	Proportion of employees in the tertiary industry	X28	0.111 ***	0.091 ***	0.13 ***	0.004	0.039	−0.029
			(0.024)	(0.026)	(0.028)	(0.033)	(0.031)	(0.037)
R2			0.89	0.868	0.918	0.827	0.868	0.851
Obs.			111	49	62	111	49	52

Note: The employment rate (%) (X19) is calculated by the ratio of the number of employed persons (including urban units, private and individual employed persons) to the total number of employed persons at the end of the year. The population of large cities is more than 5 million, and that of small and medium-sized cities is less than or equal to 5 million. Robust standard error in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.

(1) shows the regression results of some characteristics affecting urban QOL. Firstly, the natural environment has a significant impact on urban QOL. A warm climate, sufficient sunshine, and less rainfall can effectively improve the living quality, and the coefficient of sunshine duration reaches up to 0.19. Secondly, residents have a high demand for urban features related to their daily lives, such as the water usage, green space per capita, education, and the development of service industry, with the most significant impacts on water penetration rate (0.149), sewage treatment rate (0.113) and the proportion of tertiary industry personnel (0.111). In addition, strengthening the cultural development and education level will also help improve the urban QOL. On the contrary, residents are less concerned about the public characteristics, such as bus number, the density of drainage pipes, and the discharge amount of wastewater. Finally, it is also found that the population density can improve urban QOL. Although the high-density population will cause congestion and traffic jams, China has remained an overpopulated environment for a long time, and residents are accustomed to communicating with others and prefer a “human interaction” life. Therefore, a certain degree of population aggregation brings convenience and emotional interaction, thus enhancing the living quality level.

A heterogeneity analysis and robustness test were carried out on urban subsamples according to the population size and economic aggregate. In cities with different population sizes, the impact of city features on urban QOL is similar to the previous findings (Table 6 (2, 3)), as the natural environment has a significant impact (especially less rainfall) and improving water usage, green space per capita and cultural education can improve the QOL. Besides, in large cities with a population of over 5 million, the employment rate is of great importance, with a maximum coefficient of 0.165, and is significant at a level of 1%. Coversely, in small and medium-sized cities with a population of less than (or equal to) 5 million, improving the amount and quality of domestic water improves living quality more effectively. Finally, we also discussed city sub-samples with different economic aggregates (see Appendix E) and found large differences in residents’ preferences for urban features at different stages of development. In developed areas, green area per capita and the development of the service industry need to be improved, while in developing and underdeveloped areas, residents pay more attention to the popularity and quality of domestic water.

5.2. Influencing Factors of Production Amenity

According to the analysis on influencing factors of production amenity (Table 6 (4)), for natural features, although the average annual temperature does not show a significant impact on the firms’ production, less sunshine (−0.181) and more rainfall (0.073) will improve the production amenity and productivity. This is probably because cities with less sunshine and more rainfall tend to reduce agricultural activities and develop manufacturing and service industries independent of the natural climate, thus improving trade productivity and production amenity. For social and cultural features, infrastructure such as the number of buses, density of drainage pipes and green coverage rate can bring better production convenience. Interestingly, a lower sewage treatment rate and greater wastewater discharge provide higher production amenities, this indicates that firms are more willing to produce goods in cities with less environmental restrictions because production activities may cause pollutants to a certain extent, and reprocessing requires additional production costs. This may also suggest that the influence of improving the environment on productivity is not significant in the current period and may be delayed sometimes. In addition, we found that a higher employment rate, more teachers in universities and colleges, and more cinemas and theaters provide higher production convenience. Among them, the employment rate is the most significant (at a 1% level) factor with a maximum coefficient of 0.172, suggesting that human capital and education play important roles in the promotion of urban production amenities and the economic environment.

The heterogeneity analysis and robustness test obtained the same results: the employment rate, the number of buses, the density of drainage pipes, and other social features have significant impacts, and cities with less sunshine and more rainfall have high production amenities (Table 6 (5, 6)). In small and medium-sized cities with a population of less than 5 million, cultural features such as the number of cinemas and university teachers have a weakening influence on production amenity, but the influences of employment rate, the number of buses, and the density of drainage pipes remain significant and positive. Finally, there is a heterogeneity in the preference of firms for urban features among cities with different economic aggregates (shown in Appendix E). Increasing employment rate and the number of university teachers in developed areas contribute to higher production amenity and productivity. Cities in developing and underdeveloped areas need to increase their infrastructures such as buses, gas pipes, drainage pipes, and green coverage rates. Furthermore, it is beneficial for firm production and economic development to appropriately relax the restrictions on sewage discharge.

5.3. Discussion

With the hedonic model, we analyze the influencing factors of urban QOL and production amenity under the same theoretical framework, trying to find urban features and functions conducive to coordinated urban development. Results show that residents and firms have different preferences for city characteristics, and the urban factors affecting the two are heterogeneous among cities with different population sizes and economic aggregates.

For residents, the influencing factors of urban QOL are consistent with the results of previous literature. Rappaport (2007), Albouy (2008, 2013, 2016), Berger et al. (2008), Barreira (2020), Zheng, et al. (2021), and other scholars have studied urban QOL in U.S., Canadian, Russian, Portuguese, and Chinese cities, and suggested that the climate, education resources, facilities, and environment are the key factors to improve urban QOL [9,10,11,21,22,23,24,25,26,39,40,41]. Due to limits of data availability, we used numbers of hospitals and doctors as a proxy of health care, numbers of college teachers as a proxy of education level, and exclude crime rate, and traffic congestions in city features. However, unlike these studies, we found that population density and employment rate had a positive impact on urban QOL. People in China prefer a “human interaction” life, believing that living in an over-populated community can make life more convenient as they can help each other and share their material and spiritual lives. This finding is in accordance with the special national conditions of China, which indicates that our results are objective and realistic.

Firms are willing to produce in cities with less sunshine and more rainfall, perfect infrastructure, and fewer environmental restrictions. In addition, education significantly enhances the production amenity. These findings support arguments by Lee et al. (2014) that bad weather increases productivity [43], and by Geng and Zhao (2018) and Wang et al. (2020) that the transportation infrastructure and education level have positive impacts on productivity [44,45]. However, Zhang et al. (2017) and Cai et al. (2018) point out that there is an inverted-U shape relationship between temperature and labor productivity [46,47], whereas we found the temperature to be an insignificant influencing factor. This may result from the different concepts of productivity and production amenity. Since the definition of production amenity is firstly proposed to correspond to urban QOL in this paper, it focuses on the production advantages and efficiency introduced by urban characteristics, instead of production factors. There is little research on the impact of production amenity and few studies use a hedonic model (a model used to measure the quality of life) to measure production amenity, leading to the incomparability between the preference of urban features for both residents and firms. Therefore, our research enriches the understanding of urban development from a new perspective.

To sum up, our research on urban QOL in this paper (both the measurement and the influencing factors) is basically consistent with the results of existing literature, so we believe that the results of this paper are valid. This paper has the following strengths compared with other studies: (1) Based on the general equilibrium theory, urban QOL calculated in this paper has a more solid economic theoretical foundation and more objective results; (2) Through the hedonic model, this paper measures urban QOL and production amenity at the same time, providing a new perspective and method for studying the preference of firms and residents on urban characteristics; (3) Using panel data of 285 cities, we have a large sample size, leading to more convincing and effective empirical results; (4) The Chinese economy has been growing fast with sustained and rapid urbanization. Studying urban QOL and production amenities in Chinese Cities can provide a good example for other developing economies. However, due to the data limitations, we pay no attention to county-level cities with lower administrative levels and exclude influencing factors that are not easily obtainable, such as noise intensity, commuting costs, and so on. Furthermore, as we focus on the convenience brought by urban features, personal feelings are excluded, and this is the main difference between urban QOL and individual QOL.

Based on our findings, we give the following suggestions. Firstly, all types of Chinese cities should increase their employment rate, create more jobs, and provide a better employment environment. On the one hand, it brings residents higher income levels and a more stable sense of social stability, to improve their living quality. On the other hand, increasing the employment rate attracts more labor force inflow and improves human capital. Therefore, the productivity and innovation capacity of the city may be increased, and the production amenity and economic environment can also be improved. Secondly, in cities with a good economic level (with a total GDP of more than 200 billion yuan), the per capita green area and the service industry should be well-developed, and good leisure and entertainment places should be provided for residents to meet their growing needs for better lives and spiritual pursuit. In addition, the education level also affects urban QOL and production amenities. The education system and investment should be improved, giving teachers better treatment and attracting more highly educated talents. By doing so, the human capital and innovation capacity can be improved, in addition to the scientific and technological level of the city, resulting in higher production amenities and better economic development. Thirdly, in small and medium-sized cities with a population of less than (or equal to) 5 million and underdeveloped areas with poor economic levels (the total GDP is less than or equal to 200 billion yuan), we should increase the popularity of domestic water and improve the water quality to ensure residents’ basic material living standards. In addition, the construction of infrastructures such as public transportation and drainage equipment should also be improved, and the strict restrictions on sewage discharge should be appropriately relaxed, providing convenience and advantages for firm production and thus improving the overall economic strength and coordination of the region’s development.

6. Conclusions

We develop a new methodology based on the Rosen–Roback general equilibrium model to examine “production amenity” as well as the urban quality of life in a unified model, we further apply it to manually collected and matched data from Chinese cities to study the dynamic trends and influencing factors systematically. By combining the micro information of individuals with the macro characteristics of cities, the implicit price of urban quality of life and production amenity is measured using wages and housing prices, which overcomes their incomparability at the model level. Cross-sectional data are expanded to panel data from 2005 to 2018 to depict the evolution and characteristic facts of the two.

The results show that many cities which are attractive to firms are unattractive to households, and vice versa. The study on urban QOL shows that the provincial capitals and municipalities have a higher quality of life, and the cities with large populations have better living conditions than small and medium-sized cities. The results remain valid after the robustness test. We define and measure production amenity as the convenience conditions and production efficiency provided by a city’s features to firms, which is reflected in the firm’s willingness to pay for urban characteristics. The higher production costs that firms are willing to pay provide greater production amenity and higher productivity. Results show that the production amenity develops unevenly and is higher in the east and lower in the west. Cities in Jiangsu, Zhejiang, the Pearl River Delta Region, and provincial capitals provide higher production efficiency, enjoying a better production environment and amenities. The recent development of urban production amenities is relatively stable and without big fluctuations.

Taking both urban QOL and production amenity into account, we find many Chinese cities have high QOL but low production amenities. This is because most cities in China have high housing prices and low wages, thus their residents are more likely to pay a high living cost for the urban features, but firms pay less production costs for city characteristics and the production input is insufficient. Additionally, the two aspects have a low correlation and may have a substitution relationship, as cities with improved QOL have poor production amenity, while those with better production conditions have declined in QOL. Therefore, there is still a long way to go to pursue a balanced path of urban development.

There are several possible directions for future study. One is to extend the sample of Chinese prefecture-level cities to include county-level cities. The other is to construct production amenities in a broader way to include not only tradable goods (as we have done in the paper) but also other factors such as the operating system, urban governance, technology innovation policy, etc. The Chinese economy has been growing quickly with sustained and rapid urbanization. The path of Chinese cities in coordinating urban QOL and production amenities is the key to sustainable development and can provide a good example for other developing economies.