Diversification of residents’ consumption structure based on ELES model

Abstract

Based on the data of urban residents’ consumption in Jiangsu Province, this paper looks for the Statistical Yearbook of Jiangsu Province from Jiangsu Statistics Bureau, collects the required data of residents’ consumption structure, and defines the components of residents’ consumption structure in Jiangsu Province from eight aspects: clothing, food, housing, transportation and communication, medical care, culture, education, entertainment and other miscellaneous commodities. First, make a systematic theoretical explanation of these eight factors, so as to help us better understand how these eight factors are pieced together into a whole of residents’ consumption structure. Then it analyzes the present situation of consumption structure. Secondly, based on the relevant theories of residents’ consumption structure, in order to know the changes of the components of residents’ consumption structure in Jiangsu Province over the years, this paper analyzes the relevant data of the collected structural factors by using factor analysis and ELES model, based on the data from 2009 to 2018. In the last part, according to the previous analysis results, the characteristics of urban residents’ consumption structure in Jiangsu Province are summarized, and some suggestions and opinions are given. Finally, the future consumption structure is predicted according to the historical data, and some suggestions are given.

Keywords

ELES model consumption structure of residents diversification

1. Introduction

With the increasing income of residents in China, consumption has begun to show a trend of diversified development, which has led to the optimization of consumption structure. The discovery experience of some developed countries and regions that we know shows that when the economy of a country or a region develops to a certain stage, the main content will shift to improving people’s living standards. Since the country’s reform and opening up, great and profound changes have taken place in our social economy. In the days to come, upgrading the consumption structure class and expanding the internal potential of residents’ consumption will remain the key and important tasks. At this time, our economic situation is gradually rising, and people’s attitude towards life is slowly improving [1]. However, during this period of gradual relaxation of consumer psychology, some problems in the development of social consumption have been highlighted. Exploring the evolution of residents’ consumption structure helps us to understand residents’ consumption level and market supply and demand, which can help the national government lead residents’ consumption and promote economic growth. How to guide people to make correct consumption, greatly improve residents’ consumption level and optimize the consumption structure, and how to grasp the many components and characteristics that affect people’s consumption structure and improve people’s consumption awareness is very helpful for the development and progress of the country [2]. Jiangsu’s per capita income and consumption expenditure have always been very representative, so we can make an analysis of them so that we can estimate the trend of residents’ consumption in the future according to the analysis results, and provide evidence for the government’s decision-making on consumption [3]. In the long run, the contribution of consumption to economic growth is persistent and stable, so the promotion of consumption to economic growth is fundamental. Consumption structure is the embodiment of consumption quality, change and rationalization, so the study of consumption structure and its optimization and upgrading is of great significance to promote stable and sustained economic growth. The innovation of this study lies in the breakthrough point of analyzing the trend of residents’ consumption in the future. Taking Jiangsu’s per capita income and consumption expenditure as analysis cases, the factor analysis model and extended expenditure model are constructed, and the selected variables show the ratio of residents’ savings increment to total income increment, so as to obtain data such as residents’ marginal savings tendency. Based on the analysis of the present situation of the overall consumption structure, the main factors affecting the consumption structure of residents are expounded in depth, and finally the changing trend of the consumption structure of urban residents in Jiangsu Province in recent years is obtained, which can provide effective reference for the future consumption structure prediction of Jiangsu Province and the whole country.

2. Main factors affecting consumption structure

Consumption structure is a mixture of various and sufficient contents. To define its classification from different angles, the elements that make up the consumption structure can also be classified into the following categories: First, in order to meet the needs of different levels and stages, today, in the middle stage of social development, people’s class has solidified, the poor are getting poorer, and the rich are getting richer. Such a polarized scene will also bring polarization to consumption [4]. Low-income people only consume for food and clothing and daily life. People with high incomes spend money to improve their quality of life. Secondly, according to the division standard of its use form, this division is measured from the use value of consumption information. The simplest one can be divided into food, clothing, daily necessities, housing, travel and other consumption information. In order to reflect the specific content of consumption materials, we divide them in this form, which is convenient for us to make horizontal and vertical comparison in time and space. Thirdly, by analyzing and exploring the profitability and non-profit of the supply of consumption materials, consumption information can be divided into profitable commodity consumption information and consumption information that contributes to people’s social welfare. The main factors that influence the consumption structure are as follows [5]: income, social security system, government expenditure, industrial structure change and residents’ consumption tendency [6].

3. Selection of model

3.1 Factor analysis model

The basic idea of factor analysis is to decompose each research variable into a few influencing factor variables, and each original variable into two factors, one of which is the common factor shared by all variables, and the other of which is the special factor of each variable. Factor analysis can reduce the dimension of variables with multiple properties [7]. It is a multi-type variable that can explore whether there is a connection relationship among multiple quantitative samples, and analyze the hidden relationship within the data. This relationship is embodied in extracting a few representative variables to represent the general structure of the whole summary. Specifically, factor analysis means that the original variables are expressed as a few common factors with “generality” and a special factor with its own “individuality” [8]. The task of this chapter is to construct a factor analysis model, and then select the appropriate estimation method for the parameters in the model, and explain its meaning. Finally, the application of the factor analysis model is combined with the problems in this paper [8].

3.2 Extended expenditure model (ELES)

ELES function model is a demand model put forward by British Economist Si Stone in 1954. It is a function model with economic significance and wide practical value. The expression is as follows [9]:

$\displaystyle V_{i}=P_{i}X_{i}+B_{i}\left(Y-\sum\limits_{k=1}^{n}{P_{k}X_{k}}\right)$ (1)

In the above formula, $Y$ represents the per capita disposable income of people who participate in consumption, that is, consumers; $B_{i}$ is the remaining part of consumer income after deducting the basic demand consumption of various commodities, and it is also the ratio of purchasing class i commodities, which is called the marginal propensity to consume of residents $0<B_{i}<1$ , $\sum\limits_{i=1}^{n}{B_{i}<1}$ .

Because model practice is often used in statistical analysis, this method is also used here for processing and analysis. It has the following characteristics [10]:

(1)

Parameter estimation is simple and efficient. When using this function to estimate parameters, the total basic consumption expenditure and specific consumption expenditure can be calculated only by estimating $B_{i}$ and $\alpha_{i}$ , and the obtained values are unique.

(2)

Little dependence on data. When using this function to estimate parameters, there is no need to collect data about prices, which can greatly reduce the workload and eliminate the need to collect and sort out relevant data.

There are some shortcomings in the expending system [11]. It assumes that the price of goods has nothing to do with the change of income at the same time, which is applicable to a specific commodity, but not to most commodities.

We use linear expenditure system here, and its general form is:

$\displaystyle V_{i}=P_{i}X_{i}+b_{i}\left(V-\sum\limits_{k=1}^{n}{P_{k}X_{k}}\right)$ (2)

In the above formula, $V_{i}$ is the consumption of Class i commodities by residents, and $V=\sum\limits_{i=1}^{n}{V_{i}}$ is the total consumption expenditure; $P_{i}$ and $X_{i}$ respectively represent the prices of Class i commodities and people’s basic demand for them: $P_{i}X_{i}$ is the expenditure of Class i commodities to meet people’s basic needs, and $b_{i}$ is the percentage of total consumption expenditure minus the total basic consumption expenditure of our selected constituent factors, and the remaining amount will continue to purchase Class I commodities, which is called marginal budget ratio, where $0<b_{i}<1$ , $\sum\limits_{i=1}^{n}{b_{i}=1}$ .

$\sum\limits_{k=1}^{n}{P_{k}X_{k}}$ is the total basic demand of various commodities. Because the data about price is difficult to get and the data is irregular, and because $X_{i}$ and $b_{i}$ are nonlinear, it is difficult to get the parameter estimation in the model. The establishment of ELES, a functional model, has solved this problem. The specific model of ELES is as follows:

$\displaystyle V_{i}=P_{i}X_{i}+B_{i}\left(Y-\sum\limits_{k=1}^{n}{P_{k}X_{k}}\right)$ (3)

The detailed economic significance is as follows: the consumer’s demand expenditure $V_{i}$ for commodity I can be divided into two parts. The first part is the minimum basic demand expenditure $P_{i}X_{i}$ for a commodity, that is, the consumption expenditure necessary to ensure basic food and clothing. This part has nothing to do with residents’ income level. The second part is the part that per capita disposable income Y spent on commodity I after deducting the basic demand expenditure for all commodities, which is related to the consumer’s preference for a certain kind of commodity he wants to consume, and the corresponding marginal propensity to consume is $B_{i}$ .

According to relevant data, $P_{i}X_{i}$ and $\sum\limits_{k=1}^{n}{P_{k}X_{k}}$ in the model are constants, and we calculate according to the following formula:

$\displaystyle\partial_{i}=P_{i}X_{i}-B_{i}\sum\limits_{k=1}^{n}{P_{k}X_{k}}$ (4)

Combine with Eq. (2) and transform it into Eq. (4):

$\displaystyle V_{i}=\partial_{i}+B_{i}Y$ (5)

Combining Eq. (2) with Eq. (3), $i$ is obtained by summing both sides.

$\displaystyle\sum\limits_{i=1}^{n}P_{k}X_{k}=\sum\limits_{i=1}^{n}\partial_{i}% \left/\left({1-\sum\limits_{i=1}^{n}{B_{i}}}\right)\right.$ (6)

Substituting Eq. (5) into Eq. (3) to obtain Eq. (6):

$\displaystyle P_{i}X_{i}=\partial_{i}+B_{i}\sum\limits_{i=1}^{n}{\partial_{i}}% \left/\left({1-\sum\limits_{i=1}^{n}{B_{i}}}\right)\right.$ (7)

Among them, $\left({1-\sum\limits_{i=1}^{n}{B_{i}}}\right)$ represents the tendency of residents’ marginal savings and surplus amount to be used for investment. Because China’s personal investment industry chain is not yet mature, and the relevant systems and regulations have not been completely revised, the variables here represent the ratio of residents’ savings increment to total income increment by default, thus obtaining residents’ marginal savings tendency [12].

4. Analysis on the present situation of urban residents’ consumption structure

4.1 The present situation of the overall consumption structure

Simply put, Engel’s coefficient refers to the ratio of people’s expenditure on food to their total expenditure. According to the relevant consumption standards issued by the Food and Agriculture Organization of the United Nations, if the coefficient is higher than 59%, it means poverty; if it is between 50% and 59%, it means basically satiety; if it is between 40% and 50%, it means well-off; if it is between 30% and 40%, it means wealth; if it is below 30%, it means the richest. In this paper, taking named cities and towns in Jiangsu Province of China as an analysis case, the Engel coefficient of urban households from 1999 to 2013 was found in Jiangsu Statistical Yearbook 2014.

After summing up, it is found that the development process of social consumption is bound to go through the following stages: (1) The basic consumption needs such as food and clothing are the main needs. At this stage, people’s savings are low, their overall living standard is not high, and the problem of food and clothing is the most important one to solve, and there are no other savings conditions for other expenditures; (2) Housing, communication and transportation are the main consumption types. At this stage, people have sufficient savings and storage, which is close to a well-off level; (3) In the stage where emerging industries such as service industry are the main consumption hotspots, people’s savings and storage have been sufficient at this time, and their living standard has been greatly improved. In the past, only the consumption of food, clothing and other commodities has been lower than their own needs, so people began to learn to enjoy life. This is also the reason for the rapid growth of consumption expenditure in emerging industries such as service industry, and the consumption level of urban residents is also increasing.

With the deepening of China’s economic reform, the dual structure between urban and rural areas has been gradually broken, and all aspects of rural areas have changed greatly, while the consumption structure of rural residents is also changing rapidly. But there are many problems in the consumption structure of rural residents in the trend of rapid economic development. Specifically, there are the following problems: First, the proportion of rural residents’ food expenditure is still relatively large. Empirical analysis shows that food expenditure accounts for more than 50% of the total new consumption expenditure of rural residents, that is to say, more than half of the new consumption expenditure of rural residents flows to food expenditure. Second, the consumption structure tends to focus on housing and transportation and communication. With the continuous advancement of urbanization process and the continuous improvement of urbanization level in China, the urban infrastructure is constantly improved and there are more employment opportunities, which makes rural residents gradually move closer to cities and towns, and the proportion of buying houses in cities and towns is constantly increasing, which leads to the increasing trend of the proportion of residential consumption expenditure in the new consumption expenditure of rural residents. Third, the consumption of culture, education, entertainment and medical care has not been paid attention to by rural residents. In the new consumption expenditure of rural residents, the sum of culture, education, entertainment and medical care accounts for less than any one of food, transportation, communication and housing.

4.2 Main factors affecting residents’ consumption structure

4.2.1 The present situation of eating structure

In 2018, the per capita food expenditure of urban residents in Jiangsu Province was 7,687.00 yuan, an increase of 415.63 yuan compared with last year, a year-on-year increase of 6.24%. Food expenditure accounts for 34.7% of the total expenditure, but it is 0.7ï¼…% lower than last year’s 35.4%, which is consistent with Engel’s law, that is, with the increase of residents’ income, the proportion of food expenditure in the total expenditure gradually decreases.

4.2.2 Present situation of wear structure

In terms of clothing, expenditure and consumption have increased, but the growth rate has gradually decreased. Clothing, as a constituent factor, consumes clothes and shoes. Their per capita consumption decreased slightly and their consumption quality improved, which led to an increase in per capita consumption.

4.2.3 The present situation of living structure

Housing expenditure grows slowly among all kinds of expenditures. The main reason is that people have enough savings, and the consumption expenditure on the purchase and construction of real estate has greatly increased, and the growth rate has obviously increased. The pursuit of quality of life has also driven the residential consumption including decoration. In recent years, although the sales situation of commercial housing has been sluggish, a large number of rural residents have flooded into cities, which has improved the situation of purchasing houses. At the same time, with the rapid development of the service industry, residential property has been paid more and more attention, and the expenditure on housing service fees such as property has increased substantially. With the introduction of purchase restriction and loan restriction scheme by the government, the enthusiasm of many real estate investors for buying houses has been reduced. In addition, the bank’s loan interest rate does not fall but rises, and the housing credit policy changes every year, all of which have a certain impact on the sales of real estate, so this value may change greatly in the next few years.

4.2.4 The present situation of the structure of usage

In 2018, the average annual purchases of urban households per 100 households are as follows.

Table 1
Average annual purchase of urban households per 100 households in 2018

Average purchase per 100 households	Quantity
Washing machine	5.9
Air conditioner	11.1
Motorcycle	0.2
Gas stove	5
Microwave oven	3.1
Electric bicycle	13.1
Mobile phone	47.1

In terms of use, there are a wide range of products, including daily necessities, cultural and educational supplies, medical and health products, etc. According to the following table, we can know that with the improvement of residents’ living standards, the types of consumer goods are also changing day by day. With the decrease of motorcycles and the increase of electric vehicles, we can know that China’s awareness of environmental protection is increasing day by day, and the number of household goods is increasing, which also reflects the increase of residents’ living standards [13].

5. Empirical analysis

5.1 Factor analysis model

This chapter mainly makes factor analysis on the consumption structure data of urban residents in Jiangsu Province. As can be seen from the above, we have extracted eight components of urban residents’ consumption structure: food, clothing, housing, household equipment, transportation and communication, culture, education and entertainment, medical care and other miscellaneous services. We define it as $x_{1},x_{2}\ldots x_{8}$ . The first step is to determine whether the problem is suitable for factor analysis, and the analysis results are shown in Table 2.

Table 2
Analysis results

Statistic		Cities and towns
Kaiser-Meyer-Olkin test		0.2
Bartlett test	Approximate chi-square value	476.863
	df	28
	Sig	0

From Table 2, the KMO value obtained by inspection is less than the specified value, which shows that it is not suitable for factor analysis. However, the approximate chi-square is 476.863, and the significance test is 0, so it can be judged that the data provided by this question is suitable for factor analysis.

(1) Extraction of the number of common factors

Using SAS statistical software analysis, the eigenvalue table and contribution rate table are obtained, as shown in Table 3. We extracted the common factors according to the criterion that the eigenvalue is greater than 1, and got three common factors. Their cumulative variance contribution rate has reached 94.6%, and the commonness of variables (see Table 3) is all above O.8. Therefore, it can be explained that these three common factors we extracted can explain the information of the original variables well.

Table 3

Characteristic value and contribution rate (urban)

Serial number	Eigenvalue	Difference	Proportion	Accumulate
1	4.62778568	2.68316058	0.5772	0.5772
2	1.84572307	0.68285263	0.2206	0.7978
3	1.18185143	0.92077763	0.1483	0.9461
4	0.23103321	0.14369661	0.0289	0.9750
5	0.08690672	0.00670699	0.0109	0.9859
6	0.07890063	0.03320067	0.01	0.9959
7	0.04689736	0.04438935	0.0041	1
8	0.00000001		0	1

Table 4

Analysis of the common degree of each variable

Variable	X ${}_{1}$	X ${}_{2}$	X ${}_{3}$	X ${}_{4}$	X ${}_{5}$	X ${}_{6}$	X ${}_{7}$	X ${}_{8}$
Common degree	0.977	0.943	0.962	0.941	0.920	0.886	0.965	0.959

(2) Analysis results of consumption structure factors

Because the initial load matrix is an initial analysis, which is not convenient to explain, we rotate the obtained factor load matrix orthogonally. The rotation factor is as follows.

Table 5

Rotation factor

Consumption category	F ${}_{1}$	F ${}_{2}$	F ${}_{3}$
X ${}_{1}$ (food)	$-$ 0.97078	0.09316	0.14968
X ${}_{2}$ (clothing)	$-$ 0.98042	0.03285	$-$ 0.00511
X ${}_{3}$ (live)	0.79641	$-$ 0.56713	0.08402
X ${}_{4}$ (home equipment)	0.04521	0.23135	0.94136
X ${}_{5}$ (communications)	0.93885	$-$ 0.16346	$-$ 0.18565
X ${}_{6}$ (culture and education entertainment)	0.72296	0.50689	0.30268
X ${}_{7}$ (medical insurance)	0.68832	0.17316	$-$ 0.68043
X ${}_{8}$ (other)	$-$ 0.12519	0.95899	0.14843

The first common factor is heavily loaded in transportation, housing, education and entertainment, medical care, food and clothing, and the coefficient of clothing and food among these eight components is negative. In other words, if the consumption of food and clothing in this area is low, the consumption of transportation, communication, housing, education and entertainment, medical care and health care will be high. We can use the first common factor to represent components such as food, clothing, housing and transportation, and redefine it as a comprehensive factor; The second common factor has a large load on other miscellaneous services, so it can be redefined as other factors; The third factor has a large positive load on household equipment, which is mainly because the price of household equipment is falling and the replacement is fast, so it can be redefined as an improvement factor.

According to Table 4, the factor model of urban consumption structure can be obtained, as follows:

$\displaystyle\chi_{1}=-0.971F_{1}+0.093F_{2}+0.150F_{3}$ $\displaystyle\chi_{2}=-0.980F_{1}+0.036F_{2}-0.005F_{3}$ $\displaystyle\chi_{3}=0.796F_{1}-0.567F_{2}+0.084F_{3}$ $\displaystyle\chi_{4}=0.045F_{1}+0.231F_{2}-0.941F_{3}$ $\displaystyle\chi_{5}=0.939F_{1}-0.163F_{2}-0.186F_{3}$ $\displaystyle\chi_{6}=0.723F_{1}+0.507F_{2}-0.303F_{3}$ $\displaystyle\chi_{7}=0.688F_{1}+0.173F_{2}-0.680F_{3}$ $\displaystyle\chi_{8}=-0.125F_{1}+0.959F_{2}+0.148F_{3}$

Regression analysis is carried out, and the standardized scoring coefficient is obtained after analysis, as shown in Table 6 for details.

Table 6

Standardized scoring coefficient

Consumption category	F ${}_{1}$	F ${}_{2}$	F ${}_{3}$
X ${}_{1}$ (food)	$-$ 0.21655	0.0031	0.3592
X ${}_{2}$ (clothing)	$-$ 0.24864	$-$ 0.0098	$-$ 0.07231
X ${}_{3}$ (live)	0.16329	$-$ 0.35145	0.2032
X ${}_{4}$ (home equipment)	0.0785	0.00378	0.63878
X ${}_{5}$ (communications)	0.20376	$-$ 0.04617	$-$ 0.04069
X ${}_{6}$ (culture and education entertainment)	0.22055	0.32402	0.16246
X ${}_{7}$ (medical insurance)	0.13544	0.22782	$-$ 0.45079
X ${}_{8}$ (other)	0.01453	0.60839	$-$ 0.05555

The factor score function obtained from Table 6 is as follows:

$\displaystyle F_{1}=-0.217X_{1}-0.249X_{2}+0.163X_{3}+0.0785X_{4}+0.204X_{6}+0% .135X_{7}+0.015X_{8}$ $\displaystyle F_{2}=0.003X_{1}-0.01X_{2}-0.351X_{3}+0.004X_{4}-0.046X_{6}+0.22% 8X_{7}+0.608X_{8}$ $\displaystyle F_{3}=0.036X_{1}-0.072X_{2}+0.203X_{3}+0.639X_{4}-0.041X_{6}-0.4% 51X_{7}-0.056X_{8}$

It can be seen from the standardized score of factors: As the first common factor above reflects the problems of residents’ food, clothing, housing and transportation, the above figure shows that the scores of food and clothing are negative, which means that in the decade from 2009 to 2018, with the rapid economic development, people’s disposable income has increased day by day, and their savings have also increased greatly. People no longer pay attention to the requirements of food and clothing, but pay more attention to finding cost-effective products for consumption, so their scores are negative. Among the other factors presented by the first common factor, the scores of culture, education, entertainment, transportation and communication are higher, which can also reflect that people’s material life is gradually enriched, and they have increased their investment in children’s education, travel and other ways of enjoying life. For other miscellaneous items represented by the second common factor, people spend less on it, but with the increase of income in the future, the expenditure in this area will increase. The third factor is household equipment. Now, with the progress of science and technology, intelligent machines will enter our lives, so the expenditure of this part may increase slightly in the future.

5.2 The application of ELES model

The standardized data of Ahu Bureau, a constituent factor of urban residents’ consumption in Jiangsu Province from 2009 to 2018, was processed, and the Eq. (3) was regressed once by the least square method with eviews software, and the estimated values of parameters $\alpha_{i}$ and $B_{i}$ were obtained. From the result of Eq. (7), the estimated value $P_{i}X_{i}$ of basic demand expenditure of eight structural constituent factors was calculated, and then $V_{i}$ was obtained. The following table.

Table 7
Estimated basic demand expenditure of eight structural components

Age	Parameter	Food	Clothing	Live	Home equipment	Medical care	Communication	Culture and education entertainment	Other	$\sum{B_{i}}$
2009	$\partial_{i}$	2443.17	285.93	200.82	72.85	210.71	$-$ 560.12	270.40	13.69	0.49
	$B_{i}$	0.11	0.05	0.05	0.04	0.03	0.12	0.08	0.02
	$P_{i}X_{i}$	3086.25	567.57	465.03	314.56	373.97	106.67	748.85	152.54
2010	$\partial_{i}$	2958.07	384.94	277.51	57.46	253.65	$-$ 323.58	$-$ 46.17	$-$ 42.16	0.47
	$B_{i}$	0.09	0.05	004	0.04	0.02	0.10	0.10	0.02
	$P_{i}X_{i}$	3578.23	690.54	554.54	337.87	414.23	328.56	582.96	118.95
2011	$\partial_{i}$	3137.53	315.98	337.92	122.13	373.69	$-$ 357.79	97.13	$-$ 216.2	0.49
	$B_{i}$	0.11	0.05	0.03	0,04	0.02	0.10	0.10	0.03
	$P_{i}X_{i}$	3925.21	723.24	574.11	421.79	536.57	380.21	830.41	32.49
2012	$\partial_{i}$	3601.15	263.11	469.72	114.64	418.35	$-$ 231.67	$-$ 79.89	$-$ 252.1	0.49
	$B_{i}$	0.1	0.06	0.03	0.04	0.02	0.10	0.11	0.03
	$P_{i}X_{i}$	4429.34	734.68	745.34	444.84	596.89	579.53	822.10	18.93
2013	$\partial_{i}$	3440.74	349.33	591.61	74.49	556.21	$-$ 63.10	256.14	$-$ 359.3	0.48
	$B_{i}$	0.11	0.05	0.05	0.04	0.01	0.07	0.09	0.03
	$P_{i}X_{i}$	4321.12	751.14	825.44	401.29	671.24	293.31	1008.7	24.51
2014	$\partial_{i}$	3590.23	379.31	567.89	121.32	498.75	$-$ 154.56	$-$ 69.39	$-$ 297.7	0.47
	$B_{i}$	0.1	0.05	0.03	0.04	0.01	0.06	0.1	0.03
	$P_{i}X_{i}$	4671.24	762.91	844.31	423.57	698.88	356.71	1215.3	18.67
2015	$\partial_{i}$	3698.21	354.23	537.21	98.45	463.11	$-$ 98.27	$-$ 103.3	$-$ 265.2	0.48
	$B_{i}$	0.11	0.05	0.04	0.04	0.01	0.1	0.1	0.03
	$P_{i}X_{i}$	4852.12	773.21	868.25	96.31	489.67	342.76	1289.7	23.5
2016	$\partial_{i}$	3492.31	327.89	548.91	101.23	435.91	$-$ 103.44	$-$ 102.5	$-$ 276.8	0.44
	$B_{i}$	0.1	0.05	0.03	0.04	0.01	0.08	0.1	0.03
	$P_{i}X_{i}$	4931.53	813.41	872.42	101.31	503.2	336.75	1270.5	18.9
2017	$\partial_{i}$	3298.71	368.42	598.26	98.72	424.51	$-$ 123.71	$-$ 97.68	$-$ 253.4	0.46
	$B_{i}$	0.11	0.04	0.04	0.03	0.01	0.1	0.1	0.03
	$P_{i}X_{i}$	5021.43	845.24	853.91	105.6	553.64	389.21	1345.8	16.3
2018	$\partial_{i}$	3654.22	324.51	568.92	103.69	431.43	$-$ 97.47	$-$ 98.58	$-$ 269.3	0.45
	$B_{i}$	0.1	0.05	0.03	0.03	0.01	0.1	0.1	0.03
	$P_{i}X_{i}$	5322.81	867.59	870.04	107.32	579.31	403.13	1589.7	15.2

In the above picture, $\partial_{i}$ has no actual economic significance. $B_{i}$ is the marginal consumption tendency of eight structural components. From the above table, we know that the change range of $B_{i}$ is small and relatively stable. Every year, $B_{i}$ fluctuates slightly around 0.5, and the amplitude is less than 0.3. This shows that when people’s basic survival needs are met, half of the remaining disposable consumption income is used to improve their material living standard and consumption quota level. The $P_{i}X_{i}$ value represents the most basic needs of all kinds of consumer goods, among which, the consumption expenditure of basic needs of food is the largest, with a significant increase, from 3,068 yuan in 2008 to 5,322 yuan in 2018, an increase of 173%, but the increase percentage of food consumption from 1999 to 2008 is 202.8%, showing a negative growth; The consumption expenditure of basic housing needs also increased significantly, from 465 in 2009 to 187% in 870 yuan in 2018. In order to feel the changes better and more intuitively, draw a line chart of marginal consumption tendency according to the above results.

Figure 1.

Marginal consumption tendency is a line chart.

Generally speaking, the increase of people’s income now means the increase of disposable consumption. In the past, the marginal consumption tendency of the most common type of food and clothing consumption gradually declined, while the marginal consumption tendency of material enjoyment and industrial development consumption increased. We can also find that with the progress of society or the increase of residents’ income, the first type of food and clothing gradually decreases, while the second and third types of education, culture, entertainment, transportation and communication used to meet people’s material and spiritual needs gradually increase, which indicates that the development trend of urban residents’ consumption structure is reasonable.

6. Conclusion

Combined with the above empirical analysis, On the whole, Jiangsu Province’s future consumption hotspots can be summarized as follows: First, due to the increase of residents’ income and the rapid development of humanized and personalized services in the catering industry, dining out is not only the first choice for urban residents to hold wedding and birthday parties, but also holiday meals and family gatherings have become the main choices for residents to eat out. Eating out makes residents’ lives more colorful and promotes the further prosperity of the catering industry. Second, the housing situation has improved. With the improvement of living standards, urban residents are very concerned about the quality structure of housing and the surrounding environment, and pursuing a green, comfortable and environmentally friendly lifestyle has become a fashion trend. In recent years, the sustained growth of housing consumption has made housing expenditure one of the consumption hotspots of urban residents in the region and an important support for the upgrading of consumption structure. Third, the proportion of medical care has increased significantly. According to the data, with the deepening of the reform of the medical system, the medical expenses of residents have increased rapidly, while the medical expenses have decreased relatively. Fourth, transportation consumption continues to rise. With the increase of residents’ income and the decline of car prices, residents’ transportation consumption expenditure continues to rise, the number of urban residents’ family cars keeps increasing, and the expenses of fuel, insurance, repair, maintenance and taxes for vehicles increase accordingly, so family car consumption has become one of the new consumption hotspots.

Combined with the above empirical analysis, the change trend of consumption structure of urban residents in Jiangsu Province in recent years is as follows: The first is food and clothing. In recent years. The marginal propensity of urban residents in Jiangsu Province to food ranks high. Although people are no longer worried about food and clothing as their income increases, people may still have a great demand in this respect because they want high enjoyment of material life, but it shows a downward trend as a whole. The second is living. In recent years, the rising house prices have made them powerless, so they may choose to rent a house to avoid buying a house directly, so they are less inclined to buy and consume living; For this part of people whose income is above the middle, they generally choose the more developed areas to buy houses, because life is convenient; For high-income groups, they generally choose houses with good environment, large area and comfort. However, with the gradual saturation of real estate, their consumption tendency is slowly declining. The third is home equipment and services. The demand for household equipment and services has been very stable in recent years. In recent years, electronic technology has developed rapidly, new products have been updated iteratively, and some household items may tend to be scientific and technological, so their marginal consumption tendency is not high at present. The fourth is traffic communication. In 2012, due to the doomsday theory, people’s travel decreased, resulting in a significant decrease in transportation consumption. However, in the following years, the consumption of transportation and communication increased steadily, which was due to the rising number of private cars, which made it convenient and convenient to travel by car. The fifth is miscellaneous goods and services. The basic consumption demand of miscellaneous goods and services has been steadily increasing in proportion to the total expenditure, indicating that the enjoyment expenditure is increasing. Compared with other factors, miscellaneous items and others account for less of the total consumption, but as can be seen from Table 6, they also show a slow growth trend. This shows that with the increase of people’s income, the expenditure on other items is also slowly rising, and residents are inclined to enjoy consumption.

After the analysis, it is found that the consumption structure of urban residents is different from that of rural residents, and the consumption structure of urban residents needs to be optimized and upgraded on the current basis in order to adapt to the further economic development and continue to demonstrate the consumption structure of rural residents. According to the above analysis, the optimization path of urban residents’ consumption structure is also put forward from two angles: the first is to optimize the consumption structure of urban residents on the premise of certain income and consumption expenditure of urban residents. The second is to increase the income level of urban residents so as to increase their consumption power.

In order to make China’s economy develop more stably, healthily and rapidly, it is necessary to further optimize the consumption structure of Chinese residents. For how to further optimize the consumption structure of Chinese residents, we need to focus on improving the disposable income of urban and rural residents in the future. Deepen the cracking of the dual structure of urban and rural areas and promote deep-seated exchanges between urban and rural areas; At the same time, the industrial structure is optimized and upgraded.

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