Urbanization,Industrial Structure Upgrading,and Lottery Consumption based on the Sustainable Development of the Emerging Markets: Evidence from China

Abstract

Steady progress in urbanization and continuous industrial structure upgrading are important pillars for emerging economies to achieve sustainable development. As an important means of enhancing national economic development, the lottery industry has always been highly valued by all countries. For China, how to promote the sustainability of the lottery industry so as to improve residents’ subjective wellbeing and life satisfaction is particularly critical. The purpose of this study is to explore the impact of urbanization and industrial structure upgrade on lottery consumption. Based on provincial panel data running from 2008 to 2019 in China, it uses two-way fixed effects to make empirical analysis. The results show that the increase in urbanization rate and tertiary industry share has significantly positive effects on lottery consumption; the impact of urbanization and the proportion of the tertiary industry on lottery consumption would vary in categories of lottery tickets; the urbanization and tertiary industry share have a greater role in promoting lottery sales in the eastern region, followed by the central region, and finally the western region. Accordingly, this article proposes policy recommendations to promote the coordinated development of the lottery industry and other related industries in China.

Keywords

lottery consumption urbanization industrial structure consumption upgrade sustainable development

Introduction

For nearly half a century, urbanization and industrial structure upgrading have been the main trends of world development. The “Human Development Report 2015” from United Nations Development Programme has predicted that the proportion of the global urban population will reach 68% by 2050. Urbanization and industrial structure upgrading, as critical driving forces to optimize resources allocation, have played a great role in the sustainable development of emerging economies (Gu et al., 2010; Fan & Zhang, 2003; Zhang, 2009). For example, urbanization can raise farmers’ income (Li & Li, 2018; Yuan et al., 2018), alleviate the urban–rural income gap (Deng & He, 2018; Ma et al., 2018) and boost demands (Fujita et al., 1999a; Venables, 1996; Wang, 2010) to greatly promote the sustainable and healthy development of the economy. The upgrading of industrial structure is conducive to breaking through the “path dependence” and “lock-in effect” (Zhang & Hu, 2010), fostering dynamic comparative advantages and core competitive advantages (Landesmann et al., 2001; Proudman et al., 2000). According to United Nations Human Settlements Programme, despite the COVID-19 pandemic has severely hit the global urban systems, urbanization remains to be a major global growth driver.

Since the 1980s, driven by the ever-growing urbanization rate and industrial structure upgrading, emerging economies have maintained momentum in sustainable development and have become the new engine of global economic growth. As an emerging market, China has actively pushed forward urbanization and industrial restructure and upgrading since reform and opening up. After the 2008 financial crisis, the urbanization level and urban population have kept rising, and the industrial structure upgrading also has been accelerated, as China steadfastly pursuing new urbanization and supply-side structural reforms. According to data released by the National Bureau of Statistics, from 2008 to 2019, urban population grew from 624 million to 848 million with permanent urban residents exceeding 60% of the population; the secondary industry’s share of GDP has been declining, while that of the tertiary industry has exceeded 53% (see Figure 1).

Figure 1.

Urbanization, industrial structure, and lottery sales in China (2008–2019).

The 14th Five-Year Plan stipulated that the urbanization rate of China’s permanent residents will increase to 65% by 2025. On top of this, China will continue to focus on industrial restructure and upgrading as well as consumer spending to boost the service industry, especially the sports industry and sports consumption. Undoubtedly, the population size, industrial distribution, and consumption structure in urban China will undergo dramatic changes with the influx of rural population and fast-growing service industry. In the process of urbanization and industrial structure upgrading, the proportion of agricultural activities continues to decline, the resources are concentrated toward cities and towns, and the income and productivity are to be much higher, providing people a material basis for a higher quality of life, thereby facilitating consumption upgrade. Sports consumption with lottery consumption as an integral part will become more vigorous. On the other hand, the public services equalization in urban and rural areas and the steady increase in social security can increase individuals’ life satisfaction (Kotakorpi & Laamanen, 2010). A strong sense of happiness would enhance residents’ resistance to risks (Chou et al., 2007; Fehr-Duda et al., 2011), so they would be more willing to accept the lottery industry with “gambling” characteristics, and the lottery market share is expected to expand accordingly. Most of the existing studies on lotteries focuses on the relationship between people’s lottery purchase behavior and their quality of life and physical and mental health from a micro perspective (Ariyabuddhiphongs, 2011; Feng & Yao, 2016; Li et al., 2021). Eckblad et al. (1994) concluded that the majority of the Norwegian lottery winners were well-educated and of well-off families. They only took buying lottery tickets as a way of leisure because they had a stable livelihood. They would remain humble and gentle even failing to win the lottery. Some scholars also believed that individuals who win the lottery would improve their quality of life (Winkelmann et al., 2010), mental and physical health (Cesarini et al., 2016; Lindahl, 2005) along with their sense of happiness, which would further encourage lottery players to buy more.

Some others have also explored the impact of income and education (Kaizeler et al., 2014) and corporate social responsibility on lottery sales (Li et al., 2012). However, few studies have examined the relationship between urbanization, industrial structure upgrading, and lottery consumption from a macro perspective (Wei & Zhang, 2022). Especially in the context of China’s rapid economic development and substantial lottery scale growth, fewer studies on the relationship between urbanization, industrial structure upgrading, and lottery consumption have been conducted. In fact, according to the hierarchy of needs theory (Maslow, 1943), when people’s living standards develop to a certain level, they will pursue higher-level needs. Therefore, urbanization and industrial structure upgrading provide favorable basis for people to pursue higher-level needs, such as sports lottery consumption, thereby improving their living standards. Along with this logic, what is the connection between urbanization, industrial structure upgrading and lottery consumption? To answer this question clearly, this paper takes China’s macro data as a sample to analyze in depth the impact of urbanization and industrial structure upgrading on lottery consumption, and to propose suggestions for the sustainable development of sports lottery in China.

The rest of the paper is structured as follows: First, an overview of the literature on urbanization, industrial structure, and consumption is provided. Then the research hypotheses are proposed. A methodology section follows in which the measurement of variables, collection of data are explained. After presenting the results of the study, we discuss the results and conclude with a proposed future research agenda.

Literature Review

Since the 1960s, social productivity and science and technology have witnessed hyper growth, the scale of nature transformation has been unprecedented and the overwhelmed total production has led to a large amount of resource depletion. So the issue of economic sustainable development emerges as one global focus. However, the topic of sustainable consumption has caused concern until the early 1990s. In 1992, the United Nations Conference on Environment and Development passed the Rio Declaration in Brazil, which stated, “To achieve sustainable development and a higher quality of life for all people, States should reduce and eliminate unsustainable patterns of production and consumption and promote appropriate demographic policies.” Soon afterwards, the economists began to focus their research on urbanization and industrial restructure to explore their impact on consumption, especially on the sustainable growth of consumption.

Previous studies on urbanization, industrial structure, and household consumption have been explorative and systematic. Regarding the impact of urbanization on household consumption, opinions vary. For some scholars, urbanization can significantly raise residents’ consumption level (Hu & Su, 2007; Jiang et al., 2011; Liu, 2013; Pan & Gao, 2014). Urbanization can improve labor productivity through optimized resources allocation and specialized division of labor to increase labor income, thus enabling potential consumer demands (Liu, 2005; Wan, 2012). Higher level of urbanization would gradually change individual consumption habits, expand the consumer market, and indirectly raise the overall consumption level (Fu et al., 2013; Wang & Wang, 2016). In addition, agglomeration and scale effects led by urbanization would also boost consumer demands (Fujita et al., 1999b; Vernon & Wang, 2005). Moreover, better social security, social welfare, and public services brought about by urbanization can enhance individuals’ sense of happiness (Huo et al., 2018; Jiang et al., 2017), and free them from worrying about future life, which can significantly improve their consumption capacity and quality (Mei et al., 2018).Yet, a few researchers reckon that urbanization does not promote household consumption. Instead it may even bring about a reverse inhibitory effect, mainly due to the variability of future income and expenditures, division of household registration system, and changes in social security system, which may prompt individuals to hold savings and cut consumption (Aart, 2000; Chamon, 2007; Chen et al., 2015; Shi & Nie, 2014). There are also some researchers using the “U”-shaped curve to explain the relationship between urbanization and household consumption. Chen (2010) believed that urbanization can be divided into two phases: urban scale-up and citizenship. In the stage of urban scale-up, the consumption rate decreases with the urbanization rate increases, while in the citizenship stage, the consumption rate increases with the increase in urbanization rate. Lei and Gong (2014) conducted an empirical test based on data selected from 176 cities in China from 2001 to 2010, and found that urbanization can drive urban consumption rate grow, but undue urbanization would otherwise hinder it.

China’s industrial structure keeps upgrading along with urbanization. Opinions diverge on the impact of industrial structure upgrade on residents’ consumption. One is that industrial restructure is conducive to consumption upgrading, mainly due to human capital accumulation, optimal factor allocation, and better product quality (Wang & Peng, 2019; Q. Zhang, 2019; Zhao & Yue, 2019). However, opinions still differ on how to encourage the consumption of urban and rural residents. Lin and Yang (2019) argued that industrial structure upgrading is more conducive to the consumption of urban residents, but there is a certain time lag for rural residents. In contrast, Li and Liu (2015) emphasized that industrial structure upgrading shows a strong impact on rural residents’ consumption. Additionally, some researchers claim that consumption upgrade is supportive of industrial structure upgrade, because higher individual income levels lead to diversified and multi-level consumer demands, thus bringing along the industrial structure upgrading (Shi, 2009; Zhu, 2017; Yin, 1998). Dong et al.’s (2011) research showed that diverse consumption demands caused by income disparity between urban and rural in China significantly affect the transformation of industrial structure. Other researchers examined the mutual relationship between industrial structure upgrading and consumer spending. From the intrinsic link between the supply-side industrial structure and the demand-side demand structure, they found the need to coordinate industrial structure upgrade and consumer spending (Yan et al., 2018; Yin, 2003). Chen’s (2020) research showed that the upgrade of industry and consumption is interdependent. Industrial upgrading promotes consumption upgrading through structural optimization and aggregate expansion, and consumption upgrading promotes industrial upgrading through factor allocation and income growth. A healthy and sustainable development of China’s economy can be achieved in the process of consumption and industrial upgrade.

With the general improvement of national consumption ability and the continuous optimization and upgrading of industrial structure, the sports consumption demand of Chinese residents has been unprecedentedly released (Zhang & Wu, 2022), which has been significantly improved both in quantitative and qualitative dimensions, becoming a key force in boosting social and economic growth, accelerating the transformation of sports industry, expanding sports participation and meeting people’s needs (Yan & Zhou, 2021; Zhang & Zhang, 2021). Lotteries, as a part of sports consumption, are inextricably linked with a country’s industrial structure and urbanization level. However, researches on the relationship between urbanization, industrial structure, and lottery sales are relatively rare. Scholars from other countries mainly focus on the relationship between sports events and lottery industry. For example, García et al. (2008) found that having a top football team would significantly increase provincial lottery sales in Spain by constructing a dynamic panel data model. Mao et al. (2015) argued that the unpredictability of sports events would make them a very attractive gambling vehicle to attract players to spend on sports lotteries. Wann et al. (2015) and Gassmann et al. (2017) also proved that the growth of sports events can obviously contribute to higher lottery sales. Some studies have also found that the proceeds from the lottery industry can propel the growth in sporting events, as Forrest (2003) mentioned that most of the proceeds from horse racing or jai-li games came from the lottery of corresponding events in the United States, the United Kingdom, and Hong Kong, China.

Domestic scholars, such as Zhu (2013) claimed that urban development can help consolidate the foundation for sports lottery. Fang and Chen (2019) adopted econometric methods and took China’s provincial panel data from 2007 to 2016 as a sample, based on which they found that urbanization level has a positive influence on residents’ sports lottery demands. In the empirical studies, most researchers take the level of urbanization as a control variable, and the results consistently show that high level of urbanization is beneficial to lottery sales (Bai et al., 2020; Li, 2015; Xu & Zhu, 2020). Besides, there are very few papers addressing the relationship between industrial structure upgrading and lottery consumption. Niu (2019) proposed that increasing lottery issuance will support the three major industries (agriculture, manufacturing, and services). Ma and Wang (2021) argued that sports lottery, which takes events as the betting object, further meets the differentiated needs of consumers. As one of the consumption upgrade tools, sports lottery of the guessing type constitutes the basic driving force of the market demands.

Previous studies have not done enough to explore how urbanization and industrial structure affect lottery consumption, and few have addressed the impact on lottery sales with the two indicators as the core. Therefore, this paper takes urbanization and industrial structure as the starting point, constructs an empirical model with a view to examine their effects on lottery consumption and propose targeted implications.

Compared with previous studies, this paper innovates in and contributes to academic literature in several ways. First, based on two driving forces of sustainable development-urbanization level and industrial structure upgrading, this paper takes China as a representative sample of emerging markets to conduct an empirical analysis on how urbanization and industrial structure upgrading affect lottery consumption, using China’s provincial panel data from 2008 to 2019. Second, it adopts the classification standards issued by China’s lottery management institutions to divide lottery into two categories, sports lottery and welfare lottery, and further examines the differences in the effects of urbanization and industrial structure upgrading on lottery consumption. Third, it elaborates on different effects of urbanization and industrial structure optimization on lottery consumption in eastern, central, and western part based on regional division in China.

Research Hypothesis

Over the past 40 years of China’s reform and opening up, it has become a social common sense that the increase in urbanization rate has gone hand in hand with economic momentum. Although views on the relationship between urbanization and household consumption in China’ academic circles have not been unanimous, some scholars argue that rapid urbanization will have some inhibitory effects on residents’ consumption due to rising housing prices and constrained resource in quality education and medical care (Chen, 2018b). However, given the “micro-expenditure” attribute of lottery spending (Drennan et al., 2006), it is hard to imagine that pressures such as housing, education, and medical care will eventually be passed on to lottery purchase, thereby disabling lottery citizens to consume. Moreover, subject to the household registration system and other constraints, China’s urbanization involves a relatively “lengthy” process in which peasants migrate to towns and cities and then become permanent urban citizens. Even those migrant workers who have not become permanent urban citizens will have their income much higher than farming. Those who have become permanent urban citizens can enjoy better public services, which would enlarge the consumer groups and optimize the consumption structure (Tang et al., 2020). So more spending in entertainment, leisure, and cultural activities (Lin & Zhu, 2021) will invisibly attract more potential lottery consumers so as to impel the lottery industry. Therefore, hypothesis 1 is proposed:

Hypothesis 1: There is a positive correlation between urbanization and lottery consumption.

The upgrading of industrial structure is mainly manifested in the process of national economic focus shifting from the primary industry to the secondary industry, and then to the tertiary industry. From 2008 to 2019, the value-added of China’s tertiary industry has remained stable growth. Its contribution to economic growth has maintained modest growth after exceeding 50% for the first time in 2015, and reaching 53.6% of GDP in 2019. Although it is impossible to break the universal law of “GDP structural slowdown” caused by upgrading from secondary to the tertiary sector, the view that industrial structure upgrading is the internal dynamics of China’s economic growth is uncontested in China’s academic circles. Transforming production mode, adjusting industrial structure, and promoting advanced industry, which bear huge domestic demands potential, are the key paths to foster quality economic development in recent years and during the 14th Five-Year Plan period. Along with Maslow’s Hierarchy of Needs theory, this period will be a period of significant changes in the structure of social consumption, and the general trend is that people’s demand for tangible material consumer goods is slowing down and the demand for intangible service consumption is rising rapidly. As an integral part of the services, the lottery market will surely benefit from robust evolution of tertiary industry. Under the triple support of economic growth, higher income, and consumption upgrade, lottery spending and sales would surely expand. For instance, the fast growing sports event industry has engaged more attention to the related sports lottery (Funk et al., 2006; Ziemba, 2008), which would further raise people’s demand for it. Individuals can satisfy the demand for self-worth realization by winning the lottery after analyzing information related to the event. Meanwhile, the growth of sports lottery has also encouraged people to spend more in sports events, which would foster new momentum in the joint development of the sports industry and the lottery industry. Therefore, hypothesis 2 is proposed:

Hypothesis 2: There is a positive correlation between industrial structure upgrading and lottery consumption.

Methodology

In order to investigate the impact of urbanization and industrial structure upgrade on lottery consumption, this study uses ordinary least squares and two-way fixed effects making empirical analysis. In the selection of samples, it collects 2008 to 2019 provincial panel data for quantitative study.

Model Design

With regard to the model design, it takes urbanization and industrial structure as core explanatory variables, regional GDP, per capita disposable income of urban residents, population size, and number of years of education per resident as control variables, and establishes benchmark regression model. Let

lnlotter y_{it} = α_{0} + β_{1} lnurbanizatio n_{it} + β_{2} lnstructur e_{it} + γ Z'_{it} + ε_{it},

(1)

where i is a sample, t means time, lottery is lottery sales, urbanization is the urbanization, structure is industrial structure, Z’_it represents a series of control variables, α₀ is a constant term, and ε_it is an error term (Figure 2).

Figure 2.

Model design.

Variable Selection

For explanatory variables, lottery sales of each province over the years are used as substitute variable for lottery consumption.

For core variables, population urbanization (peopleurban) and land urbanization (landurban) are selected as alternative variables for urbanization, as the two indicators are accompanied in the process of urbanization. We use population urbanization (peopleurban) for benchmark regression, and land urbanization (landurban) for robust regression. Where, population urbanization (peopleurban) variable is measured by the proportion of permanent urban population in the total population of each province at the end of a year, and land urbanization (landurban) variable is measured by the proportion of built-up urban area in each province’s urban area. The industrial structure upgrading refers to the process or trend of industrial structure transformation from a low-level pattern to a high-level one, which is manifested in the process of national economical focus evolving from the primary industry to the secondary industry and then to the tertiary one. Therefore, industrial structure (structure) is measured by the proportion of tertiary industry in GDP (str3gdp) and the proportion of secondary industry in GDP (str2gdp).

Referring to previous studies (Chen et al., 2020; Liu, 2016a; Liu, 2016b), economic, demographic, and educational factors are weighted in selecting control variables. Regional GDP (gdp), per capita disposable income of urban residents (perrevenue), regional population size (people), and number of years of education per resident (education) are selected as control variables. The number of years of education per resident = (illiteracy*1 + primary school education*6 + junior high school education*9 + high school and technical secondary school education*12 + college and undergraduate degree and above *16)/population above age 6.

Data Resource and Descriptive Statistics

The panel data of 31 provinces in China from 2008 to 2019 are mainly from the Ministry of Finance of the People’s Republic of China, China Statistical Yearbook (National Bureau of Statistics, 2009–2020) and Yearbook of the Chinese Lotteries. The lottery consumption data of 2008 comes from the China Lottery Yearbook (2009), while 2009–2019 lottery consumption data come from the Ministry of Finance of the People’s Republic of China (2009–2019).

Before the empirical analysis, we use logarithm transform to treat all variables with a view to eliminate the influence of heteroscedasticity (ARCH) and improve the robustness of the regression results. The descriptive statistical results of related variables are shown in Table 1.

Table 1.

Descriptive Statistics.

Variables	No. of sample	Max	Min	Mean	SD
lnlottery	372	15.4045	10.4186	13.4564	0.9217
lnpeopleurban	372	4.4955	3.1046	3.9713	0.2592
lnlandurban	372	4.2560	2.0153	3.3425	0.4533
lnstr2gdp	372	3.8825	−6.3736	−4.6277	2.5123
lnstr3gdp	372	4.4251	−5.8816	−4.6196	2.6234
lngdp	372	20.7227	7.4371	17.9416	2.6118
lnperrevenue	372	11.2098	9.3029	10.1190	0.3877
lnpeople	372	9.3519	5.6595	8.1144	0.8453
lneducation	372	2.5417	1.4398	2.1750	0.1410

Results

This paper takes urbanization and industrial structure as core explanatory variables, regional GDP, per capita disposable income of urban residents, population size, and number of years of education per resident as control variables. Ordinary least squares and two-way fixed effects are applied to investigate the impact of urbanization and industrial structure upgrade on lottery consumption. Factors such as economy, population, education, etc., are taken into account.

Benchmark Regression

This paper uses population urbanization (peopleurban) and the proportion of tertiary industry in GDP (str3gdp) as substitute variables for urbanization and industrial structure respectively. After F-test and Hausman test, appropriate models are established to obtain the benchmark regression results, see Table 2 for details.

Table 2.

Benchmark Regression Results.

Explanatory variable	Dependent variable: lnlottery
Explanatory variable	(1)	(2)	(3)
Lnpeopleurban	1.7109*** (0.2467)		1.4164*** (0.2728)
lnstr3gdp		0.5912*** (0.1163)	0.3057** (0.1248)
lngdp	0.0395*** (0.0043)	0.6298*** (0.1161)	0.3447*** (0.1246)
lnperrevenue	0.9111*** (0.0882)	0.5828*** (0.1677)	0.5784*** (0.1616)
lnpeople	1.9111*** (0.3808)	0.9512** (0.3850)	1.6394*** (0.3939)
lneducation	0.1606 (0.2635)	0.2505 (0.2709)	0.1658 (0.2615)
Constant	−19.1235*** (2.9232)	−9.2729*** (2.8783)	−16.4565*** (3.0992)
R ² between groups	.9030	.9259	.9099
F test p value	.0000	.0000	.0000
Hausman test p value	.0000	.0001	.0000
Model	FE	FE	FE
Subjects	31	31	31
No. of sample	372	372	372

p < .050. ***p < .010.

By analyzing the regression results in Table 2, the findings are presented in the following part.

First, we select population urbanization as the core explanatory variable, and regional GDP, disposable income per capita, population size, and average years of education as control variables. The regression results are shown in Table 2 column (1). It is found that the level of population urbanization significantly affects lottery sales at the level of 1%, and every 1% increase in population urbanization will lead to 171.09% increase in lottery sales. The control variables, including regional GDP, per capita disposable income and population size, all have a significantly positive impact on lottery sales. The average years of education also shows a positive correlation, though not very significant. The R² between groups of the model is 0.9030, which means a good fit and the data explains well.

Second, the regression results are shown in column (2) of Table 2, taking the proportion of service industry in GDP as the core explanatory variable and keeping the control variables unchanged. It is found that at the 1% level, the proportion of tertiary industry in GDP significantly affects lottery sales, and for every 1% increase, the lottery sales will increase by 59.12%. The relation between control variables and lottery sales does not change significantly, basically consistent with column (1). The R² between groups of the model is 0.9259, which is a good fit and the data justifies the hypothesis.

Third, this paper comprehensively investigates the impact of population urbanization and the proportion of tertiary industry in GDP on lottery sales, while keeping the control variables unchanged. The regression results are shown in Table 2 column (3). It is found that the level of population urbanization and the proportion of tertiary industry in GDP significantly affect lottery sales at the level of 1% and 5%, respectively, and each 1% increase in the two variables will lead to lottery sales up by 141.64% and 30.57%. The influence of control variables on lottery sales does not change significantly. The R² between groups of the model is 0.9099, which shows a high degree of fit and the data explains well.

In summary, population urbanization, the proportion of tertiary industry in GDP, and lottery sales are significantly and positively correlated, indicating that the increase in urbanization level and industrial structure upgrading have a significant role in promoting lottery consumption in China, which is consistent with the hypothesis proposed in this paper. That is, urbanization and upgrading of industrial structure are changing urban population size and household consumption structure in China, and lottery, as a non-essential consumer product, remains popular among the public.

Robustness Tests

The robustness test is mainly carried out by changing the core explanatory variables, using land urbanization (landurban) as a proxy for urbanization, and the proportion of secondary industry in GDP (str2gdp) as a substitute for industrial structure. The control variables are kept constant in regression. After the F-test and Hausman test, appropriate models are adopted for regression analysis, and cross-checked with the core variables in the benchmark regression as shown in column (4) and column (5). The regression results are shown in Table 3 that urbanization and industrial structure variables are both significant at the level of 1% or 5%, and the coefficients of each variable do not change significantly, which are basically consistent with the benchmark regression results. The overall fit of the model is above 0.90, indicating that the regression results are robust.

Table 3.

Robust Test Results.

Explanatory variable	Dependent variable: lnlottery
Explanatory variable	(1)	(2)	(3)	(4)	(5)
Lnlandu	0.1914*** (0.0542)		0.1813*** (0.0534)		0.2142*** (0.0663)
lnpeopleu				1.4657*** (0.2744)
lnstr2gdp		0.2802*** (0.0572)	0.2716*** (0.0565)	0.1366** (0.0682)
lnstr3gdp					0.5736*** (0.1148)
Lngdp	0.0401*** (0.0045)	0.3139*** (0.0563)	0.3062*** (0.0556)	0.1735** (0.0670)	0.6124*** (0.1147)
lnperrevenue	1.3838*** (0.0487)	1.2466*** (0.0608)	1.1875*** (0.0624)	0.8740*** (0.0897)	0.5100*** (0.1669)
lnpeople	0.7076*** (0.0460)	0.4426*** (0.0753)	0.4267*** (0.0746)	1.7225*** (0.3906)	1.0317*** (0.3805)
lneducation	0.1798 (0.2232)	−0.1948 (0.2190)	−0.0024 (0.2234)	0.1129 (0.2634)	0.4894* (0.2772)
Constant	−8.0377*** (0.4904)	−6.6600*** (0.5477)	−6.8611*** (0.5444)	−17.9125*** (2.9722)	−10.1952*** (2.8530)
R ² between groups	.9205	.9260	.9326	.9087	.9183
F test p value	.0000	.0000	.0000	.0000	.0000
Hausman test p value	.3388	.0822	.2320	.0000	.0011
Model	RE	RE	RE	FE	FE
Subjects	31	31	31	31	31
No. of sample	372	372	372	372	372

p < .10. **p < .050. ***p < .010.

Heterogeneity Analysis

In order to further examine whether the effects of urbanization and industrial structure on lottery differ in category, this paper divides the lottery into two sub-samples, sports lottery and welfare lottery, for regression analysis according to the current classification standard issued by the Ministry of Finance of the People’s Republic of China. In addition, considering the varied levels of economic and social development across China, in-depth analysis is required at the regional level. Therefore, we divide 31 provinces (autonomous regions and municipalities) into three sub-samples of eastern, central and western regions for empirical test. After F-test and Hausman test, appropriate models are used for regression analysis, and the results are shown in Table 4.

Table 4.

Test for Heterogeneity.

Explanatory variable	Lottery category		Region division
Explanatory variable	Sports lottery	Welfare lottery	Eastern	Central	Western
lnpeopleurban	1.1980** (0.5472)	1.8101** (0.3170)	1.9675** (0.4938)	1.0761* (0.5988)	0.5339 (0.3420)
lnstr3gdp	0.5790** (0.2503)	0.0964 (0.1450)	0.4711** (0.2133)	0.3605 (0.2380)	−0.0698 (0.1851)
Lngdp	0.6010** (0.2500)	0.1528 (0.1448)	0.5074** (0.2129)	0.3988* (0.2372)	−0.0365 (0.1856)
Lnperrevenue	0.3831 (0.3241)	0.6987** (0.1877)	−0.0265 (0.2498)	0.5516* (0.2846)	1.4761** (0.2356)
Lnpeople	1.4221* (0.7902)	2.1927** (0.4577)	2.1137** (0.5623)	0.6193** (0.2873)	0.7209** (0.1805)
Lneducation	−0.1811 (0.5246)	0.5481* (0.3039)	1.4509* (0.8121)	1.8087** (0.7798)	−0.2824 (0.2873)
Constant	−15.2595** (6.2170)	−22.7256** (3.6009)	−22.0853** (4.7843)	−10.9901** (3.3121)	−8.4491** (1.6646)
R ² between groups	.8501	.8737	.9529	.7776	.9119
F test p value	.0000	.0000	.0000	.0000	.0000
Hausman test p value	.0263	.0000	.0002	.1310	.6247
Model	FE	FE	FE	RE	RE
Subjects	31	31	11	8	12
No. of sample	372	372	132	96	144

p < .10. **p < .050. **p < .010.

Table 4 shows that the effects of urbanization level and industrial structure on lottery sales differ in types. High level of population urbanization has a positive impact on both sports lottery and welfare lottery sales, and for every 1% increase, sports lottery and welfare lottery sales will increase by 119.8% and 181%, respectively. However, the impact of the tertiary industry’s GDP ratio on sports lottery and welfare lottery sales is quite different, that is, for every 1% increase in the ratio of tertiary industry to GDP, sports lottery sales will increase by 57.9%, while welfare lottery sales are not significantly related. In this regard, we believe that sports lottery sales, including football lottery, basketball lottery, and ice hockey lottery, are strongly correlated with sporting events. With the optimization and upgrading of industrial structure, China continues to introduce big top-level events to enrich and improve the rules and structure of sports lottery, which will boost sports lottery market and further expand the scale of lottery citizens, thereby lifting up sports lottery sales.

Additionally, we find that the level of urbanization and industrial structure in different regions differs in the impact on lottery sales. The level of population urbanization and the proportion of tertiary industry in GDP in the eastern region affect lottery sales at a significant level of 1% and 5% respectively. Every 1% increase in population urbanization will increase lottery sales by 196.88%. For every 1% increase in the proportion of tertiary industry in GDP, lottery sales will increase by 47.1%. Lottery sales in the central region only show a positive relation with the level of population urbanization. Every 1% increase in population urbanization will end up with 107.6% increase in lottery sales. In the western region, both indicators are not significantly correlated with lottery sales. It can be seen from the above statistics that the level of urbanization and industrial structure upgrading in the eastern region have a stronger positive effect on lottery sales than in the central and western regions. There are several possible reasons for this phenomenon. First, urbanization and industrial structure upgrading in the eastern region started earlier, and the superimposed effects of better consumer service facilities and more diversified consumer products have changed the consumption concept of residents from “basic needs” to “leisure demands.” Lottery as a part of “leisure demands” consumption will undoubtedly increase. However, as latecomers, the effects of urbanization and industrial structure optimization on the lottery have not yet fully revealed in the central and western regions. The second explanation may be due to the “micro-expenditure” attribute of lottery, the pressure on medical care, housing and education brought about by urbanization and industrial structure upgrading will not reduce lottery citizens’ enthusiasm for lottery purchases. On the contrary, a large number of migrant workers attracted by “better welfare” flow into eastern cities and have their income level higher, thus driving lottery purchasing power and lottery sales. Finally, the transition from low value-added industries to high value-added ones in the eastern region attracts a large influx of highly educated talents. They are generally more interested in the relatively complex rules of lottery games and are more willing to support national construction and public welfare undertakings through lottery purchase.

Conclusions and Discussion

Sustainability is the prevailing trend of global development at present and in the future. Urbanization and industrial structure upgrading can not only play a key role in achieving sustainable economic growth, but also drive solid the high-quality progress in the lottery industry. In this context, this paper takes China as a representative of emerging markets, collects provincial panel data running from 2008 to 2019. Urbanization and industrial structure are selected as core explanatory variables. Regional GDP, per capita disposable income of urban residents, population size, and average years of education of residents are taken as control variables, with economic, demographic, and educational factors taking into consideration. Fixed-effect models and random-effect models are built to analyze the effects of urbanization level and industrial structure upgrade on lottery consumption. The study found that (1) every 1% increase in urbanization level will result in 141.6% increase in lottery sales; every 1% increase in the upgrading of industrial structure will result in 30.6% growth in lottery sales. (2) both sports lottery and welfare lottery consumption are significantly and positively correlated with urbanization level, but the industrial structure upgrading only has a positive effect on sports lottery sales. For every 1% increase in the upgrading of industrial structure, sports lottery sales will increase by 57.9%. (3) from the perspective of the three regions, the impact of urbanization and industrial structure upgrading on lottery consumption in the eastern, central and western regions shows a decreasing trend, and the positive effects are very prominent in the eastern region. For every 1% increase in the level of population urbanization in the eastern region, lottery sales will increase by 196.8% and for every 1% increase in the tertiary industry’s GDP ratio, lottery sales will increase by 47.1%.

Therefore, this paper argues that both the increased level of urbanization and the upgrading of industrial structure contribute to the flourishing of the lottery industry. However, research in this area is mainly concentrated in developed western countries (Filer et al., 1988; Coughlin, 2006), where the lottery industry is highly marketed, and the high prizes and rich gameplay attract a large number of lottery buyers. The lack of literature in this area in emerging economies may be due to the sensitivity of the topic (Lai, 2006), or lack of interest in lottery development research due to their relatively poor economies and low urbanization levels. Scholars have found very limited research on gambling, including lotteries, in India, but a high rate of problem gambling in India can still be found in the only results available (George et al., 2016; Jaisoorya et al., 2017). Bhatia (2019) et al. further studied the current state of gambling in Goa, India, and found that its high prevalence of gambling is closely related to social issues such as interpersonal relationships, unemployment levels, and rural residence rates. The literature on Brazil is similarly scarce. Scholars have argued that Brazil’s strict gambling laws contribute to its low rates of problem gambling (Calado et al., 2017). Why do India and Brazil, both emerging economies, differ in gambling phenomena? In fact, we can find that such a result coincides with the pattern of urbanization in Brazil and India. The World Bank (2019) statistics showed that Brazil’s urbanization rate is 87%, much higher than India’s 34%. Through legislative regulation and professional training in the process of urbanization and industrial structure upgrading can well solve the problematic lottery buying behaviors. Based on this, we use data of China’s lottery to provide empirical explanations for further understanding the contribution of urbanization and industrial structure to lottery industry development in developing countries.

Practical Implications

As urbanization levels in developing countries continue to rise and industrial structures continue to be optimized and upgraded (Yang et al., 2016; Y. Zhang & Wan, 2015), it is necessary to guide and reform the traditional patterns of lottery consumption into sustainable consumption patterns to better meet people’s growing demands for lottery consumption and promote consumption upgrade. In order to achieve healthy and sustainable development in emerging economies, the lottery industry should focus on the following aspects.

First, promote the coordinated development of lottery industry and related industries to achieve the scale effect of lottery consumption. Urbanization and industrial structure upgrading have greatly reinforced a sense of well-being and belonging for residents. In this context, the sustainable development of lottery industry requires more innovation in products and services. In the short run, regions with more sound service infrastructure shall try to combine lottery with financial products to seamlessly meet the needs of contemporary young people for entertainment and financial management. In the long term, it is necessary to give full play to the strengths of lottery and actively cooperate with sport events, retailing, gaming, and other formats of industry. The relatively backward regions should exploit their advantages of local natural environment to integrate the lottery industry with tourism.

Second, strengthen lottery publicity and guidance, innovate categories and rules and playing methods, and continue to stabilize and tap into potential lottery consumer groups. At the policy level, legalization and standardization of lottery is a must and more efforts shall be made to inform of the contributions lottery has done to social welfare and sports, in a bid to enhance public awareness of lottery and then mobilize existing and potential lottery citizens’ enthusiasm to participate. At the business level, it is recommended to combine popular sporting events and projects to launch diversified lottery types, and appropriately develop lottery products with relatively complex, novel, and interesting rules, so as to encourage people to participate, to recognize and to experience, which would further generate people’s lottery consumption behavior sustainably.

Third, promote the digitalization of lottery industry to uplift the efficiency of lottery marketing in the digital age. Digital-oriented technology transformation has been driving changes in production mode globally. Countries shall firmly grasp the period of strategic opportunities for digital construction and its early advantages in the layout of digital economy. Under the premise of protecting national lottery data and personal information of lottery citizens, China shall look to integrate lottery marketing with big data, cloud computing, Internet of Things, artificial intelligence, 5G, and other new technologies and formats. We can fully exploit the potential spending power of lottery citizens in different regions and from different income classes to boost lottery sales by setting up lottery sites in regions and nationwide through intelligent layout in conjunction with differentiated marketing strategies. In this way, we will lay a solid foundation for the sustainable development of regional economies.

Research Limitations

Certainly, this paper also has some limitations. For instance, the lottery consumption is largely dependent on individual preference which is not included in the research structure of this paper. Therefore, some micro-level factors will be important research directions for our future consideration. Furthermore, this article focuses on China’s lottery industry. In fact, its development is different from other developing countries. Therefore, some comparative studies among developing countries can be carried out in order to expand the research horizon of lottery industry in developing countries.

Footnotes

Acknowledgements

The authors want to extent their gratitude toward the editors and the anonymous reviewers for their valuable suggestions that improved the quality of the paper significantly.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was funded by the National Social Science Fund of China, grant number 19ATY008.

ORCID iDs

Yufei Bai

Hemin Song

Availability of Data and Material

Publicly available datasets were analyzed in this study. The data can be found here: [http://zhs.mof.gov.cn/zonghexinxi/index.htm, ].

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