Although there are already many studies about how health-related behaviors such as alcohol consumption and physical exercise impact on earnings, most studies suggested the relation of earnings and each health-related behavior alone. This study uses longitude data from different waves to reconfirm, or to reevaluate, the relationship between 5 health-related behaviors and earnings, and to integrate locations factor into the relationship between health-related behaviors and earnings. OLS regression analysis is used to estimate the effects on earnings of various typical health-related behaviors variables including cigarette, tea, coffee, and alcohol consumption and physical exercise. In addition, using fixed effect model, the analysis reduces the bias of the estimation caused by omitted variables to the greatest extent and indicates the relation of income and other tradition variables such as occupation and experience. Finally, interaction effect model is used to examine the disparity between inland and coastal provinces. The results show that alcohol, tea, and coffee consumption, and adequately physical exercise all have significant positive effects on income. Only cigarette consumption has a negative association with income. Furthermore, most health-related behaviors variables do not show the income disparity between inland and coastal provinces when taking the location variable into interaction effect model. Health-related behaviors do not contribute more benefits neither to workers in inland provinces nor those in coastal provinces.
The relationship between health-related behaviors and income is well understood through recent studies. Poor health may cause low efficiency during work and lead to low socioeconomic status. People who perform well in health-related behaviors may have a better body to support ones daily work, thus better income. From another perspective, people with higher socioeconomic status may also have greater access to healthier behaviors such as indoor fitness activities, eventually training oneself a stronger body. It is hard to confirm whether income cause health problems or health status will impact income. Although the issue of a casual relationship is not yet proven by extant literature, the correlative relationship between health-related behaviors and income can be illustrated by data.
Furthermore, the income distribution in China is unequal as researchers have noticed. The household income from coastal provinces is higher than that from inland provinces. Under this context, it is significant to show that the unequal distribution of income still exists in China regardless of impacts of common factors that may influence income. The government can then make more efficient public policies, which need or need not related to health-related behaviors, to reduce the unequal problem.
In this research, we used data from CHNS in order to verify our propositions about the relationship between health-related behaviors and incomes.
It is well established that there is a strong positive correlation between individual’s earning and one’s health-related behaviors. I consider alcohol assumption, smoking, participation in physical exercise, and tea drinking as typical health-related behaviors and use these to evaluate the correlative relationship.
Susan L. Etna estimated the constitutional effects of income on health measures, including average daily alcohol consumption and work and functional limitations (Ettner, 1996). Susan used a two-stage instrumental variable estimation method applied to cross-sectional data to produce constant assessments of the effect of income on various health proxies. The result revealed that increase in income is strongly positive correlated to one’s health status; however, the assumption of heavy drinking did not decrease, as this result reflects the prevalence of light social drinking among those of high socioeconomic status. In 2011, a group of researchers from University of Michigan and Columbia University analyzed the socioeconomic and health information from a multi-generational sample (Cerdá, Johnson-Lawrence, & Galea, 2011). From investigation of a lifetime income patterns alcohol consumption, Magdalena Cerda concludes that if the household income is consistently low from one’s childhood to adulthood, one would have higher probability be struggle in heavy drink. People with lower incomes are generally associated with a higher likelihood of abstinence and heavy drinking, and a lower likelihood of light/moderate drinking. Moderate drinker has a higher probability to gain a better income than drinking abstention as found in M. Christopher Auld’s research (Auld, 2005). Auld asserts alcohol and cigarettes using as “endogeneity” of substance abuse to income due to the high correlation of smoking and drinking. Regard of this, the estimation shows that smoking penalty earns 24 percent less than nonsmokers after correcting for endogeneity.
In 2004, Chinese researchers published a review of studies conducted in China on the possible health benefits of tea (Zhu, Huang, & Tu, 2006). Either by reviewing past literature, or highlighting epidemiological studies and clinical trials, they reinforced the benefits of tea such as its potential inhibitory effect on carcinogenesis and its effects on reducing blood lipid levels and preventing plaque formation in the aorta. Such benefits are also mentioned and supported with further research in a paper written by Chung S. Yang and Janelle M. Landau (Yang & Landau, 2000). As mentioned in Susan Ettner’s article, standard economic theory predicts that healthy people will have higher labor force participation rates and higher wages, which translates into higher earnings. Tea has visible good effects on one’s health; hence, it is reasonable to suspect the positive correlation between tea drinking and income.
In 2020, E. D. Tovar-García used Russian longitude data to develop a regression analysis of the relationships between specific types of sports/exercise and earnings (Tovar-García, 2021). Participation in sports/exercise indicates higher productivity, which should translate into higher wages. People who earn money have a higher potential participating in physical exercise. Tovar-García concluded that high income is highly correlated to fitness activity, mainly indoor fitness activity, but not weakly correlated to team sports and even negative significant associations with activities like bicycling and pleasure walking.
As mentioned in The China Quarterly, Azizur Rahman Khan, Keith Grifn, Carl Riskin and Zhao Renwei pointed out that although urban income inequality has decreased after the reform and opening, the distribution of income still has some inequality in China (Khan, Griffin, Riskin, & Zhao, 1992). Rural disparity, for example, is greater: Public policies exacerbate this inequality—the average rural household pays 2 percent of its income as net taxes, while the average urban household receives a net subsidy of nearly two-fifths of its income. In 2005, Peter Pedroni and James Yudong Yao developed non-stationary panel techniques to empirically support that, since the reforms, the long-run trend is toward continued income divergence at the provincial level, and they concluded that economic developments of all coastal provinces are better and faster (Pedroni & Yao, 2006).
Numerous studies focused on China disparity conditions after the open-door economic reforms of the late 1970s. Nonetheless, the degree to which the inequality of average income between coastal versus inland provinces is affected by the factors of health-related behaviors has basically been ignored in most studies. Hence, this paper aims to fill in these blank areas.
Based on an analysis of past literature, I found that little analysis considered the relationship between income and overall health-related behaviors all together, and there was a lack of analysis of the extent to health-related behaviors affected the income disparity in China between coastal and inland provinces. Therefore, this study mainly focuses on the following aspects.
In terms of quantitative data, this study will also select data from recent years, from 2000 to 2015. In terms of region selections, this study also groups provinces with significant geographical location into coastal provinces and inland provinces for analysis.
1) To reconfirm the relationship between income and health-related behaviors, considered smoking, drinking tea, drinking alcohol, and physical exercise as typical health-related behaviors together.
2) Does the disparity between coastal and inland provinces still exists under all other conditions including health-related behaviors in equal status.
The data are taken from China Health and Nutrition Survey (CHNS) from 6 waves (over the years 2000-2015) to create a panel dataset.1 CHNS is an international collaborative project designed to examine the effects of government public policies related to health on the health status of its population. The sample consists of 30000 observations in 15 provinces and several municipal cities.
The sole dependent variable in this study is annual income. In the sample, the average annual income was 8180.27 yuan in 2000 and 33,807.15 in 2015. In the regression analysis, this variable is taken in logarithms to meet a more normal distribution. The explanatory variables include education and experience length, current health status, health-related habits, primary occupation, and other such as individual’s sexuality and location. Respondents reported their years of schooling, and the ages minus years of school and 6 account for the length of working or social experience as 6 is the age that usually the child attend a school. For current health status, aggregation levels are used as follows: 1) Excellent or good (43% of respondents indicating yes); 2) Fair (23%); 3) Poor (0.04%). To approach health-related habits, the research asked the respondents to indicate either they do follow behaviors or not: smoking, heavily drinking alcohol, drinking tea, adequately exercising, and drinking coffee. Mean we got from the regression analysis accounting for the percentage of observation who did do the behavior; take drinking tea (mean = 0.32, SD = 0.47) as an example: 32 percent of all 12,751 respondents have a tea-drinking habit. Different occupations will have different pay for their labor, so we classify primary occupation as technical worker, manager, office staff, farmer, worker and the other.
In addition to record their primary occupation, this study also considers other factors such as the sexuality of respondents and their living location’ whether live in urban or inland provinces.
We conclude descriptive statistics in the below chart (