CHAPTER I. INTRODUCTION
1.1 RESEARCH BACKGROUND AND MOTIVATION
Chapter I. INTRODUCTION
1.1 Research Background and Motivation 1.1.1 Current Salary issues in Taiwan
Salary issues had been widely discussed in management and social sciences fields throughout the years, because it could draw dramatic impacts on micro and macro levels.
One of the popular research fields in micro perspectives, such as organizational behavior, deals with organizational performance, job satisfaction, quit intention of employees, and other issues. Salary is a very popular factor for discussing the causal effects among these issues, thus identifying mechanism behind. On the other hand, salary in a macro scale influences every individual from a broader perspective. For example, new entrants to the workplace in Taiwan have been troubled with 22k (Taiwanese legal minimum wage) for many years. Additionally, people nowadays have been arguing about the amendment of the law of minimum wage, which not only associates with labor rights but also draws
implications on the competitiveness of a country. Overall, salary issues are so complicated but crucial to many fields that corporate leaders, policy makers, employees, and household members tend to bring them up from time to time, urging researchers to incessantly conduct analysis on various topics.
1.1.2 Past and current research on salary issues
When coping with issues about impacts of salary, lots of research compare the amount of salary among different people based on several demographic variables, such as job categories, gender, educational levels, and other sociodemographic. For instance, human capital theory talks a lot about the impacts of several variables—formal education years, experiences, working hours, on the level of salary. Human capital is a term popularized by Gary Becker (1964) and Jacob Mincer (1958) which refers to the stock of knowledge,
habits, social and personality attributes embodied in labors so as to produce economic value.
A series of human capital variables, such as formal education, working hours, seniority, marital status, were taken into the examination of wage premium and/or wage penalty (e.g.
Gupta & Shaw, 2014; Blaug, 1972; Becker, 1993; Card, 1995). In addition, issues about salary changes as a result of changes in marital status in terms of women and men, termed as “wage penalty” and “wage premium”, respectively, have been popular research topics
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these years (Hsu & Chiou, 2015). Consequently, research on salary comparisons among people has been developed for many years, and mostly has been done by cross-sectional analytical techniques.
Nevertheless, discussions about salary nowadays are not only limited to between-subject analysis, but are expanded to areas which are suitable for using temporal variables, such as age, period and cohort variables (Hsu & Chiou, 2015; Fienberg & Mason, 1985).
This kind of research requires more advanced statistical tools; therefore, traditional analytical models, such as cross-sectional models, are still inadequate. Nowadays, more advanced models driven by more comprehensive data—data obtained from individuals of a representative sample being measured across numbers of years— enable researchers to observe individual’s wage profile throughout his/her life span, and compare wage levels among different cohort groups at the same time. Compared to traditional techniques, it is more likely for modern advanced analytical tools to complete wage trajectory research from more perspectives.
1.1.3 Relevant Issues on Age, Period, and Cohort (APC) Analysis
One of the systematic studies of temporal issues is called age-period-cohort (APC) analysis, which deals with the influence of age, period and cohort variables on certain dependent variable (Jaspers & Peters, 2016; Chernyavskiy, Little, & Rosenberg, 2017; Sun
& Chen, 2017). In following sections, in order to make it more clearer, some past research as well as modern applications on age, period and cohort variables are introduced.
Age effects are variations associated with age groups chronologically. They can result from physiological changes, accumulation of social experience, social role changes, or a combination of these. Age effects therefore represent biological and social processes of aging to individuals and also reflect developmental changes across the life span. This can be seen in considerable variations across time and space in many outcomes, such as fertility, schooling, employment, marriage, disease prevalence, mortality, and other socioeconomic issues.
Due to the limitation of statistical tools and database, past researchers had difficulties tracking down and analyzing the wage profile across one’s lifespan. Most research collected cross-sectional data and compared and drew conclusions on the relationship between salary and age. For example, a prior research confirmed that there is a quadratic relationship between age and salary, indicating a downward concave of the relationship (Hsu & Chiou,
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2015). Nonetheless, due to the nature of age, we should focus more on the changing trajectory within subjects as they get older when doing research on salary trajectory.
Period effects are variations over time periods or calendar years that influence all age groups simultaneously. Shifts in social, cultural, economic, or physical environments may in turn induce similar changes in the lives of all individuals at a point in time. Period effects subsume a complex set of historical events and environmental factors, such as world wars, economic expansions and contractions, famine and pandemics of infectious diseases, public health interventions, and technology breakthroughs. For example, the financial crisis
occurred in 2007-2008 or even Taiwan's reform to labor policy and annuity implemented in 2017 are such variations that influence people of all age groups at the same time.
Cohort effects are changes across groups of individuals who experience an initial event such as birth or marriage in the same year or years. A birth cohort moves through life
together and encounters the same historical and social events at the same ages. Birth cohorts that experience different historical and social conditions at various stages of their life course therefore have diverse exposures to socioeconomic, behavioral and environmental risk factors. For instance, in the 1940s, it was the starting point of economic development in Taiwan, so people born before 1940s experienced those years of low wage level. In the 1950s, Taiwanese people encountered the painful Post-Martial Law Period together.
Whereas in 1960s and 1970s, they went through the prosperous periods of economic take-off, and the wage level had peaked in those 20 years. However, in the 1980s, Taiwan saw an economic downturn again, with people experiencing the falling wage level (Directorate General of Budget, Accounting and Statistics, Executive Yuan, R.O.C, 2013). That is to say, cohort effect means that people of different birth cohorts went through different social or economic events as a whole.
1.1.4 The importance, the main problems and solutions of APC analysis
APC analysis has the extraordinary ability to depict the whole complex of social, historical and environmental factors that affect individuals and populations of individuals in the meantime. Many research topics emphasize trajectory, such as the studies of social change, etiology of diseases, aging, and population processes and dynamics, often show that age, period and cohort effects exist in both longitudinal and cross-sectional data. That is to say, age, period and cohort effects exist in the lifespan of different people simultaneously.Researchers from all kinds of fields are interested in separating them to find out the
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mechanism behind those time-related issues.
However, the challenges posed by APC analysis are also well-known because the three variables are completely collinear. Whether time-related changes can be sorted out and separated into age effect, period effect, and cohort effect is viewed as conceptually
important but practically difficult. If we know the age of an individual and what year he/she is measured, we can also know his/her cohort, that is, the year he/she was born, which making it impossible to separate linear age, period and cohort effects from one another. It also means that if we get one of these trends wrong, we will get the others wrong as well.
Data produced by just a linear period effect would look identical to data produced by a combination of age and cohort effects of the same size, but telling them apart is impossible.
And this is the identification problem that lies in solving the APC issues:
Age = Period – Cohort, or Period = Age + Cohort
There have been many attempts to figure out solutions to identification problem.
Thanks to the evolution of advanced statistical tools, research on APC issues has gradually increased over the last few years. Compared to cross-sectional data which mostly applied in prior studies on APC issues, when longitudinal data is adapted, we have to not only focus on the theoretical and practical implications of the issue itself, but also apply rigorous
methodological design with more complex statistical models. Therefore, the rise of high-level statistical models, such as hierarchical linear modeling (HLM), structural equation model (SEM), and latent growth model (LGM) can provide better analytical solutions for processing temporal variables.
1.1.5 Applying an appropriate database to trajectory research
When dealing with broad topics like salary issues, both between-person differences in development and the development of individuals as they age are concerned. As mentioned in the previous section, age-period-cohort analysis can be used to deal with such differences across genders, races, socioeconomic classes and other characteristics, where birth cohort to which an individual belongs and period events (i.e. economic downturn) play important roles in shaping development. And this is one kind of so called “trajectory research”.
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Trajectory methods have emerged over the last several decades as important tools for investigating life course dynamics, including between-person differences in development (George, 2009). Trajectories are simply patterns of variable values over time. For example, one may be interested in trajectories of unemployment rates, stock market closing values, or other macro-level phenomena; on the other hand, one may have interests in trajectories of income or health at an individual level. In addition, trajectories can also be referred as the ordered timing of life events, such as school completion, employment, marriage,
childbearing, retirement, and death (Lynch & Taylor, 2016). In this study, models of such trajectory issues would be restricted to repeated measures (i.e. levels) of the same
phenomenon, not the timing of multiple qualitative events.
For the purpose of illustrating trajectory methods, we rely on a subset data from Panel Study of Family Dynamics (PSFD), a panel study of adults in Chinese families. It not only contains demographic information and human capital variables but also a long-term
collection of household dynamics and income information. Therefore, this study combines the use of this database with high-end statistical methods (i.e. multilevel modeling) to make salary comparisons in both between- and within-perspectives. When measuring variables within subjects, it is more difficult but at the same time more meaningful because such research is able to detect internal variations which couldn’t be achieved in most prior research. For example, when processing age and period variables in this study, it is crucial to note that they are synchronized along the trajectory. Namely, variations along a trajectory can sometimes be counted as the aging process of individuals but other times be referred to as the shifting of time. To deal with such confusing concepts and inseparable relationships between variables in trajectory research, research on age-period-cohort analysis therefore thrives and becomes more and more important.
1.1.6 How to incorporate the Human Capital Theory
Results reveal that human capital is the primary factor that explains wage differences between and within subjects. One of the most popular theories credited to it is human capital theory (Mincer, 1958; Becker, 1964; Blaug, 1976). Economist Mincer constructs an earning function that describes individuals’ wage trajectory across lifespan, and Becker (1964) proposed the word “human capital” to explain that labors’ techniques and abilities can have positive impacts on their level of production and wage. They both talked about some basic
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human capital factors, such as job experiences, personal techniques, and abilities, and their contribution to the enhancement of personal production value and wage premium. Blaug (1976) later elaborated that the enhancement of human capital can be seen as a kind of personal investment, including accumulating years of formal education and work
experiences, finding a better job, as well as looking for better medical and health supports, and the overall purpose is to exchange for monetary and non-monetary compensation. Of all the above discussions about human capital theory, we will select some critical factors that benefit our study the most on wage trajectory.