Income is considered as one of the factors to determine success in life (Liu, Kuo, He, & Liu, 2006). Banks often rely on models which predict credit risk to decide whether to lend to a loan seeker (Junjie Liang, 2011). Any event, which is subjected to uncertainty, is predicted through actuarial models.
The issue of wage projections is old in banking, insurance & pension industries. Different models are being used to predict average salary profiles. Some researchers (Shauna L. Shapiro,Gary E. Schwartz, 1998) provided a view on this issue in terms of pension industry. Carrier and Sand (1998) gave econometric perspective of salary profile and the relevance of projected wage and its relationship.
A person’s salary details are needed in the development of the pension system, however the modelling is often hamstrung by lack of adequate information. According to Carrier & Shand (1998), the challenges surrounding the projection of the income are many, one being that individuals does not easily volunteer income information. Bone & Mitchell (1997) were of the view that more data should be collected to construct better models of retirement benefits.
Decision model for pension systems
The capability to develop decision science based model and perform analysis is an integral part of pension systems right from design to planning as well as control of systems (Gustman, Alan L. Mitchell, Olivia S., 1993). Models where different variables have high correlation or causality can be used to optimize decision-making. These models can be early warning system.
Income expectations also determines schooling and profession related decisions. However, not much is known in terms of how people reacts to fluctuation in income inflow vis. a vis. income expectation (Manski,1993).
Income prediction of credit card users in the US
According to Prater( 2010), credit card companies in the US, have to comply with the Credit Card Act of 2009 and subsequently whenever an applicant applies for a new credit card or for increasing the limit on an existing credit card, there would be thorough check up of income and assets of the applicant to ascertain whether the applicant can payback. According to this law Federal Reserve allowed issuers of spot credit facility providers (such as supermarket stores) to use statistical income estimation models developed by the credit reporting bureaus. This is done to minimize default by the borrowers, and limit their potential to stress the banking/financial system.
While approving credit limit of a customer, a bank/credit company considers:
- Credit history
Determinants of a good income in the US
It is generally considered that few determinants would influence the amount of earning in adult life. Factors which are generally considered includes race, intelligence, gender, educational levels etc.(Liu et al., 2006). In this study Liu et al.,2006, considered whether as a child, an individual enrolled in a quality educational program, gender, race (six different races are considered), educational qualification (six different levels of education are considered), working hours (the number of hours in a year, an individual has worked).
Based on National Longitudinal Survey of Youth (NLSY), Murray(1997) divided youth population into five categories of intelligence, i.e. from very dull to very bright, and concluded that adults with lower intelligence would have lower income. Terman, & Oden, (1959) conducted longitudinal study of brilliant students for almost four decades starting from 1920 onwards (Renzulli). He found that earning is dependent on gender with earning of female students being less than, that of male students, in post study years. Another longitudinal study of such kind (Koskinen, Lasse Tapio Nummi, 2010) came to the same conclusion. This was corroborated in the findings of Subotnik, Karp, and Morgan, (1989).
Determinants of a good income in India
A recent study has shown that income growth rates in the context of Indian corporate sector are dependent on various macro-economic factors. According to the report, percentage increase in income is dependent on, change in GDP (Gross Domestic Product) growth rate and inflation rate, also apparently there is no correlation between index of industrial productivity (IIP) growth rate and salary growth rate (Karunanidhi, 2013).
|Financial Year||Salary Growth Rate in India||GDP Growth Rate||Consumer Price Index(CPI) Based Inflation Growth Rate||Wholesale Price Index(WPI) Based Inflation Growth Rate||CNX500 Index Return|
|2001-02||12.8 %||5.52 %||3.67 %||3.60 %||-26.70 %|
|2003-04||11.5 %||8.06 %||3.71 %||5.46 %||44.42 %|
|2004-05||13.7 %||6.97 %||3.89 %||6.48 %||50.37 %|
|2005-06||14.1 %||9.48 %||3.97 %||4.47 %||45.19 %|
|2006-07||14.4 %||9.57%||6.27 %||6.59||29.51|
|2007-08||15.1 %||9.32 %||6.37 %||4.74 %||147.29 %|
|2008-09||13.3 %||6.72 %||8.35 %||8.05 %||-59.39 %|
|2009- 10||6.6 %||8.39 %||10.88 %||3.80 %||8.41 %|
|2010-11||11.1%||8.39 %||11.99 %||9.56 %||28.69 %|
|2011-12||12.6 %||6.48 %||8.86 %||8.94 %||3.44 %|
Year on year growth rate in salary and macro economic parameters taken from understanding trends in salary escalation rates in Indian private sector, compiled by institute of actuaries of India.
In a nutshell, it can be concluded that, various personal traits, as well as macroeconomic parameters determine income growth rates, for individuals.
- Bone, Christopher, M. & Mitchell, Olivia, S. (1997). Building Better Retirement Income Models. North American Actuarial Journal, 1(1), 10–11.
- Gustman, Alan L. Mitchell, Olivia S., T. L. S. (1993). The Role of Pensions in the Labor Market.
- J., Carrier, K., S. (1998). No Title: New Salary Functions for Pension Valuations. North American Actuarial Journal, 3, 18–26.
- Junjie Liang. (2011). Predicting borrowers’ chance of defaulting on credit loans. Retrieved February 11, 2015, from http://cs229.stanford.edu/proj2011/JunjieLiang-PredictingBorrowersChanceOfDefaultingOnCreditLoans.pdf.
- Karunanidhi, M. (2013). UNDERSTANDING TRENDS IN SALARY ESCALATION RATES IN INDIAN PRIVATE SECTOR. Retrieved February 11, 2015, from http://actuariesindia.org/downloads/Research/completed/Understanding Salary Escalation Trends in Indian Private Sector.pdf.
- Koskinen, Lasse Tapio Nummi, J. S. (n.d.). Modeling and Predicting Individual Salaries: A Study of Finland’s Unique Dataset. Retrieved February 11, 2015, from http://www.actuaries.org/PBSS/Colloquia/Helsinki/Papers/Koskinen.pdf.
- Liu, H., Kuo, Y., He, Y., & Liu, J. (2006). Bayesian Regression Model For Predicting Income — Bayesian Statistics Project Final Report, 1988, 1–9.
- Manski, C. F. (1993). Adolescent Econometricians: How do Youth Infer the Returns to Schooling? (pp. 43–57).
- Murray, C. (1997). IQ and economics success.
- Prater, C. (2010). Card issuers ready to check cardholder income, assets. creditcards.com. Retrieved from http://www.creditcards.com/credit-card-news/credit-card-application-income-check-1282.php.
- Renzulli, J. S. (n.d.). The Three-Ring Conception of Giftedness: A Developmental Model For Promoting Creative Productivity. Retrieved February 11, 2015, from http://www.gifted.uconn.edu/sem/pdf/the_three-ring_conception_of_giftedness.pdf.
- Shauna L. Shapiro,Gary E. Schwartz, G. E. S. (1998). Effects of Mindfulness-Based Stress Reduction on Medical and Premedical Students. Retrieved February 11, 2015, from http://www.openground.com.au/articles/Shapiro Schwartz Bonner 1998.doc.pdf.
- Subotnik, R., Karp, D., Morgan, E. (1989). High IQ children at midlife: An investigation into the generalizability of Terman’s genetic studies of genius. Roeper Review, 11(3), 139–144.
- Terman, L.M., & Oden, M. H. (1959). The gifted group at mid-life, thirty-five years follow-up of the superior child: Genetic studies of genius. Stanford University Press.
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