The nexus between Demographics and Investment Behaviour

 

Anju K J

Assistant Professor, School of Business Studies and Social Sciences, Christ University, Bengaluru, 560076

 

ABSTRACT:

Demographics are indeed a strategic element for an economy's growth potential. IT sector plays an important role in its contribution towards India’s GDP. The aim of this research was to understand the effect of demographics on the investment behaviour of the IT professionals in Bengaluru. The rationale behind choosing this topic is that IT professionals have gained attention of economists but there still remains untapped potential of their income. Primary data was collected through questionnaire for 8 months from 439 respondents working in Bengaluru. Correlation, Factor Analysis and Multiple Linear Regression were conducted to answer the research questions. The results of the study have vital practical implications that can assist the policy makers and the investment agencies to develop appropriate strategies for promoting investments and economic growth.

 

 

 

INTRODUCTION:

Gross Domestic Product is growing faster in the developing economies than the developed because of the growth of Savings and Investment. The demographic composition of a country’s population is associated with its saving rate and investment. Information Technology (IT) sector plays an important role in its contribution towards India’s GDP and IT professionals are in high demand. Capital formation in the economy will be more effective if the incomes earned by the IT professionals are channelised into savings. Their investment behaviour have to be thoroughly studied in order to encourage them to contribute towards economic growth. There are a numerous factors that impact the individuals to make their investment decisions. Demographic factors of investors such as Upbringing Status, Qualification, Family Status, Earning Status, Gender, Annual Income,

 

Dependants, Marital Status, Age Group, Designation and Work Experience have much significance in the investment decision-making process, especially in the Indian context. Importance of demographic characteristics of individual investment behaviour has been identified as the problem for the study. The aim of this research is to understand the effect of demographics on the investment behaviour of the IT professionals in Bengaluru.

 

LITERATURE REVIEW:

A considerable amount of literature has been published on the impact of demographics on saving and investment behavior of investors. Many researchers have concluded that variables such as age, gender and income (Kapil and Jeet, 2015; Wubie, Dibabe and Wondmagegn, 2015; Velmurugan, Selvam and Nazar, 2015; Ganapathi, 2014; Geetha and Vimala, 2014; Sireesha and Laxmi, 2013; Virani, 2013; Jain and Ranawat, 2012; Bahl, 2012; Kothari, 2012) impacted the investment decision-making.

 

Previous studies revealed that education (Chaffai and Medhioub, 2014; Bishnoi, 2013) and occupation (Das and Jain, 2014; Chakraborty, 2012; Harikanth and Pragathi, 2012; Kibet et al, 2009) had a major impact on the choice of investment avenues. In recent years, there has been an increasing amount of literature on how marital status (Achar, 2012), family status (Seong, Kai and Joo, 2011), family earning status (Rehman, Bashir and Faridi, 2011), number of dependents (Gedela, 2012), upbringing status and work experience affected participants attitude toward savings (Turner and Manturuk, 2012). Kumar, Vijayabanu and Amudha (2012) found that demographic factors except gender had a positive impact on the financial literacy of the respondents. Issahaku (2011) revealed that income, occupation and expenditure excluding age had a major effect on saving. According to Bashir et al. (2013); Bhushan and Medury (2013); Arti, Julee and Sunita (2011); Barber and Odean (2001); Embrey and Fox (1997) and Bajtelsmit and Bernasek (1996) females are more risk averse than males.

 

Lewellen et al. (1977) found that age, sex, income and education affect investor’s preferences. The household saving and investment decision is influenced by different demographic variables such as age, gender, education, marital status, culture, religion and dependent family size (Graff et al., 2008). The society we live in is full of constrains likely due to variations and distinctness in the age, sex, culture, tradition, social taboos, and many more which by playing an important role determines the saving and investment behavior of any region, state or country (Oyejide, 1999). According to Odoemenem et al. (2013), finding shows that sex has significant influence on saving. Savings is low for younger groups, high for middle age groups, and again low among old age groups (Kumar and Jagadeshwara, 1985). Demographic variables like age, gender, education, occupation plays a very important role in investment decision (Jain and Mandot, 2012; Jamshidinavid et. al., 2012; Geetha and Ramesh, 2012).

 

RESEARCH OBJECTIVES:

1.    To determine the impact of demographics on the investment objectives.

2.    To identify whether demographics (Dependents, Upbringing Status, Annual Income, Family Status) predict the efficacious factors of investment decisions.

 

RESEARCH METHODOLOGY:

Judgement Sampling and Snowball Sampling were applied. Primary data was collected through self-administered structured questionnaire and secondary data consisted of Books, Periodicals, Reports, Company Websites, Articles and Journals. The statistical tools and techniques were Descriptive statistics, Reliability test, Factor Analysis, Pearson’s Correlation Coefficient and Multiple Linear Regression. The softwares used for analysis were IBM SPSS Statistics Version 23 and Microsoft Excel 2011. Questionnaire was personally administered to 50 IT professionals from the actual sample for pilot study. Feedback from six University professors and two Senior IT managers affirmed that the questionnaire had face validity. The Overall scale of 54 items had Cronbach’s Alpha of 0.81 (Hair et al., 1998). Therefore the questionnaire was confirmed and administered. Data was collected from 439 respondents. The Shapiro Wilk test confirmed that data is normally distributed.

 

DATA ANALYSIS:

1.    Percentage Analysis

The data pertaining to the demographics are presented in Table 1.

Table 1: Demographic Profile

Source: Primary data through questionnaire

Table 1 shows the Case summaries on respondent’s demography. 439 responses were received from IT professionals employed in various companies of Bengaluru.

 

2. Factor Analysis , Correlation and Multiple Linear Regression

H₀ - Demographic characteristics of IT professionals do not impact the investment objectives

H₁ - Demographic characteristics of IT professionals impacts the investment objectives

Identification of factors from Dependent Variables – To understand the investment objectives, fourteen statements were identified.

 

Table 2: KMO and Bartlett's Test

 

 

Looking at the Table 2, the KMO measure is 0.867, which falls into the range of being good (Kaiser, 1974; Hutcheson and Sofroniou, 1999). The Chi-Square value of Barlett’s test of Sphericity is 2520.222 and the significance value is 0.000 indicating that the data is suitable for factor analysis.

 

Table 3: Components and Variances Explained

 

Extraction Method: Principal Component Analysis

Rotation Method: Varimax with Kaiser Normalization

Table 3 shows that the first component accounts 40.363% of the variance, the second 9.972% and the third 8.258%.

1.    Family needs include Education – Own / Children, Risk coverage, Security after retirement, Child’s marriage, Emergency purposes (Medical etc), Other family needs, and Entertainment.

2.    Income generation includes Earn Returns, Hedge against inflation, Multiply the savings and Buying capital assets.

3.    Legal Formalities includes Tax benefits, Government Regulation and Repayment of debt.

 

Table 4: Correlations Between Demographics and Investment Objectives

Age Group

Gender

Marital Status

Qualification

Experience

Designation

Family Status

Dependants

Earning Status

Upbringing Status

Annual Income

Family Needs

Income Generation

Legal Formalities

Age Group

1

Gender

-.203**

1

Marital Status

.504**

-.053

1

Qualification

.179**

-.181**

.098*

1

Experience

.811**

-.206**

.553**

.182**

1

Designation

.644**

-.184**

.444**

.164**

.766**

1

Family Status

-.006

.045

-.024

.011

.058

.095*

1

Dependants

.285**

-.240**

.110*

.152**

.230**

.118*

-.286**

1

Earning Status

-.236**

.160**

-.275**

-.084

-.233**

-.185**

-.069

-.263**

1

Upbringing Status

.087

.132**

.100*

.010

.073

.139**

.098*

-.106*

.056

1

Annual Income

.636**

-.171**

.432**

.150**

.770**

.699**

.001

.148**

-.187**

.128**

1

Family Needs

.069

.052

-.040

.063

.032

-.036

-.144**

.171**

-.016

-.005

.032

1

Income Generation

.040

-.013

-.004

-.068

.054

.121*

.119*

-.033

.086

.135**

.122*

.000

1

Legal Formalities

-.135**

.056

-.082

.017

-.161**

-.125**

.051

.022

.064

.074

-.200**

.000

.000

1

** Correlation significant at 0.01 level (2-tailed)

*Correlation significant at 0.05 level (2-tailed)

 

Table 5: Regression between Demographics and Family Needs

 

 

The results of Regression analysis  are shown in Table 5. The multiple correlation coefficients (R) are 0.259 for the model which exhibits a fair amount of correlation among the predictors and the dependent variable. The ANOVA tab indicates that the F statistic is 2.785 with a significance level of 0.002.

 

Family Needs = (-0.176) + (.104)Age Group + (.275) Gender – (.209) Marital Status + (.119) Qualification + (.042) Experience - (.085)Designation + (.033)Annual Income - (.261)Family Status + (.112) Dependents - (.012) Earning Status + (.031) Upbringing Status

 

Table 6:  Regression between Demographics and Income Generation

 

The results of Regression analysis  are shown in Table 6. The multiple correlation coefficients (R) are 0.267 for the model which exhibits a fair amount of correlation among the predictors and the dependent variable. The ANOVA tab indicates that the F statistic is 2.985 with a significance level of 0.001.

 

Income Generation = (-1.174) - (.005)Age Group - (.088)Gender – (.049)Marital Status - (.183) Qualification - (.115) Experience + (.085) Designation + (.109) Annual Income - (.316)Family Status + (.038) Dependents - (.161) Earning Status + (.216) Upbringing Status

 

Table 7: Regression between Demographics and Legal Formalities

 

 

The results of Regression analysis  are shown in Table 7. The multiple correlation coefficients (R) are 0.254 for the model which exhibits a fair amount of correlation among the predictors and the dependent variable. The R-square value of 0.064 explains that linear relation exists between predictors and the dependent variable (predictors can bring 6.4 % of variation in the dependent variable). The ANOVA tab indicates that the F statistic is 2.674 with a significance level of 0.002.

 

Legal Formalities = (-0.885) - (.035) Age Group + (.057) Gender + (.046) Marital Status + (.088) Qualification - (.041) Experience + (.024) Designation - (.128) Annual Income + (.185) Family Status + (.090) Dependents + (.070)Earning Status + (.207) Upbringing Status

 

3. Factor Analysis , Correlation and Multiple Linear Regression

H₀ - Demographic (Dependents, Upbringing Status, Annual Income, Family Status) of IT professionals do not predict the efficacious factors of investment decisions

 

H₂ - Demographic (Dependents, Upbringing Status, Annual Income, Family Status) of IT professionals predict the efficacious factors of investment decisions.

 

Identification of factors from Dependent Variables – To understand the efficacious factors that influence the investment decision, eighteen statements were identified.

 

Table 8: KMO and Bartlett's Test

 

Looking at the Table 8, the KMO measure is 0.924, which falls into the range of being superb (Kaiser, 1974; Hutcheson and Sofroniou, 1999). The Chi-Square value of Barlett’s test of Sphericity is 3847.725 and the significant value is 0.000 indicating that the data is suitable for factor analysis.

 

 

 

 

 

Table 9 : Components and Variances Explained

 

Table 9 shows that the first component accounts for 43.331% of the variance, the second 8.373% and the third 6.778%.

 

1)   Fiscal factors include Portfolio diversification needs, Your personal experience/intuition, Your family and friends, Tax shelter, Public image of source of investment, Awareness of a new product, Major life events like retirement etc and Economic events like budget etc.

2)   Regulatory factors include Safety of principal, Maturity of investment, Low initial amount of investment, Easily available and understandable, Lock in period of funds and Your age, health, income and responsibilities.

3)   Monetary factors include Liquidity of investment, Regularity of returns, Interest rate of investment and Protection against risk.

 

 

 

 

Table 10: Correlation between demographics and the efficacious factors

** Correlation significant at 0.01 level (2-tailed)

* Correlation significant at 0.05 level (2-tailed)

 

 

 

Table 11: Regression between Demographics and Fiscal factors

 

 

 

The results of Regression analysis  are shown in Table 11. The multiple correlation coefficients (R) are 0.218 for the model which exhibits a fair amount of correlation among the predictors and the dependent variable. The ANOVA tab indicates that the F statistic is 5.424 with a significance level of 0.000.

Fiscal factors = (-0.905) - (.050)Annual Income + (.026) Family Status + (.095)Dependents + (.425) Upbringing Status

 

 

 

 

 

 

Table 12 : Regression between Demographics and Regulatory factors

 

 

 

The results of Regression analysis  are shown in Table 12. The multiple correlation coefficients (R) are 0.162 for the model which exhibits a fair amount of correlation among the predictors and the dependent variable. The ANOVA tab indicates that the F statistic is 2.939 with a significance level of 0.020.

Regulatory factors = (-0.493) - (.083)Annual Income + (.121) Family Status + (.102) Dependents + (.119) Upbringing Status.

 

 

 

 

 

Table 13: Regression between Demographics and Monetary factors

 

 

 

The results of Regression analysis  are shown in Table 13. The multiple correlation coefficients (R) are 0.230 for the model which exhibits a fair amount of correlation among the predictors and the dependent variable. The ANOVA tab indicates that the F statistic is 6.054 with a significance level of 0.000.

 

Monetary factors = (-1.013) + (.090) Annual Income + (.281) Family Status - (.036) Dependents + (.222) Upbringing Status.

 

 

FINDINGS AND DISCUSSIONS:

1.    57% of the respondents annually save upto Rs 150000 and 24% of them between Rs 150000-Rs 300000.

2.    45% of the respondents annually saved upto 10% from their salary for investment purposes and 28% of them saved between 11%-20%.

3.    The expected rates of return on investments were between 11%-20% for 45% of the respondents.

4.    Regularity of returns, Interest rate of investment, Protection against risk, Safety of principal, Maturity of investment, Ease of availability, Own age,health,income and responsibilities, Personal experience/intuition, Tax shelter and Major life events like retirement etc were the major vital factors that influenced the investment decision.

5.    The utmost significant investment objectives of IT professionals were to earn returns, avail tax benefits, multiply the savings, provide security after retirement and meet emergency purposes.

6.    Demographic characteristics of IT professionals impacted the investment objectives. Gender, family status and number of dependents had a significant impact on the family needs motive. Annual Income, family status, earning status and upbringing status influenced the income generation motive. Legal formalities were affected by annual income, number of dependents and upbringing status.

7.    Demographics (Dependents, Upbringing Status, Annual Income, Family Status) of IT professionals predicted the efficacious factors of investment decisions. The number of dependents and upbringing status influences the fiscal factors. Whereas annual income and dependents impacts the regulatory factors. Monetary factors were affected by annual income, family status and upbringing status.

 

CONCLUSION:

This research study has examined the effect of demographic characteristics on the investment behavior of IT professionals in Bengaluru. Understanding it can be of great assistance to the investors, to the policy makers, to the investment agencies, to the researchers as well as managers of the firms to adapt themselves to cater to the varying behaviour of each investor and economy as a whole.  The study revealed that demographic variables influenced the investment objectives and predicted the efficacious factors of investment decisions. The results of this research study have the following vital practical implications that can assist the policy makers and the investment agencies to develop appropriate strategies for promoting investments and economic growth.

·      To understand the various needs of the investors and develop products as per their requirement.

·    Financial literacy or awareness programmes have to be conducted to make individuals understand their financial necessities at different phases of life and the investment avenues offered to them. 


·      The financial product issuers should possess exhaustive knowledge of the different investor categories based on their life stage, emotional risk tolerance and their financial literacy level to target each of these segments.

 

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