A study on Savings and Investment Patterns of Women in ...

A study on Savings and Investment Patterns

of Women in Bangalore

Submitted in partial fulfillment of the requirements for the degree of

Master of Philosophy in Management

by

Iyer Anusha Srinivasan

(Roll No. 1130004)

Supervisor:

Dr. Ganesh L

Associate Professor

Institute of Management

CHRIST UNIVERSITY, BANGALORE

2012

Property of Christ University. Use it for fair purpose. Give credit to the author by citing properly, if your are using it.

i

ACKNOWLEDGEMENT

I take this opportunity to express my gratitude to the people who made it possible for me to

complete this dissertation.

I express my sincere gratitude to my guide, Dr Ganesh L for his step by step guidance throughout

the research. Even though the time span within which the research had to be conducted was short,

he ensured that the research was conducted in a meticulous and procedural manner.

I am extremely thankful to Ms. Keerti Mallela, Research Assistant, Christ University for

selflessly helping me with Data Analysis.

My heartfelt gratitude to my parents for the sacrifices they made to facilitate me complete this

course and to my husband for his constant support.


ii

DECLARATION

I, Iyer Anusha Srinivasan, hereby declare that the dissertation, entitled “A study on Savings

and Investment Patterns of Women in Bangalore” submitted to Christ University, in partial

fulfillment of the requirements for the award of the Degree of Master of Philosophy in

Management is a record of original and independent research work done by me during 2011-2012

under the supervision and guidance of Dr.Ganesh L of Christ University Institute of

Management, and it has not formed the basis for the award of any

Degree/Diploma/Associateship/Fellowship or other similar title to any candidate of any

University.

Date: Signature of the candidate


iii

CERTIFICATE

This is to certify that the dissertation, entitled “A study on Savings and Investment Patterns of

Women in Bangalore” submitted to Christ University, in partial fulfillment of the requirements

for the award of the Degree of Master of Philosophy in Management is a record of original

research work done by Ms. Iyer Anusha Srinivasan during the period 2011-2012 of her study in

the Institute of Management at Christ University, Bangalore, under my supervision and

guidance and the dissertation has not formed the basis for the award of any

Degree/Diploma/Associateship/Fellowship or other similar title to any candidate of any

University.

Date: Dr. Ganesh L

Associate Professor


iv

APPROVAL OF DISSERTATION

The Dissertation entitled “A study on Savings and Investment Patterns of Women in

Bangalore” by Iyer Anusha Srinivasan is approved for the degree of Master of Philosophy in

Management

Examiners:

1. ___________________ ___________________

2. ___________________ ___________________

3. ___________________ ___________________

Chairman:

___________________ (Seal)

Date: ___________

Place: __________


v

ABSTRACT

Economic growth of a nation is driven by savings and its transformation into investment. In the

last three decades, Indian economy has emerged as one of the fastest growing economies of the

world. Households are the biggest contributors to India’s savings rate; their savings equal 23

percent of India’s GDP. Though the percentage of savings by household sector in financial assets

is increasing year on year, what is worrying is that only around half of the household savings in

India are invested in financial instruments. Knowledge about saving and investment preferences,

gender-wise, will help to design effective investment instruments. Another area that requires

further examination is the role that a woman plays in influencing aggregate savings and

investment.

Till the last decade, considering the low earning potential of women to earn, save and invest, not

much research has been conducted on this subject. The coming decade, is going to see more and

more women getting higher education and aiming for heavy pay cheque jobs. There is an urgent

need to understand the savings and investment pattern of women, so as to frame policies and

develop financial products exclusively for women. Another reason for understanding the savings

and investment pattern of women is that, though they are good savers, they are unable to convert

all their savings into investments. They do not invest as much as the men do.

According to Barber (2001) though women are not active investors, they make more profits than

men when they trade because by trading more, men hurt their performance more than women.

Preda (2001) comments that women are always excluded from financial discussions, on the

explicit ground that they cannot understand investments. According to Chachoria (2000) women

are the next generation financial decision makers and they should be targeted from a financial

perspective. She suggests that marketing for financial products should be done differently for

women.

Through this study an attempt is made to understand the savings and investment pattern of

women. The factors which influence their investment decision making are identified. As 60

percent of women in India are house wives , they have been included in this study . The Property of Christ University. Use it for fair purpose. Give credit to the author by citing properly, if your are using it.

vi

variations in the investment pattern between working and non-working with respect to socio-

demographic attributes have been analyzed.

The sample size consisted of 225 women, who regularly save and invest. The study was

conducted in the city of Bangalore. Convenience sampling was used for the purpose of data

collection. Data was collected through questionnaires and was subjected to descriptive and

inferential analysis.

The major findings of the research are:

The most important reason why a woman saves is because of a “Precautionary” motive.

Saving money in Bank and in the house kitty (saving at home) are the most preferred

saving avenues.

Even though non-working women don’t have direct income of their own, they are able to

save a minimum of 5 to 10 percent from their household savings.

Safety of the principal is regarded as a very important criterion before investing, as

opposed to instruments with low initial investment.

The main motive behind investing is to fulfill their personal and financial goals. They also

recognize the importance of multiplying savings through investment.

Gold is the most preferred investment instrument, followed by real estate, insurance

products, bank deposits, chit funds, mutual funds, bonds, post office deposits, shares and

SIP.

The highest constraint in investing is found to be lack of awareness and advice. This

bursts a common myth: In India women are not able to invest as they are not able to take

decisions on their own. Property of Christ University.

Use it for fair purpose. Give credit to the author by citing properly, if your are using it.

vii

Occupation of a working woman has bearing over the choice of traditional investments

but has no effect over the choice of risky instruments. It can also be said that education

plays a role in the choice of risky instruments for a non-working women, however for the

safe- traditional investments, education has no bearing. Also, irrespective of the

occupational status (working or non-working) for safe –traditional instruments, age does

not play a role, whereas for risky instrument it plays a role.

The study will help the financial institutions in designing exclusive instruments for women and to

the Government in coming up with new policies for utilizing women’s savings for the betterment

of the economy.

Key words: Savings, Investment, Women, Working Women, Non-working women


viii

Table of Contents

1. Introduction

1.1 Savings and Investment in the present Indian Economy………………………………….2

1.2 Importance of Women as savers and Investors …………………………………………..4

1.3 Present position of women in India with regard to savings and investments……………..8

1.4 Challenges faced by Women in Saving and Investing…………………………………...11

1.5 Genesis of the problem…………………………………………………………………...12

1.6 Need and Relevance of the study………………………………………………………...14

1.7 Scope of the study………………………………………………………………………..16

1.8 Limitations……………………………………………………………………………….16

1.9 Chapeterization…………………………………………………………………………...16

2. Review of Literature

2.1 Review of Studies………………………………………………………………………...17

2.1.1 Studies regarding nature and pattern of savings……………………………………...18

2.1.2 Studies regarding nature and pattern of investment…………………………………27

2.2 Research Gap…………………………………………………………………………….31

3. Research Methodology

3.1 Statement of the problem…………………………………………………………………33

3.2 Research Objective……………………………………………………………………….33

3.3 Research Hypothesis……………………………………………………………………...34

3.4 Theoretical Framework…………………………………………………………………...37

3.5 Methodology……………………………………………………………………………..37

3.5.1 Data Collection……………………………………………………………………….37

3.5.2 Sampling Plan………………………………………………………………………..37

3.5.3 Survey instrument…………………………………………………………………….38

3.5.4 Reliability of the instrument………………………………………………………….38 Property of Christ University. Use it for fair purpose. Give credit to the author by citing properly, if your are using it.

ix

3.5.5 Scaling techniques……………………………………………………………………38

3.5.6 Statistical techniques…………………………………………………………………38

3.6 Operational Definitions…………………………………………………………………..40

3.7 Variables………………………………………………………………………………….41

3.8 Limitations………………………………………………………………………………..41

4 Analysis and Interpretation

4.1 Demographics of the Respondents……………………………………………………….44

4.2 Savings and Investment patterns of Women……………………………………………..45

4.2.1 Savings pattern ………………………………………………………………………...45

4.2.2 Investment pattern ……………………………………………………………………..52

4.3 Factor Analysis………………………………………………………………………….58

4.3.1 Factor Analysis for Independent Variables…………………………………………….58

4.3.2 Factor Analysis for Dependent Variables………………………………………………61

4.4 Variance in Investment pattern among working and non-working women……………..75

4.4.1 Differences in frequency of investing across socio-demographic attributes………....76

4.5 Variations in the choice of instrument across socio-demographic attributes…………..76

4..6 Descriptive statistics……………………………………………………………………77

5. Summary and Conclusion

5.1 Need and Rationale of the study………………………………………………………..90

5.2 Review of Literature…………………………………………………………………….91 Property of Christ University. Use it for fair purpose. Give credit to the author by citing properly, if your are using it.

x

5.3 Research Gap…………………………………………………………………………….91

5.4 Statement of the Problem………………………………………………………………..92

5.5 Research Objectives…………………………………………………………………...…92

5.6 Sampling plan…………………………………………………………………………....93

5.8 Survey Measurement……………………………………………………………………..93

5.9 Statistical Techniques…………………………………………………………………….93

5.10 Variables………………………………………………………………………………..94

5.11 Summary of findings……………………………………………………………………94

5.11.1 Savings pattern of women…………………………………………………………….94

5.11.2 Investment pattern of women…………………………………………………………95

5.11.3 Inferential Analysis……………………………………………………………………97

5.12 Implications of the Research……………………………………………………………99

5.13 Suggestions and Recommendations…………………………………………………...100

5.14 Scope for further Research…………………………………………………………….101

5.15 Limitations of the study………………………………………………………………..101

5.16 Conclusions……………………………………………………………………………102

Appendix

Questionnaire……………………………………………………………………………….103

References…………………………………………………………………………………. 108


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List of Tables

1.1 Percentage of savers by gender…………………………………………………………..9

1.2 Percentage of investor households by gender…………………………………………....9

1.3 Percentage of other households by gender……………………………………………….9

4.1 Demographic Characteristics of the Respondents……………………………………….44

4.2 Descriptive statistics for savings pattern of women…………………………………….46

4.3 Descriptive statistics for sources of saving and amount saved each month…………….50

4.4 Investment pattern …………………………………………………………………… ..53

4.5 KMO Bartlett’s Test for Independent Variables…………………………………………59

4.6 Communalities…………………………………………………………………………..59

4.7 Rotated Component Matrix……………………………………………………………..60

4.8 KMO Bartlett’s Test for Dependent Variables………………………………………….62

4.9 Communalities…………………………………………………………………………..62

4.10 Rotated Component Matrix…………………………………………………………….63

4.11 Karl Pearson’s Correlation ……………………………………………………………..65

4.12 Model Summary for Zero Risk Instrument…………..………………………………...66

4.13 Analysis of Variance for Zero Risk Instrument………………………………………..67

4.14 Co-efficients of Regression for Zero Risk Instrument…………………………………67

4.15 Model Summary for High Risk Instrument…………………………………………….69 Property of Christ University.


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4.16 Analysis of Variance for High Risk Instrument………………………………………..69

4.17 Co-efficients of Regression for High Risk Instrument…………………………………70

4.19 Model Summary for Low Risk Investment…………………………………………….72

4.20 Analysis of Variance for Low Risk Investment………………………………………..72

4.21 Co-efficient of Regression for Low Risk Investment…………………………………73

4.22 Differences in frequency of investing………………………………………………….76

4.23 Chi-square for working women (Occupation and Age)………………………………..77

4.24 Chi-square for non-working women (Education and Age)…………………………….78

4.24 Variations in Investment pattern of Working Women (Occupation)…………………..80

4.25 Variations in Investment pattern of Non-Working Women (Education)………………83

4.26 Variations in Investment pattern of Working Women (Age)…………………………..86

4.27 Variations in Investment pattern of Non working women (Age)………………………88


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List of Figures

3.1 Conceptual Framework………………………………………………………………….38

4.1 Regularity of saving……………………………………………………………………..48

4.2 Monthly savings…………………………………………………………………………48

4.3 Saving Avenues………………………………………………………………………….49

4.4 Motives of Saving……………………………………………………………………….50

4.5 Sources of saving- Working women…………………………………………………….52

4.6 Sources of saving- Non-working women………………………………………………..52

4.7 Amount saved by non-working women…………………………………………………53

4.8 Frequency of investing…………………………………………………………………..55

4.9 Motives of investing……………………………………………………………………..56

4.11 Factors affecting a woman’s choice of investment………………………………………57

4.12 Investment Avenues……………………………………………………………………..57.

4.13 Histogram for Zero Risk Investment…………………………………………………….69

4.14 Normal P Plot for Zero Risk Investment………………………………………………. .69

4.16 Histogram for High Risk Investment……………………………………………………72

4.17 Normal P Plot for High Risk Investment………………………………………………...72

4.18 Histogram for Low Risk Investment…………………………………………………….74

4.19 Normal P Plot for Low Risk Investment…………………………………………………75

Property of Christ University.


xiv

List of Abbreviations

1. ABS: Ability to save

2. CSO: Central Statistics office

3. FLCC: Financial Literacy Counseling Center

4. GDS: Gross Domestic Savings

5. GDP: Gross Domestic Product

6. HIES: Household Integrated Economic Survey

7. ITS: Incentives to save

8. MIMAP: The Micro Impact of Macro and Adjustment Policies in India

9. NCAER : National Council of Applied Economic Research

10. ROSCAS :Rotating savings and credit associations

11. NCAER : National Council of Applied Economic Research


Chapter 1

1Introduction

This chapter discusses the importance of savings and investment in the Indian economy,

importance of women as savers and investors, the present position of women with regard to

investing, the challenges they face and the qualities they posses for becoming a good investor.

Economic growth of a nation is driven by savings and its transformation into investment.

Savings made by one section of the society can be lent to another section where there might

be a need for money for production purposes. This type of saving-investing cycle creates

economic growth across many sections of the society and results in job creation. By

increasing savings within the country, the dependence on foreign direct investment reduces.

Generally, when savings rate of a country are high, the investments increase and the economy

grows. But if investment opportunities are not identified within the country, the savings flow

out of the country. If savings are hidden in homes or used to buy Gold or Real Estate they

don’t get channeled into investments by businesses.

One thing which becomes very clear is the fact that there is a pressing need to study savings

and investment side by side for all strata of the society in order to turn valuable savings into

productive investment.

The following chapter discusses the importance of savings and investment in the Indian

economy, importance of women as savers and investors, the present position of women with


2

regard to investing, the challenges they face and the qualities they posses for becoming a good

investor.

1.1 Savings and Investment in the present Indian Economy

Aggregate saving is an important source of funds for domestic investment and economic

growth of a country. Likewise, savings is also important at the household level for fulfilling

the immediate requirements of the family members, for old age and to leave bequests to

children. In simple terms, ‘Saving’ is the money left over from income after the consumption

needs of a person are satisfied. Saving is a critical variable in economic growth hence its role

as a determinant of economic growth has been emphasized by classical economists like Adam

Smith and David Ricardo in Wealth of Nations (1776) and On the Principles of Political

Economy and Taxation (1817) respectively. In the formation of physical assets and capital of

a developing economy like India, household saving plays a major role. Not only is the volume

of saving of household sector important but the form in which it is held is also equally

important.

Since independence, savings and investment have been considered as two very important

macro-economic variables in promoting economic growth of India. In the last three decades,

Indian economy has emerged as one of the fastest growing economies of the world. India is

amongst the highest savers among the emerging market economies. Gross Domestic Saving

(GDS) of the Indian economy constitutes savings of public, private, corporate and household

sectors. India reported a 33.7 percent gross domestic savings as a percentage of Gross

Domestic Product (GDP) in fiscal year 2010 as its economy picked up pace following the

economic slowdown in 2009The economic slowdown in 2009 saw gross domestic savings fall

to 32.2 percent of the GDP, down from 36.9 percent in 2008. While the household share of

gross domestic savings declined from 74 percent to 69.6 percent of total gross domestic

savings, household savings showed overall improvements, contributing INR 15,361 billion in

gross domestic savings, up from INR 13,310 billion in 2008-2009


3

Households are the biggest contributors to India’s savings rate; their savings equal 23 percent

of India’s GDP. This sector occupies a position of dominance over the other institutional

sectors like private corporate sector and the public sector in terms of generating saving. It

comprises of individuals, non-government non-corporate enterprises of farm business and

non-farm business like sole proprietorships and partnerships, and non-profit institutions.

According to data given by CSO, financial assets constituted 50.2 percent of household

savings in 2009-10, and have remained around these levels for the past four years; financial

savings were merely 10.7 percent in 1950-51. Saving in physical assets, which had a

whopping share of 89.3 percent of total household saving in 1950-51, has come down to 49.8

percent by 2009-10, reflecting growing monetization of the economy.

Though the percentage of Savings by household sector in financial Assets is increasing year

on year, what is worrying is that only around half of the household savings in India is invested

in financial instruments. This is also an important reason for the low level of financial

deepening in the country. As per RBI report (2010), only 1 percent of these savings in India

are invested in the Capital Markets. The majority of the savings are invested in physical assets

like Real Estate, Gold, Currency or Bank Deposits. If the savings continue to be invested this

way without building up the necessary infrastructure or industries, the future economic

development of the nation will be hampered.

The Planning Commission has pointed out that to achieve 9 percent GDP growth in the 12th

Plan, gross capital formation GFC (investments) need to be raised to 40 percent of the GDP in

the 12th Plan from 35.8 percent in 2009-10. The ability to finance this level of capital

formation depends largely on domestic savings. India has had 8-9 percent growth in the past,

but this could not be sustained as the economy ran into capacity constraints leading to

overheating. Therefore, to achieve and sustain a high growth rate, capacity needs to be scaled

up and for this, domestic savings need to be raised by about 5 percentage points in the next

five years. This underscores the importance of stepping up financial savings in the economy.

While addressing a conference in Mumbai, Reserve Bank of India Governor D. Subbarao said

that there is a need to raise the level of national savings and channel those savings into Property of Christ University.


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investment for a double-digit and inclusive growth rate to take place. A large share of

domestic savings is held in the form of physical assets like land and gold.

To reverse this situation the household sector needs to be encouraged to increase its savings in

the form of financial assets such as equities, insurance, pension products, etc. The Reserve

Bank of India has introduced Financial Literacy and Counseling Centers (FLCC) to provide

consumers with the tools to make better credit choices (Reserve Bank of India, 2008). They

have also targeted schoolchildren to cultivate financial literacy at an early stage via interactive

websites (Reserve Bank of India, 2007). There is a need to increase the Individual’s trust in

financial markets. One another way is by increasing the Household saving rate.

Indian Economy with its population accounting for 16.0 per cent of the global population, is

expected to benefit from the ‘demographic dividend’, as huge pool of younger population

enter into the labor force and gainfully employed in production, generating a larger national

income and high household saving rate. For capitalizing on demographic dividend, targeting

urban population seems to be a perfect choice. However, the task does not look to be very

easy. In an exercise conducted by NCAER, to know the composition of Savers, non savers

and investors from various cities of India, it was found that only 20.75 percent of urban

population consists of Investors. The rest 79.25 percent of them are non investors. This data

clearly shows that though the urban population, which we can safely assume to be better

educated, is lagging behind in undertaking investments. Studies point out that it is Risk

appetite and financial awareness of individuals which play a major role in determining the

Investment profile of the Urban Investor.

1.2 Importance of Women as Savers and Investors

According to Centre for Development Informatics, in the coming decade there will be a rise in

levels of women’s employment. Some reasons which substatiate this optimism are: India’s

IT/BPO exports have increased from USD 105 million in 1989-1990 to 50.41 billion in 2008-

09. The software and services segment accounted for 40.61 billion, or 55 percent. In a recent Property of Christ University.


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Indian study domestic IT turnover by a Dutch economic information agency (EVD) predicts

USD 50 billion for 2012. The development of the Indian IT industry is closely linked with

improvement of the country’s (higher) education system, combining technical and analytical

(mathematic) skills and English language abilities. As per World Bank edstats, females

constitute about 40 percent of all students currently enrolled in institutions of higher education

and it may be estimated that yearly 40-50,000 of these “new” IT professionals will be female.

In an effort to recruit more women employees, some companies are offering 25 percent

bonuses for female employee referrals and many companies have started providing child care

facilities to women under the Factories Act of 1948 . Thus, compared to the previous years, in

the coming decade, women will have more disposable income.

The consumer base in India consisting of educated women in urban cities is growing

immensely. Year on year the sale for Diamonds, Gold, Women’s Apparels, Imported beauty

products, footwear etc has been increases. Urban Women in India have purchasing power

with or without earning.Whether they earn by themselves or use their pocket money, or

household expenditure money, they are buyers. This gives a small hint : Women have money.

In the words of Delia Passi Smalter, founder of Medelia Communications, “Women currently

make or influence up to 85 percent of all consumer purchases, so isn’t it the time you started

marketing all products to them?” With increasing literacy amongst women, slow

disappearance of joint family system in the urban cities of India, men have started consulting

women for many decisions of the household including financial decisions. Whether working

or not working, the urban woman gets to influence the financial decisions of her household.

Thus they should be targeted from a financial perspective.

Saving for future emergencies has been an inherited and intrinsic quality of a woman. All

over the world women are known for their saving habits. In all cultures, mother’s teach their

daughters to save money for a rainy day. The oldest find of a money box dates from 2nd

century B.C. Greek colony Priene, Asia Minor, and features the shape of a little Greek

temple with a slit in the pediment. Money boxes of various forms were also excavated

in Pompeii and Herculaneum, and appear quite frequently on late ancient provincial sites,

particularly in Roman Britain and along the Rhine (Hurschmann 2009). Property of Christ University.


http://en.wikipedia.org/wiki/Greek_colony

http://en.wikipedia.org/wiki/Priene

http://en.wikipedia.org/wiki/Asia_Minor

http://en.wikipedia.org/wiki/Greek_temple

http://en.wikipedia.org/wiki/Greek_temple

http://en.wikipedia.org/wiki/Pediment

http://en.wikipedia.org/wiki/Pompeii

http://en.wikipedia.org/wiki/Herculaneum

http://en.wikipedia.org/wiki/Late_Antiquity

http://en.wikipedia.org/wiki/Roman_province

http://en.wikipedia.org/wiki/Roman_Britain

http://en.wikipedia.org/wiki/Rhine

6

Women in India are no different when it comes to savings. They usually save from their

salaries or household budget. They save for meeting emergencies or for specific goals.

Available evidence suggests that in India even young girls are more likely to save than boys,

although they are less likely to save in a bank. For example, findings of a study of adolescents

residing in the slums of Allahabad, Uttar Pradesh, India, indicate that although girls were less

likely than boys to work for pay, they were more inclined to save; and although more girls

than boys saved, a larger percentage of boys than girls saved in a formal banking institution

(Sebastian, Grant and Mensch, 2004).

As per Sibley & Law (2008) , men tend to be more optimistic than women about their

household’s ability to meet its financial obligations. This is probably because fewer men than

women have a realistic understanding of the household’s actual financial situation. Nearly

three quarters of women state that their household sometimes — or always — struggles to pay

bills and repay loans whereas less than half of men consider this to be a problem. A revealing

difference is that five times more men than women stated they do not know how well their

household is meeting its current financial commitments. Women are also more diligent at

managing household expenditures than men. They are three times more likely than men to

keep household records and are significantly more likely than men to check that household

bills and accounts are correct. Not only do more women budget than men, but women are

twice as likely to keep a written record of household income and expenditure as men.

Though women understand the importance of saving better than men, they are unable to

invest in great numbers. One reason could be that they, as mentioned above , save for a rainy

day. Hence the top priority in a woman’s mind when she invests is “Security of money”.

Thus, she is most of the times Risk averse when it comes to picking up of Investments.

Though the Risk averse nature of a woman is regarded as the greatest drawback for successful

investing in way it turns out to be their greatest strengths while investing

Of all the stereotypes about the sexes, perhaps the most enduring is that men are better with

money unless the contest is spending it. However, a research on the ways each gender earns, Property of Christ University. Use it for fair purpose. Give credit to the author by citing properly, if your are using it.

7

saves, and invests shows that women often come out ahead of men. A host of reasons why

women can be good investors are presented.

1. Women take the long view and they set goals

For most women, investment is a means of achieving life goals Goff (2003). Men want to get

rich. Women want to send their kids to college, build a house, etc. Belsky (2000) believes

that, since they match their investments with their goals, they tend to be more practical while

taking an investment decision.

2. Women sweat the details and do homework before investing

Female investors spend 40 percent or more time researching stocks than males do. They also

pay more attention to the general operations of the companies that they're putting their money

into. The result: Women -only investment clubs earn close to 7 percent more a year than all-

male groups do ( Goff 2003).

3. Women keep a close eye on household finances

Generally more wives than husbands handle the bill paying- hence they know exactly how

much cash flow is needed and where the budget can be nipped and tucked. (Goff 2003)

4. Women aren't afflicted with remote control disease

Once a woman buys a stock, she doesn't let go of it on a whim. So, she ends up earning more

in the market. (Goff 2003)

5. Women possess metaknowledge and manage overconfidence

Metaknowledge is an appreciation of what we do know and what we do not know may be

more important than primary knowledge. Women admit ignorance while taking investment

decisions. Thus they seek help. They also keep overconfidence at bay. Russo (1992). Property of Christ University. Use it for fair purpose. Give credit to the author by citing properly, if your are using it.

8

6. Women are Rational

Women stick to their investments. They can see through the emotional haze and weigh the

evidence in a detached way. Emotion and investing do not mix. Rothschild, Baruch,

Templeton, Buffett and others all understood that one should establish investment principles

that they believe in, stick to them and remain undaunted by the hysterical bleating of the

markets

7. Women are Risk Averse

Neurobiological research suggests that high testosterone levels lead to a greater sense of and a

higher likelihood of taking risks, a combination that can be toxic when it comes to securities

trading. Since the percentage of testosterone is less in women, they tend to take less risky

decisions.

Women are blessed with innate abilities to be a good saver and investor. The flip side is that

they are good spenders also. With more income in their hand, they become powerful savers

and spenders. Thus, their savings and investment is something that the economy can look

forward to.

1.3 Present Position of Women in India with regard to Savings

and Investments

Due to the increased disposable income ,even savings generated by women will increase in

the future. But, whether are they equipped to save and whether the savings will materialize

into investment is a question less pondered.Going by the current statistics from the National

Council of Applied Economic Research ‘s Report on “How Households Save and Invest :

Evidence from NCAER Household Survey (July 2011), it is understood that women lag much

behind ,when it comes to savings and investment


9

As seen in table 1.1 that only 9 percent of the post office saving, 7 percent of LIC ,12

percent of Pension scheme,7 percent of Commercial Bank and 8.77 percent of Regional Bank

savings are in the name of Women

Percentage of Savers by Gender

Gender Post Office LIC Pension Commerical Regional

Scheme Bank Bank

Male 90.87 92.14 87.81 92.98 91.23

Female 9.13 7.86 12.19 7.02 8.77

Total 100 100 100 100 100

www.rbi.com

Table 1.1 Percentage of Savers by Gender

Percentage of Investors by Gender

Gender Mutual

Fund

Bond

only Debentures IPO Secondary Derivative

Market

Male 93.59 92.55 94.42 96.43 94.77 89.6

Female 6.41 7.45 5.58 3.57 5.23 10.4

Total 100 100 100 100 100 100

www.rbi.com

Table 1.2 Percentage of Investors by Gender

Percentage of others by Gender

Gender Commodity Real Estate Business Private Art

Market

Funds Jwellery

Male 93.54 83.17 94.95 93.1 83.9

Female 6.46 16.83 5.05 6.9 16.1

Total 100 100 100 100 100

www.rbi.com

Table 1.3 Percentage of others by Gender


10

As seen in table 1.2 and table 1.3 that barring investment in Derivatives, Art Jewellery and

Real Estate which are at 10.40 percent, 16.10 percent,and 16.83 percent respectively the rest

of them are under 10 percent. The numbers are not flattering.

There is still a silver lining and optimism that investment will pick up in the women category.

Women in urban India, are far more educated than the rural areas, hence have started

contributing their thoughts and finances in the financial decisions of the household. They save

in Banks and Postoffices and invest in Mutual funds, Shares and Bonds. As most of them are

Computer literates, they are keen about investing in Stock Market. Some fairly educated

women, working and non-working both trade online and make good money. They invest in

equity of reputable companies.


11

1.4 Challenges faced by Women in Saving and Investing

Some of the constraints which stop women from investing successfully are presented below:-

1. Risk Aversion

A considerable stream of research suggests that women are more risk averse than men in

financial situations (Bajtelsmit, Bernasek, & Jianakoplos, 1999; Halek & Eisenhauer, 2001;

Hallahan, Faff, & McKenzie, 2004).Results about gender differences in risk taking in the

financial arena (Croson and Gneezy, 2009; Eckel and Grossman, 2003) are more nuanced. A

general tendency for greater risk aversion among women has been observed as early as

childhood (Hargreaves & Davies, 1996; Kass, 1964). It can also be believed that the

biological differences in women should be held responsible for their risk aversion (Olsen,

2001). This reason ranks number one as constraint for successful investing.

2. Financial Illetracy

A leading explanation for this behavior is that women are not financially literate—they lack

sufficient information about financial concepts and instruments to make informed financial

decisions. A growing literature has evaluated both the state of financial literacy and the

effectiveness of financial education programs aimed at improving financial decision-making.

Women have identified Financial Illiteracy as the main reason why they don’t fare as well as

men when it comes to investment planning. Lusardi (2008)

3. Feminine Mentality

Many women consider purchase of financial services as a masculine activity (Burton, 1995).

While they are single they, save and invest for their marriage. But once they get married, they

think planning for the future is a husband's job. The fact that they may not be able to continue

working and earning throughout their lives, stops them from long term investment planning.


12

4. Math Anxiety

Math anxiety is “a feeling of tension, apprehension, or fear that interferes with math

performance”. Related to this is gender and mathematics as women are thought to develop

anxiety towards mathematics and sciences when they become more interested in social

relations in their teen years. It is thought that women experience more anxiety in mathematics

as a group than men and this has also been suggested in regards computer programming who

explore computing and gender and especially have done experiments relating gender and

anxiety. (Copper, etal 2003). People who suffered from math anxiety as teen-agers often have

trouble making investment decisions later life, according to a survey by Dreyfus Corp. and the

national Center for Women and Retirement Research. The mail survey of nearly 1,300 people

found that those who were uncomfortable with math in their youth often put off personal

financial decisions for fear of making mistakes. Since as teenagers, women develop more

math anxiety than men (Dar-Nimrod & Heine, 2006), it can be concluded that, this may be

one of the reasons for them not turning out to be successful investors.

1.5 Genesis of the Problem

Inorder to chanelize the household Savings to productive Investment avenues, it becomes very

important to motivate and encourage women to invest in financial assets. The Government

,Banks and other Financial institutions are taking steps in this direction. Surveys are

undertaken to understand the Savings and Investment Pattern along with the Risk profile of

Individuals . Financial Literacy programmes are also undertaken by the Reserve Bank of

India to educate people in the Financial domain. However, these programmes and surveys

target the earning members of the household, which most of the times turn out to be a male

member. In houses where both the couples are earning , most of the times, it is the male

member’s Income, Saving and Investment which is reported. Housewive’s Savings or

Investment don’t get reflected in any of the surveys.

But, there are few reasons for not giving enough preference to women in such studies. Property of Christ University.


13

1. Since 1971, there has been a stabilization of women’s employment. The International

Labor Organization database shows no rise in women’s economic activity rates for India

1972-2002. These data show 31 percent of women working in 1970, 31 percent in 1980, 27

percent in 1990, and 30 percent for India in 2000. In other words there has been no

substantial change. Since women don’t have a regular income, it makes no point in taking

their perception or opinion about Savings and Investment. Perhaps this was the most

important reason why the focus for Savings and Investment have not been on women.

2. Women in India take breaks from employment for taking care of household

responsibilities, which suggests intermittent earning tenure.Thus more often, the financial

responsibility is on the shoulders of the Male members of the family. In such a situation, it

hardly makes sense to take opinion of the wife

3. Women are always excluded from financial discussions, on the explicit ground that

they cannot understand investments .(Prada ,2001)

4. A common myth that women don’t have a say in the Financial decisions of the

household.

But more than the above reasons, one concept in the Keynesian system of Saving, can be

blamed for excluding women from such studies: “Only those receiving income are "allowed"

to spend or to save.” Savings do result from income, but the fact that individuals who don’t

earn cannot save is not the truth. Walter C. Neale in his article “Who Saves? The Rich, the

Penniless, and Everyone Else” (1999) argues about this concept. Answering the question:

Who is “allowed” to save? leads to another question “Who is allowed to Spend” Thus one

who is allowed to spend can save. Going by this proposition, even housewives who don’t

earn, but get money to spend, can surely save. Studies of savings mobilization show that

economically active poor people, including women in the informal sector, can and do save but

they require services that are close to home, that do not require large opening balances or

regular deposits. In order to respond to these needs while recovering costs, institutions

recognize that they must employ innovative low-cost delivery channels (Hirschland, 2006;

Rutherford, 2006) But more importantly marketing for financial products should be done

differently for women.(Chachoria,2000) Property of Christ University.


14

It is in the benefit of the nation, if steps are taken to consider the savings of women, working

or non working and channelize them into productive investments. One more compelling fact

is that a quiet revolution has been altering greatly the mortality tables of many low-income

countries. Women are much more likely to be alone in old age, and elderly women are more

likely to be poor. The percentage of the female population over age 85 who are widows is

more than 85 percent compared to about 45 percent for men. An analysis of the National

Long Term Care Study data from 1984, 1989 and 1994 indicated that 20 percent of the

residual life expectancy at age 65 for men and 30 percent of the residual life expectancy at age

65 for women were spent in a state of chronic disability. Inflation affects people who live

longer more than those who live a shorter period so overall it is a greater issue for women. Its

impact grows over time.

1.6 Need of the Study

In order to formulate appropriate theories and policies to boost aggregate saving and

investment of the nation, it is important that economic planners have a true and fair idea about

the nature and volume of saving and investment, the behavior of people towards saving and

investment and the method by which saving can be improved for investment decisions. They

also need to know about the motives of saving and investment in order to frame policy

appeals accordingly. Knowledge about saving and investment preferences gender wise, will

help to design and implement saving and investment instruments which will effectively

stimulate investment. Sustained research in this field thus becomes imperative in order to

understand the patterns of savings and capital formation in our country.

Except for a few household surveys in the late 90s, very few studies estimate the profile of

households' saving and investment for both rural and urban areas in India. The Micro Impact

of Macro and Adjustment Policies in India (MIMAP) survey in 1996 and the detailed report

on "Household Savings and Investment Behavior in India in 2003" by EPW Research

Foundation and NCAER was an attempt in this direction. Although these studies describe

India's saving performance in detail, they do not sufficiently describe the analytical

framework of households' decision-making determinants and the factors that determine the


15

propensity to save and invest, which are important from a policy perspective. One area that

requires further examination is the role that women play in influencing aggregate saving and

investment.

Till the last decade, considering the low potential of women to earn, save and invest, not

much research has been conducted on this subject. Government surveys, policies, financial

awareness, marketing of financial products etc has been very less focussed towards woman.

The kind of enthusiasm shown by agencies and Governement for women’s development,

health, education, is yet to be seen in the subject motivating women to save and invest.

Marketing is heavily done on television ,radio or magazines for products that are bought by

women. Information regarding Women’s health or education, self-employment etc., is also

provided to them with wide coverage. However, except for Life Insurance there are very few

advertisements on investment products, financial services etc., targeting women.

The coming decade, is going to see more and more women getting higher education and

aiming for heavy pay cheque jobs. There is an urgent need to understand the savings and

investment pattern of women, so as to frame policies and develop financial products

exclusively for women. As 60 percent of women in India are house wives, they cannot be left

out of this study since they also get to save. Since working and non-working women have

different styles of living, earning and spending, their patterns differ. There is a need to bring

out these differences so that , accordingly financial products and programmes may be

designed. Above all, factors which influence them to save and invest have to be higlighted.

This study is an attempt to understand the above mentioned paradigms in depth.


16

1.7 Scope of the Study

The study focuses on financial saving and investment of women of Bangalore. The variables

tested in the empirical study are occupation, educational qualification, and age. The factors

that affect women’s investment making decision are identified. Through analysis the

differences are identified between working and non-working women with regard to their

savings and investment pattern.

1.8 Limitations

The major limitations of this study are with respect to the time frame within which the

research had to be carried out and the sample size from which it would be difficult to draw

accurate conclusions on the entire women population. The results may not be free from biased

figures as some of the responses may include deliberate falsification and incomplete

information.

1.9 Chapterization

The second chapter gives an insight into the literature reviewed for the area under study,

followed by the third chapter that provides details of the methodology used for the purpose of

this research. The fourth chapter deals with analysis and interpretation of data gathered for

the study and the last chapter summarizes the entire report, discusses the implications of the

study, the suggestions made and the conclusion arrived at the end of the study.


17

Chapter 2

2Review of Literature

The review of literature gives a fair idea about the work done in the subject area, the views

and observations made by different researchers and the gaps which need to be filled. In order

to understand and solve the research problem in question, it becomes important to get an

understanding of the various methodologies used by other researchers. During the process of

reviewing the past literature, various perceptions about the subject and concepts within the

subject get surfaced. This gives a direction to address the research problem from different

viewpoints. Several attempts have been made to explore the nature of Savings and Investment

done by people of various nationalities, cultures and age groups.

2.1 Review of Studies

The review of literature has been divided into two broad categories.

1. Studies regarding the nature and pattern of Savings

2. Studies regarding the nature and pattern of Investment


18

2.1.1 Studies Regarding Nature and Pattern of Savings

In an effort to study the saving patterns of people and the factors influencing the reasons for

saving and investment Anbarasu et al (2011), conducted a study on 1655 people of

Tiruchirappali city. Data was primarily collected through a questionnaire. The variables

considered were age, number of children, beliefs, opinions, Educational qualification and

income. Data was analysed through Econometric Analyses, Chi Square and Regression. It was

found that, out of all factors Educational Qualification played a major role for saving. Another

observation made was that people are not precise about their return and they invest their

savings carefully. The reasons for savings include Education, marriage, building a new house

and medical expenses. The amount of money saved depends on the Income level.

The socio economic and demographic factors influencing savings of various income groups

has been explored by Rehman (2011). For that purpose, data has been collected from 107

households of Multan from lower, middle and higher income group following per capita

income method. Life cycle hypothesis proposed by Ando and Modigliani in 1963 forms the

base of the study. The variables used were socio-economic and demographic in nature. It was

concluded that education, children’s educational expenditure, family size, liabilities and value

of house are reducing factors while total dependency rate and income are inducing factors for

household savings of lower income groups. Savings of middle income group was found to be

positively related to total dependency rate and total income was inversely affected by

children’s educational expenditures, liabilities, marital status, size of land holdings, and value

of house.

Dupas and Robinson (2011) conducted experiments on the participants to find whether there

was any effect of mental accounting on the psychology of saving. In one such experiment,

participants were provided with a simple metal box with a lock and key and a deposit slit in

the top. Just providing the box increased health savings by 68 percent. Based on interviews

with participants, it was concluded that this effect was due to mental accounting- meaning that

with the funds set aside, it was easier to refuse requests or it would limit consumption. It was Property of Christ University. Use it for fair purpose. Give credit to the author by citing properly, if your are using it.

19

found that the respondents felt less obligated to share funds with others when they were in the

box.

Based on primary data collected from one hundred households selected from three villages

from the state of Kerala (India), Unny (2010) tries to find the determinants of saving. After

running Regression on the data, he finds that the Level of income, value of assets and level of

education of the head of the household positively influence savings whereas number of male

children, number of earners and dependency ratio has negative influence. Among the

occupational groups, households engaged in non-farm sector have higher propensity to save.

The number of female children has been believed to have a positive influence on savings in

India due to dowry system. However; in this study this factor shows a negative influence.

Copur(2010) explored saving behavior of Turkish families in Ankara, Turkey. Data was

drawn from 600 families from the city of Ankar. This study used a trans-created adaptation of

the NCC 1172: The Complex Nature of Saving: Psychological and Economic Factors to

establish a better understanding of saving behaviour.The independent demographic variables

consisted of gender, age, education, work, marital status, and income; Willingness to Take

Financial Risks: Financial Socialization and Negative Financial Events. Results indicated that

the vast majority of Turkish families were not saving and not willing to take any financial

risk. More than half of the participants did not discuss finance with their parents when they

were growing up. Majority of the families reported that over the past year their family’s

spending exceeded their income and the current economic situation significantly impacted

more than half of the families’ saving behavior and attitudes. Most of the families indicated

that their parents were savers while they were growing up. Interestingly, families who

reported both parents were savers while they were growing up were less likely to be saving

than those who reported neither were savers. An important finding was that having experience

of negative financial events in the last two years was significantly related to the likelihood of

saving.

The study undertaken to assess the level of financial literacy among people residing in Delhi

and National Capital Region (NCR) who invest in different financial instruments, like Post


20

Office Savings Scheme, Mutual Funds, Life Insurance, Stock market etc indicated that the

financial literacy of investors was different for different financial instruments. Around 96

percent of them had knowledge about Banks, 30 percent had knowledge about National

Savings Certificate & Public Provident Fund, 98 percent of the investors knew about Life

Insurance, 92 percent of the investors knew about Mutual Funds but only 24 percent preferred

them. A Chi-square analysis has been conducted to understand whether financial literacy is

found to be affected by age, income and educational level of the individuals. The results

indicated that there is a relationship between Financial literacy and income, however no

relationship between age and educational level. Seth (2010)

Using a sample composed of 167 individuals in Bangkok, Termprasertsakul (2009), explores

the associations between the saving behavior of individuals and variables, including

demography, perceived risks, and desired benefits. The demographic variables included

gender, age, marital status, employment status, education attainment, monthly income, and

number of dependants, public medical care program membership, company’s health insurance

provision, and spending behavior. The data was collected using self-administered

questionnaires in a survey. Chi-square tests were conducted to test the hypotheses of the

associations between saving behavior and independent variables .The research showed that,

all of the demographic variables, except gender, support the hypotheses of the association

with saving behavior in terms of saving method selection. It means that there is no difference

in the way men and women save.

Boring (2007) attempts to analyze the Household Savings Behavior in Uganda, using the

Finscope 2006 national survey data. Binomial probit models have been used and thereafter

probit regressions are run to test a number of characteristics ranging from education to trust in

financial institutions to attitudes about life in General. Findings suggest that the age of the

respondent (not just the age of the head of household), literacy, higher education, formal

sector employment, entrepreneurial activity, and attitudes about life’s current state to be the

determinants of saving behavior. It was also found that the marital status and whether or not

the respondent is solely responsible for the household financial decisions are statistically Property of Christ University.


21

insignificant regarding whether or not to save but quite important regarding institutional

choice.

To analyze the relationship between savings and age structure, Demery & Duck (2006) used

data derived from the FES, an annual cross-sectional survey of around 7,000 UK households.

Results point out that household data exaggerate savings rates of young adults and the elderly

whilst underestimating those of 45- to 60-year-olds. The individual saving rates follow more

closely the 'hump shape' of the life-cycle model, although the savings rates of the elderly

remain positive for some ages. It was also observed that there is a sample selection bias when

data refers to households and not individuals because most surveys provide consumption and

income data only at the household level.

Chowa (2006) investigates savings performance among participants in a matched savings

program in Uganda, modeled after the Individual Development Accounts (IDAs) in the

United States. Comparison of savings behavior by gender, level of education, marital status,

and type of work was performed. Participants save for a minimum of six months, and an

incentive is provided to them. After successfully reaching the savings goal the participants

had to purchase productive assets that could be used to generate income. The data sample size

for the analysis was 145. Average Monthly Net Deposit (AMND) was used as the dependent

variable in the analysis. This variable was used to measure the savings performance of

participants The AMND was calculated by adding total deposits plus interest (net of fees)

minus total unmatched withdrawals, divided by the number of months of participation The

results indicate that both male and female participants were saving in the project. However,

women were found to be saving more than their male counterparts across education,

employment, and type of work.

On a study on different cultures of household structures and saving patterns, De Laiglesia &

Morrisson (2006), finds that family relations shape saving institutions. Policies can alter

saving incentives and create the conditions for household structures themselves to change. It

was found that in many African countries, where men and women have different roles in

managing family finances, Roscas (rotating savings and credit associations) are often all-


22

female. This gender segregation allows women to render their savings illiquid, and even to

hide them from their partners. This allows women to save more in order to pay for school fees

and uniforms, or to buy kitchen ware and furniture. They are then better equipped to resist

pressures to draw on them when disagreements about saving and consumption choices erupt

in the couple. Another finding is that Polygamy plays a role in the amount savings done.

Wherever polygamy exists, men must pay an important bride-price to her father. This practice

diverts savings away from productive investment.

To examine the pattern of saving among cooperative farmers in southwestern Nigeria,

Adeyemo & Bamire (2005) collected data from 400 farmers. The data was analyzed using

descriptive statistics and multiple regression technique. Two functional forms, the linear and

Cobb-Douglas were fitted to the data. The model had savings as the dependent variable while

family size, gross income, cooperative experience, distance to the nearest bank, age, level of

education, source of initial capital, technology level, total consumption and household living

expenses were independent variables. Results show that cooperative farmers in southwestern

Nigeria are mostly males, literate and of average age of 47 years. Different factors influenced

their saving and investment patterns. Income, loan repayment and amount of money borrowed

were significant variables that influenced saving patterns. Age and loan repayment positively

influenced savings pattern while family size recorded a negative influence. Also it was found

that the farmers increase their savings, as they grow old. Though this negates the life cycle

hypothesis of savings, which claims that a person would be expected to save up to a point and

then start dissaving as he grows old, the results obtained show that most farmers in the study

area are not too old and therefore tend to save to cater for their household.

In order to understand how married women use their economic resources, Schmeer (2005),

uses data from Cebu, Philippines. This study finds that the more income women earn and

control; the more households spend on food. Multiple regression analysis has been used to

assess the effect of women's resource position on food expenditures, controlling for the

individual, house-hold, and community characteristics. One important point that surfaces is

that, providing employment opportunities may not be sufficient if women must relinquish

control over their earnings. For women, in better off households, improving their financial Property of Christ University.


23

management skills may increase the likelihood that household income will be turned over to

them and thus be used to improve child welfare.

Ahmad and Asghar (2004) estimated saving function based on HIES (1998-99) primary data

in Pakistan. 8933 rural and 5374 urban respondents were chosen from the survey. It was

found that saving was directly influenced by income, employment status, age square and sex

of rural and urban respondent. Wealth, dependency ratio, and age of the respondents were

found negatively affecting savings of rural and urban respondents

After exploiting data collected from the first wave of the SAVE panel, specifically collected

to understand economic, psychological and sociological determinants of saving, Börsch-

Supan & Essig (2003) study the savings patterns of Germans. The findings suggest that nearly

everyone - whether in the middle income bracket or richer - saves substantial amounts in old

age. The research points out that 40 percent of German households save regularly a fixed

amount. About 25 percent of German households plan their savings and have a clearly defined

savings target in mind. Most of German household saving is in the form of contractual saving,

such as saving plans, whole life insurance and building society contracts. This makes the flow

of saving rather unresponsive to economic fluctuations, such as income shocks. Most

households prefer to cut consumption if ends do not meet. In particular the elderly do not like

to use credit cards, and they eschew debt.

In order to examine the determinants of private saving in the process of economic

development, in India during the period 1954 -1998, Athukorala (2002). The data obtained

was compiled from various publications like National Accounts Statistics, Government of

India, Economic Survey & Reserve Bank of India, Monthly Bulletin The methodology

involved the estimation of a saving rate function derived within the life cycle framework

while paying attention to the structural characteristics of a developing economy. It was found

that the saving rate rises with both the level and the rate of growth of disposable income and

the magnitude of the impact of the former is smaller than that of the latter. The real interest

rate on bank deposits has a significant positive impact, but the magnitude of the impact is

modest. Public saving seems to crowd out private saving, but less than proportionately,


24

suggesting that public policy can influence the national saving rate. Among the other

variables considered, the spread of banking facilities in the economy and the rate of inflation

seem to have a positive impact and changes in the external terms of trade and migrant

remittances a negative impact on private saving

Khandker (2000) does an econometric analysis on the household survey data from three

villages of Bangladesh where micro-credit programme had been in operation for three years

or more. One of the observations made was that, women were saving more in such micro-

borrowing programmes and that the informal finance impact is more pronounced for men than

for women. Also by offering membership and opportunities for borrowing, many microcredit

programmes have been able to mobilize savings (largely involuntary) from their poor and

women members, who are not considered net savers by traditional financial institutions

For exploring the saving behavior of rural industry households, Brata (1999), collected

information from 93 respondents by conducting survey of small industries in Bantul Sub

district in 1996. In the survey, he found that respondents were more interested in keeping

financial assets than real assets as their savings. They preferred to save their financial assets in

non-banking institutions like co-operations, credit unions etc. It was concluded that Income,

Education, Sex and Industry type were found to have direct significant impact on savings

Baden (1996) examines gender issues in relation to financial liberalization and financial

sector reform. The objective of the study is to demonstrate that processes of financial

liberalization and financial sector reform are not gender-neutral and that, therefore, gender

analysis has a place in the design and implementation of financial sector restructuring. The

findings suggest that women have less access to formal sector’s financial savings facilities

than men and women hold real assets in different forms. Men’s financial assets are more

likely to be in interest bearing accounts, whereas women’s savings are often in forms where

no interest accrues or else is held as real assets.


25

Panikar (1992) has studied the rural household saving pattern in few villages of two states in

India, namely Kerala and Tamil Nadu. The study was conducted with an objective of looking

into the levels of saving and the manner of its disposition and to identify the factors

underlying the rates of saving. Data was collected from 300 households. Variables like

Income of the household, consumption, expenditure, indebtness, physical investment and

financial investment were introduced apart from the basic socio-economic variables. Data was

analysed using Multiple regressing, Chi-square tests and Anova. The study found that a high

proportion of saving was absorbed in unproductive assets leading to a vicious circle of low

income and low saving.

During an analytical review of the literature on saving capacity of rural households in India,

Desai (1981) found that the existing literature has neglected “incentives to save” (ITS)

hypothesis of savings behavior. After reviewing the estimates of rural household savings

published by Reserve Bank of India (1954), he reveals the fact that there has been a

pessimistic assumption about the saving capacity of rural households in India. Most studies

have considered the “ability to save”(ABS) hypothesis alone. He says most of the studies are

Keynesian and hence consider only current income as a measure of ABS. He agrees with

Bhatty et al (1977) on the thought that the Keynesian framework of savings which assumes

that the decisions to consume and save-invest are independent, does not work for rural

household savings

Repetto & Shah (1975) studied the demographic and other influences on long term saving

behavior in India. The data for the study was collected from surveys conducted in the Kaira

district of Maharashtra from 1930 to 1965. It was found that large family size has a depressing

effect on long term household saving rate also. The study also revealed that sons in rural India

serve as substitute assets in households.

In an attempt to study the saving behavior of Indonesian households, Kelley and Williamson

(1968) run a regression analysis on per capita household saving against per capita household

income for five household age groups. They have found that the age of the head of the

household is an important determinant of household saving in rural areas and that the average


26

and marginal saving rates rise with the share of agricultural income and the presence of

positive interaction between wealth and saving.

Johnson and Chiu (1968) ran time series regression for household saving on household

income and private saving on private income for 30 countries includes developed and

developing countries. Majority of the results show a positive correlation between saving and

income. These results suggest that at best both household saving and private saving are

proportional to household income and private income respectively.

With the purpose of throwing some light on the saving behavior of urban Indian households,

Ramanathan (1969) collected data from 600 households from Delhi. Data was collected on

components of income, net worth and saving, qualitative information on past and expected

income-change, liquid assets, debts, the extent of visitors to the households, occupation of

head, family size, number of earners, age, education of head, home-ownership status,

expenditure on durable goods and dowries, motives for and attitudes toward saving, etc The

effects of income and net worth on saving are examined in detail for different socio-economic

sub-groups. The results say that income and net worth significantly influence the level of

saving. Contrary to the studies in advanced countries, home-owners had a much lower saving-

income ratio than renters although the former had a slightly higher average income. A major

reason for this may be the uncommonness of amortized mortgages in India, with no

contractual commitment to save till the age of 45 savings steadily increased and then started

dropping. After the age of 65, again increases. The author attributes this to the joint family

system in India.

While describing the use of modern banks by women in SriLanka, Dissanaike (1968) finds

that women prefer informal sources of saving and lending that afford them more flexibility. In

addition to gold SriLankan women have adopted informal group savings method called

“seetu”, where each contributes to a common fund that is then given to one member each

month. In a bid to attract women, banks offer pawning facilities .Some rural banks have even

begun to design loan schemes to accept jewellery as a collateral in order to draw more women

to their doorstep. One of the reasons found for financial exclusion of women is that in male


27

dominated societies, particularly in south Asia, men traditionally handle cash and investible

funds, even when the funds arise from women’s economic activity. Another observation made

is that, it is very difficult for a poor woman to walk into a fancy looking commercial bank to

get a loan due to lack of confidence, moreover banks lack the patience to deal with their

ignorance. The very process of going to a bank in town neglecting the whole days work is not

a happy choice.

2.1.2 Studies regarding nature and pattern of Investment.

Issahaku (2011) conducted a study that was based on a microeconomic approach of estimating

the determinants of financial saving and investment in one of the most deprived district

capitals in Ghana, Nadowli.Primary data were collected from sixty households in Nadowli.

Questionnaires were designed so as to facilitate the collection of data from households about

saving and investment. Stratified random sampling technique has been used to ensure that the

various occupational groupings were fairly represented. A multiple linear regression model

adapted from Rogg (2000) and Kibet et al (2009) has been fitted into investment. The

independent variables were Age, Education, and Occupation, number of Dependents, Income,

Assets and Saving with the dependent variable being Investment. The study reveals that there

is propensity to save and invest in Nadowli in spite of low income and the factors that drive

Saving and Investment are occupation, expenditure, assets and saving.s

To gain knowledge about key factors that influence investment behavior among men and

women, Kabra et al (2010) conducted a study based on primary data collected from 700

individuals who invest regularly. A four page questionnaire consisting of six subscales was

used. In the first subscale, demographic information such as age, gender, marital status, region

to which they belong, profession, individual income levels were sought. In the remaining five

subscales, questions were designed to measure the investment pattern of individuals on the

five variables viz. investing background, opinion leadership, duration of investment,

awareness of investments, and security. Later the statements under these variables were

reduced into smaller number of manageable variables by exploring common dimensions

available among the variables. The study found that the modern investor is a mature and


28

adequately groomed person. In spite of the phenomenal growth in the security market and

quality Initial Public Offerings (IPOs) in the market, the individual investors prefer

investments according to their risk preference. It was concluded that investors’ age and

gender predominantly decides the risk taking capacity of investors.

To find out what affects individual investors’ willingness to invest in an asset, Choi et al

(2007), use administrative panel data on 25,000 401(k) accounts of five firms. It is found that

an investor’s 401(k) contribution rate increases more if he/she has recently experienced a

higher 401(k) portfolio return and/or a lower 401(k) return variance. The results are explained

by a naïve reinforcement learning heuristic: Investors expect that investments in which they

experienced past success will be successful in the future. In addition to that, it is found that —

when there is no salient reference purchase price — investors tend to be return chasers and

variance avoiders with respect to their idiosyncratic history with the asset.

In search of reasons for preferring investment in property over other investment, particularly

financial investments, Burns & Dwyer (2007), uses three sources of information – published

studies and a review of policy settings, interviews with individuals knowledgeable about

investment and savings behavior and attitudes, and interviews with eight consumers aged

between 30-50 years. The findings suggest that Investment and savings attitudes and behavior

are influenced by the structure, complexity, transparency and perceived past and future

performance of different kinds of investment options; the general lack of independent

financial advice; the recent superior performance of property investment; perceptions and

personal tolerance of risk; the often low level of financial literacy about products other than

property; the nature of the information people use when making financial decisions; the

personal or family experience people have with investment; a general wish to have personal

control over the investment and trust in the advice of friends and family over unknown

professional advisors were the reasons for preferring investment in property over other

investments.

Agarwal et al (2006) evaluate the financial literacy of a select group of residents in India that

participate in an on-line investment service provided by a Financial Advisory Services firm.


29

The findings on financial literacy are further related to self-reported information on

respondents’ other investment activity. It was observed that the participants were generally

financially literate. It was also found that a number of relationships between literacy and

socioeconomic variables; notably: the probability of getting all the survey questions correct is

higher for male respondents, and generally increases with education level and the

aggressiveness of the investor. Further, looking at an individual’s educational attainment, it

can be said that the number of investments increase with education, with the more highly

educated individuals having lower shares of equities in their portfolios.

Adeyemo & Bamire (2005) conducted a study to understand the savings and investment

pattern of cooperative farmers in southwestern Nigeria. Data collected from four hundred

cooperators was used for the Study. The questionnaire designed contained variables such as

family size, gross income, cooperative experience, and distance to the nearest bank, age, level

of education, source of initial capital, technology level, total consumption and household

living expenses. Data were analysed using descriptive statistics and the multiple regression

technique to explain the relationship between study variables. Two functional forms, the

linear and Cobb-Douglas were fitted to the data collected. Two models were fitted, one, had

savings as the dependent variable, while the other had investment as the dependent variable.

Both models contained the same set of independent variables. Findings suggest that Income,

farming experience, family size, loan repayment, and amount of fund borrowed positively

influenced farmers’ investment pattern, while only age (in one state) had a negative influence.

For the sake of understanding the Investment Behavior of High-Income Women in America

Hira & Loibl (2004) conducted 911 interviews with couples. The survey instrument designed

included a total of thirty-eight questions, which addressed the following five guiding themes

of our analysis: (a) demographic and economic characteristics; (b) financial management

behavior; (c) sources of investment information and the investment decision-making process;

(d) investor socialization/parents’ influence on respondents’ money management and

investing; and (e) predictors of investor intentions including respondents’ investment-learning

preferences, investor beliefs, attitudes, perceived behavioral control, and opinions about

investment advisors. The results showed that women were more likely than men to have fixed Property of Christ University.


30

income investments such as savings accounts, certificates of deposits, and life insurance with

cash value. When women were involved in investment decisions, many reported making them

with their partners rather than alone. Men, however, were more likely than women to invest

on their own. Both men and women found investing to be satisfying but time-consuming.

Women were more likely than men to handle routine money management tasks.

Barber and Odean (1999) construct a theory that predicts - Men will trade more excessively

than women. This theory draws from two places. First, from theoretical models that predict -

overconfident investors trade excessively and second, from Psychological research which

demonstrates that, in areas such as finance, men are more overconfident than women. To test

this prediction by partitioning investors on the basis of gender, account data for over 35,000

households have been used from a large discount brokerage. While analyzing the common

stock investments of men and women from February 1991 through January 1997 it is revealed

that men trade 45 percent more than women. Further it is observed that trading reduces men’s

net returns by 2.65 percentage points a year as opposed to 1.72 percentage points for women.

To answer an important question, whether lifestyle characteristics can be used to differentiate

investors by the size and nature of their investment holdings, Warren et al (1990), collected

data from 600 households located in a metropolitan area. The questionnaire asked about types

of investments held, amount of total investment and proportions of total investment in stocks

and bonds, as well as demographic and lifestyle characteristics. The demographic data

included sex, marital status, presence and age of children in the household, employment status

of respondent and spouse, education and income. Lifestyle measures were obtained by asking

respondents to indicate their level of agreement. With 29 lifestyle statements such as "I am

more independent than most people"; "I think I have a lot of personal ability"; and "I think I

have more self-confidence than most people." Respondents who showed a high degree of

agreement with such questions were classified as having higher self-confidence than

respondents who disagreed. A Multiple discriminant analysis is used to determine if

investment patterns differed according to demo-graphic and lifestyle dimensions. The result

of the study shows that, respondents with a heavy investment concentration in stocks and

bonds tended not to have children in the household. They and their spouses tended to be Property of Christ University.


31

employed full-time and to have incomes over $50,000. Also the respondents who had a light

concentration of their investments in stocks and bonds could be described as dress-conscious.

With an objective to investigate whether women exhibit greater financial risk aversion than

men Jianakoplos & Bernasek (1998) empirically verify the stated and popularly perceived

notion that there are gender differences in risk taking. They estimated the impact of household

wealth and other socio economic variables like Race, kids, Age, Work,Education,Income &

Homeownership on the proportion of risky assets held, comparing single women with single

men and married couples. The dependent variable is named as RATIO. It is the ratio of risky

assets to wealth. By using maximum likelihood Tobit Regression procedure the equation was

estimated. It was observed that single women are relatively more risk averse than single men.

Another finding confirms that relative risk aversion decreases as household wealth increases

when wealth is measured excluding residential housing and human capital. Single women

reduce the proportion of risky assets they hold as the number of children in their household

increases, holding the other factors constant. Single black women are willing to hold a larger

proportion of risky assets on average than single white women, single men and married

couples. It was noticed that given the women’s greater longevity and the increasing tendency

towards self directed pensions, greater risk aversion exhibited by women can have a

significant impact on resources available to them in retirement. However, there is a limitation

to this study. The gender of financial decision makers in married household could not be

determined.

2.2 Research Gap

Research on Savings and Investment patterns on households and on Individual groups has

been considerably done. Majority of the studies observe one serious concern i.e., Savings are

not getting invested productively. Since urban woman plays a major role in saving money in

the household and are in a position to influence the financial decisions, by including them in

such studies we may be able to find a reason for the above stated concern. While Investment

pattern has been studied intensely in a generic sense, far less research exists regarding

women’s mind-set towards investment as a subject. Also, since savings is a precursor to


32

investment, it is imperative to study savings and investment patterns side by side. The present

study aims to do so. Moreover, non-working women have not been included in any study due

to a crude proxy that since they don’t earn they don’t invest. This study surges an effort to

bridge the said gap.


33

Chapter 3

3Research Methodology

This chapter begins with the statement of the problem and the objectives of the study.

Thereafter, the theoretical framework, research design, methods and procedures used for

conducting the research have been discussed.

3.1 Statement of the Problem


investment of the nation, and to convert the available savings into investment, one has to

understand how individuals in the society save and invest. The fact that women are becoming

economically powerful calls for a need to exclusively study their savings and investment

patterns, and cater to them accordingly so that they are encouraged to contribute towards

Capital Formation for the Economy.

3.2 Research Objective

Primary Objective: To study the savings and investment pattern of working and non-

working women.


34

Specific Objectives: To achieve the primary objective, the specific objectives were

formulated as follows

1. To study the variation of savings and investment pattern across socio economic

characteristics among women

2. To identify the factors those affect the investment pattern of working and non working

women

3.3 Research Hypothesis

This research proposes the following hypothesis to be tested empirically based on the

literature review.

HYPOTHESIS01: Perceptual factors do not significantly influence the investment decision.

HYPOTHESIS02: There is no significant difference in the frequency of investing across

socio-demographic attributes for working and non-working women.

HYPOTHESIS03: There is no significant difference in the choice of investment instrument

across socio demographic attributes for working and non working women.

3.4 Theoretical Framework

The following theories have contributed towards the understanding of the research problem.

Non-Income related Savings


35

The dichotomous definition of savings, given by Keynes excluded several potential savers

including individuals in receipt of inheritances, estates or money or similar assets from other

non-income related sources of funds. This was pointed out by Neale (1991). Thus including

non-income related sources as sources of Savings and Investment a new dimension is added

to Savings and Asset creation.

The Life-Cycle Hypothesis of Saving

Ando & Modigliani’s life cycle hypothesis has a basic assumption that individuals spread

their lifetime consumption evenly over their lives by accumulating savings during earning

years and maintaining consumption levels during retirement. Tests of the life cycle

hypothesis are therefore mainly concerned with the effect of demographic variables such as

age groups, birth rates, occupation of the population, skill-sets of the population and

dependency ratios on savings behavior. The life cycle hypothesis implies that demographic

variables affect savings rates

Motives of Saving and Investment

Keynes, in the famous Chapter 9 of the General Theory of Employment, Interest and Money

(1936), had eight distinct motives for saving: : (1) “Precaution” (2) ‘‘Foresight’’ (3)

“Calculation” (which refers to the wish to earn interest) (4) “Improvement”, (5)

“Independence” (6) “Enterprise”(ability to invest where the returns are maximum (7) “Pride”,

(the bequest motive); (8) “Avarice” (miserliness). Other factors at work include, in addition

to length of retirement, the timing and size of other major expenditures for which provisions

must be made, such as for the purchase of a house, children’s education, or other major

durables. The need for accumulation will be increased in the presence of credit rationing.

Finally, the saving and investment rate will be affected by socio-demographic variables.


36

Life-Cycle Funds Theory

This theory advocates creation of portfolios that that are heavily concentrated in stocks at the

beginning of the work life and gradually shift holdings to bonds as retirement nears. Several

demographic and economic factors provide some rationale for life-cycle funds. But the most

important one deals with how the value of human capital varies over time as a fraction of total

wealth. Human capital is composed of elements that are fixed (innate ability), that are largely

fixed after a certain point (formal schooling), and that vary with time (experience). A good

proxy for measuring the value of human capital is the present value of wages over an

individual’s remaining working life.

The Framework

Neale’s theory of including potential savers, who may not be at par with the other income

receiving sections of the society with regard to savings generation, forms the base to address

the problem statement. The population for this study- Woman is one such unprobed section of

the society. The theory of Life cycle hypothesis and the Life-cycle funds is instrumental in

choosing socio-demographic variables as independent variables. The presented framework

emphasizes that Savings is expected to shape investment action and thereby affect the

investment pattern. Three relationships explored in this research study are Socio demographic

variables with Savings Pattern, Socio Demographic Variables with Investment Pattern and the

Factors influencing the Investment Pattern. However, how the Savings Pattern affects the

Investment is not the focus of the scope.


37

3.5 Methodology

This section provides a description of the methods, tools and techniques used to conduct the

research.

3.5.1 Data Collection

Data has been gathered from primary sources for the purpose of this study, from women who

save and invest .The data was obtained by using the survey method through the administration

of structured questionnaires to the respondents.

3.5.2 Sampling Plan

Sampling Frame: Women who are regular savers and investors have been considered for

this study.

Figure 3.1 Savings and Investment Pattern- Conceptual Framework


38

Sampling Size: A total of 250 respondents were considered for the study but only 225

responses were usable.

Sampling Area: This study was conducted in the city of Bangalore.

Sampling Technique: Non probabilistic, convenience sampling is used for the purpose of

data collection.

3.5.3 Survey Instrument

Questionnaires were used to collect data from the respondents. An original questionnaire

consisting of three sections was used for the purpose. Details of the three sections are as

follows:

Section A consists of questions that are used for collection of data that is of personal nature

pertaining to the respondent’s age, educational qualification, occupation and monthly income.

Section B consists of questions related to sources of saving, frequency of saving, constraints

and motives of saving, and saving avenues.

Section C consists of questions related to frequency of investing, investment preferences,

motives of investing and constraints to investing

3.5.4 Reliability Of The Instrument

Cronbach Alpha test was administered for the purpose of testing the reliability of items in the

questionnaire. The higher the score of the test the more reliable the generated scale is.

However 0.7 has been indicated to be an acceptable reliability coefficient with regard to such

studies. (Santos,1999).The reliability of the instrument was tested on this basis. A pilot study

was conducted for a sample size of 50 respondents and was tested for reliability with the help Property of Christ University. Use it for fair purpose. Give credit to the author by citing properly, if your are using it.

39

of Cronbach’s Alpha- test of reliability using SPSS and a reliability of 0.766 was derived

from the test.

3.5.5 Scaling Techniques

For questions related to savings and investment pattern, an ordinal scale was used. The

responses were measured on a five point Likert scale. Since the data was non-parametric,

collapsing the responses from a five point Likert scale to a two point scale was a good

technique in order to increase the intelligibility of the outcomes of analysis (Grimbeek, 2005).

Hence the categories “Strongly agree”, “Agree” were grouped under “Agree” and “Strongly

Disagree”, “Disagree”, “Neither Agree nor Disagree” were grouped under “Disagree”. As per

Beamish (2004), the method of collapsing should be constant across items when such a

method is used. Utmost care was taken so that all. Items may be collapsed in the same

manner. For questions related to frequency of saving and investment a nominal scale was

used.

3.5.6 Statistical Techniques

Data was analyzed using the following statistical tools namely Descriptive Statistics, Factor

Analysis, Correlation, Multiple Linear Regression and Chi-square. Since the ordinal data

collected does not have an inherent order or sequence, and the items on the Likert scale are

not equidistant, mode has been used as a descriptive technique for analyzing the responses.


40

3.6 Operational Definitions

The theoretical constructs for the study have been defined as follows:-

Investment pattern: This refers to the frequency of investing and the choice of

investment instrument

Bank Deposits: The savings made in Fixed Deposits, Savings Deposits and

Recurring Deposits with nationalized and private banks

Bonds: Investments in Bonds issued by the Government of India.

Post office

Deposits:

These represent savings account with post office, Monthly

income schemes, Public Provident Fund, National Savings

Scheme and other savings schemes like Kissan Vikas Patra,

and Indira Vikas Patra

Mutual Funds:

Investments made in mutual fund schemes floated by

private and foreign Asset Management Companies like

.Kotak Mahindra, Franklin Templeton, Birla Sunlife etc.

This does not include Mutual funds by Banks like State

Bank of India, Unit Trust of India, Bank of Baroda etc.

Systematic

Investment Plan:

These include SIP’s of mutual funds comprising of equity

funds, owned by private and foreign companies’ .Eg., Birla

Sunlife equity Fund, DSP BR equity fund, Reliance Growth

Fund, Franklin India Blue Chip Fund etc.

Chit Fund: The investments in registered and un-registered chit fund

companies have been considered


41

Gold: Investing in gold schemes with a jeweler, buying of gold

bars and gold jewelry

Insurance: This includes life insurance, growth plans, retirement plans,

and money back policies floated by private and foreign

companies like Max New York and Birla Sun life, and other

nationalized institutions like Life Insurance Corporation of

India and State Bank of India.

Real Estate: The money invested in buying a piece of land or apartments

for capital appreciation.

3.7 Variables

Independent variables: The socio demographic variables and the perceptual factors

influencing the choice of investment instrument are the independent variables.

Dependent Variable: The frequency of investing and choice of investment instrument are the

dependent variables.

3.8 Limitations

The major limitations of this study are with respect to

The time frame within which the research had to be carried out and the sample size

from which it would be difficult to draw accurate conclusions on the entire population.

The results may not be free from biased figures as some of the responses include

deliberate falsification and incomplete information. Property of Christ University. Use it for fair purpose. Give credit to the author by citing properly, if your are using it.

42

Chapter 4

4Analysis and Interpretation

The chapter focuses on the analysis and interpretation of the data collected for the purpose of

studying the savings and investment pattern of women.

This chapter is divided into two parts on the basis of objectives and the nature of analysis

Part A

Demographic characteristics of the respondents

Interpretation of savings and investment pattern of women in general using descriptive

statistics.

Part B

Identification of factors that influence the investment decision using Factor analysis

and Regression analysis. Property of Christ University. Use it for fair purpose. Give credit to the author by citing properly, if your are using it.

43

Analysis of investment pattern of working and non working women using Chi-square

tests to study the variations.


44

PART A

4.1 Demographics of the Respondents

This section provides an understanding about the demographic characteristics of the sample.

Table 4.1 gives information about age, marital status, education and income.

Table 4.1 Demographic characteristics of respondents

Demographic characteristics Frequency %

Age

18-25 30 13

26-35 105 47

36-45 53 24

46-55 21 9.3

56 and above 16 7.11

Total 225 100

Marital Status

Married 211 93.78

Unmarried 14 6.22

Total 225 100

Education

High School 149 66.2

Graduate 54 24

Post-Graduate 22 9.78

Total 225 100

Occupation

Government Service 6 2.67

Private Service 39 17.3

Self Employed/Part time employed 72 32

House Wife 108 48

Total 225 100

Income

None 108 48

Less than one Lakh 37 16

2-3 Lakhs 56 24.9

3-5 Lakhs 13 5.78

5 Lakhs and above 11 4.9

Total 225 100 Property of Christ University.


45

It can be inferred from the Table 4.1 that the majority of the respondents fall under the age

group of 26-35. 93 percent of the respondents are married and 6 percent of them are

unmarried. 34 percent of the respondents are Graduates and Post graduates, whereas 66

percent of the respondents have attended the high school.52 percent of the respondents are

working women and the rest 48 percent of them are non-working.48 percent of the

respondents who are non-working report no income, and the rest 52 percent of them have

reported income with 24 percent of them reporting an income of 2-3 lakhs per annum

followed by 16 percent of them reporting an income of less than 1 lakh.

4.2 Savings and Invest Patterns of Women

To get an understanding of women’s saving and investment pattern in general, the responses

from women were analyzed with the help of descriptive statistics. The analyzed data has been

presented in a graphical form followed by an interpretation.

4.2.1 Savings Pattern of Women

The savings pattern of women can be understood by analyzing the regularity of saving,

amount of money saved every month, saving avenues and motives of saving.


46

Table 4.2 Descriptive statistics for savings pattern of women

Description Frequency Total Percentage

Regularity of Saving

Regular Savers 39 225 17.3

Irregular Savers 186 225 83.00

Amount Varies each month 13 225 6.00

Amount Fixed each month 212 225 94.00

Saving Avenues

Keep/Save in the house 153 225 68.0

Gold schemes with jeweler 103 225 45.8

Post office 42 225 18.7

Chit fund 81 225 36.0

Savings/Fixed Deposits 172 225 76.4

Motives of saving

Higher Education 26 225 11.6

To build a house 144 225 64.0

For marriage purposes 26 225 11.6

Buying gold 167 225 74.2

To afford luxury 111 225 49.3

Emergency 210 225 93.3

Helping family members

during financial crisis 170 225 75.6

Kids education 208 225 92.4

Son/Daughter's marriage 152 225 67.6

Old age 200 225 88.9


47

4.2.1.1 Regularity of Saving

It can be inferred from Fig 4.1 that only 17 percent of the respondents saved every month. 83

percent of them saved intermittently. As per Fig.4.2, only 6 percent of the respondents saved a

fixed amount each month, for the rest 94 percent, the amount of savings varied each month.

Monthly Savings

Amount Varies

94% Same Amount

94%

Figure 4.1 Regularity of Saving

Figure 4.2 Monthly Savings

Regularity of Saving

Irregular

Savings

83%

Save

Regularly

17%


48

4.2.1.2 Saving Avenues

It can be understood from fig.no 4.3 that Saving money in the Bank is a highly preferred

choice of saving at 76 percent followed by 68 percent who keep money in the house,48

percent with gold schemes,36 percent with Chit fund and 18 percent in the Post office.

4.2.1.3 Motives of saving

From Fig. no 4.4 it can be understood that saving for emergency purposes is main motive

behind saving with 93 percent of responses, then comes kid’s education at 92.44 percent

followed by saving for old age at 88.8 percent, Buying gold 74 percent, Helping family

members in need at 75 percent, kid’s marriage 67 percent and to afford luxury 49 percent.

Higher Education and saving for their own marriage was the lowest at 11 percent.

Figure 4.3 Saving Avenues


49

The findings indicate that majority of the women are irregular savers. Saving money either in

the bank or at home is a preferred mode. One of the reasons for saving money at home could

be for emergencies. Kids’ education, old age and Emergencies come up as the main motives

for saving. Women in this study have not considered their own education and marriage to be

one of the important motives for saving. This could be because majority of the respondents

were married and were in the age group of 26-35. Usually by 26 years of age, a woman in

India is through with her educational aspirations and is married.

12

64

12

74

49

93

76

92

68

89

Motives of Saving

Figure 4.4 Motives of Saving


50

Table 4.3 Descriptive statistics for sources of saving and amount saved every month

Non Working Women

Sources of Saving Frequency Percentage

Household Financial Budget 101 93.5

Pocket Money 1 0.9

Monetary Gifts Received 2 1.9

Income from any property, shares, ect 2 1.9

Income from Pension 2 1.9

Total 108 100

Working Women

Sources of Saving Frequency Percentage

Salary 53 45

Bonus & Incentives 29 25

Household financial budget 5 4

Monitary Gifts 11 9

Income from Property,Shares ect 19 16

Total 117 100

Non Working Women

Amount of Saving Frequency Percentage

Less than 5% 37 34

Less than 10% 32 30

10-20% 19 18

20-30% 11 10

Above 30% 9 8

Total 108 100

Working Women

Amount of Saving Frequency Percentage

Less than 5% 4 3

Less than 10% 12 10

10-20% 16 14

20-30% 4 3

More than 30% 81 69

Total 117 100


51

4.2.1.4 Sources of saving for working and non-working women

It can be understood from Table No.4.3 that the major source of saving for working women is

the salary 45 percent whereas for non-working women it is the household financial budget at

94 percent.

93%

2%

2% 2%

1%

Non-working Sources of invome

House budget

Income from pension

Shares

Monetary gifts

Pocket Money

Figure 4.5 Working women – Sources of income

Figure 4.6 Non Working Women – Sources of income


52

4.2.1.5 Amount saved per month by working and non-working women

It can be understood from table 4.3 that 69 percent of the working women save above 30

percent, 3 percent save 20-30 percent, 14 percent save 10-20 percent,10 percent save less than

10 percent and 3 percent of them save less than 5percent.

8 percent of the non-working women save above 30 percent, 10 percent save 20-30 percent,

18 percent save 10-20 percent, 30 percent save less than 10 percent and 34 percent save less

than 5 percent. Even though these women don’t have a direct income of their own and they

depend on Household financial budget for savings, they are able to save minimum 5percent

from their Household savings. In fact, 36 percent of the non-working women save above 10

percent.

4.2.2 Investment Pattern of Women

The investment pattern of women can be understood by analyzing the frequency of investing,

preferences of investment instrument, motives of investment, constraints to investing and

factors affecting investment decision making. The below table gives the frequencies for all of

them

Figure 4.7 Non working women Amount saved


53

Table 4.4 Investment pattern of women

Description Frequency Total Percentage

Frequency of Investing

Weekly 5 225 2.22

Monthly 166 225 73.8

Quarterly 22 225 9.78

Half yearly 19 225 8.44

Yearly 13 225 5.8

Total 225

100

Motives for Investment

Tax 18 225 8

Hedge against inflation 5 225 2.22

Personal & Fin Goals 112 225 49.78

Multiply Svngs 61 225 27.11

Annualized return 10 225 4.44

Tax benefit 19 225 8.44

Factors Affecting Investment Decisions

Easily available and understandable 184 225 81.78

Low initial investment 150 225 66.67

Safety of principal 202 225 89.78

Regular Income 192 225 85.33

Liquidity 196 225 87.11

Preferences of Investment Instrument

Gold 186 225 82.67

Real estate 144 225 64

Fixed/Bank deposit 102 225 45.33

Post office 48 225 21.33

Gov/public sector bonds 50 225 22.22

Insurance products 131 225 58.22

Shares of listed Companies 27 225 12

Mutual funds 15 225 6.67

SIP 19 225 8.44

Local Chit fund 83 225 36.89

Constraints to Investing

Lack of financial education/advice 196 225 87.11

Lack of interest /motivation willingnes 188 225 83.56

Locking of funds 167 225 74.22

Lack of sufficient funds 179 225 79.56

Unable to take decisions on your own 162 225 72 Property of Christ University. Use it for fair purpose. Give credit to the author by citing properly, if your are using it.

54

4.2.2.1 Frequency of Investing

Fig no. 4.8 shows the frequency of investing. 71 percent of women invest on a monthly basis,

followed by 22 percent on a quarterly basis, 19 percent on a half-yearly basis,13 percent on a

yearly basis and 5 percent on a weekly basis.

Figure 4.8 Frequency of investing


55

4.2.2.2 Motives of investing

It can be interpreted from Fig no 4.9 , that for 58 percent of the women, the motive behind

investing is to fulfill their personal and financial goals followed by 27 percent of them to

multiply their savings.8 percent for tax benefits, 5 percent for for Annualized return and 2

percent for hedge against inflation Annualized return and 2 percent for hedge against

inflation.

Figure 4.9 Motives of Investing


56

It can be understood from Fig no 4.11 that Safety of the principal is regarded as very

important criteria before investing. Almost 89 percent of the respondents regarded this factor

as highly important, followed by liquidity at 87 percent, receiving a regular income at 85

percent, availability and understandability of the instrument at 81 percent and low initial

investment at 67 percent.

Figure 4.10 Investment Avenues

Figure 4.11 Factors affecting a Women’s choice of Investment instrument


57

Gold is the most preferred investment instrument. 83 percent of the respondents rated Gold as

a highly desirable investment choice, followed by real estate at 64 percent, insurance products

at 58 percent, Bank deposits at 45 percent, Chit fund at 37 percent, Bonds at 22 percent, Post

office at 21 percent, Shares at 12 percent, SIP at 8 percent and Mutual Funds at 37 percent.

It can be understood from the above chart that “Lack of Financial education and advise” is

described as the highest constraint in investing at 87.11 percent, followed by “Lack of Interest

and Motivation” at 83 percent, “Lack of insufficient funds” at 79 percent, “Locking of

funds” at 74 percent and “Inability to take decisions on their own” at 72 percentPART (B)

87.11 83.5674.22 79.56

72

0

20

40

60

80

100

Constraints to investing

Figure 4.12 Constraints to investing for Women


58

4.3 Factor Analysis

In order to identify the factors that influence the investment decision, Factor Analysis was

conducted on independent as well as the dependent variables.

4.3.1 Factor Analysis of Independent Variables

1. Identification of factors from Independent Variables

To understand the factors that influence the investment decision of women, 8 statements were

identified. These statements were formed as questions on a five point scale and respondents

were asked to answer them. These eight statements are the independent variables which then

get reduced to three factors. The eight statements are listed below-

1. Interest /Willingness and Motivation to Invest

2. Availability of Funds

3. Financial decision making capacity

4. Easily available and Understandable investment

5. Safety of Principal

6. Liquidity

7. Low Initial Investment

8. Regular income

Factor analysis was used to reduce dimensions of the above mentioned eight independent

variables into three components using Principal Component Analysis


59

2. Factor analysis was used to reduce dimensions of the above mentioned eight

independent variables into three components using Principal Component Analysis.

Table 4.5 KMO and Bartlett's Test

KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling

Adequacy. .706

Bartlett's Test of

Sphericity

Approx. Chi-

Square 323.976

Df 28

Sig. .000

Based on the above output, the KMO = 0.706. This shows that the degree of common

variance among the variables is quite high; therefore factor analysis can be conducted. The

Chi-Square value for Bartlett’s test of Sphericity is 323.976 and the significant value is 0.000

indicating that the data is suitable for factor analysis.Using Principal Component Analysis as

the Extraction Method Communalities were found

Table 4.6 Communalities

Initial Extraction

IPEasily 1.000 .770

IPLow 1.000 .680

IPSecu 1.000 .351

IPIncome 1.000 .707

IPCash 1.000 .784

Ipmot 1.000 .493

Ipfunds 1.000 .457

IPDecsns 1.000 .690


60

Based on the output above, the highest variance was found in IP Cash =.784, followed by

IPEasily=.770,IPIncome=.707,IPDecsns=.690,IPLow=.680,IPMot=.493 ,IPfunds=.457 and

IPsecu=.351

Table 4.7 Rotated Component Matrix

Component

1 2 3

IPDecsns .793 .036 .244

Ipmot .699 .044 .049

Ipfunds .616 .278 -.008

IPEasily -.018 .876 .056

IPLow .281 .759 .159

IPCash -.055 -.030 .883

IPIncome .273 .268 .749

IPSecu .284 .350 .384

The Rotated Component Matrix indicates, based on factor loadings that these eight

components were reduced into three factors. The three factors were named as Individual,

Control and Monetary.

1. Individual

This factor contains three variables IPDecisions ie the financial decision making capacity of

the woman, IPMot the Motivation and willingness and IPFunds the funds available to her for

investment. All the three variables are very intrinsic to the Individual and hence they have

been named as Individual factor.

2. Control

This factor contains two variables. They are IPEasy ie the availability and understandability

of the Investment instruments and, IPLow , the preference of a low initial investment Property of Christ University.


61

instrument. This factor has been named as “Control” as the variables under this factor are pre-

requisites to taking control over an investment, which, from a women’s point of view is very

important.

3. Monetary

This factor contains three variables namely IPCash ie Liquidity, IPIncome- the need for

regular income and IPSecu- the Safety of Principal. This factor has been named as

“Monetary”

4.3.2 FACTOR ANALYSIS OF DEPENDENT VARIABLES

1. Identification of factors from Dependent Variables

To understand the choice of investment instruments ten statement about investment

instruments were identified. The opinions about these instruments were collected on a five

point Likert scale. The ten instruments are listed below-

1. Fixed Deposits/Savings Deposits

2. Government of India Bonds

3. Post office Deposits

4. Mutual Funds

5. Shares

6. Systematic Investment Plan

7. Chit Fund

8. Gold

9. Insurance

10. Real Estate Property of Christ University.


62

2. Factor analysis was used to reduce dimensions of the above mentioned ten dependent

variables into four components using Principal Component Analysis.

Table 4.8 KMO and Bartlett's Test

KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling

Adequacy. .627

Bartlett's Test of

Sphericity

Approx. Chi-Square 656.951

df 45

Sig. .000

Based on the above output the KMO = 0.627. This shows that the degree of common variance

among the variables is quite high; therefore factor analysis can be conducted. The Chi-Square

value for Bartlett’s test of Sphericity is 656.951 and the significant value is 0.000 indicating

that the data is suitable for factor analysis. The Communalities are shown below-

Table 4.9 Communalities

Initial Extraction

IPGold 1.000 .757

IPRealEst 1.000 .846

IPFD 1.000 .684

IPPost 1.000 .767

IPGov 1.000 .754

IPinsu 1.000 .582

IPShares 1.000 .506

IPMF 1.000 .850

IPSIP 1.000 .785

IPChit 1.000 .639


63

Based on the output above, the highest variance was found in IPRealEst= .846, followed by

IPMf=.850, IPSIP=.785, IPGold=.757, IPPost=.767, IPGov=.754, IPFD=.684 IPchit=.639,

IPinsu=582 and IPshares=.506

Table 4.10 Rotated Component Matrixa

Component

1 2 3 4

IPPost .867 .121 -.015 -.009

IPGov .827 .141 .208 -.077

IPFD .668 .156 -.349 .303

IPMF .139 .901 -.130 -.033

IPSIP .039 .882 -.008 -.078

IPShares .152 .627 -.057 .294

IPChit -.058 -.047 .761 -.235

IPGold .044 -.124 .725 .463

IPinsu .521 -.060 .552 .059

IPRealEst .014 .072 -.018 .917

The Rotated Component Matrix indicates, based on factor loadings that these ten components

were reduced into four factors.


64

1) Zero Risk

This factor consists of investments in Post Office,Governement Deposits, and Deposits in

Bank .

2) Low Risk

This factor consists of Investments in Real Estate.

3) Moderate Risk

This factor consists of Investments in Gold,Chit Fund and Insurance.

4) High Risk

This factor consists of Investments in Mutual Funds, Shares and SIP.

1. Regression Analysis

To identify the factors that influence the choice of investment instrument, in the first phase

Karl Pearson’s Correlation Analysis was conducted to find the association between

Independent and Dependent Factors.


65

Table 4.11 Karl Pearson’s Correlation for association between factors affecting investment

decision

Individual Control Monetary

Zero Risk

High Risk

Moderate Risk

Low Risk

Individual

Pearson Correlation

1 0 0 .162* -.253** .391** 0.1

Sig. (2-tailed)

1 1 0.015 0 0 0.136

N 225 225 225 225 225 225 225

Control

Pearson Correlation

0 1 0 .323**

0.073 .242**

-.158*

Sig. (2-tailed) 1

1 0 0.274 0 0.017

N 225 225 225 225 225 225 225

Monetary

Pearson Correlation

0 0 1 .167* -.135* .210** 0.049

Sig. (2-tailed) 1 1

0.012 0.043 0.002 0.465

N 225 225 225 225 225 225 225

Zero Risk

Pearson Correlation

.162* .323** .167* 1 0 0 0

Sig. (2-tailed) 0.015 0 0.012

1 1 1

N 225 225 225 225 225 225 225

High Risk

Pearson Correlation

-.253**

0.073 -.135* 0 1 0 0

Sig. (2-tailed) 0 0.274 0.043 1

1 1

N 225 225 225 225 225 225 225

Moderate Risk

Pearson Correlation

.391** .242** .210** 0 0 1 0

Sig. (2-tailed) 0 0 0.002 1 1

1

N 225 225 225 225 225 225 225

Low Risk

Pearson Correlation

0.1 -.158* 0.049 0 0 0 1

Sig. (2-tailed) 0.136 0.017 0.465 1 1 1

N 225 225 225 225 225 225 225

*Co-relation is significant at the 0.05 level (2- tailed)

**Co-relation is significant at the 0.01 level (2-tailed)


66

Table No. 4.11 indicates that there is an association between the below listed factors

1. ‘Individual’ Factor

‘Individual’ factor has positive correlation with “Zero risk” and “Moderate”

risk investment instruments

‘Individual’ factor has negative correlation with “High risk” investment

instrument

2. ‘Control’ Factor

‘ Control’” factor has positive correlation with “Zero risk” and “Moderate

risk” investment instrument

‘Control’ factor has negative correlation with “Low Risk” investment

instrument

3. ‘Monetary’ Factor

‘Monetary’ factor has a positive correlation with “Zero risk” and “Moderate

risk” investment instrument

‘Monetary’ factor has a negative correlation with “High Risk” investment

instrument.

In the second phase, to find the influence of the independent factors on each dependent factor,

Multiple Linear Regression was conducted.

A. Regression Analysis for “Zero Risk” Investment

Table 4.12 Model Summary for “Zero Risk” investment

Model Summaryb

Model R R Square Adjusted R

Square

Std. Error of the

Estimate

1 .398a 0.159 0.147 0.9235


67

a. Predictors: (Constant), IV_INV_Monetary, IV_INV_Control,

IV_INV_Individual

b. Dependent Variable: DV_FAC_ZeroRisk

Table 4.13 Analysis of Variance for “Zero Risk” investment

ANOVAb

Model Sum of

Squares df

Mean

Square F Sig.

1

Regression 35.51 3 11.835 13.876 .000a

Residual 188.5 221 0.853

Total 224 224

a. Predictors: (Constant), IV_INV_Monetary, IV_INV,Control, IV_INV_Individual

b. Dependent Variable: DV_FAC_ZeroRisk

Table 4.14 Coefficients of Regression for “Zero Risk” investment

Coefficientsa

Model

Unstandardized Coefficients

Standardized Coefficients

Sig.

95.0% Confidence

Interval for B

B Std.

Error Beta t

Lower Bound

Upper Bound

1

(Constant) -2.10E-17 0.062

0 1 -0.121 0.121

IV_INV_Individual 0.162 0.062 0.162 2.623 0.009 0.04 0.283

IV_INV_Control 0.323 0.062 0.323 5.238 0 0.202 0.445

IV_INV_Monetary 0.167 0.062 0.167 2.703 0.007 0.045 0.288

a. Dependent Variable: DV_FAC_Zero risk


68

Figure 4.13 Histogram for Dependent Variable “Zero Risk” investment

Figure 4.14 Normal P Plot of Regression Standardized

Residual Dependent Variable “Zero Risk” investment Property of Christ University.


69

Results indicate that the explained variance is 13.876 percent ( F statistic is 13.876 ) which is

found to be significant at 1 percent. It can also be said that all the three factors namely

“Individual”,”Control” and “Monetary” significantly influence the dependent factor “Zero

Risk”investment.Though the results indicate the existence of a fit to the relationship, it may

not be sufficed, because of a non-prominent value of adjusted R2 (0.14 ).

B. Regression Analysis for “High Risk” Investment

Table 4.15 Model Summary for “High Risk” Investment

Model Summaryb

Mod

el R R Square

Adjusted

R Square

Std. Error of the

Estimate

1 .286

a 0.082 0.074 0.96241

a. Predictors: (Constant), IV_INV_Monetary, IV_INV_Individual

b. Dependent Variable: DV_FAC_High Risk

Table 4.16 Analysis of Variance for “High Risk” Investment

ANOVAb

Model Sum of

Squares df

Mean

Square F Sig.

1

Regression 18.377 2 9.189 9.92 .000a

Residual 205.623 222 0.926

Total 224 224

a. Predictors: (Constant), IV_INV_Monetary, IV_INV_Individual

b. Dependent Variable: DV_FAC_High Risk


70

Table 4.17 Coefficients of Regression for “High Risk”Investment

Coefficientsa

Model

Unstd. Coefficients Std.Coefficients

Sig.

95.0% Confidence

Interval for B

B

Std.

Error Beta T Lower

Bound

Upper

Bound

1

(Constant) 2.751E-17 0.064

0 1 -0.126 0.126

IV_INV_Individual -0.253 0.064 -0.253 -3.929 0 -0.379 -0.126

IV_INV_Monetary -0.135 0.064 -0.135 -2.099 0.037 -0.262 -0.008

Dependent Variable: DV_FAC_“High Risk


71

Figure 4.16 Histogram Dependent Variable: “High Risk” investment

Figure 4.15 Normal P Plot of Regression Standardized Residual

Dependent Variable “High Risk ” investment


72

Results indicate that the explained variance is 9.92 percent ( F statistic is 9.92) which is found

to be significant at 1 percent.This signifies that “Individual” and “Monetary” factors both

influence the choice for opting for a “High Risk” Investment. Though the results indicate the

existence of a fit to the relationship, it may not be sufficed, because of a non-prominent value

of adjusted R2 (0.082 ).

C. Regression Analysis for “Low Risk” Investment

Table 4.18 Model Summary for “Low Risk” Investment

Model Summaryb

Model R R Square Adjusted R

Square

Std. Error of the

Estimate

1 .158a 0.025 0.021 0.98957

a. Predictors: (Constant), IV_INV_Control

b. Dependent Variable: DV_FAC_4RN

Table 4.19 Analysis of Variance for “Low Risk” Investment

ANOVAb

Model Sum of

Squares df

Mean

Square F Sig.

1

Regression 5.626 1 5.626 5.745 .017a

Residual 218.374 223 0.979

Total 224 224

a. Predictors: (Constant), IV_INV_Control

b. Dependent Variable: DV_FAC_Low Risk


73

Table 4.20 Coefficients of Regression for “Low Risk” Investment

Coefficientsa

Model

Unstandardized

Coefficients

Standardized

Coefficients Sig.

95.0%

Confidence

Interval for B

B Std.

Error Beta t

Lower

Bound

Upper

Bound

1 (Constant)

2.21E-17

0.066

0 1 -0.13 0.13

IV_INV_Control -0.158 0.066 -0.158 -2.397 0.017 -0.289 -0.028

a. Dependent Variable: DV_FAC_Low Risk

Figure 4.17 Histogram Dependent Variable: “Low Risk” investment


74

Results indicate that the explained variance is 5.745 percent ( F statistic is 5.745 ) which is

found to be significant at 1 percent. It signifies that “Control” factor is responsible for the

investment decision of women while investing in a “Low risk” investment.

Figure 4.19 No Normal P Plot of Regression Standardized

Residual Dependent Variable “Low Risk” investment


75

4.4 Variance in Investment Pattern across Working and Non-

working Women

In order to study the differences in the investment pattern across working and non-working

women, two different indicators were tested

1. Frequency of investment interval

2. Choice of investment instrument

The two hypotheses framed for the study are as follows-

H05: There is no significant difference in the frequency of investing across socio-

demographic attributes

H06: There is no significant difference in the choice of investment instrument across socio-

demographic attributes

The following steps were followed for chi-square analysis.

Firstly, the differences in investment pattern for the working women were analyzed

with “Occupation” as the reference variable

Secondly, the differences in investment pattern for the non-working women were

analyzed with “Education” as the reference variable

Finally, the differences in investment pattern for both the above mentioned groups

were analyzed with “Age” as a common reference variable.


76

4.4.1 Differences in frequency of investing across socio-demographic

attributes

Table 4.21 Chi-Square Values for frequency of investing

Working Women (Occupation)

Sl.

No Description Chi-square Sig Hypothesis

1 Frequency of

investing 25.27 0.14 Accept

Working Women (Age)

2 Frequency of

investing 18.983 0.27 Accept

Non Working (Education)

3 Frequency of

investing 46.097 0 Accept

Non Working (Age)

4 Frequency of

investing 46.097 0 Accept

The table 4.22 indicates that the frequency of investing does not differ with respect to

Occupation or Age for working women and with respect to Education or Age for non-working

women. Thus, the null Hypothesis H05: “There is no significant difference in the frequency of

investing across socio-demographic attributes” is accepted.


77

4.5 Variations in the Choice of Investment Instrument across

Socio-Demographic Attributes

Table 4.22 Chi-Square for Working Women as per Occupation and Age

Working Women (Occupation)

Description Chi-square Sig Hypothesis

Gold 29.64 0.003 Reject

Real Estate 22.26 0.035 Reject

Fixed Deposit 22.99 0.028 Reject

Post Office 29.39 0.003 Reject

Bonds 16.38 0.175 Accept

Insurance 17.05 0.148 Accept

Shares 4.39 0.975 Accept

Mutual Funds 13.08 0.363 Accept

SIP 14.51 0.27 Accept

Chit funds 28.03 0.005 Reject

Working Women (Age)

Description Chi-square Sig

Gold 19.107 0.263 Accept

Real Estate 14.54 0.559 Accept

Fixed Deposit 19.355 0.251 Accept

Post Office 19.197 0.259 Accept


Insurance 36.28 0.003 Reject

Shares 41.002 0.001 Reject

Mutual Funds 24.408 0.081 Reject

SIP 18.223 0.311 Accept

Chit funds 39.629 0.001 Reject

a) The table 4.23 indicates that there is a significant difference found in the investments

made in Gold, Real Estate, Bank Deposits, Post Office and Chit funds with respect to

Occupation of working women. However, there is no significant difference found in

investments in Bonds, Insurance, Shares, Mutual Funds and SIP.


78

b) The table 4.23 indicates that there is a significant difference found in the investments

made in Insurance, Shares, Mutual funds and Chit funds with respect to Age of working

women. However, there is no significant difference found in investments in Gold, Real Estate,

Bank Deposit, Post Office, Bonds, and SIP.

Table 4.24 Chi-Square Values for Non- working Women as per Education and Age

Non Working (Education)










SIP 29.201 0.023 Reject

Chit funds 23.16 0.11 Accept

Non Working (Age)










SIP 29.201 0.023 Reject

Chit funds 23.16 0.11 Accept


79

c) The above table indicates that there is a significant difference found in the investments

made in Gold, Mutual funds and SIP with respect to Education of non- working women.

However, there is no significant difference found in investments in Real Estate, Bank Deposit,

Post Office, Bonds, Chit funds and Insurance.

d) The table 4.24 indicates that there is a significant difference found in the investments

made in Gold, Mutual funds and SIP with respect to age of working women. However, there

is no significant difference found in investments in Real Estate, Bank Deposit, Post Office,

Bonds, Shares and Insurance

Thus, the null hypothesis H06: “There is no significant difference in the choice of investment

instrument across socio-demographic attributes” is rejected.

4.6 Descriptive statistics

With the help of frequency tables, data has been analyzed to understand whether there is any

variation in the frequency of investing and choice of investment instrument across socio-

demographic attributes.


80

Table 4.23 Variations in Investment pattern of Working Women w.r.t Occupation

Variations in Working Women (Occupation wise)


Gov % Private %

Self/Part

-time %

Weekly 0 0 2 5 2 3

Monthly 5 83 22 56 65 90

Quarterly 0 0 5 13 4 6

Half yearly 1 17 8 21 1 1

Yearly 0 0 2 5 0 0

Total 6 100 39 100 72 100

Motives of Investing

Tax benefits 0 0 18 46 1 1

Hedge against inflation 0 0 0 0 1 1

Pers/fin goals 5 83 11 28 52 72

Multiply savings 1 17 8 21 16 22

Receive annualized return 0 0 2 5 2 3

Total 6 100 39 100 72 100

Preferred Investment Instrument

Gold 5 83 23 59 69 96

Real Estate 3 50 18 46 53 74

Bank Deposits 5 83 15 38 23 32

Post Office 2 33 6 15 5 7

Bonds 3 50 9 23 8 11

Insurance 5 83 16 41 45 63

Shares 0 0 2 5 7 10

Mutual Funds 0 0 3 8 2 3

SIP 1 17 6 15 3 4

Chit fund 4 67 9 23 37 51

Total No of Employees 6

39

72


81

Based on the table 4.27 the following facts about variations in investment pattern about

working women with regard to Occupation can be recognized.

1) Frequency of Investing

Investing on a monthly basis is more prevalent amongst all types of employees. Almost 76

percent of the working women invested on a monthly basis. As per occupation, 83 percent of

Government employees and 90 percent of self employed/part time employed women invested

on a monthly basis. Compared to this, only 56 percent women employed in private sector

invest on a monthly basis. 21 percent of these employees invest half-yearly and 13 percent of

these on a quarterly basis. Half yearly investing is also popular amongst employees of Private

sector

2) Motives of investing

46 percent of women employed in private sector invest for tax benefits. Investing for tax

saving is not seen as a motive for investment for women working with Government sector and

Self employed women. 83 percent of women working with Government sector, 28 percent of

women working with Private sector and 72 percent of women who are self employed/part

time employed invest for fulfilling their personal and financial goals. Across all the

occupational groups, the need to multiply their savings is felt almost in the same magnitude.

This suggests that, irrespective of the occupation, their understanding about the fact that

investing helps in multiplying the savings is the same. Where private sector and Self-

employed/part time employed women look forward to annualized return, the Government

sector employees don’t seem to have such a necessity. To undertake investments as a hedge

against inflation does appears as an insignificant motive across all occupations. One reason

could be that, since they are working they assume that there will be a flow of income for few

more years and hence inflation may not pinch them.

3) Preferred Investment Instrument

The portfolio of investments of a women Government employee heavily consists of

traditional investment instruments like Gold, Insurance and Bank Deposits. Risky instruments Property of Christ University.


82

like Mutual funds and Shares are not preferred by them. Chit fund is a popular choice

amongst Government Employees. The portfolio of a Private sector employee is a fine balance

of risky, moderately risky, and safe traditional instruments. However, here too the traditional

investment instruments are more pronounced. Risky instruments like Mutual funds and Shares

form a tiny share. The portfolio of a Self-employed women consists of a good mix of all types

of instruments. 93 percent of Self Employed/ Part time employed women have rated Gold to

be the maximum preferred choice. Though, here too, the majority of the investments are

traditional and very safe in nature. Across all occupations, Gold is the topmost preference,

followed by Real estate and Insurance.


83

Table 4.24 Variations in Investment pattern of Non-working women w.r.t Education

Variations in Non-Working Women ( Education wise)


Under-graduates %

Graduates

and above % Total

60

46

Weekly 0 0 1 3 1

Monthly 60 91 18 45 78

Quarterly 2 3 11 28 13

Halfyearly 1 2 7 18 8

Yearly 3 5 3 8 6

Total 66 100 40 100 106


Tax Benefits 0 0 0 0 0

Hedge against

inflation 13 20 14 35 27

Personal/Financial

goals 37 56 18 45 55

Multiply savings 15 23 7 18 22

Annualized return 1 2 1 3 2

Total 66 100 40 100 106

Investment Instrument

Gold 57 86 30 75 87

Real estate 48 73 21 53 69

Bank deposits 32 48 25 63 57

Post office 25 38 15 38 40

Bonds 20 30 10 25 30

Insurance 46 70 2 5 48

Shares 10 15 8 20 18

Mutual funds 4 6 6 15 10

SIP 1 2 8 20 9

Chit funds 23 35 10 25 33

Total 66 100 40 100 106


84

Based on the Table No. 4.25, the following facts about variations in investment pattern about

Non-working women with regard to Education can be recognized.


There is a striking difference spotted in the frequency of investing with regard to educational

qualification of women. 91 percent of undergraduate women prefer investing on a monthly

basis, compared to a low figure of 45 percent of Graduate women.

28 percent of Graduates invest on a quarterly basis, followed by 18 percent of them on a half-

yearly basis and 8 percent on a yearly basis. Data shows that under-graduate women are in

the favor of monthly investments.

As the frequency of investments also depends on the choice of investment instrument, the

variance between both the groups can be justified. However, looking at the preferences of

investment instrument, it can be said that monthly investments like SIP’s and mutual funds

are not the preferred choice of these under-graduate women, rather they go for chit funds, Post

office deposits, Bank deposits and Gold. Thus it can be said that , in this case the frequency of

investment does not hold a bearing on the choice of investment instrument.


Investing to fulfill personal and financial goals is found to be the main motive behind

investment, with 56 percent of under graduate women and 45 percent of graduate women

quoting so. 23 percent of under graduates and 18 percent of graduates find the “multiply

savings” to be an important motive for investment. Since the differences in these percentages

are not high, it can safely assume that both groups understand the need to multiply savings

through investment. Annualized return is not found to be a significant motive.

Property of Christ University.


85


The portfolio of investments of undergraduate women mostly consists of traditionally safe

investment instruments like Gold, Real Estate, Bank Deposits and Insurance. The investment

preferences of Mutual funds and SIP are low at 6 percent and 2 percent respectively.

The portfolio of investments of graduate women is a mix of risky, moderately risky and safe

investments. 15 percent of the graduates invest in mutual funds, 20 percent in SIP and 20

percent in shares. However, the majority of the investments revolve around safe instruments

like Gold at 75 percent and Bank Deposits at 63 percent.

While comparing the figures of investment in Gold, Real Estate and Chit funds between both

the groups, it can be said that they are more popular amongst undergraduates. One reason

could be that, these traditional investments are getting slowly replaced by instruments like

Mutual funds and SIP’s in the graduate group.

Only 2 percent of undergraduate women have rated SIP as a preference compared to 20

percent of the graduates. Likewise, only 6 percent of the undergraduates favor Mutual funds

in comparison to 15 percent of the graduates. Thus it can be said that awareness amongst

undergraduates with respect to SIP’s and Mutual Funds is low or that their risk appetite is

low.


86

Table 4.25 Variations in the Investment pattern of working women with respect to age

Variations in Working Women (Age wise)

Working Women age

group

18-

25 %

26-

35 %

36-

45 %

46-

55 %

56 &

abov

e

%

Total

no of

women

Total no of women 18

47

36

11

5

117


Weekly 1 6 2 4 0 0 1 9 0 0 4

Monthly 9 50 36 77 34 94 9 82 5 100 93

Quarterly 3 17 3 6 2 6 1 9 0 0 9

Half yearly 4 22 5 11 0 0 0 0 0 0 9

Yearly 1 6 1 2 0 0 0 0 0 0 2

Total 18 10

0 47

10

0 36

10

0 11

10

0 5 100 117


Tax Benefits 7 39 11 23 0 0 0 0 1 20 19

Hedge against

inflation 0 0 0 0 1 3 0 0 0 0 1

Personal/Financial

goals 9 50 25 53 22 61 10 91 2 40 68

Multiply savings 2 11 9 19 11 31 1 9 2 40 25

Annualized return 0 0 2 4 2 6 0 0 0 0 4

Total 18 10

0 47

10

0 36

10

0 11

10

0 5 100 117

Investment Instrument

Gold 11 61 38 81 34 94 10 91 5 100 98

Real estate 10 56 27 57 25 69 9 82 4 80 75

Bank deposits 5 28 24 51 7 19 5 45 2 40 43

Post office 7 39 8 17 2 6 4 36 2 40 23

Bonds 2 11 9 19 17 47 2 18 4 80 34

Insurance 7 39 20 43 24 67 7 64 3 60 61

Shares 2 11 5 11 1 3 2 18 4 80 14

Mutual funds 0 0 4 9 0 0 2 18 0 0 6

SIP 1 6 8 17 0 0 1 9 0 0 10

Chit funds 1 6 18 38 23 64 7 64 1 20 50

Total 18

47

36

11

5

117

Based on the Table No 4.29 the following facts about variations in investment pattern about

working women with regard to Age can be recognized. Property of Christ University. Use it for fair purpose. Give credit to the author by citing properly, if your are using it.

87


Across all age groups, investing on a monthly basis is very popular. Almost 80 percent of the

total working women respondents invest on a monthly basis. However, it seems to be more

prevalent across the 36-45 age groups with 94 percent of them investing on a monthly basis.


Investing for personal and financial goals is the main motive behind investing for working

women with 68 percent, followed by 25 percent to multiply savings and 19 percent for tax

benefits. Under the age group of 18-25, 38 percent of them save for tax purposes followed by

23 percent of women under the age group of 26-35 percent. This motive has no significance

in the age group of 36-45 and 46-55. For the age group 36-45 and 46-55, investing for

personal and financial goals supersedes other motives at 61 percent and 91 percent

respectively.


Gold is the most preferred investment instrument with 98 percent of the working women

invest in Gold, followed by 75 percent in Real Estate and 61 percent in Insurance. Across all

age groups the preferences for investing in Mutual funds and SIP is low at 6 percent and 10

percent respectively. From the age group 36- 45 onwards investing in insurance holds a

significant importance, whereas in the age group of 18-25 and 26-35 it is comparatively low

at 39 percent and 43 percent respectively. This suggests that middle-aged women and above

prefer investing insurance related products.


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Table 4.26 Variations in Investment pattern of Non-working w.r.t to Age

Variations in Non-Working Women (Age wise)

18-25 % 26-35 % 36-45 % 46-55 %

56 &

above % Total

Total 10

51

18

9

18


Weekly 0 0 1 2 1 6 0 0 0 0 2

Monthly 8 80 35 69 10 56 8 89 6 33 67

Quarterly 1 10 6 12 1 6 0 0 2 11 10

Half

yearly 0 0 5 10 4 22 0 0 1 6 10

Yearly 1 10 4 8 0 0 1 11 9 50 15

Total 10 100 51 100 18 100 9 100 18 100 106

Motives of Investing Tax

Benefits 0 0 0 0 0 0 0 0 0 0 0

Hedging 0 0 2 4 12 67 0 0 0 0 14

Per/Fin

goals 7 70 28 55 5 28 6 67 9 50 55

Multiply

savings 5 50 23 45 17 94 3 33 0 0 48

Annualize

d return 0 0 5 10 0 0 1 11 0 0 6

Total 10 100 51 100 18 100 9 100 18 100 106

Investment Pattern

Gold 10 100 44 86 16 89 9 100 6 33 85

Real estate 7 70 35 69 13 72 6 67 8 44 69

Bank

deposits 6 60 31 61 8 44 6 67 6 33 57

Post office 4 40 18 35 7 39 7 78 3 17 39

Bonds 1 10 16 31 10 56 4 44 3 17 34

Insurance 8 80 34 67 13 72 8 89 4 22 67

Shares 3 30 12 24 3 17 2 22 2 11 22

Mutual

funds 1 10 5 10 2 11 0 0 2 11 10

SIP 0 0 7 14 2 11 0 0 0 0 9

Chit funds 3 30 18 35 6 33 3 33 3 17 33

Total 10

51

18

9

18

106


89

1. Frequency of Investing

Investing on a monthly basis is the most preferred way of investing, with 67 percent, followed

by 15 percent on a yearly basis and 10 percent on a quarterly and half yearly basis.

2. Motives of investing

Investing for personal and financial goals is the main motive behind investing across all age

groups at 55 percent, followed by 48 percent to multiply their savings and 14 percent for

hedging against inflation.

3. Preferred Investment Instrument

Gold is the most preferred investment instrument with 85 percent of the non -working women

investing in Gold, followed by 69 percent in Real Estate and 67 percent in Insurance. Across

all age groups the preferences for investing in Mutual funds and SIP is low at 10 percent and

9 percent respectively.


90

Chapter 5

5Summary and Conclusion

This chapter summarizes the entire dissertation on the study of savings and investment pattern

of women covering the need and relevance of the study, review of literature, research

methodology, results of the study and the scope for further research ending with conclusions

for the study.

5.1 Need and Relevance of the Study

Till the last decade, not much research has been conducted on savings and investment of

women ,considering the low potential of women to earn, save and invest,. Government

surveys, Policies, Financial awareness, Marketing of financial products etc has been very less

focussed towards women. This research studies the investment pattern of women along with

their savings pattern.The factors which influence the investment decision making of women

have been identified along with the constraints.As non working-women also get to save, their

savings and investment pattern has also been included as a part of the study. The findings thus

can be used by financial institutions to design products exclusively for women population, for

Government to formulate appropriate policies to boost aggregate saving and investment, and

for the research community to find out why disparities exist between what a women saves and

what she invests.


91

5.2 Review of Literature

Women are quite known for their saving habits. But they generally don’t convert all of their

savings into investments. Maltby (2006) explored the role of women in finance from

eighteenth century till date. Certain fascinating facts that come out are, women were

managing the household expenses, savings and investments and were active traders too. In

the same article, they have stressed the need for research on topics such as women’s attitude

towards money, their savings and investment patterns keeping the changing times in mind.

Chachoria ( 2000) points out that women are the next generation financial decision makers

and they should be targeted from a financial perspective. She suggests that marketing for

financial products should be done differently for women. Despite being risk averse and less

financially knowledgeable, women have made more profits than men in the stock markets and

other financial investments. Barber( 2001) points out that though women are not active

investors, they make more profits than men. He says that by trading more, men hurt their

performance more than women. Preda (2001) comments that Women are always excluded

from financial discussions, on the explicit ground that they cannot understand investments.

5.3 Research Gap

While Investment pattern has been studied intensely in a generic sense, far less research exists

regarding Women’s mind-set towards investment as a subject. Also, since Savings is a

precursor to Investment, it is imperative to study savings and investment patterns side by side.

The present study aims to do so. Secondly, Non working women have so far been kept out of

such studies. The fact that they get to save and hence can invest has been the philosophy

behind including them in this study

5.4 Statement of the Problem


investment of the nation, and to convert the available savings into investment, one has to


92

understand how individuals in the society save and invest. The fact that women are becoming

economically powerful calls for a need to exclusively study their savings & investment

patterns, and cater to them accordingly so that they are encouraged to contribute towards

capital formation of the economy.

5.5 Research Objectives

Primary objective

To study the savings and investment pattern of working and non working women.

Specific Objectives:

To achieve the primary objective, the specific objectives were formulated as follows

To study the variation of savings and investment pattern across socio economic characteristics

among women

To identify the factors those affect the investment pattern of working and non working

women

5.6 Research Hypothesis

This research proposes the following hypothesis to be tested empirically based on the

literature review.

H01: Perceptual factors do not significantly influence the investment decision. Property of Christ University. Use it for fair purpose. Give credit to the author by citing properly, if your are using it.

93

H02: There is no significant difference in the frequency of investing across socio-demographic

attributes for working and non-working women.

H03: There is no significant difference in the choice of investment instrument across socio

demographic attributes for working and non working women.

5.7 Sampling Plan

Women who are regular savers and investors have been considered for this study. A total of

250 responses were considered for the study but only 225 responses were usable. This study

was confined to the city of Bangalore. Non probabilistic, convenience sampling was used for

the purpose of data collection.

5.8 Survey Measurement

Questionnaires were used to collect data from the respondents. An original questionnaire

consisting of three sections was used for the purpose. The first section consisted of questions

that were used for collection of data of personal nature pertaining to the respondent’s age,

educational qualification, occupation and monthly income. The next section consisted of

questions related to sources of saving, frequency of saving, constraints and motives of saving,

and saving avenues. The third section comprised of questions related to frequency of

investing, investment preferences, motives of investing and constraints to investing.

5.9 Statistical Techniques

Data was analyzed using the following statistical tools namely Descriptive Statistics, Factor

Analysis, Correlation, Multiple Linear Regression and Chi-square. Since the ordinal data

collected does not have an inherent order or sequence, and the items on the likert scale are not

equidistant, mode has been used as a descriptive technique for analyzing the responses.


94

5.10 Variables

Independent Variable: The socio demographic Variables and the perceptual factors

influencing the choice of investment instrument are the independent variables.

Dependent Variable: The frequency of investing and choice of investment instrument are the

dependent Variables.

5.11 Summary of Findings

The findings are presented in four parts. They are findings with respect to

1. Savings pattern of women

2. Investment pattern of women

3. Factors that influence the investment decisions of a woman

4. Variations in the investment pattern of working and non-working women

5.11.1 Savings pattern of women

Only 17% of the respondents saved each month rest 83percent of them saved

intermittently. Also only 6percent of the respondents saved a fixed amount each month, for

the rest 94%, the amount of savings varied each month.


95

Saving money in the Bank is a highly preferred choice of saving at 76 percent

followed by 68 percent who keep money in the house.

Saving money for emergency purposes is main motive behind saving with 93 percent

of responses.

Even though non-working women don’t have direct income of their own, they are able

to save a minimum of 5 percent from their household savings. In fact, 36 percent of the non-

working women save above 10 percent.

5.11.2 Investment pattern of women

Safety of the principal is regarded as a very important criterion before investing,

followed by liquidity, receiving regular income, availability and understandability of the

instrument and finally comes the preference for a low initial investment. Thus, it can be

inferred, once a woman is satisfied about safety of the principal she will be ready to invest

further considering surplus funds.

Considering investment pattern by frequency, investing on a monthly basis is

prevalent amongst working and non-working women across all occupations, age and

education.

The main motive behind investing for women is to fulfill their personal and financial

goals. They also recognize the importance of multiplying savings through investment

Using investment as a mode to fight against inflation is not a recognized motive in the

working women category. However, non-working women are appreciative about this aspect.


96

Gold is the most preferred investment instrument, followed by real estate, insurance

products, bank deposits, chit fund, mutual funds, bonds, post office, shares and SIP.

While employees in private sector and self-employed women show some interest in

mutual funds and shares, employees in Government sector are not experimental with these.

However, this statement cannot be generalized as the sample size of Government employees

is small.

SIP’s are the least explored amongst both the groups.

It can be understood that “Lack of Financial education and advise” is described as the

highest constraint in investing , followed by “Lack of Interest and Motivation” , “Lack of

insufficient funds” , “Locking of funds” and “Inability to take decisions on their own”. This

bursts a common myth: Women are not able to invest as they are not able to take decisions or

rather they are not allowed to take decisions on their own. This also shows that there is a need

to educate and advise women particularly on the subject of investment.

The findings suggest that across all categories women prefer Chit-funds than mutual

funds or shares.

The finding suggests that occupation of working women has no bearing over the

choice of risky investments. The study also shows that non working women’s education plays

a role in investing in risky instruments.

All women under the age group of 36-45 are in favor of insurance products


97

5.11.3 Inferential Analysis to identify the factors that affect the investment

decision of a woman – Karl Pearson’s correlation

The outcome of the correlation analysis indicates that there is a good association between the

independent variables (factors affecting investment decision) and dependent variables (choice

of investment instrument). Factor Analysis:

On subjecting the independent variable (factors that affect the investment decision) to factor

analysis three components were extracted. The variables of the components were named

Individual, Control and Monetary factors.

After subjecting the dependent variables (choice of investment instrument) to factor analysis

four components were extracted. The variables of the components were named Zero Risk,

Low Risk, Moderate Risk and High Risk.

5.11.4 Regression Analysis

a) Hypothesis related findings for “Zero risk” investment

It is evident from the regression analysis that all the three factors namely

“Individual”,”Control” and “Monetary” significantly influence the dependent factor “Zero

Risk”investment.

b) Hypothesis related findings for “Low risk” investment

It is evident from the regression analysis that “Control” factor is responsible for the

investment decision of women while investing in a “Low risk

c) Hypothesis related findings for “Moderate Risk” investment Property of Christ University. Use it for fair purpose. Give credit to the author by citing properly, if your are using it.

98

It is evident from the regression analysis “Individual” ,”Control” and “Monetary” factors

influence the choice of a “Moderate Risk” Investment.

d) Hypothesis related findings for “High risk” investment

Results of regression analysis shows that “Individual” and “Monetary” factors both influence

the choice for opting for a “High Risk” Investment.

From the above inferential analysis , it can be understood that “Individual Factor” i.e., the

interest and willingness of a women to invest, ability to take decisions on own and availability

of funds has a positive influence on investing in “Zero Risk” investments like Post office,

Government Bonds and Bank deposits.

The “Control Factor” i.e. the availability and understandability of an instrument and

preference of a low initial instrument, has a negative influence over “Low Risk” instrument

like Real Estate. It can thus be inferred that the more knowledgeable she becomes about

investment instruments in the market, and the more accessible they become, the investments

in Real Estate falls. Another important inference is that, while the preference of starting with a

low investment amount goes up, the investing preference for Real Estate comes down.

The “Monetary factor” i.e. the preference of getting regular income, safety of principal and

liquidity has a negative influence over “High Risk” investment instrument like mutual funds,

shares and SIP. It can be understood that the more she prefers a safe instrument with regular

income and liquidity, the less she prefers risky instruments. The “Monetary factor” also has a

positive influence on “Zero Risk” investment instrument. Therefore it can be said that, the

preference for safe instrument with regular income and liquidity, raises the demand for Zero

Risk investments.


99

5.11.5 Inferential Analysis to Study the Variations in the Investment Pattern

of Woman Across Socio Demographic Attributes

1. Chi-square Analysis

The Chi-square analysis was conducted to study the variations in the investment pattern of

woman across socio demographic attributes

a. Hypothesis related findings for the difference in the frequency of investing across

socio-demographic attributes

It is evident from the chi-square analysis that the frequency of investing does not differ with

Occupation or Age for working women and with Education or Age for non working women.

b. Hypothesis related findings for the difference in the choice of investment

instrument across socio-demographic attributes

Chi-square analysis shows that there is a significant difference in the choice of investment

instrument with regard to occupation and age for working women and with regard to

education and age for non-working women.

Through this statistical test, it can be said that occupation of a working women has bearing

over the choice of traditional investments but has no effect over the choice of risky

instruments. It can also be said that education plays a role in the choice of risky instruments

for a non-working women, however for the safe- traditional investments, education has no

bearing. Also, irrespective of the occupational status, working or non working, for safe –

traditional instruments, age does not play a role, whereas for risky instrument it plays a role.

5.12 Implications of Research

The implications of the study with regard to various stake-holders are as follows- Property of Christ University. Use it for fair purpose. Give credit to the author by citing properly, if your are using it.

100

1) Financial Institutions

The study points out the constraints and the current pattern of savings and investment. Both

the findings are very useful to financial institutions in designing products exclusively for

women. For instance, one finding suggests that occupation of working women has no bearing

over the choice of risky investments; another says women in the age group of 36-45 favor

insurance products etc.

2) Government

This research, by providing insights into the savings and investment pattern will be useful to

the Government to raise the share of women in the productive financial investment zone. With

60 percent of women population being house-wives, with a good potential to save, if

motivated to invest would bear good results.

3) Women

Through this study, an attempt is made to understand what a women’s preferences, constraints

and wants are when it comes to investing. The Life-expectancy of women is greater than a

man and hence she outlives a man. As inflation affects them who live longer, women will face

more financial troubles in old age than men. By making the society aware about the present

condition, the first step towards empowerment of women is taken.

4) The Research Community

“Women are good savers but poor investors”. This question has baffled many researchers. So

far no research has been conducted by studying the savings and investment pattern of women.

This study becomes an useful source of information to future researchers who wish to get an

answer to the question.

5.13 Suggestions and Recommendations

Lack of Awareness and advice is found to be the main constraint in investing. The fact that

they “know” what they “don’t know”, itself suggests that they have the prerequisites of being Property of Christ University.


101

a good investor. Financial institutions and Government organizations can take initiatives in

this regard.

Marketing of financial products to women through advertisements in T.V, women’s

magazines can raise their awareness level. Attractive schemes and gifts can also persuade

women to invest.

Lack of interest and motivation to invest is also found as a constraint in investing. One big

problem which the financial advisors or insurance company agents face is in pulling women

to listen about the financial products. A Tie up with companies which sell products like

Amway, Tupperware can help marketing of financial products also.

5.14 Scope for Further Research

So far literature from Behavioral finance gives a mixed opinion about why women are good

savers but poor investors. One school of thought believes that as women are irregular savers

they don’t get to invest, another believes that it is the biological differences which make them

less interested in investing. However the third school of thought says that since they are risk

averse they don’t take up investment in risky assets and hence are poor investors. Research in

future can be conducted by studying the savings, investment and risk behavior of women so

as to get an answer to the question, “why women are good savers but poor investors”

5.15 Limitations of the Study

The major limitations of this study are with respect to

The time frame within which the research had to be carried out and the sample size

from which it would be difficult to draw accurate conclusions on the entire women

population.


102

The results may not be free from biased figures as some of the responses may include

deliberate falsification and incomplete information.

5.16 Conclusions

Analysis shows that women invest in safe traditional investments. Though risky investments

are picking up amongst the educated women, it needs to go a long way to find a considerable

share for itself in the portfolio of investments held by a woman. To get them out of this

traditional investment zone, can be a challenge. But probably the answer lies in addressing

one major constraint across all groups of women “Lack of financial awareness and advice”.

6


103

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