crime against women: the influencing factors

8/13/2019 Crime Against Women: The Influencing Factors

http://slidepdf.com/reader/full/crime-against-women-the-influencing-factors 1/19

India is home of unspeakable crimes

against women. The report attempts to

build a predictive model which explains

the factors which influence the crime

against women.

CRIME

AGAINST

WOMEN IN

INDIA

THE INFLUENCING

FACTORS

PREPARED BY:

Shobhit Bhatnagar

Divya Verma

Noopur Gupta

Prashant Dabas



1

TABLE OF CONTENTS

1. INTRODUCTION .............................................................................................................. 2

2. OBJECTIVES OF THE STUDY ............................................................................................. 3

3. METHODOLOGY .............................................................................................................. 4

3.1. SOURCES OF DATA ........................................................................................................... 4

3.2. RESOURCES USED ............................................................................................................. 4

4. DATA SET ........................................................................................................................ 5

4.1. SIZE OF DATA .................................................................................................................... 5

4.2. DATA CLEANING ............................................................................................................... 5

4.3. DATA DESCRIPTION .......................................................................................................... 5

5. EXPLORATION................................................................................................................. 7

5.1. HISTOGRAM ..................................................................................................................... 7

5.2. CORRELATIONS ................................................................................................................. 8

5.3. BOXPLOTS ......................................................................................................................... 9

5.4. PARALLEL PLOTS ............................................................................................................... 9

6. REGRESSION MODEL ..................................................................................................... 11

7. CONCLUSION & RECOMMENDATIONS........................................................................... 16

7.1. CONCLUSIONS ................................................................................................................ 16

7.2. RECOMMENDATIONS ..................................................................................................... 16

8. LIMITATIONS ................................................................................................................ 17

9. REFERENCES ................................................................................................................. 18



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1. INTRODUCTION

India is home of unspeakable crimes against women. Centuries have come, and centuries have

gone, but the plight of women in India is not likely to change. Time has helplessly watched

women suffering in the form of discrimination, oppression, exploitation, degradation,

aggression, humiliation. Indian women through the countries remained subjugated and

oppressed because society believed in clinging on to orthodox beliefs for the brunt of

violence—domestic as well as public, physical, emotional and mental.

Although, women may be victims of all kinds of crime, be it cheating, murder, robbery, etc., yet

the crimes in which only women are victims and which are directed specifically against them

are characterized as "crime against women". Broadly, crimes against women are classified

under two categories:

Crimes under the Indian Penal Code (IPC), which include seven crimes: (i) rape, (ii) kidnapping

and abduction, (iii) dowry deaths, (iv) torture physical and mental (including wife battering), (v)

molestation, and (vi) sexual harassment, and (vii) importation of girls.

Crimes under Special and Local Laws (SLL), which include seventeen crimes, of which the

important ones are: (i) immoral traffic (1956 and 1978 Act), (ii) dowry prohibition (1961 Act),

(iii) committing Sati (1987 Act), and (iv) indecent representation of women (1986 Act).

Today the crime against women in India is increasing at a very higher rate. National Crime

Record Bureau statistics show crimes against women increased by 7.1 percent nationwide since2010. There has been a rise in the number of incidents of rape recorded too. In 2011, 24,206

incidents were recorded, a rise of 9 percent from the previous year. More than half of the

victims are between 18 and 30 years of age. A total of 2,28,650 incidents of crimes against

women were reported in the country during 2011.

So facts like this pressurize us to know about the factors that are leading to such higher crime

rates in India. This study was conducted to know what are the most relevant factors influencing

the crime rates against women.



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2. OBJECTIVES OF THE STUDY

The objectives of this study are:

To identify the factors that influences the crime rates against women in India.

To better understand what are the factors that are most important leading to higher

crime rates against women in India.

To analyses all factors, their correlation with the increasing crime rate in various states.

To build a predictive regression model using factors which are highly significant in

affecting the incidents of crime against women.



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3. METHODOLOGY

3.1. SOURCES OF DATA

Data has been collected from credible and verifiable sources which mainly include government

websites which keep records for these variables. Some of the key sources of data are India’s

census data for 2011, Reserve Bank of India (RBI) data on banks in each state, and other

government databases.

3.2. RESOURCES USED

The report utilized several resources for data collection and also for processing of data for thefinal analysis. These resources include:

IBM SPSS (v19), it is a software comprising of comprehensive set of predictive analytic

tools for business users, analysts and statistical programmers.

R Statistical computing (v3.0.1), it is a language and environment for statistical

computing and graphics. It provides a wide variety of statistical and graphical

techniques, and is highly extensible.

Microsoft Excel (2013), it has grid of cells arranged in numbered rows and letter-named

columns to organize data manipulations like arithmetic operations. It has a battery of

supplied functions to answer statistical, engineering and financial needs.



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4. DATA SET

4.1. SIZE OF DATA

The data was organized so that each row represented each state and union territory and each

column represented one single of the variables, satisfying the prerequisites for Tidy Data1. The

collected data contained details about each of the 35 states and union territories of India in

regard to 17 different variables. This indicates a total of 595 data cells.

4.2. DATA CLEANING

Before carrying out any analysis the collected was cleaned to avoid any future complicationsduring the actual analysis of the data. To accomplish this following data cleaning steps were

undertaken:

All variable names were changed in acceptable formats for data processing software

such as SPSS, R Statistical Computing, etc. All variables were named in small caps.

New variables were also created to a more coherent form so as to make valid

inferences.

The range for all variables was also set appropriate and this was especially relevant as

most of the variables had numeric values.

Descriptive labeling was used to describe each variable succinctly yet effectively so as to

produce easily understandable graphs, tables, etc.

Outliers were identified using boxplots and summary statistics and any invalid entries

were traced back to the source and corrected.

All the data was also converted to a more coherent form so as to make valid inferences.

4.3. DATA DESCRIPTION

The following is the list of variables used for data collection and analysis:

NAME DESCRIPTION TYPE

incidenceIncidents of Crime Against Women (per 1,00,000 of

population)Numeric

congOrNot Congress / Non-congress Categorical

1 Wickham H., ‘Tidy data’, Journal of Statistical Software



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pop Population of the State/UT (X1,00,000) Numeric

pop_growth Population (% growth rate) Numeric

pop_density Density of population Numeric

poverty Poverty ratio (% of Population below poverty line) Numeric

literacy Literacy ratio (in %) Numericedu Number of education centers (X1,000) Numeric

power Power consumption (kwh per capita) Numeric

sex_r Females per 1 male Numeric

Feticide Female feticide (0-6 years females per 1000 0-6 years males) Numeric

gsdp Per capita Gross Domestic Product of a state Numeric

unemploymentUnemployment ratios [(per 1000) for persons of age 15 years

& above]Numeric

bank No. of commercial bank branches (x1,000) Numeric

villages No. of villages (X1,000) Numeric

The following table details about the number of entries, range, mean and standard deviation of

all of the variables in the above table.

N Minimum Maximum Mean

Std.

Deviation

Incidents of crime against women (per

1,00,000 of population) (log)

35 .00 5.20 3.0951 1.19164

Total Population (X1,00,000) 35 .65 1998.12 345.8770 444.58358

Population Growth (%) 35 -.58 55.88 18.9434 11.15292

Population Density 35 17.00 11320.00 1095.8000 2390.03028

Poverty Ratio (% of Population bpl) 35 1.00 39.93 18.4820 11.58778

Literacy Rate (%) 35 61.80 94.00 77.9171 8.59525

Power Consumption (kWh) 35 122.11 11863.64 1379.6394 2168.11137

Sex Ratio (Female per 1 male) 35 .62 1.08 .9312 .07988

Female feticide (0-6 years females per

1000 0-6 years males)

35 830.00 971.00 921.8000 38.56759

Per capita GDP of a state 35 4.08 5.12 4.2234 1.33170

Unemployment ratios [(per 1000) for

persons of age 15 years & above]

35 6.00 209.00 55.0571 47.84838

Valid N (listwise) 35



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5. EXPLORATION

Several figures, graphs and tables were generated to undertake exploration of the data and get

a better sense of the data. These figures helped in recognizing any inconsistencies in the data

and also enabled better analysis. Following are some of the initial graphs that were made to

better understand the data.

5.1. HISTOGRAM

As the incidents of crimes against women is a count variable the histogram for the total

incidents of crimes against women as left skewed as evident from the below figure.

This indicates that the data for the dependent variable is not normally distributed and is left

skewed. This violates the condition of the data being normally distributed for the purposes ofbuilding a linear regression. To counter this logarithmic transformation can be applied to the

data for the dependent variable. Doing this will result in normally distributed data for incidents

of crimes against women in India. This can be depicted by the histogram on the next page.



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5.2. CORRELATIONS

Correlation can be applied to do an exploratory check whether any of the independent

variables have correlations, this is done to verify that colinearity does not exist between the

variables.

Barring a few statistically significant and also high correlations (in yellow) most of the variables

appear not to be correlated. To ensure that the data meets the colinearity condition of

pop pop_growth pop_density poverty literacy edu power sex_r feticide gsdp unemployment banks villages cong_not

pop 1 -.078 -.129 .237 -.474 .908 -.223 .123 -.127 .759 -.276 .934 .900 -.213

pop_growth -.078 1 .091 .322 -.192 -.056 .775 -.617 -.064 -.139 -.429 -.168 -.040 .156

pop_density -.129 .091 1 -.120 .307 -.204 .088 -.314 -.346 -.052 -.032 -.093 -.204 .008

poverty .237 .322 -.120 1 -.575 .328 .128 .003 .229 -.012 -.372 .069 .403 -.172

literacy -.474 -.192 .307 -.575 1 -.580 .108 -.020 .068 -.211 .400 -.346 -.584 .203

edu .908 -.056 -.204 .328 -.580 1 -.226 .134 -.126 .666 -.349 .832 .951 -.194

power -.223 .775 .088 .128 .108 -.226 1 -.633 -.113 -.149 -.206 -.213 -.235 .175

sex_r .123 -.617 -.314 .003 -.020 .134 -.633 1 .429 .147 .180 .196 .120 -.077

feticide -.127 -.064 -.346 .229 .068 -.126 -.113 .429 1 -.275 .211 -.184 -.094 .008

gsdp .759 -.139 -.052 -.012 -.211 .666 -.149 .147 -.275 1 -.355 .881 .558 .037

unemployment -.276 -.429 -.032 -.372 .400 -.349 -.206 .180 .211 -.355 1 -.326 -.306 -.065

banks .934 -.168 -.093 .069 -.346 .832 -.213 .196 -.184 .881 -.326 1 .770 -.110

villages .900 -.040 -.204 .403 -.584 .951 -.235 .120 -.094 .558 -.306 .770 1 -.250

cong_not -.213 .156 .008 -.172 .203 -.194 .175 -.077 .008 .037 -.065 -.110 -.250 1



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regression model, the variables which have correlations between them can be removed for the

analysis. The following are the variables which were removed:

i. Number of village in a state (variable name villages)

ii. Number of banks in a state (variable name banks)

iii. Number of education centers in a state (variable name edu)

Each of these variables had high correlation with some other variable and for the purpose of

the regression model only one of these variables would be required as a high correlation

indicates that the two variables in context have similar characteristics.

5.3. BOXPLOTS

These were used to identify any invalid entries i.e. any variables with values which were toohigh or too low. This also helped in deciding on the spread of the data.

5.4. PARALLEL PLOTS

The plot below shows the value for all each of the states on all the variables simultaneously.

The red lines indicate the states with power consumption more than the median (880 kwh per

capita) of all the states and blue indicate the states with power consumption less than 880 kwh

per capita.



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Figure 1: Parallel Plot of variables Inferring from the figure above it is apparent that when power consumption of a state is high

(Red), the unemployment ratios are low indicating that the power consumption can be used assomewhat a measure of prosperity of a state. Also indicated from the figure that for most of

the states with low power consumption the incidents of crime against women was high

indicating that the states which were less prosperous had more crimes against women. These

implications are only limited to the set of variables that were identified for this study.



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6. REGRESSION MODEL

From the 14 variables only two were relevant from a regression perspective and this chapter

details about the outcomes following a linear regression of the data. The two variables which

were statistically significant for a linear regression model are:

i. Literacy ratio (%)

ii. Per GDP of the state

Per capita GDP of a state are indicators of prosperity for a state and signifies how developed a

state is in comparison to others. Whereas literacy rate describes the percentage of population

in a state that literate.

The regression model for the data is described below:

Incidents of

crime against

women

(predicted)

= -5.776 +-0.058

(Literacy rate) +

0.438

(Per capita

GSDP)

The model tells us that crime is predicted to decrease -5.776 when literacy ratio and GDP of

a state are zero.

Incidents of crime against women is predicted to decrease by 0.058 when literacy rate of a

state increases by one unit provided all other variables are constant. On the other hand

crime against women is going to increase by 0.438 when GDP goes up by one unit holding

all other variables constant.

This model describes that in order to curb crime against women a state in India needs to

improve its literacy rate.

The relation described with per capita GDP of a state indicates that in the country as a state

is getting developed crime against women is increasing indicating that even though

development is an important factor states need to focus on decreasing crime against

women as well.

Following are other relevant statistics for the regression model explained above:

Mean Std. Deviation N


1,00,000 of population) (log)3.0951 1.19164 35



12

Literacy Rate (%) 77.9171 8.59525 35

Per capita GDP of a state 4.2234 1.33170 35

The table above describes the data involved in the regression model for the data. The table

below gives the model summary for the regression.


1,00,000 of population) (log)

Literacy

Rate (%)

Per capita GDP

of a state


1,00,000 of population) (log)1.000 -.487 .547

Literacy Rate (%) -.487 1.000 -.137

Per capita GDP of a state .547 -.137 1.000

There is low correlations between incidents of crimes against women and GDP of the state. Thisindicates that there is little to no collinearity between the data for the two variables.

Model R R Square Adjusted R Square

Std. Error of the

Estimate Durbin-Watson

1 .687a .471 .438 .89305 2.284

The R square statistics is observed to measure how much standard error the model is able

to explain. The higher the value of R square the better the model is, in this study the R

square is 0.471 indicating that the model is able to explain 47.1% of the total standarderror.

The Durbin-Watson statistic is used to check the independence of error. The acceptable

range for Durbin-Watson is 1.5 to 2.5. As evident from the table above the error in the

model is independent as the Durbin Watson is 2.284.

Model

Unstandardized

Coefficients

Standardized

Coefficients

t Sig.

Collinearity

Statistics

B Std. Error Beta Tolerance VIF

1 (Constant) 5.776 1.555 3.715 .001

Literacy Rate (%) -.058 .018 -.419 -3.231 .003 .981 1.019

Per capita GDP of a

state

.438 .116 .489 3.769 .001 .981 1.019



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The unstandardized coefficients (B) are explained in the regression model and the

significance values of each of the independent variables are below 0.05.

VIF for GDP and number of villages is significantly below 3.5 and this indicates that there is

no co-linearity in the independent variables. Collinearity is further explained in the table

below.

Model Dimension Eigenvalue Condition Index

Variance Proportions

(Constant) Literacy Rate (%)

Per capita GDP of

a state

1 1 2.930 1.000 .00 .00 .01

2 .065 6.703 .02 .04 .88

3 .005 23.543 .98 .96 .11

For three independent variables the collinearity diagnostics gives three linear combinations ordimensions. Each of the three dimensions are in the ascending order of the variance that they

explain which is indicated by Eigenvalue.



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The residual graph of the dependent variable indicates that the residuals are normally

distributed. The study has already taken into account the count variable by transforming the

dependent variable data to log form.

The plot above indicates that the observed values from the model fits the expected pattern well

enough to support the conclusion that residuals are normally distributed.

Following are the partial plots for each of the variables in the regression model viz. literacy rate

and per capita GDP of a state. The partial plot for literacy rate indicates a clear negative linear

correlation with incidents of crime against women which further strengthens the model as well.

The partial plot for per capita GDP of a state indicates a positive linear correlation but it is not

as apparent as in the case of literacy rate.



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7. CONCLUSION & RECOMMENDATIONS

7.1. CONCLUSIONS

The following are the conclusions that were drawn from the analysis of the data:

The regression model built considers development indicators (per capita GDP of a state)

and the literacy rate of a state as an indicators of incidents of crimes against women.

The model suggests that if the variables are zero the crime incidents are likely to

decrease whereas if GDP of a state increases the crime is also likely to increase and if

literacy rate increase the incidents will decrease.

The model is however weak on goodness of fit (R square = 0.471) indicating a need for

more number of observations.

7.2. RECOMMENDATIONS

According to the model to control the crime incidents against women literacy rate of a state

should be increased this concurs with the common beliefs as well. Therefore to control the

spread of crime against women a state needs to focus on improving its literacy rate this can be

done by improving the education and negating the popular reasons because of which people

drop out of school.

The model also suggests that while the per capita GDP of a state increases the crime rate also

increases this indicates a gap in the part of the state, that although it is developing it is not

adequately providing for safety for women. The states need to prioritize the safety of women

so that development of a state and restriction to the incidents of crimes against women can be

undertaken simultaneously.



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8. LIMITATIONS

The study had the following limitations:

i. Due to few number of observations or states in the country the regression model wasweak.

ii. The identified variables in the study were not adequate enough to explain the incidents

of crime against women in the country.

To counter these limitations data transformation can be used to alter the values of the data and

attempt to gain a better linear regression model.



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9. REFERENCES

Wickham H., ‘Tidy Data’, Journal of Statistical Software <http://vita.had.co.nz/papers/tidy-

data.pdf>

R Core Team (2013). R: A language and environment for statistical computing. R Foundation

for Statistical Computing, Vienna, Austria. URL <http://www.R-project.org/.>

http://planningcommision.nic.on/

http://unidow.com/

http://www.mapsofindia.com/

http:/censusindia.gov.in/

http:/data.gov.in/

http:/labourbureau.nic.in/

http:/updateox.com/

http:/www.census2011.co.in/

http:/www.census2011.co.in/

http:/www.kseboa.org/

http:/www.rbi.org.in/

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