Management Journal for Advanced Research

1. Introduction

India's average age of Indian population is gradually increasing. This demographic reality, leading to new challenge in the society. Following the United Nations Population Fund's India Ageing Report (2023) ,the percentage of people aged 60 and older will rise from 10.1% in 2021 to 20.8% by 2050. This will cause increase in number of older people up to almost 347 million, surpassing the present U.S. population. Though the growing number of elderly people is a sign of progress in public health and lifespan, yet it also brings up a lot of challenges for the economy, social structure and the government, negatively affecting their stability.

In Indian traditional joint family system, fragile elderly people were always protected. But, as a consequence of economic liberalization, followed by rapid urbanization and large-scale internal migration this important part of our society has weakened (Rajan & Kumar, 2019). As more and more younger people are moving from rural areas to cities in search of better livelihoods, a new social phenomenon has emerged: a growing number of elderly people who live alone. Social isolation of elderly people are linked to higher risk of physical illness, mental weakness, and financial dependence, making them "soft targets" for a variety of crimes (Prakash, 2017). These crimes include not only serious violent crimes but also a wider range of less obvious but still serious violations, such as stealing property, committing complex financial fraud, and the deeply troubling issue of family members or caregivers abusing their charges financially, emotionally, or physically (HelpAge India, 2021).The Government of India, through the National Crime Records Bureau (NCRB), provides an annual statistical portrait of this problem. This data, while indispensable, presents a critical interpretive challenge. A cursory reading of the NCRB reports reveals a "paradox of safety": some of India's most developed, urbanized, and wealthy states, such as Maharashtra and Tamil Nadu, consistently report high rates of crime against the elderly, while some of the most populous and socio-economically challenged states, like Uttar Pradesh and Bihar, appear to be tranquil havens of safety. This counter-intuitive picture forms the central puzzle that this research paper is designed to deconstruct.

The first concept that is presented in this research is a very significant one in the field of criminology:

Here we explore foundational victimization theories, the dynamics of aging in India, and the critical problem of underreporting.

Theoretical Foundations

Criminological theories help to explain why the elderly are a vulnerable group. Following the Routine Activity Theory posits that crime occurs when a motivated offender, a suitable target, and the absence of a capable guardian converge. Unfortunately, physically frail elderly Indians with predictable routine and perceived wealth fit this model as suitable target. Crucially, the erosion of the traditional joint family system has removed the "capable guardian"—family members who once provided informal protection—leaving many seniors isolated and vulnerable.

Complementing this, Social Disorganization Theory argues that crime stems from neighbourhood characteristics. As a result of rapid urbanization, the previous tight-knit social networks of villages are replaced by urban anonymity in most of the Indian cities. The limited social interaction within the community and decrease in community willingness to intervene for a common good erodes informal social controls and increases the risk for vulnerable residents like the elderly.

The Indian Context: Socio-Cultural Dynamics

These theories are uniquely related to India’s socio-cultural characteristics. The "feminization of aging"—a higher proportion of elderly women due to longer life expectancies. So, a large number of widows face multiple types of vulnerabilities, including social marginalization and economic dependency. Furthermore, they face significant, intra-familial abuse which are often unreported. They face property disputes, caregiver stress, and financial exploitation by relatives. Yet, these crimes are rarely reported as they values family honor above justice and sometimes they fear total abundance.

Though Maintenance and Welfare of Parents and Senior Citizens Act (2007) legally obligates children to provide support and there is a provision for penalty if they fail to do so, its impact has been limited by inconsistent state-level implementation and low awareness. Many seniors remain reluctant to initiate legal proceedings against their own children.

suggesting that this single factor is insufficient to explain the variance without other controls.
• H₃: States with a higher General Crime Rate (Gen_Crime_Rate) will have a correspondingly higher rate of crime against the elderly (CRE), indicating that elderly safety is intrinsically linked to the overall public safety environment.
• H₄: In a multivariate regression model, factors representing social isolation (Elderly_Alone), state capacity (Police_per_lakh), and economic structure (Non_Agri_Wc) will be significant predictors of CRE.

4. Data and Methodology

This study is based on quantitative, cross sectional data. The unit of analysis is the Indian state/Union Territory for which complete data is available. The most recent official data has been used to construct a robust statistical analysis.

4.1. Data Sources

The sources of data used in this study are given below:

3. Multiple Linear Regression: To test the main hypothesis (H₄), an Ordinary Least Squares (OLS) regression model will be estimated. This model allows for assessing the independent impact of each variable on the CRE while holding all other variables constant.

Initially, a model was estimated with Gen_Crime_Rateas an independent variable. However, the Variance Inflation Factor (VIF) forGen_Crime_Ratethat exceeded the problematic threshold of 5, indicating severe multicollinearity with other predictors, particularlyPolice_per_lakhandNon_Agri_Wc. So,Gen_Crime_Rate was excluded from the model. This approach reduces multicollinearity but introduces a potential trade-off of omitted variable bias. The final model is therefore presented as the most robust specification for assessing the independent effects of the included socio-economic and administrative factors.

The model specified for this analysis, designed to avoid multicollinearity, is:

CREᵢ = β₀ + β₁(Non_Agri_Wcᵢ) + β₂(Grad_Densityᵢ) + β₃(Police_per_lakhᵢ) + β₄(PC_NSDPᵢ) + β₅(Elderly_Aloneᵢ) + εᵢ

4.4. Limitations of Key Variables and Proxies

While this study uses the best available official data, it is important to acknowledge the inherent limitations of the proxies used. The dependent variable, CRE, is a measure ofreportedcrime, not the true underlying victimization rate, which remains an unobservable 'dark figure.' The model, therefore, predicts the determinants of official statistics. Similarly,Police_per_lakhserves as a proxy for formal state capacity but does not capture crucial variations in police efficiency, community trust, or the strategic allocation of police resources withina state. Finally, due to non-availability of more recent data, we could not include literacy rate and urban population density in our study. Their proxies,Grad_DensityandNon_Agri_Wc are highly correlated with broader processes of urbanization and modernization, and their effects may overlap.

With a low correlation coefficient which is statistically insignificant ( p value 0.043, which is too close to 0.5), there is no string empirical support for Hypothesis H₁. This result contradicts Routine Activity Theory (Cohen & Felson, 1979).

5.2. Group-wise Comparison (ANOVA)

While a linear relationship exists, it is important to test whether this factor alone is powerful enough to create statistically distinct risk groups. States are split into three groups based on the percentage of senior citizens living alone: “low”( Elderly Alone % <=first quartile of 13.7%), “medium” (Elderly Alone % <=second quartile of 18.05%) and “high” (Elderly Alone % > 18.25%). Then we perform Analysis of Variance (ANOVA) to explore if there is a significant difference across average CREs across these three groups. The high p value (0.16) suggests that there is no significant difference across CREs for the three groups. Thus, we fail to reject H_2.

Table 2: Quartiles of Percentage of the population aged 60+ living alone or with a spouse only

Table 3: ANOVA Summary Table (CRE by Elderly Group)

States are split into three groups based on the percentage of senior citizens living alone: “low”( Elderly Alone % <=first quartile of 13.7%), “medium” (Elderly Alone % <=second quartile of 18.05%) and “high” (Elderly Alone % > 18.25%). Then we perform Analysis of Variance (ANOVA) to explore if there is a significant difference across average CREs across these three groups. In Table 3, The high p value (0.16) suggests that there is no significant difference across CREs for the three groups. Thus, we fail to reject H_2.

5.3. Multiple Regression Analysis

Finally, multiple regression analysis is performed to identify the determinants of CRE. The results are shown in Table 4.

The fact that this is the case provides strong evidence that the "absence of a capable guardian" is a significant risk factor, distinct from other developmental markers.
2. The Paradox of Police Presence: The paradox of police presence is demonstrated by the fact that the coefficient for police_per_lakh is positive and has a highly significant (p value0.00699). Statistically, it is the very definition of the "paradox of safety." There is a clear indication that a larger police density does not necessarily result in a decrease in the number of crimes that are recorded against elderly people. The Reporting Bias Hypothesis (Verma & Singh, 2016) is completely supported by this finding, which confirms the hypothesis. When it comes to reporting and recording criminal activity, states that have a stronger administrative capacity (more police) are likely to have procedures that are more easily accessible and efficient. If this is the case, then the higher CRE in these states may be a reflection of improved governmental competence and citizen trust rather than an increase in the number of crimes committed. However, the conclusion continues to be extremely significant since it provides statistical confirmation that, at the state level, a larger formal security apparatus does not correspond with a lower reported crime against seniors. This lends support to the hypothesis that official statistics are heavily influenced by state capacity.
3. Economic Structure (Non_Agri_Wc): possesses a statistically significant negative coefficient (p = 0.00785). There is an expectation that the CRE will decline by 1.2 units for every percentage point growth in the workforce that is not in the agricultural sector. This is an extremely interesting discovery. In spite of the fact that urbanization, which is frequently tied to a non-agrarian economy, is linked to the weakening of family relationships, it may also bring about various forms of community organization, increased institutional supervision, or economic opportunities that lessen some sorts of predatory crime. This indicates that the effects of modernity are not uniform; they bring about certain dangers, such as social isolation, while also posing the possibility of alleviating other dangers.
4. Education and Wealth (Grad_Density,PC_NSDP): In terms of wealth and education (Grad_Density, PC_NSDP), you have: In this particular model,

In the end, the research reveals that the concept of safety for elderly people is not a singular entity. The specific reality of their living arrangements, the administrative capacity of the state in which they reside, and the widespread economic transformations that are sweeping the nation all contribute to the formation of this phenomenon.

Policy Implications

The findings of the paper goes beyond the generic law-and-order solutions and call for a mutidimentional policy approach .

A. General Crime Control (The Foundation): one of the findings of stis study suggests (Figure1) the general safety environment matters. So, policies should aim at reducing overall crime rate (GenCrimeRate) alongwith strengthening the police force, strengthening judicial process and addressing the fundamental cause of crime like unemployment and equality to create a safer environment,

B. Targeted Interventions for the Elderly (The Specifics): The statistical significance of variables beyond the general crime rate proves that generic policies are inadequate. Tailored strategies are required:
1. For High-Capacity States (e.g., Maharashtra, Tamil Nadu): Where empowerment and reporting are likely high but so are risks of modern life, policy should focus on mitigating these specific vulnerabilities. This includes launching awareness campaigns against digital and financial fraud, introducing state supported day care for senior citizens and community senters to combat social isolation (ElderlyAlone) and ensuring mental health support.
2. For Low-Capacity States (e.g., Bihar, Jharkhand): Where the "dark figure" of crime or the problem of unreported crime is likely immense, the priority must be building foundational access to justice. Since low education (GradDensity) and institutional access are very poor in these states, trusted local intermediaries like Panchayats and ASHA workers may provide help to facilitate reporting, and launching grassroots legal literacy campaigns to build awareness on the elderly of their rights under the 2007 Act.
3. Learning from Statistical Outliers: States identified as significant outliers, such as Madhya Pradesh (unusually high CRE) and Kerala (potentially low CRE given its risk factors),

8. National Statistical Office. (2023b). Press note on provisional estimates of annual national income 2022-23, and quarterly estimates of gross domestic product for the fourth quarter (Q4) of 2022-23. Ministry of Statistics and Programme Implementation, Government of India.

9. Prakash, I. J. (2017). Ageing in India: A review of the situation and of policies, programmes and legislation. in D. N. K. P. Singh (Ed.), Gerontology: A multidisciplinary perspective. APH Publishing.

10. Rajan, S. I. (2020). Elderly in India: A critical review of the policies. In S. I. Rajan & A. James (Eds.), Demography of ageing in India. Springer.https://doi.org/10.1007/978-981-15-3033-3_13