Are crimes against women in India converging? Some empirical observations

ABSTRACT

Using the Indian state-level panel data on crimes against women for the period 2000–2019, we examined whether various rates of crime against women were converging. The second-generation unit-root test proposed by Pesaran (2007) was employed to check for absolute β-convergence after conforming to cross-sectional dependence in our panel analysis. The results showed no convergence for various rates of crime against women at the aggregate level, barring dowry deaths. The disaggregated zone-wise results revealed convergence in Eastern and North Eastern states for the Composite Crime Index (CI). The rate of rape was convergent in the west zone, while dowry deaths and cruelty by husbands were convergent only in the Eastern zone. Note that there was a weak convergence in dowry deaths in the North zone. In short, our study fails to find robust evidence of strong convergence in various categories of crime against women in India. The system-GMM results of conditional β-convergence broadly supported the no-convergence results of Pesaran’s unit-root test, as divergence was found in most crime categories. The prominent sources of divergence are women’s employment, female foeticide rate, number of pending cases, rate of arrest, and the proportion of Schedule Castes (SCs) people in the total population.

KEYWORDS:

• Crime against women
• β-convergence
• unit root test
• India

Acknowledgments

The authors are thankful to three anonymous reviewers and the editor of this journal for their insightful comments and suggestions. However, the usual disclaimer applies.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1. National Crime Records Bureau (NCRB), Government of India is the principal agency that collates crime data at different geographical regions such as levels of state and district.

2. Modernization theory argues for a convergence of criminal behaviour if developments and socioeconomic advances spread across regions. In contrast, the uneven speed of development associated with conflict theory predicts a divergence in crime rates (Cook & Winfield, 2015).

3. The NCRB does not consider eve-teasing, molestation, and cruelty by husbands and relatives as violent crimes. One can refer to the ‘Crime in India’ report of NCRB for a wider understanding of various types of crime against women.

4. The crimes against women have been rising steadily over the years. In 2019, the NCRB reported an increase in crime against women from 359 thousand in 2017 to 378 thousand cases in 2018 and further increased to 400 thousand in 2019. Concerning the composition of various crimes against women, Cruelty by the Husband or his Relatives accounts for 30.9%. The second-largest reported crime against women is Assault on Women with Intent to Outrage her Modesty (Molestation) at 21.8%, followed by Kidnapping & Abduction of Women accounting for 17.9%, and ‘Rape’ at 7.9% in 2019.

5. This Global Data Lab is developed and maintained by Nijmegen Center for Economics (NiCE), Radboud University, The Netherlands. It can be accessed at https://globaldatalab.org/areadata/.

6. The general interpolation method, having two data points (x_a,y_a) and (x_b,y_b) is expressed as:

$\mathit{y}=y_a+\left(y_b-y_a\right)\frac{x-x_a}{x_b-x_a}$ at data point (x,y). This method is easy and widely adopted, though it has some limitations.

7. In India, often male police officers interact with women victims to register First Information Reports (FIR) due to the insufficient number of female police officers in the Indian police system. This sometimes creates a situation where women victims often hesitate to properly report crime out of shame. Global evidence also suggests the same phenomenon. For example, Miller and Segal (2019) argue that police departments respond more effectively to sexual assault when they employ female officers.

8. For details about the methodology, please refer to ‘Technical Notes’ of HDR reports available at: http://hdr.undp.org/sites/default/files/hdr2018_technical_notes.pdf

9. Here, in constructing the aggregate crime index we adopt an arithmetic mean which implies the assignment of equal weights to all types of crime. However, a geometric mean is a better measure in the aggregation procedure, as it has the advantage of not influencing the ranking of the states even if there is a change in maximum value. Besides this, it also satisfies several other useful proprieties. But, the limitation of it is that if for a state a zero value of any of these individual indicators would render an overall zero value of the HDI figure. Hence, it confounds the HDI ranking of states.