Analysis of Crime Trends in the State and the Districts
4.3 Relationship between crime and select socio economic indicators
4.3.2 Relationship between crime and indices of backwardness in districts
To find the relation between backwardness and crime, one should be having an index of backwardness to place the districts in measurable scale of backwardness. In the
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IPC Crimes per 100,000 population
Per Capita NSDP At constant (1999-00) prices
present study two alternative measures of backwardness have been used, viz. the backwardness index developed by the State Government(Statistical Handbook, Assam) and the index of backwardness developed by the Planning Commission, Government of India which would allow a national level comparison. It is to be noted that the parameters used by both the indices are different.
a) Backward districts as per state indices
The districts were grouped according to the backwardness index given by Statistical Handbook, Assam1. The 5 year averages of the backwardness index for every district was calculated during the period 2007-08 and 2011-12.
The districts were then grouped into 4 categories as follows:
1) Districts with indices between 7 and 10.99 were clubbed as the very high backward districts.
2) Districts with indices between 11 and 14.99 were grouped as high backward districts.
3) Districts with indices between 15 and 18.99 were grouped as medium backward districts.
4) Districts with indices above 19 were to be classified as low backward districts.
The number of districts included in the four categories is four, sixteen, five and one respectively. The district of Guwahati has been excluded as it has very high crime rates and regarded as an outlier, as it is a metropolis with different patterns of crime.
Crime rates for each of these districts were calculated from NCRB data for the year 2011, as the total registered IPC crimes per 100000 population. Population weighted average crime rates of the four district categories were estimated based on the proportion of population of each district in that category which acted as the weight. The weighted average crime rate for each district category was plotted, as shown in the bubble graph below (Figure
1 The Composite Backwardness Index developed by the Assam Statistical Handbook uses the following indicators: geographical area, population (rural/urban), literacy rate, number of villages as per census, total number of primary schools, medical facilities in rural areas, number of primary health centres (R/U), family welfare centres, state dispensaries, community health centres, number of doctors under the existing facilities, midwives, number of scheduled commercial banks, number of villages having government water supply facilities, length of pucca PWD roads, number of villages electrified, number of registered factories, and total foodgrains available.
4.5). The size of the bubble is proportional to the total crime recorded in that district category. The larger size of the bubble for the high backward districts is due to the fact that a large number (sixteen) of districts are included in this category, and also it has the largest share of the total population of Assam.
Figure 4.5
Crime rate and Backwardness - (state index)
Crime rates per 1,00,000 Population.
Source: Author‘s calculation from NCRB data, Statistical Hand Book, Assam.
A look at the graph clearly indicates that the crime rate (which is a population weighted average of the crime rates of the respective districts in each category) increases as the backwardness of the districts falls. In other words, as development proceeds, the rate of crime increases. This finding of a positive correlation between the level of development and crime rate may not be surprising as Abraham (2011) finds such a pattern when he looks at the average crime rate (during the period 2001-2008) of various Indian states. The highest crime rates were recorded by the Union Territory of Pondicherry, followed by the highly developed
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Crime rate per 100000 population
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state of Kerala. The economically developed states such as Tamil Nadu, Rajasthan, Karnataka, Delhi and Chandigarh recorded comparatively high crime rates. The lowest crime rates were recorded in some of the poorest regions of the country such as, Uttar Pradesh, Meghalaya and Nagaland.
One can explain this apparent anomaly between the international level (as observed by UNODC) and the district and state level, as observed in the present study and by Abraham(2011) too, in the estimated correlation pattern by realizing the fact that the districts have the same state machinery and government agencies for law enforcement, unlike the different government structures that vary across countries. Thus, as the development level increases, a district is still exposed to the same level of law enforcement as before rather than concomitant rise in strength in police personnel and infrastructural support system. However, with development, the amount of economic transactions (and hence, other ancillatory activities) increases, providing greater opportunities for committing crimes. Hence, it is expected that the rate of crime increase with the level of development in a district. For instance, the highest backward district (or in other words, the least developed) Dhemaji recorded an average (for the period 2005-13) of 129 and 33 theft and burglary cases respectively, while the highest developed district Sivsagar recorded averages of 400 and 128 cases in the corresponding categories. Again, in terms of murders, Dhemaji recorded 21 cases on average, while Sivsagar recorded 74 cases on average. Over time, as the country achieves higher development, resulting in greater resources being diverted towards law enforcement and job creation one can expect the crime rates to come down in the districts as well.
b) Backward districts as per the Planning Commission index
Now districts are classified as per the all-India index devised by the Planning Commission, Government of India, for studying the development of districts. Districts with a lower index are classified as lower developed districts, that is, the development of a district increases with the increase in the index number. Based on this, we classify the districts into four categories-
1) Low development districts 2) Medium development districts, 3) High development districts 4) Very High development districts
However, one must keep in mind that the same set of districts is not included under these categories, as that under the Assam Statistical Handbook‘s index (of Very High, High, Medium and Low Backwardness respectively)- the reason being that the two indices use different sets of parameters. However, a look at the Planning Commission‘s index for district‘s backwardness yields a similar result in comparison to the Assam Statistical Handbook‘s Composite Backwardness Index. As the level of development of a district increases, the rate of crime rises as greater opportunities for committing crimes emerge, through increased transactions and better technologies. Although an unambiguous positive trend was seen based on the Assam Statistical Handbook index, a slight ambiguity is observed in this trend when one looks at the Planning Commission index. In this connection one needs to look at the parameters that have gone into building this index, and those parameters that have been either excluded or added in comparison to the Assam Statistical Handbook index.
The Planning Commission‘s index is relatively less comprehensive as compared to the Assam Statistical Handbook Index. The former index is based on five parameters only: agricultural productivity per worker, agricultural productivity per hectare, agricultural wage rate, the SC/ST population and the poverty ratio in the district (one can refer to footnote 1 to get the list of parameters used by the Assam Statistical Handbook).
Figure 4.6
Crime rate and Backwardness (Planning Commission index)
Crime rates per 1,00,000 Population.
Source: Author‘s calculation from NCRB and Planning Commission, GOI.
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Crime Rate
The lack of adequate parameters means that the Planning Commission overestimates development in some districts, and underestimates development in others, resulting in some districts with lower crime rates being clubbed in the high development and medium development categories. The Planning Commission index pays greater attention to economic factors in determining development (four of the five parameters are economic), while little weightage is given to social (only the SC/ST population is included) and infrastructural factors, such as the presence of proper roads, electrification, water supply, gender equality, etc. For instance, a district may have a low poverty ratio, but there may be a lack of better transportation facilities (as measured by the availability of metal roads). In such a case, the crime rate may be low as criminals cannot commit crimes with ease in the absence of available getaways. Under such a scenario, such a district would be classified as a high development district under the Planning Commission index, but as a ‗high backward‘ district under the Assam Statistical Handbook index. This district would display the same anomaly as was discussed in the previous paragraph. Thus, the inclusion of more parameters not only makes an index more comprehensive, but it also helps us to properly analyze the crime trend.