## Some demographic methods applied to urban and rural populations of Pakistan

##### Abstract

Summary of Findings: In this thesis, first of all I have tried to describe what is demography and different ways to collect demographic data. Then, I have applied some of the demographic techniques to the population of Pakistan. Here are my findings: First of all, I have considered the infant mortality in Pakistan and applied the test of hypothesis along with 2 x 2 table to show that there is a difference of facilities/services given by the government to the urban and rural area's population and find out the results of z and chi-square tests with p-value. The results indicate that there is really a huge difference of policies between urban and rural areas of Pakistan and I have found the p-value 0.00001 which show our hypothesis is highly significant. I have noticed that since only the 35% of the population is residing in the urban areas but still urban areas are under consideration all the times while the rest 65% areas having the less attention by the government institutions. Secondly by using the data given by Federal Bureau of Statistics, Pakistan I have set up different life tables for the total population, urban and rural population and for the male and females population of Pakistan. The results show that the life expectancy at birth in urban area (68.7 years) is 6% higher than the rural areas (64.3 years). Similarly, the probability of dying at the first age interval is also 10% smaller in the urban area then the rural one (i.e. 0.06444 & 0.07197 for urban and rural respectively). Moreover, the female life expectancy at birth (68.4 years) is found to be 7% higher than the male life expectancy (64.3 years). Third, I have applied decomposition technique introduced Kitawaga (1955) to see how much of the difference between death rates in urban and rural population is attributable to differences in their age distributions. The results shows that the original difference between the urban and rural population is -0.00210 (by equation 7.2) while the contribution of age compositional differences and contributions of age specific rate differences are -0.00052807 & -0.00157492 respectively (by equation 7.3). Further, the proportion of difference attributable to differences in age composition is found to be 25% whereas the proportion of difference attributable to differences in rate schedules is 75% which shows that both parts are contributing in the same direction to the difference. Lastly I have tried to make a population forecasting for Pakistan. For this, a few methods has been discussed and have made a forecast by using the compound rate of growth method and cohort component method. According to the first method, it shows that there might be 294.96 million population in the year 2032 (equation 8.13) whereas the second method states that it might be 258.09 million in the year 2031 (equation 8.14). It seems reasonable to say that the estimates found by the cohort component method are more reliable than the any other method as the cohort component is now the only method on which demographers are relying much.