Research Article: 2017 Vol: 21 Issue: 1

**Shah Hussain, University of Haripur**

**Muhammad Saeed Khan, University of Haripur **

Literacy rate; Urbanization; Projections; Impact; Khyber Pakhtunkhwa.

According to the Oxford Advanced Learner's Dictionary urbanization is defined as the process in which more and more people start to live and work in towns and cities rather than in the country sides. The term urbanization is defined in Encyclopedia Britannica as the process by which large numbers of people become permanently concentrated in relatively small areas, forming cities.

Urbanization is the process by which more and more people leave the countryside to live in cities. It is the process of shifting of people from undeveloped rural areas to developed urban areas. It contains escalation of human being in a specific urban area in a specific period of a year. It is the result of social, economic and political developments that escort to increase in number of more populated cities alters in ground utilization and change from rural to urban governing system. The history of urbanization began from the times when the workers migrated to cities for employments in industries during industrial revolution as there was a shortage of agricultural jobs. In this connection, urban areas became more fascinating for them to get easy living and in the urban areas there were more opportunities to earn living. This trend of urbanization is raging in both developed and developing countries. About 6.6 billion human beings dwell in urban areas of the world, which is three percent of earth’s land area (Angotti, 1993; UNFPA, 1993).

It is expected in future that over 90 percent of the people will be living in urban areas of the developing countries. Most of them are going to be poor because of lack of facilities. In 2025, it is expected that in lower urbanized countries such as Africa and Asia, 54 percent of population of these countries will be living in urban areas (United Nation, 1990).

Urbanization has strong connection with environmental degradation, insecurity, excessive air pollution, noise, wastes disposal, birth and death rate, poverty, infrastructure, crimes, health, employment, socioeconomic conditions and education. Among these factors some are affected diversely and some positively. Many researchers are of the view that urbanization is an essential factor for the betterment of the economy but many state that it affect the economy, the agricultural land (which is a source of urban food supply, loss of meadows and urban diseconomy) becomes under pressure when more and more urbanization takes place (Kasarda & Crenshaw, 1991).

According to Pakistan Economic Survey (2013-2014), literacy has pivotal role which affects national welfare. A survey report of PSLM (2014) shows that the overall literacy rate of the urban population 10 years and above in 2013-2014 was 74 percent compared to 76 percent in 2012-2013 while this ratio shows increasing trend in urban areas of Khyber Pakhtunkhwa 74 percent. In Khyber Pakhtunkhwa overall literacy rate of urban population was increased to 68 percent in 2013-2014 as compared to 66 percent in 2012-2013 (Pakistan Economic Survey, 2013-2014).

According to Pakistan Economic Survey (2014-2015), the literacy rate of both male and female in urban area is greater as compared to the literacy rate in rural area in Pakistan as given in **Figure 1**. Similarly, in Province Punjab, for both male and female, the literacy rate in urban area is higher as compared to the literacy rate in rural area as clear from **Figure 2**.

According to Pakistan Economic Survey (2014-15), the literacy rate of both male and female in urban area is higher as compared to the literacy rate in rural area in province Sindh as given in **Figure 3**. Similarly, in Province Khyber Pakhtunkhwa, for both male and female, the literacy rate in urban area is higher as compared to the literacy rate in rural area as clear from **Figure 4**.

According to Pakistan Economic Survey (2014-2015), the literacy rate of both male and female in urban area is higher as compared to the literacy rate in rural area in province Baluchistan as shown in **Figure 5**.

Similarly, about 37.7 percent of Pakistan population is living in urban areas means it is sharply increasing in Pakistan. The main reason of this is migration of the people from rural areas to urban areas. This not only affects economic indicators but also have implications for education in the country.

The researchers in the past tried to assess various dimensions of the urbanization and education. Okpala and Okpala (2014) studied the role of school life expectancy and urbanization on adult literacy rates in Sub-Saharan Africa. More specifically, they examined 1: the impact of school life expectancy on adult literacy, 2: the influence of urban population on adult literacy and 3: the effectiveness of government educational expenditure on adult literacy. OLS regression analysis showed that the percentage of the population residing in urban centers and the school life expectancy were positively significant. Government expenditure, as a percentage of GDP, was positively related.

Robertson (2014) studied the declining enrolment in Ontario. This study evaluated and investigated over a wide span of time strategy, the change in urbanization and to study about ways for policy makers who can facilitate to find ways of the unpleasant effects of declining enrolment.

De Hart and Venter (2013) studied the comparison of rural and urban dropout rates of distance student in South Africa. This study finds out, the impact of urbanization on the dropout rate of separation of participants in a basic levy course, Utilizing Kember's longitudinal-procedure model of dropout from separation. It was found that the impact of urbanization all alone represented 1% higher dropout rate for rural population over their urban companions. At the point when urbanization, as a variable, was joined with other demographic variables, the results were from time to time unanticipated. Ten multi-variable correlations demonstrated that rural population constantly dropped out the most. Despite, in four of these multi-variable examinations, rural participants were also the individuals whose dropped out the minimum.

Buang (2007) compared Madrasah and Muslim Education and its correlation with urbanization as a case study in Malaysia, Indonesia, Pakistan and Turkey. Madrasah is a Muslim religious school amongst the numerous Muslim instructive foundations that still exist today in the midst of the tides of difficulties faced by urbanization. The Muslim instructive organizations have been attributed to the correlation with and effect of urbanization, amongst other key socio-politico-financial elements. As it were, urbanization has been seen as a contributing variable to the fall of Muslim instructive organizations, including the madrasah and Muslim instruction all in all. Yet, apparently, urbanization has quickened the procedure of madrasah's educational program reconstruction, and strengthened madrasah's position as the point of convergence of Muslim people's missions for their own harmony. Such complex relation in the middle of madrasah and urbanization, and its apparently influential condition will be the center of this part. Basically, this section proposes to audit the difficulties of madrasah and Muslim instruction in and around the procedure of urbanization and its reactions and procedures in relieving the effect of the interface. These systems, for example, the Islamization of information, educational program reconstruction, and the advancement of Muslim training for human and social capitals, thusly, will be discriminatingly examined. Contextual analyses of Malaysia, Indonesia, Pakistan, and Turkey will be reviewed from its near socio-chronicled examination instead of ordered point of view, to give an outline of the madrasah and Muslim instructive framework in those nations.

Zu-chao and Wen (2006) examined and forecasted demands for education in the process of urbanization in china. In the study, available data from 2002, it was forecasted for 2015 and 2020. According to the study there will be a valuable increase in number of classes, numbers of teachers and other teaching staff in primary and junior secondary schools and higher education in china. As with the increasing number of classes, numbers of teachers and other teaching staff in primary, junior secondary schools and higher education, the demands for its allocation will also be increased in terms of budget. It was concluded that the budget for primary schools, junior secondary school. Secondary school and higher education in urban areas of the country will raise to, 8%, 8.7%, 15.2% and 9% for junior secondary schools, Secondary schools and higher education, respectively.

This present study has been conducted because firstly, in Pakistan, less attention has been given to find out the impact of urbanization on literacy rate. Secondly, none of the study has been conducted in which the issue of urbanization in connection with literacy rate at district level has been highlighted. The present study is looking to assess the impact of urbanization on literacy rate in Khyber Pakhtunkhwa. Furthermore, projections of literacy rate along with urban population have been also estimated. In this way, this study bridges this gap.

The province Khyber Pakhtunkhwa has 25 districts in total. About 15% of the total population of Pakistan is living in this province. This province has more backward areas than Punjab province. The province Khyber Pakhtunkhwa has 13% of the total education institutions in Pakistan in the year 2015-2016. The ratio of male and females were 13% and 12% of the total education Institution respectively (Government of Khyber Pakhtunkhwa, 2017). The province has also been severely affected by terrorism and extreme floods. The terrorist banned the education institutions in most of the parts of the province while a large number were destroyed or damaged. The 2010 flood also destroyed many schools of both males and females in the province. Most of the people have been displaced and shifted to the urban areas of the province. This was a challenge for the government to cope with such huge internally displaced people and also to accommodate the children in schools. The government through various initiatives supported the children and accommodated them in various males and female schools. Besides, the existing population of the cities also increased which further put pressure on the educational institutions. Similar situation also existed in various parts of Pakistan where terrorists are targeting educational institutions followed by increasing population of these cities. With this background, this is important to estimate the impact of urbanization on literacy rate and see the prospects of the literacy rate and urban population in this province. The results of the present study can be generalized to many parts of Pakistan. Although the rate of urbanization is more in overall Pakistan as compared to Province Khyber Pakhtunkhwa but both have increasing trend.

The study is significant in the sense that it provided sufficient evidences about the impact of urbanization on the education indicators in Khyber Pakhtunkhwa. More importantly, this study would be important for the policy makers and educationist to know how the literacy rate is affected due to the increase in the urban population in Khyber Pakhtunkhwa province. The policy makers would further find it interesting to know about the impact of urbanization at gender level. This would help them prioritize their policy properly.

The study is based on the hypothesis

*H1: *Urbanization affects the literacy rate in Province Khyber Pakhtunkhwa.

*H2: *The literacy rate and urban population has increasing trends over time.

This study tries to answer the questions: Does the urbanization affect literacy rate? Does the literacy rate and urban population have increasing trend over time?

Due to non-availability of data, the study covered the period 2008-2009 to 2013-2014 and also out of 25 districts, 20 districts have been selected to find out the impact of urbanization on literacy rate. There are also additional factors such as financial allocation from the government, education policies, culture and social factors which affect the education indicators but due to non-availability of data and time constraints, these dimensions were excluded from the regression models.

It is quantitative research concerning, “Impact of urbanization on literacy rate in Khyber Pakhtunkhwa”. The study is based on secondary data for the period 2009 to 2014 which has been taken from various issues of Khyber Pakhtunkhwa Development Statistics. The study covered 20 districts of Khyber Pakhtunkhwa due data availability restrictions.

This research estimated the impact of urbanization on literacy rate in Khyber Pakhtunkhwa. The diagnostics such as R-squared-statistic and Prob (F-statistic) were used for the estimated regression models. Eviews software was used for the estimation of the results. The following regression model was estimated:

Where LR represents the literacy rate, and is also the dependent variables in the above equations. The URB represents the urbanization which is the independent variable in the above equations. dst represents a particular district in Khyber Pakhtunkhwa. μi shows the random terms absorbing the effect of all those variables which are not included in the model. bo and b1 are the intercept and slop respectively. The abbreviation “ln” represents the natural logarithm in these models. These models have been estimated separately for male and female over the districts. These models were estimated for each of the year from 2008-2009 to 2013-2014. More specifically, the impact has been shown for each of the year. For projecting the literacy rate and urban population across districts in Khyber Pakhtunkhwa, two simple regression model has been used in which the time period has been taken as independent variable while literacy rate and urban population as dependent variables. The model is given as:

Where Y is the variable to be projected (literacy rate and urban population), T is the time trend and U_{i} is the error term. This method is also known as trend projection technique.

One of the objectives of this study is to estimate the impact of urbanization on literacy rate in Khyber Pakhtunkhwa. So, this section estimates the impact of urbanization on literacy rate in Khyber Pakhtunkhwa. The impact was first estimated for the overall literacy rate and then for male and female separately. The low and high impact of urbanization on literacy rate can be evaluated from the variation in the coefficient of urbanization. Besides, the impact of urbanization on the overall literacy rate was estimated for each sampled year. Furthermore, to check the robustness of the model, the diagnostic of the regression models estimated such as R-Squared, f-Statistic and Probability of f-statistic were also given. This study used regression analysis in which urbanization has been taken as independent variable, while literacy rate as dependent variable. The impact has been estimated from the year 2008-2009 to 2013-2014. The results of the impact of urbanization on literacy rate are given in **Table 1**.

Table 1: Regression Results Of The Impact Of Urbanization On Literacy Rate |
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Year | Variable | Model-1 | Model-2 | Model-3 |
---|---|---|---|---|

2008-2009 | Constant | 3.37*** | 3.94*** | 2.03*** |

ln(urbpop) | 0.09* | 0.05 | 0.22** | |

Diagnostics | ||||

R-squared | 0.24 | 0.12 | 0.28 | |

F-statistic | 4.51 | 2.00 | 5.51 | |

Prob(F-statistic) | 0.05 | 0.18 | 0.03 | |

2009-2010 | Constant | 3.40*** | 3.97*** | 2.07*** |

ln(urbpop) | 0.09* | 0.05 | 0.22** | |

Diagnostics | ||||

R-squared | 0.24 | 0.12 | 0.28 | |

F-statistic | 4.47 | 1.99 | 5.44 | |

Prob(F-statistic) | 0.05 | 0.18 | 0.03 | |

2010-2011 | Constant | 3.43*** | 3.99*** | 2.11*** |

ln(urbpop) | 0.09* | 0.05 | 0.22** | |

Diagnostics | ||||

R-squared | 0.28 | 0.12 | 0.28 | |

F-statistic | 5.41 | 2.03 | 0.03 | |

Prob(F-statistic) | 0.05 | 0.17 | 0.17 | |

2011-2012 | Constant | 4.2 | 4.44 | 3.87 |

ln(urbpop) | -0.01 | -0.01 | 0.01 | |

Diagnostics | ||||

R-squared | 0.00 | -0.01 | 0.00 | |

F-statistic | 0.03 | 0.20 | 0.04 | |

Prob(F-statistic) | 0.85 | 0.66 | 0.84 | |

2012-2013 | Constant | 4.32 | 4.49 | 3.99 |

ln(urbpop) | -0.02 | -0.02 | -0.01 | |

Diagnostics | ||||

R-squared | 0.03 | 0.05 | 0.00 | |

F-statistic | 0.61 | 1.02 | 0.02 | |

Prob(F-statistic) | 0.44 | 0.32 | 0.88 | |

2013-2014 | Constant | 4.32 | 4.52 | 4.09 |

ln(urbpop) | -0.02 | -0.02 | 0.03 | |

Diagnostics | ||||

R-squared | 0.03 | 0.12 | 0.01 | |

F-statistic | 0.63 | 2.52 | 0.24 | |

Prob(F-statistic) | 0.44 | 0.13 | 0.63 |

*** 1%, ** 5%, * 10%

Note: The dependent variables for model 1, 2 and 3 are the natural log of overall literacy rates, natural log of number of male literacy rate and natural log of number of female respectively.

One of the objectives of this study is to estimate the impact of urbanization on literacy rate in Khyber Pakhtunkhwa. So, this section estimates the impact of urbanization on literacy rate in Khyber Pakhtunkhwa. The impact was first estimated for the overall literacy rate and then for male and female separately. The low and high impact of urbanization on literacy rate can be evaluated from the variation in the coefficient of urbanization. Besides, the impact of urbanization on the overall literacy rate was estimated for each sampled year. Furthermore, to check the robustness of the model, the diagnostic of the regression models estimated such as R-Squared, f-Statistic and Probability of f-statistic were also given. This study used regression analysis in which urbanization has been taken as independent variable, while literacy rate as dependent variable. The impact has been estimated from the year 2008-2009 to 2013-2014. The results of the impact of urbanization on literacy rate are given in **Table 1**

During 2009-2010 urbanization has positive impact on the overall, male and female literacy rate. This is evident from the positive sign of the coefficient of Natural Log of Urban Population. In other words, these coefficients show that 1% increase in urban population leads to increase in the overall literacy, male literacy and female literacy rate by 0.09%, 0.05% and 0.22%, respectively. These coefficients also significant for overall literacy rate and female literacy rate with 10% and 5% significant level while insignificant for male literacy rate. Although R-squared value shows that the fit is not good but this may be attributed to excluding the important explanatory variables from the model Prob(f-statistics) favors for overall literacy rate and female literacy rate with 0.05 and 0.03, respectively while not favoring male literacy rate.

During 2010-2011 shows that urbanization has positive impact on the overall, male and female literacy rate. This is evident from the positive sign of the coefficient of Natural Log of Urban Population. In other words, these coefficients show that 1% increase in urban population leads to increase in the overall literacy, male literacy and female literacy rate by 0.09%, 0.05% and 0.22%, respectively. These coefficients also significant for overall literacy rate and female literacy rate with 10% and 5% significant level while insignificant for male literacy rate. Although R-squared value shows that the fit is not good but this may be attributed to excluding the important explanatory variables from the model. The value of f-statistics favors overall literacy rate model but does not favors male literacy rate and female literacy rate models.

During 2011-2012 urbanization has negative impact on the overall and male literacy rate, while positive impact of female literacy rate. This is evident from the negative and positive sign of the coefficient of Natural Log of Urban Population. In other words, these coefficients show that 1% decrease in urban population leads to decrease in the overall and male literacy and decrease for female literacy rate by -0.01%, -0.01% and 0.01%, respectively. These coefficients are also insignificant for male and female literacy rate. Although R-Squared value shows that the fit is not good but this may be attributed to excluding the important explanatory variables from the models. The values of F-Statistics not favor the models.

During 2012-2013 urbanization has negative impact on the overall, male and female literacy rate. This is evident from the negative sign of the coefficient of Natural Log of Urban Population. In other words, these coefficients show that 1% decrease in urban population leads to decrease in the overall, male and female literacy rate by -0.02%, -0.02% and -0.02% respectively. These coefficients are also insignificant for overall, male and female literacy rate. Although R-Squared value shows that the fit is not good but this may be attributed to excluding the important explanatory variables from the models. The values of F-Statistics not favor the models.

During 2013-2014 urbanization has negative impact on the overall and male literacy rate, while positive impact of female literacy rate. This is evident from the negative and positive sign of the coefficient of Natural Log of Urban Population. In other words, these coefficients show that 1% decrease in urban population leads to decrease in the overall and male literacy rate and decrease for female literacy rate by -0.02%, -0.02% and 0.03%, respectively. These coefficients are also insignificant for male and female literacy rate. Although R-Squared value shows that the fit is not good but this may be attributed to excluding the important explanatory variables from the models. The values of F-Statistics not favor the models.

This is also interesting to know from the results that the impact of urbanization on female literacy rate is more as compared to male as represented by the higher value of the coefficient in different years. This is due to the fact that the government took various initiatives to improve the educational institutions with special focus on the female education.

Based on the past data, this study forecasted literacy rate using trend projection method. The forecast has been made from 2014-2015 to 2017-2018. These forecasts are given in **Table 2**.

Table 2: District Wise Forecasts Of Literacy Rate In Khyber Pakhtunkhwa |
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Districts | 2014-2015 | 2015-2016 | 2016-2017 | 2017-2018 | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|

Total | Male | Female | Total | Male | Female | Total | Male | Female | Total | Male | Female | ||

Abbotabad | 89 | 95 | 83 | 90 | 95 | 84 | 91 | 96 | 85 | 93 | 97 | 87 | |

Bannu | 77 | 85 | 66 | 78 | 85 | 66 | 79 | 84 | 67 | 80 | 84 | 67 | |

Charsadda | 62 | 77 | 46 | 65 | 79 | 49 | 68 | 82 | 51 | 71 | 84 | 53 | |

Chitral | 72 | 83 | 63 | 73 | 83 | 64 | 73 | 83 | 65 | 73 | 83 | 66 | |

D.I.Khan | 65 | 71 | 58 | 63 | 69 | 57 | 62 | 67 | 56 | 61 | 65 | 56 | |

Hangu | 52 | 75 | 41 | 52 | 75 | 42 | 51 | 75 | 43 | 51 | 75 | 44 | |

Haripur | 80 | 89 | 75 | 80 | 89 | 76 | 80 | 89 | 76 | 80 | 89 | 76 | |

Karak | 72 | 92 | 56 | 73 | 92 | 57 | 74 | 93 | 58 | 75 | 94 | 58 | |

Kohat | 71 | 82 | 61 | 72 | 82 | 62 | 73 | 83 | 64 | 74 | 84 | 65 | |

Lakki | 65 | 81 | 49 | 66 | 82 | 50 | 67 | 82 | 51 | 69 | 83 | 52 | |

Lower Dir | 73 | 82 | 64 | 75 | 82 | 66 | 77 | 81 | 69 | 79 | 81 | 72 | |

Malakand | 65 | 77 | 53 | 67 | 79 | 56 | 70 | 81 | 59 | 72 | 82 | 61 | |

Mansehra | 86 | 90 | 78 | 89 | 91 | 80 | 92 | 92 | 81 | 94 | 93 | 83 | |

Mardan | 63 | 76 | 57 | 64 | 78 | 61 | 66 | 80 | 65 | 68 | 81 | 69 | |

Nowshera | 63 | 75 | 54 | 64 | 76 | 56 | 66 | 77 | 58 | 67 | 78 | 60 | |

Peshawar | 66 | 80 | 56 | 67 | 81 | 58 | 68 | 82 | 59 | 69 | 83 | 60 | |

Swabi | 51 | 66 | 42 | 50 | 65 | 43 | 49 | 64 | 43 | 48 | 63 | 44 | |

Swat | 62 | 76 | 50 | 61 | 75 | 51 | 61 | 74 | 52 | 61 | 73 | 53 | |

Tank | 67 | 80 | 52 | 69 | 81 | 54 | 71 | 82 | 56 | 73 | 83 | 57 | |

Upper Dir | 55 | 80 | 42 | 56 | 84 | 42 | 56 | 89 | 42 | 56 | 93 | 42 |

Table 3: District-Wise Forecasts Of The Urban Population In Khyber Pakhtunkhwa |
||||

Districts | Urban Population Forecasts (000) | |||
---|---|---|---|---|

2014-2015 | 2015-2016 | 2016-2017 | 2017-2018 | |

Abbotabad | 254 | 261 | 267 | 274 |

Bannu | 53 | 53 | 54 | 54 |

Charsadda | 284 | 291 | 297 | 304 |

Chitral | 47 | 48 | 49 | 50 |

D.I.Khan | 176 | 179 | 183 | 187 |

Hangu | 119 | 123 | 127 | 131 |

Haripur | 124 | 127 | 130 | 133 |

Karak | 56 | 58 | 60 | 62 |

Kohat | 263 | 271 | 279 | 287 |

Lakki | 114 | 119 | 124 | 129 |

Lower Dir | 78 | 80 | 83 | 85 |

Malakand | 75 | 78 | 80 | 82 |

Mansehra | 100 | 103 | 105 | 108 |

Mardan | 516 | 532 | 547 | 563 |

Nowshera | 343 | 352 | 360 | 369 |

Peshawar | 1713 | 1766 | 1820 | 1874 |

Swabi | 277 | 280 | 283 | 285 |

Swat | 339 | 352 | 364 | 376 |

Tank | 51 | 52 | 53 | 54 |

Upper Dir | 35 | 36 | 37 | 38 |

Source: Authors’ calculations.

The statistics given in **Table 2 **shows that for the year 2015 to 2018 the maximum forecast for overall, male and female Literacy rate is observed for district Abbottabad while the minimum is observed for district Sawabi. The rest of the statistics are given in **Table 2**. The projections for the year 2015-2017 for urban population is given in **Table 3 **show various trends over time across the districts of Khyber Pakhtunkhwa. The maximum forecast of urban population is projected for District Peshawar is 1713, 1766, 1820 and 1874 thousand during the years 2015, 2016, 2016 and 2017, respectively. While minimum forecasts are observed in District Upper Dir are 35, 36, 37 and 38 thousand for the years 2015, 2016, 2016 and 2017 respectively. The forecast has been made from 2014-2015 to 2017-2018. These forecasts are given in **Table 3**.

The present study found that there existed positive relationship between urbanization and literacy rate, however, in some of the years this relationship was negative. The negative relationship is due to the fact that with increase in the urban population, it is difficult to provide education in all the districts due to which it has negative impact. On the other hand, the positive relationship between the urban population and literacy rate is due the fact that the government took initiatives to educate all the students in all the districts due to which urban population impacted literacy rate in some of the years. The results also explore that urban population also impacted both the male and female literacy rate in KP. Similar results were also found by Chen (2010) who was of the view that urbanization and higher education have positive relationship. Our findings are also somewhat close to the findings of Song-lin (2009), who also estimated positive relationship between China's urbanization and higher education.

The projections made in this study showed that urban population as well as literacy rate has increasing trend in the future. This is due to the fact that over time the urban population increases (as also indicated by the past pattern of the data), the government also tries to cope with the increasing needs of the growing population in terms of increasing number of schools, appointing additional teachers at different levels in the province. These initiatives are taken for both male and females education in the province. The increasing trends in the literacy rate are also evident from the statistics given by the Government of Khyber Pakhtunkhwa. According to Government of Khyber Pakhtunkhwa (2015), in the year 2008-9, the overall literacy rate was 50% in the year 2008-09 which increased to 53% in the year 2013-14 in Khyber Pakhtunkhwa. The results further translated the past increasing trend and hence confirmed the robustness of the results of this study.

This study aimed to find out the impact of urbanization on literacy rate across the 20 districts in Khyber Pakhtunkhwa. For many years the urbanization has positively impacted the overall literacy rate, male literacy rate and female literacy rate. The impact also increased over time showing the increasing coefficient from 0.09 in the year 2008-2009 to 0.02 in the year 2013-2014. In majority of the years, the coefficients were found statistically significant. The projection of literacy rate for the year 2014-2017 shows increasing trends over time across the districts of KP. The highest literacy rate is projected for district Abbottabad, whereas the lowest overall literacy rate is projected for district Swabi during the year 2014-2017.

The government should take initiatives to cope with the increasing urban population in the province. Additional schools would be required to accommodate more students. As the projections show increasing trend for the future, so this will also put pressure on the available resources in the province. Therefore, the government should allocate more financial resources to fulfill the requirements in future.

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