British Journal of Humanities and Social Sciences 18 April 2013, Vol. 9 (1)

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1 British Journal of Humanities and Social Sciences 18 Poverty Level and Kenya Certificate of Primary Education (Kcpe) Performance in Kenya Nelson Jagero (PhD) Director Distance and Open (E-Learning) at Africa University Zimbabwe Abstract This study was meant to evaluate how poverty levels in 47 counties in Kenya affect the performance of Kenya Certificate of Primary Education (KCPE) examinations and how this could affect the newly elected county governors under the new Kenyan constitution. It was also meant to analyse the disparity of the performance in KCPE within the 47 counties to Kenya. The research design used in the study was research design. The population was 811,930 students whose results were released on January 28 th Data was obtained from Kenya National Examination Council (KNEC) and Ministry of Planning. Data was analysed using descriptive statistics such as simple tabulation, means and percentage. Inferential statistics used were Pearson Correlation, Analysis of Variance (ANOVA) and Linear Regression Analysis. The major findings was that Turkana county which is the poorest and was ranked number 27 in KCPE by KNEC, will rank 6 th if poverty level was taken into consideration. The top county in KCPE (Kirinyaga) will be ranked 19 th. The richest county (Kajiiado) was ranked 13 th and it will end up ranked 38 if poverty was considered. From Correlation and Regression Analysis, increase in poverty level decreased performance in examination. For the counties to improve their performance, the county government should focus on improving the quality of the lowest performing schools in order to improve the performance of their counties. Key words: Poverty index, Counties, disparity in performance of KCPE, performance in KCSE. 1.0 INTRODUCTION The World Bank estimates that Kenyan s poverty level stands at 46% which is almost the same level it has remained in the last six years ( ). In 2000 the poverty level was at 56%. Persistent poverty is due to food insecurity. Twenty percent of Kenyan s population suffered from food poverty, such that their entire income was not even enough for purchasing food (Irungu, 2012). According to Professor H. Hino, to lower domestic prices of food, you cannot reduce prices for farmers because they would stop producing and the situation would only get worse. What you need to do is to lower the cost of production and to increase the supply, and then the prices will follow (Glanaster, Kremer, Mbiti and Tacourasha, 2012) International Fund for Agricultural Development (IFAD) states that Kenya has relatively advanced agricultural and industrial sectors and substantial foreign exchange earnings from agricultural exports and tourism. Yet it ranks 128 th among 169 countries in United Nations Development Programmes Human Index, which measures development in terms of life expectancy, educational attainment and standards of living (IFAD, 2011). About 79% of Kenya s population lives in rural areas and relies on agriculture for most of its income. The rural economy depends mainly on small holder subsistence agriculture, which produces 75% of total agricultural output. The poorest communities are found in sparsely populated arid zones. Kenya poor rural people also include small holder farmers, herders, households headed by women, people with disabilities and AIDS orphans (IFAD, 2011). Free Primary Education (FPE) programme has increased access to primary education among the poor households in Kenya. Additional costs such as uniform, tuition etc continue to hinder the educational attainment of many Kenyan children. A study by Uwezo (2010) found out that there is a large disparity between private and public schools in terms of quality.

2 British Journal of Humanities and Social Sciences 19 In Kenya, distance to nearest primary school is good compared to many countries with similar income level; this is not the case in all counties. The problem is most acute in counties in northern Kenya and coastal regions with large land masses and high poverty level. In 2007 the gross enrolment rate was 35% in the North Eastern Kenya, compared to 125% in Eastern Kenya (Ministry of Education, 2010). Thus reducing distance to schools could help boost educational access for students. 2.0 OBJECTIVES 1. To evaluate poverty levels and performance of students in KCPE in all the counties in Kenya 2. To analyse the disparity of the performance in KCPE within the 47 counties in Kenya 3. To establish the relationship between poverty levels in each county and performance in KCPE in all the counties in Kenya 3.0 LITERATURE Investment in education has a great potential to contribute to poverty reduction. It has also been shown that countries where primary education is far from universal, investments in primary education benefits the poor more directly than investment in post primary education. Kenya Certificate of Primary Education (KCPE) is a very vital examination in primary education level in Kenya since it is used as a basis of selecting students to secondary schools. Due to limited chances available at secondary schools, performance in KCPE determines the quality of secondary education a pupil will achieve. For example Mandera County was the worst performing county in the 2012 KCPE examinations. Leaders in Mandera expressed concern over dismal performance and blamed it on inadequate teachers and facilities (Barasa, 2013). They also noted that most teachers from outside the county decline deployment to Mandera due to harsh conditions In some instances in Kenya, parents attack and assault teachers over poor performance of their respective schools. On February , angry parents raided Chebanyiny Primary School where they interrogated the head teacher before assaulting him in front of the pupils (Malache, 2013). The parents blamed the head teacher for poor performance of the school in 2012 KCPE in which it scored a mean of 240 from 263 in According to Maendeleo ya Wanawake (Women for Development) coast chair, Sureya Hisri, the poor performance in KCPE was due to poor leadership and high poverty index (Kerubo, 2011). She said that it is hard for coast students who lack basic needs to compete with other regions that have good resources. In Murang a county, the poor performance of pupils in 2012 KCPE was blamed on jigger infestation, according to Ahadi Kenya Trust, a lobby group (Mwangi, 2013). This is because according to Ahadi Kenya Trust many of the children infested with jiggers cannot go to school and those who attend cannot concentrate in class. According to Ministry of Education (2010), nutrition and health can affect the performance of pupils in KCPE. For example, children with poor nutrition and health maybe too sick to attend school. Poor nutrition can also hamper children s cognitive development. 4.0 METHODOLOGY 4.1 Research Design In this study research design was suitable due to the fact that the researcher wanted to find out the relationship between poverty level in each county, and how it affect the performance of students in Kenya Certificate of Primary Education (KCPE). 4.2 Research population and sample The population in this study was the 47 counties in the Republic of Kenya. All the counties were used for the study involving KCPE students who undertook their examinations in December 2012 and their results were released on January 28 th There were 811,930 candidates who wrote the examination.

3 British Journal of Humanities and Social Sciences Sampling Purposive sampling technique was used in this study since all the 47 counties in Kenya were used in the study. This sampling technique is sometimes known as saturated sampling. 4.4 Data Collection Data used in this study was obtained from the website of Kenya National Examination Council (KNEC) and Ministry of Planning. KNEC websites was used to obtain KCPE results while Ministry of Planning websites was used for poverty level in each county. 4.5 Data Analysis First, the researcher coded the data so as to enable the data to be entered into the SPSS for data analysis. Descriptive statistics consisted of simple tabulation, means and percentages, while inferential statistics used were Pearson Analysis, Analysis of Variance (ANOVA) and Linear Regression Analysis. 5.0 FINDINGS AND INTERPRETATIONS 0bjective one: Poverty levels and performance of students in KCPE in all the 47 counties in Kenya. Kajiado is the richest county, according to the new government statistics that have exposed the massive disparities in wealth among county s regions. Only 12 in every 100 people in Kajiado are classified as poor in a county where the average number of people considered not to be rich is 46 out of 100. Kajiado s wealth status is radically different from the poorest county, Turkana where 94 people out of 100 residents are considered poor (Mwakilishi, 2011). The data on education and health, water sanitation, access to electricity and road network, tell the story of historical inequalities and to some extent the failure of the state to equalize opportunities for Kenyans. Table 1: Poverty level and performance of students in KCPE Performance County Poverty level Position in KCPE(marks out of 500) Position Difference between poverty level and performance New position KIRINYAGA (4-1) 17 ELGEYO MARAKWET (31-2) 4 NANDI (22-3) 7 BARINGO (33-4) 4 UASINGISHU (18-4) 8 MAKUENI (37-6) 1 BUSIA (38-7) 1 KISUMU (17-8) 12 WEST POKOT (40-8) 1 THARAKA NTHI (11-9) 19 VIHIGA (15-11) 16 SIAYA (10-11) 23 KAKAMEGA (27-13) 8 KAJIADO (1-13) 38 NAIROBI (2-15) 39 NYERI (8-16) 34 BOMET (26-17) 13 HOMABAY (17-18) 23 MACHAKOS (32-19) 10

4 British Journal of Humanities and Social Sciences 21 KERICHO (13-19) 31 EMBU (14-21) 32 BUNGOMA (28-22) 14 TRANSZOIA (25-22) 17 HYANDARUA (24-24) 20 NAROK (9-24) 40 NAKURU (16-26) 42 TURKANA (47-27) 6 MIGORI (20-27) 32 KIAMBU (3-27) 44 SAMBURU (43-30) 10 LAIKIPIA (23-31) 34 NYAMIRA (21-31) 36 MERU (5-33) 45 KISII (34-34) 20 MOMBASA (12-35) 43 KITUI (35-36) 23 MURANG'A (7-36) 46 ISIOLO (36-38) 27 MARSABIT (44-39) 15 KILIFI (39-40) 23 TAITA TAVETA (30-41) 37 LAMU (6-42) 47 KWALE (41-43) 27 TANARIVER (42-44) 27 WAJIR (45-45) 20 GARISSA (29-46) 41 MANDERA (45-47) 27 Source: Kenya National Examinational Council 2013 and Ministry of Planning (2013) As can be seen from Table 1, those counties with positive difference between poverty level and performance were assumed to have used resources more cost effectively in providing quality education. For example Makueni County which is 37 th poorest county was ranked sixth in KCPE performance index, having a positive difference of 31 compared to Meru with which is the 4 th richest county and position 33th in performance, the difference was 29. The counties with negative difference between poverty level and performance were wealthier, but their resources were not used cost effectively in providing quality education. In some counties, there was zero difference between poverty level and performance. For example Nyandarua County is 24 th richest county, and it was also position 24 in KCPE performance. Other counties include Kisii, (34 th richest and position 34 in KCPE). The two counties show perfect relationship between performance in KCPE and poverty level. Other counties with minimum differences between performance and poverty levels includes: Siaya, Homabay, Kitui and Kilifi as shown in Table 1. Their grading should be perfect when the poverty level is considered As can be seen in Table 1, if the Kenya National Examination Council (KNEC) was to rank the performance of the 47 counties, taking into consideration resources available in those counties, the top ten

5 British Journal of Humanities and Social Sciences 22 counties in terms of performance should be: 1. Makueni,1. Busia, 1. West Pokot, 4. Elgeyo Marakwet, 4. Baringo, 6. Turkana, 7. Nandi, 8. Uasingishu, 9. Samburu, 10. Machakos Though Turkana was ranked position 27, and since it is the poorest county in Kenya, its position improved to position 6 considering the resources the county has. The top county according to KNEC was Kirinyaga, and with the new grading it will be 17 th overall. Kisumu county (8) and Tharaka Nthi (9) will be positioned 12 th and 19 th respectively. The bottom ten counties according to the new grading will be 38. Kajiado, 39. Nairobi 40. Narok, 41. Garissa, 42. Nakuru, 43. Mombasa, 44. Kiambu, 45. Meru, 46. Murang a, 47. Lamu. Even though Kajiado, Nairobi, Kiambu and Murang a counties are very rich by Kenyan standards, the resources of these counties are not used cost effectively. With these new ranking counties such as Isiolo, Marsabit, Kilifi, Taita Taveta, Kwale, Turkana, Wajir and Mandera will not be ranked in the bottom ten positions. The new county governments elected on 4 th March 2013, should try to find out why these counties with low poverty levels still perform poorly is the national examination. Objective 2: Disparity in performance in KCPE in the 47 counties Table 2: Disparity of performance in KCPE in each county Highest marks Lowest marks in County in KCPE KCPE Disparity Position NAIROBI ( ) 1 MERU ( ) 2 BUNGOMA ( ) 3 NAKURU ( ) 4 KISII ( ) 5 KIAMBU ( ) 6 KILIFI ( ) 7 KISUMU ( ) 8 KITUI ( ) 8 HYANDARUA ( ) 10 KAKAMEGA ( ) 11 MOMBASA ( ) 12 BOMET ( ) 13 HOMABAY ( ) 13 KERICHO ( ) 15 NYAMIRA ( ) 15 LAIKIPIA ( ) 17 MIGORI ( ) 17 MARSABIT ( ) 19 MAKUENI ( ) 20 NYERI ( ) 21 MURANG'A ( ) 22 NANDI ( ) 22 MACHAKOS ( ) 24 TAITA TAVETA ( ) 25 KWALE ( ) 25 KAJIADO ( ) 27 ISIOLO ( ) 27

6 British Journal of Humanities and Social Sciences 23 TANARIVER ( ) 29 KIRINYAGA ( ) 30 BUSIA ( ) 31 NAROK ( ) 31 THARAKA NTHI ( ) 31 GARISSA ( ) 34 VIHIGA ( ) 35 EMBU ( ) 36 WAJIR ( ) 37 UASINGISHU ( ) 37 TRANSZOIA ( ) 39 BARINGO ( ) 40 WEST POKOT ( ) 41 ELGEYO MARAKWET ( ) 42 LAMU ( ) 43 SIAYA ( ) 44 SAMBURU ( ) 45 TURKANA ( ) 46 MANDERA ( ) 47 Source: Kenya National Examinational Council 2013 and Ministry of Planning (2013) As can be seen in Table 2 above disparity in performance in KCPE was greatest in the following counties: 1. Nairobi 2. Meru 3. Bungoma 4. Nakuru 5. Kisii. This disparity could be due to the varying quality of primary education provided by various schools in the counties. For example, Nairobi County, which is the capital city of Kenya, has the greatest disparity in living standards. This finding concurs with the findings of Kerubo (2011) in the coast and Mwangi (2013) in Murang a County. The majority of the counties with the greatest disparity in performance were greatest in the richer counties. Those countries that performed well had the lowest disparity in performance between the best and the worst schools. These counties with the lowest disparity between performances may not be having many private academies that post good results in KCPE. The lowest disparity in performance includes counties such as: 47. Mandera, 46. Turkana, 45. Samburu, 44. Siaya and 43. Lamu. Others include Elgeyo Marakwet, West Pokot, Baringo, Transnzoia, Uasingishu and Wajir. The new county governments should focus on improving the quality of the bottom performing schools in many rich counties such as Nairobi, Kajiado and Kiambu. This shows there is a very big inequality in the living standards in those counties. Though those counties appear to be rich, there are pockets of poverty that their new county governments need to tackle urgently.

7 British Journal of Humanities and Social Sciences 24 Objective 3: The relationship between poverty levels in the counties, performance in and disparity in KCPE performance. Table 3: Pearson between poverty level, performance in KCPE and disparity in KCPE performance Poverty Performance Highest marks Lowest Disparity in in KCPE in KCPE marks in KCPE results KCPE Poverty Pearson 1 Performance in KCPE Highest marks KCPE in Lowest marks in KCPE Disparity in KCPE results Sig _ N 47 Pearson * 1 Sig _ N Pearson * 0.391** 1 Sig _ N Pearson ** Sig _ N Pearson ** ** 1 Sig _ N *Correlation is significant at the 0.05 level in a two tailed test ** Correlation is significant at the 0.01 level in a two tailed test From table 3 it can be shown that poverty levels in the counties has negative with the performance of students in KCPE examinations. The Pearson was significant of 0.05 level in a two tailed test. This shows that as poverty level increases performance in KCPE in each county decreases. This finding concurs with the observation of Hirsi (Kerubo, 2011) that showed high poverty levels leads to poor performance of primary schools in the coast region. As shown in table 3, high performing schools was negatively related to the poverty levels of the counties. The Pearson was , and was significant at 0.05 levels in two tailed test. This finding shows that majority of the schools that performed well in KCPE were in the richest counties. This could

8 British Journal of Humanities and Social Sciences 25 be explained by the fact that richer counties had most of the private academies that generally post good results in KCPE performance. The highest was between performance in KCPE in each county and the lowest scored marks in each county. The was and it was significant at 0.01 levels in a two tailed test. This effectively means that improving of performance in KCPE in any county should target the improvement in performance of their lowest performing schools. This finding concurred with the finding of the objective two above that states that counties that performed well in KCPE had the lowest disparity between the best and the worst performing school. There was a high positive between counties with the best performing schools and disparity in the performance of KCPE. The Pearson co-efficient was and it was significant of 0.01 level in a two tailed test. This can be explained by the fact that those counties with quality private schools could also be having other primary schools within the counties that offers low quality education thereby increasing the disparity in performance between schools within the same county. Table 4: Analysis of variance (ANOVA) Sum of Model squares df Mean square F Sig Regression Residual Total Predicators: (constant) Highest scores in KCPE, lowest scores in KCPE, disparity in KCPE performance, performance in KCPE. Dependant variable: poverty level in each county From Table 4 above, ANOVA shows that the F of (>2.00) and a significance of (<0.05) shows that poverty level in each county (which is the dependant variable) affects the performance of schools. Poverty levels also affect the disparity in the performance in all the schools in the counties. This shows that when merit list on the performance of KCPE is released, the Kenya National Examination Council (KNEC) should consider the poverty index of the counties. Table 5: Linear Regression Analysis between poverty level, performance in KCPE and disparity in performance in each county. Un Model standardized Beta Coefficient standard error Standard coefficient Beta t Significance Constant _ Performance in KCPE Disparity in KCPE Dependent variable: poverty level From the linear regression analysis shown in Table 5, the un standardized Beta is when poverty level of each county is regressed against performance in KCPE. This linear regression was significant at (<0.05), showing a decrease in poverty level in each county by 1% will increase the performance in KCPE by 0.344%. From this finding, the newly elected county governments should quickly find ways of reducing poverty in their respective counties in order to improve quality of education in their primary schools.

9 British Journal of Humanities and Social Sciences 26 Conclusion: Poverty affects the quality of primary education in schools in Kenya. This is because parents cannot afford other educational costs such as uniform, feeding and other charges levied by the schools, even though primary education in Kenya is free since Even though some counties in Kenya have low poverty levels, there is a great disparity in the quality of education they are providing. This is because of big gaps that exist between poverty levels among individuals in those counties. References Barasa, L. (2013, January 31). Mandera leader decry poor KCPE performance. Nation Media Group. Glennerster, R., Kremer, M., Mbiti, I., Tacavarasha, K. (2011). Access and quality in the Kenyan education system: a review of the progress, challenge and potential solution. Nairobi: Government Printer. International Fund for Agricultural Development. (2011). Enabling poor rural people to overcome poverty in Kenya. Rome: IFAD. Irungu, G. (2012, May 8). Kenya poverty level constant for six years. Daily Nation Media Group. Kerubo, L. (2011, December 30). Poverty blamed for Coast Dismal KCPE performance. Star Newspaper Kenya. Makiche, E.(2013, February 7). Parents assault head teacher over poor KCPE results. Standard Media Group. Ministry of Education.(2010). Educational Statistical Booklet Nairobi. Government Printer. Mwakilishi; (2011 December 17). Named Kenyan s Richest and Poorest counties mwakilishi.com Retrieved February 28 th 2013, from central websites: Mwangi, J. (2013, February 11). Jiggers caused Murang a KCPE drop. Star Newspaper Kenya. Uwezo. (2010). Kenya National Learning Assessment Report Nairobi Kenya: Uwezo. Author Bio Nelson Jagero (PhD) is the Director Distance and Open (E-Learning) at Africa University Zimbabwe. Before his appointment he was a Senior Lecturer at the School of Post Graduate Studies of Kampala International University, Dar es Salaam Collage Tanzania. His areas of specialization include, Research methods, Statistics in Research, Quantitative Analysis in Business and Education Management. jageronelson@yahoo.com P.O BOX 1320 MUTARE ZIMBABWE.

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