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\title{Determinants of Rural Land Owners' Migration to Urban Centers in Ethiopia}
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             \author[1]{Desta Tegegne  Assefa}

             \author[2]{Kasahun Desyalew  Mekonen}

             \author[3]{Yalemsew  Genetu}

             \affil[1]{  Wolaita Sodo University}

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\date{\small \em Received: 13 December 2015 Accepted: 1 January 2016 Published: 15 January 2016}

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\begin{abstract}
        


Without any doubt rural-urban migration of the rural land owners has its own implication on the development process of both in urban and rural areas of a country. Hence, the primary purpose of this paper is to identify the determinants contributing for rural-urban migration of the rural land owners and its negative impact on rural development in Hulet Eju Enese Woreda of Eastern Gojjam zonal administration. The study further focused on the characteristics of migrants and challenges they face at the destination. To achieve the objectives, both primary and secondary data were employed. Data collection instruments like structured questionnaire, semi-structured interview and secondary sources were largely employed. To this end, a total of 110 migrants were selected through purposive snow ball sampling technique for the survey and the data was analyzed both qualitatively and quantitatively. The result of this study revealed that both rural push and urban pull factors were determinants for rural land owners to migrate to Mota town from different parts of Hulet Eju Enese Woreda. Small land holding, poor economic condition, natural disaster and lack of social service in the rural area were serious problems that pushed rural land owners to migrate to urban centers. Where as attractive climatic conditions, existence of urban amenities and social facilities and employment opportunities in Mota town were strong pull forces.

\end{abstract}


\keywords{rural-urban migration, land owners, migrant households, rural development, Ethiopia.}

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\let\tabcellsep& 	 	 		 
\section[{Introduction}]{Introduction}\par
igration is a form of spatial mobility, which involves a change of usual residence of a person between clearly designed geographical units. Migration has been an important component of population redistribution throughout the world. It is a multifaceted phenomenon which in general involves the movement of people from one place to the other  {\ref (NGS, 2005)}. The \hyperref[b19]{UN (1970)}, defines migration as a move from one migration defining area to another that was made during a given migration interval and that involves change of residence. "A migrant is also defined as "a person who has changed his usual place of residence from one migration-defining area to another at least once during the migration interval" \hyperref[b19]{(UN, 1970)}.\par
Central to the understanding of rural-urban migration flow is the traditional push-pull factors. "Push factor" refers to circumstances at home that repel; examples include famine, drought, low agricultural productivity, unemployment etc. While "pull factor" refers to those conditions found elsewhere (abroad) that attract migrants. There are many factors that cause voluntary rural-urban migration, such as urban job opportunities, housing conditions, better income opportunities etc. There is no doubt that, apart from these factors, urban areas also offer a chance to enjoy a better lifestyle (Jahan, 2012). For \hyperref[b1]{Bhattacharya (1993)}, rural-urban migration has been historically connected with industrialization, urbanization and economic growth. Rural-urban migration eases inter-sectoral factor mobility and plays a vital role for structural changes. Moreover, migration has also been a key livelihood and survival strategy for many poor groups across the developing world, particularly in Africa.\par
In Africa, migration has been considered as a way of life where the people migrate from place to place due to political, socio-economic and demographic reasons. Rural-urban migration has contributed for half of the urban population growth in Africa in 1960s and 1970s and about 25\% of urban growth in 1980s and 1990s \hyperref[b0]{(Adepoju, 1977;}\hyperref[b16]{Lall et al, 2006)}. Concentration of investment in industries, commerce, and social services in towns has been the causes for regional inequalities and differences in economic opportunities. In addition, the productivity of the rural and agricultural sector has remained low and leading to rural out-migration to urban and industrial sectors \hyperref[b0]{(Adepoju, 1977)}.\par
Migration within Ethiopian borders has been common as well, mainly in the form of rural -urban migration flows  {\ref (Fransen \& Kuschminder, 2009)}. The rural-urban migration trend in Ethiopia can be explained by a number of so-called push and pull factors \hyperref[b15]{(Kunz, 1973)}. \hyperref[b17]{Ezra and Kiros (2001)} summarize the main push factors in Ethiopia being overpopulation, famine, poverty, land scarcity, governmental agricultural policies, and lack of agricultural resources. Many households, however, also participate in seasonal labor activities, leading to temporary rural-urban migration.\par
Researchers \hyperref[b14]{(Kidane, 1989;}\hyperref[b13]{Kibreab, 1996}; Berhanu\& White, 2000; Kiros\& \hyperref[b10]{White, 2004}) have shown how the character, direction, and the volume of migration in Ethiopia during the last two to three decades have been shaped by political instability, official objective to prevent further famine and to attain food security \hyperref[b9]{(Gebre, 2001;}\hyperref[b6]{Ezra, 2001)}. Under these circumstances, migration in Ethiopia was not only an individual and/or family response to adverse socio economic, physical and political environment, but also as a result of official government policy (Birhan, 2011).\par
Internal migration flows within Ethiopia are currently larger than international migration flows from Ethiopia (Fransen and Kuschminder, 2009). Migration is a common, yet often least desirable choice of coping strategy for poor rural families. Migration occurs in response to livelihood degradation, an inability to grow enough food, or to provide enough income for the family and is highly influenced by the five driver factor of migration, namely political, social, economic, demographic, and environmental drivers \hyperref[b12]{(Hunnes, 2012)}. In a country like Ethiopia where nearly 85 percent of the population is engaged in small-scale rain-fed agriculture, it is critical to understand how and why rural land owners' are migrated into cities from their rural origin. In developing countries like Ethiopia rural-urban migration affects development in both urban and rural areas (Birhan, 2011). Among Ethiopian regional states the Amhara region has the highest rural to urban migration. From the total population of 17,222,800 registered migrants are 2,366,972 which are 13.7\% from the total population (CSA, 2007). The same report also indicated that, from the total 2,366,972 migrants, 1,789,666 were from the rural area to urban centers and the rest 577, 306 were migrants from urban to rural areas. This indicates that in Amhara region, rural to urban migration is higher than urban to rural migration. From the Amhara region Hulet Eju Enese woreda experienced high rate of migration. From the total population of the woreda (275,638), 29, 472 are total registered migrants which constitutes around 10.7\% of the total population (CSA, 2007). From the total registered migrants, 15,579 are from the rural area and now their current place of resident is in the urban area. The rest 13,893 are migrants from urban to the rural areas. The above data indicates that ruralurban migration is more than urban-rural migration.\par
In spite of the above noted prevailing situations, there is lack of sound knowledge and understanding of rural land owners' migration to urban centers in connection to the causes and consequences in both areas of origin and destinations. At the same time, there is apparently little empirical research work on rural land owners' migration in the country at large and Hulet Eju Enese Woreda in particular. Therefore, this study is meant to analyze the determinant factors of rural land owners' migration to urban centers and its negative impact on rural development is important and thereby provides significant data and information for policy formulation for launching suitable planning and response strategies to the emerging challenges. 
\section[{II.}]{II.} 
\section[{Materials and Methods}]{Materials and Methods} 
\section[{a) Study area i. Geographical location}]{a) Study area i. Geographical location}\par
Hulet Eju Enese is one of the woreda's in the Amhara Region of Ethiopia. Being part of East Gojjam Zone, it is bordered in the south by DebayTelatgen, in the west by Bibugn, in the northwest by West Gojjam Zone (Gonji KolelaWoreda), in the north by the Abay River (which separates it from the Debub Gondar Zone), in the east by Goncha Siso Enese, and in the southeast by Enarj Enawga. Among the towns in this administrative division are Keraniyo, Mota and Sede among which Mota is the capital of the worada. The town is about 120 kilometres east of Bahir Dar, 202 km north of Debre markos and 368 km north-west of Addis Ababa. According to the current master plan, the total area of the town is 14,728 hectares and topographically the town's area is characterized by 68\% alluvial plains, 4\% gorge and 16\% ups and downs at elevation of 1800-2415 meters above sea level. The absolute location of Hulet Eju Enese Woreda is 10049'09"-10043'10"N latitude and 37040'07"-37050'45"E longitude. The total area of the worada is estimated to be 138,986 hectares (HEEWARDO, 2012). 
\section[{b) Demographic profile}]{b) Demographic profile}\par
Based on the 2007 national census conducted by the Central Statistical Authority of Ethiopia (CSA, 2007), Hulet Eju Enese worada had a total population of 275,638 of which 137,382 were male and 138,256 female (Table \hyperref[tab_1]{3}  Topographically the woreda has a relief features: 65\% of plateau, 15\% of mountainous and 20 \% of valley (HEEWARDO, 2012). There are four main seasons in the woreda, namely bega (dry) from March to May, kremt (rains) from June to August (main rainy season), tibi from September to November, and me her from December to February (harvest season). Hulet Eju Enese woreda is divided in three agro-ecological zones, namely Dega, Woinadega and Kola which accounts 32\%, 37\% and 31\% respectively. It is clear that much of the worada is found within woinadega altitudinal zone. Regarding the distribution of peoples on the basis of physiographic region, 52\% of the total population is found in woinadega altitudinal zone and the rest is found in dega (18\%) and kola (30\%) agro climatic zone (HEEWARDO, 2012).\par
The rainfall distribution in the worada varies from year to year and across seasons. Accordingly the annual rainfall distribution varies between1150mm-1189mm which is bimodal in nature, receiving the greatest rainfall in summer and the smallest portion in spring (HEEWARDO, 2012). The amount of rainfall distribution in the worada is sufficient for annual crop production. The daily temperature varies from 80c which is the lowest to 300c of the highest with the average temperature of 220c (ibid). Soils in the study area are various types. Based on their color, soil type of the worada can be divided as Red (Borebor), Brown (Bodea) and Black (Debaye) which accounts 86\%, 6\% and 8\% respectively (HEEWARDO, 2012). The worada is rich in rivers with high potential for irrigation purpose. These rivers drain annually and most of them are tributary rivers to Abay (Blue Nile). The most important ones are Teme, Azuari, Sede, Tijan, Abeya, Amberis, Zema, Mai Temeko, Silmbiye, TejiBahir, DinchWenz, etc (HEEWARDO, 2012). 
\section[{ii. Socio-economic profile of the worada}]{ii. Socio-economic profile of the worada}\par
Mixed production of crops and livestock are the cornerstone of this woreda's economy. Agriculture activities are dependent on the kremt (summer) rains which fall from May to October. Some households use irrigation. According to the data obtained from HEEWARDO, the worada has 15, 310 hectare of arable land which can be used for irrigation purpose. From this currently 13,387 hectare of land is cultivated through irrigation (HEEWARDO, 2012). The main crops cultivated are teff, barley, maize, Sorghum, Bean, potato, etc. The bulk of the produce is used for household consumption. The major types of livestock's of the worada include cattle, horse, mule, donkey, sheep, goat etc. Raising sheep, cattle and horses is a key economic strategy. Children are responsible for herding livestock.\par
The main constraints on crop production among the poor are land degradation; shortage of farmland and crop diseases. Livestock ownership is also important for building household capacity to cope with livelihood shocks. The interest in generating new stock favors the ownership of mature female animals. Sheep provide most of the regular income from livestock. Cattle are more valuable assets, and they are owned only by the middle and better-off households. They are longer term investments. Beyond a lack of money, the biggest barrier to ownership of livestock is lack of feed: livestock production in the worada is limited by diminishing availability of grazing land. The better off at times grow pasture on a portion of their land to feed their cattle. The search for work is the main livelihood strategy for poor households, and so they depend on the availability of workers in the family for a significant portion of their income.\par
Teff, Potatoes, barley, wheat, and maize are the main crops traded. They are transported from local markets to markets in Addis Ababa, Bahir Dar, and Gondar. Particularly teff is exported to different parts of Ethiopia. Sheep and cattle are the popular livestock in the market. Poorly maintained roads winding through the mountains are the biggest barrier to the inflow of traders and commodities into the woreda. In Hulet Eju Enese woreda the main determinants of wealth are the amount of land owned, the ownership of cattle and sheep, and the ownership of horses for draught power. Ownership of horses is important for productivity because access to draught power determines household capacity to utilize available land holdings. The poor who haven't the capital to obtain their own draught power, or who lack family labor, are compelled arrangements usually divide the harvest from the rented land equally between the two parties. The biggest barrier to poor household ownership of draught power is the lack of capital.\par
Other important economic activities are wage labor and the sale of eucalyptus trees. Migratory labor opportunities are available in Shindi, Humera, Wollega, and Metemma for maize and sesame weeding and harvesting. Migration is a male activity, undertaken from June to August and from November to December. 
\section[{iii. Research design}]{iii. Research design}\par
In research of this kind the use of both quantitative and qualitative methods at the same time is more advisable. Quantitative data provide precise summaries and comparisons, while the qualitative data provide general elaborations, explanations, meanings and relatively new ideas. Taking all these into account, mixed research approach, which combine both quantitative and qualitative methods is used for this study.\par
These methods are believed to be more appropriate to investigate the topic under discussioncauses and consequences of rural-land owners' migration to urban center in Hulet Eju Enese Worada. Moreover, the qualitative approach is useful to look carefully for flaws and inadequacies of quantitative data that might be induced unintentionally in this study. 
\section[{iv. Sources and method of data collection}]{iv. Sources and method of data collection}\par
Based on the research problem and objectives, both primary and secondary data sources are used. Multiple data collection strategy is more advantageous than single data collection strategy in research work. There are strengths and weaknesses to any single data collection strategy and using more than one data collection approach give opportunity to the researcher to combine the strengths and correct some of the deficiencies of any one source of data. More specifically, the methods used to collect the necessary primary data were questionnaire and interview. 
\section[{v. Survey}]{v. Survey}\par
In this data collection instrument, primary data were obtained from individual respondents who complete and return questions concerning the issue under study. Under this technique the researcher distributed printed open and close-ended questions for selected participants. A total of four enumerators (teachers of Mota preparatory school) including the researcher (as a supervisor) participated in the actual survey where all the enumerators were selected based on their previous experience of collecting data through this instrument. Accordingly those four enumerators were assigned to the four kebeles of Mota town (one enumerator to one Kebele). Each enumerator, through snow ball sampling technique, collected data from respondents who came from different parts of the worada. 
\section[{vi. In-depth interview}]{vi. In-depth interview}\par
During Interview people with ample knowledge and experiences regarding the issue raised were purposefully selected. It is believed that, employing interview is important to this study since the study aimed to investigate and in depth understanding regarding the current problem of rural land owners' migration to the capital of the Woradas under the study area. To do so, structured interview guidelines were conducted. While interviewing the key informants, the researcher followed the pre-determined questions and standardized techniques of recording the information for structured interview. Generally the researcher collected data through this method from responsible officials at worada level. For the interview, representatives were taken from Mota town municipality, Hulet Eju Enese worada agriculture and rural development office, land administration office, health office and education bureau as well as from the four Kebele officials of Mota town. 
\section[{vii. Sampling technique and sample size}]{vii. Sampling technique and sample size}\par
Hulet Eju Enese Worada is amongst the agriculturally productive areas in the country. However, according to CSA (2007) the rate of rural to urban migration is very high. As per the information obtained from Mota town municipality, there are high numbers of in-migrants in Mota town. Therefore the worada is selected based on the high magnitude of rural-urban migration of the rural land owners to urban center purposefully. But due to the absence of records of the migrants in the municipality, sample migrants were approached through snowball sampling method. This method enables to locate migrants by themselves. Accordingly, 110 sample migrants' household heads in Mota town were searched. On top of this, published and unpublished materials which include research works, books, official documents and journal articles on the issues of migration, were used in this study. 
\section[{viii. Method of data analysis}]{viii. Method of data analysis}\par
Different methods of data analysis are used depending on the nature of data. Accordingly, the quantitative data are analyzed using descriptive statistics (like percentage and frequency). Moreover, qualitative data collected through interview were used to triangulate the results of quantitative data. The researcher used SPSS 16 to carry out the statistical analysis III. 
\section[{Results and Discussion}]{Results and Discussion} 
\section[{a) Socio-demographic and economic profile of respondents i. Gender and Age structure of the respondents}]{a) Socio-demographic and economic profile of respondents i. Gender and Age structure of the respondents}\par
For this study a total of 110 sampled household heads were participated. Therefore, 110 questionnaires were analyzed. Of the surveyed household heads 68 (61.8\%) were male respondents, while 42 (38.2\%) were females (Table  {\ref 4}.1). According to table 4.1 majority of the respondents were between the age group of 40-49(32.72\%) followed by 30-39 (24.56\%), 21-29 (20.9\%),>=60(11.81\%) and 50-59(10\%). Age specific sex ratio of the study migrants indicate that females dominate above the age of 50 years, whereas males dominate below this age limit. However, this doesn't show the age specific sex ratio at the time of migration in similar with the age specific ratio of Ethiopia. Because the age-specific sex ratios of migrants from the 1994 Ethiopian census shows that males dominate females in the age groups between 30-59 years, while females dominate males at young and old ages of 0-14 and 60-65 years  {\ref (Mberu, 2006)}.\par
ii. Educational status of the respondents at the time of migration Literacy status and educational attainment are significantly linked to rural-urban mobility. This is to mean that selectivity of migration varies according to education of migrants. Those who are better educated are relatively more involved in different migration streams than those who are not. Strong association between the propensity to migrate and level of education is observed in many developing countries  {\ref (Oberai, 1978)}. The study conducted by  {\ref (Mberu, 2006)} indicates that literacy status and educational attainment are significantly linked to rural-urban mobility in the country, with more than half (51 percent) of migrants literate. On the other hand the findings of CSA (1999) in Ethiopia showed that 70\% of the internal migrants were illiterate. But the survey result of this study is different (Figure  {\ref 4}.1).\par
The survey result of this study indicates that 55(50\%) respondents are found to be as cannot read and write, while the rest can read and write. From the literate respondents 29(26.4\%) have completed first cycle elementary school (grade 1-4), 15(13.6\%) attended from 5-8 grades and 11(10\%) joined high school. But no one is found who joined college or university. Thus, this falsifies the above assumption that most migrants are literate and vice versa.  
\section[{Marital status of the respondents at the time of migration}]{Marital status of the respondents at the time of migration}\par
According to the surveyed data, out of the total respondents 55 (50\%) are married which is followed by divorced, widowed and single which accounts 26 (23.64\%), 16 (14.55\%), and 13 (11.82\%) respectively. Out of the total single and married respondents male respondents accounts 9.09\% and 45.45\%respectively which indicates that male respondents were more single and married than female respondents whereas from the total respondents of divorced and widowed, females were more divorced and widowed than males at the time of migration which accounts 20\% and 10.91\% respectively (Fig.  {\ref 4}.2). 
\section[{iii. Religion and household size of the respondents}]{iii. Religion and household size of the respondents}\par
From the surveyed respondents the majorities are found as followers of Ethiopian orthodox Christianity 87(79.09\%) and the rest 23(20.91\%) are Muslims. When the family size of household in which the migrants arise was seen, the majority of migrants came from the large family size.  {\ref Fig 4.4} shows that most of the migrants came from family size of 4-6 members (64.55\%) followed by 1-3 members (29.09\%) and a small number of migrants (6.36\%) came from a family size of 7-9 members. In addition to the decisions made by household head (father/ mother) and relatives, friends also influence the decision to migration. Getahun  {\ref (2007)} states that pioneer migrants and the existences of networks were very important in attracting potential migrants to Addis Ababa. According to the survey result of this study, 39.1\% household head respondents made the decision to migrate by the household heads (father/mother), 27.3\% migrated by the commonly agreed decision of the whole family members which indicates that family bondage for decision making is important. The survey also emphasis that family-parent decision was more important than relatives' and friends decisions. " 
\section[{Head of family at the time of migration}]{Head of family at the time of migration} 
\section[{Global Journal of Human Social Science}]{Global Journal of Human Social Science}\par
In order to assess the role of land holding as factor of migration the respondents were asked the size of their land or their family at the time of migration. From Table  {\ref 4}.5 we can understand that the farm size of 30.9\% respondents was 0.5 hectare of land whereas 36.4\% respondents reported that they had a farm land size of 0.5-1 hectare of land. From this one can understand that the majorities 74 (67.3\%) had one and below one hectare of arable land. The rest 20.9\% and 11.8\% respondents had 1-2 hectare and 2-3 hectare of farm land respectively. No one respondent migrant was found who had above three hectare of land. This implies that decline of farm land size facilitated rural land owners out migration since migrants farm land size in the rural area is an average of 0.5-1hectare per household.\par
? Respondents were also asked the type of use of their farm land and all of them reported that they utilized their farm land for annual crop production. From this it is simple to understand that crop production is the backbone for the livelihood of the migrants. Therefore, according to the data gained from interview, to engage in urban informal sectors, most people migrated to urban center from their rural origin. 
\section[{v. Determinant of rural urban migration of the rural land owners}]{v. Determinant of rural urban migration of the rural land owners}\par
There are several reasons for population mobility from place to place. Most of the studies indicate that migration is primarily motivated by push and pull factors. The survey result of this study also confirmed that both push and pull factors are determinants for rural urban migration of the rural land owners. To analyze the factors for rural to urban migration in the area a set of 14 statements (7 push factor and 7 pull factor) that determine the process of rural urban migration were studied (Table  {\ref 4}.6).\par
vi. Push factors The respondents were asked how much they are agreed about small land holding as a major push factor for their migration. They responded that 49 (44.5\%) of them strongly agreed, 42 (38.2\%) agreed, 12 (10.9\%) disagree and 1 (0.9\%) strongly disagree. This indicates small land holding is one of the determinant factors for rural land owner's migration to Mota town since the agreed and strongly agreed respondent's together accounted for 82.75\%.\par
Empirical evidences of researchers also support the result of this study. For example Hunnes 2012) suggests that in Ethiopia, land tenure laws are such that each successive generation obtains parcels of land from their parents thereby decreasing overall land size for each family. Less land provides less ability to grow adequate food or to derive an adequate income. Markos (2001) also demonstrates that declining size of landholdings is a major push factor for migration to urban center in the northern part of Ethiopia. He added that scarcity of arable land in combination with population on the smaller landholdings facilitated migration to areas with better employment opportunities. Other study done by Hossain (2001) also found that rural out-migration is closely associated with unequal distribution of resources, particularly land.\par
The other push factor examined for the rural land owners' migration to urban center in the study area is poor economic condition in the rural area. The survey data indicates that out of the total respondents 21(19.1\%) strongly agreed, 73(66.4\%) agreed and 12(10.9\%) while disagreed and strongly disagreed accounted 2.7\% and 10.9\% respectively. From the result one can understand that 94(85.5\%) respondents perceived poor economic condition in the rural area as their major factor for their migration from rural origin by leaving their farm land. The research conducted by Birhan (2011) also supports the result of this survey. He elaborates that because of lack of investment and economic growth rural areas are suffering from lack of agricultural or alternative employment opportunities and is amongst reasons for migration.\par
Respondents were also asked their agreement on natural disaster (drought, famine) as a determinant factor of rural-urban migration. The surveyed data reveals that 28 (25.5\%) respondents strongly agreed, 66 (60\%) agreed, and 6(5.5\%) disagreed. According to this data the respondents who agreed and strongly agreed together accounts 94(85.5\%) that shows it is also among the determinant factors of rural-urban migration of rural land owners in the study area.\par
Prior research has indicated that environmental degradation, population pressure, drought and famine have all been responsible for spatial mobility in Ethiopia  {\ref (Ezra, 2000;} {\ref Berhanu and White 1998)}. Factors that may increase the likelihood of migration in Ethiopia include, decreasing soil productivity and decreasing arable land area, both of which decrease a household's ability to provide for their family, thus, increasing the risk of out migration \hyperref[b12]{(Hunnes, 2012)}.\par
It is also the undeniable fact that poor infrastructure and social service in the rural area push peoples to urban areas to get better social service and infrastructure. This is because availability of social and economic infrastructures is essential both for agricultural and rural development  {\ref (FDRE, 2003)}. It is not possible to attain rapid and sustainable agricultural or overall rural development where there is a lack of services in the fields of: education, training, health, rural road and transport. Rural development and infrastructural facilities and services are almost inseparable. The expansion of rural infrastructural facilities is a major government responsibility in view of its crucial role in expanding these facilities and services in general. In fact, the government's main tasks are to expand rural infrastructure, motivate and coordinate farmers and generally create favourable conditions for development \hyperref[b12]{(Hunnes, 2012)}.\par
Though rural infrastructures and good provision of social services are essential for rural peoples, none or less existence of them currently become a major factor for rural to urban migration. Belay (2011) demonstrates that poor infrastructure and less access to basic services pushed rural people to urban areas. The survey result of this study also shows that lack of social service and poor infrastructure in the rural area are among the push factors for rural urban migration of the rural land owners. Out of the total respondents those who strongly agreed and agreed accounted 56(50.9\%) and 40(36.4\%) respectively a total of 96(87.3\%).\par
The data gained through interview from Hulet Eju Enese Worada Rural Development office strongly support the result of this survey. The interviewed expert said that "land owner farmers migrate to Mota town due to the less expansion of rural infrastructure in their former place". On the other hand the countries rural development policy and strategy give more emphasis for the expansion of rural infrastructure, health institution as well as other social service institutions particularly the provisions listed from sub article 8.1to 8.4 to bring rural development. From this one can understand that there is less implementation of the country's rural development policy and strategy in the study area which may facilitate rural land owners' migration to urban center.\par
Other push factors like lack of justice in the rural area, fear of enemy, and marital factors were also studied in this research. The surveyed data indicates that the sum of agreed and strongly agreed respondents' constituted 17(15.5\%) for lack of justice in the rural area and 37(33.7\%) for marital factors. For these three push factors the sums of disagreed and strongly disagreed respondents is by far greater than the sum of agreed and strongly agreed respondents.  
\section[{vii. Pull factors}]{vii. Pull factors}\par
The respondents were also asked about the major pull factors for their rural to urban migration. According to the surveyed data the sum of agreed and strongly agreed respondents of existence of urban amenities and social facilities as a pull factor of migration accounts 101 (91.8\%) which is followed by attractive climatic condition of Mota town 98 (89.1\%) while 90 (81.9\%) respondents considered easy access to job in Mota town as a pull factor for their migration. In addition to this 30 (27.3\%) respondents shows their agreement by perceive expectation of higher income earning in Mota town as their pull factor. Others considers political freedom, marital factor and joining relatives as a pull factor which constitutes the sum of agreed and strongly agreed respondents of 16 (14.5\%), 13 (11.8\%) and 22 ( 20\%) respectively.\par
The collected data show that lure of attractive climatic conditions, existence of urban amenities and social facilities, and easy access to job are found as the major pull factors in the worada. From the study, one can conclude that small land holding, poor economic condition, natural disaster and lack of social service in the rural area of the study woreda were serious problems that pushed rural land owners to migrate to urban centers. IV. 
\section[{Conclusion}]{Conclusion}\par
This study basically has presented the determinant push and pull factors of rural land owners', current living condition of migrants in their destination area and its negative impact on rural development in general and agricultural production and natural resource management in particular of Hulet Eju Enese Worada. The important data for this study were collected from migrants in Mota town as well as from concerned officials and experts of the worada. The empirical result of this study can be deducted as follows.\par
Rural-urban migrants of the rural land owners are selective group formed on the basis of one or combinations of characteristics, such as age, gender, educational status, marital status, family size, farm land size and the like. Accordingly, the survey result revealed that at the time of migration males household heads dominates female heads between the age group of 21-41 while females dominate between the age group of 60 and above. In terms of educational status at the time of migration, 50\% migrants were illiterate. Among literate migrants' most of them attended primary schooling. With regard to marital status, at the time of migration majority of them were married (50\%). The family size of migrants at the time of migration was found large, average family size of 4-6 per household. Migrants farm land size in the rural area was found small, an average of 0.5-1hectare per household (most of it utilized for crop production). Regarding the decision made to migrate, the survey revealed that family/parent decision was more important than relatives' and friends" decisions.\par
Both push and pull factors are found as determinants for rural urban migration in the study area. Small land holding, poor economic condition, natural disaster and lack of social service in the rural area are serious problems that pushed rural land owners to migrate to urban centers. The less realization of the country's rural development policy and strategy contributed a lot which facilitated migrants to be pushed from their rural origin to Mota town. On the other hand attractive climatic conditions, existence of urban amenities and social facilities, and easy access to job in Mota town are strong pull forces.\begin{figure}[htbp]
\noindent\textbf{31}\includegraphics[]{image-2.png}
\caption{\label{fig_1}Figure 3 . 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{32}\includegraphics[]{image-3.png}
\caption{\label{fig_2}Figure 3 . 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-4.png}
\caption{\label{fig_3}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3} \par 
\begin{longtable}{P{0.3149746192893401\textwidth}P{0.2934010152284264\textwidth}P{0.12081218274111676\textwidth}P{0.12081218274111676\textwidth}}
\tabcellsep \multicolumn{3}{l}{1 : Distribution of the Population in}\\
\multicolumn{4}{l}{Hulet Eju Enese Worada by age and sex}\\
\tabcellsep Total\tabcellsep Male\tabcellsep Female\\
0 -14\tabcellsep 120,441\tabcellsep 60, 631\tabcellsep 59,810\\
15-64\tabcellsep 147,409\tabcellsep 72400\tabcellsep 75009\\
64+\tabcellsep 7,788\tabcellsep 4,351\tabcellsep 3437\\
Total\tabcellsep 275,638\tabcellsep 137,382\tabcellsep 138,256\\
\multicolumn{2}{l}{Source: CSA (2007)}\tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_1}Table 3 .}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{32} \par 
\begin{longtable}{P{0.11056910569105692\textwidth}P{0.3800813008130081\textwidth}P{0.2073170731707317\textwidth}P{0.15203252032520326\textwidth}}
No.\tabcellsep Land use\tabcellsep Area in\tabcellsep Percentage\\
\tabcellsep \tabcellsep hectare\tabcellsep \\
1\tabcellsep Annual farm crop\tabcellsep 47, 626\tabcellsep 34.87\\
\tabcellsep production\tabcellsep \tabcellsep \\
2\tabcellsep Fallow land\tabcellsep 1, 505\tabcellsep 1.08\\
3\tabcellsep Pasture\tabcellsep 33, 549\tabcellsep 24.14\\
4\tabcellsep Construction/settlement\tabcellsep 18, 247\tabcellsep 13.13\\
5\tabcellsep Tree plantation\tabcellsep 33, 247\tabcellsep 23.92\\
\tabcellsep (forestry)\tabcellsep \tabcellsep \\
6\tabcellsep Other\tabcellsep 4812\tabcellsep 2.86\\
\tabcellsep Total\tabcellsep 138, 986\tabcellsep 100.00\\
\multicolumn{2}{l}{Source: HEEWARDO (2012)}\tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_2}Table 3 . 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{33} \par 
\begin{longtable}{P{0.2792857142857143\textwidth}P{0.0769047619047619\textwidth}P{0.11333333333333333\textwidth}P{0.0769047619047619\textwidth}P{0.10523809523809524\textwidth}P{0.09309523809523809\textwidth}P{0.10523809523809524\textwidth}}
Age group in\tabcellsep \multicolumn{2}{l}{Male}\tabcellsep \multicolumn{2}{l}{Female}\tabcellsep \multicolumn{2}{l}{Total}\\
years\tabcellsep Count\tabcellsep \%\tabcellsep Count\tabcellsep \%\tabcellsep Count\tabcellsep \%\\
21-29\tabcellsep 16\tabcellsep 14.54\tabcellsep 7\tabcellsep 6.36\tabcellsep 23\tabcellsep 20.9\\
30-39\tabcellsep 18\tabcellsep 16.36\tabcellsep 9\tabcellsep 8.2\tabcellsep 27\tabcellsep 24.56\\
40-49\tabcellsep 23\tabcellsep 20.9\tabcellsep 13\tabcellsep 11.82\tabcellsep 36\tabcellsep 32.72\\
50-59\tabcellsep 5\tabcellsep 4.55\tabcellsep 6\tabcellsep 5.45\tabcellsep 11\tabcellsep 10\\
60\tabcellsep 6\tabcellsep 5.45\tabcellsep 7\tabcellsep 6.36\tabcellsep 13\tabcellsep 11.81\\
Total\tabcellsep 68\tabcellsep 61.8\tabcellsep 42\tabcellsep 38.2\tabcellsep 110\tabcellsep 100\\
\multicolumn{2}{l}{Source: Own survey (2014)}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_3}Table 3 . 3 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{42} \par 
\begin{longtable}{P{0.3894088669950739\textwidth}P{0.03489326765188834\textwidth}P{0.3573070607553366\textwidth}P{0.03908045977011494\textwidth}P{0.029310344827586206\textwidth}}
\tabcellsep \tabcellsep iii. Decision maker for migration\\
\tabcellsep \tabcellsep Who made the decision when\\
\tabcellsep \tabcellsep you decide to migrate to\\
\tabcellsep \tabcellsep Mota?\tabcellsep Frequency Percent\\
\tabcellsep \tabcellsep Household head mother/father\tabcellsep 43\tabcellsep 39.1\\
\tabcellsep \tabcellsep Relatives who are living in Mota\tabcellsep 19\tabcellsep 17.3\\
\tabcellsep \tabcellsep Common decision by family members\tabcellsep 30\tabcellsep 27.3\\
\tabcellsep \tabcellsep Friend who are living in Mota\tabcellsep 18\tabcellsep 16.4\\
\tabcellsep \tabcellsep Total\tabcellsep 110\tabcellsep 100.0\\
\tabcellsep \tabcellsep Source: own survey (2014)\\
Head of the family\tabcellsep \multicolumn{2}{l}{Frequency Percent}\\
Father\tabcellsep 68\tabcellsep 61.8\\
Mother\tabcellsep 40\tabcellsep 36.4\\
Brother/sister\tabcellsep 2\tabcellsep 1.8\\
Total\tabcellsep 110\tabcellsep 100.0\\
Source: own survey (2014)\tabcellsep \tabcellsep \\
\multicolumn{3}{l}{According to table 4.3 rural-urban migrants from}\\
\multicolumn{3}{l}{female headed households accounted for 36.4\%, male}\\
\multicolumn{3}{l}{headed households accounted for 61.8\%, and migrants}\\
\multicolumn{3}{l}{from brother/sister headed households accounted 1.8\%.}\end{longtable} \par
 
\caption{\label{tab_4}Table 4 . 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{45} \par 
\begin{longtable}{P{0.38857142857142857\textwidth}P{0.04857142857142857\textwidth}P{0.05043956043956044\textwidth}P{0.03736263736263736\textwidth}P{0.05043956043956044\textwidth}P{0.03923076923076923\textwidth}P{0.046703296703296704\textwidth}P{0.03923076923076923\textwidth}P{0.05043956043956044\textwidth}P{0.03736263736263736\textwidth}P{0.061648351648351654\textwidth}}
Push factors\tabcellsep \multicolumn{2}{l}{Strongly}\tabcellsep \multicolumn{2}{l}{Agree}\tabcellsep \multicolumn{2}{l}{Undecided}\tabcellsep \multicolumn{2}{l}{Disagree}\tabcellsep \tabcellsep Strongly\\
\tabcellsep \multicolumn{2}{l}{agree}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{disagree}\\
\tabcellsep No\tabcellsep \%\tabcellsep No\tabcellsep \%\tabcellsep No\tabcellsep \%\tabcellsep No\tabcellsep \%\tabcellsep No\tabcellsep \%\\
Lack of social service\tabcellsep 56\tabcellsep 50.9\tabcellsep 40\tabcellsep 36.4\tabcellsep 6\tabcellsep 5.5\tabcellsep 8\tabcellsep 7.3\tabcellsep 0\tabcellsep 0.0\\
Poor economic condition in the\tabcellsep 21\tabcellsep 19.1\tabcellsep 73\tabcellsep 66.4\tabcellsep 12\tabcellsep 10.9\tabcellsep 3\tabcellsep 2.7\tabcellsep 1\tabcellsep 0.9\\
rural area\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Run away from Natural disaster\tabcellsep 28\tabcellsep 25.5\tabcellsep 66\tabcellsep 60\tabcellsep 10\tabcellsep 9.1\tabcellsep 6\tabcellsep 5.5\tabcellsep 0\tabcellsep 0.0\\
Small land holding\tabcellsep 49\tabcellsep 44.5\tabcellsep 42\tabcellsep 38.2\tabcellsep 6\tabcellsep 5.5\tabcellsep 12\tabcellsep 10.9\tabcellsep 1\tabcellsep 0.9\\
Marital factors\tabcellsep 8\tabcellsep 7.3\tabcellsep 29\tabcellsep 26.4\tabcellsep 1\tabcellsep 0.9\tabcellsep 65\tabcellsep 59.1\tabcellsep 42\tabcellsep 38.2\\
Lack of justice in the rural area\tabcellsep 0\tabcellsep 0.0\tabcellsep 17\tabcellsep 15.5\tabcellsep 43\tabcellsep 39.1\tabcellsep 40\tabcellsep 36.4\tabcellsep 19\tabcellsep 9.1\\
Fear of enemy\tabcellsep 0\tabcellsep 0.0\tabcellsep 0\tabcellsep 0.0\tabcellsep 6\tabcellsep 5.5\tabcellsep 62\tabcellsep 56.4\tabcellsep 42\tabcellsep 38.2\\
Source: Own survey (2014)\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_5}Table 4 . 5 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{46} \par 
\begin{longtable}{P{0.38240534521158126\textwidth}P{0.04922048997772828\textwidth}P{0.05111358574610245\textwidth}P{0.03596881959910913\textwidth}P{0.053006681514476614\textwidth}P{0.041648106904231624\textwidth}P{0.04732739420935412\textwidth}P{0.039755011135857464\textwidth}P{0.05111358574610245\textwidth}P{0.03596881959910913\textwidth}P{0.062472160356347436\textwidth}}
Pull factors\tabcellsep \multicolumn{2}{l}{Strongly}\tabcellsep \multicolumn{2}{l}{Agree}\tabcellsep \multicolumn{2}{l}{Undecided}\tabcellsep \multicolumn{2}{l}{Disagree}\tabcellsep \tabcellsep Strongly\\
\tabcellsep \multicolumn{2}{l}{agree}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{disagree}\\
\tabcellsep No\tabcellsep \%\tabcellsep No\tabcellsep \%\tabcellsep No\tabcellsep \%\tabcellsep No\tabcellsep \%\tabcellsep No\tabcellsep \%\\
Existence of social services\tabcellsep 45\tabcellsep 40.9\tabcellsep 56\tabcellsep 50.9\tabcellsep 5\tabcellsep 4.5\tabcellsep 4\tabcellsep 3.6\tabcellsep 0\tabcellsep 0.0\\
Attractive climatic condition\tabcellsep 23\tabcellsep 20.9\tabcellsep 75\tabcellsep 68.2\tabcellsep 7\tabcellsep 6.4\tabcellsep 5\tabcellsep 4.5\tabcellsep 0\tabcellsep 0.0\\
Easy access to job\tabcellsep 39\tabcellsep 35.5\tabcellsep 51\tabcellsep 46.4\tabcellsep 12\tabcellsep 10.9\tabcellsep 8\tabcellsep 7.3\tabcellsep 0\tabcellsep 0.0\\
Expectation of higher income\tabcellsep 1\tabcellsep 0.9\tabcellsep 29\tabcellsep 26.4\tabcellsep 65\tabcellsep 59.1\tabcellsep 14\tabcellsep 12.7\tabcellsep 1\tabcellsep 0.9\\
earning\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Joining relatives\tabcellsep 19\tabcellsep 17.3\tabcellsep 3\tabcellsep 2.7\tabcellsep 11\tabcellsep 10\tabcellsep 54\tabcellsep 49.1\tabcellsep 23\tabcellsep 20.9\\
Existence of political freedom\tabcellsep 1\tabcellsep 0.9\tabcellsep 15\tabcellsep 13.6\tabcellsep 38\tabcellsep 34.5\tabcellsep 50\tabcellsep 45.5\tabcellsep 6\tabcellsep 5.5\\
Marriage\tabcellsep 9\tabcellsep 8.2\tabcellsep 4\tabcellsep 3.6\tabcellsep 4\tabcellsep 3.6\tabcellsep 54\tabcellsep 49.1\tabcellsep 39\tabcellsep 35.5\\
Source: Own survey (2014)\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_6}Table 4 . 6 :}\end{figure}
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