Descriptive research design was applied in the present study. Pakundia upazila under Kishorgonj district was purposively selected as the locale of the present study. Three villages in Pakundia upazila viz. Narandi, Patuabhanga and Hossendi were selected from the upazila having high observed migration of labor. Lists 960 households having at least one migrant agricultural labor in these selected villages were collected from the respective Union Parishad Members. Stratified proportionate random sampling technique was followed in selecting 80 household head as respondent of the study. It constituted 8.33 percent of the total household heads. Another 10 family heads were kept as reserve. An interview schedule was prepared to collect relevant data according to the objectives of study. The instrument was pre- tested prior to the actual administration to the respondents. The pre-test was carried out on the respondents that were not part of sample of the study. Data were collected from the selected respondents using pre-tested interview schedule through face-to- face interview method during October to December 2019. A group discussion was held with a group consisting of 45 members including 15 from each of these three villages to find out the destination of migrant from the study area. In addition, direct observation was made during collection of data to understand the actual situation prevailing in the villages. Appropriate scales and measurement techniques were used to ensure correct responses to the variables of concern. The independent variables considered in this study were; age, educational level, family size, family type, annual family income, savings status of the family, cosmopoliteness, contact with the sources of information and organizational participation while impact of migration on the livelihood of the respondent was the dependent variable. Collected data were compiled, coded, categorized, and analyzed in accordance with the objectives of the study. Qualitative data were converted into quantitative form by means of suitable scoring method. Descriptive statistics; viz. frequency, percentage and rank order analysis were used for presentation of results. Age of the respondent was measured by counting years and classified into three categories according to Islam (2011). Education of the respondents was measured in formal schooling years and categorized into five categories illiterate, signature ability, primary education, secondary education and above secondary education following Tuli (2011). The family size of the respondent was measured in number of individuals in the family including himself, his wife, children and other dependents and classified into three categories on the basis of their family size according to Parvez (2007). Types of family were classified into two categories, namely; nuclear family and joint/ extended family. Cosmopoliteness was measured in score of respondents on the basis of their visit to three different places “frequently”, “occasionally”, “rarely” and “not at all” and the weights assigned to these visits were ‘3’, ‘2’, ‘1’ and ‘0’, respectively following Rahman (2011). Contact with sources of information scores of the respondents were compiled on the basis of their extent of contact with eight sources of information “frequently”, “sometimes”, “rarely” and “not at all” and corresponding assigned weights for each response were ‘3’, ‘2’, ‘1’ and ‘0’, respectively following Patwary (2018). Organizational participation of the respondent was measured by membership in six different organizations and weights assigned ‘0’ for “no participation”, ‘1’ for “not member but attend occasionally”, ‘2’ for “ordinary member”, ‘3’ for “ordinary member and attend meeting regularly” and ‘4’ for “active member”. Respondents were classified into three categories on the basis of their organizational participation according to Sultana (2015). Annual family income of the respondents was measured in BDT on the basis of total yearly earning from agriculture and non-agriculture sources by family members was categorized into three classes according to Ali (2007). Saving status was identified by asking their opinion into three types viz. ‘saving’, ‘no savings’ and ‘indebt’. Reasons for agricultural labor migration were computed by directly asking to mention their opinions in a four-point rating scale. The continuums of the scale were: ‘no’, ‘low’, ‘moderate’ and ‘high’ and the corresponding scores assigned to each of the continuum were ‘0’, ‘1’, ‘2’, and ‘3’, respectively. Rank order of the causes was prepared to get a comprehensive idea on the statements. Thus, a statement for 80 respondents could score a cumulative weightage of ‘0’ to ‘240’ where ‘0’ would indicate ‘no’ cause of migration and ‘240’ would indicate ‘high’ cause of migration. Based on their cumulative score of each statement, rank order of causes of labor migration was computed. Types of labor migration from the respondents’ family were measured by asking direct question. By adding the frequency of each type of family labor, rank order of labor migration type was calculated. Impact of labor migration on rural livelihood being the dependent variable of the study was measured in terms of changes in livelihood capitals viz. human capital, physical capital, financial capital, social capital and natural capital of the rural families following DFID (1999). Change in each capital was measured computing the changes in each of two statements. And total changes in each capital was computed adding the changes in each of two statements of each of five capitals. The respondents were asked to provide their opinions on the influence of agricultural labor migration in each of the five capital through 10 statements in four-point rating scale viz. “no change”, “low change”, “moderate change” and “high change”. Corresponding scores of ‘0’, ‘1’, ‘2’ and ‘3’ were assigned against their responses of “low change”, “moderate change” and “high change”, respectively. Thus, the assigned score of each statement for 80 respondents could range from ‘0’ to ‘240’ where ‘0’ would indicate ‘no change’ and ‘240’ would indicate high change in their livelihood. Coping strategy to adjust labor shortage in agricultural production was measured by asking their opinions against seven selected statements in a three-point rating scale. The continuums of the scale were: ‘low’, ‘moderate’ and ‘high’ against the corresponding assigned scores of ‘0’, ‘1’, ‘2’, and ‘3’, respectively. Rank order of the coping strategy statements was prepared to get a comprehensive idea on each statement. Thus, cumulative scores of a statement could range from ‘0’ to ‘240’ where ‘0’ would indicate ‘low’ means of coping up of labor shortage and ‘240’ would indicate ‘high’ means of coping up the same.