{"type":"survey","doc_desc":{"title":"PAK_2006_LEAPSM_v01_M","idno":"DDI_PAK_2006_LEAPSM_v01_M","prod_date":"2006-07-21"},"study_desc":{"title_statement":{"idno":"PAK_2006_LEAPSM_v01_M","title":"Learning and Educational Achievement in Punjab Schools (LEAPS) - Master Data 2003-2006","alt_title":"LEAPS Master 2003-2006"},"authoring_entity":[{"name":"Tahir Andrabi (Pomona College), Jishnu Das and Tara Viswanath (World Bank), Asim Ijaz Khwaja and Tristan Zajonc (Harvard University)","affiliation":""}],"production_statement":{"prod_date":"2006-07-21"},"series_statement":{"series_info":"LEAPS surveys were conducted in 2003, 2004 and 2006"},"version_statement":{"version":"Public Release Version 1.0"},"study_info":{"keywords":[{"keyword":"Education","uri":"","vocab":""},{"keyword":"Enrollmemt","uri":"","vocab":""},{"keyword":"Pakistan","uri":"","vocab":""},{"keyword":"Rural","uri":"","vocab":""},{"keyword":"Educational Achievement","uri":"","vocab":""},{"keyword":"Test Scores","uri":"","vocab":""}],"abstract":"The Master datasets comprise of four datasets: on children, schools, teachers and households. These master datasets contain key variables and identifiers which will allow users of the data to determine the progression of sample sizes and attrition of children, households, schools and teachers across the four years of the LEAPS panel data. \n\nThe children dataset contains round-by-round status of children's grades, enrollment, promotion etc. It also has variables indicating the panel child belongs to (the first panel being grade 3 children LEAPS started following in 2003, and the second one being 3rd graders followed starting in 2005 i.e. round 3 of the survey) as well as whether child was randomly selected for child questionnaire in class.\nThe school dataset contains information such as school type, survey status, construction date. Note that there is only one schoolid variable and it is constant across all rounds. To capture the fact that there is merging of some schools going on across the rounds, refer to the school_merged_into and school_merged_with variables. The school_merged_into variable only exists for the small schools that merged into a larger school whereas the school_merged_with variable exists for the larger schools that the smaller schools merged in to.\nThe teachers dataset contains information such as their round-by-round school, teaching status.\nThe household dataset contains a Mauza indicator, and a variable on whether the household was surveyed in a particular round.","coll_dates":[{"start":"2003","end":"2006","cycle":""}],"nation":[{"name":"Pakistan","abbreviation":"PAK"}],"geog_coverage":"Rural Punjab, Pakistan"},"method":{"data_collection":{"time_method":"2003-2007","sampling_procedure":"The sample comprises 112 villages in 3 districts of Punjab-Attock, Faisalabad and Rahim Yar Khan. The districts represent an accepted stratification of the province into North (Attock), Central (Faisalabad) and South (Rahim Yar Khan). The 112 villages in these districts were chosen randomly from the list of all villages with an existing private school. This allows us to look at differences between private and public schools in the same village. Although these villages are thus bigger and richer than average villages in these districts, we believe this is a forward-looking strategy and the insights earned here will soon be applicable to a significant fraction of all villages in the country.","sampling_deviation":"None","act_min":"Tracking Children: In 2004 we tested 12,000 children in 838 public and private schools in Grade 3 and re-tested them in Grades 4 (2005) and 5 (2006). In 2006, we also included Grade 3 children, increasing the total number of children tested to\n25,000. These children all need to be tracked through the year to find out where they are the next year. The table below shows for instance, the status of child-tracking as they moved from Grade 3 to Grade 4. In the transition children could (a) drop-out (b) remain in the same school and be promoted; (c) remain in the same school and not be promoted; (d) switch schools within the village and be promoted (in which case they would be tested in another school) and be promoted; (e) switch to schools within the village and not be promoted and (f) switch to schools outside the village or leave the village all together. Although close to 1800 children out of 12,000 were no longer in the same class-school combination that they would have been if they did not switch schools and were promoted, we were able to determine the status of all except 500. In one previous study, authors could track only 10 percent of the children compared to our rate of above 95 percent.\n\nSurveying Households: We have now created a longitudinal dataset of 1800 households across the 3 years, and in each of the years there are close to 750 children on whom we also have information on learning from the school testing exercise. This is the first database in low-income countries that combines detailed household information (including consumption aggregates) with school-level inputs and learning."},"analysis_info":{"response_rate":"The attrition has been remarkably small, averaging 3-4 percent in each year."}},"data_access":{"dataset_availability":{"status":"The data from year 2 will be available by December 2007."},"dataset_use":{"cit_req":"Use of the dataset must be acknowledged using a citation which would include:\n- the Identification of the Primary Investigator\n- the title of the survey (including acronym and year of implementation)\n- the survey reference number\n- the source and date of download","deposit_req":"In order to keep our database uptodate, we request that you provide us with a citation to the work you produce using the LEAPS data.","conditions":"In order to access the data, you will be required to register. Registration is a simple one-step process and is only designed to allow us to keep a track of the users of this data.","disclaimer":"To the best of our knowledge and ability, the data has been purged of identifying variables and errors of coding.\n\nThe user of the data acknowledges that the original collector of the data, the authorized distributor of the data, and the relevant funding agency bear no responsibility for use of the data or for interpretations or inferences based upon such 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