{"doc_desc":{"title":"Exploring the effects of COVID \u201319 Pandemic on Low-Cost Private School Markets in Nairobi, Kenya","idno":"DDI-KEN-APHRC-LCPS-2021-V10","producers":[{"name":"African Population and Health Research Center","abbreviation":"APHRC","affiliation":"African Population and Health Research Center","role":"Data Documentation Initiative"}],"prod_date":"2023-09-23","version_statement":{"version":"Version 1.0"}},"study_desc":{"title_statement":{"idno":"DDI-KEN-APHRC-LCPS-2021-V10","title":"Exploring the effects of COVID \u201319 Pandemic on Low-Cost Private School Markets in Nairobi, Kenya","sub_title":"LCPS-KENYA","alt_title":"LCPS-KENYA"},"authoring_entity":[{"name":"Dr. Moses Ngware","affiliation":"APHRC"}],"oth_id":[{"name":"Bonface Butichi Ingumba","affiliation":"African Population and Health Research Center","email":"","role":"Data Governance Officer"}],"production_statement":{"producers":[{"name":"John Muchira","affiliation":"APHRC","role":"Co-PI"},{"name":"Catherine Asego","affiliation":"APHRC","role":"Co-Investigator"},{"name":"Francis Kiroro","affiliation":"APHRC","role":"Co-Investigator"},{"name":"Maurice Mutisya","affiliation":"APHRC","role":"Co-Investigator"},{"name":"Vollan Ochieng","affiliation":"APHRC","role":"Co-Investigator"},{"name":"Caroline Thiongo","affiliation":"APHRC","role":"Co-Investigator"},{"name":"Rita Perakis","affiliation":"Center for Global Development","role":"Co-Investigator"},{"name":"Aisha Ali","affiliation":"Center for Global Development","role":"Co-Investigator"}],"copyright":"Copyright \u00a9 APHRC, 2023","funding_agencies":[{"name":"Center for Global Development","abbreviation":"CGD","role":"Funder"}]},"series_statement":{"series_name":"1-2-3 Survey, phase 1 [hh\/123-1]","series_info":"N\/A"},"version_statement":{"version":"1.0","version_date":"2021-08-30","version_notes":"N\/A"},"study_info":{"keywords":[{"keyword":"low cost private school","vocab":"","uri":""},{"keyword":"low fee private school","vocab":"","uri":""},{"keyword":"Nairobi","vocab":"","uri":""},{"keyword":"urban informal settlement","vocab":"","uri":""},{"keyword":"slum","vocab":"","uri":""},{"keyword":"education","vocab":"","uri":""},{"keyword":"covid-19","vocab":"","uri":""}],"topics":[{"topic":"economic shocks created by the COVID-19 pandemic on (i) households\u2019 demand for education and school choice","vocab":"","uri":""},{"topic":"links between household socio-economic status, child factors, demand for education, and supply of schools available.","vocab":"","uri":""},{"topic":"household and school-level COVID-19 responses and their effectiveness in mitigating the adverse effects of COVID-19 on schooling, enrollment and education outcomes.","vocab":"","uri":""},{"topic":"how parental perceptions on education provider\u2019s options (private and public schools) have been affected by the COVID-19 pandemic","vocab":"","uri":""},{"topic":"policy responses to the shocks to APBET schools market equilibrium caused by COVID \u201319.","vocab":"","uri":""}],"abstract":"The COVID-19 pandemic caused great disruption to the education sector, with over' 1.6 billion of the worlds  <https:\/\/en.unesco.org\/covid19\/educationresponse>school-going children affected since March 2020. Emerging research shows that lost learning opportunities during lockdowns can have medium to longer-term effects on the lives of learners. With the loss of income occasioned by school closures and the economic shocks, the pandemic could also cause significant disruptions especially to the Alternative Provision to Basic Education and Training (APBET) Institutions and the education ecosystem. \nThis study aims to examine the economic shocks created by the COVID-19 pandemic on households' demand for education and school choice; private school markets in low-resourced urban contexts; and knock-on effects on public schools. The study will be undertaken in four informal settlements in Nairobi; Korogocho, Viwandani, Mathare, and Kibra. All APBETs, public schools and villages within the four areas will be included in the survey. The study will adopt a multi-stage sampling procedure, with the household sample size estimated to be 835. Both quantitative and qualitative research approaches will be used in data collection and analyses. The expected outcomes for this study include; fostering an understanding of the implications of the pandemic on APBET schools to build a resilient education system and generate knowledge for enhancing school operations in informal settlements. The study will run from July 2021 to August 2022. The project's total direct budget is approximately KES 32,327,254 (see Table 2).","coll_dates":[{"start":"2021-07-01","end":"2021-08-20","cycle":""}],"nation":[{"name":"Kenya","abbreviation":"KEN"}],"geog_coverage":"The geographic coverage of this study is focused on four specific informal settlements within Nairobi, Kenya. These settlements are: Korogocho, Viwandani, Mathare and Kibera","analysis_unit":"Households in the four sites - Korogocho, Viwandani, Mathare, and Kibera","universe":"Households and Schools within specific Informal Settlements of Nairobi, Kenya:\nHouseholds: The survey specifically targets households residing within four identified informal settlements in Nairobi: Korogocho, Viwandani, Mathare, and Kibera. Importantly, only households with at least one child of primary or secondary school-going age (approximately 6-18 years old) are included in the survey universe.\nSchools: The survey includes all primary and secondary schools located within those same four informal settlements. This encompasses both public schools and Alternative Provision of Basic Education and Training (APBET) institutions (low-cost private schools). Additionally, schools within a 1-kilometer radius of the boundaries of these settlements are also considered part of the survey universe, acknowledging that some children from the settlements may attend schools just outside their immediate area. This includes both currently operational schools and those that permanently closed during the COVID-19 period.","notes":"The study investigated the impact of the COVID-19 pandemic on low-cost private school markets (APBET) in Nairobi's informal settlements. It examined how the pandemic's economic shocks had affected household education demand, school choices, private school operations, and the knock-on effects on public schools. The research was conducted across four informal settlements: Korogocho, Viwandani, Mathare, and Kibera. It employed both quantitative (household surveys, school surveys) and qualitative (key informant interviews) methods to collect data. The study aimed to inform policy responses to the disruptions caused by the pandemic on the education system.","study_scope":"The study investigated the impact of the COVID-19 pandemic on low-cost private school markets (APBET) in Nairobi's informal settlements. It examined how the pandemic's economic shocks had affected household education demand, school choices, private school operations, and the knock-on effects on public schools. The research was conducted across four informal settlements: Korogocho, Viwandani, Mathare, and Kibera. It employed both quantitative (household surveys, school surveys) and qualitative (key informant interviews) methods to collect data. The study aimed to inform policy responses to the disruptions caused by the pandemic on the education system."},"method":{"data_collection":{"time_method":"Cross sectional study","sampling_procedure":"The four sites - Korogocho, Viwandani, Mathare, and Kibera - were purposely selected due to their large sizes and our research knowledge of two of the sites where APHRC runs the Nairobi Urban Health and Demographic Surveillance System (NUHDSS) NUHDSS. To allow for representation of the household sample over the 4 sites, we will adopt a multistage sampling procedure. According to the (Kenya National Bureau of Statistics (KNBS), 2019), the total number of households in 2019 within the Nairobi City County was 1,506,888 which represents a proportion of 12.4 percent of the total number of households in Kenya. \n\nIn determining the sample for our study, we used a formula by the United Nations Statistics Division handbook of practical guidelines on designing household survey samples compiled by (UNSTATS, 2008) due to its clarity in the elaboration of the sample estimation specifications (Ahsan et al., 2016; Miller et al., 2020). We estimated the proportion of households with primary and\/or secondary school-going children enrolled in private schools within our study area to be about 50 percent at a confidence interval of 95 percent (the range within which a population parameter would fall). The margin of error of 5 percent, and anticipated rate of non-response and attrition of 30 percent as a result of outmigration that may be due to COVID-19, government measures on restrictions of movements, economic hardships such as loss of employment at the same time considering that the households that have been able to withstand economic hardships for over a year may exhibit more resilience than those who left shortly after initial government precautionary measures on COVID-19). Our sample accounted for the combined non-response and attrition rate of 30% based on previous experience in the studies conducted in the slums. In the computition this was considered as k.  We estimated an average household size of 2.9 based on the 2019 census for Nairobi City county and an assumed design effect of 2.0. Based on these parameters, our household sample was 883. The study was conducted in the four informal settlements within Nairobi (Korogocho, Viwandani, Mathare, and Kibera) with the number of villages in each slum. A listing exercise will be conducted as described in a later section among the four slums. Nairobi's urban informal settlements share similar characteristics, for instance, high population density, overcrowded structures, inadequate water, and sanitation services, and proliferation of low-cost private schools among others, with other informal settlements in the country due to rural-urban migration. Sample allocation for each slum site will be done in line with the sample distribution table below which was proportionally allocated based on the number of villages in each slum. In the allocation, we assumed that the number of villages is commensurate with the population of households present in a slum. \nWe used the sample estimation formula adapted from a UN guideline compiled by (UNSTATS, 2008).\n\n-\tnh is the parameter to be calculated and is the sample size in terms of the number of households to be selected;  \n-\tz is the statistic that defines the level of confidence desired, in our case 95 percent;\n-\tr is an estimate of a key indicator to be measured by the survey, in our case the key indicator is the prevalence of enrolment in an LCPS; we estimated 50 percent, a proportion that maximizes the sample (our data show 47 percent  in 2013);\n-\tf is the sample design effect, assumed to be 2.0;\n-\tk is a multiplier to account for the anticipated rate of non-response and attrition given that we will collect data in subsequent rounds;\n-\tp is provided by a product of 0.03 and the number of years in the age range that the target population of interest in a household represents, 0.03 is considered as a reasonable rule of thumb (UNSTATS, 2008) in our case the range is 6-18 years, hence a range size of 13;\n-\t\u00f1 is the average household size (total number of persons in a household) - in our case 2.9 according to 2019 Kenya Census data;\n-\te is the margin of error to be attained, in our case 5 percent.  \n\nThe following steps were used to identify an appropriate sample in the four slum areas:\n\na) All villages within each of the sites were listed. Typical slums (like our four sites) in Kenya were characterized by an overcrowded and continuous mass of dwelling structures, narrow and jammed service roads (commonly used by riders, and bicycle taxis also referred to as boda boda), and very narrow footpaths leading to dwelling areas that were off the narrow service road. We therefore worked with local guides and consulted administrative leaders such as chiefs to select an equal number of households from each listed village. In this study, existing villages were used as the boundaries were known by the local community leaders which was crucial for our design and they had some uniqueness.\n\nb) In each EA, a landmark was identified that was next to a service road. Such a landmark could be a chief's camp, a church, a school, or a 'big market among others.\n\nc) From the landmark, and using a local guide (for direction, boundary identification, and security), the enumerator started listing households moving towards a defined direction from the landmark, and along the service road. This allowed listing households that were deep inside an EA. Households along the service roads were not listed as the majority of structures were mainly used as small informal business premises.\n\nd) If the identified landmark was at an EA boundary, then the enumerator moved towards the interior of an EA, but if it was not near an EA boundary, then the enumerator started with one direction along the service road then came back to continue towards the other direction after reaching the boundary while using the first direction. The listing took place deep inside a village and was guided by the footpaths to allow for the selection of households across a village.\n\ne) To balance costs and sample efficiency (in terms of household representation of the villages), the enumerator identified every 10th household from the point where the footpath connected with the service road. Slums such as Kibra were quite huge. Due to cost implications, listing was not done for every household. However, it was acknowledged that this might create a bias - adopting a systematic sampling technique whereby every 10th household that met the set criteria was listed to develop a sampling frame thus mitigating the effect of the bias. This was a limitation of this exploratory study. The household qualified to be listed if it had at least one school-age child who was enrolled in school before school closure due to COVID-19. If it qualified, then its characteristics were enumerated such that the list of households with primary and\/or secondary school-age children with the following pieces of information was obtained: Slum, village, RoomID that was marked using a marker pen at the door, the GPS locations, phone contact (if known), number of primary-school-age children (6-13 years), number of secondary school-age children (14-17 years); household head gender, and household head age. If a HH did not qualify, the enumerator moved to the next immediate household until s\\he found a qualifying household. Thereafter the enumerator moved to the 10th household from the last to qualify and repeated the process of enumeration, until the end of the footpath and\/or EA boundary - taking into consideration dwelling structures that may be along 'mini' footpaths. It was critical to collect the GIS positioning of the household structure as well as allocation of IDs; each slum was allocated a slumID, each village, a villageID before the commencement of the listing exercise, whereas, for householdID and RoomID, each field interviewer (FI) was allocated a slot, say 001-010, another 011-020, etc. in a village. The phone number(s) of the household head was recorded for use to identify the location of the household during actual data collection. During the listing and data collection process, enumerators verified phone numbers and ensured the research team followed up with the recruited households (while adhering to research ethics) to reduce risks of high attrition for the subsequent survey rounds.\n\nf) After enumerating all eligible households in the four slums using the procedures described above, random numbers were assigned using STATA, then the required number of households in each village and\/or site were randomly selected.","sampling_deviation":"N\/A","coll_mode":"Face-to-face [f2f]","research_instrument":"This study utilized a combination of questionnaires and interview guides for data collection. The following instruments were employed:\nHousehold Questionnaire: This instrument gathered information about household demographics, children's schooling (types, funding, access, enrollment, attendance), perceptions of education quality and options, schooling costs and affordability, access to learning during closures, socio-economic characteristics (income, employment, food security), and socio-emotional support received. This questionnaire was designed to be administered in person, primarily in Kiswahili.\nSchool Institutional Questionnaire: This questionnaire was designed to gather data from school administrators. It covered school background information, enrollment and attendance rates, staffing and compensation, schooling costs, learning and inclusivity measures, quality assurance, infrastructure, resources, coping mechanisms adopted during COVID-19, and information on special needs and inclusivity. This instrument was planned for phone administration.\nPermanently Closed Schools Questionnaire: This questionnaire focused on collecting information about schools that had permanently closed due to the pandemic. It covered enrollment, attendance before closure, staffing, compensation, coping mechanisms attempted, and potential factors that could have helped the school remain open. This instrument was also planned for phone administration.\nKey Informant Interview (KII) Guides: Structured interview guides were developed to conduct in-depth interviews with key stakeholders. These included APBET Association leaders, Sub-County Education Officers, school heads, and village\/community leaders. The KIIs aimed to explore how the pandemic affected school operations, changes in enrollment and fees, the well-being of school-aged children, coping mechanisms adopted by schools and communities, and policy suggestions to mitigate future challenges. These were to be administered in person.\n\nLanguage of Questionnaires:\n\nThe primary language for the household questionnaire was Kiswahili. Other questionnaires and interview guides were designed in English, with potential for translation or adaptation into Kiswahili as needed during administration.\n\nQuestionnaire Design Process:\n\nThe development of the questionnaires and interview guides involved:\nConsultative Process: The study team engaged in a consultative process with partners and key actors in basic education.\nAdaptation from Existing Tools: Where applicable, the study borrowed and adapted suitable questions from existing tools used in previous primary school studies. This ensured some level of validation and reliability.\nValidation by Research Team: The developed tools were reviewed and validated by the research team at the African Population and Health Research Center (APHRC) in partnership with select study participants.\nPilot Testing: The tools were piloted in select schools (not participating in the actual data collection) in an urban poor settlement. This step helped refine the instruments and identify any issues before full implementation.\nStakeholder Feedback (Planned): The document mentions stakeholders being engaged through emails, phone calls, face-to-face, and virtual meet-ups during the inception meeting to discuss the purpose of the study, study tools, and buy-in.\nLearn-Adapt Approach: The study utilized a \u201clearn-adapt\u201d methodological approach, where emerging evidence from preliminary analysis and literature synthesis informed the refinement of study tools.","act_min":"Enumerators were organized into teams, each with a designated team leader who acted as a supervisor and controller. These team leaders oversaw their respective teams during data collection, ensuring data quality and adherence to ethical procedures. They conducted spot checks and sit-ins on 5 percent of randomly selected households and institutions to verify data accuracy and standards. Each urban informal settlement had a field team leader who managed the research teams and organized logistics. Senior researchers made random field visits during data collection to monitor compliance with ethical protocols.","weight":"N\/A","cleaning_operations":"Consistency and Coherence: Data was checked for consistency and coherence through a combination of automated program constraints and manual visual control.\nAutomatic Corrections: The tablet program enforced data constraints and validation rules during entry, automatically preventing certain errors.\nVisual Control: Data uploaded to the servers was visually reviewed by team leaders and research officers to identify anomalies, missing values, and inconsistencies. Corrections were then made manually based on this review.\nData Cleaning: After upload, a thorough data cleaning process was conducted. This involved:\nRectifying data entry errors not caught by the initial checks.\nStandardizing variable coding and labels.\nSystematically handling missing data.\nEnsuring data coherence across different questionnaire sections.","method_notes":"N\/A"},"analysis_info":{"response_rate":"100% of the households (883) and 96.9% (471)  of the institutions","sampling_error_estimates":"N\/A"}},"data_access":{"dataset_use":{"contact":[{"name":"African Population and Health Research Center","affiliation":"","email":"datarequests@aphrc.org\/info@aphrc.org","uri":"www.aphrc.org"}],"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 country, acronym and year of implementation)\n- the survey reference number\n- the source and date of download","conditions":"APHRC data access condition\n\nAll non-APHRC staff seeking to use data generated at the Center must obtain written approval to use the data from the Director of Research.\nThis form is developed to assess applications for data use and facilitate responsible sharing of data with external partners\/collaborators\/researchers. By entering into this agreement, the undersigned agrees to use these data only for the purpose for which they were obtained and to abide by the conditions outlined below:\n\n1.Data Ownership:\nThe data remain the property of APHRC; any unauthorized reproduction and sharing of the data is strictly prohibited. The user will, therefore, not release nor permit others to use or release the data to any other person without the written authorization from the Center.\n\n2.Purpose:\nThe provided data must be used for the purpose specified in the Data Request Form; any other use not specified in the form must receive additional or separate authorization.\n\n3.Respondent Identifiers:\nThe Center is committed to protecting the identity of the respondents who provide information in its research. All analytical data sets (both qualitative and quantitative) released by the Data Unit MUST are stripped of respondent identifiers to protect the identity of the respondents. By accepting to use APHRC data, the user is pledging that he\/she will not, under any circumstance, regenerate the identifiers or permit others to use the data to learn the identity of any individual, household or community included in any data set.\n\n4.Confidentiality pledge:\n The user will not use nor permit others to use the data to report any information in the data sets that could identify, directly or by inference, individuals or households.\n \n5.Reporting of errors or inconsistencies:\nThe user will promptly notify the Head of the Statistics and Survey Unit any errors discovered in the data as soon as the errors are discovered.\n\n6.Publications resulting from APHRC data:\nThe Center requires external collaborators to work with APHRC staff on all publications resulting from its data. In order to facilitate this, lead authors should send a detailed concept note of the paper (including the background, rationale, data, analytical methods, and preliminary findings) to the Principle Investigator (or Theme Leader) for the project (with a copy to the Director of Research), who will circulate the abstract to concerned researchers for possible expression of interest in participating in the publication as co-authors. Any exception to the involvement of APHRC staff should be approved by the Director of Research, APHRC.\n\n7.Security:\nThe user will take responsibility for the security of the data by ensuring that the data are used and stored in a secure environment where access is password protected. This will ensure that non-authorized people should not have access to the data.\n\n8.Loss of privilege to use data:\n In the event that APHRC determines that the data user is in violation of the conditions for using the data, or if the user wishes to cancel this agreement, the user will destroy the data files provided to him\/her. APHRC retains the right to revoke this agreement or informs publishers to withhold publication of any work based wholly or in part on its data if the conditions for using the data are violated.\n\n9.Acknowledgement:\nAny work\/reports from this data must acknowledge APHRC as the source of these data. For example, the suggested acknowledgement for NUHDSS data is:\n\"This research uses livelihoods data collected under the longitudinal Nairobi Urban Health and Demographic Surveillance System (NUHDSS) since 2006. The NUHDSS is carried out by the African Population and Health Research Center in two slums settlements (Korogocho and Viwandani) in Nairobi City.\"Additionally all funders, the study communities that provided the data, and staff who collected and analyzed or processed the data should be acknowledged.\n\n10.Deposit of Reports\/Papers:\nThe user should submit electronic and paper copies of all publications generated using APHRC data to the Policy Engagement and Communications Department, with copies to the Director of Research.\n\n11.Change of contact details:\nThe user will promptly inform the Director of Research of any change in your personal details as contained on this data request form.","disclaimer":"The 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 uses."}}}}