Title,Url,Last update date,Published at,First registered on,RCT_ID,DOI Number,Primary Investigator,Status,Start date,End date,Keywords,Country names,Other Primary Investigators,Jel code,Secondary IDs,Abstract,External Links,Sponsors,Partners,Intervention start date,Intervention end date,Intervention,Primary outcome end points,Primary outcome explanation,Secondary outcome end points,Secondary outcome explanation,Experimental design,Experimental design details,Randomization method,Randomization unit,Sample size number clusters,Sample size number observations,Sample size number arms,Minimum effect size,IRB,Analysis Plan Documents,Intervention completion date,Data collection completion,Data collection completion date,Number of clusters,Attrition correlated,Total number of observations,Treatment arms,Public data,Public data url,Program files,Program files url,Post trial documents csv,Relevant papers for csv Integrating value chains to improve food safety and increase smallholder incomes in Kenya,http://www.socialscienceregistry.org/trials/1373,"December 19, 2018",2018-12-19 07:49:35 -0500,2016-06-23,AEARCTR-0001373,10.1257/rct.1373-3.0,Vivian Hoffmann v.hoffmann@cgiar.org,completed,2017-04-03,2018-07-31,"[""agriculture"", ""finance"", ""governance"", ""health"", ""food safety"", ""insurance""]","Kenya (Meru, Tharaka-Nithi, and Embu Counties)",Mark Treurniet (mark.treurniet@wur.nl) Wageningen University; Sarah Kariuki (sarah.kariuki@wur.nl) Wageningen University; Erwin Bulte (erwin.bulte@wur.nl) Wageningen University; Janneke Pieters (janneke.pieters@wur.nl) Wageningen University,"","","Adoption of improved agricultural technologies in developing countries may be limited by common informational and market inefficiencies. In this study, we examine the role of such inefficiencies in the adoption of a new food safety technology, Aflasafe. Aflasafe has been shown to reduce aflatoxin contamination in maize by approximately 90%. However, this technology faces several barriers to widespread adoption. First, since Aflasafe is applied while the crop is still growing, its use increases farmers’ exposure to yield risk. In the absence of a market for crop insurance, this may limit its adoption. Second, the lack of price incentives for food safety in markets served by smallholder maize producers in Kenya may constrain the adoption of food safety technologies by these farmers. In this study, we explore the impact on adoption of Aflasafe of 1) bundling the product with a rainfall index based money-back guarantee, and 2) access to an output market that rewards aflatoxin safety. ","","","",2017-09-11,2018-07-31,"The study will include 3 interventions: an Input Linkage treatment, a Money Back Guarantee treatment and an Output Market Linkage treatment. The Input Linkage treatment groups will receive information on the benefits of Aflasafe and instruction on its use. They will also be given an opportunity to buy Aflasafe, which is not currently available in the study area. These groups will further be divided into two treatment conditions concerning money back guarantee (MBG): an Optional condition, offered the option to buy Aflasafe either with or without the MBG, and a Bundled condition, in which Aflasafe is only offered in combination with the MBG. The Money Back Guarantee treatment will be crossed with the Output Market Linkage treatment, which consists of a linkage between the farmer group and a buyer that pays a premium for safe maize. Hence, in total there will be five categories, including the pure control group, and four treatment groups (Optional MBG / Output linkage , Optional MBG / No output linkage, Bundled MBG / Output linkage, and Bundled MBG / No output linkage).","1. Adoption of Aflasafe during the 2017 season 2. Relative level of Aflasafe application to / aflatoxin levels in maize stored by households for different purposes","1. Adoption of Aflasafe Farmers’ adoption decisions will be measured by a dummy variable, equal to 1 if the farmer adopted and 0 if the farmer did not adopt. The intensity of adoption will be measured as the amount of Aflasafe purchased. Both the binary and intensity adoption variables will be constructed using Aflasafe sales data. 2. Aflatoxin contamination and Aflasafe usage: Samples of maize will be taken from the following three sources: 1) Maize stored for household consumption 2) Maize stored for later sale 3) Maize aggregated for testing and sale through the study Samples will be tested for aflatoxin using a quantitative test with an upper detection limit of 150 ppb. In the event that more than 5% of samples are at or above the upper limit of the detection range, these will be diluted and re-analyzed, to a maximum detection level of 400. A microbiological test will be used to assess whether Aflasafe was used on a batch of maize. Selection of one or both aflatoxin contamination and/or Aflasafe usage depends on the relative costs and efficiency of these two indicators for assessing farmer behavior.","","","Farmer groups are drawn from a list of 250 farmer groups obtained from the Cereal Growers Association and county Ministries of Agriculture. Based on power calculations, we allocated 160 farmer groups across the four treatment groups and the control group as follows: Optional MBG / Output linkage: 38, Optional MBG / No output linkage: 38, Bundled MBG / Output linkage: 38, and Bundled MBG / No output linkage: 38; Pure Control: 8. In order to limit spillovers across money back guarantee treatments, we created comparable, but geographically distinct, clusters of farmer groups within each of the study counties (4 in Meru, 2 in Tharaka-Nithi and 2 in Embu) and subsequently assigned these clusters to either the bundled or the optional MBG treatment. Subject to a minimum geographical distance of 5 km between clusters, we aimed to select similar farmer groups into the clusters within each county. Similarity was defined based on the Euclidean distance in the six-dimensional space formed by the standardized values of variables listed in section 5.4. To this end, we first dropped clusters close to the county borders to achieve a minimum distance of groups in different counties of at least 5 km. Subsequently, we excluded any groups within a 5 km bands dividing the remaining groups into similarly-sized clusters. The direction of this band was selected to minimize the Euclidean distance between the groups on either side of it. From the remaining groups, we then iteratively selected matched pairs across each cluster with the lowest Euclidean distance into the sample. To ensure that the MBG treatments were spread out geographically, we manually decided which clusters would receive the same treatment. Finally, we randomly assigned the bundled MBG to one of the two groups of clusters. Within each of the money back guarantee clusters, the market linkage treatment was randomly assigned at the village level. Pure control groups were selected as the 8 nearest geographical neighbors to any BY group, stratified by county approximately in proportion to the total number of groups on the initial list.","",Randomization done in office by a computer,village,160 farmer groups (cluster for data collection) across 124 villages (level of randomization),960 farmers for baseline and follow-up surveys (6 per farmer group); approximately 4000 total farmers in the groups (average of 25 / group),"Optional MBG / Output linkage: 38 farmer groups Optional MBG / No output linkage: 38 farmer groups Bundled MBG / Output linkage: 38 farmer groups Bundled MBG / No output linkage: 38 farmer groups Pure Control: 8 farmer groups","","Name: International Food Policy Research Institute Institutional Review Board Approval_number: 2016-26-MTID-M Approval_date: 2016-05-16 Name: AMREF Kenya Ethics and Scientific Review Committee Approval_number: n/a Approval_date: 2016-10-04 Name: Human Subjects Committee for Innovations for Poverty Action IRB-USA Approval_number: 14154 Approval_date: 2016-05-11 ","November 09, 2017",,,,"",,"","",,"",,"","",""