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Keeping the Doctor Away: Experimental Evidence on Investment in Preventative Health Products
Last registered on June 14, 2016

Pre-Trial

Trial Information
General Information
Title
Keeping the Doctor Away: Experimental Evidence on Investment in Preventative Health Products
RCT ID
AEARCTR-0001197
Initial registration date
June 14, 2016
Last updated
June 14, 2016 10:57 PM EDT
Location(s)
Region
Primary Investigator
Affiliation
University of New South Wales
Other Primary Investigator(s)
PI Affiliation
University of Washington
PI Affiliation
University of San Francisco
PI Affiliation
University of California, Santa Cruz and NBER
Additional Trial Information
Status
Completed
Start date
2010-01-01
End date
2010-05-31
Secondary IDs
Abstract
Household investment in preventative health products is low in developing countries even though benefits from these products are very high. What interventions most effectively stimulate demand? In this paper, we experimentally estimate demand curves for health products in Kenya, Guatemala, India, and Uganda and test whether (1) information about health risk, (2) cash liquidity, (3) peer effects, and (4) intra-household differences in preferences affect demand. We find households to be highly sensitive to price and that both liquidity and targeting women increase demand. We find no effect of providing information, although genuine learning occurred, and we find no evidence of peer effects, although subjects discussed the product purchase decision extensively.
External Link(s)
Registration Citation
Citation
Meredith, Jennifer et al. 2016. "Keeping the Doctor Away: Experimental Evidence on Investment in Preventative Health Products." AEA RCT Registry. June 14. https://doi.org/10.1257/rct.1197-1.0.
Former Citation
Meredith, Jennifer et al. 2016. "Keeping the Doctor Away: Experimental Evidence on Investment in Preventative Health Products." AEA RCT Registry. June 14. https://www.socialscienceregistry.org/trials/1197/history/8848.
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Experimental Details
Interventions
Intervention(s)
This evaluation took place in four countries. In Guatemala, India, and Uganda the researchers conducted smaller-scale studies which tested a subset of the hypotheses listed above. In Kenya, a wider set of hypotheses were tested. In Kenya, the health product studied was rubber shoes for children, a simple technology which could be effective in preventing soil-transmitted helminths (STHs). The study involved four main experimental treatments. First, we estimated how much people’s demand for health products falls as price increases by providing households with a coupon offering a random discount on the shoes (5, 15, 25, 35, 55, or 65 Ksh). Second, to measure the impact of information on health investment, they randomly selected half of the households to receive an information script on the symptoms of worms, transmission pathways, and on several strategies to prevent worms, including wearing shoes, using pit latrines, and maintaining proper hygiene. Third, to measure the role of liquidity, we provided households with randomly varying cash payments. Fourth, to measure whether there were differences within the household in terms of willingness to invest in health technology, we randomly selected either the husband or the wife to receive the intervention.

A baseline survey was administered to all sampled households, administrative records from the shops were collected (along with the redeemed coupons, which were pre-printed with the household’s ID number), and a follow-up survey was conducted with a randomly selected subset of the households about three months after the baseline, once the redemption period had ended.
Intervention Start Date
2010-02-01
Intervention End Date
2010-05-31
Primary Outcomes
Primary Outcomes (end points)
- Knowledge about intestinal worms
- Demand for specified rubber shoes (measured in number of purchase)
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
Our main experiment was conducted in the Busia and Samia Districts of Western Kenya from February to May, 2010. In our sample, parents reported that 23% of their children had a worm infection in the previous year. To obtain as representative a sample of households as possible, a door-to-door census was conducted with 1,547 households in two villages located roughly 11 kilometers apart (Ikonzo and
Bhukulungu). The census collected basic information, including whether the household had a male or female head, the number of children in the household, and the GPS location of the household. With this data, we created 51 geographic clusters based on the GPS coordinates, and randomly selected 1,069 households for project participation, stratified by geographic cluster. We were able to interview 999 of these (93.4%).

We implemented four main experimental treatments, all cross-cut against each other. All treatments were conducted after administering a baseline survey (discussed below), and obtaining informed consent. First, we estimate an experimental demand curve by implementing a methodology based on Kremer and Miguel (2007), Cohen and Dupas (2010), Dupas (2009), and Ashraf, Berry, and Shapiro (2010). In particular, we visited households and provided them with a coupon offering a random discount on the shoes. The market price at the time was about 85 Ksh ($1.13), and we provided households with coupons for 5, 15, 25, 35, 55, or 65 Ksh. Coupons were valid for a period of about 2 months. Then, to measure the impact of information on health investment, we randomly selected half of the households to receive an information script on the symptoms of worms, transmission pathways, and on several strategies to prevent infection, including wearing shoes, using pit latrines, and hygiene. We used a script, rather than a more involved educational seminar, because results from our earlier studies in Guatemala, India, and Uganda suggested that a script had similar impacts to a seminar. To measure the role of liquidity, we provided households with randomly varying cash payments. As part of our baseline survey, we elicited risk and time preferences for all households using standard laboratory techniques. For the risk preference questions, households were given a series of choices in which they could decide how much to invest (out of 40 Ksh or 100 Ksh) in an asset which paid out three times the amount invested with probability 0.5 and nothing with probability 0.5. For the time preference questions, households were given the option of accepting 40 Ksh immediately or a larger amount in the future. Lastly, to measure whether there are differences within the household in the willingness to invest in health technology in households with both a female and male head, we randomly selected either the husband or the wife for the intervention.

There are three main pieces of data that we use to evaluate the program. First, at baseline, we administered a background survey to all sampled households. In addition to standard demographic questions, we collected information on child health, worm exposure, and shoe ownership. We also collected information on household knowledge of worms, transmission pathways, and prevention strategies at the end of the survey. After the survey, we paid households their random cash payout and gave them a coupon which could be redeemed at a local shop for the price indicated. The shops were located in market centers that households would typically visit regularly for shopping (approximately 1.5 kilometers away from the average household). The coupon was pre-printed with the household’s ID number on it, so that any redeemed coupon could be matched to our household data. We hired an enumerator to supervise the redemption and maintain a log containing the name of the person redeeming the coupon, the number of coupons redeemed at one time, and the sizes of the shoes purchased. Lastly, we conducted a follow-up survey with 379 randomly selected households once the redemption period had ended (about 3 months after the first coupons had been given out). The follow-up survey included questions on shoe usage, as well as the same module used to measure worm knowledge in the baseline.
Experimental Design Details
Randomization Method
Randomization done in office by a computer.
Randomization Unit
Household level for treatment 1 (coupon offering a random discount on the shoes: price of 5, 15, 25, 35, 55, or 65 Ksh),
Household level for treatment 2 (information script on the symptoms of worms, transmission pathways, strategies to prevent worms),
Individual level for treatment 3 (either husband or wife receive coupon and script/no script treatment; for dual-headed households only).
Stratified by 51 geographic clusters, calculated from GPS coordinates.
Was the treatment clustered?
Yes
Experiment Characteristics
Sample size: planned number of clusters
51 geographic clusters.
Sample size: planned number of observations
1,069 households
Sample size (or number of clusters) by treatment arms
535 will get script. Of 535 given script: 89 price = 5 ksh, 89 price = 15 ksh; 90 price = 25 ksh; 89 price = 35 ksh; 89 price = 55 ksh; 89 price = 65 ksh.
534 will not get script. Of 534 not given script: 89 price = 5 ksh, 89 price = 15 ksh; 89 price = 25 ksh; 89 price = 35 ksh; 89 price = 55 ksh; 89 price = 65 ksh.
868 Dual headed households. Of 868: 434 females will receive price and script treatments (217 script; 217 no script); 434 Men will receive price and script treatments (217 script; 217 no script).
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
IRB Approval Date
IRB Approval Number
Post-Trial
Post Trial Information
Study Withdrawal
Intervention
Is the intervention completed?
Yes
Intervention Completion Date
May 31, 2010, 12:00 AM +00:00
Is data collection complete?
Yes
Data Collection Completion Date
May 31, 2010, 12:00 AM +00:00
Final Sample Size: Number of Clusters (Unit of Randomization)
999 households
Was attrition correlated with treatment status?
No
Final Sample Size: Total Number of Observations
999 households
Final Sample Size (or Number of Clusters) by Treatment Arms
515 given script. Of 515 given script: 83 price = 5 ksh, 88 price = 15 ksh; 91 price = 25 ksh; 86 price = 35 ksh; 79 price = 55 ksh; 88 price = 65 ksh. 484 not given script. Of 484 not given script: 82 price = 5 ksh, 76 price = 15 ksh; 79 price = 25 ksh; 77 price = 35 ksh; 86 price = 55 ksh; 84 price = 65 ksh. 812 Dual headed households. Of 812: 414 females received price and script treatments (209 script; 205 no script); 398 Men received price and script treatments (208 script; 190 no script).
Data Publication
Data Publication
Is public data available?
No
Program Files
Program Files
No
Reports and Papers
Preliminary Reports
Relevant Papers
Abstract
Household investment in preventative health products is low in developing countries even though benefits from these products are very high. What interventions most effectively stimulate demand? In this paper, we experimentally estimate demand curves for health products in Kenya, Guatemala, India, and Uganda and test whether (1) information about health risk, (2) cash liquidity, (3) peer effects, and (4) intra-household differences in preferences affect demand. We find households to be highly sensitive to price and that both liquidity and targeting women increase demand. We find no effect of providing information, although genuine learning occurred, and we find no evidence of peer effects, although subjects discussed the product purchase decision extensively.
Citation
Meredith, Jennifer, Jonathan Robinson, Sarah Walker, and Bruce Wydick. 2013. "Keeping the Doctor Away: Experimental Evidence on Investment in Preventative Health Products." Journal of Development Economics 105: 196-210.