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The Impacts of Unconditional Cash Transfers on Low-Income Families
Initial registration date
May 14, 2020
June 22, 2020 11:32 AM EDT
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University of Michigan
Other Primary Investigator(s)
University of Michigan
University of Michigan
Additional Trial Information
Low-income families in the United States struggle to make ends meet and rates of hardship are greatest among families with children. Although a number of social policies are designed to ameliorate the negative consequences of poverty, access to cash assistance is difficult to obtain or insufficient to meet families’ basic needs. Additionally, the recent coronavirus outbreak has dramatically expanded the number of families in need of assistance. This study will examine the impact of an unconditional cash transfer on the social and economic wellbeing of low-income families with children. Randomly selected low-income families with children will receive a one-time lump-sum cash payment. Families in both the treatment and control groups will be interviewed and tracked for up to six months to assess the impacts of the transfer on material hardship, mental health, parenting, child wellbeing and partner relationships. Given recent interest in unconditional cash transfers, this study has the potential to inform policy debates about the effectiveness of such transfers, especially during serious economic crises.
Jacob, Brian, Natasha Pilkauskas and Luke Shaefer. 2020. "The Impacts of Unconditional Cash Transfers on Low-Income Families." AEA RCT Registry. June 22.
As part of its charitable mission, the nonprofit organization GiveDirectly, is providing a sample of low-income families in the U.S. a one-time cash transfer of $1,000 to help mitigate the financial stress associated with the COVID-19 pandemic. The intervention is a one-time, $1,000 lump-sum cash payment. Families may use the money as they choose (i.e., there are no restrictions on how they spend the money). As a one-time, lump sum payment this aid does not impact other public assistance benefits. Families are selected randomly from a sample of individuals who are receiving federal assistance through the Supplemental Nutrition Assistance Program (SNAP). The payment is completely unanticipated and is described as an effort to provide cash relief to Americans in need during the COVID-19 pandemic. Individuals are not required to participate in the research project, or fulfill any other obligations, in order to receive the cash payment.
Intervention Start Date
Intervention End Date
Primary Outcomes (end points)
The primary outcomes in this study will be composite measures of family wellbeing in 5 domains: material hardship, mental health, relationship with partner, child wellbeing and parenting practices. We will measure these outcomes 1, 3 and 6 months following the intervention.
Primary Outcomes (explanation)
To create composites, we follow the approach outlined in Anderson (2008) and calculate a weighted mean of the standardized survey items within the domain. We will first switch the signs of all items so that the positive direction always indicates a “better” outcome. We will then standardize each outcome using the mean and standard deviation of that outcome among the control group. We will then create a weighted average of these standardized outcomes using all of the outcomes in the domain. For weights we will use the inverse of the covariance matrix of the transformed outcomes in the domain.
Secondary Outcomes (end points)
We intend to conduct a variety of secondary (i.e., exploratory) analyses guided by theory and prior literature.
Secondary Outcomes (explanation)
We intend to conduct a variety of secondary (i.e., exploratory) analyses guided by theory and prior literature. Moreover, our decisions with regard to secondary analyses may change in response to what we learn from the baseline survey or early follow-up surveys. For example, if families indicate a severe degree of hardship in one particular area on the baseline survey, we may decide to include a more detailed set of questions about this type of hardship in the follow-up survey. The discussion below is meant to be illustrative, but not exhaustive.
First, we will conduct a variety of descriptive analyses to help us understand how social and economic challenges vary by demographic characteristics and geography. We believe this type of analysis will help researchers and policymakers identify which groups are most vulnerable and best to target resources. Second, we will examine the impact of the treatment on individual items or groups of items within domains. In the case of material hardship, for example, we will explore whether the cash payment has a larger impact on certain hardships (e.g., food insecurity vs. inability to pay utilities). In the case of mental health, we will separately examine impacts on depression versus anxiety. Third, we will explore treatment effect heterogeneity along at least three dimensions: (i) degree of baseline hardship, (ii) extent to which the COVID pandemic negatively impacted the individual’s geography area; (iii) strength of social safety net in a geographic area prior to the COVID pandemic. Finally, we will examine several outcomes not included within the 5 primary domains, including income and expenditure patterns, employment and mobility.
The sample is drawn from a set of families receiving benefits through the Supplemental Nutrition Assistance Program (SNAP) who utilize the FreshEBT mobile app to manage their benefits. Each week a set of individuals are randomly selected to view a banner on the FreshEBT app that invites them to discuss their experiences during the COVID-19 pandemic. Individuals are asked a few questions, including whether they would like to participate in a research study about their experiences during COVID. After answering these questions, individuals are randomly assigned to a treatment group that is notified of the cash award and a control group that simply is thanked for their time. Individuals who agreed to participate in the research study are then sent a link to a short online survey asking them to provide additional detail on their household characteristics and wellbeing. Participants are compensated $10 for completing the survey.
Experimental Design Details
Via computer within the FreshEBT app.
As part of its internal operations, the FreshEBT app randomly assigns individual users to 1 of 1,000 different groups, which they then use for various diagnostics and user outreach campaigns. For the purpose of this study, groups of individuals (segments) will be assigned to the treatment and control group. However, because individuals are randomly assigned to segments, and there is no interaction between individuals within segment, as there would be between students within schools or workers within a firm, we did not consider the treatment to be clustered for the purpose of power calculations. Hence, we answer no to the question of whether the treatment is clustered. However, as explained in the attached analysis plan, in an effort to be as conservative as possible, our inference will take into account the clustering of individuals within segment.
Was the treatment clustered?
Sample size: planned number of clusters
Roughly 5,500 individuals
Sample size: planned number of observations
Roughly 5,500 individuals
Sample size (or number of clusters) by treatment arms
2,250 individuals in each of two arms (one treatment and one control)
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
We are powering this study to identify effects on the five pre-specified outcomes described above, adjusting for multiple hypothesis testing and incomplete take-up. Our sample size will allow us to detect an ITT (LATE) effect of 0.09 SD (0.10 SD) for the material hardship, mental health, child wellbeing and parenting practices. Because not all respondents will be in a relationship, we expect that we will be able to only detect effect of 0.111 SD (0.122 SD) for the domain of partner relationship.
INSTITUTIONAL REVIEW BOARDS (IRBs)
University of Michigan Health Sciences and Behavioral Sciences Institutional Review Board (IRB-HSBS)
IRB Approval Date
IRB Approval Number