From GiveDirectly's blog:

In 2014, GiveDirectly partnered with academic researchers to launch our largest study ever in Kenya. The ultimate goal: find out how cash transfers affect local economies, including nearby non-recipients, enterprises, and markets. Now, in 2019, the results of this research have been released.

Abstract of the paper:

How large economic stimuli generate individual and aggregate responses is a central question in economics, but has not been studied experimentally. We provided one-time cash transfers of about USD 1000 to over 10,500 poor households across 653 randomized villages in rural Kenya. The implied fiscal shock was 15 percent of local GDP. We find large impacts on consumption and assets for recipients. Importantly, we document large positive spillovers on non-recipient households and firms, and minimal price inflation. We estimate a local fiscal multiplier of 2.6. We interpret welfare implications through the lens of a simple household optimization framework.

Some interesting tidbits from the paper:

Interestingly, sales increased without noticeable changes in firm investment behavior (beyond a modest increase in inventories), and sales do not increase differentially for firms owned by cash recipient households relative to nonrecipients. Both patterns suggest a demand-led rather than an investment-led expansion in economic activity.

[...]

We next examine how these changes affect untreated households. Despite not receiving transfers, they too exhibit large consumption expenditure gains: their annualized consumption expenditure is higher by 13% eighteen months after transfers began, an increase roughly comparable to the gains contemporaneously experienced by the treated households themselves.

(Emphasis added.)

[...]

Average price inflation is 0.1%, and even during periods with the largest transfers, estimated price effects are less than 1% and precisely estimated across all categories of goods.

[...]

Real output increased, and yet there is at most limited evidence of increases in the employment of land (which is in fixed supply), labor, or capital. One plausible, albeit speculative, possibility is that the utilization of these factors was “slack” in at least some enterprises (Lewis 1954). This seems plausible because in the retail and manufacturing sectors, where output responses were concentrated, the typical firm has a single employee (i.e. the proprietor), suggesting that integer constraints may often bind. In addition, many enterprises operate “on demand” in the sense that they produce only when they have customers, and the average non-agricultural enterprise sees just 1.7 customers per hour. In addition to retail, much manufacturing in this setting is “on demand;” for example, a mill owner waits for customers to bring grain and then grinds it for them. The existence of slack may help account for the large multiplier we document, as has also recently been argued in US data, especially in poorer US regions (Michaillat and Saez 2015; Murphy 2017).

Givedirectly blog - Full paper (pdf)

When it comes to disaster relief, I often hear the refrain that it is best to donate cash, and donating boxes of things often hurts more than it helps. Is this universally true, or are there situations where donation boxes are actually helpful?

Search results on the subject ("disaster relief donation box vs cash"), all saying that boxes of stuff hurt more than help, due to the logistical costs of shipping, sorting, and storage:

• https://www.cidi.org/how-disaster-relief-works/monetary-contributions-work-bestwhy-cash-is-best/
• https://www.cbsnews.com/news/best-intentions-when-disaster-relief-brings-anything-but-relief/
• https://nonprofitquarterly.org/2017/10/27/thing-bad-disaster-donation/