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Social Learning, Technology Diffusio...

Walsh, Mark Phillip,

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Social Learning, Technology Diffusion, and Economic Development /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Social Learning, Technology Diffusion, and Economic Development // Mark Phillip Walsh.
作者:	Walsh, Mark Phillip,
面頁冊數:	1 electronic resource (282 pages)
附註:	Source: Dissertations Abstracts International, Volume: 86-04, Section: A.
Contained By:	Dissertations Abstracts International86-04A.
標題:	Technological change. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31520267
ISBN:	9798342108966

Social Learning, Technology Diffusion, and Economic Development /
Walsh, Mark Phillip,

Social Learning, Technology Diffusion, and Economic Development /Mark Phillip Walsh. - 1 electronic resource (282 pages)

Source: Dissertations Abstracts International, Volume: 86-04, Section: A.

Technology diffusion is critical to economic development. Lags in technology adoption account for, at least, 25% of cross-country GDP differences and 'growth miracles' almost always coincide with periods of technological catch up (Comin and Hobijn, 2010).As a large literature attests, people learn about new technologies through discussions with their social ties. In low- and middle-income countries, researchers have found that social learning influences the uptake of critical health care technologies, such as malaria nets (Dupas, 2014) and vaccines (Banerjee et al., 2019), as well as productivity-enhancing farming (Conley and Udry, 2010) and financial practices (Banerjee et al., 2013).However, the track record of efforts to accelerate the rate of technology diffusion by harnessing social learning is mixed. Many extension programs provide information to a few individuals and rely on these 'seeds' to share information with the rest of their social group. Unfortunately, in many cases, seeds do not share the information with others or only inform their strong social ties (Kondylis et al., 2017; Beaman et al., 2021). In other cases, efforts to foster social learning have backfired and reduced the diffusion of a new technology (e.g., Duflo et al. (2022) and Chandrasekhar et al. (2022)). Even when information does spread, diffusion through social ties can exacerbate existing inequalities (Beaman et al., 2018) and calcify inefficient hierarchies (Bolte et al., 2020).The complexities of social learning make it difficult to even evaluate the efficacy of technology diffusion programs. Recent empirical results suggest that traditional methods of estimating 'spillovers' may be misleading. For example, Banerjee et al. (2021c) estimates substantial negative spillovers from microfinance on households distant from microfinance users, and Banerjee et al. (2018) shows that seeding information with a few households may be more effective than broadcasting information to all households in some cases, challenging the assumption that increasing the saturation of a communities increases their exposure to the treatment.In my dissertation, I model how technologies diffuse through social networks, test these models through field experiments, and draw implications for policy design and evaluation. I start from the premise that conversations do not happen randomly or mechanically; people choose to bring up one topic while neglecting others. Given this assumption, it is imperative to model the motivations behind communication decisions and derive the implications for opinions about and adoption of technologies.In Chapter 1, I report on the results from a randomized field experiment encouraging parents to have conversations with their infants. The intervention entails showing recent or expectant mothers a 3-minute informational video and providing them with a themed wall calendar. Six to eight months later, mothers who participated reported a stronger belief in the benefits of verbally engaging with infants, more frequent parent-infant conversations, and more advanced language and communication skills of their infants. We find larger effects immediately after the intervention, suggesting scope for a larger long-term effect had the behavior change stuck more. The intervention's potential for low-cost implementation via health clinics makes it a promising strategy for early childhood development in low-income contexts, particularly if complemented by efforts to support habit formation and social learning.

English

ISBN: 9798342108966Subjects--Topical Terms:

3564100
Technological change.

Social Learning, Technology Diffusion, and Economic Development /
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520 $a Technology diffusion is critical to economic development. Lags in technology adoption account for, at least, 25% of cross-country GDP differences and 'growth miracles' almost always coincide with periods of technological catch up (Comin and Hobijn, 2010).As a large literature attests, people learn about new technologies through discussions with their social ties. In low- and middle-income countries, researchers have found that social learning influences the uptake of critical health care technologies, such as malaria nets (Dupas, 2014) and vaccines (Banerjee et al., 2019), as well as productivity-enhancing farming (Conley and Udry, 2010) and financial practices (Banerjee et al., 2013).However, the track record of efforts to accelerate the rate of technology diffusion by harnessing social learning is mixed. Many extension programs provide information to a few individuals and rely on these 'seeds' to share information with the rest of their social group. Unfortunately, in many cases, seeds do not share the information with others or only inform their strong social ties (Kondylis et al., 2017; Beaman et al., 2021). In other cases, efforts to foster social learning have backfired and reduced the diffusion of a new technology (e.g., Duflo et al. (2022) and Chandrasekhar et al. (2022)). Even when information does spread, diffusion through social ties can exacerbate existing inequalities (Beaman et al., 2018) and calcify inefficient hierarchies (Bolte et al., 2020).The complexities of social learning make it difficult to even evaluate the efficacy of technology diffusion programs. Recent empirical results suggest that traditional methods of estimating 'spillovers' may be misleading. For example, Banerjee et al. (2021c) estimates substantial negative spillovers from microfinance on households distant from microfinance users, and Banerjee et al. (2018) shows that seeding information with a few households may be more effective than broadcasting information to all households in some cases, challenging the assumption that increasing the saturation of a communities increases their exposure to the treatment.In my dissertation, I model how technologies diffuse through social networks, test these models through field experiments, and draw implications for policy design and evaluation. I start from the premise that conversations do not happen randomly or mechanically; people choose to bring up one topic while neglecting others. Given this assumption, it is imperative to model the motivations behind communication decisions and derive the implications for opinions about and adoption of technologies.In Chapter 1, I report on the results from a randomized field experiment encouraging parents to have conversations with their infants. The intervention entails showing recent or expectant mothers a 3-minute informational video and providing them with a themed wall calendar. Six to eight months later, mothers who participated reported a stronger belief in the benefits of verbally engaging with infants, more frequent parent-infant conversations, and more advanced language and communication skills of their infants. We find larger effects immediately after the intervention, suggesting scope for a larger long-term effect had the behavior change stuck more. The intervention's potential for low-cost implementation via health clinics makes it a promising strategy for early childhood development in low-income contexts, particularly if complemented by efforts to support habit formation and social learning.
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