Technology adoption is deeply intertwined with growth and productivity: differences in the speed with which technologies diffuse can explain a large share of the differences in wealth between countries. The adoption of new technologies can also serve as a catalyst for further innovation through improvements to, or new applications of, the original technology. To design effective policies, it is thus crucial to explore the main private sector technology gaps that innovation policies should target, and the most efficient policy instruments to address these gaps.
Experiments have shaped our knowledge of the barriers to, and benefits of, technology adoption. In this blog post, rather than offering a comprehensive review of this literature, we aim to provide a bird’s-eye view of the various insights we have learned from randomised controlled trials (RCTs) in this area. In particular, RCTs can identify and address constraints that may keep individuals and firms from taking up a profitable new technology, and allow researchers to capture the gains from adopting a given technology. In addition, lab-in-the-field experiments 1 highlight the importance of business owners’ individual traits in the adoption decision. In a follow-up blog post, we then zoom in on a special “technology”: management practices.
What stands in the way of adoption, and what to do about it?
RCTs provide a particularly useful tool to uncover hidden barriers to technology adoption, and to test ways to overcome these barriers.
Constraint I: Lack of information
A potential constraint to adoption might be the lack of information: firms may not know about a new technology at all, or they may be uncertain of its profitability. Initial subsidies that allow firms to try out a new technology, experience its benefits and learn its true value may help tackle this constraint. On the other hand, such subsidies may hinder eventual adoption if the subsidised, lower price acts as an anchor and reduces firms’ willingness to pay for the technology at its full market price. Pascaline Dupas tested these contrasting predictions in a two-stage RCT in Kenya. In the first stage, she varied the level of subsidies offered to households for a new, improved bednet, and in the second stage, she measured the share of households who wanted to buy a second bednet at the market price. She found that high subsidies increased adoption in the first stage, and this increase translated to higher demand for the product in the second stage. Her results thus suggest that the positive learning effect is more important than any potential negative anchoring effects (Dupas 2014).
Social learning, whereby firms discover new technologies and acquire information about their profitability through observing members of their business network who experiment with these technologies, presents another powerful opportunity to address knowledge constraints. In a large, randomised trial in Malawi, researchers varied the dissemination method for two new agricultural technologies: the information either came from a government-employed “outsider”, an educated, relatively wealthy local “lead farmer”, or five “peer farmers” more representative of the local population. For the latter two categories, the trial also varied whether the communicators received performance-based incentives. Their results suggest that small incentives can go a long way towards encouraging the designated communicators to experiment with the new technology and to spread their knowledge. Moreover, farmers appear most convinced by communicators who share a group identity with them, or who face comparable agricultural conditions (BenYishay and Mobarak 2019).
Inequality is an important aspect to keep in mind when relying on social learning to spread knowledge about a technology. Researchers studying information diffusion among villagers in Mali realised that targeting central, influential people in the village social network and depending on them to spread the information made women farmers less likely than men to learn about the new technology (Beaman and Dillon 2018). 2
Constraint II: Costs
Seemingly small fixed costs can create significant barriers to technology adoption, especially when the decision makers are present biased, i.e. they weigh immediate expenditures more heavily than future gains. Small, time-limited discounts can tackle procrastination: in an experiment among farmers in Kenya, offering free delivery for a limited time right after harvest increased investment in fertilisers (Duflo, Kremer, and Robinson 2011). Strikingly, the authors estimate that such small, time-limited discounts may increase welfare more than heavy subsidies.
Alternatively, we can consider eliminating some of the initial costs associated with technology adoption, as demonstrated by a trial among Kenyan SMEs (Dalton et al. 2018). Making registration costless (both in terms of money and time) and providing technology know-how training increased take-up of an electronic payment technology among treated SMEs compared to the control group. The authors also addressed the aforementioned knowledge gap by providing product information to treated businesses.
Constraint III: Credit/insurance market imperfections
Imperfections in the credit or insurance markets may also hinder technology adoption. However, adding a weather insurance policy (Giné and Yang 2009) or an indemnity component that protects against negative price shocks (Karlan et al. 2011) to a loan offer did not lead to a significant increase in technology adoption in trials in Malawi and Ghana, respectively.
Constraint IV: Misaligned incentives
Misalignment of incentives within firms may also slow down the spread of new technologies. In an RCT among football producers in Pakistan, researchers found puzzlingly low take-up of a new technology that could reduce raw material waste. Even though the new technology had clear benefits for the firms themselves, key employees (who were paid piece rates, and were initially slowed down by the new technology) resisted its adoption, and misinformed the firm owners about its value. To overcome these organisational barriers, in a second experiment the authors offered a bonus to some employees if they demonstrated competence in using the new technology, and found significantly higher adoption in response (Atkin et al. 2017).
What is the impact of new technologies?
RCTs can also boost technology adoption by demonstrating the value added of a new innovation. The trial by Dalton et al. (2018) discussed above found that firms in the treatment group who were encouraged to adopt an electronic payment technology experienced higher financial connectedness and reduced safety concerns sixteen months after the intervention. 3
Impact assessments can often capture wider benefits from technology adoption. For instance, an RCT that involved distributing a flood-tolerant seed variety to randomly selected rice farmers in India demonstrated a crowding-in of other investments by the farmers, leading to a modernisation of agricultural practices (Emerick et al. 2016). Similarly, providing fertiliser to female rice farmers in Mali induced farmers to work even harder and use more herbicide (Beaman et al. 2013).
Finally, carefully designed RCTs can also shed light on unequal gains from technology adoption. In a trial that provided training on mobile financial services and facilitated account set-up among randomly chosen participants in Bangladesh, mobile banking has improved the lives of urban households by allowing them to receive remittances but reduced the physical and emotional health of urban migrants who experienced increased pressure to send money home (Lee et al. 2018).
Do individual traits matter?
In case of small businesses, the decision to adopt technology is often made by a single individual (the owner/founder). As such, it is important to recognise the interplay between the decision makers’ personal traits and their choice to invest in a new technology. So-called “lab-in-the-field” experiments allow researchers to reliably elicit individuals’ preferences and beliefs, and to link them via survey answers to real-life decisions concerning tech adoption.
Elaine M. Liu, for instance, studied the adoption of a new form of agricultural biotechnology among Chinese farmers, and found that more risk averse or more loss averse farmers tended to adopt this technology later than their more risk tolerant peers (Liu 2013). Technology adoption has also been linked to an internal locus of control 4 (Abay, Blalock, and Berhane 2017) and lower levels of ambiguity aversion (Engle Warnick, Escobal, and Laszlo 2011). Risk aversion has also been shown to affect adoption behavior in a recent RCT in Bangladesh that provided training for randomly chosen rice farmers on a new cultivation method: the adoption decision of farmers who did not receive training was affected by their peers who underwent training, but the peers’ influence was smaller on risk-averse farmers (Islam et al. 2018).
More evidence through Business Basics
IGL is very pleased to be partnering with the UK Department of Business, Energy and Industrial Strategy (BEIS) and Innovate UK to support the Business Basics Fund (BBF) that sits within the wider Business Basics Programme. The programme embraces an experimental approach to generating much-needed further evidence on technology adoption by supporting a range of projects that test innovative ways of encouraging small and medium-sized enterprises (SMEs) to take up productivity-boosting technology. With the first experiments already in the field, we have already gathered valuable insights that Luke Nightingale (BEIS) shared with participants at the IGL2019 conference. For more details, stay tuned for future blog posts summarising initial results and lessons learned.
For those interested in running their own trial a third funding round has now opened. Within this competition up to £2 million is available for trials looking at how SMEs could be encouraged to adopt technology, including those that can limit their exposure to late payments, such as automated payment processes.
Read part two of this blog: Management practices - a special “technology” to invest in
References
Abay, Kibrom A., Garrick Blalock, and Guush Berhane. 2017. “Locus of Control and Technology Adoption in Developing Country Agriculture: Evidence from Ethiopia.” Journal of Economic Behavior & Organization 143 (November): 98–115.
Atkin, David, Azam Chaudhry, Shamyla Chaudry, Amit K. Khandelwal, and Eric Verhoogen. 2017. “Organizational Barriers to Technology Adoption: Evidence from Soccer-Ball Producers in Pakistan.” The Quarterly Journal of Economics 132 (3): 1101–64.
Beaman, Lori, and Andrew Dillon. 2018. “Diffusion of Agricultural Information within Social Networks: Evidence on Gender Inequalities from Mali.” Journal of Development Economics 133 (July): 147–61.
Beaman, Lori, Dean Karlan, Bram Thuysbaert, and Christopher Udry. 2013. “Profitability of Fertilizer: Experimental Evidence from Female Rice Farmers in Mali.” The American Economic Review 103 (3): 381–86.
BenYishay, Ariel, and A. Mushfiq Mobarak. 2019. “Social Learning and Incentives for Experimentation and Communication.” The Review of Economic Studies 86 (3): 976–1009.
Bloom, Nicholas, Benn Eifert, Aprajit Mahajan, David McKenzie, and John Roberts. 2013. “Does Management Matter? Evidence from India.” The Quarterly Journal of Economics 128 (1): 1–51.
Bloom, Nicholas, Aprajit Mahajan, David McKenzie, and John Roberts. 2018. “Do Management Interventions Last? Evidence from India.” Working Paper Series. National Bureau of Economic Research.
Bruhn, Miriam, Dean Karlan, and Antoinette Schoar. 2018. “The Impact of Consulting Services on Small and Medium Enterprises: Evidence from a Randomized Trial in Mexico.” Journal of Political Economy 126 (2): 635-687.
Cai, Jing, and Adam Szeidl. 2018. “Interfirm Relationships and Business Performance.” The Quarterly Journal of Economics 133 (3): 1229–82.
Campos, Francisco, Michael Frese, Markus Goldstein, Leonardo Iacovone, Hillary C. Johnson, David McKenzie, and Mona Mensmann. 2017. “Teaching Personal Initiative Beats Traditional Training in Boosting Small Business in West Africa.” Science 357 (6357): 1287–90.
Chatterji, Aaron, Solene Delecourt, Sharique Hasan, Rembrand Koning. 2019. “When does advice impact startup performance?” Strategic Management Journal 40: 331– 356.
Dalton, Patricio S., Haki Pamuk, Ravindra Ramrattan, Daan van Soest, and Burak Uras. 2018. “Payment Technology Adoption and Finance: A Randomized-Controlled-Trial with SMEs.”
Dalton, Patricio S., Julius Rüschenpöhler, Burak Uras, and Bilal Zia. 2019. “Local Best Practices for Business Growth.”
Duflo, Esther, Michael Kremer, and Jonathan Robinson. 2011. “Nudging Farmers to Use Fertilizer: Theory and Experimental Evidence from Kenya.” The American Economic Review 101 (6): 2350–90.
Dupas, Pascaline. 2014. “Short-run subsidies and long-run adoption of new health product: Evidence from a field experiment.” Econometrica: Journal of the Econometric Society 82 (1): 197–228.
Emerick, Kyle, Alain de Janvry, Elisabeth Sadoulet, and Manzoor H. Dar. 2016. “Technological Innovations, Downside Risk, and the Modernization of Agriculture.” The American Economic Review 106 (6): 1537–61.
Engle Warnick, James C., Javier Escobal, and Sonia C. Laszlo. 2011. “Ambiguity Aversion and Portfolio Choice in Small-Scale Peruvian Farming.” The B.E. Journal of Economic Analysis & Policy 11 (1): 83.
Giné, Xavier, and Dean Yang. 2009. “Insurance, Credit, and Technology Adoption: Field Experimental Evidence from Malawi.” Journal of Development Economics 89 (1): 1–11.
Giorcelli, Michela. 2019. “The Long-Term Effects of Management and Technology Transfers.” The American Economic Review 109 (1): 121–52.
Harrison, Glenn W., and John A. List. 2004. “Field Experiments.” Journal of Economic Literature 42 (4): 1009–55.
Iacovone, Leonardo, William F. Maloney, and David J. Mckenzie. 2018. “Improving Management with Individual and Group-Based Consulting : Results from a Randomized Experiment in Colombia.” 133230. The World Bank.
Iacovone, Leonardo; David J. Mckenzie. 2019. “Shortening Supply Chains : Experimental Evidence from Fruit and Vegetable Vendors in Bogota (English).” Policy Research working paper; no. WPS 8977
Islam, A, Ushchev, P, Zenou, Y and Zhang, X. 2018. 'The Value of Information in Technology Adoption: Theory and Evidence from Bangladesh'. London, Centre for Economic Policy Research.
Karlan, Dean, Ed Kutsoati, Margaret McMillan, and Chris Udry. 2011. “Crop Price Indemnified Loans for Farmers: A Pilot Experiment in Rural Ghana.” The Journal of Risk and Insurance 78 (1): 37–55.
Karlan, Dean, Ryan Knight, and Christopher Udry. 2015. “Consulting and capital experiments with microenterprise tailors in Ghana.” Journal of Economic Behavior & Organization 118: 281-302,
Lee, Jean, Jonathan Morduch, Saravana Ravindran, Abu Shonchoy, and Hassan Zaman.
2018. “Poverty and Migration in the Digital Age: Experimental Evidence on Mobile Banking in Bangladesh,” NYU working paper.
Liu, Elaine M. 2013. “Time to Change What to Sow: Risk Preferences and Technology Adoption Decisions of Cotton Farmers in China.” The Review of Economics and Statistics 95 (4): 1386–1403.
Mensmann, Mona, Michael Frese, Francisco Campos, Markus Goldstein, Leonardo Iacovone, Hillary Johnson, and David McKenzie. 2018. “Closing the Gender Gap – Personal Initiative Training and Female Business Performance.” Proceedings: A Conference of the American Medical Informatics Association / ... AMIA Annual Fall Symposium. AMIA Fall Symposium 2018 (1): 10669.
- 1. Also known as “artefactual field experiments” according to the terminology coined by (Harrison and List 2004), these experiments use incentivised (often abstract) choices in a controlled environment to study decision making among the population of interest - in our case, the farmers or business owners in charge of the technology adoption decision.
- 2. Side-by-side demonstration plots offer a useful alternative to relying on influential farmers to spread information and can induce learning about a new agricultural technology among less connected members of the community.
- 3. Impact evaluations can reveal unintended negative consequences of new technologies. In a recent working paper, Iacovone and McKenzie (2019) tested a mobile phone app and centralised distribution system that aimed to aggregate purchases and thus reduce travel time for fruit and vegetable sellers in Bogota, Colombia. Sellers were initially interested in the new service, and it helped them save time and money by reducing the frequency of their visits to a central market and lowering purchase prices. However, sellers abandoned the new service over time: due to less frequent market visits, sellers became less likely to offer products not covered by the service, and their short-term sales and profits fell as a consequence.
- 4. Corresponding to a belief that one’s own actions influence one’s future outcomes, and that life events can be sufficiently controlled.