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on Information and Communication Technologies |
| By: | Bessan, Eudoxie; Ayedoun, Christian |
| Abstract: | This paper aims to analyze the impact of information and communication technologies (ICTs) on bilateral trade flows among sub-Saharan African (SSA) countries. Utilizing an extended ICT gravity model, the study explores how key ICT indicators influence exports, imports, and trade in manufactured goods. The analysis covers a sample of 35 countries over the period from 2010 to 2019. To address potential over-representation of zero trade flows, the Poisson Pseudo-Maximum Likelihood (PPML) estimator is employed. The findings reveal that ICT development, particularly access to mobile telephony, mitigates the effect of distance on trade by facilitating intra-African trade flows. However, the limited availability of ICT infrastructure, especially restricted Internet access, means that physical distance remains a significant barrier to trade. Based on these insights, the study recommends strategic investments in ICT infrastructure and innovation, aimed at reducing transaction costs and improving ICT accessibility. Enhanced regional economic integration is also suggested as a pathway to facilitate these improvements and strengthen trade networks among SSA countries. |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:aer:wpaper:6d8f262b-3e69-4c6a-9831-8af9b8dc3452 |
| By: | Daniel Bj\"orkegren; Jun Ho Choi; Divya Budihal; Dominic Sobhani; Oliver Garrod; Paul Atherton |
| Abstract: | Although 85% of sub-Saharan Africa's population is covered by mobile broadband signal, only 37% use the internet, and those who do seldom use the web. The most frequently cited reason for low internet usage is the cost of data. We investigate whether AI can bridge this gap by analyzing 40, 350 queries submitted to an AI chatbot by 469 teachers in Sierra Leone over 17 months. Teachers use AI for teaching assistance more frequently than web search. We compare the AI responses to the corresponding top search results for the same queries from the most popular local web search engine, google.com.sl. Only 2% of results for corresponding web searches contain content from in country. Additionally, the average web search result consumes 3, 107 times more data than an AI response. Bandwidth alone costs \$2.41 per thousand web search results loaded, while the total cost of AI is \$0.30 per thousand responses. As a result, AI is 87% less expensive than web search. In blinded evaluations, an independent sample of teachers rate AI responses as more relevant, helpful, and correct than web search results. These findings suggest that AI-driven solutions can cost-effectively bridge information gaps in low-connectivity regions. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.12397 |
| By: | Vivarelli, Marco (Università Cattolica del Sacro Cuore); Arenas Díaz, Guillermo (Catholic University Milan) |
| Abstract: | The relationship between technology and employment has long been a topic of debate. This issue is even more pertinent today as the global economy undergoes a technological revolution driven by automation and the widespread adoption of Artificial Intelligence. The primary objective of this paper is to provide insights into the relationship between innovation and employment by proposing a conceptual framework and by discussing the state of the art of the debates and analyses surrounding this topic. |
| Keywords: | technology, employment, compensation theory, AI, robot |
| JEL: | O33 |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp17686 |
| By: | Nicholas, Gabriel |
| Abstract: | The lack of available empirical information about how people use general purpose AI systems makes it extremely challenging to develop evidence-informed policy. However, when designing new regulations, policymakers face an empirical dilemma: they must regulate AI without any access to real world data on how people and businesses are using these systems. Unlike social media and the internet, where user behavior is often public and leaves observable data traces, general-purpose AI systems are largely accessed through private, one-on-one interactions, such as chatbots. This paper proceeds in three parts. First, it describes the use case information gap, why it should be closed, and what challenges there are to doing so. Then, it gives more detail on the three approaches to providing researchers access to use case information previously mentioned. Finally, it offers recommendations for how AI companies and lawmakers can implement these approaches in ways that benefit researchers and ultimately the public, while safeguarding users’ privacy. |
| Date: | 2024–08–13 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:hvxf5_v1 |
| By: | Otis, Nicholas G.; Cranney, Katelyn; Delecourt, Solene; Koning, Rembrand (Harvard Business School) |
| Abstract: | Generative AI has the potential to transform productivity and reduce inequality, but only if adopted broadly. In this paper, we show that recently identified gender gaps in generative AI use are nearly universal. Synthesizing data from 18 studies covering more than 140, 000 individuals across the world, combined with estimates of the gender share of the hundreds of millions of users of popular generative AI platforms, we demonstrate that the gender gap in generative AI usage holds across nearly all regions, sectors, and occupations. Using newly collected data, we also document that this gap remains even when access to try this new technology is improved, highlighting the need for further research into the gap’s underlying causes. If this global disparity persists, it risks creating a self-reinforcing cycle: women’s underrepresentation in generative AI usage would lead to systems trained on data that inadequately sample women’s preferences and needs, ultimately widening existing gender disparities in technology adoption and economic opportunity. |
| Date: | 2024–10–14 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:h6a7c_v1 |