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on Information and Communication Technologies |
| By: | Erik Brynjolfsson (Stanford University and NBER); Catherine Buffington (U.S. Census Bureau); Nathan Goldschlag (U.S. Census Bureau); J. Frank Li (Stanford University); Javier Miranda (Halle Institute for Economic Research (IWH), and Friedrich-Schiller University Jena); Robert Seamans (New York University) |
| Abstract: | We use data from the Annual Survey of Manufactures to study the characteristics and geography of investments in robots across U.S. manufacturing establishments. We find that robotics adoption and robot intensity (the number of robots per employee) is much more strongly related to establishment size than age. We find that establishments that report having robotics have higher capital expenditures, including higher information technology (IT) capital expenditures. Also, establishments are more likely to have robotics if other establishments in the same Core-Based Statistical Area (CBSA) and industry also report having robotics. The distribution of robots is highly skewed across establishments’ locations. Some locations, which we call Robot Hubs, have far more robots than one would expect even after accounting for industry and manufacturing employment. We characterize these Robot Hubs along several industry, demographic, and institutional dimensions. The presence of robot integrators and higher levels of union membership are positively correlated with being a Robot Hub. |
| Keywords: | robot, technology adoption, manufacturing, labor |
| Date: | 2023–10–05 |
| URL: | http://d.repec.org/n?u=RePEc:jrp:jrpwrp:2023-014&r=ict |
| By: | Anass Arbia (Faculty of Law, Economics and Social Sciences, Salé, Mohammed V University, Rabat, Morocco.); Khalid Sobhi (Faculty of Law, Economics and Social Sciences, Salé, Mohammed V University, Rabat, Morocco.); Mohamedou Karim (Faculty of Law, Economics and Social Sciences, Salé, Mohammed V University, Rabat, Morocco.); Mohammed Eddaou (UMP - Professor, Faculty of Law, Economics and Social Sciences, University Mohammed First, Oujda, Morocco.) |
| Abstract: | Abstract The document analyzes the relationship between FDI (Foreign Direct Investment), ICT (Information and Communication Technology), and economic growth in Morocco for the period from 1990 to 2021 using the ARDL model. Three models have been evaluated, with economic growth, FDI, and ICT as dependent variables in each respective model. In model (1), the results indicate that in the short term, economic growth is not positively related to FDI and ICT. However, in the long term, FDI positively contributes to economic growth, while ICT negatively affects it. A controlled inflation rate has a positive short-term effect, and the level of education shows a positive relationship in both the short and long term. In Model (2), economic growth and government spending have a significant short-term effect on FDI, while ICT has no effect. In the long term, economic performance and inflation remain important for FDI. Model (3) confirms a significant short-term relationship between FDI and ICT, with a negative impact. However, ICT is positively influenced by the inflation rate and the level of education. In the long term, FDI, demographic changes, and education have favorable and significant effects, while economic growth has a negative impact. Regarding the Granger causality test by Toda-Yamamoto, the cause-and-effect relationship between ICT and economic growth is strong and unidirectional, while economic growth influences the level of ICT development. On the other hand, the causality between FDI and ICT concerning economic growth is indirect and depends on factors such as population growth, education level, and inflation rate. JEL classification numbers: C190, F21, F30, L96, O55. Keywords: Economic growth, FDI, Information and Communication technology, ARDL model, Toda-Yamamoto causality. |
| Keywords: | JEL classification numbers: C190 F21 F30 L96 O55 Economic growth FDI Information and Communication technology ARDL model Toda-Yamamoto causality, JEL classification numbers: C190, F21, F30, L96, O55 Economic growth, FDI, Information and Communication technology, ARDL model, Toda-Yamamoto causality |
| Date: | 2023–09–14 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-04207179&r=ict |
| By: | Georgios Petropoulos; Bertin Martens; Geoffrey Parker; Marshall Van Alstyne |
| Abstract: | Digital platforms, empowered by artificial intelligence algorithms, facilitate efficient interactions between consumers and merchants that allow the collection of profiling information which drives innovation and welfare. Private incentives, however, lead to information asymmetries resulting in market failures. This paper develops a product differentiation model of competition between two platforms to study private and social incentives to share information. Sharing information can be welfare-enhancing because it solves the data bottleneck market failure. Our findings imply that there is scope for the introduction of a mandatory information sharing mechanism from big tech to their competitors that help the latter to improve their network value proposition and become more competitive in the market. The price of information in this sharing mechanism matters. We show that price regulation over information sharing like the one applied in the EU jurisdiction increases the incentives of big platforms to collect and analyze more data. It has ambiguous effects on their competitors that depend on the exact relationship between information and network value. |
| Keywords: | information sharing, digital platforms, data bottleneck, data portability |
| JEL: | D47 D82 K21 L21 L22 L40 L41 L43 L51 L86 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_10663&r=ict |
| By: | Cusato, Antonio; Castillo, José Luis; IDB Invest |
| Abstract: | We exploit the staggered expansion of the internet broadband network to firms and bank branches locations in Peru during the last decade to study non-financial firm performance and bank credit dynamics. Access to broadband unleashes firm growth, increases the chances of entry of firms and reduces the probability of exit in benefited locations. For those firms that had a borrowing relation with a bank before the expansion of broadband, the increase in sales serves as a signal to banks about their profitability, which in turn respond by providing more credit. Entry and exit from the credit market follows a similar pattern as in the case of firms, but the results take longer to materialize after the shock. We can disentangle supply and demand effects, since there is a group of firms and bank branches with different locations and asymmetrical timing for the availability of the technology. Our analysis highlights the importance of the demand channel in the reduction of the observed interest rates, which is consistent with the fact that our credit market results are concentrated among micro and small firms, and firms with thin credit files, which are often perceived as riskier. |
| Keywords: | Compliance;accountability;Norms;Sanctions;Argentina |
| JEL: | O33 L86 G21 |
| Date: | 2023–06 |
| URL: | http://d.repec.org/n?u=RePEc:idb:brikps:12922&r=ict |
| By: | Janet Gao; Shan Ge; Lawrence D. W. Schmidt; Cristina Tello-Trillo |
| Abstract: | Employer-sponsored health insurance is a significant component of labor costs. We examine the causal effect of health insurance premiums on firms’ employment, both in terms of quantity and composition, and their technology investment decisions. To address endogeneity concerns, we instrument for insurance premiums using idiosyncratic variation in insurers’ recent losses, which is plausibly exogenous to their customers who are employers. Using Census microdata, we show that following an increase in premiums, firms reduce employment. Relative to higher-income coworkers, lower-income workers see a larger increase in their likelihood of being separated from their jobs and becoming unemployed. Firms also invest more in information technology, potentially to substitute labor. |
| Keywords: | Health insurance, insurer losses, worker skills, firm employment, labor composition, inequality, technology investment, automation |
| JEL: | G22 G31 G28 G18 J01 J08 J32 J22 J23 |
| Date: | 2023–09 |
| URL: | http://d.repec.org/n?u=RePEc:cen:wpaper:23-47&r=ict |
| By: | Taoufiq Dadouch (University Hassan II [Casablanca]); Bouchra Bennani; Malika Haoucha |
| Abstract: | The rapid proliferation of Digital marketing, due to recent digital transformation, has been accentuated by the effects of the Covid-19 pandemic. This can be noticed with changes in customer shopping behavior while adopting various digital marketing tools such as social media, E & M-commerce, and very recently AI enablers such as conversational agents/apps (Virtual Assistants & Chatbots). The purpose of this paper is to present some literature findings on consumer behavior toward mobile shopping via AI-Conversational-apps (Virtual Assistants & Chatbots), as compared to Mobile basic apps. Indeed, Mobile Shopping via AI-Conversational apps and their consumer acceptance behavior have become an important research issue worldwide in terms of involved predictors, theories, and methodologies. In summary, the literature showed that Anthropomorphism Construct (i.e., the degree to which a user perceives AI-Conversational apps to be humanlike) emerged as the primary additional predictor for acceptance of M-Shopping via AI-Conversational apps (AI-CA), in addition to mobile primary apps determinants. These determinants consist of utilitarian, hedonic & social antecedents adapted mainly from the UTAUT2 model (Unified theory of acceptance & use of technology), including mainly; performance expectation & effort expectation, hedonic motivation, social influence, and facilitating conditions. Literature findings also clarified the lack & importance of multimarket & multicultural research on M-Shopping apps' acceptance (mainly AI-CA). Indeed, not only developed markets but also developing ones, have seen surging rates of smartphone penetration conditions & mobile internet connectivity, along with changing consumer behaviors and dominating M-Shopping-apps activities. This offers great potential for research on M-Shopping-AI-CA acceptance behaviors in such developing countries, mainly in Morocco. |
| Keywords: | Digital Marketing Consumer behavior Mobile Shopping Artificial Intelligence AI-Conversational apps. JEL Classification : M21 M31 M37 M39 Paper type: Theoretical Research, Digital Marketing, Consumer behavior, Mobile Shopping, Artificial Intelligence, AI-Conversational apps. JEL Classification : M21, M31, M37, M39 Paper type: Theoretical Research |
| Date: | 2023–08–29 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-04194657&r=ict |
| By: | Lence, Sergio H.; Plastina, Alejandro |
| Abstract: | A very large number of productivity analyses have focused on Total Factor Productivity (TFP), the volume of aggregate output produced per unit of aggregate input, as the measure of choice. For example, industry-level TFP data series have been widely used to investigate many important economic issues, including whether productivity gains have been concentrated in a few industries and whether such gains were linked to the use of information technology (Stiroh 2002), whether automation is labor-displacing (Autor and Salomons 2018), whether the recent rise in the capital share can be attributed to increasing automation (Aghion, Jones, and Jones 2019), how GDP growth has been impacted by sectoral trends in TFP and labor growth (Foerster et al. 2022), the contributions of individual industries to U.S. aggregate TFP growth (Jorgenson, Ho, and Samuels 2019), and the reasons for the productivity gap between Europe and the United States in the late 1990s and early 2000s (van Ark, O’Mahony and Timmer 2008). Recently, growing concerns about environmental degradation and climate change have spurred interest in “environmentally-adjusted” TFP indicators, which take into account the production of undesirable by-products and externalities, as well as how intensely natural resources are used (OECD 2020b). For the agricultural sector in particular, studies based on TFP have analyzed public investments (Fuglie, Wang, and Ball 2012; Fuglie 2018; Ortiz-Bobea et al. 2021), international trade (Garcia-Verdu et al. 2019; Yuan et al. 2021), and the design of policies aimed at decoupling productivity growth from environmental pressure (OECD 2020a), among other issues. In the United States, agricultural TFP measures have been extensively used to evaluate returns to public investments (Fuglie and Heisey 2007; Alston et al. 2011; Jin and Huffman 2016), identify the drivers of productivity growth (Capalbo 1988; Schimmelpfennig and Thirtle 1999; Huffman and Evenson 2006; Alston et al. 2010; Andersen, Alston and Pardey 2012; O’Donnell 2012, 2014; Plastina and Lence 2018), evaluate convergence in productivity across states (McCunn and Huffman 2000; Ball, Hallahan, and Nehring 2004; Poudel, Paudel, and Zilberman 2011), assess spillovers between agriculture and other sectors of the economy (Lence and Plastina 2020), and gauge the impact of weather and climate on aggregate productivity (Njuki, Bravo-Ureta, and O’Donnell 2018; Sabasi and Shumway 2018; Chambers and Pieralli 2020; Ortiz-Bobea, Knippenberg, and Chambers 2018; Plastina, Lence, and Ortiz-Bobea 2021; Ortiz-Bobea et al. 2021). Given the vast literature that has applied TFP to analyze issues concerning productivity, it is not surprising that significant efforts have been devoted to the development of proper measures of the individual components of TFP (OECD 2001; Fuglie, Wang, and Ball 2012; Fuglie 2015; Shumway et al. 2017; USDA-ERS 2021), as well as to the evaluation of the relative merits of alternative aggregation methods (Szulc 1964; Eltetö and Köves 1964; Jorgenson and Griliches 1967; Caves, Christensen, and Diewert 1982a, 1982b; Bjurek 1996; Balk and Althin 1996; O’Donnell 2012, 2016; Färe and Zelenyuk 2021). Contrastingly, there has been a dearth of studies exploring the quality of real-world TFP data series. Interestingly, studies analyzing productivity usually rely on a single source of TFP data, even in cases where more TFP sources are available. Typically, no robustness analyses are conducted to assess the extent to which inferences hold using alternative TFP data sources. Implicitly, such studies assume that the underlying TFP data being used is of sufficiently high quality to yield valid inferences. However, Alston (2018) and Andersen, Alston, and Pardey (2011) --among the few studies analyzing more than a single TFP source-- provide evidence that calls this assumption into question. The lack of studies concerning the quality of real-world TFP series provides the main motivation of the present investigation. We contribute to the literature by examining the industry-level TFP series for the United States obtained from three alternative sources, namely, (1) Jorgenson, Ho, and Samuels (JHS), (2) the U.S. Bureau of Labor Statistics (BLS), and (3) the U.S. Bureau of Economic Analysis (BEA). These three sources are of special interest because they are highly regarded and their series have been used extensively by researchers to analyze productivity (e.g., Stiroh 2002, Autor and Salomons 2018, Aghion, Jones, and Jones 2019, Foerster et al. 2022, Jorgenson, Ho, and Samuels 2019, van Ark, O’Mahony and Timmer 2008). Besides providing an empirical assessment of the relative quality of the aforementioned series, our study contributes to the literature by proposing a general method to examine the quality of alternative time series reportedly measuring the TFP of a particular entity or sector. The main goal of our study is to spur interest in the exploration of the quality of real-world TFP data series, with the aim of finding ways to enhance them and uncovering series whose quality may be deemed questionable. Our preliminary results show that, out of the 61 industry series for which TFP data from different sources are being compared, between 34 (for JHS vs. BEA) and 46 (for BEA vs. BLS) industries have inconsistent series across sources. In other words, only 31% to 64% of the industries have TFP data consistent between source pairs. These results strongly suggest that empirical analyses based on a single data source may not be sufficiently robust to draw strong inferences and implications. The results also demonstrate the need to devote greater attention to improving the reliability of TFP data. |
| Keywords: | Productivity Analysis, Research Methods/ Statistical Methods |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:ags:iaae23:338535&r=ict |
| By: | Florian Gunsilius; David Van Dijcke |
| Abstract: | Thresholds in treatment assignments can produce discontinuities in outcomes, revealing causal insights. In many contexts, like geographic settings, these thresholds are unknown and multivariate. We propose a non-parametric method to estimate the resulting discontinuities by segmenting the regression surface into smooth and discontinuous parts. This estimator uses a convex relaxation of the Mumford-Shah functional, for which we establish identification and convergence. Using our method, we estimate that an internet shutdown in India resulted in a reduction of economic activity by over 50%, greatly surpassing previous estimates and shedding new light on the true cost of such shutdowns for digital economies globally. |
| Date: | 2023–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2309.14630&r=ict |
| By: | Plantinga, Paul; Shilongo, Kristophina; Mudongo, Oarabile; Umubyeyi, Angelique; Gastrow, Michael; Razzano, Gabriella |
| Abstract: | Several African countries are developing artificial intelligence (AI) strategies and ethics frameworks with the goal of accelerating responsible AI development and adoption. However, many of these governance actions are emerging without consideration for their suitability to local contexts, including whether the proposed policies are feasible to implement and what their impact may be on regulatory outcomes. In response, we suggest that there is a need for more explicit policy learning, by looking at existing governance capabilities and experiences related to algorithms, automation, data and digital technology in other countries and in adjacent sectors. From such learning it will be possible to identify where existing capabilities may be adapted or strengthened to address current AI-related opportunities and risks. This paper explores the potential for learning by analysing existing policy and legislation in twelve African countries across three main areas: strategy and multi-stakeholder engagement, human dignity and autonomy, and sector-specific governance. The findings point to a variety of existing capabilities that could be relevant to responsible AI; from existing model management procedures used in banking and air quality assessment, to efforts aimed at enhancing public sector skills and transparency around public-private partnerships, and the way in which existing electronic transactions legislation addresses accountability and human oversight. All of these point to the benefit of wider engagement on how existing governance mechanisms are working, and on where AI-specific adjustments or new instruments may be needed. |
| Date: | 2023–09–26 |
| URL: | http://d.repec.org/n?u=RePEc:osf:socarx:jyhae&r=ict |