Role of Monetary Incentives in the Digital and Physical Inter-Border Labor Flows

Abstract

By allowing individuals to engage in remote relationships with foreign employers, online labor markets have the potential to mitigate the inefficiency costs due to the legal barriers and other frictions deterring international physical migration. This study investigates how the supply of foreign labor in digital and physical markets responds differently to monetary incentives. We use a unique data set containing information on digital labor flows from a major global online labor platform in conjunction with data on physical labor flows. We exploit short-term fluctuations in the exchange rate as a source of econometric identification: a depreciation of a country’s currency against the U.S. dollar increases the incentives of its workers to seek digital and physical employment from employers based in the United States. Using a panel count data model, we find that monetary incentives induced by depreciations of foreign currencies against the U.S. dollar are positively associated with the supply of foreign labor in digital markets, as expected from the frictionless nature of electronic markets. However, we fail to find a positive relationship between monetary incentives and the supply of foreign labor in physical markets, which might be expected due to the substantial bureaucratic restrictions and transaction costs associated with physical migration. We further examine how countries’ income and information and communications technologies development levels moderate the positive relationship between monetary incentives and digital labor flows. Our findings are useful for gauging the extent to which digital labor flows can alleviate the economic inefficiencies from the restrictions on physical migration.

Key words and phrases:

• Economics of information systems
• electronic markets
• income elasticity
• information policy
• monetary incentive theory
• online labor markets
• outsourcing
• remote employment
• digital labor markets

Acknowledgments

An earlier version of this research—Jing Gong, Yili Hong, and Alejandro Zentner, “Vanishing Borders in the Internet Age: The Income Elasticity of the Supply of Foreign Labor in Virtual versus Physical Markets”—appeared in T. Bui (ed.), Proceedings of the 51^st Hawaii International Conference on Systems Science (Washington, DC: IEEE Computer Society Press, 2018), pp. 5156–5164. We thank the editors and anonymous reviewers for their helpful suggestions during the review process. We also thank participants at HICSS, particularly the co-chairs of the Strategy, Information, Technology, Economics, and Society (SITES) minitrack, Robert Kauffman, Thomas Weber, Eric Clemons, and Rajiv Dewan, and the Special Issue Editorial Assistant Ai-Phuong Hoang. All of the contents are the sole responsibility of the authors.

Notes

1. In reverse auctions, sellers submit bids, which differs from ordinary auctions where buyers submit bids. Note that in the context of online labor markets, workers are the sellers and employers are the buyers.

2. Due to the nature of our data, we focus on the number of bids as the main dependent variable in our empirical analysis. An increase in the number of bids can result from either an increase in the number of workers, or an increase in the number of bids that each worker submits. If the number of workers does not change but each worker makes more bids, then there would be an increase in the intensive supply of labor (i.e., how much to work conditional on working a positive number of hours) rather than in the extensive supply of labor (i.e., whether to work or not).

3. Fixed-effects regressions also limit the inclusion of variables with minimal variation during our study period (e.g., population). In addition, there is a practical limitation arising from the limited data availability at the monthly level.

4. It is worth noting that NRI scores are not comparable across years because the calculation of these scores has changed over time. Therefore, it would be inappropriate to use the NRI as a covariate in the regression. In addition, the number of countries assessed each year also differs. Because of these considerations, we choose to use the NRI rankings (instead of the raw index values) to construct the $\mathrm{H}\mathrm{i}\mathrm{g}\mathrm{h}\mathrm{N}\mathrm{R}{\mathrm{I}}_{\mathrm{i}\mathrm{t}}$ variable, and focus on the top 100 countries for each year.

5. Prior research has found that the impacts of ICT development on country development are different for countries with different levels of economic development [39]. This suggests that it is also likely that the moderating role of the level of ICT development may be different for countries with different income levels.

6. Equation (5) indicates that the marginal effect of $\mathrm{H}\mathrm{i}\mathrm{g}\mathrm{h}\mathrm{N}\mathrm{R}{\mathrm{I}}_{\mathrm{i}\mathrm{t}}$ is a function of the value of $\mathrm{l}\mathrm{o}\mathrm{g}(\mathrm{N}\mathrm{o}\mathrm{m}\mathrm{i}\mathrm{n}\mathrm{a}\mathrm{l}\mathrm{E}\mathrm{x}\mathrm{c}\mathrm{h}\mathrm{a}\mathrm{n}\mathrm{g}\mathrm{e}\mathrm{R}\mathrm{a}\mathrm{t}{\mathrm{e}}_{\mathrm{i},\mathrm{U}\mathrm{S}\mathrm{A},\mathrm{t}})$. Computing the marginal effect of $\mathrm{H}\mathrm{i}\mathrm{g}\mathrm{h}\mathrm{N}\mathrm{R}{\mathrm{I}}_{\mathrm{i}\mathrm{t}}$ using the average value for $\mathrm{l}\mathrm{o}\mathrm{g}(\mathrm{N}\mathrm{o}\mathrm{m}\mathrm{i}\mathrm{n}\mathrm{a}\mathrm{l}\mathrm{E}\mathrm{x}\mathrm{c}\mathrm{h}\mathrm{a}\mathrm{n}\mathrm{g}\mathrm{e}\mathrm{R}\mathrm{a}\mathrm{t}{\mathrm{e}}_{\mathrm{i},\mathrm{U}\mathrm{S}\mathrm{A},\mathrm{t}})$ (which amounts to 1.289) shows that the estimated marginal effect is positive but statistically insignificant for both subsamples of high GDP countries (p = 0.523) and low GDP countries (p = 0.284).

7. Whether sellers on online product platforms respond to changes in the exchange rate is a testable hypothesis, assuming that data on online commerce including the location of sellers and buyers are available.

Additional information

Notes on contributors

Jing Gong

Jing Gong ([email protected]; corresponding author) is an assistant professor of information systems (IS) in the Department of Management Information Systems at the Fox School of Business at Temple University. She holds a Ph.D. in information systems and management from Carnegie Mellon University. Dr. Gong conducts empirical research examining firm and consumer behavior in online markets, focusing primarily on two-sided online markets (peer-to-peer platforms) and digital marketing. Her work has appeared in MIS Quarterly and Journal of Retailing, as well as in the proceedings of several major conferences and workshops in IS.

Yili Hong

Yili Hong ([email protected]) is an associate professor of information systems, co-director of the Digital Society Initiative, and Ph.D. Program director in the Department of Information Systems at the W.P. Carey School of Business of Arizona State University. He obtained his Ph.D. in Management Information systems at the Fox School of Business, Temple University. He is an external research scientist for Freelancer, fits.me, Extole, Yamibuy, Meishi, and Picmonic, among others. His research interests are in the areas of the sharing economy, digital platforms, and user-generated content. His work has appeared in Management Science, Information Systems Research, Journal of Management Information Systems, MIS Quarterly, and Journal of the Association for Information Systems, among others. His research was awarded the ACM SIGMIS Best Dissertation Award. His papers have won best-paper awards at the International Conference on Information Systems, Hawaii International Conference on System Sciences, and America’s Conference on Information Systems. He is an associate editor at Information Systems Research.

Alejandro Zentner

Alejandro Zentner ([email protected]) is an associate professor of managerial economics in the Naveen Jindal School of Management at the University of Texas at Dallas. He holds a Ph.D. in economics from the University of Chicago. His research focuses on examining how information technologies affect online and offline marketplaces. He has examined cross-channel substitution, firm and consumer behavior in online markets, file-sharing impacts on sales of music and movies, and structure and competition in media and entertainment markets. His work has appeared in Journal of Law and Economics, Review of Economics and Statistics, Management Science, Journal of Economics and Management Strategy, among others.