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Analysis of Ecrime in Crowd-Sourced Labor Markets: Mechanical Turk vs. Freelancer

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The Economics of Information Security and Privacy

Abstract

Research in the economics of security has contributed more than a decade of empirical findings to the understanding of the microeconomics of (in)security, privacy, and ecrime. Here we build on insights from previous macro-level research on crime, and microeconomic analyses of ecrime to develop a set of hypotheses to predict which variables are correlated with national participation levels in crowd-sourced ecrime. Some hypotheses appear to hold, e.g. Internet penetration, English literacy, size of the labor market, and government policy all are significant indicators of crowd-sourced ecrime market participation. Greater governmental transparency, less corruption, and more consistent rule of law lower the participation rate in ecrime. Other results are counter-intuitive. GDP per person is not significant, and, unusually for crime, a greater percentage of women does not correlate to decreased crime. One finding relevant to policymaking is that deterring bidders in crowd-sourced labor markets is an ineffective approach to decreasing demand and in turn market size.

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Notes

  1. 1.

    Gross Domestic Product (GDP) indicates the aggregate worth of goods and services produced by a country in a specific time frame, typically annually. GDP per capita is an indicator of the average standard of living in a country.

  2. 2.

    http://hdl.handle.net/2451/29585, retrieved on 24 February 2012

  3. 3.

    Ideally, identical goods cost the same in two different markets, when priced in the same currency. However, transaction costs lead to different prices. Purchasing Power Parity measures the difference between prices in two different markets for identical goods and services.

  4. 4.

    http://data.worldbank.org/indicator, retrieved on 24 February 2012.

  5. 5.

    https://www.ets.org/toefl/research/topics/candidates_and_populations, retrieved on 24 February 2012.

  6. 6.

    Best-fit model indicates the subset of indicators for which the corresponding linear regression obtained the highest adjusted R 2 value.

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Acknowledgements

We would like to thank Prof. Panagiotis G. Ipeirotis who made the demographic data publicly available. We also thank Prof. Stefan Savage’s research group at UCSD for providing us with the data on Freelancer. Finally, we thank the Stat/Math Center at Indiana University for their insight on the statistical analysis. Any mistakes in this chapter are the authors’ own responsibility.

This presentation of this research was made possible by funding from the Volkswagen Foundation. This material is based upon work supported by the National Science Foundation (NSF) under award number 0916993. Any opinions, findings, and conclusions or recommendations expressed in this presentation are those of the author(s) and do not necessarily reflect the views of the NSF.

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Authors and Affiliations

  1. Indiana University, Bloomington, IN, USA

    Vaibhav Garg

  2. School of Informatics and Computing, Indiana University, Bloomington, IN, USA

    L. Jean Camp

  3. University of Illinois at Chicago, Chicago, IL, USA

    Chris Kanich

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  1. Vaibhav Garg
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Correspondence to Vaibhav Garg .

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  1. Westfälische Wilhelms-Universität Münster, Münster, Germany

    Rainer Böhme

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Garg, V., Camp, L.J., Kanich, C. (2013). Analysis of Ecrime in Crowd-Sourced Labor Markets: Mechanical Turk vs. Freelancer. In: Böhme, R. (eds) The Economics of Information Security and Privacy. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39498-0_13

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  • DOI: https://doi.org/10.1007/978-3-642-39498-0_13

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