Abstract
This chapter argues that the data-driven economy needs to figure out how to apply both competition and personal data protection laws. The implementation has to aim at preventing the situation where a group of people disproportionately gains at the expense of another group of people, whose data have been processed by the former. Without proper enforcement of competition and personal data protection laws, we argue that the feedback loop of having more data, better performance and greater talents will eventually worsen the problem of inequality. Thailand serves as a good case study of how the problem exists, while the implementation of both laws is still in retard. We propose that the line separating between personal and public interests has to be drawn and the effective enforcement of competition and personal data protection laws is at the heart of it.
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Notes
- 1.
A caveat is that personal data protection can be seen as a subset of privacy when the subject of rights is data, therefore, in the context of this chapter, personal data protection and privacy may be used interchangeably.
- 2.
Solove (2008).
- 3.
Houser and Voss (2018). The more-recent California Consumer Privacy Act (CCPA), which has just come into effect after the end of last year, does not require these legal bases in collecting data but still allow data subjects to opt out or delete their data anytime. I expect similar laws to be enacted really soon in other states within this year.
- 4.
Robinson et al. (2009).
- 5.
Cate (1994).
- 6.
Rodwin (2010).
- 7.
Mena (2003).
- 8.
Šidlauskas (2019).
- 9.
For example, Article 4 of the Thailand’s Personal Data Protection Act has exempted a few purposes of data processing from the provisions of the act, one of which is the government processes personal data for the purpose of security including fiscal and public securities. Similar exemptions exist in the Article 2 of the General Data Protection Regulation (GDPR) of the European Union.
- 10.
- 11.
Dolnicar (2002).
- 12.
- 13.
An increase in wages is not necessary. It depends largely on the skilled required and, at the same time, may lead to some further capital-labor substitutions. However, it is reasonable to say that, on average, wages should increase. Even though some middle- and low-skilled labor will be substituted partly by high-skilled labors and partly by machines.
- 14.
OECD (2020).
- 15.
Esayas (2019).
- 16.
- 17.
Acquisti et al. (2016).
- 18.
Choi (2010).
- 19.
Hayek (1945).
- 20.
Mata et al. (1995).
- 21.
Pepper and Gilbert (2015).
- 22.
It is true that we now have cloud services that smaller firms can also get access to a state of the art data infrastructure but again these cloud service providers are usually some of the biggest data processors in terms of volume of data as well. Therefore, we can consider this as the business model where they can capitalize their spare resources, by doing which they can reach a larger scale and enjoy more efficiency.
- 23.
- 24.
Evans (2003).
- 25.
Stucke and Grunes (2016).
- 26.
Ibid.
- 27.
Tepper (2018).
- 28.
Zuboff (2015).
- 29.
Longo and Drazen (2016).
- 30.
OECD (2020).
- 31.
Diker and Ünver (2017).
- 32.
- 33.
- 34.
Gregg (1996).
- 35.
Cui (2019).
- 36.
Posner and Weyl (2018).
- 37.
Chakravorti (2020).
- 38.
Newman (2015).
- 39.
Article 3 of GDPR defines the territorial scope of GDPR. The regulation applies to the processing of personal data in the context of the activities of an establishment of a controller or a processor in the Union, regardless of whether the processing takes place in the Union of not. Moreover, even those controllers or processors not established in the Union may be applied if the processing activities are related to the offering of goods and services to data subjects in the Union and to the monitoring of behavior of data subjects that takes place within the Union.
- 40.
Cui (2019).
- 41.
Stiglitz (2012).
- 42.
Furman and Stiglitz (1998).
- 43.
Evans (2011).
- 44.
Vock et al. (2013).
- 45.
Allcott et al. (2019).
- 46.
Chaloupka et al. (2000).
- 47.
Kerber (2016).
- 48.
OECD (2020).
- 49.
Botta and Wiedemann (2019).
- 50.
Taylor (2019).
- 51.
Leesa-nguansuk (2019).
- 52.
Christensen (1991).
- 53.
Haberkorn (2015).
- 54.
Phongpaichit and Baker (2015).
- 55.
Sakda (2020, p. 118).
- 56.
Zucman (2019). The figure was 58% in 2016.
- 57.
Sullivan (2019).
- 58.
Doner and Ramsay (1997).
- 59.
Phongpaichit and Baker (2015).
- 60.
Reuters (2020).
- 61.
Foo (2015).
- 62.
Lohsakul and Ungsirikul (2018).
- 63.
EEC (2019).
- 64.
CP group’s main business in China is run under the company called Ping An which is registered in the Hongkong Stock Exchange and Shanghai Stock Exchange. It now has over 200 billion USD market cap.
- 65.
Nikomborirak (2005).
- 66.
Banchongduang (2019).
- 67.
Leesa-nguansuk (2019).
- 68.
Zuboff (2015).
- 69.
Benkler et al. (2018, p. 381).
- 70.
Benkler et al. (2018).
- 71.
Wade (2006).
- 72.
Phongpaichit and Baker (2002).
- 73.
Markus and Charnysh (2017).
- 74.
Botta and Wiedemann (2019).
- 75.
Ohlhausen and Okuliar (2015).
- 76.
Custers and Uršič (2016).
- 77.
Audretsch et al. (2001).
- 78.
Clark and Lee (2008).
- 79.
Solove (2011).
- 80.
Solove (2011).
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Chokesuwattanaskul, P. (2021). The Nexus Between Competition and Personal Data Protection Laws: Thailand’s Perspective. In: Van Uytsel, S. (eds) The Digital Economy and Competition Law in Asia. Perspectives in Law, Business and Innovation. Springer, Singapore. https://doi.org/10.1007/978-981-16-0324-2_4
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