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on Payment Systems and Financial Technology |
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Issue of 2019‒07‒08
27 papers chosen by |
| By: | Neeraj Amarnani; Arpita Amarnani |
| Abstract: | Digitisation is having a significant impact in all walks of life, including the way monetary transactions take place, even in a cash-intensive society such as India. The present paper seeks to explore if digital payments have made significant progress in India and are now gaining pre-eminence as a mode of payment, by examining multiple sources of secondary data. It establishes that the Government has taken concrete, albeit controversial measures such as demonetization, as well as direct benefit transfers, designed and implemented payments infrastructure such as Immediate Payments Service (IMPS) and Unified Payments Interface (UPI). These and the proliferation of smart devices and better internet access have led to a large increase in digital payments. However, cash still seems to dominate the marketplace, though to a marginally lesser extent, and it will take a significant behavioural shift from the consumers to eventually enable digital payments to be the dominant mode of payments in India. Key Words: Digital Payments, Payment systems, Fintech, Financial Technology, IMPS, UPI Policy |
| Date: | 2019–06 |
| URL: | http://d.repec.org/n?u=RePEc:vor:issues:2019-30-07&r=all |
| By: | Vedat Akgiray |
| Abstract: | Beyond bitcoin, blockchain technology has acquired attention and importance in its own right. Today, it is conceptually accepted that blockchain stands out as a disruptive technology that will change a number of processes in financial services and could in turn impact corporate governance. This paper explores the recent applications of blockchain technology in financial services and outlines regulatory responses, to set the scene for future work in this area on corporate governance. This paper provided background for the Corporate Governance Committee’s roundtable discussion on blockchain technology and the implementation of the G20/OECD Principles of Corporate Governance on 10 April 2018. A subsequent presentation of the paper was given at the OECD Workshop on Digital Financial Assets on the 16 May 2018, and at the OECD-Asian Roundtable on Corporate Governance in Malaysia on 7-8 November 2018. This work also provides a contribution to the work of the OECD Blockchain Policy Centre. |
| Keywords: | blockchain, corporate governance, distributed ledger technology, financial market regulation, technological innovation |
| JEL: | G30 G38 O30 O33 |
| Date: | 2019–06–25 |
| URL: | http://d.repec.org/n?u=RePEc:oec:dafaae:21-en&r=all |
| By: | Annarosa Pesole (European Commission - JRC); Enrique Fernandez-Macías (European Commission - JRC); Cesira Urzi Brancati (European Commission - JRC); Estrella Gomez Herrera (European Commission - JRC) |
| Abstract: | Digital labour platforms are defined as digital networks that coordinate labour services in an algorithmic way. The rise of digital labour platforms can reshape work organisation and tasks distribution across the workforce, posing new policy challenges. A crucial problem for the design of an adequate policy response is the lack of clear estimates of the prevalence of platform workers. This paper proposes two approaches for measuring platform work. The first approach attempts to measure platform work as individual participation in the labour force through surveys, similarly to what is done by the Labour Force Survey (LFS) for traditional employment. Given the structural differences between traditional employment and platform work, the identification of the latter through surveys should include measures that assess also the regularity, intensity and significance of platform work, with a specific focus on the task performed. The second approach aims at deriving estimates of platform work as labour input. In other words, instead of asking workers if they provide services via platform, the data can be collected from the platform itself. The vast amount of information platforms collect could be used to estimate the number of hours worked via platforms and gather more detailed evidence on wages. However, the mixed use of platforms and the ambiguous identification criteria of individuals on platforms could raise issue of double counting when measuring employment using this second approach. |
| Keywords: | Digital labour platform, gig workers, technological change, work organisation, employment indicators |
| Date: | 2019–06 |
| URL: | http://d.repec.org/n?u=RePEc:ipt:laedte:201901&r=all |
| By: | Patrick Messerlin (Département d'économie) |
| Abstract: | Kritika Kultura and the European Centre for International Political Economy (ECIPE) are pleased to publish this special issue on cultural practices and cultural policies in the global and digital age. It is the outcome of a conference held in Ateneo de Manila University which has offered great opportunities for authors coming from different continents to discuss the vast changes in these domains. [First paragraph] |
| Keywords: | cultural industries; global and digital age; film and music |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:spo:wpmain:info:hdl:2441/nnqj4pgj882a9hmub79l62k07&r=all |
| By: | Tientip Subhanij (Macroeconomic Policy and Financing for Development Division, ESCAP); Ralph Chow Wen Kang (Macroeconomic Policy and Financing for Development Division, ESCAP) |
| Abstract: | Each of Pacific island countries share similar challenges and opportunities as small and remote island economies, with limited natural resources, narrowly-based economies, large distances away from major markets, and vulnerable to external shocks. Yet these countries are committed to the promotion of financial inclusion to its citizens via the harnessing of digital technology and believe that financial education plays an important role in skills transfer through the improvement of digital and financial literacy. Many Pacific Island countries have worked with agencies such as the Pacific Financial Inclusion Programme (PFIP) to fund innovative financial services |
| Date: | 2019–03 |
| URL: | http://d.repec.org/n?u=RePEc:unt:pbmpdd:pb82&r=all |
| By: | Chaikal Nuryakin (Institute for Economic and Social Research, Faculty of Economics and Business, Universitas Indonesia); Lovina Aisha; Natanael Waraney Gerald Massie |
| Abstract: | This study aims to delve deeper into the discussion on how the ?nancial inclusion progress in Indonesia could be affected by the growing ?ntech industry. We shall comprehensively discuss the current state of the platforms in the country, including the potential bene?ts and challenges. Such af?ictions include the hugely-concentrated deposit market, to begin with and the discrepancies between regulators and the technological changes, while the high internet and mobile phone penetration are only one of the many advantages the country are endowed with. The study aims to highlight the challenges faced in increasing ?nancial inclusion before the ?ntech platforms begin to ?ourish and how they differ to the current condition. Novel and relevant policy recommendations are also provided in the latter parts of the discussion. |
| Keywords: | ?nancial inclusion — ?nancial technology — Indonesia — digital divide |
| JEL: | G21 G28 |
| Date: | 2019–05 |
| URL: | http://d.repec.org/n?u=RePEc:lpe:wpaper:201936&r=all |
| By: | Mirai Igarashi; Nobuhiko Terui |
| Abstract: | This study proposes a model for identifying communities by combining two types of data: social network data and user-generated-content (UGC). The existing models for detecting the community structure of a network employ only network information. However, not all people connected in a network share the same interests. For instance, even if students belong to the same community of "school," they may have various hobbies such as music, books, or sports. Hence, targeting various networks to identify communities according to their interests uncovered by their communications on social media is more realistic and beneficial for companies. In addition, people may belong to multiple communities such as family, work, and online friends. Our model explores multiple overlapping communities according to their topics identified using two types of data jointly. By way of validating the main features of the proposed model, our simulation study shows that the model correctly identifies the community structure that could not be found without considering both network data and UGC. Furthermore, an empirical analysis using Twitter data clarifies that our model can find realistic and meaningful community structures from large social networks and has a good predictive performance. |
| Date: | 2019–04 |
| URL: | http://d.repec.org/n?u=RePEc:toh:dssraa:97&r=all |
| By: | Levin, Mark (Левин, Марк) (The Russian Presidential Academy of National Economy and Public Administration) |
| Abstract: | Since the 2000s network communication has reached a huge audience, easily winning competition from such “traditional” media as television, radio and newspapers. Network communication tools become a platform for uniting people: first around addictions and hobbies, and eventually other interests, including political ones. The Arab Spring showed that the possibility of instant messaging to a large audience could become a serious political factor, and Facebook groups created to extinguish fires in Russia in the 2010s stimulate volunteering. Numerous meetings in the spring of 2017, the rapid processes of uniting people in Moscow from the beginning of May 2017, all these are signs of the beginning of the spontaneous formation of socio-economic structures in Russia. The object of the study is the individual behavior of agents within the network structure. |
| Keywords: | socio-economic structures, institutions, socio-economic policy, networks, networking, economic analysis |
| Date: | 2019–06 |
| URL: | http://d.repec.org/n?u=RePEc:rnp:wpaper:061901&r=all |
| By: | Guglielmo Maria Caporale; Woo-Young Kang; Fabio Spagnolo; Nicola Spagnolo |
| Abstract: | This paper uses a Markov-switching non-linear specification to analyse the effects of cyber attacks on returns in the case of four cryptocurrencies (Bitcoin, Ethernam, Litecoin and Stellar) over the period 8/8/2015 - 28/2/2019. The analysis considers both cyber attacks in general and those targeting cryptocurrencies in particular, and also uses cumulative measures capturing persistence. On the whole, the results suggest the existence of significant negative effects of cyber attacks on the probability for cryptocurrencies to stay in the low volatility regime. This is an interesting finding, that confirms the importance of gaining a deeper understanding of this form of crime and of the tools used by cybercriminals in order to prevent possibly severe disruptions to markets. |
| Keywords: | crypto currencies, cyber attacks, regime switching |
| JEL: | C22 E40 G10 |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_7692&r=all |
| By: | Jäckle, Annette; Wenz, Alexander; Burton, Jonathan; Couper, Mick P. |
| Abstract: | In this paper we examine two potential ways of increasing participation in app-based data collection:1) inviting respondents to the mobile app study within an interview rather than by post, and 2)offering a browser-based alternative to the mobile app. We use experimental data from SpendingStudy 2, collected on the Understanding Society Innovation Panel and on an online access panelmanaged by Lightspeed UK. The results suggest that inviting respondents to an app study within aface-to-face interview increases participation, but does not bring in different types of participants. Incontrast the browser-based alternative can both increase participation and reduce biases in whoparticipates. |
| Date: | 2019–06–28 |
| URL: | http://d.repec.org/n?u=RePEc:ese:ukhlsp:2019-04&r=all |
| By: | Hans Gersbach (ETH Zurich, Switzerland) |
| Abstract: | We suggest that flexible majority rules for currency issuance decisions foster the stability of a cryptocurrency. With flexible majority rules, the voteshare needed to approve a particular currency issuance growth is increasing with this growth rate. By choosing suitable parameters for these flexible majority rules, we show that optimal growth rates can be achieved in simple settings. Moreover, with flexible majority rules, changes in the composition of growth-friendly and growth-adverse agents only have a comparatively moderate impact on growth rates, and extreme growth rates are avoided. Finally, we show that optimal money growth rates are realized if agents entering financial contracts anticipate ensuing inflation rates determined by these flexible majority rules. |
| Keywords: | Digital currency, central bank, voting, majority rule, flexible majority rules |
| JEL: | D72 E31 E42 E52 E58 |
| Date: | 2019–06 |
| URL: | http://d.repec.org/n?u=RePEc:eth:wpswif:19-322&r=all |
| By: | Bernd Süssmuth |
| Abstract: | Using a battery of timely multivariate time series techniques I study the Bitcoin cryptocurrency price series and web search queries with regard to their mutual predictability, Granger-causality and cause-effect delay structure. The Bitcoin is at first treated as a general currency, then as a generic asset. Google queries, although cointegrated, are found to be not helpful in predicting the USD exchange rate of Bitcoin as the speculative bubble in the latter antedates explosive behavior in the former. Chinese Baidu engine queries and compounded Baidu-Google queries predict Bitcoin price dynamics at relatively high frequencies ranging from two to five months. In the other direction, causality runs from the cryptocurrency price to queries statistics across nearly all frequencies. In both directions, the reaction time computed from a phase delay measure for the relevant frequency bands with significant causality ranges from slightly more than one month to about four months. |
| Keywords: | bitcoin, bubbles, frequency domain, causality |
| JEL: | C32 E32 E42 G12 G15 |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_7675&r=all |
| By: | Kessel, Dany (Södertörn University); Lif Hardardottir, Hulda (Stockholm University); Tyrefors, Björn (Research Institute of Industrial Economics (IFN)) |
| Abstract: | Recently, policy makers worldwide have suggested and passed legislation to ban mobile phone use in schools. The influential and only quantitative evaluation by Beland and Murphy (2016), suggests that this is a very low-cost but effective policy to improve student performance. In particular, it suggests that the lowest-achieving students have the most to gain. Using a similar empirical setup but with data from Sweden, we partly replicate their study and thereby add external validity to this policy question. Furthermore, we increase the survey response rate of schools to approximately 75 % compared to 21 % in B&M, although at the expense of the amount of information collected in the survey. In Sweden, we find no impact of mobile phone bans on student performance and can reject even small-sized gains. |
| Keywords: | Mobile phone ban; Student performance |
| JEL: | I21 I28 J24 O33 |
| Date: | 2019–06–25 |
| URL: | http://d.repec.org/n?u=RePEc:hhs:iuiwop:1288&r=all |
| By: | Natalya Apopo (Department of Economics, Nelson Mandela University); Andrew Phiri (Department of Economics, Nelson Mandela University) |
| Abstract: | The legitimacy of virtual currencies as an alternative form of monetary exchange has been the centre of an ongoing heated debated since the catastrophic global financial meltdown of 2007-2008. We contribute to the relative fresh body of empirical research on the informational market efficiency of cryptomarkets by investigating the weak-form efficiency of the top-five cryptocurrencies. In differing from previous studies, we implement random walk testing procedures which are robust to asymmetries and unobserved smooth structural breaks. Moreover, our study employs two frequencies of cryptocurrency returns, one corresponding to daily returns and the other to weekly returns. Our findings validate the random walk hypothesis for daily series hence validating the weak-form efficiency for daily returns. On the other hand, weekly returns are observed to be stationary processes which is evidence against weak-form efficiency for weekly returns. Overall, our study has important implications for market participants within cryptocurrency markets. |
| Keywords: | Efficient Market Hypothesis (EMH); Cryptocurrencies; Random Walk Model (RWM); Flexible Fourier Form (FFF) unit root tests; Smooth structural breaks. |
| JEL: | C22 C32 C51 E42 G14 |
| Date: | 2019–06 |
| URL: | http://d.repec.org/n?u=RePEc:mnd:wpaper:1904&r=all |
| By: | Richard Heuver; Ron TriepelsTriepels |
| Abstract: | Liquidity stress constitutes an ongoing threat to financial stability in the banking sector. A bank that manages its liquidity inadequately might find itself unable to meet its payment obligations. These liquidity issues, in turn, can negatively impact the liquidity position of many other banks due to contagion effects. For this reason, central banks carefully monitor the payment activities of banks in financial market infrastructures and try to detect early-warning signs of liquidity stress. In this paper, we investigate whether this monitoring task can be performed by supervised machine learning. We construct probabilistic classifiers that estimate the probability that a bank faces liquidity stress. The classifiers are trained on a dataset consisting of various payment features of European banks and which spans several known stress events. Our experimental results show that the classifiers detect the periods in which the banks faced liquidity stress reasonably well. |
| Keywords: | Risk Monitoring; Liquidity Stress; Neural Networks; Financial Market Infrastructures; Large-Value Payment Systems |
| JEL: | G32 G33 C45 E42 |
| Date: | 2019–06 |
| URL: | http://d.repec.org/n?u=RePEc:dnb:dnbwpp:642&r=all |
| By: | Alejandro Micco |
| Abstract: | Digital technologies, robotics, and artificial intelligence substitute tasks performed by labor are bringing back old fears about the impact of technology on labor markets and international trade. The aim of this paper is to provide evidence about the causal effect of automation on the labor market and sectoral US imports. We use robots per workers, instrumented by robot penetration in Europe, to study employment in almost 800 occupations in 285 industries in the US during 2002-2016. We use Autor et al (2003) and Frey and Osborne (2017) methodologies to define occupations at risk of automation and to study their behavior after robots´ penetration. We find that employment in occupations at risk has been declining at an annual rate of 2.0-2.5%, relative to other occupations. This result is mainly driven by a substitution effect within industries defined at the 4-digit NAICS level. One standard deviation increase in robots per worker reduces employment growth by 1.25-1.45% in occupations at risk compared to the other professions in the same sector. Industries with a higher share of occupation at risk have a lower rate of employment growth during the period 2002-2016. Also, imports of commodities produced by these sectors have been falling, in particular from countries with lower penetration of automation technologies. This result suggests that automation is changing countries´ comparative advantage. |
| Date: | 2019–07 |
| URL: | http://d.repec.org/n?u=RePEc:udc:wpaper:wp486&r=all |
| By: | Dmitry Dagaev (National Research University Higher School of Economics); Egor Stoyan (National Research University Higher School of Economics) |
| Abstract: | We analyse betting behaviour patterns of the visitors of the specialized betting website dedicated to the popular eSports game Counter-Strike: Global O ensive. The reverse favourite-longshot bias is found both in the in-sample and out-of-sample datasets. This phenomenon is rather unusual for parimutuel betting markets because favourite-longshot bias is more common. We de ne simple betting strategies based on the bets on underdogs and show that these strategies make a suciently large positive pro t, which is a sign of market ineciency. Next, we investigate determinants of the reverse favourite-longshot bias. We hypothesize that popular teams attract more unsophisticated gamblers which adds to the stronger reverse favourite-longshot bias in matches with such teams. Geographical proximity is found to be a signi cant factor that increases the bias, whereas the e ect of internet popularity measured by the number of team players' followers on Twitter surprisingly follows the U-shape curve |
| Keywords: | eSports; betting; market ineciency; favourite-longshot bias. |
| JEL: | G14 |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:hig:wpaper:216/ec/2019&r=all |
| By: | Huang, Yiping; Li, Xiang; Wang, Chu |
| Abstract: | This paper uses loan application-level data from a Chinese peer-to-peer lending platform to study the risk-taking channel of monetary policy. By employing a direct ex-ante measure of risk-taking and estimating the simultaneous equations of loan approval and loan amount, we are the first to provide quantitative evidence of the impact of monetary policy on the risk-taking of nonbank financial institution. We find that the search-for-yield is the main workhorse of the risk-taking effect, while we do not observe consistent findings of risk-shifting from the liquidity change. Monetary policy easing is associated with a higher probability of granting loans to risky borrowers and a greater riskiness of credit allocation, but these changes do not necessarily relate to a larger loan amount on average. |
| Keywords: | monetary policy,risk-taking,non-bank financial institution,search-for-yield,risk-shifting |
| JEL: | E52 G23 |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:iwhdps:142019&r=all |
| By: | Hitoshi Matsushima (Faculty of Economics, The University of Tokyo) |
| Abstract: | This study indicates that the improper uses of a public blockchain disable real-world governance in organizations and marketplaces. By using any basic application of smart contracts, such as escrow transactions, along with a revelation mechanism outside the blockchain, individuals can execute illegal cartel acts in a self-enforcing and non-judicial manner. Cartel members can then implement collective deviations without help from trusted intermediaries or any requirements on reputation or word-of-honor. We show that a first price auction is vulnerable to cartel threats even if the seller can hide bidders' prices because bidders take a countermeasure to hidden prices by using blockchain. |
| Date: | 2019–05 |
| URL: | http://d.repec.org/n?u=RePEc:tky:fseres:2019cf1119&r=all |
| By: | Francesco Bogliacino; Valeria Cirillo; Cristiano Codagnone; Marta Fana; Francisco Lupanez-Villanueva; Giuseppe A Veltri |
| Abstract: | Workers who perform their occupations through platforms are becoming an increasing share of the labour force. The debate is polarized between those arguing for platforms as an instrument to increase flexibility and labor force participation, and those who see it as a further mechanism to increase Non Standard Work (NSW). This debate is policy relevant because in either case, platform participation is associated to a difference in terms of willingness to contribute to the social security system. Nevertheless, the evidence is scant because we lack reliable data sources. In this contribution, we use a dedicated survey to estimate Willingness to Pay (WTP) for social security and estimate the causal impact of platform participation using a selection on observable strategy. We found that platform workers are less disposed to contribute to social security, although perception of accessibility and adequacy are not affected. Results are robust to specifications and multiple hypotheses testing. |
| Keywords: | employment; preferences; work; economic behavior. |
| Date: | 2019–06–29 |
| URL: | http://d.repec.org/n?u=RePEc:ssa:lemwps:2019/21&r=all |
| By: | Arntz, Melanie (ZEW Mannheim); Gregory, Terry (IZA); Zierahn, Ulrich (ZEW Mannheim) |
| Abstract: | Computing power continues to grow at an enormous rate. Simultaneously, more and better data is increasingly available and Machine Learning methods have seen significant breakthroughs in the recent past. All this pushes further the boundary of what machines can do. Nowadays increasingly complex tasks are automatable at a precision which seemed infeasible only few years ago. The examples range from voice and image recognition, playing Go, to self-driving vehicles. Machines are able to perform more and more manual and also cognitive tasks that previously only humans could do. As a result of these developments, some argue that large shares of jobs are “at risk of automation”, spurring public fears of massive job-losses and technological unemployment. This chapter discusses how new digital technologies might affect the labor market in the near future. First, the chapter discusses estimates of automation potentials, showing that many estimates are severely upward biased because they ignore that workers in seemingly automatable occupations already take over hard-to-automate tasks. Secondly, it highlights that these numbers only refer to what theoretically could be automated and that this must not be equated with job-losses or employment effects – a mistake that is done often in the public debate. Thirdly, the chapter develops scenarios on how digitalization is likely to affect the German labor market in the next five years and derives implications for policy makers on how to shape the future of work. Germany is an interesting case to study, as it is a developed country at the technological frontier. In particular, the main challenge will not be the number, but the structure of jobs and the corresponding need for supply side adjustments to meet the shift in demand both within and between occupations and sectors. |
| Keywords: | automation, digitalization, unemployment, inequality |
| JEL: | J23 J31 O33 |
| Date: | 2019–06 |
| URL: | http://d.repec.org/n?u=RePEc:iza:izadps:dp12428&r=all |
| By: | Matthias Pelster; Bastian Breitmayer; Tim Hasso |
| Abstract: | Are cryptocurrency traders driven by a desire to invest in a new asset class to diversify their portfolio or are they merely seeking to increase their levels of risk? To answer this question, we use individual-level brokerage data and study their behavior in stock trading around the time they engage in their first cryptocurrency trade. We find that when engaging in cryptocurrency trading investors simultaneously increase their risk-seeking behavior in stock trading as they increase their trading intensity and use of leverage. The increase in risk-seeking in stocks is particularly pronounced when volatility in cryptocurrency returns is low, suggesting that their overall behavior is driven by excitement-seeking. |
| Date: | 2019–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1906.11968&r=all |
| By: | Khlomov, Kirill (Хломов, Кирилл) (The Russian Presidential Academy of National Economy and Public Administration) |
| Abstract: | Aggressive behavior among adolescents in recent years has been actively developing in the Internet space (both in Russia and abroad), and therefore there is a growing need to study the forms, prevalence, causes and consequences of cyberbullying and develop measures for its suppression and prevention in the framework of psychological pedagogical work. The article presents the results of a study of the experience of meeting Russian adolescents with episodes of harassment on the Internet. The paper analyzes the prevalence of cyberbullying episodes, the emotional and behavioral responses of participants based on self-report, age and gender differences, and the moral evaluation of cyberbullying by adolescents. The article discusses the research and applied perspectives of psychological work in the field of Internet security of adolescents. |
| Keywords: | cyberbulling, bulling, teenager, internet, online risks, aggression |
| Date: | 2019–06 |
| URL: | http://d.repec.org/n?u=RePEc:rnp:wpaper:061907&r=all |
| By: | Arntz, Melanie; Gregory, Terry; Zierahn, Ulrich |
| Abstract: | Computing power continues to grow at an enormous rate. Simultaneously, more and better data is increasingly available and Machine Learning methods have seen significant breakthroughs in the recent past. All this pushes further the boundary of what machines can do. Nowadays increasingly complex tasks are automatable at a precision which seemed infeasible only few years ago. The examples range from voice and image recognition, playing Go, to self-driving vehicles. Machines are able to perform more and more manual and also cognitive tasks that previously only humans could do. As a result of these developments, some argue that large shares of jobs are "at risk of automation", spurring public fears of massive job-losses and technological unemployment. This chapter discusses how new digital technologies might affect the labor market in the near future. First, the chapter discusses estimates of automation potentials, showing that many estimates are severely upward biased because they ignore that workers in seemingly automatable occupations already take over hard-to-automate tasks. Secondly, it highlights that these numbers only refer to what theoretically could be automated and that this must not be equated with job-losses or employment effects - a mistake that is done often in the public debate. Thirdly, the chapter develops scenarios on how digitalization is likely to affect the German labor market in the next five years and derives implications for policy makers on how to shape the future of work. Germany is an interesting case to study, as it is a developed country at the technological frontier. In particular, the main challenge will not be the number, but the structure of jobs and the corresponding need for supply side adjustments to meet the shift in demand both within and between occupations and sectors. |
| Keywords: | Automation,Digitalization,Unemployment,Inequality |
| JEL: | J23 J31 O33 |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:zewdip:19024&r=all |
| By: | Isabel Hovdahl |
| Abstract: | One of the most important research questions in climate economics is the relationship between temperatures and human mortality. This paper develops a procedure that enables the use of machine learning for estimating the causal temperature-mortality relationship. The machine-learning model is compared to a traditional OLS model, and although both models are capturing the causal temperature-mortality relationship, they deliver very di?erent predictions of the e?ect of climate change on mortality. These di?erences are mainly caused by di?erent abilities regarding extrapolation and estimation of marginal e?ects. The procedure developed in this paper can ?nd applications in other ?elds far beyond climate economics. |
| Keywords: | Climate change, machine learning, mortality |
| Date: | 2019–06 |
| URL: | http://d.repec.org/n?u=RePEc:bny:wpaper:0077&r=all |
| By: | van Pelt, Victor (Tilburg University, School of Economics and Management) |
| Abstract: | Organizations invest significant amounts of time and money in management accounting systems, such as internal reporting systems, controls, and performance measurement systems. This dissertation presents three studies that use a laboratory experiment to examine how different users adjust and change how they use management accounting systems. The first study (chapter 2) examines how principals adjust their control over agents when the economic costs of controlling agents change. The second study (chapter 3) investigates how the prospect of rotating to another business unit impacts managers’ contemporary reports about operational distortions in performance measurement systems. The third study (chapter 4) examines how managers’ reporting decisions evolve in more complex, hierarchical systems, and whether they, next to transferring information, also communicate an intention to exhibit more cooperative behavior in the future. |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:tiu:tiutis:782413b7-2830-4e6d-bc4c-3c590e991b7d&r=all |
| By: | Aufegger, Lisa; Bicknell, Colin; Soane, Emma; Ashrafian, Hutan; Darzi, Ara |
| Abstract: | Background: Small group research in healthcare is important because it deals with interaction and decision-making processes that can help to identify and improve safer patient treatment and care. However, the number of studies is limited due to time- and resource-intensive data processing. The aim of this study was to examine the feasibility of using signal processing and machine learning techniques to understand teamwork and behaviour related to healthcare management and patient safety, and to contribute to literature and research of teamwork in healthcare. Methods: Clinical and non-clinical healthcare professionals organised into 28 teams took part in a video- and audio-recorded role-play exercise that represented a fictional healthcare system, and included the opportunity to discuss and improve healthcare management and patient safety. Group interactions were analysed using the recurrence quantification analysis (RQA; Knight et al., 2016), a signal processing method that examines stability, determinism, and complexity of group interactions. Data were benchmarked against self-reported quality of team participation and social support. Transcripts of group conversations were explored using the topic modelling approach (Blei et al., 2003), a machine learning method that helps to identify emerging themes within large corpora of qualitative data. Results: Groups exhibited stable group interactions that were positively correlated with perceived social support, and negatively correlated with predictive behaviour. Data processing of the qualitative data revealed conversations focused on: (1) the management of patient incidents; (2) the responsibilities among team members; (3) the importance of a good internal team environment; and (4) the hospital culture. Conclusions: This study has shed new light on small group research using signal processing and machine learning methods. Future studies are encouraged to use these methods in the healthcare context, and to conduct further research on how the nature of group interaction and communication processes contribute to the quality of team and task decision-making. |
| JEL: | J50 |
| Date: | 2019–06–13 |
| URL: | http://d.repec.org/n?u=RePEc:ehl:lserod:101073&r=all |