Examining the Most Important Determinants of Health- Related Quality of Life (HRQoL). The Machine Learning Approach

Kokashvili, Shin


ABSTRACT

 There are various circumstances affecting the individual health-related quality of life (HRQoL). The aim of the paper is to understand which health determinants are the most crucial while designing the efficient health policy. Using the machine learning approach, authors examine 42 health status related factors. The paper incorporates 27 individual level and 15 regional level health state determinants in empirical investigation. Results show that in terms of factor weights, the subjective health is the most influential on individual level and medical labor force - on regional level. However, in terms of frequency, the hospital visiting plays the most important role on individual level and estate condition - on regional level. In addition, empirical results indicate that individual level factors have higher impact on health status than regional level factors. Based on empirical results of the paper, authors provide policy recommendations.

Keywords: Health Determinants; Health Policy; Machine Learning; Health-related quality of life (HRQoL); 

JEL Classification: I12; I18; I15

Click here to have a look on a full paper 

Effect and measuring of the Shadow economy

                                                                                          Author: Vsevolod Klivak

First of all, it shall be mentioned, that shadow economy is an umbrella term, which includes the black market and the underground economy. Although these terms mean not the same thing, in a broader sense they can be used interchangeably. In this blog post, points from measuring and descriptive will be pointed out.   From the quantitative side, you could measure the size of the shadow economy and the influence of it, but the real essence of the understanding is in the qualitative field. There some main effects of the SE.

Negative effect

·         The government can’t measure the actual state of the economy to help make policy

·         The government isn’t collecting the taxes that it should

·         Consumers are not protected from people selling fake/unsafe goods

·         Workers injured on the job don’t have recourse to required insurance

·         Workers don’t accumulate government pension benefits

·         Informal businesses have to stay small to avoid attracting attention. Big companies are more efficient.

Positive effect

·         contracts & no contracts

·         simple terms to abide

·         easier to do business with a person that has not pet peeves and delivers profit.

Notwithstanding there are many different aspects, which may give an intuition about the relation in the economy with the shadow economy. The most curious was a connection with the intelligence level. In the paper of Raufhon Salahodjaev concluded a claim, that Intelligence level and the Informal economy are negatively correlated. We also find that the results hold when we control for endogeneity of intelligence and for the presence of influential observation.

However, it is important to note that while estimates show that higher-IQ countries are negatively associated with the size of the informal economy, they should not be treated as direct evidence that a more intelligent population is a prerequisite to constrain shadow economy. These results suggest that if a government implements policies designed to reduce underground economy, intelligence offers a reasonable estimate of the level of acceptance of these policies.

Next curious example will be the article from the Gheorghe Zamana and Zizi Goschin about the correlation between economic growth and the SE in the Romanian economy system. Since the attempts to analyze SE based on only one indicator may be misleading, was developed a new synthetic index of SE that includes three relevant indicators: SE measured in euro per inhabitant, SE as a percentage of GDP and SE of each EU member state as a percentage of the total EU-28 shadow economy. That index calculations reveal that Romania is not among the countries with very large shadow economy magnitude, and is having low and stable shadow economy index values over 1999-2012. Although OLS was not sufficient to provide significant influence of shadow economy on economic growth, it was found a counteracting relationship between GDP and SE, therefore providing empirical support to the hypothesis that SE was linked to the economic development in Romania over 1999-2012. Nevertheless, it cannot be concluded, that the synthetic index may be descriptive enough.

Another engaging research about understanding SE in Eastern Europe was made by Ligita Gasparėnienėa, Rita Remeikienėa and Markku Heikkila. In this article under scope in the relation between SE and the economic situation in Ukraine. The results of the theoretical analysis and empirical research propose the following conclusions. The general impact of the shadow economy determinants can be multidirectional. The impact can be mitigated/reinforced considering the measures applied by the government for the reduction of the shadow economy.

Given calculations were revealed that the scope of the shadow economy in Ukraine is compositely influenced by the tax rate, overall employment rate, imports of goods and services, GDP per capita and participation of working-age people in the labour market. GDP per capita, more active participation of working-age people in the labour market and an increase in volumes of imports have a negative impact, while a tax rate increase and a growth of overall labour force have a positive impact on the scope of the shadow economy in the country. The impact of the latter factor is recommended to be confirmed by applying qualitative research methods.

Next paper by Mario Solis-Garciaa and Yingtong Xie tries to use a dynamic general equilibrium model approach for measuring the shadow economy. They proposed a methodology for measuring the size and properties of the shadow economy by using a two-sector dynamic deterministic general equilibrium model with four different variables: hours worked, investment-specific productivity, formal productivity, and shadow productivity. As it was found out the shadow productivity trend is endogenous, in the sense that it is an exact function of model parameters and the other three trends. After applying a methodology to a set of seven Latin American and Asian countries and document several empirical regularities that emerge from that analysis, the most important one being that the volatility of shadow sector output is considerably larger than the one informal sector output.

Conclusions

            Still, to this day there is no general approach to measure and understand the phenomenon of the shadow economy. There are more or less the same approaches to comprehend the roots of it, but it should be mentioned, that in different countries it may influence the economy in a good and bad way. The most prominent examples are coming from the emerging and transitioning economies. From the factors shall be said, that they may be endogenous and exogenous, but there is no methodology, where the influence of each factor could be measured.

            Shadow Economy is a great problem for the government, but for most of the part, it is the only way for people to survive. Although many approaches and visions towards SE were presented, still there is no clear macroeconomic concept of general understanding of the matter.

Sources

1.      https://en.wikipedia.org/wiki/Black_market

2.      https://www.investopedia.com/terms/u/underground-economy.asp

3.      https://marketbusinessnews.com/financial-glossary/shadow-economy-definition-meaning/

4.      https://pdfs.semanticscholar.org/6e64/96c443a875ae9c4ea2ed6476ccd12d2ea7ee.pdf

5.      https://www.quora.com/What-are-the-advantages-and-disadvantages-of-an-informal-economy

6.      https://www.sciencedirect.com/science/article/pii/S016028961500015X

7.      https://www.sciencedirect.com/science/article/pii/S2212567115002294

8.      https://www.sciencedirect.com/science/article/pii/S1822801116300303

9.      https://www.sciencedirect.com/science/article/pii/S0305750X15002077

10.  https://www.sciencedirect.com/science/article/pii/S0164070417300010

Why Behavioural Economics matters?

Author: Vsevolod Klivak

                The rational choice and understanding of customer behaviour were and are quite important topics for economists to comprehend.  In the Neoclassical approach, scientists use the “homo economicus approach”, which means, that every decision, which an economic actor would make has to be about maximisation or minimisation. Thus they have an assumption, that best decision shall be made or at least close to one. Is it really the truth? Probably not quite. The human being does not machine, there are more than only efficiency numbers, which people use for making the decision. Therefore behavioural economics with more psychological approach may give a better understanding of decision making in the economy frame. The field of study known as behavioural economics initially began as a purely academic attempt at modelling irrational consumer choices, thereby challenging the notion of the rational consumer of traditional economics.

Applications

Nudge is a concept in behavioural science, political theory and economics which proposes positive reinforcement and indirect suggestions as for ways to influence the behaviour and decision making of groups or enforcement. Nudging stands out from different approaches to accomplish consistency, for example, training, enactment or requirement. The idea has affected numerous lawmakers over the world. Several nudge units exist around the world at the national level as well as at the international level (OECD, World Bank, UN).

But there are some concerns about the theory. Tammy Boyce, from public health foundation The King's Fund, has said: "We need to move away from short-term, politically motivated initiatives such as the 'nudging people' idea, which is not based on any good evidence and doesn't help people make long-term behaviour changes." Another critique comes from Hausman & Welch. They have inquired whether nudging should be permissible on grounds of distributive justice; Lepenies & Malecka have questioned whether nudges are compatible with the rule of law. Essentially, lawful researchers have talked about the role of the nudges and the law. Behavioural economists such as Bob Sugden have pointed out that the underlying normative benchmark of nudging is still homo economics, despite the proponents' claim to the contrary.

Behavioural game theory, invented by Colin Camerer, analyses interactive strategic decisions and behaviour using the methods of game theory, experimental economics, and experimental psychology. Experiments incorporate testing deviations from common disentanglements of monetary hypothesis, for example, the autonomy maxim and disregard of charitableness, decency, and surrounding impacts. On the positive side, the technique has been connected to intelligent learning and social inclinations. As an examination program, the subject is an advancement of the most recent three decades.

The central issue in behavioural finance is explaining why market participants make irrational systematic errors contrary to the assumption of rational market participants. Such errors affect prices and returns, creating market inefficiencies. The study of behavioural finance also investigates how other participants take advantage (arbitrage) of such errors and market inefficiencies. Within behavioural finance, it is assumed the information structure and the characteristics of market participants systematically influence individuals' investment decisions as well as market outcomes.

Behavioural finance highlights inefficiencies, such as under- or over-reactions to information, as causes of market trends and, in extreme cases, of bubbles and crashes. Such reactions have been attributed to limited investor attention, overconfidence, overoptimism, mimicry (herding instinct) and noise trading. Technical analysts consider behavioural finance to be behavioural economics' "academic cousin" and the theoretical basis for technical analysis.

There is the opposite approach. The efficient-market hypothesis was developed by Eugene Fama who argued that stocks always trade at their fair value, making it impossible for investors to either purchase undervalued stocks or sell stocks for inflated prices. As such, it should be impossible to outperform the overall market through expert stock selection or market timing, and that the only way an investor can possibly obtain higher returns is by chance or by purchasing riskier investments. His 2012 study with Kenneth French supported this view, showing that the distribution of abnormal returns of US mutual funds is very similar to what would be expected if no fund managers had any skill—a necessary condition for the EMH to hold. The efficient-market hypothesis (EMH) is a theory in financial economics that states that asset prices fully reflect all available information. A direct implication is that it is impossible to "beat the market" consistently on a risk-adjusted basis since market prices should only react to new information.

Conclusion

Behavioural economics is undoubtedly a fresh way to address essential economics issues. Although I have mentioned the best sides of the theory, there are some aspects, which are not so robust. First of all theory itself is best described as science between psychology and economic theory, therefore it may be some bad products of its fusion, like basic concerns about psychology. Measurement and estimation are not feasible enough. Products of the BE are mostly prognoses or suggestions, which are based on the experiments or theory of actions. Also worth mentioning, that on a macro level “homo economicus” approach are more common, thus BE is more descriptive for micro level, but still there are plenty applications of BE, which I haven’t mention. Works of Thaler and Kahneman have shaped the modern path of economic decision making, but still, there is a space for further studies.

Literature:

1)      https://en.wikipedia.org/wiki/Behavioral_economics

2)      https://www.investopedia.com/terms/b/behavioraleconomics.asp

3)      https://www.behavioraleconomics.com/resources/introduction-behavioral-economics/

4)      https://www.theguardian.com/world/2017/oct/09/what-is-behavioural-economics-richard-thaler-nobel-prize

5)      https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3680155/

6)      https://www.investopedia.com/terms/b/behavioralfinance.asp

Gig economy as new state of labour market

Author: Vsevolod Klivak

                Today we experience an alteration in many parts of the economic system, for the labour market they are pretty drastic. Although main principals of labour won’t change, a way of addressing issues, like market inequality, failures and sufficiency wages is already different. In this paper terms like “gig economy”, “independent contractor” and “withdrawal benefits” will be overlooked with approaches from digital and innovative economies point of view.

                At first, it’s expedient to work at the validation of this process. For example, there are numerous platforms for small jobs or some errands, where job seekers may find a nice proposal. Then let’s take a look at the statistics. According to Statista number of gig workers now is 57.3 m, median weekly income of male gig freelancer is 653 US dollars. Paul Oyer estimated that freelancers earn 6% less for a year, but 15% more per hour. Nearly 3-in-10 American workers earn some form of income through independent work and gig opportunities like Uber and Airbnb. There are plenty other overwhelming facts like next ones:

• ·         Nearly eight in ten (77%) freelancers said the best days of the freelance job market are still ahead.
• ·         More than half (53%) began freelancing by choice, not a necessity.
• ·         The two most common reasons for going freelance were “to earn extra money” (68%) and to “have flexibility in [their] schedule” (42%).
• ·         53 million Americans — 34 percent of the U.S. workforce — are working as freelancers.
• ·         Nearly half (43%) of freelancers said they expect their income to increase in the coming year.

                At first, I want to describe what this “market” means? In the past, it was in the newspapers, but now it came in new digital form, and it’s even in a much more advanced way that LinkedIn could give to the table. “Upwork” and “TaskRabbit” are reshaping approach to finding a one-time job and making much easier for independent contractors to find a project. As the matter of fact, these platforms could be distinguished by “friction”. It means how often employer is engaged with the temporary worker. For instance Uber is low friction platform, your ride, which every driver provides is an only small time of engagement and services like UpWork are high friction. For example, a web designer has to work approximately one week for the project, it means higher frequency with the customer. Now let’s look at some statistics and term “independent contractor” from the other perspective. An independent contractor is a person or entity contracted to perform work or provide services to another entity as a non-employee. As a result, independent contractors must pay their own Social Security and Medicare. The payer must correctly classify each payee as either an independent contractor or an employee. Another term for an independent contractor is a freelancer. Terms like freelance, temp jobs and sharing economy jobs may be used together in the umbrella term “gig job”.

                Also worth noticing the way of estimation of the population in gig world, although it was observed mostly in the USA, the main approaches may be applicable to the EU. So they are next: Observation from consultant agencies, most prominent are McKinsey and EY, some goverment sources are already gathering information specifically about freelancers, but still data may be bit dubious. Companies like Uber and AirBnB also gather data. Albeit to find data source itselt not an issue, but quality of data can be quite tricky. Firstly there is a big distiction between full-time gig workers and people, who just want to augment their main income. Also frequency of the workers could be pretty different. People, who giving a ride once per mounth and once per day are uncomparable. All of that small miscalculation or misgrooping could significantly beguile the researcher.  Therefore it's vital to choose the metrics wisely.

           New labour approach is better or just different? That’s indeed a tricky question. Let’s take a glance at benefits at first, they're quite a few for firms and jobseekers. Efficiency wage works here by other rules, that’s why for people easier to find a job and for employees, it’s more efficient, in the sense that they could find cheaper workforce and evade the social taxation and securities, threats will be discussed afterwards. Also worth mentioning, that workers may get much wider flexibility, not only with time but also with the possibility to choose clients.  What about “withdrawal benefits”?     
        Quite vital will be a discussion of threats or better to write: undersigned alternatives for the classical “social safety net”, which is provided by long-term employment. At first gig economy type of employment shall be accepted as different from others. It could be done in two ways. First one more commercial, the insurance and senior funds. Another way to make it government way of treatment independent contractors as normal workers. But where to find the money for that expenses? For fairness, it shall be said, that some tax should be introduced for that kind of workers. Although taxation is a pretty heavy topic for debates, it will be a feasible solution for making a social find for freelancers. That will probably will make at least some “safety net”. Now issues, which different freelancers encounter are overrun the funds, mini depth situation and using help from the spouses.

                For the conclusion, next may be induced. We are facing drastic changes in the labour market and other vital parts of the economic system. Rise of “gig economy” is hard not to see, because it is seemingly expedient alternative for classical type of employment in the Information Era, but like with other new wave things government and system in general just not prepared for drastic alteration and quite a few crucial things have to be considered to regulate some dubious parts of freelance. Some will say, that the rise of independent contractors won’t bring any significant benefits and want to slow it down, others praise new job platforms, as a fresh solution in the contemporary situation. The truth however somewhere in between. One thing is true though.  Art of self-employment has change and everyone has to coup with that.

References

https://whatis.techtarget.com/definition/gig-economy

https://www.investopedia.com/terms/s/statutory-employee.asp

https://www.youtube.com/watch?v=Qb4SR-uoeUM

https://www.investopedia.com/terms/e/exempt-employee.asp

https://en.wikipedia.org/wiki/Personnel_economics

https://www.investopedia.com/terms/i/independent-contractor.asp

https://www.investopedia.com/terms/g/gig-economy.asp

https://www.investopedia.com/terms/w/withdrawal-benefits.asp

https://en.wikipedia.org/wiki/Efficiency_wage

Gig economy in the U.S. Dossier from Statista

 https://www.forbes.com/sites/gregoryferenstein/2015/12/12/the-gig-economy-appears-to-be-growing-heres-why/#d9bb985288bf

https://www.bls.gov/careeroutlook/2016/article/what-is-the-gig-economy.htm#pros-and-cons-of-gig-work

http://www.pewinternet.org/2016/11/17/gig-work-online-selling-and-home-sharing/

http://www.pewresearch.org/fact-tank/2016/11/18/why-join-the-gig-economy-for-many-the-answer-is-for-fun/

http://fu-web-storage-prod.s3.amazonaws.com/content/filer_public/c2/06/c2065a8a-7f00-46db-915a-2122965df7d9/fu_freelancinginamericareport_v3-rgb.pdf

https://smallbiztrends.com/2016/07/20-surprising-stats-freelance-economy.html

https://www.fastcompany.com/3066905/the-future-of-work/how-the-gig-economy-will-change-in-2017

http://www.usnews.com/news/articles/2016-10-11/1-in-3-workers-employed-in-gig-economy-but-not-all-by-choice

https://nation1099.com/gig-economy-data-freelancer-study/

Daria Tykhonova

Name: Daria Tykhonova

Nationality: Ukrainian

Previous education: Economic Theory, National University of Kyiv Mohyla Academy, Ukraine

What did you get from MA Quantitative Economics? 

 It has developmed my analytical skills, understanding of quantitative methods and how to work with data. It gave me good background for my current position as business analyst.

Exchange, Research and Internship Experience: Erasmus exchange in Bologna University (Rimini campus) and Internship at Praxis. I was working on a research focusing on absenteeism and presenteeism, which involved building multilevel models.

Current position: Business Analyst, Finnair Business Services

Dagmar Nurges

Name: Dagmar Nurges

Nationality: Estonian

Previous education: Mathematics, University of Tartu, Estonia

What did you get from MA Quantitative Economics?

I use many skills and more importantly, the way of thinking obtained during the program in my everyday work. It is also beneficial that the program was conducted in English as it is more and more common that people of different nationalities work together, which is also the case with my current employer.

Exchange, Research and Internship Experience: Internship at Eesti Pank where I made an overview of macroprudential policies of OECD countries.

Current position: Credit Risk Officer, Bigbank

Elizaveta Lebedeva

Name: Elizaveta Lebedeva

Nationality: Russian

Previous education: Finance, Moscow State University of Economics, Statistics and Informatics, Russia

What did you get from MA Quantitative Economics? 

Wide range of programming and statistical courses helped me to step into Data Science world. Also, advanced level of quantitative courses gave a strong background which helped me to better understand Machine Learning field. The great thing is that students have the wide range of open doors after graduation, both in academia and in industries (different position as economist, analyst, Data Scientist, product managers, etc.)

Exchange, Research and Internship Experience: Exchange Semester at University of Mannheim (one of best German Universities in Business and Economics) and Delta Conference at University of Tarty, 2018 - gave a talk related to topic of Master thesis

Current position: Data Scientist, Taxify