Browsing by Subject "Taloustieteen yleinen opintosuunta"

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  • Jämsä, Pentti (Helsingin yliopisto, 2021)
    The purpose of the thesis is to explore the link between labour markets and transportation accessibility. Accessibility has been estimated with logsum and travel times of public and private transportation. Logsum as a concept has been used in the transportation system estimation, all though all in all logsum as a measure of accessibility is not as established as travel times. This way the overview of the effects of accessibility to employment is thorough. I also evaluate how impact assessment of transportation is done historically and how wider economic impacts are considered in the framework. Cost-benefit analysis is widely used, but the framework should be improved on how to include wider economic impacts. The methods section goes through how transportation system estimation is done. The used data in the estimation is collected from three dependencies. These are the interview-based traveling habits from Helsingin Seudun Liikenne, Statistics Finland metropolitan regional employment statistics and travel times of public and private transportation from Helsinki Region Travel Time Matrix. In the results I go through the empirical model. Travel time, logsum and employment are all viewed as an elasticity measure. The results of the model are reasonable and in line with the previous literature, but the coefficient of determination ends up quite low. This means that the results still need confirmation, either through better data or better estimation model. My results can be viewed as a preliminary result on the link between employment and accessibility.
  • Kuokka, Karri (Helsingin yliopisto, 2021)
    Hinnoittelualgoritmien käyttö on yleistynyt viimeisen vuosikymmenien aikana. Tähän on vaikuttanut erityisesti sähköisten markkinapaikkojen eli verkkokauppojen suosion kasvu. Lisäksi algoritmeihin liittyvä teknologia on kehittynyt ja hinnoittelualgoritmeihin liittyviä palveluita on yhä helpommin yritysten saatavilla. Tässä tutkielmassa tarkastellaan hinnoittelupäätöksissä käytettävien algoritmien vaikutuksia markkinakäyttäytymiseen, missä useat yritykset koordinoivat toimiaan saadakseen kilpailullista markkinatilannetta korkeampia voittoja. Tällainen yritysten välinen kilpailun vastainen yhteistyö, eli kolluusio, on usein kuluttajien ja kilpailun kannalta haitallista. Siksi myös hinnoittelualgoritmien käytön vaikutuksia kolluusion on syytä tarkastella. Tämän tutkielman tavoitteena on tarkastella hinnoittelualgoritmien vaikutuksia erityisesti hiljaiseen kolluusioon. Tarkastelun kohteena on myös se, kykenevätkö tekoälyyn perustuvat algoritmit kolluusion autonomisesti eli täysin ilman ihmisten ohjausta. Tutkielman kirjallisuuskatsauksessa syvennytään tekoälyyn perustuviin algoritmeihin ja hiljaiseen kolluusioon teoriatasolla. Hinnoittelualgoritmien vaikutuksia hiljaiseen kolluusion on vaikea tutkia empiirisin menetelmin, koska hiljaista kolluusiota voi olla vaikeaa havaita markkinoilta ja yritykset harvoin paljastavat hinnoittelussaan käyttämiään algoritmeja. Tästä syystä tutkielman toisena tutkimusmenetelmänä on kokeellinen tietokonesimulaatio, jonka avulla tarkastellaan hinnoittelualgoritmien vaikutuksia hiljaiseen kolluusion. Simulaation avulla pyritään kokeellisesti selvittämään, kykenevätkö tekoälyyn perustuvat hinnoittelualgoritmit kolluusioon autonomisesti. Tutkielman tulosten mukaan hinnoittelualgoritmien käytöllä voi olla vaikutuksia hiljaiseen kolluusioon. Hinnoittelualgoritmien käyttö voi muuttaa markkinarakenteita vaikuttaen hiljaiseen kolluusioon. Vaikutukset markkinarakenteisiin ovat kuitenkin hieman epäselvät ja vaikutukset hiljaiseen kolluusioon voivat olla joko lisääviä tai vähentäviä. Lisäksi hinnoittelualgoritmit voivat toimia myös välillisesti hiljaisen kolluusion fasilitaattorina. Tutkielman tuloksista erityisen mielenkiintoinen on tekoälyyn perustuvien hinnoittelualgoritmien kyky oppia kolluusio autonomisesti. Tätä tulosta tuki aiempaan kirjallisuuteen perustuva kirjallisuuskatsaus sekä tässä tutkielmassa toteutettu tietokonesimulaatio. Yleistyvä hinnoittelualgoritmien käyttö ja niiden kehittyminen voivat aiheuttaa täysin uudenlaisia ongelmia kilpailun tehokkuuden turvaamisessa ja sääntelyssä. Kokeelliseen tutkimukseen perustuvien tuloksien mukaan tekoälyyn perustuvat algoritmit näyttäisivät kykenevän autonomisesti kolluusioon. Jatkossa tutkimusta hinnoittelualgoritmien vaikutuksista kolluusioon olisi syytä laajentaa. Haasteeksi voi kuitenkin muodostua se, miten tutkimusta voidaan toteuttaa sellaisessa taloudellisessa ympäristössä, joka vastaa riittävän tarkasti todellista markkinatilannetta.
  • Kanervo, Atte Jonatan (Helsingin yliopisto, 2021)
    This thesis investigates the tax base and allocation choices in international corporate income tax architecture and provides an evaluation of the effects of the choices made in three different systems: the current system, residual profit allocation, and OECD Pillar One. International corporate income tax design has a significant effect on the functioning of the international economy and on the welfare of individuals. Thus, making the correct design choices is extremely important. This thesis argues that the international corporate income tax system should be designed following certain important principles of taxation: 1) fairness, 2) economic efficiency, 3) robustness to avoidance, 4) administrative ease, and 5) incentive compatibility. The different systems are then introduced in turn and evaluated against these criteria. The thesis finds that the current system suffers from certain conceptual weaknesses that leave significant room for improvement with regards to the set criteria. It is further argued that a reform is required for the continued functioning of the international system. Such a reform could be introduced in the form of residual profit allocation. OECD Pillar One proposal involves elements of residual profit allocation, but in comparing the different systems with each other, this thesis argues that the OECD proposal is too narrow in scope to gain the full benefits of a residual profit allocation system.
  • Kurppa, Sara (Helsingin yliopisto, 2021)
    Työvoiman riittävyys on yksi tulevaisuuden suurimmista poliittisista kysymyksistä länsimaissa. Tähän pyritään löytämään ratkaisuja muun muassa pidentämällä nykyisten työntekijöiden työuria, kuitenkaan lisäämättä työkyvyttömyyseläkkeiden aiheuttamia kustannuksia. Yhtenä mahdollisena ratkaisuna pidetään ammatillisen kuntoutuksen tarjoamia työhön paluu mahdollisuuksia työkokeilun tai uudelleen kouluttautumisen avulla terveydentilalle sopivampaan ammattiin. Tutkimuksen alussa käydään tarkemmin läpi ammatillista kuntoutusta, sen prosessia, kohderyhmää sekä mahdollisia kuntoutuskeinoja. Tavoitteena on selvittää henkilötasoisten muuttujien vaikutusta ammatilliseen kuntoutukseen osallistumiseen. Lisäksi on tarkoitus selvittää, onko osallistumista mahdollista ennustaa jo hakemusvaiheessa. Aiemmat tutkimukset ovat osoittaneet iän, sukupuolen, koulutustausta ja sairauspoissaolojen lukumäärän olevan merkittäviä muuttujia tätä tutkittaessa. Tutkimuksen aineistona on yhden eläkelaitoksen vuosina 2016-2019 antamat ammatillisen kuntoutuksen ennakkopäätökset. Tutkimusmenetelminä käytetään khiin-neliötestiä, logistista regressiota sekä satunnaismetsää. Logistisen regression tuloksien mukaan tärkeimpiä ammatilliseen kuntoutukseen osallistumiseen vaikuttavia muuttujia ovat henkilön sukupuoli, kuntoutusrahan määrä, tietyt ammatit sekä vuosiansioiden määrä viimeisen viiden vuoden aikana ennen kuntoutusoikeuspäätöksen saamista. Ennustemalliksi luodun satunnaismetsän tulosten perusteella ammatilliseen kuntoutukseen osallistumista on mahdollista ennustaa. Positiivisen luokan ennustustarkkuus on selvästi parempi kuin negatiivisen luokan. Khiin-neliötestin, logistisen regression ja satunnaismetsän tuloksissa on kuitenkin myös toisistaan eroavia tuloksia.
  • Tossavainen, Tuuli (Helsingin yliopisto, 2021)
    Asymmetric information in insurance markets is the result of policyholders, the buyers of insurance, having more information about their own risk types and preferences than the insurer. Informational asymmetry between the insurer and policyholders can lead to non-optimal insurance prices and quantities which reduce market efficiency. While the presence of asymmetric information has been widely studied in several insurance markets, it has not been empirically studied in the Finnish automobile insurance market before. This thesis aims to fill this gap in literature. The Finnish automobile insurance market consists of two types of insurance. Motor liability insurance is required by law from all vehicles used for driving in traffic. Also, voluntary automobile insurance can be acquired in addition to the mandatory motor liability insurance. In this thesis, the presence of asymmetric information is studied by comparing the occurrence of motor liability insurance claims, conditioned with the pricing variables used by the insurer, between policyholders who only have a motor liability insurance policy and policyholders with an additional automobile insurance policy. The data set used in this thesis is from a single Finnish insurance company. The data set is from the year 2019 and it contains nearly 105,000 motor liability insurance policies. The data include all variables observed by the insurer. Several regression specifications and the widely used positive correlation test are used in this thesis to study the correlation between insurance coverage and motor liability insurance claims. The results of this thesis suggest that signs of asymmetric information are not present at aggregate level in the Finnish automobile insurance market in question. However, different subgroups of policyholders show signs of asymmetric information: After controlling for the pricing variables, policyholders with an automobile insurance policy with the largest coverage show a positive correlation between buying automobile insurance and motor liability insurance claims whereas policyholders with an automobile insurance policy with the third largest coverage show a negative coverage-claims correlation. However, the results from different regression specifications regarding different automobile insurance coverages were not unanimous and thus the results are left ambiguous. In addition, new policyholders considered as experienced drivers show a negative correlation between motor liability insurance claims and having automobile insurance coverage. On the contrary, policyholders considered as experienced drivers with 1–2 years of company experience do not show signs of asymmetric information. The result suggests that the insurer learns from its repeat customers as signs of informational asymmetry disappear over time. Moreover, policyholders considered as unexperienced drivers do not show signs of asymmetric information regardless of the length of their customership in the firm. The results are in line with previous research.
  • Tiililä, Nea (Helsingin yliopisto, 2019)
    The regulatory framework for financial regulation has developed much in the Europe after the financial crisis. The use of borrower based macroprudential instruments as regulatory tools has become popular among the European Economic Area -countries. Already 21 out of 31 EEA-countries have at least one borrower based macroprudential instrument in use. The most commonly used borrower based instruments are Loan to Value (LTV) limit, Loan to Income (LTI) limit, Debt to Income (DTI) limit, Loan Service to Income (LSTI) limit, Debt Service to Income (DSTI) limit, amortisation requirement and maturity limit. As these instruments are only recently introduced as regulatory tools in Europe, their effectiveness and transmission channels are still under discussion. The aim of this master's thesis is to contribute to the ongoing discussion of the effectiveness of the instruments. This thesis provides a broad literature review in order to understand the transmission of each of the borrower based instruments and to explore previous findings of the impacts of the instruments. Further, an empirical analysis is formed by using a panel vector autoregression (PVAR) model in order to study whether borrower based macroprudential instruments have any effect on housing market stability and real economy in the Europe. The data that is used to answer this question consists of growth rates of mortgage stock, house price index, construction index, household consumption and GDP. According to the literature review, the borrower based macroprudential instruments function through four different transmission channels. These are the credit demand channel, expectations channel, resilience channel and anti-default channel. The empirical analysis provides evidence that tightening the borrower based instruments reduces mortgage growth. House prices react negatively to a policy shock in the short run but positively in the long run. Construction reacts negatively to a policy shock. Household consumption on its behalf responds to a policy shock positively in the short run but negatively in the long run. Finally, GDP responds to a policy shock negatively. However, the result concerning construction growth is the only one which is statistically significant in a 95% confidence level and all the other results lack statistical significance. Overall, the empirical results of this thesis provide slight evidence that regulating borrower based macroprudential instruments restrain the growth of mortgage stock, which for its part should enhance the stability of housing markets in Europe. Further, the impact on economic growth is likely negative. However, the results are not statistically significant in a 95% significance level. The difficulties in fitting the model and the lack of significance may implicate that the chosen model might not be the most suitable one for studying the efficiency of borrower based macroprudential instruments.
  • Walta, Veikko (Helsingin yliopisto, 2020)
    The determinants of FDI have been a topic of interest in economics since the 1980s and this paper aims to contribute to this field. This study aims to measure how associated FDI is with the political risk as well as to see the extent of this relationship in Turkey in the years 1996–2017. The political risk is measured as a change in indexes that are provided by the World Bank, Freedom House, and Transparency International. These political indicators are Political Rights, Civil Liberties, the Corruption Perceptions Index, Regulatory Quality, Voice and Accountability, Rule of Law, Government Effectiveness, Control of Corruption, and Political Stability. The earlier literature on FDI and political risks is mostly empirical and there has not been much theoretical research. Chakrabarti analyzed the past studies on FDI and its determinants in 2001 and found out that in the earlier research, almost every explanatory variable of FDI except the market size was sensitive to small changes in the conditioning information set, casting doubt on the robustness of the results. There have also been conducted studies that address political risk or equivalent concepts. The 2005 research of Busse and Hefeker had the same topic as this paper but their data consisted of many countries and they employed two different panel models. One was a fixed-effects panel analysis while the other utilized a generalized method of moments estimator. I selected three model specifications for the time-series regression analysis. All three specifications have market size as a control variable and the other two also have the economy’s growth rate and trade openness. The third has the inflation rate as the final control variable. The data have a small number of observations which limits the options available for the empirical part of the study. Out of the nine political indicators, Regulatory Quality is the only political indicator that is not associated with FDI, while the results on the Corruption Perceptions Index and Control of Corruption are inconclusive. The rest six are associated with FDI. The Rule of Law index has the highest estimated coefficient value of the World Bank indicators and the Political Rights index has the highest estimated coefficient value of the Freedom House’s indicators.
  • Ahonen, Elena Venla Maria (Helsingin yliopisto, 2017)
    The aim of this thesis is to demonstrate the importance of selecting feasible and, preferably data-based prior assumptions for the Bayesian time-varying coefficient vector autoregressive model (TVC VAR model for further reference) of Primiceri (2005) and Del Negro and Primiceri (2015). The TVC VAR model would be suitable for quantifying, for example, the impacts of different monetary policy or fiscal policy regimes. The biggest advantage of the TVC VAR model is that it takes into account both changes in economic policy and in the private sector behaviour. The latter feature makes the model very compelling to use, because the private sector plays an important role in facilitating mote stable change in monetary and fiscal policy regimes. In complex mathematical models, such as the TVC VAR model, the objectiveness of the model may be compromised by deliberate selection of parameters. The TVC VAR model uses the Bayesian approach, which means that the researcher’s choice for the prior assumptions for the model plays an important role in the estimation. Unfortunately, Primiceri’s (2005) approach for selecting hyperparameters for the model is poorly explained and difficult to follow. Given that the model depends only for a small number of hyperparameters, it might be possible that the model can be tuned in a predefined way. To investigate whether the TVC VAR model can be tuned according to a researcher’s preferences, I design a proof of concept approach for optimising the hyperparameters of the model according to a set of predefined results. In other words, my research question is: could one tune the TVC VAR model to produce results according to the researcher’s bias? In my proof of concept approach I tune the TVC VAR model for six different targets for the Finnish government consumption multiplier. Given that Finland is a small open economy, Primiceri’s (2005) original hyperparameter values for the United States are not feasible and other values have to be used. The results from my proof of concept analysis show that the TVC VAR model can be tuned for predefined results, which shows that the practical reliability of the model can be easily compromised. My findings highlight the need for a comprehensible, data-based approach for selecting the hyperparameters for the model.
  • Liukkonen, Sini (Helsingin yliopisto, 2020)
    High growth enterprises are important contributors to the aggregate economy but not much is known of their dynamics. Based on previous literature it is quite clear that their growth is usually not very persistent. The purpose of this study is to find the enterprise characteristics that positively affect the length of the growth period. In this study, extensive micro data from Statistics Finland is used. The data comprises of information from business register, financial statements, foreign trade, ownership and employee registers. Survival analysis methods are used to get information on the effects of different enterprise characteristic. The models account for the time-varying nature of the covariates and the coefficients. Based on the results, it is found that many of the characteristics have time-varying effects and the effects are not the same for all size classes of enterprises. It is quite clear though that access to foreign markets and innovativeness are important positive factors to the length of the growth period whereas size and age have negative effects. Survival analysis methods seem to fit quite well to this framework and they seem to produce robust results.
  • Kurki, Jaakko (Helsingin yliopisto, 2019)
    Wage discrimination occurs when employees of equal productivity receive different wages due to characteristics such as ethnicity, sex, or nationality, which do not affect their productivity directly. One of the common challenges in empirical research on wages has always been the challenge of determining individual employees` productivity. Professional sports leagues such as NBA (National Basketball Association) provide an ideal setting for the study of salary discrimination, as the salaries, players` backgrounds, and different statistical measures of players` performance throughout their whole careers are available publicly. Therefore, economists have used professional sports leagues when studying salary discrimination by ethnicity or nationality. The objective of this research is to find out whether salary discrimination by nationality occurs in the NBA during the period between 2016 and 2018. The research consists of a literature review that introduces previous findings on salary discrimination by nationality in the NBA, and an empirical part which aim is to find out whether this discrimination still occurs in the 2016 – 2018. The dataset of this thesis consists of statistics that measure NBA players' on-court performance and salary during the 2016 – 2017 and 2017 – 2018 seasons, as well as their nationality, and physical attributes. The empirical analysis is carried out using linear regression-analysis, which has been a standard in previous researches on salary discrimination by nationality in the NBA. Moreover, this study applies Blinder-Oaxaca decomposition, which is one of the standard tools used in salary discrimination studies in general. The statistical analysis of this study does not find discrimination by nationality against either foreign or domestic born NBA-players during our sample period. Nevertheless, foreign players earn, on average, around USD 500,000.00 higher annual salaries than their American contemporaries. However, according to our analysis, this difference is explained by foreign players' on-court performance rather than their nationality. Some previous researches find that foreign players from large economic markets receive sizeable salary premiums due to marketing possibilities in their home countries. However, this study does not find the market size of a player's home country to have a statistically significant effect on their salaries. The earliest literature on salary discrimination by nationality in the NBA dates back to the 1990s. Over the years, the results of previous researches have varied between foreign or domestic players being discriminated against by nationality. However, as different tools for statistical analysis on player performance have improved drastically and basketball has indeed become a global sport over the years, it seems that discrimination by nationality does not occur in the NBA anymore in 2019.
  • Kaur, Anmol (Helsingin yliopisto, 2020)
    This thesis aims to answer the question of whether monetary policy influences stock prices in the United Kingdom and Finland. These countries have been chosen due to their economic differences. The United Kingdom is an open relatively large economy with an independent monetary policy set by the Bank of England. Finland, on the other hand is a small open economy, and it is part of a monetary union called Eurozone. Hence, the monetary decisions are made by the European Central Bank for all the members of the union. The research is conducted for the time period of 15 years (2003-2018) with monthly time-series data. The method used in the thesis is the structural vector autoregression model which allows for solving the endogeneity issue through imposing restrictions on the structure of the model. Hence, short-run restrictions and long-run monetary neutrality are applied to the model. The model is analysed using the estimation as well as impulse response functions. In order to consider the macroeconomic environment, variables such as inflation, commodity prices, and industrial production are used in the model. Moreover, a dummy variable is used to account for the financial crisis of 2008. The results of the structural vector autoregression estimation show there to be a statistically significant negative effect of monetary policy on stock prices for both countries. The impulse responses show that as contractionary monetary policy is implemented, stock prices tend to decrease. Contrarily, expansionary monetary policy results in an increase of stock prices. The effect of a monetary policy shock is larger on the stock prices and dissipates quicker in the United Kingdom. For Finland, the effect is minor, and it takes longer to dissipate. However, the effect is statistically significant for both countries
  • Rönkkö, Niko-Petteri (Helsingin yliopisto, 2020)
    In this thesis, I analyze the causes and consequences of the Asian Crisis 1997 and simulate it with Dynare. The model includes financial accelerator mechanism, which in part explains the dynamics and the magnitude of the crisis via balance sheet effects. I find that the major components of the crisis were highly similar to other crisis that had happened in other emerging economies: High levels of foreign-currency denominated debt, unsound financial regulation, and fixed exchange rates with skewed valuation. Even though this simulation do not specifically incorporated different exchange rate regimes into the simulation, the previous literature draw a clear conclusion that flexible exchange rates lessen the shock’s effects on the economy. Thailand, as well as other ASEAN-countries during the crisis, faced severe economic contraction as well as changes in political landscape: Due to the crisis, Thailand’s GDP contracted over 10 percent, the country lost almost a million jobs, and the stock exchange index fell 75 percent. In addition, the country underwent riots, resignation of ministers, and several political changes towards more democratic institutions, even though faced some backlash and re-entry of authoritarian figures later. As the crisis worsened, IMF collected a large rescue package that was given to ASEAN-countries with preconditioned austerity policies. The simulation with recalibrated parameter-values seems to be relatively accurate. The dynamics and the impact of the crisis is captured realistically with correct magnitudes. The financial accelerator mechanism accounts a large part of the shock’s impact on investment and companies net worth, but do not account much on overall decline in output.
  • Snellman, Oliver (Helsingin yliopisto, 2019)
    It has lately become a common practice among national authorities with macroeconomic mandates to build large Dynamic Stochastic General Equilibrium (DSGE) models to assist in forecasting and policy analysis. The Finnish Ministry of Finance has also developed a small open economy New Keynesian DSGE model, “KOOMA”. As DSGE models try to emulate the key features and dynamics of the economy, the crucial question is, how well do they function in accordance with reality? An answer to this question can be searched by using Structural Vector Autoregression (SVAR) models, which are natural econometric counterparts to DSGE models and are better suited for analyzing data. The aim of this study is to evaluate the calibration of KOOMA with a SVAR model, which is identified with sign restrictions. I compare impulse response functions from the SVAR model, which are found both statistically significant and robust to changes in model specifications, to the equivalent impulse response functions from KOOMA. The findings suggest, that KOOMA generally produce impulse responses with same signs as the SVAR model, but there are some differences in the magnitudes and persistence of the responses.
  • Mostýnová, Michaela (Helsingin yliopisto, 2019)
    In Finland, entrepreneurs (both employers and self-employed) are, compared to salaried employees, free to increase their compulsory retirement insurance contributions to the public pension fund; this being an alternative to additional saving for retirement in private pension funds. This thesis seeks to identify and further examine factors which supposedly influence entrepreneurs‘ perceived sufficiency of their retirement insurance payments . The purpose is to subsequently recommend retirement policy designs which would incentivize Finnish entrepreneurs to increase their contributions to the public pension fund. The empirical section of this work was conducted on a sample of 2 294 entrepreneurs (1 533 self-employed and 761 employers) who took part in the 2017 Labor Force Ad hoc Survey on Entrepreneurship carried out by Statistics Finland. The initial hypotheses gave rise to four categories of variables, presumably affecting sufficiency of retirement insurance contributions perceived by the study sample; namely, ’Personal characteristics & Business background’, ’Motivation’, ’Future perspectives’ and ’Job satisfaction & excitement’. The obtained results suggest that the majority of the selected variables have an effect on entrepreneurs’ perceived sufficiency of their pension insurance contributions. Besides, the factors identified as negatively affecting the perceived sufficiency of retirement insurance payments were more frequently present in the group of self-employed compared to the group of entrepreneurs (employers). Therefore, it is expected that the self-employed are more prone to pay themselves insufficient pension insurance contributions. However, all these factors are considered as incorrigible since they stem from the very nature of complex human behaviour. In this sense, the behavioural approach seems to be highly relevant when forming retirement insurance policies seeking to encourage prudent saving behaviour. This study applies an alternative approach of behavioural economics to the problematics of retirement saving. The first part of the thesis outlines foundations of behavioural economics which serve as a theoretical background for further analyses. For instance, propositions of procrastination, self-control and mental accounting are discussed.
  • Sandell, Hanna (Helsingin yliopisto, 2020)
    The inaccuracy of transport infrastructure projects’ cost estimation has become large issue especially because the amount of large mega projects has been increasing during past few years. The cost estimation inaccuracy is problematic because it biases the results of cost-benefit analysis, which is used to measure the profitability of a project. Subsequently this bias can lead to the misallocation of scarce resources. Besides the construction costs, cost estimation includes the computation of owner’s indirect costs. In this thesis, owner’s indirect costs cover the construction management costs and design costs of the project. According to the current instructions, indirect costs are calculated using fixed default values. As the currently used calculation method does not take the project’s individual properties into account, need for alternative approach has increased. Thus the objective of this thesis is to forecast owner’s indirect costs in the late phases of the infrastructure projects by applying two different machine learning models: linear multiple regression model and artificial neural network model. Additionally, the aim is to study, whether machine learning models provided can outperform the currently used instructions in the prediction of indirect costs. Aim of well-functioning forecast model would be to improve the cost estimation’s accuracy level. In this thesis, owner is defined as the government and indirect costs are only forecasted in later phases of the project. Research question is attempted to solve by applying two commonly used machine learning models: artificial neural network and multiple regression model. Neural network used in this thesis is a feedforward network, which learning mechanism is based on backpropagation algorithm. Multiple regression model utilizes traditional OLS method in the estimation of parameters’ values. Models are constructed with data provided by Finnish Transport Infrastructure Agency. Data includes infrastructure projects’ initial data and the actual shares of design and construction management costs of each project. As an outcome, this thesis provides two preliminary forecast models for owner’s indirect costs. The results also indicate that the neural network and regression model are able to forecast owner’s indirect costs in both categories with higher accuracy compared to the current instructions. Furthermore, study aided to recognize influential variables affecting the indirect costs. During the research process, also few improvements for further development of the forecast models were identified. From the machine learning models, neural network performs better in forecasting the design costs and regression model is able to forecast the construction management costs with slightly better accuracy. These results support the conclusion that costs with uncertain and missing information can be forecasted more precisely with more complex machine learning models, such as the artificial neural network. On the other hand, costs with comprehensive knowledge can be accurately predicted with simpler models, such as the multiple regression.
  • Ropa, Anton (Helsingin yliopisto, 2020)
    Työn tuottavuuden merkitys kansantalouden tasolla talouskasvulle ja mikrotaloustasolla yritysten tapauksessa niiden menestymiselle on merkittävä. Työn tuottavuuteen vaikuttavien tekijöiden kirjo on laaja ja tässä tutkielmassa keskitytään tarkastelemaan henkisen pääoman vaikutusta työn tuottavuuteen. Tarkastelun keskiössä on keinot henkisen pääoman konvertoimiseksi henkilöstötuottavuudeksi ja henkilöstötuottavuuden tilan mallintamiseksi. Tutkimuskysymyksenä tarkastellaan suomalaisten yritysten kannattavuuden ja henkilöstötuottavuusindeksin korrelaatiota. Henkisen pääoman olemassaolo ei ole tae yrityksen menestymiselle. Keskeistä henkilöstötuottavuuden kannalta on ottaa yrityksen omaama henkinen pääoma hyötykäyttöön. Haasteelliseksi tilanteen tekee se, että henkinen pääoma on sidoksissa yrityksen henkilöstöön. Tutkielmassa esiteltävän aiemman kirjallisuuden mukaan henkilöstötuottavuuden stimuloimisessa johtamiskäytänteet ja toimintatavat työyhteisöissä ovat mahdollisia keinoja vaikuttaa henkilöstötuottavuuden kehittymiseen työyhteisössä. Tutkielmassa henkilöstötuottavuutta kuvataan henkilöstötuottavuusindeksin (HTI) avulla. Menetelmä perustuu Ossi Auran ja muun tutkimusryhmän kehittämään malliin kuvata työyhteisön henkilöstötuottavuuden tilaa. Mallin keskeisiä piirteitä on jaotella henkilöstötuottavuus kolmen osatekijän summaksi: motivaatio, osaaminen ja työkyky. Tiedonkeruumenetelmänä henkilöstötuottavuusindeksin laskemiseksi toimii työyhteisöiden tilaa kuvaavien työyhteisökyselyiden hyödyntäminen. Yritysten taloudellista tilaa tarkastellaan tilinpäätöstietojen perusteella ja kannattavuuden indikaattorina toimii yrityksen käyttökateprosentti. Tutkimusmenetelmänä toimii pitkittäinen tutkimus. Aineisto on kerätty Työeläkeyhtiö Elon asiakkaille tarkoitetun työyhteisökyselyn avulla siten, että aineistoon valittu yritys on suorittanut vähintään kahdesti. Henkilöstötuottavuusindeksin ja yritysten taloudellisen tilanteen yhteyttä tarkastellaan lineaarista regressiomallia hyödyntäen. Empiirisessä osiossa tarkastelun kohteena on henkilöstötuottavuusindeksin ja yritystoiminnan kannattavuuden sekä henkilöstötuottavuusindeksin ja yritystoiminnan kasvun yhteys. Kannattavuuden muutosta kuvataan sekä käyttökateprosentin prosenttiyksiköittäisen että prosentuaalisen muutoksen avulla. Liiketoiminnan kasvua kuvataan liikevaihtoprosentin prosentuaalisen muutoksen avulla. Lineaarisen regressiomallin tuloksia tarkastellaan koko aineiston tasolla sekä osajoukkojen osalta valittujen määrittävien tekijöiden osalta. Tutkielman empiirisen osion tuloksien mukaan HTI:n muutoksien osalta, motivaatio on tärkein kolmesta osatekijästä. Itse tutkimuskysymyksen osalta saavutetut tulokset osoittavat, että henkilöstötuottavuuden ja yritysten kannattavuuden välillä ei havaita selkeää yhteyttä. Yhteyttä ei ole havaittavissa myöskään henkilöstötuottavuusindeksin ja liiketoiminnan kasvun väliltä. Tutkielman empiirinen osuus antaa hyvin perspektiiviä tutkittavaan ilmiöön ja antaa syytä aiempien tutkimusten tulosten kriittiselle tarkastelulle. Henkilöstötuottavuusindeksin muutoksien osalta motivaatio on tärkein kolmesta osatekijästä.
  • Viitaharju, Olli-Pekka (Helsingin yliopisto, 2020)
    Finanssikriisin seurauksena keskuspankkien rahapolitiikan liikkumavara on vähentynyt, mikä on johtanut vaihtoehtoisten rahapolitiikan strategioiden harkitsemiseen. Yksi ehdotetuista strategioista on siirtyminen hintatasotavoitteeseen. Hintatasotavoitteessa keskuspankki pyrkii pitämään hintojen kehityksen valitsemallaan tavoiteuralla ja korjaa inflaatiotavoitteesta poiketen menneiden shokkien aikaansaamat poikkeamat. Tutkielmassa tarkastellaan viimeaikaista tutkimusta hintatasotavoitteseen liittyen ja selvitetään voisiko hintatasotavoite olla parempi rahapolitiikan strategia kuin inflaatiotavoite. Tarkastelun keskiössä ovat hintatasotavoitteen vahvat oletukset, mahdolliset edut ja siirtymän riskit. Tutkimusmenetelmänä on kirjallisuuskatsaus ja tavoitteena on muodostaa kokonaiskuva tutkimuksesta ja selvittää mihin jatkotutkimus pitäisi suunnata. Hintatasotavoitteen mahdollisia etuja ovat hintojen pitkän aikavälin ennustettavuus, inflaation ja tuotannon volatiliteetin vähentyminen sekä parempi suoriutuminen nollakorkorajalla. Edut voivat olla haastavia saavuttaa ja ne nojaavat vahvoihin oletuksiin inflaatio-odotuksien muodostumisesta uuskeynesiläisen mallin mukaan sekä keskuspankin kyvystä sitoutua siihen strategiana. Hintatasotavoitteeseen siirtymiseen liittyy paljon riskejä ja kustannuksia, joiden seurauksena nettovaikutus voi olla negatiivinen. Talouden toimijat eivät välttämättä sisäistä uutta strategiaa, jolloin vaikutus inflaatio-odotuksiin jää vaimeaksi. Lisäksi se saattaa aiheuttaa haasteita keskuspankin kommunikaatiolle ja uskottavuuden ylläpitämiselle. Hintatasotavoite suoriutuu teoriassa inflaatiotavoitetta paremmin, mutta tulokset ovat malliriippuvaisia ja usein ristiriitaisia keskenään. Lisätutkimuksen tarpeet liittyvät inflaatio-odotuksien muodostumiseen, oppimisvaiheen kestoon ja keskuspankin uskottavuuden ylläpitämiseen. Keskuspankit näkevät hintatasotavoitteen riskit edelleen merkittävinä ja siirtymä olisi monella tapaa hyppy tuntemattomaan.
  • Koski, Maaria (Helsingin yliopisto, 2019)
    The measured gross domestic product, GDP, does not consider non-paid homework in its figures. However, the relative size of the so called household production is large both from time use perspective but also as monetary wise. According to Statistics Finland, the non-salaried homework was 39.8 % of the measured Finnish GDP in 2016. Moreover, Finns spent on average three hours and 21 minutes on daily basis on household production in 2009. Yet, the standard economic theory also excludes household production in the models although individuals are known to allocate their time between market work, homework and leisure. The real business cycle theory attempts to explain and study the properties of business cycles. In this Master’s Thesis, the household production is studied within the real business cycle (RBC) theory. The purpose is to compare the benefits of including household production into the real business cycle model to the standard alternative where it is excluded. Real business cycles are studied by constituting a dynamic stochastic general equilibrium model (DSGE) for both cases: one for the household production and one for the standard non-household production. The models constituted are for a frictionless closed economy. Both models are then calibrated with Finnish figures and simulated. The results indicate that market hours are procyclical in both models. However, the correlation between output and market hours is 1.33 times larger in the household production model than in the standard model. Also, the household production model generates highly countercyclical home hours. Yet, the Finnish time use data cannot prove the procyclicality of household production hours. The main reason is that the time use research is conducted only every ten years. Also, the timing of the research does not reconcile with the Finnish recessions. Hence, the data available cannot explain the countercyclical home hours indicated by the household production real business cycle model. In this sense, the results presented can only be taken as describing facts of the Finnish economy when household production is considered.
  • Kauhanen, Arttu (Helsingin yliopisto, 2019)
    In my thesis, I estimate the childhood exposure effects of regions in Finland on the probability of completing high school matriculation examination. I estimate the degree to which the differences in high school matriculation rates across regions are driven by the causal effects of places. I study almost 180,000 children who move across regions by exploiting variation in the age of children at the time of the move. I find that neighbourhoods might have a significant childhood exposure effect on girls of low-income families. The outcomes of girls of low-income families change linearly in proportion to the amount of time they spend growing up in a new area at a rate of approximately 6 % per year of exposure. It implies that children who move at birth would pick up 90 % of the difference in permanent residents’ outcomes between their origin and destination regions by the age of 16. The results for boys support the critical age model and imply that areas have no childhood exposure effects on boys: the outcomes of boys are unrelated to their age at the time of the move. This implies that the likelihood of boys to complete high school may be unaffected by the families' choices where to live, or boys are affected by the move to a new area at similar magnitude irrespective of the age at the time of the move. The estimation using data of all girls gives a less clear result, which might imply heterogeneity of exposure effects across parents' income levels. The results are robust to alternative specifications and to the overidentification test based on different birth cohorts.
  • Lähteenmaa, Juho (Helsingin yliopisto, 2020)
    In social sciences, as in health sciences, there is an increasing interest in exploring differences in treatment effects amongst subpopulations and even individuals. In many cases, researchers must rely on observational data where the assignment mechanism of the treatment is non-randomized. Nevertheless, by including a sufficient set of covariates in the used model, it is possible to draw a causal inference. However, some causal structures have proved to cause bias in the treatment effect estimates when particular pre-treated variables in them are conditioned. In existing literature there is no consensus as to how to treat these structures, especially in the heterogeneous treatment effect estimation case. The aim of this thesis is to explore how causal structures affect covariate selection in the heterogeneous treatment effect estimation context. The theoretical background of this subject is built on the potential outcomes framework and structural causal models. This thesis provides an overview of heterogeneous treatment effect estimation methods, including a more detailed view on the causal forest method. The second stage of the thesis is carried out by executing a simulation study where the causal forest method is applied with different causal structures. In each simulation, different sets of conditioned covariates are tested. The simulation study results prove almost consistent. In every simulation except one, a higher number of variables implicates improvement in performance. Surprisingly, this result is applicable even to the cases where structural causal models literature suggests not to condition all the variables. According to the results of the simulation study, a practical recommendation would be to include as many relevant pre-treated, non-instrumental variables in the model as possible. The results are in line with practical recommendations given in potential outcomes framework literature.