Projekty badawcze

Reliance on foreign banks in emerging European countries: consequences for credit volatility and the impact of fiscal imbalances

UMO-2016/21/B/HS4/00669 - OPUS

Foreign banks play an important role in the financial markets of emerging European economies. They are important source of financing of private sector spending, including investment. However, they also extend credit to governments and hold public bonds...

Mechanisms of creating social capital - analysis on the basis of Polish empirical examples

UMO-2016/21/D/HS4/00705 - SONATA

The main research objective is to analyse microeconomic mechanisms of social capital formation. The study will cover different factors determining joining social networks and activity in them, inclination to trust others, reciprocity, and creation of collaborative attitudes. There is a focus on explaining how participation in social networks (including both active membership in organisations and taking part in more informal social networks) transfers into trust, norms of reciprocity and collaborative attitudes. Researchers of social capital, in order to explain its origins, often rely on the presumption, that participation in voluntary associations has positive external effects. They assume that by acting in organizations, people learn to cooperate and communicate, not only within one group, but in general.

However this rule is not generally applicable, because there are numerous empirical studies questioning it. If we are interested in social capital at the level of the whole community or country, and at the same time, we assume that it arises as a result of bottom-up interactions, we must try to understand the conditions under which investment in social capital at the micro level translates into its aggregation. Both quantitative and qualitative methods are going to be used.

There will be performer an analysis of data from Social Diagnosis, in order to investigate determinants of general trust and collaborative attitudes. Particular attention will be paid to participation in organisations and informal networks. However, in order to investigate the grassroots mechanisms associated with the formation of social capital, the analysis of secondary data is not sufficient. For this reason, it will be complemented by an in-depth case study of Polish and non-commercial, local exchange system and trading (LETS), based on social currency. As shown in the international literature, the creation of such a system may have beneficial socio-economic effects and contribute to building social capital in the local community. LETS allows, through the use of the internet platform, to extend the traditional neighbourly help to a wider group of people, and also contributes to the formation of social ties, especially if transactions include direct interpersonal interactions, and if they are accompanied by additional events strengthening the existence of the community.

This system allows users to access social resources existing in the neighbourhood and enables the development of entrepreneurship, and the use of own abilities and following own interests. This study will examine socio-economic mechanisms operating in the system and its impact on social capital. The main focus is to determine how interpersonal interactions within LETS may influence social norms and attitudes. It will utilise data obtained from the webbased platform, supporting the transactions and enabling to give recommendations. This data will be supplemented by information acquired from in-depth interviews and on-line survey

Parallel convergence of income and educational achievements on a regional and local level in Poland - analysis of distribution dynamics

UMO-2016/21/B/HS4/00670 - OPUS

In the last decade Poland has achieved a significant improvement of economic development indicators. Gross domestic product (GDP) per inhabitant in Poland (taking into account the differences in prices of goods and services between countries) increased from 49% of European Union average in 2004 (a year of Polish accession into UE) to 67% in 2013 showing progress in each year.

Simultaneously the impressive progres was achieved by Polish secondary schools pupils, which is confirmed by international comparisons of educational results, namely Programme for International Student Assessment (PISA) repeated every three years under OECD auspices. In mathematics literacy test Polish pupils climbed from 25. place in the world in 2000 into 13. place in the world in 2012.

In the reading test they started in 2000 from 25. place in the world, to achieve 10. place in 2012. In the science test the results improved from 22. place in 2000 to 9. place in 2012. As numerous analyses for income show, this impressive progress does not spread out proportionally on all regions. Education is widely recognized as one of the most important factors of economic growth. High level of education (or broader so called human capital) plays a major role in the success of countries and regions. Barriers in access to education are an important factor of economic and social exclusion.

However, what really matters for growth is not the level of human capital – measured for example by a share of population with secondary or higher education or the average number of schooling years, but its quality – measured for example by the average score on the final exams on particular education stages. The analyses of the relationship between convergence processes of income and educational achievements on a regional and local level and research on convergence of human capital or educational achievements on a regional and local level in Poland have not been yet performed.

The project will fill this scientific gap. The starting point of the research will be the review of the existing convergence concepts in order to seek for possibilities of their generalization to allow for verification of the parallel convergence. The analysis of paralel convergence applied within this project will relate to verification of the existence of a relationship between the convergence processes of income and educational achievements on a regional and local level. In the next step the methodology will be also applied to analyze the impact of Polish accession to European Union on the dynamics of convergence processes of the two aforementioned phenomena. Analyses will be conducted on the level of voivodeships, subregions, poviats and municipalities. The measure of income considered on the level of voivodeships and subregions will be per capita GPD, while on the level of poviats it will be proxied by the average monthly remuneration or per capita revenue in poviat budget from the share in receipts from personal income tax.

For municipalities income will be measured by the per capita revenue in municipality budget from the share in receipts from personal income tax. Educational achievements on all regional and local levels will be measured by the average results of primary school final exams, lower-secondary school leaving exams and upper-secondary school exit exams.

These exams provide detailed, standardized and comparable data on educational achievements of Polish schools pupils on all regional levels.

Economic interactions under new energy policy

UMO-2017/25/B/HS4/01143 - OPUS

Compliance with the EU climate policy will require a switch towards lower carbon energy generation in Poland. There are several potential scenarios of decarbonisation that involve increased shares of renewable energy as well as increased openness to imports of factors of energy production (e.g. gas) as well as imports of energy itself. Moreover, nuclear energy seems very efficient and should be considered as a viable option.

Decisions on the choice of the actual policy mix are difficult due to several reasons. These decisions require substantial investment outlays. They require long-term planning over periods of decades (due to both the aforementioned investments as well as a long implementation times). Moreover, changes in the use of resources have a direct impact on the structure of industry and they lead to changes in employment and living conditions of the population directly involved with the mining sector and the energy sector. Last but not least, the energy sector reform will in the long run affect the relative prices in the economy and this will in turn affect the economic conditions and economic welfare both in the short and the long term.

Energy policy requires long term planning and affects all economic agents. As a part of the project we propose a significant extension to the existing tools used to assess reform-related costs and benefits. Our economic model allows for simulations of behavior of economic agents: enterprises, government as well as households in the short and long run. Our model will take into account the investment decisions of the enterprises as well as the consumption and savings decisions of the households based on the changing prices ofgoods, services and energy. The energy sector will be modelled in a particularly detailed way, with a special focus on the nuclear technology. Our model will allow us to analyze the effects of changes in several scenarios of changes in the energy policy.It will allow us to obtain the optimal energy mix that complies with the required reform goals at the minimum economic cost.

Our model will allow us to trace the reaction of the households by looking at the changes in their consumption and savings. Moreover, it will enable us to evaluate short and long run changes in the structure of production of industry and service sectors including the changes in international trade.

Spatial econometric models with fixed and changing structure of neighbourhood. Applications to real estate valuation and business location

UMO-2016/23/B/HS4/02363 - OPUS

One of the feature of many economic phenomena is the possibility to assign them to a specific geographical location. When collecting data on such phenomena, many times one can include in databases information about the place of their occurrence. Not always, however, economic analyses take into account both the factof linking them to a specific location, as well –even being aware of such connection –the use of geolocation information already held. The results of such models, known as a-spatial, describe reality not always in a precise way. A milestone in the economic research was the introduction of the so-called spatial models, which allow to consider economic phenomena in the context of their occurrence in predefined regions, what requires the identification of the structure of the neighbourhood, which –in case of a permanent division into regions –remains constant during analysis.

This allows to identify and take into account the possible effects of the influence of neighbouring regions on the phenomenon in the other regions, the spread of economic phenomena between spatial units or the existence of the larger areas with similar level of examined variable (i.e. agglomeration areas), or to identify areas with extremely different values of tested variables (called hot spots). Predetermined constant neighbourhood structure works relatively well in case of phenomena of regional character (e.g. macroeconomic ones), but the problem arose in the analysis of phenomena which could be attributed to a precise spot location (usually microeconomic “events”). Examples of such phenomena might be the location of companies or the prices of a given real estate property (and many others).

Previously developed types of spatial models do not fully worked well for such cases. One of the reasons is that the emergence of new data (e.g. anew company, a new real estate transaction) significantly affects the structure of the neighbourhood, i.e. the relationship between (close) observations. Taking into account a new structure is inconvenient, and may cause the worse or even opposite results, mainly because of instable structure and dependence of neighbours in the phenomena examined.

The purpose of the proposed project is to develop the method for the identification of such "space division", which –independently from the occurrence of any new observation –would describe as precisely as possible the structure of the neighbourhood and the links between the location of examined phenomenon. At the same time this structure would be stable, and therefore would allow for including it to the alreadyknown spatial models. Due to the fact that for the microeconomic data, we have usually only a small sample of the whole population, the results of the models, and thus conclusions about the spatial effects of examined phenomenon, can be very dependent on the available sample data. Hence, another objective of the project is to develop a method that will allow to get the stable results for population not only for a sample. Obtaining the distribution of many possible results for the analysis, enables selecting those which are most likely (also indicating the possible range of results for a given level of confidence, i.e. with the maximum accepted by a researcher error).

The two indicated above objectives of the project will be implemented using (already known in statistical or econometric research) bootstrapping method, the method of the re-sampling of new subsamples with replacement from the date already possessed. The novelty the project brings, is the use of the proposed method to the design of adequate spatial models. On the basis of a number of randomly selected "new" samples the most probable structure of the neighbourhood for the entire population will be chosen. It will be also possible to identify the most probable, i.e. the most corresponding to the reality, spatial effects, which include the identification and describing characteristics of the area in the context of examined phenomenon and possible impacts of the events in the neighbouring locations. All of this allows for better forecasting of microeconomic point spatial phenomena in the future.

In practice, the above mentioned works will be realized on the basis of already owned database containing the information about the location of companies and real estate transaction price data. These data are geo-located point data. Owned research sample allows to carry out the entire postulated process of finding the best structure of the neighbourhood, that is the links between close or more distant units, whether in relation to the location of companies or real estate transactions, and then to develop a model that would better describe the current state and forecast the future development of the location of companies or transaction prices on the real estate market in specific points in space. This will also allow, compared to the current methods of spatial analysis, for more precise identification of areas that are characterized by a greater flow from new companies or the lack thereof, and the reasons that promote clustering of companies with similar profile. In relation to the real estate market, the project should result in forming the models that will better distinguish the areas with the similar prices, and indication of the reasons responsible for this. Due to the fact that the data used in the research have the typical point nature, the developed methods and solutions can easily be transferred to the other analyses of phenomena with the similar characteristics.

Growing inequality: within occupation wage inequality

UMO-2016/21/N/HS4/02108 - PRELUDIUM

Technological change has an enormous impact on every aspect of our lives, including how we work.The prominent framework to understand the effects states thatcomputers can be programmed to perform some of the tasks that were carried by workers, but not all of them. Workers still have a comparative advantage on tasks that require creativity, interpersonal skills and motor abilities. The adoption of new technologies not only shifted the demand for workers from some routine intensive occupations to nonroutine; but also it might have affected the task composition within occupations.

In this research project, we will study how new technologies affected wage inequality within-occupations. By doing so, we attempt to contribute to the literature on skill-biased technological change in two ways: first, we attempt to incorporate a dynamic dimension to it. We will focus not only on the levels of tasks, but also on the scope and pace of those changes. Second, we shift our interest from differences across occupations, which are usually the center of the literature, to inequality within occupations.

Both changes are relevant from a policy perspective. Analyzing dynamics could help us to understandhow occupations change, and how workers cope with those changes. Focusing on inequality withinoccupations, we can understand better who were the winners and losers from the adoption of newtechnologies, and possibly consider what policy instruments would be more effective to ameliorate the negative consequences of technological progress.

In particular, we want to test two hypothesis. First, we test whether changes in the task composition towards more complex tasks were followed by increases in relative wages. This hypothesis is driven both by considerations on changes in relative productivity, inasmuch as changes in the demand for workers. Our second hypothesis states that occupations that experienced larger changes in their tasks contents present higher wage inequalities. The hypothesis is driven by considerations on worker and firm heterogeneity.

In order to test these hypotheses, we need first to characterize the changes on the task component of different jobs and verify that in fact the share of complex tasks increased over the last twenty years. For this, we will employ the O*NET database and its predecessor the DOT. This analysis requires that we go beyond a simple description and provide a measure of the sizeand scope of the changes. Such index is currently lacking in the literature and its construction is one valuable contribution of our research as it might become an input for future developmentof the discipline.

Our analysis then provides three valuable innovations. First, an innovation in the approach to technological change, as we take into consideration the dynamic aspects of technological progres and the (in)ability of workers and firms tokeep pace with those advances. Second, we innovate with the method, as there are no available, synthetic measures of changes in the task content ofoccupations. Third, we produce our analysis in a new set of countries, transition and developed countries, who have not yet received the attention they deserve.

As stated before, our research has important policy implications, especially in the field of active labormarket policies. A confirmation of our research hypotheses would indicate that workers had difficulties in acquiring the necessary skills to profit from technological change, andwill provide further arguments for the development of lifelong learning programs. At the same time, our proposed index of task change could help to identify those occupations, and consequently those individuals who are at higher risks of falling behind due to fast technological change

EU sharOn - Opportunities for European Entrepreneurs in the Sharing Economy

- COSME

SharON project focuses on the support of collaborative economy stakeholders, essentially SMEs and social companies, in order to continuously gain the knowledge on their needs and deliver the effective support services to boost their development and achieve benefits from shared economy.

The major objective of this proposal is to increase the knowledge and provide advisory program among shared economy stakeholders resulting in better support delivered by regional/local authorities and in consequence discovering the potential of platforms owners and shared economy startups.

The main target group for the SharON consortium are SMEs, in particular, the companies and groups of people which represent the collaborative business and social platforms. The sectors covered by this project range from transport, real estate, fin tech, food, education & knowledge, open production and project design, retail, tourism and media. The project partners will adapt and match their activities to regional innovation strategies of regions covered by the consortium. The partnership will attract especially those SMEs and startups with potential for scaling-up their businesses. Special attention will be put on three subcategories of the target groups which will eventually become beneficiaries of SharON project:

• Technology startups with ideas but no business model;
• Existing SMEs, companies and organisations (foundations, associations) developing their collaborative economy business & social initiatives;
• Mature and experienced shared economy players who already validated their business models and look for expanding their services

READ MORE: https://www.sharedeconomy.eu

How determinants of commodities prices change in time? A Dynamic Model Averaging based analysis

UMO-2015/19/N/HS4/00205 - PRELUDIUM

Commodities prices have a very significant impact on economies. They influence inflation and economic activity. They are also related to the prices of other commodities, exchange rates and financial markets. So it is a very important issue from the standpoint of economic policy. Moreover, commodity prices have a direct impact on manufacturers, and thus alsofor consumers –the purchasers of final products. In the case of energy commodities, such as oil or gas, the relationship is even more direct for the common citizen.

For example, when it comes to the price of oil, the most popular method of forecasting prices in the short term is based on futures contracts. Indeed, this method is used by many central banks and the International Monetary Fund. This method has significant shortcomings. On the other hand, most of the other currently proposed, more advanced, predictive models (e.g. VAR, ARIMAGARCH, etc.) also have a major drawback. Namely, they do not care how the quality of predictions vary in time. Indeed, it turns out that the prices of commodities can be determined by different predictors in different time periods. So we can talk about ”structural changes”.

It turns out, however, that the market for commodities can be much more complicated. Now both a set of appropriate predictors may vary over time, and even coefficients for every model can also vary in time. There exists a number of studies demonstrating that certain models are good in selected periods, while at other times other models turn out to be better.

For example, when it comes to the price of oil, the supply had a very strong impact at the end of the twentieth century, but at the beginning of the twenty-first century the economic growth in India or China might be more important.

One interesting attempts to solve this problem is the use of weighted-average forecasting. The first key element of the study thus becomes the search for a large collection of potentially relevant regressors.

If there is uncertainty both about the model itself and its coefficients, then Bayesian methods seem to be very useful. Recently a dynamic model averaging has been proposed. This method, however, requires estimation of each potential model in every moment of the analysed period. If there are k potential predictors, this leads to 2k models -which is a major computational problem. Even for k = 10 we get 1024 models. If we have monthly observations from 20 years, then it is required to make at least 245 760 calculations! However, the dynamic model averaging is based on some approximation that allows to simplify calculations, and therefore, make them feasible in a reasonable time. This method allows for the uncertainty in a very broad sense.

In the initial moment each of 2k models can be regarded as equally “good”. On the other hand, assuming that each of these models at time t is assigned some weight, we can make forecasts for the time t + 1 on the basis of data available up to time t. At the time t + 1, we can verify whether the forecasts of each of the 2k models and depending on the compatibility of the predictions with the actual data “reward” or “punish” each model by modifying its assigned weight. The final forecast is formulated as a weighted average of all the forecasts from 2k models involved.

Although this method is based on the simple regression models, it results in a non-trivial methodology. Finally, this method allows to formulate the following question: which variables and to what extent in different time periods affect the prices of commodities. Of course, it would be interesting to develop this method also for more complex (than linear regression) models. Moreover, it is interesting to look more closely (and to propose possible modifications) on the procedure of choosing the weights.

Novel Bayesian model combination schemes with model uncertainty: The application to prices of selected energy commodities

UMO-2018/28/T/HS4/00095 - ETIUDA

The aim of this research is to expand the current knowledge on selected energy commodities markets. These commodities (e.g. crude oil, natural gas, coal) play a very important role in modern economy. The knowledge on how their prices can develop in the future, and what are their main determinants, is very important for individual investors, firms, companies, governments, etc. For example, this is an important issue in the context of an energy security of a country.

In particular, the research is based on the selected Bayesian models. First of all, the analysis of the selected Bayesian methods, in the context of application to energy commodities prices development, will be performed. The Bayesian methods have been chosen, because they allow to include, so called, model uncertainty and uncertainty about the model parameters (coefficients).

In practice, this means that a researcher interested in the construction of a commodity price model starts with potentially numerous explanatory variables. The number of them usually leads to serious computational problems. Therefore, it is important to carefully analyse these methods - their pros and cons -  and, if possible, modify them or improve, with an aim to optimize their usability.

The methodology used in the research allows to insert a lot of variables. Then, recursive estimations of various models (constructed out of them) allow to validate the usability of these variables, as with a time the new information comes from the market. In other words, the Bayesian econometrics allows to update model’s parameters with a time, and to update certain weights ascribed to particular variables.

Such an approach is useful, because in certain periods different variables might be the key ones. The methods used in the research allow to detect this behaviour. Recently, a few methods in this spirit have been proposed: Dynamic Model Averaging (DMA), Dynamic Model Selection (DMS) and Median Probability Model. They are based on weighted average forecasting, or from model candidates the optimal one is chosen (selected). The literature presents pros and cons of different approaches. Therefore, it is interesting to validate these methods within the particular context of energy commodities. Moreover, the particular averaging (or selection) schemes are quite flexible, and should be examined more thoroughly.

Finally, except the above methods, it seems interesting to look on, so called, mixture models. The previously presented methods are quite flexible, but they still can be made more universal. For example, DMA can be generalised by using, so called, mixed distributions and partial forgetting. This leads to the use of quasi-Bayesian algorithms.

From the interpretative point of view, the presented methods are based on an assumption that amongst the numerous models (considered in the averaging or selecting scheme) lays the „true” one. 

Importance of incentive compatibility in contingent valuation research

UMO-2014/15/N/HS4/01328 - PRELUDIUM

The proposed project addresses methodological issues related to modelling of consumer preferences and contingent valuation of non-market goods on the basis of stated preference research...