Nauki Humanistyczne i Społeczne

Central European Journal of Economic Modelling and Econometrics

Zawartość

Central European Journal of Economic Modelling and Econometrics | 2021 | No 2 |

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Abstrakt

This paper applies recently developed procedures to monitor and date so-called “financial market dislocations”, defined as periods in which substantial deviations from arbitrage parities take place. In particular, we use a cointegration perspective to focus on deviations from the triangular arbitrage parity for exchange rate triplets. Due to increasing attention on and importance of mispricing in the market for cryptocurrencies, we include the cryptocurrency Bitcoin in addition to fiat currencies in our analysis. We do not find evidence for substantial deviations from the triangular arbitrage parity when only traditional fiat currencies are considered, but document significant deviations from triangular arbitrage parities in the newer market for Bitcoin. We tentatively confirm the importance of our results for portfolio strategies by showing that a currency portfolio that trades based on our detected break-points outperforms a simple buy-and-hold strategy.
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Bibliografia

[1] Akram Q. F., Rime D., Sarno L., (2008), Arbitrage in the foreign exchange market: Turning on the microscope, Journal of International Economics 76, 237– 253.
[2] Andrews D. W. K., (1991), Heteroskedasticity and autocorrelation consistent covariance matrix estimation, Econometrica 59, 817–854.
[3] Bain B., (2013), Mexican peso falls to two-year low as central bank dims outlook, Bloomberg, available at: https://www.bloomberg.com/news/articles/2014- 12-05/mexican-peso-falls-to-five-year-low-as-central-bank-dimsoutlook.
[4] Barro R. J., (2006), Rare disasters and asset markets in the twentieth century, The Quarterly Journal of Economics 121, 823–866.
[5] Barro R. J., (2009), Rare disasters, asset prices, and welfare costs, The American Economic Review 99, 243–264.
[6] Baur D. G., Hong K., Lee A. D., (2018), Bitcoin: Medium of exchange or speculative assets?, Journal of International Financial Markets, Institutions and Money 54, 177–189.
[7] Ben-David I., Franzoni F., Moussawi R., Sedunov J., (forthcoming), The Granular Nature of Large Institutional Investors, Management Science.
[8] Bollerslev T., Todorov V., (2011), Tails, fears, and risk premia, The Journal of Finance 66, 2165–2211.
[9] Brauneis A., Mestel R., Riordan R., Theissen E., (2019), A high-frequency analysis of bitcoin markets, Unpublished Manuscript.
[10] Campbell J. Y., Serfaty-De Medeiros K., Viceira L. M., (2010), Global currency hedging, The Journal of Finance 65, 87–121.
[11] Campbell J. Y., Viceira L. M., White J. S., (2003), Foreign currency for long-term investors, The Economic Journal 113, C1–C25.
[12] Chu C.-S. J., Stinchcombe M., White H., (1996), Monitoring structural change, Econometrica 64, 1045–1065.
[13] Courtois N. T., Emirdag P., Nagy D. A., (2014), Could Bitcoin transactions be 100x faster?, [in:] Proceedings of the 11th International Conference on Security and Cryptography (SECRYPT), [ed.:] M. S. Obaidat, A. Holzinger, P. Samarati, Vienna, 426–431.
[14] Decker C., Wattenhofer R. , (2014), Bitcoin transaction malleability and MtGox, [in:] Proceedings of the 19th European Symposium on Research in Computer Security, [ed.:] M. Kutylowski, J. Vaidya, Wroclaw, 313–326.
[15] Dickey D. A., Fuller W. A., (1979), Distribution of the estimators for autoregressive time series with a unit root, Journal of the American Statistical Association 74, 427–431.
[16] Dong H., Dong W., (2014), Bitcoin: Exchange rate parity, risk premium, and arbitrage stickiness, British Journal of Economics, Management & Trade 5, 105–113.
[17] Eha B. P., (2013), Why regulate bitcoin?, The New Yorker, available at: https://www.newyorker.com/business/currency/why-regulate-bitcoin.
[18] Engel C., Rogers J., (1996), How wide is the border?, The American Economic Review 86, 1112–1125.
[19] Evans M. D., (2018), Forex trading and the WMR fix, Journal of Banking and Finance 87, 233–247.
[20] Fink C., Johann T., (2014), Bitcoin markets, Unpublished Manuscript.
[21] Fleckenstein M., Longstaff F. A., Lustig H., (2014), The TIPS-treasury bond puzzle, The Journal of Finance 69, 2151–2197.
[22] Foley S., Karlsen J. R., Putnins T. J., (2019), Sex, drugs, and bitcoin: How much illegal activity is financed through cryptocurrencies?, The Review of Financial Studies 32, 1798–1853.
[23] Foucault T., Kozhan R., Tham W. W., (2016), Toxic arbitrage, The Review of Financial Studies 30, 1053–1094.
[24] Gagnon L., Karolyi A., (2010), Multi-market trading and arbitrage, Journal of Financial Economics 97, 53–80.
[25] Gromb D., Vayanos D., (2010), Limits to arbitrage: The state of the theory, Annual Review of Financial Economics 2, 251–275.
[26] Hamilton E., (2016), itBit Bitcoin OTC market recap: May 2016 infographic, itBit, available at: https://web.archive.org/web/20171216101048/https://www.itbit.com/blog/itbit-bitcoin-otc-market-recap-may-2016- infographic.
[27] Hau H., Rey H., (2006), Exchange rates, equity prices, and capital flows, The Review of Financial Studies 19, 273–317.
[28] Ito T., Yamada K., Takayasu M., Takayasu H., (2012), Free lunch! Arbitrage opportunities in the foreign exchange markets, Unpublished Manuscript.
[29] Jolly D., Irwin N., (2015), Swiss franc soars after central bank drops cap, The New York Times, available at : https://www.nytimes.com/2015/01/16/business/ swiss-national-bank-euro-franc-exchange-rate.html.
[30] Jopson B., Foley S., (2013), Big US online retailer to accept bitcoin, Financial Times, available at: https://www.ft.com/content/d5f2f096-6907-11e3- 996a-00144feabdc0.
[31] Kim T., (2017), On the transaction cost of bitcoin, Finance Research Letters 23, 300–305.
[32] Klemkosky R. C., Lee J. H., (1991), The intraday ex post and ex ante profitability of index arbitrage, Journal of Futures Markets 11, 291–311.
[33] Kozhan R., Tham W. W., (2012), Execution risk in high-frequency arbitrage, Management Science 58, 2131–2149.
[34] Kwiatkowski D., Phillips P. C., Schmidt P., Shin Y., (1992), Testing the null hypothesis of stationarity against the alternative of a unit root, Journal of Econometrics 54, 159–178.
[35] Lielacher A., (2017), Bitcoin 2018: ’Show me the (institutional) money!’, Brave New Coin, available at: https://bravenewcoin.com/insights/bitcoin- 2018-show-me-the-institutional-money.
[36] Lyons R. K., Moore M. J., (2009), An information approach to international currencies, Journal of International Economics 79, 211–221.
[37] Makarov I., Schoar A., (2020), Trading and arbitrage in cryptocurrency markets, Journal of Financial Economics 135, 293–319.
[38] Marsh I. W., Panagiotou P., Payne R., (2017), The WMR fix and its impact on currency markets, Unpublished Manuscript.
[39] Matvos G., Seru A., (2014), Resource allocation within firms and financial market dislocation: Evidence from diversified conglomerates, The Review of Financial Studies 27, 1143–1189.
[40] Modest D. M., Sundaresan M., (1983), The relationship between spot and futures prices in stock index futures markets: Some preliminary evidence, Journal of Futures Markets 3, 15–41.
[41] Moore T., Christin N.. (2013), Beware the middleman: Empirical analysis of Bitcoin-exchange risk, [in:] Financial Cryptography and Data Security, 17th International Conference, FC 2013, [ed.:] A.-R. Sadeghi, Okinawa, 25–33.
[42] Osipovich A., Jeong E.-Y., (2018), Bitcoin’s crashing? That won’t stop arbitrage traders from raking in millions, The Wall Street Journal, available at: https://www.wsj.com/articles/bitcoins-crashing-that-wont-stoparbitrage- traders-from-raking-in-millions-1517749201.
[43] Pasquariello P., (2014), Financial market dislocations, Review of Financial Studies 27, 1868–1914.
[44] Phillips P. C. B., Hansen B. E., (1990), Statistical inference in instrumental variables regression with I(1) processes, Review of Economic Studies 57, 99–125.
[45] Phillips P. C. B., Perron P., (1988), Testing for a unit root in time series regression, Biometrika 75, 345–356.
[46] Pieters G. C., (2016), Does bitcoin reveal new information about exchange rates and financial integration?, Unpublished Manuscript.
[47] Pieters G., Vivanco S., (2017), Financial regulations and price inconsistencies across Bitcoin markets, Information Economics and Policy 39, 1–14.
[48] Saikkonen P., (1991), Asymptotically efficient estimation of cointegration regressions, Econometric Theory 7, 1–21.
[49] Shleifer A., Vishny R., (1997), The limits of arbitrage, The Journal of Finance 52, 35–55.
[50] Stock J. H., Watson M. W., (1993), A simple estimator of cointegrating vectors in higher order integrated systems, Econometrica 61, 783–820.
[51] Veronesi P., (2004), The peso problem hypothesis and stock market returns, Journal of Economic Dynamics and Control 28, 707–725.
[52] Vogelsang T. J., Wagner M., (2014), Integrated modified OLS estimation and fixed-b inference for cointegrating regressions, Journal of Econometrics 178, 741–760.
[53] Wagner M., (2018), Estimation and inference for cointegrating regressions, [in:] Oxford Research Enyclopedia in Economics and Finance.
[54] Wagner M., Wied D., (2015), Monitoring stationarity and cointegration, Unpublished Manuscript.
[55] Wagner M., Wied D., (2017), Consistent monitoring of cointegrating relationships: The US housing market and the subprime crisis, Journal of Time Series Analysis 38, 960–980.
[56] Weisenthal J., (2014), Bitcoin plunges as major exchange Mt. Gox halts all withdrawals, Business Insider, available at: https://www.businessinsider. com/mtgox-halts-withdrawals-2014-2.
[57] Wied D., Arnold M., Bissantz N., Ziggel D., (2012), A new fluctuation test for constant variances with applications to finance, Metrika 75, 1111–1127.
[58] Wied D., Ziggel D., Berens T., (2013), On the application of new tests for structural changes on global minimum-variance portfolios, Statistical Papers 54, 955–975.
[59] Yu G., Zhang J., (2018), A revisit to capital controls policies when bitcoin is in town, Unpublished Manuscript.
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Autorzy i Afiliacje

Julia Reynolds
1
ORCID: ORCID
Leopold Sögner
2 3 4
ORCID: ORCID
Martin Wagner
5 6 7
ORCID: ORCID

  1. U.S. Securities and Exchange Commission, Washington, USA
  2. Department of Economics and Finance, Institute for Advanced Studies, Vienna, Austria
  3. Vienna Graduate School of Finance, Vienna, Austria
  4. NYU Abu Dhabi , Emirate of Abu Dhabi, United Arab Emirates
  5. Department of Economics, University of Klagenfurt, Austria
  6. Bank of Slovenia, Ljubljana
  7. Institute for Advanced Studies, Vienna
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Abstrakt

Indian states exhibit considerable heterogeneity in terms of revenue mobilizing capacities and efforts, development spending and fiscal dependence on the central government. In this context, the paper compares the fiscal performance of major Indian states in terms of two non-parametric performance evaluation models for the period 2009–10 to 2014–15. The study thus uses the conventional two stage framework for efficiency evaluation as well as the two stage conditional performance model. The outcomes enable us to identify front-runners as well as laggards in the area of fiscal management. Further, the study showed that the gross capital formation experienced by the states significantly influences state performance in India. However, the impact of outstanding liabilities on efficiency performance was statistically insignificant.
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Bibliografia

[1] Acharya D., Sahoo B. K., (2017), Dynamic Macroeconomic Performance of Indian States: Some Post-Reform Evidence [in:] Regional Growth and Sustainable Development in Asia. New Frontiers in Regional Science: Asian Perspectives, [eds.:] Batabyal A. A., Nijkamp P., Springer International Publishing Switzerland.
[2] Banker R. D., (1993), Maximum Likelihood, Consistency and Data Envelopment Analysis: A Statistical Foundation, Management Science 39(10), 1265–1273.
[3] Banker R. D., Charnes A., Cooper W. W., (1984), Some models for estimating technical and scale efficiency, Management Science 30(9), 1078–1092.
[4] Banker R. D., Natarajan R, (2008), Evaluating contextual variables affecting productivity using data envelopment analysis, Operations Research 56(1), 48–58.
[5] Banker R. D., Natarajan R., Zhang, D., (2019), Two-stage estimation of the impact of con- textual variables in stochastic frontier production function models using data envelopment analysis: second stage OLS versus bootstrap approaches, European Journal of Operational Research 278(2), 368–84.
[6] Barro R. J., (1990), Government spending in a simple model of endogenous growth, Journal of Political Economy 98, S103–S125.
[7] Barro R. J., (1991), Economic growth in a cross section of countries, Quarterly Journal of Economics 106, 407–444.
[8] Bhide S., Manoj P., (2002), Evaluating Quality of Budgets with a Composite Index, Economic and Political Weekly 37(13), 1177–1180.
[9] Bjurek H., Kjulin U., Gustafsson B., (1992), Efficiency, productivity and determinants of inefficiency at public day care centers in Sweden. Scandinavian Journal of Economics 94 (Supplement), 173–187.
[10] Basílio M. S., Pires M. C. P., Reis J. F. P., (2016), Portuguese banks’ performance: comparing efficiency with their Spanish counterparts, Eurasian Economic Review 6(1), 27–44, https://doi.org/10.1007/ s40822-015-0033-6.
[11] Battese G. E., Coelli T. J., (1995), A model for technical inefficiency effects in a stochastic frontier production function for panel data, Empirical Economics 20 (2), 325–332.
[12] Charnes A., Cooper W. W., Rhodes E. (1978), Measuring Efficiency of Decision Making Unit, European Journal of Operational Research 2(6), 429–444.
[13] Chillingerian J. A., (1995), Evaluating physician efficiency in hospitals: A multivariate analysis of best practices. European Journal of Operational Research 80, 548–574.
[14] Coondoo D., Majumder A., Mukherjee R., Neogi C., (2001), Relative Tax Performances-Analysis for Selected States in India, Economic and political weekly 36(40), 3869–3871.
[15] Devarajan S., Swaroop V., Heng-fu Z.,(1996), The composition of public expenditure and economic growth, Journal of Monetary Economics 37(2-3), 313– 344.
[16] Dholakia A., (2005), Measuring Fiscal Performance of Indian States An Alternative Approach, Economic and Political Weekly 40(30), 3421-28.
[17] Dholakia R. H., Karan N., (2004), Consistent Measurement of Fiscal Deficit and Debt of States in India, Working Paper WP 2004-07-05, Indian Institute of Management, Ahmedabad.
[18] Diamond J., (1989), Government expenditure and economic growth: An empirical investigation, IMF working paper no. 89/45, International Monetary Fund, Washington, DC.
[19] Efron B., (1979), Bootstrap Methods: Another Look at the Jackknife, Annals of Statistics 7(1), 1–26.
[20] Erden L., Holcombe R. G., (2005), The Effects of Public Investment on Private Investment In Developing Economies, Public Finance Review 33(5), 575–602.
[21] Färe R., Grosskopf S., Lovell C. A. K., (1994), Production Frontiers, Cambridge University Press.
[22] Färe R., Grosskopf S., (1985), A Nonparametric Cost Approach to Scale Efficiency, The Scandinavian Journal of Economics 87(4), 594–604.
[23] Farrell M. J., (1957), The Measurement of Productive Efficiency, Journal of The Royal Statistical Society, Series A, General, 120(3), 253–281.
[24] Garg S., Goyal A., Rupayan P., (2017), Why Tax Effort Falls Short of Tax Capacity in Indian States, Public Finance Review 45(2), 232–259.
[25] Goyal R., Khundrakpam J., Ray P., (2004), Is India’s Public Finance Unsustainable? Or, Are the Claims Exaggregated?, Journal of Policy Modelling 26, 401–420.
[26] Jha R., Mohanty, M., Chatterjee S., Chitkara P., (1999), Tax efficiency in selected Indian states, Empirical Economics 24(4), 641–654.
[27] Klopp G. A., (1985), The analysis of the efficiency of production system with multiple inputs and output, PhD dissertation, University of Illinois, Industrial and System Engineering College, Chicago.
[28] Koopmans T. C., (1951), An Analysis of Production as an Efficient Combination of Activities [in:] Activity Analysis of Production and Allocation, [ed.:] Koopmans T. C., New York Cowles Commission for Research in Economics, Monograph No. 13, John Wiley and Sons.
[29] Mohanty A,. Mishra B. R., (2016), Fiscal Performance Index of the States in India, Prajnan 44(3), 247–267.
[30] Mohanty R. K., Sahoo B. K., Chaudhuri P. K., (2020), Assessing the (eco)macroeconomic performance index of India: A data envelopment analysis approach, Public Affairs, https://doi.org/10.1002/pa.2122.
[31] Mundle S., Chowdhury S., Sikdar S., (2016), Governance Performance of Indian States 2001–02 and 2011–12, NIPFP Working Paper No. 2016-164.
[32] Rajaraman I., Bhide S., Pattnaik R. K., (2005), A Study of Debt Sustainability at State Level in India, Reserve Bank of India.
[33] Rangarajan C., Prasad A., (2012), Managing State Debt and Ensuring Solvency- The Indian Experience, WPS6039, Policy Research Working Paper, The World Bank, Economic Policy and Debt Department.
[34] Ramirez M. D., (2004), Is public infrastructure spending productive in the Mexican case? A vector error correction analysis, The Journal of International Trade & Economic Development 13(2), 159–178.
[35] Ray S. C., Neogi C., (2007), A Non-Radial Measure of Efficiency in Indian Textile Industry: An Analysis of Unit-level Data, working paper No. 38, Department of Economics, University of Connecticut, USA.
[36] Reserve Bank of India, (2018), State Finances-A Study of State Budgets 2015–16, RBI, Mumbai.
[37] Reserve Bank of India, (2017), State Finances-A Study of State Budgets 2014–15, RBI, Mumbai.
[38] Sahoo B. K., Acharya D., (2012), Constructing macroeconomic performance index of Indian states using DEA, Journal of Economic Studies 39(1), 63–83.
[39] Shephard R. W., (1953), Cost and Productions, Princeton: Princeton University Press.
[40] Shephard R. W., (1970), Theory of Cost and Productions, Princeton: Princeton University Press.
[41] Shephard R. W., (1974), Indirect Production Functions, Meisenheim am Glan, Verlag Anton Hain.
[42] Sinha R. P., (2017), Fiscal Performance Benchmarking of Indian States – a Robust Frontier Approach, Central European Review of Economics and Management 1(4), 225–249.
[43] Sinha R. P., (2018), Fiscal Performance of Indian States – a Comparison of Convex and Non-Convex Frontier Approaches, Journal of Governance & Public Policy 8(2), 2–17.
[44] Silverman B. W., (1986), Density Estimation for Statistics and Data Analysis, Chapman and Hall, New York, NY.
[45] Simar L., Wilson P. W., (1998), Sensitivity Analysis of Efficiency Scores: How to Bootstrap in Nonparametric Frontier Models, Management Science 44(11), 49–61.
[46] Simar L., Wilson P. W., (2000), A general methodology of bootstrapping in Nonparametric Frontier Models, Journal of Applied Statistics 27(6), 779–802.
[47] Simar L., Wilson P. W., (2007), Estimation and inference in two-stage, semiparametric models of productive efficiency, Journal of Econometrics 136(1), 31– 64.
[48] Simar L., Wilson P. W., (2011), Two-stage DEA: caveat emptor, Journal of Productivity Analysis 36(2), 205–218.
[49] Simm J., Besstremyannaya G., (2016), “Package ‘rDEA’, Version 1.2-5”, available at: https://cran.rproject.org/web/packages/rDEA/rDEA.pdf.
[50] Simone A. S., Topalova P., (2009), India’s Experience with Fiscal Rules: An Evaluation and The Way Forward, IMF working paper no. 09/175, International Monetary Fund, Washington, DC.
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Autorzy i Afiliacje

Ram Pratap Sinha
1
ORCID: ORCID

  1. Government College of Engineering and Leather Technology, Kolkata, India
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Abstrakt

Global Vector Autoregressive models came to be used quite widely in empirical studies using macroeconomic non-stationary panel data for the global economy. In this paper, it is shown that when the loading matrix of the cointegrating vectors is not block-diagonal and the cross-sectional spillovers of disequilibrium exist, the use of the GVAR model leads to spurious cross-sectional long-run relationships. Moreover, the results of Monte Carlo simulation show that the GVAR model is outperformed by other valid econometric approaches in terms of the maximum likelihood estimator of long-run coefficients, when the cointegrating vectors matrix is block-diagonal.
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Bibliografia

[1] Bai J., (2009), Panel data models with interactive fixed effects, Econometrica 77, 1229–1279.
[2] Bi Y., Anwar S., (2017), US monetary policy shocks and the Chinese economy: a GVAR approach, Applied Economics Letters 24, 553–558.
[3] Bussière M., Chudik A., Sestieri G., (2009), Modelling global trade flows. Results from a GVAR model, ECB Working Paper Series 1087.
[4] Chudik A., Pesaran M. H., (2016), Theory and practice of GVAR modelling, Journal of Economic Surveys 30, 165–197.
[5] Dees S., di Mauro F., Pesaran M. H., Smith V., (2007), Exploring the international linkages of the euro area: A global VAR analysis, Journal of Applied Econometrics 22, 1–38.
[6] Favero C., (2013), Modelling and forecasting government bond spreads in the euro area: A GVAR model, Journal of Econometrics 177, 343–356.
[7] Harbo L., Johansen S., Nielsen B., Rahbek A., (1998), Asymptotic inference on cointegrating rank in partial system, Journal of Business and Economic Statistics 16, 388–399.
[8] Jacobson T., Lyhagen J., Larsson R., Nessén M., (2008), Inflation, exchange rates and PPP in a multivariate panel cointegration model, Econometrics Journal 11, 58–79.
[9] Johansen S., (1991), Estimation and hypothesis testing of cointegration vectors in Gaussian vector autoregressive models, Econometrica 59, 1551–1580.
[10] Johansen S., Juselius K., (1994), Identification of the long-run and the short-run structure. An application to the ISLM model, Journal of Econometrics 63, 7–36.
[11] Kebłowski P., (2016), Canonical correlation analysis in panel vector error correction model. Performance Comparison, Central European Journal of Economic Modelling and Econometrics 8(4), 203–217.
[12] Larsson R., Lyhagen J., (2007), Inference in panel cointegration models with long panels, Journal of Business & Economic Statistics 25, 473–483.
[13] Larsson R., Villani M., (2001), A distance measure between cointegration spaces, Economics Letters 70, 21–27.
[14] Pesaran M. H., (2006), Estimation and inference in large heterogeneous panels with multifactor error structure, Econometrica 74, 967–1012.
[15] Pesaran M. H., Schuermann T., Weiner S., (2004), Modeling regional interdependencies using a global error-correcting macroeconomic model, Journal of Business & Economic Statistics 22(2), 129–162.
[16] Temizsoy A., Montes-Rojas G., (2019), Measuring the effect of monetary shocks on European sovereign country risk: An application of GVAR models, Journal of Applied Econometrics 22, 484–503.
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Autorzy i Afiliacje

Piotr Kłębowski
1
ORCID: ORCID

  1. University of Łódz, Poland
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Abstrakt

In this paper we study the relationship between foreign firm ownership and innovation activities in a wide group of West European and Central and East European countries. Based on a dataset including more than 100,000 firms covered by the 2014 edition of the Community Innovation Survey, we examine the role of home- and host country effects in firms’ decisions to introduce various forms of innovation. In addition, we identify a group of foreign-owned firms that specialize in exporting and interpret them as participants of hierarchic global value chains organized by multinational enterprises. We show that while foreign direct investment, especially from Germany, is positively associated with innovation, the opposite effect is observed in the case of hierarchic global value chains’ participants. The negative impact of within-multinationals global value chains on innovation is more pronounced in the affiliates located in the Central and East European countries.
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Bibliografia

[1] Aghion P., (2004), Growth and development: A Schumpeterian approach, Annals of Economics and Finance 5, 1–25.
[2] Aghion P., Howitt P., (1992), A model of growth through creative destruction, Econometrica 60, 323–351.
[3] Aghion P., Howitt P., (1998), Endogenous growth theory, MIT Press, Cambridge.
[4] Amador J., Cabral S., (2016), Global value chains: A survey of drivers and measures, Journal of Economic Surveys 30(2), 278–301.
[5] Amador J., Cabral S., Mastrandrea R., Ruzzenenti F., (2018), Who’s who in global value chains? A weighted network approach, Open Economies Review 29(5), 1039–1059.
[6] Ambroziak Ł., (2018), The CEECs in global value chains: The role of Germany, Acta Oeconomica 68(1), 1–29.
[7] Antràs P., (2020), Conceptual aspects of global value chains, The World Bank.
[8] Andersson T., (2005), Linking national science, technology and innovation policies with FDI policies, [in:] Proceedings of UNCTAD Expert Meeting on the Impact of FDI on Development, Geneva.
[9] Bair J., Gereffi G., (2001), Local clusters in global chains: The causes and consequences of export dynamism in Torreon’s blue jeans industry, World Development 29(11), 1885–1903.
[10] Bazan L., Navas-Aleman L., (2004), The underground revolution in the Sinos Valley: A comparison of upgrading in global and national value-chains, [in:] Local Enterprises in the Global Economy: Issues of Governance and Upgrading, [ed.:] Schmitz H., Edward Elgar, Cheltenham, 110–140.
[11] Bedi A. S., Cieslik A., (2002), Wages and wage growth in Poland: The role of foreign direct investment, Economics of Transition 10(1), 1–27.
[12] Benkovskis K., Masso J., Tkacevs O., Vahter P., Yashiro N., (2019), Export and productivity in global value chains: Comparative evidence from Latvia and Estonia, Review of World Economics, 1–21.
[13] Berger M., Diez J. R., (2008), Can host innovation systems in late industrializing countries benefit from the presence of transnational corporations? Insights from Thailand’s manufacturing industry, European Planning Studies 16(8), 1047–1074.
[14] Birkinshaw J., (1996), World mandate strategies for Canadian subsidiaries, Industry Canada Working Paper 9.
[15] Birkinshaw J., (1998), Foreign owned subsidiaries and regional development: the case of Sweden, [in:] Multinational Corporate Evolution and Subsidiary Development, [eds.:] Birkinshaw J., Hood N., McMillian, London, 268–298.
[16] Birkinshaw J., Hood N., (1998), Multinational subsidiary evolution: capability and charter change in foreign-owned subsidiary companies, Academy of Management Review 23(4), 773–95.
[17] Birkinshaw J., Hood N., (2001), Unleashing innovation in foreign subsidiaries, Harvard Business Review 79(3), 131–137.
[18] Blomstrom M., Kokko A., (2002), FDI and human capital: A research agenda, OECD Development Centre, Working Paper no. 195.
[19] Brancati E., Brancati R., Maresca A., (2017), Global value chains, innovation and performance: firm-level evidence from the Great Recession, Journal of Economic Geography 17(5), 1039–1073.
[20] Cieslik A., Hagemejer J., (2014), Multinational enterprises, absorptive capacity and export spillovers: evidence from Polish firm-level data, Review of Development Economics 18(4), 709–726.
[21] Cieslik A., Michałek J. J., Szczygielski K., (2019), What matters for firms’ participation in Global Value Chains in Central and East European countries?, Equilibrium. Quarterly Journal of Economics and Economic Policy 14(3), 481– 502.
[22] Cohen W. M., Levinthal D. A., (1990), Absorptive capacity: A new perspective on learning and innovation, Administrative Science Quarterly 35, 128–152.
[23] Costa I., Filippov S., (2007), A new nexus between foreign direct investment, industrial and innovation policies, UNU-MERIT Working Paper #2007-030.
[24] De Marchi V., Giuliani E., Rabellotti R., (2018), Do global value chains offer developing countries learning and innovation opportunities?, The European Journal of Development Research 30(3), 389–407.
[25] D’Este P., Rentocchini F., Vega-Jurado J., (2014), The role of human capital in lowering the barriers to engaging in innovation: evidence from the Spanish innovation survey, Industry and Innovation 21(1), 1–19.
[26] Dolan C., Humphrey J., (2000), Governance and trade in fresh vegetables: The impact of UK supermarkets on the African horticulture industry, Journal of Development Studies 37(2), 147–176.
[27] Fagerberg J., Martin S., Mark K., (2007), The competitiveness of nations: Why some countries prosper while others fall behind, World Development 35, 1595– 1620.
[28] Fagerberg J., Srholec M., (2017), Capabilities, economic development, sustainability, Cambridge Journal of Economics 41(3), 905–926.
[29] Foss N. J., Pedersen T., (2004), Organizing knowledge processes in the multinational corporation: an introduction, Journal of International Business Studies 35, 340–349.
[30] Furman J. L., Porter M. E., Stern S., (2002), The determinants of national innovative capacity, Research Policy 31, 899–933.
[31] Gereffi G., Humphrey J., Sturgeon T., (2005), The governance of global value chains, Review of International Political Economy 12(1), 78–104.
[32] Geroski P. A., (1989), Entry, innovation and productivity growth, Review of Economics and Statistics 71, 572–578.
[33] Ghoshal S., Nohria N., (1989), Internal differentiation within multinational corporations, Strategic Management Journal 10(4), 323–337.
[34] Giuliani E., Pietrobelli C., Rabellotti R., (2005), Upgrading in global value chains: Lessons from Latin American clusters, World Development 33(4), 549– 573.
[35] Grilliches Z., (1990), Patent statistics as economic indicators: A survey, Journal of Economic Literature 28, 1646–1661.
[36] Hashi I., N. Stojcic, (2013), The impact of innovation activities on firm performance using a multi-stage model: Evidence from the Community Innovation Survey 4, Research Policy 42(2), 353–366.
[37] Hayter R., Han S., (1998), Reflections on China’s open policy towards direct foreign investment, Regional Studies 32, 1–16.
[38] Helpman E., (1984), A simple theory of trade with multinational corporations, Journal of Political Economy 92, 451–471.
[39] Helpman E., (1985), Multinational corporations and trade structure, Review of Economic Studies 52, 443–458.
[40] Helpman E., Krugman P. R., (1985), Market structure and foreign trade: Increasing returns, imperfect competition and the international economy, MIT Press, Cambridge.
[41] Holm U., Malmberg A., and Sölvell Ö., (2003), Subsidiary impact on hostcountry economies: the case of foreign-owned subsidiaries attracting investment into Sweden, Journal of Economic Geography 3(4), 389–408.
[42] Katz J. M., Bercovich N. A., (1993), National systems of innovation supporting technical advance in industry: the case of Argentina, [in:] National Innovation Systems: A comparative analysis, [ed.:] Nelson R. R., 451–475.
[43] Kolasa M., (2008), How does FDI inflow affect productivity of domestic firms? The role of horizontal and vertical spillovers, absorptive capacity and competition, Journal of International Trade & Economic Development 17(1), 155–173.
[44] Krugman P. R., (1983), The “New Theories” of international trade and the multinational enterprise, [in:] The multinational corporation in the 1980s, [eds.:] Kindleberger Ch. P., Audretsch D. B., MIT Press, Cambridge, 57–73.
[45] Lacasa I. D., Giebler A., Radoševic S., (2017), Technological capabilities in Central and Eastern Europe: an analysis based on priority patents, Scientometrics 111(1), 83–102.
[46] Lall S., (1992), Technological capabilities and industrialization, World Development 20(2), 165–186.
[47] Lema R., Pietrobelli C., Rabellotti R., (2019), Innovation in Global Value Chains, [in:] Handbook of Global Value Chains, [eds.:] Gereffi G., Ponte S., Raj-Reichert G., Edward Elga, Cheltenham and Lyme, 370–384.
[48] Lema R., Quadros R., Schmitz H., (2015), Reorganising global value chains and building innovation capabilities in Brazil and India, Research Policy 44(7), 1376– 1386.
[49] Marin A., Bell M., (2006), Technology Spillovers from Foreign Direct Investment (FDI): an Exploration of the Active Role of MNC Subsidiaries in the Case of Argentina in the 1990s, Journal of Development Studies 42(4), 678–697.
[50] Morrison A., Pietrobelli C., Rabellotti R., (2008), Global value chains and technological capabilities: a framework to study learning and innovation in developing countries, Oxford Development Studies 36(1), 39–58.
[51] Pearce R., (2001), Multinationals and industrialisation: the bases of ’inward investment’ policy, International Journal of the Economics of Business 8(1), 51– 73.
[52] Pearce R., (2005), The globalization of R&D: key features and the role of TNCs, [in:] Proceedings of UNCTAD Expert Meeting on the Impact of FDI on Development, Geneva.
[53] Pearce R., Papanastassiou M., (2006), Multinationals and national systems of innovation: strategy and policy issues, [in:] Multinationals, clusters and innovation, [eds.:] Tavares A. T., Teixeira A., Palgrave Macmillan.
[54] Pedersen T., (2006), Determining factors of subsidiary development, [in:] Multinationals, clusters and innovation, [eds.:] Tavares A. T., Teixeira A., Palgrave Macmillan.
[55] Penrose E. T., (1956), Foreign investment and the growth of the firm, Economic Journal 66, 220–236.
[56] Pietrobelli C., Rabellotti R., (2011), Global value chains meet innovation systems: are there learning opportunities for developing countries?, World Development 39(7), 1261–1269.
[57] Rigo D., (2020), Global value chains and technology transfer: new evidence from developing countries, Review of World Economics, 1–24.
[58] Romer P. M., (1993), Idea gaps and object gaps in economic development, Journal of Monetary Economics 32, 543–573.
[59] Romer P. M., (1990), Endogenous technological change, Journal of Political Economy 98, 71–102.
[60] Schmitz H., (2007), Transitions and trajectories in the build-up of innovation capabilities: Insights from the global value chain approach, Asian Journal of Technology Innovation 15(2), 151–160.
[61] Szczygielski K., Grabowski W., Pamukcu M. T., Tandogan V. S., (2017), Does government support for private innovation matter? Firm-level evidence from two catching-up countries, Research Policy 46(1), 219–237.
[62] Tavares A. T., (2002), Multinational subsidiary evolution and public policy: two tales from the European periphery, Journal of Industry, Competition and Trade 2(3), 195–213.
[63] Varsakelis N. C., (2006), Education, political institutions and innovative activity: A cross-country empirical investigation, Research Policy 35, 1083–1090.
[64] Taglioni D., Winkler D., (2016), Making Global Value Chains Work for Development. Trade and Development, World Bank, Washington.
[65] World Bank, (1999), World Development Report, Oxford University Press, Oxford and New York.
[66] WTO, (2019), Global Value Chain Development Report 2019: Technological Innovation, Supply Chain Trade, and Workers in a Globalized World.
[67] Yokota K., Tomohara A., (2010), Modeling FDI-Induced Technology Spillovers, International Trade Journal 24(1), 5–34.
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Autorzy i Afiliacje

Andrzej Cieślik
1
ORCID: ORCID
Jan Jakub Michałek
1
ORCID: ORCID
Krzysztof Szczygielski
1
ORCID: ORCID
Jacek Lewkowicz
1
ORCID: ORCID
Jerzy Mycielski
1
ORCID: ORCID

  1. University of Warsaw, Faculty of Economic Sciences, Warsaw, Poland

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