GOOGLE’DA YAPILAN BAZI KELİME ARAMALARI SEFALET ENDEKSİNİ ETKİLER Mİ?

1970 yılında Arthur Okun tarafından literatüre sunulan Sefalet Endeksi, işsizlik ve enflasyon oranlarının toplamıyla elde edilmektedir. Özellikle 2019 yılının son günlerinde ortaya çıkan COVİD-19 salgını neticesinde dünya genelinde sefalet endeksinde bir artış gözlemlenmiştir. İletişim teknolojilerinde yaşanan hızlı gelişmeler, insanların hayat tarzlarında da çok ciddi değişimlere öncülük etmiş ve sosyal yaşantının online dünyada şekillenmesini sağlamıştır. Alışveriş, eğlence ve iletişim konusunda internetin önemi yadsınamaz hale gelmiştir. İnsanlar merak ettikleri her konuyu internet üzerinden araştırmaya başlamıştır. Bu husus, internet arama motorlarının da işlevselliğini artırmıştır. İnternette yapılan aramalar, servis sağlayıcılar tarafından kamuoyuna sunulmaktadır. Bunların en popüler olanı ise Google Trend’dir. Bu mecradan elde edilen verilerin bazı ekonomik göstergeler ile ilişkisinin olabileceği literatürde tartışılmaktadır. Bu çalışmada da, Türkiye özelinde 2006-2022 tarihleri arasında aylık olarak ortaya çıkan sefalet endeksi ve yine bu dönemi kapsayan Google Trend’den elde edilen sefalet endeksini gösteren kelimelerin aranma endeksi verileri arasındaki ilişki incelenmektedir. Dönem dönem yaşanan keskin değişimlerin etkisini göz ardı etmemek adına yapısal kırılmalı modeller tercih edilmiş ve sefalet endeksi ve arama trendi arasındaki eşbütünleşme ve nedensellik ilişkisi incelenmiştir. Elde edilen bulgular, her iki değişkenin de birbirlerini etkilediğini ortaya koymaktadır.

DO SOME WORD SEARCHES ON GOOGLE AFFECT THE MİSERY INDEX?

The Misery Index, which was presented to the literature by Arthur Okun in 1970, is obtained by the sum of unemployment and inflation rates. In particular, as a result of the COVID-19 epidemic that emerged in the last days of 2019, an increase in the misery index was observed throughout the world. The rapid developments in communication technologies have led to serious changes in people's lifestyles and have enabled social life to be shaped in the online world. The importance of the internet in shopping, entertainment and communication has become undeniable. People have started to research every subject they are curious about on the internet. This has also increased the functionality of internet search engines. Internet searches are made available to the public by service providers. The most popular of these is Google trending. It is discussed in the literature that the data obtained from this channel may be related to some economic indicators. In this study, the relationship between the monthly misery index between 2006-2022 in Turkey and the search index data of words showing the misery index obtained from Google trend covering this period is examined. In order not to ignore the effect of the sharp changes experienced from time to time, structural break models were preferred and the cointegration and causality relationship between the misery index and the search trend were examined. The findings reveal that both variables affect each other.

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  • Anzoátegui-Zapata, J. C., & Galvis-Ciro, J. C. (2020). “Disagreements in Consumer Inflation Expectations: Empirical Evidence for a Latin American Economy”. Journal of Business Cycle Research, 16, 99-122.
  • Bicchal, M., & Raja Sethu Durai, S. (2019). “Rationality of Inflation Expectations: An Interpretation of Google Trends Data”. Macroeconomics and Finance in Emerging Market Economies, 12(3), 229-239.
  • Blanchflower, David G. (2007). “Is Unemployment More Costly than Inflation?” NBER Working Paper, 13505.
  • Bleher, J., & Dimpfl, T. (2022). “Knitting Multi-Annual High-Frequency Google Trends to Predict Inflation and Consumption”. Econometrics and Statistics, 24, 1-26.
  • Chadwick, M. G., & Sengül, G. (2015). “Nowcasting the unemployment rate in Turkey: Let's ask Google”. Central Bank Review, 15(3), 15.
  • Da, Z., Engelberg, J., & Gao, P. (2015). “The sum of all FEARS investor sentiment and asset prices”. The Review of Financial Studies, 28(1), 1-32.
  • Dadgar, Y., & Nazari, R. (2018). “The Impact Of Economic Growth And Good Governance On Misery Index in Iranian Economy”. European Journal of Law and Economics, 45(1), 175-193.
  • Di Tella, R., MacCulloch R. J. & Oswald A.J. (2001). “Preferences over Inflation and Unemployment: Evidence from Surveys of Happiness”. The American Economic Review. Sayı:91(1). 335-341.
  • Fajar, M., Prasetyo, O. R., Nonalisa, S., & Wahyudi, W. (2020). “Forecasting Unemployment Rate in the Time of COVID-19 Pandemic Using Google Trends Data (case of Indonesia)”. International Journal of Scientific Research in Multidisciplinary Studies, 6(11), 29-33.
  • Fondeur, Y., & Karamé, F. (2013). “Can Google Data Help Predict French Youth Unemployment?. Economic Modelling, 30, 117-125.
  • Gamal, A. A. M., & Dahalan, J. (2015). “Estimating the Size of the Underground Economy in the UAE: Evidence from Gregory-Hansen Cointegration Based Currency Demand Approach”. Review of Integrative Business and Economics Research, 4(3), 183.
  • GLYNN, John, PERERA, Nelson ve VERMA, Reetu (2007). “Unit Root Tests and Structural Breaks: A Survey With Applications” (http://ro.uow.edu.au/commpapers/455/)
  • Google, (2023), https://trends.google.com/trends/, Erişim Tarihi: 13/09/2023)
  • GREGORY, Allan W. ve Hansen Bruce E. ( 1996). “Test for Cointegration in Models with Regime and Trend Shifts”, Oxford Bulletin of Economics and Statics, 58, 99-126
  • Hariadhy, R. P., Danutirta, A. S., & Lubis, M. (2022, September). “Implementation of Data Science Algorithm for Monthly Inflation Prediction Based on Financial Technology Awareness Levels”. In 2022 10th International Conference on Cyber and IT Service Management (CITSM) (pp. 01-05). IEEE
  • Hassani, H., & Silva, E. S. (2018). “Forecasting UK Consumer Price Inflation Using Inflation Forecasts”. Research in Economics, 72(3), 367-378.
  • Jha, S., & Sahu, S. (2020). “Forecasting Inflation for India With the Phillips Curve: Evidence From Internet Search Data”. Economics Bulletin, 40(3), 2372-2379.
  • Kiewiet, D. R. (1981). “Policy-Oriented Voting in Response to Economic Issues”. American Political Science Review, 75(2), 448-459.
  • Korenok, O., Munro, D., & Chen, J. (2022). “Inflation and Attention Thresholds”. Available at SSRN 4230600.
  • Li, X., Shang, W., Wang, S., & Ma, J. (2015). “A MIDAS Modelling Framework for Chinese Inflation Index Forecast Incorporating Google Search Data”. Electronic Commerce Research and Applications, 14(2), 112-125.
  • Mihaela, S. (2020). “Improving Unemployment Rate Forecasts at Regional Level in Romania Using Google Trends”. Technological Forecasting and Social Change, 155, 120026.
  • Mulero, R., & García-Hiernaux, A. (2021). “Forecasting Spanish Unemployment With Google Trends And Dimension Reduction Techniques”. SERIEs, 12(3), 329-349.
  • Naccarato, A., Falorsi, S., Loriga, S., & Pierini, A. (2018). “Combining Official and Google Trends Data to Forecast the Italian Youth Unemployment Rate”. Technological Forecasting and Social Change, 130, 114-122. Nagao, S., Takeda, F., & Tanaka, R. (2019). “Nowcasting of the US Unemployment Rate Using Google Trends”. Finance Research Letters, 30, 103-109.
  • Powell Jr, G. B., & Whitten, G. D. (1993). “A cross-national Analysis of Economic Voting: Taking Account of The Political Context”. American journal of political science, 37(2), 391-414.
  • Sahu, S., & Chattopadhyay, S. (2020). “Epidemiology of İnflation Expectations and Internet Search: An Analysis for India”. Journal of Economic Interaction and Coordination, 15, 649-671.
  • Simionescu, M., & Cifuentes-Faura, J. (2022). “Can Unemployment Forecasts Based on Google Trends Help Government Design Better Policies? An İnvestigation Based on Spain And Portugal”. Journal of Policy Modeling, 44(1), 1-21.
  • Sotis, C. (2021). “How do Google Searches For Symptoms, News and Unemployment Interact During COVID-19? A Lotka–Volterra Analysis of Google Trends Data”. Quality & quantity, 55(6), 2001-2016.
  • Şentürk, G. (2022). “Can Google Search Data Improve the Unemployment Rate Forecasting Model? An Empirical Analysis for Turkey”. Journal of Economic Policy Researches, 9(2), 229-244.
  • TÜİK, (2023a), (https://data.tuik.gov.tr/Kategori/GetKategori?p=enflasyon-ve-fiyat-106&dil=1, Erişim Tarihi: 13/09/2023)
  • TÜİK, (2023b), (https://data.tuik.gov.tr/Kategori/GetKategori?p=istihdam-issizlik-ve-ucret-108&dil=1, Erişim Tarihi: 13/09/2023)
  • Veiga, F. J., & Veiga, L. G. (2004). “The Determinants of Vote Intentions in Portugal”. Public Choice, 118(3-4), 341-364.
  • Wei, Y., Zhang, X., & Wang, S. (2017, December). “Can Search Data Help Forecast Inflation? Evidence From A 13-Country Panel”. In 2017 IEEE International Conference on Big Data (Big Data) (pp. 4184-4188). IEEE.
  • World Bank, (2023a) (https://data.worldbank.org/indicator/FP.CPI.TOTL.ZG?end=2022&start=1960&view=chart, Erişim Tarihi: 13/09/2023)
  • World Bank, (2023b), (https://data.worldbank.org/indicator/SL.UEM.TOTL.ZS?end=2022&start=2012&view=chart, Erişim Tarihi: 13/09/2023)
  • Worthington, A. C., & Pahlavani, M. (2007). “Gold Investment as an Inflationary Hedge: Cointegration Evidence with Allowance for Endogenous Structural Breaks”. Applied Financial Economics Letters, 3(4), 259-262.
  • Zıvot, E. ve Donald W. K. Andrews (1992), “Further Evidence on the Great Crash, The Oil- Price Shock, and the Unit-Root Hypothesis”, Journal of Business and Economic Statistics, 10(3), 25-44