Academic literature on the topic 'Gross domestic product'

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Journal articles on the topic "Gross domestic product"

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KULYK, Volodymy. "Gross domestic product and critical infrastructure." Naukovi pratsi NDFI 2021, no. 2 (2021): 25–43. http://dx.doi.org/10.33763/npndfi2021.02.025.

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GDP is considered as a critical infrastructure that characterizes the global interaction of consumers and manufacturers of the final product in the national economy. For the analysis and modelling of GDP, the Leontiev model is used to analyze and to model GDP as a critical infrastructure, where costs are interpreted in two ways - as consumer spending and as producer costs. This allows you to determine the degree of their influence on each other through the established production structure. Based on Leontiev's aggregated three-sector model, two scenarios were modelled: a) the impact of final consumer spending on output and the formation of added value and b) the impact of changes in added value on output and final consumer spending. The model includes three sectors – the agricultural sector, industry and service sectors. In the first scenario, the increase in the final consumed product in the agricultural sector by 1000 yen causes an increase in output in the agricultural sector by 1153.5 yen, in industry by 594.4 yen and in the service sector by 358.1 yen. At the same time, total production costs will increase by 1106.0 yen; added value will increase in the agricultural sector by 549.8 yen, in industry – 216.8 yen, in the service sector – 233.4 yen. The volume of production growth can be determined only through the Leontiev model. In the second scenario, an increase in added value in the agricultural sector by 1000 yen brings about an increase in output in the agricultural sector by 1153.5 yen, in industry by 1470.4 yen and in the service sector by 480.7 yen. For both scenarios, the assessment and verification of calculations of GDP growth and output are carried out by the balance method – output and GDP growth is described within the Leontiev model. The three-sectoral model is useful for educational purposes, scientific research; to reach the method in a specific example. The approach can be extended to models of greater dimension and large size.
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Rajewski, Zenon. "Gross Domestic Product." Eastern European Economics 32, no. 4 (1994): 71–80. http://dx.doi.org/10.1080/00128775.1994.11648537.

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Lowie, Jefferson, Sunarji Harahap, Elidawati Elidawati, and Thomas Sumarson Goh. "PENGARUH KEMISKINAN, INFLASI, TENAGA KERJA, EKSPOR, KONSUMSI DAN INVESTASI TERHADAP PRODUK DOMESTIK REGIONAL BRUTO SUMATERA UTARA." Progress: Jurnal Pendidikan, Akuntansi dan Keuangan 6, no. 1 (2023): 1–14. http://dx.doi.org/10.47080/progress.v6i1.2181.

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The purpose of this study was to determine and analyze the effect of poverty, inflation, labor, exports, consumption and investment on the gross regional domestic product of North Sumatra. The population of this study is 33 data on poverty, inflation, labor, exports, consumption, investment and gross regional domestic product of North Sumatra for the period 2017-2020. The sample of this study is 33 data on poverty, inflation, labor, exports, consumption, investment and products. North Sumatra's gross regional domestic product for the period 2017 - 2020. The data collection technique is based on the documentation on the data presented on the website https://sumut.bps.go.id/site/selectdata.html. This study uses multiple linear regression analysis. The results showed that poverty had no effect on the grossw regional domestic product of North Sumatra. Inflation has no effect on the gross regional domestic product of North Sumatra.
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XU, Xianchun. "China's gross domestic product estimation." China Economic Review 15, no. 3 (2004): 302–22. http://dx.doi.org/10.1016/j.chieco.2003.09.021.

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Rustam Qizi, Saparboyeva Sohiba. "ECONOMETRIC ANALYSIS OF THE GROSS DOMESTIC PRODUCT OF UZBEKISTAN." American Journal Of Social Sciences And Humanity Research 3, no. 10 (2023): 140–44. http://dx.doi.org/10.37547/ajsshr/volume03issue10-21.

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Serquina, Melanie Aban. "Prediction Model for Gross Domestic Product: A Production Approach." International Journal of Research Publication and Reviews 5, no. 7 (2024): 1089–97. http://dx.doi.org/10.55248/gengpi.5.0724.1720.

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Silkha Magfira Br Solin, Sri Rosmiati Sani, and Jariah Abubakar. "GROSS (GDP) IN INDONESIA." International Journal of Social Science, Educational, Economics, Agriculture Research and Technology (IJSET) 2, no. 8 (2023): 327–39. http://dx.doi.org/10.54443/ijset.v2i8.188.

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This study aims to analyze tea exports and imports of gross domestic product in Indonesia. The data used in this study is secondary data sourced from BPS, the research model uses multiple linear regression. The results of the study partially show that tea exports have a positive and significant effect on Indonesia's gross domestic product, tea imports have a negative and significant effect on gross domestic product in Indonesia. Taken together, tea exports and imports have a positive and significant effect on gross domestic product in Indonesia. The recommendation is that the government is expected to develop the existing tea export market. Producing Indonesian tea is not only for the domestic market, but also for exporting Indonesian tea products abroad.
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Shafizada, Elnure, and Nigar Aslanova. "Innovative Approaches to Model and Forecast of Azerbaijan’s Economic Growth." Marketing and Management of Innovations 2, no. 1 (2022): 198–208. http://dx.doi.org/10.21272/mmi.2022.2-18.

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One of the indicators of the country's economic growth is the gross domestic product, and one of the factors of economic growth is capital. The main criteria and source of economic development is economic growth. Economic growth is a sustainable increasing tendency of the main indicators of national theory production. Furthermore, absolute value and growth per capita are also considered. The objective and subjective aspects and components of the processes of formation and development of economic growth are investigated. The essence and characteristics of objective and subjective factors of the country's economic growth are given. In economics and statistics, various indicators are used to measure the volume of national production. The most important of these is the gross domestic product. Gross domestic product is expressed by the monetary unit of the final products and services produced in the economy. The fact that gross domestic product comprises final products and services produced within a particular country should be considered. Moreover, the most important is the diversification of sources of gross domestic product growth and ensuring the multiplier influence of the interconnected sectors of the country's economy to increase gross domestic product growth. In this work, the dynamic model that demonstrates the dependence of gross domestic product on investments in the case of the Azerbaijan economy is created. Based on this model, it is possible to more optimally plan and predict promising sources and strategic directions for developing the national economy and ensuring its intensive growth. This approach provides an opportunity for the country's strategic planning of the gross domestic product. In this work, to achieve the desired level of gross domestic product, the volume of investment is used as the independent variable in the dynamic model. However, as indicated above, many other factors affect the amount of investment in gross domestic product. Nevertheless, the dynamic model of the optimal gross domestic product trajectory yielded good results. In modern conditions, optimal and effective modelling is essential to ensure the dynamic development of the country's economies and gross domestic product based on global changes and factors. Similar approaches create opportunities to strengthen the immunity of the national economy to counter global impacts and risks. Further research will consider the other most influential factors on gross domestic product. In this case, a dynamic model of the optimal trajectory of the gross domestic product will give even more adequate results.
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Lochan Jena, Rajiv. "A Time Series Analysis of Gross Domestic Product and Gross Domestic Capital Formation: The Post Reform Analysis in India." International Journal of Science and Research (IJSR) 13, no. 2 (2024): 1821–26. http://dx.doi.org/10.21275/sr24228070842.

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Razack, Sarah, and S. Indumati. "Empirical Relationship between Gross Domestic Product and Gross Domestic Savings in India." Asian Journal of Research in Business Economics and Management 5, no. 3 (2015): 10. http://dx.doi.org/10.5958/2249-7307.2015.00054.7.

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Dissertations / Theses on the topic "Gross domestic product"

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Mirza, Ata Miran. "Estimates of gross domestic product by provinces in Pakistan." Thesis, University of Dundee, 1986. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.253453.

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Ahmed, A. K. M. Zasheem Uddin. "Industrialisation and economic growth : a case study of Bangladesh." Thesis, University of Strathclyde, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.249883.

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Fotoyi, Asanda. "An analysis of revisions to the South African quarterly gross domestic product." Thesis, Nelson Mandela Metropolitan University, 2016. http://hdl.handle.net/10948/3635.

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Gross domestic product (GDP) is one of the most important economic performance indicators for any country that is closely watched by governments, businesses and financial communities. GDP often influences economic decisions and policy-making. These decisions are however often based on preliminary initial announcements by statistical agencies. The preliminary estimates are then revised as more comprehensive information becomes available. Therefore, it is important to ensure that the initial GDP announcements are unbiased, efficient and can be relied on. This study focuses on seasonally adjusted and annualised (qq) growth rates at constant prices for the following national accounts aggregates: GDP, gross domestic expenditure (GDE), final consumption expenditure by households and general government, gross fixed capital formation (GFCF), Exports and imports of goods and services, gross national income (GNI) and disposable income of households. The research methodology used is based on descriptive statistics and Ordinary Least Squares (OLS) regression estimations. The results of the study indicate that little confidence should be attributed to interpretations of the initially announced estimates for GDP, GDE, final consumption expenditure by households, GNI and disposable income by households. The study found evidence that suggests that the initial announcements are biased and inefficient. This implies that the initial announcements contain measurement errors that could be eliminated in order to become a better forecast of the final or true value. This further suggests a lack of expected reliability of existing and future initial announcements of the estimates. For short-term analysis the users of GDP data are encouraged to focus on different measures. It is also recommended that the statistical agents producing South Africa’s national accounts aggregates improve their statistical compilation processes. The agencies are also encouraged to conduct periodic revisions studies and make these available to the users.
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Biddle, Elyce Anne. "Estimating the impact of occupational fatal injuries on the U.S. gross domestic product." Morgantown, W. Va. : [West Virginia University Libraries], 2004. https://eidr.wvu.edu/eidr/documentdata.eIDR?documentid=3704.

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Thesis (Ph. D.)--West Virginia University, 2004.<br>Title from document title page. Document formatted into pages; contains vii, 146 p. : ill. (some col.). Vita. Includes abstract. Includes bibliographical references (p. 74-83).
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Attia, Khalid Abd El-Aziz. "Environmental accounting in a developing country : a case study of Egypt." Thesis, University of Strathclyde, 1999. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=21342.

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The existing United Nations System of National Accounting (IJNSNA) provides useful indicators of economic performance in terms of traditional macroeconomic variables such as Gross Domestic Product (GDP), investment, savings and depreciation of capital. However, they fail to account for the depletion and degradation of environmental capital and hence give a misleading picture of sustainable development. The need for a broader assessment of growth and welfare in terms of modified accounts has therefore become a pressing concern. The main objective of this research is to modify the current Egyptian System of National Accounting (SNA) to include environmental factors, in order to provide a basis for calculating Egyptian sustainable income. Firstly, an environmental accounting approach and model is developed for Egypt to value the depletion and degradation of natural resources caused by economic activities. Secondly, valued environmental costs are incorporated into the Egyptian System of National Accounts to build up the Egyptian Environmental Macro and Sectoral Accounting, which will be helpful in decision-making, planning and policy analysis. The main findings of this research are as follows. Firstly, the environmentally adjusted macro accounting indicators portray a totally different picture of the growth and development of the Egyptian economy compared to the one resulting from conventional SNA. In addition, they indicate that Egypt has experienced an unsustainable path in at least half of the ten-year study period. Secondly, sectoral concerns, which involve measurement of sectoral productivity and performance, indicate that both the performance and the productivity of tradable sectors decrease when their depletion and degradation costs are incorporated. On the other hand, the opposite result is found for the service sectors, which may indicate a potential leading role for the service sectors in the Egyptian economy. Finally, the results indicate that Egypt's natural wealth, which lies in its people, land, the Nile river, oil and gas, and the surrounding seas, has been depleted by many economic "development' programmes that have been carried out to date.
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Sharify, Nooraddin. "An economic regional planning model for the Golastan Province of Iran." Thesis, University of Liverpool, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.367094.

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Golodenko, Olga. "The Impact of Foreign Direct Investment on Gross Domestic Product Growth in Lithuania." Bachelor's thesis, Lithuanian Academic Libraries Network (LABT), 2010. http://vddb.laba.lt/obj/LT-eLABa-0001:E.02~2010~D_20100712_110906-50447.

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The purpose of this bachelor thesis is to determine what type of causative relationship between FDI and GDP exists in Lithuania. The analysis includes assessment of the overall economic situation in the country, analysis of historical statistical data on FDI, overview of existing studies and regression analysis. The regression is performed in order to reveal the impact of various economic factors on GDP growth. The model in question includes such economic indicators as corruption perceptions level index, harmonized consumer price index, net export, foreign investments via liabilities and FDI. Firstly, Granger causality test is performed in order to see whether FDI Granger causes GDP. Then, after making corrections for sequences to be stationary, a regression is performed using ordinary least squares method. The results of the analysis show that there is no statistically significant impact of FDI on economic growth in Lithuania. Nevertheless, foreign investments of other type had a great influence on economic performance in the past several years. However, due to their nature economic growth could not be sustained. The reasons for FDI having no influence over the economic growth in Lithuania are seen in the fact of scarcity of the investments, country’s inability to attract foreign investors, corruption existence, and unstable taxing system. Recommendations are provided on the matter.<br>Darbo tikslas – nustatyti koks egzistuoja ryšys tarp tiesioginių užsienio investicijų (TUI) ir BVP augimo Lietuvoje. Darbe analizuojama dabartinė šalies ekonominė situacija, TUI statistiniai duomenys, apžvelgiama susijusi literatūra bei moksliniai straipsniai, atliekama regresinė analizė. Regresinės analizės tikslas – nustatyti kokią įtaką ekonominiam augimui daro į modelį įtraukti kintamieji. Į regresinį modelį įeina korupcijos lygio indeksas, suderintas vartotojų kainų indeksas, grynasis eksportas, kitos užsienio investicijos ir TUI. Pirmiausia atliekamas Grendžerio duomenų analizės testas, kuris nustato ar TUI gali dinamiškai paaiškinti BVP augimo tempus. Tuomet, atlikus laiko eilučių stacionarumo korekcijas, mažiausių kvadratų metodu įvertinama tiesinė regresija. Analizės rezultatai parodė, kad TUI neturi statistiškai reikšmingos įtakos ekonominiam augimui Lietuvoje. Tačiau kitos užsienio investicijos, pastaraisiais metais ekonominiam vystymuisi turėjo didelę įtaką, nors dėl jų pobūdžio, stabilus ekonomikos augimas nebuvo užtikrintas. Galima įvardinti pagrindines priežastis, dėl kurių TUI neturėjo įtakos ekonominiam augimui Lietuvoje – tai investicijų trūkumas, šalies nesugebėjimas pritraukti užsienio investuotojus, korupcija ir nestabili mokesčių sistema. Darbo pabaigoje pateikiamos rekomendacijos.
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Kasongo, Atoko. "Essays on savings in South Africa." University of the Western Cape, 2019. http://hdl.handle.net/11394/7616.

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Philosophiae Doctor - PhD<br>Savings is essential for boosting economic growth. Low savings in a country will have negative consequences for both investment and economic growth. South Africa has continued to expe rience declining saving rates and in recent years, accompanied by declining economic growth. The study evaluated savings in South Africa by decomposing it into household saving, cor porate saving and public saving. The focus was to investigate the determinants of household savings, corporate and public savings. In addition to examining the determinants of savings, the research has also analysed the saving-investment relationship for South Africa. The study used a Bayesian vector auto regressive model to investigate the determinants of household sav ing from 1980Q1 to 2017Q4. The results of the investigation on household saving showed that GDP, inflation rate, and financial deepening determine household saving in South Africa. The Bayesian VAR was also used to identify the determinant of budget deficit between 1980Q1 to 2017Q and found Real GDP, inflation rate, total government debt, investment by general government and the inflation rate to be determinants. The Blundel-Bond Generalized Method of Moment (GMM) was used to investigate the determinants of corporate saving in form of cash holding for 80 non-financial firms listed on the JSE between 2007 and 2017. The results showed leverage, cash flow, debt maturity and previous amounts of cash holding to have significant effect on cash holding in SA. Lastly, the study examined the saving-investment nexus for South Africa using yearly data from 1980 to 2016. Using the Autoregressive Distributed lag (ARDL) and the Error Correction Model, (ECM), the study found a cointegrating relationship between domestic saving and domestic investment. It further found a positive relationship between domestic saving and domestic investment in both the short and long run. Causality analysis showed a unidirectional causality from domestic saving to domestic investment.
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Sekáč, Martin. "Vývoj čínské ekonomiky na začátku 21. století." Master's thesis, Vysoká škola ekonomická v Praze, 2015. http://www.nusl.cz/ntk/nusl-205684.

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This paper is focused on economic development of the People's Republic of China during the last decade. The aim of this paper is to analyse the economic transformation and identify key factors holding back the economic growth. In the theoretical part of this paper there will be a short introduction of China. Some Chinese social and economic specifics will also be explained. The end of this chapter will be focused on historical economic reforms and current goals for the economy. Next chapter will be focused on analysis of the economy in the last decade using the official available data. In the conclusion of this paper there will be presented findings and key factors holding back the economic growth of China.
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Vijakkhana, Charumporn Fon Jackson John D. "Differential economic performance in developing countries." Auburn, Ala, 2008. http://repo.lib.auburn.edu/EtdRoot/2008/FALL/Economics/Thesis/Phoka_Charumporn_54.pdf.

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Books on the topic "Gross domestic product"

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Hong Kong (China). Census and Statistics Department. Gross domestic product, 1961-1999. Census and Statistics Dept., Hong Kong Special Administrative Region, 2000.

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Sharp, Peter. Gross Domestic Product: Output approach (Gross Value Added). Office for National Statistics, 1999.

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Sharp, Peter. Gross domestic product: Output methodological guide. Office for National Statistics, 1998.

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Great Britain. Office for National Statistics., ed. News release: Regional gross domestic product. Office for National Statistics., 1999.

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Cashell, Brian. Gross national product or gross domestic product: What difference does it make? Congressional Research Service, Library of Congress, 1993.

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Great Britain. Office for National Statistics., ed. Gross Domestic Product: Output approach (Gross Value Added) - revised. Office for National Statistics, 2003.

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Dept, Hong Kong Census and Statistics. Estimates of gross domestic product, 1966-1987. J. R. Lee, Government Printer, 1988.

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New Zealand. Statistics New Zealand., ed. Quarterly gross domestic product: Sources and methods. Statistics New Zealand, 1996.

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Strengthening the National Accounts of Bangladesh (Project) and Bangladesh Parisaṃkhyāna Byuro, eds. Preliminary estimates of gross domestic product, 1999-2000 and final estimates of gross domestic product, 1998-99. Bangladesh Bureau of Statistics], 2000.

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Statistique Canada. Système de comptabilité nationale. Produit intérieur brut par industrie =: Gross Domestic Product by Industry. Approv. & Serv, 1990.

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Book chapters on the topic "Gross domestic product"

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Egerer, Matthias, Erich Langmantel, and Markus Zimmer. "Gross Domestic Product." In Regional Assessment of Global Change Impacts. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-16751-0_17.

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Nahler, Gerhard. "Gross Domestic Product (GDP)." In Dictionary of Pharmaceutical Medicine. Springer Vienna, 2009. http://dx.doi.org/10.1007/978-3-211-89836-9_621.

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Fernandez, Rosa Maria. "Gross Domestic Product and Health." In Encyclopedia of the UN Sustainable Development Goals. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-95681-7_83.

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Fernandez, Rosa Maria. "Gross Domestic Product and Health." In Encyclopedia of the UN Sustainable Development Goals. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-69627-0_83-1.

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McCrone, Gavin. "Gross Domestic Product and its Composition." In Scotland's Economic Progress 1951–1960. Routledge, 2021. http://dx.doi.org/10.4324/9781003208402-2.

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Carbaugh, Robert. "Gross Domestic Product and Economic Growth." In Contemporary Economics, 9th ed. Routledge, 2024. http://dx.doi.org/10.4324/9781003438571-13.

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Češka, Jaroslav. "Comparison of Gross Domestic Product and Net Material Product." In The Future of the World Economy. Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/978-3-662-02586-4_30.

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Gupta, Yashika, and Shalini Aggarwal. "Global Gross Domestic Product and Sustainable Development." In Encyclopedia of the UN Sustainable Development Goals. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-319-71058-7_132-1.

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Ghosh, Sarada, and G. P. Samanta. "Gross Domestic Product Modeling Using “Panel-Data” Concept." In Computational Management. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72929-5_9.

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Pabreja, Kavita. "Anticipation of Gross Domestic Product Using World Development Indicators." In Advances in Intelligent Systems and Computing. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6602-3_14.

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Conference papers on the topic "Gross domestic product"

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Onyeisi, Ogbonna Samuel. "Gross Domestic Product and Gross Domestic Happiness: A Review of Asian Economic Development Models." In The Asian Conference on Asian Studies 2021. The International Academic Forum(IAFOR), 2021. http://dx.doi.org/10.22492/issn.2187-4735.2021.10.

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Roush, John, Keith Siopes, and Gongzhu Hu. "Predicting gross domestic product using autoregressive models." In 2017 IEEE 15th International Conference on Software Engineering Research, Management and Applications (SERA). IEEE, 2017. http://dx.doi.org/10.1109/sera.2017.7965745.

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Hudakova, Jarmila. "RELATIONSHIP BETWEEN GROSS DOMESTIC PRODUCT AND HUMAN DEVELOPMENT INDEX." In 4th International Multidisciplinary Scientific Conference on Social Sciences and Arts SGEM2017. Stef92 Technology, 2017. http://dx.doi.org/10.5593/sgemsocial2017/14/s04.087.

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Dimitrova, Violeta. "ELECTRONIC COMMERCE AND GROSS DOMESTIC PRODUCT GROWTH IN BULGARIA." In 5th SGEM International Multidisciplinary Scientific Conferences on SOCIAL SCIENCES and ARTS SGEM2018. STEF92 Technology, 2018. http://dx.doi.org/10.5593/sgemsocial2018/1.5/s05.037.

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Hamiane, Sana, Hamid Khalifi, Youssef Ghanou, and Gabriella Casalino. "Forecasting the Gross Domestic Product using LSTM and ARIMA." In 2023 IEEE International Conference on Technology Management, Operations and Decisions (ICTMOD). IEEE, 2023. http://dx.doi.org/10.1109/ictmod59086.2023.10438159.

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"REGIONAL GROSS DOMESTIC PRODUCT PREDICTION USING TWITTER DEEP LEARNING REPRESENTATIONS." In 17th International Conference on Applied Computing. IADIS Press, 2020. http://dx.doi.org/10.33965/ac2020_202013l011.

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Rahim, Rahela Abdul, and Mohd Aliff Zikri. "Forecasting a state gross domestic product with logistic growth model." In THE 4TH INNOVATION AND ANALYTICS CONFERENCE & EXHIBITION (IACE 2019). AIP Publishing, 2019. http://dx.doi.org/10.1063/1.5121067.

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Bataev, A. V. "Comparative analysis of the dynamics of world and Russian gross domestic product product." In ТЕНДЕНЦИИ РАЗВИТИЯ НАУКИ И ОБРАЗОВАНИЯ. НИЦ «Л-Журнал», 2018. http://dx.doi.org/10.18411/lj-06-2018-17.

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Savu, Nicoleta Andreea. "WHO IS BUILDING GROSS DOMESTIC PRODUCT - A VIEW FROM NATIONAL ACCOUNTS." In SGEM 2014 Scientific SubConference on POLITICAL SCIENCES, LAW, FINANCE, ECONOMICS AND TOURISM. Stef92 Technology, 2014. http://dx.doi.org/10.5593/sgemsocial2014/b22/s6.112.

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Hronec, Martin. "UNPAID WORK IN THE CALCULATION OF THE SLOVAK GROSS DOMESTIC PRODUCT." In 2nd International Multidisciplinary Scientific Conference on Social Sciences and Arts SGEM2015. Stef92 Technology, 2015. http://dx.doi.org/10.5593/sgemsocial2015/b23/s7.125.

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Reports on the topic "Gross domestic product"

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McCarty, Tanner J., Zuyi Wang Wang, Man-Keun Kim, and James Evans. The Economic Contribution of Utah’s Energy and Mining Industries. Utah Geological Survey, 2022. http://dx.doi.org/10.34191/mp-176.

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Utah’s abundant energy and mineral resources are used to heat homes, drive industry, and keep the lights on. Effectively managing these energy and mineral resources and their associated supply chains requires knowledge of their economic value and how they currently contribute to Utah’s economy. This report calculates the total economic contribution of Utah’s energy and mining industries for 2019 and 2020. Each industry makes substantial contributions to Utah’s gross domestic product, employment, and tax base. Collectively, these industries contributed over 10% to Utah's total Gross Domestic Product (GDP), supported over 7% of its jobs, and contributed 20% to state tax revenue. Not only do these industries produce sizable economic value, they also create high-paying jobs. Average wages for the energy and mining industries were calculated to be $22,415 (45%) and $13,652 (28%) above the overall state average wage, respectively.
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2

Whitaker, Stephan D. The impact of the additional appropriations on federal spending in the Fourth District. Federal Reserve Bank of Cleveland, 2023. http://dx.doi.org/10.26509/frbc-ddb-20230829.

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Headlines regularly report large changes in federal spending. But despite recent additional appropriations, it appears that the federal funds flowing into the Fourth District in 2023 have returned to their prepandemic levels relative to the District’s gross domestic product.
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De Salvo, Carmine Paolo, Gonzalo Muñoz, Gonzalo Rondinone, and Olga Shik. Agrimonitor Agricultural Policy Indicators: Total Support Estimate (TSE) 2023. Inter-American Development Bank, 2023. http://dx.doi.org/10.18235/0004987.

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Governments across the world utilize agricultural policies to intervene in their respective agricultural sectors. This infographic displays the level of total support offered to the agricultural sector, encompassing support for individual producers, the agricultural industry overall, and consumers. The Total Support Estimate (TSE) serves as the indicator, represented as a percentage of the Gross Domestic Product (GDP) of each country.
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4

Javid, Muhammad, Fakhri Hasanov, Carlo Bollino, and Marzio Galeotti. Sectoral Investment Analysis for Saudi Arabia. King Abdullah Petroleum Studies and Research Center, 2021. http://dx.doi.org/10.30573/ks--2021-dp011.

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This study aims to investigate the determinants of short- and long-run investment behavior in Saudi Arabia for eight non-oil sectors. Saudi Arabia is currently proceeding with its historic Vision 2030 reform plan, which aims to significantly increase the private sector’s contribution to the country’s gross domestic product. Thus, analyzing investments at the sectoral level is important for Saudi Arabia. Such an analysis can provide policymakers with a deeper understanding of potential opportunities for boosting private sector growth.
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Niyonsenga, Seraphin, Paul Guthiga, Khadim Dia, and Aissatou Ndoye. Impacts of El Niño-Induced Drought in Zambia. AKADEMIYA2063, 2024. http://dx.doi.org/10.54067/elnino2024insas.03.

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El Niño – induced droughts have inflicted a heavy toll on Zambia's economy, with far-reaching adverse effects on the most vulnerable due to failed crops and livestock losses. Over 50 percent of Zambia's population is employed in agriculture, contributing up to 20 percent of the country's Gross Domestic Product (GDP). The present brief focuses on several spatial aspects of the drought that impact community vulnerability to shed light on hot-spot areas that require priority intervention and attention.
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6

Kikkawa, Aiko, Raymond Gaspar, Kijin Kim, Mahinthan J. Mariasingham, and Christian Marvin Zamora. Measuring the Contribution of International Remittances to Household Expenditures and Economic Output: A Micro–Macro Analysis for the Philippines. Asian Development Bank, 2024. http://dx.doi.org/10.22617/wps240025-2.

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This study assesses the simultaneous contribution of international remittance income reported by recipient households to the overall economy of the Philippines by integrating micro data and macro simulation based on an input–output framework. It finds that remittance-driven household expenditures and investments contributed 3.5% of the country’s total output, 3.4% of the gross domestic product, and 3.7% of the total employment in 2018. Industries that received the bulk of remittances in the form of final consumption are manufacturing, agriculture, and wholesale and retail trade.
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7

Rossi, José Luiz, José Eduardo Gonçalves de Sousa, and Jose Alejandro Gutiérrez Briceño. Minding the Output Gap: A Hamilton Filter Approach and Updated Estimates for the Brazilian Economy. Inter-American Development Bank, 2023. http://dx.doi.org/10.18235/0004981.

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This paper develops an alternative approach for estimating the potential output and the output gap, intending to serve as a good balance between a simple low data requiring method and a powerful but complex structural approach. We rely on the Hamiltons Regression Filter properties to generate a statistically robust estimator of the potential Gross Domestic Product level which overcomes the problems associated to the Hodrick-Prescott filter and improves the Production Function Approach (PFA). Furthermore, we use this methodology to update the estimates of the potential output and output gap for the Brazilian economy.
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Gravina, Antonio Francesco, and Matteo Lanzafame. “What’s Your Shape?”: A Data-Driven Approach to Estimating the Environmental Kuznets Curve. Asian Development Bank, 2024. http://dx.doi.org/10.22617/wps240334-2.

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This paper uses Bayesian Model Averaging techniques to explore the relationship between environmental degradation and economic growth and finds that per capita gross domestic product (GDP) and carbon dioxide emissions are positively associated in emerging economies. Due to model and variable selection uncertainty, studies on the inverted U-shaped relationship between environmental degradation and economic growth—known as the Environmental Kuznets Curve—have produced very mixed evidence. This paper addresses these issues by using Bayesian Model Averaging techniques. The results indicate that the Environmental Kuznets Curve has an inverted-N shape. Almost all of the emerging economies analyzed display a positive association between per capita GDP and carbon dioxide emissions, whereas most advanced economies analyzed are on the second downward segment of the curve.
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Luo, Yan, Shu Tian, and Hao Yang. Green Bonds, Air Quality, and Mortality: Evidence from the People’s Republic of China. Asian Development Bank, 2021. http://dx.doi.org/10.22617/wps210435-2.

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This study uses city-level data from the People’s Republic of China to examine links between green bond market development and air quality as well as mortality rates. It finds that cities with more green bond financing as a share of total bond financing tend to have better air quality. The effect is stronger when certified green bonds are examined and in cities with higher gross domestic product growth. Further, local green bond issuance is also negatively related to mortality rates. The findings support the argument that green bond issuance is a credible signal of corporates’ commitment to be environmentally responsible.
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Srinivasan, Madhumitha. Climate Finance in India 2023. Indian Institute for Human Settlements, 2023. http://dx.doi.org/10.24943/cfi11.2023.

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This report takes stock of the current climate finance landscape in India, along with the estimated financing requirements, enabling conditions and macro policy instruments to achieve national 1.5oC and 2oC goals. Climate change will negatively affect India’s economy leading to annual Gross Domestic Product (GDP) loss of 3 per cent to 10 per cent by 2100 (Kompas et al., 2018; RBI, 2023). In a business-as-usual scenario, India is estimated to face GDP per capita loss of 2.6 per cent, 6.7 per cent and 16.9 per cent in 2030, 2050, and 2100 respectively (Kahn, 2019).
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