17 datasets found
  1. w

    Dataset of book series where books equals Will China's economy collapse?

    • workwithdata.com
    Updated Aug 15, 2024
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    Work With Data (2024). Dataset of book series where books equals Will China's economy collapse? [Dataset]. https://www.workwithdata.com/datasets/book-series?f=1&fcol0=j0-book&fop0=%3D&fval0=Will+China's+economy+collapse?&j=1&j0=books
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    Dataset updated
    Aug 15, 2024
    Dataset authored and provided by
    Work With Data
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    China
    Description

    This dataset is about book series, has 1 rows. and is filtered where the books is Will China's economy collapse?. It features 10 columns including book series, number of authors, number of books, earliest publication date, and latest publication date. The preview is ordered by number of books (descending).

  2. Great Recession: GDP growth for the E7 emerging economies 2007-2011

    • statista.com
    Updated Sep 2, 2024
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    Statista (2024). Great Recession: GDP growth for the E7 emerging economies 2007-2011 [Dataset]. https://www.statista.com/statistics/1346915/great-recession-e7-emerging-economies-gdp-growth/
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    Dataset updated
    Sep 2, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2007 - 2011
    Area covered
    Worldwide
    Description

    The Global Financial Crisis (2007-2008), which began due to the collapse of the U.S. housing market, had a negative effect in many regions across the globe. The global recession which followed the crisis in 2008 and 2009 showed how interdependent and synchronized many of the world's economies had become, with the largest advanced economies showing very similar patterns of negative GDP growth during the crisis. Among the largest emerging economies (commonly referred to as the 'E7'), however, a different pattern emerged, with some countries avoiding a recession altogether. Some commentators have particularly pointed to 2008-2009 as the moment in which China emerged on the world stage as an economic superpower and a key driver of global economic growth. The Great Recession in the developing world While some countries, such as Russia, Mexico, and Turkey, experienced severe recessions due to their connections to the United States and Europe, others such as China, India, and Indonesia managed to record significant economic growth during the period. This can be partly explained by the decoupling from western financial systems which these countries undertook following the Asian financial crises of 1997, making many Asian nations more wary of opening their countries to 'hot money' from other countries. Other likely explanations of this trend are that these countries have large domestic economies which are not entirely reliant on the advanced economies, that their export sectors produce goods which are inelastic (meaning they are still bought during recessions), and that the Chinese economic stimulus worth almost 600 billion U.S. dollars in 2008/2009 increased growth in the region.

  3. f

    Descriptive statistics.

    • plos.figshare.com
    xls
    Updated Jan 11, 2024
    + more versions
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    Wei Fan; Yun He; Liang Hao; Fan Wu (2024). Descriptive statistics. [Dataset]. http://doi.org/10.1371/journal.pone.0295311.t001
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    xlsAvailable download formats
    Dataset updated
    Jan 11, 2024
    Dataset provided by
    PLOS ONE
    Authors
    Wei Fan; Yun He; Liang Hao; Fan Wu
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Moderate rising of house prices are beneficial to the economic development. However, over high house prices worsen the economic distortions and thus hinder the development of the real economy. We use the stochastic frontier models to calculate the fundamental value in the housing in Chinese large and medium cities, and then obtain indexes which could measure the house prices’ deviations from the fundamental value. With the macroeconomic data in the city-level, this paper empirically investigates the effects of the house prices’ deviations on macro-economic variables like consumption, investment and output. The study reveals that the housing bubble exists in most Chinese cities, and first-tier cities fare the worst. House prices over the fundamental value, which could increase the scale of real estate investment, bring adverse impacts on GDP, as it causes declining civilian consumption and discourages real economy’s investment and production. The encouragement and the discouragement on macroeconomy caused by house prices’ deviation from its basic value take turns to play a key role in the process of China’ eco-nomic growth. In the early stage of China’s economic growth, the encouragement effect predominates. As urbanization and industrialization gradually upgrade to a higher level, the discouragement effect takes charge.

  4. Great Recession: global gross domestic product (GDP) growth from 2007 to...

    • statista.com
    Updated Sep 2, 2024
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    Statista (2024). Great Recession: global gross domestic product (GDP) growth from 2007 to 2011 [Dataset]. https://www.statista.com/statistics/1347029/great-recession-global-gdp-growth/
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    Dataset updated
    Sep 2, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2007 - 2011
    Area covered
    Worldwide
    Description

    From the Summer of 2007 until the end of 2009 (at least), the world was gripped by a series of economic crises commonly known as the Global Financial Crisis (2007-2008) and the Great Recession (2008-2009). The financial crisis was triggered by the collapse of the U.S. housing market, which caused panic on Wall Street, the center of global finance in New York. Due to the outsized nature of the U.S. economy compared to other countries and particularly the centrality of U.S. finance for the world economy, the crisis spread quickly to other countries, affecting most regions across the globe. By 2009, global GDP growth was in negative territory, with international credit markets frozen, international trade contracting, and tens of millions of workers being made unemployed.

    Global similarities, global differences

    Since the 1980s, the world economy had entered a period of integration and globalization. This process particularly accelerated after the collapse of the Soviet Union ended the Cold War (1947-1991). This was the period of the 'Washington Consensus', whereby the U.S. and international institutions such as the World Bank and IMF promoted policies of economic liberalization across the globe. This increasing interdependence and openness to the global economy meant that when the crisis hit in 2007, many countries experienced the same issues. This is particularly evident in the synchronization of the recessions in the most advanced economies of the G7. Nevertheless, the aggregate global GDP number masks the important regional differences which occurred during the recession. While the more advanced economies of North America, Western Europe, and Japan were all hit hard, along with countries who are reliant on them for trade or finance, large emerging economies such as India and China bucked this trend. In particular, China's huge fiscal stimulus in 2008-2009 likely did much to prevent the global economy from sliding further into a depression. In 2009, while the United States' GDP sank to -2.6 percent, China's GDP, as reported by national authorities, was almost 10 percent.

  5. o

    Replication data for: The Great Housing Boom of China

    • openicpsr.org
    Updated Oct 12, 2019
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    Kaiji Chen; Yi Wen (2019). Replication data for: The Great Housing Boom of China [Dataset]. http://doi.org/10.3886/E114102V1
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    Dataset updated
    Oct 12, 2019
    Dataset provided by
    American Economic Association
    Authors
    Kaiji Chen; Yi Wen
    Area covered
    China
    Description

    China's housing prices have been growing nearly twice as fast as national income over the past decade, despite a high vacancy rate and a high rate of return to capital. This paper interprets China's housing boom as a rational bubble emerging naturally from its economic transition. The bubble arises because high capital returns driven by resource reallocation are not sustainable in the long run. Rational expectations of a strong future demand for alternative stores of value can thus induce currently productive agents to speculate in the housing market. Our model can quantitatively account for China's paradoxical housing boom.

  6. Replication dataset for PIIE WP 23-5, Why China's housing policies have...

    • piie.com
    Updated Jun 14, 2023
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    Tianlei Huang (2023). Replication dataset for PIIE WP 23-5, Why China's housing policies have failedby Tianlei Huang (2023). [Dataset]. https://www.piie.com/publications/working-papers/2023/why-chinas-housing-policies-have-failed
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    Dataset updated
    Jun 14, 2023
    Dataset provided by
    Peterson Institute for International Economicshttp://www.piie.com/
    Authors
    Tianlei Huang
    Area covered
    China
    Description

    This data package includes the underlying data files to replicate the data and charts presented in Why China's housing policies have failed, PIIE Working Paper 23-5.

    If you use the data, please cite as: Huang, Tianlei. 2023. Why China's housing policies have failed. PIIE Working Paper 23-5. Washington, DC: Peterson Institute for International Economics.

  7. f

    Robustness test (Random effect).

    • figshare.com
    xls
    Updated Jan 11, 2024
    + more versions
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    Wei Fan; Yun He; Liang Hao; Fan Wu (2024). Robustness test (Random effect). [Dataset]. http://doi.org/10.1371/journal.pone.0295311.t018
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    xlsAvailable download formats
    Dataset updated
    Jan 11, 2024
    Dataset provided by
    PLOS ONE
    Authors
    Wei Fan; Yun He; Liang Hao; Fan Wu
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Moderate rising of house prices are beneficial to the economic development. However, over high house prices worsen the economic distortions and thus hinder the development of the real economy. We use the stochastic frontier models to calculate the fundamental value in the housing in Chinese large and medium cities, and then obtain indexes which could measure the house prices’ deviations from the fundamental value. With the macroeconomic data in the city-level, this paper empirically investigates the effects of the house prices’ deviations on macro-economic variables like consumption, investment and output. The study reveals that the housing bubble exists in most Chinese cities, and first-tier cities fare the worst. House prices over the fundamental value, which could increase the scale of real estate investment, bring adverse impacts on GDP, as it causes declining civilian consumption and discourages real economy’s investment and production. The encouragement and the discouragement on macroeconomy caused by house prices’ deviation from its basic value take turns to play a key role in the process of China’ eco-nomic growth. In the early stage of China’s economic growth, the encouragement effect predominates. As urbanization and industrialization gradually upgrade to a higher level, the discouragement effect takes charge.

  8. Banquet Bubble Machine Market Report | Global Forecast From 2025 To 2033

    • dataintelo.com
    csv, pdf, pptx
    Updated Oct 5, 2024
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    Dataintelo (2024). Banquet Bubble Machine Market Report | Global Forecast From 2025 To 2033 [Dataset]. https://dataintelo.com/report/banquet-bubble-machine-market
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    pdf, csv, pptxAvailable download formats
    Dataset updated
    Oct 5, 2024
    Dataset authored and provided by
    Dataintelo
    License

    https://dataintelo.com/privacy-and-policyhttps://dataintelo.com/privacy-and-policy

    Time period covered
    2024 - 2032
    Area covered
    Global
    Description

    Banquet Bubble Machine Market Outlook




    The global banquet bubble machine market size was valued at approximately USD 1.2 billion in 2023 and is projected to reach an impressive USD 2.5 billion by 2032, growing at a Compound Annual Growth Rate (CAGR) of 8.2% during the forecast period. This substantial growth can be attributed to the rising demand for unique and captivating event experiences, advancements in bubble machine technology, and the increasing frequency of social and corporate gatherings.




    One of the primary growth factors driving the banquet bubble machine market is the escalating trend of personalized and themed events. From weddings to corporate events, clients are increasingly seeking distinctive ways to entertain and engage their guests. Bubble machines offer a visually stunning and cost-effective solution that enhances the overall ambiance of various events. This growing consumer inclination towards creating memorable experiences is fostering the demand for innovative bubble machines. Additionally, the social media culture, where visually appealing events are often shared and promoted, further propels market growth.




    Technological advancements in bubble machines are another significant contributor to market growth. The development of automatic bubble machines equipped with advanced features such as remote control operation, adjustable bubble sizes, and LED lights has broadened their application scope. These innovative machines offer ease of use and versatility, making them appealing to both event organizers and individual consumers. Furthermore, advancements in battery technology have resulted in longer-lasting, portable bubble machines, thereby expanding their usability in outdoor and remote locations.




    The increasing number of corporate events and social gatherings worldwide is also augmenting the demand for banquet bubble machines. As the global economy recovers and businesses resume in-person meetings and celebrations, the event management industry witnesses a surge in activity. Corporate events, product launches, and trade shows are increasingly incorporating bubble machines to create an engaging and dynamic environment, thus driving the market growth. Additionally, the growing disposable income and changing lifestyle preferences of consumers contribute significantly to the rising popularity of bubble machines in personal events such as weddings and birthday parties.




    From a regional perspective, North America and Europe are the dominant markets for banquet bubble machines, owing to the high frequency of social and corporate events in these regions. However, the Asia Pacific region is expected to witness the fastest growth rate during the forecast period. The rising middle-class population, increasing disposable income, and expanding event management industry in emerging economies such as China and India are key factors driving the market growth in the Asia Pacific. Additionally, the growing trend of destination weddings in exotic Asian locations further boosts the demand for bubble machines.



    Product Type Analysis




    The banquet bubble machine market is segmented into automatic bubble machines and manual bubble machines. Automatic bubble machines are witnessing a higher demand due to their ease of operation and efficiency. These machines are equipped with advanced features such as remote control, adjustable bubble output, and LED lights, which make them suitable for a wide range of applications. Event organizers prefer automatic bubble machines as they allow for seamless integration into event setups, providing consistent bubble production with minimal manual intervention. The convenience offered by automatic bubble machines is a significant factor contributing to their growing popularity.




    Manual bubble machines, on the other hand, are typically favored for smaller, more intimate gatherings. These machines require manual operation, which can be ideal for events where the bubble output can be controlled by an individual. Manual bubble machines are often more affordable than their automatic counterparts, making them accessible to a broader audience. They are popular among hobbyists and small event organizers who seek an economical solution for adding a whimsical touch to their events. Despite the growing preference for automatic machines, manual bubble machines continue to hold a substantial share in the market, particularly in segments where cost-effectiveness is a priority.

    <

  9. B

    Bubble Film Machinery Report

    • datainsightsmarket.com
    doc, pdf, ppt
    Updated Mar 15, 2025
    + more versions
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    Data Insights Market (2025). Bubble Film Machinery Report [Dataset]. https://www.datainsightsmarket.com/reports/bubble-film-machinery-37778
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    ppt, doc, pdfAvailable download formats
    Dataset updated
    Mar 15, 2025
    Dataset authored and provided by
    Data Insights Market
    License

    https://www.datainsightsmarket.com/privacy-policyhttps://www.datainsightsmarket.com/privacy-policy

    Time period covered
    2025 - 2033
    Area covered
    Global
    Variables measured
    Market Size
    Description

    The global bubble film machinery market, valued at $118 million in 2025, is projected to experience robust growth, driven by increasing demand across diverse sectors. The Compound Annual Growth Rate (CAGR) of 4.4% from 2025 to 2033 indicates a steady expansion fueled by several key factors. The burgeoning e-commerce industry necessitates efficient packaging solutions, significantly boosting the demand for bubble film, and consequently, the machinery used in its production. Furthermore, the growth of the food and beverage, pharmaceuticals, and electronics industries contributes significantly to market expansion. These sectors require reliable and cost-effective packaging to ensure product protection and maintain quality during transportation and storage. The market segmentation reveals a preference for single-layer bubble film, followed by double- and triple-layer options, reflecting cost-benefit analyses in different applications. Technological advancements in bubble film machinery, including automation and increased production efficiency, are also contributing to market growth. Competition among established players like Guangdong Zhongrui Plastic Machinery Technology Co.,Ltd., Shanghai Shenmeng Machinery Equipment Co.,Ltd., and others drives innovation and competitive pricing. Geographic expansion, particularly in rapidly developing economies in Asia-Pacific, presents lucrative opportunities for market players. However, fluctuating raw material prices and increasing labor costs pose potential restraints on market growth. The forecast period of 2025-2033 suggests a continuous upward trajectory for the bubble film machinery market. The expanding global economy and the sustained emphasis on efficient packaging solutions across various industries will likely propel market growth beyond the projected figures. Factors like stricter regulations regarding packaging materials and a growing emphasis on sustainability will further influence market dynamics. Companies will need to focus on innovation, cost-efficiency, and sustainability to maintain a competitive edge. This includes developing machinery that optimizes resource utilization and minimizes environmental impact while providing high-quality production outputs. The continued growth in e-commerce and the ongoing demand for safe and effective packaging across various goods will remain fundamental drivers, shaping the future trajectory of this dynamic market.

  10. f

    Impacts of housing price’s deviation from the basic price on real economic...

    • plos.figshare.com
    xls
    Updated Jan 11, 2024
    + more versions
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    Wei Fan; Yun He; Liang Hao; Fan Wu (2024). Impacts of housing price’s deviation from the basic price on real economic investment. [Dataset]. http://doi.org/10.1371/journal.pone.0295311.t007
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jan 11, 2024
    Dataset provided by
    PLOS ONE
    Authors
    Wei Fan; Yun He; Liang Hao; Fan Wu
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Impacts of housing price’s deviation from the basic price on real economic investment.

  11. Direct tourism GDP growth rate Australia FY 2006-2024

    • ai-chatbox.pro
    • statista.com
    Updated Apr 9, 2024
    + more versions
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    Statista Research Department (2024). Direct tourism GDP growth rate Australia FY 2006-2024 [Dataset]. https://www.ai-chatbox.pro/?_=%2Ftopics%2F4551%2Ftravel-and-tourism-industry-in-australia%2F%23XgboDwS6a1rKoGJjSPEePEUG%2FVFd%2Bik%3D
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    Dataset updated
    Apr 9, 2024
    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Statista Research Department
    Area covered
    Australia
    Description

    Australia's tourism gross domestic product (GDP) bounced back strong in 2023, recording an increase of 90.8 percent. After witnessing a significant decline in tourism GDP in 2020 and 2021, with tourism GDP taking a massive plunge of 36.2 percent in 2021 as a result of the coronavirus outbreak, the industry appears to be on the road to recovery. The state of the tourism industry in 2021 The coronavirus pandemic had an enormous negative effect on the travel and tourism industry worldwide. In Australia, all major tourism-related industries reported a decline in GVA on the previous year. International visitors were also restricted from entering the country, resulting in a significant drop in revenue from international visitors. China, as the origin of the COVID-19 virus, was the first country to be subjected to travel bans. This was particularly damaging to the Australian economy due to the high volume of Chinese visitors that visit Australia for work, leisure, and study. Hopes for a trans-Tasman travel bubble  Just as visitors to Australia were restricted, international travel for Australians became increasingly limited throughout 2020 and 2021. However, with New Zealand’s success at containing the virus, and incidents of COVID-19 in Australia declining at the end of April, the two countries opened negotiations for a “trans-Tasman travel bubble”. The concept would open travel for Australian and New Zealand residents across the Tasman sea, without the need to undergo quarantine in Australia or New Zealand. Unfortunately, after a second wave outbreak of coronavirus in Melbourne and subsequent outbreaks later in the year, the trans-Tasman bubble did not come to pass in 2020.

  12. Inflation rate in Japan 2030

    • statista.com
    Updated Apr 25, 2025
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    Statista (2025). Inflation rate in Japan 2030 [Dataset]. https://www.statista.com/statistics/270095/inflation-rate-in-japan/
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    Dataset updated
    Apr 25, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    Japan
    Description

    In 2024, Japan had an average inflation rate estimated at 2.74 percent, marking the highest rate of inflation in Japan in almost a decade. However, this figure was still very low compared to most other major economies, such as Japan's fellow G7 members, four of which had inflation rates around six or seven percent in 2023 due to the global inflation crisis. Why is Japan's inflation rate lower? There are a number of contributing factors to Japan's relatively low inflation rate, even during economic crises. Japan eased its Covid restrictions more slowly than most other major economies, this prevented post-pandemic consumer spending that may have driven inflation through supply chain issues caused by higher demand. As the majority of Japan's food and energy comes from overseas, and has done so for decades, the government has mechanisms in place to prevent energy and wheat prices from rising too quickly. Because of this, Japan was able to shield its private sector from many of the negative knock on effects from Russia's invasion of Ukraine, which had a significant impact on both sectors globally. Persistent deflation and national debt An additional factor that has eased the impact of inflation on Japan's economy is the fact that it experienced deflation before the pandemic. Deflation has been a persistent problem in Japan since the asset price bubble burst in 1992, and has been symptomatic of Japan's staggering national debt thereafter. For almost 30 years, a combination of quantitative easing, low interest rates (below 0.5 percent since 1995, and at -0.1% since 2016), and a lack of spending due to low wages and an aging population have combined to give Japan the highest national debt in the world in absolute terms, and second-highest debt in relation to its GDP, after Venezuela. Despite this soaring debt, Japan remains the fourth-largest economy in the world, behind the U.S., China, and Germany.

  13. f

    Output responses to changes in house prices’ deviation from the fundamental...

    • figshare.com
    xls
    Updated Jan 11, 2024
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    Wei Fan; Yun He; Liang Hao; Fan Wu (2024). Output responses to changes in house prices’ deviation from the fundamental prices. [Dataset]. http://doi.org/10.1371/journal.pone.0295311.t009
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jan 11, 2024
    Dataset provided by
    PLOS ONE
    Authors
    Wei Fan; Yun He; Liang Hao; Fan Wu
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Output responses to changes in house prices’ deviation from the fundamental prices.

  14. f

    Selection of the optimal lag order.

    • figshare.com
    • plos.figshare.com
    xls
    Updated Jun 8, 2023
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    Chen Wang; Xiaowei Ma; Hyoungsuk Lee; Zhen Chu (2023). Selection of the optimal lag order. [Dataset]. http://doi.org/10.1371/journal.pone.0257106.t005
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    xlsAvailable download formats
    Dataset updated
    Jun 8, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Chen Wang; Xiaowei Ma; Hyoungsuk Lee; Zhen Chu
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Selection of the optimal lag order.

  15. f

    Johansen cointegration test results for each given variable.

    • plos.figshare.com
    xls
    Updated Jun 9, 2023
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    Chen Wang; Xiaowei Ma; Hyoungsuk Lee; Zhen Chu (2023). Johansen cointegration test results for each given variable. [Dataset]. http://doi.org/10.1371/journal.pone.0257106.t004
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    xlsAvailable download formats
    Dataset updated
    Jun 9, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Chen Wang; Xiaowei Ma; Hyoungsuk Lee; Zhen Chu
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Johansen cointegration test results for each given variable.

  16. f

    Comparison of the effectiveness of measurement models.

    • plos.figshare.com
    xls
    Updated Jun 9, 2023
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    Chen Wang; Xiaowei Ma; Hyoungsuk Lee; Zhen Chu (2023). Comparison of the effectiveness of measurement models. [Dataset]. http://doi.org/10.1371/journal.pone.0257106.t002
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    xlsAvailable download formats
    Dataset updated
    Jun 9, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Chen Wang; Xiaowei Ma; Hyoungsuk Lee; Zhen Chu
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Comparison of the effectiveness of measurement models.

  17. f

    Descriptive statistics.

    • figshare.com
    xls
    Updated Sep 6, 2024
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    Kwangwon Ahn; Minhyuk Jeong; Jinu Kim; Domenico Tarzia; Ping Zhang (2024). Descriptive statistics. [Dataset]. http://doi.org/10.1371/journal.pone.0309483.t001
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    xlsAvailable download formats
    Dataset updated
    Sep 6, 2024
    Dataset provided by
    PLOS ONE
    Authors
    Kwangwon Ahn; Minhyuk Jeong; Jinu Kim; Domenico Tarzia; Ping Zhang
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Housing markets are often characterized by price bubbles, and governments have instituted policies to stabilize them. Under this circumstance, this study addresses the following questions. (1) Does policy tightening change expectations in housing prices, revealing a regime change? (2) If so, what determines the housing market’s reaction to policy tightening? To answer these questions, we examine the effects of policy tightening that occurred in 2016 on the Chinese housing market where a price boom persisted in the post-2000 period. Using a log-periodic power law model and employing a modified multi-population genetic algorithm for parameter estimation, we find that tightening policy in China did not cause a market crash; instead, shifting the Chinese housing market from faster-than-exponential growth to a soft landing. We attribute this regime shift to low sensitivity in the Chinese housing market to global perturbations. Our findings suggest that government policies can help stabilize housing prices and improve market conditions when implemented expediently. Moreover, policymakers should consider preparedness for the possibility of an economic crisis and other social needs (e.g., housing affordability) for overall social welfare when managing housing price bubbles.

  18. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

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Work With Data (2024). Dataset of book series where books equals Will China's economy collapse? [Dataset]. https://www.workwithdata.com/datasets/book-series?f=1&fcol0=j0-book&fop0=%3D&fval0=Will+China's+economy+collapse?&j=1&j0=books

Dataset of book series where books equals Will China's economy collapse?

Explore at:
Dataset updated
Aug 15, 2024
Dataset authored and provided by
Work With Data
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Area covered
China
Description

This dataset is about book series, has 1 rows. and is filtered where the books is Will China's economy collapse?. It features 10 columns including book series, number of authors, number of books, earliest publication date, and latest publication date. The preview is ordered by number of books (descending).

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