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題名 中國貧困縣經濟成長的評估 — 行政資料與光照資料的對比
Evaluating Economic Growth in China’s Poverty County: Contrasting Administrative Data with Nighttime Light Data作者 吳莉雯
Wu, Li-Wen貢獻者 胡偉民<br>黃柏鈞
Hu, Wei-Min<br>Huang, Po-Chun
吳莉雯
Wu, Li-Wen關鍵詞 夜間光照
經濟成長
貧困縣政策
Nighttime light
Economic growth
Poverty county policies日期 2024 上傳時間 3-Jun-2024 11:46:20 (UTC+8) 摘要 在2012年，中國政府提出「2020年全面脫貧」的計劃，要求現有接受貧困縣政策的地方政府於2020年退出貧困縣名單，達到全中國脫離絕對貧困的目標。因此，脫離貧困縣成為接受扶助之地方政府的目標。 當地方政府存在目標，地方官員就有誇大經濟成長的誘因(Movshuk, 2002 ; Gilley, 2001)，造成虛假脫貧的現象。因此，本文參考並修改 Martínez (2022) 設計的模型，估計中國貧困縣與非貧困縣之間地區生產總值與夜間光照彈性的差異，推估中國貧困縣地方政府是否會因有來自中央政府的目標高報地區生產總值。 主要假設為在沒有官方操縱時，地區生產總值與夜間光照數據的彈性在各個縣市之間應一致。研究結果顯示在宣佈2020年全面脫貧的目標後，貧困縣高報的GDP比例約為23%，即貧困縣真實經濟成為率若為1%，則官方公佈之GDP成長約為1.23%。除此之外，事件分析法的研究結果也顯示，誇大的比例會隨著距離目標截止日越近而越高，該結果可以反映，目標到期日越近則政績壓力越大，政績壓力會成為地方政府官員誇大經濟成長的誘因。
In 2012, the Chinese government set the goal of " absolute poverty eliminated by 2020", which required local governments currently receiving poverty county policy support to be removed from the list of poverty counties by 2020. Consequently, exiting the poverty county status became the objective for local governments receiving assistance. When local governments have targets, local officials have incentives to exaggerate economic growth (Movshuk, 2002; Gilley, 2001), resulting in the phenomenon of false poverty alleviation. Consequently, this paper references and modifies the model designed by Martínez (2022) to estimate the differences in the nighttime lights elasticity of GDP between poverty and non-poverty counties in China. This estimation assesses whether local governments in China's poverty counties are likely to overreport GDP due to targets set by the central government. The primary assumption is that in the absence of official manipulation, the nighttime lights elasticity of GDP should be consistent across different counties. The result indicate that after the announcement of the comprehensive poverty alleviation goal for 2020, the proportion of GDP overstate by poverty counties was approximately 23%. This implies that if the actual economic growth rate in a poverty county is 1%, the officially reported GDP growth would be about 1.23%. Furthermore, results from the event study method also show that the proportion of exaggeration increases as the deadline for the target approaches. This outcome reflects that the closer the deadline for the target, the greater the performance pressure on officials, which becomes an incentive for local government officials to exaggerate economic growth.參考文獻 一、英文文獻 Adams, F. G., & Chen, Y. (1996). Skepticism about Chinese GDP growth--The Chinese GDP elasticity of energy consumption. Journal of Economic and Social Measurement, 22(4), 231–240. Bluhm, R., & Krause, M. (2022). Top lights: Bright cities and their contribution to economic development. Journal of Development Economics, 157, 102880-. Chen, Z., Yu, B., Yang, C., Zhou, Y., Yao, S., Qian, X., Wang, C., Wu, B., & Wu, J. (2021). An extended time series (2000–2018) of global NPP-VIIRS-like nighttime light data from a cross-sensor calibration. Earth System Science Data, 13(3), 889–906. Chen, S., Qiao, X., & Zhu, Z. (2021). Chasing or cheating? Theory and evidence on China’s GDP manipulation. Journal of Economic Behavior & Organization, 189, 657–671. Chen, X., & Nordhaus, W. D. (2011). Using luminosity data as a proxy for economic statistics. Proceedings of the National Academy of Sciences - PNAS, 108(21), 8589–8594. Clark, H., Pinkovskiy, M., & Sala-i-Martin, X. (2020). China’s GDP growth may be understated. China Economic Review, 62, 101243-. Croft, T. A. (1978). Nighttime Images of the Earth from Space. Scientific American Vol. 239, Issue 1, pp. 86–101. Doll, C.N. H., Muller, J.-P., & Elvidge, C. D. (2000). Night-time Imagery as a Tool for Global Mapping of Socioeconomic Parameters and Greenhouse Gas Emissions. Ambio, 29(3), 157–162. Doll, C. N. H., Muller, J.-P., & Morley, J. G. (2006). Mapping regional economic activity from night-time light satellite imagery. Ecological Economics, 57(1), 75–92. Elvidge, C. D., Baugh, K. E., Kihn, E. A., Kroehl, H. W., Davis, E. R., & Davis, C. W. (1997). Relation between satellite observed visible-near infrared emissions, population, economic activity and electric power consumption. International Journal of Remote Sensing, 18(6), 1373–1379. Ebener, S., Murray, C., Tandon, A., & Elvidge, C. C. (2005). From wealth to health: modelling the distribution of income per capita at the sub-national level using night-time light imagery. International Journal of Health Geographics, 4(1), 5–5. Galimberti, J. K. (2020). Forecasting GDP Growth from Outer Space. Oxford Bulletin of Economics and Statistics, 82(4), 697–722. Gibson, J., Olivia, S., & Boe‐Gibson, G. (2020). NIGHT LIGHTS IN ECONOMICS: SOURCES AND USES1. Journal of Economic Surveys, 34(5), 955–980. Gibson, J., Olivia, S., Boe-Gibson, G., & Li, C. (2021). Which night lights data should we use in economics, and where? Journal of Development Economics, 149, 102602-. Gilley, B. (2001). Breaking barriers. Far Eastern Economic Review, 14–19. HOLZ, C. A. (2004). Deconstructing China’s GDP statistics. China Economic Review, 15(2), 164–202. Holz, C. A. (2008). China’s 2004 Economic Census and 2006 Benchmark Revision of GDP Statistics: More Questions than Answers? The China Quarterly (London), 193(193), 150–163. Holz, C. A. (2014). The quality of China’s GDP statistics. China Economic Review, 30, 309–338. Hamman, N., & Phiri, A. (2022). Using Nighttime Luminosity as a Proxy for Economic Growth in Africa: Is It a Bright Idea? Managing Global Transitions, 20(2), 139–165. Henderson, J. V., Storeygard, A., & Weil, D. N. (2012). Measuring Economic Growth from Outer Space. The American Economic Review, 102(2), 994–1028. Park, A., Wang, S., & Wu, G. (2002). Regional poverty targeting in China. Journal of Public Economics, 86(1), 123–153. Pérez-Sindín, X. S., Chen, T.-H. K., & Prishchepov, A. V. (2021). Are night-time lights a good proxy of economic activity in rural areas in middle and low-income countries? Examining the empirical evidence from Colombia. Remote Sensing Applications, 24, 100647-. Phan, D. H. (2023). Lights and GDP relationship: What does the computer tell us? Empirical Economics, 65(3), 1215–1252. Pagaduan, J. A. (2022). Do higher‐quality nighttime lights and net primary productivity predict subnational GDP in developing countries? Evidence from the Philippines. Asian Economic Journal, 36(3), 288–317. Rawski, T. G. (2001). What is happening to China’s GDP statistics? China Economic Review, 12(4), 347–354. Rosenbaum, P. R., & Rubin, D. B. (1985). Constructing a Control Group Using Multivariate Matched Sampling Methods That Incorporate the Propensity Score. The American Statistician, 39(1), 33-. Siying Wei.(2023) Research on the Impact of Multidimensional Policy-Making Complexity on Achieving Sustainable Development in China.Proceedings of the 2023 2nd International Conference on Social Sciences and Humanities and Arts, SSHA 2023, ASSEHR 752, pp. 258–275, 2023. Sutton, P. C., & Costanza, R. (2002). Global estimates of market and non-market values derived from nighttime satellite imagery, land cover, and ecosystem service valuation. Ecological Economics, 41(3), 509–527. Sutton, Paul & Elvidge, Christopher & Tilottama, Ghosh. (2007). Estimation of Gross Domestic Product at Sub-National Scales Using Nighttime Satellite Imagery. International Journal of Ecological Economics & Statistics. Vol. 8, No. S07; Int. J. Ecol. Econ. Stat.; 5-21. Movshuk, Oleksandr.(2002). The Reliability of China’s Growth Figures: A Survey of Recent Statistical Controversies. The Journal of Econometric Study of Northeast Asia 4, No.1: 31-45. Martínez, L. R. (2022). How Much Should We Trust the Dictator’s GDP Growth Estimates? The Journal of Political Economy, 130(10), 2731–2769 Mellander, C., Lobo, J., Stolarick, K., & Matheson, Z. (2015). Night-Time Light Data: A Good Proxy Measure for Economic Activity? PloS One, 10(10), e0139779–e0139779. Meng, L. (2013). Evaluating China’s poverty alleviation program: A regression discontinuity approach. Journal of Public Economics, 101, 1–11. Wu, H. X. (2007). The Chinese GDP Growth Rate Puzzle: How Fast Has the Chinese Economy Grown? Asian Economic Papers, 6(1), 1–23. Zhou, Y., Tong, C., & Wang, Y. (2022). Road construction, economic growth, and poverty alleviation in China. Growth and Change, 53(3), 1306–1332. 二、中文文獻 黃志平 (2018)，「國家級貧困縣的設立推動了當地經濟發展嗎？-基於PSM-DID方法的實證研究」，《中國農村經濟》，5，98−111。 徐康寧、陳豐龍與劉修岩 (2015)， 「中國經濟增長的真實性：基於全球夜間燈光數據的檢驗」，《經濟研究》，050(009)，17-29,57。 仇童偉與彭嫦燕 (2023)。「脫貧摘帽的經濟影響及財政扶持績效—來自中國地方政府財政預決算的證據」，《中國經濟學》， 2（5）， 104-139。 李丹、裴育與陳歡 (2019)，「財政轉移支付是輸血還是造血—基於國定扶貧縣的實證研究」，《財貿經濟》，06，22-39。 范燕麗、叢樹與郗曼 (2021)，「從爭窮保帽到主動摘帽：正向激勵與農民持續增收」，《財政研究》，12，63-78。 賴胡與成天柱. (2014)，「財政扶貧的效率損失—基於財政激勵視角的縣級面板數據分析」，《經濟問題》，5，33–37。 鄭家喜與江帆 (2016)，「國家扶貧開發工作重點縣政策：驅動增長、縮小差距，還是政策失靈—基於PSM-DID方法的研究」，《經濟問題探索》，12，43–52。 李紹平。李帆與董永慶 (2018)，「集中連片特困地區減貧政策效應評估：基於PSM-DID方法的檢驗」，《改革》，12，142-155。 佟大建與應瑞瑤 (2019)，「扶貧政策的減貧效應及其可持續性—基於貧困縣名單調整的准自然試驗」，《改革》，11，126-135。 李小雲、唐麗霞與許漢澤 (2015)，「論我國的扶貧治理：基於扶貧資源瞄準和傳遞的分析」，《吉林大學社會科學學報》，04，90-98250-251。 周敏慧與陶然 (2016)，「市場還是政府：評估中國農村減貧政策」，《國際經濟評論》，6，63- 76。 鄧金錢與蔣雲亮 (2023)，「財政依賴、數位普惠金融與農民增收—基於縣域面板數據的實證分析」，《農林經濟管理學報》，03，330-340。 郗曼、付文林與范燕麗 (2021)《財政依賴與地區減貧增收—基於國家級貧困縣面板數據的實證研究》，《財政研究》，07，66-79。 方迎風 (2019)，「國家級貧困縣的經濟增長與減貧效應—基於中國縣級面板數據的實證分析」，《社會科學研究》，1，15–25。 汪三貴與鍾宇 (2021)，「貧困縣何以摘帽 —脫貧攻堅中的央地關係與幹部激勵」，《貴州財經大學學報》，5，1–7。 方迎風 (2022)，「中國縣域經濟發展差距的異質性與動力機制分析」，《河南社會科學》，9，46–55。 陶然、蘇福兵、陸曦與朱昱銘 (2010)，「經濟增長能夠帶來晉陞嗎？—對晉陞錦標競賽理論的邏輯挑戰與省級實證重估」，《中國經濟學》，00，200-239。 描述 碩士
國立政治大學
財政學系
111255017資料來源 http://thesis.lib.nccu.edu.tw/record/#G0111255017 資料類型 thesis dc.contributor.advisor 胡偉民<br>黃柏鈞 zh_TW dc.contributor.advisor Hu, Wei-Min<br>Huang, Po-Chun en_US dc.contributor.author (Authors) 吳莉雯 zh_TW dc.contributor.author (Authors) Wu, Li-Wen en_US dc.creator (作者) 吳莉雯 zh_TW dc.creator (作者) Wu, Li-Wen en_US dc.date (日期) 2024 en_US dc.date.accessioned 3-Jun-2024 11:46:20 (UTC+8) - dc.date.available 3-Jun-2024 11:46:20 (UTC+8) - dc.date.issued (上傳時間) 3-Jun-2024 11:46:20 (UTC+8) - dc.identifier (Other Identifiers) G0111255017 en_US dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/151514 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 財政學系 zh_TW dc.description (描述) 111255017 zh_TW dc.description.abstract (摘要) 在2012年，中國政府提出「2020年全面脫貧」的計劃，要求現有接受貧困縣政策的地方政府於2020年退出貧困縣名單，達到全中國脫離絕對貧困的目標。因此，脫離貧困縣成為接受扶助之地方政府的目標。 當地方政府存在目標，地方官員就有誇大經濟成長的誘因(Movshuk, 2002 ; Gilley, 2001)，造成虛假脫貧的現象。因此，本文參考並修改 Martínez (2022) 設計的模型，估計中國貧困縣與非貧困縣之間地區生產總值與夜間光照彈性的差異，推估中國貧困縣地方政府是否會因有來自中央政府的目標高報地區生產總值。 主要假設為在沒有官方操縱時，地區生產總值與夜間光照數據的彈性在各個縣市之間應一致。研究結果顯示在宣佈2020年全面脫貧的目標後，貧困縣高報的GDP比例約為23%，即貧困縣真實經濟成為率若為1%，則官方公佈之GDP成長約為1.23%。除此之外，事件分析法的研究結果也顯示，誇大的比例會隨著距離目標截止日越近而越高，該結果可以反映，目標到期日越近則政績壓力越大，政績壓力會成為地方政府官員誇大經濟成長的誘因。 zh_TW dc.description.abstract (摘要) In 2012, the Chinese government set the goal of " absolute poverty eliminated by 2020", which required local governments currently receiving poverty county policy support to be removed from the list of poverty counties by 2020. Consequently, exiting the poverty county status became the objective for local governments receiving assistance. When local governments have targets, local officials have incentives to exaggerate economic growth (Movshuk, 2002; Gilley, 2001), resulting in the phenomenon of false poverty alleviation. Consequently, this paper references and modifies the model designed by Martínez (2022) to estimate the differences in the nighttime lights elasticity of GDP between poverty and non-poverty counties in China. This estimation assesses whether local governments in China's poverty counties are likely to overreport GDP due to targets set by the central government. The primary assumption is that in the absence of official manipulation, the nighttime lights elasticity of GDP should be consistent across different counties. The result indicate that after the announcement of the comprehensive poverty alleviation goal for 2020, the proportion of GDP overstate by poverty counties was approximately 23%. This implies that if the actual economic growth rate in a poverty county is 1%, the officially reported GDP growth would be about 1.23%. Furthermore, results from the event study method also show that the proportion of exaggeration increases as the deadline for the target approaches. This outcome reflects that the closer the deadline for the target, the greater the performance pressure on officials, which becomes an incentive for local government officials to exaggerate economic growth. en_US dc.description.tableofcontents 第一章 緒論 1 第二章 文獻回顧 3 第一節 中國貧困縣制度發展 3 第二節 官方公佈數據的準確性 9 第三節 貧困縣經濟研究 13 第三章 研究設計 15 第一節 資料來源 15 第二節 研究方法 15 第三節 樣本敘述性統計與說明 19 第四章 實證結果 21 第一節 主要結果 21 第二節 穩健性分析 23 第五章 結論與建議 30 參考文獻 31 附錄 36 zh_TW dc.format.extent 1392924 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0111255017 en_US dc.subject (關鍵詞) 夜間光照 zh_TW dc.subject (關鍵詞) 經濟成長 zh_TW dc.subject (關鍵詞) 貧困縣政策 zh_TW dc.subject (關鍵詞) Nighttime light en_US dc.subject (關鍵詞) Economic growth en_US dc.subject (關鍵詞) Poverty county policies en_US dc.title (題名) 中國貧困縣經濟成長的評估 — 行政資料與光照資料的對比 zh_TW dc.title (題名) Evaluating Economic Growth in China’s Poverty County: Contrasting Administrative Data with Nighttime Light Data en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 一、英文文獻 Adams, F. G., & Chen, Y. (1996). Skepticism about Chinese GDP growth--The Chinese GDP elasticity of energy consumption. Journal of Economic and Social Measurement, 22(4), 231–240. Bluhm, R., & Krause, M. (2022). Top lights: Bright cities and their contribution to economic development. Journal of Development Economics, 157, 102880-. Chen, Z., Yu, B., Yang, C., Zhou, Y., Yao, S., Qian, X., Wang, C., Wu, B., & Wu, J. (2021). An extended time series (2000–2018) of global NPP-VIIRS-like nighttime light data from a cross-sensor calibration. Earth System Science Data, 13(3), 889–906. Chen, S., Qiao, X., & Zhu, Z. (2021). Chasing or cheating? Theory and evidence on China’s GDP manipulation. Journal of Economic Behavior & Organization, 189, 657–671. Chen, X., & Nordhaus, W. D. (2011). Using luminosity data as a proxy for economic statistics. Proceedings of the National Academy of Sciences - PNAS, 108(21), 8589–8594. Clark, H., Pinkovskiy, M., & Sala-i-Martin, X. (2020). China’s GDP growth may be understated. China Economic Review, 62, 101243-. Croft, T. A. (1978). Nighttime Images of the Earth from Space. Scientific American Vol. 239, Issue 1, pp. 86–101. Doll, C.N. H., Muller, J.-P., & Elvidge, C. D. (2000). Night-time Imagery as a Tool for Global Mapping of Socioeconomic Parameters and Greenhouse Gas Emissions. Ambio, 29(3), 157–162. Doll, C. N. H., Muller, J.-P., & Morley, J. G. (2006). Mapping regional economic activity from night-time light satellite imagery. Ecological Economics, 57(1), 75–92. Elvidge, C. D., Baugh, K. E., Kihn, E. A., Kroehl, H. W., Davis, E. R., & Davis, C. W. (1997). Relation between satellite observed visible-near infrared emissions, population, economic activity and electric power consumption. International Journal of Remote Sensing, 18(6), 1373–1379. Ebener, S., Murray, C., Tandon, A., & Elvidge, C. C. (2005). From wealth to health: modelling the distribution of income per capita at the sub-national level using night-time light imagery. International Journal of Health Geographics, 4(1), 5–5. Galimberti, J. K. (2020). Forecasting GDP Growth from Outer Space. Oxford Bulletin of Economics and Statistics, 82(4), 697–722. Gibson, J., Olivia, S., & Boe‐Gibson, G. (2020). NIGHT LIGHTS IN ECONOMICS: SOURCES AND USES1. Journal of Economic Surveys, 34(5), 955–980. Gibson, J., Olivia, S., Boe-Gibson, G., & Li, C. (2021). Which night lights data should we use in economics, and where? Journal of Development Economics, 149, 102602-. Gilley, B. (2001). Breaking barriers. Far Eastern Economic Review, 14–19. HOLZ, C. A. (2004). Deconstructing China’s GDP statistics. China Economic Review, 15(2), 164–202. Holz, C. A. (2008). China’s 2004 Economic Census and 2006 Benchmark Revision of GDP Statistics: More Questions than Answers? The China Quarterly (London), 193(193), 150–163. Holz, C. A. (2014). The quality of China’s GDP statistics. China Economic Review, 30, 309–338. Hamman, N., & Phiri, A. (2022). Using Nighttime Luminosity as a Proxy for Economic Growth in Africa: Is It a Bright Idea? Managing Global Transitions, 20(2), 139–165. Henderson, J. V., Storeygard, A., & Weil, D. N. (2012). Measuring Economic Growth from Outer Space. The American Economic Review, 102(2), 994–1028. Park, A., Wang, S., & Wu, G. (2002). Regional poverty targeting in China. Journal of Public Economics, 86(1), 123–153. Pérez-Sindín, X. S., Chen, T.-H. K., & Prishchepov, A. V. (2021). Are night-time lights a good proxy of economic activity in rural areas in middle and low-income countries? Examining the empirical evidence from Colombia. Remote Sensing Applications, 24, 100647-. Phan, D. H. (2023). Lights and GDP relationship: What does the computer tell us? Empirical Economics, 65(3), 1215–1252. Pagaduan, J. A. (2022). 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