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題名 縱向因素分析與中介效應模型：資源錯置與能源對區域所得影響
Longitudinal Factor Analysis and Mediation Models: The Impact of Resource Misallocation and Energy Use on Regional Income
作者 胡芷瑄
Hu, Chih-Hsuan
貢獻者 鄭宗記
Cheng, Tsung-Chi
胡芷瑄
Hu, Chih-Hsuan
關鍵詞 資源錯置
收益生產力
線性混合效應模型
縱向因素分析
縱向因素分析混合模型
區域所得差異
日期 2025
上傳時間 2-Oct-2025 10:57:33 (UTC+8)
摘要 本研究運用多個政府行政資料，探討台灣地區發展失衡的問題，並納入鄉鎮市區層級的企業績效指標進行分析。地區發展失衡係以總要素收益生產力（Total Factor Revenue Productivity, TFPR）的變異程度作為衡量指標，該指標反映各鄉鎮內企業資源錯置與產出扭曲的程度，並作為區域扭曲程度的反向指標。研究進一步探討影響區域扭曲的潛在因素，特別是人口結構等社會經濟特徵與之間的關聯。為掌握區域異質性與潛在結構特徵，研究採用縱向混合效應因素分析模型，從多項社經變數中提取潛在結構因素。接著，構建兩個時間滯後中介模型，將企業固定資產淨額與薪資成本納入模型中作為中介變項，以評估 TFPR 與工業售電度數對各鄉鎮所得中位數的間接影響。實證結果說明了 TFPR 如何透過資本與勞動成本的傳導路徑影響地區所得表現，並突顯生產結構調整對地方經濟發展的重要性，最終提供對區域政策設計之理論洞見與實證依據。
參考文獻 Adler, G., Duval, R. A., Furceri, D., Çelik, S. K., Koloskova, K., & Poplawski-Ribeiro, M.(2017). Gone with the headwinds: Global productivity. International Monetary Fund. Allison, P. D. (2009). Fixed Effects Regression Models. SAGE Publications. Alviarez, V., Cravino, J., & Ramondo, N. (2023). Firm-embedded productivity and cross-country income differences. Journal of Political Economy, 131(9), 2289-2327. An, X., Yang, Q., & Bentler, P. M. (2013). A latent factor linear mixed model for high-dimensional longitudinal data analysis. Statistics in Medicine, 32(24), 4229-4239. Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist’s Companion. Princeton University Press. Banerjee, A. V., & Duflo, E. (2005). Growth theory through the lens of development economics. Handbook of Economic Growth, 1, 473-552. Banerjee, A. V., & Moll, B. (2010). Why does misallocation persist?. American Economic Journal: Macroeconomics, 2(1), 189-206. Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6),1173-1182. Barro, R. J., & Sala-i-Martin, X. (1992). Convergence. Journal of Political Economy, 100(2), 223-251. Barro, R. J., Sala-i-Martin, X. (2004). Economic Growth (2nd ed.). MIT Press. Bauer, D. J., Preacher, K. J., & Gil, K. M. (2006). Conceptualizing and testing random indirect effects and moderated mediation in multilevel models: new procedures and recommendations. Psychological Methods, 11(2), 142– 163. https://doi.org/10.1037/1082-989X.11.2.142 Bates, D., Mächler, M., Bolker, B., & Walker, S.(2015). Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software, 67(1),1-48. Bils, M., Klenow, P. J., & Ruane, C. (2021). Misallocation or mismeasurement? Journal of Monetary Economics, 124, S39-S56. Blood, E. A., & Cheng, D. M. (2011). The use of mixed models for the analysis of mediated data with time-dependent predictors. Journal of Environmental and Public Health,Article 2011.https://doi.org/10.1155/2011/435078。 Bollen, K. A., Harden, J. J., Ray, S., & Zavisca, J. (2014). BIC and alternative Bayesian information criteria in the selection of structural equation models. Structural Equation Modeling, 21(1), 1-19. https://doi.org/10.1080/10705511.2014.856691 Buera, F. J., Kaboski, J. P., & Shin, Y. (2011). Finance and development: A tale of two sectors. American Economic Review, 101(5), 1964-2002. Buera, F. J., & Shin, Y. (2013). Financial frictions and the persistence of history: A quantitative exploration. Journal of Political Economy, 121(2), 221–272. https://doi.org/10.1086/670271 Busso, M., Madrigal, L., & Pagés, C. (2012). Productivity and resource misallocation in latin america. The BE Journal of Macroeconomics, 13(1), 903-932. Cashin, A. G., & Lee, H. (2021). An introduction to mediation analyses of randomized controlled trials. Journal of Clinical Epidemiology, 131,161-164. Cheung, M. W.-L. (2007). Comparison of approaches to constructing confidence intervals for mediating effects using structural equation models. Structural Equation Modeling: A Multidisciplinary Journal, 14(2),227– 246. Cole, D. A., & Maxwell, S. E. (2003). Testing mediational models with longitudinal data: questions and tips in the use of structural equation modeling. Journal of Abnormal Psychology, 112(4), 558-577. International Monetary Fund. (2015). Causes and consequences of income inequality: A global perspective. IMF Staff Discussion Note, SDN/15/13. https://www.imf.org/external/pubs/ft/sdn/2015/sdn1513.pdf Delattre, M., Lavielle, M., & Poursat, M. A. (2014). A note on BIC in mixed-effects models. Electronic Journal of Statistics, 8(1), 456-475. Demirer, M. (2020). Production function estimation with factor-augmenting technology: An application to markups. Job Market Paper. Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society: Series B (Methodological), 39(1), 1-38. Diggle, P. J., Heagerty, P., Liang, K. Y., & Zeger, S. L. (2002). Analysis of Longitudinal Data(2nd ed.). Oxford University Press. Eberly, J. C., Rebelo, S., & Vincent, N. (2012). What explains the lagged investment effect? Journal of Monetary Economics, 59(4), 370– 380. https://doi.org/10.1016/j.jmoneco.2012.04.006 Fan, S., & Zhang, X. (2004). Infrastructure and regional economic development in rural China. China Economic Review, 15(2), 203-214. Fisher, R.A. (1925). Statistical Methods for Research Workers. Edinburgh: Oliver and Boyd. Fitzmaurice, G. M., Laird, N. M., & Ware, J. H. (2011). Applied Longitudinal Analysis (2nd ed.). Wiley. Gennaioli, N., La Porta, R., Lopez-de-Silanes, F., Shleifer, A. (2014). Growth in regions. Journal of Economic Growth, 19(3), 259– 309. Gandhi, A., Navarro, S., & Rivers, D. A. (2020). On the identification of gross output production functions. Journal of Political Economy, 12(8), 2973-3016. Gelman, A., Carlin, J. B., Stern, H. S.,Dunson, D. B., Vehtari, A., & Rubin, D. B.(2013). Bayesian Data Analysis(3rd ed.). Chapman and Hall/CRC. Gelman, A., & Hill, J. (2007). Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press. Goldstein, H. (2011) Multilevel Statistical Models.Multilevel Statistical Models. New Jersey: John Wiley & Sons. Hall, R. E., & Jones, C. I. (1999). Why do some countries produce so much more output per worker than others? The Quarterly Journal of Economics, 114(1), 83-116. Hedeker, D., & Gibbons, R. D. (2006). Longitudinal Data Analysis. New Jersey: John Wiley & Sons Hirschfeld, G., & Brachel, R. (2014). Multiple-Group confirmatory factor analysis in R – A tutorial in measurement invariance with continuous and ordinal indicators. Practical Assessment, Research & Evaluation, 19, 1-12. https://doi.org/10.7275/qazy-2946 Hox, J. J.(2010). Multilevel Analysis:Techniques and Applications(2nd ed.). Routledge. Hsieh, C.-T., & Klenow, P. (2009). Misallocation and manufacturing tfp in China and India. Quarterly Journal of Economics, 124(4), 1403-1448. Hsieh, C.-T., & Moretti, E. (2019). Housing constraints and spatial misallocation. American Economic Journal: Macroeconomics, 11(2), 1-39. Jones, C. I. (2013). Misallocation, input-output economics, and economic growth. In Advances in Economics and Econometrics: Tenth World Congress (Vol. 2, pp. 419-458). Cambridge University Press. Jose, P. E. (2016). Doing Statistical Mediation and Moderation. New York, NY: Guilford Press. Jovanovic, B. (2014). Misallocation and growth. American Economic Review, 104(4), 1149-1171. Kim, E. S., Cao, C., Wang, Y., & Nguyen, D. T. (2017). Measurement invariance testing with many groups: a comparison of five approaches. Structural Equation Modeling: A Multidisciplinary Journal, 24(4), 524-544. https://doi.org/10.1080/10705511.2017.1304822 Klenow, P. J., & Rodriguez-Clare, A. (1997). The neoclassical revival in growth economics: Has it gone too far? NBER Macroeconomics Annual, 12, 73-103. Laird, N. M., & Ware, J. H. (1982). Random-effects models for longitudinal data. Biometrics, 38(4), 963– 974. Li, S., E, H., Wang, L., & Xue, H. (2023). Factor misallocation and optimization in China’s manufacturing industry. Sustainability, 15(5), 4279. Liang, K.-Y., & Zeger, S. L. (1986). Longitudinal data analysis using generalized linear models. Biometrika, 73(1), 13– 22. Maas, C. J. M., & Hox, J. J.(2005). Sufficient sample sizes for multilevel modeling. Methodology: European Journal of Research Methods for the Behavioral and Social Sciences, 1(3),86-92. MacKinnon, D. P. (2008). Introduction to Statistical Mediation Analysis. Lawrence Erlbaum Associates. MacKinnon, D. P., Fairchild, A. J., & Fritz, M. S. (2007). Mediation analysis. Annual Review of Psychology, 58, 593– 614. MacKinnon, D. P., Lockwood, C. M., & Williams, J. (2004). Confidence limits for the indirect effect: distribution of the product and resampling methods.Multivariate Behavioral Research, 39(1), 99-128. Markovits, R. S. (1975). The causes and policy significance of pareto resource misallocation: a checklist for micro-economic policy analysis. Stanford Law Review, 28(1), 1-46. McLachlan, G., Peel, D., & Bean, R. (2003). Modelling high-dimensional data by mixtures of factor analyzers. Computational Statistics & Data Analysis, 41(3), 379-388. https://doi.org/10.1016/S0167-9473(02)00183-4 Meredith, W., & Horn, J. L. (2001). The role of measurement invariance in understanding group differences in multivariate data. Multivariate Behavioral Research, 36(3), 527–552. Midrigan, V., & Xu, D. Y. (2014). Finance and misallocation: Evidence from plant-level data. American Economic Review, 104(2), 422-458. Millsap, R. E. (2011). Statistical Approaches to Measurement Invariance. Routledge. Moll, B. (2014). Productivity losses from financial frictions：can self-financing undo capital misallocation? American Economic Review, 104(10), 3186-3221. Ounajim, A., Slaoui, Y., Louis, P.-Y., Billot, M., Frasca, D., & Rigoard, P. (2023). Mixture of longitudinal factor analyzers and their application to the assessment of chronic pain. Statistics in Medicine, 42(18), 3259– 3282. https://doi.org/10.1002/sim.9804 Parente, S. L., & Prescott, E. C. (1994). Barriers to technology adoption and development. Journal of Political Economy, 102(2), 298-321. Parente, S. L., & Prescott, E. C. (1999). Monopoly rights: A barrier to riches. American Economic Review, 89(5), 1216-1233. Pearl, J. (2009). Causality: Models, Reasoning, and Inference. Cambridge University Press. Pinheiro, J. C., & Bates, D. M.(2000). Mixed-Effects Models in S and S-PLUS. New York:Springer. Preacher, K. J., Curran, P. J., & Bauer, D. J. (2006). Computational tools for probing interactions in multiple linear regression, multilevel modeling, and latent curve analysis. Journal of Educational and Behavioral Statistics, 34(1),437– 448. Preacher, K. J., & Hayes, A. F. (2004). SPSS and SAS procedures for estimating indirect effects in simple mediation models. Behavior Research Methods, Instruments, & Computers, 36(4),717-731. Proust, C., Jacqmin-Gadda, H., Taylor, J. M. G., Ganiayre, J., & Commenges, D. (2006). A nonlinear model with latent process for cognitive evolution using multivariate longitudinal data. Biometrics, 62(4), 1014-1024. https://doi.org/10.1111/j.1541-0420.2006.00573.x Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods(2nd ed.). Thousand Oaks, CA: Sage Publications. Restuccia, D., & Rogerson, R. (2008). Policy distortions and aggregate productivity with heterogeneous establishments. Review of Economic Dynamics, 11(4), 707-720. Restuccia, D., & Rogerson, R. (2017). The causes and costs of misallocation. Journal of Economic Perspectives, 31(3), 151-174. Rijnhart, J. J. M., Twisk, J. W. R., Valente, M. J., & Heymans, M. W. (2022). Time lags and time interactions in mixed effects models impacted longitudinal mediation effect estimates. Journal of Clinical Epidemiology, 151, 143-150. https://doi.org/10.1016/j.jclinepi.2022.07.004 Rodrigue, J., Shi, Q., & Tan, Y. (2024). Trade policy uncertainty & resource misallocation. European Economic Review, 164, 104720. Rodríguez-Pose, A. (2018). The revenge of the places that don’t matter (and what to do about it). Cambridge Journal of Regions, Economy and Society, 11(1), 189-209. Rohrer, J. M. (2018). Thinking clearly about correlations and causation: Graphical causal models for observational data. Advances in Methods and Practices in Psychological Science, 1(1), 27– 42. Rotemberg, M., & White, T. K. (2021). Plant‐to‐Table(s and Figures): Processed Manufacturing Data and Measured Misallocation. Mimeo. Roy, J., & Lin, X. (2000). Latent variable models for longitudinal data with multiple continuous outcomes. Biometrics, 56(4),1047-1054. Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6(2), 461– 464. Singer, J. D., & Willett, J. B. (2003). Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence. Oxford University Press. Snijders, T. A. B., & Bosker, R. J. (2012). Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling (2nd ed.). Sage. Spearman, C. (1904). ”General intelligence,” objectively determined and measured. American Journal of Psychology, 15(2),201-292. Twisk, J. W. R. (2013). Applied Longitudinal Data Analysis for Epidemiology: A Practical Guide. Cambridge, United Kingdom: Cambridge University Press. Uras, B. R. (2014). Journal of Banking & Finance, 39, 177-191. Corporate financial structure, misallocation and total factor productivity. West, B. T., Welch, K. B., & Galecki, A. T.(2014). 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描述 碩士
國立政治大學
統計學系
112354027
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0112354027
資料類型 thesis
dc.contributor.advisor 鄭宗記zh_TW
dc.contributor.advisor Cheng, Tsung-Chien_US
dc.contributor.author (Authors) 胡芷瑄zh_TW
dc.contributor.author (Authors) Hu, Chih-Hsuanen_US
dc.creator (作者) 胡芷瑄zh_TW
dc.creator (作者) Hu, Chih-Hsuanen_US
dc.date (日期) 2025en_US
dc.date.accessioned 2-Oct-2025 10:57:33 (UTC+8)-
dc.date.available 2-Oct-2025 10:57:33 (UTC+8)-
dc.date.issued (上傳時間) 2-Oct-2025 10:57:33 (UTC+8)-
dc.identifier (Other Identifiers) G0112354027en_US
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/159688-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 統計學系zh_TW
dc.description (描述) 112354027zh_TW
dc.description.abstract (摘要) 本研究運用多個政府行政資料，探討台灣地區發展失衡的問題，並納入鄉鎮市區層級的企業績效指標進行分析。地區發展失衡係以總要素收益生產力（Total Factor Revenue Productivity, TFPR）的變異程度作為衡量指標，該指標反映各鄉鎮內企業資源錯置與產出扭曲的程度，並作為區域扭曲程度的反向指標。研究進一步探討影響區域扭曲的潛在因素，特別是人口結構等社會經濟特徵與之間的關聯。為掌握區域異質性與潛在結構特徵，研究採用縱向混合效應因素分析模型，從多項社經變數中提取潛在結構因素。接著，構建兩個時間滯後中介模型，將企業固定資產淨額與薪資成本納入模型中作為中介變項，以評估 TFPR 與工業售電度數對各鄉鎮所得中位數的間接影響。實證結果說明了 TFPR 如何透過資本與勞動成本的傳導路徑影響地區所得表現，並突顯生產結構調整對地方經濟發展的重要性，最終提供對區域政策設計之理論洞見與實證依據。zh_TW
dc.description.tableofcontents 第一章 緒論 1 第一節 研究動機 1 第二節 研究目的 3 第二章 文獻探討 4 第一節 縱向數據分析 4 第二節 線性混合效應模型 5 第三節 縱向因素分析 7 第四節 中介變數效應 13 第五節 LMM 結合時間滯後之中介變數分析 15 第三章 資源錯置 19 第一節 何為資源錯置？ 19 第二節 產生資源錯置後對地區經濟發展的影響 20 第三節 衡量資源錯置的理論模型 21 第四章 實證分析 24 第一節 分析資料 24 第二節 縱向因素分析 26 第三節 資源錯置與能源投入的中介效應 30 第四節 區域經濟中介機制 38 第五章 結論 40 參考文獻 42zh_TW
dc.format.extent 1890049 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0112354027en_US
dc.subject (關鍵詞) 資源錯置zh_TW
dc.subject (關鍵詞) 收益生產力zh_TW
dc.subject (關鍵詞) 線性混合效應模型zh_TW
dc.subject (關鍵詞) 縱向因素分析zh_TW
dc.subject (關鍵詞) 縱向因素分析混合模型zh_TW
dc.subject (關鍵詞) 區域所得差異zh_TW
dc.title (題名) 縱向因素分析與中介效應模型：資源錯置與能源對區域所得影響zh_TW
dc.title (題名) Longitudinal Factor Analysis and Mediation Models: The Impact of Resource Misallocation and Energy Use on Regional Incomeen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) Adler, G., Duval, R. A., Furceri, D., Çelik, S. K., Koloskova, K., & Poplawski-Ribeiro, M.(2017). Gone with the headwinds: Global productivity. International Monetary Fund. Allison, P. D. (2009). Fixed Effects Regression Models. SAGE Publications. Alviarez, V., Cravino, J., & Ramondo, N. (2023). Firm-embedded productivity and cross-country income differences. Journal of Political Economy, 131(9), 2289-2327. An, X., Yang, Q., & Bentler, P. M. (2013). A latent factor linear mixed model for high-dimensional longitudinal data analysis. Statistics in Medicine, 32(24), 4229-4239. Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist’s Companion. Princeton University Press. Banerjee, A. V., & Duflo, E. (2005). Growth theory through the lens of development economics. Handbook of Economic Growth, 1, 473-552. Banerjee, A. V., & Moll, B. (2010). Why does misallocation persist?. American Economic Journal: Macroeconomics, 2(1), 189-206. Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6),1173-1182. Barro, R. J., & Sala-i-Martin, X. (1992). Convergence. Journal of Political Economy, 100(2), 223-251. Barro, R. J., Sala-i-Martin, X. (2004). Economic Growth (2nd ed.). MIT Press. Bauer, D. J., Preacher, K. J., & Gil, K. M. (2006). Conceptualizing and testing random indirect effects and moderated mediation in multilevel models: new procedures and recommendations. Psychological Methods, 11(2), 142– 163. https://doi.org/10.1037/1082-989X.11.2.142 Bates, D., Mächler, M., Bolker, B., & Walker, S.(2015). Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software, 67(1),1-48. Bils, M., Klenow, P. J., & Ruane, C. (2021). Misallocation or mismeasurement? Journal of Monetary Economics, 124, S39-S56. Blood, E. A., & Cheng, D. M. (2011). The use of mixed models for the analysis of mediated data with time-dependent predictors. Journal of Environmental and Public Health,Article 2011.https://doi.org/10.1155/2011/435078。 Bollen, K. A., Harden, J. J., Ray, S., & Zavisca, J. (2014). BIC and alternative Bayesian information criteria in the selection of structural equation models. Structural Equation Modeling, 21(1), 1-19. https://doi.org/10.1080/10705511.2014.856691 Buera, F. J., Kaboski, J. P., & Shin, Y. (2011). Finance and development: A tale of two sectors. American Economic Review, 101(5), 1964-2002. Buera, F. J., & Shin, Y. (2013). Financial frictions and the persistence of history: A quantitative exploration. Journal of Political Economy, 121(2), 221–272. https://doi.org/10.1086/670271 Busso, M., Madrigal, L., & Pagés, C. (2012). Productivity and resource misallocation in latin america. The BE Journal of Macroeconomics, 13(1), 903-932. Cashin, A. G., & Lee, H. (2021). 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