Max Weber, Taha Chaiechi and Rabiul Beg

Correspondence: Max Weber, max.p.weber@gmail.com

School of Management and Law, Zurich University of Applied Sciences (ZHAW)

pdf (1202.13 Kb) | doi: https://doi.org/10.47260/bae/9212

Abstract

Addressing urgent global climate change and inequality issues has been a major challenge for the ten AMS (ASEAN Member States) given the diversity and local agendas with divergent political, economic, and social objectives. While regional policy frameworks prioritise harmonisation and inter-regional integration outcomes over national inequality and climate change policy goals, governments typically address the latter through discretionary policy add-ons that lead to policy fragmentation and competing fiscal goals that interfere with intermediate development plans. To achieve the aspired inequality outcomes (Regional Framework and Action Plan, ASEAN Declaration, 2013) and the ratified global climate targets (Paris Treaty 2015) simultaneously, such policy outcomes and commitments to collaborative policy action will need to be aligned within an integrated policy framework at the regional or global level, and externalities of economic activity internalised into a sustainable and inclusive fiscal model. Focusing on policy design, integration and evaluation aspects, this paper tests the hypothesis that, given the urgency of these issues, the ASEAN governments devised corrective and preventive measures to systematically mitigate these externalities through intervention at the policy level and multilateral coordination at the regional and global levels to achieve pro-green and pro-equity policy outcomes with a net social surplus. Adopting a qualitative methodology, this study conducts a structured literature search and subsequent document analysis, using advanced text mining techniques to extract, contextualise and map policy-relevant themes by geopolitical scope, policy intent and outcomes. Literature evidence confirmed that the ASEAN Member States have recognised and acknowledged the urgency of climate change and inequality challenges, and that these governments intervene at the local policy level and also engage in multilateral discussions, which lack, however, formal commitment and transparency. This study could not produce literature evidence of a systematic approach by these governments internalising mitigation mechanisms into the fiscal policy frameworks to achieve the aspired inclusive and sustainable outcomes - by design, rather than discretionary policy add-ons - and thus, the hypothesis was rejected.

Keywords:

  Sustainable fiscal policy, inclusive growth, climate change mitigation, policy integration, multilateral intervention, document analysis.

References

Al-Khatib, W., Day, Y. F., Ghafoor, A., & Berra, P. B. (1999). Semantic modelling and     knowledge representation in multimedia databases. IEEE transactions on knowledge and data engineering, 11(1), 64-80.Arksey, H., & O'Malley, L. (2005). Scoping studies: towards a methodological framework.   International journal of social research methodology, 8(1), 19-32.Bauer, M. W. (2000). Classical content analysis: A review. Qualitative researching with text, image and sound, 131-151.Birkland, T. (2005). Models of the Policy Process. J Rabin, Encyclopedia of Public Administration and Public Policy, Taylor & Francis Group, Boca Raton, 188-191.Blackstone, L., Kamara, S., & Moataz, T. (2019). Revisiting leakage abuse attacks. Cryptology ePrint Archive.Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of machine Learning research, 3(Jan), 993-1022.Blei, D. M., & Jordan, M. I. (2003, July). Modeling annotated data. In Proceedings of the 26th annual international ACM SIGIR conference on research and development in informaion retrieval (pp. 127-134).Bowen, G. A. (2005). Preparing a qualitative research-based dissertation: Lessons learned. The qualitative report, 10(2), 208-222.Boyatzis, R. E. (1998). Transforming qualitative information: Thematic analysis and code development. sage.Braun, V., & Clarke, V. (2019). Reflecting on reflexive thematic analysis. Qualitative research in sport, exercise and health, 11(4), 589-597.Bowen, G. A. (2009). Document analysis as a qualitative research method. Qualitative Research Journal, 9(2), 27-40. doi:10.3316/QRJ0902027Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative research in psychology, 3(2), 77-101.Cao, N., Sun, J., Lin, Y. R., Gotz, D., Liu, S., & Qu, H. (2010). Facetatlas: Multifaceted visualisation for rich text corpora. IEEE transactions on visualisation and computer graphics, 16(6), 1172-1181.Cardno, C. (2018). Policy Document Analysis: A Practical Educational Leadership Tool and a Qualitative Research Method. Educational Administration: Theory & Practice, 24(4), 623-640.Chau, M., & Chen, H. (2008). A machine learning approach to web page filtering using content and structure analysis. Decision Support Systems, 44(2), 482-494.Chýla, R. (2012). Automated methods of textual content analysis and description of text structures.Clarke, V., & Braun, V. (2013). Teaching thematic analysis: Overcoming challenges and developing strategies for effective learning. The psychologist, 26(2).Clarke, V., & Braun, V. (2014, 2015). Thematic Analysis. SAGE Publications, London.Collier, P. (2010). The plundered planet: Why we must--and how we can--manage nature for global prosperity. Oxford University Press.Collins, C., Carpendale, S., & Penn, G. (2009, June). Docuburst: Visualising document content using language structure. In Computer graphics forum (Vol. 28, No. 3, pp. 1039-1046). Oxford, UK: Blackwell Publishing Ltd.Courtney, M., Breen, M., McMenamin, I., & McNulty, G. (2020). Automatic translation, context, and supervised learning in comparative politics. Journal of Information Technology & Politics, 17(3), 208-217.CROTTY, M. (1998). The Foundations of Social Research. London. Sage.Crotty, M. (2020). The foundations of social research: Meaning and perspective in the research process. Routledge.Darlington, Y., & Scott, D. (2020). Qualitative research in practice stories from the field. Routledge.Data, I. Q., & Silverman, D. (2011). A Guide to the Principles of Qualitative Research.Fernández-Gómez, M., & Mendes, S. (2021). LDAShiny: An R package for exploratory review of scientific literature based on a Bayesian probabilistic model and machine learning tools. Mathematics, 9(14), 1671.England, H. (1986). Social work as art: Making sense for good practice. Taylor & Francis.Flick, U. (2018). Triangulation in data collection. The SAGE handbook of qualitative data collection, 527-544.Frith, H., & Gleeson, K. (2004). Clothing and embodiment: Men managing body image and appearance. Psychology of men & masculinity, 5(1), 40.Havre, S., Hetzler, E., Whitney, P., & Nowell, L. (2002). Themeriver: Visualising thematic changes in large document collections. IEEE transactions on visualisation and computer graphics, 8(1), 9-20.Hayes, N. (1997). Theory-led thematic analysis: social identification in small companies. Doing qualitative analysis in psychology, 93, 114.Hill, H. (1998). An overview of the issues. ASEAN Economic Bulletin, 261-271.Havi, E. D. K., & Enu, P. (2014). The effect of fiscal policy and monetary policy on Ghana’s economic growth: which policy is more potent. International Journal of Empirical Finance, 3(2), 61-75.Hofmann, T. (2001). Unsupervised learning by probabilistic latent semantic analysis. Machine learning, 42(1), 177-196.Hofmann, T. (2013). Probabilistic latent semantic analysis. arXiv preprint arXiv:1301.6705.Hopkins, D. J., & King, G. (2010). A method of automated nonparametric content analysis for social science. American Journal of Political Science, 54(1), 229-247.Howarth, R. B., & Norgaard, R. B. (1990). Intergenerational resource rights, efficiency, and social optimality. Land economics, 66(1), 1-11.Jordan, B. (2008). Welfare and well-being: Social value in public policy. Policy Press.Kanakia, A., Wang, K., Dong, Y., Xie, B., Lo, K., Shen, Z., ... & Wu, C. H. (2020). Mitigating biases in cord-19 for analysing covid-19 literature. Frontiers in research metrics and analytics, 5, 14.Kapadia, S. (2019). Evaluate topic models: latent dirichlet allocation (LDA). Towards Data Science. Web accessed 14, April 2022; https://towardsdatascience.com/evaluate-topic-model-in-python-latent-dirichlet-allocation-lda-7d57484bb5d0Kiyavitskaya, N. (2006). Tool support for semantic annotation. International Doctorate School in Information and Communication Technologies DIT-University of Trento.Lee, L. W., Dabirian, A., McCarthy, I. P., & Kietzmann, J. (2020). Making sense of text: artificial intelligence-enabled content analysis. European Journal of Marketing.Liu, L., Tang, L., Dong, W., Yao, S., & Zhou, W. (2016). An overview of topic modeling and its current applications in bioinformatics. SpringerPlus, 5(1), 1-22.Majchrzak, A., & Markus, M. L. (2013). Methods for policy research: Taking socially responsible action (Vol. 3). SAGE publications.McDermott, R. C., Kilmartin, C., McKelvey, D. K., & Kridel, M. M. (2015). College male sexual assault of women and the psychology of men: Past, present, and future directions for research. Psychology of Men & Masculinity, 16(4), 355.Merriam, S. B., & Tisdell, E. J. (2015). Qualitative research: A guide to design and implementation. John Wiley & Sons.Merriam, S. B., & Tisdell, E. J. (2016). Designing your study and selecting a sample. Qualitative research: A guide to design and implementation, 67(1), 73-104.Morgan, H. (2022). Conducting a Qualitative Document Analysis. The Qualitative Report, 27(1), 64-77.Neves, M., & Ševa, J. (2021). An extensive review of tools for manual annotation of documents. Briefings in bioinformatics, 22(1), 146-163.O’Leary, M. B., Wilson, J. M., & Metiu, A. (2014). Beyond being there. MIS quarterly, 38(4), 1219-1244.O’Leary, Z. (2014). The essential guide to doing your research project (2nd ed.). Thousand Oaks, CA: SAGE Publications, Inc.Onwuegbuzie, A. J., & Leech, N. L. (2007). A call for qualitative power analyses. Quality & quantity, 41(1), 105-121.Owen, G. T. (2014). Qualitative methods in higher education policy analysis: Using interviews and document analysis. The qualitative report, 19(26), 1.Patton, M. Q. (2015). The sociological roots of utilisation-focused evaluation. The American Sociologist, 46(4), 457-462.Pham, M. T., Rajić, A., Greig, J. D., Sargeant, J. M., Papadopoulos, A., & McEwen, S. A. (2014). A scoping review of scoping reviews: advancing the approach and enhancing the consistency. Research synthesis methods, 5(4), 371-385.Redlich-Amirav, D., & Higginbottom, G. (2014). New emerging technologies in qualitative research. Qualitative Report, 19(26).Rees, W. E. (1995). Achieving sustainability: reform or transformation?. Journal of planning literature, 9(4), 343-361.Ritchie, J., & Spencer, L. (2002). Qualitative data analysis for applied policy research. The qualitative researcher’s companion, 573(2002), 305-29.Scott, I. (1989). Political change and the crisis of legitimacy in Hong Kong. University of Hawaii Press.Scott, D. (2002). Adding meaning to measurement: The value of qualitative methods in practice research. The British Journal of Social Work, 32(7), 923-930.Scott, J. (1990). A matter of Record: Documentary Sources. Social Research.Seale, C., & Tonkiss, F. (2012). Content and comparative keyword analysis. Researching society and culture, 459, 478.Seale, C., Silverman, D., Gubrium, J. F., & Gobo, G. (2003). Qualitative research practice. Qualitative Research Practice, 1-640.Seale, C. (2002). Quality issues in qualitative inquiry. Qualitative Social Work, 1(1), 97-110.Seale, C. (1999). Grounding theory. The quality of qualitative research, 1, 87-105.Seale, C. (1999). Quality in qualitative research. Qualitative inquiry, 5(4), 465-478.Seale, C., & Silverman, D. (1997). Ensuring rigour in qualitative research. The European journal of public health, 7(4), 379-384.Silverman, D. (Ed.). (2020). Qualitative research. Sage.Silverman, D. (2015). Interpreting qualitative data. Sage.Silverman, D. (2013). Doing qualitative research: A practical handbook. Sage.Singh, A. K., Issac, J., & Narayanan, K. G. S. (2019). Measurement of environmental sustainability index and its association with socio-economic indicators in selected Asian economies: An empirical investigation. International Journal of Environment and Sustainable Development, 18(1), 57-100.Slater, S., Joksimović, S., Kovanovic, V., Baker, R. S., & Gasevic, D. (2017). Tools for educational data mining: A review. Journal of Educational and Behavioral Statistics, 42(1), 85-106.Stiglitz, J. (2015). Reduce Inequality, Increase Economic Growth: A Conversation with Joseph Stiglitz (Vol. 6). NYU Press.Tang, J., Yao, L., & Chen, D. (2009, April). Multi-topic based query-oriented summarisation. In Proceedings of the 2009 SIAM international conference on data mining (pp. 1148-1159).Society for Industrial and Applied Mathematics.Tijare, P., & Rani, P. J. (2020, December). Exploring popular topic models. In Journal of Physics: Conference Series (Vol. 1706, No. 1, p. 012171). IOP Publishing.Tight, M. (2019). Documentary research in the social sciences. Sage.Ullah, A., & Baharudin, B. (2016). Pattern and semantic analysis to improve unsupervised techniques for opinion target identification. Kuwait Journal of Science, 43(1).Weber, M., & Chaiechi, T. (2022). Fiscal Implications–Inclusive Growth and Climate Change Resilience: A Scoping Study of Existing Policy in Selected ASEAN Countries. In Community Empowerment, Sustainable Cities, and Transformative Economies (pp. 587-604). Springer, Singapore.Webster, K., Recasens, M., Axelrod, V., & Baldridge, J. (2018). Mind the GAP: A balanced corpus of gendered ambiguous pronouns. Transactions of the Association for Computational Linguistics, 6, 605-617.Weimer, D. L., & Vining, A. R. (2017). Policy analysis: Concepts and practice. Routledge.Yanow, D. (2007). Interpretation in policy analysis: On methods and practice. Critical policy analysis, 1(1), 110-122.Yao, L., Mimno, D., & McCallum, A. (2009, June). Efficient methods for topic model inference on streaming document collections. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 937-946).Yau, C. K., Porter, A., Newman, N., & Suominen, A. (2014). Clustering scientific documents with topic modeling. Scientometrics, 100(3), 767-786.