{"title":"越开心越富有?印尼人口统计与发展的多维分类","authors":"D. Wardhana","doi":"10.1108/ijdi-05-2023-0115","DOIUrl":null,"url":null,"abstract":"\nPurpose\nThis paper aims to unpack the nexus of development and demography controlling for three important variables to represent the meaning of development, that is, poverty rate, unemployment rate and human development index (HDI). Demographic variables are proxied with total fertility rate (TFR) and net migration rate (NMR).\n\n\nDesign/methodology/approach\nThis research applies cluster analysis at the provincial level using INDO-DAPOER and 2015 Intercensal Population Survey data sets.\n\n\nFindings\nDemographic and development status of Indonesian provinces can be classified into four clusters, and members of these clusters are mostly dissimilar with those of previous groupings on demographic dividends (Adioetomo, 2018). With only less than 50% matching rate, the author argues that there is no simple linear relationship between demographic and development variables.\n\n\nResearch limitations/implications\nThe most recent data set on Population Census Year 2020 has not been made available at the time of the writing. Also sometimes known as unsupervised classification, cluster analysis is about finding groups in a set of objects characterised only by certain measurements; therefore, findings of this study need to be positioned solely within the context of development and demography.\n\n\nPractical implications\nTaxonomy in this study offers a more nuanced and contextual understanding of the diverse challenges at the local and regional levels. Recommendations from this study lead to asymmetrical design in development policies and budget proportions at local levels.\n\n\nSocial implications\nIt is expected that the findings are relevant to the input of policymaking process within the sphere of development and demography, especially for countries with significant size of populations and grappling with development issues.\n\n\nOriginality/value\nTo the author’s understanding, this paper is the first to discuss the impact of “demographic dividend” to economic development in Indonesia using the approach of cluster analysis. The expected contribution of this work is twofold: Firstly, the author would like to ignite a discourse on the nexus of development and demography using the most recent data set and cutting-edge method. Secondly, the findings are relevant to the input of policymaking process within the sphere of development and demography, especially for countries with significant size of populations and grappling with development issues.\n","PeriodicalId":37830,"journal":{"name":"International Journal of Development Issues","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The more the merrier the wealthier? Multi-dimensional taxonomy of demography and development in Indonesia\",\"authors\":\"D. Wardhana\",\"doi\":\"10.1108/ijdi-05-2023-0115\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\nPurpose\\nThis paper aims to unpack the nexus of development and demography controlling for three important variables to represent the meaning of development, that is, poverty rate, unemployment rate and human development index (HDI). Demographic variables are proxied with total fertility rate (TFR) and net migration rate (NMR).\\n\\n\\nDesign/methodology/approach\\nThis research applies cluster analysis at the provincial level using INDO-DAPOER and 2015 Intercensal Population Survey data sets.\\n\\n\\nFindings\\nDemographic and development status of Indonesian provinces can be classified into four clusters, and members of these clusters are mostly dissimilar with those of previous groupings on demographic dividends (Adioetomo, 2018). With only less than 50% matching rate, the author argues that there is no simple linear relationship between demographic and development variables.\\n\\n\\nResearch limitations/implications\\nThe most recent data set on Population Census Year 2020 has not been made available at the time of the writing. Also sometimes known as unsupervised classification, cluster analysis is about finding groups in a set of objects characterised only by certain measurements; therefore, findings of this study need to be positioned solely within the context of development and demography.\\n\\n\\nPractical implications\\nTaxonomy in this study offers a more nuanced and contextual understanding of the diverse challenges at the local and regional levels. Recommendations from this study lead to asymmetrical design in development policies and budget proportions at local levels.\\n\\n\\nSocial implications\\nIt is expected that the findings are relevant to the input of policymaking process within the sphere of development and demography, especially for countries with significant size of populations and grappling with development issues.\\n\\n\\nOriginality/value\\nTo the author’s understanding, this paper is the first to discuss the impact of “demographic dividend” to economic development in Indonesia using the approach of cluster analysis. The expected contribution of this work is twofold: Firstly, the author would like to ignite a discourse on the nexus of development and demography using the most recent data set and cutting-edge method. Secondly, the findings are relevant to the input of policymaking process within the sphere of development and demography, especially for countries with significant size of populations and grappling with development issues.\\n\",\"PeriodicalId\":37830,\"journal\":{\"name\":\"International Journal of Development Issues\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-08-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Development Issues\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1108/ijdi-05-2023-0115\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"Social Sciences\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Development Issues","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1108/ijdi-05-2023-0115","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Social Sciences","Score":null,"Total":0}
The more the merrier the wealthier? Multi-dimensional taxonomy of demography and development in Indonesia
Purpose
This paper aims to unpack the nexus of development and demography controlling for three important variables to represent the meaning of development, that is, poverty rate, unemployment rate and human development index (HDI). Demographic variables are proxied with total fertility rate (TFR) and net migration rate (NMR).
Design/methodology/approach
This research applies cluster analysis at the provincial level using INDO-DAPOER and 2015 Intercensal Population Survey data sets.
Findings
Demographic and development status of Indonesian provinces can be classified into four clusters, and members of these clusters are mostly dissimilar with those of previous groupings on demographic dividends (Adioetomo, 2018). With only less than 50% matching rate, the author argues that there is no simple linear relationship between demographic and development variables.
Research limitations/implications
The most recent data set on Population Census Year 2020 has not been made available at the time of the writing. Also sometimes known as unsupervised classification, cluster analysis is about finding groups in a set of objects characterised only by certain measurements; therefore, findings of this study need to be positioned solely within the context of development and demography.
Practical implications
Taxonomy in this study offers a more nuanced and contextual understanding of the diverse challenges at the local and regional levels. Recommendations from this study lead to asymmetrical design in development policies and budget proportions at local levels.
Social implications
It is expected that the findings are relevant to the input of policymaking process within the sphere of development and demography, especially for countries with significant size of populations and grappling with development issues.
Originality/value
To the author’s understanding, this paper is the first to discuss the impact of “demographic dividend” to economic development in Indonesia using the approach of cluster analysis. The expected contribution of this work is twofold: Firstly, the author would like to ignite a discourse on the nexus of development and demography using the most recent data set and cutting-edge method. Secondly, the findings are relevant to the input of policymaking process within the sphere of development and demography, especially for countries with significant size of populations and grappling with development issues.
期刊介绍:
The International Journal of Development Issues (IJDI) publishes scholarly research on important development issues, with a particular focus on development dynamism and a leaning towards inter-disciplinary research. IJDI welcomes papers that are empirically oriented but such work should have solid methodological foundations based on realism and pragmatism rather than on idealism. Critical analysis of development issues from both the heteredox viewpoint and the neo-liberalist viewpoint, in orthodox tradition, are equally encouraged. The journal publishes authoritative, intelligent articles and research of direct relevance to those investigating and/or working within areas closely associated with development processes. Special consideration is given to research papers that consider development issues from either a socio-economic, political, historical or sociological, anthropological, ecological and technological standpoint.