Paper ini mengkaji pengaruh transisi struktur penduduk terhadap pertumbuhan ekonomi menggunakan data panel dari 33 provinsi di Indonesia dari tahun 2000 hingga 2015. Indonesia tengah memasuki masa transisi penduduk yang berakibat pada meningkatnya penduduk usia kerja dan menurunnya rasio ketergantungan. Kelompok usia kerja utama berpengaruh positif terhadap pertumbuhan ekonomi, sedangkan kelompok usia paruh baya berpengaruh negatif. Selanjutnya pertumbuhan penduduk berpengaruh negatif terhadap pertumbuhan ekonomi. Pengaruh kepadatan penduduk terhadap pertumbuhan ekonomi sangat kecil. Tidak ada perbedaan rata-rata pertumbuhan ekonomi antar pulau di Indonesia kecuali Pulau Sulawesi yang memiliki rata-rata pertumbuhan ekonomi lebih tinggi dibandingkan dengan Pulau Jawa.

This study aims to analyse the impact of the 2006 Yogyakarta earthquake on the mental health and welfare of victims in Bantul and to examine whether disasters they experienced before might increase or decrease their mental health when facing indirect exposure to repeated disasters. This study employs several methods: descriptive analysis; logit; a combination of propensity score matching; and difference in differences. The results find that 7 people (1.78%) in 2007 and 31 people (7.89%) in 2014 have experienced symptoms of depression. Age is confirmed as a significant factor in increasing this probability. The results of this study reveal that direct exposure to the 2006 Yogyakarta earthquake increased the average mental disorder score and decreased the victims’ education expenditure 1 year after the disaster. The direct exposure to the 2006 Yogyakarta earthquake also affected the increase in mental disorder score and decrease in household expenditure of victims 1 year after the disaster, although the effects were insignificant.

This study aims to review methods of artificial intelligence (AI) in land use modelling. Data were extracted from journals in the Scopus and Google Scholar databases using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) method. The review demonstrates that modelling land use predictions is a complex matter that involves land use maps and driving forces. AI technology can support land use forecasting by interpreting land use data, analyzing drivers, and modeling. However, AI has limitations in terms of broad contextual understanding and algorithmic errors. To anticipate this, it is necessary to select the appropriate image resolution and interpretation method in accordance with digital data segmentation. It is also recommended to use spatial regression methods to determine the driving forces that affect land use. Hybrid models such as multilayer perceptron neural network Markov chain (MLPNN-MC), random forest algorithm (RFA), and cellular automata (CA)-Markov chain (MC) are recommended for modelling. The selection of a model should be based on the data's characteristics and tested for accuracy. The use of AI for land use prediction modelling is expected to provide accurate predictions that can be used as a basis for land use policy.

This study examines the role of farmer wives in managing circular economy-based dairy farms and its impact on family income. The research focuses on the utilization of dairy waste through the production of organic fertilizer (manure) and biogas. The processed manure is used for fertilizing fodder crops and vegetables, both for personal consumption and sale. The study was conducted among farmers in the Tegalombo District of Pacitan, East Java. Regression analysis is employed to assess the factors influencing farmers' income and determine the potential of women's participation in farming to enhance household income. Based on a survey of 101 families, the findings reveal that households can save up to 145,949.35 rupiahs per month by reducing expenses on animal feed (grass). The regression results indicate that the adoption of circular economy practices positively impacts farmers' income, including income derived from dairy farming and overall farming activities. Moreover, by promoting the involvement of women in farm management, the study aligns with the second Sustainable Development Goal (SDG) to address hunger, achieve food security, and promote sustainable agriculture, as well as the fifth SDG to attain gender equality and empower women and girls.

Introduction/Main Objectives: This study aims to see whether the retirement consumption puzzle occurs in Indonesia. The retirement consumption puzzle refers to when there is a decline in consumption in retirement which is not in line with the life-cycle hypothesis. Background Problems: Previous studies have revealed that consump­tion shifts when entering retirement due to efficient spending. This research analyzes the consumption pattern of the Indonesian elderly peculiarly in the frame of the retirement consumption puzzle. Novelty: There is a lack of studies on the effect of retirement on consumption by Indonesian retirees and prior research has focused on the impact of retirement on household expenditure. Research Methods: This study uses data from the Indonesian Family Life Survey (IFLS) wave 4 and 5 from the years 2007 and 2014. It uses panel data and the total sample is 2,556. It also uses the difference in differences (DiD) method to see whether the change in labor status toward retirement causes a decrease in consumption in households in Indonesia. Then, this study also uses the division of age categories as the robustness check. Finding/Results: The results of this study show that there is a retirement consumption puzzle in Indonesia indicated by a decrease of 19.9% in total expenditure per capita. Conclusion: The consumption decrease in retirement demons­trates that the government should consider pension funds and create social security programs to maintain the welfare of elderly people.

This study aims to determine the impact of assistance from the rice program for people experiencing poverty on food security for poor households. The data in this paper are sourced from the fourth (2007) and fifth (2014) Indonesian Family Life Survey (IFLS). A total of 1346 household samples were used in this study. The sample consisted of 401 treatment groups and 945 control groups. The treatment group is poor households that receive Raskin assistance, and the control group is poor households that do not receive Raskin assistance. The analysis used is Propensity Score Matching (PSM) with Difference in Difference (DiD). The study's results prove that the Rice for the Poor program has no significant impact on the proportion of household food expenditure. The interaction between the variables year (after) and treatment (Raskin) has no significant effect on the proportion of household food. The provision of the Raskin program in a longitudinal period has no impact on household per capita food expenditure.