Low productivity and quality of employment have always been a big problem in Indonesia, caused by the lack and mismatch of skills in the workforce. Labor productivity (LP) describes the company’s ability to produce something. This can be determined by added value or output. Several factors influence LP, such as education, age, and training. The concept of global value chains (GVCs) has become an integral part of economic activity, and trade within global production networks has grown more rapidly than conventional trade in final goods. GVCs have both positive and negative impacts on employment opportunities. The automotive industry is categorised as one of the ten primary priority industries in the Indonesian manufacturing sector. This research analyses the impact of LP on GVC integration in the Indonesian automotive industry. The data used in this research comes from the World Input–Output Database (WIOD) and the Central Statistics Agency (BPS) from 1995 to 2014. The variables used include the number of workers, added value, real wages, and GVC, which were calculated by the author using the Inter-Country Input–Output (ICIO) approach. Using ICIO analysis, the following matrix should be constructed to determine involvement in GVCs. This research shows that LP in the Indonesian automotive industry has a positive trend, and the GVC position in the Indonesian automotive industry has a positive trend line. The domestic value chain increased from 4% to 33%. This improves the ability to produce higher value-added and intermediate export goods. LP increases the domestic value chain in the Indonesian automotive industry, leading to global value chain integration. Thus, LP goes hand in hand with integrating GVCs.

Indonesia is a country located on the equator and in the form of an archipelago. It has a high potential for various types of hydrometeorological-related disasters, such as floods, flash floods, droughts, extreme weather, etc. Almost all cities in Indonesia experience flooding every year, including DKI Jakarta, the capital city of Indonesia. Based on data from the National Disaster Management Agency (BNPB) in 2020, East Jakarta is a city that is prone to flooding. According to BNPB (2013), flooding is a disaster that relatively causes the most losses. Losses caused by floods, especially indirect losses, may rank first or second after an earthquake or tsunami. Floods cause so many losses, and it is necessary to have disaster mitigation efforts to minimize the possibility of flood risks. One risk mitigation due to natural disasters is buying insurance products. However, not everyone buys flood-impact insurance products due to economic and social factors. This study aims to create a model with the Logistics Regression Model to determine the factors influencing Indonesian people's interest in purchasing flood-impact insurance products. The research data is from 140 households in East Jakarta, Indonesia, using a non-probability purposive sampling technique. Furthermore, with a significance level of 10%, the logistic regression model obtained 14 significant regression coefficients. In the end, the obtained model is evaluated based on its level of accuracy. The results showed that the accuracy rate was almost excellent, namely 89.3%.

Menurut data BPS, tingkat pertumbuhan populasi penduduk di Indonesia secara konsisten meningkat setiap tahun. Kondisi pertumbuhan populasi penduduk yang tidak dapat ditekan akan menyebabkan berbagai masalah. Salah satu masalah yang mungkin terjadi di Indonesia dan sulit diselesaikan adalah kesehatan masyarakat Indonesia. Provinsi Papua menjadi provinsi dengan persentase rumah tangga kumuh perkotaan tertinggi di Indonesia, persentase rumah tangga yang memiliki akses terhadap layanan sanitasi layak dan berkelanjutan terendah, menempati peringkat kelima persentase terendah yang memiliki akses terhadap layanan sumber air minum layak dan berkelanjutan, serta menjadi provinsi dengan angka kematian balita per 1000 kelahiran hidup tertinggi di Indonesia. Salah satu penyebabnya adalah rendahnya persentase balita yang pernah mendapatkan imunisasi. Penelitian ini melakukan pemodelan untuk mendapatkan faktor-faktor yang memengaruhi kesehatan di Provinsi Papua. Penelitian ini melibatkan pengaruh spasial (model panel spasial) dan membandingkannya dengan model panel biasa untuk mendapatkan model terbaik. Model panel spasial yang dipilih dalam penelitian ini adalah model SAR, SEM, dan GSM. Hasil menunjukkan bahwa model SAR dengan pengaruh tetap adalah model terbaik dalam penelitian ini.According to BPS data, the rate of population growth in Indonesia consistently increasing every year. Conditions of population growth that cannot be suppressed will cause several problems. One of them and difficult to solve is the public health problem. Papua Province is the province with the highest percentage of urban slum households, the lowest percentage of households that has access to decent and sustainable sanitation services, ranks fifth lowest who has access to decent and sustainable drinking water services, and the highest number infant mortality per 1000 live births in Indonesia. One of the reasons is the low percentage of children under five who have been immunized. This research is modeling to find the factors that influence health in Papua Province. This research involves spatial influence (spatial panel model) and compares it with the ordinary panel models to get the best model. The spatial panel models selected in this research are SAR, SEM, and GSM models. The results show that the SAR model with the fixed effect is the best in this research.

One way to achieve profits in a company is through investment activities. However, everything has risks. Investing can also be risky. Therefore, the relationship between risk and investment is important because it will influence the determination of investment selection. The problem faced by investors is choosing an efficient portfolio, or a portfolio that provides the smallest risk. This risk can be done by measuring risk, one of which is using the Value at Risk (VaR) measure. Measurement using Value at Risk has several methods that are quite popular, namely the Historical Method, Variance-Covariance, and Monte Carlo. In this research, the Historical and Monte Carlo methods will be used to measure Value at Risk in stock portfolios. The research results show that Monte Carlo simulations provide greater results at 95% and 90% confidence levels. This is because the Monte Carlo simulation method carries out repeated random generation by including data so that the data samples become larger and make the calculations bigger.

Despite the remarkable improvements in key economic and social standards, the Indonesian footwear industry still struggles to ensure decent work, particularly for women workers. In this paper, female workers are divided into production and non-production workers. Women production workers are used as a proxy for occupational segregation based on sex, called non-skilled workers, while women non-production workers are used as a proxy for women’s share of work in managerial and administrative work, called skilled workers. This paper examines the close links between decent work (DW) and global production networks (GPNs) in the Indonesian footwear industry. More specifically, this article scrutinizes fair employment treatment for local female employees within the DW framework in the country’s footwear industry with the expansion of GPNs based on Indonesian footwear firm-level panel data from 2001 to 2015. Vertical specialization is a proxy for GPNs and is the main independent variable in this paper. The results show that vertical specialization is in line with the fair treatment indicators and has a significant level for female production workers. In addition, the other independent variable, namely the wages of production workers, has a significant level, and the results are inversely proportional to the fair treatment indicator, while the wages of non-production workers show results that are inversely proportional to female production workers but positive for female non-production workers. This shows that an increase in the wages of production workers is less profitable for female production workers than for female non-production workers. Thus, the results show that the expansion of GPNs in the Indonesian footwear sector has essentially led to improved fair employment treatment, especially for women workers.

Menghitung Cadangan Klaim pada suatu perusahaan asuransi merupakan hal penting untuk dilakukan. Cadangan Klaim dapat merepresentasikan seberapa besar kewajiban perusahaan terhadap tertanggung di masa yang akan datang serta menjadi tolak ukur kondisi keuangan dari suatu perusahaan. Cadangan Klaim terbagi menjadi dua, yaitu IBNR (Incurred But Not Reported) dan RBNS (Reported But Not Settled). Pada dasarnya, ada banyak metode dalam menghitung Cadangan Klaim, salah satu yang umum dikenal adalah Metode Chain Ladder. Oleh karena itu, penelitian ini menggunakan Metode Chain Ladder dalam menghitung Cadangan Klaim. Dalam penerapannya, hampir segala jenis perhitungan akan dipermudah menggunakan beberapa software yang belakangan dikembangkan, diantaranya adalah Microsoft Excel dan RStudio. Tujuan penelitian ini adalah untuk menghitung nilai Cadangan Klaim yang dihasilkan dari kedua software tersebut dengan menggunakan metode yang sama, yaitu Chain Ladder, serta mengetahui efektifitas serta efisiensi dalam perhitungan. Hasil menunjukkan bahwa hasil perhitungan antara kedua software adalah sama tetapi dari sisi kecepatan pengerjaan, penggunaan RStudio lebih unggul dibandingkan dengan Microsoft Excel.