Linear Regression: Literature Review: Linear regression can be used to analyze the relationship between various socio-economic factors and women's participation in global value chains (GVCs). Studies employing linear regression might investigate how factors like education level, access to vocatio
Global Value Added Theory This theory emphasizes the importance of integration into global value chains to enhance productivity and economic competitiveness. In this context, GVCs can create jobs with higher value-added accessible to women, increasing their participation and contribution to the n
Human Capital Theory According to human capital theory, investment in education and training enhances individual productivity and their capacity to participate in the labor force (Schultz, 1961). Better education and specialized skill training are crucial for women to engage in the manufacturing
Macroeconomic Impacts Research by Kabeer and Mahmud (2004) emphasizes that women's participation in the formal sector not only impacts household income but also overall economic growth. Studies by the World Bank (2012) show that increasing women's labor force participation can positively impact G
=======literature review Literature Review Global Value Chains (GVCs) and Women's Participation Research indicates that integration into GVCs can have significant impacts on women's labor force participation (Barrientos et al., 2004). GVCs often create new job opportunities, especially in lab
Gender and Development: The gender and development framework focuses on how economic policies and practices affect men and women differently. GVCs can provide more job opportunities for women, especially in labor-intensive manufacturing sectors. However, for women to benefit fully, supportive pol...
Economic Theory Analysis Grand Theory of Economic Development Economic development theories provide a framework for understanding how various factors such as foreign direct investment (FDI), manufacturing value addition, and education impact labor markets and societal structures. Structural Tra
Analysis Linear Regression: Shows the highest MSE and MAPE, indicating it does not capture the complexity of the data well. Polynomial Regression: Better than linear regression but still not the best in capturing non-linear relationships. Support Vector Regression (SVR): Performs moderately well
Key Independent Variables Foreign Direct Investment (FDI) Net Inflows (% of GDP) Manufacturing Value Added (% of GDP) Gender Parity Index (GPI) in School Enrollment Female Literacy Rate (% of females ages 15 and above) Exports of Goods and Services (% of GDP) Model Performance Comparison To
============resume === Do Women Benefit from Global Value Chains? A Tale from the Indonesian Manufacturing Sector Analysis and Prediction of Female Labor Force Participation Rate (FLFPR) in Indonesia's Manufacturing Sector This analysis examines whether women benefit from global value chains (GVC
# Extracting the features X_future = future_data.drop(columns=) # Making predictions future_predictions = nn_model.predict(X_future) # Adding predictions to the future data future_data = future_predictions print(future_data]) ======================== Predicted FLFPR for 2024 and 2025 Based on th
kode python nya ========== import numpy as np import pandas as pd from sklearn.neural_network import MLPRegressor from sklearn.metrics import mean_squared_error, mean_absolute_percentage_error from sklearn.model_selection import train_test_split # Assuming the data is in a pandas DataFrame data = p
Literacy rate, adult female (% of females ages 15 and above) Year Literacy Rate (% of females ages 15 and above) 2003 86.10 2004 86.80 2005 87.60 2006 88.79 2007 89.00 2008 89.10 2009 89.68 2010 90.00 2011 90.07 2012 90.50 2013 91.00 2014 93.45 2015 93.34 2016 93.59 2017 93.80 2018 93.99 2019 94.20
========================================================= data datanya variabel terikat Tingkat Partisipasi Tenaga Kerja Wanita (Persen dari Populasi Wanita Usia 15+) di Indonesia - Tahun 2003 - 2022 Female Labor Force Participation Rate (Percent of Female Population Ages 15+) in Indonesia - 2003
101.Poverty gap at $2.15 a day (2017 PPP) (%): Ini mengukur seberapa jauh pendapatan rata-rata orang miskin berada di bawah garis kemiskinan $2.15 per hari. Nilai skala 9 menunjukkan bahwa kesenjangan kemiskinan ini sangat besar. 102.Poverty gap at $3.65 a day (2017 PPP) (%): Ini mirip dengan yang
57.Current account balance (BoP, current US$): Neraca berjalan yang positif atau stabil menunjukkan kesehatan ekonomi makro, yang dapat mencerminkan stabilitas sektor manufaktur. Stabilitas ini penting untuk menciptakan lingkungan kerja yang kondusif bagi wanita. 58.Net official development assista
1.Access to electricity (% of population) - Akses terhadap listrik sangat penting bagi sektor manufaktur. Manufaktur modern memerlukan listrik yang andal untuk operasional sehari-hari. Akses terhadap listrik yang baik juga mempengaruhi kesejahteraan perempuan yang bekerja di sektor tersebut, karena
1.Access to electricity (% of population) 2.Electric power consumption (kWh per capita) 3.Electricity production from renewable sources, excluding hydroelectric (% of total) 4.CO2 emissions (kt) 5.Forest area (% of land area) 6.Foreign direct investment, net inflows (% of GDP) 7.Prevalence
Pertanian & Pembangunan Pedesaan Akses terhadap listrik, pedesaan (% populasi pedesaan) Lahan irigasi pertanian (% dari total lahan pertanian) Lahan pertanian (% luas lahan) Lahan pertanian (km persegi) Mesin pertanian, traktor Mesin pertanian, traktor per 100 meter persegi. km lahan subur Emis
sumber data : https://data.worldbank.org/indicator?tab=all === perubahan iklim Akses terhadap listrik (% populasi) Lahan irigasi pertanian (% dari total lahan pertanian) Lahan pertanian (% luas lahan) Lahan pertanian (km persegi) Pertanian, kehutanan, dan perikanan, nilai tambah (% PDB) Pengambilan