Main Article Content
Abstract
Crime in Lampung province is among the 10 highest in Indonesia in 2021. This study aims to obtain a model of the number of crimes and factors influencing it using negative binomial panel regression. The data used is in the form of panel data from the Lampung Province BPS website and publications for 2017-2021. The condition of data on the number of crimes as discrete and overdispersed data makes the negative binomial panel regression method more suitable than Poisson panel regression. Overdispersion is a state where the variance of the data is greater than the mean value of the data. Overdispersion causes the standard error (SE) of the estimated value to decrease, so that variables that should not be significant become significant. The factors thought to be the cause of crime are percentage of poverty (X1), population density (X2), expenditure per capita (X3), unemployment rate (X4), regional gross domestic income (X5), and the average duration of schooling (X6). The results of the analysis obtained for the selected panel data model are the negative binomial random effects (REBN), the influencing factors being X1, X3, X4 and X5. The districts/cities with the largest individual random effects were in the Way Kanan district and the smallest were in Metro City.
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References
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References
S. Rahmalia, Ariusni, dan M. Triani, “Pengaruh Tingkat Pendidikan, Pengangguran, dan Kemiskinan Terhadap Kriminalitas di Indonesia,” J. Kaji. Ekon. dan Pembang., vol. 1, no. 1, hal. 21–36, 2019.
S. Adri, S. Karimi, dan I. Indrawari, “Pengaruh Faktor Sosial Ekonomi terhadap Perilaku Kriminalitas,” J. Ilm. Adm. Publik, vol. 5, no. 2, hal. 181–186, 2019.
A. Harefa, “Faktor-faktor Penyebab Terjadinya Tindak Pidana Kekerasan dalam Rumah Tangga,” J. Panah Keadilan, vol. 1, no. 1, hal. 18–21, 2021.
A. H. Youssef, M. R. Abonazel, dan E. G. Ahmed, “Estimating the Number of Patents in the World Using Count Panel Data Models,” Asian J. Probab. Stat., hal. 24–33, 2020.
A. C. Cameron dan P. K. Trivedi, “Count Panel Data,” Oxford Handb. Panel Data, hal. 233–256, 2013.
D. W. Osgood, “Poisson-Based Regression Analysis of Aggregate Crime Rates,” J. Quant. Criminol., vol. 16, no. 1, hal. 21–43, 2000.
S. Sun, J. Bi, M. Guillen, dan A. M. Pérez-Marín, “Driving risk assessment using near-miss events based on panel poisson regression and panel negative binomial regression,” Entropy, vol. 23, no. 7, hal. 1–22, 2021.
M. O. Adenomon dan G. S. Akinyemi, “Statistical Analysis of Tuberculosis and HIV Cases in West Africa Using Panel Poisson and Negative Binomial Regression Models,” 2020 Int. Conf. Math. Comput. Eng. Comput. Sci. ICMCECS 2020, 2020.
F. Liu dan D. Pitt, “Application of bivariate negative binomial regression model in analysing insurance count data,” Ann. Actuar. Sci., vol. 11, no. 2, hal. 390–411, 2017.
Boswell dan Patil, Statistical Distributions in Scientific Work, vol. 2. Boston: D. Reidel Publishing Company, 1974.
B. J. Park dan D. Lord, “Adjustment for maximum likelihood estimate of negative binomial dispersion parameter,” Transp. Res. Rec., vol. 2061, no. 1, hal. 9–19, 2008.
C. A. Colin dan T. Pravin, Regression analysis of count data, 2 ed. United States: Cambridge University Press, 2013.
J. M. Hilbe, Negative Binomial Regression, Second., vol. 4, no. 1. New York: Cambridge University Press, 2011.
S. Angnitha Purba, “Estimasi Parameter Data Berdistribusi Normal Menggunakan Maksimum Likelihood Berdasarkan Newton Raphson,” J. Sains Dasar, vol. 9, no. 1, hal. 16–18, 2021.
R. U. Datangeji, A. Warsito, H. I. Sutaji, dan L. A. S. Lapono, “Kajian Distribusi Intensitas Cahaya pada Fenomena Difraksi Celah Tunggal dengan Metode Bagi Dua dan Metode Newton Raphson,” J. Fis. Fis. Sains dan Apl., vol. 4, no. 2, hal. 56–69, 2019, doi: 10.35508/fisa.v4i2.976.
B. H. Baltagi, The Oxford Handbook of Panel Data. New York: Oxford University Press, 2015.
W. H. Greene, LIMDEP Econometric Modeling Guide, 10 ed. New York: Econometric Software Inc, 2012.
J. Hausman, B. H. Hall, dan Z. Griliches, “Econometric Models for Count Data with an Application to the Patents-R & D Relationship,” vol. 52, no. 4, hal. 909–938, 1984.
R. R. Hocking, “Methods and Applications of Linear Models,” in Technometrics, vol. 39, no. 3, 1997, hal. 332. doi: 10.2307/1271138.
P. R. Sihombing dan M. Sundari, “Pemodelan Data Diskrit Dengan MenggunakanRegresi Panel Poisson,” 2021.
B. H. Baltagi, Econometrics, 5 ed. New York: Springer, 2011.
W. H. Greene, Econometric Analysis, 5 ed. New York: Pearson Education Inc, 2002.
A. D. Putra, G. S. Martha, M. Fikram, dan R. J. Yuhan, “Faktor-Faktor yang Memengaruhi Tingkat Kriminalitas di Indonesia Tahun 2018,” Indones. J. Appl. Stat., vol. 3, no. 2, hal. 123, 2021.
M. E. Ervina, “Hubungan Rata-rata Lama Sekolah, Gini Ratio, dan Pengeluaran Per Kapita dengan Tingkat Kejahatan Tahun 2011-2018,” Euclid, vol. 7, no. 2, hal. 97, 2020.
F. Anata, “Pengaruh Tingkat Pengangguran Terbuka, PDRB perkapita, Jumlah Penduduk dan Indeks Williamson Terhadap Tingkat Kriminalitas (Studi pada 31 Provinsi Di Indonesia Tahun 2007-2012),” J. Ilm. Mhs. FEB Univ. Brawijaya, vol. 1, no. 2, 2013.
D. G. Omotor, “Demographic and Socio-Economic Determinants of Crimes in Nigeria (A Panel Data Analysis),” J. Appl. Bus. Econ., vol. 11, no. 1, hal. 185–195, 2010.
E. Y. Purwanti dan E. Widyaningsih, “Analisis Faktor Ekonomi Yang Mempengaruhi Kriminalitas Di Jawa Timur,” J. Ekon., vol. 9, no. 2, 2019.