Demand Analysis of Termite Control Service in Jakarta
Authors
-
Mohamad Miftah Rahman
Department of Forest Products, Faculty of Forestry, IPB University
https://orcid.org/0000-0002-9610-9646
- Dodi Nandika Department of Forest Products, Faculty of Forestry, IPB University
- Bintang Charles Hamonangan Simangunsong Department of Forest Products, Faculty of Forestry, IPB University
DOI:
https://doi.org/10.23960/jsl1810-19Abstract
Termite is an economically important pest species in the pest control industry and considered as one of the urban pests. Although it had caused a great loss, only a few studies on termite control demand were found. This study attempted to identify determinants and build the econometric model of termite control demand in Jakarta. The findings are expected to give the pest control industry a better understanding of the pest control market. Two ad-hoc models, linear and double log models, were investigated using the Least Square Dummy Variable (LSDV) technique. The results showed that the double log model was found better than the linear model based on sign expectation and significance. The price of termite control service, building permits, price of structural metals and dummy variables for the large company were statistically significant determinants, whereas dummy variables for risk class were not. Termite control demand for medium and small companies was not significantly different, but both of them were significantly lower than demand for the large company. This study also found that the demand for termite control was elastic.
Keywords: demand analysis, econometric modeling, pest control, termite control, urban pest
Downloads
References
Arinana. 2017. Prediction Model Development of Subterranean Termite Hazard Risk Class in DKI Jakarta Province Based on the Species Composition, Soil and Climate Characteristics. IPB University.
Badan Pusat Statistik. 2017a. Konstruksi dalam Angka 2017. Jakarta.
Badan Pusat Statistik. 2017b. Provinsi DKI Jakarta dalam Angka 2017. Jakarta.
Bhandari, D. 2003. An Economic Analysis of Homeowners’s Preferences and Perceptions Regarding Termite Prevention and Control in Lousiana. Louisiana State University and Agricultural and Mechanical College.
Chen, Z., Qu, Y., Xiao, D., Song, L., Zhang, S., Gao, X., Desneux, N., and Song, D. 2015. Lethal and Social-Mediated Effects of Ten Insecticides on the Subterranean Termite Reticulitermes speratus. Journal of Pest Science 88(4): 741–751. DOI: 10.1007/s10340-015-0656-0
Evans, T. A. 2002. Assessing efficacy of Termatrac???; A new microwave based technology for non-destructive detection of termites (Isoptera). Sociobiology 40(3): 575–583. DOI: 10.1167/2.7.86
Gober, R. W., and Freeman, G. L. 2005. Interpretation of Shifted Binary Interpretive Framework Coefficients Using a Classical Regression Problem. Journal of Management Information and Decision Sciences 8: 115–123.
Gujarati, D. N., and Porter, D. C. 2009. Basic Econometrics. McGraw-Hill/Irwin, Boston.
Johnston, R., Jones, K., and Manley, D. 2018. Confounding and Collinearity in Regression Analysis: A Cautionary Tale and an Alternative Procedure, Illustrated by Studies of British Voting Behaviour. Quality & Quantity 52(4): 1957–1976. DOI: 10.1007/s11135-017-0584-6
Long, J. S., and Ervin, L. H. 2010. Using Heteroscedasticity Consistent Standard Errors in the Linear Regression Model. The American Statistician 54(3): 217–224. DOI: 10.1080/00031305.2000.10474549
Murray, L., Nguyen, H., Lee, Y. F., Remmenga, M. D., and Smith, D. W. 2012. Variance Inflation Factors in Regression Models with Dummy Variables. in: 24th Annual Conference on Applied Statistics in Agriculture 1–18.
Nanda, M. A., Seminar, K. B., Nandika, D., and Maddu, A. 2018. Discriminant Analysis as a Tool for Detecting the Acoustic Signals of Termites Coptotermes Curvignathus (Isoptera: Rhinotermitidae). International Journal of Technology 4: 840–851. DOI: 10.14716/ijtech.v9i4.455
Nandika, D., Rismayadi, Y., and Diba, F. 2015. Rayap: Biologi dan Pengendaliannya. Universitas Muhamadiyah, Surakarta.
O’Brien, R. M. 2017. Dropping Highly Collinear Variables from a Model: Why it Typically is Not a Good Idea. Social Science Quarterly. DOI: 10.1111/ssqu.12273
Oliver-Villanueva, J. V., and Abián-Pérez, M. A. 2013. Advanced Wireless Sensors for Termite Detection in Wood Constructions. Wood Science and Technology 47(2): 269–280. DOI: 10.1007/s00226-012-0485-8
Paudel, K. P., Dunn, M. A., Bhandari, D., Vlosky, R. P., and Guidry, K. M. 2007. Alternative Methods to Analyze the Rank Ordered Data: A Case of Invasive Species Control. Natural Resource Modeling 20(3): 451–471. DOI: 10.1111/j.1939-7445.2007.tb00216.x
Pindyck, R. S., and Rubinfeld, D. L. 1998. Econometric models and economic forecasts. Mcgraw-Hill College.
Rentokil. 2018. The Rentokil Pest Control Report 2018: Insight from Pest Control Markets Across the Globe. London.
Rusiawan, W., Tjiptoherijanto, P., Suganda, E., and Darmajanti, L. 2015. System Dynamics Modeling for Urban Economic Growth and CO2 Emission: A Case Study of Jakarta, Indonesia. Procedia Environmental Sciences. DOI: 10.1016/j.proenv.2015.07.042
Rust, M. K., and Su, N. 2012. Managing Social Insects of Urban Importance. Annual Review of Entomology 57: 355–375. DOI: 10.1146/annurev-ento-120710-100634
Simangunsong, B. C. H., and Buongiorno, J. 2001. International Demand Equations for Forest Products: A Comparison of Methods. Scandinavian Journal of Forest Research 16(2): 155–172. DOI: 10.1080/028275801300088242
Steinberg, F. 2007. Jakarta: Environmental Problems and Sustainability. Habitat International 31(3–4): 354–365. DOI: 10.1016/j.habitatint.2007.06.002
Sugiarman. 2014. Analisis Pengaruh Kualitas Produk dan Layanan Pelanggan Terhadap Kepuasan dalam Membentuk Loyalitas Pest Control Operator pada PT Bayer Indonesia. Sekolah Pascasarjana Institut Pertanian Bogor.
Suparno, S. M., and Marlina, E. 2006. Perencanaan dan Pengembangan Perumahan. Penerbit Andi, Yogyakarta.
White, H. 1980. A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity. Econometrica. DOI: 10.2307/1912934
Wooldridge, M. 2013. Introductory Econometrics. Cengage Learning. DOI: 10.1016/j.jconhyd.2010.08.009
Zarnikau, J. 2003. Functional Forms in Energy Demand Modeling. Energy Economics 25(6): 603–613. DOI: 10.1016/S0140-9883(03)00043-4
Downloads
Published
How to Cite
Issue
Section
Statistics
Abstract views: 151 times
PDF downloaded: 109 times
Metrics
License
Copyright (c) 2020 Mohamad Miftah Rahman, Dodi Nandika, Bintang C.H. Simangunsong
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-NonCommercial 4.0 Licence that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
