Gıda Güvencesizliğinin Bazı Belirleyicileri (Kantil Regresyon Yöntemi ve Sabit Etki Panel Yönteminin Karşılaştırılması)
Abstract
Gıda güvencesizliği son yıllarda Birleşmiş Milletler ve Dünya Bankası tarafından çıkarılan raporlarda tartışılmaktadır. Gıda güvencesizliği özellikle gıda kıtlığı yaşayan az gelişmiş ve gelişmekte olan ülkeler için önemli bir meseledir. Literatürde gıda güvencesizliği yatay kesit ya da zaman serisi verileri ile farklı yöntemler kullanılarak incelenmiştir. Yapılan çalışmalarda daha çok bir ülkeye ya da hane halkı seviyesinde bir ülkenin belirli bir bölgesine odaklanıldığı görülmektedir. Bu çalışmanın amacı gıda kıtlığı yaşayan az gelişmiş ya da gelişmekte olan 80 ülkede 2000-2015 yılları arasında gıda güvencesizliğinin bazı belirleyicilerini tespit etmektir. Bu amaç doğrultusunda Kantil Regresyon Yöntemi kullanılmaktadır. Oluşturulan modelde bağımlı değişken, gıda güvencesizliği yerine kullanılan yetersiz beslenme yaygınlığının nüfus içerisindeki yüzdesi olarak belirlenirken, bağımsız değişkenler; 2010 sabit fiyatlarıyla ABD dolarına göre hesaplanmış kişi başına GSYH, sadece yenilebilir ve besleyici gıdaları içeren net gıda üretim endeksi, FAO tarafından belirlenen gıda güvencesi göstergeleri içerisinde yer alan gelişmiş su kaynaklarına erişimin yüzdesi ve gelişmiş sanitasyon olanaklarına erişim yüzdesi olarak belirlenmiştir. Analiz sonuçlarına göre kişi başına GSYH, net gıda üretim endeksi, gelişmiş su kaynaklarına ve gelişmiş sanitasyon olanaklarına erişimin gıda güvencesizliği üzerindeki etkisinin farklı kantillerde (τ = 0.25, 0.50, 0.75, 0.95), yani gıda güvencesizliğinin farklı dilimlerinde değiştiği gözlenmiştir. Oysa Gaussian sabit etki tahmincileri sadece gıda güvencesizliği üzerindeki ortalama etkiyi tahmin edebilmektedir. İncelenen örneklem için kişi başına GSYH ve net gıda üretim endeksi ile gıda güvencesizliği arasında güçlü bir ilişki bulunmuşken, gelişmiş su kaynaklarına erişimin yüzdesi ve gelişmiş sanitasyon olanaklarına erişimin yüzdesi ile gıda güvencesizliği arasında daha zayıf bir ilişki bulunmuştur.
References
- Abrevaya, J. ve C. M. Dahl (2008). The Effects of Birth İnputs on Birthweight: Evidence from Quantile Estimation on Panel Data. Journal of Business and Economic Statistics, pp.379-397.
- Akter, S., Basher S. A. (2014). The İmpacts of Food Price And İncome Shocks on Household Food Security and Economic Well-Being: Evidence from Rural Bangladesh. Global Environmental Change, Volume 25, March, pp.150-162.
- Canay, I. A. (2011). A Simple Approach to Quantile Regression for Panel Data, Econometrics Journal, volume 14, pp. 368–386.
- Candidato, Francesco Burc (2008). On the Relation Among Education, Development and Food Security Through The Capability Approach. PhD Tesi, Università degli Studi “Roma Tre”, Dipartimento di Economia.
- D’Souza A., ve Jolliffe D. (2012). Food Security and Wheat Prices in Afghanistan: A Distribution-Sensitive Analysis of Household-Level Impacts. Policy Research Working Paper; No. 6024. WorldBank, Washington DC. © World Bank.
- Elmola S.A.F., ve Ibrahim A. H. (2012). Household Food Security Under the Conditions of Poverty: Evidence from Kordofan Region, Central-West of Sudan. Conference on International Research on Food Security, Tropentag 2012, Göttingen, Germany September 19-21.
- FAO (2009a). Declaration of the World Summit on Food Security. Rome 16-18 November, pp.1-2, http://www.fao.org/fileadmin/templates/wsfs/Summit/Docs/Final_Declaration/WSFS09_Declaration.pdf, (20.3.2017).
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- Galvao, A. (2008). Quantile Regression for Dynamic Panel Data with Fixed Effects. Journal of Econometrics, 2011, vol. 164, issue 1, pages 142-157.
- Geraci, M. ve M. Bottai (2007). Quantile regression for longitudinal data using the asymmetric laplace distribution. Biostatistics, 2007 Jan;8(1):140-54. Epub 2006 Apr 24, pp.140-154.
- Guloglu, B., Kangalli Uyar, S. G. ve Uyar, U. (2016). Dynamic Quantile Panel Data Analysis of Stock Returns Predictability, International Journal of Economics and Finance, 8, No 2, 115- 126.
- Koenker, R (2011). Quantile Regression LSE Short Course, CEMMAP and University of Illinois, Urbana-Champaign, 16-17 May 2011.
- Koenker, R. (2004). Quantile Regression for Longitudinal Data. Journal of Multivariate Analysis, Volüme.91, Issue.1, pp.74–89.
- Lamarche, C. (2010). Robust Penalized Quantile Regression Estimation for Panel Data. Journal of Econometrics, Volume.157, Issue.2, p.396–408.
- Lopéz-Carra A. C., Grant L., Weeks J., Lopéz-Carr D. (2010). The Spaces and Places of Food Security: Learning from Spatial, Hierarchical, and Econometric Models in Urban Data-poor Areas. Conference on International Research on Food Security, Natural Resource Management and Rural Development, Tropentag 2010, ETH Zurich, September 14 – 16.
- Machado J.A.F., Parente P.M.D.C ve Santos Silva J.M.C. (2011). QREG2: Stata Module to Perform Quantile Regression with Robust and Clustered Standard Errors. Statistical Software Components S457369, Boston College Department of Economics, revised 07 Feb 2015.
- Parente P.M.D.C ve Santos Silva J.M.C. (2016). Quantile Regression with Clustered Data. Journal of Econometric Methods, 5(1), 1-15.
- Powel David (2015). Quantile Regression with Nonadditive Fixed Effects. Unpublished Paper. Rand Corporation, April.
- Powel David (2016). Quantile Treatment Effects in the Presence of Covariates. Unpublished Paper. Rand Corporation, April.
- Rosen, A. (2009). Set Identification via Quantile Restrictions in Short Panels. CEMMAP, UCL, and IFS September 16, Working paper, University College, London.
Some Determinants of Food Insecurity (Comparison of Quantile Regression Method and Fixed Effect Panel Method)
Abstract
Food security has been discussed by reports issued by the United Nations and World Bank in recent years. Food insecurity is an important topic for underdevepoled and developing countries with food shortage. In the literatüre, food insecurity/security has been investigated using different econometric methods with cross section and time series data. It seems that the studies focused on one country or a specific region of one country at the household level. The aim of this study is to identify some determinants of food security in the underdeveloped or developing 80 countries with food shortages between 2000 and 2015. Fort his purpose, Kntil Regression Method is used. While the dependent variable in the generated model is the percentage of undernutrition prevalence in place of food insecurity, independent variables; per capita real GDP calculated based on US dollars at fixed prices in 2010, defined as the net food production index containing only edible and nutritious foods, the percentage of Access to developed water resources within the food safety indicators set by FAO and the percentage of acsess to improved sanitasion facilities within the food safety indicators set by FAO. Results show that the effects of explanatory variables (per capita real GDP, net food production index, access to improved water source, accses to improved sanitation facilities) are changing on food insecurity for different quantiles (τ = 0.25, 0.50, 0.75, 0.95) whereas Gaussian fixed effect estimators can only predict the avarage effect on food insecurity. It is found that strong relationship between per capita real GDP and net food prodcution index with food insecurity while it is found that weak relationship between access to improved water source and access to improved sanitation facilities with food insecurity.
References
- Abrevaya, J. ve C. M. Dahl (2008). The Effects of Birth İnputs on Birthweight: Evidence from Quantile Estimation on Panel Data. Journal of Business and Economic Statistics, pp.379-397.
- Akter, S., Basher S. A. (2014). The İmpacts of Food Price And İncome Shocks on Household Food Security and Economic Well-Being: Evidence from Rural Bangladesh. Global Environmental Change, Volume 25, March, pp.150-162.
- Canay, I. A. (2011). A Simple Approach to Quantile Regression for Panel Data, Econometrics Journal, volume 14, pp. 368–386.
- Candidato, Francesco Burc (2008). On the Relation Among Education, Development and Food Security Through The Capability Approach. PhD Tesi, Università degli Studi “Roma Tre”, Dipartimento di Economia.
- D’Souza A., ve Jolliffe D. (2012). Food Security and Wheat Prices in Afghanistan: A Distribution-Sensitive Analysis of Household-Level Impacts. Policy Research Working Paper; No. 6024. WorldBank, Washington DC. © World Bank.
- Elmola S.A.F., ve Ibrahim A. H. (2012). Household Food Security Under the Conditions of Poverty: Evidence from Kordofan Region, Central-West of Sudan. Conference on International Research on Food Security, Tropentag 2012, Göttingen, Germany September 19-21.
- FAO (2009a). Declaration of the World Summit on Food Security. Rome 16-18 November, pp.1-2, http://www.fao.org/fileadmin/templates/wsfs/Summit/Docs/Final_Declaration/WSFS09_Declaration.pdf, (20.3.2017).
- FAO (2016a) FAO Hunger Map 2015. FAO, ss.1, http://www.fao.org/economic/ess/ess-fs/en/, (27.9.2016).
- Food and Agriculture Organization Agricultural and Development Economics Division. (2006). The State of Food Insecurity in the World, 2006 : Eradicating world hunger – taking stock ten years after the World Food Summit. Food and Agriculture Organization of the United Nations, p. 8., http://www.fao.org/docrep/009/a0750e/a0750e00.htm, (15.11.2016).
- Galvao, A. (2008). Quantile Regression for Dynamic Panel Data with Fixed Effects. Journal of Econometrics, 2011, vol. 164, issue 1, pages 142-157.
- Geraci, M. ve M. Bottai (2007). Quantile regression for longitudinal data using the asymmetric laplace distribution. Biostatistics, 2007 Jan;8(1):140-54. Epub 2006 Apr 24, pp.140-154.
- Guloglu, B., Kangalli Uyar, S. G. ve Uyar, U. (2016). Dynamic Quantile Panel Data Analysis of Stock Returns Predictability, International Journal of Economics and Finance, 8, No 2, 115- 126.
- Koenker, R (2011). Quantile Regression LSE Short Course, CEMMAP and University of Illinois, Urbana-Champaign, 16-17 May 2011.
- Koenker, R. (2004). Quantile Regression for Longitudinal Data. Journal of Multivariate Analysis, Volüme.91, Issue.1, pp.74–89.
- Lamarche, C. (2010). Robust Penalized Quantile Regression Estimation for Panel Data. Journal of Econometrics, Volume.157, Issue.2, p.396–408.
- Lopéz-Carra A. C., Grant L., Weeks J., Lopéz-Carr D. (2010). The Spaces and Places of Food Security: Learning from Spatial, Hierarchical, and Econometric Models in Urban Data-poor Areas. Conference on International Research on Food Security, Natural Resource Management and Rural Development, Tropentag 2010, ETH Zurich, September 14 – 16.
- Machado J.A.F., Parente P.M.D.C ve Santos Silva J.M.C. (2011). QREG2: Stata Module to Perform Quantile Regression with Robust and Clustered Standard Errors. Statistical Software Components S457369, Boston College Department of Economics, revised 07 Feb 2015.
- Parente P.M.D.C ve Santos Silva J.M.C. (2016). Quantile Regression with Clustered Data. Journal of Econometric Methods, 5(1), 1-15.
- Powel David (2015). Quantile Regression with Nonadditive Fixed Effects. Unpublished Paper. Rand Corporation, April.
- Powel David (2016). Quantile Treatment Effects in the Presence of Covariates. Unpublished Paper. Rand Corporation, April.
- Rosen, A. (2009). Set Identification via Quantile Restrictions in Short Panels. CEMMAP, UCL, and IFS September 16, Working paper, University College, London.
Details
| Journal Section | Articles |
|---|---|
| Authors | |
| Publication Date | January 31, 2018 |
| Submission Date | June 16, 2017 |
| Published in Issue | Year 2018 Volume: 26 Issue: 35 |
