Phân tích tác động của thiên tai đến tăng trưởng kinh tế và lạm phát tại việt nam

BỘ GIÁO DỤC VÀ ĐÀO TẠO TRƢỜNG ĐẠI HỌC KINH TẾ THÀNH PHỐ HỒ CHÍ MINH NGUYỄN KHẮC HIẾU PHÂN TÍCH TÁC ĐỘNG CỦA THIÊN TAI ĐẾN TĂNG TRƢỞNG KINH TẾ VÀ LẠM PHÁT TẠI VIỆT NAM Chuyên ngành: Kinh tế phát triển Mã số: 62310105 LUẬN ÁN TIẾN SĨ KINH TẾ TP.HCM, Năm 2017 BỘ GIÁO DỤC VÀ ĐÀO TẠO TRƢỜNG ĐẠI HỌC KINH TẾ THÀNH PHỐ HỒ CHÍ MINH NGUYỄN KHẮC HIẾU PHÂN TÍCH TÁC ĐỘNG CỦA THIÊN TAI ĐẾN TĂNG TRƢỞNG KINH TẾ VÀ LẠM PHÁT TẠI VIỆT NAM Chuyên ngành: Kinh tế phát triển Mã số: 62310105 LUẬN ÁN TIẾN SĨ KINH TẾ Ngƣời Hƣớng Dẫn Khoa Học: TS NGUYỄN HOÀNG BẢO TS PHẠM THỊ THU TRÀ TP.HCM, Năm 2017 i LỜI CAM ĐOAN Tôi cam đoan cơng trình nghiên cứu tơi thực Các số liệu thu thập, kết phân tích luận án trung thực chƣa đƣợc cơng bố cơng trình khác Nguyễn Khắc Hiếu ii LỜI CẢM ƠN Trong q trình thực luận án, tơi gặp nhiều khó khăn từ việc định hƣớng nghiên cứu, phƣơng pháp nghiên cứu, thu thập liệu việc làm để xếp công việc hợp lý để có thời gian thực nghiên cứu Để vƣợt qua khó khăn trên, tơi nhận đƣợc giúp đỡ nhiều từ GVHD, Thầy Cô công tác trƣờng Đại học Kinh Tế Tp.HCM nhƣ động viên từ gia đình đồng nghiệp Lời đầu tiên, xin chân thành cảm ơn thầy Nguyễn Hồng Bảo, ngƣời tận tình hƣớng dẫn luận án Thầy cho hƣớng giúp đỡ vƣợt qua khó khăn đƣờng thầy định hƣớng Thầy hƣớng dẫn từ bố cục đến cách trình bày câu ý luận án Tôi xin chân thành cảm ơn cô Phạm Thị Thu Trà, ngƣời có góp ý quan trọng giúp tơi hồn thiện tốt viết Tơi xin chân thành cảm ơn đến tất quý Thầy Cô Khoa Kinh Tế, Trƣờng đại học Kinh Tế Tp.HCM tạo điều kiện thuận lợi, mơi trƣờng học thuật tốt để tơi hồn thành đề tài nghiên cứu Đặc biệt, xin gởi lời cảm ơn đến thầy Nguyễn Trọng Hoài, Nguyễn Hữu Dũng, Phạm Khánh Nam, Trƣơng Đăng Thụy thầy Trần Tiến Khai có góp ý giúp tơi hồn thiện đề cƣơng nghiên cứu nhƣ hồn thiện ba chun đề nghiên cứu Tôi xin chân thành cảm ơn đến tất quý Thầy Cô Khoa Kinh Tế, Trƣờng đại học Sƣ Phạm Kỹ Thuật Tp.HCM Q Thầy Cơ có góp ý cho luận án đồng thời tạo điều kiện thuận lợi công việc để thực tốt nghiên cứu Cuối cùng, xin gởi lời cảm ơn đến bố mẹ vợ tôi, ngƣời cho chỗ dựa mặt tinh thần động viên lúc cần thiết để tơi hồn thành chặng đƣờng đầy chông gai Nguyễn Khắc Hiếu iii MỤC LỤC LỜI CAM ĐOAN LỜI CẢM ƠN MỤC LỤC DANH MỤC CÁC KÝ HIỆU CÁC TỪ VIẾT TẮT DANH MỤC CÁC BẢNG DANH MỤC CÁC HÌNH VẼ TÓM TẮT LUẬN ÁN Chƣơng 1: TỔNG QUAN ĐỀ TÀI 1.1Bối cảnh nghiên cứu 1.1.1Bối cảnh thực tiễn 1.1.2Bối cảnh lý thuyết 1.2Mục tiêu nghiên cứu 1.3Câu hỏi nghiên cứu 1.4Đối tƣợng phạm vi nghiên cứu 1.5Phƣơng pháp nghiên cứu liệu 1.6Ý nghĩa đề tài 1.7Bố cục luận án Chƣơng 2: CƠ SỞ LÝ THUYẾT 2.1Các khái niệm liên quan 2.1.1Thiên tai 2.1.2Tăng trƣởng kinh 2.1.3Lạm phát iv 2.1.4Thu nhập bình q 2.2 Mơ hình Solow tác động thiên tai dài h 2.3 Mô hình tổng cung-tổng cầu 2.3.1Đƣờng tổng cầu 2.3.2Đƣờng tổng cung 2.3.3Phân tích tác động 2.4 Mơ hình IB-EB 2.4.1Trạng thái cân bằn 2.4.2Tác động thiên 2.5 Các nghiên cứu thực nghiệm liên quan 2.5.1Tác động thiên tai 2.5.2Tác động thiên tai 2.5.3Tác động thiên tai 2.5.4Tác động thiên tai 2.6 Tóm tắt chƣơng Chƣơng 3: PHƢƠNG PHÁP NGHIÊN CỨU 3.1 Phƣơng pháp tự hồi quy vectơ có cấu trúc (SVAR) 3.1.1Mơ hình tốn 3.1.2 Vấn đề xác định SVAR 3.2 Phƣơng pháp Synthetic Control 3.2.1Giới thiệu phƣơng 3.2.2Mơ hình hóa phƣơ 3.2.3Kiểm định ý nghĩa v 3.3Tóm tắt chƣơng Chƣơng 4: TÁC ĐỘNG CỦA THIÊN TAI ĐẾN TĂNG TRƢỞNG KINH TẾ 4.1Mô hình nghiên cứu liệu nghiên cứu 4.1.1Mơ hình nghiên 4.1.2Dữ liệu nghiên cứu 4.2Kết nghiên cứu 4.2.1Kiểm tra tính dừng 4.2.2Ƣớc lƣợng kiểm 4.2.3Kiểm định nhân qu 4.2.4Phân tích hàm phả 4.2.5Phân tích phân rã p 4.2.6Kiểm tra tính vững 4.3Thảo luận kết nghiên cứu 4.4Tóm tắt chƣơng Chƣơng 5: TÁC ĐỘNG CỦA THIÊN TAI ĐẾN LẠM PHÁT 5.1Tác động thiên tai đến mức giá từ mơ hình tổng 5.1.1Các yếu tố tác độn 5.1.2Tác động thiên 5.2Mô hình nghiên cứu liệu nghiên cứu 5.2.1Mơ hình nghiên 5.2.2Dữ liệu nghiên cứu 5.3Kết nghiên cứu 5.3.1Tác động thiên vi 5.3.2 Tác động thiên tai đến giá loại hàng hóa khác 5.4Thảo luận kết nghiên cứu 5.5Tóm tắt chƣơng Chƣơng 6: TÁC ĐỘNG CỦA THIÊN TAI ĐẾN THU NHẬP BÌNH QUÂN ĐẦU NGƢỜI 6.1Mơ hình nghiên cứu liệu nghiên cứu 6.1.1Lựa chọn tình hu 6.1.2Mơ hình nghiên c 6.1.3Dữ liệu nghiên 6.2Kết nghiên cứu 6.2.1Tác động bão 6.2.2Tác động bão 6.2.2.1 Tác động bão Durian đến thu nhập nông 6.2.2.2 Tác động bão Durian đến thu nhập từ lƣ 6.2.2.3 Tác động bão Durian đến thu nhập từ công n mại dịch vụ 6.3Thảo luận kết nghiên cứu 6.4Tóm tắt chƣơng Chƣơng 7: KẾT LUẬN 7.1Tóm lƣợc phƣơng pháp liệu nghiên cứu 7.2Những phát đề tài 7.3Những hàm ý sách 7.3.1Chính sách ổn địn 7.3.2Những sách vii 7.4 Hạn chế hƣớng nghiên cứu 135 DANH MỤC CÁC CƠNG TRÌNH CỦA TÁC GIẢ 137 TÀI LIỆU THAM KHẢO TIẾNG VIỆT 138 TÀI LIỆU THAM KHẢO TIẾNG ANH 141 PHỤ LỤC 149 Phụ lục 1.1: Tần số xuất thiên tai Việt Nam từ 1989-2016 149 Phụ lục 1.2: Thiệt hại ngƣời thiên tai Việt Nam từ 1989-2016 150 Phụ lục 1.3: Thiệt hại tài sản thiên tai Việt Nam từ 1989-2016 151 Phụ lục 1.4: Tần số xuất thiên tai theo khu vực địa lý .152 Phụ lục 1.5: Số ngƣời chết số nhà cửa bị phá hủy thiên tai theo khu vực địa lý 155 Phụ lục 1.6: Tần số xuất loại thiên tai khác 156 Phụ lục 2.1: Đƣờng IS 157 Phụ lục 2.2: Đƣờng LM 157 Phụ lục 4.1: Tăng trƣởng kinh tế Việt Nam giai đoạn 2004Q1-2016Q2 158 Phụ lục 4.2: Thiệt hại ngƣời tài sản giai đoạn 2004Q1-2016Q2 159 Phụ lục 4.3: Kiểm định tính dừng 160 Phụ lục 4.4: Các tiêu chí lựa chọn độ trễ cho mơ hình 160 Phụ lục 4.5: Kết ƣớc lƣợng mơ hình SVAR 161 Phụ lục 4.6: Kiểm định phần dƣ tuân theo phân phối chuẩn 163 Phụ lục 4.7: Kiểm định tƣợng tự tƣơng quan 163 Phụ lục 4.8: Kiểm định phƣơng sai sai số thay đổi 164 Phụ lục 4.9: Phân phối thiệt hại tài sản thiên tai từ 2004Q1-2016Q2 165 viii Phụ lục 5.1: Thiệt hại thiên tai giai đoạn 2004T1-2014T12 166 Phụ lục 5.2: Lạm phát Việt Nam giai đoạn 2004T1-2014T12 166 Phụ lục 5.3: Kiểm định tính dừng 167 Phụ lục 5.4: Các tiêu chí lựa chọn độ trễ cho mơ hình 167 Phụ lục 5.5: Mơ hình SVAR phân tích tác động thiên tai đến lạm phát .168 Phụ lục 5.6: Kiểm định tƣợng tự tƣơng quan 170 Phụ lục 5.7: Kiểm định phƣơng sai thay đổi với biến phụ thuộc lạm phát 171 Phụ lục 5.8: Kiểm định phƣơng sai thay đổi với biến phụ thuộc thiệt hại tài sản 172 Phụ lục 5.9: Kiểm định phƣơng sai thay đổi với biến phụ thuộc giá dầu 173 Phụ lục 5.10: Kiểm định phƣơng sai thay đổi với biến phụ thuộc cung tiền 174 Phụ lục 5.11: Kiểm định phƣơng sai thay đổi với biến phụ thuộc tỷ giá 175 Phụ lục 5.12: Ma trận A B xác định cấu trúc mơ hình VAR 176 Phụ lục 5.13: Ảnh hƣởng thiên tai đến giá lƣơng thực, thực phẩm 177 Phụ lục 5.14: Ảnh hƣởng thiên tai đến giá đồ uống, thuốc .178 Phụ lục 5.15: Ảnh hƣởng thiên tai đến giá nhà vật liệu xây dựng .179 Phụ lục 5.16: Ảnh hƣởng thiên tai đến giá y tế, dƣợc phẩm 180 Phụ lục 5.17: Ảnh hƣởng thiên tai đến giá giáo dục 181 Phụ lục 5.18: Ảnh hƣởng thiên tai đến giá du lịch, giải trí 182 Phụ lục 5.19: Ảnh hƣởng thiên tai đến giá hàng may mặc 183 Phụ lục 5.20: Ảnh hƣởng thiên tai đến giá thiết bị gia đình 184 Phụ lục 5.21: Ma trận A, B ƣớc lƣợng với biến FOOD-PRICE 186 Phụ lục 5.22: Ma trận A,B ƣớc lƣợng với biến HOUSE-PRICE 187 Jensen (2000) Boustan et al (2012) 10 Chƣơng 3: METHODOLOGY 3.1 Structural Vector Auto Regression (SVAR) method The SVAR model proposed by Sims (1986) is the special case of the VAR model VAR or SVAR is used to analyze the impact of a shock on economic variables for time-series data However, there is little difference between VAR and SVAR The VAR model proposed by Sims (1980) considers that all variables are endogenous variables and may affect other variables This makes setting up an econometric model become easy because researchers not need to distinguish between endogenous variables and exogenous variables nor need more information from economic theories This ease of use makes the VAR model popular in economic studies, particularly in the forecasting field However, some economists have suggested that the VAR model is not suitable for policy analysis (Cooley Leroy, 1985; Sargent Hansen, 1984) because there is no cause and effect distinction between variables in the model From the criticism above, Sims (1986) wrote a paper named "Are forecasting models useful for policy analysis" to provide a solution for VAR weakness In this paper, Sims (1986) introduced the reduced form, structural, and identification concepts which allowed for restricting the impact direction of variables and supporting analysis of policies Since then, SVAR had been used not only for forecasting but also for analyzing economic policies 3.2 Synthetic Control method Synthetic Control is considered quasi-experiment method This method was first introduced by Abadie and Gardeazabal (2003) in a study of the impact of political conflicts on economic growth in the Basque Country The results show that political conflicts reduced the GDP by 10% Synthetic control was then repeated in the study by Abadie et al (2010) when the team investigated the tobacco control campaign in California in 1988 Research results shows that the campaign reduced the amount of tobacco consumption 26 packs per capita in 2000 11 Synthetic control is often used to evaluate the impact of an event on a given variables by measuring the difference in the output of the two groups: the control group and the treatment group In this study, the situation was chosen as Durian typhoon which occurred in 2006 affecting the Southern Provinces of Vietnam The treatment group is Ben Tre province and the control group is other provinces that were not affected by Durian typhoon and were not affected by other natural disasters after 2006 Dependent variable of this study is the per capita income 12 Chƣơng 4: IMPACTS OF NATURAL DISASTERS ON ECONOMIC GROWTH 4.1 Research model and data 4.1.1 Research model From empirical reviews and from relevant theoretical studies, the following seven variables were proposed in research models: G, FDI, R, MX, INF, DAMAGE, and DEAD G is dependent variable and represents economic growth FDI is foreign direct investment, R is lending rate, MX is total import-export value, INF is inflation, DAMAGE is asset damaged due to natural disasters, and DEAD is the death toll due to natural disasters Or the vector of variables in the model are denoted by Yt=(Gt, FDIt, Rt, MXt, INFt, DAMAGEt, DEADt) 4.1.2 Research data In this study, data were collected quarterly between 2004Q1 and 2016Q2 from the General Statistics Office and the IMF There is total of 50 observations In particular, economic growth (G) was collected from GSO (2017) Damage caused by natural disasters measured by two variables: total damage (DAMAGE) and number of deaths (DEAD) Both variables are also collected from GSO (2017) Implemented FDI is also collected from GSO (2017) Inflation was calculated from the consumer price index (CPI) by formula INF t=(CPIt-CPIt-4)/CPIt-4, in which CPI is collected from the International Monetary Fund IMF (2017) Similarly, lending rate (R) is also collected from the IMF (2017) Finally, total import-export value (MX) is collected from GSO (2017) All of above variables are converted to real values with the base year of 2010 13 4.2 Results 4.2.1 Model estimations Unit root test results show that G, FDI, DAMAGE and DEAD are stationary at level with the significance of 1% to 5%, INF is stationary at level with the significance of 10% MX and R are stationary at first difference From unit root test results, vector of variables in the model are denoted by Yt=(Gt, FDIt, D(R)t, D(MX)t, INFt, DAMAGEt, DEADt) The optimal lag is chosen two (p=2) following LR, FPE and HQ criteria In order to obtain a good model, some regression assumptions had been tested such normality test, autocorrelation test and heteroscedasticity test The results show that the model does not violate regression assumptions above Therefore, we can use this regression model to analyze the impact of natural disasters on economic growth as well as the impact of natural disasters on other economic variables 4.2.2 Ranger causality test From the SVAR model estimated above, the author examines the causality Granger (1969) test to assess the impact of natural disasters on other economic variables The testing results show that damage caused by natural disasters represented by DAMAGE and DEAD (causality) affected economic growth at a significance level of 5% 4.2.3 Impulse response function With the optimal lag selected two (p=2), the author analyzed impulse response function to see the effects of natural disasters on economic growth and other economic variables over the time The order of variables is chosen as DAMAGE, DEAD, D(R), INF, D(MX), FDI and G The identification method for converting 62 from reduced form to general form used is Cholesky Ordering 62 The author intended to use the identification method as Short-run However, the A, B matrix estimated by this method is not convergent due to small sample size (50 observations) 14 From analyzing the impulse response function, we see that if damage due to natural disasters (DAMAGE) increases one standard deviation (equivalent to 5,474 billion VND / quarter, equal 0.25% 63 GDP of year 2010), economic growth (G) will reduce 0,6% The impacts lasted four quarter after natural disasters 4.2.4 Robustness check The basic result of this chapter is that natural disasters have a negative impact on economic growth in Vietnam However, is the result robust when we change some parameters of the model? In the robustness analysis, the results show that model is relatively robust when we change the order of some variables and the lag of the model is changed from two to four 63 According to the General Statistics Office, Vietnam's GDP in 2010 was VND 2,157,828 billion 15 Chƣơng 5: IMPACTS OF NATURAL DISASTERS ON INFLATION 5.1 Research model and data 5.1.1 Research model In this chapter, the variables included in the model are the damage caused by natural disasters (DAMAGE) as independent variables, the dependent variable is INFLATION Control variables include oil price (OIL_PRICE), money supply (DM2) and exchange rate (EX_RATE) or vector of SVAR variable is Yt=(DAMAGEt, OIL_PRICEt, DM2t, EX_RATEt INFLATIONt) 5.1.2 Research data In this section, data were collected by month from 2004T1-2014T12 of total 132 observations In particular, disaster damage (DAMAGE) was collected from CRED (2015) Monthly change in commodity prices (%) or monthly inflation is calculated according to the formula INFLATIONt=100*(CPIt-CPIt-1)/CPIt-1 In which the consumer price index (CPI) was collected from the GSO (2015b) World oil prices (OIL_PRICE) were collected from Indexmudi (2015) EX_RATE is the exchange rate between VND/USD collected from IMF (2015) Finally, the money supply (M2) was also collected from the IMF (2015) Monthly change in money supply (%) is calculated according to the formula DM2t=100*(M2t-M2t-1)/M2t-1 5.2 Results 5.2.1 Impacts of natural disasters on overall commodity price Unit root test results show that all variables are stationary at level except exchange rate (EX_RATE) EX_RATE is stationary at first difference with the significance of 1% Next, optimal lag was chosen two (p=2) following AIC, LR and FPE criteria After estimating the model, some regression assumptions had been tested such normality test, autocorrelation test and heteroscedasticity test The model does not seriously violate the above assumptions, so the model is used for subsequent analyzes From the SVAR model estimated above, the Granger causality test was 16 conducted to assess the impact of natural disasters on commodity prices and other economic variables The results show that damage of natural disaster has a causal effect on commodity prices with the significance of 1% In order to see the impacts of natural disasters on commodity price over the time, Impulse Response Function (IRF) was analyzed by SVAR model in which the structure of the model determined by A and B matrix Results of IRF show that if damage of natural disaster increase one standard deviation (Equivalent to $ 27 64 million) commodity prices will rise by 0.2% over the next three to five months 5.2.2 Impacts of natural disasters on prices of different commodities In order to see the impact of natural disasters on different commodity prices, the author uses the same SVAR model as analyzing the effects of natural disasters on overall commodity prices The results of the analysis show that there are three groups of commodities whose prices are affected by natural disasters: food and foodstuff (FOOD); beverage and cigarette (DRINK); housing and construction materials (HOUSE) There are five commodity groups whose prices are not affected by the disaster including: medicine and health care (MEDICAL); education services (EDU); culture, entertainment and tourism (ENTERTAIN); garment, footwear and hat (CLOTH) and household appliances (EQUIP) 64 $ 27 million, equivalent to the floods that occurred on December 24, 2005 in Khanh Hoa, Dac Lac and Phu Yen provinces causing 69 deaths and 18,000 people affected 17 Chƣơng 6: IMPACTS OF NATURAL DISASTERS ON INCOME PER CAPITA 6.1 Research model and data 6.1.1 Choosing case study and control group In this study, Durian storm, which occurred in 2006, affects Southern Provinces of Vietnam was selected as a typical disaster for the case study because of the following two reasons In terms of location, typhoon Durian most affected the provinces of Ba Ria - Vung Tau, Ho Chi Minh City and Ben Tre These areas have low frequency of disasters compared to the Central and Northern provinces so that impact analysis will reduce interference of other disasters occurring after 2006 In terms of time, 2006 is the right time to analyze since from this point we will have some data before the disaster to select the control group and some data after the disaster to assess the impact (study time is from 2002 to 2012) In this study, the control group was defined as the group of provinces which are unaffected by Durian typhoon and unaffected by "severe disasters" after Durian From the above definition, the author has selected 29 provinces which meet the conditions of the control group 6.1.2 Research model In this study, the dependent variable was income per captita (INCOME) and more typically was income from salary (S_INCOME), income from agriculture-forestryfishery (AFF_INCOME) and income from industry, commerce and service (NAFF_INCOME) Independent variable is natural disaster (DISASTER) which is measured by property damage From empirical reviews and from relevant theoretical studies, the author suggest following variables as control variables for income per capita Retail sales per capita (SALES) make a representative variable toward total demand The volume of goods transported (INFR), land square per capita (LAND), capital for provinces (CAP) 18 represents the supply side as well as the inputs of the economy Number of doctors per capita (DOCTOR) and number of high school students per capita (STUDENT) represents investment in human capital Or the research model can be rewritten by the equation as follows: INCOME = F (DISASTER, SALES, INFR, CAP, DOCTOR, STUDENT, LAND) 6.1.3 Research data The research data includes data of natural disasters and data of economic variables for the period 2002-2012 Data of natural disasters serve as building control group, while the data of economic variables will be used to assess the impact of natural disasters on per capita income Data of natural disasters are collected from Desinventar Data of income per captita (INCOME), income from salary (S_INCOME), income from agriculture-forestry-fishery (AFF_INCOME) and income from industry, commerce and service (NAFF_INCOME) are collected from GSO (2014h) Other control variables are collected from the General Statistics Office All of above variables are converted to real values with the base year of 2005 6.2 Results 6.2.1 Impact of natural disasters on income per capita The results of detailed analysis show that Durian typhoon reduces Ben Tre's capita income for the period 2007-2012 up to VND 51,000 / month The results were statistically significant under 5% for year 2008, under 10% for year 2007 and 2009 while other years were not statistically significant (Figure 6.1) In other words, Durian typhoon has negative impact on per capita income in short-run 19 Figure 6.1: Difference of income per capita between Ben Tre and control group Source: Author’s analysis 6.2.2 Impact of Durian typhoon on components of per capita income The more detailed analysis shows that Durian typhoon reduced income per capita from agriculture-forestry-fishery 166,000 VND per month, accounting for 28% of the total income of Ben Tre residents The results are statistically significant years after the disaster For income from salary, the results of the analysis show that Durian typhoon reduce income from salary However, the above results are not statistically significant after performing permutation test Therefore, we not have enough evidence to conclude that Durian typhoon reduces income from salary For income from industry, construction, trade and services, Synthetic Control analysis shows that Durian typhoon increased income from the industry However, the results are also not statistically significant after performing the permutation test 20 Chƣơng 7: CONCLUSIONS AND POLICY IMPLICATIONS 7.1 Conclusions From the results of the analysis in chapter 4, we see that natural disasters have negative impact on economic growth of Vietnam If damage due to natural disasters (DAMAGE) increases one standard deviation (equivalent to 5,474 billion VND / quarter) economic growth (G) will reduce 0.6% The impacts lasted four quarter after natural disasters Next, following the results of chapter 5, if damage of natural disaster increases one standard deviation (Equivalent to $ 27 million), commodity prices will rise by 0.2% over the next three to five months For more details, price of food and foodstuff increases 0.3%, price of beverage and cigarette increase 0.3%, price of housing and construction materials increase 0.1% Price of other commodity was not affected by natural disasters Lastly, results in chapter show that Durian typhoon has negative impacts on income per capita The reduction in per capita income due to Durian typhoon is estimated at 51,000 VND / month This impact exists within years after a disaster When analyzing in more detail Durian typhoon reduce income from agriculture, forestry and fisheries in the long-run (5 year after disaster) 7.2 Policy Implications 7.2.1 Macro policies From the research results, we see that natural disasters reduce the output and increase the price level of the economy According to aggregate supply-demand theory (Keynes, 1936), the volatility of the output is a shift in the supply curve in the short-run To mitigate these impacts, policy makers can use (expansionary) fiscal policy and (expansionary) monetary policy to influence the aggregate demand However, the application of the two policies should be very cautious because fiscal and monetary policy may cause undesirable consequences In addition, it may take some time for these policies to take effect Therefore, fiscal 21 policy and monetary policy should only be applied to very large natural disasters and accompanied by certain economic conditions 7.2.2 Post-disaster aid policies Firstly, the State needs to call for international assistance for large and very large disasters Secondly, it is necessary to repair the damages of infrastructure quickly after natural disasters in order to avoid the increase prices locally This is a prerequisite for the post-disaster aid and reconstruction activities to work effectively Thirdly, it is necessary to intervene in the market after large natural disasters in order to avoid price increases suddenly after natural disasters The State should prioritize intervention in food markets because food prices mostly increase after natural disasters Finally, aid organizations should prioritize relief to people whose incomes are from agriculture, forestry and fisheries for disaster similar to Durian typhoon because they are the ones most likely to suffer from natural disasters 7.3 Limitations and suggestions for further research Besides contributions, this study has some limitations The first limitation is research data The time series data for some economic variables at Vietnam are short (under 30 years), some data are not synthesized monthly (GDP for example) In addition, another limitation of the study is the measurement of the intensity of the disaster through the number of dead and property damaged The research has not measured the intensity of the disaster through wind speed, water level, rainfall, Richter and other parameters Finally, the research has not analyzed the spillover of disasters from the damaged regions to other regions SPECIALIST REPUBLIC OF VIETNAM Independence - Freedom - Happiness st Ho Chi Minh City, August 07 2017 NEW CONTRIBUTIONS OF THE THESIS Name of the thesis: The impacts of natural disasters on economic growth and inflation in vietnam Major: Development Economics Code: 62310105 Research student: Nguyen Khac Hieu Training year: 2012 Trained by: University of Economics Ho Chi Minh City Academic Advisors: Dr Nguyen Hoang Bao; Dr Pham Thi Thu Tra THEORETICAL CONTRIBUTIONS  This study tests the appropriateness of the aggregate supply-aggregate demand model (Keynes, 1936), IB-EB model (Salter, 1959) and the economic growth model (Solow, 1956) in the explanation of the impact of natural disasters on economic growth and inflation, especially in the context of one country like Vietnam  Synthetic Control method was used to assess the impact of natural disasters on per capita income in Vietnam This has contributed to the dissemination of a new methodology used for impact evaluating of new policy in Vietnam  Another new contribution of the thesis is the study of the impacts of natural disasters on inflation This research issue is relatively new in Vietnam In the world, there is also little research on this subject because research results are not statistically significant due to sticky price phenomenon and suppliers fear customers angry if prices rise after a natural disaster PRACTICAL CONTRIBUTIONS  This study has quantified the impacts of natural disasters on economic growth and inflation These results can help policy-makers have more scientific evidence in order to make appropriate decisions which can mitigate the negative effects of natural disasters This study also suggests some econometric models to predict the economic fluctuations in the case of natural disaster happened  The results of this study have confirmed that natural disasters have a negative impact on economic growth which is reflected in two aspects: the country's GDP is reduced and per capita income is reduced after natural disasters Income is reduced mainly from agriculture-forestry-fishery Therefore, the state should prioritize the aid for people whose incomes are from agriculture, forestry and fishery after natural disasters as they are the most affected by natural disasters  Finally, inflation is one of the economic variables that the state needs to control regularly to stabilize the macro economy This study also confirmed that natural disasters increased inflation in Vietnam The most affected commodities are food, foodstuff and construction materials The policy makers should intervene in the market for severe disasters (damage over 5000 billion VND) to avoid rising commodity prices after disaster Signature of research student Nguyen Khac Hieu ... động thiên tai đến tăng trƣởng kinh tế lạm phát Việt Nam Từ việc lƣợng hóa tác động thiên tai, đề tài đề xuất mô hình kinh tế lƣợng nhằm dự báo tác động thiên tai tăng trƣởng kinh tế lạm phát Việt. .. GIÁO DỤC VÀ ĐÀO TẠO TRƢỜNG ĐẠI HỌC KINH TẾ THÀNH PHỐ HỒ CHÍ MINH NGUYỄN KHẮC HIẾU PHÂN TÍCH TÁC ĐỘNG CỦA THIÊN TAI ĐẾN TĂNG TRƢỞNG KINH TẾ VÀ LẠM PHÁT TẠI VIỆT NAM Chuyên ngành: Kinh tế phát triển... tai tác động thiên tai hoạt động kinh tế Các lƣợc khảo đƣợc chia thành bốn nhóm tác động thiên tai đến tăng trƣởng kinh tế, tác động thiên tai đến giá hàng hóa, tác động thiên tai đến thu nhập