Analysis of rural transportation infrastructure impact on rural household income in vietnam

VIETNAM NATIONAL UNIVERSITY, HANOI VIETNAM JAPAN UNIVERSITY HA THI DIU ANALYSIS OF RURAL TRANSPORTATION INFRASTRUCTURE IMPACT ON RURAL HOUSEHOLD INCOME IN VIETNAM MATER’S THESIS VIETNAM NATIONAL UNIVERSITY, HANOI VIETNAM JAPAN UNIVERSITY HA THI DIU ANALYSIS OF RURAL TRANSPORTATION INFRASTRUCTURE IMPACT ON RURAL HOUSEHOLD INCOME IN VIETNAM MAJOR: MASTER PUBLIC POLICY CODE: 8340402.01 RESEARCH SUPERVISOR: Dr TRUONG THI THU TRANG Hanoi, 2022 COMMITMENT I declare that this dissertation is my own work and that where material is obtained from published or unpublished works, this has been fully acknowledged in the references I assume full responsibility for this study ACKNOWLEDGMENTS I want to thank Vietnam Japan University and Dr Nguyen Thuy Anh for allowing me to complete my Master’s Degree in Public Policy and provide me with quality education I want to thank Dr Truong Thi Thu Trang who is my thesis supervisor Her constant guidance, support, and encouragement, and for generously sharing her precious time and expertise to help me make this thesis The authors thank all the Center for Agricultural Policy (CAP) for allowing me to use information and data about the World Bank project I want to express a special thank you to Mr Cao Duc Son at The Center for Agricultural Policy for his support and advice for my thesis I am also grateful to staff from the VJU department who helped me complete this thesis process successfully My heartfelt gratitude also goes to VJU lectures who unselfishly share their expertise, knowledge and skills, and for the utmost patience and understanding to give us quality comment for my thesis CONTENTS The list of figures i The list of tables i List of abbreviations ii Chapter 1: Overview 1.1 Introduction 1.2 Research Objective 1.3 Object and Scope of Research .4 1.4 Research Methods 1.5 Structure of Thesis: .5 Chapter 2: Literature review on the impact of rural transportation infrastructure on rural household income in Vietnam 2.1 International Study 2.2 Domestic Study 10 2.3 Research Question and Hypotheses 14 Chapter 3: Status of rural infrastructure and rural household income in Vietnam in the period 2010-2020 .15 3.1 Rural Transport Infrastructure in Vietnam 15 3.2 Rural Household Income in Vietnam 16 Chapter 4: Determining the impact of rural transportation infrastructure on rural household income in Vietnam for the period 2018-2020, a case study of Lai Chau and Son La provinces 22 4.1 Model and Data Set .22 4.2 Estimation Results .30 Chapter 5: Conclusions and policy implications 37 Reference .38 Appendix .41 THE LIST OF FIGURES Figure 2.1 Impact of infrastructure development on total household income .13 Figure 3.1 Volume of freight carried by road, thousand tons, 2000-2020 16 Figure 3.2 Vietnam’s population, 2021 17 Figure 3.3 Employees 15 years and older work annually in urban and rural areas in the period 2005-2020, thousand people 18 Figure 3.4 GDP per capita in the period 2001-2020, USD 18 Figure 3.5 Monthly average income per capita at current prices by urban and rural areas for the period 2002-2020, thousand VND .19 Figure 3.6 Monthly average income per capita at current prices by region for the period 2002-2020, thousand VND 20 Figure 3.7 The correlation between monthly average income per capita and volume of freight carried by the road by six areas in Vietnam, 2020 21 Figure 4.1 The location map is implemented by the National Target Programs for New Rural Development and the Sustainable Poverty Reduction Support Program of the World Bank 23 Figure 4.2 Graphical explanation of the DID estimation 26 Figure 4.3 Drying of freshly harvested agricultural products on the road in Nam Dinh province 32 THE LIST OF TABLES Table 4.1 Description statistics in 2018 .27 Table 4.2 Description statistics in 2020 .28 Table 4.3 Description variable .29 Table 4.4 Impact of transportation investment on household income 30 Table 4.5 Impact of the distance from home to the nearest road on income per capita 33 Table 4.6 The impact of distance from home to market and coach stop on household income per capita 34 Table 4.7 Impact of transportation infrastructure on nonfarm income per capita 34 i ADB: DID: DRVN: GDP: GSO: Km: PCA: PSM: US: USD: VHLSS VND LIST OF ABBREVIATIONS ASIA development Bank Difference-in-difference Directorate For Road of Vietnam Gross Domestic Product General Statistics Office Kilometer Principal Component Analysis Propensity Score Methods United State US dollar Household Living Standards Survey Vietnam Dong ii CHAPTER 1: OVERVIEW 1.1 Introduction Vietnam is a developing country in Asia with 98.51 million people in 2021 (GSO, 2021) 62.9% of the total national population lives in rural areas 67.3% of workers over 15 years of age or older currently work in rural areas (GSO 2021) The poor in Vietnam are mainly concentrated in rural areas and areas of ethnic minority Their income depends mainly on agricultural income (Nguyen, CV, 2010; Word Bank, 2018) Agriculture plays a critical role in economic development It ensures the survival of society without development (Schultz, 1953) Agriculture contributed to economic growth by providing affordable food for urban citizens and by providing labor for industry and cities (Dethier & Effenberger, 2012) According to World Bank data, in 1991, the share of agricultural GDP in total GDP was 40.5% The agricultural sector's contribution to economic growth tended to decrease (accounting for 14.9% of GDP in 2020) In the field of employment, there were more than 70% of workers who participated in production activities in the agricultural sector in 1991 This proportion tended to decrease and represented only 37% in 2019 According to FAO data, the rate of agricultural land per total land use in Vietnam increased from 18.97% (1961) to 37.39% (2020) The export reached 17.45 billion USD (6% of the total export) in 2019 (GSO data) The agricultural sector played an important role in Vietnam’s economy However, relying entirely on agriculture to reduce poverty and inequality is not as effective as the non-agricultural sector (Nguyen, VC, 2010) The poor are mainly concentrated in rural areas and ethnic minority areas Their income depends mainly on agricultural income (Nguyen, CV, 2010; The Word Bank, 2018) The reason why the poor are especially vulnerable is in developing countries Agriculture in these countries depends a lot on weather, climate, soil quality, etc When the shock occurred, the poor were greatly affected by economic insecurity Diversifying income for the poor is an effective way to increase the income of poor households and quickly reduce poverty (Dethier & Effenberger, 2012; Word Bank, 2018) Increasing income from nonfarm activities helped to significantly reduce poverty in rural households, especially ethnic minorities Households with wage income have more opportunities for macroeconomic exposure than households without Therefore, increasing the household's tolerance to shock (Ali & Pernia, 2003; Ghalib et al., 2015) There are four main reasons for the diversified income sources of rural households in most developing countries (Barrett, 2001; Nielen, 2013) (1) Agricultural activities depend on the seasonality of crops and livestock, creating seasonality in employment opportunities (2) The rate of labor profit gradually decreases in agricultural production (3) Excessive dependence on income from agricultural activities poses risks to households Household income from more than two different sources better copes with income risk due to a lack of insurance markets (4) In the rural area, there is a failure in the capital markets Households in rural areas have difficulty accessing capital due to the working mechanism of this market (World Bank, 2016) Therefore, wage income helps households accumulate capital which makes it easier to invest in agricultural production Furthermore, in most developing countries, agricultural productivity is low because farmers have difficulty applying science and technology to production Furthermore, the high income from nonfarm jobs makes agricultural workers tend to leave the agricultural industry (Dethier & Effenberger, 2012; Mu & van de Walle, 2011; Nguyen, VC et al., 2017; Setboonsarng, 2008) Barriers to the adoption of technology in agriculture stem from the lack of adequate infrastructure Rural roads are a key factor contributing to the promotion of rural household income and poverty reduction (Jalan, 2001) The population density is sparse in rural areas, and poor transportation leads to high transportation costs Therefore, it affects the price of input materials, such as fertilizer and seed prices The high price causes farmers to limit the use of input materials and the difficulty of applying science and technology to production, which affects agricultural productivity (Dethier & Effenberger, 2012; Servén & Calderón, M., 2004; Setboonsarng, 2008) Good infrastructure makes it easier for households or businesses to transport agricultural products, access markets, facilitate trade, and reduce intermediary costs in commodity transactions It affects income from agricultural activities (Charlery et al., 2016; Dethier & Effenberger, 2012; Mu & van de Walle, 2011; Per Pinstrup-Andersen, 2007; Setboonsarng, 2008) A good rural transport infrastructure increases job opportunities for rural workers by making it easier for workers to move to more developed economic regions (ADB, 2019; Ali & Pernia, 2003; Charlery et al., 2016; Ut et al., 2000) Infrastructure development promotes the creation through the development of small and medium enterprises and craft villages in rural areas (Hoàng, 2012; C V Nguyen et al., 2017) Thus, total household income increases, reducing poverty Through previous studies, village infrastructure has direct and indirect impacts on total rural household income However, most previous studies only used the length of roads to estimate the impact of infrastructure on household income Meanwhile, the distance from home to the road, along with the quality of the road, affects the impact of rural infrastructure (Medeiros et al., 2021; Mu & van de Walle, 2011; Nguyen, V.C., 2010; Servén & Calderón, 2004) Therefore, in this study, I use these two factors as representatives to assess the impact of rural infrastructure in Vietnam on household income in rural areas The limitations of previous research on rural infrastructure are discussed in more detail in Chapter Realizing the importance of agricultural development and diversification of sources of rural household income in socioeconomic development, I would like to further research the impact of rural transportation infrastructure on rural household income in Vietnam How does the rural household benefit from the development of rural transport infrastructure? The construction of new rural infrastructure is one of the critical policies in the investment policy of the national target program for the construction of new rural areas of many countries around the world and Vietnam 1.2 Research Objective - To describe the actual situation of rural transportation infrastructure and rural household income in Vietnam in the period 2002-2020 - To analyze the impacts of investment in rural transportation infrastructure in rural household income in the period 2018-2020 - Provide knowledge about practical rural transportation infrastructure and rural household income; the relationship between them so that the central government can have better policies to enforce rural infrastructure regulation CHAPTER 5: CONCLUSION AND POLICY IMPLICATIONS Through the results of the study, the investment in rural transportation infrastructure has a positive impact on the total income and agriculture income of Vietnamese rural households in the period 2018-2020 Improving transportation infrastructure to the market can improve the diversification of income sources The distance between the household and the difference roads has an diffence effect on the household income However, with a small number of observations, uncertainty data; the results of regression may be biased It is a weakness of the study The article can be developed in a more stable economic context and in more provinces of Vietnam The regression results in this study are only true for the province studied in the period 2018-2020 and cannot be generalized to other research scopes and contexts Through the results of the study, I suggest several policy implications for Vietnam Rural roads are a crucial factor for rural household income per capita, agriculture income per capita and nonfarm income per capita Policies geared toward improving rural transportation infrastructure investment in rural areas, especially in mountain areas in Vietnam There is a difference in the impact of quality roads on rural household income per capita Improving the quality and quantity of rural roads is important and necessary The local government or the private sector should promote investment and upgrade old roads into roads that can be ridden by cars Investment in transportation infrastructure can improve income diversification Infrastructure planners should be careful in investing in new infrastructure and planning the appropriate infrastructure to bring the greatest economic benefits to the economy, avoiding land speculation Education level has a significant effect on household income and maintains sustainable development of the household economy Improving rural roads alone is not enough There should improve other infrastructures, such as schools and markets 37 REFERENCE ADB (2019) Gender Equality in Vietnam: Gender Equality in Transport Ali, I & Pernia, E M (2003) Infrastructure and Poverty Reduction: What is the Connection ? Asian Development Bank.ERD Policy Brief No 13, 13 Charlery, L C., Qaim, M., & Smith-Hall, C (2016) Impact of infrastructure on rural household income and inequality in Nepal Journal of Development Effectiveness, 8(2), 266–286 https://doi.org/10.1080/19439342.2015.1079794 Cuong, N.V (2011) Estimation of the Impact of Rural Roads Asia-Pacific Development Journal, 18(2), 105–135 http://www.unescap.org/pdd/publications/index_apdj.asp%5Cnhttp://search.ebsco host.com/login.aspx?direct=true&db=ecn&AN=1301043&site=ehostlive&scope=site Dethier, JJ, & Effenberger, A (2012) Agriculture and development: A brief review of the literature Economic Systems, 36(2), 175–205 https://doi.org/10.1016/j.ecosys.2011.09.003 Ghalib, A K., Malki, I., & Imai, K.S (2015) Microfinance and Household Poverty Reduction: Empirical evidence from Rural Pakistan Oxford Development Studies, 43(1), 84–104 https://doi.org/10.1080/13600818.2014.980228 Hoàng, N H (2012) Tác động viện trợ Nhật Bản cho phát triển sở hạ tầ ng ( ) 28, 177–184 Medeiros, V., Ribeiro, R S M., & Amaral, P.V.M (2021) Infrastructure and household poverty in Brazil: A regional approach using multilevel models World Development, 137, 105118 https://doi.org/10.1016/j.worlddev.2020.105118 Mu, R & Van de Walle, D (2011) Rural roads and development of the local market in Vietnam Journal of Development Studies, 47(5), 709–734 https://doi.org/10.1080/00220381003599436 Mu, R & Walle, D Van De (2007) Rural roads and Poor Area Development in 38 Vietnam In World Bank Policy Research Working Paper 4340 (Issue August) Nguyen, C V., Phung, T D., Ta, V.K., & Tran, D T (2017) The Impact of Rural Roads and Irrigation on household welfare: evidence from Vietnam International Review of Applied Economics, 31(6), 734–753 https://doi.org/10.1080/02692171.2017.1324408 Nguyen, V.C (2010) Does agriculture help to reduce poverty and inequality? 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S H., Jensen, K., & De La Torre Ugarte, D (2013) Evaluating the spatial spillover effects of transportation infrastructure on agricultural output across the United States Journal of Transport Geography, 30, 47–55 https://doi.org/10.1016/j.jtrangeo.2013.03.001 UN Women (2020) Tóm tt Báo cáo đánh giá độc lập Rà soát đánh giá Tác ng v Gii Ut, T T., Hossain, M., & Janaiah, A (2000) Modern farm technology and infrastructure 39 in Vietnam: Impact on income distribution and poverty Economic and Political Weekly, 35(52), 4638–4643 http://www.jstor.org/stable/4410109%5Cnhttp://www.jstor.org/action/cookieAbse nt World Bank (2016) Chuyn i Nông nghiệp Việt Nam : Tăng giá tr, gim u vào Báo Cáo Phát Trin Viet Nam, Washington, DC Ngân hàng Th gii World Bank (2019) Completion of implementation and report of results, Northern Mountains Poverty Reduction Project II, Vietnam World Bank (2020) Climbing the ladder poverty reduction and shared prosperity in Vietnam Power and Passion in Egypt https://doi.org/10.5040/9780755609871.ch005 Wu, Q., Guan, X., Zhang, J., & Xu, Y (2019) The role of rural infrastructure in reducing production costs and promoting resource-conserving agriculture International Journal of Environmental Research and Public Health, 16(18) https://doi.org/10.3390/ijerph16183493 Yoshino, N., & Truong Thi Hoa (2020) Effects of infrastructure projects on government revenues: the case of expressway projects in the northern midlands and mountainous area of Vietnam ADBI Working Papers Nos 1171, 1171 Cong Luat (2020) Nam Định: Phơi thóc lúa đường gây an tồn giao thơng https://vnanet.vn/vi/anh/anh-thoi-su-trong-nuoc-1014/nam-dinh-phoi-thoc-luatren-duong-gay-mat-an-toan-giao-thong-4762846.html access in 20/5/2022 Lechner, M (2011) The estimation of causal effects by difference-in-difference methods Foundations and Trends® in Econometrics, 4(3), 165-224 40 APPENDIX OLS regression with DID estimation, the dependent variable is the total income per capita lnYj,t =β0 + Tt β1+ Treat β2+ Tt *Treat β3+ Road1,j β4 + Road2,j β5+ nonfarmjβ6 + eduj β7 +agejβ8 + genderjβ9+ εj,t (1) ln_inctt_percap Coef Robust t P>t [95% Std Err Interval] Conf t 0287863 0502296 0.57 0.567 -.0702167 1277893 treat 259553 052579 0.000 1559195 3631866 T*treat -.0016757 0601158 -0.03 0.978 -.1201643 1168129 Road1 -.0089079 0372373 -0.24 0.811 -.0823028 0644871 Road2 -.0158001 0092352 -1.71 0.089 -.0340027 0024025 nonfarm 158238 038179 4.14 0.000 082987 233489 edu 0353409 0125653 2.81 0.005 0105747 0601071 age 0009375 0012743 0.74 0.463 -.0015741 0034491 gender -.0255596 0513785 -0.50 0.619 -.1268269 0757077 _cons 8831467 0.000 7102964 1.055997 4.94 0876964 10.07 Variable VIF 1/VIF T*treat 3.00 0.333637 treat 2.76 0.362762 t 2.09 0.477474 41 nonfarm 1.31 0.762949 Road1 1.31 0.764742 Road2 1.14 0.875907 edu 1.10 0.912912 age 1.09 0.919999 gender 1.04 0.959440 Mean VIF 1.65 Ramsey RESET test using powers of the fitted values of ln_inctt_percap Ho: The model has no omitted variables F(3, 213) = 1.06 Prob > F = 0.3678 ln_inctt_percap Coef Std Err t P>t [95% Interval] Conf _hat 1.100223 _hatsq _cons 1.172704 0.94 0.349 -1.210776 3.411223 -.0454098 5287517 -0.09 0.932 -1.087399 9965794 -.0545305 6480756 -0.08 0.933 -1.331667 1.222605 OLS regression with DID estimation, the dependent variable is the agriculture income per capita lnYj,t =β0 + Tt β1+ Treat β2+ Tt *Treat β3+ Road1,j β4 + Road2,j β5+ nonfarmjβ6 + eduj β7 +agejβ8 + genderjβ9+ εj,t (1) 42 ln_incagr_percap Coef Robust t P>t [95% Std Err Interval] Conf t 0211584 0543798 0.39 0.698 -.0860247 1283415 treat 2470768 0566928 4.36 0.000 1353349 T*treat -.0000374 0674245 -0.00 1.000 -.1329315 1328567 Road1 0304474 0.484 -.0551825 1160773 Road2 -.0114648 0095071 -1.21 0.229 -.0302034 0072738 nonfarm -.1028143 0458957 -2.24 0.026 -.1932752 -.0123535 edu 032086 0158972 2.02 0.045 0007526 age 0016617 0014588 1.14 0.256 -.0012136 0045369 gender -.0681152 0480845 -1.42 0.158 -.16289 0266597 _cons 8884798 0.000 6974641 1.079495 0434448 0.70 0969127 9.17 Variable VIF 1/VIF T*treat 3.00 0.333637 treat 2.76 0.362762 t 2.09 0.477474 nonfarm 1.31 0.762949 Road1 1.31 0.764742 Road2 1.14 0.875907 edu 1.10 0.912912 age 1.09 0.919999 3588187 0634194 43 gender 1.04 Mean VIF 1.65 0.959440 Ramsey RESET test using powers of the fitted values of ln_incagr_percap Ho: The model has no omitted variables F(3, 213) = 0.51 Prob > F = 0.6749 ln_incagr_percap Coef Std Err t P>t [95% Interval] Conf _hat 1.541122 _hatsq _cons 1.935218 0.80 0.427 -2.272533 5.354776 -.2643531 9439067 -0.28 0.780 -2.124471 1.595765 -.2708372 9736826 -0.28 0.781 -2.189634 1.647959 44 OLS regression with DID estimation, dependent variable is total income per capita lnYj,t =β0 + Tt β1+ Treat β2+ Tt *Treat β3+ Market ,j β4 + Coach stopj β5 +Tt β3+ Treat β4+ Tt *Treat β5 + nonfarmjβ6 + eduj β7 +agejβ8 + genderjβ9+ εj,t (2) ln_incagr_percap Coef Robust t P>t [95% Std Err Interval] Conf t 0269357 0492178 0.55 0.585 -.0700729 1239443 treat 2055981 058956 0.001 0893955 T*treat -.0001758 0591041 -0.00 0.998 -.1166705 1163188 Market -.0011013 0010073 -1.09 0.275 -.0030867 0008841 Coach_stop -.0025807 0013763 -1.88 0.062 -.0052935 0001321 nonfarm 1300194 0409425 3.18 0.002 0493214 2107173 edu 0328716 012406 2.65 0.009 0084193 0573239 age 0012196 0012394 0.98 0.326 -.0012231 0036624 gender -.0437707 0524713 -0.83 0.405 -.1471921 0596507 _cons 9584936 0.000 7562738 3.49 1025972 9.34 3218008 1.160713 Ramsey RESET test using powers of the fitted values of ln_inctt_percap Ho: The model has no omitted variables F(3, 213) = 0.76 Prob > F = 0.5193 Variable VIF 1/VIF treat 3.24 0.308960 45 T*treat 3.03 0.330407 t 2.06 0.485787 Coach_stop 1.53 0.652595 Market 1.50 0.666595 nonfarm 1.41 0.709835 age 1.09 0.921547 edu 1.07 0.930369 Mean VIF 1.77 ln_inctt_percap Coef Std Err t P>t [95% Interval] Conf _hat 2.000531 _hatsq _cons 1.370673 1.46 0.146 -.7005975 4.70166 -.4566237 6234665 -0.73 0.465 -1.685264 7720163 -.5398467 7477222 -0.72 0.471 -2.013352 9336587 OLS regression with DID estimation, dependent variable is agriculture income per capita lnYj,t =β0 + Tt β1+ Treat β2+ Tt *Treat β3+ Market ,j β4 + Coach stopj β5 +Tt β3+ Treat β4+ Tt *Treat β5 + nonfarmjβ6 + eduj β7 +agejβ8 + genderjβ9+ εj,t (2) ln_incagr_percap Coef Std Err t P>t [95% Interval] Conf t 0036024 0513575 0.07 0.944 -.0976235 1048284 treat 1320083 0645883 2.04 0.042 0047042 2593124 46 T*treat 0151404 Market 0649178 0.23 0.816 -.1128131 1430939 -.0013687 0010373 -1.32 0.188 -.0034132 0006759 Coach_stop -.0042256 001487 -2.84 0.005 -.0071564 -.0012948 nonfarm -.1443959 0489664 -2.95 0.004 -.2409091 -.0478827 edu 0295157 0156697 1.88 0.061 -.0013694 0604008 age 0019128 0013559 1.41 0.160 -.0007597 0045852 gender -.0858268 0477393 -1.80 0.074 -.1799212 0082677 _cons 1.052407 0.000 8463677 1045349 10.07 Variable VIF 1/VIF treat 3.24 0.308960 T*treat 3.03 0.330407 t 2.06 0.485787 Coach_stop 1.53 0.652595 Market 1.50 0.666595 nonfarm 1.41 0.709835 age 1.09 0.921547 edu 1.07 0.930369 gender 1.04 0.960789 Mean VIF 1.77 1.258446 Ramsey RESET test using powers of the fitted values of ln_incagr_percap Ho: The model has no omitted variables 47 F(3, 213) = 0.69 Prob > F = 0.5609 ln_incagr_percap Coef Std Err t P>t [95% Interval] Conf _hat 2.645473 _hatsq _cons 1.337546 1.98 0.049 009626 5.28132 -.8131669 6591619 -1.23 0.219 -2.11215 4858164 -.8110354 6655548 -1.22 0.224 - 5005461 2.122617 48 OLS regression with DID estimation, dependent variable is non-farm income per capita Inc_nonfarm_percapj,t =β0 +Treat,j β1+ Tt β2+ Tt *Treatj β3+ Road1,j β4 + Road2,j β5+Marketj β6 + Coach stopjβ7 + eduj β8 +agejβ9 + genderjβ10 + εj,t (3) Inc_nonfarm Coef Robust t P>t [95% Std Err _percap t 067307 treat 9711691 0.07 Interval] Conf 0.945 -1.846925 1.981539 -2.929866 8063176 -3.63 0.000 -4.519166 -1.340567 T*treat 2021077 0.885 -2.541793 2.946008 Road1 -.8835441 8376007 -1.05 0.293 -2.534505 7674164 Road2 -.1417364 1118102 -1.27 0.206 -.362121 Market -.0246709 0111224 -2.22 0.028 -.0465939 -.002748 Coach_stop -.0171027 0207814 -0.82 0.411 -.058064 edu 5244277 0.234 -.3414866 1.390342 age -.0043184 0265211 -0.16 0.871 -.0565931 0479562 gender 1235282 1.101019 0.11 0.911 -2.046646 2.293702 _cons 4.485527 1.913142 2.34 0.020 7146119 1.392094 0.15 4393142 1.19 Variable VIF 1/VIF T*treat 3.05 0.328266 treat 2.95 0.338411 t 2.13 0.470092 Coach_stop 1.56 0.641401 0786482 0238586 8.256443 49 Market 1.48 0.677076 Road1 1.31 0.762140 Road2 1.20 0.831491 edu 1.09 0.913637 age 1.09 0.914123 gender 1.04 0.959493 Mean VIF 1.69 50 Ramsey RESET test using powers of the fitted values of inc_nonagr_percap Ho: The model has no omitted variables F(3, 212) = 1.02 Prob > F = 0.3834 Inc_nonfarm_percap Coef Std Err t P>t [95% Interval] Conf _hat 7560112 550769 1.37 0.171 -.3293666 1.841389 _hatsq 0591335 1199826 0.49 0.623 -.1773112 2955783 _cons 1392183 7195768 0.19 0.847 -1.278822 1.557259 51