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Spatial Spillover Effects of Transport Infrastructure on Economic Growth of Vietnam Regions: A Spatial Regression Approach

Spatial Spillover Effects of Transport Infrastructure on Economic Growth of Vietnam Regions: A... This article examines the spatial spillover effects of transport infrastructure in regions of Vietnam. We apply the spatial Durbin model to estimate the regional spillover of transport infrastructure to Vietnam's economic growth from 2000-2019. The results show that positive evidence exists in each period due to the connective nature of the transport infrastructure at the national level. At the regional level, transport infrastructure spillover effects vary considerably over time among four macro-regions of Vietnam: the southern region always has a positive spillover effect; the Northern region had adverse spillover effects in the period 2000-2009 and positive in the period 2010-2019; the Central region had negative spillovers in both periods; in the case of the economic region of the Mekong Delta, negative spillovers can be observed after 2010. The analysis has shown that changes in spillover among regions are closely related to the shift of production factors in Vietnam over the past two decades. Key words: Spillover, Transport infrastructure, Economic growth, econometrics, Vietnam. JEL Classification: C22, H54, N1, 033. Citation: Nguyen, H.M. (2022). Spatial spillover effects of transport infrastructure on economic growth of Vietnam regions: a spatial regression approach. Real Estate Management and Valuation, 30(2), 12-20. DOI: https://doi.org/10.2478/remav-2022-0010 1. Introduction In developing countries, there are often stages of spatial transformation through the development of transport infrastructure. Good transport infrastructure will reshape geographic connectivity, reduce transport costs, open up trade flows between countries, positively affect commodity prices and increase export competitiveness (Ramajo et al., 2008; Cohen, 2010; Saima & Inayat, 2018). The benefits of transport infrastructure are not limited to specific areas but can also have positive spillover effects on neighboring regions (Chen & Haynes, 2015b). Many studies on the impact of the development of transport infrastructure on regional economic growth have been carried out over the past decades, mainly to examine the economic returns of investments in transport to determine those which are reasonable. The pioneering work of Aschauer et al. (1989) has inspired a series of follow-up studies. However, Aschauer et al., (1989) did not find evidence of the contribution of transport infrastructure to economic growth. Subsequent research indicates a negative association between transport infrastructure and economic growth (Holtz-Eakin & Schwartz, 1995b, 1996; Moreno & Lopez-Bazo, 2007). Other efforts show positive spillovers from transport infrastructure to economic growth (Pereira & Roca-Sagalés, 2003; Cohen, 2010). Two reasons sum up the difference between the above conclusions: firstly, the difference in the definition of public REAL ESTATE MANAGEMENT AND VALUATION, eISSN: 2300-5289 vol. 30, no.2, 2022 www.degruyter.com/view/j/remav capital in investment in transport infrastructure (Yu et al., 2013; Saima & Inayat, 2018). The remaining problem is due to studies that ignore the cross-region spillover effect to linearize the parameters (Mikelbank & Jackson, 2000; Saima & Inayat, 2018). For Vietnam, most of the studies are published at domestic conferences, so they usually only focus on considering the low returns of investment in transport to find a reasonable investment method. Very few publications mention the spatial advantage of transport infrastructure (Tran, 2009; Truong et al., 2019; Phi et al., 2019). There are no studies to estimate spillover effects at the local level, and this is very useful for community decision-making regarding the planning of major transport projects. Accordingly, the study is structured as follows. The following section briefly describes the regional distribution of transport infrastructure in Vietnam. Section 3 introduces the methodology and database to quantify spatial spillovers of transport investment. Section 4 provides an in-depth analysis of the spatial spillover effects of transport infrastructure in key economic regions. The paper ends with conclusions and policy implications. 2. Transport infrastructure in Vietnam: an overview Before the 2000s, investment resources in transport infrastructure were mainly focused on maintenance work to ensure traffic safety and only implemented the construction of some urgent projects. As of 2004, the transport sector has made breakthroughs, upgrading and renewing more than 16,000 km of roads, 1,400 km of railways, more than 130,000 m of road bridges, 11,000 m of railway bridges. Vietnam's transport infrastructure is particularly observed to thrive in the Socio-Economic Development Strategy 2010-2019, with the scale of the road system skyrocketing to 668,000 km, nearly three times higher than at the end of 2004. Vietnam now has more than 1,800 km of expressways, of which almost 1,500 km are in use. Table 1 describes the length and density of traffic routes in Vietnam in 2019. Table 1 The length and density of traffic routes in Vietnam in 2019 Type Road Year 2000 Year 2009 Year 2019 Avg Road Avg Road Avg Road Length Length Length speed density speed density speed density (km) (km) (km) 2 2 2 (km/h) (km/km ) (km/h) (km/km ) (km/h) (km/km ) Expressway 1900 100 0.003 National 11.068 29.54 0.031 19.068 37.67 0.045 25.875 47.61 0.060 highways Provincial 17.491 23.12 0.041 24.491 28.12 0.052 37.700 38.52 0.072 road Other roads 101.326 13.32 0.369 152.826 13.32 0.469 256.000 23.14 0.713 Source: World Bank’s GIS work using data from Directorate for Roads of Vietnam (DRVN). The data in Table 1 shows that Vietnam's transport network has expanded significantly since the US lifted the trade embargo. With administrative characteristics by province and economy by region, the national transport network tends to cluster by region, considering spatial factors before estimating the potential economic benefits of different modes of transport is essential. Will transport provide more economic benefits than their direct impacts on the region? How can we measure spatial spillover effects of transport infrastructure if there is evidence of the existence of spillovers? The study will examine the spatial spillover effects of transport infrastructure at the national and regional levels in the next section to answer these questions. 3. Measuring spatial spillover effects of transport infrastructure in Vietnam 3.1.Model specification This study will use the approach commonly used in most previous studies, i.e. the Cobb-Douglas production function, to test the spatial spillover from infrastructure onto economic growth (Holtz- Eakin & Schwartz, 1995a; Del Bo & Florio, 2012). The critical variable is GDP, and the interaction REAL ESTATE MANAGEMENT AND VALUATION, eISSN: 2300-5289 vol. 30, no. 2, 2022 www.degruyter.com/view/j/remav variables between GDP are labor and investment capital. The extended Cobb-Douglas production function has the following form:    12 4 5 Yf (,L K ,KK , ,K ) AL K K K K (1) C C T P PT C C T P PT where Y- output; L- labor, K , K - are private investments and public investment not for traffic, C P respectively; K , K – is a private investment and public investment capital for transport, CT PT respectively. Taking the logarithm of both sides of the equation (1), we get a linear regression where each estimated parameter is estimated to be each GDP elasticity coefficient: LnY ln L LnK  LnK  LnK  LnK  (2) 01 2 CC 3 T 4 P 5 PT The study uses Moran's index as a measure of spatial autocorrelation. Moran's I statistics suggest a spatial correlation in the research data, so it is necessary to consider the extent of the contribution of spatial factors to regional economic growth. According to Auselin (2001), who wants to feel the spatial interaction between the dependent and independent variables, the spatial econometric model is suitable. Based on the general spatial model (LeSage et al., 2009), the study proposes to choose the spatial Durbin model (SDM) as the empirical analysis model: YW  X WXI (3) yn In which, ρ is the autocorrelation coefficient in space, W is the spatial weighting matrix, X is the matrix of control variables ( L, K , K , K , K ); I is the level unit matrix (n x1); α, θ and β are vectors C CT P PT n of the estimated coefficient; ε is the error. The SDM model will contain the spatial delay of both the dependent variable (Wy) and the explanatory variable (WX). The implication is that if there is a change in the dependent variable in a specific region, that change can affect the dependent variable in all other regions by the transport network effect. A change in the explanatory variable for one observation can affect the dependent variable in all other observations. The combination of equations (2) and (3) gives us an empirical model that estimates the spillover effects in space: Ln() Y w Ln() Y  Ln( L) Ln( K ) Ln( K ) it  ij jt 01 it 2 C it 3 CT it j1 nn  Ln() K LnK( ) w LnL( ) w LnK( ) 45 Pit PT it 1 ij jt 2 ij C jt jj 11 nn n  wLn() K wLn(K ) w Ln() K  (4)   34 ij CT jt ij P jt5 ij PT jt it jj 11 j1 Where: Y is real GDP; i and tcorrespond to the index of the ith province in year t; jrepresents neighboring provinces (j ≠i); wij is an element of the spatial matrix W that describes the spatial arrangement between different regions of the variables. To build the spatial weight matrix W = (w ), ij the study uses a contiguous binary matrix (wbin), assuming that adjacent provinces can influence each other. The formula determines the elements of the space matrix W: 1, th e p ro vinceij h as a b o d er w ith provin ce , w  ij  0, o th erw is e. 3.2. Data The data used in this study was collected from various sources, including the General Statistics Office of Vietnam (GSO) from 2000-2019; Statistical yearbook of the province/city directly under it. Accurate GDP data, private sector investment, employed population (labor input), transport infrastructure investment, and public investment are available from (GSO). Particularly for investment data on transport infrastructure, the study uses data on "transport infrastructure and postal services" from statistical yearbooks of provinces and cities. 3.3. Results and discussion Next, to determine the change in the regional spillover effect, we conducted model estimation (4) REAL ESTATE MANAGEMENT AND VALUATION, eISSN: 2300-5289 vol. 30, no.2, 2022 www.degruyter.com/view/j/remav during the following periods: 2000-2009 and 2010-2019. Table 2-4 summarizes the main results of model (4) at the national and regional levels. The results show that the table's spatial autocorrelation coefficient ρ in the tables is positive and statistically significant, implying a positive spillover effect between the regions in Vietnam. 3.3.1. The spillover effect at the national level Table 2 summarizes the SDM model estimation results, showing that the coefficients of L, K , K , K , C CT P K are positive and statistically significant. At the same time, their respective spatial lag coefficients PT are positive and statistically significant, except the sign of labor's spatial lag is negative and not significant. Table 2 Estimation results of the SDM model from 2000-2019 and each period Variable 2000-2019 Period 2000-2009 Period 2010-2019 Constant 1.204(9.23)*** 0.689(6.87)*** 1.524(14.34)*** 0.573(19.80)*** 0.472(14.37)*** 0.571(13.52)*** C 0.152(12.47)*** 0.122 (17.05)*** 0.101(13.64)*** K 0.112(12.47)*** 0.068(17.05)*** 0.132(13.64)*** CT K 0.179(13.58)*** 0.249(13.35)*** 0.162(13.58)*** K 0.124(15.78)*** 0.256(15.63)*** 0.178(15.45)*** PT ρ 0.231(4.25)*** 0.307(3.16)*** 0.279(13.01)*** WxL -0.382(1.65) - 0.157(0.32) - 0.198(1.38) WxK C 0.073(6.38)*** 0.046(2.33)** 0.063(7.58)*** WxK CT 0.043(6.38)*** 0.028(2.33)** 0.038(7.58)*** WxK 0.025(11.34)*** 0.003(5.56)*** 0.031(9.43)*** WxK 0.046(7.33)*** 0.019(12.52)*** 0.043(7.36)*** PT Adj. R 0.797 0.521 0.878 Log likelihood 178.47 154.44 165.36 Source: The author’s calculations from GIS software. Table 3 presents the direction and extent of direct and indirect spillovers of the explanatory variables. The experiment shows that the total effect of private and public investment in transport is positive and highly significant. Moreover, the elasticity coefficients of the two types of capital as an investment are pretty close (coefficients are 0.24 and 0.22), showing that the contribution of private capital and public capital to economic growth is nearly equal. The insignificant difference in these two types of capital from a personal point of view is due to the fact that market factors have played an essential role in the Vietnamese economy. The results also show that the labor coefficient (0.45) is quite reasonable, implying that labor input growth has the most significant impact on Vietnam's real GDP growth; coefficient values are very consistent with the findings of previous studies (Le et al., 2011, Pham et al., 2016). Table 3 The direct and indirect effects of explanatory variables Period Period Variables Effect 2000-2019 2000-2009 2010- 2019 Direct effect 0.577(23.24)*** 0.465(14.77)*** 0.538(6.38)*** Indirect - 0.124(1.28) - 0.114(1.27 -0.146(0.38) Labor effect REAL ESTATE MANAGEMENT AND VALUATION, eISSN: 2300-5289 vol. 30, no. 2, 2022 www.degruyter.com/view/j/remav Total 0.453(9.45)*** 0.351(14.43)*** 0.392(4.75)*** Direct effect 0.155(6.03)*** 0.072(14.65)*** 0.116(21.24) *** Indirect 0.081(15.36) *** 0.063(9.32) *** 0.066(4.27) *** CT effect Total 0.235(22.65) *** 0.135(5.67) *** 0.182(11.23)** Direct effect 0.190(19.24) *** 0.261(3.65) *** 0.167(11.46) *** Indirect 0.033(7.36) *** 0.003(2.27)** 0.036(8.26) *** PT effect Total 0.224(13.14) *** 0.265(11.79) *** 0.203(12.55) *** Direct effect 0.118(18.22) *** 0.259(13.46) *** 0.172(19.47) *** Transport Indirect 0.055(16.37) *** 0.026(13.41) *** 0.052 (2.59)** infrastructure effect Total 0.173(15.54) *** 0.285(27.45) *** 0.224(3.36)** Source: The author’s calculations from the model. Considering the impact of transport infrastructure, we find that transport infrastructure has a positive effect on GDP growth (coefficient is 0.17), the aggregate effect tends to decrease over time because direct impacts show signs of decline in different periods (the coefficients are 0.26 and 0.17 in the periods 2000-2009, 2010-2019, respectively). The impact of transport infrastructure declines over time, possibly due to economic reforms, investment in transport projects continues to increase, and marginal returns begin to increase after a while. The findings of this experiment are consistent with previous domestic studies (Dang, 2021; Nguyen, 2020). Table 3 also shows that the indirect effect of transport infrastructure is highly statistically significant (coefficient is 0.06), which means that the transport volume contributes both directly and indirectly to GDP. Indirect impacts increase over time: coefficients are 0.03 for the period 2000-2009; 0.05 for the period 2010-2019. This shows that the increasing spillover effects contribute more significantly to economic growth due to the expanded transport network. Next, to analyze spillover effects at the research area level, we will consider the indirect influence of transport infrastructure. 3.3.2. The spillover effects of transport infrastructure at the regional level Table 4 shows a significant difference in indirect influence between the key economic regions of Vietnam during the study period (μ = 0.07; - 0.04; 0.14; and - 0.06 respectively for different regions). The coefficient of indirect effects on the southern critical economic region is very high, 0.14, which means that the GDP of the critical southern region will increase by 0.14% if the proportion of transport in the vicinity increases by 1%. The spillover effects in the northern critical economic region also exhibit positive signs, although these are weaker than those in the southern critical economic region (coefficient is 0.07). However, the findings indicate that increased investment in the neighboring transport infrastructure of the two key economic regions of the Central region and the Mekong River Delta may constrain the local economy (negative coefficient). Table 4 Estimation results of SDM model at the regional level Regions Variable 2000-2019 Period 2000-2009 Period 2010 - 2019 Constant 2.503(9.32)*** 2.261(6.92)*** 3.521(14.24)*** 0.538(13.09)*** 0.576(16.13) *** 0.510 (3.23)** K 0.098 (10.12)*** 0.051(2.98)* 0.112 (7.82)*** K 0.068 (13.12)*** 0.031(1.89)* 0.132 (5.75)*** CT K 0.181(7.62)*** 0.224(11.35)*** 0.167(3.34)** Northern K 0.143(4.13)*** 0.221(22.56)*** 0.194 (22.86)* PT critical ρ 0.215(24.67)*** 0.269(4.75)*** 0.197(12.73)*** economic WxL -0.193(0.51) -0.114(2.63)** -0.112(0.73) region WxK 0.063(14.72)*** 0.074(1.87)* 0.105(0.59) WxK 0.034(4.77)*** 0.035(2.86)* 0.105(0.09) CT WxK 0.061(8.49)*** 0.052 (0.93) 0.056(6.75)*** REAL ESTATE MANAGEMENT AND VALUATION, eISSN: 2300-5289 vol. 30, no.2, 2022 www.degruyter.com/view/j/remav WxK 0.021(12.42)*** 0.121(0.64) 0.074(2.37)** PT μ 0.071(9.26)*** 0.206(0.47) 0.052(4.14)* Adj. R 0.780 0.831 0.667 Log likelihood 178.57 103.25 121.12 Constant 4.503(9.32)*** 2.271(6.92)*** 3.571(14.24)*** L 0.537(6.36)*** 0.566(1.86)* 0.524(2.05)* K 0.144(11.22)*** 0.078(2.33)** 0.113(9.73)*** CT 0.074(12.22)*** 0.045(6.33)** 0.083(11.73)*** P 0.171(11.32)*** 0.183(10.24)*** 0.202(9.45)*** PT 0.193(15.85)*** 0.172(2.25)** 0.162(17.34)*** Central 0.265(5.35)*** 0.315(7.15)*** 0.193(15.27)*** critical WxL -0.098(1.12) -0.116(0.75) -0.094(1.37) economic WxK C 0.054(16.33)*** 0.063(8.32)*** 0.057(15.29)*** region WxK -0.024(15.26)*** -0.078(6.86)*** -0.065(12.29)*** CT WxK 0.037(6.84)*** 0.071(1.04) 0.102(2.45)** WxK -0.072(16.45)*** -0.033(7.59)*** -0.134(1.98)* PT μ -0.043(8.29)*** -0.015(6.43)*** -0.06(2.51)** Adj. R 0.705 0.636 0.464 Log likelihood 132.63 145.89 161.27 Constant 2.273(9.21)*** 1.694(6.94)*** 2.533(14.54)*** 0.626(16.61)*** 0.495(16.12)** 0.597(11.91)** C 0.167(22.05)*** 0.091(21.24)*** 0.151(17.98)*** CT 0.137(18.35)*** 0.071(17.24)*** 0.111(13.98)*** P 0.135(16.56)*** 0.137(11.42)*** 0.139(18.55)*** PT 0.093(2.35)** 0.204(3.59)** 0.105(2.34)** Southern ρ 0.391(14.54)*** 0.326(6.47)*** 0.294(15.35)*** critical WxL -1.425(1.46) -1.075(1.47) -1.710(0.79) economic WxK 0.044(17.34)*** 0.039(11.45)*** 0.006(2.45)* region WxK 0.104(16.21)*** 0.092(12.25)*** 0.065(1.99)* CT WxK 0.033(3.45)*** 0.023(1.95)* 0.023(7.37)*** WxK 0.128(14.56)*** 0.036(16.34)*** 0.154(14.44)*** PT 0.143(16.32)*** 0.063(7.87)*** 0.172 (7.42)*** Adj. R 0.647 0.831 0.536 Log likelihood 149.34 137.65 114.36 Constant 4.503(8.92)*** 2.471(7.85)*** 3.371(13.34)*** L 0.463(14.43)*** 0.405(32.93) 0.345(2.78)** K 0.127(12.63)*** 0.170(6.86)** 0.062(10.67)*** K 0.042(15.45)*** 0.058(3.89)** 0.065(6.56)*** CT K 0.228(13.25)*** 0.194(15.37)*** 0.252(2.35)** K 0.074(13.12)*** 0.082(2.77)** 0.086(2.06)* PT Cuu Long ρ 0.235(7.47)*** 0.169(16.76)*** 0.148(21.67)*** Delta WxL -0.051(1.36) -0.131(0.16) -0.067(2.26)** critical WxK 0.038(13.65)*** 0.032(7.62)*** 0.055(2.39)** Economic WxK CT -0.078(12.45)*** -0.123(0.87) -0.065(1.86)** region WxK P 0.023(5.18)*** 0.013(1.98)* 0.044(2.14)* WxK PT -0.084(4.25)*** -0.117(0.16) -0.135(8.57)*** -0.063 (2.22)** -0.071(0.98) -0.062(12.15)*** Adj. R 0.548 0.496 0.522 Log likelihood 162.56 181.46 166.52 Source: The author’s calculations from GIS software. Table 4 shows that there are significant differences in the extent of spillovers across regions in each REAL ESTATE MANAGEMENT AND VALUATION, eISSN: 2300-5289 vol. 30, no. 2, 2022 www.degruyter.com/view/j/remav period. For the southern region, the spillover in the surrounding areas had a positive impact the entire time covered by the research. The regression results illustrate that the output elasticity of neighboring transport infrastructures for the two sub-periods are significant and positive (the coefficients are 0.06, 0.17). For the Northern region, no spillovers were noted in the period 2000-2009, but there was a positive spillover in 2010-2019 (coefficient of 0.05). In the central critical economic region, the estimated coefficients of spillover effects are -0.02 and -0.06 for the period 2000-2009, and 2010-2019 respectively, which means that the growth of transport investment in the surrounding areas harms economic growth in the Central region during the period. For the critical economic region of the Mekong River Delta, a negative spillover effect in the period 2010-2019 was observed (coefficient is - 0.06). In the first period, the coefficient is insignificant, showing no spillover effects in the first period (1). The findings of the study partially support those of previous studies (Tran, 2009; Tran et al., 2019; Phi et al., 2019) at the national level, but also point to some contradictory results compared with others at the regional level. This can be explained by applying the advanced spatial Durbin model, which considers the lagging factor of both dependent and independent variables and measures spatial spillovers from all possible regions, which makes the estimates of the present study more convincing. For an in-depth analysis of the differences between the macroeconomic regions, we will next study how the spillover of transport infrastructure works in Vietnam. 4. Spillover effect arising from transport network characteristics According to Banister & Berechman (2001), an increase in transport investment in an area will upgrade the transport network of that region and thus enlarge the market size. An expanding market size means attracting a more specialized workforce, which allows the economy to expand more. Therefore, it can be argued that the expansion of the transport network can stimulate the economy in both the investment area and the surrounding areas. For the case of Vietnam, Pham et al., (2016) conclude that both economic openness and regional integration have a clear positive impact on Vietnam's regional TFP; Le et al., (2011) found that expanding market size has a positive effect on economic growth. Therefore, the rapid expansion of market size due to improved transportation brings along many economic benefits. Based on the analysis, we can explain that the existence of positive spillover effects of Vietnam's transport network at the national level in both the research period and the change in each period is caused by the shift from factors of production. Although, the policy of Doi Moi - comprehensive reform covering the economy and many other aspects of social life, was initiated by the Communist Party of Vietnam in the 1980s. Vietnamese politics changed slowly, continuing to maintain the model of socialism and communism under Marxism-Leninism; as a result of this, the movement of factors of production is still limited. Since 2000, the shift to a market economy has radically changed the economy, making the exchange of production and goods much more favorable due to lower transport costs. In the North, there has been a shift in factors of production from the poor provinces, i.e. the Northwest poor region (14 provinces), the Central-Central Highlands region (10 provinces), and the Mekong River Delta region (13 provinces), to the southern region. Since the last years of 2000-2009, the southern critical economic region has witnessed a strong development that is expected to benefit other economic sectors by redistributing industry. The study will continue to examine how the movement of factors of production has changed over time between the four regions in Table 5. Table 5 The migration of production factors among regions in various periods Northern critical Central critical Southern critical Cuu Long Delta economic region economic region economic region critical region Period K L trend K L trend K L trend K L trend 2000- + -31 -25 - - 367 -312 - +1036 + -352 -298 - 2010- +65 +78 + +35 +53 + -115 -156 - +46 +46 + Source: Author's own calculations. REAL ESTATE MANAGEMENT AND VALUATION, eISSN: 2300-5289 vol. 30, no.2, 2022 www.degruyter.com/view/j/remav Table 5 shows the net movement trend of production factors (capital and labor) between regions in different periods. There was a shift of production factors from the two regions of the Central - Central Highlands and the South West in the early years of the 2000-2009 period, contributing to a significant increase in production activities in southern areas. Particularly in the northern region, production factors are relatively stable, possibly due to the long geographical distance compared to the southern region. In the recent decade, factors of production have begun to shift from the southern region towards the less developed regions. These findings seem to be utterly consistent with the current situation of Vietnam in the current context. 5. Conclusion and policy implications Based on the research results, transport capital is associated with increased production in the region. A convenient transportation network causes positive spillover effects at the national level affecting all of Vietnam. There is a significant difference in the degree of spillover among critical economic regions of Vietnam in the different periods. In terms of policy implications, the following conclusions can be drawn: Firstly, identifying the transport network as the lifeblood of the economy - society, the Vietnamese Government should prioritize the development of the inter-regional transport network instead of building an intra-regional transport network. The existence of spatial externalities emerging from the contribution of transport infrastructure to regional growth should be viewed from a hyper-regional perspective. Secondly, with the characteristics of the current Vietnamese transport network, the Government should pay special attention to the coordination of traffic construction between provinces to balance and harmonize geography, economic and regional development orientation, avoiding centralized investment methods. Therefore, the central Government needs to provide guidelines and constraints on local government decision-making regarding their investment models. Third, industrial policies related to the lagging sector are needed due to adverse spillover effects. The effects of industrial accumulation from transportation development lead to an increase in industrial activities from the central region and the Mekong Delta to the southern part, especially industries that use many technologies. Since there can be negative spillovers of transport infrastructure from migration factors, local governments in less developed areas should change their industrial policies to avoid allocating economic activities. Targeted region-specific industrial policies, such as favorable tax policies and lower interest rates on loans to invest in local technology and labor- intensive industries, are needed. 6. Possible future research To increase the significance of this study, the following opinions can be developed more comprehensively. First, it should be assessed whether transport infrastructure development is the leading cause of the "fever" observed in real estate prices in Vietnam. Second, it is necessary to consider the relationship between urban planning and the real estate market. These questions, however, extend beyond the scope of the present research and require enormous effort. They should therefore be considered in a follow-up study. References Aschauer, D. A. (1989). 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Jounal Problems and Perspectives in Management, 17(2), 2019. Saima, N., & Inayat, U. M. (2018). The Economic Geography of Infrastructure in Asia: The Role of Institutions and Regional Integration .Conference: PSDE & AGM At: Pakistan. Ramajo, J., Marquez, M. A., Hewings, G. J. D., & Salinas, M. M. (2008). Spatial heterogeneity and Interregional spillovers in the European Union: Do cohesion policies encourage convergence across regions? European Economic Review, 52(3), 551–567. https://doi.org/10.1016/j.euroecorev.2007.05.006 Yu, N., de Jong, M., Storm, S., & Mi, J. (2013). Spatial spillover effects of transport infrastructure: Evidence from Chinese regions. Journal of Transport Geography, 28, 56–66. https://doi.org/10.1016/j.jtrangeo.2012.10.009 Trương, C. B., & Phan, Q. V. (2019). The spatial autocorrelation analysis for volume of freight in different regions: A case of Vietnam [IJESRT]. International Journal of Engineering Sciences & Research Technology, 8(12), 2019. Tran, T. Q. (2009). Sudden Surge in FDI and Infrastructure Bottlenecks: The Case in Vietnam. ASEAN Economic Bulletin, 26(1), 58–76. https://doi.org/10.1355/AE26-1E REAL ESTATE MANAGEMENT AND VALUATION, eISSN: 2300-5289 vol. 30, no.2, 2022 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Real Estate Management and Valuation de Gruyter

Spatial Spillover Effects of Transport Infrastructure on Economic Growth of Vietnam Regions: A Spatial Regression Approach

Real Estate Management and Valuation , Volume 30 (2): 9 – Jun 1, 2022

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de Gruyter
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© 2022 Hai Minh Nguyen, published by Sciendo
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1733-2478
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10.2478/remav-2022-0010
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Abstract

This article examines the spatial spillover effects of transport infrastructure in regions of Vietnam. We apply the spatial Durbin model to estimate the regional spillover of transport infrastructure to Vietnam's economic growth from 2000-2019. The results show that positive evidence exists in each period due to the connective nature of the transport infrastructure at the national level. At the regional level, transport infrastructure spillover effects vary considerably over time among four macro-regions of Vietnam: the southern region always has a positive spillover effect; the Northern region had adverse spillover effects in the period 2000-2009 and positive in the period 2010-2019; the Central region had negative spillovers in both periods; in the case of the economic region of the Mekong Delta, negative spillovers can be observed after 2010. The analysis has shown that changes in spillover among regions are closely related to the shift of production factors in Vietnam over the past two decades. Key words: Spillover, Transport infrastructure, Economic growth, econometrics, Vietnam. JEL Classification: C22, H54, N1, 033. Citation: Nguyen, H.M. (2022). Spatial spillover effects of transport infrastructure on economic growth of Vietnam regions: a spatial regression approach. Real Estate Management and Valuation, 30(2), 12-20. DOI: https://doi.org/10.2478/remav-2022-0010 1. Introduction In developing countries, there are often stages of spatial transformation through the development of transport infrastructure. Good transport infrastructure will reshape geographic connectivity, reduce transport costs, open up trade flows between countries, positively affect commodity prices and increase export competitiveness (Ramajo et al., 2008; Cohen, 2010; Saima & Inayat, 2018). The benefits of transport infrastructure are not limited to specific areas but can also have positive spillover effects on neighboring regions (Chen & Haynes, 2015b). Many studies on the impact of the development of transport infrastructure on regional economic growth have been carried out over the past decades, mainly to examine the economic returns of investments in transport to determine those which are reasonable. The pioneering work of Aschauer et al. (1989) has inspired a series of follow-up studies. However, Aschauer et al., (1989) did not find evidence of the contribution of transport infrastructure to economic growth. Subsequent research indicates a negative association between transport infrastructure and economic growth (Holtz-Eakin & Schwartz, 1995b, 1996; Moreno & Lopez-Bazo, 2007). Other efforts show positive spillovers from transport infrastructure to economic growth (Pereira & Roca-Sagalés, 2003; Cohen, 2010). Two reasons sum up the difference between the above conclusions: firstly, the difference in the definition of public REAL ESTATE MANAGEMENT AND VALUATION, eISSN: 2300-5289 vol. 30, no.2, 2022 www.degruyter.com/view/j/remav capital in investment in transport infrastructure (Yu et al., 2013; Saima & Inayat, 2018). The remaining problem is due to studies that ignore the cross-region spillover effect to linearize the parameters (Mikelbank & Jackson, 2000; Saima & Inayat, 2018). For Vietnam, most of the studies are published at domestic conferences, so they usually only focus on considering the low returns of investment in transport to find a reasonable investment method. Very few publications mention the spatial advantage of transport infrastructure (Tran, 2009; Truong et al., 2019; Phi et al., 2019). There are no studies to estimate spillover effects at the local level, and this is very useful for community decision-making regarding the planning of major transport projects. Accordingly, the study is structured as follows. The following section briefly describes the regional distribution of transport infrastructure in Vietnam. Section 3 introduces the methodology and database to quantify spatial spillovers of transport investment. Section 4 provides an in-depth analysis of the spatial spillover effects of transport infrastructure in key economic regions. The paper ends with conclusions and policy implications. 2. Transport infrastructure in Vietnam: an overview Before the 2000s, investment resources in transport infrastructure were mainly focused on maintenance work to ensure traffic safety and only implemented the construction of some urgent projects. As of 2004, the transport sector has made breakthroughs, upgrading and renewing more than 16,000 km of roads, 1,400 km of railways, more than 130,000 m of road bridges, 11,000 m of railway bridges. Vietnam's transport infrastructure is particularly observed to thrive in the Socio-Economic Development Strategy 2010-2019, with the scale of the road system skyrocketing to 668,000 km, nearly three times higher than at the end of 2004. Vietnam now has more than 1,800 km of expressways, of which almost 1,500 km are in use. Table 1 describes the length and density of traffic routes in Vietnam in 2019. Table 1 The length and density of traffic routes in Vietnam in 2019 Type Road Year 2000 Year 2009 Year 2019 Avg Road Avg Road Avg Road Length Length Length speed density speed density speed density (km) (km) (km) 2 2 2 (km/h) (km/km ) (km/h) (km/km ) (km/h) (km/km ) Expressway 1900 100 0.003 National 11.068 29.54 0.031 19.068 37.67 0.045 25.875 47.61 0.060 highways Provincial 17.491 23.12 0.041 24.491 28.12 0.052 37.700 38.52 0.072 road Other roads 101.326 13.32 0.369 152.826 13.32 0.469 256.000 23.14 0.713 Source: World Bank’s GIS work using data from Directorate for Roads of Vietnam (DRVN). The data in Table 1 shows that Vietnam's transport network has expanded significantly since the US lifted the trade embargo. With administrative characteristics by province and economy by region, the national transport network tends to cluster by region, considering spatial factors before estimating the potential economic benefits of different modes of transport is essential. Will transport provide more economic benefits than their direct impacts on the region? How can we measure spatial spillover effects of transport infrastructure if there is evidence of the existence of spillovers? The study will examine the spatial spillover effects of transport infrastructure at the national and regional levels in the next section to answer these questions. 3. Measuring spatial spillover effects of transport infrastructure in Vietnam 3.1.Model specification This study will use the approach commonly used in most previous studies, i.e. the Cobb-Douglas production function, to test the spatial spillover from infrastructure onto economic growth (Holtz- Eakin & Schwartz, 1995a; Del Bo & Florio, 2012). The critical variable is GDP, and the interaction REAL ESTATE MANAGEMENT AND VALUATION, eISSN: 2300-5289 vol. 30, no. 2, 2022 www.degruyter.com/view/j/remav variables between GDP are labor and investment capital. The extended Cobb-Douglas production function has the following form:    12 4 5 Yf (,L K ,KK , ,K ) AL K K K K (1) C C T P PT C C T P PT where Y- output; L- labor, K , K - are private investments and public investment not for traffic, C P respectively; K , K – is a private investment and public investment capital for transport, CT PT respectively. Taking the logarithm of both sides of the equation (1), we get a linear regression where each estimated parameter is estimated to be each GDP elasticity coefficient: LnY ln L LnK  LnK  LnK  LnK  (2) 01 2 CC 3 T 4 P 5 PT The study uses Moran's index as a measure of spatial autocorrelation. Moran's I statistics suggest a spatial correlation in the research data, so it is necessary to consider the extent of the contribution of spatial factors to regional economic growth. According to Auselin (2001), who wants to feel the spatial interaction between the dependent and independent variables, the spatial econometric model is suitable. Based on the general spatial model (LeSage et al., 2009), the study proposes to choose the spatial Durbin model (SDM) as the empirical analysis model: YW  X WXI (3) yn In which, ρ is the autocorrelation coefficient in space, W is the spatial weighting matrix, X is the matrix of control variables ( L, K , K , K , K ); I is the level unit matrix (n x1); α, θ and β are vectors C CT P PT n of the estimated coefficient; ε is the error. The SDM model will contain the spatial delay of both the dependent variable (Wy) and the explanatory variable (WX). The implication is that if there is a change in the dependent variable in a specific region, that change can affect the dependent variable in all other regions by the transport network effect. A change in the explanatory variable for one observation can affect the dependent variable in all other observations. The combination of equations (2) and (3) gives us an empirical model that estimates the spillover effects in space: Ln() Y w Ln() Y  Ln( L) Ln( K ) Ln( K ) it  ij jt 01 it 2 C it 3 CT it j1 nn  Ln() K LnK( ) w LnL( ) w LnK( ) 45 Pit PT it 1 ij jt 2 ij C jt jj 11 nn n  wLn() K wLn(K ) w Ln() K  (4)   34 ij CT jt ij P jt5 ij PT jt it jj 11 j1 Where: Y is real GDP; i and tcorrespond to the index of the ith province in year t; jrepresents neighboring provinces (j ≠i); wij is an element of the spatial matrix W that describes the spatial arrangement between different regions of the variables. To build the spatial weight matrix W = (w ), ij the study uses a contiguous binary matrix (wbin), assuming that adjacent provinces can influence each other. The formula determines the elements of the space matrix W: 1, th e p ro vinceij h as a b o d er w ith provin ce , w  ij  0, o th erw is e. 3.2. Data The data used in this study was collected from various sources, including the General Statistics Office of Vietnam (GSO) from 2000-2019; Statistical yearbook of the province/city directly under it. Accurate GDP data, private sector investment, employed population (labor input), transport infrastructure investment, and public investment are available from (GSO). Particularly for investment data on transport infrastructure, the study uses data on "transport infrastructure and postal services" from statistical yearbooks of provinces and cities. 3.3. Results and discussion Next, to determine the change in the regional spillover effect, we conducted model estimation (4) REAL ESTATE MANAGEMENT AND VALUATION, eISSN: 2300-5289 vol. 30, no.2, 2022 www.degruyter.com/view/j/remav during the following periods: 2000-2009 and 2010-2019. Table 2-4 summarizes the main results of model (4) at the national and regional levels. The results show that the table's spatial autocorrelation coefficient ρ in the tables is positive and statistically significant, implying a positive spillover effect between the regions in Vietnam. 3.3.1. The spillover effect at the national level Table 2 summarizes the SDM model estimation results, showing that the coefficients of L, K , K , K , C CT P K are positive and statistically significant. At the same time, their respective spatial lag coefficients PT are positive and statistically significant, except the sign of labor's spatial lag is negative and not significant. Table 2 Estimation results of the SDM model from 2000-2019 and each period Variable 2000-2019 Period 2000-2009 Period 2010-2019 Constant 1.204(9.23)*** 0.689(6.87)*** 1.524(14.34)*** 0.573(19.80)*** 0.472(14.37)*** 0.571(13.52)*** C 0.152(12.47)*** 0.122 (17.05)*** 0.101(13.64)*** K 0.112(12.47)*** 0.068(17.05)*** 0.132(13.64)*** CT K 0.179(13.58)*** 0.249(13.35)*** 0.162(13.58)*** K 0.124(15.78)*** 0.256(15.63)*** 0.178(15.45)*** PT ρ 0.231(4.25)*** 0.307(3.16)*** 0.279(13.01)*** WxL -0.382(1.65) - 0.157(0.32) - 0.198(1.38) WxK C 0.073(6.38)*** 0.046(2.33)** 0.063(7.58)*** WxK CT 0.043(6.38)*** 0.028(2.33)** 0.038(7.58)*** WxK 0.025(11.34)*** 0.003(5.56)*** 0.031(9.43)*** WxK 0.046(7.33)*** 0.019(12.52)*** 0.043(7.36)*** PT Adj. R 0.797 0.521 0.878 Log likelihood 178.47 154.44 165.36 Source: The author’s calculations from GIS software. Table 3 presents the direction and extent of direct and indirect spillovers of the explanatory variables. The experiment shows that the total effect of private and public investment in transport is positive and highly significant. Moreover, the elasticity coefficients of the two types of capital as an investment are pretty close (coefficients are 0.24 and 0.22), showing that the contribution of private capital and public capital to economic growth is nearly equal. The insignificant difference in these two types of capital from a personal point of view is due to the fact that market factors have played an essential role in the Vietnamese economy. The results also show that the labor coefficient (0.45) is quite reasonable, implying that labor input growth has the most significant impact on Vietnam's real GDP growth; coefficient values are very consistent with the findings of previous studies (Le et al., 2011, Pham et al., 2016). Table 3 The direct and indirect effects of explanatory variables Period Period Variables Effect 2000-2019 2000-2009 2010- 2019 Direct effect 0.577(23.24)*** 0.465(14.77)*** 0.538(6.38)*** Indirect - 0.124(1.28) - 0.114(1.27 -0.146(0.38) Labor effect REAL ESTATE MANAGEMENT AND VALUATION, eISSN: 2300-5289 vol. 30, no. 2, 2022 www.degruyter.com/view/j/remav Total 0.453(9.45)*** 0.351(14.43)*** 0.392(4.75)*** Direct effect 0.155(6.03)*** 0.072(14.65)*** 0.116(21.24) *** Indirect 0.081(15.36) *** 0.063(9.32) *** 0.066(4.27) *** CT effect Total 0.235(22.65) *** 0.135(5.67) *** 0.182(11.23)** Direct effect 0.190(19.24) *** 0.261(3.65) *** 0.167(11.46) *** Indirect 0.033(7.36) *** 0.003(2.27)** 0.036(8.26) *** PT effect Total 0.224(13.14) *** 0.265(11.79) *** 0.203(12.55) *** Direct effect 0.118(18.22) *** 0.259(13.46) *** 0.172(19.47) *** Transport Indirect 0.055(16.37) *** 0.026(13.41) *** 0.052 (2.59)** infrastructure effect Total 0.173(15.54) *** 0.285(27.45) *** 0.224(3.36)** Source: The author’s calculations from the model. Considering the impact of transport infrastructure, we find that transport infrastructure has a positive effect on GDP growth (coefficient is 0.17), the aggregate effect tends to decrease over time because direct impacts show signs of decline in different periods (the coefficients are 0.26 and 0.17 in the periods 2000-2009, 2010-2019, respectively). The impact of transport infrastructure declines over time, possibly due to economic reforms, investment in transport projects continues to increase, and marginal returns begin to increase after a while. The findings of this experiment are consistent with previous domestic studies (Dang, 2021; Nguyen, 2020). Table 3 also shows that the indirect effect of transport infrastructure is highly statistically significant (coefficient is 0.06), which means that the transport volume contributes both directly and indirectly to GDP. Indirect impacts increase over time: coefficients are 0.03 for the period 2000-2009; 0.05 for the period 2010-2019. This shows that the increasing spillover effects contribute more significantly to economic growth due to the expanded transport network. Next, to analyze spillover effects at the research area level, we will consider the indirect influence of transport infrastructure. 3.3.2. The spillover effects of transport infrastructure at the regional level Table 4 shows a significant difference in indirect influence between the key economic regions of Vietnam during the study period (μ = 0.07; - 0.04; 0.14; and - 0.06 respectively for different regions). The coefficient of indirect effects on the southern critical economic region is very high, 0.14, which means that the GDP of the critical southern region will increase by 0.14% if the proportion of transport in the vicinity increases by 1%. The spillover effects in the northern critical economic region also exhibit positive signs, although these are weaker than those in the southern critical economic region (coefficient is 0.07). However, the findings indicate that increased investment in the neighboring transport infrastructure of the two key economic regions of the Central region and the Mekong River Delta may constrain the local economy (negative coefficient). Table 4 Estimation results of SDM model at the regional level Regions Variable 2000-2019 Period 2000-2009 Period 2010 - 2019 Constant 2.503(9.32)*** 2.261(6.92)*** 3.521(14.24)*** 0.538(13.09)*** 0.576(16.13) *** 0.510 (3.23)** K 0.098 (10.12)*** 0.051(2.98)* 0.112 (7.82)*** K 0.068 (13.12)*** 0.031(1.89)* 0.132 (5.75)*** CT K 0.181(7.62)*** 0.224(11.35)*** 0.167(3.34)** Northern K 0.143(4.13)*** 0.221(22.56)*** 0.194 (22.86)* PT critical ρ 0.215(24.67)*** 0.269(4.75)*** 0.197(12.73)*** economic WxL -0.193(0.51) -0.114(2.63)** -0.112(0.73) region WxK 0.063(14.72)*** 0.074(1.87)* 0.105(0.59) WxK 0.034(4.77)*** 0.035(2.86)* 0.105(0.09) CT WxK 0.061(8.49)*** 0.052 (0.93) 0.056(6.75)*** REAL ESTATE MANAGEMENT AND VALUATION, eISSN: 2300-5289 vol. 30, no.2, 2022 www.degruyter.com/view/j/remav WxK 0.021(12.42)*** 0.121(0.64) 0.074(2.37)** PT μ 0.071(9.26)*** 0.206(0.47) 0.052(4.14)* Adj. R 0.780 0.831 0.667 Log likelihood 178.57 103.25 121.12 Constant 4.503(9.32)*** 2.271(6.92)*** 3.571(14.24)*** L 0.537(6.36)*** 0.566(1.86)* 0.524(2.05)* K 0.144(11.22)*** 0.078(2.33)** 0.113(9.73)*** CT 0.074(12.22)*** 0.045(6.33)** 0.083(11.73)*** P 0.171(11.32)*** 0.183(10.24)*** 0.202(9.45)*** PT 0.193(15.85)*** 0.172(2.25)** 0.162(17.34)*** Central 0.265(5.35)*** 0.315(7.15)*** 0.193(15.27)*** critical WxL -0.098(1.12) -0.116(0.75) -0.094(1.37) economic WxK C 0.054(16.33)*** 0.063(8.32)*** 0.057(15.29)*** region WxK -0.024(15.26)*** -0.078(6.86)*** -0.065(12.29)*** CT WxK 0.037(6.84)*** 0.071(1.04) 0.102(2.45)** WxK -0.072(16.45)*** -0.033(7.59)*** -0.134(1.98)* PT μ -0.043(8.29)*** -0.015(6.43)*** -0.06(2.51)** Adj. R 0.705 0.636 0.464 Log likelihood 132.63 145.89 161.27 Constant 2.273(9.21)*** 1.694(6.94)*** 2.533(14.54)*** 0.626(16.61)*** 0.495(16.12)** 0.597(11.91)** C 0.167(22.05)*** 0.091(21.24)*** 0.151(17.98)*** CT 0.137(18.35)*** 0.071(17.24)*** 0.111(13.98)*** P 0.135(16.56)*** 0.137(11.42)*** 0.139(18.55)*** PT 0.093(2.35)** 0.204(3.59)** 0.105(2.34)** Southern ρ 0.391(14.54)*** 0.326(6.47)*** 0.294(15.35)*** critical WxL -1.425(1.46) -1.075(1.47) -1.710(0.79) economic WxK 0.044(17.34)*** 0.039(11.45)*** 0.006(2.45)* region WxK 0.104(16.21)*** 0.092(12.25)*** 0.065(1.99)* CT WxK 0.033(3.45)*** 0.023(1.95)* 0.023(7.37)*** WxK 0.128(14.56)*** 0.036(16.34)*** 0.154(14.44)*** PT 0.143(16.32)*** 0.063(7.87)*** 0.172 (7.42)*** Adj. R 0.647 0.831 0.536 Log likelihood 149.34 137.65 114.36 Constant 4.503(8.92)*** 2.471(7.85)*** 3.371(13.34)*** L 0.463(14.43)*** 0.405(32.93) 0.345(2.78)** K 0.127(12.63)*** 0.170(6.86)** 0.062(10.67)*** K 0.042(15.45)*** 0.058(3.89)** 0.065(6.56)*** CT K 0.228(13.25)*** 0.194(15.37)*** 0.252(2.35)** K 0.074(13.12)*** 0.082(2.77)** 0.086(2.06)* PT Cuu Long ρ 0.235(7.47)*** 0.169(16.76)*** 0.148(21.67)*** Delta WxL -0.051(1.36) -0.131(0.16) -0.067(2.26)** critical WxK 0.038(13.65)*** 0.032(7.62)*** 0.055(2.39)** Economic WxK CT -0.078(12.45)*** -0.123(0.87) -0.065(1.86)** region WxK P 0.023(5.18)*** 0.013(1.98)* 0.044(2.14)* WxK PT -0.084(4.25)*** -0.117(0.16) -0.135(8.57)*** -0.063 (2.22)** -0.071(0.98) -0.062(12.15)*** Adj. R 0.548 0.496 0.522 Log likelihood 162.56 181.46 166.52 Source: The author’s calculations from GIS software. Table 4 shows that there are significant differences in the extent of spillovers across regions in each REAL ESTATE MANAGEMENT AND VALUATION, eISSN: 2300-5289 vol. 30, no. 2, 2022 www.degruyter.com/view/j/remav period. For the southern region, the spillover in the surrounding areas had a positive impact the entire time covered by the research. The regression results illustrate that the output elasticity of neighboring transport infrastructures for the two sub-periods are significant and positive (the coefficients are 0.06, 0.17). For the Northern region, no spillovers were noted in the period 2000-2009, but there was a positive spillover in 2010-2019 (coefficient of 0.05). In the central critical economic region, the estimated coefficients of spillover effects are -0.02 and -0.06 for the period 2000-2009, and 2010-2019 respectively, which means that the growth of transport investment in the surrounding areas harms economic growth in the Central region during the period. For the critical economic region of the Mekong River Delta, a negative spillover effect in the period 2010-2019 was observed (coefficient is - 0.06). In the first period, the coefficient is insignificant, showing no spillover effects in the first period (1). The findings of the study partially support those of previous studies (Tran, 2009; Tran et al., 2019; Phi et al., 2019) at the national level, but also point to some contradictory results compared with others at the regional level. This can be explained by applying the advanced spatial Durbin model, which considers the lagging factor of both dependent and independent variables and measures spatial spillovers from all possible regions, which makes the estimates of the present study more convincing. For an in-depth analysis of the differences between the macroeconomic regions, we will next study how the spillover of transport infrastructure works in Vietnam. 4. Spillover effect arising from transport network characteristics According to Banister & Berechman (2001), an increase in transport investment in an area will upgrade the transport network of that region and thus enlarge the market size. An expanding market size means attracting a more specialized workforce, which allows the economy to expand more. Therefore, it can be argued that the expansion of the transport network can stimulate the economy in both the investment area and the surrounding areas. For the case of Vietnam, Pham et al., (2016) conclude that both economic openness and regional integration have a clear positive impact on Vietnam's regional TFP; Le et al., (2011) found that expanding market size has a positive effect on economic growth. Therefore, the rapid expansion of market size due to improved transportation brings along many economic benefits. Based on the analysis, we can explain that the existence of positive spillover effects of Vietnam's transport network at the national level in both the research period and the change in each period is caused by the shift from factors of production. Although, the policy of Doi Moi - comprehensive reform covering the economy and many other aspects of social life, was initiated by the Communist Party of Vietnam in the 1980s. Vietnamese politics changed slowly, continuing to maintain the model of socialism and communism under Marxism-Leninism; as a result of this, the movement of factors of production is still limited. Since 2000, the shift to a market economy has radically changed the economy, making the exchange of production and goods much more favorable due to lower transport costs. In the North, there has been a shift in factors of production from the poor provinces, i.e. the Northwest poor region (14 provinces), the Central-Central Highlands region (10 provinces), and the Mekong River Delta region (13 provinces), to the southern region. Since the last years of 2000-2009, the southern critical economic region has witnessed a strong development that is expected to benefit other economic sectors by redistributing industry. The study will continue to examine how the movement of factors of production has changed over time between the four regions in Table 5. Table 5 The migration of production factors among regions in various periods Northern critical Central critical Southern critical Cuu Long Delta economic region economic region economic region critical region Period K L trend K L trend K L trend K L trend 2000- + -31 -25 - - 367 -312 - +1036 + -352 -298 - 2010- +65 +78 + +35 +53 + -115 -156 - +46 +46 + Source: Author's own calculations. REAL ESTATE MANAGEMENT AND VALUATION, eISSN: 2300-5289 vol. 30, no.2, 2022 www.degruyter.com/view/j/remav Table 5 shows the net movement trend of production factors (capital and labor) between regions in different periods. There was a shift of production factors from the two regions of the Central - Central Highlands and the South West in the early years of the 2000-2009 period, contributing to a significant increase in production activities in southern areas. Particularly in the northern region, production factors are relatively stable, possibly due to the long geographical distance compared to the southern region. In the recent decade, factors of production have begun to shift from the southern region towards the less developed regions. These findings seem to be utterly consistent with the current situation of Vietnam in the current context. 5. Conclusion and policy implications Based on the research results, transport capital is associated with increased production in the region. A convenient transportation network causes positive spillover effects at the national level affecting all of Vietnam. There is a significant difference in the degree of spillover among critical economic regions of Vietnam in the different periods. In terms of policy implications, the following conclusions can be drawn: Firstly, identifying the transport network as the lifeblood of the economy - society, the Vietnamese Government should prioritize the development of the inter-regional transport network instead of building an intra-regional transport network. The existence of spatial externalities emerging from the contribution of transport infrastructure to regional growth should be viewed from a hyper-regional perspective. Secondly, with the characteristics of the current Vietnamese transport network, the Government should pay special attention to the coordination of traffic construction between provinces to balance and harmonize geography, economic and regional development orientation, avoiding centralized investment methods. Therefore, the central Government needs to provide guidelines and constraints on local government decision-making regarding their investment models. Third, industrial policies related to the lagging sector are needed due to adverse spillover effects. The effects of industrial accumulation from transportation development lead to an increase in industrial activities from the central region and the Mekong Delta to the southern part, especially industries that use many technologies. Since there can be negative spillovers of transport infrastructure from migration factors, local governments in less developed areas should change their industrial policies to avoid allocating economic activities. Targeted region-specific industrial policies, such as favorable tax policies and lower interest rates on loans to invest in local technology and labor- intensive industries, are needed. 6. Possible future research To increase the significance of this study, the following opinions can be developed more comprehensively. First, it should be assessed whether transport infrastructure development is the leading cause of the "fever" observed in real estate prices in Vietnam. Second, it is necessary to consider the relationship between urban planning and the real estate market. These questions, however, extend beyond the scope of the present research and require enormous effort. They should therefore be considered in a follow-up study. References Aschauer, D. A. (1989). 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Journal

Real Estate Management and Valuationde Gruyter

Published: Jun 1, 2022

Keywords: Spillover; Transport infrastructure; Economic growth; econometrics; Vietnam; C22; H54; N1; 033

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