Overview
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.
Object and Scope of Research
Rural areas can be defined in various ways, and this research focuses on specific definitions related to rural transportation infrastructure, rural households, and household income To maintain consistency within the research context, only these particular definitions will be utilized.
Rural areas in Vietnam refer to regions outside urban centers, including towns and townships, and are governed by local administrative bodies, specifically the commune people's committee These areas primarily engage in agriculture, serving as the main livelihood for their residents.
Rural households engaged are households primarily in agricultural activities, including forestry, fishing, and nonagricultural activities in rural areas
Rural household income is the total income of a household, including income from agricultural and nonfarm activities
Rural transportation infrastructure plays a crucial role in linking national highways and provincial roads to villages, towns, farms, and production facilities, thereby supporting agricultural, forestry, and fishery activities This infrastructure is essential for promoting the economic, cultural, and social development of local communities and encompasses various elements such as roads, irrigation systems, and transportation hubs like bus stations.
Research subjects: Rural transportation infrastructure and rural household income
This study examines the effects of rural transport infrastructure in Lai Chau and Son La provinces from 2018 to 2020, coinciding with the implementation of the World Bank's "National Target Programs for New Rural Development and Sustainable Poverty Reduction Support Program" in Lai Chau during 2016-2020.
Research Methods
This study employs a quantitative method utilizing difference-in-differences estimation to assess the impact of rural transportation infrastructure on household income in Vietnam from 2018 to 2020 A comprehensive analysis of the differences in difference estimation is provided in Chapter 4.
The study has five chapters as follows:
Chapter 2: Review of the literature on the impact of rural transport infrastructure on rural household income in Vietnam
Chapter 3: Status of rural infrastructure and rural household income in Vietnam in the period 2010-2020
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
Chapter 5: Conclusion and Implications for Policy
Literature review on the impact of rural transportation infrastructure on
International Study
Numerous global studies examine how transportation infrastructure influences agricultural productivity and household income This article focuses on a selection of significant research that specifically evaluates the impact of transportation infrastructure on household income, highlighting studies that align closely with the objectives of this investigation.
Medeiros et al (2021) analyze 2010 household microdata from the Demographic Census to explore the relationship between infrastructure characteristics and household poverty in Brazil Their findings reveal that improvements in both the quantity and quality of transport infrastructure significantly reduce poverty levels The authors emphasize that enhanced transport quality and efficiency can boost productivity, particularly benefiting lower-income households by creating more opportunities Due to a lack of regional studies examining infrastructure heterogeneities, they calculated average infrastructure supply and quality from 2004 to 2010 and employed principal component analysis (PCA) to develop multidimensional infrastructure indexes However, the survey results may not fully capture the comprehensive impact of infrastructure on poverty.
A study by Setboonsarng (2008) demonstrated that the Agrarian Reform Community Project significantly improved household conditions, facilitating a transition from poverty to financial stability Enhanced rural infrastructure positively influenced the transportation of goods to market and agricultural input production, leading to reduced costs and increased income from farming activities The ADB project notably boosted agricultural household incomes, enabling greater asset ownership and improved access to utilities through enhanced transport services However, the reliance on trucks and other vehicles diminished manual transport opportunities, shifting labor from the non-farm sector to agriculture and resulting in decreased non-farm income Consequently, the poorest households faced greater challenges, losing employment and income opportunities, while the non-poor benefited from better access to equipment and resources The study utilized statistical methods and a binary response model, analyzing data from 2,290 households in the Philippines over a two-year period from 2001 to 2003 to evaluate the project's impact.
Inadequate infrastructure significantly limits farmers' options in agricultural production, employment, and distribution, ultimately affecting the consumption of agricultural products (Per Pinstrup-Andersen, 2007) In many developing countries, challenges such as poor transportation, inadequate storage facilities, and weak communication hinder market integration Investing in infrastructure positively influences capital market development and enhances income across both agricultural and non-agricultural sectors, leading to increased household income and a reduction in poverty levels.
Ali & Pernia (2003) highlight the crucial connection between infrastructure investments and improvements in household income, consumption, and poverty alleviation By enhancing agricultural and non-agricultural productivity, creating job opportunities, and increasing access to wage employment, rural infrastructure can significantly reduce poverty levels Improved road access allows households to better engage in production activities, thereby minimizing their vulnerability to economic shocks The impact of rural roads on poverty reduction is both direct and indirect, and this effect is amplified when combined with complementary investments like education and community facilities The authors emphasize that the effectiveness of infrastructure interventions is significantly influenced by their location, suggesting that targeted investments in strategic areas can accelerate poverty reduction efforts This perspective is echoed by Medeiros et al (2021).
Research conducted in Nepal highlights the critical role of roads in enhancing rural incomes in developing countries, with new road infrastructure leading to an average increase of $235 (28%) in total household income (Charlery et al., 2016) Improved road conditions lower transaction costs and reduce transit times, enabling households to achieve higher profits through more efficient transactions This efficiency allows families to diversify their labor into various paid jobs and facilitates travel to economically developed towns, further boosting income Notably, the poorest households benefit the most from road construction, underscoring its effectiveness as a pro-poor development strategy The study utilized a difference-in-difference approach with panel data from 177 observations between 2006 and 2012; however, limitations such as the impact of the 2008 economic recession, a small sample size drawn from only two villages, and potential inaccuracies in past income data collected via online sources were acknowledged Additionally, the long-term economic impacts may not be fully captured, as the treatment data was gathered four years post-construction.
A study in the United States revealed that road allocation positively influenced not only a state’s agricultural production but also the agricultural development of neighboring states (Tong et al., 2013) The research highlighted that infrastructure investment plays a crucial role in promoting agricultural growth, with broader effects observed across 44 states from 1981 to 2004 Utilizing a spatial Durbin panel data model, the study aimed to capture spatial dependencies in agricultural production and transportation infrastructure However, the author noted that the dataset may not be optimal, suggesting the need for separate spatial data on agricultural outputs and inputs Additionally, the spatial weight matrix employed in the analysis could provide a more intuitive understanding compared to the spatial contiguity matrices, potentially offering a better examination of the complexities within the U.S highway and transportation network.
Standard roads have significantly lowered agricultural production costs in China, while substandard roads only negatively impacted costs when they were high, but had a positive effect when costs were low (Wu et al., 2019) The authors utilized a public expenditure model and an agricultural production function with an unconditional quantile regression model, analyzing provincial data from 1995 to 2017 to assess the effects of irrigation infrastructure and road quality on production costs However, the study did not consider the impact of infrastructure on micro producers due to the absence of household data, nor did it explore how infrastructure affects agricultural profit, which is crucial for sustainable agricultural development.
An evaluation by Servén & Calderón (2004) demonstrates that expanding infrastructure positively influences economic growth and mitigates income inequality Their analysis, which assessed both the quantity and quality of infrastructure, reveals a significant correlation between these factors and income distribution The research indicates that infrastructure development enhances overall income levels, particularly benefiting the poor by providing them with greater access to productive opportunities Consequently, prioritizing infrastructure development is essential for effective poverty alleviation strategies This conclusion is supported by regressions using data from 121 countries spanning 1960 to 2000.
A study by Takada et al (2021) reveals that improvements to rural roads in Cambodia enhance household income by increasing the frequency of travel to local markets, although they do not significantly affect travel time or costs Respondents noted that reduced commuting time allows them to engage more in household activities, such as rice planting and childcare, while others have leveraged this time to start small businesses This increased travel frequency not only boosts employment opportunities but also contributes to higher overall household income However, the study's findings may lack external validity, as data were collected from only three rural areas through retrospective inquiries, and pre-rehabilitation data are unavailable The research employed a Difference-in-Differences (DID) approach.
Domestic Study
A study by Mu & Walle (2007) assessed the impact of rural road improvements on market and institutional development in rural Vietnam, highlighting that poor road conditions significantly hinder local development Improved roads enhance household incomes and create job opportunities for unskilled workers; however, the benefits primarily favor wealthier households reliant on service industries, while agricultural families often experience negative effects The project has led to a shift from agriculture to non-agricultural activities, particularly in the service sector Additionally, better roads facilitate freight services and increase motorcycle ownership among households Poorer communes benefit more through market development and commercialization, especially in mountainous areas with high ethnic minority populations and illiteracy rates, where road improvements positively impact job opportunities and access to goods The study utilized a combination of difference-in-difference (DID) and propensity score methods (PSM) with data from 3,000 households across 200 communes to estimate the average impacts of the road project, effectively controlling for changes in road placement and outcomes over time.
Nguyen, V.C (2010) conducted a fixed effect regression analysis using the VHLSS data from 2004 to 2006, revealing that rural roads positively influence household income Households with access to good roads tend to have longer working hours and increased employment opportunities compared to those without However, the presence of rural roads does not significantly impact the proportion of nonfarm income within total household income The study assessed poverty reduction through household consumption indicators, concluding that rural road policies are not effective in alleviating poverty While rural roads enhance per capita income, their effect on per capita expenditure is considerably lower.
It implied that rural roads have positive effects on household investments and savings
Enhancements in infrastructure have significantly improved citizens' access to public services and markets However, a limitation of the study was the absence of data indicating the distance between villages and communes The author posits that the effectiveness of rural roads may vary based on how close a village is to its commune road.
Research indicates that improved road transportation has provided families with greater access to non-agricultural employment opportunities, facilitating a shift from agriculture to other sectors (Nguyen et al., 2017) Infrastructure investments have led to decreased transit times and costs, while also reducing annual cropland size by approximately 31% Despite the reduction in land size, the number of households cultivating rice has increased, as alternative tree cultivation offers better income benefits Villages with road projects exhibit a higher wealth index due to improved access to essential services like electricity and water However, the study faced limitations due to insufficient data, preventing an analysis of the project's effects on formal or informal employment Utilizing a difference-in-difference estimator and a two-part model, the authors assessed the impact of the Irish Aid project on the livelihoods of households in Vietnam's poorest and most remote regions.
Mu & van de Walle (2011) assessed the Vietnam Rural Transport Project I (RTPI) and its effects on rural market development using a combined difference-in-difference (DID) and propensity score matching approach The findings revealed significant average impacts on local market development, allowing households to transition from agriculture to non-farm activities Specifically, there was a notable 2% decrease in households relying on agriculture as their primary income source and a 1.7% increase in those dependent on the service sector, indicating a shift towards alternative livelihoods, particularly in poorer villages The study highlighted that poor communes faced challenges such as distance to central markets, low population density, and high adult illiteracy, which diminished the project's effectiveness However, the research was limited by the non-random location of the project and ambiguous interpretations regarding road length.
Access to inadequate infrastructure restricts labor market opportunities, escalates production costs, and reduces goods volume (ADB, 2019) Enhancing transport accessibility can significantly boost women's productivity and advance gender equality by allowing equal access (UN Women, 2020) Improved transport reduces the time spent on fetching water, fuel, and firewood, enabling women to allocate that time to educational pursuits and non-farm employment Infrastructure development has been linked to job creation and wage growth without compromising competitiveness (World Bank, 2020) Additionally, smaller public investments in irrigation and infrastructure yield lower profits per hectare for impoverished households Road projects have been shown to generate more job opportunities and enhance income levels (Yoshino & Truong Thi Hoa, 2020).
Research by Ut et al (2000) in eight Vietnamese villages revealed that infrastructure significantly influences rural poverty and household income, with a notable positive effect on income distribution The development of infrastructure created more non-farm job opportunities for low-income households, leading to an increase in total income, especially in less developed villages Additionally, Hoang (2012) demonstrated that infrastructure development enhances non-farm activities by fostering small and medium enterprises and traditional craft villages, while also reducing transit time for employment and businesses.
The World Bank's Second Northern Mountains Poverty Reduction Project was implemented in six of the poorest mountain provinces in Vietnam in the period 2010-
In 2018, a project focused on infrastructure investment aimed to enhance access to basic services and empower communities, resulting in a 16% income increase for 192,000 households (World Bank, 2019) By improving productive infrastructure, agricultural productivity and market connections were strengthened, leading to better incomes and elevated living standards Key benefits of this initiative included cost savings, reduced travel time, enhanced access to markets and facilities, and overall improvements in household living conditions The study utilized difference-in-difference estimations for evaluation.
Through the overview of the research, the impact of infrastructure development investment on household income in rural areas is as follows
Figure 2.1 Impact of infrastructure development on total household income
Improved transport infrastructure significantly enhances agricultural productivity by reducing transit times and costs, while also expanding market access This advancement not only boosts the supply and local pricing of agricultural products but also creates non-farm employment opportunities, thereby increasing overall productivity Furthermore, investments in roads lead to higher household incomes and job creation, particularly benefiting poorer households Enhanced access to services and a rise in total household income further influence household expenditure patterns.
Agriculture productivity Non-farm opportunities Non-farm productivity
Supply and cost of agriculture product
Income and employment for poor households
Access market developed Transaction cost and transit time
Investment in rural infrastructure has a positive impact on total household income and promotes the diversification of income sources.
Research Question and Hypotheses
An analysis of previous studies reveals the significant influence of rural transportation infrastructure on household income in rural areas This research, set in Vietnam, aims to address the question: How does rural transportation infrastructure impact the income of households in these regions? The study proposes a hypothesis to explore this relationship further.
Hypotheses 1: The rural transport infrastructure has a positive impact on the income of Vietnamese rural households
Hypotheses 2: The rural transportation infrastructure has an impact on the diversification of rural household income sources.
Status of rural infrastructure and rural household income in Vietnam in
Rural Transport Infrastructure in Vietnam
On 10 April 10, 2022, Vietnam had 10,599 commune-level administrative units, including 656 communes belonging to provincial cities and towns, and 7,599 communes belonging to districts (GSO, 2022) In 2019, Vietnam has 159,082km of commune roads,
As of 2020, Vietnam has an extensive road network comprising 57,032 km of district roads and 24,866 km of national roads, with 99.16% of communes having access to asphalted or concreted roads By mid-2020, 99.67% of rural communes were connected by motorways to their District People's Committee headquarters Additionally, 96.36% of villages have concrete and paved roads, while 89.97% of alleys are well-maintained The National Target Program for New Rural Development has facilitated significant improvements in connectivity among villages and administrative centers Under decision 263/QD-TTg, the program for 2021-2025 aims to further enhance transport infrastructure, with a goal of achieving 98% of communes having solidified roads by 2025, ensuring year-round accessibility for vehicles.
In 2021, the Ministry of Transport allocated 10,486 billion VND for the maintenance and management of the national highway system, significantly enhancing the quality of transportation This investment plays a crucial role in facilitating the movement of goods across the country, promoting efficient transport and circulation.
Figure 3.1 Volume of freight carried by road, thousand tons, 2000-2020
The total volume of goods transported by road in Vietnam tends to increase over the years, increasing from 142,955 thousand tons (2020) to 1,213,940.6 thousand tons
(2020), 9 times higher than in 2000 The annual average growth rate for the period 2000-
2020 is approximately 11.8% Due to the impact of the COVID-19 epidemic in 2020-
In 2021, the volume of goods transported by road experienced a slight decline compared to 2019, with significant figures reported in 2020 The Red River Delta led the country with 444,018.1 thousand tons, followed closely by the North Central Coast and Central Coast at 372,556.3 thousand tons In contrast, the Mekong Delta showed lower transportation volumes, relying heavily on its extensive river network for goods transport, which accounted for 102,265.8 thousand tons, representing one-third of the nation's total waterway transport.
Rural Household Income in Vietnam
Vietnam ranks among the 15 most populous countries globally and is the sixth most populous in Asia, with a population of 98.51 million as of 2021 Of this total, 61.94 million people, or 62.9%, reside in rural areas In 2020, the country's population density was recorded at 295 people per square kilometer, with the highest density found in the Red River region.
Red River Delta Northern midlands and mountain areas
Northern Central area and Central coastal area Central Highlands
South East Mekong River Delta
Delta with 1078 people/km2, the lowest in the northern Midlands and mountains with
134 people/km2 (especially in the Lai Chau province with 52 people/km2, the lowest in the entire country)
In 2021, 49.75% of Vietnam's working-age population was employed, with 67.3% of those aged 15 and older working in rural areas (GSO 2021) However, the share of workers in rural regions has been declining, dropping from 75% in 2005 to 67.3% in 2020 This trend is largely attributed to urbanization and underemployment, contributing to the rising unemployment rate among working-age individuals in rural areas.
2.48% (2021), lower than in urban areas The unemployment rate in rural areas in 2021 is 2.96% The job market in rural areas does not meet the employment needs of workers
Figure 3.3 Employees 15 years and older work annually in urban and rural areas in the period 2005-2020, thousand people
In 2021, the agriculture, forestry, and fisheries sector employed 14.2 million workers, accounting for 28.9% of the total workforce in the country However, this proportion has been declining over the years as more individuals transition into the service sector.
Figure 3.4 GDP per capita in the period 2001-2020, USD
Between 2001 and 2020, Vietnam's GDP per capita showed a consistent upward trend, transitioning from a low-income country in 2001 to a low-middle-income country with a GDP per capita of $2,655.77 in 2020 The average annual growth rate for GDP per capita during 2001-2019 was 5.43% However, in 2020, this growth experienced a decline of 1.97% due to the adverse effects of the COVID-19 pandemic.
Figure 3.5 Monthly average income per capita at current prices by urban and rural areas for the period 2002-2020, thousand VND
The average monthly income per capita of the whole country tends to increase in the period 2002-2020, increasing from 356 thousand VND (2002) to 4249 thousand VND
Between 2002 and 2020, per capita income in both urban and rural areas generally increased However, in 2020, there was a significant decline of 7.17% in urban per capita income compared to 2019, primarily due to the COVID-19 pandemic, which led to social distancing and job losses Conversely, rural areas experienced a modest increase of 2.38% in monthly income per capita, indicating that the job market in these regions remained more stable despite pandemic-related challenges The disparity in monthly income per capita between urban and rural areas has been growing over time, highlighting a widening income gap.
Total Urban Rural lower 347 thousand VND than in urban areas By 2020, this difference reached 2100
000 VND, 6 times higher than in 2002 It implies that there is income inequality in 2 areas
From 2002 to 2020, the Southeast region of Vietnam boasted the highest monthly per capita income among the country's six regions, while the Northern Midlands and mountainous region recorded the lowest, with only 2,745 thousand VND in 2020 This disparity in income can be attributed to several factors, including a poorly developed economic market, low population density, limited educational opportunities, and a predominantly ethnic minority population in the Northern Midlands.
Figure 3.6 Monthly average income per capita at current prices by region for the period 2002-2020, thousand VND
Red River Delta Northern midlands and mountain areas
North Central area and Central coastal area Central Highlands
South East Mekong River Delta
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
Figure 3.7 illustrates the positive correlation between the volume of freight transported by road and the monthly average income per capita in Vietnam for 2020, indicating that improved transportation can positively influence income levels.
Northern midlands and mountain areas
North Central area and Central coastal area Central Highlands
In come p er cap ita (t h o u san d VN D)
Volume of freight carried by the road (thousand ton)
Determining the impact of rural transportation infrastructure on rural
Model and Data Set
The difference-in-difference estimation method is widely recognized for evaluating the effects of infrastructure investments on rural household income (Charlery et al., 2016; Nguyen et al., 2017; Mu & Van de Walle, 2011; Mu & Walle, 2007; World Bank, 2019) This study employs this method, focusing on the impact of rural transportation infrastructure by using the distance from households to various road types and their quality as proxies Previous literature indicates that these factors significantly influence rural infrastructure (Medeiros et al., 2021; Mu & Van de Walle, 2011; Nguyen, V C., 2010; Servén & Calderón, 2004) The research utilizes secondary data from a survey conducted by the Center for Agricultural Policy, which analyzed the World Bank’s ‘National Target Programs for New Rural Development and Sustainable Poverty Reduction Support Program for 2016-2020’ This project aimed to enhance rural livelihoods through investments in transportation, irrigation, and public services across 18 provinces, particularly in minority and mountainous regions The study uses baseline data from 2018 and follow-up data from 2020, collected during the survey period from 2018 to 2021.
Based on characteristic data, in the study, the distance from household to roads, the market, coach stop location to represent the investment in transportation infrastructure
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
The difference-in-difference estimator is a widely used method for estimating causal effects, particularly in evaluating the impacts of policy interventions that affect different groups at different times According to Lechner (2010), this approach allows researchers to assess the effects of specific policy changes that do not uniformly influence all individuals.
Yi = α + βTi + γti + δ (Ti* ti) + εi α: constant β: treatment group specific effect (to account for average permanent differences between treatment and control) γ: time δ: effect of treatment
This study utilizes a difference-in-difference estimator, building on the methodologies established by Charlery et al (2016) and Nguyen et al (2017) The model can be expressed as follows: lnYj,t = β0 + Tt β1 + Treat β2 + Tt * Treat β3 + Road1,j β4 + Road2,j β5 + nonfarmj β6 + eduj β7 This formulation incorporates key variables such as treatment effects, road infrastructure, non-farm employment, and education, allowing for a comprehensive analysis of the research hypothesis.
+agejβ8 + genderjβ9+ εj,t (1) 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 (1)
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)
Yj,t is an indicator of householdincome per capita in year t (million VND)
In this study, the dependent variable lnY is utilized to represent the outcome variable in natural logarithms, enhancing the analysis of medium to large data sets This transformation aims to reduce bias, normalize the data, and minimize variance and standard deviations, ultimately leading to more reliable estimation results The primary outcome variables examined are total household income and agricultural income.
The household nonfarm income per capita, measured in million VND, is represented by Inc_nonfarm_percapj Due to the presence of numerous zero values in the non-farm income data, the dependent variable in equation (3) is not expressed in logarithmic form.
In the dataset, Road1,j is a dummy variable indicating whether a household can access the nearest normal quality road, with a value of 1 for access and 0 for no access Notably, 35.4% of households, totaling 40, lack access to this road Furthermore, 56% of households, or 64 in total, are located less than 10 meters from a normal road, while only 8% of households are situated at distances ranging from 23 meters to 3 kilometers.
Road2,j is the distance between the house and the nearest good quality road (automobiles can move on the good quality road) (km);
Marketj is the distance between household and market (km);
Coach stopj is the distance between the home and the coach stop location (km);
Treat are dummy variables that are equal to 1 for the treatment group and 0 for the control group;
Tt is the dummy year that equals 1 for the year 2020, and 0 for the year 2018;
Nonfarmj is the dummy variables for household nonfarm income equal to 1 for household nonfarm income and 0 for the household not nonfarm income;
Poorj are dummy variables that are equal to 1 for the poor and near poor of the household and 0 for the nonpoor household;
Genderj is the gender of the head of household;
Agej is the age of the head of household
Eduj is Head of household education level; (0 for unlettered, 1 for elementary, 2 for junior high school, 3 for high school, 4 for above); εi,j,t is unobserved variables
The coefficient β3 for the interaction variable is the difference in difference (DID) estimate of the effect of the project
The parallel assumption is that without a program, the tendency to change is the same for the treatment group and the control group
To analyze the actual deep situation, I chose two provinces (Lai Chau Province and Son
Lai Chau and Son La are neighboring provinces in Vietnam's Northern Midlands and Mountainous region, characterized by a low population density primarily composed of ethnic minorities The per capita monthly income in this area is the lowest in the country, with Lai Chau being the least economically developed province in the region Both provinces share similar geographical and topographical features, including mountainous terrain, limited water resources, and a tropical monsoon climate with cold winters Additionally, neither province has railway infrastructure, which further hinders economic development, particularly in agriculture These factors underscore the need for research into Lai Chau's economic potential and investment opportunities.
Figure 4.2 Graphical explanation of the DID estimation
Source: The summarize of author (2022)
The World Bank project in Lai Chau Province serves as the targeted province, while Son La Province acts as the control province The study involves a sample of 113 households, with 55 from Lai Chau and 58 from Son La, highlighting a limitation due to the small sample size This limited number of observations may lead to inaccurate and skewed regression results Additionally, the assumption of parallel trends may be uncertain, as investments in transportation infrastructure in Son La from 2018 to 2020 could have been influenced by the National Target Program for the New Rural Development Policy.
Control province (Son La province)
Source: Calculation of CAP survey data in the period 2018-2021
Year 2020 Treatment province (Lai Chau province)
Control province (Son La province)
Source: Calculation of CAP survey data in the period 2018-2021
In 2018 and 2020, the mean total income per capita and agriculture income per capita in Lai Chau province (treatment province) surpassed that of Son La province (control province) While total income per capita increased in both provinces by 2020, agriculture income per capita in Lai Chau decreased from 16.93 million dong in 2018 to 16.5 million dong Additionally, the growth rates for total and agriculture income per capita in Lai Chau were lower compared to Son La Nonfarm income and its growth rate also lagged behind in Lai Chau However, significant infrastructure investments in Lai Chau have improved transportation, leading to a notable reduction in the distance from homes to quality roads and markets.
Variables Obs Mean Std Dev Min Max
Coach_stop 226 9.631877 13.72062 0 60 gender 226 1.070796 2570539 1 2 edu 226 1.066372 1.166199 0 4 poor 226 0.5 5011099 0 1 age 226 43.66372 12.09526 24 89 nonfarm 226 0.3584071 480597 0 1 t 226 0.5 0.50111 0 1 treat 226 0.486726 0.500933 0 1
Source: Calculation of CAP survey data in the period 2018-2021
Estimation Results
The study employs ordinary least squares (OLS) regression to analyze household income, utilizing first and second regressions with cluster-robust standard errors for the difference-in-differences (DID) estimation Given that the characteristics data and the DID estimation involve variables that remain constant over time, OLS regression is deemed appropriate for this analysis.
In the third regression analysis, a dummy variable serves as the dependent variable, and Logit regression is employed To ensure the validity of the regressions, they are subjected to the Ramsey test, model specification, and multicollinearity assessments The findings indicate that the regressions are correctly specified, with detailed results of these tests provided in the appendix.
The table in this section showcases regression analyses, with comprehensive results available in the Appendix Specifically, Table 4.4 highlights the influence of investments in rural transportation infrastructure on household income.
Table 4.4 Impact of transportation investment on household income
Variables Log(Total income per capita)
Log(Agricultural income per capita)
Note: *** Significant at 0.01, ** significant at 0.05, * significant at 0.1
Number in (…) is Robust Standard Error
Source: Calculation of CAP survey data in the period 2018-2021
In Table 4.4, we examine the impact of the project on the household income per capita
The household income in the treatment province is 19% higher than that in the control province, with agricultural income surpassing by 28.3% Although the project appears to negatively impact total household income per capita and agricultural income per capita, these effects are not statistically significant Challenges related to road construction may hinder travel and work, affecting household income Additionally, the COVID-19 pandemic has influenced the overall economy, potentially skewing household income data for 2020 and not fully reflecting the project's impact Nevertheless, households in the treatment province still enjoy a higher income per capita compared to those in the control group.
Table 4.5 highlights the significant impact of road quality on household incomes, revealing that increased distance from good roads leads to a decline in both total and agricultural income due to higher transportation costs and challenges in transporting goods This situation ultimately results in lower agricultural product prices, negatively affecting overall household earnings (Dethier & Effenberger, 2012; Servén & Calderón, M., 2004; Setboonsarng, 2008; Charlery et al., 2016) Conversely, households with access to normal roads, despite their limitations for vehicle travel, experience a modest increase in agricultural income, as these roads provide a convenient space for drying freshly harvested products, thus supporting agricultural production.
Figure 4.3 Drying of freshly harvested agricultural products on the road in Nam
In Lai Chau province, total income per capita and agricultural income per capita surpass those of Son La province by 26% and 24.7%, respectively Households with nonfarm income report a total income per capita that is 15.8% higher than their nonfarm income However, their agricultural income per capita lags behind that of other households This wage disparity between the agricultural sector and other employment opportunities may lead individuals to allocate less time to agriculture in favor of paid work, thereby impacting agricultural income Additionally, the education level of the household head positively influences both total income and agricultural income per capita, with each level of education increasing total income by 3.5% and agricultural income by 3.2% Meanwhile, the age and gender of the household head do not significantly affect income outcomes.
Table 4.5 Impact of the distance from home to the nearest road on income per capita
Variables Log(Total income per capita)
Log(Agricultural income per capita) t 0.0288
Note: *** Significant at 0.01, ** significant at 0.05, * significant at 0.1
Number in (…) is Robust Standard Error
Source: Calculation from CAP survey data in the period 2018-2021
Table 4.6 analyzes how the distance from home to the market and the location of the coach stop affect household income per capita and agricultural income per capita An increase in distance to the market negatively impacts both total and farm income per capita, although the market variable itself is not statistically significant Similarly, for each additional kilometer from the stop location, household income per capita decreases by 0.2% Greater distances to markets raise transit costs, putting households at a disadvantage in transporting products and accessing development areas, while also limiting non-farm job opportunities (Takada et al 2021).
Table 4.6 The impact of distance from home to market and coach stop on household income per capita
Variables Log(Total income per capita)
Log(Agricultural income per capita) t 0.0269
Note: *** Significant at 0.01, ** significant at 0.05, * significant at 0.1
Number in (…) is Robust Standard Error
Source: Calculation from CAP survey data in the period 2018-2021
Table 4.7 Impact of transportation infrastructure on nonfarm income per capita
Variables Nonfarm income per capita t 0.0673
Note: *** Significant at 0.01, ** significant at 0.05, * significant at 0.1
Number in (…) is Robust Standard Error
Source: Calculation of CAP survey data in the period 2018-2021
Table 4.7 estimates the impacts on household nonfarm income per capita, revealing that Lai Chau province's non-farm income per capita is lower than that of Son La province by 2.9 According to Setboonsarng (2008), projects can reduce nonfarm income by diminishing manual transport job opportunities for the poor However, the introduction of new roads tends to lead to a slight increase in household non-farm income per capita, although this change is not statistically significant The findings suggest that improved access to good roads and bus stops correlates with increased nonfarm income, while normal roads may have the opposite effect, albeit insignificantly Notably, if a household is located more than 1 km from the market, their non-farm income per capita decreases by 24,000 dong Households can enhance their earnings by renting properties near good roads, markets, or bus stops, facilitating better access to transport and customers This proximity also enables household members to secure additional employment opportunities, positively affecting nonfarm income Therefore, enhancing transportation infrastructure to markets can significantly boost household nonfarm income.
The COVID pandemic and non-essential travel restrictions in Vietnam from 2020 to 2022 likely caused a downward bias in household income data, which may not accurately represent the true impact of the project.
In general, the results show evidence that the infrastructure project has a positive impact on household income and agricultural income in Lai Chau province in the period 2018-
Research conducted in 2020 aligns with findings from previous studies by Setboonsarng (2008), Pinstrup-Andersen (2007), Charlery et al (2016), Dethier & Effenberger (2012), Mu & Walle (2011), and Nguyen (2010), indicating that households involved in projects generally have a higher per capita income compared to those without such initiatives Specifically, treatment households in Lai Chau province have experienced an increase in non-farm income, although it remains lower than that of the control province Additionally, the proximity of households to higher-quality roads significantly influences their income levels, supporting the conclusions drawn by Medeiros et al (2021) and Mu.
Improving transportation infrastructure can enhance household nonfarm income, as supported by Takada et al (2021) and Mu & Walle (2007) However, it is noted that household agricultural income often decreases when family members engage in nonfarm employment.
7 CHAPTER 5: CONCLUSION AND POLICY IMPLICATIONS
The study reveals that investment in rural transportation infrastructure significantly enhances both total and agricultural income for Vietnamese rural households between 2018 and 2020 Improved access to markets fosters income diversification, while the proximity of households to various roads affects their income levels However, the study's limited sample size and uncertain data introduce potential bias in the regression results, highlighting a key limitation Future research could benefit from a more stable economic environment and a broader geographic scope across Vietnam Consequently, the findings are specific to the studied province and timeframe, limiting their generalizability to other contexts.
The study highlights the significant impact of rural roads on household income in Vietnam, emphasizing their role in enhancing per capita income from agriculture and nonfarm activities To boost economic development, it is essential to implement policies that prioritize investment in rural transportation infrastructure, particularly in mountainous regions.
The quality and quantity of rural roads significantly influence household income per capita in rural areas Enhancing these roads is essential, and both local governments and private sectors should prioritize investment to upgrade existing roads for vehicle access Improved transportation infrastructure fosters income diversification, making careful planning and investment critical Infrastructure planners must focus on creating effective systems that maximize economic benefits while preventing land speculation.
Education level plays a crucial role in determining household income and fostering sustainable economic development Simply enhancing rural roads is insufficient; it is essential to also invest in other infrastructures, including schools and markets, to support overall community growth.
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