Impacts of rural roads on household welfare in Vietnam: evidence from a replication study

Cuong Viet Nguyen (National Economics University, Hanoi, Vietnam)

Journal of Economics and Development

ISSN: 1859-0020

Article publication date: 2 October 2019

Issue publication date: 3 October 2019

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Abstract

Purpose

Recently, there has been a call for replication research to validate empirical findings, especially findings that are important for development policies. Thus, the purpose of this paper is to replicate the estimation results from Mu and van de Walle (2011).

Design/methodology/approach

The author used raw data sets provided by Mu Ren and Dominique van de Walle and the same methods of Mu and van de Walle (2011). In addition to the pure replication, the author conducted the two extensions: sensitivity analysis of covariates and bandwidth selection and analysis of the effect of the road project on additional outcome variables.

Findings

Overall, the author ables to replicate most estimates from Mu and van de Walle (2011). The author find a positive effect of rural roads on local market development. The impact estimates of the road project are not sensitive to the selection of the bandwidth in kernel propensity score (PS) matching. There are no significant effects of road projects on additional outcomes, including access to credit and migration.

Practical implications

The study confirms a positive effect of rural roads on local market development. Thus, the government can provide investment in rural roads to improve the local market and its welfare.

Originality/value

This study tried to replicate and verify an important study on the impact of the rural road in Vietnam.

Keywords

Citation

Nguyen, C.V. (2019), "Impacts of rural roads on household welfare in Vietnam: evidence from a replication study", Journal of Economics and Development, Vol. 21 No. 1, pp. 83-112. https://doi.org/10.1108/JED-06-2019-0002

Publisher

:

Emerald Publishing Limited

Copyright © 2019, Cuong Viet Nguyen

License

Published in Journal of Economics and Development. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode


1. Introduction

In recent years, there has been a remarkably increasing number of empirical socioeconomic studies. Empirical studies are important for not only researchers but also policy makers in designing socioeconomic policies. Most empirical studies rely on large-scale data sets and econometric methods to test research hypotheses. Findings from empirical studies depend heavily on the methodology selection and how data are analyzed. Even by using the same method and data sets, there can be different ways that researchers can define and select variables for model estimation, and as a result, these different ways can lead to different findings and policy recommendations. Thus, there is a call for replication research to validate empirical findings, especially important findings for development policies (Brown et al,, 2014). Replication research not only confirms the validity of replicated studies but also raises the importance of analyzing, documenting and keeping empirical data during the research.

In this study, I tried to replicate the study of Mu and van de Walle (2011, pp. 709-34)[1]. Mu and van de Walle (2011) aim to measure the effect of rural roads on local market development in Vietnam. They test a hypothesis called “transport-induced local-market development” using data from surveys of “Vietnam Rural Transport Project I” and double differences with propensity score-matching methods. They conclude that rural roads raise local market development. By using regressions, they also find that there is heterogeneity in the impact of rural roads. The impact of rural roads tends to be higher for poorer communes, since the poorer communes have low base levels of market development.

There are several reasons for selection of this study for replication. First, rural roads play a crucial role in the socioeconomic development of rural areas (World Bank, 1994; Gannon and Liu, 1997; Lipton and Ravallion, 1995; Jalan and Ravallion, 2001). Jalan and Ravallion (2001) point out that rural roads are a necessary element for fostering rural income growth and reducing poverty. Rural roads can increase household income, including both farm and nonfarm income. Rural roads increase agricultural productivity by reducing transportation costs, increasing access to advanced technology, increasing capital and enabling the employment of labor from outside local areas. In addition, rural roads can also increase nonfarm production and nonfarm employment opportunities for local people. Mu and van de Walle (2011) provide findings on the important role of rural roads in nonfarm employment and market development. Until the end of 2013, according to the Google Scholar citation system, this paper (together with the working paper version) has been cited in 125 studies. It is important to validate its estimates and results using the original data sets.

Second, there are a large number of arguments that local market development can increase household welfare. However, there is little if anything known about the effect of public investment in transport on local market development. Most empirical studies focus on the effect of rural roads on household income and find a positive effect of rural roads on nonfarm income, e.g., Balisacan et al. (2002), Fan et al. (2002), Corral and Reardon (2001), Escobal (2001) and Nguyen (2011)[2]. Thus, Mu and van de Walle (2011) provide important evidence on the effect of rural roads on local market development. As is known, market accessibility is an important channel through which rural roads can help local people to improve nonfarm activities, income and consumption and expenditure.

Third, Vietnam is a developing country with more than two-thirds of the population living in rural areas and 95 percent of the poor living in rural areas. An important poverty reduction program in Vietnam is to improve the infrastructure for rural areas, especially those with a high poverty rate and a higher proportion of ethnic minorities. State and international agencies work continuously to improve and maintain the infrastructure, including roads[3]. In Mu and van de Walle (2011), rural roads are found to be an important factor in local market development and the effect of rural roads is higher for the poor areas. This finding is very important for policy makers in designing poverty reduction programs in Vietnam.

Fourth, the findings from Mu and van de Walle (2011) can be used for other developing countries, especially for some Asian developing countries with similar economic structures as Vietnam, such as the Philippines, Indonesia, Laos and Cambodia. Rural roads can help local market development in the short run, as a result, enhancing nonfarm employment, increasing income and reducing poverty in the long run.

In this study, I first conduct a pure replication of the study of Mu and van de Walle (2011). Mu Ren and Dominique van de Walle provided us with the raw original data sets, which allow us to replicate their published estimates. The pure replication includes the following basic steps: Reconstruct all the variables used in the study; Recalculate descriptive statistics of all the variables using the raw data; Re-estimate the results in the original study using the original specifications.

Second, I also conducted the so-called statistical replication to examine the sensitivity of the impact estimates to different sets of covariates and bandwidth used in the propensity score (PS) matching. One of the key issues in the propensity score-matching method is to select covariates and bandwidth and there are no standard criteria for this selection. Different selections produce different comparison groups and as a result different estimates of the program impacts. Thus, it is important to investigate whether the main findings from an empirical study are robust to different model specifications.

Third, I will go beyond the outcomes that are considered in Mu and van de Walle (2011) (including market accessibility, nonfarm employment, and child education), and estimate the effect of the road project on additional outcome variables, including access to credit and migration[4]. These outcomes are important for the livelihood and nonfarm diversification of rural households, and can provide policy-relevant findings.

The report is structured into five sections. The second section describes the method and data in Mu and van de Walle (2011). The third section presents the pure replication results. The fourth section presents the results from statistical replication. Finally, the fifth section describes the conclusion.

2. Data and methods in Mu and van de Walle (2011)

Mu and van de Walle (2011) assess the impact of “the Vietnam Rural Transport Project I,” which implemented the rehabilitation of 5,000 km of rural roads in communes in 18 provinces in Vietnam. The project was implemented during 1997–2001. Data used in Mu and van de Walle (2011) were collected before and after the project. This data set is called the Survey of Impacts of Rural Roads in Vietnam (SIRRV). More specifically, a panel data of 3000 households in 200 communes were conducted in 1997, 1999, 2001 and 2003. In total, 15 households were sampled from each commune. There are 100 communes in the project areas, and 100 communes from the non-project areas. Mu and van de Walle (2011) use commune data sets in 1997 (the baseline survey), 2001, and 2003 (the mid-term and endline surveys) for impact evaluation.

The endogeneity bias in the impact evaluation of “the Vietnam Rural Transport Project I” can happen because the project placement is not random. Provinces were allowed to select communes for the projects and the road links to be rehabilitated. There are several criteria for the selection of communes and road links such as cost, population density, and share of the ethnic minority population. However, these criteria are not well documented in the project documents, and it is not clear how the selection process actually happened (Mu and van de Walle, 2011). For most large-scale projects in Vietnam, it is very difficult to conduct a randomization or well-defined regression discontinuity impact evaluation (Nguyen, 2013). To solve the problem of endogeneity, Mu and van de Walle (2011) used the difference-in-difference (DD) estimator. This method controls the difference in outcomes between the treatment and control groups caused by observed variables and the time-invariant difference caused by unobserved variables. In other words, it assumes that the difference in no-project outcomes between the treatment and control groups (once observed variables are controlled for) was the same before and after the project.

Mu and van de Walle (2011) combine the DD with PS matching to estimate the effect of the rural road project on communes’ market development. They estimate the average treatment effect on the treated group. According to their denotation, the estimator is expressed as follows:

(1) D D = N P D D i / N P ,
where:
(2) D D i = ( Y i1 P Y i0 P ) j W i j ( Y j1 N P Y j0 N P ) ,

where DDi is the estimate for the project commune i. P and NP denote the treatment (project commune) and control (non-project commune), respectively. Subscripts “1” and “0” denote the outcome after and before the project, respectively. W indicates weights applied to the comparison communes when they are matched with the treatment communes.

Mu and van de Walle (2011) use the kernel PS matching (Heckman et al., 1997) and propensity score-weighted difference-in-differences (Hirano and Imbens, 2002; Hirano et al., 2003) to estimate the impact. A logit regression is used to predict the propensity score. Control variables are commune characteristics in the base year 1997. The list of control variables is presented in Tables AIII and AIV. The list of outcome variables is presented in Table II in the next section.

After estimating the effect of the rural roads on the outcomes for each commune (i.e., DDi), Mu and van de Walle (2011) run regression of DDi on commune characteristic variables to examine whether the effect of rural roads varies across communes of different characteristics as follows:

(3) D D i = α + X i β + ε i ,
where DDi is the estimated impact on an outcome for commune i, and Xi is a vector of explanatory variables of commune i.

3. Replication results

In this section, I aim to conduct pure replication of the results from Mu and van de Walle (2011). The pure replication includes the three following basic steps: reconstruct all the variables used in the study; recalculate descriptive statistics of all the variables using the raw data; and re-estimate the results in the original study using the original specifications.

3.1 Raw data sets and do-files

As mentioned, Mu and van de Walle (2011) use commune data sets in 1997 (the baseline survey), 2001, and 2003 (the mid-term and endline surveys) for impact evaluation of the rural road project. The original authors (Mu and Van de Walle) are very generous to provide me with not only the raw original data sets but also their analysis do-files (they used Stata for analysis). These data sets and do-files are used for estimation for not only the study by Mu and van de Walle (2011) but also for the study by Van de Walle and Mu (2007). The authors mentioned that they sent all the data and do-files available in their current computers. However, since the analysis was conducted by the authors a very long time ago (before 2007), do-files that are used to estimate the results of Mu and van de Walle (2011) are not fully available. It means that I cannot simply rerun the do-files sent by Mu and van de Walle to replicate their results, since some do-files are missing.

Figure 1 summarizes the data sets and do-files provided by Ren Mu and Dominique van de Walle. The Shapes 1, 2, 3 and 4 mean that data or do-files are fully available, while the “pink” shapes mean that data or do-files are just partially available. Shape 7, i.e., “Do-files to create data for analysis,” is not available. Running “Do-files to estimate the impacts” (Shape 6) using “Data for impact estimation” (Shape 5) does not produce the results of Mu and van de Walle (2011), since some do-files as well as data variables are missing. I checked all the available do-files including those to create data sets and those to estimate the project impact, and find no problems.

3.2 Reconstruct all variables and recalculate descriptive statistics

In the next step, I use the raw data sets provided by the authors to create the outcome variables and the control variables that are used to estimate the project impact. Table I is replicated in Mu and van de Walle (2011). After checking the do-files, data, and questionnaires carefully, I still cannot produce the same estimates as Table I in Mu and van de Walle (2011). Table I in this study adds the column reporting the percentage difference in the outcome means between the replication and the original paper. Variables with 0 percent difference have the same values as the original papers. There are 12 variables that are the same. There are four variables that differ by more than 10 percent from those from the original papers. For the remaining seven variables, the difference in the mean is less than 10 percent.

Next, I estimated the outcome variables for the years 1997, 2001 and 2003. Table AI replicates the results of Table II in Mu and van de Walle (2011). The outcomes are estimated for communes within the common support of the predicted propensity scores. In Mu and van de Walle (2011), there are 94 project and 95 non-project communes on common support. In this study, I estimated the PS using the same model specification. However, the regression results are not the same (see the next section for detailed presentation). As a result, the predicted PS is not the same, and the common support is different from Mu and van de Walle (2011). There are 85 project and 83 non-project communes on common support. The mean outcomes of project and non-project communes cannot be the same as those in Mu and van de Walle (2011) due to different common supports. However, the difference in the replicated results and the original results is not large.

I found a variable of the predicted PS in the data sets sent by Mu and Van de Walle. By using this propensity score, I am able to define the common support as Mu and van de Walle (2011) (including 94 project and 95 non-project communes). Using this common support, I re-estimated the outcomes of project and non-project communes, and reported the results in Table AII. Now, there are five outcome variables (which are marked with a star *) which have the same value as the original paper.

There is a problem of the variable “Primary school completion (<15 years)” which has very high values in 1997 but low values in 2001 and 2003. My estimates of “Primary school completion (<15 years)” for 2001 and 2003 are close to the estimates in Mu and van de Walle (2011). However, my estimate for 1997 is substantially higher than that in Mu and van de Walle (2011). I checked the data set carefully, but cannot find the reason for this problem. A possible reason for the difference might be that the raw data sets that Mu and Van de Walle provided for me are not the same raw data sets used for Mu and van de Walle (2011). Data collectors sometimes clean and update cleaned data sets. As a result, different versions of data sets might exist.

3.3 Re-estimate the results in the original study using the original specifications

After constructing the variables and producing descriptive analysis, I estimate the impact of the rural road project on commune outcomes using the original specifications. The first step is to estimate the PS using logit regression. The logit estimation is presented in Van de Walle and Mu (2007, pp. 667–685). I am not able to produce the same logit result as Van de Walle and Mu (2007). The summary statistics of the explanatory variables (covariates) in the logit regression is presented in Table AIII. In Van de Walle and Mu (2007), the number of observations is 200. The number of observations in this logit regression is 198. There are missing values in some variables, and I do not know how these missing values are treated in Van de Walle and Mu (2007). In this replication study, I dropped two observations with missing values. It means that these dropped two communes are not used for impact estimation. In the logit regression (Table AIV), most explanatory variables have the same sign and close point estimates as the original paper of Van de Walle and Mu (2007). Since the logit regression results are different, the predicted propensity scores are also different from the original paper.

Figure A1 presents the predicted PS for the treatment (project communes) and control groups (non-project communes). There are 85 project and 83 non-project communes on common support. This is different from Mu and van de Walle (2011), in which there are 94 project and 95 non-project communes on common support.

Tables II and III present the impact estimation of the rural road project using the original specifications and methods (these estimates replicate Table III in Mu and van de Walle, 2011). In Stata, I used the command “psmatch2” like Mu and van de Walle, 2011. Mu and van de Walle (2011) used the default bandwidth which is 0.06 in the kernel PS matching. The original estimates in Mu and van de Walle (2011) are also reported in Tables II and III for comparison. The replicated estimates are not the same as the original paper, since the predicted PS as well as the common support are different. However, most of the impact estimates for 2003 have the same sign as the impact estimates in the original paper.

As mentioned, I found a variable of the predicted PS in the data sets sent by Mu and Van de Walle. I used this predicted PS variable to estimate the effect of the project on the five outcome variables that have the same value as the original paper. Table IV presents the results of this analysis. I cannot replicate the impact estimates for the year 2001. However, for the year 2003, I am able to replicate the same impact estimates as the original paper. It means that the difference between the replicated results and the original results lies in the construction of variables, not in the methodology.

An interesting analysis in Mu and van de Walle (2011) is to examine the determinants of heterogeneous impacts of the rural road project. More specifically, after estimating the effect of the rural roads on the outcomes for each commune, Mu and van de Walle (2011) run ordinary least-square (OLS) regressions of these specific impact estimates on commune characteristic variables to examine whether the effect of rural roads varies across communes of different characteristics. Overall, they find that there is some evidence on heterogeneity in the impact of rural roads. The impact of rural roads tends to be higher for the poorer communes, since the poorer communes have low base levels of market development.

In this study, I also run regressions of the predicted impact of the rural project on explanatory variables using commune-level data. The regression results are presented in Tables from AV to AX. None of our estimates are the same as Mu and van de Walle (2011), since their common supports are different, and some of the control variables are also different. However, most of the replicated estimates have the same sign as the point estimates in Mu and van de Walle (2011).

4. Statistical replication

After conducting pure replication, I conducted the so-called statistical replication. In the statistical replication, I conduct the two extensions: sensitivity analysis of covariates and bandwidth selection, and analysis of the effect of the road project on additional outcome variables.

4.1 Sensitivity analysis of covariates and bandwidth selection

Analysis methods

The main advantage of PS matching is that it does not rely on assumptions of functional forms of outcomes. However, the point estimates as well as the standard errors of the propensity score-matching estimators can be sensitive to the selection of control variables used in the logit (or probit) model to estimate the propensity score. The estimates might also be sensitive to the magnitude of the bandwidth in kernel matching. Thus, in the replication study, I also examine the sensitivity of the impact estimates to different bandwidths used in kernel matching.

The list of control variables (covariates) used in Mu and van de Walle (2011) is presented in Tables AIII and AIV. Variables that affect outcomes and program selection should be controlled in PS estimation. Obviously, variables which affect both the program participation and outcomes should be included in the PS model (e.g., Ravallion, 2001; Caliendo and Kopeinig, 2008). Bryson et al. (2002) argue that inclusion of irrelevant variables can increase the standard error of estimates. Zhao (2008) finds that overspecification of the model of the PS can bias impact estimates. However, using simulation, Nguyen (2013) shows that efficiency in the estimation of the average treatment effect on the treated group can be gained if all the variables in the outcome equation are included in the estimation of propensity scores.

A challenge in measuring the impact of “Vietnam Rural Transport Project I” is that the project selection is not fully observed. Although there are several criteria for the selection of communes and road links such as cost, population density, and share of the ethnic minority population, the actual selection of the project communes is not clear and documented (Mu and van de Walle, 2011). In addition, there are a number of outcomes, and different outcomes can be affected by different explanatory variables. Thus, Mu and van de Walle (2011) control variables that are important for program selection and other variables that can affect the program selection and outcomes. The control variables are listed in Tables AIII and AIV.

In the replication study, I can examine the sensitivity of the program impact to two additional sets of control variables as follows:

  1. Add pretreatment outcomes to the logit regression of the program selection. The pretreatment outcome can be used as control in the regression of the PS to reduce the difference in outcomes between the treatment and control groups in the baseline (Dehejia and Wahba, 1998; Smith and Todd, 2005).

  2. Limit the covariates to those that are statistically significant in the logit regression of the program selection. Several control variables are statistically significant in Mu and van de Walle (2011). They can be dropped, since these variables might affect the quality of matching of the key variables (Bryson et al., 2002; Zhao, 2008).

I can also examine the sensitivity of the program impact estimates to the selection of bandwidth. Mu and van de Walle (2011) used the default bandwidth which is 0.06 in the kernel matching. In the study, I can use other bandwidths, e.g., 0.01, 0.03 and 0.09 for robust analysis. In addition, I can use a cross-validation method − a widely used selection method of bandwidth in PS matching (Frolich, 2004; Galdo et al., 2010). This method selects the bandwidth as follows:

(4) h C V = arg min h ( 1 n 0 j = 1 n 0 ( y 0 j m ˆ j ( p j , h ) ) 2 ) ,
where n0 is the number of control units, y0j is the outcome of the control unit j, and m ˆ j ( p j , h ) is the estimated conditional mean for the control unit j at the PS pj using all the control units within the bandwidth but with the exception of unit j. The bandwidth that has the smallest value of hCV will be selected.

Empirical results

Table V presents the impact estimates of the road project using difference-in-differences with the PS kernel-matching method. It replicates the PS kernel-matched DD estimates in Tables II and III. The difference between the estimation method in Table V and the estimation method in Tables II and III is that the propensity scores used in Table V are estimated by using not only the covariates but also the baseline outcome variable (variable in 1997). For each outcome, the corresponding baseline variable is added to the logit regression. Thus, the logit model differs for different outcomes. Although the results are not the same as those of Mu and van de Walle (2011), most impact estimates have the same sign as those of Mu and van de Walle (2011). Similar to Mu and van de Walle (2011), the effect of the project on the market and the percentage of farming households is statistically significant.

In Table VI, the propensity scores are estimated using the logit regressions in which only covariates significant at the 10% level are kept. The results show that most estimates have the same sign as those in Mu and van de Walle (2011). However, the effect is not significant for almost all outcomes.

As mentioned, Mu and van de Walle (2011) used the default bandwidth, which is 0.06 in the kernel matching. There are no standard criteria to select the bandwidth. Using a large bandwidth results in a larger number of matched controls. This reduces the standard error, but increases potential bias, since I can match a participant with a very different nonparticipant. On the contrary, using a small bandwidth can reduce the bias but increase the standard error of the impact estimates. I can vary the bandwidth to examine whether the impact estimates are sensitive to different bandwidths. In Tables from AXI to AXIII, I used other bandwidths, e.g., 0.01, 0.03 and 0.09 for robust analysis. Three bandwidth schemes produce the same sign of the effect estimates of the project in 2003. However, the significance is slightly different between the three bandwidth schemes. For example, the effect of the road project on market availability is not significant, using a bandwidth of 0.01, while the effect of the road project on market availability is significant, using bandwidths of 0.03 and 0.09.

Finally, Table VII presents the estimates when an optimal bandwidth is used (Frolich, 2004; Galdo et al., 2010). For each outcome, a bandwidth is estimated so that the difference in baseline outcomes between the treatment and control communes is minimized. The results are quite similar to those estimated using other bandwidths.

4.2 Additional outcome variables

Mu and van de Walle (2011) focus on the effect of the road project on market development, employment and education. Roads are very important for the rural economy. Thus, in this study, I examine the effect of the road project on additional outcome variables, by using the same method and data used by Mu and van de Walle (2011). The surveys contain very detailed data on commune living standards. The outcome variables are selected based on the data availability. The road project is also expected to have a significant effect on these outcomes.

The first outcome is the access to credit. The distance to banks and a credit institution is negatively correlated with the access to credit in Vietnam (Nguyen, 2008). Rural roads are expected to reduce the distance to lenders and increase the credit access of households. The second outcome is migration, out-migration and in-migration. Roads can reduce the cost of mobility and increase migration (Lucas, 2001).

Tables VIII and IX present the impact estimates of the project on credit and migration, using the same three methods as those by Mu and van de Walle (2011). Overall, there are no significant effects of the road project on credit access and migration of households in project communes.

5. Conclusions

Rural roads are one of the key factors for rural development. Mu and van de Walle (2011) is an influential study, which finds a positive effect of rural roads on local market development in Vietnam. In this study, I tried to replicate the estimates of Mu and van de Walle (2011) using the raw data sets provided by the authors. I am able to produce quite similar results as those of the original paper. However, several estimates are not the same as those from the original paper. A possible reason for the difference is that the raw data sets that Mu and Van de Walle provided for me might not be the same raw data sets used for Mu and van de Walle (2011). Data collectors sometimes clean and update cleaned data sets. As a result, different versions of data sets might exist.

In addition to the pure replication, I conducted a so-called statistical replication. In the statistical replication, I conducted two extensions: Sensitivity analysis of covariates and bandwidth selection, and analysis of the effect of the road project on additional outcome variables. I find that the impact estimates of the road project are not sensitive to the selection of the bandwidth in kernel PS matching. However, using only covariates that are significant in the logit regression tends to reduce the statistical significance of the impact estimates. Finally, there are no significant effects of the road project on credit access and migration of households in project communes.

Overall, I find similar findings on the impact of the rural road project as those of Mu and van de Walle (2011). It indicates that there is a positive effect of rural roads on local market development. Thus, the government can provide investment in rural roads to improve the local market and its welfare.

Figures

Data sets and do-files

Figure 1

Data sets and do-files

Predicted propensity score

Figure A1

Predicted propensity score

Mean baseline characteristics and outcome variables for communes classified by median household per capita consumption (log)

Commune characteristics Variable type Below median (1) Above median (2) Difference Difference between these and the original paper (%)
Typology: mountain Binary 0.70 0.33 0.37*** 0
Distance to the closest central market (km) Continuous 16.09 10.46 5.63*** <10
Share of households owning motorcycles Continuous 6.32 10.00 −3.68*** <10
Population density Continuous 2.14 5.20 −3.06*** <10
Ethnic minority share Continuous 0.67 0.20 0.48*** 0
Adult illiteracy rate Continuous 0.11 0.03 0.07*** >10
Flood and storm prevalence Binary 0.60 0.64 −0.04 0
Credit availability Binary 0.27 0.30 −0.03 >10
North provinces Binary 0.54 0.66 −0.12* 0
Transportation accessibility Binary 0.23 0.31 −0.09*** 0
Road density Continuous 0.01 0.02 −0.01*** 0
Market availability Binary 0.31 0.66 −0.35*** <10
Market frequency Discrete 0.72 1.43 −0.71*** 0
Shop Binary 0.39 0.58 −0.19*** 0
Bicycle repair shop Binary 0.54 0.88 −0.34*** <10
Pharmacy Binary 0.34 0.75 −0.41*** 0
Restaurant Binary 0.23 0.44 −0.21*** 0
Women’s hair dressing/Men’s barber Binary 0.33 0.74 −0.41*** >10
Men and women’s tailoring Binary 0.56 0.92 −0.36*** <10
% farm households Continuous 93.64 86.34 7.29*** 0
% trade households Continuous 1.17 1.70 −0.53* 0
% service sector households Continuous 0.69 1.08 −0.39 <10
Primary school completion (less than 15 years) Continuous 53.78 68.89 −15.11*** >10
Secondary school enrollment rate Continuous 76.81 94.13 −17.32*** <10

Notes: Table I replicates the estimates of Table I in Mu and van de Walle (2011). The definition of variables and sample is the same as the Mu and van de Walle (2011). *,**,***Significant at 10, 5 and 1 percent levels, respectively

Source: Author’s estimation

Impacts of road rehabilitation/building for year 2001

Simple DD PS kernel matched DD PS weighted DD
Outcomes DD t-ratio Original estimates in Mu and van de Walle (2011) PS kernel matched DD t-ratio Original estimates in Mu and van de Walle (2011) PS weighted DD t-ratio Original estimates in Mu and van de Walle (2011)
Market
Market availability −0.01 −0.16 0.00 0.03 0.91 0.03 0.03 0.85 0.04
Market frequency 0.07 0.49 0.01 0.14 1.57 0.08 0.15 1.44 0.10
Shop −0.05 −0.57 −0.02 −0.13 −1.23 0.01 −0.15 −1.35 0.08
Bicycle repair shop −0.09 −1.60 −0.08* −0.06 −1.26 −0.06 −0.06 −1.04 −0.04
Pharmacy 0.09 1.44 0.08 0.05 0.70 0.04 0.04 0.57 −0.06
Restaurant 0.11* 1.89 −0.03 0.13* 1.69 −0.01 0.14* 1.94 −0.01
Women’s hair dressing/Men’s barber 0.02 0.33 −0.04 0.06 0.73 −0.07 0.06 1.05 −0.07
Men and women’s tailoring 0.01 0.19 0.12 0.00 0.04 0.11 0.00 0.08 0.10
Employment: % households whose main occupation is
% farm households −0.77 −0.47 0.04 −0.73 −0.45 0.05 −0.42 −0.29 0.03
% trade households 0.10 0.23 −0.05 −0.23 −0.34 0.03 −0.59 −0.68 0.03
% service sector households −0.65 −1.61 −0.06 −0.18 −0.40 −1.54 0.07 0.14 −1.03
School enrollments
Primary school completion (<15 years) −3.71 −0.65 0.00 1.82 0.27 0.15** 4.08 0.65 0.25**
Secondary school enrollment rate −0.52 −0.16 0.06 1.03 0.33 0.10 0.56 0.19 0.25

Notes: Table II replicates the estimates of Table III in Mu and van de Walle (2011); the sample consists of the 85 project and 83 non-project communes on common support as determined by propensity score matching. t-Ratio of kernel matching is obtained from bootstrapping (100 repetitions); standard errors of weighted DD estimations are robust to heteroskedasticity and serial correlation of communes within the same district. *,**Significant at 10 and 5 percent levels, respectively

Source: Author’s estimation

Impacts of road rehabilitation/building for year 2003

Simple DD PS kernel matched DD PS weighted DD
Outcomes DD t-ratio Original estimates in Mu and van de Walle (2011) PS kernel matched DD t-ratio Original estimates in Mu and van de Walle (2011) PS weighted DD t-ratio Original estimates in Mu and van de Walle (2011)
Market
Market availability 0.07 1.27 0.09* 0.08** 2.28 0.08* 0.08** 2.00 0.09**
Market frequency 0.16 1.02 0.19 0.18 1.60 0.23* 0.18 1.28 0.25**
Shop −0.05 −0.71 0.03 −0.14 −1.52 0.08 −0.17* −1.70 0.14
Bicycle repair shop −0.05 −0.94 −0.04 −0.05 −0.73 0.02 −0.05 −0.92 0.03
Pharmacy 0.14* 1.93 0.14* 0.16* 1.74 0.12 0.14 1.54 0.16
Restaurant 0.08 0.83 0.05 0.04 0.47 0.01 0.04 0.36 0.05
Women’s hair dressing/Men’s barber 0.05 0.95 0.14* 0.08 1.04 0.18** 0.08 1.31 0.20**
Men and women’s tailoring 0.03 0.56 0.09 0.03 0.42 0.10 0.02 0.36 0.12*
Employment: % households whose main occupation is
% farm households −2.10 −1.35 −1.99 −2.49 −1.56 −2.04* −2.81** −2.11 −2.06**
% trade households 0.70 1.41 0.57 0.80 1.47 0.36 0.70 1.22 0.58
% service sector households 0.75** 2.40 1.01* 1.09** 2.16 1.68** 1.31* 2.04 1.72**
School enrollments
Primary school completion (<15 years) 2.52 0.37 0.04 10.13 1.45 0.17** 9.89 1.35 0.30**
Secondary school enrollment rate −0.92 −0.31 0.10** 0.58 0.20 0.05 0.35 0.13 0.07*

Notes: Table III replicates the estimates of Table III in Mu and van de Walle (2011); The sample consists of the 85 project and 83 non-project communes on common support as determined by propensity score matching. t-Ratio of kernel matching is obtained from bootstrapping (100 repetitions). Standard errors of weighted DD estimations are robust to heteroskedasticity and serial correlation of communes within the same district. *,**Significant at 10 and 5 percent levels, respectively

Source: Author’s estimation

Impacts of road rehabilitation/building on market access for years 2001 and 2003

Simple DD PS kernel matched DD PS weighted DD
Outcomes DD t-ratio Original estimates in Mu and van de Walle (2011) PS kernel matched DD t-ratio Original estimates in Mu and van de Walle (2011) PS weighted DD t-ratio Original estimates in Mu and van de Walle (2011)
Impacts in 2001
Market availability −0.00 −0.09 0.00 0.04* 1.90 0.03 0.04 1.06 0.04
Bicycle repair shop −0.08* −1.76 −0.08* 0.01 0.26 −0.06 −0.04 −0.76 −0.04
% farm households −0.28 −0.18 0.04 −1.02 −0.62 0.05 1.31 0.79 0.03
% trade households −0.06 −0.14 −0.05 0.18 0.16 0.03 −1.03 −0.94 0.03
% service sector households −0.68 −1.60 −0.06 0.84* 2.05 −1.54 0.10 0.26 −1.03
Impacts in 2003
Market availability 0.09* 1.83 0.09* 0.08* 1.85 0.08* 0.09** 2.19 0.09**
Bicycle repair shop −0.04 −0.89 −0.04 0.02 0.37 0.02 0.03 0.58 0.03
% farm households −1.99 −1.25 −1.99 −2.04* −1.67 −2.04* −2.06* −1.87 −2.06**
% trade households 0.57 1.26 0.57 0.36 0.71 0.36 0.58 1.35 0.58
% service sector households 1.01** 2.52 1.01* 1.68*** 2.43 1.68** 1.72*** 3.10 1.72**

Notes: Table IV replicates the estimates of Table III in Mu and van de Walle (2011); The sample consists of the 94 project and 95 non-project communes on common support as determined by the propensity score obtained from the original paper. t-Ratio of kernel matching is obtained from bootstrapping (100 repetitions) Standard errors of weighted DD estimations are robust to heteroskedasticity and serial correlation of communes within the same district. *,**Significant at 10 and 5 percent levels, respectively

Source: Author’s estimation

Estimated impact of the road project using PS kernel matched DD − baseline outcome variable is controlled in estimating propensity scores

2001 2003
Outcomes PS kernel matched DD t-ratio Original estimates in Mu and van de Walle (2011) PS kernel matched DD t-ratio Original estimates in Mu and van de Walle (2011)
Market availability 0.029 0.771 0.03 0.084** 2.260 0.08*
Market frequency 0.119 1.298 0.08 0.199* 1.803 0.23*
Shop −0.080 −0.618 0.01 −0.115 −0.905 0.08
Bicycle repair shop −0.012 −0.273 −0.06 0.020 0.438 0.02
Pharmacy 0.035 0.377 0.04 0.098 0.789 0.12
Restaurant 0.103 1.546 −0.01 0.003 0.029 0.01
Women’s hair dressing/ Men’s barber 0.071 1.038 −0.07 0.078 1.184 0.18**
Men and women’s tailoring 0.026 0.523 0.11 0.039 0.674 0.10
% farm households −0.263 −0.182 0.05 −3.293* −1.872 −2.04*
% trade households −1.575 −1.596 0.03 0.514 1.130 0.36
% service sector households 0.524 0.950 −1.54 2.273 2.562 1.68**
Primary school completion (<15 years) 9.670* 1.777 0.15** 12.483** 1.992 0.17**
Secondary school enrollment rate 0.594 0.115 0.10 1.245 0.276 0.05

Notes: The sample consists of project and non-project communes on common support as determined by propensity score matching. t-Ratio of kernel matching is obtained from bootstrapping (100 repetitions). The propensity scores are estimated using logit models, which include covariates as Table AII and also outcome variables. *,**Significant at 10 and 5 percent levels, respectively

Source: Author’s estimation

PS kernel matched DD − only covariates and baseline outcome variables, which are significant at the 10 percentlevel are controlled in estimating propensity scores

2001 2003
Outcomes PS kernel matched DD t-ratio Original estimates in Mu and van de Walle (2011) PS kernel matched DD t-ratio Original estimates in Mu and van de Walle (2011)
Market availability 0.000 0.004 0.03 0.064 1.198 0.08*
Market frequency 0.049 0.336 0.08 0.154 1.016 0.23*
Shop 0.001 0.014 0.01 −0.027 −0.316 0.08
Bicycle repair shop −0.036 −0.703 −0.06 −0.013 −0.241 0.02
Pharmacy 0.044 0.554 0.04 0.063 0.732 0.12
Restaurant 0.100* 1.679 −0.01 0.050 0.492 0.01
Women’s hair dressing/ Men’s barber 0.045 0.639 −0.07 0.038 0.514 0.18**
Men and women’s tailoring 0.040 0.790 0.11 0.022 0.361 0.10
% farm households 0.138 0.092 0.05 −1.349 −0.883 −2.04*
% trade households −0.409 −0.703 0.03 0.317 0.677 0.36
% service sector households −0.271 −0.736 −1.54 1.194** 1.976 1.68**
Primary school completion (<15 years) 2.530 0.411 0.15** 6.056 1.169 0.17**
Secondary school enrollment rate 1.610 0.458 0.10 2.680 0.869 0.05

Notes: The sample consists of project and non-project communes on common support as determined by propensity score matching. The propensity scores are estimated using logit models in Table AIII. t-Ratio of kernel matching is obtained from bootstrapping (100 repetitions). *,**Significant at 10 and 5 percent levels, respectively

Source: Author’s estimation

PS kernel matched DD − Optimal bandwidth

2001 2003
Outcomes PS kernel matched DD t-ratio Original estimates in Mu and van de Walle (2011) PS kernel matched DD t-ratio Original estimates in Mu and van de Walle (2011)
Market availability 0.026 0.692 0.03 0.081** 2.201 0.08*
Market frequency 0.116 1.269 0.08 0.194* 1.782 0.23*
Shop −0.058 −0.645 0.01 −0.083 −0.955 0.08
Bicycle repair shop −0.050 −0.726 −0.06 −0.025 −0.306 0.02
Pharmacy 0.068 1.126 0.04 0.108* 1.727 0.12
Restaurant 0.087 1.542 −0.01 0.058 0.725 0.01
Women’s hair dressing/Men’s barber 0.040 0.677 −0.07 0.048 0.828 0.18**
Men and women’s tailoring 0.016 0.324 0.11 0.020 0.380 0.10
% farm households −0.677 −0.440 0.05 −3.623 −1.935 −2.04*
% trade households −0.066 −0.168 0.03 0.436 0.979 0.36
% service sector households 0.593 0.926 −1.54 2.447** 2.505 1.68**
Primary school completion (<15 years) 4.230 0.805 0.15** 9.605 1.628 0.17**
Secondary school enrollment rate 2.480 0.614 0.10 1.632 0.488 0.05

Notes: The sample consists of 85 project and 83 non-project communes on common support as determined by propensity score matching. The propensity score is estimated by the logit model in Table AII. t-Ratio of kernel matching is obtained from bootstrapping (100 repetitions). *,**Significant at 10 and 5 percent levels, respectively

Source: Author’s estimation

Impact of the road project on credit and migration in 2001

Simple DD PS kernel matched DD PS weighted DD
Estimates t-ratio Estimates t-ratio Estimates t-ratio
Number of credit sources available in communes −0.050 −0.330 −0.090 −0.410 −0.148 −0.841
There is a branch of Agricultural Bank in commune 0.082 1.501 0.055 0.739 0.071 1.317
Number of households borrowing from a credit source 192.8** 1.997 139.1 1.098 95.05 0.676
% households in commune who borrowing from a credit source 8.171 1.367 6.992 1.109 5.393 0.723
Loan size per borrowing household (million VND) −0.722 −1.093 −0.455 −0.815 −0.426 −0.521
There are private lenders in commune −6.166 −0.671 1.685* 0.187 2.704 0.260
Percentage of people leaving commune temporarily 0.100 0.230 −0.096 −0.163 −0.191 −0.348
Percentage of men leaving commune temporarily −0.041 −0.062 −0.255 −0.298 −0.349 −0.411
Percentage of women leaving commune temporarily 0.210 0.857 0.032 0.094 −0.057 −0.201
Percentage of households having member permanently leaving 1.015 0.906 1.789 1.069 2.115 1.189
Percentage of people coming to commune temporarily 0.006 0.018 −0.218 −0.885 −0.368 −1.384
Percentage of households coming to commune permanently 0.005 1.349 0.004 1.160 0.003 0.961

Notes: The sample consists of 85 project and 83 non-project communes on common support as determined by propensity score matching. The propensity score is estimated by the logit model in Table AII. t-Ratio of kernel matching is obtained from bootstrapping (100 repetitions). *,**Significant at 10 and 5 percent levels, respectively

Source: Author’s estimation

Impact of the road project on credit and migration in 2003

Simple DD PS kernel matched DD PS weighted DD
Estimates t-ratio Estimates t-ratio Estimates t-ratio
Number of credit sources available in communes 0.230 1.495 0.196 0.712 0.109 0.487
There is a branch of Agricultural Bank in commune −0.036 −0.692 −0.013 −0.216 −0.001 −0.009
Number of households borrowing from a credit source 262.8* 1.909 236.5 1.590 192.4 1.125
% households in commune who borrowing from a credit source 10.400 1.613 9.307 1.267 7.416 0.887
Loan size per borrowing household (million VND) 41.243 1.010 0.975 0.876 41.167 1.009
There are private lenders in commune −9.639 −0.920 −1.566 −0.143 −3.774 −0.388
Percentage of people leaving commune temporarily −0.087 −0.218 −0.403 −0.818 −0.562 −1.265
Percentage of men leaving commune temporarily −0.337 −0.611 −0.693 −1.067 −0.895 −1.535
Percentage of women leaving commune temporarily 0.174 0.588 −0.111 −0.288 −0.219 −0.630
Percentage of households having member permanently leaving 1.461 1.445 2.011 1.285 2.233 1.263
Percentage of people coming to commune temporarily −0.437 −0.883 −0.989* −1.645 −1.156 −1.560
Percentage of households coming to commune permanently 0.002 1.060 0.001 1.208 0.001 0.815

Notes: The sample consists of 85 project and 83 non-project communes on common support as determined by propensity score matching. The propensity score is estimated by the logit model in Table AII. t-Ratio of kernel matching is obtained from bootstrapping (100 repetitions). *,**Significant at 10 and 5 percent levels, respectively

Source:f Author’s estimation

Outcome variable means using the same propensity score estimated from the replication study

1997 2001 2003
Variable Project Non-project Difference between these and the original paper (%) Project Non-project Difference between these and the original paper (%) Project Non-project Difference between these and the original paper (%)
Local market development
Market availabilitya 0.51 0.45 <10 0.57 0.52 <10 0.61 0.48 <10
Market frequency 1.09 0.98 <10 1.35 1.17 <10 1.39 1.11 <10
Shop 0.53 0.46 <10 0.76 0.75 <10 0.74 0.72 <10
Bicycle repair shopa 0.75 0.65 <10 0.80 0.78 <10 0.86 0.81 <10
Pharmacy 0.52 0.52 <10 0.68 0.59 <10 0.66 0.52 <10
Restaurant 0.32 0.35 <10 0.46 0.39 <10 0.49 0.45 <10
Women’s hair dressing/Men’s barber 0.54 0.52 >10 0.74 0.70 >10 0.76 0.69 >10
Men and women’s tailoring 0.76 0.71 >10 0.82 0.76 <10 0.84 0.75 <10
Employment: % households whose main occupation is
% farm householdsa 90.31 90.85 <10 90.18 91.50 <10 87.57 90.22 <10
% trade householdsa 1.18 1.34 <10 1.62 1.69 <10 3.13 2.59 <10
% service sector householdsa 0.97 0.52 <10 1.36 1.55 <10 2.80 1.61 <10
School enrollments (%)
Primary school completion (<15 years) 62.19 60.70 >10 29.77 31.98 >10 39.00 34.99 >10
Secondary school enrollment rate 86.53 84.30 >10 93.58 91.87 >10 94.53 93.21 >10

Notes: Table AI replicates the estimates of Table II in Mu and van de Walle (2011); the sample consists of the 94 project and 95 non-project communes on common support as determined by propensity score matching. Many outcome variables are dichotomous referring to whether the outcome is present in the commune. The exceptions are: market frequency which takes the values 0 for no market, 1 for once per week or less, 2 for more than once a week and 3 for permanent market; the percentage of households in various occupations refers to their main source of income; the primary completion rate is defined as the share of children aged 15 years and under who completed primary school; the secondary school enrollment rate is the share of children who graduated from primary school in the previous year who are enrolled in secondary school. aOutcomes have the same value as in Table II in Mu and van de Walle (2011)

Source: Author’s estimation

Outcome variable means using the same propensity score variable

1997 2001 2003
Variable Project Non-project Difference between these and the original paper (%) Project Non-project Difference between these and the original paper (%) Project Non-project Difference between these and the original paper (%)
Local market development
Market availability 0.51 0.44 0 0.57 0.51 0 0.62 0.46 0
Market frequency 1.07 1.00 <10 1.30 1.17 <10 1.38 1.08 <10
Shop 0.54 0.44 <10 0.79 0.73 <10 0.76 0.71 <10
Bicycle repair shop 0.76 0.65 0 0.80 0.78 0 0.87 0.81 0
Pharmacy 0.55 0.53 <10 0.70 0.62 <10 0.66 0.52 0
Restaurant 0.33 0.33 <10 0.48 0.39 <10 0.49 0.43 <10
Women’s hair dressing/Men’s barber 0.53 0.51 >10 0.74 0.69 >10 0.77 0.68 >10
Men and women’s tailoring 0.76 0.72 >10 0.82 0.75 <10 0.82 0.75 <10
Employment: % households whose main occupation is
% farm households 89.53 90.67 0 89.65 91.07 0 87.02 90.15 0
% trade households 1.45 1.41 0 1.73 1.75 0 3.17 2.56 0
% service sector households 1.12 0.54 0 1.42 1.51 0 3.20 1.60 0
School enrollments (%)
Primary school completion (<15 years) 62.93 60.20 >10 31.22 31.81 >10 38.55 34.85 >10
Secondary school enrollment rate 86.64 84.89 >10 93.20 92.14 >10 94.52 93.41 >10

Notes: Table AII replicates the estimates of Table II in Mu and van de Walle (2011). The sample consists of the 85 project and 83 non-project communes on common support as determined by propensity score matching. Many outcome variables are dichotomous referring to whether the outcome is present in the commune. The exceptions are: market frequency which takes the values 0 for no market, 1 for once per week or less, 2 for more than once a week and 3 for permanent market; the percentage of households in various occupations refers to their main source of income; the primary completion rate is defined as the share of children aged 15 years and under who completed primary school; the secondary school enrollment rate is the share of children who graduated from primary school in the previous year who are enrolled in secondary school

Source: Author’s estimation, Mu and van de Walle (2011)

Summary statistics of explanatory variables in Logit regression of commune participation in the project

Explanatory variables Obs. Mean SD Min. Max.
Terrain: coast
 Mountains 200 0.5150 0.5010 0 1
 Uplands 200 0.1800 0.3852 0 1
 Plains 200 0.2550 0.4370 0 1
Province: Tra Vinh
 Lao Cai 200 0.1500 0.3580 0 1
 Thai Nguyen 200 0.2000 0.4010 0 1
 Nghe An 200 0.2500 0.4341 0 1
 Binh Thuan 200 0.1250 0.3315 0 1
 Kon Tum 200 0.1250 0.3315 0 1
Population (log) 199 8.5394 0.7088 6.86 10.15
Population density (log) 199 0.6083 1.3208 −2.51 3.00
Minority population share 199 0.4338 0.3974 0 1
National road passes through commune 200 0.3700 0.4840 0 1
Railway passes through commune without stop 200 0.1350 0.3426 0 1
Waterway passes through commune 200 0.2200 0.4153 0 1
Distance to province center (km) (log) 200 48.823 37.627 2 160
Commune has a passenger transport service 200 0.6150 0.4878 0 1
Share of households engaged in non-agricultural activities 200 0.0506 0.1226 0 1.00
Share of population working in government 199 0.0027 0.0049 0 0.04
Share of population working in private enterprises 199 0.0028 0.0165 0 0.19
Share of population working in state enterprises 199 0.0006 0.0024 0 0.02
Share of crop land 198 0.3191 0.2715 0.003 0.87
Share of perennial crop land 198 0.0544 0.0800 0 0.39
Land rental market exists in commune 200 0.4300 0.4963 0 1
Number of production organizations 200 1.2450 2.2383 0 14
Commune has a radio broadcasting station 200 0.2000 0.4010 0 1
Commune has a market 200 0.4850 0.5010 0 1
Agricultural crop land adversely affected by natural disaster (1996) 200 0.6200 0.4866 0 1
Commune has an agricultural bank 200 0.1300 0.3371 0 1
Number of official credit sources 200 2.2950 1.2270 0 5
Enrollment rate for children age 6 to 15 200 85.435 19.237 0 100
Commune has a lower secondary school 200 0.7350 0.4424 0 1
Predicted consumption per capita (log) 200 7.6354 0.2766 6.91 8.14
Share of households owning motorcycles 200 8.1613 8.3419 0 49.70
Road density (commune and district level roads) 199 0.0178 0.0235 0 0.16
Share of earth and car impassable roads in total road km 200 0.3752 0.3032 0 1

Source: Author’s estimation

Logit regression of commune participation in the project

Explanatory variables Coeff. SE Same sign as Van de Walle, D. and Mu, R. (2007)
Terrain: Coast Reference
 Mountains −0.331 1.194 Yes
 Uplands 0.029 0.962 Yes
 Plains −0.834 1.047 Yes
Province: Tra Vinh Reference
 Lao Cai 0.762 1.244 Yes
 Thai Nguyen 0.699 1.162 Yes
 Nghe An 1.296 1.211 Yes
 Binh Thuan 1.226 1.079 Yes
 Kon Tum 3.007*** 1.046 Yes
Population (log) 0.814* 0.424 Yes
Population density (log) 0.536 0.411 Yes
Minority population share 2.608** 1.139 Yes
National road passes through commune −1.827*** 0.559 Yes
Railway passes through commune without stop 1.492* 0.772 Yes
Waterway passes through commune 0.343 0.551 Yes
Distance to province center (km) (log) −0.006 0.0097 Yes
Commune has a passenger transport service 0.396 0.426 No
Share of households engaged in non-agricultural activities 0.371 1.407 No
Share of population working in government −0.639* 0.365 Yes
Share of population working in private enterprises −0.265* 0.155 Yes
Share of population working in state enterprises 0.711 0.741 Yes
Share of crop land 1.145 2.187 Yes
Share of perennial crop land −1.899 3.552 No
Land rental market exists in commune 0.333 0.455 Yes
Number of production organizations 0.012 0.083 Yes
Commune has a radio broadcasting station −1.079** 0.452 Yes
Commune has a market 0.338 0.431 Yes
Agricultural crop land adversely affected by natural disaster (1996) 0.202 0.448 Yes
Commune has an agricultural bank 0.977** 0.431 No
Number of official credit sources −0.407*** 0.152 Yes
Enrollment rate for children age 6 to 15 −0.012 0.018 Yes
Commune has a lower secondary school 0.167 0.626 Yes
Predicted consumption per capita (log) 1.030 1.159 Yes
Share of households owning motorcycles 0.076** 0.036 No
Road density (commune and district level roads) −12.21 11.40 Yes
Share of earth and car impassable roads in total road km 1.102 0.712 Yes
Constant −15.96* 9.418 Yes
Observations 198
Pseudo R2 0.204

Source: Author’s estimation

Impact heterogeneity: market and market frequency

Market Market frequency
Explanatory variables Model 1 Model 2 Same sign as the original paper Model 1 Model 2 Same sign as the original paper
1997 value −0.236** (−3.07) −0.234** (−4.36) Yes −0.265** (−3.22) −0.283** (−3.86) Yes
Distance to central district 0.006 (1.57) 0.003 (0.87) Yes 0.008 (0.53) No
North province −0.011 (−0.16) Yes −0.208 (−1.07) −0.202 (−1.15) Yes
Typology: mountain 0.038 (0.27) Yes 0.229 (0.54) Yes
Flood and storm prevalence 0.123** (2.04) 0.133** (2.58) No 0.553** (2.90) 0.612** (3.74) No
Population density −0.098 (−0.09) No 0.72 (0.18) No
Ethnic minority share −0.082 (−0.55) Yes −0.131 (−0.30) Yes
Adult illiteracy rate 0.018 (0.060) Yes 0.049 (0.07) Yes
Share of households owning motorcycles 1.057** (2.10) 1.363** (2.90) Yes 2.143 (1.43) 2.210** (1.99) Yes
Credit availability 0.305* (1.74) 0.328 (1.60) Yes 1.018 (1.47) 0.974* (1.70) Yes
Length of road rehabilitated/100 −0.014 (−1.52) Yes −0.032 (−1.16) −0.017** (−2.19) Yes
Length squared/10,000 0.01 (0.50) Yes 0.019 (0.31) Yes
Month since project completion/100 0.044 (1.63) 0.018 (0.96) Yes 0.165** (2.34) 0.172** (2.72) Yes
Month squared/10,000 −0.045* (−1.71) −0.02 (−1.10) Yes −0.174** (−2.51) −0.183** (−2.92) No
Constant −0.976 (−1.52) −0.505 (−1.03) Yes −3.689** (−2.01) −3.792** (−2.51) Yes
R2 0.42 0.39 0.41 0.39

Notes: Table AV replicates the estimates of Table IV in Mu and van de Walle (2011). The dependent variables are the 85 estimated commune specific impacts for 2003. Standard errors are clustered at the district level of which there are 29. Market is a zero/one dummy for whether a market exists in the commune. Market frequency takes the value 0 for no market; 1 for once a week or less; 2 for more than once a week and 3 for permanent market. t-Statistics are given in parentheses. *,**Significant at 10 and 5 percent levels, respectively

Source: Author’s estimation

Impact heterogeneity: shop and bicycle repair shop

Shop Repair
Explanatory variables Model 1 Model 2 Same sign as the original paper Model 1 Model 2 Same sign as the original paper
1997 value −0.962** (−7.01) −0.969** (−8.03) Yes −0.738** (−6.27) −0.729** (−6.48) Yes
Distance to central district 0.004 (0.52) Yes −0.003 (−0.83) Yes
North province −0.084 (−0.67) Yes −0.012 (−0.18) Yes
Typology: mountain 0.033 (0.17) Yes −0.016 (−0.28) No
Flood and storm prevalence −0.264** (−2.37) −0.218** (−2.23) Yes 0.111 (1.54) 0.106* (1.68) Yes
Population density 2.100 (1.11) 1.381 (1.00) Yes 0.242 (0.29) Yes
Ethnic minority share 0.451** (2.12) 0.483** (3.22) Yes −0.047 (−0.37) Yes
Adult illiteracy rate −1.196** (−2.23) −1.207** (−2.48) Yes −0.477 (−1.16) −0.589 (−1.49) Yes
Share of households owning motorcycles −0.819 (−0.92) No 0.716* (1.72) 0.714* (1.80) Yes
Credit availability 0.983** (2.60) 0.894** (2.32) No −0.053 (−0.28) Yes
Commune has a market in 1997 0.161 (1.18) 0.123 (1.15) Yes 0.115** (2.16) 0.132** (2.35) Yes
Length of road rehabilitated/100 −0.009 (−0.53) Yes −0.005 (−0.39) −0.010** (−3.34) Yes
Length squared/10,000 0.015 (0.33) Yes −0.006 (−0.19) No
Month since project completion/100 0.068* (1.69) 0.057 (1.34) Yes 0.063** (2.17) 0.062** (2.55) Yes
Month squared/10,000 −0.064 (−1.60) −0.054 (−1.29) Yes −0.063** (−2.26) −0.061** (−2.65) Yes
Constant −1.681 (−1.63) −1.448 (−1.34) No −0.957 (−1.29) −1.008 (−1.57) No
R2 0.58 0.57 0.62 0.61

Notes: Table AVI replicates the estimates of Table V in Mu and van de Walle (2011). The dependent variables are the 85 estimated commune specific impacts for 2003. Standard errors are clustered at the district level of which there are 29. All outcomes refer to availability in the commune. t-Statistics are given in parentheses; *,**Significant at 10 and 5 percent levels, respectively

Source: Author’s estimation

Impact heterogeneity − pharmacy and restaurant

Pharmacy Restaurant
Explanatory variables Model 1 Model 2 Same sign as the original paper Model 1 Model 2 Same sign as the original paper
1997 value −0.656** (−4.61) −0.660** (−5.38) Yes −0.614** (−4.59) −0.570** (−5.82) Yes
Distance to central district −0.002 (−0.36) Yes −0.006 (−0.83) −0.003 (−0.44) Yes
North province 0.095 (0.84) Yes 0.171 (1.21) Yes
Typology: mountain −0.094 (−0.61) No 0.019 (0.10) Yes
Flood and storm prevalence −0.095 (−0.73) Yes 0.023 (0.18) No
Population density 0.858 (0.57) Yes −1.017 (−0.37) Yes
Ethnic minority share 0.043 (0.21) No 0.068 (0.36) Yes
Adult illiteracy rate −0.788 (−1.51) −0.910** (−2.34) Yes −0.376 (−0.54) Yes
Share of households owning motorcycles 0.369 (0.36) 0.483 (0.77) Yes −0.454 (−0.57) −0.826 (−1.25) No
Credit availability 0.295 (0.80) Yes −0.022 (−0.05) Yes
Commune has a market in 1997 0.304** (2.53) 0.348** (3.07) Yes 0.242** (2.58) 0.258** (2.72) No
Length of road rehabilitated/100 −0.009 (−0.66) −0.004 (−1.03) Yes 0.009 (0.60) Yes
Length squared/10,000 0.010 (0.30) Yes −0.012 (−0.35) Yes
Month since project completion/100 0.055 (1.33) 0.042 (1.14) Yes 0.035 (0.76) 0.015** (2.95) No
Month squared/10,000 −0.055 (−1.37) −0.042 (−1.17) Yes −0.022 (−0.47) No
Constant −0.881 (−0.88) −0.605 (−0.69) No −1.110 (−1.02) −0.565* (−1.73) Yes
R2 0.50 0.44 0.44 0.39

Notes: Table A7 replicates the estimates of Table V in Mu and van de Walle (2011). The dependent variables are the 85 estimated commune specific impacts for 2003. Standard errors are clustered at the district level of which there are 29. All outcomes refer to availability in the commune. t-statistics are given in parentheses. *,**Significant at 10 and 5 percent levels, respectively

Source: Author’s estimation

Impact heterogeneity − service availability

Pharmacy Restaurant
Explanatory variables Model 1 Model 2 Same sign as the original paper Model 1 Model 2 Same sign as the original paper
1997 value −0.857** (−8.13) −0.818** (−8.81) Yes −0.853** (−6.28) −0.849** (−7.03) Yes
Distance to central district −0.002 (−0.35) −0.000 (−0.10) Yes 0.002 (0.32) No
North province −0.213* (−1.73) −0.154* (−1.94) Yes −0.011 (−0.14) Yes
Typology: mountain 0.110 (0.78) Yes −0.076 (−0.96) −0.092 (−1.13) No
Flood and storm prevalence 0.037 (0.33) Yes −0.063 (−0.85) Yes
Population density 2.711* (1.72) 2.415** (2.15) Yes −0.080 (−0.08) No
Ethnic minority share −0.156 (−0.89) Yes −0.212 (−1.50) −0.203 (−1.54) Yes
Adult illiteracy rate −0.671 (−1.17) −0.615 (−1.36) Yes −1.078** (−2.21) −1.011** (−2.37) Yes
Share of households owning motorcycles 0.993* (1.69) 1.015** (2.75) Yes 0.470 (1.24) 0.613* (1.72) Yes
Credit availability 0.224 (0.78) Yes 0.344 (1.57) 0.312* (1.80) Yes
Commune has a market in 1997 0.092 (0.94) 0.093 (1.12) Yes 0.055 (0.79) Yes
Length of road rehabilitated/100 0.003 (0.21) −0.005 (−1.14) Yes −0.005 (−0.42) −0.006** (−2.01) No
Length squared/10,000 −0.011 (−0.31) Yes −0.001 (−0.02) Yes
Month since project completion/100 0.000 (0.00) Yes 0.077** (2.60) 0.080** (2.26) Yes
Month squared/10,000 −0.001 (−0.05) Yes −0.077** (−2.74) −0.080** (−2.38) Yes
Constant 0.495 (0.67) 0.514** (3.54) Yes −1.041 (−1.35) −1.078 (−1.26) No
R2 0.58 0.55 0.63 0.62

Notes: Table AVIII replicates the estimates of Table VI in Mu and van de Walle (2011). The dependent variables are the 85 estimated commune specific impacts for 2003. Standard errors are clustered at the district level of which there are 29. All outcomes refer to availability in the commune. t-Statistics are given in parentheses. *,**Significant at 10 and 5 percent levels, respectively

Source: Author’s estimation

Impact heterogeneity: employment

Farming Services Trade
Explanatory variables Model 1 Model 2 Same sign as the original paper Model 1 Model 2 Same sign as the original paper Model 1 Model 2 Same sign as the original paper
1997 value −0.118 (−1.55) −0.118* (−1.70) Yes −0.308 (−0.86) −0.235 (−0.67) Yes −0.315 (−0.92) −0.198 (−0.64) Yes
Distance to central district −0.010 (−0.08) Yes −0.090 (−1.07) −0.099 (−1.37) Yes −0.014 (−0.24) Yes
North province −2.474 (−1.25) −2.712 (−1.51) Yes 1.172 (0.97) 1.732 (1.25) Yes −1.985** (−1.96) −1.170 (−1.29) Yes
Typology: mountain −2.086 (−0.66) Yes −1.744 (−1.08) −2.829** (−2.22) Yes 0.820 (0.44) Yes
Flood and storm prevalence −2.534 (−1.17) −3.189* (−1.91) Yes 2.401* (1.79) 2.713* (1.65) Yes −0.140 (−0.13) No
Population density −37.572 (−0.87) −21.625 (−0.80) Yes 28.058 (0.79) Yes 28.536 (0.92) Yes
Ethnic minority share 0.313 (0.08) Yes 0.132 (0.07) Yes 0.906 (0.55) No
Adult illiteracy rate 3.369 (0.41) Yes 5.120 (0.90) 5.318 (1.14) No −3.947 (−0.88) −4.938* (−1.67) No
Share of households owning motorcycles −12.164 (−0.79) −11.355 (−0.72) Yes 23.844** (2.83) 23.250** (3.16) Yes 12.545 (1.64) 11.471* (1.73) Yes
Credit availability 2.259 (0.39) 3.202 (0.51) Yes −8.094** (−2.16) −9.260** (−2.59) Yes −4.298 (−1.56) −4.783* (−1.76) Yes
Commune has a market in 1997 −2.765 (−1.52) −2.543* (−1.69) Yes −0.476 (−0.33) Yes 1.612* (1.76) 1.486* (1.93) Yes
Length of road rehabilitated/100 −0.006 (−0.03) Yes 0.038 (0.20) No 0.002 (0.02) Yes
Length squared/10,000 −0.078 (−0.21) No −0.030 (−0.08) No −0.020 (−0.11) Yes
Month since project completion/100 0.324 (0.41) −0.015 (−0.22) Yes 0.532 (1.22) 0.569* (1.65) Yes 0.410 (1.04) 0.295 (0.96) Yes
Month squared/10,000 −0.324 (−0.41) Yes −0.662 (−1.59) −0.686** (−1.98) Yes −0.439 (−1.15) −0.317 (−1.10) Yes
Constant 7.292 (0.34) 14.134* (1.91) Yes −8.280 (−0.74) −7.830 (−0.93) Yes −8.649 (−0.87) −4.857 (−0.61) Yes
R2 0.21 0.19 0.29 0.27 0.19 0.16

Notes: Table AIX replicates the estimates of Table VII in Mu and van de Walle (2011). The dependent variables are the 85 estimated commune specific impacts for 2003. Standard errors are clustered at the district level of which there are 29. t-Statistics are given in parentheses. All outcomes refer to availability in the commune. *,**Significant at 10 and 5 percent levels, respectively

Source: Author’s estimation

Impact heterogeneity − schooling

Secondary school enrollment Primary school completion
Explanatory variables Model 1 Model 2 Same sign as the original paper Model 1 Model 2 Same sign as the original paper
1997 value −0.915** (−8.96) −0.961** (−14.47) Yes −0.999** (−9.83) −0.932** (−8.91) Yes
Distance to central district 0.068 (0.39) No −0.190 (−0.40) Yes
North province 2.052 (0.79) 3.268 (1.58) No 7.322 (0.76) 5.700 (0.95) Yes
Typology: mountain 1.061 (0.27) No −6.896 (−0.66) Yes
Flood and storm prevalence 1.703 (0.66) No 16.182** (2.49) 16.717** (2.87) Yes
Population density 7.611 (0.22) No 58.566 (0.46) Yes
Ethnic minority share −5.906 (−1.50) −6.363* (−1.88) Yes 3.152 (0.26) Yes
Adult illiteracy rate 7.168 (0.46) No 43.797 (1.22) 21.340 (0.93) No
Share of households owning motorcycles −8.500 (−0.55) −8.096 (−0.50) No 97.884* (1.66) 94.383* (1.94) Yes
Credit availability 3.814 (0.60) 5.519 (0.97) Yes 4.141 (0.22) No
Commune has a market in 1997 1.940 (0.86) 1.720 (0.98) Yes 9.817 (1.29) 9.344 (1.42) Yes
Length of road rehabilitated/100 −0.024 (−0.10) −0.173* (−1.85) Yes −0.633 (−0.73) −0.368 (−1.35) No
Length squared/10,000 −0.286 (−0.45) No 0.327 (0.16) No
Month since project completion/100 −0.192 (−0.25) 0.084 (0.82) No 0.188 (0.07) No
Month squared/10,000 0.274 (0.37) No −0.422 (−0.16) No
Constant 80.464** (3.93) 81.035** (9.67) Yes 52.136 (0.76) 45.989** (3.50) Yes
R2 0.87 0.86 0.71 0.67

Notes: Table AX replicates the estimates of Table VIII in Mu and van de Walle (2011). The dependent variables are the 85 estimated commune specific impacts for 2003. Standard errors are clustered at the district level of which there are 29. All outcomes refer to availability in the commune. t-statistics are given in parentheses. *,**Significant at 10 and 5 percent levels, respectively

Source: Author’s estimation

PS kernel matched DD: bandwidth = 0.01

2001 2003
Outcomes PS kernel matched DD t-ratio Original estimates in Mu and van de Walle (2011) PS kernel matched DD t-ratio Original estimates in Mu and van de Walle (2011)
Market availability 0.023 0.537 0.03 0.068 1.380 0.08*
Market frequency 0.124 0.941 0.08 0.137 0.930 0.23*
Shop −0.203 −1.617 0.01 −0.194* −1.827 0.08
Bicycle repair shop −0.057 −1.027 −0.06 −0.044 −0.626 0.02
Pharmacy 0.096 1.337 0.04 0.260** 2.367 0.12
Restaurant 0.145** 2.007 −0.01 0.089 0.829 0.01
Women’s hair dressing/Men’s barber 0.077 1.032 −0.07 0.102 1.373 0.18**
Men and women’s tailoring 0.012 0.248 0.11 0.034 0.585 0.10
% farm households −1.961 −0.943 0.05 −3.035 −1.418 −2.04*
% trade households 0.064 0.083 0.03 1.218 1.582 0.36
% service sector households −0.044 −0.086 −1.54 1.353** 2.306 1.68**
Primary school completion (<15 years) 7.150 0.850 0.15** 13.848** 1.943 0.17**
Secondary school enrollment rate 2.948 0.834 0.10 0.837 0.290 0.05

Notes: The sample consists of 85 project and 83 non-project communes on common support as determined by propensity score matching. The propensity score is estimated by the logit model in Table AII. t-Ratio of kernel matching is obtained from bootstrapping (100 repetitions). *,**Significant at 10 and 5 percent levels, respectively

PS kernel matched DD: bandwidth = 0.03

2001 2003
Outcomes PS kernel matched DD t-ratio Original estimates in Mu and van de Walle (2011) PS kernel matched DD t-ratio Original estimates in Mu and van de Walle (2011)
Market availability 0.028 0.776 0.03 0.079** 2.003 0.08*
Market frequency 0.137 1.398 0.08 0.171 1.477 0.23*
Shop −0.173 −1.553 0.01 −0.178* −1.850 0.08
Bicycle repair shop −0.059 −1.152 −0.06 −0.038 −0.575 0.02
Pharmacy 0.074 1.030 0.04 0.206* 1.883 0.12
Restaurant 0.139** 1.946 −0.01 0.073 0.795 0.01
Women’s hair dressing/men’s barber 0.068 0.894 −0.07 0.092 1.231 0.18**
Men and women’s tailoring 0.004 0.080 0.11 0.033 0.551 0.10
% farm households −1.208 −0.686 0.05 −2.782 −1.529 −2.04*
% trade households −0.191 −0.244 0.03 1.069 1.544 0.36
% service sector households −0.032 −0.068 −1.54 1.330** 2.439 1.68**
Primary school completion (<15 years) 4.141 0.551 0.15** 11.986* 1.718 0.17**
Secondary school enrollment rate 1.565 0.526 0.10 0.890 0.308 0.05

Notes: The sample consists of 85 project and 83 non-project communes on common support as determined by propensity score matching. The propensity score is estimated by the logit model in Table AII. t-ratio of kernel matching is obtained from bootstrapping (100 repetitions). *,**Significant at 10 and 5 percent levels, respectively

PS kernel matched DD: bandwidth = 0.09

2001 2003
Outcomes PS kernel matched DD t-ratio Original estimates in Mu and van de Walle (2011) PS kernel matched DD t-ratio Original estimates in Mu and van de Walle (2011)
Market availability 0.028 0.819 0.03 0.082** 2.196 0.08*
Market frequency 0.134 1.430 0.08 0.173 1.503 0.23*
Shop −0.103 −1.011 0.01 −0.115 −1.272 0.08
Bicycle repair shop −0.071 −1.373 −0.06 −0.058 −0.813 0.02
Pharmacy 0.045 0.601 0.04 0.140* 1.681 0.12
Restaurant 0.129 1.614 −0.01 0.038 0.393 0.01
Women’s hair dressing/men’s barber 0.047 0.627 −0.07 0.069 0.926 0.18**
Men and women’s tailoring 0.000 0.003 0.11 0.022 0.329 0.10
% farm households −0.534 −0.341 0.05 −2.263 −1.527 −2.04*
% trade households −0.161 −0.261 0.03 0.692 1.343 0.36
% service sector households −0.325 −0.759 −1.54 0.877* 1.890 1.68**
Primary school completion (<15 years) 0.552 0.086 0.15** 8.896 1.260 0.17**
Secondary school enrollment rate 0.915 0.293 0.10 0.607 0.205 0.05

Notes: The sample consists of 85 project and 83 non-project communes on common support as determined by propensity score matching. The propensity score is estimated by the logit model in Table AII; t-Ratio of kernel matching is obtained from bootstrapping (100 repetitions). *,**Significant at 10 and 5 percent levels, respectively

Notes

1.

Two-related papers of this article are Van de Walle and Mu (2007) and Mu and van de Walle (2007).

2.

A review on empirical studies of the impact of rural roads can be found in Ali and Pernia (2003).

3.

According to Donnges et al. (2007), Vietnam had a rural road network consisting of approximately 175,000 km in 2007. Around 73 percent of rural villages can be accessed by a good road (tar on gravel) (according to VietNam Household Living Standard Survey in 2010).

4.

There are no data on consumption expenditure in the data set.

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Acknowledgements

The author would like to thank Mu Ren and Dominique van de Walle for generously providing me with not only the raw original data sets but also analysis do-files. Without their help, this replication work cannot be done. They also gave me useful comments on the reports. The author would also like to thank Benjamin Wood and anonymous reviewers for his help and very useful comments during this study.

Corresponding author

Dr Cuong Viet Nguyen can be contacted at: c_nguyenviet@yahoo.com

About the author

Cuong Viet Nguyen holds PhD and MSc degrees in development economics from Wageningen University, the Netherlands. Dr Cuong has extensive experience in impact evaluation, poverty analysis, ethnic minority issues, education and health issues. Dr Cuong recent studies have been published in well-respected journals such as the American Political Science Review, World Bank Economic Review, the Journal of Comparative Economics the Journal of Health Economics, World Development, the Journal of Development Studies, etc.

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