AgBioForum, 21 (3): 1-14. ©2019 AgBioForum. Influence of New Countryside Construction Program on household welfare: Evidence from the Mekong River Delta of Vietnam Lai, Thi Cam Phan Vietnam National University Ho Chi Minh City (VNU-HCM), 71309, Ho Chi Minh City, Vietnam. Email: ptclai@vnuhcm.edu.vn Quang-Thanh, Ngo* Institute of Policy and Strategy for Agriculture and Rural Development, Ho Chi Minh City, Vietnam. Email: ngoqthanh@gmail.com; ORCID: http://orcid.org/0000-0001-8357-1957 In developing countries, comprehensive rural development programs contribute significantly to exports and generate domestic demand for food as well as provide capital and labor for further industrialization and development. However, the efficacy of these programs remains in question. The National Target Program on New Rural Development, known as New Countryside Construction Program, in Vietnam has been implemented since 2010 as a new policy initiative and approach to rural development with the following key objectives: (i) to improve rural infrastructure; (ii) to foster linkages between the agricultural sector and the industrial and service sectors, and between rural economies and urban economies; (iii) and to improve rural living standards in terms of economic, social, and environmental qualities. The current study uses commune fixed- effect and two-stages-least-squared regressions to estimate the effect of the New Countryside Construction Program on household welfare in The Mekong River Delta of Vietnam in two kinds of models: The small model contains only demographical variables and commune-level variables such as commune general conditions, and initial infrastructure conditions. The large models include additional variables of education, occupation, and commune-level variables as in the small model. Controlling for the endogeneity of the New Countryside Construction Program variable, we find that New Countryside Construction Program has positive effects on household expenditure and the New Countryside Construction Program tends to prone to top 20 expenditure quintiles than to bottom 20 expenditure quintiles. Key words: New Countryside Construction Program, household welfare, Mekong River Delta, Vietnam. Introduction In most developing countries, industrialization and modernization is seen as a pre-requisite fora prosperous, equitable and democratic society, which have been emblemed in many modern theories of development such as the stages of economic growth (Rostow, 1959), the dual economy (Lewis (1954); Lewis (1979)), the structural change model (Clark (1940); Kuznets (1957); Chenery (1960)). During the development process, agriculture and rural society may be adversely affected as a result, and this in turn, can hamper the socio- economic development and growth of the country as a whole (Long et al. (2010); Perry (2011)). Governments in rapidly developing countries in the world such as South Korea, China, have invested a huge number of resources to foster agriculture and rural development (Im et al. (2016); Jacka (2013); Ahlers and Schubert (2009)). In these countries, to build a new countryside, comprehensive programs have been launched, under the names of “the New Village Movement” (also known as the New Community Movement or Saemaul Undong in Korean)) in South Korea in the 1970s, in rural development program Taiwan in 1950s, and in the “new socialist countryside” in China in 2006. These programs are known to contribute significantly to exports, domestic demand for food, and more capital and labor for industrialization and successful development in these countries (Looney, 2012). The Vietnam National Target Program on New Rural Development (NTP-NRD) during 2010-2020 has been launched under the name of New Countryside Construction Program (NCCP) nationwide in over 9.008 communes (Vietnam National Assembly, 2016). The general objectives of the program are: (1) To build a new countryside with gradually modem socio-economic infrastructure, rational economic structure, and forms of production organization; (2) To associate agriculture with the quick development of industries and services, and rural with urban development under planning; (3) To assure a democratic and stable rural community deeply imbued with national cultural identity; to protect the eco-environment and maintain security and order, and to raise people's material and spiritual lives along with the socialist orientation. The NTP-NRD is an mailto:ptclai@vnuhcm.edu.vn mailto:ngoqthanh@gmail.com http://orcid.org/0000-0001-8357-1957 AgBioForum, 21(3), 2019 | 2 Lai and Quang-Thanh. — Influence of New Countryside Construction Program overall socio-economic development, political and security, and defense program, covering the following 11 activities: (1) New rural planning, (2) Socio- economic infrastructure development, (3) Rural economic development and income raising, (4) Poverty reduction and social protection, (5) Renovation and development of production organization, (6) Education and training development, (7) Health care, (8) Rural culture, information, and communication, (9) Clean water and rural environment, (10) Operations of local party/government system, and (11) Rural security and public order. From 2010 to 2015, the NCCP program has mobilized 851,380 billion VND to invest in rural areas across Vietnam. By early 2016, 1761 communes out of a total of 8,920 communes in Vietnam had achieved a set of 19 criteria developed by the NCCP program, equal to 19.7% (Vietnam National Assembly, 2016). The Vietnamese government has considered the NCCP program to be successful (Vietnam National Assembly, 2016). The impact of the NCCP program, however, remains debatable. A study jointly conducted by the International Fund for Agricultural Development (IFAD) and the World Bank (WB) mentions that although the NCCP program in Vietnam has upgraded rural infrastructure and conditions for economic and social improvements in rural Vietnam between 2010 and 2015 (IFAD-World Bank, 2016), it is lacking evidence to support the success of the NRD program (IFAD- World Bank, 2016). Some shortcomings of NCCP programs are indicated in this regard, including (1) the inflexibility of the set of criteria neutralizes any priority local needs in rural transformation, (2) local entities are not empowered when important policy decisions are taken, and (3) local communes are forced to rush for the fulfillment of the criteria regardless of the resources’ capacity and management ability (IFAD-World Bank, 2016). The objective of this study is to assess the impacts of the NCCP program on households’ income and living standards in the Mekong River Delta (MRD) of Vietnam. To address the above issue, this study uses surveyed data from Vietnam Household Living Standard Surveys (VHLSS) 2010 when the NCCP program is started and the survey of 2014 when the NRD program has been implemented and successfully recorded in some communes for the first phase of development 2010-2015. The data from VHLSS 2010 and 2014 for the same households in the MRD are selected and information on NCCP is added to each household following NCCP and non-NCCP communes, and each NCCP criteria achieved by those communes as well. The first research question addressed by this study concerns the impact of the NCCP program on household income in the affected communes? The hypothesis is that households’ total income in the treatment communes experienced a significant increase as a result of the NCCP program. The hypothesis is that the NCCP program generally increases income level in a fulfilled commune. The second and final research question is: how are different wealth groups affected by the NCCP program? To answer this question, the population was sub-divided into deciles and we estimated the NCCP program effects on each decile. A greater change in income for the poorest decile would indicate pro-poor attributes of the NCCP program and vice versa. The hypothesis is that the NCCP program increases income inequality by favoring the top-income group due to the inflexibility of the criteria set. The paper follows the following structure. Section 2 provides a literature review of the paper subject while Section 3 presents the dataset as well as a description of the methodology used to design this research. Section 4 shows empirical results obtained on the basis of data collected and collated in the previous section. Lastly, Section 5 provides a conclusion of the study findings as well as recommendations vis-à-vis future research work in this area. Literature review NRD is expected to increase the welfare outcomes of households in Vietnam (Liêng, 2015), like in China (see, for example, Ahlers and Schubert (2009)). It happens through several channels such as infrastructure of various types (Charlery, Qaim and Smith-Hall (2016), Shenggen and Zhang (2004), Im et al. (2016), Kara,Taş and Ada (2016), Rahman (2014)), social capital (Narayan and Pritchett, 1999). Charlery, Qaim and Smith-Hall (2016) find that the new road had a significantly positive impact on mean household income. Shenggen and Zhang (2004) also find that rural infrastructure and education play a more important role in explaining the difference between rural nonfarm productivity and agricultural productivity. Because the rural nonfarm economy is a major determinant of rural income, investing more in rural infrastructure is key to the growth of overall income of the rural population. Kara,Taş and Ada (2016) consider two categories of infrastructure investments: Economic infrastructure investments (i.e., highways, power generation and water facilities), and social infrastructure investments (i.e., education and healthcare). The authors identify how different types of infrastructure expenditures affect regional incomes in Turkey and find that infrastructure expenditures enhanced regional income in Turkey, and social infrastructure investments and education AgBioForum, 21(3), 2019 | 3 Lai and Quang-Thanh. — Influence of New Countryside Construction Program expenditures demonstrate a significant impact on regional income. Rahman (2014) studies the impact of rural infrastructure on the decision to choose between farm and non-farm enterprises vis-à-vis income by Bangladeshi rural households and finds that rural infrastructure has a significant but inverse impact on enterprise choices vis-à-vis income. Concerning social capital, Narayan and Pritchett (1999) find that the social capital of a household’s village is as important in determining the household’s income as are many of the household’s characteristics, such as schooling, assets, distance to markets, or gender of the household head. NRD can also affect household livelihood. Households might be more diversified in income under the impact of infrastructure (Escobal (2001); Abdulai and CroleRees (2001); Deininger and Olinto (2001)). Escobal (2001) examines the determinants of non-farm income diversification in Peru and finds that access to public assets such as rural electrification and roads is an important factor in diversification. Deininger and Olinto (2001), using data from Colombian rural households, confirm the importance of non-farm activities as a source of income and employment. The authors also find that a significant share of poor households engages in a combination of wage labor in jobs with low entry requirements plus self-employment in “marginal” on- farm or informal sector activities. NRD can affect income distribution (Calderon and Servén (2004), Calderón and Chong (2004)) through several ways. One is through the impact of infrastructure (see, for example, Charlery, Qaim and Smith-Hall (2016)), non-farm employment (see, for example, Reardon et al. (2000)). Charlery, Qaim and Smith-Hall (2016) find that the new road had a significantly positive impact on mean household income and contributed to decreasing income inequality, and the poorest households gained most from the construction of the road. Reardon, Berdegué and Escobar (2001) find that the effect of non-farm employment on inequality is mixed. Materials and methods Data sources This study relies on Vietnam Household Living Standard Surveys (VHLSS) 2010, 2012, and 2014. The VHLSSs were conducted by the General Statistics Office of Vietnam (GSO) with technical assistance from the World Bank. The surveys contain household and community data. Data on households include basic demography, employment and labor force participation, education, health, income, expenditure, housing, fixed assets and durable goods, the participation of households in poverty alleviation programs. Commune data include demographic status of communes, general economic conditions, non-farm employment, agriculture production, local infrastructure and transportation, education, health, and social affairs. The commune data contained information on natural disasters happening in communes in previous years. Commune data can be merged with household data. Each of the VHLSS covers more than nine thousand households. The data are representative of urban/rural and eight geographic regions. The entire dataset of 2010, 2012, and 2014 household-level VHLSSs covered 6,750, 6,696, and 6,618 rural households, respectively. The entire data set of 2010, 2012, and 2014 commune- level VHLSSs covered 2,199, 669, and 1,716 communes, respectively.In this study, we use the rural samples for the Mekong River Delta. The selected sample of 2010 and 2014 household-level VHLSSs covered 1,455 and 1,440 rural households, respectively. The selected sample of 2010 and 2014 commune-level VHLSSs covered 470 and 278 communes, respectively. Table 1 presents the summary of 2-wave household- level panel data in 2010-2014 with 628 households in each year of which 51 households live in NCCP- qualified communes. Table 1: Household-level sample, 2010-2014 Year Non-NCCP NCCP Total Obs. Row (%) Obs. Row (%) Obs. Row (%) 2010 679 51.95 0 0.00 679 50.00 2014 628 48.05 51 100.00 679 50.00 Total 1,307 100.00 51 100.00 1,358 100.00 Source: Authors’ compilation from VHLSS 2010-2014 (Household survey) Table 2: Commune-level sample, 2010-2014 Year Non-NCCP NCCP Total Obs. Row (%) Obs. Row (%) Obs. Row (%) 2010 268 51.94 0 0.00 268 50.00 2014 248 48.06 20 100.00 268 50.00 Total 516 100.00 20 100.00 536 100.00 Source: Authors’ compilation from VHLSS 2010-2014 (Commune survey) AgBioForum, 21(3), 2019 | 4 Lai and Quang-Thanh. — Influence of New Countryside Construction Program Table 2 presents the summary of 2-wave commune-level panel data in 2010-2014 with 268 communes in each year. In our data set, there are 20 communes, which have been qualified as NCCP ones in 2014. Methods Model specification We assume a household welfare indicator is a function of characteristics of households and communities as follows (Glewwe, 1991): ln⁡ 𝑌𝑖𝑗𝑡 =⁡𝛼0 +⁡𝑋𝑖𝑗𝑡𝛽1 +⁡𝐶𝑗𝑡𝛾1 ⁡+ ⁡𝑁𝐶𝐶𝑃𝑗𝑡𝛿1 +⁡𝜏𝑡 + ⁡휀𝑗𝑗𝑡 (1) Where the script it denotes for household i in commune j in the year t; Y is a welfare indicator of households; X is a vector of characteristics of households such as demographical variables and socio-economic variables; C is a vector of characteristics of communities such as commune general conditions, and initial infrastructure conditions; NCCP is a dummy variable indicating whether a commune is qualified for NCCP criteria or not;  is the dummy variable of years;  is unobserved variables. We use different indicators of household welfare including per capita income, per capita expenditure by levels and by quintiles, and share of incomes by different sources. We use similar specifications as equation (1) for different dependent variables. The effect of NCCP on households is measured by parameters 𝛿1, 𝛿2, and⁡𝛿3. One challenge faced when estimating the effect of NCCP is the endogeneity of NCPP. The unobserved variables can be correlated with the NCCP. In equation (1), unobserved variables ijt include both commune- level (vj) and household-level variables (ui). Since our NCCP is the commune-level variables, they are more likely to be correlated with unobserved commune-level variables. The unobserved commune-level variables can be decomposed into time-variant (𝑣𝑗1𝑡) and time- invariant commune-level variables (𝑣𝑗𝑜) (Equation (2)). In this study, we use the commune fixed-effect regression to eliminate unobserved time-invariant commune-level variables. It is expected that the endogeneity bias will be negligible after the elimination of these unobserved time-invariant variables and the control of observed variables. 휀𝑗𝑗𝑡 =⁡𝑢𝑖𝑡 +⁡𝑣𝑗𝑡 = 𝑢𝑖𝑜 + 𝑢𝑖1𝑡 + 𝑣𝑗𝑜 + 𝑣𝑗1𝑡 (2) NCCP and Outcomes We use a dummy variable that takes the value of 1 if a commune is rewarded as an NCCP one, and takes the value of 0 if otherwise. The unit of analysis is the household and both consumption and income in per capita terms and in terms of quintile, and share of income sources as well are under consideration. Both consumption and incomes have been deflated to January 2010 national prices through the use of monthly and regional price indices calculated as part of the survey and using the General Statistics Office CPI to adjust prices across rounds of the survey. Table 3: Changes in outcome variables by Non-NCCP and NCCP groups, 2010-2014 Outcome Non-NCCP NCCP Diff in Diff 2010 2014 Diff 2010 2014 Diff Expenditure 13,798 16,724 2,926 16,488 19,885 3,397 471 Food 6,470 7,596 1,126 6,987 8,287 1,300 174 Nonfood 7,328 3,716 -3,612 9,501 4,654 -4,847 -1,235 Durables 861 1,728 867 5,167 2,927 -2,240 -3,107 Income 16,093 52,352 36,259 17,694 63,865 46,171 9,912 Housing 70 81 11 68 83 15 4 Land 7,096 8,852 1,756 6,656 12,872 6,216 4,460 Fixed assets 17,167 30,774 13,607 30,395 47,472 17,077 3,470 Health 872 1,014 142 778 874 96 -46 Incidence 0.0211 0.0666 0 0.0262 0.067 0 0 Education 380 446 66 532 595 63 -3 Source: Authors’ estimation from VHLSS 2010-14 (Household Survey) Table 3 presents a comparison of outcome variables between 2010 and 2014 and between non-NCCP and NCCP groups as well. There are significant increases between 2010 and 2014 within non-NCCP and NCCP groups in terms of total expenditure per capita, food expenditure per capita, income per capita, housing area by household, land by household, and fixed capital assets. Increases in these indicators also occur between non-NCCP and NCCP groups. There are significant improvements between non-NCCP and NCCP groups in terms of health expenditure per capita, health incidence, and education expenditure per capita. Table 4 presents a comparison of income shares between 2010 and 2014 and between non-NCCP and NCCP groups as well. There are significant increases between 2010 and 2014 within non-NCCP and NCCP groups in terms of income share from livestock, and a small increase in income share from services, and income share from agriculture. There are significant improvements between non-NCCP and NCCP groups in terms of income share from the enterprise. AgBioForum, 21(3), 2019 | 5 Lai and Quang-Thanh. — Influence of New Countryside Construction Program Table 4: Changes in income shares by non-NCCP and NCCP groups, 2010-2014 Outcome Non-NCCP NCCP Diff in Diff 2010 2014 Diff 2010 2014 Diff From labor 38.14 39 0.86 53.95 35.56 -18.39 -19.25 From crops 5.07 0.36 -4.71*** 5.81 0.67 -5.14*** -0.43 From livestock 0.77 1.89 1.12*** 0.1 3.51 3.41 2.29 From forestry 0.18 0.35 0.17 0 0.1 0.1 -0.07 From aquaculture 1.47 10.61 9.14 0 7.61 7.61 -1.53 From services 0 0.01 0.01 0 0.52 0.52 0.51 From enterprise 31.74 20.33 -11.41*** 22.97 26.04 3.07*** 14.48 From others 22.62 27.45 4.83*** 17.17 26 8.83** 4 From agriculture 7.5 13.22 5.72*** 5.91 12.41 6.5*** 0.78 Note: *** significant at 1% level, ** at 5% level. Source: Authors’ estimation from VHLSS10-14 (Household survey) Table 5 presents changes in expenditure deciles (percentage) and a comparison of expenditure deciles between 2010 and 2014 and between non-NCCP and NCCP groups as well. For the Non-NCCP group, expenditure shares of seven deciles increase between 2010 and 2014, whereas for the NCCP group the first 30 deciles decrease between 2010 and 2014. However, expenditure shares from the fourth to the ninth decile increase between 2010 and 2014. NCCP is unlikely to benefit the bottom 30 and the top. Table 5: Changes in expenditure deciles (percentage) by Non-NCCP and NCCP groups, 2010-2014 Outcome Non-NCCP NCCP 2010 2014 Ratio 2010 2014 Ratio Bottom 3.77 3.79 1.00*** 3.96 3.81 0.96*** 2nd decile 5.18 5.39 1.04*** 5.05 4.89 0.97*** 3rd decile 6.09 6.32 1.04*** 5.69 5.65 0.99*** 4th decile 6.96 7.26 1.04*** 6.12 6.63 1.08*** 5th decile 8.05 8.15 1.01*** 7.37 7.68 1.04*** 6th decile 9.14 9.22 1.01*** 8.16 8.88 1.09*** 7th decile 10.30 10.39 1.01*** 9.37 10.63 1.13*** 8th decile 12.22 11.98 0.98*** 10.86 11.76 1.08*** 9th decile 14.98 14.57 0.97*** 12.91 14.03 1.09*** Top 23.32 22.92 0.98*** 30.51 26.04 0.85*** Note: *** significant at 1% level. Source: Authors’ estimation from VHLSS 2010-14 (Household Survey) Table 6 presents changes in expenditure deciles (in terms of the mean of expenditure) and a comparison of expenditure deciles between 2010 and 2014 and between non-NCCP and NCCP groups as well. All deciles are better between 2010 and 2014 for both groups of non-NCCP and NCCP. Only the bottom shows to be better off between NCCP vs. non-NNCP. Table 6: Changes in expenditure deciles (expenditure mean) by Non-NCCP and NCCP groups, 2010-2014 Outcome Non-NCCP NCCP Diff in Diff 2010 2014 Diff 2010 2014 Diff Bottom 5,268 6,388 1,120 5,452 7,214 1,762 642 2nd decile 7,235 9,071 1,836 7,305 8,959 1,654 -182 3rd decile 8,494 10,612 2,118 8,456 10,884 2,428 310 4th decile 9,669 12,219 2,550 9,609 11,627 2,018 -532 5th decile 11,162 13,757 2,595 11,111 13,551 2,440 -155 6th decile 12,673 15,568 2,895 12,860 15,435 2,575 -320 7th decile 14,310 17,579 3,269 14,360 17,193 2,833 -436 8th decile 16,973 20,368 3,395 16,315 20,693 4,378 983 9th decile 20,786 24,777 3,991 20,761 24,185 3,424 -567 Top 32,239 38,809 6,570 49,401 44,485 -4,916 -11,486 Source: Authors’ estimation from VHLSS 2010-14 (Household Survey) Table 7 presents changes in expenditure quintiles (percentage) and a comparison of expenditure quintiles between 2010 and 2014 and between non-NCCP and NCCP groups as well. The results are consistent with expenditure deciles in Error! Reference source not found.. For the Non-NCCP group, expenditure shares of three quintiles increase between 2010 and 2014, whereas for the NCCP group the poorest quintile and the richest quintile decrease between 2010 and 2014. However, income shares from the near poorest to the near richest quintiles increase between 2010 and 2014. NCCP is unlikely to benefit both the poorest and the richest. AgBioForum, 21(3), 2019 | 6 Lai and Quang-Thanh. — Influence of New Countryside Construction Program Table 7: Changes in expenditure quintiles (percentage) by Non-NCCP and NCCP groups, 2010-2014 Outcome Non-NCCP NCCP 2010 2014 Ratio 2010 2014 Ratio Poorest 8.95 9.18 1.03*** 9.01 8.70 0.97*** Near poorest 13.05 13.58 1.04*** 11.81 12.29 1.04*** Middle 17.18 17.38 1.01*** 15.53 16.56 1.07*** Near richest 22.52 22.38 0.99*** 20.23 22.38 1.11*** Richest 38.30 37.49 0.98*** 43.42 40.07 0.92*** Note: *** significant at 1% level. Source: Authors’ estimation from VHLSS 2010-14 (Household Survey) Table 8 presents changes in expenditure quintiles (in terms of the mean of expenditure) and a comparison of expenditure quintiles between 2010 and 2014 and between non-NCCP and NCCP groups as well. All quintiles are better between 2010 and 2014 for both groups of non-NCCP and NCCP. Only the near richest quintile shows to be better off between NCCP vs. non- NNCP. Table 8: Changes in expenditure quintiles (expenditure mean) by Non-NCCP and NCCP groups, 2010-2014 Outcome Non-NCCP NCCP Diff in Diff 2010 2014 Diff 2010 2014 Diff Poorest 6,225 7,711 1,486 6,800 8,211 1,411 -75 Near poorest 9,054 11,427 2,373 9,279 11,149 1,870 -503 Middle 11,918 14,654 2,736 12,044 14,659 2,615 -121 Near richest 15,654 18,948 3,294 15,286 19,526 4,240 946 Richest 26,594 31,827 5,233 33,035 33,959 924 -4,309 Source: Authors’ estimation from VHLSS 2010-14 (Household Survey) Table 9 presents changes in income deciles (percentage) and a comparison of income deciles between 2010 and 2014 and between non-NCCP and NCCP groups as well. For the non-NCCP group, income shares of seven deciles decrease between 2010 and 2014, whereas for the NCCP group, the first six deciles decrease between 2010 and 2014. In terms of income, NCCP is unlikely to benefit the six bottom deciles. Table 9: Changes in income deciles (percentage) by Non-NCCP and NCCP groups, 2010-2014 Outcome Non-NCCP NCCP 2010 2014 Ratio 2010 2014 Ratio Bottom 2.80 0.59 0.21*** 2.60 0.56 0.22*** 2nd decile 4.10 2.23 0.54*** 4.16 2.01 0.48*** 3rd decile 5.16 3.64 0.71*** 5.57 3.34 0.60*** 4th decile 6.15 4.91 0.80*** 6.12 4.14 0.68*** 5th decile 7.19 6.24 0.87*** 6.78 5.47 0.81*** 6th decile 8.35 7.81 0.93*** 8.10 6.79 0.84*** 7th decile 10.04 9.72 0.97*** 9.13 9.41 1.03*** 8th decile 12.22 12.41 1.02*** 11.22 12.30 1.10*** 9th decile 15.72 16.75 1.07*** 15.00 16.92 1.13*** Top 28.25 35.70 1.26*** 31.33 39.04 1.25*** Note: *** significant at 1% level, ** at 5% level. Source: Authors’ estimation from VHLSS10-14 (Household survey) Table 10 presents changes in income deciles (in terms of the mean of income) and a comparison of income deciles between 2010 and 2014 and between non-NCCP and NCCP groups as well. All deciles, except for the bottom, are better between 2010 and 2014 for both groups of non-NCCP and NCCP. The bottom 20 shows to be worse off between NCCP vs. non-NNCP. Table 10: Changes in income deciles (income mean) by Non-NCCP and NCCP groups, 2010-2014 Outcome Non-NCCP NCCP Diff in Diff 2010 2014 Diff 2010 2014 Diff Bottom 4,536 3,646 -890 4,131 3,906 -225 665 2nd decile 6,654 13,890 7,236 6,745 11,607 4,862 -2,374 3rd decile 8,393 22,587 14,194 8,295 22,611 14,316 122 4th decile 10,006 30,291 20,285 10,272 30,159 19,887 -398 5th decile 11,609 38,413 26,804 11,518 36,788 25,270 -1,534 6th decile 13,512 48,268 34,756 13,454 47,447 33,993 -763 7th decile 16,181 60,038 43,857 16,106 57,620 41,514 -2,343 8th decile 19,682 77,237 57,555 19,740 82,272 62,532 4,977 9th decile 25,414 104,835 79,421 25,913 103,232 77,319 -2,102 Top 45,563 221,700 176,137 54,625 258,809 204,184 28,047 Source: Authors’ estimation from VHLSS 2010-14 (Household Survey) Table 11 presents changes in income quintiles (percentage) and a comparison of income quintiles between 2010 and 2014 and between non-NCCP and NCCP groups as well. The results are consistent with AgBioForum, 21(3), 2019 | 7 Lai and Quang-Thanh. — Influence of New Countryside Construction Program income deciles in. For the Non-NCCP group, income shares of the four bottom quintiles decrease between 2010 and 2014, whereas for the NCCP group the three bottom quintiles decrease between 2010 and 2014. However, income shares of the richest quintiles increase between 2010 and 2014. NCCP is unlikely to benefit the three bottom quintiles. Table 11: Changes in income quintiles (percentage) by Non-NCCP and NCCP groups, 2010-2014 Outcome Non-NCCP NCCP 2010 2014 Ratio 2010 2014 Ratio Poorest 6.91 2.82 0.41*** 6.76 2.58 0.38*** Near poorest 11.32 8.56 0.76*** 11.69 7.48 0.64*** Middle 15.54 14.04 0.90*** 14.87 12.26 0.82*** Near richest 22.26 22.13 0.99*** 20.34 21.72 1.07*** Richest 43.97 52.45 1.19*** 46.33 55.97 1.21*** Note: *** significant at 1% level, ** at 5% level. Source: Authors’ estimation from VHLSS10-14 (Household survey) Table 12 presents changes in income quintiles (in terms of the mean of income) and a comparison of income quintiles between 2010 and 2014 and between non- NCCP and NCCP groups as well. All quintiles are better between 2010 and 2014 for both groups of non-NCCP and NCCP. However, the poorest quintile shows to be worse off between NCCP vs. non-NNCP. Table 12: Changes in income quintiles (income mean) by Non-NCCP and NCCP groups, 2010-2014 Outcome Non-NCCP NCCP Diff in Diff 2010 2014 Diff 2010 2014 Diff Poorest 5,590 8,791 3,201 5,438 7,207 1,769 -1,432 Near poorest 9,158 26,348 17,190 9,832 27,139 17,307 117 Middle 12,569 43,205 30,636 12,429 44,605 32,176 1,540 Near richest 18,013 68,754 50,741 17,063 67,891 50,828 87 Richest 35,536 163,835 128,299 39,513 175,037 135,524 7,225 Source: Authors’ estimation from VHLSS 2010-14 (Household Survey) Table 13 shows changes in income inequality between 2010 and 2014 and between non-NCCP and NCCP groups as well. Within both non-NCCP and NCCP groups, income inequality increases over 2010-2014. Table 13: Changes in income inequality by Non-NCCP and NCCP groups, 2010-2014 Outcome Non-NCCP NCCP 2010 2014 2010 2014 Top 10/bottom 10 10.04 60.81 13.22 66.26 Top 20/bottom 20 6.36 18.64 7.27 24.29 Income Gini 0.37 0.49 0.39 0.52 Source: Authors’ estimation from VHLSS 2010-14 (Household Survey) Table 14 shows changes in expenditure inequality between 2010 and 2014 and between non-NCCP and NCCP groups as well. Within both, non-NCCP and NCCP groups, expenditure inequality decreases over 2010-2014. Table 14: Changes in expenditure inequality by Non-NCCP and NCCP groups, 2010-2014 Outcome Non-NCCP NCCP 2010 2014 2010 2014 Top 10/bottom 10 4.27 4.13 4.86 4.14 Top 40/bottom 40 2.77 2.65 3.01 2.76 Expenditure Gini 0.29 0.28 0.35 0.32 Source: Authors’ estimation from VHLSS 2010-14 (Household Survey) Household-level confounding variables Time variant household-level explanatory variables that could be correlated with outcome variables have also been obtained from the data set to serve as controls in the fixed effects regression including, demographic characteristics and socio-economic characteristics. Demographic characteristics are compiled from the household roster and include household size and proportion of members in different age/sex groups to capture changes in household composition resulting from births, deaths and marriages. Socio-economic variables include household head’s characteristics, and the proportion of members in a different occupation, and education as well. Summary statistics are reported in Table 15. AgBioForum, 21(3), 2019 | 8 Lai and Quang-Thanh. — Influence of New Countryside Construction Program Table 15: Summary statistics on household-level covariates, 2010-2014 Covariates Baseline (2010) Follow-up (2014) Mean Std. Dev. Mean Std. Dev. Demographic characteristics Household size 3.910 1.536 3.823 1.510 Kinh majority (=1) 0.946 0.227 0.923 0.266 Proportion of elderly 0.119 0.247 0.131 0.237 Proportion of child 0.231 0.205 0.226 0.201 Proportion of female 0.520 0.197 0.501 0.197 Socio-economic characteristics Head’s age 49 14.143 51 13.393 Head male (=1) 0.753 0.432 0.769 0.422 Head married (=1) 0.795 0.404 0.000 0.000 HH member’s occupation "Leaders/ Managers" (%) 0.042 0.136 0.035 0.108 "Professionals/ Technicians" (%) 0.015 0.079 0.018 0.099 "Clerks/ Service Workers" (%) 0.123 0.207 0.115 0.211 "Agriculture/ Forestry/ Fishery" (%) 0.003 0.028 0.007 0.060 "Skilled Workers/Machine Operators" (%) 0.004 0.057 0.002 0.025 "Unskilled Workers" (%) 0.000 0.013 0.000 0.000 HH member’s educational level "No degree" (%) 0.355 0.295 0.322 0.295 "Primary school" (%) 0.308 0.265 0.333 0.275 "Lower Secondary School" (%) 0.141 0.204 0.142 0.205 "Upper Secondary School" (%) 0.058 0.139 0.063 0.151 "College and above" (%) 0.016 0.079 0.023 0.094 Note: HH: Household. Source: Authors’ calculation from VHLSS 2010-2014 (Household Survey) Commune-level confounding variables Time variant commune-level explanatory variables that could be correlated with outcome variables have also been obtained from the data-set to serve as controls in the fixed effects regression including: (1) commune general conditions, (2) social support programs within three years before 2010, and (3) initial infrastructure conditions three years before 2010. Commune conditions include several natural disasters, commune with specific natural disaster, communes that self-reported no improvement or improvement in living standards compared to 5 years before the survey, were asked about the reasons, with possible responses including a natural disaster or production risk or number of enterprises (firms, or factories) per 1000 commune members in 2006-2010, and 2001-2005. Summary statistics are reported in Table 16. Table 16: Summary statistics on commune-level covariates: Commune general conditions Covariates Baseline (2010) Follow-up (2014) Mean Std. Dev. Mean Std. Dev. Storm in the survey year (=1) 0.074 0.261 0.037 0.188 Storm in the last year (=1) 0.119 0.324 0.140 0.347 Storm in the last two years (=1) 0.047 0.212 0.108 0.310 Storm in the last three years (=1) 0.016 0.126 0.040 0.196 Flood in the survey year (=1) 0.006 0.077 0.007 0.086 Flood in the last year (=1) 0.013 0.114 0.031 0.173 Flood in the last two years (=1) 0.007 0.086 0.018 0.132 Flood in the last three years (=1) 0.000 0.000 0.046 0.209 Drought in the survey year (=1) 0.031 0.173 0.003 0.054 Drought in the last year (=1) 0.004 0.066 0.009 0.094 Drought in the last two years (=1) 0.010 0.101 0.000 0.000 Drought in the last three years (=1) 0.000 0.000 0.001 0.038 Epidemic in the survey year (=1) 0.028 0.165 0.003 0.054 Epidemic in the last year (=1) 0.013 0.114 0.016 0.126 Epidemic in the last two years (=1) 0.012 0.108 0.019 0.137 Epidemic in the last three years (=1) 0.000 0.000 0.013 0.114 Insect in the survey year (=1) 0.242 0.428 0.059 0.236 Insect in the last year (=1) 0.060 0.238 0.090 0.286 Insect in the last two years (=1) 0.069 0.254 0.066 0.249 Insect in the last three years (=1) 0.027 0.161 0.029 0.169 Number of enterprises per 1000 commune members in 2006-2010 0.473 0.574 0.507 0.631 Number of enterprises per 1000 commune members in 2001-2005 0.329 0.431 0.297 0.423 Source: Authors’ calculation from VHLSS2010-2014 (Commune survey) AgBioForum, 21(3), 2019 | 9 Lai and Quang-Thanh. — Influence of New Countryside Construction Program Initial infrastructure conditions include infrastructure programs started three years before 2010 and completed three years before 2010 as well. Summary statistics are reported in Table 17. Table 17: Summary statistics on commune-level covariates: Infrastructure programs Covariates Infrastructure programs started 3 years before 2010 Infrastructure programs completed 3 years before 2010 Mean Std. Dev. Mean Std. Dev. Road to district or province (=1) 0.203 0.402 0.178 0.383 Road within commune (=1) 0.502 0.500 0.524 0.500 Bridge 3 (=1) 0.409 0.492 0.391 0.488 Expand irrigation (=1) 0.242 0.429 0.236 0.425 Concrete irrigation canals (=1) 0.138 0.345 0.138 0.345 Electricity (=1) 0.149 0.356 0.133 0.340 Drinking water (=1) 0.230 0.421 0.232 0.423 Health center (=1) 0.223 0.417 0.186 0.389 School (=1) 0.480 0.500 0.393 0.489 Source: Authors’ calculation from VHLSS 2010 (Commune Survey) Estimation steps We examine two sets of models: (1) small model and (2) large model. The small model contains only demographical variables and commune-level variables such as commune general conditions, and initial infrastructure conditions. The large models include additional socio-economic variables such as education, occupation, and commune-level variables as in the small model. We tend to use a small set of control variables that are more exogenous or less likely to be affected by NCCP. The control variables should not be affected by the treatment variable of interest, i.e., the NCCP in this study (Heckman, LaLonde and Smith (1999); Angrist and Pischke (2008)). We use consumption expenditure instead of income as the dependent variable since consumption expenditure is widely used as an aggregate indicator of household welfare and expenditure data contain fewer measurement errors than income data. NCCP variable is suspected to be endogenous in model (1), and thus the estimators can be inconsistent. We apply commune fixed effect-2SLS (FE-2SLS) regressions to estimate the effect of the New Countryside Construction Program (NCCP) on household welfare. The Stata xtivreg2 command is explored (Schaffer, 2015). NCCP variable is instrumented by a set of variables related to social support programs within three years before 2010. Social support programs from the Vietnamese governments and other organizations (such as job creation, hunger elimination and poverty reduction, investment in economic development and infrastructure, investment in culture and education, health and public health, environment/clean water) within three years before 2010. Summary statistics are reported in Table 18. Table 18: Summary statistics on commune-level covariates: Government programs or/and support programs within three years before 2010 Covariates Mean Std. Dev. Job creation (=1) 0.467 0.499 Hunger elimination and poverty reduction (=1) 0.717 0.451 Investment on economic development and infrastructure (=1) 0.592 0.492 Investment on culture and education (=1) 0.244 0.430 Health and public health (=1) 0.150 0.358 Environment/clean water (=1) 0.236 0.425 Source: Authors’ calculation from VHLSS 2010 (Commune Survey) Empirical results and discussion We used two models which differ in the number of explanatory variables in order to examine the sensitivity of the estimates of NCCP impacts to the selection of explanatory variables. The small model contains only demographical variables (such as household size, the proportion of adults above 60 in households, proportion of children below 15 in the household, the proportion of female members in the household, and the ethnicity of the household) and commune-level variables such as commune general conditions (such as specific natural disaster in the last three years, number of enterprises per 1,000 people in 5 and 10 years before), and initial infrastructure conditions (such as infrastructure programs started 3 years before 2010 (namely, the road to district or province, the road within the commune, bridge, irrigation, canals, electricity, drinking water, health center, school), and infrastructure programs completed 3 years before 2010 (namely, the road to district or province, the road within the commune, bridge, irrigation, canals, electricity, drinking water, health center, school). The large models include additional household-level variables related to socio-economic characteristics (such AgBioForum, 21(3), 2019 | 10 Lai and Quang-Thanh. — Influence of New Countryside Construction Program as the age of household head, the gender of household head, the proportion of household members in occupations, the proportion of household members in education), and commune-level variables such as commune general conditions, and initial infrastructure conditions as in small model. The effect of NCCP on expenditure level In Table 19, we present the commune fixed-effects regression of per capita expenditure. In all alternatives of the model with expenditure, results indicate that households in NCCP-qualified commune have a higher real expenditure per capita of around 119 per cent. Although the present study primarily focuses on the impact of NCCP, we observe the negative effects of household size/proportion of children on real expenditure per capita. In addition, households living in commune affected by flood during the survey year, or in commune affected by drought during the last three years have the probability of lower real expenditure per capita of 68,1 percent (e-0.383) and 36 percent (e-1.03), respectively. Table 19: Commune fixed-effects regressions of household expenditure Variable Small model: Real exp. pc Large model: Real exp. pc NCCP (Yes=1; No=0) 1.192*** (0.386) 1.022*** (0.348) Household size -0.0543*** (0.0152) -0.0811*** (0.0129) Proportion of child -0.591*** (0.103) -0.258*** (0.0914) Member occupation "Leaders/ Managers" (%) -0.0374 (0.103) "Professionals/ Technicians" (%) 0.0371 (0.196) "Clerks/ Service Workers" (%) 0.0371 (0.0853) "Agriculture/ Forestry/ Fishery" (%) 0.217 (0.224) "Skilled Workers/ Machine Operators" (%) 0.838*** (0.235) "Unskilled Workers" (%) 0.249 (0.227) Member education "No degree" (%) -0.00151 (0.0993) "Primary school" (%) 0.282*** (0.104) "Lower Secondary School" (%) 0.511*** (0.113) "Upper Secondary School" (%) 1.064*** (0.152) "College and above" (%) 1.238*** (0.354) Flood in the survey year (=1) -0.363* (0.211) -0.305 (0.267) Number of enterprises per 1000 commune members in 2001-2005 0.151** (0.0657) 0.155*** (0.0581) Commune affected by drought during the last three years (=1) -1.030*** (0.394) Observations 1,358 1,358 R-squared 0.010 0.200 Number of communes 679 679 Under identification test (Kleibergen-Paap LM statistic): 16.529 Chi-sq(6) P-value 0.0112 Note: Robust standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1. Source: Authors’ estimation from VHLSS2010 The effect of NCCP on income distribution In Table 20, we present the commune fixed-effects regression of expenditure quantiles for the small model. In all quantiles, (we only present 6 quantiles here to save space), results indicate that households in NCCP- qualified communes have a higher real expenditure per capita than those living in non-NCCP communes. Estimates of the differential impact of NCCP between the lowest three deciles and the top three deciles indicate that households in NCCP-qualified commune have the higher real expenditure per capita of around 41.5 per cent for the bottom, 34.4 per cent for the 2nd decile, and 33.9 per cent for the 3rd decile. NCCP likely affects the top at the most, about 63,7%. However, the effects on the 8th and 9th deciles are less compared with those on the 2nd and the 3rd ones. Table 20: Commune fixed-effects regressions of household expenditure quantiles, small model Variable Bottom 10 2nd decile 9th decile Top 10 NCCP (Yes=1; No=0) 0.415*** (0.0064) 0.343*** (0.0071) 0.0561*** (0.0120) 0.637*** (0.0442) Household size -0.0277*** -0.0385*** -0.0696*** -0.0468*** (0.0020) (0.0009) (0.0010) (0.0149) Proportion of elderly -0.342*** -0.379*** -0.0831*** -0.652*** (0.0052) (0.0054) (0.0055) (0.126) Proportion of child -0.749*** -0.753*** -0.433*** -0.974*** (0.0222) (0.0092) (0.0073) (0.0368) Storm in the last year (=1) -0.0586*** -0.122 AgBioForum, 21(3), 2019 | 11 Lai and Quang-Thanh. — Influence of New Countryside Construction Program (0.0076) (0.150) Storm in the last three years (=1) -0.0691*** -0.0353 -0.0975*** (0.0116) (0.0260) (0.0102) Drought in the last year (=1) -0.00971 (0.0240) Drought in the last two years (=1) -0.229*** -0.379*** (0.0240) (0.00647) Epidemic in the survey year (=1) -0.253*** -0.200*** -0.483*** (0.0045) (0.0135) (0.0048) Number of enterprises per 1000 commune members in 2001-2005 0.123*** (0.0057) 0.110*** (0.0031) 0.0921*** (0.0034) -0.143*** (0.0413) Number of enterprises per 1000 commune members in 2006-2010 0.0282*** (0.0028) 0.0433*** (0.0044) 0.0581*** (0.0033) -0.0301 (0.0549) Storm in the last two years (=1) -0.0334*** (0.0032) Flood in the survey year (=1) -0.288*** (0.0038) Epidemic in the last three years (=1) -0.318*** (0.0057) Drought in the last three years (=1) -0.0715*** (0.0129) Constant 9.191*** (0.0044) 9.437*** (0.0054) 10.48*** (0.0079) 8.926*** (0.0555) Observations 1,358 1,358 1,358 1,358 Note: Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1. Source: Authors’ estimation from VHLSS 2010 In Table 21, we present the commune fixed-effects regression of expenditure quantiles for a large model. In all quantiles, (we only present 6 quantiles here to save space), results indicate that households in NCCP- qualified communes have a higher real expenditure per capita than those living in non-NCCP communes. Estimates of the differential impact of NCCP between the lowest three deciles and the top three deciles indicate that households in NCCP-qualified commune have the higher real expenditure per capita of around 24.8 per cent for the bottom, 16.9 per cent for the 2nd decile, and 14.8 per cent for the 3rd decile. NCCP likely affects the top at the most, about 37,1%. However, the effects on the 8th and 9th deciles are less compared with those on the 2nd and the 3rd ones. Table 21: Commune fixed-effects regressions of household expenditure quantiles, large model Variable Bottom 10 2nd decile 9th decile Top 10 NCCP (Yes=1; No=0) 0.248*** 0.169*** 0.111*** 0.371*** (0.0039) (0.0076) (0.0014) (0.0046) Household size -0.0279*** -0.0509*** -0.102*** -0.0573*** (0.0010) (0.0006) (0.0011) (0.0017) Proportion of elderly -0.216*** -0.0985*** 0.205*** -0.334*** (0.0073) (0.0031) (0.0009) (0.0305) Proportion of child -0.429*** -0.308*** -0.185*** -0.130*** (0.0154) (0.0075) (0.0011) (0.0072) Age’s head -0.0006*** 0.0005*** -0.0025*** -0.0016*** (0.0002) (8.48x10-5) (0.0001) (0.0002) Male head (=1) 0.0455*** 0.0796*** 0.00255 0.00467 (0.00279) (0.0027) (0.0026) (0.0138) Head married (=1) -0.141*** -0.137*** -0.0642*** -0.113*** (0.0026) (0.0024) (0.0007) (0.0032) HH member’s occupation "Leaders/ Managers" (%) 0.282*** 0.191*** -0.0657*** 0.586*** (0.0087) (0.0086) (0.0012) (0.0194) "Professionals/ Technicians" (%) 0.0107 -0.0399*** -0.0365*** 0.0203* (0.0076) (0.0112) (0.00207) (0.0113) "Clerks/ Service Workers" (%) 0.233*** 0.211*** 0.234*** 0.413*** (0.0086) (0.0033) (0.0019) (0.0044) "Agriculture/ Forestry/ Fishery" (%) 0.0540 (0.0401) 0.0219** (0.0102) 0.119*** (0.0014) 0.149*** (0.0145) "Skilled Workers/ Machine Operators" (%) 0.936*** (0.0259) 0.766*** (0.0203) 0.337*** (0.0024) 1.502*** (0.0116) "Unskilled Workers" (%) 1.412*** 0.509*** -2.696*** 3.610*** (0.0074) (0.0162) (0.0011) (0.0616) HH member’s educational level "No degree" (%) 0.239*** 0.157*** -0.0755*** 0.0899*** (0.0096) (0.0042) (0.0021) (0.0242) "Primary school" (%) 0.434*** 0.440*** 0.323*** 0.349*** (0.0054) (0.0082) (0.0011) (0.0473) "Lower Secondary School" (%) 0.798*** 0.760*** 0.533*** 0.612*** (0.0056) (0.0041) (0.0013) (0.0462) "Upper Secondary School" (%) 0.816*** 0.927*** 1.055*** 0.948*** (0.0137) (0.0095) (0.0005) (0.0258) AgBioForum, 21(3), 2019 | 12 Lai and Quang-Thanh. — Influence of New Countryside Construction Program Variable Bottom 10 2nd decile 9th decile Top 10 "College and above" (%) 1.421*** 1.605*** 1.204*** 1.829*** (0.00893) (0.0168) (0.00136) (0.0333) Storm during the last year (=1) -0.0231*** -0.0248*** -0.0643*** (0.0034) (0.0033) (0.0034) Storm in the last three years (=1) -0.0701*** (0.0069) -0.0873*** (0.0026) Flood in the last three years (=1) -0.0267** (0.0127) -0.0242*** (0.0091) Drought in the last year (=1) -0.0662*** (0.0044) Drought in the last two years (=1) -0.264*** (0.0059) -0.512*** (0.0008) Number of enterprises per 1000 commune members in 2001-2005 0.0782*** (0.0047) 0.0952*** (0.0017) 0.0372*** (0.0012) -0.256*** (0.0080) Number of enterprises per 1000 commune members in 2006-2010 0.0171*** (0.0022) 0.0138*** (0.0012) 0.104*** (0.0013) 0.0565*** (0.0021) Epidemic in the survey year (=1) -0.163*** (0.0056) -0.208*** (0.0010) Storm in the last two years (=1) -0.163*** (0.0017) -0.0087* (0.0045) Flood in the survey year (=1) -0.472*** (0.0037) Drought in the last three years (=1) -0.0867*** (0.0011) Epidemic in the last three years (=1) -0.250*** (0.0009) Constant 8.803*** 8.948*** 10.32*** 8.582*** (0.0158) (0.0075) (0.0092) (0.0121) Observations 1,358 1,358 1,358 1,358 Note: Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1. Source: Authors’ estimation from VHLSS2010 Conclusion The paper estimates the effect of NCCP on household welfare in the Mekong River Delta of Vietnam. It finds that NCCP has a positive effect on household expenditure in all models, both in terms of expenditure level, and expenditure quintile. In all alternatives of the model with expenditure, results indicate that households in NCCP-qualified commune have a higher real expenditure per capita of around 1.1 times. Estimates of the differential impact of NCCP between the lowest three deciles and the top three deciles in both small and large models indicate that households in NCCP-qualified commune have a higher real expenditure per capita of around 41.5 percent for the bottom, 34.4 percent for the 2nd decile, and 33.9 percent for the 3rd decile. NCCP likely affects the top at the most, about 63.7%. However, the effects on the 8th and 9th deciles are less when compared with those on the 2nd and the 3rd ones. While efforts were made to identify the impact of NCCP, it is not possible to completely differentiate its impact from the spillover effect since NCCP is said to be a profound and comprehensive social mobilization (in economic, socio-cultural development, productivity, living standards, lifestyle, customs, and traditions). It is also not possible to completely disaggregate NCCP into separated programs to evaluate the total and specific impacts of NCCP. References Abdulai, A. and CroleRees, A. (2001) “Determinants of income diversification amongst rural households in Southern Mali”, Food Policy, Vol. 26, No. 4, pp. 437-452. ISSN 0306-9192. DOI 10.1016/S0306- 9192(01)00013-6. Ahlers, A.L. and Schubert, G. (2009) “`Building a new socialist countryside’–only a political slogan?”, Journal of Current Chinese Affairs, Vol. 38, No. 4, pp. 35-62. ISSN 1868-4874. DOI 10.1177/186810260903800403. Angrist, J.D. and Pischke, J.-S. (2008) Mostly harmless econometrics: An empiricist's companion. Princeton university press. Calderón, C. and Chong, A. (2004) “Volume and quality of infrastructure and the distribution of income: an empirical investigation”, Review of Income and Wealth, Vol. 50, No. 1, pp. 87-106. ISSN 0034- 6586, 1475-4991. DOI 10.1111/j.0034- 6586.2004.00113.x. Calderon, C.A. and Servén, L. (2004) 'The effects of infrastructure development on growth and income distribution', The World Bank. DOI 10.1596/1813- 9450-3400. Charlery, L.C., Qaim, M. and Smith-Hall, C. (2016) “Impact of infrastructure on rural household income and inequality in Nepal”, Journal of Development Effectiveness, Vol. 8, No. 2, pp. 266-286. ISSN https://doi.org/10.1016/S0306-9192(01)00013-6 https://doi.org/10.1016/S0306-9192(01)00013-6 https://doi.org/10.1177%2F186810260903800403 https://doi.org/10.1111/j.0034-6586.2004.00113.x https://doi.org/10.1111/j.0034-6586.2004.00113.x https://doi.org/10.1596/1813-9450-3400 https://doi.org/10.1596/1813-9450-3400 AgBioForum, 21(3), 2019 | 13 Lai and Quang-Thanh. — Influence of New Countryside Construction Program 1943-9407, 1943-9342. DOI 10.1080/19439342.2015.1079794. Chenery, H.B. (1960) “Patterns of industrial growth”, The American Economic Review, Vol. 50, No. 4, pp. 624-654. ISSN 0002-8282. Clark, C. (1940) “The morphology of economic growth”, The Conditions of Economic Progress, Macmillan, London, pp. 337-373. Deininger, K. and Olinto, P. (2001) “Rural nonfarm employment and income diversification in Colombia”, World Development, Vol. 29, No. 3, pp. 455-465. ISSN 0305-750X, 1873-5991. DOI 10.1016/S0305-750X(00)00106-6. Escobal, J. (2001) “The determinants of nonfarm income diversification in rural Peru”, World Development, Vol. 29, No. 3, pp. 497-508. ISSN 0305-750X, 1873-5991. DOI 10.1016/S0305- 750X(00)00104-2. Glewwe, P. (1991) “Investigating the determinants of household welfare in Côte d'Ivoire”, Journal of Development Economics, Vol. 35, No. 2, pp. 307- 337. ISSN 0304-3878. DOI 10.1016/0304- 3878(91)90053-X. Heckman, J.J., LaLonde, R.J. and Smith, J.A. (1999) “The economics and econometrics of active labor market programs”, Handbook of Labor Economics, Vol.3, pp. 1865-2097. DOI 10.1016/S1573- 4463(99)03012-6. IFAD-World Bank (2016) “Assessment of the National Targeted Program on New Rural Development (NTP-NRD) Phase 1. Im, S. B., Lee, S. H., Lee, J. and Kim, T. (2016) “Contribution of Agricultural Infrastructure to Rural Development in the Republic of Korea”, Irrigation and Drainage, Vol. 65, No. S1, pp. 40-47. ISSN 1531-0353. DOI 10.1002/ird.1997. Jacka, T. (2013) “Chinese discourses on rurality, gender and development: a feminist critique”, Journal of Peasant Studies, Vol. 40, No. 6, pp. 983-1007. ISSN 0306-6150. DOI 10.1080/03066150.2013.855723. Kara, M.A., Taş, S. and Ada, S. (2016) “The Impact of Infrastructure Expenditure Types on Regional Income in Turkey”, Regional Studies, Vol. 50, No. 9, pp. 1509-1519. ISSN 1360-0591. DOI 10.1080/00343404.2015.1041369. Kuznets, S. (1957) “Quantitative aspects of the economic growth of nations: II. industrial distribution of national product and labor force”, Economic Development and Cultural Change, Vol. 5, No. S4, pp. 1-111. ISSN 0013-0079. Lewis, W.A. (1954) “Economic development with unlimited supplies of labour”, Manchester School of Economic and Scial Studies, Vol. 22, pp. 139-191. Lewis, W.A. (1979) “The dual economy revisited”, The Manchester School, Vol. 47, No. 3, pp. 211-229. ISSN . Liêng, N.T.D. (2015) “The reality and solutions in building new countryside in Tri Ton district, An Giang province”, Journal of Science, Vol. 2, No. 2, pp. 92-103. Long, H., Liu, Y., Li, X. and Chen, Y. (2010) “Building new countryside in China: A geographical perspective”, Land Use Policy, Vol. 27, No. 2, pp. 457-470. ISSN 0264-8377. DOI 10.1016/j.landusepol.2009.06.006. Looney, K. (2012) The rural developmental state: Modernization campaigns and peasant politics in China, Taiwan and South Korea, Doctoral Dissertataion, Havard University. Narayan, D. and Pritchett, L. (1999) “Cents and sociability: Household income and social capital in rural Tanzania”, Economic Development and Cultural Change, Vol. 47, No. 4, pp. 871-897. ISSN 0013-0079. DOI 10.1086/452436. Perry, E.J. (2011) “From mass campaigns to managed campaigns: `Constructing a new socialist countryside’”, Mao's Invisible Hand. Brill, pp. 30- 61. ISSN . Rahman, S. (2014) “Impact of rural infrastructure on farm and non-farm enterprise choice and income in Bangladesh”, The Journal of Developing Areas, Vol. 48, No. 1, pp. 275-290. ISSN 1548-2278. Reardon, T., Berdegué, J. and Escobar, G. (2001) “Rural nonfarm employment and incomes in Latin America: overview and policy implications”, World Development, Vol. 29, No. 3, pp. 395-409. ISSN 0305-750X, 1873-5991. DOI 10.1016/S0305- 750X(00)00112-1. Reardon, T., Taylor, J. E., Stamoulis, K., Lanjouw, P. and Balisacan, A. (2000) “Effects of non‐farm employment on rural income inequality in developing countries: An investment perspective”, Journal of Agricultural Economics, Vol. 51, No. 2, pp. 266-288. ISSN 1477-9552. DOI 10.1111/j.1477- 9552.2000.tb01228.x. Rostow, W.W. (1959) “The stages of economic growth”, The Economic History Review, Vol. 12, No. 1, pp. 1- 16. ISSN 1468-0289. DOI 10.2307/2591077. Schaffer, M.E. (2015) “xtivreg2: Stata module to perform extended IV/2SLS, GMM and AC/HAC, LIML and k-class regression for panel data models”, https://doi.org/10.1080/19439342.2015.1079794 https://doi.org/10.1016/S0305-750X(00)00106-6 https://doi.org/10.1016/S0305-750X(00)00104-2 https://doi.org/10.1016/S0305-750X(00)00104-2 https://doi.org/10.1016/0304-3878(91)90053-X https://doi.org/10.1016/0304-3878(91)90053-X https://doi.org/10.1016/S1573-4463(99)03012-6 https://doi.org/10.1016/S1573-4463(99)03012-6 https://doi.org/10.1002/ird.1997 https://doi.org/10.1080/03066150.2013.855723 https://doi.org/10.1080/00343404.2015.1041369 https://doi.org/10.1016/j.landusepol.2009.06.006 https://doi.org/10.1086/452436 https://doi.org/10.1016/S0305-750X(00)00112-1 https://doi.org/10.1016/S0305-750X(00)00112-1 https://doi.org/10.1111/j.1477-9552.2000.tb01228.x https://doi.org/10.1111/j.1477-9552.2000.tb01228.x https://doi.org/10.2307/2591077 AgBioForum, 21(3), 2019 | 14 Lai and Quang-Thanh. — Influence of New Countryside Construction Program Statistical Software Components. Shenggen, F. and Zhang, X. (2004) “Infrastructure and regional economic development in rural China”, China Economic Review, Vol. 15, No. 2, pp. 203- 214. ISSN 1043951X. Vietnam National Assembly (2016) “Monitoring Report on the Implementation of the National Target Program on New Rural Construction for the period of 2010-2015 and Agricultural Restructuring in Vietnam”.