The Impact of Remittances on Saving Behaviour and Expenditure Patterns in Vietnam
Abstract
:1. Introduction
2. Methodology
2.1. Methods
2.2. Data
3. Results
3.1. Results of the Estimated Propensity Score by Logit Regression
- The probability of receiving remittances increases steeply with the age of the household head until the age of 70 is reached, after which the probability decreases.
- There are significant differences in the probability of receiving remittances between households in different regions. According to the coefficients of the different regions, the probability of households receiving remittances is the highest for the Midlands and Northern Mountainous Areas, followed by the Northern and Coastal Central Region, the Red River Delta, the Mekong River Delta, the Central Highlands, and the South-Eastern Area. However, the difference in the probability of receiving remittances of households in the Mekong River Delta and households in the Red River Delta is not significant.
- Rural households have a higher probability of receiving remittances than urban households.
- As far as education is concerned, the probability of receiving remittances depends negatively on the number of well-educated members. Migrants from a well-educated household could have less strong motives to send remittances to support their home families.
- With regard to the marital status of household head, married household heads have a higher probability of receiving remittances than the others.
- Older children negatively affect the probability of receiving remittances. Migrant members could have less responsibility to support their home families in cases where these consist of older children. As in Hua and Erreygers (2020), we considered older children as belonging to the household labour force, and not as dependent members, as in other empirical papers. This result confirms the role of older children as labourers in households.
- The probability of receiving remittances depends negatively on household size. This implies that small families tend to receive remittances more often than larger families.
- Lastly, the effect of the ethnicity covariate reveals that the Kinh have a higher probability of receiving remittances than other ethnic groups. This result supports the conclusion of Nguyen and Vu (2018) and Coxhead et al. (2019), who found that people from minor ethnicities were less likely to migrate than Kinh people.
3.2. Defining the Common Support for Propensity Scores of Treated and Non-Treated Groups
3.3. Impact of Remittances on Saving Behaviour and Expenditure Patterns
3.3.1. Effect of Remittances on Saving, Adjusted Income, and Total Expenditure
3.3.2. Effect of Remittances on Household Expenditure Patterns
4. Discussion
4.1. Assessing the Quality of the Matching
4.2. Analysing Sensitivity
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Aakvik, Arild. 2001. Bounding a matching estimator: The case of a Norwegian training program. Oxford Bulletin of Economics and Statistics 63: 115–43. [Google Scholar]
- Adams, Richard H., Jr., and Alfredo Cuecuecha. 2010. Remittances, household expenditure and investment in Guatemala. World Development 38: 1626–41. [Google Scholar] [CrossRef]
- Adams, Richard H., Jr., Alfredo Cuecuecha, and John Page. 2008. Remittances, Consumption and Investment in Ghana. Policy Research Working Paper No. 4515. Washington, DC: World Bank. [Google Scholar]
- Aggarwal, Reena, Asli Demirgüç-Kunt, and Maria Soledad Martínez Pería. 2011. Do remittances promote financial development? Journal of Development Economics 96: 255–64. [Google Scholar] [CrossRef]
- Ait Benhamou, Zouhair, and Lesly Cassin. 2021. The impact of remittances on savings, capital and economic growth in small emerging countries. Economic Modelling 94: 789–803. [Google Scholar] [CrossRef]
- Ang, Alvin P., Guntur Sugiyarto, and Shikha Jha. 2009. Remittances and Household Behavior in the Philippines. Asian Development Bank Economics Working Paper No. 188. Manila: Asian Development Bank. Available online: https://www.adb.org/publications/remittances-and-household-behavior-philippines (accessed on 15 July 2022).
- Austin, Peter C. 2009. Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples. Statistics in Medicine 28: 3083–107. [Google Scholar] [CrossRef] [PubMed]
- Becker, Sascha O., and Marco Caliendo. 2007. Sensitivity analysis for average treatment effects. The Stata Journal 7: 71–83. [Google Scholar] [CrossRef]
- Berloffa, Gabriella, and Sara Giunti. 2019. Remittances and healthcare expenditure: Human capital investment or responses to shocks? Evidence from Peru. Review of Development Economics 23: 1540–61. [Google Scholar] [CrossRef]
- Caliendo, Marco, and Sabine Kopeinig. 2008. Some practical guidance for the implementation of propensity score matching. Journal of Economic Surveys 22: 31–72. [Google Scholar] [CrossRef]
- Cardona-Sosa, Lina, and Carlos Medina. 2006. Migration as a Safety Net and Effects of Remittances on Household Consumption: The Case of Colombia. Borradores de Economía No. 414. Bogotá: Banco de la Republica de Colombia. [Google Scholar]
- Castaldo, Adriana, and Barry Reilly. 2007. Do migrant remittances affect the consumption patterns of Albanian households? South-Eastern Europe Journal of Economics 5: 25–44. [Google Scholar]
- Clément, Matthieu. 2011. Remittances and household expenditure patterns in Tajikistan: A propensity score matching analysis. Asian Development Review 28: 58–87. [Google Scholar] [CrossRef]
- Coxhead, Ian, Viet Cuong Nguyen, and Hoang Linh Vu. 2019. Internal migration in Vietnam: 2002–2012. In Rural-Urban Migration in Vietnam. Edited by Amy Y. C. Liu and Xin Meng. Cham: Springer, pp. 67–96. [Google Scholar]
- Deaton, Angus S. 2019. The Analysis of Household Surveys: A Microeconometric Approach to Development Policy. Washington, DC: World Bank. [Google Scholar]
- Deaton, Angus S., Javier Ruiz-Castillo, and Duncan Thomas. 1989. The influence of household composition on household expenditure patterns: Theory and Spanish evidence. Journal of Political Economy 97: 179–200. [Google Scholar] [CrossRef]
- de Brauw, Alan, and Scott Rozelle. 2008. Migration and household investment in rural China. China Economic Review 19: 320–35. [Google Scholar] [CrossRef]
- Démurger, Sylvie, and Xiaoqian Wang. 2016. Remittances and expenditure patterns of the left behinds in rural China. China Economic Review 37: 177–90. [Google Scholar] [CrossRef]
- Esquivel, Gerardo, and Alejandra Huerta-Pineda. 2007. Remittances and poverty in Mexico: A propensity score matching approach. Integration and Trade 27: 45–71. [Google Scholar]
- Friedman, Milton. 1957. A Theory of the Consumption Function. Princeton: National Bureau of Economic Research, Inc. [Google Scholar]
- Gangl, Markus. 2004. RBOUNDS: Stata Module to Perform Rosenbaum Sensitivity Analysis for Average Treatment Effects on the Treated. Statistical Software Components S438301, Boston College Department of Economics. Available online: https://ideas.repec.org/c/boc/bocode/s438301.html (accessed on 28 August 2022).
- Garrido, Melissa M., Amy S. Kelley, Julia Paris, Katherine Roza, Diane E. Meier, R. Sean Morrison, and Melissa D. Aldridge. 2014. Methods for constructing and assessing propensity scores. Health Services Research 49: 1701–20. [Google Scholar] [CrossRef]
- Haider, Mohammed Ziaul, Tanbir Hossain, and Ohidul Islam Siddiqui. 2016. Impact of remittance on consumption and savings behavior in rural areas of Bangladesh. Journal of Business 1: 25–34. [Google Scholar] [CrossRef]
- Heckman, James J., Hidehiko Ichimura, and Petra E. Todd. 1997. Matching as an econometric evaluation estimator: Evidence from evaluating a job training programme. Review of Economic Studies 64: 605–54. [Google Scholar] [CrossRef]
- Heckman, James J., Hidehiko Ichimura, Jeffrey Smith, and Petra E. Todd. 1998. Characterizing selection bias using experimental data. Econometrica 66: 1017–98. [Google Scholar] [CrossRef]
- Ho, Daniel E., Kosuke Imai, Gary King, and Elizabeth A. Stuart. 2007. Matching as nonparametric preprocessing for reducing model dependence in parametric causal inference. Political Analysis 15: 199–236. [Google Scholar] [CrossRef]
- Hua, Thanh Xuan, and Guido Erreygers. 2020. Applying quantile regression to determine the effects of household characteristics on household saving rates in Vietnam. Journal of Asian Business and Economic Studies 27: 175–93. [Google Scholar] [CrossRef]
- Jimenez-Soto, Eliana V., and Richard P. C. Brown. 2012. Assessing the poverty impacts of migrants’ remittances using propensity score matching: The case of Tonga. Economic Record 88: 425–39. [Google Scholar] [CrossRef]
- Junge, Vera, Javier Revilla Diez, and Ludwig Schätzl. 2015. Determinants and consequences of internal return migration in Thailand and Vietnam. World Development 71: 94–106. [Google Scholar] [CrossRef]
- Kessels, Roselinde, and Guido Erreygers. 2019. A direct regression approach to decomposing socioeconomic inequality of health. Health Economics 28: 884–905. [Google Scholar] [CrossRef]
- Leser, Conrad E. V. 1963. Forms of Engel functions. Econometrica 31: 694–703. [Google Scholar] [CrossRef]
- Leuven, Edwin, and Barbara Sianesi. 2018. PSMATCH2: Stata Module to Perform Full Mahalanobis and Propensity Score Matching, Common Support Graphing, and Covariate Imbalance Testing. Available online: https://EconPapers.repec.org/RePEc:boc:bocode:s432001 (accessed on 15 July 2022).
- Li, Mingxiang. 2012. Using the propensity score method to estimate causal effects: A review and practical guide. Organizational Research Methods 16: 188–226. [Google Scholar] [CrossRef]
- Liu, Amy Y. C., and Xin Meng, eds. 2019. Rural-Urban Migration in Vietnam. Cham: Springer. [Google Scholar]
- Luong, Hy V. 2018. The changing configuration of rural-urban migration and remittance flows in Vietnam. Sojourn: Journal of Social Issues in Southeast Asia 33: 602–46. [Google Scholar] [CrossRef]
- McKenzie, David, and Marcin J. Sasin. 2007. Migration, Remittances, Poverty, and Human Capital: Conceptual and Empirical Challenges. Policy Research Working Paper No. 4272. Washington, DC: World Bank. [Google Scholar]
- McKenzie, David, Steven Stillman, and John Gibson. 2010. How important is selection? Experimental vs. non-experimental measures of the income gains from migration. Journal of the European Economic Association 8: 913–45. [Google Scholar] [CrossRef]
- Ministry of Foreign Affairs of Vietnam. 2012. Review of Vietnamese Migration Abroad; Ha Noi: Consular Department, Ministry of Foreign Affairs of Vietnam.
- Modigliani, Franco, and Richard Brumberg. 1954. Utility analysis and the consumption function: An interpretation of cross-section data. In Post-Keynesian Economics. Edited by Kenneth K. Kurihara. New Brunswick: Rutgers University Press, pp. 388–436. [Google Scholar]
- Nguyen, Duc Loc, Ulrike Grote, and Trung Thanh Nguyen. 2017. Migration and rural household expenditures: A case study from Vietnam. Economic Analysis and Policy 56: 163–75. [Google Scholar] [CrossRef]
- Nguyen, Thu Phuong, Ngo Thi Minh Tam Tran, Thi Nguyet Nguyen, and Remco Oostendorp. 2008. Determinants and Impacts of Migration in Vietnam. Working Papers 2008/01. Ha Noi: Development and Policies Research Center (DEPOCEN). [Google Scholar]
- Nguyen, Viet Cuong. 2008. Impacts of international and internal remittances on household welfare: Evidence from Viet Nam. Asia-Pacific Development Journal 16: 59–92. [Google Scholar] [CrossRef]
- Nguyen, Viet Cuong, and Daniel Mont. 2012. Economic impacts of international migration and remittances on household welfare in Vietnam. International Journal of Development Issues 11: 144–63. [Google Scholar]
- Nguyen, Viet Cuong, and Huang Linh Vu. 2018. The impact of migration and remittances on household welfare: Evidence from Vietnam. Journal of International Migration and Integration 19: 945–63. [Google Scholar]
- Opiniano, Jeremaiah M. 2021. Remittances and the financial capabilities of migrant households in the Philippines. Asian and Pacific Migration Journal 30: 370–85. [Google Scholar] [CrossRef]
- Ponce, Juan, Iliana Olivié, and Mercedes Onofa. 2011. The role of international remittances in health outcomes in Ecuador: Prevention and response to shocks. International Migration Review 45: 727–45. [Google Scholar] [CrossRef]
- Quartey, Peter, Charles Ackah, and Monica Puoma Lambon-Quayefio. 2019. Inter-linkages between remittance and savings in Ghana. International Journal of Social Economics 46: 152–66. [Google Scholar] [CrossRef]
- Randazzo, Teresa, and Matloob Piracha. 2019. Remittances and household expenditure behaviour: Evidence from Senegal. Economic Modelling 79: 141–53. [Google Scholar] [CrossRef] [Green Version]
- Ratha, Dilip, Eung Ju Kim, Sonia Plaza, Elliott J. Riordan, and Vandana Chandra. 2022. Migration and Development Brief 36: A War in a Pandemic: Implications of the Russian Invasion of Ukraine and the COVID-19 Crisis on Global Governance of Migration and Remittance Flows. Washington, DC: KNOMAD-World Bank. [Google Scholar]
- Rosenbaum, Paul R. 2002. Observational Studies. New York: Springer. [Google Scholar]
- Rosenbaum, Paul R., and Donald B. Rubin. 1983. The central role of the propensity score in observational studies for causal effects. Biometrika 70: 41–55. [Google Scholar] [CrossRef]
- Rosenbaum, Paul R., and Donald B. Rubin. 1985. Constructing a control group using multivariate matched sampling methods that incorporate the propensity score. The American Statistician 39: 33–38. [Google Scholar]
- Salahuddin, Sarah, Muhammad Mehedi Masud, and Kian Teng Kwek. 2022. Remittances and households’ savings behaviour in Bangladesh. Society and Business Review 17: 120–40. [Google Scholar] [CrossRef]
- Sianesi, Barbara. 2004. An evaluation of the Swedish system of active labor market programs in the 1990s. Review of Economics and Statistics 86: 133–55. [Google Scholar] [CrossRef]
- Stark, Oded, and David E. Bloom. 1985. The new economics of labor migration. American Economic Review 75: 173–78. [Google Scholar]
- Tabuga, Aubrey D. 2010. International remittances and family expenditure patterns: The Philippines’ case. Philippine Journal of Development 35: 103–23. [Google Scholar]
- Taylor, J. Edward, and Jorge Mora. 2006. Does Migration Reshape Expenditures in Rural Households? Evidence from Mexico. Policy Research Working Paper No. 3842. Washington, DC: World Bank. [Google Scholar]
- Wen, Ming, and Danhua Lin. 2012. Child development in rural China: Children left behind by their migrant parents and children of nonmigrant families. Child Development 83: 120–36. [Google Scholar] [CrossRef]
- World Bank. 2012. Well Begun, Not Yet Done: Vietnam’s Remarkable Progress on Poverty Reduction and the Emerging Challenges. Hanoi: World Bank. [Google Scholar]
- World Bank. 2016. Transforming Vietnamese Agriculture: Gaining More from Less. Washington, DC: World Bank. [Google Scholar]
- Working, Holbrook. 1943. Statistical laws of family expenditure. Journal of the American Statistical Association 38: 43–56. [Google Scholar] [CrossRef]
- Yang, Dean. 2008. International migration, remittances and household investment: Evidence from Phillipine migrants’ exchange rate shocks. Economic Journal 118: 591–630. [Google Scholar] [CrossRef]
- Yoshino, Naoyuki, Farhad Taghizadeh-Hesary, and Miyu Otsuka. 2020. Determinants of international remittance inflow in Asia-Pacific middle-income countries. Economic Analysis and Policy 68: 29–43. [Google Scholar] [CrossRef]
- Zhu, Yu, Zhongmin Wu, Liquan Peng, and Laiyun Sheng. 2014. Where did all the remittances go? Understanding the impact of remittances on consumption patterns in rural China. Applied Economics 46: 1312–22. [Google Scholar] [CrossRef] [Green Version]
Category | Description |
---|---|
Education | All education expenses of the household members, including tuition fees, contributions to class, school, uniforms, books, study instruments (paper, pen, etc.), coaching sessions, and others (such as exam fees, travel, rent, and student body insurance). |
Health | All expenses for healthcare and health checks, such as doctor fees, lab fees, hospitalisation, prescription, travel, and insurance fees. |
Assets | All expenditures on house equipment, such as bikes, motorbike, car, boat, phone, air conditioner, and washing machine. |
House repairs | All costs for repairing and maintaining the house. |
Food | All expenditures on food and drink. |
Non-food | All expenditures on non-food consumption categories. |
Utilities | All expenditures on water, electricity, and waste. |
Whole Sample | Households with Remittances | Households without Remittances | ||||
---|---|---|---|---|---|---|
(N = 8778 and W = 21,870,190) | (N = 2174 and W = 5,427,473) | (N = 6604 and W = 16,442,717) | ||||
Numerical variables | Mean | Std. Dev. | Mean | Std. Dev. | Mean | Std. Dev. |
Household size | 3.836 | 1.516 | 3.478 | 1.686 | 3.954 | 1.437 |
Number of members with at least high-school degree | 0.881 | 1.128 | 0.738 | 1.017 | 0.928 | 1.158 |
Age of the household head | 49.736 | 13.817 | 58.116 | 11.748 | 46.969 | 13.324 |
Number of elderly members | 0.210 | 0.498 | 0.329 | 0.595 | 0.171 | 0.454 |
Number of children 0–5 years | 0.342 | 0.589 | 0.244 | 0.512 | 0.375 | 0.609 |
Number of children 6–14 years | 0.539 | 0.760 | 0.283 | 0.597 | 0.623 | 0.789 |
Categorical variables (%) | ||||||
Living area household | ||||||
Urban | 30.79 | 20.99 | 34.02 | |||
Rural | 69.21 | 79.01 | 65.98 | |||
Ethnicity household head | ||||||
Kinh | 87.95 | 91.37 | 86.81 | |||
Minor ethnicity | 12.05 | 8.63 | 13.19 | |||
Marital status household head | ||||||
Married | 81.93 | 78.24 | 83.14 | |||
Otherwise | 18.07 | 21.76 | 16.86 | |||
Region of household living | ||||||
Red River Delta | 24.92 | 28.36 | 23.78 | |||
Midlands and Northern Mountainous Areas | 12.54 | 12.39 | 12.59 | |||
Northern and Coastal Central Region | 22.39 | 26.96 | 20.88 | |||
Central Highlands | 5.17 | 2.92 | 5.91 | |||
South-Eastern Area | 16.81 | 8.75 | 19.47 | |||
Mekong River Delta | 18.17 | 20.62 | 17.37 |
Whole Sample | Households with Remittances | Households without Remittances | ||||
---|---|---|---|---|---|---|
Numerical Variables | Mean | Std. Dev. | Mean | Std. Dev. | Mean | Std. Dev. |
Adjusted income (1000 VND) | 99,406.460 | 99,510.700 | 89,412.370 | 80,718.890 | 102,705.400 | 104,774.100 |
Remittances (1000 VND) | 3259.227 | 15,572.290 | 13,133.170 | 29,116.130 | ||
Saving amount (1000 VND) | 22,027.620 | 71,713.220 | 22,227.710 | 67,708.120 | 21,961.570 | 72,991.840 |
Saving rate | −0.005 | 1.456 | 0.059 | 0.642 | −0.026 | 1.638 |
Total expenditure (1000 VND) | 77,378.850 | 61,546.320 | 67,184.660 | 56,730.740 | 80,743.780 | 62,696.420 |
Share of education expenditure (%) | 0.044 | 0.069 | 0.034 | 0.064 | 0.048 | 0.070 |
Share of health expenditure (%) | 0.048 | 0.079 | 0.064 | 0.096 | 0.043 | 0.072 |
Share of assets expenditure (%) | 0.040 | 0.089 | 0.041 | 0.095 | 0.040 | 0.087 |
Share of house repairs (%) | 0.012 | 0.056 | 0.015 | 0.066 | 0.010 | 0.052 |
Share of food expenditure (%) | 0.542 | 0.129 | 0.536 | 0.133 | 0.544 | 0.128 |
Share of non-food expenditure (%) | 0.280 | 0.096 | 0.278 | 0.101 | 0.280 | 0.095 |
Share of utilities (%) | 0.034 | 0.033 | 0.031 | 0.025 | 0.035 | 0.035 |
Per capita expenditure (PCE) (1000 VND) | 21,329.770 | 17,054.220 | 20,383.920 | 16,176.260 | 21,641.980 | 17,324.120 |
PCE for education (1000 VND) | 1086.043 | 4559.845 | 718.995 | 1839.826 | 1207.199 | 5145.896 |
PCE for health (1000 VND) | 1072.264 | 2672.007 | 1420.606 | 3260.805 | 957.283 | 2436.128 |
PCE for assets (1000 VND) | 1223.634 | 6611.444 | 1426.143 | 10,424.190 | 1156.789 | 4718.759 |
PCE for house repairs (1000 VND) | 3582.293 | 2255.531 | 537.692 | 3030.480 | 298.992 | 1929.331 |
PCE for food (1000 VND) | 10,704.140 | 6671.854 | 10,007.310 | 5234.823 | 10,934.150 | 7067.761 |
PCE for non-food (1000 VND) | 6111.310 | 6252.114 | 5649.425 | 4550.661 | 6263.771 | 6713.151 |
PCE for utilities (1000 VND) | 99,406.460 | 99,510.700 | 623.750 | 787.180 | 823.795 | 1278.468 |
Variable | Coefficients Initial Model (Chi-Square Value) | Coefficients Final Model (Chi-Square Value) |
---|---|---|
Age of the household head | 0.101 *** (677.99) | 0.101 *** (700.42) |
Squared age of the household head (mean-centred) | −0.003 *** (208.09) | −0.002 *** (252.88) |
South-Eastern Area | −0.625 *** (31.04) | −0.627 *** (31.28) |
Central Highlands | −0.529 *** (13.05) | −0.527 *** (12.95) |
Northern and Coastal Central Region | 0.162 ** (3.88) | 0.165 ** (4.00) |
Midlands and Northern Mountainous Areas | 0.169 * (2.71) | 0.178 * (3.00) |
Red River Delta | 0.004 (0.00) | 0.015 (0.03) |
Urban | −0.491 *** (53.99) | −0.488 *** (53.49) |
Number of members with at least high-school degree | −0.193 *** (41.81) | −0.200 *** (45.94) |
Married household head | 0.389 *** (25.70) | 0.388 *** (26.70) |
Number of children 6–14 years | −0.249 *** (21.25) | −0.273 *** (28.48) |
Household size | −0.097 *** (12.80) | −0.074 *** (12.08) |
Kinh household head | 0.287 *** (8.17) | 0.294 *** (8.61) |
Number of elderly members | 0.116 (2.64) | / |
Number of children 0–5 years | 0.079 (1.41) | / |
Constant | −5.791 *** (533.53) | −5.857 *** (559.30) |
Pseudo R2 | 0.189 | 0.189 |
−Log-likelihood (full model–constant model) | 928 *** (1856.23) | 926 *** (1852.70) |
Observations, N | 8778 | 8778 |
Household Group | N | Mean | Std. Dev. | Min | Max |
---|---|---|---|---|---|
With remittances | 2174 | 0.3965 | 0.1662 | 0.0017 | 0.7063 |
Without remittances | 6604 | 0.1987 | 0.1732 | 0.0007 | 0.7048 |
Treated | Non-Treated | Difference | S.E. | t-Statistic | |
---|---|---|---|---|---|
Saving amount | |||||
Unmatched | 21,322.069 | 18,775.257 | 2546.813 | 1680.144 | 1.52 |
5NN | 21,353.926 | 16,441.609 | 4912.318 | 2000.714 | 2.46 ** |
Radius | 21,362.624 | 16,712.992 | 4649.633 | 2044.478 | 2.27 ** |
Kernel | 21,353.926 | 16,537.695 | 4816.232 | 1932.336 | 2.49 ** |
Saving rate | |||||
Unmatched | 0.047 | −0.049 | 0.096 | 0.039 | 2.44 ** |
5NN | 0.048 | −0.125 | 0.173 | 0.055 | 3.17 *** |
Radius | 0.048 | −0.119 | 0.168 | 0.041 | 4.14 *** |
Kernel | 0.048 | −0.159 | 0.207 | 0.037 | 5.66 *** |
Adjusted income | |||||
Unmatched | 89,125.092 | 96,554.327 | −7429.236 | 2291.376 | −3.24 *** |
5NN | 89,214.019 | 83,375.030 | 5838.989 | 2617.316 | 2.23 ** |
Radius | 89,509.807 | 84,803.818 | 4705.989 | 2651.937 | 1.77 * |
Kernel | 89,214.019 | 83,836.196 | 5377.824 | 2483.378 | 2.17 ** |
Total expenditure | |||||
Unmatched | 67,803.022 | 77,779.071 | −9976.049 | 1486.577 | −6.71 *** |
5NN | 67,860.093 | 66,933.422 | 926.670 | 1753.725 | 0.53 |
Radius | 68,147.183 | 68,090.826 | 56.356 | 1780.121 | 0.03 |
Kernel | 67,860.093 | 67,298.501 | 561.592 | 1681.281 | 0.33 |
Treated | Non-Treated | Difference | S.E. | t-Statistic | |
---|---|---|---|---|---|
Education | |||||
Unmatched | 0.033 | 0.046 | −0.013 | 0.002 | −7.92 *** |
5NN | 0.033 | 0.033 | 0.000 | 0.002 | 0.22 |
Radius | 0.033 | 0.033 | 0.000 | 0.002 | 0.08 |
Kernel | 0.033 | 0.033 | 0.000 | 0.002 | 0.05 |
Health | |||||
Unmatched | 0.065 | 0.043 | 0.022 | 0.002 | 11.17 *** |
5NN | 0.065 | 0.055 | 0.010 | 0.003 | 3.50 *** |
Radius | 0.065 | 0.056 | 0.010 | 0.003 | 3.68 *** |
Kernel | 0.065 | 0.056 | 0.009 | 0.003 | 3.49 *** |
Assets | |||||
Unmatched | 0.043 | 0.043 | 0.000 | 0.002 | 0.03 |
5NN | 0.043 | 0.036 | 0.006 | 0.003 | 2.24 ** |
Radius | 0.043 | 0.037 | 0.006 | 0.003 | 2.04 ** |
Kernel | 0.043 | 0.036 | 0.007 | 0.003 | 2.47 ** |
House repairs | |||||
Unmatched | 0.015 | 0.011 | 0.005 | 0.001 | 3.36 *** |
5NN | 0.015 | 0.012 | 0.003 | 0.002 | 1.80 * |
Radius | 0.015 | 0.011 | 0.004 | 0.002 | 2.21 ** |
Kernel | 0.015 | 0.012 | 0.004 | 0.002 | 2.19 ** |
Food | |||||
Unmatched | 0.537 | 0.548 | −0.012 | 0.003 | −3.63 *** |
5NN | 0.536 | 0.559 | −0.022 | 0.004 | −5.34 *** |
Radius | 0.536 | 0.555 | −0.020 | 0.004 | −4.93 *** |
Kernel | 0.536 | 0.559 | −0.022 | 0.004 | −5.78 *** |
Non-food | |||||
Unmatched | 0.278 | 0.278 | 0.000 | 0.002 | 0.09 |
5NN | 0.278 | 0.275 | 0.003 | 0.003 | 1.07 |
Radius | 0.278 | 0.277 | 0.001 | 0.003 | 0.43 |
Kernel | 0.278 | 0.274 | 0.004 | 0.003 | 1.30 |
Utilities | |||||
Unmatched | 0.030 | 0.032 | −0.002 | 0.001 | −2.93 *** |
5NN | 0.030 | 0.031 | −0.001 | 0.001 | −1.32 |
Radius | 0.030 | 0.031 | −0.001 | 0.001 | −1.44 |
Kernel | 0.030 | 0.031 | −0.001 | 0.001 | −1.37 |
Treated | Non-Treated | Difference | S.E. | t-Statistic | |
---|---|---|---|---|---|
Expenditure per capita | |||||
Unmatched | 20,331.619 | 20,633.493 | −301.874 | 413.994 | −0.73 |
5NN | 20,341.403 | 19,483.273 | 858.130 | 477.386 | 1.80 * |
Radius | 20,368.527 | 19,768.648 | 599.879 | 498.593 | 1.20 |
Kernel | 20,341.403 | 19,563.238 | 778.165 | 472.459 | 1.65 * |
Education | |||||
Unmatched | 700.610 | 1129.769 | −429.158 | 112.147 | −3.83 *** |
5NN | 701.579 | 737.116 | −35.538 | 64.935 | −0.55 |
Radius | 708.431 | 798.690 | −90.258 | 115.047 | −0.78 |
Kernel | 701.579 | 782.200 | −80.622 | 103.507 | −0.78 |
Health | |||||
Unmatched | 1448.926 | 935.685 | 513.241 | 68.120 | 7.53 *** |
5NN | 1449.837 | 1207.229 | 242.608 | 95.503 | 2.54 ** |
Radius | 1457.375 | 1218.209 | 239.166 | 90.199 | 2.65 *** |
Kernel | 1449.837 | 1221.525 | 228.311 | 86.381 | 2.64 *** |
Assets | |||||
Unmatched | 1488.071 | 1220.869 | 267.201 | 175.100 | 1.53 |
5NN | 1489.008 | 952.164 | 536.843 | 247.270 | 2.17 ** |
Radius | 1491.888 | 995.499 | 496.390 | 258.983 | 1.92 ** |
Kernel | 1489.008 | 969.065 | 519.942 | 251.939 | 2.06 ** |
House repairs | |||||
Unmatched | 536.503 | 307.776 | 228.727 | 57.022 | 4.01 *** |
5NN | 537.245 | 321.867 | 215.378 | 79.441 | 2.71 *** |
Radius | 519.236 | 303.151 | 216.086 | 74.544 | 2.90 *** |
Kernel | 537.245 | 301.387 | 235.858 | 75.382 | 3.13 *** |
Food | |||||
Unmatched | 9956.491 | 10,489.693 | −533.203 | 159.427 | −3.34 *** |
5NN | 9958.977 | 10,202.106 | −243.129 | 184.490 | −1.32 |
Radius | 9973.769 | 10,244.294 | −270.525 | 180.117 | −1.50 |
Kernel | 9958.977 | 10,184.127 | −225.150 | 169.063 | −1.33 |
Non-food | |||||
Unmatched | 5614.585 | 5847.380 | −232.795 | 143.296 | −1.62 |
5NN | 5618.003 | 5455.790 | 162.212 | 163.256 | 0.99 |
Radius | 5629.198 | 5592.111 | 37.086 | 161.619 | 0.23 |
Kernel | 5618.003 | 5500.074 | 117.929 | 150.432 | 0.78 |
Utilities | |||||
Unmatched | 586.433 | 702.320 | −115.888 | 24.676 | −4.70 *** |
5NN | 586.756 | 607.001 | −20.245 | 23.182 | −0.87 |
Radius | 588.630 | 616.696 | −28.065 | 25.842 | −1.09 |
Kernel | 586.756 | 604.858 | −18.103 | 24.316 | −0.74 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Hua, T.X.; Kessels, R.; Erreygers, G. The Impact of Remittances on Saving Behaviour and Expenditure Patterns in Vietnam. Economies 2022, 10, 223. https://doi.org/10.3390/economies10090223
Hua TX, Kessels R, Erreygers G. The Impact of Remittances on Saving Behaviour and Expenditure Patterns in Vietnam. Economies. 2022; 10(9):223. https://doi.org/10.3390/economies10090223
Chicago/Turabian StyleHua, Thanh Xuan, Roselinde Kessels, and Guido Erreygers. 2022. "The Impact of Remittances on Saving Behaviour and Expenditure Patterns in Vietnam" Economies 10, no. 9: 223. https://doi.org/10.3390/economies10090223