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www.postersession.com Food Market Modernization and Diet-Related Health Outcomes: Evidence from Urban Vietnam Di Zeng, Wendy J. Umberger, Jesmin Rupa Centre for Global Food and Resources, University of Adelaide Selected Paper prepared for presentation at the 2017 Agricultural & Applied Economics Association Annual Meeting, Chicago, Illinois, July 30-August 1 Copyright 2017 by [authors]. All rights reserved. Readers may make verbatim copies of this document for non-commercial purposes by any means, provided that this copyright notice appears on all such copies.
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Page 1: Food Market Modernization and Diet-Related Health Outcomes…ageconsearch.umn.edu/record/258470/files/Abstracts_17_05_24_16_17... · Food Market Modernization and Diet-Related Health

www.postersession.com

Food Market Modernization and Diet-Related Health Outcomes: Evidence from Urban Vietnam

Di Zeng, Wendy J. Umberger, Jesmin Rupa Centre for Global Food and Resources, University of Adelaide

Selected Paper prepared for presentation at the 2017 Agricultural & Applied Economics

Association Annual Meeting, Chicago, Illinois, July 30-August 1

Copyright 2017 by [authors]. All rights reserved. Readers may make verbatim copies of this document for non-commercial purposes by any means, provided that this copyright notice

appears on all such copies.

Page 2: Food Market Modernization and Diet-Related Health Outcomes…ageconsearch.umn.edu/record/258470/files/Abstracts_17_05_24_16_17... · Food Market Modernization and Diet-Related Health

www.postersession.com

Food Market Modernization and Diet-Related Health Outcomes: Evidence from Urban Vietnam

Di Zeng1, Wendy J. Umberger2, Jesmin Rupa3 1Lecturer, 2Professor and 3PhD Student, Centre for Global Food and Resources, The University of Adelaide

q Food systems in many Asian economies are rapidly transforming. q Specifically, increasing penetration of modern food retail outlets are fundamentally connected to the

health and welfare of society. Modern retail outlets (e.g. hypermarkets, supermarkets and minimarkets) are having profound and multi-faceted implications on food markets, including consumers (e.g. Umberger et al., 2015; Gorton et al., 2011).

q Food purchase from modern food retail outlets has potential positive implications for urban consumers, yet it can result in unintended diet-related non-communicable diseases (e.g. obesity, Type II diabetes) if the result in increased access to unhealthy foods that are less nutritious (Toiba et al., 2015).

q Empirical studies on the obesogenic impacts of supermarkets in Asian countries have mixed results (e.g. Umberger et al., 2015).

q The current study aims to narrow the knowledge gap through an assessment of the possible obesogenic impacts of supermarkets among Vietnamese urban consumers.

Objectives: q  To identify the overall obesogenic impacts of supermarket food purchase on weight outcomes; q  To detect possible impact heterogeneity through subsample analysis; and q  To identify demographic and socioeconomic factors that possibly affect weight outcomes. q The analysis is facilitated by a large Vietnamese urban consumer survey (Dec. 2016 - Mar. 2017). q The full sample (n = 1700) consists of 700 Hanoi and 1,000 Ho Chi Minh City households. q In each city, wards were first selected using a proportional sampling strategy where the probability is

determined by ward-level population. 14 households from each ward were then randomly selected and surveys were completed via face-to-face interviews using a CommCare data collection application.

q The survey provided detailed information about §  Monthly food expenditure at both modern food retail outlets (hypermarket, supermarket and

minimarket) and traditional outlets (formal wet market, informal street market, semi-permanent stand, peddler, traditional family shop)

§  The height and weight of each individual in surveyed households, which were converted into Body Mass Index (BMI) and BMI z-scores

§  Conventional socioeconomic characteristics and information on food attitudes and perceptions

q OLS and instrumental variable regression (2SLS) models predicting BMI z-scores are estimated. q The coefficient of household supermarket food expenditure share is of main interest. q The excluded instrument in the 2SLS regressions is the average supermarket food expenditure share of

other surveyed households in the same ward. q Both full sample and subsample are analysed.

1 The outcome variable is BMI z-score. The excluded instrument in 2SLS regression is the average supermarket food expenditure share of other surveyed households in the same ward. 2 The education of household head is used for children, while own education is used for adults. 3 Standard errors are presented in parentheses. *, ** and *** indicate statistical significance at 10%, 5% and 1% levels, respectively. 4 The reference income group is low income (1st quantile).

q There is little overall impact of supermarket food expenditure share on BMI z-scores. q Child weight generally decreases with household size q Child weight is higher in Ho Chi Minh City than in Hanoi. q Adult weight is higher if household head is married.

1 The outcome variable is BMI z-score. Each estimate comes from a separate 2SLS regression where the excluded instrument is the average supermarket food expenditure share of other surveyed households in the same ward. 2 Standard errors are presented in parentheses. *, ** and *** indicate statistical significance at 10%, 5% and 1% levels, respectively.

q Supermarket food expenditure share can affect girls’ weight outcomes. q Girls during adiposity rebound are heavier with higher supermarket food expenditure. q Girls in upper middle income households are likely heavier with higher supermarket food expenditure. q There is no overall evidence that the supermarket revolution is leading to obesogenic diets in Vietnam. q There is evidence that supermarket food expenditure is positively associated with the weight outcomes

of girls during adiposity rebound and those from upper middle income households. q National public health policies may focus on these specific subgroups to minimize obesity risks.

References •  Gorton, M., Sauer, J. and Supatpongkul, P. 2011. Wet Markets, Supermarkets and the Big Middle for Food Retailing in

Developing Countries: Evidence from Thailand. World Development 39 (9): 1624–37. •  Toiba, H., Umberger, W.J. and Minot, N. 2015, Diet transition and Supermarket Shopping Behaviour: Is There a Link?,

Bulletin of Indonesian Economic Studies, 51, 3, 389-403. •  Umberger, W.J., He, X., Minot, N. and Toiba, H. 2015. Examining the Relationship between the Use of Supermarkets and

Over-nutrition in Indonesia, American Journal of Agricultural Economics, 97 (2): 510-525.

Acknowledgements •  The project was funded by the Australian Centre for International Agricultural Research (ACIAR) projects AGB/

2015/029 and AGB/2012/059 and the Centre for Global Food and Resources at the University of Adelaide. We acknowledge and sincerely thank, without implicating, intellectual contributions during the development of research from Professor James Seale (University of Florida, USA), Professor Junfei Bai (China Agricultural University), Dr. Nick Minot (IFPRI), and researchers at the Institute of Policy and Strategy for Agriculture and Rural Development Hanoi University of Agriculture, THe Vietnam Fruit and Vegetable Research Institute, and the Vietnam Women’s Union.

Introduction & Objectives

Data

Baseline Results

  Children  (n=1,596)  

Adults  (n=4,073)  

  OLS   2SLS   OLS   2SLS  

Supermarket food expenditure share   0.482***  (0.160)  

0.218  (0.358)  

0.134  (0.096)  

0.220  (0.181)  

Gender (1=male; 0=female)   0.001  (0.049)  

0.003  (0.053)  

-0.005  (0.031)  

-0.004  (0.031)  

Age   0.009  (0.024)  

0.010  (0.027)  

-0.015  (0.010)  

-0.015  (0.010)  

Age square   -0.001  (0.001)  

-0.001  (0.001)  

0.001  (0.001)  

0.000  (0.000)  

Religion (1=yes; 0=no)   -0.067  (0.087)  

-0.057  (0.090)  

0.105**  (0.051)  

0.102**  (0.052)  

Household size   -0.046*  (0.027)  

-0.048*  (0.027)  

-0.004  (0.013)  

-0.004  (0.013)  

Head marriage (1=yes; 0=no)   0.228  (0.496)  

0.212  (0.752)  

0.119***  (0.041)  

0.117***  (0.041)  

Education (years)   -0.003  (0.005)  

-0.002  (0.006)  

-0.007  (0.006)  

-0.007  (0.006)  

Refrigerator (1=yes; 0=no)   -0.608  (0.437)  

-0.652  (0.441)  

0.233  (0.188)  

0.226  (0.189)  

Vehicle (1=yes; 0=no)   -0.214  (0.191)  

-0.200  (0.196)  

0.024  (0.112)  

0.021  (0.112)  

Low middle income (2nd quantile)   -0.024  (0.070)  

-0.019  (0.074)  

-0.074*  (0.044)  

-0.076*  (0.044)  

Upper middle income (3rd quantile)   -0.085  (0.070)  

-0.078  (0.085)  

-0.051  (0.044)  

0.053  (0.043)  

High income (4th quantile)   0.047  (0.070)  

-0.049  (0.081)  

0.019  (0.043)  

0.018  (0.044)  

Ho Chi Minh City (1=yes; 0=no)   0.349***  (0.089)  

0.378***  (0.101)  

0.096*  (0.054)  

0.085  (0.058)  

Constant   0.744  (0.506)  

0.701  (0.683)  

-0.016  (0.294)  

0.001  (0.295)  

         R-squared   0.045   0.043   0.015   0.015  

First-stage F-statistic     99.10***  (0.000)     60.67***  

(0.000)  

Impact Heterogeneity

Key Messages

Children     Male   Female  

  -0.136  (0.396)  

0.558  (0.359)  

Age cohort: 2-5   -0.184  (0.629)  

-1.352*  (0.742)  

Age cohort: 6-9   -0.536  (0.907)  

1.544***  (0.582)  

Age cohort: 10-17   0.189  (0.600)  

0.975*  (0.548)  

Low income  (1st quantile)  

-0.198  (0.789)  

-0.249  (0.646)  

Lower middle income (2nd quantile)  

-0.419  (0.782)  

0.048  (0.773)  

Upper middle income (3rd quantile)  

-0.220  (0.654)  

2.219***  (0.659)  

High income  (4th quantile)  

0.894  (1.161)  

-0.085  (0.763)  

Adults     Male   Female  

  0.071  (0.270)  

0.295  (0.234)  

Age cohort: 18-34   0.107  (0.441)  

0.531  (0.400)  

Age cohort: 35-54   0.056  (0.341)  

0.178  (0.313)  

Age cohort: 55-65   -1.217  (0.796)  

0.349  (0.884)  

Low income  (1st quantile)  

0.200  (0.505)  

0.766  (0.471)  

Lower middle income (2nd quantile)  

0.171  (0.635)  

0.404  (0.560)  

Upper middle income (3rd quantile)  

0.578  (0.499)  

0.337  (0.447)  

High income  (4th quantile)  

-0.387  (0.522)  

-0.073  (0.417)  

Methods


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