Final Report:
Food Prices, Time Values, and Time Spent on Food Shopping and Meal Preparation: RuralUrban Disparities
Principal Investigator and Collaborators:
Principal Investigator:
Ning Zhang, Department of Health Promotion and Policy, University of Massachusetts School
of Public Health and Health Sciences.
Other Significant Contributors:
Chad D. Meyerhoefer, Department of Economics, Lehigh University
1. Introduction and Problem Statement
Rural areas are home to approximately 70 million people, or 23% of the US population.1
Compared to their urban counterparts, rural residents have higher risks for chronic diseases
and higher all-cause mortality.2-4 Research has suggested that rural-urban differences in
obesity may be a major contributor to this demographic health disparity.5-11 In one study,
almost 40% of rural adults were obese, compared to 33% of urban residents.12 Controlling for
many confounders, little difference has been found in levels of participation in physical activity
between rural and urban residents.12-13 Instead, studies have shown that people who live in
rural areas consume more fat from food than urban dwellers.14 However, differences in the
determinants of the eating habits between rural and urban residents are still unknown.
Although research suggests that food prices and time values may influence individuals’
eating patterns and decisions, very little research exists on the complex relationships between
food costs, constraints on time, and time allocated to shopping, meal preparation, obtaining
pre-prepared food, and choosing where to eat (at-home eating vs. restaurant eating). Even
less effort has been devoted to determining whether food prices and time values have different
effects on eating patterns for rural and urban residents. Understanding the structure of time
allocation decisions is critical to the formulation of policies designed to combat obesity and
reduce rural-urban disparities in obesity.15 This proposed study will address the influence of
price and time values on eating patterns between people living in rural or urban locations and
whether such price effects contribute to rural-urban disparities in obesity.
The primary objectives of this proposed study are to examine 1) whether there exist
differences in food preparation and dining locations between rural and urban residents, 2) how
prices and time values influence time allocation, and 3) whether and to what extent differences
in time allocation contribute to rural-urban obesity disparity. In particular, we will focus on the
mechanisms through which food prices and time values influence individuals’ opportunity cost
of time, or how time values vary across regions. In addition, we will identify how changes in the
prices of foods with different nutritional attributes as well as households’ time values impact the
amount of time households allocate to food shopping, meal preparation, at-home eating, and
away-from-home eating for rural and urban residents.
2. Methods
A. Theoretical Framework
This proposed work is built on the model established by Behrman and Deolalikar.16 This
model suggests that a household can be described in terms of a preference function that
1
includes the number of children (S), health of each household member (𝐻 𝑖 ), and their
consumption (𝐶 𝑖 ), leisure time (𝑇 𝑖 ) , education (𝐸 𝑖 ) and taste norms (𝜍), as follows:
𝑈 = 𝑈(𝐻 𝑖 , 𝐶 𝑖 , 𝑇 𝑖 , 𝐸 𝑖 , 𝑆; 𝜍), 𝑖 = 1, … , 𝐼,
where I is the total number of individuals in this household. Therefore the health production
function will take the following format,
𝐻 = 𝐻(𝑁 𝑖 , 𝐶 𝑖 , 𝑇 𝑖 , 𝑇𝑁𝑖 , 𝐸 𝑖 , 𝑆; 𝜙),
where N is the nutrient intake and 𝜙 is the endowment of the household. 𝑇𝑁𝑖 is the time of the
responsible individual devoted to securing and preparing nutrients, which includes multiple
components, i.e., the time spent on shopping, home meal preparation, and eating at
restaurants. Multiple factors, such as the prices of food (P), time values (W), as well as socioeconomic status (SES), resident location (L, rural vs. urban), and other unobserved (𝜁) factors
should contribute to the time allocated to these nutrient-related activities, that is,
(1) 𝑇𝑁 = 𝑇(𝑃, 𝑊, 𝑆𝐸𝑆 𝑖 , 𝐿; 𝜁),
Equation (1) guides the empirical analysis. First, resident location (L) has an explicit role
in determining the time allocated to nutrient acquisition. Second, the price of food and time
values (measured by wage rates) are important factors. We will have multiple ways of measuring
food prices and wages (see the Data Analysis section).
B. Data Sources
This study will use three secondary data sources for the years 2002-2009: the American
Time Use Survey (ATUS), March Current Population Survey (CPS), and Nielsen Homescan
Data. The ATUS is the nation’s first federally administered, continuous survey on time use in the
United States. This unique survey measures how people divide their time among life’s activities
within a typical 24-hour day. We limit our sample to adults 20-64 years. ATUS provides
information on time spent on food shopping and preparation, location of meals, as well as
resident location and family background. The top module of ATUS will be used in the first two
aims. To examine aim 3, the Eating and Health Module of ATUS (2006-2009) will be used, as
only this specific module includes the weight status of each respondent.
March CPS is the Annual Social and Economic Supplement of the CPS. This is the
primary source of labor force statistics for the population of the United States. March CSP
provides employment status, salary base, and income received in the previous calendar year.
This information will be used as the measure for the time value of adults who are employed in
non-farming industries. For farmers, the wage rate will be obtained directly from the national
estimates provided by the Bureau of Labor Statistics (BLS). Various county-level
characteristics will also be obtained from the March CPS. ATUS was merged with the March
CPS to obtain the wage rate of individuals and county-level characteristics. Forty-five percent
March CPS respondents had valid county code, hence merged with ATUS respondents.
Quarterly Food-at-Home Price Database were used to provide the food prices at market
level by food categories (e.g., fruit & vegetables). This is constructed by Nielsen Homescan
Data. Quarterly food-at-home price database was merged with ATUS-CPS data at the county
level to obtain the county-level price.
Rural-urban commuting area codes (RUCA) includes classification of areas as rural or
urban, based on the Census and commuting data and zip codes, following the same
theoretical concepts as those used by the Office of Management and Budget (OMB) to define
county-level metropolitan and micropolitan areas.
2
We used two measures to define rural and urban area. The first measure is
metropolitan vs. non-metropolitan area. The second is to create a rural or urban indicator. The
second measure should be more accurate; however, as only 45% respondents in March CPS
had valid county code, only 45% ATUS respondents were able to merge with March CPS.
Since codes from RUCA are provided at the census tract level and ATUS only provides county
codes these need be aggregated prior to merging. To do this we first labeled each census tract
urban if it's RUCA code equaled 4.1, 5.1, 7.1, 8.1, or 10.1, or if it's RUCA code was less then
four. Then for each county we summed the population that was in urban tracts and the
population that was in rural tracts. The category (rural or urban) which had a larger share of
the population was then assigned to the whole county.
Results
Aim 1: Identify rural-urban differences in time spent on a) food shopping, b) meal preparation,
c) at-home eating, and d) eating at restaurants.
There were 74,600 respondents in the full sample and 33,154 in the county-only
sample. Table 1 shows the summary statistics of our study sample. The first column indicated
the sample from full sample, and the second column indicating the sample from those who had
valid county-code (in March CPS). Most statistics between these two samples were similar.
For instance, the average age for the full sample was 46.4 ages, and was 46.1 ages in the
county only sample. Sixty-nine percent (69%) in full sample, vs. 61% in county-only sample,
were white non-Hispanic, 13% (vs. 14%) were black non-Hispanic. Larger proportion in countyonly sample were Hispanic (19%) compared to full sample (13%). Overall, half respondents
were married (50% in full sample, and 48% in county-only sample), 13% (for both samples)
divorced, less than 10% widowed (8.6% in full sample) vs. 7.9% in county-code sample), one
quarter had never married by the time of interview (both samples). In both samples, 16% had
no high school degree, and one-quarter respondents who attended college had not finished
their college program, while 26% in full sample while 23% in county-code sample had high
school diploma. Sixty percent respondents in either sample were employed, while 4.9% in full
sample and 5.2% in county-code sample were unemployed.
The third and fourth column compared the demographic characteristics of respondents
between non-metropolitan and metropolitan areas. Seventy-nine percent respondents in nonmetropolitan area were white, 10% were black, and 7.5% were Hispanic, compared to 66%
white, 14% black and 15% Hispanic in Metropolitan area. Marital status was similar between
non-metropolitan and metropolitan areas, including proportion of respondents married (53%
vs. 49%), widowed (10% vs. 8%), divorce (14% vs. 13%), separation (2.6% vs. 2.7%) and
never married (20% vs. 25%). The level of education was different between these two areas.
In non-metropolitan areas, 19% had no high school degree, 33% high school degree, and 21%
college degree, compared to 15% no high degree, 24% high school degree and 32% college
degree in metropolitan area. During the study period, 60% respondents who lived in nonmetropolitan areas were employed, compared to 62% in metropolitan area.
The last two columns show the comparison between respondents lived in rural (Column
5) and urban areas (Column 6). The patterns were similar to the first measure (nonmetropolitan vs. metropolitan area).
Figure 1 represents the comparisons in all food related activities between nonmetropolitan and metropolitan areas. On average on a weekly basis, people living in urban
area (defined as metropolitan area) spent 28 minutes while those living in rural area spent 22
3
minutes on grocery shopping including travel time. Urban dwellers spent 34 minutes while rural
residents spent 38 minutes on food preparation. The differences in these two measures were
persistent over time through 2002-2009, even considering the economic recession in 2008.
People living in urban areas spent 50 minutes on eating at home and 17 minutes eating out of
home, whereas those living in rural area spent 51 minutes eating at home and 13 minutes
eating out. It is not surprising that people increased their time eating at home, and reducing
their time eating out in both areas. Analysis also shows that the differences in eating out
between urban and rural areas were larger in 2009 than in 2002. These results reveal the
differences in food related activities between rural and urban areas.
Figure 2 represents the comparisons in all food related activities between rural and
urban areas. The patterns were not clear in these defined areas.
Based on data from Step 2, the following equation will be examined,
(2) 𝑦𝑖 = 𝛽0 + 𝛽1 (𝑅𝑢𝑟𝑎𝑙) + 𝛽2 𝑋𝑖 + 𝛽3 𝐶𝑐 + 𝜇 + 𝑡 + 𝜀𝑖 .
In this equation, 𝑦𝑖 represents one of the four outcome variables, time (minutes) spent on
shopping, food preparation, eating at home, and eating at restaurants. 𝑋 represents individual
characteristics including age, gender, employment status, marital status, health status, etc. 𝐶
denotes county-level macro-economic conditions. State and year fixed effects are controlled.
Other individual unobserved characteristics are included in the error term 𝜀. The main
explainatory variable is the resident location, whether rural (1) or urban (0). 𝛽1 shows the
differences between urban and rural residents. A t-test will be used for significance testing.
Table 2 showed the regression results. On average, people living in non-metropolitan area
spent 1.56 less minutes weekly to get grocery, spent 2.01 more minutes preparing food, 3.1
more minutes eating at home, and 2.3 less minutes eating out in a restaurant, comparing to
those living in metropolitan area. When we use another measure (rural vs. urban), the
differences in average patterns were similar but with smaller magnitude. Rural residents spent
0.77 minutes in grocery shopping, 0.55 more minutes in food preparation, 2.94 more minutes
eating at home, and 2.51 less minutes eating out in a restaurant.
As a conclusion, people living in non-metropolitan (or rural) areas spent more time on
food preparation and eating at home, compared to their counterparts living in metropolitan (or
urban) areas, on a weekly basis.
A manuscript based on these preliminary results has been submitted to The Journal of
Rural Health (revise and resubmit).
Aim 2: Identify the relationship between food prices and individual time values with time spent
on food shopping, meal preparation, at-home and away-from-home eating.
The following equation will be examined,
(3) 𝑦𝑖 = 𝛽0 + 𝛿1 (𝑃𝑖 ) + 𝛿2 (𝑊) + 𝛽2 𝑋𝑖 + 𝛽3 𝐶𝑐 + 𝜇 + 𝑡 + 𝜀𝑖 .
𝑦𝑖 follows the previous definition. P is the food price, and W is the wage rate. In this step, we
will use various measures of prices. First, we will use aggregated food prices by category in
the model. These categories include: grains, fruit, vegetable and meat. The coefficient 𝛿1 is the
price effect. 𝛿1 will be compared to various measures of prices. If individuals were employed in
a non-farm industry, the hourly wage will be used in W. If individuals were farmers, the national
estimates for agricultural workers will be obtained from the Major Occupation Group, National
Occupational Employment and Wage Estimates (http://www.bls.gov/oes/current/oes_nat.htm).
State-wide average wage rates and average wage rates within metropolitan areas will be
4
included in the model. Though neither of these wage rates for farmers is at the county level or
individual level, this estimate can be used as a proxy for wage rates for farmers.
The indicator for resident location was added to equation (2). In addition, interaction
terms of resident location and prices (𝑅𝑢𝑟𝑎𝑙 × 𝑃) and resident location and wage (𝑅𝑢𝑟𝑎𝑙 × 𝑊)
will also be included in the model to investigate how time allocation among rural residents may
further vary by price effects and time value effects.
(5) 𝑦𝑖 = 𝛽0 + 𝛽1 (𝑅𝑢𝑟𝑎𝑙) + 𝛿1 (𝑃𝑖 ) + 𝛿2 (𝑊) + 𝛿3 (𝑅𝑢𝑟𝑎𝑙 × 𝑃) + 𝛿4 (𝑅𝑢𝑟𝑎𝑙 × 𝑊) + 𝛽2 𝑋𝑖 + 𝛽3 𝐶𝑐 +
𝜇 + 𝑡 + 𝜀𝑖 .
Table 3 reports the regression results. In this file, we only report results that used
metropolitan area as the indicator for rural urban residency. Part A included results when the
dependent variable is the number of minutes getting grocery every week. Any 10 cents
increase in meat prices would reduce 14.5 minutes in getting grocery weekly. When grains
prices were included, any $10 increase in weekly wage reduced 1.3 minutes in getting grocery
on a weekly basis. When meat price interacted with non-metropolitan area, this number
increased to 45 minutes. These results indicated that meat price may be an important factor
related to individuals’ decision to get grocery, particularly for those living in non-metropolitan
areas.
Panel B included results when the dependent variable is the number of minutes meal
preparation in an average week. No significance results have been found. Panel C showed the
results when the dependent variable is the number of minutes people eat at home. No prices
seemed to have statically significant results in determining the time spent on eating at home.
However, when grains prices were included, any $10 increase in hourly salary reduced half
minutes in preparing for meals every week; when meat prices were included, any $10 increase
in hourly wage reduced 1.1 minutes in preparing for a meal. When indicator for nonmetropolitan area interacted with prices, people spent almost 1 minutes less in preparing for
meal given any 10 cents in vegetable or meat for those living in the non-metropolitan areas.
Moreover, when the indicator for non-metropolitan area interacted with salary, any $10
increase in weekly wage reduced almost 1 minute in preparing for food in residents living in
non-metropolitan areas.
The last panel (Panel D) showed the results when the dependent variable is the number
of minutes spent on eating out in a restaurant every week. Any 10 cents increase in grains is
associated with 1 less minute eating out, and any 10 cents increase in vegetable would reduce
time spent on eating in a restaurant by more than half minute, on a weekly basis. When
vegetable prices were included, any $10 increase in weekly salary was associated with 0.79
more minutes in eating out. Compared to people living in metropolitan areas, people in nonmetropolitan areas spent less time eating out, when any of the four types of food prices were
included in the model. The associations remain when non-MSA interacted with food prices.
These results indicated that food prices, salary, and resident locations may have influence on
time spent on eating out in a restaurant.
In a manuscript, we not only reported these results above, but also link this relationship
regards the 2008 economic recess. This manuscript is under review of Journal of Health
Economics.
Aim 3: Identify how changes in food shopping, meal preparation, and at-home and away-fromhome eating affect weight status.
5
In this aim, we move our focus from simply time spent of food related activities to weight
status. Only the Eating and Health Module of ATUS (2006-2009) were used to merge with
other data sets. The estimated model is:
(6) 𝑦𝑖 = 𝛽0 + 𝛾1(𝑇𝑖 ) + 𝛽1 (𝑅𝑢𝑟𝑎𝑙) + 𝛾2 (𝑇𝑖 × 𝑅𝑢𝑟𝑎𝑙) + 𝛽2 𝑋𝑖 + 𝛽3 𝐶𝑐 + 𝜇 + 𝑡 + 𝜀𝑖 ,
In this equation (6), 𝑦 represents the likelihood of being obese. Obesity is defined as occurring
when the body mass index (BMI = weight / height2) is equal to or higher than 30.17 T
represents the time allocation on shopping, food preparation, at-home eating and restaurant
eating. It is obvious that T is an endogenous variable. Weight status will also contribute to the
time allocation. Hence, we will generally use food price (P) and wage rate (W) as instrumental
variables and employ two-stage residual inclusion analysis (2SRI). The reason to use 2SRI is
that the second stage is not linear. Hence, according to many scholars, a 2SRI should be used
to obtain consistent estimates.18-19 The procedures are:
Stage 1: 𝑇𝑖 = 𝛽0 + 𝛿1 (𝑃𝑖 ) + 𝛿2 (𝑊) + 𝜀𝑖 , Obtain residual 𝜀̂.
Stage 2: (9) 𝑦𝑖 = 𝛽0 + 𝛾1 (𝜀̂) + 𝛽1 (𝑅𝑢𝑟𝑎𝑙) + 𝛾2 (𝜀̂ × 𝑅𝑢𝑟𝑎𝑙) + 𝛽2 𝑋𝑖 + 𝛽3 𝐶𝑐 + 𝜇 + 𝑡 + 𝜀𝑖 ,
Various price measures and wage rate measures, as explained previously, will be used
in this 2SRI procedure. Table 4 showed the second stage result. As the dependent variable is
the likelihood of being obese, odds ratio (OR) are reported. A 10 cent increase in vegetable is
associated with 8% reduction in becoming obese. However, a resident living in a nonmetrapolitan area is 8% more likely to become obese than a resident in metropolitan area.
More analyses are under going to see if this is the best result. The plan is to write a manuscript
based on these results. The target journal is Journal of Biology, Agriculture and Healthcare.
Conclusions
This project examined the rural urban disparities in food related activities, including
grocery shopping, meal preparation, eating at home and eating out in a restaurant. Results
showed that people living in rural areas spent more time eating home than people living in
urban areas. However, the current results failed to find any significance results when the focus
outcome is weight status. We are currently still conducting more analyses to figure out this
problem.
8. References
1. United States Census Bureau. 2008 American Community Survey 1-year estimates.
B01003. Total population. Washington, DC. Available at http://factfinder.census.gov. Accessed
May 6, 2011.
2. Eberhardt MS, Ingram DD, Makuc DM. Urban and Rural Health Chartbook. Health, United
States, 200. Hyattsville, Maryland: National Center for Health Statistics; 2001.
3. McIntosh WA, Sobal J. Rural eating, diet, nutrition, and body weight. In Glasgow N, Morton
LW, Johnson NE, eds. Critical Issues in Rural Health. Ames, IA: Blackwell; 2004:113-126.
6
4. Cossman JS, James WL, Cosby AG, Cossman RE. Underlying causes of the emerging nonmetropolitan mortality penalty. Am J Public Health. 2010; 100(8): 1417-1419.
5. Jackson JE, Doescher MP, Jerant AF, Hart LG. A national study of obesity prevalence and
trends by type of rural county. J Rural Health. 2005; 21(2):140-148.
6. Patterson PD, Moore CG, Probst JC, Shinogle JA. Obesity and physical inactivity in rural
America. J Rural Health. 2004; 20(2):151-159.
7. Gorber SC, Tremblay M, Moher D, Gorber B. A comparison of direct versus self-report
measures for assessing height, weight, and body mass index: a systematic review. Obes Rev.
2007; 8(4):307-326.
8. Rogers C. The older population in 21st century rural America. Rural America. 2002; 17(3):110.
9. Kuczmarski MF, Kuczmarski RJ, Najjar M. Effects of age on validity of self-reported height,
weight, and body mass index: findings from the Third National Health and Nutrition
Examination Survey, 1988-1994. J Am Diet Assoc. 2001; 101(1):28-34.
10. Nyholm M, Gullberg B, Merlo J, Lundqvist-Persson C, Rastam L, Lindblad U. The validity
of obesity based on self-reported weight and height: implications for population studies.
Obesity. 2007; 15(1):197-208.
11. Sobal J, Troiano RP, Frongillo EA. Rural-urban differences in obesity. Rural Sociology.
1996; 61(2): 289-305.
12. Befort CA, Nazir N, Perri, M. Prevalence of Obesity among Adults from Rural and Urban
Areas of the United States: Findings from NHANES (2005-2008). J of Rural Health 2012;
28(4):392-397.
16. Behrman JR, Deolalikar AB. Health and Nutrition. Handbook of Developed Economics, Vol
1, Edited by H. Chenery and TN Srinivasan, Elsevier Science Publishers, B.V. 1988.
17. NHLBI Expert Panel. (2002). Clinical guidelines on the identification, evaluation, and
treatment of overweight and obesity in adults: Evidence report. Bethesda, MD: NIH (NIH
publication no. 02-4084).
18. Terza JV, Basu A, Rathouz PJ. Two-Stage Residual Inclusion Estimation: Addressing
Endogeneity in Health Econometric Modeling. J Health Econ (2008); 27(3):531-543.
19. Stuart BC, Doshi J, Terza JV. Assessing the Impact of Drug Use on Hospital Costs. Health
Services Research (2009); 44:128-144.
7
Figure 1. Time spent in food shopping, meal preparation, eating at home and eating out in a
restaurant between non-metropolitan and metropolitan areas.
8
Figure 2. Time spent in food shopping, meal preparation, eating at home and eating out in a
restaurant between rural and urban areas.
9
Table 1. Summary Statistics.
Age
Race:
White
Black; Non-Hisp.
Hispanic
Other
Female
Number of
Children
Marital Status:
Married
Widowed
Divorced
Separated
Never Married
Education
Completed:
No Degree
High School
Degree
Some College
College Degree
Full
Countyonly
Non-MSA
MSA
Rural
Urban
Mean/(SD)
Mean/(SD)
Mean/(SD)
Mean/(SD)
Mean/(SD)
Mean/(SD)
47.6
(18.1)
46.0
(17.5)
47.6
(18.1)
46.0
(17.5)
47.1
(18.4)
46.1
(17.6)
0.79
(0.41)
0.099
(0.30)
0.075
(0.26)
0.034
(0.18)
0.57
(0.50)
0.66
(0.47)
0.14
(0.35)
0.15
(0.36)
0.053
(0.22)
0.55
(0.50)
0.79
(0.41)
0.099
(0.30)
0.075
(0.26)
0.034
(0.18)
0.57
(0.50)
0.66
(0.47)
0.14
(0.35)
0.15
(0.36)
0.053
(0.22)
0.55
(0.50)
0.87
(0.34)
0.016
(0.12)
0.066
(0.25)
0.049
(0.22)
0.17
(0.37)
0.61
(0.49)
0.14
(0.35)
0.19
(0.39)
0.061
(0.24)
0.55
(0.50)
0.86
(1.15)
0.89
(1.15)
0.86
(1.15)
0.89
(1.15)
0.96
(1.26)
0.89
(1.15)
0.53
(0.50)
0.10
(0.30)
0.14
(0.34)
0.026
(0.16)
0.20
(0.40)
0.49
(0.50)
0.081
(0.27)
0.13
(0.34)
0.027
(0.16)
0.25
(0.43)
0.53
(0.50)
0.10
(0.30)
0.14
(0.34)
0.026
(0.16)
0.20
(0.40)
0.49
(0.50)
0.081
(0.27)
0.13
(0.34)
0.027
(0.16)
0.25
(0.43)
0.15
(0.35)
0.032
(0.17)
0.046
(0.21)
0.0071
(0.084)
0.069
(0.25)
0.49
(0.50)
0.081
(0.27)
0.13
(0.33)
0.028
(0.17)
0.26
(0.44)
0.19
(0.39)
0.15
(0.36)
0.19
(0.39)
0.15
(0.36)
0.046
(0.21)
0.16
(0.37)
0.33
(0.47)
0.27
(0.44)
0.21
(0.41)
0.24
(0.43)
0.26
(0.44)
0.32
(0.47)
0.33
(0.47)
0.27
(0.44)
0.21
(0.41)
0.24
(0.43)
0.26
(0.44)
0.32
(0.47)
0.12
(0.32)
0.087
(0.28)
0.051
(0.22)
0.23
(0.42)
0.26
(0.44)
0.33
(0.47)
Employment
Status:
10
Employed
Unemployed
Not in Labor
Force
Self Employed
Work Week Hours
0.60
(0.49)
0.045
(0.21)
0.62
(0.49)
0.051
(0.22)
0.60
(0.49)
0.045
(0.21)
0.62
(0.49)
0.051
(0.22)
0.18
(0.39)
0.013
(0.11)
0.61
(0.49)
0.053
(0.23)
0.35
(0.48)
0.12
(0.32)
40.4
(14.5)
0.31
(0.46)
0.092
(0.29)
40.1
(13.3)
0.35
(0.48)
0.12
(0.32)
40.4
(14.5)
0.31
(0.46)
0.092
(0.29)
40.1
(13.3)
0.10
(0.30)
0.031
(0.17)
40.0
(15.1)
0.31
(0.46)
0.098
(0.30)
40.0
(13.3)
11
Table 2. Regression results for Aim 1.
Getting Grocery
Meal
preparation
Eating at home
Eating out
Non-MSA (vs. MSA)
Coefficient
95% CI
-1.56
(-2.41, -1.08)
2.01
(1.80, 2.11)
Rural (vs. Urban)
Coefficient
95% CI
-0.77
(-1.03, 0.09)
0.55
(0.32, 0.79)
3.1
-2.3
2.94
-2.51
(2.56, 3.99)
(-2.71, -2.19)
12
(2.54, 3.01)
(-2.99, -2.01)
Table 3. Regression results for Aim 2.
Grains
Fruit
Panel A: Dependent variable: Grocery shopping
Prices
-0.05
-1.57
0.03
1.22
***
Salary
1.31
0.044
0.89
0.13
Non-MSA
-1.70
2.05
0.89
0.99
Interaction
-1.14
-0.44
between nonMSA and prices
0.72
0.78
Interaction
-0.87
-0.06
between nonMSA and salary
0.55
0.11
Panel B: Dependent variable: Meal preparation
Prices
-0.35
-0.84
0.45
0.62
Salary
-0.77
0.032
0.14
0.06
Non-MSA
0.799
0.097
0.52
0.14
Interaction
-0.07
-0.001
between nonMSA and prices
0.05
0.005
Interaction
-0.13
-0.001
between nonMSA and salary
0.06
0.07
Panel C: Dependent variable: Eating at home
Prices
0.79
0.55
0.04
0.11
***
Salary
-0.55
0.32
0.04
0.56
Non-MSA
1.87
0.84
0.24
0.33
13
Vegetable
Meat
-0.09
0.12
1.13
0.24
-1.11***
0.33
-.004
-14.5***
1.42
1.55
0.49
-1.07***
0.04
-45.01***
0.03
-0.087
0.05
-0.12
0.099
0.88
-0.33
0.29
-0.007
0.14
0.004
0.05
-0.002
-1.27
1.18
-0.198
0.59
0.019
0.09
-0.78
0.03
-0.002
0.44
-0.78
0.05
0.44
0.15
0.11
-0.79
0.52
1.09
0.19
1.01
0.33
-1.10***
0.29
1.44
0.53
Interaction
between nonMSA and prices
Interaction
between nonMSA and salary
0.14
0.004
0.89***
0.59***
0.05
-0.33
0.04
-0.05
0.17
-0.84***
0.09
-0.81**
0.11
0.18
0.22
0.14
-0.58***
0.09
0.79
0.12
-1.02
0.41
-0.77***
-1.01
0.52
0.74
0.08
-1.96***
0.32
-0.65***
0.13
0.44
0.14
0.54
0.12
0.21
Panel D: Dependent variable: Eating out in a restaurant
Prices
-0.98***
0.17
0.11
0.13
Salary
0.39
0.78
0.14
0.92
***
Non-MSA
-1.79
-1.52***
0.14
0.04
Interaction
-0.41***
-0.02
between nonMSA and prices
0.03
0.09
Interaction
0.09
0.004
between nonMSA and salary
0.10
0.04
14
Table 4. Results for Aim 3 (2nd stage results, Odds Ratio (OR) reported in the table)
Prices
Salary
Non-MSA
Interaction
between
non-MSA
and prices
Interaction
between
non-MSA
and salary
Grains
OR
0.98
0.81
1.09
1.02
95% CI
(0.76,1.01)
(0.69, 1.09)
(0.91, 1.29)
(0.82, 1.11)
Fruit
OR
0.94
0.97
1.04
1.08
95% CI
(0.89,1.09)
(0.81, 1.13)
(0.82, 1.21)
(0.93, 1.39)
Vegetable
OR
95% CI
0.92 (0.62,0.95)
1.01 (0.88, 1.20)
1.08 (1.02, 1.29)
1.01 (0.88, 1.29)
Meat
OR
0.91
0.82
1.04
1.04
95% CI
(0.79, 0.93)
(0.67, 0.91)
(0.92, 1.22)
(0.78, 1.30)
1.03
(0.79, 1.25)
1.01
(0.82, 1.22)
1.08
1.05
(0.92, 1.31)
15
(1.03-1.27)