Are fast food restaurants responsible for diabetes epidemic?

Instantaneous cut evidence from China.

Estelle Zemmour.

Objective• The first time this research is conducted• Benchmark papers:

-Kaushal, « Do food stamps cause obesity? Evidence from immigrant experience », Journal of Health Economics, 2007- Davis, Carpenter, « Proximity to Fast Food Restaurants to Schools and Adolescent Obesity », American Journal of Public Health, 2009.

• Raises 3 main literature reviews: 1) impact of diabetes on the economy2) impact of nutrition on diabetes3) impact of fast food on obesity (used for methodology).

Some explanations on Diabetes.• « Disease of the century »: touches rich over-nourished,as

well as under-nourished people.« Characterized by a disturbance in the use of sugar by the body due to a malfunction of the pancreas, which does not release enough insulin in the body ».

• International measure ( American Diabetes Asociation): Hba1c (measures glycemic indexes over 3 past months, this is an average). -if higher than 6,5 declared « diabetic »

• 2 types: second one due to environmental factors: touches people after 40. Represents 95% of cases.

Measuring the main key variables for Nutrition

• First index « Nutrition_1 »=Body Mass IndexBMI=weight/(height²)

• Second index « Nutrition_2 »= Average Glycemic IndexAGI= (total servings per day* carbohydrates content of food* glycemic index)/total carbohydrates content in diet (Salmeron and al, 1981)

Socio-economic determinants of BMI5

5.5

66.

57

Fitte

d va

lues

10 20 30 40 50Body Mass Index (BMI)

Huan urban woman age>40 non-Huan rural man age<40

Socio-economic determinants of AGI4

68

1012

Fitte

d va

lues

0 5 10 15 20Average Glycemic Index (AGI)

Huan urban woman age>40 non-Huan rural man age<40

Specification• 2 models: Nutrition_1Nutrition_2

linear regression models probit models

• Problems: reverse causality suspected between the index of Nutrition and Diabetes

• Proposed solution: instrumental variable approach.

Linear regression models• 1rst model:Diabetes= a + b*Nutrition_1 + c*Income + d*Education+ e*Occupation + f*Healthcare + g*Insurance + h*Subsidies+ i*Media + j*Age + k*Urbanization+ l* Origin +m* Sex

• 2nd model:Diabetes= a +b*Nutrition_2+ c*Income + d*Education+ e*Occupation + f*Healthcare + g*Insurance + h*Subsidies+ i*Media + j*Age + k*Urbanization+ l* Origin +m* Sex

Probit models• Diabetes_2= 1 if Diabetes>6.5O otherwise

sum Diabetes, detail

hba1c(mmol/l) hemoglobin a1c - field------------------------------------------------------------- Percentiles Smallest 1% 4.1 2.7 5% 4.7 3.110% 4.9 3.4 Obs 908825% 5.2 3.5 Sum of Wgt. 9088

50% 5.5 Mean 5.593376 Largest Std. Dev. .856635475% 5.8 13.390% 6.2 14 Variance .733824395% 6.7 15.2 Skewness 3.63884799% 9.7 16.1 Kurtosis 25.81249

Probit models

• 1rst model: Diabetes_2= a + b*Nutrition_1 + c*Income + d*Education+ e*Occupation + f*Healthcare + g*Insurance + h*Subsidies+ i*Media + j*Age + k*Urbanization+ l* Origin +m* Sex• 2nd model:Diabetes_2= a + b*Nutrition_2 + c*Income + d*Education+ e*Occupation + f*Healthcare + g*Insurance + h*Subsidies+ i*Media + j*Age + k*Urbanization+ l* Origin +m* Sex

Data

• China Health and Nutrition Survey(CHNS)

• 1989-20092009: data for Diabetes

• Merge and create delays

• 1rst model: Diabetes_2= a + b*∑Nutrition_1 + c*∑Income + d*Education+ e*Occupation + f*Healthcare + g*Insurance + h*Subsidies+ i*Media + j*Age + k*Urbanization+ l* Origin +m* Sex• 2nd model:Diabetes_2= a + b*Nutrition_2 + c*∑Income + d*Education+ e*Occupation + f*Healthcare + g*Insurance + h*Subsidies+ i*Media + j*Age + k*Urbanization+ l* Origin +m* Sex

Data.

Foodlikes_5 byte %8.0g food likes: soft/sugared drinksFoodlikes_4 byte %8.0g food likes: vegatablesFoodlikes_3 byte %8.0g food likes: fruitsFoodlikes_2 byte %8.0g food likes: salty snack foodFoodlikes_1 byte %8.0g food likes: fast foodInsurance_1 byte %8.0g do you have medical insurance?Occupation byte %8.0g presently working?Education_3 byte %8.0g currently in school?Education_1 byte %8.0g completed years of formal ed. in regular schoolIncome double %10.0g total net individual incomesex float %9.0g =1 if woman, 0 otherwiseorigin float %9.0g =1 if Huan, 0 otherwiseUrbanization byte %8.0g 1=urban, 0=ruralage float %9.0g calculated age in years to 2 decimal pointsNutrition_2 float %9.0g Average Glycemic Index (AGI)Nutrition_1 float %9.0g Body Mass Index (BMI)Diabetes float %9.0g hba1c(mmol/l) hemoglobin a1c - field variable name type format label variable label storage display value

> ce_1 Foodlikes_1 Foodlikes_2 Foodlikes_3 Foodlikes_4 Foodlikes_5. describe Diabetes Nutrition_1 Nutrition_2 age Urbanization origin sex Income Education_1 Education_3 Occupation Insuran

Data

Foodlikes_5 8634 2.799398 1.239834 1 9 Foodlikes_4 8635 3.944181 .6226271 1 9 Foodlikes_3 8635 3.808454 .7399377 1 9 Foodlikes_2 8635 2.769658 1.810096 1 9 Foodlikes_1 8635 3.095773 2.384992 1 9 Insurance_1 9058 .9067123 .2908512 0 1 Occupation 8294 .5764408 .494152 0 1 Education_3 9004 .1048423 .3063671 0 1 Education_1 9030 18.93876 8.674618 0 36 Income 6124 16169.59 25271.07 0 657000 sex 9059 .5204769 .4996081 0 1 origin 9058 .8870612 .3165355 0 1 Urbanization 6124 .3024167 .4593422 0 1 age 9026 47.20006 18.2311 1.93 98.94 Nutrition_2 8915 4.955387 1.389529 .4241872 21.08974 Nutrition_1 8876 22.87695 3.825774 11.63445 42.72461 Diabetes 9088 5.593376 .8566354 2.7 16.1 Variable Obs Mean Std. Dev. Min Max

> nce_1 Foodlikes_1 Foodlikes_2 Foodlikes_3 Foodlikes_4 Foodlikes_5. summarize Diabetes Nutrition_1 Nutrition_2 age Urbanization origin sex Income Education_1 Education_3 Occupation Insura

Stylized facts

Insurance_1 0.0118 0.0200 0.0143 0.0579 -0.0790 -0.0092 0.0169 -0.0277 -0.0022 -0.0119 1.0000 Occupation -0.1378 -0.0763 -0.0396 -0.4969 -0.2095 -0.0670 0.0383 0.1176 -0.0040 1.0000 Education_3 0.0199 0.0117 0.0001 0.0168 -0.0177 0.0206 -0.0183 -0.0757 1.0000 Education_1 -0.0557 0.0076 -0.0998 -0.3588 0.3048 0.0722 0.2012 1.0000 Income 0.0356 0.0538 -0.0245 -0.0238 0.1435 0.0428 1.0000 origin 0.0783 0.0747 -0.0319 -0.0108 0.0943 1.0000Urbanization 0.1037 0.0680 -0.0266 0.1101 1.0000 age 0.2249 0.0854 0.1405 1.0000 Nutrition_2 0.5659 0.1365 1.0000 Nutrition_1 0.2254 1.0000 Diabetes 1.0000 Diabetes Nutri~_1 Nutrit~2 age Urbani~n origin Income Educat~1 Educat~3 Occupa~n Insura~1

(obs=5796). corr Diabetes Nutrition_1 Nutrition_2 age Urbanization origin Income Education_1 Education_3 Occupation Insurance_1

Results: Estimating linear models with Nutrition_1

M1_N1 M2_N1 M1_N1_ivreg1 M2_N1_ivreg2coef t coef t coef t coef t

Diabetes

Body Mass Index (BMI) 0,051*** 15,811 -0,007 -0,553 0,202** 3,110 0,411* 1,782

calculated age in years to 2 decimal points 0,012*** 12,617 0,015*** 7,717 0,010*** 5,971 0,021*** 4,419

1=urban, 0=rural 0,113*** 4,320 0,037 0,752 0,068* 1,877 0,021 0,221

=1 if Huan, 0 otherwise 0,159*** 4,700 0,112** 2,228 0,052 0,861 -0,174 -0,877

2009 Income 0,000* 1,693 0,000* 1,657 -0,000 -0,225 -0,000 -0,304

completed years of formal ed. in regular school -0,001 -0,778 -0,001 -0,339 -0,002 -1,025 0,002 0,388

currently in school? 0,090 1,137 0,019 0,143 0,055 0,582 0,148 0,651

presently working? -0,017 -0,595 0,025 0,550 0,018 0,487 0,179 1,312

do you have medical insurance? 0,005 0,124 0,072 0,817 -0,035 -0,651 0,148 0,932

2006 Nutrition_1 0,017 1,193

2004 Nutrition_1 -0,011 -0,743

2000 Nutrition_1 0,067*** 5,821

2006 Income 0,000 0,778 -0,000 -0,972

2004 Income -0,000** -2,172 -0,000 -1,544

2000 Income -0,000 -0,561 -0,000 -0,964

constante 3,634*** 32,067 3,106*** 14,317 0,395 0,282 -5,187 -0,950

Number of observations 5 879 1 805 5 872 2 159

Adjusted R2 0,101 0,118 . .

F 74,261 17,089 35,020 3,650

note: *** p<0.001, ** p<0.05, * p<0.1

Wald test 0,00655 0,00527Sargan test 0,1242 0,2158

Results:Estimating probit models with Nutrition_1.

P1_N1 P2_N1 P_ivprobit1_N1 P_ivprobit2_N1 P_ivprob3_N1

coef t coef t coef t coef t coef t

Diabetes_2

Body Mass Index (BMI) 0,094*** 11,592 -0,034 -0,995 0,592** 2,954 0,990 1,563 0,660** 3,265

calculated age in years to 2 decimal points 0,023*** 8,216 0,026*** 4,353 0,015** 3,083 0,042** 3,214 0,036*** 5,089

1=urban, 0=rural 0,251*** 3,915 0,254* 1,876 0,105 1,029 0,223 0,928 0,527** 3,125

=1 if Huan, 0 otherwise 0,215** 2,066 0,305* 1,708 -0,137 -0,729 -0,446 -0,821 0,297 1,543

2009 Income -0,000 -0,016 -0,000 -0,277 -0,000 -1,561 -0,000 -1,068 -0,000 -0,459

completed years of formal ed. in regular school 0,001 0,237 0,006 0,933 -0,001 -0,229 0,011 0,982 0,009 1,178

currently in school? 0,111 0,580 0,282 0,815 0,001 0,006 0,203 0,364 0,385 0,990

presently working? -0,048 -0,672 -0,049 -0,377 0,074 0,709 0,362 0,995 -0,028 -0,195

do you have medical insurance? 0,083 0,688 0,713* 1,732 -0,045 -0,286 0,833 1,624 0,845* 1,956

2006 Nutrition_1 0,066 1,553 -0,283** -2,372

2004 Nutrition_1 -0,034 -0,809 -0,230** -2,597

2000 Nutrition_1 0,132*** 4,109

2006 Income 0,000 1,520 -0,000 -0,484 0,000 1,498

2004 Income -0,000 -1,319 -0,000 -1,564 -0,000 -1,331

2000 Income -0,000** -2,529 -0,000** -2,182 -0,000** -2,551

constante -5,436*** -16,271 -6,998*** -9,301 -16,218*** -3,730 -27,625* -1,840 -8,346*** -8,659

Number of observations 5 879 1 805 5 872 2 159 1 804

chi2 339,027 138,066 131,338 30,534 89,770

chi2_exog 9,063 4,561 14,315

Pseudo R2 0,127 0,168

note: *** p<0.001, ** p<0.05, * p<0.1

Wald test 0 0,0023 0Chi² test 0,0026 0,0327 0,0002

Results:Estimating linear models with Nutrition_2.

N2_M1 N2_M2 N2_ivreg1 N2_ivreg2coef t coef t coef t coef t

DiabetesAverage Glycemic Index (AGI) 0,356*** 52,855 0,336*** 30,543 0,714** 2,692 1,070** 2,230

calculated age in years to 2 decimal points 0,008*** 9,708 0,009*** 5,368 0,003 0,743 -0,005 -0,507

1=urban, 0=rural 0,160*** 7,187 0,157*** 3,822 0,183*** 5,658 0,297** 2,556

=1 if Huan, 0 otherwise 0,230*** 8,044 0,224*** 5,168 0,265*** 6,121 0,409** 2,870

2009 Income 0,000** 3,284 0,000** 2,935 0,000** 2,982 0,000* 1,764

completed years of formal ed. in regular school 0,001 1,145 0,000 0,091 0,004 1,631 0,001 0,157

currently in school? 0,121* 1,797 0,298** 2,704 0,143* 1,704 0,440** 2,059

presently working? -0,059** -2,446 -0,043 -1,099 -0,096** -2,380 -0,117 -1,402

do you have medical insurance? 0,006 0,169 0,072 0,961 -0,002 -0,034 -0,019 -0,132

2006 Income 0,000 0,280 -0,000 -0,390

2004 Income 0,000 0,007 0,000 1,275

2000 Income 0,000 1,528 0,000* 1,715

constante 3,166*** 39,144 3,113*** 20,611 1,582 1,344 -0,022 -0,011

Number of observations 5 902 2 183 5 894 2 180

Adjusted R2 0,363 0,333 0,057 .

F 374,413 91,725 43,980 4,996

note: *** p<0.001, ** p<0.05, * p<0.1

Wu-Hausman test 0,1 0,00754Sargan test 0,0063 0,1719

Results:Estimating probit models with Nutrition_2.

R1_N2 R2_N2 R3_ivprob R4_ivprob

coef t coef t coef t coef t

Diabetes_2

Average Glycemic Index (AGI) 0,486*** 22,761 0,482*** 13,684 3,922* 1,768 1,235** 2,019

calculated age in years to 2 decimal points 0,020*** 5,933 0,018** 2,953 -0,029 -0,897 0,005 0,377

1=urban, 0=rural 0,407*** 5,392 0,632*** 4,766 0,638** 2,847 0,785*** 3,986

=1 if Huan, 0 otherwise 0,333** 2,750 0,431** 2,341 0,664** 2,124 0,633** 2,451

2009 Income -0,000 -0,076 -0,000 -0,826 0,000 0,553 -0,000 -0,599

completed years of formal ed. in regular school 0,002 0,496 0,005 0,800 0,024 1,411 0,007 0,909

currently in school? 0,233 1,104 0,669** 2,165 0,450 0,872 0,805** 2,161

presently working? -0,151* -1,825 -0,172 -1,284 -0,510* -1,728 -0,249 -1,532

do you have medical insurance? 0,212 1,400 0,355 1,089 0,145 0,496 0,262 0,735

2006 Income 0,000 1,640 0,000 1,351

2004 Income -0,000 -0,074 -0,000 -0,784

2000 Income -0,000 -1,587 -21,193** -2,152 -9,357*** -3,442

constante -5,944*** -17,726 -6,113*** -10,047

constante

Number of observations 5 902 2 183 5 894 2 180

chi2 982,580 349,809 47,442 62,827

chi2_exog 8,626 1,925

Pseudo R2 0,366 0,349

note: *** p<0.001, ** p<0.05, * p<0.1

Wald test 0 0Chi² test 0,0033 0,0474

Investigation prospects• Try to ameliorate instrumentation including community

variables as: « distance to fast food » and « is there a fast food in the community? »

• Create binary variables for foodlikes and use this instrumentation instead

• Create Nutrition variables by age (z-scores)

Challenges ahead• Use to have some arguments to tax fast foods in China from 2006, but

nothing really happened. The reasons? high cost of taxation, which will damage interest groups (KFC, Mac Donald’s) may derive from the unefficiency of China’s health system

• We suspect overweight and obesity to increase diabetes intensity. That’s why fast food are suspected to increase diabetes intensity indirectly, through BMI (we are going to use it as a possible instrument).

• Then it raises nutritional and policitical recommandations: (1) space out fast foods from schools to a sustainable development(2) why not taxing fast food restaurants?(3) at least, changing their policies (who knows in the USA and in China, buying a glas of water is more expensive than a Coke?)

Thank you for your attention!