Cecilia Alexandri a , Bianca Păuna b , Lucian Luca a

1. Using of Family Budget Survey microdata to estimate food demand and diet diversity demand parameters - implications for population’s food security - Cecilia Alexandria, Bianca Păunab, Lucian Lucaa aInstitute of Agricultural Economics, Bucharest, Romania b INCE Macroeconomic Modelling Center, Bucharest, Romania

2. Contains • Context 2. Data base HBS provided by National Institute of Statistics 3. Estimation of food demand system 4. Measures of food diversity 5. Vulnerable households 6. Policy implications and conclusions

3. Explorândviitorulsecurităţiialimentareşinutriţionale la nivel globalExploringtheFuture of Global FoodandNutritionSecurity Proiect FP7, martie 2012 – februarie 2017

4. FoodSecure partners • 1. LEI Wageningen UR (Olanda) • 2. ZEF, Center for Development Research (Germania) • 3. International Food Policy Research Institute (Washington)) • 4. Institut National de la Recherche Agronomique / AgroParisTech I (Franţa) • 5. LICOS, KatholiekeUniversiteit Leuven (Belgia) • 6. Centre for Chinese Agricultural Policy, Chinese Academy Of Sciences (China) • 7. Institute of Agricultural Economics (România) • 8. Fondation pour l‘Etude des Relations Internationales et du Developpement (Elveţia) • 9. IIASA, Internationales Institut fuer Angewandte Systemanalyse (Austria) • 10. JRC, Joint Research Centre - European Commission • 11. PBL Netherlands Environmental Assessment Agency (Olanda) • 12. ProspexBvba (Belgia) • 13. Slovak Agricultural University in Nitra (Slovacia) • 14. Universita degli Studi Roma Tre (Italia) • 15. Fondation Institut de Recherche pour le Developpement Durable et les Relations Internationales (Franţa) • 16. Ethiopian Economics Association (Etiopia) • 17. Empresa Brasileira de Pesquisa Agropecuaria (Brazilia) • 18. Centre de Cooperation International en Recherche Agronomique pour le Developpement (Franţa)

5. The role of the Institute of Agricultural Economics in the FoodSecure project • D2.5 Food security in the EU: overview and case study • Food secure in the European Union: an overview and case studies • European Union • Slovakia • Romania • D2.6 The role domestic and international policies can play in mitigating FNS

6. The data base used is the Household Budget Survey from the first quarter of 2011 • Food categories, aggregated in eight broad groups: • Bread, cereals and pasta (Cereals); • Meat and meat products, fish and sea food (Meat); • Milk, dairy products (Milk); • Fruits and fruit derivatives (Fruits); • Vegetables and vegetable derivatives (Vegetables); • Sweets and non-alcoholic beverages (Sweets); • Adult goods, as coffee and alcoholic beverages (Alcohol); • Other. • Purchased quantity vs. household consumption • option for household expenditure in the specific month • Price for each food group • computed by dividing the group expenditure by the group quantity • Aggregate price index of food (in log form) • computed as a weighted sum of the index of prices for each group (in log form) • Censoring issue (zero purchases/ consumption) • 4450 households from 7843 purchase food items from all eight groups • 6573households from 7843 consume food items from all eight groups

7. Elements of methodology regarding Almost Ideal Demand System model • Functional form of the AIDS model: • Linear approximate AIDS model: • Properties of the demand system coefficients: • Uncompensated price elasticity of demand: • Compensated price elasticity of demand: • Expenditure elasticity of demand:

8. The influence of the demographic characteristics over the demand of the food groups • The place of residence is significant and in terms of the demand (expressed as share in total food expenditure) for the specific food groups • the rural households have larger share of cereals, fruits, sweets and alcohol, in comparison to the urban households • If the household head is female, than there is a reduced demand for meat and alcohol and increased demand for sweets in comparison to the case when the household head is male • in the urban population sample • A more educatedhousehold head is associated with reduced shares for cereals and fruit groups, increased shares for meat, milk and alcohol groups • in the urban sample • The number of household members is an important determinant of the household demand • larger urban households demand more cereals and meat at the expense of the other food • larger rural household demand more cereals, and less milk and vegetables • The presence of babies in the household decreases the share of cereals and increases the share for milk, while the presence of children decreases meat and increases milk • in both the rural and the urban households • The age of both the household head and the second person in the household do not seem to influence significantly the share for food groups • with the exception of the urban household, cereals share

9. The groups of products bread, meat and alcohol appear as necessity goodsExpenditureelasticities calculated for the urban area are comparable to other countries, but in the rural area the interaction with self-consumption can be important, as many food groups are considered luxury goods (the cereal group inclusively) Estimated elasticities for Q1 2011

10. The own price elasticities and cross price elasticities of the analyzed groups of products Uncompensated price elasticities for the total sample

11. Urban households are less vulnerable to changes in prices compared to the rural households Price elasticities in the urban area Price elasticities in the rural area The own price elasticities less than one for meat and fruit indicate an inelastic demand for these products the traditional food consumption pattern implies a higher consumption of meat in winter time • Estimated own price elasticities are less than one for the cereals, meat, fruit and vegetables groups • Milk group has an elastic demand • explained by the seasonal variations in the milk and dairy price (with significant price increases in winter time)

12. Demand for food diversity Methodology Data Household Budget Survey for two quarters of the 2011 The survey records approximately 100 items for food expenditure Number of observation used: for first quarter = 7843 and for third quarter = 7724 For Q1, CM and TBI were computed also for consumption data • Two measures of diversity: first, the count measure (CM) and a transformed Berry Index (TBI) . • The explanatory variables used being: • the logarithm of the household income • the household composition variables: the number of household members and the number of children. • the household characteristics that were included: age, education, occupational status. • in terms of residence, we have included a dummy for the urban households and the county of residence. • in addition, the education of the household head was interacted with the zone of residence (urban/rural) in order to assess whether educated/un-educated urban households made different choices in terms of diversity in comparison to educated/un-educated rural households.

13. Results for food diversity Count Measure Q1: 22.50, Q3: 24.11, Q1cons: 36.18 Transformed Berry Index Q1: 2.22, Q3: 2.23, Q1cons: 2.72 results are not very different from the results of the count measure the constant is 0.30 for Q1, 0.79 for Q3 and 1.73 for consumption the income elasticity is somewhat larger (0.23 in the first quarter, 0.17 in the third quarter, and 0.15 for consumption) a female household head has a positive influence on the TBI measure (for consumption too) self employment in agriculture decreases diversity in the third quarter only (not for consumption) household composition greatly affects the TBI diversity, the increase in the number of members decreases diversity (for consumption too) but if the members are children, they increase diversity (the same for consumption) • the diversity in the third quarter is higher • the constant in the regression is significant higher in the summer data in comparison to the winter one (1.65 in comparison to 1.08, and 2.53 for consumption) • the diversity is more elastic with respect to income in winter than in summer (0.21 in comparison to 0.15, but 0.12 for consumption) • a female household head is associated to higher diversity (for consumption too) • if the household head is self-employed in agriculture food diversity is lower (it is not the case for consumption) • larger households, especially households with children have larger number of foods items purchased (for consumption too)

14. The situation of Roma households is more difficult than the other households, even from the perspective of food diversity. • The economic vulnerability of the Roma households is indicated by the excessively high share of food consumption expenditures in total consumption expenditures. • Almost half of Roma households spend on food more than 50% of total consumption expenditures, and a large part even more than 60% • In the case of Roma households, the food consumption expressed in calories is under the minimum nutritional requirements defined by FAO (about 2000 kcal/person/day) for almost half of the registered cases (roma households represent 1.8% of the sample).

15. The differences between the urban and the rural households and between Roma and non-Roma households remain high, as regards the share of food expenditures in total consumption expenditures Distribution of households by the share of food expenditures in total consumption expenditures in the 1st quarter of the year 2011, by area Distribution of households by the share of food expenditures in total consumption expenditures in the 1st quarter of the year 2011, for certain population categories

16. Conclusions • The expenditure elasticities are higher in the rural area than in the urban area mainly due to the rural population’s lower cash incomes. • Expenditure elasticitiesfor basic products like bread, meat or milk have values larger than one, which suggests that they are perceived as luxury products by the population. • An explanation of this issue is the low income level and the large share of food expenditure in total expenditure due to the poverty level of the rural households. • Low income, which has a negative influence to food diversity demand induce a negative influence in food commodity markets. • The residence area is also important for the household diet, as the rural households have a less diversified food diet. • The evolution of the food consumption pattern in Romania in the last decade suggests that the increase of cash incomes can accelerate the shift to a Western type of consumption pattern, with less bread and more meat, as well as with a significant share of fruit in people’s diet. • As regards the consumption stimulation measures dedicated to a certain group of products (for instance through the differentiated VAT diminution), these seem to be effective only if they support the evolution already manifested into trend.