Estimating demand elasticities in the recent crises

Transcription

1 Estimating demand elasticities in the recent crises Concetta Rondinelli PRELIMINARY AND INCOMPLETE. Abstract This paper studies consumer behavior in Italy during the recent recessions by estimating a demand system in which demand depends on income and prices, but also on other factors such as age, education, geographical area and position in the labour market of the household head. I combine Household Budget Survey data with information on prices between 2004 and The results point to a strong negative income effect for each of the expenditure items during the recession; less support is found for the existence of a substitution effect. The proportion of total expenditure geared toward the satisfaction of basic and difficult to compress needs (such as rents) is higher the lower is disposable income. The results suggest that the recovery of disposable income expected in the coming years should result in a significant recomposition of expenditure, towards the composition prevailing before the crisis. Keywords: consumer behavior, demand systems, price and income elasticities. JEL Codes: D12, E21. Bank of Italy, Economic Outlook and Monetary Policy Directorate, via Nazionale, 91, Roma Italy. concetta.rondinelli@bancaditalia.it. I am grateful to A. Brandolini, S. Siviero, R. Zizza and F. Zollino for their helpful comments and suggestions on a preliminary version of the paper. I would also like to thank O. Maizza (Ministero dello Sviluppo) for kindly providing me with price level data from 2004 to The usual disclaimer applies: the opinions expressed in this paper are those of the author and do not involve the Bank of Italy. 1

2 1 Introduction About two thirds of the downturn in Italian GDP recorded between mid 2011 and end 2013 was due to factors affecting domestic demand (Busetti and Cova, 2013). At the end of 2014 Italian household consumption, which had been recovering slowly since the summer of 2013, was still nearly 8 per cent below the pre-crises level. Two consecutive recessions in five years may have induced Italian households not only to reduce their expenditure level, but also to adjust its composition. These adjustments stem from several mechanisms: a) the shift in the budget constrain determined by the fall in real disposable income (through unemployment, pay cuts, lower return from asset holdings; Rodano and Rondinelli, 2014); b) variations in the slope of the budget constraint due to changes in relative prices (Faiella and Mistretta, 2015); c) changes in preferences (Pozzolo, 2011). The severe contraction in income (-8.3% in , experienced over the recent years) is likely to have induced a re-composition in consumption: in order to minimize the impact of the fall of income on basic, not-easy-to compress expenses (such as rents and health which are inelastic to income), households may have reduced their consumption for leisure items, such as clothing and footwear, furniture and household services (Rondinelli et al., 2014). The crises may have also induced a change in households preferences, with a reduction in the quality of purchased goods and a more widespread recourse to discounts. Pozzolo (2011), for example, finds that during the Global Financial crisis Italian families consumed more chicken, reducing the quantity of veal, and preferred cheaper outlet types, such as discounts. As to the outlet type dimension, Lamey et al. (2007) show that during a recession consumers substitute towards private label products, similarly to what is expected for an inferior good if income falls. However, unlike what is expected for an inferior good, consumers continue to buy private label products after the economic recovery begins, resulting in a permanent increase in private label market share. The asymmetry in the response of the sales of private label products to the business cycle may arise because of differences between the perceived and intrinsic quality of private label goods. 1 1 Richardson et al. (1994) show that consumers perceive private label products to be of lower quality because of extrinsic cues such as branding or low prices. 2

3 Research on how consumer adjust their purchasing behavior across the business cycle has been (primarily) developed in the field of marketing, focussing on sales and profitability of individual firms. Deleersnyder et al. (2004) for example show that consumer behavior over the business cycle depends on both the rate of economic growth or decline and the size of the peaks and troughs, i.e. sales fall much quicker during contractions than they recover during economic expansions. Similarly, Gordon et al. (2013) find that price sensitivity is countercyclical: it rises when the economy weakens. Using the sharp changes in fuel prices to estimate the impact of short run changes in disposable income, Gicheva et al. (2010) find that consumers who experience a fall in income substitute toward lower-cost food and toward on-sale items. Coibon et al. (2012), using store level data, show that the average prices paid by consumers decline significantly with higher unemployment, by means of outlet substitution. Kamakura and Du (2012) suggest that these cyclical variations in consumption patterns are not exclusively a function of changes in the budget constraint but rather due to changes in the preferences of consumers. They state that during a recession, as consumers see others spending less on positional categories (i.e.purchases that signal a higher social status), they also adapt by reducing their expenditure on these categories as they can do so and still maintain their relative social standing. This paper studies consumer behavior in Italy during the recent recessions by estimating a demand system in which demand depends on income and prices, but also on other factors such as age, education, geographical area and position in the labour market of the household head. My contribution to the existing literature is twofold. The paper builds a novel dataset matching Household Budget Survey data and information on price levels to estimate whether over the recent Italian crises the consumption pattern of different expenditure components may have reflected (1) an income effect, (2) a change in the relative prices or both of them. In practice I estimate a set of equations describing how households with given characteristics allocate their total expenditure across different goods/services, given their price and households income. To the best of my knowledge, it is the first paper dealing with this issue for Italy. Secondly, given the evidence of possible non linearity of Engel curves for Italy, I allow the original Almost Ideal Demand System (AIDS; Deaton and Muellbauer 1980) to include 3

4 a quadratic term in log(expenditure) (QUAIDS as in Banks, Blundell, and Lewbel, 1997), so that the demand system depends on income, both linearly and quadratically. Adding demographic variables and obtaining price and expenditure elasticities in these models is not an easy goal, as it requires a modification in the likelihood function (see Poi 2012), subject to parameter constraints, that makes the achievement of a maximum very difficult. The results point to a strong negative income effect for each of the expenditure items during the recession; less support is found for the existence of a substitution effect. This may also be due to the high degree of commodity aggregation into commodity bundles. The proportion of total expenditure geared toward the satisfaction of basic and difficult to compress needs (such as rents) is higher the lower is disposable income. The reminder of the paper is organized as follows. In Section 2 I present the micro data on consumption and prices used to estimate the demand system, while Section 3 provides a descriptive analysis of the drop in consumption neglecting relative prices. Section 4 illustrates the estimation procedure and the main results. Section 5 wraps up the main findings of the paper discussing possible policy implications. 2 Data In this paper I construct a novel dataset matching survey data on consumption (Household Budget Survey) with price level data (underlining the construction of the Consumer Price Index). The matching is based on the regional dimension. 2.1 The Household Budget Survey The Household Budget Survey (HBS) is an annual survey conducted by the National Institute of Statistics (ISTAT). It provides information on the patterns and the level of consumption of Italian resident households according to various demographic and social characteristics. It is a continuous survey, involving approximately 28,000 households each year, sampled at random from the residence records of the municipalities involved in the Survey. The data is collected for 278 elementary consumption items, providing a very detailed picture of con- 4

5 sumption patterns. Sampling weights allow to expand the sample to the whole population. 2 Real consumption measures are obtained by deflating elementary nominal consumption items with the corresponding price indices from the Consumer Price Index (CPI). There have been several discussion about the quality of survey data and their ability to reproduce the movements in aggregate consumption from National Accounts (see Seslnick 1992 and Paulin et al for USA data; Banks and Johnson 1997 for UK). The studies stress that aggregate individual data and National Accounts (NA) are expected to diverge given 1. the different definition of goods and services, 2. the reference population and 3. measurement errors. For Italy, using a COICOP 3 classification (i.e. Classification of Individual Consumption by Purpose), Rondinelli et al. (2014) find that the main consumption aggregates in NA and HBS show differences in levels but similar dynamics, once the definition of households and the classification of services and goods in the two datasets are harmonized. Consequently, the results obtained from studying the evolution of expenditure on the basis of micro data may be viewed as consistent with the evolution of macro data. In Table 1 I regressed each of the expenditure components on a linear trend, a dummy variable for the crises (i.e. taking the value one over the years ) and some demographic characteristics of the household. It emerges that monthly total expenditure for durable goods was lower than in the pre-crises period by 2% on average, reflecting mainly the contraction of motoring. From official statistics (ACI, 2014) we know that new car registrations fell on average by 24% in the period , compared to Durable 2 The survey is based on three questionnaires: (1) dairy that records daily expenses, such as the amount spent for food (bread, pasta, meat, etc...) and current goods and services (newspapers, tobacco, bus tickets, etc...). (2) self-consumption, that records self-produced and auto-consumed goods, during the reference period (one week); (3) summary of expenditure, compiled by the interviewer at the beginning of the month following the period of reference. On this occasion the interviewer also records the socio-demographic characteristics of the households, expenses for housing, the cost of furniture and equipments, clothing and footwear, health, transport and communications, leisure, entertainment and education, other goods and services. 3 The 12 COICOP chapters are (1) food and non-alcoholic beverages; (2) alcoholic beverages and tobacco; (3) clothing and footwear; (4) housing, water, electricity and fuel; (5) furniture, and household services; (6) health services and health expenditures; (7) transport; (8) communications; (9) recreation, entertainment and culture; (10) education; (11) accommodation services and restaurants; (12) other goods and services. 5

6 Table 1: Average increase in expenditure in Coef. Std. errors Total expenditure (1.070) Durables *** (0.503) Household goods (0.0412) Leisure goods (0.252) Motoring *** (0.424) Semi-durables (0.515) Clothing and footwear (0.153) Housing 1.331*** (0.369) Non durables (0.317) Food (0.178) Alcohol *** (0.0245) Tobacco (0.0263) Energy 0.483*** (0.0674) Goods *** (0.0244) Medicines (0.0475) Fuel *** (0.0793) Leisure items ** (0.0418) Personal goods ** (0.0284) Notes: Author s calculation from the HBS, monthly data. Sample weights included. Each row reports the estimated coefficients obtained from regressing the different expenditure items on a dummy variable for the crises (taking value 1 in the years ), controlling also for a linear trend, age, geographical area, occupation, education of the household head and housing tenure. Durable expenditure includes household goods (washing machine, refrigerator, dishwashing), leisure goods (furniture, tv, suitcases, costume jewelry) and motoring (car, motorcycle); housing covers actual and imputed rents of the main and secondary house and maintenance; non durable expenses incorporate food, alcohol and tobacco, energy (bills like electricity, water, gas), goods (paper towels), medicines, fuels (petrol, diesel fuel), leisure items (newspapers, magazines) and personal goods (shampoo, soap, toothpaste). Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1. goods were seriously affected by the recession: they mainly represent luxuries, which are easier to postpone than necessities (see Browning and Crossley, 2000 and Crossley et al., 2012). Among non durables, leisure items (like newspapers and magazines) and fuels contributed to the drop in household consumption, the latter mainly reflecting the overall increase in Brent quotation observed over that period. Housing expenditure and payment of bills (mainly electricity and gas tariffs) went up during the crises. 2.2 Price levels The second dataset used in the paper includes price levels from Osservatorio Prezzi (OP, Ministero dello sviluppo). They are obtained from elementary items collected from the municipal offices of statistics within the construction of Consumer Price Index (CPI); these prices are available for each Italian province (NUTS3 level) as a minimum, maximum and average price. Since region (NUTS2) is the most detailed geographical dimension available 6

7 in HBS and OP, I evaluate mean price for goods for each of the 20 Italian regions. 4 Sampled goods, that include food, alcohol, clothing and footwear, rents, fuels, restaurant and other goods and services, represent about 17% of the items included in the official CPI by the National Institute of Statistics (see Rondinelli and Veronese, 2007). I checked that the selected goods were representative of the main COICOP aggregates. 5 Food products include meat, fruit and vegetables, milk, fish, oil and fats, bread and cereals accounting for about 80% of the processed and unprocessed foods covered in the CPI. To proxy COICOP 2 (alcoholic beverages and tobacco) wine and beer were included in the alcohol category; washing and ironing dresses for men and women is used to mimic the evolution of clothing and footwear (COICOP 3). The fuel class includes diesel and gasoline, while other goods and services (COICOP 12) include groceries such as shampoo, shower gel, toothpaste, laundry detergents, diapers and hair cut (for ladies and gentleman). Restaurant was verified to proxy the evolution over time of COICOP 11 and includes coffee, cappuccino, panini, etc. To proxy COICOP4, I enrich the OP dataset with actual rent prices at the provincial level recovered from the Consulente Immobiliare (CI), a semiannual survey conducted for a special review published by Il Sole 24 Ore media group: along with new house prices it collects data on new contract rents for a very large sample of Italian municipalities. In each sampled town, CI provides estimates of the average rent level (per square meter) of an apartment located in three areas: center, semi-center and suburbs. Rents are further distinguished into new and renewed contracts, the latter defined as contracts negotiated with previously sitting tenants upon contract expiration (see also Rondinelli and Veronese, 2011). 4 To recover the regional dimension I aggregated observations weighted by the resident population in each province. 5 Results are available upon request. 7

8 Table 2: Average price levels in the Italian regions. Food Alcohol Cloth. Rents Fuel Rest. Other Food Alcohol Cloth. Rents Fuel Rest. Other Food Alcohol Cloth. Rents Fuel Rest. Other Piemonte Valle d Lombardia Trentino Veneto Friuli Liguria Emilia Toscana Umbria Marche Lazio Abruzzi Molise Campania Puglia Basilicata Calabria Sicilia Sardegna Source: Author s calculation from OP and Consulente Immobiliare. Unit of measure is kg for food, liter for alcohol and fuel, one piece for clothing, restaurants and other goods and services, square meters for rents. 8

9 I will focus on the period to conduct a comparison before and after the recession (see also Fabiani and Porqueddu, 2014 for a description of price durations in the latest years). Price levels for selected years and across the Italian regions are reported in Table 2. I observe a general increase in price levels between 2005 and 2012 common to all Italian regions and products. Except for fuel, the highest increase has occurred between 2005 and Since the beginning of the recession food and fuel prices have almost doubled in all Italian regions; the latter reflected the rapid rise experienced by oil quotations in that years. Prices for other goods and services went up by 40% between 2005 and 2012; rents increased by 10% on average, the highest rise has been observed in Lazio. A downward trend was marked by clothing between 2005 and 2009, especially in Northern regions; a slight increase was observed between 2009 and Prices for coffee, cappuccino, panini, which are included in the restaurant category, increased by one quarter during the recession. 3 Neglecting relative prices in the expenditure drop To understand the role of the recent crises in consumption fall, I split the households along four dimensions, i.e. age, education, ownership of the household and geographical area. I first exploit the age dimension of the household head: the young (those aged less than 44), the middle aged (those aged 45 or greater and less than aged 64), and the old (those aged 65 or older). I estimate the following equation in which, for the time being, the effects of variation in relative prices are ignored: c it = α i + f(t) + β 1 crises + β 2 crises age it + β 3 age it + γz it + ɛ it (1) where c it indicates real expenditure for different items (food, energy, housing, etc...), Z it accounts for the main socio-demographic characteristics of the household head (age, education, geographical area and housing tenure), crises is a dummy variable taking value one for the years and f(t) is a linear trend capturing common trend in consumption. In Table 3 I show that during the crises monthly total expenditure was lower than in the pre-crises period by 3% on average for young households and by 5% for old ones, while higher by 17% for middle aged: the test of equality indicates that the three values, taken 9

10 pairwise, are not statistically equivalent at 1% level. The drop in durable goods, common to all age groups, has been more severe for retired households (about 6%), due to a fall in both leisure goods and, especially, motoring (values for young and old and for middle aged and old households are not statistically equivalent at 1% level). Semidurable and nondurable goods were higher for middle aged household; for them household bills were higher than in the precrises period by 2% on average compared to 1.5% for old households (t-stat of equality=0.06). Fuel expenditure (petrol and diesel fuel) was lower by 0.6% for young households and by 3% household aged 65 and over, possibly reflecting their reduced need of cars (to go to work, for example; see also Faiella and Mistretta, 2015). As to the leisure items, their consumption was higher for household aged 45 and over, while adjusting downwards for Table 3: Average effect of the crises. Crises X 0-44 Crises X Crises X 65+ Crises X Low Crises X Medium Crises X High Crises X Owner (1) (2) (3) (4) (5) (6) (7) Total expenditure ** *** *** *** *** *** *** Durables *** *** *** *** *** 1.61 *** 2.61 *** Household goods *** 0.41 *** Leisure goods *** *** *** *** 1.82 *** Motoring *** *** *** *** *** Semi-durables * 7.93 *** *** *** ** *** *** Clothing and footwear *** *** *** *** 3.77 *** Housing 1.55 *** 1.72 *** 3.18 *** *** 1.41 *** *** *** Non durables *** 1.18 *** *** *** *** *** Food *** 2.08 *** *** *** *** 4.57 *** Alcohol *** 0.08 ** *** *** *** Tobacco *** *** *** *** *** Energy 0.48 *** 2.19 *** 1.50 *** *** 0.60 *** 0.65 *** 2.48 *** Goods 0.12 *** 0.33 *** 0.14 *** *** *** Medicines *** 1.13 *** ** * *** Fuel *** 0.53 *** *** *** *** *** 2.03 *** Leisure *** 0.86 *** 0.28 *** *** *** 0.63 *** 0.50 *** Personal *** ** *** *** 0.35 *** Notes: Author s calculation from the HBS. Sample weights included. Columns (1)-(3) report the estimated coefficients obtained regressing the expenditure categories on age, education, occupation, geographical area of the household head, housing tenure, a linear trend, a dummy variable taking value one over the years and the interaction of the dummy variable with age. Column (4)-(6) report the estimated coefficients obtained regressing the expenditure categories on age, education, occupation, geographical area of the household head, housing tenure, a linear trend, a dummy variable taking value one over the years and the interaction of the dummy variable with education. Durable expenditure includes household goods (washing machine, refrigerator, dishwashing), leisure goods (furniture, tv, suitcases, costume jewelry) and motoring (car, motorcycle); housing covers actual and imputed rents of the main and secondary house and maintenance; non durable expenses incorporate food, alcohol and tobacco, energy (bills like electricity, water, gas), goods (paper towels), medicines, fuels (petrol, diesel fuel), leisure items (newspapers, magazines) and personal goods (shampoo, soap, toothpaste). *** p<0.01, ** p<0.05, * p<0.1. I repeated the same exercise looking at the effect of the recession across three educational levels: low (including none and primary school), medium and high education (Table 3, 10

11 columns (4)-(6)). Over the period total monthly expenditure was lower than in the pre-crises period by 30% on average for low educated households, while higher by 35% for high educated (t-stat on equality=0.000). The fall in durable goods has been concentrated among households with medium or low education. Among non durable goods, food expenditure decreased more for low educated, by about 4% (two times more than high educated); also the reduction of fuel consumption was concentrated in the lowest part of the education distribution. The expenditure for leisure items was higher than in the pre-crises period by about 1% for high educated, while lower (by the same amount) for low ones (test of equality of coefficient reject that the two coefficients are statistically equivalent). House expenditure significantly reduced for low educated households, reflecting a compression of maintenance expenditure in a period of a downward trend in house prices; compared to renters housing tenure constituted an insurance against the drop in total consumption and its main components (Table 3, column (7)). As to the geographical dimension, the effect of the crises was less perceived in Northern regions, compared to Southern ones. 6 All in all, younger, and to a less extent older, households were most affected by the economic downturn; those middle aged, living in Northern regions, high educated and owners of a house could instead easily cope with the recession. In Figure 1 I plotted the empirical non parametric Engel curve, representing the relationship between the budget share for good i and household expenditure, for the seven COICOP aggregation as illustrated in Section Although the linear formulation appears a reasonable approximation for the food share curve, a clear nonlinear behavior is evident, in the raw data, for alcohol, clothing, restaurant and other goods and services. Notice also that the Engel curve as a clear positive slope for fuel, suggesting that petrol and diesel are luxury goods, and a negative one for food, indicating it is a necessary item. 6 Results available upon request. 7 Notice that, to proxy COICOP 2 (alcoholic beverages and tobacco) wine and beer were included in the alcohol category; washing and ironing dresses for men and women is used to mimic the evolution of clothing and footwear (COICOP 3). The fuel class is composite of diesel and gasoline, while other goods and services (COICOP 12) include groceries like shampoo, shower gel, toothpaste, laundry detergents, diapers and hair cut (for ladies and gentleman). Restaurant was verified to proxy the evolution over time for COICOP 11 and include coffee, cappuccino, panini, etc. To proxy COICOP4, I use actual rents. 11

12 Though the exercise presented in the Section is telling about the distributive effect of the recession, it neglects that the reduction in expenditure operated by households may also be due to a variation in relative prices. Between 2009 and 2012 indeed brent quotation increased by 80%, given that fuel prices impact on expenditure for heating, private transport and electricity we could expect that to cope with the increase in energy prices households adjusted the composition of the expenditure of the remaining items. At the same time, house and rent prices decreased. To account for these issues I estimate a system of demand equations in the next Section. 4 Estimating demand elasticities 4.1 Model Specification One of the most widely used specifications in applied demand analysis is the Almost Ideal Demand System (AIDS) model proposed by Deaton and Muellbauer (1980). The model features budget shares as dependent variables and logarithm of prices and real expenditure/income as regressors. Although AIDS model allows for a linear approximation in prices and expenditure it is not easy to be fitted with commonly used software (like STATA), as it requires to write a function evaluator program as illustrated in Poi (2008). Additionally, various empirical Engel curve studies suggest that further terms in income may be required to achieve reliable estimations. Banks, Blundell, and Lewbel (1997) show that although the linear approximation is a reasonable approximation for the food and fuel share curves, for alcohol and clothing a non linear specification is more reliable. The original AIDS model was subsequently extended to permit non-linear Engel curves. The resulting model, proposed by Banks, Blundell, and Lewbel (1997), is the Quadratic Almost Ideal Demand System (QUAIDS). Under QUAIDS, the i th budget share (w i ) equation for household h is given by w ih = α i + n j=1 where i = 1,..., n indicates good i, w ih = p ih q ih /x h and { } xh γ ij lnp j + β i ln + λ [ { }] 2 i xh ln (2) a(p) b(p) a(p) 12

13 ln(a(p)) = α 0 + n α k lnp k k=1 n k=1 j=1 n γ kj lnp k lnp j where p i is the price of good i and x stands for the total consumption expenditure; b(p) is the Cobb-Douglas price aggregator b(p) = We need to estimate αs, βs, γs and λs. n k=1 From the utility maximization problem under a budget constraint we need to impose the following restrictions: p β k k n n α i = 1; γ ij = 0; i=1 i=1,j=1 i=1 n β j = 0; i λ i = 0 (3) γ ij = 0 (4) j γ ij = γ ij (5) The equalities in (3) are the adding-up restrictions. They express the property that the sum of the budget shares equals 1 (i.e. ih w ih = 1). The restriction in (4) accounts for the prediction that the demand functions are homogenous of degree zero in prices and income. Satisfaction of the restriction in (5) ensures that Slutsky symmetry would hold true. 8 Appendix A I prove how to generalize equation 2 to allow for the demographic characteristic of the household. 8 The Slutsky equation decomposes the change in demand for good i in response to a change in the price of good j into two terms, a substitution and an income effect: x i (p, w) = h i(p, w) x i(p, w) x j (p, w) p j p j w In 13

14 4.2 Estimation Procedure In this Section I estimate demand elasticities by using the HBS integrated with price levels data available from Osservatorio Prezzi, exploiting the regional dimension. I will focus on seven groups of consumer s expenditure. I estimate QUAIDS incorporating demographic variables as in Ray s (1983), as illustrated in the Appendix A. To estimate the parameters of the QUAIDS, I use the maximum likelihood approach (Poi, 2002; Poi, 2008). The results of the estimates for αs, βs, γs and λs of equation (2) with demographic variables, ηs and ρs, are provided in Table 4. Most of the parameters are statistically significant at the 1% level. In particular, the estimates of the parameters λ, that regulate the effect of the second order coefficient on budget shares (thus allowing for nonlinear Engel curves), are statistically significant for most of the seven commodity groups; this confirms the relevance of the quadratic term extension of the linear AIDS. The quadratic term in the logarithm of expenditure is close to zero only in case of restaurants (in 2013, 2012 and 2004) and other goods and services (in 2011, 2008 and 2005). Thus, omitting the quadratic term of the remaining commodities from the analysis would bias the estimates. Even if we are generally interested in income elasticities, I say something on the estimated βs by splitting the population according the equivalised consumption distribution. As changes in real expenditure operates through the βs, in Figure 2 I report the estimated βs from the budget share equation (2) from 2004 to These coefficients add to zero (by construction) and are positive for luxuries and negative for necessities for each year. Excluding 2013, over the last recession we have observed an increase (in absolute value) of the estimated coefficients, particularly marked at the end of the sample. Even if the estimated βs do not change considerably across years, a formal test of equality of means reveals that the estimated coefficients are not statistically different over two consecutive years for alcohol, clothing and footwear and restaurants. If I split the sample considering households below and above the median of the equivalised consumption distribution, I find that clothing, restaurants and other good and services are considered a luxury for poor households. Rents 9 Notice that the βs are different from Table 4, as they are estimated without demographics to allow for a break of the population below and above the median of equivalised distribution. 14

15 are a luxury good for households at the top of the distribution and a necessity for those at the bottom. The same remark applies to fuel. As we are often more interested in the expenditure and price elasticities rather than in the estimated coefficients per se, I present the results in the two Sections below Expenditure elasticities I now consider income elasticities across the relevant characteristics of the households. Income elasticities measure the responsiveness of demand of a specific good to changes in expenditure, i.e. it shows how the quantity purchased changes in response to a change in consumer expenditure, which is a proxy for total household income. 10 The higher the income elasticity, the more sensitive consumer demand is to income changes. 11 In Figure 3 I plot the income elasticities; they are calculated for each individual household and subsequently averaged. Based on the estimates, the commodity bundle of food, alcohol (that includes wine and beer) and housing are necessary goods, as their budget elasticity is positive and below one at the same time. On the contrary, I identified clothing, fuel, restaurants and other goods and services as luxury goods, with income elasticity well above one. Although income elasticities do not change considerably across years, a formal test of 10 Although expenditure is commonly used as a proxy for income, notice that over during the Sovereign Debt crisis consumption initially diminished at about the same pace as income and then, starting in the second quarter of 2013, fell much more steeply, owing in part to the simultaneous drop in household wealth (Rodano and Rondinelli, 2014). Additionally, if total expenditure is jointly determined with the budget shares of the specific commodities in the demand model, total expenditure becomes endogenous in the budget share equations. This may induce inconsistent parameter estimates if not taken care of; Blundell and Robin (1999) illustrate a two step augmented regression technique to solve the problem. First, total expenditure is regressed on a set of exogenous variables including those which may directly influence budget shares. In the second step, the residual from this reduced-form regression is added, as an explanatory variable in the budget share equations together with total expenditure. This extension is left for future research. 11 A good is called a normal good if its budget elasticity is positive. Specifically, so-called normal necessities have an income elasticity between 0 and 1; demand for such goods increases with income, but their budget share decreases. Luxury goods are goods with income elasticity of demand above 1; demand is highly sensitive to any change in income and the budget share increases with income. Finally, inferior goods have negative income elasticity. Thus, demand for this type of good falls as income rises. 15

16 equality of means reveals that means are statistically different from each other at any level greater or equal than 1% for the years before and after the recession. This evidence confirms the importance of the income effect for each of the seven expenditure items. Before the crisis started, a 10% increase in income would have induced an increase of 4% in food expenditure for youngest households and of 7% for oldest ones; I observe a gradual convergence of all age groups to a value of 6% in 2013 (Figure 4). Looking at the distribution of equivalised income we notice that poorer households are more sensitive than richer ones to a change in income: a 10% increase in income would induce food expenditure to raise by 4% for high income households and by 8% for low ones; however I do not find any significant change in the recession. No relevant variation is found for housing. More interestingly, the estimated expenditure elasticity for clothing and footwear remained roughly unchanged for households heads aged less than 44 and decreased for retired ones, meaning that cloth expenditure became for them less sensitive to an income increase. Additionally, an upward trend is observed for restaurant elasticity for households 65 and over from , while a downward for youngest between I also present the fitted Engel curves, once controlling for relative prices and estimating a system of demand equations. The Engel curves in Figure 5 confirm the importance of nonlinearities and suggest that food and rents are necessary goods, while cloths, fuel, restaurants and other goods and services luxuries. More interestingly, the inverse U-shape empirical Engel curve for clothing (Banks et al., 1997), that includes services like the washing and ironing dresses for men and women, turns out to have an upward slope once estimating a QUAIDS model. This suggests, that for high income households the budget share allocated to cloth services is higher than for low income ones, differently from the empirical curves that instead indicate an equal budget share for high and low income. I also notice that during the recession the Engel curve shifted downwards for high income households. The same figure applies to restaurants. For fuel the fitted Engel curve has the same shape than empirical one, although with a downward shift for richer households. There is also evidence that the share allocated to rents decreases with income, while the empirical Engel curve suggests an increase in the budget share for wealthier households. 16

17 Given the severe contraction in income over the recent recession and the importance of the income effect estimated for each of the seven expenditure items, the analysis suggests that the satisfaction of basic needs and difficult to compress in the short term (such as spending on rents) absorbs a larger proportion of total expenditure, especially for poor households Price elasticities In Tables 5 and 6 I provide estimates of compensated 12 and uncompensated 13 price elasticities. Notice that, the own-price elasticities are negative for all commodity groups, as expected; the cross elasticities are higher than the own elasticities. This indicates that individual commodity groups do not have any strong substitutes or complements among the remaining ones; this observation could have been affected by the degree of commodity aggregation into commodity bundles. Using more detailed commodity bundles, one might find a higher degree of substitutability, for example between wine and beer. 14 The use of more aggregated expenditure items, however, helps in solving the problem of zero-expenditure, a common feature of survey consumption data. 15 Based on the size of the own-price elasticities, I found demand for alcohol, clothing, fuel and restaurants to be the most affected by changes in their own prices. In addition, looking only at the substitution effect of a price change, presented in Table 5, fuel and restaurants are classified as goods with price elastic demand: since the commodity bundle of fuels includes 12 The Hicksian demand curve is called the compensated demand curve because consumers are compensated for the price change. That is, when prices change they receive a compensation that allows them to remain on their original indifference curve (substitution effect). 13 Marshallian demand indicates how the quantity of a good changes to a price variation, when income is held constant. 14 A greater detail in the number of goods considered would however compromise the maximization of the likelihood. 15 Apart from imperfect recall, three main reasons for zero-expenditures can be identified: (i) permanent zero expenditure, (ii) zero expenditure during the survey period, (iii) recall bias. Unfortunately, it is not possible to identify which of these reasons is responsible for each of the reported zero-expenditures from HBS data. In presence of zero-expenditure or censored dependent variable, we know that the econometric estimates that neglect censoring (due to a sample selection problem) are biased and inconsistent (Maddala, 1983). However, aggregation over commodities helps in reducing the problem. 17

18 diesel and gasoline, a 1% increase in prices of crude materials reduces household expenditure for fuel by 1.4%. Clothing and alcohol have also a price elasticity close to 1. On the contrary, I find food, housing and other goods to be relatively less affected by changes in prices. The stories told by compensated and uncompensated price elasticities seem to be comparable. Additionally, compared to 2005 during the recession compensated price elasticities increased for food, housing and other goods, while decreasing for the remaining categories. I now simulate a 30% increase in prices of a necessary good (housing) and of a leisure service (restaurant). The results, reported in Table 7, show that a rise in rents would induce a reduction in cloths expenditure both in normal times and, especially (about twice as much), in period of crises; households continue to buy restaurant related items (coffee, cappuccino, etc...) in normal times, while they compress the related consumption in recession. Additionally, an increase in restaurant prices, would result in a rise in housing expenditure in non recessionary periods about twice as much than in recessionary ones; in particular household react increasing (decreasing) cloth services expenditure by about 4% in normal (recession) times. 5 Conclusions This study estimates demand elasticities in Italy. I use the Household Budget Survey and Osservatorio Prezzi dataset to implement the QUAIDS model of Banks et al. (1997), allowing for a detailed analysis of commodities and including characteristics of consumers. I estimate the stochastic version of the demand system for a representative household extended to include demographic variables, such as age, geographical area, education and the position in the labour market of the household head. I focuss on goods representing about 17% of the items included in the official CPI by the National Institute of Statistics; food products as a whole cover about 80% of the processed and unprocessed food in the CPI. I threat the recent recession as a whole episode of downturn and focuss on years to conduct a comparison before and after the recession. The estimated Engel curves appear to be non linear for most of the goods, justifying the relevance of the quadratic term in the logarithm of expenditure. I estimate a downward slope 18

19 for housing and food expenditure (whose income elasticity is below one) and a positive slope for cloth, fuel, restaurant (income elasticity well above one). In particular, the estimated curves differ from the empirical ones, with a U-shaped reverse, for clothing and footwear and restaurants, especially for the wealthiest families. In addition, the satisfaction of basic needs and difficult to compress in the short term (such as spending on rents) absorbs, following to a reduction in income, a larger proportion of total expenditure. I find that households tend to promptly adjust their fuel consumption due to a change in price of diesel and gasoline; however we do not point out any particular substitution effect across the seven group of items considered, possibly due to the high degree of commodity aggregation into commodity bundles. The estimates of a system of demand equation show a strong negative income effect during the recent crises for each of the expenditure items. It is also possible that the income effect masks a change in household s preferences with a reduction in the quality of purchased goods, a more widespread recourse to discounts or an extension in the mean duration of durable goods, that I cannot control with these models. Given the fall in the purchasing power of households in the course of the recession, this paper documents a greater difficulty of poorer households in addressing basic needs. The recovery of disposable income expected in the coming years should result in a significant recomposition of expenditure, towards the composition prevailing before the crisis. 19

20 Table 4: Estimated coefficients for the budget share equation Coef. Std. Coef. Std. Coef. Std. Coef. Std. Coef. Std. Coef. Std. alpha lambda alpha lambda alpha lambda alpha lambda alpha lambda alpha lambda alpha lambda alpha lambda beta eta beta etaage beta etaage beta etaage beta etaage beta etaage beta etaage beta etaage gamma etaeduc gamma etaeduc gamma etaeduc gamma etaeduc gamma etaeduc gamma etaeduc gamma etaeduc gamma etaoccup gamma etaoccup gamma etaoccup gamma etaoccup gamma etaoccup gamma etaoccup gamma etaoccup gamma etaarea gamma etaarea gamma etaarea gamma etaarea gamma etaarea gamma etaarea gamma etaarea gamma rho gamma rhoage gamma rhoeduc gamma rhooccup gamma rhoarea gamma gamma gamma Source: Author s calculation from the HBS and PO. 1=food, 2=alcohol, 3=clothing, 4=rents, 5=fuel, 6=restaurant, 7=other goods and services. Age, educ, occup and area are the age, educational level, occupation and geographical area of residence of the household head. 20

21 Table 5: Compensated price elasticity Food Alcohol Clothing Rents Fuel Restaurant Other goods Food Alcohol Clothing Rents Fuel Restaurant Other goods Food Alcohol Clothing Rents Fuel Restaurant Other goods Food Alcohol Clothing Rents Fuel Restaurant Other goods Notes: Author s calculation from the HBS and PO. The entry in row i, column j informs about a percentage change in quantity demanded for a good in row i as price of a good in column j increases by 1%. Table 6: Uncompensated price elasticity Food Alcohol Clothing Rents Fuel Restaurant Other goods Food Alcohol Clothing Rents Fuel Restaurant Other goods Food Alcohol Clothing Rents Fuel Restaurant Other goods Food Alcohol Clothing Rents Fuel Restaurant Other goods Source: Author s calculation from the HBS and PO. The entry in row i, column j informs about a percentage change in quantity demanded for a good in row i as price of a good in column j increases by 1%. 21

22 Table 7: A simulation exercise. 30% increase in rent prices 30% increase in restaurant prices Food Alcohol Clothing Rents Fuel Restaurant Other goods Notes: Author s calculation from the HBS and PO. The entry in row i informs about a percentage change in quantity demanded for a good i as prices of rents and restaurant increase by 30%. 22

23 Budget Share Budget Share Budget Share Budget Share Budget Share Budget Share.2.4 Budget Share Figure 1: Non parametric empirical Engel curves Food Alcol Log (expenditure) Log (expenditure) Cloth Rents Log (expenditure) Log (expenditure) Fuel Rest Log (expenditure) Log (expenditure) Other Log (expenditure) Notes: My calculation from HBS 23

24 Figure 2: Estimated betas Notes: My calculation from HBS and OP. Estimated βs for total population and households below and above median of equivalised distribution (QUAIDS estimated without demographics). 24

25 Figure 3: Income elasticities Notes: My calculation from HBS and OP. 25