UNIVERSIT `
A DEGLI STUDI DI PISA
Dipartimento di Economia e Management
CORSO DI LAUREA MAGISTRALE IN ECONOMICS
Prospect theory and perceived
inflation: application to Italian data
Relatore
Alessio Moneta
Candidato
Valeriia Budiakivska
Prospect theory and perceived
inflation: application to Italian
1
Introduction
This study evaluates the perceived inflation in Italy, combining the psycho-logical mechanisms and economic theory.
Policy makers need to be aware of the true perceptions of inflation in the country which influence the actual and expected inflation. This is supported by Dr¨ager (2011) [11] and Maag (2010)[38], who demonstrate that households tend to base their inflation expectations largely on perceived, rather than ac-tual inflation rate. Blanchflower and Kelly (2008)[4] show that groups with biased perceptions also form biased expectations. Therefore, perceptions of inflation influence the behavior of agents in all the markets and have a certain impact on expectations about the future prices, which, in their turn, deter-mine the actual inflation rate together with many important macroeconomic indicators such as nominal wages, exchange rates, interest rates, unemploy-ment rates and many others.
The first part of the paper examines the methods and procedures conducted by ISTAT to calculate the national consumer price index. The procedure, though well-founded and complex, takes the consumer’s perspective, in a sense that certain COICOP1 items are purchased with different frequencies,
causing a divergence between the official and the perceived prices changes. This implies that official CPI index measures something different from what attracts the public interest.
In the second part of the study, the balanced perceived inflation (BPI) is discussed, together with its mechanism to capture the perceived inflation. The BPI, initiated by the European Commission, is a qualitative indicator of perceptions of inflation, which means that it can be compared with CPI only in terms of behavior, but not in terms of the magnitude.
Thus, to be able to compare perceived and official inflation properly, the Index of perceived inflation (IPI), suggested by Brachinger (2008)[5], is con-structed. The IPI takes into account some key psychological patterns of consumers. Based on the Prospect theory (1979) [30], the index assumes that consumers compare price changes to a certain reference point and these price changes are considered as gains or losses subject to this reference point. Furthermore, IPI takes into account the crucial psychological hypothesis of decision makers: inflation perceptions are influenced more by a price increase
than by an equivalent price decrease. Another psychological mechanism con-sidered in the construction of IPI is the fact that consumers perceive inflation stronger for frequently purchased goods than for those purchased seldom. The Index of perceived inflation is computed using the Italian data pro-vided by the Ministry of economic development of Italy (Ministero dello Sviluppo Economico) as well as some additional data sources and materials. The period from 1997 to 2015 is analyzed, with a particular interest of the behavior of IPI during the main economic events such as the introduction of euro and the 2007-2008 financial crisis. For the first time in the economic literature, the perceived inflation is compared across different Italian regions and geographical areas. A small theoretical extension of Brachinger’s index of perceived inflation is suggested, regarding the reference years used in the construction of the index. Last part of the paper examines the influence of the media on the consumers’ perceptions of inflation in Italy.
2
ISTAT measures of consumer price indices
Inflation is usually defined as a rate of variation of the level of prices in an economy. More precisely, it is a variation of the value of money. This relationship between the flow of money and changes in prices is the reason why central banks tend focus on inflation. Measuring inflation has a crucial meaning for both current and future generations. Inflation is an integral part of any country since it affects every aspect of the economy from daily purchases and pensions to national debt and government receipts. However, inflation cannot be measured directly and without any assumptions. There are several tools to capture inflation in the economy with the most common one being the consumer price index.
Consumer price indices measure the variation of prices of goods and ser-vices consumed over time in a given economy and, thus, provide information on the average price changes in a given period. Consumer price indices are constructed using the prices of a sample of representative items collected periodically as well as the weights of the items based on the shares of the total expenditure of households. Since consumer price indices are used to analyze the dynamics of price changes, they are important determinants of the economic and social conditions of any economy.
The Italian National Institute of Statistics (ISTAT) is the main producer of official statistics in Italy. ISTAT constructs three different indices of con-sumer prices: NIC2, FOI3 and IPCA4. NIC represents the national index of
consumer prices for the entire community, meaning that the national com-munity is seen as one big family of consumers in which the spending habits are highly differentiated. The index takes into account the set of all goods and services purchased by households and each Italian consumer is treated as a single homogeneous unit. The NIC is considered to be the main measure of inflation of the Italian economic system. FOI can be seen as a consumer price index for blue and white-collar worker households and is based on con-sumptions of households whose reference person is an employee. This index is generally used to periodically adjust monetary values such as rents or maintenance payments to a separated spouse. The harmonised index of con-sumer prices (HICP or IPCA in Italian), was developed according to the EU regulations and gives comparable measures of inflation across the European Union as well as the European Economic Area and other countries including accession and candidate countries. The index provides the official measure of consumer price inflation in the euro area being a key indicator for the monetary policy of the European Central Bank and for the assessment of inflation convergence required by the Maastricht criteria. HCPI is published by ISTAT on a monthly basis.
All the three indices have several elements in common, namely: the price collection, the methodology of calculation, the territorial basis and the clas-sification of the basket divided into 12 divisions. Obviously, the indices differ for some specific aspects. In particular, NIC and FOI are calculated based on the same basket but the product weights differ subject to the importance of the consumption of the reference population (in case of NIC - the entire Italian population, in case of FOI - employees).
HICP index, is calculated with reference to the entire population (as NIC), but is based on a slightly different basket defined by the EU. The latter basket excludes those goods which are hardly comparable between different countries: for example, the lotteries, sports pools and the life insurance ser-vices. Furthermore, the first two indices consider the full sale price, while the HICP refers to the actual price paid by consumers. In addition, the HICP takes account the temporary price reductions (sales and promotions).
2l’indice nazionale per l’intera collettivit´a (consumer price index for the whole nation)
3l’indice per le famiglie di operai e impiegati (consumer price index for blue and
white-collar worker households)
2.1
Calculation procedure
For the consumer price indices the Laspeyres type formulas are used, fixing the quantities relative to the chosen base period. In order to obtain the over-all national index of consumer prices the process of calculating the indices goes through four stages of aggregation. In the first stage the provincial index of representative position is calculated, also called the elementary aggregate, since weighting coefficients are not used for its construction. Provincial in-dex of representative position is obtained as a geometric mean of the micro-indices of the prices recorded for each product in the capital of the province. These micro-indices are obtained considering the relationship between the prices in the current month in a specific selling point and the price in the same place in the base month. The provincial index of representative posi-tion is a basic departure point for the subsequent synthesis which leads to the determination of the general national index of consumer prices. For the provincial index of representative position a non-probabilistic sample of rep-resentative prices is selected. The minimum number of observations of prices is fixed at 7 for food related products and 5 for other non-food related goods and services, even though, in some cases, this minimum may be insufficient to adequately represent the dynamics of prices. The following calculation procedure is taken from the ISTAT’s methodology manual (2012) [25]. As mentioned before, the provincial index of representative position iIhm,a
is obtained as a geometric mean of the indices of the prices recorded for each product in the provincial capital:
iIhm,a= [ N (h,i) Y n=1 iIhm,a(n)] 1 N (h,i) (1)
where i is a generic province, h is the elementary product index, N(h,i) are the observations of prices corresponding to month m of the year a and
iIhm,a(n) is the micro-index of the n-th reference:
iIhm,a(n) =
ipm,ah (n) ipbase,ah (n)
(2) whereipm,ah (n) stands for the price of the n-th reference of product h observed
in a specific selling point in month m and year a, whereas ip base,a
h (n) stands
for the price of the same reference observed in the same selling point in the base period.
aggregates: national index, general provincial index, general regional index and the geographical area index.
2.1.1 The national index
In order to compute the national consumer price index, first, the provincial indices of representative position are aggregated together to build the re-gional index of representative position. To do so the weighting coefficients are used which are based on the weight of each provincial capital in terms of population of residents. RIhm,a = X i∈R (Piπ i∈R iπ )iIhm,a (3) where iπ P
i∈R iπ represents the amount of residents in the capital of the province
i in region R relative to the residents of the region.
Later, regional indices of representative position are aggregated to build the index of national representative position. The weighting coefficients used are based on the weight of each region in terms of household consumption. Algebraically, the index is calculated in the following way:
Ihm,a = 20 X R=1 ( Rπh P20 R=1 Rπh )RIhm,a (4) where πh PH h=1πh
corresponds to the households expenditure of product h in region R relative to the national household expenditure of the same product. Finally, the national general consumer price index is obtained as a weighted average of the indices of national representative position. The weighting coef-ficients used are based on the weight of each representative position in terms of household consumption. Im,a= H X h=1 ( πh PH h=1πh )Ihm,a (5) where πh PH h=1πh
corresponds to the households expenditure of product h rela-tive to the total expenditure.
2.1.2 Geographical area index
The geographical area index is calculated simultaneously with the national index. It describes the geographical location of groups of regions like North-East, North-West, Center etc. First, the regional indices of representative position are aggregated to build the geographical area index of representative position. The weighting coefficients used are based on the weight of each region in terms of household consumption.
GIhm,a= X R∈G (P Rπh R∈G Rπh )RIhm,a (6) where Rπh P
R∈G Rπh corresponds to the households expenditure of product h in
region R relative to household expenditure of the same product in the ge-ographical area. Then, the general index for the gege-ographical area of prices is obtained as a weighted average of geographical area index of representa-tive position. The weighting coefficients used are based on weights of each representative position in terms of household consumption.
GIm,a= H X h=1 ( Gπh PH h=1 Gπh )GIhm,a (7) where Gπh PH
h=1 Gπh stands for the households expenditure of product h relative
to the total expenditure, within the geographical area. 2.1.3 General regional index
The regional index is obtained by aggregating the regional indices of repre-sentative position. The weighting coefficients used are based on the weights of each representative position in terms of household consumption at the regional level. RIm,a= H X h=1 ( Rπh PH h=1 Rπh )RI m,a h (8) where Rπh PH
h=1 Rπh represents the households expenditure of product h in region
R relative to the total expenditure measured within the same region. 2.1.4 General provincial index
To construct the general provincial index the provincial indices of represen-tative position are aggregated together. The weighting coefficients are based
on the weight of each representative position in terms of household consump-tion. iIm,a= H X h=1 ( Rπh PH h=1 Rπh )iIhm,a (9)
In this part of the paper an overview of the official methods in calculating the consumer price index was presented. The procedure, though well-founded and complex, takes the consumer’s perspective, in a sense that certain goods can cause a remarkable divergence between the official and the perceived movement of prices. For example, bread and sofa are purchased with abso-lutely different frequencies and, thus, consumers follow better prices of bread than prices of sofas. The consumption basket used by ISTAT contains big va-riety of goods and services purchased with very different frequencies, ranging from every day to once in several years. Moreover, due to those differences and certain behavioral patterns, consumers perceive inflation differently from the official institutions. Several ways to capture this perceived inflation are analysed further.
3
Measuring perceived inflation
Stable inflation expectations reflect the credibility of monetary policy while its efficiency is thought to depend more heavily on the perceived inflation rate than the actual, calculated inflation rate (Bernanke, 2007)[3]. Accord-ing to Del Giovane and Sabbatini (2006)[10], who analysed the perceived and measured inflation in Italy after adopting the euro currency, the gap between perceived and measured inflation is a crucial phenomenon due to various reasons. The gap deteriorates the ability of firms and consumers to evaluate the prices correctly, and thus, it puts in doubt the efficiency of the current price system. Inability to evaluate price changes properly results in misleading forecasts of future price trends which can distort decisions on prices and wages. Moreover, if the quality of price indices is publicly put into question, it may result in undetermined credibility of monetary policy and massive disbelief in institutional framework. Therefore, it is crucial to anal-yse and measure not only the actual price indices, but also the perception of consumers.
Ranyard et al. (2008)[44] presents a conceptual framework of perceptions and expectations of price changes and inflation in the socio-economic envi-ronment (Figure 1). Changes in prices in the economy are captured by
in-dividuals via two channels: direct personal experience of those changes and social media with word of mouth. Perceptions of inflation are also affected by personal income and variation of income growth.
Figure 1: Conceptual framework of perceptions and expectations of price changes and inflation in the socio-economic environment
Source: Ranyard et al. (2008) Perceptions and expectations of price changes and inflation: a review and conceptual framework. J. Econ. Psychol. 29, p.379.
3.1
Measures based on surveys
In order to capture the perceived inflation, the European Commission launched The Joint Harmonised EU Programme of Business and Consumer Surveys. One of the tasks of this program is to conduct consumer surveys whose pur-pose is “first, to collect information on households’ spending and savings intentions, and second, to assess their perception of the factors influencing these decisions”[46]. The consumer survey is mainly qualitative and is based on the questionnaire which can be found in Appendix. Starting from 2003, consumer survey includes also quantitative questions concerning perceived and expected price changes (but these questions cannot be used to observe
the situation before 2003).
For each question, aggregate balances are calculated which capture the differ-ence between positive and negative answers computed as percentage points of total answers. In addition to other questions, the respondents are asked to specify how, in their opinion, consumer prices have developed over the last 12 months and how they will develop in the next 12 months. If a question has three alternative answers: “positive” (increased, P), “neutral” (unchanged, E) and “negative” (decreased, M), and if P, E and M (P+E+M=100%) denote the percentages of respondents having chosen a certain option, the balance is computed as B = P − M . The procedure is similar in case of questions with six options: P, E and M have the same meaning as before, while PP denotes the percentage of respondents having chosen the option “very positive”, MM the percentage of respondents having chosen the op-tion “very negative” and N is the percentage of respondents without any opinion (PP+P+E+M+MM+N=100%). In this case aggregate balances are calculated as:
B = (P P +1
2P ) − ( 1
2M + M M ). (10)
The key point of such balanced perceived inflation measure is to quantify the discrepancy between the “positive” and the “negative” side of the answers. It can be clearly noticed that balance values range from −100, when all re-spondents choose the most negative option to +100, when all rere-spondents choose the most positive one. In Italy to obtain the balanced perceived infla-tion ISTAT conducts phone interviews with monthly periodicity. The final sample is composed of 2000 units.
The higher is the balance, the bigger is the proportion of those, in target population, who perceive prices as having increased in the last 12 months. The evolution of BPI and CPI time series for Italy from January 1985 till January 2016 is shown on Figure 2. CPI in this case is measured as the an-nual percentage growth rates. BPI and CPI cannot, obviously, be measured on the same scale, otherwise, if plotted on the same graph, both measures can actually be fitted closely to any desired picture.
Comparing the levels of balanced perceived inflation with the levels of con-sumer price index can be misleading since both indicators are measured on different scales. Therefore, one can only compare the behaviour of the curves, not their levels. One can observe that from 1986 till 1999, the BPI time se-ries displays a relatively strong co-movement with the CPI growth rate. In
Figure 2: Annual percentage growth rates of the Italian CPI and the Bal-anced Perceived Inflation Measure for Italy
2003-2005 the BPI for Italy demonstrates that the proportion of those who considered inflation to have increased a lot in the last 12 months have gone up considerably: 47,28% of responses on average (while this number has never exceeded 27,13% before). Also in 2008 the perceptions of inflation to “increase a lot” accounted for 41,80%. Consequently, one can imply that the balanced perceived inflation reflected quite well the increased inflation perceptions after the introduction of euro and during the financial crisis of 2007-2008.
However, according to Brachinger (2006, 2008)[5] [6] balanced perceived in-flation has no satisfying explicative power to provide an explanation about the gap between official and perceived inflation. Balanced perceived inflation index can take on negative values which makes it not comparable with CPI directly. Several methods exist to convert the European Commission’s quali-tative measure of perceived inflation into a quantiquali-tative indicator comparable with the CPI. Del Giovane (2004)[9] presents a method which is based on the estimation of the relation between the official CPI index and the BPI. The official inflation is regressed on the BPI, which reflects the perceptions of inflation in the last 12 months. The fitted values of such a regression can be considered a quantitative measure of perceived inflation. However, in this quantification method, the choice of the period has a strong influence on the
results: in case of Italy, if the regression starts from 1997 then the perceived inflation in 2003 is equal to 3,5%, however, if the regression starts from 1992, this number for 2003 is equal to 6%. Thus, it can be seen that the BPI, when quantified, varies considerably from one approach to another. Therefore, the quantification of the BPI measure appears to be highly non-robust.
To make the comparison of magnitude of perceived inflation with the CPI possible, one should have a price index which measures the rate at which inflation perception changes.
3.2
Index of perceived inflation
Index of perceived inflation (IPI) was developed by Hans Wolfgang Brachinger[6] to measure perceived inflation. The aim of IPI is to quantify the subjective price change perception of a representative household during its daily pur-chases. To analyse the perspective of the buyer, Brachinger created his theory of perceived inflation based on the decision making process and some funda-mental insights of psychology proposed by Kahneman and Tversky in their Prospect Theory (1979)[30]. The main goal of IPI is to measure the annual perceived inflation rate directly or indirectly from the available official data on price changes without any need to rely on survey data.
3.2.1 Consumers’ perspective vs purchasers’ perspective
CPIs worldwide focus on consumer’s perspective, meaning that a good is more important the higher is its proportion of the total costs in consump-tion. However, the aggregate expenditure shares are heavily influenced by the larger purchases of high-income individuals (Ranyard, 2008)[44]. Since the median consumer’s basket of goods contains bigger amount of essential items than the CPI basket, the median perception differs from CPI signifi-cantly. Purchaser’s perspective instead focuses on the frequency with which the goods are purchased. A good is more important the higher is its share of the total number of purchasing acts. Contrary to CPI, IPI takes the pur-chasers’ perspective.
Thus, one of hypotheses about inflation perceptions is that consumers form their inflation expectations by mentally aggregating acts of price changes that they recall. This implies that inflation will be perceived to be higher, the more often the purchaser experiences price increases. It is easier to recall from memory examples of price increases of frequently purchased goods, whereas
price changes of goods purchased seldom would have almost no effect on in-flation perception. When frequently bought items like bread or milk have a higher than average price increase, then perceived inflation will be higher than what summary measures of inflation announce (O’Donoghue, 2007[43]). Georganas (2014)[21] proved that consumers tend to bias their perceptions of economy-wide inflation towards the inflation rates of the more-frequently purchased goods and suggested that macroeconomic analysts should con-sider adjusting inflation perceptions for frequency. Also, Antonides, Heijman and Schouten (2006)[1] demonstrated that consumers are likely to remem-ber price changes for items bought frequently. Another finding, suggested by Mastrobuoni (2004)[40], suggests that there is higher inflation for lower priced goods, which is partially in line with the frequency matter discussed above. The author states that this effect of lower priced goods having higher inflation disappears as variance of noise goes to zero. All this implies that individuals give greater recall weight to goods or services bought frequently, which have increased in price. The frequency hypothesis suggests that ease of memory retrieval is an important determinant of inflation perception. The key interest is to capture the inflation individuals face in their daily shopping. Thus, changes in perceptions which could be induced by changes in purchasing frequencies are disregarded.
Therefore, it seems appropriate to take as weights the relative purchasing frequency of good i with which this good is bought by the average consumer. To make IPI be an index of the Laspeyres type, the purchasing frequencies must characterise the conditions during the base period.
3.2.2 Prospect theory
Other hypotheses about inflation perceptions are based on the famous Prospect Theory developed by Kahneman and Tversky in 1979 [30]. Before stat-ing next hypotheses, the basic intuition of some behavioural concepts and Prospect theory is presented.
The relativity concept plays a significant role when analysing various be-havioural phenomena. Individuals treat gains and losses as relative values, comparing each new state with a reference point which they focus on, for instance, expectations, past values, peers’ assets, other alternative gains and losses etc. Figure 3 shows the relevant psychological perspective on relativity.
Figure 3: Reference-dependence in the perception of brightness
Source: Kahneman, D. (2002) Maps of Bounded Rationality: Psychology for Behavioral Economics, The Nobel Foundation 2002, Stockholm.
The two squares in the middle of Figure 3 have the same colour even though they do not seem to be equally bright. The idea of showing these squares is that brightness of an area is not an absolute value or a single-parameter function of the light elements which reach the eye. The colour of the middle squares is a relative value which is seen differently due to couple of reference points used for comparison. The middle square on the left is relatively bright (the reference point is a dark fond) whereas the one on the right is relatively dark (the reference point is the bright fond) (Kahneman, 2002[31]). The no-tions of reference points and relativity are the core elements of the Prospect theory.
Therefore, another hypothesis about inflation perception assumes that, in a preliminary perception phase, the price of each good consumer faces, iso-lated from other goods, is perceived as a gain or a loss with respect to a reference price related to that specific good. Consumers do not perceive a price change in terms of a monetary value but only consider whether prices have increased or decreased when compared to their point of reference. For instance, if asked to consider inflation of fuel prices, an individual may recall only that the price has increased since the last time he re-fuelled his car. The reference price concept in economic studies is not a novel technique. In fact, vast research evidence has been focused on the reference price litera-ture. Kalyanaram and Winer (1995)[32] summarized the effects of reference
prices on consumers’ demand using empirical generalization in marketing field. Researchers underlined some theories about the existence of reference price and how consumers react to price changes relative to reference prices. Mazumdar et al. (2005)[41] provides a detailed assessment of the way refer-ence prices are formed, retrieved and used, and how referrefer-ence prices affect brand choice, quantity and timing decisions. Koszegi and Rabin (2004) [33] proved that it is one’s expectations about future outcomes that serve as ref-erence point among investors, not the original purchase price.
Hack and Lammers (2008)[22] analysed the impact of expectations on the reference point formation using experimental approach. Both economists de-veloped a peculiar experimental design employing an indirect approach (when individual’s risky choices allow making an inference of the reference point) and the direct one (when participants are asked to rate their satisfaction concerning a certain outcome). Scientists conducted an experimental study proving that expectations do indeed influence the adaptation of reference points, and the higher the margin of expectations of individual is, the higher the new reference point will be. Hack and Lammers (2008) also found out that individuals “shift reference points upward more strongly when expected values exceed the information for adaptation contained in the recent status quo, and they adapt less strongly if expected values are lower”.
Relating reference point concept to inflation, historic inflation rates may serve as a reference point in forming the inflation perceptions and expec-tations of future inflation rates. Malmendier and Nagel (2013)[39] proved that personal experience plays a crucial role in inflation expectations: when forming macroeconomic expectations, individuals set up a reference point based on realizations of macroeconomic data experienced during their life-times rather than on other available historical data. Developing their own learning from experience model authors suggest that perceptions of inflation are history-dependent and, moreover, are heterogeneous, meaning that young individuals place more weight on recent data than older individuals since re-cent experiences of young constitutes a considerable part of their life-times so far. In this work the main focus is on the reference point concept while the heterogeneity of agents who evaluate this reference point is not considered. Herd behaviour and other people’s state may have an impact on the reference point. Herding effect is the tendency of individuals to copy the actions of a large group or the rivals. Economou, Kostakis and Philippas (2010)[13] in their research proved that herding was present in the Portuguese stock mar-ket during periods of reduced returns as well as in the period of the global
financial crisis of 2008. Crowd behaviour makes the reference point of indi-vidual change towards the value of the analytical suggestion, index figures, or other similar sources of herding. However, here, the herd behaviour is not taken into account, focusing more on the reference point adaptation in time, rather than its formation across space or across individuals.
Since the main goal of IPI is to be comparable with CPI, which refers to prices of the base period, also in the IPI, the prices of the base period are taken as reference prices, which is in line with Malmendier and Nagel’s (2013)[39] demonstration of inflation being a history-dependent variable.
Last hypothesis about inflation perception suggests that each price change is evaluated according to a value function, meaning that a price increase (a loss) has a higher value than a price reduction (a gain).
Proposed by Kahneman and Tversky (1979)[30], the value function is treated as a function of asset position that serves as a reference point and the de-viation from that reference point (positive or negative in magnitude). The function is defined in terms of relative gains and losses in initial wealth rather than in terms of final states. The main result of Kahneman and Tversky’s findings and experiments is that the S-shaped value function is concave for gains, convex for losses, and is steeper for losses than for gains (shown on Figure 4). The theory proves that people are risk-averse towards gains and pro risk towards losses. It also states that human beings place much more weight on the outcomes which are certain rather than on those which are just probable, a feature known as the “certainty effect”. Moreover, the value function is the steepest at the reference point. This means that a particular gain or loss has a smaller effect on the value experienced by a person when the distance to the reference point is large.
Thus, according to value function (Figure 4), each consumer reacts much more sensitively to price increases (losses) than to price reductions (gains). The value function formulation is a result of numerous experiments conducted by Kahneman and Tversky [30]. Figure 5 shows some of the experimental findings with gambles. It can be noticed that preferences between negative prospects is the mirror image of preferences with positive prospects. This “reflection effect” demonstrates the existence of risk aversion in the positive domain and risk seeking in the negative one. First two prospects are repre-sented in detail:
Figure 4: The S-shaped value function of prospect theory
Source: Kahneman, D. and Tversky, A. (1979) Prospect theory: An Analysis of Decision Under Risk, Econometrica, 47(2), pp. 263-291.
Figure 5: Preferences between positive and negative prospects
Source: Kahneman, D. and Tversky, A. (1979) Prospect theory: An Analysis of Decision Under Risk, Econometrica, 47(2), pp. 263-291.
Problem 3: choose among:
A: 4000 with probability 0.80 B: 3000 for sure
N = 95 [20] [80]∗
Problem 4: choose among:
A: 4000 with probability 0.20 B: 3000 with probability 0.25
N = 95 [65]∗ [35]
In Problem 3, 0, 80u(4000) < u(3000) holds. However, in Problem 4, the reverse situation can be noticed:
0, 20u(4000) > 0, 25u(3000) | × 4 (11)
0, 80u(4000) > u(3000). (12)
Both results contradict each other and cannot be explained by commonly used expected utility theory. Prospect theory explains the problem through the value function and the certainty effect, stating that in case of gains in-dividuals prefer certain options and are risk averse, whereas in the case of losses individuals tend to be pro risk and choose the less probable options. The asymmetric impact of gains and losses, referred to as loss aversion, has been of a particular interest of various researchers. Observed by Kahne-man and Tversky in Kahne-many experiments of choice under risk and uncertainty (Kahneman and Tversky, 1984[29]; Kahneman and Tversky, 1991[28]), it is considered that the coefficient of loss aversion (for the consumption good in their case) is slightly greater than two.
To sum up, the index of perceived inflation developed by Brachinger [5] is based on three hypotheses, namely:
• consumers compare price changes to a certain reference point and these price changes are considered as gains or losses subject to the reference point;
• individuals place more weight on price increases than price decreases; • consumers perceive inflation stronger for frequently purchased goods
than for those purchased seldom5.
To conduct the IPI, the same basket of goods and prices is used as in the construction of the CPI. However, later it is adjusted based on the three hy-potheses mentioned. To be comparable with the CPI, Laspeyres construction principle is used to build the IPI. It is based on a market basket of n goods that represents the consumption habits of an average household. The purpose of measuring inflation is to capture the pure price evolution in an economy. To achieve that goal, the quantities of the goods consumed are held constant. Laspeyres type price indices can be generally represented by the following formula: p0,tL = n X i=1 pt(i) p0(i) × Pnp0(i)q0(i)
i=1p0(i)q0(i)
(13) where pt(i) and p0(i) stand for the prices of the i th good for the analysed
period t and for the base period 0, respectively. The term q0(i) refers to
the quantities consumed by the average household of the good i in the base period. It can be seen that in this formula, the price relatives of all the goods in the basket are weighted by the corresponding expenditure shares of the base period.
A class of generalised Laspeyres type price indices can be formed from this formula if we allow for optional reference prices pr(i) (to compare them with
the actual ones) and if we admit the transformations of the price relatives
pt(i)
p0(i) and weights, as was suggested by Brachinger (2008)[5]. Weighting
fac-tors have to demonstrate the conditions in the base period in order to keep on being of the Laspeyres type. Such class of generalised Laspeyres type price indices is captured by:
pr,tgL = n X i=1 Gi pt(i) pr(i) × gi (14)
where Gi is any non-negative real-valued transformation of some price
rela-tive of the i th good and the gi denotes discretionary base period weights with
which the individual price relatives are weighted. These weights are positive and normalised to one.
Now, to construct the IPI, it is needed to select the transformation func-tion as well as the weights in the generalized Laspeyres index formula in such a way that they take account the hypotheses outlined before.
As mentioned previously, the reference prices correspond to the base pe-riod of the CPI index, so pr(i) = p0(i). It is assumed that value functions
are independent of the type of good and its price level. As any price index formula, IPI is not a function of absolute but is of relative price changes:
pt(i) p0(i) = pt(i) − p0(i) p0(i) + 1 i = (1, ..., n). (15) And so, Gi pt(i) p0(i) = Gi pt(i) − p0(i) p0(i) + 1 = G∗i pt(i) − p0(i) p0(i) + 1 (16)
Each transformation function Gi can be seen as a function of relative price
changes.
Weber (1834)[47] and later Fechner (1860)[20], a scholar of Weber, made an attempt to quantify the perception of change in a given stimulus. They later summarized the relationship between stimulus and perception in a Weber-Fechner Psychophysical Law: equal relative changes in a stimulus bring about equal absolute changes in perception and absolute changes in perception are a linear function of relative changes in the stimulus. Assuming that the Weber-Fechner Psychophysical Law also holds for the perception of price changes, the term G∗i pt(i)−p0(i)
p0(i)
can be interpreted as the change in perception in-duced by a price change pt(i) − p0(i). Consequently, perception of a price
relative pt(i)
p0(i) only depends on the perceived price change and is independent
of the price level. Moreover, one can state that the perceived absolute price changes are a linear function of relative price changes, meaning that for every i holds: G∗i pt(i) − p0(i) p0(i) = G∗ pt(i) − p0(i) p0(i) = c pt(i) − p0(i) p0(i) , (17) and, Gi pt(i) p0(i) = Gi pt(i) − p0(i) p0(i) + 1 = c pt(i) − p0(i) p0(i) + 1. (18)
According to Kahneman and Tversky (1979)[30], since the value function is steeper in the negative than in the positive domain, consumers react much more sensitively to price increases (losses) that to price reductions (gains). This implies that constant c should be higher for price increases than for price reductions. Assuming that this constant is already normalised to 1
for price decreases the transformation function loss aversion assumption is described by the following formula:
Gi pt(i) p0(i) = (pt(i)
p0(i), if pt(i) ≤ p0(i)
cpt(i)−p0(i)
p0(i)
+ 1, if pt(i) > p0(i)
(19)
Since the following holds:
c pt(i) − p0(i) p0(i) = c(pt(i) − p0(i)) p0(i) , (20)
the transformation function is based on a value function which is independent of the price level p0(i) which follows from the Weber - Fechner Psychophysical
Law:
V (pt(i)) =
(
pt(i) − p0(i), if pt(i) ≤ p0(i)
c(pt(i) − p0(i)), if pt(i) > p0(i)
(21)
Combining Kahneman and Tversky’s loss aversion coefficient with Weber-Fechner Psychophysical Law, it can be seen that the ratio between perceived price increases and perceived price decreases (the loss aversion coefficient) equals to c. The loss aversion assumption implies that c > 1 and, based on the set of various experimental findings on individual decision-making under risk by Tversky and Kahneman, the loss aversion coefficient was estimated to be “slightly greater than two”. Hardie, Johnson, and Fader (1993)[23] experimentally estimated that the coefficient lies in an interval between 1.5 and 2.5. Therefore, Brachinger (2008)[5] assumes that the IPI with the loss aversion coefficient of 2 represents a decent estimation of the “true” index of perceived inflation. The interval between the IPI with the loss aversion coefficients of 2.5 and 1.5 can be seen as a confidence band with the “true” IPI lying with high probability within this band.
As stated before, in the index formula the weight will be represented by the relative purchasing frequency of good i with which this good is purchased by average consumer in the base period.
Combining the statements mentioned above, the index of perceived inflation IP I0,t is described by the following formula:
IP I0,t = X
i:pt(i)>p0(i)
c pt(i) − p0(i) p0(i) + 1 fi0+ X
i:pt(i)≤p0(i)
pt(i)
p0(i)
where fi0 is a relative frequency of good i in the base period.
Dr¨ager (2009)[12], using balance statistics for inflation perceptions for 12 European countries, finds solid empirical support for Brachinger’s assump-tions. The author reports the presence of loss aversion for the whole panel in the period before the euro introduction, suggesting that loss aversion is a predominantly long-run phenomenon. To test for this existence of loss aver-sion Dr¨ager (2009)[12] constructed two threshold-dummies that were aimed to capture the periods where losses in terms of rising inflation occurred. How-ever, the results show that while prior to the euro introduction loss aversion was quite pronounced for the EMU countries, there is no sign of loss aversion after the introduction of euro, meaning that there a strong structural break in the relation of perception-inflation.
Moreover, Dr¨ager (2009)[12] states that price inflation of frequently bought goods (and services) has a significant effect on perceived inflation both be-fore and after the introduction of euro. While inflation rates of other non-frequently purchased goods (and services) do not seem to be significant prior to the introduction of euro, they become significant afterwards.
The IPI can then be seen as a special case of a price index of the gener-alised Laspeyres type and thus can be directly compared with the CPI which is also a special case of that same family of price indices. However, there are several differences between the two indices. In IPI price increases are treated to be higher than price decreases through boosting the growth rates by the factor c. In the CPI the price relatives of all market basket goods and services are not transformed at all, meaning that the transformation functions are all the identity, Gi ≡ G ≡ 1, while in the IPI only the price relatives which
are decreasing or constant go untransformed. The price relatives which are increasing are transformed by the factor c. In other words, each increasing price relative is split into the growth rate part and unity. Then, the growth rate part is multiplied by the loss aversion coefficient c and is re-added to unity to obtain the final price relative. So, prices which decreased or re-mained the same go directly into the IPI, whereas prices that increased are transformed according to equation (22).
Another difference between CPI and IPI is the weighting scheme. CPI fo-cuses on consumer’s perspective while IPI fofo-cuses on purchaser’s perspective, meaning that in the CPI expenditure weights of an average household are used as weights whereas in the IPI buying frequencies are used instead. Both weighting schemes use base period weights which allows for comparability
be-tween them.
4
Application to Italian data
The CPI monthly data for the period 1996-2015, already converted to the base year of 2010, was taken from the ISTAT data archive6. Prices that had decreased or stayed the same with respect to 2010, went directly to the IPI, being multiplied by the relative frequency. While prices which had increased were transformed according to the equation (22), being adjusted for the loss aversion coefficient c (which is assumed to be equal to 2, based on Kahneman and Tversky’s (1991)[28] estimates).
In order to be comparable with CPI, the IPI has to be applied to the same basket of goods and services which are used to determine CPI. The main ob-stacle to calculate IPI was to determine the relative purchasing frequencies for the 2010 base year. The Brachinger’s approach was taken: the relative purchasing frequencies were determined from the actual expenditure shares, which were deduced from the average monthly household expenditure data taken from the ISTAT online database. The expenditure shares of good i were multiplied by the total expenditure of the index household and were then divided by the average base period price of that good:
q0(i) = w0(i) × Pn j=1p0(j)q0(j) p0(i) (23) where Pn
j=1p0(j)q0(j) is the total expenditure of the index household, w0(i)
is the expenditure share of good i and w0(i) =
p0(i)q0(i)
Pn
j=1p0(j)q0(j)
(24) is the quantity of good i consumed by the index household during the base period.
The main problem was to find the corresponding average base period price. The majority of the data was provided by the Ministry of economic devel-opment of Italy (Ministero dello Sviluppo Economico), such as the data of average prices for different Italian provinces for various food, fuel, heath,
leisure, some transport and personal care categories. Other COICOP cate-gories were estimated using the following additional sources:
• average 2010 price of new cars, used cars and motorcycles for every region were determined from the Findomestic Bank’s observatory pro-vided by BNB Paribas7;
• average regional prices of bills of gas and energy which consumers re-ceive were deducted from the independent administrative authority for Electricity and Gas (AEEG)8;
• water regional 2010 prices of bills which consumers receive were taken from the VIII national sample survey on fees of the national water ser-vice provided by Centre for Economic Research, Education and Train-ing “Federconsumatori” (2010)9;
• average prices citizens pay for rents were taken from the Italian Union of Labour10;
• various prices such as average price of CDs, various clothes, plants, pots, glasses, fridges etc. were taken from the European Commission’s price research 11, and from the research paper “The household appliances market and its temporal evolution” conducted by the National Agency for New Technologies, Energy and Sustainable Economic Development (2010);
• average 2010 prices of books of various genres (apart from the educa-tional books) were taken from Italian Publishers Association’s report (2012) 12;
• average 2010 prices of educational books, enrollment at schools and universities, prices of pencils, backpacks and other items needed for studying were determined from the Italian Union of Labour and Altro-consumo magazine’s articles.
Obviously, due to lack of data the mentioned basket is not exactly the one provided by ISTAT, it is an approximation of the ISTAT’s basket of con-sumer goods and services. Due to this reason, the CPI is calculated using
7Marina Beccantini, Findomestic Banca S.p.A., Edizione 2012
8Relazione annuale sullo stato dei servizi e sull’attivita’ svolta; Autorita’ per l’energia
elettrica e il gas (AEEG), 2012
9Available at: www.federconsumatori.lombardia.it
10Rapporto UIL Famiglia Reddito Casa, 2007 e 2012
11Available at: ec.europa.eu
the created approximate basket and is later compared to the “real” CPI. All the goods were aggregated into the wider categories suggested by ISTAT. Concerning the units of measurement of each good or service, ISTAT’s fixed average units were considered, e.g. 1 kilo of rice, 250ml of shampoo, 1 haircut service, 1 public transport ticket etc.
The relative frequencies were obtained using Equation (23). Similarly to Brachinger’s findings, the mean values of both expenditure and frequency weights are the same since they are normalized to unity. The distribution of the relative buying frequencies is less steep than the distribution of the expenditure shares. Contrary to Brachinger’s findings though, standard de-viation seems to be almost the same in both variables. From the scatter plot (Figure 6), one can notice that the groups of goods with the highest purchasing frequency but relatively low expenditure share are the “Potatoes, fruits and vegetables”, “Bread and cereals”, “Other goods and services”. While “Housing” and “Transport” are the categories which show the highest expenditure shares and the lowest frequency. Expenditure weights and the purchasing frequency weights are hardly correlated: the correlation coeffi-cient is 0.04984.
Looking at geographical NUTS1 distribution (Figure 7), one can observe that expenditure shares of food related goods are the highest in South of Italy and the Islands, 25%. Similarly, relative frequencies are the highest in the Islands (78%), followed by the South (77%). The lowest expenditure shares and relative frequency of food are in the North-East geographical area of Italy.
Using the expenditure shares of the created basket the CPI was determined. The correlation between the obtained CPI and actual CPI is 0,99758, their means are similar (0,9152 and 0,9182 respectively) while the created CPI is slightly more volatile than actual CPI with standard deviation of 0,1261 and 0,1101 respectively. Therefore, the chosen basket can be considered a decent approximation of the actual ISTAT’s basket of consumer goods and services. The IPI was determined using the Equation (22), assuming that the loss aversion coefficient c=2. Figure 8 shows the evolution of annual percentage growth rates of the actual CPI and the determined IPI since 1997.
Figure 6: Expenditure and relative purhasing frequency weights The periods when the perceived inflation lies below the CPI are peculiar since the price increases entered with a double weight to the IPI compared to CPI. Such a behavior during the period from 1999 till 2001 coincides with Brachinger’s findings for Germany (Figure 9). The periods of IPI being lower than CPI in 1997 and late 2004 also coincide with German’s evolution of perceived inflation, but seem to be more long lasting and more dramatic in Italy. The only period of significant decline of IPI which is more pronounced in Germany than in Italy is at the beginning of 2003. Germans perceived a very strong deflation during that period (the strongest in magnitude in the ten years analyzed by Brachinger) while Italians had CPI being equal to IPI during that period.
Brachinger examined inflation perceptions till the year 2005, which means that other cases of IPI being lower than CPI cannot be compared with Brachinger after 2005. In the middle of 2010 and 2014 Italians had very strong perceptions of deflation. The highest values of IPI occurred during the period from January, 2011 to December, 2013, which is the most long
Figure 7: Expenditure shares and relative purhasing frequencies across NUTS1 areas
Red colour - the highest purchasing frequency (and expenditure shares) of food items, yellow - the lowest.
lasting period of high perceived inflation since 1997. During this period, the difference between average yearly rate of IPI and CPI was the highest: 2,38% in 2011, 2,35% in 2012 and 3,50% in 2013. Also in 2015 the IPI was 2,48% higher than CPI. In the opposite magnitude, the difference between average yearly rate of IPI and CPI was the highest in 2005 (1,49%) followed by 2010 (1,15%). The mean of Italian rate of annual variation of CPI is 1,74% (with standard deviation of 0,86%) while the one of IPI is higher – 2,25% (with more than twice higher standard deviation of 1,92%).
Figure 8: Comparison of IPI(2.0) and CPI evolutions in Italy
Figure 9: Comparison of IPI(2.0) and CPI evolutions in Germany
Source: Brachinger, H. (2008) A new index of perceived inflation: Assumptions, method, and application to Germany, Journal of Economic Psychology 29(2008), pp.433-457
Relatively high and long perceived inflation took place during the introduc-tion of euro as well as during the 2007-2008 financial crisis. One has probably noticed that the period of euro introduction is characterized by level of IPI which is higher in Germany than in Italy. This matter is covered more in de-tail in section 4.3, here I would just suggest that this difference in magnitude is not due to the fact that Italians perceived lower inflation than Germans when euro was introduced, it is rather the consequence of using the later base period (reference point) than the one chosen by Brachinger.
4.1
Comparison of IPI and CPI in different Italian
ter-ritorial areas
According to Eurostat’s Nomenclature of Territorial Units for Statistics (NUTS), Italy is divided in 5 NUTS1 units (Nord-Ovest, Nord-Est, Centro, Sud, Isole) and 20 NUTS2 units which represent 20 Italian regions.
4.1.1 CPI and IPI evolutions in NUTS1 geographical areas
Since the main data of average price levels provided by the Ministry of eco-nomic development of Italy was available at the level of provinces, it was split and aggregated into the NUTS1 areas and into wider consumption categories. Due to lack of data, price levels of clothes and the majority of furniture cate-gories were kept constant using values at country level. To calculate relative frequencies, the expenditure shares for each area were determined from the ISTAT’s data on the household expenditure.
Looking at the evolutions of the annual growth rates of Italian CPI and IPIs in different Italian NUTS1 zones (Figure 10) one can observe that on average differences of perceptions of inflation in different geographical areas are not big, but become more pronounced at margins. During the periods of high perceived inflation (or deflation) differences between the areas become more observable. For instance, the average difference between average rate of IPIs and CPI in 2002 across all the areas was 1,13% with South having the highest difference (1,44%) followed by Islands (1,24%) while North-East having the lowest difference (0,90%). This implies that in 2002 the gap between CPI and IPI in South was 1,6 times bigger than the gap between CPI and IPI in North East. Same tendency occurs in case of negative inflation perceptions: in 2010 the difference between the average yearly rate of IPIs and CPI was 1,23% while it was 1,51% in South and 0,94% in North-East. For example,
Figure 10: Comparison of NUTS1 evolutions of CPI and IPIs(2.0)
the gap is smaller in 2005 when the difference between the average rate of IPIs and CPI was 1,59%, it was 1,81% in South, 1,75% in Islands and 1,35% in the North-East area. However, the gap is bigger in 2014: 1,64% in South, 0,92% in North-East with the average difference being 1,25%. From July, 2011 till May, 2012 the inflation perceptions difference between the IPIs and the national CPI was the highest in North-East (2,43%) and the lowest in South (1,99%). Therefore, one can imply that during “big” variations of per-ceived inflation different geographical areas have different “feelings” about inflation even though, in general, there are no big geographical disparities of perceptions of inflation across Italy.
Indeed, the North-East area has the lowest mean (2,24%) and the lowest standard deviation (1,86%) followed by West and Center with North-West being less volatile than Center but having the same mean of 2,24%. South appears to be the area with the highest mean (2,32%) and the highest standard deviation (2,21%) followed by Islands with lower standard deviation (2,13%) and the mean of 2,28%. As mentioned before, the national CPI’s mean is 1,74% (with standard deviation of 0,86%).
Analysing monetary policy is not the goal of this paper, however, since CPI growth rates in the period 1997-2015 on average are equal to 1,74%, Italian inflation is in line with the ECB’s target to keep inflation rates below, but
close to 2% over the medium term. On the other hand, when considering the average rates of IPI, in none of the NUTS1 areas it was below or equal to 2% (the lowest mean of the rate of IPI was 2,24%). This is an important issue because in case of inflation targeting, central banks should be forward-looking, therefore, they need to be aware of the true perceptions of inflation in the country which influence the actual and expected inflation. This is sup-ported by Dr¨ager (2011)[11] and Maag (2010)[38], who report that Swedish households base their inflation expectations largely on perceived, rather than actual inflation rate. Dr¨ager (2011)[11] also states that for stable inflation periods, expectations are affected by shocks to perceived inflation. Using survey data from Bulgaria, Carlson and Valev (2003)[7] also underline that dispersion in perceived past inflation generates significant dispersion in in-flation expectations. While the variation in perceived inin-flation, in turn, is influenced by the combination of observed relative price shifts and various expenditure patterns across consumers. Blanchflower and Kelly (2008)[4] outline that groups with biased perceptions also form biased expectations. While Jonung (1981)[26] reports that households’ inflation expectations are highly correlated with their perceptions of past inflation, using the Swedish Consumer Tendency Survey. Thus, one can imply that perceptions of infla-tion influence the behavior of agents in all the markets and have a certain impact on expectations about the future prices, which in their turn deter-mine the actual inflation rate together with many important macroeconomic indicators such as: nominal wages, exchange rates, interest rates, unemploy-ment rates and many others.
4.1.2 CPI and IPI evolutions across Italian regions
When analyzing perceived inflation across Italian regions the same approach as with NUTS1 areas was used: available average prices at province levels were aggregated into regions and were put into wider COICOP categories. Due to lack of data two regions did not enter into the analysis: Valle D’aosta (Vall´ee D’aoste) and Abruzzo.
Looking at the evolutions of the annual growth rates of Italian CPI and IPIs in different Italian regions (Figure 11), it can be noticed again that the difference between regions is the highest at margins (while regional dispar-ities in inflationary perceptions decrease when getting closer to the level of CPI).
Figure 11: Comparison of NUTS2 evolutions of CPI and IPIs(2.0)
Legend for this graph can be found in Appendix.
Lombardia, Emilia-Romagna, Veneto, Friuli-Venezia Giulia and Piemonte appear to be the least volatile regions (all located in West and North-East areas) while Puglia, Campania and Molise have the highest standard deviation (all located in South). Central regions have the “central” standard deviation of about 2,06%. When looking at means of the annual variations of IPIs (Figure 12), the geographical differences are less observable: Southern regions still have the highest means, but all the other regions have mixed geographical pattern of means with no big differences among them.
Looking closer at those “margins” of movements of the growth rates (Figure 11), in 1997 (and in 2010) the average IPI monthly growth rate is the lowest in Molise, meaning that the region showed the biggest gap between IPI and CPI rates across the regions in that year while Emilia-Romagna showed the lowest one (in 2010 - Lombardia had the lowest gap). During the
introduc-Figure 12: Standard deviations and means of regional IPIs in Italy
Sorted from minimum to maximum.
tion of euro currency, again, Molise seems to have suffered from the highest perceived inflation rate (and Emilia-Romagna and Lombardia suffered least). In the period of the Financial crisis, Campania had the highest IPI growth rate and North-Eastern regions had the lowest one. The gap between the perceived and actual inflation rate in 2011-2012 is the lowest in Molise and the highest in Calabria (though already in 2013 and 2015 Molise shows the highest IPI rate again). Campania had the biggest gap between the perceived inflation rate and the average Italian CPI rate in 2014 while Lombardia - the smallest one.
When analyzing the average yearly growth rates of CPI across regions, ac-cording to ISTAT’s official data (Figure 13), in 2011, the inflation is not clearly split between Southern and Northern regions, on contrary, the evi-dence is rather mixed: the highest rates of CPI were noticed in Valle D’Aosta, Puglia, Basilicata and Lazio while the lowest ones - in Molise, Veneto, Sicilia and Campania. Similarly, in 2012 the highest CPI rate was in Basilicata (4,43%) and Trentino (3,6%), in 2013 - Calabria (1,68%), Molise (1,67%) and Liguria (1,52%), in 2014 - Sicilia (0,78%), Abruzzo (0,72%) and Trentino (0,67%), in 2015 - Abruzzo (0,36%) and Trentino (0,29%). The lowest aver-age yearly CPI rates in 2011 and 2012 were those of Molise, in 2013 - Valle d’Aosta, in 2014 - Friuli-Venezia Giulia, in 2015 - Umbria. Therefore, when
one looks at the growth rates of CPI the South-North disparities are not present, contrary to relatively clear geographical differences at peaks and troughs of the perceived inflation.
Figure 13: CPI growth rates across Italian regions, 2011-2016
With key maximum values labeled.
4.2
Loss aversion coefficient
Jungermann et al. (2007)[27] present an experimental study evaluating the impact of purchasing frequency and loss aversion on the individual percep-tions of price changes. The author estimates the loss aversion of a value of about two in the experiment.
Figure 14 demonstrates the time series of the CPI together with the time series of the IPIs for three loss aversion coefficients: c=1,5, c=2,0 and c=2,5. While Figure 15 shows the growth rates of CPI and IPI for the loss aversion coefficients mentioned above. From these figures one can imply that before 2009 CPI and IPI with different loss aversion coefficients lie close to each
Figure 14: Evolution of CPI and IPI indices with different loss aversion coefficients
Figure 15: CPI and IPI growth rates evolution with different loss aversion coefficients
other. While from 2009 and, especially in 2011-2013, IPIs diverge remark-ably. The average value of CPI from the beginning of 2011 till the end of
2013 is 1,053 while the average value of the IPI(2,0) is 1,08, which is around 5,07% higher than shown by CPI. This gap between CPI and IPI(1,5) is about 2,54% while for CPI and IPI(2,5) is about 7,61%, which can be con-sidered a confidence interval for the difference between CPI and IPI(2,0). Throughout the whole period IPIs seem to evolve parallel to each other. According to ISTAT’s annual 2012 report[24], the reason behind the high actual and perceived inflation behavior in 2011-2012, as in other economies of the euro area, might be the tension of prices of energy, raw materials, in-dustrial materials and food, the tendency which was recorded in international markets since 2010. The depreciation of euro mainly caused an increase in the procurement costs of inputs. On average in 2011, the national consumer price index rose by 2.8 percent, almost twice the growth recorded the previ-ous year. Consumer prices of energy goods increased in middle of 2011 by 11.3 percent (from 4.2 percent in 2010), exceeding the previous maximum recorded in 2008.
Figure 16: Evolution of CPI and IPI indices in Germany
Source: Brachinger, H. (2008) A new index of perceived inflation: Assumptions, method, and application to Germany, Journal of Economic Psychology 29(2008), pp. 433-457
Brachinger’s findings (Figure 16) are somewhat different: he obtained the highest differences between CPI and IPIs after the introduction of euro. In his case, after the introduction of euro notes and coins, the perceived infla-tion with the loss aversion coefficient c=2 lied 8,2% higher than shown by CPI (this gap between CPI and IPI(1,5) was around 4,3% while for CPI and IPI(2,5) it was close to 12,9%).
The fact that in case of Italy the introduction of euro is not characterized by such high differences between CPI and IPIs can be explained through the dif-ferent choice of reference (base) years between this research and Brachinger’s work.
4.3
Behind the reference year change in the IPI
esti-mations
The key element of IPI which is not considered by Brachinger (2008)[5] is the change of the base year. There are significant differences in magnitude of the Brachinger’s data, who considered 2000 as a base year and the dataset analysed in this paper, with 2010 as a base year. For instance, the period of euro introduction in case of Germany seems to be much more pronounced than in case of Italy, which is quite a strange pattern since many findings suggest that Italians faced higher perceived inflation during the euro intro-duction than Germans.
For example, in the Eurobarometer Flash survey (2002)[16] realized upon the request of the European Commission 11 months after the euro introduc-tion, Netherlands and Italy are the countries in which the highest proportion of respondents agreed that the conversion to euro was harmful for consumers ( 94% in Netherlands and 91% in Italy). Luxembourg (62%) and Finland (63%) are the countries where the respondents showed the smallest percent-ages on agreeing with the harmfulness of the currency change. Germany followed Italy in this survey with 88% of respondents feeling that the euro conversion was detrimental towards consumers. While according to the Euro-barometer survey (2003)[17], conducted two years after the euro introduction, France (62%) and Italy (57%) appear to be the only two countries where the majority of respondents still seemed to have difficulties in adjusting to euro. Furthermore, Italy, according to the survey, is the only country where many
respondents (46%), state they are using former national currency more often than euro as a mental benchmark scale for everyday purchases. In addition, in Italy there is the highest number of people who believe that prices were rounded off to a higher value (95%), followed the Netherlands (93%), Ger-many (92%) and Greece (92%). In Eurobarometer (2004)[18], Italy (64%) and France (58%) continue to remain the only two countries where the ma-jority of respondents still appear to have difficulties in adjusting to euro. According to Mastrobuoni (2004)[40], who proposed a model where indi-viduals need some time to adapt to new currency, among the 15 European countries, Spain, Italy and France proved to be the ones which encountered the biggest problems with the introduction of euro. These countries proved to have higher proportions of consumers that evaluate and judge the ap-propriateness of prices using their old currency. The author assumes that consumers observe the prices in euros which is a noisy signal for individuals about the price in their old currency. Moreover, Mastrobuoni (2004)[40] finds that in Spain, Italy and France consumers had a hard time in recalling and comparing price levels expressed in new currency.
The following theoretical interpretation of the change of the base year will be suggested. First, I would suggest to treat the CPI’s trendline (base year = 2010) as a special case of consecutive gambles, to be precise, consecu-tive losses since there is the long lasting trend of increasing inflation (Figure 17) and perceived inflation (Figure 18). Second, following Schwartz et al. (2008)[45] who investigated prospect theory and reference point adaptation in health industry, let us assume that the initial reference point from which evaluation takes place is the 2000 one (the base year chosen by Brachinger), shown on Figure 19. The decision makers evaluate a certain event (let us say, the euro-introduction event, shown as a red dot on the graph) from that reference year. From 2000 reference point, v1 represents the disutility
of uncertainty and potentially high inflation that the decision makers expect because of the euro-introduction13.
From CPI’s trend line (Figure 17) we can observe that inflation was steadily increasing, which makes the 2010-year reference point adapt towards the “losses” side. From this new base year of 2010, which lies on the “loss” side of the previous reference point, the euro introduction event (the red dot on
13According to Eurobarometer (2001)[19], before the euro introduction, 49% of EU
citi-zens agreed that the introduction of the euro would drive up prices of consumer goods (as opposed to 29% who disagree) and 33% believed it would increase the difference between the rich and the poor.
Figure 17: Time series of Italian CPI
Figure 18: Time series of Italian IPI
Figure 19) seems to promise a smaller disutility (v2) comparing to the
sit-uation in the initial state. From the 2010 reference point decision makers would thus see the euro introduction as less “negative” event, causing rela-tively smaller perceived inflation than with 2000 reference point (as shown on Figure 19). From the 2015 reference point (the next base year suggested by ISTAT), located further on the loss part relative to the previous reference points, if the theory holds, the euro introduction is predicted to show even
smaller perceived inflation.
Figure 19: Graphical depiction of Prospect Theory applied to perceived in-flation with shifted reference points
The euro-introduction event is not located on the loss axis in chronolog-ical manner: we saw previously that with the 2010 base year, still, euro introduction causes relatively high perceived inflation, though smaller than in case of Brachinger. The key point is that after a certain threshold, the euro introduction would not be perceived as a loss any more: let us assume that this threshold occurs when the decision makers adapt their reference point to the 2020 base year. From there, the euro introduction event is per-ceived to be on the gain curve, making the decision makers consider its value as “utility”, contrary to “disutilities” previously associated with the euro adoption. Thus, the new currency will not be perceived as the one promis-ing high inflation any more: due to the adaptation of the reference point (caused by other various and perception-volatile events followed after the euro introduction), individuals forget the perceived inflation associated with distant events. Due to various expected and unexpected events changing the perceived inflation, the impact of past experience related to the euro
