household saving situation evidence from the consumer ... · related to employment, illness and...

Household saving situation

Evidence from the consumer survey according to

household income in Albania

Iris Metani1

EU WORKSHOP ON BUSINESS AND CONSUMER SURVEYS

BRUSSELS, 15 – 16 November 2018

SUMMARY

Confidence surveys in Albania are carried out under the Joint Harmonized EU Programme of Business

and Consumer Surveys since May 2016. The harmonization has enriched the frequency of confidence

indicators, as well as the micro – dimension of the database for socio – demographic factors. This

material tries to make use of the monthly data stemming from the harmonized consumer survey in order

to provide some descriptive evidence regarding the household financial and saving situation in Albania.

Following the approach discussed by Friz in the 8th Joint EC/OECD Workshop (2017), the material

looks more closely at the Albanian household saving pattern in relationship to the income component.

On the basis of the descriptive evidence, the share of savings for precautionary reasons in Albania

results to be low, but particularly higher amongst the richest consumers. This material is considered as

a first and still preliminary analysis of the Albanian consumer survey data in terms of income subgroups

of the population. However, it highlights the importance of the micro – data collected from confidence

surveys, for their additional and valuable information content regarding specific macro questions.

1 Bank of Albania, Monetary Policy Department, Economic Analysis Sector. The views expressed in this material represent exclusively the position of the author and do not necessarily correspond to those of the Bank of Albania. Corresponding address: [email protected]

mailto:[email protected]

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Content

1. Introduction ................................................................................................................................... 2

2. Theoretical considerations and the approach to saving pattern from the consumer survey . 3

2.1 Some theoretical considerations ................................................................................................ 3

2.2 The approach to the household saving pattern from the consumer survey ........................... 5

2.3 Consumer survey in Albania ...................................................................................................... 6

3. The empirical analysis of the results from micro – data ........................................................... 8

3.1 Income quartiles in the context of Albanian consumer survey ............................................... 8

3.2 Household financial situation according to income ................................................................. 9

3.3 Household saving pattern according to income ..................................................................... 11

4. Concluding remarks ................................................................................................................... 17

References ............................................................................................................................................ 18

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1. Introduction

It remains fundamental to understand aspects of the Albanian household financial and saving situation,

as well as the related motives, in the context of an economy where the household sector plays an

important role. To broaden the knowledge about this subject, this material exploits household micro –

data from the harmonized Albanian consumer survey. The evidence provided follows the approach

discussed by Friz in the 8th Joint EC/OECD Workshop (2017) and the analysis focuses on household

saving behaviour in Albania, with a particular emphasis on the precautionary motive to save. As

uncertainty from income shocks might be one of the factors driving precautionary savings, I take a look

at the income dimension.

The main contribution of the material lies in conducting an examination of the consumer survey data to

check particularly for possible evidence of the precautionary saving behaviour in Albania and how it

varies for different groups of individuals according to their income. Furthermore, it explores for the

first time disaggregated consumer survey indicators per income quartiles since the conduct of the survey

under the EC programme in May 2016. The analysis of consumer survey data at disaggregated levels

offers a more detailed picture regarding different aspects of consumer behaviour. And as Dominitz and

Manski (2004) highlight, the examination at this dimension enhances the information power of the

indicators that are generated from surveys. The household data approach has the advantage of providing

details on the saving behaviour and the socioeconomic profile of each interviewed family, allowing the

data to speak in a much less filtered way than a structural estimation.

The material is organized as follows. Section 2 briefly reviews some theoretical considerations. It also

explains the approach used in the analysis and provides a summary on the consumer survey in Albania.

Section 3 presents and discusses the descriptive evidence. The final section concludes.

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2. Theoretical considerations and the approach to saving pattern from the consumer

survey

2.1 Some theoretical considerations

An extensive international research has examined the household saving behaviour. In 1936 Keynes

identified several saving motives, listed by Browning and Lusardi (1996) as the precautionary motive,

the life – cycle motive, the inter – temporal substitution motive, the improvement motive, the

independence motive, the enterprise motive, the bequest motive, the avarice motive and the down –

payment motive. This latter added by Browning and Lusardi. However, Curtin (2000) points out that it

was first Katona who provided evidence related to the importance of each motive and their variations

across population groups. According to Katona (1960) the different saving reasons were primarily a

function of the life – cycle stage, as for instance people falling in the age group between 45 – 65 years

old save primarily for retirement or old age, whereas younger families report children’s education, down

payment on a house and major purchases among the motives to save. As Curtin highlights most research

has found that precautionary savings motives are particularly present at younger life – cycle stages,

whereas older consumers are more concerned in saving for retirement.

In the context of the post recent financial crises and in the uncertain environment that characterizes

developing economies, in this material I will particularly focus on the precautionary motive of savings.

Keynes refers to savings for precautionary reasons as a form of building up reserves against unforeseen

contingencies, while Browning and Lusardi explain that this precautionary behaviour is reflected in the

building up of financial reserves to be used as a buffer to pre – retirement income and consumption

shocks. Katona found that precautionary saving behaviour mirrors primarily concerns about future risks

related to employment, illness and disability (uncertainty about potential future medical expenses and

longevity).

As Lugilde et al. (2017) clearly point out consumers saving decisions might be affected by their

economic situation, other personal household characteristics, uncertainty and features of the

environment where these decisions are made (related mainly to public insurances, credit constraints,

welfare system). The authors argue that also the motive for precautionary saving is shaped by both

characteristics of individuals and the environment, which influence as well consumers’ perceived

uncertainty. Lugilde et al. explain that the need to save for precautionary motives is linked to uncertainty

on future income. They argue that when consumption decisions are made under uncertainty, and

individuals are prudent and seek protection from risk, a negative impact is produced on current

consumption and an extra saving, more exactly precautionary saving, is generated.

Saito and Shiratsuka (2003) argue that precautionary reasons are most likely to affect savings in

uncertain environments. The authors explain that in order to prepare for risky events in the future,

consumer save by transferring their current resources over time to stabilize future consumption.

According to them precautionary savings are enhanced by the magnitude of risks. In addition, in the

material of EC (2013) among the well-known channels analysed by the economic theory through which

uncertainty can negatively impact economic activity, as investments, employment, consumption and

risk premium, are stated also precautionary savings. When in the economy uncertainty is higher

regarding expected income, economic agents may decrease their consumption, generating an increase

in the current level of savings in order to smooth consumption in the future.

As Börsch – Supan and Lusardi (2003) point out when the precautionary savings motive is present

among economic agents, economies might experience an increase of their saving rates in the case of a

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higher degree of uncertainty in income and other future economic circumstances. Also, Carroll and

Kimball (2007) define precautionary saving as an additional saving stemming from the uncertainty for

the future. According to the authors in a standard analysis context, the precautionary saving behaviour

is a function of the solution of the consumer optimization problem related to the allocation of disposable

resources in the present and future. Toussaint – Comeau and DiFranco (2009) argue that consumers

tend to accumulate precautionary savings in times of economic uncertainty or in times of an increase in

unemployment in order to insure against a sudden loss in income.

Curtin highlights several empirical works that have found that consumers are expected to accumulate

precautionary savings as a hedge against uncertainty, among which he mentions Kimball (1990),

Caballero (1991), Carroll (1996), Carroll (1997), Carroll and Samwick (1997) and Carroll and Samwick

(1998). Some more recent evidence regarding the impact a higher uncertainty has on greater saving

rates is also summarized by Kłopocka (2017), as for instance the works of Carroll et al. (2012), Mody

et al. (2012), Bande and Riveiro (2013), Ceritoglu (2013), Chamon et al. (2013), and Mastrogiacomo

and Alessie (2014).

Kłopocka points out also the fact that some research finds little or no evidence on the precautionary

motive as Fossen and Rostam-Afschar (2013). In the same line also Lugilde et al. in their study highlight

that empirical evidence, explored at both macro and micro levels, are not conclusive regarding

precautionary saving. According to them there is neither consensus on the intensity of the precautionary

behaviour to save, nor on the best measure of uncertainty, what in itself poses problems when it comes

to the analysis of saving decisions. The authors clarify that the lack of consensus has to do not only with

the adequate measure of uncertainty, but also with the type of data to be used in order to better estimate

the effect of uncertainty on saving decisions. Lugilde et al. argue that as saving decisions are taken at

the individual level, micro data might be a better alternative compared to macro data, since individuals

might have more information about their future income. Micro – level data might be a good measure

when it comes to estimate the effect of a specific risk to consumers.2

Le Blanc et al. (2015) provide also other authors’ arguments relevant to explain and to further

investigate empirically what might replace the consumers need for precautionary savings. The authors

mention that Hubbard et al. (1995) state unemployment benefits and other welfare policies as reasons

behind lower precautionary savings, due to their aimed targets in reducing changes and shocks to life –

time income. A higher proportion of government transfers and of remittances in overall household

income might reduce the saving rate, revealing that these sources of income appear to discourage saving.

Le Blanc et al. provide also another reason that might be relevant in developing economies to replace

the need for precautionary saving. This has to do with the role of the network of relatives and friends to

offset shocks, which according to Börsch – Supan and Lusardi might substitute the formal capital

market requirements and binding liquidity constraints, what finally can have a negative impact on

savings for precautionary reasons. However, at the end as Le Blanc et al. point out, it has to be kept in

mind that saving remains fundamentally an attitude, a personal trait. As such, some individuals save

because not only they can, but also they are patient and prudent, while some others not only cannot

afford saving, but it might be that they are impatient and risk takers.

2 The uncertainty measure approximated through micro – data has also disadvantages, which Lugilde et al. discuss in their paper, but this issue is beyond the scope of this material. In addition, it is important to mention that another indicator discussed by Lugilde et al. to estimate

uncertainty is the probability of continuing being employed. During economic downturns, unemployment is likely to rise, what might drive

the income variation. The authors argue that as most of the consumers’ income are coming from labour, becoming unemployed is one of the largest negative shocks on income, so the risk of becoming unemployed in the future might be also a good indicator of uncertainty.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5569149/#CR11







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2.2 The approach to the household saving pattern from the consumer survey

Following the discussion presented by Friz in the 8th Joint EC/OECD Workshop (2017), I make use of

the data collected from several questions of the consumer survey, starting from question 11 that is

related to savings expectations. The exact formulation of question 11 is:

“Over the next 12 months, how likely is that you save any money?

(++ ) Very likely; (+) Fairly likely; (-) Not likely; (--) Not at all likely; (N) Don’t know.”

Friz points out two facets when interpreting the results from question 11, which aim to understand if

consumers might be saving in the near future for opportunity reasons or precautionary reasons. One of

the facets has to do with saving for opportunity reasons, which might be when consumers think their

future revenues will increase, and therefore they will have the possibility to save. The other facet is

linked to saving for precautionary reasons that is more related to a perception of a future uncertainty

period, as for example when consumers know that they will have to face more difficult periods due to

decreased future revenues.

I explore these two facets of question 11 in order to shed some light mainly regarding precautionary

saving behaviour in the case of Albania using micro – data from the consumer survey and in line with

the questions’ combination as discussed by Friz. The author argues that what could help in

understanding if consumers expect to save for precautionary reasons is looking at both question 11

(savings expectations) and question 2 (expected household financial situation). The exact formulation

of question 2 is:

“How do you expect the financial position of your household to change over the next 12 months?

(++ ) Get a lot better; (+) Get a little better; (=) Stay the same; (-) Get a little worse; (--) Get a lot worse; (N)

Don’t know.”

The interpretation would be that, if a considerable share of consumers saying that they expect their

financial position to get worse, are also expecting to save money, this can be a signal they want to save

for precautionary reasons.

An additional check suggested by Friz, in order to confirm the precautionary nature of savings is

explored using responses from question 9, which tries to capture consumers’ expectations on spending

in major purchases. The exact formulation of question 9 is:

“Compared to the past 12 months, do you expect to spend more or less money on major purchases (furniture,

electrical/electronic devices, etc.) over the next 12 months? I will spend…

(++ ) Much more; (+) A little more; (=) About the same; (-) A little less; (--) Much less; (N) Don’t know.”

Friz points out that an additional signal that savings are done for precautionary reasons would be that

households expect also to spend less on major purchases, so that they could be able to cover a potential

distress of their financial situation.

The analysis is completed with two other indicators that I construct using the consumer survey answers

and following the approach presented by Malgarini and Margani in 4th Joint EC/OECD Workshop

(2009) for the financial distress indicator and the methodology proposed by Girardi and Reuter (2017)

for the uncertainty indicator as discussed in Ivanov (2018).

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The financial distress indicator is build from the data provided from question 12, which exactly asks:

“Which of these statements best describes the current financial situation of your household?

(++) We are saving a lot; (+) We are saving a little; (=) We are just managing to make ends meet on our income;

(-) We are having to draw on our savings; (--) We are running into debt; (N) Don’t know.”

This indicator of financial distress as suggested by Malgarini and Margani, is constructed as the sum of

consumers frequencies that have answered they have to draw on their savings and of those that are

running into debt to sustain their expenditures.

As also Lugilde et al. argue if there is precautionary saving, uncertainty in the current period should

increase savings. Accounting for the effect uncertainty plays in precautionary savings, I include in the

material an uncertainty indicator to help the analysis. However, the focus of the material is not to discuss

uncertainty, and as such I have chosen to consider only one type of uncertainty measure, referred by

Ivanov as the U1 indicator. This represents an expectations – based uncertainty indicator and captures

the dispersion in survey responses by computing the cross – sectional standard deviation of individual

survey responses, as in Bachmann et al. (2013). The formula is:

𝑺𝒕𝒂𝒏𝒅𝒂𝒓𝒅 𝒅𝒆𝒗𝒊𝒂𝒕𝒊𝒐𝒏 𝒐𝒏 𝑸𝒕 = √𝒑𝒕+ + 𝒑𝒕

− − (𝒑𝒕+ − 𝒑𝒕

−)𝟐

where 𝑝𝑡+ is the fraction of "increase", whereas 𝑝𝑡

− the fraction of "decrease" responses to 𝑄𝑡 3

This standard deviation is computed for 5 questions that in the consumer survey are:

Question 2, Question 9 that have been both formulated above.

Question 4: “How do you expect the general economic situation in this country to develop over the next 12

months? It will…”

Question 6: “By comparison with the past 12 months, how do you expect that consumer prices will develop in the

next 12 months? They will…”

Question 7: “How do you expect the number of people unemployed in this country to change over the next 12

months? The number will…”

The dispersions for each question are then standardized and the uncertainty indicator for consumers is

computed as the average of the standardised results from the 5 questions stated above.

2.3 Consumer survey in Albania

Bank of Albania, in cooperation with the Institute of Statistics (INSTAT), as well as initially with the

assistance of Ifo Institute for Economic Research, began conducting confidence surveys from 2002 at

a quarterly basis. These surveys have provided useful data to better understand domestic economic

developments and to complete official statistics, as by measuring inflation expectations or by

constructing uncertainty indicators. Confidence surveys serve as a valuable input for periodic analysis,

reports and short term forecasting models of economic growth prepared by the Bank staff.

Since May 2016, Bank of Albania, in cooperation with INSTAT, conducts confidence surveys at a

monthly frequency under the Joint Harmonized European Union Programme of Business and Consumer

Surveys, carried out in member and candidate countries. The conduct under the European Commission

3 As in the consumer survey there are five possible responses, 𝑝+ is the sum of of the fraction of consumers that have given very positive and

positive answers, whereas the 𝑝− is the sum of the fraction of those consumers that have provided very negative and negative answers.

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programme brought several advantages. It enriched the frequency of the database, and it applied a

sample selection methodology as well as a weighting scheme, which better represents the population,

ensuring at the same time a better alignment with methodologies in other EU countries. This allows for

comparative analysis of synchronization of business cycles.

Each month, 1200 individuals are randomly selected in Albania to participate in the consumer

confidence survey. The consumer’s list, which serves as population for the survey, is taken from the

Population and Housing Census 2011. This register contains the complete count of all persons,

households and dwellings in Albania.4 The method used to extract information from the given

population is random sampling, and the sample is created to represent all consumers aged above 15

years old. The criteria for the selection of strata are geography and density of the region where

consumers belong to. After the questionnaire is filled out and data entry has finished, the consumer’s

answers are weighted. The weighting criteria are: geographic area, age of the respondent, gender and

size/density of the population.5 The questions asked cover different areas of consumer confidence

related broadly to personal finances, economic conditions and future plans.

Nationally, household spending on final goods and services represent about 80% of all expenditures in

the economy. Since private consumption accounts for such a large share of aggregate demand, consumer

confidence can signal changes in the direction of the economy. The consumer confidence indicator is

an official component of the Economic Sentiment Indicator (ESI), and its importance has been

recognized and it is regularly monitored in Albania. Furthermore, since the harmonization the survey

offers a rich database, what allows for a better understanding of the importance of influence of consumer

spending and saving decisions on the course of the national economy.

4 On 1 January 2016, the number of population over 15 years old, which serves as population of CCS, consists of 2.376 million individuals. 5 As Kristo (2017) explains the weighting process is done after the data collection, because parts of consumers’ characteristics are not known at the time of the sample creation (e.g. age or gender). In the weighting phase, replies of each consumer are weighted at the individual level

to evaluate the population, by using the sample weight (1𝜋𝑖

⁄ where 𝜋𝑖 is the inclusion probability). These weights are revised each month and

are redistributed in case there are individuals that have not answered.

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3. The empirical analysis of the results from micro – data

3.1 Income quartiles in the context of Albanian consumer survey

As result of the harmonization with the EU methodology, consumer survey in Albania has been enriched

for data on several socio – demographic groups, as income, occupation, working regime, education, age

and gender. This allows looking more closely at the information provided by the survey, accounting

also for specific characteristics of consumers. In this analysis, the focus will be on the income

dimension, checking its influence in confidence, financial situation and more particularly in the saving

pattern of the Albanian household.

To determine the values of the four quartiles, two questions were included in the consumers’

questionnaire. The first one is an open question and consumers are directly asked for the average family

income. Taking into consideration that some of the respondents would be hesitant to report a number

for their income in an otherwise qualitative questionnaire, and after consultations with INSTAT, another

question was introduced in the questionnaire. This second question asks the respondent to choose

between different income brackets where his/her respective family average monthly income falls.

Income quartiles of average income per household are calculated by INSTAT, based on the values of

quartiles from the Household Budget Survey.6

Chart 1. Income breakdown by quartiles, an average during 2016:M5 – 2018:M10. Other household

characteristics by income quartiles

Source: Albanian consumer survey data and author’s calculations.

6 The values of the intervals are fixed, but it will be considered to change the values based on the responses from the questionnaire. The

Household Budget Survey is the only updated data source disposed by INSTAT to compute household average income. Certainly, it has to be taken into account that income quartiles generated from this source of data have several constraints that are mainly related to the fact that the

survey has not as objective the collection of income, but indirectly collects information regarding this topic through expenditures done by

households. Therefore, data might be underreported or might lack coherence with expenditures. However, INSTAT judges these data as the most suitable for analysis.

17%

36%

39%

8%First quartile

Second quartile

Third quartile

Fourth quartile

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% of unemployed per income quartile

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Chart 1 provides an allocation of households in terms of income quartiles, showing that almost 75% of

the households fall under the second and third quartiles during the period 2016:M5 – 2018:M10. Over

this time period, the third income quartile shows the highest volatility relatively to other quartiles. Also,

over time it results a slight downward tendency of the fractions of the first and second quartiles.

In addition, the first quartile is characterized by a slight predominance of households belonging to the

older age classes (50 – 64 years old and 65 years old and above). In the second quartile, households

result as more roughly equally represented (even though those aged between 50 – 64 years old account

for the highest share around 29%). The third and fourth quartiles have more households from the

younger age classes, 15 – 29 years old and 30 – 49 years old, respectively by around 61% the third

quartile and 69% the fourth quartile. Regarding gender characteristics, it can be noticed a slightly higher

predominance of female respondents in the first (56%) and second quartiles (51%). The third and fourth

quartiles seem to have more equally distributed households in terms of gender, although female

respondents account for the larger share of total respondents per quartile. When looking at occupations

status per income quartiles, it can be observed that the first quartile has the highest share of unemployed

people (40% of total respondents in the first quartile), whereas this share falls at around 4.5% of all

households in the fourth income quartile.

3.2 Household financial situation according to income

Both this part and the next one, report the main observations. They build on the conceptual arguments

and references already presented in section 2. The present part focuses solely on specific consumer

survey indicators, as consumer confidence, household financial situation and consumer financial

distress indicator by income groups.

Chart 2 provides an overview of the consumer confidence indicator for the overall population and a

breakdown of this variable by income groups for the period 2016:M5 – 2018:M10. The chart shows

that the lower income groups are generally less optimistic then their respective counterparts. On the

basis of the available evidence it results that confidence is particularly higher for the richest group of

consumers.

Chart 2. Consumer confidence indicator (CCI)7 by income quartiles


7 Since the monthly series are too short for seasonal adjustment, charts in this material are constructed using non-seasonally adjusted data.

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M5 M6 M7 M8 M9 M10M11M12 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10M11M12 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10

2016 2017 2018

First Quartile Second Quartile Third Quartile Fourth Quartile CCI

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In addition, taking into consideration that the time span is short, it can however be noticed that

differences in sentiment tend to persist and remain on average constant. Results are consistent with

other international empirical evidence, which has also shown that consumer confidence varies across

demographic characteristics and socioeconomic groups. As Souleles (2004) argues, differences in

groups' expectations may be due to time varying or group level shocks. As he pointed out, during

economic expansions, high income households received relatively good shocks, while on the contrary

low income households continued to receive negative shocks. In addition, Toussaint – Comeau and

McGranahan (2006) provide supportive evidence that individual's expectations are partly shaped by

their own subjective experiences.

As the consumer confidence is made up of four components, I take a closer look at specific questions

that are also part of the index8, as well as other questions more relevant to understand the saving

situation of the Albanian household. The lower confidence level among consumers with lower levels of

income can be attributed to their lower confidence in both the overall economy, as well as their personal

financial situation. This is confirmed by the household financial situation indicators, as shown in chart

3. Groups with relatively lower income are significantly less optimistic and have lower assessments of

their past and future financial situation than their complements. Household financial situation seems to

ameliorate with higher levels of income.

Chart 3. Household past financial situation (question 1 of the consumer survey) and household expected

financial situation (question 2 of the consumer survey) by income quartiles9


Household financial distress indicator results higher for the poorest group, as it can be seen from chart

4. As with the other indicators, the share of consumers being in a situation of distress differs remarkably

among quartiles and the distance between the first and fourth quartile results particularly large. This

also confirms that it is not surprising that the poor differ most from the non-poor in their sentiment or

assessments for economic conditions, because being poor is partly the result of current financial distress.

8 The Consumer Confidence Indicator (CCI) is based on the arithmetic average formula that takes into account respondents' opinions about: 1. how they expect their financial situation to be over the next twelve months; 2. how they expect the general economic situation to change

over the next twelve months; 3. whether they think it is the right moment for people to make major purchases; and 4. which of the statements

best describes the current financial situation of their household (respectively questions 2, 4, 8 and 12 from the questionnaire of consumers). For further details on how the index is generated, please refer to

https://www.bankofalbania.org/Monetary_Policy/Surveys_11282/Business_and_consumers_survey/ 9 The exact formulation of question 1 is: “How has the financial situation of your household changed over the last 12 months? It has... (++) Got a lot better; (+) Got a little better; (=) Stayed the same; (-) Got a little worse; (--) Got a lot worse; (N) Don’t know.”

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2016 2017 2018

First Quartile Second Quartile Third Quartile

Fourth Quartile Past financial situation

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2016 2017 2018


Fourth Quartile Expected financial situation

https://www.bankofalbania.org/Monetary_Policy/Surveys_11282/Business_and_consumers_survey/

11

Chart 4. Household financial distress indicator by income quartiles


In addition, by looking carefully at the two components used to build up the indicator of financial

distress, namely “Drawing from savings” and “Running into debt”, it emerges that the component

related to debt is relatively higher than the other component and it is larger for households in the first

quartile (Chart 5).

Chart 5. Household financial distress components by income quartiles


So when checked for the income dimension to understand why consumers think the way they think

about confidence, financial situation and financial distress, it seems that different income segments of

consumers react in a systematic way.

3.3 Household saving pattern according to income

This part of the analysis looks carefully at the evidence regarding the saving behaviour of the Albanian

consumers by income quartiles, following the approach proposed by Friz. Before analysing the

descriptive evidence focusing mainly on precautionary saving, I preliminary examine if it exists any

particular relationship between uncertainty and precautionary saving. The rationale behind that is

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Fourth Quartile Financial distress

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First Quartile Second QuartileThird Quartile Fourth QuartileDrawing from savings component

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2016 2017 2018

First Quartile Second QuartileThird Quartile Fourth QuartileRunning into debt component

12

explained also earlier in the theoretical considerations. During times of greater economic uncertainty,

as consumers perceive greater risk, they tend to accumulate precautionary savings to insure against a

sudden loss in income. So, precautionary savings may appear because under an uncertainty context

individuals behave prudently and they increase the rate of saving. Based on the uncertainty indicator

that I have constructed, I check if there is any positive correlation between uncertainty and saving at

present, as indicated by question 10. Question 10 exactly asks:

“Over the next 12 months, how likely is it that you save any money?

(++) Very likely; (+) Fairly likely; (-) Not likely; (--) Not at all likely; (N) Don’t know.”

Saving at present for all the population appears to have a positive correlation with uncertainty (around

0.5), being in line with the assumption that if the precautionary motive exists under uncertainty

conditions, current savings tend to increase (Chart 6). Even though correlation does not necessarily

mean causality, this might give signals that the precautionary motive is present among the Albanian

consumers.

Chart 6. Consumer uncertainty indicator and saving at present10


When decomposed in terms of income quartiles, it seems that saving at present has a relatively higher

correlation with the uncertainty indicators for the third and the second quartile (the correlation

coefficients are respectively 0.3 and 0.2) (Chart 7). However, this positive correlation is low, what

might suggest that the strength of the precautionary motive might be weak.

10 The uncertainty indicator is standardized. As such a value of 0 would mean that the level of uncertainty is “average”, whereas values above

0 are periods with heightened uncertainty and values below 0 are periods with uncertainty “below average”. To smooth for high fluctuations, the uncertainty indicator and the saving at present indicator represent a moving average with three terms. To check for the relevance of the

uncertainty indicator, I have also looked at the presence of correlation with the macro indicator of household savings, this latter approximated

with the sum of deposits and other forms of savings among individuals (treasury bills, bonds). It seems to exist a considerable positive correlation among the uncertainty indicator and the rate of growth of the approximated savings for individuals in the economy.

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0.8

1.0

M7 M8 M9 M10 M11 M12 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 M12 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10

2016 2017 2018

Uncertainty indicator Saving at present (rhs.)

13

Chart 7. Consumer uncertainty indicator and saving at present according to income quartiles


Turning now to question 11, the following chart graphs household saving expectations over the

specified time period for the overall population and by income quartiles. According to the results, the

upper income quartile exhibits higher saving expectations.

Chart 8. Households saving expectations (question 11 of the consumer survey) by income quartiles


-90

-85

-80

-75

-70

-65

-60

-55

-50

-45

-40

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0


2016 2017 2018

First income quartile Uncertainty indicator Saving at present (rhs.)

-90

-85

-80

-75

-70

-65

-60

-55

-50

-45

-40

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0


2016 2017 2018

Second income quartile Uncertainty indicator Saving at present (rhs.)

-50

-45

-40

-35

-30

-25

-20

-15

-10

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8


2016 2017 2018

Third income quartile Uncertainty indicator Saving at present (rhs.)

0

5

10

15

20

25

30

35

40

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0


2016 2017 2018

Fourth income quartile Uncertainty indicator Saving at present (rhs.)

-100

-80

-60

-40

-20

0

20

40

60


2016 2017 2018


Fourth Quartile Saving expectations

14

When I take a closer look at the responses of individuals on question 11, it results that among the

consumers who expect to save over the next 12 months (22% of all respondents), the majority of them

fall respectively in the third (58%) and fourth (27%) quartile (Table 1)11.

Table 1. Household saving expectations (question 11)

In %

2016:M5 – 2018:M10

Increase in savings Decrease in savings Don’t know Share of respondents

by quartile

First quartile 2 21 11 17

Second quartile 13 43 32 36

Third quartile 58 33 46 39

Fourth quartile 27 2 10 8

Total 100 100 100 100

Share of respondents by

question answers 22 74 3 100


In the third quartile, only 33% of the respondents expect an increase in savings, while in the fourth

quartile 76% of the respondents (Table 2).

Table 2. Household saving expectations (question 11)

In % 2016:M5 – 2018:M10

Increase in savings Decrease in savings Don’t know Total


Second quartile 8 89 3 100 Third quartile 33 63 4 100 Fourth quartile 76 19 5 100


When I look at the micro – data on question 2 it seems that around 14% of respondents report that they

expect a worsening in the household financial situation over the next 12 months, and among those

consumers the majority of them falls in the second (44%) and first (36%) quartile (Table 3).

Table 3. Expected household financial situation (question 2)

In %

2016:M5 – 2018:M10

respondents

who expect a

better financial

situation

respondents who

expect a worse

financial situation

respondents who

expect no changes

in their financial

situation

Don’t

know

Share of

respondents

by quartile

First quartile 6 36 18 11 17

Second quartile 27 44 40 36 36

Third quartile 51 19 37 45 39

Fourth quartile 15 1 5 8 8

Total 100 100 100 100 100

Share of respondents

by question answers 30 14 53 4 100


However, only 16% of the respondents that fall in the second quartile expect a worsening of the financial

situation, while in the first quartile this share is 29% (Table 4).

11 The data presented in Table 1 and all the following tables are averages of the results for the period under investigation, 2016:M5 – 2018:M10, and as such are rounded. Due to this, the summation might reflect slight divergences from 100%.

15

Table 4. Expected household financial situation (question 2)

In %

2016:M5 – 2018:M10

respondents who

expect a better

financial situation

respondents who

expect a worse

financial situation

respondents who expect

no changes in their

financial situation

Don’t

know Total

First quartile 11 29 57 2 100

Second quartile 23 16 57 4 100

Third quartile 39 7 49 4 100

Fourth quartile 59 2 35 4 100


Furthermore, I examine responses to question 11, as well as responses to question 2 of the survey, by

combining both questions. The evidence shows that on average the share of savings that are done for

precautionary reasons by Albanian consumers is low (around 2%). This share is mostly concentrated

among consumers that fall in the third (57%) and second quartiles (26%) (Table 5).

Table 5. Results when combining data from question 2 and 11

In %

2016:M5 – 2018:M10

Worsening of the

financial situation

and increase in

savings

Worsening of the

financial situation

and decrease in

savings

Worsening of the

financial situation

and don’t know

Share of

respondents by

quartile





Total 100 100 100 100

Share of respondents

by question answers 2 96 2 100


However, only 7% of the respondents that fall in the third quartile expect to increase their savings while

their financial situation is expected also to worsen. This fraction becomes even smaller for the second

quartile, around 1%, whereas it is the highest for the fourth quartile, around 27% (Table 6).

Table 6. Results when combining data from question 2 and 11

In %

2016:M5 – 2018:M10

Worsening of the

financial situation and

increase in savings

Worsening of the

financial situation and

decrease in savings

Worsening of the

financial situation

and don’t know

Total






In addition, when accounting also for the responses to question 9, it results that during the period under

analysis 2016:M5 – 2018:M10, on average 50.4% of consumers who expect to save in view of a

worsening of their expected household financial situation, expect to spend less money on major

purchases. It results that this fraction is higher during 2017, which might be linked also to the higher

uncertainty in the first half of the year12 related mainly the parliamentary elections.

12 The uncertainty indicator in Chart 6 shows an increase in uncertainty among consumers up to the first half of 2017. The parlamentiary elections in Albania were held on 25 June 2017.

16

Chart 9. Share of consumers expecting to save in view of a worsening of their expected household financial

situation, expecting also to spend less money on major purchases


Finally, according to the income breakdown, the share of savings for precautionary reasons results on

average relatively higher for the upper quartile of income. It suggests that it could exist a positive

income effect on saving for unexpected events. Nevertheless, since the data do not cover fully three

years and start after the financial crisis, it remains difficult to draw strong conclusions.

Chart 10. Consumers that expect a worsening of their financial situation, a reduction of their expenditures on

major purchases, an increase in their savings, as % of consumers expecting a worsening of their financial

situation.


33.5%

60.6%

51.6%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2016:M5 - 2016:M12 2017 2018:M1 - 2018:M10

0%

5%

10%

15%

20%

25%

30%

2016:M5 - 2016:M12 2017 2018:M1 - 2018:M10

First quartile Second quartile

Third quartile Fourth quartile

In all income quartiles

17

4. Concluding remarks

There is a growing interest among countries to provide early behavioural signals of different groups of

consumers. Data from the consumer confidence survey are among the sources that could help in this

direction both researchers and policy makers. Consumer attitudes may incorporate their estimates of the

impacts of shocks whose effects cannot be directly measured from history or hard data. They might also

reflect changes in expectations or uncertainties about the future. So, disaggregated consumer data are

useful in highlighting differences by group attributes, remaining particularly informative also in a

context of greater uncertainty.

The analysis of consumer survey data for sub groups of the population, as income quartiles, can provide

very valuable information in terms of confidence levels, household financial situation and saving

situation in Albania. The charts presented in the material reveal that consumers hold different opinions

regarding the sentiment, their financial situation and saving situation depending on their income. It

seems that this gaps among income quartiles seem to remain constant over the period of time under

consideration. My approach to investigate more in particular the theory of precautionary savings in

Albania is based on the question combination discussed by Friz and using the Albanian consumer survey

data. Results suggest that the share of savings that are done for precautionary reasons in Albania is low.

Differences emerge by income quartiles, and according to the data, most of the precautionary saving is

generated by the highest income quartile.

However, it is crucial to highlight that results remain descriptive and the analysis provides a first

evidence of the precautionary saving motive. Results are just a first exploration at a micro dimension

and by subgroups of the population, as well as they are based on non-seasonally adjusted data and

should be treated as provisional. It still remains crucial to understand the reasons behind the saving

attitudes of the Albanian consumer, as further evidence is provided, in order to be taken into account

when modelling saving behaviour. Future research should focus on other factors that might influence

the precautionary motive. It would be interesting checking the relation of precautionary saving with

age, geographic regions, gender and education. In addition, with the further enrichment of the consumer

survey database, it would be possible to determine whether differences in consumer sentiment might

explain or predict groups’ differences in saving behaviour, investigate what happens to the gap in

attitudes during periods of slowdown or check for the presence of a disproportional impact of a

recession among consumer’ groups.

18

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