measuring the crisis-related economic uncertainty with...

EU WORKSHOP ON BUSINESS AND CONSUMER SURVEYS – Brussels, 14-15 November 2016

Measuring the crisis-related economic uncertainty with Italian survey

data

October, 2016

Luciana Crosilla*, Solange Leproux*

Abstract

The recent literature, taking into account the advantages of data stemmed from the monthly

business and consumer surveys (in particular, their high frequency and their coming directly from

agents who make consumption and investment decisions), proposes some survey-based indicators

to proxy the economic uncertainty. In line with these approaches, in this work we use data coming

from the business surveys carried out by ISTAT to calculate three uncertainty measures for each

Italian productive sector (manufacturing, construction, services and retail trade). More specifically,

we calculate a first uncertainty measure based on the Bachmann’s formula (Bachmann et al., 2010),

a second uncertainty indicator applying a version modified of the Bachmann’s formula (Friz, 2016),

and, finally, a third uncertainty indicator using the Theil’s entropy formula (European Commission,

2013a). This application has had the aim to verify what kind of relation the so-obtained uncertainty

measures present with respect to the positive or negative evolution – with special attention to the

recent economic crisis - of the Italian economy. Although the interpretation of the results obtained

is not straightforward, the analysis seem to suggest that uncertainty measures obtained applying the

Bachmann’s formula and the Theil’s entropy formula on service and retail trade survey data are,

among all, the ones characterized by a higher level of symmetry.

______________________________________________________________________________

* Istat - Short Term Business Statistics Division – Rome, Italy.

The opinions expressed in this paper are solely the responsibility of the authors and should not be interpreted as

reflecting the views of ISTAT or its staff.

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Contents

1. Introduction

2. The theoretical measurement of uncertainty using survey data

3. The Italian survey data used to quantify uncertainty in the country

4. How uncertainty has evolved in the Italian economic sectors: the empirical results

5. Concluding remarks

6. References

Appendix A

1. Introduction

Already in 1979, George Katona, father of the application of psychological principles to

macroeconomics and of the Consumer Confidence Indicator, asked whether survey data could asses

if the consumer really was uncertain and to what degree. As a matter of fact, Katona originally

considered his Confidence Indicator an all-comprehensive measure of the optimism or pessimism,

but also of the certainty or uncertainty that was attached to the expectations of the respondents1. To

recognize this dualism, Katona defined the dimension of consumer confidence as ranging from

optimism and confidence to pessimism and uncertainty2. The perspective that defines uncertainty

as the variability of opinion, but distinct from the overall survey measures of confidence was early

proposed by Lazarsfeld, Berelson and Gaudet3. The authors wrote that “if the [ period-to-period

survey response] turnover is large, it indicates that the opinion or behaviour is unstable. We know

that people feel uncertain”.

Hence, it is so long that the literature has linked the possibility to measure uncertainty to the

use of the survey data as well as the uncertainty concept to the one of opinion variability.

Regarding this, both survey-based and variability-based measures of uncertainty have been

defined in the recent literature. In this work, leaving aside the criticism and the doubts that have

been recently raised about the possibility of using the dispersion as measure of uncertainty4, we

1 “At a time when almost everybody in a representative sample expresses optimistic expectations, or almost everybody

expresses pessimistic expectations, we may say that the people are optimistic or pessimistic. On the other hand, when a

substantial proportion is optimistic and a similar proportion is pessimistic, the people as a whole may be viewed as

uncertain in their expectations about future developments. Thus the smaller the difference between expecting good or

better times and expecting bad or worse times, the greater the uncertainty on the aggregate or macro level. This measure

[is] constructed irrespective of whether optimists or pessimists are more frequent…It indicates not only…that growing

optimism dispels uncertainty but also that growing pessimism dispels uncertainty.” Drawn from “Towards a

Macropsychology”, G. Katona. American Psychologist, February, 1979, p. 122. 2 See “Changing Sources of Economic Uncertainty”. R. Curtin. Paper presented at 26

th CIRET Conference, Taipei,

October 2002. 3 “The People’s Choice”. P. Lazarsfeld, et al. Columbia University Press, New York, 1948

4 Part of the most recent literature about the issue, argues that heterogeneity among firms can invalidate the reliability of

uncertainty measures based on the reply dispersion. Nevertheless, other analysis have shown that the heterogeneity of

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apply a few of these formulas to verify whether the uncertainty measures based on Italian data

present a symmetric relation with respect to positive or negative economic evolution of the country,

or whether they display an asymmetric behaviour, confirming the results already highlighted by

previous studies.

More specifically, we calculate an uncertainty measure proposed by Bachmann et al. (2010),

where the indicator is defined as the cross-sectional standard deviation of the responses. Since in

this measure the "unchanged" replies are not taken into account, following the latest studies, a

modified Bachmann’s formula measure has been also calculated in which the responses to the

"Unchanged" category are equally distributed among the positive and negative shares. Finally,

according to the European Commission, we have calculated a third measure of Italian economic

uncertainty, using the Theil’s entropy formula.

Moreover, the empirical evolution of the so-obtained indicators is examined in the

manufacturing, services, retail trade and construction sectors of the national economy during the

period 2003Q1-2016Q2. To verify what kind of relation exists between the evolution of uncertainty

among Italian managers and the national economic developments, the sector-level indicators are

analysed against the respective quantitative reference series (year on year Industrial Production

Index, Value Added and Private Final Consumption growth). From this point of view, the period

taken into account appears particularly suitable to the analysis because it includes the 2008 financial

crisis which had a strong, deteriorating effect on the fragile Italian economy.

In basis of the results obtained, we can conclude that the uncertainty measures derived from the

Italian business survey data are generally characterized by unclear behaviors compared to economic

evolution of the country. In particular, the uncertainty indicator obtained applying the Bachmann’s

formula seems not to respect entirely the principle of symmetry of uncertainty: it, in fact, tends to

decrease when the economic evolution is negative, but not when the latter is positive. Moreover,

contrary to what would be expected, the signals are still unclear also when the indicators are based

on the modified Bachmann’s formula and on the Theil’s entropy formula: they only occasionally

show a symmetric behaviour in respect to the respective reference series.

However, summarizing the main results obtained, the measures based on the Bachmann’s

formula and on the Theil’s entropy formula applied to service and to retail trade survey data seem to

be, among all, the ones more in line with the principle of symmetry of uncertainty.

The widespread lack of symmetry of the uncertainty measures here presented, especially during

abrupt economic fluctuations (e.g. recent economic crisis), may be a signal that Italian managers,

not recognizing immediately the economic worsening/recovery, show expectations discordant.

2. The theoretical measurement of uncertainty using survey data

The indicators presented in this analysis are calculated following, in particular, two approaches that

are well-known in the up-to-date literature: the formula of Bachmann et al.(2010) and the formula

of Theil’s entropy5. Moreover, taking into consideration the lack of symmetry in uncertainty

respondents is not so empirically important. Bachmann (2010) has shown that an alternative uncertainty measure, which

conceptually excludes the effect of (e.g. structural or informative) heterogeneity, delivers results similar to the ones

obtained using an uncertainty measure based on dispersion. 5 Formula used in the Quarterly report on the Euro Area, European Commission (2013a).

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changes that has been recently pointed out by certain analysis6, another measure has been used.

More specifically, we have employed the modified Bachmann’s formula in which the “Unchanged”

replies are not ignored, but, on the contrary, are equally distributed among the positive and negative

shares7.

The first measure applied is the Bachmann’s formula. It, in particular, defines uncertainty as:

Ut = √(𝐹𝑟𝑎𝑐𝑡(+) + 𝐹𝑟𝑎𝑐𝑡(−) − (𝐹𝑟𝑎𝑐𝑡(+) − 𝐹𝑟𝑎𝑐𝑡(−))2)

where the expression “Fract” indicates the fraction of positive (+) and negative (-) responses to

the survey question , at time t.

The second method to measure uncertainty that we propose is based on the “corrected” version of

Bachmann’s formula. In the latter, as above mentioned, the “Unchanged” replies are divided

equally among the positive and negative shares in order to simplify the interpretation of the

uncertainty changes. We know, in fact, that the increase or decrease of uncertainty can be really due

to the movement of balance values (e.g. the rising or falling dominance of “increase” over

“decrease” replies, or vice versa), but also to the movement of the share of “Unchanged” replies.

The higher the “Unchanged” fraction, the lower the uncertainty. Thus, changes in uncertainty are

particularly difficult to interpret in particularly when the changes in the “Unchanged” share are

important and frequent.

The modified formula of Bachmann, undoing the effect of the intermediate share, makes the

interpretation of the measure easier.

The corrected positive and negative fractions of the modified Bachmann’s formula are defined as:

Fract*(+) = Fract(+) + 0.5* Fract(=)

Fract*(-) = Fract(-) + 0.5* Fract(=)

Thus the Uncertainty modified indicator will be:

Ut*= √(𝐹𝑟𝑎𝑐𝑡

∗(+) + 𝐹𝑟𝑎𝑐𝑡∗(−) – (𝐹𝑟𝑎𝑐𝑡

∗(+) – 𝐹𝑟𝑎𝑐𝑡∗(−))2)

Finally, to account for all three response options, we also have looked at the Theil’s entropy

formula. The latter has been used as follows:

Te = - Σi(αi Log(αi))/3 i = 1,2,3

6 You can see, for example, European Commission (2013b).

7 R. Friz (2016), Paper presented at the 33rd CIRET Conference in Copenhagen.

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Where αi represents the share of responses to each of the three possible categories of reply

(“Increase”; “Remain Unchanged”; “Decrease”) .

3. The Italian survey data used to quantify uncertainty in the country

This section begins with a description of data used to quantify uncertainty. Following the latest

literature, the replies of managers to the questions about their economic expectations, may be more

or less concordant in their views, depending on the degree of uncertainty about the economic

scenario.

At this aim, the following questions have been investigated:

- for the manufacturing sector, EC question 5:

“How do you expect your production to develop over the next 3 months?”

- for the services sector, EC question 3:

“How do you expect the demand (turnover) for your company’s services to change over the

next 3 months?”

- for the retail trade sector, EC question 4:

“How do you expect your business activity (sales) to change over the next 3 months?”

- for the construction sector, EC question 4:

“How do you expect your firm's total employment to change over the next 3 months?”

The answers to these questions fall into three main qualitative categories: Increase, Decrease and

Unchanged category. All the survey data are available for the period 2003M1 – 2016M8. To assess the

empirical evolution of our survey-based uncertainty indicators, we compare them to the

developments in year-on-year growth of the quantitative reference series for each sector. Indeed, for

manufacturing and construction sector we use monthly Industrial Production Index and Production

Index in construction, respectively; Value Added is used for services and Private Final

Consumption for retail trade sector. All the uncertainty indicators were become quarterly8, which

facilitated the comparison with the reference series. All in all, the time span analyzed is 2003Q1-

2016Q2.

4. How uncertainty has evolved in the Italian economic sectors: the empirical results

In this section, we examine the empirical evolution of the uncertainty indicators calculated

employing both the Bachmann’ formulas (the original as well as the modified one) and the Theil’s

entropy formula.

In particular, we wonder whether the uncertainty measures based on Italian data present a

symmetric relation with respect to positive or negative news on the economic developments of the

8 In order to improve graphic comparison, uncertainty indicators and reference series were become quarterly for

manufacturing and construction sectors, too.

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country, or whether they display an asymmetric behaviour, confirming the results already

highlighted by the previous studies9.

Starting from the uncertainty indicator derived from the manufacturing survey data, Graph.1

shows the uncertainty measure obtained using the Bachmann’s formula, plotted against the annual

growth rate of the Italian Industrial Production Index in Manufacturing. The empirical results seem

to confirm the ones already obtained, for the same economic sector, by the European Commission

at the Euro area level10. In fact, apart from the first period of observation during which the indicator

wigwags up and down (until 2005Q2), the measure tends to decline when the Italian IPI in

Manufacturing shows an upward trend (more specifically, between 2005Q3-2007Q1, more

markedly from 2009Q2 to 2010Q2), but increases when the reference series highlights a

deterioration of the economic environment (2008Q2-2009Q1). However, apart from the local peak

in 2013Q4 (that corresponds to a local trough in the “Unchanged” replies11), the measure seems to

be characterized by a more respectful behaviour of the principle of symmetry of uncertainty over

the final period of observation (mid2010-mid2016).

Source: Our calculations on ISTAT Manufacturing survey-data

To show the net relationship between the uncertainty and its reference series nullifying the impact

on the measure calculation of the “Unchanged” replies, in Graph 2 the development of the indicator

obtained using the modified Bachmann’s formula12

is plotted.

At first sight, the new measure appears to swing more compared to the previous one, in particular,

until the beginning of 2008. The “corrected” indicator seems to mirror very clearly the net effect of

the changes in the “Increase” or “Decrease” fractions occurred over these first years13

. However, the

signals are not entirely clear. In fact, focusing attention on the period between 2008Q3-2009Q4, the

modified indicator decreases in conjunction with the deteriorating of the reference series (that

9 One can see European Commission (2013b).

10 European Commission (2013b).

11 That can be observed in Graph 1, Appendix A.

12 Graphs in Appendix A show, for every sector, the impact of the changes in “Unchanged” share on the uncertainty

measure obtained applying the Bachmann’s formula. 13

It is interesting to note that, just in correspondence of that period, the share of “Unchanged” replies is quite low

(between 60% and 67%). See Appendix for the “Unchanged” replies graphs.

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Graph 1: Manufacturing - Uncertainty Indicator (Bachmann's formula) and Industrial Production Index (year-on-year growth). 2003Q1-2016Q2

Uncertainty Indicator in Manufacturing Industrial Production in Manufacturing (rhs)

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reaches its lowest levels at 2009Q2), then improves, reaching its highest levels (2009Q4), exactly

while the reference series is increasing. Afterwards, it reaches a new local trough at the beginning

of 2011, in correspondence with a downward evolution of the Industrial Production Index, and then

increases again and remains broadly stable until the end of the period taken into consideration.

Overall, we can say that also the uncertainty indicator obtained applying the modified Bachmann’s

formula seems not to respect entirely the principle of symmetry of uncertainty, in particular,

between 2008Q3 and 2009Q4. In fact, it decreases when the economic evolution is negative, but not

when the latter is positive.


As it is immediately apparent in Graph 3, the uncertainty measure based on Theil’s entropy formula

exhibits a very similar path in respect to that obtained by applying the Bachmann's formula. In fact,

it presents the same asymmetric behaviour, showing a decreasing uncertainty when the situation is

clearly improving (2009Q1-2010Q2) and then an increasing when economic developments are

markedly worsening (2008Q2-2009Q1). After that, first the measure worsens until 2011Q3 and then

increases again reaching a new local peak at 2013Q4. Since then, while the IPI has already entered

in a slight upward trend, it shows a coherent and marked downward evolution.


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Graph 2: Manufacturing - Uncertainty Indicator (modified Bachmann's formula) and and Industrial Production Index. 2003Q1-2016Q2

Modified Uncertainty Indicator in Manufacturing Industrial Production in Manufacturing (rhs)

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Graph 3: Manufacturing - Uncertainty Indicator (Theil's entropy formula) and Industrial Production Index. 2003Q1-2016Q2

Uncertainty Indicator in Manufacturing Industrial Production in Manufacturing (rhs)

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A similar asymmetric relation emerges also for managers’ expectations which are part of panel of

the construction survey. Graph 4 presents the evolution of the uncertainty indicator based on the

Bachmann’s formula and the Industrial Production in Construction (which is the reference series the

survey is supposed to track). Until the beginning of 2005, the indicator presents an up and down

trend. Afterwards, the measure first decreases, while the economic situation is improving and then

enters in an upward evolution just while the reference series, on the contrary, is worsening

(2007Q3-2009Q1). Starting from mid-2010, the measure fluctuates, remaining broadly stable on

values quite high. Thus, to conclude, also in this case one can note an asymmetric reaction of the

measure: it, in fact, decreases when economic developments are improving, but does not decrease

when they are worsening. That is observable, in particular, in the first part of the time span taken

into consideration.

Source: Our calculations on ISTAT Construction survey-data

In Graph 5, the line of the indicator obtained using the modified Bachmann’s formula appears

higher in the first part of the graph compared to the previous one. It reflects the large share of

“unchanged” replies that characterizes the survey results, in particular, until 2007Q314

. Overall, also

in this case the relation between the indicator and the Production Index in construction seems not to

be in line with the principle of symmetry of uncertainty: after a first period of substantial stability,

from 2003Q1 to 2006Q4, the indicator enters in a downward path (2007Q1-2009Q1) when the

economic situation worsens. After that, the measure increases when the economic outlook becomes

more positive. Starting from the beginning of 2011, the uncertainty indicator appears more

fluctuating and enters in a downward path until the local trough at 2014Q4. That happens while the

economic situation is improving, in particular from 2013Q1.

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Graph 2 in Appendix A.

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Graph 4: Construction - Uncertainty Indicator (Bachmann's formula) and Industrial Production in Construction. 2003Q1-2016Q2

Uncertainty Indicator in Construction Industrial Production in Construction (year-on-year growth), rhs

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As Graph 6 shows, with the Theil’s formula the empirical results obtained confirm the asymmetric

relation that has been just observed in the previous graphics. Moreover, one can see that the

measure swings but remains broadly stable starting mid-2011 until the end of the time span

observed.


A slightly different picture is observable when the uncertainty indicator is based on the service

survey data (Graph 7). When the measure is elaborated using the Bachmann’s formula, the

dispersion among managers’ replies does not increase in worsening periods (between the end of

2007 and the beginning of 2009) and confirms its negative trend, finding itself at a minimum just

when the Value Added in Services reaches its maximum (2011Q1). However, again the respondents

become more discordant in their opinions about the future demand when the Value Added in the

sector shows an economic improvement (2012Q3-2013Q4). Finally, the measure returns to decrease

when the reference series enters in the final rather stable trend.

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Graph 5: Construction - Uncertainty Indicator (modified Bachmann's formula) and Industrial Production in Construction. 2003Q1-2016Q2


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Graph 6: Construction - Uncertainty Indicator (Theil's formula) and Industrial Production in Construction. 2003Q1-2016Q2


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Source: Our calculations on ISTAT Service survey-data

* Net of financial services and inclusive of the wholesale and retail trade, repair of motor vehicles and motorcycles

Graph 8 shows the uncertainty measures for the services using the modified Bachmann’s formula.

Overall, also in this case the relation between the indicator and the Value Added in services appears

different. Showing a coherent behaviour, the indicator touches its minimums (2006Q2 and 2007Q2)

when the Value Added reaches its highest levels. However, starting from 2008Q3, it remains

broadly stable on high levels independently of the movements upwards and then downwards of the

Value Added. Apparently, the managers of this sector show themselves continuously discordant in

their opinions both when the economic evolution is negative, but also when it is positive.


* Net of financial services and inclusive of the wholesale and retail trade, repair of motor vehicles and motorcycles

To conclude with the service sector, in Graph 9 the empirical results obtained employing the Theil’s

formula to work out the uncertainty measure are shown. All in all, they appear very similar to the

ones obtained using the Bachmann’s formula (Graph n° 7). Also in this case, in fact, the measure

doesn’t increase when the Value Added decreases and it goes down even while the reference series

is improving (2009Q1-2011Q1). Nevertheless, between 2012Q3 and 2014Q1, the uncertainty

measure based on the Theil’s entropy formula increases when the economic signals improve. The

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Graph 7: Services - Uncertainty Indicator (Bachmann's formula) and Value Added. 2003Q1-2016Q2

Uncertainty Indicator in Services Value added in Services* (year-on-year growth), rhs

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Graph 8: Services - Uncertainty Indicator (modified Bachmann's formula) and Value Added. 2003Q1-2016Q2


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indicator regains a symmetrical behaviour between mid-2014 and mid-2016 showing a negative

trend when a more positive economic evolution seems to become stable .

Source: Our calculations on ISTAT Service survey-data * Net of financial services and inclusive of the wholesale and retail trade, repair of motor vehicles and motorcycles

Looking at the uncertainty measure obtained applying the Bachmann’s formula on the retail trade

survey data (Graph 10), the disagreement among the retailers intensifies in the worsening period

and reaches its peak when the Final Consumption Expenditure reaches its minimum (end

2008/beginning 2009). Then, it remains broadly stable in conjunction with the improvement of the

Final Consumption Expenditure and, starting from 2011Q4, enters in an downward path. In this

period, it points out a more coherent behaviour: the indicator, in fact, does not exhibit the increase

in the uncertainty that should have been observed when the reference series started to decline

(during 2011Q3-2012Q3) and continues to decrease when the Final Consumption Expenditure goes

back to increasing (2012Q3-2016Q1).

Source: Our calculations on ISTAT Retail Trade survey-data

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Graph 9: Services - Uncertainty Indicator (Theil's formula) and Value Added. 2003Q1-2016Q2


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Graph 10: Retail Trade - Uncertainty Indicator (Bachmann's formula) and Final Consumption Expenditure. 2003Q1-2016Q2

Uncertainty Indicator in Retail Trade Final consumption expenditure (year-on-year growth), rhs

12

A slightly different relation between the retailers’ uncertainty and Final Consumption Expenditure

is pointed out by the measure based on the modified Bachmann’s formula. In Graph 11 the indicator

presents ups and downs between the end of 2006 and the end of 2008, as in the previous picture,

however, later tends not to increase too much when the Final Consumption Expenditure decreases

and remains broadly stable, even if on high levels, showing even falls when the reference series

improves (in 2010Q4 and in 2015Q4).


Finally, in Graph 12 the indicator elaborated using the Theil’s formula presents an upward trend

until 2009Q1, independently of the reference series movements. After that, it remains rather stable

between 2009Q3 and 2011Q3. Starting from 2011Q4, the indicator seems to respect the principle

of symmetry of uncertainty: it shows a downward trend until the end of the period taken into

consideration in conjunction with the ups and downs of the reference series.


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Graph 11: Retail Trade - Uncertainty Indicator (modified Bachmann's formula) and Final Consumption Expenditure. 2003Q1-2016Q2


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Graph 12: Retail Trade - Uncertainty Indicator (Theil's formula) and Final Consumption Expenditure. 2003Q1-2016Q2


13

5. Concluding remarks

This work involves the calculation of three uncertainty indicators based on Italian survey data.

The measures at sector level are evaluated against the developments of the respective reference

series, using in particular the Industrial Production Index growth for manufacturing sector, the

Production Index growth for construction sector, the Value Added growth for services and, finally,

the Final Consumption Expenditure growth for retail trade sector.

With regard to the characteristics of symmetry of the three measures, our main findings are that

the uncertainty indicator, obtained applying the Bachmann’s formula, seems not to respect entirely

the principle of symmetry of uncertainty: it tends to decrease when the economic evolution is

negative, but not when the latter is positive. However, even the modified Bachmann’s formula and

the Theil’s entropy formula provide results not completely clear. In theory, they would be more

reliable, but the empirical results actually underline behaviours irregular and only occasionally

respectful of the principle of symmetry.

Moreover, some uncertainty measures obtained seem to assume different behaviours in the

course of the period taken into consideration showing themselves more fluctuating in the first part

of the time span and more stable in the second one. This is the case, for example, of the uncertainty

measures obtained using the modified Bachmann’s formula on data of manufacturing and service

surveys (Graph 2 and Graph 8).

As for the behaviour of the uncertainty measures across Italian economic sectors, all in all, the

empirical analysis carried out on data of manufacturing, construction, service and retail trade

surveys shows rather homogeneous behaviours of the three indicators (they do not respect entirely

the principle of symmetry of uncertainty). On the base of the results, we could argue that abrupt

economic fluctuations make the Italian managers unprepared and make their views more discordant.

As a consequence, in conjunction with marked worsening and improvement economic evolutions

the uncertainty indicators present in all the economic sectors considered trends that are generally

hard to reconcile with a symmetric reaction pattern.

However, among the sectors analysed, the ones of services and of retail trade seem to present

the uncertainty indicators more respectful of the principle of symmetry of uncertainty. In particular,

the measures based on the Bachmann’s formula and on the Theil’s entropy formula seem to be the

more coherent. As regards the service sector, the measure shows a symmetric reaction in respect

with the evolution of the Value Added (which is the reference series the survey is supposed to

track) in particular between the end of 2007 and the beginning of 2011. Finally, as regards the retail

trade, the results show a good level of symmetry of uncertainty from the end of 2011 to the end of

the period taken into consideration.

14

6. References

Bachmann, R., Elstner, S. and E. R., Sims (2010), “Uncertainty and Economic Activity: Evidence

from Business Survey Data”, National Bureau of Economic Research, Working Paper No. 16143,

June. (2010, 2012 o 2013?)

Curtin, R., (2002),“Changing Sources of Economic Uncertainty”, Paper presented at 26th

CIRET

Conference, Taipei, October .

European Commission, DG ECFIN (2013a), "Assessing the impact of uncertainty on consumption

and investment", Quarterly Report on the Euro Area, Volume 12 No. 2, 7 -16.

European Commission, DG ECFIN (2013b), "Using survey data for measuring uncertainty",

European Business Cycle indicators, 3rd

quarter, ISSN: 1831 – 5704.

Friz, R. (2016), “Using survey data for measuring uncertainty. Which measure better captures

disagreement in expectations? “, presented at 33rd CIRET Conference, Copenaghen, September .

Fuss, C. and P. Vermeulen, (2004), “Firms’ investment decisions in response to demand and price

uncertainty”, European Central Bank, Working Paper Series No.347, April.

Katona, G. (1979), “Towards a Macropsychology”, American Psychologist, February, p. 122.

Lazarsfeld, P., Berelson,B. and H. Gaudet, (1948), “The People’s Choice”, Columbia University

Press, New York.

Monthly Bulletin (2013), European Central Bank, October, box No.4.

15

APPENDIX A



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Graph 1: Manufacturing - Uncertainty Indicator based on the Bachmann's formula and "Unchanged" share. 2003Q1-2016Q2

Manufacturing - Expectations on production - Unchanged (frequence of reply)

Uncertainty Indicator in Manufacturing (Bachmann's formula)

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Graph 2: Construction - Uncertainty Indicator based on Bachmann's formula and "Unchenged" share. 2003Q1-2016Q2

Construction - Expectations on employment - Unchanged (frequence of reply)

Uncertainty Indicator in Construction (Bachmann's formula)

16



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Graph 3: Services - Uncertainty Indicator based on Bachmann's formula and "Unchanged" share. 2003Q1-2016Q2

Services - Expectations on demand - Unchanged (frequence of reply)

Uncertainty Indicator in Services (Bachmann's formula)

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Graph 4: Retail Trade- Uncertainty Indicator based on Bachmann's formula and "Unchanged" share. 203Q1-2016Q2

Retail Trade - Expectations on business trend - Unchanged (frequence of reply)

Bachmann's formula Uncertainty Indicator in Retail Trade

measuring the crisis-related economic uncertainty with...

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