To Be, or Not to Be: The Role of Self-Perception in European Countries’ Performance Assessment

Abstract

Performance evaluation is commonly based on objective indicators which do not explicitly consider the role of perceptions. Especially when evaluating countries’ performance, citizens’ perceptions can influence public debate and socio-economic narratives. Since these may influence policy making and countries’ performance, perceptions should not be ignored. The objective of this article is to investigate the presence of discrepancies between objective performance and self-perception (subjective performance) among European Union countries. The aim is to raise awareness of the importance of recognizing biases in performance perception as factors that may hamper European debate, countries’ relations and, thus, the political and social sustainability of the European project. The article considers five spending areas that may influence the public opinion’s assessment about countries’ performance (education, environmental protection, health, public order and safety and social protection) and compares objective and subjective indicators for 28 EU countries from 2007 to 2017 using the distance-to-frontier score methodology. The results indicate that the discrepancies are significant, with a generalized tendency toward overestimation, especially among some Central and Northern European countries. The opposite occurred in Greece and some Eastern European countries. These results represent a starting point for grasping an undervalued aspect of the complexity of the European socio-economic environment.

1. Introduction

Performance evaluation, be it economic, social, political or environmental, is extremely important in assessing whether objectives have been achieved and whether inefficiencies or difficulties remain unsolved. In this task, a well-established methodology is recommended to be “objective”, in the sense of not being influenced by opinions, feelings and impressions. However, the importance of a “subjective” evaluation should not be underestimated. The citizens’ perception of the performance of their country vis-à-vis other countries forms public opinion and influences public and political debate, socio-economic narratives and relations among states. The issue is particularly important in the context of the European integration process, which is based on the coexistence of heterogeneous countries in both the political-economic and cultural sense.

As will be explored in Section 2, the economic literature on managerial studies, public administration and the measurement of public sector performance is deeply influenced by the recurrent epistemological debate contrasting quantitative, positivist and objective methods of investigation to narrative and subjective methodologies. However, most scholars are apparently aware that it is erroneous to reject subjective measures of performance, and the important methodological advances guaranteeing the reliability of international datasets based on subjective indicators should be pursued further. These developments seem to meet the need to improve empirical research about the role of citizens’ perceptions. This is a particularly relevant topic in European studies. Indeed, many scholars hold that the expectations and opinions of European citizens are important for the success of the European project. Moreover, trust in national governments depends on their ability to meet citizens’ expectations—for example, through the proper use of financial resources to provide public services.

On the basis of this theoretical and methodological background, the article empirically investigates whether there exist discrepancies between objective performance and self-perception (subjective performance) among European Union (EU) countries. Although the EU is united by common objectives and the effort to promote the improvement of national institutional frameworks, strong cultural heterogeneity remains a prominent feature. Our hypothesis is that this heterogeneity has an impact on the perception that citizens of different European countries have of the integration process. This perception, in turn, influences the choice of the ways to improve socio-economic performance and has important implications for countries’ mutual relations.

The methodology is described in Section 3. This article considers five spending areas that are particularly important in influencing the public opinion’s assessment about countries’ performance: education, environmental protection, health, public order and safety and social protection. The analysis considers the decade following the global financial crisis, an event with a significant impact on the European integration process and the objective and subjective performance of EU countries. The pandemic and the international crisis following the conflict in Ukraine will not be considered, since they are ongoing events whose impact on performance and perceptions needs further investigations. Objective and subjective indicators have been selected in accordance with the way that these spending areas are evaluated and described in the literature. We utilized the World Bank’s distance-to-frontier (DTF) score methodology to make these spending areas comparable among different countries over time. The DTF score methodology is an absolute score that allows for a comparison of a country’s performance against the best and the worst performance over time and for whatever indicator is used.

In Section 4, the results are presented and commented on. Section 5 concludes the article.

2. Literature Review and Conceptual Framework

2.1. Assessing Objective and Subjective Performance

Most scholars seek to use objective data in their empirical analyses to evaluate performance, rather than subjective data. The first data type is inter-observable, measurable and systematically collected, independent from investigators; the second data type derives from opinions and judgments collected through surveys, limited to a sample of the population. This distinction stresses that objective performance is the actual achievement of certain objectives or the actual capacity to accomplish tasks. Subjective performance is the perception that people have regarding the achievement of certain objectives or the ability to accomplish tasks.

The distinction between objective and subjective performance is particularly important in managerial studies (e.g., [1,2]). It is also relevant in the literature on the measurement of public sector performance, which is grappling with the recurrent epistemological debate contrasting “quantitative positivist methods” with “interpretative narrative methodologies” [3] (p. 550). In the public administration literature, subjective measures of performance are criticized not only for their alleged unreliability but also because some scholars maintain that objective performance gains are often unnoticed by citizens ([4,5]).

These debates not only concern particular research fields. They involve, in various ways, the whole economic discipline and empirical analysis. Indeed, the distinction between objective and subjective becomes crucial even in measuring the performance of institutions. In this regard, [6] (p. 2) claims that “objective measures are generally preferable over subjective measures”; indeed “institutional measures should explicitly take the factual enforcement of the respective institution into account and the measures should be as objective as possible” (p. 4). Of course, it is not always possible to rely exclusively on objective data, especially when investigating socio-political phenomena or, for example, informal institutions. In this regard, the most recurrent proxies are social capital, trust and culture. In this context, opinions, perceptions, beliefs and values play a crucial role, and subjective data are used for empirical analysis (e.g., [7,8]).

However, subjective data involve particular methodological issues which warrant caution when using them. As noted by [9] (p. 1) “analyzing comparative survey data requires the fulfillment of specific assumptions, i.e., that these values are comparable over time and across countries”. This implies that it is necessary to guarantee that people understand the survey questions in the same way and do not systematically answer the same questions differently. However, it is difficult to guarantee this cross-cultural measurement invariance: “given the large number of groups that can be compared in repeated cross-national datasets, establishing measurement invariance has been, however, considered unrealistic”. Various methodologies have been developed to guarantee the reliability of international datasets such as the European Social Survey (ESS), the European Values Study (EVS), the Executive Opinion Survey (EOS) and the World Values Survey (WVS). Important steps for the achievement of more reliable subjective data have been made by [10,11], who introduce experimentally validated survey datasets. Their Global Preference Survey dataset provides interesting insights about altruism, reciprocity, risk and time preferences and trust. Although not yet suitable for inter-temporal comparisons, the survey is promising for economic analysis [12].

2.2. Performance Evaluation within the EU: The Role of Perceptions

Objective performance evaluation in the EU is fundamental for assessing whether countries have reached goals and solved inefficiencies. However, since the EU is a project of social and political integration, the expectations and opinions of European citizens are important for the success of the European project and its long-term sustainability.

According to [13] (p. 542), public support for European integration depends on the level of trust, and “trust originates from evaluations about the (actual and perceived) performances and procedures of the European Union”. In their analysis, they compare the objective and subjective performance of national governments and find that “to an important degree, citizens’ trust in the EU can be predicted by their trust in national institutions” (p. 561).

Trust in national governments depends on their ability to meet citizens’ expectations. A particularly relevant expectation concerns the government’s ability to efficiently use the financial resources raised through taxation to provide quality public services. It is not surprising that the efficiency and effectiveness of public spending is considered fundamental for promoting economic growth, a particularly relevant topic within the EU. Indeed “improved efficiency and effectiveness of public spending not only helps maintain the fiscal discipline […] but also is instrumental in promoting the structural reform agenda” [14] (p. 2).

Measuring efficiency and effectiveness means verifying that resources are not wasted and that goals are reached. As noted by [14] (p. 2), “the measurement of efficiency and effectiveness of public spending remains a conceptual challenge”; moreover “efficiency and effectiveness are not always easy to isolate” (p. 3). In addition, the problem of cross-country comparisons is also important given that public spending differs significantly within the EU. However, “analyses based upon individual spending areas (function-by-function approach) seem to be a more promising approach to measure efficiency and effectiveness on a cross-country basis” [14] (p. 1).

The function-by-function approach is compatible with both objective and subjective data. Indeed, objective data could be used to estimate the objective performance of public spending, i.e., the concrete ability to reach some goals and standards of efficiency and effectiveness in selected spending areas. Subjective data could be used to estimate subjective performance, i.e., the perceptions of citizens and country experts regarding the ability of governments to reach some targets and comply with satisfaction criteria. Although this type of analysis is not widespread in the literature, it is possible to find some interesting contributions with reference to specific spending areas. For example, [15] analyze the effect of healthcare spending on health outcomes by comparing a worldwide sample of countries. They recognize that there is an “objective (observed) health status and subjective (perceived) health status” (p. 88) and that “subjective health status is based on health feeling self-assessment and self-reported” (p. 89). [15] (p. 90) admit that it is difficult to provide reliable measures of subjective health status because it is “strongly influenced by emotional experiences and cognitive biases created through [the] distinction between experiencing-self and remembering-self that [was] introduced by [16]”. Despite this, they find a “linkage between subjective health status and health expenditures” and, on the basis of the analysis of [17], they do not exclude the possibility that the “perception of ill health may increase with income because of more accessibility to health care and more education that makes people understand more about their health. As a result, health expenditures will be increase, although it is only loosely related to objective health”. Other recent studies have dealt with the comparison between subjective and objective performance in the healthcare sector (see, for example, [18,19]).

There are studies that have also taken into consideration other spending areas. For example, [20] (p. 126) considers police services and finds that “objective and subjective indicators that were conceptually similar were found to be associated statistically”. [21] consider citizens’ satisfaction with public schools by testing the link between official objective measures of public-school performance and the subjective satisfaction of parents. Their contribution is in line with the growing literature supporting the idea that a stronger link exists between subjective and objective measures of government performance (e.g., [22,23]) and that both subjective and objective measures of performance deserve further investigations.

Further, recent analyses confirm the importance of considering both objective and subjective data and demonstrate the current relevance of both types of measurements and evaluations in various contexts (see, for example, [24,25,26,27,28,29,30,31]).

In summary, although the use of subjective performance measures faces some relevant methodological issues, there is no doubt that citizens’ perceptions cannot be neglected. The literature recognizes the role of self-perception in many contexts: from socio-economic policies to electoral behavior (e.g., [32,33,34]) and investment decisions (e.g., [35,36]).

The solidity of each national government and, consequently, of each supranational integration project depends not only on objective performance but also on the expectations of citizens and their level of trust. Although these expectations and perceptions can be distorted by contextual or cultural factors, they should be taken into account because they influence public debate and relations between citizens and governments.

3. Data and Methodology

Based on the theoretical background discussed in Section 2, and considering five relevant spending areas, the article empirically investigates whether there are relevant differences between objective performance and self-perception (subjective performance) among EU countries. Our hypothesis is that the strong institutional and cultural heterogeneity that characterizes the EU may determine differences in performance perception and that these differences may be useful for better understanding EU debates. This section will discuss the methodology used in the empirical investigation.

3.1. From the Definition of the Spending Areas to the Selection of the Indicators

In the empirical analysis, objective data aimed at measuring objective public performance in selected key spending areas are compared with subjective data. The latter aim at measuring the degree of satisfaction and the perceptions of citizens regarding performance in the selected areas.

Only those spending areas that are particularly important in influencing the public opinion’s assessment about countries’ performance are considered. These spending areas are: education, environment protection, health, public order and safety and social protection. They are five out of the ten functions identified by the classification of the Functions of Government (COFOG), developed within the framework of the European System of National Accounts (ESA2010) by OECD.

Objective and subjective indicators have been selected for each spending area. Although objective and subjective indicators sometimes do not measure the same concept, we expect that they do not generate considerable conflicting results if the topic is chosen with care. In other words, and in accordance with the literature discussed in Section 2, we expect that the subjective judgment of the quality of a given spending area is not systematically in sharp contrast with an objective assessment of its performance.

The purpose of the analysis is to compare objective and subjective performance within the EU. Although European countries are also internally heterogeneous, we take into consideration objective and subjective indicators at the national level. This is because this analysis aims to understand the perception that citizens have of their own country, leaving a more detailed analysis of the regional dynamics to future research.

Indicators have been selected according to the criteria reported in

Table 1. Definition of the spending areas: objective and subjective perspectives.

	Objective	Subjective
public order and safety	recorded crimes by type	safety perception, criminal events reported by the population
health	medical equipment, hospital beds, ability to cope with diseases and prevent deaths	satisfaction with the health system, accessibility/affordability of services
education	educational qualifications, quality of school results and researchers	satisfaction with the education system, ability to meet the needs of society
social protection	working and living conditions, poverty	social tensions, perceived poverty and standard of living
environmental protection	emissions, waste, recycling, renewable energy	satisfaction with the government’s commitment to protect the environment

, which describe each spending area from an objective and subjective point of view. These descriptions are consistent with the way that these spending areas are assessed in the literature. The list of the indicators chosen is reported in Appendix A (see

Table A2. List of indicators used in the spending area of public order and safety.

Type	Series Name	Sources and Dataset	Description
objective	Recorded crimes police data: Intentional or attempted homicide	Eurostat. Data coverage: 2008–2016.	Data per one-hundred-thousand inhabitants.
	Recorded crimes police data: Assault and kidnapping	Eurostat. Data coverage: 2008–2016.	Data per one-hundred-thousand inhabitants.
	Recorded crimes police data: Sexual violence, rape or sexual assault	Eurostat. Data coverage: 2008–2016.	Data per one-hundred-thousand inhabitants.
	Recorded crimes police data: Robbery	Eurostat. Data coverage: 2008–2016.	Data per one-hundred-thousand inhabitants.
	Recorded crimes police data: Burglary and theft	Eurostat. Data coverage: 2008–2016.	Burglary, burglary of private residential premises, theft, theft of a motorized land vehicle. Data per one-hundred-thousand inhabitants.
	Recorded crimes police data: Unlawful acts involving controlled drugs or precursors	Eurostat. Data coverage: 2008–2016.	Data per one-hundred-thousand inhabitants.
subjective	Reliability of police services	Global Competitiveness Index, World Economic Forum, Executive Opinion Survey. Data coverage: 2007–2017.	In your country, to what extent can police services be relied upon to enforce law and order? [1 = not at all; 7 = to a great extent].
	Safe Living Conditions	Sustainable Governance Indicators (SGI). SGI Survey. Data coverage: 2014–2017.	Qualitative indicator (phrased as a question) administered to country experts. These experts provide a written assessment and score, which are scaled from 1 (worst) to 10 (best). There are four response options for each indicator. Question: How effectively does internal security policy protect citizens against security risks?
	Population reporting the occurrence of crime, violence or vandalism in their area	Eurostat. Data coverage: 2007–2017.	The indicator shows the share of the population that reported that they face the problem of crime, violence or vandalism in their local area (% of population). This describes the situation where the respondent feels that crime, violence or vandalism in the area is a problem for the household, although this perception is not necessarily based on personal experience.

,

Table A3. List of indicators used in the spending area of health.

Type	Series Name	Sources and Dataset	Description
objective	Medical technology facilities	Eurostat. Data coverage: 2008–2017.	The series is the result of the average number of different medical facilities (Computed Tomography Scanners, Magnetic Resonance Imaging Units, Gamma Cameras, Angiography Units and Lithotriptors) per one-hundred-thousand inhabitants.
	Available beds in hospitals	Eurostat. Data coverage: 2007–2016.	Available beds in hospitals per one-hundred-thousand inhabitants.
	Infant mortality, deaths/1000 live births	Global Competitiveness Index Data coverage: 2007–2017.	Infant (children aged 0–12 months) mortality per 1000 live births. Infant mortality rate is the number of infants dying before reaching one year of age per 1000 live births in a given year.
	Deaths related to infectious diseases	Eurostat. Data coverage: 2011–2017.	The series is the result of the elaboration of the Eurostat series about infectious diseases: certain infectious diseases (A00-A40, A42-B99), other sepsis, other infectious diseases, pneumonia, organism unspecified and total population. This crude death rate describes mortality in relation to the total population, expressed in deaths per 100,000 inhabitants.
	Amenable and preventable deaths of residents	Eurostat. Data coverage: 2011–2015.	The series is the result of the elaboration of three Eurostat series: amenable deaths, preventable deaths and total population. This crude death rate describes mortality in relation to the total population, expressed in deaths per 100,000 inhabitants.
subjective	Cost of seeing the doctor	Eurofound. European Quality of Life Survey. Data coverage: 2007, 2011, 2016.	Percentages of people per country who answered “Very difficult” when asked “Difficulty: cost of seeing the doctor”.
	Delay in getting an appointment for the doctor	Eurofound. European Quality of Life Survey. Data coverage: 2007, 2011, 2016.	Percentages of people per country who answered “Very difficult” when asked “Difficulty: delay in getting an appointment for the doctor”.
	Distance to the doctor’s office	Eurofound. European Quality of Life Survey. Data coverage: 2007, 2011, 2016.	Percentages of people per country who answered “Very difficult” when asked “Difficulty: distance to doctor’s office (or hospital for 2011 and 2007)”.
	Quality of health services	Eurofound. European Quality of Life Survey. Data coverage: 2007, 2011, 2016.	Mean score (0–10) expressed by people when asked “How would you rate the quality of health services in your country?”.

,

Table A4. List of indicators used in the spending area of education.

Type	Series Name	Sources and Dataset	Description
objective	Upper Secondary Attainment	Sustainable Governance Indicators (SGI). Eurostat. Data coverage: 2014–2017.	Population with at least upper-secondary attainment (ISCED 3 and above); age group: 25–64 years. Percentage values.
	Tertiary Attainment	Sustainable Governance Indicators (SGI). Eurostat. Data coverage: 2014–2017.	Population with tertiary attainment (ISCED 5 and above); age group: 25–64 years. Percentage values.
	PISA results	Sustainable Governance Indicators (SGI). OECD PISA. Data coverage: 2014–2017.	PISA results, mean of scores on the reading, mathematics and science scales.
	Total Researchers	Sustainable Governance Indicators (SGI). Eurostat. Data coverage: 2014–2017.	Total researchers per 1000 employees (full-time equivalents).
subjective	Quality of the education system and needs of a competitive economy	Global Competitiveness Index, UNESCO Institute for Statistics. Data coverage: 2007–2017.	In your country, how well does the education system meet the needs of a competitive economy? [1 = not well at all; 7 = extremely well].
	Quality of math and science education	Global Competitiveness Index, UNESCO Institute for Statistics. Data coverage: 2007–2017.	In your country, how do you assess the quality of math and science education? [1 = extremely poor—among the worst in the world; 7 = excellent—among the best in the world].
	Quality of scientific research institutions	Global Competitiveness Index, World Economic Forum, Executive Opinion Survey. Data coverage: 2007–2017.	In your country, how do you assess the quality of scientific research institutions? [1 = extremely poor—among the worst in the world; 7 = extremely good—among the best in the world].
	University–industry collaboration in R&D	Global Competitiveness Index, World Economic Forum, Executive Opinion Survey. Data coverage: 2007–2017.	In your country, to what extent do businesses and universities collaborate on research and development (R&D)? [1 = do not collaborate at all; 7 = collaborate extensively].
	Rating the quality of the education system	Eurofound. European Quality of Life Survey. Data coverage: 2007, 2011, 2016.	Mean score (0–10) expressed by people when asked “How would you rate the quality of the education system in your country?”

,

Table A5. List of indicators used in the spending area of social protection.

Type	Series Name	Sources and Dataset	Description
objective	Poverty Rate	Sustainable Governance Indicators (SGI). Eurostat. Data coverage: 2014–2017.	Poverty rate, total population, cut-off point—50 percent of median equivalized disposable income. Percentage values.
	Low Pay Incidence	Sustainable Governance Indicators (SGI). Eurostat. Data coverage: 2014–2017.	Share of workers earning less than 2/3 of median earnings. Percentage values.
	Living conditions (house)	EU-SILC survey, Eurostat. Data coverage: 2007–2017.	Total population living in a dwelling with a leaking roof, damp walls, floors or foundation or rot in the window frames or floor. Percentage values.
	Severe material deprivation rate	EU-SILC survey, Eurostat. Data coverage: 2007–2017.	Inability to afford some items considered by most people to be desirable or even necessary to lead an adequate life. The indicator distinguishes between individuals who cannot afford a certain good or service and those who do not have this good or service for another reason, e.g., because they do not want or do not need it. Severe material deprivation rate is defined as the enforced inability to pay for at least four of the deprivation items.
subjective	Pay and productivity	Global Competitiveness Index, World Economic Forum, Executive Opinion Survey. Data coverage: 2007–2017.	In your country, to what extent is pay related to employee productivity? [1 = not at all; 7 = to a great extent].
	Perceived tension between poor and rich people	Eurofound. European Quality of Life Survey. Data coverage: 2007, 2011, 2016.	Percentages of people per country who answered to have perceived “a lot of tension” between poor and rich people.
	Households can afford meat and fish	Eurofound. European Quality of Life Survey. Data coverage: 2007, 2011, 2016.	Percentages of people per country who answered “Yes, can afford it if I want” when asked “Can your household afford a meal with meat, chicken or fish every second day (if you wanted it)?”
	Satisfaction with present standard of living	Eurofound. European Quality of Life Survey. Data coverage: 2007, 2011, 2016.	Mean score (0–10) expressed by people when asked “Satisfaction with present standard of living”.
	Households that can afford to replace worn-out furniture	Eurofound. European Quality of Life Survey. Data coverage: 2007, 2011, 2016.	Percentages of people per country who answered “Yes, can afford it if I want” when asked “Can you afford to replace any worn-out furniture?”

and

Table A6. List of indicators used in the spending area of environmental protection.

Type	Series Name	Sources and Dataset	Description
objective	Generation of waste	Eurostat. Data coverage: 2008, 2010, 2012, 2014, 2016.	Generation of waste excluding major mineral wastes by hazardousness (kg per capita—total hazardous and non-hazardous) generated in a country. Due to the strong fluctuations in waste generation in the mining and construction sectors and their limited data quality and comparability, major mineral wastes, dredging spoils and soils are excluded. This exclusion enhances comparability across countries, as mineral waste accounts for high quantities in some countries with important economic activities such as mining and construction.
	Material recycling	Sustainable Governance Indicators (SGI). Eurostat Online Database. Data coverage: 2014–2017.	Proportion of municipal waste recovered by material recycling. Percentage values.
	Renewable energy	SGI. World Bank Sustainable Energy Database. Data coverage: 2014–2017	Share of renewable energy in total final energy consumption. Percentage values.
	Greenhouse gas emissions	SGI, UNFCCC, World Bank World Development Indicators. Data coverage: 2014–2017.	Greenhouse gas emissions, tonnes in CO2 equivalents per capita, including land use, land-use change and forestry and indirect CO2.
subjective	Government protection of the environment	Special Eurobarometer 416 and 468. Data coverage: 2014 and 2017	Attitudes of European citizens towards the environment. “In your opinion, is each of the following currently doing too much, doing about the right amount or not doing enough to protect the environment?” Answer: “Not doing enough” (with reference to the government).
	Environmental policy effectiveness	SGI Survey. Data coverage: 2014–2017.	Qualitative indicator (phrased as a question) administered to country experts. These experts provide a written assessment and score, which are scaled from 1 (worst) to 10 (best). There are four response options for each indicator. Question: “How effectively does environmental policy protect and preserve the sustainability of natural resources and the quality of the environment?”
	Contribution to global environmental policy	SGI Survey. Data coverage: 2014–2017.	Qualitative indicator (phrased as a question) administered to country experts. These experts provide a written assessment and score, which are scaled from 1 (worst) to 10 (best). There are four response options for each indicator. Question: “To what extent does the government actively contribute to the design and advancement of global environmental protection regimes?”

). Since various databases combine data from surveys, data collected in the registers or administrative data (e.g., the EU Statistics on Income and Living Conditions EU-SILC), subjective indicators are those for which it is possible to trace a clear question posed to the interviewee. Alternatively, it should be clear that the indicator depends significantly on the opinion of the interviewees. It is worth remembering that, although there are important databases measuring the perceptions of citizens in various areas (e.g., EVS), only databases capable of providing data for all countries of the EU and for at least two periods from 2007 to 2017 are chosen.

3.2. Comparing Indicators and Countries: The Distance-to-Frontier Score Methodology

We utilize the distance to frontier (DTF) score methodology to make these spending areas and countries comparable. The DTF score methodology was developed by the World Bank (see [37]) and is used in the Doing Business project. It is an absolute score that allows for comparisons over time, for whatever indicator, of a country’s performance against the best and worst performance. DTF scores are widely used in the economic literature for country comparisons in different contexts (e.g., [38,39,40,41]). DTF scores range from a minimum score of zero (worst performance) to a maximum score of 100 (best performance, the frontier). The frontier represents, for each indicator, a situation of efficient and effective public spending that is coherent with the potential performance of all member countries. This implies that the frontier does not necessarily match the performance of a particular country. The computation of the DTF scores considers the data of all 28 European member countries in the period 2007–2017. Indicators are based on different units of measurement (e.g., percentages, quantities, billions, years). They are normalized to a common unit of measurement, and each component indicator y is rescaled using linear transformation (worst–y)/(worst–frontier). The scores obtained for each indicator are aggregated into one DTF score for each spending area, country and period using the geometric mean in order to avoid the poor performance of one indicator being compensated by a better performance of another indicator. Missing data are handled according to the methodology suggested in the Asia-Pacific Trade and Investment Report of the United Nations Economic and Social Commission for Asia and the Pacific (ESCAP): when possible, missing values are imputed using linear interpolation, the nearest non-missing value is used in the other cases; in any case, no information is taken from other countries for imputing the missing values (see [42], p. 182). See [43] for more details about the DTF score methodology, computation and its use for comparisons among European countries.

4. Results and Discussion

4.1. Comparing Objective and Subjective DTF Scores

Figure 1 reports the average DTF scores with reference to subjective and objective data for each spending area and each country (see Appendix A 

Table A1. European member countries’ codes.

Code	European Member Country
AUT	Austria
BEL	Belgium
BGR	Bulgaria
CYP	Cyprus
CZE	Czech Republic
DEU	Germany
DNK	Denmark
ESP	Spain
EST	Estonia
FIN	Finland
FRA	France
GBR	United Kingdom
GRC	Greece
HRV	Croatia
HUN	Hungary
IRL	Ireland
ITA	Italy
LTU	Lithuania
LUX	Luxembourg
LVA	Latvia
MLT	Malta
NLD	Netherlands
POL	Poland
PRT	Portugal
ROU	Romania
SLO	Slovenia
SVK	Slovak Republic
SWE	Sweden

for country codes; all figures have been produced using MATLAB). The quadrants allow for an evaluation of the DTF scores (greater or lower than 50), while the bisector allows for a distinction between cases of an overestimation of performance (subjective performance greater than objective performance) and cases of an underestimation of performance.

A significant dispersion can be noted among European countries in terms of objective and subjective performance. In terms of objective performance, many Eastern European and some Mediterranean countries obtain high DTF scores only in the order and safety area, and, surprisingly, it is only in this area that there is a sharp and generalized underestimation of their performance. The opposite happens for some Central and Northern European countries that overestimate their performance in the order and safety area. There are also significant phenomena of the overestimation of performance in the health and environment protection area, as well as in the education area for some Eastern European and Mediterranean countries. It seems that countries with similar institutional frameworks exhibit quite similar combinations of objective and subjective performance, although some differences persist. An important aspect is that, when considering the average DTF scores for all areas (last plot in Figure 1), there seems to be greater dispersion in terms of subjective performance vs. objective performance.

Figure 2 considers the differences between the subjective and objective DTF scores for each spending area, and on average (average all years). The black horizontal line indicates a value equal to zero, i.e., when the objective performance coincides with the subjective one. Figure 2 graphically represents how some countries tend to exhibit a tendency to overestimate or underestimate performance in some spending areas. The most evident examples are the overestimation by the Netherlands and the underestimation by Greece. On average, Belgium, Denmark, Luxembourg and Sweden are also interesting cases of overestimation of performance, while the opposite occurs in Bulgaria and Hungary, in particular.

4.2. Time Trends

Figure 3 presents an overview of the extent of the overvaluation or underestimation of performance by considering the average of all spending areas and comparing the average values for 2007–2017 with the values at the beginning and at the end of the period. The overestimation of performance is most evident among the countries of Northern and Central Europe. Estonia, Malta, Portugal, Romania and Spain should be added to this group, although, in their case, the overestimation is less intense compared to Denmark, Luxembourg, the Netherlands and Sweden. The overestimation (underestimation) of performance seems to be a constant phenomenon over time, as the comparison between the average scores of 2007 and 2017 demonstrates. The Greek case is also interesting: starting from a marked underestimation of performance, Greece significantly worsened this underestimation. Conversely, Bulgaria, Croatia, Hungary, Italy, Latvia, the Slovak Republic and Slovenia reduced their underestimation of performance. The Austrian, Finnish, French and Lithuanian cases show rather moderate over/underestimation phenomena compared to other countries.

Noteworthy is that Figure 3 does not allow for an assessment of the determinants of these over/underestimations. Indeed, overestimation can be caused by an unperceived worsening of objective performance or a new optimistic perception of an unaltered objective performance. The opposite is true in the case of underestimation. To verify these trends, Figure 4 represents how objective and subjective performance changed from the first to the last period considered (see Figure 5 for an analysis of the single spending areas).

Greece displays a collapse in subjective performance against a slightly decreased objective performance. This seems to show that the collapse in terms of perception and, therefore, confidence has been rapid and dramatic, although the impact of the crisis on these spending areas is not yet fully evident and is rather limited. A similar dynamic has been experienced in Cyprus.

Most Northern and Central European countries show little variation. Interesting exceptions are Ireland, Luxembourg and the Netherlands, which have further increased their overestimation while experiencing a worsening of their objective performance. Similar are the situations of Bulgaria, Hungary and Italy. In general, objective performance appears to have changed less intensely than subjective performance.

4.3. Objective and Subjective Performance and Spending Profiles: Some Notes

It is interesting to analyze, in Figure 6, the public spending profiles of the various European countries. There seems to be a positive correlation between the level of government expenditure and the objective performance in all spending areas. The health spending area is quite puzzling: although Denmark, the Netherlands and Sweden invest more than the average of other countries, their objective performance is, on average, modest. In the environmental spending area, the objective performance of countries such as Austria, Denmark, Finland and Sweden is better than that of many other countries, in spite of these countries investing less. It is interesting to note how many countries have decreased their expenditure in the environmental protection area, starting from a very low percentage of GDP.

It is clear that, according to objective performance, most Northern and Central European countries should invest more, or more effectively, into the order and safety spending area, while the remaining countries should invest more or more efficiently in all other spending areas. The generalized overestimation of performance may have prevented that social pressure and could lead policy makers to pay more attention to important spending areas. In addition, it should be considered that, during the European sovereign debt crisis, many countries have been forced to adopt austerity policies. This led to the shrinking of important public spending areas, a phenomenon that can be seen in Figure 6.

4.4. Grasping the Role and the Roots of the Over/Underestimation of Performance

The comparison between objective and subjective performance revealed the presence of discrepancies. This phenomenon is relevant not because such differences exist but because these differences are often significant and concern several spending areas. The discrepancy seems to indicate a possible systematic tendency to overestimate or underestimate performance, and both tendencies can cause problems. Indeed, a systematic overestimation of performance can influence public opinion, induce policy makers in a country to underestimate problems and invest insufficiently or ineffectively in some sectors and lead to a distorted image of both one’s own country and other countries. A systematic underestimation of performance indicates a probable latent distrust in institutions that may hinder growth prospects. Greece is probably a case of systematic underestimation of performance, while the Netherlands is an example (with Luxembourg) of systematic overestimation of performance, which seems to persist despite the worsening of objective performance.

Overestimating performance may be associated with strong confidence in the validity of a country’s socio-economic model. However, overestimation may also be connected to a self-perception bias or self-enhancement bias, which can be recognized as the “the tendency to emphasize or exaggerate one’s desirable qualities relative to other people’s” [44] (p. 1254), with potential discrimination effects towards other countries. There is a wide social psychology literature that analyzes how the desire to self-enhance varies among countries. As noted by [44] (p. 1254) “the magnitude of this bias varies across cultures, with people reporting higher levels of self-enhancement in some nations (e.g., the United States) than in others (e.g., Japan)”, and they hold that some economic dimensions such as income inequality may also influence self-enhancement. If we observe our results regarding the EU, it is not surprising that the two countries not belonging to the Eurozone (Sweden and the UK) have high average levels of overestimation of performance. The UK, in particular, is now out of the EU as a result of Brexit. This could mean that the overestimation of performance may make it difficult to participate in a complex integration process such as the European case.

The role of national stereotypes should not be forgotten in this context. Stereotypes influence behavior, the perception of others and self-perception. As confirmed by [45] (p. 81): “Social psychologists have a long history of studying stereotypes and their effects on judgment and behavior. […] stereotypes people have about others can influence how those others are treated and in turn elicit particular behaviors from the others that are consistent with those stereotypes [46]. In addition, stereotypes can exert a direct influence on the stereotype holder. In particular, activation of a stereotype can cause people to act in a manner consistent with the stereotype [47], regardless of whether they are members of the stereotyped group or not [48]”.

National stereotypes “can be thought of as the correlation between trait dimensions and national affiliations” [49] (p. 213) and have been studied as part of accentuation theory since the late 1950s. National stereotypes play a role not only in political geography and psychology (e.g., [50]) but also within economics, including their impact on discriminating behavior in the workplace (e.g., [49]) and their influence on product evaluation (e.g., [51]) and product country image (e.g., [52]).

Some scholars have explicitly mentioned the role of stereotypes within the EU and their role before and during the crisis [53,54]. In particular, the stereotype of lazy Southern Europeans played an important role during the sovereign debt crisis [53]. According to [55] (p. 132), “the image of the Southerner as a lazy consumer of free time was also a fruit of the industrious revolution”, and “in order to look for the history of the construction of the Southerner as a lazy consumer of a surplus of time and goods, we shall turn from economic history to cultural history”. However, as confirmed by [56], the myth of the “lazy Southern Europeans” is not supported by economic data. We cannot exclude the possibility that these factors contributed to increasing the overestimation of performance in some countries of Northern and Central Europe and damaged the self-perception of peripheral countries such as Greece.

The outbreak of the COVID-19 emergency seems to demonstrate that national stereotypes continue to matter within the EU [57]. Indeed, there has been a strong contrast between the so-called frugal countries (i.e., Austria, Denmark, Sweden and the Netherlands) and the Southern countries, despite the French–German support for the introduction of a recovery plan. This contrast was substantially due to the fear that European resources would have been wasted by the irresponsible and excessive spending of Southern countries [57]. However, as the Spanish Minister of Foreign Affairs, Arancha González Laya, noted in May 2020, “this is not a battle between some frugal countries, and some free-spending countries. The battle today is between a swift recovery that generates jobs or a long recession that creates pain” [58]. The long and difficult negotiations that led to the approval of the Recovery Fund in July 2020 demonstrate that national stereotypes and narratives matter and can create distrust among member countries if they are not taken seriously.

Overestimating performance may appear to be associated with a good objective performance, which probably causes the public to have greater confidence in the socio-economic model. This statement is not fully confirmed by the data. For example, the country with the best objective performance in the average of all the spending areas, Finland, has a subjective performance similar to the objective one. Mediterranean countries perceive order and safety as problematic in spite of the low incidence of crimes, while the opposite occurs in most core countries of Northern Europe. This could be influenced by cultural factors that induce politicians and the public to remove issues incompatible with the perception of their own social model, as appears to be the case in Sweden (see [59]). This tendency seems to be reflected in the public spending profiles, as shown in Figure 6.

With reference to the health spending area, many countries seem to overestimate their healthcare system. Many countries invest relatively little in healthcare despite their modest objective performance. This could be due to the fact that many healthcare systems in Europe are accessible and affordable, and few people are able to judge their true efficiency and effectiveness in dealing with disease and preventing deaths through adequate structures and facilities. After years of austerity and cuts in public spending, the COVID-19 emergency severely tested the healthcare systems of many European countries. This was not an entirely unpredictable emergency, given the previous cases of Sars (2002) and H1N1 (2009) that had already prompted the World Health Organization to recommend that countries develop adequate emergency plans to deal with potential pandemics on a global scale. The data seem to confirm that these warnings have been underestimated.

Similar observations can also be made with reference to the spending areas of education and social protection. In these cases, some Mediterranean and Eastern European countries seem to not be very aware of the problems of the educational and social system and invest little (or cannot invest more) in these sectors.

5. Conclusions

The article presents an empirical comparison between objective and perceived performance among European member countries. Our analysis is limited to five spending areas from 2007 to 2017. The comparison between objective and subjective performance has long been at the center of a debate in the literature and has highlighted that various methodological problems are linked to the use of subjective data. However, many scholars recognize the importance of subjective analysis for the correct understanding of the role of public perception in performance evaluation and its social and political influence.

According to the results of our analysis, there are cases of strong overestimation and underestimation of performance. There are good reasons to assume that both may have negative effects. Underestimation, as in the case of Greece, can hide a chronic mistrust in institutions that can damage the socio-economic environment and future growth prospects. Overestimation, although it may be an indication of self-esteem and self-enhancement, can make it difficult to correctly compare with other European countries and to share a common integration project. According to our empirical findings, the countries that, on average, overestimate their performance most are the Netherlands, Luxembourg, Denmark and Sweden.

Denmark, the Netherlands and Sweden are part of that group of so-called frugal countries that have opposed the Recovery Fund as a response to the COVID-19 emergency. Compared to the European sovereign debt crisis, the COVID-19 crisis represents a shock for which no member country can be considered responsible. Nevertheless, the narrative that aims to contrast the efficiency of the North with the wastefulness of the South reappeared and played a role in the approval process of the Recovery Fund [60]. [61] (p. 288) notes that the hostility that emerged between the European core and the periphery countries can be recognized as a sort of “second asymmetry”. This asymmetry had a considerable impact not only during the European sovereign debt crisis but also during the COVID-19 emergency.

Our analysis shows that we cannot exclude the possibility that this asymmetry is influenced by the perception that countries have of themselves and other European countries. When analyzing the objective average performance of core countries compared to peripheral countries, although it is true that some of the former perform better than some of the latter, it is clear that core countries also face serious challenges. We can see a greater dispersion of subjective performance compared to objective performance when considering the average DTF scores for all areas. Moreover, many peripheral countries are not investing enough in important spending areas, with negative repercussions on objective and subjective performance. The austerity policies of recent years have certainly played a role in this dynamic, an issue that needs further investigations.

Our research framework is not without limitations either, and various aspects need to be further explored. Most importantly, the choice of objective and subjective indicators, albeit oriented by the literature, should be compared with other possible sets of indicators. This analysis should not be limited to the period 2007–2017 but should also be carried out for longer periods. The time horizon is a crucial aspect: while objective indicators have an analytical value that does not change dramatically over time, subjective indicators are sensitive to social and political events and changes. Our analysis appears to testify to major variability in time in terms of subjective performance. Further research is needed to investigate this aspect.

The main contribution of this article is to raise awareness among scholars and policy makers about the importance of recognizing biases in performance perception as factors that may hamper the European debate, countries’ relations and, consequently, the political and social sustainability of the European project in the long term. It is urgent to deepen our methodological ability to compare objective and subjective indicators because discrepancies in performance evaluation exist and seem to be significant, as our empirical results show.

Overall, the Recovery plan for Europe, NextGenerationEU and the temporary suspension of the Stability and Growth Pact (extended to the end of 2023) can represent important opportunities to pursue a better understanding of the real problems and needs of EU countries. These actions are important because they can contribute to improving objective performance. At the same time, it is important to not underestimate the role of subjective performance, self-perception and national stereotypes, which have greatly influenced and will continue to influence the European public debate and decision making.