WHAT STATISTICS HIDE? SECONDARY ANALYSIS OF UNEMPLOYMENT IN SPAIN
DIANA AMBER University of Jaén, Spain
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Official statistical sources report an alarming unemployment rate among people over 45 years old in Spain. However, much of the information is either silenced or requires previously nonexistent secondary analyses, that which facilitates the social lessening of the problems, making it a worthwhile endeavour to critically analyse both the data and their weaknesses and gaps. This study performs a secondary analysis of the data of the National Institute of Statistics on unemployment in this collective. The results provide: a general profile of unemployed people over 45 years old; an educational profile of unemployed people over 45 years old; and the silenced data by the sources. The study concludes that the information offered does not allow us to refine and identify all the indicators that determine employability. Nationality, educational level and sex are identified as main factors of employment.
Key words: Secondary analysis, Over 45 years old, Unemployment, Statistics
JESUS DOMINGO University of Granada, Spain
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Diana Amber, Jesús Domingo
PRESENTATION AND JUSTIFICATION OF THE RESEARCH PROBLEM1
Unemployment is currently a social scourge common to the countries of the Euro-Mediterranean region (Genaro and González 2014). In particular, it affects more than 5 million people in Spain, according to the data from the Active Population Survey (EPA) of the National Institute of Statistics for the first trimester of 2015 (INE 2015a). The high rates of unemployment point to the existence of a serious social problem, but they are just the "tip of the iceberg" (Martos and Domingo 2011) of a much more complex and profound problem that involves specific people and cases. The economic crisis and unemployment are especially cruel to some collectives. This is the case of people over 45 years old, whose rate of unemployment has quadrupled in recent years according to official statistical sources. For this reason, they are officially considered a high-priority group. However, the group of older people is not homogeneous, and not all of them experience employment to the same degree. Therefore, it is necessary to consider a set of contingencies that describe subprofiles with different degrees of vulnerability.
Statistics allow us to have a general idea of the basic characteristics of the population with regard to each aspect being studied, organizing and synthesizing the information until reaching approachable values. Moreover, they are a tool that allows us to "quantify uncertainty" (Monleón-Getino 2010), predicting the probability that a certain event will occur. This predictive property of statistics can be useful in the study of unemployment, as it will determine which profiles are most likely to be excluded from the job market.
Understanding the limitations of the statistics, the search for reality based on the complexity paradigm (Morin 2008) leads us, from one of its perspectives, to the secondary analysis of the data on unemployment of people over 45 years old as a part of the complex framework the situation creates. This collective is characterized by situations of economic and emotional
1 The study was finished in June 2015
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instability (McKee-Ryan, Song, Wanberg, and Kinicki 2005), which are increased by their stigmatization (Mazibuko 2014), the resistance of many companies to hire them, social deafness and lack of interest in their problem, even by research (Izquierdo and López 2013).
By examining the information offered by the statistics on these individuals, this study contributes to clarifying the employment data and the profiles most affected by this situation.
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LITERATURE REVIEW
Large numbers and statistical data give readers a sense of security because they usually trust in their accuracy. However, the numbers can be used to conceal information (through omission, categorizing, generalizing, relativizing, etc.), which may not be noticed by innocent interpreters who accept them without question.
This turns them into a tool with a great political and economic interest, as Brachinger (2007) confirms, who deliberates on the veracity of the statistical data and its usage's suitability, on account of the reductionism required which Statistics implies in contrast to the complexity of the social difficulties. With the purpose of simplify complex realities, statistics categorize the particular cases, which stay masked as cold numbers that show general data, leaving out the particular casuistries. This peculiarity proper to statistics and quantitative data, linked to the fact that they allow using strategies and artifices to their analysis and presentation, can mislead the population which interpret them or intentionally guide their interpretations, trying to find a way to lessen this problem. Although the statistical data are available on the Internet, interpreting and searching for information on the topic of our study is a complex process (Peset and Fernández-López 2014). This study focuses on interpreting the data on unemployed people over 45 years old presented by the Active Population Survey. Thus, in order to enhance the comprehension of the numbers analysed, it is necessary to start with the definition of the terminology used by this source.
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The previously mentioned survey produces a system of population categories related to the job market, based on the employment situation of people over 16 years old. Thus, the population is divided into "active" and "inactive". On the one hand, the active population includes people who are working (for themselves or for others) and the unemployed, understood as those who do not have jobs, but are actively looking and available for work (INE 2015b).
On the other hand, the inactive population consists of people who are not classified as unemployed or employed. People who find themselves in one of the following situations are included in this category: those who take care of their homes; students; retired or people or those with early retirement; people who receive a pension other than the one for retired or pre-retired people; people who perform voluntary social work, charity activities, etc.; those who are incapable of working; among others (INE 2015b). This terminological categorization leaves a lot of people who are unemployed or in precarious situations out of the unemployment statistics, that which helps to silence vulnerability (Barton 2008; Füredi 2004; Kihato 2007; López-Fogués 2014; Luengo 2007).
In this terminological framework, the collective of unemployed people over 45 years old has a lacking social visibility (Amber and Domingo 2015), even being one of the age ranges about which long-term unemployment affects with higher harshness (Rubio 2013). The prolonged unemployment situation, together with the fact that these people used to take responsibility for their families sustenance (Arnal, Finkel, and Parra 2013), affects negatively to their personal motivation, which in turn reduces the possibilities to access the labour market (Izquierdo 2012; Piqueras, Rodríguez, and Rueda 2008). The situation gets worse as for women, who find greater obstacles to access to the employment (Poveda 2006), despite the fact that they show a slightly higher tendency to continuous training than men (Pineda 2007).
Training, as a key to access to employment (Nodaa and Kim 2013; Schömann 2011), goes against older unemployees, as their early training is away from the current requirements of an
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ever changing labour market. In addition, digital divide (Castells 1998) creates impassable virtual barriers to access employment. Within this demanding labour context, lifelong learning becomes a necessity (more than an alternative) for those who want to be part of the labour market. However, there is a clear tendency, on the part of the most trained, towards their training's improvement known as Mateo Effect (Bonitz 2005; Jiménez 2009), which is not habitually shared by the fewer trained, accentuating the lack of training as a labour exclusion factor (Subirats 2004).
Facing this panorama, the study agrees with Touraine's proposals (2005) by looking for new ways to understand and relocate the different social or cultural realities from a perspective of the right to "be" with "dignity".
METHODOLOGY
This study is carried out using a quantitative approach with interpretative and hermeneutic nuances, in order to extract the essence contained in the statistical data on unemployment in people over 45 years old. However, from the moment when the informants are selected and the statistical data are presented, it is possible to mask or slant the information or make interpretation errors (Garavito 2012), so that other complementary approaches are needed.
To this end, and based on the information offered by the EPA, a secondary analysis of the data is carried out (Thorne 2005), understood as "all the posterior analyses of a set of primary data that offer additional interpretations and conclusions or in a different way from the one presented in the original research report" (Sierra-Bravo 2003, 292). The baseline information is used to look for tendencies and profiles that define the probabilities of accessing the job market, based on the variables studied, and specify especially vulnerable profiles, in order to design specific job market insertion measures.
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SELECTION OF THE STUDY SAMPLE
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DATA COLLECTION AND INFORMATION ANALYSIS PROCESS
We selected all the national results offered by the EPA on unemployed people that included the age variable, in relation to other variables, and showed the figures in absolute values. Based on this information, and with the help of the filtering and consultation tool offered by the INE on its website, the information was limited to the study sample, selecting only the data pertaining to unemployed people over 45 years old.
All of the data collected were exported to Microsoft Excel software, where they were processed. The absolute unemployment values were divided into two age categories (unemployed people between 45 and 54 years old and those older than 55). These two categories, in turn, were divided into three others, based on sex (men, women and both). The age and sex variables were crossed with the other variables available in the data.
Finally, an analysis is performed of the gaps found in the information and the categories not contemplated. To shed light
This study is based on the national unemployment information presented by the National Institute of Statistics (INE) in the Survey of the Active Population (EPA). This source is chosen because it offers more specific and reliable data on unemployment than other official sources. In most of the analyses that contemplate age, it is possible to select the specific age range of interest and cross it with other variables. This survey is often used to make international comparisons because it strictly follows the unemployment measurement criteria established by the International Labour Organization (ILO).
To guarantee the current validity of the study, the sample was limited to the information offered by the EPA for the first trimester of 2015. This was the latest information offered by this source at the time the analyses were performed.
As this is a survey, the data presented by this source are estimations and, therefore, subject to errors related to sampling and other causes (INE 2012).
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on some of the grey areas found in the data on national unemployed people, the calculation of the difference between active and employed is used, thus finding the unemployment numbers for some cloudy categories.
Through the process described above, three analyses are performed that make up the three blocks of results in this study: 1) General profile of unemployed people over 45 years old according to the statistics; 2) Educational profile of unemployed people over 45 years old according to the statistics; and 3) Gaps in the data: grey and concealed areas.
RESULTS
GENERAL PROFILE OF UNEMPLOYED PEOPLE OVER 45 YEARS OLD ACCORDING TO THE STATISTICS
This first analysis starts with the total national number of unemployed people over 45 years old collected in the statistics of the Survey of the Active Population (EPA). The number of unemployed people in this age range is 1,841,700 at the national level. Using this number, the percentages in Table 1 are calculated, showing the percentage of unemployed older people by sex and age, and crossing it with the variables of marital status, previous employment, job search time, and type of timetable sought.
The statistical data included in Table 1 show that the majority of the unemployed people over 45 years old are between 45 and 54 (67.49%), with a slightly higher number of women than men, specifically 33.78% and 33.71% of the total, respectively. The percentage of unemployed people over 55 years old declines considerably to less than half, 32.51% for both sexes together, with a greater number of men (18.83%) than women (13.68%). Higher percentages are maintained for all the variables in unemployed people between 45 and 54 years old. The results for the different variables studied are very similar in both age ranges, showing higher percentages on the same values in almost all cases.
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Table 1: Percentage of unemployed by sex and age compared to the national total of unemployed people over 45 years old
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Age		45-54 (%)			Over 55 (%)			Total
Sex		M	W	Both	M	W	Both	(%)
General		33,71	33,78	67,49	18,83	13,68	32,51	100,00
	Single	7,88	4,25	12,13	2,68	1,17	3,85	15,98
Marital	Married	20,74	22,83	43,58	13,28	9,16	22,44	66,01
status	Widowed	0,40	1,17	1,57	0,52	0,83	1,35	2,93
	Divorced	4,68	5,53	10,21	2,35	2,52	4,87	15,08
Prior	Have worked before	33,45	32,92	66,37	18,82	13,25	32,08	98,45
employment	Looking for first job	0,26	0,86	1,12	0,01	0,43	0,43	1,55
	Has already found work	1,70	1,32	3,02	0,57	0,17	0,74	3,76
	Less than one month	1,17	0,68	1,85	0,41	0,17	0,58	2,43
	From 1 month							
	to less than 3	2,68	2,68	5,36	0,90	0,70	1,60	6,96
Job search time	months From 3 months to less than 6 months	2,18	2,67	4,85	1,09	0,81	1,89	6,74
	From 6 months to 1 year	3,03	2,96	5,99	1,59	1,07	2,66	8,65
	From 1 year to less than 2 years	5,31	5,39	10,70	2,62	1,78	4,39	15,09
	2 years or more	17,64	18,07	35,72	11,67	8,99	20,66	56,37
	Full time but							
	would accept	6,13	3,90	10,03	2,88	1,03	3,91	13,94
	part-time							
	Part-time	5,43	4,75	10,17	3,32	1,84	5,16	15,33
Type of timetable	Part-time but							
	would accept full time	0,35	2,44	2,79	0,17	1,11	1,28	4,07
sought	Whatever he/ she can find	0,12	0,73	0,85	0,09	0,17	0,26	1,11
	Doesn't know	20,75	21,29	42,05	11,90	9,43	21,33	63,38
		0,16	0,23	0,39	0,09	0,03	0,12	0,50
	Not classifiable	0,77	0,43	1,20	0,38	0,09	0,47	1,67
Source: Own elaboration based on INE data (2015a).
Regarding marital status, more than half of the unemployed people, 66.01%, regardless of the age range or gender, are married, which shows that the majority have family responsibilities.
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While 1.55% is looking for their first job after the age of 45 (mostly women), 98.45% have worked before. Table 1 highlights the high percentage of unemployed people who have been unsuccessfully looking for work for two years or more, with 56.37% of the total in this situation.
In addition, 63.38% of the unemployed people over 45 years old do not specify the type of timetable they are looking for, stating that they would accept whatever they can find. The fulltime schedule is requested more by men than women, while a | 37 greater number of women than men are seeking part-time work: in the age range from 45 to 54 years old, 2.44% are women and 0.35% are men.
EDUCATIONAL PROFILE OF UNEMPLOYED PEOPLE OVER 45 YEARS OLD ACCORDING TO THE STATISTICS
In this second analysis, we examined the unemployment and occupation data according to the subjects' educational level.
Table 2 shows the percentage of the total number of unemployed people over 45 years old with regard to their educational level, indicated by the EPA data in the period studied.
Table 2: Percentage of unemployed people according to their educational level compared to the total number of unemployed people over 45 years old
	45-54 (%)			Over 55 (%)			Total
	M	W	Both	M	W	Both	(%)
Illiterate	0,61	0,22	0,83	0,30	0,32	0,62	1,45
Incomplete primary studies	1,53	0,88	2,41	1,14	0,97	2,11	4,52
Primary Education	5,02	3,45	8,47	4,59	3,42	8,01	16,48
Lower secondary education and similar	15,14	14,4	29,54	6,78	5,34	10,12	41,66
Upper secondary education with a general focus	3,85	4,53	8,38	2,32	1,33	3,65	12,03
Upper secondary education with a professional focus	2,53	3,68	6,21	0,71	0,93	1,64	7,85
Higher education	5,04	6,61	11,65	2,99	1,37	5,29	16,01
Source: Own elaboration based on INE data (2015a).
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The highest proportion of unemployed people (41.46%) have finished Lower Secondary Education. Next, 16.48% have only finished Primary Education. The number of unemployed people with Higher Education stands out, making up 16.01% of the unemployed people of this age.
While the unemployment rate by educational level in most cases is lower in women over 55 years old than in men, it is surprising to observe that the tendency changes and follows a pattern in people from 45 to 54 years old. For this age range, the men have higher unemployment rates in the lower educational levels, but the number declines, compared to women, in the higher levels.
Taking into account that the Spanish population has increased its educational level considerably, to bring out the real data on the probability of being unemployed depending on education, Table 3 is constructed. This table presents the unemployment data by educational levels based on the total percentage of the active population that currently has this training.
Thus, the table shows that of the total number of illiterate unemployed people over 45 years old in Spain, 63.19% are unemployed. This number declines as the educational level increases, with 10.40% of people over 45 with Higher Education unemployed.
Table 3: Percentage of unemployed people based on their educational level compared to the active population over 45 years old with this educational level
	45-55 (%)			Over 55 (%)		
	M	W	Both	M	W	Both
Illiterate	52,34	60,61	54,29	61,54	64,84	63,19
Incomplete primary studies	46,92	48,50	47,49	33,49	31,56	32,58
Primary Education	37,95	36,74	37,45	28,43	28,37	28,40
Lower secondary education and similar	24,11	31,20	27,11	22,01	22,22	18,46
Upper secondary education with a general focus	15,26	21,37	18,05	15,52	13,13	14,55
Upper secondary education with a professional focus	16,93	24,43	20,70	15,56	16,44	16,05
Higher education	9,36	12,46	10,90	10,04	6,50	10,40
Source: Own elaboration based on INE data (2015a).
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The number of unemployed people participating in courses and training sessions to update their skills is another type of data offered by the EPA. These data indicate whether people are taking training courses or not in the first trimester of 2015.
Table 4: Percentage of unemployed people over 45 years old according to whether they take courses and the type of courses taken
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Take courses	45-55 (%)			Over 55 (%)			
	M	W	Both	M	W	Both	Total
NO	31,51	30,47	61,97	18,24	12,92	31,16	93,13
YES	2,21	3,31	5,52	0,59	0,76	1,35	6,87
Only accredited	7,03	15,64	22,67	1,58	3,00	4,58	27,25
Only non-accredited	24,64	31,52	56,16	7,03	7,27	14,30	70,46
Both	0,47	1,03	1,50	0,00	0,79	0,79	2,29
Source: Own elaboration based on INE data (2015a) * In cursive, the percentages according to the population sector studied
Table 4 shows that the majority of unemployed people (93.13%) are not taking courses during the trimester consulted. Only 6.87% (126,600 people in absolute values) of the unemployed people of this age are taking training courses. A 70.46% of the unemployed people who take courses, participate in non-accredited courses, while a 27.25% of them take accredited courses. The data reveal that women show higher percentages of participation in training activities in all cases than men, and this difference is especially pronounced in the case of accredited courses.
Table 5 is elaborated for comparison purposes and shows the same values, but for the employed population over 45 years old.
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Table 5: Percentage of employed people over 45 years old according to whether they take courses and the type of courses taken
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Take courses	45-55 (%)			Over 55 (%)			
	M	W	Both	M	W	Both	Total
NO	32,84	25,65	58,48	19,46	14,71	34,16	92,65
YES	2,52	2,80	5,32	0,95	1,08	2,04	7,35
Only accredited	5,90	7,77	13,67	1,28	1,97	3,25	16,92
Only non-accredited	28,14	29,48	57,61	11,63	12,65	24,28	81,90
Both	0,26	0,78	1,04	0,04	0,11	0,15	1,19
Source: Own elaboration based on INE data (2015a) . * In cursive, the percentages according to the population sector studied
The data on the employed again confirm women's greater interest in carrying out training activities. The percentage of people who take courses increases, although only slightly, among those with jobs, to 7.35%.
In order to show whether there is a relationship between taking courses to update skills and the educational level of the unemployed and employed, Table 6 crosses these two sets of data.
Table 6: Percentage of unemployed and employed people over 45 years old who take courses, according to their educational level
Educational level of those who take courses	Over 45 years old Unemployed (%) Employed (%)
Illiterate Incomplete primary studies Primary Education Lower secondary education and similar Upper secondary education, with a general focus Upper secondary education with a professional focus Higher education	0,87 0,15 3,08 0,19 6,15 1,76 29,02 12,29 19,16 13,85 8,83 6,95 32,89 64,81
Source: Own elaboration based on INE data (2015a).
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The higher percentages of updating skills are shown by people with Higher Education, representing 32.89% of unemployed and 64.81% of employed people over 45 years old who are taking training courses.
People with lower Secondary Education make up the second group of unemployed people, based on education, who take courses, with 29.02%. Meanwhile -among the employed- second place in taking courses corresponds to upper Secondary Education with a general focus (13.85%). The information for | 41 | both the unemployed and employed coincides in assigning the lowest percentages of training courses to the groups of people with lower educational levels.
GAPS IN THE DATA: GREY AND CONCEALED AREAS
During the analysis of the information available on the Internet about unemployment in people over 45 years old, certain gaps were detected that limited the baseline data for this study.
The first observation is the disproportionate amount of information offered about employed and unemployed people. While there are 113 databases with crossed information about employed people, there are only 38 about the unemployed.
Sometimes the databases did not allow the information to be filtered by the age of the unemployed. A revealing example would be the case of information about the economic sectors of unemployed individuals. This information is shown by age, but with rigid previously established ranges (from 25 to 54 years old and 55 and up), which prohibits us from narrowing down specific information about the collective examined in this study. The data about people over 55 years old also exclude information about the economic sector of people who have been unemployed for more than one year. Therefore, they do not provide information about the sectors of 73.88% of these people. The services sector has more unemployed individuals in this age range (16.08% for both sexes), followed by construction, with 3.88% for men.
Other variables are completely missing, such as the ethnic group or functional diversity of the unemployed. Through the
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information about the active population and their differences compared to the employed population, Table 8 sheds some light on the nationality variable and its percentile representation for the total number of unemployed people over 45 years old. The same table includes the contrasted data for the percentage of unemployment by nationality based on the total active population for each nationality.
42 | Table 7: Percentage of unemployment of people over 45 years old by nationality
	Over 45 years old	
NATIONALITY	Unemployed compared to the total (%)	Unemployed compared to the active population by nationality (%)
Spanish	84,49	18,84
Dual nationality	3,42	31,28
foreign: Total	12,09	31,12
Ext. European Union	31,46	23,90
Ext. Rest of Europe	7,32	30,35
Ext. Latin America	27,74	31,48
Ext. Rest of world and stateless	33,48	43,27
Source: Own elaboration based on INE data (2015a).
* In cursive and in right column, the percentages for the population sector
studied
As Table 8 shows, 84.49% of unemployed people over 45 years old have Spanish nationality, while 12.09% have a foreign nationality. However, considering the percentage of unemployment compared to the total number of active people of each nationality, the percentages become inverted. Thus, unemployment affects 18.84% of Spaniards, while it is especially hard on people with dual nationality and foreigners (with 31.28% and 31.12%, respectively). Those most harmed by unemployment are foreigners catalogued by the EPA as being of foreign nationality from the "Rest of the world and stateless", with 43.27% of these people finding themselves unemployed and making up 33.48% of unemployed foreigners.
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CONCLUSIONS
The official statistical data sketch a very general picture of unemployed people over 45 years old. Based on these data, it is not possible to establish a clear profile of all the factors and indicators that influence employability. This gap seriously interferes with the comprehension of the reality and the ability to establish appropriate and differentiated measures that can positively influence employability. However, the results show that nationality, educational level and, sometimes, sex is clearly conditioning factors.
Analysing what exists and what is hidden, in the official Spanish statistics there is a lack of proportion between factors that influence employment and data on the unemployed. Furthermore, in this case it would seem that the most gruelling data about the social reality studied are ignored or disguised in generalities. An information gap that feeds the silencing of the problems and invites to social resignation. These data are scrutinized and visualized through the secondary analysis performed in this study.
Having family responsibilities stands out, reflected in the predominant marital status of the unemployed in this age range (married). These are collectives that could traditionally maintain the family unit. The majority of people of this age have worked in the past, as very few (and mainly women) are seeking access to the job market for the first time. This fact may indicate that the economic crisis is causing women in families that now feel vulnerable to have to look for work for the first time. The most dramatic data show that more than half have been unemployed for more than 2 years, which produces lack of motivation, isolation and, consequently, less probability of accessing the job market. This situation is maintained in spite of the fact that the majority do not make any demands as far as the type of timetable is concerned. They are looking for work, any work, but they have lost faith in the system and its possibilities.
The percentages of unemployment are higher in women between 45 and 54 years old; and they drop considerably in women over the age of 55 (compared to men's unemployment). This tendency shows the lower participation of women over 55 years
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old in the world of work. They are excluded from the unemployment numbers offered by the INE, which places them in the category of "inactive" because they are not actively searching for employment.
The data on education reveal that people with higher educational levels have a greater probability of finding a job, which shows the existence of a close relationship between education and employment. However, numerically there are more unem-44 | ployed professionals with higher studies than professionals without qualifications, although the latter find themselves in a more vulnerable situation. The increase in higher education in Spain in recent years, along with the economic crisis, may provide a possible explanation.
Regarding skills updating, there is a greater participation of women than men in courses, and this is especially pronounced in accredited courses. With regard to non-accredited courses, the women's percentages of participation are only slightly higher than those of the men. This tendency shows women's greater interest in education. Another noteworthy result as far as education is concerned is that there is a direct proportional relationship between educational level and taking training courses. The well-known Mateo Effect is produced; that is, there is a greater tendency to continue studying in people with a higher educational level. Meanwhile, people with lower qualifications statistically show less inclination to take training courses.
The information analysed in this study, reveals silences and gaps in the statistical data that conceal the reality. The unemployment data based on ethnic group and functional diversity are completely obscured due to their omission, which could respond to a culture of silencing vulnerability in complex times. In spite of this, this study manages to shed light on the hidden data about the nationality of unemployed people by rescuing and contrasting information about employed and active people. The information processed increases the variations in the unemployment rate that accompany the "nationality" factor, especially affecting people of non-European Union nationalities.
In conclusion, the data found in the official statistics on unemployment in people over 45 years old define a general profile
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that is clearly not sufficient to comprehend the situation affecting these people. This informative gap impedes establishing priority indexes and reduces the possibility of designing specific actions to favour job insertion. The limited statistical data and their opacity, in addition to the silences and gaps found in the study, encourage us to advance in the analysis of the collective through other means, research alternatives and sources, in order to achieve a more exhaustive understanding and further examine the hidden details contained in this reality.	| 45
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