Un análisis sobre la desigualdad de los ingresos (ganadores y perdedores de la crisis financiera mundial) (página 5)

Las estimaciones de la Organización Internacional
del Trabajo reflejan la presencia de estabilidad y mejoras en la
mayoría de los países desde fines del decenio de
1990 hasta la primera década de este siglo. El aumento
brusco del desempleo normalmente obedece a crisis
macroeconómicas, ya sean financieras o cambiarias. Tal es
el caso de la crisis financiera mundial, que ha generado un
marcado incremento de los despidos y el desempleo, sobre todo en
países desarrollados y en Europa y Asia Central (figura
4.5).

La crisis financiera mundial se
precipitó por el estallido de la burbuja de precios del
mercado inmobiliario y el derrumbe bancario en Estados Unidos;
ésta se propagó rápidamente por el resto del
mundo. Se trata de la peor crisis financiera desde la Gran
Depresión, al menos en los países desarrollados, y
ciertamente no será la última.

El desempleo y la pobreza recrudecieron:
34 millones de individuos perdieron su empleo y 64 millones
más cayeron bajo la línea de pobreza de US$ 1,25
diarios. Esto se suma a los entre 160 millones y 200 millones que
se convirtieron en pobres a raíz del aumento del precio de
los productos básicos en años anteriores. En 2010,
la tasa de desempleo rondó la media de 9% en los
países desarrollados, alcanzó el 10% en Estados
Unidos y se empinó sobre el 20% en
España.

La reactivación comenzó en
2009, pero no está de ningún modo garantizada: el
riesgo de recesión doble persiste y la plena
recuperación podría tardar años. La
aplicación de políticas públicas innovadoras
y enormes estímulos fiscales en muchos países,
sumado a la rápida coordinación mundial, ayudaron a
evitar una crisis mayor. En los países en desarrollo que
habían administrado bien los réditos de
períodos anteriores de bonanza económica el impacto
de la crisis fue más leve. Algunos gobiernos mantuvieron o
aumentaron el gasto social, al contrario de lo ocurrido a fines
de la década de 1990, tras las crisis de Asia Oriental y
Rusia.

Las consecuencias de las crisis pueden
perdurar incluso después de recuperar el crecimiento, ya
que el mercado laboral suele tener rezagos con respecto a la
producción cuando ocurre la recuperación. La OIT
prevé que 43 millones de individuos que perdieron su
empleo durante la crisis financiera mundial hasta 2009
están en riesgo de pasar a ser desempleados de largo
plazo. Otros podrían decepcionarse y abandonar
completamente el mercado laboral. Puede repetirse el
fenómeno observado tras la crisis de Asia Oriental de
fines de la década de 1990, cuando los índices de
participación en la fuerza laboral nunca se
recuperaron.

Sin embargo, han surgido nuevos riesgos, ya que
aumentó la preocupación por la sostenibilidad
fiscal de algunos países desarrollados (como Grecia) y el
fantasma del contagio persiste. Por lo general, las
economías que crecieron más rápido en la
primera década de este siglo fueron las más
golpeadas, aunque Australia y China son apenas dos de las
excepciones. En América Latina y el Caribe, el crecimiento
del PIB bajó, especialmente en Chile, México y
Perú. África Subsahariana siguió creciendo,
aunque a una tasa mucho menor: pasó de 5% en 2008 a apenas
2% en 2009. En los países desarrollados, el crecimiento
anual cayó cerca de 6 puntos porcentuales hasta -3,4% en
2009. Algunos países de Europa y Asia Central parecen
haber sido los más golpeados: las economías de la
ex Unión Soviética pasaron de tener un crecimiento
superior a 5% en 2008 a sufrir una contracción de casi 7%
en 2009, mientras la pobreza aumentó en forma
marcada.

Mientras los países desarrollados han sido los
más afectados por la crisis, la capacidad de algunas
naciones en desarrollo para lidiar con sus efectos es más
limitada. Cerca de 40% de los países que están
enfrentando una desaceleración del crecimiento ya
tenían altos índices de pobreza en 2009 y
capacidades fiscales e institucionales limitadas para hacer
frente a la volatilidad económica.

Respuestas de políticas
públicas

El empleo y los ingresos fluctúan
en todas las economías, pero la calidad de la respuesta de
los seguros y de otros mecanismos a esas fluctuaciones
varía ampliamente. El sistema estadounidense de seguro de
desempleo difiere mucho del europeo. Sin embargo, tienen en
común que a medida que los países se enriquecen,
aumenta la protección social y el papel del Estado en
ella. Dani Rodrik afirma que el crecimiento del aparato estatal
ha sido un corolario del aumento del riesgo que acarrea la
globalización. Esto se pudo apreciar durante la crisis
reciente: casi la mitad de los países del Grupo de los 20
prolongaron los beneficios de desempleo durante el período
2009–2010 y más de un tercera parte expandió
la cobertura.

Un repaso por la experiencia
internacional sugiere que es imposible identificar una
configuración de normas e instituciones que reduzcan el
desempleo. Esta conclusión pesimista contrasta con los
firmes supuestos sobre el tipo de instituciones y la flexibilidad
que serían óptimas en el mercado laboral
según, por ejemplo, los indicadores Doing Business del
Banco Mundial.

Al mismo tiempo, son cada vez más los gobiernos
que están respondiendo ante la volatilidad del empleo y
del desempleo juvenil. Un ejemplo son los Estados Árabes,
donde tales problemas precedían a la crisis mundial
reciente. Los desafíos se deben no sólo al
rápido crecimiento de la fuerza laboral y al crecimiento
económico no favorable a los pobres, sino también a
los límites a la creación de nuevos puestos de
trabajo, impuestos por la protección al empleo, sobre todo
en el sector público.

Elaborar políticas públicas viables tanto
en términos financieros como institucionales y que eviten
las dificultades de los países desarrollados es un reto
enorme. En países con grandes sectores informales y a
menudo instituciones débiles, parecería apropiado
implementar una combinación entre seguros públicos
y privados (recuadro 4.5).

Cómo afectan las crisis al desarrollo
humano

Los grandes aumentos en los niveles de
pobreza son frecuentes en las crisis financieras. La que
afectó a Asia Oriental a fines de la década de 1990
dejó a 19 millones de indonesios y a 1,1 millones de
tailandeses en la pobreza. La crisis financiera de Argentina en
2001 incrementó los índices de pobreza nacional en
15 puntos porcentuales, mientras que la de 1998 en Ecuador
aumentó la pobreza en 13 puntos porcentuales.

El impacto de una crisis en los ingresos
depende de la existencia de planes adecuados de desempleo. La
preocupación por la seguridad laboral y la pérdida
de empleos ha llevado a la mayoría de los gobiernos a
abordar el problema, si bien la cobertura y los beneficios son a
menudo, parciales e insuficientes (recuadro 4.5). Cuando no hay
protección social, quienes pierden el trabajo deben
transitar a la economía informal, donde los salarios son
más bajos y la vulnerabilidad es mayor.

Los efectos de las crisis en el
desarrollo humano van evidentemente más allá de los
ingresos y pueden tener mayor duración. Por ejemplo, las
familias pobres pueden decidir sacar a sus hijos de la escuela,
en desmedro de sus oportunidades futuras. Las crisis
también aumentan la mortalidad infantil y la
desnutrición; el retraso del crecimiento impone un alto
costo cuyas consecuencias perduran en el tiempo. Las estimaciones
sugieren que en África, al menos entre 30.000 y 50.000
niños morirán debido a la crisis financiera
reciente.

Otros efectos negativos incluyen el aumento del
número de niños de la calle y de las tasas de
suicidio y delincuencia, así como el recrudecimiento del
maltrato y la violencia doméstica, y también de las
tensiones étnicas. Datos recientes sugieren que el aumento
del desempleo durará más que la caída en la
producción.

El impacto de las crisis en la mortalidad infantil
golpea con mayor severidad a las niñas. Datos sobre 1,7
millones de partos en 59 países en desarrollo para el
período entre 1975 y 2004 muestran que una caída de
1% en el PIB se relaciona con un aumento en la mortalidad
infantil promedio de 7,4 muertes por cada 1.000 nacimientos en el
caso de las niñas y de 1,5 entre los
niños.

En la reciente crisis, algunos países en
desarrollo han protegido el presupuesto para el sector social.
Sudáfrica destinó 56% de su estímulo a este
ítem. Sin embargo, en Myanmar y la República
Democrática del Congo, los salarios reales de los maestros
cayeron hasta 40%, y en Madagascar, Sudán y Yemen se
redujeron entre 20% y 30%. En muchos países subsaharianos
se retrasaron los pagos de los salarios a maestros y trabajadores
de la salud. En ocasiones, los recortes presupuestarios se
consideran una respuesta necesaria a la caída de los
ingresos, pero muchos países en desarrollo tienen hoy
bastante más espacio para aplicar políticas
fiscales anticíclicas.

Las crisis a menudo crean más
desigualdad.

Mientras millones han perdido su empleo,
otros, como algunos inversionistas, están protegidos por
seguros a los depósitos o se benefician con los rescates
financieros. Quienes ganan -en términos relativos y en
ocasiones absolutos- son generalmente los que tienen más
bienes, mejor información y mayor agilidad financiera y,
por supuesto, aquellos con influencia.

Una perspectiva de largo plazo

Pese a los duros efectos, es importante mantener la
crisis actual dentro de una perspectiva de largo plazo. Al menos
para los países desarrollados, fue la peor crisis desde la
Gran Depresión.

La mayoría de los países en desarrollo
tuvo peores caídas a comienzos de la década de 1980
y algunos -como China e India– han mantenido su vigoroso ritmo de
crecimiento. En realidad, se prevé que la
producción mundial será un 1% más alta a
fines de 2010 que antes de la crisis. Nuestras estimaciones
también indican que la esperanza de vida y la tasa de
matriculación siguieron aumentando y se traducirán
en 2010 en un IDH de 0,68, es decir, 2% más alto que en
2007. En los países desarrollados, sin embargo, el IDH
apenas ha crecido, ya que las fuertes caídas en los
ingresos han contrarrestado los avances en salud y
educación.

Al mismo tiempo, la crisis ha dado
aún más importancia al tema de la regulación
de los mercados y ha planteado preguntas importantes sobre la
sostenibilidad del modelo y de los enfoques que impulsaron el
auge económico de la primera década de este siglo.
Este año, Estados Unidos aprobó una reforma general
de su sistema de regulación financiera que aumenta la
cantidad de entidades del ramo sujetas a fiscalización,
regula muchos de los contratos derivados que estuvieron en la
raíz de la crisis y crea un órgano regulador para
proteger a los consumidores de servicios financieros.

RECUADRO 4.5

¿Hacia dónde apunta la protección
del empleo?

En la actualidad, cerca de 150
países ejecutan algún tipo de programa de
compensación por desempleo. En muchos países
desarrollados, el riesgo de desempleo ha sido cubierto
ampliamente -en particular en Europa Occidental- mediante una
variedad de programas de bienestar, entre los que se destaca el
seguro de desempleo. El gasto en protección social en la
mayoría de las naciones de Europa Occidental alcanza ahora
al 25%-30% del PIB. Mientras el diseño y la cobertura de
tales planes se han mantenido mucho más austeros en
Estados Unidos, la tendencia ha sido ofrecer más
alternativas ante la pérdida del empleo. El gasto social
de libre disposición -incluidos los beneficios de
desempleo- ha representado cerca de 40% del gasto fiscal
adicional, aunque menos de la mitad de los desempleados de
Canadá y Estados Unidos recibe beneficios.

Sin embargo, en los países en desarrollo, incluso
menos desempleados perciben algún tipo de
compensación. Una estimación sugiere que apenas uno
de cada cinco desempleados en América Latina y el Caribe
cuenta con algún tipo de compensación por
desempleo. Esta proporción baja a 1 de cada 33–50 en
los Estados Árabes y en África Subsahariana.
Argentina, Brasil, Sudáfrica y Turquía tienen una
cobertura de desempleo que va de 7% a 12%, mientras que en la
Federación de Rusia la cobertura es cercana a 25%.
Más aún, donde existe cobertura, el monto de los
beneficios es bajo. El beneficio promedio -reemplazo de la
pérdida salarial- se mantiene en alrededor de 10%. El
seguro privado y otros mecanismos informales de adaptación
siguen siendo la manera preferida de lidiar con la pérdida
del empleo en los países en desarrollo.

Algunos países, en particular Chile, tienen
cuentas obligatorias de capitalización individual, que
exigen a los empleadores y, en ocasiones, a los trabajadores,
depositar entre 3% y 9% de sus ingresos. Si bien la
macroeconomía y los incentivos, pueden ser el motivo
detrás de tales estrategias (elevar los índices de
ahorro), éstas presentan desafíos de diseño
y capacidad y arrojan dudas sobre la equidad. Algunos
trabajadores podrían no acumular suficientes ahorros como
para retirarlos durante un período de desempleo, sobre
todo los trabajadores jóvenes y aquellos con salarios
bajos en el sector informal.

Los planes de seguro con subsidio estatal se han
adoptado ampliamente. Por ejemplo, en Corea del Sur y en
Turquía el seguro de desempleo es obligatorio. Los
trabajadores deben aportar una contribución
específica y cumplir ciertos requisitos para recibir
beneficios durante 7-10 meses. En China, los beneficios de
desempleo están disponibles para una pequeña
porción de la fuerza laboral urbana y los beneficios
establecidos por los gobiernos locales son inferiores al salario
mínimo local.

UE: Cuando los juegos
virtuales no alivian el dolor (el futuro ya no es lo que
era)

Mientras los culpables de la crisis (banqueros
-codiciosos y mendaces-, bancos centrales -cómplices y
prevaricadores-, gobiernos -irresponsables y corruptos-…)
siguen disparando con pólvora del contribuyente, un 80 por
ciento de la población europea se debate entre la
incertidumbre y el miedo. La sociedad "20/80" citada
anteriormente.

Sabido es que la participación de las
remuneraciones en el Ingreso Nacional ha caído paulatina y
persistentemente en los países de la periferia desde la
"Década Perdida" de los ochenta y el proceso de
globalización. Pero resulta que el descenso también
ha sido la norma en los países del Norte, sin
excepción. El gráfico siguiente, que abarca el
extendido periodo que se inicia en 1960 hasta 2009, aunque
sólo incluye las economías más importantes,
permite rastrear la tendencia progresiva en la
distribución funcional hasta 1974-75 y la regresividad que
se impuso a partir de entonces.

Obviamente esa tendencia negativa es consecuencia,
primero debido al primer choque petrolero y, posteriormente, a
resultas del buen funcionamiento del mercado global de trabajo.
Con la duplicación de la fuerza de trabajo a escala
mundial (de 1.500 a 3.000 millones de empleados y obreros), era
de esperar la pérdida de influencia de los trabajadores en
general y de los sindicatos en particular. Probablemente,
también el progreso técnico ha jugado un papel
importante en ese proceso, especialmente desde los años
noventa.

También debe considerarse, en parte, que la
crisis de los países centrales responde a una
típica tendencia a la "sobreproducción", como
consecuencia de la compresión relativa del poder de compra
de la clase trabajadora.

Fuente: European Commission (2009). Annual
Macro-economic Database (AMECO)

Entrando en ciertos detalles (algunos de los que no se
pueden observar en el Gráfico) tenemos lo
siguiente:

• La caída más espectacular en la
  participación de sueldos y salarios fue la que se dio
  en Italia, que era de 69,7% en 1975, para desplomarse a un
  promedio de 54% en esta primera década del siglo XXI;
  es decir, perdieron 16 puntos porcentuales o 23%. De cerca le
  sigue Japón, que mostraba un 75% a mediados de los
  años setenta y cayó a 60% en el último
  quinquenio; o sea,  15 p.p. o 20% menos. De 68% a 56% se
  desplomó, aunque con altibajos, la
  participación del trabajo en el caso de España
  (-12 p.p. o -18%). También Alemania, en parte por la
  unificación (1990), sintió el golpe: la
  participación cayó de 64,4% en 1974 a 55%
  (-15%) en los últimos años. Un caso que llama
  poderosamente la atención en ese sentido es el de
  Noruega, que cae de un 62% a mediados de los setenta a 45% en
  este segundo lustro del nuevo siglo.

• Llama la atención la recuperación leve
  de la participación laboral en los últimos tres
  años de "crisis global". Lo que se debería,
  más que al aumento real de las remuneraciones, a la
  caída de las ganancias en términos
  absolutos.

• Durante el trienio pasado los países que
  tuvieron una participación superior al 60% fueron unos
  pocos, pero que tampoco llegaron a recuperar los niveles de
  mediados de los setenta: Bélgica, Corea, Dinamarca,
  Eslovaquia, EEUU, Gran Bretaña, Japón y Suiza.
  En cambio, la participación es menor al 50% en
  Bulgaria, Lituania, Luxemburgo, Malta, Polonia,
  Turquía, Nueva Zelandia y Noruega (sic); y aún
  menor al 40% en Eslovaquia, Macedonia y México
  (obviamente también gran parte del resto de
  América Latina, pero cuyos datos no presenta nuestra
  fuente).

Dentro de la Unión Europea, tal vez, la
situación más dramática esté
representada por España que, con una tasa de paro anclada
en el 20% y del 43% para los jóvenes entre 16 y 24
años, vuelve a las andadas de la década de los
ochenta y mediados de los noventa.

La crisis es especialmente cruel con los jóvenes
que entran en el mercado de trabajo con ánimo de
independizarse y al no encontrar empleo durante un largo tiempo,
terminan quedándose en el hogar familiar. Por otro lado,
los jóvenes llevan una ajetreada vida laboral si es que se
incorporan al mercado de trabajo, dado que ésta es un
continuo trasiego entre el paro, la economía sumergida, el
trabajo temporal y el indefinido. Este baile marca profundamente
a los jóvenes y como bien señalan Víctor
Pérez Díaz y Juan Carlos Rodríguez en su
nuevo libro "Alerta y Desconfianza: La Sociedad Española
ante la Crisis", la clave de sostenibilidad de la sociedad
española es la familia que evita que todo salte por los
aires.

Otro caso relevante es el de los emigrantes, donde el
paro oficial ronda el 30%. Ellos representan un nuevo apartado al
paro crónico español, y su trasiego laboral es algo
más complicado, concluyendo con el retorno a su
país natal como una de las opciones ante el paro.
Mientras, la familia, incluyendo las redes sociales como
Cáritas, acoge y evita males mayores ante la
situación desesperada en la que se encuentran.

Hasta cierto punto podríamos decir que la
sociedad española moderna abusa, una vez más, de la
familia en momentos de crisis. Se le exige que ante la avalancha
que le acecha, reaccione y se adapte sin rechistar a la grave
situación económica y a los abruptos cambios
sociales, eso sí, en total soledad y sin paliativos. Estas
circunstancias son palpables en instituciones dedicadas a la
ayuda desinteresada y es también a través del
entorno de las propias instituciones donde se oyen las voces que
claman y reclaman por el día a día.

El Observatorio Laboral de la Crisis, elaborado
desde FEDEA, realiza trimestralmente un análisis
sistemático de la información longitudinal que
aporta la EPA (Encuesta de Población Activa) con las
Estadísticas de Flujos de la Población Activa.
Aunque la información que sigue corresponde a la
situación española, muchos aspectos pueden
trasladarse fácilmente a gran parte de los países
europeos. Por ello se citan algunos párrafos de
análisis publicado el 5/2/11:

• (i) Del Empleo al Desempleo:

Características que afectan a la pérdida
de un empleo

Tras el análisis descriptivo, este
Observatorio "cuantifica" la importancia relativa de cada una de
las características analizadas previamente –
género, edad, educación, nacionalidad, tipo de
contrato y ocupación en la probabilidad de pérdida
de empleo.Para esto, se estima la probabilidad de perder el
empleo de cada individuo ocupado en el trimestre anterior. Los
resultados detallados se ofrecen en la tabla 2 del
boletín. Presentamos aquí un resumen de los
mismos:

• El género en sí mismo
no contribuye a explicar las diferencias observadas en la
pérdida de empleo entre hombres y mujeres de similares
características.

• Ser menor de 35 años aumenta
el riesgo de pérdida de empleo alrededor de un 30% con
respecto a trabajadores similares pero mayores de 35 años.
Este resultado contrasta con los encontrados en trimestres
anteriores, donde la edad en sí misma no provocaba
diferencias en el riesgo de pérdida de empleo. En este
trimestre, el ser menor de 35 años parece añadir un
factor de riesgo en la pérdida de empleo con respecto a
trabajadores similares pero de edad superior.

• Tener estudios universitarios disminuye el riesgo
de pérdida de empleo a la mitad.

• Ser extranjero aumenta la probabilidad de perder
el empleo en un 26% con respecto a un trabajador de similares
características pero de origen nacional.

• Tener un empleo temporal – relativamente a un
indefinido, multiplica por cuatro el riesgo de perderlo. En otras
palabras, si se comparan dos individuos de similares
características en términos de edad, género,
nivel educativo y nacionalidad pero que difieren en el tipo de
contrato, encontramos que la probabilidad de pérdida de
empleo del que tiene el contrato temporal es más de casi 4
veces superior a la que se enfrenta el trabajador con contrato
indefinido.

• Trabajar en la agricultura o en la
construcción aumenta la probabilidad de perder el empleo
con respecto a trabajar en servicios o en industria.

• (ii) Del Desempleo al Empleo

¿Qué características son
importantes para encontrar un empleo?

Para responder a esta pregunta se estima la probabilidad
de que un individuo desempleado encuentre empleo en el trimestre
siguiente. Tomando tanto a individuos que han accedido al empleo
como aquellos que han continuado desempleados, es inmediato
obtener la importancia relativa de variables como género,
edad, educación, nacionalidad y duración del
desempleo en el acceso al empleo. Destacan los siguientes
resultados:

• Las mujeres se enfrentan a dificultades
ligeramente superiores de acceso a un empleo que los varones con
características similares.

• No se encuentran diferencias significativas en el
acceso a empleo entre los desempleados nacionales y los
desempleados extranjeros.

• Tener una edad comprendida entre 35 y 44
años duplica la probabilidad de acceder a un empleo con
respecto al tramo de edad más joven – 16-24
años.

• El tipo de empleo al que acceden los desempleados
es fundamentalmente temporal – un 80% frente a un 13% de empleo
indefinido. Es cierto que la proporción de acceso a un
empleo indefinido ha aumentado en este trimestre, pero estas
diferencias tienen un componente estacional, dado que las
proporciones observadas en este trimestre son idénticas a
las observadas hace exactamente un año.

• La duración del desempleo se manifiesta
como el factor más importante para la salida hacia el
empleo. Llevar desempleado menos de un mes multiplica por seis la
probabilidad de acceso a un empleo con respecto a llevar
desempleado más de un año.

• Finalmente, no cobrar subsidio multiplica por dos
la probabilidad de acceder a un empleo desde el desempleo. Esto
indica que el cobro de subsidio provoca un efecto desincentivador
en la búsqueda de empleo, al elevar las condiciones que se
exigen para aceptar un trabajo. La consecuencia fundamental es
que el cobro del subsidio retrasa la salida al empleo de los
individuos desempleados.

Aunque muchas de las "características" del
desempleo y empleo español resultan representativas de la
problemática europea, para aquellos que puedan tener dudas
o presuponer cierta exageración por mi parte,
permítanme adjuntarles las "conclusiones" del
Capítulo 5 – Income poverty and income inequality
correspondientes al Informe Income and living conditions in
Europe – 2010 del Eurostat Statistical Books (que en
el siguiente Anexo se transcribe en su integridad). Se recomienda
muy especialmente analizar los Gráficos y Tablas del
Informe y de otras publicaciones de Eurostat adjuntas.

Conclusions (en
inglés en el original)

"The EU-SILC data on income inequality and poverty are
rich and varied. Here we bring together in telegraphic form some
of the main findings:

• 1 in 6 citizens are at-risk-of-poverty, and they
are to be found in all Member States;

• in three-quarters of Member States, the
proportion of children at risk of poverty exceeds the overall
proportion; there are real grounds for concern about child
poverty in Europe;

• success in reducing income poverty tends to go
with success in reducing income inequality; there are no
instances of countries pursuing a low poverty/high inequality
strategy;

• we do not yet know the impact of the economic
crisis, but the picture prior to 2008 was not a static one. Some
countries achieved sustained reductions in the proportions at
risk- of-poverty, but in the EU as a whole this progress has been
offset by reversals in other Member States;

• it is widely believed that income inequality was
increasing globally prior to the economic crisis, but the EU-SILC
data suggest that the EU picture is more nuanced, with some
Member States exhibiting declining inequality".

Se acabó. El Primer mundo se derrumba. Hace mucho
tiempo, dice el creciente lamento plañidero en Europa y
Estados Unidos, que nosotros mismos necesitamos ayuda. Nosotros
mismos, así lo sienten millones de electores incluso en
las regiones urbanas en expansión, somos los estafados por
los nuevos tiempos.

Sálvese quien pueda, es el lema. Sólo que:
¿Quién puede? Porque tras la victoria del
capitalismo no se ha alcanzado en modo alguno el "fin de la
historia", que el filósofo americano Francis Fukuyama
proclamaba en 1989, sino el fin del proyecto que tan osadamente
se llamó "la modernidad".

Un cambio de época de dimensiones globales ha
comenzado, dado que no son el ascenso y el bienestar, sino la
decadencia, la destrucción ecológica y la
degeneración cultural las que determinan a ojos vista la
vida cotidiana de la mayoría de la Humanidad.

Los datos son conocidos, pero debido a las fuerzas
liberadas por la globalización aparecerán en breve
bajo una nueva luz: la quinta parte rica de todos los Estados
decide sobre el 85% del PIB mundial, sus ciudadanos desarrollan
el 85% del comercio mundial y poseen el 85% de todos los ahorros
internos… también esto es una declaración de
bancarrota.

Anexo VI –

Eurostat Statistical
Books

Income and living conditions in Europe –
2010

Capítulo 5 – Income poverty and income
inequality

5.1 Introduction

5.1.1 Aim of this chapter

This chapter focuses on the financial dimensions of
poverty and inequality.

Income is an important variable for Europe"s households.
People are naturally concerned with how much they receive each
month in the form of earnings (from employment or self
employment), pensions, government transfers (such as unemployment
benefits, family benefits or sick pay), and from their savings.
In this chapter, we examine the distribution of income in the 27
Member States of the European Union (EU-27). Are there large
differences within and across countries? In which countries are
the differences largest? Particular concern attaches to those
households which, according to the EU definition, are
"at-risk-of-poverty" as this is one of the three indicators that
form the new EU Headline Target on social inclusion adopted by
the June 2010 European Council in the context of the Europe 2020
Agenda.

The chapter has four main aims:

1. to identify (in the remainder of Section 5.1) the
particular role of the EU-SILC data as a source of evidence about
income inequality and poverty

2. to analyse (Section 5.2) headline indicators for
income poverty and inequality that has been agreed at EU level,
with particular reference to the cross-country
patterns

3. to examine (Section 5.3) changes over time in income
inequality and poverty

4. to consider (Section 5.4) how the EU indicators based
on the EU-SILC data can be used in monitoring the Europe 2020
Agenda.

From the chapter, the reader will, we hope, learn about
the income dimension of poverty and social exclusion in the
EU-27, as shown in the EU-SILC data, and how this evidence
relates to that from other sources. The chapter looks back in
time, to see how (income) poverty and inequality have changed in
recent years, and forward in time to consider the implications of
the Europe 2020 Agenda.

5.1.2 Role of EU-SILC

As described in Chapter 2, EU-SILC is not a common
survey across countries. In this respect, it differs from its
predecessor, the European Community Household Panel (ECHP), which
was based on a standardised questionnaire (the ECHP ran from 1994
to 2001 in most of the then 15 EU countries, providing
comparative data on income and living conditions for the years
1993 to 2000). EU-SILC is a harmonised data framework involving
ex ante standardisation but allowing countries a large degree of
flexibility in the underlying source(s) and some flexibility in
the concepts and definitions. For example, while in the ECHP the
income reference period was the previous year, the EU-SILC income
reference period may be a fixed 12-month period (such as the
previous calendar year or tax year) or a moving 12-month period
(such as the 12 months preceding the interview) or be based on a
comparable measure. (2)

EU-SILC is not based on a common questionnaire used in
all countries, but on a common ex ante framework that defines the
harmonised "target variables" to be collected/produced and
provided to Eurostat by the national statistical institutions.
The aim of this procedure was to facilitate EU-SILC being
embedded within the national statistical systems, allowing the
results to be produced at a lower additional cost in terms of
resources, while serving a common EU purpose. The intention in
allowing a degree of flexibility is to secure, not input
harmonisation, but output harmonisation. Output harmonisation in
EU-SILC is sought through the use of common guidelines and
procedures, common concepts (e.g. that of "household") and of the
information produced. In this respect, it may be contrasted with
ex post standardisation, where data from different sources are
processed to put them as far as possible on a common basis, as in
the Luxembourg Income Study (LIS). In this case, the aim is again
output harmonisation, but without an ex ante framework. The scope
for ex post standardisation is limited by the constraints imposed
by the original survey designs or other sources (such as data
from administrative/ register records).

Finally, EU-SILC may be contrasted with meta analyses
that take, not the microdata, but the results from different
sources and seek to put them in a common framework. In the study
of income inequality, this approach was particularly developed by
Simon Kuznets (1963). In the case of both income inequality and
poverty, a lead was taken by the OECD, who published the study by
Sawyer (1976), assembling results from some dozen countries, and
later Atkinson, Rainwater and Smeeding (1995) which covered 17
countries.

The current OECD work involves "a regular data
collection … (at around 5-year intervals) through a
network of national consultants" (2008, p. 47). The national
experts "apply common conventions and definitions to unit record
data from different national data sources and supply detailed
cross-tabulations to the OECD" (2008, p. 41). This procedure of
"customising results" may be seen as lying between that of LIS,
which produces microdata, and that of Kuznets, where the results
are pre-defined. It has the advantage over meta-analyses of
pre-imposing a degree of standardisation but "its disadvantage is
that it does not allow accessing the original microdata, which
constrains the analysis that can be performed" (OECD, 2008, p.
41); directly related to this disadvantage, it also seriously
hampers the possibility of controlling the quality of the data
received.

In short, we have a "hierarchy" of degrees of
standardisation:

1. common survey instrument (ECHP);

2. ex ante harmonised framework (EU-SILC);

3. ex post standardised microdata (LIS);

4. ex post customised results (OECD);

5. meta-analyses of results (Kuznets).

Presenting them in this rank order may seem to imply a
quality ranking (with 1 at the top). However, it should be borne
in mind that tighter requirements of standardisation may have a
cost in terms of reduced accuracy in the final statistical
outcomes. In particular, a common set of variables may have
differing significance in different countries, and a degree of
flexibility may allow national statistical institutions to
provide data better suited to purpose. Input harmonisation does
not necessarily ensure output harmonisation. Different sources
may be appropriate in different countries. For example, the use
of tax records may allow superior income data to be collected in
some countries but may not be possible or reliable in other
countries. The ultimate validity of the results may be greater
where countries are allowed to make use of register data, and not
constrained to take income data from survey
interviews.

The EU-SILC procedure may therefore be seen as a balance
of considerations. There is a cost in that greater flexibility
may lead to lower comparability, but this may allow data to be
drawn from different sources including sources other than
household surveys. It may also have been instrumental in allowing
Member States to reach agreement that EU-SILC could be adopted on
a continuing annual basis. In this respect, there is an important
difference between EUSILC, on the one hand, and the LIS and OECD
data, on the other hand. The results in the OECD report Growing
Unequal? (OECD, 2008) relate to the mid-80s, mid-90s, and
mid-2000s. Such decadal observations are valuable but of limited
use to policy-makers. LIS has more frequent observations,
approximately semi-decadal:

Waves I (around 1980), II (around 1985), III (around
1990), IV (around 1995), V (around 2000), and VI (around 2004).
But the data are not annual.

(2) In practice, except for Ireland and the United
Kingdom, the income reference period is for all EU countries the
calendar year prior to the Survey Year. In Ireland, the survey is
continuous and the reference period is the last 12 months. In the
UK, current income is collected and annualised with the aim of
referring to the current (survey) year – i.e. weekly estimates
are multiplied by 52, monthly estimates by 12, etc. (Eurostat,
2009).

The essential requirement of (timely) annual data is
apparent from the recent economic and financial crisis. The
occurrence of such events will only by chance correspond to the
decadal or semi-decadal measurements. Data for 2004, the central
year for Wave VI in LIS, and the year taken for 23 of the 30
observations analysed by the OECD in their 2008 report (2008,
Table 1.A2.3), are too far distant to provide a benchmark for
monitoring the impact of the crisis and the subsequent recession.
(Indeed, even annual data may not always be sufficient for
monitoring purposes – see the discussion on timeliness and
frequency at the OECD March 2009 Roundtable on Monitoring the
effects of the financial crisis on vulnerable groups of society
(3) and Section 18.2.3 of Chapter 18.)

EU-SILC has therefore a distinctive role on the
international scene. At the same time, it is important to examine
how the findings relate to those in other cross-country sources.
The OECD in its 2008 report makes exactly such a comparative
analysis, and the present chapter uses this analysis in Section
5.2 when comparing the EU-SILC evidence on income inequality and
poverty with that in other international sources.

5.2 Income poverty/inequality across countries and
comparison with international sources

5.2.1 Evidence from EU-SILC on the risk of
poverty

The chapter begins with the key income-based indicators
from EU-SILC Survey Year 2008.

"Income" refers here to the total household disposable
income; it includes cash transfers and is net of income taxes and
social insurance (4)

In order to reflect differences in household size and
composition, total household income is divided by an equivalence
scale (called the modified OECD scale), which gives a weight of 1
to the first adult, 0.5 to other household members aged 14 and
over and 0.3 to each child aged under 14. This means that, for a
couple and 2 children, income is divided by 2.1 (1 + 0.5 + 0.3 +
0.3), so that an annual income of € 10.500 becomes an
equivalised income of € 5.000 which is artificially assigned
to each of the four household members (i.e. also to each of the
two children). As explained above, the data in the 2008 Survey
are based on the income reference year 2007 (except in Ireland
and the United Kingdom). The reader should bear in mind that we
are considering annual income in 2007 in relation to the
household circumstances at the time of interview in 2008. There
may have been changes in these circumstances, such as the arrival
of a new baby.

(3)See:http://www.oecd.org/document/2/0,3343,en_2649_33933_42507906_1_1_1_1,00.html.contributions.

(4) The definition of income used here excludes imputed
rent, i.e. the money that one saves on full (market) rent by
living in one"s own accommodation or accommodation rented at
below-market rent. It also excludes non-cash transfers, such as
education and healthcare provided free or subsidised by the
government. Finally, as explained in Chapter 2, it also excludes
pensions from private plans (which as from the second half of
2010 will be incorporated in the EU-SILC income definition for
all – past and future – waves) and most non-monetary income
components. Income is neither top-coded nor
bottom-coded.

The EU headline indicator of (income) poverty/inequality
is the proportion of the population living "at-risk-of-poverty",
defined as those living in households whose total equivalised
income is below 60 per cent of the median national equivalised
household income. It is thus a relative concept. The equivalised
income of € 5.000 for the four members of the family
described above is compared with 60 per cent of the median in the
Member State in which they live. Table 5.1 provides the value of
the national income poverty thresholds for each Member State for
a family consisting of 2 adults and 2 children below 14. To make
them more comparable, because the cost of living can vary a lot
from one country to the next, these thresholds are expressed in
Purchasing Power Standards. (5) So, if we take our example above
and assume that this family has an income of 10 500 Purchasing
Power Standards (rather than euros), then the four members of
this family would not be considered at risk of poverty in eight
EU countries (all of them are New Member States: Bulgaria, the
three Baltic States, Hungary, Poland, Romania and Slovakia); in
the remaining 19 EU countries, they would be considered income
poor.

Figure 5.1 shows the standard bar chart
for the percentage of people living in households at risk of
poverty. The countries covered are those in EU-27. The average
for the EU-27 as a whole is 16.6 per cent, which means that 1 in
every 6 of EU citizens are at risk of poverty, or around 80
million people. (6) The rate for the 12 "new" Member States
(NMS12) was 17.3 per cent, a little but not much higher than for
EU-15 with a rate of 16.4 per cent. It is certainly not the case
that those at risk of poverty on the EU definition are mostly to
be found in the New Member States: of the 80+ million at risk of
poverty in EU-27, 64 million are to be found in the EU-15. In
Germany, alone, there are 12½ million; in the United
Kingdom 11½ million; in Italy 11 million; and France and
Spain together account for a further 17 million. In the largest
New Member State, Poland, the number of people at risk of poverty
is about 11½ million.

On this relative poverty measure, New Member States are
to be found at both ends of the national figures, which range
from 9-11 per cent (in the Czech Republic, the Netherlands, and
Slovakia) to 20 per cent or more in Lithuania, Greece, Bulgaria,
Romania and Latvia. The picture shows that, in terms of
cross-country variation, there is a relatively continuous
gradation. It is not easy to draw sharp dividing lines on the
basis of income poverty performance. There are only four jumps
from an adjacent country in excess of 1 percentage point:
Finland/ Malta (1.1), Poland/ Portugal (1.6), Bulgaria/ Romania
(2), and Romania/ Latvia (2.2).

(5) On the basis of Purchasing Power Parities (PPP),
Purchasing Power Standards (PPS) convert amounts expressed in a
national currency to an artificial common currency that equalises
the purchasing power of different national currencies (including
those countries that share a common currency).

(6) This "EU-27 average" is a weighted average of the 27
EU Member States" percentages, in which each country percentage
is weighted by the country"s population size. EU-15, NMS10 and
NMS12 averages presented in this chapter are calculated in the
same way. For the countries included in the various geographical
aggregates, see the list of "Country official abbreviations and
geographical aggregates" (Appendix 2). of income poverty
performance. There are only four jumps from an adjacent country
in excess of 1 percentage point: Finland/ Malta (1.1), Poland/
Portugal (1.6), Bulgaria/ Romania (2), and Romania/ Latvia
(2.2).

From Figure 5.1, we can assess the ambition of the
Europe 2020 Agenda "to lift at least 20 million people out of the
risk of poverty and social exclusion" (European Council, 2010).
Measured in terms of the at-risk-of-poverty rate, (7) it would
mean reducing poverty and social exclusion by 4 percentage
points. The EU-27 as a whole would have to match the performance
of Austria. It is also clear that attainment of this ambition
requires, as far as the at-risk-of-poverty indicator is
concerned, action by the six largest Member States. France,
Germany, Italy, Poland, Spain and the United Kingdom cannot stand
aside. If they were to do so, then reaching the 20 million
targets would require the virtual elimination of income poverty
in the other 21 Member States.

Who is "at-risk-of-poverty"? EU-SILC allows income
poverty rates to be calculated for many groups within the
population. Here we focus on just one group which has (rightly)
received a great deal of attention in recent years: the
proportion of children living in households at risk of poverty.
(8) This is referred to for short as "child poverty", although it
should be emphasised that what is being measured is the status of
the household where the child lives (see above example). It
should also be emphasised that no account is taken of the
possibly unequal sharing of income within the household. Figure
5.2 shows the child poverty risk rate in each country compared
with the overall poverty risk rate for Survey Year 2008.
Countries lying on the heavy line have the same rate of child
poverty risk as overall population poverty risk. The cause for
concern about child poverty is that relatively few (only about a
quarter of the 27 EU Member States) are below this line. For
seven Member States, the child poverty rates are more than 5
percentage points above the overall rate – shown by those above
the dashed line in Figure 5.2. So that while in Hungary child
poverty rate is slightly below the EU average (19.7 vs. 20.1 per
cent), it is 7.3 per cent higher than the overall population
poverty rate. Above the dashed line are Luxembourg and Italy, but
the other 5 countries are New Member States. The overall child
poverty rate for the 12 New Member States is indeed 4 percentage
points higher than for EU-15 (23.1 vs. 19.3 per cent).

(7) This is in fact only one of three
indicators.

(8) See, for instance: Frazer and Marlier (2007), Social
Protection Committee (2008), Tárki (2010), Frazer, Marlier
and Nicaise (2010).

So far, we have been counting the number of people, or
the number of children, at risk of poverty. But how far do they
fall below? The final EU indicator considered here is the total
poverty risk gap. What is the total income shortfall? Figure 5.3
shows, in addition to the at-risk-of-poverty rate, the median
percentage by which households fall below the income poverty
line. For EU-27, the figure is 22 per cent, which means that half
of the at-risk-of-poverty population is living on less than 78
per cent of the income poverty threshold. Since the threshold is
60 per cent of median income, this means that the shortfall is
some 13 per cent of median income. What is of interest is that
the graduation is now much less smooth as we move across
countries. For half the Member States (those to the left of
Germany in Figure 5.3), the shortfall is between 15 and 20 per
cent, but for Germany and countries to its right the gaps range
from 16.5 to 32.3 per cent.

EU-SILC contains much further rich data about the risk
of poverty, but the evidence presented above from the 2008 Survey
(income year 2007) shows that the risk is pervasive, affecting
all Member States. New Member States are not concentrated at the
top of the scale. Looking to the future, achievement of a 20
million reduction requires action by the large Member States: the
largest six accounts for nearly three-quarters of the total at
risk of poverty.

5.2.2 Evidence from EU-SILC on income
inequality

To this juncture, we have focused on the bottom of the
income distribution. What is the overall extent of inequality?
Many are concerned that inequality was a factor contributing to
the economic crisis; others are concerned that the crisis will
exacerbate inequality. But just how unequal are incomes? The two
main indicators of income inequality used at EU level are shown
in Figure 5.4. The first is the ratio of the share of income
going to the top 20 per cent of the population (referred to as
the top quintile share) to that going to the bottom 20 per cent
(the bottom quintile share).

This ratio, also called S80/S20, varies from 3.4 to 7.3
across the EU Member States. There is an interesting geographical
pattern. The lowest ratios are found in some of the New Member
States (Slovenia, Slovakia, the Czech Republic and Hungary) as
well as in Austria and the Nordic countries. Then come Malta,
Benelux, Cyprus and France. In Southern Europe (except Cyprus and
Malta), Poland, the United Kingdom and Lithuania, the ratios are
between 5.1 and 6.1, and they are 6.5 or more in Bulgaria,
Romania and Latvia. For the EU-27 as a whole, the S80/ S20 ratio
is 5. It should be noted that the latter is the weighted average
of the 27 national ratios, in which each country ratio is
weighted by the country"s population size; it is thus not the
same as the ratio of the top to bottom quintile shares in the
EU-27 as a whole, which can be expected to be higher.

The second indicator of income inequality shown in
Figure 5.4 is the Gini coefficient, a summary measure, based on
the cumulative share of income accounted for by the cumulative
percentages of the number of individuals, with values ranging
from 0 per cent (complete equality) to 100 per cent (complete
inequality). The Gini coefficients vary a lot across countries,
from 23 per cent in Slovenia to 38 per cent in Latvia. (9) For
the EU-27 as a whole, the (weighted) averaged value is 31 per
cent. What do such values mean? The following hypothetical
calculation may be helpful. Suppose that the tax and transfer
system is approximately of the form of a uniform tax credit and a
constant tax rate on all incomes, that the government spending on
goods and services absorbs 20 per cent of tax revenue, and that
the Gini coefficient for disposable income is 48 per cent in the
absence of redistribution. Then, an increase in the tax rate of 5
percentage points would be needed to reduce the Gini coefficient
by 3 percentage points. (10) Since a tax rise of 5 percentage
points would be a challenge for any Finance Minister, this
suggests that a 3 point difference would be salient. This means
that moving across a vertical division in Figure 5.4 represents a
significant -in economic terms- difference.

(9) The scales for the two inequality indicators in
Figure 5.4 are different but the indicators move very closely
together. There is no reason why this should necessarily be the
case. A redistribution that affected only those between the
bottom quintile and the top quintile would have no impact on the
S80/S20 ratio but would affect the Gini coefficient as this
indicator considers the entire income distribution and not just
the top and bottom quintiles.

(10) See Atkinson (2003), p. 484. The Gini coefficient
is equal to half the mean difference divided by the mean.
Taxation with a constant marginal tax rate implies that the mean
difference is reduced by (1-marginal tax rate); the mean is
reduced by (1-average tax rate). 1 minus the average tax rate is
what is left for households after paying for government goods and
services: in this example, 80 per cent. With no redistribution,
the tax rate would be 20 per cent. So that the Gini coefficient
for disposable income would be the same as for pre-tax income. If
the marginal tax rate is raised to 25 per cent to finance
redistribution via a uniform tax credit, then (1-marginal tax
rate) becomes 75 per cent, while the average tax rate (allowing
for the credit) is unchanged. The Gini coefficient is therefore
reduced to 75/80 of its previous value: i.e. from 48 per cent to
48 per cent times 75/80, which equals 45 per cent.

Applying the criterion that 3 percentage points
represents a "salient" difference in the Gini coefficient, we
obtain a partial ranking of Member States. We cannot say that
inequality is different in France from that in Germany (in Survey
Year 2008), but there is a salient difference between the Gini
coefficients for France and the United Kingdom, as there is
between those for Sweden and France. On this basis, income
inequality is higher in Latvia than in any other country apart
from Romania, Bulgaria and Portugal. Income inequality can be
said to be lower to a salient degree in Slovenia than in all
Member States apart from Slovakia, the Czech Republic, Austria,
Hungary and the Nordic countries.

How is inequality in income related to income poverty?
Do the same countries have both low at-risk-of-poverty
proportions and low income inequality? There is no reason why
this should necessarily be the case. The share of the bottom 20
per cent may reasonably be taken as closely linked to the
incidence of income poverty, but this leaves considerable room
for differences in the other quintile group shares. A country may
for example have a share for the bottom 20 per cent of 11 per
cent, which -if equally distributed- would ensure an income equal
to 55 per cent of the mean. (11) Since the mean is typically
higher than the median, this could well be above 60 per cent of
the median and the poverty risk score could be zero. Such a (low
poverty risk) bottom quintile share could however be combined
with a relatively unequal distribution, such as 12, 13, 14 per
cent for the second to fourth quintile groups and 50 per cent for
the top 20 per cent. The S80/ S20 ratio would then be 4.55, which
is not much lower than the EU-15 average (4.88).

(11) The figure of 55 per cent is obtained by dividing
11 per cent by the group"s proportionate share (20 per cent):
11/20 = 0.55.

In fact, as may be seen from Figure 5.5, the at
risk-of-poverty rate is closely correlated with the degree of
income inequality as measured by the S80/S20 ratio (the same is
true with the Gini coefficient in place of the S80/S20 ratio,
although this is not shown here). There do not appear to be
countries with medium/high inequality and low poverty risk. A
simple regression shows that the inequality ratio explains 85 per
cent of the variance in the poverty rate, and that an increase in
the ratio from 3.5 to 4.5 is associated with a 3.4 percentage
point increase in the poverty rate.

5.2.3 Comparison with other cross-country
sources

There are now a variety of sources of internationally
comparative data on income inequality and income poverty. The
best known is perhaps the World Bank"s World Development
Indicators (WDI), which shows in its 2009 edition estimates of
the distribution of income or consumption for 136 countries in
the form of the Gini coefficient and the shares of income
quintile groups (World Bank, 2009, Table 2.9). The values for 24
out of the 27 EU countries (data for Cyprus, Luxembourg and Malta
are not included in the WDI table) are shown in Table 5.2,
together with the sources. There are two evident problems. The
first is that the data come from two different sources. It is
stated that data for "the high-income countries" are income data
taken from the LIS database, and this applies for 16 of the
countries. But for eight countries, all New Member States, the
data relate to expenditure and come from other sources. Secondly,
as explained earlier, the LIS data are not annual, and those used
in the 2009 WDI relate mostly to the year 2000 or, in seven
cases, even earlier. This latter point reduces significantly the
value of the WDI compilation. It certainly appears a little odd
that the data in the 2009 WDI table for Liberia and Morocco
relate to 2007, whereas the data for France are no more recent
than 1995. The former problem limits the comparability within the
EU, although the expenditure data may be more comparable with
those for middle-income and developing countries.

The question naturally arises as to why the WDI does not
employ the EU-SILC data, which would have the definite advantages
of being more current and of not mixing income-based and
expenditure-based estimates? The answer may depend on the
comparison of this new source with the longer established LIS and
with official sources such as the OECD. Here we may turn to the
OECD report (OECD, 2008), which contained a most helpful
comparison of the OECD estimates with EU-SILC (2005 data, income
reference year 2004) and LIS (mostly relating to years around
2000). There is relatively little discussion of the findings of
the comparison in the OECD report, perhaps because the results
appear reassuring. Their figures for the at-risk of- poverty
definition based on 60 per cent of the median are reproduced in
Figure 5.6. (12) The three bars show the estimates for each
country for the OECD, EU-SILC and LIS (in some cases one of the
latter two is missing).

(12) The comparison also includes four non-EU countries:
Iceland (IS), Norway (NO), Switzerland (CH) and Turkey
(TR).

In almost all cases, the estimates of poverty risk in
the three sources are close. Only for 9 of the 57 possible
comparisons is the difference equal to 3 percentage points or
more (although the estimates are rounded to the nearest integer,
so that some of the differences may be only 2.1). Three countries
(Germany, the Netherlands and the United Kingdom) account for six
of these discrepancies, and these differences are identified by
the OECD as a matter for concern. The differences in the case of
Germany are four (LIS/OECD) and five (EU-SILC/OECD) percentage
points. These differences are among those discussed further in
Section 5.3. It should also be noted that only one of the nine
discrepancies (for Sweden) concerns the comparison of the EU-SILC
and LIS estimates, which are generally closer.

The Gini coefficients of income inequality from the
three sources are compared in Figure 5.7. The general pattern is
similar. It has to be borne in mind, and this applies to both the
poverty risk figures (Figure 5.6) and the Gini coefficients
(Figure 5.7), that the definitions are not identical. The EU-SILC
estimates use the modified OECD equivalence scale described
above, whereas, a little strangely, the OECD does not use the
scale that bears its name, but uses a square root equivalence
scale, as in the LIS data. Use of this latter scale means that
income is divided by the square root of the household size (two
in the case of the four person household example), which means
that the relative position of different households will be
affected. This may well affect the comparison, as may the fact
that the OECD and EU-SILC data refer mostly to 2004, whereas the
LIS data refer to a variety of years around 2000.

All in all, there appears to be a high level of
coherence between the cross-country datasets. The data for
certain countries needs to be examined, but data created by the
EU-SILC framework approach do not seem to be out of line with
those assembled by the LIS or OECD methods.

5.3 Changes in income poverty and inequality over
time

5.3.1 Monitoring trends in EU-SILC

In the previous section, we have described the situation
in the EU in 2007 (the 2008 Survey Year related in nearly all
countries to incomes in 2007). But much of the interest of the
figures lies in how inequality and poverty are changing over
time. In this respect, it is frustrating that we can say little
about what has happened since 2007. At a time of economic crisis,
everyone, citizens and politicians alike, wants to be able to
monitor what is happening to living standards following the
financial crisis and the subsequent world recession. Who is
bearing the burden?

It is also important, however, to understand what was
happening before the economic crisis. How far had the EU been
successful in its 2000 declared ambition of achieving a
significant reduction in poverty and social exclusion? Was it the
case that there had been rising inequality, a factor which some
commentators have treated as a contributing to the crisis? Here
too we are limited as to what we can say. EU-SILC was launched in
2003, with income reference year 2002, on the basis of a
"gentleman"s agreement" in six Member States. The official
starting date for EU-SILC was Survey Year 2004 for EU-15 (minus
Germany, the Netherlands and the United Kingdom, plus Estonia),
with income reference year 2003. The New Member States that
joined the EU in 2004 (apart from Estonia) as well as Germany,
Netherlands and the United Kingdom, started with respect to
Survey Year 2005. Bulgaria entered in Survey Year 2006, and
Romania in Survey Year 2007. This means that there are data for
between 2 and 6 years -see Table 5.3. (As indicated previously,
the income reference year is different for Ireland and the United
Kingdom.)

Can we identify from this short EU-SILC time series
countries where income poverty and inequality are decreasing or
increasing? In the case of year-to-year changes, sampling errors
are clearly relevant. In the case of the at-risk of- poverty
rate, Lelkes et al (2009, Figure 1.10) show for Survey Years
2004-2006 10 countries where there were changes outside the 95
per cent confidence interval for the preceding year. (13) The
countries are equally divided in their direction of movement. The
"improvers" were Estonia, Ireland, Netherlands, Poland and
Slovakia. Those moving towards higher poverty risk were Finland,
Italy, Latvia, Luxembourg and Sweden.

Year-to-year variation on account of sampling error
certainly means that we should not attach weight to modest
changes in the at-risk-of-poverty rate over time. The sampling
errors reported for the 2005 EU-SILC for the proportion
at-risk-of poverty imply a one-sided 95 per cent confidence
interval of less than 1 percentage point for 11 of the 23
countries analysed and in all cases it is less than 2 percentage
points (Eurostat, 2008). Account has also to be taken of
non-sampling errors, as has been discussed in Chapter 3. These
considerations refer to the "supply side": the accuracy of the
estimates supplied by EUSILC (or other sources). It is indeed a
prerequisite that the observed performances are different. But we
have also to ask about the "demand" side. What differences are of
interest to the user? Here the Europe 2020 targets provide a
point of reference. The ambition of the EU is to reduce those at
risk of poverty and social exclusion by 20 million. In terms of
the at-risk of- poverty rate, this would mean a reduction of
approximately a quarter (20 million out of 80 million) or, put
differently, a reduction of about 4 percentage points for the
EU-27 as a whole. Applied at the level of individual countries, a
reduction of a quarter would mean between 2½ and 6½
percentage points. Taking account of both supply and demand side
considerations, we pay particular attention in what follows to
changes of 2 percentage points or larger. (13) We have here
excluded Hungary on the grounds explained by Lelkes et al, that
there appear to be problems with the estimate for 2006 (Survey
Year).

(13) We have here excluded Hungary on the grounds
explained by Lelkes et al, that there appear to be problems with
the estimate for 2006 (Survey Year).

5.3.2 Changes in poverty risk

What do we learn from Table 5.3 if we run our cursor
over the figures identifying cases where the Survey Year 2008
data represent a change of 2 percentage points of more in the
proportion at-risk-of-poverty relative to an earlier year? For
six Member States, we have EU-SILC data covering six years. For
only one -Ireland- did an earlier year have a proportion that
differed by 2 percentage points or more. Between 2003 and 2008,
Ireland moved from having an above EU- 27 average
at-risk-of-poverty rate to one that is below it. In the other
five countries there were falls, but these were smaller and in
some cases reversed: for example, in Greece the proportion fell,
then rose, and then fell.

For the countries with five years of data, Finland saw
an increase in the at-risk-of-poverty rate in each year and ended
with a figure 2½ percentage points higher – an increase of
nearly a quarter. In the opposite direction, Portugal, with an
initially high at-risk-of-poverty poverty rate, showed a
reduction of 2 percentage points. Sweden showed both falls and
rises of at least 2 percentage points, but ended in 2008 with an
at-risk-of-poverty rate less than 1 percentage point different
from that in Survey Year 2004.

There is some tendency for convergence, with high
poverty risk countries tending to show reductions in income
poverty rates (although not universally) and for there to be
slippage in the opposite direction among the previous better
performers. This is illustrated by the fall between Survey Years
2005 and 2008 in the at-risk-of poverty rate for the NMS10 group,
i.e. the 10 countries that joined the EU in 2004, where the rise
in Latvia was more than offset by the falls in Poland and
Slovakia.

In sum, the picture prior to 2008 was not a static one.
Some countries have achieved sustained reductions in the
proportions at-risk-of-poverty, but in the EU as a whole this
progress has been offset by reversals in other Member
States.

5.3.3 Changes in income inequality

It is widely believed that income inequality has been on
the increase. This belief is much influenced by the experience of
the United States, but has the same happened in
Europe?

The EU-SILC data suggest that the EU picture is more
nuanced. Tables 5.4a and 5.4b show the EU-SILC results for the
two inequality indicators used in the previous section. Overall
the weighted-average indicator for EU-27 hardly changed between
Survey Years 2005 and 2008. (Again it has to be remembered that
this is the average of national inequalities, not the overall EU
inequality taking account of between-country differences.) This
did not reflect stasis. There were country changes, and indeed
some degree of convergence. The average for the 10 New Member
States showed a reduction in inequality: the S80/S20 ratio went
from 5.6 to 4.6, and the Gini coefficient fell by nearly the 3
percentage points that we described as a "salient" change in the
previous section. There were falls of more than 3 percentage
points in the Gini coefficient in Estonia and Poland.

If we look at EU-15, then among the larger countries
there is little evidence of change in France, Italy, Spain and
the United Kingdom. The most evident change in the EU-SILC data
is the rise in the S80/S20 ratio (from 3.8 to 4.8) and in the
Gini coefficient (from 26 to 30 per cent) in Germany. (During the
same period, the at-risk of- poverty rate measured on the basis
of EUSILC also increased sharply in Germany, from 12.3 per cent
to 15.3 per cent; we come back to these estimates in Section
5.3.4.)

These country differences underline the need to compare
the EU-SILC findings with those from national sources, to which
we now turn.

5.3.4 Comparison with national sources: a case
study

The provision of data on income inequality and poverty
has a long history in individual Member States. Whereas in some
countries the launching of ECHP, and now EU-SILC, was a stimulus
to collect distributional data on a regular basis, and the EU
reference data provide the main national source, in quite a
number of countries there are long running regular series,
typically annual, for income inequality and poverty. In the
latter cases, it is important to compare the findings from EUSILC
with those from the national sources. (14)

Differences between the results from EU-SILC and from
national sources do not imply that one source is necessarily in
error or that one source is to be preferred. Differences may
arise for several reasons, including the following
ones:

• differences in the population covered (for
example, the exclusion in EU-SILC of the non-household
population, whereas national sources may cover people living in
collective households or institutions);

• differences in the definitions adopted (for
example, of the unit of analysis or of total income or of the
equivalence scale);

• differences in timing (for example, in the
definition of the income reference period or

in the scheduling of the interviews).

On the other hand, differences may be attributable to
identifiable shortcomings. Response rates may be different,
particularly where there is attrition from a panel survey. The
extent of reporting may vary, as may be indicated by checks
against known income totals.

In this section, we take one comparison with national
sources as a case study. The case study is that of Germany. There
are three reasons for this choice. First, Germany is the largest
Member State. Secondly, the EU-SILC findings show that Germany
was one of the countries to exhibit rising income poverty and
inequality. Thirdly, there have been a number of academic studies
making comparisons between the EU-SILC results and those from
other sources.

(14) It would also be possible to use the findings from
the ECHP – see Lelkes et al (2009). The issue of the continuity
of indicators during the transition between ECHP and EU-SILC is
considered by Eurostat (2005).

The main national sources of household data in Germany
are the Microcensus, the Income and Expenditure Survey and the
German Socio- Economic Panel (GSOEP) conducted by the Deutsches
Institut für Wirtschaftsforschung (DIW). The relationship
between these sources has given rise to considerable discussion.
Hauser (2008) has compared the EU-SILC results for 2005 with the
Microcensus and GSOEP. He noted that two features of the German
EU-SILC (reliance on a postal survey and delay in developing a
fully random sample) led there to be ex ante doubts about the
EU-SILC German data. He reported that there were "significant
deviations in the coverage of poorly integrated foreigners, small
children and the level of education, as well as the ratio of
house/apartment owners and the employment ratio" (2008, p.
2).

The implications for the EU commonly agreed indicators
have been discussed by Lelkes et al. Drawing on Frick and Grabka
(2008), they note that "the proportion of the population at risk
of poverty is about 5 percentage points lower when calculated
from the EU-SILC data than when calculated from (GSOEP)" (2009,
p. 44). They cite figures from GSOEP (EU-SILC figures in
brackets) of income poverty rates of 16.3 per cent for Survey
Year 2004, 16.7 (12.0) per cent (15) for Survey Year 2005, and
18.0 (12.7) per cent for Survey Year 2006. These are large and
disconcerting differences, but since then the GSOEP methodology
has been revised with regard to weighting and the imputation of
missing income. The estimates given by Frick and Krell (2010,
Table 2) show income poverty rates of 13.9 per cent for Survey
Year 2005 and 14.3 per cent for Survey Year 2006. For these two
years, the difference is now reduced.

If that were the end of the story, then one might be
reassured. However, a correspondence between the aggregate
(income) poverty rates does not imply that the constitution of
the poverty population is the same. We need to go further and
examine, for example, the household composition. We need to
consider the implications of the differences in the degree of
mobility found in the longitudinal data by Frick and Krell
(2010). Moreover, the EU-SILC data for Survey Year 2007 show (see
Table 5.3) a rise in the income poverty rate by 2.5 points (to
15.2 per cent), maintained as 15.3 per cent in Survey Year 2008;
by contrast, GSOEP estimates decrease between these two years
(from 14.3 to 13.6 per cent). Not only is the direction of
movement in the opposite direction from the GSOEP figures, but
the magnitude of the increase in the EU-SILC values is hard to
understand.

(15) The figure of 12.0 from EU-SILC corresponds to that
of 12.3 in Table 5.3.

In the same way, for the income inequality measures, the
GSOEP (calculations of Frick and Krell, Table 2) show a broadly
stable S80/S20 ratio (4.4 for Survey Year 2006 and 4.3 for Survey
Year 2007), whereas the EU-SILC data show a rise from 4.1 to 5.0.
Frick and Krell comment that the size of the latter increase is
"exceptionally difficult to comprehend or explain based on the
evolution of income inequality in Germany over the last few
decades – particularly given the positive labour market
conditions at the end of the period" (2010, p. 18). They go on to
explore the sources of the discrepancy in the sample composition
and weighting methods.

The issues raised by this comparison with national
sources are technical ones, but there is clearly need to invest
in their resolution. Such comparisons are necessary to secure
acceptance of the EU reference source at the national level.
Results that indicate income poverty rates very different
(whether higher or lower) from those reported nationally are
likely to raise questions and potentially generate political
debate. Where levels and/or trends over time are different in
EUSILC and in national sources, it becomes difficult to draw
conclusions about the effectiveness of policy measures taken to
reduce income poverty and inequality.

5.4 Monitoring progress

EU-SILC data play a central role in the promotion of the
Social Agenda of the EU. (16) In this section, we consider the
use of EU-SILC data in forensic policy analysis, particularly for
monitoring the Europe 2020 Agenda. As we emphasised earlier in
this chapter, the significance of changes in income inequality
and poverty depends on both supply and demand side
considerations. The suppliers of the data can advise on the
statistical validity of observed changes, and the demanders can
calibrate the policy significance of the changes. Both of these
are relevant to monitoring, but we focus here on the less
discussed side: the criteria stemming from the use of the EU-SILC
data.

5.4.1 An at-risk-of-poverty target

The original proposal by the Commission was of a
Headline Target set in terms of the numbers at-risk-of-poverty,
with the aim of reducing these by 20 million, and we begin by
considering this case. As we have seen in Section 5.2, such a
target is ambitious; it is also in need of further amplification.
We discuss two aspects here. First, it needs to be anchored in
time. (17) The 80+ million figure for those at risk of poverty
relates to Survey Year 2008, typically income year 2007. Even
though it is still being discussed, it is likely that this is to
be taken as the base figure. This -perfectly reasonable- choice
would imply that, in the early years of monitoring, performance
will be affected by the economic crisis. The lags mean that the
incomes of the present year (2010) will only enter the assessment
based on EUSILC Survey Year 2011 whose data will become available
at the end of 2012. Does this mean that the at-risk-of-poverty
percentage will initially rise? The implications are not in fact
clear. The economic crisis has affected both the incomes of those
at the bottom of the income distribution and the median income
against which poverty risk is being measured. If, for example,
pensions have been maintained but incomes in work have fallen,
then fewer pensioners may be below the income poverty threshold.
On the other hand, there are reasons to fear that the unemployed
living in households where there is a single earner have suffered
falls in income.

(16) On the "Renewed Social Agenda" adopted by the
European Commission on 2 July 2008, see:
http://ec.europa.eu/social/main.jsp?catId=547.

(17) We are grateful to Holly Sutherland for a helpful
discussion about these income poverty threshold.

To the delays in monitoring, we have to add the likely
delays in policy impact. Some policies adopted by Member States
may have immediate impact. An increase in child benefit payments
can raise family incomes immediately. However, other policies,
such as investment in early childhood, or in education, may only
yield fruit after a number of years. These two sources of delay
mean that we should look to a mid-decade review in 2015 as a
crucial stage in the evaluation of the Europe 2020
agenda.

Secondly, the overall EU target has to be translated
into national targets. As discussed by Marlier et al (2007, p.
216), this can be done in different ways. One approach is to
require each country to scale down their at-risk-of-poverty
percentage by the same amount – around a quarter. Countries with
a rate of 20 per cent would have a target of 15 per cent;
countries with a rate of 12 per cent would have a target of 9 per
cent. Alternatively, Member States may be set the task of
emulating the best performers. The underlying arithmetic does not
however allow great flexibility. Even if we start with the Member
States with the highest proportions at risk, the total of 20
million is only reached when the majority of Member States are
contributing. The trade-off is illustrated for Survey Year 2008
in Figure 5.8, which shows the reduction in the number of income
poor in the EU-27 as a whole achieved if the maximum national
at-risk-of-poverty percentages are reduced to x per cent, with x
being progressively lowered as we move to the left. For example,
if all countries with at-risk-of-poverty rates above 17 per cent
reduced their rates to 17 per cent, and if the proportions at
risk in the other Member States remained unchanged, then the
total number of income poor in the EU would be reduced by 6
million. This would require action by 10 of the 27 EU Member
States. To achieve a reduction of 20 million, the maximum income
poverty percentage would have to be reduced to below 13 per cent,
and would require action by 19 Member States. Put another way,
reducing the total by 20 million implies an overall income
poverty rate of 12.6 per cent, and there are not many Member
States with rates below this: Austria, the Czech Republic,
Denmark, Hungary, the Netherlands, Slovakia, Slovenia, and
Sweden.

5.4.2 Three indicators (18)

The June 2010 European Council finally opted for a more
complex Headline Target for promoting social inclusion at EU
level. The target is defined on the basis of three indicators:
the number of people at risk of poverty (EU definition, as used
above), the number of materially deprived people, and the number
of people aged 0-59 living in "jobless" households (defined, for
the purpose of the EU target, as households where none of the
members aged 18-59 are working or where members aged 18-59 have,
on average, very limited work attachment). The target consists of
lowering by 20 million the number of people who are at risk of
poverty and/or severely deprived and/or living in "jobless"
households. The European Council Conclusions indicated that this
"would leave Member States free to set their national targets on
the basis of the most appropriate indicators, taking into account
their national circumstances and priorities" (European Council,
2010, p. 12).

This decision introduces further complexity into the
monitoring process, and it is not obvious how the decisions of
individual Member States can be reconciled. The extension to more
indicators means that the target population is larger, as is
illustrated schematically in Figure 5.9 for the three indicators
according to the EU-SILC 2008 results. A little over 80 million
people live in households at risk of poverty, but a further 40
million live in households that are not at risk of poverty but
are defined as jobless and/or materially deprived according to
the two newly agreed headline indicators. The total is 120
million for the EU-27 as a whole. The union is quite a lot larger
than the intersection. Only some 7 million people (or less than 6
per cent) live in households identified under all three criteria,
and only 28 million are identified fewer than two of the
criteria. Well over two-thirds are identified under only one of
the criteria. Put differently, it would be quite possible for the
20 million reduction target to be achieved by reducing the
proportion living in jobless households, without any reduction in
the number living in households at risk of poverty.

(18) For further information on the "Europe 2020"
indicators, see: http://
epp.eurostat.ec.europa.eu/portal/page/portal/europe_2020_indicators/headline_indicators.

The degree of overlap between the households identified
under the three criteria varies across Member States, and this
has to be taken into account when monitoring progress. Figure
5.10 shows for each of the 27 Member States the proportions
living in households identified under all three criteria and by
two of the three criteria. The differences across countries do
not follow any evident pattern. The intersection is smaller than
average in Luxembourg, the Netherlands and the Nordic countries,
but also in Spain, Cyprus, Greece and Portugal; it is larger than
average in a number of the New Member States, but also in
Ireland, France, Austria, Germany and Belgium.

It is evident that progress in terms of combating
poverty and social exclusion will depend very much on (1) the
national choice of priorities and (2) the extent to which the
chosen policies are directed at households where the criteria
overlap. Of particular concern is the possibility that a country
targeting one indicator may adopt policies that worsen the
situation according to the other indicators. There is already
evidence that fiscal pressures are leading countries to scale
back income support for the unemployed. It is possible that this
may lead some people to take jobs, and hence reduce the
proportion of jobless households, but at the cost of reduced
household incomes and the risk of falling below the income
poverty threshold.

The one conclusion that is clear is that the European
Commission will need to monitor the three indicators for all
Member States, regardless of national priorities. It is only in
this way that coherence can be maintained at an EU level. What
seems also important is that if the Europe 2020 Agenda has
highlighted three indicators of poverty and social exclusion,
Member States – and the EU as a whole- should however continue to
monitor performance according to the full set of commonly agreed
indicators underpinning EU coordination and cooperation in the
social field.

5.5 Conclusions

The EU-SILC data on income inequality
and poverty are rich and varied. Here we bring together in
telegraphic form some of the main findings:

• 1 in 6 citizens are at-risk-of-poverty, and they
are to be found in all Member States;

• in three-quarters of Member States, the
proportion of children at risk of poverty exceeds the overall
proportion; there are real grounds for concern about child
poverty in Europe;

• success in reducing income poverty tends to go
with success in reducing income inequality; there are no
instances of countries pursuing a low poverty/high inequality
strategy;

• we do not yet know the impact of the economic
crisis, but the picture prior to 2008 was not a static one. Some
countries achieved sustained reductions in the proportions at
risk- of-poverty, but in the EU as a whole this progress has been
offset by reversals in other Member States;

• it is widely believed that income inequality was
increasing globally prior to the economic crisis, but the EU-SILC
data suggest that the EU picture is more nuanced, with some
Member States exhibiting declining inequality.