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.