Research Overview and Preliminary Analysis on CBD and SAMIP: Setting the conditions for a longitudinal study Rosalba Manna Research Fellow in Statistics, University ”Parthenope” of Naples, Italy Statistician at the National Institute for Educational Research, Florence, Italy and Rocco Palumbo Research Fellow in Organization Studies, University of Salerno, Italy
Who we are Rocco Palumbo Research Fellow in Organization Studies Dept. Management and Innovation Systems, University of Salerno Lecturer of Human Resource Management and Organizational behavior at the School of Medicine, University “Federico II” of Naples Scientific coordinator of the HLS-IT and FLS-IT projects
Who we are Rosalba Manna Research Fellow in Statistics Dept. Management and Quantitative Studies, University “Parthenope” of Naples Data analyst and statistician at the National Institute for Educational Innovation and Research (INDIRE), Florence Fellow statistician at the Italian Institute of Statistics (ISTAT) Rome, Italy
Aims of our visiting at SOFI 1. To measure the effect of child benefits on income inequality in two group of countries using Child Benefit Dataset (CBD) of Social Policy Indicators Database (SPIN). Income data provided by EUROSTAT were also used. The following two groups of Countries were contemplated: • Italy, France, Austria and Belgium • Sweden, Denmark, Norway and Finland The time span ranged : • from 1995 to 2010
Aims of our visiting at SOFI 2. To investigate the relationship between different forms of social assistances – as assessed by the SAMID interim dataset – and a set of socio- economic outcomes, including net earnings, material deprivation and crude divorce rates. The following European Countries were contemplated: Austria, Belgium, Denmark, Finland, France, Germany, Ireland, Italy, Lichtenstein, Spain, Portugal, and UK. The time span ranged : • From 1990 to 2013 for most of Countries • From 1990 to 2009 for Italy • From 1996 to 2013 for Portugal
Decomposition methods A regression based decomposition technique was implemented, following the Pyatt (1976) approach The analysis exploited the potential of panel data, with reference to the pooling of observations on a cross-section of countries over several time periods.
Pyatt`s decomposition Pyatt`s decomposition allows to compute Gini index within and between group inequality decomposition. It provides the Gini coefficient for the whole population, for each subgroup specified. Any group is a categorical variable (in our case: country) which determines the subgroups in which the population is grouped. We choose this method to capture the effects both across countries (from panel data estimation with fixed effects) and within different countries.
Overview decomposition methods: Traditional approaches • INCOME SOURCE: This method decomposes the Gini coefficient by income source, using the approach described in Lerman and Yitzhaki (1985) and Stark, Taylor, and Yitzhaki (1986), which allows the calculation of the impact that a marginal change in a particular income source has on inequality. • POPULATION SUBGROUPS: This method estimates a range of inequality and related indices commonly used by economist to calculate inequality measures by subgroups (gender gap, educational levels, etc…).
Regression-based decomposition methods SHAPLEY VALUE AND FIELDS APPROACH : The regression-based method allows to quantify the contribution of a set of factors to inequality, while taking into account the correlations among them. Shapley Value and Fields methods allow to measure in micro and macro analysis the relative contributions of any factors to inequality. OAXACA-BLINDER (1973) This method performs a decomposition of the mean outcome differential of linear and nonlinear regression models, as described by Bauer and Sinning (2008). Daymond and Andrisani (1984) proposed an extension of the B-O decomposition.
Income generating model 1. First step: • Specification and estimation of an income generating function • The log of income is regressed on some explanatory variables: • Employment-based child benefit • Total child benefit (Child tax allowance+Child tax credit+Child tax rebate) • Etc.
Model Selection • Second step: A model for longitudinal data with fixed effects is selected: Y X i i i i i Where: • Y i is the vector Y = (Y1, Y2, …, Y T ) of the dependent variable; •The observations of the explicative variables (i.e. the regressors) are included in the matrix X, whose dimensions are (T * K); •The parameters to be estimated are K and they are part of the vector: • The factor α i is the individual effect.
Model Assumptions • The panel data model with fixed effects allows to capture the heterogeneity between countries in several time periods; • The effects are correlated with the regressors; • This hypothesis does not allow to include time-invariant covariates, but in our case this is not a problem, because the analysis is conducted on countries, and not on individuals.
Longitudinal Data Models • LINEAR REGRESSION • LINEAR REGRESSION WITH AR • BINARY OUTCOMES • ORDINAL OUTCOMES • COUNT OUTCOMES • ENDOGENOUS COVARIATE • Etc...
Longitudinal Data Models • LINEAR REGRESSION • LINEAR REGRESSION WITH AR BINARY AUCOMES FE BE • ORDINAL OUTCOMES • COUNT OUTCOMES RE PA • CENSORED OUTCOMES • SURVIVAL MODELS • ENDOGENOUS COVARIATE
Linear Regression • RE • BE • MLE • AR
Binary Outcomes If we assume a normal distribution for the RE , where The panel-level likelihood is given by
Alternative Data Models BINARY OUTCOMES • Logistic Regression (FE,RE, PA) • Probit Regression (RE, PA) ORDINAL OUTCOMES • Logistic (RE) • Probit (RE) COUNT OUTCOMES • Poisson regression (FE, RE, PA) • Negative Binomial regression (FE, RE, PA)
Alternative Data Models DYNAMIC PANEL DATA • Arellano/Bond estimation • Arellano/Bover e Blundell/Bond ENDOGENOUS COVARIATE • Instrumental variables regression (FE; RE; FD, BE) • Hausman Taylor (RE)
SPIN • As previously anticipated, data were collected from the SPIN database; they were handled to perform comparative and longitudinal research, in association with the EUROSTAT database. • It covers 34 countries from 1930 to 2013 and includes the following modules: • OUTWB (Out of Work Benefit) • CBD (Child Benefit Database) • PLB (Parental Leave Benefit) • SAMIP (Social Assistance and Minimum Income Protection) • SCIP (Social Citizenship Indicator Program) • SIED (Social Insurance Entitlements Dataset) • SPEAD (Social Policy in East Asia Dataset) • CCD (Child Care Dataset)
CB dataset • The current version of CBD includes detailed information about the generosity of child benefit in 18 countries between 1960 and 2010. • We conveniently selected data on 8 countries from 1995 to 2010. • This choice was dictated by availability of data and it is inspired by the purpose of analyzing 2 group of countries in function of their geographical, social and cultural proximity. • The first group includes: Sweden, Finland, Norway and Denmark • The second group includes: Italy, France, Austria and Belgium
Variables CBD • Dependent variable : income expressed in € and transformed in log, in order to allow the appropriate estimation of the model; • Benefits levels are calculated for a two-parent model family with two children aged 2 and 7; • All benefit in CBD are expressed in national currency and converted in € to allows comparisons.
All Countries
DK, FI,NO,SE AU, BE, FR, IT
DK, FI,NO,SE AU, BE, FR, IT
DK, FI,NO,SE AU, BE, FR, IT
Social Benefit (%) Consist of transfers, in cash or in kind, by social protection schemes to households and individuals to relieve them of the burden of a defined set of risk or needs. The risk are: sickness/healthcare, disability, old age, survivors, children, unemployment, housing, social exclusion. In this work, we consider social benefit to children.
Total Child Benefits trend
GINI* before the crisis (2007) *The Gini coefficient measures the extent to which the distribution of income within a country deviates from a perfectly equal distribution. A coefficient of 0 expresses perfect equality where everyone has the same income, while a coefficient of 100 expresses full inequality where only one person has all the income.
GINI during the crisis (2008)
GINI during the crisis (2009)
People at risk of poverty* or social exclusion** (%), 2007 *At risk-of-poverty are persons with an equivalised disposable income below the risk-of- poverty threshold, which is set at 60 % of the national median equivalised disposable income (after social transfers) ** Severely materially deprived persons have living conditions severely constrained by a lack of resources.
People at risk of poverty or social exclusion (%) , 2008
People at risk of poverty or social exclusion (%), 2009
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