This paper analyses financial incentive across different age, gender, region, household composition and level of education. The most important contribution is to provide an empirical test of whether replacement rates vary across different age, gender, region, household composition and level of education. This paper using correlation supported the strong positive relationship between replacement rates and unemployment rates, indicates higher replacement rates of one group of individuals with have higher unemployment rates (lower financial incentive to work). In addition, this paper through simulation techniquences to calculate the counterfactual income to estimate the replacement rates. The finding support hypotheses on the financial incentive vary across different sub groups.

Key Words: Replacement rates;incentive; level of education; age; family; gender; region

1. Introduction

From the middle of the 1990s to 2007 the growing economic environment in Ireland resulted in the Irish government reducing income tax and rising social welfare at the same time. This paper empirically analyse the impact of social benefit on different type of family incentive to work. The Irish government needs to design tax threshold and welfare carefully to balance cost and tax (IMF, 2014) after the fiscal crisis in 2008. Since 2008, Ireland has experienced unemployment level up to 14.8% (SUR, July 2012) and from early 1980s to mid-1990s high unemployment rates between 13% to 17%. One of the factors which result in this weak employment rate may be tax or social welfare(TSG, 12/11). This paper use various techniques to quantify the incentive to work and presents how incentives change across different individuals. The main measure in this paper is the replacement rate shows which sorts of population has weaker or stronger financial incentive to work. The replacement rate has been widely used in policy debate(e.g. NESC, 2011) not only the nationally also internationally(e.g. OECD, 2014). Consequently, the replacement rate be focused for this paper.

Originating in the research of Brewer(2005), Callen(2014) and Crilly(2012), the replacement rate is the most important representation of the financial incentive to work. Also, the marginal effective tax rate may whether influence the individual’s incentive to work(Callen, 2012). One of the goals of this paper is to test the replacement rate has a positive relationship with the unemployment rate. Some of literature also address this test, Savage(2014) and Callen(2012) explains that the replacement rate keeps in line with Microeconomic Theory(Duncan and Giles, 1997), which can explain the relationship between the replacement rate and unemployment rate(this is discussed further in section 2). This paper also test replacement rate whether be differential across different age, gender, region, household composition and level of education.

In Ireland, O’Donoghue(2011) and Mitchell(2010) compares the replacement rate across different age, gender, region, household composition and level of education. Form the existing literature in general female have higher replacement rates than males’; rural have higher replacement rate than urban; elder people have higher replacement rate; female with children with have highest replacement rate than others family composition and individuals have higher level of education will have lower replacement rate. Callen(2012) using OLS tests whether replacement rate differs in different individuals.  Also, O’Donoghue(2011) describes a simulation model which calculate the replacement rate to measure the monetary incentive for different individuals to work. However, replacement rate is seen as the most important tools to measure individual incentive to work. Comparison replacement rate across different type of group can help government to publish more precise policy.

This paper examines (i)the relationship between replacement rates and unemployment rates and (ii) comparison the replacement rate in different sub group: age, gender, region, household composition and level of education. The method used is ordinary least square(OLS). By performing OLS with the data[1] can be easier understood, it is possible to analyze the replacement rate in different sub group. This paper used equation to calculate the counterfactual income to estimate the replacement rates(Engen et al., 1999; Scholz et al., 2004) argument whether replacement rate be differential in different age, gender, region, household composition and level of ed