1 Short and Medium-Term Projections of Household Income in Ireland using a Spatial Microsimulation Model Cathal O’Donoghue, John Lennon, Jason Loughrey and David Meredith Rural Economy and Development Programme, Teagasc Abstract This paper provides a set of methodologies that can be used to estimate changes in household income and relative income poverty at the district level during an intercensal period. The methodologies are applied to overcome data limitations due to the absence of spatially disaggregated household income data, significant time lags in the availability of micro- data on household income at the national level and additional constraints posed by the five year interval between each census. We implement these methodologies using the example of districts in Ireland from 2006 to 2011. Both of these are census years but lengthy time lags in the availability of data for 2011 and the economic transformation during this intercensal period make this a useful example. Keywords: Micro-simulation, Household Income, Employment Rates, Live-Register, Spatial, Calibration, Alignment, Projections, Age-Specific Employment Rates J.E.L. Classification: C15, C54, D31, J21, J24, J31, R12, R13 I. INTRODUCTION This paper provides a methodology that can be used to make projections of employment, household income and poverty at a local level in the absence of up-to-date spatially disaggregated data on these variables. The development of these projection methods can help researchers overcome obstacles associated with a complete absence of spatially disaggregated data on household income and time lags in the availability of spatially disaggregated data on employment. A pre-requisite for this research is that small area population statistics (SAPS) from a census of population are available for a year in the recent past. In the case of Ireland, the SAPS from the 2006 Census are the most spatially disaggregated dataset on demographics and employment. The Census data excludes information on household income but matching the SAPS to a national level micro dataset, with household income variables, is a route towards overcoming this particular obstacle. The procedures developed to match the SAPS to the micro- dataset are described in Farrell, O’Donoghue and Morrissey (2011). The work of this paper builds upon that of (Morrissey and O’Donoghue, 2011) which outlined a methodology to impute household income at the district level in a census year. The added value of this paper is that we project forward the spatial distribution of earnings beyond that of the census year and in addition we apply a tax-benefit model in order to estimate changes in household disposable income and poverty at the district level. The projections of employment and household income for 2011 are made using alignment and calibration techniques based upon external data sources. The large changes in the Irish economy since 2006 make Ireland an interesting case upon which to apply these methods. Over the period of this analysis the biggest impacts on welfare have been labour market change, policy change and price changes. In this paper we shall concentrate on the first two sets of changes, simulating the impact of labour
Transcript
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Short and Medium-Term Projections of Household Income in Ireland using a Spatial Microsimulation Model
Cathal O’Donoghue, John Lennon, Jason Loughrey and David Meredith
Rural Economy and Development Programme, Teagasc
Abstract
This paper provides a set of methodologies that can be used to estimate changes in household income and relative income poverty at the district level during an intercensal period. The methodologies are applied to overcome data limitations due to the absence of spatially disaggregated household income data, significant time lags in the availability of micro-data on household income at the national level and additional constraints posed by the five year interval between each census. We implement these methodologies using the example of districts in Ireland from 2006 to 2011. Both of these are census years but lengthy time lags in the availability of data for 2011 and the economic transformation during this intercensal period make this a useful example.
This paper provides a methodology that can be used to make projections of employment, household income and poverty at a local level in the absence of up-to-date spatially disaggregated data on these variables. The development of these projection methods can help researchers overcome obstacles associated with a complete absence of spatially disaggregated data on household income and time lags in the availability of spatially disaggregated data on employment. A pre-requisite for this research is that small area population statistics (SAPS) from a census of population are available for a year in the recent past. In the case of Ireland, the SAPS from the 2006 Census are the most spatially disaggregated dataset on demographics and employment. The Census data excludes information on household income but matching the SAPS to a national level micro dataset, with household income variables, is a route towards overcoming this particular obstacle. The procedures developed to match the SAPS to the micro-dataset are described in Farrell, O’Donoghue and Morrissey (2011).
The work of this paper builds upon that of (Morrissey and O’Donoghue, 2011) which outlined a methodology to impute household income at the district level in a census year. The added value of this paper is that we project forward the spatial distribution of earnings beyond that of the census year and in addition we apply a tax-benefit model in order to estimate changes in household disposable income and poverty at the district level. The projections of employment and household income for 2011 are made using alignment and calibration techniques based upon external data sources. The large changes in the Irish economy since 2006 make Ireland an interesting case upon which to apply these methods. Over the period of this analysis the biggest impacts on welfare have been labour market change, policy change and price changes. In this paper we shall concentrate on the first two sets of changes, simulating the impact of labour
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market change using a spatial microsimulation model and developing a new methodology for static ageing, appropriate to the available data in Ireland. As in other types of microsimulation model, ageing or updating can take a number of forms.
There is an extensive literature on the ageing of aspatial microsimulation models. This literature has been divided into static ageing (See Immervoll et al., 2005) and dynamic ageing (See O’Donoghue, 2000). Immervoll et al. (2005) describe the process of static ageing or adjusting the weights of the population to correspond to the changes over the relevant period. Static ageing thus takes macro-aggregates and then adjusts the underlying distribution to produce projections of the population distribution over time. The same issues apply in spatial microsimulation modelling. Static ageing has been used on a number of occasions. For example Ballas et al., (2005b) use a different method known as GHOSTs generate demographic projections of the UK population, which validated against the population census produced reasonably good estimates. The Australian models in NATSEM also use static ageing reweighting techniques to update the population (Phillips and Kelly, 2006) for use in a large range of analyses.
On the other hand, dynamic ageing (See O’Donoghue, 2001) involves the estimation systems of a system of econometric equations and then simulates changes in the population, It is an ageing procedure that takes a sample whose underlying characteristics X, are held constant, while the weights given to different parts of the sample is changed through the use of a dynamic simulation mechanism to produce different weighted distributions corresponding to expected characteristics in the future. This method essentially involves the estimation of a system of equations that replicate the distribution of incomes by sources.
Dynamic ageing methods have also been used within spatial microsimulation. The SVERIGE model dynamically simulates a number of demographic processes (Fertility, Education, Marriage, Divorce, Leaving Home, Migration, Mortality, Immigration and Emigration) in Sweden for use in a range of applications (Rephann, 2001). Lundevaller et al., (2007) using a similar methodology undertook a simulated population for a small area in Sweden. The MOSES model (Birkin et al., 2009) uses dynamic microsimulation to underpin socio-economic analysis such as education and transport. In Ireland Ballas et al., (2005a) simulated the Irish population forward from 1991 to 1996 as benchmark comparison against the 1996 Census at a county level and projected the population forward into the future.
Pudney (1992) argues that neither approach should be used in isolation. Dynamic ageing by focusing on the individual takes no account of processes at the level of the market such as labour demand and has impossible requirements in terms of data and modelling to jointly estimate all the required processes. Static ageing has a number of theoretical objections. Static ageing cannot be used where there are no individuals in the sample in a particular state. If there are a small number of cases of a particular household category, a very high weight may have to be applied, resulting in unstable predictions. Changing demographic and economic trends over time may mean that increasing weight is placed on population types with very few cases in the sample (See Klevmarken 1996). Static ageing procedures are relatively well suited to short to medium term forecasts where changes in the structure of the population is small.
In the case of a spatial microsimulation model, particularly when focusing on an entire country, dynamic microsimulation poses very significant computational requirements. Also panel data is only available at a national scale and so we cannot observe any spatial heterogeneity in mobility patterns. Also as the projection period is relatively short although we experience significant volatility, the main theoretical objections to static ageing do not hold. In this paper we use a combination of the two methods. Within the labour market, because of issues associated with spatial heterogeneity and conditional independence assumptions associated with the statistical matching within spatial microsimulation models, we use a calibration and alignment approach
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(See Morrissey and O’Donoghue, 2010) drawn from the dynamic microsimulation literature described below. This method requires the generation of new external control totals and calibration of micro data via a system of equations as per the dynamic ageing approach. However in this paper we ignore labour market dynamics, recreating instead a cross-sectional pattern of incomes across space consistent with the control totals. Also abstracting from population change, we focus on the labour market, ignoring both demographic dynamics as well as demographic cross-sectional changes. Drawing upon the static ageing literature, we apply updating parameters to account for differential earnings growth described below.
II. DATA AND TRENDS
Our capacity to project our spatial microsimulation model beyond our baseline dataset depends to a large part on the data availability. In this paper, we project the labour market characteristics over the intercensal period 2006-2011. As outlined above, this was a period of substantial economic change, corresponding to the peak of the business cycle in 2007 and a major economic collapse from 2008 to 2010.
In modelling changes in the labour market between 2006 and 2011, we focus on changes to a number of labour market variables. These are
In-work Employment Status Industry Earnings Growth
Labour Market Status
The main labour market status dataset available in Ireland is the Quarterly National Household Survey. This is a survey undertaken each quarter, containing a rolling 5 quarter panel with 20 per cent replacement every quarter and a sample size of about 100,000. While it contains no income data, it is the main source of information on labour market status. It forms part of the EUROSTAT Labour Force Surveys. This data is available very shortly after data collection, so at most it is 5 months out of date. The short time-lag means that this dataset can be used for the projection of a microsimulation model.
Table 1, describes the trend in the age-specific in-work rate over the period 2006-2011 for both males and females. The QNHS definition of inwork is consistent with the Census definition of at-work. Overall the male employment rate has fallen from a peak of 68.1 per cent of males aged 15 or higher in 2007 to 55.1 per cent in 2011. The female employment rate has fallen from a peak of 49.4 per cent of females aged 15 or higher in 2008 to 46.3 per cent in 2011. These falls in employment are disproportionately concentrated at the bottom of the age distribution, with a fall of 30.3 per cent in the employment rate for males aged 20 to 24 and 14.1 per cent for females in the same age bracket. The employment rate of males aged 25-34 fell by 18.7 per cent and 5.9 per cent for females in the same age bracket. The employment rate of males aged 35-44 fell by 12.1 per cent and by 10.4 per cent among males aged 45 to 54 and 7.5 per cent among males aged 60 to 64. Thus the employment decline has hit young males the hardest with large but nonetheless less severe declines among older males and younger females. We also notice that while the recession began in 2008, the onset was preceded by a decline in male employment from 2006. The employment rates among older women are an exception in that rates have increased among this cohort. Explanation+++
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Table 1: Trends in the Age-Specific In-Work Rate in Ireland 2006-2011
The QNHS has relatively limited declared spatial information, with only the NUTS3 region being reported. We report these trends in table 2. The largest declines from peak to trough occurred in the south-east with a decline of 12.1 per cent in the employment rate from peak in 2007 to 2011, while Dublin has experienced the smallest decline of 7.7 per cent from peak.
Table 2: Trends in the NUTS3 Region-Specific Employment Rate in Ireland 2006-2011
However NUTS3 is far too disaggregated a level for our projection purposes. Another spatially disaggregated dataset that can be used to provide relevant information is the Central Statistics Office’s Live Register Statistics. While not as finely disaggregated as the 3400 districts in the Irish Census, it is available for 124 local office areas. It is released on a monthly basis and has a particular advantage in that data is reported within a week of the end of the relevant month. Allocating each district to its closest SW Office, in figure 1, we report the growth rate in recipients of the Unemployment related payments in each district in Ireland.
The main pattern we observe is that the biggest growth in male unemployment has occurred in the commuting areas around the cities, while the smallest has occurred in the peripheral regions around the coast. This latter point however is not related to the fact that the recession was weak, rather employment did not grow as much in these districts during the growth period. The pattern of rising unemployment is particularly striking in the east of the country in the commuting areas
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surrounding Dublin City. For instance, the highest growth rate (558 per cent) applies to the Trim social welfare office in County Meath. The neighbouring towns of Kells in County Meath (359 per cent) and Maynooth in County Kildare (401 per cent) have offices which are among the seven worst affected in the country. Portarlington and Portlaoise in County Laois are also among the worst affected areas with growth rates of 419 per cent and 422 per cent respectively. The SW offices located within County Dublin are not among the worst affected in terms of employment growth rates.
A similar but less striking pattern appears to be evident from the area surrounding Cork City in the south west of the country. The number of registered unemployed in the Macroom Social Welfare Office increased by 522 per cent while Skibbereen, Killorglin and Bantry have all witnessed increases in excess of 340 per cent.
Income Data
The income data is due to a number of different sources. The Survey of Income and Living Conditions (SILC) of 2005 is used to impute earnings for households in the baseline population in 2006. The imputation process is discussed in the methodology section. This data is not available for 2011 and we must therefore rely upon alternative data sources for the updating of earnings. For this purpose, the main source of data is the Earnings Hours and Employment Costs Survey (EHECS). This survey, carried out by the CSO on a quarterly basis provides an index of labour costs for three occupational groups according to sixteen industry groups (NACE Rev. 2). We make use of the data spanning the period from the 2nd quarter of 2005 to the 2nd quarter of 2011 although data is available for more recent quarters. The EHECS does not provide information on the spatial distribution of movements in labour costs and earnings. As in the case of (Morrissey and O’Donoghue, 2011), we avail of the data on County Level incomes which are reported in the National Accounts. This provides us with the means to capture some of the changes in the spatial distribution of income over time.
Small Area Population Statistics
The above data sources provide us with the necessary information to incorporate the evolution of earnings over the relevant time period. However, this data alone is incapable of producing reasonably precise estimates of changes in household income and poverty at a local level. We must have prior knowledge about aggregate levels of employment, educational achievement and demographic statistics for each district. In the case of Ireland, these aggregate numbers are provided via the Small Area Population Statistics (SAPS) from the Census of Population. The most recently available SAPS are due to the 2006 census. These statistics are provided for 3,440 districts in Ireland. May need to revise #
Quota Sampling The availability of spatially disaggregated micro data at the level of the household and individual is crucial for this analysis. The reasoning is twofold. The estimates of average household disposable income for each district require that a tax-benefit model is applied to each individual and/or tax unit. The inevitable variations in market incomes within each district implies that average changes in market incomes are not a sufficiently reliable indicator of
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changes in disposable incomes. This is particularly true in the case of a progressive income tax system. The SAPS contains the necessary information on demographics and employment at the district level but provides no information at the household level. To overcome this obstacle, the SAPS are matched with the 2005 SILC data to produce the baseline dataset. The matching process is carried out using a quota sampling method developed by Farrell, O’Donoghue and Morrissey (2011). Quota sampling randomly assigns households from the SILC data to the SAPS. The resulting dataset forms the basis for a new population model of the local economy called SMILE (Spatial Model of the Irish Local Economy). The SMILE model also contains an agri-environmental component that may be used as a stand alone model or linked with the population model to address agri-environmental policy issues (Hynes et al., 2009).
III. METHODOLOGY – PROJECTIONS USING SPATIAL CALIBRATION TOTALS
In this paper, our objective is to develop a methodology to understand the impact of labour market and policy on the spatial distribution of income during the period from 2006 to 2011. The chosen methodology involves a dynamic modelling approach utilising a system of equations or income generation model taken from the inequality decomposition literature and a calibration or alignment method to adjust the models to meet external control totals for 2011. A tax-benefit model is used to incorporate changes to the tax-benefit system from 2006 and 2011. This tax-benefit model is described in (O’Donoghue et. al, 2011).
In this methodology we do not focus on dynamics, in the sense of tracking individuals moving through states, as is generally presented in the dynamic microsimulation modelling literature. Instead, we follow the same objective as in the development economics literature and reproduce cross-sectional distributions of income. For this reason, we can utilise a simpler error component structure without the requirement of time based components and in addition can base our estimations upon cross-sectional data. On the other hand, focusing on cross-sections does not allow us to say anything about the change in the welfare of winners or analyse the numbers of winners and losers. Rather we can merely discuss the impact of external changes on the shape of the distribution and assess these impacts in relation to groups such as income quintiles or labour market groups.
Estimations
The projection of employment status for the projection year of 2011 requires the estimation of a set of equations in three stages; the presence of employment, the type of employment (employee, self-employed or farmer) and the occupation and industry of that employment. Bourguignon et al. (2002) disentangled the impact of macro-economic changes on inequality by utilising parameters B from one year to simulate employment participation effects in different years. In this analysis, however, the objective is slightly different; to project the distribution forward over a period using a single set of estimated parameters, B and single set of explanatory factors X, calibrated to external control totals at the district level.
In our model, we utilise three types of alignment for binary discrete data, discrete data with more than two choices and continuous data. We concentrate for now on the first two types of alignment. Models of binary events such as in-work may be modelled using a logit model due to the computational ease of undertaking simulations. In order to use the estimated models for
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Monte Carlo simulation, we draw a set of random numbers such that we predict the actual dependent variable in the base year.
We define our logit model as follows
ik
kio
i
iii XB
P
Ppy
1
ln)(logit* (1)
In order to create the stochastic term, i , we use the following relationship:
i
ii u
u
1ln (2)
A value of iu that satisfies this is:
iiii prpYprYu 1*0*1 (3)
where r is a uniform random number.
Even with a detailed system of equations with many explanatory variables, there remains unexplained spatial heterogeneity due to the failure of conditional independence. We therefore calibrate the predicted values from Eq.1 to a set of external control totals at the district level.
Calibration - External Control Totals
To produce the control totals, we draw upon the dynamic micro-simulation literature, using an alignment or calibration technique described in O’Donoghue et al. (2008), Caldwell, (1996) and Morrison, (2006). Using alignment, we calibrate the variables simulated via our system of equations to the exogenous constraints. These constraints are established using data from the QNHS and the Live Register Data from the Social Welfare Offices.
The Binary choice models (inwork) are calibrated by ranking *y defined in (1) above and
selecting the highest N cases from each electoral division until the values of the external control totals Nj for each electoral division are reached. In the case of occupational choice, a similar method is developed, ranking *
jy for each choice j in turn to be consistent with externally
defined Nj. The following details the process undertaken to estimate the values of the external control totals.
In Work
The Live Register data from the social welfare offices is the most spatially disaggregated data on out of work or inwork statistics for 2011 but excludes sufficiently rich data on the demographic characteristics of recipients. This justifies the use of the QNHS which can be used to capture the uneven changes in employment across demographic groups.
The (QNHS) age-specific employment rates can be used to make inferences about the relative employment strength of the district in 2006. To make these inferences, we firstly apply the national age-specific employment rate from the (QNHS) from quarter 2, 2006 to the age-sex structure of the district and compare the resulting aggregate employment rate with the actual employment rate in 2006.
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AgeSex
AgeSexAgeSexTot xPopIWWI,
2006,,2006,,2006,ˆ (8)
2006,
2006,
ˆTot
TotS
WI
IWLM (4)
The ratio of these is our measure of labour market strength.
Leaving aside the use of the live register data, initial estimates of district level employment for 2011 can be made by multiplying the 2011 age-specific employment rate of the district by the value of the labour market strength variable.
AgeSex
AgeSexiAgeSexSiTot xPopIWLMWI,
2006,,,,2006
,ˆ
, i = 2011 (5)
However, while we incorporate spatial heterogeneity in the starting position through the Labour Market strength variable, there is the assumption of no spatially differential change in employment other than the spatial change that is captured by changes in age-specific employment rates. A large number of districts may have employment rates that closely track the age-specific employment rates at the national level but this is not necessarily true of all districts and there remains scope to better capture changes in employment at a spatial scale.
To differentiate the growth rate in employment across space, we utilise the trends in the local office Live Register data. The national growth rate in Live-Register based unemployment
iSexLR , is as follows:
2006,
,,
Sex
iSexiSex LR
LRLR , i = 2011 (6)
It is widely appreciated that the number of people on the live register at a particular point in time
iSexLR , is not an accurate measure of unemployment in Ireland. Furthermore, the live register data is not an accurate measure of out of work in Ireland given that part-time and casual workers are technically in-work and elderly people are unlikely to be included in the live register. The number of people on the Live Register must be converted, using reasonable assumptions, in order to become consistent with the QNHS definition of out of work. The national growth rate in
QNHS based Out of Work numbers ( iSexOW , ) is calculated for this purpose.
The national growth rate in QNHS based Out of Work numbers ( iSexOW , ) is calculated as follows:
2006,
,,
Sex
iSexiSex OW
OWOW , i = 2011 (7)
Dividing the national growth rate in QNHS based Out of Work numbers ( iSexOW , ) from (Eq.7)
by the national growth rate in Live-Register based unemployment ( iSexLR , ) from (Eq.6) provides the Ratio of Growth in QNHS Out of Work to Growth in Live Register based Unemployment.
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2006,
,
Sex
iSex
LR
OW
, i = 2011 (8)
This ratio can be used to convert live register numbers from the local social welfare office into numbers out-of-work at the level of the local social welfare office and therefore be consistent with the QNHS definition of out-of-work.1
Table 3 shows the values of this ratio according to NUTS 3 region and gender. The decline in the growth rate of QNHS based out-of work relative to Live Register based unemployment is consistent with rapid growth in unemployment as the numbers in retirement, education, sick and caring etc do not change as quickly as unemployment in a period of economic transformation. We see that by 2010, that the numbers out of work had grown by less than half the growth rate of Live Register based Unemployment. With the Mid-East having the lowest ratio, reflecting the low age profile of families in the commuting counties around Dublin.
Table 3: Ratio of Growth in QNHS Out of Work to Growth in Live Register based Unemployment
We apply the national level ratio to the growth rate in Live Register numbers for each social welfare office ( iOfficeSexLR ,, ) to produce a spatially (at the local office level) out of work growth
rate iOfficeSexOW ,, . This out of work growth rate is consistent with the QNHS definition of out of
work.
iOfficeSexiSex
iSexiOfficeSex LR
LR
OWOW ,,
,
,,,
, i = 2011 (9)
The value of this growth rate differs between local social welfare office areas but applies equally to all districts that are assigned to a particular social welfare office. Recall that Eq.5 provides an initial estimate of the absolute change in the number in-work and therefore out-of-work at the district level but does not adequately deal with spatial heterogeneity in employment change.
1 It should be noted that the Live Register incorporates unemployed, seasonal and causal workers, part‐time workers and those signing on for credits. Utilising the growth rate in Live Register based unemployment assumes that the growth rate in these other non‐unemployed categories is independent of area. While a strong assumption, it is made in projections of the ILO based unemployment rate by the CSO within the Quarterly National Household Survey.
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Eq.9 accounts for spatial heterogeneity but only at the local office level and not at the district level. Merging the information from Eq.9 to Eq.5 allows us to improve upon the initial estimates of employment change at the local office level. We can capture a portion of the changes at the local office level i.e. at the district level through the Labour market strength variable provided under Eq.5. This involves the assumption that the value of the labour market strength variable remains constant between 2006 and 2011.
The merging of Equations 5 and 9 requires some further explanation. There is some overlap in the information provided in both equations and the merging process requires that the possibility of double-counting is removed. 2 The age-specific employment rates at the district level explicitly form part of Eq.5 and can influence Eq.9, given that the differential change in numbers on the live register between social welfare offices can be due to age-specific effects. Before combining (5) and (9), the age-specific effects and the national trend in out-of-work must be removed from Eq.9.
To make this adjustment, we estimate the numbers out-of-work (at the local office level) for the projection year of 2011, under the assumption that the QNHS age-specific employment rate applies equally to all districts in the country. We have 2006
,ˆ
iTotWI from Eq.9 i.e. the numbers in-work
(at the district level) for the projection year of 2011 under this assumption. Summing 2006,
ˆiTotWI for
all districts assigned to a particular social welfare office provides Office
iTotWI 2006,
ˆ .
Subtracting this total from the local population ( 2006,,OfficeTotPop ) produces 2006,,
ˆiOfficeTotWO i.e. the total
number out-of-work at the local office level under the assumption that the QNHS age-specific employment rates apply equally to all districts in the country.
Office
iTotOfficeTotiOfficeTot WIPopQNHSWO 2006,2006,,
2006,,
ˆ)(ˆ, i = 2011 (10)
The hypothetical growth rate in the QNHS based Out of Work numbers (at the local office level) under the above assumption is the following:
2006,
2006,,
,
)(ˆ)(
OfficeTotal
iOfficeTotalioffice
OW
QNHSWOQNHSOW , i = 2011 (11)
The denominator is the baseline total number out-of work at the local office level as provided in the Small Area Population statistics from the census.
Dividing Eq.9 by Eq.11 provides estimates of the growth rate in out-of work at the local office level relative to the national average growth rate in out-of work (with age-specific effects removed) in the following:
iSex
Sex
ioffice
iofficeioffice Pop
Popx
QNHSOW
OWOWR
,
2006,
,
,, )(
)(
, i = 2011
(12)
2 It should be recognised that summing (9) for all social welfare offices equals the sum of (5) for all districts in both the baseline and projection years as both equations provide the absolute change of in-work (out-of-work) at a national level.
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This is the number used to adjust for spatial heterogeneity in changes of employment between local office areas. The notation R() indicates that this is a relative score for each local social welfare office area. An additional component is added to account for changes in the size of the national population of people aged 15 and above between the baseline year and the projection year.
Eq.12 must be combined with a variant of Eq.5 to form estimates of employment at the district
level for 2011. The initial estimate of in-work in 2011,i
jTotWI ,ˆ (Eq.5) can be used to form initial
estimates of the growth rate in out-of-work at the district level in the following:
2006
,2006,,,
,2006,,,,,
ˆˆ
jTotjAgeSex
ijTotjAgeSex
ijSexIWPop
WIPopQNHSWO
, i = 2011 (13)
These initial estimates at the district level must be combined with Eq.12 to form estimates of out-of-work at the district level in 2011.
Multiplying (12) by (13), we generate out-of-work growth rates for each district j for each year i.
QNHSWOOWRWO ijSexiOfficeijSex ,,,,,ˆ)(
, i = 2011 (14)
The absolute number of people out-of-work in each district in 2011, is calculated in the following:
2006,,,,,,,ˆ
jAgeSexijSexijSex PopWOWO
, i = 2011 (15)
Note that the population age structure 2006,,, jAgeSexPop is held constant for each district at 2006
levels.
Eq.15 can be transformed into the number in-work at the district level in the following:
ijSexjAgeSexijSex WOPopWI ,2006,,,,,
ˆˆ , i = 2011 (16)
For the other labour market variables, (employee, industry, occupation), we utilise changes in the national level QNHS applied to the differential employment rate to produce a set of control totals for the following labour market states:
Employee
Self-employed
Farmer
Retired
Unemployed
Occupation
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The trends in the QNHS occupation statistics indicate that some significant changes have recently occurred in the occupational profile of the working population in Ireland. In our methodology, these national level changes impact directly on the size of the control totals for each occupation category in each district. The national level share of total employment due to each occupation group from 2006 to 2011 is presented in the following table. The statistics show the approximate halving in the share for construction among males and the relative increases for commerce, transport, public administration and professional services. Among females, there does not appear to have been any large movements in the share of occupation group. This suggests that the declines in female employment are less involved in the changing structural mix of the economy and more to do with the absolute decline in demand both globally and domestically.
Table 4: Occupation Share of Total Employment by Gender
Once we have estimated, whether an individual is in-work or not, their work status, employee, farmer, etc, multi-category choices such as occupation are simulated using a reduced form multinomial logit model. Multinomial models may be used when the explanatory variables are not choice specific. 3 Disturbance terms for multi-category dependent variables such as occupation or industry are derived from multinomial logit models using the following method. We firstly generate a set of random variables for counter-factual choices using the extreme value distribution:
uv j lnln (17)
Where u is a uniform random number and j is choice j not the actual choice chosen by the individual in the original data. Our objective now is to choose a random variable from the extreme value distribution, vi for the actual choice i such that:
3 There is a large literature on using choice specific models for modelling multi-category choices as in the case of structural labour supply equations (See, Van Soest, 1995; Callan et al., 2009). However we use a calibration mechanism described below which dominates the behavioural operation of these models.
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ijvxbvxb ji (18)
Earnings Growth
Having established the labour force characteristics of each individual and hence the profile of the labour force in each district, income variables can then be modelled using an OLS equation.
BXYi*exp (19)
where the disturbance term is normally distributed, recovered directly from the 2005 SILC data for those with observed incomes, or generated stochastically for those without a specific income source.
Earnings Alignment
Within this paper, we utilise the system of equations described above to simulate the heterogeneity of earnings between individuals across space. This is an insufficient for the projection of household income. We must therefore exploit alternative data sources in order to update earnings to the projection year and the distribution of those earnings.
Unlike Bourguignon et al. (2002), who had information from historical surveys on the standard deviation of earnings over different periods, we do not directly adjust the earnings distribution. Rather we use the sector and occupation specific earnings growth indices from the Earnings, Hours and Employment Costs Survey. These growth index numbers are reported in figure 3 for professionals, clerical staff and manual workers across different industries. There is significant heterogeneity across sectors. Across all occupational categories, health and social care performs strongly. Information and Communications also did well across all occupation groups. Industry was among the sectors with biggest earnings improvement in the case of professionals but not in the case of clerical or manual workers. The poorest earnings performance occurred in real estate and construction across all occupation groups. Earnings growth amongst clerical and manual workers was in general lower than professionals, a trend that should be expected to increase inequality of the income distribution.
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Figure 2: Earnings Growth Rate 2005-2011 – Note Assumption for Q2 to Q2 2011
Professionals
75
100
125
150
2005 2006 2007 2008 2009 2010 2011 2012
Real estate activities Professional, scientific and technical activities
Administrative and support service activities Public admin. Defence, Social
Education Human health and social work activities
75
100
125
150
2005 2006 2007 2008 2009 2010 2011 2012
Construction IndustryWholesale and retail trade; Transportation and storage Accommodation and food service activities Information and communication Financial and insurance activities
Clerical
75
100
125
150
2005 2006 2007 2008 2009 2010 2011 2012
Real estate activities Professional, scientific and technical activities
Administrative and support service activities Public admin. Defence, Social
Education Human health and social work activities
75
100
125
150
2005 2006 2007 2008 2009 2010 2011 2012
Construction IndustryWholesale and retail trade; Transportation and storage Accommodation and food service activities Information and communication Financial and insurance activities
Manual
75
100
125
150
2005 2006 2007 2008 2009 2010 2011 2012
Real estate activities Professional, scientific and technical activities
Administrative and support service activities Public admin. Defence, Social
Education Human health and social work activities
75
100
125
150
2005 2006 2007 2008 2009 2010 2011 2012
Construction IndustryWholesale and retail trade; Transportation and storage Accommodation and food service activities Information and communication Financial and insurance activities
At this point, we have initial income estimates for each household based upon socio-economic characteristics but there remains some scope to better capture the spatial heterogeneity of earnings. As in the case of (Morrissey and O’Donoghue, 2011), the estimated wages for each worker are aligned to the exogenously specified incomes at the county level. These external forecasts are due to the Irish National accounts (NA) and are provided according to the source of income (Employee Earnings, Self-Employment Income, Rent, Net Interest and Dividends). The relevant statistics are released according to the average earnings per person and the total value of earnings for all people residing in each county. We adjust these statistics further to calculate the average earnings per recipient of each income source. The statistics on the number of recipients are largely due to our equations 1-18 from this paper. This provides an alignment co-efficient . The alignment co-efficient may be defined as follows:
Y
EC (20)
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The average earnings per recipient in each county forms the numerator and the national average earnings per recipient forms the denominator.
Tax-Benefit System
Changes in income inequality depend not only upon changes in market income, but also changes in tax-benefit policy. Disposable income, defined as income after direct taxation and social benefits is calculated through the use of a static tax-benefit model, programmed in Stata. The model simulates the main direct tax and transfer instruments:
Income Taxation Social Insurance Contributions (Employee, Self-Employed and Employer) Income Levies Family Benefits Social Assistance Benefits Social Insurance Benefits
The tax-benefit system is described further within (O’Donoghue et. al, 2011).
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Results I: Labour Market Change
In this section we review the results of our projection exercise in relation to employment rates, household income and poverty. As outlined above, we utilise the most recent official statistics for
Employment rate (QNHS Quarter 2 2011 and Local Social Welfare Office, April 2011)
Labour Market Status (QNHS Quarter 2, 2011)
Earnings (Earnings, Hours and Employment Costs Survey , Q2 2011)
Other Market Income (National Accounts Q2 2011)
Figure 3: Female Employment Rates in 2006 and 2011 by District
2006 2011
**Estimates of county level employment rates are included in the appendix.
Figure 3 illustrates the changing pattern of female employment rates between 2006 and 2011 i.e. the percentage of females aged 15 and above in employment. It appears that the areas with the largest declines in female employment lie in the outskirts of Dublin city. In 2006, the percentage of females in employment stood at greater than 45 per cent in most of the mid-east region4 which surrounds Dublin city. This is in contrast to the picture in 2011 as the majority of these districts now have a female employment rate less than 45 per cent. Appendix 2 shows that the 4 The mid‐east region includes the counties of Meath, Wicklow and Kildare
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estimated female employment rate fell by almost 12 per cent in County Kildare and thus far exceeding the national decline of 1.7 per cent. Most parts of the south-west region appear to have much lower female employment rates in 2011 relative to 2006. This is perhaps a little deceiving as the estimates in Appendix 2 suggest that female employment rates increased in County Kerry as a whole. The variations in population density and district size are such that the patterns suggested by the above maps can lead to some misinterpretation. The estimates detailed in Appendix 2 should therefore accompany a reading of the above.
We know from table 1 that the overall female employment rate declined by less than 2 per cent between 2006 and 2011. There must therefore be areas which have experienced some increase in employment among women. The above graph indicates that areas such as West Clare, West Kerry, County Waterford, North Mayo and West Donegal have witnessed increases in female employment. This is supported by the county level estimates outlined in the appendix. These are largely rural areas with particular demographic population profiles. This is not a universal trend whereby rural areas have experienced lower declines in female employment. Large parts of the midlands including the counties of Roscommon, Offaly, Laois and North Tipperary have experienced declines in female employment rates that far exceed the national average. The most densely populated areas of the major cities appear to follow closely the national level trend in terms of female employment rates.
Figure 4: Male Employment Rates in 2006 and 2011 by District
2006 2011
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In figure 4, we report the male employment rate from our baseline year, 2006 and our projection year 2011. Table 1 shows that male employment rates fell to a much greater extent than female employment rates. This is partly due to the initially higher labour force participation rates among men and the greater life expectancy of women relative to men. The large decline in employment within construction is likely to be a further and perhaps more significant contributor to the divergence of employment rates by gender. The above maps confirm the national trend and illustrate the variability of these large employment declines across the country.
It appears from the map of 2006 that the male employment rate stood at greater than 69 per cent in most of the area surrounding Dublin city. This is no longer the case in 2011 as employment rates have declined to below 59 per cent. The estimates outlined in Appendix 1 show that the male employment rate declined by about 16 per cent in counties of Kildare and Meath. The largest declines in male employment rates are in counties with predominantly rural settlement patterns. These include Carlow, Cavan, Roscommon, Laois and Leitrim. Male employment rates appear to be holding up much better in the cities relative to elsewhere in the country. This pattern is somewhat evident from the trends of female employment but is much more striking in the case of males. It remains the case however, that suburban areas in Dublin county have closely followed the national trend and have been affected to a much greater extent than the inner parts of the city. A similar divide between city and county is evident from Galway, Limerick, Cork and Waterford.
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Figure 5: Equivalised Disposable Income 2006, 2011 and change 2006-2011 by District
2006 2011 Change 2006 to 2011
**The OECD Equivalence scales are used in this analysis. The OECD system allocates a value of one to the first adult in each household, a value of 0.5 to each additional adult and 0.3 for each child within each household.
The relatively large variation of change in employment rates ought to provide some indication about the differential income change across the country during the relevant period. Figure 5 illustrates the change in household disposable income between 2006 and 2011 for each district in the country. Focusing on the change in disposable income, it appears that large parts of the south-west region have been particularly affected by the recession in terms of declines in household income as indicated by the red colouring. We should again be cautious in interpreting these results given differences in population density and district size. Large parts of the commuter belt surrounding Dublin city have districts with declines in excess of 10 per cent. The coastal areas do not appear to be among the worst affected areas and thus mirroring the employment trends. The declines in household income are
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lower in the five major cities relative to the rest of the country. It appears that household income was initially lower in the cities in 2006 relative to the national level with the exception of Dublin. The lower decline of household income in the cities relative to the rest of the country points therefore towards a narrowing gap in household income between city and non-city based populations outside of Dublin.
Figure 6: Poverty Rates 2006, 2011 and change 2006-2011 by District
2006 2011 Change 2006 to 2011
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The change in average or median household income is not always a reliable indicator of the change in poverty given that the extent of income inequality can vary across time and space among other things. Figure 6 shows that poverty rates increased in most but not all districts within the country. This is partly due to the measurement of poverty in this instance. We hold the poverty line constant at a level of real income in 2006 i.e. €12,000 in equivalised household disposable income. The Consumer Price Index grew by 4.4 per cent between the second quarter of 2006 and the second quarter of 2011. Therefore declines in nominal household disposable income of less than 4.4 per cent are construed to be increases in real income over the relevant period. Despite these measurement issues, the spatial pattern of poverty change follows closely that of household income change. The percentage of households living below the poverty line actually declined in some districts, particularly those located in the five major cities and along some coastal areas. The poverty trends show that the major cities outside of Dublin had initially higher poverty rates than the rest of the country. A number of counties in the east of the country appear to be particularly badly hit by increases in poverty including Kildare, Carlow, Laois and Meath and this is not surprising given the large employment declines in these areas.
Conclusion
This paper provides a set of methodologies that can be used to estimate changes in household income and relative income poverty at the district level during an intercensal period. The methodologies are applied to overcome data limitations due to the absence of spatially disaggregated household income data, significant time lags in the availability of micro-data on household income at the national level and additional constraints posed by the five year interval between each census. We implement these methodologies using the example of districts in Ireland from 2006 to 2011. Both of these are census years but lengthy time lags in the availability of data for 2011 and the economic transformation during this intercensal period make this a useful example.
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Appendix
Appendix1: Male Employment Rates by County in 2006 and 2011 and Percentage Change
County Male Emp Rate 2006 Male Emp Rate 2011 Change Emp Rate
Co 01 Carlow 65.5 39.3 26.3
Co 02 Dublin City 62.9 59.3 3.6
Co 03 South Dublin 69.5 57.0 12.5
Co 04 Fingal 73.2 61.5 11.8
Co 05 Dun Laoghaire-Rathdown 63.9 48.8 15.1
Co 06 Kildare 73.6 57.3 16.3
Co 07 Kilkenny 66.8 49.9 17.0
Co 08 Laoighis 67.9 39.3 28.5
Co 09 Longford 64.3 55.7 8.6
Co 10 Louth 68.4 58.3 10.1
Co 11 Meath 73.7 57.0 16.6
Co 12 Offaly 67.4 52.9 14.5
Co 13 Westmeath 67.2 55.8 11.4
Co 14 Wexford 64.9 44.9 20.0
Co 15 Wicklow 67.5 59.4 8.1
Co 16 Clare 67.4 55.1 12.4
Co 17 Cork City 55.9 53.0 2.9
Co 18 Cork County 69.3 51.7 17.6
Co 19 Kerry 63.1 46.4 16.7
Co 20 Limerick City 55.7 49.5 6.3
Co 21 Limerick County 65.7 50.2 15.5
Co 22 Tipperary North 66.5 50.1 16.4
Co 23 Tipperary South 64.1 41.9 22.1
Co 24 Waterford City 59.6 56.8 2.8
Co 25 Waterford County 64.6 57.3 7.3
Co 26 Galway City 61.2 56.8 4.4
Co 27 Galway County 65.8 49.8 15.9
Co 28 Leitrim 63.1 33.3 29.8
Co 29 Mayo 61.9 44.4 17.5
Co 30 Roscommon 64.5 39.6 24.9
Co 31 Sligo 62.8 49.3 13.5
Co 32 Cavan 67.2 42.4 24.9
Co 33 Donegal 57.7 43.2 14.5
Co 34 Monaghan 67.4 45.6 21.8
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Appendix2: Female Employment Rates by County in 2006 and 2011 and Percentage Change
