1. Regional inequality in decentralized countries:
a multi-country analysis using LIS
Javier Martín-Román
UNED (Madrid)
Luis Ayala
Universidad Rey Juan Carlos (Madrid)
Juan Vicente
Universidad de Valladolid
2017 LIS/LWS User Conference
April 27th-28th, Luxembourg

2. Outline 1) Motivation 2) Background 3) Database and methodology
4) Preliminary results
5) Concluding remarks
LIS/LWS User Conference / April 27th-28th, 2017

3. 1) Motivation
This paper focuses on the role played by the territorial variable in explaining the disposable income inequality:
The study examines six decentralized countries (Australia, Canada, Germany, Italy, Spain and United States) during a decade.
We have carried out a double analysis: a traditional decomposition by population subgroups and a regression analysis.
LIS/LWS User Conference / April 27th-28th, 2017

4. 1) Motivation
This paper focuses on the role played by the territorial variable in explaining the disposable income inequality:
Private sector
Public sector
Regional tax policy
Benefits policy / Public services policy
Level of decentralization
Regional financing system
LIS/LWS User Conference / April 27th-28th, 2017

5. 1) Motivation Purpose: managing the LIS database.
Regional Inequality, redistribution and decentralization.
(1) First step: regional variable and inequality.
Australia, Canada, Germany, Italy, Spain and United States.
LIS Waves V and VIII (around around 2010).
(a) Decomposition by subgroups / (b) DFL reweighting method
(2) Future plans:
Build on previous works using LIS (Mahler, 2002; Ravishankar, 2003; Mahler and Jesuit, 2006; Wang et al., 2014; Jesuit and Mahler, 2017; Guillaud et al., 2017).
Compare and combine data from other sources (IMF, OECD…) (measures of fiscal decentralization).
The aim of our project is to operate LIS database to look for more evidence about regional inequality, redistribution and decentralization. Our intention is
1) To built on previous work included that of our discussant today and other participants in this conference to which I will come back later and
2) To use other sources to compare an to complete the information needed and not provided by LIS (“Li s h”) such that of measures of fiscal decentralization
As a first step, in this paper we focus on the relevance of the regional variable to explain inequality in a sample of six representative decentralised countries during the period using two complementary approaches:
The classical decomposition by groups and the reweighting method proposed by DiNardo Fortin and Lemieux
LIS/LWS User Conference / April 27th-28th, 2017

6. 1) Motivation Purpose: managing the LIS database.
Regional Inequality, redistribution and decentralization.
(1) First step: regional variable and inequality.
Australia, Canada, Germany, Italy, Spain and United States.
LIS Waves V and VIII (around around 2010).
(a) Decomposition by subgroups / (b) DFL reweighting method
(2) Future plans:
Build on previous works using LIS (Mahler, 2002; Ravishankar, 2003; Mahler and Jesuit, 2006; Wang et al., 2014; Jesuit and Mahler, 2017; Guillaud et al., 2017).
Compare and combine data from other sources (IMF, OECD…) (measures of fiscal decentralization).
The aim of our project is to operate LIS database to look for more evidence about regional inequality, redistribution and decentralization. Our intention is
1) To built on previous work included that of our discussant today and other participants in this conference to which I will come back later and
2) To use other sources to compare an to complete the information needed and not provided by LIS (“Li s h”) such that of measures of fiscal decentralization
As a first step, in this paper we focus on the relevance of the regional variable to explain inequality in a sample of six representative decentralised countries during the period using two complementary approaches:
The classical decomposition by groups and the reweighting method proposed by DiNardo Fortin and Lemieux
LIS/LWS User Conference / April 27th-28th, 2017

7. Fiscal Federalism theory
2) Background
Fiscal Federalism theory
First generation theory assigns the redistributive function to the Central Government (mobility constraints local policies);
(Tiebout, 1956; Prud’homme, 1995)
Second generation theory states that redistribution is more efficient when it is carried out by subcentral entities financed with own resources;
(Padovano, 2007; Oates, 2008)
According to Oates (“Outs”) the fiscal federalism theory has evolved, from a first generation that assigned the redistributive function to the central government (because the mobility of factors limits the potential of local policies), to the current theories in which the redistributive impact depends on the form adopted by the decentralization process).
In fact, some models predict that decentralised redistribution could be more efficient if assigned to decentralised levels of government when they are financed with their own resources instead of intergovernmental transfers.
LIS/LWS User Conference / April 27th-28th, 2017

8. Empirical evidence (IMF, 2014 and OECD, 2016)
2) Background
Empirical evidence (IMF, 2014 and OECD, 2016)
Regional disparities are dampened if intergovernmental transfers are replaced by sub-national own-source revenue.
National inequality benefits from the decentralization of expenditure when the government size is sufficient and is financed by own sources.
Decentralization advantages are biased in favor of middle income earners.
Goerl, C. A. and M. Seiferling (2014): “Income Inequality, Fiscal Decentralization and Transfer Dependency”. International Monetary Fund Working Paper 14/64.
Stossberg, S., D. Bartolini and H. Blöchliger (2016): “Fiscal decentralisation and income inequality: Empirical evidence from OECD countries”, OECD Economics Department WP nº
The empirical evidence seems to be in line with the evolution of the theory. Recent papers of IMF and OECD (“aienef and oisidí”) shows that:
… read the slide …
___________________________
Goerl, C. A. and M. Seiferling (2014):“Income Inequality, Fiscal Decentralization and Transfer Dependency”. International Monetary Fund Working Paper 14/64
Stossberg, S., D. Bartolini and H. Blöchliger (2016): “Fiscal decentralisation and income inequality: Empirical evidence from OECD countries”, OECD Economics Department WP nº 1331.
LIS/LWS User Conference / April 27th-28th, 2017

9. 3) Database and methodology
Several databases offering inequality statistics (JEI 2015).
Luxembourg Income Study (LIS) database:
Information at regional level
Access to microdata
(enabling researchers to make choices)
21 out of the over 700 LIS WP are classified as “regional”.
Very few report calculations of inequality at regional level.
There are several databases that offer inequality statistics for multiple countries and years, the most important have been reviewed in a recent special issue of the Journal of economic inequality
Among them, LIS is the only one that provide information at a subnational level and that allows us to access to microdata, enabling researchers to make their own methodological decisions
21 out of the over 700 LIS WP are classified under the heading “regional” and a very few compute inequality measures at a subnational level
LIS/LWS User Conference / April 27th-28th, 2017

10. 3) Database and methodology
3.1) Inequality decomposition by population subgroups
Between regional inequality:
Inequality that remains after each individual’s income is replaced with the mean income of the subgroup to which he/she belongs.
Within regional inequality:
Difference between the total and the between regional term.
Weighted sum of the inequality within the regions.
In order to look for more evidence about the contribution of regional inequality to the above mentioned trends we perform in this paper two types of analysis:
A classical decomposition by subgroups and a reweighting method to test contrafactual distributions.
Starting with the first analysis, total inequality is decomposed in two terms:
The Between regional component or inequality that remains after each person’s income is replaced with the mean income of the subgroup to which she belongs and
The Within regional inequality which is the difference between the total and the between regional term and it can be, in turn, decomposed as a weighted sum of the inequality within the regions
LIS/LWS User Conference / April 27th-28th, 2017

11. 3) Database and methodology
3.1) Inequality decomposition by population subgroups
𝜽: Generalized Entropy Family
2 ► Coefficient of variation (C2/2)
1 ► Theil index
0 ► Mean logarithmic deviation
𝑻=𝑩𝑻+𝑾𝑻=𝑩𝑻+ 𝒈 𝒒 𝒈 𝑳 𝒈
𝑳=𝑩𝑳+𝑾𝑳=𝑩𝑳+ 𝒈 𝒑 𝒈 𝑳 𝒈
The lower the parameter θ, the greater the transfer impact when it occurs at the bottom end of the distribution
In this paper we compute decompositions for three members of the generalized entropy family corresponding to three classical measures of inequality
The coefficient of variation, the theil index and the Mean logarithmic deviation
On the one hand they give different relevance to the place where a transfer occurs: The lower the parameter θ the greater the transfer impact when it occurs at the bottom end of the distribution
On the other hand they have simple decompositions. In particular the within regional inequalities are weighted by the income shares in the case of the Theil index and by the population shares in the case of the Mean logarithmic deviation.
weighted by income shares
weighted by population shares
LIS/LWS User Conference / April 27th-28th, 2017

12. 3) Database and methodology
3.2) DFL reweighting method
We implement a simulation exercise inspired by the semiparametric approach proposed by DiNardo et al. (1996).
The purpose is to determine what the regional per capita disposable income would have looked like in t=2 if the population weights had remain as they were in t=1.
We consider a joint distribution function that characterizes each one of our households: F(d, x, t).
LIS/LWS User Conference / April 27th-28th, 2017

13. 3) Database and methodology
3.2) DFL reweighting method
We asume that the marginal distribution of the per capita disposable income, in the final period, is the following:
𝒇 𝒅 𝒕=𝟐 = 𝒙 𝒇(𝒅,𝒙|𝒕=𝟐) 𝒅𝒙
Formally, DFL describe this goal this way:
𝒇 𝒅 𝒕 𝒅 =𝟐, 𝒕 𝒙 =𝟏 = 𝒙 𝒇 𝒅 𝒙, 𝒕=𝟐 𝑭(𝒙|𝒕=𝟏)
LIS/LWS User Conference / April 27th-28th, 2017

14. 3) Database and methodology
3.2) DFL reweighting method
The key to calculate this counterfactual distribution is to rewrite it as follows:
𝒇 𝒅 𝒕 𝒅 =𝟐, 𝒕 𝒙 =𝟏 = 𝒙 𝒇 𝒅 𝒙, 𝒕=𝟐 𝑭(𝒙|𝒕=𝟏) 𝑭(𝒙|𝒕=𝟐) 𝒅𝑭(𝒙|𝒕=𝟐)
Where:
𝚿 𝒙 = 𝑭(𝒙|𝒕=𝟏) 𝑭(𝒙|𝒕=𝟐)
𝚿 𝒙 = 𝑭 𝒕=𝟏 𝒙 𝑭 𝒙 𝑭 𝒕=𝟏 𝑭 𝒕=𝟐 𝒙 𝑭 𝒙 𝑭(𝒕=𝟐 = 𝑷𝒓 𝒕=𝟏 𝒙 𝑷𝒓 𝒕=𝟏 𝑷𝒓 𝒕=𝟐 𝒙 𝑷𝒓 𝒕=𝟐 = 𝑷𝒓 𝒕=𝟏 𝒙 𝑷𝒓 𝒕=𝟐 𝑷𝒓 𝒕=𝟐 𝒙 𝑷𝒓 𝒕=𝟏
LIS/LWS User Conference / April 27th-28th, 2017

15. 4) Preliminary results
4.1) Inequality decomposition by population subgroups
With regard to the decomposition of inequality. We present the results for the theil index and the Mean logarithmic deviation.
According with previous works the between component (or interregional inequality) explains in general a small share of total inequality.
Although in some cases like Italy and and to a lesser extent Spain the part explained is so significant.
The relevance of the issue has probably more to do with the redistribution process which will be addressed in a subsequent paper.
LIS/LWS User Conference / April 27th-28th, 2017

16. Scenario 1: Distribution (t=2) - Distribution (t=1)
4) Preliminary results
4.2) DFL reweighting method
Scenario 1: Distribution (t=2) - Distribution (t=1)
LIS/LWS User Conference / April 27th-28th, 2017

17. Scenario 2: Distribution (t=2) - Distribution (t=2; cf=1)
4) Preliminary results
4.2) DFL reweighting method
Scenario 2: Distribution (t=2) - Distribution (t=2; cf=1)
LIS/LWS User Conference / April 27th-28th, 2017

18. Scenario 3: Distribution (t=2; cf=1) - Distribution (t=2; cf=2)
4) Preliminary results
4.2) DFL reweighting method
Scenario 3: Distribution (t=2; cf=1) - Distribution (t=2; cf=2)
LIS/LWS User Conference / April 27th-28th, 2017

19. Scenario 4: Distribution (t=2; cf=2) - Distribution (t=1)
4) Preliminary results
4.2) DFL reweighting method
Scenario 4: Distribution (t=2; cf=2) - Distribution (t=1)
LIS/LWS User Conference / April 27th-28th, 2017

20. 4) Preliminary results 4.2) DFL reweighting method
LIS/LWS User Conference / April 27th-28th, 2017

21. 5) Concluding remarks
The literature on regional inequality and decentralization is very broad. However, there are not many papers that examine in a particular way the relevance of the territorial variable (our main contribution).
The aditive decomposition by population subgroups reveals the same evidence observed in the majority of works on regional inequality: the high weight of the within component.
The DFL approach allows us to find:
important disparities in the contribution of the regional variable.
remarkable differences not only in the magnitude of the results, but also in the sign of variation.
LIS/LWS User Conference / April 27th-28th, 2017

22. Thank you for your attention
LIS/LWS User Conference / April 27th-28th, 2017