How Inclusive Is Abenomics?; by Chie Aoyagi, Giovanni Ganelli, and Kentaro Murayama; imf working Paper No. 15/54; March 1, 2015

 5 

foster growth and increase equality along the growth path. All policy variables included in 

our analysis are found to have a positive effect on average income growth (ranging from 0.31 

to 0.83 percentage points); and increasing female labor participation is found to have a 

positive impact on income equality by 0.20-0.35 percentage points. Those estimates are 

significant in magnitude relative to the recent levels of low (or even negative) growth.  

 

The rest of the paper is organized as follows. Next section discusses various concepts and 

definitions of inclusive growth. Section III presents a descriptive analysis of recent trends in  

inequality and poverty  in Japan along various dimensions, such as age groups, gender, 

employment status. Section IV presents our measure of inclusive growth, based on the 

measure developed by Anand et al. (2013). Section V illustrates the results of our empirical 

analysis. Section VI presents some scenario analysis based on our regressions, and discussed 

policy implications. Section VII concludes.  

 

II.   I

NCLUSIVE 

G

ROWTH

:

 

M

ULTIPLE 

D

EFINITIONS

 

Inclusive growth is a multidimensional concept and the notions of inclusiveness and 

inclusive growth have varying definitions, interpretations and connotations. Since the mid-

2000s, the term earned a significant popularity in the operational work of various 

international institutions, although it had been used sparsely in the scholarly literature before 

that. Various “inclusive growth” measures have been used to define policy orientation and 

priorities in resource allocation, and to evaluate and monitor projects. The wide range of 

definitions used can sometimes be cause of confusion, although it also provides flexibility in 

the operationalization of the inclusive growth concept. According to the existing literature, a 

broad classification of inclusive growth measures can be done according to two criteria, 

which we will now discuss in detail.  

First, inclusiveness measures in the literature can be classified by whether the inclusiveness 

is scaled by monetary or non-monetary measures. The monetary approach is less demanding 

in terms of data collection and analysis and highly compatible with conventional notions of 

poverty. However, it may fail to capture some important non-monetary aspects of poverty 

and inequality, and of the impact of policies to address them. The second approach, on the 

other hand, gives proper consideration to non-monetary factors, such as opportunities and 

access to social services across socioeconomic groups. Ali and Son (2007), for instance, 

propose a measure, which takes into account the varying degree of access to social services 

and health benefits across income groups. Like multi-dimensional poverty measures, non-

monetary measures of inclusive growth are informative but difficult to interpret. A balanced 

analysis, then, should use both types of measures to achieve a manageable but useful 

assessment of the growth strategies. 

Another conceptual discussion, following Klasen (2010), is whether inclusiveness is 

measured by a process or an outcome. Inclusive growth in process often refers to labor 

participation during economic growth. This dimension of inclusiveness is a core issue for 

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 6 

many developing countries and natural resource rich countries, where growth in certain 

industries such as oil production does not necessarily lead to employment and higher wage 

for the whole population. Another instance is exclusion of certain segments of the population 

from economic activities. Inclusiveness in outcome, in contrast, looks at the gains from the 

growth such as the income (distribution) or access to public services such as education and 

health care. Clearly those two measures of inclusive growth are related to each other, and the 

distinction is a matter of analytical framework. In summary, different measures capture 

different aspects of inclusiveness, and they are most informative when they are used 

complementarily rather than exclusively. A complementary approach reduces the risk of 

focusing exclusively on some aspects of inclusiveness while failing to address others.  

In light of the above discussion, in the remainder of the paper we try to capture various 

aspects of inclusiveness in the Japanese economy, although the primary focus is placed on 

the monetary measure of inclusive growth. First, we examine measures of income inequality 

to highlight current trends. Second, we look at poverty measures. Then we investigate the 

extent to which certain demographic groups are disadvantaged compared to others in terms 

of income. Lastly, we run multivariate regressions to determine and quantify key factors of 

inclusive growth and derive policy implications. In the econometric part, we use as proxy of 

inclusive growth the measure developed by Anand et al. (2013), which takes into account 

both average income growth and its equity impact.  

 

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 7 

III.   I

NCLUSIVENESS IN 

J

APAN

:

 

T

RENDS AND 

S

TYLIZED 

F

ACTS

 

A.   Income Inequality 

When measured using the Gini coefficient of market income (before fiscal redistribution), 

inequality in Japan has increased steadily in the last three decades.  While an upward trend 

and some degree of 

convergence can be observed 

amongst all G7 countries, 

Japan’s pace of increasing 

inequality has been 

exceptionally high, marking a 

15 points increase in about 25 

years. The latest available 

figure imply that income 

inequality in Japan, starting 

from the lowest G7 level in the 

mid 1980s, has almost 

converged to the G7 average of 

0.50 (text chart).  

Part of the increase in market inequality might be related to the exceptionally rapid pace of 

aging of the Japanese population. As Jones (2007) suggests, an increasing share of elderly 

population increases income inequality for various reasons: the elderly population earn less 

income than the working population; inequality among the elderly population is greater than 

amongst working population; and an increasing number of elderly people have been forming 

small households consisting of elderly only, instead of forming households with working-age 

population.  

Another measure of income inequality, which takes into account the impact of fiscal 

redistribution, is the Gini 

coefficient of disposable income. 

This measure reflects the actual 

livelihood status of households, as 

disposable income represents how 

much each household, including 

those who retired, is capable to 

spend after tax and transfers. In 

Japan, the disposable income Gini 

coefficient rose moderately, yet 

consistently (with the exception of 

a temporary drop in the early 

2000s) over the last three decades. 

0.3

0.35

0.4

0.45

0.5

0.55

Canada

France

Germany

Italy

Japan

United Kingdom

Gini before Taxes and Transfers (Market  Income)

Source: OECD

0.2

0.25

0.3

0.35

0.4

Canada

France

Germany

Italy

Japan

United Kingdom

United States

G7 Average

Gini after Taxes and Transfers (Disposable  Income)

Source: OECD

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