How does monetary policy affect income inequality in Japan? Evidence from grouped data

We examine the effects of monetary policy on income inequality in Japan. For that purpose, we use a novel Bayesian approach that jointly estimates the Gini coefficient from grouped income data and the dynamics of macroeconomic quantities. Our results indicate different effects on income inequality for different types of households: A monetary tightening decreases inequality when we consider a broad definition of household data that also includes the unemployed and retirees. Higher unemployment and tighter borrowing conditions make the richer (i.e., employed) comparably worse off. The result reverses, if we focus on the subsample of households whose head is employed. Through the same channels, inequality increases if monetary policy is tightened. A counterfactual analysis reveals that indeed the financial channel and the job destruction channel are the most important transmission mechanisms.

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Notes

Alternatively one could estimate a Pareto distribution or more generally a generalized beta distribution; for the latter, however, it is not straightforward to calculate the Gini coefficient; see, for example, McDonald and Ransom (2008). More recently, Kobayashi et al. (2021) proposed a method to estimate Lorenz curves assuming more flexible distributions than the log-normal distribution. However, their method is not directly applicable to our modeling framework. That said, it should be noted that there is empirical evidence that the log-normal distribution fits Japanese income data well (Nishino and Kakamu 2011).

It should be mentioned that this model is a straightforward extension of the simple stochastic volatility model. The state variable \(h_\) means the income inequality, and this variable is used as a variable in the VAR system. Therefore, it can be seen as the joint estimation of a stochastic volatility model and VAR model. In the recent financial time series analysis, the leverage effect in the stochastic volatility model, which is a drop in the return followed by an increase in the volatility, plays an important role. However, in the context Footnote 2 Continued of income data, stylized facts do not report such a leverage effect. Thus, we assume independence between the variance and the distribution over time. Moreover, the estimation methods follow the previous standard methods in the estimation of the stochastic volatility model and VAR model.

More precisely, the simulated data are constructed as follows. First, 100, 000 observations are simulated from the log-normal distribution. The observations are sorted in an ascending order and are divided into quintile groups. Then, we calculate the class income means. Using the frequencies and class income means, we can finally draw the Lorenz curve from grouped data.

The survey excludes one-person student households, inpatients in hospital, inmates of reformatory institutions, households which manage restaurants, hotels, boarding houses or dormitories, sharing their dwellings, households which serve meals to the boarders even though not managing boarding houses as an occupation, households with 4 or more living-in employees, households whose heads are absent for a long time (three months or more) and foreigner households. For more details, see http://www.stat.go.jp/english/.

Data mnemonics are as follows: rgdp: JPNRGDPEXP , ltir: IRLTLT01JPM156N , eq: NIKKEI225 and m3: MABMM301JPM189S .

In theory, since older households tend to depend more strongly on interest rate income—which is not covered in the income data available for this study—quantitative easing should decrease income inequality (Amaral 2017).

For example, Nakashima et al. (2017) attribute these short-run anomalies in output and price behavior to revision of expectations of market participants regarding future economic conditions.

As an alternative, consider the approaches of Baumeister and Benati (2013) and Ludvigson et al. (2002) who manipulate the VAR coefficients and corresponding elements in the variance covariance matrix directly to offset the effects of a particular variable.

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Funding

Kakamu acknowledges the financial support by KAKENHI (#16KK0081, #16K03592, #25245035, #20H00080 and #20K01590).

Author information

Authors and Affiliations

1. Vienna School of International Studies (DA), Favoritenstraße 15a, 1040, Vienna, Austria Martin Feldkircher
2. Oesterreichische Nationalbank (OeNB), Vienna, Austria Martin Feldkircher
3. Graduate School of Economics, Nagoya City University, Yamanohata 1, Mizuho-cho, Mizuho-ku, Nagoya, 467-8501, Japan Kazuhiko Kakamu

1. Martin Feldkircher