This piece is a more technical appendix of what I’ve wrote in Birokreasi about inequality. You can read the article here http://birokreasi.com/2017/08/rusak/.
In that article, I mentioned the research of Martinez-Vazquez, et al. (2014) concerning what fiscal policies (i.e., taxation and expenditure policies) that can reduce inequality.
The aforementioned study measures inequality by using Gini ratio, in which the closer it is to zero, the more equal society is. Martinez-Vazquez and co. found that progressive taxes such as corporate and personal income tax can reduce Gini ratio. Therefore, the better a country collects income taxes, the better she is in reducing inequality. It is also understandable as progressive taxation levies higher rate for entity with higher income.
On the other hand, Martinez-Vazquez, et al. found that regressive taxation (such as Value Added Tax) will increase inequality. It is understandable because in regressive taxation, the tax burden is similar across the population, regardless of their levels of income. Meaning that a billionaire and a farmer will pay the same amount of taxes when they purchase similar goods/services.
With regards to expenditure policy, generally it is found that the more a government dedicate their revenue to fund social expenditure, housing, education, and healthcare, the better she is in reducing inequality. As middle and lower income households are provided better access towards such needs by the government, not only they have better living standards, but they will also have more disposable income.
I am not interested in testing whether their findings are similar in the case of Indonesia. (My friend, Mikhail Nugroho Adi Setiawan, replicated Martinez-Vazquez, et al.’s study in his junior thesis. He found that their conclusions also hold for ASEAN countries.)
What I am interested to find out is how those policies play out during the periods after they were implemented. One of the tools for this is Vector Autoregression (VAR).
VAR models the how a variable (for instance, inequality) changes over time by the influence of its own lagged value (inequality in the previous year) and the past value of the variable that influences it (say, last year’s education expenditure). VAR modeling is also useful because it does not require as much a priori assumption about how the forces influencing a variable. Therefore, we can set aside econometric issues such as simultaneity and endogeneity. What is needed is a list of variables which can be hypothesized to affect each other within certain period of time.
The data used in this post is collected from IMF Government Financial Statistics, Laporan Keuangan Pemerintah Pusat and Anggaran Pendapatan dan Belanja Negara of various years, also Badan Pusat Statistik. The data I used in VAR modeling is from fiscal year 1990-2014.
To implement VAR, we must test how much lags are needed for the independent variable to affect dependent variable. For instance, increasing education expenditure for the current year may not directly reduce inequality for that year. The effect may be felt 2 or 3 years after the policy was implemented.
To obtain the best lags, we can subject the variables to a battery of tests. We can then select which one is the best based on several criteria. The usual criteria for lag selection test are, inter alia, final prediction error (FPE), Akaike’s information criterion (AIC), Schwarz’s Bayesian information criterion (SBIC), and the Hannan and Quinn information criterion (HQIC). Luckily, the command “varsoc” in statistical software STATA can do a comprehensive job.
After obtaining the appropriate lags, I subject all variables into VAR equations. However, reading VAR statistical output can be tedious.
To ease how we picture the relationship between fiscal policies and Gini ratio, I transform the VAR results into Impulse Response Function (IRF). In this case, fiscal policies such as corporate income tax (CIT), personal income tax (PIT), indirect taxes (INDIRECT), social expenditure (SOCIAL), housing expenditure (HOUSE), education expenditure (EDU), and healthcare expenditure (HEALTH) can be seen as the “impulse”. That is, they trigger a response in Gini ratio in the form of ups (worsening inequality) and downs (reducing inequality) in the periods that follow. To better illustrate how IRF works, imagine you throw a stone into a pond. The stone is the impulse, and the waves that show on the surface are the responses.
So, in terms of taxation policy, the IRF results are as follow:
In general, we can expect personal income tax to reduce Gini ratio in the next year after policy implementation. On the contrary, increasing VAT in the current year will lead to an increase in Gini ratio in the next year.
Interestingly, I found that an increase in corporate income tax may even worsen the inequality. There may be several causes. First, following the finding of Martinez-Vazquez, et al., the more open the economy, the higher share of the CIT that would fall on labor income, making this corporate income tax less progressive (Martinez-Vazquez, et al., 2014:110). Secondly, as in the case of Indonesia, our corporate income tax is not progressive to begin with. Currently, our corporate income tax rate is flat at 25%. (It started to be a flat tax since fiscal 2009, with the issuance of Law Number 36 Year 2008 concerning Income Tax, in which the corporate tax rate changed from progressive into single 28% rate). This may be the case to redesign our corporate income tax rate to be progressive again.
On the public spending side, the results are as follows:
An increased spending in healthcare and social expenditure will lead to a decrease in Gini ratio at least two years after implementation. An increase in education expenditure, however, increases Gini ratio one year after implementation, yet reduces it afterward in year two. Similarly, an increase in housing expenditure will increase Gini ratio in the first year.
It can be theorized that housing expenditure (at least in Indonesia) generally benefits middle-to-higher class instead of the poorest part of the population. In other words, it only enables middle to high class – who already have a steady stream of income – to accumulate more wealth. But this conjecture may be wrong. Thus, a further study is needed.
Conclusion: to reduce inequality, we need to better redistribute income via personal income tax. We also want to increase healthcare and social spending. A more progressive corporate income tax and a better-targeted housing policy are also needed if we want to reduce inequality.
(H/T to Mikhail for providing the necessary dataset.)
Reference:
Martínez-Vazquez, Jorge, Violeta Vulovic, and Blanca Moreno Dodson. 2014. “The Impact of Tax and Expenditure Policies on Income Distribution: Evidence from a Large Panel of Countries.” Hacienda Pública Española 200 (2012): October, 6th. 2014
