The Effects of Government Spending Shocks on Income Distribution in South Africa

Abstract

The main aim of this study is to examine the effect of government spending and its components’

shocks on the distribution of income between labour and capital in South Africa for the period

between 1994Q2 up to 2019Q3. The effects of government spending shocks on income

distribution are analyzed using Jorda’s (2005) local projection method. The shocks, however, are

identified by applying short-run contemporaneous restrictions in a vector autoregressive model

based on cholesky identification scheme. The results indicate that government spending shock

has a positive and significant effect on labor share after the first quarter. This means that

expansionary government spending has a paramount role in reducing income inequality in the

economy. Both government investment and government consumption shocks have also

contributed to a reduction in income inequality, though the magnitude effect is smaller for

government consumption.

Keywords: Government spending, government consumption, government investment, local

projection, income distribution, South Africa.

1. Introduction

South Africa is one of the world countries with high income inequality. This has been the case

since the advent of the post-apartheid era. Although reducing poverty and inequality has been the

overriding concern in South Africa’s development policies and programmes since the advent of

the post-apartheid era as revealed for example in free primary healthcare, non-fee-paying

schools, old age and child support grants, housing, social wage interventions, and free basic

services, measures of inequality do not exhibit a clear improvement in the country and the

country continues to face the challenge of high poverty and inequality. South Africa’s

consumption expenditure per capita Gini coefficient was 0.63 in 2015, the highest in the world

and observed an increase since 1994 (World Bank, 2018).

The labour income share, another measure of income inequality from functional distribution of

income perspective, in South Africa has also exhibited short-term volatility, in contrast to the

stability of factor shares that economic theory might lead us to anticipate in the absence of short

term technology volatility. After the introduction of the new democracy in 1994, labor income

share fell significantly from its stable range of 54–56 per cent to a lowest point of approximately

48 per cent in 2008, while the share of those who own capital rose (Goodness and Harris, 2019).

Improvement in labour’s income share followed, but the post-1994 fall and likely hysteresis

effects led to high poverty, inequality, and unemployment being recognized as the three

challenges in South Africa’s National Development Plan 2030 (World Bank 2018).

At the same time, like many other developing countries, public debt-to-GDP ratios have recently

increased in South Africa because of weaker economic activity and subdued commodity prices.

Hence, South Africa like the other emerging economies is encountering the challenge of

addressing high inequality while maintaining fiscal sustainability. This research, therefore,

intends to answer two research questions. First, what is the effect of government spending shocks

on income distribution in South Africa? Second, what is the effect of its components’ shocks on

income distribution in South Africa? Addressing these two research questions are essential

because a worsening in income inequality could diminish the political support for the

government to execute consolidation measures, but also because higher levels of inequality could

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hurt long-run growth (Berg and Ostry, 2017). It is also the first study to answer these two crucial

research questions for the South African economy.

2. Review of Related Literature

Empirical studies that deal with the effects of government spending shocks on the distribution of

income for the South African economy is non-existent. This paper, therefore, contributes to the

empirical literature on the effect of government spending shock on labour income share for this

small open economy.

Furceri and Li (2017), using public investment forecast errors to identify unanticipated changes

in public investment, exhibit that increases in public investment tend to lower income inequality

in developing economies. Furceri et al. (2018) also study the distributional effects of public

spending shocks in a large panel of emerging and low income countries using a panel local

projection method. They find that unanticipated fiscal consolidations lead to a long-lasting

increase in income inequality while fiscal expansions lower inequality. They also calculate

medium-term inequality multiplier and show that a cumulative decrease in government spending

of 1 percent of GDP over 5 years is associated with a cumulative increase in the Gini coefficient

over the same period of about 1 percentage point.

Using an annual data set covering 17 OECD countries over the time period 1978–2013, Philipp

(2009) analyzes the dynamic effects of fiscal consolidation episodes on income inequality in the

short- and medium-run. The result of the study show that fiscal consolidations typically lead to

an increase in income inequality. Baseline results suggest that in the aftermath of the start of a

fiscal adjustment episode, the Gini coefficient of disposable income increases by about 0.4%

points in the short-run (in year three), and by 0.6% points in the medium-run (in year seven).

Woo et al. (2017) also analyzes the effects of fiscal adjustments for a panel of 17 OECD

countries over the last 30 years, complemented by a case study of selected fiscal adjustment

episodes. The paper shows that fiscal adjustments are likely to raise inequality through various

channels including their effects on unemployment.

Ball et al. (2013) examines the distributional effects of fiscal consolidation using episodes of

fiscal consolidation for a sample of 17 OECD countries over the period 1978–2009. Their result

show that fiscal consolidation has typically had significant distributional effects by raising

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inequality, decreasing wage income shares and increasing long-term unemployment. Agnello and

Sousa (2014) also assess the impact of fiscal consolidation on income inequality for 18

industrialized countries from 1978 to 2009 and find that income inequality significantly rises

during periods of fiscal consolidation. Agnello and Sousa (2012), on the other hand, find that

fiscal austerity plans that succeed in bringing public debt to a sustainable path seem to be more

likely to reduce inequality. 

Djeneba and Kinda (2019), using newly assembled data on spending composition for 83

countries across all income groups, assess the effect of public expenditure on income inequality

by changing the composition of public spending while keeping the total level of expenditure

fixed. Their result shows that reallocating spending toward social protection and infrastructure is

associated with reduced income inequality, particularly when it is financed through cuts in

defense spending. However, the political and security situation matters.

Roine, Vlachos, and Waldenström (2009) explore the determinants of income inequality using a

sample of 16 countries spanning the whole of the twentieth century. Using panel estimations, the

authors show that the relative amount of government spending negatively affects high-income

shares (except for 1% of the highest incomes), and they document the rise of the income share in

the bottom nine deciles. Milanovic and Ersado (2012) study the determinants of income

distribution (using decile shares) in 26 post-communist economies during 1990-2005. In their

study, government expenditure is confirmed distribution-neutral in all of their specifications.

This result contrasts with Aristei and Perugini (2014), who document that a larger government

expenditure significantly reduced income inequality in 27 post-communist economies during the

period 1989–2009. Kahanec and Zimmermann (2014) identify a negative correlation between

inequality and government expenditure on a sample of 16 OECD countries. Doerrenberg and

Peichl (2014) use data for 30 OECD countries from 1981 to 2005 and provide evidence that

redistributive policy measures can reduce income inequality.

Martin and Martin (2018) apply instrumental variable estimation techniques to identify a causal

relationship between income inequality and government size for 30 European countries over the

period 2004-2015. They show that much of the literature underestimates the true role of the

government in attenuating income inequality.

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3. Methodology

3.1. Data type and source

The sample period that is used in the estimation is from 1994Q2 to 2019Q3. To keep the analysis

as parsimonious as possible, we four variables in the baseline model, namely real government

spending, real tax revenues, real gdp and labour income share. Labour income share which is the

measure of functional distribution of income is used as a proxy for income inequality. As in

Goodness and Harris (2019), labour income share is defined as the ratio of compensation of

employees to gross value added at factor cost. The data source is the South African Reserve

Bank.

3.2. Empirical Approach

Jorda’s (2005) local projection method is used to show the aggregate as well as disaggregate

effects of government spending shocks on income distribution for South African economy. The

local projection method requires running a sequence of predictive regressions of a variable of

interest on a structural shock for different prediction horizons. The impulse response is then

obtained from the sequence of regression coefficients of the structural shock (Goodness, and

Harris, 2019; Goodness, 2019). Therefore, the method can produce the response of inequality to

government spending shock at different horizons.

As clearly explained in Auerbach and Gorodnichenko (2013), Ramey and Zubairy (2014),

Goodness and Harris (2019), and Goodness (2019), this method has some advantages compared

to conventional VAR model. First, the estimation relies on robust standard errors and is simple to

implement. Second, impulse responses from the local projection are consistent and

asymptotically normal. Last but not least, it is robust to misspecification of the data generating

process.

The baseline model can be written as follows:

x t +h = α h+φh(L)y t−1+βh shockt + linear trend +ε t+ h (1)

where x denotes the variable of interest, y represents a vector of control variables, φ(L) is a

polynomial in the lag operator, and shock is the VAR-based government spending shock. In our

study, x contains real government spending and labour income share, y consists of lags of the

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values of government spending, labour income share, output and taxes. The coefficient βh

represents response of x at time t + h to the shock at time t. Hence, one can construct impulse

responses by estimating a set of regressions for each horizon h. We include a constant α h and a

linear trend in the model.

Impulse response coefficient estimates can suffer from serial correlation, which may lead to

wider marginal error bands. Conditional error bands help remove the variability caused by the

serial correlation. Conditional error bands are consistent with the joint null of significance and

give a better sense about the significance of individual responses. In the absence of correlation

among impulse response coefficients, marginal and conditional bands would be similar (Jorda,

2009). In this paper, therefore, results are produced using the conditional error bands to remove

serial correlation, if any.

Another issue arises from identification of government spending shocks. There are four different

frameworks to achieve identification of government spending shocks in the literature. These are

VAR-based government spending shocks (Blanchard and Perotti, 2002; Ilzetzki et al., 2013),

narrative approach based on news about future defense spending (Barro, 1981; Ramey and

Shapiro, 1998; Ramey, 2011a; Ramey, 2011b; Owyang, et al., 2013), loans from official

creditors as exogenous sources of fluctuations in government spending (Kraay, 2012; Kraay,

2014) and forecast error for growth rate of government spending (Auerbach and Gorodnichenko,

2012; Auerbach and Gorodnichenko, 2013).

To construct government spending shocks, we use VAR-based government spending shocks in

our local projection model and establish a four variable VAR framework based on cholesky

decomposition. Accordingly, government spending is ordered first followed by government

revenue, output and lastly labour income share. Government investment and consumption are

ordered first and second in order to identify the government expenditure components’ shocks,

respectively while maintaining the order of the rest of the endogenous variables as in the

identification of the total government spending shock.

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4. Main Results

Empirical results are presented for the baseline aggregate government spending shock model as

well as its components’ effects on labour income share. The impulse response functions over 10

horizons for the effect of one standard deviation government spending shock on labour income

share is given in Figure 1.

Figure 1: Response of endogenous variables to one standard deviation government spending

shock with 95 per cent conditional confidence bands

Given the importance of government spending in the economy, we examine the dynamics of tax

revenue, real gross domestic product (output), and labor income share in response to a shock to

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government spending, using impulse response functions (IRFs) computed from the local

projection model. Figure 1 shows the response of the variables to a one standard deviation shock

to the government spending. The first figure indicates the response of government spending to its

own shock while the other figures show the responses of GDP, tax revenue and labour income

share to a shock to government spending.

Government spending shock is persistent. The effect of government spending shock on tax

revenue is positive and insignificant on impact. Real output, however, increases significantly for

the first eight quarters significantly. As to its effect on our variable of interest, labour income

share increases significantly to around 25 percentage points on the tenth quarter and persists to

increase onwards. This indicates us that an increase in government spending leads to a reduction

in income inequality in this highly income unequal economy.

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Figure 2: Response of endogenous variables to one standard deviation government investment

shock with 95 per cent conditional confidence bands

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Similar to government spending shock, government investment shock is persistent. Government

consumption decreases significantly in response to an exogenous government investment shock

after the fifth quarter. The effect of government investment shock on both tax revenue and output

is positive and insignificant on impact. Labour income share increases significantly to around 30

percentage points at the fourth quarter in response to an increase in government investment.

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Figure 3: Response of endogenous variables to one standard deviation government consumption

shock with 95 per cent conditional confidence bands

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The effect of government consumption shock on government investment is negative and

insignificant on impact. Government consumption shock is persistent. Its effect on tax revenue is

insignificant. Real output, however, increases significantly over the whole period horizons

considered. The effect of government consumption shock on labour income share is also positive

and significant from quarter two onwards.

5. Conclusion

This study shows the effect of government spending and its components’ shocks on the

distribution of income between labour and capital in South Africa using local projection method

over the sample period between 1994Q2 up to 2019Q3. The results indicate that expansionary

government spending shock has a positive and significant effect on labour income share after the

first quarter. Labour income share increases significantly to around 25 percentage points on the

tenth quarter. This shows us that an increase in government spending leads to a reduction in

income inequality in this highly income unequal small open economy. The effect of government

spending shock on tax revenue is positive and insignificant on impact. Real output, however,

increases significantly for the first eight quarters significantly.

As for the components of government spending, the results exhibit that government consumption

decreases significantly in response to an increase in government investment shock after the fifth

quarter. The effect of government investment shock on both tax revenue and output is positive

and insignificant on impact. Labour income share increases significantly to around 30 percentage

points at the fourth quarter in response to expansionary government investment shock.

The effect of government consumption shock on government investment is negative and

insignificant on impact. Its effect on tax revenue is insignificant. Real output, however, increases

significantly over the whole period horizons considered. The effect of government consumption

shock on labour income share is also positive and significant from quarter two onwards.

Although both government investment and government consumption shocks have contributed to

a reduction in income inequality, the magnitude effect is smaller for government consumption. In

our future research work, we will try to examine the asymmetric effects of government spending

shocks on income distribution for this small open economy.

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References

Agnello L, and Sousa R (2012). Fiscal adjustment and income inequality: a first assessment.

Appl Econ Lett 19(16):1627–1632.

Agnello L, and Sousa R (2014). How does fiscal consolidation impact on income inequality. Rev

Income Wealth 60(4):702–726.

Aristei, D., and Perugini, C. (2014). Speed and sequencing of transition reforms and income

inequality: A panel data analysis. Review of Income and Wealth, 60(3), 542–570.

http://doi.org/10.1111/roiw.12090.

Auerbach, A.J., and Y. Gorodnichenko (2012). Measuring the output responses to fiscal policy.

American Economic Journal: Economic Policy 4, no. 2: 1-27.

Auerbach, A.J., and Y. Gorodnichenko (2013). Fiscal multipliers in recession and expansion. In

Fiscal Policy after the Financial Crisis, ed. A. Alesina and F. Giavazzi, 63-98. Chicago:

University of Chicago Press.

Ball L, Furceri D, Leigh D, and Loungani P (2013). The distributional effects of fiscal

consolidation. IMF Working Papers no. 13/151, International Monetary Fund.

Barro, R. (1981). Output Effects of Government Purchases, Journal of Political Economy, vol.

89, no. 6, 1981, pp. 1086-1121.

Berg, A. G., and J. D. Ostry (2017). Inequality and Unsustainable Growth: Two Sides of the

Same Coin?, IMF Economic Review, in press.

Blanchard, O., and R. Perotti (2002). An Empirical Characterization of the Dynamic Effects of

Changes in Government Spending and Taxes on Output, The Quarterly Journal of Economics,

vol. 117, no. 4, pp. 1329–1368.

Djeneba D. and T. Kinda (2019). Reallocating Public Spending to Reduce Income Inequality:

Can It Work? IMF Working Paper. WP/19/188.

Doerrenberg, P., and Peichl, A. (2014). The impact of redistributive policies on inequality in

OECD countries. Applied Economics, 46(17), 2066–2086.

Furceri D., J. Ge, P. Loungani, and G. Melina (2018). The Distributional Effects of Government

Spending Shocks in Developing Economies, IMF Working Papers, No. 18/57, International

Monetary Fund.

12

Furceri, D., and B. G. Li (2017). The Macroeconomic (and Distributional) Effects of Public

Investment in Developing Economies, IMF Working Papers, No. 17/217, International Monetary

Fund.

Goodness C. A., and L. Harris (2019). The effect of real exchange rate volatility on income

distribution in South Africa, WIDER Working Paper 2019/29.

Goodness C. A. (2019). Fiscal policy uncertainty and economic activity in South Africa: An

asymmetric analysis, UP Working Paper 2019/22, University of Pretoria.

Ilzetzki, E., Enrique G M., and C. A. Vegh (2013). How Big (Small?) are Fiscal Multipliers?,

Journal of Monetary Economics, vol. 60, no. 2, pp. 239-254.

Jorda, O. (2005). Estimation and inference of impulse responses by local projections. The

American Economic Review, vol. 95, no. 1, pp. 161-182.

Kahanec, M., and Zimmermann, K. F. (2014). How skilled immigration may improve economic

equality. IZA Journal of Migration, 3(1). http://doi.org/10.1186/2193-9039-3-2.

Kraay, A. (2012). How Large Is the Government Spending Multiplier? Evidence from World

Bank Lending, The Quarterly Journal of Economics, vol. 127, no. 2, pp. 829–887.

Kraay, A. (2014). Government Spending Multipliers in Developing Countries: Evidence from

Lending by Official Creditors, American Economic Journal: Macroeconomics, vol. 6, no. 4, pp.

170-208.

Martin G., and Martin K. (2018). Income Inequality and the Size of Government: A Causal

Analysis. IZA DP No. 12015.

Milanovic, B., and Ersado, L. (2012). Reform and Inequality during the Transition: An Analysis

Using Panel Household Survey Data, 1990–2005. In G. Roland (Ed.), Economies in Transition

(pp. 84– 108). London: Palgrave Macmillan.

Owyang M. T., V. A. Ramey, and S. Zubairy (2013). Are Government Spending Multipliers

Greater during Periods of Slack? Evidence from Twentieth-Century Historical Data, American

Economic Review, vol. 103, no. 3, pp. 129-134.

Philipp H. (2018). The dynamic effects of fiscal consolidation episodes on income inequality:

evidence for 17 OECD countries over 1978–2013. Empirica. 47, pages 53–81.

Ramey, V. A. (2011a). Identifying Government Spending Shocks: It’s all in the Timing. The

Quarterly Journal of Economics, vol. 126, no. 1, pp. 1-50.

13

Ramey, V. A. (2011b). Can Government Purchases Stimulate the Economy?, Journal of

Economic Literature, vol. 49, no. 3, pp. 673-685.

Ramey, V. A., and M. D. Shapiro (1998). Costly capital reallocation and the effects of

government spending,” Carnegie-Rochester Conference Series on Public Policy, vol. 48, no. 1,

pp. 145-194.

Ramey, V.A., and S. Zubairy (2014). Government spending multipliers in good times and in bad:

Evidence from U.S. historical data, NBER Working Paper, no. 20719.

Roine, J., Vlachos, J., and Waldenström, D. (2009). The long-run determinants of inequality:

What can we learn from top income data? Journal of Public Economics, 93(7–8), 974–988.

http://doi.org/10.1016/j.jpubeco.2009.04.003.

Woo J., Elva B., Tidiane K., and Y. S. Zhang (2017). Distributional Consequences of Fiscal

Adjustments: What Do the Data Say? IMF Economic Review. 65, pages 273–307.

World Bank (2018). Overcoming Poverty and Inequality in South Africa: An Assessment of

Drivers, Constraints and Opportunities. Washington, DC: World Bank.

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