Defense Expenditure and Income Inequality

Defence and Peace Economics

ISSN: 1024-2694 (Print) 1476-8267 (Online) Journal homepage: http://www.tandfonline.com/loi/gdpe20

Defense Expenditure and Income Inequality: Evidence on Co-integration and Causality for China Binbin Meng, William Lucyshyn & Xiangqian Li To cite this article: Binbin Meng, William Lucyshyn & Xiangqian Li (2015) Defense Expenditure and Income Inequality: Evidence on Co-integration and Causality for China, Defence and Peace Economics, 26:3, 327-339, DOI: 10.1080/10242694.2013.810026 To link to this article: http://dx.doi.org/10.1080/10242694.2013.810026

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Date: 27 October 2015, At: 22:15

Defence and Peace Economics, 2015 Vol. 26, No. 3, 327–339, http://dx.doi.org/10.1080/10242694.2013.810026

DEFENSE EXPENDITURE AND INCOME INEQUALITY: EVIDENCE ON CO-INTEGRATION AND CAUSALITY FOR CHINA BINBIN MENGa*, WILLIAM LUCYSHYNa and XIANGQIAN LIb a

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School of Social Sciences and Humanities, National University of Defense Technology, Changsha, China; bSchool of Public Policy, University of Maryland, College Park, MD, USA (Received 16 January 2013; in final form 28 May 2013) There are conflicting views as to the relationship between a nation’s defense expenditure (DE) and its population’s income inequality (INEQ). DE, always an important part of government budget, can easily crowd out transfer payments, necessary to improve INEQ; however, these payments may also create a demand that may raise the income levels of the lower income earners. Consequently, the relationship between DE and INEQ is an important question. This paper examines the relationship between DE and INEQ in China for the period of 1989–2012. Utilizing basic cointegration and causality tests, our objective is to add to the literature by providing evidence that China’s DE, in fact, do have an impact on INEQ. Keywords: Defense expenditure; Income inequality; Granger causality; China JEL Codes: H56; D30

INTRODUCTION China’s rapid economic growth over the past 30 years has made it possible for China to increase its investment in its national defense. At same time, the income gap of China’s residents has been growing persistently. A reasonable question then is, what is the relationship of China’s defense expenditure (DE), and its population’s income inequality (INEQ)? We believe that the mechanisms between DE and INEQ are quite complex, and are potentially influenced by many other factors in the economy. Clearly, unbiased, rigorous study is necessary. Currently, there are a few studies addressing this interaction on a global basis, but there are no studies specifically examining for China. The purpose of this paper is to address two important questions. First, what are the mechanisms between DE and INEQ in China? Second, what is the consequence of the interaction of these mechanisms? Our study examines the period from 1989 to 2012; this time frame covers the beginning of the ‘China’s reform and opening-up policy.’ The paper is organized as follows. Section 2 reviews the literature of the relationship of DEs and income gaps, both in China and globally. Section 3 describes the mechanisms between DE and INEQ, both theoretically and practically. Section 4 presents the empirical methodology and the data. Section 5 presents the results of the regression. The final section presents the conclusions.

*Corresponding author. Email: [email protected]

Ó 2013 Taylor & Francis

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LITERATURE REVIEW A substantial body of literature has uncovered relationships between INEQ and economic and political institutions (Ali, 2007). The idea of ‘anti-stratifying phenomenon’ by Willam Parish showed that the income distribution is equal in the socialist planned economy; meanwhile, Ivan Szelenyi, and Victor Nee strongly suggested that the redistribution in the socialist planned economy cannot improve the INEQ, but aggravates the gap between the poor and the rich. The Kuznets curve attempts to describe that as a country develops, there is a natural cycle of economic inequality driven by market forces. At first, these forces increase inequality, and then, after a certain average income is attained, decrease it. Alternatively, Akos Rona et al. suggested that as a nation transitions from socialism to capitalism, the INEQ would decrease; this result is ascribed to the equality effect resulting from market reform. And, with this transition develops, the equality effect would disappear and be followed by the deterioration of the income distribution. The former effect can be aptly named an inverse U-shape curve, while the latter effect is named U-shape curve. Further study examining this issue is clearly required. Ali (2007) provided a good review of the current research, and following are summarized from his paper. Gradstein, Milanovic, and Ying (2001) suggested that democratization can reduce inequality. Further, Lipset, Seong, and Torres (1993) and Diamond (1992) concluded that affluence can be correlated with the presence of democratic institutions. Other studies showed that higher wages are always associated with democratic institutions. Additionally, Rodrik showed that institutions also matter to distributive outcomes (Rodrik, 1999). Moreover, deunionization was found to play a vital role in income distribution outcomes. Dinardo et al. (1996) determined that deunionization is a critical factor that helps to explain the rise in wage inequality in America, between 1979 and 1988. The comparison studies on wage inequality (in the US and the other OECD countries) strongly suggest that the diversity in an economy’s institutions, especially in manpower intensive market institutions, with their relative decentralization of wage-decision mechanisms have a significant impact (Blau and Kahn, 1996). As to the INEQ in China, there are some intersting results. Wu (2010) assessed that the equality of access to public wealth and public goods is one of the main factors that leads to the inequality of income. As a result, administrative corruption and the monopoly of production resources are the central reasons for the growing gulf between China’s rich and poor. Wang (2010) showed that the opportunities for rent-seeking, or gray income, widen the gap between the privileged and underprivileged, and erode the resource base of the state’s welfare distribution. Additionally, after the more than 30 years of economic reform, the transformation of China’s political system lags significantly. The existing power structure, combined with the capital, prevents the economy from improving, and may lead to serious consequences. Further, Gui, Chen, and Yin (2012) believed that the rent-setting and rent-seeking behavior is one of the main causes of the emergence of the social income gap. Successful social production requires government officials to provide resource aid and public goods, and this is generally facilitated by hiring junior officials. When senior officials care about their own private interests, they can create ‘artificial’ research shortages; this enables junior officials to then charge rents from the producers. It can also facilitate charging junior officials’ position rents, since resource rents are higher than external optional income. A society with greater limits on power will have fewer public officials, a more efficient supply of public goods, and a smaller social income gap.


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Furthemore, Li, et al. (2009) provided views on inequality of labor income. Labor’s share of Gross Domestic Product (GDP) is important to understanding income distribution; it is the foundation for analyzing investment, savings, and consumption in an economy, and it also reveals an economy’s microeconomic behavior. This paper documents the evolution of labor’s share in the Chinese economy, and explores possible explanations of the evolution of that share. Specifically, labor’s share in the Chinese economy has been decreasing, and is lower than those of developed countries. In our study, we find that labor’s share in economic development seems to follow a U-shaped curve; the lowest point is USD 6000 per capita PPP (2000 constant). The Chinese economy seems to be also following this pattern, although there are other factors affecting labor’s share in China. These include industry structure and labor’s bargaining power. Some work has also been done on the relationship between DEs and public INEQ. Knight et al. (1996) extended a standard growth model and obtained consistent data estimates of the growth-retarding effects of DE via its adverse impact on capital formation and resource allocation. DEs reduce the resources available for importing capital goods that promote sustainable long-term prosperity (Dunne, Perlo-Freeman, and Soydan, 2004). During the period 1988–2002, researchers observed military expenditures, as a percentage of GDP, trending downward, as a result of rapid economic growth during this time period (with cyclical fluctuations and spikes in times of war) (Smyth and Narayan, 2009). However, the per capita military expenditure has continued to rise, and so has the level of inequality in the region. Earlier studies (Ali, 2004, 2007) explored the relationship between military expenditure and inequality, grounded in standard economic theory that suggests that defense-related industries pay higher wages; and, that an increase in demand for military expenditure can cause the demand to shift outward and INEQ to rise. Using panel data for more than 150 countries, Ali (2007) replicated Knight et al. (1996) and emphasized the relationship between DE and inequality, which has shown that military expenditure is an important factor in influencing INEQ. As Elveren (2012) summarized, the following are the most recent results on this subject. Vadlamannati, 2008 set up panel data analysis for the period of 1975–2005 for India, Pakistan, Sri Lanka, and Bangladesh and found that there is a positive effect for military spending on INEQ, controlling for major macroeconomic and institutional factors. Hirnissa, Habibullah, and Baharom (2009) adopted the ARDL method to investigate causality between defense spending and INEQ for Malaysia, Indonesia, Singapore, the Philippines, South Korea, and India, for the period 1970–2005 based on the UTIP-UNIDO data. They concluded that there is a one-way causality from defense spending to INEQ for Malaysia, two-way causality for Singapore, and no causality for the rest. Lin and Ali (2009a) strengthen the causality study in a significantly more comprehensive way, it included 58 countries for the 1987–1999 period. Based on the data from UTIP and Estimated Household INEQ Data set, the panel non-Granger test is conducted. They found that there is a positive relationship between INEQ and defense spending. Ali (2012) extended the same model, taking into consideration new variables that pertain to MENA countries – for example, the Arab–Israeli dynamics of military expenditure, in which countries export the negative externality of the arms race across their borders, and the presence of oil as an instrument that helps to finance this arms race. Elveren (2012) examined the relationship between DE and INEQ in Turkey for the period of 1963–2007. Utilizing basic cointegration and causality tests, Elveren (2012) added to the literature by providing evidence that DE has an impact on INEQ in this specific case. For now, no research on DEs and INEQ in China is publicly available. This paper emulates Elveren’s purpose and approach. However, we treat the INEQ as Gini coefficient, rather than Theil Index, and pay attention instead to the relationship between DE and

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inequality in China. During the same time frame, we examined the relationship between DE and INEQ in China for the period of 1989–2012. Moreover, the unique relationship between DE and INEQ, using data from China, is discussed with detail in the following sections. DE AND INEQ Theoretical Basis There is no theory that adequately describes to the relationship between DE and INEQ; no unique model explains the interaction between the two. The possible causality mechanisms that DE may affect economic inequality from different perspectives are listed below (Lin and Ali, 2009b; Elveren, 2012). First, from a Keynesian point of view, DE can boost the income in defense-related sectors and result in increased aggregate demand and employment. Since the level of INEQ increases during downswings in the economy, and decreases during the upswings, the implication is that the low-income earners gain relative to the rich during peaks in the business cycle. Then, by implication, such expenditure should provide opportunities to reduce INEQ (Lin and Ali, 2009b). Second, increases in military expenditure are potentially made at the expense of other public expenditures on social programs, such as health and education, which have an equalizing effect. The military as an institution, therefore, competes for scarce resources with other social entitlements, and reduces the resources available to those social programs (Ali, 2004). Third, the results may be different, since the detail composition of DE is complex. When DEs are used to pay for a less-skilled labor force, the INEQ should be reduced. Similarly, when DEs are used to pay for a higher-skilled labor force, the INEQ would be exacerbated. The actual effect then depends on the specific structure and nature of the DEs (Ali, 2007). Finally, the inequality is analyzed at the industry level. The labor revenue of the defense industry base in many cases is higher than that of other industries. Then, the increase of DE aggravates the wage gap of different industries (Ali, 2007). Empirical Analysis According to China’s economic development and national defense practices, the impact of its DE on the nation’s income gap is not adequately explained by the current theoretical summary. The actual mechanism is more complex than the existing theory. Which part of the previous research helps to describe the observed behavior in China? Moreover, what is the actual status? Is there any new mechanism that should be explored from the China’s experience? The following would require a detailed analysis of the DEs and INEQ in China, with a focus on their interaction. Firstly, the general status of China DE and INEQ is introduced. Then, the mechanism of DE and INEQ is examined in detail. According to China’s State Council white paper, entitled China’s National Defense 2010, China adheres to the principle of a coordinated development of both their national defense and their economy. In line with the demands of national defense and economic development, China decides on the size of DE in an appropriate way, and then manages and uses the defense funds, in accordance with the law. Moreover, as China’s economy has grown, its expenditure on national security needs has increased accordingly. Approximately, one-third of China’s defense spending is dedicated to cover personnel,



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training and maintenance, and equipment costs. Personnel expenses include salaries, allowances, housing, insurance, subsistence, and clothing for officers, enlisted men, and contracted civilians. Training and maintenance expenses include troop training, institutional education, construction and maintenance of installations and facilities, and other expenses on routine consumables. Equipment expenses include Research and Development, experimentation, procurement, maintenance, transportation, and storage of weapons and equipment. China’s DE covers costs to support the active forces, reserve forces, and militia. It also covers part of the costs to support retired servicemen, and servicemen’s children, as well as national and local economic development and other social expenditures. Generally, China’s income gap could be divided into two parts: before the reform and opening up from 1949 to 1979; and after the reform and opening up from 1979 to now (Li, 1997). As a whole, the income gap in the former period is quite small while the income gap in the latter period is large. The sample of this paper starts at 1989, which related to the latter period with the INEQ deteriorating gradually. With that introduction of China’s defense spending and INEQ, the following will discuss the mechanism of how China’s DE influences that INEQ. The redistribution channel DE is, in part, the financial redistribution of national income. However, this spending to the greater national finance pressures and competes with spending on public welfare expenditure, such as rural education, non-defense research, health and medical, and social support. When a nation’s financial income is held constant, an increase in DE is accompanied by a proportional decrease in spending on the public welfare. China’s income distribution system is characterized as efficiency and equality (Hu, 2012). In China, the initial distribution focus is on efficiency, and the redistribution focus on equality. So, the redistribution plays an important role in reducing INEQ in China. The ‘crowding-out’ effect of defense spending decreases the redistribution resources available for addressing INEQ. In light of that, the conclusion can be drawn that defense spending can decrease INEQ. The personnel expenditure channel China’s defense development has lagged behind that of developed countries. During the period that is the focus of this research, the modernization of national defense and the armed forces was a key strategic task (Information Office of the State Council 2010). The status of the China’s national defense and the armed forces significantly lags, particularly in terms of the mechanization and IT application. There are many low-skilled members of the military who received very little education in the army; the proportion of this category of personnel is quite large. However, these low-skilled forces received the social average wage, which is generally higher than that would be received in non-defense sector employment. Based on this logic, China’s DE improved a specific certain low-income group’s wages; therefore DEs can reduce INEQ. The army industry channel When examining China’s weapon and equipment acquisition, the supply side is comprised primarily of nation-owned firms (Lin, 2006). These firms have a strong administrative character. There is a saying that the China’s equipment acquisition process is to just put the money from the left pocket into the right pocket, which accurately describes China’s

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weapon equipment acquisition system. The R&D, production, maintenance and disposal of their weapons equipment are largely accomplished by the state-owned firms. DEs mainly include R&D, experimentation, procurement, maintenance, transportation, and storage of weaponry and equipment. As a result, the income of these firms relies largely on these equipment expenses. During the period of our analysis, as China’s economy was being transformed, the state-owned firms bore much of the administrative social responsibility. The goal of these firms then is not only their own self-interest, but also broader their social needs. Consequently, many of the less-skilled workers that may have been let go in pursuit of greater efficiency by private sector firms continued to be employed for the national goal of greater social stability. And, the state-owned firms must also cover the costs necessary to support retired servicemen, and their children, education, medical care, and so on. All these costs are ultimately transferred to the cost of the equipment, and then the total DEs. These relationships reduce economic efficiency. Even so, it does reduce INEQ to some extent, especially when the economy is in the state of transformation, and there are no other public sources of funding for China’s effort to achieve greater income equality.

The household register channel The household register is a special institution in China. Only when individuals are recorded in their cities household register, can they live and work there (Wang, 2011). The process requires people to identify where they are from, and is intended to limit unplanned migration. There are two categories of household registration that differ significantly: the city household register and the rural household register. China’s dual economy is very much interrelated with this formal process of household registration. Joining the army is not limited by household registration (Lin et al., 2006). Young people from both rural and urban area serve. The army then acts as a mechanism to link the dual economy, and accelerate the flow of labor between the two. It especially improves the chances of young adults to overcome the circumstances of their birth. Moreover, the development of a single individual helps to drive his or her whole family’s improvement, both from the perceived and their economic level. This effect is quite large in China, since there is a long history of a strong family culture – this increases the effect of the labor flow. Historically, joining the army in China is often the only opportunity young people from rural areas have to gain an education and change their destiny. This helps to confirm the above analysis. Just as the channel of the dual economy, the DE improves the INEQ in China.

The R&D channel The spillover of defense R&D and defense technology provides a positive externality that, plays and important role in reducing INEQ, and cannot be ignored. As is known, modern military weapons are depend on advanced technologies. Often, these same technologies are also a key factor in the improvement of industrial productivity. As a result, investments in defense technologies not only do improve the capability and quality of the weapons, but also can improve the social productivity. For example, there were 400 specific defense technologies transformed for civilian use in 1983, and 8000 in 1984; this accounted for 4.3 billion yuan. In the following year 1985, the quantity was up to 20,000 examples and accounted for 10 billion yuan (Xiao, 2003). The development of technology raises the


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productivity of the whole country. As a result of these spillover effects, the total financial income is increased; this provides more resources that can be used to reduce INEQ.


The regional economy channel Remote military bases help to improve the development of the affected region’s economy. Military bases are often located in remote districts. Since economic development in China is not distributed evenly, remote districts often have lower levels of economic development. Infrastructure developed to support military facilities, such as highways, railways, hospitals, schools etc., provide a good foundation for region’s economic development (Zhou, 1992). Furthermore, military bases can create demand for supplies and services, and the effective multiplier effect increases opportunities for employment in the local economy. In China, ‘the three front constructions’ is a variation of the defense industrial base. It changed the industrial development structure in China, which reduce the imbalance of the regional economic development. With the ‘The three front construction’ strategy, military bases were widely spread, with most located in mountainous areas. As a result, many defense industries were developed in remote districts. The defense firms introduced higher levels of productivity to the local economy; this improved the development and helped to reduce the imbalance of the whole economy of China. In large part, China’s INEQ is the result of the gap in regional development (Li and Zhao, 1999). The defense construction reduces this gap, and, consequently, reduces INEQ. China’s social policy rests on the concept of the economy as an interlocking system of markets that automatically adjusts supply and demand through the price mechanism. During this period, the national policy did not focus on people’s livelihood (Wang, 2008). As discussed above, however, defense spending is not necessarily crowding out the resources that would be used to improve INEQ. The complex mechanism between DE and INEQ in China is discussed above from the theoretical and practical perspectives. Defense spending can increase or decrease INEQ in different situation. Despite this complex mechanism, there are valid reasons to approach the issue in a more straightforward way (Ali, 2012). METHODOLOGY AND DATA Methodology The two main variables in this analysis are DE and INEQ. We apply the econometric methodology to confirm the ideas discussed in part three with the data from 1989 to 2012, which is to explore the long-term relationship between DE and INEQ. The Engle– Granger’s two-step procedure method is used in this analysis. The Granger approach answers the question whether DE causes INEQ by answering the question: can the current value of INEQ be explained by the past values of DE. INEQ is said to be Granger-caused by DE if DE is helpful in predicting the value of INEQ. That is to say, if the coefficients of the lagged DE are statistically significant in the regression of INEQ on DE, then we can conclude that INEQ is Granger caused by DE. The process is listed below following Elveren (2012) and Lin and Ali (2009b)’s econometric modeling procedure. The first step, investigate the stationary of the data. The unit root test is adopted to investigate whether the time-series data are stationary or not. For detail, we employ the Augmented Dickey–Fuller (ADF) and Phillips–Perron (PP) unit roots test to investigate

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how stationary the variables of DE (DS) and INEQ are. We test the variables both with and without trends. The second step is cointegration test. The Ordinary Least Squares (OLSs) method is adopted to test the cointegration of the two time-series data. DEt ¼ a0 þ b0 INEQt þ lt

ð1Þ

INEQt ¼ a1 þ b1 DEt þ l0t

ð2Þ

In the Equations (1) and (2), a0 and a1 are constants, lt and l0t are residual terms. The third step is causality test. The Error Correction Model (ECM) is adopted to test the residual-term coefficient based on the variables for Granger causality test. The ECM is not modeled by the levels or differencing of variables, but modeled by the full integration of two variables, which enables us a full use of these data. The ECM is shown as follows: m n X X DDEt ¼ a0 þ b0 lt1 þ C0i DDEti þ d0i DINEQtj þ et ð3Þ i¼1

DINEQt ¼ a1 þ b1 l0t1 þ

q X i¼1

j¼1

c1i DINEQti þ

r X

d1i DDEtj þ e0t

ð4Þ

j¼1

In Equations (3) and (4), l1 and l0t1 are the lagged residual terms. The residual terms are derived from regressions of Equations (1) and (2). D is difference operator – after differencing to enable the time-series data meet the stationary requirement. According to the ECM, if two variables are cointegrated, there exists at least one direction in the Ganger sense. If b0 and b1 are significant in statistics, then time-series DE is the cause of INEQ in the Granger sense, and INEQ is the effect of DE in the Granger sense. Data DE data used in this study are from Stockholm International Peace Research Institute (SIPRI). GINI coefficients for INEQ measures are from Tian (2012), who used GINI coefficient calculation method – nonuniform grouping method – used by Thomas et al. (2000) to work out the GINI coefficients of the urban resident income and the rural resident income. Based on that, we have used a grouping weighing method (Sundrum, 1990) and have adjusted urban–rural weighing method (Dong and Li, 2004) to calculate the Gini coefficient for the national resident income. The data span selected is from 1989 to 2012 (Figure 1). RESULTS AND DISCUSSION Unit Root Test In order to test the Granger causality of DE and INEQ, first of all, we used the Phillips– Perron (PP) method, along with the one-sided probability estimate data (Mackinnon, 1996) to conduct a unit root test, to test whether the time-series data are stationary or not. The table below shows the PP test of the time-series data, and the corresponding p value to test the hypothesis that there exists a unit root. The test results indicate that under any


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2.6 2.4 2.2

5.2

2.0 4.8

1.8 1.6

4.4

1.4 4.0 3.6 90

92

94

96

98

00

02

GINI*10

04

06

08

10

12

DE


NOTE: The left axis is GINI while the right is DE.

FIGURE 1 GINI coefficient and DE

condition, DE and INEQ data are not stationary. We then adjust the two variables by taking the first differencing. Results show that DE and INEQ data are stationary after the first differencing. This result indicates that DE and INEQ variables are stationary, and of the same order of integration after first differencing (Table I). Cointegration Test Since DE and INEQ are stationary after the first differencing, we can use the Engle– Granger two-step procedure to test whether the two variables are cointegrated or not. First, we estimate the long-run equilibrium between DE and INEQ data, and then test the stationary of the residuals. As the results in Table II indicate, residuals are stationary after this regression. Therefore, we can conclude that DE and INEQ series data are cointegrated when first differenced. This shows that there exists a long-run equilibrium relationship between DE and INEQ variables. TABLE I

Unit Root Test ADF unit root test analysis (level value)

P-P unit root test (level value)

Var DE GINI

CV ADF Stat Prob. 2.669 0.95063 0.2948 2.679 1.07 0.9199 ADF unit root test analysis (first difference)

CV PP Stat Prob. 4.41 1.94 0.6011 4.41 1.56 0.7753 P-P unit root test (first difference)

Var DE GINI

CV 2.67 2.68

CV 3.76 3.76

ADF Stat 4.06 3.54

Prob. 0.0003 0.0012

PP Stat 4.04 2.69

Prob. 0.0055 0.0909

Note: CV is critical values at 1%.

TABLE II Cointegration Analysis Cointegrated regressions DE = f(GINI) GINI = f(DE)

CV

ADF Stat

Prob.

Results

3.8 3.8

8.37 8.38

0 0

Cointegrated Cointegrated

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Granger Causality Test


Due to the cointegration relationship between DE and INEQ after first differencing, the causality between the two variables cannot be analyzed using VAR model; but vector error correction model must be used, i.e. Equations (3) and (4). The results are tabulated in Table III: The econometric results cannot reject that INEQ is not the cause of DE; but can reject the original hypothesis that DE is not the Granger cause of INEQ. Therefore, DE is the Granger cause of INEQ, that is, DE ‘causes’ INEQ, INEQ does not ‘cause’ DE. The econometric results indicate that in the long run, DE data and INEQ data have a uni-directional relationship in the Granger sense. These results support the conclusion in the related literature, that DE crowds out other budgetary expenditure, and thus widens INEQ. However, the economic implications reflected by the long-run equilibrium do not necessarily reflect the short term results. ECM Traditional economic model usually depicts the ‘long-run’ relationship between variables; however, the practical economic data are produced by ‘disequilibrium processes.’ Therefore, we can approach the long-run equilibrium processes through the short-run disequilibrium processes of data. In order to test the short-run relationship between DE and INEQ, the ECM test is conducted in terms of Equations (3) and (4). The ECM can reflect short-run dynamic relationship between variables. The regression-lagged terms are selected by AIC criterion. The regression result of the ECM reflects adjustment from the disequilibrium condition in the short run to the equilibrium condition in the long run, which is called ‘error adjustment coefficient.’ In the model, differencing term reflects the impact of short-run fluctuations. Short-run fluctuations can be divided into two parts: the impact of short-run independent fluctuations, and the impact of deviating from long-run equilibrium. When the error adjustment term is minus, and short-run fluctuations deviate from long-run equilibrium, it requires a -13% adjustment factor, turning disequilibrium condition back to equilibrium condition, which indicates that the relationship between the two variables do exist, in the long run, as well as in the short run. In the short run, the fluctuations of the variable to be explained are affected by a more stable long-run tendency, and short-run fluctuations; the TABLE III Pairwise Granger Causality Tests Null hypothesis DE does not granger cause GINI GINI does not granger cause DE

TABLE IV

F-statistic

Prob.

3.41801 0.01584

0.0793 0.9011

Error Correction Coefficient

Variable

Coefficient

Std. error

t-statistic

Prob.

D(DE(-2)) D(GINI(-1)) RESID_GINI_C_DE(-1)

0.028588 0.467225 0.131055

0.015264 0.171392 0.07287

1.872896 2.726064 1.798482

0.0774 0.0139 0.0889

EVIDENCE ON CO-INTEGRATION AND CAUSALITY FOR CHINA Response of GINI to GINI

Response of GINI to DE

.016

.016

.012

.012

.008

.008

.004

.004

.000

.000 1

2

3

4

5

6

7

8

9

10

1

2

3

Response of DE to GINI


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4

5

6

7

8

9

10

9

10

Response of DE to DE

.20

.20

.16

.16

.12

.12

.08

.08

.04

.04

.00

.00

-.04

-.04 1

2

3

4

5

6

7

8

9

10

1

2

3

4

5

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7

8

FIGURE 2 Response to cholesky one S.D. innovations

degree of deviating from equilibrium condition will directly result in the degree of the fluctuation swing. In the long run, cointegration relationship will exert a ‘gravitational line’ function, drawing the disequilibrium condition back to the equilibrium condition (Table IV). Impulse Response Analysis Based on the estimate of the VEC model, we perform an impulse response analysis. As Figure 2 displays that, DE–SD shocks quickly impact the current and later DE and last for a long time; the shock result is still robust after 10 years. GINI response to DE–SD gradually increases and tends to be stable without an increase, after 6 years. GINI response to GINI–SD shocks gradually increases, and tends to stabilize without an increase after five years; DE response from negative to positive at the very beginning and subsequently decreases, reaches the lowest at the fourth year, and then tends to be stable. CONCLUSION Currently, there are few empirical models that analyze the interactive relationship between DE and INEQ in economic theories. This paper attempts to tap into the relationship between the two variables in light of cointegration analysis and Granger causality test. There exists a long-run equilibrium relationship between DE and INEQ, and the lagged DE can help forecast the current INEQ data. The conclusion based on China’s data agrees with the previous research by Abell (1994), using US time-series data, and Ali (2007), using panel data, which show that military spending is indeed positively associated with


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INEQ. DE is the Granger cause of INEQ, which indicates that DE leads to the expansion of INEQ due to the ‘crowding-out’ effect. The reasons are related to the size of China’s DE, and China’s social transformation from ‘economic society’ to ‘social economy.’ From the results of impulse response functions, DE increases will result in INEQ increases; this agrees with the previous assessments. Consequently, current DEs have an impact on the GINI coefficient. A proportion of resources in DE do indeed increase the income of lower earning individuals, and thus decreases the INEQ. Seen from the overall level of military personnel, a significant proportion of them are low-skilled workers. If they were employed in other sectors, it would be difficult for them to earn a salary equivalent to their military compensation. Defense-related sector has always been conservative in economic structural reform. These firms not only enjoy a maximum of profits, but also assume very important social roles. They generally cannot dismiss unneeded personnel, based on the agreed to benefits, and they will still assume social functions of retired elderly employees. Payments for defense equipment purchases also play a key social role through defense-related corporations, increasing the income of lower-skilled workers and decreasing INEQ. Moreover, military bases are often built in poverty-stricken regions, with associated benefits. Although there are a number of mechanisms that indicate that DE improves income distribution, the total impact of DE is to exacerbate INEQ. Faced with the seriously growing INEQ and the increasing national security demand, the relationship between DE and INEQ should be seriously analyzed. China’s defense spending, its industrial structure, and the mechanisms that improve INEQ should be rationalized, enabling the DEs to better influence income distribution for its new-phase of economic development. An improved interaction of this defense build-up and economic development can pioneer a path to a better military–civil integration; then, the goal of developing strong economic growth and military can be achieved. Certainly, this study does not fully address this issue. DEs are only one of the various factors in affecting INEQ, and there are alternative methods to probe into the relationship between the two variables. Whether GINI coefficients can reflect the real state of INEQ is still a question requiring further analysis. However, this paper aims to achieve the following: (1) to analyze the channels and functioning mechanisms of DE to improve income distribution, laying a foundation for better agreement of defense spending and economic development; and (2) to raise a new question for the academic world about China, seeking alternatives to study the relationship between DE and INEQ. Empirical analyses of the working mechanisms of DE and INEQ, as well as factors impacting the working mechanisms, are areas that still require further research. ACKNOWLEDGMENTS This paper is supported by National Social Science Foundation Project (11BJY134) & NUDT Excellent PhD Students Foundation. The insightful comments of an anonymous referee on an earlier version are gratefully acknowledged. References Abell, D. (1994) Military Spending and Inequality. Journal of Peace Research 31(1) 35–43. Ali, Hamid Eltgani (2004) Essays on economic development and conflict. Ph.D. dissertation. Austin, TX: University of Texas at Austin. Ali, H. (2007) Military expenditures and income inequality: Evidence from global data. Journal of Defence and Peace Economics 18(6) 519–535.



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