^{1}

^{2}

Current study examines the relevance of twin deficit hypothesis in Indian context by considering the endogenously determined structural breaks in both unit root and cointegration tests. The cointegration analysis shows that there is no long term relationship between the study variables. But the Granger causality test results indicate that bidirectional granger causality is running between the variables. Results showed no long run relationship between the variables; twin divergence in the long run, while in the short term, variables are related. The findings of the study are based on data for the period 1973-1974 to 2013-2014.

Economic theory provides different views on the relationship between budget and current account deficit. The Keynesian economists are of the view that the budget and current account deficits are related and an increase in fiscal shocks will make the current account balance worst. On the other hand, the Ricardian Equivalence Hypothesis (REH) postulates that these two variables are not related. The Keynesian view supports the popular hypothesis known as “Twin deficit hypothesis, while the Ricardian view is in favour of twin divergence hypothesis.

Over the years many researchers have tested the validity of both the views on current account and fiscal deficit in different origins of contexts by using different econometric methodologies. But these studies did not provide conclusive evidence on the issue. This topic is now attracting the attention of the researchers, since many countries face the problem of high fiscal deficit in the context of the stimulus packages to fight the recent economic crisis [

Therefore, the objective of current study is to examine the relevance of twin deficit hypothesis in Indian context by considering the endogenously determined structural breaks in both unit root and cointegration tests. The issue of structural breaks in the estimation of unit root test and cointegration analysis has been ignored in the literature especially in Indian context and in this study, we have considered the possible breaks in both unit root as well as cointegration analysis. For considering the structural breaks in unit root test, we have used a recently developed [

Here we present a connection between budget deficit and current account balance that might be traced from the national income identity,

where Y, C, I, G, X and M denotes national income, consumption expenditure, investment spending, government expenditure, exports and imports of goods and services respectively. Here we can define current account (CA) balance as:

where NITF stands for net income and transfer flows (that is income received from abroad or paid abroad and unilateral transfers) and it is added to the net balance from goods and services flows. However, if we assume for simplicity, that NITF are not large enough to affect CA significantly or proportion of NITF is negligible therefore, we can omit this variable and our CA will be just equal to trade balance.

Further, national savings (S) in an open economy based on the national income identity, can be written as follows:

Alternatively, we can write the above equation as:

where

Since,

where

Hence, Equation (5) in an identity form can be written as:

Further, we can modify Equation (8) as follows if we allow the effects of government saving decisions in an open economy:

Or alternatively we can write Equation (8) as:

where the term in parenthesis is consolidated public sector budget deficit (BDEF), that is, as government saving preceded by a minus sign. The government deficit measures the extent to which the government is borrowing to finance its expenditures. Equation (9) states that a country’s private savings can take three forms: investment in domestic capital (I), purchases of wealth from foreigners (CA), and purchases of the domestic government’s newly issued debt (G + R ? T).

Looking at the macroeconomic identity (10), we can see that two extreme cases are possible. If we assume that difference between private savings and investment is stable over time, the fluctuations in the public sector deficit will be fully translated to current account and the twin deficits hypothesis will hold. The Public sector includes general government (local and central) and non-financial public enterprises (state enterprises like railroads, public utility and other nationalized industries). The second extreme case is known as Ricardian Equivalence Hypothesis, which assumes that change in the budget deficit will be fully offset by change in savings. The explanation is the following; a tax cut does not affect households’ lifetime wealth because future taxes will go up to compensate for the current tax decrease. So, current private households save the income received from the tax cut in order to pay for the future tax increase. Hence, a budget deficit would not cause a twin deficit.

Although there are so many studies in the literature on twin deficit hypothesis, there hardly exists any consent. Here we are reviewing some past literature to get clarity of concept and state of research in the concerned field of knowledge.

Those studies supporting the twin deficit hypothesis includes, inter alia, [

In the context of India one of the first systematic study in this area was of [

The current literature on this issue especially in Indian context provides contradicting results on the effect of fiscal deficit on current account deficit. Secondly, to the best of our knowledge, none of the above mentioned studies considered the effect of structural breaks while analyzing cointegration analysis between budget deficit and current account deficit. Our main contribution in this study is considering the endogenously determined structural breaks in both unit root analysis and cointegration.

We have used the Current account and Gross Fiscal deficit as percentage of GDP for the period 1973-1974 to 2013-2014. This data is available in the Reserve Bank of India (RBI) website.

Since the study period is long and during this period India experienced many economic policy changes such as the economic reforms in the year of 1991, which includes reforms in external as well as domestic sectors of the economy. Therefore, it is appropriate to consider the possible structural breaks in estimating the unit root test. [_{t}_{−1} rather than TB) and the bias in estimating the persistence parameter is maximized and spurious rejections are the greatest. [

But, here we are using a recent unit root test developed by [

[_{t} has two components, a deterministic component (d_{t}) and a stochastic component (u_{t}), as follows:

e_{t} is a white noise process, such that ^{*}(L) and B(L), which are of order p and q, respectively, lie outside the unit circle [_{t},:

With

where, _{i} and γ_{i}, indicate the magnitude of the level and slope breaks, respectively. The inclusion of _{t} [

with

where, Equations (13) and (14) are IO-type test regression for M1 and M2 respectively,

In order to test the unit root null hypothesis of ρ = 1 against the alternative hypothesis of ρ < 1, we use the t- statistics of

Since it is assumed that true break dates are unknown,

The first step in this case is the search for a single break according to the maximum absolute t-value of the break dummy coefficient θ_{1} for M1 and κ_{1} for M2. Thereafter, we impose the restriction θ_{2} = δ_{2} = 0 for M1 and κ_{2} = δ = γ = 0 for M2 and hence, we have:

So, in the first step, the test procedure reduces to the case described in [_{2} for M1 and κ_{2} for M2. Hence, we have:

After determining the order of integration of each variable, we tested for cointegration to find out whether any long-run relationship exists between the variables (if cointegration exists, it will imply the sustainability of trade). Standard cointegration techniques are biased towards accepting the null of no cointegration and if there is a structural break in the relationship as [

where

In both trend and level sift(C/T)

And a full shift of the regime shift model(C/S)

where _{tτ} is defined as

If there is no cointegration between the study variables, we use the Granger Causality test to examine the short term casual relationship between the variables. For this we estimate the following equation:

The null hypothesis (H_{0}) for the Equation (25) is _{0}) for the Equation (24) is^{2}) test.

The unit root test results using [

The NP test results indicate that the study variables are nonstationary at level form, since the test statistics are not significant at the conventional significance levels. First differencing of the data series makes it stationary as shown in

The Gregory-Hanson cointegration test results are given in

It is evident from the

Since there is no long term relationship between CAD and FD as per the Gregory Hanson cointegration test

M1 | M2 | |||||
---|---|---|---|---|---|---|

At level form | ||||||

Test statistic | 1^{st} break | 2^{nd} break | Test statistic | 1^{st} break | 2^{nd} break | |

CAD | −3.45(2) | 2002-2003 | 2004-2005 | −4.13(4) | 2003-2004 | 2004-2005 |

FD | −3.05(2) | 2002-2003 | 2004-2005 | −4.39(4) | 2003-2004 | 2004-2005 |

At first difference form | ||||||

CAD | −3.37(5) | 1979-1980 | 1990-1991 | −4.989(0)^{**} | 1990-1991 | 1993-1994 |

FD | −6.14(0)^{*} | 1977-1978 | 1990-1991 | −6.57(0)^{*} | 1980 | 1990-1991 |

Note: ^{**} and ^{*} indicates significance at 5% and 1% level.

Model | Break date | Lags included | T statistics |
---|---|---|---|

Break in intercept: no trend | 2000-2001 | 1 | −3.12556 |

Break in trend | 1999-2000 | 1 | −3.45266 |

Trend and intercept | 1999-2000 | 1 | −3.28418 |

Note: Critical values are −5.13 and −4.61 for 1% and 5% respectively for model with constant. For model with trend, the Critical Values are −5.45 at 1% level and −4.99 at 5% level. For model with intercept and trend the respective critical values are −5.47 and −4.95. Authors’ calculation.

Null hypothesis | Lag used | F Statistics | P value | H1 |
---|---|---|---|---|

FD does not Granger cause CAD | 1 | 17.34 | 0.002 | FD→CAD |

CAD does not granger cause FD | 1 | 4.42 | 0.04 | CAD→FD |

Note: “→” indicates the direction of causality.

result, further we examined the short term casual relationship between the variables using the Granger causality test. The Granger causality test result is sensitive to the no of lags used. We used the lag selection criteria in Eviews 7 to select the lag length for the test. The selection criteria such as SIC, AIC, HQ and LR provide the same result; selecting the first lag.

As shown in the above table the Granger causality test results indicate the presence of a bidirectional causal relationship between the variables.

We have analyzed the relevance of twin deficit hypothesis in Indian context using the annual data on current account deficit and fiscal deficit for the period 1973-1974 to 2013-2014. We have used a recently developed [

The cointegration analysis shows that there is no long term relationship between the study variables. That is current account deficit and fiscal deficit are not related in the long term in Indian context. This is against the postulates of “Twin deficit hypothesis”; which assumes a long term relationship between the study variables. So, our results indicate that in Indian context twin deficit hypothesis is not valid in the long term. But the Granger causality test results indicate that bidirectional granger causality is running between the variables.

Regarding the long run relationship between fiscal deficit and current account deficit, our results are in tandem with the results of [