On the flow of funds accounts and inter-sectoral mobility of capital in India

ABSTRACT

This study uses the flow of funds accounts framework and undertakes an in-depth analysis of the inter-sectoral mobility of capital in India. Unlike previous studies, the FH model is estimated at the sectoral level using annual data for the period 1950–51 to 2012–13. The model estimated in one-regime setting with no structural break provides a weak and mixed support and that estimated in a sample-split setting with a single structural break provides no support for the presence of a long-run relationship between saving and investment for all the sectors. In contrast, the model estimated with multiple structural breaks provides dominant support for the presence of cointegration between saving and investment for all the sectors. The end-of-sample cointegration breakdown tests suggest the breakdowns of cointegration between saving and investment in all the sub-sample periods for the household and PCB sectors, but not for the public sector. The FOF accounts could be used to monitor the borrowing and lending operations of both financial and non-financial sectors and to identify any deformities in the system. The regulatory and supervisory policies need to be put in place promptly to resolve the identified deformities at their early stages, before they magnify and make the entire system dysfunctional.

KEYWORDS:

• FOF accounts
• saving
• investment
• capital mobility
• cointegration

JEL CLASSIFICATION:

• C30
• C32
• G10
• G20
• E44

Acknowledgments

I am grateful to an anonymous Referee of the journal for very useful comments and suggestions. I am, however, solely responsible for any errors and omissions that may remain in the paper.

Disclosure statement

No potential conflict of interest was reported by the author.

Notes

¹ The current account comprises (i) exports net of imports of goods and services, (ii) net factor income (such as interest and dividends) from the rest of the world and (iii) net current transfers (such as foreign aid) from the rest of the world. The exports net of imports of goods and services are, however, normally a major component of the current account.

² Consider a model with p number of variables. The q test of Stock and Watson (1988) tests the null hypothesis of p unit roots (zero cointegrating vector) against the alternative hypothesis of m unit roots; where m < p. Alternatively stated, the null hypothesis in q test is p common stochastic trends (no cointegration) and the alternative hypothesis is m common stochastic trends. The alternative hypothesis thus corresponds to r = (p – m) number of cointegrating vectors. The rejection of the null hypothesis, H₀: q(p, m), implies the presence of r = (p – m) number of cointegrating vectors.

³ The LB–Q (13) = 8.40 {0.82} for the household sector, LB–Q (13) = 18.90 {0.13} for the PCB sector and LB–Q (13) = 12.74 {0.47} for the public sector in the DOLS estimates. The LB–Q (14) = 9.40 {0.80} for the household sector, LB–Q (14) = 18.39 {0.19} for the PCB sector and LB–Q (15) = 15.64 {0.41} for the public sector in the NLLS estimates. Figures in round parentheses associated with LB–Q statistics are the k number of lags for the residual autocorrelations. Figures in curly brackets corresponding to LB–Q statistics are the p-values. The LB–Q statistics thus do not reject the null hypothesis of no serial-correlation in the model residuals for all sectors in both DOLS and NLLS estimates.

⁴ The multiple structural break tests are also performed on the DOLS estimations carried out using one lower and one higher lag and lead structure of I(0) regressors for all the sectors so as to assess the robustness of results. The results were generally consistent in terms of the number and locations of the breakpoints (break years) – these results are not reported to conserve space, but are available from the author on request.

⁵ The unduly large (more than unity) magnitudes of the slope parameter of saving, $\hat{\beta }$, found in some cases do not carry any meaningful economic interpretation. The OLS, FMOLS and FIML estimations are also carried out to test the null hypothesis of cointegration for the full-sample period (1951–2013) against the alternative hypotheses of cointegration breakdowns during the sub-sample periods of (i) 1992–2013, m = 22 and (ii) 1995–2013, m = 19; where m denotes the number of observations in the breakdown periods. The results generally remain robust to the choice of breakdown period. The sets of both $\mathrm{P}\in \left\{{\mathrm{P}}_{\mathrm{a}},{\mathrm{P}}_{\mathrm{b}},{\mathrm{P}}_{\mathrm{c}}\right\}$ and $\mathrm{R}\in \left\{{\mathrm{R}}_{\mathrm{a}},{\mathrm{R}}_{\mathrm{b}},{\mathrm{R}}_{\mathrm{c}}\right\}$ tests largely reject the null hypothesis of cointegration against the alternative hypotheses of cointegration breakdowns during the sub-sample periods of (i) 1992–2013 and (ii) 1995–2013 for both household and PCB sectors, but not for the public sector. The results for these sub-sample periods are not reported to conserve space, but are available from the author on request.