Impact of Remittances on Poverty in India: Empirical Evidence

Abstract

Remittances have been an important source of external funding for India. The span of Indian diaspora stretches across the globe in all continents. The Ministry of Overseas Indian Affairs has registered the presence of non-resident Indians (NRIs) in 180 of the 183 countries of the world. The numbers have varied from just two in Lebanon to almost a million in the USA. Estimated at over 30 million, India ranks second to Chinese diaspora. The growing number of migrants from India has added to the remittance inflow over the years. Data in this regard reveals that, even though the remittance flows to the Indian economy during the 1980s remained more or less stable, the postreform period from 1991 onwards has experienced a significant increase in remittances. There has been an annual average trend growth of 16 % during the period 1990 to 2008. In 2008, after the outbreak of economic crisis, India reported 34 % growth over 2007.

Appendix 1

Using time-series data from 1973–1974 to 2007–2008, the unit root test is undertaken to examine the stationarity of the dataset. The stationarity of variables such as remittances, GDP, poverty ratio, GFCF, personal disposable income and private final consumption expenditure has been checked for the available 28 years dataset. For the said purpose, the augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) test is used. The ADF is conducted by adding the lagged values of the dependent variables. The idea is to include enough lag terms so that the error term in the equation is serially uncorrelated. Here, while testing the ADF and the PP test, the null and the alternative hypothesis is that, when b ₀  =  0, the series is non-stationary and when b ₀  <  0 then the series is stationary.

The ADF test is obtained by using the following format of the equation:

$$ \delta {x}_{t}={a}_{0}+{b}_{0}{x}_{t-1}+{\displaystyle \sum _{i=1}^{k}{C}_{0}\delta {x}_{t-1}+{w}_{t}}$$

where δ is the difference operator, a _0, b ₀ and c ₀ are the coefficients to be estimated, x is the variable whose time-series operators are examined and w is the white noise error term. The results are reported in following tables.

6.1.1 Appendix 1.1 Stationary Test of the Following Variables: ADF Test and Phillips-Perron (Kerala)

Variables	Aug. Dickey-Fuller test		Remarks
		t-Statistics
Per capita NSDP	ADF test statistics	−4.522***	Per capita NSDP was not stationary at level and first difference. It is observed stationary at second difference
	1% level	−3.887
	5% level	−3.052
	10% level	−2.667
Remittances	ADF test statistics	−4.299**	Remittances were found stationary at level
	1% level	−4.533
	5% level	−3.674
	10% level	−3.277
Per capita NSDP	Phillips-Perron test		Per capita NSDP was not stationary at level and first difference. It is observed stationary at second difference
	Phil.-P statistics	−18.567***
	1% level	−3.959
	5% level	−3.081
	10% level	−2.681
Remittances	Phil.-P statistics	−4.299**	Remittances were found stationary at level
	1% level	−4.533
	5% level	−3.674
	10% level	−3.277

6.1.2 Appendix 1.2 Stationary Test of the Following Variables: ADF Test (All India)

Variables	Aug. Dickey-Fuller test		Remarks
		t-Statistics
Remittances	ADF test statistics	−8.2695***	FDI was not stationary at level, first difference. It is observed stationary at second difference
	1% level	−4.3561
	5% level	−3.595
	10% level	−3.2335
GDP	ADF test statistics	−3.3917*	GDP was not stationary at level, but observed stationary at first difference
	1% level	−4.324
	5% level	−3.5806
	10% level	−3.2253
GFCF	ADF test statistics	−6.3119***	GFCF was not stationary at level, first difference. It is observed stationary at second difference
	1% level	−4.3561
	5% level	−3.595
	10% level	−3.2335
Disp. income	ADF test statistics	−5.8736***	Disposable income was not stationary at level, first difference. It is observed stationary at second difference
	1% level	−4.3743
	5% level	−3.6032
	10% level	−3.2381
Pvt. final consumption exp	ADF test statistics	−4.4596***	Disposable income was not stationary at level, but it was found stationary at first difference
	1% level	−4.3393
	5% level	−3.5875
	10% level	−3.2292

1. Note: * and *** represent the significance level at 10 and 1% level

Interestingly all the variables under consideration are found non-stationary at levels. In other words, it shows that the past results cannot be used to predict future results of any variables. This is because the non-stationary in the data reveals that the mean and the variance do not remain constant over time. However, the variables such as GDP and private final consumption expenditure are found stationary when they are first differenced, whereas all other variables become stationary at second difference. The level of significance for ADF statistics for all variables is at one percentage level except GDP (at 10% level). Appendix-1.3 reports results of Phillips-Perron (PP) test.

6.1.3 Appendix 1.3 Stationary Test of the Following Variables: Phillips-Perron Test (All India)

Variables	Phillips-Perron
		t-Statistics	Remarks
Remittances	Phil-Perron test stat	−8.8452***	FDI was not stationary at level, first difference. It is observed stationary at second difference
	1% level	−4.3561
	5% level	−3.595
	10% level	−3.2335
GDP	Phil-Perron test stat	−3.37*	GDP was not stationary at level, but observed stationary at first difference
	1% level	−4.324
	5% level	−3.5806
	10% level	−3.2253
GFCF	Phil-Perron test stat	−6.6006***	GFCF was not stationary at level, first difference. It is observed stationary at second difference
	1% level	−4.3561
	5% level	−3.595
	10% level	−3.2335
Disp. income	Phil-Perron test stat	−4.5374***	Disposable income was not stationary at level but it was found stationary at first difference
	1% level	−4.3393
	5% level	−3.5875
	10% level	−3.2292
Pvt. final consumption exp.	Phil-Perron test stat	−4.4997***	Disposable income was not stationary at level but it was found stationary at first difference
	1% level	−4.3393
	5% level	−3.5875
	10% level	−3.2292

1. Note: * and *** represent the significance level at 10 and 1% level

The results show that, on using the Phillips-Perron (PP) test the variables such as GDP, personal disposable income and private final consumption expenditure are found stationary when they are first differenced, whereas other variables become stationary at second difference.