Does inflation reduce remittance outflows in Saudi Arabia?

Figures & data

Table 1. Dependent and Independent Variables

Variable	Definition	Coefficient	Expected Sign
$LRE{M}_t$	The Log of remittance outflows at time (t)	Dependent
$LRE{M}_{t-i}$	The lag of the dependent variable (explicative variable):	$\gamma$	+
$IN{F}_t$	Inflation rate at time (t) (The main independent variable)	β	-
$Z_t$	Vector of control variables (other explicative variables):	${\phi }_{\hspace{0.17em}}^{/}$
LGDPC	Log of gross domestic product at constant prices (2010 = 100) at time (t). The level of economic activity.	${\phi }_{\hspace{0.17em}}^{LGDPC}$	+
LGF	Log of gross fixed capital formation at time (t). A proxy for investment.	${\phi }_{\hspace{0.17em}}^{LGF}$	-
LOPEN	Log of Exportation + Importation /GDP at time (t)	${\phi }_{\hspace{0.17em}}^{LOPEN}$	±

Table 2. Migrants and remittance outflows in the KSA

Year	# of Migrant Workers^1	% of total population	Remittance Outflows (US $ million)^2	Remit in % of GDP at current prices*
1980	1,920,478.00	30.55	4,090	2.49
1985	3,401,020.00	30.55	5,200	5.00
1990	4,998,445.00	30.55	11,200	9.59
1995	5,122,702.00	27.17	16,600	11.65
2000	5,263,387.00	24.60	15,400	8.17
2005	6,501,819.00	26.28	14,303	4.35
2010	8,429,956.00	30.01	27,069	5.14
2015	10,185,945.00	32.29	38,787	5.93
2016	10,884,335.00	33.54	37,842	5.87
2017	10,694,320.00	32.31	36,119	5.25
2018	9,578,055.00	28.42	33,882	4.31
2019	9,827,096.00	28.68	33,466	4.73

Source: (1) Annual outflows remittance data, World Bank (April 2020); (2) International migrant stock, total/World Bank online data; (3) Data from 2016 to 2019 are from Saudi General Authority for Statistics (https://www.stats.gov.sa/en).

*Calculated by the authors based on GDP/World Bank dataset 2019 (Available online at the website).

Table 3. Descriptive statistics during the period from 1971 to 2019

	REMITGDP (%)	INF (%)	GFGDP (%)		OPEN XMGDP (%)	GDPC
Mean	5.774701	3.693471		20.96660	76.80579	3.95E+11
Median	5.003559	1.206073		20.97991	75.08284	3.59E+11
Max	13.37369	34.57611		29.99081	120.6203	7.04E+11
Min	1.141326	−3.203331		8.834239	56.08838	1.56E+11
SD	2.999484	7.624659		4.255244	12.90819	1.52E+11
J-B	3.638603	172.6770		3.088748	6.337822	4.734024
Prob.	0.162139	0.000000		0.213445	0.042049	0.093760
Obs.	49	49		49	49	49

This table is elaborated by the authors based on results obtained by Eviews 9. All values are expressed as percentages, except GDP, which represents constant prices.

Figure 1. Evolution of outflows remittance and inflation rates in the KSA from 1980 to 2019.

Table 4. Break Point Unit Root Tests (Model with Constant and Linear Trend)

	Level				1st Differenced
Variable	Breakpoint	ADF t-statistic	Prob.	Lags #	ADF t-statistic	Prob.	Lags #	Integrated Level
LREMIT	2015	−3.161	0.94	1	−4.75**	0.027	0	I(1)
INF	1984	−4.240	0.37	1	−5.38**	0.029	0	I(1)
LCPI	2009	−7.29***	< 0.01	1				I(0)
LGDPC	1986	−4.17	0.42	0	−7.6***	< 0.01	0	I(1)
LOPEN	2004	−5.52**	0.02	0				I(0)
LGF	2002	−5.67***	0.01	1				I(0)

Null hypothesis: The series has a unit root. Break selection: Minimize Dickey-Fuller t-statistic. Lag length: Based on Schwarz information criterion Asterisks indicate significant differences (*** significant at 1%, ** significant at 5%, * significant at 10%).

Table 5. Optimal Lag selection creterion

Lag	LogL	LR	FPE	AIC	SC	HQ
0	−49.24541	NA	9.58e-06	2.633129	3.034610	2.782797
1	207.5855	433.7589	3.25e-10	−7.670467	−6.265285*	−7.146629
2	250.8430	63.44436	1.52e-10	−8.481912	−6.073029	−7.583905*
3	268.4900	21.96067	2.41e-10	−8.155111	−4.742526	−6.882934
4	315.7608	48.32123*	1.16e-10*	−9.144923*	−4.728637	−7.498576

LogL: indicates lag order selected by the criterion, LR: sequential modified LR test statistic (each test at 5% level),

FPE: Final prediction error, AIC: Akaike information criterion, SC: Schwarz information criterion, HQ: Hannan-Quinn

information criterion.

Table 6. VAR residual diagnostics test results

VAR Residual Serial Correlation LM Tests
Lags	LM-Stat	Prob
1	34.68942	0.094
2	28.14718	0.3011
3	15.75869	0.9218
4	25.62861	0.4276
5	27.96227	0.3096
Probs from chi-square with 25 df.
VAR Residual Heteroscedasticity Tests
Chi-sq	df	Prob.
618.7603	615	0.4499
VAR Residual Normality Tests
Skewness	Chi-sq	df	Prob.
	3.206606	5	0.6682
Kurtosis	Chi-sq	df	Prob.
	2.936385	5	0.7098
Jarque-Bera	df	Prob
6.142991	10	0.8031

Table 7. Johansen cointegration test summary

Test Type	Model1 No: C and t	Model2 Only C	Model3 Linear (Only c)	Model4 Linear (C &t)	Model5 Quadratic (c &t)
Trace	1	1	1	2	2
Max-Eig	1	1	1	1	1

C and t indicate the intercept and the trend, respectively. r is the rank of the matrix Π, that is, the number of cointegrated equations.

Λ trace and λ_max are the trace and max-eigenvalue statistics, respectively

Table 8. Johansen cointegration test results for type (model2)

Null Hypothesis	$r=0$	$r\le 1$	$r\le 2$
${\lambda }_{Trace}$	75.38393*	33.49338	12.32267
${\lambda }_{max}$	20.96851*	41.89054	9.074578

*** denotes rejection of the null hypothesis at the 5% significance levels.

Table 9. ARDL Optimal Model Lags (1971–2019)

Dependent Variable: LREMIT- Selected Model: ARDL(1, 0, 1, 2, 3)
Variable	Coefficient	Std. Error	t-Statistic	Prob.*
LREMIT(−1)	0.795975	0.066644	11.94371	0.0000
INF	−0.001993	0.001806	−1.103666	0.2777
LGDPC	−0.032996	0.281235	−0.117327	0.9073
LGDPC(−1)	−0.322118	0.236902	−1.359709	0.1831
LOPEN	−0.393794	0.229286	−1.717476	0.0953
LOPEN(−1)	0.264478	0.166486	1.588584	0.1217
LOPEN(−2)	−0.283078	0.191388	−1.479076	0.1486
LGF	−49.04811	9.884969	−4.961888	0.0000
LGF(−1)	142.1488	28.58327	4.973148	0.0000
LGF(−2)	−137.8665	27.62439	−4.990755	0.0000
LGF(−3)	45.00677	8.987789	5.007546	0.0000
DUM	−0.030494	0.026595	−1.146603	0.2598
C	4.458340	2.139137	2.084177	0.0450
R-squared	0.993761	Mean dependent var		10.02382
Adjusted R-squared	0.991492	S.D. dependent var		0.465553
S.E. of regression	0.042943	Akaike info criterion		−3.224808
Sum squared resid	0.060855	Schwarz criterion		−2.708018
Log likelihood	87.17058	Hannan-Quinn criter.		−3.031215
F-statistic	437.9939	Durbin-Watson stat		1.754060
Prob(F-statistic)	0.000000

This table is elaborated by the authors based on the results obtained by E-Views 9, where the regressors are: LREMIT INF LGDPC LOPEN GF. Model selection method: Akaike Information Criterion (AIC).

Table 10. Bounds-Test of Cointegration

Test Statistic	Value	k
F-statistic	6.486129	4
Critical Value Bounds
Significance	I0 Bound	I1 Bound
5%	2.86	4.01
1%	3.74	5.06

Null Hypothesis: No long-run relationship.

Table 11. Residuals and Stability Tests

A: Heteroscedasticity	B: Autocorrelation	C: Normality	D: Stability
F-statistic (Probability)	F-statistic (Probability)	Jarque-Bera (Probability)	F-statistic (Probability)
1.19204 (0.3287)	0.469725 (0.6296)	0.042531 (0.978959)	2.19954 (0.1478)

Figure 2. Plot of cumulative sum and cumulative sum of squared recursive residuals.

Null hypothesis: parameters are stable

Table 12. Short-Run ARDL (1, 0, 1, 2, 3)

Variable	Coefficient	Std. Error	t-Statistic	Prob.
D(INF)	−0.268975	0.001806	−148.923831	0.0000
D(LGDPC)	1.676421	0.281235	5.960924	0.0000
D(LOPEN)	1.786532	0.229286	7.791705	0.0000
D(LOPEN(−1))	8.037240	0.191388	41.994411	0.0000
D(LGF)	5.725817	9.884969	0.579245	0.5664
D(LGF(−1))	10.090104	27.624388	0.365261	0.7172
D(LGF(−2))	−11.618519	8.987789	−1.292700	0.2051
D(DUM)	0.186681	0.026595	7.019388	0.0000
CointEq(−1)	−0.829526	0.066644	−12.447133	0.0000

Table 13. Long-Run Estimation Results of ARDL model

Variable	Coefficient	Std. Error	t-Statistic	Prob.
INF	−0.324251	0.025099	−12.918997	0.0000
LGDPC	1.381101	0.257345	5.366737	0.0000
LOPEN	−15.640718	1.340918	−11.664183	0.0000
LGF	−11.629037	0.952450	−12.209606	0.0000
DUM	0.225045	0.035692	6.305222	0.0000
C	0.644224	2.600067	0.247772	0.8058
Cointeq = LREMIT—(−0.3243*INF + 1.3811*LGDPC −15.6407*LOPEN −11.6290*LGF + 0.2250*DUM + 0.6442)

Table 14. Toda-Yamamoto VAR Granger Causality/Block Exogeneity Wald Tests

Null Hypothesis	Chi-sq	P-value	DF
INF does not granger cause LREM	13.24130	0.0102	4
LREM does not granger cause GDP	3.217958	0.5220	4

Note: The rest of causality directions results between control variables and dependent and main independent