The effects of climate change on food production in Ghana: evidence from Maki (2012) cointegration and frequency domain causality models

Figures & data

Table 1. Data description and sources

Variable	Proxy/definition	Notation	Data source
Food production	Food production index: Edible food crops that contain nutrients, excluding coffee and tea (2004–2006 = 100).	FP	WDI (2020)
	Cereal production: Crops harvested (metric tons) for dry grain only (rice, maize and wheat accounts for more than three-forth). This excludes cereal crops harvested for hay or harvested green for food, feed, or silage and those used for grazing	CP	Knoema database
	Maize production: Zea mays Corn, Indian corn, mealies. A grain with a high germ content. Measured in metric tons	MP	Knoema database
	Rice production: Rice grain after threshing and winnowing, measured in metric tons. Also known as rice in the husk and rough rice	RP	Knoema database
	Root and tubers: Cassava, yam, cocoyam and sweet potato production, measured in tons	RTP	Knoema database
	Vegetable production: Onion (shallot), tomato, sweet and chillipepper (hot pepper), local spinach (Indian and Gambian spinach), okra, eggplant, measured in metric tons.	VP	Knoema database
Carbon dioxide emission	Carbon dioxide emissions are those stemming from the burning of fossil fuels and the manufacture of cement. They include carbon dioxide produced during consumption of solid, liquid, and gas fuels and gas flaring, measure in metric tons per capita	CO2	WDI (2020)
Temperature	Average annual temperature measured in degrees Celsius recorded on land, sea as well as some satellite measurements.	TEM	Climate Change Knowledge Portal
Rainfall (Precipitation)	Water released from clouds in the form of rain, measured in millimeters (mm). It is the primary connection in the water cycle that provides for the delivery of atmospheric water to the Earth.	PRE	Climate Change Knowledge Portal
Economic growth	Real gross domestic product per capita	RGDP	WDI (2020)
Inflation, consumer price index	Annual percentage change in the prices of goods and services.	INF	WDI (2020)
Population growth	Population growth (annual %)	POP	WDI (2020)

Table 2. Descriptive statistics

	$\mathit{l}\mathit{n}\mathit{F}\mathit{P}$	$\mathit{l}\mathit{n}\mathit{C}\mathit{P}$	$\mathit{l}\mathit{n}\mathit{M}\mathit{P}$	$\mathit{l}\mathit{n}\mathit{R}\mathit{P}$	$\mathit{l}\mathit{n}\mathit{V}\mathit{P}$	$lnRTP$	$\mathit{l}\mathit{n}\mathit{T}\mathit{E}\mathit{M}$	$\mathit{l}\mathit{n}\mathit{P}\mathit{R}\mathit{E}$	$\mathit{l}\mathit{n}\mathit{C}{O}_2$	$\mathit{l}\mathit{n}\mathit{I}\mathit{N}\mathit{F}$	$\mathit{l}\mathit{n}\mathit{R}\mathit{G}\mathit{D}\mathit{P}$	$\mathit{l}\mathit{n}\mathit{R}\mathit{G}\mathit{D}\mathit{P}$
Mean	3.75	14.15	6.69	12.04	13.07	15.99	3.31	4.61	1.67	3.05	6.94	16.62
Median	3.69	14.31	6.86	12.06	13.08	16.12	3.31	4.58	1.63	2.89	6.85	16.64
Maximum	4.72	15.25	7.92	13.73	13.61	17.29	3.34	6.97	2.81	4.81	7.54	17.23
Minimum	2.96	12.98	5.15	10.49	12.26	14.94	3.19	4.23	0.83	1.12	6.54	15.98
Std. Dev.	0.58	0.57	0.68	0.87	0.38	0.80	0.02	0.35	0.63	0.78	0.26	0.38
Skewness	0.21	−0.09	−0.25	0.23	−0.35	0.01	−2.87	6.00	0.38	0.34	0.81	−0.04
Kurtosis	1.60	1.93	2.08	1.98	2.10	1.50	15.49	40.72	1.83	3.02	2.64	1.73
Observation	50	50	50	50	50	50	50	50	50	50	50	50

Source: Authors’ estimation

Table 3. ADF and KPSS unit root test results

	Level				First-difference
	ADF		KPSS		ADF		KPSS
	($c)$	($c,t)$	($c)$	$\left(c,t\right)$	($c)$	$c)$	($c)$	$c)$
$lnFP$	0.746	−2.56	0.89	0.16[0]^a	−8.57[1]^a	−8.82[1]^a	0.30[1]^a	0.13[1]^a
$lnFP$	−4.13[0]^a	−4.55[0]^a	0.68[0]^b	0.07[0]^a
$lnPRE$	−7.70[0]^a	−7.63[0]^a	0.09[0]^a	0.08[0]^a
$lnPRE$	0.64	−3.69[0]^a	0.90	0.19[0]^a	−6.28[1]^a	−6.38[1]^a	0.31[1]^a	0.21[1]^a
$lnINF$	−3.72[0]^a	−4.95[0]^a	0.40[0]^a	0.12[0]^a
$lnINF$	−1.04	−2.04	0.61[0]^b	0.23	−1.60	−2.78	0.73	0.07[1]^a
$lnPOP$	−4.83[0]^a	0.71	0.94	0.15[0]^a	−0.80	−3.23[1]^c	0.22[1]^a
$lnPOP$	0.23	4.68[0]^a	0.89	0.09[0]^a	−7.28[1]^a	−7.35[1]^a	0.19[1]^a	0.11[1]^a
$lnCP$	0.32	−3.08	0.88	0.09[0]^a	−4.69[1]^a	−4.81[1]^a	0.20[1]^a
$lnRP$	0.28	−2.88	0.89	0.13[0]^a	−6.78[1]^a	−6.84[1]	0.12[1]^a
$lnRP$	−1.44	−2.55	0.70[0]^a	0.09[0]^a	−0.87[1]^a	−7.99[1]^a
$lnRTP$	0.38	−2.73	0.91	0.10[0]^a	−9.80[1]^a	−9.84[1]

Note: ^A,b and ^c denote significance levels at 1%,5% and 10% respectively. The figures in parenthesis indicate the integration orders, where $lnRTP$ means stationary at levels and $\left[1\right]$ means stationary at first difference. $\left[1\right]$) denotes constant and $\left(c,t\right)$ represents constant and deterministic time trend. Critical values for ADF with constant at 1%, 5% and 10% are −3.574, −2.923, and −2.560 accordingly, while the critical values are −4.157, −3.508, and −3.182 for the model with constant and time trend. The critical values for the KPSS test with a constant term are 0.739, 0.463, and 0.347 respectively and those of the model with both constant and time trend 0.216, 0.146, and 0.119 respectively. The AIC and the Newey-West Bandwidth are used to select the optimal lag for the ADF and the KPSS tests respectively. The critical values for the ADF test are obtained using Mackinnon (1996) while that of KPSS is obtained from Kwiatkowski et al. (1992).

Source: Authors’ estimation

Table 4. Cointegration results

Model	Test statistics	Year of break	Conclusion
Level shift with trend	−5.838*	1976,1992,1999	Reject $H_0$
Trend and Regime shifts	−6.165	1975,1982,1989	Cannot reject $H_0$
Regime shifts	−6.018	1981,1989,2007	Cannot reject $H_0$
Level shift	−5.769**	1975,1982,1994	Reject $H_0$

Notes: **, * denote significance level at 5% and 10% respectively. The null hypothesis is rejected when the test statistics is greater than the critical value.

Source: Authors’ estimation

Table 5. DOLS and FMOLS results

Dependent variable = $\mathit{l}nFP$
	DOLS Results			FMOLS Results
Variables	Coefficients	Std. errors	t-statistics	Coefficients	Std. errors	t-statistics
$lnTEM$	−1.707	1.260	−1.355	−2.132*	1.201	−1.763
$lnPRE$	0.010	0.058	0.177	0.002	0.052	0.960
$lnC{O}_2$	0.023	0.196	0.120	0.140	0.183	0.449
$lnRGDP$	0.496***	0.176	2.809	0.336**	0.160	2.105
$lnPOP$	1.193***	0.273	4.361	1.074***	0.270	3.973
$lnINF$	−0.069**	0.032	−2.195	−0.096***	0.033	−2.877
Constant	−13.733**	6.028	−2.278	−9.315*	5.463	−1.704
$R^2$	0.977			0.975
$Adj.R^2$	0.974			0.972

Note: ***, **, * denote significance level at 1%, 5% and 10% accordingly.

Source: Authors’ estimation

Table 6. DOLS and FMOLS results for cereal, maize, rice, and root and tuber production

Dep. Var.	$\mathit{l}\mathit{n}\mathit{C}\mathit{P}$		$\mathit{l}\mathit{n}\mathit{M}\mathit{P}$		$\mathit{l}\mathit{n}\mathit{R}\mathit{P}$		$\mathit{l}\mathit{n}\mathit{R}\mathit{T}\mathit{P}$
	DOLS	FMOLS	DOLS	FMOLS	DOLS	FMOLS	DOLS	FMOLS
Variables	Coef.	Coef.	Coef.	Coef.	Coef.	Coef.	Coef.	Coef
$lnTEM$	−1.770 (−1.037)	−2.189 (−1.365)	−2.781 (−1.454)	−3.236 (−1.826)^c	−1.252 (−0.480)	−2.889 (−1.088)	−2.271 (−1.303)	−3.220 (−1.888)^c
$lnPRE$	0.121 (1.542)	0.142 (2.052)^b	0.134 (1.527)	0.159 (2.076)^b	0.083 (0.692)	0.111 (0.976)	0.087 (1.084)	0.104 (1.422)
$lnC{O}_2$	−0.385 (−1.449)	−0.292 (−1.204)	−0.536 (−1.799)^b	−0.477 (−1.782)^c	−0.174 (−0.428)	0.024 (0.061)	0.142 (0.522)	0.288 (1.117)
$lnY$	0.404 (1.701)^b	0.239 (1.133)	0.378 (1.412)	0.196 (0.878)	1.379 (3.776)^a	1.182 (3.370)^a	0.084 (0.346)	−0.146 (−0.649)
$lnPOP$	1.808 (4.882)^a	1.724 (4.813)^a	2.321 (5.590)^a	2.305 (5.819)^a	1.919 (3.390)^a	1.727 (2.911)^a	1.777 (4.696)^a	1.668 (4.380)^a
$lnINF$	−0.105 (−2.452)^b	−0.121 (−2.750)^a	−0.156 (−3.228)^a	−0.177 (−3.637)^a	0.140 (2.128)^b	0.149 (2.047)^b	−0.110 (−2.508)^b	−0.138 (−2.944)^a
Constant	−12.430 (−1.523)	−8.712 (−1.202)	−24.55 (−2.683)^b	−21.656 (−2.270)^b	−25.789 (−2.068)	−16.309 (−1.359)	−6.904 (−0.828)	−0.598 (−0.078)
$R^2$	0.937	0.936	0.934	0.933	0.944	0.940	0.974	0.973
$Adj.R^2$	0.928	0.927	0.925	0.924	0.936	0.931	0.971	0.969

Note: Values in parentheses are T-statistics; ^a,b, and ^c represent significance level at 1%, 5% and 10% respectively. The t-values are in parenthesis.

Source: Authors’ estimation.

Table 7. Frequency domain granger causality test results

	High frequency (short run)		Low frequency (long-run)
Direction of causality ($\to )$	$\omega =2.50$	$\omega =2.00$	$\omega =0.05$	$\omega =0.01$
Panel A
$lnTEM\to lnFP$	0.896 (0.639)	0.348 (0.840)	13.264 (0.001)***	13.265 (0.001)***
$lnPRE\to lnFP$	1.579 (0.454)	1.818 (0.403)	0.032 (0.984)	0.032 (0.984)
$lnC{O}_2\to lnFP$	1.448 (0.485)	1.828 (0.101)	5.536 (0.063)*	5.543 (0.063)*
$lnRGDP\to lnFP$	0.884 (0.643)	0.652 (0.722)	5.997 (0.050)*	5.953 (0.051)*
$lnPOP\to lnFP$	1.811 (0.404)	1.842 (0.398)	16.263 (0.000)***	18.695 (0.000)***
$lnINF\to lnFP$	1.492 (0.474)	1.968 (0.374)	0.142 (0.932)	0.141 (0.932)
Panel B
$lnTEM\to lnCP$	2.902 (0.234)	2.197 (0.333)	8.752 (0.013)***	8.762 (0.012)***
$lnPRE\to lnCP$	0.339 (0.844)	0.705 (0.703)	0.649 (0.723)	0.649 (0.723)
$lnC{O}_2\to lnCP$	0.292 (0.864)	0.372 (0.830)	2.251 (0.325)	2.239 (0.326)
$lnRGDP\to lnCP$	1.812 (0.404)	1.574 (0.455)	0.804 (0.669)	0.791 (0.673)
$lnPOP\to lnCP$	0.914 (0.633)	0.909 (0.635)	13.220 (0.001)***	14.301 (0.001)***
$lnINF\to lnCP$	4.631 (0.099)*	5.058 (0.079)*	0.203 (0.903)	0.205 (0.903)
$lnTEM\to lnMP$	2.829 (0.243)	1.604 (0.448)	14.219 (0.001)***	14.230 (0.001)***
$lnTEM\to lnMP$	0.115 (0.944)	0.313 (0.855)	0.818 (0.664)	0.818 (0.664)
$lnC{O}_2\to lnMP$	0.228 (0.892)	0.264 (0.876)	2.313 (0.314)	2.306 (0.316)
$lnRGDP\to lnMP$	3.311 (0.191)	2.282 (0.319)	4.191 (0.123)	4.168 (0.124)
$lnPOP\to lnMP$	1.862 (0.394)	1.798 (0.407)	14.595 (0.001)***	15.864 (0.000)***
$lnINF\to lnMP$	6.853 (0.032)**	7.412 (0.025)**	0.938 (0.625)	0.943 (0.624)
$lnTEM\to lnRP$	2.723 (0.256)	1.699 (0.428)	1.259 (0.533)	1.262 (0.532)
$lnTEM\to lnRP$	0.837 (0.658)	0.996 (0.608)	0.047 (0.977)	0.046 (0.977)
$lnC{O}_2\to lnRP$	4.432 (0.109)	5.152 (0.076)*	9.242 (0.009)***	9.192 (0.010)***
$lnC{O}_2\to lnRP$	4.701 (0.095)*	3.547 (0.169)	1.694 (0.429)	1.696 (0.428)
$lnPOP\to lnRP$	15.244 (0.000)***	15.272 (0.000)***	15.007 (0.000)***	10.821 (0.004)***
$lnPOP\to lnRP$	0.137 (0.934)	0.010 (0.995)	5.473 (0.065)*	5.463 (0.065)*
$lnTEM\to lnVP$	0.362 (0.834)	0.001 (0.999)	6.085 (0.048)*	6.080 (0.048)*
$lnTEM\to lnVP$	0.299 (0.861)	0.089 (0.956)	1.547 (0.461)	1.546 (0.461)
$lnC{O}_2\to lnVP$	2.687 (0.263)	4.386 (0.112)	11.941 (0.003)***	12.024 (0.002)***
$lnRGDP\to lnVP$	0.821 (0.663)	0.916 (0.632)	2.884 (0.236)	2.895 (0.235)
$lnPOP\to lnVP$	3.546 (0.170)	3.564 (0.168)	13.272 (0.001)***	13.855 (0.001)***
$lnINF\to lnVP$	15.981 (0.000)***	13.627 (0.001)***	5.694 (0.058)*	5.717 (0.057)*
$lnINF\to lnVP$	1.565 (0.457)	0.980 (0.612)	7.323 (0.026)**	7.329 (0.026)**
$lnPRE\to lnRTP$	3.200 (0.202)	2.868 (0.238)	2.464 (0.292)	2.466 (0.291)
$lnPRE\to lnRTP$	1.553 (0.460)	1.026 (0.599)	0.705 (0.703)	0.708 (0.712)
$lnRGDP\to lnRTP$	0.443 (0.801)	0.280 (0.869)	2.833 (0.243)	2.813 (0.245)
$lnRGDP\to lnRTP$	0.470 (0.791)	0.439 (0.803)	12.363 (0.002)***	13.413 (0.001)***
$lnINF\to lnRTP$	0.358 (0.836)	1.004 (0.605)	0.853 (0.653)	0.853 (0.653)

Note: ***, **, * denote significance level at 1%, 5% and 10%. In parentheses are the probability values.

Source: Authors’ estimation

Kwiatkowski, D., Phillips, P. C. B., Schmidt, P., & Shin, Y. (1992). Testing the null hypothesis of stationarity against the alternative of a unit root. how sure are we that economic time series have a unit root? Journal of Econometrics, 54(1–3), 159–178. https://doi.org/10.1016/0304-4076(92)90104-Y

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