Access to credit and agricultural productivity: Evidence from maize producers in Benin

Figures & data

Table 1. Financing of the agricultural sector strategic recovery plan (PSRSA)

Indicators		2011	2012		2013	2014	2015	Cumulus between 2011 and 2015
Annual forecast	(a)	209.98		253.79	292.9	353	421.36	1531.03
	(b)	320.11		386.9	446.52	538.15	642.36	2334
Annual Realizations	(a)	84.41		119.5	160.68	180.49	197.22	742.3
	(b)	128.68		182.18	244.96	275.16	300.66	1131.6
Annual Deviation	(a)	125.57		134.29	132.22	172.51	224.14	788.73
	(b)	191.43		204.72	201.57	262.99	341.70	1202.4
Annual financial execution rate (%)		40.20		47.09	54.86	51.13	46.81	48.48

Note: All amounts on line (a) are in billions of FCFA and those on line (b) are in millions of Euros. The conversions are based on the exchange rate in effect on 10 April 2019, i.e. € 1 = FCFA 655.957. The annual financial execution rate is obtained by dividing the annual achievements to the annual forecasts. The annual deviations are given by the difference between the objectives set at the start of the year (annual forecasts) and the annual achievements.

Source: Authors, MAEP (2016).

Table 2. Descriptive statistics of the main variables

	Yield (Kg/ha)	Age (years)	Land area (ha)	Quantity of inputs used per hectare (kg/ha)	Quantity of seeds used on the plot (Kg)	Capital per hectare (FCFA/ha)
Mean	1283.38	52.05	2.39	198.83	94.74	8247.53
Minimum	183.33	30	0.1	0	5	6574.00
Maximum	5650.00	95	4	1342	250	69661.80
Std, Dev,	761.25	10.49	0.84	102.17	58.52	5468.80
Obs.	356	356	356	356	356	356

Source: Authors from PAPA data, 2016.

Table 3. Socioeconomic characteristics of producers according to access to credit

Variables	All	No access to credit	Access to credit	T-test
Yield (kg/ha)	1283.38	1186.499	1672.268	−4.969***
Area (ha)	2.386	2.409	2.294	1.034
Capital productivity	0.172	0.160	0.221	−4.607***
Age (years)	52.05	52.05	52.06	−0.008
Gender (Man=1) ($\pm$)	96.63	96.14	98.59	−1.023
Agriculture as main activity ($\pm$)	90.73	90.53	91.55	−0.265
Education (At least primary=1, No level=0) ($\pm$)	50.3	49.8	52.1	−0.344
Adoption of improved seed (Yes=1) ($\pm$)	57.3	53.7	71.8	−2.788***
Observations	356	285	71

Notes: ***, ** and * indicate the significance levels at the 1%, 5% and 10% thresholds respectively.

($\pm$) indicates that the statistics presented are the frequencies (in percent).

Source: Authors from PAPA’s data 2016.

Figure 1. Access to credit by region and average regional yield by access to credit.

Note: The proportions are in %. The yield is measured by relating the volume of production (Kg) to the area planted (ha).Source: Authors from PAPA’s data 2016.

Table 4. Estimation of the endogenous switching regression model

	Dependent variable: Yield (kg/ha)
Variables	Not access to credit (1)	Access to credit (2)	Selection Equation (3)
Age (years)	0.001	0.002	0.000
	(0.002)	(0.005)	(0.008)
Gender (Male=1, Female=0)	−0.195	0.275	0.591
	(0.121)	(0.277)	(0.401)
Education (At least Primary School=1, None=0)	0.098**	0.175	0.152
	(0.042)	(0.124)	(0.149)
Log farm size (ha)	−0.364***	−0.883***	0.456***
	(0.045)	(0.194)	(0.134)
Use of improved seeds (Yes=1, No=0)	0.005	0.306*	0.590***
	(0.050)	(0.167)	(0.202)
Central region	0.424	0.387*	−0.404*
	(0.065)	(0.208)	(0.233)
Northern region	0.088	−0.181	−0.032
	(0.066)	(0.333)	(0.248)
Agriculture as main activity (Yes=1, No=0)	0.018	−0.246	0.113
	(0.047)	(0.218)	(0.218)
Extension services (Yes=1, No=0)	0.036	−0.010	0.193
	(0.037)	(0.131)	(0.184)
Use of fertilizer (Kg/ha)	0.068***	0.092
	(0.015)	(0.068)
Total labour (in Man/day)	0.064***	0.064
	(0.019)	(0.059)
Ownership of non-farm assets (Yes=1, No=0)			0.289*
			(0.146)
Constant	6.619***	5.790***	−1.820***
	(0.210)	(0.633)	(0.617)
$\sigma$	0.358***	0.636***
	(0.024)	(0.104)
$\rho$	−0.877***	0.900***
	(0.041)	(0.098)
Wald test of indep. eqns. (chi2(2)	63.930
Prob	0.000
Log pseudolikelihood	−255.402
N	356

Note: Standard Errors are in parenthesis. * p<0.1; ** p<0.05; *** p<0.01

Source: Authors from PAPA’s data 2016.

Figure 2. Kernel density of the logarithm of yield according to access to credit. (Source: Author based on estimating results).

Table 5. Productivity difference according to access to credit or not

Sub-samples	Access to credit	No access to credit	Average treatment effect on treated (ATT) C=A-B
Farmers have access to credit	(a) 1602.625	(b) 1144.151	458.473***
Farmers do not have access to credit	(c) 746.721	(d) 463.352	283.369***

Notes: (a) and (d) represent the observed yield of maize (kg/ha).

(b) and (c) refer to counterfactual expected yield of maize (kg/ha).

$d_{it}=1$ if farmers have access to agricultural credit; $d_{it}=0$ otherwise.

${\hat{y}}_{it}^a$: Maize yield if farmers have access to agricultural credit.

${\hat{y}}_{it}^n$: Maize yield if farmers do not have access to agricultural credit.

Source: Authors, based on estimation results.

Table 6. Capital productivity difference according to access to credit or not

Sub-samples	Access to credit	No access to credit	Average treatment effect on treated (ATT) C=A-B
Farmers have access to credit	(a) 0.194	(b) 0.147	0.047***
Farmers do not have access to credit	(c) 0.239	(d) 0.085	0.154***

Notes: (a) and (d) represent the observed capital per hectare (FCFA/ha).

(b) and (c) refer to counterfactual expected capital per hectare (FCFA/ha).

$d_{it}=1$ if farmers have access to agricultural credit; $d_{it}=0$ otherwise.

${\hat{y}}_{it}^a$: Maize yield if farmers have access to agricultural credit.

${\hat{y}}_{it}^n$: Maize yield if farmers do not have access to agricultural credit.

Source: Authors, based on estimation results.

Table A1. Definitions and measures of study variables

Variables	Measure	Source
Dependent variable
Productivity	Total yield per hectare (Kg/ha).
Explanatory variables
Age (in years)	Producer’s completed age measured in years	SeeDiamoutene and Jatoe (2020);Agbodji and Johnson (2019); Saqib et al. (2018)
Type	Binary variable to capture the gender of the producer (Female = 0, Male = 1,)	SeeDiamoutene and Jatoe (2020);D. A. Ali et al. (2014)
Education	Refers to producers with at least a primary level of education (None = 0, At least primary level = 1)	SeeDiamoutene and Jatoe (2020); Saqib et al. (2018);D. A. Ali et al. (2014)
Fertilizer (kg/ha)	Quantity of fertilizer used expressed in kg per hectare	SeeDiamoutene and Jatoe (2020);Asante et al. (2019)
Use of improved seeds	Producer adoption of improved corn seed (No = 0, Yes = 1)	Improved seed is considered a primary source of productivity improvement (Houeninvo et al, 2020)
Location	The producer’s region of residence is measured by considering the Southern (0), Central (1) and Northern (2) regions.	Agbodji and Johnson (2019) use geographic location by considering the village.
Extension Services	Producer who has benefited from the extension services of official producer support structures. (No = 0, Yes = 1)	SeeAsante et al. (2019)
Capital per ha (FCFA)	Represents the amount of capital held by the producer and valued in CFAF.
Agriculture as main activity	Measures producers who have farming as their primary activity. This variable takes the value 1 if farming is a main source of income for the producer and 0 otherwise.
Member of a Farmers’ Organization	Binary variable measuring the producer’s membership in a farmers’ association (No = 0, Yes = 1)	Agbodji and Johnson (2019);D. A. Ali et al. (2014)

Table A2. Estimation of the endogenous switching regression model

Variables	Dependent Variable: Productivity of capital (Kg/FCFA)		Selection Equation (3)
	Not access to credit (1)	Access to credit (2)
Age (years)	0.002	0.006	−0.001
	(0.002)	(0.007)	(0.008)
Gender (Male = 1, Female = 0)	−0.075	0.302	0.993
	(0.178)	(0.324)	(0.692)
Education (At least Primary School = 1, None = 0)	−0.054	0.052	0.200
	(0.053)	(0.163)	(0.168)
Use of improved seeds (Yes = 1, No = 0)	0.008	0.091	0.485***
	(0.072)	(0.172)	(0.186)
Central region	−0.151*	−0.522**	−0.300
	(0.082)	(0.250)	(0.273)
Northern region	0.008	−0.156	0.014
	(0.089)	(0.337)	(0.246)
Log area planted (ha)	−0.160**	−0.762***	0.421***
	(0.063)	(0.230)	(0.143)
Agriculture as main activity (Yes = 1, No = 0)	0.006	−0.255	0.262
	(0.055)	(0.270)	(0.269)
Extension services (Yes = 1, No = 0)	0.101	0.310	0.204
	(0.062)	(0.286)	(0.192)
Total labor (in Man/day)	0.033	0.092
	(0.023)	(0.077)
Fertilizer (Kg/ha)	0.038**	0.122*
	(0.017)	(0.070)
Ownership of non-farm assets (Yes = 1, No = 0)			0.491***
			(0.173)
Constant	−2.132***	−3.417***	−2.218***
	(0.286)	(0.929)	(0.806)
$\sigma$	0,439***	0.653**
	(0.042)	(0.111)
$\rho$	0.631*	0.583***
	(0.238)	(0.154)
Wald test of indep. Eqns. (chi2(2)	10,880
Prob	0,004
Log pseudolikelihood	−369,345
N	356

Note: Standard Errors are in parenthesis. * p < 0.1; ** p < 0.05; *** p < 0.01

Source: Authors from PAPA’s data 2016.

Figure A1. Maize yield from 2011 to 2020 (Kg/ha).

Source: Authors from FAOSTAT data (2022)

MAEP. (2016). Rapport de Performance du Secteur Agricole, Gestion 2016. https://api.sitetest.agriculture.gouv.bj//media/63ce96fa517c5_7_Rapport%20de%20performance%202016%20sect%20%20agricole.pdf

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