By Ajijola, S; Saka, JO; Omonona, BT (2022). Greener Journal of Agricultural Sciences, 12(3): 185194.
Greener Journal of Agricultural SciencesISSN: 22767770 Vol. 12(3), pp. 185194, 2022 Copyright ©2022, the copyright of this article is retained by the author(s) 
Determinants of Transition in Economic Growth among Farming Households in Rural Nigeria.
^{*1}Ajijola S., ^{1}Saka J. O. and^{ 2}Omonona B. T.
^{1}Institute of Agricultural Research and Training, Moor Plantation, Ibadan, Nigeria
^{2}Department of Agricultural Economics, University of Ibadan, Nigeria
ARTICLE INFO  ABSTRACT 
Article No.: 061522063
Type: Research 
This study analysed the determinants of transition in economic growth among rural households and matched it to the economic growth rate to categorise households into being in Inclusive Growth (IG) and NonInclusive Growth (NIG) groups in Nigeria. Secondary data from General Household Surveys for 2010, 2013 and 2016 were used. Data were analysed using descriptive statistics, FosterGreerThorbecke (FGT), Probit model and Markov chain. The result shows that mean age of the rural households were 41.8, 43.7, and 46.9 years for 2010, 2013 and 2016 respectively. Majority (65.0%, 65.4% and 65.5%) were male while 64.3%, 63.1% and 63.4% were married in 2010, 2013 and 2016 respectively. Markov probability transition matrix revealed that rural households (29.9%) remained in NIG in both periods 2010–2013 and 2013–2016 while 70.1% of rural households contributed to the economic growth in 2013–2016. However, rural households (46.6%) that are inclusive in period 2010–2013 worsened in the period 2013–2016. In the long run, rural households (40.2%) were noninclusive while 59.8% were inclusive. Probit results show that household size, education, access to energy, residency in zones (South East and South South) influenced rural households moving into NIG while age, access to health facilities, being married, access to credit, involvement in agriculture and residency in zones (North East and North Central) influenced rural households to be in IG. It was concluded that with equitable resources, rural households have the probability to be inclusive and contributed into economic growth in Nigeria. 
Accepted: 18/06/2022
Published: 12/07/2022 

*Corresponding Author
Ajijola S. Email: ajsik1967@yahoo.ca Phone: +2348033906398 

Keywords: Non–inclusive growth, Economic growth, Rural households, Poverty, Transition, Nigeria.  
INTRODUCTION
Growth is noninclusive when individual members of a society are not contributing and participating in the growth process in an equitable basis irrespective of their individual conditions. Growth inclusiveness therefore laid emphasis on making opportunities and focusing on how the opportunities would be available to all and also ensuring equitable access to them. The significance of equal opportunities for individual lies in its inherent worth which depends on the fundamental right of every individual that equal opportunity should be circulated to all Adepoju and Adejare, 2013). It is impossible to overemphasize the importance of equitable access to services, creating employment and properties, as such access is critical in stimulating the economy to longterm development (Omonona, 2009). The promotion of inclusive growth needs a policy that is intentionally developed to help the poor thereby allowing the engagement and contribution of members to have equal advantage proportionally to the growth. Therefore, the group at the bottom end, that is, the poor will be able to meet their basic requirements. This will invariably reduce the incidence of poverty especially in the rural settings (Akinlade et al., 2011). The concept of inclusiveness of growth can be used interchangeably with propoor growth which ensures equitable access by all strata of individual in the society (including the disadvantaged and marginalized) to opportunities created by growth (Ali and Son, 2007).
Inclusive growth centres consideration around the degree to which the marginalized, the youth, poor men and women are engaged in and add value to economic growth; as assessed through improvements in household living standards and the available resources they require in enhancing higher incomes in the future (OECD, 2014). Mendoza and Mahurkar (2012) also opined that noninclusive growth is a growth process which advances nonequitable resources for economic agent such as the marginalized, poor women, youth and unemployed.
Inclusive growth with high sustainability in the economy can only be accomplished when all the more vulnerable segments in the society including those that are dedicated to agriculture, both small and medium scale firm, are encouraged and equivalent with the other members of the society in order to have equitable growth which is fundamental for a sustained inclusive growth (Omotola and Okoruwa, 2016). Economies in Africa are growing rapidly and remarkably with an average of 5.6 percent in year 2012, while the growth in Gross Domestic Products (GDP) in Africa was 6.7 percent and the GDP growth in Nigeria was 4.21 percent. It (Nigeria GDP) increas es to 6.22 percent in 2014 and dropped drastically to 2.8 percent in 2015 (NBS, 2017). The noninclusiveness of growth was influenced by living characteristics (such as availability of resources, accessibility to various resources and geographical location) and socio economic characteristics (for example, employment status, health facilities, household size, educational attainment, human capability and ownership of assets). Each of these parameters has a dimension that can be improved for better living conditions in order to benefit from growth.
The impressive growth in the economy has not been accompanied by increased employment generation. Unemployment rate has assumed an upward trend, rising from an average of 9.2% between 1991 and 2000 to 23.1% over the period of 20112014. The unemployment rate increased from 14.2% in 2016 to 18.8% in the third quarter of 2017 (Aderounmu, 2018). Similarly, people’s welfare had worsened over time in spite of the persistent economic growth in term of access to employment, social amenities and the basic necessity of life. The growth achieved over the years has not translated into poverty reduction despite the fact that the Nigeria economy recorded significant growth. This is because rural households in Nigeria faced a high level of income inequality due to factors such as poor infrastructural facilities and poor access to incentives coupled with their poverty that make them particularly being marginalized (Adeleye et al., 2020; Aderounmu et al, 2021). There is disparity between rural and urban households, (both rich and poor) when considering their socio economic characteristics and living characteristics (Amaechi, 2018). It is therefore pertinent to provide an insight into the extent to which the interventions of the implemented programmes have been achieved This study therefore, examined the long run or equilibrium transition probability between inclusive and noninclusive growth condition among rural households in Nigeria and determine the factors influencing rural households’ transitions between noninclusive growth categories (Always non–inclusive, Exiting noninclusive, Entering non–inclusive and Never non–inclusive) in Nigeria.
MATERIALS AND METHODS
Data Requirement and Sources
The data used for this study were sourced from General Household Survey (GHS) carried out periodically throughout the country in periods 2010, 2013 and 2016. The General Household Survey (GHS) survey is a panel survey of 5,000 households carried out periodically throughout the country by National Bureau of statistics (NBS). The first GHS survey conducted in 2010 is referred to as wave 1 while the second survey in 2013 and third survey in 2016 are referred to as wave 2 and wave 3 respectively.
Analytical Techniques
The analytical techniques used include descriptive statistics, FosterGreerThorbecke and Markov chain. The descriptive statistics involves the use of percentages, tables, figures, frequency distribution and standard deviation. The socioeconomic characteristics of the rural households between periods 2010 and 2013; 2013 and 2016 and; 2010 and 2016 was examined with the use of descriptive statistics such as frequency distribution, percentages, ratios, mean and standard deviation.
Poverty Gap Index
The use of the consumer price indexes for capturing the poverty lines was necessary in order to remove the influence of poverty and for the comparison of individual households for two periods (Omonona and Agoi, 2007). The poverty gap index was created using the quantitative poverty measure developed by Foster, Greer and Thorbecke (1984). This measure of poverty gaps was captured with the use of the Consumer Price Indexes (CPI) and the poverty line of year 2009 (Table 1).
Markov Chain Processes
Markov chain is a stochastic interaction that fulfills the Markov property, which implies that when the present is realized the past and future are free. That is, there is no extra data of its past states that may be needed to make the most ideal expectations of its future (Jerumeh and Omonona, 2018). Markov chains are mainly used to estimate the probabilities of occasions happening by review them as states changing into similar states as in the past or progress into another state.
The consumer price index (CPI) / Raising Factor
The consumer price index (CPI) of 95.78 in 2009 and the poverty line N54,401.16 in 2009 (NBS, 2010) were used in order to scale up the poverty lines produced by CBN (2010) in 2009 to 2010, 2013 and 2016 values. The consumer price index for years 2010, 2013 and 2016 were 108.92, 135.48 and 173.13 respectively. The raising factor was used to multiply the poverty line N54,401.16 of 2009 to upscale the poverty lines to N61,864.42 in 2010; N76,949.98 in 2013 and N98,334.44 in 2016 as shown in Table 1. Therefore, to know that growth between two periods was noninclusive, if the difference in poverty gap between the two periods is positive, this shows that, as expenditure increases, poverty level is also increasing indicating that households in the growth process is poor and noninclusive and if the difference in poverty gap is negative, it shows that there is reduction in poverty and therefore there is growth inclusiveness.
Table 1. CPI and Estimated Poverty Lines for years 2010, 2013 and 2016
Year  CPI  Poverty line  Raising factor  Estimated Poverty line (N) 
2009  95.78  N54,401.16  1.0000  54,401.16 
2010  108.92  –  1.1372  61,864.42 
2013  135.48  –  1.4145  76,949.98 
2016  173.13  –  1.8076  98,334.44 
Source: NBS, 2017
Markov Chain Probability Transition Matrix
The Markov chain probability transition matrix was used to determine the rural households’ non inclusive transition into non – inclusive, remain noninclusive, exiting non – inclusive and never noninclusive; and determine the long run or equilibrium probability transition of rural households between periods (2010 – 2013 and 2013 – 2016). The probability transition of the rural households was a 2 x 2 matrix (periods 2010 – 2013 and 2013 – 2016).
The 2 x 2 matrix (periods 2010 – 2013 and 2013 – 2016) in Table 2 shows the transition into four categories. That is, transitioning from;
e_{1} in period 2010 – 2013 to e_{1 }in period 2013 – 2016 (always non–inclusive, p11),
e_{1} in period 2010 – 2013 to e_{2 }in period 2013 – 2016 (exiting non–inclusive, p12),
e_{2} in period 2010 – 2013 to e_{1 }in period 2013 – 2016 (entering non–inclusive, p21)
e_{2} in period 2010 – 2013 to e_{2 }in period 2013 – 2016 (never non–inclusive, p22).
Table 2. FirstOrder Markov Model of Growth Probability Transitions of Rural Households
Period  Period 2013 – 2016  
Period
2010 – 2013 
NonInclusive (e_{1})  Inclusive (e_{2})  Total  
Non–Inclusive (e_{1})  p11  p12  r^{1}  
Inclusive (e_{2})  p21  p22  r^{2}  
Total  p1  p2 
The Table 2 was obtained by using;
The above matrix produced r_{1 }and r_{2, }which were the proportions of households that would be noninclusive and inclusive at equilibrium in the long run respectively. The long run equilibrium is attained when the total numbers of rural households entering a given category equals the numbers of rural households exiting the category.
The proportion of households that would be in each category in the periods is given as;
Where;
k is the time periods (2010 – 2013 and 2013 – 2016),
P(o) = the vector of initial probability,
P_{ij }= the probability transition matrix, the probability of households transitioning from i to j (from one category of growth to the other),
i = ith household,
j = jth period ,
r_{1 }= the probability of rural households that would be in noninclusive growth category at equilibrium in the long run, and
r_{2 }= the probability of rural households that would be in inclusive growth category at equilibrium in the long run.
RESULTS AND DISCUSSION
SocioEconomic Characteristics of Households in Rural Nigeria
The distribution of socioeconomic characteristics of rural households in Nigeria in year 2010, 2013 and 2016 is presented in Table 3. The mean value of 41.8 ± 9.4, 43.7 ± 9.46, and 46 .93 ± 9.39 years in years 2010, 2013 and 2016 respectively, which implies that a significant proportion of the respondents were middleaged and may be physically capable, indicating that they should be healthy and agile to engage in economic activities. The mean household sizes were 8 ± 2.03, 7.3 ± 3.12 and 7.6 ± 1.6 in years 2010, 2013 and 2016 respectively. Most (64.3%) were married while majority of the rural households (65.0%) were male across the years. This indicates that more males were involved in various activities than the females especially farming in rural Nigeria while the females might be involved in small farming and engaged more in processing of agricultural produce.
For human capital assets, the result shows that 43.4%, 45.3% and 40.2% of rural households had no formal education in years 2010, 2013 and 2016 respectively. The results revealed that educational status in 2013 worsened as higher proportions of rural households were recorded with no education. The number of rural households that had no education was reduced in 2016 and there was appreciable proportion (20.6%) of rural households in the year 2016 that attained postsecondary education. Considering the importance of education as human capital asset, inadequate access is a disincentive to abilities of population to explore growth opportunities especially in rural communities. Majority of the rural households were selfemployed. The higher proportions that were recorded in the self–employed among the rural households might not be unconnected to the fact that majority (96.4%, 94.1% and 88.9% in 2010, 2013 and 2016 respectively) in the rural areas were involved in agricultural activities as their major occupation. This corroborates Adeoti (2014) that a large proportion of the rural sector is primarily an agrarian society and larger number of people living in the rural areas were mostly farming households.
Table 3. Socioeconomic Characteristics of Rural Households in Nigeria
Variable  20102011  20122013  20152016  
Frequency  %  Frequency  %  Frequency  %  
Age (yr.)  
<40  592  17.7  1475  44.06  1267  37.84 
41 – 60  2,582  77.15  1660  49.60  1801  53.82 
>60  173  5.15  212  6.34  279  8.34 
Mean  41.77  43.69  46.93  
SD  9.38  9.46  9.39  
Household size  
<5  43  1.28  43  1.30  0  0.00 
6 – 10  3,026  90.42  2844  84.97  2726  81.45 
>10  278  8.3  460  13.73  621  18.55 
Mean  7.95  7.3  7.56  
SD  2.03  3.12  1.76  
Sex  
Male  2176  65.01  2189  65.40  2192  65.49 
Female  1171  34.99  1158  34.60  1155  34.51 
Occupation  
Agric.  3226  96.38  3148  94.05  2978  88.96 
NonAgric.  121  3.62  199  5.95  369  11.02 
Marital status  
Single  1009  30.13  1046  31.25  714  21.34 
Married  2151  64.25  2111  63.08  2123  63.42 
Divorced  107  3.21  139  4.15  332  9.92 
Widowed  80  2.4  41  1.23  178  5.32 
Education  
No education  1,451  43.35  1515  45.26  1344  40.15 
Primary  509  15.21  632  18.88  673  20.12 
Secondary  760  22.71  595  17.77  642  19.17 
Postsecondary  627  18.72  606  18.09  688  20.56 
Employment  
Self employed  2,728  81.51  2756  82.36  2650  79.18 
Paid employment  526  15.72  512  15.28  591  17.67 
Unemployed  68  2.04  62  1.85  70  2.10 
Retired  24  0.73  17  0.51  35  1.05 
Transitions of Rural Households from period 1 (2010 – 2013) to Period 2 (2013 – 2016)
The results of the transition of the rural households were shown in Table 4. The results of the transition probability matrix was estimated by converting the probability transition matrix into probability values by dividing each item of the corresponding rows by the corresponding total.
Table 4. Transition Matrix of Rural Households between Period 2010 / 2013 and Period 2013 / 2016
2010/2013  Status  2013/2016  
NonInclusive growth (NIG)  Inclusive growth (IG)  Total  
NonInclusive growth (NIG)  162  380  542  
Inclusive growth (IG)  1,308  `1,497  2,805  
Total  1,470  1,877  3,347 
Table 5 revealed that 29.9% of the rural household that were in non–inclusive group in periods 2010 – 2013 were also in non–inclusive group in period 2013 – 2016 which of the rural household who were in the non–inclusive group in period 20102013 transited to inclusive group, that is, exiting non–inclusive growth group in period 2013 – 2016. The result revealed that larger proportion of the rural household exited non–inclusive growth group and transited into inclusive growth group. Similarly, 46.6% of the rural households who were in the inclusive growth group in the period 2010 –2013 transited to non–inclusive group in the period 2013 – 2016, while 53.4% of the household who were in inclusive group in the period 2010 – 2013 remained in the inclusive group (never noninclusive) in the period 2013 – 2016. This indicates that the transition probability of rural households moving from one period to another that would never be in the noninclusive group was 53.4%. This showed that the proportion of rural households that would always remain in inclusive growth group was higher than those that would remain in noninclusive growth group. The results indicate that there was an improvement in the non–inclusiveness of growth from periods 2010 – 2013 to periods 2013 – 2016 because higher percentage of rural households that were worseoff in 2010 – 2013 transited into inclusive growth group in periods 2013 – 2016.
Table 5. Probability Transition Matrix of Rural households
2010/2013  Status  2013/2016  
NonInclusive growth (NIG)  Inclusive growth (IG)  
NonInclusive growth (NIG)  0.299  0.701  
Inclusive growth (IG)  0.466  0.534  
P(o) Vector of Initial Probability  0.4392  0.5608 
Rural Households Equilibrium (Long Run Probabilities Transition) between Periods 2010 – 2013 and 2013 – 2016
The analyses of the Markov chain probability transition matrix of rural households were estimated with a 2 x 2 matrix to generate how the observed population in a given period is distributed in different times. Following Ayantoye et al. (2011), the Markov chain processes for long run probability of the 2 x 2 matrix was calculated as;
Solving the above matrix, the vector of probabilities as the long run is obtained as;
(r_{1}, r_{2}) = (0.402, 0.598)
At equilibrium, that is, in the long run, the probability of the rural household that would be in the non–inclusive group (r_{1}) is 40.2% while the probability that the rural household would transit to inclusive growth group (r_{2}) is 59.8%. The result indicates that higher proportion of the rural households (59.8%) would be in inclusive growth group in the future. It also shows that the long term projection of rural households that would be moving out from non–inclusive growth group, that is, that would be inclusive in long run is higher than the rural households that would be transitioning into non–inclusive growth.
Similarly, in short run, the results in Table 5 were converted into probability values by dividing the probability matrix values under each item in the different categories (always non inclusive, exiting non–inclusive, entering non inclusive and never non–inclusive) by the corresponding row total. The results also revealed the vector of initial probability that, in short run, the probability of the rural households in Nigeria that would be transited into non–inclusive growth group is 43.9% while the probability that the rural households would transit into inclusive growth group in short run is 56.1%. The results revealed that the probability that the rural households would transit into inclusive growth group in long run is higher than the probability of transition in short run. Therefore, there would be a reduction in the proportion of rural households that would be in non–inclusive growth in long run.
Factors Influencing Rural Households’ Transition In and Out of NonInclusive Growth between periods
The Probit regression model was used to determine factors influencing rural households’ transition in and out of growth categories in Nigeria. The model was adopted for its suitability in capturing non–inclusive growth transition of rural households into four categories namely always non–inclusive growth, exiting noninclusive growth, entering non–inclusive growth and never non–inclusive growth.
Where:
Y_{ij} = the dependent variable for the different categories of non – inclusive transition
i = ith household (1………… 3,347)
j = jth categories of noninclusive transition (1………4)
The four categories of non – inclusive growth transition are as stated below;
Y_{11 }= 1 if always non–inclusive, 0 if otherwise,
Y_{12} = 1 if exiting non–inclusive, 0 if otherwise,
Y_{13 }= 1 if entering non–inclusive, 0 if otherwise,
Y_{14 }= 1 if never non–inclusive, 0 if otherwise,
ß_{0 }= constant term,
ßs = coefficients estimated,
Xs = Vector of explanatory variables, and
E_{i} = Random error
The independent variables, which are the socio–economic and demographic variables, are captured as:
X_{1 }= sex of household (1 if male, 0 if female),
X_{2 }= age of household (years),
X_{3 }= household size (number of persons),
X_{4 }= access to health facilities by household (1 if yes, 0 otherwise),
X_{5} = educational attainment of household (years),
X_{6} = marital status of household (1 if married, 0 otherwise)
X_{7 } = access to credit by household (1 if yes, 0 otherwise),
X_{8 }= access to electricity by household (1 if yes, 0 otherwise),
X_{9} = occupational status (agriculture) of household (1 if yes, 0 otherwise),
X_{10} = North east regional (1 if yes, 0 otherwise),
X_{11} = North Central regional (1 if yes, 0 otherwise),
X_{12} = North West regional (1 if yes, 0 otherwise),
X_{13} = South East regional (1 if yes, 0 otherwise),
X_{14} = South South regional (1 if yes, 0 otherwise),
X_{15 }= South West region (1 if yes, 0 otherwise), and
E_{i} = Random error.
Factors Influencing Rural Households Transition In and Out of Non–Inclusive Growth Group in Nigeria
Table 6 presents factors influencing rural households transition in and out of the non–inclusive growth category in Nigeria between periods 2010 – 2013 and 2013 – 2016. The transition of the households in and out of non–inclusive growth categories were made up of 4 categories; namely, always non–inclusive growth category, exiting noninclusive growth category, entering non–inclusive growth category and never non–inclusive growth category.
The results show that rural households have the probability to be in always non–inclusive growth category with increase in household age and size. This supported the findings of Adeoti (2014), that a rise in household size was correlated with a higher likelihood of being noninclusive, which is linked to poverty due to increase in dependency ratio. The probability of always non–inclusive would also be reduced by 0.0178 with increased in access to health facilities. The result revealed that healthy farmers would be able to work and utilize available resources effectively thereby increasing in productivity. The probability transition of the households to always remain in non–inclusive growth decreases with marital status (14.4%). The result indicates that being married will invariably decrease the probability of households that would always remain noninclusive. Also being engaged in agricultural activities, the probability of households to remain non–inclusive would be decreased by 0.0385. The regional dummies shows that increased in the residency of households in Northeastern region will reduce the non–inclusiveness of growth by 25.1%, while increasing in the residency in the North Central would reduce the probability of non–inclusive by 36.6%. However, the North central region had the highest tendency of probability of reducing the number of rural population that would remain in noninclusive growth.
The probability of rural households exiting non–inclusive growth group increase by 0.0804, 0.0216, 0.1953, 0.0621 and 0.1673 with access to health facilities, educational attainment, marital status, access to credit and engagement in agricultural activities respectively while it reduces by 0.0025 with age. The results indicate that having access to health facilities and educational attainment in the rural areas would increase the probability of rural households exiting noninclusive growth by 8.04% and 2.16% respectively, while marital status had the probability of increasing members that exiting noninclusive growth by 0.1953. Similarly, the results show that gaining access to credit and being engaged in agricultural activities have the probability of increasing the number of rural households exiting non–inclusive growth category by 6.2% and 16.7% respectively. Also, the probability of rural household exiting noninclusive would increase by 0.1168 and 0.2227 with increase in the number of residencies in the North East and North Central regions respectively.
The probability of rural household entering into non–inclusive growth category increase by 0.0223 (p<0.05) with household size while the probability of rural households moving into non–inclusive growth category decrease by 0.0132 (p<0.05) and 0.2164 (p<0.05) with educational attainment and access to electricity respectively. This result indicates that the probability of entering into noninclusive growth category is associated with large household size.
Access to electricity had a significant influence on rural households and it is negatively related to the rural households entering into non–inclusive growth category. Being educated would decrease the projection of households into noninclusive growth category by 0.0132 at 5% level of significant. The results also show the significant influence of residency in the geopolitical zones on rural household head per capita expenditure. It indicates that, residing in the SE and SS have the probabilities of increasing the households entering non–inclusive growth category by 0.3541 (p<0.05) and 0.3459 (p<0.05) respectively. These also show that being resident in these areas hardly added value to the welfare of the people in terms of increasing their income but increasing the per capita expenditure of the rural households which is also associated with poverty.
The probability of rural households to be never non–inclusive decreased by 0.0358 (p<0.05) and 0.2170 (p <0.1) with household size and marital status respectively while it increases by 0.0638 (p<0.01), 0.0625 (p<0.05) and 0.1802 (p<0.05) due to access to health facilities, access to credit and being engaged in agriculture, respectively. Also being engaged in agriculture (0.1802) would increase the probability of rural households to remain noninclusive. Rural household that never noninclusive also increased by 0.3336 (p<0.1) and 0.3287 (p<0.1) with a regional increase in the number residencies among households in North Central and South East respectively.
Determinants of Rural Households Transitioning In and Out of Non–Inclusive Growth Group
Variable  Household Always NI  Household Exiting NI  Household Entering NI  Household Never NI 
Constant  0.2544***
(0.0681) 
0.3063* (0.1693)  1.2666*** (0.2226)  1.9440***
(0.2982) 
Sex  0.0913
(0.0789) 
0.1082
(0.0801) 
0.0779
(0.1013) 
0.1968
(0.1382) 
Age  0.0036*
(0.0014) 
0.0025**
(0.0015) 
0.00067
(0.0019) 
0.0033
(0 .0026) 
Household size  0.0219**
(0.0093) 
0.0105
(0.0089) 
0.0223***
(0.0038) 
0.0358**
(0 .0135) 
Access to health facilities  0.0178**
0.0083 
0.0804**
(0.0524) 
0.2393
(0.3632) 
0.0638***
(0.0258) 
Educational attainment  0.0013
(0.0044) 
0.0216**
(0 .0075) 
0.0132**
(0.0057) 
0.0049
(0.0044) 
Marital status  0.1438*
(0.0789) 
0.1953*
(0.0806) 
0.0403
(0.1025) 
0.2170*
(0.1332) 
Access to credit  0.0168
(0.0442) 
0.0621***
(0.0145) 
0.0517
(0 .0580) 
0.0625 **
(0.0253) 
Access to electricity  0.0509
(0.1090) 
0.0559
(0.1093) 
0.2164**
(0.1348) 
0.0104
(0.1857) 
Occupational status (agric)  0.0385**
(0.0193) 
0.1673***
(0.0345) 
0.0165
(0.1552) 
0.1802**
(0.0541) 
Nonagric  0.0399
(0.0701) 
0.0381
(0.071) 
0.1218
(0.0881) 
0.0424
(0.1169) 
North East  0.2509**
(0.0938) 
0.1168**
(0.0952) 
0.2031
(0.1356) 
0.2692
(0 .1831) 
North Central  0.3661***
(0.0966) 
0.2227*
(0.0976) 
0.1823
(0.1392) 
0.3336*
(0.1865) 
North West  0.0856
(0.0929) 
0.0061
(0.0947) 
0.1321
(0.1359) 
0.2043
(0.1832) 
South East  0 .2284*
(0.0952) 
0.0039
(0.0969) 
0.3541**
(0 .1350) 
0.3287*
(0.1838) 
South South  0.1519
(0.0966) 
0.0738
(0.0987) 
0.3459**
(0.1364) 
0.3090
(0.1868) 
Pro > chi2
Loglikelihood LR ch2 Pseudo R2 
0.0003
2280.78 37.86 0.6205 
0.0007
2221.88 33.23 0.5803 
0.0069
1171.13 25.87 0.4814 
0.0000
639.514 15.35 0.5224 
The coefficients ***, ** and * denote significance at 1%, 5% and 10% respectively
SUMMARY AND CONCLUSION
The socio economic characteristics of the rural households in Nigeria show that, the average age of the rural households across the three waves was 42 which imply that the rural households were still agile and can be very active in terms of agricultural production. Majority (64%) of the rural households were married while households that were never married recorded below average. The transition probability matrix results show the projection of rural households in and out of non–inclusive growth category over time. The result showed that larger number (70%) of the rural households would move out of noninclusive growth category (exiting non inclusive growth) from year 2010 to year 2016. The transition matrix also revealed that 53% of the rural households had the probability of being inclusive (never non–inclusive) while the 30% and 47% of the rural households had the probability of remaining in noninclusive growth (always non–inclusive) and transiting into non–inclusive growth category (entering non–inclusive growth) respectively.
However, the long run probability of the households show that larger percentage (59.8%) would be moving into inclusive growth category while 40.2% would be non–inclusive which indicates that the long term projection of rural households that would be moving out from poverty, that is, that would be inclusive at long run was higher than the rural households that would be transiting into non–inclusive growth. The probability of the rural households that would move into noninclusive growth category in short run was 43.9%, while the probability of the rural households moving out of noninclusive growth category, that is, inclusive was 56.1%. Therefore, the vector at short run shows that there was also a reduction in the proportion of households that were non– inclusive at short run to a long term projection.
The study shows that there is still significant disparity in terms of access to facilities, social amenities and the basic necessity of life. In Nigeria’s rural households, there is a lack of inclusion; unemployment and poverty remain high, and the vast majority of the population is denied access to health care, electricity, credit, and educational opportunities. The probability of the rural households that would be inclusive in long run is higher than the rural households that would not be participating in economic growth. Therefore, Nigeria should incorporate distributive features and pursue growth that is inclusive as this would support positive multiplier effects.
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Cite this Article: Ajijola, S; Saka, JO; Omonona, BT (2022). Determinants of Transition in Economic Growth among Farming Households in Rural Nigeria. Greener Journal of Agricultural Sciences, 12(3): 185194. 
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