




































In ternationa l
Scholars
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African Journal of Agricultural Marketing ISSN 2375-1061 Vol. 7 (2), pp. 001-010, February, 2019. Available online at 
www.internationalscholarsjournals.org © International Scholars Journals 

 

Author(s)                                           Author(s) retain the copyright of this article. 
 
 

 

Full Length Research Paper 

 

Rural livelihood diversification and agricultural 
household welfare in Ghana 

 
Emmanuel Ekow Asmah 

 
1755 Massachusetts Avenue, NW, Washington, DC, 20036-2103, U.S.A. E-mail: EAsmah@brookings.edu 

Tel: +1-703-629-6174. 
 

Accepted 19 August, 2018 
 
Sufficient time has elapsed since agricultural sector reforms got underway in Ghana and so this study 
examined how some selected proxies of the reforms have changed overtime and evaluated their relative 
importance in influencing rural livelihood diversification and household welfare. In doing this, data was pooled 
from the 1991/1992 and 2005/2006 Ghana Living Standards Survey (GLSS) and the endogenous switching 
regression technique was employed. Diversified households and less diversified households differed 
significantly in terms of variables related to household assets, markets and institutions. Both household 
welfare and rural non-farm diversification decisions are mostly driven by household assets including good 
health, education, and household age composition. Households who live in communities with access to 
fertilizers, public transports and local produce markets are more likely to engage in non-farm diversification 
and enjoy improved welfare. The importance of access to TV and radio as effective mass media tools in 
influencing household behavior is underscored in the analysis. Targeting interventions that enhance 
livelihood diversification would ultimately have a positive impact on household welfare. 
 
Key words: Rural, diversification, agriculture, household welfare, switching regression. 

 
INTRODUCTION 

 
Ghana has for over the past two decades been involved 
in a wide-range of economic reforms aimed at creating an 
enabling environment for sustainable growth and 
development. These reforms which began under the 
framework of the Structural Adjustment Program marked 
the beginning of the deregulation of the economy and its 
transformation from an inefficient and import-dependent 
economy to one that is diversified, dynamic, efficient and 
export-oriented with a greater role for the private sector 
(Aryeetey et al., 2000). Within the agricultural sector, the 
reforms were implemented within the context to the 
Medium Term Agricultural Development Program 
(MTADP, 1991-2000). Agricultural policies were 
supported by a massive rural development scheme, 
designed to provide the basic infrastructure of roads, 
water and electricity that would encourage people to stay 
in the rural areas rather than migrate to the overpopu-
lated urban areas. In the cocoa sub-sector, the multiple 
buying systems, involving several companies, was re-
established to replace the monopoly enjoyed by the 
United Ghana Farmers Co-operative Council. As part of 

 
 
 

 
the liberalization program, the guaranteed minimum 
prices for maize and rice were abolished and all subsidies 
were removed including those for agricultural inputs, 
notably fertilizers, insecticides and fungicides  
(Seini, 2002). In addition, the procurement and 
distribution of these inputs (which was hitherto the 
responsibility of the MOFA and COCOBOD) were 
privatized in order to enhance competition and efficiency 
in agricultural input marketing.  

Even though the reforms seem to be paying off on 
many aspects of the economy, a number of structural 
challenges still remain. For example, the agricultural 
sector continues to be characterized by low levels of 
productivity. Farm yields per hectare in Ghana are among 
the lowest in the world. Cocoa yields for example in 
Ghana are much lower than in neighboring Cote d'Ivoire 
at 350 kg per hectare compared with 800 kg per hectare 
(Africa Growth Initiative Report, 2010). While advanced 
economies are using more than 100 kg of fertilizer per 
hectare and producing thousands of kilograms of cereal 
per hectare, Ghana‟s use of fertilizer is about 20 kg per 

file:///C:\Users\user\Documents\REPUBLICATION\AGRICULTURAL%20SCIENCES\AppData\Local\Temp\www.internationalscholarsjournals.org


 
 
 

 

ha (ISSER, 2009). The traditional dependence on rain-fed 
agriculture is still prevalent in Ghana and weather 
patterns are increasingly unpredictable and unreliable. 
The sector continues to be dominated by small holder 
farms (less than 3 hectors) with low use of new 
technology.  

Given that agriculture is a large sector in Ghana‟s 
economy and provides livelihood for over 60% of the 
population, it is also reasonable to suspect that the 
sector‟s lack of transformation may be a significant 
contributory factor to the food security and poverty 
challenges in the country. According to a USAID (2010) 
report, Ghana currently has nearly two million people still 
vulnerable to food insecurity and food remains a serious 
concern in many parts of the country. Analysis of poverty 
trends in Ghana based on the results of the Ghana Living 
Standards Survey, though impressive also leaves much 
to be desired. Significant intra-regional differences in 
poverty levels exist and the speed of the reduction in 
poverty still remain a source for concern. Poverty levels 
have remained strikingly high (at between 52-88%) in the 
three northern - Northern, Upper East and Upper West 
(GSS, 2007).  

While recognizing the urgent need to maintain a robust 
agricultural sector, it is increasingly becoming clear that 
the agricultural sector alone cannot be relied upon as the 
core activity for rural households as a means of improving 
livelihood and reducing poverty. One phenomenon that is 
gaining prominence in the rural development literature is 
the promotion and support for nonfarm diversification 
opportunities (Stifel, 2010). Non-farm economic activities 
include seasonal migration off the farm to engage in 
wage employment, handicraft production, trading and 
processing of agricultural produce, provision of 
agricultural services, etc. Such non-farm activities provide 
a way of off-setting the diverse form of risks and 
uncertainties (relating to climate, finance, markets, etc) 
associated with agriculture and creates a way of 
smoothing income over years and seasons. The relative 
importance of non-farm activities in rural areas is well 
documented in Reardon (1997), Reardon et al. (2001) 
and Barret et al. (2001).  

Already, there is evidence that non-farm activities in 
both the rural and urban areas are widespread in Ghana. 
The fifth round of the Ghana Living Standards Survey 
(GLSS 5) estimates that approximately three million, two 
hundred thousand households representing about 
(46.4%) of households in Ghana operate non-farm 
enterprises. A case study of four rural communities in 
three ecological zones of Ghana by Oduro and Osei-
Akoto (2007) gives further credence to this observation. 
Residents in the villages were found to be employed in a 
number of non-farm activities, such as hairdressing, car-
pentry, tailoring, trading, „pito‟ brewing, food processing, 
charcoal trading, masonry, sewing, teaching, and nursing. 
Lay and Schuler (2008) analyzed changes in income 
portfolios of rural households and found that 

 
 
 
 

 

asset-poor households, which account for an important 
share of the rural population, are likely to be pushed into 
activities off the farm to meet subsistence needs. Al-
Hassan and Poulton (2009) document a study by the 
Ministry of Food and Agriculture (MOFA) which disaggre-
gates households in 16 households in the three northern 
regions (12), Brong Ahafo 3) and Ashanti Region (1) 
according to their livelihood strategies as shown in Table 
1.  

These micro level evidences of diversification in Ghana 
are also mirrored at more aggregate sectoral levels. The 
agricultural sector which has for long dominated 
economic activity has in recent years given way to the 
services sector (GOG, 2010).  

While some analysts see the growing trend of non-farm 
activities as a natural progression from a predominantly 
agrarian economy into a diversified and productivity 
economy dominated by manufacturing and services 
(„push‟ factors), others (Ellis and Freeman, 2004) attribute 
the signs to a distressed agricultural sector that is losing 
its labor force not as a consequence of agricultural 
growth triggering growth in other sectors of the economy, 
but as a consequence of lack of growth or income 
opportunities in agriculture („pull‟ factors). Whichever way 
one looks at it, policy makers ultimately have a role to 
play either by way of providing the necessary incentives 
for agricultural households to maximize on existing 
opportunities or try to minimize the constraints 
households face in their effort to construct viable 
livelihood activities. Sound empirical information on 
issues at the household and community level that require 
attention would be necessary in this regard.  

Even though some extensive literature already exists 
on the causes and consequences of livelihood diversifi-
cation, the evidence is somewhat mixed and ambiguous 
(Stifel, 2010; Bezabiw et al., 2010). The multitude of 
constraints and incentives faced by a largely 
heterogeneous households engaged in a multiple set of 
heterogeneous non-farm activities makes broad 
generalizations problematic (Reardon et al., 1994; Barrett 
et al., 2002; Haggblade et al., 2007). Attractive livelihood 
opportunities, according to Barret et al. (2002) are 
normally accessible to those households who have better 
endowments in terms of human, financial and physical 
assets. And even where households have similar endow-
ments, production techniques, preferences, constraints 
and incentives attached to particular livelihood activities 
may be different (Iiyama, 2006).  

In order to have a deeper understanding of the micro-
economic constraints and incentives that influence 
livelihood diversification and the welfare implications of 
such decisions by agricultural households, this study 
examines and evaluates the importance of some selected 
proxies of the reforms in Ghana. Following the 2007 
World Development Report framework for thinking about 
an agriculture-for-development agenda, the study 
specifically focuses on variables related to (i) assets (for 



  
 
 

 
Table 1. Livelihood strategies of households in Northern Ghana.  

 
Group Characteristics Assets Activities   
Vulnerable (5%) High proportion of orphans, 

school drop-outs, youth  
economic migrants, widows 
with children, elderly,  
handicapped, sick 

  
0-0.5 acres of land per 
active member; no livestock 
except 0-5 poultry; basic 
house & cooking equipment, 
clothes 

  
Sale of firewood, making 
baskets or ropes, collecting 
wild products, sheanut 
gathering, buy & sell foodstuffs 

 
 

Poor (35%) High proportion of widows 0.3-2.5 acres per active Food crops and livestock 
 with children, youth semi- member; 0-5 sheep/goats, farming, petty trading, 
 permanent migrants, 0-3 cattle (per household); collection/processing/sale of 
 migrants creating farms Bicycle, roofing sheets NR products, seasonal and 
 outside their tribal areas,  semi-permanent migration 
 small-scale farmers with   

 weak labour capacities   

 
 

Medium (51%) Large family and high labour 
capacity (that is, low 
dependency ratio) 

  
1.5-4 acres per active Farm and non-farm activities 
member; 10-40 sheep/goats, 
3-30 cattle; (semi-permanent 
house; modest education  
and assets (e.g. sewing 
machine, shop, TV) 
 

 
Well-off (9%) Large family and high labor  

capacity, higher proportion of 
skilled labor 

  
1-25 acres per active 
member; 0-120 
sheep/goats; 0-1000 cattle; 
larger, permanent house 
with water, electricity, 
kitchen, toilet, fridge; tractor, 
car/truck. May have two 
houses-one in town, more 
modest on farm  

  
Agricultural: perennial (cocoa, 
rubber, mango), non-traditional 
or food crops (all on 
commercial scale); livestock 
(including commercial poultry). 
No-agric: tractor or transport 
services, medium-large-scale 
trading, shop/house rental, 
salaried positions 
 

Source:  Al-Hassan and Poulton (2009). 
 

 

example, access to land, education, finance, etc), (ii) 
markets (for example, access to local markets, motorable 
roads) and (iii) institutions (for example, extension 
services, producer organizations, sharecropping, access 
to radio, TV, etc). To implement these objectives, data 
from the 1991/1992 and 2005/2006 Ghana Living 
Standards Survey (GLSS) were used to first describe the 
changes that have taken place among the chosen 
variables between the two periods. Second, based on the 
suspicion of unobserved heterogeneity and possible 
endogeneity in establishing the econometric relationship 
between livelihood diversification and household welfare, 
the endogenous switching regression approach was 
employed for the analysis. This technique, following 
Lokshin and Sajaia (2004) relies on joint normality of the 
error terms and allows us to simultaneously estimate the 
binary and continuous parts of the model in the binary 
and continuous equations in order to yield consistent 
standard errors. By doing this, the paper provides 
additional insights to related studies on Ghana such as 
Oduro and Osei-Akoto (2007), Owusu and Abdulai 
(2009), Knudsen (2007) and Anriquez and Daidone  
(2008). The results provide guidelines that are helpful  for 

 
 

 

governments in their effort to define concrete plans to 
reduce poverty and vulnerability as well as to enhance 
household well-being.. 
 

 
MATERIALS AND METHODS 
 
Study framework 
 
The framework (Fc helps in conceptualizing the causes and 
consequences of rural livelihood diversification in Ghana.  

The basic framework is predicated on the assumption that a 
household‟s portfolio of non-farm activities and how they impact on 
welfare is decided based on selected micro-economic constraints 
and incentives created through access to public and private 
resources embodied in assets, markets and institutions. By assets, 
we are referring to the natural, physical, social, financial and human 
resources of value to the household. Changes in the portfolio of 
assets, their productivity and the extent to which households have 
access to them are the attributes that are critical in determining 
livelihood diversification and ultimately household welfare (Dorward 
et al., 2003). The limitations from access to credit and lack of 
education, for example, have been highlighted by Bezabiw et al. 
(2010) in their case study on Ethiopia.  

For small and marginal farmers, the importance of well-
functioning markets helps in reducing transaction costs and risks 



     
 
 

 

ASSETS 

 

Physical (access to land, fertilizers), social (education,  
health status, household structure), financial (money),  
natural (time), etc 

 

 

INSTITUTIONS 

 

Producer Organizations, Extension  
Services, R and D, Sharecropping  
arrangements, mass media access 

 
 

 

MARKETS 

 

Access to motorable roads, product markets, 
public transport, etc 

 

 

 

LIVELIHOOD  DIVERSIFICATION 
 

On-farm, off farm and Non-farm  
activities 

 
 
 

 

HOUSEHOLD  WELFARE 
 

 
Figure 1. Study framework: Causes and consequences of livelihood diversification. Source: Author. 

 
 

 
involved in acquiring inputs and profitably selling outputs. For 
example, access to rural infrastructure including the presence of 
local markets, motorable roads, electricity, telecommunications, etc 
provide important means of intervening directly in market transact-
tions in order to change costs or returns of economic activities. This 
creates direct and indirect non-farm employment and income 
opportunities for the poor to improve welfare. The acknowledge-
ment of the role of institutions in non-farm diversification is derived 
from the recognition that much of human interaction and activity is 
structured in terms of overt or implicit rules that define the 
incentives and sanctions households face (Hodgson, 2006). While 
the idea of institutions could be defined to include many compo-
nents, the study examines three of these that are particularly 
important for rural farm households namely, producer cooperatives, 
extension services and sharecropping (Figure 1). 
 

 
Estimation technique 
 
The endogenous switching regression analysis, also known as the 
Mover or Stayer model, is applied to situations where one wishes to 
establish the effect of being in one of two different positions (status, 
regimes or states) on desired outcomes and the possibility of 
moving or staying in that particular position, regime or state (Tauer, 
2005). In this study, the outcome of interest is household welfare 
and the two regimes or decision states are whether or not 
households are more or less diversified. The endogenous switching 
regression approach is used because the decision whether or not to 
engage in livelihood diversification is voluntary and may be based 

 
 
 

 
on individual self-selection, causing a biased sample with non-
probability sampling. Self-selection makes it difficult to determine 
causation. For example, one might notice a significantly higher 
welfare among those participate in off-farm participation and credit 
this difference to the off-farm participation decision. However, farm 
households that engage in non-farm activities may have 
systematically different characteristics from households that do not 
due to self selection. Those who choose to participate in off-farm 
activities might be more hard-working, studious and dedicated than 
those who did not participate, explaining the difference between the 
two groups. Neglecting these effects is likely to give a false picture 
of the relative welfare status among the diversified and non-
diversified farm groups. These reasons warrant estimating distinct 
regressions for the two different groups (diversified and less 
diversified) instead of a homogenous and single welfare function. 
Doing this however leads to observations that are no longer random 
draws from the population which makes the use of ordinary least 
squares not appropriate.  

Following Lokshin and Sajaia (2004), the first step in the 
switching regression model is to determine the factors influencing 
livelihood diversification among the farm households based on a 
probit function is specified as: 
 

D 
*
   α 

'
 Z 

I 
ε 

0I 
 

 

I    (1)  

      
 

Where  D *  is the  latent  dependent  variable  which  we  observe 
 

 I      
  

through the decision to engage in livelihood diversification activities 



       
 

* 
 0  (diversified)  and 

 * 
≤ 0  (less 

  
 

and  DI     1  if  DI DI  0  if DI       the „unobservables‟ of household welfare equations.  
 

diversified).  Diversification  is  defined  as  participation  in  the  local   
 

non-farm sector through wage or self-employment. The subscript  I 
Data 

 
 

denotes  farm-households,  Z I  is  a  vector of exogenous variables 
 

 

  
 

 

 
' 

α I   are vectors of unknown parameters and ε 0I 
 

 

W1I    β1 X1I    ε1I    
If

  DI    1  

W
2 I   


 
β

 2 

X
 2I   


 
ε

 2I If  DI    0 

 

Data for the study are derived from the nationally representative 
multi-purpose Ghana Living Standards Survey (GLSS) for 
1991/1992 and 2005/2006. These surveys provide a valuable 
source of detailed data including socio-economic situation of 
individuals, households, communities, and regions in Ghana. It 
includes data on demographic characteristics, health, 
education, economic activities and migration. The survey 
consists of both a household questionnaire and a community 
questionnaire, and data from either of these are combined for 
this paper. In order to also analyze the role of media access 
(TV, radio, newspapers) on diversification and welfare, 
additional data was sourced from the Demographic and Health 
Surveys (DHS) for the years 1993 and 2003. 

 

(3) 
 

 

Where W1 and W2 represent  welfare functions  for  households 
 
engaged with non-farm activities and those who do not. Household 
welfare is defined here as a household‟s command over market and 
non-market goods and services at the household level. The proxy 
used to measure household welfare is the log of household 

consumption expenditure adjusted by adult equivalent units. X1 and 

X 2 are vectors of weakly exogenous variables; β1 and β 2 are 
 

vectors of parameters; and ε 1 and ε 2 are random disturbance 

terms. The underlying assumption here is that diversification is 

endogenous to household welfare. Also, by splitting the sample into 

two, the problem of sample selection bias may arise. In order to 

deal with these challenges, the switching regression technique 

relies on joint normality of the error terms in the binary and 
continuous equations. The error terms, ε 0  , ε 1 and ε 2 are 

I I I 
 
assumed to have a trivariate normal distribution with zero mean and 
non-singular covariance matrix specified as: 

      
2

 σ
12 

σ
10 

  
 

      σ1   
 

COV(ε ,ε 
2 
,ε 

0 
)   σ σ 

2
 σ 

20  (4)  

1    12 2  
 

       

σ
 20 

 2  
 

      
σ

10 
σ

 0   
 

where σ1
2
 and σ 2

2
 are variances of the error terms, ε 1 and ε 2 ,  in 

  

equations (2) and (3);  σ 0
2 is the variance of the error term,  ε 0 , in 

equation (1); σ 12 ,  σ 10 and  σ 20 are the 

covariance of ε 1 and 
 

ε 2 , ε1 and ε 0 , ε 2 and  ε 0 , respectively. 
 

The simultaneous estimation based on the full information 
maximum likelihood (FIML) estimation of the Equations 1- 3 
corrects for the selection bias in the household welfare estimates. 
This is implemented using the move-stay command in STATA. The 
estimates generated through this technique include the inverse 
Mill's ratio, which measures the ratio of the ordinate of a standard 
normal to the tail area of the distribution and reflects the probability 
that an observation belongs to the selected sample (Heckman, 
1979). Other estimates from interaction of the error terms show the 
correlation of the „unobservables‟ of the diversification equation with 

 
RESULTS AND DISCUSSION 

 

Descriptive statistics 

 

The first part of the results provides a description of how 
the household, community and institutional variables for 
the sample households have changed between 
1991/1992 and 2005/2006 in terms of the percentage of 
distribution of the survey and t-tests. As shown in Table 
2, significant increases in household welfare, measured 
as household consumption expenditure adjusted by adult 
equivalent units were found. 
 

Similar significant increases were also observed in the 
percentage of farm households engaged in non-farm 
diversification. No significant difference is observed in the 
education of the head of household and members aged 
above 60 years. Significant increases were found in the 
age structure of household members within the two 
periods. 
 

For the 242 community variables surveyed in 1991 as 
against 328 in 2005, the results showed that the 
increases in access to motorable roads, farmers‟ use of 
fertilizers and insecticides were not significantly different 
from zero. Noticeable and significant reductions were 
however observed for the practice of sharecropping and 
the existence of local markets. With regards to 
communities with access to land markets, extensions 
 

services and where existence of agricultural 
cooperatives, no meaningful changes were found 
between 1991 and 2006. Given that rural households are 
spread over large areas and that information transmission 
to farming communities was problematic, the study 
decided to explore the role of access to TVs and radios 
on welfare and livelihood diversification. Data obtained 
from the Ghana Demographic and Health Surveys over a 
10 year period between 1993 and 2003 showed 
significant increases in the proportion of households who 
listen to radio and watch television at least once a week. 

term. 

The second step in the switching regression model is to define 
separate welfare functions for the two groups of farm households. 
Their welfare functions are expressed as: 

gender, age, level of education, number of dependants, land size 
and selected community infrastructure variables that account for 
community differences in income generation that may affect 
diversification as well as the level of household expenditures. Also, 

is the disturbance 

                           (2) 



 
 
 

 
Table 2. Characteristics of surveyed households and communities.  

 
Variable 1991/1992 2005/2006 Significance of change 

Welfare 1,061,975 1,438,117 *** 

Non-farm diversification 0.81 0.85 ** 

Total acres of land 251.72 211.73 * 

Remittances 0.58 0.51 ** 

Disease burden 0.07 0.08 * 

Gender of household head 1.25 1.22 ** 

Age of household head 45.43 46.82 ** 

Education of household head 0.65 0.68 increase not significant 

Size of household 4.84 4.99 * 

Members aged 5-14 1.55 1.45 ** 

Members aged 15-24 0.77 0.87 ** 

Members aged 25-39 0.76 0.85 ** 

Members 40-59 0.63 0.72 ** 

Members aged above 60 0.32 0.35 increase not significant 

Motorable road in community 0.81 0.84 increase not significant 

Bank in community 0.09 0.05 reduction not significant 

Local community market 0.39 0.29 *** 

Extension worker in community 0.25 0.24 Decrease not significant 

Agric cooperative in community 0.29 0.31 Increase not significant 

Farmers use fertilizer 0.55 0.68 *** 

Farmers use insecticide 0.58 0.70 *** 

Land Market 0.11 0.12 increase not significant 

Sharecroppers in community 0.66 0.55 *** 
 

*Means difference is significant at 10%, ** means difference is significant at 5% and *** means difference is significant at 1%. 
 

 

The endogenous switching regression results 

 

The full information maximum likelihood estimates of the 
endogenous switching model based on pooled cross-
sections data are reported in Table 3. The first and 
second columns present the estimated coefficients of the 
welfare functions of the less diversified and diversified 
groups respectively whiles the probit selection equation 
for the off-farm diversification equation is shown in the 
third column. A Wald test of whether the estimated 
coefficients as a group are different between the more 
diversified and less diversified equations produced a chi-
squared value of 184.08 with 25 degrees of freedom. This 
means that the coefficients are statistically different. The 
likelihood ratio test for joint independence of the three 
equations rejected the null hypothesis that all slope 
coefficients are equal to zero at the 1% level (chi-squared 
value was 71.77). The simultaneous modeling 
coefficients are equal to zero at the 1% level (chi-squared 
value was 71.77). The simultaneous modeling based on 
the switching regression technique was justified given the 
highly significant off-diagonal values of the error 
covariance matrix and the error correlations.  

The correlation coefficients rho_1 and rho_2 are both 
positive and significant. This means both observed and 
unobserved factors influenced the decision to participate 

 
 

 

in off-farm employment and welfare resulting from those 
decisions. This also indicates that self-selection occurred 
in the off-farm participation decision and welfare given the 
participation decision. In other words, farm households 
who participate in off-farm employment have higher 
welfare than random households from the random 
sample who have not participated in non-farm work. 
Engaging in off-farm diversification had a significant 
impact on welfare among those who participated in it. 
 

 

Household assets and composition 

 

The findings from for variables related to household 
assets and composition are quite interesting. The age 
structure of the household which attempts to capture the 
life-cycle effects was found to be significant correlates of 
household welfare and livelihood diversification. The 
coefficient of household head‟s age is positive and 
significant which means that a farm household‟s welfare 
is improved as age increases. This coefficient was 
however significantly negative in the probit selection 
equation, implying that the likelihood to engage in non-
farm diversification decreases as the head of household 
grows in age. Furthermore, households where there are 
members aged 5 or older have a greater probability to 



 
 
 

 
Table 3. Full information maximum likelihood estimates of the switching regression model based on pooled data for 1991 and 2005.  

 

Variable 
Welfare =0 Welfare =1 Non-farm diversification 

 

Less diversified Diversified (select equation)  

 
 

Education of household 0.2382**(0.0489) 0.1528*** (0.01445) 0.1902** (0.05256) 
 

Age of household head 0.0739**(0.0036) -0.00125 (0.0013) -.01445*** (0.00422) 
 

Gender of household head 0.4986**(0.1044) 0.1266 *** (0.02986) 0.8656*** (0.1064) 
 

Size of household -0.0139 (0.0508) -0.1239*** (0.01233) Dropped 
 

Members aged 5-14 0.1257* (0.0761) 0.0354** (0.0155) 0.65586*** (0.0486) 
 

Members aged 15-24 Dropped 0.0655** (0.01693) 0.50343*** (0.04848) 
 

Members aged 25-39 0.2415**((0.0629) 0.09044*** (0.02413) 0.85054*** (0.06303) 
 

Members aged 40-59 -0.0995 (0.08103) 0.05255** (0.0255) 0.43886*** (0.07436) 
 

Members aged above 60 -0.1685 (0.1241) 0.0755** (0.0323) 0.55888** (0.1175) 
 

Sector of employment of head -0.0911 (0.0656) -0.03031** (0.0080) -0.19631*** (0.03613) 
 

Access to remittances 0.00912 (0.0605) 0.0008 (0.02099) -0.05868 (0.07096) 
 

Acres of land owned 0.01916 (0.0138) 0.01048 ** (0.00474) -0.02113 (0.01544) 
 

Agricultural land sales 0.02513 (0.0863) 0.0307 (0.0303) 0.05455 (0.10318) 
 

Access to motorable ROADS 0.0784 (0.0977) -0.07156 * (0.03688) -0.08697 (0.11655) 
 

Access to banking center -0.0282 (0.1415) 0.00132 (0.04449) -0.00244 (0.16449) 
 

Access to produce market 0.12156*  (0.06706) 0.06706** (0.0238) 0.04126  (0.07999) 
 

Member of agric cooperative -0.0406* (0.06415) -0.04821** (0.02242) -0.0665  (0.07463) 
 

Access to public transport 0.1997**(0.07175) 0.17969** (0.02742) 0.18464** (0.08584) 
 

Agricultural sharecropping 0.06995 (0.0732) 0.1213**(0.0259) 0.02327 (0.08681) 
 

Access to extension service -0.03022 (0.0747) 0.00418  (0.0258) -0.00805 (0.087777) 
 

Households use fertilizers 0.1089* (0.0667) 0.0.07252** (0.02399) 0.07172 (0.07796) 
 

Listens to radio at least once a week -1.1431*  (0.60843) -0.87463** (0.22954) -0.8596***  (0.27064) 
 

Watches TV at least once a week 0.3956 (0.3631) 0.61385*** (0.12974) 0.17891  (0.40968) 
 

Read newspapers at least once a week 1.5548** (0.76699) 1.3685*** (0.27167) -0.37409 (0.79786) 
 

Time dummy (2006) 0.6284***(0.1818) 0.53607*** (0.07078) Dropped 
 

Constant 14.1017***(0.48165) 13.8454*** (0.13764) -0.87211**  (0.31743) 
 

 
Rho_1 = 0.80906 *** (0.0531), Rho_2 = 0.59552 *** (0.054255), Number of Observations = 3264, Wald chi 2 (25) = 184.08, Prob > chi2 = 0.00000, 
LR test of independent equations: chi 2 (2) = 71.77 Prob > chi2 = 0.0000. *Means difference is significant at 10%, ** means difference is significant 
at 5% and *** means difference is significant at 1%. Also standard errors are shown in parenthesis. 

 
 

 

engage in non-farm work with the likelihood for positive 
dividends on welfare. This could be due to the fact that 
participation in off-farm work is critically dependent on 
labor availability. But in terms of the size of the 
household, we find that the coefficient of household 
membership size appeared to be negative but statistically 
insignificant for the welfare of non-diversified households. 
This means that, holding all other variables constant, 
each additional child decreases the probability of 
increased household welfare and puts a greater burden 
on the household. The gender dummy variable repre-
sents the gender segregation between men and women 
household heads. The estimated sign of the gender 
variable is positive for all the models, which indicates that 
relative to female-headed households, the level of welfare 
and non-farm diversification is likely to be high for male-
headed households.  

The level of education and the health status of house-
hold members in the model represent human capital 

 
 
 

 

endowment. The results from Table 3 show that edu-
cation of the head of the household has a significant and 
positive effect on household‟s non-farm diversification as 
well as household welfare.  

The higher the level of education, the greater the 
probability that households will engage in non-farm work 
and ultimately have improved welfare. With regards to the 
health status variable, the study find from the analysis 
that the tendency to engage in non-farm work reduces 
when the burden of disease is high, which is what was 
generally expect in theory. Owning land is an important 
asset in improving the welfare of households who engage 
in non-farm work. We surprisingly find no significant effect 
of land ownership on the selection decision whether or 
not to participate in non-farm work. Applying fertilizers is 
shown in the results to be justifiable as the study find out 
that those farming households who use fertilizers 
(specifically, those who engage in non-farm 
diversification) have a greater likelihood of enjoying 



 
 
 

 

improved welfare. The impact of access to insecticides 
was statistically insignificant and was dropped from the 
model. The problem of the exclusion of rural populations 
from financial services is widely acknowledged. It was 
therefore not too surprising we uncovered no significant 
impact of access to remittances and banking services on 
welfare and the non-farm participation decision. The 
importance of the sector of employment of the household 
head is clearly underscored in the analysis. The results 
show that relative to other jobs other than agriculture, the 
level of welfare and non-farm diversification is likely to be 
lower for household heads who work in the agricultural 
sector. 
 

 

Market access 

 

For small and marginal farmers, having access to 
markets helped in reducing transaction costs and risks 
involved in acquiring inputs and profitably selling outputs. 
Having access to local community markets was found to 
be positive and significant in promoting welfare of 
diversified households. This was not the case for less 
diversified households. We also find that access to local 
markets has no direct correlation with the livelihood 
diversification decision. As was expected, households 
who live in communities with better access to public 
transport have a higher probability to engage in non-farm 
work and also enjoy higher welfare. Access to public 
transport facilitates movement of persons, farm inputs 
and outputs in a cost effective way. We realized that this 
finding was robust for all the groups studied. Strikingly, 
however, we find that access to motorable roads turns 
out to be negative and significant at 10% in the welfare 
function of diversified households and insignificant in the 
case of the selection decision and welfare of less 
diversified groups. 
 

 

Institutions 

 

Transfer of information and knowledge to small farm 
households working in diverse settings, remote locations 
and some of whom are illiterate is very challenging task. 
The traditional models of transferring knowledge in 
Ghana are largely based on extension activities and 
agricultural cooperatives. In this last set of results, how 
traditional and non-traditional sources of obtaining 
agricultural information affect non-farm diversification and 
household welfare are discussed. No significant effect of 
access to extension services on non-farm diversification 
and also household welfare was found. This may not be 
too surprising considering the fact that agriculture 
extension departments in Ghana lack the resources and 
state-of-the-art technologies to deliver the required 
services to farming communities. With regards to the 
importance of agricultural cooperatives, a negative and 

 
 
 
 

 

significant likelihood effect on the welfare of diversified 
households was observed. The effect of agricultural co-
operatives on the diversification decision and the welfare 
of less diversified households are insignificant. Looking at 
the history, culture, type and structure of co-operative 
organizations in Ghana and elsewhere in Africa, this 
result may not necessarily be surprising. Traditional 
agricultural cooperatives are normally sees as disinter-
grated stand alone groups promoted through collective 
ownership with minimal capital investment who are 
unable to see problems of their members in terms of 
solutions generated by the co-operative movement as a 
whole, but rather, they look to the government when 
seeking co-operative solutions (Chambo, 2009). One 
aged-long risk-sharing institution in Ghana that turned out 
to have a positive and significant effect on household 
welfare is the idea of sharecropping. This is the system 
where a landowner allows a tenant to use the land in 
return for a share of the farm produce. The likelihood 
impact of sharecropping in the selection equation and in 
the welfare of less diversified households was however 
not significantly different from zero.  

Beyond the traditional knowledge institutions that are 
typically available to rural farmers, access to TV and 
radio networks are important channels through which 
various kinds of information can be transmitted to farm 
households. After controlling for other variables in the 
model, some interesting results were found when mass 
media variables were included. Households who listen to 
radio at least once a week were found to have a greater 
likelihood to engage in non-farm work. With regards to 
promoting household welfare, instead of a positive effect, 
we found a significantly negative effect of access to radio 
for both diversified and less diversified groups. In the 
case of households who watch television at least once a 
week, a positive and significant welfare impact was found 
for the more diversified group. Though difficult to 
interpret, these findings underscore the significance of 
such mass media tools to either positively or negatively 
influence household behavior (Appendix). 
 

 

Conclusions 

 

Since the implementation of the market-oriented 
agricultural sector reforms in the late 1980s, there has 
been a remarkable diversification trend in rural Ghana 
characterized by developments of non-agricultural rural 
enterprises. The paper draws from the 1991/1992 and 
2005/2006 Ghana Living Standards Household Surveys 
to also throw light on how selected elements of the 
agricultural sector reforms impact on non-farm diversi-
fication and household welfare. The study finds that non-
farm diversification activities and household welfare are 
mostly driven by household assets and compositions 
including household age structure, education level and 
gender. The role of market access, fertilizer use and 



 
 
 

 

public transportation are also critical dimensions of rural 
livelihood diversification and household welfare and 
merits attention by policy makers. The idea of 
sharecropping as a risk-sharing mechanism is also not 
misplaced and needs to be supported. Among the 
information variables, listening to radio and providing 
access to televisions are effective tools in influencing 
household livelihood diversification and welfare, con-
ditional on other relevant variables. All in all, the paper 
supports the emerging consensus that the livelihood 
diversifications are important means of enhancing welfare 
and deserves attention. 
 

 
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APPENDIX 

 
Definition and measurement of variables 

 

i) Household welfare: This is defined as a household‟s command over market and non-market goods and services at the 
household level. The proxy used to measure household welfare is the log of household consumption expenditure 
adjusted by adult equivalent units.  
ii) Non –farm diversification: It is defined as participation in the non-farm sector through wage or self-employment. We 
use a dummy variable which is 1 if households engage in non-farm activities and 0 if otherwise. 
iii) Gender of the household head:  A dummy variable with male = 1 and female = 0. 
iv) Remittances: A dummy for access to remittances. 
v) Total acres of land owned:  Value of land owned. 
vi) Size of household:  Number of members in the household 
vii) Age of the household head:  Age of the household head (in years) 
viii) Education of the household: Highest level of education completed by household head where 0 is defined for those  
with no education, 1 = Primary; 2 = Secondary, and 3 = Higher.  
ix) Disease burden: It is defined as the number of days (over a two week period) individual falls sick plus the number of 
days (over a two week period) individual does not work. This is then expressed as an index between 0 and 1 where 
lower indices represent good health or low disease burden and higher indices mean poor health status or high disease 
burden.  
x) Access to motorable roads: 1 for respondents who have access or 0 otherwise, 
xi) Access to a bank: 1 for those who said yes and 0 for those who said no. 
xii) Access to community market:  1 for those who have access and 0 for those who do  not. 
xiii) Access to fertilizers: 1 for respondents who use fertilizers or 0 otherwise. 
xiv) Agricultural sharecropping:  1 if sharecropping exists in community or 0 otherwise. 
xv) Access to extension worker: 1 if respondents have access or 0 otherwise. 
xvi) Agricultural cooperatives:  1 if farmer belongs to cooperative, 0 otherwise. 

xvii) Access to public transport: 1 if farmer has access to public transport or 0 otherwise. 


