determinants of household … of household participation in rural nonfarm employment activities... 3...

Determinants of Household Participation in Rural Nonfarm Employment Activities... 1

* Ministry of Defence, Justice and Security, Government of Botswana, Gaborone, Botswana** Senior Lecturer, Department of Economics, University of Botswana, Private Bag UB 705, Gaborone,

Botswana, E-mail: [email protected]; [email protected]*** Professor of Economics, University of Botswana, Private Bag UB 705, Gaborone, Botswana, E-mail:

[email protected]

Asian-African Journal of Economics and Econometrics, Vol. 14, No. 1, 2014: 1-22

DETERMINANTS OF HOUSEHOLD PARTICIPATIONIN RURAL NONFARM EMPLOYMENT

ACTIVITIES IN BOTSWANA

Ipuseng Zambo*, J.B. Tlhalefang**, O. Galebotswe** and N. Narayana***

ABSTRACT

Non-farm employment (NFE) is important to the rural households because it directly increasestotal household income and thus contributes to food security by allowing better access to food.This study uses a multinomial logit model to investigate the factors influencing rural households’participation decisions in the NFE in Botswana. For this study, rural non-farm employment(RNFE) is categorized into three types: (i) non-farm wage employment (ii) non-farm self-employment with hired employees, and (iii) non-farm self-employment without hired employees.In addition to the types of non-farm employment, the factors that influence participation in thefarm employment counterpart are also investigated. The results of this study show that genderinfluences households’ participation decisions in all the types of RNFE. Other important factorsinfluencing households’ decision to participate in the non-farm employment activities are educationand age and marital status.The findings of the study reveal that the policies aimed at increasingemployment in the rural nonfarm employment activities should target more women than men.

INTRODUCTION

Despite rapid economic growth, which saw Botswana transition from one of the poorest countriesat independence in 1966 to a middle income country by 1993, unemployment and poverty haveremained high, especially in the rural areas. For instance, rural poverty averaged around 38 percent between 1993/94 and 2009/10 (MFDP, 2010). According to Maundeni and Mookodi (2004),the majority of the people in rural areas remain poor due to the constraints of a largelyundiversified economy that is highly dependent on cattle and diamonds.

Consequently, the government of Botswana, in its rural poverty reduction strategies hasidentified the non-farm sector as the leading activity in the rural economy. The governmentreviewed its national policy for rural development in 2002 to emphasise formulation andimplementation of rural nonfarm employment (RNFE) generating activities. This policy shifthas also been emphasised in the National Vision-2016, annual budget speeches and ruraldevelopment policies. However for policymakers to design and implement appropriate policies

2 Ipuseng Zambo, J. B. Tlhalefang, O. Galebotswe and N. Narayana

that can promote growth of rural nonfarm activities they need to have accurate knowledge andunderstanding of the main determinants of the decision of households to participate in thoseactivities.

Empirical studies show that RNFE comprises of a set of diverse activities that range fromemployment in high productivity sectors to low productivity subsistence sectors (Reardon,1997). This heterogeneity is attributable to differences in incentives and capacity to undertakenonfarm activities among rural households. Furthermore, the nature and importance of thesefactors vary among different regions (Isgut, 2004). For instance, in rural African countriessuch as Rwanda where farm income and landholding are unevenly distributed, farm assetssuch as landholding and cattle ownership were found to influence decisions to participate inthe RNFE (Barrett et al, 2000). In contrast Dercon (1998) found this link to be less common inother low and middle-income regions like Burkina Faso and Kenya and instead the variationsdepended on location and labour market opportunities. Consequently, each country needs toidentify those factors germane to it to inform policy design and implementation.

Despite the large number of studies analysing the nature and importance of factorsinfluencing households’ participation in RNFE, we are not aware of any such studies forBotswana. This study attempts to fill this gap in the literature by providing a quantitativeanalysis of household characteristics that influence decision to participate in RNFE in Botswana.The rest of the paper is organised as follows. The next section discusses the context of thestudy. The third section discusses the methodological issues. Section four discusses resultsfrom the empirical models and the conclusions are presented in section 5.

RURAL NONFARM EMPLOYMENT ACTIVITY IN BOTSWANA

This section reviews the rural non-farm employment (RNFE) activities in Botswana in order togain an understanding of the structure of RNFE and how the rural households earn theirlivelihoods. This information subsequently instructs the choice of the multinomial logit modelused in this study. Furthermore, it is useful in interpretation of the results.

STRUCTURE OF RNFE

According to the Tenth National Development Plan (NDP 10) of Botswana, the rural-employment sector comprises of a wide range of activities that provide employment opportunitiesto rural dwellers. These employment-generating activities differ in terms of size and the type oflabour market integration. More specifically, rural employment activities includes: (i) micro-enterprises operated by only one person, who is the sole owner or the non-farm self-employmentwithout hired employees; (ii) micro, small and medium-scale enterprises with a number ofemployees, hereafter referred to as non-farm self-employment with hired employees; and (iii)paid-workers by employer, hereafter referred to as wage employment. The wage-employmentactivities include professional wage employment (e.g., nurses, teachers, and lecturers), skilled-manual labourers (e.g., mechanics) and unskilled labourers.

Figure 1 illustrates the structure of employment in rural areas in Botswana in 2005. Ruralpopulation derives their livelihoods mainly from non-farm wage employment. As seen in Figure


1, 60.5 per cent of rural households derived their livelihoods from non-farm wage employmentin year 2005. Included in the nonfarm wage employees are the permanent and pensionable, thetemporary and the part-time employees in government, parastatals, non-governmentalorganizations and private sectors. Farm employment is the second most important source oflivelihood in rural areas, accounting for 24 per cent in total employment. The agriculturalworkers consist of traditional farmers, who are either working on their own arable farms orrearing and selling their livestock and livestock products such as milk and skins. About 8.6 percent of the rural households are employed in the nonfarm self-employment activity withouthired employees, that is, they operate their businesses themselves and have not hired employees.The proportion of the rural population employed in the nonfarm self-employment activity withhired employees is 3.7 per cent.

Figure 1: Structure of Rural Employment in Botswana in Year 2005

Source: Central Statistics Office, 2008

The dominance of non-farm employment is a feature of fast growing economies (Davies,2006). These are countries with great diversity among rural regions and both endogenous andexogenous factors that affect rural employment growth. According to Davies (2006), in theseeconomies, economic sectors like manufacturing and services usually grow faster than theagricultural sector, leading to a fall in agricultural labour. The fall in agricultural labour iscompensated by increased employment in services-particularly tourism and, in some casesmanufacturing. The structure of rural employment thus, explains diversity of rural areas inBotswana.


RURAL NON-FARM SECTOR DEVELOPMENT

At independence in 1966, agriculture was the mainstay of the rural economy, contributingabout 40 per cent to Botswana’s Gross Domestic Product (GDP) and 27 per cent of formalemployment. The agricultural sector’s contribution to both GDP and formal employment fellsubstantially over the years. Whereas in 1988 the sector contributed 4 per cent to both GDP andformal sector employment, it contributed less than 3 per cent of both GDP and formal employmentin 2010. The country’s declining contribution of agriculture to GDP could be attributed todiversification of activities on one hand and the effects of natural disasters such as recurringdroughts, pests and diseases on the other.

Botswana’s first national policy on rural development was adopted in 1973. The objectivesof this policy were to, promote rural industrialization; improve infrastructure; and promoteagricultural productivity. An analysis of the evolution of policy objectives since independenceshows that the major policy change was the incorporation of the non-farm employment (NFE)generating activities in 2002. The revised policy emphasizes the creation of NFE as a way toalleviate rural poverty. The creation of micro and small enterprises, for example, small scaletourism, small scale mining, processing and selling of veld products are envisaged as a way tocreate non-farm employment opportunities for the poor in the rural areas (MFDP, 2002).

In an effort to support the rural sector, micro and small enterprises are given special treatmentthrough exemption. Moreover, economy wide schemes such as the Citizen EntrepreneurialDevelopment Agency (CEDA) established in 2001, and the Local Enterprise Authority (LEA)established in 2004 have also extended support to non-farm employment generating activities.The schemes provide grants, low interest loans and support services to Small Medium andMicro Enterprises (SMME), including training, market access facilitation, mentoring andfinalization of business plans in manufacturing, tourism and services (MFDP, 2010).

A number of technology development activities were also undertaken during the NationalDevelopment Plan 9 (NDP 9) to promote both the rural non-farm and on-farm employmentactivities. In this effort, the key institution was the Rural Industries Promotion Company(RIPCO). RIPCO has been engaged in a number of projects that transfer technology to ruralcommunities including the development of fodder processors, milling plants, row planters andmulti-purpose threshers. Moreover, the institution also contributed to rural development throughtransfer of technologies to village communities in textiles, blacksmithing, bread-making, tanneryand hammer milling (MFDP, 2010).

THEORETICAL FRAMEWORK

The agricultural household model has been the workhorse in the analysis of rural householdsbehaviour regarding participation in the non-farm employment. This model assumes that ruralhouseholds are both producers and consumers in subsistence economies (Singh et al, 1986). Assuch, they allocate their labour between on-farm and non-farm employment activities. The decisionsas to the amount of labour to allocate to each of these activities are made jointly within the family.

In its basic form, households participate in both on-farm and the non-farm labour marketsbecause they want to maximize their utilities. This model views household i’s decision to


supply labour to activity j as a function of the incentives and capacity variables. Coral andReardon (2001) express the incentives as the potentially higher returns to labour that wouldeither “pull” or “push” the individuals into the labour market. These include higher profits thatone derives from own enterprise and higher wages earned in the non-farm employment. Thecapacity variables are expressed as the vector of individual’s and household’s characteristicsthat enable them to respond to the incentives. Examples of the capacity variables include thelevel of education, age, experience and skills. Therefore, the utility function is expressed as:

U = U(Yf, Y

m, L

S; Z

h) (1)

Where U is the total household utility, Yf is farm output, Y

m, goods purchased from market, L

s is

leisure time and Zh is household’s characteristics.

Equation 1 says that rural household derives utility from the consumption of farm output,goods purchased from the market and leisure time, conditional of the household’s characteristics.

Since the rural household depends on both farm output and consumption of market goodsfor its livelihood, it allocates its labour time between on-farm employment and non-farmemployment. Thus, the basic assumption of the model is that total household’s labour timeavailable (L

t) is split among farm work, non-farm employment and leisure. Equation 2 gives

the household’s labour allocation constraint as:

Lt = L

0 + L

f + L

S(2)

Where Lo the labour time spent on non-farm employment, L

f is labour time spent on farm

employment, Ls is time spent on home activities like cooking and cleaning. In studying the

household’s consumption behavior on the basis of the unitary household model, it is importantto note that consumption and production decisions are made within the household (Reardon,2006). Therefore, in this study we posit that individuals within a household have similarpreferences.

Thus, total household’s income is assumed to be the sum of incomes of various familymembers. That is, we do not include the intra-household dimensions, hence, total family’sconsumption is assumed to be positively related to the total household’s income. Thus, thehousehold maximizes utility subject to the household income constraint given by the followingequation.

Y = �(p, v, �, Lf; Z

h) + w

0 L

0(3)

Where Y is household income, p is price of farm output, v prices of variable inputs, � fixed farminputs such as land and w

o is wage rate in the non-farm employment. The other variables are as

defined before.

According to Lanjouw and Lanjouw (2001), a change in the wage rate of an individualfamily member will affect both the individual’s own labour supply decision and labour supplydecision of other family members, through different and conceptually distinct channels. Forinstance, assume that a family is made of husband and wife only. Initially, the husband worksfull-time in the non-farm labour market whilst the wife works in the home. Also, assume that,due to economic growth, there is an increase in the non-farm market wage that the wife canearn if she decides to participate in the non-farm labour market. This would have implications


on the family’s labour allocation decisions. Firstly, the rise in wages increases the opportunitycost of staying at home. This will motivate her to participate in the non-farm labour market.Secondly, when the wife decides to participate in the non-farm labour market, her income willincrease and this also makes the income available to her husband to increase. Thus, the husbandmay choose to reduce his hours of work and increase leisure, leading to a negative incomeeffect on his labour supply. Moreover, just like the general production case, the amount ofhousehold labour time allocated to farming (L

f) is determined not only by the household’s

assets endowments, but also by the production technology. The household assets’ endowmentswhich influence farm production include land, number of livestock owned, ploughs and otherinputs used.

The household’s problem is to maximize utility represented by equation 1 subject to thetotal labour time available (L

t), defined by equation 2, and total household income (Y), defined

by equation 3, i.e.,

Max U = U(Yf, Y

m, L

t; Z

h) st: L

t = L

0 + L

f + L

S and Y = �(p, v, �, L

f; Z

h) + w

0L

0(4)

of which the Langrangian function for this optimization problem is stated as;

� = U(Yf, Y

m, L

t; Z

h) + �

1(L

t – (L

0 + L

f + L

S)) + �

2 (Y – �(p, v, �, L

f; Z

h)– w

0L

0) (5)

Where �1 and �

2 denote the langrangian multipliers, which are associated with constraints

(capacity variables) that influence allocation of labour to non-farm employment activities.

To derive the amount of labour time allocated to non-farm and farm employment activities,we differentiate equation 5 with respect to the household’s total income (Y), which yields thefollowing optimality conditions:

��

� � �/

/f

f

U L

U Y L (6)

��

� � � �0 2

0

/

/ /

U Lw

U Y U Y(7)

Equation 6 indicates that if a household member participates in the farm employmentactivity, the marginal rate of substitution of family labour allocated to the farm employmentactivity for money income should equal the shadow price of labour. Likewise, equation 7indicates that if the household member decides to participate in the non-farm employmentactivity, the marginal rate of substitution of the non-farm employment activity for incomeshould equal the non-farm market wage rate. In short, the individual household member’sdecision of whether to participate in the non-farm employment activity depends on a comparisonof the market wage rate (w

o) and the individual’s reservation wage (w

r), such that:

L0 = 0, if w

r � w

0(8)

Equation 8 states that a household member will not allocate labour time to any non-farmemployment activity whose wage rate is less-than-or-equal to his/her reservation wage. Thereservation wage is assumed to be influenced by exogenous variables, such as input and outputprices, fixed farm input and household characteristics. Variables that raise the reservation


wage reduce the probability for the decision to search for non-farm employment and those thatreduce the reservation wage increase the probability of participation.

EMPIRICAL EVIDENCE

A large volume of recent empirical literature has been directed at analysing the determinants ofRNFE activities, especially in developing countries. These literatures indicate that diverse factorsinfluence household’s participation in non-farm employment activities. Moreover, the factorsdiffer from country to country and according to the modelling approaches used. Some of suchstudies include Taylor and Yunez-naude (2001), Sanchez (2005) and Zahonogo (2011) whofound education of the household head to be the most important determinant of decision toparticipate in RNFE activity in Mexico, Bolivia and Burkina Faso, respectively. However, Bayene(2008) found education of household to have no influence on the decision to participate inRNFE in Ethiopia. Instead age of the household head was the most influential variable. Ingeneral studies found gender, ownership of assets, access to electricity and household size topositively influence decision to participate in RNFE activities (see, e.g., Ackah, 2011; Timothy,2011).

EMPIRICAL METHODOLOGY

In order to investigate the main determinants of household decisions to participate in RNFEactivities, this paper follows most of the recent literature that applies the McFadden’s (1974)multinomial logit model to the household problem of incentives and capacity. The multinomiallogit model is inspired by the utility maximization hypothesis of decision makers (Haussmanand McFadden, 1984). It is suitable for modelling choice between two or more participationalternatives.

Along the lines of joint utility maximization behaviour of farm households, it is assumedthat each household chooses a type of non-farm employment which maximizes its utility. Thehousehold’s utility cannot be observed. In this case we use probabilities of household’s choiceof any particular non-farm employment activity to predict the probability of household’s decisionto participate in the types of non-farm activities (Pryanishnikov and Zigova, 2003). Thus,following Zahonogo (2011) the relations between our objectives, theory and data allow us tospecify the following multinomial logit model:

��

exp( )

exp( )i j

ijj i j

xP

x (9)

where i is the index of rural households, j is the index of rural non-farm employment (RNFE)activities. P

ij is the probability that household i decide to participate in the non-farm activity j, x

ij

is a vector of the factors that are related to household characteristics and the socio-economicvariables (capacity variables) which are expected to influence household participation decisionsin the RNFE, �

j is the vector of all regression coefficients in the vector j and x’

i�

j denotes a

certain level of utility a household is expected to drive from participating in specific RNFEactivity.


In equation 9, the empirical problem can be described as determining the probability ofhousehold i deciding to participate in the non-farm employment activity j. It is assumed thathouseholds take into consideration that their household characteristics to represent their capacityto participate in the different non-farm employment activities. That is, the household maydecide to participate in a combination of non-farm employment activities depending on itscapacity. The different combinations of non-farm employment activities that households maydecide to participate in could be explained well in a multinomial model because we consider aset of mutually exclusive and exhaustive choices of non-farm activities by the households(Pryanishnikov and Zigova, 2003).

Given the focus on the factors that influence the decision to participate in the non-farmemployment activities, it is convenient to further present the estimator for explanatory variables(capacity variables) for the study. In this study, the effects of the explanatory variables (x

i’s)

on the dependent variable will be estimated using maximum likelihood estimator. The maximumlikelihood estimator is a pre-requisite for estimating random effects (Myung, 2003). Themaximum-likelihood log function of vector of parameter values � is given by:

� � � ��

� ��

N N

1 1 1 1j

exp xln L ln

exp x

J Ji i

ij iji j i ji j

y y D (10)

where D is logarithm of the probability that household i participate in non-farm employment j.

That is, D equals �

��

'

'

exp( )ln ln

exp( )i i

iji jj

xP

x , y is the latent dependent variable, thus, yij denotes

choice of a specific non-farm activity category j by household i. yij equals one if a specific non-

farm activity category is chosen, ijy equals zero for all non-chosen non-farm categories. Equation

10 shows that each non-farm participation decision is explained by a probability (Myung, 2003).The probability is given by theterm D. We maximise the likelihood function in order to getvalue of parameters that maximise the likelihood of household i�S decision to participate inparticular type of non-farm employment activity j.

Multinomial Logit Models often exhibit the problem of Independence of IrrelevantAlternatives (IIA). The IIA problem arises from the assumption of the Multinomial logit modelthat the probability of choosing between two alternatives remains unaffected when the 3rd,4th..., jth choice alternatives are involved. For example, with the multinomial logit model it isassumed that the relative probabilities of participating in non-farm wage employment andfarm employment do not change if non-farm self-employment opportunities are made availableto rural dwellers. This assumption is not always desirable. It may in some situations imposetoo much constraint on the relative preferences between the different employment alternatives(Myung, 2003). Hence, in this study, from the results of the estimated probabilities of choicebetween alternative non-farm employment activities, the test for the IIA problem was carriedout. The study used the Hausman test statistic to test for the IIA problem.


VARIABLES DEFINITIONS AND THEIR EXPECTED SIGNS

Dependent Variable: Participate or Not Participate

Participate or not participate in the on-farm and non-farm employment an activity is used inthis study as the dependent variable. The dependent variable has four categories as dummieswhich indicate the possibility of household’s decision to participate. These are: (i) non-farmwage employment (ii) non-farm self-employment with hired employees (iii) non-farm self-employment without hired employees and, (v) farm employment. All of these categoriesmeasure the probability of a household participating in a rural employment activity. Thisvariable takes the value 1 if the household participates in the non-farm employment activityand 0 otherwise.

Independent Variables

This study considers a number of possible factors that could influence the decision to participatein a given non-farm employment activity. These were chosen based on empirical findings ofother studies.

Age and age-squared of household head: These are continuous variables measuringnumber of years of household head. They are used to capture the non-linear life cycle effectsof one’s number of years on the decision to participate in the non-farm employment activity(Huffman and Lange, 1989). The age variable is used to reflect the effects of early age whilstage-squared is used to capture the effects of elderly age on participation. Following the life-cycle effects of age on labour market participation, a strong positive relationship is expectedbetween age and probability for the decision to participate in a given non-farm employmentactivity. That is, at a younger age the probability of working in a given non-farm employmentactivity will increase. At older ages the overall labour hours supplied to non-farm employmentwill diminish as stock of health declines and the demand for leisure increases. As a result, aninverted U-shaped curve is expected for the effect of age on the decision to participate in thenon-farm employment.

Gender of household head: This dummy variable represents the gender segregationbetween male household heads and female ones. The sign of this variable is expected to bepositive, which will indicate that men are more likely to participate in non-farm employment.This will reflect the fact that men in developing economies have more time commitment to jobparticipation in the non-farm labour market and have less time commitment to the householdactivities as compared to women.

Marital status: This represents household head’s status of being married. For this study,marital status is used to capture the effects of marriage on participation decisions in the RNFEactivities. It is defined as a dummy variable that takes value 1 if household head is married,and 0 otherwise. It is anticipated that the effect of being married on the decision to participateon non-farm employment is negative. Specifically, married household heads are associatedwith greater access to resources such as land and credit compared to those staying together,widowed, separated and those who were never married. Married women get resource support


from their husbands and this enable them prefer leisure and self-income generating activitiesrather than participating more in job search.

Education level: This represents human capital endowment.It isdefined with five levels:(i) no education (ii) primary (iii) secondary (iv) tertiary (v) non-formal education. The levelsare used as dummies such that if the level is the highest attained by household head then ittakes a value of 1, and zero otherwise. Following the human-capital theory, a strong positiverelationship is expected between education and the decision to participate in non-farmemployment activities. Education changes the tastes and attitudes of individuals and householdswith respect to desire to work (Davis, 2003). It is an investment for higher earnings in thesense that one has to suffer large direct costs (tuition) and opportunity cost (forgone earningsfrom work). The level of education attained reflects one’s potential labour market wage.

Household size: This is the number of individuals living in a household. This variable isused to capture whether household structure has an important role on the household’s decisionto participate in the non-farm employment activities. This is because the household size reflectsthe size of income and the reservation wage of a household (Lanjouw and Lanjouw, 2001). Onbasis of the consumption theory more people living in a household indicates a greater burdenon actively working individuals (Huffman and Lange, 1989). Members of the household whoare not working are supported economically by the household members who are working. Bycontrast, small households are likely to have few dependents, thus they are less likely toparticipate in the non-farm employment.It is therefore, expected that staying in large familiesincrease the likelihood of participation in the non-farm employment, so as to meet the familyneeds.

DATA

The data for this study is obtained from the 2005/06 Labour Force Survey (LFS). The surveywas conducted by the Central Statistics Office in Botswana in 2005. The 2005/06 LFS providesthe latest data on employment and characteristics of the workforce. It is for this reason that thisdata is used in this study.

The 2005/06 LFS covered three geographical zones; rural districts, urban villages andcities and towns. A two-stage stratified sampling was adopted. At first-stage, EnumerationAreas (EAs) (a cluster of housing units) were selected as primary sampling units. Selection ofEAs was done with a probability that is proportional to the number of households. Altogether,488 EAs were selected. Then, a list of households was created. In the second stage, householdswere systematically selected from the lists of households in the selected EAs. Overall, 9760households were selected. Information was collected from the various members of the 9760households by means of questionnaires. Data on household head variables included demographiccharacteristics of both rural and urban household members. These characteristics entail age,gender, state, education level, size of household, employment (agriculture and non-agriculture)by location, and the household’s non-labour income sources (CSO, 2008). Out of the 9760households, 4351 or 45 per cent were in the rural areas. Furthermore, 3887 of rural householdheads were in the labour force.


EMPIRICAL RESULTS

5 Descriptive Statistics

The dependent variable has four categories. These are: (i) non-farm wage employment (ii) non-farm self-employment with hired employees (iii) non-farm self-employment without hiredemployees and, (iv) farm employment. The LFS defined non-farm wage employment as thosewho performed some work for a wage or salary in non-agricultural activity. Persons involved inthe non-farm self-employment were identified as those who performed some work for paymentor income in-kind but not on own lands or cattle-posts. They include small and largeentrepreneurs. This category is split into: (i) self-employment with hired employees and (i)self-employment without hired employees. Lastly, persons involved in farm employment arethose who worked either in agriculture on their own lands or cattle-post or fishing as either self-employed persons or unpaid family helpers (CSO, 2008). Table 1 shows the types of employmentactivities in the rural areas that households participate in.

Table 1Types of Rural Nonfarm Employment

Participation Frequency Percentage

Non-farm wage employment 2,321 59.71

Non-farm self -employment with hired employees 128 3.29

Non-farm self-employment with no hired employees 338 8.70

Farm employment 1,100 28.30

Total 3,887 100

Source: estimations based on 2005/06 LFS data

Table 1 shows that approximately 28.3 per cent of the rural labour-force in the sampleparticipated in agricultural employment. The rest of the labour-force in rural areas was engagedin non-farm employment activities, which suggests a transition out of agriculture. Non-farmwage employment alone represents more than half of employment of the labour-force in ruralareas. This indicates that non-farm wage employment is the major source of employment forthe majority of the rural people.

To explain decision/choice of participation on the four categories of RNFE, sixcharacteristics of the household head (explanatory variables) were used. These are age, age-squared, household size, and three dummy variables, which are gender, marital status andeducation level. For the gender dummy, 1 was reserved for male headed and 0 for femaleheaded household. There are six 6 categories for the marital dummy. These are never married,married, separated, living together, divorced and widowed. The 6 dummies for marital statuswere all allocated 1 or 0 to represent the status of marriage. Likewise, education level wasdefined as a dummy with categories: no education, primary, secondary, tertiary and non-formal,all allocated 1 or 0 otherwise to represent the five levels of education The summary statisticsare presented in Table 5.


Table 2Summary Statistics of Household Characteristics

Variable Obs. Mean Std. dev. Min. Max.

Age 3887 43.02 15.28 12 64

Age-squared 3887 2083.97 1483.60 144 9604

Gender 3887 1.42 0.49 1 2

Marital status 3887 2.02 1.39 1 6

Education level 3887 3.21 4.52 2 4

Household size 3887 10.48 5.82 0 51

Source: Descriptive statistics based on 2005/06 LFS data

From table 2, it can be observed that average age of household heads in the sample was 43years. Age of household head is important in this study because, age in some instances couldbe an entry determination criterion for some employment and livelihood opportunities (Reardon,2006). The statistics also show that, households in rural Botswana are large, averagingapproximately 10 people per household. The maximum number of people in a household was51 people. The household size is important to this study because it could be helpful indetermining if sharing of household sustenance leads to less involvement in various forms ofnon-farm employment. Another important characteristic of the households is the gender ofhousehold head. In traditional family setting, the household head, normally the man/father isthe key decision maker and has all the authority, he also provides for the family. In the sampleconsidered the majority (58.5 per cent) of the households in rural areas were headed by males.

It is also essential to look at the marital status of the household heads. Being marriedgenerally can be a sign that somebody is the bread winner. As such married household headswould prefer to go on working off-farm for as long as the income they earn from the RNFE ishigher than they obtain on-farm (Reardon, 2006). In the sample 46 per cent of the householdheads were found to be never married, 30.6 per cent were married and 14.0 per cent were notmarried but living together. Moreover, 9.1 per cent of the rural household heads were singleby divorce, widowhood and separation.

Lastly, the summary statistics of the highest education levels attained by the householdheads are presented. Table 3 shows that, the most common level of education attained by thehousehold heads was secondary education (39.7 per cent). The proportions of household headsthat have attained primary and tertiary education are also considerably larger (30.98 per centand 27.6 per cent respectively). It is interesting to note from these results that all the householdheads have gone to school. The importance of education in this study lies in the need toinvestigate if exposure to education has any impact in influencing participation in varioustypes of non-farm employment activities in order to sustain the household.

ECONOMETRICS RESULTS

Table 4 presents results of probability estimates and the marginal effects of households’participation decisions in RNFEfrom the multinomial logit model. STATA 11 econometricsoftware was used to estimate the model. Four equations were estimated for rural households’


participation decisions in the non-farm employment activities. These are: non-farm wageemployment, non-farm self-employment with hired employees, non-farm self-employmentwithout hired employees and farm employment. The results of the model show the marginaleffects on participation given a unit change in the explanatory variables and associatedprobabilities that households will choose to participate in the given employment activities.

As shown by the chi-square statistic, the correlation between errors was significant at 1%level, suggesting that the decisions of the household members are not independent. The pseudoR-squared which measures the goodness of fit of the model was 0.1842, which means that that18.4 per cent of the variables are explained. This result indicates that the multinomial logitmodel is good in explaining the factors influencing rural household participation decisions innon-farm employment for the case of Botswana. The results also indicate that all the estimatesare significantly different from zero with a chi-square (24) of 1417.30, and a p-value equal to0.0000.

A common property associated with the use of the multinomial logit model is theIndependence of Irrelevant Alternatives (IIA) assumption. To test for this property, Hausmantests were computed automatically by STATA. Computation of the Hausman test was basedon the null hypothesis that the types of rural non-farm employment activities are not independent.The null hypothesis was rejected, suggesting that, the given types of RNFE are independenthence; the multinomial logit model was suitable for the data set used in analysis.

Among the dependent variables, non-farm wage employment was set as the base outcome,in the first stage (equation 1). The results show that age, age-squared, gender, secondaryeducation level and marital status significantly influence the decision to participate in the non-farm wage employment. Age showed that at an early age the chances of participation in thenon-farm wage employment increases by 2.6 per cent. The coefficient for age is positive andsignificant at 1 per cent level. It shows that participation in the non-farm wage employment islikely to be high early in life. This is because at young ages one would prefer the non-agriculturalactivities due to opportunities that are available outside agriculture. The results show that age-squared decrease chances of participation in the non-farm wage employment by 0.04 per cent.These two results indicate that participation in non-farm wage employment increases early inlife as experience increases and later decreases as the individual gets older. The effect of ageon participation in the non-farm wage-employment is illustrated by the non-linear, hump-

Table 3Education Levels of Household Heads

Education level Frequency Percentage

Primary 1 204 30.98

Secondary 1 542 39.67Tertiary 1 073 27.6

Non-formal 68 1.76

Total 3887 100

Source: estimations based on 2005/06 LFS data

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shaped curve presented in Figure 2 below. This curve is well known in the empirical humancapital literature as the life-cycle curve for the effect of age on employment.

Figure 2: Effect of Additional Years of Age (Age-squared) on Participation inNon-farm Wage Employment

Source: sensitivity results of age based on 2005/06 LFS

Figure 2 indicates that younger household heads are more likely to participate in the non-farm wage employment. Participation rises on average until the age of approximately 43 yearsand falls thereafter. This shows that the minimum age before the household heads begin toreduce their choice for participation in the non-farm wage employment is 43 years.

Another important result in Table 4 is that the probability of participation in non-farmwage employment increases with secondary education but declines with tertiary education.Results of the marginal effects indicate that the probability of participation in non-farm wageemployment increased by 19.7 for those with secondary education, while it reduces by 14.4per cent for those with tertiary education. The coefficients for secondary and tertiary educationlevels were significant at 1 per cent and 5 per cent respectively. These results suggest thathousehold heads with secondary education are more likely to choose nonfarm wage employmentactivities, while those with tertiary education are less likely to take part in the non-farm wageemployment activities. The positive relation between non-farm wage employment and secondaryeducation could be because of the availability of opportunities for wage paying non-farmemployment which require a minimum of secondary education in the rural areas. Likewise, the


possible justification for a negative relationship between non-farm wage employment andtertiary education could be the nature of non-farm wage-employment activities in the ruralareas.

The probability of participation in the non-farm wage employment decreases significantlywith marital status (being married). The results of marginal effects show that the probability ofparticipation in the nonfarm wage employment falls by 10.9 per cent if the household head ismarried and this result is significant at 1 per cent level. It indicates that married householdheads are less likely to choose the wage-paying non-farm employment. The possible justificationfor this could be that married household heads are normally associated with higher assetendowment. Therefore, they would mostly prefer to spare their own time on leisure than paidwork. The results also indicated that, the probability of participation in the non-farm wage-employment decreases with gender (male). Households headed by males are less likely tochoose the non-farm wage employment. The effect of gender of household head (male) on thenon-farm wage employment was statistically significant at 10 per cent level. The fact thatparticipation in non-farm wage employment decreases with gender (male head of household)was unexpected. However, the possible justification for this could be the nature of the non-farm wage employment activities in the rural areas. The other variables, namely, primaryeducation and household size did not show any significant influence on the households’ decisionsto participate in the non-farm wage employment.

The results from the second stage (equation 2) indicate that only three of all explanatoryvariables affected the probability of participation in the non-farm self-employment with hiredemployees. Variables that significantly influenced the decision to participate in the non-farmself-employment with employees include age and marital status. The coefficient for age on thedecision to participate in non-farm self-employment with hired employees had a positive sign.This result indicates that young heads of households are likely to participate in the non-farmself-employment with hired employees (own enterprises). However, the coefficient for thisvariable was only significant at 10 per cent. This is an indication that the non-farm self-employment with hired employees (own enterprises) like small general dealers and tuck-shopsin the rural areas are mostly initiated by young household heads in the rural areas. A unitchange in age of household head will have the impact of raising the probability of participationin non-farm self-employment with employees by 5.9 per cent.

Similarly, marital status (married dummy) increases the probability for the decision toparticipate in non-farm self-employment with hired employees. The coefficient for maritalstatus on the decision to participate in non-farm self-employment with hired employees wassignificant at 1 per cent level. The results of marginal effects showed that a change of statusfrom being never married, living together, separated, divorced and widowed to being marriedwould increase the probability to participate in the non-farm self-employment with hiredemployees by 2.3 per cent.This may be because married women are normally associated withhigher asset endowment. This enables them to invest into self-employment income-generatingactivities (own enterprises) like small general dealers and tuck-shops in the rural areas. Theyhire labour in their businesses as they are at the same time expected to perform householdchores (Beyene, 2008).


On the contrary, male household heads have less probability of participating in the non-farm self-employment with hired employees. The results of the marginal effects indicated aunit change from being headed by a female to being headed by a male reduces the probabilityof the decision to participate in non-farm self-employment with hired employees by 2.5 percent and the coefficient is statistically significant at 1 per cent level. This could mean that thenon-farm self-employment with hired employees (own enterprises) like small general dealersand tuck-shops are mostly initiated by women, therefore, households headed by women aremore likely to rely in this activities.

Consistent with prior expectations, the probability for the decision to participate in non-farm self-employment with hired employees is negatively related to age-squared and householdsize. Likewise, increasing the level of education of household head from no education to primary,increases the probability to choose non-farm self-employment with hired employees by 0.9per cent. However, this result is not statistically significant. Unexpectedly, tertiary educationalso shows a negative relationship on the decision to participate in non-farm self-employmentwith hired employees, although its coefficient is not significant.

The results for the third stage (equation 3) of household’s participation decisions in non-farm self-employment without hired employees indicate that most of the variables do not affectthe probability of participation, except gender (male). The results showed that gender (beingmale) increases chances for decision to participate in the non-farm self-employment withouthired employees. In addition, results of the marginal effects showed that a unit change frombeing headed by a female to being headed by male raises the probability of participation innon-farm self-employment without employees hired by 11.1 per cent and the coefficient issignificant at 1 per cent level. This result is in line with the argument that small businesses, forexample trading, own selling of craftwork, selling handicrafts and own operation of taxi/transportservices are generally initiated by males. The results for household size, marital status andprimary and secondary education also carried the expected signs, but were not significant.

In the last stage (equation 4) the effects of the explanatory variables on participationdecisions on the farm employment counterpart were determined. The results indicated thatmost of the explanatory variables affected the probability of the decision to participate in farmemployment, except household size and primary education. The variables that positively affectedthe decision to participate in farm employment include age-squared, gender, marital status andtertiary education. The results showed that age-squared has positive and significant coefficientsfor farm employment. The results of the marginal effects indicated that a unit change frombeing headed by a young household head to being headed by an elderly household head raisesthe chances of participation in farm employment by 0.04 per cent. This result is as it wasexpected. It indicates that at older ages the probability of the decision to participate in farmemployment increases. This is because at older ages individuals are normally associated withdiminishing stock of health. This reduces the chances of participation in the non-farm labourmarket and a need for leisure. Hence, they decide to move-out of the non-farm labour marketinto farming activities.

Similarly, the results showed that gender (being male) increase chances for the decision topartake farm employment by 6.9 per cent and the coefficient is significant at 1 per cent level.


The possible justification for this could be that farming is generally carried out, or at-leastinitiated, by men. Households headed by men are likely to rely of this activities compared tothose headed by females. In contrast to prior expectations, tertiary education increases theprobability of decision to choose farm employment. For instance, a unit increase of the educationlevel of the household head from secondary to tertiary increased the probability of participationin farm employment by 15.3 per cent. A possible justification for this could be the nature ofnon-farm employment activities in the country. These results reflect that the returns in thefarm employment in the country are perhaps higher than the returns in rural non-farmemployment activities. The agriculture returns could be influenced by the agriculturaldevelopment programmes such as the Young Farmer’s Fund and the Citizen EntrepreneurshipDevelopment Agency (CEDA). This programmes support youth with funds to invest intocommercial agricultural activities.

Age and secondary education are negatively related to farm employment, as expected.Increasing the age of household head by one unit reduces the probability of participation infarm employment by 2.9 per cent whereas increasing secondary education of household headby one unit reduces the probability of participation in farm employment by 18.4 per cent. Thefact that participation in farm employment is lower at early age is in line with the expectationthat at young age no one would like to work on farm. This is because young educated peoplehave more access to the non-farm labour market because they experience fewer health problems.This relationship is shown by the U-shaped curve in Figure 3.

Figure 3: Effects of Additional Years of Age (Age-squared) on Participation in Farm Employment

Source: sensitivity results of age based on 2005/06 LFS


Figure 3 indicates that household heads of elderly age are more likely to choose farmemployment. In other words, the probability of the decision to undertake farm work is lower atyoung age when the non-farm employment is the dominant form of employment. During theolder ages, household heads shifts to farm employment. The minimum age before the householdheads begin to increase their likelihood for participation in the farm employment is 451 years.This result is similar to that of Timothy (2011) who found that in rural Nigeria householdheads above 45 years were more likely to be found in farm employment than the non-farmemployment. The results for household size and primary education for participation in farmemployment had the expected signs but were not significant.

CONCLUSION

The paper used a multinomial logit model to investigate the factors that influence ruralhouseholds’ decision to undertake nonfarm employment activities. The results show that severalhousehold characteristics influence these decisions to varying degrees. Gender and educationof the household head have emerged has emerged as the most influential factors. First, male-headed households are less likely to take nonfarm self-employment as compared to female-headed households. This is also true for nonfarm self-employment with hired workers. However,male-headed households are more likely to participate in self-employment where the owner isthe only employee. These results could be a reflection of the nature of the activities involved.Male-headed households are also more likely to participate in farm employment. The policyimplication of this is that policies aimed at increasing employment in the rural nonfarmemployment activities should target more women than men. Second, tertiary education showssimilar effects as gender, although the coefficients are significant only for nonfarm wageemployment and farm employment. In contrast, secondary education positively influencesnonfarm employment and negatively influences participation in farm employment. Thereforepolicy intervention aimed at encouraging movement from farm to nonfarm employment shouldencourage completion of secondary school education among household heads in the rural areas.

Note

1. The number of years (45years) is calculated by obtaining the square-root of age-squared (2025)plotted onhorizontal axis of figure 3 at the lowest point of the curve.

References

Ackah, C. (2011), “Non-farm Employment and Incomes in Rural Ghana”. Journal of International Development.DOI: 10.10002/jid.1896: pp 1-15.

Adewumni, I. O., T. T. Awoyemi, A. O. Falusi and B. T. Omonona. “Non-farm Income Diversification andPoverty Reduction among Farm Households in South West Nigeria”. European Journal of Social Sciences,21(1): pp. 1-4.

Barrett, C. B., M. Bezuneh, D. C. Clay and T. Reardon. (2000), “Heterogeneous Constraints, Incentives andIncome Diversification Strategies in Rural Africa”. WARREN-HALL Working paper No. 25. Departmentof Applied Economics and Management. Cornel University, New York, USA.

Barrett, C. B., Readon, T., & Webb, P. (2001), “Non-farm Income Diversification and Household LivelihoodStrategies in Rural Africa: Concepts, Dynamics, and Policy Implications”. Food Policy, 26: 315-31.


BIDPA. (1997), Study of Poverty and Poverty Reduction in Botswana.

Beyene, A. D. (2008), “Determinants of Off-farm Participation Decisions of Farm Households in Ethiopia”.Agrekon, 47(1): pp 1-22.

Central Statistics Office, CSO. (2008), Labour Force Survey report-2005/06: Government Printer: Gaborone,Republic of Botswana.

Central Statistics Office, CSO. (2011), Preliminary Results of the Botswana Core-welfare Indicators (poverty)Survey-2009/10: Government Printer: Gaborone, Republic of Botswana.

Corral, L., T. Reardon (2001), “Rural Non-farm Incomes in Nicaragua.” World Development, 29(3): pp. 427-442.

Davies, Jr. (2003), “The Rural Non-farm Economy, Livelihoods and their Diversification: Issues and Options”.NRI Report No: 2753. Natural Resource Institute. Department of International Development, World Bank.

Davies Jr. (2006), “Rural Non-farm Livelihoods in Transition Economies: Emerging Issues and Policies”.Journal of Agricultural and Development Economies, 3(2): pp. 180-224.

Dercon, S. (1998), Insurance Against Poverty, Oxford University Press, New York.

Haussman, J. and D. M. McFadden. (1984), “Specification Tests for Multinomial Logit Model”. Econometrica,52(5): pp. 1219-1240.

Huffman, W. E. and M. D. Lange. (1989), “Off-farm Work Decisions of Husbands and Wives: Joint DecisionMaking”. The Review of Economics and Statistics, 71 (3): pp. 471-480.

Isgut, A. E. (2004), Non-farm Income and Employment in Rural Honduras: Assessing the Role of LocationalFactors. Journal of Development Studies, 40(3): pp. 59-86.

Kijima, Y., T. Matsumoto and T. Yamano. (2006), Non-farm Employment, Agricultural Shocks and PovertyDynamics: Evidence from Rural Uganda. Agricultural Economics. 35: pp. 459-467.

Kimhi, A. (2004), “Family Composition and Off-farm Participation Decisions in Israeli Farm Households”.American Journal of Agricultural Economics, 86 (2): pp. 502-512.

Lanjouw, J. O., and P. Lanjouw. (2001), “The Rural Non-farm Sector: Issues and Evidence from DevelopingCountries”. Agricultural Economics, 26: pp. 1-23.

Maundeni, T., and G. Mookodi. (2004), Critical Social Challenges in Contemporary Botswana. BotswanaJournal of African Studies 18(1): pp. 1-6.

Ministry of Finance and Development Planning MFDP. (2012), Budget Speech, Government Printer: Gaborone,Republic of Botswana.

Ministry of Finance and Development Planning MFDP. (2010), National Development Plan 10, GovernmentPrinter: Gaborone, Republic of Botswana.

Ministry of Finance and Development Planning MFDP. (2002), Revised National Policy for Rural Development,Government Paper No: 3 of 2002, Government Printer: Gaborone, Republic of Botswana.

Myung, I. J. (2001), “Tutorial on Maximum Likelihood Estimate”. Journal of Mathematical Psychology, 47:pp. 90-100.

Pindyck, R. S. and D. L. Rubinfeld. (1998), Econometric Models and Economic Forecasts. 4th Edition, NewYork: McGraw-Hill.

Pryanishnikov, I. and K. Zigova. (2003), “Multinomial Logit Models for the Australian Labour Market”.Australian Journal of Statistics, 32(4): pp. 267-282.

Reardon, T. (2006), “Rural Non-farm Incomes in Developing Countries”. Paper Prepared for the FinancialAssistance Policy, FAO. Washington, D.C, 20433, USA.


Stat, D. (2001), “The Rise and Fall of the Rural Non-farm Economy: Poverty Impacts and Policy Options”.Development Policy Review, 19(4): pp. 491-505.

Statistics Botswana. (2010), “Formal Sector Employment: Stats Brief”. Report No: 2011/13. Government Printer:Gaborone, Republic of Botswana.

Statistics Botswana. (2011), “ Preliminary Results of the Core Welfare Indicators (Poverty) Survey-2009/10”.Report No: 2011/15. Government Printer: Gaborone, Republic of Botswana.

Timothy, A. T. (2011), Rural Non-farm Incomes and Poverty Reduction in Nigeria. AERC Research paper no.224. African Economic Research Consortium, Nairobi, Kenya.

UNDP-United Nations Development Programme. (1998), Keeping our Commitments to Development. AnnualReport.

Wikan, G. (2003), Rural Development in Botswana. The Notwegian Research Council and Hedmark College.Report No: 5

Yúnez-Naude, A., and E. Taylor. (2001), “The Determinants of Non-farm Activities and Incomes of RuralHouseholds in Mexico With Emphasis on Education”. World Development, 29(3): pp. 561-572.

Zahonogo, P. (2011), “Determinants of Non-farm Activities Participation Decisions of Farm Households inBurkina Faso”. Journal of Development and Agricultural Economics, 3(4): pp. 174-182.


Appendix

Definitions and descriptions of Variables

Table A1Variable Definitions

Variable Definition

Dependent Variables

Participation This is a latent dependent variable (it cannot be observed) representingwhether the rural household is involved in the given type of NFE ornot. That is, it can only be observed if it takes the value 1, meaningthat the rural household is involved in the given NFE, and the valuezero otherwise.

Non-farm wage employment It is a type of participation category (dependent variable or outcome).Participation in this type of non-farm employment holds if householdhead is engaged in a paid job by cash, other than a job in cropproduction, livestock production, fishing, forestry or hunting.

Non-farm self-employment with employees Head of household operates micro, small or medium enterprises apartfrom agricultural business or profession with a number of hiredworkers, employed as either temporary or permanent employees.

Non-farm self-employment without Head of household operates a micro-enterprise on his/her own (doeshired employees. not hire any workers).

Independent variables

Age Age of head of household in years; continuous variable

Age-squared Age of head of household in years squared; continuous variablerepresenting the life-cycle effects of age on employment.

Gender Dummy variable for gender of household head. If the household headis male the dummy takes 1, and 0 if female.

Education level Highest level of education attained by head household. It is made upof 5 categories as dummies, classified according to whether householdhead attained (i) pre-school (ii) primary (iii) secondary (iv) tertiary,or (v) tertiary education, or not. It takes values:

Pre-school 1=yes, 0=otherwise

Primary school 1=yes, 0=otherwise

Secondary school 1=yes , 0=otherwise

tertiary school 1=yes, 0=otherwise

Non-formal education 1=yes, 0=otherwise

Household size Number of household members; continuous variable

Marital status Indicates state of household head being married or not. It is a dummyvariable. If the household head is married the dummy takes 1, and 0otherwise.

Source: Author’s modified names and definitions of selected variables, based on the 2005/06 Labour Force Survey(LFS) report, CSO (2008)

determinants of household … of household participation in rural nonfarm employment activities... 3...

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