Remittances from Internal Migration and Poverty in Botswana

doi:10.4236/sm.2011.13016

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Sociology Mind

2011. Vol.1, No.3, 130-137

Remittances from Internal Migration and Poverty in Botswana*

Eugene K. Campbell1, Ngianga-Bakwin Kandala2

1Department of Population Studies, University of Botswana, Botswana;

2University of Warwick, Warwick Medical School, Coventry, England.

Email: [email protected]

Received April 19th, 2011; revised May 23rd, 2011; accepted June 24th, 2011.

This study seeks to address the question of the existence of a relationship between remittances from internal la-

bour migrants and poverty. Data was obtained from a stratified random sample survey of internal migrants and

poverty in Botswana in 2004. A structured questionnaire was used to collect data from the migrants. A total of

1160 migrant households were enumerated. The lived poverty index method is used to estimate the level of pov-

erty. It takes the social aspect of development into consideration, thereby reducing the limitations of the eco-

nomic measurement of poverty. Logistic regression analysis is used to examine the remittance-poverty relation-

ship. Though female-headed households are transitorily poorer than their male counterparts, there is no signifi-

cant gender difference among the extremely poor. The results do not show conclusively that migrant remittances

have moderating effect on poverty in the country. Policy implications are addressed.

Keywords: Botswana, Migrant, Poverty, Remittance

Introduction

Though migrant remittances have increased considerably

since the 1980s, it is still not certain what impact remittances

from internal migration make on poverty mitigation in develop-

ing countries and especially in Sub Saharan Africa (SSA). This

is partly because, until recently, much of the funding for popula-

tion studies went to reproductive health while the effects of

globalization focused recent research on international migration.

It is general knowledge that pre-1990 remittances were generally

quite low in SSA (Caldwell, 1969; Adepoju, 1974; Lucas, 1982).

Rempel & Lobdell (1978) concluded that the monies migrants

remitted from urban areas in Kenya made no significant contri-

bution to economic development in rural areas. These findings

may have been due to the assumption that urban-rural remit-

tances were made primarily to develop rural communities, as

Gould (1995) also appears to have done. An additional contrib-

uting factor may be the relatively low real wages received in

urban areas plus the negative effects of structural adjustment

programmes on human development. As Taylor et al. (2005) and

several others have observed, not much has been done to inves-

tigate the relationship between migrants’ remittances and pov-

erty. Using data from 71 developing countries throughout the

world, Adams and Page (2005) found that international migra-

tion reduces poverty significantly. This position is supported by

studies in Latin America (Acosta et al., 2007) and Sub Saharan

Africa (Gupta et al., 2009). But while there seems to be certainty

at the international level, there is considerable uncertainty about

the effect of internal migration on poverty. Moreover there still

exist pessimists who fail to see the relationship between migra-

tion and poverty mitigation. Among the principle areas of con-

tention is the definition of poverty. However, personal incomes

have increased substantially in many African countries and

much more so in Botswana. Recent studies indicate increases in

urban-rural remittances in South Africa and North Africa (Posel

& Casale, 2003; Deshingkar & Grimm, 2004). This provides

incentives to investigate changes that might have occurred in the

ability of internal migrants to remit.

The current paper re-examines the hypothesis that remit-

tances assist families in Botswana to live above the poverty line.

In a recent book on the Zimbabwean exodus, the resonance of

remittance confirms the dominance of remittance as a determi-

nant of African emigration (Crush & Tevera, 2010). The com-

bination of poverty and altruism in rural African families has

helped to fuel migrant remittances in the continent. There is

considerable evidence to the effect that, in addition to the joy of

having children, traditional and early transitional families in

SSA and Asia value children particularly for the anticipated

financial rewards that the parents accrue from them. This is the

basis of the intergeneration wealth flows theory by John Cald-

well (1978) and it forms a crucial part of the social behaviour of

African families up till now. Though traditional African parents

have not invested nearly as much financially on their children

as Western parents have done since the early 20th century, it is

within the framework of this theory that strong social bonds

have developed between parent and child to the extent that the

latter willingly offer to assist their parents (poor and not-poor)

to obtain their basic and other needs.

According to Foster (1998), a poor person or family is one

whose resources are below the poverty threshold. Our under-

standing of poverty has greatly improved since the 1960s when

development was defined predominantly from an economic

perspective, with per capita income as the primary factor. Hu-

man development now includes social, political, psychological

and biological factors and is widely acknowledged as a variable

that is no longer restricted to individual and family income but

serves as an index of living standards in societies (Seers, 1972;

Deininger & Squire, 1996; Mattes, Bratton, & Davids, 2003).

*This study was funded by the Canadian International Development Agency

(CIDA) and the Department for International Development (DFID) through

the executing agency, Southern African Migration Project (SAMP). The

authors thank these organizations and peer reviewers for their useful contri-

bution to this paper.

E. K. CAMPBELL ET AL. 131

This modern interpretation of the meaning of development, and

hence poverty, has led to numerous examinations of the distinc-

tions between absolute and relative poverty and transitory and

extreme/chronic poverty. Transitory poverty refers to a tempo-

rary state of poverty (frequently associated with students in ter-

tiary institutions) from which individuals may emerge through

education, employment increased income and self-control

(Thaler & Shefrin, 1981; Jacoby, 1994; Foster, 1998; Rodgers &

Rodgers, 2006).

While several studies have attested to the importance of re-

mittances in supplementing household income, there is no evi-

dence yet that remittances have significant influence on poverty

in Botswana. Since Lucas (1982) observed this, with apparent

support from Izzard (1985), no study has been done about this

subject from primary sources.

Botswana Profile

Since attaining political independence in 1966, Botswana has

grown from being among the poorest countries in the world to

having one of the most developed economies and societies in

Africa. Notwithstanding the devastating effects of the current

global economic depression, the government produced an im-

pressive budget for the 2009/2010 financial year, thus indicat-

ing that the state of the national economy is strong (Gaolathe,

2009). However, two of the issues that concern the government

and public a great deal are unemployment (especially among

young professionals) and poverty. It was generally acknowl-

edged that rural households were generally poor with the ma-

jority being female-headed households (Akinsola & Popovich,

2002). The Botswana Institute for Development Policy Analy-

sis estimated that in 1993/94, 47% of Botswana citizens lived

below the poverty line and it was argued that this figure re-

flected substantial decline in poverty since 1985/86 (BIDPA,

1997). The government of Botswana also revealed considerable

decline in poverty between the 1980s and now. Reportedly,

“the poverty rates (fell) from 46.6 percent in 1985/86 to 30.2

percent in 2002/03” and it was assumed to be 23 percent five

years later (Gaolathe, 2007: p. 1). It is difficult to determine the

current official position on poverty because the budget speeches

of 2008 and 2009 exclude specific figures of the country’s pov-

erty level (Gaolathe, 2008, 2009).

Much of the poverty in Botswana is due to unemployment.

Though employment opportunities have increased over ten times

since 1966, the production of skilled nationals has surpassed

available jobs. Since 2000, the government has found it difficult

to increase employment nationally (Anonymous, 2005). In a

paper presented at the Review of the National Population Policy

Delivery in June 2007, the UNECA revealed that unemployment

began to rise in the 1990s especially among the youth. The

Household Income and Expenditure Survey in 2002/2003 also

revealed a national unemployment rate of 24% with school

leavers between 15 and 24 years old being the worst affected

(Botswana, 2007). The observations about unemployment in

Botswana and the higher poverty figures provided by other

sources make contentious the government’s poverty rate.

Meanwhile, since the 19th Century, poverty has been influential

in encouraging spouses, offspring and siblings to migrate in

anticipation of financial returns to families at home (Schapera,

1947). Though Lucas (1982) observed that remittances from

internal and international sources were quite low previously, the

situation has changed considerably for the better especially due

to accelerated increase in education and income since the 1990s.

Research Methodology

This paper is part of a major study of migration, remittances

and poverty which was undertaken by the Southern African

Migration Project (SAMP) in eight countries in the Southern

African Development Community (SADC) region between June

and August 2004. The objectives, organization, sample design

and questionnaires used in these surveys are described elsewhere

(see Campbell, 2010). The survey was done from a random

sample of internal migrants and the design used is appropriate to

minimize bias in the selection of migrant households.

Briefly, the data collection was national and was based on

stratified and cluster sampling. The sample was selected from

internal migrant households and was designed to be propor-

tional to population size in each stratum. The households were

drawn from all major land use areas of the country including

primary and secondary urban centers as well as major commu-

nal areas. Hence the major areas of origin and destination of

internal migrants were represented. A two-stage sample selec-

tion was done. The first stage was a random selection of district

and urban areas from 36 census districts. This determined the

number of primary sampling units (PSU) selected countrywide.

Where the population was very small no selection was done as

it was not cost effective. In the second stage, enumeration areas

(EA) were randomly selected from each of the rural and urban

areas. The sample EAs were selected to be proportionate to the

size of the PSU. A sample of 30 EAs was randomly selected

from each of the enumeration areas. From the 2001 national

census population, an initial sample of 1200 migrant house-

holds was selected. An example of the proportional selection is

that in Gaborone about 11.8% of the households were drawn as

this was the proportion of households in the city in relation to

the national population of households. In Gaborone and Fran-

cistown (the two cities in the country) the sample EAs were

further stratified into clusters of sub-EAs so that each cluster

had approximately 30 to 50 households. The sub-EAs were

numbered continuously in a more or less serpentine manner and

the sample clusters were selected randomly thereafter. All mi-

grant households in each sample cluster were enumerated. In

other urban and rural areas the sample households in each sam-

ple EA were selected as in simple random selection. With few

non-responses, 1160 households were enumerated.

Interviews were conducted by students of the University of

Botswana and supervision was rigorously done by the author

and a collaborating lecturer. Hence quality assurance was

maximized, ensuring the data were free of bias and errors. In

each sample household the respondent was the head of house-

hold. In few cases where the household head was unavailable

after several recalls, a responsible adult who was present was

interviewed. Individual data were provided by the household

member or (in the case of children and absentees) the house-

hold head or a knowledgeable person in the household.Quality

assurance was maximized to minimize bias and errors. SPSS

was used in all analyses. Logistic regression analysis was used

to determine the relationship between remittances and poverty.

Reflecting on Poverty Measurement

Though extreme and chronic poverty have similar connota-

E. K. CAMPBELL ET AL.

132

tions, the first may be measured from cross-sectional data while

chronic poverty is obtained from time series data. Considering

that both types may be conceptualized within the “culture of

poverty”, Oscar Lewis (1971: p. 21) observed that people in

these categories “have a strong feeling of marginality, of help-

lessness, of dependency (and) of not belonging. They are like

aliens in their own country, convinced that the existing institu-

tions do not serve their interests and needs. They also experi-

ence feelings of “powerlessness…inferiority (and) personal

unworthiness”. It is within the context of self-gratification that

Africans tended to value children to the extent that fertility in

SSA was persistently high until the 1990s; and the expectation

was that at least one of the offspring would attain the ability in

future to assist the parents financially and materially. Migration

and remittances have been established as effective mechanisms

with which these aspirations are being realized.

The unit of measurement (at individual or household level) is

also crucial to achieving plausible and comparable measures of

poverty. Much of the ensuing debates about the feminization of

poverty (i.e. dominance of females among the poor) derive

from the units of measurement that various researchers have

used. Thus while the results of several studies of households are

consistent with the theory of the feminization of poverty the

intra-household approach indicates otherwise. Hence increas-

ingly, questions are being raised about the reliability of the

feminization of household poverty in developing countries

(Buvini & Gupta, 1997; Quisumbing et al., 2001; Medeiros &

Costa, 2006). On the other hand, while there is relatively little

conflict over the definition of remittances, the primary unit of

investigation could significantly influence the results obtained.

For instance, expenditure and consumption of remittance are

best studied where the primary sample unit is the recipient

while knowledge about remitting attitudes and behaviour may

be best obtained from the remitter as the primary unit of inves-

tigation (Taylor, 1999).

A poverty index is developed here to assist investigation of

the remittance-poverty hypothesis. The household is defined

here as a group of people who share food from a common

source, sleep in the same house or compound at least 15 days in

the past year and share in a common resource pool. Unlike

the de jure definition of household which excludes migrants

from the home household (Sanni, 2006), the culture of Bat-

swana permits their inclusion in the household (Lucas, 1982;

Izzard, 1985). A migrant is a person who moved from one re-

gion (district) in Botswana to another to work or seek work.

Transitory poverty occurs within populations with annual in-

come that is above poverty level and extreme poverty refers to

sustained poverty throughout the 12 months preceding the date

of this study.

Poverty Measurement

The official (BIDPA and Government) estimates of absolute

poverty in Botswana were obtained by methods that are basi-

cally economic and therefore restrictive, given the poor treat-

ment of social factors which contribute significantly to human

development. Probably mindful of this, Mattes et al. (2003)

developed the lived poverty index (henceforth referred to as LPI)

which was applied to data from surveys of the living conditions

of people in southern Africa in 2000. It implied cognizance of

the hierarchy of needs by Maslow (1943) and Sen’s (1999) view

that poverty should be measured from the standpoint of access to

basic needs. Mattes et al. (2003) obtained that citizens of Bot-

swana and South Africa enjoyed the best standard of living in

southern Africa. Among the merits of the LPI method are that it

is simple, it provides a direct measure of people’s access to basic

assets and it considers the multi-dimensionality of human well-

being. This method was applied to the data on which the current

study is based, using twenty-one questions. The questions asked

how frequently households were forced to live without basic

assets (such as money, food, clean water, etc.) as well as fear of

crime, domestic violence, etc. (Table 1). For example, “how

often have you or your family gone without food, etc?” and

“how often have you or your family feared crime, etc. in your

home?” In the first question, “food” was subsequently replaced

by “clean water”, “medical treatment”, etc. while in the second

question “crime” was subsequently replaced by “house break-

ing”, “physical assault”, etc.

The joint use of attitude and behaviour approaches in the

measurement of poverty was mainly influenced by expected

affirmative responses to questions about people’s fears, given

the existence of that which they fear. Experiences have shown

that to a large extent people’s attitudes tend to be realized.

Throughout the developing world, exposure to shortage of cash,

water, electricity, etc. is a continuous factor, given the transi-

tory nature of its occurrence. On the contrary, exposure to

crime, physical abuse, etc. is subject to factors that may be less

difficult to control; and this therefore reduces the actual ex-

perience. The control of crime, etc. is assisted by government

interventions which include continuous maintenance of peace-

keeping forces such as the police and military and support of

non-governmental organizations (NGO). The second reason for

using the actual experience/fear approach was premised on the

attitude-behaviour relationship. Notwithstanding the on-going

debate about this relationship, several studies have demon-

strated that to a large extent it is significant (Ajzen & Fishbein,

1977; Bankole, 1995; Holland et al., 2002). A study by Kim &

Hunter (1993) points toward strong and significant relationship

between attitude and behaviour while Elliot et al. (2007) re-

vealed intention as the sole predictor of observed speeding

behaviour.

The LPI ranges from 0 to 4 (0 = zero poverty and 4 = ex-

treme poverty). The means of the poverty ratios for food, etc.

were computed from grouped poverty levels (0-1, 1-2, 2-3, 3-4)

as m = Σfx/n. For example, the index for food was computed as

Σ (720(0.5), 220(1.5), 182(2.5), 13(3.5))/1135. Table 1 shows

the computed LPI of the basic assets and fear or experience of

negative events. With the exception of fear of crime, much of

the hindrance to attaining satisfactory living standard is appar-

ently influenced by access to basic assets. Medical treatment

and clean water are the only two assets that have LPI less than

1. Most of the credit for good access to medical treatment goes

to the national government for its vigorous implementation of

the health policy that was designed to improve the state of

wellness of the population (Botswana, 1991). Electricity and

cash income seem to be the most troublesome obstacles to liv-

ing above poverty. Crime has increased in Botswana since 1990;

and while this is largely an effect of development, it is fre-

quently associated with irregular immigration. Where basic

assets only are considered, the LPI is 1.13 (i.e. Σ(1.05, 0.86,

0.80, 1.59, 1.04, 1.46)/6. This is lower than Mattes et al.’s

(2003) estimate (1.98) for Botswana in 2000 (the lowest in

E. K. CAMPBELL ET AL. 133

southern Africa then). When fear (related to crime, violence,

etc.) was included among the units of analysis, the overall LPI

dropped to 0.84, implying a good feeling of security among the

study population.

A better understanding of poverty levels in Botswana was

obtained from an examination of the dummy variables that

were created from six basic assets of our sample population (i.e.

cash, food, water, medicine, electricity and fuel) for use as re-

sponse variables in the logistic regression analysis. Each vari-

able represents poverty where people lived without these basic

assets most of the time and always. From these variables a

composite variable (POVERTY) was computed to represent the

population that was not poor (0) and those that were at a level

of poverty (1…..6). The six are cases where households lived

without one or more of the six basic assets. Poverty was subse-

quently divided into 1 = Transitory Poverty and 2-6 = Extreme

Poverty. This grouping was influenced by the need to maximize

the valid sample size in each category when using logistic re-

gression method.

The choice of predictors to include in the logistic regression

models was influenced by the need to produce models with the

best fit (Pregibon, 1981; Hosmer & Lemeshow, 2000). The

intention was to produce the best model in each case. Thus only

variables which assisted the goodness of fit were finally entered.

In each model the economic predictors are: migrant remits to

household (yes = 1; no = 0), amount of cash remitted by mi-

grant (less than P1,000 = 1; P1,000 or more = 0), frequency of

migrant remittance (monthly = 1; every 3 months = 2; six to

twelve months = 0), household remits to migrant (yes = 1; no =

0), household borrowed money in past 12 months (yes = 1; no =

0), and annual income of household head (less than P30,000 = 1;

P30,000 or more = 0). Income includes total earnings from

wages, formal and informal business, farm and pension. The

demographic variables are: age of respondent (defined as <45

years = 1; 45 + years = 0), sex (male = 1; female = 0), educa-

tion (none and primary = 1; secondary and tertiary = 0) and

marital status (married = 1; otherwise [includes separated, di-

vorced and cohabiting] = 0). The socio-cultural variables are:

frequency of migrant’s home visits (monthly = 1; once in three

months = 2; once in six months = 3; once a year = 0) and

household member visit migrant (yes = 1; no = 0) and the geo-

graphic variable is the region of household (rural = 1; urban =

0). In each analysis, the last category is the reference category.

Results

Table 2 indicates that the majority (63%) of households was

never forced to live without sufficient food during the twelve

months preceding August 2004. Seven percent always lived

without sufficient food throughout this period. Much higher

proportion of households (77% and 79%, respectively) had

never been without clean water and medicine/medical treatment.

Electricity ranked lowest among the basic assets that the popula-

tion had access to. However, given the financial cost versus

utility function of this power source to rural populations’ pref-

erences for cheaper alternative sources of fuel (such as candle,

firewood, paraffin and gas), the satisfaction derived from using

non-electrical appliances probably puts the wellbeing of house-

holds higher than the figures suggest. This position tends to

receive support from the observation that only 7% of households

always lacked sufficient cooking fuel during the reference pe-

riod. Cash income ranked lowest among the basic factors that

households never lacked.

The composite variable (POVERTY) indicates that 48% of

the households were not poor while 27% were transitorily poor

and 25% were extremely poor. There seemed to be significant

gender difference in the exposure to poverty (X2 = 10.84, p

< .01). Table 3 shows a dominance of male households (i.e.

male-headed households) among those which were not poor.

Substantially more female than male households were transito-

rily poor; but contrary to general opinion that extreme poverty

affects significantly more female than male households in

Botswana, the results suggest otherwise. The difference be-

tween extremely poor male-headed households and women-

headed households is apparently not significant.

Table 1.

Lived poverty index (LPI).

Unit of Index LPI N

Food 1.05 1135

Clean water 0.86 1152

Medicine 0.80 1131

Electricity 1.59 984

Fuel 1.04 1138

Money 1.46 1136

Crime 1.30 1143

House breaking 0.84 1146

Physically assaulted 0.66 1144

Domestic violence 0.60 1139

Communal violence 0.61 1137

Fear of being raped 0.53 1143

Fear of being murdered0.53 1127

Witchcraft 0.94 985

Livestock stolen 0.71 1127

Land dispute 0.60 1137

Serious illness 0.91 1147

Young child die 0.67 1142

Young adult die 0.68 1144

Drought 0.72 1136

Flood 0.55 1141

Total 0.84 1125

Table 2.

Percentage of household members’ experience of having gone without

food, etc.

Unit of basic

necessity Never Just Once or Twice/

Several Times

Many times/

Always TotalN

Enough food

to eat 63.430.0 6.6 100.01136

Enough clean

water 76.817.8 5.4 100.01142

Medi-

cine/medical

treatment

79.117.1 3.8 100.01132

Electricity in

the home 55.113.8 31.1 100.0984

Enough cook-

ing fuel 65.127.6 7.3 100.01138

Cash income45.536.8 17.7 100.01137

E. K. CAMPBELL ET AL.

134

Table 3.

Percentage distribution of peopl e t h at were not poor and poor by sex.

Sex

Level of Poverty Male Female

Not poor 51.4 42.4

Transitory poverty 23.0 31.7

Extreme poverty 25.6 25.9

Total 100.0 100.0

N 591 394

X2 = 10.836, p < .01

From individual data analysis, it was observed that 65% of

the internal labour migrants in the household remitted to their

families at home. Due to frequent visits by migrants to their

home place, the majority preferred to carry remittances along

with them. Though substantially more women than men remit-

ted, there was no significant difference between the frequency

of receipt of remittance by male and female household heads.

Table 4 suggests that remittances were considered favourably

by household heads; and this further encourages investigation

to determine whether or not remittances influenced the level of

poverty in Botswana.

Logistic Regression Analysis

As noted earlier (Table 3), the households in the composite

variable (poverty) were grouped into (1) not poor, (2) transito-

rily poor and (3) extremely poor. The responses in the two

models of poverty that are examined here, and appear in Table

5, are (a) aggregate poverty (Model 1) and (b) extreme poverty

(Model 2). Aggregate poverty is the total of transitory and ex-

treme poverty. In Model 1, the “not poor” population (recoded

0) is the reference category. In Model 2 the reference category

is a combination of the “not poor” and “transitorily poor”.

Due to multicoliniarity effect an interaction term (education

+ income) was computed and included in the second model

(response being extreme poverty) with education and income

(the main effect variables). Though several results in Table 5

are significant at the 0.05 level and are therefore exposed to

type II error, it is assumed here that they are free of this error.

Model 1 in Table 5 indicates that maximizing education

minimizes the risk of being poor in Botswana. It appears that

households where the heads had primary or no education were

four times more likely to be poor (aggregate poverty) than those

who attained secondary or more education (OR = 4.16; p <

0.001). However, model 2 indicates no relationship between

education and aggregate poverty. Meanwhile, there are mixed

results about the effects of remittances on aggregate and ex-

treme poverty (OR = 4.75 and 0.75, respectively) and in both

cases, they were not significant. It is therefore apparent that

there is no association between remittances from internal mi-

grants and poverty. However, this may not be entirely true.

Model 2 suggests that households receiving remittances fre-

quently (every month or every three months) are less likely to

be extremely poor than in cases where migrants remit once

every six to twelve months (OR = 0.17 (p < 0.01) and 0.21 (p <

0.05), respectively). It may be conjectured therefore that fami-

lies which encourage one or more of their offspring or siblings

to become labour migrants have better chances (albeit remote)

of minimizing the risk of living in extreme poverty. Unlike the

theory which relates migrant visitation with remittances, neither

Table 4.

Importance of remittances to survival of households (in percentage)

regarding availability of basic items.

Item ImportantNeutral Not Important Total N

Enough food to eat 87.9 7.7 4.4 100.0 775

Enough clean water75.3 12.3 12.4 100.0 775

Medicine/medical

treatment 72.2 15.0 12.8 100.0 769

Electricity 75.1 10.9 14.0 100.0 675

Enough cooking fuel82.4 10.6 7.0 100.0 771

Cash income 87.5 7.3 5.2 100.0 772

Sending children to

school 78.2 7.3 14.5 100.0 757

Table 5.

Odds ratios from logistic regression of poverty on demographic, eco-

nomic and socio-cultural factors.

Model 1 Model 2

Variable

Aggregate Poverty

(Transitory & Extreme)

Odds Ratio

Extreme Poverty

Odds Ratio

Demographic

Sex 0.471** 0.704

Age 1.843* 2.489*

Marital status 1.173 1.456

Economic

Education 4.155*** 3.887

Income - 1.259

Interaction term

(income + education) - 1.926

Migrant remits to household4.748 0.748

Amount of cash remitted

by migrant 0.827 0.711

Migrant remits Monthly 0.445 0.169**

Every three months 0.625 0.208*

Household remits to migrant1.233 1.538

Household borrowed money1.022 0.759

Socio-cultural

Frequency of migrant visit

home

Monthly 0.961 1.251

Once in three months 1.138 1.072

Once in six months 1.354 1.610

Household member visit

migrant 0.829 1.610

Geographic

Region of household 1.174 1.119

N 378 242

***p < 0.001, **p < 0.01, *p < 0.05

visits by migrant nor visits by household members have any

effect on household poverty.

The results for the feminization of poverty are also quite

mixed. From Model 1 the indications are that female-headed

households were less likely to be poor (in aggregate terms) than

their male counterparts (OR = 0.47, p < 0.01). But Model 2

does not support the existence of female dominance among the

extremely poor. To the contrary, the likelihood of extreme pov-

erty seems to be a bit more among male than female headed

households. But the result is not significant. It is apparent also

that where households are located (rural or urban areas) is mu-

tually exclusive of chances of becoming poor. Age seems to be

a significant predictor of poverty. Households were about two

times more likely to be extremely poor where the head was

younger (< 45 years) than where they were over 44 years old

(OR = 2.49, p < 0.05). This is plausible because maximization

of income depends on higher education and accumulated ex-

E. K. CAMPBELL ET AL. 135

perience (through skilled employment); and these are realized

through increased time (hence increased age). Younger house-

hold heads are more likely to have very limited skills/education

and are therefore more likely to be unemployed.

Discussion and Policy Implications

It is worth mentioning some of the limitation of our study.

Due to the cross-sectional nature of the data, it is not possible to

infer causality or investigate endogenenity among variables

inherent to cross-sectional design. We are only able to attribute

relationship found here to statistical associations. The investiga-

tion of causality or endogenenity calls for a better study design.

Notwithstanding these limitations, the results of this study pro-

vide information that may be useful in policy development.

As observed earlier, remittances currently have no significant

influence on poverty (transitory and extreme) in Botswana. In

other words, the ability of very poor households to attain sig-

nificantly improved economic status may not be assisted by the

remittances that migrants send. From the observation that more

frequent remitting (monthly and every three months) aids pov-

erty mitigation, it may be surmised that migrants’ willingness

to remit has positive impact on poverty. However, the signifi-

cance levels (even at 0.01) are not strong enough to attract con-

fidence in the results. Hence, considering all the results of this

study, it is apparent that migrant remittances do not have sig-

nificant impact on poverty moderation. This conclusion is con-

sistent with that of Lucas (1982) and seems to strengthen the

observation (noted inter alia) by Rempel & Lobdell (1978).

Lucas’ conclusion was made almost three decades ago

(Botswana, 1982) when income and educational levels were

quite low and wages from work in South African mines were

significantly higher than those in Botswana. The amount of

money remitted by migrants was small even after including the

effect of mine workers’ total remittances (Lucas, 1982). In

another study in Botswana, Izzard (1985, 274) observed that

“rural women complained that the remittances they received

were inadequate and irregular”. Hence it seems that notwith-

standing that Table 4 suggests receivers’ appreciation of mi-

grant remittances, they may not be high enough to make sig-

nificant and sustainable economic changes in the household.

Much of the remittances that went from urban to rural areas

recently were motivated by altruism as well as self-interest.

While the magnitude of the two cannot be determined, it is

likely that the difference between the two partly explains the

insignificant impact of remittances. In view of this and Lucas’

(1982) observation that remittances from migrants were quite

low, it appears that the net effect of internal migrants’ remit-

tances is not enough to make significant impact on poverty in

rural (and urban) areas. Still, the expressed satisfaction that

some households derived from migrants’ remittance should not

be overlooked. Poor households may likely have been worse off

in the absence of remittance. What this study reveals is that

remittances from migrants are not significantly high enough to

help raise households out of poverty.

On the poverty measurement used in this study, it is note-

worthy that the proportion of this study’s sample that lived in

extreme poverty is more or less consistent with the govern-

ment’s level of poverty in Botswana. However, bearing in mind

the controversy related to measurement of poverty (Aaberge &

Mogstad, 2007), the poverty levels deserve further considera-

tion. In view of the feminization of poverty theory, our obser-

vation that female headed households were more likely to be

transitorily poorer than their male counterparts is consistent

with other findings (Akinsola & Popovich, 2002). However, we

find no evidence of gender difference in the experience of ex-

treme poverty and this requires some comment. Notwith-

standing the methodology on which this finding was obtained,

there is good reason to keep it. Increasingly, questions are being

raised about the reliability of the feminization of household

poverty in developing countries (Buvini & Gupta, 1997; Qui-

sumbing et al., 2001; Medeiros & Costa, 2006). From a study

of ten developing countries (including Botswana), Quisumbing

et al. (2001) found that while poverty exists more in fe-

male-headed than male-headed households, in many developing

countries the difference between these two groups of house-

holds is really not significant. In Botswana, three measures

indicated that poverty was greater among male than female

headed households, though the difference was insignificant.

Chant (2007) further observed the appearance of a dominance

of female headed households among those that were not poor.

Several cultural props which fueled gender discrimination in

Botswana have weakened considerably partly due to the gov-

ernment’s educational policy and changes in fertility attitudes

which have significantly moderated preferences for sons.

The results point towards a need to review the poverty, educa-

tion, employment, gender and urbanization policies in Botswana.

However, our results on household poverty do not lend them-

selves easily to policy formulation because the survey was de-

signed to investigate poverty at a macro (household) level. As

Fuwa (2000) noted, a micro-level investigation is required to

fully understand the dynamics between individuals in the house-

hold in order to arrive at policy statements that would benefit

governments in their quest to minimize poverty. In this regard, it

would be premature to recommend policies from our observation

that female-headed households experience extreme poverty as

much as male-headed households. What this study brings out is

the immediate need for further in-depth research in this area.

While this could be costly, it is expedient to guide government

and non-governmental organizations on where to focus future

plans and projects that address poverty in Botswana.

It may be conjectured that the remittances do not assist in

poverty alleviation partly because it is only one of many op-

tions available to households wishing to reduce poverty risk.

The government has provided its citizens with many poverty

alleviating options by establishing economic development

agencies and programmes, including the Botswana Institute of

Development and Policy Analysis, Botswana Productivity Cen-

tre, and Citizen Entrepreneurial Development Agency (Matiage,

2002). Given that the poor have limited access to personal and

other loans, government interventions leave the public with few

options to lift themselves out of poverty from individual efforts.

Due to economic and other factors, there is high preference

among young professionals to emigrate to work in other coun-

tries (Crush, 2006). This may, in future, provide substantial

remittances to assist in moderating poverty in especially rural

areas. Hence, now is the time for the government to consider

policies that would attract remittances from its citizens who

would have left the country to work elsewhere.

From primary data and regression analyses, this study reveals

E. K. CAMPBELL ET AL.

136

that remittances from internal migrants still has no significant

effect in moderating poverty among migrant-sending families

in Botswana. This is notwithstanding the remarkable economic

and social development that has occurred in the country since

its independence in 1966. This result should not be interpreted

as implying zero benefit from internal migration. But it does

leave unanswered the question of whether or not internal mi-

gration truly rewards sending families. Though the result is not

consistent with the new economics of labour migration theory

(NELM) that remittance is very helpful to sending families, it

does not challenge its validity because the theory was devel-

oped particularly from observations about international (not

internal) migrants’ remittances.

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