The Pakistan DevelQpment Review
Vol. XXIV, No.2 (Summer 1985)
Landholding, Rural Fertility and Internal
Migration in Developing Countries: Econometric Evidence
from Cross-National Data
R. E. BILSBORROW
andC.
R. WINEGARDEN*
We develop an empirical model of the interaction of rural fertility and ruralurban migration which incorporates the effects of landholding patterns. Crosssection data for 26 developing countries are used to test the model. The statistical
results support the hypothesis of a positive relationship between fertility and outmigration in the rural sector and lend credence to some of the propositions regarding the impact of landholding patterns. A reduced form of the model is derived
from the statistical results, and its policy implications are considered.
I. INTRODUCTION
The importance of the agricultural sector in the process of economic development has been widely documented [43; 44; 76]. Government policy statements at
the World Population Conference in Bucharest (1974) and subsequently, together
with a mushrooming literature, have stressed the interrelationships between demographic variables and socio-economic change. Since most of the population in developing countries lives in rural areas, it follows that the relationships between the
demographic factors and agricultural change must be especially important in the
development process. It is in these areas that poverty is deepest and continued high
fertility frustrates efforts to slow population growth. Rural areas also supply
growing numbers of migrants to hard-pressed cities, a paramount concern of many
governments in the Third World [85]. A better understanding of the economicdemographic interrelationships within the rural sectors of developing countries is
thus crucial for a better policy formulation.
*The authors have made equal contributions and are listed in alphabetical order. They
are grateful to the Carolina Population Center, University of North Carolina at Chapel Hill, and
the Department of Economics, University of Toledo, with which they are respectively associated,
for logistical support; and to Jack Molyneaux for yeoman research assistance. They are also
grateful to Scott Grosse and Frances Kobrin for helpful comments on an earlier version of this
paper which was presented at the Annual Meeting. of the Population Association of America,
San Diego, California, April 29 - May 1, 1982; and to two anonymous referees for helpful
comments.
127
Bilsborrow and Winegarden
Landholding, Rural Fertility and Internal Migration
Nevertheless, little is known about the interrelationships between population
changes and economic development in rural areas, and even less about the role of
landholding patterns. The existing literature has been concerned mainly with isolated
aspects of the relationships, or with specialized studies of individual countries.
Little formal statistical analysis has been carried out, and the interaction of landholding, rural fertility, and out-migration appears to have been particularly neglected.
In this paper, we try to fill some of this gap.
The paper is organized as follows. Section II reviews the relevant theoretic~
and empirical literature, Section III discusses the data used, Section IV presents the
econometric model, and Sections V and VI set forth the empirical findings and their
policy implications. The concluding section (Section VII) summarizesthe results and
provides some caveats.
in the present-day developed countries. In the absence of adequate time-series for
most contemporary developing countries, investigators interested in examining the
factors influencing their patterns of fertility decline have relied mainly on crosscountry statistics. (Recent references include Faruquee [27] and Winegarden [89;
90] . For a survey see Mauldin and Berelson [52]).
Unfortunately, this considerable literature has little to say about the relationships of special interest in the present paper. That is, there is little evidence on the
effects of agricultural land on rural fertility, although some limited evidence exists.
For example, from the historical studies of European populations, Knodel
[47, p. 125 ff] and Coale et al. [15, pp. 60-67] observed positive relationships
between the size of landholdings and fertility in the nineteenth century Germany and
Russia, respectively. In an early and influential piece, Stys [78] observed a strong
positive relationship between family size and women's fertility in Poland. In
Sweden, smaller landholdings were also associated with lower fertility [25].
Similarly, in the U.S., Easterline [23] argued that fertility declined over time with
126
n. THEORETICAL AND EMPIRICAL BACKGROUND
In this section, we first briefly review the relevant literature on the determinants of fertility and migration, and then consider how changes in fertility and migration may be interrelated over time.
(A) The Detenninants of Fertility
A variety of theoretical approaches to the determinants of fertility exist, based
Jn different disciplinary perspectives. Until the 1970s, most of this work was carried
Jut by sociologists and demographers, and suggested a wide range of factors as influencing fertility, including education, women's employment status and location of
work, place of residence (current as well as original), family background (parents'
.ocio-economic characteristics and fertility), social class, health, land and other
lssets, and family planning use [20; 28; 31 ; 32; 39]. The complementary approach
I
)f economists has its origins in Leibenstein [49] and Becker [6] and was further
leveloped in T. W. Schultz [68; 69], T. P. Schultz [67], Turchi [83] and others. It
:onceptualizes the various factors influencing the demand for children through
'price" and "income" effects. For example, under normal conditions the demand for
:hildren will increase with an increase in the family income and a reduction in the
osts of raising children. Since a major cost includes the opportunity cost of the
!lother's time in child rearing, and since this cost is a function of level of education,
he demand for children is negatively related to level of education.! Actual fertility
) a function of factors influencing not only the "demand" for children but also their
llpply, viz. the knowledge and use of fertility-regulating methods [12; 20; 21J .
While the theory generally refers to the micro or household level, there does
xist a considerable body of literature on the aggregatechanges in fertility over time
! See, for example, UN [84]; Simon [74]; Williams [88]; McGreevey et al. [53]; and
iochrane [16]
~
increasing population
density
-
the ability of farmers to bequeath land to their
children declined with the disappearance of unused "frontier" land.
The effect of land on fertility has received far less attention with respect to
contemporary LDCs.2 Moreover, the evidence is not conclusive. Merrick [54]
found only a slight negative relationship between rural population density and
fertility for "microregions" in Brazil, while Collver et al. [17], Chaplin [13],
Kleinman [46], Hicks [41], Rosenzweig and Evenson [65], Aghajanian [1] and
Seligson [71] observed stronger relationships for Taiwan, Peru, India, Mexico, Iran,
and Costa Rica, respectively. Irfan and Farooq [42] observed a positive relationship
up to 20 acres in Pakistan and a:negative one thereafter. Hermalin and Lavely [40]
recently observed a negative relationship between farm size and fertility in Taiwan.3
In empirical work, a complicating and unresolved issue has been how to measure the
land availability variable: is it the size of the cultivable plot or its ownership that
influences fertility? Schutjer and Stokes [70] have asserted that land ownership is
anti-natalist because it provides a form of old age security which is an alternative to
that provided by children. But ownership also provides a more secure basis for
children to contribute to family income when they are young. Thus, the net effects
of ownershipare indeterminatea priori. Some evidenceon this questionwill be
presented below.
(B) The Detenninants of Migration
The substantial literature on the determinants of migration has recently been
surveyed in UN [84], Shaw [73], Greenwood [36], Todaro [81J, Ritchey [62J,
h
2 Recent surveys are provided by Stokes et al. [77] and Schutjer and Stokes [70].
3 Stokes and Schutjer
would
interpret this as refle_ctin~ a tendency for higher-incD.
. .
'.'
128
Bilsborrowand
Winegarden
Landholding,
Da Vanzo [18] and Bilsborrow et at. [10]. Factors influencing people's decisions
to migrate are thought to include relative income and employment conditions in
origin and destination areas, educational levels and access to education, land availability and population density, family ties, and the relative availability of health and
other amenities across areas. Beginningwith Sjaastad [75], economists developed a
theory of migration which asserts that individuals strive to maximize expected income over. time and space, but it is clear that many other factors are also involved
in individual migration decisions. Unfortunately, except perhaps for education, these
other factors are often difficult to measure at the areal or country level; so macromodelling of rural-urban or interregional migration flows has focused on income
differences and other economic variables [63; 64] . One investigation which has gone
beyond this approach is Mundlak [57]. He investigated factors influencing rates of
out-migration of the labour force from agriculture across a mixture of 70 developed
countries and LDCs. He found the rate of out-migration positively related (and
statistically significant) to the urban-rural income differential, the ratio of the nonagricultural to the agricultural labour force (a measure of absorptive capacity), the
level of education, and the rate of population growth. The results for the lastmentioned variable are of particular interest, suggesting a role for demographic
"push" variables. However, both of the last two variables were measures for the
country as a whole rather than for rural areas. Moreover, since the rate of population growth is not a direct measure of either fertility or population density, the
mixture of countries raises questions about the relevance of the findings for LDCs
[7] , and the dependent variable is the migration of only a select portion of the rural
population.
Although the literature on migration is rich and rapidly growing, empirical
evidence of the explanatory variables of particular interest for the present study is
limited and inconclusive. Again, some evidence from the European fertility studies is
relevant.4 Knodel [47, Ch. 5] observed that the (expected positive) relationship between rural density and out-migration across administrative areas of Germany largely
disappeared when the level of development was taken into account, and Anderson
[3] observed a positive effect of rural density on out-migration from rural areas of
European Russia to Asiatic Russia but not to urban areas of European Russia.s
Given the importance of land to all aspects of life in rural areas of LDCs,it is
surprising that there has not been more empirical work on the effects of size of landholding on out-migration in contemporary LDCs at either the micro or areal level.
But Shaw [72] observeda significantpositiveeffectin severalLatinAmericancountries, as did Kessinger [45] in an in-depth, longitudinal study of a village in India.
and Internal
Migration
129
Other examples, largely descriptive and derived from the anthropological and geographical literatures, are cited in Bilsborrow [8] for Uganda, Nigeria, Puerto Rico,
and Chile. Finally, there are three other relevant cross-country studies on factors influencing rural-urban migration rates. Firebaugh [29] observed the expected effect
of land in a cross-section of Latin American countries, using census data. Preston
found a significant positive relationship between rates of rural natural increase and
rural-urban (out-) migration, but specifically cited his omission of "important unmeasured variables such as rural density" [61, p. 12]. Nevertheless, in an earlier
cross-country analysis based on 1950-1960s data, Annable [2] found rural density
to be insignificant in his rural-urban migration function.6 And, finally, as with
fertility change, the form in which land availability influences out-migration remains
to be determined empirically.
(C) The Interrelationships between Migration and Fertility
The theory of the demographic transition was expanded by K. Davis [19] and
Friedlander [33] to allow a wider range of demographic responses to increased rural
population density instead of considering fertility decline only. Davisconceptualized
the responses as "multiphasic", including increases in the age at marriage, outmigration, and even infanticide, in addition to reductions in fertility. But a linked
inverse relationship between the two most general possible responses - decline in
fertility and out-migration - was not explicitly postulated until Friedlander noted
the interrelationship in contrasting the historical responses to increased density in
France and Sweden. Wemay infer that the greater the out-migration the less the need
for fertility to fall; and the greater the fertility decline the less the "vent for surplus"
of out-migration [8] .7
6 Annable's work was based on 27 countries and included as the dependent (endogenous)
variables the rate of rural-urban migration and the size of the urban traditional sector. There are
a number of problems, unfortunately, with the variables in the migration function, including the
way the dependent variable is measured as the rate of urban population growth minus that of the
total population. The extent to which the procedure yields valid measures cross-country depends
not on compensating differences in age structure (as the author states on p. 400) but on the
extent to which urban-rural fertility and mortality differences cancel out [85]. His measures of
the size of the urban traditional sector and of the urban-rural wage gap leave much to be desired
as well, leaving moot the question of whether rural density has positive effects on out-migration.
7 This conceptualization of the responses to rural population pressures may be too
narrowly demographic. It does not admit the possibility of major economic responses, as it
assumes the supply of land and technology to be inflexible. Boserup [11] suggests that its
Malthusian-Ricardian assumption of constant technology is incorrect: as arable land becomes
scarcer relative to population, land may be used more intensively. For example, more of the land
may be irrigated, or devoted to multiple cropping (more than one crop per year on the same
land). Examples illustrating increases in land-intensifying technology are indicated in Grigg [38]
and Bilsborrow [8]. To the extent such land intensification occurs, the other responses are less
likely. In a little-known aspect of his article on fertility, Stys [78] noted an inverse relation
between the size of the family's landholding in Poland and out-migration of children. General
surveys on the interrelationships between migration and fertility at the micro level include
Goldstein [35] and Oberai [58] .
4 Among the many references are Knodel [47], Coale [14], Coale et al. [15], and Tilly
[80].
s However, the statistical results are quite weak. A lack of relationships was found in the
historical studies of Knodel [47, Ch. 5] on Germany and Anderson [3] on Russia.
- ..wo.' '"
Rural Fertility
..L-..
130
131
Bilshorrow and Winegarden
Landhokling. Rural Fertility and Internal Migration
III. THE DATA
which computed migration rates by subtracting observed national growth rates from
observed rural rates of population growth. This approach was accepted after noting
that there were only minor discrepancies with estimates which incorporated adjustments for differential urban and rural survival rates [87, p. 22] P Inability to
separate out effects of reclassification also creates "noise" in the data, but in the
only available sample of (four) developing countries it was found to be only 20
percent of net migration on the average.
The analytical limitations of the cross-sectionalapproach are widely known. In
the present case, however, it has not only the virtue of necessity, given the extreme
scarcity of historical data for developing countries, but also the positive advantage
of utilizing the widely varying conditions prevailing in these countries. This great
range of variation probably simulates the effects of long-term processes far more
realistically than the limited experience of a particular country.
The observations relate to the 26 developing countries for which all necessary
data could be obtained for the relevant time periods.8 The smallnumber of observations reflects the combined effect of the paucity of both demographic and economic
data for the rural sectors of developing countries.9 By limiting the observations to
LDCs, we may have made it more difficult to produce significant regression results,
but we have acquired some immunity to cluster and outlier effects: see [7] .
Apart from sample size, there is the problem of data quality. In addition to
the usual caveats pertaining to cross-national data for LDCs, there are further difficulties associated with the measurement of rural fertility and the estimation of
internal migration rates, the two key dependent variables in this paper. The estimation of fertility (total fertility rates) was particularly complex because of the wellknown lack of reliable published estimates for rural areas. Moreover, for purposes
of this project, it was not desirable to use the crude birth rates employed in most
cross-country studies because of the effects of migration on the age-sexdistribution.
A number of data sources were used to ferret out what we believe to be reliable
IV. THEMODEL
In order to test the hypotheses regarding interrelationships among fertility,
migration, and landholding patterns in the rural sector, we develop a two-equation
stochastic model. The general form of the model is:
estimates, mainly using the U.S. Bureau of the Census [87].10 In the majority of
cases, no separate estimates of rural fertility were available, so they were approxi-
mated usingdata on total fertilityrates - childreneverborn or child-womanratios,
and the proportion of women of childbearing age livingin rural areas.11
The computation of net rates of rural-urban migration is even more problematic because of the different definitions of "urban" and because of the difficulties
in separating out that proportion of city/town growth resulting from reclassification
of contiguous areas through annexation. In general, the data source was the UN [87] ,
Let F = total (national) fertility, F R = rural total fertility rate, F u = urban total fertility
rate, CRand Cu = the corresponding child-woman ratios or children ever born, and w = proportion of women of child-bearing age in the country in rural areas. Then
-.
.-"
,
= [(Mit-k'
Mit+k
= [(RFit'
Lit' Xit)
Lit' Xit)
(1)
(2)
where RF is the (rural) total fertility rate, M the rate of rural-urban migration, L a
vector of exogenous variables measuring land use and landholding, and X a vector of
exogenous control variables. The i subscript denotes the rural sectors of a crosssection of developing countries. Both t and k are time subscripts which pertain, reo
spectively, to circa 1960 and to the decade preceding (t-k) or following (t+k) that
year. On the basis of the discussion in Section II above, the rural fertility and migration variables are hypothesized to be positively related. The land variables,it should
be noted, are intended to gauge landholding effects independently of the influence
of income factors which are included among the control variables.
It is evident that this model is recursive. To some degree this may be justified
a priori. Only after a period in which population pressures are reduced by outmigration does it seem likely that fertility will increase; conversely, pressures on
living standards associated with high fertility also intensify over time, as young children grow into workers and adult consumers. There is, in addition, a straightforward
8The data are available on request from the authors. The countries included in the
sample, listed by present names, are; Benin, Brazil, Chile, Colombia, Costa Rica, Dominican
Republic, Egypt, Guatemala, India, Indonesia, Iran, Jamaica, Kenya, Korea (Republic of),
Malaysia, Mexico, Nicaragua, Pakistan, Panama, Paraguay, Peru, Philippines, Sri Lanka, Thailand,
Uganda, and Venezuela.
9Data on landholdings are from Food and Agriculture Organization [30].
Other
economic data (except as noted below), literacy, and life expectancy at birth are taken from the
World Bank [91]. Other demographic data are described below in the text.
IOOther sources for the 1960s period, for one or more countries, were Ominde [59],
and "Women in Development" data tape of the U.S. Bureau of the Census. Census data, reported
in the UN Demographic Yearbooks, provided figures for the child-woman ratios and the urbanrural numbers of women aged 15-49. Far more fertility data are available for the 1970s (from
the WFS and other sources), but we could not use them because the latest detailed agricultural
data from the FAa World Census of Agriculture relate to the period around 1960 [30] .
II The procedure was as follows:
FR = F' CR/[CR w + Cu(l-2)].
RFit
12Mundlak [57] made a parallel assumption regarding the measure of migration of the
agricultural labour force. Ledent [48] noted that our approach rarely results in non-trivial
errors.
~..
132
Bilsborrow and Winegarden
Landholding,
empirical consideration in that efforts to validate a simultaneous (non-recursive)
model were not unsuccessful, whereas the recursive structure yielded conceptually
defensible results. This suggested to us that the true relationships may be best reflected with a lag structure, although the specific periods used in this model represent
mainly an accommodation to available data rather than dominant theoretical considerations. Also, it should be noted that temporal ordering increases the plausibility
of a causalinterpretation, although it does not conclusivelyestablish causation.
The generalized relationships of equation (1) are specified in detail below.
Variables are presented in unsubscripted form to simplify notation. Except where
otherwisenoted, the timereferenceis to 1960or the availableyearclosestto 1960.
RTFR
=
ao + a2 MLAG + a3 Ln AGY + a4 SMALL + as CONC + a6 OWN
+ a7 ADENS + as LIT + a9 EX + alO (EX)2 + URTFR
(1.0)
where
R TFR
MLAG
=
=
SMALL
=
total fertility rate in rural areas;
mral-urban migration, average annual rate, 1950-60 (in percent of
rural population at the beginning of the period, viz. 1950);
Ln AGY= natural logarithm of GDP per agriculturalworker (in U.S.dollars)13;
small-holder index: i.e. number of agricultural holdings in the 1-5
hectare range as percentage of all agricultural holdings of one
hectare or over14;
CONC
=
concentration
index: i.e. percentage of total area of all agriculture
holdings (1+ hectares) in holdings over 50 hectares, divided by
percentage of total area of holdings in the 1-5 hectare range;
land ownership index: i.e. percentage of all agricultural holdings
owned by their operator;
agricultural density: i.e. number of persons in the agricultural
labour force per hectare of agricultural land ;
OWN
=
ADENS
=
LIT
EX
=
literacy rate for the adult population (in percentage); and
=
mean expectation of life at birth, 1955-60 (in years).
Predicted signs of the estimated parameters are indicated below, followed by
a brief justification for the expectations.
13GDP in local currency was converted to dollars, using the prevailing exchange rates,
then deflated to 1967 -69 prices for purposes of uniformity, and further adjusted for differences
in internal purchasing power parity (per Summers, Kravis, and Heston [79]).
14Except for a subsample of countries, the F AO data exclude holdings under one hectare.
It appears, however, that the extent of holdings under one hectare is reasonably well represented
by the 1-5 hectare data.
a2, a3, a6, a9
a4, as, as, alO
Rural Fertility
and Internal Migration
133
>0
<0
a7 ?
A positive (and significant) estimate of a2 would be consistent with the hypothesis that out-migration serves as a demographic safety valvein helping to maintain high rural fertility.
Inasmuch as AGY is a proxy for the income of agricultural workers, a positive
sign is anticipated for its coefficient when the impact of other variables has been
taken into account. This conforms to prevailing micro-economic theory, which
postulates that the "pure" income effect on fertility is positive; see reviewsin Simon
[74] and Muellerand Short [56].
The next three variables in the equation - SMALL, CONC, and OWN - pertain to the distribution of land and the prevailing forms of tenure. The greater the
concentration of landholdings in the small (1-5 hectare) size category, the lower the
productivity of child labour on the farm, the smaller the economic value of children,
and, in turn, the lower the expected fertility (following a line of reasoning developed
by Mueller [55]). Concentration in the ownership of agricultural land is expected to
be antinatal in that it implies that a few landowners have very large plots and the
majority of these plots are too small to benefit from additional family workers.
Ownership by those who work the land (as distinguished from tenancy and sharecropping arrangements) seem likely to have a positive effect on fertility, in the
sense that children make a positive contribution to output on family farms (as well
as provide old-age security).
The ADENS variable has been used to take into account the influence of the
labour-intensity of land use in the base period. Such intensity varies greatly among
countries, depending on the quality of land, crops grown, extent of livestock grazing,
farming technology, and other long-standing historical differences. The expected
effects of changes in density are reflected in the coefficient of MLAG; the sign at the
ADENS coefficient is, therefore, not predicted.
Literacy generally acts to lower birth rates, according to a now substantial
body of literature on the effects of education on fertility [16; 52; 53; 88]. In the
present case, however, data on the extent of literacy in the rural sector are not available for most of the developing countries; so national rates must serveas a less than
ideal proxy.
A quadratic functional form is suggested for the effect of life expectancy on
fertility [4; 24; 90]. (This form generates an inverted-U curve.) A rise in mean expectation of life implies an improvement in health conditions that acts to enhance
fecundity. However, the gain in EX also acts to reduce fertility because of the wellknown inverse relationship between the probability of child survivaland the desired
Landholding, Rural Fertility and Internal Migration
134
Bilsborrowand
number of births. At low levels of EX, therefore, the positive effect will dominate,
but as EX rises, a net negative influence will emerge. Positive effects of mortality
reductions on fertility in the early stages of modernization have been observed by
Arriaga [5] for Latin America and Page and Lesthaeghe [60] for Africa. Again, as
with literacy, we lack direct data on life expectation in rural areas and must make do
with national statistics.
Turning now to the determinants of rural-urban migration, the general relationships of equation (2) are specified below.
M=bo +b1 RTFR +b3 LnAGY+b4
SMALL+bs CONC+b6 OWN+
b7 ADENS + bs LIT + b9 EX + bu URB + b12 GAP + UM
...
(2.0)
where
M
=
rural-urban migration: average annual rate of out-migration,
1960-69
(as percent of the 1960 rural population);
URB = urban population in relation to total population (percent); and
GAP = the ratio of GDP per non-agricultural worker is GDP per agricultural
worker.
Other variables are as defined for the preceding equation, and the time reference is also to circa 1960, except for the dependent variable which leads the explanatory variables and therefore pertains to the 1960-69 decade.
Predicted signs of the estimated parameters are as follows:
bl> bs, bs. b9. bu, b12 > 0
b3. b6 <0
b4, b7 ?
Again, a proposition
135
Winegarden
embodying the "multiphasic
response" has been incor-
porated into the estimating equation - the positive sign for rural fertility hypothesizes that out-migration occurs in part as a response to an intensification of demographic pressures (actual or anticipated) arising from high fertility.
All else being equal, low rural incomes should induce (or force) out-migration
by the poor. The various facets of landholding - SMALL. CONC. and OWN - are
expected to have mixed effects. Concentration in landholdings should be associated
with the departure of landless workers and very small landholders (or members of
their families) to the city. Ownership of land should have the opposite effect. The
net effect of the relative incidence of smallholding is uncertain. If small size implies
inadequate amounts of land, b4 will be positive; if smallness does not carry this
implication, a negative coefficient should result. The sign can, therefore, be determined only by empirical means. As in the fertility equation, ADENS has the function of taking into account the impact of intensity of land use and, therefore, its sign
is open to empirical determination.
Literacy increases both the awareness of urban opportunities and the capacity
to benefit from them. Unfortunately, the available data are national averagesthat
do not take into account urban-rural educational differences that may impinge on
the decision to migrate. In using national averages, we are implicitly forced to
assume that they are correlated across countries with rural literacy rates (which may
not be implausible).
The role of the mean expectation of life at birth in this equation is simply to
account for differential probabilities of survivalin different countries which should
condition the effect of fertility (Le. for any given level of the rural TFR, a lower
value of EX implies a reduction in the rate of increase in demographic pressures
resulting from fertility). Once again, national averagesmust substitute for specifically rural data for mortality.
A priori expectations regarding the URB and GAP variables are straightforward. The higher the degree of prior urbanization, the greater the absorptive capacity
of urban areas and, therefore, the greater the ease for rural people to migrate. GAP
is expected to exert a positive influence on the migration rate, followingthe discussion in Section II above, which noted the urban-rural income differential as a main
causal factor in migration flows.
V. EMPIRICALRESULTS
Regression results for the rural fertility and migration equations are presented
in Table 1. These results provide substantial support for our major propositions as
well as considerable evidence on behalf of most of the secondary hypotheses. Let us
examine each equation in turn.
Starting with the initial formulation of the fertility equation (1.0), we see that
the coefficient for lagged migration is positive, as hypothesized, although less than
twice its standard error. Given the smallnessof the sample and the deficiencies of
the data, this may be viewed as reasonably satisfactory. Moreover, there is the
likelihood that migration also acted to depress fertility (by reducing the masculinity
ratio in the ages of marriage and reproduction), so that a positive net effect provides
more support for our thesis than the results directly reveal.
Whenoutcomesarecomparedwith a priori expectationsforthe other variables
in equation (1.0), it is evident that none of the specific predictions has been directly
contradicted with respect to sign, although at least two of the t-ratios leave something to be desired. The lack of an effect of literacy is surprising, and may indicate
that urban-rural differentials in literacy rates did in fact vary widely across countries,
Bilsbo"owand Winegarden
136
Landholding, Rural Fertility and Internal Migration
Table I
or that education'seffect on fertilityis largelyan urban phenomenonin developing
countries (as expected by some scholars, including Cochrane [16]). The insignificant
result for agricultural density may perhaps be explained by an approximate balance
between the opposing forces previously discussed.
The positive coefficient for rural income provides evidenceof a positive income
effect on fertility in rural areas as it is statistically significant at the .05 (two-tailed)
level. The anticipated quadratic form of the relationship between life expectancy
and fertility is also confirmed. This function yields a turning point at 51.6 years,
which is very close to the fmdings from other investigations [24; 90] .
Hypotheses regarding the three landholding variables receive strong statistical
support (significant at the 5-percent level), and are of particular interest for the
present paper. The CONC variable provides evidence that the greater the concentration of land ownership, the lower the fertility, apart from its effects through
income (since that is already included in the equation). Note that the SMALL
variable turns out to be both negative and significant, whereas OWN is significant in
the opposite direction. These results may suggest that, in the first instance, some
smallholders seek additional land more than extra family labour (farm units are so
small that additional children are more a cost than a benefit), and that, in the second
instance, land ownership per se generates demand for family workers by providing a
more secure basis for a contribution from child labour.
The initial fertility equation was also re-estimated, in equation (1.1),15 with
ADENS deleted. Parameters for the other variables, as noted, show virtually no
changes as a result of this operation.
Estimation of the migration equation (2.0) also yields results generally supportive of the hypotheses. Most importantly, the coefficient for rural fertility
is both positive and significant at the .05 level.
Comparing outcomes with expectations for the remaining variablesin equation
(2.0) produces a somewhat mixed picture. The best results are the very highly
significant coefficients in the expected direction for agricultural income and the
degree of urbanization (negative for the former and positive for the latter). Our
results for URB parallel those of Mundlak [57] and Annable [2]. The URB variable
is so powerful that it indicates that macro-level studies of migration in low-income
countries are likely to be seriously biased if they do not take into account the
absorptive capacity of cities. The negative impact of the ADENS variable suggests
that, as intended, it measures mainly the intensity of land use, with the effects
through scarcity captured by other variables in the equation. In particular, the
positive and significant effect of the smallholdiilg variable may indicate that many
such holdings were perceived as inadequate in size, resulting in out-migration.
RegressionResults
Explanatory
Variable
Intercept
RTFR
(1.0)
Dependent Variable and Equation Number
M
RTFR
M
(2.1)
(2.0)
(1.1)
-9.1424
(1.23)
-7.7614
(1.14)
RTFR
MLAG
LnAGY
SMALL
CONC
OWN
ADENS
LIT
EX
(EXl
.2980
(1.50)
.9806*
(2.43)
-.0336*
(3.00)
-.0106*
(2.20)
.0209*
(2.53)
.2410
(.54)
-.0076
(.77)
.4365
(1.66)
-.0042
(1.74)
.2756
(1.45)
.8786*
(2.52)
-.0295*
(3.62)
_..oIOO*
(2.18)
.0189*
(2.62)
-.0064
(.69)
.4096
(1.62)
-.0040
(1.71)
URB
GAP
5.6374
(2.01)
.4804*
(2.45)
4.3467
(1.78)
.3871 *
(2.37)
-1.7823*
(3.37)
.0226*
(2.44)
-.0054
(1.31)
.0061
(.92)
-.7916*
(2.39)
.0063
(.96)
-.0060
(.28)
-.9484*
(2.99)
.0192*
(2.24)
-.0058
(1.45)
.0088
(1.41)
-.6543*
(2.14)
.0890*
(7.28)
-.1404
(2.04)
.0907*
(7.65)
-.5241
(2.05)
.83
(16.02)
LnNAGY
"R2
(F)
.51
(3.86)
.53
(4.49)
.82
(12.25)
.
.
15F
or this estimate d equation as well as for estimated equation (2.1) mentioned later in
this section, see Table I.
Notes: Unstandardized regression coefficients in upper rows; t-ratios in lower rows (in
parentheses).
*Indicates significant at the 5% level.
.-
137
L
138
Bi/shorrowand
Landholding, Rural Fertility and Internal Migration
Winegarden
139
explanatory variables. Because changes in rural fertility and out-migration are
mutually reinforcing, cross-equation feedback intensifies the final impact of altering
the value of an explanatory variable, except in those instances where the coefficients
differ in sign between the two structural equations. Thus, for example, OWN has a
greater total effect on RTFR and M than its structural coefficients would indicate,
because its coefficient has the same (positive) sign in both equations. Similarly,
although URB's. direct influence is limited to the migration equation, it indirectly
affects rural fertility, which, in turn, affects migration, thus contributing to the total
effect of URB on both dependent variables. AGY, however, operates on migration
and fertility in opposite directions so that cross-equation feedback diminishes its
ultimate influence.
In order to gauge the total effect of changes in the explanatory values,
particularly those with distinct policy implications, reduced-form equations have
been derived from the structural results, using equations (1.1) and (2.1). Reducedform coefficients were computed at mean values of the non-linear variables. The
results of this operation, restated as beta coefficient and elasticities, are shown in
Table 2.17
Viewed in a policy perspective, both opportunities and obstacles are highlighted by the reduced-form results. On the assumption that the usual policy orientation is to reduce rural birth rates and diminish the propensity to migrate to cities,
we find only three variablesthat operate in the same desired direction with respect to
both goals. These are ADENS, which accounts for the intensity of land use, LIT, and
EX. The effect of ADENS, however, is mainly limited to migration; its influence on
fertility is quite small. The impact of literacy is also largely limited to a single
objective function, fertility in this case. It will be recalled, moreover, that our proxy
for this variable is crude and indirect. The reduced-form coefficients for expectation
of life at birth, although formally correct, are deceptively small in absolute magnitude. The quadratic function (in the fertility equation) is evaluated at the sample
mean, which, at 51.6 years, is only slightly above the turning point for that function.
Given the continuing improvement in mortality conditions achieved in most LDCs
since 1960, the effects of this variable on both rural fertility and out-migration
should now be much more substantial.
Several variables appear to have mixed effects in the policy contex~. Raising
the real incomes of the agricultural labour force seemsto reduce migratory outflows,
Contrary to expectations, the diffusion of land ownership per se apparently did not
reduce migration rates. Perhaps the mere availability of land to the cultivator
(through rent or share-cropping arrangements) was generally sufficient to ameliorate
out-migration. Alternatively, land ownership may have facilitated the out-migration
of individuals (even heads of households, as in parts of Africa) who had the security
of being able to return to their rural home. To the extent the size of landholding was
insufficient to support the entire rural household, this would seem especially likely
(since income per worker is already in the equation). The coefficient of the literacy
variable has the predicted sign,but its error varianceis large, perhaps again indicating
the drawbacks of using national averagesas a proxy for specifically rural data.
The remaining three variables in equation (2.0) have "incorrect" signs. Fortunately, two of them are not statistically different from zero. We take up these
two first. With regard to EX, the problem may lie in the necessary substitution of
national for rural data. And, while the coefficient of the concentration variable is
only slightly larger than its standard error, we can offer no ready explanation for
its sign. The only real problem is the significant and negative coefficient estimated
for GAP. Even when we re-specified the equation by substituting a direct measure
of income in the non-agricultural sector (the natural log of GDP per non-agricultural
worker or In NAGY), the results are the same: see equation (2.1). Simultaneousequation bias may in part explain this unexplained outcome, if migration itself
affects the urban-rural income differential as is likely.16 However, estimation with
GAP as an instrumental variable (not shown here) did not produce a materially
different result. Another possibility, which cannot be explored with the available
data, is omitted variable bias. If urban unemployment, a potentially important
variable - see Todaro [81; 82] - is positively correlated with non-agricultural wage
levels and negatively correlated with rural-urban migration, then the estimated
coefficient of NAG Y will be biased in a negative direction. Still another possibility
is suggested by the work of Greenwood et al. [37] on Mexican migration. Beyond
a threshold distance from the destination, higher incomes in the areas of origin make
migration easier to afford.
In any event, the regression results for the structural equations generally
support our main hypotheses. These results may therefore be used for the reducedform analysiswhich follows.
VI. REDUCED-FORMANALYSIS:POLICY IMPLICATIONS
17In this process, we substituted the right side of the migration equation for MLA G. in
equation (1.1) and the right side of the fertility equation for RTFR in equation (2.1). This
requires the assumption that the parameters of these functions remained invariant over the
relevant time periods. The derived reduced form used here should be distinguished from the
direct reduced form obtained by regressing the endogenous variables on all predetermined
variables in the equation system. The former is a mathematical process that utilizes the estimated
parameters provided by the structural equations [34].
Estimation of structural equations reveals only direct, or first-order, impacts.
In a system of two (or more) equations, these estimates do not include crossequation effects, and, thus, cannot fully reflect the consequences of variation in the
16Such bias may arise where the dependent variable affects a supposedly exogenous
explanatory variable. The resulting correlation between the explanatory variable and the error
term may distort the "true" value of the estimated regression coefficient.
L.
140
Bilsborrowand
Winegarden
Landholding, Rural Fertility and Internal Migration
Table 2
Beta Coefficients and Elasticities of the Derived Reduced-Form
Equations
Explanatory Variables
AGY
Dependent Variable and Equation Number
RTFR
M
(1.2)
(2.2)
.415
[.102]
SMALL
-.694
[-.252]
CONC
-.653
[-.046]
OWN
.608
[.212]
-.332
[-.601]
.197
[.525]
-.482
[-.252]
.405
[1.037]
ADENS
-.124
[-.015]
-.398
[-.341]
LIT
-.202
[-.054]
-.069
[-.136]
EX
-.033
[-.028]
-.016
[-.071]
URB
NAGY
.558
[.128]
-.117
[-.025]
1.781
[3.001]
-.354
[-.140]
Notes: Beta coefficients are shown in the upper rows; elasticities in the lower rows [in
brackets] .
Based on the structural parameters of equations (1.1) and (2.1), respectively, in Table 1.
Reduced-form coefficients are computed at variable means. AGY and NAGY have been
transformed. from logarithmic to natural numbers.
but at the cost of higher rural birth rates. Fortunately, the elasticity of the latter is
only one-sixth as high as that of the former.
Two of the policy-linked variables apparently operate in the wrong direction
vis-a-vis both
policy goals. Decreasedconcentrationin landholdingsand greater
diffusion of landownership - both basic aspects of what is generally meant by "land
reform" - unfortunatelyshow net effectshere that both raisefertilityandincrease
rural-urban migration rates.
This outcome, it should be stressed, does not eliminate
141
the case for land reform which is highly desirable for reasons of equity and perhaps
efficiency of land use as well. But the results here suggest that land reform be
accompanied by family-planning programmes as well as measures to reduce outmigration.
But the problem here may not be as serious as first appears. When all three
of the landholding variables are considered together (SMALL, CONC, and OWN),
we see from the elasticities in Table 2 that the effects largely cancel each other,
especially for fertility. The strong effect of ownership on migration, however, is
still disturbing. But it may mainly reflect the vast amount of non-family (individual)
migration, which rural households find necessary, together with the receipt of subsequent remittances, given the persistence of rural poverty in most low-income
countries.
Finally, the reduced-form results suggest that the level of urbanization may
a~ravate the problems of the rural sector with respect to fertility as well as outmigration. We do not suggest direct restrictions on the growth of cities, but to the
extent that rural-urban migration has net negative effects on the society, the
observed relationships point to the need to slow down rural population growth
(e.g. through making family planning facilities more accessible in rural areas) and to
improve the attractiveness of life in the countryside. The latter is desirable in any
case to improve the standard of living of the majority of the population who live
in rural areas and often continue to be neglected in government's development
decisions [50J .
VII. SUMMARY
The purpose of this paper is to investigate the interrelationships between
rural fertility, rural-urban migration, and landholding patterns in developing
countries. We develop a two-equation recursive model in which the endogenous
variables are the level of rural fertility and the rate of out-migration from rural to
urban areas, with each (with appropriate attention to lags) influencing the other.
Landholding patterns as well as a number of other variables are included in each
equation. The model is tested for a cross-sectionof those (26) developing countries
for which the necessary data were available on all variables for the decade of the
1960s.
The statistical results provide strong support for the major hypotheses regarding the effects of landholding patterns on rural fertility and rural-urban
migration. Moreover, the results indicate that fertility and out-migration are interrelated - the higher the fertility the higher the subsequent rate of out-migration, and
the higher the out-migration in the previous decade the higher the subsequent fertility. The former relationship is stronger than the latter which was expected.
142
Landholding,
Bilsborrow and Winegarden
and Internal
Migration
143
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