migration from the land and urban unemployment in sierra leone

MIGRATION FROM THE LAND AND URBAN UNEMPLOY-MENT IN SIERRA LEONE*

by J. F. S. LEVI

IntroductionThe phenomenon of large-scale urban unemployment in Africa seems to have

developed generally within the last two decades, probably in line with the growthof urban industry. It is not in general the same as the involuntary unemploymentfound in industrial economies; rather, it is voluntary in the sense that it ischosen from alternatives and is not generally the result of the laying-off ofworkers. For the most part, the alternative rejected in favour of urban unemploy-ment is agricultural employment, or rural life as a whole, and hence migrationfrom the land is at the heart of the problem. Because of this it could be arguedthat urban unemployment is simply a locally intensified manifestation of ruralpoverty, with the added troubles of greater social unrest and crime, and with theincidental implication that it is less easy for politicians to ignore.'

The expansion of formal education seems to have exacerbated the problem inrecent years. It is said that those with some education tend to accept unemploy-ment while rejecting some urban employment opportunities as well as the ruralones, while they have an even greater distaste for the latter than those withouteducation. They tend to want 'white-collar' jobs rather than manual work andmay by-pass the latter in favour of waiting for office jobs even if there are manualemployment opportunities available to them. Until recently it was not at allclear what sort of policy should be used to cure unemployment in African town sbecause its causes were not understood. In spite of apparently falling chancesof obtaining employment (in some countries, falling employment), migration tothe cities has continued unabated, and even accelerated. Lately, however, econo-mistsnotably Todaro and Harris2have had some success in formulating theo-retical models of the phenomenon and suggesting policies to deal with it.

The purpose of this paper is to examine the factors affecting the rate ofmigration from the land and the level of urban unemployment in Sierra Leone,in a further effort to contribute to our understanding of the issue. Cross-sectiondata are employed in an attempt to determine why the rate of migration variesacross the country, and time-series data are used in an econometric study of

* I am grateful for helpful comments on earlier drafts from the following: C. J. Doyle.O. E. G. Johnson, G. T. Jones, G. L. Karr, J. B. Knight, S. J. Luckett and G. J. Tyler.

1 John Weeks, 'Does Employment Matter?' Manpower and Unemploymeni Research in.lfrica, Vol. 4 No. 1, April, 1971, while making this point, also argues forcibly that capital hasbeen made of the use of the emotive term 'unemployment' as if it were the same as the GreatDepression variety. Poverty and inequality are the real difficulties, but these terms are thoughtfo carry less weight with the economically advanced countries.

2 M. P. Todaro, 'A Model of Labour Migration and Urban Unemployment in Less DevelopedCountries', American Economic Review, Vol. LIX, March 1969, pp. 138-149. J. R. Harris andM. P. Todaro, 'Migration, Unemployment and Development: A Two-Sector Analysis',AmevicanEconomic Review, Vol, LX, March 1970, pp. 126-142

309

310 BULLETIN

unemployment, based on a theoretical model similar to those mentioned above.1

BACKGROUND

The principal migrant flow is from the rural areas to Freetown (and its en-virons), the capital city, with a population at the 1963 census of 2 128,000, com-pared with the second largest town, Bo, whose population was 27,000 and thethird largest, Kenema, with 13,000. Other urban places neighbouring Freetownmake an urban complex which had a population of about 150,000 in 1963. Usingthe 1948 and 1963 census results, we obtain a growth rate of the Freetown popula-tion over that 15-year period of 4.7 per cent per annum. The natural growthrate over the same period has been estimated at 2.4 per cent,3 subtractingwhich leaves a growth rate attributable to net migration of 2.3 per cent.

In 1966 and 1967 a household survey of the Western Province, in which Free-town is situated, was conducted.4 The Freetown population was estimated at160,000 at the end of 1967, implying a growth between 1963 and 1967 of 5.6 percent per annum. Of course, the later population is derived from a sample (ap-proximately one-ninth) and so the growth rate should be treated with due reserva-tion. Sampling errors apart, however, it is misleading to deal only with the cityof Freetown proper, because of the growth of contiguous industrial and urbanareas outside the city boundary during the 1960s; there will have been growingmigration to these areas, as well as to Freetown itself, and also movement betweenthem and the city. The 1967 population of these 'suburbs' was estimated as55,000 which, added to the Freetown population, gives a total of 215,000. In1963, the population of the same localities was 166,000,6 implying a growth rateof 6.5 per cent per annum over the four-year period. The crude rate of naturalincrease for Freetown in the 1960s has been estimated at 3.4 per cent per annum,7which, after subtracting from the total growth rate, leaves a growth rate of 3.1per cent attributable to migration. The Survey tells us that 2.8 per cent of theurban population moved in from outside the Western Province during 1967,8 so

The approach bears sorne similarity to that of J. B. Knight, 'Rural-Urban Income Com-parisons and Migration in Ghana', BULLETIN, Vol. 34, No. 2. 1972, pp. 199-228, especially withregard to the econometric analysis, in which the data and models are similar in some respects.Similar notation to Knight's has been employed therein, to facilitate comparison.

2 1963 Population Census of Sierra Leone, Vol. 1, Number of Inhabitants. Ccntral StatisticsOffice, Freetown, 1965.

Household Survey of the Western Area, Nov. 1966Jan. 1968. Final Report. HouseholdExpenditure and Income. Economic Characteristics and Migration. Central Statistics Office,Freetown, 1968, p. 18.

Ibid., The Western Province, or Western Area, is much smaller than the other three Pro-vinces of Sierra Leone-215 square miles out of a total of 28,000

Ibid. Table 20. The Survey refers to them as the Western Urban Area, but two smalllocalities out of the seven concerned are a few miles from Freetown the remainder, togetherwith the city, could accurately be termed 'Greater Freetown'.

6 1963 Population Census of Sierra Leone. Vol. 1, Number of Inhabitants. Central StatisticsOffice, Freetown, 1965.

Thomas E. Dow Jr., Sierra Leone, Country Profiles, The Population Council and The Inter-national Institute for the Study of Human Reproduction, Columbia University, September,1969, p. 1.

B Household Survey of the Western Area. Final Report. 1968, from Table 28. The two figuresare not exactly comparable because the estimate for 1967 from the survey refers to gross migra-tion, while the other is net. Also the 2.8 per cent excludes migration from the rural parts of theWestern Area. These discrepancies are probably not very significant, however.

MIGRATION AND URBAN UNEMPLOYMENT IN SIERRA LEONE 311

that a round figure of 3 per cent seems fairly acceptable; that is, an absoluteannual net inflow of roughly 6,000 persons.

The Household Survey also gives considerable detail about the compositionof the labour force, including the unemployed. Unfortunately we can gatherlittle information from this about any consideration of the problems involved inprecisely defining the term 'unemployed', apart from two things: a phrase usedin conjunction with the term, in the Report, is 'looking for work', and secondly,'No attempt was made to determine the extent of underemployment'.1 It wouldseem fairly safe to assume that an 'unemployed' person is truly doing no paidwork at all and is not self-employed, while being potentially employable; he haspresumably replied to questioning, that he is without work and wishes to havework. However, what is perhaps more important about the definition of the term'unemployment' is the period of time it refers to. In the 1963 census, which weshall come back to later on, questions concerning economic activities, includingunemployment, related to what people were doing most of the time during theprevious year.2 But it is not obvious that the same applies to the Survey.

Out of a total civilian labour force (aged 15 and over) of about 79,000 about12,000, or roughly 15 per cent, were unemployed, i.e. looking for work.3 Although25 per cent of the unemployed were classed as Clerical Workers, the majoritywere manual workers, comprising some 60 per cent of the unemployed.4 Howeverthe unemployment rate among Clerical Workers was highest at 31 per cent, com-pared with 23 per cent among Craftsmen and Labourers, the second highest rate.ilost of the unemployed were in the young age group (40 per cent were aged15-24 years), and these had the highest unemployment rates (48 per cent forthose aged 15-19 years and 27 per cent for those aged 20-24 years)

Table 1 gives, for the city of Freetown only, a two-way classification of un-employment rates, by age and occupation. Nearly 90 per cent of Clerical Workersaged 15-49 years were unemployed, but probably many of these were would-beschool boys as well as would-be white-collar workers: the two are inseparable.Even among manual workers, however, in the same age-group, unemploymentrates appear to have been very high: 70 per cent among Transport and Communi-cations workers and 50 per cent in the mai or group, Craftsmen and Labourers;so these high unemployment rates appear to be as much a function of age as ofoccupational aspiration.6 The rates of unemployment drop fairly sharply withincreasing age in all groups but still remain relatively high among Clerical andManual Workers.

About 50 per cent of the unemployed liad no formal education, about 20 percent had primary and about 30 per cent secondary and similar education. The

1 Ibid., p. 13. Attempts to ascertain the exact definition from the Central Statistics Officehave been in vain.

2 1963 Population Census of Sierra Leona, Vol. 3, Economic Characteristics. Central StatisticsOffice, Freetown, 1965, P. xvii,

Household Survey of the Western Area. Final Report, 1968. Tablc 20.i.e. Transportation and Communication Workers, Craftsmen and Labourers, and Service aiid

Recreation Workers.Ibid. ThIde 20.lt is not clear whether 'occulyItion refers to previous employnient or iiieielv to espiration

presumably it is more often the latter among the young.

Source: Household Survey of the Western Area, 1968.

Ordinary labourers receive about half the average earnings of clerks,2 so thatin so far as education offers the prospect of clerical work, the variation in rates ofunemployment by education level could, at least in part, reflect the differentincome levels attainable with education rather than the effects of education onwork preferences. Also the Household Survey indicates some relationship be-tween level of education and a ge (although this is not so apparent at the primarylevel). For example 22 per cent of the 15-19 age group had no education, com-pared with at least 44 per cent in the higher age groups; nearly 60 per cent of the15-19 year-olds had secondary education, compared with 36 per cent of 20-24year-olds and less than 30 per cent in the other age groups.3 Hence, the apparentrelationship between unemployment rates and education may to some extentreflect the influence of age on both variables. Thus even the relatively weakapparent relationship may be spurious.4

1 Ibid. Table 18.2 ibid. Table 16.

Ibid., Table 24.The data necessary to separate the effects of education and age were not available.

Occupation 15-19 20-24 25-29 30-34Age Group35-39 40-44 45-49 50-54 55+

% of thelabour

All force

Professional, tech-nical and relatedworkers .,, 33.3 14.3 8.7 4.8 7.1 5.7

Administrative, ex-ecutive & manage-erial workers 1 .6

Clericalworkers 89.7 47.0 31.6 7.9 23.1 22.2 12.1 5.0 14.3 31.9 13.2Sales workers ....M.3 3.8 2.2 - .9 1.1 - - - 1.3 26.4Farmers, fishermen,miners, etc....- _ .6

Transportation &communicationsworkers ... 70.0 9.5 12.2 18.0 6.7 12.7 17.9 7.7 13.6 14.6 10.7

Craftsmen & lab-ourers ,.. ,,. 50.0 28.0 24.8 16.3 19.1 16.7 17.5 11.8 21.9 21.5 29.0

Service & recrea-tionworkers .,. 18.2 26.1 5.1 12.3 5.7 13.6 12.9 9.8 9.3

Occupation notreported ... 100.0 100.0 100.0 100.0 100.0 75.0 100.0 50.0 42,9 90.2 2.2

Total ... .., 50.0 27.1 15.8 10.4 10.0 10.3 11.6 6.1 11.4 15.5 100.0Per cent of the

labour force ,,. 5.0 13.8 16.8 14.2 12.8 13.1 10.2 6.1 8.0 100.0

312 BULLETIN

highest unemployment rate was among those with primary education, at 19 percent compared with 14 per cent among those with no education and 16 per centamong people with secondary, technical and vocational education.' The varia-tion in unemployment rates with educational level is thus not great, and far lessthan the variation by occupation and by age especially.

Table 1Unemployment Rates by Age and Occupation Group, Freetown, End 1967

MIGRATION AN1) URBAN UNEMPLOYMENT IN SIERRA LEONE 313

We can gain a certain amount of further information about unemploymentfrom an earlier household survey of Freetown, conducted in 1960.1 This wasbased on a smaller sample than the 1967 survey, so that sampling errors weregreater; also the sampling frame omitted 'non-African' areas, so that the samplewas not strictly representative of Freetown as a whole. There was an estimatedoverall unemployment rate of 11 per cent, while the rate in the 15-24 years agegroup was about one quarter (compared with about one third in 1967). The 1963Census Report also allows the calculation of Freetown's unemployment rate (forthe civilian labour force aged 15 years and over) at nearly 13 per cent. Theseearlier figures compare with Household Survey estimates of 14 per cent at the endof 1966 and about 16 per cent at the end of 1967,2 suggesting a persistent rise inthe rate of unemployment during the 1960s.

The other source of data on unemployment in Sierra Leone is that providedmonthly by the employment exchanges, situated in the mai or towns, on numbersregistered as unemployed. Table 5 shows the average monthly numbers for theyears 1956 to 1970, and it can be seen that there has been a strong upwardtrend3. The largest number registering is, as is to be expected, at the Freetownexchange, which accounts for about 70 per cent of the total (including the MaritimePool, which is also in Freetown), with a further 20 per cent registered in theneighbouring Wellington industrial estate. The other five exchanges account foronly a few hundred each.4

Of course, registered unemployment is not necessarily actual unemployment.In theory, those looking for work are required by law to register at their nearestemployment exchange, but it is said that in practice many do not register.However, we have a rough means of checking the order of magnitude of theemployment exchange figures, at least for the Freetown area, by comparing themwith the figures obtained from the household surveys and the 1963 Census ofPopulation. The 1967 survey renders the number unemployed at the end ofDecember in Freetown as approximately 9,000 persons;5 an estimate subject tosampling errors, being derived from approximately a one-ninth sample. Thenumber registered at the Freetown employment exchange in December 1967 was10,143 (including the Maritime pool).6 This is close to the sample figure andstrongly suggests that non-registration is not as extensive as might have beenexpected, especially as the employment exchange figure is the greater of the two.Again from the 1963 Census Report, we obtain the number unemployed in Free-town as 5,794 (aged 15 and over)7 compared with 5,755 registered in 1963.8

H. T. Kumin, Report to the Government of Sierra Leone on Revision of the Consumer PriceinderFreetown: The Mine Workers' Price Index and Statistics of Employment. InternationalLabour Organisation, 1962.

2 Household Survey of the Western Area 1968, Table 4Although in 1971 there was a fall of about 1,500 to 13,766.Labour Department, Sierra Leone Ministry of Lands, Mines and Labour.Household Survey of the Western Area. Final Report. 1968. Table 20.Labour Department.1963 Census of Sierre Leone, Vol. 111. Econonsic Characteristics, Central Statistical Office,

Freetown, 1965. Calculated from Tables 2, 3, and 6.J. K. E. Cole and 1). Davies, 'Employment and Unemployment in Sierra Leone', lien/e of

»iena Leone Economic Review, Vol. 4. No. 1, June 1969, and international Labour Office,Bulletin of Labour Statistics.

314 BULLETIN

A similar, though somewhat rougher, comparison can be made for 1960-61using data from the earlier survey. The numbers unemployed in Freetown in1960 are estimated at about 4,400. Registered unemployment in 1960 is notavailable, but in 1961 the monthly average was 4,637 in Freetown.' Registeredunemployment in 1967 at the Freetown and Wellington exchanges totalled 12,448,while the Household Survey gives neàrly 12,000 unemployed in Freetown plusthe Western Urban Area. For the other five towns with employment exchanges,registrations in 1963 compare closely with the 1963 Census figures of unemploy-ment. For the country as a whole, the 1963 Census Report gives the numberunemployed as 30,795 (10 years and over), compared with only 9,226 registeredas such. The evidence is, then, that registered unemployment represents fairlywell the unemployment situation in the major urban areas, but not that in thecountry as a whole.

There is virtually no information on the extent of urban under-employment,but some notion of the relative sizes of the urban 'traditional' and 'modern' sectorscan be got from the 1963 Census, although, of course, the dividing line betweenthe two is rather nebulous. If we define the traditional sector as self-employedsales workers, craftsmen, labourers, etc., we obtain a figure of 11,383 or about30 per cent of the employed labour force in Freetown.

THE RATE OF MIGRATION

There has, unfortunately, been no comprehensive survey in the rural areasseeking to determine the factors affecting the rate of migration, as there has inGhana. There Caldwell2 found that economic motivation seemed to be dominant,and moreover, that the desire for extra income, was important.

the vast mai ority of the respondents explained rural-urban migration interms of more money and a better standard of living in the town rather thaninsufferable economic conditions in the village.'3For Sierra Leone there is little direct evidence. Ruth Finnegan, in her study

of the Limba,' who exhibit a high propenstity to migrate, gives as the mostcommon reason for leaving home: 'going to get work and money'; but underlyingthis are a variety of reasons for wanting the money, apart from the growingdemand for modern commodities; for example the need to pay bride-price, taxes,debts and money for law-cases; or indeed they may migrate to escape some ofthese payments.

Some of the circumstances causing migration are connected with farming it-self, for instance, the shortage of land:

'This applies mainly in Safroko where much of the land is degraded or in shortsupply. The average density per square mile is high for such poor land (nearly150 per square mile).5 Men therefore leave either because they have no landCole and Davies, op. cit., and ILO toc. cit.

2 J C. Caidwell, A/rico Rural-Urban Migration: The Movement to Ghana's Towns, AustrailianNational University Press, Canberra, 1969.

Ibid., p. 89.R. Finnegan, Survey of the J_imbu People uf Northern Sierra Leofle, Overseas Research

'ublication No. 8, Department of Technical Cooperation. HMSO, London, 1965, Chapter 7.128 per square mile, according to the 1963 Census.


to farm, if they are to give the bush the reasonable fallow period required bythe principles of shifting cultivation, or because their land has been given asa pledge for a debt or law case. The population is too large to be supported bythe available land and methods, and conditions are therefore unattractiveeven for those who had decided to stay and work their own farms."Banton2 has similar things to say about the area he studied in the north of

the country. In particular, he implies that high population density and the con-sequent land degradation where shifting cultivation is practised, are the dominantreasons for migration, since emigration seems to be heaviest from areas wherethis is the case.

We use three sources to provide data on migration froni the twelve 'Provin-cial' Districts (i.e. those outside the Western Province). These are, first, Banton'schapter on Migration,3 secondly, the 1960-61 household survey of Freetown4 andthirdly, the 1967 household Survey.5 Table 2 summarises the information theyprovide on migration to Freetown. It should be noted that the data under column

Table 2Migration to Freetown by District

Sources: (a) Banton, op. cit.(b) Kumin, op. cit.(cl Central Statistics Office, Freetown.

(b) obtained from the 1960-61 survey refer to the numbers born in each district,and hence to migration over quite a long period of years, whereas Banton'sfigures under (a) are essentially of migration within the preceding year. In spiteof this difference, and the difference in time between the two investigations (1953and 1960-61), the proportions of migrants to Freetown coming from each district

1 Ibid., p. 128.2 M. A. Bariton, West African City: A Study of Tribal Life in Freetown, London, Oxford

lTniversity Press, 1957. Chapter 3 and 4.Ibid., Chapter 5.Komm, op. cit.Household Survey of the Western Area. Final Report. 1968. I am grateful to the Central

Statistics Office, Freetown, for allowing me access to the raw data.

(a)New registrants at

Freetown Employment

(b)Sample No. of house-

hold heads born us

(c)Estimated numbers

migrating, 1967District Exchange, 1953 from

each Districtsuch district 1961 from each District

0/ 0/ 0//0 /0 /0

Bo ... .., 110 7.5 15 5.8 529 10.7Bombali 405 27.7 78 30.3 463 9.3Jionthe ... ... 23 1.6 7 2.7 110 2.2Kailahun .. ... 64 4.4 5 1.9 66 1.3Kambja ... ... 174 11.9 38 14.7 441 8.9Kenema ... 83 5.7 6 2.3 595 12.0Koinadugu ... ... 57 3.9 8 3.1 529 10.7Kono ... 45 3.1 1 0.4 265 5.3Moyamba ... ... 171 11.7 32 12.4 397 8.0l'ortLoko ... .,. 238 16.3 56 21.7 1345 27.1Pujehun ... ... 36 2.5 6 2.3 44 0.9Tonkolili ... ... 56 3.8 6 2.3 176 3 .6Total 1462 100.0 258 100.0 4960 100.0

316 BULLETIN

appear to be remarkably stable. Any change that occurred seems to have takenthe form of an even greater concentration of migrants from those Districts whichalready had heavy outward movements of population. Thus in 1953 the highestnumbers carne from Bombali, Port Loko, Kambia and Moyamba (68 per centcoming from these four Districts); which was also true in 1961, but the proportionswere larger, 79 per cent coming from all four districts. The 1967 figures of column(c) however, display quite a different pattern. In particular, the proportionsmigrating from Kenema, Bo and Koinadugu increased markedly at the expenseof the four previously dominant districts, except that the percentage from PortLoko district increased still further. The drop in the percentage corning fromBombali is very marked, this being from 30 per cent in 1961 to only 9 per centIt may be that 1967 was not a representative year. In particular, agriculturalcash incomes in the South and East of the country were low in that year becauseof the failure of the Produce Marketing Board to buy the export crops, especiallypalm kernels.'

This perhaps explains why migration from Bo and Kenema districts increased,and indeed why it increased from Port Loko, for in these districts the concentra-tion of palm kernel output is heaviest. The increase from Bo and Kenema, andalso that from Kono, may also reflect the drop in diamond-mining revenue in 1967,the mining operations being mainly in those areas.2 The drop in migration fromBombali and the increase from Koinadugu are difficult to explain, but again, itmay simpy be that 1967 was an unusual year, not only because of the Sierra LeoneProduce Marketing Board's malfunctioning, but also because of the occurrence ofpolitical disturbances which may have in general disturbed the migration patternto Freetown. On the other hand, the figures may reflect genuine long-term changesin the pattern; for example, in Bombali the scheme for the mechanical cultivationof rice has expanded more rapidly than anywhere else in the country, and in themid-60s, between five and ten thousand acres a year were being ploughed.3 Thisrepresented a not insignificant increase in rural incomes in the district and migra-tion may have fallen for that reason.4

A possible reason for the increase in migration from Koinaduga might be theprobably high natural rate of growth of the population there and rapidly in-creasing population pressure. Dow5 estimates gross reproduction rates for thetwelve districts, which are roughly proportional to fertility rates, and Koinaduguhas the highest of them all. A further reason might be that the border of Koina-dugu is probably a popular gateway from neighbouring Guinea, and that migrantsmight simply report their starting point within Sierra Leone when asked wherethey came from. But in any case, one has to remember that the sample involved

1 Estimates of real agricultural export earnings per head are given in Table 5.2 Diamond mining in Sierra Leone has an important labour-intensive (alluvial) clement.

See M. A. Havinden, G. L. Karr, O. E. G. Johnson, and J. F. S. Levï, Agricultural Develop-ment in West Africa: A Case Study of Sierece Leone. Forthcoming. Chapter 5.

In this context, it is interesting to note that there is evidence for migration to one area inthe South where mechanical cultivation has been expanded. A. O. Njoku, Labour Utilisation inTraditional Agriculture: The Case of Sierra Leone Rice Farms, unpublished Ph.D. dissertation,University of Illinois, 1971.

'1 E Dow Jr I ertihty in Sierra Leone Sierra Leone Ceographical Journ'il No 131)69.


is quite small, and that the apparent changes in the pattern of migration in 1967could well be only sampling accidents. In this context it is interesting to notethat of tile 24 migrants in the sample coming from Koinadugu 18 came in threegroups of six.

The 1960-61 survey' also provides data on migration to the mining areas ofMarampa, Yengema, Tongo and Bambawo in the form of Districts of birth of theheads of mineworkers' households. The distance of the place of origin from thedestination seems to be the dominant factor determining the flow of migrantsfrom any area. The greatest proportion of household heads (42 per cent) in theiron-mining area of Marampa came from Port Loko, the District in which tilemine is located; over half (56 per cent) of households head in Yengema,a diamondarea, came from the home District of Kono; in Tongo and Bambawo, the greatestnumbers came from Kenema, where the mines are situated, and neighbouringKailahun. Conversely, few migrants come from the more distant parts of tilecountry.

What determines the rate of migration to the Freetown area from differentparts of the country? One obvious factor, as with migration to the mining areas,is the distance from the city, representing the cost of making the journeyanimportant consideration in countries such as Sierra Leoneand the degree ofcontact with the city. But a further hypothesis put forward is that the rate ofmigration is strongly influenced by the degree of population pressure on the land.The references to Banton and Finnegan above are relevant here. In Sierra Leonethe predominant method of cultivation is the bush-fallow technique, or shiftingcultivation: a patch of bush is cut and burnt and rice is grown for a year only,followed perhaps by a year or two of other crops. The next rice crop is planted ona different burnt patch while old patches are left fallow for some years so thatthe fertility of the soil is renewed. With a fixed total area of land, as populationrises, the length of fallow becomes shorter as the same patch of bush is returnedto more and more frequently, i.e. the age of the bush at the time of clearing forcultivation declines with rising population pressure, and yields fall as fertilityis less and less fully restored. An agricultural survey conducted in the mid-60s2provides data on the age distribution of newly-cleared bush by District, whichcan be used to provide an indicator of population pressure. We expect a greaterrate of migration the lower the median age of the bush, cet cris jt'aribus. Distancewas measured crudely as the distance from the subjectively judged 'centre' of aDistrict to Freetown. Obviously, other factors must influence the rate of migra-tion, but because of the nature of the data and the small number of observations,we must satisfy ourselves with only fairly crude hypothesis-testing, and thatinvolving only what are considered to be the most important variables.

Rates of migration are obtained from columns (b) and (c) of Table 2 by divid-ing by the 1963 District population; in the former case the numerator is thesample number of Freetown household heads in 1961 born in a particular District;

1 Kumin, o. cii.2 Central Statistics Office, Agricultural Statistical Survey of Sierra Leone, 1965-66. Govern-

ment Printer, Freetown, 1967.

318 BULLETIN

in the latter case it is the estimated number of migrants to Freetown from aDistrict. Table 3 gives details of these data which are used in a multiple regres-sion analysis with the rate of migration as dependent variable, and with distancefrom Freetown and median age of bush as independent variables.

Table 3Estimated Ilotes of Migration from the Provincial Districts of Freetown, Distances

from Freetown, Age of Bush and Populations

Sources: Table 8; 1963 Population Census of Sierra Leone, Vol. 1; Agricultural StatisticalSurvey. 1965-66.

The regression results were not very conclusive when using tile 1967 migra-tion rates for the dependent variable and are not worth reporting, but, for thereasons outlined above, 1967 may have been an untypical year. The best equa-tion from the point of view of explanatory power and standard errors, using the1961 data, was as follows:

log M65 =8.65-2.80 log A -1.18 log D(1.61) (.57)

where the bracketed figures are standard errors, R2=.5563, and M61 is the rateof migration, A is the median age of bush and D is distance.

The coefficient of distance is significantly less than zero at the 5 per cent level.That of age is almost significant at the 5 per cent level. Although the coefficientof the age of the bush is not quite statistically significant, the magnitude of theeffect on the rate of migration seems to be large; ceteris j5aribus, a drop in themedian age by 1 per cent increases the rate of migration by 2.8 per cent.

An interesting insight into the causes of migration can he gained by lookingat the numbers of migrants coming from each Chiefdom-a smaller administra-tive unit than the District-there being 146 of these in Sierra Leone. Table 4shows the numbers of migrants (new registrants) from the seven Chiefdoms withthe highest numbers migrating together with their percentages out of all thosecoming from the Provinces (1,462).

Sample No. Estimated nos.of household migrating from

heads born in each DistrictDistance

fromMedian ageof newly

Population('OOOs)

District each District 1967 ± 1963 Freetown cleared bush1961 ± 1963 District pop. (miles) (years)District pop. (x 100) (%)

('OOOs)

Bo ... .072 .25 102 9.2 209.8Bombali 392 .23 85 7.1 198.8Bonthe .096 .15 86 9.6 73.2Kailahun .033 .04 157 8.6 150.2Kambia .276 .32 52 6.0 137.8Kenerna .026 .26 132 10.2 227.4Koinadugu .062 .41 138 7.6 129.1Kono ... .006 .16 146 11.0 167.9Moyamba .191 .24 54 9.2 167.4Port Loko ... .226 .54 31 7.1 247.5Pujehun ... .071 .05 126 8.7 84.9Tonkolili .033 .10 88 7.4 184.5

F

Source: Banton, o. cit., Chapter 4, Table 11, p. 64.

One quarter of all migrants came from four Chiefdoms, while over one thirdcame from the seven Chiefdoms shown, and this out of a total of 146 Chiefdoms.These seven Chiefdoms are all known to be areas with high population pressureand very degraded land. They have relatively high population densities withlittle swampland for possible intensive cultivation of the staple food, rice, as analternative to the bush-fallow cultivation of the upland areas. Hence, whenfallow periods and soil fertility fall as population rises, there is a strong incentiveto move out of the area as it is not feasible to make changes within agriculturethat will remedy the situation.1 For example, 174 new registrants in 1953 camefrom Kambia District, of whom 107 (61 per cent) came from Tonko LimbaChiefdom (the Chiefdom with the highest rate of migration of all), where there islittle swamp land, whereas only 8 came froni neighbouring Samu Chiefdom, whichis of similar area, has a greater population and is closer to Freetown, but is wellendowed with tidal swamps used for intensive rice production.

The rate of seasonal migration seems to be determined by more or less the samefactors as those that affect permanent migration. Banton2 gives the Districts oforigin of registrants at the Freetown Employment Exchange during January andFebruary of 1953, who had registered previously but had evidently been back tothe provinces. The percentages of seasonal migrants coming from each Districtare very similar to the percentages of supposedly permanent migrants shown inthe second column of Table 2.

THE DETERMINANTS OF UNEMPLOYMENT1. Modes

Todaro3 recently put forward the idea that the rate of migration and thesupply of urban labour depended not merely on income levels in rural and urbanoccupations, but also on the probability of getting urban employment. Theprobability itself he makes a function of urban employment and unemployment,with the implication that there will be an equilibrium rate of unemployment (oremployment).

See the quotation from Finnegan above.O. cit.M. P. Todaro, op. cit.


Table 4Numbers of Migrants from the Chi efdoms with the highest rates of migration, 1953

Clziefdonz DistrictNo. of

migrantsPer cent of

total migrantsCumulativeper cent

Tonko Limba ,.. Kambia 107 7.3 7.3Pendembu-Gowahun Bombali 98 6.7 14.0Biruva ... ... Bombali 82 5.6 21.6Maforki ... ... Port Loko 52 3.6 25.2Safroko Limba ... Bombali 49 3.4 28.6Sela Limba ... Bombali 44 3.0 31.6Bombali Sebora Bombali 37 2.5 34.1

Total ... ,.. 469 34.1Total, all migrants 1462 100.0

320 BULLETIN

In his first article, Todaro mistakenly made the rate of change of the urbanlabour force (the constant natural rate of increase plus the rate of change as aresult of migration) a function of the expected rural-urban income differential,i.e. the probability of getting an urban job multiplied by the differential. How-ever, he and Harris1 later correctly made the rate of change of the urban labourforce a function of the probability times the urban wage, minus the rural incomelevel. They postulated as an equilibrium condition that this difference be zero,i.e. the expected urban wage be equal to the agricultural real wage.

Knight2 formulates a similar but static model. He has no function explainingthe rate of change of the urban labour force or the rate of migration, but simplysays that migration occurs until the equilibrium condition is reached, i.e. aswith Harris and Todarowhere the expected urban wage3 equals rural income:

(a+ß)L(1)

Uwhere a =rate of job turn-over in urban sector

ß =new jobs as a proportion of existing jobs= (exogenuosly determined) urban wage

f = rural income (independent of number of rural workers)L =urban employment

and U = urban unemploymentRe-arranging, the equilibrium level (not the proportion) of unemployment

is given by:

In his later article on Ghana,4 Knight uses this as a basis for an econometricstudy with a measure of unemployment as dependent variable and measures ofthe other variables above as independents, in a multiple regression equation.The implied assumption in using such a static model is that adjustments in theequilibrium level of unemployment take place in a year (using annual data in theeconometric study). This seems unlikely, or at any rate cannot be assumed.Instead, we make the assumption that adjustments towards equilibrium may helagged, and use the well-known partial adjustment formulation :

i.e. the change in actual unemployment is a fraction of the difference betweenequilibrium unemployment in the current period and actual unemployment inthe previous period. Hence

U=U"+(1) Ut_l (4)

1 J R. Harris arnd M. P. Todaro, op. cit.2 J B. Knight, 'Wages and Employment in Developed and Underdeveloped Economies,

Oxford Economic Papers, NS., Vol. 23, No. 1, March 1971, pp. 42-58.The money value placed on the probability of getting a job is assumed equal to the wage

rate multiplied by the probability. Ibid., p. 53.Knight, J. B., 'Rural-Urban Income Comparisons and Migration in Ghana', ¡oc. cii.See Nerlove, M., The Dynamics of Supply. Estimation of Farmers' Respone to Price, Bal-

timore, 1958. Note that this precludes the necessity for a function explaining the rate ofmigration, or rate of change of the urban labour force as in the Harris-Todaro model.

MiGRATION AND URBAN UNEMPLOYMENT IN SIERRA LEONE 321

We modify Knight's model further, by including as part of urban income anelement of maintenance income channelled from those who are employed to thosewho are unemployed. Otherwise, the model implies that if people are not em-ployed they have zero real income. Let be an average fraction of ' madeavailable to the unemployed (usually directly given to kinsmen, but perhaps madeavailable via petty jobs).' Thus a fraction of the total wage bill, Çy L is ef-fectively shared among the unemployed, and each on average receives « L.

UTherefore the total subjective income level of the urban unemployed is this plusthe value placed on the probability of getting a job, as above, and migrationoccurs until this is equal to the rural income per worker, i.e. when:

(a+L+b LfU U

Equilibrium unemployment is given by:Ue=WL(a+p+b) (6)

r

==' (A L+(a+13) L) (7)

r

since ß =AL/L. Assuming a and are constant, the above reasoning justifies amultiple regression equation of the following form:2

Ue=f(W, R, A L, L) (8)

where the letters on the right hand side now refer to appropriate measured ver-sions of the corresponding theoretical variables.

Substituting into (4) (and referring to measured variables) we now have:

Ut=g(Wt, R, A L, L, U1) (9)

where partial derivatives with respect to Wt, AL, L and U1 are positive, andthat with respect to R is negative.

2. The Data

Details of the data are presented in Table 5. The degree to which registeredunemployment, which we use as the dependent variable, reflects actual unemploy-ment has been discussed above. The evidence is that for the towns containingemployment exchanges correspondence is very close, but that total unemploy-ment is much larger than the unemployment in those towns. However, we areonly concerned with urban unemployment, the extent of which depends on how

See Caroline Hutton, 'How the Unemployed Survive in Town: Kampala and Jifia,Uganda', Manpower and unemployemnt Research in Africa, Vol. 3., No. 2., November 1970.This is also suggested by George E. Johnson, 'The Structure of Rural-Urban Migration Models',Easf eye Africa Economic Review, Vol. 3, No. 1, June 1971, pp. 21-28.

a and b may not he constant. Knight ('Wages and Employment. . .', p. 53) suggests a =f(w), f' <o, while Johnson (op. cit.) suggests b might also depend on w. 'There is little evidence,but one suspects the year-to-year effects are weak, and in any case the wage variable shouldtake up most of them,

322 BULLETIN

Table 5Data for Regression Analysis of Unemployment

Definitions, Notes and Sources.U. Average monthly registered unemployment (including the Maritime Pool). Source:

International Labour Office, Bulletin of Labour Statistics.W. Average hourly earnings, cents, unskilled building and construction work, deflated by

the Freetown Consumer Price Index. 1964, 1965, 1968, 1969 and 1970 are minimum wage rates,but it is unlikely that these are very different from earnings. Sources: Labour Department,Sierra Leone Ministry of Lands, Mines and Labour; International Labour Office, Bulletin ofLabour Statistics. (100 cents=1 Leone==0.50 sterling).

R. Index of per capita agricultural real incomes, 1956 = 100. Sources: United StatesDepartment of Agriculture, Indices of Agricultural Production in Africa and the Near East,USDA Economic Research Service, ERS Foreign, 265, and private correspondence; M. A.Havinden, G. L. Karr, O. E. G. Johnson, and J. F. S. Levi, Agricultural Development in WestAfrica: A Case Study of Sierra Leone. Forthcoming.

L. Index of non-agricultural employment in establishments with six or more employees.December of preceding year. 1969=100. Source: International Labour Office, Bulletin ofLabour Statistics.

Sources: USDA op. cit., and private correspondence. 1970 is preliminary.1955-1961: based on index of food prices, 1955=100. 1962-1970: index of food and

drink prices, 1961 = 100. (The weight for drink is less than one-thirteenth of the total). Sources:J. M. Due, Changes in Incomes and Imports of Consumer Goods in Sierra Leone. University ofIllinois College of Agriculture, Agricultural Experiment Station, Bulletin 719, 1966. Bank ofSierra Leone, Economic Review.

Le Million at 1961 prices. For the three most important export crops, palm kernels,cocoa and coffee, Sierra Leone Produce Marketing Board purcheses were multiplied by deflatedup-country Buying Station prices, (or for some years, for coffee, volume of exports by estimatedproducer prices). For the other three minor crops included, the deflated value of exports wasused. Source: Havinden, et. al., op. cit.

we define 'urban'. All except one of the seven towns with populations greaterthan 10,000 in 1963 are served by employment exchanges, but only two out ofthe ten with populations between 5,000 and 10,000. Registered unemploymentis therefore representative of actual urban unemployment provided we take10,000 as the lower limit of 'urban'.

For the urban wage variable we use average hourly earnings of unskilledbuilding and construction workers deflated by the Freetown Consumer PriceIndex. A point to note is the theoretical importance of using hourly earnings inview of the fact that rural workers often put in few hours relative to those put in

W R L

Index ofper cap.food pro-

ductiois (1)

FreetownFood PriceIndex (2)

Real percapita

agricultu-val exports

earnings (3)

1956 4834 70.51957 5598 6.6 100.0 81.3 100.0 110.8 3.141958 6586 9.5 102.0 84.0 101.3 98.9 3.391959 7233 9.0 102.4 81.3 100.9 99.3 3.591960 7889 10.5 103.7 83.3 103.4 97.3 3.321961 6382 8.9 105.3 84.6 104.6 100.0 3.511962 8539 12.0 104.6 89.4 105.7 95.6 2.991963 9226 8.8 103.7 94.1 106.8 89.8 2.451964 9863 9.2 102.9 100.0 105.8 98.5 2.461965 11566 9.5 97.5 104.2 101.9 102.3 1.931966 12894 8.5 98.1 113.4 99.7 104.8 2.661967 13690 8.8 93.3 113.8 99.8 109.5 1.211968 14113 8.6 98.6 110.1 102.7 108.5 2.011969 14723 9.3 95.6 109.2 98.5 113.5 2.231970 15269 8.6 97.5 112.4 100.5 129.4 2.28


by non-agricultural workers. This series was probably the best available, butone difficulty is that it may not reflect the wage variable to which those witheducation respond; one would expect this to be the earnings of clerical workers,which may be double those of unskilled workers. A refinement, had the data beenavailable, would have been a weighted average wage of say clerical earnings andlabourers' earnings, using as weights the proportions unemployed in each cate-gory.1

The most problematic variable, of course, is rural income. For this, physicalproduction figures obtained from the United States Department of Agriculturewere used in the main. These were multiplied by a set of constant price weightsand divided by population2 to give per capita agricultural production. Thereasons for using this approach rather than multiplying by current price weightsand deflating, are firstly, that it is not clear what deflator should be used, andsecondly, that a large proportion of agricultural production is not sold and of theamount that is sold, relatively little goes out of the agricultural sector. Someattempt was made, however, to allow for cash income from production sold outof the rural sector by including deflated estimates of earnings from agriculturalexport crops (see Table 5).

The food production figures included only those for the major commodities,namely: rice (husk), millet and sorghum, maize, cassava, sweet potatoes andyams, groundnuts (in shell), citrus fruits, bananas and plantains, and palm oil.The data for minor products were not considered sufficiently accurate to be in-cluded and in any case showed little variation through time.

The price weights were averages of 1961-1965 farmers' prices. It is probablethat the official rice price used understates the actual figure because of the govern-ment's policy of price-pegging; even so in 1968, for example, rice accounted fornearly half of the total value of the above products. Thus, variation in riceproduction has a predominant influence on variation in the aggregate figure.Second in importance to rice is cassava, which in 1968 accounted for 18 per centof the value of the products included.

The orders of magnitude of the production figures and the relative quantitiesof the different products are probably fairly reasonable as they are based on anational agricultural survey conducted in 1965-66g. It is, of course, the annualvariation in production that presents the difficulty. Generally, production isarrived at by estimating yields in different parts of the country and for differenttypes of production (for example, mangrove swamp rice, upland rice, etc.) andapplying these estimates globally. Probably it is as good a technique as is possibleunder the circumstances.

A simple test of the broad validity of these statistics was made by looking at

1 Judging from the figures presented in the Household Survey, these weights would be about25 and 75 respectively for 1967. The simple variable used would be highly correlated with theimproved version, however, so this short-coming need not trouble us unduly.

2 Based on the 1963 Census population and a 2 per cent growth rate. The latter figure wasprovided by Dr. Thomas E. Dow, Jr., formerly of the Population Council, New York, a demo-grapher who has done considerable research on the population of Sierra Leone.

Central Statistics Office, Agricultural Statistical Survey of Sierra Leone, 1965-66. Goverii-ment Printer, Freetown, 1967.

Regression Coefficients with Standard Errors Beneath

The other coefficients are all of expected sign, although the coefficient of t

is less titan twice its standard error. However, in equation III it is significantlyless than zero at the 5 per cent level.

Taking approximate orders of magnitude for the coefficients, it seems reason-able to draw the following conclusions about the short-run effects of the variableson uneniployment from these equations. Other things being equal:

1. An increase in the real level of hourly earnings by 1 cent2 (the averagebeing about 9 cents in recent years) would increase absolute unemployment byabout 400 (i.e. by about 4 per cent from the mean level).

1 The point for 1970 is seen to lie away from the main scatter and suggests that the produc-tion estimates for that year (which are preliminary) are too high. For this reason, regressionswere tried excluding the observations for 1970.

2 100 cents=1 Leone=0,50 Sterling. approximately.

Time Period Equation Constant W Rt JL Lt TJt1 R2 DW d.f.

1957-1970

1957-1969

1

11

III

1V

7923

5771

6574

6370

411.4(170.2)363.7

(142.2)386.6

(175.7)382.3

(141.9)

-140.9(79.7)

-126.7(72.7)

-137.3(81.3)

-136.1(72.5)

35.1(61.9)

3.6(73.2)

64.0(51.9)85.1

(34.7)89.6

(60.9)92,0

(35.0)

0.67(0.19)0.59

(0.12)0.54

(0.25)0.53

(0.13)

0.972

0.974

0.968

0.972

2.38

2.33

2.48

2.48

8

9

78

324 BULLETIN

the relationship between the Freetown Food Price Index and the estimated indexof per capita food production. Figure 1 shows a scatter diagram for these twovariables, and it can be seen that the negative correlation is fairly strong.1 This,together with the fact that there is also some negative correlation between theestimates of rice production and imports of rice, lends some support to the use ofthese data.

For the employment variable we use non-agricultural employment in estab-lishments with six or more employees, which is, of course, unsatisfactory not onlybecause it does not cover all employment but also because the response to theregular enquiry that determines this series may vary. The hope is that the varia-tion in the data reflects the variation in the general employment situation. Thedata are also unsatisfactory because the employment variable refers to the Decem-ber of the preceding year, whereas unemployment is the average monthly valuefor the whole year. The change in employment is also lagged and refers to thechange over the preceding year, but this may be thought to indicate the expectedchange in the current year, or a lagged response of unemployment to a change inemployment.

3. Resulls

The principal regression equations are shown in Table 6. AL is seen to benon-significant, especially in equation III, and dropping it improved the sig-nificance of the other variables.

Table 6Regression Equations Explaining Unemployment

MIGRATION ANT) URBAN UNEMPLOYMENT IN SIERRA LEONE 325

An increase in tile index of real per capita agricultural incomes by 10, orabout 10 per cent above the average (and recent) levels, would reduce absoluteunemployment by about 1,300, or approximately 13 per cent from the mean.

An increase in the index of employment by 10, or about 10 per cent aboveaverage (and recent) levels, would increase absolute unemployment by about900, or approximately 9 per cent from the mean level.

It would appear then that changes in agricultural incomes have the strongesteffect, in proportionate terms, on unemployment in the same year, while theeffect of changes in the level of employment is not much less. The effects ofchanges in real wages appear to be relatively weak. To obtain the long-run in-fluence of these variables, we divide the regression coefficients by 1 minus thecoefficient of Ut.' Since this coefficient is a little more than 0.5, the long-runeffects are more than twice the short-run effects. Thus, an increase of 10 per centin per capita agricultural incomes would reduce unemployment in the long-runby over 2,600, while an increase in employment of 10 per cent would increaseunemployment by over 1,800. Of course, in view of the nature and quality of thedata, these results should be treated with caution. Nevertheless, the approximateorders of magnitude and the signs of the coefficients, seem at least moderatelyconvincing.

CONCLUSIONSUnemployment rates in the Freetown area vary to the greatest extent with

age-group, nearly half those aged 15-19 being unemployed, for example. Varia-tion by occupation is not marked although clerical workers tend to have higherunemployment rates than manual workers; but there are many more unemployedamong the latter. Unemployment rates also appeared to depend to some extenton educational level. However, the positive relationship is not as strong as mighthave been expected, and it may even be spurious since educational level is relatedto other variables that are likely to affect unemployment rates, especially age.The evidence does not lend itself to the hypothesis that more education alonebrings about a greater willingness to be unemployed, i.e. a greater aversion tosome, especially manual, work among the educated than among those withouteducation.

There is fairly strong evidence, not only that absolute urban unemploymentlias risen since the 'SOs, but also that the rate of unemployment has increased inthe '60s, from around 10 per cent to about 16 per cent at the end of 1967 inFreetown.

Evidence is provided in support of the hypothesis that population pressure isan important factor determining the rate of migration to Freetown, althoughother factors may be at work. There is some suggestion that many migrantscome from relatively small pockets of the country where land degradation andpopulation pressure are great and where there is little opportunity for changingagricultural techniques. The apparently great reduction in the rate of migration

Long-run equilibrium unemployment is reached when Ut=Ut1=Ue, say.Hence )Je=a+bWt+cRt+dLt+e IJe

1 (a+bWt+cRt4-dL)1e

326 BULLETIN

from a district which contains some of the most degraded land (Bombali), maywell be due to the provision of new techniques by government, in the form ofextensive mechanical cultivation of rice land. These conclusions are reinforcedby the results of the time series regression analysis which indicates a strong(negative) influence of agricultural incomes on the level of urban unemployment.

The regression study was based on a dynamic model of unemployment, andindicated that two variables exercise a dominant influence on unemployment,namely, rural incomes, as mentioned, and the amount of employment; moreover,the long-run effects of changes in these variables are apparently about double theshort-run effects. Changes in real wages do not appear to have as much effect,perhaps because they are so much higher than rural incomes anyway, that thesmall changes that can occur are not of much significance to would-be entrantsto the urban labour force.Institute of Agricultural Economics,Oxford University.

Figure I,food price index

Freetown130 - '70

120

'6.9

'57

'67 '68

'66

'65

'59 '62

I J I

98 99 100 101 102 103 104 105 106 107

index of per capita food production

'64

'62

'63

'58 '60

110

100

90

migration from the land and urban unemployment in sierra leone

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