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Non‐farm income andinequality in ruralPakistan: A decompositionanalysisRichard H. Adams Jr. aa International Food Policy ResearchInstitute, Washington, DCPublished online: 23 Nov 2007.

To cite this article: Richard H. Adams Jr. (1994): Non‐farm income andinequality in rural Pakistan: A decomposition analysis, The Journal ofDevelopment Studies, 31:1, 110-133

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Non-Farm Income and Inequality in RuralPakistan: A Decomposition Analysis

RICHARD H. ADAMS, JR.

This article uses three-year panel data to analyse the impact ofnon-farm income on income inequality in rural Pakistan. Afterpinpointing the importance of rural non-farm income for thepoor, the article decomposes total rural income among fivesources: non-farm, agricultural, livestock, rental and transfer.This decomposition shows that non-farm income represents aninequality-decreasing source of income. The study then decom-poses the sources of non-farm income. This analysis reveals thatwhile non-farm unskilled labour income has an equalising effecton income distribution, non-farm government income has adisequalising effect.

In the past many researchers and policy-makers have viewed the ruraleconomy of the Third World as being synonymous with agriculture.According to this view, rural households receive the bulk of their incomefrom the production of food and export crops.

Within the past few years this view has begun to change. There is nowa growing recognition that the rural non-farm sector - which includessuch diverse activities as government, commerce, manufacturing andservices - also plays a vital role in the economies of many rural ThirdWorld households.

This changed view is largely due to the results of rural budget surveysin a number of developing countries, which suggest that non-farmincome represents between 13 and 67 per cent of total rural householdincome.1 According to these surveys, the contribution of non-farmincome to total rural income is especially high in those areas where

Richard H. Adams, Jr., International Food Policy Research Institute, Washington, DC. Anearlier version of this article was presented as a paper at the annual meeting of the PakistanSociety of Development Economists, Islamabad, Pakistan, January 1993. The author isgrateful to Jane He for computer assistance. The article benefited from the comments ofHarold Alderman, Aly Ercelawn, Ivo Havinga, Sohail Malik and an anonymous reviewer.

The Journal of Development Studies, Vol.31, No.l, October 1994, pp.110-133PUBLISHED BY FRANK CASS, LONDON

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NON-FARM INCOME AND INEQUALITY IN PAKISTAN 111

unfavourable labour-to-land ratios constrain income-earning opportuni-ties in agriculture. In land-restricted areas of the Third World - likeSouth and South-east Asia - the rural non-farm sector is now oftenviewed as a key source of income for rural households.

Despite such considerations, there is still no general agreement onone central issue, namely, what is the impact of rural non-farm incomeon income distribution? On the one hand, studies by Chinn [1979] andHo [1979] in Taiwan indicate that non-farm income reduces ruralincome inequality. According to Chinn, non-farm income benefits thepoor because the share of non-farm income varies inversely with farmsize. On the other hand, some studies have produced quite differentresults. For example, Reardon, Delgado and Matlon [1992] in BurkinaFaso, Collier, Radwan and Wangwe [1986] in Tanzania and Matlon[1979] in Nigeria all find that non-farm income has a negative impact onrural income distribution.

Part of this inconsistency is perhaps due to differences in study sites. Inland-scarce, labour-rich settings - like Taiwan and much of Asia - smalland inadequate landholdings may tend to 'push' poorer households outof agriculture and into the non-farm sector. Thus, in these settings non-farm income may be expected to have a favourable impact on equity. Theobverse, then, could hold in land-rich settings - such as Africa - whereabundant land and scarce labour may tend to keep most people in agri-culture and to 'pull' only richer households into the non-farm sector.2

As tempting as such a hypothesis may seem, it is clouded by theresults of a recent study by Shand [1987] of the Kemubu Project innorth-eastern Malaysia. According to Shand, in this land-scarce ruralproject area non-farm income does not have a favourable impact onincome distribution. When households in this rural project area areranked by income, non-farm income has a disequalising impact onincome distribution because of the lack of local unskilled labour oppor-tunities for the rural poor.

This article proposes to clarify the impact of rural non-farm incomeon income distribution by analysing the results of a new rural householdsurvey in one specific South Asian setting: Pakistan. The appraisal seeksto make two contributions. First, it uses decomposition techniques topinpoint the contribution of five different sources of rural income -including non-farm income - to total inequality. This is useful becausepast studies have often attempted to evaluate the distributional impactof non-farm income by merely comparing the size distribution of non-farm income with that of rural income as a whole.3 Second, the articledecomposes the sources of non-farm income inequality with a view tounderstanding the differential impact of various types of non-farm

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112 THE JOURNAL OF DEVELOPMENT STUDIES

income on income distribution. To the best of my knowledge, none ofthe previous studies have attempted to do this.

The study proceeds in five sections. Section I presents the decompo-sition of several inequality measures. Section II presents the data setfrom the three-year study in rural Pakistan. Section III discusses thecontribution of non-farm income to total income inequality and sectionIV decomposes the sources of non-farm income inequality. Section Vsummarises the results.

I. THE DECOMPOSITION OF INCOME INEQUALITY

At the start of any decomposition exercise, the question arises: whatmeasure of inequality should be chosen for the analysis? Several differ-ent inequality measures have been proposed in the literature [Fields,1980]. Following Foster [i9&5] and others, the chosen measure shouldhave five basic properties. They are: (1) Pigou-Dalton transfer sensitiv-ity; (2) symmetry; (3) mean independence; (4) population homogeneity;(5) decomposability.

Pigou-Dalton transfer sensitivity holds if the measure of inequalityincreases whenever income is transferred from one person to someonericher. Symmetry holds if the measure of inequality remains unchangedwhen individuals switch places in the income order. Mean independenceholds if a proportionate change in all incomes leaves the measure ofinequality unchanged. Population homogeneity holds if increasing (ordecreasing) the population size across all income levels has no effect onthe measured level of inequality.

The property of decomposability allows inequality to be partitionedeither over sub-populations or sources. It is the latter type of decompo-sition that is the subject of this analysis. Ideally, an inequality measurecan be regarded as source decomposable if total inequality can bebroken down into a weighted sum of inequality by various incomesources (for example, non-farm and agricultural income). However,since activities which influence a particular source of income are likelyto have an impact on other activities from which total income is com-prised, any inequality measure which is source decomposable mustaddress covariance among the income sources.

There are several measures of inequality which meet the five preced-ing properties. These measures include Theil's entropy index T, Theil'ssecond measure L, the coefficient of variation and the Gini coefficient.The two Theil measures, however, are not decomposable when sourcesof income are overlapping and not disjoint. While the need for non-overlapping groups is not restrictive when inequality is decomposed

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NON-FARM INCOME AND INEQUALITY IN PAKISTAN 113

over regions, this restriction rules out using the two Theil measures inthis study because many of the survey households receive income fromseveral different sources.

Shorrocks [1982] has shown that the results of decomposing anyinequality measure depend on the rule used in the decompositionprocedure. In the absence of any restrictions, for any inequality measurethe inequality of total income can be allocated in many ways betweenthe components of total income [Shorrocks, 1982:199]. For this reason,it seems best to base the decomposition analysis in this study on the tworemaining inequality measures: the coefficient of variation and the Ginicoefficient.

The source decomposition based on the coefficient of variation can bedeveloped following Shorrocks [1982] and Ercelawn [1984]. Let totalincome, y, consist of income from k sources. The variance of totalincome, a2, can be written as the sum of variances of each source ofincome, a2^ and of the covariances between sources of income, ov:

a2=ScT2.+ 2 ex (1)i 5̂ j

The contribution of the i-th source of income to total income varianceconsists of the i-th income variance and the part of the covariances allo-cated to the i-th source. According to Shorrocks [1982], the 'natural'decomposition of the variance assigns to the i-th source exactly one-halfof all covariances involving the i-th income source. This leads to theexpression:

CT2=2(Tiy (2)

where the (absolute) contribution of the i-th source is measured by itscovariance with total income, y. This relationship can be rewritten so asto express the contribution in relative terms. As is apparent, the relativecontributions remain the same whether inequality is measured by thevariance or by the coefficient of variation. Since the variance does notmeet the axiom of mean independence (that is, it is not invariant to pro-portional changes in all incomes) the coefficient of variation will beadopted here. The decomposition corresponding to the coefficient ofvariation can be further elucidated by defining the following terms:

Swfi-l.w^.c^Sfi (3)

where WjCj is the so-called 'factor inequality weight' of the i-th source inoverall inequality; |X; and (x are the mean income from the i-th source

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114 THE JOURNAL OF DEVELOPMENT STUDIES

and from all sources, respectively; c; is the relative concentration coeffi-cient of the i-th source in overall inequality; and p is the correlationcoefficient between the i-th source and total income.

The decomposition of the Gini coefficient can be developed as fol-lows. Pyatt et al. [1980] have shown that the Gini coefficient of totalincome, G, can be written as:

G = 4 Cov (y> r> (4)where n is the number of observations, y refers to the series of totalincomes and r refers to the series of corresponding ranks. On this basisthe Gini coefficient of the i-th source of income, G;, can be expressed as:

G=njk Cov (yi, r,) (5)

where y; and r{ refers to the series of incomes from the i-th source andcorresponding ranks, respectively. Since total income is the sum ofsource incomes, the covariance between total income and its rank canbe written as the sum of covariances between each source income andrank of total income. Equations (4) and (5) can then be used to expressthe total income Gini as a function of the source Ginis:

^ (6)

where R is the 'correlation ratio' expressed as:

covariance between sourceincome amount and total income rank

R. = cov (y.,r) = : (7)1 y^ ' covariance between source v '

COV (y^rj) income amount and total income rank

The decomposition corresponding to the Gini coefficient can thenexpressed by defining the following terms:

(8)

where Wjgj is the 'factor inequality weight' of the i-th source in overallinequality; and g; is the relative concentration coefficient of the i-thsource in overall inequality.

An income source can be defined as inequality-increasing or inequal-ity-decreasing on the basis of whether or not an enlarged share of that

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NON-FARM INCOME AND INEQUALITY IN PAKISTAN 115

income source leads to an increase or decrease in overall incomeinequality. From the decomposition equations (3) and (8), it follows thatthe i-th income source is inequality-increasing or inequality-decreasingaccording to whether C; (or g() is greater than or less than unity.4

II. DATA SET

Data come from a three-year survey of 734 households in threeprovinces in rural Pakistan.5 Since the goal of this survey was to analyzethe determinants of rural poverty, the survey was not designed to be rep-resentative of the rural population as a whole in Pakistan. In eachprovince the poorest district was selected on the basis of a productionand infrastructure index elaborated by Pasha and Hasan [1982], Theselected districts included Attock (Punjab province), Badin (Sindprovince) and Dir (North-west Frontier province). Since rural povertyalso exists in relatively prosperous areas, a fourth district Faisalabad(Punjab province) was added to the survey.6

Surveying of the households continued over a three-year period,1986-87, 1987-88 and 1988-89. Of the total 734 households, sevenhouseholds were excluded because of missing or incomplete data. Theanalysis is therefore based on data from 727 households.7

Total income for each household was divided into five sources:

(1) Non-farm - Includes wage earnings from non-farm labour, govern-ment and private sector employment plus profits from non-farmenterprises;

(2) Agricultural - Includes net income from all crop productionincluding imputed values from home production and crop by-productsplus returns to own agricultural labour;

(3) Livestock - Includes net returns from traded livestock (cattle,poultry) plus imputed values of home-consumed livestock plus tractionpower;

(4) Rental - Includes rents received from ownership of assets such asland, machinery and water;

(5) Transfer - Includes pensions (government), internal and interna-tional remittances and zakat (payments to poor).

Although the reasons for dividing income into these five sources

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116 THE JOURNAL OF DEVELOPMENT STUDIES

should be apparent, the rationale for distinguishing between agriculturaland livestock income may need clarification. On the one hand, someobservers may claim that within a rural subsistence economy it is artifi-cial (and empirically difficult) to distinguish between agricultural andlivestock income, since outputs from one - such as straw and cropresiduals from agriculture and draft power and manure from livestock -are used as inputs in the other. On the other hand, the goal of this studyis to disaggregate the sources of income inequality as finely as possible.For this reason, it seems essential to distinguish between agriculturaland livestock income, because these two income sources have verydifferent effects on inequality.

As shown in Table 1, the three-year mean correlation betweenagricultural income and total income is the highest of all five incomesources: 0.636. By contrast, the three-year mean correlation betweenlivestock income and total income is the lowest of all income sources:0.174. One of the main reasons for this difference has to do with land.In Pakistan, as in many developing countries, land is distributed farmore unevenly than income. While the Gini coefficient for three-yearmean income in the survey sites is 0.381, the Gini for landholding (thatis, land owned plus land rented in minus land rented out) is 0.630. In thisstudy agricultural income is highly correlated with landholding: a simple

TABLE 1

SIMPLE CORRELATIONS BETWEEN TOTAL INCOME AND SOURCEINCOMES FROM SURVEYS IN RURAL PAKISTAN

Sourceof

Income*'

Total Income*

1986-87 1987-88 1988-89 Three-Year Mean

Non-farm 0.161** 0.179** 0.302** 0.213**

Agricultural 0.632** 0.634** 0.645** 0.636**

Transfer 0.465** 0.436** 0.318** 0.413**

Livestock 0.142** 0.307** 0.040** 0.174**

Rental 0.468** 0.521** 0.655** 0.549**

N = 727 HouseholdsNotes: * All income figures based on mean annual per capita household income

expressed in constant 1986 terms.** Significant at the 0.1 level

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NON-FARM INCOME AND INEQUALITY IN PAKISTAN 117

correlation between the two yields a positive and significant relationshipin two of the three survey years. By contrast, livestock income and land-holding are statistically correlated in only one of the survey years and inthat year they are inversely related. These results suggest that while agri-cultural income is closely linked with landholding, which is unevenlydistributed in favor of the rich, livestock income is not linked with land-holding and thus of more potential importance to the poor and landless.

Table 2 presents summary data for the five income sources. This tableshows quite clearly the importance of rural income other than agricul-ture. In each of the three years agricultural income accounts for less thana third of total income: between 23.2 and 27.1 per cent of total income.In each of these years non-farm income represents the single mostimportant income source, accounting for between 30.7 and 34.6 per centof total rural income. Although definitions of non-farm income varywidely,8 these percentage figures for non-farm income are comparableto those recorded elsewhere. For example, studies in Pakistan havefound that the share of non-farm income ranges between 39 and 43 percent [Klennart. 1986: 45], and that the share of such income in otherSouth Asian countries ranges between 36 and 43 per cent [Liedholm andKilby, 1989:346].9

The Gini coefficient of income for the sample increased over the threesurvey years: from 0.311 in 1986-87 to 0.355 in 1987-88 to 0.428 in1988-89. While these Ginis are slightly higher than that (0.327) whichcan be calculated from the rural portion of the 1987-88 PakistanHousehold Income and Expenditure Survey (HIES),10 they are wellwithin the range of Gini coefficients for household income recorded forother Asian countries.11

In Table 3 the five sources of income are presented by income quin-tile group aggregated over the entire three-year period. The results showthe importance of non-farm income for the poor. According to the data,households in the lowest quintile group receive almost 50 per cent oftheir mean per capita income from non-farm income. This percentagefigure is more than twice that received by the poor from any otherincome source, and more than seven times that received from agricul-tural income! Evidently, the very real land constraints in rural Pakistan- 37.1 per cent of the households in the sample own no land12 - 'force'the poor to seek the bulk of their livelihood from outside agriculture.13

Table 4 presents another way of demonstrating the dependence of thepoor on non-farm income. In this table the households are ranked by sizeof three-year mean landholding. Like other studies,14 the data reveal aninverse relationship between size of landholding and the share of non-farm income. For the poorest (that is, landless) group, non-farm income

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TABLE 2

SUMMARY OF INCOME DATA FROM 1986-87, 1987-88 AND 1988-89SURVEYS IN RURAL PAKISTAN

00

Sourceof

Income

Non-farm

Agricultural

Transfer

Livestock

Rental

Total

1986-87

Mean AnnualPer CapitaHouseholdIncome"inRupees'*

1,007.39

763.75

554.01

534.88

425.07

3,285.10

StandardDeviation

1,158.40

2,170.35

1,497.76

641.98

1,429.80

3,015.60

1987-88

Mean AnnualPer CapitaHouseholdIncome1' inRupees"7

1,204.65

851.39

573.35

444.21

405.46

3,479.06

StandardDeviation

1,364.28

2,188.16

1,591.70

832.35

1,357.63

3,288.21

1988-89

Mean AnnualPer CapitaHouseholdIncome'7 inRupees'*

959.54

832.90

369.38

435.05

473.84

3,070.71

StandardDeviation

1,086.19

2,048.37

1,176.10

718.71

1,610.71

3,107.57

TH

E JO

fitZ>r°0w

ffl

N= 727 HouseholdsNotes: * Mean income figures include negative source incomes recorded for some households in various years.

b/ In 1986,1 Pakistani Rupee = US$0,062. All rupee figures in constant 1986 terms.

3dom

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TABLE 3SOURCES OF INCOME RANKED BY THREE YEAR MEAN AVERAGE PER CAPITA HOUSEHOLD INCOME QUINTILE

GROUP

O

IJO

Percent of 727Households Rankedby 3 Year MeanAverage, Per CapitaIncome'

Lowest 20%

Second 20%

Third 20%

Fourth 20%

Highest 20%

Total

3 Year MeanAveragePer CanitaIncome?', inRupees'

1,008.47

1,818.35

2,535.99

3,538.61

7,353.50

3,271.18

Percent fromNon-farmIncome

49.9

48.4

43.6

42.7

16.8

40.3

Percent fromAgriculturalIncome

6.8

9.3

14.3

21.4

36.5

17.7

PercentfromTransferIncome

13.9

13.4

15.1

12.7

17.1

14.4

PercentfromLivestockIncome

24.5

23.5

18.3

15.6

8.8

18.2

PercentfromRentalIncome

4.9

5.3

8.7

7.6

20.8

9.4

:OM

E A

ND

IN

aO

N = 727 HouseholdsNotes: ** Mean income figures calculated by averaging household income over the three years (1986-87 to 1988-89) and then dividing

by average household size.b/ In 1986,1 Pakistani Rupee = US$0,062. All rupee figures in constant 1986 terms.

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TABLE 4

SOURCES OF INCOME RANKED BY SIZE OF THREE YEAR MEAN AVERAGE LANDHOLDING

Is)O

Size of3 Year MeanAverage .Land-Holding1'

(acres)

0

< 1

1 - <5

5 - <10

slO

N = 727 Households

Number ofHouseholds1n Group

126

82

180

171

168

3 Year MeanAveragePer CapitaIncome:' 1nRupees''

2,650.75

3,167.33

3,203.80

3,096.22

4,068.23

Percent fromNon-farmIncome

57.1

46.6

39.4

23.0

15.8

Percent fromAgriculturalIncome

1.4

0.5

12.5

32.4

50.3

PercentfromTransferIncome

15.8

28.3

17.2

12.9

10.1

PercentfromLivestockIncome

11.3

13.2

18.2

18.0

10.3

PercentfromRentalIncome

14.4

11.4

12.7

13.7

13.5

o

IO

IPIfflNotes: * Landhoiding includes land owned plus land rented in minus land rent out.

bl Mean income calculated by averaging household income over the three years (1986-87 to 1988-89) and then dividing by averagehousehold size.

" In 1986,1 Pakistani Rupee = US$0,062. All rupee figures in constant 1986 terms.

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NON-FARM INCOME AND INEQUALITY IN PAKISTAN 121

accounts for 57 per cent of three-year mean per capita household income.Not only do the poor receive over half of their total income from non-farmsources, but the share of non-farm income decreases monotonically by sizeof landholding. By contrast, the share of agricultural income generallyrises with landholding: not until the size of landholding reaches five to tenacres does the share of agricultural income exceed that of non-farm.

III. INCOME INEQUALITY IN RURAL PAKISTAN, 1986-89

Decomposing the coefficient of variation and the Gini coefficientprovides two ways for measuring the contribution of any income sourceto overall income inequality. First, it can be asked whether inequality inan income source serves to increase or decrease overall incomeinequality.15 Second, it is possible to identify how much of the overallinequality is due to any particular income source.

Table 5 reports the decomposition results with respect to the distinc-tion between inequality-increasing versus inequality-decreasing sourcesof income. Both decompositions agree that for all three years twoincome sources - non-farm and livestock - represent inequality-

TABLE 5

RELATIVE CONCENTRATION COEFFICIENTS OF SOURCE INCOMES INOVERALL INCOME INEQUALITY

Source ofIncome

Non-farm

Agricultural

Transfer

Livestock

Rental

1986-1987

c

0.202

1.961

1.375

0.184

1.703

N = 727 households

9

0.555

1.622

1.111

0.397

1.551

1987-88

c

0.214

1.719

1.280

0.607

1.843

9

0.495

1.452

1.209

0.857

1.410

1988-89

c

0.336

1.570

1.000

0.064

2.194

9

0.598

1.427

1.063

0.424

1.543

N = 727 Households

cr/y. G

All estimates based on annual per capita income expressed in constant 1986 terms.

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122 THE JOURNAL OF DEVELOPMENT STUDIES

decreasing sources of income. This means that, ceteris paribus, addi-tional increments of non-farm or livestock income will reduce overallincome inequality. Both decompositions also agree that for all threeyears three sources of income - agricultural, transfer and rental -represent inequality-increasing sources of income.

Table 6 presents the decomposition results for relative factor inequalityweights of source incomes in overall income inequality. The resultsshow that non-farm income makes a relatively small contribution tooverall inequality. Depending on the year, the two decompositionssuggest that non-farm income accounts for between 6.2 and 18.7 per centof overall inequality. By contrast, the data also reveal that in each of thethree years agricultural income makes the largest contribution to overallinequality. Depending on the year and the decomposition measure,agricultural income contributes between 35.5 and 45.6 per cent of overallinequality.

The results of Table 6 can be further explained by analysing the resultsof the Gini decomposition. This is done in Table 7, which presents thethree elements of the Gini decomposition procedure: (1) source incomeweight; (2) source gini (G); and (3) correlation ratio between sourceincome and total income (R).

Row (2) of Table 7 shows that non-farm income has the lowest sourcegini in each of the three years and is thus the most equally distributedincome source. This is a reflection of the fact that - depending on theyear - between 71.3 and 76.2 percent of all 727 households receive non-farm income. Row (3) of Table 7 reports the correlation ratios betweensource income and total income. The figures reveal that non-farmincome has a low degree of correlation with overall income.

The data in Table 7 serve to explain the factor inequality weightsreported in the preceding table. Despite the fact that it represents alarge share of total income, non-farm income makes a small contributionto income inequality because it has a low source gini and is poorlycorrelated with total income. From these results, a clear policy prescrip-tion emerges, namely, that efforts to improve income distribution inrural Pakistan should focus on expanding non-farm income.

IV. SOURCES OF NON-FARM INCOME INEQUALITY IN RURAL PAKISTAN

Since non-farm income has such a favorable impact on income distribu-tion, it seems useful to decompose the sources of non-farm income. Suchan analysis can answer the question: Do all types of non-farm incomehave a favorable effect on inequality?

In this study non-farm income can be divided into five sources:

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TABLE 6FACTOR INEQUALITY WEIGHTS OF SOURCE INCOMES IN OVERALL INCOME INEQUALITY

o

oow>zd

tnO

SourceofIncome

1986-87 1987-88 1988-89

SourceofIncome

wgSourceofIncome

SourceofIncome

wgSourceofIncome

SourceofIncome

wg

Agricultural 0.456 Agricultural 0.377

Transfer 0.232 Rental 0.201

Rental 0.220 Transfer 0.187

Non-farm 0.062 Non-farm 0.170

Livestock 0.030 Livestock 0.065

Agricultural 0.421 Agricultural

Rental

Transfer

Livestock

Non-farm

0.216 Transfer

0.211 Non-farm

0.077 Rental

0.074 Livestock

0.355 Agricultural 0.426 Agricultural 0.387

0.199 Rental 0.339 Rental 0.238

0.172 Transfer 0.120 Non-farm 0.187

0.164 Non-farm 0.105 Transfer 0.128

0.110 Livestock 0.009 Livestock 0.060

Total 1.000 1.000 1.000 1.000 1.000 1.000

N = 727 HouseholdsNotes:

wiCi> where W j = — C j = p

G iH iwigi> where w i= — g^RjQ-All estimates based on annual per capita household income expressed in constant 1986 terms.

5

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TABLE 7

DECOMPOSITION OF OVERALL INCOME INEQUALITY USING GINI COEFFICIENT

Overall G1n1Coefficient ofIncome

(1) SourceIncomeWeight

(2) SourceGini (G,)

(3) Correlationratiobetweensource Incomeand totalIncome (R)

N = 727 HouseholdsNotes:

G; = 2 covfy, Tj), R =

1986-87

0.311

Non-FarmAgriculturalTransferLivestockRental

AgriculturalRentalTransferLivestockNon-farm

AgriculturalRentalTransferNon-farmLivestock

cov(yi, r)

0.3070.2320.1690.1630.129

1.000

0.9320.9030.7850.6170.586

0.6970.6880.5660.3790.258

1987-88

0.355

Non-FarmAgriculturalTransferLivestockRental

AgriculturalRentalLivestockTransferNon-farm

AgriculturalRentalTransferLivestockNon-farm

0.3450.2450.1650.1280.117

1.000

0.9080.9010.8860.8610.387

0.7170.7020.6300.4340.387

1988-89

0.428

Non-FarmAgriculturalRentalLivestockTransfer

RentalTransferAgriculturalLivestockNon-farm

RentalAgriculturalTransferNon-farmLivestock

0.3120.2720.1540.1420.120

1.000

0.9020.8770.8660.7410.580

0.7780.7490.5520.4690.260

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NON-FARM INCOME AND INEQUALITY IN PAKISTAN 125

(1) Unskilled labour - Includes wages from any unskilled non-farmactivity, such as construction and ditch digging;

(2) Self-employment - Includes profits and earnings from shopkeepingand artisan activities (for example, bricklaying, shoe repair) pluslabour/construction contracting;

(3) Government employment - Includes wages from all grades (grades1 to 22) of government service;

(4) Private sector - Includes wages from a private sector company (forexample, Dawood Hercules Fertilizer Company);

(5) Other - Includes other non-farm wages.

Table 8 presents summary data for the five sources of non-farmincome. The data reveal that three sources of non-farm incomepredominate: self-employment, unskilled labour and governmentemployment. In any given year these three sources account for between74.1 and 78.8 per cent of total mean per capita non-farm income.

In Table 9 the five sources of non-farm income are presented byincome quintile group aggregated over the three-year period. Theresults show the dependence of the poor on two particular sources ofnon-farm income: self-employment and unskilled labour. Households inthe lowest income quintile receive more than their quintile shares ofnon-farm income - 24.9 and 32.3 per cent, respectively - from self-employment and unskilled labour. By contrast the poor receive only14.6 per cent of their non-farm income from government employment.

Table 10 reports the decomposition results with respect to thedistinction between inequality-increasing and inequality-decreasingsources of non-farm income. The decomposition results for non-farm self-employment are mixed. However, with only one exception both decom-positions agree that unskilled labor represents an inequality-decreasingsource of non-farm income. In comparison, both decompositions agreethat government employment represents an inequality-increasing sourceof non-farm income.

These results suggest that non-farm income has a kind of 'dual impact'on income distribution.16 On the one hand, poor households are heavilydependent on non-farm unskilled labour and therefore additionalincrements of such income have a favourable impact on inequality. Onthe other hand, rich households depend on non-farm income fromgovernment employment and thus more income from this source tendsto increase inequality.

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TABLE 8

SUMMARY OF NON-FARM INCOME DATA

SourceofNon-FarmIncome

Self-employment

Unskilled labor

Governmentemployment

Privatesector

Other

Total

1986-87

Mean AnnualPer CapitaHouseholdIncome" inRupees

305.61

237.48

209.80

139.06

115.45

1,007.39 1

StandardDeviation

764.79

588.43

618.50

466.48

369.28

,158.40

1987-88

Mean AnnualPer CapitaHouseholdIncome" InRupees

361.64

239.60

322.09

200.31

81.01

1,204.65

StandardDeviation

893.07

608.48

810.93

512.97

300.51

1,364.28

1988-89

Mean AnnualPer CapitaHouseholdIncome" inRupees

228.07

269.05

259.49

177.60

25.33

959.54 1

StandardDeviation

586.89

681.45

683.84

507.84

123.70

,086.19

aa

s

N = 727 HouseholdsNote: To ensure comparability with Table 2, the mean income figures in this table include those households with no income in various non-

farm categorie."/ All rupee figures in constant 1986 terms.

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TABLE 9SOURCES OF NON-FARM INCOME RANKED BY MEAN ANNUAL PER CAPITA HOUSEHOLD INCOME QUINTILE GROUP

S

Percent of 727Households Rankedby 3-Year MeanAverage Per CapitaIncome

Lowest 20%

Second 20%

Third 20%

Fourth 20%

Highest 20%

Total

3-Year MeanAverage PerCapitaNon-FarmIncome "In Rupees v

503.2

880.5

1,107.1

1,553.5

1,235.5

1,057.2

PercentNon-FarmIncome fromSelf-Employment

24.9

24.1

22.6

29.6

30.6

26.4

PercentNon-FarmIncome fromUnskilledLabor

32.3

28.7

24.0

18.6

21.1

24.9

PercentNon-FarmIncome fromGovernmentEmployment

14.6

24.0

24.0

32.9

29.3

24.9

PercentNon-FarmIncome fromPrivateSector

15.6

14.2

18.6

15.2

18.1

16.4

PercentNon-FarmIncomefrom Other

12.6

9.0

10.8

3.7

0.9

7.4

I>o

moI

52

N = 727 Households

Notes: "I Mean income figures calculated by averaging household income over the three years (1986-87 to 1988-89) and then dividing byaverage household size.

V In 1986,1 Pakistan Rupee = US$0,062. All rupee figures in constant 1986 terms. tojD

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TABLE 10

RELATIVE CONCENTRATION COEFFICIENTS OF SOURCE INCOMESIN NON-FARM INCOME INEQUALITY

1986-87 1987-88 1988-89Source of

Non-farm Income c g c g c

Self-employment 1.223 1.110 1.335 1.094 0.852 0.893

Unskilled labor 0.870 0.947 0.736 0.881 0.980 1.036

Government 1.032 1.035 1.072 1.122 1.116 1.099employment

Private sector 1.002 0.984 0.888 0.955 0.984 1.008

Other 0.615 0.774 0.281 0.561 0.279 0.505

N = 727 Households

Notes: *

All estimates based on annual per capita household income expressed in constant 1986terms.

Why is this so? Does non-farm government employment have higherentry costs - especially in the form of education - than non-farmunskilled labour which makes the former more accessible to richerhouseholds?

To answer this question, the determinants of non-farm governmentand unskilled labour employment can be estimated using a reduced-form labor participation model. To avoid problems of fitting dummydependent variables by linear methods, a probit function is selected. Theprobit is estimated for two types of non-farm employment: governmentemployment and unskilled labour. It is estimated on 1461 males over 15years of age in the sample.

Table 11 shows the variables, expected coefficients and results of themodel. The results indicate that non-farm government employment doeshave higher entry costs - in the form of education - than non-farmunskilled labour. As expected, for government employment the resultsfor the EDUC (no schooling) and EDUCHS (high school) variables arenegative and positive, respectively, and statistically significant. Forunskilled labour employment the variables EDUC and EDUCHS are

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PROBIT ANALYSIS OF INDIVIDUAL PARTICIPATION IN NON-FARM GOVERNMENT AND UNSKILLED LABOUR EMPLOYMENT

Variable ExpectedSign forGovernmentEmployment

CoefficientforGovernmentEmployment

.093(5.243)**

-.001(-5.250)**

.082(0.265)

-.280(-2.690)**

.786(7.096)**

-.010(-1.579)

-2.562(-7.738)**

NFGOVa/

-527

ExpectedSign forUnskilled LaborEmployment

1

+

+

-

-

Coefficientfor UnskilledLaborEmployment

.023(2.040)*

-.001(-3.617)**

.169(0.651)

.681(8.234)**

-.756(-5.668)**

-.025(-3.857)**

-.861(-3.645)**

NFLABb/

-776

o

IoO

w>awo%

AGE (age of male)

AGESQ (age of male squared)

MALE15 (number of males in householdover 15 years of age)

EDUC (education of male, one ifno schooling, zero otherwise)

EDUCHS (education of male, oneif high school or higher, zerootherwise)

IRLAND (irrigated land in village)(acres)

CONSTANT

Dependent variableLog likelihood

Notes: "Includes 1,461 males over 15 years of age. Numbers in parentheses are t-statistics (two-tailed)."/ NFGOV = 1 if individual receives income from non-farm government employment in any year, zero otherwise.b/ NFLABOUR = 1 if individual receives income from non-farm unskilled labour employement in any year, zero otherwise.* Difference is significant at 0.05 level.** Difference is significant at 0.01 level.

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130 THE JOURNAL OF DEVELOPMENT STUDIES

also as expected: positive and negative, respectively, and significant.These two sets of results are consistent with the view of education rep-resenting a 'barrier' to certain forms of non-farm employment.

V CONCLUSION

This study has examined the impact of non-farm income on inequalityin rural Pakistan using three-year panel data from 727 households.Three key findings - and one caveat - emerge.

First, the study shows the importance of rural non-farm income forthe poor. When the sample households are ranked by per capita income,those in the lowest income quintile group receive almost 50 per cent oftheir total income from non-farm sources. This relationship also holdswhen the households are ranked by size of landholding. For the poorest(that is, landless) group, non-farm income accounts for 57 per cent oftotal household income.

Second, the study shows that while non-farm income represents thelargest single source of rural household income, it also has a favourableimpact on income distribution. The decomposition analysis reveals thatnon-farm income represents an inequality-decreasing source of income.In any given year of the study, non-farm income accounts for only a smallproportion - between 6.2 and 18.7 per cent - of overall income inequality.Of the five sources of rural income analysed in this study, only livestockincome makes a smaller contribution to overall income inequality.

Third, the decomposition analysis shows that while non-farm incomeas a whole reduces income inequality, not all sources of non-farmincome have such a favourable impact on income distribution. Of thethree main sources of rural non-farm income - self-employment,unskilled labour and government employment - only non-farmunskilled labour represents an inequality-decreasing source of income.In comparison, non-farm government employment represents aninequality-increasing source of income. Non-farm government employ-ment has higher entry costs - especially in the form of education - whichmake this source of non-farm income more accessible to richer house-holds.

Finally, a caveat - and a plea - need to be expressed. Although thepreceding findings are based on a detailed three-year panel data set, thedata set itself was never designed to be representative of rural Pakistanas a whole. It is therefore difficult, if not impossible, to generalise theresults presented here. In particular, since three of the four rural studydistricts were chosen because they were 'poor', it is likely that meanincomes - as well as the variances of such incomes - are smaller than

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NON-FARM INCOME AND INEQUALITY IN PAKISTAN 131

they would be in more 'representative' rural districts in Pakistan. Howsuch biases might affect the distributional impact of different sources ofrural income is unknown. For this reason, it is useful to end with a pleato other researchers to develop data sets which are both sufficiently dis-aggregated and broadly representative enough to test the robustness ofthese findings concerning the impact of non-farm income on ruralinequality in the Third World.

final version received January 1994

NOTES

1. In their review of 13 rural household budget surveys, Braun and Pandya-Lorch (eds.)[1991] find that the share of rural non-farm income in total income ranges from 13 percent (Brazil) to 67 per cent (Burkina Faso). For other estimates of the share of rural non-farm income, see Liedholm and Kilby [1989] and Haggblade, Hazell and Brown [1989].

2. For more on this point, see Haggblade, Hazell and Brown [1989].3. Because it neglects the twin issues of income weights and covariance between income

sources, any approach which merely compares the size distribution of non-farm incomewith that of total income is likely to arrive at erroneous conclusions regarding the dis-tributional impact of non-farm income. For examples of this approach, see Reardon,Delgado and Matlon [1992] and Matlon [1979].

4. This analysis ignores feedback effects, that is, the effects that a change in any sourceincome share might have on distribution within any source income. Of course, such anassumption might be quite unrealistic for large changes in any source income share.

5. This study was undertaken by the International Food Policy Research Institute (IFPRI)working in collaboration with Pakistani research institutes - Applied EconomicResearch Centre (University of Karachi), Punjab Economic Research Institute(Lahore), the University of Baluchistan (Quetta) and the Center for Applied EconomicStudies (University of Peshawar). For more detail, see Alderman and Garcia [1993].

6. The sample was randomly drawn with all rural residents in the selected districts havingan equal probability of being included. Landowners who reside in urban areas, there-fore, are not included in the sample. Since unweighted samples generally tend to missthe apex of a distribution, the fact that there are, for example, far fewer householdsowning 3,000 acres of land than there are households owning three acres may lead to aslight underrepresentation of the skew of landholding in any moderately-sized sample.

7. The 727 households were distributed as follows: 148 from Attock District (Punjab), 239from Badin District (Sind), 193 from Dir District (North-west Frontier) and 147 fromFaisalabad District (Punjab).

8. It should be noted that the definition of non-farm income used here is narrower thanthose used in other studies. For example, Chinn [1979] includes rental income in non-farm income, while Matlon [1979] includes livestock income.

9. As noted above, the share of rural non-farm income in total household income tendsto be higher in Asia and South Asia than in Africa. See Haggblade, Hazell and Brown[1989].

10. This 1987-88 Household Income and Expenditure Survey (HIES) was a national-levelsurvey which included 9,760 rural Pakistani households.

11. The Gini coefficients of household income recorded for ten Asian countries inLecaillon et al. [1984: Table 3] range from a low of 0.351 (Korea) to a high of 0.561(Iran). It should, however, be noted that the Gini coefficients for these Asian countriesare based on the distribution of overall (that is rural and urban), while the Ginis usedin this study are based on rural household income. In theory, one would expect thatthe distribution of rural household income to be more egalitarian than that of overallhousehold income. See Lecaillon et al. [1984: 67-8].

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132 THE JOURNAL OF DEVELOPMENT STUDIES

12. There is an active rental market for land in rural Pakistan. Thus, while 37.1 percent of thesurvey households own no land, in terms of landholding (that is, land owned plus landrented in minus land rented out), only 17.3 per cent of the survey households are landless.

13. For more on this point, see Klennart [1986; 1988].14. For Pakistan, see Mohammad and Badar [1985]. For other countries, see Chinn [1979],

Shand [1986] and Walker and Ryan [1990].15. In analysing whether an income source is inequality-increasing or -decreasing, it is

assumed that additional increments of that income source are distributed in the samefashion as the original units.

16. Other researchers have noted the 'dual character' of rural non-farm income. See, forexample, Hasbullah [1988] and Stokke, Yapa and Dias [1991] in Sri Lanka.

REFERENCESAlderman, H. and M. Garcia, 1993, Poverty, Household Food Security and Nutrition in

Rural Pakistan, Research Report 96, International Food Policy Research Institute,Washington, DC, March.

Braun, J. von and R. Pandya-Lorch (eds.), 1991, 'Income Sources of Malnourished Peoplein Rural Areas: Microlevel Information and Policy Implications', Working Papers onCommercialization of Agriculture No.5 (International Food Policy Research Institute,Washington).

Chinn, D., 1979, 'Rural Poverty and the Structure of Farm Household Income inDeveloping Countries: Evidence from Taiwan', Economic Development and CulturalChange Vol.27, No.2, pp.283-301.

Collier, P., Radwan, S. and S. Wangwe, 1986, Labour and Poverty in Rural Tanzania,Oxford: Clarendon Press.

Ercelawn, A., 1984, 'Income Inequality in Rural Pakistan: A Study of Sample Villages',Pakistan Journal of Applied Economics, Vol.3, No.1, pp.1-28.

Fields, G., 1980, Poverty, Inequality and Development, New York: Cambridge UniversityPress.

Foster, J., 1985, 'Inequality Measurement', in H. Peyton Young (ed.), Proceedings ofSymposia in Applied Mathematics, Vol.33, Providence, RI: American MathematicalSociety.

Haggblade, S., Hazell, P. and J. Brown, 1989, 'Farm-Nonfarm Linkages in Rural Sub-Saharan Africa', World Development Vol.17, No.8, pp.1173-201.

Hasbullah, B., 1988, 'The Growth and Variations in Rural Non-farm Activities in SriLanka Since Independence', unpublished Ph.D. thesis, University of British Columbia.

Ho, S., 1979, 'Decentralized Industrialization and Rural Development: Evidence fromTaiwan', Economic Development and Cultural Change Vol.28, No.1, pp.77-96.

Klennart, K., 1986, 'Off-Farm Employment in Marginal Farm Households: A HiddenDevelopment of Parts of Pakistan's Rural Poor', Quarterly Journal of InternationalAgriculture Vol.25, No.1, pp.37-48.

Klennart, K., 1988, Off-Farm Employment and Rural Development: Pakistan, Socio-Economic Studies on Rural Development, Vol.81, Institut fur Rurale Entwicklung derGeorg-August-Universitat, Gottingen: Alano Verlag.

Lecaillon, J., Paukert, E, Morrisson, C. and D. Germidis, 1984, Income Distribution andEconomic Development: An Analytical Survey, Geneva: International Labour Office.

Liedholm, C. and P. Kilby, 1989, 'The Role of Non-farm Activities in the Rural Economy',in The Balance Between Industry and Agriculture in Economic Development:Proceedings of the Eighth World Congress of the International Economic Association,London: Macmillan Press.

Matlon, P., 1979, 'Income Distribution Among Farmers in Northern Nigeria: EmpiricalResults and Policy Implications', African Rural Economy Paper No. 18, Michigan StateUniversity.

Mohammad, F. and G. Badar, 1985, 'Structure of Rural Income in Pakistan: SomePreliminary Estimates', Pakistan Development Review Vol.24, No. 2, pp.385-403.

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NON-FARM INCOME AND INEQUALITY IN PAKISTAN 133

Pakistan, Federal Bureau of Statistics, 1989, 1987-88 Household Income and ExpenditureSurvey, Islamabad, Pakistan.

Pasha, H. and T. Hasan, 1982, 'Development Ranking of Districts of Pakistan', PakistanJournal of Applied Economics Vol.1, pp.157-92.

Pyatt, G., Chen, C.N. and J. Fei, 1980, 'The Distribution of Income by Factor Components',Quarterly Journal of Economics, Vol.95, No.3, pp.451-73.

Reardon, T., Delgado, C. and P. Matlon, 1992, 'Determinants and Effects of IncomeDistribution Amongst Farm Households in Burkina Faso', Journal of DevelopmentStudies Vol.28, No.2, pp.264-96.

Shand, R.T., 1986, 'Off-farm Employment in the Development of Rural Asia: Issues', inR.T. Shand (ed.), Off-Farm Employment in the Development of Rural Asia, Vol.1,National Centre for Development Studies, Australian National University.

Shand, R.T., 1987, 'Income Distribution in a Dynamic Rural Sector: Some Evidence fromMalaysia', Economic Development and Cultural Change Vol.36, No.1, pp.35-50.

Shorrocks, A.F, 1982, 'Inequality Decomposition by Factor Components', EconometricaVol.50, No.l, pp.193-211.

Stokke, K., Yapa, L. and H. Dias, 1991, 'Growth Linkages, the Non-farm Sector and RuralInequality: A Study in Southern Sri Lanka', Economic Geography Vol.67, No.2,pp.223-39.

Walker, T. and J. Ryan, 1990, Village and Household Economies in India's Semi-AridTropics, Baltimore, MD: Johns Hopkins University Press.

APPENDIX TABLEMEANS OF INDEPENDENT VARIABLES FOR PROBIT REGRESSION

Variable

AGE

AGESQ

MALE15

EDUC

EDUCHS

IRLAND

Allmales

(N=1461)

34.94(16.93)

1507.16(1424.52)

3.29(1.77)

0.48(0.50)

0.17(0.38)

3.23(7.83)

Males withNon-FarmIncome fromGovernmentEmployment(N=210)

31.10(11-70)

1103.25(817.16)

3.23(1.76)

0.26(0.44)

0.43(0.50)

2.62(7.14)

Males withNon-Farm Incomefrom UnskilledLabor Employment

(N=432)

32.66(14.33)

1271.91(1147.47)

2.99(1.62)

0.68(0.47)

0.05(0.21)

1.78(5.08)

Notes: Variables are defined in Table 11. Standard deviation in parenthesis.

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