84

* PSP Discussion Paper Series19706

November 1995

Accounting for the Reduction in RuralPoverty in Ghana 1988-1992

Christine Jones

Xiao Ye

November 1995

Poverty and Social Policy DepartmentHuman Capital Development and Operations Policy

The World Bank

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Abstracts

This Booklet of Abstracts contains short summaries of recent PSP Discussion Papers; copies of specific papersmay be requested from Patricia G. Sanchez via All-in-One. The views expressed in the papers are those of theauthors and do not necessarily represent the official policy of the Bank. Rather, the papers reflect work inprogress. They are intended to make lessons emerging from the current work program available to operationalstaff quickly and easily, as well as to stimulate discussion and comment. They also serve as the building blocksfor subsequent policy and best practice papers.

Preface

This paper is one of a series undertaken as part of the Extended Poverty Study in Ghana. Asynthesis of this work is available in Ghana: Poverty Past, Present and Future (Report No.140504-GH, Population and Human Resources Division, West Central Africa Department, WorldBank, Washington D.C., June 29, 1995). Four of the background papers are appearing in thePoverty and Social Policy Discussion Paper Series:

Harold Coulombe and Andrew McKay, 'An Assessment of Trends in Poverty inGhana, 1988-92.' Poverty and Social Policy Discussion Paper No. 81, WorldBank, Washington D.C. (November 1995)

Lionel Demery, Shiyan Chao, Rene Bernier and Kalpana Mehra, 'The Incidence ofSocial Spending in Ghana.' Poverty and Social Policy Discussion Paper No. 82,World Bank, Washington D.C. (November 1995)

Andy Norton, David Korboe, Ellen Bortei-Dorku and D.K. Tony Dogbe, 'PovertyAssessment in Ghana using Qualitative and Participatory Research Methods.'Poverty and Social Policy Discussion Paper No. 83, World Bank, WashingtonD.C. (November 1995)

Christine Jones, and Ye Xiao, 'Accounting for the Reduction in Rural Poverty imGhana, 1988-1992.' Poverty and Social Policy Discussion Paper No. 84, WorldBank, Washington D.C. (November 1995)

T'he papers draw upon the work of many. The contributions of the Ghana Statistical Service,Ministry of Health, and Ministry of Education in the Government of Ghana, and of UNICEF andthe Canadian International Development Agency (CIDA), are gratefully acknowledged. However,the views expressed are those of the authors. They should not be attributed to the World Bank, itsBoard of Directors, its management or any of its member countries.

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Abstract

Poverty appears to have declined in the three rural localities of Ghana between 1988 and1992. It has been hypothesized that this was largely due to the growth in nonfarm self-employmentin the context of a stagnating agricultural sector. This explanation is problematic, since there is noclear source of demand for the output from the rural nonfarm sector. An alternative hypothesis isthat the food production did increase. This explanation also poses some problems, in view of thedifficulty of accounting for the factors that would have led to an increase in production.

These questions are the basis for the hypotheses explored in this paper. The first hypothesis isthat the strong growth in per capita expenditure revealed by the GLSS data may be partially theresult of changes in the design of the GLSS3 questionnaire which resulted in an upward bias in theGLSS3 data relative to GLSS 1/2 data. Thus the increase in expenditure is an artifact of changesin questionnaire design. The second hypothesis is that agricultural incomes, rather than stagnating,actually did increase, providing an impetus for the growth in rural nonfarm self-employment. Thepaper looks at altemative data sources on agricultural production and prices to come up with aview on what happened to agricultural revenues. The conclusion of the paper is that bothhypotheses appear to have some merit. It is plausible that there was a small reduction in povertydue to the growth in agricultural incomes, but that the magnitude of the reduction is overstated bythe GLSS data due to changes in the questionnaire that bias the GLSS3 estimates upwards relativeto earlier years.

3

CONTENTS

1. Introduction

2. Questions About the Comparability of the Surveys

3. What Happened to Agricultural Revenues?

4. Accounting for the Very Large Reduction in Poverty in the Savannah

5. Conclusion

Annex

4

1. INTRODUCTION

Poverty appears to have declined in the three rural localities of Ghana between 1988and 1992, according to the GLSS data (Table 1), as reported in the Extended PovertyStudy (World Bank, 1995a). The Ghana Country Economic Memorandum (World Bank,1995b) hypothesizes that the decline in poverty was largely a function of the growth innonfarm self-employment in the context of a stagnating agricultural sector. Thisexplanation raises some additional questions. If agricultural incomes did indeed stagnate,what was the source of demand for output from rural nonfarm self-employment? It wouldnot appear to be the formal urban sector, since there was little growth in wages. TheExtended Poverty Study raises the question of whether agricultural production did indeedstagnate. An increase in agricultural production would explain at least part of the growthin rural expenditures, but raises the question of what accounts for the growth inagricultural production. It is difficult to identify shifts in price incentives or nonpricefactors that would have contributed to a strong increase in agricultural in the post 1987period, since most of the large relative price changes took place during the initial years(1983-1986) of the Economic Recovery Program initiated by the government in 1983.

So what does account for the decline in poverty? The first hypothesis explored in thepaper is that the strong growth in per capita expenditure revealed by the GLSS data maybe partly the result of changes in the design of the GLSS3 questionnaire which resulted inan upward bias (in estimated household expenditures) in the GLSS3 data relative toGLSS1/2 data. The GLSS data show a much stronger increase in expenditure, about 3.3percent per annum between GLSS1 and 3, than the national accounts data, which show agrowth rate of private consumption per capita of about 1.1 percent per annum between1987 and 1992. Below we look at how the changes in the questionnaire design may haveresulted in the apparent increase in expenditures.

The second hypothesis is that agricultural incomes, rather than stagnating, actually didincrease, providing an irnpetus for the growth in rural nonfarm self-employment. Thepaper looks at alternative data sources on agricultural production and prices to come upwith a view on what happened to agricultural revenues. The conclusion of the paper is thatboth hypotheses appear to have some merit. It is plausible that there was a smallreduction in poverty due to the apparent growth in agricultural food incomes, but that themagnitude of the reduction is overstated by the GLSS data due to changes in thequestionnaire that bias the GLSS3 estimates upwards relative to the GLSS 1/2 data.

It is worth noting that the changes in poverty in Ghana are largely due to the growthin mean expenditure, and not to an improvement in distribution of expenditure favoringthe poor. This is evident from the decomposition of the change in poverty into its growthand redistribution components following the methodology used in Datt and Ravallion(1992), presented in Table 2. Between 1988 and 1992, the redistribution effect wasadverse in the Rural Forest and Coastal localities, and in the Savannah, it was responsiblefor less than a quarter of the reduction in poverty. Similarly, the decline in expenditureaccounts for most of the increase in poverty between 1988 and 1989. Only in the Rural

5

Table 1. Rural Ghana: Poverty Incidence and Poverty Gapa"

...................................................................... ............................................................. I.......... ..................... ................................................................................................

Po Po Po PI PI PiGLSSI GLSS2 GLSS3 GLSSI GLSS2 GLSS3

...... ......................................................................................................... ...............................................................................................................

LocalityRural Coastal 0.377 0.446 0.286 0.124 0.149 0.068Rural Forest 0.381 0.419 0.330 0.114 0.128 0.083Rural Savannah 0.494 0.548 0.383 0.178 0.204 0.105Rural Ghana 0.419 0.467 0.339 0.138 0.157 0(087a/ These estimates are based on adjustments for the effects of the shortened the recall period for food expenditures in GLSS3 (see WorldBank, 1995a and discussion below).

Coastal locality was there a large adverse redistribution effect that contributed to theincrease in poverty. The Savannah region stands out as having the largest increase inpoverty between 1988 and 1989 as well as the largest decrease between 1988 and 1992.Clearly any explanation for the change in poverty must also come to terms with the largechange in welfare in the Savannah over this period.

Table 2. Rural Ghana: Growth-Redistribution Decomposition

Growth DistributionTotal change Component Component Residual

GLSSI-GLSS3 (1988-1992)

Rural Coastal - 9.09 -6.67 0.65 -3.06Rural Forest - 5.18 -8.38 4.65 -1.46Rural Savannah -11.12 -8.39 -2.56 -0.16Rural Ghana - 8.00 -7.70 1.21 -1.51

GLSSI-GLSS2 (1988-1989)

Rural Coastal 6.90 5.13 2.86 -1.09Rural Forest 3.78 2.11 0.32 1.36Rural Savannah 5.37 8.54 -2.61 -0.57Rural Ghana 4.85 4.93 0.43 -0.52

GLSS2-GLSS3 (1989-1992)

Rural Coastal -15.99 -11.37 0.27 4.89Rural Forest -8.96 -11.78 2.93 -0.12Rural Savannah -16.49 -15.8 1.09 -1.78Rural Ghana -12.85 -12.42 1.58 -2.01

6

2. QUESTIONS ABOUT THE COMPARABILITY OF THE SURVEYS

Given the increase in rural poverty between GLSS1 and 2, the question arises as towhich survey would make the better baseline for the purposes of comparison with GLSS3.One hypothesis that has been advanced to explain the decrease in expenditure between thefirst and second rounds of the GLSS is that there may have been sampling errors inGLS SI leading to a substantial undercount of the poor, thus biasing the GLS S 1 estimatesupwards relative to the GLSS2 estimates. According to this hypothesis, GLSS2 wouldmake the better baseline comparison for the GLSS3 data. However, the evidence onhousehold size does not support the hypothesis that there was an undercount of poor ruralhouseholds in GLSS1. Mean household size fell between GLSS I and 2 indicating thatpoverty estimates would tend to fall in GLSS2, all other things being equal (see Table 3).Moreover, food shares declined between GLSS1 and 2, also suggesting that poorhouseholds were not under-represented in the GLSS1 survey (see Table 4).

The decline in the food share between 1988 and 1989 is unexpected, given the declinein total expenditures. Ordinarily, one would have expected the food share to rise asexpenditure falls. The relative price of food and nonfood remained unchanged between1988 and 1989, so that would not explain the declining food share.

A possible explanation for the fall in expenditure between 1988 and 1989 could bethat the food deflator was misestimated for the 1987/88 period. There is some evidence(discussed below in the section on producer prices) suggesting that the food deflator forthe 1987/88 might not have captured the spike in staple food prices, particularly maize andcassava, two of the main staple food crops. If the food deflator were underestimated in1987/88, the overall rural deflator would be biased downwards for GLSS1. This wouldbias expenditures upwards, thereby exaggerating decline in expenditure between 1988 and1989. Moreover, had the food deflator been higher in 1987/88 than reported, the relativeprice of food to non food would have fallen between 1988 and 1989. A fall in relativefood prices would also be consistent with the decline in the food share between GLSS1and 2. If so, GLSS2 might be the better baseline survey. However, we do not haveenough information about the construction of the food deflator and sufficientlydisaggregated price data to reach a conclusion.

As for the GLSS1/2 and GLSS3 comparisons, the evidence suggests that the changein expenditures may be biased upwards due to changes in the GLSS3 questionnaire designfor which no corrections to the data were made. We look at two possible sources of bias:1) changes in the recall period and 2) the more detailed breakdown of the expendituredata. It should be noted that the changes in GLSS3 may have improved the estimates ofexpenditure and thus in an absolute sense are less biased than estimates based on theGLSSI/2 questionnaire. Our concern in this paper is whether the changes in GLSS3substantially affect the comparability of the data, by introducing a bias relative to theGLSS1/2 estimates, and if so, whether the estimates for GLSS3 are biased upwards ordownwards relative to the GLSS 1/2.

7

Table 3. Rural Ghana: Mean Household Size, GLSS1-3

..................................... ...... ........... ............................. -.-.... .............. ...... .................... ................... ................. .................... ........................................................................

Mean Household SizeGLSS1 GLSS2 GLSS3

................................................................... ................................................................... ......................................................................... .......................................................................................LocalityRural Coastal 4.48 4.52 4.00Rural Forest 4.86 4.36 4.38Rural Savannah 5.80 5.67 5.46Rural Ghana 5.05 4.75 4.60

Table 4. Rural Ghana: Per Capita Expenditure by Category, GLSS1-GLSS3

............................................................................................................................................................................. ............................................................

Consumption of Purchased Total Food Non food Total Mear HH Share ofHome Production* Food** Expenditure Expenditure Expenditure Food Expenditure

........... I........................................................ ..................................................................... ...............................................................................................

Rural CoastalGLSSI 41265 88784 130049 59766 189815 69.3GLSS2 29563 83518 113081 60897 173978 66.0GLSS3 36815 98229 135044 82761 217805 63.0

GLSS2-GLSSl -11702 -15837

Rural ForestGLSS1 64068 68645 132713 53950 186663 70.8GLSS2 42994 73535 116529 63713 180242 66.2GLSS3 56074 72501 128575 84929 213504 61.7

GLSS2-GLSSl -21074 -6421

Rural SavannahGLSS1 87928 39752 127680 39406 167086 74.4GLSS2 65343 41211 106554 38364 144918 74.0GLSS3 72280 65856 138136 50333 188469 73.1

GLSS2-GLSS1 -22585 -22168

Rural GhanaGLSS1 67305 63126 130431 50246 180677 71.7GLSS2 47224 65265 112489 54800 167289 68.7GLSS3 57592 75653 133245 72526 205771 65.9

GLSS2-GLSS1 -20081 -13388

* Includes in-kind payment from employment.** Food expenditure includes expenditure on alcohol, but not tobacco. Tobacco expenditures were subtracted from the

aggregate food expenditure for GLSS3 used by Coulombe and Mckay (1995) to be consistent with the GLSS I and 2aggregate.

8

Change in the recall period

The GLSS3 questionnaire in general used a much shorter recall period in collectingexpenditure data both for food and non food. For purchased food expenditures, the recallperiod was changed from 14 days in the GLSS1/2 questionnaire to 3 days. Based onwork done by Scott and Amenuvegbe (1990) which showed that expenditures were afactor of 1.029 higher for each day the recall period was shortened up to a week,Coulombe and McKay (1995) multiplied the GLSS1/2 food purchase data by a factor of(1.029)5 to correct for the distortion introduced by the change in the recall period.

Unlike food expenditures, however, the GLSSI/2 frequent nonfood expenditure datawere not corrected for the shift in recall period from fourteen days in GLSS 1/2 to threedays in GLSS3. If this correction were made, mean expenditures in GLSS1 would rise by2,000 cedis (less than 1 percent), with a small impact on the incidence of poverty (seeTable 5). For the sake of uniformity, this correction should also be made even though itsimpact on the poverty estimates is small.

Another way that the change in recall period affected the estimates was the shift incertain items non food from the infrequent category of expenditure in the GLSSI/2questionnaire to the frequent category in the GLSS3 questionnaire. We can get some ideafrom the GLSS1/2 surveys of the extent of the bias resulting from the shift from theinfrequent to frequent section of the questionnaire. The surveys asked households toreport their less frequent expenditures on an annual basis as well as what they actuallyspent in the last two weeks. Mean expenditure based on the two week estimates is about60 percent higher than the annual estimate (see Table 6). It is sometimes argued that theannual estimates are preferable, since using the two week estimates introduces morevariance and thus would tend to increase poverty, all other things being equal. While thecoefficients of variation for the two week recall are about double those of the annualestimates, mean expenditures based on the two week recall are sufficiently higher suchthat the poverty incidence using the two week estimates differed by more than onepercentage point in GLSS1 and 0.6 percentage points in GLSS2 from the povertyincidence based on the annual estimates.

This only accounts for the bias in shifting from an annual to a two week recall period.A further bias is introduced in GLSS3, because the recall period used for frequentexpenditures shifted from 14 to 3 days. Purchased food expenditures in GLSS1/2 wererevised upwards by 15 percent to take account of the bias introduced in the change from a14 day to a 3 day recall period. If the expenditures based on a two week recall are 60percent higher than the estimates based on annual recall, and those based on a three dayrecall are 15 percent higher than those based on two week, then the combined effect ofshifting nonfood expenditures from an infrequent (annual) to a frequent three day estimatewould be an upward bias in the GLSS3 estimates of about 70 percent.

9

Table 5. Rural Ghana: Poverty Incidence Corrected for Change in Recall Period forNonfood Daily Expenditure*

Correctedfor Recall Error Not Correctedfor Recall ErrorPo Po Po P0 Po PO

Locality GLSS1 GLSS2 GLSS3 GLSSI GLSS2 GLSS3................................. ....................................................................................... ........................ ................................... ...............................................

Rural Coastal 0.368 0.434 0.286 0.377 0.446 0.286Rural Forest 0.374 0.408 0.330 0.381 0.419 0.330Rural Savannah 0.490 0.546 0.383 0.494 0.548 0.383Rural Ghana 0.412 0.459 0.339 0.419 0.467 0.339

* The variable EXPDAY in file IE34 was multiplied by (1.029)5 for GLSS 1 and 2.

Table 6. Rural Ghana: Per Capita Annual Other Expenditure, 2-week and 12-month Estimates

Per Capita Infrequent Expenditure Poverty Incidence2-week 12-month Ratio 2-week 12-month

Estimates Estimates Estimates Estimates

(a) (b) (a)/(b)

Rural CoastalGLSS1 39010 25194 1.55 0.341 0.377GLSS2 44468 23784 1.87 0.435 0.446

Rural ForestGLSS1 40814 24635 1.66 0.384 0.381GLSS2 52749 29024 1.82 0.406 0.419

Rural SavannahGLSS1 20369 15745 1.29 0.481 0.494GLSS2 21770 17963 1.21 0.555 0.548

Rural GhanaGLSS1 33472 21730 1.54 0.408 0.419GLSS2 40755 24221 1.68 0.461 0.467

10

It is difficult to estimate what the overall impact of the shift from the infrequent tofrequent categories is, given the lack of exact correspondence between the categories ofexpenditure used in GLSS1/2 and 3. However, two relatively important categories ofexpenditure, public transport expenditure and medical expenses (other than hospital-related medical expenses), shifted from the infrequent to the frequent section. Transportand medical expenditures ( 5,400 cedis) alone accounted for almost 3 percent of meantotal expenditure in GLSSI, the bias could have amounted to about 15 percent of the25,000 cedis difference in mean total expenditure between GLSSI and 3, assuming thatthe shift in recall period from annual to 3 day increases mean expenditure by 70 percent.

And even for those items that remained in the infrequent category, the recall period inGLSS3 was reduced from one year to 3 months if the household purchased the item morethan 4 times a year. This would presumably also introduce some bias in the estimate ofinfrequent expenditures, though we have no way to estimate the impact.

A final way in which the recall period change affected the estimates was forconsumption of home production. The method used to estimate consumption of homeproduction (CBP) changed substantially between GLSSI/2 and GLSS3. The GLSS1 and2 questionnaires asked for information concerning the number of months a home-produced good was usually consumed, the number of times per month it was consumed,and the average value of consumption each time the commodity was consumed. Thisinformation was then aggregated to arrive at an estimate of consumption of homeproduction. The GLSS3 survey relied on a direct recall method in which households wereinterviewed every 3 days over a two week period regarding the value of home productionconsumed since the last interview. These responses were then multiplied by 26 toannualize the data. The latter procedure introduces greater variance in the estimates ofCHP, to the extent that some households may be interviewed in a period in which they arenot consuming a home-produced commodity that they consume during other months of ayear. Its advantage is that it offers greater precision in the estimates of what was actuallyconsumed.

There are various ways in which the comparability of the GLSS1/2 and GLSS3estimates could be affected. Errors in estimating consumption of home production couldbe introduced in the GLSSI/2 method of estimation if a household does not correctlyestimate the number of months it consumed a home-produced good, the number of timesper month, or the average value of consumption each time a commodity was consumed.While the evidence suggests that lengthening the recall period tends to reduce the estimateof consumption, it is not entirely clear in this case what the impact would be of going tothe three day recall period used in GLSS3 since the method of estimating homeconsumption in GLSS1/2 is the product of three values. If households systematicallyunderreport the number of months they consume, but compensate by overestimating thenumber of times per month they consume or their consumption each time, then theresulting differences between the two methods of estimation would be smaller.

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We can get some idea of the bias introduced by the difference between the reportedmonths of consumption and the actual months a good is consumed from the GLSS3questionnaire. For each home-produced item, the GLSS 3 questionnaire first askedhouseholds whether they consumed of the item in the last 12 months, and then asked asecond question as to whether they eat the item all through the year or only in somemonths, and if so, which months. While the question on the number of months is notphrased specifically with respect to the last twelve months, the fact that it follows thequestion on whether the household consumed any of the home-produced item in the lasttwelve months introduces the possibility that the household will reply with respect to thelast twelve months. If we assume that this is the case, then under the assumption thathouseholds are interviewed evenly throughout the year, the percentage of households withnon-zero home consumption of a given item multiplied by twelve months provides anestimate of the mean "effective" months of home consumption.' This can then becompared with the mean reported months of consumption, which are presented in Table 7.

We also computed the mean "effective" and reported months after correction for the seasonality biasintroduced by non-uniform sampling throughout the year. Results are reported in Table 7. In general thedifferences between the seasonally unweighted and weighted ratios are very small.

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Table 7. Rural Ghana: A Comparison of Effective and Reported Months, 1992

Not Adjustedfor Seasonality Adjustedfor Seasonalty

Effective Actual Ratio of Effective Actual Ratio of

Months Months Eff/Actual Months Months EfifActual........... .......................................................................................................................................................................................................

Rirtal Ghana:

nce 0.91 0.6 1.51 0.84 0.6 1.39

maize 5.02 4.4 1.14 5.22 4.4 1.19

millet 2.11 1.3 1.62 2.23 1.4 [.59

cassava 7.61 6.7 1.14 7.46 6.6 1.13

yarn 3.52 2.9 1.22 3.39 2.8 1.21

cocoyam 4.02 3.5 1.15 3.93 3.5 1.12

plaintain 4.63 4.0 1.16 4.61 4.0 1.15

Rural Savannah:

rice 2.12 1.3 1.63 1.90 1.3 1.46

maize 5.97 4.3 1.39 6.20 4.4 1.41

niillet 6.09 3.7 1.65 6.46 4.1 1.58

caasava 4.66 4 1.17 4.03 3.5 1.15

yam 4.63 3.2 1.45 4.10 2.9 1.41

cocoyam 1.40 1.2 1.16 1.10 1 1.10

plaintain 1.34 1.2 1.11 1.11 1 1.11

Rural Forest:

rice 0.39 0.4 0.98 0.41 0.4 1.02

maize 4.52 4.6 0.98 4.71 4.6 1.02

niillet 0.00 0 N/A 0.00 0 N/A

cassava 10.01 8.9 1.12 10.09 8.9 1.13

yamn 3.91 3.5 1.12 3.85 3.6 1.07

cocoyam 7.09 6.3 1.13 7.04 6.3 1.12

plaintain 8.03 7.1 1.13 7.92 7 1.13

Rural Coastal:

rice 0.00 0 N/A 0.00 0 N/A

maize 4.50 4 1.12 4.69 3.9 1.20

millet 0.00 0 N/A 0.00 0 N/A

cassava 7.39 6.6 1.12 7.56 6.6 1.15

yamn 0.91 0.8 1.14 1.28 1 1.28

cocoyam 1.89 1.5 1.26 2.01 1.5 1.34

plaintain 2.89 2.3 1.26 3.41 2.5 1.36

Source: GLSS3

The mean effective months of consumption were about 15 percent greater than themean reported months for most of the major staple food crops. For millet/sorghum, thedifference is about 60 percent. Thus, the procedure used to calculate the homeconsumption would tend to bias the mean GLSS3 subsistence consumption upwardsrelative to the method employed in GLSS1/2, assuming that households were reportingthe number of months they consumed an item over the last twelve months (and assumingthere was no offsetting bias in the GLSS1/2 estimates in the direction of an overestimate

13

of the number of times per rnonth or the value of consumption each time the item isconsumed). If we adjust mean subsistence expenditures for the major staple food crops toreflect the proportionate difference between reported and effective consumption, nneansubsistence expenditures would fall by about 7,400 cedis (see Table 8a). The difference inmean total expenditures between GLSS1 and GLSS3 would be reduced by about 30percent, and between GLSS2 and GLSS3 to almost 20 percent. The difference isparticularly large for the Savannah: correcting for it would reduce the difference in meanexpenditures between GLSS I and 3 by more than 50 percent (see below).

Table 8a: Rural Ghana: Per Capita Staple Food Consumption Revised for Reported Month Bias

All Rural AreasHome consumption of self-produced food..............................................................................................................................................................................................................................

Millet! Coco- Total

Rice Maize Sorghum Cassava Yams yams Plantain Stcple........ ...................................................................................................................................................................................................................

GLSS1 732 10879 9203 14761 5248 4764 6452 52039GLSS2 722 7507 6900 9308 4198 3401 3923 35959GLSS3 1102 4538 5069 11518 6731 4424 6632 40014

GLSS3 Revised 739 3834 3073 10210 5069 3860 5803 32588

GLSS3 Revised -GLSS3 -363 -704 -1996 -1308 -1662 -564 -829 -7426

To obtain an idea of how important a source of bias this might be, we adjusted thehome-produced consumption of the main staple foods for each households by the ratio ofmean reported to effective months of consumption. The poverty estimates were thenrecalculated (see Table gb). The poverty index for rural areas for GLSS3 rises by 2.7percentage points, implying a reduction of 5.3 percentage points between GLSS1 and 3instead of 8 percentage points. The Savannah showed a major change: poverty wasreduced by only 6 percentage points, instead of 11 percentage points. This suggests thatthe change in the questionnaire with respect to the calculation of the consumption of homeproduction could be a relatively important source of bias.

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Table 8b: Rural Ghana: Poverty Incidence With Per Capita Staple Food Consumption Revised andUnrevised for Reported Month Bias

Revised Unrevised

Po Po Po Po Po Po

GLSS1 GLSS2 GLSS3 GLSS1 GLSS2 GLSS3............. I.......................................................................I........................... ............... I...............................................I.......................................... .....

Rural Coastal 0.377 0.446 0.294 0.377 0.446 0.286Rural Forest 0.381 0.419 0.347 0.381 0.419 0.330Rural Savannah 0.494 0.548 0.436 0.494 0.548 0.383Rural Ghana 0.419 0.467 0.366 0.419 0.467 0.339

Of course, this is just an indicative calculation, as there remain many unknowns, including thequestion of how households actually interpreted the question on the number of months they consumed ahome-produced item. It would seem, however, that there are enough questions about the comparabilityof the methods to raise serious question about the comparability of the data on consumption of homeproduction.

Expanding the number of categories

A second large change in the questionnaire between GLSS1/2 and 3 was the large expansion inthe number of items listed in the questionnaire. The impact of this expansion on expenditure has notbeen assessed. A study carried out in Indonesia suggests that it may have little impact, while a study onEl Salvador came to the opposite conclusion. For some of the categories in the GLSS3, an increase inthe number of expenditure subcomponents may not have mattered too much, since mean consumptionfor some of the subcomponents was very low. However, there are a few categories in the foodexpenditure section, for example, that might have been affected by the increase in the number ofcategories, as the subcomponents turned out to be fairly important in their own right.

GLSS1 and 2 asked one question about consumption of alcohol beverages. The GLSS 3questionnaire broke it down into six categories. Between 1988 and 1992, real mean per capitaconsumption of alcoholic beverages went up from 2925 to 5296 cedis, an 81 percent increase (seeTable 9). Another example is dry pepper. There was no specific item in the GLSS1/2 question onconsumption of dry pepper, so presumably households would have reported it under "other foodexpenditure." It was listed as a separate item in the GLSS3 questionnaire, however, with meanconsumption of 995 cedis, a rather large food expenditure to relegate to "other," which amounted toonly 196 cedis in GLSS1. A third example is the consumption of non dairy oils/fats. There were twoquestions on consumption of refined oil/fats in GLSS1/2 (palm oil and shea butter; refined oils)compared with seven questions in GLSS3 (coconut oil, groundnut oil, palm kemel oil, red palm oil,shea butter, animal fat, other vegetable oils). Purchases of oil/fats between GLSS 1 and GLSS3 in realterms went up from 2560 to 4039 cedis. Additional expenditures on these categories of expenditurealone represent about 20 percent of the increase in mean per capita food expenditure between GLSS1

2

and 3 .

2 It is possible that the price of these items may have increased substantially more than the overall CPI, explaining part of theapparent increase in consumption. While this seems unlikely, it is worth investigating.

15

Table 9. Rural Ghana: Per Capita Food Consumption by Categories

................................. I.... ................................... ....... I...................................................... ................................................................ I.......................O...............I.................... . . . . . . . I. .. . . .. . . . . .. . . .. . . . . . . .. . . . .. . .

Pulse Oilseeds Oi/ Fruits Vege- Meat Poul-try Milk Beverage Misc Alcohol Cereal Starch Fish Sugar Spice Prepared TOT1Fats tables food

fi m..e.consump tion..ofse"fp.. odu"edfood * ...........................................................................................................................................................................................................................................................Home consumption of self-produced food s

GL.SSI 1974 1257 630 1998 5334 987 1160 64 29 227 223 20814 31305 812 66814GLSS2 1476 766 483 1389 4106 836 1079 34 1 72 132 15129 20929 242 46674GLSS3 3684 1640 541 1199 5374 1335 2665 49 8 168 10710 29454 581 57408

Consumiption ofpurchasedfood

GLSSI 1872 1182 1930 709 6983 3374 1161 925 285 196 2702 8464 8041 15749 2044 1219 6289 63127

GLSS2 1687 1039 2145 692 7105 2987 1194 849 343 457 2862 7655 7488 18798 2033 1201 6733 65267GLSS3 2195 1860 3498 593 6404 3810 1761 812 1115 250 5128 11856 8184 17942 1610 2402 6233 75653

Total consumption

GLSSI 3846 2439 2560 2707 12317 4361 2321 989 314 423 2925 29278 39346 16561 2044 1219 6289 129941GLSS2 3163 1805 2628 2081 11211 3823 2273 883 344 529 2994 22784 28417 19040 2033 1201 6733 111941GLSS3 5879 3500 4039 1792 11778 5145 4426 861 1123 250 5296 22566 37638 18523 1610 2402 6233 133061% Change

GLSSI-3 53% 43% 58% -34% -4% 18% 91% -13% 258% -41% 81% -23% -4% 12% -21% 97% -1% 2%

* Excludes in-kind payment for employment.

16

The number of categories in nonfood expenditures also increased substantially. There were45 separate categories of frequent expenditure in GLSS3, compared with 9 in GLSS1/2. Therewere 63 categories of infrequent expenditure in GLSS3 compared with 23 in GLSS1/2.3 Toexamine whether there are any large increases that appear to be out of line, the expenditureestimates need to be redeflated by the price index relevant to a particular subcompenent since alarge increase in consumption may be partly due to a large price increase relative to the overallincrease in the CPI. Table 10 breaks down nonfood expenditure by category and also showsredeflated expenditures in GLSS1 and 2 by the relevant subcomponent of the CPI index to give amore accurate picture of the increase in each subcomponent. Medical expenditures increased themost -- 135%, but how much due to the shortening in the recall period and increase in thenumber of questions unclear.

Table 10. Rural Ghana: Real Per Capita Nonfood Expenditure by Categories............................................................................................................................................................................................................................................................

.H. . ...... ... ... ... ... .. ......H.............TFTA......... Cloting ..... 11.Gods ... Transp0zion ... aicaL ..... Recrt ... L Edcaon ... MivceJ1aneous.. T ba-co .. T.TALAlRural

GLSS1 145 3624 9626 7383 2617 2799 1793 2733 5080 2386 38186GLSS2 218 3450 11248 7355 2582 2830 2200 3330 5752 2148 41113GLSS3 764 6832 13301 9181 7300 6635 3253 5574 6458 1468 60766% change,GLSSI-3 427% 89% 38% 24% 179% 137% 81% 104% 27% -38% 59%RedeflatedGLSS3 16942 11878 4785 6583 2489 4265 7870% change,GLSS1-3 76% 61% 83% 135% 39% 56% 55%

Rural SavannahGLSSI 65 3020 7402 5256 1873 1884 1216 1616 4349 3161 29842GLSS2 55 2547 7967 5264 1405 1797 1031 1645 5664 2946 30321GLSS3 271 6152 9075 6940 4222 4596 2360 2740 4729 1810 42895% change,GLSS1-3 317% 104% 23% 32% 125% 144% 94% 70% 9% -43% 44%RedeflatedGLSS3 11559 8979 2767 4560 1806 2097 5763

Rural CoastalGLSSI 151 5033 10212 8828 3745 3057 1911 3795 6055 1800 44587GLSS2 607 4993 10961 8500 2979 2720 2447 3819 5495 1802 44323GLSS3 1665 8331 12343 10778 7824 7633 3755 8387 6808 1331 68855% changeGLSSI-3 1003% 66% 21% 22% 109% 150% 96% 121% 12% -26% 54%RedeflatedGLSS3 15722 13944 5128 7573 2873 6418 8297

Rural ForestGLSSI 204 3407 11043 8312 2644 3374 2177 3075 5171 2076 41483GLSS2 139 3323 13796 8300 3240 3643 2930 4313 5947 1742 47373GLSS3 717 6646 17051 10163 9448 7747 3709 6439 7639 1267 70826% change,GLSSI-3 251% 95% 54% 22% 257% 130% 70% 109% 48% -39% 71%RedeflatedGLSS3 21719 13148 6193 7686 2838 4927 9309

Note: Redeflation factor is the change in the CPI between 1988-92 divided by the change in the subcomponent CPI over the same period.

3 Three items i' the infrequent expenditure section of the GLSSI/2 (weddings, funerals, taxes) were shifted to anotherpart of the GLSS3 questionnaire and four other items were not included in the total, bringing the total number of items inthe infrequent section of the GLSSI/2 questionnaire for purposes of comparison with GLSS3 down from 30 to 23.

17

3. WHAT HAPPENED TO AGRICULTURAL REVENUES?

Thus, it would seem that while there may have been some growth in expenditures betweenGLSS 1/2 and GLSS3, there is reason to think that the rate is probably not nearly as large as datasuggest. One approach to checking whether the growth in expenditure is reasonable is toexamine what happened to the main sources of agricultural income. If agricultural incomes didindeed stagnate, it is unlikely there was much growth in rural incomes. On the other hand, ifagricultural incomes showed a strong increase, one would also expect to see an increase inhousehold expenditures. A problem with relying solely on the agricultural income data in theGLSS survey is that it is heavily influenced by the estimation of consumption of home production.If there are biases in that data, then agricultural income will be sirnilarly biased. So we looked atother sources of agricultural production and price data to investigate what happened toagricultural revenues.

Production Trends

Unfortunately, the analysis of what happened to agricultural production over the 1.987/92period is complicated by the fact that there exist two series on agricultural production: onepublished by FAO and the other published by the Ghana Statistical Service (Appendix table 1).For most crops, they are quite sirnilar in the second half of the 1980s. The two series differprincipally in the estimates for rnillet, sorghum and maize production in 1988 (see Charts 1,2,3).With respect to maize the FAO estimate is 25 percent higher than the GSS estimate, whereas theFAO estimates are lower for millet and sorghum, and dramatically so for millet.

The discrepancies are more important in the 1990s, however. According to the GSS datathere is a large jump in yam and cassava production in 1991-93 relative to the 1987-89 levels,whereas the FAO estimates show a decline for yam and a more modest increase for cassava (seeCharts 4 & 5). According to the GSS data, yam production increased by 119 percent between1991 and 1989 while cassava increased by 72 percent. Moreover, this was not a one yearincrease: production levels for both yam and cassava, according to the GSS data, remained atthese higher levels through 1994. Both sets of data also show a considerable increase in maizeproduction: in 1991, 1993, and 1994 production exceed 930,000 thousand metric tons comparedto production in the range of 600,000 to 700,000 thousand metric tons in the 1987-1989 period,an increase of 33 - 50 percent. Both sets of data indicate that production dropped in 1990, a yearof poor rainfall. For plantains, there is no difference in the two series after 1985 (see Chart 6).

18

Millet Production Sorghum Production200000 350000180000 - 300000 -

140000 -..... . . .. .... 250000 ... .... ........ .. .......... . -120000 .... ... .. .100000 ... ...... 200000 -80000 .. - -- -............. 150000 ...... .../ ..6 0 0 0 0 .-- .... .\ 1 ------ --- -- -............................. ..... ........... .......40000 , 100000 ............ ........ ... ... .......... .......

1980 1982 1984 1986 1988 1990 1992 1994 50000 Li

1980 1982 1984 1986 1988 1990 1992

| FAO - GSS| | FAO . ES|]

Chart 1 Chart 2

Maize Production Plantain Production1000000 1800000

800000 - ----- ---------- -- --- . . --- 1600000 *--- --.--.-----800000 ..... ............ .. ..................................... ......140000........... ........ ..0...... .... .. ........ ........

19 /> / 1400000 *-.- . -

600000 -------- -- - --;--- ---- IA 600000 . ........ ../ .............. ...... 1200000.....'..

400000ooooo - . .... .... . . .

200000 ... . ........................ 800000............. .

0 600000 I I I I I I I- -1980 1982 1984 1986 1988 1990 1992 1980 1982 1984 1986 1988 1990 1992

| _ FAO - GSS I |-FAO GSGSS]

Chart 3 Chart 4

Cassava Production Yam Production6000000 3000000

. 1 ~~2500000 . . .-

5000000-. .. ... I ~~~~~~~~~~~~~~~~~~~~2 5 0 0 0 0 00 . ....... .............. ..... ......... ................ ..... ......... . ............

4 0 0 0 0 0 - --------- 2 0 0 0 0 00------............4000000 - .. _..

1500000 ......... ... ................ ..... ......... . .

3000000 -- 1000000 . . . ....---- -- -

2000000 . .................... ........ 500000 . ......................

1000000 O1980 1982 1984 1986 1988 1990 1992 1980 1982 984 1C86A1R 1 390 1992

FAO GSS |FAO GS

Chart 6Chart 5

19

Producer price trends

Producer price trends may help to shed some light on which production series is likelymore accurate. The price data appear to be consistent with an increase in staple foodproduction after 1990. They do not however provide strong support for the view thatthere was a spectacular increase in yam production such as is suggested by the GSS data.

Table 11 and Chart 7 show producer prices for the four major crops, deflated by therural CPI index.4 For maize and cassava, real producer prices peaked in 1987, followingthe drought in 1986, then came down between 1988-89, rose again in 1990 due to thedrought and then fell in 1991. This is consistent with the pattern of production. Between1987 and 1989, maize and cassava production increased by roughly 21 percent for bothcrops, and prices fell by roughly 50 percent. Real maize and cassava prices increased withthe drought in 1990, and then fall again in 1991. The 1991 prices are roughly similar tothe 1989 prices. To the extent that both cassava and maize are among the most priceinelastic comnrnodities, one would expected a further fall in prices in 1991 and 1992 if therehad been another major increase in output. The price data would seem to indicate thatthere has not been a dramatic increase in production of either crop.

Table 11. Rural Ghana: Producer Prices Deflated by Consumer Price Index

........................... ...... .......................................... I..................................... .....................................................................................................

Year Plantain Yam Cassava Maize198 107 92 76 901986 108 114 142 1141987 138 138 215 1531988 130 129 126 1181989 137 146 105 821990 155 123 142 1291991 105 110 111 891992 100 100 100 100

As for yams, real yam prices peaked in 1989, and then began to drop in 1990, andcontinued to fall in the 1991 and 1992. Between 1989 and 1992, yam prices fell by 53percent. It is somewhat surprisinp that prices were not higher in 1990, given the decline inproduction in 1990 due to the drought. Since yam production increased dramatically in1991 according to the GSS data, it is somewhat puzzling that the decline in yam pricebegan in 1989. The production and price data do not seem totally consistent, but the largedecline in real yam prices suggests that there has been an increase in yam production.

4 Producer price data are taken from Leenhardt (1993). They are annual data. Ideally one would use monthlydata and look at price trends more closely ied to the crop calendar.

20

Real plantain prices rise in 1990, which is consistent with the poor harvest reported in1990. Prices then fall sharply between 1990 and 1991 as production recovered to the1988 level. Given the strong decline after 1990, however, in plantain prices, one wouldhave expected to see an increase in plantain production in the 1990s compared to thesecond half of the 1980s.

Real Producer Prices240220 ................... ..200 . A.. .

160

12 0 -; : ..... .... ..... ........ . o s140 ......12010080

1985 1986 1987 1988 1989 1990 1991 1992

-Pantain -. Yam - cassa _

Chart 7

Producer Revenues

Using the GSS production data, we find that producer revenues fell for three out ofthe four main staple food crops 1987 and 1988 (except for plantains) (see Table 12).There was a further drop between 1988 and 1989 for cassava and maize. Weighted byeach crop's share in total rural consumption, crop revenue from these four crops droppedby more than 10 percent between 1988 and 1989--due, except for plantains, to a fall inreal crop prices. Revenues rebounded between 1989 and 1992, as the increases inproduction outweighed the drop in prices. Producer revenues increased by nine percentbetween 1988 and 1992, and by twenty percent between 1989 and 1992. The FAO data,on the other hand, indicate that income from these staple crops fell by 20 percent between1988 and 1989 and by three percent between 1989 and 1992.

21

Table 12. Ghana: Real Producer Revenue

GSS Production Data FA 0 Production Data

Index of Real Crop Revenue Weighted Index of Real Crop Revenue Weighted

Year Plantain Yam Cassava Maize Total* Plantain Yam Cassava Maize Total*-- i... ... ........... .............-. ........ ............. ...-........ ........................ ............... ........-.. ..... ... ... ... ........... ................ ................ .............................................................

1988 145 66 73 97 91 145 154 104 121 122

1989 131 75 62 81 79 131 187 88 81 103

1992 100 100 100 100 100 100 100 100 100 100

* Weighted by follow ing consumption shares of respective crops:................................................................. .......................... ................ I...................

Year Plantan Yarn C'assava Maize.... ...................................... ......................................................................................I........

1988 016 0.14 0.41 0.29

1989 0.13 0.12 0.39 0.35

1992 0.19 0.21 0.40 0.20

Given the likely possibility that there was some increase in yam production in 1991,there is reason to think that agricultural incomes from the staple food crops may haveincreased between 1989 and 1992-- probably not as much as the 20 percent indicated bythe GSS production data, but probably more than indicated by the 3 per cent decline basedon the FAO data. According to the GLSS data, the increase in agricultural incomesbetween 1989 and 1992 was 18 percent (see Table 13). Remember, however, that theconsumption of home production estimates for GLSS3 may be biased upwards relative toGLSS 1/2. Since consumption of home production accounts for a large share ofagriculture income, if such a bias exists, then the recovery in agricultural incomes in 1992would be less than what is indicated by the GLSS data.

Whether or not agricultural incomes increased between 1988 and 1992 is morequestionable. The GSS producer revenue estimate shows a 10 percent increase, while theFAG estimate gives a 25 percent decrease in income between 1988 and 1992. Accordingto the GLSS data, agricultural incomes fell by 22 percent. However, there is an anomalyin the GLSS agricultural income for the rural coastal region; consumption of homeproduction was an unusually low fraction of agricultural income for the rural coastalregion for 1988, suggesting that agricultural incomes from sources other thanconsumption of home production might have been upwardly biased in 1988. If so, thiswould tend to overstate the fall in incomes between 1988 and 1992. Another factor thatwould tend to overstate the fall in incomes would be a bias in the CPI deflator for GLSS1.If the CPI deflator were underestimated for the GLSSI, agricultural incomes would alsobe biased upwards in GLSS1, thus exaggerating the decline in income between 1988 and1992. However, shifts in the method of estimating consumption of home production in

22

GLSS3 may give rise to an offsetting bias, as the method used in GLSS3 appears to leadto a higher estimate of consumption of home production and thus agricultural income inGLSS3. The net effect of these three potential sources of bias cannot be determined.

Real cocoa revenues were also declining during this period (see Table 14]. Realcocoa producer prices peaked in the 1987/88 season, which was a particularly poorharvest. Real cocoa revenues peaked in 1988/89 season, a year of record production, andthen declined steadily thereafter. The only exception would have been in the northern partof Western region, which experience a 144 percent increase in production between1987/88 and 1991/92 (Jacquet, 1995). It is difficult to link the comparison of cocoarevenues between GLSS1 and 2 to the real income trends, because it is not clear exactlywhich cocoa harvest households are reporting on. 5 However, it is evident from theCOCOBOD data that there was a large decline in both the real producer price and realcocoa revenues between GLSS1/2 and GLSS3. The GLSS data show a similar trend:mean cocoa revenue increased slightly between GLSSI and 2, and then declined inGLSS3.

Table 14. Ghana: Real CocoaRevenue

Production Real producer Real producer Real Revenue Real Cocoa Revenue Indexprice price index (COCOBOD) (GLSS)

1985/86 219044 6211 104 1360482 94

1986/87 227764 7506 126 1709597 118

1987/88 188171 9474 159 1782732 123 129

1988/89 300101 7933 133 2380701 165 147

1989/90 295052 6914 116 2039990 141

1990/91 293352 6266 105 1838144 127

1991/92 242807 5952 100 1445187 100 100

1992/93 212122 5556 93 1178550 82

In conclusion, what seems to be driving the increase in agricultural revenues is thesharp increase in yam production and, to a lesser extent, cassava production. Examiningwhether yam production actually did increase by 119 percent between 1989 and 1991 andremained high thereafter is a question worth pursuing, but beyond the scope of this papergiven the data available.

5 The 1987/88 season covers the principal harvest (October 1987-Februaay 1988) and the secondary season(May -August 1988). This is essentially the same period covered by GLSS1. However households surveyedearly in the survey year would more likely be reporting their revenues from the 1986/87 season, while thosesurveyed later in the year would more likely be reporting on revenues from the principal harvest in 1987/88.

23

Questions about the food component of the CPI index

There is some reason to think that the food price index is underestimated in the period1987/88. Table 15 compares the nominal producer price index with the food and nonfood indices used to derive the CPI. What is interesting is that between 1987 and 1989the producer price indices are substantially higher than the food index, except for maizeand cassava in 1989.

Table 15. Ghana: Producer Price Itdices (1992=100)

Nominal Producer Price Index Food Price Nonfood Total CPIYear Plantain Yam Cassava Maize Crop index Index Index Index1985 23 20 17 20 19 23 21 221986 29 30 37 30 33 28 25 261987 49 50 77 55 62 37 34 361988 61 60 59 55 58 49 45 471989 79 85 61 48 64 61 55 581990 121 96 111 100 107 82 74 781991 95 99 100 80 93 91 90 901992 100 100 100 100 100 100 100 100Note: The nominal crop index of producer price is the weighted average of norninal producer price indices of plantain, yam, cassava, andmaize. Weights are the average share of consumption each crop in total rural consumption of the four crops for GLSS 1,2 and 3.

Producer Price and Food Price Indices

120

100-

80 -

60

403

201

01985 1986 1987 1988 1989 1990 1991 1992

-4- Producer price index Food Price Index

Chart 8

24

Constructing a producer price index weighted by the share of each crop's value intotal consumption of the four crops for each year, we find that the producer price index issubstantially higher than the food price index in 1987 and 1988 (see Chart 8). Since thesecommodities represent a large share of consumption, either the price index for other foodcommodities was substantially lower than the overall food index in the late 1980s, andthus rose rapidly between the late 1980s and 1992, or alternatively, and more plausibly,that food staples may be under-represented in the construction of the food price indexused to calculate the CPI. If so, then the degree of underestimation of the CPI food indexwould be greatest in the 1987-88 period when real prices of maize and cassava peaked,biasing expenditure upwards in the 1987-88 period.

If expenditures in GLSSI were biased upwards, the fall in expenditure betweenGLSS1 and 2 would tend be exaggerated. GLSS2 might then be the more appropriatebaseline, with the caveat that it might represent a poor agricultural year in some areas. Toget some idea of how much bias an inappropriately specified deflator might introduce, werecalculated the total deflator assuming that the food deflator were equal to the producerprice index. The CPI index for GLSS1 would be 26 percent higher and expenditures forGLSSI would be 20 percent lower, bringing them below mean expenditures in GLSS2.While such a comparison is probably an extreme upper bound, it does illustrate thepotential biases that could result from a misspecification of the food deflator.

Trends in consumption of staple foods and producer incomes

It is also usefuil to examine how the trends in consumption of the major food grainand root crops compare with the production and income trends. Table 16a indicates thatconsumption in rural areas, which is mostly consumption of home produced goods, fellbetween GLSSI and 2 and then recovered in GLSS3, though not to the same level as inGLSS L. Total consumption of maize, millet/sorghum, cassava and cocoyams was lower inGLSS3 than in GLSSI, and consumption of these cereal crops fell in GLSS2 relative toGLSS1. In contrast, consumption of yam and plantains was higher in year 3 comparedwith GLSS1.

Table 16a. Rural Ghana: Per Capita Staple Food ConsumptionMillet/

Rice Maize Sorghum Cassava Yams Cocoyams PlantainsConsumption of home-produced foodGLSS1 732 10879 9203 14761 5248 4764 6452GLSS2 722 7507 6900 9298 4198 3401 3923GLSS3 1102 4538 5069 11518 6731 4424 6632Consumption of purchased foodGLSS1 3194 1949 652 4616 1514 420 1442GLSS2 2614 1735 446 3845 1454 471 1649GLSS3 3268 3626 1994 4749 1704 391 1305Total consumptionGLSSI 3926 12828 9855 19377 6762 5184 7894GLSS2 3336 9242 7346 13143 5652 3872 5572GLSS3 4370 8164 7063 16267 8435 4815 7937

25

However, these figures do not give a good idea of real consumption levels, because ofshifts in relative prices. The GLSS consumption data are deflated by the overall deflatorfor each region, which is a weighted average of the food and non-food deflator based onfood shares for each region calculated from in GLSS3. To the extent that staple foodprices rose less rapidly than other food and non food prices, the procedure of deflatingeach commodity by the overall price deflator would tend to overestimate in the early yearsconsumption of those commodities whose relative prices have grown less rapidly than theoverall price index, and underestimate consumption of those commodities whose relativeprices grew more rapidly.

To get an idea of the change in real quantity consumed, we could take the nominalconsumption of the commodities and deflate by the commodity specific price index. Sincewe only have annual producer price data not disaggregated by rural area (and sinceconsumption, particularly of home production, is not tied to a specific reference period inGLSS1 and 2), we multiplied the commodity specific producer price index by the overallCPI price deflator to approximate nominal consumption, and then divided by theconmmodity specific price index. Table 16b shows how this would change consumption ofthe four commodities for which we have price data. For maize, the data still show adecline in consumption between GLSS1 and 3. For cassava consumption increases by 6percent, plantain by 31 percent, and yam by 60 percent between GLSS1 and 3.

Table 16b. Rural Ghana: Per Capita Total Staple Food Consumption Redeflated by CommodityPrice Deflators

Maize Cassava Yams PlantainGLSS1 1(909 15368 5260 6055GLSS2 11215 12473 3864 4076GLSS3 8164 16267 8435 7937

% Change GLSS1-3 -25% 6% 60% 31%% Change GLSS2-3 -27% 30% 118% 95%

If we correct for change in the bias introduced by shifting from the effective to thereported months in the calculation of consumption of home production in GLSS3,consumption of home production in GLSS3 falls. This reduces the increase betweenGLSSI/2 and GLSS3 (see Table 16c). However, there remain fairly large increases inyam and plantain consumption between GLSS2 and 3. The increase in yam consumptionis consistent with a strong increase in yam production, and also with an increase inexpenditure as the elasticity of yam consumption with respect to income is higher thanmaize, cassava or plantain consumption. The rather large decline in maize consumptionbetween GLSS1 and 3 is somewhat surprising. However, maize does have the secondlowest expenditure elasticity of the main staple food crops (although positive), and withthe fall in yam and plantain prices after 1989, perhaps consumers shifted out of maize into

26

other crops.6 If the income elasticity of demand for maize were actually negative, it mightalso explain why maize is the only major staple food crop in which consumption increasedbetween GLSS I and 2 as expenditure fell.7

Table 16c. Rural Ghana: Per capita Total Staple Food Consumption Redeflatedand GLSS3 Corrected for Bias in Home Consumption

Maize Cassava Yams PlantainGLSS1 10909 15368 5260 6055GLSS2 11215 12473 3864 4076GLSS3 7619 14885 7223 7009

% Change GLSS1-3 -30% -3% 37% 16%% Change GLSS2-3 -32% 19% 87% 72%

Home consumption correction factor:GLSS3 0.88 0.88 0.82 0.86

Redeflation correction factor:GLSS1 0.85 0.79 0.78 0.77GLSS2 1.21 0.95 0.68 0.73

6 See Alderman and Higgins (1992) for a discussion of income and price elasticities.

7 There was also a change in the questionnaire regarding the various components of maize consurnption, butthis change does not appear to be significant. GLSS1/2 asked two questions, one about maize (cob, grain, orflour) and one about kenkey. GLSS3 asked two questions one about maize (cob-fresh) and the other maize(flour/dough), but there was no question about consumption of home-produced kenkey. However, kenkey wasnot a large expenditure item in the CHP section in GLSS1/2.

27

4. ACCOUNTING FOR THE VERY LARGE REDUCTION IN POVERTY IN THE SAVANNAH

The headcount index for the Savannah region fell by some 11 percentage pointsbetween 1988 and 1989, and 16 percentage points between 1989 and 1992, mostly due tothe growth in mean expenditures. While both food and nonfood expenditures in GLSS3appear to be biased upwards for all rural areas, there are some reasons to think that thegrowth in expenditure and consequent reduction in poverty is exaggerated for theSavannah than for other two localities for two reasons. First, the GLSS3 expenditure dataseem more biased for the Savannah than for the other two localities. Second, the 1989 -1992 comparison may provide an overly optimistic view of the growth in expenditures,because 1989 was a worse agricultural year in the Savannah than in the other two rurallocalities.

The Savannah region had by far the largest growth in total food expenditures between1988 and 1992, almost double that of the rural coastal region. While there was a largedrop in the food share in other regions between 1988 and 1992, there was virtually none atall in the Savannah, despite the fact that total expenditures increased and the relative priceof food fell. This suggests that it may be worth looking into the large increase inSavannah food expenditures (see Table 4).

The Savannah had a particularly largely increase in several categories of real foodexpenditure between GLSS2 and 3 (see Table 17). Pulses increased by roughly 7,300cedis, largely due to spectacular increase in consumption of home-produced groundnuts.Other categories that showed a particularly dramatic increase in oilseeds (roughly 2,800cedis), poultry (4,300 cedis), vegetables (2,200), and alcohol (3,800 cedis). Whetherthese increases are "real," or whether they are due to large relative price shifts or tochanges in the questionnaire or sampling bias is not clear. However, the increase ingroundnuts is particularly suspect. According to the FAO data, the 1991 groundnutharvest was about a third of the 1987 harvest, and the Savannah is essentially the ordyregion in which groundnuts are grown. Groundnut prices would have had to haveskyrocketed to account for the huge increase in the value of groundnut consumption. Ifthe price increase were that large, it should have had an important effect on the fooddeflator for the Savannah region. We do not know whether the rural CPI adequatelycaptures changes in the prices across regions.

28

Table 17. Rural Ghana: Per Capita Food Consumption by Categories*

Pulse Oil- Oils/ Fruits Vege- Meat Poultry Milk Beve- Misc Alcohol Cereal Starch Fish Sugar Spice Prepared TOTAL

seeds Fats tables rage food

..CoastaI....................................................................................................................................................................................................................................................................................Rural Coastal

GLSS1 2889 4261 3067 2855 12307 2852 2793 1367 658 683 3188 19009 34780 23155 2762 943 11672 129239

GLSS2 2355 3252 2871 1862 11639 2514 2730 1509 680 933 2848 15662 25431 26665 2272 945 8467 112634

GLSS3 2190 3946 4755 2046 11462 3203 2991 1177 1689 288 5892 18501 33982 28665 1949 2259 9837 134832

% Change

GLSSI-3 -24% -7% 55% -28% -7% 12% 7% -14% 157% -58% 85% -3% -2% 24% -29% 140% -16% 4%

Rural Forest

GLSSI 2865 2541 2825 3817 13752 5918 2816 1086 196 556 3005 15609 48157 19345 1844 1246 6599 132177

GLSS2 2816 1794 2920 2893 12697 4829 2736 870 328 668 2670 11776 34899 22613 2099 1176 8049 115830

GLSS3 2049 2900 4363 2296 11293 6717 4121 830 1323 224 4087 11158 45991 20928 1538 1962 6539 128319

% Change

GLSS1-3 -28% 14% 54% -40% -18% 13% 46% -24% 575% -60% 36% -29% -4% 8% -17% 57% -1% -3%

Rural Savannah

GLSSI 5735 1158 1893 1162 10442 3271 1377 622 251 83 2653 53647 30676 8764 1855 1360 2498 127448

GLSS2 4198 807 2058 1123 8882 3361 1319 463 132 57 3542 42799 21649 8831 1777 1414 3723 106136

GlSS3 13055 3995 3187 990 12595 4317 5696 707 517 260 6482 39700 29160 9216 1495 3054 3630 138056

% Change

GLSSI-3 128% 245% 68% -15% 21% 32% 314% 14% 106% 213% 144% -26% -5% 5% -19% 125% 45% 8%

* Excludes in-kind payment for employment.

29

Moreover, the discrepancy between the reported months of consumption of homeproduction and the effective months is particularly large for the Savannah primarilybecause of the large discrepancy for millet/sorghum (see Table 18). If we correct theconsumption of home production of staple food crops by the ratio of mean reportedmonths to effective months, consumption of home production in GLSS3 would drop byalmost 14,000 cedis. This is equivalent to the increase in total food expenditure betweenGLSS1 and GLSS3. While a fall of this magnitude is probably not plausible, it does raisesome doubts about the comparability of the data.

Table 18. Rural Savannah: Per Capita Consumption of Home-produced Staples

Consumption of home-produced staples..................................... I....................................................................- .................. .................. ............................................... .................................................................. ..............

Rice Maize Millet! Cassava Yams Cocoyams Plantain TOTAL TotalSorghum Expenditure

GLSS1I 1592 17769 27004 12381 10516 1770 1676 72708 167086GLSS2 1526 13068 21143 6391 8279 1902 957 53266 144918GLSS3 2744 6382 14684 5552 13375 1296 2358 46391 188469GLSS3Revised* 1683 4595 8957 4775 9229 1115 2122 32476

* GLSS3 correction factor for consumption of home-produced staplesRice Maize Milletl Cassava Yams Cocoyams Plantain

Sorghum0.61 0.72 0.6:1 0.86 0.69 0.86 0.90

On the agricultural side, there is reason to think that the very large decrease inpoverty in the Savannah between 1989 and 1992 was due to a rebound in combined milletand sorghum production between 1988 and 1991 harvest, in addition to the increase inyam production. The Savannah consumes twice as much yam per capita as the ruralcoastal and rural forest regions combined, and thus it stands to reason that if yamproduction did show a spectacular increase from 1991 onwards, that there would havebeen a large reduction in poverty in the Savannah. Also the largest increase in percentageof the population engaged in retail trade (virtually all women) took place in the Savannah,suggesting that the increase in yam production may have fostered an increase in trade (seeTable 19).

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Table 19. Rural Ghana: Retail Trade Nonfarm Self Employment

............................................................................................................................. .......... .......................................................................................

Percent of Women Percent of Self-employed Mean Percentagein Retail Trade Population in Retail Trade Self-employment Income

from Retail Trade................... .......................................................................................................................................................................................................Rural GhanaGLSSI 82.5 39.8 33.1GLSS2 85.8 40.0 32.9GLSS3 88.9 50.1 42.5

Rural SavannahGLSS1 83.0 30.9 25,3GLSS2 81.7 28.4 23.0GLSS3 87.2 55.0 48.1

In addition, the reduction in poverty between GLSS2 and GLSS3 may also bemagnified by the apparent poor harvest of sorghum and millet (crops basically grown onlyin the Savannah region) in 1988. The fall 1988 harvest would have affected crop revenueand consumption in 1988/89 GLSS2 survey. According to the FAO data, the milletharvest was down by 34 thousand tons (a drop of 20 percent), while the sorghum harvestwas down by 45 thousand tons (a decline of 22 percent). The GSS data are moreoptimistic: they show the 1988 sorghum harvest falling by 28 thousand tons, with anincrease in millet production of 19 thousand tons. If the production trends are in line withthe FAO data, they would explain the severe increase in poverty in the Savannah region inGLSS2. And by the same token, it would also be misleading to take the GLSS2 data as abaseline, since doing so would give an overoptirnistic view of the reduction in povertybetween 1989 and 1992. So for the Savannah, at least, GLSSI might be the betterbaseline, depending on the magnitude of the bias, if any, introduced by an improperlyspecified food deflator.

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E. CONCLUSION

Thus by and large the evidence seems to be suggesting that there was a large incr.easein production of non cereal staple food crops in the early 1990s. Agricultural proclucerincomes likely increased between 1989 and 1992. Some increase in total expenditureswould thus be consistent with this trend. However, there is also reason to believe that theGLSS data strongly overstate the increase because of upward biases in the GLSS3 datarelative to the GLSS1/2 data due to changes in the questionnaire. The GLSS expendituredata for the Savannah seem particularly suspect, given some very large increases in certainconsumption items.

To the extent that there was a significant increase in yam (and perhaps cassava)production, it would not be surprising to see a strong growth in rural non farm income.The data indicate that much of the growth came in retail trade by women. Other accountsalso suggest that there has been a major increase in yam marketing over the decade.

If so, this explanation opens up a whole new puzzle: accounting for the spectacularincrease in root crop production in the absence of any apparent technologicalimprovement or major infrastnucture improvements. One possible explanation might bethat households shifted out of cocoa and into yam production, particularly in areas wherethere are problems with cocoa diseases. Since the GLSS3 data suggest a strong increasein yam consumption in the Savannah (and a strong increase in yam production), which isnot a cocoa growing region, this explanation would not seem to account fully for theincrease in yam production Further research is needed to ascertain whether there was amajor increase in the production of yam and other non cereal staple food crops, and if so,why.

To the extent that there was a reduction in poverty based on the increase inagricultural production, it is worth asking the question of the extent to which the majorpolicy changes undertaken in Ghana were responsible for the increase. As it appears thatnontradables crops benefited the most, the real devaluation of the cedi in the mid 1980swould not seem to be a major factor in agricultural growth and poverty reduction. Thus,while casting serious doubt on the comparability of the GLSS data over time, this papersuggests that we need to look at what happened to the agricultural sector in Ghana tounderstand what role-if any-policy changes may played in what is most likely a smallerreduction in poverty than what is suggested by the GLSS data.

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References

Alderman, Harold, and Paul Higgins. 1992. "Food and Nutritional Adequacy in Ghana."Cornell Food and Nutrition Policy Program Working Paper 27. Ithaca, N.Y.:Cornell University.

Coulombe, Harold and Andrew McKay. 1995. "An Assessment of Trends in Poverty inGhana, 1988-92." PSP Discussion Paper, The World Bank, Washington, D.C.(November).

Datt, Gaurav and Martin Ravallion. 1992. "Growth and Redistribution Components ofChanges in Poverty Measures. A Decomposition with Applications to Brazil and Indiain the 1980s." Journal of Development Economics, 38 (1992) 275-295.

Jones, Christine and Xiao Ye. 1995. "Understanding Poverty Trends in Ghana, 1988-1992." Draft for the Ghana Extended Poverty Study, The World Bank.

Leenhardt, Blaise. 1993. "Prix des Vivriers et Biais Urbain au Ghana." Caisse Francaisede Developpement. Processed.

Jacquet, Laurent. 1995. "Le cacao demeure la deuxieme ressource du Ghana." MarchesTropicaux, 722, April 7, 1995, pp722-725.

Scott, Chris, and Ben Amenuvegbe. 1990. Effect of Recall Duration on Reporting ofHousehold Expenditures: An Experimental Study in Ghana. Social Dimensions ofAdjustment Working Paper No. 6, World Bank, Washington D.C.

World Bank. 1995a. A Synthesis of the Extended Poverty Study. Report No. 14504-GH. Washington, D.C.: The World Bank.

World Bank. 1995b. Ghana: Growth, Private Sector, andPoverty Reduction -- ACountry EconomicMemorandum. Report No. 14111- GH. Washington, D.C.: TheWorld Bank.

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Annex

Table Al. GHANA: Crop Production Statistics, Reported by Ghana Statistical ServiceYear Yams Cassava Plantain Cocoyam Rice Millet Sorghum Maize

1980 525000 2896000 931000 848000 64000 136000 156000 3540001981 463000 2721000 835000 972000 44000 131000 171000 3340001982 374000 1986000 763000 756000 37000 120000 126000 2640001983 354000 1375000 755000 613000 27000 114000 106000 1410001984 725000 4005000 1234000 2835000 76000 139000 176000 5740001985 660000 3075000 1360000 900000 80000 120000 185000 3950001986 1048000 2876000 1088000 1005000 70000 110000 128000 5590001987 1185000 2725000 1079000 1012000 81000 173000 206000 5970001988 1200000 3300000 1200000 1115000 105000 192000 178000 6000001989 1200000 3320000 1040000 1200000 67000 180000 215000 7150001990 877000 2717000 799000 815000 81000 75000 136000 5530001991 2632000 5701000 1178000 1297000 151000 112000 241000 9320001992 2331000 5662000 1082000 1202000 132000 133000 259000 7310001993 2720000 5973000 1322000 1236000 157000 198000 328000 9610001994 1700000 6025000 1475000 1148000 162000 168000 324000 940000

Table A2. GEIANA: Crop Production Statistics, Reported by FAOYear Yams Cassava Plantain Cocoyam Rice Millet Sorghum Maize

1980 650000 1857600 734000 643000 78000 82000 132000 3820001981 591000 2065000 829000 631000 97000 119000 131000 3780001982 588000 2470000 745000 628000 36000 76000 86000 3460001983 866000 1720000 755000 720000 40000 40000 56000 1720001984 1178000 2200000 1400000 800000 65000 133000 172000 6960001985 987000 2300000 1629000 900000 80000 112000 145000 5840001986 1048000 2876000 1087000 1005000 69600 110000 128000 5591001987 1185400 2725800 1077600 1011800 80700 173100 205900 5977001988 1200000 3300000 1200000 1115000 95000 139000 161000 7510001989 1280000 3327200 1040000 1200000 73700 180000 215000 7150001990 877000 2717000 799000 815000 80900 74500 135800 5526001991 1000000 3600000 1178300 1296800 150900 112400 241400 9315001992 1000000 4000000 1082000 1202200 131500 133300 258800 7306001993 1000000 4200000 1321500 1235500 157400 198100 328300 960900

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