1

CHAPTER ONE

1.0: INTRODUCTION

1.1: Background information

Rice is one of the important staple foods in Tanzania. Its per capita consumption is

about 16Kg, contributing 8% of caloric intake among Tanzanians (Minot, 2010). The

per capita consumption of maize and cassava are estimated at 73Kg and 157Kg

respectively. According to Kadigi (2003), the bulky of paddy consumed in Tanzania is

produced from five regions namely Mbeya, Shinyanga, Mwanza, Morogoro and

Tabora. Morogoro town is located nearly 200Km west of Dar-es-salaam city. It is a

good supplier of rice to Dar-es-salaam markets which receive even rice imported from

outside countries such as Thailand and Vietnam as well.

1.1.1: Characteristics of rice/paddy producers

1.1.1.1: Small farmers

Small rice farmers can further be classified into small tradition farmers and small

irrigation farmers.

i. Small tradition farmers

They cultivate 1-5 acres using tradition methods.

Use hand hoes or hire oxen with a plough.

In some cases (rarely) they hire tractors.

Hire local labour (for planting, weeding, and harvesting/threshing).

2

Earns 150,000 TSH per acre (Assuming all rice is sold).

ii. Small irrigation farmers

Grow about 1ha of rice in an irrigation scheme often controlled by the

government.

Rents the land from the scheme.

Also they rent out their services.

Gross margin can be 175,000 TSH per acre.

1.1.1.2: Larger rice farmers

Grow more than 5 ha of rice in irrigation scheme.

Hires labours to involve in various field operations.

They are cash intensive.

They enjoy economies of scale as a result of operating in large scale cultivation

In addition, both small and large farmers involve in irrigation schemes of paddy.

However, larger farmers are the ones who involve mainly in irrigation schemes than

small farmers.

1.2: PRODUCTION OF PADDY/RICE IN TANZANIA

In general, rice involves about 281,000 rice growing households (Ebony Consulting

International, 2003). Moreover, rice is mainly grown by smallholders under rain fed

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conditions, where about 74% of total rice area is rain fed lowland rice, 20% is upland

rice and 6% is irrigated.

According to Hamilton and DAI (2010) rice production in Tanzania is taken by small

scale farmers where less than 1% of the rice crop is produced by large scale farmers.

Although more than 99% is produced by smallholder farmers, some of them are part of

large scale rice irrigation schemes that were formerly state managed farms (NBS,

2006). Paddy production is estimated at 1.2 million metric tones annually or 750,000

metric tone of milled rice (Hamilton and DAI, 2010).

1.3: STATEMENT OF THE PROBLEM AND JUSTIFICATION

Prices of staple foods in Tanzania have been fluctuating a lot due to various reasons

such as poor harvests leading to shortage of food relative to the demand by the people.

This fluctuation tends to affect the spatial price difference of staple foods between

regions. This is due to the fact that once there is a change in price at one market,

usually a surplus market; this change must be shifted into the deficit region especially if

they are well integrated.

Furthermore, this price fluctuation affects the welfare of both producers and consumers.

Producers are affected once there is a decrease in prices of their produce mainly during

a bumper season while consumers especially those in a deficit region are affected when

there is an increase in prices of those produce (Dawe and Opazo, 2009).

According to Gjolberg et al (2004), the wholesale prices of rice over 1992-2002 were

consistently higher in Dar-es-salaam city than in Morogoro municipality, with the

4

average difference being 4000Tsh/100KG. This suggests that Dar-es-salaam is a deficit

region having higher prices while Morogoro is the surplus region experiencing

relatively lower prices. In addition, this fluctuation of rice prices depicts an upward

trend.

A more recent study by Minot (2010) also indicates fluctuation in wholesale prices of

staple foods. Wholesale prices of maize in Dar-es-salaam were falling in 2006 while

they started to increase sharply in September 2007 and eventually decreased in March

2008. For rice, wholesale price in Dar-es-salaam started to rise in August 2007 and

falling sharply in April 2008. This shows to what extent the staple food prices fluctuate

over time.

While the above two studies have indicated fluctuation of prices of the two staple

foods (rice and maize) no attempt has been made to carry out a detailed spatial price

analysis to show how prices in the two markets are related and factors influencing the

difference in prices between Morogoro and Dar-es-salaam. This study was intended to

analyze spatial price differences of rice between Morogoro municipality and Dar-es-

salaam city.

2.0: OBJECTIVES OF THE STUDY

2.1: GENERAL OBJECTIVE

To analyze the spatial price difference of rice between Morogoro and Dar-es-

salaam so as to check to what extent prices of rice in these regions are related.

2.2: SPECIFIC OBJECTIVES

a) To examine the trend of rice prices in Morogoro and Dar-es-salaam.

5

b) To identify determinants of rice price differences between these regions.

3.0: HYPOTHESES

a) There is no significant difference between the average wholesale prices/Kg

charged in these two regions (Average wholesale price/Kg in both regions are

statistically equal).

b) Marketing costs are not the major determinants of price differences in these

regions.

4.0: LIMITATIONS/ASSUMPTIONS OF THE STUDY

1. All rice is of the same type and quality.

It was assumed that all rice traded by traders had the same quality and of the same type.

Therefore the differences in quality and type of rice were not captured in this study.

2. Morogoro and Dar-es-Salaam were assumed to have equal variances.

Samples of wholesale monthly rice prices for three years i.e. 2001, 2002 and 2003 in

these regions were assumed to be collected in the population having equal variances.

3. Marketing costs were assumed to be the same in all three situations.

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CHAPTER TWO

2.0: LITERATURE REVIEW

2.1: Staple food prices variation

World food prices have been varying dramatically in recent years due to number of

reasons such as fluctuation in world fuel prices. This is also the case in Tanzania for the

case of major staple foods namely maize, rice and cassava. Therefore, this part is going

to make a review about the variation of staple food prices.

According to the quarterly bulletin on food prices in Africa by FAO regional office for

Africa (2009), region prices remain higher than 2007 levels. For example, price of

maize in Tanzania was 68% higher in October than two years earlier. However, in Dar-

es-salaam the wholesale price of maize dropped to $419/tonne.

Food retail price series were collected and showed seasonal fluctuations as well as a

considerable variation across a large sample of markets in Tanzania (Delgado et al.,

2003). The overview in staple food prices trends by WFP (2009) shows that prices in

Tanzania experience an upward trend relatively to the past five years. Another study by

WFP (2009) on trends in staple food prices in selected vulnerable countries shows that

maize prices in Tanzania increased by 17%.

The shortage of food due to failure of rainfall in 2009, left about 280,000 people in

Tanzania food insecure (FEWSNET, 2009). Another factor that contributed to the

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shortage of food in some of the areas in Tanzania in 2009 is the increase in maize, rice

and bean prices to 40%-60% above their five year averages (FEWSNET, 2009).

According to the latest edition of Food price watch, the World Bank’s food price index

rose by 15% between October 2010 and January 2011. Moreover, this is 29% above its

level a year earlier and it is only 30% below its 2008 peak.

A more recent study by Falcon and Naylor (2010) reveals an upheaval of staple food

prices in the world in 2008. This sharp rise captured the interest of Economists,

creating some questions about the state of food security, the nature of price variability

and the appropriate strategies for international agricultural development.

Rice prices have been increased in all markets as the case on prices of other staple

foods in Tanzania (USAID, 2008; FEWS NET, 2009). The trend in 2009 shows that

prices of staple food are higher than the last year as well as above the five year

averages. This rise in prices was probably due to increased transportation costs and

traders’ speculation during the hunger season.

According to Minot (2010) wholesale prices of maize tend to move together in the late

2003 and late 2005 in almost all markets in Tanzania. Furthermore, this increase in

price was due to poor harvests in 2003 and 2005 when output fell by 42% and 33%

respectively.

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A recent study by Dawe and Opazo (2009) using data from new FAO price database

shows that domestic staple food prices in developing countries typically increased by

48% in real terms during the world food crisis in 2008.

In recent years, food prices have increased throughout the world (USAID, 2008; FAO

2008). These reports indicate that during the first three months of 2008, international

nominal prices of all major food commodities reached their highest levels in nearly 50

years while for the case of real prices it is nearly 30 years.

Global food prices started rising sharply in 2006 and reached record levels in the

second quarter of 2008 (Macharia et al., 2009). However, in June 2008 it started to

decline even though it was different in some of the east African countries which

experienced increasing prices in June 2008 and Early 2009. For example, Tanzania

experienced a sharp increase in its food price index (FPI) between the last quarter of

2007 and the first quarter of 2008 i.e. about 9% increase. Eventually, prices dropped

down following a bumper harvest for maize and rice in May 2008.

According to a recent discussion paper by Minot (2011) staple food prices in Tanzanian

markets tend to move with the world staple food prices. For example, Arusha showed

to have a long-run relationship with the world price of maize. For rice, four of the

height rice markets in Tanzania appeared to be linked to world rice markets.

Furthermore, the elasticity of price transmission ranges from 0.24 to 0.54 implies that

24% to 54% of the world rice prices changes are transmitted to the Tanzanian markets.

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A report by USAID (2008) shows that regions in Tanzania such as Mbeya, Iringa and

Morogoro have shown increased prices of major food crops. In addition, there are

various reasons for this increase in prices such as poor harvests of major food crops,

higher transport cost of major food, increased demand for food by consumers, Oil and

energy supplies and rising cost in energy which contribute to the increasing of

agricultural production cost which is eventually reflected into food prices.

2.2: Determinants of staple food prices variation

According to Nyange and Wobst (2005), the price volatility is determined by market

forces i.e. demand and supply of a product. This implies once there is an imbalance

between demand and supply of a product, and then prices are likely to fluctuate in the

process of re-gaining an equilibrium point. The demand side factors (rising incomes,

increasing world population and urbanization) and supply side factors (high agricultural

input prices and declining agricultural input resources) influence the price increase of

the product (ILRI, 2009).

The behavior of the domestic food prices depends highly on the degree of tradability of

the commodity (Delgado et al., 2004; Haggblade and Dewina, 2010; Minot, 2010). For

international commodities, domestic prices are expected to follow the world prices of

the same commodities unless otherwise there are significant barriers to trade.

Conversely, if not, it is likely to be determined by domestic supply and demand of that

particular commodity.

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Naylor and Falcon (2010) argues that weather variability, changes on population,

fluctuation of Petroleum prices, speculation and stockholding and changes on exchange

rates are the major determinants of staple food price variation. They insist that any

changes in these factors are likely to be shifted into the staple food price and hence its

variation.

A recent report by a Central Bank of Lesotho (2010) also suggests that the increasing

demand on food along with the world population growth is one among the determinants

of variation of staple food prices. This increase in demand on food due to an increase in

world population may result to an imbalance between demand and supply of food, the

former being relatively higher. This might be compensated by the price increase as it is

the case since July 2010.

According to the report by FEWS NET (2009) on the Tanzania food security, it is

postulated that increased demand at the markets and increased transportation costs

caused by high fuel prices are likely to contribute to high food prices. This report

suggests that variability in food prices may be caused by changes in demand and

transport costs

.

Agricultural prices vary because production and consumption are variable (Gilbert and

Morgan, 2010). However, predictable and unpredictable should be distinguished from

the economic point of view because the latter is characterized by the elements of

shocks. This implies that, shocks in production and consumption are transmitted into

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price variability. Furthermore, it postulates that speculation and stockholding or

purchase currently and sell in future at relatively higher prices also determine the

degree of food price variability.

A more recent study by Rashid and Minot (2010) suggests that the variation of

commodity prices between locations and over time is a natural market phenomenon

and, in addition, excessive variability of staple food prices to a large extent is a

reflection of a lack of market integration across space.

Kilima et al (2008) studies changes in the variability of maize prices using monthly

maize wholesale price data from seven regions of Tanzania between 1983 and 1998.

The results revealed that market liberalization increased both the level and the

variability in the maize prices. This suggests that market forces namely demand and

supply are major determinants of price variability.

According to Jayne et al (2005) government intervention contributes potentially to the

staple food prices variability particularly in maize market. These interventions are

maize export bans as what is the case in recent years in Tanzania, unexpected changes

on import tariffs and government importation and sell to selected buyers. Once there

are changes in at least one of these mentioned interventions, it is likely that changes to

be shifted into prices resulting to its variability.

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Schlosser (2006) applied regression models using the actual spatial price variation so as

to determine the causes of spatial price variation over time. He used mean price,

standard deviation and coefficient of variation as the tools to measure price variations.

In addition, coefficient of variation for each item was regressed against a dummy

variable commodity (good or service) so as to determine whether an item is a good or

service while assigning 1 to a good and 0 for service. In this study, he mentioned

supply and demand, transport costs and market characteristics as the major

determinants of price variation. For the case of market characteristics, the study

describes that characteristics such as monopoly situation in the industry leads to higher

price variation and conversely if there are relatively many competitors in the industry,

then price variability is likely to be relatively low since there is a stiff competition.

2.3: Analysis of spatial prices variation

The major economic approaches that are used to measure the degree of spatial price

integration are the Law of One Price (LOP), Co-integration (regression analysis),

Ravallion model and Granger-Causality (Baulch, 1997).

Nyange and Wobst (2005) applied Autoregressive Conditional Heteroskedastic

(ARCH) regression to analyse the monthly price data over the period of 1992-2000 in

predominantly consumer, producer and border markets of Dar-es-salaam, Dodoma and

Arusha respectively. The essence of this analysis was to examine changes in maize

price levels and variability in Tanzania so as to evaluate effects of Strategic Grain

Reserve on stabilizing domestic market prices against alternative food security policies

such as regional food stock and trade.

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Rashid and Minot (2010) suggested Cointegration analysis, Threshold Autoregressive

(TAR) and Parity Bound Model as the suitable methods in analyzing spatial arbitration.

Cointegration takes non-stationary into account and allows long-run relationship as

well as the speed of adjustment; however, it does not distinguish between lack of

integration because of market inefficiency and lack of integration because the

difference is too small.

Karfakis and Rapsomanikis (2008) used threshold co-integration to examine the

relationship between prices in a number of well connected and remote markets in

Tanzania. In addition, this approach was used to approximate the magnitude of

transport and other transaction costs between the markets. The threshold co-integration

suggested that regional markets in Tanzania are integrated.

Brempong and Asare (2007) also used co-integration to study the monthly time series

price data from January 1996 to December 2003 so as to measure the extent to which

the rice prices in spatially markets are integrated or co-move. This study suggests that

prices of imported rice in Ghana do not share the common properties with the local

prices trends in the central market.

Separate regressions were used by Minot (2010) to study the relationship between

domestic staple foods prices as well as the transmission of changes in international food

price to domestic markets in Tanzania.

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Campenhout (2007) used Threshold Auto-regressive (TAR) model and Parity Bound

Model (PBM) to analyze the relationship between maize prices in Iringa and other six

markets in Tanzania. Weekly price data over the period of 1989 to 2000 were used in

this analysis.

Korir, et al (2003) applied Cointegration analysis in analyzing the average monthly

wholesale bean prices (secondary data) from four markets namely Arusha, Moshi,

Taveta and Nairobi. In addition, Augmented Dickey Fuller (ADF) test, Granger

causality test and Pearson’s bivariate correlation coefficient were used to test the

stationarity of prices and first difference, capturing the direction of causality in price

changes and analysig the market integration respectively.

Boysen (2009) used descriptive statistics and regression methods to assess the spatial

variability and transmission of prices in Uganda. Time series data were used and

classified into two datasets namely time series of retail prices for six major local

markets in Uganda and national household survey 2002/2003 which includes detailed

information on expenditure and unit value data for 9711 households.

Standard Vector Error Correlation Model and Threshold Vector Error Correlation

Model (TVECM) were used by Amikuzuno (2010) to estimate the speed of price

adjustments between the net producer and net consumer market pairs. Two datasets

were used in this analysis namely High Frequency Data (HFD) and Low Frequency

Data (LFD) in five major Tomato markets in Ghana.

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Barry and Piggot (2001) applied Threshold Cointegration models to analyze and

evaluate the spatial price dynamics among regional corn and Soybean markets in North

Carolina. They found that Threshold reflecting the influences of transaction costs is

confirmed and markets are strongly spatially integrated.

It is important to note that two spatially separated markets are said to work efficiently if

and only if the price difference between them is not larger than the transaction costs

required to move the good from surplus market to deficit market (Rapsomanikis, 2003).

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CHAPTER THREE

3.0: METHODOLOGY

3.1: Description of the research design

The design of the research was a case study of two regions i.e. Dar-es-salaam city and

Morogoro municipality and the item concerned was rice.

These two regions are located almost 200Km apart, Morogoro being located at the

western side of Dar-es-salaam. Morogoro is one among the surplus regions that

produce rice in Tanzania.

3.1.1: Ifakara town

Ifakara is a small rural town in Kilombero district, Morogoro region, south central

Tanzania. It is the headquarter of the Kilombero district administration and the main

trading centre for Kilombero and Ulanga districts. The town is located near the

Tanzania-Zambia Railway (TAZARA) line, at the edge of the Kilombero valley, a vast

swampland flooded by the mighty Kilombero River.

3.2: Sampling methods

Methods that were used to select sample are simple random sampling and purposive

sampling methods. The sample was selected randomly from the groups of traders who

are involved in rice trade in these two regions particularly those who transport rice from

Ifakara to different markets in Dar-es-Salaam.

3.3: Sample size

Total of 21 traders who transport rice from Morogoro (Ifakara) to Dar-es-Salaam city

were interviewed in order to get data which were later used to estimate rice prices in

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both good and bad seasons. In addition, these data were also used to estimate average

prices of buying and selling rice for both traders and farmers.

3.4: Data collection

Both primary and secondary data were collected for the study as described below.

3.4.1: Primary data collection

Structured questionnaires as well as face to face interviews were used to collect

primary data from traders who transport rice from Morogoro (Ifakara) to Dar-es-salaam

city.

3.4.2: Secondary data collection

Secondary data on wholesale monthly prices of rice in both regions for the period of

time 2001, 2002 and 2003 were collected from the Ministry of Industry, Trade and

Marketing of Tanzania located in Dar-es-Salaam. In addition, secondary data on the

trends of fuels prices in Tanzania were collected from various previous studies so as to

ensure critical analysis of the rice prices patterns in these two regions as far as fuels

prices trends are concern.

3.5: Survey and questionnaire administration

A survey was conducted by the researcher himself for two days in the mid-May 2011.

The data were collected at market place and at milling machines by using structured

questionnaires prepared in English but translated in Kiswahili during the data

collection. In addition, face to face interviews with traders using Kiswahili was also

done since Kiswahili is understood better by all respondents and was therefore a useful

language for the purpose of the study. This helped in making the response rate from the

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traders more useful and, indeed, they seemed to be aware about the research on their

business. Respondents were asked on the following important variables:

a) Traders were asked on the quantities of rice they purchase from farmers as well

as whether they resale all of them or not. Also they were asked to mention the

buying and selling prices of the rice they purchase from farmers.

b) Also traders were asked to to mention the buying and selling prices during good

and bad season regarding the availability of rice(supply).

c) Moreover, they were asked to specify the cost for each marketing function they

incur namely packaging, storage, processing, loading, transportation and

unloading.

d) Eventually they were asked to mention taxes and/or market charge they pay if

any.

3.6: Data analysis

3.6.1: Software

Data from the questionnaire survey were analyzed using Excel software computer

program. Excel was used to simplify the analysis of quantitative secondary data on

wholesale monthly prices of rice in 2001, 2002 and 2003 for both regions. Also it was

used to calculate percentages and averages for primary data on different cost elements

of the marketing costs. In addition, it displayed quantitative statistics which were t-

statistics.

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3.6.2: Descriptive analysis

Descriptive statistics were used in the analysis of the study data. It included deduction

of means and percentages of marketing costs during different situations of rice

availability.

3.6.3: Quantitative analysis

The mean difference t-test was used to test the hypothesis that there is no significant

difference in wholesale monthly rice prices charged in Morogoro Municipality and in

Dar-es-Salaam city.

3.7: Tools of testing hypotheses

1. T-test was used to test the significance of hypothesis that there is no significant

difference in wholesale monthly prices of rice charged in these regions.

2. Percentages and pie chart were used to show the proportion of marketing costs

to the average price difference.

3.8: Decision rule

For T-test, the rule of thumb was to reject the null hypothesis if and only if the

corresponding P value is less than the predetermined significant level of 5%, and fail to

reject if p value is greater than the predetermined significant level of 5%.

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CHAPTER FOUR

4.0: RESULTS AND DISCUSSION

4.1: Gender of the respondents.

All 21 respondents who were interviewed were males i.e. 100% of the respondents.

This suggests that males dominate this trade by being more involved directly in this

trade rather than females. However, this does not conclude that males are the owners of

the businesses since they might be sent on behalf of the owners who are probably

females.

4.2: Statistical significance of price gap between Morogoro and Dar-es-Salaam

regions.

Results from Table 1 on the T-test show that the price gap of rice per 100KG between

these two regions is statistically significant, (P<0.05). This can be explained by the fact

that the profit margin attached by the rice traders as well as marketing costs incurred

are expected to contribute substantially in the price difference in these regions.

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Table 1: T-test of wholesale prices of rice for Morogoro and Dar-es-Salaam

refions

Mean

Wholesale

price for

Morogoro

Mean Wholesale

price for Dar-es-

Salaam

df Sig. (2-tailed) t-value

Price

TSH/100KG

33321.56

38588.56

70

0.0000*

-4.174

* Implies statistically significant at 5%.

4.3: Trend of wholesale monthly rice prices in Morogoro town and Dar-es-salaam

city for the year 2001, 2002 and 2003.

Figure 1 of this report shows that wholesale monthly prices of rice in both regions are

almost moving together. However, prices in Morogoro are relatively lower throughout

the specified period of time i.e. 2001, 2002 and 2003 even though there are some

occasions whereby the prices in Morogoro are higher than that of Dar-es-Salaam.

The tendency of wholesale prices of rice in Dar-es-Salaam being higher than that of

Morogoro helps us to explain the fact that the former is the deficit region relatively to

the latter. In addition, the average gap is 5,267 TSH per 100KG. This result is the same

to what was concluded by Gjolberg et al (2004) who studied the movement of the

prices of maize, beans, and rice over 1992-2002 between Dar es Salaam and Morogoro

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(a rice surplus region).Their study showed that monthly wholesale prices in Dar-es-

Salaam is relatively higher than that in Morogoro and the average gap was 4,000TSH

per 100 KG. Therefore from this comparison, it can be concluded that the average gap

of rice prices in these regions increased from 4,000TSH per bag over 1992-2002 to

5,267 TSH per bag over 2001 and 2003.

Also from figure 2 of this report, it depicts that the gap of monthly wholesale rice

prices between these regions fluctuate a lot due to various reasons such as seasonality

due to rainfall. Indeed, it should be noted that Dar-es-Salaam is a deficit region, hence

we expect it to have higher prices than Morogoro town. However, this is not always the

case since the gaps in some months are negative following the fact that prices in

Morogoro being higher than that in Dar-es-Salaam because the latter is also receiving

rice from other surplus regions in the country as well as rice from outside the country.

This makes supply of rice in Dar-es-Salaam to increase and hence lowering the price of

rice and sometimes become relatively lower comparing to that prevail in Morogoro.

Table 2: Wholesale monthly rice prices in Morogoro town and Dar-es-salaam city

S/N TIME WHOLESALE RICE

PRICES

IN MOROGORO

(TSH/100KG)

WHOLESALE RICE PRICES

IN

DAR-ES-SALAAM

(TSH/100KG)

1. Jan/2001 36,833 43,500

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2. Feb/2001 35,500 45,250

3. Mar/2001 35,400 37,692

4. Apr/2001 35,564 38,410

5. May/2001 38,125 38,542

6. Jun/2001 29,167 40,000

7. July/2001 29,875 35,583

8. Aug/2001 27,722 33,796

9. Sept/2001 25,357 36,750

10. Oct/2001 27,667 36,250

11. Nov/2001 28,143 38,000

12. Dec/2001 28,500 38,000

13. Jan/2002 31,714 38,917

14. Feb/2002 32,333 37,542

15. Mar/2002 34,455 35,925

16. Apr/2002 32,875 35,063

17. May/2002 28,500 36,385

18. Jun/2002 25,727 37,000

19. July/2002 23,750 33,000

20. Aug/2002 23,000 32,500

21. Sept/2002 39,021 41,211

22. Oct/2002 24,396 33,889

23. Nov/2002 24,667 33,000

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24. Dec/2002 36,000 40,000

25. Jan/2003 28,900 39,542

26. Feb/2003 31,773 37,136

27. Mar/2003 32,318 39,273

28. Apr/2003 34,132 39,447

29. May/2003 39,667 37,200

30. Jun/2003 39,729 38,667

31. July/2003 39,200 37,883

32. Aug/2003 38,688 40,091

33. Sept/2003 41,667 45,000

34. Oct/2003 43,607 46,286

35. Nov/2003 45,375 46,125

36. Dec/2003 50,229 46,333

SOURCE: Ministry of Trade, Industry and Marketing of Tanzania.

4.4. Average quantity of rice purchased and sold, purchasing and selling prices of

rice/KG (A case of traders).

According to the analyzed data on prices of rice per KG during good and bad seasons

with respect to the availability of rice from the farmers, it showed that the average

prices per KG paid by traders during good and bad seasons are 597.9TSH/KG and

832.5TSH/KG respectively. In addition, normal average buying and selling price of rice

by rice traders under normal situation are 575.7TSH/KG and 1162.7TSH/KG

respectively. However, it should be noted that the research was not able to capture the

25

type and quality attributes of rice which are among the factors that differentiate prices

of rice classified in different grades. Therefore, this study assumed that all rice

purchased by different traders was the same as far as their types and qualities are

concern, and the major emphasis was given in estimating the averages prices of rice in

three situations namely normal situation whereby rice is neither shortage nor plenty,

good season whereby rice is available in abundance and bad season whereby rice is

available not in abundance.

Also results show that quantities of rice purchased by rice traders are all sold. This can

be explained by the fact that majority of rice traders who purchase rice from Ifakara to

Dar-es-Salaam markets are small traders i.e. majority purchase on average between 1-3

tones while few of them purchase between 4-7 tones. This difference in quantities

purchased from one trader to another is due to their difference level of their capital.

Therefore, the quantity purchased on average by each trader is about 1.82 tones.

Table 3: Prices, quantities of rice purchased and sold by rice traders at Ifakara

Item Value

Average quantity of rice purchased by rice traders (Tone)

1.83

Average quantity of rice sold by rice traders (Tone)

1.83

26

Average price of rice paid by rice traders under Normal situation

(TSH/KG)

575.7

Average price of rice received by rice traders under normal season

(TSH/KG)

(TSH/KG)

1162.7

Average price of rice paid by traders during bad season (TSH/KG)

832.5

Average price of rice paid by rice traders during good season (TSH/KG)

597.9

Average price of rice received by rice traders during good season (TSH/KG)

1169.05

Average price of rice received by rice traders during bad season (TSH/KG)

1359.5

SOURCE: Own calculations from the data collected.

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From Table 4, Storage services are offered free of charge by the owners of the

processing (milling) machines and that is why it is not included in estimating marketing

costs. However, for the case of processing costs, it is not included in Table 4 since it is

paid by farmers themselves before selling their rice to traders.

Table 4: Estimation for marketing costs incurred by traders

LOADING

(TSH/KG)

PACKAGING

(TSH/KG)

TRANSPORT

(TSH/KG)

UNLOADING

(TSH/KG)

TOTAL

MARKETING

COSTS

(TSH/KG)

4.01 6.31 61.11 3.756 75.186

SOURCE: Own calculations from the data collected.

Table 5: Marketing margin, costs and proportion of marketing costs in the price

gap between Morogoro and Dar-es-Salaam.

Duration Marketing

margin(Farmer –

Trader, TSH/KG)

Marketing

costs(TSH/KG)

Proportion of

Marketing costs in

price gap (%)

Normal season 587 75.186 12.8

Bad season 571.11 75.186 13.164

28

Good season 526.98 75.186 14.267

Figure 3.

4.5. Determinants of staple food prices variation

From the results presented in the Table 6, it shows that prices of rice vary according to

the variation in availability of rice i.e. supply of rice. This can be evidenced by the

variation of both buying and selling prices of rice by traders during good and bad

seasons. These variations of rice prices are also the results of weather variation. In

addition, prices are lower in good season whereby supply of rice is relatively higher

than demand while prices are higher during bad season from October to January (off-

season) whereby supply of rice is relatively low comparing to demand. The results are

29

comparable with other studies on the same topic. Variation of staple food prices is due

to forces of demand and supply in the domestic markets and variation in weather

conditions (Nyange and Wobst, 2005; Naylor and Falcon, 2010; Delgado et al., 2004;

Haggblade and Dewina, 2010; Minot, 2010).

Moreover, the analysis done from the prices of fuel collected from other studies show

that prices of rice in both regions tend to move together with the variation in the prices

of fuel in Tanzania. These results are the same to the results by Naylor and Falcon

(2010) and FEWS NET (2009) whereby in both studies, it was found that prices of

fuels play a significant role in determining prices of staple foods. Consider the

Movement of rice prices in both regions and the movement of prices for Diesel and

Gasoline.

30

Trends of avearage annual wholesale

rice prices

0

10000

20000

30000

40000

50000

60000

19951997

20002001

20022003

2004

YEAR

PR

ICES

(TSH

/100

KG

)

Morogoro

Dar-es-Salaam

56

Figure 4.

31

Trends of fuels prices

0

10

20

30

40

50

60

70

80

90

100

19951997

20002001

20022003

2004

YEAR

PR

ICES

(U

S C

ENTS

/LIT

RE)

Gasoline

Diesel

Figure 5.

32

CHAPTER FIVE

5.0: CONCLUSION AND RECOMMENDATIONS

5.1: Conclusion

The conclusion made is based on the statistical significance of the gap between these

two regions, trend of wholesale rice prices, marketing margin during different

situations of regarding availability of rice and determinants of staple food prices

variation.

5.1.1: significance of the gap between these two regions

The critical question of whether average wholesale rice prices in these regions are

statistically the same was one among the important issues covered in this study. It was

shown that the price gap between these regions is statistically significant at 5% using t-

test tool.

5.1.2: Trend of wholesale rice prices between these regions

It was shown that monthly wholesale prices in these regions tend to move together

throughout the specified period of time i.e. 2001, 2002 and 2003. However, there are

some occasions whereby prices in Morogoro are higher than that of Dar-es-Salaam.

This may be the result of importation of rice from outside countries into the latter

which increases supply of rice and hence relative lower prices than the former.

5.1.3: Marketing margin

Marketing margin between farmers and traders differ in different situations regarding

the availability/supply of rice. The marketing margin normal, good and bad situations

are 587TSH/KG, 571.11TSH/KG and 526.98TSH/KG respectively. In addition, the

33

proportion of marketing costs in the marketing margin for the three mentioned

situations are 12.85, 13.164% and 14.267% respectively.

5.1.4: Determinants of staple food prices variation.

The study revealed that the major determinants of staple food prices variation are

supply and demand of the product regarding the climatic conditions and on-season as

well as off-season (October to January), variation in the fuels’ prices.

5.2: Recommendations

The government should make an effort to reduce prices of fuels because are the

essential element of transport for commodities particularly rice. As we have seen in the

results and discussion part, transport costs account about 81.28% in the marketing costs

incurred by rice traders. The government can achieve this strategy through reducing

taxation on fuels imported in our country. This will reduce cost burden which is usually

shifted to final consumers by traders involving in fuels industry in our country.

Also the government should improve its monitoring system on provision of farm inputs

particularly to small holder farmers so as to make sure that the targeted farmers get

these inputs at lower prices as planned. This will help to increase number of those who

get these inputs which will help to reduce costs of production and eventually reduction

in prices of rice to the final consumers of rice as well as its volatility.

Indeed, in depth research should further be done to capture the attributes of quality and

type of rice and how they affect the prices of rice due to the fact that this study ended

assuming all rice are the same as far as quality and type attributes are concern.

34

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