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Analysing Income Distribution

Page history last edited by Vong Phalla 15 years, 1 month ago

Tool 7 - Analysing Income Distribution

 


Introduction 

 

Analysing incomes within the value chain is central to understanding how the participation of the poor can be increased.  Understanding how income is distributed along the value chain provides the necessary start to determine opportunities for income generation.  Income analysis is different from the analysis in Tool 6 - Analysing Costs and Margins.  While costs and margins analysis focuses on the profitability of an activity and the individual actor, analysis of income looks at all of the actors of the value chain.  

 

Analysing distribution of income is not only an analysis within a particular value chain but also recognises that individual actors participate in a number of different value chains at the same time. For example, a farmer may be involved in several agricultural crops and several handicraft activities as a means of income diversification. A trader might be involved in trading multiple agricultural products at the same time or at different times of the year depending on the season. Therefore, livelihood strategies made by various actors are influenced by the sum of their income sources and any analysis must take this into account.

 

Objectives

 

  1. To analyse the impact of value chain participation on the distribution of incomes within and between various levels of the value chain at the level of the individual actor.
  2. To  analyse the impact of different value chain governance systems on income distribution and on final product price.
  3. To analyse the distribution of income at a whole of enterprise level and to analyse how that impacts on value chain participation and decision making

  4. To describe the impact of income distribution on the poor and other disadvantaged groups and the potential for poverty alleviation from different value chains.

 

Key Questions

 

  • Are there differences in incomes within and between different levels of the value chain?
  • What is the impact of various governance systems on income distribution between and within various levels of the value chain?
  • What are the impacts of the distributional outcomes of the value chain on the poor and other disadvantaged groups, both currently and in the future?
  • What are the changes in incomes that result from the development of various types of value chains?
  • What is the variability of incomes and risks to livelihoods within and between various levels of the value chain?
  • What is the contribution of the particular value chain to the whole of enterprise income and how does this influence decision making?
 

Terminology 

 

Income is defined as the earnings accruing to an economic unit during a given period of time. Income comprises the money received from the sale of goods plus the value of self-consumed output minus the costs of production.

 

The costs of production comprise the costs of inputs, depreciation on capital equipment, interest payments and taxes. 

 

Unlike profits (sales minus costs), where costs of production include the opportunity cost of own labour, income does not deduct the cost of own labour (since this accrues to the enterprise as "income" from labour). However, the cost of hired labour is deducted as this is a cost to the enterprise.

 

Cash income can be distinguished from non-cash income where a barter system occurs. For example, hired labour is sometimes paid for in a combination of cash as well as benefits (food, healthcare, pensions).

 

Steps

 

Step 1     Define categories

 

To analyse incomes within value chains it is important to first categorise actors. The mapping of the value chain as discussed in Value Chain Toolbook - Part Two (Tool 2) generally provides a map of actors within categories and this can be used as a basis to add income specific information. Categorisation should include a distinction between poor and non-poor actors as a starting point for analysis of incomes

 

An example is given below for the value chain for rice in Cambodia. The value chain is divided into the size of operations at each level of the value chain (low, medium and high technology and volumes), as well as the mode of operations (contract milling, medium and large mills). In this example, each level of the value chain is separated into different categories for poor, medium wealth and better-off actors (distinguished by different colour coding). Thus, low and medium level technology farmers are more likely to comprise poor households, while the high technology and contract farming households are more likely to comprise medium wealth households. 

 

 

Rice Value Chain - Categorization of Stakeholders 

Tool 7 - Figure 1: Rice value chain - Categorisation of actors 

 

Take Note

Poverty levels are a relative measure and it is difficult (and perhaps unwise) to be comparing poverty (as defined by income) between value chain levels. For example, a poor farming household earning $1 per day cannot be compared against a poor factory worker in the city earning $4 per day. Both are poor relative to other actors within their particular level of the value chain but there is clearly a difference between $1 and $4.

Other measures of income (such as purchasing power) may be a better reflection of differences between different levels of the value chain. Use can also be made of official poverty lines, which are often different between urban and rural areas or between mountainous rural areas and flat land agricultural areas.

 

Step 2     Calculating incomes per unit of output

 

After the actors at each level of the value chain have been categorised and mapped the calculation of income per unit of output can be carried out at each level of the chain and for each actor. Income per unit at each level is determined using the tools outlined in Value Chain Toolbook - Part Two (Tool 6). Recall from above that income is different from profit in that the cost of own labour is not deducted from the calculation.

 

Step 3     Calculating the net income at each level of a value chain

 

Comparing the distribution of net income across each level of a value chain means that the benefits accruing to actors at various levels of the chain can be compared. This is in addition to the analysis of the margins and the profits accruing at each level of the chain. The analysis of income gives a more accurate picture of the true distribution of benefits at each level of the value chain, as it reflects the often vastly different volumes handled by actors at each level of the chain.

 

To determine income distribution the net income per unit at each level is multiplied by the sales volume at each level. Net income per unit is calculated as total revenue minus total costs (where total costs include hired labour costs but do not include own labour costs). In the example in Table 1 below, the net income and sales volume are used to calculate income earned by each actor at each process level in the value chain[1].

 

Tool 7 - Table 1: Example of income distribution along the value chain for silk in Thailand

 

 

Cocoon - Farmer

Yarn - Farmer

Total Farmer

Trader

Weaver

Small Retailer

Total Cost (Baht)

67

725

704

715

437

744

Total Revenue (Baht)

70

834

834

750

660

812

Net Income per unit (Baht)

3

109

130

35

223

68

Sales Volume

137 kg 

18 kg

18 kg

18 kg 

100 pieces 

100 pieces

Total Income - Baht (US$)

378 ($9.45)

1962 ($49.05) 

2340 ($58.50)

630 ($15.78) 

22266 ($556.65) 

6822 ($170.55) 

 

The average net income level accruing to actors at each level of the chain should be benchmarked (compared with) the official poverty line and a subsistence level of expenditure to determine if the income level generated by the activity at that level of the value chain is sufficient to maintain or improve livelihoods. Using the benchmark level of poverty, and the profit margin and income information, a calculation can be made to determine how much of a particular activity would need to be undertaken in order to generate an income higher than the poverty line. Examples could include: how many hectares of rice cropped or how many tons of fruit traded.

 

Benchmarking incomes relative to the poverty line is a first way to consider the involvement of the poor in the value chain. A study of supermarket and street vendors[2] compared street vendors’ incomes with the 2005 poverty threshold in Hanoi, (500,000 VND/month) and found that 18% of street vendors are poor, while no poor households were found in the formal markets, nor in the shops or supermarkets.

 

Comparing income with subsistence level expenses is another way to appraise the role of the participation in the value chain in livelihood strategies. For example, the incomes of peri-urban vegetable commercial farmers in different African cities have been compared with the income necessary for subsistence[3]. In Brazzaville and Bangui, at the time of surveys, market gardening yielded enough income to provide for the basic food requirements of the family, plus housing, clothing and schooling expenses; see Table 2. In this case, even if the total number of farms is small as compared with total urban population, their functioning demonstrates that urban agriculture is one of the sources of stable income that should be protected and considered a portfolio of cash-earning activities that require limited starting capital.

 

Tool 7 - Table 2: Estimates of family commercial farmers’ incomes compared with subsistence income

City (year)  (source)

Number

Estimation of average monthly income $

Estimation of minimum subsistence food expenditures $

Brazzaville (1989)

(Moustier, 1996)

1000 producers

1700 retailers

150

120

100

 

Bangui (1991) 

(David, 1992)

300 producers

300 wholesalers

280

290

60

 

Source:[4] 

 

In the example in Figure 2 below, net incomes from the production of rice in the Red River Delta of Vietnam was calculated according to land area, and compared against the official poverty line. The example shows that 0.57 ha of paddy would be needed to increase the net income of the household from rice production to take that household up to the poverty line. Given the allocation of land per household usually is around 0.144 ha (360m2 per person and up to four people per household), the analysis implies that unless yields can be dramatically improved poverty alleviation cannot be achieved by rice production alone. Therefore, alternative income generating activities and value chains need to be considered.

 

 

Source: Adapted from[5]  

Tool 7 - Figure 2: Comparison of net incomes from rice production with the official poverty line – Minimum area of rice land required to support a four person household in the Red River Delta of Vietnam. The official poverty line is shown by the red horizontal line. The graph demonstrates that 0.57 ha of paddy is required for the harvest to generate enough income to equal the official poverty line.

 

Step 4     Calculate the wage income distribution

 

Since the calculation of incomes is profit + own wage income, it is useful to look at the combined components of wage incomes (own wages and hired wages) to see how wages are distributed over the value chain. Looking only at income accruing to the enterprise itself does not capture the contribution of each level of the value chain to the whole sector.

 

In order to calculate the wage income distribution along the value chain, separate the wage components in the partial budget calculations for margins and incomes. The value of costs (represented by wages multiplied by the value of sales at each level) will give the level of wage income at each level of the value chain. The comparison of wage incomes over different levels of the chain, combined with the categorisation completed in Step 1, gives a picture of the distribution of benefits to individuals within the framework of enterprises at each level of the value chain. Wage costs can be especially high for large-scale farms, as well as processing companies. An example calculation is provided in Table 3 below.

 

Tool 7 - Table 3: A virtual example of calculation of total wage costs for a farmer to process 50 tonnes

of vegetables

Item

$/kg

kg/actor

No. of actors

Total ($)

Farmers’ input costs

1

 

 

 

Farmers’ wage costs

0.5

500

100

25,000

Farmers’ other costs (depreciation, taxes, interest rates)

0.5

 

 

 

Farmers’ total costs

2

 

 

 

 

 

 

 

 

 

Farmers’ Revenue

3

 

 

 

 

 

 

 

 

Farmers’ Profit

1

500

100

50,000

 

 

 

 

 

Processors’ input costs

2

 

 

 

Processors’ wage costs

3

5000

10

150,000

Processor’s other costs

3

 

 

 

Processors’ total costs

8

 

 

 

 

 

 

 

 

Processors’ Revenue

10

 

 

 

 

 

 

 

 

Processors’ profit

2

5000

10

100,000

 

 

 

 

 

Total farmers and processors’ profits

3

 

 

150,000

Total farmers’ and processors’ wage costs

3.5

 

 

175,000

 

In this example total wage costs, as paid by farmers and processors, are a little more than farmers’ and processors’ profits. If all profits and wages are used as household incomes (which means that some of the profits are not used for investments) it can be concluded that the chain generates $325,000 in terms of incomes ($150,000 profit and $175,000 wage costs).

 

In the example in Table 4 below looking at profits along the chain would suggest that farmers earn $15.9 million and processors earn $0.99 million. When wages are taken into consideration it can be shown that the processing industry contributes $9.6 million to the Zambian economy in hired labour alone, while the farm level contributes $7.3 million. 

 

Tool 7 - Table 4: Distribution of incomes and profits in the Zambian cotton value chain

Wage Costs/Profits

$/tonne

tonne/actor

No. Actors

Total ($)

Farmer

 

 

 

 

Wage Costs

$40.00

0.655

280,000

7,336,000

Profit

$86.75

0.655

280,000

15,910,000

 

 

 

 

 

Processor

 

 

 

 

Wage Costs

$52.20

30,566

6

9,573,000

Profit

$5.40

30,566

6

990,000

 

 

 

 

 

Total Wages

 

 

 

16,909,000

Total Profit

 

 

 

16,900,000

 Source:[6]

 

In the analysis of income distribution, care should be taken to differentiate between paid labour and unpaid family labour. Although unpaid family labour does not incur a cash cost, it does incur an opportunity cost, frequently calculated using the local paid labour rate. This is explained in more detail in Value Chain Toolbook - Part Two (Tool 6).

 

Step 5     Calculate income variability over time

 

Seasonality in income is important to model, as substantial variations can occur. Therefore, value chain investigations based on a single estimate of income (at a particular point in time) may result in biased estimates of income. Variability in income increases risk of production and affects actors’ decisions to invest in particular activities. This is particularly important for farmers who grow staple crops (such as rice or maize).

 

Overall, cash is most constrained in the period just prior to harvest. After a large harvest, households often have sufficient cash for their needs before planting begins and inputs need to be purchased. There may be large differences between households in different locations. This is a function of market access as households in remote areas have to rely on their own resources to make ends meet during the lean months. There may also be significant differences between the cash constraint profiles of poor, average, and better-off households.  Box 1 below gives an example of a simple survey instrument designed to determine seasonal levels of cash constraint.

 

Tool 7 - Box 1: Example of survey question to examine seasonal cash constraints 

What are the seasonal cash constraints for the farmers? Get the farmer to place a Ăľ or a x in the appropriate row for each month. 

  Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Surplus Cash r r  r  r  r  r  r  r  r  r  r  r 
Enough Cash r r  r  r  r  r  r  r  r  r  r  r 
Lack of Cash r r  r  r  r  r  r  r  r  r  r  r 

 

 Source:[7]

 

By cross-referencing the data collected using the survey tool above with the categorisation of poverty levels, a graph can be produced focusing on cash constraints.  This type of analysis can highlight the seasonality of cash constraint and surplus in certain value chains.  This is not limited to agricultural crop cycles but can also be a result of changes in consumer demand, for example tourist seasons.  

 

  

 

Source: [8]

Tool 7 - Figure 3: Monthly cash constraint by wealth category

 

Step 6     Appraising the place of income in livelihood strategies

 

It is important to consider the place of income generated by the value chain in total household incomes. Due to income diversification strategies, the income brought by one value chain may be only a small fraction of a household’s total. The share of income represented by the value chain should be calculated in order to accurately model livelihoods and livelihood responses

 

In the example previously of street vendors in Hanoi and peri-urban agriculture in Africa, the business represented more than 90% of cash income of the household, which means that an improvement of the income generated by the value chain will have significant impact on the family incomes. Therefore, the participants in the value chain will be particularly willing to invest their energy in the upgrading of the value chain, which may not be the case if the commodity had a more minor contribution to the household income.

 

In the example in Box 2 below, the contribution of different household activities to total household livelihood is calculated using a survey questionnaire. It is important to distinguish between activities that derive income (through cash sales) and those that are carried out for household consumption purposes.

 

Tool 7 - Box 2: Extract of survey questionnaire on calculating household income

Weighting Activities

 

Get the farmer to list all farm and non-farm activities and sources of income and livelihood. Put them into the categories below. Using 100 seeds, ask the farmer to partition and weight each activity between what the household consumes/uses and what is either sold for income or kept as capital accumulation. For example, livestock is typically kept for capital accumulation and draught power purposes (own use). After the farmer has finished weighting review the results with the farmer. Do pair-wise comparisons between the cells, asking the farmer to verify that the relative weightings are correct. Some common problem that arise:

 

  • Farmers giving a “consumption” weighting to off-farm labour or salaries (people can’t “eat” labour).
  • Farmers weighting activities between income and consumption, but not between activities.

 

Farm and non-farm activities 

Weighting

Income and capital accumulation 

Consumption / Own Use 

C.1

Rice

  __________%

  __________%

C.2

Root and Tuber Crops (cassava, potato etc)

__________%

__________%

C.3

Upland Crops (maize, other cereals, legumes etc)

__________%

__________%

C.4

Vegetables

 __________%

  __________%

C.5

Perennial Crops (rubber, coffee, pepper etc)

 __________%

__________%

C.6

Annual Industrial Crops (Sugarcane, Cotton, etc)

 __________%

_________%

C.7

Fruit Trees

__________%

  __________%

C.8

Fishing and Aquaculture

  __________%

  __________%

C.9

Small livestock (poultry, pigs, goats, etc)

 __________%

__________%

C.10

Large Livestock (Cattle, buffalo, etc)

__________%

__________%

C.11

Non-Timber Forest Products

  __________%

 __________%

C.12

Forest Products

__________%

__________%

C.13

Other Farm Activities

 __________%

__________%

C.14

Handicrafts and Weaving

__________%

 __________%

C.15

Off-Farm Work and Remittances

__________%

__________% 

Check Sum Total=100% 

Valuing Activities

 

Identify the activity with the highest income weighting. Ask the farmer to estimate what the value of that activity was in terms of sales. Reconfirm the relative weightings of each activity for the Income column in terms of value. Calculate total Farm Income below.

 

Farm and non-farm activity

Income and capital accumulation Value (in Local Currency)

Weighting (From Above)

C.16

 

  (A)

          %    (B)

C.17

TOTAL FARM INCOME (Cash and Consumption)

=A/B*100

 100% 

Once total Farm Income has been calculated, the percentages for each activity can be then re-calculated into monetary value for comparison between farmers.  
 

Source: [9]

 

The results of the above survey can be averaged across categories of respondents and then re-calculated in % terms for comparison purposes[10]In the example in Table 5 below, focusing on the cash returns alone results in the conclusion that off-farm work and remittances are the most important income sources. This is followed by small livestock production and upland crops whereas rice is clearly the most important activity after own consumption is factored in.

 

Tool 7 - Table 5: Source of farm family incomes in Lao PDR - average percentage reported

  

Overall

Total by Income Group

Farm and Non-farm Activities

Income and Capital Accumulation

Consumption/ Own Use

Total

Poor

Average

Better-Off

Rice

4.4

24.0

28.4

34.7

27.1

27.1

Root and Tuber Crops (cassava, potato etc) 

0.2

0.6

0.8

1.1

0.8

0.8

Upland Crops (maize, other cereals, legumes etc)

5.7

3.1

8.8

12.7

8.1

8.1

Vegetables

5.5

2.5

7.9

8.2

8.6

8.6

Perennial Crops (rubber, coffee, pepper etc) 

0.9

1.0

1.9

1.0

2.4

2.4

Annual Industrial Crops (Sugarcane, Cotton, etc) 

0.1

0.3

0.4

0.7

0.3

0.3

Fruit Trees

2.4 

1.3

3.7

2.6

3.8

3.8

Fishing and Aquaculture

0.8

0.3

1.1 

0.0

1.8

1.8

Small livestock (poultry, pigs, goats, etc) 

5.9

3.9

9.8

6.8

10.6

10.6

Large Livestock (Cattle, buffalo, etc)

3.5

2.3

5.7

2.5

6.5

6.5

Non-Timber Forest Products

2.7

0.9

3.6

5.4

3.2

3.2

Forest Products

1.5

0.6

2.1

3.2

1.7

1.7

Other Farm Activities

0.8

0.9

1.8

2.9

1.6

1.6

Handicrafts and Weaving

0.8

0.2

1.0

1.2

0.9

0.9

Off-Farm Work and Remittances

20.4 

2.5

23.0

17.0

22.5

22.5

  Total

55.7

44.3

100.0 

100.0

100.0

100.0

Source:[11].

 

Traders are also likely to have multiple income sources. One trader may be involved in maize, cassava, and soybeans either simultaneously or on a seasonal basis. This means that decisions to participate in any particular value chain are contingent on factors which could be outside the single value chain. For example, a trader may decide to liquidate maize stocks at a loss rather than wait for an imminent price rise if he has to use the storage space and cash liquidity to engage in the upcoming soybean season.

 

Step 7     Comparing incomes across different value chains

 

The comparison of incomes generated by different value chains characterised by different governance structures or different upgrading strategies (the two being often related) enables recommendations regarding the promotion of governance and upgrading which generates the highest incomes and/or the most balanced ones across different actors. For instance an analysis of an aromatic-rice value chain in Vietnam shows that the association-driven chain, with the labelling of aromatic rice by a farmers’ association and sales to supermarkets, generates more income to the farmers than the traditional chain[12].

 

A researcher may wish to compare incomes across different value chains, such as within a commodity but across different governance structures, or across commodities (value chains) within a particular area. It is important to recognise that comparing different value chains in different areas without considering the different agro-ecological systems (for production) or the different technologies available (low technology milling versus high technology milling) may result in incorrect conclusions.

 

For the first case, comparing across different governance structures, the following example shows profit margins for producers and processors across three different value chain governance systems for cotton in Zambia. The first governance system is called the Distributor System. This system follows a Principle-Agent model of organisation where the processor makes contracts with traders who are then responsible for the distribution of inputs and services and the collection of the crop. The second governance system is called the Contact Farmer System where the processor has a system of field agents and extension advisors who are employees of the company. The third governance system is a Side-Buyer System where the processor does not invest in providing inputs or services to farmers but relies on attracting farmers currently under the two other systems to renege on their contracts by offering a slightly higher price.

 

 

Source:[13]

Tool 7 - Figure 4: Comparison of profit margins across governance systems in cotton in Zambia

 

The analysis shows that farmers are better off in the side-buyer value chain as the profits are slightly higher than the other two systems. However, as the discussion in Value Chain Toolbook - Part Two (Tool 3) indicates, such a strategy may not be sustainable in the long run as it could force the other governance systems out of the market and farmers would lose the advantages of having their inputs and services provided by the lead firms. The analysis also shows that while the side-buyer processor has the greatest profit (since they do not have to spend any money on inputs or extension), the distributor model is more profitable than the Contact Farmer model since the Contact Farmer processor has to spend their own money on the logistics of providing inputs and services as well as collection of the harvest.

 

Comparing the incomes in the value chains before and after upgrading is also a good way to assess the economic impact of value chain upgrading. Yet it is often difficult and time-consuming to carry out “before” and “after” evaluation, and comparing “with” and “without” situation at the same period of time, for different actors, is generally more feasible.

 

Similarly, comparing incomes across value chains is a good indicator of alternative activities which households could undertake. In the example below, the value chains for five different sectors in Zambia are compared for employment and income. The results indicate that sugarcane and export horticulture value chains are the two chains with the highest income per capita; the domestic horticulture and cotton chains have the two lowest incomes per capita. This suggests that interventions to get greater numbers of people into the sugarcane and export horticulture chain would have the most benefit. However, a deeper analysis of the five chains suggest that barriers to entry for these two chains are significant (hence their greater returns) and that improvements in the cotton and domestic horticulture chain would yield more significant benefits, and impact on more households.

 

Tool 7 - Table 6: Income distribution and employment across value chains in Zambia

Value Chain

Sector Earnings

(US$ million)

Wage Employment

Small Farmers

Earnings per Person (US$/day)

Cotton

81

2,300

280,000

$1.30

Tobacco

63

92,000

23,000

$2.49

Sugarcane

65

4,000

1,692

$51.91

Export Horticulture

55

14,500

2,500

$14.71

Domestic Horticulture

116

10,000

525,406

$0.98

Source:[14]

 

 

Source:[15]

Tool 7 - Figure 5: Income distribution and employment across value chains in Zambia

 

What Should be Known after Analysis is Complete

 

After having followed all the steps it should be possible to answer the key questions outlined below:

 

  • Are there differences in incomes within and between different levels of the value chain?  
  • What is the impact of various governance systems on income distribution between and within various levels of the value chain?
  • What  are the impacts of the distributional outcomes of the value chain on the poor and other disadvantaged groups, both currently and into the future?
  • What  are the changes in incomes that result from the development of various types of value chains?
  • What is the variability of incomes and risks to livelihoods within and between various levels of the value chain?

 

Useful Examples

 

Tool 7 - Example 1: Differences between the distribution of unit profits and incomes. 

 

Moustier et al[16] assessed the distribution of costs and profits* between the different actors of the following off-season tomato chains in Northern Vietnam: 

  • Among the different value chain actors, it is the collectors and wholesalers selling vegetables of Moc Chau who get the highest incomes. This is due to the large quantities traded as their profits per kg are smaller than other actors; e.g., 19-5 Cooperative and Van Tri Cooperative (for tomato, 105 t/year for collectors, 132 t/year for wholesaler, 6 ton/year for Bao Ha, 13 ton/year for 19-5, 12 ton/year for Van Tri). It is worth investigating the reasons behind these differences in quantities traded. It may be a function of the number of years in the business, or the fact that the cooperatives prefer the reliability of their suppliers in terms of product quality rather than the number of suppliers and their large scale.
  • Compared with the other actors, supermarkets get relatively low margins (less than 20% of final price, while the farmer's margin is more than 25%);
  • Selling to supermarkets does not bring more income to farmers than selling to safe vegetable shops, even though the retail price is 20% higher. The price difference is distributed into increased profits for the assembling and distribution cooperatives (Van Tri, Van Noi) and company (Bao Ha), and into the supermarket margin. Compared with safe vegetable shops, supermarkets represent more constraints for their suppliers, in particular as regards the possibility of returned products.

Note: in this calculation, we assume that the actors get the same profit per kg for all vegetables traded; therefore, the figures of total incomes should be taken for comparison rather than in absolute terms.

 

* Profits = Sales revenue – Cash costs – Depreciation (see Tool 6 - Analysing Costs and Margins).

 

Tool 7 - Table 7: Estimation of incomes of various actors of the vegetable chains (USD) 

 

Tomato

 

 

All commodities

 

 

Profit/kg

Qty/year

Income/year

Qty/year

Income/year

Farmers Moc Chau inside coop

0,06

3340

203,18

9200

559,67

Collectors Moc Chau (local)

0,02

2100

42,94

13440

274,83

19-5 Cooperative

0,01

12600

129,23

500000

5128,21

Van Tri Cooperative

0,04

11900

530,16

612000

27265,38

Farmers Moc Chau outside coop

0,06

8400

474,38

15000

847,12

Collector Moc Chau (to Hanoi)

0,02

105000

2147,12

105000

2147,12

Wholesaler Hadong

0,02

132000

3206,92

148000

3595,64

Farmer Soc Son

0,14

2374

322,77

8700

1182,87

Collector Soc Son

0,04

20130

771,65

82500

3162,50

Company Bao Ha

0,03

5610

150,32

132000

3536,92

Safe vegetable shop

0,02

3400

78,24

40800

938,92

Source:[17]

 

Tool 7 - Example 2: Unit Profits and incomes along the value chain for onions.

 

The analysis of distribution of incomes among actors in the onion value chain from Niger to Ivory Coast in 1995 shows that incomes are higher by far for urban wholesalers, and lower for producers and retailers, even though the retail stage has the highest profit per kg.

 

A significant part of wholesalers’ incomes is actually distributed to other actors of the chain in the form of gifts, in kind and cash, to help them in difficult times.

 

Tool 7 - Table 8: Distribution of incomes from onion production in Niger to retail sale in Abidjan in 1995

 

Number of actors

Tons/Actor

Sales price (USD/kilo)

Costs/kilo (apart from purchase price)

Profit/Kilo/ actors

Total income/

actors/year (USD)

Producers

 

4

0.14

0.04

0.10

400

Assemblers

6950

1565

0.16

0.01

0.01

12520

Mobile wholesalers

15

703

0.30

0.13

0.01

8436

Urban wholesalers

30

1984

0.38

0.02

0.07

134912

Semi-wholesalers

175

113

0.53

0.02

0.13

14238

Retailers

11200

2

0.95

0.04

0.37

744

Source:[18]

 

Links to Other Examples

Author Title Description of Tool URL Link
       
       
       
       
       

 

 

Footnotes

  1. The analysis indicates an immediate opportunity for intervention in the value chain; providing opportunities for farm households to also undertake weaving activities. The weaving step is where the majority of the value added occurs, so any intervention which promotes upgrading will enable poor farming households to increase their income.
  2. Moustier, P., D. T. Anh, et al., Eds. (2006). Supermarkets and the Poor in Vietnam. Hanoi, Vietnam, CIRAD and ADB.
  3. Moustier, P. and G. Danso (2006). Local Economic Development and Marketing of Urban Produced Food. Cities Farming for the Future. R. Veenhuizen. Ottawa, IDRC.
  4. Moustier, P. and G. Danso (2006). Local Economic Development and Marketing of Urban Produced Food. Cities Farming for the Future. R. Veenhuizen. Ottawa, IDRC.
  5. CIEM (2004). Adding Value to Viet Nam's Rice Industry and Improving the Incomes of the Poor. Hanoi, Vietnam, Central Institute for Economic Management, ADB and IFC MPDF.
  6. Purcell, T., R. v. Gent, et al. (2008). Zambia Participatory Value Chain Management for Poverty Reduction. Lusaka, Zambia, Report Prepared for the World Bank.
  7. UNDP and NERI (2005). Macroeconomics of Poverty Reduction Project - Improving Farm Family Incomes in Lao PDR. Vientiane, Lao PDR, Prepared for the UNDP and the National Economic Research Institute of Lao PDR.
  8. UNDP and NERI (2005). Macroeconomics of Poverty Reduction Project - Improving Farm Family Incomes in Lao PDR. Vientiane, Lao PDR, Prepared for the UNDP and the National Economic Research Institute of Lao PDR.
  9. UNDP and NERI (2005). Macroeconomics of Poverty Reduction Project - Improving Farm Family Incomes in Lao PDR. Vientiane, Lao PDR, Prepared for the UNDP and the National Economic Research Institute of Lao PDR.
  10. It is important to recognize that just using percentages will not allow a comparison across different groups, as all income sources add up to 100%. The data need to be converted into $ values and then averaged within stratification groups. Once averages (means) have been calculated, these can then be converted back into percentages for comparison between groups.
  11. UNDP and NERI (2005). Macroeconomics of Poverty Reduction Project - Improving Farm Family Incomes in Lao PDR. Vientiane, Lao PDR, Prepared for the UNDP and the National Economic Research Institute of Lao PDR.
  12. Binh, V. T., D. D. Huan, et al. (2005). Assessing Poor Consumers' Access in DVCs: The Case of Fragrant Tam Xoan Rice from Hai Hau. Hanoi, MALICA/M4P: 38.
  13. Purcell, T., R. v. Gent, et al. (2008). Zambia Participatory Value Chain Management for Poverty Reduction. Lusaka, Zambia, Report Prepared for the World Bank.
  14. Purcell, T., R. v. Gent, et al. (2008). Zambia Participatory Value Chain Management for Poverty Reduction. Lusaka, Zambia, Report Prepared for the World Bank.
  15. Purcell, T., R. v. Gent, et al. (2008). Zambia Participatory Value Chain Management for Poverty Reduction. Lusaka, Zambia, Report Prepared for the World Bank.
  16. Moustier, P., D. T. Anh, et al., Eds. (2006). Supermarkets and the Poor in Vietnam. Hanoi, Vietnam, CIRAD and ADB.
  17. Moustier, P. and A. Leplaideur (1999). Cadre d’analyse des acteurs du commerce vivrier africain. Urbanisation, alimentation et filières vivrières. Montpellier, CIRAD,. 4: 42.
  18. Moustier, P. and Zebus (2002). The effects of produce properties on the organisation of vegetable commodity systems supplying selected African cities. Montpellier, INRA/MOISA.

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