Africa Lead II Scaling up, Leveraging Momentum Market Linkages - - PowerPoint PPT Presentation

SLIDE 1

Africa Lead II

Scaling up, Leveraging Momentum

Market Linkages Initiative (MLI) Evaluation Results

Nairobi, Kenya

March 11, 2014

Presented by: Bill Wolfe

SLIDE 2

Brief Overview of the MLI Program

• MLI was an USD$11.5 million two year project implemented in

Burundi, Democratic Republic of Congo, Kenya, Malawi, Rwanda and Uganda that begun in Sept. 2009 and ended in Sept. 2011.

• The project focused on increasing marketing and storage

efficiencies in staple foods by stimulating investments and backward linkages in commercial grain markets. The end objective was to create a “pull-through effect”, stimulating increased commercialization of smallholder staple food production and thereby increasing regional and national food security.

• A key element was the use of a co-investment mechanism for the

provision of grain storage and processing facilities (buildings), equipment and training.

2

MLI Value-Chain Scheme

     

Individual Farmers Village Aggregation Centers (VACs) Grain Bulking Centers (GBCs) Wholesale Buyers

           

• Exporter
• Miller
• Brewer
• Gov’t Entity
• Other Processor

SLIDE 3

Executive Summary

• Evaluation Scope:

– Examine the performance of the investments made, including:

• Gauge the wider economic impact created by the MLI investments and

their influence on professional practices

• Try to compare the performance of the MLI participants with non-

participants

– Identify key factors/variables that determined success or failure – Make recommendations regarding any future activities involving similar investments

• A wide range of entities participated in the MLI program and the investment

results reflect a range of outcomes from “total write-off” to “home run”.

• Using return on investment (ROI) metrics as part of an evaluation is a good

concept as it forces you to precisely define costs and related benefits. In practice this can be challenging, especially ex-post.

• Using co-investments can be a powerful way to influence the development of a

value chain and the resulting economic positions of its participants. However, much consideration is advised in areas such as the scale and type of investment, management capabilities, other necessary resources and the type of “end-user” buyer.

• Careful considerations of USAID’s (investment) objectives, risk tolerance and its

wider “public policy” responsibilities is suggested as these may conflict in subtle, but important ways.

3

SLIDE 4

Evaluation Design and Methodology

• The evaluation was focused on examining the usage and

economic returns on the assets acquired via the MLI project. Since GBCs and VACs were where the fixed asset investments occurred, these were the entities where data were collected and evaluated.

• An informal interview design was used since it is less

intimidating than a big form to fill out, allows for explanations to ensure the right data are received, and the interviewer can better determine if the interviewee loses interest and/or when answers are being “made up”.

• Key elements captured were of two general types, factual

and team observations. Given the investments are only two years old and the lack of record keeping by most participants, the team observations, although subjective, take

• n greater importance.

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SLIDE 5

• For several reasons the evaluation focused on the

volumes versus monetary values transacted:

– Since fixed (physical) assets were a focus, physical volume provides a more direct relationship. – To minimize price and currency impacts that make inter-country comparisons difficult. – Interviewees tend to remember physical quantities better than values.

• Answers were not forced. We took a “no data are

better than bad data” approach.

• Warning: We quantified many aspects, although the

data given were far from precise. Thus, our numerical analysis can convey a “false precision” if interpreted

• literally. Our goal and approach was to be “generally

correct” versus “precisely wrong”.

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Evaluation Design and Methodology (cont’d)

SLIDE 6

The Entities Evaluated: GBCs and VACs

Both the GBCs and VACs varied dramatically as to their size and nature:

– GBCs: from units within sophisticated, large, national or regional entities to small local enterprises – VACs: from micro and informal sites of small daily aggregation to more sophisticated operations where storage, testing, cleaning and drying occurs – Not a “bright line” distinction between larger VACs and smaller GBCs 6

Accounting system used Count Commercial Program 18 Double-entry Spreadsheet 1 Spreadsheet 6 Ledger/Receipt Books 7 None 6 Unknown 3 41 Count 1 3 2 3 3 4 4 3 5 2 6 6 7 5 8 5 9 5 10 3 Unknown (DRC) 2 41 Level of Business Complexity (1-10 scale) Small/Micro Type SME Type Large & Complex

Business Sophistication and Complexity Indicators

MT Handled Annually Count unknown or not applicable 5 less than 10 8 between 100 and 500 6 between 501 and 1000 4 between 1001 and 5000 9 between 5001 and 10,000 4 between 10,001 and 20,000 3 greater than 20,000 2 41

Legal Entity Type MLI GBC MLI VAC GBC Non- MLI VAC Non- MLI Totals Unknown 2 9 11 Co/op 6 1 1 8 Informal/Unidentified 1 1 2 Limited Liability Company 13 3 16 Ngo 1 1 2 Self-Help Group 3 1 4 Sole Proprietorship 11 1 1 13 Totals 33 8 11 4 56 MLI Total Non-MLI Total 41 15

Country MLI GBC MLI VAC GBC Non- MLI VAC Non- MLI Totals Burundi 3 3 DRC 2 2 Kenya 6 5 5 16 Malawi 11 2 4 17 Rwanda 5 2 7 Uganda 6 3 2 11 Totals 33 8 11 4 56 MLI Total Non-MLI Total 41 15

Descriptive Statistics: Who Were the MLI Participants and the Survey Population

Country Maize Sorghum Rice Soya

• Gr. Nuts

Beans Barley unknown n/a Burundi 1 1 1 Kenya 9 6 1 Malawi 10 2 2 2 1 Rwanda 6 1 Uganda 7 3 1 DRC 2 33 9 5 2 2 1 1 2 1 Identified by Respondents as a “Top Three” Crop in 2013: Frequency of Responses

Principal Commodities Handled by the Entities Surveyed

SLIDE 7

The Entities Evaluated: GBCs and VACs (cont’d)

• Our subjective definition of successful versus

unsuccessful GBC and VAC businesses and investments

– A successful business

• provides valued service to customers
• generates a positive cash flow

– A successful MLI investment

• generates an acceptable return on the investment
• can be sustained without external support
• utilizes its assets well
• generates a positive economic impact (e.g. employment)
• increased the focus on quality and improved handling of crops (higher

value product with less losses)

• creates demand (price competition) for customers
• Is where the recipient needed the support in order to expand

– A VAC or GBC can be successful from a business standpoint while being a failure from an investment perspective.

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SLIDE 8

Analysis of MLI Investments

• The various statistics were gathered by the survey

teams fell into three general categories:

1. Asset usage/utilization related 2. Return on investment related 3. Wider business/community impact and the influence of MLI

The survey teams’ subjective opinion as to the return on the MLI investments was as follows:

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ROI Score 1 2 3 4 5 6 7 8 9 10 Frequency 1 4 2 1 1 2 7 4 4 3 ROI Score 1 2 3 4 5 6 7 8 9 10 Frequency 2 2 2 2 Rating Rating VACs 25% 25% 0% 50% 0% "write offs" poor

• k

good "home runs" "write offs" GBCs 17% poor 10%

• k

10% good 38% "home runs" 24%

SLIDE 9

Descriptive Overview of the Investments

Amount Invested (USD), Cost Sharing, and Funds Usage by Country and Organization Type

Country Org Type Total Invested Total Facilities Total VAC Facilities Total GBC Facilities Total Equipment Total Salary, Training & Other Minimum MLI Cost Share %'age Maximum MLI Cost Share %'age Burundi GBC 471,799 232,918 32,096 200,822 233,749 5,132 51% 68% DRC GBC 290,334 112,696 177,638 74% 91% Kenya GBC 1,952,965 663,920 320,080 343,840 1,029,760 259,284 39% 71% VAC 52,740 47,090 47,090 5,650 50% 87% Malawi GBC 1,989,511 1,331,972 38,877 1,293,095 411,539 246,000 32% 51% Rwanda GBC 1,245,578 683,087 26,411 656,676 489,151 73,331 21% 57% Uganda GBC 3,457,338 2,884,582 179,341 2,705,241 329,783 242,974 8% 61% VAC 36,231 31,472 31,472 4,759 8% 61% Totals 9,496,495 5,875,040 675,366 5,199,674 2,617,088 1,004,359 VAC Facilities/Buildings 7% GBC Facilities/Buildings 55% Total Facilities/Buildings 62% Total Equipment 28% Total Salary, Training & Other 11% Grand Total 100% Percent of Total

SLIDE 10

Asset Utilization Measures

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Storage: Annual Turn-Over Ratio Storage: Peak Usage Ratios

Entity Name Org Type Peak Storage Utilization Mulli Namphasa GBC Sodea GBC ETS Muyinga GBC KPMC GBC Nyantare VAC 2% Kambu VAC 2% Zwii Enterprise GBC 3% Ikeesu VAC 5% Cheka GBC 9% Mwailu GBC 10% Farmers World GBC 31% Dalitso GBC 36% Mulli Thuchila GBC 38% Uplands Rice GBC 38% Mwandama GBC 50% ETS Bujumbura GBC 50% MC Agronomy GBC 50% FACE GBC 57% Smart Logistics GBC 60% Namwenda VAC 67% Ke Maikuu VAC 67% Kisita GBC 75% Chitsosa GBC 83% Export Trading GBC 87% Agroway GBC 100% UZ Investments GBC 100% COAMV GBC 100% UCORIBU GBC 100% ENAS GBC 100% SOSOMA GBC 100% ProDev GBC 100% Nuru GBC 100% God's Favour Womens VAC 100% Mama Millers GBC 100% Kapeeka GBC 100% Kasfa GBC 100% Abei VAC 100% Sarah Musaga VAC 100% Maximum/ Good Probably OK Low No Data Entity Name Org Type StorageTurn-

• ver Ratio

Cheka GBC Zwii Enterprise GBC Export Trading GBC Kambu VAC 0.01 Namwenda VAC 0.02 Ikeesu VAC 0.02 God's Favour Womens VAC 0.12 Mulli Namphasa GBC 0.15 Abei VAC 0.2 Mulli Thuchila GBC 0.23 Mwailu GBC 0.29 Nyantare VAC 0.3 Farmers World GBC 0.33 Kisita GBC 0.47 Mwandama GBC 0.5 ETS Bujumbura GBC 0.63 MC Agronomy GBC 0.65 ETS Muyinga GBC 0.71 Sodea GBC 0.75 KPMC GBC 0.81 FACE GBC 1 Kasfa GBC 1 Nuru GBC 1.2 Sarah Musaga VAC 1.2 Ke Maikuu VAC 1.67 UZ Investments GBC 1.67 Dalitso GBC 1.73 COAMV GBC 1.83 Agroway GBC 2.04 ENAS GBC 2.07 Smart Logistics GBC 2.14 SOSOMA GBC 2.34 Chitsosa GBC 3.5 Kapeeka GBC 4 ProDev GBC 4.5 Mama Millers GBC 4.7 UCORIBU GBC 10.53 Uplands Rice GBC 20 Low/Poor No Data Needs Improvement Good to Excellent

SLIDE 11

11

Asset Utilization Measures (cont’d)

Has equipment been calibrated Count %'age Undetermined 17 31% No 13 24% Yes 24 44% Total 54 100% Condition/ maintenance of buildings Count %'age Undetermined 2 4% Poor 2 4% Adequate 36 67% Very Good 14 26% Total 54 100% Are facilities well organized and kept? Count %'age Undetermined 4 7% No 13 24% Yes 37 69% Total 54 100% Condition/ maintenance of equipment Count Not Seen 7 13% Very Poor 1 2% Poor 1 2% Adequate 37 69% Very Good 8 15% Total 54 100% Utilization of equipment Count %'age Undetermined 15 28% Not Used 4 7% Low 7 13% Moderate 11 20% High 7 13% Very High 10 19% Total 54 100%

SLIDE 12

Return on Investment (ROI) Measures

Summary of Investments Made (USD) 12

Practical Issues in ROI Measurement

• ROI = Return (net income after-tax, or “profit”) ÷ Amount Invested
• ROI Calculation Challenges:

– Many businesses do not keep the records needed to show net income – If there are records, you may see those that are used for tax reporting purposes, i.e. dubious – This is private business information, many do not want to share it – It is tough/impossible to separate the return generated by one specific asset from another. Usually they “work together” to create value/profits.

• What We Did

– Tried to get data on gross margin, knowing that this would overstate ROI/profitability – Tried to obtain volume data on specific MLI related assets/buildings

Country Org Type Total Invested Total Facilities Total VAC Facilities Total GBC Facilities Total Equipment Total Salary, Training & Other

• Min. MLI

%'age

• Max. MLI

%'age Burundi GBC 471,799 232,918 32,096 200,822 233,749 5,132 51% 68% DRC GBC 290,334 112,696 177,638 74% 91% Kenya GBC 1,952,965 663,920 320,080 343,840 1,029,760 259,284 39% 71% VAC 52,740 47,090 47,090 5,650 50% 87% Malawi GBC 1,989,511 1,331,972 38,877 1,293,095 411,539 246,000 32% 51% Rwanda GBC 1,245,578 683,087 26,411 656,676 489,151 73,331 21% 57% Uganda GBC 3,457,338 2,884,582 179,341 2,705,241 329,783 242,974 8% 61% VAC 36,231 31,472 31,472 4,759 8% 61% Totals 9,496,495 5,875,040 675,366 5,199,674 2,617,088 1,004,359 100% 62% 7% 55% 28% 11%

SLIDE 13

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Return on Investment (cont’d)

What should it be?... On what investment?… Who’s return?... Is it enough? Question: What rate of return should be expected?

Answer: The rate required by an investor in the market

This required, or “hurdle”, rate is comprised of the following:

Risk Free Rate + Risk Premium = Required ROI In practice, this requires subjectivity… What I did was…

Risk Free Rate + Bank Lending Premium + Equity Risk Premium = Required ROI (in local currency)

Country Risk Free Rate Bank Lending Premium Bank Lending Rate Equity Risk Premium Required ROI Burundi 9.6% 5.8% 15.4% 8.7% 24.1% DRC Kenya 13.5% 2.4% 15.8% 3.5% 19.4% Malawi 6.9% 10.9% 17.8% 16.3% 34.1% Rwanda 7.2% 9.6% 16.8% 14.4% 31.2% Uganda 16.5% 6.7% 23.2% 10.1% 33.3%

SLIDE 14

Example: FACE GBC in Uganda (Uganda Required ROI = 33.3%)

• ROI on what investment (denominator)?

– Total Amount? = $353,100 – Total Invested in Buildings? = $220,000 – Total Invested in Equipment? = $121,269 – Total Invested in Fixed Assets? = $341,269

• Who’s return?

– All investors (USAID + Recipient)? = 100% – USAID’s portion? = 61% in the case of FACE GBC – Recipient’s portion? = 39% in the case of FACE GBC

14

Return on Investment (cont’d)

Total Investment Buildings + Equipt. Buildings Only Total Investment Buildings + Equipt. Buildings Only Total Investment Buildings + Equipt. Buildings Only Investment Amount: USD 353,100 341,269 220,000 215,391 208,174 134,200 137,709 133,095 85,800 FX Rate 2,282 2,282 2,282 2,282 2,282 2,282 2,282 2,282 2,282 Investment Amount: UGX 805,774,200 778,775,858 502,040,000 491,522,262 475,053,273 306,244,400 314,251,938 303,722,585 195,795,600 Required ROI 33.3% 33.3% 33.3% 33.3% 33.3% 33.3% 33.3% 33.3% 33.3% Required Profit/Margin: UGX 268,322,809 259,332,361 167,179,320 163,676,913 158,192,740 101,979,385 104,645,895 101,139,621 65,199,935 Actual Gross Profit: 2013 741,685,651 741,685,651 741,685,651 741,685,651 741,685,651 741,685,651 741,685,651 741,685,651 741,685,651 Actual ROI: 2013 92% 95% 148% 151% 156% 242% 236% 244% 379% Total Perspective MLI Perspective Recipient Perspective Entity Name Total Investment Buildings + Equipt. Buildings Only Total Investment Buildings + Equipt. Buildings Only Total Investment Buildings + Equipt. Buildings Only FACE 353,100 341,269 220,000 215,391 208,174 134,200 137,709 133,095 85,800 MLI Perspective Total Perspective Recipient Perspective

SLIDE 15

ROIs from Raw Survey Data 15

Return on Total MLI Investment

• Wow!... There are some fantastic

returns.. …Are they real? ……short answer…. no

• Two factors are artificially boosting the

ROI numbers 1. Using gross margin versus net income (a numerator effect), and; 2. Many participants had pre-existing

• perations. MLI added some

assets to those pre-existing ones. But our data captured the benefits/returns of all the assets (another numerator issue) while attributing them to only the MLI investment amount (a denominator issue). You can see a correlation between the calculated ROI and the volume of grain purchased (volume being a strong indicator of pre-existing operations).

• Where does that leave us?

Entity Name MT Purchased/ Year ROI ETS Bujumbura 5,000 5772% Chitsosa 5,000 1353% ETS Muyinga 7,500 1272% Uplands Rice 8,500 592% UCORIBU 5,285 375% ProDev 9,000 360% Smart Logistics 7,000 239% ENAS 9,000 196% Mama Millers 767 167% Agroway 21,169 145% Mwandama 1,250 139% Mulli Namphasa 17,300 122% COAMV 3,200 100% FACE 760 92% Sodea 1,500 79% SOSOMA 265 74% Farmers World 1,405 71% Kasfa 750 56% MC Agronomy 325 52% Mulli Thuchila 1,157 33% Sarah Musaga 153 20% Kisita 6 18% God's Favour Womens 6 14% Mwailu 138 12% KPMC 486 1% Nuru 0%

SLIDE 16

Towards a More Realistic ROI Figure

• Drawing on previous work, we
• btained an average observed net

margin of 4% for businesses of this type in the region. Net margin brings us much closer to net profit.

• As for trying to isolate the MLI

assets and their returns from the

• ther assets, this was well beyond

the scope of the evaluation. Thus, this overstatement effect remains.

• The adjoining table shows the

results using the 4% net margin figure and compares them to the country specific return/hurdle rate. Since the returns are still

• verstated, we can only opine on

those not meeting their hurdle rate by saying that they are mostly likely underperforming from a total investment perspective.

16

ROIs Adjusting Gross Margin

Entity Name Country Adjusted ROI Country ROI Adjusted - Country ROI ETS Bujumbura Burundi 770% 24% 746% Chitsosa Malawi 193% 34% 159% ETS Muyinga Burundi 170% 24% 146% Dalitso Malawi 164% 34% 130% Uplands Rice Uganda 125% 33% 91% UCORIBU Rwanda 100% 31% 69% UZ Investments Malawi 88% 34% 54% Mama Millers Kenya 67% 19% 47% ProDev Rwanda 58% 31% 26% Smart Logistics Kenya 56% 19% 37% Mulli Namphasa Malawi 49% 34% 15% Kasfa Malawi 43% 34% 9% ENAS Rwanda 39% 31% 8% Agroway Uganda 39% 33% 5% COAMV Rwanda 23% 31%

• 8%

SOSOMA Rwanda 20% 31%

• 11%

Mwandama Malawi 18% 34%

• 16%

FACE Uganda 18% 33%

• 15%

Sodea Burundi 18% 24%

• 6%

Mulli Thuchila Malawi 13% 34%

• 21%

Farmers World Malawi 11% 34%

• 23%

Sarah Musaga Uganda 8% 33%

• 25%

MC Agronomy Malawi 7% 34%

• 27%

Ke Maikuu Kenya 6% 19%

• 13%

God's Favour Womens Kenya 5% 19%

• 14%

Kapeeka Uganda 4% 33%

• 30%

Kisita Uganda 3% 33%

• 30%

KPMC Kenya 0.0234 0.1939

• 17%

Abei Uganda 0.0212 0.3332

• 31%

Mwailu Kenya 0.014 0.1939

• 18%

Ikeesu Kenya 0.0095 0.1939

• 18%

Nyantare Kenya 0.009 0.1939

• 18%

Kambu Kenya 0.0083 0.1939

• 19%

Namwenda Uganda 0.0032 0.3332

• 33%

Nuru Kenya 0.1939

• 19%

Most Likely Sub-Performing (from Total Investment Perspective)

Boarder Line

Can't Determine with Current Data

SLIDE 17

Payback Period Perspective

Concept: Given the amount of profit earned, how long will it take before total profits equal the cost of the asset purchased? Q: What is an acceptable payback period? A: Well, it depends on….

• 1. the type of asset
• 2. the risk of the activity that the

asset is engaged in

• 3. the risk existing where the

asset is located

17

Return on Investment (cont’d)

Entity Name Country Payback Period - Years ETS Bujumbura Burundi 0.1 Chitsosa Malawi 0.5 ETS Muyinga Burundi 0.6 Dalitso Malawi 0.6 Uplands Rice Uganda 0.8 UCORIBU Rwanda 1.0 UZ Investments Malawi 1.1 Mama Millers Kenya 1.5 ProDev Rwanda 1.7 Smart Logistics Kenya 1.8 Mulli Namphasa Malawi 2.0 Kasfa Malawi 2.3 ENAS Rwanda 2.6 Agroway Uganda 2.6 COAMV Rwanda 4.3 SOSOMA Rwanda 5.1 Mwandama Malawi 5.4 FACE Uganda 5.4 Sodea Burundi 5.5 Mulli Thuchila Malawi 7.6 Farmers World Malawi 8.8 Sarah Musaga Uganda 12.6 MC Agronomy Malawi 14.3 Ke Maikuu Kenya 16.7 God's Favour Womens Kenya 18.3 Kapeeka Uganda 27.2 Kisita Uganda 34.3 KPMC Kenya 42.7 Abei Uganda 47.2 Mwailu Kenya 71.7 Ikeesu Kenya 105.4 Nyantare Kenya 111.3 Kambu Kenya 120.1 Namwenda Uganda 311.8 Nuru Kenya Never Not Good Way Too Long Excellent…. If True!!! Good

SLIDE 18

Business Environment & Impact Measures

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Employment MLI Return & Business Success Gender

Full Time Employees 2012 2013 Change Kenya 113 138 25 Malawi 69 185 116 Rwanda 90 87

• 3

Uganda 93 94 1 Total 365 504 139 Part Time Employees 2012 2013 Change Kenya 685 685 Malawi 309 360 51 Rwanda 386 380

• 6

Uganda 224 294 70 1,604 1,719 115

Mli's Overall Return on Investment (1-10 scale) Overall business performance assessment Count 10 Highly Successful 3 9 Highly Successful 3 9 Successful 1 8 Highly Successful 4 8 Successful 2 7 Highly Successful 3 7 Successful 5 7 Viability Questionable 1 6 Successful 2 5 Highly Successful 2 5 Struggling 1 4 Successful 1 3 Highly Successful 1 3 Successful 2 2 Successful 2 2 Viability Questionable 4 1 Viability Questionable 1

Country Org Type Owner/ Mgr Count Burundi GBC Female 1 Male 2 Kenya GBC Female 2 Male 4 VAC Female 3 Male 2 Malawi GBC Female 1 Male 10 Rwanda GBC Male 5 Uganda GBC Female 1 Male 5 VAC Female 1 Male 2

Country <40% 40%-60% >60% Burundi Kenya 1 2 Malawi 1 3 Rwanda Uganda 1 1 Percentage of Volume Purchased by VACs from Women Sellers

Quality Focus

Primary Buyer Type Do you buy at different prices for quality? Count End-user Processors Yes 7 No 6 Wfp/crs & Like Yes 1 Own Use (processing) Yes 1 No 1 Government Entities Yes 2 No 4 Not Sure 2 GBC No 5 Not Sure 1 Individuals/farmers Yes 1 No 2 Local Traders No 1 Regional/national Traders No 3 Undefined 4

Country Do you buy at different prices for quality? Count Burundi No 3 Kenya Yes 4 No 5 undetermined 2 Malawi Yes 3 No 8 Rwanda Yes 3 No 2 Uganda Yes 2 no 6 undetermined 1 DRC n/a 2

Country Do you grade the grains you handle? Count Burundi No 3 Kenya Yes 8 No 3 Malawi Yes 8 No 3 Rwanda Yes 3 No 2 Uganda yes 5 No 4 DRC n/a 2

SLIDE 19

MLI versus Non-MLI Participants

Given the small sample, our observations fall firmly into the “anecdotal ” category.

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Org Type Number Surveyed Number w/ Meters Number of Meters GBC 31 27 219 VAC 8 5 5 GBC Non-MLI 11 5 9 VAC Non-MLI 4

Quality Testing Equipment Storage & Handling Spoilage/Losses

Org Type Overall business performance assessment Count GBC Highly Successful 15 Successful 9 Struggling 1 Viability Questionable 5 Undefined 1 VAC Highly Successful 1 Successful 6 Viability Questionable 1 GBC Non-MLI Successful 8 Struggling 3 VAC Non-MLI Successful 2 Struggling 1 Undefined 1

Business Condition Assessment

Org Type Awareness of Formal Grain Standards Count GBC Yes 17 No 9 undetermined GBC Non-MLI Yes 6 No 4 VAC Yes 1 No 3 undetermined 1 VAC Non-MLI undetermined No 1

Awareness of Industry Standards

Org Type Minimum %'age Losses/Spoilage Maximum %'age Losses/Spoilage Avgerage %'age Losses/Spoilage GBC 0.0% 12.0% 2.0% GBC Non-MLI 0.0% 25.0% 5.9% VAC 0.0% 3.0% 1.3% VAC Non-MLI 0.0% 0.0% 0.0%

SLIDE 20

Qualitative Impacts and Returns of the MLI Program

• For those MLI participants that were successfully supplying end-user

processors, almost all mentioned the importance of the post-harvest cleaning and handling training, and that they did not realize the importance at that time. This training, which extended to the farm level, allowed them to better participate in the market for high quality crops.

• Related to the above were meetings where end-user business people

met with farmers, GBCs and VACs to explain their business and

• needs. This was mentioned as having a large impact on “downstream”

participants, allowing them to hear first-hand what the market demanded of their products. It also allowed those end-users an

• pportunity to understand the “downstream” issues and how to work

together.

• The mere presence of a new and high quality physical store facility

gives the operator a higher stature among local farmers. It conveys a certain credibility to the local farming community and sets them apart from local traders.

• The storage manual produced by MLI has received distribution and

positive comments from other projects.

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SLIDE 21

Observations, Lessons and Recommendations

• From an investment perspective, the MLI portfolio fell

into two general categories:

– Low risk/conservative: These were co-investments made with sophisticated, established, large national or regional entities. By definition, these entities know how to utilize assets well.

• The equity market corollary of this group would be

investing in “blue chip” stocks... i.e. low risk of “losing your shirt” along with a low probability of big upside gain. – High risk/speculative: These were co-investments made with newer, smaller more localized entities. Not surprisingly, this is where both the “write-offs” and the “home runs” were found.

• The equity market corollaries of this group would be

venture capital and “high growth” investing... i.e. a real risk and expectation of “losing your shirt” on many, but also the possibility of a few big winners.

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SLIDE 22

• The type of “end buyer” from the GBCs (and VACs)

had a very large influence on the professionalism and handling practices down to the farmer level.

– With local/regional traders as buyers, quality and price differentiation was much less prevalent. Consequently the care and quality of the crops down the chain was poor. – Where end-user/processors were the buyers, they enforced strict quality standards and paid a premium for that quality. This influence was strongly transmitted down the value chain.

• Even where available, farmers made very little use of

public storage options (e.g. warehouse receipts and grain receipt notes) even though the economics were positive (at least superficially).

• Equipment is best placed where there is an

expressed and demonstrated need versus where it “should” be needed.

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Observations, Lessons and Recommendations

SLIDE 23

• Many observed storage facilities were much larger

than current and potential usage required. Storage facilities need to be appropriately sized for the realities of the location and how they are managed. This is most pronounced at the VAC level.

• As with all start-up/venture investing with private

entities, the person/people with whom you are investing is paramount.

– Investing with cooperatives hold special risks. Key factors are stability and professionalism of management and whether the

• bjectives are business related or social.

– Investing with NGOs includes a sustainability risk, namely what happens when the NGO’s support ends? – Investing with private entities works best when the owner has a (real) significant personal stake at risk in the business and the investment represents a large portion of that investment.

23

Observations, Lessons and Recommendations

SLIDE 24

• VAC investments, while no doubt helpful for those

recipients, appear to have a limited impact. In order to achieve “transformational” results they might be necessary, but they are not sufficient in themselves.

• As to the sustainability of a program of shared cost

investment in long-lived fixed assets, it is most likely superior than programs that focus on subsidizing annual operating aspects. This is so because once the subsidy “boosts” the asset return to the recipient (effectively lowers the cost) above the hurdle rate, the asset should keep producing for the rest of its life (i.e. needs no further subsidies to be useful). The large caveat is that care needs to be taken in upfront selection as the decision is large and permanent.

24

Observations, Lessons and Recommendations

SLIDE 25

• The existence of an (actively utilized) GBC or VAC

facility in a region had an upward influence on the price received by the local farmers.

• Lack of working capital was identified by a vast

majority of interviewees (MLI and non-MLI) as the major factor limiting their ability to grow. Any future fixed-asset program needs to consider this issue.

• The wide variety in the size and type of MLI

participants was extremely important in being able to identify differentiating features.

• A program such as MLI could be scaled in a

consistent manner. For recipient selection, the use of a pre-determined check-list (or “scorecard”) that utilized the factors identified in this evaluation would be highly recommended.

25

Observations, Lessons and Recommendations

SLIDE 26

• If any return related (ROI) measures are to be

incorporated into a project’s evaluation scheme, it needs to be done ex-ante. The following methodology is suggested:

a) Isolate the cost of the specific item:

• e.g. machine, building, service, training program, whatever..

b) Identify and quantify the incremental changes/benefits that are expected from incurring the above cost:

• e.g. increased production volume, lower wastage, reduced

transportation cost.

c) Calculate the monetary value of the above incremental changes (annualized). d) Expected ROI is “c” ÷ ”a”. e) For ongoing monitoring, evaluate the items identified in “b”

• above. Actual results are then used to calculate the actual

ROI.

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Observations, Lessons and Recommendations

SLIDE 27

The Investment Process: Generic Steps

1. Define your specific objective(s)… e.g.:

• Increase farm income; decrease post harvest losses between farm

and end-user; create employment

2. Determine your risk tolerance level and limits… e.g:

• Minimize failed investments; maximum losses < USD x

3. Identify any absolute boundaries… e.g.:

• No tobacco investments; no investments in state sponsored entities:

no investment in entities with assets > x

4. Determine the return target “hurdle” rate and the appropriate denominator value. 5. Determine appropriate metrics (direct and indirect) and how they will be captured… e.g.:

• Change in farm-gate price vs. local market; volume handled; change

in amount sold to end-users; profitability of recipient

6. Determine the type and structure of investments… e.g.:

• Grant; matching grant; forgivable loan; equity stake

7. Begin evaluation and selection of specific candidates

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SLIDE 28

Potential Conflicts: The Investment Perspective vs. “Public Policy” (and other) Requirements Illustrative Example:

• From a policy perspective…..

– USAID, as a government entity, should strive to maximize the probability that any fixed assets acquired with USAID (US taxpayer) support are used in a productive manner (not wasted).

• From an investment perspective…

– This means that any support should go to entities known to have the skills to utilize such assets and are least likely to fail, i.e. large and well established entities.

• However……

– Does giving support (i.e. money) to large and well established private entities create any potential downside risks or conflict with other policy requirements? – It also precludes the possibility of funding of “home runs”.

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SLIDE 29

• Investment metrics (e.g. ROI, asset utilization, cost per unit

produced) can add significantly to project evaluation but need to be kept in context with other indicators. They are not a “stand- alone silver bullets”.

• In most instances, identifying the cost (denominator of ROI) of an

activity is straightforward. Investment metrics thus have greatest value where the numerator can be calculated, i.e. where the:

a) change in output/effect of the project can be measured, and b) a monetary figure can be attached to the output/effect

• Many types of projects do not directly lend themselves to

“investment analysis”. For example, what is the “return” on reduced infant mortality? Although these types of values can be, and are, calculated explicitly (e.g. by actuaries) or implicitly (e.g. setting of a budget), economic and moral complexities abound.

• In the above situations, “second-order investment type” metrics

may serve well (e.g. cost per infant examined/treated). These are most useful where there exist comparative statistics that help in setting realistic objectives and thus can covey relative value.

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Investment Metrics: Uses and Limitations

SLIDE 30

Q&A

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Many Thanks To….

Team Members:

• Peninah Gichuru – EAGC
• Paul Thangata – Africa Lead II
• Peter Boone – CARANA

Kimberly Smith – Africa Lead II Stephen Gudz - USAID Charlee Doom – USAID Sophie Walker – ACDI/VOCA All of You…………