The Economic Benefits of Supporting Private Social Enterprise at the Nexus of Water and Agriculture: A Social Rate of Return Analysis of the Securing Water for Food Grand Challenge for Development

Abstract

This article attempts to evaluate the comparative socioeconomic benefits of funding performance-based private sector social enterprises and posits that the social rate of return of such investments is significant and exceeds expectations for similar investment portfolios. Using the case study of the Securing Water for Food Grand Challenge for Development, we perform a social rate of return analysis on 16 water conservation technologies (WCTs) in 10 nations. Through using an extensive benefit cost ratio, we obtain the Marginal Internal Rate of Return whereby the discounted value of future benefits is equal to the reinvestment cost of capital for the SWFF portfolio. This allows the impact of SWFF to be comparable to other investments and serve as a benchmark. The resulting social rate of return metrics exceed the conservative expectations of impact investment funds as well as comparable foreign aid investments. The reasons behind this high rate of social impact are further explored and recommendations are provided accordingly for an alternative performance-based investment model of foreign aid disbursement that prioritizes scalable small and medium-sized social agribusiness enterprises in developing nations.

1. Introduction

Securing Water for Food (SWFF) was a $34 million program founded by the United States Agency for International Development (USAID) in conjunction with Sweden through the Swedish International Development Cooperation Agency (SIDA), the South African Department of Science and Technology (DST), and the Foreign Ministry of the Kingdom of the Netherlands (MFA-NL). SWFF provided technical assistance to 40 innovators at the nexus of water and agriculture. In addition, SWFF innovators received 3-year milestone-based grants with up to $500,000 of grant funding for Tier 1 applicants or up to $2.5 million for Tier 2 applicants.

Of the 40 innovators, 23 met their milestone target for the full 3 years of this program and became SWFF graduates while the remaining 17 failed to meet their milestone targets and are classified as alumni, all but one of whom only participated in SWFF for only one year. SWFF performed a comprehensive social rate of return analysis (SROR) on 16 of the 23 graduate innovators in the SWFF portfolio. The results demonstrate that, on average, SWFF innovators exceed the average SRORs of comparable foreign aid investment portfolios. SWFF then compared and analyzed the large range of returns on investment and the subsequent reasoning for the differences in return on investments of the SWFF graduates. SWFF compared the socio-economic benefits of the SWFF program to other comparable foreign aid programming and examined the difference between the SWFF program’s average SROR and that of the DIV (Development Innovation Ventures) program, as well as another USAID agriculture program based in Nigeria. As of March 2020, SWFF’s innovation portfolio has currently reached over 7.21 million beneficiaries in 38 countries and also impacted more than 8 million hectares of land and produced 6.8 million tons of produce.

SWFF-sponsored inventions are diverse and include aquaponics units, wastewater recycling systems, drone monitoring, saline tolerant seeds, more efficient water pumps, and technologically advanced agricultural alert text message systems. Sponsored SWFF innovators had to have sufficient evidence to prove their respective technologies would be adapted and modifiable to the needs of their customers, which indicates greater future adoption [1,2]. This included giving free or cheap access to the respective technology including access to credit, which further increases the chances of adoption [3]. Evidence based rules and Collaborating, Learning, and Adapting (CLA) monitoring procedures contributed to the portfolio mix of high tech and low-tech innovations being context specific as was shown to be optimal in Africa and with CLA showing evidence for improvement in development outcomes [4,5]. The positive effects on the households of Ignitia, a higher tech SMS based communication system and biggest innovator is an example of a “digital dividend”, which are often scalable but lack the necessary capital that SWFF provided [6] Further evidence of the heterogeneity of impact of the same agricultural value chain interventions across different locations also supports the tailored diversity of SWFF technological interventions [7]. As the impact of many innovators are undervalued if only monetary benefits are to be included, the net present value (NPV) of social benefits is accrued according to a modified USAID Nigeria Markets II Program CBA template, formatted to each individual SWFF innovator [1]. From a financial perspective, the SROR of this program evaluates the NPV of the socially discounted benefits of an innovation for its beneficiaries and from an economic perspective it evaluates the Modified Internal Rate of Return (MIRR) explained below. By using the benchmarked portfolio social rate of return we can then compare this MIRR to the expected 10–20% return on investment target for conventional private and foreign aid investments.

In the past 20 years, a variety of private and public organizations have funded innovation programming in international development. These include the Bill and Melinda Gates Foundation in addressing global health issues, Grand Challenges Canada, and USAID to address agricultural water needs of resource poor communities. In addition to numerous social entrepreneurship and angel investor networks that increasingly attract impact investors, institutions such as the World Bank and the UK Department of International Development have also promoted the testing of development innovations through randomized controlled trials [2].

As much of the evidence of the efficacy of innovation for development schemes is anecdotal, it becomes imperative to evaluate and empirically compare the SRORs of foreign aid programs and with likewise innovation venture-based programming. Foreign aid evaluations would be further enhanced by the detailed cost effectiveness exhibited in SROR analyses as opposed to the current Development Assistance Criteria of the OECD [8]. Since the emphasis of only the successful returns of an innovation portfolio can be potentially biased and unrepresentative of its total funding, in the context of this paper we compare the returns from the SWFF SROR portfolio to the overall costs of SWFF programming.

SWFF’s 17 alumni innovators only received 1 year of funding and most alumni failed to reach sustainable scale with insignificant development impacts. As such, the 17 alumni were excluded from the analysis of SWFF’s development impact and benefits. Instead, the $1.7 million in costs from their $100,000 SWFF grants were included in the costs only.

A total of 98% of SWFF’s development impact was created by the 16 graduate innovators in our analysis, which represents a proportionate representation of the entire SWFF portfolio and makes it possible to evaluate a SWFF overall rate of portfolio return. Sixteen out of the 23 graduate innovators will be the focus of this paper. As such, those 16 graduate innovators received a combined total of $15,500,000 in grants and technical assistance. This is despite the previously laid out difficulties of said study in estimating entire portfolio returns including a lack of time, hard to quantify monetary benefits, and hard to collect data.

For this assessment we focus on the SROR portfolio of SWFF which comprises 16 innovation investments spanning 3 years each in 10 nations between May 2015 and March 2020. These innovation investments can be further broken down into three rounds of applications as well as graduate and alumni innovators and Tier 1 and Tier 2 innovators as earlier mentioned. On average, these SWFF graduate innovators increased farmer per capita income by 182% compared to before the introduction of a SWFF innovation. This can alternatively be represented as an average increase of $1850 over the 3 years of SWFF programming ($617 annually) for a total of over 1 million households resulting in an annual net gain of approximately $500 million for 3 years.

This average income increase was despite the fact that input costs increased $185 on average or 7% for farmers during SWFF. These average input cost differences range drastically from a decrease of 76% in input costs to an increase of up to 125%. It is important to note that the income described in the context of this SROR portfolio is inclusive of all the produce/crops a household grew regardless of how much was consumed at home. This was done as the majority of farmers serviced by SWFF innovators were subsistence level or sold only small amounts of produce.

The SWFF portfolio’s SRORs are varied greatly with MIRRs in the range of −5% to 93% with an average of 42%. The average income per farmer ranged from $53 to $2885 with an average of $1849 before SWFF intervention, with after SWFF incomes averaging $3453. This skewing of benefits is most evident in the high MIRRs for the most impactful innovations, with the top three accounting for 90% of the beneficiary households in the entire SROR portfolio.

The SROR analysis performed in this paper was created by the United States Agency for International Development (USAID) for the “Maximizing Agricultural Revenue and Key Enterprise in Targeted Sites (MARKETS II), a flagship project between USAID and Nigeria and a successor to a 7-year long MARKETS I project before it. It is important to note that only the numerical format of the MARKETS II project was used in the SWFF SROR, and they are fundamentally different projects in their implementation. The objective of this project was to assist Nigerian farmers who owned between 1 and 5 hectares of land in increasing their crop performance, incomes, and food security through private sector market interventions and development [1].

In order to compensate for the differences between Nigeria MARKETS II and SWFF, the cost-benefit analysis (CBA) template used in the former was modified. Whereas in the former, solely Nigerian farmers were separated by their crop profiles, for SWFF, they were separated into the 10 nations where the innovations operated. In this case the macroeconomic indicators involved in the SROR such as exchange rates, appreciation, interest rates, and prime lending rates were of much more importance rather than physiological and economic differences in crop production in determining income impacts. Additionally, whereas extensive input cost specific data for Nigerian smallholders was collected before and after project implementation, in the SWFF analysis, due to SWFF beneficiary households growing of over a dozen crops in 10 differing national and economic contexts, input cost data was significantly reduced to 3–6 inputs for the purpose of the SWFF SROR. This was mainly because, as data collections methods were retroactive, they were then also dependent on the rough estimations and distant memories of mostly subsistence or small-scale commercial farmers regarding previous and current harvests, and it thus became necessary to standardize the main, common inputs across countries.

In comparison, the SWFF SROR was performed retroactively during the last 2 years of its 6-year duration for projects that spanned 3 years each. Without a firmly established baseline for SWFF project beneficiaries, secondary information such as impact evaluation reports were first analyzed where available regarding each innovation.

2. Materials and Methods

Section 2.1 will define the benefit cost ratio (BCR) and SRORs for our SWFF portfolio. In Section 2.2, we will use a conservative assumption to establish the lower and upper bounds for our SRORs. Section 2.3 discusses values we will use for two key parameters in the analysis, such as the discount rate. Section 2.4 discusses and summarizes the innovations as well as their impacts on income and input usage. Section 2.5 discusses, compares, and contrasts the two indicators we use in our SROR analysis, NPV and MIRR.

The SROR document consists of 11 headings under 4 categories and compares indicators before and after SWFF intervention in the innovation. Macroeconomic variables include factors such as the real exchange rate, currency appreciation/depreciation, domestic and US inflation, as well as interest rates and the discount rates used to value investments through time. Under farmer information, data regarding crop yields, prices, and farm size are examined. Cost information includes fixed costs such as land rent and equipment, input costs and quantities with the most common inputs being fertilizer, pesticides, and fuel, and labor, transport, and storage costs. Lastly, we look at the price per kilogram of produce, any loans farmers took to finance their respective innovation, and project variables such as the adoption number of an innovation, the attrition rate, and the amount of funding. The above indicators are then compiled into an inflationary table and growth indices that take crop yield increases and prices into account. These tables are used to enumerate the financial and economic portions of the analysis with the objective functions being incremental Net Present Value (NPV) and the Economic MIRR, respectively. Both portions project the 3 years of SWFF programming as well as 6 additional years into the future for a 9-year study.

It is worthy to note that this study is in addition to and theoretically modelled after an earlier DIV study by Kremer et al. 2018 in which two highly successful innovations were used to justify the overall portfolio of 41 innovations due to a lack of comprehensive time series data. The justification of using only two innovations rested on the premise that an innovation portfolio exceeding a benchmark, defined as the opportunity cost of conventional development assistance or the general economy gains on capital, is more of a realistic result than estimating a total return on an innovation portfolio.

2.1. Definition of SRORs

SRORs attempt to remedy the underestimation of social value for antipoverty transfers characterized by current economic utilitarian value functions [9]. We use the benefit-cost ratio (BCR) and the SROR to assess USAID investment in the 16 innovations constituting the SWFF portfolio and establish lower bounds for their overall performance. In the formulas below, we denote the number of people reached by innovation I in time period t as $N_{i,t}$, the estimated benefits per person reached (net of operating costs and capital investment) of innovation i in time period t as $B_{i,t}$, and the innovation costs as $C_{i,t}$.

Let $I$ denote the total number of innovations in the USAID portfolio and let $S_{i,t}$ denote the share of innovation costs covered by the donor. $r$ is the discount rate used to make monetary values from different time periods comparable.

2.1.1. Benefit-Cost Ratio

The first measure of social impact of an innovation is the BCR, which is the ratio of discounted value of net benefits of an innovation allocated to the investor to the discounted value of the innovation costs. If the innovation operates from t = 0 to t = T, and i represents each innovation in the portfolio then the return on the investor’s investment in innovation i, as noted in Kremer, et al., 2018 is:

$$BC{R}_i=\frac{{{\displaystyle \sum }}_{t=0}^T\frac{S_{i,t}{N}_{i,t}{B}_{i,t} }{{\left(1+r\right)}^t}}{{{\displaystyle \sum }}_{t=0}^T\frac{C_{i,t}^{INVESTOR}}{{\left(1+r\right)}^t}}$$

(1)

Since we are interested in the social return on each dollar from a particular investor, the BCR for the portfolio is the ratio of the sum of the discounted allocated innovation benefits to the discounted portfolio cost

$$BC{R}_{portfolio}=\frac{{{\displaystyle \sum }}_{t=0}^T{{\displaystyle \sum }}_{i=1}^I\frac{S_{i,t}{N}_{i,t}{B}_{i,t}}{{\left(1+r\right)}^t}}{{{\displaystyle \sum }}_{t=0}^T{{\displaystyle \sum }}_{i=1}^I\frac{C_{i,t}^{INVESTOR}}{{\left(1+r\right)}^t}+{{\displaystyle \sum }}_{t=0}^T\frac{C_t^{INVESTOR}}{{\left(1+r\right)}^t}}$$

(2)

The portfolio pays for itself if the portfolio benefit-cost ratio is greater than 1 [4].

2.1.2. Social Rate of Return

A second measure of social impact is the SROR. The SROR of an investment in an innovation is the discount rate that equalizes the discounted value of the benefits allocated to the investor and the discounted value of investment in the innovation:

$${\displaystyle \sum }_{t=0}^T\frac{S_{i,t}{N}_{i,t}{B}_{i,t}}{{\left(1+SRO{R}_i\right)}^t}={\displaystyle \sum }_{t=0}^T\frac{C_{i,t}^{INVESTOR}}{{\left(1+SRO{R}_i\right)}^t}$$

(3)

Following the same example used for the BCR, the social rate of return is 80%. This is because if we use an 80% discount rate (instead of 10% as in the example above), the discounted value of benefits and costs balance out: $\left(\frac{0.9\times \$2,000,000}{{\left(1+0.8\right)}^1}=\frac{\$1,000,000}{{\left(1+0.8\right)}^0}\right)$ [2].

The portfolio-level SROR is the rate that equalizes the discounted benefits and costs of the entire portfolio:

$${\displaystyle \sum }_{t=0}^T{\displaystyle \sum }_{i=1}^I\frac{S_{i,t}{N}_{i,t}{B}_{i,t}}{{\left(1+SRO{R}_{portfolio}\right)}^t}={\displaystyle \sum }_{t=0}^T{\displaystyle \sum }_{i=1}^I\frac{C_{i,t}^{INVESTOR}}{{\left(1+SRO{R}_{portfolio}\right)}^t}+{\displaystyle \sum }_{t=0}^T\frac{C_t^{INVESTOR}}{{\left(1+SRO{R}_{portfolio}\right)}^t}$$

(4)

This can be compared with a benchmark (e.g., an alternative investment or the market rate of return) to assess a portfolio’s relative performance [4].

$${}_{J,T} {}_{I,T^{\prime }}{}_{portfolio}{\displaystyle \sum }_{}^{T^{\prime }}{\displaystyle \sum }_{}^I\frac{{{}_{i,t}}_{i,t} {}_{i,t}}{{{}_{portfolio}}^t}{\displaystyle \sum }_{}^T{\displaystyle \sum }_{}^I\frac{{}_{i,t}^{INVESTOR}}{{{}_{portfolio}}^t}{\displaystyle \sum }_{}^T\frac{{}_t^{INVESTOR,admin}}{{{}_{portfolio}}^t} {}_{subset}{\displaystyle \sum }_{}^T{\displaystyle \sum }_{}^J\frac{{{}_{i,t}}_{i,t} {}_{i,t}}{{{}_{subset}}^t}{\displaystyle \sum }_{}^T{\displaystyle \sum }_{}^I\frac{{}_{i,t}^{INVESTOR}}{{{}_{subset}}^t}{\displaystyle \sum }_{}^T\frac{{}_t^{INVESTOR,admin}}{{{}_{subset}}^t}$$

2.2. Net Present Value (NPV) Versus Economic Modified Internal Rate of Return (MIRR)

The SROR is essentially a decision-making metric for the social benefit generated by socially impactful enterprises. The SROR’s main objective is calculating the costs and benefits of a social enterprise over its lifespan using a discounted value through time known as the NPV. If this discounted value is high, more future costs and benefits are discounted. For the purpose of our SROR we use the national prime lending rate of the respective SWFF innovator. This prime lending rate is the best national interest rate where beneficiaries can obtain credit. NPV can be defined as:

$$\mathrm{NPV}=-I+\left(t=0\right){\displaystyle \sum }n\frac{Bt}{\left(1+r\right)t}$$

where I is the initial investment, B is the benefits accrued over time period t, and r is the prime lending rate [6]. As per the requirements of capital budgeting, projects can be ranked according to how positive this NPV value is, indicating the present benefits outweigh costs. For instance, citing numbers similar to SWFF, for an investment of $550,000 with returns of $250,000, $100,000, and $400,000 over the course of 3 years with a target discount rate of 12%:

$$\mathrm{NPV}=\frac{\$250,000}{{\left(1+0.12\right)}^1}+\frac{\$100,000}{{\left(1+0.12\right)}^2}+\frac{\$400,000}{{\left(1+0.12\right)}^3}-\$550,000$$

NPV = $587,630.5

Alternatively, the IRR is simply the discount rate when NPV equals 0, and the selection criterion for an IRR is that it is greater than the cost of capital. As the IRR is frequently a favored method in SROR analysis, its weaknesses in regard to the SWFF SROR study is its inability to compare mutually exclusive projects, to compare the size differences of our innovators, and its main assumption that all future cash flows are reinvested with equal returns to that IRR [10]. The latter is not feasible for most real-world projects. Consequently, MIRR arises as a problem to fix the scale and reinvestment at capital returns issue IRR analysis faces. It is calculated thusly in our analysis:

$$\begin{array}{c}MIRR={\displaystyle \sum }\frac{Bt}{{\left(1+r\right)}^t}-{\displaystyle \sum }\frac{Ct}{{\left(1+r\right)}^t}\ast {\displaystyle \sum }[Ar\ast {\displaystyle \sum }Nt\ast \left(1-Att{r}_t\right)-C\ast \frac{Nt}{N}\ast \frac{ER}{1000}\\ \mathrm{where} r=0.12 \mathrm{and} t=1,2,3\end{array}$$

where B is incremental benefits, C is investment costs, Ar is adoption rate percentage, N is total number of beneficiaries at time t, Attr is the attrition rate percentage of beneficiaries, ER is the local currency exchange rate with the US dollar, and r is the discount rate of capital, set at 12% for our analysis. This can alternatively be expressed in MS Excel using the formula: MIRR (values, finance rate, reinvest rate) where the latter two are equal to each other at 12%. For the purpose of performing a holistic analysis, we will be looking at both NPV and MIRR for the 16 SRORs in the SWFF portfolio.

2.3. Innovation and Data Selection

SWFF Innovations are categorized by rounds, in which Round 1, Round 3, and Round 4 started in 2015, 2016, and 2017, respectively. During the formulation of the SWFF SROR portfolio, all Round 1 and 3 graduate innovators, which comprise 11 of the 16 in this study, had already completed their 3 years of SWFF programming while out of the remaining 6 Round 4 innovators, 2 were still actively participating with the closing of their award scheduled towards May 2020, and 4 had graduated by March 2020.

Due to the division of active and graduate innovators, two methods of data collection were utilized in order to perform our SROR analysis. For 9 of the 16 innovations studied, the SROR information was provided via e-mail and face to face communication by the innovator themselves. This process began with an introductory communication via e-mail to the individual innovators informing them of the objectives and purpose in performing SRORs as well as the data needed for the SWFF program. This data was simplified into a tailored template and included input usage and quantities before and after SWFF intervention as well as attrition rates, other production costs such as storage, transport, and equipment, and crop yield and prices for farmers. The template was then enumerated by a team member for the innovator and sent to the M&E Specialist of the SWFF team, who independently verified it and compared it to available country specific smallholder farmer data. Lastly, through a series of phone calls with the innovator, the data was fact checked, validated, and confirmed by the innovator as well as two USAID economists.

For the remaining seven innovations in our SROR analysis, data was obtained on the ground by field evaluators who were tasked with writing field evaluation reports as part of SWFF M&E activities. Each field evaluation consisted of a minimum of 50 interviewees of the beneficiaries of an innovation and were selected by cluster randomized selection based on GPS coordinates. Whereas previously these reports were based on perceptions of smallholders on how an individual innovation impacted them, new questions were added into the standard questionnaire to specifically gather data pertaining to the SROR indicators. The field evaluators would then clean their data and include a section in their field evaluation report discussing differences in input costs and usage, income impacts, differences in crop yield and variety, as well as basic demographic information. The Excel template with said cleaned data would then be reviewed as well as the field evaluation report and accordingly placed in the SROR document wherever relevant. Any gaps in data or potential miscalculations were then clarified through follow up calls with the field evaluators during their report writing process as well as via email. In the case of two innovations (Skyfox and Meat Naturally) the former method of innovator provided data was first performed, and a field evaluator was later sent. The findings from their respective field evaluation reports echoed the findings from the innovator provided data with both innovators being high impact and having high MIRRs.

As summarized below in

Table 1. Innovator Summary Table.

Innovator	Country	Description of Innovation	Impacts on Beneficiaries	Total Number of Households Impacted in SROR
Adaptive Symbiotic Technologies	India	Fungal treatment of seeds	Dramatic increase in crop yield Reduction in input costs	395
aQysta	Nepal	Solar powered water pump	Lower fuel and water costs Substantial crop yield increase	748
Aybar	Ethiopia	Broad Bed and Furrow for increased cultivation	Potential doubling of crop yield in 3 years	64,586
Central University of Technology	Kenya	SMS based drought early warning system	More optimal input usage and timing	8610
Green Heat	Uganda	Slurry separation system for fertilizer	Dramatic reductions in fertilizer, pesticide, and charcoal High crop yield increase	337
Hydroponics Africa	Kenya	Locally sourced hydroponics units	Elimination of fertilizer, pesticide, and manure costs Production of higher value crops	4132
ICU Peru	Peru	Irrigation scheduling system for farmers	Reduction of labor and equipment costs for asparagus, water for corn, and fertilizer for quinoa	1541
Ignitia	Ghana	SMS based multi-variate weather model and alert system	Halving of labor costs More optimal planting and harvesting Substantial crop yield increase	689,935
Lal Teer Seed	Bangladesh	Saline tolerant vegetable seeds	Reduction in gasoline and water usage 20% crop yield increase and doubling of income	41,260
Meat Naturally	South Africa	Communal Livestock Management with Mobile Abattoirs	Lower labor and transport costs Elimination of fencing and watering costs	4006
Practical Action	Bangladesh	Sandbar cultivation for pumpkin farming	Dramatic crop yield increase Provision of loans to farmers	3101
Reel Gardening	South Africa	Seed Tape for household and community gardens	Provision of free vegetables for schoolchildren through a home garden seed tape	259,207
Skyfox	Ghana	Top of the hill aquaculture and fish wastewater irrigation system	Significant reduction of gasoline usage New source of revenue from fish	27,400
WASTE Stichting	India	Wastewater recycling for exotic vegetables	Halving of usage of manure Sales from wastewater byproduct	2243
Water Governance Institute	Uganda	Aquaponics system with horticultural crop production	Less water usage Additional income from horticultural crops and fish	117
World Hope	Mozambique	Greenhouses for mushroom farming	Additional income from sales of mushrooms	929

, the SWFF SROR portfolio of innovations has varying national contexts, innovation types, and adoption numbers. The total number of households impacted by SWFF is slightly over 1 million (representing 80% of the entire SWFF portfolio) with a highly skewed range of 117–689,935 and an average of approximately 50,000 households per innovator. The innovator Reel Gardening benefitted a further 160,709 households, most of whom were schoolchildren and community members through a home garden seed tape. Innovations included aquaponics, hydroponics, greenhouses, livestock management, solar pumps, treated seeds, broad bed furrows, and text-based weather alert and irrigation scheduling systems. Due to the diversity of innovations, their resultant market intervention varied greatly as well, with two common underlying themes being large increases in crop yields and input substitution, replacement, or reduction. All 16 innovations envisaged the former theme as their primary means of impact while for the latter theme, 10 innovations played an important part in reducing, replacing, or substituting fertilizer (5), labor (4), water and fuel (8), and transport (1).

It is also important to note that these innovations have many externalities and effects that are not quantifiable for the sake of our SROR analysis. These include increased food security from better crop production, increase in nutrition from fish and growing different, more nutritious crops varieties, market effects along the supply chain and value-added processing chain, spillover effects in neighboring communities of the innovation, and substantial savings in time for farmers. In addition, 4 of the 16 innovations operate in more than one country, which is not reflected in the SROR as it is based on national macroeconomic indicators.

2.4. Lower Bound Estimation

In this section we demonstrate how it is possible to establish lower bounds on the SROR using data on the realized returns to a subset of the investment portfolio up to any given date, based on two conservative assumptions.

Assumption 1: Innovations did not lead to net social costs beyond USAID’s investment.

As we discussed in Section 2, the benefits of many innovations in USAID’s portfolio are unknown or cannot be easily expressed in monetary terms. We assume that the other 24 innovations not included in our analysis did not result in net social costs (excluding USAID’s investments). Thus, we allow for the possibility that USAID investments created no net benefits, but we assume that they did not lead other investors or consumers to waste their money (as would be implied under rational expectations), or create negative net externalities, which seems reasonable given USAID’s environmental and other safeguards.

This assumption is highly conservative because, as seems clear from the discussion in Section 2, other innovations in the USAID portfolio, despite not graduating from the SWFF program, likely generated substantial benefits and managed to scale afterwards because of the connections and assistance SWFF provided either during their award or after. Moreover, even for the 16 innovations we examine in detail, we only value the innovations’ direct income impacts on immediate beneficiaries (households who personally participated in farming). We do not account for the indirect benefits of the innovations (e.g., environmental and ecosystem benefits in terms of water savings, reduced charcoal usage, optimal fertilizer use; the nutritional effects of increased food security and provision of fish in diets; the reduction of poverty or ease in future economic mobility).

Assumption 2: Net future benefits of portfolio innovations are either positive or zero, but not negative.

The true performance of an innovation depends on both already realized and future net benefits, but since we do not know the future benefits of innovations in our main analysis, we assume the innovations generate either zero or positive net benefits beyond March 2020. This is a conservative assumption because multiple USAID-supported innovations may continue to generate benefits. In a worst-case scenario in which innovations shut down, it is unlikely that they would create social costs in the process.

Using Assumptions 1 and 2, we arrive the proposition that underlies the lower bound approach [11]:

(1)

$SRO{R}_{J,T}\le SRO{R}_{I,T^{\prime }}$ where: $SRO{R}_{portfolio}$ is such that

$${{\displaystyle \sum }}_{t=0}^{T^{\prime }}{{\displaystyle \sum }}_{i=1}^I\frac{A_{i,t}{N}_{i,t}{B}_{i,t}}{{\left(1+SRO{R}_{portfolio}\right)}^t}={{\displaystyle \sum }}_{t=0}^T{{\displaystyle \sum }}_{i=1}^I\frac{C_{i,t}^{INVESTOR}}{{\left(1+SRO{R}_{portfolio}\right)}^t}+{{\displaystyle \sum }}_{t=0}^T\frac{C_t^{INVESTOR,admin}}{{\left(1+SRO{R}_{portfolio}\right)}^t}$$

(2)

$SRO{R}_{subset}$ is such that

$${{\displaystyle \sum }}_{t=0}^T{{\displaystyle \sum }}_{i=1}^J\frac{A_{i,t}{N}_{i,t}{B}_{i,t}}{{\left(1+SRO{R}_{subset}\right)}^t}={{\displaystyle \sum }}_{t=0}^T{{\displaystyle \sum }}_{i=1}^I\frac{C_{i,t}^{INVESTOR}}{{\left(1+SRO{R}_{subset}\right)}^t}+{{\displaystyle \sum }}_{t=0}^T\frac{C_t^{INVESTOR,admin}}{{\left(1+SRO{R}_{subset}\right)}^t}$$

(3)

$T\le T^{\prime }$

(4)

$J\subseteq I$

In words, calculating the social rate of return including net benefits from a subset of innovations and investment cost of all innovations up to the present gives a lower bound on the social rate of return for the portfolio over a longer horizon [11].

2.5. Parameters

Below, we discuss two key parameters in the analysis.

2.5.1. Parameter 1: Discount Rate

We will treat the opportunity cost of the capital used to fund an investment as generating a 120% return. This is the rate in which the financing rate of capital is equal to the reinvestment rate in the SWFF MIRR. A standard threshold rate of return for foreign aid is 10%. Ten percent is also in line with rates typically used for cost-benefit analysis by development banks and developing country governments [11].

2.5.2. Parameter 2: Cost of SWFF DIV’s Portfolio

SWFF’s 2015–2020 portfolio comprised 40 awards, totaling $29.2 million. Of these 40 awards, 16 alumni innovators participated for only 1 year of SWFF programming, and 1 alumnus participated for 2. The SWFF SROR portfolio contains 16 of the remaining 23 graduate innovators who participated for the full 3 years of SWFF programming. A total of $15.5 million went to the 16 innovations we analyze, and $13.65 million went to the other 24. These awards were obligated in USAID’s fiscal years 2015–2020, and funding was then disbursed according to milestone-based contracts over three years.

In summary, our performance estimates thus represent a worst-case scenario, in which the other 24 SWFF-funded innovations incurred investment costs but generated no benefits, and all innovations are assumed to generate no net benefits after March 2020.

3. Results

All value chains have positive financial net present values (NPVs) at the farm level (see

Table 2. FINANCIAL NPV AND ECONOMIC MIRR.

Innovator	Country	MIRR	Incremental NPV	Whole Economy NPV
Adaptive Symbiotic Technologies	India	35%	$407	$104,489
aQysta	Nepal	34%	$139	$79,417
Aybar	Ethiopia	73%	$145	$9,745,841
Central University of Technology	Kenya	45%	$0.27	$9040
Green Heat	Uganda	47%	$10.60	$3773
Hydroponics Africa	Kenya	38%	$20.16	$106,151
ICU Peru	Peru	59%, 42%, 43%	$5498	$8,041,534
Ignitia	Ghana	95%	$1584	$1,275,093,222
Lal Teer Seed	Bangladesh	79%	$118	$5,374,545
Meat Naturally	South Africa	45%	$1158	$3,711,056
Practical Action	Bangladesh	30%	$23.96	$61,531
Reel Gardening	South Africa	27%	$9.60	$738,676
Skyfox	Ghana	54%	$879	$48,030,042
WASTE Stichting	India	25%	$600	$521,645
Water Governance Institute	Uganda	−5%	$0.46	−$254
World Hope	Mozambique	3%	$0.04	−$32

below) for farmers during each year of project implementation, each evaluated over a 9-year time span. This suggests that beneficiary households in each value chain are experiencing increased incomes as a result of their respective innovations. In terms of economic impacts, we estimate that the SWFF SROR portfolio will be adding $1.1 billion to their respective 10 nations over 9 years. The vast majority of this value added comes from the 5 most impactful innovations, namely, Ignitia, Skyfox, ICU Peru, Lal Teer Seed, and Aybar. For ICU Peru, the three MIRR values represent three farmer groups each growing a different crop (asparagus, corn, and quinoa) with a weighted average used to calculate their overall MIRR. Incremental NPV refers to the increase or decrease in NPV per household while the whole economy NPV refers to the overall national economic impact of the innovation. Ignitia, in particular, impacting over 600,000 households in Ghana with the highest MIRR of 93%, accounts for 97% of the whole economy NPV of the entire SWFF SROR portfolio and is estimated to add over $1 billion to the Ghanaian economy over 9 years and this excludes its operations and their effects in 4 other West African nations and subsequent, expected scaling in Ghana after SWFF programming ends.

The MIRR estimates ranged from −5%–95% with an average of 42%, which far exceeds the standard estimates of the rate of return on foreign aid (~10%), historical stock market returns (7%), the typical financial returns on impact investment funds (~6%), and the 15% target set for the DIV portfolio of 41 innovations [9].

As seen in

Table 3. Differences in Input Costs, Incomes, and Crop Yields.

Innovator	$ Difference in Input Costs	% Change in Inputs Costs	$ Difference in Annual Income	% Change in Annual Income	% Annual Crop Yield Increase
AST	$550	−31%	$3000	460%	24%
aQysta	$140	−17%	$2080	175%	24%
Aybar	($90)	82%	$498	135%	38%
CSA	$1650	−61%	$3030	42%	N/A
CUT	$0	0%	$3	0%	12%
Green Heat	$270	−76%	$3480	205%	90%
Hydroponics Africa	$0	0%	$220	414%	39%
ICU Peru	$1667	−11%	$5627	95%	6%
Ignitia	$50	−22%	$2000	73%	22%
Lal Teer Seed	($870)	95%	$2360	200%	20%
Practical Action	($200)	125%	$280	165%	30%
Reel Gardening	($1.15)	NA	$7.83	NA	N/A
Skyfox	($200)	24%	$1120	560%	33%
WASTE	$0	0%	$3890	171%	35%
WGI	$0	0%	$140	29%	18%
World Hope	$0	0%	$7	0%	52%
Average	$185	7%	$1850	182%	32%

below, the difference in input costs ranged from a savings of $667 to an increase of $870, or alternatively a decrease of 31% to an increase of 125% averaging at a modest increase of 7% for individual farmers. All farmers had an increase in income ranging from $7 to small greenhouses and home gardens (WGI and Reel Gardening) to $3890 in the case of treated wastewater fertilizer that led to higher value exotic vegetable cultivation. The increase in income averaged $1850 that works out to be an overall income increase of 182% per household. Crop yield increases averaged 32% annually per farmer for 3 years and were also skewed from 6% (ICU Peru) due to already having high producing commercial farmers to 90% (Green Heat), in which case the main impact was an observed 270% increase in crop yield over 3 years due to its nutrient rich slurry byproduct and again changes to higher value crop production.

Sensitivity Analysis

To perform a sensitivity analysis, we take into consideration household gross income, the physical input costs, and labor costs of farm production to estimate net income (as shown in

Table 4. Innovator Gross and Net Income.

Innovator	$ Gross Income		Physical Input Costs		Labor Costs		Net Income
AST	$	4862	$	260	$	950	$	3652
aQysta	$	3949	$	190	$	490	$	3269
Aybar	$	1107	$	200	$	30	$	877
CSA	$	27	$	20	$	-	$	7
CUT	$	5268	$	90	$	-	$	5178
Green Heat	$	503	$	220	$	10	$	273
Hydroponics Africa	$	17173	$	2900	$	5760	$	8513
ICU Peru	$	4326	$	1110	$	60	$	3156
Ignitia	$	4350	$	620	$	190	$	3540
Lal Teer Seed	$	5364	$	200	$	-	$	5164
Practical Action	$	570	$	80	$	40	$	450
Reel Gardening	$	7	$	-	$	-	$	7
Skyfox	$	1660	$	200	$	140	$	1320
WASTE	$	9705	$	3340	$	200	$	6165
WGI	$	249	$	-	$	-	$	249
World Hope	$	3	$	-	$	-	$	3

) and calculate a baseline household NPV. Any expenses outside of input and labor costs are negligible in farm production for SWFF innovators. The disproportionate impact on our baseline NPV due to changes in our parameters of the most impactful innovator Ignitia has been taken into account by utilizing a weighted baseline NPV based on the number of households impacted by each innovator.

For innovator gross income, we use currency appreciation or depreciation as a parameter, with input usage and labor sensitivity being parameters for the physical input costs and labor costs, respectively. As shown in

Table 5. Sensitivity Analysis on NPV per Household.

Currency Value			Input Usage			Labor Sensitivity
	$	929		$	929		$	929
0.8	$	675	0.8	$	992	0.8	$	934
0.9	$	802	0.9	$	961	0.9	$	931
1	$	929	1	$	929	1	$	929
1.1	$	1056	1.1	$	897	1.1	$	927
1.2	$	1183	1.2	$	865	1.2	$	924
1.3	$	1310	1.3	$	834	1.3	$	922
1.4	$	1437	1.4	$	802	1.4	$	919

below, by using a range of −20% to 40% for our parameters we see that the most sensitive parameter is the currency value with which a 20% depreciation results in a 27% decrease in the NPV and a 40% appreciation leading to a 55% increase in NPV. This indicates the vulnerability of farmers to inflation or a fall of their respective currency against the USD. There is also a marked difference in the input usage of farmers with less inputs leading to gains in NPV and anything above the baseline leading to NPV losses. Specifically, 80% of current input usage leads to a rise of 7% in NPV whereas an input usage of 140% leads to an NPV decrease of 14%. The price of labor has minimal effect on NPV as it is a small component of the overall costs of production and is a minimal to zero costs for a number of the innovators.

By performing a Monte Carlo simulation with 5000 iterations using our above parameters, we find a mean NPV of $988 which is approximately 6% higher than our baseline NPV of $929, and a large range between −40% and 48% with a standard deviation of $126 as shown below in

Table 6. Monte Carlo Simulation Statistics.

Min	543
Max	1377
Mean	988
Std Dev	126

. This suggests high variability of our baseline NPV according to changes in our parameters under a normal distribution. This high variability to the uncertainty of the specified parameters is to be expected given the lack of a baseline survey for SWFF beneficiaries and having to thus retroactively collect data.

4. Discussion

The SWFF SROR portfolio consists of 16 innovators as compared to roughly a handful of high impact interventions in similar sized portfolios and thus is more comprehensively representative of the rate of return on funding for similar technologies and market interventions. This average MIRR of 42% of the SWFF portfolio, which greatly exceeds likewise investment portfolios, is also a worthwhile investment in itself and thus justifies a financial and investment-based approach to international agricultural development. Including metrics such as SROR, which attempt to clarify the ambiguity of social impact measurement, is crucial considering that social impact investing (SII), valued at $500 billion and growing, is creating uneven investment geographies and altering the behavior of existing international development agents, which creates a need for more socially inclusive financial metrics [12,13]. In a respect, SWFF donors eschewed the behavior of foreign aid agents or even traditional investors and served a role as impact investors in that their exiting of the SWFF graduate investments after 3 years indicated to mainstream funding partners readiness for funding, which was received and helped them expand [14].

The moral framework implicit in SII require new, nontraditional evaluative metrics for impact investors [15]. The existing indicators of development referred to in SII, namely the Impact Investment and Reporting Standards system, and the UN Sustainable Development Goals lack financial rigor while even lean data approaches such as the Progress out of Poverty Index developed by the Grameen Foundation is limited to sensitivity changes in poverty status that SRORs include [16,17]. True Cost Accounting, another financial metric that attempts to capture non-financial attributes, also cannot accommodate catalytic change and require more standardized profit loss statements such as SRORs [18]. Metrics inclusive of any spillover effects in turn improve the qualitative measures of impact evaluations and between outcomes and development [19]. Moreover, there is a great demand by impact investors for more accurate tools to measure environmental returns on agricultural investments which SRORs can provide through various environmental discounting methods [20]. It is worthy to note that the unevenness of social benefits is very apparent both in funding and depth of impact. The top three innovators in the SWFF SROR Portfolio (Ignitia, Lal Teer Seed, and Aybar) account for 92% of beneficiary households of the entire SWFF portfolio and have a significantly higher average MIRR at 81% as opposed to the SWFF average of 42%. In terms of funding, the 3 Tier 2 innovators that were funded at five times the level of the other 14 Tier 1 innovators also accounted for 90% of impact beneficiaries and had a likewise higher than average MIRR at 66%. Both these indicators point towards using a portfolio approach through which most funds are concentrated in higher amounts of funding to fewer, more impactful and scalable innovators to maximize social impact, and then a lesser number of funds are dispersed in smaller amounts among a larger set of smaller innovators that will have less near-term development impact.

These results are in line with findings that foreign aid reduces poverty when it is directed towards pro-poor public expenditures such as agriculture and, in particular, agricultural production [21]. Studies have also proven that foreign aid investments in agriculture improve agricultural productivity, reduce malnutrition, and promote food security [22]. Agricultural aid, in particular, is more effective in Africa compared to Asia and South America [23]. This paper essentially argues that foreign aid assistance for smallholder farmers is better utilized in funding business entities that have managed to adequately scale operations and offer impactful technologies and/or market interventions. Foreign aid investments have also been proven to be more effective in bettering the institutional quality of host countries and attract more foreign direct investment (FDI) in African countries with better institutional quality and financial environments [24,25]. This is further evidenced in foreign aid having a positive effect on economic growth only in lower middle-income countries and it being limited in lower income countries [26], Moreover, aid is only effective at promoting economic growth in more favorable policy environments with macroeconomic stability, and even then is ineffective in reducing income inequality [27,28,29].

Developed country FDI in the agricultural land of developing countries positively influences food security whereas increased agricultural investments in Africa can counteract the effects of climate change and increase crop yields by 50% from 2010–2030 [30,31]. As FDI is a greater determinant for the GDP growth of developing nations than foreign aid, programs that support private initiatives such as SWFF also can help promote FDI and better address the needs of specific communities [32,33]. However, it is also worth noting FDI has a positive effect on GDP in only middle-income countries and a very limited effect on low-income countries with studies of 29 African countries showing both FDI and foreign aid having an insignificant effect on poverty reduction. [26,34]. In Africa, agricultural innovation is also better utilized in governance structures capable of creating institutional arrangements [35]. This is noteworthy because integrating climate smart agricultural technologies such as those in SWFF with institutionally enabling factors can lead to more effecting scaling [36].

The necessity for scalability of the business operations, which not all SWFF innovators fulfilled, was a key determinant of social impact. Scaling is further aided by flexible, stepwise, and reflective strategies that seek to understand market and non-market constraints as was part of SWFF’s incubation process and noted to work in a fellow USAID agricultural innovation scaling project in Ethiopia named Africa RISING [37]. This was taken into consideration in the development of the successor program to SWFF, Water Energy for Food (WE4F), starting May 2020, which utilizes milestone-based funding to fund technologies representing the nexus of power and agricultural needs of developing nations face and includes only innovators who have already successfully scaled. In addition, regional centers of operation will be created closer to the countries of impact to further increase the efficiency of service delivery. This ineffectiveness is further exacerbated by aid allocations still traditionally being used as export promotion tools and falling short of the Paris Declaration in 2015 which promised disbursing aid to poorer nations with good governance, [38]. This use of aid and trade as an export promotion tool is exemplified by Chinese FDI and foreign aid which contrarily to US and OECD foreign aid and FDI, has little to no evaluative framework, is concentrated in African countries with weaker governance for resource extraction purposes, and in recipient countries with higher political risk [39,40]. Chinese aid projects also fuel local corruption and anti-union labor policies [41]. This is despite Chinese FDI and trade being better utilized by improved domestic institutions, such as US and OECD FDI [42]. All these characteristics and deficiencies of foreign aid and FDI support the need for pro-poor, private sector agricultural impact investments in poorer, rapidly growing nations with adequate governance and socially inclusive evaluative metrics.

5. Conclusions

Despite the setbacks in many developing nations of a lack of resources or endemic corruption, the capital gains in the private agricultural sector of the 10 developing nations of the SWFF SROR portfolio is indicative of fast growth and was better utilized by stable, middle income private sector partners. This disproportionate uptake of investments in specific African countries is supported by the most impactful SWFF innovators coming from Ghana (Ignitia and Skyfox) and South Africa (Reel Gardening and Meat Naturally), both being high growth economies that are comparatively financially sound with better governance in Africa. With the exception of South Africa, the other nine nations under their 3 years of SWFF operation exhibited robust rates of annual GDP growth ranging from 4% (Peru) to over 6% (Uganda) with growth in the agricultural sector often being double those figures. The exceptional rate of return for SWFF and similar DIV investments is primarily due to these high rates of economic development and its consequences on returns to capital. With the right targeting of economically vulnerable households, great opportunity to progress exists with minimal investments by smallholder farmers due to the variety of technologies and resources available to them for substantial livelihood improvement.

Economically, foreign aid should be invested where it receives the highest rates of return on investment and development impact. Due to the varying inadequacies of the public sector in dispersing these economic benefits of agricultural growth in the developing world, it then is up to local capitalist entities to provide said technologies and interventions in a profitable manner. Our results provide clear evidence in the profitability in the development of these socially impactful enterprises that necessitates an investment based and business minded approach in which the SROR is crucial in determining financial viability over time. As demonstrated by the performance SWFF SROR portfolio, investments in technologically based and scalable private social enterprises achieve significant rates of return. Social enterprises that have already reached a certain scale of operations or with provable scalable business models should be given priority with larger investments being more optimal than smaller grants. This is due to the significant barrier in raising capital and accessing reasonable rates of credit in most developing nations. In the case of companies that have not yet successfully scaled but have strong financial fundamentals, it is also worthwhile, from a foreign donor’s perspective, to provide technical facilitation and advisory services to assist in expanding its social impact.

In terms of the overall national economic effect of said enterprises, the results of the SWFF SROR portfolio provide further reasoning for concentrating funding to fewer, high impact innovations. In order to most impact smallholder farmers, focus should be placed on agribusinesses that supply technologies that increase crop yields, shift to more valuable crop production patterns, and result in moderate increases in input cost with more efficient usage while solving a regional farming need such as salinity, aridity, or irrigation water scarcity. Further studies are necessary on innovation based agricultural development projects and the substantial externalities they incur but are not included in current SROR analyses.