#### Transcript TOA-MD Model - Tradeoff Analysis Project

**What is the TOA-MD Model? **

*Basic Concepts and an Example*

*Basic Concepts and an Example*

**John Antle Roberto Valdivia**

*Agricultural and Resource Economics Oregon State University www.tradeoffs.oregonstate.edu*

*Agricultural and Resource Economics Oregon State University www.tradeoffs.oregonstate.edu*

## What is the TOA-MD Model?

• The **TOA-MD Model **is a unique simulation tool for **multi-** **dimensional impact assessment **that uses a statistical description of a heterogeneous farm population to simulate the adoption and impacts of a new technology or a change in environmental conditions. • TOA-MD is designed to simulate what would be observed if it were possible to conduct a controlled experiment. In this experiment, a population of farms is offered the choice of continuing to use the current or “base” production system (System 1), or choosing to adopt a new system (System 2).

• In fact it is never possible to carry out such ideal experiments, so TOA-MD is designed to utilize the available data to attain the best possible approximation, given the available time and other resources available to conduct the analysis. • Additionally, TOA-MD is designed to facilitate analysis of the inevitable uncertainties associated with impact assessment.

• There are two components in the TOA-MD analysis: • First, the model simulates the proportion of farms that would adopt a new system (system 2), and the proportion that would continue to use the “base” system (system 1) • Second, based on the adoption rate of the system 2, the TOA-MD model simulates selected economic, environmental and social *impact indicators * for adopters, non-adopters and the entire population.

• How is the TOA-MD simulation approach related to the experimental and non-experimental statistical approach to impact assessment (estimation of “treatment effects”)? • The underlying conceptual model is very similar: economic agents (farms in this case) self-select into “treatment” or choose to adopt a technology (or adapt to an exogenous change such as climate change), and related “outcomes” occur to those self-selected into “treatment” (adoption) • For “ex post” assessment the simulation approach can be used together with experimental methods to parameterize the model and simulate outcomes of interest, including both “mean” indicators and other “threshold” indicators such as poverty rates and indicators based on other quantifiable outcomes. It can also be used to simulate “policy relevant” effects, such as technology adoption induced by payments for ecosystem services. • For “ex ante” assessment, where statistical methods cannot be used, the simulation approach provides a “laboratory” to explore potential impacts, using various types of available data to construct the future “counterfactual,” e.g., the impacts of and adaptation to climate change.

## TOA-MD approach: modeling systems used by heterogeneous populations

A system is defined in terms of household, crop, livestock and aquaculture sub-systems Systems are being used in heterogeneous populations

(ω)

**Opportunity cost, system choice and adoption**

Opportunity cost = v 1 – v 2 follows distribution ( ) v 1 = returns to system 1 V 2 = returns to system 2 System 2: (adopters) < 0 System 1: > 0 (non-adopters) 0 opportunity cost Map of a heterogeneous region

## Adoption, Outcome Distributions and Impact Indicators

• Outcome distributions are associated with system choice – Farms select themselves into “non-adopter” and “adopter” sub populations, generating corresponding outcome distributions for these sub-populations • Impact indicators are based on system choice and outcome distributions – TOA-MD produces mean indicators and threshold-based indicators • Analysis shows that impacts depend on the correlations between adoption (opportunity cost) and outcomes – Many impact assessments ignore correlations – Yet these correlations are often important for accurate impact assessment!

(*z*|1,a)

### Adoption and outcome distributions

(*z*|1) System 1 before adoption: 25% > threshold r(1,*a*)% non adopters (*z*|2,*a*) Outcome *z* r(2,*a*)% adopters System 1: 20% > (*z*|a*)* System 2: 90% > Entire Population with adoption: 55% >

Components of TOA-MD Analysis Design Population (Strata) System characterization Impact indicator design Data Opportunity cost distribution Outcome distributions Simulation Adoption rate Indicators and Tradeoffs

### An Example: Integrated Agriculture-Aquaculture

• • Based on Dey et al (2010) *Agricultural Economics: *economic analysis of IAA • stratified survey of farms, without and with IAA Design of TOA-MD analysis • population: farm households in southern Malawi where aquaculture is • • feasible strata: 5 southern districts systems: • • Subsistence crops Crops + aquaculture, low or high integration Subsistence crops Aquaculture Irrigated vegetables

Adoption Rate and Opportunity Cost of Adopting IAA in Southern Malawi – Predicted Adoption Rate is Point Where Curves Cross the Horizontal Axis 800 600 -600 -800 -1000 400 200 0 0 -200 -400 10 20 30 40 50 60 70 Zomba Mwanza

**Adoption Rate (%)**

Mulanje Thyolo Mangochi 80 90 100

Poverty Rate and Adoption Rate of IAA, Southern Malawi 75 70 65 60 0 100 85 80 95 90 10 20 Zomba 30 Mwanza 40 50

**Adoption Rate (%)**

60 Mulanje Thyolo 70 Mangochi 80 90 100

Mean Monthly Protein Consumption and Adoption of IAA, Southern Malawi 2.5

2 1.5

1 0.5

Note most improvement occurs for those districts with lowest protein consumption 0 0 10 20 Zomba 30 Mwanza 40 50

**Adoption Rate (%)**

60 Mulanje Thyolo 70 Mangochi 80 90 100

### Relationship between adoption and Protein Consumption, Non-adopters and Adopters of IAA, Mulanje Dist., Malawi

Slope of relationship between indicator and adoption rate has same sign as the correlation between opp cost and the outcome variable (negative in this case) Adopter sub-population and entire population are equal at 100% adoption Non-adopter sub-population and entire population are equal at 0% adoption

Relationship between adoption and Mean Returns per Farm, Non Adopters and Adopters of IAA, Mulanje District, Malawi Economic outcomes that are positively related to net returns have a maximum in the entire population at the predicted adoption rate (41% in this example) 800 600 400 200 0 0 -200 -400 -600 -800 -1000 10 20 30 40 50 60 70 80 90 100 Zomba Mwanza

**Adoption Rate (%)**

Mulanje Thyolo Mangochi

**The various “treatment effects” discussed in the experimental and non-experimental statistical literatures are either equivalent to or closely related to the mean indicators generated by the TOA-MD model: **

• • • • AveTreatment Effect = mean population indicator Marginal Treatment Effect = change in mean indicator Treatment on Treated = related to mean indicator of adopters Treatment on Untreated = related to mean indicator of non • adopters Policy relevant treatment effect = derived from mean indicators

### Summary: Impacts of IAA Adoption on Farm Population and IAA Adopters

1

**Strata **

ZOMBA MWANZA MULANJE THYOLO MANGOCHI

**REGION Adoption rate (%) **

49.22 49.40 40.81 41.92 37.95

**44.49 Ave. farm income ($/year) base (no adoption) **

112.47 89.01 81.01 170.85 188.62 123.90

**% Change on population **

54.60% 50.77% 54.46% 41.85% 30.77% 45.23%

**% Change on adopters **

135.62% 137.61% 179.51% 116.92% 116.63% 132.70%

**base (no adoption) **

87.50 99.16 84.30 95.93 72.24 87.11

**Poverty rate (%) % Change on population **

-15.81% -16.01% -11.38% -16.48% -10.53% -11.25%

**% Change on adopters **

-42.48% -51.88% -44.26% -56.11% -53.66% -30.45%

**Mean Monthly Protein Consumption (kg/person) base (no adoption) **

1.41 1.94 0.65 1.75 0.77 1.29

**% Change on population **

12.86% 0.30% 53.10% -0.49% 56.42% 15.32%

**% Change on adopters **

38.95% 10.64% 191.35% 28.63% 178.33% 59.00%

# Conclusions

• TOA-MD is a unique simulation tool for multi-dimensional impact assessment of agricultural systems • The Malawi case study illustrates how it can be used with available data to simulate: • the adoption rate of a new technology • the economic, environmental or social impacts of the new technology • The model can also be used for analysis of ecosystem services, and impacts of climate change and other environmental change • Training in use of the model, and the model software are available from the TOA Team.

• More info is available at : http://tradeoffs.oregonstate.edu