evaluating the effect of rural finance on african economies

15 July 2013Slide 1 Christian Kuhlgatz

Evaluating the Effect of Rural Finance on African Economies

Farm- and Market-based MethodsEvaluating the Effect of Rural Finance on African Economies

Dr. Christian H. KuhlgatzThünen Institute of Market Analysis

Accra, Ghana15. July 2013



Access to finance for enhanced agric. productivity

• Agricultural supply: variable, affected by climate change

• Price volatility on world markets

• Incomplete financial markets impede consumption smoothing ability of households• Precautionary savings to prevent food insecurity• Focus on short-term income generation, lower expected return Reduced human capital accumulation Adoption of new technologies hindered

Which tools of TI could be useful in the African context?



Outline

- Investigate African markets with simulation models- Impact assessment methods to measure the causal

effect of rural finance- Inter-regional comparisons of farms with the agri

benchmark network- Conclusions



Price development: Staple food (wheat)

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Wheat, US, n° 2 Hard Red Winter (ordinary), FOB Gulf hist. Vola width = 12)hist. Vola in %

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1970s food crisis Food price crisis



Price development: Export markets (cocoa)

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Cocoa beans, avg daily prices New York/London (¢/lb.) hist. Vola (width = 12)Nominal Price ¢/lb. hist. Vola in %



AGMEMOD: Using partial equilibrium models for policy consultancy in Africa

• At TI: AGMEMOD model used for simulatingthe effects of EU agricultural policies

• Extending AGMEMOD to Africa• June 2013: Visit of researchers from Kenya and

Ethiopia at TI

• In the current process, country models for Ethiopia, Kenya, and Uganda with intended extensions to other African countries

• Reduced set of 5 markets for the start• Ethiopia with wheat, corn, sorghum, teff, and haricot beans• Kenya with wheat, corn, sorghum, haricot beans, sweet potatoes or milk• Uganda with corn, sorghum, cassava, haricot beans, and sweet potatoes



AGMEMOD goes Africa

Markets represented by area, yields, productions, trade, different demand and prices

Drivers (exogenous variables) • Policies – trade measures, board prices, investment support, input support• Macro economic variables – GDP, inflation, exchange rates, population• Others – rainfall, oil price, fertilizer price

Build a solid base for policy consultancy in African countries so that African economies and farmers can respond adequately

on external shocks and build a resilient, productive agriculture Capture regional interactions and investigate multiplier effects



Identifying the causal effect of finance on agricultural productivity

- Ex-post analysis: What would have happened if the household had no access to finance? - Measurement problems: selection bias, spill-over effects- Experiments (RCTs) or quasi-experimental approaches

- Typical impact assessment tools- Propensity score matching, Regression Discontinuity, DiD,

Instrumental Variables, Heckman selection model…- Pitt & Khandker vs. Roodman & Morduch debate:

- Still no consensus on the impact of microfinance reached



Sources of selection bias in capital markets (examples)

- Monitoring costs - Areas with high population density are preferred

- Adverse selection- Higher interest rates attracts riskier borrowers- Higher collateral requirements attracts riskier borrowers

- Moral hazard - Insurances encourage farmers to behave riskier



Example of an impact assessment: Ghana

• Causal effect of export crop cultivation on hh-income• Self selection problem. E.g.: some farms cannot afford

participation in profitable but volatile export markets

• 1st part: Identification of the determinants of export cropping• Heckman selection model

• 2nd part: Impact assessment• Propensity score matching

• GLSS 5 data of farm households, 2005-6



Determinants of export crop cultivation in Ghana (excerpt)

Participation in export cropping

Intensity of export cropping

coefficient (t-value) coefficient (t-value)Female hh-head -0.139 (-1.43) -4.585* (-1.82)Age of hh-head 0.013*** (5.25) 0.197*** (2.8)Number of children -0.0007 (-0.04) -2.12*** (-4.63)Institutional loans 0.0001 (1.04) 0.0011 (0.5)Private loans 0.0001 (1.35) 0.0022** (2.46)Savings -0.000001 (-0.05) 0.0011** (2.05)Land with deed (%) 0.0021* (1.81) -0.029 (-0.99)

Owned land 0.00006*** (4.63) 0.0002*** (2.88)

Motor vehicle 0.221 (1.48) 7.673* (1.94)Eco-zone: forest 0.228 (1.1) 13.41*** (3.37)…

λ (Inverse Mills ratio) -9.673*** (-3.12)F-test [p-value] 11.10 [0.00]

*, ** and *** indicate significance at 10%, 5% and 1% levels, respectively.



Impact of export crop cultivation

• Results of propensity score matching• Compares income and poverty of households that are

similar in their observable characteristics

Outcome ATT Critical level of hidden bias (Γ) No. of treated No. of controlsIncome/capita 97.58 ( 2.20)** 1.15-1.20 438 2,351Poverty status -0.053 (-2.18)** 1.25-1.30 435 2,351Poverty gap -6.16 (-2.67)** 1.15-1.20 438 2,351Monetary values are reported in 10,000 cedi. Numbers in parentheses are t-values. ** indicate 5% significance levels.



Identifying the reasons of a causal relation

- Impact assessments can quantify the causal effect, BUT:- “Impact” is most often context specific and changes over time- Even if impact is identified without bias: can it be repeated in other

places or circumstances? - For a better understanding of what mechanisms are at work,

there is need for in-depth analyses of farms- Aim: identify impact pathways that explain the effect of access to

finance- TI farm economics: agri benchmark network has the ability

to perform rigorous investigations by comparing results of typical farms from different regions



Unique features of agri benchmark

• Production systems approach>>> more than financial data and reasons behind differences

• Cooperation with producers and advisors>>> get the story behind the data

• Global coverage>>> big players and emerging economies

• Using standardised methods world-wide>>> global comparability

• Works in countries without / with limited statistics and accounting>>> global comparability

• Expert knowledge>>> access local expertise and overcome language issues

Main supporting partner



Countries in the agri benchmark Network

Participating countries 2013

Contacts for further growth

New countries 2013Ireland (beef/sheep)Uruguay (beef/sheep)China (sheep)Myanmar, Laos, Zambia,Mozambique (cash crop)

2013 CountriesFarms

Cash crop 2775

Cow-calf 2355

Beef finishing 2970

Sheep 1425



Financial market analysis in Africa

- TI can assist African research on capital markets with our policy analysis toolkit- Knowledge transfer in trade analysis methods & impact

assessments- Providing access to the agri benchmark network

- Ex-post analyses within single countries- Evaluating the impact of improved financial access on productivity

- Model-based simulations- Identify probable multiplier effects on other regions- Analyze the effect of external shocks on whole economies



Thank you for your interest

[email protected]ünen Institute of Market Analysis

www.ti.bund.de

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