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Evaluating the Effect of Rural Finance on African Economies

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Evaluating the Effect of Rural Finance on African Economies

  1. 1. 15 July 2013 Slide 0 Christian Kuhlgatz Evaluating the Effect of Rural Finance on African Economies Farm- and Market-based Methods Evaluating the Effect of Rural Finance on African Economies Dr. Christian H. Kuhlgatz Thünen Institute of Market Analysis Accra, Ghana 15. July 2013
  2. 2. 15 July 2013 Slide 1 Christian Kuhlgatz Evaluating the Effect of Rural Finance on African Economies 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?
  3. 3. 15 July 2013 Slide 2 Christian Kuhlgatz Evaluating the Effect of Rural Finance on African Economies 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
  4. 4. 15 July 2013 Slide 3 Christian Kuhlgatz Evaluating the Effect of Rural Finance on African Economies Price development: Staple food (wheat) 0 2 4 6 8 10 12 14 16 18 0 50 100 150 200 250 300 350 400 450 500 Wheat, US, n° 2 Hard Red Winter (ordinary), FOB Gulf hist. Vola width = 12) hist. Vola in %Nominal Price US$ 1970s food crisis Food price crisis
  5. 5. 15 July 2013 Slide 4 Christian Kuhlgatz Evaluating the Effect of Rural Finance on African Economies Price development: Export markets (cocoa) 0 2 4 6 8 10 12 0 20 40 60 80 100 120 140 160 180 Jan1980 Dec1980 Nov1981 Oct1982 Sep1983 Aug1984 Jul1985 Jun1986 May1987 Apr1988 Mar1989 Feb1990 Jan1991 Dec1991 Nov1992 Oct1993 Sep1994 Aug1995 Jul1996 Jun1997 May1998 Apr1999 Mar2000 Feb2001 Jan2002 Dec2002 Nov2003 Oct2004 Sep2005 Aug2006 Jul2007 Jun2008 May2009 Apr2010 Mar2011 Feb2012 Jan2013 Cocoa beans, avg daily prices New York/London (¢/lb.) hist. Vola (width = 12) Nominal Price ¢/lb. hist. Vola in %
  6. 6. 15 July 2013 Slide 5 Christian Kuhlgatz Evaluating the Effect of Rural Finance on African Economies AGMEMOD: Using partial equilibrium models for policy consultancy in Africa • At TI: AGMEMOD model used for simulating the 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
  7. 7. 15 July 2013 Slide 6 Christian Kuhlgatz Evaluating the Effect of Rural Finance on African Economies 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
  8. 8. 15 July 2013 Slide 7 Christian Kuhlgatz Evaluating the Effect of Rural Finance on African Economies 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
  9. 9. 15 July 2013 Slide 8 Christian Kuhlgatz Evaluating the Effect of Rural Finance on African Economies 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
  10. 10. 15 July 2013 Slide 9 Christian Kuhlgatz Evaluating the Effect of Rural Finance on African Economies 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
  11. 11. 15 July 2013 Slide 10 Christian Kuhlgatz Evaluating the Effect of Rural Finance on African Economies 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.
  12. 12. 15 July 2013 Slide 11 Christian Kuhlgatz Evaluating the Effect of Rural Finance on African Economies 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 controls Income/capita 97.58 ( 2.20)** 1.15-1.20 438 2,351 Poverty status -0.053 (-2.18)** 1.25-1.30 435 2,351 Poverty gap -6.16 (-2.67)** 1.15-1.20 438 2,351 Monetary values are reported in 10,000 cedi. Numbers in parentheses are t-values. ** indicate 5% significance levels.
  13. 13. 15 July 2013 Slide 12 Christian Kuhlgatz Evaluating the Effect of Rural Finance on African Economies 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
  14. 14. 15 July 2013 Slide 13 Christian Kuhlgatz Evaluating the Effect of Rural Finance on African Economies 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
  15. 15. 15 July 2013 Slide 14 Christian Kuhlgatz Evaluating the Effect of Rural Finance on African Economies Countries in the agri benchmark Network Participating countries 2013 Contacts for further growth New countries 2013 Ireland (beef/sheep) Uruguay (beef/sheep) China (sheep) Myanmar, Laos, Zambia, Mozambique (cash crop) 2013 Countries Farms Cash crop 27 75 Cow-calf 23 55 Beef finishing 29 70 Sheep 14 25
  16. 16. 15 July 2013 Slide 15 Christian Kuhlgatz Evaluating the Effect of Rural Finance on African Economies 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
  17. 17. 15 July 2013 Slide 16 Christian Kuhlgatz Evaluating the Effect of Rural Finance on African Economies Thank you for your interest christian.kuhlgatz@ti.bund.de Thünen Institute of Market Analysis www.ti.bund.de

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