Assessing the inverse farm size-productivity relationship in Malawi

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Presentation transcript:

Assessing the inverse farm size-productivity relationship in Malawi

Main questions & policy relevance Does addition of larger farms eliminate IR? Most studies use household-based samples Excludes commercial farms & may limit larger ones May matter as some studies find U-shaped relationship What can explain IR – of many options suggested Omitted variables Measurement error Edge effects Factor market imperfections Policy relevance: Debate on farm size heating up Small is beautiful -> Redistribution Large is beautiful -> Large acquisition

Data: Interesting combination 2016/17 Nat. hh. survey (IHS4) 779 EAs in all 28 districts Extensive qnr with land/credit questions 2017 Estate survey Used IHS4 qnr; in 8 districts w. highest no. of estates Frame is digitized estate registry Many estates subdivided/operate as smallholder farms Significant level of non-utilization (only 45% of land used) We focus only on cultivated area Includes commercial estates

Owned size distribution   IHS4 Estate survey All Overl Subdiv. HH Est. Corp. est 1% 0.00 5% 0.04 10% 0.10 25% 0.23 50% 0.43 75% 0.75 90% 1.17 95% 1.55 99% 2.64

Owned size distribution   IHS4 Estate survey All Overl Subdiv. HH Est. Corp. est 1% 0.00 5% 0.04 0.02 10% 0.10 0.15 25% 0.23 0.36 50% 0.43 0.63 75% 0.75 1.06 90% 1.17 1.61 95% 1.55 2.08 99% 2.64 3.49

Owned size distribution   IHS4 Estate survey All Overl Subdiv. HH Est. Corp. est 1% 0.00 5% 0.04 0.02 0.27 10% 0.10 0.15 0.45 25% 0.23 0.36 0.85 50% 0.43 0.63 1.47 75% 0.75 1.06 2.56 90% 1.17 1.61 4.18 95% 1.55 2.08 6.03 99% 2.64 3.49 14.33

Owned size distribution   IHS4 Estate survey All Overl Subdiv. HH Est. Corp. est 1% 0.00 0.26 5% 0.04 0.02 0.27 0.54 10% 0.10 0.15 0.45 0.81 25% 0.23 0.36 0.85 1.42 50% 0.43 0.63 1.47 2.56 75% 0.75 1.06 5.53 90% 1.17 1.61 4.18 11.52 95% 1.55 2.08 6.03 18.24 99% 2.64 3.49 14.33 42.41

Owned size distribution   IHS4 Estate survey All Overl Subdiv. HH Est. Corp. est 1% 0.00 0.26 0.38 5% 0.04 0.02 0.27 0.54 0.68 10% 0.10 0.15 0.45 0.81 1.01 25% 0.23 0.36 0.85 1.42 2.03 50% 0.43 0.63 1.47 2.56 4.60 75% 0.75 1.06 5.53 11.50 90% 1.17 1.61 4.18 11.52 19.13 95% 1.55 2.08 6.03 18.24 39.57 99% 2.64 3.49 14.33 42.41 272.00

Desc. Statistics farm level   IHS4 Estate survey All Overl Subdiv. HH Corp Owned land size (GPS ha) 0.57 0.81 7.30 11.32 15.47 Operated land size (GPS ha) 0.60 0.84 10.70 14.91 Number of operated plots 1.44 1.63 2.07 2.56 2.70 Output value (US$/ha) 296 294 370 449 371 Profit at shadow wage (US$/ha) 210 216 234 256 275 Profit at market wage (US$/ha) 75.70 101 166 199 269 Hired labor days/ha 5.74 5.84 9.11 37.34 15.14 Family labor days/ha 119 98.54 71.71 58.65 15.05 Value of chemical fertilizer (US$/ha) 69.96 64.48 104 142 96.13 Value of ag. assets (US$) 24.41 36.38 325 663 1,885 Share of quantity rationed 0.22 0.25 0.21 0.06 Share of risk rationed 0.10 0.08 0.09 0.00 Share of transaction costs rationed 0.18 0.17 0.11 Number of observations (farms) 7,153 1,993 828 1,028 64

Desc. Statistics field level   IHS4 Estate survey All Overl Subdiv. HH Corp GPS Plot size (ha) 0.36 0.43 0.91 1.25 2.64 Yield (US$/ha) 311 314 396 489 360 Value of organic fertilizer (US$/ha) 5.79 2.71 3.87 3.54 1.82 Value of chemical fertilizer (US$/ha) 72.05 64.94 90.14 134 84.60 Value of pesticide/herbicide (US$/ha) 1.23 1.73 4.53 11.64 9.16 Hired labor days per ha 6.57 6.34 8.92 27.81 18.97 Family labor days per ha 124 107 83.90 69.58 14.49 Profit at shadow wage (US$/ha) 221 233 285 308 260 Profit at market wage (US$/ha) 83.40 110 202 242 249 Distance to dwelling (km) 1.17 1.08 0.82 0.76 0.38 Share planted maize 0.77 0.62 0.49 0.45 0.40 Share planted groundnut 0.10 0.15 0.24 0.22 0.16 Number of observations (plots) 10,309 3,241 1,714 2,634 173

Does IR change with large farms?

Does IR change with large farms?

Yield regressions Panel A: IHS4 smallholders Plot size -0.202***   Plot size -0.202*** -0.264*** -0.181*** -0.207*** (0.026) (0.055) (0.028) (0.064) Farm size -0.186*** -0.197*** (0.027) Obs. 10,000 9,894 R2 0.464 0.892 0.475 0.896 Panel B: IHS4 smallholders & estates in overlap districts -0.273*** -0.369*** -0.258*** -0.262*** (0.022) (0.037) (0.025) (0.048) -0.024 -0.041 (0.023) 7,375 7,594 7,052 7,089 0.415 0.765 0.440 0.771 Note: Cols. 1& 3 include district and cols. 2 & 4 farm fixed effects. Farm characteristics, crops, plot characteristics, and inputs controlled for.

Implications Missing large farmers – does not seem relevant 779 EAs in all 28 districts Extensive qnr with land/credit questions Unobserved land quality Not an issue at farm level But of course we do not have plot panel (could test)

Profits for smallholders

Profits for smallholders & estates

Profit regression w. shadow wage Panel A: IHS4 smallholders   Plot size -0.129*** -0.206** -0.127*** (0.036) (0.084) (0.085) Farm size -0.335*** (0.037) Obs. 9,894 R2 0.280 0.828 Panel B: IHS4 smallholders & estates in overlap districts -0.277*** -0.351*** -0.350*** (0.042) (0.067) -0.085* -0.083* (0.049) 7,052 7,089 0.230 0.679 Note: Cols. 1& 3 incl. district and cols. 2 & 4 farm fixed effects. Farm char’s, crops, plot characteristics, inputs, credit constrained status controlled for.

Profit regression w. market wage Panel A: IHS4 smallholders   Plot size 0.018 -0.111 0.015 -0.123 (0.044) (0.108) Farm size -0.330*** -0.326*** (0.048) Obs. 9,919 9,894 R2 0.216 0.822 Panel B: IHS4 smallholders & estates in overlap districts -0.056 -0.135 -0.060 -0.137 (0.055) (0.084) -0.042 -0.041 (0.061) (0.062) 7,056 7,093 7,052 7,089 0.196 0.694 0.197 Note: Cols. 1& 3 incl. district and cols. 2 & 4 farm fixed effects. Farm char’s, crops, plot characteristics, inputs, credit constrained status controlled for.

Implication for explanations Statistical artifact or edge effects Idiosyncratic measurement error can be excluded Plot-specific measurement error? But within-farm labor allocation also matters Cannot (yet) test edge effect directly; plot shapes still cleaned Edge effect cannot explain disappearance of IR with profit Labor market imperfection matters HH FEs control for them Shadow wage rate: still higher yield/profit on small plots Profits insig. but larger with FEs Within-farm plot specific decisions increase IR

Labor days/ha smallholders

Labor days/ha smallholders & estates

Total labor days per ha vs. size Panel A: IHS4 smallholders   Plot size -0.399*** -0.429*** -0.359*** -0.393*** (0.015) (0.031) (0.033) Farm size -0.032** -0.033** (0.016) Obs. 10,186 9,881 R2 0.262 0.891 0.280 0.895 Panel B: IHS4 smallholders & estates in overlap districts -0.596*** -0.608*** -0.559*** -0.589*** (0.020) (0.028) (0.030) -0.021 -0.026 (0.019) 7,167 7,028 0.373 0.872 0.388 0.876 Note: Cols. 1& 3 incl. district and cols. 2 & 4 farm fixed effects. Farm char’s, crops, plot characteristics, inputs, credit constrained status controlled for.

Household vs. corporate estates Rationale Corporate estates have less labor market constraints Within-farm allocation should not differ if behavioral issue What do we find? No IR for corporate estates but for household estates Corporates also allocate more labor on small plots Implication Donors pulling together can get us most of the way But leadership to close the gap is needed

Profit for household estates

Profit for corporate estates

Regressions of yield/profit on size Panel A: Output value for household vs. corporate estates   Plot size (β1) -0.277*** -0.370*** -0.249*** -0.196*** (0.032) (0.054) (0.041) (0.072) Plot size * Corp. 0.141 0.248 0.173* 0.206   (β2) (0.096) (0.161) (0.097) (0.178) Farm size 0.035 0.015 (0.027) (0.030) Test: β1+ β2=0 2.04 0.62 0.60 0.00 Panel B: Profit at shadow wage for household vs. corporate estates -0.260*** -0.296*** -0.263*** -0.295*** (0.064) (0.102) (0.065) 0.055 0.262 0.028 0.241 (0.182) (0.461) (0.183) (0.456) 0.011 0.008 (0.068) 1.20 0.01 1.56

Labor use for household estates

Labor use for corporate estates

Regressions of profit/labor on size Panel A: Profit market wage for household vs. corporate estates   Plot size (β1) 0.033 -0.006 0.026 -0.005 (0.074) (0.119) Plot size * Corp. -0.340 -0.094 -0.361* -0.108   (β2) (0.216) (0.435) (0.432) Farm size 0.089 0.084 (0.070) (0.072) Test: β1+ β2=0 1.99 0.06 2.35 0.07 Panel B: Total labor days/ha for household vs. corporate estates -0.756*** -0.721*** -0.720*** -0.703*** (0.026) (0.036) (0.027) (0.038) 0.287** 0.180 0.260** 0.164 (0.175) (0.120) -0.020 -0.026 (0.021) (0.020) 15.92*** 9.77*** 15.24*** 9.72***

Conclusion Here, inverse relationship is unaffected by truncation Many of the estates adjusted to smaller sizes Exploring size dynamics of estates interesting for follow-up Labor market imperfections as key explanation No IR (farm or plot) for profits with labor at market wage No IR for corporate estates Measurement error/edge effect may not explain this Interesting within-farm dynamics Higher labor use (own + hired) on smaller plots Plot location (observed) or quality (unobs.) could explain Will use IHS4 parcel panel to explore these further

Thank you!

Field size distribution   IHS4 Estate survey All Overl Subdiv. HH Est. Corp. est 1% 0.02 0.03 0.11 0.12 0.21 5% 0.06 0.08 0.17 0.20 0.39 10% 0.09 0.23 0.27 0.48 25% 0.16 0.44 0.77 50% 0.29 0.36 0.65 0.81 1.29 75% 0.56 1.10 1.45 3.13 90% 0.72 0.82 1.90 2.62 7.93 95% 0.91 1.07 2.54 3.89 9.65 99% 1.41 1.53 4.30 8.00 11.26