1. Assessing the inverse farm size-productivity relationship in Malawi

2. 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

3. 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

4. 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

5. 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

6. 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

7. 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

8. 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

9. 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

10. 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

11. Does IR change with large farms?

12. Does IR change with large farms?

13. 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.

14. 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)

15. Profits for smallholders

16. Profits for smallholders & estates

17. 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.

18. 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.

19. 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

20. Labor days/ha smallholders

21. Labor days/ha smallholders & estates

22. 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.

23. 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

24. Profit for household estates

25. Profit for corporate estates

26. 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

27. Labor use for household estates

28. Labor use for corporate estates

29. 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***

30. 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

31. Thank you!

32. 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