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Farm Size and Productivity: Lessons from Recent Literature

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CGIAR Research Program on Policies, Institutions, and Markets Workshop on Rural Transformation in the 21st Century (Vancouver, BC – 28 July 2018, 30th International Conference of Agricultural Economists). Presentation by Douglas Gollin, Oxford University

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Farm Size and Productivity: Lessons from Recent Literature

  1. 1. Farm Size and Productivity Lessons from Recent Literature Douglas Gollin Oxford University PIM Pre-Conference Workshop: Rural Transformation in the 21st Century ICAE 2018 - Vancouver 28 July 2018 D. Gollin (2018) Farm Size and Productivity PIM Pre-Conference 1 / 36
  2. 2. I. Introduction D. Gollin (2018) Farm Size and Productivity PIM Pre-Conference 2 / 36
  3. 3. Introduction Issues of farm size and productivity recur in discussions of agricultural development. Questions relate to development strategy and growth. What determines the size of farms? Are large farms more productive than small? Are small farms more productive than large? How much is farm size distorted by policies and by missing markets? Should we care about farm size for development strategy? Specic questions about smallholder agriculture: To what extent should development strategies prioritize smallholder agriculture? D. Gollin (2018) Farm Size and Productivity PIM Pre-Conference 3 / 36
  4. 4. Why Do We Care about Farm Size and Productivity? Farm size in developing countries is very small, on average. Is this ecient? Or does it reect land market failures (which are very much in evidence)? If smallholder agriculture is highly productive, then: Trade-os between equity and eciency may not be very important. Land market failures may not be very signicant in explaining low levels of agricultural productivity. Targeting smallholder systems will also help to solve other development problems. If smallholder agriculture is unproductive, then these issues all need to be revisited. D. Gollin (2018) Farm Size and Productivity PIM Pre-Conference 4 / 36
  5. 5. II. Descriptive Data on Farm Size D. Gollin (2018) Farm Size and Productivity PIM Pre-Conference 5 / 36
  6. 6. Background on Farm Size Most of the world's farms are small very small. Most of the world's farms are family managed... True also in the U.S. and Europe. Both US and EU dene farms in ways that allow for many small farms. D. Gollin (2018) Farm Size and Productivity PIM Pre-Conference 6 / 36
  7. 7. US Smallholdings 20 percent of farms report $1,000 in sales. Smallest 75% of US farms accounted for 6% of gross sales, ≈25% of farmed area Largest 10% of farms accounted for 75% of gross sales, 40% of farmed area D. Gollin (2018) Farm Size and Productivity PIM Pre-Conference 7 / 36
  8. 8. EU Smallholdings Similarly, in the EU, there are large numbers of smallholdings. Very small farms accounted for: 47% of the holdings 39% of the regular farm workers and 23% of the total farm labour. But... only 7% of the farmed area, 2.5% of the total livestock units, 1.6% of the gross value. D. Gollin (2018) Farm Size and Productivity PIM Pre-Conference 8 / 36
  9. 9. Average Farm Size across Countries Wide dispersion in farm size, but many developing countries have much smaller farm size and rely more heavily on small farms: Malawi: only 8% of holdings 2 ha or larger. Rwanda: only 6% of holdings 2 ha or larger Mozambique: 95% of holdings smaller than 4 ha EU-12: average farm size 6.0 ha EU-15: average farm size 22.0 ha EU-27: average farm size 12.6 ha United Kingdom (53.8 ha) France (52.1 ha) Germany (45.7 ha) Netherlands (25.0 ha) Spain (23.9 ha) US (169 ha) D. Gollin (2018) Farm Size and Productivity PIM Pre-Conference 9 / 36
  10. 10. III. Productivity Dierences across Countries D. Gollin (2018) Farm Size and Productivity PIM Pre-Conference 10 / 36
  11. 11. Labour Productivity in Agriculture Dierences across countries in average labour productivity are very large. These dierences are bigger for agriculture than for other sectors. Average labour productivity dierences in agriculture are arithmetically important in explaining the cross-country dispersion of income per capita; see Caselli (2005). Examine data to see what we can learn from cross-country patterns. D. Gollin (2018) Farm Size and Productivity PIM Pre-Conference 11 / 36
  12. 12. Labour Productivity Data from FAO Numerator: gross agricultural output at international prices - at common set of prices of agricultural goods - no adjustment for intermediate usage Denominator: economically active population in agriculture D. Gollin (2018) Farm Size and Productivity PIM Pre-Conference 12 / 36
  13. 13. Agricultural Productivity Dierences Across Countries Ratio Richest Ten Percent over Poorest Ten Percent 50.1 Richest Quarter over Poorest Quarter 29.9 These are very large dierences in comparison to GDP per worker. D. Gollin (2018) Farm Size and Productivity PIM Pre-Conference 13 / 36
  14. 14. Decomposing Agricultural Output per Worker Output per worker in agriculture: output worker = output land × land worker output/land - i.e. yield, widely measured in both micro and macro studies land/worker - also widely measured in both micro and macro studies D. Gollin (2018) Farm Size and Productivity PIM Pre-Conference 14 / 36
  15. 15. Decomposing Agricultural Output per Worker Output per worker in agriculture: output worker = output land × land worker output/land - i.e. yield, widely measured in both micro and macro studies land/worker - also widely measured in both micro and macro studies Where do the big dierences in output per worker come from? D. Gollin (2018) Farm Size and Productivity PIM Pre-Conference 14 / 36
  16. 16. FAO Estimates of Output per Unit Land Table: Tons Produced per Hectare of Staple Grains by Country Group Maize Rice Wheat Richest Ten Percent of Countries 9.2 8.1 4.9 Richest Quarter 8.2 6.8 4.8 Poorest Quarter 2.4 3.2 2.1 Poorest Ten Percent of Countries 2.0 2.9 2.0 Ratio of Top to Bottom Ten Percent 4.7 2.8 2.5 Ratio of Top to Bottom Quarter 3.4 2.1 2.2 D. Gollin (2018) Farm Size and Productivity PIM Pre-Conference 15 / 36
  17. 17. Output per Unit Land: Comments Substantial dierences between rich and poor countries. Richest countries produce at least twice as much grain per hectare as poorest. Clearly a signicant source of dierences across countries in agricultural productivity. D. Gollin (2018) Farm Size and Productivity PIM Pre-Conference 16 / 36
  18. 18. Output per Unit Land: Comments Substantial dierences between rich and poor countries. Richest countries produce at least twice as much grain per hectare as poorest. Clearly a signicant source of dierences across countries in agricultural productivity. What about land per worker? D. Gollin (2018) Farm Size and Productivity PIM Pre-Conference 16 / 36
  19. 19. FAO Estimates of Land per Worker Arable Cropland (ha) / Agricultural Labour Fource Richest Ten Percent of Countries 44.6 Top Quarter 23.9 Bottom Quarter 1.3 Poorest Ten Percent of Countries 1.4 Ratio of Top to Bottom Ten Percent 31.2 Ratio of Top to Bottom Quarter 18.0 D. Gollin (2018) Farm Size and Productivity PIM Pre-Conference 17 / 36
  20. 20. Direct Micro Measures of Output per Worker Some micro studies compute not only yield but also labor. Allows for direct comparisons of output per unit of labor. Data show very large dierences across agricultural production systems. D. Gollin (2018) Farm Size and Productivity PIM Pre-Conference 18 / 36
  21. 21. Output per Unit of Labor, Maize Maize Country Year Yield Hours Output / Hour Source Kenya 1997 367.6 kg/acre 390.0 0.9 Kg/Hr Suri (2011) Kenya 2004 572.5 kg/acre 471.2 1.2 Kg/Hr Suri (2011) Malawi 1989-90 745.0 kg/ha 306.0 2.4 Kg/Hr Smale (1995) South Africa 2003-04 1060.8 kg/ha 249.0 4.3 Kg/Hr Gouse et al. (2006) USA 2001 3429.1 kg/acre 2.6 1318.9 Kg/Hr Foreman (2006) USA (Iowa) 2011 4191.2 kg/acre 2.9 1470.6 Kg/Hr Iowa State D. Gollin (2018) Farm Size and Productivity PIM Pre-Conference 19 / 36
  22. 22. Output per Unit of Labor, Rice Rice Country Year Yield (kg/ha) Hours Output (kg) / Hour Java 1875-78 1600 1350 1.19 Java 1920-30 1800 1224 1.47 Java 1968-71 2700 948 2.85 Java 1977-80 3700 864 4.28 Burma 1930s 1548 282.6 5.48 Thailand 1960s 1321 339 3.90 Sri Lanka 1972-73 2917 972.6 3.00 Philippines 1974-75 2449 489.6 5.00 Taiwan 1980-85 2020 1110 1.82 Nigeria (Semi- Irrigated) 2002 3700 1350 2.74 California 2010 8282 12.9 641.39 USA 2000 7734 11.1 696.97 D. Gollin (2018) Farm Size and Productivity PIM Pre-Conference 20 / 36
  23. 23. Output per Unit of Labor, Rice - Time Series Rice Country Year Yield (kg/ha) Hours Output (kg) / Hour USA 2000 7734 11.1 696.97 Japan 1888-91 2600 1626 1.60 Japan 1956 4200 1374 3.06 Japan 1971 5100 846 6.03 Korea (local vars) 1974 6.5 Korea (HYVs) 1974 5.6 Korea 1995 6052 34.7 174.4 Korea 2005 6568 20.8 315.8 Korea 2013 6764 12.7 532.6 Japan (per 10a) 2011 533 26.1 20.03 Taiwan (Central) 1936-37 3100 756 4.10 Taiwan (Central) 1961 4100 828 4.95 Taiwan (Central) 1972 5200 504 10.32 Taiwan (National) 2015 7430 99 75.1 D. Gollin (2018) Farm Size and Productivity PIM Pre-Conference 21 / 36
  24. 24. Summary Points on Productivity Data Huge dierences across countries in output per unit of labour. Output per hour varies across contexts by an order of magnitude more than yield. Perhaps the focus should be on labour productivity dierences, not yield dierences! Dierences in output per unit of land are substantial but small in comparison to dispersion in land per unit of labor. Hours per hectare vary by enormous amounts across settings. Output per unit of labour is much higher in contexts where farm size is large. D. Gollin (2018) Farm Size and Productivity PIM Pre-Conference 22 / 36
  25. 25. IV. The Inverse Relationship? D. Gollin (2018) Farm Size and Productivity PIM Pre-Conference 23 / 36
  26. 26. Farm Size and Measured Productivity A large literature has tested for yield dierences across farm size. Frequent nding: an inverse relationship between farm size and measured productivity. Typical approach regress an outcome measure (usually yield) on farm size, with a number of controls. A negative and signicant coecient is seen as evidence for the inverse relationship. Typical conclusion: small farms are highly productive. Consistent with a story dating back to Chayanov (1966) and characterized by Sen (1962)... Small farms self-exploit labor. D. Gollin (2018) Farm Size and Productivity PIM Pre-Conference 24 / 36
  27. 27. Empirical Diculties Farm size is a choice; farms are typically smaller in more fertile areas. Much of the literature also compares small farms with slightly larger farms, rather than small with very large... Small farms are highly productive precisely because of failures in labor and capital markets. Not necessarily the case that they are technologically more productive. D. Gollin (2018) Farm Size and Productivity PIM Pre-Conference 25 / 36
  28. 28. Measurement Issues Perhaps the inverse relationship simply reects measurement error. Small farms may underestimate land area or large farms may overestimate their area, systematically... No; the opposite may be true; but the inverse relationship holds even with GPS estimates of area instead of farmer reported area. [Carletto, Gourlay, and Winters (2015)]. Small farms may systematically over-report crop output. Yes, possibly. [Gourlay, Kilic, and Lobell (2017)]. Other measurement errors are also present; e.g., dierences in land quality. D. Gollin (2018) Farm Size and Productivity PIM Pre-Conference 26 / 36
  29. 29. Inverse Relationship: A Summary Recent evidence casts some doubt on the inverse relationship. Even if it is present, farm size is a relatively weak variable in explaining output per unit of land. Much farm-level variation in yield is not explained by observable characteristics of farms or households [Gollin and Udry (2017)]. Production shocks, measurement error and heterogeneity in input quality are important. Even where the inverse relationship holds for yields, output per unit labour may be higher on large farms. D. Gollin (2018) Farm Size and Productivity PIM Pre-Conference 27 / 36
  30. 30. V. Policy Issues D. Gollin (2018) Farm Size and Productivity PIM Pre-Conference 28 / 36
  31. 31. Policies Aecting Farm Size Many countries pursue policies intended to inuence farm size. Some seek to promote consolidation; e.g., through land market reforms intended to facilitate rental and transaction. Land titling and registry Direct support for investors seeking to assemble large tracts of land Growth corridors Other countries aim to limit consolidation Restrictions on sale or rental of land Legally determined limits on maximum farm size; e.g., through land redistribution or limits on land holdings. D. Gollin (2018) Farm Size and Productivity PIM Pre-Conference 29 / 36
  32. 32. Do Policies Induce Misallocation Some striking evidence from macro analyses suggests that policies limiting land markets can lead to quantitatively important misallocation Examples: Adamopoulos, Brandt, Leight, and Restuccia (2017); Restuccia and Santaeulalia-Llopis (2017); Adamopoulos and Restuccia (2015) Land market restrictions may reduce sector output value by two-thirds or more! Potential methodological caveats discussed in Gollin and Udry (2017), but no doubt that land market failures are important for development. Look hard at policies restricting land markets or limiting farm size. But land distribution has important implications for equity and poverty. D. Gollin (2018) Farm Size and Productivity PIM Pre-Conference 30 / 36
  33. 33. VI. Conclusions D. Gollin (2018) Farm Size and Productivity PIM Pre-Conference 31 / 36
  34. 34. Conclusions (1) All countries seem to have a large number of small, often part-time farms. Productivity per unit of land may be quite high. Productivity per unit of labor is often very low. Income from these farms is low. Small farms may produce higher output per unit of land than large farms, although measurement problems may aect this nding. However, large farms typically have higher output per unit of labour. D. Gollin (2018) Farm Size and Productivity PIM Pre-Conference 32 / 36
  35. 35. Conclusions (2) Small farms are ubiquitous and will persist in developing countries (as they do in developed countries). In many developing countries, average farm size will continue to shrink due to demographic trends and limited availability of land. Many reasons to work towards making these smallholder systems productive. Equity Poverty Food security Nutrition Employment Promote sustainable management of natural resources But we should be realistic about the potential of small farms to generate income. Limits to what value / income can be produced on farms of 1-2 ha, even if yields are at the world frontier. D. Gollin (2018) Farm Size and Productivity PIM Pre-Conference 33 / 36
  36. 36. Conclusions (3) Some smallholders will win the geographic lottery: Locations with access to lucrative markets Locations allowing for production of high-value commodities that cannot be produced in many places Locations permitting easy access to o-farm labour opportunities But many (most?) smallholders will nd that their farms cannot yield a middle class income, leading to an eventual (decades-long) process of labour exiting agriculture. Not necessarily movement out of rural areas; some will move into rural non-farm economy. Some into part-time farming (and part-time or full-time o-farm work) Roles for policy: Try to create more winners of the geographic lottery. Reduce frictions that limit movements out of agriculture. Plan and manage urban growth. D. Gollin (2018) Farm Size and Productivity PIM Pre-Conference 34 / 36
  37. 37. VII. References D. Gollin (2018) Farm Size and Productivity PIM Pre-Conference 35 / 36
  38. 38. References Adamopoulos, T., L. Brandt, J. Leight, and D. Restuccia (2017): Misallocation, selection and productivity: A quantitative analysis with panel data from China, Discussion paper, National Bureau of Economic Research. Adamopoulos, T., and D. Restuccia (2015): Land Reform and Productivity: A Quantitative Analysis with Micro Data, . Carletto, C., S. Gourlay, and P. Winters (2015): From Guesstimates to GPStimates: Land Area Measurement and Implications for Agricultural Analysis, Journal of African Economies, 24(5), 593628. Caselli, F. (2005): Accounting for cross-country income dierences, Handbook of Economic Growth, 1, 679741. Chayanov, A. V. (1966): The theory of peasant economy. Irwin, New York. Gollin, D., and C. Udry (2017): Heterogeneity, Measurement Error, and Misallocation: Evidence from African Agriculture, . Gourlay, S., T. Kilic, and D. Lobell (2017): Could the debate be over? Errors in farmer-reported production and their implications for the inverse scale-productivity relationship in Uganda, . Restuccia, D., and R. Santaeulalia-Llopis (2017): Land misallocation and productivity, Discussion paper, National Bureau of Economic Research. Sen, A. K. (1962): An aspect of Indian agriculture, Economic Weekly, 14(4-6), 243246. D. Gollin (2018) Farm Size and Productivity PIM Pre-Conference 36 / 36

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