1. Productivity and Growth in U.S. Agriculture: 1948- 2008 Eldon Ball and Sun Ling Wang Economic Research Service USDA Prepared for Presentation at National Academy of Economic Sciences Buenos Aires-UBA-FCE-Economic Department Buenos Aires-UBA-FCE-Economic Department, Argentina 30 September 2010, Argentina 27 September 2010 The views expressed herein are those of the authors, and not necessarily those of the U.S. Department of Agriculture.

2. 2 Productivity and Growth The growth of agricultural productivity has long been chronicled as the single most important source of economic growth in the U.S. farm sector Though their methods differ in important ways, the major sectoral productivity studies (Kendrick and Grossman, 1980; Jorgenson, Gollop, and Fraumeni, 1987; Jorgenson and Gollop, 1992; Jorgenson, Ho, and Stiroh, 2005) share this common conclusion

3. 3 There are, however, dissenting points of view regarding the sector’s performance Alston et al. (2010) find evidence of a slowdown in the rate of productivity growth If productivity growth falters, growing global demand will likely lead to sharply higher food prices and to increased environmental stress The U.S. Department of Agriculture’s estimates do not support the hypothesis of slowing productivity growth

4. 4 The USDA’s Productivity Estimates The U.S. Department of Agriculture (USDA) has been monitoring the industry’s productivity performance for decades In fact, in 1960, the USDA was the first agency to introduce multifactor productivity measurement into the Federal statistical program Today the USDA’s Economic Research Service (ERS) routinely publishes total factor productivity (TFP) measures from production accounts distinguishing multiple outputs and inputs and adjusting for quality change in each input category

5. 5 Indexes of productivity are constructed for each state and the aggregate farm sector The state series provide estimates of the growth and relative levels of productivity There is also an ongoing effort to provide international comparisons of agricultural productivity (Ball et al., 2001; 2008; 2010)

6. 6 Methodology The USDA constructs the following “translog” index of total factor productivity: (1) (1) where k and l are adjacent time periods, the Y i are the output indexes, the X j are input indexes, the R i are output revenue shares, and the W j are input cost shares

7. 7 The subscripts k and l in (1) can be interpreted as time periods or as states/countries When the number (N) of states/countries exceeds two, the application of (1) to the [N(N- 1)]/2 possible pairs of states yields a matrix of bilateral comparisons that may not satisfy transitivity

8. 86 Caves, Christensen and Diewert (1982) proposed the following index for bilateral comparisons: (2) where a bar indicates the arithmetic mean and a tilde indicates the geometric mean The use of (2) for bilateral comparisons results in transitive multilateral comparisons that retain a high degree of characteristicity

9. 9 The Production Accounts: Output The USDA constructs both state and aggregate farm sector accounts The accounts are consistent with a gross output model of production Output is defined as gross production leaving the farm, as opposed to real value added Inputs are not limited to capital and labor, but include intermediate inputs as well

10. 10 Both state and aggregate models view agriculture within their respective geographic boundaries as a single farm Output includes deliveries to final demand and to intermediate demand in the non-farm sector State output accounts include these deliveries plus interstate shipments to intermediate farm demand

11. 11 An unconventional aspect of our measure of total output is the inclusion of output of “inseparable” secondary activities These activities are defined as activities whose costs cannot be observed separately from those of the primary agricultural activity Services relating to agricultural production (e.g., machine services for hire; contract feeding of livestock) are typical of these activities

12. 12 Translog output indexes are formed by aggregating over agricultural goods and goods and services from secondary activities using revenue-share weights based on shadow prices

13. 13 Intermediate Input Intermediate input consists of all goods and services used in production during the calendar year, whether withdrawn from beginning inventories or purchased from outside the farm sector In the case of the state accounts, intermediate input also includes purchases from farms in other states

14. 14 Price and quantity data corresponding to purchases of feed, seed, and livestock are available and enter the calculation of intermediate input Translog indexes of energy input are constructed by weighting the growth rates of petroleum fuels, natural gas, and electricity by their shares in the overall value of energy inputs

15. 15 Pesticides and fertilizer are important intermediate inputs, but price/consumption data require adjustment since these inputs have undergone significant changes in input quality Price indexes for pesticides and fertilizer are constructed using hedonic methods The corresponding quantity indexes are formed implicitly by taking the ratio of the value of each aggregate to corresponding hedonic price index

16. 16 Finally, price and implicit quantity indexes of purchased services (e.g., custom machine services; contract feeding of livestock; contract labor services) are constructed A translog index of total intermediate input is constructed for each state and the aggregate farm sector by weighting the growth rates of each category of intermediate input by their value shares in the overall value of intermediate input

17. 17 Labor The labor accounts incorporate demographic cross-classifications of the agricultural labor force developed by Jorgenson, Gollop, and Fraumeni (1987) Hours worked and compensation per hour are cross-classified by sex, age, education, and employment class (employee versus self- employed and unpaid family workers) Self-employed and unpaid family workers are imputed the mean wage of hired workers with the same demographic characteristics

18. 18 Indexes of labor input are constructed for each state and the aggregate farm sector using demographically cross-classified hours and compensation data Labor hours having higher marginal productivity (wages) are given higher weights in the index than hours having lower marginal productivities This procedure explicitly adjusts state and aggregate farm sector time series of labor input for “quality” changes in labor hours

19. 19 Capital Measurement of capital input begins with estimating the capital stock and rental price for each asset type Stocks of depreciable capital are the cumulation of all past investments adjusted for discards of worn-out assets and loss of efficiency of assets over their service life

20. 20 Asset discards are calculated based on an assumed mean service life for a homogeneous group of assets and a distribution of actual discards around this mean service life Efficiency loss is assumed to be a function of age of the asset The function relating efficiency to age of the asset is approximated by a rectangular hyperbola concave to the origin

21. 21 The USDA constructs an ex ante measure of the user cost of capital The ex ante rate of return is calculated as the nominal yield on investment grade corporate bonds adjusted for expected rather than actual price inflation The same pattern of decline in efficiency is used both for the capital stock and the price of capital services so that the requirement for internal consistency of a measure of capital input is met

22. 22 To estimate the stock of land we construct price and implicit quantities of land in farms We assume that land in each county is homogeneous, hence aggregation is at the county level Beginning inventories of crops and livestock are treated as capital inputs, while net additions to inventory during the calendar year are considered a component of output

23. 23 Translog indexes of capital input in each state and the aggregate farm sector are formed by aggregating over the various capital assets using cost-share weights based on asset-specific rental prices As is the case for labor, the resulting measure of capital input is adjusted for changes in asset quality

24. 24 Sources of Growth Input growth typically has been the dominant source of economic growth for the aggregate economy and for each of its producing sectors Jorgenson, Gollop, and Fraumeni (1987) find that output growth relies most heavily on input growth in forty-two of forty-seven private non- farm business sectors, and in a more aggregate study (Jorgenson and Gollop, 1992) in eight of nine sectors Agriculture turns out to be one of the few exceptions; productivity growth dominates input growth

25. 25 Using equation (1), output growth equals the sum of the contributions of labor, capital (including land) and intermediate inputs and TFP growth The contribution of each input equals the product of the input’s growth rate and its respective share in total cost Over the full 1948-2008 period labor input declined at a 2.6% annual rate When weighted by its 20% cost share, the contraction in labor contributes an average -0.51 percentage points to output growth

26. 26 Growth of capital input (excluding land) contributed just 0.01 percentage points to output growth Land input declined at a -0.55% average annual rate; its contribution to output growth averaged -0.10 percentage points Intermediate input’s contribution averaged a substantial positive rate equal to 0.66% per year The net contribution of all inputs was less than one-tenth of one percent, leaving responsibility for positive growth in farm sector output to productivity growth

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28. 28 Do we or do we not have an agricultural productivity slowdown? That’s the question!

29. 29 Historical Trends Output expanded at 1.57% average annual rate Aggregate input use contracted since early 1980s Productivity growth was responsible for positive growth in farm sector output

30. 30 Unit root and structural break tests TFP growth is not stable for the 1948-2008 period TFP series is stationary with trend Unit root tests confirm there is a break in both trend and intercept indicating a structural break and TFP growth change

31. 31 Productivity Slowdown Considering break in intercept only, productivity growth is significantly different pre- and post-break with 1980 or 1990 as break point. TFP grows faster in the after break-point period. Considering both breaks in intercept and trend, productivity growth is significantly different with 1980 as break point. Again, TFP grows faster in the after break-point period.

32. 32 Productivity Slowdown Tests Productivity grew faster in the “after-break” period with any year between 1976 and 1984 as break point Chow test confirms significant structural change in any year between 1976 and 1985

33. 33 Group Mean Tests Considering only differences in mean values for each period, productivity growth is not significantly different between “during” and “after” periods

34. 34 Conclusions U.S. Agricultural Productivity: Slowdown, Structural Change, or Short-term Shocks? That’s the question! The results say…. Short-term shocks affect output and productivity growth with input growth being more stable There is evidence of significant structural breaks in both TFP level and TFP growth rate Considering the structural break effects, there is no evidence of a productivity slowdown

35. Trends of Relative Productivity Levels

36. Trends of Relative Productivity Levels (Cont.)

37. 37 A more detailed discussion of methods and data and complete references can be found at our website http://www.ers.usda.gov/Data/AgProductivity/ THANK YOU! Q&A