The Role of Productivity Growth in U.S. Agriculture
The U.S. Department of Agriculture (USDA) has been monitoring agriculture's productivity performance for decades. In fact, in 1960, USDA was the first agency to introduce multifactor productivity measurement into the Federal statistical program. Today, the Department's Economic Research Service (ERS) routinely publishes total factor productivity (TFP) measures based on a sophisticated system of farm production accounts. Its TFP model is based on the transcendental logarithmic (translog) transformation frontier. Annual TFP growth rates are measured using the Törnqvist-Thiel Index number approach. It relates the growth rates of multiple outputs to the cost-share weighted growth rates of labor, capital, and intermediate inputs. (See Shumway et al. (2017) and Ball et al. (2016) for more details regarding the background and a complete description of the USDA model.)
The applied USDA model is quite detailed. The changing demographic character of the agricultural workforce is used to build a quality-adjusted index of labor input. Similarly, much asset-specific detail underlies the measure of capital input. The index of land input, for example, is constructed by aggregating county-level data. The contributions of feed, seed, energy, agricultural chemicals, purchased services, and other materials are captured in the index of intermediate inputs. An important innovation is the use of hedonic price indexes in constructing measures of fertilizer, pesticide, and purchased contract labor services. The result is a time series of total factor productivity indexes for the aggregate farm sector spanning the period 1948 to 2017 (see table 1), along with price and quantity indices for ten outputs and twelve inputs (see table 1a). State-specific measures are available for the 1960-2004 period. The State series provides estimates of both the growth and relative levels of total factor productivity (see tables 3-23).
National Agricultural Productivity
Output growth derives from growth in the use of inputs (capital, land, labor, and intermediate goods) and TFP. Input growth has been the main source of economic growth for the U.S. aggregate economy and for most sectors, but the agricultural sector seems to be an exception (Jorgenson et al. 2014). According to ERS estimates, while total farm output nearly tripled, total inputs used in agriculture grew by only 5 percent in total over the last seven decades. The composition of inputs, however, changed markedly, shifting from labor and land toward machinery and intermediate goods—including energy, agricultural chemicals, purchased services, and other materials (see table 1). Between 1948 and 2017, labor and land inputs declined by 76 and 28 percent, respectively, while intermediate goods grew by 133 percent.
TFP growth measures output growth that cannot be explained by growth in inputs, such as innovations in onfarm tasks, changes in the organization and structure of the farm sector (O’Donoghue et al. 2011; Macdonald et al. 2016), improvements in animal and crop genetics, or other embodied and disembodied technical changes. Between 1948 and 2017, farm output grew at 1.53 percent per year on average. With total inputs (including land, labor, capital, and intermediate inputs) growing by merely 7 percent per year, total factor productivity grew at 1.46 percent pear, nearly single-handedly leading farm output to grow 170 percent above its 1948 level.
Long-term TFP growth is driven mainly by technical change, which is primarily fueled by research and development (R&D) investment from public and private sectors (Alston et al. 2010; Huffman and Evenson, 2006; Fuglie and Heisey 2007; Plastina and Fulginiti, 2012; and Wang et al. 2013). It can also be enhanced by public infrastructure, extension, and technology spillover from other sectors or neighboring regions (Alston et al. 2010; Huffman and Evenson, 2006; and Wang et al. 2012). Yet, in the short term, estimated agricultural TFP can fluctuate considerably from year to year, largely in response to transitory events—such as bad weather (Liang et al. 2017; Wang et al. 2019) and pest outbreaks—or to changes in input use affected by macroeconomic activities or short-term policies (Wang and McPhail 2014). Eventually, TFP growth will return to its long-term trend following these temporary shocks.
Sources of Agricultural Output Growth
In addition to long-term trends, ERS also examines the sources of agricultural output growth—apportioning increases in output among changes in inputs (both quantities and quality/composition) and TFP—for the entire 1948-2017 period and 12 peak-to-peak subperiods (see table 2). The subperiods are not chosen arbitrarily, but are measured from cyclical peak to peak in aggregate economic activity. Since the data reported for each subperiod are average annual growth rates, the unequal lengths of the subperiods do not affect the comparisons across subperiods. Applying the USDA model, output growth equals the sum of contributions of labor, capital, and materials 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.
Input growth typically has been the dominant source of economic growth for the aggregate economy and for each of its producing sectors while agriculture turns out to be one of the few exceptions where productivity growth dominates input growth (Jorgenson, Ho, and Samuels, 2014). According to USDA estimates, the singularly important role of productivity growth in agriculture is made all the more remarkable by the dramatic contraction in labor input in the sector, a pattern that persists through every subperiod. Over the full 1948-2017 period, labor input declined at an average annual rate of 2.06 percent. When weighted by its 20-percent share in total costs, the contraction in labor input contributes an annual average -0.45 percentage point per year to output growth.
Capital input in the sector exhibits a different history. Its contribution to output growth alternates between positive and negative over the 1948-2017 period. On average, however, capital, like labor, contracts over the full period. Its negative growth contributes an annual -0.06 percentage point to output growth.
The negative contributions of both labor and capital are all the more notable given the positive contributions offered through improvements in both labor and capital quality, the recomposition of demographic characteristics in the labor hours worked, and the shifting use of the types of capital assets in total capital stocks. As shown in table 2, farms have shifted to higher-quality labor. This is primarily due to a more highly educated labor force. To account for changes in the quality and composition of agricultural family and hired labor, labor hours worked are cross-classified by sex, employment class, age, and education. Average hourly wages of each cross-classification is used to indicate the marginal productivity of each type of worker. If the composition of the workforce changes to include a higher proportion of more highly-paid workers, it then reflects an improvement in the average quality of the farm labor force. Analysis of the changing composition of hours over the 1948-2017 period reveals that the decline in labor hours was coincident with an increase in the proportion of more highly educated workers. This was sufficient to offset the negative effects of the changing sex, class, and age composition of hours and results in the persistent pattern of improving labor quality throughout the full 1948-2017 period. Increased labor quality made a positive contribution to output growth in 11 of the 12 subperiods, averaging 0.11 percentage points per year.
While the changes from three capital subtypes (excluding land)—durable equipment, service buildings, and inventory—made positive contributions to output growth in 9 of 12 subperiods (Wang 2019), the persistent shrinking use in farm land has resulted in a negative contribution to output growth from the overall capital growth (including land) at -0.06 percentage points per year.
Intermediate goods' contribution was positive in 9 of the 12 subperiods, and averaged a substantial positive rate of nearly 0.60 percent per year. Though large, this positive contribution just offsets the negative contributions through labor and capital. The net contribution of all three inputs was 0.07 percentage points per year, leaving responsibility for positive growth in farm sector output to productivity growth in all but 1948-1953 and 1973-79 subperiods.
Examining table 1 makes clear that the 1973-79 period is an outlier. Output, labor, and capital growth rates did not deviate much from trend. Materials input, however, exhibited significant positive growth at a rate far in excess of the incremental growth in output, accounting single-handedly for the measured decline in TFP growth. This anomaly appears to be due to rapid growth in export demand during this subperiod which resulted in both the increased consumption of intermediate inputs as well as a significant withdrawal of goods from inventory. Both led to a reduction in productivity growth (see Wang et al. 2015 for further discussion).
The early 2000s saw the emergence of biofuels as a major source of demand for grains and oilseeds. Corn used in ethanol production in 2007 accounted for roughly one-quarter of total corn production. The land area planted to corn increased some 15 million acres between 2006 and 2007, which could have resulted in the cultivation of more marginal lands. Consumption of agricultural chemicals (i.e., fertilizer and pesticides) increased more than 10 percent. Yet yields per acre were largely unchanged.
In spite of these anomalous subperiods, over the 1948-2017 period, TFP grew an average of 1.46 percent annually. Cumulated over the full 70 year period, this average annual rate (compounded annually) implies that farm sector productivity in 2017 was 2.7 times its 1948 level. Given the less than one percent annual increase in total input growth between 1948 and 2017, productivity growth caused agricultural output to grow significantly in every subperiod so that by 2017, farm output was about 2.9 times its level in 1948.
Note that updates of the State-level statistics are suspended.
A national measure of productivity growth for the aggregate farm sector provides a useful summary statistic indicating how economic welfare is being advanced through productivity gains in agriculture, but it may mask important State-specific or regional trends. For this reason, USDA has constructed estimates of the growth and relative levels of productivity for the 48 contiguous States for the 1960-2004 period (estimates are not made for Alaska and Hawaii as some major data are not available for the entire period). These indexes, expressed relative to the level of TFP in Alabama in 1996, are presented in table 19 along with their percentage rates of growth. In tables 20, 21, and 22, we rank the States by their level and growth of farm output, input use, and TFP, in 1960 and 2004.
One remarkable similarity exists across all States for the full 1960-2004 period. Every State exhibited a positive and generally substantial average annual rate of TFP growth. There is considerable variance, however. The median TFP growth rate over the 1960-2004 period was 1.67 percent per year. However, 9 of the 48 States had productivity growth rates averaging more than 2 percent per year. Only Oklahoma and Wyoming had average annual rates of growth less than 1 percent per year. The reported average annual rates of growth ranged from 0.58 percent for Oklahoma to 2.58 percent for Oregon (see maps below). Accumulated over the entire 45-year period, productivity growth in Oklahoma was responsible for only a 30-percent increase in that State's output. Over the same period, TFP growth in Oregon resulted in a 319-percent increase in that State's agricultural output. A key economic question is whether States with lower levels of productivity tend to grow faster than the technology leaders: are there forces (e.g., the diffusion of technical knowledge from the leading States to the more backward ones) that lead to convergence over time in the levels of productivity (see Ball et al. 2014 for more information).