Review of Agricultural Economics Volume 22, Number 2 Pages Technology Adoption and Its Impact on Production Performance of Dairy Operations

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1 Review of Agricultural Economics Volume 22, Number 2 Pages Technology Adoption and Its Impact on Production Performance of Dairy Operations Hisham S. El-Osta and Mitchell J. Morehart Data from the 1993 Agricultural Resource Management Study were used to examine the impact of technology adoption on production performance of a sample of dairy farms. Findings showed that the adoption of a capital- or a management-intense technology would measurably lower the likelihood of a farmer being in the lowest quartile of production performance. The economic costs of milk production by the top-performance group were estimated to be 53% lower than those by the low-performance group, providing evidence of the importance of improved production practices to the viability of many dairy operations. Technological advances in the dairy industry have contributed greatly toward the financial success of farmers through increased productivity and lower perunit costs. Average milk production per cow in the United States has increased from 12,505 lbin 1984 to 17,192 lbin 1998 (U.S. Department of Agriculture, 1996 and 1999a). Because technological advances work at extending the size over which costs remain low due to gains in productivity, farms are becoming larger and fewer in number (Johnson and Grabanski). Matulich attributes the consolidation of dairy farms and herd expansion to the economic incentives that were provided by advances in milking systems, feed, and herd management. Over the period, the number of dairy farms in the United States declined by about 59% (from 282,430 to 116,430), and the average size of operation when measured in milk cows increased from 38 to 79 (Manchester and Blayney; U.S. Department of Agriculture, 1999a). Increases in productivity resulting from technological advances have not been unique to the dairy sector, since sustained productivity increases in other sectors of U.S. agriculture have been attributed to the acceleration of technical change as Hisham El-Osta and Mitchell Morehart are agricultural economists with the Resource Economics Division, Economic Research Service, U.S. Department of Agriculture.

2 478 Review of Agricultural Economics well (Huffman and Evenson). Over the period, while the number of farms in agriculture as a whole declined by about 6% (from 2.33 million to 2.19 million), the average farm size remained almost unchanged at 435 acres despite the slight increase in size to 473 acres in 1993 (U.S. Department of Agriculture, 1996 and 1999b). The trend in the dairy industry in particular, and to some extent in U.S. agriculture in general, toward fewer and larger farms is consistent with the view held among economists (Cochrane, 1965, 1979; Musser and White; Weersink and Tauer) that technological change is a major determinant of structural change. While the importance of technology to structural change is well documented, other influential factors include availability of financial opportunities, institutional innovations, and a productive human capital (Boehlje). Contributing factors toward the rise in productivity in dairy production include, among others, use of technologies that fall within two broad categories: capital-intense (e.g., advanced milking parlors, genetically superior milking cows) and management-intense (e.g., use of record-keeping systems for total management, bovine somatotropin, improved nutrition and feeding practices). 1 Because of their high startup costs, capital-intensive technologies can only be afforded by larger and more specialized operations and, as such, may act at restricting open entry into dairy farming. In contrast, management-intensive technologies are inexpensive, but their success requires higher levels of human capital by farmers. Efficient dairy farmers have a better chance at staying competitive and financially solvent as milk prices become increasingly volatile, a direct result of the market-oriented dairy policy prescribed by the 1996 farm bill. The main objective of this study is to examine the determinants of production performance of a sample of dairy farms using multinomial regression procedures and data from the 1993 Agricultural Resource Management Study (formerly known as Farm Costs and Returns Survey). Special emphasis is given to the role of capital- and management-intense technologies in terms of having an impact on the likelihood of farms being in the low-, middle-, or high-performance group of farms. In the context of this article, these groups are defined, respectively, as the lower quarter, middle two quarters, and top quarter of farms when farming units are ranked in order of their level of technical efficiency. Also, the capitalintense technology here refers to the use of an array of advanced milking parlors (e.g., herringbone, side opening, polygon, or carousel), while the managementintense technology refers to the participation in Dairy Herd Improvement (DHI) Association. The second objective of the article is to provide a microeconomic analysis of production performance of dairy operations by means of determining the extent of farm-level competitiveness (in terms of both costs and returns) that could be achieved by moving from a lower to a higher efficiency category. Expected increases in the level of risk exposure resulting from the new U.S. dairy policy will force producers to either control production costs or increase productivity, thereby increasing their chances of financial survival, or to exit from farming. Whereas a number of studies have examined the efficient use of resources in dairy production based on individual milk-producing states (Matulich; Tauer and Belbase; Grisley and Gitu; Grisley and Mascarenhas; Bravo-Ureta), this study differs in that it uses representative and probability-based data from multiple milk-producing states and in its use of measured efficiency as an indicator of

3 Technology Adoption and Its Impact 479 potential dairy production performance. The study is organized as follows: The next section gives a detailed discussion of the empirical model used to measure the determinants of production performance of dairy operations. This is followed by two sections that describe data sources and empirical results. The final section summarizes the major findings of the study along with some concluding remarks. Empirical Specification The analysis starts with delineation of the method used to estimate technical efficiency index. This is followed by a description of the multinomial logit regression used to examine how technology and certain other factors (e.g., farm, enterprise, and farm operator characteristics) affect the likelihood of a dairy operation being among the group of top-producing operations. Efficiency Index Production performance of the ith farm (i = 1 n) is measured as the ratio of its observed milk output relative to a maximum output that potentially could be produced when resources are allocated most efficiently. The trace of all potential outputs by all farms is referred to here as the production frontier. Since the production frontier is not known, its estimation usually is conducted using parametric or nonparametric methods. Several articles have provided a thorough description of the advantages and limitations of these methods (e.g., Bravo-Ureta and Rieger; Kalaitzandonakes et al.; Hallam). This article estimates a deterministic parametric frontier, as was proposed originally by Greene (1980). The method entails first the specification of the following output frontier: (1) Y i = [α + f(x ij ; β)] exp[(d iq T im ; γ) + u i ] u i 0 where Y i is output of milk sold by the ith farm (i = 1 n) measured in hundredweights (cwt); X ij (j = 1 k) is a vector of aggregate inputs including per-farm dollar expenditures on concentrate, hay, other feed (by-products, liquid whey, silage, pasture, and other forage), miscellaneous inputs (other than feed, including veterinary and medicine; custom services and supplies; fuel, lube, and electricity; repairs; hauling; artificial insemination; bedding and litter; and marketing), capital (capital replacement, operating capital, other nonland capital, and land), labor hours (operator s paid and unpaid labor; unpaid labor by partners, operator s spouse, household members; and paid labor by hired workers), and managerial ability, which is proxied here by operator s years of experience in dairy production; f(x ij ; β) is a milk production function; D iq is a dummy variable of region q; T im is a dummy variable denoting technology m (i.e., T im = 1if adoption occurs, 0 otherwise); α is a constant; β and γ are vectors of unknown parameters; exp is the exponential function; and u i is deviation from the production frontier and, as specified, represents technical inefficiency. Management experience, as was pointed out by Stefanou and Saxena, can lead to gains in efficiency through better organization and knowledge gained from experimenting with alternative production methods, which justifies its inclusion in equation (1).

4 480 Review of Agricultural Economics The technical efficiency for the ith farm, which reflects the firm s ability to produce maximum output given a set of inputs and technology, is calculated as: (2) TE i = Y o i Ŷ i where Y o i and Ŷi are the ith farm s observed and frontier levels of milk output, respectively. A complete discussion of relevant econometric concerns in terms of the estimation of f(x ij ; β) and TE i is found in section (I) of the Appendix. Likelihoods of Production Performance Farming units were ranked in order of their TE levels. This was followed by grouping ranked farms in the top quarter, the middle two quarters, or the lower quarter of the efficiency distribution, denoted here as C 1 C 2 and C 3, respectively. The probability that the ith farm was in the qth efficiency category (P iq ) was estimated as follows [see Zepeda (1990a) for more detail]: (3) P iq = exp(z i ζ q) M q=1 exp(z i ζ q) q = (1 M) where Z i is a vector of k explanatory variables including instruments for technology adoption and for correcting potential selectivity concerns, and ζ q is a vector of parameters associated with Z i. 2 Because elements of ζ q enter equation (3) nonlinearly, their interpretation becomes difficult. McFadden corrects for this by taking the logarithm of the probabilities, which yields, in the context of this study, the following multinomial logit model (MNL): (4) ( ) Piq ln = Z i P ζ q Z i ζ M = Z i ζ qm q = (1 M 1) im where ζ qm is the marginal effect of Z i on the odds ratio. In the context of this study, the coefficients depicted in ζ qm measure the marginal effect of the explanatory variables on the logarithm of the odds of a dairy farm being in efficiency category C 1 relative to C 3 and in efficiency category C 2 relative to C 3 [see section (II) of the Appendix for the estimation of the logarithm of the odds of being in C 1 relative to C 2 ]. The interpretation of ζ qm is simplified even further by computing the marginal effect of Z i on the probabilities of being in C 1 C 2 or C 3 as in [for more detail, see Greene (1997)]: (5) δ q = P q Z q = P q ( ζq ζ ) where ζ is a vector whose elements are the averages of all estimated ζ q (q = 1 2 3). The signs of any particular ζ q and δ q need not be the same.

5 Technology Adoption and Its Impact 481 Data Description Data for the analysis were from the Dairy Cost of Production version of the 1993 Agricultural Resource Management Study (ARMS). The ARMS, which is conducted annually (only every fifth year for a specific commodity) by the Economic Research Service and the National Agricultural Statistics Service (NASS), has a complex stratified multiframe sample design (U.S. Department of Agriculture, 1991, p. 6). This survey design allows each sampled farm to represent a number of similar farms, which are commonly referred to as the survey expansion factor. Because the expansion factor, by definition, is the inverse of the probability of the surveyed farm being selected (U.S. Department of Agriculture, 1991, p. 6), it allows for the expansion of the data to derive estimates for the population of all farms producing the commodity. Farms surveyed in the 1993 ARMS were selected from a NASS list of known milk producers and whose businesses were identified as dairy during all of While the survey of milk operations collects information specific to the farm business (e.g., ownership of land, type of commodity produced, legal form of organization, government payments, debts, assets, among others) and the operator (e.g., age, education, occupation, experience, off-farm work, among others), its primary purpose is to collect information used to estimate the average cost of milk production for the United States and various milk production regions (for a detailed discussion on USDA s method of estimating milk-cost-of-production, see Short and McBride). Another important aspect of the dairy survey is the collection of information on items such as the breed of the dairy cows, types of milking parlors, participation in record-keeping associations, manure handling, feed storage facilities, cow death rate, hours per day milking system was in operation, frequency of milk pickup, number and capacity of bulk tanks and/or milk silos, etc. The size of the sample used after excluding one outlying observation was 679, which represented 102,725 dairy operations from 15 major milk-producing states. The 15 milk-producing states are divided in three groups (see figure 1): the North, Figure 1. Agricultural Resource Management Study s sampling coverage of milk production by milk-producing area, 1993

6 482 Review of Agricultural Economics which includes production regions from the Northeast (New York, Pennsylvania, Vermont), the Corn Belt (Wisconsin, Minnesota, Michigan), and the Upper Midwest; the West, which includes the Pacific production region (Arizona, California, and Washington); and the South, which includes the Southeast production region (Florida, Georgia) and the Southern Plains region (Texas). It is important to note that dairy farms in the North comprised 92% of the 1993 ARMS sampling coverage; dairies in the South and West accounted for 3% and 5%. Results The logit models describing the decision to adopt capital- and managementintense technologies (ci and mi, respectively) were estimated using maximumlikelihood procedures. Table 1 shows higher correct classification rate for the ci adoption model compared with the mi adoption model, at 81.7% versus 52.9%. 3 This goodness of fit measure (Amemyia), along with the McFadden s R 2 of 0.21 and 0.04 for the ci and mi logit models, demonstrate the predictive superiority of the ci adoption model. Estimated parameters shown in table 1 indicate how changes in the explanatory variables change the likelihood (in logarithmic terms) of technology adoption. Experience in dairy farming appeared to positively influence the likelihood of adopting both types of technologies. The significance and the signs of COWS and Table 1. Logit estimates of technology choice in dairy production, 1993 Capital-Intensive Management-Intensive Variables Coefficients t-statistics Coefficients t-statistics Intercept Experience in dairy production (OPEXP) Experience, squared (OPEXPSQ) Expected size ( COWS) Expected size, squared ( COWSSQ) 4 6E E Expected government payments (ĜOV T ) Farm located in the North (NORTH) Farm located in the West (WEST) McFadden s R F-statistic (D.F.) (7 612) 2 88 (7 612) Percentage correct prediction Note: D.F. is degrees of freedom, with 7 denoting the number of exogenous variables and 612 denoting the number of survey design s segments minus the number of survey design s strata. Significant at 10% level. Significant at 5% level. Significant at 1% level. Data source: USDA, Economic Research Service, Agricultural Resource Management Study, 1993.

7 Technology Adoption and Its Impact 483 COWSSQ indicate that the likelihood of adopting a capital-intensive technology increases with size and then reaches a peak at a size of operation equivalent to 358 milking cows. While farms with more than 358 milking cows are less likely to adopt a ci technology, a possible explanation is that the majority of farms in the survey are from the North, where the average herd size is only 57 cows (1993 ARMS), making investment in such a costly technology too prohibitive to many operations. Evidence from Minnesota suggests that some DHI farms are able to grow to a size equivalent to over 150% of barn capacity by using calf hutches, by housing dry cows separately from the milking cows, and by milking in shifts (Conlin). When these results are contrasted against those in the mi technology adoption model, the signs of COWS and COWSSQ show that the likelihood of adopting a management-intensive technology decreases (although void of any statistical significance) as farm size becomes larger and then reaches its lowest level at a size of operation equivalent to 129 milking cows, beyond which it starts to rise with further increases in farm size. At larger farm sizes, dairy farms generally are more likely to invest in the mi technology as the need for better monitoring of production, feeding, and animal health and reproduction becomes more critical. The coefficients of ĜOV T in the two technology models are significant but have opposite directions. An increase in direct government payments, which reflects an increase in the level of diversification, while it tends to decrease the likelihood of adopting a capital-intensive technology, increases the likelihood of adopting a management-intensive technology. Table 2 presents the regression results of per-farm milk output. Findings show that all inputs are significant determinants of milk output. Of the seven inputs used in the model, a 1% increase in the per-farm expenditure on miscellaneous items contributes the most to dairy output, as indicated by the magnitude of its estimated coefficient. Second in importance is per-farm expenditure on concentrates, which is not surprising considering the fact that they are an important source of protein and energy and as such tend to be correlated with high output levels (see Grisley and Gitu). Summing the estimated coefficients (i.e., elasticities) across all inputs produced an elasticity of output with respect to scale (EOS) in the magnitude of 1.079, which, based on an F-test result, allowed for rejection of the hypothesis of constant returns to scale at the 5% significance level in favor of an increasing returns to scale. In other words, an increase of all resources by 1% would result in an increase in farm s total milk output by more than 1%. These findings are in accord to those reported by Bravo-Ureta, who also used a C-D specification of milk output for a selected sample of New England dairy farms. Figures 2 and 3 show, respectively, estimated milk production surfaces based on equation (1) where labor and capital and hay and concentrate are allowed to substitute for each other (within certain selected ranges), while all other inputs are kept fixed at their mean levels. The negative and significant coefficient of NORTH indicates that dairy farms in the northern milk-producing states produce, on average, 22% less in total milk output than those in the southern milk-producing states. The statistical significance of λ ci and λ mi indicates that self-selectivity is a valid concern, which fur-

8 484 Review of Agricultural Economics Table 2. Estimated Cobb-Douglas dairy farm production function, 1993 Coefficients of Estimated Separate Variables Coefficients t-ratios Determination Intercept Consumption of concentrates (dollars) Consumption of hay (dollars) Consumption of other feed (dollars) Miscellaneous inputs (dollars) Labor (hours) Capital (dollars) Experience in dairy production (years) NORTH WEST P ci P mi λ ci λ mi R F-statistic (D.F.) (13 612) Note: P ci, predicted probability of adopting a capital-intense technology (ci); P mi, predicted probability of adopting a management-intense technology (mi); λ ci ci s self-selection variable; λ mi mi s selfselection variable. D.F. is degrees of freedom, with the first number indicating number of explanatory variables and the second indicating number of survey segments minus number of survey strata, respectively. All inputs are in logarithmic form. Significant at 10% level. Significant at 5% level. $ Significant at 1% level. Data source: USDA, Economic Research Service, Agricultural Resource Management Study, ther indicates that estimation of the milk output in the absence of these variables would be biased. Based on the R 2 of 0.87 in table 2, the C-D model explained 87% of the variation in per-farm milk production. Column (4) shows the results of decomposing the R 2 into relative contributing components using the method of coefficients of separate determination, as described in section (III) of the Appendix (see Burt and Finley; Langemeier et al.; El-Osta and Johnson). The results show that consumption of concentrate-feed and expenditures on miscellaneous items (e.g., veterinary and medicine, custom services, etc.) contribute more toward explaining the variability of milk output than all other variables. Table 3 presents the estimated coefficients of the multinomial logit model of factors affecting production performance of dairy operations. The interpretation

9 Technology Adoption and Its Impact 485 Figure 2. Dairy farm s milk production surface with concentrate, hay, other feed, intermediate inputs, and experience at mean levels, 1993 Figure 3. Dairy farm s milk production surface with other feed, intermediate inputs, labor, capital, and experience at mean levels, 1993

10 Table 3. Multinomial logit estimates of factors affecting production performance of dairy operations, 1993 a ln(p 1 /P 3 )ln(p 2 /P 3 )ln(p 1 / P 3 ) b Variables Coefficients t-statistics Coefficients t-statistics Coefficients t-statistics Intercept h g h Experience in dairy production (OPEXP) g Experience, squared (OPEXPSQ) g Sole proprietorship (SOLEOP) c f h Hired labor to total labor (PAIDHRS) f f Milk sold per cow (YIELD) h h h Cows death rate (DRATE) Ratio of calves to cows (CALF) Milk sales/farm gross income (SPEC) h h Debt-to-asset ratio (%) (DA) Farm located in the North (NORTH) d Farm located in the West (WEST) e h h P ci P mi h f λ ci g λ mi f McFadden s R F-statistic (D.F.) 4 44 c (30 ) 486 Review of Agricultural Economics a P 1, P 2, and P 3 are the probabilities of a dairy operation being in a high-performance group, middle-performance group, or a low-performance group, respectively. In turn, low, middle, and high performance denote the lower quartile, middle two quartiles, and upper quartile of the technical efficiency distribution, respectively. D.F. is degrees of freedom. b See section (II) of Appendix for the computation of estimated coefficients and of their corresponding standard errors. c Dummy variable coded 1 if farm is organized as sole proprietorship; 0 otherwise. d Dummy variable coded 1 if farm is located in the Northern region; 0 otherwise. e Dummy variable coded 1 if farm is located in the Western region; 0 otherwise. f Significant at 10% level. g Significant at 5% level. h Significant at 1% level. Data source: USDA, Economic Research Service, Agricultural Resource Management Study, 1993.

11 Technology Adoption and Its Impact 487 of the estimated coefficients is awkward because they describe the marginal effect of the explanatory variables on the logarithm of the odds of being in one production performance group relative to another. Looking at the results of ln(p 1 /P 3 ), the negative and positive significant coefficients of OPEXP and OPEXPSQ indicate that although farmers with little experience in dairy production are more likely to be in the low-performance group instead of the high-performance group, their chances of changing the outcome by moving to the high-performance group increase dramatically as they become more experienced. Other factors such as using a higher proportion of hired labor (PAIDHRS), producing milk more productively (YIELD), specializing in dairy production, and using a managementintense technology are all significant in increasing the odds of being in the high- versus the low-performance group. In terms of how farm location affects the likelihood of being in the high-performance group as opposed to the lowperformance group, dairy farms are much less likely to be among the most efficient group if they are located in the West instead of the South. As evident from the ln(p 1 /P 2 ) results, higher use of paid labor, higher productivity levels, and the use of management-intense technology all tend to increase the likelihood of a farmer being in the high-performance group as opposed to the middle-performance group. The importance of the operator being the sole proprietor of the farm business along with specializing in dairy production and of owning a productive stock is evident from the ln(p 2 /P 3 ) model, where these factors are shown to increase the probability of a farmer being in the middleperformance group versus the low-performance group. Table 4 presents the predicted marginal probabilities of production performance for the sample s dairy farms (see equation 5). An increase in the ratio of calves to cows (CALF), which indicates an increase in a farm s production of its own heifers, decreases the probability of a dairy farm being in the low-performance group by as much as 11%. While an 11% drop in the probability of being in the low efficiency group is sizable, considerably larger reductions are likely to occur if the dairy operation becomes more apt at using a capital- or a managementintense technology, if it switches from being multiowned to being owned singly by the operator, and most important, if it increases its level of specialization in dairy production. In contrast, producing milk in the West increases the probability of low production performance by 47%. The probability of being in the top quartile of the technical efficiency distribution appears to increase dramatically (66%) as the likelihood of a dairy farm using a management-intense technology (P mi ) increases (see table 4). Next in importance is specialization in dairy production. The results indicate that as the operation becomes more specialized in dairy production, the likelihood of becoming a top producer increases by 23%. Likewise, using a higher proportion of hired labor increases the likelihood of high performance by 13%. The MNL results, along with the corresponding predicted marginal probabilities, point to the importance of management-intense technology and, to a lesser extent, of capital-intense technology on the production performance of dairy farms. Figure 4 presents the simulated probabilities of being in a particular production group as the dairy farm moves from production practices that involve no technology use (P ci = P mi = 0) to those involving the use of the technology (P ci = P mi = 1). The upper chart shows a sizable decrease in the likeli-

12 488 Review of Agricultural Economics Table 4. Predicted marginal probabilities of production performance of dairy farms, 1993 Low Middle High Variables Performance Performance Performance Experience in dairy production (OPEXP) Experience, squared (OPEXPSQ) Sole proprietorship (SOLEOP) Hired labor to total labor (PIADHRS) Milk sold per cow (YIELD) Cows death rate (DRATE) Ratio of calves to cows (CALF) Milk sales/farm gross income (SPEC) Debt-to-asset ratio (%) (DA) Farm located in the North (NORTH) Farm located in the West (WEST) P ci P mi Note: Low, middle, and high performance denote the lower quartile, middle two quartiles, and upper quartile of the technical efficiency distribution, respectively. Data source: USDA, Economic Research Service, Agricultural Resource Management Study, hood of being in the low-performance group as the farm moves from producing milk without using a capital-intense technology to that where the technology is used (from to 0.048). Such a movement in the level of adoption of the capital-intense technology increases a dairy farm s chance of being among the topperforming group by a moderate amount (from to 0.573). The lower chart of figure 4 demonstrates the importance of a management-intense technology to production performance as use of the technology; while it decreases significantly the farm s chance of being in the lower quartile of production performance (from to 0.004), it increases in a dramatic way its chance of being in the top quartile of production performance (from to 0.903). Table 5 presents the average technical efficiency and the costs and returns characteristics of dairy farms by the level of production performance. While the average overall technical efficiency of the dairy farms in the full sample is at 87%, the average of the low-performance group is 83%, and that of the high-performance group is significantly higher, at 91%. Dairy operations in the low-performance group tend to be located in areas where milk prices are high, which explains their significantly larger per-unit gross value of production. Despite the higher value of production, lower yields and inability to control costs by using inputs more efficiently are behind this group s high per-unit cost of production. Farmers in the high-performance group produce milk at significantly less per-unit cost than their counterparts in the lowperformance group, with most of the cost savings resulting from the group s higher feed and labor efficiency. For example, data from the 1993 ARMS show

13 Technology Adoption and Its Impact 489 Figure 4. Effect of technology adoption on production performance of dairy operations, 1993 that to produce a hundredweight of milk, farmers in the high-performance group need only 165 lbof feed; this is in comparison with the 270 lbof feed needed by farmers in the low-performance group. In terms of total labor (paid and unpaid), producers in the high-performance group use 0.19 hours per hundredweight of milk sold, compared with 0.61 hours by producers in the low-performance group. Lower machinery and equipment costs for capital replacement and lower unpaid labor costs contribute significantly toward the lowering of the economic costs for producers in the high-performance category. By moving from the lowto the high-performance group, a dairy producer will be able to improve his or her per-unit economic cost by around 53% (from $27.64 to $13.06). Being in the top-performance group brings an additional economic benefit because it allows for a positive residual return to management and risk (gross revenue less economic costs), at 0.88 per hundredweight of milk sold. Figure 5 shows the cumulative distributions of costs and returns per hundredweight of milk sold, with the upper and the lower charts exhibiting the distributions of economic costs and of residual returns to management and risks, respectively. In 1993, while the aver-

14 490 Review of Agricultural Economics Table 5. Costs and returns characteristics of dairy farms, by production performance, 1993 Low Middle High Item Performance Performance Performance All Technical efficiency Dollars per cwt of milk sold Cash costs and returns: Gross value of production: Milk Cattle Other income Total, gross value of production Cash expenses: Feed Concentrates By-products Liquid whey Hay Silage Pasture and other forage Total feed cost Other Hauling Artificial insemination Veterinary and medicine Bedding and litter Marketing Custom services and supplies Fuel, lube, and electricity Repairs Hired labor DHIA fees Dairy assessment Total, variable cash expenses General farm overhead Taxes and insurance Interest [3pt] Total, fixed cash expenses Total, cash expenses Gross value of production less cash expenses Economic (full-ownership) costs and returns: Variable cash expenses General farm overhead Taxes and insurance Capital replacement Operating capital Other nonland capital

15 Technology Adoption and Its Impact 491 Table 5. Continued Low Middle High Item Performance Performance Performance All Land Unpaid labor Total, economic (full-ownership) costs Residual returns to management and risks Note: Estimates that are underlined have coefficients of variation (CVs) in the range of 55% to 85%. Denotes that difference of mean of this item relative to same item in middle-performance category is significant at α = Denotes that difference of mean of this item relative to same item in high-performance category is significant at α = Data source: USDA, Economic Research Service, Agricultural Resource Management Study, age economic cost for all producers in the full sample (i.e., regardless of the level of production performance) was $16.43, the corresponding average of residual returns was $2 09. Nearly all the dairy producers in the low-performance group had economic costs exceeding the sample s average, compared with only 13% of the farms in the high-performance group. In contrast, the sample s average of residual returns was exceeded by only 2% of dairy farms in the low-performance group and by nearly 87% of dairy farms in the high-performance group. Summary and Conclusions The primary purpose of this article was to examine the determinants of production performance of a sample of dairy farms, particularly those pertaining to technology adoption. To achieve this, a milk production frontier, corrected for endogeneity and self-selectivity of the technology variables, was estimated. Results from the estimated production frontier showed that per-farm annual consumption of concentrate-feed along with expenditures on miscellaneous items (e.g., veterinary and medicine, custom services and supplies, etc.) were most important in explaining the variability in milk production. The estimated average technical efficiency of all dairy farms was 87%, indicating that farms were producing 13% less of their potential due to inefficient means of production. Estimated marginal probabilities of production performance indicated that operating as a sole proprietorship and an increase in the level of specialization in dairy production would lower the likelihood of a farmer being in the lower quartile of the technical efficiency distribution by as much as 22% and 36%, respectively. Measurably less in importance was technological adoption, since findings indicated that use of a capital- and a management-intense technology would lower the probability of a farmer being in the low production performance group by 16% and 17%, respectively. In contrast, factors that were found most important in positively affecting the likelihood of a farmer being in the top-performance group were specialization in dairy production, use of hired labor, and to a large extent, use of a management-intense technology. Adoption of a capital-intense

16 492 Review of Agricultural Economics Figure 5. Cumulative (smoothed) distributions of costs and returns per hundredweight of milk sold, by level of production performance, 1993 technology had a positive but rather a mild impact on the likelihood of a farmer being in the upper quartile of the efficiency distribution. Results showed that dairy farmers could reap a sizable economic gain if they were to produce milk more efficiently. Specifically, by moving from a low- to a high-performance group, dairy farmers could improve their per-unit economic costs by about 53%, from $27.64 to $ Such a move also would allow for a positive return to management and risks, up from $12 23 per hundredweight of milk sold to nearly $0.88. Because of a strong demand, milk prices in the late 1990s were on the rebound, allowing many producers to sell their milk at record levels, at more than $16 per hundredweight. To many producers, particularly to those in the lower production performance groups (see table 5 and figure 5), even these prices are not high enough to cover the per-unit economic costs of production. Increased and sustained production efficiency may help these farmers in narrowing the gap between the price they receive for their output and the cost of producing that output. However, improved management and production practices can, on their own, increase the pressure on dairy farms if their facilities are old and dilapidated. Widening access to relatively inexpensive credit would provide producers with the means for investing in newer capital structures, thereby allowing for a

17 Technology Adoption and Its Impact 493 fuller use of their managerial ability and for higher levels of milk output. This is in light of the finding that producers could lower the likelihood of being in the low production performance group by nearly 16% if they were to invest in a capital-intense technology. Acknowledgments The views expressed are the authors and do not necessarily represent those of the Economic Research Service or the U.S. Department of Agriculture. We acknowledge the helpful comments of the editors and of two anonymous reviewers. Any remaining errors are our responsibility. Endnotes 1 Following Zepeda (1990b), a capital-intensive technology is defined as one for which the largest single cost share of its implementation is capital cost. A management-intensive technology is defined likewise. 2 Where attending to the problem of sample selection bias has appeared mostly in the context of the linear regression model, it also has appeared in limited dependent-variable models and in count-data models (see Greene, 1997, p. 983). 3 Because of the complex nature of the ARMS sampling design, all relevant computations were conducted using appropriate statistical algorithms (see Dubman) and computer software (e.g., PC CARP by Fuller et al.). Appendix (I) In estimating equation (1), several econometric issues need to be addressed: 1. As in most technical efficiency studies (see Dawson and Lingard, 1982), the functional form of f(x ij ; β) chosen is a Cobb-Douglas (C-D) because of the ease in interpreting parameter estimates. Specifically, the estimated jth parameter is interpreted as an elasticity that measures the percentage change in milk output given a 1% change in that particular input, and sum of all estimated j(j = 1 k) parameters is interpreted as the elasticity of output with respect to scale (EOS), as in: EOS = k ˆβ j A Wald F test (see Johnston, p. 192) can be used to examine whether a firm is experiencing increasing, constant, or decreasing returns to scale based on whether estimated EOS is greater than 1, equal to 1, or less than 1, respectively. Although C-D has well-known limitations, its use here should not cause concern because functional specification, as was noted in the literature (Kopp and Smith; Bravo-Ureta and Rieger; Bravo-Ureta and Pinheiro), has a small impact on measured efficiency. 2. If the simultaneity and the self-selectivity problems associated with the technology variable T im are left uncorrected, estimation of equation (1) would yield inconsistent parameter estimates. Simultaneity arises because productivity and technology choice are jointly determined (Zepeda, 1994). j=1

18 494 Review of Agricultural Economics Self-selectivity arises because not all farmers are adopters of the technologies considered. Because the decision to adopt is based on the relative marginal utility of adoption, which in turn is related to a vector of personal characteristics, adopting operators are not a random sample of all operators. As in Burrows and in Fernandez-Cornejo, remedies for these concerns are provided using a two-stage procedure. The initial stage entails two tasks. First, use logistic regression to estimate the probability of technology adoption by producer i as described in the following: P i (T i = 1) = exp( x i ϑ) where x is a vector of characteristics (e.g., years of experience in dairy farming, expected number of milking cows, expected government payments, and regional location of farm), and ϑ is a vector of parameters to be estimated. Because size of farm, as measured by the number of cows (COWS), and government payments (GOVT) are jointly determined with the technology adoption decision, predicted values of these variables were used instead. These predicted values were obtained from regressing the observed values of COWS and GOVT on such variables as human capital (operator experience and years of education in the COWS model and operator experience and experience squared in the GOVT model), regional location, state average corn price, state average wage rate of hired labor, and state average monthly temperature and precipitation. Because the study considers the adoption of capital- and management-intense technologies (ci and mi), two separate logistic regressions are estimated, with results yielding two probability vectors P ci and P mi, respectively. Where a binomial logit approach is used here to measure the probability of technology adoption, Zepeda (1990a) used a multinomial logit technique to predict the use of bovine somatotropin by California dairy farmers. Lee et al. used mean variance and stochastic efficiency criteria to predict adoption of a reduced tillage practice in a watershed in central Indiana. The second task involves the estimation of a selectivity variable λ, which is done using Lee s two-stage procedure (Lee, 1979, 1982, and 1983), as in λ i = ϕ(s i) (S i ) where S i = ϕ 1 (P i ), ϕ( ) and ( ) are the probability density function and the cumulative distribution function of the standard normal distribution, respectively, evaluated at the argument. Using the logistic regression results from the two technology adoption models, λ i allows for the

19 Technology Adoption and Its Impact 495 estimation of two separate selectivity variables λ ci and λ mi, respectively. While selectivity variables λ ci and λ mi are estimated from separate probability distributions using binomial logit models, a preferred procedure is to estimate these variables jointly. To do just this, however, would require the use of bivariate logit or probit models. This, however, is not feasible due to the lack of appropriate computing algorithms when the underlying data are based on surveys with complex design as in ARMS. In the second stage of estimating the output frontier, the resulting estimates for the technology variables (i.e., P ci and P mi ) and for the selectivity variables (i.e., λ ci and λ mi ) are treated as exogenous variables, as in (1a) Y i = [α + f(x ij ; β)] exp[(d iq P ci P mi λ ci λ mi ; γ) + u i ] Because P ci and P mi are the predicted probabilities for adopting capitaland management-intense technologies, their use in equation (1a) as instruments for the endogenous variable T im acts in mitigating bias due to simultaneity concerns. Likewise, appending λ ci and λ mi in equation (1a) allows for unbiased and consistent estimates of model s parameters. 3. There exists a potential for estimation bias due to the distribution of u i. Specifically, because the distribution of u i is not known a priori, using OLS to estimate equation (1a) produces a best linear unbiased parameter estimate, except for the intercept, which tends to be biased downward. This problem can be avoided by using corrected OLS (COLS), whereby the production frontier described in equation (1a) is estimated first by OLS, and then the intercept α is shifted upward by an amount equal to the largest positive residual. This shift will cause all residuals to become nonnegative and at least one to take the value of zero (Bravo-Ureta and Rieger). 4. There exists an inherent limitation in the deterministic parametric frontier approach of measuring production efficiency. Since production inefficiency is relegated to a deviation from the frontier as measured by u i, any deviation that might have been caused by bad weather, statistical noise, or measurement error is lumped with actual technical inefficiency (Kalaitzandonakes et al.). A consequence of this is that this method of measuring efficiency is susceptible to data outliers. To mitigate the potential for this problem, this study identified one outlying observation based on the studentized deleted residuals method and Cook s distance measures (Neter et al.; Cook, 1977, 1979), which was later deleted from the data set. (II) For a set of M categories in MNL models, computing algorithms estimate only M 1 sets of coefficients that measure the marginal effect of the regressors on the logarithm of the odds of being in one category versus another. Here, while the coefficients and their corresponding standard errors of the ln(p 1 /P 3 ) and ln(p 2 /P 3 ) models can be estimated directly by available computing software, the coefficients (ζ 12 ) and their standard errors (SE ζ 12 ) for

20 496 Review of Agricultural Economics the ln(p 1 /P 2 ) model need to be imput indirectly, as in the following: ln ( ( ) ) Pi1 /P P i1 /P i2 = ln i3 P i2 /P i3 = ln ( P i1 /P i3 ) ln ( Pi2 /P i3 ) = ( Z i ζ 1 Z i ζ ) ( 3 Z i ζ 2 Z i ζ ) 3 = Z iˆζ 13 Z iˆζ 23 where and where = Z i ζ 12 ζ 12 = ˆζ 13 ˆζ 23 SEζ 12 = [var(ˆζ 13 ˆζ 23 )] 0 5 = [varˆζ 13 + varˆζ 23 2cov(ˆζ 13ˆζ 23 )] 0 5 In the preceding formulation of SE ζ 12, var and cov denote the variance and covariance of estimated parameters. (III) The relative contributions to variability ϕ g (g = 1 k), also known as coefficients of separate determination, are obtained from regressing a random variable against a set of explanatory variables X 1 X k, and their sum, accordingly, is equivalent to the goodness-of-fit measure R 2. For the C-D function used in the analysis, and under the assumption that Y is a function of only two inputs, this can be demonstrated by regressing the logarithm of Y on the logarithms of X 1 and X 2 (i.e., per-farm output, concentrate-feed, and hay, respectively), as in ln Y i = ln α 0 + α 1 ln X 1 i + α 2 ln X 2 i + ε i (i = 1 n) The variability in ln Y(σ ln Y ) can be decomposed into: [ α 2 σ ln Y = σ(ln Y α 0 α 1 α 2 ) = 1 σ ] 11 +α 1 α 2 σ 12 α 2 α 1 σ 21 +α 2 2 σ 22 Consequently, ϕ 1 = ( α 2 1 σ 11 + α 1 α 2 σ 12 ) /σln Y and ϕ 2 = (α 2 α 1 σ 21 + α 2 2 σ 22)/σ ln Y R 2 = ϕ 1 + ϕ 2

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