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


 Elmer Evans
 9 months ago
 Views:
Transcription
1 Review of Agricultural Economics Volume 22, Number 2 Pages Technology Adoption and Its Impact on Production Performance of Dairy Operations Hisham S. ElOsta 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 managementintense 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 topperformance group were estimated to be 53% lower than those by the lowperformance 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 ElOsta 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: capitalintense (e.g., advanced milking parlors, genetically superior milking cows) and managementintense (e.g., use of recordkeeping systems for total management, bovine somatotropin, improved nutrition and feeding practices). 1 Because of their high startup costs, capitalintensive 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, managementintensive 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 marketoriented 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 managementintense technologies in terms of having an impact on the likelihood of farms being in the low, middle, or highperformance 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 farmlevel 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 milkproducing states (Matulich; Tauer and Belbase; Grisley and Gitu; Grisley and Mascarenhas; BravoUreta), this study differs in that it uses representative and probabilitybased data from multiple milkproducing 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 topproducing 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., BravoUreta 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 perfarm dollar expenditures on concentrate, hay, other feed (byproducts, 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, offfarm 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 milkcostofproduction, 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 recordkeeping 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 milkproducing states. The 15 milkproducing states are divided in three groups (see figure 1): the North, Figure 1. Agricultural Resource Management Study s sampling coverage of milk production by milkproducing 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 CapitalIntensive ManagementIntensive Variables Coefficients tstatistics Coefficients tstatistics 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 Fstatistic (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 capitalintensive 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 managementintensive 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 capitalintensive technology, increases the likelihood of adopting a managementintensive technology. Table 2 presents the regression results of perfarm 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 perfarm expenditure on miscellaneous items contributes the most to dairy output, as indicated by the magnitude of its estimated coefficient. Second in importance is perfarm 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 Ftest 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 BravoUreta, who also used a CD 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 milkproducing states produce, on average, 22% less in total milk output than those in the southern milkproducing states. The statistical significance of λ ci and λ mi indicates that selfselectivity is a valid concern, which fur
8 484 Review of Agricultural Economics Table 2. Estimated CobbDouglas dairy farm production function, 1993 Coefficients of Estimated Separate Variables Coefficients tratios 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 Fstatistic (D.F.) (13 612) Note: P ci, predicted probability of adopting a capitalintense technology (ci); P mi, predicted probability of adopting a managementintense technology (mi); λ ci ci s selfselection 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 CD model explained 87% of the variation in perfarm 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.; ElOsta and Johnson). The results show that consumption of concentratefeed 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 tstatistics Coefficients tstatistics Coefficients tstatistics 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 Debttoasset 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 Fstatistic (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 highperformance group, middleperformance group, or a lowperformance 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 lowperformance group instead of the highperformance group, their chances of changing the outcome by moving to the highperformance 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 lowperformance group. In terms of how farm location affects the likelihood of being in the highperformance 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 managementintense technology all tend to increase the likelihood of a farmer being in the highperformance group as opposed to the middleperformance 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 lowperformance 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 lowperformance 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 managementintense 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 managementintense technology and, to a lesser extent, of capitalintense 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) Debttoasset 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 lowperformance group as the farm moves from producing milk without using a capitalintense technology to that where the technology is used (from to 0.048). Such a movement in the level of adoption of the capitalintense 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 managementintense 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 lowperformance group is 83%, and that of the highperformance group is significantly higher, at 91%. Dairy operations in the lowperformance group tend to be located in areas where milk prices are high, which explains their significantly larger perunit 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 perunit cost of production. Farmers in the highperformance group produce milk at significantly less perunit 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 highperformance group need only 165 lbof feed; this is in comparison with the 270 lbof feed needed by farmers in the lowperformance group. In terms of total labor (paid and unpaid), producers in the highperformance group use 0.19 hours per hundredweight of milk sold, compared with 0.61 hours by producers in the lowperformance 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 highperformance category. By moving from the lowto the highperformance group, a dairy producer will be able to improve his or her perunit economic cost by around 53% (from $27.64 to $13.06). Being in the topperformance 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 Byproducts 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 (fullownership) 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 (fullownership) 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 middleperformance category is significant at α = Denotes that difference of mean of this item relative to same item in highperformance 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 lowperformance group had economic costs exceeding the sample s average, compared with only 13% of the farms in the highperformance group. In contrast, the sample s average of residual returns was exceeded by only 2% of dairy farms in the lowperformance group and by nearly 87% of dairy farms in the highperformance 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 selfselectivity of the technology variables, was estimated. Results from the estimated production frontier showed that perfarm annual consumption of concentratefeed 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 managementintense 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 topperformance group were specialization in dairy production, use of hired labor, and to a large extent, use of a managementintense technology. Adoption of a capitalintense
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 highperformance group, dairy farmers could improve their perunit 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 perunit 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 capitalintense 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 capitalintensive technology is defined as one for which the largest single cost share of its implementation is capital cost. A managementintensive 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 dependentvariable models and in countdata 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 CobbDouglas (CD) 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 CD has wellknown limitations, its use here should not cause concern because functional specification, as was noted in the literature (Kopp and Smith; BravoUreta and Rieger; BravoUreta and Pinheiro), has a small impact on measured efficiency. 2. If the simultaneity and the selfselectivity 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 Selfselectivity 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 FernandezCornejo, remedies for these concerns are provided using a twostage 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 managementintense 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 twostage 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 managementintense 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 (BravoUreta 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 goodnessoffit measure R 2. For the CD 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., perfarm output, concentratefeed, 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
21 Technology Adoption and Its Impact 497 References Amemiya, T. Qualitative Response Models: A Survey. J. Econ. Lit. 19(December 1981): Boehlje, M. Alternative Models of Structural Change in Agriculture and Related Industries. Agribusiness 8(May 1992): BravoUreta, B. E. Technical Efficiency Measures for Dairy Farms Based on a Probabilistic Frontier Function Model. Can. J. Agr. Econ. 34(November 1986): BravoUreta, B. E., and A. E. Pinheiro. Efficiency Analysis of Developing Country Agriculture: A Review of the Frontier Function Literature. Agr. Res. Econ. Rev. 22(April 1993): BravoUreta, B. E., and L. Rieger. Alternative Production Frontier Methodologies and Dairy Farm Efficiency. J. Agr. Econ. 41(May 1990): Burrows, T. M. Pesticide Demand and Integrated Pest Management: A Limited Dependent Variable Analysis. Am. J. Agr. Econ. 65(November 1983): Burt, O. R., and R. M. Finely. Statistical Analysis of Identities in Random Variables. Am. J. Agr. Econ. 50(August 1968): Cochrane, W. W. The City Man s Guide to the Farm Problem. Minneapolis: University of Minnesota Press, The Development of Agriculture: A Historical Analysis. Minneapolis: University of Minnesota Press, Conlin, B. J. Managing $10 Milk Prospectives on Dairy Inputs. October Available at DAIRY INPUTS AND OUTPUTS.html (May 1997). Cook, R. D. Detection of Influential Observations in Linear Regression. Technometrics 19(February 1977): Dawson, P. J., and J. Lingard. Management Bias and Returns to Scale in a CobbDouglas Production Function for Agriculture. Europ. Rev. Agr. Econ. 9(1982): Influential Observations in Linear Regression. J. Am. Stat. Assoc. 74(March 1979): Dubman, R. Variance Estimation with USDA s Farm Costs and Returns Survey and Agricultural Resource Management Study Surveys (AGES 0001). Washington: U.S. Department of Agriculture, Economic Research Service, April ElOsta, H. S., and J. D. Johnson. Determinants of Financial Performance of Commercial Dairy Farms (Technical Bulletin No. 1859). Washington: U.S. Department of Agriculture, Economic Research Service, July FernandezCornejo, G. The Microeconomic Impact of IPM Adoption: Theory and Application. Agr. Res. Econ. Rev. 25(October 1996): Fuller, W.A., W. Kennedy, D. Schnell, G. Sullivan, and H. J. Park. PC CARP. Ames, IA: Statistical Laboratory, Iowa State University, Greene, W. H. Maximum Likelihood Estimation of Econometric Frontier Functions. J. Econometrics 13(May 1980): Econometric Analysis. Englewood Cliffs, NJ: PrenticeHall, 1997 Grisley, W., and K. W. Gitu. The Production Structure of Pennsylvania Dairy Farms. N.E. J. Agr. Res. Econ. 13(October 1984): Grisley, W., and J. Mascarenhas. Operating Cost Efficiency on Pennsylvania Dairy Farms. N.E. J. Agr. Res. Econ. 14(April 1985): Hallam, A. A Brief Overview of Nonparametric Methods in Economics. N.E. J. Agr. Res. Econ. 21(October 1992): Huffman, W. E., and R. E. Evenson. Science for Agriculture: A Long Term Perspective. Ames, IA: Iowa State University Press, Johnson, R. G., and R. L. Grabanski. Technology Adoption and Farm Size. In: Determinants of Farm Size and Structure, Arne Hallam, ed. Ames, IA: Iowa State University Press, Johnston, J. Econometric Methods. New York: McGrawHill, Kalaitzandonakes, N. G., S. Wu, and J. Ma. The Relationship between Technical Efficiency and Firm Size Revisited. Can. J. Agr. Econ. 40(November 1992): Kopp, R. J., and V. K. Smith. Frontier Production Function Estimates for Steam Electric Generation: A Comparative Analysis. South. Econ. J. 46 (April 1980): Langemeir, M., T. Schroeder, and J. Mintert. Determinants of Cattle Finishing Profitability. South. J. Agr. Econ. 24(December 1992):41 7. Lee, J., D. J. Brown, and S. Lovejoy. Stochastic Efficiency versus MeanVariance Criteria as Predictors of Adoption of Reduced Tillage. Amer. J. Agr. Econ. 67(November 1985): Lee, L. Identification and Estimation in Binary Choice Models with Limited (Censored) Dependent Variables. Econometrica 47(July 1979): Some Approaches to the Correction of Selectivity Bias. Rev. Econ. Stud. 49(January 1982): Generalized Econometric Models with Selectivity. Econometrica 51(March 1983):
Appendix I Whole Farm Analysis Procedures and Measures
Appendix I Whole Farm Analysis Procedures and Measures The wholefarm reports (except for the balance sheets) include the same number of farms, which were all of the farms whose records were judged to
More informationFactors Impacting Dairy Profitability: An Analysis of Kansas Farm Management Association Dairy Enterprise Data
www.agmanager.info Factors Impacting Dairy Profitability: An Analysis of Kansas Farm Management Association Dairy Enterprise Data August 2011 (available at www.agmanager.info) Kevin Dhuyvetter, (785) 5323527,
More informationCalculating Your Milk Production Costs and Using the Results to Manage Your Expenses
Calculating Your Milk Production Costs and Using the Results to Manage Your Expenses by Gary G. Frank 1 Introduction Dairy farms producing milk have numerous sources of income: milk, cull cows, calves,
More informationChapter 11: Two Variable Regression Analysis
Department of Mathematics Izmir University of Economics Week 1415 20142015 In this chapter, we will focus on linear models and extend our analysis to relationships between variables, the definitions
More informationComparative Study of Artificial Insemination and Natural Service Cost Effectiveness in Dairy Cattle
Comparative Study of Artificial Insemination and Natural Service Cost Effectiveness in Dairy Cattle Valergakis G.E., Banos G., Arsenos G. Department of Animal Production, School of Veterinary Medicine,
More informationEnterprise Budgeting. By: Rod Sharp and Dennis Kaan Colorado State University
Enterprise Budgeting By: Rod Sharp and Dennis Kaan Colorado State University One of the most basic and important production decisions is choosing the combination of products or enterprises to produce.
More informationEstimating Cash Rental Rates for Farmland
Estimating Cash Rental Rates for Farmland Tenant operators farm more than half of the crop land in Iowa. Moreover, nearly 70 percent of the rented crop land is operated under a cash lease. Cash leases
More informationCHAPTER 5. Exercise Solutions
CHAPTER 5 Exercise Solutions 91 Chapter 5, Exercise Solutions, Principles of Econometrics, e 9 EXERCISE 5.1 (a) y = 1, x =, x = x * * i x i 1 1 1 1 1 1 1 1 1 1 1 1 1 1 y * i (b) (c) yx = 1, x = 16, yx
More information7. Tests of association and Linear Regression
7. Tests of association and Linear Regression In this chapter we consider 1. Tests of Association for 2 qualitative variables. 2. Measures of the strength of linear association between 2 quantitative variables.
More informationFacts about Organic Dairying
Facts about Organic Dairying Juan S. Velez, M.V., M.S., DACT Aurora Organic Dairy The Organic Industry Today: Organic farming has been one of the fastest growing segments of U.S. agriculture since 1990.
More informationGrowth in Dairy Farms: The Consequences of Taking Big Steps or Small Ones When Expanding
Growth in Dairy Farms: The Consequences of Taking Big Steps or Small Ones When Expanding Presented by Bruce L. Jones, Director UW Center for Dairy Profitability University of Wisconsin Through the years
More informationLinear and Piecewise Linear Regressions
Tarigan Statistical Consulting & Coaching statisticalcoaching.ch Doctoral Program in Computer Science of the Universities of Fribourg, Geneva, Lausanne, Neuchâtel, Bern and the EPFL Handson Data Analysis
More informationECONOMETRIC THEORY. MODULE I Lecture  1 Introduction to Econometrics
ECONOMETRIC THEORY MODULE I Lecture  1 Introduction to Econometrics Dr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur 2 Econometrics deals with the measurement
More informationCharacteristics, Costs, and Issues for Organic Dairy Farming
United States Department of Agriculture Economic Research Service Economic Research Report Number 82 Characteristics, Costs, and Issues for Organic Dairy Farming William D. McBride Catherine Greene November
More informationThe Effects of Drought and Disaster Payments on the Missouri Cattle Industry
The Effects of Drought and Disaster Payments on the Missouri Cattle Industry July 2016 Dr. Scott Brown Assistant Extension Professor 215 Mumford Hall Columbia, MO 65211 (573) 882 3861 brownsc@missouri.edu
More informationCosts to Produce Hogs in IllinoisC2007
Costs to Produce Hogs in IllinoisC2007 University of Illinois Farm Business Management Resources FBM0150 Costs to Produce Hogs in IllinoisC2007 Dale H. Lattz Extension Specialist, Farm Management Department
More informationEconomic Impact of Washington Dairy Farms: An InputOutput Analysis
Economic Impact of Washington Dairy Farms: An InputOutput Analysis Working Paper Draft October 16, 2007 J. Shannon Neibergs Associate Professor Extension Economist David Holland Professor Emeritus School
More informationEconometric Analysis of Cross Section and Panel Data Second Edition. Jeffrey M. Wooldridge. The MIT Press Cambridge, Massachusetts London, England
Econometric Analysis of Cross Section and Panel Data Second Edition Jeffrey M. Wooldridge The MIT Press Cambridge, Massachusetts London, England Preface Acknowledgments xxi xxix I INTRODUCTION AND BACKGROUND
More informationIntegrated Resource Plan
Integrated Resource Plan March 19, 2004 PREPARED FOR KAUA I ISLAND UTILITY COOPERATIVE LCG Consulting 4962 El Camino Real, Suite 112 Los Altos, CA 94022 6509629670 1 IRP 1 ELECTRIC LOAD FORECASTING 1.1
More informationHow much financing will your farm business
Twelve Steps to Ag Decision Maker Cash Flow Budgeting File C315 How much financing will your farm business require this year? When will money be needed and from where will it come? A little advance planning
More informationThe data for this report were collected by Iowa Farm Business Association consultants and compiled by Iowa State University Extension and Outreach.
2015 Cash Iowa Rental Farm Rates Costs for Iowa Ag Decision Maker Returns 2015 Survey File C110 The farm record data utilized in this report were obtained from the Iowa Farm Business Association. The
More informationThe Fall Run: Cull Cows & Trading Calves
Canfax Research Services A Division of the Canadian Cattlemen s Association Publication Sponsored by: A lot happens during the fall run and the decisions made now determine if a cow/calf operation is profitable
More informationEstablishing and Using a Farm Financial RecordKeeping System
PB 1540 Establishing and Using a Farm Financial RecordKeeping System Delton C. Gerloff, Associate Professor, Agricultural Economics Robert W. Holland Jr., Assistant Area Specialist  Farm Management For
More information
'DLU\%XVLQHVV$QDO\VLV3URMHFW&ULWLFDO)LQDQFLDO 3HUIRUPDQFH)DFWRUVIURP7KUHH
'DLU\%XVLQHVV$QDO\VLV3URMHFW&ULWLFDO)LQDQFLDO 3HUIRUPDQFH)DFWRUVIURP7KUHH
More informationEastern Kentucky Meat Goat Budget Analysis
Eastern Kentucky Meat Goat Budget Analysis Agricultural Economics Extension No. 200011 May 2000 By: ALIOUNE DIAW AND A. LEE MEYER University of Kentucky Department of Agricultural Economics 400 Charles
More informationBalance Sheet of Wisconsin Dairy Farms by Gary Frank 1 August 11, 2001
Balance Sheet of Wisconsin Dairy Farms  2000 by Gary Frank 1 August 11, 2001 Introduction One of the most important indicators of financial progress in a farm business is the Balance Sheet, sometimes
More informationMPP Decision Guide 15 01. MPP Dairy Financial Stress test Calculator: A User s Guide
MPP Decision Guide 15 01 MPP Dairy Financial Stress test Calculator: A User s Guide Christopher Wolf and Marin Bozic Michigan State University and the University of Minnesota A financial stress test calculator
More informationStatistics for Management IISTAT 362Final Review
Statistics for Management IISTAT 362Final Review Multiple Choice Identify the letter of the choice that best completes the statement or answers the question. 1. The ability of an interval estimate to
More informationHow Profitable is Backgrounding Cattle? Dr. John Lawrence and Cody Ostendorf, Iowa State University
How Profitable is Backgrounding Cattle? Dr. John Lawrence and Cody Ostendorf, Iowa State University Many beef producers question the profitability of backgrounding cattle before selling them. Many variables
More informationSTATISTICA Formula Guide: Logistic Regression. Table of Contents
: Table of Contents... 1 Overview of Model... 1 Dispersion... 2 Parameterization... 3 SigmaRestricted Model... 3 Overparameterized Model... 4 Reference Coding... 4 Model Summary (Summary Tab)... 5 Summary
More information1. The Classical Linear Regression Model: The Bivariate Case
Business School, Brunel University MSc. EC5501/5509 Modelling Financial Decisions and Markets/Introduction to Quantitative Methods Prof. Menelaos Karanasos (Room SS69, Tel. 018956584) Lecture Notes 3 1.
More informationMISSING DATA TECHNIQUES WITH SAS. IDRE Statistical Consulting Group
MISSING DATA TECHNIQUES WITH SAS IDRE Statistical Consulting Group ROAD MAP FOR TODAY To discuss: 1. Commonly used techniques for handling missing data, focusing on multiple imputation 2. Issues that could
More information2. Linear regression with multiple regressors
2. Linear regression with multiple regressors Aim of this section: Introduction of the multiple regression model OLS estimation in multiple regression Measuresoffit in multiple regression Assumptions
More informationModern Efficiency Analysis:
Timo Kuosmanen Modern Efficiency Analysis: Combining axiomatic nonparametric frontier with stochastic noise Outline Applications of efficiency analysis The classic DEA and SFA approaches Modern synthesis
More informationPrice Indices for Estimating Cattle Prices in the Pacific Northwest
PNW 666 Price Indices for Estimating Cattle Prices in the Pacific Northwest C. Wilson Gray Introduction The history of the cattle industry since statistics were first collected in 1865 has been cyclical.
More informationChapter 4: Business Planning & Financials What Attitude Can Do for Aptitude
Chapter 4: Business Planning & Financials What Attitude Can Do for Aptitude 25 Introduction An often overlooked yet vital resource to our industry is a cadre of effective dairy managers and consultants.
More informationChapter 1 Introduction to Econometrics
Chapter 1 Introduction to Econometrics Econometrics deals with the measurement of economic relationships. It is an integration of economics, mathematical economics and statistics with an objective to provide
More informationSimultaneous Equation Models As discussed last week, one important form of endogeneity is simultaneity. This arises when one or more of the
Simultaneous Equation Models As discussed last week, one important form of endogeneity is simultaneity. This arises when one or more of the explanatory variables is jointly determined with the dependent
More informationSAS Software to Fit the Generalized Linear Model
SAS Software to Fit the Generalized Linear Model Gordon Johnston, SAS Institute Inc., Cary, NC Abstract In recent years, the class of generalized linear models has gained popularity as a statistical modeling
More informationThe Demand for Nitrogen, Phosphorous and Potash Fertilizer Nutrients in the Western United States
The Demand for Nitrogen, Phosphorous and Potash Fertilizer Nutrients in the Western United States Hoy F. Carman An economic model of the demand for fertilizer is specified and equations for nitrogen, phosphorous
More informationSELFTEST: SIMPLE REGRESSION
ECO 22000 McRAE SELFTEST: SIMPLE REGRESSION Note: Those questions indicated with an (N) are unlikely to appear in this form on an inclass examination, but you should be able to describe the procedures
More informationPart Three Major Opportunities Exist for U.S. and Canadian Beef Producers For (1) Those Who Start Paying Much Closer Attention to What Consumers are
Part Three Major Opportunities Exist for U.S. and Canadian Beef Producers For (1) Those Who Start Paying Much Closer Attention to What Consumers are Spending Their Stretched Protein Dollars on and Why
More informationSimple Linear Regression
Inference for Regression Simple Linear Regression IPS Chapter 10.1 2009 W.H. Freeman and Company Objectives (IPS Chapter 10.1) Simple linear regression Statistical model for linear regression Estimating
More informationMissouri Soybean Economic Impact Report
Missouri Soybean Economic Report State Analysis March 2014 The following soybean economic impact values were estimated by Value Ag, LLC, as part of a Missouri Soybean Merchandising Council funded project.
More informationTESTING THE ONEPART FRACTIONAL RESPONSE MODEL AGAINST AN ALTERNATIVE TWOPART MODEL
TESTING THE ONEPART FRACTIONAL RESPONSE MODEL AGAINST AN ALTERNATIVE TWOPART MODEL HARALD OBERHOFER AND MICHAEL PFAFFERMAYR WORKING PAPER NO. 201101 Testing the OnePart Fractional Response Model against
More informationWooldridge, Introductory Econometrics, 4th ed. Chapter 15: Instrumental variables and two stage least squares
Wooldridge, Introductory Econometrics, 4th ed. Chapter 15: Instrumental variables and two stage least squares Many economic models involve endogeneity: that is, a theoretical relationship does not fit
More informationNew York Dairy Production Trends by County
New York Dairy Production Trends by County Dairy production trends in New York are highly regional. The Finger Lakes and some western counties have grown in milk production and cow numbers. The North Country
More informationAnalysis of Financial Time Series with EViews
Analysis of Financial Time Series with EViews Enrico Foscolo Contents 1 Asset Returns 2 1.1 Empirical Properties of Returns................. 2 2 Heteroskedasticity and Autocorrelation 4 2.1 Testing for
More informationReplacement Heifers Costs and Return Calculation Decision Aids
Replacement Heifers Costs and Return Calculation Decision Aids The purpose of these replacement heifer cost decision aids is to calculate total production costs and return on investment (ROI) to evaluate
More informationECONOMETICS FOR THE UNINITIATED. by D.S.G. POLLOCK University of Leicester
1 ECONOMETICS FOR THE UNINITIATED by D.S.G. POLLOCK University of Leicester This lecture can be supplemented by the texts that are available at the following website, where each of the topics is pursued
More informationUsing Enterprise Budgets to Compute Crop Breakeven Prices Michael Langemeier, Associate Director, Center for Commercial Agriculture
October 2015 Using Enterprise Budgets to Compute Crop Breakeven Prices Michael Langemeier, Associate Director, Center for Commercial Agriculture Enterprise budgets provide an estimate of potential revenue,
More informationEcon 371 Problem Set #3 Answer Sheet
Econ 371 Problem Set #3 Answer Sheet 4.1 In this question, you are told that a OLS regression analysis of third grade test scores as a function of class size yields the following estimated model. T estscore
More informationA Comparative Analysis of Recombinant Bovine Somatotropin Adoption across Major U.S. Dairy Regions 1
1 A Comparative Analysis of Recombinant Bovine Somatotropin Adoption across Major U.S. Dairy Regions 1 Bradford Barham, Jeremy D. Foltz, Sunung Moon, and Douglas JacksonSmith Abstract: Trends and determinants
More informationRegression Analysis: Basic Concepts
The simple linear model Regression Analysis: Basic Concepts Allin Cottrell Represents the dependent variable, y i, as a linear function of one independent variable, x i, subject to a random disturbance
More informationManagement and Marketing Series Series No. 19 (Revised)
Agricultural and Resource Economics C O L L E G E O F A G R I C U L T U R E & L I F E S C I E N C E S Management and Marketing Series Series No. 19 (Revised) July 2004 Dairy Cow Leasing Geoff Benson, Ph.D
More informationSuggestions for Setting up Expense and Revenue Accounts in Quick Books Pro for Cow Calf and Retained Ownership Ranches
Suggestions for Setting up Expense and Revenue Accounts in Quick Books Pro for Cow Calf and Retained Ownership Ranches The business accounting system first must provide the data for compliance reporting
More informationSuggestions for Setting up Expense and Revenue Accounts in Quick Books Pro for Cow Calf and Retained Ownership Ranches
Suggestions for Setting up Expense and Revenue Accounts in Quick Books Pro for Cow Calf and Retained Ownership Ranches The business accounting system first must provide the data for compliance reporting
More informationMEASURES OF DISPERSION
MEASURES OF DISPERSION Measures of Dispersion While measures of central tendency indicate what value of a variable is (in one sense or other) average or central or typical in a set of data, measures of
More information11. Analysis of Casecontrol Studies Logistic Regression
Research methods II 113 11. Analysis of Casecontrol Studies Logistic Regression This chapter builds upon and further develops the concepts and strategies described in Ch.6 of Mother and Child Health:
More informationReplacement Heifers Costs and Return on Investment Calculation Decision Aids
Replacement Heifers Costs and Return on Investment Calculation Decision Aids The purpose of this replacement heifer cost decision aid is to calculate total production costs and return on investment (ROI)
More informationHousehold s Life Insurance Demand  a Multivariate Two Part Model
Household s Life Insurance Demand  a Multivariate Two Part Model Edward (Jed) W. Frees School of Business, University of WisconsinMadison July 30, 1 / 19 Outline 1 2 3 4 2 / 19 Objective To understand
More informationHow to Write a Dairy Job Description
G951224A How to Write a Dairy Job Description This NebGuide leads you through the process of developing a job description for positions in the dairy industry. Jeffrey F. Keown, Extension Dairy Specialist
More informationX X X a) perfect linear correlation b) no correlation c) positive correlation (r = 1) (r = 0) (0 < r < 1)
CORRELATION AND REGRESSION / 47 CHAPTER EIGHT CORRELATION AND REGRESSION Correlation and regression are statistical methods that are commonly used in the medical literature to compare two or more variables.
More informationFarm Financial Management
Farm Financial Management Your Farm Income Statement How much did your farm business earn last year? There are many ways to answer this question. A farm income statement (sometimes called a profit and
More informationLOGIT AND PROBIT ANALYSIS
LOGIT AND PROBIT ANALYSIS A.K. Vasisht I.A.S.R.I., Library Avenue, New Delhi 110 012 amitvasisht@iasri.res.in In dummy regression variable models, it is assumed implicitly that the dependent variable Y
More informationManaging Feed and Milk Price Risk: Futures Markets and Insurance Alternatives
Managing Feed and Milk Price Risk: Futures Markets and Insurance Alternatives Dillon M. Feuz Department of Applied Economics Utah State University 3530 Old Main Hill Logan, UT 843223530 4357972296 dillon.feuz@usu.edu
More informationC: LEVEL 800 {MASTERS OF ECONOMICS( ECONOMETRICS)}
C: LEVEL 800 {MASTERS OF ECONOMICS( ECONOMETRICS)} 1. EES 800: Econometrics I Simple linear regression and correlation analysis. Specification and estimation of a regression model. Interpretation of regression
More informationThe key tools of farm business analyses
10 The key tools of farm business analyses This chapter explains the benefits of accurately documenting farm assets and liabilities, as well as farm costs and income, to monitor the business performance
More informationPASS Sample Size Software. Linear Regression
Chapter 855 Introduction Linear regression is a commonly used procedure in statistical analysis. One of the main objectives in linear regression analysis is to test hypotheses about the slope (sometimes
More informationEstimation and Inference in Cointegration Models Economics 582
Estimation and Inference in Cointegration Models Economics 582 Eric Zivot May 17, 2012 Tests for Cointegration Let the ( 1) vector Y be (1). Recall, Y is cointegrated with 0 cointegrating vectors if there
More informationOverview of Violations of the Basic Assumptions in the Classical Normal Linear Regression Model
Overview of Violations of the Basic Assumptions in the Classical Normal Linear Regression Model 1 September 004 A. Introduction and assumptions The classical normal linear regression model can be written
More informationFE670 Algorithmic Trading Strategies. Stevens Institute of Technology
FE670 Algorithmic Trading Strategies Lecture 6. Portfolio Optimization: Basic Theory and Practice Steve Yang Stevens Institute of Technology 10/03/2013 Outline 1 MeanVariance Analysis: Overview 2 Classical
More informationA Primer on Forecasting Business Performance
A Primer on Forecasting Business Performance There are two common approaches to forecasting: qualitative and quantitative. Qualitative forecasting methods are important when historical data is not available.
More informationThe Economic Impacts of Immigrant Labor on U.S. Dairy Farms
The Economic of Immigrant Labor on U.S. Dairy Farms Flynn Adcock, David Anderson, and Parr Rosson 1 Research Support Provided by Dan Hanselka Prepared Under Contract for National Milk Producers Federation
More informationInstrumental Variables Regression. Instrumental Variables (IV) estimation is used when the model has endogenous s.
Instrumental Variables Regression Instrumental Variables (IV) estimation is used when the model has endogenous s. IV can thus be used to address the following important threats to internal validity: Omitted
More informationWIDENING GENDER WAGE GAP IN ECONOMIC SLOWDOWN: THE PHILIPPINE CASE
WIDENING GENDER WAGE GAP IN ECONOMIC SLOWDOWN: THE PHILIPPINE CASE Emily Christi A. Cabegin University of the Philippines/ School of Labor and Industrial Relations Diliman, Quezon City Philippines Email:
More information(ii) as measure of dispersion standard deviation statistic, which, in turn, can be further classified as (a) standard deviation statistics based on sh
Strategy Formulation for Quality Control in Process Industries Bikash Bhadury, Professor, Department of Industrial Engineering & Management, Indian Institute of Technology, Kharagpur, India. Email: bikash@vgsom.iitkgp.ernet.in
More informationEU Milk Margin Estimate up to 2014
Ref. Ares(215)28824999/7/215 EU Agricultural and Farm Economics Briefs No 7 June 215 EU Milk Margin Estimate up to 214 An overview of estimates of of production and gross margins of milk production in
More informationRegression analysis in the Assistant fits a model with one continuous predictor and one continuous response and can fit two types of models:
This paper explains the research conducted by Minitab statisticians to develop the methods and data checks used in the Assistant in Minitab 17 Statistical Software. The simple regression procedure in the
More informationMeasuring BDC s impact on its clients
From BDC s Economic Research and Analysis Team July 213 INCLUDED IN THIS REPORT This report is based on a Statistics Canada analysis that assessed the impact of the Business Development Bank of Canada
More informationSimple Linear Regression Chapter 11
Simple Linear Regression Chapter 11 Rationale Frequently decisionmaking situations require modeling of relationships among business variables. For instance, the amount of sale of a product may be related
More informationFirm and Product Life Cycles and Firm Survival
TECHNOLOGICAL CHANGE Firm and Product Life Cycles and Firm Survival By RAJSHREE AGARWAL AND MICHAEL GORT* On average, roughly 5 10 percent of the firms in a given market leave that market over the span
More information1 OLS under Measurement Error
ECON 370: Measurement Error 1 Violations of Gauss Markov Assumptions: Measurement Error Econometric Methods, ECON 370 1 OLS under Measurement Error We have found out that another source of endogeneity
More informationModule 5: Multiple Regression Analysis
Using Statistical Data Using to Make Statistical Decisions: Data Multiple to Make Regression Decisions Analysis Page 1 Module 5: Multiple Regression Analysis Tom Ilvento, University of Delaware, College
More informationAuxiliary Variables in Mixture Modeling: 3Step Approaches Using Mplus
Auxiliary Variables in Mixture Modeling: 3Step Approaches Using Mplus Tihomir Asparouhov and Bengt Muthén Mplus Web Notes: No. 15 Version 8, August 5, 2014 1 Abstract This paper discusses alternatives
More informationBusiness Planning and Economics of Sheep Farm Establishment and Cost of Production in Nova Scotia
Business Planning and Economics of Sheep Farm Establishment and Cost of Production in Nova Scotia Prepared by: Christina Jones, Economist, Nova Scotia Department of Agriculture Although care has been taken
More informationQuantitative Methods in Regulation
Quantitative Methods in Regulation (DEA) Data envelopment analysis is one of the methods commonly used in assessing efficiency for regulatory purposes, as an alternative to regression. The theoretical
More informationNotes 10: An Equation Based Model of the Macroeconomy
Notes 10: An Equation Based Model of the Macroeconomy In this note, I am going to provide a simple mathematical framework for 8 of the 9 major curves in our class (excluding only the labor supply curve).
More informationStructural Econometric Modeling in Industrial Organization Handout 1
Structural Econometric Modeling in Industrial Organization Handout 1 Professor Matthijs Wildenbeest 16 May 2011 1 Reading Peter C. Reiss and Frank A. Wolak A. Structural Econometric Modeling: Rationales
More informationThe LifeCycle Motive and Money Demand: Further Evidence. Abstract
The LifeCycle Motive and Money Demand: Further Evidence Jan Tin Commerce Department Abstract This study takes a closer look at the relationship between money demand and the lifecycle motive using panel
More informationAge to Age Factor Selection under Changing Development Chris G. Gross, ACAS, MAAA
Age to Age Factor Selection under Changing Development Chris G. Gross, ACAS, MAAA Introduction A common question faced by many actuaries when selecting loss development factors is whether to base the selected
More informationUSDA Acreage and Quarterly Grain Stocks Reports
USDA Acreage and Quarterly Grain Stocks Reports June 30, 2016 Aaron Smith and Chuck Danehower Department of Agricultural & Resource Economics University of Tennessee Extension USDA Acreage and Quarterly
More informationUsing the Futures Market to Predict Prices and Calculate Breakevens for Feeder Cattle Kenny Burdine 1 and Greg Halich 2
Introduction Using the Futures Market to Predict Prices and Calculate Breakevens for Feeder Cattle Kenny Burdine 1 and Greg Halich 2 AEC 201309 August 2013 Futures markets are used by cattle producers
More informationWiswall, Labor Economics (Undergraduate), Fall QUIZ. Instructions: Write all answers on the separate answer sheet.
Wiswall, Labor Economics (Undergraduate), Fall 2005 1 QUIZ Instructions: Write all answers on the separate answer sheet. Make sure you write your name on this answer sheet. (68 points total) Multiple Choice
More informationRobust procedures for Canadian Test Day Model final report for the Holstein breed
Robust procedures for Canadian Test Day Model final report for the Holstein breed J. Jamrozik, J. Fatehi and L.R. Schaeffer Centre for Genetic Improvement of Livestock, University of Guelph Introduction
More informationStatistics in Geophysics: Linear Regression II
Statistics in Geophysics: Linear Regression II Steffen Unkel Department of Statistics LudwigMaximiliansUniversity Munich, Germany Winter Term 2013/14 1/28 Model definition Suppose we have the following
More informationThe CobbDouglas Production Function
171 10 The CobbDouglas Production Function This chapter describes in detail the most famous of all production functions used to represent production processes both in and out of agriculture. First used
More informationThe Impact of MarketingInduced Versus WordofMouth Customer Acquisition on Customer Equity Growth
The Impact of MarketingInduced Versus WordofMouth Customer Acquisition on Customer Equity Growth Journal Journal of marketing research Vol/No Vol. 45, Issue 1, 2008 Authors JULIAN VILLANUEVA, SHIJIN
More informationProfits, Costs, and the Changing Structure of Dairy Farming
United States Department of Agriculture Economic Research Service Economic Research Report Number 47 Profits, Costs, and the Changing Structure of Dairy Farming James M. MacDonald, Erik J. O Donoghue,
More informationCost implications of nofault automobile insurance. By: Joseph E. Johnson, George B. Flanigan, and Daniel T. Winkler
Cost implications of nofault automobile insurance By: Joseph E. Johnson, George B. Flanigan, and Daniel T. Winkler Johnson, J. E., G. B. Flanigan, and D. T. Winkler. "Cost Implications of NoFault Automobile
More information