The primary methodological challenges of empirical work examining why livestock production is scaling-up are, first, to define a quantitative measure of relative farm competitiveness in production and, second, to consider in a structured way all of the factors that differ across farms that might explain higher relative competitiveness across farms. Those factors include technical and allocative efficiency; asymmetries in access to assets (e.g., credit, liquidity, fixed capital) and information (e.g., education, experience); externalities (i.e., some farmers get away with uncompensated pollution while others do not); and policies (i.e., some farmers get a better deal from the state than others).
The failure to consider all the relevant factors when explaining competitiveness (e.g., looking at the differential efficiency across farms while neglecting transaction cost differences, or vice versa) leads to biased estimates. Furthermore, the inclusion of explanatory factors of relative competitiveness that are themselves functions of relative competitiveness leads to simultaneity bias. For example, the relative competitiveness of a farm might be enhanced by its being recognized as a salesleader, but being recognized as a salesleader may depend on being more competitive than other farms. The two-way causality among the variables leads to bias in the empirical estimation of the effect of all variables unless appropriate procedures are used.
Relative competitiveness might be thought of as having the ability to produce at a lower unit cost of production than one's competitors. In fact, if large farms can produce livestock at a lower unit cost than small ones, they will clearly drive small farms out of the market over time. The market price that applies to both large and small farms will, by this reasoning, fall as large-scale producers expand production, and the small farmers will get squeezed out. The only way for smallholders to survive then will be if they produce for a few higher-priced niche markets - if they exist - that are not economically feasible for larger farms to serve, and to cut costs by remunerating their own (family) labor force at a wage lower than that a large farmer pays to hired labor. Even if small farmers act in such a way, it is unlikely that they will be able to stay in business long by doing so.
The reverse is not necessarily true, however. If small farms can produce at lower unit costs than large farms, they may still be squeezed out because large-scale farms can remain profitable with very thin profit margins; they make up in volume what they lose in per unit profit. Very low per unit profits, coupled with a small sales volume, may not provide enough income for a smallholder to stay in business. Thus if large farms have lower per unit costs of production when all labor is costed at market wages, the next question to consider is whether this finding still holds true if smallholders do not cost their family labor. If the finding does hold true, then there would be little reason for smallholders to stay in poultry business.
If, on the other hand, small farms can produce at lower per unit costs in the same markets as large farms - perhaps by not costing their own labor at full market wage rates or for some other reason - then there is at least some hope that they will continue to exist. Thus having higher unit profit, with or without the cost of family labor, is a necessary but not sufficient condition for the competitiveness of smallholders.
To obtain a more satisfactory measure of relative competitiveness that is not dependent on the issue of larger farms being able to expand production while small ones cannot it is necessary to examine the notion of efficiency. Small farmers are most likely to be able to stay in business - and perhaps even to gain market share - if they are more efficient users of farm resources, both in a technical sense (i.e., being on the production possibility frontier, given existing technology) and an allocative sense (being on the right place on the production frontier, given prevailing prices). If small farms are more efficient users of farm resources, perhaps because they put more care in producing per unit of input, then they have a market advantage over large-scale producers that will be difficult to surpass. This market advantage then yields a measurable index of relative competitiveness - relative farm efficiency in securing profit per unit of output. Ceteris paribus, farmers that are more efficient users of farm resources to secure profits per unit of output, are more likely to be able to maintain market share than larger producers who are less efficient in their use of resources. Over time, the more efficient producers are in a better position to invest more in their farm enterprise and to grow, regardless of their initial size.
A standard way of assessing farm-specific relative profit efficiency is to estimate a is profit frontierly across a sample of farms, and then to measure how far each farm in the sample lies below the frontier. Conceptually, such a frontier can be thought of as a function mapping profit per unit to relative input and output prices and quantities of non-traded factors of production, where each point is the maximum profit per unit that a farm can achieve, given those relative prices and access to resources. Given a set of prices, the average farm with a given level of resources will fall below the frontier. Thus an ordinary least squares regression on data from a sample of farms with different sizes of profits per unit of output against input and output prices and fixed factor of production (e.g., land, labor) will always lie below the theoretical frontier. The frontier itself has to be estimated in some fashion by looking at data for farms that perform well at each level of resources. A variety of approaches to this are described by Fried, Lovell and Schmidt (1993).
The measurement of if "most efficient" can be improved by estimating a stochastic profit frontier, which allows for measurement error in the econometric estimation of the frontier itself and thus for the fact that observations for some farms will lie above the estimated "best" frontier (see Battese 1992 for a survey of this literature). In the case of this study, the dependent variable is profit per unit of output, and the explanatory variables are farm-specific fixed resources (e.g., land, family labor, sunk capital), farm-specific input prices (e.g., for feed, medicines, stock), and farm-specific output prices. In the Philippines situation that was studied, farm resources such as land may be non-tradable inputs and must be accounted for in the frontier in terms of the amount available, and not in terms of their price. The unit prices received for output and prices paid for inputs can also be expected to vary greatly, and to reflect (and control for) quality differences and differential transactions costs such as bargaining power and riskiness.
The actual performance of each farm in terms of unit profit can then be compared to an ideal unit profit for that farm, taking into account its resources and prevailing input prices. The difference between the ideal and the actual profit per unit for that farm is the farm's relative profit inefficiency. Following Ali and Flynn (1989), Figure 3.1 traces a profit frontier for a sample of farms: each dot corresponds to the actual outcome in terms of profit per unit of output for a specific farm relative to input prices; points on the stochastic frontier curve (estimated by maximum likelihood methods and labeled MLE. Maximum Likelihood Estimation) represent fully efficient farms (on the frontier); and all points below the stochastic frontier curve represent inefficient farms in terms of their specific resources at prevailing input prices
Figure 3.1: Frontier (MLE) Stochastic Profit Function for Sample Farms
Farm-specific profit efficiency (deviations below the frontier) are measured as the ratio of actual profit per unit (Yi in figure 3.1 for farm i) and ideal profit (Y*). Note that the curve denoting average profit for any given level of resources (shown as the locus of points Y in Figure 3.1) - estimated by Ordinary Least Squares Regression (OLS). [indicates?] less than ideal profit. The measure of farm efficiency embodied in Yi/Y* is bounded by 1 ("best" on the frontier), and 0 ("worst") or no profit. Farm-specific inefficiency is the distance below the frontier, (Y* - Yi).
If small farms average significantly higher profit efficiency per unit of output when family labor is not costed the outlook for those farms is more encouraging. This is even more true if the higher profit efficiency holds when family labor is costed at the market wage rate. This methodology, however, allows one to go beyond simply making this determination; it also permits the investigation into which elements contribute most to explaining relative unit profit efficiency for large and small farms. Individual farms - large or small - may lie well below the profit frontier for reasons other than technical or allocative inefficiency: farm-specific transaction cost barriers or policy distortions may also affect a farm's position relative to the frontier.
Discussion of the measurement of a farm's relative competitiveness leads us to the principal methodological approach to deriving a farm-specific measure of relative inefficiency in securing profit per unit of output - an approach that estimates a stochastic profit frontier (SPF) in the usual way. Once the SPF curve is developed, the relative profit inefficiency of each farm is explained - following the lead of Jondrow, et. al. (1982), Ali and Flynn (1989), and Battese and Coelli (1995) - in terms of the transaction cost barriers and farm-specific policy distortions faced by that farm. This approach first explains each farm's unit profit performance in terms of technical and allocative efficiency and, subsequently, in a second stage, explains differences in efficiency between farms in terms of farm-specific variations in transaction costs and policy distortions.
3.3.1 First stage: The Stochastic Profit Frontier
The stochastic profit frontier (SPF), used in this study to estimate profit efficiency is defined as
Yi = f(Xi,Wi,Pi; b)exp(vi - ui) (1)
where Yi = profit per farm i normalized by quantity of output Qi (i.e., unit profit) defined as:
Yi = ((PiQi - CiQi)/Qi) (2)
PiQi represents total revenue from poultry (broiler and layer) per farm i in question (manure sales included);
CiQi represents total variable costs, including costs for feed, day old chicks (DOC), hired labor, electricity, medicines, vaccines, water, depreciation, and securing revenue, and excluding the cost of family labor per farm i; and
Qi represents the quantity of output per farm i;
Xi = vector of fixed factors used to obtain i Y (e.g., stock of family labor, land, buildings and equipment, and fixed capital stock to control for differences in farm resources), normalized by quantity of output Qi;
Wi = vector of farm-specific input prices;
Pi = weighted-average output price (weights are the farm-specific transaction quantities);
b = vector of unknown parameters to be estimated; and
vi, ui, are random error terms.
The error term (v) is distributed independently and identically as a two-sided normal random variable around the frontier to account for measurement error on both sides of the frontier, and the inefficiency term (u) is distributed independently as a one-sided (downwards) random variable relative to the frontier to allow for the fact that farms actually fall below the ideal efficiency point. Average efficiency can be easily estimated by OLS regression of unit profit per farm against farm-specific input and output prices and farm-specific fixed factors of production. The frontier, which shows ideal unit profits for any given level of farm resources and prevailing price levels, can only beestimated, however, if specific assumptions are made about the distributions of u and v across farms - and even then only by using a non-linear estimation technique such as Maximum Likelihood Estimation (MLE). The critical assumption is the distribution of the u (inefficiency) term. The approach of Battese and Coelli (1995), which allows for systematic differences across farms in the distribution of u, is adopted in order to not "assume away" what we want to investigate. For example, transaction cost factors and policy distortions that are different for different farms help to determine their relative profit inefficiency.
3.3.2 Second Stage: Technical Inefficiency Determinant Model
Battese and Coelli (1995) base their approach on the assumption that the expected value of the farm-specific inefficiency effect for farm i can be modeled as a function of farm-specific characteristics (which of course vary across farms) and fixed coefficients, which do not. In other words, , where is the mean of a truncated-normal distribution of ui. The zik are k explanatory variables observed for farm i, associated with technical inefficiency effects (ui) and d is a vector of unknown coefficients to be estimated simultaneously with equation (1). Thus, the technical inefficiency effect, ui in equation (1), can then be specified as , where ei is the inefficiency error term, defined by the truncation of the normal distribution with mean equal to zero and variance s2. The truncation of ei occurs at (Battese and Coelli, 1995).
Specification of an estimation model A translog profit frontier is used to specify an estimation model because of the flexibility it allows in estimating parameters where it is not desirable to build in, through model specification, rigid assumptions about substitution relationships among inputs and factors. The full form of the model is:
where Yi is the normalized profit of the i-th farm defined in equation (2); Wij is the price of input j (j= hired labor, capital, feed, and DOC used by the i-th farm); Xik is the fixed factor k used by the i-th farm (k =is the normalized value of buildings and equipment, normalized total farm labor in hours and so on); and Di is a vector of scale dummy variables for farm i. The vi,ui are as previously defined. The akj, bjk are coefficients to be estimated by MLE using Frontier 4.1 software (Coelli, 1992).
Normalizing by output quantity builds in an assumption of constant returns to scale. In order to allow for the fact that larger producers may be using higher grade technology than others one needs to control for non-Hicks neutral technical change on the right hand side (RHS) of the first stage of the stochastic profit functions. To deal with this problem, the parameterization of Hicks non-neutral technological change is allowed by including the feed conversion ratio (FCR), which is probably closely correlated with both technology and managerial ability. The weighted average FCR per farm is calculated as: total feed used in kg. divided by total output in kg. of live-weight in the case of broilers (and in number of eggs in the case of layers).
Translog profit frontiers share the use of logarithms in the dependent variables, and thus do not handle cases of negative or zero unit profits. Yet it is not unreasonable to suppose that some farms in lose money in some years. There is in fact no perfect fix for this problem, thus a "lesser-of-the-evils" approach is employed that is adequate for present purposes. A constant scalar is added to the unit profit data in each sample such that the unit profit of every farm is positive. As long as the cases of negative average farm profits are few (less than, say, five percent), and they are not very negative relative to average farm unit profit (so that the scalar is small relative to the mean), the resulting bias from a non-linear transformation of the data is judged to be of minor importance compared with the bias that would arise form using a less appropriate functional form or arbitrarily dropping the least efficient sample members.
The technical inefficiency effects (ui s) generated in equation (3) are estimated within the MLE model specified above as:
where Zi is the ith farm characteristic determining relative inefficiency and ei is distributed as above.
The RHS variables of equation (4), represented by the Z, cover all the farm characteristics that proxy different levels of transaction costs faced by each farm. Those characteristics include access to credit for capital, experience in poultry production, age and education of household head, formal/informal trainings attended, subsidies represented by regional and contract dummies (for differences in policy distortions), access to markets for output, access to information, and the "instrumented" environmental cost (discussed in the next section).
Two remaining methodological problems concern the measurement of the farm-specific data. First, some of the explanatory variables that one may wish to include in the second stage may not be observable at all, or in any event may be very hard to observe. This is especially true of transaction cost and externality variables. Second, some of the explanatory variables that one may wish to include in the second stage may be endogenous in the sense discussed above: the causality goes both ways, introducing simultaneity bias in estimation. This is particularly a problem for environmental externalities, since farm-specific differences here will help to determine relative unit profit efficiency as we define it, but themselves may be a function of the latter in some cases.
Two problems arise in trying to account for why some farms pollute more per unit of output than others when assessing why some farms have higher profits than others. The environmental externalities of livestock production are both hard to measure and, in many cases, determined simultaneously with the level of actual profit per unit. An externality is defined here as a return to an economic agent where part of the cost (or benefit) of undertaking an activity accrues to another entity that is not compensated (or charged) in the market. Negative externalities may be created in the production process for animal agriculture through odor, flies, and the nutrient-loading effects on soil of manure that is either mishandled or supplied in excess. Producers capture the benefit of negative externalities by receiving payment for livestock output but not bearing the full cost of their enterprise's impact, in terms of odor, flies, and poor water quality, for example, on surrounding communities. Producers that do not pay the full cost of production per unit may show up as "more efficient" (in financial terms) than producers who are otherwise similar but internalize some of the externality by cleaning up after their enterprise, or by making compensatory payments to surrounding communities.
The first problem relates to how the value of not paying for pollution created is measured, particularly if this differs by scale of farm, since that will lead to erroneous comparisons of unit profits across scale categories. Externalities of the sort involved are exceedingly difficult to measure. There is the "non-point source" issue, which basically concerns how to decide which farm's pollution run-off ended up in a river. Another issue that can arise concerns farmers themselves suffering from some of their own pollution, a fact that needs to be separated out of the externality. Yet another issue is that the negative effects of pollution carry over into future time periods. The costs of decreased sustainability are also very difficult to measure physically. Furthermore, the true consequences for sustainability from a given amount of manure will differ depending on soil type, temperature, rainfall, and so forth.
In view of these many difficulties in conducting measurements, it is not practical in the present study to attempt to measure actual negative externalities. Instead, the focus is on differences across farms in terms of the amount of externality "internalized" when a farmer invests in pollution abatement by handling manure and dead stock in an ecologically sound manner. Higher expenditure per unit of output on a given farm for abatement of environmental externalities, with other things being equal, should be inversely correlated with the incursion of net negative environmental externalities per unit of output, under the assumptions above. Thus a farm that spends more per unit of output on environmental abatement is postulated to incur less negative environmental externalities than a farm that spends less on environmental services per unit of output.
The heroic assumption that allows us to proxy environmental mitigation with the monetary value of manure management is that any amount of manure of any sort is equally polluting regardless of which farm it comes from if it is not spread on one's own or someone else's fields. This assumes that spreading manure on crops is uniformly good (despite run-off going into watercourses in some cases), and ignores the fact that farms close to population centers and watercourses probably produce more ecological harm per ton of manure - other things being equal - than farms farther away from people and watercourses. By the same logic, if one is willing to assume that the relationship is cardinal as well as ordinal - $1 per 100 kg. of output in abatement on farm A is twice as environmentally friendly as $0.50 per 100 kg. of output on a different farm - then we have a workable index that differentiates (inversely) across farms in terms of the amount of negative environmental externalities incurred. The assumptions are not perfect but the only feasible alternative - to ignore negative externalities altogether in econometric production work - seems worse.
The components that go into the measurement of environmental mitigation include all costs of disposing of manure, including transport and other costs associated with manure disposal, the costs of disposing dead birds, the cost of controlling flies, and the cost of pollution payments. In addition, the spreading of manure on crops is considered to transform a potential externality (pollution) into a positive contribution to soil structure and fertility. It is difficult to cost this benefit accurately. The simple approach adopted is to value all the manure sold for spreading on others' fields (the reason for which it is purchased) at its sale value at the producing farm gate. Manure spread on one's own fields is valued at what it could have been sold for at the farm gate. Thus if manure is spread in the field and has any market value (i.e., people are not just dumping the manure) the latter is included in the internalization of the externality. The worst that any farm can do under this approach is to have no abatement expenditure at all per unit of output, yet this is actually the case for many farms.
Having a working index of environmental mitigation creates a new problem and a new opportunity. The new problem is that this index, measured in rupees (Rp) per kg. Of broilers (or per egg in the case of layers), is in many cases simultaneously determined ith profits per unit. Thus profit per unit depends on environmental mitigation expenditures, but environmental mitigation expenditures are also influenced by profit. The new opportunity is the solution to the econometric problem, which involves creating an instrumental variable for environmental mitigation by regressing it on a series of exogenous determinants of environmental mitigation. Opportunity lies in the insights that the solution to this problem gives into why some farms are prone to spend more on environmental mitigation than others.
Among the factors accounted for in this study that might influence the amount of environmental mitigation expenditure across farms are farms' access to assets and information (transactions costs), farm characteristics such as location, and policy subsidies. Other examples of such variables include the education, experience, gender, and age of the household head; access to technical assistance; mortality rate; family labor; scale dummy; and other locational variables. The predicted value of the dependent variable from these regressions - environmental mitigation - can then be used as an explanatory variable in the second stage regression that explains why some farms are closer to the profit frontier than others.
The measurement of environmental mitigation by the procedure above is only one approach to measuring the important environmental impact of livestock. The development of the measurement procedure was motivated by the need to incorporate environmental factors in the analysis of efficiency. More direct measures of environmental impact are possible, however, outside of this framework. The next section explores a methodology for directly assessing the interaction of animal density and environment.
A proper application rate is the principal manure management practice that affects whether or not water resources are contaminated by manure nutrients, and in fact has very little to do with manure management technology per se. The above approach can be used to look at the effect on profitability of different efforts to mitigate environmental problems, yet this actually says little about the effectiveness of these measures or whether they are necessary. To know the latter one would have to actually follow the nutrient chain from each household to the final uptake source. Furthermore, to ensure that the uptake was actually occurring, one would have to conduct specific measurements of the disposal of the nutrients and the uptake. As noted above, such a measurement is beyond the ability of this project to address.
It is possible, however, to estimate the potential of the externality in terms of the ability of a farm to utilize all the nutrients that it produces from the household survey conducted. If the manure produced exceeds the potential for on-farm use one needs to either (1) sell the manure, (2) transport the manure to an area where there is enough land for application, or (3) utilize a processing technology to transform the manure into a product that is profitable being transported a long distance or that does not need to be transported. Estimating the potential of the externality in this way may also help us understand why some farms may be investing more in manure mitigation technology than others, and it may also help us understand the differences across size of operations, particularly if large farms have limited land on which to dispose of manure.
To determine whether a farmer has the ability to utilize all the manure on his own farm the balance of manure nutrients on the farm relative to the farm's potential to utilize the nutrients through crop production is calculated, based on the farm-level data collected in the household survey. From these numbers, the amount of nutrients in the manure is estimated in terms of organic nitrogen (N) and phosphate (P2O5). These two nutrients were chosen because they are the nutrients for which regulations are primarily written, assuming that there is any regulation at all. The amount of chemical fertilizer applied per land unit was also included in the calculation, when available, to compute the mass balance of nutrients applied to the land. Crop assimilation capacity was estimated to determine whether a crop could assimilate all the nutrients produced on the farm. The amount of manure, if any, sold off-farm was then subtracted.
3.5.1 Animal Unit Calculations
Nutrient values from poultry were calculated based on animal units, the most commonly used metric measurement unit for calculating nutrient levels. Animal unit conversions are used because it is recognized that the amount of manure produced from one broiler is not equivalent to the amount produced by one swine. This method is used to equate excretion across species. One animal unit is equal to the amount of manure produced by 250 broilers or layers, which is based on averages used by the U.S. Extension Service. Given that the level of nutrients present in the manure may also differ by species, based on what is eaten, the amount of different nutrients for each species also varies. For instance, 250 chickens produce 298 pounds (135.1 kg.) of nitrogen per year and 209 pounds (94.8 kg.) of P2O5 per year.
Different countries have different conversions/limits. According to the European Community Directive, for example, the number of manure-producing animals per hectare of land for 0 to 16 weeks is limited to two dairy cows, four ground stock/beef cattle, 16 fattening pigs, five sows with piglets, 100 turkeys or ducks, 133 laying hens, or 285 ground hens. This numerical limit is equivalent to a limit of 170 kg/ha/yr of total nitrogen (including that deposited while grazing) in zones deemed vulnerable in terms of nitrate leaching (Williams 1992). It is expected that the above conversion factors will be lower for many of the developing countries, since the amount of nitrogen and phosphate excreted in animal manure depends on diets, species, and age of animal (Faassen and van Dijk, 1987).
3.5.2 Total Nutrient Production
From animal unit estimations, the total nutrient deposition from poultry for each household was estimated such that the total nutrient deposited by household h was the sum of the nutrient produced by animal units of poultry type l in household h. If data on commercial fertilizer use was available it was added to this calculation in order to come up with total nutrient use on the farm, including both organic and inorganic nutrients, using the following formula:
l = Poultry category (broilers and layers)
n = Nutrient type
h = Household
AUlh = Animal units of poultry type l in household h
Estimation of crop up-take the capacity of these nutrients to be utilized at the household level is estimated by assuming that all the available land was planted with crops that would uptake the nutrients. This estimation is done to determine whether a household has the potential to utilize all the nutrients produced, given the current number of animals in the household.
The capacity for each household to use the nutrients produced by livestock operations is computed as the area of cropland available to the household multiplied by the nutrient uptake of the crops planted on the land. To determine this, we calculated the potential for rice to take up these nutrients under the assumption that all the available cropland was planted with rice. And, for the purpose of the calculation, we assumed that the nitrogen uptake for rice production is 100 kg. per hectare and that phosphorous uptake is 32 kg. per hectare. Again, it is recognized that the actual figure arrived at depends on the type of soil and the ability of the soil to utilize nutrients, and that tropical soils require far more nutrients than other types.
3.5.3 Mass Balance
In order to determine the nutrient balance on the farm the difference between manure nutrient production and nutrient consumption is calculated. The mass balance (MB) for each nutrient of interest (nitrogen or phosphorous) is expressed by the following equation:
Ah = Area of cropland on household h
bn = Total nutrient uptake by rice
The result is indicative of a household's potential assimilative capacity for nutrients based on the current number of animals on their property. A positive mass balance would imply that there is sufficient land to assimilate the nutrients produced, while a negative mass balance suggests that there is not enough land to absorb the nutrients.
3.5.4 Manure Sold Off-Farm
Although manure is a potentially valuable fertilizer - and soil conditioner - areas with concentrated poultry production may not have adequate cropland for nutrient utilization stemming from by-products on livestock. Therefore, exporting nutrients from concentrated areas to surrounding areas may be both environmentally and economically beneficial. Markets for poultry manure do exist in India. Though there may be a market for poultry manure, the market for unprocessed manure may be seasonal as crops need fertilizer only during certain periods of their growing cycle throughout the year.
To determine whether the households in this study that do not have the ability to absorb all the nutrients on their own farms are getting rid of excess nutrients through the market the amount of that which is sold or given away is subtracted from the amount of that which is produced. The results are then compared across various sizes of operations to see how various sizes of producers are handling the potential problem.
 The log-likelihood
function of this model is presented in the appendix of Battese and Coelli
(1993). Estimation of the likelihood function also requires the specification of
a relationship among the variance parameters such that
, where the parameter
ã has value between 0 and 1.|
 It is recognized that in this analysis the ability for the household to absorb on all land overestimates what can be absorbed. Unfortunately, it is necessary to use this estimate as most of the surveys are not detailed enough to delineate crop acreage from building area.