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Supplemental Material, sj-pdf-1-jpa-10.1177_07342829211047005 for Developing and Piloting a Computerized Adaptive Test for a Culturally Appropriate Measure of Adaptive Behavior by Mo Chen, Yong-Hwee Nah, Nicolette Waschl, Kenneth Poon and Ping Chen in Journal of Psychoeducational Assessment

Supplemental Material, sj-pdf-1-jpa-10.1177_07342829211047005 for Developing and Piloting a Computerized Adaptive Test for a Culturally Appropriate Measure of Adaptive Behavior by Mo Chen, Yong-Hwee Nah, Nicolette Waschl, Kenneth Poon and Ping Chen in Journal of Psychoeducational Assessment

Business risk management (BRM) continues to be the central objective of agricultural policy in many countries, including Canada and the US. The unprecedented volatility that has characterized the farming sector in recent years is only expected to rise. Thus, governments continue to implement comprehensive suites of BRM programs to assist farmers in coping with these gyrations. This paper aims to examine 1) the relationships between the main Canadian BRM programs (AgriInsurance, AgriStability, and AgriInvest), and 2) if and how those relationships differ across different farms. Understanding the interlinkages between government BRM programs is central for policy makers in order to achieve the desired objectives. The analysis uses data from the Ontario Farm Income Database (OFID), which is a longitudinal farm-level dataset compiled from Ontario farm tax-file records from 2003 to 2011. The dataset contains detailed financial, production and program payment (except for Production Insurance/AgriInsurance payments) data for all Ontario tax-filling farm operations. Additional operator-level Production Insurance/AgriInsurance payment data is linked to farm-level OFID records to complement the program payment data. The paper uses a two-stage approach to examine the relationships between AgriInsurance, AgriStability, and AgriInvest. First, a multinomial probit model is estimated in which the dependent variables are dummies for all eight possible combinations of participation in the three programs and the independent variables include program participation in previous year, operating profit margin, operating expense ratio, leverage, diversification index, size, and sector. In the second stage, the predicted probabilities of the eight participation states from the first stage multinomial function is used as regressors against percentage falls in gross margin (operating revenue minus operating expense), controlled for size and sector. We find that participation in the previous year has a strong and positive effect on participation in the current year, and the three programs are generally treated as compliments. We also find that in general, farms that participate in some combination programs have smaller drops in gross margin compared to those that participate in no programs. Despite this effect, operators continue to drop out of BRM programs.

The risk balancing literature suggests that business risk management (BRM) programs may, through risk balancing (offsetting adjustments between business risk and financial risk), lead farmers to take on more financial risk than they would take otherwise, which, in turn, increases the risk of equity loss. Business risk management continues to be the central objective of Canadian agricultural policy, and this was re-enforced with the recent introduction of the Growing Forward II policy framework. However, it is not known whether Canadian BRM programs designed to offset business risk lead to increased financial risk and possibly higher levels of overall risk for individual farm operations. This paper aims to empirically examine the impact of Canadian BRM programs on the financial riskiness of farms using a longitudinal farm data set from Ontario. Results show that: 1) the lag of payment of Canadian Agricultural Income Stabilization/AgriStability diminishes the effectiveness of BRM programs in reducing business risk; 2) a relatively small share of farms exhibit risk balancing behaviour, and 3) BRM payments have no impact on the likelihood of risk balancing. Taken together, these findings suggest that the impact of BRM programs on the financial riskiness of farms is limited.

Canadian governments collect data on the agricultural sector through a variety of surveys, income tax filings, and support program applications. The definition of a census farm provides the benchmark to estimate aggregate commodity supply which is most critical to analyze and forecast commodity prices. Prices are also collected and made available, but there is growing concern about the lack of price data for contracted commodities. The farm and rural population characteristics are also adequately portrayed through the linkage of the Census of Agriculture with the long-form Census of Population (up to 2006) and the voluntary National Household Survey in 2011. Agriculture and Agri-Food Canada’s typology captures different size and motivation for units of production, but broad inferences on the sector based on this characterization must recognize that it is only for unincorporated farms. These farms tend to be smaller and represent a shrinking percentage of the sector. In contrast, incorporated farms are becoming common place and produce the majority of farm cash receipts. The growing complexity within agricultural production units extends to the relationships with these units back to input suppliers and forward to processors. These components of the supply chain are becoming intricately linked over time and these linkages cannot be presently described with the publically available data on the sector. As the heterogeneity in the agricultural sector grows, the data and information demanded for various analytical and administrative purposes grows along with it. The demands for data to determine the need, design, and evaluation of public policy are simultaneously narrower and greater than the broad requirements by private and public users for aggregate production numbers. Farm support programs are meant as a safety net for viable farms in times of unexpected need so the first step involves assessing financial health and identify farms that are “normally competitive” from the ones that are never competitive. Information from an income statement and balance sheet are required along with input and output levels by farm units over time. No single data set supplies all the data necessary to determine need and effect of farm support programs. Performance measures based on tax data are limited to production margin so this data, while detailed in many respects, lacks information on assets/liabilities, inputs /outputs, and demographics. The latter may become important if future support programs are to target “active” farms. The proposal to consider the individuals behind a unit of production in the determination of support eligibility would drive a large gap between data demands and the current supply of publically provided data. The potential change reflects the growing heterogeneity of the agricultural sector. The initial focus on the unit of production was appropriate for all users of the data. The unit of production was the farm and it was generally operated by a single farmer and it supported a single farm family. This no longer generally applies. This growing heterogeneity forces the objectives of agricultural policy to be clearly specified, i.e. efficiency vs. equity, but the political rhetoric has yet to catch up with the changes as the terms farm, farmer, and farm family are interchanged as if they were still one. The growing distinction between them may result in changes in the nature of the data collected on the sector.

The purpose of this paper is to examine the factors affecting the relative variability in farm and off-farm income for Canadian farm operators. Previous attempts have been limited by the lack of available data combining both farm and off-farm income levels for farm operations over time. Statistic Canada’s Farm Micro-Longtidinal Dataset of 17,000 farm operators from 2001 to 2006 allowed such an analysis. The coefficient of variation (CV) in farm income is significantly greater than that for off-farm income but both measures are inversely related to the permanence of the income source to the operation. The greater the reliance on farm income and the greater the labour demand within the farm, the lower (greater) the relative variability in farm (off-farm) income. Larger commercial operations tend to experience larger farm income volatility either because they are less risk averse and/or have the ability to manage more risk. Diversification and off-farm employment appear to be substitute for risk management strategies for commercial operations. Pension and lifestyle farms have lower coefficient of variation for both farm and off-farm income compared to business-focused farms since they are possibly more risk averse and benefit from a permanent stream of off-farm revenue. Government payments have mixed effects on the relative variability of both income sources, which may be due the lag between the time of the income reduction and the time at which the aid is received.