We then used this model as the foundation for simulation experiments which compare the effects of alternative scenarios regarding agricultural trade and support policies, both before and after accession to the European Union. The purpose of these experiments was to analyze the interactions between the accession “contract,” transition policies, sectoral perfonnance, and the pace of enterprise restructuring. A robust conclusion of the model is that the long-term health of the agricultural sector in these nations is likely to depend more on the choice of transition policies than on the terms of accession to the EU. The defining feature of successful transition programs is that they provide some form of subsidy to long-term investment, some mechanism by which landowners can overcome credit constraints and enhance the productivity of their enterprises. Mechanisms involving price supports and tariff barriers do have this desired effect. This result follows from the theory of the second-best, due to the presence of the distorted credit market. At the same time, however, and somewhat counter-intuitively, these distortive policies create price instability. Free trade can substitute for price support as a market-stabilizing mechanism,container raspberries operating more effectively and at lower cost. Both distortive and laissez-faire approaches are dominated by policies that address the credit constraining directly by subsidizing credit.
Such targeted approaches provide superior outcomes at lower cost. Our results also have a methodological implication, viz., that static analyses, or analyses that assume near-equilibrium market behavior, can fail to pick up or properly to address the importance of the transition dynamics associated with enterprise restructuring. A robust conclusion of the model is that land will tend to shift toward large, efficient holdings. This outcome reflects the lower effective interest rates available to these units. Thus, not only the availability of long-term credit, but the price of short-term credit, are central determinants of the model dynamics. The shift in land towards large farms also reflect to some degree the model’s inability to capture the advantage of smaller units in production of commodities such as vegetables. On the policy front, our analysis suggests that a focus on achieving “convergence” with EU norms may constitute an unwise distraction from the real business at hand: to create the conditions for enterprise restructuring that will improve the productivity of land and other factors. The central problem with such thinking is that it confuses the behavior of developed nations with behavior that will make a nation develop. It is no more intelligent for the CEEes to undertake the burdens of lavish agricultural price supports than it is for the poor to spend their scarce resources on champagne and caviar in the hope of thereby becoming rich.
A desire for structural alignment with the ED in no way implies the advisability of policy alignment during the transition period. At the same time, we find a basis for rejecting the laissez-faire approaches advocated by “Big Bang” theorists. Indeed, in a situation in which market institutions are badly underdeveloped, price support can provide a mechanism-albeit a very inefficient one-to counter the deleterious effects of these imperfections. Governments can play their most constructive role, however, by fostering the creation of functional market institutions that allow for productivity increases. Identifying the factors that impede such improvements, and designing the mechanisms to correct them, should be the goal for future research on agricultural policy in transition economies. The first task is to take a careful, elaborated look at enterprise restructuring, and of the factors that determine farmers’ investment behavior.Public health concerns about pesticide exposure to young children have received increased attention following the publication of “Pesticides in the diets of infants and children” in 1993. In 1996, the U.S. Food Quality Protection Act required the U.S. Environmental Protection Agency to set food tolerances that account for dietary and non-dietary exposure and protect sensitive populations. Biomonitoring studies have confirmed that children are widely exposed to pesticides, including organophosphorus , pyrethroid, fungicide, and organochlorine pesticides. Diet is an important source of pesticide exposure in children. For example, Lu et al.reported that the median urinary concentrations of the specific metabolites for malathion and chlorpyrifos decreased to undetectable levels after the introduction of organic diets in school-aged children. Several studies have confirmed that children may also be exposed to pesticide contamination in home and daycare environments. Children living in agricultural areas may also be exposed to pesticides through drift during applications or volatilization from nearby fields and parental take-home exposures. Lu et al. found that children who live in agricultural communities had five times higher OP metabolite levels in their urine compared to children who resided in non-agricultural communities.
These researchers also found higher residential OP pesticide contamination and/or elevated urinary metabolite levels in children living near orchards. Higher exposure to children living in agricultural areas has raised environmental justice concerns and has resulted in proposals to define farm worker children as a vulnerable population that need additional protections by the U.S. EPA. Identifying pesticide exposure determinants is needed to identify sources and pathways of pesticide exposure in children and contribute to policies aiming to reduce exposure. To date, no longitudinal studies have investigated factors associated with pesticide exposure in very young children. We hypothesize that exposure factors will vary over time given the changes in diet, behavior, and family practices that occur as children age. In this study, we report levels of OP pesticide metabolites in 6, 12, and 24 month old children participating in the CHAMACOS birth cohort study in the Salinas Valley of California, an agricultural area. We examined potential determinants of exposure associated with OP urinary metabolite levels at each age point, including sex, child behavior, diet, home pesticide use, season, parental work status, and proximity of homes to fields. We focused on OPs because they are commonly used in the Salinas Valley and were the first pesticide class re-examined under the FQPA. Mothers were interviewed when the children were 6, 12, and 24 months old. Interviews were conducted in Spanish or English by bilingual interviewers. Information collected included demographics, household enumeration, occupational status, whether work clothes were worn into the home, home pesticide use, presence of pets, daily servings of child fruit and vegetable consumption based on a modified food frequency questionnaire, time spent in child care, location of child care relative to fields, and frequency of hand washing and how often child fingers, hands, or toes are placed in the mouth. The interview also included a Child Behavior Checklist which uses a standardized format to assess parent-reported behavioral characteristics of children. Based on the CBCL, we selected child temperament indicators that we hypothesized could be associated with behaviors that affect pesticide exposure: “Can’t sit still, restless, or hyperactive”, “Gets into everything”, “Quickly shifts from one activity to another”, and “Underactive, slow moving, or lacks energy.” Shortly after each interview,draining pots study staff conducted a home inspection. Recorded information included distance between the home and agricultural fields, carpeting, housekeeping quality, and adetailed inventory of home pesticides. Home visits were completed for 87%, 84%, and 87% of the enrolled children at 6-, 12-, and 24-months, respectively. All data analyses were performed with Stata Version 10 . We first computed descriptive statistics and percentiles for individual and total DMAP and DEAP metabolites at each sampling time point. We used Pearson correlations and ANOVA to assess bivariate associations between the metabolite levels and potential exposure determinants selected a priori, including sex, age, produce intake, breastfeeding, season, distance to agricultural fields, occupation of household members, wearing work clothes or shoes into the home, home pesticide use, presence of carpets, presence of pets, and housekeeping quality. We examined post facto additional determinants which may be related to drift of pesticides from fields, including daily rainfall, behaviors which may modify exposures , time spent in child care, and proximity of child care to agricultural fields. We then constructed generalized linear mixed models with log10-transformed DMAP or DEAP metabolite levels as the dependent variables and potential exposure determinants found to have significant bivariate relationships. The models included a random effects term to adjust for the lack of independence of repeated measures on the same subject. Because children’s development, diet, and behavior differ at different age points, we also examined whether age modified any associations, with 12-month olds and 24-month olds compared to 6-month olds as the reference. All interaction terms were included in the final DMAP and DEAP models. Based on the final models, we used linear combination equations to compute the percent differences in log DMAP and DEAP metabolites for the predictor variables to determine the effect of these predictors on metabolite levels among the 6-, 12- and 24-month old children. To assess bias due to loss to follow up, we ran the models with weights equal to the inverse probability of inclusion in the final sample at each time-point. We then performed the analyses without the weights for comparison. For statistical analyses, we present results that are not adjusted for creatinine.
We also included urinary creatinine as an independent variable in the final multi-variable mixed DMAP and DEAP models for comparison with models without the urinary creatinine variable. We investigated the relationship between potential exposure determinants and urinary pesticide metabolite levels in ~400 children followed through infancy and toddlerhood living in an agricultural community. All children had detectable levels of OP metabolites in their urine. Consistent with previous studies, the DMAP metabolite levels were higher than the DEAP metabolite levels. We observed three-fold higher DMAP levels in 24-month olds and two-fold higher levels in 12-month olds relative to 6 month olds; however DEAPs declined between 12 and 24 months. Nearby agricultural use of dimethyl and diethyl OP pesticides was generally stable over the study period, however, most residential uses of chlorpyrifos and diazinon, two diethyl OP pesticides, were cancelled. CHAMACOS children turned 12 months during the first year of the residential ban, which was phased in gradually. Thus, the decrease in DEAP metabolite levels among 24-month olds may be related to reduced indoor contamination of chlorpyrifos and diazinon , due to the residential use ban. This hypothesis is supported by our finding in a separate study that chlorpyrifos and diazinon house dust levels declined in Salinas Valley homes between 2000 and 2006. However, the ontogenetic increase in DMAP levels cannot be explained by changes in dimethyl pesticide use which did not change substantially during this time. The increase in DMAP levels may be due to increasing exposure-related behaviors and changes in diet as the children age in an environment where dimethyl OP pesticide use was relatively constant. Associations between the two classes of DAP metabolites and exposure determinants were not consistent at different age points. Possible reasons include differences in usage patterns, physical-chemical properties of the pesticides, field degradation, environmental transport, and metabolism of the dimethyl versus the diethyl OP pesticides. For example, malathion, which devolves to a DMAP metabolite, has a relatively high vapor pressure compared to other OP pesticides, and, thus, may result in greater exposures via inhalation. The spring/summer season, when malathion use is higher, was associated with higher DMAP levels in six-month olds, who are not yet crawling, suggesting an inhalation exposure pathway. We also found that recent rainfall was associated with lower DMAP levels in the younger children, a finding consistent with our previous study that showed rainfall was associated with lower OP levels in air. Together, these findings support the hypothesis that inhalation may be an important pesticide exposure route for very young children. Overall, our findings suggest that agriculture-related determinants of pesticide exposure may be associated with measured exposure at some ages, but we did not observe consistent associations across age points, or between DMAP and DEAP metabolites. The high variability in pesticide application frequency and the nature of transient, non-persistent exposures in young children may create too much variability to statistically model the association of these variables and child exposures. In contrast, intake of fruits and vegetables was consistently and positively associated with both classes of urinary metabolites in children at all ages, and was statistically significant for DMAP metabolites in 6- and 24-month old children, suggesting that diet is an important pesticide exposure pathway. This finding is consistent with recent studies that indicate diet is an important source of pesticide exposure to children.