With respect to alternative accession arrangements, as we move from full CAP to two-tier CAP to no CAP, we are simultaneously decreasing price supports and tariffs while we increase moneys targeted to public goods. This movement corresponds to increase in output decreases in commodity prices, and a dramatic increase in agricultural social welfare. Thus, in this model, entry to CAP can be counter-productive if it requires a diversion of funds away from the maintenance of infrastructure and credit support. In this paper, a model of the agricultural sector for a generic Central and East European nation is developed that attempts to represent the key characteristics of these transition economies. 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 performance, 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,dutch bucket hydroponic 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, 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. 16 The 42 countries of continental sub-Saharan Africa have a combined population of 960 million people. Despite recent economic growth, these countries have a GDP per capita of just 3.71 USD per day and include 21 of the 24 countries worldwide with a GDP per capita less than 2 USD per day.

Agriculture is the dominant sector in most African economies: 64% of the labor force in sub-Saharan Africa works in agriculture and 44% of consumer expenditure is on food, with even higher numbers for the poorest countries. Although there are large areas of land well-suited to agricultural production in sub-Saharan Africa, productivity in African agriculture is extremely low with output per hectare 5 times lower and output per worker 78 times lower than in North America, facts which can explain most of the income differences between sub-Saharan Africa and the rest of the world . One of the most striking facts that emerges from data on the African agricultural sector is that the prices of agricultural products vary tremendously across space. The left panel of figure 1.1 shows monthly maize prices from four large hub markets in East Africa on a 2000 kilometer south-north axis from Songea — a maize surplus area in southern Tanzania — to Mandera — a maize deficit area in northern Kenya. The right panel shows equivalent maize prices from three markets in the US on a 2000 kilometer north-south axis from inland surplus area Minneapolis to the major export ports near New Orleans. By December 2011, maize prices in Mandera had exceeded 0.85 USD/kg during the height of the Horn of Africa famine — the first UN-declared famine in 30 years. Meanwhile, maize prices in New Orleans were 0.25 USD/kg and maize prices in Songea were a mere 0.15 USD/kg. Empirical evidence from African agricultural markets suggests that traders in large hub markets like those in figure 1.1 behave competitively, facing low four-firm concentration ratios and not deviating detectably from marginal cost pricing . The large price gaps between markets within African countries, between African countries, and between Africa and the world market must therefore be reflective of large trade costs — the total costs involved in getting a product from a producer or trader in one location to a trader or consumer in another. There are several reasons why one might expect ex ante that trade costs in Africa are higher than elsewhere in the world,dutch buckets system including poor infrastructure, lots of borders with formal and informal tariffs and delays, vast interior areas far from ports including 16 countries that are completely landlocked, high fuel costs, etc. Several recent studies have provided evidence that freight rates and the distance-dependent component of total trade costs are two to five times higher in particular African countries than elsewhere in the world . Given the importance of agricultural production and consumption in Africa, high trade costs and the large spatial price gaps they cause have significant consequences. In surplus regions like Songea, trade costs confine African farmers to local markets with low prices and inelastic demand, limiting their incentives for productivity-enhancing technology adoption.

In deficit regions like Mandera, trade costs mean that African consumers face high food prices that fluctuate with volatile local harvests, leading to regular food security crises. How big are trade costs in the agricultural sector in Africa? What would be the gains from lowering them to match levels in other parts of the world? What is the most efficient way to achieve these gains? And how do trade costs alter the potential effects of productivity-enhancing technology adoption? This chapter addresses these questions by building and estimating a dynamic model of African agricultural storage and trade. I start by assembling a new intra-national dataset including ten years of monthly price and production data for the 6 major staple cereal grains in 230 regional markets covering all 42countries of continental sub-Saharan Africa. While many previous studies have made use of spatial data on grain prices from individual countries or regions within sub-Saharan Africa1 , I am the first to compile and use monthly intra-national grain price data from all countries in the entire sub-continent. I combine these price data with GIS grid cell level production data, which I allocate to individual markets using a market catchment methodology based on minimum travel time . With data in hand, I proceed to write down, estimate, and solve a two-part model including a model of consumer demand for staple grains and an outside numeraire good, from which I derive an expression for welfare; and a rational expectations model of monthly grain storage and trade under uncertainty including storage in each of the 230 markets, overland trade between them, and trade with the world market through 30 ports. Although the focus of this chapter is trade and the consequences of high trade costs, forward-looking storage is inextricably linked to trade in a sector where uncertain harvests occur once or twice a year, harvest periods vary by location, and both harvests and prices fluctuate dramatically . Dimensionality problems have traditionally restricted the use of this class of dynamic models to contexts involving two markets and a single commodity . I make use of an additional assumption about trader expectations that converts the intractable stochastic problem into a series of tractable deterministic problems, and I show that this assumption does not significantly affect my results. My estimation strategy includes a new, iterative approach to inferring trade costs from price differences when precise data on where trade occurs is not available . My median estimated intra-national trade cost using this approach is over 5 times higher than benchmark freight rates elsewhere in the world. My estimates appear to be in line with the results of trucking surveys and studies of the distance-dependent component of trade costs , with larger magnitudes reflective of additional components of overall trade costs not previously captured. In reduced-form regressions, I find that higher trade costs are correlated with lower road quality, international borders, the absence of regional trade agreements, and lower scores on the Transparency International Corruption Perceptions Index and the World Bank Logistics Performance Index. My port-to-world trade cost estimates are also over five times higher than benchmark shipping rates. After verifying the goodness of fit of my model-generated equilibrium, I begin my counterfactual analysis. In my main counterfactual, I lower trade costs in Africa to match benchmark freight rates in other parts of the world. Lowering trade costs leads to a 46.4% drop in the average price index for staple grains across all markets, a decrease in continent-wide agricultural revenues net of storage and trade costs of $117.4 billion over ten years , and a welfare gain equivalent to 2.2% of GDP . The aggregate drop in prices and revenues is largely attributable to increased penetration of imports from the world market , with the gains from lower food prices outweighing the lost income for farmers. However, there is significant heterogeneity in my results, with exporting regions experiencing increases in prices, revenues, and welfare and some regions experiencing welfare losses due to terms-of-trade effects. My results are robust to different demand specifications and allowing for a long-run reallocation of factors of production between sectors. Reducing trade costs everywhere in Africa may be politically and financially infeasible in the near future. However, in additional counter factuals, I show that 86% of the aggregate welfare gain from lower trade costs can be achieved by lowering trade costs through ports and along key links representing just 18% of the trade network. This suggests that a corridor-based approach of the kind advanced by multilateral donors may be effective . In two additional counter factuals I estimate the effects of widespread agricultural technology adoption under existing high trade costs and counterfactual low trade costs. In 2013, African cereal grain yields were half of South Asia’s and a third of Latin America’s due largely to the low use of inputs like fertilizer. Institutional donors and organizations like the Alliance for a Green Revolution in Africa are promoting widespread technology adoption to increase smallholder incomes and decrease food prices.