metadata: authors: 0: author: Bastos, Paulo
1: author: Straume, Odd Rune
2: author: Urrego, Jaime A.
count: World
docna: 0: docna: Rain, agriculture, and tariffs
docty: Policy Research Working Paper
theme: Other trade and integration
majtheme: Trade and integration
subtopic: Trade Policy,International Trade and Trade Rules,Rules of Origin,Trade and Services,Food Security,Trade and Multilateral Issues
teratopic: Agriculture,Macroeconomics and Economic Growth,International Economics and Trade,Science and Technology Development
abstracts: cdata!: This paper examines whether and how
            rainfall shocks affect tariff setting in the agricultural
            sector. In a model of strategic trade policy, the authors
            show that the impact of a negative rainfall shock on optimal
            import tariffs is generally ambiguous, depending on the
            weight placed by the domestic policy maker on tariff
            revenue, profits and the consumer surplus. The more weight
            placed on domestic profits, the more likely it is that the
            policy maker will respond to a rainfall shortage by reducing
            import tariffs. These findings are robust to alternative
            assumptions about market structure and the timing of the
            game. Using detailed panel data on applied tariffs and
            rainfall for 70 nations, the authors find robust evidence
            that rainfall shortages generally induce policy makers to
            set lower tariffs on agricultural imports.
repnme: repnme: Rain, agriculture, and tariffs
display_title: Rain, agriculture, and tariffs
listing_relative_url: /research/2013/03/17493992/rain-agriculture-tariffs
topicv3: Agriculture and Food Security m1326205 435,Trade m1326924 698,Trade in Services m1326928 780,Trade Policy and Integration m1327407 775,Trade Policy m1327640 776
publishtoextweb_dt: 2013-03-01T00:00:00Z
historic_topic: Agriculture,Macroeconomics and Economic Growth,International Economics and Trade,Science and Technology Development
no_of_pages: 38
model_extractions: WPS6394 Policy Research Working Paper 6394 Rain, Agriculture, and Tariffs _Paulo Bastos Odd Rune Straume Jaime A. Urrego_ The World Bank Development Research Group Trade and Integration Team March 2013
Policy Research Working Paper 6394

# **Abstract**

This paper examines whether and how rainfall shocks affect tariff setting in the agricultural sector. In a model of strategic trade policy, the authors show that the impact of a negative rainfall shock on optimal import tariffs is generally ambiguous, depending on the weight placed by the domestic policy maker on tariff revenue, profits and the consumer surplus. The more weight placed on domestic profits, the more likely it is that the policy maker will respond to a rainfall shortage by reducing import tariffs. These findings are robust to alternative assumptions about market structure and the timing of the game. Using detailed panel data on applied tariffs and rainfall for 70 nations, the authors find robust evidence that rainfall shortages generally induce policy makers to set lower tariffs on agricultural imports.

This paper is a product of the Trade and Integration Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at pbastos@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Rain, agriculture, and tari¤s<sup>�</sup>

Paulo Bastos<sup>y</sup> Odd Rune Straume<sup>z</sup> Jaime A. Urrego<sup>x</sup> Keywords: Rainfall shocks; optimal tari¤s; strategic trade policy; agriculture.

> �We would like to thank the Editor, two anonymous referees, and Will Martin for very helpful comments. The views expressed herein are those of the authors and not of the institutions they are a¢ liated with.

> yCorresponding author. Development Research Group–Trade, The World Bank, 1818 H Street NW, Washington DC, United States. E-mail: pbastos@worldbank.org. Tel. +1 202 473 4332.

> zDepartment of Economics/NIPE, University of Minho, Portugal; and Department of Economics, University of Bergen. E-mail: o.r.straume@eeg.uminho.pt

> xResearch Department, Inter-American Development Bank, United States. E-mail: jaimeu@iadb.org

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"...drought [in "rice countries"] is, perhaps, scarce ever so universal as necessarily to occasion a famine, if the government would allow a free trade."–Adam Smith (1776, IV.5.45)

# 1 Introduction

Random ‡uctuations in weather may have severe adverse e¤ects on agricultural producers and consumers. How can policy makers mitigate these impacts? Recent empirical research suggests that greater openness to trade alleviates the adverse e¤ects of weather shocks on domestic consumers. Using district-level panel data for colonial India in 1875-1919, and exploiting the construction of the railroad network to identify the e¤ects of increased openness, Burgess and Donaldson (2011) document that the arrival of railroads dramatically constrained the ability of rainfall shortages to cause famine.<sup>1</sup> But while this evidence points to the existence of a causal link between openness and weather-related famine, import tari¤s on agricultural goods remain high in many nations, and relatively little is known about the extent to which countries use trade policy strategically to mitigate the impacts of weather shocks on domestic welfare.<sup>2</sup> In this paper, we examine whether and how rainfall shocks a¤ect tari¤ setting in the agricultural sector. To identify key mechanisms at play, we ...rst set up a model of international oligopoly in which domestic and foreign agricultural producers compete in the home market. International trade is potentially costly due to import tari¤s optimally set by the domestic policy maker. A rainfall shortage increases the marginal costs of domestic producers, thus generating a shortfall in agricultural output that foreign producers have an incentive to meet. Consequently, the shock a¤ects the marginal e¤ects of import tari¤s on tari¤ revenue, domestic pro...ts and the consumer surplus, and thereby the optimal policy response.

We ...nd that the impact of a rainfall shock on optimal import tari¤s is not clear-cut. A rainfall shortage leads to a higher volume of agricultural imports and therefore higher marginal tari¤ revenues. All else equal, this increases the optimal tari¤. On the other hand, by making home production more costly, the shock reduces incentives for using tari¤s as an instrument to shift rents from foreign to domestic producers. This leads, ceteris paribus, to a lower optimal tari¤. Finally, a rainfall shortage also reduces domestic consumers’surplus due to a lower total supply of the agricultural good. This increases (reduces) the negative e¤ect of import tari¤s on domestic consumers if food demand is

> 1 This evidence is consistent with Donaldson (2013), who shows that railroads contributed to reduce the exposure of agricultural prices and real incomes to rainfall shocks.

> 2 Gibson et al. (2001) emphasize that high protection for agricultural commodities in the form of tari¤s continues to be the major factor restricting world trade.

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su¢ ciently (not too) convex, leading, all else equal, to lower (higher) tari¤s. The overall impact of the rainfall shock on optimal import tari¤s is, therefore, generally ambiguous, depending on the weight placed by the domestic policy maker on each of these policy objectives, and on the shape of the food demand function. A larger weight placed on domestic pro...ts will enlarge the scope for a rainfall shortage to cause tari¤ reductions. A stronger concern for domestic consumers may have a similar e¤ect, but only if demand is su¢ ciently convex, i.e., if the demand for the agricultural good becomes su¢ ciently inelastic at lower consumption levels.

We proceed by verifying the extent to which the main predictions of our theory are sensitive to assumptions about the timing of the game and the mode of competition. In the basic model, we assume that a rainfall shortage increases the marginal costs of agricultural production, as the a¤ected producers must spend more resources on irrigation in order to achieve a given level of output. However, in some agricultural markets producers may have limited access to irrigation technology, and hence have limited ability to adjust output following the shock. We therefore modify the timing of the game, letting the policy maker, and subsequently the foreign producers, make their optimal decisions in response to an exogenous drop in domestic output. Reassuringly, we ...nd that the results of the basic model remain qualitatively unchanged under this alternative setting, while also showing that a rainfall shortage is more likely to induce tari¤ cuts when producers have greater access to irrigation technology. In another extension to the basic model, we examine if and how our main results depend on the assumption of oligopolistic competition. Applying the theoretical framework of Bagwell and Staiger (2001), who analyze strategic trade policy under perfect competition, we re-examine the e¤ect of a rainfall shortage on optimal import tari¤s when ...rms are competitive rather than oligopolistic. We ...nd, once again, that the key predictions of our basic model remain qualitatively similar.

We then examine empirically the impact of rainfall shocks on e¤ectively applied tari¤s. Using detailed panel data on applied tari¤s and rainfall for 70 nations over 1988-2006, we ...nd that rainfall shortages generally lead to lower applied tari¤s on agricultural imports. The positive relationship between rainfall and import tari¤s is robust to several alternative empirical speci...cations, tends to be stronger in countries with greater access to irrigation, and applies fairly widely across the set of agricultural products and regions considered. The main exception refers to nations in Africa, for which we ...nd no evidence of signi...cant tari¤ adjustment in response to rainfall shocks. This lack of response may plausibly re‡ect the fact that governments of African nations tend to depend more heavily on import tari¤s for revenue (Devarajan et al., 1999). In this case, tari¤ revenue is likely to carry a larger weight on the governments’objective function, and thereby o¤set opposite incentives via pro...ts and/or the consumer surplus. Overall, however, our empirical ...ndings suggest 3
that policy makers around the world often adjust agricultural import tari¤s strategically in response to rainfall shocks.

In addition to the work cited above, our paper is related to the theoretical and empirical literature on optimal tari¤s and strategic trade policy, including Bickerdike (1907), Graaf (1949–1950), Spencer and Brander (1983), Dixit (1984), Brander and Spencer (1985), Eaton and Grossman (1986), Grossman and Helpman (1994, 1995), Brander (1995), Gawande et al. (2000), Broda et al. (2008) and Bagwell and Staiger (2001, 2012a, b). We are not aware of previous research, either theoretical or empirical, focusing on the e¤ects of rainfall shocks on optimal tari¤s in the agricultural sector. Our work is also broadly related to the emerging literature on how weather shocks shape economic, social and political outcomes (Deschênes and Greenstone, 2007; Maccini and Yang, 2009; Burgess et al., 2009; Brückner et al., 2011; Jones and Olken, 2010; Dell et al., 2012).

The remainder of the paper is organized as follows. Section 2 develops a theoretical model in which rainfall shocks impact on the optimal agricultural tari¤s set by the domestic policy maker. Section 3 describes the empirical strategy, while section 4 presents the data employed in the empirical analysis. Section 5 presents the main results, before section 6 examines their robustness. Section 7 examines heterogeneity of e¤ects across products and countries. Section 8 concludes.

# 2 A model of weather shocks and optimal agricultural tari¤s

Consider an oligopolistic domestic market for a homogeneous agricultural good that is supplied by n domestic and m foreign producers.<sup>3</sup> Domestic demand for the agricultural good is given by the inverse demand function where is total supply of the good, with qi and qbj being quantities supplied by the domestic producer i and the foreign producer j, respectively.<sup>4</sup> There is a constant marginal cost of

> 3 It is common to adopt oligopoly models to investigate international trade in agricultural markets. Early theoretical and empirical research in this literature includes Sarris and Freebairn (1983), Karp and McCalla (1983), Kolstad and Burris (1986), Paarlberg and Abbott (1986) and Pick and Park (1991). Krishna and Thursby (1992) note that agricultural trade is often conducted through marketing boards for the product in question, both in developed and developing countries. Building on this observation, they examine optimal tax/subsidy policies with export marketing boards that compete as oligopolists in world markets.

> 4 We adopt the simplifying assumption of linear demand in order to identify key mechanisms whereby a rainfall shortage shapes tari¤ setting incentives. Below we examine the implications of considering

4
production equal to c (bc) for domestic (foreign) producers. In addition, foreign producers must pay a per-unit tari¤ t for supplying the domestic market. This tari¤ is set by a domestic policy maker with the following objective function: where is total tari¤ revenue, is the pro...t of the domestic producer i, and is domestic consumers’surplus. The speci...cation of � is su¢ ciently general to encompass a variety of di¤erent policy objectives, where we allow the policy maker to place di¤erent weights on domestic pro...ts and consumers’surplus: � � 0 and � � 0, respectively.<sup>5</sup> The pro...t of a foreign producer j is given by We consider the following two-stage game: Stage 1: The domestic policy maker sets the import tari¤ t.

Stage 2: The domestic and foreign producers choose quantities simultaneously and noncooperatively.

We look for a subgame-perfect Nash equilibrium, solving the model by backwards induction.

# 2.1 Equilibrium supply of the agricultural good

Each producer chooses its supply of the agricultural good to maximize its pro...ts. The ...rst-order conditions for a domestic and foreign producer, respectively, are given by alternative speci...cations for food demand, and discuss their empirical relevance.

> 5 Notice that these weights are relative to the weight on tari¤ revenues. For example, if �< (>) 1, the policy maker places a smaller (larger) weight on domestic pro...ts than on tari¤ revenues. The case of �> 1 is referred to by Bagwell and Staiger (2001) as a case where the government is in‡uenced by ‘political-economy considerations’(e.g., due to industry lobbying).

5
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The optimal tari¤ balances three di¤erent policy concerns: (i) raising tari¤ revenues, (ii) shifting oligopoly rents from foreign to domestic producers, and (iii) increasing consumers’ surplus. Notice that more rent shifting and an increase in consumers’ surplus are con‡icting policy targets. Thus, a larger weight on domestic pro...ts leads to a higher optimal tari¤, while a larger weight on consumers’surplus leads to a lower optimal tari¤,

# 2.3 Rainfall shortage

A negative rainfall shock implies that the a¤ected producers must spend more resources on irrigation in order to achieve a given level of output, making agricultural production more expensive. We therefore model a domestic rainfall shortage as an increase in the marginal cost of domestic production, c. For simplicity, we assume that domestic and foreign producers have the same marginal production cost initially; i.e., we evaluate the domestic cost increase at c = bc. The e¤ect on the optimal tari¤ can then be summarized as follows:<sup>7</sup> Proposition 1 Suppose that markets are oligopolistic and that domestic and foreign ...rms have initially the same production costs. In this case, (i) a domestic rainfall shortage always leads to a higher import tari¤ if � is su¢ ciently low, (ii) a domestic rainfall shortage leads to a lower import tari¤ if � is su¢ ciently high and if m is su¢ ciently large relative to n, (iii) the higher is �, the larger is the scope for a higher import tari¤ in response to a domestic rainfall shortage.

Proof. (i) From (18) we have (ii) From (21) we see that @t<sup>�</sup> =@c < 0 if �> <u>�.</u> However, it remains to show whether this case is relevant, i.e., whether <u>�</u> 2 � for some parameter con...gurations. Setting c = bc and

> 7 We evaluate the e¤ect of a domestic rainfall shortage by considering a marginal increase in the domestic cost of agricultural production. However, it can easily be shown that the e¤ect on the optimal tari¤ is qualitatively identical if we instead consider a discrete increase in marginal production costs.

7
inserting the optimal import tari¤ into (10)-(11), an interior solution exists if �<<sup>n(1+</sup> 2n<sup><u>�)+1</u></sup> and �<<sup>m+2</sup> m<sup><u>(n+1)</u></sup> . It is straightforward to verify that, if these two inequalities hold, then the second-order condition �< �e is satis...ed. Furthermore,<sup>n(1+</sup> 2n<sup><u>�)+1</u></sup> > <u>�, implying �</u> 2 �, if m ><sup><u>n2�1</u>Part(iii)ofthepropositionfollowsdirectlyfromthede...nitionof</sup> <u>�.</u> n�+1<sup>.</sup> For a given level of the import tari¤, a domestic rainfall shortage, by increasing the domestic cost of agricultural production, leads to lower market shares for domestic producers. Although some of the fall in domestic production is replaced by increased imports, there will also be a reduction in the total supply of the agricultural good to the domestic market. Thus, domestic producers as well as consumers are hurt by a negative rainfall shock. Since domestic producers (consumers) would su¤er (bene...t) from a lower import tari¤, it might seem somewhat counterintuitive that a lower import tari¤ can be an optimal policy response to a rainfall shortage only if the policy maker places a su¢ ciently large weight on domestic pro...ts, and that the scope for such a policy response is larger the less weight the policy maker places on consumers’surplus. The intuition for this result, though, can be found by considering how a domestic cost increase a¤ects the policy maker’s trade-o¤ among its three di¤erent policy targets. These are given by the three terms in (14), and we will consider each of the three di¤erent policy targets in turn.

(i) The e¤ect of using import tari¤s to raise tax revenues is given by An increase in domestic production costs leads to a higher import volume (since qb is increasing in c) and therefore higher marginal tari¤ revenues. All else equal, this increases the optimal tari¤.

(ii) The e¤ect of using import tari¤s to shift oligopoly rents from foreign to domestic producers is given by Foreign producers will respond to a higher import tari¤ by reducing their supplies to the domestic market. This leads to a shift in oligopoly rents from foreign to domestic producers through two di¤erent channels, represented by the two terms in (23). The price increase that results from less imports makes domestic production more pro...table (term 1) and domestic producers will respond by expanding their output, which increases pro...ts in proportion to the price-cost margin (term 2). A domestic rainfall shortage reduces both of these e¤ects, since higher domestic production costs reduce both the output and the price-cost margin of domestic producers (notice that p � c = q in equilibrium), which in turn reduces the marginal rent-shifting e¤ect of import tari¤s. In other words, import tari¤s become less e¤ective as a rent-shifting instrument if domestic producers become 8
more cost-disadvantaged. All else equal, this reduces the optimal tari¤ and, naturally, the e¤ect is stronger the larger weight the policy maker places on domestic pro...ts.

(iii) The e¤ect of using import tari¤s to increase domestic consumers’surplus is given by An increase in domestic production costs reduces total supply of food in the domestic market. If consumers’surplus is convex in output, which is true for linear demand functions, this means that the reduction in consumers’surplus due to a marginal tari¤ increase is lower. All else equal, this increases the optimal tari¤ and, naturally, the e¤ect is stronger the larger weight the policy maker places on consumers’surplus.

Thus, we can conclude that the policy maker will optimally respond to a domestic rainfall shortage by lowering import tari¤s if the second of the three above described e¤ects –the reduced e¤ectiveness of import tari¤s as a rent-shifting instrument –is su¢ ciently strong to outweigh the other two e¤ects. Otherwise, the import tari¤ will increase.

The above analysis is based on the simplifying assumption of linear demand. Although the main mechanisms of the model, as given by the three di¤erent e¤ects described above, generalize well beyond the assumption of linear demand, it is worth considering the extent to which our results are robust to alternative demand assumptions. In particular, the result that a tari¤ increase has a larger negative impact on consumers at higher consumption levels relies on the assumption that consumers’surplus is convex in output. For a general inverse demand function p (Q), this requires that In other words, consumers’surplus is convex in output for concave, linear and ‘not-tooconvex’ demand functions. However, since demand for food is likely to become quite inelastic for low consumption levels, consumers’surplus might be concave in output for the range of output levels that are relevant for rainfall shortages that cause a serious cut-back in domestic production.<sup>8</sup> If this is the case, a rainfall shortage will increase the negative e¤ect of import tari¤s on consumers’surplus, thereby increasing the scope for tari¤ reductions as an optimal response to a negative rainfall shock.

> 8 In empirical studies, demand for food is generally found to be inelastic (Andreyeva et. al. 2010). O’Hare and Kammen (2008) argue that it seems reasonable to assume that demand for food becomes less and less elastic as agricultural output declines. As food consumption falls, consequences like malnutrition and starvation begin to appear, impacts much more compelling than the hedonic costs of consuming a less-preferred diet or wasting less food. Consistent with this hypothesis, Andreyeva et. al. (2010) survey a large body of evidence on the price elasticity of demand for food and conclude that demand is relatively more elastic for categories like "food away from home", "soft drinks" and "juice".

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# 2.4 Extensions

In this subsection we consider two di¤erent extensions to the model. First, we modify the timing of the game by considering the case where domestic producers are not able to adjust their output decisions in response to a rainfall shortage. Second, we abandon the oligopoly assumption and consider the case of perfectly competitive markets.

## 2.4.1 No domestic output adjustment

In some agricultural markets, due to a lack of access to su¢ cient amounts of irrigated water, it might not be feasible to use irrigation in order to compensate for a rainfall shortage. If this is the case, domestic producers will have limited ability to adjust output in response to rainfall-induced tari¤ changes. We explore this scenario by modifying the timing of the game, letting the policy maker, and subsequently the foreign producers, make their optimal decisions in response to an exogenous drop in domestic production.

Let total domestic output be denoted by <u>q</u> :=<sup>Pn</sup> i=1<sup>qi.Foragiventari¤level,the</sup> best-response from foreign suppliers are given by (9). In the symmetric equilibrium, with b b qj = q for all j = 1; :::; m, the output chosen by each foreign producer is given by b b Substituting q from (25) into (3), where<sup>Pn</sup> i=1<sup>�i=(p �c)</sup> <u>q</u> and S = 2<sup><u>1(</u></sup> <u>q</u> + mq)<sup>2</sup> , and maximizing with respect to t, the optimal import tari¤, as a function of an exogenous domestic output level, is given by The second-order condition of the maximization problem holds if �<<sup>2(m</sup> m<sup><u>+1)</u></sup> .<sup>9</sup> We also assume that <u>q</u> is su¢ ciently low to ensure an interior solution with strictly positive import volumes.

We analyze the import tari¤ response to a domestic rainfall shortage by considering a marginal reduction in domestic production, <u>q.</u><sup>10</sup> Proposition 2 Suppose that markets are oligopolistic but domestic ...rms are not able to adjust output in response to a rainfall shock. In this case,

> 9 The second-order condition is explicitly given by

> 10 Due to the linearity assumptions of the model, the tari¤ response is qualitatively identical if we instead consider a discrete fall in domestic production.

10
(i) a domestic rainfall shortage leads to a higher (lower) import tari¤ if � is su¢ ciently low (high), (ii) the higher is �, the larger is the scope for a higher import tari¤ in response to a domestic rainfall shortage.

Proof. (i) From (26) we have An interior solution always exists for �> 1+ m�+1<sup>if</sup> <u>q</u> is su¢ ciently low, which we assume to be the case. Part (ii) of the proposition follows directly from (27).

Comparing Propositions 1 and 2, it is clear that the e¤ect of a domestic rainfall shortage on import tari¤s, in qualitative terms, does not depend on whether domestic producers are able to adjust their output in response to the rainfall shock or not. Notice also that the results in Proposition 2 hold for any initial level of <u>q</u> (that yields positive import volumes).

The intuition behind these results follows closely the intuition given for Proposition 1. The e¤ects that go through the policy maker’s incentive for raising tari¤ revenues and increasing consumers’surplus are almost identical under the two di¤erent scenarios, as the equilibrium e¤ect of higher marginal production costs is similar to an exogenous cut-back in production. The incentives for using import tari¤s to shift oligopoly rents from foreign to domestic producers are also qualitatively similar, although the quantitative e¤ect is somewhat di¤erent when domestic producers cannot respond strategically to a change in import tari¤s. Formally, the e¤ect of a tari¤ increase on domestic pro...ts in the case of no domestic output adjustment, is given by A higher import tari¤ increases the domestic market price, which increases domestic profits. However, if domestic producers are not able to adjust their output in response to the tari¤ change, the positive pro...t e¤ect is smaller than it would have been if the domestic producers could meet the price increase by increasing their output. Thus, the incentive to use import tari¤s to increase domestic pro...ts is smaller in the absence of domestic output adjustment. Nevertheless, a drop in domestic production due to rainfall shortage will reduce this policy incentive even further, contributing, all else equal, to a lower import tari¤. As before, this e¤ect establishes a negative relationship between rainfall shortage and import tari¤s if the policy maker places a su¢ ciently large weight on domestic pro...ts in his objectives.

11
# 2.4.2 Perfect competition

The oligopoly assumption used in the main version of the model may be less suitable for some agricultural markets, particularly in developing countries. We will therefore analyze if and how our main results might be a¤ected by the assumption of imperfect competition. Suppose instead that the domestic market is supplied by competitive (domestic and foreign) ...rms. As in Bagwell and Staiger (2001), we assume that the domestic and foreign industries are characterized by positively sloped supply functions and associated pro...t (producer surplus) functions. The domestic and foreign supply functions are given by, respectively, and where p is the domestic price, given by (1), and pb is the export price for foreign suppliers. The parameter c is a shift parameter that shifts down the domestic supply function in b case of a rainfall shortage.<sup>11</sup> The prices p and p satisfy the arbitrage condition and the competitive equilibrium is characterized by the market-clearing condition From (1) and (29)-(32), the domestic price in the competitive equilibrium is given by The domestic policy maker optimally sets the import tari¤ to maximize an objective function similar to (3), now given by b b where T = tq (p) is tari¤ revenue, � is domestic producer surplus, and S is consumers’ surplus, given by (6). Since @�=@p = q (p), domestic producer surplus is given by

> 11 In contrast to the cost assumption in the oligopoly model, these supply functions imply decreasing returns to scale. This di¤erence is consistent with the often held notion that market structure (oligopoly versus perfect competition) is endogenously determined by technology. However, it is straightforward to show that the results from the oligopoly model would be qualitatively similar if we changed the cost functions to a linear-quadratic form C (q) = cq + q<sup>2</sup> , with c being the cost-shifting parameter, which would correspond to a supply function given by (29). Details of these calculations are available upon request.

12
Solving the policy maker’s maximization problem, it is straightforward to derive an explicit expression for the optimal import tari¤: The second-order condition is which is satis...ed if �< 24 � 2�.<sup>12</sup> As in the oligopoly model, optimal import tari¤ setting balances three di¤erent considerations: (i) raising tari¤ revenues, (ii) increasing domestic producer surplus, and (iii) increasing consumer surplus. Once more, (ii) and (iii) are con‡icting policy targets, as domestic producers bene...t from a higher import tari¤ while consumers bene...t from lower tari¤ level. Thus, A negative rainfall shock leads to a downward shift in the domestic supply curve. As before, due to the linearity of the model, the e¤ects on optimal tari¤ setting of a marginal increase in c and a discrete increase in c are identical. In line with our previous analysis, we also assume that domestic and foreign production costs are ex ante identical; i.e., we evaluate the e¤ect of the rainfall shock at c = 0.

Proposition 3 Suppose that markets are perfectly competitive and that domestic and foreign ...rms have initially the same production costs. In this case, (i) a domestic rainfall shortage leads to a higher (lower) import tari¤ if � is su¢ ciently low (high), (ii) the higher is �, the larger is the scope for a higher import tari¤ in response to a domestic rainfall shortage.

Proof. (i) From (36) we have We also need to show that an interior solution always exists for �> <u>13</u><sup>(4 + 2�).</sup> For c = 0, an interior solution requires �< 7 and �< 3 + �. The latter inequality also ensures that the second-order condition �< 24 � 2� is satis...ed. Equilibrium existence for �> 3<sup><u>1</u>(4 + 2�)iscon...rmedbynoticingthat3 + �></sup><sup><u>1</u></sup> 3<sup>(4 + 2�)forall��0.Part(ii)of</sup> the proposition follows directly from (39).

> 12 In addition, an interior solution with positive domestic and foreign supply requires that �< <u>142(1��17c)c</u> and �<<sup>2�+</sup> 2(1<sup>3c�</sup> �<sup>2</sup> c<sup>c</sup> )<sup><u>�+6</u></sup> .

13
A comparison of Propositions 1 and 3 reveals that the e¤ect of domestic rainfall shortage on import tari¤s does not depend crucially on whether agricultural markets are oligopolistic or competitive. In either case, a su¢ ciently large weight on domestic producer surplus in the policy maker’s objective function is necessary for import tari¤s to fall in response to a negative rainfall shock.

The decomposition of e¤ects shown in Section 2.2 also applies in the case of perfect competition. A downward shift in the domestic supply curve implies that some domestic production is replaced by imports but total supply to the market goes down. The increased import volume increases marginal tari¤ revenues, while the lower total supply implies that the reduction in consumer surplus due to a tari¤ increase is lower (since consumers’surplus is convex in output). Both of these e¤ects contribute to a higher import tari¤ in response to a domestic rainfall shortage.

As before, the counteracting e¤ect is the concern for domestic producer surplus. A higher import tari¤ increases the domestic producer price, and the corresponding increase in domestic producer surplus is positively correlated with total domestic output. Technically, this e¤ect is given by A negative rainfall shock (i.e., an increase in c) reduces domestic output and therefore reduces the marginal pro...t gain of increasing the domestic price through higher import tari¤s. All else equal, this reduces the optimal tari¤, and, if this e¤ect is su¢ ciently strong (which requires a su¢ ciently high value of �), it will outweigh the two other e¤ects and enforce a negative equilibrium relationship between rainfall shortage and import tari¤s.

# 3 Empirical strategy

To estimate the impact of rainfall shocks on applied import tari¤s we adopt the following econometric speci...cation: where tari¤ict is the mean of import tari¤s applied on product i by country c in year t; rainct is the amount of rainfall recorded in country c in year t; agrici is a dummy variable that takes the value of one for agricultural products; �i is an industry ...xed-e¤ect, � c is a country ...xed-e¤ect and � t is a year ...xed-e¤ect. �ict is an idiosyncratic error term.

Our main interest lies on �, which reveals whether and how countries use trade policy strategically to mitigate the adverse e¤ects of rainfall shortages on the agricultural sector. Conditional on industry and year ...xed-e¤ects, identi...cation of the causal e¤ect of interest 14
relies on the plausible assumption that within-country variation in rainfall over time is orthogonal to other determinants of import tari¤s.

# 4 Data

Our empirical analysis makes use of the following sets of data:

1. Import tari¤s We use annual data on bilateral applied tari¤s by country and product (SITC 2-dig., Rev. 4) from the TRAINS database of the United Nations Conference on Trade and Development. These data are generally available from 1988 onwards, but the extent of time coverage di¤ers considerably across countries. We construct both simple and import-weighted averages of bilateral applied tari¤s by countryproduct-year. For a given country-product pair, the former measures are de...ned as the simple average of applied tari¤s on imports from the various source countries in year t. The latter measures are de...ned as the weighted average of such bilateral import tari¤s, where the weights are the share of each source country in total countryproduct imports in a base period.<sup>13</sup> Data on bilateral imports used to construct these weights come from the United Nations Commodity Trade Statistics Database (COMTRADE). To account for tari¤ binding, we will further make use of analogous information on bound tari¤ rates.<sup>14</sup> These data also come from TRAINS, but are only available from 1995.

2. Rainfall and irrigation We use the data set on population-weighted rainfall levels by country-year constructed and made available online by Dell et al. (2012). The original source of the historical rainfall data is the Terrestrial Air Temperature and Precipitation: 1900-2006 Gridded Monthly Time Series (Version 1.01), compiled by Kenji Matsuura and Cort Willmott (2007) in conjunction with NASA. This database provides worldwide (terrestrial) monthly precipitation information at 0:5�0:5 degree resolution. Dell et al. (2012) use geospatial software to aggregate these rainfall data to the country-year level, weighting by the population distribution within the country.<sup>15</sup> We further use country-level data on the proportion of agricultural land that is irrigated. These data come from the World Development Indicators of the World Bank, and are generally available for the period 2001-2009.

We combine these sets of data to construct an unbalanced panel at the product-countryyear level, covering 70 nations over the period 1988-2006. Table A.1 in the appendix

> 13 The base period is the ...rst year for which tari¤ data are available for the tari¤-setting country.

> 14 The most-favored-nation tari¤ rate resulting from negotiations associated with GATT/WTO membership.

> 15 These data are also used by Jones and Olken (2010).

15
summarizes the data sources and variable de...nitions. To ensure adequate coverage for each country, we excluded information for nations that do not have at least three consecutive years of tari¤ and rainfall data. Table A.2 lists the countries and corresponding time periods covered in our sample, while Table A.3 details the product classi...cation we employ. To identify agricultural products we use the UN statistics division of the SITC classi...cation. For robustness, we also consider a broader de...nition that includes as well other agricultural products.<sup>16</sup> Irrigation data are available for 32 of the 70 countries initially considered. These data generally refer to the 2001-2009 period, but the extent of time coverage di¤ers across countries. We therefore use the average value of this variable among non-missing observations for each of the 32 countries as a (time-invariant) proxy for access to irrigation at the country-level.

Figure 1 provides illustrative evidence on the rainfall variable we employ for a subset of countries. The dashed lines in these diagrams refer to major, widely-documented droughts observed in each nation. Many of these extreme weather events were associated with the El Niño or La Niña phenomena, including Argentina (1999), Bolivia (1998), Brazil (1998, 2003), Colombia (1992, 1997), Chile (1998), Costa Rica (2001), Guatemala (1997, 2001) and Peru (2000).

# [Figure 1 about here]

Our theory applies to countries that have discretion to set import tari¤s on agricultural goods. A potential concern is that, in reality, tari¤ setting might be constrained by limits associated with GATT/WTO membership. As shown in Figure 2, however, countries typically set their tari¤s on agricultural goods well below the bound tari¤s imposed by such multilateral tari¤ agreements; see Gibson et. al (2001, pp. 20-21) for further data and discussion. This implies that policy makers have ample policy space to adjust tari¤s strategically in response to rainfall shortages. We will nevertheless address this concern directly in the econometric analysis below.

# [Figure 2 about here]

Table 1 reports summary statistics on the ...nal panel data set employed in the regression analysis. As can be seen, most observations refer to developing countries in Latin America and Caribbean, Asia-Paci...c and Africa.<sup>17</sup>

[Table 1 about here]

> 16 As shown in Table A.3, this latter de...nition includes as well SITC product categories "08", "09", "11", "12" and "29".

> 17 We adopt a broad de...nition of the term "Asia-Paci...c", comprising all nations in Asia and Oceania.

16
# 5 Main results

Table 2 reports our baseline results. Columns (1) and (2) report the estimates obtained using a simple average of import tari¤s as the dependent variable, while columns (3) and (4) report results from using import-weighted tari¤s. By de...nition, the former measures give equal weights to all bilateral applied tari¤s within each country-product-year. They are therefore less sensitive to potential bias arising from the fact that a higher tari¤ imposed on a given source country may lead to zero or little equilibrium imports from that nation. The latter measures have the advantage of placing a larger weight on tari¤s imposed on countries for which import volumes of that product are larger to begin with. If such di¤erential import shares re‡ect heterogeneity in fundamentals across source countries (e.g., due to stronger comparative advantage in agriculture), the policy maker may optimally favor larger adjustments to import tari¤s imposed on major food exporters, so as to obtain larger impacts from the policy response. It is therefore important to use both measures.

## [Table 2 about here]

The estimates reported in columns (1) and (3) of Table 2 show no signi...cant e¤ects of rainfall shocks on overall import tari¤s. This result is not surprising, considering that rainfall shortages would not be expected to a¤ect tari¤ setting in the non-agricultural sector, and that agricultural goods represent a relatively small proportion of the full spectrum of products. Columns (2) and (4) point, however, to signi...cant impacts of rainfall shocks on import tari¤s of agricultural products: the coe¢ cient of interest is positive and statistically signi...cant at the 1% level, suggesting that a rainfall shortage induces policy makers to set lower tari¤s on agricultural imports. When seen in conjunction with our theoretical model, this result suggests that policy makers place a considerable weight on domestic producer surplus when setting import tari¤s.

# 6 Robustness

We conduct a number of checks to verify the robustness of our empirical ...ndings. First, we examine the extent to which the results are sensitive to composition of our sample. To do this, we restrict the sample to include only countries that report at least four consecutive years of tari¤ and rainfall data. This restriction reduces the number of countries in the estimation sample to 51, down from the 70 initially considered. Reassuringly, the estimates yielded by this restricted sample remain very similar, both qualitatively and quantitatively (Table 3).

[Table 3 about here] 17
We proceed by verifying the extent to which the results are sensitive to the de...nition of agricultural products we employ. In our baseline analysis, we rely on the UN statistics division of the SITC classi...cation. For robustness, in Table 4 we consider a broader de...nition that includes as well other agriculture-related products (Table A.3). The results reported in this table show that our ...ndings remain very similar when this alternative de...nition is used.

# [Table 4 about here]

Table 5 reports the estimates obtained when considering this broader de...nition of agricultural products and the restricted sample of countries that report at least four consecutive years of tari¤ and rainfall data. Once again, the results remain very similar.

# [Table 5 about here]

As mentioned above, a potential concern for our empirical analysis is that tari¤ setting might be constrained by limits associated with GATT/WTO membership. We adopt two alternative strategies to verify the extent to which our results are sensitive to tari¤ binding. First, we restrict the sample to include only country-product-year observations in which applied tari¤s are considerably smaller than the corresponding bound tari¤s. Second, we account for censoring by adopting a Tobit random e¤ects model on the full estimation sample.

Table 6 reports the results yielded by the ...rst of these strategies. While the analysis in the previous section spanned the 1988-2006 period, information on bound tari¤s is only available from 1995. Hence we begin by showing in column (1) that our baseline results remain qualitatively similar when we restrict the sample to include this sub-period only. In columns (2)-(4) we report results from analogous regressions, but now imposing progressively more stringent restrictions on the distance between applied and bound tari¤s. Reassuringly, we ...nd that the coe¢ cient of interest remains remarkably stable across all the subsamples considered.

# [Table 6 about here]

A potential concern with the results reported in Table 6 is that this approach introduces sample selection, which may a¤ect the estimated coe¢ cients directly. We therefore turn to the Tobit random e¤ects estimator. This method makes it possible to account for censoring due to tari¤ binding without introducing sample selection. A downside of the Tobit model, however, is that it is not possible to correct the standard errors for potential idiosyncratic correlation across products and over time within each country, implying that the standard errors are likely to be biased downwards. Table 7 reports the estimated marginal e¤ects. Columns (1) and (3) refer to models with country random-e¤ects (including 18
also product and year dummies), while columns (2) and (4) refer to models with product ...xed-e¤ects (including also country and year dummies). We ...nd that the marginal e¤ects of interest are always remarkably similar to the OLS point estimates reported in column (1) of Table 6. Taken together, the results from these two strategies provide evidence that the potential bias imposed by tari¤ binding does not appear to be an important concern in our application.

In the analysis thus far, we have adopted a linear function of rainfall. To account for potential nonlinearity in the policy response to extreme events such as drought, we modify out basic speci...cation by considering instead a set of dummy variables that are intended to capture extreme realizations of rainfall. In columns (1) and (4) of Table 8, we consider a dummy variable that takes the value of one when a realization of rainfall for a given country-year is below the 15th percentile of the rainfall distribution in the period 1988-2006.<sup>18</sup> Consistent with the results reported earlier, the estimates indicate that severe rainfall shortages induce policy makers to set lower tari¤s on agricultural imports. In columns (2) and (5), we introduce a dummy variable that equals one when the level of rainfall for a given country-year is above the 85th percentile of this distribution. The results indicate that larger volumes of rainfall are associated with higher agricultural tari¤s, what is again consistent with the linear speci...cation.<sup>19</sup> The results in columns (3) and (6) reveal that these conclusions remain unchanged when including both these dummy-variables and interaction terms together in the same regression.

# 7 Heterogeneity of e¤ects across products and countries

We now examine the extent to which the impacts of rainfall shocks on agricultural tari¤s are heterogeneous across products and countries. Table 9 presents the results from regressions in which the rainfall variable is interacted with dummy variables for each of the agricultural products included in the UN statistics division of the SITC classi...cation (see Table A.3). The results in columns (1) and (3) suggest that a rainfall shortage leads to tari¤ reductions in most of the products considered. When considering both simple and weighted import tari¤s, we ...nd positive and signi...cant e¤ects for all product categories,

> 18 We de...ne this variable based on the full country-level data on the log of rainfall for this period. We have considered alternative thresholds to de...ne the dummy variable and have obtained qualitatively similar results.

> 19 Extremely high realizations of rainfall in a short period of time can cause ‡oods. Floods may (or may not) reduce agricultural output in a given year. While a ‡ood typically reduces agricultural output in the months it occurs, it also tends to increase agricultural productivity in the following growing season, what has the opposite e¤ect (Banerjee, 2007). Based on annual data on country-level rainfall, it is however di¢ cult to separate ‡oods from consistently higher volumes of rainfall spread over the year.

19
with the exception of the categories "live animals" and "oilseeds and oleaginous fruits". In columns (2) and (4), we conduct a similar analysis, but including now interactions terms between log rainfall and individual dummy variables for each agricultural product considered in the broader de...nition de...ned earlier.

# [Table 9 about here]

Table 10 reports separate estimates by region. Since our sample comprises a very small number of jurisdictions in North America and Europe, we report results for countries in Africa, Latin America and Caribbean and Asia-Paci...c. The estimates for the latter two regions are very similar to those obtained for the full sample. For Africa, however, we obtain insigni...cant coe¢ cients on the interaction term of interest.<sup>20</sup> In the context of our model, how can we rationalize these insigni...cant results? One potential explanation is that agricultural producers in Africa tend to have less access to irrigation technologies. As discussed in section 2.4, for a given weight placed by domestic policy maker on domestic pro...ts, the inability to adjust output in response to a rainfall shortage will reduce the likelihood that such shock leads to a tari¤ reduction. Alternatively, policy makers in Africa may generally place a smaller weight on domestic pro...ts, perhaps because agricultural producers there tend to be relatively smaller and less organized collectively. On the other hand, as also discussed above, a negative rainfall shortage is more likely to induce tari¤ reductions when demand for food is more inelastic for low consumption levels. This would appear to be more likely the case in African countries, where food consumption tends to be closer to subsistence levels, making tari¤ reductions in response to a rainfall shortage more likely there. The lack of response revealed by the data may however re‡ect the fact that governments of African nations tend to depend more heavily on import tari¤s for revenue (Devarajan et al., 1999). In such a case, tari¤ revenue is likely to carry a larger weight on the governments’objective function, and thereby o¤set opposite incentives via pro...ts and/or the consumer surplus.

# [Table 10 about here]

We proceed by examining more directly the predictions of our theory about the role of access to irrigation in shaping the e¤ects of a rainfall shock on agricultural import tari¤s. As noted above, our theory predicts that a greater ability to adjust output in response to a rainfall shortage increases the incentives to shift oligopoly rents from foreign to domestic

> 20 Nevertheless, there are reasons to remain cautious in interpreting these insigni...cant results as evidence that African nations do not lower import tari¤s strategically in response to rainfall shortages. First, although still sizable, sample size is considerably smaller for Africa than for the other two regions. Second, when using the main sample and weighted import tari¤s as the dependent variable, the magnitude of the coe¢ cient of interest is fairly similar across regions.

20
producers. All else being equal, greater access to irrigation would therefore be expected to magnify the positive relationship between rainfall and agricultural import tari¤s. To examine this prediction, we use country-level data on the proportion of total agricultural land that is irrigated. As mentioned above, these data are available for 32 of the 70 countries initially considered. Hence our estimation sample is reduced accordingly. Table 11 reports the corresponding results. In line with the predictions of our theoretical model, the estimates in columns (2) and (4) suggest that greater access to irrigation contributes to magnify the positive relationship between rainfall and agricultural import tari¤s.

## [Table 11 about here]

# 8 Concluding remarks

We have examined theoretically and empirically the e¤ects of rainfall shocks on tari¤ setting in the agricultural sector. In a model of strategic trade policy, we have shown that the impact of a negative rainfall shock on optimal tari¤s is generally not clear-cut, depending crucially on the weight placed by the domestic policy maker on each of the various policy objectives, and on the shape of the food demand function. We have then estimated the impact of rainfall shocks on applied tari¤s. Using detailed panel data on applied tari¤s and rainfall for 70 nations, we have found robust evidence that rainfall shortages generally lead to lower tari¤s on agricultural imports. When interpreted in conjunction with our theory, this result suggests that policy makers place a considerable weight on domestic producer surplus when setting import tari¤s.

# Appendix

[Tables A.1-A.3 about here]

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24
Table 1: Descriptive statistics

|Variable|Obs|Mean|Std. Dev.|
|---|---|---|---|
|ln simple tarifs|41,043|2.034|1.221|
|ln weighted tarifs|40,606|1.582|1.680|
|ln rain|41,043|2.469|0.757|
|agric|41,043|0.129|0.335|
|agric2|41,043|0.209|0.406|
|irrig|18,812|13.626|17.476|
|North America|41,043|0.046|0.211|
|Latin America and Caribbean|41,043|0.369|0.482|
|Europe|41,043|0.053|0.225|
|Africa|41,043|0.200|0.400|
|Asia-Pacifc|41,043|0.329|0.469|
|Number of countries||70||
|Number of products||65||

Country-level data on the proportion of agricultural land that is irrigated are available for 32 countries.

Table 2: Baseline estimates

|Dep. variable|ln simp<br>(1)|le tarifs<br>(2)|ln weig<br>(3)|hted tarifs<br>(4)|
|---|---|---|---|---|
|ln rain_jt_|0.064<br>[0.076]|0.035<br>[0.076]|0.125<br>[0.094]|0.082<br>[0.093]|
|ln rain_jt ∗_agric_i_||0.230***<br>[0.047]||0.338***<br>[0.064]|
|Observations|41,043|41,043|40,606|40,606|
|Number of countries|70|70|70|70|
|Number of products|65|65|65|65|
|R-squared|0.572|0.574|0.401|0.403|

The estimation method is OLS. Each model includes product, country and year fixed effects. Robust standard errors clustered by country in brackets. *** _p ≤_ 0 _._ 01 ,** _p ≤_ 0 _._ 05, * _p ≤_ 0 _._ 1 1
Table 3: Robustness, restricted sample

|Dep. variable|ln simp<br>(1)|le tarifs<br>(2)|ln weig<br>(3)|hted tarifs<br>(4)|
|---|---|---|---|---|
|ln rain_jt_|0.039|0.006|0.106|0.061|
||[0.092]|[0.091]|[0.113]|[0.111]|
|ln rain_jt ∗_agric_i_||0.263***<br>[0.049]||0.354***<br>[0.068]|
|Observations|32,436|32,436|32,117|32,117|
|Number of countries|51|51|51|51|
|Number of products|65|65|65|65|
|R-squared|0.579|0.582|0.415|0.418|

The estimation method is OLS. Each model includes product, country and year fixed effects. Robust standard errors clustered by country in brackets. *** _p ≤_ 0 _._ 01 ,** _p ≤_ 0 _._ 05, * _p ≤_ 0 _._ 1 Table 4: Robustness, alternative definition of agricultural goods

|Dep. variable|ln simp<br>(1)|le tarifs<br>(2)|ln weig<br>(3)|hted tarifs<br>(4)|
|---|---|---|---|---|
|ln rain_jt_|0.064|0.015|0.125|0.062|
||[0.076]|[0.074]|[0.094]|[0.092]|
|ln rain_jt ∗_agric_i_||0.235***<br>[0.051]||0.304***<br>[0.059]|
|Observations|41,043|41,043|40,606|40,606|
|Number of countries|70|70|70|70|
|Number of products|65|65|65|65|
|R-squared|0.572|0.574|0.401|0.403|

The estimation method is OLS. Each model includes product, country and year fixed effects. Robust standard errors clustered by country in brackets. *** _p ≤_ 0 _._ 01 ,** _p ≤_ 0 _._ 05, * _p ≤_ 0 _._ 1 2
Table 5: Robustness, alternative definition of agricultural goods and restricted sample

|Dep. variable|ln simp<br>(1)|le tarifs<br>(2)|ln weig<br>(3)|hted tarifs<br>(4)|
|---|---|---|---|---|
|ln rain_jt_|0.039|-0.015|0.106|0.040|
||[0.092]|[0.089]|[0.113]|[0.110]|
|ln rain_jt ∗_agric_i_||0.261***<br>[0.056]||0.317***<br>[0.063]|
|Observations|32,436|32,436|32,117|32,117|
|Number of countries|51|51|51|51|
|Number of products|65|65|65|65|
|R-squared|0.579|0.583|0.415|0.418|

The estimation method is OLS. Each model includes product, country and year fixed effects. Robust standard errors clustered by country in brackets. *** _p ≤_ 0 _._ 01 ,** _p ≤_ 0 _._ 05, * _p ≤_ 0 _._ 1 3
|Applied _<_ 0.7 Bound<br>(4)<br>0.011<br>[0.071]<br>0.223***<br>[0.048]<br>29,798<br>69<br>65<br>0.583<br>Applied _<_ 0.7 Bound<br>(4)<br>0.065<br>[0.098]<br>0.265***<br>[0.064]<br>28,726<br>69<br>65<br>0.411<br> 1995-2006. Each model includes<br>_≤_0_._05, * _p ≤_0_._1|
|---|
|Dep. variable: ln simple tarifs<br>Applied _<_ 0.9 Bound<br>Applied _<_ 0.8 Bound<br>(2)<br>(3)<br>0.004<br>0.007<br>[0.071]<br>[0.071]<br>0.228***<br>0.225***<br>[0.050]<br>[0.049]<br>31,128<br>30,538<br>69<br>69<br>65<br>65<br>0.582<br>0.582<br>Dep. variable: ln weighted tarifs<br>Applied _<_ 0.9 Bound<br>Applied _<_ 0.8 Bound<br>(2)<br>(3)<br>0.069<br>0.072<br>[0.094]<br>[0.095]<br>0.277***<br>0.272***<br>[0.066]<br>[0.065]<br>29,980<br>29,403<br>69<br>69<br>65<br>65<br>0.415<br>0.414<br> of data for bound tarifs, the period of analysis is restricted to <br>ndard errors clustered by country in brackets. *** _p ≤_0_._01 ,** _p _|
|Full sample<br>(1)<br>0.022<br>[0.070]<br>0.218***<br>[0.053]<br>33,454<br>69<br>65<br>0.580<br>Full sample<br>(1)<br>0.071<br>[0.087]<br>0.271***<br>[0.068]<br>33,114<br>69<br>65<br>0.416<br> Due to availability<br> efects. Robust sta|
|_Panel A_<br>ln rain_jt_<br>ln rain_jt ∗_agric_i_<br>Observations<br>Number of countries<br>Number of products<br>R-squared<br>_Panel B_<br>ln rain_jt_<br>ln rain_jt ∗_agric_i_<br>Observations<br>Number of countries<br>Number of products<br>R-squared<br>The estimation method is OLS.<br>product, country and year fxed|

4
Table 7: Accounting for tariff binding, tobit random effects

|Dep. variable|ln simpl|e tarifs|ln weight|ed tarifs|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|ln rain_jt_|0.030|0.034|0.091**|0.105**|
||[0.026]|[0.026]|[0.043]|[0.043]|
|ln rain_jt ∗_agric_i_|0.220***<br>[0.018]|0.220***<br>[0.017]|0.273***<br>[0.029]|0.267***<br>[0.028]|
|Observations|33,454|33,454|33,114|33,114|
|Number of countries|69|69|69|69|
|Number of products|65|65|65|65|
|Country efects|Random|Fixed|Random|Fixed|
|Product efects|Fixed|Random|Fixed|Random|
|Log likelihood|-38777.222|-38718.094|-52882.104|-52832.222|

The estimation method is Tobit random effects. Due to availability of data for bound tariffs, the period of analysis is restricted to 1995-2006. Each model includes year fixed-effects. Marginal effects resported. Standard errors in brackets. *** _p ≤_ 0 _._ 01 ,** _p ≤_ 0 _._ 05, * _p ≤_ 0 _._ 1 Table 8: Non-linear specification of rainfall

|Dep. variable|ln|simple ta|rifs|ln w|eighted ta|rifs|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|low rain_jt_|0.137||0.131|0.081||0.073|
||[0.086]||[0.086]|[0.098]||[0.098]|
|low rain_jt ∗_agric_i_|-0.340**||-0.296**|-0.407**||-0.343*|
||[0.143]||[0.143]|[0.182]||[0.186]|
|high rain_jt_||-0.062|-0.060||-0.044|-0.042|
|||[0.068]|[0.068]||[0.094]|[0.094]|
|high rain_jt ∗_agric_i_||0.265*|0.253*||0.379**|0.366**|
|||[0.143]|[0.144]||[0.181]|[0.183]|
|Observations|41,043|41,043|41,043|40,606|40,606|40,606|
|Number of countries|70|70|70|70|70|70|
|Number of products|65|65|65|65|65|65|
|R-squared|0.572|0.572|0.572|0.401|0.401|0.402|

The estimation method is OLS. Each model includes product, country and year fixed effects. Robust standard errors clustered by country in brackets. *** _p ≤_ 0 _._ 01 ,** _p ≤_ 0 _._ 05, * _p ≤_ 0 _._ 1 5
Table 9: Estimates by individual agricultural product

|Dep. variable|ln simpl|e tarifs|ln weigh|ted tarifs|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|ln rain_jt_|0.034|0.015|0.082|0.062|
||[0.076]|[0.074]|[0.093]|[0.092]|
|ln rain_jt ∗_live animals|0.118|0.137|0.306|0.326*|
||[0.114]|[0.115]|[0.184]|[0.182]|
|ln rain_jt ∗_meat products|0.286***<br>[0.074]|0.305***<br>[0.075]|0.507**<br>[0.195]|0.526***<br>[0.194]|
|ln rain_jt ∗_dairy products and birds eggs|0.262**<br>[0.114]|0.281**<br>[0.118]|0.536***<br>[0.159]|0.555***<br>[0.161]|
|ln rain_jt ∗_cereals|0.320***<br>[0.062]|0.339***<br>[0.066]|0.348***<br>[0.088]|0.368***<br>[0.092]|
|ln rain_jt ∗_vegetables and fruit|0.296***<br>[0.059]|0.315***<br>[0.062]|0.336***<br>[0.090]|0.355***<br>[0.094]|
|ln rain_jt ∗_sugar and honey|0.301***|0.320***|0.354**|0.374**|
||[0.072]|[0.077]|[0.148]|[0.151]|
|ln rain_jt ∗_cofee_,_ tea, cocoa and spices|0.245***|0.264***|0.266***|0.286***|
||[0.062]|[0.063]|[0.092]|[0.094]|
|ln rain_jt ∗_oilseeds and oleaginious fruits|-0.015|0.004|0.045|0.064|
||[0.085]|[0.084]|[0.108]|[0.110]|
|ln rain_jt ∗_feeding stuf for animals||0.001||0.205|
|||[0.125]||[0.151]|
|ln rain_jt ∗_miscellaneous edible products||0.252***||0.256***|
|||[0.065]||[0.078]|
|ln rain_jt ∗_beverages||0.335***||0.126|
|||[0.075]||[0.150]|
|ln rain_jt ∗_tobacco||0.292*||0.379**|
|||[0.159]||[0.151]|
|ln rain_jt ∗_crude animal and vegetable materials||0.180*<br>[0.102]||0.127<br>[0.118]|
|Observations|41,043|41,043|40,606|40,606|
|Number of countries|70|70|70|70|
|Number of products|65|65|65|65|
|R-squared|0.574|0.576|0.404|0.405|

The estimation method is OLS. Each model includes product, country and year fixed-effects. Robust standard errors clustered by country in brackets. *** _p ≤_ 0 _._ 01 ,** _≤_ 0 _._ 05, * _p ≤_ 0 _._ 1 6
Table 10: Estimates by region

|_Panel A_|l|n simple t|arifs|
|---|---|---|---|
||LAC|Africa|Asia-Pacifc|
|ln rain_jt_|0.142|-0.058|-0.075|
||[0.095]|[0.127]|[0.142]|
|ln rain_jt ∗_agric_i_|0.345***|-0.030|0.303**|
||[0.076]|[0.81]|[0.115]|
|Observations|15,151|8,248|13,512|
|Number of countries|22|18|23|
|Number of products|65|65|65|
|R-squared|0.528|0.611|0.589|
|_Panel B_|ln|weighted|tarifs|
||LAC|Africa|Asia-Pacifc|
|ln rain_jt_|0.256*|-0.125|-0.113|
||[0.134]|[0.217]|[0.183]|
|ln rain_jt ∗_agric_i_|0.398***|0.274|0.388**|
||[0.128]|[0.316]|[0.155]|
|Observations|14,969|8,069|13,458|
|Number of countries|22|18|23|
|Number of products|65|65|65|
|R-squared|0.331|0.398|0.448|

The estimation method is OLS. Each model includes product, country and year fixed effects. Robust standard errors clustered by country in brackets. *** _p ≤_ 0 _._ 01 ,** _p ≤_ 0 _._ 05, * _p ≤_ 0 _._ 1 Table 11: The role of irrigation

|Dep. variable|ln simpl<br>(1)|e tarifs<br>(2)|ln weight<br>(3)|ed tarifs<br>(4)|
|---|---|---|---|---|
|ln rain_jt_|0.048|0.056|0.013|0.024|
||[0.074]|[0.074]|[0.096]|[0.096]|
|ln rain_jt ∗_agric_i_|0.243***<br>[0.048]|0.132**<br>[0.051]|0.447***<br>[0.074]|0.291***<br>[0.069]|
|ln rain_jt ∗_agric_i ∗_irrig_j_||0.004***<br>[0.001]||0.006***<br>[0.001]|
|Observations|18,812|18,812|18,683|18,683|
|Number of countries|32|32|32|32|
|Number of products|65|65|65|65|
|R-squared|0.586|0.588|0.422|0.423|

The estimation method is OLS. Each model includes product, country and year fixed effects. Robust standard errors clustered by country in brackets. *** _p ≤_ 0 _._ 01 ,** _p ≤_ 0 _._ 05, * _p ≤_ 0 _._ 1 7
Table A.1 : Variables and data sources

|Variable|Defnition|Source|
|---|---|---|
|Rainfall|Population-weighted average rainfall by<br>country-year in 100s mm.|Kenji Matsuura and Cort Willmott<br>(2007) and Dell et al. (2012)|
|Irrigation|Proportion of agricultural land that is<br>irrigated by country (average of non-<br>missing country-year observations for<br>the period 2001-2009).|World Bank WDI|
|Simple Tarifs|Simple average of bilateral applied im-<br>port tarifs by country-product-year,<br>SITC classifcation.|UNCTAD TRAINS|
|Weighted Tarifs|Weighted average of bilateral applied<br>import tarifs by country-product-year,<br>based on the share of each source coun-<br>try in total country-product imports in<br>the base year, SITC classifcation.|UNCTAD TRAINS and UN COM-<br>TRADE|
|Imports|Bilateral imports value by country-<br>product in the base year, SITC classif-<br>cation.|UN COMTRADE|

8
Table A.2 : Sample period by country

|Country|Period|Country|Period|
|---|---|---|---|
|Algeria|1993, 1997-1998, 2001-2003, 2005-2006|Lesotho|2001, 2004-2006|
|Argentina|1992-1993, 1995-2006|Macedonia|2001, 2004-2006|
|Australia|1991, 1993, 1996-2006|Malawi|1994, 1996-1998, 2001, 2006|
|Bangladesh|1989, 1994,1999-2000, 2002-2006|Malasyia|1988, 1991-1993, 1996-1997, 2001-2003, 2005-2006|
|Belize|1996, 1999, 2001-2003,2006|Mali|1995, 2001-2006|
|Benin|2001-2006|Mexico|1991, 1995, 1997-2006|
|Bolivia|1993-2002, 2004-2006|Morocco|1993, 1997, 2000-2003, 2005-2006|
|Botswana|2001, 2004-2006|Mozambique|1994, 1997, 2001-2003, 2005-2006|
|Brazil|1989-2006|Myanmar|2001-2006|
|Brunei|1992, 2001-2006|Nepal|1993, 1998-2000, 2002-2006|
|Burkina Faso|1993, 2001-2006|New Zealand|1992-1993, 1996-2000, 2002-2006|
|Cambodia|2001-2003, 2005|Nicaragua|1995, 1998-2002, 2004-2005|
|Canada|1989, 1993, 1995-2006|Niger|2001-2006|
|Chile|1992-1995, 1997-2002, 2004-2006|Nigeria|1988-1990, 1992, 1995-2002, 2005-2006|
|China|1992-1994, 1996-2001, 2003-2006|Norway|1988, 1993, 1995-1996, 1998, 2000-2003, 2006|
|Colombia|1991-1992, 1994-1997, 1999-2002, 2004-2006|Pakistan|1995, 1998, 2001-2006|
|Costa Rica|1995, 1999-2005|Papua New Guinea|1997, 2002-2006|
|Croatia|2001, 2004-2006|Paraguay|1991, 1994-2006|
|Cuba|1993, 1997, 2002-2006|Peru|1993, 1995, 1997-2000, 2004-2006|
|Dominican Republic|1997, 2000-2006|Philippines|1988-1990, 1992-1995, 1998-2006|
|Ecuador|1993-1999, 2002, 2004-2006|Saudi Arabia|1994, 1999-2000, 2003-2006|
|El Salvador|1995, 1997-1998, 2000-2002, 2004-2006|Senegal|2001-2006|
|European Union|1988-2006|South Africa|1990-1991, 1993, 1996-1997, 1999, 2001, 2004-2006|
|Guatemala|1995, 1997-1998, 2000-2002, 2004-2005|Sri Lanka|1990, 1993-1994, 1997, 2000-2001, 2004-2006|
|Guyana|1996, 1999-2003, 2006|Switzerland|1990, 1993, 1995-2006|
|Honduras|1995, 1999-2002, 2004-2005|Taiwan, China|1989, 1992, 1996, 1999-2003, 2005-2006|
|Indonesia|1989-1990, 1993, 1995-1996, 1999-2006|Togo|2001-2006|
|Israel|1993, 2004-2006|Trinidad & Tobago|1991-1992, 1996, 1999, 2001-2003, 2006|
|Jamaica|1996, 2000-2003, 2006|Tunisia|1990, 1992, 1995, 1998, 2002-2006|
|Japan|1988-2006|Uganda|1994, 2000-2006|
|Jordan|2000-2003, 2005-2006|United States|1989-2006|
|Kenya|1994, 2000-2001, 2004-2006|Uruguay|1992, 1995-2002, 2004-2006|
|Korea, Republic of|1988-1990, 1992, 1995-1996, 1999, 2002, 2004, 2006|Venezuela|1992, 1995, 1997-2000, 2002, 2004-2006|
|Laos|2000-2001, 2004-2006|Vietnam|1994, 1999, 2001-2006|
|Lebanon|1999-2002, 2004-2006|Zimbabwe|1996-1999, 2001-2003|

9
Table A.3 : SITC Classification

|SITC<br>00|Agr<br>1|ic Agr<br>1|ic2<br>Description<br>Liilththilfdiii03|
|---|---|---|---|
|01|1|1|ve anmas oer an anmas o vson <br>Meat and meat preparations|
|02|1|1|Dairy products and birds eggs|
|04|1|1|Cereals and cereal preparations|
|05|1|1|Vegetables and fruit|
|06|1|1|Sugars, sugar preparations and honey|
|07|1|1|Cofee, tea, cocoa, spices, and manufactures thereof|
|22|1|1|Oil-seeds and oleaginous fruits|
|08|0|1|Feeding stuf for animals|
|09|0|1|Miscellaneous edible products and preparations|
|11|0|1|Beverages|
|12|0|1|Tobacco and tobacco manufactures|
|29|0|1|Crude animal and vegetable materials, n.e.s.|
|03<br>|0<br>|0<br>|Fish, crustaceans, molluscs and aquatic invertebrates, and preparations thereof<br>|
|21|0|0|Hides, skins and furskins, raw|
|23|0|0|Crude rubber (including synthetic and reclaimed)|
|24|0|0|Cork and wood|
|25|0|0|Pulp and waste paper|
|26|0|0|Textile fbres (other than wool tops and other combed wool) and their wastes|
|27<br>|0<br>|0<br>|Crude fertilizers, other than those of Division 56, and crude minerals<br>|
|28|0|0|Metalliferous ores and metal scrap|
|32|0|0|Coalcokeandbriquettes|
|33|0|0|, <br>Petroleum, petroleum products and related materials|
|34<br>|0<br>|0<br>|Gas, natural and manufactured<br>|
|35|0|0|Electric current|
|41<br>42|0<br>0|0<br>0|Animal oils and fats<br>Fixed vegetable fats and oils, crude, refned or fractionated|
|43|0|0|Animalorvegetablefatsandoilsprocessed;waxesandmixtures|
|51|0|0|, <br>Organic chemicals|
|52<br>53|0<br>0|0<br>0|Inorganic chemicals<br>Dyeing, tanning and colouring materials|
|54|0|0|Medicinalandpharmaceuticalproducts|
|55|0|0|<br>Essential oils and resinoids and perfume materials; toilet, polishing and cleansing preparations|
|56|0|0|Fertilizers(otherthanthoseofgroup272)|
|57|0|0|<br>Plastics in primary forms|
|58|0|0|Plasticsinnon-primaryforms|
|59|0|0|<br>Chemical materials and products, n.e.s.|
|61|0|0|Leatherleathermanufacturesn.e.s.anddressedfurskins|
|62|0|0|, , , <br>Rubber manufactures, n.e.s.|
|63<br>|0<br>|0<br>|Cork and wood manufactures (excluding furniture)<br>|
|64|0|0|Paper, paperboard and articles of paper pulp, of paper or of paperboard|
|65|0|0|Textileyarnfabricsmade-uparticlesnesandrelatedproducts|
|66|0|0|, , , ..., <br>Non-metallic mineral manufactures, n.e.s.|
|67|0|0|Ironandsteel|
|68|0|0|<br>Non-ferrous metals|
|69|0|0|Manufacturesofmetalsnes|
|71|0|0|, ...<br>Power-generating machinery and equipment|
|72|0|0|Machineryspecializedforparticularindustries|
|73|0|0|<br>Metalworking machinery|
|74|0|0|Generalindustrialmachineryandequipmentn.e.s.andmachinepartsn.e.s.|
|75|0|0|, , , <br>Ofce machines and automatic data-processing machines|
|76<br>|0<br>|0<br>|Telecommunications and sound-recording and reproducing apparatus and equipment<br>|
|77<br>78|0<br>0|0<br>0|Electrical machinery, apparatus and appliances, n.e.s., and electrical parts thereof<br>Roadvehicles(includingair-cushionvehicles)|
|79|0|0|<br>Other transport equipment|
|81<br>|0<br>|0<br>|Prefabricated buildings; sanitary, plumbing, heating and lighting fxtures and fttings, n.e.s.<br>|
|82|0|0|Furniture and parts thereof; bedding, mattresses, mattress supports, cushions and similar stuf|
|83|0|0|Travelgoodshandbagsandsimilarcontainers|
|84|0|0|, <br>Articles of apparel and clothing accessories|
|85|0|0|Footwear|
|87|0|0|Professional, scientifc and controlling instruments and apparatus, n.e.s.|
|88<br>|0<br>|0<br>|Photographic apparatus, equipment and supplies and optical goods, n.e.s.; watches and clocks<br>|
|89<br>96|0<br>0|0<br>0|Miscellaneous manufactured articles, n.e.s.<br>Coin(otherthanoldcoin)notbeinlealtender|
|97|0|0|g , g g <br>Gold, non-monetary (excluding gold ores and concentrates)|

_Note:_ Agricultural products are defined based on Standard International Trade Classification, Revision 4; United Nations Statistics Division 10
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**Figure 2: Average bound and applied import tariffs on agricultural goods**

<!-- Start of picture text --> 120<br>Bound tariff<br>Applied tariff in1998<br>100<br>80<br>60<br>40<br>20<br>0<br>Argentina Brazil Colombia Costa Rica Ecuador El Salvador Guatemala India Indonesia Mexico Morocco Nicaragua Pakistan Panama Paraguay Philippines Republic of Korea Tunisia Uruguay Vanezuela<br><!-- End of picture text --> _Note_ : Bound tariffs are MFN rates based on final URAA implementation. Applied tariffs represent annual average. Data on average applied import tariffs refer to 1998, with the exception of Costa Rica (1995), Republic of Korea (1996), Indonesia (1996), India (1997) and Morocco (1997). Source: Economic Research Service, USDA.