The next step is to see whether measures of market power are linked to asymmetry.
For this two variables are used. The first is the importance of exports in the mar-ket. By looking at the quantity exported with respect to the quantity produced, we will be able to see whether asymmetry is influenced by a larger share of ex-ports.10 One might expect the fact of exporting more of local production to lead to more price transmission. However, this would not explain more asymmetry. What is of interest here is whether the mechanism specific to exporting plays a role in explaining asymmetry. A larger local presence of intermediaries will be a prereq-uisite for exporting a larger share of local production. A difference in asymmetry for high versus low shares of exports will point to the impact of intermediaries in the asymmetric pattern of price transmission. We will therefore use two groups of countries, those exporting a large share of their production and those exporting a small share. Results are given in table 5. One sees that in markets where exports
9This is implemented in the statistical package STATA using the nlcom command.
10A benchmark value of 30 percent is used to distinguish a large share from a small share.
consist of a large share with respect to local production there is a significant amount of asymmetry whereas in the other markets there is no significant asymmetry.
This is complemented by another measure of market power. This measure is a regression run specifically on items where the presence of one of the main wholesalers is present, namely Cargill. As mentioned in section 3, this data has been collected from Cargill’s country websites. This is an item specific information that will enable us to separate the regressions into two groups, one where Cargill is present and the other where it isn’t. A larger asymmetry for the group where Cargill is present will point to an influence of the wholesaler on asymmetry. The results of these regressions are given in table 6. We see that market power plays a role in explaining asymmetry. The presence of Cargill significantly increases the asymmetry of price transmission whereas when it is absent from a market, the asymmetry term isn’t significantly different from zero.
The presence of one of the main intermediaries as well as the share of local production exported abroad are both explaining asymmetry, supporting the theory presented above.
6 Conclusion
A model of price transmission from international agricultural prices to producer prices is presented in order to understand the mechanisms behind price transmission.
Due to the geographical dispersion of farmers and economies of scale in wholesaling, agricultural markets will we characterized by market power on the demand side. The model predicts that this power of intermediaries buying the products from farmers leads to an asymmetric price transmission when intermediaries have sufficiently convex marginal cost curves. The asymmetry is such that there is more price transmission when prices fall.
The results are shown using a Two Stage Least Squares estimator to control for endogeneity problems. This approach has the advantage, as noted by Acharya et al.
(2011), of avoiding some of the disadvantages of many studies in the recent litera-ture that focus on the time-series properties of the data such that it is sometimes unclear whether rejection of symmetry isn’t simply due to specification error. The instruments used are variations in rainfall, temperature, climate disasters and cloud cover in other geographic regions. The exclusion of neighbouring countries within the region ensures that these instruments aren’t correlated to the local weather conditions, thus local prices. The results are clear in pointing out the presence of asymmetry, with the transmission being stronger for price decreases. Robustness checks using two different specifications of a Generalized Method of Moments esti-mator confirm the presence of an asymmetric price transmission. These results also imply hysteresis, where an international price increase followed by a decrease of the same magnitude will not bring the producer price back to its initial equilibrium.
The link between market power and asymmetry is then tested. A variable indicating the presence of intermediaries on specific markets is used to test this link.
More specifically it is the presence of Cargill, one of the largest intermediaries in commodity markets, that is used as a variable and the results show that asymmetry
is stronger when Cargill is present. This result is supported by another regression where the larger the share of local production exported abroad, the higher the degree of asymmetric price transmission.
This points towards some policy issues, notably the fact that governments should be aware of the effect of market power of intermediaries and the role they play in influencing the price received by farmers, and therefore the gains from trade liberal-ization in agricultural markets. Abuse of monopsony power by large intermediaries in agricultural markets can be particularly harmful in poor countries where farmers often live close to the poverty line.
Future research could be done in the collection of market power data at the item and country level on a yearly basis. Other private companies than the one looked at here could also be integrated into such studies. Measures of search costs should also be considered.
Appendix
Appendix 1: Sample of items.
Item Cargill Item Cargill
1 Almonds, with shell No 27 Cherries No
2 Anise, badian, fennel, corian. No 28 Chestnuts Yes
3 Apples Yes 29 Chick peas No
4 Apricots No 30 Chicken meat Yes
5 Arecanuts No 31 Chicory roots No
6 Artichokes No 32 Chillies and peppers, dry No
7 Asparagus No 33 Chillies and peppers, green No
8 Avocados No 34 Citrus fruit, nes No
9 Bananas No 35 Cloves No
10 Barley Yes 36 Cocoa beans Yes
11 Beans, dry Yes 37 Coconuts No
12 Beans, green Yes 38 Coffee, green Yes
13 Beeswax No 39 Cotton lint Yes
14 Berries Nes No 40 Cottonseed Yes
15 Blueberries No 41 Cow milk, whole, fresh No
16 Broad beans, horse beans, dry No 42 Cow peas, dry No
17 Buckwheat No 43 Cranberries No
18 Cabbages and other brassicas No 44 Cucumbers and gherkins No
19 Canary seed No 45 Currants No
20 Carobs No 46 Dates No
21 Carrots and turnips No 47 Duck meat Yes
22 Cashew nuts, with shell No 48 Eggplants (aubergines) No
23 Castor oil seed No 49 Fibre Crops Nes No
24 Cattle meat Yes 50 Figs No
25 Cauliflowers and broccoli No 51 Flax fibre and tow No
26 Cereals, nes No 52 Fruit Fresh Nes No
Appendix 1: Sample of items (continued).
Item Cargill Item Cargill
53 Fruit, tropical fresh nes No 90 Natural rubber No
54 Game meat No 91 Nutmeg, mace and cardamoms No
55 Garlic No 92 Nuts, nes Yes
56 Ginger No 93 Oats Yes
57 Goat meat Yes 94 Oilseeds, Nes Yes
58 Goose and guinea fowl meat Yes 95 Okra No
59 Gooseberries No 96 Olives No
60 Grapefruit (inc. pomelos) Yes 97 Onions (inc. shallots), green No
61 Grapes No 98 Onions, dry No
62 Groundnuts, with shell Yes 99 Oranges No
63 Hazelnuts, with shell No 100 Other Bastfibres No
64 Hemp Tow Waste No 101 Other bird eggs,in shell No
65 Hempseed No 102 Other melons (inc.cantaloupes) No
66 Hen eggs, in shell Yes 103 Palm kernels Yes
67 Hops Yes 104 Palm oil Yes
68 Horse meat Yes 105 Papayas No
69 Jute No 106 Peaches and nectarines No
70 Karite Nuts (Sheanuts) No 107 Pears No
71 Kiwi fruit No 108 Peas, dry Yes
72 Leeks, other alliaceous veg No 109 Peas, green Yes
73 Leguminous vegetables, nes No 110 Pepper (Piper spp.) No
74 Lemons and limes No 111 Persimmons No
75 Lentils No 112 Pig meat Yes
76 Lettuce and chicory Yes 113 Pigeon peas No
77 Linseed No 114 Pineapples No
78 Lupins No 115 Pistachios No
79 Maize Yes 116 Plantains No
80 Maize, green Yes 117 Plums and sloes No
81 Mangoes, mangosteens, guavas No 118 Poppy seed No
82 Manila Fibre (Abaca) No 119 Potatoes No
83 Mat´e No 120 Pulses, nes No
84 Meat nes Yes 121 Pumpkins, squash and gourds No
85 Millet Yes 122 Pyrethrum,Dried No
86 Mixed grain No 123 Quinces No
87 Mushrooms and truffles No 124 Rabbit meat Yes
88 Mustard seed No 125 Ramie No
89 Natural honey No 126 Rapeseed Yes
Appendix 1: Sample of items (continued).
Item Cargill
127 Raspberries No
128 Rice, paddy Yes
129 Roots and Tubers, nes No
130 Rye Yes
131 Safflower seed No
132 Sesame seed No
133 Sheep meat Yes
134 Silk-worm cocoons, reelable No
135 Sisal No
136 Sorghum Yes
137 Sour cherries No
138 Soybeans Yes
139 Spices, nes Yes
140 Spinach No
141 Stone fruit, nes No
142 Strawberries No
143 Sugar beet Yes
144 Sugar cane Yes
145 Sunflower seed Yes
146 Sweet potatoes No
147 Tangerines, mandarins, clem. No
148 Taro (cocoyam) No
149 Tea Yes
150 Tobacco, unmanufactured No
151 Tomatoes No
152 Triticale No
153 Turkey meat Yes
154 Vanilla No
155 Vegetables fresh nes No
156 Walnuts, with shell No
157 Watermelons No
158 Wheat Yes
159 Wool, greasy No
160 Yams No
161 Yautia (cocoyam) No
Appendix 2: Sample of countries.
1 Albania 38 Estonia
2 Algeria 39 Ethiopia
3 Argentina 40 Finland
4 Armenia 41 France
5 Australia 42 Georgia
6 Austria 43 Germany
7 Azerbaijan 44 Ghana
8 Bangladesh 45 Greece
9 Barbados 46 Guinea
10 Belarus 47 Honduras
11 Belgium 48 Hungary
12 Belize 49 India
13 Bhutan 50 Indonesia
14 Bolivia 51 Iran, Islamic Republic of 15 Bosnia and Herzegovina 52 Ireland
16 Brazil 53 Israel
17 Brunei Darussalam 54 Italy
18 Bulgaria 55 Jamaica
19 Burkina Faso 56 Japan
20 Burundi 57 Jordan
21 Cambodia 58 Kazakhstan
22 Cameroon 59 Kenya
23 Canada 60 Korea, Republic of
24 Chile 61 Kyrgyzstan
25 China 62 Lao People’s Democratic Republic
26 Congo 63 Latvia
27 Costa Rica 64 Lebanon
28 Croatia 65 Lithuania
29 Cuba 66 Luxembourg
30 Cyprus 67 Madagascar
31 Czech Republic 68 Malawi
32 Denmark 69 Malaysia
33 Dominican Republic 70 Mali
34 Ecuador 71 Mauritius
35 Egypt 72 Mexico
36 El Salvador 73 Moldova
37 Eritrea 74 Mongolia
Appendix 2: Sample of countries (continued).
75 Morocco 112 Ukraine
76 Mozambique 113 United Kingdom
77 Myanmar 114 United States of America
78 Namibia 115 Uruguay
79 Nepal 116 Yemen
80 Netherlands 117 Zimbabwe
81 New Zealand 82 Nicaragua 83 Niger 84 Nigeria 85 Norway 86 Pakistan 87 Panama 88 Paraguay 89 Peru 90 Philippines 91 Poland 92 Portugal 93 Romania
94 Russian Federation 95 Rwanda
96 Saudi Arabia 97 Slovakia 98 Slovenia 99 South Africa 100 Spain
101 Sri Lanka 102 Sudan 103 Sweden 104 Switzerland
105 Syrian Arab Republic 106 Tajikistan
107 Thailand 108 Togo
109 Trinidad and Tobago 110 Tunisia
111 Turkey
Table 1: Asymmetry of agricultural price transmission.
OLS IV OLS IV
VARIABLES ln(prod price) ln(prod price) ln(prod price) ln(prod price)
ln(exp price) 0.416*** 0.734*** 0.439*** 0.978***
(0.008) (0.028) (0.008) (0.121)
price up -0.001 2.235**
(0.023) (1.089)
price up * ln(exp price) -0.012*** -0.375**
(0.004) (0.171)
N 40174 40174 40174 40174
R2 0.330 0.329
Number of clusters 4978 4978 4978 4978
Number of instruments 4 12
Kleibergen-Paap rk LM statistic P-val = 0.000 P-val = 0.000
Hansen J statistic P-val = 0.059 P-val = 0.426
Standard errors in parentheses: *** p<0.01, ** p<0.05, * p<0.1 Clustering by item-country.
Table 2: First stage regressions of the 2SLS.
ln(exp price) ln(exp price) price up ln(exp price * pr. up)
ln(temperature IV) -0.486*** -0.504*** -0.029* -0.518***
0.038 -0.04 -0.017 (0.117)
ln(cloud cover IV) 0.289*** 0.289*** 0.028 0.358
0.068 -0.073 -0.036 (0.249)
ln(climate disaster IV) 0.205*** 0.203*** -0.037*** -0.100***
0.01 -0.011 -0.004 (0.026)
ln(rainfall IV) -0.036 -0.05 -0.073*** -0.528***
0.048 -0.05 -0.022 (0.151)
ln(temp * price up) 0.039 0.046* 0.383**
-0.033 -0.024 (0.166)
ln(cloud cover * price up) -0.098 0.032 -0.046
-0.068 -0.057 (0.405)
ln(climate disaster * price up) -0.014 0.139*** 0.870***
-0.012 -0.008 (0.052)
ln(rainfall * price up) 0.071 0.048 0.239
-0.071 -0.05 (0.340)
ln(temp * temp up) 0.007 0.042*** 0.228**
-0.019 -0.014 (0.095)
ln(cloud cover * cloud cover up) 0.038 0.013 0.191
-0.032 -0.027 (0.189)
ln(climate disaster * cl. dis. up) 0.008 0.016*** 0.116***
-0.01 -0.005 (0.033)
ln(rainfall * rainfall up) -0.01 0.012 0.105
-0.021 -0.015 (0.100)
N 40174 40174 40174 40174
Partial R2 0.054 0.055 0.032 0.028
F(x, 4977) 131.75 46.49 156.95 132.05
Standard errors in parentheses
*p <0.10, **p <0.05, *** p <0.01
Table 3: GMM regressions.
Lags as IV’s Adding economic instruments lag ln(producer price) 0.803*** 0.813***
(0.013) (0.013)
price up 0.198*** 0.189***
(0.058) (0.058)
ln(exp price) 0.174*** 0.162***
(0.013) (0.013)
price up * ln(exp price) -0.019** -0.018**
(0.009) (0.009)
year dummies Yes Yes
N 28483 28483
AR(1) 0.000 0.000
AR(2) 0.114 0.121
Hansen J statistic 0.805 0.725
Standard errors in parentheses
*p <0.10, **p <0.05, *** p <0.01
Table 4: Long and short run transmission.
Variable lags as IV’s Adding economic instruments
short run long run short run long run ln(exp price) 0.174*** 0.884*** 0.162*** 0.863***
(0.013) (0.037) (0.013) (0.038) price up * ln(exp price) -0.019** -0.179** -0.018** -0.147*
(0.009) (0.074) (0.009) (0.077)
Table 5: Asymmetry explained by the export importance.
large exp share small exp share
OLS 2SLS OLS 2SLS
ln(exp price) 0.640*** 1.108*** 0.467*** 0.672***
(0.011) (0.191) (0.011) (0.162)
price up -0.175*** 2.399 -0.042 -0.677
(0.056) (1.558) (0.032) (1.597) price up * ln(exp price) 0.0020 -0.4344* -0.0069 0.050
(0.008) (0.238) (0.005) (0.240)
N 16343 16343 23831 23831
Standard errors in parentheses
*p <0.10, **p <0.05, *** p <0.01
Table 6: Cargill presence.
Cargill No Cargill
OLS 2SLS OLS 2SLS
ln(exp price) 0.517*** 0.998*** 0.471*** 0.926***
(0.013) (0.120) (0.011) (0.158)
price up -0.018 1.385 -0.018 1.737
(0.036) (0.978) (0.033) (1.560) price up * ln(exp price) -0.014*** -0.253* -0.010* -0.289
(0.006) (0.154) (0.005) (0.244)
N 14692 14692 25482 25482
Standard errors in parentheses
*p <0.10, **p <0.05, *** p <0.01
Figure 1: Proportion of population in agriculture and wealth.
Q P
∗,p
fmc(Q)
w(Q)
P∗1 P∗0 P∗2
Q1 Q0 Q2 p1f
p0f p2f
Figure 2: Price transmission for various international prices.
P
∗p
fFigure 3: Relationship between international price and producer price.
Figure 4: Rainfall IV. In parenthesis the export share and the annual standard deviation of rainfall in millimeters.
Market Access, Export
performance and Survival:
Evidence from Peruvian Firms. ∗
1 Introduction
Exemptions or partial exemptions from Most-Favoured-Nation tariff rates, namely tariff preferences, are still crucial in the eyes of policy makers especially in developing countries. Tariff preferences, by providing a competitive edge that could foster exports of domestic firms, remains the most cost efficient trade promotion policy.
This could explain why countries have been engaging in an almost frenetic race to trade agreements. This is particularly true for the so-called south-south trade agreements, which are trade agreements whose members are developing countries.
It certainly reflects the fact, although not exclusively,1 that potential preference margins are higher for southern destinations relative to northern ones due to higher average levels of tariffs.
However, together with the generalized fall in applied MFN tariffs observed in the aftermath of the Uruguay round due to multilateral trade negotiations and/or unilateral liberalization, the sequential proliferation of regional trade agreements has raised serious concerns especially in terms of preference erosion.2 Preference erosion implies a de facto reduction in the competitive edge tariff preferences pro-vided and could nullify the impact on exports of the implementation of any trade agreement.
Over time it has become increasingly complex for firms but also for policy makers to identify precisely the true advantage in terms of tariff generated by any trade
∗This paper is co-authored with Marco Fugazza. This paper was published in the Review of International Economics 22(3), 599-624. An earlier version was presented at the ”University of Geneva Young Reseachers Seminar” in November 2011.
1See for instance Freund & Ornelas (2010) for a comprehensive review of elements motivating regional trade agreements.
2Preference erosion can occur without any change in MFN rates. For instance this would happen if export partners eliminate preferences of if they expand the number of preference bene-ficiaries.
31
agreement. As a consequence gains in terms of effective market access and their impact on trade remain unclear.
Despite the relative importance of economic growth and development that one attributes to exports, there is very little evidence at the firm level of the impact of market access conditions on exports. Most evidence is based at best on dis-aggregated trade data. Moreover, very little attention has been paid to a correct identification and definition of market access indicators.
This paper contributes to filling up both gaps. First, it empirically assesses the impact of tariff-wise market access that Peruvian firms face when exporting. This is done using firm level customs data over the period 2002-2008. Market access is qualified in both absolute and relative terms. The former refers to the average tariff prevailing in a given sector (corrected for the reactivity of demand in that sector).
The latter refers to the difference between the average tariff faced by a Peruvian firm and the average tariff faced by competing firms from other countries (once again corrected for the reactivity of demand in that sector). We see this as an appropriate measure of the advantage firms have due to the preference they are given. Our approach to the measurement of preferential market access is comprehensive and allows us to fully identify its incidence. Second, the paper assesses the impact of market access conditions on two major components of export dynamics. We consider both the survival of export relationships and export performance as our dependent variables.
From an empirical point of view Peru had several policy developments which could help identify the impact of market access conditions on exports. Since the 1990s, successive governments have sought to restructure Peru’s economy. The pe-riod under investigation in this paper corresponds to a phase in which Peru took an active approach to joining the global economy through regional integration and a commitment to pursuing bilateral Free Trade Agreements (FTAs). Peru has been a member of the Andean Community Customs Union since its establishment in 1969.
In August 2003, MERCOSUR and Peru signed an FTA. Brazil and Peru reached an agreement on major investments in road infrastructure, multimodal transport, energy and communications. These agreements have been part of the basis for a South American trade bloc comprising the Andean Community and MERCO-SUR countries.3 As a consequence trade barriers have been cut, direct subsidies to exporters and domestic producers have been eliminated and equal treatment has been granted to foreign and domestic investors. On the other hand Peruvian ex-porters have seen tariffs imposed on their products falling steadily. Market access for Peruvian firms exporting towards MERCOSUR countries can be seen in Figure 1 whereas the same access for non MERCOSUR countries is presented in Figure 2. The relative preferential margin mentioned higher up will be presented in detail in Subsection 3.2. To interpret the figure one must mainly see it as the difference
3Peru has further concluded negotiations on FTAs with Chile, the United States, Singapore and Canada. However, the impact of such negotiations on market access cannot be captured by our dataset. The 2006 United States-Peru ”Trade Promotion Agreement” (PTPA) entered into force on February 1, 2009. FTAs with Canada and with Singapore were signed in May 2008.
Peru, along with Australia, Malaysia, Vietnam and the United States, is negotiating an expanded Trans-Pacific Strategic Economic Partnership (TPP) agreement (between New Zealand, Brunei, Chile and Singapore).
between the average tariff faced by a Peruvian firm and the average tariff faced by competing firms from other countries. When Peruvian firms face a lower tariff when exporting to a given country than the tariff faced by firms from other countries, the relative preferential margin is positive. Otherwise, the value will be negative.
In 2002, Peru’s preferential margin with MERCOSUR was minus 5.35 percent-age points. In 2008 it reached almost 6.5 percentpercent-age points. For non MERCOSUR destinations the preferential margin was almost nil in 2002 and went down to minus 3.5 percentage points in 2008. Exports to MERCOSUR partners increased almost fivefold during the 2002-2008 period while exports to non MERCOSUR partners increased about fourfold. The number of firms exporting to MERCOSUR mar-kets almost doubled while at the same time the number of firms exporting to non MERCOSUR markets increased by 54 percent. This has implied that in 2002 firms exporting to non MERCOSUR markets were 12 times more numerous than those exporting to MERCOSUR markets. The corresponding figure was 9 in 2008.4 As a consequence the number of trade relationships with a positive preference margin has almost tripled in seven years, as shown in Figure 3.
Our results from both survival analysis and OLS estimation of export perfor-mance determinants suggest that both absolute and relative market access con-ditions play a significant role in framing export dynamics. However, results also indicate that relative market access conditions predominantly determine survival and performance. We also obtain that better diversified firms, both in terms of products and destinations, survive more easily on international markets. The re-sults concerning the importer status could lead to believe that being part of some form of an international production network also positively affects survival and slightly improves export performance.
The rest of the paper is organized as follows: the next section briefly discusses some results and insights from the related literature. Section 3 provides a descrip-tion of the variables used and their sources with a close look given to the indices measuring market access conditions. It also provides some elementary descriptive statistics. Section 4 reports and comments some major results and the last section concludes and presents some possible directions for future research.
2 Related literature
This paper investigates two essential dimensions of export dynamics, namely sur-vival and export performance. The literature dealing with these topics is vast. We present the results of only a restricted list of papers, those we think are the most closely related to our empirical work. A recent extensive review of the latter is provided by Bernard et al. (2012).
4Figures on firms are not sensitive to whether we count firms exporting to both types of destinations or not.